Life Cycle Assessment of
conventional and source-separation systems
for urban wastewater management
vorgelegt von
Dipl.-Ing. Christian Remy
von der Fakultät III – Prozesswissenschaften –
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
– Dr.-Ing. –
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr.-Ing. Matthias Kraume
Gutachter: Prof. Dr.-Ing. Martin Jekel
Gutachter: Prof. Dr. rer.nat. Matthias Finkbeiner
Tag der wissenschaftlichen Aussprache: 28. Januar 2010
Berlin 2010
D 83
Vorwort
Nachhaltigkeit ist als Leitbild der zukünftigen Entwicklung in unserer Gesellschaft
weithin anerkannt und wird von Wissenschaft, Öffentlichkeit und Politik zunehmend
eingefordert. Damit ist auch im Bereich des Abwassermanagements eine Bewertung der
bestehenden Systeme und eine Entwicklung hin zu nachhaltigeren Lösungen gefragt.
Neben dem traditionellen System der Abwasserableitung und -behandlung wurden
daher in den vergangenen Jahren neue Ansätze entwickelt, die eine kreislauforientierte
Nutzung der im Abwasser vorhandenen Ressourcen ermöglichen. Die vorliegende
Arbeit bietet nun einen systematischen Vergleich der ökologischen Nachhaltigkeit von
konventionellen und neuen Systemen des kommunalen Abwassermanagements mittels
der Ökobilanz-Methodik. Durch die Systemanalyse konnten die entscheidenden
Vorteile der neuen Systeme nachgewiesen und damit Möglichkeiten zur weiteren
Verbesserung der Nachhaltigkeit aufgezeigt werden.
Die vorliegende Arbeit entstand während meiner Tätigkeit als wissenschaftlicher
Mitarbeiter am Fachgebiet Wasserreinhaltung der Technischen Universität Berlin. Sie
basiert zu einem erheblichen Teil auf Ergebnissen des Projekts „Sanitary Concepts for
Separate Treatment of Urine, Faeces and Greywater“ (SCST), finanziert vom
Kompetenzzentrum Wasser Berlin gGmbH, Anjou Recherche (Paris) und der EU-
Kommission (EU LIFE 03ENV/D/000025).
Danksagung
Zuerst möchte ich meinem Betreuer Herrn Prof. Dr.-Ing. Martin Jekel ganz herzlich für
das Vertrauen, die gute Zusammenarbeit und die Unterstützung meiner Arbeit in den
vergangenen Jahren danken. Er gab mir die Möglichkeit, an seinem Fachgebiet
unterschiedliche spannende Projekte zu bearbeiten und ließ mir zudem die notwendigen
Freiräume zur Erstellung dieser Dissertation. Sein wissenschaftliches Vorbild in der
Wasserforschung hat diese Arbeit geprägt, und dafür bin ich ihm sehr verbunden. Als
zweiten Gutachter konnte ich mit Herrn Prof. Dr. rer. nat. Matthias Finkbeiner einen
Experten im Bereich der Ökobilanzen gewinnen. Ihm danke ich für die kritische
Begutachtung des methodischen Vorgehens dieser Arbeit und seine vielen Anregungen
für die schriftliche Ausarbeitung dieser Dissertation. Herrn Prof. Dr.-Ing. Matthias
Kraume danke ich für die Übernahme des Vorsitzes des Prüfungsausschusses.
Diese Arbeit baut auf den umfangreichen Vorarbeiten von Dr.-Ing. Alexander Ruhland
auf, dem ich für seine Einführung in die Ökobilanz-Methodik und den Umgang mit der
Software UMBERTO® herzlich danke. Erwähnt werden soll auch Ralf Mühleck (†),
der das Thema der neuen Sanitärsysteme durch seine langjährigen Recherchen am
Fachgebiet eingeführt hat.
Meinen Kollegen im Projekt SCST danke ich für die gute Zusammenarbeit und die
vielen anregenden Gespräche, speziell Anton Peter-Fröhlich, Alexandre Bonhomme,
Martin Oldenburg, Martina Winker, Felix Tettenborn und Andreas Muskolus. Den
vielen Kolleginnen und Kollegen aus der Wasserreinhaltung danke ich allen ganz
herzlich für die sehr angenehme und offene Atmosphäre und den guten Zusammenhalt,
die meine Arbeit am Fachgebiet in den vergangenen Jahren so angenehm und spannend
gemacht haben.
Am Schluss möchte ich besonders meiner Familie danken, meinen Eltern und
Geschwistern, die mich die ganzen Jahre so wunderbar auf meinem Weg unterstützt
haben und ohne die diese Arbeit nicht möglich gewesen wäre.
Berlin, im Dezember 2009
Christian Remy
Zusammenfassung
Trennsysteme für die Behandlung von urbanem Abwasser erfassen die verschiedenen
Abwasserteilströme separat und ermöglichen so die Rückgewinnung wertvoller
Ressourcen (Energie, Nährstoffe) aus dem Abwasser. Daher werden sie allgemein als
nachhaltiger im Vergleich zum konventionellen System der gemeinsamen Erfassung
und Behandlung erachtet. Diese Hypothese wird in der vorliegenden Arbeit überprüft,
indem die Umweltauswirkungen von konventionellem und stoffstrom-separierenden
Systemen mit der Methodik der Ökobilanz (ISO 14040/44) verglichen werden. Für eine
hypothetische Fallstudie eines Stadtgebiets mit 5000 Einwohnern werden zwölf
verschiedene Szenarien für die integrierte Behandlung von Haushaltsabwasser und
Bioabfall in einem Stoffstrommodel abgebildet. Die benötigten Sachbilanzdaten für alle
relevanten Prozesse der Abwassererfassung und –behandlung sind aus Pilotprojekten
und der Literatur zusammengestellt und werden durch qualifizierte Abschätzungen
ergänzt. Sekundärfunktionen der Trennsysteme (Bereitstellung von Energie und
Nährstoffen) werden berücksichtigt, indem das konventionelle System durch die
entsprechenden Produktionprozesse für Netzstrom und Mineraldünger erweitert wird.
Der Ressourcenverbrauch und die Emissionen werden für jedes Szenario aggregiert und
in der Wirkungsabschätzung anhand von acht Indikatoren ausgewertet, darunter
Energie- und Ressourcenverbrauch, globale Erwärmung, Eutrophierung, Versauerung
sowie Human- und Ökotoxizität.
Die Ergebnisse der Wirkungsabschätzung zeigen, dass Trennsysteme signifikante
Potentiale für ein nachhaltigeres Abwassermanagement bieten. Die Rückgewinnung von
Energie aus Toilettenabwasser und vor allem Bioabfall in einem Vergärungsprozess
kann den kumulierten Energieaufwand um bis zu 40% und die verbundenen Emissionen
von Treibhausgasen um bis zu 46% reduzieren. Energetische Vorteile der Substitution
von Mineraldünger sind relativ gering, aber die Qualität der organischen Dünger aus
Urin und Fäkalien ist der von Mineraldünger oder Klärschlamm in Bezug auf
Schwermetallgehalte überlegen. Das verbleibende Grauwasser kann in einem
Belebtschlammverfahren mit geringerem Energieaufwand und besserer Ablaufqualität
gereinigt werden als im konventionellen System. Eine naturnahe Reinigung des
Grauwassers in Bodenfiltern senkt den Energieverbrauch erheblich, aber die
eingeschränkte Entfernung von Phosphor kann hier das Eutrophierungspotential um bis
zu 140% erhöhen. Grauwasser kann zudem für die Abwasserwiederverwendung in
Membranbioreaktoren adequat gereinigt werden, obwohl die energetischen Vorteile der
Wiederverwendung marginal sind. Bei der Ausbringung von flüssigen organischen
Düngern aus Urin oder Fäkalien führen hohe Ammoniakemissionen zu einem um 60-
110% erhöhten Versauerungspotential und sollten daher durch geeignete
Ausbringungstechniken minimiert werden.
Insgesamt zeigt die Gruppierung und Wichtung der Indikatoren signifikante Vorteile
von Trennsystemen in Bezug auf ökologische Nachhaltigkeit. Dennoch ist die Auswahl
einer geeigneten Prozesskombination für Trennsysteme essentiell, um diese Vorteile zu
realisieren, da das konventionelle System in Bezug auf Energieverbrauch und
Ablaufqualität bereits optimiert wurde. Durch Sensitivitätsanalysen wurden
entscheidende Schlüsselparameter der Sachbilanz identifiziert. Funktionelle
Definitionen und die Auswahl sowohl der Indikatoren zur Wirkungsabschätzung als
auch der Bewertungsmethode können die Ergebnisse der Ökobilanz erheblich
beeinflussen.
Abstract
Source-separation systems for urban wastewater management collect the different
wastewater flows separately to facilitate the recovery of valuable resources from
wastewater (energy, nutrients). Thus, they are claimed to be more sustainable than the
conventional concept of combined drainage and treatment. This hypothesis is verified in
this study by comparing the environmental impacts of conventional and source-
separation systems with the methodology of Life Cycle Assessment (ISO 14040/44). In
a hypothetical case study for an urban area (5000 inhabitants), twelve different
scenarios for the integrated management of household wastewater and biowaste are set
up in a substance flow model. Required inventory data for all relevant core processes of
wastewater collection and treatment is compiled from pilot projects and literature and is
complemented by qualified assumptions. Secondary functions of separation systems
(supply of energy and nutrients) are considered by expanding the conventional system
with the respective production processes for grid energy and mineral fertilizer. Resource
demand and emissions are aggregated for each scenario and evaluated in Life Cycle
Impact Assessment with a set of eight indicators for energy and resource demand,
global warming, eutrophication, acidification, and human and ecotoxicity.
Results of the impact assessment show that separation systems offer significant
potentials for an increase in sustainability. Recovering energy from the organic matter
of toilet wastewater and especially biowaste in a digestion process can decrease the
cumulative energy demand by up to 40% and related emissions of greenhouse gases by
up to 46%. Energetic benefits of mineral fertilizer substitution are relatively low, but the
quality of organic fertilizers from urine and faeces is superior to mineral fertilizer or
sewage sludge in terms of lower heavy metal content. The remaining greywater can be
treated in an activated sludge process with less energy demand and better effluent
quality than in the conventional system. Natural treatment in soil filters can further
reduce the energy demand considerably, but the insufficient retention of phosphorus in
soil filters can seriously increase the eutrophication potential by up to 140%. Greywater
can also be adequately treated for non-potable reuse with membrane bioreactors,
although the energetic benefits of wastewater reuse are marginal. During the application
of liquid organic fertilizers from urine and faeces, increased emissions of ammonia lead
to a higher potential for acidification (+ 60-110%) and should be minimized by
adequate application techniques.
Overall, grouping and weighting of the indicators reveal significant benefits in
ecological sustainability for separation systems. However, the choice of an appropriate
combination of process technology for separation systems is essential for a realization
of these potential benefits, because the conventional system has already been optimized
in terms of energy demand and effluent quality. In sensitivity analysis, decisive key
parameters of the inventory are identified. Functional definitions and the choice of both
indicators for impact assessment and valuation methods can have a considerable impact
on the results of this LCA.
Contents
1 Introduction......................................................................12
2 Literature review and approach of this study ...............20
2.1 Sustainability assessment in water management .............................. 20
2.2 Review of LCA case studies for wastewater treatment...................... 22
2.3 Approach of this study ....................................................................... 29
3 Definition of goal and scope...........................................30
3.1 Goal and target group........................................................................ 30
3.2 Function and functional unit ............................................................... 30
3.3 Reference input flows ........................................................................ 31
3.4 System expansion ............................................................................. 35
3.5 Description of the investigated sanitation scenarios .......................... 37
3.5.1 Reference scenarios (R)............................................................. 40
3.5.2 Separation scenarios with vacuum collection and digestion of
urine and faeces (V).................................................................................. 41
3.5.3 Separation scenarios with urine separation, vacuum drainage and
digestion of faeces (SV)............................................................................ 44
3.5.4 Separation scenarios with urine separation and composting of
faeces (SC)............................................................................................... 46
3.6 System boundaries ............................................................................ 49
3.6.1 System boundaries between the analysed economic system and
the environment ........................................................................................ 49
3.6.2 Multi function processes and recycling ....................................... 50
3.6.3 Considered life cycle phases and sub-systems .......................... 52
3.6.4 Considered elementary flows ..................................................... 55
3.6.5 Geographical and temporal scope.............................................. 57
3.7 Data quality of Life Cycle Inventory.................................................... 58
3.7.1 Data quality of the present LCI ................................................... 59
3.7.2 Important assumptions and limitations of this study.................... 60
3.8 Life Cycle Impact Assessment........................................................... 64
3.8.1 Selection of LCIA methodology .................................................. 65
3.8.2 Classification............................................................................... 68
3.8.3 Characterization.......................................................................... 69
3.8.4 Normalization .............................................................................. 75
3.8.5 Grouping and weighting .............................................................. 77
3.9 Interpretation and sensitivity analysis................................................. 80
4 Life Cycle Inventory........................................................ 82
4.1 System operation ...............................................................................82
4.1.1 Conventional sanitation system................................................... 83
4.1.2 Separation systems..................................................................... 91
4.1.3 Fertilizer application .................................................................. 124
4.2 System construction......................................................................... 132
4.2.1 Settlement structure.................................................................. 132
4.2.2 Inventory ................................................................................... 133
4.2.3 Service life................................................................................. 138
4.2.4 Materials for system construction.............................................. 140
4.3 Background processes.....................................................................142
4.3.1 Energy Supply........................................................................... 142
4.3.2 Transport by truck ..................................................................... 144
4.3.3 Incineration plant....................................................................... 145
4.3.4 Auxiliary material....................................................................... 146
4.3.5 Industrial fertilizer production .................................................... 147
5 Results........................................................................... 150
5.1 Selected results of the Life Cycle Inventory ..................................... 150
5.1.1 Demand of electric energy for operation ...................................151
5.1.2 Supply of organic fertilizers ....................................................... 154
5.1.3 Processes for system expansion............................................... 156
5.1.4 Effluent concentrations and loads from wastewater and greywater
treatment plants.......................................................................................158
5.1.5 Heavy metals loads emitted to surface waters and soil............. 164
5.2 Results of the Life Cycle Impact Assessment .................................. 168
5.2.1 Cumulative energy demand ...................................................... 168
5.2.2 Depletion of abiotic resources................................................... 173
5.2.3 Global warming ......................................................................... 174
5.2.4 Acidification............................................................................... 177
5.2.5 Eutrophication ........................................................................... 178
5.2.6 Human toxicity........................................................................... 182
5.2.7 Freshwater ecotoxicity ..............................................................184
5.2.8 Terrestrial ecotoxicity................................................................ 186
5.2.9 Summary of LCIA results.......................................................... 187
5.2.10 Normalization............................................................................ 190
5.2.11 Grouping and Weighting ........................................................... 192
5.3 Sensitivity analysis........................................................................... 195
5.3.1 Energy recovery without biowaste ............................................ 196
5.3.2 Transport distance of organic fertilizers.................................... 197
5.3.3 Energy demand of urine treatment ........................................... 198
5.3.4 Energy demand for vacuum plant............................................. 199
5.3.5 Efficiency of urine separation toilets ......................................... 200
5.3.6 Reuse volume of and energy demand for water supply............ 203
5.3.7 Comparable effluent concentrations in SBR ............................. 204
5.3.8 NH3 emissions during application of liquid fertilizer .................. 206
5.3.9 Concentrations of Cu and Zn in drinking water......................... 207
5.3.10 Heavy metal data for mineral fertilizer ...................................... 208
5.3.11 Plant availability of phosphorus in sewage sludge.................... 209
5.3.12 Alternative indicators for eutrophication.................................... 210
5.3.13 Alternative indicators for ecotoxicity.......................................... 211
5.3.14 Valuation with original UBA method.......................................... 213
6 Interpretation..................................................................215
6.1 Relevant findings of this LCA........................................................... 215
6.2 Methodological issues...................................................................... 227
6.3 Additional remarks ........................................................................... 229
6.3.1 Freshwater use......................................................................... 229
6.3.2 Economic sustainability of separation systems......................... 231
6.3.3 Social LCA................................................................................ 232
7 Conclusions ...................................................................233
8 References......................................................................237
9 List of Abbreviations.....................................................258
10 List of Tables..................................................................259
11 List of Figures................................................................262
12 ..............................................................................266 Annex
1 Introduction
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1 Introduction
„Sustainable development is development that meets the needs of the present without
compromising the ability of future generations to meet their own needs”
The Brundtland Report (WCED, 1987)
The concept of sustainable development has been postulated by the United Nations in
1987 in view of the rising pressure on natural resources and ecosystems caused by
human activities. Thus, the global community recognized the limitation of global
resources and the need for a paradigm shift in development towards sustainability. First
announced by the Club of Rome in 1973 (Meadows et al., 1973), the limits of growth
have since then become obvious in many fields of human activities, e.g. regarding the
depletion of resources such as fossil fuels, agricultural land, or local availability of
freshwater. Continuous growth of global population, rapid urbanisation, and the
consequences of climate change will certainly put additional pressure on these resources
in the future, further increasing the need for sustainable development (UN, 2008).
Hence, the need to design and operate sustainable systems in society has also been
recognized for the management and disposal of municipal wastewater (Wilderer, 2004).
Traditionally, the functions of urban wastewater management are the protection of
human health by ensuring the safe disposal of wastewater and the protection of aquatic
ecosystems from negative impacts of effluent discharge. The latter function also
includes the protection of surface waters which serve as drinking water supply. These
functions can be adequately fulfilled with the historically grown concept of wastewater
collection and treatment as it is practised in industrialized countries. This concept is
based on the combined collection of municipal wastewater in a flushing sewer and its
treatment in a centralized wastewater treatment plant prior to the discharge into surface
waters (hereafter denoted as “the conventional system”). However, the sustainability of
this concept has been questioned within the scientific community (Wilderer, 2004). To
further elaborate this issue, a closer look at the historical development of the
conventional system of wastewater management can be helpful.
Historical development of urban wastewater management
Historically developed in central Europe in the second half of the 19th century, open
drainage channels were built targeting the disposal of wastewater outside of the urban
area without hygienic risks. The concept of flushing away all wastewater flows and
pathogens with large amounts of water was originally designed to prevent the inner-city
outbreaks of water-borne diseases such as cholera and typhus, causing a severe amount
of deaths in regular intervals at that time. The immediate success in improving the
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1 Introduction
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hygienic situation of the urban population led to the rapid implementation of flushing
sewers in urban and also rural areas of industrialized countries in the 20th century.
Thus, the linear disposal-oriented system of combined collection of wastewater in a
flushing sewer (Figure 1) has survived many decades and is still the guiding principle of
urban sanitation today. Additionally, the need for a protection of receiving surface
waters from negative impacts of wastewater discharge led to the development of
sophisticated treatment processes for the combined wastewater, targeting the removal of
solids, organic matter, and nutrients. In Germany, conventional wastewater treatment
plants (WWTP) usually include a combination of physical, biological and chemical
processes to ensure the compliance with stringent effluent standards. Recently, the
question of implementing additional treatment stages has been raised for improving the
hygienic quality of the effluent or removing potentially harmful micropollutants such as
pharmaceuticals or endocrine disruptors (DWA, 2008a).
Figure 1: Conventional system of urban wastewater management
Inherent drawbacks of the traditional approach
Despite its successful history, the conventional wastewater system is originally designed
as a linear disposal-oriented concept. Its focus is the safe disposal of wastewater while
minimizing negative impacts on receiving surface waters, thus being a typical “end-of-
pipe” technology. In terms of sustainability, the conventional system has been
questioned for its capability to meet future requirements for sustainable system design.
In particular, the following drawbacks have been identified for the conventional system
(Wilderer, 2005):
The use of freshwater to transport human excreta in large flushing sewers
leads to a high drinking water demand. In water-scarce regions, additional
pressure is put on limited freshwater resources.
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1 Introduction
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Organic matter and nutrients have to be eliminated in the WWTP with a
considerable effort in energy and chemicals to protect the receiving surface
waters.
The recovery potential of valuable constituents of wastewater such as
organic matter and nutrients is limited to the application of sewage sludge in
agriculture. However, this option is discussed controversially due to the
function of sewage sludge as a pollutant sink (Hartmann et al., 2004). Other
options for nutrient recovery are being proposed, but their large-scale
implementation is still pending and can be energy-intensive (Cornel and
Schaum, 2009).
The reuse of treated wastewater for non-potable purposes requires an
extensive treatment to comply with the relevant standards, especially
concerning hygienic quality and nutrient content (DWA, 2008b).
Conventional wastewater collection and treatment requires large investments
in relatively inflexible infrastructure. In industrialized countries, existing
sewers have to be maintained and renewed with considerable financial effort,
whereas the installation of completely new systems in developing or
threshold countries may be delayed or entirely inhibited by the high
investment needs.
The need for sustainable solutions for urban wastewater management
Hence, the sustainability of the conventional approach of urban sanitation is challenged
with regard to high resource demand, limited recovery potential for valuable resources,
and large investment needs. However, the conventional system has already been fully
implemented in most industrialized countries: 95% of the population is connected to a
public sewer system in Germany (DESTATIS, 2007). Nevertheless, considerations to
improve the sustainability of wastewater management are necessary for a slow but
constant shift towards a sustainable society. Existing systems have to be maintained or
reconstructed: 20% of the existing urban sewer systems in Germany have been
identified as requiring restoration in the next years, with an estimated investment of 53
billion Euro (Berger and Lohaus, 2005). Here lies an opportunity to gradually improve
the sustainability of our wastewater management systems.
On a global scale, the situation is much more pressing. Many developing and threshold
countries do not have sufficient coverage of urban areas with sanitation systems at all,
with 2.4 billion people living without adequate sanitation today (WHO/UNICEF,
2008). Thus, large efforts are being undertaken to build up sanitation systems in these
countries, with a focus on rapidly growing urban areas. Providing adequate sanitation
for the global population is one of the Millennium Development Goals of the United
Nations (UN, 2000). Without adequate sanitation, the negative impacts on human
health (e.g. through water-borne diseases), economic development and ecosystems (e.g.
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1 Introduction
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hypoxic zones in coastal areas (Diaz and Rosenberg, 2008)) are obvious in the
developing world (UN-WWAP, 2003).
In all, there is a rising demand for sustainable systems for urban wastewater
management, more distinct and pressing in developing countries, but also relevant for
industrialized societies. Having identified the inherent drawbacks of the conventional
disposal-oriented approach, several research groups started to develop future concepts
for urban sanitation in the 1990s, targeting the recovery of resources from municipal
wastewater (Larsen and Gujer, 1996; Hanaeus et al., 1997; Otterpohl et al., 1997).
Source separation systems
The difficulty of recovering potentially valuable resources from wastewater mainly
originates from the commingling of wastewater flows with different characteristics. The
exemplary distribution of nutrients in urine, faeces, and greywater (i.e. wastewater from
kitchen, bathrooms, washing etc) shows that nutrients are mainly contained in human
excreta, whereas greywater has low nutrient content in a high volume (Figure 2). By
mixing these different wastewater flows, a high dilution of nutrients and organic matter
occurs, making their recovery a difficult task. Hence, the basic principle of recovery-
oriented systems is the separate collection of the different wastewater flows at the
source (“source separation”) (Figure 3).
0%
20%
40%
60%
80%
100%
NPKVolume
Nutrients NPK and Volume
Share of Total [%]
Greywater Faeces Urine
Figure 2: Volume and distribution of nutrients in urine, faeces, and greywater
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1 Introduction
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Figure 3: Source separation system for urban wastewater management
If the different wastewater flows are separately collected, resource recovery from highly
concentrated flows (urine and faeces = toilet wastewater) is achievable with a
reasonable effort. Separation systems can include various combinations of available
technology for the different flows (Otterpohl et al., 1999; Wilderer, 2004). For example,
the following features can be part of a separation system:
The use of vacuum drainage for urine and faeces enables their transport with
low amounts of flushwater, resulting in low dilution of the concentrated flows.
With urine separation toilets, undiluted urine (“yellow water”) can be
collected separately from faeces and flushwater (“brown water”). Thus, urine
can be directly applied in agriculture as a valuable fertilizer.
Organic matter from faeces can be converted to biogas in a digestion process,
recovering a substantial part of the energy bound in the organic matter. In an
integrated approach for waste handling, biowaste from households can be easily
added to the biogas plant. The residual sludge of the digestion process still
contains valuable nutrients and can be applied as fertilizer in agriculture.
Faeces and biowaste can also be treated in a composting process to obtain a
valuable soil conditioner with some nutrients. Therefore, flushwater has to be
separated from faeces beforehand to maintain aerobic conditions during
composting.
The remaining greywater – high in volume, but low in nutrient content – can be
easily treated in a conventional WWTP. Moreover, it can be treated adequately
in a low-energy natural process with a planted soil filter (“constructed
wetlands”). Finally, a sufficient degree of purification for non-potable reuse of
treated greywater can be reached with a membrane bioreactor (MBR).
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1 Introduction
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The concept of source separation is not new, but has only recently been reconsidered for
modern urban wastewater management. Following its rediscovery in the 1990s, multiple
research projects targeted the development and optimization of source separation (e.g.
Stockholm Vatten, 2000; Oldenburg et al., 2002; Otterpohl and Oldenburg, 2002; Peter-
Frohlich et al., 2007; Larsen and Lienert, 2007). Several pilot plants were built to gain
experience in installation, operation and maintenance of the different components of
separation systems. Despite the positive results of the pilot projects, large-scale
implementation of separation systems in industrialized countries is still pending.
Nevertheless, the possibility for a paradigm shift in wastewater handling towards source
separation is being intensively discussed by researchers and practitioners (DWA, 2008c;
Larsen et al., 2009; Guest et al., 2009).
Assessing environmental sustainability with Life Cycle Assessment
As shown above, separation systems are usually claimed to be more sustainable than the
conventional sanitation system, a thesis often supported with conceptual benefits and
qualitative arguments. Quantitative evidence of the sustainability of separation systems
is scarce. Evaluating the economic sustainability of different options for urban
sanitation is relatively easy, because costs for installation, operation and maintenance
can be directly compared between the systems. Quantifying environmental impacts for a
comparison between conventional and separation systems is a more difficult task.
A suitable tool for the quantification of environmental impacts of a technical system is a
method called “Life Cycle Assessment” (LCA), a methodology originally developed in
the 1970s for assessing the environmental impacts of industrial products (Siegenthaler,
2006). Meanwhile, LCA has become a widely applied method for sustainability
assessment of products or services (Klöpffer and Grahl, 2009). Rules for its application
are precisely regulated in ISO standards 14040/44 (ISO 14040, 2006; ISO 14044, 2006),
which have been updated recently (Finkbeiner et al., 2006). The LCA approach is
characterized by taking a “life cycle perspective”: the assessment includes all relevant
processes for the product or service under investigation “from the cradle to the grave”,
following the entire life cycle. Additionally, all results are related to a functional unit,
allowing the quantitative comparison of environmental impacts of different systems
which provide the same function. Thus, LCA is an appropriate tool for a quantitative
comparison of two or more technical systems in their environmental impacts.
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1 Introduction
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Goals of this thesis
The present thesis aims at a quantitative comparison of environmental impacts of
conventional and separation systems for urban sanitation in an industrialized
environment. Thus, the ecological sustainability of separation systems shall be verified
in a reproducible and transparent manner. Therefore, the study follows the methodology
of Life Cycle Assessment as defined in ISO 14040/44. During the assessment, potential
hotspots of separation systems in terms of environmental impacts shall be identified for
further optimization of processes and system layout. Finally, key parameters for the
environmental comparison shall be identified for a simplification of future LCA studies
in this field. In particular, the following questions shall be discussed:
Is source-separation more sustainable in terms of environmental impacts than a
conventional system of combined drainage and treatment?
What are the decisive benefits of separation systems?
Which environmental drawbacks of separation systems can be identified, and
how could they be minimized?
Which are the important key parameters for the impact assessment?
Structure of this study
This study is structured according to the requirements of LCA as defined in ISO
14040/44 except for the critical review (ISO 14040, 2006; ISO 14044, 2006). After a
review of relevant literature in the field of sustainability assessment of wastewater
systems, the approach of this study is precisely illustrated (chapter 2). In the subsequent
chapters, the various parts of LCA are performed for a hypothetical case study. In detail,
this includes the following steps (Figure 4):
Definition of goal and scope (chapter 3), including system functions,
scenarios, system boundaries, and methodological issues of LCI and LCIA
Life Cycle Inventory or LCI (chapter 4), documenting the inventory data for
all relevant processes which is fed into a substance flow model
Life Cycle Impact Assessment or LCIA (chapter 5), calculating the
environmental impacts of aggregated emissions and resource demand from LCI
Interpretation of results (chapter 6), including a discussion of relevant
outcomes with special regard to data quality and sensitivity analysis.
The thesis is completed by summarizing the results of this case study and presenting
relevant conclusions (chapter 7). Additional information (e.g. sources for inventory
data, model descriptions, and numerical results of inventory analysis) is documented in
the annex of this thesis.
18
1 Introduction
______________________________________________________________________
Figure 4: Structure of this thesis
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2 Literature review and approach of this study
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2 Literature review and approach of this study
2.1 Sustainability assessment in water management
The rising pressure on limited global resources and ecosystems through anthropogenic
activities has raised awareness of the need to design and operate sustainable systems. In
the field of water management, numerous studies have been published which aim at
assessing the sustainability of water supply and wastewater treatment options
Development of methods for sustainability assessment
Several attempts of developing a consistent methodology for sustainability assessment
of water systems have been described in literature. Some of these studies include all
three dimensions of sustainability, i.e. ecological, economic, and social issues, also
known as “triple bottom line” (e.g. Ellis and Tang, 1990; Otterpohl et al., 1997;
Hellström et al., 2000; Steinberg et al., 2002; Balkema, 2003). While evaluating
ecological, economic and social issues, the high number of indicators (up to 35 different
criteria) often complicates a conclusive comparison between two different systems. A
carefully defined procedure for weighting the indicators against each other is required to
come to a distinct result. An overview of relevant studies and sustainability indicators
developed for water systems can be found in the thesis of Huegel and Balkema (Huegel,
2000; Balkema, 2003).
Other studies focus on the environmental performance of water systems, developing
specific sets of environmental indicators applicable in the water sector (e.g. Lundin and
Morrison, 2002; Mühleck et al., 2003). These environmental impacts are often
characterized following the methodology of LCA, i.e. adopting the life cycle
perspective and broadening the system boundaries to include associated background
processes. In addition to LCA specific impact indicators, other types of indicators on the
level of LCI are introduced here, e.g. energy demand, total water use, or recycling rate.
Again, systematic difficulties arise while aggregating the different indicator results into
a conclusive outcome.
Some authors finally deduct a numeric decision support tool from their sustainability
assessment to facilitate the choice of an optimum solution under certain conditions
(Ellis and Tang, 1990 ; Balkema et al., 2001; Norstrom et al., 2008). However,
boundary conditions of water systems have a high influence on the choice for an
appropriate solution and are very much depending on local conditions. Thus, the
development of a universal decision support tool covering a wider area of application is
a daunting task.
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2 Literature review and approach of this study
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Life Cycle Assessment for wastewater management
The use of Life Cycle Assessment (ISO 14040, 2006) as an appropriate tool for the
environmental assessment of wastewater management systems has been systematically
evaluated by Huegel (Huegel, 2000). On the basis of four case studies, Huegel
investigates methodological issues of LCA for four different targets of study: WWTP as
downstream process in other LCA, comparative LCA for optimization of WWTP,
ecological efficiency of sub-processes in WWTP, and strategic planning of wastewater
management. The author identifies systematic problems in the methodology of LCA,
particularly for the definition of functional unit, impact assessment and data
management:
Although the defined function (= wastewater treatment) should be comparable
between different scenarios, different wastewater treatment technologies can
differ in the treatment efficiency (= effluent loads) that they deliver. Thus, they
may not provide exactly the same function and are per definition not comparable
with LCA. The careful definition of system functions is necessary for a valid
comparison between different scenarios.
In impact assessment, short-term, acute and local impacts are usually omitted in
LCA methodology, even though a major task of wastewater treatment is to
control in particular short-term, acute and local emissions.
The extensive amount of data required for assessing a complete wastewater
system complicates the setup of the Life Cycle Inventory, often lacking high-
quality data for certain processes or infrastructure.
Nevertheless, LCA is seen as a useful tool for a systematic investigation of the
environmental impacts of wastewater systems (Huegel, 2000). For LCAs designed for
strategic planning of wastewater management, further development of impact
assessment and evaluation procedure is strongly recommended based on the available
methods at that time.
Case studies
Aside from methodological improvement of sustainability assessment, a number of case
studies have been carried out to compare different scenarios of water and wastewater
management in their impacts on the environment. These studies are often limited in
their system boundaries to a specific sub-part of the overall water management system,
e.g. drinking water supply or installations (Crettaz et al., 1999, Gabriel and Kreissig,
2006; Stokes and Horvath, 2006; Barrios et al., 2008), wastewater treatment (Roeleveld
et al., 1997; Hospido et al., 2004; Hoibye et al., 2008; Wenzel et al., 2008), or sludge
disposal (Dennison et al., 1998; IFEU, 2002; Schubert, 2006). Studies which include the
21
2 Literature review and approach of this study
______________________________________________________________________
complete water management system or even the water cycle (e.g. with catchment areas)
are scarce due to the complexity of the system and the high amount of data required
(Mühleck et al., 2003; Lundie et al., 2004; Lassaux et al., 2007). Separation systems
have also been assessed with LCA, and the following chapter describes a selection of
relevant case studies.
2.2 Review of LCA case studies for wastewater treatment
The methodology of LCA has already been applied in previous research since the mid
1990s to study the environmental impacts of different options for wastewater treatment.
Several studies have been carried out to compare different scenarios of wastewater
management, including both conventional and separation systems. However, the
existing studies differ widely from each other in terms of layout and scale of the
investigated systems, system boundaries, level of detail for Life Cycle Inventory, and
the applied methodology of impact assessment (Table 1). In the following, the most
important LCA case studies in the field of wastewater treatment are shortly summarized
in chronological order.
Bengtsson et al 1997
This study evaluates different options for wastewater and sludge treatment for three
case studies in Sweden with the LCA methodology (Bengtsson et al., 1997).
Wastewater treatment options include urine separation or liquid aerobic composting of
blackwater and organic kitchen waste, both with nutrient recycling to agriculture. The
substitution of mineral fertilizer is accounted for in the LCA. Both separation systems
show less environmental impacts than the conventional system, mainly due to a
reduction of water emissions from the wastewater treatment plant and a conservation of
fossil fuels due to the substitution of mineral fertilizer. In another publication, the
authors emphasize the influence of system boundaries and scale on the calculated
environmental loads (Lundin et al., 2000). Large economies of scale in environmental
terms can be gained both for the operation and construction phase (smaller systems have
higher relative impacts in relation to larger systems). Some of the most important
environmental benefits of separation systems emerge only when system boundaries
include associated processes such as substituted mineral fertilizer production.
Roeleveld et al 1997
Roeleveld and co-workers use the LCA method to quantify the environmental impacts
of different options for conventional wastewater treatment in the Netherlands
(Roeleveld et al., 1997). The scenarios cover an increasing degree of treatment, starting
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2 Literature review and approach of this study
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with the removal of organic matter, and adding nitrification, denitrification, biological
and chemical phosphorus elimination, and finally tertiary filtration plus activated
carbon. Sludge handling and disposal is not considered within this study. With
increasing nutrient removal, the contribution of the wastewater treatment process to the
total environmental impacts in the Netherlands is relatively low. Eutrophication and
aquatic ecotoxicity are the decisive indicators after normalisation. Consequently, the
authors emphasize the importance of effluent-derived impacts from nutrients, heavy
metals and organic pesticides, whereas the energy consumption of the process is of
minor relevance. Likewise, the construction of the plant itself and the production of
auxiliary chemicals for its operation are not decisive for the environmental performance
in this LCA.
Tillman et al 1998
Tillman and co-workers apply the LCA instrument to municipal planning of wastewater
systems in two Swedish settlements, a suburban area (12,000 inhabitants) and a coastal
village (900 inhabitants) (Tillman et al., 1998). They compare the existing conventional
wastewater systems with a separation system consisting of urine separation and
application in agriculture, faeces co-digestion with biowaste, and greywater treatment in
sand filter beds. The authors distinguish a core system of wastewater collection,
treatment, and sludge disposal, and an enlarged system where effects on the surrounding
technical systems are taken into account (e.g. energy production, drinking water
production, fertilizer substitution). Whereas the core system can be modelled with
higher accuracy, the enlarged system is more relevant for the assessment of the overall
environmental impacts, although it is naturally based on data with higher uncertainty.
For the core system, the study includes the construction expenditures. The separation
scenario requires more electricity and fossil fuels for the construction than the
conventional system, mostly due to the collection tanks for urine separation. Results for
the enlarged system show that the separation scenario can save considerable amounts of
energetic resources in the small village. The suburban area has a heat recovery system
from the wastewater, which makes it energetically favourable to the separation scenario
(existing system is a net producer of energy). The nitrogen emissions to surface waters
are substantially reduced by urine separation in both settlements. Through different
weighting procedures, the authors identify those impact categories which are decisive
for the comparison, namely fossil fuel use, CO2 emissions, use of raw phosphate, and N
and P emissions to water. Finally, the urine separation scenario is assessed as preferable
to the conventional system, with a high sensitivity to the subjective weighting procedure
and to several assumptions concerning the surrounding technical systems.
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2 Literature review and approach of this study
______________________________________________________________________
Schneidmadl et al 2000
This LCA study evaluates different options for wastewater management and is
associated with a pilot project testing new sanitation concepts in Freiburg, Germany
(“Vauban”) (Schneidmadl et al., 2000). The conventional system of wastewater and
stormwater treatment is compared to several separation systems, including blackwater
digestion, local infiltration of stormwater and the treatment of greywater in a sand filter
with partial reuse as toilet flush water. For water-related emissions, the separation
systems can substantially reduce the load of nutrients and COD to surface waters in
comparison to the conventional system, especially due to the prevention of combined
sewage overflow events. Similarly, the reduction of heavy metal emissions into surface
waters leads to a lower aquatic ecotoxicity potential of the alternative systems.
Concerning the energetic resources and related indicators such as the global warming
potential, their advantages are less pronounced. In fact, some separation systems exhibit
an even higher demand of fossil fuels and related CO2 emissions, although the
substitution of mineral fertilizer is taken into account. The construction of the
infrastructure needs more resources for the separation systems, however the authors
point out that the energy-related indicators are only of minor relevance for the
comparison after normalisation. Consequently, the construction phase is found to be less
important for the overall environmental performance of a sanitation system.
Jeppsson and Hellström 2002
This Swedish study compares two scenarios for urban wastewater systems using
material flow analysis in combination with evaluation methods based on Life Cycle
Assessment (Jeppsson and Hellström, 2002). The conventional centralised wastewater
system includes activated sludge treatment with denitrification and chemical P
elimination. Additionally, phosphorus is recovered from sewage sludge via the
KREPRO process and recycled to agriculture. The second scenario is a separation
system with urine separation, blackwater digestion, and greywater treatment in an
activated sludge plant without enhanced phosphorus removal. The material flows of
these two systems are simulated with ORWARE software and evaluated with LCA
indicators. Due to the high recycling rate of phosphorus through the KREPRO process
(80%), the conventional system offers a higher amount of recycled phosphorus and less
cadmium input to agricultural soil than the separation system. The separation system
features higher recycling of nitrogen and potassium and decreases the fresh water
consumption. Both systems are net producers of energy due to the assumed use of heat
pumps for the recovery of thermal energy from the wastewater. The authors conclude
that neither of the investigated systems can be described as an absolute best solution
based on the priority environmental criteria developed in this study. The outcome
depends on local conditions and political priorities of decision-makers concerning
relevant impact categories.
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2 Literature review and approach of this study
______________________________________________________________________
Mühleck et al 2003
Mühleck and co-authors describe a decision support system (DSS) which is developed
for the assessment of environmental impacts of different wastewater management
options in urban areas (Mühleck et al., 2003). The DSS is based on a material flow
analysis (MFA) of the technical system and its evaluation with a set of environmental
indicators. The authors want to establish a methodology for the assessment of
wastewater management strategies on a regional level in contrast to site-specific studies.
All relevant components of the Berlin drinking water and wastewater system are
included in the MFA. The set of indicators is a mixture of typical LCA-derived
indicators, water management-specific figures (e.g. water consumption per capita and
year), and more legislative-oriented parameters (e.g. emissions to water are weighted in
“damage units” of the German Wastewater Charges Act). The comparison of the
conventional system with separation scenarios (blackwater separation with vacuum
toilets or urine separation, both with nutrient recycling to agriculture) results in a
significant reduction of nutrient emissions into receiving surface waters. The authors
also identify a substantial reduction in heavy metal input to farmland in comparison to
the spreading of sewage sludge in the conventional scenario. The transport of digested
blackwater to farmland is seen as a possible hotspot with increased greenhouse gas
emissions due to the high volume and low nutrient concentration of the organic
fertilizers. However, if the substitution of mineral fertilizer products is accounted for,
the separation scenarios show comparable or less emissions of greenhouse gases than
the conventional system.
Hospido et al 2004
This LCA study is designed to evaluate the environmental impacts corresponding to a
municipal wastewater treatment plant (90,000 inhabitant equivalents) with primary and
secondary treatment (no denitrification or chemical P elimination) (Hospido et al.,
2004). The inventory is based on real data from a two year measurement campaign
characterizing all input and output flows of the WWTP. The categories of
eutrophication (WWTP effluent) and terrestrial ecotoxicity (sludge application in
agriculture) are identified as decisive for the environmental performance. An enhanced
nitrogen removal via denitrification results in a substantial reduction of the
environmental impacts. Heavy metals are responsible for the high terrestrial ecotoxicity
(mainly Cr, Hg, Zn), which may lead to alternative treatment strategies for WWTP
sludge in the future.
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2 Literature review and approach of this study
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Lundie et al 2004
This is a prospective LCA study of water supply and wastewater collection and
treatment in the greater Sydney area for the year 2021 (Lundie et al., 2004). The
complete metropolitan water system including water filtration plants, distribution, sewer
system, wastewater treatment plants, and sludge treatment and disposal is modelled with
LCI data from site-specific measurements. The environmental performance of the actual
system is extrapolated into the future and compared to several scenarios including water
demand management, increased energy efficiency, desalination of seawater, and
upgrading of major WWTPs. The impact assessment reveals that demand management
and energy efficiency measures result in an improvement in all impact categories, while
desalination and WWTP upgrade show environmental trade-offs. However, the missing
normalisation and weighting of the results prevents a comprehensive evaluation of the
different scenarios. A combination of ecological measures in a new suburb (“greenfield
scenario”) reveals environmental benefits in all indicator categories on a per household
basis. In general, LCA is found to be a useful tool for the examination of alternative
future scenarios for strategic planning.
Lassaux et al 2007
Lassaux and co-workers conduct an LCA for the water cycle in the Walloon Region of
Belgium (Lassaux et al., 2007). They include the water catchment, water treatment and
supply, the sewer system, the wastewater treatment plants including sludge treatment,
and water discharge without any treatment. Based on the data of more than 100
wastewater treatment plants, different scenarios are calculated which reflect an
increasing connection rate to the public sewer and consequently less discharge of
untreated wastewater. The inventory includes construction and operation of all facilities,
and it is evaluated with Eco-Indicator 99 and CML 2001. Nitrogen is excluded from the
impact assessment in the category of eutrophication. The authors conclude that an
increasing rate of connection to a public sewer and wastewater treatment plants
decreases the global environmental burden of the water cycle. Although wastewater
treatment and sewer construction consume considerable amounts of energy and
chemicals, this disadvantage is far outweighed by the reduced discharge of P and COD
to surface waters due to increased wastewater treatment.
Benetto et al 2009
The authors describe a comparative LCA case study for an office building (40 persons)
in Luxembourg (Benetto et al., 2009). They compare conventional wastewater treatment
in a centralized activated sludge plant with a scenario of ecological sanitation, including
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2 Literature review and approach of this study
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27
urine separation, faeces composting, and greywater treatment in a reed bed. The
secondary functions of fertilizer substitution are accounted in three different ways: “cut
off”, i.e. no allocation of environmental burdens of disposal (transport and application),
“waste”, i.e. urine and faeces are considered as waste that has to be disposed, and
“system expansion” where the substitution of mineral fertilizer is accounted for. The
authors emphasize that the system expansion approach is discussed controversially in
the LCA community. Impact assessment is described with several indicators on the
midpoint and endpoint level. Their endpoint results show that separation systems
decrease ecosystem quality damage (particularly due to decreased emissions of Al from
precipitation, Zn, and Cu), whereas effects on climate change, human health and
resource demand are worse than for the conventional system. On the midpoint level, the
authors state benefits in eutrophication and drawbacks in acidification for the separation
system. Transport distance of organic fertilizers and the allocation of the secondary
functions are identified as decisive points in sensitivity analysis.
Table 1: Overview of previous LCA case studies for wastewater systems
Source Scale
Faeces
treatment Urine Grey-
water System
including Substi-
tution Impact assessment Remarks
Country
Inhabitant
equivalents
CAS system
digestion
composting
separation
Alternative
treatment
Infrastructure
Stormwater
Energy
Fertilizer
Resources
Water and air
emissions
Soil emissions
Normalisation
Bengtsson et al.,
1997 S 200 +
2700 X X X (X) X X X (X)
Two case studies
Roeleveld et al., 1997 NL 100000 X X X X X
Tillman et al., 1998 S 900 +
12600 X X X X X X X X X
Heat recovery from WW
in CAS system
Schneidmadl et al.,
2000 D 40 X X X X X X X X X X
Greywater reuse
Jeppsson and
Hellström, 2002 S 15000 X X X X X X (X) CAS with KREPRO
(P recycling)
Mühleck et al., 2003 D 4.1 Mio X X X X (X) X X X X (X) Berlin water supply and
wastewater treatment
Hospido et al., 2004 E 90000 X X X X X X
Lundie et al., 2004 AU ~ 4 Mio X X X X X X X Detailed model of
Sydney water system
Lassaux et al., 2007 B Walloon
region X X (X) X X X X
Complete water cycle
Benetto et al., 2009 L 40 X X X X X (X) X X (X) X
This study D 5000 X X X X X X X X X X X X Greywater reuse via
MBR treatment
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2 Literature review and approach of this study
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2.3 Approach of this study
The approach of this study targeting a comparison of environmental impacts of different
wastewater management systems is developed in consideration of the outcomes of
previous studies. It attempts to combine all relevant issues addressed in previous
research (Table 1), especially concerning the applied methodology of LCA and the
investigated types of separation systems. In detail, the following aspects are
implemented (Figure 5):
The study follows the methodology of LCA as described in ISO 14040/44. All
relevant steps shall be considered (excluding critical review).
As a prospective LCA for strategic planning, a hypothetical case study is set up
in which different options for the management of municipal wastewater
(excluding stormwater) and biowaste are compared.
The scale of the case study is set to 5000 inhabitants, thus reflecting an urban
environment in which an implementation of separation systems could be
possible within a reasonable timeframe.
Different options for the layout of separation systems (2-flow, 3-flow, non-
potable reuse) are included, combining available technology for each partial
flow of wastewater.
The system boundaries include operation and construction of wastewater
systems, associated background processes, and secondary functions.
Specific modular substance flow models for each relevant process of the core
system of wastewater management are set up with available LCI data compiled
from pilot projects and literature, using the LCA software UMBERTO® (IFU
and IFEU, 2005).
Figure 5: Approach of this study
29
3 Definition of goal and scope
______________________________________________________________________
3 Definition of goal and scope
3.1 Goal and target group
In this study, different systems for the management of urban wastewater are analysed in
order to compare the systems in terms of selected impacts on the environment. The goal
is to identify advantages and disadvantages of the investigated systems with special
regard to possible benefits of source-separation technologies compared to conventional
wastewater systems. On the other hand, potential ecological hotspots of separation
systems shall be revealed to specify those parts of the system which need further
improvement. Finally, key parameters with high relevance for the ecological assessment
shall be identified, so that future LCA studies in this field may use a simplified
approach for setting up the Life Cycle Inventory.
No existing marketable products are compared, but integrated concepts of urban
wastewater management. The results of this work are primarily intended for experts
from research and sanitary engineers working in the fields of urban wastewater
management and environmental assessment of water systems. The results may also be
interesting for professionals in administrative or political functions related to decision-
making in the wastewater sector.
3.2 Function and functional unit
The core or primary function of the sanitation systems under investigation is the
transport and disposal of all fractions of urban household wastewater. To reflect
possible advantages of an integrated approach of managing municipal waste, the
disposal of solid biowaste is also defined as a primary function. Additionally, the supply
of drinking water is included as a primary function to reflect the effects of varying
freshwater demand between the scenarios. In summary, the primary functions include
the following services:
Primary functions
supply of drinking water for urban households
transport and disposal of human urine and faeces from urban households
drainage and treatment of domestic urban wastewater charged with
substances from kitchen, personal hygiene, and washing machines
(greywater)
collection and disposal of solid domestic biowaste from kitchen and garden
30
3 Definition of goal and scope
______________________________________________________________________
This study explicitly excludes all other fractions of wastewater which may arise in a
settlement, i.e. stormwater which is often collected together with domestic sewage or
wastewater from industrial or agricultural activity. The scope of the study is purposely
limited to domestic wastewater from households, because the influence of other
wastewater fractions on the wastewater system is heavily depending on local conditions
(e.g. combined or separate collection of rainwater, amount of rainfall, type and size of
industrial or agricultural activity). This limitation keeps the size of the study
manageable without compromising the general validity of its results for urban
wastewater systems.
The material and energy flows associated with the above mentioned primary functions,
respectively the LCA results, are related to the provision of these services per person
and year. Consequently, the functional unit is defined as:
Functional unit: Provision of the primary functions per person and year
Unit: (pe*a)-1
Besides their primary functions, separation systems additionally provide secondary
functions, i.e. the supply of organic fertilizers and electric energy. For proper system
comparison, these secondary functions are taken into account by the method of system
expansion (see chapter 3.4).
Although the functional unit is related to the provision of sanitation services per person,
the overall size of the settlement under investigation is important as well. For certain
facilities such as collection systems or treatment plants, a minimum scale is required for
reliable and cost-efficient operation. Additionally, the settlement size affects other
boundary conditions (e.g. transport distances). It is assumed that the sanitation systems
shall be installed in an urban settlement with 5000 inhabitants with low to medium
population density (~ 40 inhabitants per ha). This setting can be seen as exemplary for a
small town or a suburban city district with a mixture of single houses and apartment
blocks. Consequently, the results of this study are not directly applicable for smaller or
rural settlements (< 500 inhabitants), because the technical implementation and process
parameters would be different for small-scale systems.
3.3 Reference input flows
The amount and elemental composition of the reference input flows of drinking water,
wastewater constituents and biowaste are defined based on data collected in an
extensive review of relevant literature (cf. annex 12.1). Thus, average values are
generated for each input flow which are supposed to represent a typical composition of
domestic wastewater in Germany. Recently, a similar compilation of literature data was
31
3 Definition of goal and scope
______________________________________________________________________
published to determine key figures for the composition of household wastewater flows
in Germany (Oldenburg et al., 2008).
Daily mass flows of urine, faeces (+ toilet paper), and greywater per person are defined
as 1.5, 0.14, and 80 kg/(pe*d), respectively (Table 2). The average daily excretion of
urine and faeces of an average adult is relatively well-known from medical studies
(Ciba-Geigy, 1977). Greywater volume and quality can vary considerably depending on
its origin and user behaviour (Li et al., 2009). This study assumes an average amount of
80 L of greywater per person and day, which is on the lower range of published values
and reflects a general trend of decreasing water consumption in Germany, mostly due to
water-saving household appliances. The total domestic water demand in Germany
(including toilet flush water) ranges from 90 L to 143 L per person and day, with an
average of 126 L/(pe*d) in 2004 (DESTATIS, 2006).
The average quantity of kitchen and garden biowaste is characterized by large variations
depending e.g. on user behaviour, the structure of the urban area and the annual seasons.
The amount of garden biowaste which is assumed in this study represents the average
potential of organic garden waste in Germany (0.3 kg/pe*d). In addition, there is an
average potential of 0.2 kg/(pe*d) of loppings from municipal greens (Wintzer et al.,
1996). The annual range of the mass flow of mixed urban biowaste is about factor 2, the
range of the volume flow about factor 3 (Fricke, 1990). It is emphasized that the
potential amount of domestic garden biowaste can be higher than assumed in this study.
Table 2: Mass flow of reference input flows
Input flow Mass flow
kg/(pe*a) kg/(pe*d)
Urine 547.5 1.5
Faeces* + toilet paper 51.1 0.14
Toilet flush water** 8760 – 13140
1898 – 8760
24 – 36 (conventional)
5.2 – 24 (separation)
Greywater 29200 80
Kitchen biowaste* 58.4 0.16
Garden biowaste* 109.5 0.3
Sources: annex 12.1
* wet mass
** depending on type of toilet used in scenario
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3 Definition of goal and scope
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The defined elemental composition of the reference input flows in terms of organic
matter, nutrients, salts, and heavy metals is assumed to represent average values for
Germany (Table 3 and Table 4). It should be pointed out that the amount of nutrients in
human excreta is heavily depending on the nutritional diet (Ciba-Geigy, 1977). The
composition of drinking water is estimated (contributing to both toilet wastewater via
flush water and greywater loads) and represents typical concentrations after contact with
pipe materials (Schulz et al., 2008). The dissolution of copper and zinc from pipe
materials is responsible for the elevated concentrations of these heavy metals in
drinking water (here: Cu = 0.16 mg/L, Zn = 0.37 mg/L). Thus, drinking water
contributes a major fraction of the total loads of these metals in the input flows. In
scenarios with wastewater reuse for toilet flushing, the composition of flush water is
calculated according to effluent concentrations of the respective wastewater treatment
process.
Table 3: Average composition of faeces, urine, and greywater
Flow Urine Faeces
(+ toilet paper) Greywater*
Quantity kg/(pe*d)
1.50 0.14 80
Main constituents and nutrients
Dry matter g/(pe*d) 60 45 120
Organic dry matter g/(pe*d) 45 42
COD g/(pe*d) 15 35 60
TOC g/(pe*d) 7 21 18
N total g/(pe*d) 10 1.5 1.3
P total g/(pe*d) 10.5 0.5
K g/(pe*d) 2.6 0.55 2
Na g/(pe*d) 3.5 0.15 6
Ca g/(pe*d) 0.21 1 14
Mg g/(pe*d) 0.12 0.2 3
Cl g/(pe*d) 4.8 0.06 7
S total g/(pe*d) 0.8 0.2 7.5
Heavy metals
Cd mg/(pe*d) 0.0002 0.02 0.2
Cr mg/(pe*d) 0.01 0.02 3
Cu mg/(pe*d) 0.05 1.5 20
Hg mg/(pe*d) 0.0004 0.02 0.02
Ni mg/(pe*d) 0.04 0.2 2
Pb mg/(pe*d) 0.01 0.02 3
Zn mg/(pe*d) 0.25 10 46
* including loads from drinking water (80 L, for composition see Table 4)
Sources: annex 12.1
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3 Definition of goal and scope
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Table 4: Average composition of kitchen and garden biowaste and
drinking water
Flow Kitchen
biowaste Garden
biowaste Drinking water*
Quantity kg/(pe*d) 0.16 0.30 (amount of flush water
depends on scenario)
Main constituents and nutrients
Dry matter % wet mass 50 41 520 mg/L
Organic dry matter g/(pe*d) 36 87
TOC g/(pe*d) 13 45.5 1.10 mg/L
N total g/(pe*d) 0.9 1.4 1.00 mg/L
P total g/(pe*d) 0.2 0.6 0.08 mg/L
K g/(pe*d) 0.6 13.6 7.50 mg/L
Na g/(pe*d) 1.2 0.2 36.00 mg/L
Ca g/(pe*d) 1 4.1 103.00 mg/L
Mg g/(pe*d) 0.22 0.6 10.00 mg/L
Cl g/(pe*d) 3 0.04 18.00 mg/L
S total g/(pe*d) 0.1 0.06 40.50 mg/L
Metals
Cd mg/(pe*d) 0.01 0.05 0.0005 mg/L
Cr mg/(pe*d) 0.5 0.6 0.005 mg/L
Cu mg/(pe*d) 1 2.3 0.16** mg/L
Hg mg/(pe*d) 0.01 0.02 0.0002 mg/L
Ni mg/(pe*d) 0.2 0.5 0.005 mg/L
Pb mg/(pe*d) 0.6 0.6 0.005 mg/L
Zn mg/(pe*d) 7.3 13.5 0.37** mg/L
Sources: annex 12.1
* used as flush water (except wastewater reuse scenarios)
** elevated due to contact with pipe materials
Assumed total amounts of organic matter, nitrogen and phosphorus in wastewater flows
are comparable to population equivalents for municipal wastewater in Germany (Table
5). Population equivalents of DWA are empirical values used for the dimensioning of
wastewater treatment plants and represent a maximum daily load which is not exceeded
in 85% of days (ATV, 2000). They offer a reasonable basis for estimating the validity of
assumed wastewater composition in this study.
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3 Definition of goal and scope
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Table 5: Allocation of organic matter, nitrogen and phosphorus
in wastewater flows compared to ATV population equivalents
Urine Faeces Greywater SUM Population
equivalent* of
ATV A131
COD g/(pe*d) 15 35 60 110 120
N g/(pe*d) 10 1.5 1.3 12.8 11
P g/(pe*d) 1 0.5 0.5 2.0 1.8
* daily load per person that is not exceeded on 85% of days (ATV, 2000)
3.4 System expansion
The primary function of the investigated sanitation systems is defined as the collection
and safe disposal of wastewater and biowaste from households. Besides the primary
function, sanitation systems can provide secondary functions of interest, namely the
supply of secondary fertilizers and energy. Secondary fertilizers can be in the form of
sewage sludge, compost, urine, faeces and related products (e.g. sludge from faeces
digestion).
For a comprehensive comparison of all investigated systems, these secondary
functions have to be included in the environmental assessment to avoid an allocation
problem. A previous LCA study comparing urine separation to conventional wastewater
treatment has revealed a significant influence of secondary functions of fertilizer
substitution on the results of the impact assessment (Lundin et al., 2000).
A suitable procedure to consider secondary functions in LCA is the system expansion
approach which is used in many LCA case studies. There are two different ways to
implement system expansion in an LCA study: One approach broadens the system
boundaries and introduces a new function or product to make the two systems being
compared equal in scope (Figure 6). Another approach subtracts the environmental
burdens of an alternative way of supplying the secondary function from the respective
system (“avoided burden”) (Curran, 2007).
Figure 6 Principle of system expansion by broadening the system boundaries
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3 Definition of goal and scope
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In this study, the relevant scenarios are expanded with additional processes which
deliver equivalent products, i.e. mineral fertilizer and electric energy (Table 6).
Nitrogen, phosphorus, and potassium are taken into account as relevant macronutrients
for agriculture. Regarding the potential of secondary fertilizers to substitute industrially
produced mineral fertilizer, the nutrient availability of sewage sludge, urine, faeces and
related products in relation to that of mineral fertilizer is decisive for calculating the
respective amounts of mineral fertilizer that can be effectively substituted (= fertilizing
equivalents). With the system expansion, all scenarios in this study supply exactly the
same amount of fertilizing equivalents and electric energy.
Table 6: Secondary functions delivered by sanitation systems and
their respective equivalent products in system expansion
Secondary functions of sanitation
systems Equivalent products for system
expansion
Supply of macronutrients N, P, K with organic
fertilizers from:
Sewage sludge
Urine
Composted faeces or biowaste
Residual of faeces digestion
Industrially produced mineral fertilizers
Supply of electric energy from combustion of:
sewage gas from sludge digestion
biogas from digestion of faeces and
biowaste
Electric energy from the grid
Note: thermal energy and fertilizing value of organic carbon are excluded in this study
Exclusions
Two possible secondary functions have explicitly been excluded from system
expansion: a) thermal energy (= heat) and b) organic carbon content of secondary
fertilizers. For both products, the effective benefit is difficult to quantify in terms of
equivalent products, which led to their exclusion in this assessment.
For thermal energy, the utility value of a possible surplus of heat heavily depends on
the heat demand near the place of origin. Losses during heat transmission and transport
can be substantial and depend on local conditions. However, thermal energy can be
utilized within the system, e.g. waste heat from biogas combustion is used for the
hygienisation process of digester substrate.
The beneficial effect of the organic carbon content of secondary fertilizers on soil
fertility and crop growth cannot be precisely quantified. Organic fertilizers are mainly
applied as supplementary fertilizers for soil conditioning. Thus, it is not clear whether
the organic content of secondary fertilizers will in fact substitute other organic
fertilizers (e.g. peat or bark mulch). It should be pointed out that this exclusion may lead
to an underestimation of the beneficial secondary functions of organic fertilizers,
especially in areas where the soil is depleted in organics.
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3 Definition of goal and scope
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3.5 Description of the investigated sanitation scenarios
The sanitation scenarios which are investigated in this study represent different options
for collection and treatment of the various flows of household wastewater and biowaste
within a settlement of 5000 inhabitants. The collection and treatment of stormwater as
well as wastewater from industrial or agricultural activities is excluded in this study.
Reference scenarios
The conventional system with combined collection of all household wastewater flows in
one sewer system and its treatment in a conventional activated sludge (CAS) plant is
taken as a reference scenario. More precisely, three configurations of the conventional
system are taken into account, representing different levels of treatment technology and
options for sludge handling:
- Scenario R: The baseline scenario represents conventional wastewater treatment
with extended removal of nutrients N and P and optimized energy demand
(anaerobic sludge digestion and use of sewage gas). Stabilised sludge is
incinerated and disposed in a landfill. Many wastewater treatment plants in
Germany are operated with extended nutrient removal to minimize nutrient
loads into surface waters (DWA, 2005), even though it is not explicitly required
by legal standards for this dimension (5000 population equivalents = size range
2, in: AbwV, 2004).
- Scenario Rmin: In this scenario, wastewater is only treated to comply with
current legal discharge standards for a wastewater treatment plant of this
dimension, i.e. no extended nutrient removal is required. This scenario reflects
the minimum requirements in terms of effluent quality. Sewage sludge is
stabilised by extended aeration and incinerated without the use of sewage gas,
i.e. the plant is not energetically optimized.
- Scenario Ragri: The third reference scenario applies advanced wastewater
treatment similar to scenario R, but the digester residual from sludge
stabilisation is directly applied in agriculture. This scenario reflects the
maximum nutrient recovery potential in a conventional system.
In all reference scenarios, household biowaste is separately collected before it is
transported to incineration or composting. While incineration is a traditional disposal
route for all types of municipal solid waste, composting represents a specific option for
biowaste handling to recover a valuable product (compost). It is assumed here that 50%
of the garden biowaste and 20% of the kitchen biowaste is incinerated, while the
remaining biowaste is converted into compost.
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3 Definition of goal and scope
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Separation scenarios
In these scenarios, the different wastewater flows are collected and treated separately.
Thus, an enhanced recycling of valuable resources such as nutrients and freshwater is
possible. If wastewater flows are separately collected, different treatment processes are
available for the respective flows of urine, faeces, and greywater. This study
investigates three different options for collection and treatment of urine and faeces (=
toilet wastewater):
- Scenario group V: combined collection of urine and faeces with a vacuum
system and co-digestion with biowaste, application of digester residual in
agriculture
- Scenario group SV: separate collection of undiluted urine and direct application
as fertilizer, vacuum collection of faeces and co-digestion with biowaste,
application of digester residual in agriculture
- Scenario group SC: separate collection of undiluted urine and direct application
as fertilizer, gravity collection of faeces and composting with biowaste,
application of compost in agriculture
Furthermore, there are different options for the treatment of greywater (Li et al., 2009),
which is collected and treated separately in all separation scenarios. This study includes
three options for greywater treatment:
- 1st option: treatment in a conventional activated sludge plant which is operated
as a sequencing batch reactor (= SBR)
- 2nd option: near-natural treatment in a planted soil filter (= constructed
wetlands)
- 3rd option: treatment in a membrane bioreactor (= MBR) with subsequent non-
potable reuse of a part of the effluent for toilet flushing
Each option for greywater treatment is combined with the three scenario groups for
treatment of urine and faeces, resulting in a total of nine separation scenarios. While
scenario group V represents a two-flow system (toilet wastewater and greywater),
scenario groups SV and SC are three-flow systems (undiluted urine, faeces plus flush
water, and greywater).
Including the three reference scenarios, this study investigates twelve different scenarios
for wastewater treatment (Table 7). In the following chapter, each scenario is described
in detail for a precise definition of the processes and flows within the systems which are
relevant for this study.
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3 Definition of goal and scope
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Table 7: Overview of the investigated sanitation scenarios
Scenario Urine Faeces Greywater Biowaste*
R Combined collection and treatment in CAS plant (extended
nutrient removal, sludge digestion and incineration)
Rmin Combined collection and treatment in CAS plant (only
nitrification, aerobic sludge stabilisation and incineration)
Ragri Combined collection and treatment in CAS plant
(extended nutrient removal, sludge digestion and
application in agriculture)
Composting
fertilizer
V1 SBR
V2 Soil filter
V3
Vacuum drainage
and digestion
energy + fertilizer
MBR + reuse
Digestion
energy and
fertilizer
SV1 SBR
SV2 Soil filter
SV3
Separate
collection
fertilizer
Vacuum drainage
and digestion
energy + fertilizer
MBR + reuse
Digestion
energy and
fertilizer
SC1 SBR
SC2 Soil filter
SC3
Separate
collection
fertilizer
Gravity drainage
and composting
fertilizer
MBR + reuse
Composting
fertilizer
* 50% of garden biowaste and 20% of kitchen biowaste is incinerated in each scenario
CAS: conventional activated sludge
SBR: sequencing batch reactor
MBR: membrane bioreactor
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3 Definition of goal and scope
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3.5.1 Reference scenarios (R)
Scenario R
The reference scenario uses modern low-flush toilets (6L flush volume). The different
household wastewater streams are collected in a combined sewer system and drained by
gravity. Wastewater is treated in an activated sludge plant operating with extended
nutrient removal, including the elimination of organic matter, nitrogen (conversion of
dissolved nitrogen to N2 via nitrification and denitrification) and phosphorus (chemical
precipitation with ferric salts) (Figure 7). The excess sludge is stabilised separately by
anaerobic digestion, dewatered and co-incinerated in a municipal waste incineration
plant. Sludge liquor is recycled back to the influent of the plant. Sewage gas is
combusted in a central heat and power (CHP) plant to produce electricity and heat.
Collected biowaste is composted and applied in agriculture.
System expansion:
industrial production of
N/P/K- fertilizers
Kitchen,
bathroom,
washing machine
Low-flush
conventional
toilets
Gravity drainage +
activated sludge plant
with nutrient removal
Anaerobic digestion +
dewatering
Greywater
Faeces, urine +
flush water Biowaste
Sludge
Composting
Discharge
Garden
biowaste
Compost
Agriculture
Incineration
System expansion:
production of
electrical energy
T
T
T
T= Truck transport
Effluent
Sewage gas
CHP plant
Scenario R Scenario Ragri
T
Figure 7: System setup of reference scenarios with advanced treatment (R and Ragri)
Scenario Rmin
This scenario features conventional toilets with normal flush volume (9L), a combined
gravity sewer, and a conventional activated sludge plant for elimination of organics and
ammonia (nitrification = conversion of NH4 into NO3). The sludge is aerobically
stabilised by extended aeration, dewatered and co-incinerated in a municipal waste
incineration plant (Figure 8). Sludge liquor is recycled back to the influent of the plant.
Collected biowaste is composted and applied in agriculture.
40
3 Definition of goal and scope
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System expansion:
industrial production of
N/P/K- fertilizers
Kitchen,
bathroom,
washing machine
Conventional
toilets
Gravity drainage +
activated sludge plant
Aerobic stabilisation +
dewatering
Greywater
Faeces, urine +
flush water Biowaste
Sludge
Composting
Discharge
Garden
biowaste
Compost
Agriculture
Incineration
System expansion:
production of
electrical energy
T
T
T
T
T= Truck transport
Effluent
Figure 8: System setup of reference scenario with minimum treatment (Rmin)
Scenario Ragri
System setup of this scenario is exactly the same as in the baseline scenario R, except
for the sludge disposal. In this scenario, stabilised sludge is dewatered and directly
applied in agriculture as organic fertilizer (Figure 7). Thus, nutrient recycling is
maximised in this scenario for a conventional sanitation system.
3.5.2 Separation scenarios with vacuum collection and digestion of
urine and faeces (V)
In this group of separating scenarios, vacuum toilets are used to drain the mixture of
faeces and urine with small amounts of flush water (0.7 – 1.2 L per flush). Mixed toilet
wastewater is pumped from the vacuum station to a biogas plant, where it is digested
together with household biowaste. Biogas is combusted in a CHP plant to generate
electricity and heat. The residual digester sludge contains large amounts of valuable
nutrients, but also a lot of water. The majority of the nutrients in this sludge derive from
urine and are likely to be lost for recycling purposes with the filtrate if the residual
sludge is dewatered and the filtrate is separated. Hence, it is assumed that this sludge is
directly applied in agriculture without dewatering. This allows a high amount of
nutrients to be recycled, but also creates large volumes of sludge which have to be
transported to the point of application.
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3 Definition of goal and scope
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Scenario V1
Toilet wastewater is drained in a vacuum sewer and treated by anaerobic digestion in a
biogas plant together with household biowaste. The residual sludge is directly applied in
agriculture. Greywater is drained by gravity and treated in a conventional activated
sludge process. This process is operated as a sequencing batch reactor (SBR) with
partial denitrification and chemical P elimination (Figure 9). Excess sludge is
aerobically stabilised, dewatered and incinerated.
Figure 9: System setup of digestion scenario with SBR (V1)
Scenario V2
In this two-flow scenario, greywater is treated in a soil filter after extended settling in a
sedimentation tank (Figure 10). The soil filter is planted with reed to prevent clogging
of the filter and provide additional oxygen for biological processes in the filter bed. The
reed is mowed once each year, and the plants are disposed in the biogas plant. Primary
sludge from sedimentation is incinerated without dewatering.
Scenario V3
Here, greywater is treated in a membrane bioreactor to reach a high quality effluent.
This effluent is partially reused for toilet flushing (Figure 11). Therefore, a separate pipe
network for reused water has to be installed in the settlement. Excess sludge from the
MBR is aerobically stabilised, dewatered and incinerated.
42
3 Definition of goal and scope
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Figure 10: System setup of digestion scenario with soil filter (V2)
Agriculture
Garden biowaste
Vacuum toilets Kitchen,
bathroom,
washing machine
Vacuum
drainage
Biowaste
Faeces, urine
+ flush water
Greywater
Sequencing
batch reactor
Digestion
T= Truck transport
Aerobic stabilisation
+ dewatering
Incineration
Effluent
Discharge
T
Sludge
T
Residual CHP
plant
Biogas
Greywater for reuse
System expansion:
electrical energy
and fertilizer
Figure 11: System setup of digestion scenario with MBR and reuse (V3)
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3 Definition of goal and scope
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3.5.3 Separation scenarios with urine separation, vacuum drainage
and digestion of faeces (SV)
In this scenario group, vacuum separation toilets are applied. These toilets combine the
urine separation technology (Roediger, 2007) with a vacuum system for the transport of
faeces and flush waster. A special valve construction allows the separate collection of
urine which is not diluted with flush water. Although this type of toilets is not yet
available on the market, prototypes have been successfully tested in pilot projects
(Peter-Fröhlich et al., 2007). Depending on user behaviour, it was found that 60-90% of
total urine can be collected (Jönsson, 2001).
Undiluted urine is collected in separation toilets and drained by gravity to large
storage tanks. After oxidative treatment by ozone for the removal of potentially harmful
pharmaceuticals and other micro-pollutants, it is directly applied as liquid fertilizer in
agriculture. Faeces and small amounts of flush water (0.7 – 1.2 L) are drained by a
vacuum system and pumped to a biogas plant, where they are digested together with
household biowaste. Generated biogas is combusted in a CHP plant. The residual
digester sludge still contains valuable nutrients, and it is used as fertilizer in agriculture
after dewatering and aerobic stabilisation (composting).
The toilet flush water separated as sludge liquor of the digester residual is now highly
loaded with organics and nutrients. Therefore, it is decided to treat this concentrate
separately in a biological reactor (activated sludge process) with high efficiencies in
organics and nutrient removal. This is in contrast to scenario group SC (see 3.5.4),
where the toilet flush water is separated from faeces before composting and treated
together with greywater. Thus, the influent load for greywater treatment is considerably
lower in SV scenarios than in SC scenarios.
Scenario SV1
In scenario SV1, greywater is treated in an activated sludge process operated as a
sequencing batch reactor (Figure 12). The process includes nitrification, partial
denitrification, and chemical P elimination. Excess sludge is aerobically stabilised,
dewatered and incinerated.
Scenario SV2
Scenario SV2 includes greywater treatment in a planted soil filter after extended settling
in a sedimentation tank (Figure 13).
Scenario SV3
In scenario SV3, greywater is treated in a membrane bioreactor with high effluent
quality. The effluent is partially reused for toilet flushing (Figure 14).
44
3 Definition of goal and scope
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Storage
Agriculture
Vacuum
separation toilets
Vacuum
drainage
Faeces +
flush water
Sludge
liquor
Urine
Sequencing
batch reactor
Digestion
Treatment
T= Truck transport
T
T
Aerobic
stabilisation +
dewatering
Incineration
Effluent
Discharge
T
Sludge
Dewatering and
composting
T
Residual
SBR
CHP
plant
Biogas
Garden biowaste Kitchen,
bathroom,
washing machine
Biowaste Greywater
System expansion:
electrical energy
and fertilizer
Figure 12: System setup for digestion scenario with urine separation and SBR (SV1)
Storage
Agriculture
Vacuum
separation toilets
Vacuum
drainage
Faeces +
flush water
Sludge
liquor
Urine
Digestion
Treatment
T= Truck transport
T
TDewatering and
composting
T
Residual
SBR
CHP
plant
Biogas
Sedimentation
Soil filter
Effluent
Discharge
Sludge
Reed Incineration
T
T
Greywater
Garden biowaste Kitchen,
bathroom,
washing machine
Biowaste
System expansion:
electrical energy
and fertilizer
Figure 13: System setup of digestion scenario with urine separation and soil filter (SV2)
45
3 Definition of goal and scope
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Storage
Agriculture
Vacuum
separation toilets
Vacuum
drainage
Faeces +
flush water
Sludge
liquor
Urine
Membrane
bioreactor
Digestion
Treatment
T= Truck transport
T
T
Aerobic
stabilisation +
dewatering
Incineration
Effluent
Discharge
T
Sludge
Dewatering and
composting
T
Residual
SBR
CHP
plant
Biogas
Greywater for reuse
Greywater
Garden biowaste Kitchen,
bathroom,
washing machine
Biowaste
System expansion:
electrical energy
and fertilizer
Figure 14: System setup for digestion scenario with urine separation, MBR and reuse (SV3)
3.5.4 Separation scenarios with urine separation and composting of
faeces (SC)
In this scenario group, urine separation toilets are used to separately collect undiluted
urine at the source. Undiluted urine is drained by gravity and collected in tanks, stored,
and treated by ozonation for the elimination of micro-pollutants. Thereafter, it is
transported to farms and directly applied as a multi-nutrient fertilizer in agriculture.
Faeces are drained with flush water (6 L per flush) in a gravity system. After flush water
has been mechanically separated from solids, faecal matter is stabilised in an aerobic
composting process together with biowaste. Whereas compost is used as soil
conditioner in agriculture, faeces filtrate with high volume and low nutrient loads is
pumped to greywater treatment.
Scenario SC1
In the first scenario, greywater and faeces filtrate are treated in an activated sludge plant
operated as a sequencing batch reactor with extended nutrient removal (Figure 15).
Excess sludge is stabilised aerobically, dewatered and incinerated.
46
3 Definition of goal and scope
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Scenario SC2
The second composting scenario includes a soil filter for the treatment of greywater and
faeces filtrate (Figure 16). The process comprises of primary sedimentation, addition of
faeces filtrate, and a planted soil filter which eliminates organics and a part of the
nutrients. Reed from the soil filter is mowed once each year and added to the
composting process. Sludge from greywater sedimentation stage is directly dewatered
and incinerated. An additional stabilisation process for primary sludge is neglected here
due to the small amounts of this sludge.
Storage
Agriculture
Garden biowaste
Gravity separation
toilets
Kitchen,
bathroom,
washing machine
Solid-liquid
separation
Biowaste
Faeces +
flush water Greywater
Filtrate
Compost
Urine
Sequencing
batch reactor
Composting
Faeces
System expansion:
electrical energy
and fertilizer
Treatment
T= Truck transport
T
T
T
T
Aerobic stabilisation
+ dewatering
Incineration
Effluent
Discharge
T
Sludge
Figure 15: System setup of composting scenario with SBR (SC1)
Scenario SC3
The third composting scenario is targeting the reuse of treated greywater for toilet
flushing (Figure 17). Therefore, the mixture of greywater and faeces filtrate is treated in
a membrane bioreactor, which is a combination of an activated sludge process and a
membrane stage for separation of sludge from the effluent. Due to the excellent effluent
quality of the membrane separation step, the effluent can be reused for toilet flushing
without further treatment. It is pumped back to the households via a separate pipe
network and used for toilet flushing.
47
3 Definition of goal and scope
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Storage
Agriculture
Gravity separation
toilets
Solid-liquid
separation
Faeces +
flush water Greywater
Filtrate
Compost
Urine
Sedimentation
Composting
Faeces
System expansion:
electrical energy
and fertilizer
Treatment
T= Truck transport
T
T
T
Soil filter
Effluent
Discharge
Sludge
TReed
TIncineration
T
Garden biowaste Kitchen,
bathroom,
washing machine
T
Biowaste
Figure 16: System setup of composting scenario with soil filter (SC2)
Storage
Agriculture
Gravity separation
toilets
Solid-liquid
separation
Faeces +
flush water
Filtrate
Compost
Urine
Membrane
bioreactor
Composting
Faeces
System expansion:
electrical energy
and fertilizer
Treatment
T= Truck transport
T
T
T
Aerobic stabilisation
+ dewatering
Incineration
Effluent
Discharge
T
Sludge
Greywater for reuse
Greywater
Garden biowaste Kitchen,
bathroom,
washing machine
Biowaste
T
Figure 17: System setup of composting scenario with MBR and reuse (SC3)
48
3 Definition of goal and scope
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3.6 System boundaries
The careful definition of system boundaries is an essential step in LCA methodology
(ISO 14040, 2006). The choice of system boundaries determines the scope of the study
and consequently the validity of its results. Moreover, the scope of a study has a major
impact on the time and effort required for the Life Cycle Inventory. The availability of
appropriate LCI data can be a crucial limit while defining the system boundaries of an
LCA.
In order to make a specific statement about the investigated processes (i.e. the
different sanitation systems) and to keep the LCA study manageable, system boundaries
must be defined that run between (Guinée et al., 1993):
the analysed economic system and the environment
the analysed economic system and other economic systems (allocation problem)
relevant and irrelevant life cycle phases and unit processes
relevant and irrelevant substance and energy flows
considered and not considered geographical regions (local, regional, global)
considered and not considered time periods (period of production, life time of
products, time horizon of emissions, etc.)
In the following chapter, important definitions with regard to the system boundaries of
this study are specified and explained in detail.
3.6.1 System boundaries between the analysed economic system
and the environment
The system boundary between the economic system under investigation and the
environment is usually defined intuitively: Resources from the environment for
consumption within the economic system cross this boundary (“input”), while emissions
from economic processes to water, air, and soil pass the system boundary into the
environment (“output”).
However, processes in agriculture or landfill sites have characteristics that apply to
ecosystems as well as to economic systems (Guinée et al., 2002). Agricultural soil is
primarily used for production processes and thus can be seen as a part of the economic
system. Consequently, emissions to agricultural soil stay within the economy and are
not emitted into the environment. On the other hand, the soil itself is naturally a part of
the environment and has a multitude of functions (“multifunctionality”) including
ecological functions, even if it is mainly used for agricultural purposes. Previous LCA
studies of agricultural processes regard agricultural soil as part of the economic system
right down to the groundwater table (Audsley et al., 1997) or as part of the environment
to include long-term impacts on soil quality (Wegener Sleeswijk et al., 1996).
49
3 Definition of goal and scope
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In this study, agricultural soil is regarded as a part of the environmental system.
However, only heavy metals in fertilizers applied to agricultural soil are treated as
emissions into the environment. The incorporation of nutrients and other substances into
the crops is not further specified, because there is no relevance concerning the interests
of this LCA study. The percentage of nutrient availability to the crop is considered with
regard to the amount of mineral fertilisers that can be substituted by urine or other
secondary fertilisers. Furthermore, primary emissions during fertilizer application
(volatilization of NH3, N2O) are included in the inventory as well. Secondary emissions
by subsequent processes in the soil (e.g. soil erosion, nitrification and denitrification, or
migration of nitrate into the groundwater) are not considered in the inventory. These
effects are difficult to quantify in a consistent way for the different types of fertilizers
and are therefore excluded in this study, argueing that secondary emissions are
comparable between all scenarios.
The landfill deposition of slags and ashes originating from incineration of biowaste or
sewage sludge is not included in the scope of this study. Usually, landfill processes are
considered as a part of the economic system. However, the environmental impacts of
gaseous or leachate emissions from deposition of inert ashes and slags are estimated to
be negligible for the result of this LCA. A major fraction of these incineration residues
is reused today as filling material in road construction (Hirschmann, 1999).
3.6.2 Multi function processes and recycling
Economic systems often imply processes that generate several products or fulfil more
than one function. This fact of multi-function processes applies particularly in the
following cases:
Combined production of several products at the same time (co-products)
Combined waste air, wastewater and solid waste treatment as well as combined
services such as transports in different economic systems
Reuse and recycling processes
In such cases, the associated input and output flows must be allocated between the
functions of interest and other functions on the base of physical or economical
relationships. LCA methodology recommends to avoid allocation, because it is often
regarded as subjective (ISO 14044, 2006). Several procedures are available to avoid an
allocation problem, for example dividing the respective unit processes in suitable sub
processes or including the additional functions within the systems. The latter procedure
is known as system expansion and is the preferred approach in many LCA studies
(Curran, 2007). However, system expansion may lead to a larger and more complicated
model that requires more LCI data.
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3 Definition of goal and scope
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System expansion for secondary functions
In this study, the primary function of all investigated systems is the collection and
disposal of wastewater and biowaste. However, some scenarios deliver secondary
functions such as the supply of organic fertilizers and energy. This allocation problem is
overcome with the approach of system expansion by broadening the system boundaries
(see chapter 3.4).
Allocation of co-products
Co-production applies mainly to energy and fuel production as well as to the production
of caustic soda and chlorine during the supply of auxiliary materials. For these
purposes, pre-allocated datasets from literature and Umberto® are used, which are
widely accepted (e.g. IFU and IFEU, 2004; Boustead, 1998; Boustead, 1999a). In
general, the allocation in these datasets is calculated on the basis of physical
relationships (mass, energy, or molar proportions).
The combined treatment of solid waste in waste incineration plants and the treatment
of wastewater in a municipal wastewater treatment plant are modelled widely on the
basis of physical and chemical relationships. Emissions, residuals, and the subsidiary
material demand of the waste incineration plant are calculated based on the waste
composition and boundary conditions of operation (IFU and IFEU, 2004). For
wastewater treatment, a substance flow model is used that has been specifically
developed for LCA purposes at TU Berlin.
Recycling of construction materials
The recycling or disposal of construction materials after their prospected life cycle is
defined depending on the type of material (Table 8). No recycling or system expansion
is considered for concrete and vitrified clay, although these materials can potentially be
recycled. Recycling of concrete is estimated to require approximately the same amount
of energy that can be substituted with the recycled product (Baitz et al., 2004). Finally,
concrete is often used as substitute for primary gravel, split, or sand in other
applications.
Scrap metals made of steel or cast iron are supposed to be partially recycled, molten
and converted to new metal products. This recycling procedure is taken into account by
quasi-closed loop recycling, adopting actual recycling shares from Germany (BDSV,
2005). Plastic components are assumed to be incinerated for disposal, including the
recovery of feedstock energy. Possible expenditures during recycling (transport, energy
etc) are neglected here.
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Table 8: Modes of disposal and recycling for construction materials
Material Mode of disposal
Concrete Landfill or filling material
Vitrified clay Landfill or filling material
Steel Quasi closed-loop recycling, recycling share: 42% (BDSV, 2005)
Cast iron Quasi closed-loop recycling, recycling share: 88% (BDSV, 2005)
Plastics Incineration + recovery of feedstock energy
3.6.3 Considered life cycle phases and sub-systems
For the definition of considered life cycle phases and sub-systems, the Life Cycle
Inventory is differentiated between processes of system operation and those associated
with the production of capital equipment (infrastructure).
Operation
All relevant processes required for the operation of the analysed sanitation systems are
included in the Life Cycle Inventory. Processes which are identical in all systems can be
excluded without compromising the environmental evaluation. In general, each single
process is linked again with all preceding und succeeding processes which are required
for the production of energy, raw and auxiliary materials, or for the treatment and
disposal of residuals. In particular the following life cycle phases are included:
Domestic: Toilets (urination and defecation including toilet flushing), water
consumption and pollution by personal hygiene and laundry washing;
production of kitchen and garden biowaste
Drinking water supply (energy demand for pumping and delivery)
Conventional wastewater treatment (activated sludge process, including
stabilisation of sewage sludge, dewatering and incineration or application in
agriculture)
Biowaste treatment (composting or co-incineration with municipal waste)
Greywater treatment
Faeces treatment: Dewatering and composting or vacuum drainage and
anaerobic digestion
Urine separation, storage, and treatment by ozonation
Application of secondary fertilizers from urine, faeces, and biowaste
Production and application of mineral fertilizers
Production and supply of electric and thermal energy
Transports
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The inventory does not include inspection and maintenance, human labour or transport
of workers. Although expenditures for maintenance may be significant, no suitable data
is available for quantifying them and determine their environmentally relevant inputs
and outputs.
Finally, drainage and treatment of stormwater, industrial or agricultural wastewater,
and additional groundwater that is infiltrating through pipe leakages is not included in
this study.
Capital equipment
The expenditures for the production of capital equipment (= infrastructure) are often
small in relation to those for the operation of a technical process. Due to the relatively
long lifetime of technical infrastructure, the time-related environmental impacts are
negligible compared to operational flows and emissions. Consequently, the production
of capital equipment is excluded in many LCA studies to minimize the complexity of
the LCI model and the effort for data acquisition (Frischknecht et al., 2007).
However, the role of infrastructure in LCAs of wastewater treatment can be
significant and is controversially discussed in literature. A case study of alternative
sanitation systems in Sweden found that infrastructure had only a small and non-
decisive impact on the environmental comparison (Tillman et al., 1998). Consequently,
infrastructure is neglected in another Swedish LCA case study (Bengtsson et al., 1997).
Other studies reveal a considerable influence of infrastructure in LCAs of wastewater
treatment systems (Zimmermann et al., 1996). A recent review states that the share of
impacts caused by capital goods manufacture is substantial in wastewater treatment,
claiming the inclusion of infrastructure in these types of LCA (Frischknecht et al.,
2007).
The present study includes both the necessary infrastructure and the operation of the
respective sanitation systems in the assessment. The separation systems investigated in
this study are characterized by the use of multiple pipe networks for the different
wastewater flows, presumably leading to higher expenses for the production of pipes
compared to the conventional system. It is therefore decided to include all capital
equipment required for the construction of the different sanitation systems as far as the
respective components are not identical in each system. Capital equipment of the
background system (i.e. energy supply, any industrial production plant, road
construction, etc.) is excluded from the inventory.
For the inventory of the infrastructure, it is assumed that the sanitation systems are
integrated in a new urban development area together with the provision of other
infrastructure (roads, power lines etc). The systems are also integrated inside the
buildings during the initial construction (no rehabilitation of existing systems). The
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following components are considered in the infrastructure inventory (for details cf.
chapter 4.2):
Materials for pipes for in-house and external drainage of combined wastewater,
greywater, urine, and faeces (including inspection manholes)
Excavation of trenches
Conventional wastewater treatment plant
Greywater treatment plants (SBR, soil filter, MBR)
Storage tanks and pipe network for greywater reuse
Urine collection and storage (pumping wells, pumps, storage tanks)
Vacuum system
Digester with stabilisation area for residual sludge and CHP plant
Solid-liquid separators for faeces dewatering
Service buildings for facilities of separation systems
The following infrastructure components of sanitation systems are excluded:
Production of toilets (it is assumed that the associated expenses are roughly
comparable for conventional and separation systems)
Construction of composting plant: Both conventional and separation systems can
include the composting of biowaste. Typically, the scale of a composting plant
depends on the volume to be treated rather than on the mass (Bidlingmaier,
2000). As the expected volume variation of kitchen and garden biowaste is
larger than the additional volume of faeces for composting, the scale of the
composting plant is assumed to be the same in all systems. It is therefore
excluded from the inventory.
Facilities for urine treatment (ozonation plant)
Electric measurement and control systems
Drainage and treatment of stormwater and infiltrating groundwater: This is of
particular relevance with regard to the size of the sewer and the wastewater
treatment plant.
Energy demand for the installation
Human work and transport of workers
Figure 18 illustrates the procedure with regard to the consideration of capital equipment
for sanitation systems and the background system which is not included in the
inventory.
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Figure 18: Consideration of capital equipment for sanitation systems and the background system
Cut-off criteria
In order to keep the complexity of the Life Cycle Inventory and the related substance
flow model within reasonable limits, those processes and subsystems which are
assumed to be not relevant for the environmental assessment are usually excluded from
the inventory. Usually, a general cut-off criterion is defined in an LCA for this purpose.
For example, material or energy flows falling below a certain limit in proportion to the
total mass or energy flow (e.g. 1%) are cut off from further consideration.
In this study, no general cut-off criterion is defined due to the heterogeneity of the
data quality in the LCI. For each process, it is decided separately whether or not an
intermediate product (or waste) flow is linked to its preceding or succeeding processes
according to its estimated environmental relevance and the availability of data.
However, the aggregated share of all substances cut off may not exceed 5% of the total
intermediate product input or output mass or amount of energy.
3.6.4 Considered elementary flows
According to ISO 14044, environmental interventions linked to unit processes are
denoted as elementary flows. Elementary flows can be in the form of resources (e.g.
crude oil), emissions to environmental compartments, or land use. In view of the large
number of chemicals which are industrially produced or generated during combustion
processes, the number of elementary flows potentially released to the environment can
be extremely high. The latest datasets from LCI databases provide a large number of
single substances in their inventory (e.g. > 1000 in Ecoinvent, 2007).
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In this study, only selected elementary flows are considered with regard to the following
criteria:
elementary flows which in particular have a known significance in wastewater
and biowaste management as well as in agricultural processes
elementary flows of general environmental interest
the selected elementary flows should quantify the considered impact categories
in a sufficient way
the respective elementary flows should be known for each process of the system
(at least for the most important processes) in order to provide a symmetrical
database
elementary flows which are considered as possibly relevant but where no
appropriate data is available have to be neglected (e.g. organic micro-pollutants)
Relevant elementary flows are defined before setting up the Life Cycle Inventory (Table
9). The listed elementary flows are assumed to be the most relevant for the ecological
comparison. For the reference input flows of wastewater and biowaste, the inventory
includes the distribution of organic material, nutrients, salts, and heavy metals to water,
air, and soil. It should be again pointed out that organic micro-pollutants are not
included in this assessment due to missing data for their occurrence in household
wastewater and for their environmental relevance.
Datasets for material production, incineration plant, and background processes
(transport, energy, mineral fertilizer) contain more emission flows of organic and
inorganic substances to air (e.g. from combustion processes) and also a demand for
mineral resources.
Table 9: Relevant elementary flows
Emissions to air Emissions to water Emissions to soil Resources in
geological deposits
CO2, CO, CH4
N2O, NH3, NOx
SOx
HCl, HF
PM 10, particles
NMVOC
Benzene, BaP, PAH
Formaldehyde
PCDD / PCDF (TE)
As, Be, Pb, Cd,
Cr, Co, Cu, Ni,
Hg, Se, Th, Zn
C, N, K, P, S
in respective species
As, Cd, Cr, Cu,
Hg, Ni, Pb, Zn
Ca, Mg, Na, Cl
AOX
C, N, K, P, S
in respective species
As, Cd, Cr, Cu,
Hg, Ni, Pb, Zn
Fe, U
Ca, Mg, Na, Cl
AOX
Lignite, Hard coal
Uranium
Natural gas
Crude oil
Phosphorus
Lead
Iron ore
Copper ore
Zinc
Bauxite, Sulphur
Nickel ore
Potash, Gypsum
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Due to the simultaneous use of readily available datasets from LCI databases for
background processes and own process models for the sanitation systems, the symmetry
of the LCI data is not completely consistent. Datasets for background processes
generate up to 130 different elementary flows of emissions (e.g. during the production
of electric energy), but most of them are not decisive for the impact assessment and are
neglected in the further documentation of this study. Material flows in the system
(wastewater, biowaste etc) and related process emissions from wastewater treatment are
decomposed into their elementary composition as far as suitable data is available (Table
10).
Table 10: Material flows and their elementary composition
Material flows in the system Elementary composition
Reference input flows
(Combined wastewater or
urine, faeces, greywater,
biowaste)
C, N, K, P, S
Ca, Mg, Na, Cl
Cd, Cr, Cu, Hg, Ni, Pb, Zn
AOX
Mineral fertilizers
N, K, P, S,
Ca,Cl
As, Cd, Cr, Cu, Hg, Ni, Pb, U, Zn
Precipitation chemicals Fe, S, Cl
Construction material No elementary decomposition is considered
Electric, thermal
and mechanical energy
3.6.5 Geographical and temporal scope
The present study is a prospective LCA, because the separation systems under
investigation have not been realized in large scale. However, the process design and
technology of the different scenarios is available at the time of this study or will be
available in the near future. For definition purposes, it is assumed that all sanitation
systems are operated in Germany in 2007.
Geographical and temporal scope: Germany 2007
The process technology reflects available state-of-the-art technology of the year 2007.
Still, several assumptions have been adopted for process performance and emission
data, because respective data has not been generated or published.
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The background data of this study (energy, transport, materials) mainly refers to
conditions in Germany during 1990 – 2000 as available in the UMBERTO® database
(IFU and IFEU, 2005). Updates of datasets are identified accordingly in the inventory.
The international origin of some materials and resources (crude oil, phosphate rock,
etc.) is taken into account by local production conditions and respective transport
distances.
Impact assessment
Environmental interventions are aggregated without accounting for time and exact
location of emissions. With regard to impact assessment, spatial differentiation on a
regional level (Germany) is adopted if possible. The indicators used in the impact
assessment mostly assume a defined time horizon for the impacts of emissions (Guinée
et al., 2002). For global warming potential, a time horizon of 100 years is considered.
Validity of the results
This study reflects conditions in Germany concerning available process technology and
performance, spatial differentiation in impact assessment, and interpretation of LCIA
results (normalisation and weighting). The outcomes of this study may also be valid for
other geographical regions with similar technological development (e.g. industrialized
countries of Western Europe). However, regional conditions such as the local power
mix or legal requirements for effluent discharge (= discharge standards, disinfection,
micro-pollutants, recycling quotas etc.) can have a significant impact on the results of
the assessment.
3.7 Data quality of Life Cycle Inventory
General remarks
In general, the quality and significance of LCA results highly depends on the quality of
data that is used for the inventory analysis. At best, the inventory is based on consistent
measurement data from an existing system which resembles the analysed scenario in
system layout, dimension, and location and time of operation (e.g. a conventional
sanitation system for combined sewage without stormwater in a settlement with 5,000
inhabitants in Germany in the year 2007). However, this optimum data quality cannot
be reached in most cases due to lack of adequate time and financial means for data
collection. Sometimes, adequate data with high quality may be existent, but it is
confidential or simply not published by the operator. Hence, there is always a trade-off
between the data quality of the LCI and the required time and effort for the study.
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Frequently, Life Cycle Inventories are based on data from pilot studies, databases or
literature, which have to be readjusted (e.g. recalculation or upscaling) to fit into the
specific boundary conditions of the LCA study. Where no appropriate data is available,
qualified assumptions have to be made to allow for a coherent inventory analysis. Thus,
the assessment of data quality in an LCA study is an important indicator for its
representativeness and hence for the validity and significance of its conclusions.
Data quality of this study
The present study is designed to assess the environmental impacts of different sanitation
systems with LCA methodology. Modelling the construction and operation of these
systems for the Life Cycle Inventory requires an extensive collection and review of
data. To keep the study effort within reasonable limits, there has to be a compromise
between the level of detail and data quality on the one hand and the effort and time
required for data collection on the other hand.
Moreover, separation systems have not yet been implemented in a larger settlement in
Germany. Thus, high quality primary data of full-scale plants is not available for most
of the processes of separation scenarios. Secondary data from pilot-scale tests or
laboratory experiments has to be used to generate a defensible basis for the LCI.
Upscaling this data for a relatively large settlement is a delicate task and requires
reasonable assumptions. The prospective nature of this study – assessing systems that
are not in operation yet – can justify the use of data which may not be representative for
large-scale systems. However, the origin and the quality of LCI data have to be clearly
identified to show the limitations of this study. The following chapter gives a short
overview about data quality of the LCI, necessary assumptions and methodological
limitations of this study.
3.7.1 Data quality of the present LCI
LCI data of core processes (Table 11)
The composition of the different wastewater flows is determined from an extensive
review of respective literature. Although the specific composition can vary depending
on local user pattern and habits, the chosen values are thought to represent a reasonable
average of the German population. Process data for the conventional sanitation system
is based on an elaborate LCA model of an activated sludge plant. The process model is
originally designed for larger plants (> 10000 inhabitant equivalents), but is adjusted to
the present dimension in terms of elimination ratios and energy demand.
The processes of separation systems are mainly based on experiences from pilot
projects, pilot plants, and laboratory experiments. Pilot projects include Berlin-
Stahnsdorf (~ 35 inhabitants), Lübeck-Flintenbreite (108), Freiburg-Vauban (30), and
several Swedish sites (160+160). Results from these pilot studies have to be adapted to
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the conditions of this study. However, data for urine separation, storage and application
as well as for greywater treatment in soil filters and membrane bioreactor is regarded as
appropriate to reflect conditions in a larger settlement. Ozonation of urine and co-
digestion of faeces and biowaste have only been investigated in small-scale laboratory
experiments, so that LCA data for these processes may be different in full-scale
applications. Data for the digestion process is further adopted from an LCA study of
biowaste treatment, assuming comparable energy demand and transformations of
elemental composition.
The processes of faeces composting with biowaste and the aerobic stabilisation of
digester residual from faeces co-digestion have not been investigated in terms of
emission data, so that LCI data from biowaste treatment is used here. The process of
faeces dewatering prior to composting has not been technically realized in a satisfactory
way, so that the efficiency of the process and the quality of the faeces filtrate have to be
estimated.
LCI data of background processes (Table 12)
Data for the supply of energy and transport expenditures is adopted from approved
databases. The electrical energy data is updated with national data of the energy mix in
Germany in 2003. Transport distances in the system are heavily depending on local
conditions, so that reasonable estimates are used and their influence is quantified in
sensitivity analysis. The construction inventory is set up with a high level of detail in
cooperation with experts for the design of sanitation systems. LCI data for the supply of
construction materials is calculated with a combination of literature data and datasets
from Umberto®. The LCI datasets for the production of mineral fertilizers are relatively
outdated, but more recent comprehensive datasets have been unaccessible for this study.
Heavy metal contents of mineral fertilizers is based on information of 1992 and should
be updated (the effect of more recent, but not representative data is calculated in
sensitivity analysis). Emissions during fertilizer application are estimated according to
emission inventories for the European agriculture.
3.7.2 Important assumptions and limitations of this study
General limitations
LCA considers only loads of emissions, without quantifying the concentration of
pollutants and without differentiation of time and location of the emission.
Particularly with regard to the evaluation of toxic impacts on humans and the
environment, this is a crucial limitation.
Sum parameters (COD, NMVOC, etc.) allow only a restricted evaluation of the
ecological significance of emissions
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61
Specific limitations
Potentially harmful organic micropollutants (pharmaceuticals, etc.) in human
excreta are not evaluated due to missing data of occurrence and toxicity
The beneficial effects of organic carbon in compost (e.g. for soil fertility) are not
quantified here and are neglected regarding fertilizer substitution. This
constraint may lead to an underestimation of the fertilizer substitution potential
of separation systems.
The migration of nitrate into the groundwater and associated impacts are not
considered. It is assumed that mineral and secondary fertilizers exhibit
comparable leaching rates for nitrate
The constructional design of wastewater systems, the operational expenses and
the associated environmental impacts depend heavily on local conditions.
Emissions linked with fertilising also depend strongly on several variable
boundary conditions (atmospheric conditions, dilution of fertilisers, technique
and agricultural machines used, etc.).
Only a limited set of environmental interventions and impact categories are
included (e.g. no odour exposure) because of lack of data and missing symmetry
of data
The results are strongly influenced by the system expansion with the production
of mineral fertilizers. However, the LCI datasets for fertilizer production and
heavy metal content are relatively old
Table 11: Origin and quality of inventory data for core processes
Processes Main data sources Data origin Reference
period Data quality Remarks
Composition of
wastewater flows Average values from literature Literature 1995-2000 High Literature reviews and
case studies
General operational
parameters*
Peter-Fröhlich et al., 2007
Otterwasser, 2005 Pilot projects 2003-2006 High ~ 35 inhabitants
~ 108 inhabitants
Activated sludge
plant/SBR TU Berlin Literature
+ national surveys 2003 High LCA model by TU Berlin
Membrane
bioreactor Pinnekamp and Friedrich, 2006
Peter-Fröhlich et al., 2007
Review
Pilot plant
2000-2006
2006
Moderate
High
MBR characteristics
Treatment of greywater
Soil filter Peter-Fröhlich et al., 2007
Otterwasser, 2005 Pilot plants 2003-2006 High ~ 35 inhabitants
~ 108 inhabitants
Incineration IFU and IFEU, 2004 + literature Operational data of
full-scale plants 1990-2000 Moderate Waste incineration plant
modified for sludge incin.
Composting Vogt et al., 2002 LCA study 2000 Low LCA study of biowaste
composting
Faeces co-digestion Vogt et al., 2002
Otterwasser, 2005 + literature
LCA study
Lab experiments
2000
2003-2007
Low
Moderate
Biowaste digestion
Blackwater digestion
Urine separation Stockholm Vatten, 2000
Peter-Fröhlich et al., 2007 Pilot projects 1995-2000
2006
High
Moderate
~ 320 inhabitants
~ 35 inhabitants
Urine treatment Stockholm Vatten, 2000
Escher et al., 2006
Pilot experiments
Lab experiments
1995-2000
2006
High
Moderate
Storage
Ozonation
Urine application Stockholm Vatten, 2000
Peter-Fröhlich et al., 2007
Field tests
Field tests
1995-2000
2006
High
High
Availability of urine-N
+ N emissions
* flush water demand for toilets, energy demand for vacuum system, faeces dewatering etc
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Table 12: Origin and quality of inventory data for background processes
Datasets Main data sources Data origin Reference
period Data quality Remarks
Energy electrical
Fritsche et al., 2001
Frischknecht et al., 1996
BMWi, 2005
Database
LCI
National data
1990 + 1998
1995
2003
High
Moderate
Moderate
Power plant technology
Some emission data
Energy mix
Energy thermal Fritsche et al., 2001 Database 1990 Low Power plant technology
Transport by truck TREMOD (Knörr et al., 1997) Database 1996 High Emission model
Transport distances Assumptions based on literature LCA studies 1997-2000 Moderate Dependent on local
conditions
Materials
plastics Boustead, 1999a LCI 1995 Low APME data (old)
concrete Frischknecht et al., 1996 + literature LCI 1995 + 2002 Moderate Updated LCI
limestone Patyk and Reinhardt, 1997 LCI 1995 Moderate
metals Frischknecht et al., 1996 + literature LCI 1990-2000 Low Updated LCI (old)
flocculant Manufacturer information + literature Literature 2000 Moderate
Construction inventory Peter-Fröhlich et al., 2007 Consultants 2006 High Exemplary system layout
Industrial fertilizer
production
Patyk and Reinhardt, 1997
Gaillard et al., 1997
Boysen, 1992
Drescher-Hartung et al., 2001
LCI
LCI
Literature
Market data
1990-1995
1992
1992
1998/99
Moderate
Moderate
Low
Moderate
Resources + emissions
Water emissions
Heavy metal contents
Market shares
Fertilizer application EMEP/CORINAIR, 2004
Borken et al., 1999
Review
LCI
1992
1995
Moderate
Moderate
Emission inventory
Tractor emissions
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3.8 Life Cycle Impact Assessment
The procedure of Life Cycle Impact Assessment (LCIA) aims at connecting the results
of the Life Cycle Inventory (e.g. extraction of resources, emissions) to their potential
environmental impacts on the basis of impact pathways. Impact pathways consist of
linked environmental processes, and they express the causal chain of subsequent effects
originating from an emission or extraction. It has to be kept in mind that LCIA cannot
determine whether actual effects occur, but it delivers information about potential
environmental impacts or hazards.
Figure 19: Elements of Life Cycle Impact Assessment (ISO 14040, 2006)
In general, LCIA consists of the following steps (ISO 14044, 2006) (Figure 19):
1. Selection of LCIA methodology: Suitable impact categories, related indicators
and characterization models have to be chosen in relation to the goal and scope
of the study. This can be an iterative process.
2. Classification: LCI results are assigned to the impact categories for which they
are relevant.
3. Characterization: Extractions and emissions are characterized by scientifically
derived factors. Aggregation of all relevant LCI flows multiplied with their
characterization factors results in the overall category indicators.
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4. Normalization (optional): In order to gain a better understanding of the relative
size and importance of a category indicator result, a normalization step can
optionally be integrated. Each indicator is calculated in relation to reference
information (e.g. total impact in a reference area on a per capita basis) as a
benchmark.
5. Grouping and weighting (optional): Indicators can be grouped according to
the goal and scope of the study, based either on a nominal basis (sorting) or on
value-choices (ranking). Weighting includes the conversion of indicator results
of different impact categories by using numerical factors based on value-
choices. These weighted indicators can be evaluated individually or aggregated
across impact categories.
Finally, the LCIA results are evaluated and interpreted with regard to the goal and scope
of the study. This includes the identification of significant issues or system parameters
based on the LCI and LCIA results, and an evaluation of completeness, sensitivity, and
consistency of the study. With a sensitivity analysis for the LCI and LCIA phase, the
reliability of the final results can be assessed by determining how they are affected by
uncertainties in LCI data, LCIA methodology, or other relevant parts of the study.
Ultimately, conclusions from the LCA study can be drawn while identifying possible
limitations of the study and making recommendations for the audience.
3.8.1 Selection of LCIA methodology
A number of different methodologies for Life Cycle Impact Assessment have been
developed in recent years according to the requirements of ISO 14040/44. These LCIA
methods include among others the CML guide (Guinée et al., 2002), Ecoindicator 99
(Goedkoop and Spriensma, 2000), EDIP (Hauschild and Potting, 2003), IMPACT
2002+ (Jolliet et al., 2003a), TRACI (Bare et al., 2003) and LUCAS (Toffoletto et al.,
2007). In general, Life Cycle Impact Assessment methods can be grouped into two
families (Jolliet et al., 2004):
A) Classical impact assessment methods that stop quantitative modelling before the
end of the impact pathways and link LCI results to so-defined midpoint
categories (“midpoint-oriented”)
B) Damage-oriented methods which aim at LCA outcomes that are more easily
interpretable fur further weighting, by modelling the cause-effect chain up to
the environmental damage, i.e. the damage to human health, ecosystem quality
etc. (“endpoint- or damage-oriented”)
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The difference between these types of LCIA lies in the modelling depth of the cause-
effect chain (Figure 20). Both methods have specific benefits and drawbacks, and the
choice for a certain LCIA method depends on the specific goal and scope of the LCA
study. Recently, a new LCIA method providing midpoint and endpoint characterization
factors has been published (Goedkoop et al., 2009), combining methodologies of CML
and Ecoindicator. Thus, both modelling depths are available in the same methodological
framework.
Results of Life Cycle
Inventory
Midpoint
categories
Environmental
damage
Emission of 100 kg
CO2-equivalents Climate
change Human health
Ecosystems
MIDPOINT-ORIENTED
DAMAGE-ORIENTED
Impact pathway of environmental impacts
Modelling depth of Life Cycle Impact Assessment methods
e.g.
Figure 20: General structure of methods for Life Cycle Impact Assessment
For the impact assessment in this study, a well-established midpoint-oriented approach
is used based on the methodological LCA guide developed at the Centrum voor
Milieukunde at Leiden University (CML method, Guinée et al., 2002). The
environmental impacts of the respective sanitation systems can be compared regarding
each impact category for itself. The results are more transparent and comprehensible. A
damage-oriented approach would produce results which are easier to compare as there
are usually only a small number of endpoint categories, but the second step of impact
modelling (from midpoint to endpoint categories) would lead to higher uncertainties of
the results. Furthermore, the evaluation of differences in endpoint categories between
the compared sanitation systems is more difficult, as the cause of the difference is not
necessarily traceable.
The focus of the present study is not to give a conclusive answer which sanitation
system has the lowest total environmental impacts, but to identify the benefits and
drawbacks of each system by a systematic analysis of the specific impacts. Therefore, a
midpoint-oriented LCIA approach is more appropriate for the specific goals of this
study.
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Impact categories and category indicators
The CML method distinguishes three sets of impact categories, depending on the
environmental relevance in relation to LCA and the availability of adequate
characterization methods (Guinée et al., 2002):
1) Group A comprises the baseline impact categories which are typically included
in many LCA studies
2) Group B comprises study-specific impact categories which may be included
depending on the goal and scope of the study and whether appropriate data are
available
3) Group C comprises several categories for which no baseline characterization
method is available and which require further research and elaboration
It is decided that only baseline impact categories (Group A) are considered in this LCA
study. These categories represent the most important environmental impacts of
sanitation systems and are mainly based on a well-established methodology. The
specific categories of Group B and C are either not applicable in the context of
sanitation systems (e.g. impacts of ionising radiation, noise, waste heat, desiccation etc)
or the required LCI data is inconsistent or not available (loss of life support function and
biodiversity due to land use, odour etc).
The CML method includes 11 baseline impact categories, of which 7 are applied in
this LCA study (Table 13). Four categories have been excluded, basically because the
relevant inventory data for these categories is not available or largely inconsistent in the
LCI (land use, stratospheric ozone depletion, photo-oxidant formation). For ecotoxicity,
it is decided to reduce the impact assessment to the effects on terrestrial and freshwater
aquatic ecosystems (excluding marine ecosystems and sediments).
The baseline impact categories of the CML method are complemented by a specific
impact category related to the demand of non-renewable energy resources (VDI, 1997).
The rising demand for finite energy resources is a major concern for society today, and
precise information on this sensitive issue should be included in each assessment of
sustainability.
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Table 13: Categories for Life Cycle Impact Assessment
Impact category Description of the environmental impact
Resource-related
Demand of non-renewable
energy sources
consumption of finite fuels for energy production, i.e.
fossil and nuclear fuels
Depletion of abiotic resources
consumption of finite non-living natural resources,
including fossil fuels and mineral resources (ores
etc)
Emission-related
Climate change impact of human emissions on the radiative forcing
(i.e. heat radiation absorption) of the atmosphere
Acidification release of protons in surface waters and soil due to
oxidation and hydrolysis of atmospheric gases
Eutrophication
excessive levels of macronutrients (nitrogen and
phosphorus) and degradable organic carbon in
aquatic and terrestrial ecosystems, causing oxygen
deficiency through elevated biomass production and
decomposition
Human toxicity impacts of toxic substances present in the
environment on human health.
Freshwater aquatic ecotoxicity impacts of toxic substances on freshwater aquatic
ecosystems
Terrestrial ecotoxicity impacts of toxic substances on terrestrial
ecosystems
3.8.2 Classification
In a next step, the emissions and extractions from the Life Cycle Inventory are
attributed to the impact categories to which they are contributing (Table 14). The LCI
data in this study is heterogeneous in quality and in the number of considered material
flows. Whereas LCI datasets for the background system can comprise more than 100
different input and output flows (e.g. supply of electric energy, steel production etc), the
inventory for the core processes is limited to the elementary flows. For reasons of
clarity, the classification of emissions and extractions is limited to the substance flows
which are expected to be relevant for the impact assessment. The contribution of
substance flows which are not listed in Table 14 to the respective impact categories is
estimated to be < 0.1% of the total impact category.
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Table 14: Classification of emissions and extractions to their related
impact categories
Impact category Relevant emissions/resources
(R: resources, W: emission to water, A: emission to air, S:
emission to soil)
Depletion of abiotic
resources
(R): lignite, hard coal, natural gas, crude oil, uranium, raw
phosphate, lead, iron/copper/nickel/chromium ore,
zinc, bauxite, sulphur, potash
Demand of non-renewable
energy sources
(R): all forms of primary energy from fossil and nuclear
resources
Climate change (A): CO2, CH4, N2O
Acidification (A): NH3, NOx, SO2, HCl, HF
Eutrophication (W): P species, N species, COD/TOC
(A): NH3, NOx
Human toxicity
(W): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn, F
(A): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn, NH3, NOx, SO2, HCl,
HF, particles, PM10, benzene, CH2O, BaP, PAH,
PCDD/PCDF
(S): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn
Aquatic Ecotoxicity
Terrestrial Ecotoxicity
(W): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn, F
(A): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn, HF, benzene,
formaldehyde, BaP, PAH, PCDD/PCDF
(S): Cd, CrIII+IV, Cu, Hg, Ni, Pb, Zn
Note: listed are only relevant emission flows (> 0.1% contribution to impact category)
3.8.3 Characterization
All relevant emissions or extractions from the Life Cycle Inventory are characterized by
scientifically derived factors (“characterization factors”) which describe the respective
environmental impact in relation to a fixed reference, e.g. the emission of a certain
reference substance with a known effect in this category. Thus, all environmental
impacts in a certain impact category can be aggregated to one indicator score with a
common unit.
For the impact assessment, a set of seven indicators from CML has been selected
together with the cumulative energy demand of VDI (Table 15). In sensitivity analysis,
four indicators from EDIP and IMPACT methodology for eutrophication and
ecotoxicity are calculated and compared to the baseline indicators to reveal possible
influences of the indicator choice on the results.
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Table 15: Midpoint indicators for Life Cycle Impact Assessment
Indicator Abbr (Unit) Remarks LCIA
method
Resource-related indicators
Cumulative energy demand CED (MJ) fossil + nuclear fuels VDI
Abiotic depletion potential ADP (kg Sb-eq) CML
Emission-related indicators
Global warming potential GWP (kg CO2-eq) factors for GWP100a
(IPCC, 2001) CML
Acidification potential AP (kg SO2-eq) regional factors for
NH3/NOx/SO2 in air CML
Eutrophication potential EP (kg PO4-eq) regional factors for
NH3/NOx in air CML
Human toxicity potential HTP (kg DCB-eq) CML
Freshwater aquatic
ecotoxicity potential FAETP (kg DCB-eq) CML
Terrestrial ecotoxicity
potential TETP (kg DCB-eq)
fate and
exposure
included
CML
Indicators for sensitivity analysis
Terrestrial eutrophication TEU (m² UES) regional factors for
NH3/NOx in air EDIP
Aquatic eutrophication of
inland waters AEU (kg NO3-eq) regional factors for N
and P in water EDIP
Aquatic ecotoxicity AET (kg TEG) fate and exposure
included IMPACT
Terrestrial ecotoxicity TET (kg TEG) fate and exposure
included IMPACT
DCB: 1,4-dichlorobenzene, TEG: triethylene glycol, UES: unprotected ecosystem
Sources: Guinée et al., 2002 (CML); Hauschild and Potting, 2003 (EDIP); Jolliet et al.,
2003a (IMPACT)
The calculation of characterization factors is based on distinct scientific models for the
different indicators. The following part describes in short the underlying scientific
models for the indicators applied in this study. The characterization factors which have
been used for the impact assessment are listed for each indicator in annex 12.2.
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Fate and exposure
In general, emissions into the environment can cause impacts on a local, regional, or
global scale. While global impacts are usually not dependent on the point of emission,
local and regional impacts can vary between different media, different regions, and
different ecosystems. Local impacts are determined by the sensitivity of the local
environment and local concentrations, which depend on the environmental fate of the
substance and the quantity of emissions. Therefore, the impact assessment of emissions
causing local and regional effects should take the fate of the substances into account by
adding a factor for fate and exposure to the calculation of the category indicator:
mmm
ii i
i
SFEM
m
with S = category indicator
m = medium, i = substance
F = fate and exposure factor (≤ 1)
E = effect factor
F*E = characterization factor
M = mass emitted
However, the calculation of consistent factors for fate and exposure and effect is
dependent on the availability of appropriate data from local or regional ecosystems and
adequate scientific models for transport, fate and effect of the emissions. For the
majority of indicators in this study, these factors are available and are thus applied if
possible. Other indicators are still lacking consistent models, so that maximum
characterization factors have to be used. Thus, these factors represent a “maximum”
potential of environmental impact without consideration of local or regional conditions.
Resource-related indicators
Abiotic depletion potential (ADP)
The calculation of this indicator relates the demand for each specific resource to the
reference substance of antimony (Sb). The characterization factors are based on the ratio
of the ultimate reserve of each resource and its annual global de-accumulation, relative
to those for antimony (Guinée et al., 2002). It has to be noted that exact data of reserve
and consumption is subjected to a considerable level of uncertainty for many resources
(exploration of new deposits, cost effectiveness of exploitation etc).
Cumulative energy demand (CED)
This indicator directly quantifies the amount of primary energy which correlates to the
amount of fossil and nuclear fuels that are consumed for a particular process (VDI,
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1997). It is a well-established indicator which has been determined for a large number
of products and services, thus making it a simple and effective measure for the total
energy demand. It has been shown that cumulative fossil energy demand can be a good
screening indicator for the overall environmental performance of certain products or
systems (Huijbregts et al., 2006).
Emission-related indicators
Global warming potential (GWP)
This indicator quantifies the potential of greenhouse gases to contribute to global
warming. It is related to CO2 as a reference substance. The characterisation factors are
based on an elaborate model developed by the Intergovernmental Panel on Climate
Change (IPCC, 2001) for a time horizon of 100 years. CO2 emissions from renewable
fuels (e.g. from biogas combustion) are not accounted for this indicator, neither are
gases with indirect effects on climate change.
Acidification potential (AP)
Factors for acidification describe the potential of the emitted gases to form H+ ions,
related to the reference substance of SO2. The sensitivity of the region in which the
acidifying substances are emitted and the subsequent fate are considered for the three
main acidifying air pollutants (NH3, NOx, SO2) with a dispersion model for long-range
transport of air pollutants (EMEP) and the acidification model RAINS-LCA
(Huijbregts, 1999a). The effect factors are based on a ratio of deposition and critical
load, taking into account only emissions above a specific threshold (baseline emissions:
1995) and Germany as the area of emission.
Eutrophication potential (EP)
This indicator describes the eutrophication of aquatic and terrestrial ecosystems due to
aquatic emissions of nitrogen, phosphorus, and degradable organic carbon and air
emissions of NH3 and NOx. Usually, phosphorus is the limiting macronutrient in
freshwater ecosystems, while nitrogen is limiting in marine and terrestrial ecosystems.
The effect factors for eutrophication are generally based on the Redfield ratio which
represents the average chemical composition of aquatic phytoplankton (C : N : P = 106 :
16 : 1 on a molar basis). Hence, they should be seen as a highest potential contribution
to biomass growth rather than an effective effect on biomass growth in the respective
ecosystems. While the fate of aquatic emissions is not taken into account (fate factor =
1), site-dependent characterisation factors for air emissions of NH3 and NOx are
calculated for Germany with the RAINS-LCA model (Huijbregts, 1999a). The reference
substance for the characterization factors is PO4.
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Human toxicity potential (HTP)
Characterization factors for human toxicity have been calculated including degradation
of substances and intermedia transport with the multi-media fate, exposure and effect
model USES-LCA (Huijbregts, 1999b). This model includes five spatial scales, six
environmental compartments, and six exposure routes. The factors are related to 1,4-
dichlorobenzene as a reference substance, considering a global scale and an infinite time
horizon (HTPglobal, ∞). The selection of an infinite time horizon results in a high
influence of persistent heavy metals on this indicator, while degradable organic
chemicals are less dominant.
Terrestrial and freshwater aquatic ecotoxicity potential (TETP, FAETP)
The calculation of characterization factors for these indicators is basically similar to that
described for the human toxicity potential. Degradation of substances and intermedia
transport is considered using the multi-media fate, exposure and effect model USES-
LCA (Huijbregts, 1999b). The characterization factors relate to 1,4-dichlorobenzene as
a reference substance. The effect factors are calculated from the ratio of Predicted
Environmental Concentration (PEC) and Predicted No Effect Concentration (PNEC) for
aquatic and terrestrial ecosystems (= risk characterization factor).
Indicators for sensitivity analysis
Terrestrial eutrophication (TEU)
This indicator calculates the impact of nitrogen gases NH3 and NOx on the
eutrophication of terrestrial ecosystems. Critical loads, fate and exposure are modelled
based on the RAINS model for emissions in Germany and the predicted emission levels
of the year 2010 (Hauschild and Potting, 2003). The impact is expressed as the area of
terrestrial ecosystem that exceeds the critical load (m² of unprotected ecosystem or
UES).
Aquatic eutrophication of inland waters (AEU)
This indicator quantifies the impact of nitrogen and phosphorus emissions on the
eutrophication of aquatic ecosystems of inland waters (rivers and lakes). The influence
of degradable organic carbon and all air emissions are excluded in this method. Fate and
exposure of all relevant emissions to water is modelled in high spatial resolution with
the CARMEN model (Hauschild and Potting, 2003). Characterization factors relate to
NO3 as a reference substance and Germany as the place of emission and impact. The
method is designed to include the effect of nitrogen and phosphorus input on
agricultural soil and subsequent leaching/run-off in aquatic ecosystems. However, the
contribution of nutrient application in agriculture is excluded here for reasons of
consistency to the other eutrophication indicators. Furthermore, the impact of
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agricultural emissions would be comparable for all investigated scenarios as equal
amounts of nutrients are applied in each scenario by definition.
Terrestrial and aquatic ecotoxicity (TET, AET)
This method for the assessment of ecotoxicity is based on a different approach. The fate
factor is defined as the residence time of a substance in a “global” freshwater or
terrestrial ecosystem divided by a dilution factor (Jolliet et al., 2003a). The overall
residence time is determined by modelling volatilization, sedimentation, and
degradation processes. Intermedia transfer is included by fractions transferred between
air, soil, and water. The effect factor is calculated as the inverse PNEC value. The result
can be interpreted as an area polluted up to the PNEC over a certain time determined by
the fate of the substance (“critical surface time”). For communication purposes, the
impacts are related to triethylene glycol as a reference substance. This method was
originally developed for non-polar organics and has been adapted to include metals.
Review of toxicity assessment in LCIA
The characterization of toxic impacts on human health or ecosystems is a complex task.
Toxic effects of emissions into the environment are dependent on the fate of the
substance, threshold phenomena, the exposure time (acute vs. chronic) and pathways,
and potential cumulative effects for mixtures of toxic substances. For ecotoxicity, the
impact of toxic substances further depends on the affected species and characteristics of
the ecosystem.
Hence, the modelling of toxic impacts for LCIA is still under development. Present
state-of-the-art models take into account fate and exposure and have different
approaches to calculate effect factors. However, the calculated characterization factors
for human and ecotoxicity can still be affected with high uncertainty. For example,
characterization factors of the CML method which have been calculated for different
time horizons (20/100/500a or infinite) show differences of up to 6.5 orders of
magnitude for some toxicity potentials of heavy metals (Huijbregts, 2000). This is
particularly attributed to the persistence of heavy metals and the weak modelling of
their speciation and intermedia transport without spatial differentiation of the
environmental conditions.
The problem of a realistic assessment of toxic impacts in LCIA is intensively
discussed in the international LCA community (e.g. Guinee et al., 2004). A major task
is seen in calculating improved characterization factors for heavy metal emissions by
extending available models to include effects of speciation, persistence, essentiality, and
bioavailability along with further elaboration of fate and effect models (Ligthard et al.,
2004). Until then, the deficiency of current LCIA methods in terms of toxicity
assessment should be clearly communicated.
Due to the availability of different methods for toxicity assessment in LCIA, the
choice of the evaluation method may have a significant influence on the outcome of the
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LCIA. In comparative studies, different LCIA methods for aquatic ecotoxicity have
been applied to a fixed Life Cycle Inventory, revealing a distinct influence on the
contributions of specific substances to the overall indicator (Schulze et al., 2001; Gloria
et al., 2006). Another recent work compares different approaches to calculate toxic
effect factors, concluding that none of the existing approaches can be recommended as
optimal in their present form (Larsen and Hauschild, 2007). A combined model for
toxicity assessment of chemicals (USEtox) has been presented recently (Rosenbaum et
al., 2008), but it is not yet ready for application.
In view of these difficulties with toxicity assessment in LCIA, the results of human and
ecotoxicity in this study should be regarded with care. The exclusion of organic
micropollutants from the elementary flows will result in a dominant influence of heavy
metals on the toxicity indicators. Hence, the major uncertainties associated with the
toxicity assessment for heavy metals are reflected in these indicators. One should keep
that in mind while interpreting the toxicity indicators and their influence on the overall
comparison. Apart from the baseline indicators from CML, toxicity indicators from
IMPACT methodology are applied in sensitivity analysis to reveal possible influences
of indicator choice on the results.
3.8.4 Normalization
Normalization is an optional step for LCA that can be applied for a better understanding
of the relative magnitude for each indicator result (ISO 14044, 2006). It is commonly
used for checking for inconsistencies, providing information on the relative significance
of the indicator results, and as a first step in weighting. A normalized score for a certain
impact category is obtained by determining the ratio of the category indicator result of
the product system and that of a reference system:
,
,
,
isystem
i norm
i reference
I
II
with Ii, norm = normalized value of indicator i
I
i, system = score of indicator i for the product system
I
i, reference = score of indicator i for the reference system
The reference systems applied for normalization purposes in LCA are typically defined
as global or regional annual values, e.g. the world in a certain year or the population of
a certain country in a certain year. The indicator values for the reference system are
calculated with emission data for the respective area, using the same characterization
factors as for the LCIA. Normalization data is usually included in LCIA methods, and
the CML method which is basically adopted in this study provides normalization data
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for different geographical and temporal scopes (Netherlands 1997, Western Europe
1995, World 1995).
However, the scope of the normalization data should correspond to the scope of the
study if possible. The present work investigates different sanitation systems which are
operated in Germany, using technology that is available for the year 2007. Hence,
normalization should be carried out in relation to the latest available emission data for
Germany. Consequently, emission data for Germany is collected for the year 2004 (or
latest available data) and used for the calculation of specific normalization scores (Table
16):
,2004
,2004
2004
ij j GER
j
iGER
GER
Cm
Ipop
with Ii,GER2004 = normalization score for indicator i in Germany 2004
(unit: inhabitant equivalents [pe*a])
Cij = characterization factor for flow j and indicator i
mj, GER2004 = total flow j in Germany 2004 (or latest available data)
popGER2004 = population of Germany 2004 (82.523.000 inhab.)
The emission data used for the calculation of normalization scores for Germany 2004
eventually suffers from incompleteness due to the lack of emission data. For some
indicators (CED, GWP, AP, EP), the most relevant emission data is readily available.
For toxicity indicators, emissions to air are available for heavy metals and some organic
and inorganic pollutants. Data for emissions to surface waters and soil are limited to
heavy metals.
If emission data for the calculation of normalization scores is inconsistent, it can lead
to a biased normalization (Heijungs et al., 2007). The normalized indicator results are
then calculated either too high or too low, and their use in further interpretation can be
misleading. It is recommended to detect possible biases in normalization scores (e.g. by
a thorough analysis of the contributing substances) and discuss their influence on the
interpretation. Biased normalization seems to be particularly important for impact
categories where many substances are relevant, i.e. any form of toxicity assessment and
resource depletion (Heijungs et al., 2007).
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Table 16: Normalization data
Indicator Germany
2004
Western
Europe
1995
[1] [2]
Cumulative energy demand MJ/(pe*a) 168468 -
Abiotic depletion potential kg Sb-eq/(pe*a) 32.6* 32.6
Global warming potential kg CO2-eq/(pe*a) 12202 14600
Acidification potential kg SO2-eq/(pe*a) 13.5 84.2**
Eutrophication potential kg PO4-eq/(pe*a) 6.5 38.4**
Human toxicity potential kg DCB-eq/(pe*a) 7266 23300
Freshwater aquatic ecotoxicity
potential kg DCB-eq/(pe*a) 88.9 1550
Terrestrial ecotoxicity potential kg DCB-eq/(pe*a) 70.1 146
Terrestrial eutrophication m² UES/(pe*a) 1011.3 -
Aquatic eutrophication of
inland waters kg NO3-eq/(pe*a) 37.1 -
Aquatic ecotoxicity kg TEG-eq/(pe*a) 1722063 -
Terrestrial ecotoxicity kg TEG-eq/(pe*a) 1969880 -
DCB: 1,4-dichlorobenzene, TEG: triethylene glycol, UES: unprotected ecosystem
* ADP adopted from CML method due to lack of data
** calculated with baseline characterization factors (fate and exposure excluded)
Sources:
[1] see annex 12.3
[2] CML method (Guinée et al., 2002)
3.8.5 Grouping and weighting
Grouping and weighting the different indicator results of Life Cycle Impact Assessment
is another optional step in LCA to come to conclusive statements in an environmental
comparison (ISO 14040, 2006). In comparing two or more systems, the environmental
pros and cons usually have to be grouped and weighted against each other to identify
the preferable system in terms of environmental impacts. This so-called valuation of the
results is both based on scientific findings and certain standards of value. Thus, it
combines scientific findings with subjective value judgements, and the application of
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certain criteria for valuation of LCIA results has to be addressed in a transparent and
reproducible manner to comply with the ISO standard.
Different schools of valuation exist for grouping and weighting of LCIA results in the
scientific community. Some methods try to reduce the comparison to just one
representative “overall score” of the systems or products under investigation (e.g.
Ecoindicator 99), weighting the different endpoint categories (e.g. human health,
ecosystem quality etc) to add up the numeric results to one final score. These
approaches are traditionally targeting LCA for product development, so that the
designer can easily choose the preferable product with lower environmental impact
without further knowledge of the LCA background.
Other valuation methods are based on ranking of the various impact categories, i.e.
allocating a higher priority to a specific impact category than to another. The method
applied in this work has been developed by the German Federal Environmental Agency
(UBA method, Schmitz and Paulini, 1999) and is based on a subjective ranking of the
indicators following a certain methodology.
In detail, the UBA method is designed in alignment with the superior protected assets of
environmental policy, i.e. human health, the structure and functionality of ecosystems,
and natural resources. Thus, it assigns different priorities for each indicator on a relative
ordinal scale (A = highest priority to E = lowest priority) based on three different
criteria:
1. Ecological hazard: how serious the potential hazard for the protected asset is
for the respective impact category, in particular concerning potential effects of a
damage, reversibility, spatial extent, and uncertainties of the cause-effect-chain.
2. Distance to target: how distant the existing environmental status is to the status
of ecological sustainability, i.e. regarding the distance to quantifiable
environmental quality goals, the extent of necessary reduction, actual emission
trends (rising, stable, lowering), and the feasibility of abatement measures.
3. Specific contribution: how large the normalized impact score is in relation to
the total impact in Germany.
Whereas the first two criteria are derived by subjective valuation, the third criterion is
determined simply by computation, relating the various normalized indicator results to
the score of the indicator with highest normalized score (for calculation details see
Schmitz and Paulini, 1999). Finally, the evaluations of all three criteria are combined to
formulate a final statement about the ecological priority of the respective impact
category.
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Based on the proposal of the Federal Environmental Agency (UBA) for the ranking of
impact categories (Schmitz and Paulini, 1999), priorities for all impact categories are
derived in an ordinal scale (Table 17). It has to be noted that choices for ecological
hazard and distance to target are adopted from UBA except for the toxicity indicators
(HTP, FAETP, TETP) which are explicitly not included in the UBA proposal. For a
comprehensive valuation of all indicators, it has been decided to include toxicity
indicators here (= “modified UBA method”). For the ecological hazard, level A is
attributed for all three toxicity indicators. The distance to target is estimated to level C
for all three indicators, based on the relatively good state of environment that is the
result of environmental policy in Germany for the last 50 years. The resulting ecological
priority of each indicator is defined based on a specific scheme. In the valuation of this
study, all indicators are attributed either medium or large priority (Table 17). To reveal
the influence of the modification of the UBA valuation method, the original UBA
ranking (without toxicity indicators) is applied in sensitivity analysis.
For a comparison between two scenarios, the relative indicator results are illustrated in a
T-diagram. Now, relative scores with the same priority can be counterbalanced against
each other. If this comparison leads to a definitive advantage of one scenario over the
other, the result can be seen as significant. Otherwise, the differences between the
scenarios should be described as “insignificant”.
Table 17: Ranking of LCIA indicators based on ecological
hazard, distance to target, and specific contribution
Ecological
hazard Distance
to target Specific
contribution
Resulting
ecological
priority
CED C B D
Medium
GWP A A E
Large
ADP C B D
Medium
EP B C A
Large
AP B B B
Large
HTP A C E
Medium
FAETP A C A
Large
TETP A C A
Large
A = highest priority, E = lowest priority
Ranking based on UBA method (Schmitz and Paulini, 1999) extended with
toxicity indicators
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3.9 Interpretation and sensitivity analysis
This last step of an LCA brings together the results of classification, characterization,
normalization and grouping or weighting to an overall interpretation. Significant issues
influencing the result of the LCA study should be addressed in this part. The
interpretation of the results should explicitly take into account the
- goal and scope of the study
- data quality of the Life Cycle Inventory
- significant parameters of LCI
- methodological limitations
- the influence of value-choices (e.g. in weighting)
- results of sensitivity analysis.
Completeness, sensitivity and consistency have to be checked to assess the reliability of
the results. For a comprehensive LCA, the uncertainties connected to the results of the
study should be clearly communicated to support the significance of the study (ISO
14044, 2006). Finally, conclusions and recommendations are presented together with
limitations of the study.
Sensitivity analysis
Sensitivity analysis is a systematic procedure for the estimation of the influence of
variations in LCI data or LCIA methodology on the final results of the LCA. In this
study, two types of sensitivity analysis are implemented:
A) sensitivity analysis of LCI data
B) sensitivity analysis of LCA methodology
Sensitivity analysis of LCI data
In this part, specific parameters of the inventory (e.g. energy demand of sub-processes,
transport distances, process layout, flow characteristics) are varied over a defined range
to observe the resulting effect on single LCIA indicators. Thus, the robustness of the
LCA results and the related conclusions can be tested to:
- quantify the influence of boundary conditions, assumptions, or other
uncertainties for specific LCI data
- identify relevant processes which should be further optimized
- identify relevant key parameters for the ecological assessment to facilitate data
acquisition for future LCA studies in this field.
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Sometimes, the variation of a system parameter can lead to necessary adjustments in the
respective scenario or in other scenarios. If e.g. improved urine separation efficiency
leads to more urine available for fertilizing, this results in a) increased working time and
emissions of the tractor and b) more production and application of mineral fertilizer in
other scenarios. All necessary adjustments are fully implemented while calculating the
indicator results of sensitivity analysis.
Sensitivity analysis of LCA methodology
This part of sensitivity analysis relates to methodological choices and their influence on
the results and conclusions of this study. For certain impact categories (eutrophication,
ecotoxicity), alternative indicators are calculated to reveal a possible influence of the
indicator choice on the results. Other methodological issues are e.g. related to functional
definitions inside the inventory data (e.g. effluent concentrations) or modification of the
valuation method. Sensitivity analysis of LCA methodology can be helpful to identify
important definitions or choices that have been made prior to the setup of the substance
flow model (LCI) and their influence on the outcomes. Thus, it can be revealed to what
extent these definitions can determine the LCA results, and the respective findings can
be seen in relation to these issues.
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4 Life Cycle Inventory
This chapter contains a detailed description of the Life Cycle Inventory, i.e. the input
data that is used to model the relevant system components of the different scenarios and
related subsystems. Core processes of wastewater collection and treatment (“system
operation”) are described with a high level of detail, explicitly stating process layout,
underlying assumptions, data origin, and parameter adjustment. Data for system
construction (infrastructure) is generated by a simplified layout of the systems in
combination with secondary data from a real city area. Data for background processes
such as energy supply and transport is adopted from existing databases and is described
briefly. The resulting substance flow models for all scenarios are implemented in
UMBERTO® software (IFU and IFEU, 2005).
4.1 System operation
This part describes the Life Cycle Inventories for the operation of the different
processes in each scenario. It includes the following processes:
A) Conventional sanitation system
- drainage
- activated sludge plant with sludge stabilisation
- composting of biowaste
B) Source-separation systems
- urine separation, storage and treatment
- gravity drainage and composting of faeces with biowaste
- vacuum drainage and digestion of faeces with biowaste
- vacuum drainage and digestion of faeces and urine with biowaste
- greywater treatment in
activated sludge plant (sequencing batch reactor)
planted soil filter
membrane bioreactor with non-potable reuse
C) Application of mineral and organic fertilizers
The different scenarios of conventional and source-separation systems are built by
different combinations of the processes listed above. An exact definition of each
scenario and its relevant processes is given in chapter 3.5.
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4.1.1 Conventional sanitation system
Scenario R
The activated sludge plant is operated with extended nutrient removal, i.e.
denitrification and chemical P elimination. Excess sludge is stabilised by anaerobic
digestion, dewatered and co-incinerated with municipal waste. The energy demand of
wastewater treatment is minimized by using energy-efficient aggregates and surplus
energy from sewage gas combustion (Figure 21).
Scenario Rmin
Wastewater treatment in this scenario is operated according to minimum standards for
wastewater discharge, i.e. without extended nutrient removal. The activated sludge plant
eliminates organic matter and ammonia (nitrification). The excess sludge is stabilised
simultaneously by extended aeration (Figure 22). Stabilised sludge is co-incinerated in a
waste incineration plant.
Scenario Ragri
This scenario resembles the second reference scenario (R) in terms of wastewater
treatment (extended nutrient removal, sludge digestion, optimized energy demand).
Here, the stabilised sludge is directly applied in agriculture as organic fertilizer (Figure
21).
Sedimentation
tank
Activated sludge
Activated sluge with
nitrification,
denitrification, and
chemical P elimination
(sludge age: 20 d)
Wastewater
Discharge
Primary
sludge
Incineration
Excess
sludge
Anaerobic
digestion
+ dewatering
Truck transport
Clarifier
CHP plant
Sewage
gas Waste
heat
Return
sludge
Agriculture
Scenario R Scenario Ragri
(20 km)(30 km)
Figure 21: System layout of conventional wastewater treatment with anaerobic sludge stabilisation
(R and Ragri)
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Figure 22: System layout for conventional wastewater treatment with aerobic sludge stabilisation
(Rmin)
4.1.1.1 Drainage
The drainage of mixed wastewater in the reference scenarios is done by gravity. The
operation of lifting or pumping stations in the sewer system and the associated energy
demand is not considered here. Even though terrain properties often require lifting or
pumping of wastewater (e.g. in the Berlin sewer network), gravity drainage per se does
not need energy for operation. For reasons of simplicity, the investigated area is
assumed to be plain, so that pumping is not necessary in the conventional system.
Possible transformations of wastewater components during drainage (e.g. the
formation of H2S or NH3) are neglected. The influence of infiltrating groundwater or
possible leakages of wastewater pipes are not considered as well.
4.1.1.2 Activated sludge plant
For the operation of the conventional wastewater treatment plant, an LCA process
model is applied that has been developed at TU Berlin. It describes the operation of an
activated sludge plant and covers the processes of mechanical, biological and (optional)
chemical treatment of conventional wastewater, including the stabilisation of sewage
sludge.
The model calculates the allocation of the different elemental components (C, N, P
etc) of influent wastewater to the output flows of effluent, air, and sewage sludge. It
does not provide a dynamic load-dependent modelling of the wastewater treatment
process, but allocates the elements based on linear input-output relationships via
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specific factors. In addition, the demand of energy and chemicals is determined as a
function of process parameters and wastewater loads. The model can be adjusted to
specific operational conditions with a set of parameters (Table 18). A detailed
description of the process model is provided in annex 12.4. Here, the most important
process parameters are briefly discussed:
Sedimentation: Particulate fractions of COD (35% of influent), N (11%), and
P (17%) are removed via sedimentation and do not contribute to aeration
energy or chemical demand.
Elimination of organic matter: Chemical oxygen demand (COD) is
eliminated to 95% in all scenarios.
Nitrification/denitrification: Removal of dissolved N is specifically targeted
only in scenarios R and Ragri (90% removal of dissolved N). Dissolved N
removal in scenario Rmin (50%) is due to nitrogen demand for bacterial
growth and partially due to unintended denitrification processes (e.g. in
clarifier).
Chemical P elimination: Dissolved P is chemically precipitated by ferric salt
addition in scenarios R and Ragri (β factor = 1.5). In the minimum scenario
Rmin, P is removed only by microbial uptake for biomass growth. Rates of
biological P removal are estimated according to common German rules for
the design of activated sludge plants (ATV, 2000).
Heavy metals: Heavy metals are bound in element-specific fractions to
sewage sludge.
Sludge age: The sludge age is estimated to 25 days in case of simultaneous
aerobic sludge stabilisation (= extended aeration). For anaerobic sludge
stabilisation, a sludge age of 20 days is sufficient to achieve denitrification
(ATV, 2000). The stabilised sludge is dewatered by a mobile centrifuge to
40% dry matter content. The sludge liquor (including significant loads of
NH4) is recycled to the influent.
Sludge yield: The amount of excess sludge is determined by the yield
coefficient of 0.67 g Csludge/g Celiminated (ATV, 2000).
Energy demand
The demand for electric energy is calculated via the particular energy requirements of
the relevant processes (e.g. aeration, mixing, pumping, sludge treatment etc) (Table 19).
The resulting total energy demand (23.4 kWh/(pe*a) for scenarios R and Ragri and 27.8
kWh/(pe*a) for scenario Rmin) is in the lower range of average values for German
WWTPs (Table 20), reflecting modern process design and machinery. The volume-
related energy demand is substantially higher than the average of German WWTPs due
to the treatment of concentrated wastewater without stormwater.
In scenarios with optimized energy demand (R and Ragri), the net energy balance is
further improved by energy production from sewage gas combustion. The sewage gas is
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combusted in a combined heat and power plant (electrical/thermal efficiency:
32%/57%). The thermal energy demand for digester heating is met by the waste heat
from the CHP plant.
Table 18: Process parameters of conventional WWTP
Parameter
Scenario
R + Ragri Scenario
Rmin Remarks
Removal in sedimentation
COD 35 35
N 11 11
P
[%]
17 17
Particulate fraction
of influent load
Removal in biological stage
COD 95 95 Total incl. CODparticulate
NH4 > 93 > 93 Nitrification
Ndissolved 90 50 Denitrification
Pdissolved 95 40 Total elimination
by chemicals 60 - Addition of Fe (β = 1.5)
Heavy metals
[%]
60 – 85 60 – 85 Depending on element
Sludge treatment
Stabilisation anaerobic aerobic
Sludge age [d] 20 25 ATV, 2000
Sludge yield Y [g Csludge /
g Celim] 0.67 0.67 ATV, 2000
Dry matter content
of stabilised sludge [%] 40 40
Dewatering with
centrifuge, sludge liquor
recycled to influent
Energy demand
Electric [kWh/m³] 0.61* 0.65
[kWh/pe*a] 23.4* 27.8
Total (incl. sludge
stabilisation and dewatering)
Thermal [MJ/m³] 0 - Digester heating
by heat of CHP plant
* without benefit from biogas use (12.1 kWh/pe*a)
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Table 19: Energy demand for WWTP in reference scenarios
Process Scenarios R + Ragri Scenario Rmin
[kWh/pe*a] [kWh/pe*a]
Pumping + primary treatment 2.6 2.5
Aeration* 18.3 22.8
Benefit from denitrification - 5.1 - 2.8
Mixing 1.9 3.2
Sludge treatment 4 0.9
Other 1.7 1.2
Total 23.4 27.8
Benefit from sewage gas usage -12.1 0
Net energy demand 11.3 27.8
Calculated values (LfU, 1998; Müller et al., 1999), see annex 12.4.4 for details
* Aeration energy required for carbon removal and nitrification
Table 20: Comparison of energy demand for wastewater treatment
with average values of municipal WWTPs in Germany
Energy demand This study Average values of Germany*
Scenarios
R + Ragri Scenario Rmin Median 80%-
percentile
weighted
average
kWh/(design-pe*a) 23.4 27.8 27 41 24.3
kWh/(pe * a) 41.5 64 31.7
kWh/m³ 0.61 0.65 0.32 0.56 0.32
* values from 1097 municipal wastewater treatment plants (LfU, 1998)
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Transfer coefficients
For the wastewater treatment process, exemplary transfer coefficients for each scenario
are calculated with the default parameters. They describe the allocation of the elemental
input flows to the different output flows for all three reference scenarios (Table 21).
Resulting effluent loads and effluent concentrations of wastewater treatment are
calculated in chapter 5.1.4 for each reference scenario.
The stabilised and dewatered excess sludge contains 25% of influent carbon, 18% of
influent nitrogen, and 50% or 96% of influent phosphorus depending on whether
chemical P elimination is applied. These potentially valuable nutrients are recycled to
agriculture in scenario Ragri, whereas they are lost for recycling if sewage sludge is
incinerated (scenarios R and Rmin).
Table 21: Transfer coefficients of elemental flows in conventional
wastewater treatment
Input Emissions Scenarios R + Ragri Scenario Rmin
Effluent Air Sludge
Sewage
gas Effluent Air Sludge
COD-C COD-C 5.0 5.0
HCO3-C 1.8 1.7
CO2-C 44.7 8.0 68.7
CH4-C 15.4
C
org, sludge 25.1 24.6
Ntotal NH4-N 0.9 4.5
NO3-N 6.2 31.1
N org 1.8 8.9
N
2-N 72.7 0.1 37.5
NH3-N 0.3 0.3
N
2O-N 0.4 0.2
N
sludge 17.6 17.5
Ptotal P-species 4.2 95.8 49.8 50.2
K K 95 5 95 5
Heavy metals
Pb, Cr, Hg 20 80 20 80
Cd 30 70 30 70
Cu 15 85 15 85
Ni 40 60 40 60
Zn 25 75 25 75
Transfer coefficients calculated with LCA model for wastewater treatment (TU Berlin)
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4.1.1.3 Collection and composting of biowaste
The kitchen and garden biowaste from households is collected by truck in a stop-and-go
mode (7.5 km) and transported to a composting plant (20 km). Possible emissions
during storage and collection of biowaste are neglected.
The process layout of the composting plant consists of a pretreatment of biowaste,
followed by an encapsulated intensive composting phase in boxes (average retention
time: 11 days) and a final stabilisation in open composting for eight weeks (Figure 23).
The product is stabilised compost which can be stored without further treatment before
direct application in agriculture. Exhaust air from the intensive composting process can
contain harmful and malodorous gases and is therefore cleaned in a biofilter. Excess
leachate from intensive composting is discharged to the wastewater treatment plant.
Open
composting
Intensive
composting
(encapsulated)
Excess
leachate
Pretreatment
Biowaste
Agriculture
To wastewater
treatment
Exhaust
air
Biofilter
Stop-and-go collection (7.5 km)
and truck transport (20 km)
Truck transport (20 km)
Figure 23: System layout for composting of biowaste
Process inventory
Inventory data of the composting process is basically adopted from a previous LCA
study of biowaste composting (Vogt et al., 2002). The pretreatment of biowaste includes
chopping and elimination of metals and plastics and requires a considerable portion of
the total energy demand (Table 22). The intensive composting is done with active
aeration to provide enough oxygen for the aerobic process. During the final open
composting stage, the composted material is turned over biweekly with a special turning
machine powered by a diesel engine.
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Table 22: Energy demand for the composting of biowaste
Process Energy demand
Pretreatment [kWh/Mg biowaste] 14.2
Intensive composting [kWh/Mg biowaste] 10.0
Open composting [L diesel/Mg biowaste] 0.5
Source: Vogt et al., 2002
Leachate of the intensive composting process is recycled for the moistening of the piles
during open composting. Excess leachate (~ 60 L/Mg biowaste) is discharged into the
sewer and treated in conventional wastewater treatment. This leachate is usually heavily
loaded with organic matter (COD = 20000 – 100000 mg/L (Bidlingmaier, 2000)) and
nitrogen. Transfer of phosphorus and salts (K, Cl, heavy metals, etc) into the leachate is
neglected.
The final product of the composting process is a stabilized organic fertilizer with a dry
matter content of 60%. It can be stored and transported to farms by truck prior to
application as a slow-release fertilizer and soil conditioner.
Transfer coefficients
Transfer coefficients of the composting process are calculated for the most important
elemental flows (Table 23). A detailed allocation of elemental flows, energy demand,
and emissions is given in chapter 4.1.2.2 for the composting of faeces and biowaste,
which is described with the same process model.
In total, it is assumed that 60% of the organic carbon and 40% of the nitrogen of
biowaste is converted into off-gases or leachate during the composting process.
Consequently, gaseous emissions of nitrogen (NH3, NOx, and N2O) and methane (CH4)
are most relevant for the environmental evaluation.
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Table 23: Transfer coefficients of biowaste composting
Input Emission as Compost Air
Excess
leachate
[%] [%] [%]
Organic carbon Corg in compost 40.6
TOC 1.2
CO2-C 55.8
CH4-C 1.8
COD-C 0.6
Ntotal N in compost 60.1
NH3-N 32.3
N
2O-N 2.3
N
2-N 0.8
NOx-N (as NO2) 4.2
N
total 0.3
Ptotal P in compost 100
K K in compost 100
Source: Vogt et al., 2002
4.1.2 Separation systems
4.1.2.1 Urine separation, storage and treatment
Undiluted urine is separately collected by using separation toilets and drained by gravity
to holding tanks (Figure 24). Here it is collected biweekly with tanker trucks and
transported (5 km) to a treatment unit, where micropollutants are oxidized by ozone.
Treated urine is stored for at least 6 months for hygienisation, before it is transported to
farms (20 km) and applied as organic fertilizer in agriculture.
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Holding tank
Max 14 d
Storage tank
Storage time > 6 months
hygienisation
Undiluted urine
Agriculture
Emissions
Emissions
Dilution with
service water (1:1)
70% separation
efficiency Separation toilets
Ozonation
Ozone dose: 1 g/L
(15 kWh/kg O3)
Truck transport (5 km)
Truck transport (20 km)
Figure 24: System layout of urine separation and treatment
Separation efficiency
Urine can be separated from remaining toilet wastewater by separation toilets, which are
available from various manufacturers (SwedEnviro, 2001). These toilets have two
separate outlets: one for urine in the front part and one for faeces and toilet paper in the
rear part of the bowl. If the urine outlet is fitted with a movable plug, the collection of
undiluted urine without flush water is possible (Roediger, 2007). However, due to
incorrect use of the toilets and imperfect design of the bowl, it is not possible to collect
100% of the daily urine of the inhabitants. Experiences from Swedish pilot plants
indicate that 60 – 90% of the total urine flow can be separated depending on the
motivation of the tenants (Jönsson, 2001). Results from a German pilot plant suggest a
separation efficiency of only 33 – 41% (Peter-Fröhlich et al., 2007), but this is mainly
attributed to user behaviour and construction deficiencies of the system. In a Swiss pilot
project, 70-75% of the expected urine quantity could be recovered with separation
toilets (Rossi et al., 2009).
In this study, it is assumed that 70% of the daily urine can be collected without flush
water, while 30% of the urine is drained together with faeces and flush water. This
seems to be a reasonable mean value which should be achievable with optimized toilet
systems and user education. The influence of separation efficiency on the overall results
is further investigated in sensitivity analysis.
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Urine treatment: micropollutants
Human urine can contain considerable amounts of pharmaceuticals (Lienert et al., 2007;
Winker et al., 2008) and hormones, and the fate of these micropollutants during
agricultural application of source-separated urine is not properly understood (Winker,
2009). In a Swedish pilot project, untreated human urine is applied as fertilizer after
long-time storage, and micropollutants are assessed to pose only minor risks to
consumers or the environment (Stockholm Vatten, 2000). However, the need for more
research in this field is clearly identified in the Swedish report. In Germany, legal
regulations for fertilizers are strict (DüMV, 2003). The approval of new types of organic
fertilizers requires evidence for their harmlessness for the environment and the
consumers. Following the precautionary principle, human urine should be treated prior
to its application as a fertilizer to reach inactivation of pharmaceuticals and hormones.
For the treatment of source-separated urine, a wide range of technical options is
available to reach different purposes which could be hygienisation, stabilisation, volume
reduction, recovery of nutrients, or handling of micropollutants (Maurer et al., 2006).
Treatment options include storage, evaporation, stripping, biological processes,
precipitation, ion exchange, membrane filtration, and ozonation.
In this study, the main goal of urine treatment is the inactivation of micropollutants to
eliminate possible risks of urine application. Pilot studies have shown the ability of
ozonation to inactivate and metabolize pharmaceuticals in human urine (Escher et al.,
2006; Fitzke and Geissen, 2007), and ozone is a well-known oxidant in water treatment.
Consequently, the present work includes an ozonation step for the oxidation of
micropollutants in urine prior to its application in agriculture. Pilot experiments for the
ozonation of human urine have determined a suitable ozone dose of 0.6 – 1.3 g O3/L to
oxidize a set of micropollutants below the level of detection (Escher et al., 2006; Dodd
et al., 2008). Based on these results, it is assumed that an ozone dose of 1 g O3/L is
adequate to achieve a sufficient inactivation of potentially harmful micropollutants.
According to comparable units used in water treatment, an overall energy demand of 15
kWh per kg ozone is estimated for this process (ITT, 2007). Process emissions of
ozonation (e.g. via waste air) are neglected.
Urine treatment: volume reduction
Volume reduction may be another target of interest, but the energy demand of suitable
treatment technologies can be high and may offset the advantages of small transport
volumes (Maurer et al., 2003). Many treatment options still have to be optimized and
tested in full-scale to generate reliable data of process performance and energy demand.
A Suisse pilot project recently tested the feasibility of a combination of electrodialysis
and ozonation for the production of a concentrated fertilizer without micropollutants
from source-separated urine (Pronk et al., 2007; Dodd et al., 2008). It should therefore
be emphasized here that other options for urine treatment will probably be available in
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the near future to facilitate the recovery of urine-bound nutrients in agriculture. Whether
the implementation of these technologies has a significant impact on the LCA results,
this has to be checked when reliable process data is available.
Storage and application
After ozonation, the urine is stored in large tanks for more than six months, following
the recommendations of Swedish studies on hygienisation of stored urine (Stockholm
Vatten, 2000). Cross contamination of separated urine with faecal microorganisms
cannot be completely avoided (Hoglund et al., 1998 ; Schonning et al., 2002) and
sufficient storage at reasonable temperature leads to the die-off of pathogenic bacteria
(Höglund, 2001). After that storing period, the urine is considered to be stabilised and
hygienically safe for application as fertilizer. The urine is mixed to stir up possible
precipitates and transported to the farms (20 km). Prior to the application, it is diluted
with service water (1:1) to minimize potential losses of ammonia during application.
The energy demand for the various pumping processes is estimated to 0.08 kWh/m³
urine. The treatment and supply of service water for dilution is assumed to consume 0.1
kWh/m³.
Atmospheric emissions during collection, storage, and application
During the collection and storage of urine, the urea content is hydrolysed to NH3, and
pH rises from 6 to 9. A part of the nitrogen is lost via atmospheric emissions during
collection, transport and storage. Estimated transfer coefficients can be found in
literature (Table 24).
NH3 losses in pipe networks (0.01%) and storage tanks (0.003%) are relatively small.
Most of the NH3 evaporates during the application of urine on the fields. Depending on
the application technique and weather conditions, the losses of nitrogen via NH3
evaporation can amount to 2 – 10 % (Johansson et al., 2001). Pilot-scale field tests
conducted in Germany resulted in a nitrogen loss of 3 – 10% (Peter-Fröhlich et al.,
2007; Muskolus, 2008). In a conservative approach, this study assumes an average loss
of 10% of the nitrogen through ammonia evaporation. If suitable application techniques
(trailing hoses, injection etc (Döhler, 2001)) are applied, NH3 volatilisation can
presumably be reduced. Losses via N2O and NOx are estimated to be equivalent to
mineral fertilizer application (EMEP/CORINAIR, 2004.
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Table 24: Nitrogen losses during urine collection, storage and
application
Losses Pipes and
holding tanks Storage
tanks Application in
agriculture Source
NH3-N [% N in urine] <1 / 0.01 / <0.3 0.1 / 0.003 / – 2 - 10 / 6 / 1 - 10 1 / 2 / 4
– / 5 / – 6 / 5 / 3 - 10 3 / 5 / 7
N2O-N [% N in urine] 1.25 / 1.25 / 0.3 5 / 6 / 7
NOx-N [% N in urine] 0.1 / 0.7 5 / 6
Bold: this study
Sources:
1) Johansson et al., 2001
2) Vinneras et al., 1998
3) Simons and Clemens, 2004
4) Palm et al., 2002
5) Tidaker, 2003
6) EMEP/CORINAIR, 2004 (equivalent to mineral fertilizers)
7) Peter-Fröhlich et al., 2007; Muskolus, 2008
4.1.2.2 Gravity drainage and composting of faeces with biowaste
For the composting of faeces with biowaste, faeces are collected in standard toilets
(preferably with low flush water volume, i.e. 6 L per flush) and drained in a gravity
sewer. Flush water is separated from the solids by a solid-liquid separation process to
obtain faecal matter with sufficient dry matter content for the aerobic composting
process. Solid faecal matter is transported to a composting plant, where it is mixed with
pretreated biowaste. The composting plant is a two-stage process: an intensive
composting in boxes is followed by an open composting stage for stabilisation (Figure
25). The final product is stabilised compost with high organic matter content, which can
be transported to the farms and used in agriculture for soil conditioning.
Composting of faeces for the production of organic fertilizer is a well-known
treatment for small-scale sanitation units or dry toilets. If operated correctly, it has
proven to effectively reduce pathogens and odour problems of faecal matter, producing
a stabilised soil conditioner with some nutrients (Gajurel, 2003; Winblad and Simpson-
Hébert, 2004; Muskolus, 2008). Laboratory experiments have confirmed that
composting with worms (“vermicomposting”) seems to be a promising process to
convert faeces into an odourless, earth-like material within three months (Shalabi,
2006).
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Figure 25: System layout for composting of faeces and biowaste
However, the composting of human faeces together with biowaste has been realized to
date only in single houses or small scale units, mostly in a rural context. Its
implementation in a high-tech urban environment requires fully automated processes for
solid-liquid separation, storage and transport, and large-scale composting of faeces. This
may pose difficulties for process engineering in terms of efficiency and reliability.
In particular, the separation of the flush water from the faecal matter without major
losses of nutrients and organic matter is a difficult task. First field trials of a separation
process based on filter bags are unsatisfactory for large-scale implementation due to
insufficient separation efficiency and impractical handling (Peter-Fröhlich et al., 2007).
A novel rotating disc filter with ceramic membranes is currently developed at the
Fraunhofer Institute in Stuttgart which may allow the continuous operation of a
filtration process for blackwater (IGB, 2007). Another approach uses a simple whirlpool
surface tension separator (“Aquatron”) to separate flush water from faecal matter inside
the house (Vinneras, 2004). However, none of these processes has so far been
successfully applied in larger scale. For this study, the process data for solid-liquid
separation is estimated from dewatering and thickening devices of sewage sludge
treatment.
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Similarly, there is only few data on the material flows during faeces composting. To
overcome this lack of data, it is decided that the inventory of this process should be
based on inventory data of biowaste composting (Vogt et al., 2002). Although the
process configuration of biowaste composting may be different from faeces composting,
the data is thought to provide a good estimation of resource demand and emissions of
the composting process. More research on large-scale composting of faeces is required
to accurately predict energy demand and emissions as well as the allocation of the
elemental flows on the different output fractions (compost, exhaust air, and leachate).
Solid-liquid separation
The solid-liquid separation process is assumed to be a two-step process with a
sedimentation stage followed by a mechanical thickening device (e.g. disc thickener).
By adding organic coagulation aids (polyacrylamide), a residual sludge with 10% dry
matter content is obtained. The demand of electric energy and chemicals is estimated
from sewage sludge treatment (Table 25). Residual sludge is temporarily stored and
transported to the composting plant by truck (20 km). Possible anaerobic decomposition
of the faeces sludge during storage is neglected.
Table 25: Energy and chemical demand of solid-liquid separation
Input Amount Remarks
Polyacrylamide 10 g/kg dry matter
Electric energy 0.03 kWh/kg dry matter
adapted from the thickening
of sewage sludge (Müller et al., 1994;
Schumann et al., 1997)
Electric energy 0.13 kWh/m³ pumping
Filtrate
The separated flush water (“faeces filtrate”) is heavily loaded with water-soluble
nutrients and organic matter from faeces and also from urine which is not properly
separated in the separation toilets (Gajurel et al., 2003). In this study, faeces filtrate is
pumped to the greywater sewer and treated in greywater treatment.
The quality of the filtrate is decisive for the amount of nutrients which are lost for
recycling purposes and have to be removed in greywater treatment. The filtrate
composition is assumed based on data from a pilot plant for faeces dewatering in bags
(Peter-Fröhlich et al., 2007), faeces dewatering in specific composting systems
(“Rottebehälter”,Gajurel et al., 2003) and consideration of input flows (Table 26):
Organic matter mainly originates from dissolving of faecal matter
N and P loads originate from misdirected urine
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Salts and heavy metals: concentrations in filtrate are calculated as average
concentrations of urine and flush water (i.e. it is assumed that faeces-derived
salts and heavy metals do not dissolve in the flush water)
In case of greywater treatment in MBR and non-potable reuse (scenario SC3), the
faeces filtrate which is treated together with greywater is partially used for toilet
flushing, thus creating a closed loop in the model which cannot be simulated by the
UMBERTO® software. For this scenario, the quality of the faeces filtrate is
approximated by an iterative calculation.
Table 26: Composition of filtrate from faeces dewatering
Substance Concentration Load
[mg/L] [kg/(pe*a)]
[1] [2] This study This study
Volume 8780
Total organic carbon 398-758* 1023* 200 1.8
Nitrogen (total) 17-152 141 121 1.1
Phosphorus 1-35 16 12 0.1
Salts, heavy metals average concentration of urine and flush water
* COD
1) Gajurel, 2003 (Rottebehaelter)
2) Peter-Fröhlich et al., 2007 (mean values with filter bags)
Pretreatment of biowaste
Biowaste has to be conditioned and homogenised prior to the composting process.
Possibly hazardous material for the compost quality has to be removed (e.g. plastics,
metals). Biowaste with high water content has to be chopped and mixed with structure
material if necessary (e.g. saw dust) to achieve a sufficient pore volume for maintaining
aerobic conditions during the composting process. The additional input of structure
material is neglected in the mass balance as well as the further handling of the removed
plastics and metal. The energy demand for the pretreatment steps (Table 27) includes an
exhaust air treatment.
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Table 27: Energy demand for pretreatment of biowaste
Process Energy demand
[kWh/Mg biowaste]
Exhaust air treatment 8.1
Drum screen 3.0
Magnetic separator 0.5
Shredder 2.6
Source: Vogt et al., 2002
Intensive composting
The automated process of intensive composting is conducted in closed boxes with heat
insulation over a period of 11 days. The respective allocation coefficients of the
different elemental components of biowaste to the output fractions (Table 28) are
adopted from biowaste composting and are related to an average biowaste composition.
Due to the lack of adequate data for the composting process of faeces and biowaste, the
allocation coefficients are adopted par to par. In total, 16% of carbon and 10.6% of
nitrogen are lost with exhaust air or leachate.
Leachate
Salts and heavy metals are supposed to accumulate in the compost: The leachate is used
to moisten the raw materials prior to the composting process, and hence the salts and
heavy metals contained in the leachate are recycled to the compost. Therefore, the
output loads with excess leachate are neglected here (Gronauer et al., 1997). Data for
heavy metal concentration in composting leachate indicates that the proportion of heavy
metals lost with the leachate is small (Fricke, 1990). It is assumed that excess leachate is
only charged with nutrients and organic matter (COD > 10000 mg/L). This leachate is
treated in a separate activated sludge plant (sequencing batch reactor) next to the
composting plant. The process model for the activated sludge plant is similar to that for
greywater treatment (see chapter 4.1.2.5) with nitrification, denitrification, and chemical
P elimination. Due to the high COD load of the leachate, a total energy demand of 2
kWh/m³ is assumed for this process.
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Table 28: Substance flows of intensive composting of faeces and
biowaste
Input Output Source
In compost: 50% water content 1
In biofilter: difference
Water content
In leachate: 125 L/Mg biowaste input 1
In compost: Cout = (1 – 0.16) * Cin 1
In biofilter:
CO2-Cout = 0.14 * Cin
CH4-Cout = 0.004 * Cin
NMVOC-Cout = 0.003 * Cin
1, 3
Organic carbon
In leachate:
TOC = 0.013 * Cin 1
In compost:
Nout = (1 – 0.106) * Nin 1
In biofilter:
NH3-Nout = 0.096 * Nin
N2O-Nout = 0.002 * Nin
N2-Nout = 0.002 * Nin
1
Nitrogen
In leachate:
NH4-Nout = 0.003 * Nin
N org. out = 0.003 * Nin
1
In compost:
Pout = 0.997 * Pin
Phosphorus
In leachate:
Pout = 0.003 * Pin
assumption:
Pout = 20 mg/L
Salts and heavy metals Accumulation in compost, fraction in
leachate is neglected 1, 2
Inert substances Remain in compost
Energy demand
(incl. biofilter) 10 kWh/Mg waste 1
Sources:
1) Vogt et al. 2002 (for biowaste composting in closed boxes, retention time : 11 days)
2) Gronauer et al. 1997
3) Leinemann 1998 (similar values from tunnel reactors)
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Exhaust air
In most composting plants, the exhaust air is cleaned in a biofilter to prevent the output
of malodorous gases and ammonia. Allocation coefficients of the biofilter are compiled
from different sources (Table 29). It is assumed that 60% of ammonia is partially
converted to N2O and NO in the biofilter (Cuhls, 2001; Clemens and Cuhls, 2003). Data
for nitrogenous air emissions from a biofilter varies considerably in literature. The
amount of N2O emissions is likely to depend on the ammonia load to the filter.
Table 29: Allocation coefficients of biofilter
Input to biofilter Output Source
H2O-vapor H2Oout = H2Oin
CO2-C CO2-Cout = CO2-Cin 1, 2
CH4-C CH4-Cout = CH4-Cin 1
NMVOC-C NMVOC-Cout = 0.5 * NMVOC-Cin 2
NH3-N
NH3-Nout = 0.4 * NH3-Nin
N2O-Nout1 = 0.6 * 0.26 * NH3-Nin
NO-Nout = 0.6 * 0.74 * NH3-Nin
1,3
N2O-N N2O-Nout2 = N2O-Nin 1,2,3
N2-N N2-Nout = N2-Nin 2
Sources :
1) Clemens und Cuhls 2003
2) Vogt et al. 2002
3) Cuhls 2001
Open composting
After the intensive degradation stage in boxes, the composted mix of faeces and
biowaste is stabilised in open piles for 8 weeks to obtain a marketable product. A
mechanical circulation of the piles with appropriate machinery is necessary biweekly to
ensure oxygen supply and prevent anaerobic decomposition. The allocation coefficients
for open composting (Table 30) are basically adopted from biowaste composting (Vogt
et al., 2002). During the stabilisation phase, 52% of organic carbon and 33% of nitrogen
are converted to gaseous emissions. No leachate is produced in this stage, because
excess moisture evaporates directly to the atmosphere. The final product is stabilised
compost with 40% water content which can be stored or directly transported to farms
for application in agriculture.
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Faeces can be contaminated with pathogenic microorganisms. These pathogens have to
be reliably inactivated in the composting process to prevent any potential hazard for
farmers and agricultural products. Usually, the long stabilisation phase leads to a
substantial reduction in pathogens due to elevated temperatures and microbial
competition. In this study, it is assumed that pathogens are eliminated in open
composting so that no further hygienisation of the final compost is necessary.
Table 30: Substance flows of open composting of pre-composted
faeces and biowaste
Input Output Source
Compost (stabilised): 40% water content 1
In off-gas: difference
Water content + water demand
Leachate: 0 L/Mg input 1
In compost: Cout= (1 – 0.52) * Cin 1
Organic carbon In off-gas:
CO2-Cout = 0.494 * Cin
CH4-Cout = 0.016 * Cin
NMVOC-Cout = 0.010 * Cin
1
In compost:
Nout = (1 – 0.33) * Nin 1
Nitrogen In off-gas:
NH3-Nout = 0.317 * Nin
N2O-Nout = 0.007 * Nin
N2-Nout = 0.007 * Nin
1
Phosphorus, salts, heavy metals
and inert substances Accumulation in compost, no leachate 1,2
Pathogenic microorganisms Reliable inactivation of pathogens
Diesel fuel (circulation) 0.76 L/Mg waste 1
Sources:
1) Vogt et al. 2002 (open composting in piles, retention time: 56 days)
2) Gronauer et al. 1997
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4.1.2.3 Vacuum drainage and digestion of faeces with biowaste
For the digestion of faeces with biowaste, faeces are drained in a vacuum system with
very small amounts of flush water. From the vacuum station, the mixture is pumped
directly to the biogas plant where it is mixed with pretreated biowaste. Faeces can be
heavily contaminated with pathogenic microorganisms, so the mixture has to pass a
hygienisation stage. Pathogens are reliably inactivated by pasteurization of the substrate
(70°C for 1 h). The hygienisation step is usually applied ahead of the digester to
minimize the risk of recontamination (ATV, 1996). In the digester, the organic content
is partially degraded under anaerobic conditions and transformed into biogas (mainly
CO2 and CH4) which is used in a combined heat and power plant (CHP plant) to
generate electricity and heat. The remaining residual sludge still contains valuable
nutrients and some organic material, and it can be used as fertilizer in agriculture after
stabilisation (Figure 26). Hence, this option of faeces treatment uses the energetic
content of the faecal organic matter without major losses of nutrients N and P while
delivering both energy and fertilizer (Wendland et al., 2007).
Vacuum drainage
Faeces + flush water
Open composting
HygienisationPretreatment
Biowaste
Agriculture
Mesophilic (40°C)
Retention time 30 days
In piles for 28 days
Digester
CHP plant Biogas
Residual sludge
Pasteurisation
70°C, 1h
15 kWh/(pe*a)
Waste
heat
DewateringSBR Sludge
liquor Final SS of dewatered
residual: 35%
Vacuum
separation toilets
Truck transport (20 km)
Figure 26: System layout for vacuum drainage and co-digestion of faeces with biowaste
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Vacuum drainage
The vacuum system for the drainage of faeces can operate with very small amounts of
flush water (1.2 L/flush). A vacuum pump imposes a slight negative pressure (ca. -0.5
bar) on the pipe network, sucking the mixture of faeces and flush water towards the
vacuum tank. Interface valves and small interim holding tanks inside the houses allow
for a comfortable operation without inconvenience for the customer. From the main
vacuum station, faeces are pumped in pressure lines to the biogas plant. The energy
demand for the vacuum system can vary considerably depending on the size and layout
of the system and the wastewater volume (Table 31). For this study, the energy demand
for the vacuum pumps is estimated to 15 kWh/(pe*a). Pressure pumps deliver the faeces
mixture to the biogas plant (0.05 kWh/m³).
Table 31: Energy demand of vacuum systems
System Amount of
wastewater Size Energy
demand Remarks Source
[L/(pe*d)] [pe] [kWh/(pe*a)]
Flintenbreite 5 108 51
Not working to
capacity 1
17 Possible 1
Vauban 8.4 40 7 2
Hannover 9 80 27 Annual period 3
9 Possible 3
Stahnsdorf 9 9 3.1 Calculated 4
ATV 150 -- 36 Combined wastewater 5
This study 5.8 5000 15
Sources:
1) Otterwasser, 2005
2) Schneidmadl, 1999 (calculated from operating time)
3) Herrmann and Hesse, 2002
4) Peter-Fröhlich et al., 2007 (calculated for optimized system)
5) ATV, 1995
Pretreatment
An automated rake system protects the digester from interfering objects in faeces
sludge. Biowaste is shredded in smaller parts, and plastics and metals are eliminated in
a wet separation process (“swim-and-sink-process”) combined with a pulper to support
the hydrolysis of the biowaste. The energy demand of the different processing steps is
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adopted from biowaste digestion. After the pretreatment and mixing of the substrates,
the water content is adjusted to 90 – 97% with service water if necessary.
Table 32: Energy demand for the pretreatment of digester substrate
Process Electric energy demand Source
Rake 0.07 kWh/m³ MURL, 1999
Shredder 1.2 kWh/Mg (1) Vogt et al., 2002
Exhaust air treatment 8.1 kWh/Mg Vogt et al., 2002
Wet processing / pulper 150 kWh/Mg Vogt et al., 2002
1) related to wet mass of waste
2) related to dry matter
Hygienisation
The hygienisation of the substrate is ensured by a pasteurisation step prior to the
digester. Pathogenic microorganisms are inactivated by means of elevated temperature
(70°C for one hour). The waste heat from the CHP plant is used for hygienisation. A
heat exchanger reclaims a major part of the thermal energy from the hot effluent of the
hygienisation unit. Process parameters are estimated from sewage sludge hygienisation
(Table 33). If the waste heat of biogas combustion is not sufficient for the hygienisation
process, extra fuel can be added to the CHP plant.
Table 33: Parameters for the thermal energy balance of
hygienisation
Parameter Remarks
Specific heat capacity of dry matter 1.05 MJ/(Mg*K) MURL, 1999
Specific heat capacity of water 4.19 MJ/(Mg*K)
Hygienisation temperature 70 °C Retention time: 1h
Starting temperature of substrate
(annual mean) 15 °C Assumption
Substrate temperature after heat
exchanger 35 °C
Proportion of the recovered waste heat
from output 85 % Assumption
Energy losses through transmission 5 % According to sludge
digestion (MURL, 1999)
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Digester
During the anaerobic fermentation process, the organic matter is dissolved in water
(hydrolysis) and converted by microorganisms to acetate, hydrogen and carbon dioxide
(acido- and acetogenesis) and finally to methane. Methanogenic bacteria are strictly
anaerobic and die off quickly in the presence of oxygen. Hence, the optimum water
content of the substrate is between 90 and 97%. Digesters can be operated at different
temperatures, however most of the digesters in Germany are operated in mesophilic
conditions (30 – 42°C). Typically, retention times are around 30 – 40 days, in which
around 50% of the degradable substrate is converted into biogas (BLU, 2004;
Stadtmüller, 2004).
This study assumes a mesophilic digestion process (40°C) with an average retention
time of 30 days. Co-digestion of faeces and biowaste is a reliable process under these
conditions and has been tested successfully in various pilot studies (Otterwasser, 2005;
Kujawa-Roeleveld et al., 2006; Wendland et al., 2007). However, the process has not
yet been realized in full-scale. Consequently, the operating conditions and the energy
demand have to be estimated from literature (Table 34). It is assumed that the operating
temperature of the digestion process can be maintained without heating. The process
itself generates thermal energy from microbial activity and the digester substrate is
delivered with a temperature of 35°C from hygienisation.
Table 34: Parameters of the mesophilic digestion process
Parameter Unit
Digester
Digester temperature 40 °C
Retention time (Digester and stabilisation) 30 days
Water content of digester substrate 95 %
Electric energy demand of digester 3 kWh/Mg fresh mass
Thermal energy demand of digester 0*
Dewatering of residual
Water content of dewatered residual 65 %
Dosage of coagulation aid 4 kg/Mg dry matter
Water demand for solving of coagulation aid 200 kg/kg
Electric energy demand for dewatering 30 kWh/Mg dry matter
* energy transfer from hygienisation is sufficient to maintain operating temperature
Source: Vogt et al. 2002
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The expected methane yield of the relevant substrates (faeces, misdirected urine, and
biowaste) is estimated from pilot studies and literature (Table 35). Methane yield is
calculated based on the content of organic dry matter of the different substrates,
assuming a certain amount of generated biogas with a specific methane content (ρCH4=
0.72 kg/m³).
After the digestion process, the substrate is concentrated and stabilised in a closed
secondary tank, where the biological processes are gradually stopped. The biogas which
is still generated during this post-digestion process is also combusted in the CHP plant.
Prior to composting, the stabilised digester residual is dewatered to 65% dry matter
content to establish aerobic conditions during the subsequent composting process.
Table 35: Biogas and methane yield of different substrates in relation
to input mass to digester
Substrate Organic dry
matter (oDM) Biogas Methane Methane Source
[% dry matter] [m3/kg oDM] [m3/kg oDM] [kg/kg oDM]
Faeces 93 0.45 0.29 0.21 1
Urine* 75 0.34 0.22 0.16 2
Kitchen biowaste 72 0.45 0.28 0.20 3
Garden biowaste 71 0.48 0.30 0.21 4
* misdirected urine which is not properly separated in toilets
Sources:
1) Estimation according to Otterwasser, 2005; assumed methane content in biogas 65%:
Kujawa-Roeleveld et al., 2003 report a methane content of 70% for blackwater digestion
2) Data from Otterwasser, 2005 suggests a significantly lower TOC proportion from
organic dry matter in urine than in faeces; hence, the biogas yield is assumed to be
smaller
3) Estimation according to mean values for kitchen biowaste (BLU, 2004)
4) Estimation according to mean values for loppings (BLU, 2004)
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Dewatering of digester residual
Dewatering of digester residual is done mechanically with a centrifuge and coagulation
aids. Sludge liquor from digester residual can contain considerable amounts of organics
and nutrients (Loll, 1999). Here, transfer ratios are adopted from biowaste digestion
(Vogt et al., 2002). Concentrations of Ntotal and P are assumed to be higher than in the
literature due to the contribution of misdirected urine (Table 36). The elution of heavy
metals is estimated according to the dewatering of sewage sludge.
Sludge liquor has to be treated in an activated sludge plant (SBR) with nutrient
elimination prior to discharge. Process data for the SBR plant is basically adopted from
the SBR model for greywater treatment (see 4.1.2.5). Elimination ratios are set to 95%
for TOC, 97% for NH4-N, 85% for Ntotal, and 95% for P to reach legally requested
effluent concentrations. Due to the high load of organics and NH4-N, the energy
demand for the treatment of sludge liquor is relatively high (0.68 kWh/m³).
Table 36: Transfer ratios from digester residual to
sludge liquor during dewatering
Substance Transfer ratio [%] Source/Remarks
Corg 2 Vogt et al., 2002
Ntotal 35* 85% as NH4-N, 15% as Norg
P 15*
K 50 Vogt et al., 2002
Mg, Ca 10 Vogt et al., 2002
Heavy metals
Pb, Cd, Cr, Hg 20
Cu 15
Ni 40
Zn 25
estimation according to
dewatering of sewage
sludge
* elevated due to misdirected urine
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Open composting
The dewatered digester residual is further stabilized in open composting in piles. Due to
lack of appropriate data, the relevant substance flows are based on an LCA model for
composting of residual from biowaste digestion (Vogt et al., 2002). The required time
of composting to reach a sufficient degree of stabilization is set to 28 days. Piles are
mechanically turned over biweekly, requiring the listed amount of diesel fuel. Leachate
containing organic carbon and nitrogen is treated together with sludge liquor. Losses of
phosphorus, salts and heavy metals with the leachate are neglected.
Table 37: Substance flows of open composting of digester
residual
Input digester residual Output Source
Compost (stabilised): 40% water content 1
In off-gas: difference
Water content
+ water demand
Leachate: 22.5 L/Mg input 1
In compost: Cout = 0.69 * Cin 1
Organic carbon
In off-gas:
CO2-Cout = 0.290 * Cin
CH4-Cout = 0.01 * Cin
NMVOC-Cout = 0.006 * Cin
In leachate:
TOCout = 0.004 * Cin
1
In compost:
Nout = 0.83 * Nin 1
Nitrogen
In off-gas:
NH3-Nout = 0.163 * Nin
N2O-Nout = 0.003 * Nin
N2-Nout = 0.003 * Nin
In leachate:
NH4-Nout = 0.0005 * Nin
N orgout = 0.0005 * Nin
1
Phosphorus, salts, heavy
metals and inert substances Assumption: accumulation in compost 1, 2
Diesel fuel (circulation) 2.2 L/Mg waste 1
Sources:
1) Vogt et al. 2002 (open composting of digester residual, retention time: 28 days)
2) Gronauer et al. 1997
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Combined heat and power plant
The biogas is combusted in a CHP plant to generate electric and thermal energy. A
potentially required gas conditioning prior to the combustion process is neglected. A
part of the generated biogas has to be flared in case of system malfunction or storage
overflow. It is assumed that 5% of the total sewage gas volume is flared (Ronchetti et
al., 2002), generating emissions which are comparable to the combustion in the CHP
plant. A small proportion of the sewage gas (0.75%) is lost by accidental leakage
(Ronchetti et al., 2002), causing respective emissions of methane.
The CHP plant is equipped with a spark-ignition engine in lean combustion mode
with high excess air, so that legal air emission standards can be met easily. CHP
parameters and emission factors (Table 38) are compiled from an LCA study (Ronchetti
et al., 2002) and the Umberto® database (IFU and IFEU, 2005)
Table 38: Parameters and emission factors for CHP plant
This study Biogas-CHP Natural gas-CHP
Source Ronchetti et al., 2002 Umberto® database
(IFU and IFEU, 2005)
Engine 60 kW
Lean burn engine
60 kW
Lean burn engine
50 kW (elec)
Catalysator engine
Efficiency 32% electrical,
57% thermal
32% electrical,
57% thermal
29,3% electrical
58,6% thermal
Emissions in mg/MJ
CH4, combustion 2,5 2,48 3,78
CO2 * 81.308 55.151
NOx (as NO2) 38 37,85 62,98
N2O 1,6 -- 1,57
CO 51 50,93 51,17
SOx (as SO2) 30 29,91 0,43
NMVOC 2,5 2,48 4,72
Dust 1,6 -- 1,57
* depending on input (CO2 + CH4) minus CO
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4.1.2.4 Vacuum drainage and digestion of faeces and urine with
biowaste
In scenarios using vacuum toilets without urine separation, the mixture of faeces and
urine is drained by a vacuum system and co-digested with biowaste. Basically, this
process is comparable to the digestion of faeces and biowaste described in chapter
4.1.2.3. Vacuum system, pretreatment of digester substrate, hygienisation and digestion
are modelled using the same data sets and assumptions.
The digester residual contains a large amount of dissolved nutrients due to the
contribution of urine. These valuable nutrients would be lost to a great extent for
fertilizing purposes if the residual sludge would be dewatered and composted. Hence, it
is assumed that the digester residual is directly applied as fertilizer, comparable to liquid
animal manure (Figure 27). Although the transport volume is relatively large and NH3
emissions during application are presumably higher than for composted residual, the
direct application of digester residual seems to be the best solution for optimum
utilization of the nutrients if urine is not separated in the toilets.
Vacuum drainage
Faeces + urine + flush water
HygienisationPretreatment
Biowaste
Agriculture
Mesophilic (40°C)
Retention time 30 days
Digester
CHP plant Biogas
Residual sludge
Pasteurisation
70°C, 1h
15 kWh/(pe*a)
Waste
heat
Vacuum toilets
Truck transport (20 km)
Figure 27: System layout for vacuum drainage and co-digestion of faeces and urine with biowaste
If digester residual is directly applied in agriculture, no emissions from sludge liquor
treatment and open composting arise. The complete process is encapsulated without any
atmospheric emissions (except from biogas combustion in CHP plant). Possible
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emissions during storage of digester residual are difficult to quantify and are neglected
here. However, the emissions during application of liquid digester residual in
agriculture are significantly higher than for dewatered and stabilised residual.
4.1.2.5 Sequencing batch reactor for greywater treatment
Greywater can easily be treated in a conventional activated sludge process due to its low
load of organic matter and nutrients (Figure 28). Particulate matter is separated in an
upstream sedimentation tank. The activated sludge process for greywater treatment is
designed as a sequencing batch reactor (SBR) providing carbon removal, nitrification,
and chemical P elimination. All stages of the process proceed in a single reactor within
a temporal sequence. If nitrogen load to greywater treatment is increased by the co-
treatment of faeces filtrate (scenario SC2), a denitrification phase (mixing and no
aeration) has to be implemented to comply with legal effluent standards. Excess sludge
is withdrawn and stabilised aerobically together with primary sludge from
sedimentation. Digestion of the greywater sludge for energy recovery is not considered
here due to the low load of organic matter in the sludge. Stabilised sludge is dewatered
and incinerated.
Figure 28: System layout for greywater treatment in sequencing batch reactor
The LCA process model is basically adapted from conventional wastewater treatment
(see chapter 4.1.1.2). However, the different composition of greywater compared to
combined wastewater requires some adjustments (Table 39):
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Particulate matter (15% of TOC, 11% of Ntotal, 10% of Ptotal) is separated in
sedimentation, and faeces filtrate is optionally added after this stage
The distribution of nitrogen and phosphorus to the different output flows is
determined with the same calculatory approach as for the conventional
WWTP, but with different elimination ratios.
For the carbon balance, excess sludge production is estimated via a constant
factor (0.6 g dry matter per g TOC eliminated) for an average sludge age of
25 days.
The influence of recycling of sludge liquor to the influent is neglected here.
Sludge liquor is directly discharged to surface waters.
Table 39: Process parameters of SBR for greywater treatment
Parameter Remarks
Removal in sedimentation
TOC [%] 15
N [%] 11
P [%] 10
particulate fractions of
influent loads
Removal in biological stage
TOCdissolved [%] 93
NH4-N [%] 96
Ndissolved [%] 70
reflecting typical removal
efficiencies of C and N in SBR
plants (Helmreich et al., 2000;
Steinmetz et al., 2002)
Pdissolved [%] 86 with 20% biological P uptake
Heavy metals [%] 60 – 85 depending on element
Inorganic salts [%] 0 neglected
Sludge age [d] 25 for aerobic sludge stabilisation
(ATV, 1997)
Sludge production [g dry m./g TOCel] 0.6
assumption: 70% of dry matter
are organic matter, which has
50% carbon content
Energy demand [kWh/(pe*a)] 14.5* without faeces filtrate
[kWh/(pe*a)] 16.1* with faeces filtrate
* calculated according to LCA model of conventional WWTP (except energy
demand for aerobic stabilisation: 1.8 kWh/kg Corg,degraded)
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Elimination of nitrogen and phosphorus
The removal ratios for N and P are lower in greywater treatment (70 and 86%) than for
combined wastewater in the conventional scenario (90 and 95%), even though both
waters are treated with the same technology. This is due to the lower influent N and P
loads of greywater, which would lead to unrealistic low effluent concentrations of N
and P if the removal ratios of conventional wastewater treatment would be applied.
Consequently, N and P removal in greywater treatment is adjusted to smaller total
elimination ratios to result in reasonable effluent concentrations. Effluent of greywater
treatment still has lower N and P concentrations than conventional wastewater treatment
(cf chapter 5.1.4), thus reflecting improved effluent quality of low-loaded greywater
treated with conventional activated sludge technology.
Energy demand
The energy demand is calculated from the particular processes (greywater lifting,
aeration, mixing, co-precipitation, sludge dewatering, and auxiliaries) according to the
LCA model for the conventional activated sludge plant. For aerobic stabilisation, an
additional energy demand of 1.8 kWh per kg of eliminated Corg is assumed for aeration
and mixing (estimated from conventional WWTP with aerobic stabilisation).
Transfer coefficients
The resulting transfer coefficients include the entire process with sedimentation, SBR,
sludge stabilisation and dewatering (Table 40). They are calculated here for scenarios
without co-treatment of faeces filtrate. The transfer of salts into sludge is neglected
here, while the transfer of heavy metals into sludge is calculated with elimination ratios
of conventional activated sludge plant (60-85% depending on element).
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Table 40: Transfer coefficients of elemental flows in
greywater treatment with sequencing batch reactor*
Input Emission as Effluent Air Sludge
[%] [%] [%]
TOC TOC 5.8
CO2-C 78.2
C
org in sludge 16.0
Ntotal NH4-N 2.0
NO3-N 15.5
N org 10.2
N
2-N 39.5
NH3-N 0.3
N
2O-N 0.2
N in sludge 32.3
Ptotal P-species 9.3 90.7
K K 100
Heavy metals
Pb, Cr, Hg 20 80
Cd 30 70
Cu 15 85
Ni 40 60
Zn 75 25
* including sedimentation, SBR, sludge stabilisation and dewatering; without
faeces filtrate
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4.1.2.6 Soil filter for greywater treatment
Due to its low load of nutrients and organic matter, greywater can be treated
appropriately in a planted soil filter (“constructed wetland”). This natural treatment
process is based on the elimination of pollutants during the passage of wastewater
through a soil filter that is planted with reed. The root system of the plants helps to
prevent clogging of the soil filter, facilitates oxygen transport to the lower filter layers,
and provides surface area for the growth of attached microorganisms. These
microorganisms are mainly responsible for the degradation of organic matter and
nitrogen, whereas the contribution of the reeds is relatively small (Langergraber, 2005).
Most of the COD and nitrogen is removed by microbial activity in the soil filter, while
phosphorus is mainly retained by adsorption on soil particles (Wissing and Hofmann,
2002; Geller and Höner, 2003). The effluent is characterized by low turbidity, good
bacteriological quality, and low carbon and nutrient load. Soil filters are known as a
reliable process for wastewater treatment if sufficient surface area is provided to ensure
adequate hydraulic loading rates (ATV, 1998; Cooper, 2005).
Process layout
Upstream of the soil filter, particulate matter is removed by sedimentation to reduce the
pollutant load on the filter and prevent clogging by particle aggregation on the filter
surface. Sludge from sedimentation stage is dewatered and transported to an
incineration plant (stabilisation is neglected here due to low sludge volume). The soil
filter is operated as a vertical-flow filter with intermittent loading of greywater (Figure
29). Optionally, faeces filtrate can be added to particle-free greywater after
sedimentation stage. Long-term experience has shown that no excess sludge is
accumulating in the filter (Geller and Thum, 1999). Reed is mowed once each year and
added to composting or digestion process.
Process inventory
Particulate matter (15% of TOC, 11% of N, 10% of P) is removed by sedimentation.
The subsequent soil filter provides organic carbon removal, nitrification and partial
denitrification, and phosphorus removal (Table 41). Nitrogen removal occurs by
denitrification processes in anoxic micro-zones of the soil filter and by plant uptake.
While carbon and nitrogen are eliminated by microbial activity, phosphorus is mainly
eliminated by adsorption on soil particles. Phosphorus elimination can be as high as
90% in fresh soil, especially if the iron content of the filter material is high (Peter-
Fröhlich et al., 2007). However, the long-term elimination rate for phosphorus is
expected to be around 50% throughout the lifetime of the filter material (Otterwasser,
2005), because it will eventually reach saturation with phosphorus. Adsorption capacity
can be extended by the addition of specific adsorption materials into the filter (Arias
and Brix, 2005; Molle et al., 2005).
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Figure 29: System layout for greywater treatment in soil filter
The removal of inorganic salts (K, S, Ca) occurs only via plant uptake. Heavy metal
elimination efficiencies in soil filters have been investigated for Cu and Zn (Vymazal
and Krasa, 2003; Otterwasser, 2005). For other heavy metals, elimination has been
roughly estimated to be equivalent to conventional wastewater treatment. Their removal
in the soil filter occurs mainly by adsorption on soil particles, while plant uptake is
negligible.
Energy demand
The energy demand of the soil filter system is determined by the energy consumption of
the feed pump. Beside the static pressure head (4 m), a dynamic pressure head of 8 m is
calculated for the distribution system (plastic pipes with drilled holes) according to the
recommendations of DWA (DWA, 2006).
The loss of water through evapotranspiration from soil or plant surface can reach a
significant fraction of the inflow. Depending on plants and weather conditions,
evapotranspiration rates of 1 – 18 mm/d are possible (Geller and Höner, 2003). An
average rate of 5 mm/d (= 5 L/m²*d) is assumed in this study.
Surface area
The surface area of a soil filter system has to be adapted to the volume and quality of
the influent water. Usually, a surface area of 1 – 3 m² per inhabitant equivalent is
sufficient for greywater treatment to prevent clogging of the filter surface, hydraulic
overload or oxygen deficiency in the filter (Oldenburg, 2002; Peter-Fröhlich et al.,
2007). In this study, the soil filter is designed with a surface area of 2 m² per inhabitant
(2.5 m²/pe for treatment of faeces filtrate with greywater).
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Table 41: Process parameters of soil filter for greywater treatment
Parameter Remarks Sources
Removal in sedimentation
TOC [%] 15
Ntotal [%] 11 particulate
fraction
Ptotal [%] 10
Removal in soil filter
TOCdissolved [%] 90
NH4-N [%] 96
Ndissolved [%] 40 partial denitrification
Pdissolved [%] 50 adsorption (long-term)
1, 2, 3
Heavy metals [%] 60-80 depending on element see
Table 43
Salts [%] removal by plant uptake
Energy demand [kWh/(pe*a)] 1.6 (2.1*) pumping with static and
dynamic pressure head 4
Evapotranspiration [L/(m²*d)] 5 1 – 18 mm/d 2, 5
Surface area [m²/pe] 2 (2.5*) 1 – 3 m²/pe 5, 6
* in scenario SC2 due to extra volume of faeces filtrate
Sources:
1) Bahlo, 1999
2) Langergraber, 2005
3) Otterwasser, 2005
4) DWA, 2006
5) Oldenburg, 2002,
6) Peter-Fröhlich et al., 2007
Plant uptake
The plant uptake of organic carbon, nutrients, and inorganic salts is estimated from
literature (Table 42). By recycling of the mowed reed to the composting or digestion
process, a small amount of nutrients and organic carbon can be reused as fertilizer. The
soil filter produces an average amount of 2.44 kg reed per m² and year above ground
with a dry matter content of 41% (Peverly et al., 1995). Sludge production from
microbial growth or clogging inside the soil filter is neglected.
Uptake of inorganic salts is roughly estimated. The accumulation of heavy metals in
plants is negligible (~ 1%) (Geller and Thum, 1999). Most of the heavy metals seem to
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accumulate in the roots, which are not harvested and remain in the soil filter (Vymazal
and Krasa, 2003).
Table 42: Plant uptake of specific elements in soil filter
Substance Remarks Sources
Dry matter [g/(m²*a)] 1000 Plant production Peverly et al., 1995
Corg [g/(m²*a)] 370 Peverly et al., 1995
N [g/(m²*a)] 10 Langergraber, 2005
P [g/(m²*a)] 3 Langergraber, 2005
K [g/(m²*a)] 10 Estimated
S, Ca [%] 1 Estimated
Cl, Na [%] 0 Estimated
Heavy metals [%] 1 Estimated Geller and Thum, 1999
Transfer coefficients
Overall transfer coefficients for sedimentation and soil filter treatment are calculated
with the LCA model (Table 43). Compared to the SBR process, the soil filter is
characterized by relatively low nutrient removal and higher effluent loads of nitrogen
and phosphorus. The advantages of the soil filter are its low energy demand and low
sludge production.
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Table 43: Transfer coefficients for greywater treatment in soil filter
Input Emission as Effluent Air Reed
Filter
material Sludge
[%] [%] [%] [%] [%]
Organic carbon TOC 8.5 15.0
CO2-C 76.5
Ntotal-N NH4-N 2.2
NO3-N 30.7
N org 20.5
NH3-N 0.3
N
2O-N 0.2
N
2-N 30.9
N 4.2 11.0
Ptotal-P P-species 45 3.3 41.7 10
K K-species 97.3 2.7
Heavy metals*
Pb, Cr, Cu, Hg, Zn 20 1 79
Cd 30 1 69
Ni 40 1 59
Transfer coefficients calculated with LCA model including sedimentation + soil filter,
without co-treatment of faeces filtrate
* estimated with transfer coefficients of conventional wastewater treatment except Cu
and Zn (Vymazal and Krasa, 2003; Otterwasser, 2005), transfer into reed roughly
estimated to 1% of influent load (Geller and Thum, 1999)
4.1.2.7 Membrane bioreactor for greywater treatment and reuse
If non-potable reuse of purified greywater is targeted (e.g. for toilet flushing), greywater
can be treated in an activated sludge process combined with a membrane stage, a
membrane bioreactor (MBR). Ultrafiltration membranes are submerged directly into the
activated sludge tank to replace the conventional clarification stage for sludge
separation. The greywater is drawn through the membranes by a slight negative pressure
(outside-in mode), leading to an efficient separation from the activated sludge and an
effluent with low turbidity and good hygienic quality. Effluent from MBR processes
usually has a sufficient quality for direct reuse as service water (Jefferson et al., 2000).
Another advantage of MBR technology is the reduction of required tank volume by a
factor of 3 – 4 compared to conventional activated sludge plants (Pinnekamp and
Friedrich, 2006). A drawback of this advanced technology is the high energy demand
for aeration and the need for periodic cleaning of the membranes with chemicals to
ensure constant flow rates and prevent membrane fouling.
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Process layout
For the protection of the membrane modules against coarse materials, hair etc, a
pretreatment of the greywater with fine sieving is recommended (Frechen et al., 2006).
After the pretreatment, faeces filtrate can be added to the process if required. The
membrane bioreactor operates with nitrification, denitrification, and chemical P
elimination. Sludge is separated by passing the effluent through ultrafiltration
membranes. Excess and primary sludge is stabilised via extended aeration, dewatered,
and incinerated. The recycling of sludge liquor to the influent is neglected due to the
linear model structure. Additionally, a proportion of the effluent is stored in holding
tanks to be reused for toilet flushing (Figure 30).
Fine sieving
Membrane
bioreactor
Activated sluge with
nitrification, denitrification,
and chemical P elimination
Greywater
Discharge Incineration
Excess
sludge
Faeces filtrate
(optional)
Aerobic
stabilisation +
dewatering
Truck transport
(30 km)
Primary sludge
Holding
tank
Reuse for
toilet flushing
Figure 30: System layout for greywater treatment in membrane bioreactor with partial reuse
Process inventory
In principle, the process model of the MBR is adapted from the conventional SBR plant
for greywater treatment (see chapter 4.1.2.5) in terms of emissions and resource
demand. Adjustments have been made in process performance (= elimination ratios),
sludge production, and energy demand (Table 44):
Particulate matter (15% of TOC, 11% of N, 10% of P) is separated by fine
sieving or inside the MBR without demand for aeration energy or chemicals.
Energy demand for fine sieving is assumed to be 0.01 kWh/m³.
Process performance in terms of carbon oxidation, nitrification, and
denitrification is estimated following experiences from an MBR pilot plant
treating municipal greywater (Peter-Fröhlich et al., 2007).
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Phosphorus removal via chemical precipitation is assumed to be more efficient
(95%) than in the SBR plant (86%), because MBR processes are known to
have superior particle retention capacity compared to traditional clarifiers,
leading to lower P effluent concentrations (Pinnekamp and Friedrich, 2006).
Transfer of heavy metals into sludge is estimated according to the SBR
process, while transfer of inorganic salts is neglected.
Excess sludge production is typically lower than in an SBR process. Pilot
experiments resulted in an average value of 0.11 g dry matter/g CODelim (~ 0.3
g dry matter/g TOCelim) for a sludge age of 20 d (Peter-Fröhlich et al., 2007).
Extensive bubble aeration to prevent blockage and fouling of the membrane
modules leads to a high energy demand of MBR systems. They have an
average energy demand of 0.7 – 1.5 kWh/m³ for treatment of combined
wastewater (Pinnekamp and Friedrich, 2006) and 0.5 – 0.9 kWh/m³ for
greywater (Peter-Fröhlich et al., 2007).
Energy and chemical demand for membrane cleaning is neglected here.
Table 44: Process parameters of MBR for greywater treatment
Parameter Remarks
Elimination in fine sieving Energy demand: 0.01 kWh/m³
TOC [%] 15
N [%] 11
P [%] 10
particulate fractions of
influent loads
Elimination in MBR
TOCdissolved [%] 93
NH4-N [%] 96
Ndissolved [%] 60
estimated from MBR pilot
plant for greywater
treatment (Peter-Fröhlich et
al., 2007)
Pdissolved [%] 95 with 20% biological P uptake
Heavy metals [%] 60 – 85 depending on element
Inorganic salts [%] 0 neglected
Excess sludge
production [g dry m./g TOCel] 0.3 for sludge age of 20 d
(Peter-Fröhlich et al., 2007)
Energy demand [kWh/m³] 0.6 0.5 – 0.9 kWh/m³
(Peter-Fröhlich et al., 2007)
[kWh/(pe*a)] 20.2
(25.6*)
incl. pretreatment, aerobic sludge
stabilisation (1.8 kWh/kg Cdegraded)
and dewatering
* with faeces filtrate (scenario SC3)
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Transfer coefficients
The transfer coefficients for the greywater treatment in an MBR process (Table 45) are
calculated for the entire process with fine sieving, MBR, sludge stabilisation and
dewatering. Compared to the SBR process, the MBR process is characterized by less
organic matter in sludge, higher concentrations of nitrogen in the effluent, and better
elimination of phosphorus.
Table 45: Transfer coefficients of elemental flows in
greywater treatment with membrane bioreactor*
Input Emission as Effluent Air Sludge
[%] [%] [%]
TOC TOC 6.0
CO2-C 82.4
C
org in sludge 11.6
Ntotal NH4-N 2.0
NO3-N 20.7
N org. 13.7
N
2-N 30.7
NH3-N 0.3
N
2O-N 0.2
N in sludge 32.4
Ptotal P-species 4.6 95.4
K K 100
Heavy metals
Pb, Cr, Hg 20 80
Cd 30 70
Cu 15 85
Ni 40 60
Zn 75 25
* for fine sieving, MBR, sludge stabilisation and dewatering; without faeces
filtrate
Greywater reuse
Greywater which has passed the ultrafiltration modules is assumed to be hygienically
safe for non-potable reuse (e.g. toilet flushing) without further disinfection (Lazarova et
al., 2003). Hence, a part of the MBR effluent is stored in holding tanks and pumped
back to the households in a separate service water pipe, providing a pressure of 5 bar (=
0.23 kWh/m³). The calculated elemental composition of reused greywater is adopted for
toilet flush water in the substance flow model. This leads to a recursive model structure
in scenario SC3 (greywater flush water faeces filtrate greywater), which is
overcome by an iterative calculation of greywater composition.
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4.1.3 Fertilizer application
For the inventory of the application of different types of fertilizers in agriculture,
several processes have to be considered (Figure 31):
The operation of the agricultural tractor requires diesel fuel for operation and
causes emissions
Emissions arise from the transformation and evaporation of fertilizer
components. Emission factors are specific for each type of fertilizer.
Fertilizing equivalents of the respective fertilizer are determined by the plant
availability of nutrients, which are specific for each type of fertilizer.
Figure 31: Processes during fertilizer application
4.1.3.1 Agricultural tractor
The inventory for the operation of the agricultural tractor is described with a dataset
from Umberto® (IFU and IFEU, 2005). This dataset calculates the energy demand and
the emissions from tractor operation depending on engine power, total working time,
and engine load levels (Borken et al., 1999):
Engine load levels for the fertilizer-specific application techniques (solid or
liquid) are compiled from a study of agricultural machinery (Rinaldi et al.,
2005).
The total working time for the application of mineral and organic fertilizers
is calculated from the field area and the area-related working time (Table
46).
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The field area on which the fertilizers are applied is estimated by assuming a
maximum application rate for nitrogen of 110 kg N/(ha*a). For compost, the
applicable amount is limited by the total mass (25 t/(ha*a)) due to its low
content of plant-available nitrogen. For sewage sludge, the applicable
amount is limited to 1.66 t/(ha*a) dry matter by law (AbfKlärV, 1992).
Table 46: Calculation of total working time during fertilizer
application
Scenarios Type of fertilizer Field
area* Time
per area Regime** Total
working
time
[ha] [h/ha] [doses/a] [h/a]
Mineral 147 0.7 3 308
Reference
(R + Rmin)
Compost 7 1.5 + 2.3 1 11 + 16
Mineral 128
0.7 3 270
Reference Compost 7 1.5 + 2.3 1 11 + 16
(Ragri)
Sewage sludge 51 1.6 2 165
Faeces and urine
digestion (V)
Digester residual 148 1.6 2 472
(liquid)
Mineral 31 0.7 3 65
Faeces digestion +
urine separation Urine 102 1.6 2 328
(SV)
Digester residual
(stabilised) 15 1.5 + 2.3 1 23 + 35
Mineral 43 0.7 3 89
Urine 102 1.6 2 328
Faeces composting
+ urine separation
(SC)
Compost 12 1.5 + 2.3 1 18 + 28
* calculated with maximum amounts: 110 kg N/ha*a or 25 t compost/ha*a or 1.66 t/ha*a
sewage sludge (dry matter)
** assumed number of doses per year
Sources: see annex 12.5
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4.1.3.2 Volatilisation and transformation of fertilizer
components
In general, the gaseous losses of nitrogen during application of the various types of
fertilizer heavily depend on the application methods (e.g. spreading, splash plates,
trailing hoses, injection, incorporation by ploughing), weather conditions (temperature,
wind, precipitation), and soil type and conditions (pH, infiltration capacity, vegetation)
(ECETOC, 1994). Consequently, average emission factors in the literature are often
given in ranges, depending on boundary conditions. In the field of agricultural LCA, a
structured method has been proposed to estimate on-field nitrogen emissions from
fertilizer application (Brentrup et al., 2000). It is based on specific parameters for
calculating ammonia volatilisation in relation to air temperature, soil infiltration rate,
time between application and incorporation, and precipitation. N2O and NOx emissions
are estimated by a constant factor (Bouwman, 1995). The European emission inventory
for agriculture provides similar methods, proposing a simplified or a detailed
calculation methodology in relation to the available data (EMEP/CORINAIR, 2004).
However, no specific information about the soil type and further conditions during
application are applicable for the present study. Therefore, it is decided to follow a
simplified method with constant factors for the calculation of emissions. These figures
are adopted from emission inventories and other LCA studies, amended by field data
from pilot projects of source-separation systems (Table 47). They can vary significantly
under real conditions, but they are expected to be representative for an average loss over
a longer period of time.
Mineral fertilizer
Mineral fertilizers are delivered as solid stable chemicals without the potential for
gaseous emissions. However, their application on agricultural fields may cause
emissions of NH3, N2O, NOx, and CO2 through hydrolysis and various chemical
reactions. The rate of emissions is influenced by the chemical composition of the
fertilizer and soil parameters (pH, moisture, composition) (ECETOC, 1994). A
simplified calculation of ammonia emissions can be performed with average emission
factors (EMEP/CORINAIR, 2004). Emission factors for six different nitrogen fertilizers
are connected to nutrient content and market share to calculate the average NH3
emission rates. For nitrous oxides, constant factors of 1.25% N2O-N/kg N (Bouwman,
1995) and 0.7% NOx-N/kg N (Bouwman et al., 2002) are used. These emissions occur
during microbial processes of nitrification and denitrification. The factors are related to
the nitrogen input after NH3 volatilisation, which predominantly occurs earlier than the
nitrous oxide emissions (Brentrup et al., 2000). The carbon dioxide emitted from urea
hydrolysis (0.59 g CO2/g N) originates from fossil sources and contributes to climate
change.
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Table 47: Emission factors for mineral and secondary fertilizers during
agricultural application
NH3 N
2O# NOx# CO2 fossil CO2 regen. Source
g NH3-N/
g N g N2O-N/
g N g NOx-N/
g N g CO2/g N g CO2-C/g C
Mineral N fertilizer 0.05 0.0125 0.007 0.59 0 1,2
Sewage sludge 0.08 0.0125 0.007 0 0 3
Compost from
biowaste/faeces 0.05 0.0125 0.007 0 0.5** 4
Urine 0.1 0.0125 0.007 0* 0 5
Digester residual,
stabilised 0.063 0.0125 0.007 0 0.5** 4
Digester residual,
liquid 0.22 0.0125 0.007 0 0 6
# emissions from nitrification and denitrification, factor is related to applied N after NH3
volatilisation
* CO2 from urea hydrolysis is from regenerative sources (human food)
** long-term degradation of 50% of organic carbon content
1) ECETOC, 1994
2) EMEP/CORINAIR, 2004
3) NH3 emissions estimated between mineral fertilizer and urine due to lack of data
4) according to compost from biowaste/digester residual in Vogt et al., 2002
5) see chapter 4.1.2.1 for details
6) NH3 emissions from spreading of cattle slurry (ECETOC, 1994)
Organic fertilizers
The application of organic fertilizers such as sewage sludge, stabilised urine or compost
is connected with gaseous nitrogen emissions as well (Table 47). NH3 emissions of
urine application are estimated to 10% of Napplied from data of field trials. For liquid
digester residual, no data from field trials is available. Due to the mixture of digested
faeces and urine, the respective NH3 emissions during application are estimated in
analogy to cattle slurry (22% of Napplied). For compost, assumptions from an LCA study
of biowaste composting are adopted.
Specific emission factors for N2O and NOx are not available for organic fertilizers, so
emission factors are estimated to be equivalent to those of mineral fertilizer. Carbon
dioxide from urea hydrolysis of urine and from carbon degradation of compost does not
contribute to climate change due to the regenerative source of the carbon (= human
food).
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4.1.3.3 Plant availability of nutrients
The plant availability of the nutrients nitrogen, phosphorus, and potassium is a decisive
factor to determine the effective potential of organic fertilizers from source-separation
systems to substitute industrially produced fertilizer. Mineral fertilizers are usually
developed to supply the total amount of nutrients in a short term. Thus, they can be
applied precisely at a particular time to meet the nutritional demands of the agricultural
crops, which is usually high during the growing period. The short-term availability is of
particular importance for nitrogen and less important for phosphorus and potassium.
In general, multiple factors can influence the plant availability of nutrients from a
certain fertilizer, including soil properties (pH, moisture content, oxygen, organic
matter), time of application, and weather conditions (temperature and precipitation)
(Finck, 1992). For this study, it is important to which extent organic fertilizers can
effectively substitute industrially produced mineral fertilizers. The relevant figure is
consequently a relative plant availability compared to mineral fertilizer:
plant availability of nutrient from secondary fertilizer
plant availability [%] plant availability of nutrient in mineral fertilizer
The relative plant availability of nutrients in mineral and organic fertilizers is different
for each nutrient, because the biogeochemical cycles of the nutrients have distinct
characteristics. A short background of the most important aspects of the fate of nitrogen,
phosphorus and potassium in soil is given below.
Nitrogen
Nitrogen can be present either in a soluble inorganic form (NH4-N and NO3-N) or in
organic material such as amino acids, proteins etc (Norg). Plants can only take up
inorganic nitrogen, so the organic nitrogen has to be transformed in a soluble form
(“mineralised”) to be plant-available. The organic part of the nitrogen is therefore not
completely accounted for when determining the nutrient equivalents of a certain
fertilizer, because the mineralization of nitrogen can require a considerable time.
Several biological processes can transform nitrogen in the soil, e.g. nitrification,
denitrification etc. Pools of nitrogen in the soil include organic matter and ammonia
absorbed in clay particles. Due to the complexity of the various interrelationships
between physical, chemical and biological characteristics of soil, estimations of the
plant availability of nitrogen highly depend on the specific conditions during fertilizer
application (Bengtsson et al., 1997). Empirical field tests are a simple measure to
estimate the relative plant availability of nitrogen from secondary fertilizers by
comparing the yields for different types of fertilizer. Volatilisation of nitrogen species
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has to be taken into account while determining the substitution potential of organic
fertilizers to replace mineral fertilizers.
Phosphorus and potassium
For phosphorus and potassium, the short-term availability is less important for the
fertilizing effect due to the more balanced demand of most crops throughout the year.
Phosphorus is found in many inorganic forms as well as in constituents of soil organic
matter and in living organisms. Only a very small part (less than 0.1% in soil solution)
is present in the plant-available soluble forms of HPO4- and H2PO42- (Bengtsson et al.,
1997). Due to the relatively large pool of phosphorus in the soil, the availability of
phosphorus for plant uptake is both a matter of chemical equilibriums and kinetics. Soil
pH is one of the most important factors affecting the transformation of phosphorus in
soil since these chemical processes are sensitive to changes in pH. Plant-availability of
phosphorus may be limited in the short term if phosphorus is chemically fixed. Organic
phosphorus has to be transformed into inorganic phosphorus by microbial activity
before plant uptake. Due to the strong adsorption of phosphorus to soil particles,
leaching is negligible for inorganic phosphate.
The plant availability of potassium in soil is determined by similar factors. A large
pool of potassium is present in the soil in inorganic minerals (feldspar, clay etc) with a
major fraction of irreversibly bound potassium. The kinetics of dissolution of potassium
into the soil solution determines its plant availability. Potassium in organic fertilizers is
assumed to be in a soluble form which is readily plant-available (Stadtmüller, 2004).
Leaching of potassium from secondary fertilizers is comparable to that of mineral
fertilizer.
Losses by volatilisation of phosphorus or potassium are neglected due to the physical
characteristics of these inorganic ions and their species.
Plant availability
The relative availability of nutrients from secondary fertilizers has to be estimated
according to pilot studies and field trials (Table 48). Values for plant availability of
nitrogen are assumed after possible losses due to volatilisation of NH3, N2O, and NOx.
If no data is available for the specific substrate, figures for organic fertilizers with
similar composition are applied.
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Table 48: Plant availability of nutrients from organic fertilizers with
regard to the substitution potential for mineral fertilizer
Substrate Nitrogen Phosphorus Potassium Source
[% of total N*] [% of total P] [% of total K]
10-63 / 50 20-70 / 70 100 / – 1 / 2
Sewage
sludge
50 70 100
90-100 / 60-90 / 80-90 – / 80-120 / – – / – / – 3 / 2 / 4
Urine
100 100 100
50 / 10 / 5-15 100 / 100 / 20-40 100 / 100 / >85 5 / 6 / 7
Biowaste
compost
10 70 100
30 (20-60) 8
Digester
residual
(stabilised)
30 100 100
contains 100% of urine with high nutrient availability 9
Digester
residual
(liquid)
90 100 100
Bold: this study
*plant availability of nitrogen after volatilisation of NH3, N2O, NOx, leaching of NO3 to
groundwater is not taken into account
1) Schneidmadl, 1999 (literature review)
2) Bengtsson et al., 1997 (including volatilisation of nitrogen during storage and handling)
3) Peter-Fröhlich et al., 2007 (pot and field tests)
4) Stockholm Vatten, 2000 (field tests)
5) assumptions in Vogt et al., 2002
6) EPEA, 2004
7) Stadtmüller, 2004
8) Roschke, 2003 for dewatered digested manure
9) Jönsson et al., 2004 for blackwater
Sewage sludge
The plant availability of nutrients from sewage sludge depends on the chemical forms of
the nutrients. If the sludge is stabilised and dewatered, a major part of the inorganic
nitrogen is lost with the sludge liquor, leading to limited nitrogen availability. Typically,
25% of organic N and 90% of mineral N in sewage sludge are estimated to be plant-
available (ATV, 1996). This study assumes a 50% availability of nitrogen for the
dewatered sewage sludge. For phosphorus, the availability is heavily depending on the
mode of P elimination in the wastewater treatment plant. While P in sludge from
biological P elimination should be completely plant-available, chemically eliminated P
may not be readily soluble due to the strong chemical fixation in the precipitates (Coker
and Carlton-Smith, 1986; Bengtsson et al., 1997; Suntheim, 2001; IME, 2005).
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Therefore, a limited P availability of 70% is assumed for phosphorus from chemical P
elimination in this study. However, the overall plant availability can eventually reach
100% in the long term (ATV, 1996; Onnen, 2001; IME, 2005).
Urine
For urine, the availability of all nutrients seems to be equal to that of mineral fertilizer.
Results from pot and field tests in Germany revealed no significant difference to
industrial fertilizer in terms of nitrogen availability (Peter-Fröhlich et al., 2007; Simons
and Clemens, 2004), presumably due to the fast transformation of urea into ammonium
by microbial activity. Other studies assume a reduced availability of nitrogen in urine
(60-90%), but here losses during storage and handling (volatilisation and leaching) are
included in the figures. Phosphorus and potassium are present in their soluble inorganic
form which is readily plant-available.
Biowaste compost
In biowaste compost, the major part of nitrogen is bound to the organic matrix due to
high microbial activity. Inorganic nitrogen is mainly lost by volatilisation of NH4-N to
the atmosphere during the composting process and NO3-N in leachate, leading to low
nitrogen efficiency (10%). The P availability is estimated to be limited to 70% due to
the high fraction of organic phosphorus (Stadtmüller, 2004).
Digester residual
For the residual of the co-digestion process of faeces and biowaste, the nitrogen
efficiency particularly depends on the post-treatment. If the digester residual is
dewatered and further stabilised by open composting/storage, a large part of the plant-
available inorganic nitrogen is lost with the leachate (NO3-N) or through atmospheric
emissions (NH4-N) similar to biowaste compost. Literature values for dewatered
digester residual from animal manure suggest that only 30% of the remaining nitrogen
can be used by the plants (Roschke, 2003). Phosphorus and potassium is also lost to a
smaller extent with the leachate, but the phosphorus remaining in the composted
residual should be completely usable for the crops.
In case of direct application of the liquid digester residual, the nitrogen availability is
estimated to be almost comparable to mineral fertilizer (90%). Data for the fertilizing
equivalence of digested blackwater is scarce. Pilot studies indicate a reduced N
availability for undigested blackwater (Simons and Clemens, 2004). A major part of the
nutrients contained in this residual derives from the urine, for which a complete nutrient
efficiency is assumed. The digestion process itself increases the plant-available nitrogen
by the conversion of Norg into NH4-N without significant losses of gaseous nitrogen.
Consequently, liquid digester residual is assumed to be a good fertilizer with high
nutrient availability.
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4.2 System construction
The implementation of a source-separation sanitation system has not been realized to
date in an urban settlement with 5000 inhabitants. Pilot plants for several houses or
small settlements (10 – 150 inhabitants) have been built in Sweden (Johansson et al.,
2001) and Germany (Oldenburg et al., 2002; Otterwasser, 2005; Peter-Fröhlich et al.,
2007), but related construction data – if available – cannot be directly transferred to a
larger settlement.
Thus, construction data has to be generated for the source-separation systems. For
reasons of symmetry, construction data of the conventional system is generated with the
same methodology, even though respective data for the construction of conventional
sewers and treatment would be available. The housing structure of the settlement is
adopted from an existing quarter of the Berlin area (Berlin-Nikolassee) to reflect
realistic conditions. Data from the geographical information system (GIS) of the local
water supplier is used for the layout of the sanitation system. Conventional and source-
separation systems have been exemplary designed in close cooperation with consulting
engineers (Otterwasser GmbH, Lübeck, Germany) and have been published before
(Peter-Fröhlich et al., 2007). Consultants are experts with considerable experience in the
design of source-separation systems (e.g. Lübeck-Flintenbreite).
4.2.1 Settlement structure
The settlement structure is adopted from an existing quarter of Berlin with
approxiamtely 5000 inhabitants. The quarter is located in the south-west of the Berlin
city area and can be characterized as a sub-urban area (Berlin-Nikolassee). The
distribution of houses, house types and other structural data are taken from data of the
geographical information system (GIS) of Berliner Wasserbetriebe (Berlin water works,
Table 49). The quarter is divided by a large road into a northern part and a southern part
(see annex 12.6.1 for a map).
The layout of the sewer systems and the treatment facilities is based on the real
distribution of inhabitants throughout the area. For simplification of sewer dimensioning
and slope, it was assumed that the whole terrain is plain (i.e. no elevation or hills).
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Table 49: Structural data of the urban settlement
Parameter Remarks
Population 5000
inhabitants
Area 126
ha
Population density 40 inhabitants/ha
Buildings 1000
public and commercial
buildings excluded
One-family houses 649 units
Semi-detached house 98 units
Row houses 123 units
870 house units
Apartment houses 130 units = 1170 apartments (assumption:
three-storey buildings with
three apartments per floor)
Data adopted from GIS data of existing city quarter (Berlin-Nikolassee)
4.2.2 Inventory
The construction inventory of the sanitation systems includes sanitary in-house
installations, the complete sewer system, and the treatment facilities. Although the
wastewater system layout can heavily depend on local conditions, the following
calculations are supposed to provide a defensible estimation of the different
infrastructural needs of the scenarios. More details of the construction inventory are
provided in annex 12.6.
4.2.2.1 Sanitary in-house installations
The realization of the sanitary in-house piping strongly depends on the particular layout
of the respective buildings. For reasons of simplicity, a prototype layout for a house unit
and an apartment unit is assumed by estimating necessary pipe lengths and diameters.
The dimensioning is done in accordance with the relevant legal regulations in Germany
(DIN EN 12056-2, 2000).
Pipe lengths and diameters are multiplied with the number of houses and apartments
to calculate the total material demand (Table 50). In-house piping is made from
polypropylene (PP) or high-density polyethylene (HD-PE) for houses and cast iron for
apartments (due to legally required fire prevention measures). The additional weight of
fittings and plug-in connections is estimated with a proportional factor. The sanitary in-
house piping includes the base pipe below the basement which runs up to the house
shaft.
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Table 50: Inventory data for pipes of sanitary in-house installations
Material Ø Weight*
WW GW SWS F Vac** U Fitting
factor***
[mm] [kg/m] [m] [m] [m] [m] [m] [m] [%]
PP 50 0.285 12180 13050 2910
15
70 0.454 6253 8948
15
100 0.938 10875 7246 10875 6300
15
HD-PE 20 0.172 18156 10
50 0.666 17070
10
150 1.943 6300 6300
10
Cast iron 50 4.3 7020 7020 10
70 5.9 3510 10
100 8.4 6338 6338
10
Total length [m/pe] 8.5 7.4 3.6 4.7 3.4 3.6
Total plastics [kg/pe] 5.8 3.1 0.7 5.0 3.76 2.5
Total cast iron [kg/pe] 18.4 11.2 11.7 0.6
WW: combined wastewater, GW: greywater, SWS: service water supply (for greywater
reuse), F: gravity drainage of faeces, Vac: vacuum system, U: urine
* Sources: Ostendorf, 2005; Simona, 2005; Dueker, 2005
** plus vacuum collection tanks: 1260 units (each with 5 kg PE and 2.2 kg cast iron)
*** additional factor to account for fittings (10%, for PP: + 5% for plug-in connections)
4.2.2.2 Sewer system
The sewer system includes house shafts, house connections to the main sewer, and the
sewer system with piping and inspection chambers, ending at the respective treatment
facilities. The sewer system design follows common German rules in sewer layout
(ATV, 1999; DIN EN 752, 1997). The pipes of the conventional system have the largest
diameter (DN 150 - 400) due to the high volume of combined wastewater. For the
source-separation scenarios, the dimensioning of the gravity drainage systems is
adjusted to the reduced volume of greywater or toilet wastewater. The urine collection
system has a separate pipe for gravity drainage of separated urine to pumping shafts,
from where it is pumped to holding tanks. Purified greywater is supplied via service
water net for non-potable reuse.
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The resulting total length of the sewer pipes for each sanitation system is presented in
Table 51. Materials for the pipes are vitrified clay (combined wastewater, toilet
wastewater) or plastics (greywater, service water, urine, vacuum, pressure pipes).
Table 51: Total length of sewer systems
System Material
House
connections Drainage Pressure pipes**
Diameter Length Diameter Length
[m DN 150] [mm DN] [m] [mm DN] [m]
Combined wastewater Vitr. clay 10000 150-400 13805
Greywater PE 10000 150-200 13638
Service water supply PE 10000* 150 11108
Faeces Vitr. clay 10000 150 11108 50 7816
Vacuum PE 10000* 65-100 13308 65 2241
Urine PP 10000 150 11108 50 7050
* DN 32 for service water supply, DN 50 for vacuum system
** Faeces: to solid-liquid separation, vacuum: to biogas plant, urine: to holding tanks
Major additional parts (i.e. inspection chambers, pumping shafts and pressure pipes) are
included in the construction inventory. Material demand for pipes and additional parts is
aggregated to calculate the total material demand for the sewer system (Table 52). The
amount of soil material from excavation is calculated from the required depth of the
piping, providing a sufficient slope for gravity drainage. Pipes of source-separation
systems are laid in one combined trench wherever possible to minimize the required
excavation volume.
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Table 52: Total length and total material demand for sewer
systems per person
System WW GW SWS F Vac U
Total length [m/pe] 4.8 4.7 4.2 5.8 5.1 5.6
Total material demand*
Vitrified clay [kg/pe] 145 0 0 111.5 0 0
PP/PE [kg/pe] 1.3 13.6 12.6 1.9 4.1 12.5
Concrete [kg/pe] 552.4 508.6** 0 20 18.8 20
Cast iron [kg/pe] 25.6 21.7** 0 1.2 1.7 1.2
Excavation [m³/pe] 8.9 9** 0 0.1 0.1 0.4
WW: combined wastewater, GW: greywater, SWS: service water supply (for greywater
reuse), F: gravity drainage of faeces, Vac: vacuum system, U: urine
* including pipes + fitting factors, sealing, inspection chambers, and pumping shafts
** house shafts, inspection chambers and excavation for source-separation systems
are assigned to greywater system
The comparison of the calculated total length of the sewer system and the in-house
installations for each scenario reflects the multiple piping that is required in source-
separating systems (Figure 32). Two-flow systems (scenarios V) and three-flow systems
(i.e. with urine separation, scenarios SC and SV) enhance the necessary sewer length by
a factor of 2 or 3, respectively. Greywater reuse requires another piping system for the
recycling of purified greywater to the households. For the in-house installations, the
additional piping is less than proportional to the number of separated flows due to
constructional reasons (e.g. one ventilation pipe for multiple systems).
0
5
10
15
20
25
30
35
40
45
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Length of piping [m/pe]
Sewer In-house installations
Figure 32: Total length of sewer systems and in-house installations per person for each scenario
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4.2.2.3 Treatment facilities
The construction expenditures for the treatment facilities in the different sanitation
scenarios are estimated by a simplified layout. Major parts of the systems are included
in the inventory (Table 53), whereas smaller parts are neglected. Only the composting
plant is excluded from the construction inventory due to a lack of data. However,
composting plants are usually large facilities for high volume throughput, so that most
likely existing plants would be used for biowaste and faeces treatment.
Adequate inventory data for construction is not directly available for most of the
facilities, so inventory data has to be adopted from other LCA studies or pilot plants.
Where no comparable data could be acquired, material data is roughly estimated.
Table 53: Treatment facilities included in the construction inventory
System WW GW SWS F Vac U
Vacuum
system
SBR or
MBR or
soil filter
Storage
tanks
Solid-liquid
separation Biogas
plant
Urine
holding
tanks
Conventional
activated sludge
plant with
digester
Service buildings for source-separation systems
WW: combined wastewater, GW: greywater, SWS: service water supply (for greywater
reuse), F: gravity drainage of faeces, Vac: vacuum system, U: urine
Inventory data for the particular facilities is compiled from the following sources:
Conventional activated sludge plant: A detailed inventory is adopted from an LCA
study on sanitation systems (Schneidmadl, 1999). Inventory data originates from an
activated sludge plant with digester (21000 inhabitant equivalents) and is
recalculated in proportion to the influent volume.
SBR for greywater treatment: plant data is recalculated from data of the
conventional activated sludge plant with reduced daily influent volume
MBR for greywater treatment: plant data is basically adopted from activated sludge
plant. The compact construction of this process is taken into account by reducing the
material demand for tank construction (concrete, non-alloy steel, excavation) by a
proportional factor. Typically, aeration tanks for the activated sludge process can be
smaller by a factor of 3 – 4 for an MBR process, and the process does not need a
final clarifier (Pinnekamp and Friedrich, 2006). Additional material demand for
membranes and other equipment is neglected.
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Soil filter: Inventory data is adopted from material specifications of the pilot plant in
Lübeck-Flintenbreite (Oldenburg, 2002) and related to the required surface area.
Sedimentation tanks are additionally provided as primary treatment (HRT > 3 h).
Greywater reuse: Storage tanks for purified greywater prior to reuse (= service
water) are designed to provide the average service water demand of one day (Fbr,
2005).
Urine tanks: Holding tanks for urine have a sufficient volume to store separated
urine of 14 days as a minimum (= 21 L/pe).
Vacuum system: Inventory is estimated from manufacturer information for steel
tanks and pumps.
Biogas plant: Inventory data for the biogas plant is adopted from other LCA studies
(Edelmann et al., 2001; Ronchetti et al., 2002) and recalculated in proportion to the
required digester volume.
Solid-liquid separation: The equipment for the solid-liquid separation process for
faeces dewatering is not yet commercially available, so the required material is
roughly estimated.
Service buildings: For source-separation systems, three service buildings (70 m²) are
provided in the settlement to contain the various treatment facilities (vacuum plant,
solid-liquid separation etc).
In general, the construction inventory for the treatment facilities heavily depends on
actual design and local boundary conditions. The construction data calculated in this
study can only provide a rough estimation of the material demand for facility
construction. However, the facilities constitute only a minor part compared to in-house
piping and sewer system, so the uncertainty in this part of the inventory should not
compromise the overall results significantly.
4.2.3 Service life
In order to allow the aggregation of the inventory data from construction and operation
of the sanitation systems, the construction expenditures have to be scaled to a time
frame. This can be done by relating the material and energy flows of the construction to
the estimated service life of the system components. The estimation of an adequate
service life of sanitation systems for LCA purposes is a difficult task. Depending on a
rather high or low estimation, the influence of the construction on the overall LCA
comparison can be manipulated. For economic calculations, the estimated service life
for all components of a sanitary system has been assessed (LAWA, 2005). However,
these values relate more to an economic amortisation than to a realistic material-
dependent service life.
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In this study, service life for the different components of the sanitation systems is
estimated from other LCA studies in this field (Bengtsson et al., 1997; Schneidmadl,
1999) and data for economic purposes (LAWA, 2005). The chosen values reflect a more
conservative approach and are probably smaller than the realistic material lifetimes
(Table 54). On the other hand, this LCA study considers the construction of sanitation
systems, but excludes expenditures for both maintenance and repair. These exclusions
are neglecting the effort necessary to reach a long service life for the sanitation systems.
The assumption of a relatively short service life may roughly offset these limitations.
While evaluating the influence of the construction phase on the results of the LCA, the
question of adequate assumptions for service life has to be kept in mind.
Table 54: Estimated service life of infrastructure components
Bengtsson
et al., 1997 Schneidmadl,
1999 LAWA, 2005 This study
Drainage Service life in years
In-house pipes 30 – – 40*
Sewer pipes 30 75 50 – 80 50*
Manholes/shafts – – 50 – 80 50*
Pumps 15 15 8
12.5
Facilities
Buildings 30 – 30 – 50 40
Tanks 30 35 30 – 40 40
Machinery 15 15 8 – 30 12.5
Vacuum plant 15 15 25 – 40 40
Soil filter 30 50 12 – 15 40
* independent of material
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4.2.4 Materials for system construction
The components of a sanitation system are made from different materials. Sanitary
installations inside the houses are mainly made of cast iron or plastic, whereas drainage
pipes of the sewer system are usually made of plastic or vitrified clay. Manholes and
pumping shafts are made of concrete, and facilities for treatment (e.g. activated sludge
plant, soil filter etc) can include various materials.
Most of the inventories for material supply are delivered with Umberto® software (IFU
and IFEU, 2004). They are based on relatively old LCI datasets and are not described in
detail in this report. Other LCI data sets (e.g. steel, copper) are developed from
literature data of material and energy flows by connecting them to background datasets
from Umberto®. More recent LCI datasets (e.g. from ecoinvent database) were not
accessible by the author of this study. The transport distance for all materials from the
supplier to the settlement is assumed to be 300 km. For concrete, a shorter transport
distance of 50 km is assumed which is done in accordance to other LCA studies (e.g.
Reckerzügl, 1997; Frischknecht and Jungbluth, 2002). Concrete production is typically
located close to a building site to minimize transport costs for this heavy material which
is usually required in high amounts.
Aside from the production of building materials, the excavation of trenches forms
another major part of the construction expenditures. The energy demand for excavation
is calculated assuming a demand of 115 g diesel fuel for 1 m³ of excavated material
(Frischknecht et al., 1996), which is equivalent to an amount of mechanical energy of
1.425 MJ per m³ soil (IFU and IFEU, 2005). The operational emissions of the excavator
are calculated with a dataset for the operation of construction machinery (IFU and
IFEU, 2005).
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Table 55: Construction materials and inventory datasets
Material Components Source Remarks
Pipes
Polypropylene In-house pipes, urine pipes 1 Boustead, 1999a
Polyethylene Sewer pipes, vacuum pipes 1 Boustead, 1999a
+ welding energy
Cast iron In-house pipes 1,3 Recycling share: 88%
Vitrified clay Sewer pipes 1,3 Including sealing
Other
Concrete Manholes, tanks,
buildings, digester 2
Construction steel Buildings, tanks 2,4 Recycling share: 42%
Stainless steel Machinery, vacuum plant 2,4 Recycling share: 42%
Cast iron Manhole covers, pumps 1,3 Recycling share: 88%
Copper Activated sludge plant 2,5
Aluminium Activated sludge plant 1 Boustead, 2000
Polypropylene Urine pumping shafts 1 Mouldings
Polyethylene Activated sludge plant,
soil filter 1 Mouldings
Glass fibre
reinforced plastic
Urine tanks,
service water storage 1 Epoxy resin
and glass (1:1)
Limestone Activated sludge plant 1 Patyk and Reinhardt,
1997
Excavation Trenches, tanks 1 1.425 MJ Emech/m³ soil,
incl. excavator emissions
Transport to construction site is 300 km by truck for all material except concrete (50 km)
Sources:
1) IFU and IFEU, 2005
2) Frischknecht et al., 1996
3) Jeschar et al., 1995; Jeschar et al., 1996
4) BDSV, 1998; Corradini and Köhler, 1999; Fritsche et al., 2001
5) Kraus et al., 1999; Kippenberger, 2001
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4.3 Background processes
Background processes are processes or services that are included within the system
boundaries, but do not belong to the core system under investigation. Here, background
processes include the supply of electric and thermal energy, transport by truck, waste
incineration, and the supply of auxiliary materials for system operation (e.g. chemicals).
Additionally, the system expansion process of industrial fertilizer production is
described in this section.
4.3.1 Energy Supply
The supply of energy includes the forms of electric and thermal (heat) energy. The
datasets are related to conditions in Germany in terms of power plant technology and
energy mix.
Electric energy
The expenditures for the production and supply of electric energy are calculated with
datasets provided by Umberto® (IFU and IFEU, 2005). These datasets are mainly based
on data from databases GEMIS (Fritsche et al., 2001) and ECOINVENT (Frischknecht
et al., 1996). The process chain includes all steps of energy supply, including the
extraction and transport of fuels and the energy transport from power plant to customer.
The technology of the different types of power plants represents German conditions of
1990 – 2000. Data for the German energy mix in 2003 (Table 56) is derived from the
federal ministry of economics (BMWi, 2005). Heating values for the different fuels are
assumed according to Umberto® datasets (Table 56). It is further assumed that all
electricity is drawn at medium voltage, so that the energy losses from transport and
transforming add up to 1.8% of the total energy production (Frischknecht et al., 1996).
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Table 56: Power mix and efficiencies of electricity supply in Germany 2003
and respective heating values
Power plant type Proportion
in power mix1 Energy
efficiency2 Fuel Heating value
[%] [%] [MJ/kg]
Hard coal 23.7 37.2 Hard coal 29.2
Lignite 28.1 30.1 Lignite 21.1
Nuclear 33.0 31.0 Uranium 6375
Oil 1.3 41.7 Crude oil 40.6
Gas 9.3 35.7 Natural gas3 45.0
Hydro 2.9 100.0 -- --
1) BMWA, 2005; missing to 100%: wind, solar, waste incineration (1.7%)
2) electric energy output to thermal input (heating value)
3) density 0.776 kg/m³
Thermal energy
The supply of thermal energy is described with Umberto® datasets based on GEMIS
data (Fritsche et al., 2001). Power plant technology represents German conditions from
1990. The power mix for thermal energy production is heavily depending on the
particular situation and location of interest. Thermal energy production is frequently
coupled with electric energy production, as most power plants produce both types of
energy. For this study, thermal energy is produced in dedicated heating plants, and an
average fuel mix is assumed (Table 57).
Table 57: Power mix and efficiencies of thermal energy supply
Power plant type Proportion in
power mix Thermal efficiency*
[%] [%]
Hard coal 20 85
Lignite 10 85
Light fuel oil 20 85
Heavy fuel oil 10 85
Gas 40 90
* thermal output to thermal input (heating value)
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4.3.2 Transport by truck
The transport processes in this study are described with the respective dataset for truck
transport from Umberto® (IFU and IFEU, 2005). This dataset is based on the
TREMOD model which was developed for the German environmental agency (Knörr et
al., 1997). The parameterized model calculates fuel consumption and emissions as a
function of the transport distance, the cargo weight, the load factor, the type of truck,
and the road categories. The dataset is based on truck stocks operated in Germany in
1996.
In this study, only the transport distance and the cargo weight are adjusted depending
on the respective transport process in the system. The remaining parameters of the
dataset are assumed to be constant for each transport process. The load factor is set to
100% (= fully loaded) for one way and 0% (= empty trip) for the return. The truck type
is assumed to be a single truck with a total weight > 20 tons. The distribution of road
categories is set to 37% on highways, 41% on country roads, and 22% on municipal
roads according to average German data for this type of truck (IFU and IFEU, 2005).
With these assumptions, the TREMOD dataset calculates an overall primary energy
demand of 2 MJ per ton*km for the return trip, including the pre-chain for the provision
of fuel.
The transport distances of the respective cargo are heavily depending on local boundary
conditions. In an urban context, the distance of suitable farmland for application of
organic fertilizers can be relatively high, whereas it should be shorter for a rural setting.
If animal manure is readily available or has to be disposed, the demand for other
organic fertilizers in the proximity can be limited, resulting in longer transports
distances. Especially the transport of high-volume organic fertilizers (e.g. urine) may
eventually have a high influence on the energy balance of the system. Consequently,
this parameter is varied in sensitivity analysis to quantify its impact on the results of the
study.
The preset distances for all transport processes are estimated according to comparable
LCA studies and own assumptions (Table 58, cf. annex 12.7):
The transport of industrial products (mineral fertilizer, precipitation chemicals, and
construction materials) is assumed with 300 km, except for concrete which is
typically produced closer to the construction site (50 km)
Composting plants and waste incineration plants are large facilities with wide
service areas, resulting in a longer transport distance (20 and 30 km, respectively)
Biogas plants are smaller facilities that can be located close to the settlement, so that
truck transport is not necessary at all if pipes are used for the substrates
For the organic fertilizers, a distance of 20 km is assumed from the treatment
facilities (WWTP, composting or biogas plant, urine treatment) to the farms
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The urine collection and transport to the ozone treatment is estimated to 5 km
Biowaste from households is collected in a stop-and-go mode before it is
transported to the composting plant. Umberto® provides a dataset for biowaste
collection with an average distance of 7.5 km in stop-and-go mode
Table 58: Transport processes and distances
Cargo From To Distance
Fertilizers [km]
Mineral fertilizer Manufacturer Farms 300
Sewage sludge WWTP Farms 20
Biowaste and faeces Collection* Compost plant 20
Biowaste, faeces Collection* Biogas plant 0
Compost Compost plant Farms 20
Digester residual Biogas plant Farms 20
Urine Holding tanks Ozone treatment 5
Ozone treatment Farms 20
Waste
Sewage sludge WWTP Incineration 30
Biowaste Collection* Incineration 30
Auxiliary
Chemicals Manufacturer WWTP 300
Construction materials
Concrete Manufacturer Settlement 50
Settlement Disposal 50
Plastics, metals, etc Manufacturer Settlement 300
Settlement Disposal 100
* plus stop-and-go collection of biowaste (7.5 km)
Sources: annex 12.7
4.3.3 Incineration plant
The co-incineration of biowaste or dewatered sewage sludge in an incineration plant for
domestic waste is described by an Umberto® dataset for a municipal solid waste
incineration plant (IFU and IFEU, 2005). It represents an average technology of the
German plants in 1990 (grate firing, four-step flue gas cleaning with electrostatic filter,
spray absorber, baghouse filter with coke as absorbent, and Denox catalytic converter).
The dataset calculates the demand for auxiliary material and energy, the emissions and
residues, and the electric or thermal energy derived from the incineration of the waste.
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The calculation is based on the amount and elemental composition of the waste and its
heating value.
The heating value of the biowaste or sewage sludge is calculated roughly via Dulong´s
formula from the elemental composition (including C, H, O, S, H2O). In case of a
heating value of less than 1.85 MJ/kg waste or a dry matter content of less than 30%,
extra fuel (light heating oil) is required to maintain the incineration process. The default
parameters for the energy output are set to 10% electricity and 30% effective heat of the
net calorific value of the waste input. The supply of auxiliary material (coke, lime, light
fuel oil) required for plant operation is included with datasets from Umberto® (IFU and
IFEU, 2005).
4.3.4 Auxiliary material
Data for the supply of auxiliary materials is based on Umberto® datasets (IFU and
IFEU, 2005. Flocculants (FeCl3, FeSO4) are produced from industrial waste acids with
energy demand as the most relevant input (Ruhland, 2004). Coagulation aid
(polyacrylamide) is produced via polymerisation of acryl amide which is synthesized
from acrylonitrile. Coke, lime, and light fuel oil are required for the operation and flue
gas cleaning of the incineration plant. The respective datasets are calculated with
Umberto® data for basic materials, connected to specific literature data of production
processes (Table 59). The relevance of auxiliary materials for the present study is
limited, so that details of these datasets are not provided here. Further information can
be found in other LCA studies (Ruhland, 2004).
Table 59: Auxiliary materials for system operation
Material Application Source Remarks
Ferric chloride
(40% solution) Flocculant 1)
Production from waste acid, scrap iron and
chlorine (CED = 2.74 MJ/kg FeCl3)
Ferrous sulfate
(96% solution) Flocculant 1) Drying of FeSO4 (214 kWh/t FeSO4)
Polyacrylamide Coagulation aid 1)
Based on data for acrylonitrile production
(Boustead, 1999b)
Lime 1) Calcination of limestone + hydration
Coke
flue gas cleaning in
waste incineration
plant 2) Based on Corradini and Köhler, 1999
Light fuel oil additional fuel in
incineration plant 2) Based on Fritsche et al., 2001 and
Frischknecht et al., 1996
1) Ruhland, 2004
2) IFU and IFEU, 2004
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4.3.5 Industrial fertilizer production
Numerous different products of single or multi-nutrient fertilizers are produced in the
fertilizer industry, and they offer a range of different nutrient contents. The main
macronutrients are nitrogen (quantified as N), phosphorus (as P2O5), potassium (as
K2O), calcium (as CaO) and magnesium (as MgO). Beside the valuable nutrient content,
industrial fertilizers can contain considerable amounts of heavy metals, originating from
raw materials (e.g. raw phosphate ores) and possibly enriched during the production
process. For this study, it is important to characterize the substitutable mineral fertilizers
in terms of resource usage and emissions in their production process and regarding their
heavy metal content.
Heavy metal contents
For calculating the heavy metal content of an average P, N, or K fertilizer related to its
nutrient content, the nutrient concentration and market shares of the different fertilizers
have to be combined with their average heavy metal content.
Data for the average nutrient content of mineral fertilizers (Patyk and Reinhardt,
1997) is combined with market shares in the German fertilizer market in 1998/99
(Drescher-Hartung et al., 2001). The average heavy metal contents for each fertilizer are
compiled from several studies (Hackenberg and Wegener, 1999; Drescher-Hartung et
al., 2001), where the original source relates to the year 1992 (Boysen, 1992). Thus,
calculated mean concentrations of heavy metals (Table 60) are based on relatively old
data. A comprehensive up-to-date dataset for heavy metals in mineral fertilizers is not
publicly available.
Table 60: Mean concentrations of heavy metals and As for average
mineral fertilizers, related to the single nutrients
[mg/kg nutrient] As Cd Cr Cu Ni Hg Pb U Zn
N-fertilizer (as N) 9.3 6.0 77.9 26.0 20.9 0.07 54.9 51.5 203.0
P-fertilizer (as P2O5) 14.5 39.5 543.2 90.5 88.3 0.3 67.0 349.2 839.2
K-fertilizer (as K2O) 0.1 0.1 5.8 4.8 2.5 0.03 0.8 1.0 6.2
Sources: Patyk and Reinhardt, 1997; Drescher-Hartung et al., 2001; Hackenberg and
Wegener, 1999 (see annex 12.8.1 for calculation details)
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Recently, new values for heavy metal contaminations of several mineral fertilizers sold
in the EU were published (UBA, 2007a), suggesting significant changes for certain
heavy metals compared to the dataset from Boysen (cf. annex 12.8.1). However, UBA
data is not considered to be representative for the German fertilizer market due to the
low number of analysed samples. Thus, it is decided to use comprehensive data from
Boysen despite its relatively high age (15 years). The effect of using updated heavy
metal data from UBA on the toxicity assessment is shown in sensitivity analysis.
The contamination of phosphate fertilizer with elevated levels of elemental Cd, Cr, and
Zn originates mainly from raw phosphate ores. Recently, the toxic element uranium (U)
was detected in significant concentrations in mineral phosphate fertilizers (Kratz, 2004;
Fink, 2005). Due to lacking LCIA characterization factors for its toxic effects, uranium
is not included in the impact assessment method of this study.
Inventory for production
The production of mineral fertilizers in Germany and the associated substance and
energy flows have been carefully documented (Patyk and Reinhardt, 1997). However,
emissions in surface waters are not listed in this data, although they may be important
for the environmental evaluation. Hence, aquatic emissions are adopted from a Suisse
study (Gaillard et al., 1997) and recalculated, relating them to the average single-
nutrient fertilizer via market shares and nutrient content. Aquatic emissions of
phosphate and fluoride are updated with recent data from other studies (see annex ).
The inventory data takes into account the complete processes of production and supply
of mineral fertilizers, including transport and energy supply, starting with the extraction
of resources until the packing of the marketable product (Table 61). The transport of
mineral fertilizers from the production to the customer is not included.
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Table 61: Life cycle inventories of mineral fertilizer
production
Reference value 1000 kg N 1000 kg P2O51000 kg K2O
Input
Use of resources
Raw potash kg 10500
Limestone kg 550
Raw phosphate ore kg 4060
Sulphur kg 272
Cumulated energy demand MJ 48896 17432 10382
Output
Emissions (air)
CO2 (fossil) kg 2820 1117 617
CO kg 2.80 1.42 0.42
CH4 kg 7.45 2.07 1.38
NMVOC kg 0.54 0.49 0.12
NH3 kg 6.69 0.01 0
N2O kg 15.05 0.04 0.05
NOx kg 15.76 8.58 1.15
SO2 kg 5.16 11.98 0.27
Dust (> PM10) kg 2.31 1.11 0.85
HF kg 0 0.023 0
Emissions (water)
Metals
Al g 476.09 94.71 23.4
As g 0.96 4.59 0.05
Cd g 0.03 4.40 0
Cr g 4.94 23.04 0.28
Cu g 2.40 22.47 0.12
Ni g 2.43 18.11 0.12
Hg g 0 4.18 0.00
Pb g 2.67 19.58 0.19
Zn g 4.95 27.48 0,27
Nutrients
NH3 g 2.68 9.17 1.72
NO3 g 189.15 8.16 1.20
PO4 g 28.62 *4400 1.40
Chloride g 6219 5827 825.00
Fluoride g 1.65 *2200 0.27
Sources: Patyk and Reinhardt, 1997; Gaillard et al., 1997 for aquatic emissions
* updated with other data, see annex 12.8.2 for details
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5 Results
This chapter presents the results of the Life Cycle Assessment along with a discussion
of relevant findings. This includes:
selected results on the level of Life Cycle Inventory
all indicator results from the Life Cycle Impact Assessment with contribution
analysis
normalisation of indicator results to total impacts in Germany to reveal their
relative contribution to the respective impact categories
exemplary procedure of grouping and weighting of indicators to come up with a
conclusive evaluation while comparing two scenarios
sensitivity analysis to test the stability of the results against the variation of
important system parameters
5.1 Selected results of the Life Cycle Inventory
The setup of the Life Cycle Inventory (cf chapter 4) results in the collection of a huge
amount of data for all relevant processes. Input-Output balances can be compiled for
each single process or for the entire system under investigation. Due to the large number
of sub-processes and scenarios, these inventory results cannot be shown entirely in this
study. However, it can be useful to show selected results of the Life Cycle Inventory on
the level of cumulative emissions or resource demand before these results are
transferred into indicators during Life Cycle Impact Assessment. On the one hand,
inventory results can give a first hint of important differences between the investigated
systems. On the other hand, the consistency of the inventory data can be checked, and
the indicator results of the impact assessment are more transparent and comprehensible.
Detailed data on the level of LCI is listed in annex 12.9.
Selected results of the Life Cycle inventory are presented here for:
demand of electric energy for system operation
supply of nutrients to agriculture with organic fertilizers
system expansion processes (industrial fertilizer and grid energy)
effluent loads of COD, nitrogen and phosphorus from wastewater and
greywater treatment plants
heavy metal loads emitted to surface waters and agricultural soil
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5.1.1 Demand of electric energy for operation
Many processes require electric energy during the operation of the different sanitation
systems. The input-output balance for each scenario shows the composition of electric
energy input (= demand) and output (= supply), separated for the different sub-
processes (Figure 33). The resulting net energy demand gives a picture of the effective
demand for electric energy in each scenario (Figure 34).
-60
-40
-20
0
20
40
60
80
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Energy demand [kWh/(pe*a)]
Urine separation
Digestion
Composting
Wastewater
treatment
Pumping/Vacuum
system
Drinking water
supply
Incineration of
sludge
Feedstock
Incineration of
biowaste
Sewage
gas/biogas
Figure 33: Input-Output balance of electric energy for system operation
-10
0
10
20
30
40
50
60
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Net electricity demand [kWh/(pe*a)]
Figure 34: Net electricity demand for system operation
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Reference scenarios
In the reference systems, a major part of the demand is due to the supply of drinking
water (~ 40%) and the treatment of combined wastewater (~ 55%). The use of low-flush
toilets lowers the demand for drinking water demand by 12% (= 2 kWh/(pe*a)) in
scenarios R and Ragri compared to the Rmin scenario. The energetically optimized
wastewater treatment in scenarios R and Ragri leads to energy savings of 16 kWh/(pe*a),
of which 4 kWh/(pe*a) are due to more efficient machinery and 12 kWh/(pe*a) are due
to energy recovery via sludge digestion with sewage gas electrification. Thus, the net
energy demand of the Rmin scenario can be lowered from 48 kWh/(pe*a) to 30
kWh/(pe*a) (-38%) if the wastewater treatment is energetically optimized and low-flush
toilets are used.
Separation scenarios
For the separation systems, the input of electric energy is generally higher than for the
reference system. Even though the use of low-flush or vacuum toilets saves some
energy in drinking water production (~ 3 – 7 kWh/(pe*a)), additional processes such as
the vacuum system (15 kWh/(pe*a)), the operation of the digestion process (14 – 17
kWh/(pe*a)) or urine treatment (6 kWh/(pe*a)) offset these savings and increase the
total energy input compared to the conventional wastewater treatment system (Figure
33).
Energy recovery
However, the potential for energy recovery via digestion of faeces and biowaste is
substantial. In digestion scenarios, 41 – 48 kWh/(pe*a) can be recovered via biogas
production (= 63 – 109% of the total demand for electric energy in scenarios SV and V).
It has to be noted though that a substantial part of the recovered energy does not
originate from wastewater constituents itself, but from the organic biowaste. In fact, 23
kWh/(pe*a) of the energy from the biogas plant is due to the digestion of biowaste,
corresponding to 49-57% of the total recovered energy. Offsetting this energy with the
energy demand for biowaste pretreatment for digestion (6 kWh/(pe*a)), a net energy
potential of 17 kWh/(pe*a) can be allocated to the biowaste. This part of the energy
recovery potential could also be used in conventional wastewater treatment systems if
biowaste would be treated in an external biogas plant.
Scenarios with faeces composting (SC) do not use the energy potential of the organic
matter. Consequentially, composting scenarios are comparable (SC2) or higher (SC1
and SC3) in net energy demand in relation to the reference system, while digestion
scenarios offer the potential for significant energy savings.
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Greywater treatment
The technology for greywater treatment is another decisive issue for the net energy
demand of separation systems. Obviously, natural treatment of greywater in soil filters
has by far the lowest energy demand (~ 2 kWh/(pe*a)) followed by greywater treatment
in SBR (15-19 kWh/(pe*a)) or MBR plants (19-25 kWh/(pe*a)). In comparison,
conventional wastewater treatment requires 24-28 kWh/(pe*a) in the reference
scenarios. Hence, separation scenarios with soil filter and energy recovery (V2, SV2)
are those systems with the smallest net demand of electric energy.
Greywater reuse
The non-potable reuse of treated greywater for the substitution of drinking water does
not lead to an energetic benefit for the reuse scenarios V3/SV3/SC3. The substituted
amounts of toilet flush water are too small (5-24 L/(pe*d)) to equalize the higher energy
demand of the MBR process. Additionally, pump energy for delivering purified
greywater back to the households is estimated rather high (= 0.23 kWh/m³),
corresponding to about 50% of the energy demand for drinking water supply (= 0.5
kWh/m³). Hence, the reuse scenarios are not energetically favourable under these
conditions. It is decided to further investigate this issue in sensitivity analysis to reveal
the conditions (e.g. amount of substituted drinking water, energy demand for drinking
water supply) in which the non-potable reuse of greywater becomes energetically
beneficial.
In all, the separation scenarios with energy recovery (digestion of faeces and biowaste)
offer potentials for a substantial decrease in the net energy demand of wastewater
treatment. If energy from organic matter of faeces and biowaste is fully recovered, the
net energy demand can be reduced by 30-60% with greywater treatment in an activated
sludge process, resulting in a net energy demand of 12-21 kWh/(pe*a). If a low-energy
process is used for greywater treatment (soil filter), the net energy demand amounts to
only ~ 6 kWh/(pe*a). The lowest energy demand of all scenarios is equivalent to a net
energy surplus (+ 4 kWh/(pe*a)) in the scenario V2 with energy recovery and low-
energy greywater treatment. In this scenario, the supply of drinking water and the
treatment of wastewater can even be achieved with a small overall energy benefit.
In general, the higher energy demand for the operation of separation systems is offset
by the energy recovered from the organic matter of faeces and biowaste. About half of
the energy recovery potential originates from biowaste. Separation scenarios without
energy recovery (composting of faeces and biowaste, SC scenarios) are not
energetically favourable to the conventional system. In fact, these systems tend to
require more energy than the optimized conventional system. Similarly, the non-potable
reuse of greywater does not lead to energetic benefits under the boundary conditions
specified in this study.
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5.1.2 Supply of organic fertilizers
The amount of plant-available nutrients nitrogen, phosphorus and potassium supplied in
the form of organic fertilizers varies significantly between the scenarios (Figure 35). It
depends on the extent in which wastewater nutrients are contained in or transferred to
the respective fertilizer, possible losses during treatment, handling and application, and
the physical and chemical properties of the organic fertilizer (e.g. availability of
nutrients for plant uptake).
Reference scenarios
In the reference scenarios without agricultural application of sewage sludge (R and
Rmin), wastewater-derived nutrients are completely lost for recycling purposes.
Recovered nutrients originate only from application of biowaste compost, amounting to
0.03 kg N, 0.12 kg P and 0.48 kg K per person and year. If sewage sludge is applied in
agriculture (Ragri), these amounts rise to 0.43 kg N, 0.61 kg P, and 0.49 kg K per person
and year (Figure 35). Sewage sludge contains only a part of wastewater N (~ 18% of
influent N), but the majority of wastewater P (~ 96% of influent P). Nitrogen is mostly
transferred to gaseous N2 in denitrification and is thus lost for recycling purposes.
Phosphorus is eliminated by chemical precipitation and transferred completely to
sewage sludge. However, the plant availability of chemically-bound P is assumed to be
limited (70%). Hence, the relative share of total nutrients in wastewater and biowaste
which can be recovered by the agricultural application of sewage sludge amounts to 8%
for nitrogen, 68% for phosphorus, and 27% for potassium (Figure 36).
0
0.5
1
1.5
2
2.5
3
3.5
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Nutrient equivalents from
organic fertilizers [kg/pe*a]
N P K
Figure 35: Nutrient equivalents (plant-available nutrients) provided by organic fertilizers
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Separation scenarios
In the separation scenarios, nutrient recycling is one of the intended targets of
wastewater treatment and disposal. Consequently, the amounts of recovered nutrients
are comparable or higher than in conventional wastewater treatment, ranging between
2.32-3.25 kg N/(pe*a), 0.51-0.72 kg P/(pe*a), and 1.16-1.68 kg K/(pe*a). Surprisingly,
the two-flow systems without urine separation are superior in nutrient recovery to the
three-flow systems where urine is separately collected. This is due to the low efficiency
of the separation toilets, where only 70% of the urine can be effectively collected. The
remaining urine is lost for recycling purposes in the three-flow systems, either with the
faeces filtrate (composting systems) or during the dewatering of the digester residual
(digestion systems). In the two-flow systems where faeces and urine are collected
together by vacuum and treated in digestion, no nutrient-rich urine is lost, because the
digester residual is applied without dewatering. Even though the nitrogen emissions
during application of organic fertilizers are assumed to be higher for the two-flow
systems (23% of N) than for the three-flow systems (11% of urine-N, 6-7% of faeces-
N), the two-flow systems are still superior in nitrogen recovery. The recovery ratio is
highest in the two flow systems (scenarios V: 63/80/78% for N/P/K), smaller for the
three flow systems with digestion (scenarios SV: 50/72/54), and smallest for the three-
flow systems with composting (scenarios SC: 45/56/63).
0
10
20
30
40
50
60
70
80
90
100
Recovery [% of total nutrient content]
N P K
N1 8 63 50 45
P13 68 80 72 56
K22 27 78 54 63
R + Rmin Ragri V SV SC
Only plant-available nutrients are accounted
as recovered nutrients!
Figure 36: Recovery of nutrients in agriculture in relation to total amount of nutrients in
wastewater and biowaste
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The comparison of conventional and separation systems in terms of nutrient recovery
potential is depending on the nutrient. For nitrogen and potassium, separation systems
can significantly increase the recovery potential. For phosphorus, the application of
sewage sludge in agriculture can lead to a comparable recovery potential between
conventional and separation systems. The question of a possible limitation of the plant
availability of P in sewage sludge is the crucial point here. Advantages for the
separation systems result only from differences in plant availability of sewage sludge P
(70%) and urine-derived P (100%).
In all, the comparison reveals a significant influence of the assumptions for a) plant
availability of P in sewage sludge and b) the separation efficiency of the urine
separation toilets. Hence, the influence of both parameters on the results is further
investigated in sensitivity analysis.
5.1.3 Processes for system expansion
The secondary functions of separation systems are accounted for by expanding the
scenarios with processes which deliver equivalent products (cf chapter 3.4). Two
secondary functions are included in this study: the supply of electric energy and the
supply of nutrients with organic fertilizers. The equivalent products are electric energy
from the grid and industrial mineral fertilizer.
The calculation of the respective amount of equivalent products is based on the
scenario delivering the highest amount of electricity and nutrients. Here, scenario V2
delivers maximum amounts for both secondary products. Consequently, all other
scenarios are expanded in relation to scenario V2 (Table 62).
Electric energy
The amount of electric energy which has to be delivered by system expansion amounts
to 3-48 kWh/(pe*a) depending on the scenario. Scenarios without energy recovery
(R/Rmin/Ragri and SC1/2/3) naturally have to deliver the highest amounts of additional
electricity with 36-48 kWh/(pe*a). Energy recovery scenarios are more or less
comparable in their electricity output, so that these scenarios need only minor
corrections via system expansion (3-7 kWh/(pe*a)). Scenario V2 has the maximum
output of electricity as a secondary function and thus serves as the reference scenario
for the calculation. In general, the secondary function of energy recovery in separation
systems has a strong influence on the energetic comparison in this study.
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Table 62: Electric energy and mineral fertilizer provided with
system expansion processes
Industrial mineral fertilizer
Scenario Electric
energy from
the grid Nitrogen Phosphorus Potassium
[kWh/pe*a] [kg/pe*a] [kg/pe*a] [kg/pe*a]
R 35.9 3.2 0.6 1.2
Rmin 47.7 3.2 0.6 1.2
Ragri 36.6 2.8 0.1 1.2
V1 3.5 0 0 0
V2 0 0 0 0
V3 3.4 0 0 0
SV1 6.7 0.7 0.1 0.5
SV2 3.2 0.7 0.1 0.5
SV3 6.5 0.7 0.1 0.5
SC1 47.9 0.9 0.2 0.3
SC2 47.2 0.9 0.2 0.3
SC3 47.5 0.9 0.2 0.3
scenario V2 delivers the maximum amount of energy and nutrients
Fertilizer
The supply of additional nutrients with mineral fertilizer amounts to 0.7-3.2 kg N, 0.1-
0.6 kg P, and 0.3-1.2 kg K per person and year depending on the scenario. Total annual
sales of mineral fertilizer in Germany (related to its total population) amount to 21.7 kg
N, 1.5 kg P, and 4.3 kg K per person and year (DESTATIS, 2007). Thus, the proportion
of the total demand for mineral fertilizer in Germany which can be substituted with
organic fertilizers from wastewater and biowaste is relatively small for nitrogen (15%),
but significant for potassium (28%) and especially phosphorus (40%). In other words,
the recovery of nitrogen from wastewater and biowaste has only a minor impact on the
total demand for mineral nitrogen fertilizer, whereas the recovery of potassium and
phosphorus significantly lowers the demand for their mineral equivalents. This is
especially important for phosphorus which is produced from phosphate rock, a finite
resource which is depleted continuously (USGS, 2008).
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5.1.4 Effluent concentrations and loads from wastewater and
greywater treatment plants
The elimination of pollutants in the different treatment processes for wastewater and
greywater is calculated by relative elimination ratios for the influent load of each
substance (= % elimination) in the inventory. The relative elimination ratios for each
process (activated sludge plant, soil filter, and membrane bioreactor) are estimated from
characteristic performance data of wastewater treatment plants, based on nation-wide
surveys or literature information of full-scale or pilot plants.
However, actual elimination ratios are typically within a certain range and depend on
the specific wastewater composition, the load situation, and other specific features of
the treatment plant. The exact prediction of average elimination ratios for a
representative plant is difficult and thus connected to uncertainty, especially in
quantifying the differences between the treatment of concentrated wastewater and
greywater with low pollutant loads. To check the plausibility of the estimated
elimination ratios of all wastewater treatment processes, average effluent concentrations
are calculated for each scenario. These concentrations are compared between the
scenarios and in relation to the legal discharge limits in Germany (AbwV, 2004). Mean
effluent concentrations are calculated for each scenario in total by dividing the total
effluent loads of all processes by the total effluent volume. For the reference scenarios,
this refers only to the effluent from the wastewater treatment plant. For the separation
scenarios, the considered processes include greywater treatment (eventually with faeces
filtrate), but also the treatment of composting leachate or sludge liquor from digestion.
Chemical oxygen demand
The chemical oxygen demand is a sum parameter for the organic matter content of
wastewater. All calculated effluent concentrations are between 31-53 mg/L COD and
therefore comply with the legal discharge limit of 90 mg/L (Figure 37). Effluent
concentrations of the reference scenarios are in the range of 47-52 mg/L COD, which
are reasonable concentrations for the effluent of a conventional activated sludge plant
treating concentrated wastewater without stormwater (DWA, 2005).
COD concentrations in the effluent of the greywater treatment plants are different
between activated sludge plants (SBR, MBR = 32 mg/L COD) and soil filters (~ 50
mg/L COD). Due to the considerable evaporation of water in the soil filter (~
10 L/pe*d), the reduced effluent volume leads to higher effluent concentrations in this
calculation. In general, the low content of organic matter in greywater is expected to
result in equal or lower effluent concentrations of COD compared to the reference
systems.
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0
10
20
30
40
50
60
R +
Ragri Rmin V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
COD [mg/L]
Discharge limit (GER): 90 mg/L
Figure 37: Calculated effluent concentrations for chemical oxygen demand
Nitrogen
Two parameters are regulated by law for the nitrogen content of the effluent: the
ammonia nitrogen (NH4-N) and the total inorganic nitrogen (Ntotal, inorg). Legal discharge
limits are set at 10 mg/L NH4-N (size category 3, > 5000 inhabitant equivalents) and 18
mg/L Ntotal, inorg for larger plants (category 4, > 10000 inh.-eq). All scenarios safely
comply with the limit for NH4-N (Figure 38), and all but one comply with the standard
for Ntotal, inorg.
0
5
10
15
20
25
R +
Ragri Rmin V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Nitrogen [mg/L]
NH4-N
N total, inorg
Discharge limit (GER):
10 mg/L NH
4
-N (> 5000 inh.eq)
18 mg/L N
total, inorg
(> 10000 inh.eq)
38.9 mg/L
Figure 38: Calculated effluent concentrations for ammonia and total inorganic nitrogen
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The reference system without extended nutrient removal (Rmin) has the highest effluent
concentration of 5 mg/L NH4-N. The reference systems with advanced wastewater
treatment (R + Ragri) and the separation systems with urine separation (SC + SV) are
characterized by a comparable effluent concentration of 1 mg/L NH4-N. In the
separation systems, the co-treatment of concentrated faeces filtrate or sludge liquor
from digestion (with concentrations of 100 and 315 mg/L NH4-N, respectively) with
greywater is responsible for the relatively high ammonia load in the effluent. The
treatment of “pure” greywater with low influent concentration (~ 7 mg/L NH4-N) results
in effluent concentrations of 0.3 mg/L NH4-N (scenarios V).
For the inorganic nitrogen, the denitrification capacity of the respective treatment
process is decisive. The reference system without extended denitrification (Rmin) has a
high effluent concentration of 39 mg/L Ntotal, inorg, thus exceeding the limit for larger
treatment plants (18 mg/L for plants > 10000 inh.-eq). However, the present study
describes a system for 5000 inhabitants, so that this limit is not applicable here. It has to
be kept in mind that the minimum reference system does not comply with the limits for
effluent quality of larger plants. If the conventional system is equipped with
denitrification (R + Ragri), the effluent has a concentration of 9 mg/L Ntotal, inorg.
In the separation scenarios, greywater treatment in soil filter leads to the highest effluent
concentrations for inorganic nitrogen (6-17 mg/L) due to the low denitrification
capacity (40%). MBR systems have a higher denitrification ratio (60% = 4-11 mg/L),
followed by SBR plants (70% = 3-8 mg/L). Highest effluent concentrations are
generally reached in the composting scenarios where concentrated faeces filtrate is
treated together with greywater. In vacuum scenarios with dewatering of digester
residual (SV), sludge liquor is treated separately at the biogas plant in an SBR process
with high denitrification ratio (85%), leading to lower effluent concentrations of
inorganic nitrogen. For treatment of “pure” greywater (scenarios V), effluent
concentrations are between 3-6 mg/L Ntotal, inorg.
Phosphorus
A legal discharge limit for phosphorus only applies for larger treatment plants (2 mg/L
for plants >10000 inh.-eq). Hence, it is not directly relevant for the scope of this study.
All scenarios without extended phosphorus removal (Rmin and soil filter scenarios SC2,
SV2, V2) do not comply with this standard due to high predicted effluent concentrations
of 3-9 mg/L P (Figure 39). In all other scenarios, chemical phosphorus removal via
precipitation of ferric salts is applied. While the conventional activated sludge processes
(R, Ragri, and SC1, SV1, V1) have comparable effluent P concentrations (0.6-0.8 mg/L),
the treatment of greywater in a membrane bioreactor leads to lower effluent
concentrations (0.3-0.4 mg/L in scenarios SC3, SV3, V3). This advantage of the MBR
process is due to a better separation of particulate matter (flocculation of ferric iron) by
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the membrane and is well-known for this type of treatment (Pinnekamp and Friedrich,
2006).
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
R +
Ragri Rmin V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
P
total
[mg/L]
Discharge limit (GER):
2 mg/L P
total
(> 10000 inh.eq)
8.5 mg/L
Figure 39: Calculated effluent concentrations for total phosphorus
Effluent loads
After the effluent concentrations have been checked for their plausibility, the effluent
loads of the separation systems are compared to the reference system R with advanced
wastewater treatment (Figure 40). These loads are the basis for the calculation of the
eutrophication potential of each scenario, which is an important indicator in the impact
assessment of wastewater treatment systems.
COD: The COD load in the effluent can be reduced by 16-57% in separation
scenarios. The separate treatment and recycling of organic matter (mostly
faeces-derived) relieves the treatment process and should lead to lower
effluent loads.
Nitrogen: For nitrogen, the comparison is inconclusive (Figure 40).
Composting scenarios may increase the effluent load of Ntotal compared to
conventional wastewater treatment. The nitrogen load of concentrated faeces
filtrate treated together with greywater increases effluent loads for Ntotal by 7%
in SBR scenario (SC1) and 22% in MBR scenario (SC3). For the soil filter
scenario (SC2) with low denitrification, this increase is even higher and
amounts to 114%. It is therefore questionable if the soil filter is an adequate
treatment process for greywater mixed with faeces filtrate. Vacuum scenarios
(SV +V) do not produce faeces filtrate due to the processing of the complete
toilet wastewater in the digestor. Consequently, these scenarios decrease the
effluent loads for Ntotal by 12-70% compared to the conventional system. The
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highest decrease (- 39-70%) can be reached in the vacuum scenarios V where
the digester residual is applied without dewatering (= no sludge liquor).
Phosphorus: The comparison reveals that soil filters are not comparable to
advanced wastewater treatment in terms of P removal. Even though the
separate treatment of toilet wastewater leads to relatively low phosphorus
loads to the soil filters, the total P effluent loads increase by 170-345% in soil
filter scenarios SC2/SV2/V2 compared to the reference system R. The low P
removal capacity of the soil filter systems (50%) is a major disadvantage
compared to advanced wastewater treatment with chemical P elimination
(>95%). This drawback is especially significant in case of co-treatment of
faeces filtrate with greywater (SC2), but also if “pure” greywater with low
phosphorus content is treated in soil filters (V2 and SV2).
All separation systems with activated sludge technology (SBR or MBR) and
chemical P elimination decrease P effluent loads by 11-75% compared to the
reference system R. The highest reductions are reached with MBR systems
due to their superior particle retention capacity (- 63-75%).
-100
-50
0
50
100
150
200
Relative change of effluent loads
compared to scenario R [%]
COD N total P
COD -53 -34 -57 -51 -31 -54 -41 -16 -55
N total -70 -39 -62 -43 -12 -35 7 114 22
P-46 171 -75 -34 183 -63 -11 345 -65
V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
345 %
Figure 40: Variation of effluent loads in separation scenarios compared to wastewater treatment
with extended nutrient removal (scenario R)
In all, it can be concluded that separation systems can offer potentials for a decrease of
pollutant loads in the effluent. However, a careful examination of the system
configuration is required to realize these potentials:
Soil filter systems typically provide only limited nutrient elimination. These
natural systems can be inferior to advanced wastewater treatment with
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163
extended nutrient removal, especially concerning long-term phosphorus
removal. Thus, total nutrient loads in the effluent may increase in soil filter
scenarios compared to the reference system, even though nutrient-rich toilet
wastewater is treated separately.
The treatment of high-strength flows (e.g. faeces filtrate) in a soil filter leads
to very high effluent loads for N and P and eventually exceeds the legal
effluent standards for larger plants.
Greywater treatment in an activated sludge process (SBR or MBR) results
most likely in a decrease in pollutant loads compared to the reference
systems. The MBR process is characterized by slightly higher nitrogen loads
in the effluent, but has a superior P removal capacity due to the excellent
particle separation with the membrane.
Further remarks
The above calculation of effluent loads and concentrations is based on estimations for
the performance of the different treatment processes under varying conditions (influent
concentrations, load profiles etc). More data from pilot and full-scale plants is required
to confirm the reduction potentials of separation systems. The estimation of
representative elimination ratios for pollutant removal from literature and plant data
determines the effluent loads in each scenario and consequently has a strong influence
on the respective environmental impact indicator for eutrophication.
In sensitivity analysis, the influence of an alternative approach to this issue is
investigated: it is assumed that all scenarios with comparable technology produce the
same effluent quality (= pollutant concentrations). Thus, all activated sludge plants with
extended nutrient removal (scenarios R + Ragri and SC1/SV1/V1) are characterized by
the same effluent concentrations for COD, N, and P regardless of the different influent
loads. The advantage of superior particle retention of MBR systems is maintained in
this case, as is the limited nutrient elimination of soil filters.
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5.1.5 Heavy metals loads emitted to surface waters and soil
The emission of heavy metals into the environment is an important factor determining
the ecotoxicological impacts of the sanitation systems. The heavy metal content of
wastewater constituents can be transferred to receiving surface waters with the effluents
or to agricultural soil with organic fertilizers. In case of sewage sludge incineration, the
heavy metals bound in the sludge are disposed in a landfill or e.g. in road construction
and are not accounted as emissions into the environment. Mineral fertilizer as an
equivalence product in system expansion is also contaminated with heavy metals and
contributes to the loads emitted to agricultural soil.
Heavy metal emissions to surface waters
Heavy metal emissions to surface waters predominantly occur via the effluent of the
wastewater treatment process. Copper and zinc are selected to exemplify the differences
between conventional and separation systems:
Emissions of zinc are lower in all separation scenarios compared to the
reference system (Figure 41). Emissions of copper are lower in separation
scenarios with activated sludge for greywater treatment, whereas soil filter
scenarios lead to a small increase in Cu effluent loads.
Elimination ratios for these metals are comparable between wastewater and
greywater treatment (activated sludge plants: 85% for Cu, 75% for Zn; soil
filters: 80%/80%).
Copper and zinc mainly originate from elevated concentrations in drinking
water (0.16 mg/L Cu and 0.37 mg/L Zn) due to corrosion of pipe materials.
Consequently, reduced water consumption with low-flush and vacuum toilets
decreases input loads of these metals and results in fewer emissions to surface
waters. Similarly, greywater reuse lowers Cu and Zn input by substituting
drinking water.
The separate treatment of faecal matter transfers the faecal-derived Cu and Zn
to organic fertilizer and not to wastewater treatment.
For other heavy metals, relevant inputs originate mainly from greywater composition
and to a smaller extent from drinking water and faeces, but also from the production of
mineral P fertilizer (e.g. Cd, Cr). Hence, total effluent loads are slightly reduced due to
substitution of mineral P fertilizer, reduction of drinking water consumption and
separate treatment of faeces (e.g. reduction of Pb: 5–23%, Cr: 12-31%, Cd: 19-36%, cf.
annex 12.9). In general, more data from pilot and full-scale plants is required to exactly
predict heavy metal removal in greywater treatment. Currently, relative elimination is
assumed to be comparable for wastewater and greywater treatment processes.
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-50
-40
-30
-20
-10
0
10
20
30
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Relative change of emissions to water
compared to scenario R [%]
Cu Zn
Figure 41: Emissions of Cu and Zn to surface waters (relative to scenario R)
Heavy metal emissions to agricultural soil
Input of heavy metals into agricultural soil occurs via organic or mineral fertilizers. For
the essential trace elements of copper and zinc, the substitution of mineral with organic
fertilizers in separation systems leads to an increase in the loads to agricultural soils
(Figure 42). This corresponds to the lower emissions of Cu and Zn to surface waters:
these metals are partially diverted from the water path to the soil in separation systems.
Furthermore, concentrations of these metals in organic fertilizers from faeces and
biowaste are higher than in mineral fertilizers. The application of sewage sludge in
agriculture even extends this effect: copper loads to agricultural soil increase by 825%
and zinc loads by 260% in scenario Ragri.
For other heavy metals, the application of organic fertilizers in separation systems leads
to a significant reduction (Figure 43). Loads to agricultural soil decrease by 48-76% for
Cd, 52-78% for Cr, 31-48% for Pb, 11-47% for Ni, and 65-100% for U compared to the
application of mineral fertilizer (scenario R). Organic fertilizers from urine, faeces and
biowaste have a low content of these heavy metals compared to an average mineral
fertilizer. The quality of sewage sludge from conventional wastewater treatment is even
worse than mineral fertilizer: in scenario Ragri, agricultural application of sewage sludge
increases metal loads to soil by 14% (Cd), 22% (Cr), 120% (Ni), 141% (Hg), and 145%
(Pb). In general, sewage sludge is characterized by a high content of heavy metals
compared to organic or mineral fertilizers. An exemption is the radioactive element
uranium, which is only contained in mineral phosphate fertilizer.
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0
20
40
60
80
100
120
140
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Relative change of emissions to agricultural
soil compared to scenario R [%]
Cu Zn
825 / 260
Figure 42: Emissions of Cu and Zn to agricultural soil (relative to scenario R)
-100
-50
0
50
100
150
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Relative change of emissions to agricultural
soil compared to scenario R =0 [%]
Cd Cr Pb Ni U
Figure 43: Emissions of Cd, Cr, Pb, Ni, and U to agricultural soil (relative to scenario R)
Overall, it becomes apparent that organic fertilizers from urine, faeces and biowaste are
less contaminated with toxic heavy metals Cd/Cr/Ni/Hg/Pb than mineral fertilizer or
sewage sludge. For Cu and Zn, organic fertilizers have a higher content than mineral
fertilizer, but significantly less than sewage sludge. The substitution of mineral P
fertilizer with organic fertilizer from secondary resources leads to a significant
reduction in heavy metal loads to agricultural soil. If the nutrients are collected in a
separation system, they are not contaminated with heavy metals from other wastewater
sources. Thus, organic fertilizers from separation systems have a superior quality for
agricultural application compared to sewage sludge from the treatment of combined
wastewater.
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Quality of sewage sludge
The quality of the sewage sludge calculated in this study is compared to mean values of
sewage sludge applied in agriculture in Germany and to legal limits for agricultural
application of sewage sludge (Table 63). The calculated quality of the sewage sludge in
scenario Ragri is higher than mean values of sewage sludge applied in agriculture in
Germany. However, they are still safely within the current legal limits for sewage
sludge application established in 1992 (AbfKlärV, 1992). This comparison confirms
that the quality of the sewage sludge calculated in this study is a realistic estimation of
typical sewage sludge in Germany.
The low heavy metal content of the sludge which is effectively applied to agriculture
in Germany illustrates the efforts to improve the quality of the sludge. By limiting
industrial heavy metal emissions to the combined sewer, heavy metal content of
municipal sewage sludge has been decreasing continuously over the last decades (BMU,
2007). This improvement should facilitate the safe disposal of sewage sludge in
agriculture, especially concerning the public reception of this disposal route. Otherwise,
sludge disposal can be costly, e.g. through fees for incineration or landfill deposition.
However, if lower limits for heavy metal contamination will be established in the
future (BMU, 2006), the proportion of sewage sludge that can be disposed in agriculture
may be heavily restricted by its heavy metal content. In other European countries (CH,
NL), the agricultural application of sewage sludge is completely forbidden or heavily
restricted. The problematic risk assessment between benefits and drawbacks of
agricultural disposal of sewage sludge could be overcome with the use of separation
systems delivering an organic fertilizer with low content of inorganic and organic
pollutants.
Table 63: Comparison of calculated heavy metal loads in
sewage sludge with German mean values and legal limits
Element This study
Mean values for
Germany 2006 Limits for sewage
sludge application
[mg/kg dry matter] scenario Ragri [1] [2] ([3])
Cd 4 1 10 (2)
Cr 54 37 900 (80)
Cu 461 300 800 (600)
Ni 30 25 200 (60)
Hg 0.7 0.6 8 (1.4)
Pb 54 37 900 (100)
Zn 1043 714 2500 (1500)
1) for sewage sludge used in agriculture (BMU, 2007)
2) AbfKlärV, 1992
3) proposal of new limits for amendment of AbfKlärV (BMU, 2006)
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5.2 Results of the Life Cycle Impact Assessment
In Life Cycle Impact Assessment, the results of the Life Cycle Inventory are transferred
into indicator results via classification and characterization (cf chapter 3.8). Here, all
calculated indicator results are presented and analysed by contribution analysis, thus
determining the processes or emissions which contribute most to the indicator category.
5.2.1 Cumulative energy demand
The cumulative energy demand (CED) of non-renewable resources (fossil and nuclear
fuels) accounts to 750 to 1430 MJ per person and year for all scenarios (Figure 44). The
reference systems require 1200 to 1430 MJ/(pe*a) depending on whether energy
recovery via sewage sludge digestion is applied (R + Ragri) or not (Rmin). Major parts are
contributed by the operation of the systems (~ 45%) and the supply of equivalence
products (~ 50%), whereas the infrastructure constitutes only a small part of the total
CED (6-8%). It is remarkable that the CED for the secondary functions (fertilizer and
energy supply) is in the same range as CED for operation and infrastructure in the
reference systems.
For the separation systems, the total CED ranges between 750 and 1430 MJ/(pe*a)
depending on the system configuration. As expected, separation systems with energy
recovery are superior to systems with faeces composting. The recovery of energy bound
in the organic matter of faeces and biowaste outweighs the increased operational energy
demand of vacuum systems. Consequently, the CED for the supply of equivalence
products (= energy) is significantly higher in composting systems.
0
250
500
750
1000
1250
1500
1750
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
CED [MJ/(pe*a)]
Infrastructure Operation Equivalent products
Figure 44: Cumulative energy demand
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Greywater treatment is another important factor for the CED of separation systems. In
each scenario group, the second scenario with a low-energy process (soil filter) has a
significantly smaller CED than the first and third scenarios with technical processes
(SBR or MBR). The reuse of greywater for toilet flushing does not result in an energetic
benefit. The amounts of substituted drinking water are too small (5 L/(pe*d) in case of
vacuum toilets and 24 L/(pe*d) for low-flush gravity toilets) to equalize the increased
energy demand of the membrane system.
A detailed contribution analysis for the CED of infrastructure, operation and the
supply of equivalence products is carried out to provide further insights in the energetic
comparison.
Infrastructure
The cumulative energy demand for the construction of a conventional sanitation
infrastructure with combined sewer is around 90 MJ per person and year (Figure 45).
More than 50% are due to the construction of the sewer, while in-house piping and
sewage treatment plant each require around 25% of total CED for infrastructure.
Separation systems are characterized by multiple flows, which need multiple piping
networks for their transport. Consequently, the energy demand for the infrastructure is
increased by 10-30% for two-flow systems (black-/greywater: V1-3) and 55-90% for
three-flow systems (urine/brown-/greywater, urine: SC1-3 + SV1-3) compared to the
reference scenario. This increase is in a large part caused by the additional sewer pipes
(Figure 45). In case of greywater reuse, the required extra pipe network for service
water supply needs additional energy.
0
20
40
60
80
100
120
140
160
180
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
CED [MJ/pe*a]
In-house Sewer Facilities
Figure 45: Cumulative energy demand for infrastructure
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In all, the separation scenarios have a higher CED for their infrastructure due to the
additional piping. However, the increase in relation to the conventional system is less
than a factor of 2 or 3, which could be expected by their layout (two-flow or three-flow
systems vs one combined sewer). The small diameter of the additional sewer pipes (e.g.
for vacuum system or urine transport) and the installation of all pipes in one trench
minimizes the extra energy demand of separation systems to 10-90%.
Operation
The operational energy demand is in the range of 560-620 MJ/(pe*a) for the
conventional systems and 470-840 MJ/(pe*a) for the separation scenarios. In relation to
the respective CED for infrastructure, the operational CED is higher by a factor of 6-7
for conventional systems and 3-8 for separation systems.
The contribution analysis reveals that the operational CED is largely dominated by the
electricity supply (Figure 46). This process contributes 75-90% to the total operational
CED, whereas the contribution of chemical supply (e.g. flocculants) and transport are
small. Only in vacuum scenarios without urine separation (V1-3), the high volume of
the organic fertilizer (= digester sludge, not dewatered) leads to a significant
contribution of transport to the operational CED (15-19%).
In general, separation scenarios have a higher operational CED (+13-47%) than the
conventional systems, particularly due to higher electricity demand (cf. chapter 5.1.1)
and to a lesser extent due to increased transport volumes. An exemption is scenario SC2
with faeces composting and soil filter, which has the lowest operational CED of all
scenarios (467 MJ/pe*a).
0
100
200
300
400
500
600
700
800
900
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
CED [MJ/pe*a]
Electricity Transport Chemicals Others
Figure 46: Cumulative energy demand for operation
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Equivalent products
For the supply of equivalent products (grid energy and mineral fertilizer), the CED is
highest for scenario Rmin (720 MJ/pe*a) without nutrient recycling or energy recovery
of sewage sludge (Figure 47). Energy recovery via sewage sludge digestion decreases
the CED for equivalent products substantially to 600 MJ/(pe*a) in scenario R. If sewage
sludge is applied in agriculture for nutrient recycling, this amount further decreases
slightly to 561 MJ/(pe*a) in scenario Ragri. The energetic benefits of nutrient recycling
via sewage sludge are limited due to the low content of nitrogen in the sludge and its
low plant availability (50%). The production of mineral nitrogen fertilizer is by far more
energy intensive than mineral phosphate fertilizer. Hence, the high ratio of phosphorus
recycling with sewage sludge (cf. chapter 5.1.2) results only in a small energetic benefit
(< 40 MJ/(pe*a)).
For the separation systems, the option of energy recovery from organic matter is crucial
for the comparison with the reference system. Composting systems without energy
recovery are comparable to the reference scenarios, whereas vacuum systems with
energy recovery offer substantial benefits. Two-flow vacuum systems without urine
separation have the highest energy recovery (including energy bound in organic matter
of urine) and supply the most organic nitrogen fertilizer. Consequently, these scenarios
have to supply the smallest amount of equivalent products.
While comparing the different separation systems, the amount of substituted mineral
fertilizer plays only a minor role, whereas the energy recovery option is again crucial
for this comparison (Figure 47).
0
100
200
300
400
500
600
700
800
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
CED [MJ/pe*a]
Mineral fertilizer Grid energy
Figure 47: Cumulative energy demand for supply of equivalent products (system expansion)
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In general, the following conclusions can be drawn from the contribution analysis of
cumulative energy demand:
The supply of equivalent products has a decisive impact on the energetic
comparison of conventional and separation systems.
The choice of the greywater treatment process is equally important. Natural low-
energy treatment systems (soil filter) result in substantial energetic benefits.
Separation systems require more energy for the construction of infrastructure,
but this is not decisive for the overall comparison due to its low contribution to
the total CED (11-17%).
Energy recovery via conversion of organic matter into biogas offers significant
net energetic benefits only if biowaste is co-digested with toilet wastewater.
In this study, urine separation (group SV) does not offer an energetic advantage
compared to vacuum systems (group V) treating blackwater (= faeces and
urine). By applying the digested blackwater directly to agriculture without
dewatering, more nitrogen is supplied to the fields than with urine separation. In
contrast, nutrients in urine which is not properly separated in separation toilets
(30% of total urine) are mainly lost for recycling purposes. It has to be noted
though that urine separation allows the partial containment of micropollutants
via ozonation of separated urine. Direct application of digested blackwater does
not provide a feasible option for micropollutant removal.
Composting systems have the lowest energy demand for operation, but no
option for energy recovery. Vacuum systems are energetically superior to
composting systems.
Transports have only a negligible impact on the overall comparison at the
estimated distances for the transport of organic fertilizers (20 km).
The reuse of greywater for toilet flushing does not lead to energetic benefits due
to the low volume of substituted drinking water (5-24L/pe*d).
Hence, separation systems do not necessarily lead to energetic benefits compared to an
optimized conventional system. If both the substitution of nitrogen fertilizer and the
recovery of energy from the organic matter are implemented, the higher operational
energy demand of separation systems is offset and substantial energetic benefits can be
realized. Additional benefits can be realized if low-polluted greywater is treated in low-
energy natural systems. The maximum reduction potential can be realized in scenario
V2 if vacuum systems with energy recovery are combined with greywater treatment in
soil filters (-40% compared to reference system).
Many system parameters of the Life Cycle Inventory have a significant influence on the
cumulative energy demand. System configurations that were initially expected to yield
an energetic benefit (urine separation, greywater reuse) may do so if certain system
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parameters are optimized or assumptions are changed. For sensitivity analysis, the
following parameters are chosen to determine their influence on the energetic
comparison:
Addition of biowaste in co-digestion
Separation efficiency of urine separation toilets
Energy demand for urine treatment and vacuum plant
Transport distances for organic fertilizers
Volume of reused greywater
5.2.2 Depletion of abiotic resources
The depletion of abiotic resources evaluates the demand for non-renewable energetic
resources (fossil and nuclear fuels) as well as mineral resources (Fe, Al, Cu, P etc). In
this study, the calculated indicator results are predominantly determined by the energy
resources. The contribution of minerals to the abiotic depletion potential (ADP) is
negligible for all scenarios, accounting for less than 0.1% of total ADP. Scenarios with
energy recovery have benefits compared to the reference system, while composting
scenarios are comparable or slightly worse than the conventional system (Figure 48).
The benefit from recycling of wastewater-derived P as a substitution of raw phosphate
being a limited resource is not reflected in ADP. Characterization factors for ADP of
raw phosphate are too low to show this effect in the present indicator category. The
limited availability of raw phosphate (demand vs projected reserves) seems to be
negligible compared to the limited reserve and high demand of fossil fuels.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
ADP [kg Sb-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 48: Abiotic depletion potential
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5.2.3 Global warming
Typically, the indicator of global warming potential (GWP) is strongly related to the
fossil energy demand due to CO2 emissions from the burning of fossil fuels. However,
emissions of CH4 and N2O can also have a strong influence on GWP, especially for
agricultural processes.
In this study, the global warming potential is in the range of 75-150 kg CO2-eq per
person and year for all scenarios (Figure 49). The construction of the infrastructure
constitutes only a minor part of the total GWP (5-13%), whereas system operation and
the supply of equivalent products cause the major part of GWP. Similar to the
cumulative energy demand, separation systems perform worse compared to the
reference system for the GWP caused by infrastructure (+14-63%) and system operation
(+12-45%). However, GWP from the supply of equivalent products offsets these
amounts and results in an overall benefit for most separation scenarios. Vacuum
scenarios reduce total GWP by 24-46% , while composting scenarios are comparable to
the reference system.
0
25
50
75
100
125
150
175
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
GWP [kg CO2-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 49: Global warming potential
Composting systems have a comparable GWP with the reference system, only the soil
filter scenario has a slight benefit (-8%). The differences between the scenarios can be
partially explained by the results from the energetic comparison (cf. chapter 5.2.1):
separation systems require more energy for infrastructure and system operation, but
deliver secondary products and thus avoid emissions involved in their production.
Additionally, a contribution analysis for the equivalent products reveals that the
substitution of mineral fertilizer plays a more significant role for the benefits in GWP
than the supply of additional energy (Figure 50). Two factors are important here: a) the
substitution of mineral nitrogen fertilizer whose production is energy-intensive and b)
emissions of N2O during the production of mineral N fertilizer.
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0
10
20
30
40
50
60
70
80
90
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
GWP [kg CO2-eq/pe*a]
Mineral fertilizer Grid energy
Figure 50: Global warming potential for supply of equivalent products (grid energy and mineral
fertilizer)
Apparently, both CO2 and N2O emissions are important to assess the global warming
potential of sanitation systems. Hence, a detailed contribution analysis of the different
greenhouse gases is calculated to point out the decisive factors (Figure 51).
CO2
Emissions of CO2 from fossil sources mainly derive from energy production and
transport processes. Consequently, the amount of CO2 emissions is strongly correlated
with the cumulative energy demand. In the contribution analysis for gases, the vacuum
scenarios with low CED reduce CO2 emissions in relation to the conventional system
(Figure 51). Scenarios SV3 and SC1/2 have emissions of CO2 that are comparable to
the conventional system, whereas scenario SC3 increases CO2 emissions. In general,
benefits in CO2 emissions do not seem to play a decisive role for the benefits in GWP of
separation systems.
In all, the contribution of CO2 is determined by the demand for electric energy in most
scenarios. The contribution of transport processes to CO2 emissions is negligible in
most scenarios (<1% of CO2 emissions). The maximum contribution of transport is
calculated for the scenario with low electric energy demand and high transport volumes
(scenario V2: 15% contribution of transport to CO2 emissions, data not shown).
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0
20
40
60
80
100
120
140
160
180
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
CED [MJ/pe*a]
CO2 N2O CH4
Figure 51: Greenhouse gases contributing to global warming
N2O
Emissions of N2O mainly arise during the production and application of nitrogen
fertilizers and from the denitrification process in wastewater treatment:
In the inventory, N2O emission factors for the application of nitrogen fertilizers
are assumed to be comparable for both mineral and organic fertilizers, so
fertilizer application is not responsible for the benefits of separation systems.
N2O emissions during the production of mineral N fertilizer are substantial
(~ 30% of total N2O emissions in reference scenarios) and can be avoided in
separation scenarios. In composting scenarios, N2O emissions from composting
partially offset this benefit.
N2O emissions from denitrification (~ 18% of total N2O in reference scenarios)
are reduced considerably due to lower influent loads of nitrogen and in
greywater treatment.
Hence, two decisive factors have been revealed: a) avoiding N2O emissions during
fertilizer production and b) less N2O emissions from denitrification. In total, composting
scenarios reduce N2O emissions by ~ 20% and vacuum scenarios by 40-56% compared
to the reference system (Figure 51). This reduction of N2O emissions is mainly
responsible for the reduction of total GWP.
CH4
Methane is mainly emitted in composting and to a smaller extent in energy production.
Consequently, composting scenarios increase CH4 emissions by 34-37% compared to
the reference system, while vacuum scenarios reduce CH4 emissions by 25-65%.
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However, the contribution of CH4 to the total GWP is small (4-12%) and it is not
decisive for the overall comparison.
5.2.4 Acidification
The acidification potential (AP) is determined by the emissions of acidifying gases
(mainly NH3, NOx, and SO2). In this study, NH3 emissions are responsible for the
greatest share (70-89%) of the acidification potential in all scenarios due to high
emissions from nitrogen fertilizer application and composting. Emissions of NOx and
SO2 which typically occur in combustion processes are less important in this study (6-
16% and 5-14%, respectively).
Overall, the acidification potential is between 0.5 and 1.2 kg SO2-equvialents per
person and year (Figure 52). Separation systems increase AP by 59-111% compared to
the reference system. The substantial increase originates from higher emissions during
system operation, whereas the infrastructure has a negligible impact on this indicator.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
AP [kg SO2-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 52: Acidification potential
By calculating the contribution of different sub-processes to acidification (Figure 53),
the following conclusions can be drawn:
The acidification potential is predominantly determined by the processes of
fertilizer application and composting.
The application of organic fertilizers is presumably associated with high NH3
emissions. The respective NH3 emission factors are estimated to be
considerably higher for urine (10% of applied N) and digester sludge (22%)
than for mineral fertilizer (5%).
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Composting processes also contribute substantially to NH3 emissions,
especially if faeces are co-treated with biowaste. Emissions originate mainly
from open composting without encapsulation and off-gas cleaning.
Emissions from other processes (transports, energy production etc) are
comparable between all scenarios and play only a minor role for acidification
(9-21% of total AP).
0.0
0.2
0.4
0.6
0.8
1.0
1.2
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
AP [MJ/pe*a]
Fertilizer application Fertilizer production Composting Rest
Figure 53: Contribution of sub-processes to acidification potential
In all, the substitution of mineral with organic fertilizers leads to a substantial increase
in NH3 emissions and thus in the acidification potential. Therefore, available emission
reduction measures should be implemented to minimize this drawback for the
separation systems. These measures include special application techniques for urine and
digester sludge (e.g. direct injection in the ground, drag hoses, instant ploughing for
incorporation of applied fertilizer) and encapsulation of the complete composting
process with adequate off-gas cleaning in a biofilter. The effect of emission reduction
measures for fertilizer application and composting on the acidification potential is
quantified in sensitivity analysis.
5.2.5 Eutrophication
Eutrophication is mainly caused by the emission of nitrogen, phosphorus, and organic
matter (as COD) to surface waters. Additionally, atmospheric deposition of nitrogen
gases (NH3, NOx) contributes to this impact category to a lesser extent. Consequently,
the decisive process for eutrophication is the treatment of wastewater and the associated
effluent loads of nutrients. The contribution of infrastructure and equivalent products is
negligible for the comparison of the eutrophication potential (EP) (Figure 54).
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0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
EP [kg PO4-eq/(pe*a)]
Infrastructure Operation Equivalent products
2.25
Figure 54: Eutrophication potential
The reference scenario with minimum standards for wastewater treatment (Rmin) has an
EP of 2.15 kg PO4-equivalents per person and year. Per definition, this scenario
represents conventional wastewater treatment without extended nutrient removal and
consequently has the highest EP of all scenarios. It should be pointed out that this
scenario still complies with all German legal regulations for wastewater treatment plants
of this dimension. If extended nutrient removal is implemented in conventional
wastewater treatment (scenarios R + Ragri), EP is reduced substantially (-81%) to 0.4 kg
PO4-eq/(pe*a). The scenarios with extended nutrient removal represent the capabilities
of modern wastewater treatment technology. Despite the less stringent legal
requirements, new or upgraded plants of the present dimension (5000 inhabitant
equivalents) are often operated with denitrification and extended P elimination to
minimize nutrient loads in the effluent.
For the separation scenarios, two aspects are decisive for eutrophication: a) the process
for greywater treatment (SBR/MBR or soil filter) and b) the co-treatment of faeces
filtrate in composting scenarios.
In composting scenarios, faeces are separated from flush water to obtain high dry
matter content for the composting process. The flush water (“faeces filtrate”) is heavily
loaded with nutrients, particularly due to the high amounts of misled urine (30% of total
urine) which is not separated properly in the toilets. This filtrate is treated together with
greywater and substantially increases nutrient loads to the greywater treatment process.
Composting scenarios with technical greywater treatment in SBR or MBR (SC1 + 3) do
not offer advantages in EP compared to advanced wastewater treatment (R). The
combination of faeces composting and GW treatment in soil filter (scenario SC2) even
leads to a significant increase of EP (+140%), because the soil filter has only limited
capacity for nutrient removal.
In vacuum scenarios, the complete mixture of faeces and flush water is treated together
in the digestion process. In case of urine separation, the digester residual is dewatered
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prior to stabilisation in open composting. The resulting sludge liquor is heavily loaded
with nutrients (comparable to faeces filtrate). However, it is treated in a separate process
(SBR with denitrification and chemical P elimination) close to the digester tank and not
together with greywater. Hence, the nutrient load to greywater treatment is relatively
low and results in better effluent quality than in composting scenarios: Scenarios with
GW treatment in SBR/MBR (SV1 + 3) lead to a reduction of 30% in EP compared to
the reference system R. Despite the low nutrient load, GW treatment in soil filter (SV2)
again results in a significant increase in EP (+38% compared to R).
Vacuum scenarios without urine separation (V1-3) operate without any effluents from
faeces treatment: the complete residual of the digestion process is directly applied in
agriculture. Consequently, nutrient loads in the effluent are lowest for these scenarios
(cf. chapter 5.1.4), but this benefit is partially offset by higher emissions of atmospheric
NH3. Hence, EP reduction potential between vacuum scenarios with and without urine
separation is comparable (Figure 54).
The detailed contribution analysis of EP shows that for the soil filter scenarios, limited
phosphorus removal (50% elimination) is the main reason for the increase in EP (Figure
55). In all other scenarios, the contribution of nitrogen to EP is larger than that of
phosphorus, while COD generally plays only a minor role (< 10%). Atmospheric
emissions of nitrogen gases are responsible for 20-30% of eutrophication except in
those scenarios with lowest nutrient loads in the effluent: in scenarios V1 and V3,
nitrogen gases contribute more than 50% to EP.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
EP [kg PO4-eq/pe*a]
Effluent N Effluent P Effluent COD Gases
2.15
Figure 55: Contribution of effluent emissions of nitrogen, phosphorus and chemical oxygen demand
and gaseous nitrogen emissions to eutrophication potential
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Overall, the following conclusions can be drawn for the impact category of
eutrophication:
Composting systems do not decrease eutrophication potential due to the co-
treatment of faeces filtrate (high volume = 24 L/(pe*d), high nutrient loads)
with greywater.
Soil filters are inferior to advanced wastewater treatment in nutrient removal,
even if they treat only low-polluted greywater. The limited long-term
retention of phosphorus is the main reason for this drawback.
Vacuum systems using greywater treatment in technical systems with
nutrient removal (SBR/MBR) offer benefits in EP. If digester residual is
dewatered, sludge liquor (low volume = 6 L/(pe*d), high nutrient loads)
should be treated in a separate technical process with high nutrient removal
capacity and should not be mixed with greywater.
The contribution of nitrogen gases to eutrophication is relatively small, but
can become important for scenarios with low effluent loads and high NH3
emissions (V1 + 3).
All separation scenarios have a significantly lower eutrophication potential
than conventional wastewater treatment without extended nutrient removal
(Rmin).
Thus, some separation systems can reduce the emission of nitrogen and phosphorus,
while others may potentially increase them. Crucial points are a general load reduction
to wastewater treatment due to the separate treatment of nutrient-rich toilet wastewater,
the limited phosphorus elimination capacity in soil filters and the treatment options for
concentrates (faeces filtrate, sludge liquor).
In general, the assumed elimination ratios for the different treatment processes have a
strong influence on the results in this impact category (cf. chapter 5.1.4). Elimination
ratios are qualified estimates for mean load-based elimination ratios based on data from
pilot plants and literature. In sensitivity analysis, another approach to this issue is
investigated: it is assumed that all scenarios with comparable technology (e.g. SBR)
deliver the same effluent quality. Furthermore, two other indicators for this impact
category are calculated based on site-dependent characterization factors of EDIP
(Hauschild and Potting, 2003).
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5.2.6 Human toxicity
This impact category evaluates all emissions which can have a toxic impact on human
health, including emissions to air, water, and agricultural soil. The related indicator of
human toxicity potential (HTP) ranges between 10 and 28 kg DCB-equivalents per
person and year for all scenarios (Figure 56). In general, all three sub-parts of the
systems (infrastructure, operation, and supply of equivalent products) contribute
substantially to this indicator. Separation systems have significant benefits in HTP,
decreasing total HTP by 53-63% with vacuum systems and 30-38% with composting
systems compared to scenario R.
0
5
10
15
20
25
30
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
HTP [kg DCB-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 56: Human toxicity potential
A detailed contribution analysis reveals that the production and application of mineral
fertilizer or sewage sludge causes a substantial part of HTP in the reference scenarios
(Figure 57). In the reference systems without sewage sludge application in agriculture
(R + Rmin), the production and application of mineral fertilizer constitutes 62% of the
total HTP, mainly due to the transfer of toxic heavy metals in mineral P fertilizer to
agricultural soil. Additionally, the production of mineral P fertilizer causes considerable
aquatic emissions of metals and especially fluoride from the processing of raw
phosphate ores. If sewage sludge is applied in agriculture (Ragri), heavy metal content of
the sludge contributes with 56% to the total HTP.
Interestingly, the total HTP of reference systems with or without sewage sludge
application in agriculture is comparable. Thus, mineral fertilizer and sewage sludge are
characterized by the same human toxicity potential, even though the specific heavy
metal content of mineral fertilizer is much lower than that of sewage sludge (cf chapter
5.1.5). Process emissions during the production of mineral P fertilizer equalize these
benefits of mineral fertilizer and result in an overall comparable HTP for all reference
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scenarios. However, it has to be noted that process emissions of P fertilizer production
usually occur at the place of phosphate mining (e.g. Morocco), whereas the pollution of
agricultural soil takes place “on-site”, i.e. in the proximity of the settlement. Therefore,
the usage of mineral fertilizer shifts a part of the associated environmental burden to the
country of fertilizer production.
0
5
10
15
20
25
30
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
HTP [kg DCB-eq/pe*a]
Rest
Infrastructure
Electricity
Application org. fertilizer
Application min. fertilizer
Production min. fertilizer
Figure 57: Contribution of sub-processes to human toxicity potential
Comparing conventional and separation systems, the substitution of mineral fertilizer or
sewage sludge with organic fertilizers from faeces and urine leads to significant benefits
in HTP for the separation systems. The transfer of toxic heavy metals to agricultural soil
can be substantially reduced by applying secondary fertilizers with low heavy metal
content. Additionally, toxic emissions from the production of mineral P fertilizer can be
avoided. The minor increases in HTP due to the additional infrastructure do not offset
the benefits of separation systems (Figure 57). Vacuum systems are superior to
composting systems in HTP, because the substituted amount of mineral fertilizer is
lower in the composting scenarios.
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5.2.7 Freshwater ecotoxicity
For the impact category of aquatic ecotoxicity, emissions of eco-toxic substances to
each environmental compartment (air, water, and soil) are relevant due to the inter-
media transport of substances into surface waters. In this study, the respective indicator
is mainly determined by the emissions of heavy metals, particularly by direct emissions
into the water (from wastewater treatment) and indirect emissions via agricultural soil
(from fertilizer application).
The calculated freshwater aquatic ecotoxicity potential (FAETP) of all scenarios is
between 4 and 5.5 kg DCB-equivalents per person and year (Figure 58), i.e. in a
relatively close range. An exemption is scenario Ragri which has an FAETP of 11 kg
DCB-eq/(pe*a). In general, the major part of FAETP is caused by the operation of
sanitation systems, with a small contribution from the supply of equivalent products
(fertilizer).
0
2
4
6
8
10
12
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
FAETP [kg DCB-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 58: Freshwater aquatic ecotoxicity potential
The contribution analysis shows that direct emissions with the effluent of water
treatment processes are responsible for the major part (52-66%) of FAETP in all
scenarios, while the application of mineral and organic fertilizers contributes a smaller
but relevant part to this indicator (29-43%). The significant increase in FAETP in
scenario Ragri is due to sewage sludge application in agriculture (Figure 59).
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0
2
4
6
8
10
12
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
FAETP [kg DCB-eq/pe*a]
Rest
Application min. fertilizer
Application org. fertilizer
Effluent
Figure 59: Contribution of sub-processes to freshwater aquatic ecotoxicity potential
The elimination of heavy metals in wastewater or greywater treatment is estimated with
comparable elimination ratios for all treatment processes in the inventory. Thus, FAETP
of direct heavy metal emissions to surface waters are roughly similar between all
scenarios (Figure 59). MBR scenarios with greywater reuse (V3, SV3, SC3) have a
slight advantage due to the partial substitution of drinking water with a high content of
Cu and Zn. These two heavy metals have a distinct influence on this indicator, as both
have high impact factors for aquatic ecotoxicity. Sewage sludge contains large amounts
of Cu and Zn (particularly from input via drinking water, cf. Figure 42) and thus leads
to the significant increase of FAETP in case of sewage sludge application in agriculture
(Ragri).
The assumed concentrations of Cu and Zn in drinking water (0.16 and 0.37 mg/L,
respectively) seem to have a strong impact on this indicator due to the transfer of these
metals to agricultural soil via sewage sludge. For sensitivity analysis, the effect of a
reduced concentration of Cu and Zn in drinking water is quantified for this impact
category.
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5.2.8 Terrestrial ecotoxicity
The second impact category for ecotoxicity evaluates toxic impacts on the terrestrial
ecosystem. It is strongly determined by the transfer of heavy metals to agricultural soil
with the different types of fertilizer. Emissions to air and water are also accounted for in
the respective indicator, but they play a negligible role for the result in all scenarios (<
1-3% of total TETP).
0
2
4
6
8
10
12
14
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
TETP [kg DCB-eq/(pe*a)]
Infrastructure Operation Equivalent products
Figure 60: Terrestrial ecotoxicity potential
The comparison shows that terrestrial ecotoxicity is substantially reduced in separation
scenarios (- 42-67%) by the substitution of mineral fertilizer with organic fertilizers
from faeces and urine (Figure 60). Compared to the application of sewage sludge
(scenario Ragri), the reduction potential for separation systems is even higher (- 56-75%).
In other words, the heavy metal content of mineral fertilizer and sewage sludge causes a
significantly higher terrestrial ecotoxicity potential than the organic fertilizers from
faeces and urine.
This is confirmed by a contribution analysis for the different fertilizers to the indicator
of TETP (Figure 61). In the reference systems, mineral fertilizer and sewage sludge are
mainly responsible for TETP. For the separation systems, compost or digester sludge
constitutes a major share of the total TETP. It has to be pointed out that separated urine
has a negligible impact on TETP. Together with its high nutrient content (cf. chapter
5.1.2), the low heavy metal content of urine makes it a highly attractive fertilizer
compared to other organic fertilizers such as faeces compost or digester sludge. From an
ecotoxicological point of view, the separation of urine from faeces can be
recommended.
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0
2
4
6
8
10
12
14
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
TETP [kg DCB-eq/pe*a]
Rest
Urine
Digester sludge
Compost
Sewage sludge
Mineral fertilizer
Figure 61: Contribution of different types of fertilizer to terrestrial ecotoxicity potential
5.2.9 Summary of LCIA results
This section summarizes all indicator results and relevant findings from Life Cycle
Impact Assessment, thus giving an overview of the calculated results and the
contribution analysis at a glance. Indicator results and findings are presented in different
ways:
Comparison of LCIA results for all indicators in relation to the reference
scenario R (Figure 62)
Comparison of LCIA results for all scenarios in relation to the reference
scenarios R (Figure 63)
For a detailed analysis of each indicator results, the relevant chapters for the specific
indicators can be referred to.
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Comparison of LCIA results for all indicators
The overview of all LCIA indicators shows that the comparison between conventional
and separation systems yields different profiles for each indicator (Figure 62):
Resource-oriented indicators (CED, ADP) are ambiguous: most separation
systems have benefits, but some scenarios have drawbacks.
Emission-related indicators can be divided into indicators of macro-elemental
emissions (C, N, P, S emissions for GWP, AP, EP) and those determined by
micro-elements (heavy metals for HTP, FAETP, TETP).
For macro-elemental emissions, the comparison between the conventional and
separation systems is not conclusive: some indicators have benefits, while others
show considerable drawbacks. The potential for acidification is significantly
increased in all separation systems, while global warming can be reduced by the
majority of them. Eutrophication is a twofold case: some scenarios decrease this
impact considerably, while others will potentially increase it.
Concerning micro-elemental emissions, all separation systems are superior in
toxicity indicators due to the avoided heavy metal emissions to the environment.
This overview of all indicators gives a first hint on the difficulties in summarizing the
results of this LCIA into one conclusive statement for or against separation systems. It
seems to depend heavily on the respective impact category if a conclusive statement is
possible or not.
-100
-50
0
50
100
150
CED GWP ADP AP EP HTP FAETP TETP
Indicator relative to scenario R [%]
RRmin Ragri
V1 V2 V3
SV1 SV2 SV3
SC1 SC2 SC3
440%
Figure 62: Comparison of all LCIA indicators (relative to scenario R)
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Comparison of LCIA results for all scenarios
The comparison of all scenarios in relation to scenario R shows that some scenarios
have benefits in the majority of impact categories, while others show a more distinct
picture of benefits and drawbacks (Figure 63):
Following its definition, the reference system with minimum standards (Rmin) is
clearly inferior to advanced wastewater treatment, especially in nutrient
emissions, energy demand and related emissions.
The agricultural application of sewage sludge (Ragri) leads to an increased
ecotoxicity due to the transfer of wastewater-derived heavy metals to
agricultural soil.
Vacuum systems without urine separation (two-flow systems) show benefits in
all impact categories except acidification. Only the soil filter scenario has a
higher impact in eutrophication.
Vacuum systems with urine separation (three-flow systems) also have benefits
in most impact categories. The drawback of increasing acidification is less
distinct than in the two-flow vacuum scenarios. Again, the soil filter leads to
enhanced eutrophication.
Composting systems are those separation systems with the smallest benefits.
Benefits in toxicity indicators are smaller due to the lower amount of substituted
mineral fertilizer. Energy demand is higher due to the unexploited energy
recovery potential, so that only the soil filter scenario yields energetic benefits.
The latter scenario leads to a significant increase in eutrophication due to high
nutrient emissions in the effluent.
-100
-50
0
50
100
150
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Indicator relative to scenario R [%]
CED GWP ADP AP EP HTP FAETP TETP
440%
Figure 63: Comparison of all scenarios (relative to scenario R)
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5.2.10 Normalization
In normalization, LCIA indicator results are related to the total impacts of the respective
impact category in Germany. Thus, the relative share of each indicator result to the total
actual environmental impact can be calculated. In fact, the normalized score of 1 pe*a
inhabitant equivalent is equal to 100% of the total environmental impact, whereas 0.01
pe*a inhabitant equivalent means 1% of the total impact.
Normalization is an optional tool in valuation of LCIA to allow a first assessment of
the quantitative importance of a certain indicator result in relation to the environmental
situation in a society as a whole. It is stressed here that normalized indicator scores
provide no final information on the importance of a certain indicator for the overall
comparison.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
CED GWP ADP AP EP HTP FAETP TETP
Inhabitant equivalents [pe*a]
RRmin Ragri
V1 V2 V3
SV1 SV2 SV3
SC1 SC2 SC3
0.33
Figure 64: Normalization of all indicators from Life Cycle Impact Assessment
In detail, the normalized indicator scores (Figure 64) are evaluated as follows:
Resource-related indicators (CED, ADP) have a low normalised score,
accounting for < 3% of the total resource demand in Germany. Hence,
wastewater management has only a marginal share of the total energy demand in
Germany.
The contribution to global warming is even lower: GWP normalized scores are
below 1.2% for all scenarios. Considering the possible correlation of GWP to
energy demand (CED), it seems that wastewater management has a relatively
low GWP compared to its demand of non-renewable energetic resources.
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In acidification, the calculated normalized scores are relatively high (3.5-7.5%
of total AP). This contribution is not caused by wastewater treatment itself, but
mostly by application of different fertilizers and related emissions of NH3.
Agriculture is known to contribute a significant share to total NH3 emissions in
Germany (ECETOC, 1994).
As expected, normalized scores for eutrophication are reasonably high, i.e.
between 4-15% for all scenarios except the reference scenario with minimum
standards (Rmin). The latter scenario contributes with 33% to total eutrophication
in Germany, mostly due to insufficient elimination of nutrients. If extended
nutrient removal is not applied, municipal wastewater treatment contributes
significantly to eutrophication. With extended nutrient removal, the share of
municipal wastewater treatment can be minimized to ~ 6%. Diffuse pollution
with nutrients is the major cause for eutrophication of surface waters in
Germany (UBA, 2007b).
Human toxicity has the lowest normalized score of all indicators (< 0.4%),
indicating that wastewater management including the application of secondary
fertilizers has a marginal toxic effect on humans compared with other activities
in society. Thus, the effective protection of human health with common or future
wastewater management options seems to be secured.
For aquatic ecotoxicity, the normalized scores account to 4-6% of total FAETP
for most scenarios. Loads of toxic heavy metals in WWTP effluent are mainly
responsible for the ecotoxicological impact of wastewater management. If
sewage sludge is applied in agriculture (Rmin), the relative contribution rises
significantly to 13% due to the possible transfer of wastewater-derived heavy
metals to surface waters via agricultural soil.
Normalized scores for terrestrial ecotoxicity are between 13-17% for the
reference systems and 4-7% for separation systems. Input of heavy metals via
mineral fertilizer or sewage sludge is mainly responsible for the high
contribution of this impact category in the reference scenarios. Wastewater
treatment itself has a marginal share of TETP, but the secondary functions of
fertilizer supply extend the impacts of wastewater management to agriculture,
thus causing a significant impact on terrestrial ecotoxicity.
In summary, normalization of indicator results showed a marginal share of wastewater
management for energy and resource demand, global warming, and human toxicity.
Moderate to high contribution to total environmental impacts is calculated for
acidification, eutrophication, and ecotoxicity.
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5.2.11 Grouping and Weighting
Grouping and weighting of indicator results from LCIA is another optional step of
valuation. It can be helpful to come to a conclusive statement while comparing two
scenarios in their environmental impacts. Here, a modified evaluation method based on
the UBA method (Schmitz and Paulini, 1999) is applied, adding toxicity indicators to
the original method. It is based on the ranking of the various impact categories
considering ecological hazard, distance to target, and specific contribution of each
indicator.
This method is used for a direct comparison of two scenarios. Out of the large number
of possible combinations, three combinations are chosen to exemplify the valuation
procedure and possible outcomes in detail.
Comparison of scenario R vs V1 (FEASIBLE)
Scenario V1 is a separation system with a relatively low level of complexity: toilet
wastewater is collected by a vacuum system, digested and applied to agriculture, while
the remaining greywater is treated in a conventional activated sludge plant. The actual
implementation of such a system is not very different in infrastructural needs to a
conventional system: it just requires a second pipe network and vacuum toilets.
Furthermore, all required process technology (vacuum drainage, biogas plant, SBR) is
well-known and thus easy to handle. Hence, the feasibility of scenario V1 is estimated
to be high compared to other separation scenarios.
-250 -200 -150 -100 -50 0 50 100 150
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario V1
[%]
Figure 65: Comparison of scenario R and V1 with valuated indicator results in T diagram
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The direct comparison between scenarios R and V1 shows distinct benefits for the
separation scenario after valuation (Figure 65). Except for acidification, environmental
impacts of the reference scenario are all significantly higher. If indicators with similar
priority are offset against each other, the valuated comparison leads to a conclusive
result: scenario V1 is clearly favourable in terms of environmental impacts.
Comparison of scenario R vs SV3 (SOPHISTICATED)
In contrast, scenario SV3 can be described as the most sophisticated separation scenario
in terms of technical effort. It employs vacuum drainage, urine separation, collection
and treatment, and greywater reuse. Thus, four different pipe networks are required and
a variety of technical processes of which some have not been tested successfully in
larger scale (e.g. vacuum separation toilets, urine treatment, greywater reuse). The
feasibility of a large-scale implementation of scenario SV3 is probably limited in the
near future.
However, the direct comparison between scenarios R and SV3 also yields distinct
benefits for the separation scenario (Figure 66). Offsetting the indicators with
comparable priority, scenario SV3 is superior to the reference system: drawbacks in
acidification are clearly outweighed by the benefits in energy and resource demand,
global warming, eutrophication, and toxicity. Scenario SV3 is favourable in terms of
environmental impacts compared to the reference system.
-200 -150 -100 -50 0 50
Cum. energy demand
Global warming
Abiotic resource depl.
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotox.
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario SV3
[%]
Figure 66: Comparison of scenario R and SV3 with valuated indicator results in T diagram
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Comparison of scenario R vs SC2 (LOW-TECH)
Scenario SC2 is a separation system using low-tech natural processes. Faeces are
drained by gravity and processed in composting after dewatering, whereas greywater is
treated in a soil filter. Only the treatment of separately collected urine may pose some
technical difficulties in this scenario.
Here, the valuated comparison of indicators does not result in a distinct statement of
environmental benefits for this separation system (Figure 67). Drawbacks in
acidification and eutrophication are critical and do not offset benefits in indicators with
the same priority (GWP, TETP). Even though the separation scenario has benefits in all
indicators with medium priority, the overall comparison cannot come to a conclusive
statement, following the definition of UBA (Schmitz and Paulini, 1999). In contrast, the
result of this comparison has to be declared as inconclusive, because the environmental
benefits of medium priority indicators (reduced resource demand and human toxicity)
cannot be compensated directly with the drawbacks in high priority indicators.
-100 -50 0 50 100 150 200
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario SC2
[%]
Figure 67: Comparison of scenario R and SC2 with valuated indicator results in T diagram
Remaining separation scenarios
For completion, all separation scenarios are compared to the reference scenario, using
the valuation procedure described above. The respective results in the form of T
diagrams with valuated indicator results are presented in annex 12.10. The extent of this
study does not allow a verbal comment for each comparison, but the graphical results
are attached for information of the interested reader.
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5.3 Sensitivity analysis
In sensitivity analysis, the robustness of the results is tested by varying certain
parameters of the Life Cycle Inventory and calculating their effect on the indicator
results of LCIA. Due to the high number of parameters in the inventory, the sensitivity
analysis is limited to those parameters which have been identified during contribution
analysis of LCIA indicators, presumably having a distinct effect on the results. Table 64
presents an overview of the tested parameters and the respective indicators. In addition
to the sensitivity analysis for inventory data, four additional impact indicators and an
alternative valuation method are calculated to reveal the influence of indicator choice
and LCIA methodology on the outcomes of this LCA.
Table 64: Parameters and indicators for sensitivity analysis
Observed Indicators CED GWP EP AP FAETP TETP
Data of Life Cycle Inventory
Energy recovery without biowaste X X
Transport distance of organic fertilizers X X
Energy demand of urine treatment X
Energy demand of vacuum plant X
Efficiency of urine separation toilets X X X X
Volume of reused greywater and
energy demand for drinking water X
Effluent concentrations of SBR X
NH3 emissions in application of
liquid organic fertilizer X
Cu/Zn content in drinking water X X
Heavy metal data of mineral fertilizer X
Plant availability of P in sewage sludge X
Alternative indicators
Aquatic eutrophication (AEU)* X
Terrestrial eutrophication (TEU)* X
Aquatic ecotoxicity (AET)** X
Terrestrial ecotoxicity (TET)** X
Valuation with original UBA method
* EDIP 2003 (Hauschild and Potting, 2003)
** IMPACT 2002+ (Jolliet et al., 2003a)
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5.3.1 Energy recovery without biowaste
If biowaste is not co-digested together with toilet wastewater, the amount of energy that
can be recovered from organic matter is significantly lower (-17 kWh/(pe*a)). For most
energy recovery scenarios, total CED is higher than the reference scenario without
biowaste co-digestion (Figure 68). Only those scenarios with low-energy greywater
treatment (V2 and SV2) can maintain an energetic benefit without biowaste co-
digestion. For global warming, benefits of energy recovery are significantly reduced
without biowaste, but energy recovery scenarios are still superior to the reference
system (Figure 69).
These calculations underline the importance of an integrated approach treating both
toilet wastewater and biowaste if energy recovery is targeted. Biowaste contains a high
amount of organic matter (= energy recovery potential) and thus improves the overall
energy balance of the separation scenarios decisively. Without biowaste co-digestion,
the high energy demand for operating the vacuum plant and the digestion process
offsets the recovered energy from toilet wastewater, leading to a higher total CED.
-50
-40
-30
-20
-10
0
10
20
30
40
without biowaste with biowaste
Amount of biowaste in co-digestion
Cumulated energy demand
[in % relative to R]
RV1 V2 V3 SV1 SV2 SV3
Original value in LCI
Figure 68: Variation of CED with and without biowaste in co-digestion
-50
-40
-30
-20
-10
0
10
without biowaste with biowaste
Amount of biowaste in co-digestion
Global warming potential
[in % relative to R]
RV1 V2 V3 SV1 SV2 SV3
Original value in LCI
Figure 69: Variation of GWP with and without biowaste in co-digestion
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5.3.2 Transport distance of organic fertilizers
Transport distance of organic fertilizers (urine, compost from faeces and biowaste,
digester sludge, sewage sludge) is set to 20 km in the inventory, thus representing the
application of secondary fertilizers in a relatively short distance to the settlement. If no
suitable agricultural land is available in the proximity, this transport distance might be
increased considerably.
If transport distance is increased to 100 km, cumulated energy demand and global
warming potential rise due to the additional fuel required for the trucks. However, this
effect is relatively small in the urine separation scenarios SV and SC: CED increases by
2% and GWP by 4%, respectively (Figure 70 and Figure 71). This increase does not
lead to a different qualitative result of the comparison of urine separation scenarios with
the reference scenario R. In vacuum scenarios without urine separation (V), the
complete volume of urine, faeces, and flush water is transported to the fields after the
digestion process. The effect of a longer transport distance is significant: the increase of
CED and GWP amounts to 13 and 26%, respectively, for a transport distance of 100
km. Hence, the benefits of V scenarios in CED and GWP are partially offset if digester
sludge is transported over long distances. The transport distance up to which the
vacuum scenario V1 is still superior to the reference system are calculated to 189 km for
CED and 156 km for GWP.
Overall, the transport distance has a significant influence in vacuum scenarios without
urine separation (V), whereas its influence in urine separation scenarios SV and SC can
be described as minor. The volume of urine and faeces itself in relation to their specific
nutrient content justifies a transport over longer distances, even for urine with relatively
high volume. If flush water is not separated from these fertilizers, transport should be
limited to a minimum.
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0
10
20
0 20 40 60 80 100 120
Transport distance of organic fertilizers [km]
Cumulated energy demand
[in % relative to R]
RRagri V1 SV1 SC1
Original value in LCI
Figure 70: Variation of CED with transport distance of organic fertilizers
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0
10
0 20 40 60 80 100 120
Transport distance of organic fertilizers [km]
Global warming potential
[in % relative to R]
RRagri V1 SV1 SC1
Original value in LCI
Figure 71: Variation of GWP with transport distance of organic fertilizers
5.3.3 Energy demand of urine treatment
The treatment of separated urine for the elimination of organic micropollutants is an
important feature to allow its agricultural application without potential risks for humans
or the agro-ecosystem. However, the treatment of urine has not been tested in full-scale,
so that the energy demand had to be roughly estimated in the inventory (1 kg ozone per
m³ = 15 kWh/m³).
If this energy demand is doubled to 30 kWh/m³, cumulated energy demand rises by
6% for all urine separation scenarios compared to the reference scenario R (Figure 72).
A qualitative change in the comparison is detectable only for scenario SC2. If no urine
treatment is applied (= 0 kWh/m³), the results of the qualitative comparison are stable
for all scenarios.
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0
25
50
0 5 10 15 20 25 30 35
Energy demand of urine treatment [kWh/m³]
Cumulated energy demand
[in % relative to R]
RSV1 SV2 SV3
SC1 SC2 SC3
Original value in LCI
Figure 72: Variation of CED with energy demand of urine treatment
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In all, the energy demand of urine treatment does not have a decisive influence on the
energetic comparison if estimated in realistic ranges (15-30 kWh/m³). It has to be noted
though that urine separation does not lead to energetic benefits if combined with faeces
composting (SC1/3), even if no treatment is considered.
5.3.4 Energy demand for vacuum plant
The energy demand for the operation of the vacuum plant is estimated to 15 kWh per
inhabitant and year in the LCI. However, literature data shows a wide range of possible
values, with a minimum value of 3.1 kWh/(pe*a) postulated in the Berlin pilot project
and a maximum of 51 kWh/(pe*a) for the pilot plant in Flintenbreite (cf chapter
4.1.2.3). Hence, the influence of a variation in energy demand for operating the vacuum
plant is calculated for the indicator CED, covering a range of 7.5 to 30 kWh/(pe*a).
If the energy demand of the vacuum plant is increased to 30 kWh/(pe*a), CED of
vacuum scenarios rises by 45% compared to the reference scenario R (Figure 73). Thus,
all separation scenarios are now inferior in CED to the reference scenario. The
respective maximum energy demand of the vacuum plant for realizing an energy benefit
ranges from 18 to 28 kWh/(pe*a) for scenario SV3 and V2, respectively.
The vacuum plant is one of the major energy consuming processes in energy recovery
scenarios, partially offsetting the energetic benefit of these systems in the comparison
with the conventional system. Therefore, its energy demand should be minimized to
recover a net maximum amount of energy from wastewater-derived organic matter. A
reduction of the energy demand to 7.5 kWh/(pe*a) reduces CED of separation systems
by 22% compared to the reference scenario R.
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-60
-40
-20
0
20
40
5 1015202530
Energy demand of vacuum plant [kWh/pe*a]
Cumulated energy demand
[% relative to scenario R = 100%]
35
RSV1 SV2 SV3 V1 V2 V3
Original value in LCI
Figure 73: Variation of CED with energy demand of vacuum plant
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5.3.5 Efficiency of urine separation toilets
The separation efficiency of the urine separation toilets is an important parameter in
urine separation scenarios, determining the relative amount of undiluted urine that can
be collected and applied as fertilizer in agriculture. However, the correct long-term
operation of the separation toilets depends on technical issues (valve operation,
blockages of urine pipes with precipitates etc) as well as user behaviour. Therefore, the
estimation of average separation efficiency is affected with uncertainty. In this study, it
is assumed that 70% of the daily urine can be effectively separated with separation
toilets. However, the actual efficiency during long-term operation could be significantly
higher (due to improved toilet design or user education) or lower (due to improper
usage and maintenance of the separation mechanism).
The efficiency of urine separation has an effect on a variety of LCIA indicators:
energy demand and global warming are affected through the amount of substituted
fertilizer and the related energy demand. Acidification is affected by urine application
causing considerable NH3 emissions, whereas additional misdirected urine can lead to
increased eutrophication by increasing nutrient loads of faeces filtrate which is
eventually treated with greywater. All relevant changes in system expansion processes
(amount of substituted fertilizer and energy) are considered during the calculation of
this sensitivity analysis.
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-20
-10
0
10
20
30
50 55 60 65 70 75 80 85 90
Efficiency of urine separation toilets [%]
Cumulated energy demand
[% relative to scenario R = 100%]
RSV1 SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 74: Variation of CED with efficiency of urine separation toilets
Variation of CED with urine separation efficiency is marginal for all scenarios (Figure
74): if the separation efficiency is increased to 90%, CED of urine separation scenarios
slightly decreases for scenarios SC1 (-2%) and SC3 (-0.3%) due to a higher amount of
substituted N fertilizer. In vacuum separation scenarios (SV), CED slightly increases
with separation efficiency (+0.3%), probably due to the missing organic matter of urine
in digestion. However, the qualitative comparison to reference scenario R does not
change for any of the urine separation scenarios.
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Changes in global warming are more significant: GWP decreases significantly with
increasing separation efficiency. An improvement of separation efficiency to 90% leads
to a 4-5% reduction of GWP, whereas a decline to 50% efficiency results in a 4-6%
increase in GWP of separation scenarios. However, composting scenarios SC1 and SC3
change their relative comparison to the reference system from slightly worse (50%
separation) to slightly better (90% separation).
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-30
-20
-10
0
10
50 55 60 65 70 75 80 85 90
Efficiency of urine separation toilets [%]
Global warming potential
[% relative to scenario R = 100%]
RSV1 SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 75: Variation of GWP with efficiency of urine separation toilets
The acidification potential gives a twofold picture (Figure 76): in vacuum separation
scenarios, AP significantly decreases by 11% with better separation (90%), presumably
due to less nitrogen emissions in open composting of digester sludge: properly
separated urine does not contribute to NH3 emissions from processing of the digester
residual. This amount of avoided NH3 emissions offsets an increase in NH3 emissions
during urine application. In contrast, scenarios SC1 and 2 show a small increase in AP
(+3%) when increasing the separation efficiency to 90%. Here, the increased NH3
emissions from urine application are responsible for the higher AP. Scenario SC3 has an
optimum: higher and lower separation lead to an increased AP. In all, the separation
efficiency has no influence on the qualitative comparison of urine separation scenarios
with the reference system: all separation scenarios have a significantly higher impact in
AP, independent of the separation efficiency.
In eutrophication, the increase of separation efficiency results in a reduction in EP for
all scenarios (Figure 77). The reduction is more significant in composting scenarios SC1
and 3 (-29%) and especially for the soil filter scenario SC2 (-74%). The co-treatment of
faeces filtrate (containing misdirected urine with high nutrient content) with greywater
is responsible for this effect, particularly pronounced in soil filter scenario with low
nutrient elimination. Nevertheless, scenario SC2 remains inferior in EP even in case of
high separation efficiency (90%), whereas scenarios SC1 and 3 become superior to the
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reference system in EP. For vacuum separation scenarios, the effect in EP is not that
strong (-7% in EP fro 90% separation). Here, misdirected urine ends up in sludge liquor
which is treated in a separate process with high nutrient elimination.
-10
0
10
20
30
40
50
60
70
50 55 60 65 70 75 80 85 90
Efficiency of urine separation toilets [%]
Acidification potential
[% relative to scenario R = 100%]
RSV1 SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 76: Variation of AP with efficiency of urine separation toilets
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0
50
100
150
200
250
50 55 60 65 70 75 80 85 90
Efficiency of urine separation toilets [%]
Eutrophication potential
[% relative to scenario R = 100%]
RSV1 SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 77: Variation of EP with efficiency of urine separation toilets
In all, an increase in urine separation efficiency can have a distinct effect on GWP, AP
and EP, thus changing the results of the comparison for certain impact categories. The
energetic comparison (CED) is not influenced much by this parameter. Nevertheless,
the optimization of the separation process itself should be in the focus of technical
development of urine separation.
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5.3.6 Reuse volume of and energy demand for water supply
The simplified feasibility of non-potable reuse of purified greywater is an important
feature of separation systems. However, it was found that the substitution of drinking
water in toilet flushing does not lead to an energetic benefit in reuse scenarios V3, SV3,
and SC3. The volume of substituted drinking water is too low with 5.4 L/(pe*d) using
vacuum toilets and 24 L/(pe*d) using gravity toilets. Additionally, the energy demand
for drinking water production is estimated relatively low (0.5 kWh/m³) in relation to the
pumping energy for the reused greywater (0.27 kWh/m³). Thus, the variation of CED
with the volume of reused greywater is calculated assuming simple (0.5 kWh/m³) or
complex drinking water treatment (1 kWh/m³) to determine the conditions under which
reuse of greywater can be beneficial in energy demand.
For simple drinking water treatment, a volume increase to 50 L/(pe*d) of substituted
drinking water results in a 4% decrease of CED for vacuum and 2% for gravity systems
(Figure 78). This does not result in a change in the relative comparison to the reference
system or the separation systems without reuse. Assuming complex drinking water
treatment, increased reuse volume results in a 9% increase in CED for vacuum and 5%
for separation systems (Figure 79). Now, scenario SV3 and V3 are energetically
favourable to the reference system and to the respective separation scenarios without
reuse (SV1 and V1).
In all, it can be concluded that the reuse of purified greywater only results in energetic
benefits if a complex drinking water treatment is assumed and the volume of substituted
drinking water is high (> 25 L/(pe*d), i.e. more than 30% of total greywater flow).
Otherwise, the additional energy demand during treatment (MBR) and the pumping
energy for delivery of reused greywater back to the households offset the energetic
benefits of drinking water substitution.
70
75
80
85
90
95
100
105
110
115
120
0 1020304050
Volume of reused greywater [L/pe*d]
Cumulated energy demand
[% relative to scenario R = 100%]
60
RV1 V3 SV1 SV3 SC1 SC3
5.4
Original value of V3
and SV3 in LCI
24
Original value of SC3 in LCI
Figure 78: Variation of CED with volume of reused greywater
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60
70
80
90
100
110
120
0 10203040506
Volume of reused greywater [L/pe*d]
Cumulated energy demand
[% relative to scenario R = 100%]
0
RV1 V3 SV1 SV3 SC1 SC3
5.4
Original value of V3
and SV3 in LCI
24
Original value of SC3 in LCI
Figure 79: Variation of CED with volume of reused greywater, assuming complex drinking water
treatment (1 kWh/m³)
5.3.7 Comparable effluent concentrations in SBR
Effluent concentrations of activated sludge plants in scenarios R, V, SV, and SC are
different in conventional and separation scenarios, because elimination ratios for COD,
N and P are estimated for each scenario separately based on total loads of influent to
effluent. This refers to wastewater and greywater treatment plants (SBR or MBR), but
also to the treatment of concentrates (sludge liquor of digestion). However, the actual
elimination ratio of a technical process also depends on the operational parameters, e.g.
hydraulic retention time, aeration, or applied amount of chemicals (Fe for precipitation).
Consequently, another systematic approach to this issue would be to assume equal
effluent concentrations for comparable technical processes (SBR and MBR) if
operational parameters are adapted to influent loads.
Hence, it is assumed that all SBR processes (wastewater treatment in scenario R,
greywater treatment in V1/SV1/SC1, and sludge liquor treatment in SV2 and SV3) have
a comparable effluent quality of 50 mg/L COD, 1 mg/L NH4-N, 7 mg/L NO3-N, and 0.6
mg/L PO4-P. Energy and chemical demand is adjusted accordingly. For MBR plants in
reuse scenarios V3/SV3/SC3, effluent concentrations are assumed to 25 mg/L COD, 1
mg/L NH4-N, 7 mg/L NO3-N, and 0.3 mg/L PO4-P. The low concentrations of COD and
PO4-P are justified by the superior particle retention capacity of membrane processes, so
that retention of small sludge particles or Fe precipitates is better in MBR compared to
SBR processes. For the soil filters, effluent concentrations are not adjusted, because
their elimination performance can only be partially controlled by operational
parameters.
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0
50
100
150
200
variable equal
Effluent concentrations
Eutrophication potential
[% relative to scenario R = 100%]
RV1 V2 V3 SV1
SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 80: Variation of EP with definition of effluent concentrations of SBR and MBR
With equal effluent concentrations in SBR and MBR plants, the calculated
eutrophication potential of separation systems is significantly different in relation to the
reference scenario R (Figure 80). EP of most separation scenarios increases compared to
scenario R, while only scenario SC3 has a lower EP while assuming equal
concentrations. Scenario V1 is now slightly inferior to the reference system in EP, as
well as all soil filter scenarios and scenario SC1. Benefits in EP are calculated for reuse
scenarios V3, SV3, and SC3 due to higher effluent quality of MBR. Additionally,
scenario SV1 has lower EP due to lower effluent volumes, leading to lower effluent
loads if equal concentrations are assumed.
That issue is identified as a major drawback of this approach: with comparable
effluent concentrations, a volume reduction in effluent automatically results in a lower
EP, which is not supposed to be a realistic assumption. The treatment of concentrates
should eventually lead to higher effluent concentrations compared to the treatment of
diluted wastewater.
Overall, the adequate comparison of effluent loads or concentrations of the different
water treatment processes can only be achieved if original effluent data from full-scale
plants can be implemented. Both methodological approaches of this study to estimate
effluent loads and resulting eutrophication have their drawbacks. However, the
approach of estimating elimination via the ratio of influent to effluent loads (= variable
concentrations) is considered a reasonable basis for a first assessment of EP in
separation systems, if no suitable full-scale data is available.
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5.3.8 NH3 emissions during application of liquid fertilizer
The evaporation of nitrogen in the form of NH3 during the application of liquid organic
fertilizer is a major source for acidification in separation systems. However, the
respective ratios of NH3 emissions in relation to total applied nitrogen are estimated
from pilot studies or data of comparable fertilizers. Emission factors for sewage sludge
(8% of applied N), urine (10%) or digester sludge (stabilised: 6.3%, liquid: 22%) are
estimated relatively high in comparison to mineral fertilizer (5%). If specific emission
reduction techniques are applied (e.g. drag hoses, injection, instant ploughing after
application), NH3 emissions could be considerably reduced, resulting in lower
acidification.
If NH3 emissions during application of liquid organic fertilizer are reduced by 50%
for each fertilizer, drawbacks in AP can be significantly reduced for separation
scenarios (Figure 81). AP of urine separation scenarios SV and SC are decreased by
23%, and AP of vacuum scenarios without urine separation is even decreased by 92% in
relation to the reference scenario. However, all separation scenarios are still
significantly inferior in AP compared to scenario R.
Increased acidification in separation scenarios due to high NH3 emissions during the
application of liquid organic fertilizers has been identified as a major drawback for
separation systems. Hence, emission reduction techniques should be implemented to
minimize this drawback. Even with considerable efforts to minimize these emissions,
separation systems most likely cause more emissions of NH3 than mineral fertilizer
application. The liquid form of organic fertilizers is seen as a major cause of this
drawback, a fact that is inherent in the separation systems. The production of a solid
fertilizer product would be an alternative.
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0
20
40
60
80
100
120
40 50 60 70 80 90 100 110
NH
3
emissions during application of liquid organic fertilizer [%]
Acidification potential
[in % relative to R]
RRagri V1 SV1 SC1
Original value in LCI
Figure 81: Variation of AP with NH3 emissions during application of liquid organic fertilizer
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5.3.9 Concentrations of Cu and Zn in drinking water
The contribution of drinking water to heavy metal loads in wastewater-derived
fertilizers plays an important role for the two elements of Cu and Zn. Increased
concentrations of these metals due to corrosion of pipe materials can lead to elevated
concentrations in drinking water and organic fertilizers. In this study, concentrations of
0.16 mg/L Cu and 0.37 mg/L Zn are assumed, representing relatively high
concentrations after contact with pipe materials.
If assumed concentrations of Cu and Zn are reduced by 50%, changes in freshwater
aquatic ecotoxicity can be detected (Figure 82). While FAETP of separation scenarios
with SBR or soil filter are decreasing by 1-3% in relation to the reference scenario,
FAETP of reuse scenarios is slightly increasing (+1-3%). In this study, calculated
concentrations of Cu and Zn in reused greywater are smaller than in drinking water, so
that this advantage is partially cancelled for low concentrations of Cu and Zn in
drinking water. The highest effect can be detected for scenario Ragri: the application of
sewage sludge in agriculture transfers a high amount of Cu and Zn from drinking water
to agricultural soil. Consequently, this drawback of sewage sludge application decreases
with lower Cu and Zn content (-22% in FAETP for 50% lower Cu and Zn) and
increases with higher Cu and Zn content (+30% in FAETP for doubling of Cu and Zn).
The transfer of Cu and Zn from drinking water to wastewater-derived fertilizers can
contribute significantly to freshwater aquatic ecotoxicity, especially if sewage sludge is
applied in agriculture. However, the qualitative comparison of separation scenarios with
the reference system is not affected by assuming lower or higher concentrations of Cu
and Zn in drinking water.
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0
20
40
60
80
100
120
140
160
0 50 100 150 200 250
Cu and Zn concentration in drinking water [%]
(100%: Cu = 0.16 mg/L, Zn = 0.37 mg/L)
Freshwater aquatic ecotoxicity potential
[% relative to scenario R = 100%]
RRagri V1
V2 V3 SV1
SV2 SV3 SC1
SC2 SC3
Original value in LCI
Figure 82: Variation of FAETP with concentrations of Cu and Zn in drinking water
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5.3.10 Heavy metal data for mineral fertilizer
The content of heavy metals in mineral fertilizers (especially P fertilizer) is responsible
for the high input of heavy metals in agricultural soil in the reference scenarios.
Secondary fertilizers from urine and faeces have a lower heavy metal content, so that
the separation scenarios are superior in terrestrial ecotoxicity to the conventional
system. However, heavy metal data for mineral fertilizers used in the inventory relates
to the year 1992. More recent data published in 2007 suggests a decreasing heavy metal
content in mineral fertilizers, but this dataset is not representative due to a low number
of samples (cf. annex 12.8.1).
If the 2007 dataset is used in the inventory, the benefits in TETP are slightly
decreasing, but still stable for all separation scenarios (Figure 83). The relative decrease
of the benefits of separation scenarios is due to a decreasing TETP of the reference
scenario (-34%). A reduced content of toxic metals Cd, Cr, Ni, and Pb in mineral P
fertilizer decreases the ecotoxicity potential caused by mineral fertilizer application.
However, secondary fertilizers still contain less of these heavy metals.
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-20
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0
10
20
Data 1992 Data 2007
Heavy metals in mineral fertilizer
Terrestrial ecotoxicity potential
[in % relative to R]
RV1 V2 V3 SV1
SV2 SV3 SC1 SC2 SC3
Original value in LCI
Figure 83: Variation in TETP with updated heavy metal data for mineral fertilizers
In general, the quality of mineral fertilizers seems to improve gradually in terms of
heavy metal content, so that this specific benefits of secondary fertilizers (= lower
heavy metal content) may diminish in the future. However, available resources of raw
phosphate with low heavy metal content are limited (USGS, 2008). Hence, a
sophisticated separation process is required to produce mineral P fertilizer with low
heavy metal content, probably increasing resource demand and related emissions in the
production process. If available, new and representative datasets for both the production
of mineral fertilizers and their heavy metal content should be used to clarify this issue.
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5.3.11 Plant availability of phosphorus in sewage sludge
Sewage sludge contains a major part of the wastewater-derived phosphorus. However,
its plant availability is assumed to be limited (70%), basically due to the strong binding
to ferric precipitates. Thus, the agricultural application of sewage sludge leads to a
limited recycling of wastewater P to agriculture, so that mineral P fertilizer has to be
amended to supply comparable P loads to agriculture. In consequence, scenario Ragri
shows the highest human toxicity and terrestrial ecotoxicity of all scenarios.
If phosphorus in sewage sludge is estimated to be 100% plant available, TETP of
scenario Ragri decreases significantly (-20%) in comparison to the reference system R
(mineral fertilizer) (Figure 84). For human toxicity, the decrease is smaller (-15%), but
it leads to a lower total HTP in relation to scenario R (Figure 85). This underlines the
importance of estimating the correct plant availability of phosphorus in sewage sludge
while assessing the human and ecotoxicological impact of sewage sludge application.
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0
50
100
20 30 40 50 60 70 80 90 100 110
Plant availability of P in sewage sludge [%]
Terrestrial ecotoxicity potential
[in % relative to R]
RRagri V1 SV1 SC1
Original value in LCI
Figure 84: Variation of TETP with plant availability of phosphorus in sewage sludge
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0
50
100
20 30 40 50 60 70 80 90 100 110
Plant availability of P in sewage sludge [%]
Human toxicity potential
[in % relative to R]
RRagri V1 SV1 SC1
Original value in LCI
Figure 85: Variation of HTP with plant availability of phosphorus in sewage sludge
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5.3.12 Alternative indicators for eutrophication
Eutrophication is an important impact category while assessing the environmental
impacts of wastewater systems. However, calculation of the respective indicator can be
performed following different methodologies, basically characterized by different
impact factors for COD, nitrogen and phosphorus. To reveal possible influences of the
indicator choice on the evaluation of this impact category, site-dependent
characterization factors of EDIP 2003 (Hauschild and Potting, 2003) for Germany are
used. Additionally, two different indicators are calculated: aquatic eutrophication
(AEU) considering N and P emissions to surface waters (COD or TOC and N emissions
to air are not included), and terrestrial eutrophication (TEU) considering only nitrogen
emissions to air (NH3, NOx).
For aquatic eutrophication, the comparison between reference scenario and separation
systems results in a similar trend than for the original indicator EP (cf. chapter 5.2.5).
Soil filter scenarios are inferior in AEU (+ 47-205%), whereas vacuum scenarios with
SBR or MBR are superior by 40-68% (Figure 86). Composting scenarios with SBR or
MBR are comparable to the reference system. Thus, results of the new indicator AEU
come to the same conclusion than EP, but the relative benefit or drawback of the
respective separation scenario is more distinct. In other words, the relative benefits or
drawbacks of separation scenarios are even more pronounced with this indicator. Hence,
the indicator choice has no influence on the general findings in this impact category, but
on their intensity.
0
1
2
3
4
5
6
7
8
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
AEU [kg NO3-eq/(pe*a)]
Infrastructure Operation Equivalent products
17.5
Figure 86: Aquatic eutrophication calculated with EDIP2003
For terrestrial eutrophication, the comparison tends to comparable results than the
acidification indicator AP: all separation scenarios are inferior to the reference system,
mostly due to high NH3 emissions from application of liquid organic fertilizer (Figure
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87). This fact of enhanced eutrophication of terrestrial ecosystems due to an increase in
nitrogen emissions to air is not properly reflected in the original indicator of EP. Thus,
the new indicator TEU has revealed another drawback of separation systems, i.e. the
risk of enhanced eutrophication of terrestrial ecosystems. However, emission reduction
techniques should be able to reduce this drawback of separation systems considerably
by decreasing NH3 emissions in fertilizer application (cf. chapter 0).
0
20
40
60
80
100
120
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
TEU [m2 UES/(pe*a)]
Infrastructure Operation Equivalent products
Figure 87: Terrestrial eutrophication calculated with EDIP 2003
5.3.13 Alternative indicators for ecotoxicity
The high uncertainty in calculating indicators for ecotoxicity has been addressed above
(cf chapter 3.8.3). Especially the role of heavy metals in this assessment has to be
evaluated with care. Two alternative indicators for ecotoxicity are calculated here to
reveal possible influences of different approaches of toxicity assessment on the
outcomes of this study, including aquatic ecotoxicity (AET) and terrestrial ecotoxicity
(TET) following the methodology of IMPACT 2002+ (Jolliet et al., 2003a).
For aquatic ecotoxicity, the results are not comparable to the original indicator of
FAETP: reuse scenarios are slightly superior to the reference system due to substitution
of drinking water (decreasing Cu and Zn loads), while all other separation scenarios are
inferior to scenario R with an increase in AET of 2-25% (Figure 88). This is particularly
due to increased loads of Cu and Zn to agricultural soil (cf. chapter 5.1.5), because these
emissions are characterized by high impact factors in the calculation of AET. The
different characterization of aquatic ecotoxicity of Cu and Zn transferred to agricultural
soil leads to a distinctively other result of the comparison in aquatic ecotoxicity
depending on indicator choice.
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0
10
20
30
40
50
60
70
80
90
100
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
AET [1000 kg TEG-eq/(pe*a)]
Infrastructure Operation Equivalent products
251
Figure 88: Aquatic ecotoxicity calculated with IMPACT 2002+
For terrestrial ecotoxicity, the differences between original and alternative indicator are
even higher. While the original indicator TETP revealed significant benefits for all
separation systems, the alternative indicator of TET calculates comparable or higher
impacts in this category for the separation systems (Figure 89). Again, the role of Cu
and Zn is crucial: high loads of these metals to agricultural soil are responsible for the
drawbacks of separation systems, even though all other heavy metal loads (Cr, Cd, Hg,
Ni, Pb) are decreased significantly (cf. chapter 5.1.5).
0
10
20
30
40
50
60
70
80
90
100
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
TET [1000 kg TEG-eq/(pe*a)]
Infrastructure Operation Equivalent products
206
Figure 89: Terrestrial ecotoxicity calculated with IMPACT 2002+
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Overall, it can be concluded that the assessment of ecotoxicity delivers different results
in the comparison of reference and separation systems, depending on the respective
indicator choice. Particularly the evaluation of Cu and Zn loads to agricultural soil is a
crucial point here, because these heavy metals are contained in organic fertilizers from
wastewater in high amounts. However, both metals belong to the group of essential
trace metals, and their ecotoxicity is probably depending heavily on the specific
conditions in the environment. Thus, the results of the ecotoxicity assessment from the
original indicators of FAETP and TETP is taken as a basis for the comparison, even
though these results should be seen with care before further knowledge in ecotoxicity
assessment of metals is available.
5.3.14 Valuation with original UBA method
Valuation in this LCA study is based on an approach developed by UBA (Schmitz and
Paulini, 1999) which is explicitly modified to include the indicators for toxicity. Thus,
benefits of separation scenarios in toxicity indicators have a strong influence in
valuation. However, the original UBA method has excluded these indicator categories
on purpose due to methodical uncertainties in the calculation of the respective
indicators.
If the original UBA method without toxicitiy indicators is used for valuation, the
overall results of comparing each separation scenario with the reference system R are
less significant. For example, the comparison between scenarios V1 and R results in a
non-significant conclusion (Figure 90). The high acidification potential in scenario R
more than offsets benefits in GWP and EP, and benefits in CED and ADP have another
priority than AP, leading to a non-significant overall result by definition.
-60 -40 -20 0 20 40 60 80 100 120
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Additional impact
of reference scenario R
[%]
Additional impact
of scenario V1
High priority indicator Medium priority indicator
Figure 90: Comparison of scenario R and V1 with original UBA method in T diagram
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In contrast, scenario SV3 remains significantly superior to the reference system
applying the original UBA method (Figure 91). In total, only two separation scenarios
(SV1 and SV3) remain significantly superior to the reference system using the original
UBA method, whereas the results for all other separation scenarios are either non-
significant or significantly worse (SC1 and SC3) than the reference system. The
respective t-diagrams are shown in chapter 5.2.11 or in annex 12.10 (exclude toxicity
indicators).
-40 -30 -20 -10 0 10 20 30 40
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Additional impact
of reference scenario R
[%]
Additional impact
of scenario SV3
High priority indicator Medium priority indicator
Figure 91: Comparison of scenario R and SV3 with original UBA method in T diagram
As a conclusion, it is a crucial decision for the overall result of this LCA whether to
include or exclude the toxicity indicators in valuation. If toxicity indicators are
included, more separation scenarios are rated better than the reference system. Due to
the high uncertainties in the scientific basis for calculating toxicity indicators (cf.
discussion in chapter 3.8.3), quantitative results of the toxicity assessment should be
regarded with care. Nevertheless, stated benefits of separation systems in ecotoxicity are
based on a lower input of toxic heavy metals into the environment. This benefit should
in some way be included while comparing the scenarios. However, the quantitative
uncertainty with toxicity indicators may lead to a biased valuation: a 50% drawback in
acidification is then offset with a 50% benefit in TETP, although the calculation of the
indicators is based on scientific models with different uncertainty. Thus, a valuation
procedure including toxicity indicators is prone to a biased result until the impact
models for toxicity are further improved.
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6 Interpretation
The interpretation of the results of inventory analysis and impact assessment is the final
step of an LCA. Relevant findings of LCI and LCIA are considered in relation to the
specified goal of the study. Life cycle interpretation should reach conclusions, explain
limitations and provide recommendations based on the results of the LCA (ISO 14040,
2006). The following chapter is divided into a discussion of relevant findings of this
LCA, methodological issues and additional remarks.
6.1 Relevant findings of this LCA
For a comprehensive interpretation of the results of this LCA case study, findings of
inventory analysis and impact assessment are summarized and discussed. As a starting
point for the discussion, important results of the impact assessment are presented in two
different schemes:
a) Comparison of separation systems with baseline scenario R
A matrix summarizes the benefits and drawbacks of separation systems for each
indicator in relation to the baseline scenario R (Table 65). The simplified diagram
accounts for the uncertainty of the LCIA results. Due to the large amount of
inventory data, uncertainty is not quantified exactly in this study. However, the
simplified presentation considers a defined uncertainty by postulating impact
changes less than 10% as “not significant”, changes of 10-25% as “slight”, and
changes greater than 25% as “significant”. Even though these numbers are set
arbitrary, they are supposed to represent a rather conservative approach in
accounting for uncertainty in this LCA. For toxicity indicators with high uncertainty
in impact modelling, changes up to 50% are defined as “not significant”. The
stability of the respective indicator results in sensitivity analysis is indicated by a
background color (green = stable, yellow = partially unstable).
b) Synopsis of relevant findings in LCIA
For each impact category, a verbal summary lists the decisive processes, emissions
and resources determining the indicator results (Table 66). Additionally, specific
benefits and drawbacks of separation systems are mentioned in keywords. It has to
be noted though that this verbal summary is by definition a synopsis of the results of
this LCA. The reduction of relevant findings to certain keywords should not lead to
a “shortcut” conclusion of this study.
Table 65: Matrix of indicator comparison between separation systems and reference scenario R
Scenario V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Faeces treatment |–––––––––––––––––––––––– Digestion ––––––––––––––––––––––––| |–––––––– Composting ––––––––|
Urine treatment |––––––––– Digestion –––––––––| |––––––––––––––––––––––– Separation ––––––––––––––––––––––– |
Greywater treatment SBR Soil filter MBR
+ reuse SBR Soil filter MBR
+ reuse SBR Soil filter MBR
+ reuse
Cumulative energy demand ++ ++ + + + + 0 0 -
Global warming ++ ++ ++ ++ ++ + 0 0 0
Abiotic resource depletion ++ ++ + + ++ + 0 0 -
Acidification – – – – – – – – – – – – – – – – – –
Eutrophication ++ – – ++ ++ – – ++ 0 – – 0
Human toxicity* + + + + + + 0 0 0
Aquatic ecotoxicity* 0 0 0 0 0 0 0 0 0
Terrestrial ecotoxicity* + + + + + + 0 0 0
Note: indicator is ++ = better than 25% + = better than 10% 0 = comparable (± 10%) – = worse than 10% – – = worse than 25%
* for toxicity indicators: + = better than 50% 0 = comparable (± 50%) – = worse than 50%
Stability of results in sensitivity analysis: GREEN = STABLE YELLOW = PARTIALLY UNSTABLE
216
Table 66: Synopsis of relevant findings in Life Cycle Impact Assessment
Indicator Decisive processes Important
emissions or
resources
Benefits of
separation systems Drawbacks of
separation systems Remarks
CED Greywater treatment,
Faeces treatment,
Biowaste?
- Energy recovery from
organic matter
Higher operational
energy demand
Urine separation/
N fertilizer substitution/
GW reuse not decisive
GWP Energy supply,
Fertilizer application,
Denitrification
CO2, N2O
Less N2O from N fertilizer
production and
denitrification
More CH4 from
composting –
ADP Energy supply Fossil fuels Energy recovery from
organic matter
Higher operational
energy demand
Substitution of raw P ores
has negligible influence
AP Fertilizer application NH3 –
NH3 emissions from
fertilizer application and
composting
(Emission factors
estimated)
EP Effluent of WWTP P, N Reduction of nutrient
loads to WWTP
Insufficient P retention in
soil filters, treatment of
concentrates?
Depends on functional
definition
HTP Production and
application of MF / SS
Fluoride,
heavy metals Substitution of MF / SS – MF and SS have
comparable HTP
FAETP Effluent of WWTP,
Fertilizer application Heavy metals
Substitution of MF / SS
contaminated with HM,
(GW reuse)
– SS application causes
highest FAETP
TETP Fertilizer application Heavy metals Substitution of MF / SS
contaminated with HM – –
MF: mineral fertilizer, SS: sewage sludge, GW: greywater, WWTP: wastewater treatment plant, HM: heavy metals
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The following discussion is structured by the questions which were raised in the
introduction of this thesis. Additionally, results are evaluated in relation to the data
quality and the findings of sensitivity analysis.
1. Are separation systems more sustainable than the conventional
system?
The comparison of environmental impacts between separation systems and the
conventional approach reveals potential benefits in most of the impact categories (Table
65). Some indicators show distinct benefits or comparable impacts for separation
scenarios, whereas other indicators reveal benefits for some scenarios, and comparable
or even worse impacts for others:
Human toxicity, terrestrial and aquatic ecotoxicity and global warming are
comparable or lower for all separation scenarios.
Cumulative energy demand, abiotic resource depletion, and eutrophication
are lower, comparable, or higher depending on the layout of the separation
system.
Acidification is higher in all separation scenarios.
Regarding the different scenario groups of separation systems, which represent different
options for handling of separated wastewater flows, the following trends in comparison
to the reference system can be identified:
Vacuum scenarios without urine separation (2-flow systems) have the lowest
environmental impact scores of the three scenario groups. They are
significantly better than the conventional system in many impact categories.
In total, V scenarios have less environmental impacts than the reference
system after grouping and weighting of the indicators if toxicity indicators
are included in valuation.
Vacuum scenarios with urine separation (3-flow systems) are also
significantly better than the conventional system in some impact categories.
In total, these scenarios are also superior to the reference system after
grouping and weighting. However, their benefits are not as large as for the V
scenarios, especially in terms of energy demand and resource depletion.
Composting scenarios with urine separation (3-flow systems) have the
highest environmental impact of all investigated separation systems. All
indicator scores are comparable or worse than for the reference system. If
indicators are grouped and weighted, the valuation result for the composting
systems is either insignificant or worse than for the reference system.
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In summary, it can be concluded from the results of this LCA case study that separation
systems obviously can offer significant potentials for an increase in sustainability of
wastewater management systems if compared to a conventional system. However, it
requires a careful examination of system configuration and design to end up with a new
sanitation system with less environmental impacts, especially if the conventional system
is optimized in terms of nutrient removal (denitrification and P elimination) and energy
demand (sludge digestion). The comparison between a conventional system with
minimum standards (scenario Rmin) and various separation systems shows a clear
benefit for the separation scenarios. In other words, replacing a non-optimized
conventional system with separation systems will most likely lead to a more sustainable
solution, whereas the replacement of an optimized conventional system with separation
systems is more difficult to evaluate. Nevertheless, the appropriate choice of
configuration for a separation system should also lead to a more sustainable wastewater
management in the latter case.
2. What are the decisive benefits of separation systems?
The decisive benefits of separation systems are – as expected – their improved
capability to recover valuable resources from wastewater. The substitution of grid
energy and mineral fertilizer is a crucial feature for the increased sustainability of
separation systems. Another benefit is the reduced effort for the treatment of the
remaining wastewater (= greywater) and presumably the better quality of the WWTP
effluent. Below, the decisive benefits are discussed in detail.
a) Resource recovery: energy and nutrients
Energy recovery
Regarding energy recovery from organic matter, the energy content of faecal matter
and urine alone (18-23 kWh/(pe*a)) does not justify the increased energy demand
for its drainage with a vacuum system (15 kWh/(pe*a)) and digestion (8-11
kWh/(pe*a)). Only the addition of organic-rich household biowaste (+17
kWh/(pe*a)) to the digestion process results in an overall energetic benefit of the
energy recovery option. Therefore, energy recovery from wastewater should
definitely include the processing of biowaste. Alternatively, the energy demand of
the vacuum system should be minimized by improved pump efficiency and system
operation. With optimum energy recovery from wastewater and biowaste, separation
systems require less energy if all relevant processes are considered in the system
analysis.
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Nutrient recovery
The nutrients which can be recovered from wastewater include the plant nutrients N,
P, and K. Effective recovery rates are between 45-63% for nitrogen, 56-80% for
phosphorus, and 54-78% for potassium depending on the system configuration of
separation scenarios. The substitution of mineral fertilizers results in a reduction of
environmental impacts by avoiding resource consumption and emissions during
fertilizer production and by delivering fertilizers with better quality regarding their
heavy metal content.
The environmental impact of mineral N fertilizer production is mainly associated
with its energy demand, but the amount of substituted N fertilizer and its energetic
equivalent do not play a major role for the energetic comparison of the different
scenarios. Additionally, N2O emissions from the production of mineral N fertilizer
can be avoided and lead to a reduction in global warming potential. Overall, the
substitution of mineral N fertilizer is not decisive for the environmental benefits of
separation systems.
The main environmental benefit of nutrient recovery is associated with the
substitution of mineral P fertilizer. On the one hand, the depletion of the finite
resource of raw phosphate rock is reduced, and toxic emissions of fluoride and
heavy metals during its exploitation can be avoided. On the other hand, mineral P
fertilizer contains considerable amounts of heavy metals. Its substitution with
organic fertilizer with low heavy metal content leads to a significant reduction of
heavy metal inputs to agricultural soil. This benefit is also evident in comparison to
the conventional method of phosphorus recovery via sewage sludge: phosphate
fertilizers from separation systems are less contaminated with heavy metals than
sewage sludge.
The substitution of mineral K fertilizer only results in marginal environmental
benefits, because its production is not associated with relevant energy demand or
emissions in this LCA.
b) Simplified treatment of remaining wastewater
Reducing energy demand for WWTP
Due to the low content of organic matter and nutrients, the remaining wastewater (=
greywater) can be sufficiently treated with less energy demand than the combined
wastewater. In case of an activated sludge plant (SBR), energy savings caused by a
reduced aeration demand for carbon elimination and nitrification are relatively small
(5-9 kWh/(pe*a)). Another suitable option is the low-energy treatment in a soil
filter, which results in significant energy savings compared to the conventional
system (22 kWh/(pe*a)). If faeces are composted, the co-treatment of nutrient-rich
flushwater of gravity toilets offsets the benefits of simplified wastewater treatment
considerably.
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Improving WWTP effluent quality
Influent loads of nutrients and organic matter are considerably reduced for the
wastewater treatment process in separation systems. Thus, effluent loads of N, P,
and COD are expected to be lower in separation systems if the same process
technology (activated sludge plant) is used than in the conventional system.
However, the projected benefit of improved effluent quality has to be confirmed by
more data from full-scale greywater treatment plants. The co-treatment of high
strength flows with greywater (e.g. faeces filtrate) partially offsets this benefit.
Greywater treatment in soil filters does not lead to a better effluent quality due to
their limited potential for nutrient removal.
Overall, decisive benefits arise from energy recovery from wastewater and biowaste,
substitution of mineral P fertilizer, and reduced energy demand for treatment of the
remaining greywater. Minor benefits can be attributed to the substitution of mineral N
and K fertilizer and the improved effluent quality of the wastewater treatment process.
Contrary to expectations prior to this LCA, the following issues have been identified as
being not decisive for the environmental comparison of conventional and separation
systems:
The increased expenditures for the infrastructure of separation systems
(especially for multiple drainage pipes) lead to a higher cumulative energy
demand for their construction. However, this additional burden is clearly
offset by energetic benefits from system operation. Thus, it is recommended
to include the infrastructure in a simplified LCA, but with a simplified
inventory which is limited to the sewer system. The contribution of
infrastructure to total CED is less than 10%.
The reduced water consumption with vacuum toilets does not lead to a
substantial decrease in energy demand for separation systems. Likewise, the
reuse of purified greywater for toilet flushing does not result in energetic
benefits. An increased energy demand for treatment (MBR) and pumping
energy for delivering greywater back to the households offset the energy
savings in drinking water production. Substantial energetic benefits can
presumably be realized if the volume of substituted drinking water is high (>
25 L/(pe*d)) and drinking water treatment is complex (~ 1 kWh/m³). It has
to be noted here that a reduced demand of limited freshwater resources is not
evaluated in the framework of this LCA (cf. chapter 6.3.1).
The separation of urine does not lead to additional environmental benefits in
this study if compared to vacuum scenarios without urine separation.
Misdirected urine which is not properly separated in the toilet (30% of total
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urine) leads to higher nutrient losses during treatment, so that the amount of
substituted mineral fertilizer is smaller with urine separation. In terms of
energy demand, low volume of nutrient-rich urine does result in lower fuel
demand for transport, but these benefits are offset by lower biogas yield in
digestion (3.5 kWh/(pe*a) due to loss of organic matter of separated urine
for energy recovery) and additional energy demand for urine treatment
(6 kWh/(pe*a) for abatement of organic micropollutants). However, it has to
be noted here that the latter function is not included in vacuum scenarios
without urine separation: the mixture of digested urine and faeces is not
treated to remove micropollutants, mainly because an adequate process has
not yet been successfully tested. Thus, the issue of micropollutant removal
has been neglected in V scenarios.
3. Which environmental drawbacks of separation systems can be
identified, and how could they be minimized?
In the course of this LCA, the following major environmental drawbacks of separation
systems and possible options for their minimization could be identified:
NH3 emissions during application of liquid organic fertilizer
The application of organic fertilizers will most likely lead to increased atmospheric
emissions of NH3. The liquid nature of the fertilizers (urine, digester residual) results in
a high volatilization rate of nitrogen, decreasing the amount of available nitrogen and
increasing the potential acidification of soils and surface waters via wet deposition of
NH3. Additionally, the input of excessive nitrogen can lead to eutrophication
phenomena in terrestrial or marine ecosystems. Therefore, specific regulations should
be established for the application of liquid organic fertilizers to minimize the losses of
nitrogen, including mandatory low-emission application techniques (trail hoses,
injection, or incorporation by ploughing) and rules for application (appropriate weather
and soil conditions etc).
Phosphorus retention in soil filters
The long-term retention of phosphorus in soil filters is not sufficient to reach a
reasonably low effluent concentration in greywater treatment. Phosphorus loads in
greywater are substantial, so that total P effluent loads are likely to be increased if
greywater treatment in soil filters is compared to conventional wastewater treatment.
This drawback is especially important if nutrient-rich faces filtrate or sludge liquor from
digestion is treated together with greywater. The implementation of an additional post-
treatment stage for phosphorus removal (e.g. precipitation or adsorption on suitable
filter media) should be seriously considered to prevent the increased eutrophication of
surface waters.
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Treatment of concentrates
The treatment of highly concentrated flows from processing of toilet wastewater (e.g.
faeces filtrate in composting systems of sludge liquor from dewatering of digestor
residual) should be separated from greywater treatment. The total amounts of nutrients
and organic matter in concentrates can be considerable, deriving e.g. from urine which
is not properly separated in the toilets. The mixing of these high-strength flows with
greywater partially offsets the benefits of source separation. A separate treatment of
concentrates with suitable technology (e.g. a high-load activated sludge process or
membrane process) is strongly recommended, especially if soil filters are used for
greywater treatment. Vacuum systems without urine separation do not produce
concentrates, because the complete amount of toilet wastewater is applied in agriculture.
4. Which are the important key parameters for the impact
assessment?
In the course of performing this LCA, key parameters for the impact assessment have
been identified (Table 67). These parameters of the Life Cycle Inventory are found to be
decisive for the results of this LCA and thus should be based on data with a sufficient
quality. In particular, assumptions concerning these parameters should be justified
carefully. In case of unavoidable uncertainty, sensitivity analysis should be performed
to quantify the influence of variation of these parameters on the outcomes of the LCA.
For a simplified LCA assessment of different separation scenarios, the listed parameters
constitute the essential basis of a simplified inventory.
The listed key parameters of the inventory are not determined in a defined calculatory
approach. A numerical analysis of all LCI parameters would be beyond the scope of this
thesis. Identification of key parameters is rather based on experience from the setup of
LCI, the impact assessment, and sensitivity analysis. Qualitative criteria for their
selection are the significance or contribution of the related processes to certain impact
categories and their relative importance for the conclusions of this LCA case study.
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Table 67: Key parameters for LCI of separation systems
Process Key parameters Remarks
Wastewater flows - Distribution of COD, N, P
- Heavy metal content Biowaste included?
Wastewater or greywater
treatment
- Energy demand
- Elimination of COD, N, P
Energy recovery from
sludge digestion?
Vacuum system - Energy demand
Urine separation - Separation efficiency
- Energy demand of treatment Micropollutant removal?
Digestion - Energy demand
- Quality of sludge liquor
Post-treatment of
sludge?
Composting - Quality of faeces filtrate Solid-liquid separation?
Fertilizer application - Plant availability of nutrients
- NH3 emissions
Nutrient equivalents of
secondary fertilizers?
Mineral fertilizer - Production data
- Heavy metal content
Note: The choice of key parameters is not based on a calculatory approach, but on
experience gathered in inventory analysis, impact assessment and sensitivity analysis
Direct impacts versus indirect impacts
In general, direct emissions from core processes of wastewater treatment and fertilizer
application are very important for the results of the impact assessment. Naturally,
effluent emissions play a major role while evaluating the sustainability of wastewater
treatment options. Additionally, fertilizer application has a significant environmental
impact for separation systems due to the secondary functions of fertilizer supply. The
impact categories of eutrophication, acidification, and ecotoxicity are determined by
direct emissions to a large extent. The importance of direct emissions is confirmed by
normalisation results where the respective indicators (EP, AP, TETP, FAETP) have a
significant share to the total environmental burden in Germany (4-15%).
On the contrary, indirect environmental impacts from background processes (i.e. energy
supply, transport, fertilizer production, supply of construction materials) are less
significant for the comparison. Background processes mainly determine the energy
demand (CED) and related emissions (GWP) or resource demand (ADP). Normalised
results for these indicators show that the contribution of the investigated systems to total
impacts in the respective impact categories is relatively small (< 3%). Nevertheless, the
impact categories represent important environmental issues (i.e. the depletion of non-
renewable energy resources and climate change) and should not be overlooked in the
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comparison. Consequently, they are attributed a high relevance in grouping and
weighting of indicators.
Data quality in relation to goal and scope
The evaluation of the quality of inventory data is an essential part of the interpretation
of results in an LCA. The inventory data constitutes the basis of the LCA and
consequently of its outcomes. Data quality should be evaluated in relation to the goal
and scope of LCA, particularly concerning representativeness, consistency,
completeness, and uncertainty (ISO 14044, 2006).
In this study, the quality of the LCI data has been discussed in detail in chapter 3.7.1.
In summary, it has been found that the data quality of the inventory data used in this
study can be described as sufficient in relation to the goal of this study. This thesis aims
at a system analysis of prospective nature for the strategic planning of wastewater
management options in the next decades. Thus, the mix of primary data from large-scale
or pilot plants and secondary data from literature can be accepted for this case study, but
has to be clearly communicated. For a case study targeting a decision support in a real
situation, additional efforts should be invested to improve the data quality and thus
make the LCA outcomes more defensible. Especially data from long-term operation of
separation systems has to be generated to reach a sufficient level of accuracy for a
decision support. Background data for materials, energy mix, transport and fertilizer
production should be updated with more recent LCI datasets.
Sensitivity of results
The sensitivity of indicator results has been checked concerning the variation of
important parameters of the Life Cycle Inventory and concerning the choice of
indicators and valuation method. For the LCI parameters, the following conclusions can
be drawn:
The addition of biowaste into the digestion process is crucial for a net energetic
benefit in the energy recovery scenarios.
Transport distance of organic fertilizers is not decisive for CED and GWP of
separation systems. Only if the complete toilet wastewater (= urine, faeces and
flushwater) is transported over long distances (100 km in V scenarios), a
significant influence can be detected (+13% in CED and +22% in GWP).
Doubling the energy demand for urine treatment to 30 kWh/m³ is not decisive
for the energetic comparison (+6% in CED).
The energy demand of the vacuum system is a decisive parameter in the energy
recovery scenarios. It should be minimized to yield a maximum energetic
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benefit from energy recovery. An energy demand of more than 18 kWh/(pe*a)
may offset benefits from biogas production.
The separation efficiency of urine separation toilets is an influential parameter.
Increasing the separation efficiency from 70 to 90%, several indicators are
significantly influenced. Whereas GWP decreases by 4-5%, AP is either
decreased by 11% (SV scenarios) or slightly increased by 3% (SC scenarios). EP
decreases with increasing separation efficiency significantly for composting
scenarios due to the reduction in nutrient loads to greywater treatment (co-
treatment of faeces filtrate containing misdirected urine). EP reduction amounts
to 29% for greywater treatment in SBR and 74% in soil filter.
Increasing the volume of reused greywater to 50 L/(pe*d) results in marginal
energetic benefits for the reuse scenarios (CED – 2-4%). Only if a complex
drinking water treatment is assumed (1 kWh/m³), the energetic benefit can be
substantial (CED – 5-9%).
Assuming equal effluent concentrations for all activated sludge processes, the
calculated EP for separation systems can increase considerably (+15-33%). The
question of assuming variable or equal effluent concentrations for the different
scenarios can be decisive for the comparison in EP.
Minimizing NH3 emissions during application of liquid organic fertilizers by
50% with adequate application techniques decreases the drawbacks in AP
considerably.
Increasing concentrations of Cu and Zn in drinking water have only marginal
effects on the aquatic ecotoxicity of separation systems.
An update of data for heavy metal content of mineral fertilizers still confirms
benefits of secondary fertilizers in separation systems in terrestrial ecotoxicity.
If P availability in sewage sludge is assumed to 100%, benefits of separation
systems in HTP and TETP are still consistent if compared to scenario Ragri.
Overall, adopted LCI data evidently has an influence on specific indicators. However,
just a few LCI parameters are found to be really influential on the results, whereas most
of the other parameters do affect the results only marginal. Boundary conditions such as
transport distance or heavy metal content of drinking water are less important for the
comparison, whereas technical optimization of systems (e.g. separation toilets, vacuum
system, or fertilizer application) should be seriously targeted to improve the
sustainability of separation systems. Functional definitions can have a major influence
on certain indicators (e.g. biowaste addition, effluent concentrations on EP) and should
be carefully justified.
Concerning the sensitivity analysis for the indicator choice in LCIA, a twofold picture is
evident. Some indicators are based on a widely accepted scientific basis, so that
choosing a different LCIA model affects only the absolute scores, but not the qualitative
results of the scenario comparison (e.g. aquatic eutrophication impact model). It has to
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be noted that in case of aquatic eutrophication, the underlying model is fairly simple and
may not be very precise in predicting actual impacts, but it is the only valid scientific
model available. The splitting of eutrophication into separate indicators for aquatic and
terrestrial effects leads to an additional indicator which reveals drawbacks for separation
systems (increased terrestrial eutrophication). Here, the choice of an additional indicator
can point out new hotspots and may influence the overall outcomes after grouping and
weighting.
For other impact categories, various approaches of impact modelling are available
which differ widely in the scientific basis for the calculation of indicator scores. The
quantification of ecotoxicity can be performed with completely different approaches,
and the indicator choice is highly relevant for the outcomes of the comparison. These
limitations of the respective results (TETP, FAETP) should be clearly communicated
here. Divergent results of other indicators can be additionally presented to underline the
possibilities of a different outcome.
In total, the indicator choice has a strong influence on the outcomes in specific impact
categories. Thus, it can seriously change the overall results of the comparison,
especially after their evaluation via grouping and weighting. These issues underline the
importance of further scientific improvement of impact assessment methods and
probably the need for a generally accepted method for impact assessment. If the results
of an LCA depend on the choice of LCIA method, the validity and relevance of its
conclusions is obviously reduced.
Sensitivity analysis of the valuation method reveals a strong influence of the
modification of the UBA method by including toxicity indicators. The high uncertainty
of impact modelling demands a careful interpretation and thus may favour an exclusion
of toxicity indicators from weighting. On the other hand, this would exclude a clear
advantageous feature of separation systems from the overall evaluation, i.e. the superior
quality of secondary fertilizers over their industrially produced equivalents. In the end,
it is decided to include toxicity indicators in valuation with a distinct communication of
the uncertainties involved.
6.2 Methodological issues
In the course of performing the present LCA case study, some methodological issues
have been found to be important for further research in this area. In general, the
structured approach of LCA is very helpful for a system analysis in wastewater
management. Following the clearly defined framework of the ISO standard, a step-by-
step procedure guides the researcher through all required parts of the assessment:
functional definitions, inventory, impact assessment, and interpretation. At the same
time, the ISO norm leaves enough room in its definitions to adapt the methodology
closely to the specific needs and features of the respective case study.
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However, the application of LCA in the field of strategic planning of wastewater
management also implicates the acceptance of specific drawbacks which are connected
to this methodology at present. Some of these issues can probably be overcome by a
further progression in LCA development. In particular, the following methodological
issues have been raised while applying LCA for wastewater management options,
especially concerning the evaluation of new approaches such as source separation:
Goal and scope definition
The definition of the primary system function should be as broad as possible to
include all possible options for wastewater management.
The careful consideration of all secondary functions of wastewater management
is decisive for the comparison, preferably via system expansion.
Life Cycle Inventory
The prospective nature of strategic studies naturally requires assumptions in the
inventory data. Unavoidable high uncertainty in important parameters should be
overcome by sensitivity analysis.
Efforts in data collection should be focussed on improving data quality of key
parameters, not on gathering detailed data for sub-processes of minor relevance.
The quality of the results is presumably more influenced by the data quality of
key parameters than by the completeness of the inventory. Key parameters of the
LCI have been listed in chapter 6.1.
The considerable effort for a detailed inventory of infrastructure in wastewater
management is not justified by its relatively small impact on the results. A
simplified inventory for infrastructure is recommended for strategic studies.
Life Cycle Impact Assessment
The static nature of the substance flow model for the inventory and the scientific
models for impact assessment prevents the evaluation of acute impacts of
wastewater management. Effects of process dynamics (e.g. morning peak loads
of nitrogen) which are important for WWTP layout, operation, and resulting
effluent concentrations are not properly considered in LCA. Likewise, the
effects of shock loads of emissions into aquatic ecosystems are not evaluated in
LCIA.
Specific effects of wastewater discharge on local ecosystems are not adequately
evaluated in LCIA. Site-specific effect models are not available to predict the
potential impacts of local emissions on a certain ecosystem (e.g. for
eutrophication or ecotoxicity). Eventually, LCIA has to be connected to
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available watershed models (e.g. MONERIS (Behrendt, 1999)) for a more
precise prediction of impacts on local ecosystems.
The assessment of human and ecotoxicity for important wastewater pollutants is
connected to high uncertainty (e.g. heavy metals) or not available at present (e.g.
organic micropollutants). Thus, the evaluation of these important environmental
impacts of wastewater discharge is not based on a sufficient scientific basis to
allow for a definitive statement.
The evaluation of water use (i.e. water abstraction from a natural source) as an
environmental impact is not yet included in LCIA. This impact category is
essential for a comprehensive assessment of the ecological consequences of
wastewater reuse. The implementation of water use into the LCIA framework is
currently under development (see chapter 6.3.1 below for details).
Summarizing the issues above, it is obvious that Life Cycle Impact Assessment is still
lacking a number of important features for a precise evaluation of the environmental
impacts of wastewater management. However, the available LCIA methods are
sufficient for a prospective system analysis in the author’s opinion, targeting the
comparison of different systems of wastewater management for strategic planning. If
LCA is used as a decision support in a real case study, the impact assessment should be
improved to include acute and local effects of wastewater discharge.
6.3 Additional remarks
6.3.1 Freshwater use
Freshwater is one of the most valuable resources, as it is an essential precondition for
life and is not substitutable. For humans, it is indispensable as both drinking water and
the basis for hygiene and food supply. On a global scale, many regions of the world
suffer from limited or decreasing freshwater resource availability, with 900 million
people around the globe lacking sufficient access to safe drinking water
(WHO/UNICEF, 2008). Furthermore, the upcoming trends of urbanization, population
growth and climate change are likely to intensify water stress on a regional scale.
Therefore, a prospective sustainability analysis of water management systems should
include an evaluation of the impact on freshwater resources.
However, traditional LCIA methods do not include an impact category for freshwater
use. Only recently, the implementation of freshwater use at the level of LCI and LCIA
is addressed in a joint effort (Koehler, 2008). Proposals of a methodology for inventory
modelling, characterization to midpoint or even endpoint categories have been
published (Frischknecht et al., 2008; Pfister et al., 2009), but are still being intensively
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discussed in the scientific community. First attempts to implement the use of freshwater
into the framework of LCA have addressed the following issues (Koehler, 2008):
As water is an abiotic resource, three types of resources can be identified:
deposits (non-regenerative within human lifetimes, i.e. fossil groundwater
stocks), funds (regenerative stocks, i.e. aquifers and lakes), and flows (i.e.
streams and rivers)
Freshwater use can be divided into “utilization” (flows which are returned into
the river basin where they have been abstracted) and “consumption” (ultimate
withdrawal from a watershed, e.g. inter-basin transfer, evaporation, and
incorporation into products).
Impact assessment should evaluate both qualitative and quantitative issues.
Water quantity should take into account the ratio between withdrawal and
natural replenishment (water stress indicator). Chemical water quality
impairments are already covered by LCIA (eutrophication, acidification,
ecotoxicity), but other parameters (e.g. hygienic quality) are not yet
characterized.
Hence, the use of freshwater resources cannot be evaluated within the LCA framework
of this study. Nevertheless, separation scenarios offer the possibility of decreasing
freshwater consumption in urban sanitation, either by decreasing toilet flushwater
volume with vacuum toilets or by non-potable reuse of greywater. It is decided to
quantify this potential for a decrease in freshwater consumption by showing the total
daily water consumption per person for the different scenarios (Figure 92).
0
20
40
60
80
100
120
140
R Rmin Ragri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Freshwater utilization [L/(pe*d)]
Greywater Toilet flushwater
Figure 92: Freshwater utilization in each scenario
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6 Interpretation
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The potential for a reduction in freshwater utilization is limited with the separation
systems investigated in this study: composting systems require the same amount of
freshwater than the conventional system, whereas vacuum systems save around 19
L/(pe*d) of freshwater (-18%). Reuse scenarios substitute the complete toilet flushwater
with purified greywater, so that freshwater demand is lowest in these systems. However,
the largest part of the utilized freshwater is greywater (80 L/pe*d), and this fraction is
not decreased by any scenario investigated in this study. The non-potable reuse of
greywater for other purposes (e.g. garden irrigation, cleaning, washing etc) is an option
to further reduce the freshwater demand substantially. Additionally, the amount of toilet
flushwater used in conventional systems can be significantly higher than assumed in
this study, depending on user behaviour and single flush volume. Thus, the reduction
potential of vacuum toilets or greywater reuse for toilet flushing can be more important
for the overall freshwater consumption, together with water saving appliances (washing
machine, shower etc).
6.3.2 Economic sustainability of separation systems
Economic sustainability is one of the three integral parts of the concept of sustainability
(WCED, 1987). The present work is limited to the comparison of the ecological impacts
between the different sanitation scenarios with the methodology of Life Cycle
Assessment. However, economic costs can be assessed with a similar concept which is
called Life Cycle Costing (LCC). In analogy to LCA, LCC also takes into account all
parts of the economic life cycle, i.e. construction, operation and maintenance, and (if
applicable) disposal. Furthermore, the system boundaries can be broadened in LCC to
include associated costs of other product systems.
Assessments of the economic sustainability of separation systems are difficult to find in
the scientific literature. Reliable cost data of large-scale separation systems is not
available, so that data from smaller pilot plants has to be used as a basis for cost
projections. Lindert et al. estimated costs for a vacuum system for faeces drainage and
found that the separation system can be economically beneficial if greywater can be
treated in decentralized units and digested faeces can be disposed in agriculture (Lindert
et al., 1997). Oldenburg and co-workers published cost calculations comparing
different separation systems with a conventional system, stating that operational costs
can be lower with separation systems, whereas total project costs are presumably
comparable or higher (Oldenburg et al., 2007). Previous calculations of the same group
resulted in economic benefits for separation systems over a 50 year period, but cost data
was based on rough assumptions (Peter-Froehlich et al., 2004). Both publications stress
the influence of local boundary conditions (e.g. population density, energy prices, water
prices etc) on the results of the cost comparison. Dockhorn made a detailed cost analysis
of a large urban sanitation system (350000 inhabitants) and identified economic benefits
for separation systems (Dockhorn, 2007). These benefits derive from reduced costs for
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6 Interpretation
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the treatment of the remaining wastewater and saved costs for mineral fertilizer. Even
for a gradual change of wastewater management systems towards source separation,
economic incentives are existent which may stimulate further development and gradual
implementation of separation systems.
Overall, the economic sustainability of separation systems has yet to be proven in a real
full-scale case study, as reliable cost data for the new sanitation options is not available
at the moment. However, projections have shown that separation systems have the
potential to yield economic benefits by reducing costs for the wastewater treatment
plant and by substituting mineral fertilizer. Rising prices for energy and fertilizer will
certainly increase the economic benefits of resource recovery from wastewater in the
future. At the moment, separation systems seem to be at least comparable to the
conventional system regarding the total costs.
6.3.3 Social LCA
The third pillar of sustainability is related to the social impacts of a certain activity in
society. However, consistent methodologies for societal Life Cycle Assessment are still
under development (Jorgensen et al., 2008). Specific problems arise during the
assessment of the social impacts of a certain product, e.g. how to establish a quantitative
relation between indicators and functional unit, how to obtain regionalized inventory
data, and how to quantify the impacts properly (Klopffer, 2008). Recently, the
UNEP/SETAC initiative released a framework for social LCAs to establish mandatory
guidelines (UNEP, 2009). Some studies have already tried to include the social aspects
into their sustainability assessment of wastewater systems (Balkema, 2003). Suggested
indicators in this study are cultural acceptance, level of required expertise, institutional
requirements, and possibilities for participation. In all, more research is required to
systematically assess the social impacts of different systems for wastewater
management.
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7 Conclusions
The target of increasing sustainability for all technical systems in society includes the
field of urban wastewater management. The conventional system of combined
wastewater drainage and treatment is characterized by a disposal-oriented approach,
historically developed for the safe disposal of human excreta to prevent water-borne
diseases. For the protection of receiving surface waters, combined wastewater is treated
in activated sludge plants, thus consuming high amounts of energy and chemicals (“end-
of-pipe” technology). Recently, new approaches for urban wastewater management
have been described which aim at the recovery of valuable resources from wastewater.
Low-volume, nutrient-rich flows of urine and faeces are separated at the source to
facilitate the recovery of energy and nutrients, whereas the remaining greywater can be
treated with less effort to reach sufficient effluent quality for discharge or even
wastewater reuse. However, the environmental sustainability of these separation
systems has to be systematically investigated to identify and quantify decisive benefits
and drawbacks in comparison to the conventional approach. Therefore, the
methodology of Life Cycle Assessment (ISO 14040/44) is applied in this thesis to
compare the conventional and various separation systems for urban wastewater
management in their environmental impacts.
Applying LCA methodology
The definition of system functions, boundaries and scenarios is based on previous
research in this area and conforms to the ISO standards 14040/44. As a hypothetical
case study, twelve different scenarios for the integrated management of urban
wastewater and biowaste from 5000 inhabitants are investigated, including both
infrastructure and operation of all relevant processes. Secondary functions of separation
systems (supply of energy and nutrients) are considered by expanding the conventional
system with the respective processes of energy and mineral fertilizer production. A
detailed Life Cycle Inventory for all relevant processes is set up, describing the
elemental flows through the system and related emissions and resource demand.
Inventory data of core processes is based on data from pilot projects, literature and
qualified assumptions, whereas background processes (energy supply, transport etc) are
described with available process modules from databases. A sophisticated substance
flow model is developed using the LCA software UMBERTO®. Finally, aggregated
resource demand and emissions are evaluated in Life Cycle Impact Assessment. Eight
indicators are calculated which describe the environmental impacts of fossil energy
demand, global warming, abiotic resource depletion, eutrophication, acidification, and
human and ecotoxicity.
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7 Conclusions
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Findings of the LCA case study
The results of the impact assessment show that separation systems offer potentials for a
reduction in environmental impacts of urban wastewater management. The energy
recovery from organic matter of faeces, urine, and household biowaste via digestion in a
biogas plant can decrease the cumulative energy demand by up to 40% and related
emissions of greenhouse gases by up to 46%. Energetic benefits of substituting mineral
fertilizer with recovered nutrients are relatively low, but the quality of the organic
fertilizer is significantly better: heavy metal content of mineral P fertilizer is relatively
high, and its substitution with organic P fertilizer from urine and faeces leads to a
reduction of heavy metal input on agricultural soils. This benefit is consistent if
separation systems are compared to agricultural application of sewage sludge, the
conventional way of nutrient recycling.
The reduction of energy demand for the WWTP is relatively small if comparable
technology (activated sludge) is applied in greywater treatment. With natural treatment
in soil filters, this energy demand can be considerably decreased. However, insufficient
retention of phosphorus in soil filters can lead to an increased eutrophication potential
(+ 40-140%) of receiving surface waters. With activated sludge technology, effluent
quality of the WWTP is comparable or better than in a conventional system, resulting in
comparable or less eutrophication potential and aquatic ecotoxicity. Using membrane
bioreactors, greywater can be easily treated for non-potable reuse to reduce freshwater
demand. Energetic benefits of wastewater reuse can only be detected if drinking water
treatment is complex and reuse volume is high.
Serious drawbacks of separation systems are the increased emission of ammonia during
the application of liquid organic fertilizers, leading to increased acidification (+ 30-
110%) and eutrophication of terrestrial ecosystems. Concentrated flows such as faeces
filtrate or sludge liquor should be treated separately and not together with greywater, as
they contain considerable amounts of nutrients. Soil filters for greywater treatment
should be equipped with an additional treatment stage for phosphorus removal. Each of
these drawbacks is not system-inherent, but can be overcome by suitable improvements
of technology.
If indicator results are summarized by grouping and weighting, separation systems can
offer significant potentials for an overall increase in environmental sustainability of
wastewater management. However, the choice of an appropriate combination of process
technology is essential for the realization of these potentials, because the conventional
system has already been optimized in terms of energy demand and effluent quality in
the last decades. The higher demand for infrastructure (multiple pipe networks) in
separation systems is not decisive for the environmental comparison.
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7 Conclusions
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Validity and robustness of results
Data quality of the present LCA can be described as sufficient for a prospective study of
strategic planning and evaluation of wastewater management options. For a decision
support in a real case study, data quality of key parameters should be improved to end
up with more defensible results, and background data should be updated. Respective
key parameters of the inventory have been identified in this thesis to facilitate the data
acquisition in future LCA studies of wastewater management.
Sensitivity analysis revealed that only a few inventory parameters have a decisive
influence on the results in this LCA. Whereas boundary conditions have only marginal
effects on indicator results, specific process parameters (e.g. energy demand for vacuum
plant, urine separation efficiency, techniques for application of organic fertilizer) may
heavily influence the results and thus offer major potentials for system optimization.
Functional definitions and the choice of scientific models for calculation of indicators
have a distinct influence on the comparison and should therefore be carefully justified.
Methodological issues of LCA in wastewater management
In general, the structured approach of LCA is apparently valuable for a systematic
assessment of the environmental impacts of wastewater management systems.
Following the step-by-step procedure of the ISO standard and adapting it to the specific
case study, a detailed system analysis is possible which draws the attention to the
decisive issues of the comparison. Thus, the identification of important environmental
benefits and drawbacks of the different scenarios as well as potentials for further
process optimization is easily achievable. This holds especially true if scenarios with
different underlying principles (combined vs. separation systems) are compared in their
sustainability. Therefore, the life cycle approach is definitely recommended by the
author for further studies in this area.
Life Cycle Assessment in its current state is suitable for the tasks described above.
However, the available methods of impact assessment do not consider spatial or
temporal dynamics of emissions, so that the actual impacts of effluent discharge in a
local ecosystem may not be adequately characterized with the presented indicators.
Additionally, an indicator for freshwater withdrawal from natural sources should be
included in the impact assessment to account for a major environmental impact in
water-scarce regions, especially concerning the evaluation of wastewater reuse
scenarios. The further development of existing and new indicators for Life Cycle Impact
Assessment is a major task for the LCA community.
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Outlook on further research
Increasing sustainability of our technical systems in society is a major challenge of the
near future. As shown in this thesis, new approaches in wastewater management can
obviously contribute to this task by changing the functions of the existing systems from
disposal and end-of-pipe technology to separation and recovery of resources. This
requires a fundamental change of our perception of wastewater, away from a waste that
has to be disposed to a resource that can be exploited. Comparable transitions to a new
perception have been successfully implemented in solid waste management in the past.
The technology for a change in wastewater management is available today and will be
further optimized in the future. However, it is important to carefully evaluate the new
approaches in terms of their sustainability, including environmental, economic and
social impacts. Only if all three pillars of sustainability are adequately considered, the
new approaches can prove that they are superior to the old concepts.
Therefore, more research is required in the field of sustainability assessment of
wastewater systems. Methods for sustainability assessment have to be improved to
deliver a more realistic picture of the true impacts of wastewater management on the
whole water cycle, including the production of drinking water and other functions of
natural water resources. Following the methodological approach of this thesis, more
case studies should be carried out to evaluate the benefits of separation systems in
specific situations, with local boundary conditions and specific technologies available in
different countries. Only then, society will gradually change its picture of wastewater
management from a 19th century concept of urban hygiene to a 21st century concept of
resource recycling.
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257
9 List of Abbreviations
______________________________________________________________________
9 List of Abbreviations
ADP Abiotic Depletion Potential
AET Aquatic Ecotoxicity
AEU Aquatic Eutrophication
AP Acidification Potential
CAS Conventional activated sludge
CED Cumulative Energy Demand
COD Chemical Oxygen Demand
EP Eutrophication Potential
FAETP Freshwater Aquatic Ecotoxicity Potential
GWP Global Warming Potential
HTP Human Toxicity Potential
ISO International Standardization Organisation
LCA Life Cycle Assessment
LCI Life Cycle Inventory
LCIA Life Cycle Impact Assessment
MBR Membrane Bioreactor
SBR Sequencing Batch Reactor
TETP Terrestrial Ecotoxicity Potential
TET Terrestrial Ecotoxicity
TEU Terrestrial Eutrophication
TOC Total Organic Carbon
WWTP Wastewater Treatment Plant
258
10 List of Tables
______________________________________________________________________
10 List of Tables
Table 1: Overview of previous LCA case studies for wastewater systems....... 28
Table 2: Mass flow of reference input flows ..................................................... 32
Table 3: Average composition of faeces, urine, and greywater........................ 33
Table 4: Average composition of kitchen and garden biowaste and drinking
water ................................................................................................................ 34
Table 5: Allocation of organic matter, nitrogen and phosphorus in wastewater
flows compared to ATV population equivalents................................................ 35
Table 6: Secondary functions delivered by sanitation systems and their
respective equivalent products in system expansion ....................................... 36
Table 7: Overview of the investigated sanitation scenarios.............................. 39
Table 8: Modes of disposal and recycling for construction materials ............... 52
Table 9: Relevant elementary flows ................................................................. 56
Table 10: Material flows and their elementary composition.............................. 57
Table 11: Origin and quality of inventory data for core processes.................... 62
Table 12: Origin and quality of inventory data for background processes........ 63
Table 13: Categories for Life Cycle Impact Assessment.................................. 68
Table 14: Classification of emissions and extractions to their related impact
categories ........................................................................................................ 69
Table 15: Midpoint indicators for Life Cycle Impact Assessment ..................... 70
Table 16: Normalization data ........................................................................... 77
Table 17: Ranking of LCIA indicators based on ecological hazard, distance to
target, and specific contribution........................................................................ 79
Table 18: Process parameters of conventional WWTP.................................... 86
Table 19: Energy demand for WWTP in reference scenarios .......................... 87
Table 20: Comparison of energy demand for wastewater treatment with average
values of municipal WWTPs in Germany ......................................................... 87
Table 21: Transfer coefficients of elemental flows in conventional wastewater
treatment.......................................................................................................... 88
Table 22: Energy demand for the composting of biowaste .............................. 90
Table 23: Transfer coefficients of biowaste composting................................... 91
Table 24: Nitrogen losses during urine collection, storage and application...... 95
Table 25: Energy and chemical demand of solid-liquid separation .................. 97
259
10 List of Tables
______________________________________________________________________
Table 26: Composition of filtrate from faeces dewatering................................. 98
Table 27: Energy demand for pretreatment of biowaste................................... 99
Table 28: Substance flows of intensive composting of faeces and biowaste.. 100
Table 29: Allocation coefficients of biofilter..................................................... 101
Table 30: Substance flows of open composting of pre-composted faeces and
biowaste ......................................................................................................... 102
Table 31: Energy demand of vacuum systems............................................... 104
Table 32: Energy demand for the pretreatment of digester substrate............. 105
Table 33: Parameters for the thermal energy balance of hygienisation.......... 105
Table 34: Parameters of the mesophilic digestion process ............................ 106
Table 35: Biogas and methane yield of different substrates in relation to input
mass to digester ............................................................................................. 107
Table 36: Transfer ratios from digester residual to sludge liquor during
dewatering...................................................................................................... 108
Table 37: Substance flows of open composting of digester residual .............. 109
Table 38: Parameters and emission factors for CHP plant............................. 110
Table 39: Process parameters of SBR for greywater treatment ..................... 113
Table 40: Transfer coefficients of elemental flows in greywater treatment with
sequencing batch reactor*.............................................................................. 115
Table 41: Process parameters of soil filter for greywater treatment................ 118
Table 42: Plant uptake of specific elements in soil filter ................................. 119
Table 43: Transfer coefficients for greywater treatment in soil filter................ 120
Table 44: Process parameters of MBR for greywater treatment..................... 122
Table 45: Transfer coefficients of elemental flows in greywater treatment with
membrane bioreactor* .................................................................................... 123
Table 46: Calculation of total working time during fertilizer application........... 125
Table 47: Emission factors for mineral and secondary fertilizers during
agricultural application.................................................................................... 127
Table 48: Plant availability of nutrients from organic fertilizers with regard to the
substitution potential for mineral fertilizer ....................................................... 130
Table 49: Structural data of the urban settlement........................................... 133
Table 50: Inventory data for pipes of sanitary in-house installations .............. 134
Table 51: Total length of sewer systems ........................................................ 135
260
10 List of Tables
______________________________________________________________________
Table 52: Total length and total material demand for sewer systems per person
....................................................................................................................... 136
Table 53: Treatment facilities included in the construction inventory ............. 137
Table 54: Estimated service life of infrastructure components ....................... 139
Table 55: Construction materials and inventory datasets............................... 141
Table 56: Power mix and efficiencies of electricity supply in Germany 2003 and
respective heating values............................................................................... 143
Table 57: Power mix and efficiencies of thermal energy supply..................... 143
Table 58: Transport processes and distances ............................................... 145
Table 59: Auxiliary materials for system operation......................................... 146
Table 60: Mean concentrations of heavy metals and As for average mineral
fertilizers, related to the single nutrients......................................................... 147
Table 61: Life cycle inventories of mineral fertilizer production ...................... 149
Table 62: Electric energy and mineral fertilizer provided with system expansion
processes....................................................................................................... 157
Table 63: Comparison of calculated heavy metal loads in sewage sludge with
German mean values and legal limits ............................................................ 167
Table 64: Parameters and indicators for sensitivity analysis.......................... 195
Table 65: Matrix of indicator comparison between separation systems and
reference scenario R...................................................................................... 216
Table 66: Synopsis of relevant findings in Life Cycle Impact Assessment ..... 217
Table 67: Key parameters for LCI of separation systems .............................. 224
261
11 List of Figures
______________________________________________________________________
11 List of Figures
Figure 1: Conventional system of urban wastewater management .................. 13
Figure 2: Volume and distribution of nutrients in urine, faeces, and greywater.15
Figure 3: Source separation system for urban wastewater management......... 16
Figure 4: Structure of this thesis ....................................................................... 19
Figure 5: Approach of this study.......................................................................29
Figure 6 Principle of system expansion by broadening the system boundaries 35
Figure 7: System setup of reference scenarios with advanced treatment (R and
Ragri).................................................................................................................. 40
Figure 8: System setup of reference scenario with minimum treatment (Rmin).41
Figure 9: System setup of digestion scenario with SBR (V1)............................42
Figure 10: System setup of digestion scenario with soil filter (V2).................... 43
Figure 11: System setup of digestion scenario with MBR and reuse (V3) ........ 43
Figure 12: System setup for digestion scenario with urine separation and SBR
(SV1) ................................................................................................................ 45
Figure 13: System setup of digestion scenario with urine separation and soil
filter (SV2)......................................................................................................... 45
Figure 14: System setup for digestion scenario with urine separation, MBR and
reuse (SV3) ...................................................................................................... 46
Figure 15: System setup of composting scenario with SBR (SC1)................... 47
Figure 16: System setup of composting scenario with soil filter (SC2) ............. 48
Figure 17: System setup of composting scenario with MBR and reuse (SC3).. 48
Figure 18: Consideration of capital equipment for sanitation systems and the
background system .......................................................................................... 55
Figure 19: Elements of Life Cycle Impact Assessment (ISO 14040, 2006) ...... 64
Figure 20: General structure of methods for Life Cycle Impact Assessment .... 66
Figure 21: System layout of conventional wastewater treatment with anaerobic
sludge stabilisation (R and Ragri)....................................................................... 83
Figure 22: System layout for conventional wastewater treatment with aerobic
sludge stabilisation (Rmin)................................................................................. 84
Figure 23: System layout for composting of biowaste ......................................89
Figure 24: System layout of urine separation and treatment ............................ 92
Figure 25: System layout for composting of faeces and biowaste.................... 96
262
11 List of Figures
______________________________________________________________________
Figure 26: System layout for vacuum drainage and co-digestion of faeces with
biowaste......................................................................................................... 103
Figure 27: System layout for vacuum drainage and co-digestion of faeces and
urine with biowaste......................................................................................... 111
Figure 28: System layout for greywater treatment in sequencing batch reactor
....................................................................................................................... 112
Figure 29: System layout for greywater treatment in soil filter........................ 117
Figure 30: System layout for greywater treatment in membrane bioreactor with
partial reuse ................................................................................................... 121
Figure 31: Processes during fertilizer application........................................... 124
Figure 32: Total length of sewer systems and in-house installations per person
for each scenario............................................................................................ 136
Figure 33: Input-Output balance of electric energy for system operation....... 151
Figure 34: Net electricity demand for system operation ................................. 151
Figure 35: Nutrient equivalents (plant-available nutrients) provided by organic
fertilizers......................................................................................................... 154
Figure 36: Recovery of nutrients in agriculture in relation to total amount of
nutrients in wastewater and biowaste ............................................................ 155
Figure 37: Calculated effluent concentrations for chemical oxygen demand . 159
Figure 38: Calculated effluent concentrations for ammonia and total inorganic
nitrogen .......................................................................................................... 159
Figure 39: Calculated effluent concentrations for total phosphorus................ 161
Figure 40: Variation of effluent loads in separation scenarios compared to
wastewater treatment with extended nutrient removal (scenario R)............... 162
Figure 41: Emissions of Cu and Zn to surface waters (relative to scenario R)165
Figure 42: Emissions of Cu and Zn to agricultural soil (relative to scenario R)
....................................................................................................................... 166
Figure 43: Emissions of Cd, Cr, Pb, Ni, and U to agricultural soil (relative to
scenario R)..................................................................................................... 166
Figure 44: Cumulative energy demand .......................................................... 168
Figure 45: Cumulative energy demand for infrastructure ............................... 169
Figure 46: Cumulative energy demand for operation ..................................... 170
Figure 47: Cumulative energy demand for supply of equivalent products
(system expansion) ........................................................................................ 171
263
11 List of Figures
______________________________________________________________________
Figure 48: Abiotic depletion potential..............................................................173
Figure 49: Global warming potential ............................................................... 174
Figure 50: Global warming potential for supply of equivalent products (grid
energy and mineral fertilizer) .......................................................................... 175
Figure 51: Greenhouse gases contributing to global warming........................ 176
Figure 52: Acidification potential..................................................................... 177
Figure 53: Contribution of sub-processes to acidification potential................. 178
Figure 54: Eutrophication potential ................................................................. 179
Figure 55: Contribution of effluent emissions of nitrogen, phosphorus and
chemical oxygen demand and gaseous nitrogen emissions to eutrophication
potential.......................................................................................................... 180
Figure 56: Human toxicity potential ................................................................ 182
Figure 57: Contribution of sub-processes to human toxicity potential............. 183
Figure 58: Freshwater aquatic ecotoxicity potential........................................ 184
Figure 59: Contribution of sub-processes to freshwater aquatic ecotoxicity
potential.......................................................................................................... 185
Figure 60: Terrestrial ecotoxicity potential ...................................................... 186
Figure 61: Contribution of different types of fertilizer to terrestrial ecotoxicity
potential.......................................................................................................... 187
Figure 62: Comparison of all LCIA indicators (relative to scenario R) ............ 188
Figure 63: Comparison of all scenarios (relative to scenario R) ..................... 189
Figure 64: Normalization of all indicators from Life Cycle Impact Assessment
....................................................................................................................... 190
Figure 65: Comparison of scenario R and V1 with valuated indicator results in T
diagram .......................................................................................................... 192
Figure 66: Comparison of scenario R and SV3 with valuated indicator results in
T diagram ....................................................................................................... 193
Figure 67: Comparison of scenario R and SC2 with valuated indicator results in
T diagram ....................................................................................................... 194
Figure 68: Variation of CED with and without biowaste in co-digestion .......... 196
Figure 69: Variation of GWP with and without biowaste in co-digestion......... 196
Figure 70: Variation of CED with transport distance of organic fertilizers ....... 197
Figure 71: Variation of GWP with transport distance of organic fertilizers ...... 198
Figure 72: Variation of CED with energy demand of urine treatment.............. 198
264
11 List of Figures
______________________________________________________________________
265
Figure 73: Variation of CED with energy demand of vacuum plant................ 199
Figure 74: Variation of CED with efficiency of urine separation toilets ........... 200
Figure 75: Variation of GWP with efficiency of urine separation toilets .......... 201
Figure 76: Variation of AP with efficiency of urine separation toilets.............. 202
Figure 77: Variation of EP with efficiency of urine separation toilets.............. 202
Figure 78: Variation of CED with volume of reused greywater....................... 203
Figure 79: Variation of CED with volume of reused greywater, assuming
complex drinking water treatment (1 kWh/m³)................................................ 204
Figure 80: Variation of EP with definition of effluent concentrations of SBR and
MBR ............................................................................................................... 205
Figure 81: Variation of AP with NH3 emissions during application of liquid
organic fertilizer.............................................................................................. 206
Figure 82: Variation of FAETP with concentrations of Cu and Zn in drinking
water .............................................................................................................. 207
Figure 83: Variation in TETP with updated heavy metal data for mineral
fertilizers......................................................................................................... 208
Figure 84: Variation of TETP with plant availability of phosphorus in sewage
sludge ............................................................................................................ 209
Figure 85: Variation of HTP with plant availability of phosphorus in sewage
sludge ............................................................................................................ 209
Figure 86: Aquatic eutrophication calculated with EDIP2003......................... 210
Figure 87: Terrestrial eutrophication calculated with EDIP 2003.................... 211
Figure 88: Aquatic ecotoxicity calculated with IMPACT 2002+ ...................... 212
Figure 89: Terrestrial ecotoxicity calculated with IMPACT 2002+ .................. 212
Figure 90: Comparison of scenario R and V1 with original UBA method in T
diagram .......................................................................................................... 213
Figure 91: Comparison of scenario R and SV3 with original UBA method in T
diagram .......................................................................................................... 214
Figure 92: Freshwater utilization in each scenario ......................................... 230
12 Annex
______________________________________________________________________
12 Annex
Contents
12.1 Composition of partial flows of water, wastewater and waste ..267
12.2 Characterization factors for Life Cycle Impact Assessment .....272
12.3 Normalization data for Germany 2004......................................278
12.4 LCA model for wastewater treatment in activated sludge plant283
12.4.1 Basic considerations for the LCA model ...........................283
12.4.2 Model description ..............................................................284
12.4.3 Sludge treatment...............................................................289
12.4.4 Energy demand.................................................................293
12.5 Agricultural tractor ....................................................................296
12.6 Data for construction of sanitary systems.................................300
12.6.1 Map of the settlement area ...............................................300
12.6.2 Sanitary in-house installations...........................................301
12.6.3 Drainage systems .............................................................303
12.6.4 Treatment facilities............................................................306
12.7 Transport distances..................................................................313
12.8 Composition and production of mineral fertilizer ......................314
12.8.1 Nutrient and heavy metal contents of mineral fertilizers....314
12.8.2 Life cycle inventory of the production and supply of commercial
mineral fertilizers .............................................................................317
12.9 Data from Life Cycle Inventory .................................................321
12.10 Valuation results ...................................................................326
12.11 List of Tables in Annex .........................................................329
12.12 List of Figures in Annex ........................................................332
12.13 Literature in Annex................................................................333
Annex 266
12 Annex
______________________________________________________________________
Annex 267
12.1 Composition of partial flows of water, wastewater and
waste
The following tables list the results of an extensive literature research concerning the
average composition of human urine (Table 68) and faeces (Table 69), greywater from
kitchen, bath and washing machine (Table 70), biowaste from households and loppings
(Table 71).
Table 68: Volume flow and composition of human urine
Parameters unit 1) 2) 3) 4) 5) 6) 7) 8) 9) This study
Volume flow L/(pe*d) 1.37 1.73 1.57 1.20 1.5
Dry matter g/(pe*d) 19.18 72.4 60
Organic dry matter g/(pe*d) 39.1 45
COD g/(pe*d) 12.97 6.00 10.19 18.00
15
N total g/(pe*d) 13.70 8.42 11.5 10.80 11.00 10.30 10.49 10.30 10
P total g/(pe*d) 1.10 0.73 0.8 - 2.0 0.93 1.00 0.68 1.10 1
K g/(pe*d) 1.94 2.32 2.70 2.60 2.25 2.20
2.6
Na g/(pe*d) 2.81 3.50 6.00 3.5
Ca g/(pe*d) 0.21 1.40 0.21
Mg g/(pe*d) 0.12 0.12
Cl g/(pe*d) 4.10 4.80 6.50 4.8
S total g/(pe*d) 1.32 0.63 0.8
Cu mg/(pe*d) 5.80 0.03 0.10 0.05 0.05
Zn mg/(pe*d) 0.46 0.35 – 0.53 0.04 0.29 0.25
Cd mg/(pe*d) ≤ 0.002 0.00 0.0002 0.0002
Ni mg/(pe*d) 0.14 0.00 0.01
0.04
Hg mg/(pe*d) 0.00 0.001 – 0.009 0.01 0.00 0.0004 0.0004
Pb mg/(pe*d) ≤ 0.02 0.04 0.012 0.01
Cr mg/(pe*d) 0.04 0.01 0.01 0.00 0.01
AOX mg/(pe*d) 2.00 2
1) Lange and Otterpohl, 2000: collection of literature values
2) Jönsson et al., 1997: average values after storage (14 d)
3) Average range values from Ciba-Geigy, 1977
4) Fittschen and Hahn, 1998
5) Jönsson and Vinneras, 2003
6) Koppe and Stozek, 1999
7) Calculated from Palmquist and Jönsson, 2003, assumption: 24h
attendance of settlement inhabitants
8) Oldenburg, 2002
9) Becker et al., 2002
Annex 268
Table 69: Volume flow and composition of human faeces
Parameters unit 1) 2) 3) 4) 5) 6) 7) This study
Volume flow (wet mass) kg/(pe*d) 0.14 0.14 0.22 0.13 0.18 0.14
Dry matter g/(pe*d) 35.11 50.96 21 – 34 44.70 45
Organic dry matter g/(pe*d) 29.23 ca. 18 44.80 42
COD g/(pe*d) 78.27 4.57 37.00 33.00 35
BOD g/(pe*d) 3.35 19.00 11.10
14
TOC g/(pe*d) 46.58 21.40 21
N total g/(pe*d) 1.95 1.80 1.90 2.00 1.5
P total g/(pe*d) 0.96 0.55 0.68 0.31 – 0.77 0.70 0.5
K g/(pe*d) 0.33 0.77 0.44 0.70
0.55
Na g/(pe*d) 0.15 0.15
Ca g/(pe*d) 0.81 1.10
1
Mg g/(pe*d) 0.24 0.2
Cl g/(pe*d) 0.06 0.10 0.06
S total g/(pe*d) 0.21 0.2
Cu mg/(pe*d)
1.74 1.96 1.10
1.5
Zn mg/(pe*d) 46.41 5.1 – 10.3 10.80 10
Cd mg/(pe*d) 0.016 0.16 0.015 – 0.06 0.01 0.02
Ni mg/(pe*d) 0.22 0.26 0.26 0.07
0.2
Hg mg/(pe*d) 0.01 0.02
0.02
Pb mg/(pe*d) 0.04 0.32 0.02
0.02
Cr mg/(pe*d) 0.13 0.06 0.02
0.02
AOX mg/(pe*d) 2
1) Calculated from Kujawa-Roeleveld et al., 2003
2) Lange and Otterpohl, 2000: collection of literature values
3) Palmquist and Jönsson, 2003 (faeces + toilet paper)
4) Ciba-Geigy, 1977
5) Koppe and Stozek, 1999: calculation assuming 150 g faeces/(pe*d)
6) Oldenburg, 2002: average values, including toilet paper
7) Vinneras, 2001
Annex 269
Table 70: Volume flow and composition of greywater from households
Parameters unit 1) 2) 3) 4)* 5) 6) 7) 8) 9) This study This
study*
Volume flow L/(pe*d) 110.00 82.19 70.79 69.00 80.00 80.00
Dry matter g/(pe*d) 0.04 73.71 120.00 78.400
COD g/(pe*d) 47.95 95.00 62.00 35.00 62.76 33.00 56.83 101.53 60.00 60.000
TOC g/(pe*d) 15.07 17.40 18.00 17.912
N total g/(pe*d) 1.40 1.30 1.26 0.50 1.00 1.50 1.40 1.30 1.220
P total g/(pe*d) 0.60 0.50 0.30 0.67 0.14 0.15 3.49 0.65 0.50 0.494
K g/(pe*d) 0.96 1.85 0.37 2.50 2.00 1.400
Na g/(pe*d) 4.00 6.00 3.120
Ca g/(pe*d) 14.10
14.00 5.760
Mg g/(pe*d) 3.00 2.200
Cl g/(pe*d) 10.60 6.95 7.00 5.560
S total g/(pe*d) 1.60 7.70 3.80 7.17 7.50 4.260
Cu mg/(pe*d) 6.49 6.00 18.00 20.00 7.200
Zn mg/(pe*d) 6.18 33.40 61.00 46.00 16.400
Cd mg/(pe*d) 0.02 0.18 0.48 0.08 0.20 0.160
Ni mg/(pe*d) 0.66 1.97 6.30 2.00 1.600
Hg mg/(pe*d) 0.00 0.02 0.19 0.07 0.02 0.004
Pb mg/(pe*d) 0.24 3.00 19.00 18.01 3.00 2.600
Cr mg/(pe*d) 0.41 3.01 5.70 3.00 2.600
AOX mg/(pe*d) 10.00 10.00 10.000
*) without loads from tap water
1) Palmquist and Jönsson, 2003
2) Koppe and Stozek, 1999: loads from tap water calculated with 200 L/(pe*d)
3) Vinneras, 2001
4) Schneidmadl, 1999: average values from household wastewater, subtracting
urine/faeces/tap water loads
5) Lange and Otterpohl, 2000: collection of literature values
6) Almeida et al., 1999 (recalculated)
7) Bahlo, 1999
8) Butler et al., 1995: average values from the U.S. 1974-1986
9) Raach et al., 1999 (calculated)
Annex 270
Table 71: Mass flow and composition of kitchen and garden biowaste
Parameters for
kitchen biowaste unit 1) 2) 3) 4) 5) 6)
This
study Parameters for
garden biowaste 1) 7) 8) This
study
Mass flow kg/(pe*d) 0.16 0.18 0.18 0.20 0.20 kg/(pe*d) 0.33 0.30
Dry matter g/(pe*d) 50.37 32.00 50.00 g/(kg wet mass) 424.62 400.00 410.00
Organic dry matter g/(pe*d) 35.81 49.01 30.80 46.09 27.20 36.00 g/(kg d.m.) 72.29 70.00 710.00
TOC g/(pe*d) 13.16 10.90 13.60
13.00 g/(kg d.m.) 391.22 348.60 370.00
N total g/(pe*d) 0.82 0.89 1.00 0.43 0.90 g/(kg d.m.) 13.93 12.00 6.80 11.00
P total g/(pe*d) 0.20 0.16 0.17 0.13 0.18 0.20 g/(kg d.m.) 9.00 5.28 5.00
K g/(pe*d) 0.60 0.50 0.28
0.60 g/(kg d.m.) 12.24 14.94 13.60
Na g/(pe*d) 6.729
1.20 g/(kg d.m.) 0.20
Ca g/(pe*d) 0.98 0.35
1.00 g/(kg d.m.) 21.95 44.02 33.00
Mg g/(pe*d) 0.22 0.03 0.22 g/(kg d.m.) 3.87 4.80 4.50
Cl g/(pe*d) 10.37
3.00 g/(kg d.m.) < 0.3 0.30
S total g/(pe*d) 0.09 0.10 g/(kg d.m.) 0.50 0.50
Cu mg/(pe*d) 1.06 0.44 0.34
1.00 mg/(kg d.m.) 27.82 10.00 10.00 19.00
Zn mg/(pe*d) 7.28 1.85 0.98
7.30 mg/(kg d.m.) 152.82 60.00 57.00 110.00
Cd mg/(pe*d) 0.01 0.007 0.00
0.01 mg/(kg d.m.) 0.60 0.27 < 2.2 0.40
Ni mg/(pe*d) 0.24 0.30
0.20 mg/(kg d.m.) 3.70 3.70
Hg mg/(pe*d) 0.01 0.01 0.00
0.01 mg/(kg d.m.) 0.15 0.23 0.20
Pb mg/(pe*d) 1.06 0.11 0.06
0.60 mg/(kg d.m.) 47.27 4.80 < 6.6 4.80
Cr mg/(pe*d) 0.50 0.05
0.50 mg/(kg d.m.) 4.60 54.00 4.60
1) Average value, calculated from Wintzer et al., 1996
2) Palmquist and Jönsson, 2003
3) Expected values in Kujawa-Roeleveld et al., 2003
4) Paik et al. (1999), cited in Kujawa-Roeleveld et al., 2003: assumption 0.2 L/(pe*d)
5) Kübler et al. (1999), cited in Kujawa-Roeleveld et al., 2003:
assumption 0.18 L/(pe*d)
6) Vogt et al., 2002: calculated assuming 0.2 kg/(pe*d)
7) Vogt et al., 2002
8) Wolff, 2004
Annex 271
12 Annex
______________________________________________________________________
12.2 Characterization factors for Life Cycle Impact
Assessment
The following tables list the characterization factors of Life Cycle Impact Assessment
for all calculated indicators
Table 72: LCIA characterization factors for emissions to air:
GWP, AP, EP, TEU, AEM
Name GWP AP EP TEU
Indicator
Global
Warming
Potential
(100a)
Acidification
Potential
Eutrophication
Potential
Terrestrial
eutrophication
Source [1] [2] [3] [4]
Unit kg CO2-eq/kg kg SO2-eq/kg kg PO4-eq/kg m² UES/kg
CH4 23
CO2 (fossil) 1
HCl 0.88
HF 1.6
H2S 1.88
NH3 0.77 0.11 80
N2O 296
NOx 0.19 0.05 23.6
SO2 0.6
[1]: IPCC, 2001 (related to a time horizon of 100 years)
[2]: Guinée et al., 2002 (including site-dependent fate and effect factors for NOx,
NH3, SO2, scenario OA 1995 for Germany)
[3]: Huijbregts and Seppala, 2001 (including fate and effect factors for Europe)
[4]: Hauschild and Potting, 2003 (site-dependent factors for Germany)
Annex 272
12 Annex
______________________________________________________________________
Table 73: LCIA characterization factors for emissions to air: HTP,
FAETP, TETP, AET, TET
Name HTP FAETP TETP AET TET
Indicator Human
Toxicity
Potential
Freshwater
Aquatic
Ecotoxicity
Potential
Terrestrial
Ecotoxicity
Potential
Aquatic
Ecotoxicity
Terrestrial
Ecotoxicity
Source [1] [1] [1] [2] [2]
Unit kg 1,4-dichlorobenzene-eq/kg kg of triethylene-glycol-
eq/kg
Particles 8.20E-01
Dust (PM10) 8.20E-01
Sb 6.71E+03 3.72E+00 6.11E-01 2.96E+05 2.01E+04
As 3.48E+05 4.95E+01 1.61E+03 5.48E+04 4.19E+05
Be 2.27E+05 1.71E+04 1.77E+03
Pb 4.67E+02 2.40E+00 1.57E+01 4.01E+04 1.31E+05
Cd 1.45E+05 2.89E+02 8.12E+01 4.28E+05 9.12E+05
CrIII 6.47E+02 1.92E+00 3.03E+03
CrVI 3.43E+06 7.69E+00 3.03E+03
CrIII+VI 3.49E+04 1.98E+00 3.03E+03 6.70E+04 3.82E+05
Co 1.75E+04 6.39E+02 1.09E+02
Cu 4.30E+03 2.22E+02 6.99E+00 2.94E+06 1.18E+06
Ni 3.50E+04 6.29E+02 1.16E+02 1.79E+05 5.63E+05
Hg 6.01E+03 3.17E+02 2.83E+04 7.86E+05 3.84E+06
Se 4.77E+04 5.46E+02 5.35E+01 4.78E+05 9.32E+03
TI 4.32E+05 1.55E+03 3.40E+02
V 6.24E+03 1.73E+03 6.65E+02 2.04E+05
Zn 1.04E+02 1.78E+01 1.20E+01 2.04E+05 1.01E+06
Sn 1.73E+00 2.54E+00 1.44E+01
Benzo(a)pyrene 5.72E+05* 8.78E+01 2.41E-01 2.50E+04 2.42E+01
Benzene 1.90E+03 8.37E-05 1.56E-05 2.44E-02 4.72E-03
Chlorobenzene 9.23E+00 4.68E-04 7.29E-04 5.07E+00 3.36E-01
Formaldehyde 8.31E-01 8.26E+00 9.40E-01 1.67E+00 2.54E+00
PAH w/o B(a)P 5.72E+05 1.72E+02 1.02E+00
PAH, unspecified 5.72E+05 1.72E+02 1.02E+00
PCDD, PCDF 1.93E+09 2.13E+06 1.20E+04 3.94E+05 2.59E+02
NH3 1.00E-01 3.90E+00 9.80E+00
HCl 5.00E-01
HF 2.85E+03 4.64E+00 2.95E-03 7.84E-01** 1.79E+02**
NOx 1.20E+00
SO2 9.60E-02
H2S 2.20E-01
* factor for PAH w/o benzo(a)pyrene is adopted
** calculated from fluorene
[1]: Huijbregts et al., 2000
[2]: Jolliet et al., 2003b
Annex 273
12 Annex
______________________________________________________________________
Table 74: LCIA characterization factors for emissions to soil: HTP,
FAETP, TETP, AET, TET
Name HTP FAETP TETP TET AET
Indicator Human
Toxicity
Potential
Freshwater
Aquatic
Ecotoxicity
Potential
Terrestrial
Ecotoxicity
Potential
Terrestrial
Ecotoxicity
Aquatic
Ecotoxicity
Source [1] [1] [1] [2] [2]
Unit kg 1,4-dichlorobenzene-eq/kg kg of triethylene-glycol-eq/kg
As 3.18E+04 1.34E+02 3.34E+03 2.43E+06 3.87E+05
Pb 3.28E+03 6.53E+00 2.34E+01 7.54E+05 2.64E+05
Cd 1.96E+04 7.76E+02 1.67E+02 5.28E+06 2.91E+06
CrIII 5.13E+03 5.25E+00 6.30E+03
CrVI 8.55E+03 2.10E+01 6.30E+03
CrIII+VI 5.16E+03 5.41E+00 6.30E+03 2.25E+06 4.49E+05
Co 2.39E+03 1.71E+03 2.23E+02
Cu 9.39E+01 5.95E+02 1.44E+01 6.92E+06 2.04E+07
Ni 2.68E+03 1.69E+03 2.39E+02 3.30E+06 1.26E+06
Hg 5.92E+03 8.48E+02 5.60E+04 2.65E+07 1.58E+07
Zn 6.37E+01 4.77E+01 2.46E+01 5.91E+06 1.40E+06
Sn 1.31E+01 6.90E+00 2.98E+01
[1]: Huijbregts et al., 2000
[2]: Jolliet et al., 2003b
Annex 274
12 Annex
______________________________________________________________________
Table 75: LCIA characterization factors for emissions to water: HTP,
FAETP, TETP, AET, TET
Name HTP FAETP TETP TET AET
Indicator Human
Toxicity
Potential
Freshwater
Aquatic
Ecotoxicity
Potential
Terrestrial
Ecotoxicity
Potential
Terrestrial
Ecotoxicity
Aquatic
Ecotoxicity
Source [1] [1] [1] [2] [2]
Unit kg 1,4-dichlorobenzene-eq/kg kg of triethylene-glycol-eq/kg
Sb 5.14E+03 1.97E+01 1.66E-20 1.22E-09 2.10E+06
As 9.51E+02 2.07E+02 1.04E-17 3.88E+05
Ba 6.30E+02 2.28E+02 5.08E-19 1.53E-10 8.05E+04
Be 1.40E+04 9.13E+04 3.30E-16
Pb 1.23E+01 9.62E+00 4.77E-22 2.64E+05
Cd 2.29E+01 1.52E+03 1.42E-20 1.52E-09 2.92E+06
CrIII 2.05E+00 6.91E+00 2.27E-19
CrVI 3.42E+00 2.77E+01 2.27E-19
CrIII+VI 2.07E+00 7.12E+00 2.27E-19 4.53E+05
Co 9.67E+01 3.41E+03 2.69E-18
Cu 1.34E+00 1.16E+03 4.06E-21 2.06E+07
Mo 5.51E+03 4.76E+02 2.31E-18
Ni 3.31E+02 3.24E+03 1.03E-18 1.27E+06
Hg 1.43E+03 1.72E+03 9.30E+02 6.93E-08 1.58E+07
Se 5.60E+04 2.92E+03 1.55E-17 3.40E+06
V 3.16E+03 8.97E+03 1.02E-17
Zn 5.84E-01 9.17E+01 2.53E-21 1.40E+06
Sn 1.73E-02 1.02E+01 7.86E-22
Fluoride 3.42E+03 1.81E+01 4.28E-05
Fluorine 3.42E+03 1.81E+01 4.28E-05
Ammonium 2.18E-02 4.98E+02
NH3-N 2.80E-02 6.40E+02
NH4-N 2.80E-02 6.40E+02
Benzo(a)pyrene 2.80E+05 2.52E+05 2.53E-03 2.27E-01 1.71E+06
PAH w/o B(a)P 2.80E+05 2.75E+04 2.12E-03
Phenols 4.92E-02 2.37E+02 2.49E-06
[1]: Huijbregts et al., 2000
[2]: Jolliet et al., 2003b
Annex 275
12 Annex
______________________________________________________________________
Table 76: LCIA characterization factors for
emissions to water: EP, AEU
Name EP AEU
Indicator Eutrophication
potential
Aquatic eutrophication
of inland waters
Source [1] [2]
Unit kg PO4-eq/kg kg NO3-eq/kg
COD 0.022
TOC 0.044*
NH3 0.35 2.45
NH4 0.33 2.31
NH3-N 0.42 2.97
NH4-N 0.42 2.97
NO3 0.1 0.67
NO3-N 0.443 2.97
Norg 0.42 2.97
HNO3 0.1 0.67
P species as P 3.06 30.75
PO4 1.0 10.03
PO4-P 3.06 30.75
Phosphates (as P2O5) 1.34 13.44
* assumption: TOC/COD = 0.5 for treated effluent
[1]: Guinée et al., 2002
[2]: Hauschild and Potting, 2003 (site-dependent factors for
Germany)
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Table 77: LCIA characterization factors for extraction
of resources: ADP
Name ADP
Indicator Abiotic Depletion Potential
Source [1]
Unit kg Sb-eq/kg
Lignite 6.71E-03
Natural gas 1.87E-02
Crude oil 2.01E-02
Coal 1.34E-02
Hard coal 1.34E-02
Uranium 2.87E-03
Lead 6.77E-04
Iron 4.80E-08
Ferromanganese 6.20E-06
Zinc 3.95E-05
Bauxite 2.10E-09
Sulphur 3.58E-04
Raw phosphate 9.29E-06
Nickel ore, sulphured 5.38E-06
Nickel ore, lateritic 1.08E-06
Raw potassium 3.76E-09
Copper ore (0,99% Cu) 2.20E-05
Chromium ore 2.58E-04
Chromium 8.58E-04
[1]: Guinée et al., 2002 and updated factors from CML method
(version 2.7, April 2004, http://www.leidenuniv.nl/cml/ssp/index.html)
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12.3 Normalization data for Germany 2004
The following data is used to generate normalization data for the present LCA study
which corresponds to the scope of this study. The normalization scores are calculated
for Germany as the reference area and 2004 as the reference year. The latest available
emission data is used to calculate the normalization scores. They are expressed in
relation to a single inhabitant by dividing the total indicator score for Germany by its
population (82.532.000 inhabitants in 2004).
Cumulated energy demand
Table 78: Normalization data for cumulated energy demand
Annual consumption
in Germany 2004 CED per inhabitant
[Petajoule/a] [MJ/(pe*a)]
CED fossil 12081 146380
CED nuclear 1823 22088
CED renewable 164 1987
CED miscellaneous 370 4483
CED (fossil + nuclear) 168468
Source: BMWi, 2005
Climate change
Some substances are neglected (i.e. fluorinated hydrocarbons, chlorofluorocarbons etc),
because no emission data for single substances is available. Additionally, these gases
are not included in the inventory for the majority of the processes. It is estimated that
they contribute less than 2% to the GWP in Germany (UBA, 2007b). Gases with
indirect effects on climate change (CO, SO2, NOx etc) are neglected due to high
uncertainty of their effect factors.
Table 79: Normalization data for global warming potential
Substance Annual emissions
in Germany 2002 Characterization
factor GWP100a per
inhabitant
[1000 Mg/a] [kg/(pe*a)] [kg CO2-eq/kg] [kg CO2-eq/a]
CO2 864117 10470 1 10470
CH4 3878 47 23 1081
N2O 180 2.2 296 651
Global warming potential (100a) 12202
Source: UBA, 2003
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Acidification
Some substances are neglected (i.e. HCl, H2S) due to lacking emission data. Emission
data for HF is estimated as 70% of emission level in 1991 (124000 Mg/a, from IFU and
IFEU, 2005).
Table 80: Normalization data for acidification potential
Substance Annual emissions
in Germany 2002 Characterization
factor AP per
inhabitant
[1000 Mg/a] [kg/(pe*a)] [kg SO2-eq/kg] [kg SO2-eq/a]
NH3 607000 7.4 0.77 5.7
HF 86800* 1.1 1.1 1.2
SO2 608000 7.4 0.6 4.4
NOx (as NO2) 1479000 17.9 0.19 3.4
Acidification potential 14.6
* estimated with 70% of 1991 emission levels (124000 Mg/a from IFU and IFEU, 2005)
Source: UBA, 2003
Eutrophication
For COD emissions, emission data is estimated with 120 g COD/(pe*d) (ATV, 2000)
and an average elimination of 90% in wastewater plants (DWA, 2005). Normalization
scores for eutrophication potential (CML method) are calculated in Table 81, and for
terrestrial eutrophication and aquatic eutrophication of inland waters (EDIP method) in
Table 82.
Table 81: Normalization data for eutrophication potential
Substance Annual emissions
in Germany 2002 Characterization
factor EP per
inhabitant
[1000 Mg/a] [kg/(pe*a)] [kg PO4-eq/kg] [kg PO4-eq/a]
Emissions to air
NH3 607000 7.4 0.11 0.81
NOx 1479000 17.9 0.05 0.9
Emissions to water
COD 360500* 4.4 0.022 0.1
P species as P 33164 0.4 3.06 1.23
N species as N 687960 8.3 0.42 3.5
Eutrophication potential 6.5
*COD emissions are estimated from total COD load per person and day (120 g/(pe*d),
ATV, 2000) and mean elimination ratio in wastewater treatment (90%,DWA, 2005 )
Source: UBA, 2003
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Table 82: Normalization data for terrestrial eutrophication and aquatic
eutrophication of inland waters
Substance
Annual
emissions in
Germany
2002
Characterization
factor Indicator
per inhabitant
TEU AEI TEU AEI
[kg/(pe*a)]
[m² UES
per kg]
[kg NO3-eq
per kg]
[m² UES/
(pe*a)]
[kg NO3-eq/
(pe*a)]
Emissions to air
NH3 7.4 80 588
NOx 17.9 23.6 423
Emissions to water
P species as P 0.4 30.75 12.4
N species as N 8.3 2.97 24.7
Terrestrial eutrophication
1011
Aquatic eutrophication of inland waters 37.1
TEU: terrestrial eutrophication
AEI: aquatic eutrophication of inland waters
UES: unprotected ecosystem
Source: UBA, 2003
Human toxicity
Data for calculating normalization scores for human toxicity potential is presented in
Table 83 and Table 84. For characterization factors of all relevant substance flows, the
reader is referred to annex 12.2. The calculation includes only substances where
emission data is available.
Ecotoxicity
Emission data used to calculate normalization scores for ecotoxicity is presented in
Table 83 and Table 84. Normalization scores are calculated for terrestrial and
freshwater aquatic ecotoxicity potential (indicators from CML method) and terrestrial
and aquatic ecotoxicity (IMPACT method). For characterization factors of all relevant
substance flows, the reader is referred to annex 12.2. The calculation includes only
substances where emission data is available.
Table 83: Normalization data for human and ecotoxicity
Substance Annual emission in
Germany Source Indicator
[Mg/a] [g/(pe*a] HTP FAETP TETP AET TET
Emissions to air [kg 1,4-DCB-eq/a] [kg TEG-eq/a]
As 33 0.40 1) 1.15E+10 1.63E+06 5.31E+07 1.81E+09 1.38E+10
Be 2 0.02 1) 4.53E+08 3.43E+07 3.53E+06
Pb 632 7.66 1) 2.95E+08 1.52E+06 9.90E+06 2.53E+10 8.28E+10
Cd 11 0.13 1) 1.60E+09 3.18E+06 8.94E+05 4.71E+09 1.00E+10
CrIII 113.85 1.38 2) 7.36E+07 2.19E+05 3.45E+08
CrVI 1.15 0.01 2) 3.94E+09 8.84E+03 3.49E+06
CrIII+VI 115 1.39 1) 6.98E+09 4.39E+10
Co 12 0.15 1) 2.10E+08 7.67E+06 1.30E+06 6.49E+09 2.54E+09
Cu 79 0.96 1) 3.39E+08 1.75E+07 5.52E+05 2.32E+11 9.32E+10
Ni 159 1.93 1) 5.57E+09 1.00E+08 1.85E+07 2.85E+10 8.95E+10
Hg 31 0.38 1) 1.86E+08 9.82E+06 8.78E+08 2.44E+10 1.19E+11
Se 25 0.30 1) 1.19E+09 1.37E+07 1.34E+06 1.20E+10 2.33E+08
TI 8 0.10 1) 3.46E+09 1.24E+07 2.72E+06
Zn 452 5.48 1) 4.72E+07 8.04E+06 5.41E+06 9.22E+10 4.57E+11
Benzo(a)pyrene 13.757 0.17 3) 7.87E+09 1.21E+06 3.32E+03 3.44E+08 3.33E+05
Benzene 42900 519.80 3) 8.15E+10 3.59E+03 6.68E+02 1.05E+06 2.02E+05
PAH w/o B(a)P 382.243 4.63 1) 2.19E+11 6.57E+07 3.90E+05
PCDD, PCDF 3.09E-4 4.00E-6 1) 5.98E+08 6.57E+05 3.71E+03 1.22E+05 8.00E+01
NH3 607000 7354 4) 6.07E+07
HF 86800 1051 5) 2.47E+11 4.03E+08 2.56E+05 1.55E+10
SO2 608000 7367 4) 5.84E+07
NOx (as NO2) 1479000 17920 4) 1.77E+09
Dust (PM10) 247000 2993 4) 2.03E+08
Sources:
1) UBA, 2003 (data from 1995)
2) assumption: 99% CrIII and 1% CrVI
3) UBA, 2000 (data from 1993/1994)
4) UBA, 2003
5) assumption with 70% of 1991 emission levels (124000 Mg/a from IFU and IFEU, 2005)
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Table 84: Normalization data for human and ecotoxicity (continued)
Substance Annual emission in
Germany Source Indicator
[Mg/a] [kg/(pe*a] HTP FAETP TETP AET TET
Emissions to water [kg 1,4-DCB-eq/a] [kg TEG-eq/a]
Pb 287.4 3.48 1) 1.15E+10 1.63E+06 5.31E+07 7.59E+10
Cd 9.9 0.12 1) 4.53E+08 3.43E+07 3.53E+06 2.89E+10
CrIII+VI 261.2 3.17 1) 7.36E+07 2.19E+05 3.45E+08 1.18E+11
Cu 594.6 7.21 1) 3.94E+09 8.84E+03 3.49E+06 1.22E+13
Ni 324 3.93 1) 4.01E+09 2.28E+05 3.49E+08 4.11E+11
Hg 4.2 0.05 1) 2.10E+08 7.67E+06 1.30E+06 6.70E+10
Zn 2931 35.51 1) 3.39E+08 1.75E+07 5.52E+05 4.10E+12
Emissions to soil
Pb 1464 17.74 2) 1.86E+08 9.82E+06 8.78E+08 3.86E+11 1.10E+12
Cd 84 1.02 2) 1.19E+09 1.37E+07 1.34E+06 2.44E+11 4.43E+11
CrIII+VI 561 6.80 3) 3.46E+09 1.24E+07 2.72E+06 2.52E+11 1.26E+12
Cu 4577 55.46 2) 4.72E+07 8.04E+06 5.41E+06 9.34E+13 3.17E+13
Ni 476 5.77 3) 7.87E+09 1.21E+06 3.32E+03 6.00E+11 1.57E+12
Hg 3.06 0.04 4) 8.15E+10 3.59E+03 6.68E+02 4.83E+10 8.11E+10
Zn 21237 257.32 2) 2.19E+11 6.57E+07 3.90E+05 2.97E+13 1.26E+14
TOTAL per inhabitant (Emissions to air, water, and soil) 7266 88.9 70.1 1722063 1969880
Sources:
1) Fuchs et al., 2002
2) Wilcke and Döhler, 1995
3) calculated from Eckel et al., 2005
4) estimation according to data from Bannick et al., 2001
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12.4 LCA model for wastewater treatment in activated sludge
plant
This part describes in detail the LCA model for conventional wastewater treatment in an
activated sludge plant with extended nutrient removal. After basic considerations for
LCA models of wastewater treatment, a detailed description of the model explains the
allocation of elemental flows and energy demand.
12.4.1 Basic considerations for the LCA model
Concept
This LCA process model is mainly based on linear input-output relations: substance and
energy inputs are related to output via specific factors. The model describes the
activated sludge process with advanced nutrient removal, including oxidation of ,
nitrification, optional denitrification and chemical P elimination, considering typical
parameters of German wastewater treatment plants (WWTP). Sludge treatment
comprises of simultaneous aerobic stabilisation or anaerobic digestion, followed by
dewatering of residual sludge. If the excess sludge is digested, biogas from the digestion
process is combusted in a combined heat and power plant (CHP plant) to provide
electrical and thermal energy for the operation of the WWTP.
The input substance flows are allocated to the output flows of discharged water,
sludge and air specifically for each elemental flow. In addition, material and energy
demands for WWTP operation are calculated from input flows and operating
conditions.
Product-specific relation
Municipal wastewater treatment plants treat wastewater from different origin
(households, light and heavy industry, stormwater etc) which is mixed in the sewer. Due
to the product relation of LCA, the model has to be capable to calculate the inputs and
outputs of a specific wastewater being treated in the WWTP (problem of allocation in
multi-input-processes). The present model tries to solve this allocation problem by
considering causal relations between single wastewater components and emissions or
input flows
Temporal scope
LCA models do not consider the time-related dynamic behaviour of the analysed
systems. Hence, the present model is of a static type, calculating average loads and
emissions.
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12.4.2 Model description
12.4.2.1 Wastewater composition
The input wastewater is characterized in terms of its elemental composition (COD, N, P
etc). For the process model, the speciation of the elemental flows is important (e.g. the
distribution of nitrogen to NH4-N, organic nitrogen, particulate nitrogen). Therefore,
exemplary parameters describe the different fractions of the elemental flows (Table 85).
While particulate species can be separated physically by sedimentation, dissolved
species have to be treated biologically (activated sludge) or chemically (precipitation).
Table 85: Assumed composition of influent wastewater
Species Proportion [%]
Nitrogen NH4-N 54
NO3-N 0
N organic 35
N particulate 11
Phosphorus PO4-P 83
P particulate 17
Sulphur SO4-S 96
S particulate 4
12.4.2.2 Wastewater treatment
The present WWTP model describes the processes of mechanical, biological and
chemical (or primary, secondary and tertiary) wastewater treatment, including sludge
treatment with thickening, stabilisation and dewatering. The removal efficiency for the
most important wastewater constituents (COD, nitrogen, and phosphorus, all of which
are regulated in legal standards for municipal wastewater treatment (ATV, 2000)) is
adjustable via parameters (Table 86). The model calculates the specific elemental
allocation of wastewater components to the different output flows (discharged water,
air, stabilized sludge, biogas) and the associated demand for chemicals or energy. The
removal efficiency for nitrogen elimination is greater than 25%, due to the fact that
around 25% of the nitrogen is usually incorporated into the excess sludge during
biomass production. In the following, the most important elements are discussed in
detail.
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Table 86: Parameters of LCA model for conventional WWTP
Parameter Description
ABBAU Rate of decomposition of organic substance in stabilisation 50 %
CSBE Efficiency of COD elimination 95 %
CVK Proportion of COD eliminated in sedimentation 35 %
EG* Proportion of thermal energy supply from gas 50 %
ETH* Thermal energy demand per m³ of raw sludge 150 MJ/m³
FH Static head of wastewater lifting at WWTP inlet 4 m
FHMEW Demand of coagulation aid for dewatering 7 g/kg TS
FHMVD Demand of coagulation aid in thickening 10 g/kg TS
NE* Efficiency of dissolved nitrogen removal (>25%) 90 %
PE* Efficiency of dissolved phosphorus removal 95 %
PFE* Proportion of phosphorus removal via precipitation
(rest: biological P elimination) 60 %
T Temperature 15 °C
TOC Ratio TOC / COD 0.35 --
TRR Dry matter content of thickened raw sludge, 2 < TRR < 5 5 %
TRST Dry matter content of dewatered stabilised sludge
Dewatering with centrifuge: 25 < TRST < 40 40 %
TTS* Sludge age 20 days
YC Yield coefficient (g C biomass per g C substrate) 0.67 --
* effective parameter values in this study depend on scenario
Carbon
Carbon content of influent and effluent is related to the chemical oxygen demand
(COD). The relation of total organic carbon (TOC) and COD is usually between 0.28
and 0.4, in household wastewater it is typically around 0.35 (Zimmermann et al., 1996).
The average elimination rate in German WWTP is ca. 94% for COD and 98% for
biological oxygen demand (BOD) (DWA, 2005). The particulate fraction of COD is
separated by sedimentation in primary treatment, so no aeration energy is required for
its removal in the activated sludge process. In case of sludge digestion, sludge from
primary treatment increases the production of biogas.
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The dissolved COD is partially transformed into biomass (assimilation) and partially
into CO2 and water (dissimilation) during the activated sludge process. The ratio
between assimilation and dissimilation is described by the yield coefficient Y, which is
typically around 0.67 (ATV, 2000). The formation and endogenous decomposition of
biomass is described by the following equation:
*(1 * * )
biomass decomposed
TS T
Y
COD COD bt F
with CODbiomass = COD of formed biomass [mg/L]
COD
decomposed = difference of COD in influent and effluent [mg/L]
F
T = 1.072(T-15)
Y = 0.67 [g Cbiomass/g Cdecomposed]
b = 0.17 [d-1] (at 15°C)
t
TS = sludge age [d]
T = temperature
The inert solids remaining from endogenous decomposition are estimated to account for
20% of decomposed biomass:
COD
biomass, inert = 0.2 * CODbiomass * tTS * b * FT
Neglecting the inert particulate influent COD, which is separated in primary treatment,
the COD transfer in excess sludge is:
COD
excess sludge = CODbiomass + CODbiomass, inert
Nitrogen
Nitrogen content of WWTP influent consists of fractions of NH4-N, organic-bound
nitrogen, and particulate nitrogen. The removal of total nitrogen amounts to 70 – 90% in
average WWTPs if specific denitrification is employed. In average, 81% of total
nitrogen load are eliminated in German WWTPs (DWA, 2005). Around 20 – 30% of
nitrogen are incorporated in biomass during microbial growth, and 5 – 10% are
denitrified in anaerobic sludge of clarifier (Zimmermann et al., 1996). The particulate
nitrogen is separated by sedimentation in primary treatment. The nitrogen speciation in
the effluent is assumed to 70% NO3-N, 20% N org., and 10% NH4-N. The low fraction
of NH4-N in the effluent reflects an almost complete nitrification process (> 90%
oxidation of NH4,in).
Dissolved nitrogen is eliminated via incorporation in biomass or via denitrification to
elemental N2. During this process, minor emissions of N2O and NH3 arise from the
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tanks. In this study, 0.6 % of denitrified N is transformed into N2O (Wicht, 1996) and
0.6 % of influent NH4 into gaseous NH3 (adopted from Bardtke et al., 1994).
There are different concepts for a nitrogen balance of wastewater treatment. The
nitrified NH4-N can be estimated as follows (Scheer, 1998):
(NH
4-N)NIT = TKNinfluent – Nexcess sludge – NH4-Neffluent – N orgeffluent
Denitrified nitrogen is calculated by the following equation:
(NO
3-N)DEN = (NH4-N)NIT + NO3-Ninfluent – NO3-Neffluent
For the calculation of nitrified and denitrified nitrogen, the fraction of nitrogen
incorporated into the excess sludge has to be estimated:
N
excess sludge = 0.25 * TKN
Phosphorus
Phosphorus content of wastewater consists of dissolved and particulate phosphorus
species. It is assumed that particulate phosphorus is completely separated by
sedimentation during primary treatment. Dissolved phosphorus can be eliminated by
biological processes (incorporation in biomass or enhanced biological phosphorus
removal (EBPR)) and chemical precipitation with ferric or alum salts. In modern
WWTP with intended P elimination, the efficiency of P removal is 93 – 97% (ATV-
DVWK, 2004).
EBPR requires a specific process engineering and favourable wastewater composition
(Scheer and Seyfried, 1996). In this study, biological P elimination is attributed only to
usual incorporation in biomass, without intended EBPR mechanisms. Thus, 40% of
dissolved phosphorus is eliminated without chemical P elimination. In case of extended
nutrient elimination, P removal (> 95%) is reached by the addition of ferric salts (FeCl3
and FeSO4).
Sulphur
Sulphur is present in wastewater as sulphate or particulate sulphur. It is assumed that
particulate sulphur is separated in primary treatment and remains in sludge, whereas
dissolved sulphate remains in the effluent except the proportion that is dissolved in
sludge water content. Sulfate from addition of flocculants (FeSO4) is added to the
effluent.
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Heavy metals
Inorganic trace substances like heavy metals are removed in varying proportions from
the wastewater and transferred into the sludge. The degree of removal is determined by
their speciation and different adsorption properties to the wastewater sludge. The
remaining heavy metals are discharged with the effluent. Transfer coefficients of heavy
metals are compiled from various literature sources (Table 87).
Table 87: Transfer of heavy metals to sewage sludge in
activated sludge plant
Element Transfer to sewage sludge
in % of influent load
This study Literature Source
Lead 80 88 / 62 1 / 2
Cadmium 70 73 / 50 1 / 3
Chrome 80 85 / 50 1 / 4
Copper 85 88 / 46 – 93 1 / 5
Nickel 60 63 / 40 1 / 4
Mercury 80 79 / 91 / 70 1 / 2 / 3
Zinc 75 79 / 60 – 70 1 / 3
1) Fuchs et al., 2002 (mean values of literature review for activated
sludge plants with P elimination)
2) Raach et al., 1999
3) Koppe and Stozek, 1999
4) Zimmermann et al., 1996
5) Overath et al., 1997
Inorganic salts
Most inorganic salts are highly soluble in water. Hence, it is estimated that they remain
in the discharged water except for the fractions in sludge water content. Chloride from
addition of flocculants (FeCl3) is added to the effluent. 43% of calcium and 5% of
potassium are estimated to be bound in the sewage sludge.
AOX
AOX is a sum parameter for adsorbable organic halogenated compounds, which are
typical wastewater components of anthropogenic nature, e.g. chlorinated chemicals. For
this study, a transfer coefficient of 50% of AOX into the effluent is assumed (Koppe
and Stozek, 1999). The remaining fraction of AOX is not metabolized, but absorbed to
the sludge.
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Sand and screenings
The further treatment of inorganic inert material (mainly sand) and screenings from
mechanical treatment are neglected in this study. Usually, the screenings are disposed of
in a landfill after washing, dewatering, and compaction. The sand or grit is washed to
remove residual organic material, and it can be reused e.g. for road construction after
classification.
Flocculation chemicals
Chemical P elimination in wastewater treatment can be achieved by the addition of
ferric salts (= flocculation). The two salts which are usually used for flocculation are
ferric (III) chloride and ferrous (II) sulfate. Both chemicals have basically the same
effect for P elimination, although the use of FeSO4 consumes less alkalinity. In this
study, both flocculants are used in equal amounts, i.e. 50% of the required molar mass
of iron is delivered as FeCl3 (ferric chloride, 40% solution) and 50% as FeSO4 (ferrous
sulfate 7-hydrate, 96% solution). Potential contamination of flocculants with heavy
metals is neglected here.
Decisive for the applied flocculant dosage is the beta factor, which describes the
molar ratio between eliminated phosphorus and applied iron. Usually, the dosage is in
excess of regular stoichiometry. In this study, the beta factor is assumed to 1.5 which
represents a common dosage for wastewater treatment (Scheer, 1998).
12.4.3 Sludge treatment
Sewage sludge contains a considerable fraction of degradable organic substances, which
can lead to the formation of malodorous or toxic gases during storage and disposal of
the sludge. Hence, the sludge has to be stabilised prior to its disposal to prevent
uncontrolled degradation processes. It can either be stabilised by continuous aeration of
the sludge (= simultaneous aerobic stabilisation) or by digestion (= anaerobic
stabilisation). Both types of stabilisation are included within the reference scenarios.
The LCA model for conventional wastewater treatment developed at TU Berlin was
initially designed to describe the operation of large wastewater treatment plants. These
plants are typically equipped with a digester for anaerobic sludge stabilisation. Thus, the
process inventory for sludge digestion and sewage gas usage is readily available and is
described below.
For reasons of simplicity, the present model is also used to describe the process of
simultaneous aerobic stabilisation. Minor changes in the calculation of energy demand
and emissions are necessary to adopt the process model for sludge digestion to aerobic
sludge stabilisation with adequate accuracy (see below).
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12.4.3.1 Sludge digestion and sewage gas usage
Raw sludge
Raw sludge is composed of the organic and inorganic particulate substances from
sedimentation (primary sludge), the generated biomass of the activated sludge process
(excess sludge), and the remaining inert solids from endogenous decomposition of
biomass (ATV, 2000). A high sludge age leads to more respiration of carbon into CO2
and less organic matter in the excess sludge. In case of chemical P elimination, the
excess sludge additionally contains precipitated iron from flocculation with ferric salts.
The elemental composition of the raw sludge is calculated as follows:
The contents of carbon, nitrogen, phosphorus, sulphur, iron, heavy metals and
salts are calculated from input loads with transfer coefficients
Organic matter is mainly composed of carbon, oxygen and hydrogen. The
contributory fraction of oxygen and hydrogen to the organic matter is calculated
with proportional factors related to carbon (oxygen: 70% of carbon, hydrogen:
15% of carbon).
In case of chemical P elimination, the equivalent oxygen and hydrogen content
of the precipitated iron is calculated by assuming iron in the form of Fe(OH)3.
The mass of the inert fraction in the raw sludge is estimated to 45% of its
organic content.
For the conversion of chemical oxygen demand in excess sludge into organic
matter, the factor 1.45 g COD/g organic dry matter is applied.
Sludge thickening
The raw sludge is thickened by gravity in a settling tank. The process is supported by
the dosage of coagulation aids (polyacrylamide). A dose of 10 g polyacrylamide per kg
dry matter in sludge is assumed, leading to a final dry matter content of 5 % (Schumann
et al., 1997).
Digestion
This process model describes a mesophilic sludge digestion process (as for example in
Gujer, 1999). The thickened raw sludge is digested in a mesophilic reactor (33 – 37°C).
Within an average retention time of 15 – 30 days, degradable organic matter is
converted into biogas. The process needs a thermal energy input of 130-180 MJ/m³ raw
sludge to maintain the required operating temperature.
Around 40 – 50% of the organic matter content of the raw sludge is converted to
biogas, generating ca. 0.9 m³ biogas per kg decomposed organic matter. The biogas
(density: 1.15 kg/m³) contains 63 – 68 % CH4, 32 – 37 % CO2, 0 – 2 % N2 and 0 – 1 %
H2S. Based on the average composition of the substrate, the theoretical content of CH4,
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CO2, NH3, and H2S of the biogas can be calculated by the disproportioning equation
(Tidden, 2003):
2
42
(4 2 3 2 )
11
(4 2 3 2 ) (4 2 3 2 )
88
abcde
CHONS a b c d e HO
ab c d eCH ab c d eCO dNH eHS
32
3
The organic matter of raw sewage sludge is usually composed of 49% starch, 38%
proteins and 13% fats (Tidden, 2003). If this composition is assumed for the above
equation, the proportional allocation of converted carbon can be estimated with the
following transfer coefficients:
,42
100% 61% 32% 7%
raw sludge decomposed
C CH C CO C HCO C
These transfer coefficients are used for the carbon conversion in the digestion process.
The decomposition of proteins leads to the production of NH4, which is recycled to the
influent with the sludge liquor. This results in an additional ammonia load for the
nitrification stage. Depending on the efficiency of sludge dewatering, 10 – 15% of the
influent ammonia load is from sludge water (Gujer, 1999). This model considers the
recycled NH4 load in terms of energy demand for the aeration process. Around 10% of
the sulphur content of raw sludge is converted into H2S (Raach et al., 1999).
Dewatering
The stabilised sludge is dewatered by a high performance centrifuge with addition of
organic coagulation aids (polyacrylamide). The dosage of polyacrylamide is set to 7 g/g
dry matter. A stabilised sludge with good properties can be dewatered to a final dry
matter content of 40%.
Sewage gas usage in CHP plant
The sewage gas is combusted in a combined heat and power plant (CHP plant) to
generate electrical and thermal energy. A potentially required gas conditioning (e.g.
drying) prior to the combustion process is neglected. A part of the generated sewage gas
has to be flared in case of system malfunction or storage overflow. It is assumed that
5% of the total sewage gas volume is flared (Ronchetti et al., 2002), generating
emissions which are comparable to the combustion in the CHP plant. A small
proportion of the sewage gas (0.75%) is lost by accidental leakage (Ronchetti et al.,
2002), causing respective emissions of methane.
The CHP plant is equipped with a spark-ignition engine (“otto engine”) in lean
combustion mode with high excess air, so that legal air emission standards can be met
easily. CHP parameters and emission factors are compiled from an LCA study
(Ronchetti et al., 2002) and the Umberto® database (IFU and IFEU, 2005)
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Table 88: Parameters and emission factors for CHP plant
This study Biogas-CHP Natural gas-CHP
Source Ronchetti et al., 2002 Umberto® database
(IFU and IFEU, 2005)
Engine 60 kW
Lean burn engine
60 kW
Lean burn engine
50 kW (elec)
Catalysator engine
Efficiency 32% electrical,
57% thermal
32% electrical,
57% thermal
29,3% electrical
58,6% thermal
Emissions in mg/MJ
CH4, combustion 2,5 2,48 3,78
CO2 * 81.308 55.151
NOx (as NO2) 38 37,85 62,98
N2O 1,6 -- 1,57
CO 51 50,93 51,17
SOx (as SO2) 30 29,91 0,43
NMVOC 2,5 2,48 4,72
Dust 1,6 -- 1,57
* depending on input (CO2 + CH4) minus CO
12.4.3.2 Aerobic stabilisation of sludge
For a simplified model of aerobic stabilisation of wastewater sludge, the LCA model of
anaerobic sludge treatment is slightly modified. Aerobic stabilisation of wastewater
sludge is done simultaneously to the normal activated sludge process just by extended
aeration of the sludge (Gujer, 1999). This process is typically operated at smaller
wastewater plants. For adopting the LCA model to describe simultaneous aerobic
sludge stabilisation, the following modifications are implemented:
Sludge age is increased to 25 days (ATV, 2000), leading to a decrease in excess
sludge production
Generated gas in stabilisation is only CO2 (= transfer of Craw-sludge, decomposed into
93% CO2-C and 7% HCO3-C)
Energy demand for aeration of wastewater and for recirculation and mixing of
aeration tank is increased by 50% (Müller et al., 1999)
No energy demand for digester operation
No thermal energy required for sludge heating
Thickening, dewatering, and sludge transport is similar to sludge digestion
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12.4.4 Energy demand
The energy demand of wastewater treatment plants has been extensively studied in
literature (e.g. LfU, 1998; Müller et al., 1999) to identify important electric consumers
and optimize the energy balance of wastewater treatment. Many factors influence the
actual energy demand of a certain WWTP: Input-related parameters (influent volume,
COD and N load), the process layout and the plant dimension are important. Average
energy data from 1097 municipal wastewater treatment plants in Germany (Table 20)
show a wide range in energy demand related to influent volume, degraded COD, and
population equivalents.
Table 89: Energy demand of 1097 municipal WWTP in Germany
Relation Median 80%-percentile weighted average
kWh/m³ 0,32 0,56 0,32
kWh/(design-pe*a) 27,0 41,0 24,3
kWh/(pe * a) 41,5 64,0 31,7
kWh/ (kg COD) 1,06 1,7 0,88
Source: LfU, 1998
For this LCA model, specific energy coefficients for the different sub-processes of
wastewater treatment are calculated (Table 90). The calculation of the coefficients is
based on index figures of energy demand and specific data of wastewater volume, load
and removal efficiency (Müller et al., 1999; LfU, 1998). With these coefficients, the
LCA model calculates the effective energy demand for wastewater treatment in relation
to influent quality and quantity and preset removal efficiencies.
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Table 90: Allocation of energy demand in wastewater treatment
plants
Electric
energy Unit / allocation
Wastewater lifting facility 3.5 Wh/(m³*m pressure head)
Primary treatment
Mechanical treatment and
sedimentation 0.08 kWh/kg dry matter
(primary sludge + sand)
Primary sludge pumps 6 Wh/(m3 m pressure head)
Secondary treatment
Aeration for carbon degradation 0.55* kWh/kg CODrespirated
( Table 92)
Aeration for nitrification 2.34 kWh/kg Nnitrified
Benefit from denitrification -1.58 kWh/kg Ndenitrified
Internal circulation 0.01 kWh/m3 wastewater
Recirculation and mixing 0.05* kWh/m3 wastewater
Phosphate precipitation 0.37 kWh/kg P eliminated
Clarifier 0.01 kWh/m3 wastewater
Sludge treatment
Sludge pumping 0.01 kWh/kg dry matter sludge
Thickening of raw sludge 0.03 kWh/kg dry matter raw sludge
Raw sludge and digester heating** 150 MJ/m³ thickened raw sludge
Digester with mixing 0.12 kWh/kg dry matter raw sludge
Dewatering stabilised sludge
(high performance centrifuge) 0.06 kWh/kg dry matter stab. sludge
Auxiliary
Room heating**, lighting etc 0.03 kWh/m³ wastewater
38 kJ/m³ wastewater
Sources: LfU, 1998; Müller et al., 1999
* for WWTP with aerobic stabilisation, these values are increased by 50%
** thermal energy
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Oxygen demand and aeration energy for carbon degradation and nitrification
The oxygen demand for carbon degradation and endogenous respiration, nitrification
and denitrification (Table 91) is estimated according to common design rules for
wastewater treatment plants (ATV, 2000). 62.5% of the oxygen demand for nitrification
is reclaimed for microbial processes via the use of NO3-oxygen during denitrification.
Table 91: Specific oxygen demand of biological wastewater treatment
Process Value or calculation
Carbon degradation +
endogenous respiration kg O2/kg CODrespirated CODinfluent – CODeffluent – CODexcess sludge
Nitrification kg O2/kg Nnitrified 4.3
Denitrification kg O2/kg Ndenitrified - 2.9 (= oxygen benefit from NO3)
Source: ATV, 2000
For the calculation of the effective oxygen demand, it has to be considered that oxygen
introduced into the aeration tank is only partially exploited by microorganisms. Hence,
the oxygen input has to be significantly higher than the values calculated above. The
specific energy demand for effective oxygen transfer (0.55 kWh/kg O2, effective) in fine-
bubble aeration is calculated with common design parameters for activated sludge
plants (Imhoff, 1990). The energy demand for oxygen transfer is used to calculate the
specific energy demand for aeration for carbon degradation, nitrification, and
denitrification (Table 92).
Table 92: Calculation of specific energy demand for aeration
Parameter Value
Energy demand for fine-bubble aeration kWh/(kg O2*m) 0.02*
Injection depth m 3*
Oxygen utilization (3 m injection depth) % 11*
Specific energy demand for oxygen transfer kWh/kg O2,effective 0.55
Specific energy demand for aeration
Carbon degradation + endogenous respiration kWh/kg CODrespirated 0.55
Nitrification kWh/kg Nnitrified 2.34
Denitrification kWh/kg Ndenitrified - 1.58
*Source: Imhoff, 1990
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12.5 Agricultural tractor
For the calculation of the respective emissions and fuel demand of the tractor, a three
step procedure is necessary:
1. calculation of field size (in ha) to which the specific fertilizer is applied
2. definition of working time per ha and engine load levels during fertilizer
application
3. calculation of total working time and inserting all information into a given data
set for tractor operation
Calculation of the area of farmland for fertilizer application
From exemplary data for winter wheat, the following fertilizer doses are necessary for
its cultivation (Finck, 1992):
P2O5: 90 kg/ha, one dose per year
K2O: 160 kg/ha, one dose per year
N: 200 kg/ha, split into four doses per year
Table 93 lists an exemplary distribution of the required nutrients on different types of
fertilizer, taking into account the approximate nutrient content. It is assumed that
farmers will apply a mixture of all available types of fertilizer to provide suitable
amounts of nutrients to their crops.
Table 93: Exemplary distribution of the nutrient amounts of manure,
mineral and organic fertilizers for the cultivation of winter wheat
Nutrient Demand Manure Mineral
fertilizer Secondary
organic fertilizers
[kg/ha*a] [kg/ha*a] [kg/ha*a] [kg/ha*a]
P2O5 90 63 0 27
K2O 160 95 34 31
N 200 90 0 110
Of all nutrients, nitrogen is the most important during the growing period of the crops.
Consequently, the field size to which the fertilizers are applied is calculated based on
the nitrogen demand. The source separated urine contains around 6.7 kg/m³ N, which is
equivalent to 11272 kg plant-available N per year in total for the settlement (N losses
are already subtracted).
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Assuming the fertilizer management of Table 93, the collected urine can be applied to
an area of 102 ha (Table 94). The application area for the other fertilizers is calculated
accordingly (110 kg N/(ha*a)). For the different composts, the application rate is
limited by a maximum applicable amount of 25 t compost per ha and year. Due to its
low content of plant-available nitrogen, the N input on the respective field area via
compost is relatively low. For the application of sewage sludge, a maximum amount of
1.66 t/(ha*a) dry matter is regulated by law (AbfKlärV, 1992).
Table 94: Calculation of field area for fertilizer application in each
scenario
Scenarios Fertilizer Plant-
available
nitrogen
Nitrogen
dosage Field
area
[kg/a] [kg N/(ha*a)] [ha]
Mineral 16132 110 147
Reference
(Rmin + R) Compost (biowaste) 143 20.4* 7
Mineral 14121 110 128
Compost (biowaste) 143 20.4* 7
Reference (Ragri)
Sewage sludge 2011 39.4** 51
Mineral 4683 110 43
Urine 11272 110 102
Faeces
composting +
urine separation
(SC)
Compost (faeces +biow.) 320 26.7* 12
Mineral 3380 110 31
Urine 11272 110 102
Faeces digestion
+ urine separation
(SV)
Digester residual
(stabilised) 1630 110 15
Faeces and urine
digestion
(V)
Digester residual (liquid) 16280 110 148
* limited by a maximum applicable amount for compost: 25 t/(ha*a)
** limited by a maximum applicable amount for sewage sludge: 1.66 t/(ha*a) dry matter
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Specific working time per ha and engine load levels of agricultural tractor for each
fertilizer
Solid mineral fertilizers are applied with various methods (e.g. centrifugal spreader).
Liquid fertilizers are usually applied with splash plates, nozzles, trailing hoses, or liquid
injection. To minimize nitrogen losses during urine application, trailing hoses or
injection systems should be applied. In addition, the liquid fertilizer can be incorporated
into the ground by harrowing. A dilution of urine with service water (3:2) prior to the
application further reduces N losses. Composts are loaded on trailers with a crane and
applied via manure spreaders.
Table 95 lists the estimated working time and the distribution of engine load levels for
the application of mineral fertilizer, urine or liquid digester residual, and composts. Data
is compiled from charts of a detailed study on agricultural machinery (Rinaldi et al.,
2005).
Table 95: Working time and distribution of engine load levels during
the application of different fertilizers
Type of fertilizer Mineral
fertilizer
Urine#, sewage
sludge or
liquid digester
residual
Compost
Nominal engine power [kw] 50 50 50 50
Remarks
Spreader
width: 15 m
Pressure drum:
6.5 m³ Loading Spreading
Working time* [h/ha] 0.7 1.6 1.5 2.3
Applied amount -- 30 m³/ha 25 t/ha
Engine load levels
Heavy load [%] 10 10 0 20
Medium load [%] 60 50 0 30
Easy load [%] 0 0 95 10
Road [%] 20 30 0 20
Engine idle [%] 10 10 5 20
Calculated fuel
demand** [L/ha] 3.8 8.5 16.7
Source: Rinaldi et al., 2005
* incl.all working steps, road traffic etc; field size: 2 ha, distance farm to field: 1000 m
** calculated with data set “agricultural tractor” (IFU and IFEU, 2004)
# diluted with service water (3:2)
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Total working time for fertilizer application
In Table 96, the total working time for the application of the respective amounts of
fertilizer is listed for each scenario. The calculated times are based on the assumption of
different fertilizing regimes. For compost, a single application per year is assumed to
fulfil its function as a long-term fertilizer and soil conditioner. Urine, sewage sludge and
liquid fertilizer are applied in two doses per year due to their relatively high nutrient
content. Mineral fertilizer is applied in three doses per year (two for nitrogen, one for
phosphorus and potassium).
The respective values are fed into the data set “agricultural tractor” provided by
UMBERTO® (IFU and IFEU, 2004). This data set calculates the energy demand and
the emissions from agricultural tractor operation depending on engine power, total
working time, and engine load levels (dataset based on Borken et al., 1999).
Table 96: Calculation of total working time during fertilizer application
Scenarios Fertilizer Field
area Time
per area Regime* Total
working
time
[ha] [h/ha] [doses/a] [h/a]
Mineral 147 0.7 3 308
Reference
(Rmin + R) Compost (biowaste) 7 1.5 + 2.3 1 11 + 16
Mineral 128 0.7 3 270
Compost (biowaste) 7 1.5 + 2.3 1 11 + 16
Reference (Ragri)
Sewage sludge 51 1.6 2 165
Mineral 43 0.7 3 89
Urine 102 1.6 2 328
Faeces
composting +
urine separation
(SC) Compost (faeces +biow.) 12 1.5 + 2.3 1 18 + 28
Mineral 31 0.7 3 65
Urine 102 1.6 2 328
Faeces
digestion +
urine separation
(SV) Digester residual
(stabilised) 15 1.5 + 2.3 1 23 + 35
Faeces and
urine digestion
(V)
Digester residual (liquid) 148 1.6 2 472
* assumed number of doses per year
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12.6 Data for construction of sanitary systems
This part describes in detail the calculation of the material demand for the construction
of the infrastructure of all scenarios. Infrastructure can be divided into three main parts:
the sanitary installations inside the buildings, the sewer system, and the facilities for
treatment and storage. A map of the settlement area in Berlin which is used for the
exemplary layout of the sewer system is provided below (Figure 93).
12.6.1 Map of the settlement area
Figure 93: Map of the settlement area (Berlin-Nicolassee, study area is marked in yellow), source:
Berliner Wasserbetriebe (www.bwb.de), screenshot from GIS data software ARCVIEW®
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12.6.2 Sanitary in-house installations
The sanitary piping of prototype houses (Figure 94) and apartments (Figure 95) is
designed according to the relevant German legal norm (DIN EN 12056-2, 2001).
Details of the calculation of sanitary in-house-piping for conventional and source-
separation systems are presented in Table 97 and Table 98.
Ground level
SAL IV
SAL IIISAL II
GL I
SAL I
SAL = Collection pipe
FL = Down pipe
GL = Base pipe
To sewer system
2m 2m
4m 2m
1m
1m 1m 1m
5m
To roof for
ventilation
2m
Figure 94: Prototype house for dimensioning of sanitary in-house installations
FL I
SAL II
SAL I
GL I (1x per down pipe)
SAL = Collection pipe
FL = Down pipe
GL = Base pipe
To sewer system
To roof for
ventilation
(1x per down
pipe)
3m
1m
1m 2m
One downpipe and base pipe for
three superposed apartments
5m
2.75m3m 2m
Figure 95: Prototype apartment for dimensioning of sanitary in-house installations
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Table 97: Inventory of in-house installations (conventional system)
Length Sum Total Final length**
Building Line Pipe material
and diameter [m] [m]
[m] [m]
House SAL I + II PP 50 4 + 4 14 12180 14007
SAL III + IV PP 50 2 + 4
SAL III + IV PP 100 1
Down pipe PP 100 10.5 12.5 10875 12506
Base pipe PE 150 5 5 4350 4785
Apartment SAL I + II Cast iron 50 3 + 3 6 7020 7722
SAL II Cast iron 100 1
Down pipe* Cast iron 100 13.25 16.25 6337.5 6971
Base pipe* PE 150 5 15 1950 2243
* one down pipe and base pipe for three superimposed apartments
** including proportional factors for fittings (+10% for PE/cast iron, +15% for PP)
Table 98: Inventory of in-house installations (source-separation)
Length Sum Total Final
length***
Building System** Line Pipe material
and diameter [m] [m] [m] [m]
House F SAL III + IV PP 100 1 + 1
Down pipe PP 100 10.5 12.5 10875 12506
Base pipe PE 150 5 5 4350 4785
GW SAL I + II PP 50 4 + 4
SAL III + IV PP 50 3 + 4 15 13050 15008
Down pipe PP 70 6.25 6.25 5437.5 6253
Base pipe PP 100 5 5 4350 5003
U SAL III + IV PP 50 1 + 1 2 1740 2001
Down pipe PP 70 6.25 6.25 5437.5 6253
Base pipe PP 100 5 5 4350 5003
Vac Total PE 50 12 12 10440 11484
SWS Total PE 20 13.25 13.25 11527.5 13257
Apartment F SAL II Cast iron 100 1
Down pipe* Cast iron 100 13.25 5.42 6337.5 6971
Base pipe* PE 150 5 1.67 1950 2145
GW SAL I + II Cast iron 50 3 + 3 6 7020 7722
Down pipe* Cast iron 70 9 3 3510 3861
Base pipe* PP 100 5 1.67 1950 2243
U SAL II PP 50 1 1 1170 1346
Down pipe* PP 70 9 3 3510 4037
Base pipe* PP 100 5 1.67 1950 2243
Vac Total* PE 50 17 5.67 6630 7293
SWS Total PE 20 5.67 5.67 6630 7625
* one down pipe and base pipe for three superimposed apartments
** F: gravity drainage of faeces, GW: greywater, U: urine, Vac: vacuum system, SWS: service
water supply (for greywater reuse)
*** including proportional factors for fittings (+10% for PE/cast iron, +15% for PP)
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12.6.3 Drainage systems
The layout of the drainage systems is designed following common German regulations
(ATV, 1999; DIN EN 752, 1997). Figure 96 shows an exemplary map of the
conventional sewer system in the northern part of the study area. Resulting pipe lengths
for the different systems (Table 99) are used to calculate total material demand with
weight factors from manufacturers. Additional components such as house shafts,
inspection chambers or holding tanks are included in the inventory through quantity
(Table 100) and weight of single components (Table 101).
Figure 96: Exemplary layout of the conventional sewer system in the northern part of the
settlement area (Red: sewage pipes)
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Table 99: Pipe dimensions, materials, and total lengths for sewer systems
Faeces
Material Ø Weight
Ref Grey-
water Service
water Gravity Vacuum
Urine Fitting
factor*
[mm] [kg/m] [m] [m] [m] [m] [m] [m] [%]
House connections
Vitr.clay 150 24 10000 0 0 10000 0 0 10
PP 150 1.943 0 0 0 0 0 10000 15
PE 32 0.433 0 0 10000 0 0 0 15
50 0.457 0 0 0 0 10000 0 20
150 2.46 0 10000 0 0 0 0 20
Drainage
Vitr.clay 150 24 11250 0 0 11108 0 0 10
200 36 1010 0 0 0 0 0 10
250 51 645 0 0 0 0 0 10
300 67 370 0 0 0 0 0 10
400 104 530 0 0 0 0 0 10
PE 50 0.457 0 0 0 7816 0 7050 15
65 0.727 0 0 0 0 14185 0 15
100 2.18 0 0 0 0 1364 0 15
150 2.46 0 13168 0 0 0 0 15
(pressure) 150 4.56 0 0 11108 0 0 0 15
200 4.82 0 470 0 0 0 0 15
PP 150 1.943 0 0 0 0 0 11108 20
*proportional addition for fittings (PP: including 5% for plug-in connections)
Sources for weight factors:
Vitrified clay: Euroceramic, 2005
Polypropylene (PP):Ostendorf, 2005
Polyethylene (PE): Simona, 2005
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Table 100: Additional components of sewer systems
Dimensions
System Component Material Depth
[m]
Quantity
House shafts Concrete DN 1000 2 1000
Conventional
Inspection chambers Concrete DN 1000 3 342
House shafts Concrete DN 1000 2 -*
Inspection chambers Concrete DN 1000 3 -*
Faeces
with gravity
drainage
Pumping shafts Concrete DN 1000 3 32
Vacuum Inspection chambers Concrete DN 1000 1 67
House shafts Concrete DN 1000 2 1000*
Greywater
Inspection chambers Concrete DN 1000 3 273*
Inspection chambers Plastic DN 400 3 223
Pumping shafts Concrete DN 1000 3 32
Urine
Holding tanks GRP 12 m³ - 12
* one house shaft and inspection chamber for brownwater, greywater and service
water
Table 101: Material weight of chambers and shafts
Dimensions Weight
Component Material
Depth
[m] [kg]
Source
Inspection chambers Concrete DN 1000 3 3050 Mall, 2005
Cast iron 35 estimated
House shafts Concrete DN 1000 2 1610 Mall, 2005
Cast iron 26 estimated
Pumping shafts Concrete DN 1000 3 3050 Mall, 2005
Cast iron 35 estimated
Urine inspection shafts Polypropylene DN 400 3 40 Ostendorf,
2005
Manhole covers Concrete DN 800 80/75* Mall, 2005
Cast iron 40/120* estimated
* class B125/D400: house shafts are B125, rest is D400
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12.6.4 Treatment facilities
The design of the treatment facilities heavily depends on local conditions and specific
process layout. Therefore, the assessment of the material demand for the treatment
facilities can only roughly estimate the material demand for a potential sanitation
system. In general, the difficult acquisition of appropriate material data for the various
installations often leads to the use of qualified estimates. Estimates are based on data of
comparable processes, manufacturer product sheets, or other available information. This
section provides information about the material demand of:
conventional wastewater treatment plant
greywater treatment units (SBR, MBR + service water storage, soil filter)
urine collection tanks
solid-liquid separation process for faeces dewatering
vacuum system
biogas plant.
The composting plant for biowaste treatment or combined treatment of faeces and
biowaste is excluded from this study as well as the ozonation unit for urine treatment.
Conventional wastewater treatment plant
Construction data for the conventional wastewater treatment plant is adopted from a
detailed construction inventory of a German activated sludge plant (design dimension:
21000 inhabitant equivalents) (Schneidmadl, 1999). This data is converted to the plant
dimension of this study (~ 5000 inhabitant equivalents) by recalculating the material
demand in relation to the daily influent volume (m³/d) with a 10% safety margin (Vdesign
calc = Vdaily wastewater * 1.1) (Table 102).
SBR and MBR for greywater treatment
For the construction of greywater treatment plants (SBR and MBR), adequate inventory
data is not available. Hence, it is roughly estimated that both processes (conventional
activated sludge plant and SBR/MBR for greywater treatment) have a comparable
material demand in relation to the treated volume. Both SBR and MBR for greywater
treatment are described with the same inventory dataset which is recalculated in relation
to the daily influent volume (Table 102).
For MBR plants, the tank volume of the activated sludge tank can typically be
reduced by a factor of 3-4 (Pinnekamp and Friedrich, 2006), and no clarifier is required.
Consequently, construction data for the MBR plants for greywater treatment is
corrected by a factor of 4 for those materials which are relevant for tank construction
(concrete, non-alloy steel, and excavation). The possibility of a decentralized layout of
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the greywater reuse system (with several small MBR units distributed across the
settlement) to minimize the transport distances for greywater drainage and service water
supply is not accounted for in this study.
The purified greywater is temporarily stored in holding tanks before the distribution as
service water. The volume of the stored water should be large enough to balance
variations in the amount of treated greywater and service water demand. On the
contrary, a long retention time in the storage tank promotes the risk of bacterial
regrowth and associated problems (e.g. odour). Following the recommendations of the
German association for the use of service and storm water (Fbr, 2005), the volume of
the storage tanks is calculated to provide the average service water demand of one day
(= 120 m³ as maximum daily demand in scenario SC3). The material demand for the
storage tanks (10 x 12 m³) is estimated in analogy to the urine storage tanks: a 12 m³
tank made of glass fibre reinforced plastic weights 800 kg according to manufacturer
data (Mannschott, 2005).
Table 102: Construction data for a conventional wastewater treatment plant and calculated material demand
for wastewater or greywater treatment plants in this study
System SBR greywater MBR greywater
Conventional
activated
sludge plant
(Schneidmadl, 1999)
Combined
wastewater with
faeces filtrate without
faeces filtrate with
faeces filtrate without
faeces filtrate
Influent volume [m³/d] 7540 638 572 440 572 440
Influent volume* [L/pe*d] 127.6 114.4 88 114.4 88
Material [kg]
Concrete 30809720 2606976 2337289 1797915 584322# 449479#
Steel, high-alloy 57390 4856 4354 3349 4354 3349
Steel, non-alloy 498020 42140 37781 29062 9445# 7266#
Polyethylene HD 28820 2439 2186 1682 2186 1682
Limestone 602580 50988 45713 35164 45713 35164
Copper 13310 1126 1010 777 1010 777
Aluminium 3090 261 234 180 234 180
Glas 1130 96 86 66 86 66
Excavation 96382600 8155451 7311783 5624448 1827946# 1406112#
values are calculated in relation to influent volume (m³/d)
* influent volume is calculated as daily volume + 10% safety factor
# assumption for MBR plants: tank volume can be reduced by a factor of 4 (Pinnekamp and Friedrich, 2006), relevant for concrete, non-alloy
steel, and excavation
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Soil filter for greywater treatment
Greywater and faeces filtrate are treated in planted soil filters in scenarios SC2, SV2,
and V2. For this treatment, three large soil filters are designated for greywater treatment
near the settlement. Each soil filter consists of an upstream sedimentation tank
(HRTmin~ 3h, volume: 40 m³), a feeding pump, and a planted soil filter with ground
sealing (PE foil + PES fleece) and pipes for feeding and drainage (Table 103).
For the dimensioning, the necessary surface area for the soil filters is calculated
according to recommendations of ATV (ATV, 1998) and operational experience from
pilot sites in Lübeck-Flintenbreite (Oldenburg, 2002) and Stahnsdorf (Peter-Fröhlich et
al., 2007). In scenarios without faeces filtrate (SV2 and V2), the required surface area is
set to 2 m² per inhabitant. In case of combined treatment of greywater and faeces filtrate
(SC2), the required surface area is increased to 2.5 m² per inhabitant to prevent
hydraulic overloading and clogging of the filter.
Whereas the material demand for the sedimentation tank is estimated from
manufacturer data, the soil filter components are adopted from existing pilot plants or
are qualified estimates of consultants.
Table 103: Material data for system components of a soil filter with
upstream sedimentation tank
Component Material Source
Sedimentation tank Concrete 29582 kg Mall, 2005
(volume: 40 m³) Steel, non-alloy 444 kg
Excavation 60 m³ Estimation
Pumps (2x) Cast iron 100 kg Bengtsson et al., 1997
Pumping sump Polyethylene 110 kg
Cast iron 100 kg
Romold, 2004
Soil filter* Excavation 1 m³/m² estimation
Sand 1 m³/m²
PE foil 1.4 kg/m²
PES fleece 0.16 kg/m²
Spiess-Wallbaum, 2002
Pipe DN50 (Feed) PE 0.5 kg/m²
Pipe DN100 (Drainage) PE 1.44 kg/m²
Length: Oldenburg, 2002
Weight: Simona, 2005
* area for soil filter: 2 m²/pe in scenarios SV2 and V2, 2.5 m²/pe in scenario SC2
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Urine tanks
Urine is interim stored in underground tanks made of glass fibre reinforced plastic. The
required volume of the tanks is calculated from the daily amount of urine (1.5 L/(pe*d)),
the separation efficiency (70%), and a minimum holding time of 14 days. The urine is
withdrawn from the tanks by a suction vehicle and transported to the ozonation plant for
micropollutant oxidation. The ozonation plant is excluded from the construction
inventory due to lack of appropriate material data. From the plant, the urine is carried to
the nearby farms for storage (> 6 months) and subsequent application on the fields.
Storage tanks at the farms are not included in the inventory data, because farmers are
assumed to use existing tanks for liquid manure to store the urine.
Table 104: Material data of interim storage tanks for separated urine
Component Material Weight Qty
[kg]
Interim storage tank (12 m³) 800 9
Interim storage tank (11 m³)
Glass fibre
reinforced plastic 730 3
Source: Mannschott, 2005
Solid-liquid separation system
For the separation of flush water from faeces in composting scenarios, three technical
solid-liquid separators are operated in the settlement. At present, a reliable and efficient
system is not commercially available. Thus, the material demand for a separator has to
be roughly estimated (Table 105). It is assumed that an intermediate storage tank (10
m³) is provided for compensation of volume variations. The separator and subsequent
thickener are protected by an automated rake system against damage. The filtrate is
pumped to the greywater sewer and treated with greywater. The separated solid matter
is stored in dewatering containers before it is brought to the composting plant by truck.
Vacuum system
The vacuum system for the collection and transport of faeces comprises of three
vacuum stations which are located in the settlement. A vacuum station consists of two
steel tanks, two vacuum pumps to induce the vacuum into the tank, and two pressure
pumps to deliver the collected faeces to the biogas plant. Material and weight of all
system parts is estimated due to lack of appropriate material data (Table 106).
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Table 105: Material data for a solid-liquid separator
Component Material Qty Source
Pumps Cast iron 100 kg 6
Bengtsson et al.,
1997
Storage tank (10 m³) Concrete 7500 kg 1 Mall, 2005
Steel, non-alloy 115 kg
Flocculation aid
dosage Polyethylene 100 kg 1 Estimation
Rake system Steel high alloy 250 kg 1 Estimation
Separator/thickener Steel high alloy 500 kg 1 Estimation
Dewatering container Steel, non-alloy 1000 kg 5 Estimation
Pressure pipe DN50 PE 0.457 kg/m 100 m Simona, 2005
Table 106: Material data for a vacuum station
Component Material Qty Source
Vacuum tank (4 m³) Sheet steel 1000 kg 2 Dehoust, 2005
Excavation 16 m³ Estimation
Vacuum pump Cast iron 100 kg 2 Bengtsson et al., 1997
Pressure pump Cast iron 100 kg 2 Bengtsson et al., 1997
Small parts Polyethylene 100 kg Estimation
Biogas plant
The biogas plant for the co-digestion of faeces and biowaste consists of the following
parts:
Pretreatment of biowaste (“pulper”)
Hygienisation tank
Digester and gas storage
Post-digestion tank
Central heat and power plant for biogas usage
Service building
For pretreatment and hygienisation, two tanks with 25 m³ each are provided, which can
simultaneously act as buffer tanks. For the main biogas unit, available data describes the
material demand for a biogas plant with a digester volume (DV) of 600 m³ (Edelmann et
al., 2001; Ronchetti et al., 2002). The post-digestion tank has a volume of 250 m³, and
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two gas storage tanks with 225 m³ each can store the generated biogas. This data is
adjusted to the amount of substrate load in this study via linear extrapolation.
In scenarios with urine separation (SC and SV), there is a daily load of ca. 30 m³ of
digester substrate (faeces + biowaste). This load increases to 36 m³/d if urine is not
separated (urine + faeces + biowaste, scenarios V). With a DV of 600 m³, calculated
average residence times would be 20 or 17 days for the substrate inputs in this study,
respectively. However, the targeted average residence time for the digestion process is
30 days. To account for the increased demand for DV at higher residence times, the
material demand is increased proportionally for all materials related to the DV
(pretreatment tank, digestor, post-digestion tank). Resulting factors are 1.5 in case of
digestion of faeces and biowaste (30 m³/d for 30 days = 900 m³ DV = 1.5 * 600 m³) and
1.8 in case of digestion of urine, faeces, and biowaste (36 m³/d for 30 days = 1080 m³
DV = 1.8 * 600 m³).
The two CHP engines have a maximum power output of 25 kWel/ 50 kWth. For the
housing of CHP engines and other equipment, a service building is provided.
Table 107: Material data for a biogas plant with 600 m³ digester
volume
Component Material Qty Source
Concrete* 10000 kg 2
Tanks (25 m³)
Steel non-alloy* 345 kg
Edelmann et al.,
2001
Pumps Cast iron 100 kg 8
Bengtsson et al.,
1997
Stirrer Cast iron 150 kg 4 Estimation
Concrete* 202200 kg
Digester (600 m³) incl.
gas storage (450 m³) Steel non-alloy* 4533 kg
1 Ronchetti et al.,
2002
Concrete* 100000 kg
Post-digestion tank
(250 m³) Steel non-alloy* 3450 kg
1 Edelmann et al.,
2001
Steel, alloy 118 kg
CHP engine
(25 kWelec/50 kWtherm) Polyethylene 2,6 kg
2 Ronchetti et al.,
2002
Small parts Polyethylene 1000 kg Estimation
Concrete 112200 kg 1
Service building (70
m²) Steel, non-alloy 4100 kg
Reckerzügl, 1997
*for these materials, linear factors are applied additionally to the listed values to account
for increased demand of digester volume: factor 1.5 with urine separation (scenarios SV),
factor 1.8 without urine separation (scenarios V)
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12.7 Transport distances
Table 108: Transport distances in LCA studies
in km 1) 2) 3) 4) 5) This
study
Fertilizers
Mineral fertilizer 1200 600 + 100* 300
Sewage sludge 25 30 20
Compost to composting 20
Compost to farms 2 (30) 20
Faeces to composting 20
Digester residual (20) 25 15 (30) 30 20
Urine to treatment 5
Urine to farms 8 30 20
Waste
Sludge to incineration 30 20 30
Biowaste to incineration 30
Chemicals
Flocculation 50 1200 600 + 100* 300
Construction materials
Concrete to site 57 50 50
Concrete to disposal 50
Others to site 57 200 + 100** 300
Others to disposal 100
1) Frischknecht et al., 1996
2) Bengtsson et al., 1997
3) Reckerzügl, 1997
4) Frischknecht and Jungbluth, 2002
5) Jönsson et al., 2004
* for basic chemicals (train + truck)
** for plastic and metals (train + truck)
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12.8 Composition and production of mineral fertilizer
12.8.1 Nutrient and heavy metal contents of mineral fertilizers
Commercial mineral fertilizers are single- or multi-nutrient fertilizers with variable
nutrient content. The main nutrients nitrogen, phosphorus, potassium, lime and
magnesium are usually specified in the form of N, P2O5, K2O, CaO, and MgO. Table
109 and Table 110 show the most relevant types of mineral fertilizer together with their
mean nutrient content and their market shares related to the nutrient content (Patyk and
Reinhardt, 1997; Hackenberg and Wegener, 1999; Drescher-Hartung et al., 2001). The
basis for the calculated market shares are the amounts of fertilizer applied in Germany
in 1998/99 (Drescher-Hartung et al., 2001). Fertilizers with marginal market shares are
neglected.
The multi-nutrient fertilizers (NP, NPK, and PK) are composed of defined parts of
single nutrient fertilizers or contain chemical compounds with two types of nutrient
(e.g. (NH4)2HPO4). Lime fertilizer is assumed to be composed of 85% CaCO3 and 15%
CaO (Patyk and Reinhardt, 1997). Potassium chloride (KCl) is considered as the most
important K-fertilizer. Magnesium is added to the production process of multi-nutrient
fertilizers in the form of dolomite, and is then transformed to magnesium nitrate.
However, the production of Mg-fertilizers is neglected in this study due to its minor
contribution to the overall resource usage and heavy metal content of all fertilizers.
Table 109: Nutrient content and market shares of relevant
mineral N- and P-fertilizers
Fertilizer Nutrient content [%] Market share [%]
N P2O5 related to
N
related to
P2O5
Calciumammoniumnitrate 26.8 55
Urea 46.7 12
Ureaammoniumnitrate 32 18
Singlesuperphosphate 20 2
Triplesuperphosphate 48.5 11
Raw phosphate 26 3
NP-fertilizer (20/20/0) 20 20 7 29
NPK- fertilizer (15/15/15) 15 15 9 40
PK- fertilizer (0/15/20) 15 15
Sources: Patyk and Reinhardt, 1997; Hackenberg and Wegener, 1999;
Drescher-Hartung et al., 2001
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Table 110: Nutrient content and shares of lime and K-fertilizer
Fertilizer Nutrient content [%] Market share [%]
K
2O CaO rel. to K rel. to CaO
Potassium chloride 60 100
CaCO3 (lime) 54.3 85
Quick lime 97 15
Calciumammoniumnitrate (CAN) 21 --
Sources: Patyk and Reinhardt, 1997; Hackenberg and Wegener, 1999; Drescher-
Hartung et al., 2001
When assessing the environmental impacts of mineral fertilizers, their heavy metal
content is of particular importance. The partially high contamination of phosphate
fertilizers with Cd, Cr, and Zn is well-known (Boysen, 1992). Recent research results
from the federal agency for agricultural research indicate considerable concentrations of
uranium in certain fertilizers (Kratz, 2004; Fink, 2005). Uranium is present in raw
phosphate rock, and is further enriched during the production process of superphosphate
or triplesuperphosphate, resulting in concentrations of 13 – 191 mg U/kg in these
fertilizers. Due to the limited output of uranium via groundwater or agricultural
products, an accumulation of toxic uranium in agricultural soils is likely, which may
result in an enhanced transfer into agricultural products.
Table 111: Concentrations of heavy metals and As in relevant mineral
fertilizers
Fertilizer Concentrations of heavy metals and As [mg/kg dry matter]
As
1) Cd
2) Cr 2) Cu 2) Ni 2) Hg 1) Pb
2) U
3) Zn 2)
Calciumammoniumnitrate 3.3 0.25 8.7 4 3.8 0.01 21.4 < 1 38.3
Urea 0.04 0.13 0.5 0.5 0.7 0.01 0.6 < 1 1.9
Ureaammoniumnitrate 0.03 1.3 6.3 0.3 0.2 < 1 2.3
Singlesuperphosphate 3.7 10.8 114 17.2 28.8 0.02 18.5 138 236
Triplesuperphosphate 3.7 26.8 288 27.3 36.3 0.04 12 138 489
Raw phosphate 3.6 7.8 168 15.6 15.6 0.07 1.3 44 199
NP-fertilizer (20/20/0) 9.15 91.4 21.5 18 0.022) 5.5 93 151
NPK-fertilizer (15/15/15) 3.78 45.8 11.3 10.9 0.062) 14.8 14 116
PK-fertilizer (0/15/20) 7.98 191 19.3 19.9 0.082) 14.4 93 152
Potassium chloride 0.01 0.08 3.5 2.9 1.5 0.02 0.5 < 1 3.7
Lime (CaCO3) 0.05 0.3 7.5 8.2 6.1 0.04 5.9 < 1 41.2
Quick lime 0.1 19.2 11.1 6 2.8 < 1 15.8
1) Vogt et al. (2002)
2) Drescher-Hartung et al. (2001)
3) Estimations according to Kratz (2004)
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Table 111 lists the average heavy metal content of mineral fertilizers. The literature data
is essentially based on the work of Boysen (Boysen, 1992). The values for uranium are
mean values derived from concentration ranges (Fink, 2005), and are therefore
representing rough estimations. In general, the heavy metal content of fertilizers shows
a wide variation limit due to the variable origin of raw materials.
For the calculation of mean heavy metal contents for the average N-, P-, K, and Ca-
fertilizers (Table 112), the data from Table 111 is connected with the nutrient content
and market shares of the respective fertilizers. Data for arsenic in multi-nutrient
fertilizers is estimated via single-nutrient fertilizers.
Table 112: Calculated mean concentrations of heavy metals and As for
average mineral fertilizers, related to the single nutrients
Values in mg/kg nutrient As Cd Cr Cu Ni Hg Pb U Zn
N-fertilizer (as N) 9.3 6.0 77.9 26.0 20.9 0.07 54.9 51.5 203.0
P-fertilizer (as P2O5) 14.5 39.5 543.2 90.5 88.3 0.3 67.0 349.2 839.2
K-fertilizer (as K2O) 0.1 0.1 5.8 4.8 2.5 0.03 0.8 1.0 6.2
Lime (as CaO) 0.1 0.5 14.7 14.6 10.5 0.06 9.7 1.0 66.9
Sources: Patyk and Reinhardt, 1997; Hackenberg and Wegener, 1999; Drescher-Hartung
et al., 2001
Recently, updated values for heavy metal contaminations of several mineral fertilizers
sold in the EU were published (UBA, 2007a). These values from the database of the
FAL (Federal Agricultural Research Centre) indicate substantial changes in relation to
the Boysen study. If the updated values are taken as the basis for calculation (Table
114), the contamination with some heavy metals decreases significantly (Cr, Ni, Pb),
while the content of Cu (added as micronutrient) and Zn increases. However, the UBA
study suffers from a lack of adequate data (small number of samples), and the published
values are not considered as representative for the national average by the authors.
Hence, the present study is based on the comprehensive results of the Boysen study
despite its relatively high age of 15 years.
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Table 113: Updated concentrations of heavy metals and As in mineral
fertilizers
Fertilizer Concentrations of heavy metals and As [mg/kg dry matter]
As Cd Cr Cu Ni Hg Pb U Zn
Calciumammoniumnitrate 0.31 0.17 2.9 4.0 2.3 0.01 16.0 0.23 32.3
Urea 0.03 0.33 0.4 0.26 0.07 0.09 1.7
Singlesuperphosphate 3.9 3.3 23.9 24.8 16.3 0.04 20.9 72.2 93.6
Triplesuperphosphate 10.7 14.4 131 5.2 17.6 0.09 10.1 197 159
NP-fertilizer (20/20/0) 11.2 7.8 63 4.6 16.4 0.12 5.1 65.2 121
NPK-fertilizer (15/15/15) 2.0 4.5 18.2 172 5.5 0.02 6.1 22.6 283
PK-fertilizer (0/15/20) 5.2 4.5 79.5 12.3 17.5 0.07 4.2 87.6 128
Potassium chloride 0.25 1.0 6.5 1.6 2.3 0.09 3.1 0.56 2.2
Source: UBA, 2007a, based on small number of samples from FAL database
Table 114: Mean concentrations of heavy metals and As for average
mineral fertilizers, calculated with values based on UBA study (2007)
Values in mg/kg nutrient As Cd Cr Cu Ni Hg Pb U Zn
N-fertilizer (as N) 5.7 5.8 39.3 120.4 13.9 0.1 38.2 36.8 284.6
P-fertilizer (as P2O5) 30.0 32.3 269.8 483.8 63.6 0.3 32.7 301.8 1.126.9
K-fertilizer (as K2O) 0.1 1.7 10.8 2.7 3.8 0.2 5.2 0.9 3.7
Lime (as CaO) 0.1 0.5 14.7 14.6 10.5 0.1 9.7 1.0 66.9
Sources: Patyk and Reinhardt, 1997; Hackenberg and Wegener, 1999; Drescher-Hartung et
al., 2001; UBA, 2007a
12.8.2 Life cycle inventory of the production and supply of
commercial mineral fertilizers
The production of mineral fertilizers and the associated substance and energy flows are
documented in detail by Patyk and Reinhardt (Patyk and Reinhardt, 1997). However,
emissions in surface waters are not considered within their study, although they can
play an important role for the environmental evaluation (e.g. phosphate and fluoride
emissions in processing of raw phosphates). Hence, aquatic emissions are adopted from
a Suisse study (Gaillard et al., 1997) and recalculated, relating them to the average
single-nutrient fertilizer via market shares and nutrient content. The Suisse study does
not include all types of multi-nutrient fertilizers, so NP- and NPK-fertilizers are
accounted for as ammonium-nitrate-phosphate and PK-fertilizers, single
superphosphates and raw phosphates are calculated as triplesuperphosphate (conversion
relative to the nutrient content).
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Table 115 shows an abstract of important LCI data regarding the production process of
mineral fertilizers. The data takes into account the complete processes of production and
supply of mineral fertilizers, including transport and energy supply, starting with the
extraction of resources until the packing of the marketable product.
Table 115a: Life cycle inventories of mineral fertilizer production
Reference value 1000 kg N 1000 kg P2O51000 kg K2O 1000 kg CaO
Input unit
Use of resources
Primary energy carrier
Lignite kg 35 69 57 77
Natural gas kg 881 180 173 17
Crude oil kg 229 166 29 1,75
Hard coal kg 107 62 40 15
Minerals and ores
Raw potash kg 10.500
Limestone kg 550 2.045
Raw phosphate ore kg 4.060
Sulphur kg 272
Cumulated energy demand (CED)
CED (fossil) MJ 48.264 16.337 9.866 1.842
CED (nuclear) MJ 632 1.095 516 428
CED (regenerative) MJ 178 278 79 14
Output
Emissions (air)
Particles kg 0.10 0.51 0.05 0.60
Dust (>PM10) kg 2.31 1.11 0.85 0.38
NH3 kg 6.69 0.01 0.00 0.00
HCl kg 0.07 0.02 0.07 0.01
N2O kg 15.05 0.04 0.05 0.03
HF kg 0 0.022 0 0
CO2, fossil kg 28201) 1117 617 3432)
CO kg 2.80 1.42 0.42 4.64
NOx kg 15.76 8.58 1.15 0.24
SO2 kg 5.16 11.98 0.27 0.07
VOC
Benzo(a)pyrene kg 7.07E-07 9.65E-07 2.01E-07 1.37E-08
Benzene kg 0.01 0.01 0.00 0.00
Formaldehyde kg 0.02 0.03 0.01 0.00
Methane kg 7.45 2.07 1.38 0.31
NMVOC, unspecified kg 0.54 0.49 0.12 0.01
PCDD, PCDF kg 1.29E-09 2.38E-10 2.51E-10 3.74E-11
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Table 115b: Life cycle inventories of mineral fertilizer production
(ctd)
Reference value 1000 kg N 1000 kg P2O51000 kg K2O 1000 kg CaO
Emissions (water)
Metals
Al g 476.09 94.71 23.4 29.4
As g 0.96 4.59 0.05 0.06
Cd g 0.03 4.40 0.00 0.00
Co g 0.95 0.19 0.05 0.06
Cr g 4.94 23.04 0.28 0.30
Cu g 2.40 22.47 0.12 0.15
Fe g 334.19 146.89 33.10 119.00
Ni g 2.43 18.11 0.12 0.15
Hg g 0.00 4.18 0.00 0.00
Pb g 2.67 19.58 0.19 0.18
Se g 2.39 0.48 0.12 0.15
Zn g 4.95 27.48 0.27 0.31
Nutrients
Ammonia as NH3 g 2.68 9.17 1.72 0.69
Nitrate as NO3 g 189.15 8.16 1.20 0.75
Phosphate as PO4 g 28.62 436631.40 1.75
Salts
Chloride g 6219.00 5826.89 825.00 459.00
Cyanide g 0.09 0.06 0.02 0.01
Fluoride g 1.65 131030.27 0.15
Sulphide g 0.17 0.29 0.04 0.01
Hydrocarbons g 0.027 0.05 0.007 0.005
1) Including credit for the bonding of CO2 in urea production; without credit, i.e.
including CO2 emissions from urea hydrolysis in soil: 2980 kg if urea has 10% market
share (rel. to applied N)
2) For a fertilizing lime with the assumed main components (85% CaCO3, 15% CaO),
670 kg CO2 is being emitted in the soil per ton of applied CaO. These emissions are not
included in this data, and therefore have to be considered during application of the lime
3) Data for fluoride and phosphate emissions are estimated (see text)
Sources: Patyk and Reinhardt, 1997; Gaillard et al., 1997 for aquatic emissions
Different production processes and countries of origin are considered in relation to their
market share in Germany 1993 (aquatic emissions: 1998/99). Transport of the fertilizers
to the farms and emissions during application are not included, and neither is the
construction and maintenance of infrastructure.
Regarding the data quality, recent changes in market shares or production processes are
not accounted for in this data set. However, the data for the emissions of fluoride and
phosphate during P-fertilizer production presented in Gaillard et al., 1997 is based on
production data from 1992 (Audsley et al., 1997). The emission factors are very high
compared to more recent data (Table 116) and have a strong influence on the impact
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assessment due to the high toxicity of fluoride. Hence, it is decided to adjust these
emission factors in view of the advances in exhaust gas and wastewater treatment.
In the multi-stage production process of P-fertilizer, considerable emissions of gaseous
fluorides (HF, SiF4) occur due to the high fluoride content of raw phosphates (2 – 5%).
In modern production facilities, these air-borne emissions are filtered from the exhaust
air by gas scrubbers. Removal efficiencies of 99% are achievable with regard to HF
emissions (Wiesenberger, 2002). Literature data shows a wide range of emission factors
for fluoride and phosphate emissions (Table 116) depending on production route,
emission control measures, and environmental legislation. Hence, moderate emission
factors for the present inventory are estimated based on the collected data, assuming
state-of-the art exhaust air cleaning and wastewater treatment according to European
standards.
Table 116: Emissions of fluoride and phosphate in P fertilizer
production
[mg/kg P] F (as fluorides) PO4 Remarks
Source Air Water Water
Audsley et al., 1997 460 167000 138000 Data from 1992 for
triplesuperphosphate
HELCOM, 1996 690 351 Recommendations
for critical load
Wiesenberger, 2002 13.331/2171 2680 4141
Plant data, air emissions for
triplesuperphosphate/NPK
on mixed acid route
Wiesenberger, 2002 671 290 530
Plant data for NPK fertilizer
with nitro-phosphate route
EPA, 1995 874
Emission factors for
superphosphate production
(US)
EPA, 2000 11 – 227
EPA standards for new
plants (depending on
production route)
EC, 2007 6 – 309 4580 –
71000
4912 –
9123
Reported emissions in
phosphoric acid production
IFC, 2007 23 1603 3677 Industry benchmark for
nitro-phosphate route
IFC, 2007 167 250 Industry benchmark for
mixed acid route
This study 501 3000 10000 estimated
1) as HF
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Annex 321
12.9 Data from Life Cycle Inventory
This section presents selected data from the Life Cycle Inventory. The presented data
constitutes the basis for the results of LCI as presented in chapter 5.1. In particular, the
following information is listed:
1. Input-output balance of electrical energy for operation (Table 117)
2. Calculated plant-available nutrient content for mineral and organic fertilizers
(Table 118)
3. Calculated effluent volume, loads, and concentrations from wastewater and
greywater treatment (Table 119)
4. Calculated heavy metal emissions to surface waters and agricultural soil (Table
120)
Table 117: Input-output balance of electrical energy for operation
in kWh/(pe*a) R Rmin R
agri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
INPUT
Drinking water 19 21.2 19 15.5 15.5 14.6 15.5 15.5 14.6 19 19 14.6
Pumping/Vacuum 0 0 0 16.0 15.8 17.0 16.0 15.8 17.0 3 1.9 5.6
Composting 2.4 2.4 2.4 0 0 0 0 0 0 5.2 6.4 5.4
Digestion 0 0 0 14.1 14.8 14.1 15.0 15.8 15.0 0 0 0
Wastewater treatment 24 28.3 24 14.7 1.6 19.4 14.9 1.6 19.6 19.1 2.1 25
Urine separation 0 0 0 0 0 0 5.8 5.8 5.8 5.8 5.8 5.8
SUM INPUT 45.4 51.9 45.4 60.3 47.7 65.1 67.2 54.5 72 52.1 35.2 56.4
OUTPUT
Sewage gas/biogas 12.1 0 12.1 44.4 47.6 44.5 40.9 44 40.9 0 0 0
Incineration of biowaste 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1
Incineration of sludge 0.7 1.0 0 0.2 0.4 0 0.2 0.3 0 0 0.4 0
Feedstock* 0.2 0.2 0.2 0.7 1.0 1.1 1.1 1.4 1.5 1.0 1.3 1.4
SUM OUTPUT 16.1 4.3 15.4 48.4 52.1 48.7 45.3 48.8 45.5 4.1 4.8 4.5
NET ENERGY DEMAND 29.3 47.6 30 11.9 -4.4 16.4 21.9 5.7 26.5 48 30.3 51.9
SYSTEM EXPANSION 36 47.8 36.7 3.7 0 3.4 6.8 3.3 6.6 48 47.3 47.6
* energy credit from infrastructure (incineration of plastic materials)
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Table 118: Calculated plant-available nutrient content of mineral and organic fertilizers
in g/(pe*a) R Rmin R
agri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
NITROGEN
Compost 29 29 29 0 0 0 0 0 0 64 65 63
Urine 0 0 0 0 0 0 2254 2254 2254 2254 2254 2254
Digester sludge 0 0 402* 3241 3255 3247 325 323 326 0 0 0
Mineral fertilizer 3226 3226 2824 14 0 8 675 677 674 937 935 938
PHOSPHORUS
Compost 119 119 119 0 0 0 0 0 0 250 254 246
Urine 0 0 0 0 0 0 256 256 256 256 256 256
Digester sludge 0 0 490* 718 724 719 393 398 394 0 0 0
Mineral fertilizer 605 605 115 6 0 5 75 70 74 218 214 222
POTASSIUM
Compost 480 480 480 0 0 0 0 0 0 689 709 692
Urine 0 0 0 0 0 0 664 664 664 664 664 664
Digester sludge 0 0 10* 1644 1664 1678 490 500 507 0 0 0
Mineral fertilizer 1198 1198 1188 34 14 0 524 514 507 325 305 322
* sewage sludge (digested)
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Table 119: Calculated effluent volume, loads, and concentrations from wastewater and greywater treatment
R Rmin R
agri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Effluent volume m³/(pe*a) 38.5 42.9 38.5 28.7 25.5 27.2 30.8 27.5 29.2 37.3 33.4 29
Effluent loads
COD kg/(pe*a) 2.0 2.0 2.0 0.9 1.3 0.9 1.0 1.4 0.9 1.2 1.7 0.9
NH4-N kg/(pe*a) 0.04 0.2 0.04 0.01 0.01 0.01 0.03 0.03 0.03 0.04 0.05 0.03
N total, inorg kg/(pe*a) 0.3 1.7 0.3 0.08 0.16 0.1 0.15 0.23 0.17 0.3 0.6 0.3
N total kg/(pe*a) 0.4 2.1 0.4 0.13 0.25 0.16 0.24 0.37 0.27 0.45 0.9 0.5
P g/(pe*a) 30.4 364 30.4 16.4 82.2 7.6 20 85.8 11.2 27 135 10.8
Effluent concentrations
COD mg/L 52.3 47.0 52.3 32.7 52.5 32.3 32.0 50.5 31.5 31.7 50.7 31.3
NH4-N mg/L 1.1 4.9 1.1 0.3 0.4 0.3 0.9 1.1 0.9 1.2 1.6 1.2
N total, inorg mg/L 8.7 38.9 8.7 2.8 6.1 3.6 5.0 8.4 5.9 7.6
16.7 10.9
N total mg/L 10.8 48.6 10.8 4.4 9.9 5.8 7.8 13.4 9.3 11.9 26.7 17.5
P mg/L 0.8 8.5 0.8 0.7 4.0 0.4 0.6 3.1 0.4 0.7 4.0 0.4
Note:
- in scenarios SV1+2+3, values include effluent from separate treatment of sludge liquor from digestion
- in scenarios SC1+2+3, faeces filtrate is co-treated with greywater
Annex 324
R
Table 120: Calculated heavy metal emissions to surface waters and agricultural soil
in mg/(pe*a) R Rmin agri V1 V2 V3 SV1 SV2 SV3 SC1 SC2 SC3
Emissions to surface waters
Cd 32 32 27 22 22 21 25 25 23 26 25 20
Cr 281 285 253 221 220 207 239 239 225 247 246 195
Cu 1429 1535 1403 1096 1460 1024 1137 1503 1060 1320 1755 886
Ni 379 388 357 293 293 274 335 335 317 322 322 261
Hg 9 9 4 2 2 2 3 3 3 4 4 4
Pb 295 306 272 247 237 234 270 260 257 280 267 225
U* 8 10 8 7 8 5 9 10 6 10 7 11
Zn 5999 6405 5966 4203 3361 3928 4773 3942 4471 5036 4031 3450
Emissions to agricultural soil
Cd 88 88 100 21 21 21 25 25 26 45 45 46
Cr 1280 1280 1555 279 281 282 370 373 373 613 619 619
Cu 941 941 8706 1590 1661 1357 1389 1450 1192 1354 1440 1360
Ni 340 340 748 240 246 249 179 183 185 295 302 293
Hg 8 8 20 15 15 15 12 12 12 13 13 15
Pb 567 567 1388 305 314 309 295 303 299 383 393 385
U* 664 664 249 6 5 0 98 98 94 227 224 230
Zn 6475 6475 23278 9059 9212 8628 7095 7212 6773 8961 9153 8965
* uranium in water from energy production (nuclear power), uranium in soil from mineral phosphate fertilizer
Annex 325
12 Annex
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12.10 Valuation results
-250 -200 -150 -100 -50 0 50 100 150
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario V2
Figure 97: Comparison of scenario R and V2 with valuated indicator results in T diagram
-250 -200 -150 -100 -50 0 50 100 150
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario V3
[%]
Figure 98: Comparison of scenario R and V3 with valuated indicator results in T diagram
Annex 326
12 Annex
______________________________________________________________________
-200 -150 -100 -50 0 50
Cum. energy demand
Global warming
Abiotic resource depl.
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R
Additional impact
of scenario SV1
[%]
Figure 99: Comparison of scenario R and SV1 with valuated indicator results in T diagram
-200 -150 -100 -50 0 50
Cum. energy demand
Global warming
Abiotic resource depl.
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario SV2
[%]
Figure 100: Comparison of scenario R and SV2 with valuated indicator results in T diagram
Annex 327
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______________________________________________________________________
-100 -80 -60 -40 -20 0 20 40 60 80
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario SC1
[%]
Figure 101: Comparison of scenario R and SC1 with valuated indicator results in T diagram
-80 -60 -40 -20 0 20 40 60 80
Cumulated energy demand
Global warming
Abiotic resource depletion
Eutrophication
Acidification
Human toxicity
Aquatic ecotoxicity
Terrestrial ecotoxicity
High priority indicator Medium priority indicator
Additional impact
of reference scenario R Additional impact
of scenario SC3
[%]
Figure 102: Comparison of scenario R and SC3 with valuated indicator results in T diagram
Annex 328
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12.11 List of Tables in Annex
Table 68: Volume flow and composition of human urine................................ 268
Table 69: Volume flow and composition of human faeces ............................. 269
Table 70: Volume flow and composition of greywater from households......... 270
Table 71: Mass flow and composition of kitchen and garden biowaste.......... 271
Table 72: LCIA characterization factors for emissions to air: GWP, AP, EP,
TEU, AEM ...................................................................................................... 272
Table 73: LCIA characterization factors for emissions to air: HTP, FAETP,
TETP, AET, TET ............................................................................................ 273
Table 74: LCIA characterization factors for emissions to soil: HTP, FAETP,
TETP, AET, TET ............................................................................................ 274
Table 75: LCIA characterization factors for emissions to water: HTP, FAETP,
TETP, AET, TET ............................................................................................ 275
Table 76: LCIA characterization factors for emissions to water: EP, AEU ..... 276
Table 77: LCIA characterization factors for extraction of resources: ADP...... 277
Table 78: Normalization data for cumulated energy demand......................... 278
Table 79: Normalization data for global warming potential............................. 278
Table 80: Normalization data for acidification potential .................................. 279
Table 81: Normalization data for eutrophication potential .............................. 279
Table 82: Normalization data for terrestrial eutrophication and aquatic
eutrophication of inland waters....................................................................... 280
Table 83: Normalization data for human and ecotoxicity................................ 281
Table 84: Normalization data for human and ecotoxicity (continued)............. 282
Table 85: Assumed composition of influent wastewater................................. 284
Table 86: Parameters of LCA model for conventional WWTP ....................... 285
Table 87: Transfer of heavy metals to sewage sludge in activated sludge plant
....................................................................................................................... 288
Table 88: Parameters and emission factors for CHP plant ............................ 292
Table 89: Energy demand of 1097 municipal WWTP in Germany ................. 293
Table 90: Allocation of energy demand in wastewater treatment plants ........ 294
Table 91: Specific oxygen demand of biological wastewater treatment ......... 295
Annex 329
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Table 92: Calculation of specific energy demand for aeration ........................ 295
Table 93: Exemplary distribution of the nutrient amounts of manure, mineral and
organic fertilizers for the cultivation of winter wheat ....................................... 296
Table 94: Calculation of field area for fertilizer application in each scenario... 297
Table 95: Working time and distribution of engine load levels during the
application of different fertilizers ..................................................................... 298
Table 96: Calculation of total working time during fertilizer application........... 299
Table 97: Inventory of in-house installations (conventional system)............... 302
Table 98: Inventory of in-house installations (source-separation)................... 302
Table 99: Pipe dimensions, materials, and total lengths for sewer systems... 304
Table 100: Additional components of sewer systems..................................... 305
Table 101: Material weight of chambers and shafts ....................................... 305
Table 102: Construction data for a conventional wastewater treatment plant and
calculated material demand for wastewater or greywater treatment plants in this
study............................................................................................................... 308
Table 103: Material data for system components of a soil filter with upstream
sedimentation tank ......................................................................................... 309
Table 104: Material data of interim storage tanks for separated urine............ 310
Table 105: Material data for a solid-liquid separator....................................... 311
Table 106: Material data for a vacuum station................................................ 311
Table 107: Material data for a biogas plant with 600 m³ digester volume....... 312
Table 108: Transport distances in LCA studies .............................................. 313
Table 109: Nutrient content and market shares of relevant mineral N- and P-
fertilizers ......................................................................................................... 314
Table 110: Nutrient content and shares of lime and K-fertilizer ...................... 315
Table 111: Concentrations of heavy metals and As in relevant mineral fertilizers
....................................................................................................................... 315
Table 112: Calculated mean concentrations of heavy metals and As for average
mineral fertilizers, related to the single nutrients............................................. 316
Table 113: Updated concentrations of heavy metals and As in mineral fertilizers
....................................................................................................................... 317
Annex 330
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Table 114: Mean concentrations of heavy metals and As for average mineral
fertilizers, calculated with values based on UBA study (2007) ....................... 317
Table 115a: Life cycle inventories of mineral fertilizer production .................. 318
Table 116: Emissions of fluoride and phosphate in P fertilizer production ..... 320
Table 117: Input-output balance of electrical energy for operation................. 322
Table 118: Calculated plant-available nutrient content of mineral and organic
fertilizers......................................................................................................... 323
Table 119: Calculated effluent volume, loads, and concentrations from
wastewater and greywater treatment ............................................................. 324
Table 120: Calculated heavy metal emissions to surface waters and agricultural
soil.................................................................................................................. 325
Annex 331
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12.12 List of Figures in Annex
Figure 93: Map of the settlement area (Berlin-Nicolassee, study area is marked
in yellow), source: Berliner Wasserbetriebe (www.bwb.de), screenshot from GIS
data software ARCVIEW®.............................................................................. 300
Figure 94: Prototype house for dimensioning of sanitary in-house installations
....................................................................................................................... 301
Figure 95: Prototype apartment for dimensioning of sanitary in-house
installations..................................................................................................... 301
Figure 96: Exemplary layout of the conventional sewer system in the northern
part of the settlement area (Red: sewage pipes)............................................ 303
Figure 97: Comparison of scenario R and V2 with valuated indicator results in T
diagram .......................................................................................................... 326
Figure 98: Comparison of scenario R and V3 with valuated indicator results in T
diagram .......................................................................................................... 326
Figure 99: Comparison of scenario R and SV1 with valuated indicator results in
T diagram ....................................................................................................... 327
Figure 100: Comparison of scenario R and SV2 with valuated indicator results
in T diagram.................................................................................................... 327
Figure 101: Comparison of scenario R and SC1 with valuated indicator results
in T diagram.................................................................................................... 328
Figure 102: Comparison of scenario R and SC3 with valuated indicator results
in T diagram.................................................................................................... 328
Annex 332
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______________________________________________________________________
12.13 Literature in Annex
AbfKlärV (1992): Klärschlammverordnung (Sewage sludge ordinance),
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ATV (1998): A262: Grundlagen für Bemessung, Bau und Betrieb von Pflanzenbeeten
für kommunales Abwasser bei Ausbaugrößen bis 1000 Einwohnerwerte (A262:
Fundamentals for dimensioning, construction and operation of constructed
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Abwassertechnische Vereinigung e.V. Hennef, Germany.
ATV (1999): A118: Hydraulische Bemessung und Nachweis von
Entwässerungssystemen (A118: Hydraulic dimensioning and verification of
drainage systems). Abwassertechnische Vereinigung e.V. Hennef, Germany.
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Vereinigung e.V. Hennef, Germany.
Audsley, E., Alber, S., Clift, R., Cowell, S., Crettaz, P., Gaillard, A., Hausheer, J.,
Jolliet, O., Kleijn, R., Mortensen, B., Pearce, D., Roger, E., Teulon, H.,
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2028), European Commission DG VI Agriculture, Silsoe, United Kingdom.
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for greywater treatment), pp. 85-95: Grauwasser-Recycling, Fachvereinigung
Betriebs- und Regenwassernutzung e.V., Darmstadt, Germany.
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und Maßnahmen für eine vorsorgeorientierte Begrenzung von
Schadstoffeinträgen in landbaulich genutzte Böden (Fundamentals and measures
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Annex 333
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Becker, K., Kaus, S., Krause, C., Lepom, P., Schulz, C., Seiwert, M., and Seifert, B.
(2002): Umwelt Survey 1998, Bd. III: Human-Biomonitoring: Stoffgehalte in
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systems outside of buildings). Deutsches Institut für Normung e.V. Berlin,
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Drescher-Hartung, S., Fruth, F., and Kranert, M. (2001): Umverteilung von
Schwermetallen in der Umwelt durch Komposte im Vergleich zu anderen
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Annex 334
12 Annex
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Chemicals - Ammonia, Acids and Fertilisers, European Commission, Brussels,
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Mengen an Uran enthalten (Sometimes it contains more than listed - phosphate
fertilizers can contain high amounts of uranium): Forschungsergebnisse der
Bundesforschungsanstalt für Landwirtschaft (FAL), veröffentlicht am
15.02.2005 im Informationsdienst Wissenschaft, Bundesforschungsanstalt für
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of energy systems and the inclusion of LCA in Switzerland), ETH, Zürich,
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Arbeitspapier v5.7 (Quality guidelines ecoinvent 2000,working paper v5.7),
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Annex 335
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Fuchs, S., Scherer, U., Hillenbrand, T., Marscheider-Weidemann, F., Behrendt, H., and
Opitz, D. (2002): Schwermetalleinträge in die Oberflächengewässer
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landwirtschaftlichen Inputs im Pflanzenbau (Life cycle inventories of
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Annex 336
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Annex 337
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Annex 340
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