Document [original]

WATER USE IN LCA

Addressing water quality in water footprinting: current status,

methods and limitations

Natalia Mikosch

&Markus Berger

&Matthias Finkbeiner

Received: 17 September 2019 /Accepted: 1 November 2020

#The Author(s) 2020

Abstract

Purpose In contrast to water consumption, water pollution has gained less attention in water footprinting so far. Unlike water

scarcity impact assessment, on which a consensus has recently been achieved, there is no agreement on how to address water

quality deterioration in water footprinting. This paper provides an overview of existing water footprint methods to calculate

impacts associated with water pollution and discusses their strengths and limitations using an illustrative example.

Methods The methods are described and applied to a case study for the wastewater generated in textile processing. The results for

two scenarios with different water quality parameters are evaluated against each other and the water scarcity footprint (WSF).

Finally, methodological aspects, strengths and limitations of each method are analysed and discussed and recommendations for

the methods application are provided.

Results and discussion Two general impact assessment approaches exist to address water quality in water footprinting: the Water

Degradation Footprint (WDF) calculates the impacts associated with the propagation of released pollutants in the environment

and their uptake by the population and ecosystem, while the Water Availability Footprint (WAF) quantifies the impacts related to

the water deprivation, when polluted water cannot be used. Overall, seven methods to consider water quality in water footprinting

were identified, which rely upon one or a combination of WDF, WAF and WSF. Methodological scopes significantly vary

regarding the inventory requirements and provided results (a single-score or several impact categories). The case study demon-

strated that the methods provide conflicting results concerning which scenario is less harmful with regard to the water pollution.

Conclusions This paper provides a review of the water pollution assessment methods in water footprinting and analyses their

modelling choices and resulting effects on the WF. With regard to the identified inconsistencies, we reveal the urgent need for a

guidance for the methods application to provide robust results and allow a consistent evaluation of the water quality in water

footprinting.

Keywords Water footprint .Water quality .Water pollution .Life cycle impact assessment .Water deprivation footprint

1 Introduction

Global risks associated with water pollution have been em-

phasized in several studies, e.g. spread of waterborne diseases

(Schwarzenbach et al. 2010; UNEP/WHO 1996), ecosystem

deterioration (UN-Water 2011) and reduced drinking water

availability (FAO and IWMI 2017). The impacts on human

health resulting from the use of unsafe water rank 4th out of 19

major risk factors with the highest effects observed in low-

income counties (WHO 2009). Despite setting global targets

for achieving better water quality within the sustainable de-

velopment goal 6 (SDG #6) (UN 2015), water pollution due to

the agriculture, industrial production and domestic water use

is predicted to increase in the coming years (FAO and IWMI

2017;OECD2012).

Water footprint (WF) is a widely applied tool for the quan-

tification of the impacts associated with water use throughout

the life cycle of products. The general procedure for the WF

quantification is set up in ISO 14046:2014, which requires an

evaluation of both water quantity and quality. The standard

determines water quality as “physical (e.g. thermal), chemical

and biological characteristics of water with respect to its

Editor: Stephan Pfister

*Natalia Mikosch

natalia.mikosch@tu-berlin.de

Sustainable Engineering, Technische Universität Berlin, Straße des

17. Juni 135, 10623 Berlin, Germany

https://doi.org/10.1007/s11367-020-01838-1

/ Published online: 16 November 2020

The International Journal of Life Cycle Assessment (2021) 26:157–174

suitability for an intended use by humans or ecosystems”(ISO

2014).

Until now, most WF studies have been focussing only on

the quantitative aspects of the water use. Water quality, in

contrast, has gained less attention in water footprinting.

Lovarelli et al. (2016) conducted a review of 96 WF studies

and discovered that only 46% of them included a water pol-

lution assessment, while the rest considered only the water

consumption. Nevertheless, several industries cause signifi-

cant impacts on water resources mainly due to water pollution,

while having a relatively low water consumption. For exam-

ple, Chapagain et al. (2006) calculated a WF of around 800 l

per kg of cotton fabric resulting from the wastewater generat-

ed during the textile processing stage. At the same time, water

consumption in the textile processing was negligible.

Gerbens-Leenes et al. (2018) discovered that for steel produc-

tion, the impacts associated with water pollution are about 20

to 220 times higher than that of water consumption. Therefore,

the need to include water quality aspects in water footprinting

has been recently emphasized by several authors (Berger and

Finkbeiner 2013; Liu et al. 2017; Pradinaud et al. 2018;van

Vliet et al. 2017).

Currently, there is a lack of harmonization in the terminol-

ogy used across the publications in the field of water

footprinting. Therefore, for consistency reasons, the nomen-

clature proposed by ISO 14046:2014 is adopted throughout

this article.

Water degradation: negative change in water quality.

Water availability: extent to which humans and ecosys-

tems have sufficient water resources for their needs. If

water availability only considers water quantity, it is

called water scarcity.

Water scarcity: extent to which demand for water com-

pares to the replenishment of water in an area, e.g. a

drainage basin, without taking into account the water

quality (ISO 2014).

In water footprinting, two consequences of water pollution

are modelled: (1) reduced water availability—Water

Availability Footprint (WAF); and the intake of or contact to

the pollutants—Water Degradation Footprint (WDF). Both

WAF and WDF can be calculated at the midpoint or endpoint

levels for the areas of protection (AoP) human health, natural

environment and resources. The WAF quantifies the impacts

originating from the water deprivation, which occurs when

contaminated water does not fulfil the quality requirements

of a user and therefore cannot be used (ISO 2014). The im-

pacts of water deprivation depend on the affected user; for

example, malnutrition is caused by agricultural water depriva-

tion (Motoshita et al. 2014; Pfister et al. 2009) and infectious

diseases by domestic water deprivation (Boulay et al. 2011a;

Motoshita et al. 2011). The WDF quantifies the impacts

caused by the spreading of the contaminants in the environ-

ment (i.e. pollution of water bodies) and their effect on the

target (humans or ecosystem), e.g. toxicity or eutrophication

potential. Water Scarcity Footprint (WSF) quantifies the im-

pacts associated with reduced water availability resulting from

water consumption, i.e. it does not consider the changes in

water quality in the inventory analysis (Fig. 1). The term

WF is used throughout this article for all three terms WDF,

WAF and WSF.

The goal of this paper is to provide a review of existing

methods to consider water quality in water footprinting. For

this purpose, each method is described and applied to a case

study, which allows for comparing the results provided by

different methods. The paper is structured as follows: in

Section 2, the methods are introduced, Section 3describes

the scope of the case study and Section 4provides the results

including an overview of the methodological aspects of all

introduced methods and the case study results. The strengths

and shortcomings of the methods are discussed in Section 5.

In Section 6, the conclusion and outlook for future research

are provided.

2 Methods to address water quality in water

footprinting

As described in the introduction part of this paper, existing

methods to consider water quality in water footprinting rely

upon one or combine two overarching impact assessment ap-

proaches: WAF (impacts due to water deprivation) and WDF

(impacts due to the intake of or contact to the discharged

pollutants).

The WAF quantifies the amount of or impacts related to

water deprivation and is calculated by means of the distance-

to-target (DtT) or functionality approach. The DtT approach

relates the inventory (emissions to water) to a desired value,

usually a water quality threshold. Thus, a DtT value above 1

indicates an exceedance of water quality thresholds. Resulting

WAF is derived from the most penalizing pollutant, i.e. the

one with the greatest threshold exceedance. The functionality

approach classifies water into different categories depending

on for which users (e.g. domestic, agriculture) it is functional

based on its quality properties. If water of a lower class

is discharged compared to the withdrawn water, it is not

functional anymore for a specific user group. In this

case, water withdrawal is accounted for as being con-

sumed (i.e. unavailable) for these specific users

(UNESCO/WHO/UNEP 1996).

The WDF is calculated by means of the impact assessment

models for water quality degradation, which quantify the

propagation and transformation of discharged contaminants

in different environmental compartments (e.g. air, freshwater,

soil), their transport to and effect on the target organisms or

158 Int J Life Cycle Assess (2021) 26:157–174

ecosystems (Rosenbaum et al. 2018). Resulting impacts are

attributed to the contact to or intake of the pollutants and are

calculated usually for four impact categories: eutrophication,

acidification, aquatic ecotoxicity and human toxicity

(ISO 2014).

2.1 Methods based on the WAF

2.1.1 Grey water footprint (Hoekstra et al. 2011)

The grey water footprint (GWF) is a part of the water footprint

assessment proposed by Hoekstra et al. (2011) alongside the

blue and green WF.

The GWF relies upon the DtT approach and represents the

amount of water needed to dilute a contamination to a

certain quality threshold (Hoekstra et al. 2011). It is

calculated by dividing the pollutant load L(mass/time)

by the threshold value c

i,max

(mass/volume) or the dif-

ference between the threshold and natural concentration

i,nat

in cases when the pollutant occurs naturally in the

environment (see Eq. (1)).

GWF ¼Li

ci;max−ci;nat

ð1Þ

where iis the pollutant.

For the non-point sources of pollution (e.g. agriculture), the

pollutant load is calculated by multiplying the application rate

Appl (e.g. kg/ha) with the leaching rate α(%) of the corre-

sponding pollutant (Eq. (2)).

GWF ¼αi*Appli

ci;max−ci;nat

ð2Þ

When more than one water quality parameter is evaluated,

the GWF is calculated for each pollutant. Total GWF is then

determined based on the most penalizing pollutant, i.e. caus-

ing the highest GWF (Hoekstra et al. 2011). This implies that

calculated amount of dilution water would be sufficient to

assimilate all other discharged contaminants. The result is

expressed in l/kg of product and reported separately or

summed up with the blue and green WF.

2.1.2 Pollution-induced water scarcity (Zeng et al. 2013)

Zeng et al. (2013) proposed a method to integrate the GWF

) results into a water scarcity assessment for a river basin.

For this, the indices for water quantity scarcity I

blue

and pol-

lution induced water scarcity I

grey

in a basin bare summed up

to a water scarcity index I(dimensionless) (Eq. (3)).

Ib¼Iblue;bþIgrey;bð3Þ

The authors calculate the GWF according to the method

proposed by Hoekstra et al. (2011) as described in

Section 2.1. Then, I

grey

is calculated by dividing the GWF

by total renewable freshwater resources Q(m

)availablein

the river basin (Eq. (4)).

Igrey;b¼GWFb

ð4Þ

If I

grey

< 1, the total contamination can be assimilated in the

available freshwater resources.

blue

is calculated by dividing water withdrawal by total

renewable freshwater resources available in the river ba-

sin (Q

Fig. 1 Impact assessment in

water footprinting (based on

Boulay and Pfister 2013)

159Int J Life Cycle Assess (2021) 26:157–174

2.1.3 Water Impact Index (Bayart et al. 2014)

The Water Impact Index (WII) is proposed as a screening

assessment for the water use including both water consump-

tion and pollution (Bayart et al. 2014). WII is calculated as the

difference between the amounts of water withdrawn and

returned to a water body, both values multiplied by the corre-

sponding water scarcity and water quality indices (Eq. (5)).

The results are expressed in m

impact index equivalent

(Bayart et al. 2014).

WII ¼∑iWi*QW;i*WSIi



−∑jRj*QR;j*WSI j



ð5Þ

where W(m

) is the amount of water withdrawn from a water

body i,R(m

) is the amount of water returned to a water body

j,Q

and Q

(dimensionless) stand for the quality index of the

withdrawn and returned water, respectively, and WSI

(dimensionless) is the water scarcity index provided by the

model of Pfister et al. (2009).

The quality index follows the (inverse) DtT approach and

is calculated as the ratio of the reference concentration of the

pollutant (a threshold) to the actual concentration present in

the withdrawn and discharged water. In the same manner as

the GWF calculation, when several water quality parameters

are evaluated, the result is based on the most penalizing pol-

lutant. The water quality index lays between 0 and 1 and

reaches the maximum of 1 when the reference concentration

p, ref

is equal to or higher than the actual concentration c

the withdrawn or discharged water (Eq. (6)).

Q¼min 1; cp;ref

 ð6Þ

When the water quality index turns 1, it is assumed that the

entire volume of water returned to the water body remains

available for human use (irrespective of whether c

is equal

to or substantially lower than the threshold (see Eq. (5)).

Otherwise, discharged water becomes partly unavailable due

to insufficient quality and therefore is accounted for as being

consumed. In this case, the more c

exceeds c

p, ref

, the larger

the amount of water consumed.

2.1.4 The method of Boulay et al. (2011a,b)

The method proposed by Boulay et al. (2011a,b)isbasedon

the functionality approach, which implies that water quality

degradation can render water unavailable for certain users.

Since the water quality requirements of different users vary,

water polluted with certain contaminants can be unfit, for ex-

ample, for domestic users, but still be suitable for irrigation

purposes. To address this issue, the authors introduce eleven

water user types: three domestic, two agricultural, and one

industrial user, cooling, fisheries, hydropower, transport, and

recreation. For each user, the desired water quality is speci-

fied, which results in eight water categories from excellent to

unsuitable for both surface and groundwater. Furthermore,

rain is introduced as a separate water category being function-

al for all users. For each category, water quality thresholds are

set based on the national and international water quality guide-

lines. Overall, eleven general parameters (e.g. pH), 38 param-

eters for inorganics (salts and heavy metals) and 87 parameters

for organics are considered. Parameters relevant for the WDF

calculationhavetobeselecteddependingontheindustrybe-

ing assessed (Boulay et al. 2011a).

The method allows for quantifying the impacts on the mid-

point (Water Stress Indicator) and endpoint (diseases due to

lack of hygiene and/or malnutrition) level (Boulay et al.

2011b). The Water Stress Indicator is calculated as the differ-

ence between the volumes of withdrawn and discharged water

of a certain category, each multiplied by the corresponding

water stress index (Eq. (7)). The results are expressed in m

equivalent of water.

Water Stress Indicator ¼∑

αi*Vi;in



−∑

αi*Vi;out



ð7Þ

where αis the water stress index (dimensionless), and V

i,in

and V

i,out

) stand for the volume of withdrawn and

discharged water, respectively, and iis the water category.

The impacts on human health are measured in disability

adjusted life years (DALYs) and calculated similarly by mul-

tiplying the volume of withdrawn and discharged water by the

human health impact factors for the corresponding water cat-

egory. Resulting damage on human health depends on the

users who are deprived. For example, malnutrition arises from

water deprivation in agriculture and fisheries, while impacts

associated with lack of hygiene and sanitation result from

domestic water use deprivation.

2.2 Methods based on the WDF or combined

approaches

2.2.1 Impact assessment models for water quality

degradation

Impact assessment models for water quality degradation quan-

tify the WDF, i.e. the impacts resulting from the intake of or

contact to the pollutants. The calculation is based on the ISO

14040 and ISO 14044 standards (ISO 2006a,b) for the life

cycle assessment (LCA) and can be carried out at midpoint

and/or endpoint level. The method includes two steps:

assigning the elementary flows compiled in the inventory

(emissions to water) to the relevant impact categories

(classification) and multiplying them by the corresponding

characterization factors (CFs) (characterization) (Eq. (8)).

Ic¼∑pEp*CFpð8Þ

160 Int J Life Cycle Assess (2021) 26:157–174

where Eis the emission, pis the pollutant and cstands for the

impact category.

The CFs themselves are calculated using the environmental

mechanism models that describe the cause-effect chains be-

tween the inventory and resulting impacts on the environment

for all elementary flows that contribute to the selected impact

category (ISO 2006b). The cause-effect chains are modelled

based on the contaminants’propagation and transforma-

tion in the environment (fate), transport and contact to

targets including ecosystems and humans (exposure),

and negative impacts on the targets (effect), e.g. a dis-

ease (Rosenbaum et al. 2018).

In contrast to the LCA studies, where a comprehensive set

of environmental impacts are considered, for the WDF, only

the impact categories related to water quality, e.g. aquatic

eutrophication, acidification, aquatic ecotoxicity and human

toxicity, are accounted for (Boulay et al. 2015;ISO2014).

Aquatic eutrophication impacts are calculated based on the

waterborne emissions of nutrients (N and P) and organic com-

pounds. The cause-effect chain includes the emissions’trans-

port, increased nutrient concentration in water compartments

and subsequent increased algal growth and oxygen depletion

in lakes. Acidification impacts are related to the emissions of

acidifying compounds, e.g. sulphur oxides and ammonia. The

latter can be absorbed by water and form acids, which reach

water bodies via rainfall and leaching from soils (Rosenbaum

et al. 2018). Human and ecotoxicity are attributed to emissions

of heavy metals and organic compounds that have adverse

impacts (carcinogenic and non-carcinogenic) on human health

and ecosystems. The cause-effect chains model the contami-

nants’spread in the environment, contact to or intake by the

organism and resulting negative impact on the target

(Rosenbaum et al. 2008).

Calculating WDF at the endpoint level requires further

modelling steps, which cover the entire cause-effect chain

for the areas of protection human health, natural environment

and resources. An example of the cause-effect chain for the

AoP natural environment is species extinction and the subse-

quent damage to aquatic ecosystems due to eutrophication or

acidification. Human health damage can be attributed to

the intake of pollutants via food and drinking water

(impacts due to diseases). Providing results at the end-

point level allows the aggregatation of impacts from

both water consumption and pollution (i.e. WSF and

WDF) into one value, e.g. impact on the human health

expressed in DALY (disability adjusted life years)

(Huijbregts et al. 2017).

2.2.2 The method of Ridoutt and Pfister (2013)

Ridoutt and Pfister (2013) emphasize the need for a stand-

alone water footprint indicator considering both quantitative

and qualitative aspects of the water use, pointing out that the

GWF has not gained a broad acceptence. The authors propose

a method, which combines the WSF and WDF in the units

“water-equivalents”(H

O-eq.) and allows the results to be

summed up into a single score.

WDF is addressed by the authors as degradative water use

(DWU) and quantified using the Life Cycle Impact

Assessment (LCIA) model ReCiPE for each emission

(Huijbregts et al. 2017). The recommended impact categories

are freshwater eutrophication, freshwater ecotoxicity and hu-

man toxicity, which are quantified at the endpoint level for the

AoPs human health and natural environment. Normalization

and weighting are then conducted. During the normalization

step, the results in each category are related to a reference,

which are the European factors in the method of Ridoutt and

Pfister (2013). Weighting aggregates the results of different

impact categories into a single score through multiplication

with selected factors (value choice) (ISO 2006b). This pro-

vides a single value for all considered pollutants in the units

ReCiPe points. Finally, the result is divided by the WSF (also

expressed in ReCiPe points) of 1 l water consumption (CWU)

(global average consumption weighted value) (Eq. (9)). The

authors calculated 1.86E-06 ReCiPe points for the global av-

erage for 1 l CWU (based on Ridoutt and Pfister 2013).

DWU ¼ReCiPe points emissions to water from the product systemðÞ

1:86E−06 ReCiPe points global average for 1 litre CWUðÞ

ð9Þ

Then, the DWU is summed up with the impacts associated

with the CWU to obtain a single score. The CWU (H

O-eq.) is

calculated by multiplying water consumption with the water

scarcity index (WSI) of the corresponding river basin and

dividing the result by the global average WSI.

2.2.3 Pollution Water Indicator (Lovarelli et al. 2018)

Lovarelli et al. (2018) point out that the GWF method is in-

sufficient to reflect water contamination comprehensively, be-

cause it is based on only one (the most penalizing) pollutant.

Nevertheless, other contaminants may cause damage to hu-

man health and environment, even if present in low concen-

trations. To address this issue, the authors introduce the

Pollution Water Indicator (PWI), which combines the WDF

and WAF. The PWI calculates the GWF according to the

method of Hoekstra et al. (2011) and three impact categories

for water quality degradation freshwater eutrophication, ma-

rine eutrophication and freshwater ecotoxicity. The results of

the GWF and impact categories are plotted on the axes of a

spider diagram and connected to each other, with each axis

representing a vector of an environmental impact. To allow for

plotting, the results’unit and order of magnitude in different

impact categories are neglected (each vector varies between 0

and 1). The PWI (dimensionless) is calculated as the area of

the obtained rhombus, thus, the smaller the area of the rhom-

bus, the lower the resulting PWI.

161Int J Life Cycle Assess (2021) 26:157–174

3 Case study

This section describes the scope of the case study including

the inventory data and calculation procedure, while the results

are provided in Section 4.2. The case study is carried out for

textile production, which represents one of the most water

polluting industries worldwide (Ellen MacArthur Foundation

2017). The impact assessment is conducted for Pakistan, one

of the world’s major textile exporters that simultaneously suf-

fers from acute water shortage and pollution (Statista 2020;

WWF-Pakistan 2007).

The WAF and WDF are calculated for two generic scenar-

ios of the textile processing step. Only wastewater emissions

discharged directly into the environment from the textile pro-

duction are considered. Overall, eight water quality parame-

ters are included in the calculation (see Table 1). The results

are compared to the WSF, which is calculated based on the

water withdrawal and discharge data (Table 1). All results

refer to one ton of fabric.

The inventory for the first scenario represents the average

wastewater from ten textile processing plants and literature

data (InoCottonGROW 2019; Manzoor et al. 2006). In the

second scenario, considered wastewater parameters are equal

to the foundational thresholds of the Zero Discharge of

Hazardous Chemicals (ZDHC) standard, which is an interna-

tional guideline for the wastewater quality in the textile pro-

duction (Stichting ZDHC Foundation 2016) (see Table 1).

The volumes of withdrawn and discharged water are assumed

to remain the same in both scenarios. It should be noted that

the case study serves as a theoretical comparison of the WF

methods and not for deriving conclusions on the case.

In order to determine WAF and WDF according to the

methods described in Section 2, the following parameter set-

tings have been chosen: the thresholds applied for the calcu-

lation using the methods based on the DtT approach (GWF,

pollution induced water scarcity, WII) are derived from the

National Environmental Quality Standard (NEQS) of Pakistan

for industrial wastewater (PEPA 1999)(seeTable1). For the

calculation of the pollution induced water scarcity according

to Zeng et al. (2013), the data on total renewable freshwater

resources of Pakistan is used (FAO AQUASTAT 2019). For

the WII calculation, the WSI is set to 0.967 according to

Pfister et al. (2009). In the method of Boulay et al. (2011a,

b), the water stress index αis set to 1 and withdrawn ground-

water belongs to the category 1 (fits for all users) according to

Boulay et al. (2011a). The method is applied to calculate the

impacts on the midpoint (Water Stress Indicator) and endpoint

(human health damage) level.

For the WDF calculation, the impact categories freshwater

ecotoxicity (FETP), human toxicity (HTP) and marine eutro-

phication (ME) are calculated by means of the ReCiPe 2016

method (Huijbregts et al. 2017). At the endpoint level, the

damage on human health and ecosystems is calculated. For

the PWI calculation, the GWF and results for the impact cat-

egories FETP, HTP and ME are used. The impact category

HTP is used instead of freshwater eutrophication originally

proposed by Lovarelli et al. (2018), since none of the emis-

sions compiled in the inventory contributes to this impact

category. The possibilities and effects of applying different

impact categories are discussed in Section 5.7.

The WSF is calculated as the share of WAF related to water

consumption for the methods based on the WAF approach.

For the method of Ridoutt and Pfister (2013), the WSF is

determined as described in Section 2.2.2.The WSF is not

quantified for the impact assessment models for water quality

degradation at the midpoint level, since in this case, the results

of the WSF are not comparable to the WDF due to different

units. At the endpoint level, the WSF is calculated for the

human health impacts due to malnutrition using the character-

ization model of Motoshita et al. (2014). The WSF was not

Table 1 Case study inventory

data: water use and wastewater

quality parameters in textile

processing

Scenario 1 Scenario 2 NEQS

Water use data

Water withdrawal [l/kg] 128 128 -

Water discharge [l/kg] 101 101 -

Effluent data

Chemical oxygen demand (COD) [mg/l] 738 150 150

Biological oxygen demand (BOD

) [mg/l] 297 30 80

Total suspended solids (TSS) [mg/l] 152 50 200

Total dissolved solids [mg/l] 4980 3500

3500

Total nitrogen (Total N) [mg/l] 30 20 no threshold

Oil and grease [mg/l] 14 10 10

Total chromium [mg/l] 0.06 0.20 1

Copper [mg/l] 0.19 1 1

Based on NEQS, since no threshold is provided by ZDHC

162 Int J Life Cycle Assess (2021) 26:157–174

quantified for the PWI, since this method calculates WDF and

WAF as a single score (it is not possible to separate the shares

of water pollution and consumption).

4 Results

4.1 Methods overview

Four of seven introduced methods calculate the WAF: GWF,

pollution induced water scarcity and WII (all use the DtT

approach) and the method of Boulay et al. (2011a,b)thatis

based on the functionality approach. The WDF is calculated

by the impact assessment models for water quality

degradation and the method of Ridoutt and Pfister (2013)

(combined WDF and WSF). Lovarelli et al. (2018)combine

the WAF and WDF (see Fig. 2and Table 2).

All methods that calculate the WDF consider all pollutants

compiled in the inventory as long as they contribute to select-

ed impact categories. This means that for each relevant pol-

lutant, a cause-effect chain is modelled and the resulting

impact is quantified, irrespective of whether this pollutant is

emitted in a concentration below or above the water quality

threshold. The method of Boulay et al. (2011a,b)adoptsthe

functionality approach and therefore considers all pollutants

whose concentrations exceed the water quality thresholds

specified by the method for a total of 136 parameters.

Therefore, several contaminants influence the resulting water

functionality, while others are neglected if emitted in the con-

centrations lower than the thresholds. The methods based on

the DtT approach (GWF, pollution induced water scarcity and

WII) consider only one most penalizing pollutant, while the

impacts of other emissions are neglected, even if their concen-

trations are higher than the thresholds. None of the methods

based on the DtT approach specify the quality thresholds to be

applied in the calculation. The quality of withdrawn water is

considered in the method of Boulay et al. (2011a,b) and WII.

Except the GWF, which does not consider local conse-

quences of water pollution, all methods allow the user to con-

duct a regionalized impact assessment. This is achieved

through the application of country-specific water scarcity fac-

tors (in the case of WAF) or spatially explicit cause-effect

chains including fate and exposure modelling (WDF).

Three methods provide results at the endpoint level: the

method of Boulay et al. (2011a,b), impact assessment models

for water quality degradation and the method of Ridoutt and

Pfister (2013). While the method of Boulay et al. (2011a,b)

calculates impacts only for the AoP human health, the other

two methods provide results for all three AoPs human health,

natural environment and resources.

Six methods provide results as a stand-alone indicator as

WAF or a combination of two different approaches (WDF/

Fig. 2 Modelling steps of the WF methods addressing water quality. Each method is highlighted in a different colour. The results obtained by the

methods are highlighted in italics and with bold frames

163Int J Life Cycle Assess (2021) 26:157–174

Table 2 Summary of the methodological aspects of the WF methods addressing water quality

Type

of WF

Impact

assessment

approach

Considered

pollutants

Thresholds

provided?

Quality of

withdrawn

water considered?

Region-specific

impact assessment?

Covered AoPs

at the endpoint level

Stand-alone indicator Comparison to

the WSF possible?

GWF (Hoekstra

et al. 2011)

WAF DtT One, most penalizing No Yes No No Yes Yes (Blue Water

Footprint)

Pollution induced

water scarcity

(Zheng et al. 2013)

WAF DtT One, most penalizing No No Yes, based on

water scarcity

No Yes Yes (WSF is calculated

as WAF related to

only water

consumption)

WII (Bayart et al. 2014) WAF DtT One, most penalizing No Yes Yes, based on

water scarcity

No Yes Yes (WSF is calculated

as WAF related to

only water

consumption)

Method of Boulay

et al. (2011a,2011b)

WAF Functionality All for which

thresholds are

available and in

concentrations

higher than the

thresholds

Yes, for 136

water quality

parameters

Yes Yes, based on

water scarcity

Human health Yes Yes (WSF is calculated

as WAF related to

only water

consumption)

Impact assessment

models for water

quality degradation

WDF Environmental

mechanism

All which contribute

to selected impact

categories

Not applicable No Yes, based on

the cause-effect

chains

Human health,

Natural environment

Yes Yes

PWI (Lovarelli

et al. 2018)

Combines

WDF and

WAF

Environmental

mechanism

and DtT

All which contribute

to selected impact

categories and one

most penalizing for

the GWF calculation

No No Yes, based on

the cause-effect

chains

No Yes No

164 Int J Life Cycle Assess (2021) 26:157–174

WAF or WDF/WSF). The impact assessment models for wa-

ter quality degradation are the exception since they produce

results for different impact categories, which cannot be

summed up into a single score.

The GWF, pollution induced water scarcity, impact cate-

gories for water quality degradation at the endpoint level and

the method of Ridoutt and Pfister (2013) allow comparing the

WDF/WAF and WSF, since both results are provided in same

units (e.g. m

or DALY). The WAF calculated by means of

the WII and the method of Boulay et al. (2011a,b)canbe

compared to the WSF, when the water quality index is set to

one. In this case, the WAF results from the water consumption

only and thus is equal to WSF. Methodological aspects of all

methods are summarized in Table 2.

4.2 Case study results

The case study demonstrates that the WDF and WAF calcu-

lated by means of different methods significantly vary (see

Table 3and Fig. 3). Overall, a general trend can be observed:

the methods that rely upon the DtT approach yield a higher

WAF in the first scenario: GWF, pollution-induced water

scarcity and WII. This can be explained by the fact that while

in the first scenario, several pollutants are present in concen-

trations higher than the applied thresholds; in the second sce-

nario, no pollutants exceed these thresholds (see Table 1),

which makes the water pollution indices equal zero. For the

WII result in the second scenario, the impact of 25.7 m

im-

pact index eq. is driven by the water consumption only.

The method of Boulay et al. (2011a,b) provides the same

results for both scenarios, since discharged water is unusable

for all users in both cases (Fig. 3). This happens because in

both scenarios, the emissions significantly exceed drinking

water quality thresholds adopted by the method. Apart from

COD and TDS, which are not considered in the method, all

water quality parameters are accounted for by the WAF

calculation.

The impact assessment models for water quality degrada-

tion and method of Ridoutt and Pfister (2013)) provide a

higher WDF for the second scenario (except for the impact

category ME), which can be explained by increased chromi-

um and copper concentrations. The latter determine toxicity

related impact categories (HTP and FETP) and the results at

the endpoint level. Marine eutrophication is calculated based

on the nitrogen emissions (total-N), which are emitted in a

lower concentration in the second scenario and therefore lead

to a lower result in the impact category ME compared to the

first scenario. Other contaminants included in the inventory

are not reflected by the WDF since they do not contribute to

any impact category. Therefore, high concentrations of COD,

BOD, TDS and oil and grease (over the thresholds) in the first

scenario and their potential impacts are not reflected by the

WDF (see Table 3).

The PWI combines the impact categories’results with the

GWF and therefore considers COD (used for the GWF calcu-

lation) in addition to the emissions included in the calculation

of ME, MEPT and HTP. Calculated PWI is about 2.4-times

higher in the first scenario compared to the second one.

As described in Section 4.1, all methods except the impact

assessment models for water quality degradation and PWI

allow for comparing the WF results related to water pollution

(WDF/WAF related to water pollution) to WSF. In case of the

models based on the WAF approach, this can be achieved by

setting the quality indices to zero, which turns the WAF being

equal to WSF (i.e. WAF is caused by water consumption

only). Figure 4demonstrates the shares of water pollution

(WDF/WAF related to water pollution) and water consump-

tion (WSF/WAF related to water consumption) in the total

WF. The methods based on the DtT approach have a higher

contribution of the pollution related WAF to the total WF in

the first scenario: GWF (96%), pollution-induced water scar-

city (80%) and the WII (80%). In the second scenario, these

methods do not account for water pollution (because the water

quality thresholds are not exceeded); therefore, resulting WF

is attributed to WSF only. According to the method of Boulay

et al. (2011a,b), water pollution contributes to 86% of the total

WF in both scenarios. The same result is yielded because

water belongs to the category “unusable”in both scenarios

as described above. According to the impact assessment

models for water quality degradation at the endpoint level

(AoP human health), WDF accounts for only 0.01% of the

total WF in the first scenario and 0.04% in the second one. The

WDF calculated by means of the method of Ridoutt and

Pfister (2013) contributes to 7% and 30% of the total WF in

the first and second scenario, respectively (Fig. 4). Detailed

results for each method are provided in ESM S.1.

5 Discussion and recommendations

In the previous sections, the methods to consider water quality

in water footprinting were described and applied to a case

study. Each method is based on one or combines two impact

assessment approaches (DtT, functionality and pollution

based environmental mechanism), each addressing water pol-

lution in a different way. This leads to diverse and in some

cases conflicting results as demonstrated in the case study.

5.1 GWF

The GWF calculation is based on the DtT approach and there-

fore requires selecting water quality thresholds, which are then

set in relation to the inventory. This step is a value choice,

since there is no consensus on which thresholds should be

used in water footprinting. The water quality limits are usually

taken from national or international water quality standards or,

165Int J Life Cycle Assess (2021) 26:157–174

Table 3 Case study results: WDF and WAF for the scenario 1 and 2 and WSF. Pollutants exceeding the thresholds are highlighted in bold (in the second scenario, none of the pollutants is in the

concentration over the thresholds). It should be noted that the WDF considers only water pollution, while the WAF considers both water pollution and consumption; the WSF considers only water

consumption

Method Unit Scenario 1 Scenario 2 WSF

Pollutants [inventory] Pollutants [considered] WDF/

WAF

Pollutants [inventory] Pollutants [considered] WDF/WAF

GWF (Hoekstra et al. 2011)m

COD

BOD

TSS

TDS

Oil and grease

Total N

COD 499 COD

BOD

TSS

TDS

Oil and grease

Total N

none 0 21

Pollution induced water scarcity

(Zheng et al. 2013)

dimensionless COD 2.09E-09 none 0 0.5E-09

WII (Bayart et al. 2014)m

impact index eq. COD 103.8 none 25.7 25.7

Method of Boulay et al.

(2011a,2011b)

eq. of water (midpoint) BOD, TSS, oil and

grease, Total N,

Cr, Cu

128 BOD, TSS, oil and

grease, Total N,

Cr, Cu

128 21

DALYs (endpoint) 9.64E-03 9.64E-03 1.58E-03

Impact assessment models

for water quality degradation

Midpoint:

FETP (kg 1.4-dB-eq.)

Cr, Cu 3.31 Cr, Cu 17.10 -

Midpoint: HTP (kg 1.4-dB-eq.) Cr, Cu 0.228 Cr, Cu 0.860 -

Midpoint: ME (kg N-eq.) Total N 0.912 Total N 0.609 -

Endpoint: Human toxicity (DALY) Cr, Cu 5.20E-08 Cr, Cu 1.96E-07 -

Endpoint: Natural

environment (species.yr)

Cr, Cu, Total N 3.84E-09 Cr, Cu, Total N 1.29E-08 -

Method of Ridoutt and

Pfister (2013)

H2O-eq. Cr, Cu 2.72 Cr, Cu 14.62 34

PWI (Lovarelli et al. 2018) dimensionless COD, Cr, Cu, Total N 0.303 COD, Cr, Cu, Total N 0.126 -

166 Int J Life Cycle Assess (2021) 26:157–174

sometimes, industry specific guidelines. Since the thresholds

may significantly vary, resulting WAF depends on the stan-

dard selected for the calculation, as already addressed by

Berger and Finkbeiner (2010). For example, the GWF calcu-

lated in the case study for the first scenario is based on the

national water quality standards of Pakistan (NEQS) (PEPA

1999) and amounts to 499 m

. Applying more ambitious

aspirational thresholds (Stichting ZDHC Foundation 2016)

or the thresholds for drinking water used in the method of

Boulay et al. (2011a,b)(EEC1975) will lead to a two- and

twelvefold GWF increase, respectively (Fig. 5).

Overall, the GWF can be misinterpreted for endorsing the

potential to assimilate water pollution in the available fresh-

water resources (Wichelns 2015). Furthermore, the method

Fig. 3 Case study results. The

highest result of each method is

set to 100% and the lowest result

is set in relation to it

Fig. 4 Case study results.

Relative contribution of

WDF/WAF and WSF to the total

WF for the scenario 1 and 2

167Int J Life Cycle Assess (2021) 26:157–174

does not consider local water scarcity; thus, it may be unclear

whether the result is problematic or not (Wichelns 2017).

Nevertheless, GWF is widely applied to address water quality

in water footprinting and has been calculated for a broad range

of products from agricultural goods, e.g. maize (Chukalla

et al. 2018) and wheat (Chu et al. 2016) to wastewater treat-

ment plants (Morera et al. 2016).

5.2 Pollution-induced water scarcity

The pollution-induced water scarcity indicator proposed by

Zeng et al. (2013) advances the GWF method by introducing

spatial differentiation into the impact assessment, which is

achieved through setting GWF in relation to available fresh-

water resources in the study area. In the case study, calculated

pollution-induced water scarcity amounts to 2.0E-09 and,

thus, lays significantly below the threshold of 1 set by the

authors (see Section 2.2). The very low value is yielded be-

cause the inventory data for one ton of fabric is set in relation

to the total freshwater resources of Pakistan. The threshold of

1 will be not exceeded even if the data for local water re-

sources (e.g. on a province level) is used for the calculation.

Thus, the method is applicable only for large inventories, e.g.

water pollution of a whole city as demonstrated by Zeng et al.

(2013) using the example of Beijing, but does not fit for the

calculation on a product level.

5.3 WII

The WII adopts the DtT approach and provides results for both

water quantity and quality as a single score. As addressed

above, relating the inventory to a threshold leads to the fact

that only one most penalizing pollutant is considered in the

calculation, while other emissions are neglected. Same as by

the GWF calculation, applying different thresholds has an in-

fluence on the result. Furthermore, for the WII calculation,

both water consumption and pollution (as the water quality

index) are multiplied by the water scarcity factor of the

production region (see Eq. 5), which therefore strongly affects

the WII. For example, if the water scarcity factor for Brazil

(0.0659 according to Pfister et al. (2009)) is applied in the case

study, resulting WII amounts to only 8.4 m

impact index eq.,

which is more than twelve times lower than the result for

Pakistan obtained in the case study. This may lead to an un-

derestimation of the WAF or provide incentives for compa-

nies to locate polluting industries in water-rich countries in-

stead of reducing the emissions.

5.4 The method of Boulay et al. (2011a,b)

The method of Boulay et al. (2011a,b) is based on strict water

quality thresholds that allow a direct water use, e.g. for drink-

ing or irrigation. Therefore, in both scenarios calculated within

the case study, water is unsuitable for all users except transport

and hydropower, even though in the second scenario it com-

plies with the ZDHC foundational thresholds. These results

may lead to loss of incentives for reducing water pollution

from a company perspective, because the WAF remains same

even by achieving strict industry-specific quality thresholds.

The method specifies overall 136 water quality thresholds;

however, the authors let the user decide which parameters to

select for the WAF calculation depending on the data avail-

ability and industry being evaluated. This may provide mis-

leading results, particularly if relevant (present in high con-

centrations) substances are not considered. Therefore, deter-

mining a set of industry- or process-specific water quality

parameters that have to be included in the inventory analysis

could support practitioners in the method’sapplication.Inthe

same manner as the WII calculation, water scarcity factors are

directly applied in the calculation and therefore may signifi-

cantly influence the result (see Eq. 7).

5.5 Impact assessment models for water quality

degradation

Three impact categories were calculated in the case study at

the midpoint level: FETP, HTP and ME. While the results for

ME are higher in the first scenario, toxicity-related impacts

(FETP and HTP) are higher in the second scenario due to

increased chromium and copper concentrations. For the same

reason, higher WDF is yielded in the second scenario at the

endpoint level for both human health and natural environment

AoPs. Apart from nitrogen (as total N), chromium and copper,

other water quality parameters compiled in the inventory are

not considered, since none of them contributes to any impact

category. As a result, neglecting some pollutants may lead to

significant underestimation of the WDF. Particularly non-

biodegradable organics (reported as COD) may cause severe

damage to human health, e.g. due to intake of dyestuffs, res-

idues of the auxiliary materials and breakdown products in

case of the textile production (Roos et al. 2019). Since in the

Fig. 5 GWF calculated using different thresholds: NEQS (applied in the

case study), ZDHC foundational thresholds and drinking water thresholds

of the Council of the European Union

168 Int J Life Cycle Assess (2021) 26:157–174

first scenario the COD concentration is five times higher than

in the second one, a higher toxicity level can be anticipated as

well. Thus, completeness of the inventory (coverage of all

pollutants instead of providing a sum parameter as COD) is

essential to conduct a comprehensive impact assessment and

provide robust results; however, this might be challenging

with regard to the data collection.

At the endpoint level, the WDF (human health damage due

to toxicity) is four orders of magnitude lower than the WSF

(damage due to malnutrition) (see Fig. 4). To validate these

results, the WSF is calculated by means of two other models:

malnutrition impacts according to Pfister et al. (2009) and

health damage due to lack of water for domestic use and

resulting infectious diseases (Motoshita et al. 2011). The com-

parison is carried out for the second scenario of the case study.

The WDF contributes to only 0.1% of the total WF when

applying the method of Pfister et al. (2009) and over one third

of the total WF when using the method of Motoshita et al.

(2011)(Fig.6). These results demonstrate that the WDF cal-

culated by means of the impact assessment models for water

quality degradation at the endpoint level might be

underestimated compared to the WSF. At the same time, this

result can also be caused by the fact that several toxic pollut-

ants reported as COD were not considered in the WDF calcu-

lation as discussed above.

5.6 The method of Ridoutt and Pfister (2013)

The method of Ridoutt and Pfister (2013) calculates the “sin-

gle stand-alone weighted indicator”including both WDF and

WSF. The method is based on the impact assessment models

for water quality degradation and includes additional impact

assessment steps: normalization and weighting. These addi-

tional steps enable aggregation of the results into a single

score, but at the same time they may distort the results, e.g.

due to normalization with the European factors and weighting.

Due to the long calculation procedure (alone the DWU

calculation includes four steps), some information may be

lost, so that analysing the hotspots attributed to individual

emissions becomes problematic. Furthermore, the results pro-

vided by this method cannot be used for public reporting due

to the weighting step (ISO 2006a,b). Calculated DWU con-

tributes to only 7% in the first and 30% of the total WF in the

second scenario (see Fig. 4). In the same way as the toxicity-

related impact categories FETP and HTP, this can be ex-

plained by not individually considering organic pollutants in-

cluded in the sum parameter COD.

5.7 PWI

The PWI developed by Lovarelli et al. (2018)combinesthe

GWF and impact assessment models for water quality degra-

dation. The calculation may lead to loss of information or

distort the results since the units and orders of magnitude of

the individual results obtained in different impact categories

are disregarded for the PWI calculation. Furthermore, the au-

thors do not provide any guidance on how to plot different

results (GWF and impact categories) to calculate the surface

area of the rhombus. Therefore, the resulting PWI depends on

the way the chart is built. For example, in the case study, the

GWF result is plotted on the same diagonal with the impact

category FETP. In this case, the PWI obtained in the first

scenario is about 2.4-times higher than the PWI of the second

scenario (see Fig. 7a, b). If plotted in a different way (GWF

and ME on the same diagonal), the PWI calculated for the

second scenario is around 1.7-times higher compared to the

first one (Fig. 7c, d). This inconsistency may lead to misinter-

pretation or misuse of the results since, as demonstrated, dif-

ferent plotting may lead to controversial results regarding

which scenario is less detrimental with regard to water

pollution.

For the case study calculation, the impact category HTP

was applied instead of freshwater eutrophication as proposed

by the authors, since none of the emissions compiled in the

inventory contribute to this impact category. Lovarelli et al.

(2018) do not specify whether the impact categories can be

substituted, e.g. depending on the inventory or focus on spe-

cific environmental impacts. Providing a guidance for

selecting the impact categories and plotting results could sup-

port practitioners and facilitate the application of the method.

5.8 Applicability and limitations

When selecting a method to calculate the impacts associated

with the water pollution, practitioners first need to decide be-

tween the WAF and WDF, as these two impact assessment

approaches differ in the way they address environmental is-

sues. The WAF implies that if the water quality exceeds de-

fined thresholds, it will not be available for users whose water

quality requirements are not met. In this case, the damage

Fig. 6 Relative contribution of WDF and WSF to the total WF:

comparison of the case study results (based on Motoshita et al. (2014))

to the method of Pfister et al. (2009)andMotoshitaetal.(2011)

169Int J Life Cycle Assess (2021) 26:157–174

associated with water pollution results from the lack of water.

Several WF models exist that calculate the health damage

resulting from the water deprivation, e.g. due to malnutrition

(for agricultural water deprivation) (Motoshita et al. 2014;

Pfister et al. 2009) and infectious diseases (for domestic water

deprivation) (Boulay et al. 2011a; Motoshita et al. 2011).

However, as addressed by Pradinaud et al. (2018), water is

often used despite the contamination. On the one hand, the

presence of a contaminant may be not visible for the users, e.g.

if it does not influence such water properties as colour, odour

or taste. This applies to many contaminants, e.g. pesticides,

pharmaceuticals and some heavy metals. On the other hand,

even when being aware of the water contamination, some

users would rather withdraw polluted water than suffer from

water scarcity. This applies to many regions in the world,

where people have to use polluted water (e.g. for irrigation)

due to either inexistence or lack of access to a proper water

supply (UN-Water 2019). In this case, water pollution will

result in impacts associated with the contaminants taken up

by the population rather than lack of water due to not using

polluted water.

The quality of withdrawn water is considered in the GWF,

WII calculation and the method of Boulay et al. (2011a,b).

This allows for considering the initial pollution of the input

water and is important in particular for the regions with an

overall high pollution of the freshwater resources. For exam-

ple, withdrawing water of a lower quality will result in a lower

WAF compared to the case when unpolluted water (i.e. with a

high water quality class) is discharged. This can even lead to a

negative WAF in an extreme case when discharged water has

a higher class than the withdrawn, which means that the eval-

uated process is providing additional water for the users.

However, the inventory data for the withdrawn water is not

reported in LCI databases and difficult to obtain, because the

quality of the withdrawn water used in the industrial produc-

tion is usually not measured apart from the quality parameters

that are essential for the production process (e.g. hardness).

Thus, currently, considering the quality of the withdrawn wa-

ter is challenging and can be conducted either by gathering

primary data or using highly aggregated datasets on a country

or regional level.

All methods that rely upon the DtT approach (GWF,

pollution-induced water scarcity and WII) perform low with

regard to the completeness of scope (pollutant coverage),

since the results are calculated based on one pollutant.

However, most penalizing pollutant might be not the most

harmful (Lovarelli et al. 2018). As demonstrated in the case

study, heavy metals discharged with the wastewater are not

addressed when applying the DtT approach, even with in-

creased (but still below the thresholds) concentrations in the

second scenario. Thus, neglecting some pollutants may lead to

an underestimation of the impacts associated with water pol-

lution. The method of Boulay et al. (2011a,b)andthe

methods that calculate the WDF have a much broader scope

Fig. 7 Different plotting of the

GWF and impact categories and

its effect on the PWI

170 Int J Life Cycle Assess (2021) 26:157–174

with regard to considered pollutants, which however might be

limited by data availability, i.e. emissions included in the in-

ventory. Particularly for the impact assessment models for

water quality degradation, considering individual substances

instead of the sum parameters (e.g. COD, TDS, TSS) is cru-

cial, but might be very time-consuming and challenging due

to low data availability and high costs for carrying out a waste-

water analysis. Furthermore, all water-related impact catego-

ries have to be considered to ensure full coverage of the water

pollution-related impacts.

Apart from the GWF, all methods allow the impact assess-

ment to be conducted considering regional context by means

of country-specific water scarcity factors or regionalized

cause-effect chains. As demonstrated in Section 5.3,applying

water scarcity factors significantly influences the results,

which should be taken into account when using the WII or

method of Boulay et al. (2011a,b).

While the methods based on the WAF approach consider

both water consumption and pollution and provide result as a

single-score, WDF-based models, in contrast, require addi-

tional calculation of the WSF to consider the impacts related

to water consumption. Therefore, a WAF assessment may be

more straightforward (i.e. less calculation effort) and benefi-

cial when communicating the results to stakeholders, while

WDF allows for a more comprehensive impact assessment.

When applying the WAF, it should be noted that the water

consumption is already considered in the result to avoid dou-

ble-counting, e.g. by calculating the WSF and adding it to the

WAF results. Methods’limitations are summarized in

Table 4.

5.9 Recommendations

As described in the previous section, when choosing a method

for the quantification of the impacts resulting from water pol-

lution, first, the underlying impact assessment approach

(WDF or WAF) needs to be selected. This decision needs to

be made by the practitioners, since currently, there is no guid-

ance that specifies when the one or another approach should

be applied. Therefore, the choice between these two ap-

proaches needs to be made by analysing which impact path-

way (water deprivation or intake of the contaminants) is the

most probable for the study area. For example, considering

increasing water scarcity in many parts of the world, the usage

of the wastewater for irrigation and resulting impacts on hu-

man health is more likely to occur than agricultural water

deprivation due to water pollution (UN-Water 2017,2020).

We propose to distinguish between three general situa-

tions with regard to possible impact pathways and avail-

ability of the inventory data for water pollution. These

archetypal situations can serve as a guidance for the

practitioners to select the most appropriate WF method

for their study:

–Most probable impact pathway is the intake of or contact

to the emitted contaminants; a comprehensive inventory

is available (i.e. the inventory data includes the emissions

of all contaminants relevant for the study); the inventory

can be classified to an impact category (e.g. acidification,

eutrophication etc.). We recommend to quantify WDF by

means of the impact assessment models for water quality

degradation. This allows to determine the full range of the

impacts associated with water pollution and to identify

potential trade-offs between different impact categories

(e.g. eutrophication vs. human toxicity as demonstrated

in the case study)

–Most probable impact pathway is water deprivation due

to water pollution; a comprehensive inventory is avail-

able. We recommend to quantify WAF by means of the

method of Boulay et al. (2011a,b), which allows to con-

sider all pollutants included in the inventory (in contrast

to other WAF methods, which are based on the DtT ap-

proach and therefore consider only one most penalizing

pollutant)

–Most probable impact pathway is water deprivation due

to water pollution; only one or few water quality param-

eters are available in the inventory. We recommend to

quantify WAF by means of the WII. It should be taken

into account that the WF result might be significantly

underestimated, since only one pollutant is considered.

We do not recommend using the GWF, since the method

does not allow to conduct a regionalized impact assess-

ment. We also do not recommend using the pollution-

induced water scarcity method, since as demonstrated in

the case study, it does not fit well for the impact assess-

ment on a product level, which is typical for WF studies.

None of the methods can be recommended for the situation

when the impacts originate from the intake of the contami-

nants, but only one or few water quality parameters are avail-

able in the inventory. This emphasizes the importance of the

availability of inventory data. As addressed above, we do not

recommend the application of GWF and the pollution-induced

water scarcity method. We also do not recommend using the

method of Ridoutt and Pfister (2013) due to two reasons: (1)

the normalization and weighting steps may partly distort the

results and (2) due to weighting, the results cannot be used for

external communication. We also do not recommend to use

the PWI, because as demonstrated in Section 5.7, the method

is currently not robust enough.

The selection of a method can also be made depending on

whether the results need to be provided at the midpoint or

endpoint level. The latter can be quantified by means of im-

pact assessment models for water quality degradation and the

method of Boulay et al. (2011a,b).

As addressed above, a comprehensive inventory is essen-

tial to provide reliable results, in particular when using the

171Int J Life Cycle Assess (2021) 26:157–174

methods based on the functionality approach and impact as-

sessment models for water quality degradation. Nevertheless,

data availability remains a significant challenge for LCA prac-

titioners. Therefore, initiatives that provide quality assess-

ments of global water resources and process-specific waste-

water quality datasets play a significant role in enhancing

water quality evaluation in water footprinting. Furthermore,

determining industry-specific water quality parameters that

have to be considered in the inventory analysis can signifi-

cantly support practitioners in the application of the methods

by (1) ensuring that all relevant pollutants are considered and

(2) reducing the number of water quality parameters that need

to be determined to the required ones. Providing a set of con-

sistent water quality thresholds can increase the transparency

and comparability of the results provided by the methods

based on the DtT approach. Finally, further impact assessment

models, e.g. for the pathogen pollution, need to be developed

and included into the scope of water footprinting to address all

impacts associated with the water quality deterioration.

5.10 Providing a comprehensive WF assessment

As stated in ISO 14046 (ISO 2014), the term water footprint

can only be used if a comprehensive WF assessment is con-

ducted; otherwise, a qualifier (e.g. WSF or “water eutrophica-

tion footprint”) needs to be applied. At the same time, the

standard allows for selecting the environmental impacts to

be considered (e.g. water scarcity and/or water degradation)

depending on the goal and scope of the study. As described in

the introduction part of this article, currently, less than 50% of

WF studies consider water quality (Lovarelli et al. 2016). This

small share seems to be inappropriate considering the fact,

that, on the one hand, there is hardly an industry that does

not contribute to water pollution, and on the other hand,

80% of globally released wastewater is untreated (UN-Water

2020). Therefore, including both water quantity and quality in

the WF assessment is the only proper way to address impacts

related to the water use comprehensively. To achieve this

goal, the WF can be determined either as the combination of

WDF and WSF or as WAF (considering both water and emis-

sion flows in the inventory analysis). Combining WDF and

WAF (e.g. as it is done in the PWI) may lead to the overesti-

mation of the impacts due to the double-counting of the im-

pacts as it is described by Berger and Finkbeiner (2013). Still,

there might be cases when the application of both WDF and

WAF is reasonable. For example, ecosystems and agricultural

water users might be affected by the pollutants due to appli-

cation of untreated wastewater (WDF), while domestic water

users will suffer from water deprivation (WAF). In this case,

the WDF and WAF should be applied based on the shares of

affected water users, while the difference between the origin

of the impacts (intake of the pollutants vs. lack of water) needs

to be taken into account when interpreting the result.

Table 4 Summary of the methodological aspects and limitations

Method Type of WF Inventory requirements Completeness of scope

(pollutant coverage)

Regionalization Main methodological aspects

influencing the result

GWF (Hoekstra et al. 2011) WAF Emissions of pollutants to water Low Not regionalized Selected threshold

Pollution induced water scarcity

(Zeng et al. 2013)

WAF Emissions of pollutants to water Low Yes, based on water scarcity Selected threshold

Water resources in the study area

WII (Bayart et al. 2014) WAF Emissions of pollutants to

water and water flows

Low Yes, based on water scarcity Selected threshold

Water scarcity factor

Method of Boulay et al.

(2011a,2011b)

WAF Emissions of pollutants to

water and water flows

High Yes, based on water scarcity Pollutants included in the inventory

Water scarcity factor

Impact assessment models for

water quality degradation

WDF Emissions of pollutants to water High Yes, based on cause-effect chains Pollutants included in the inventory

Selected impact categories

Method of Ridoutt and Pfister (2013) Combines WDF and WSF Emissions of pollutants to

water and water flows

High Yes, based on cause-effect chains Pollutants included in the inventory

PWI (Lovarelli et al. 2018) Combines WDF and WAF Emissions of pollutants towater High Yes,basedoncause-effectchains Pollutants included in the inventory

Selected impact categories

Results’plotting on the graph

172 Int J Life Cycle Assess (2021) 26:157–174

5.11 Limitations of the case study

The case study serves for comparing the WF methods and is not

intended to be used as a representative example of the textile

production processes. Only eight water quality parameters were

included in the case study. As discussed above, considering all

relevant contaminants is crucial to provide robust results

particularly when using the method of Boulay et al. (2011a,b)

and methods calculating the WDF. However, comprehensive

wastewater quality datasets are usually not available in literature

andaredifficulttoobtainsincewastewater quality analysis is

time-consuming and costly, particularly when measuring addi-

tional parameters apart from the general ones as COD and TSS.

This limitation regarding the inventory completeness applies for

the WDF and WAF calculation in general irrespective of the

industry being evaluated.

6 Conclusions

This paper provides an overview on existing methods to cal-

culate impacts associated with water pollution and an analysis

of the methodological aspects, strengths and shortcomings of

each method. Decomposing modelled impact pathways and

highlighting their methodological choices can support practi-

tioners in choosing an appropriate way to implement a water

quality assessment according to their goals and data availabil-

ity when conducting a water footprint study.

It is an alarming fact that different methods provide con-

flicting results for the two evaluated scenarios. This can lead

to wrong interpretation of the results regarding which scenario

is more beneficial or misuse of the results for communication

purposes. Comparing WDF and WAF to WSF calculated in

the case study also demonstrated a large difference between

the results provided by different methods, which should be

investigated in future research. Therefore, a clear guidance

for the application of the methods to account for water pollu-

tion in water footprinting, particularly with regard to the in-

ventory requirements and applied thresholds, is essential to

provide robust results and facilitate method application for

decision-support in politics and industry.

Supplementary Information The online version contains supplementary

material available at https://doi.org/10.1007/s11367-020-01838-1.

Acknowledgements Open Access funding enabled and organized by

Projekt DEAL.

Funding This study was conducted within the research project

InoCottonGROW funded by the German Federal Ministry of Education

and Research (BMBF), funding measure “Water as a global resource

(GRoW)”, grant number 02WGR1422B. The authors would like to thank

the Federal Ministry of Education and Research for the financial support.

Open Access This article is licensed under a Creative Commons

Attribution 4.0 International License, which permits use, sharing,

adaptation, distribution and reproduction in any medium or format, as

long as you give appropriate credit to the original author(s) and the

source, provide a link to the Creative Commons licence, and indicate if

changes were made. The images or other third party material in this article

are included in the article's Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not included in the

article's Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will

need to obtain permission directly from the copyright holder. To view a

copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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