Citation: Kiwfo, K.; Grudpan, K.;
Held, A.; Frenzel, W.
Smartphone-Based Color Evaluation
of Passive Samplers for Gases: A
Review. Atmosphere 2024,15, 451.
https://doi.org/10.3390/
atmos15040451
Academic Editors: Adrianos Retalis,
Vasiliki Assimakopoulos and
Kyriaki-Maria Fameli
Received: 27 February 2024
Revised: 26 March 2024
Accepted: 28 March 2024
Published: 4 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
atmosphere
Review
Smartphone-Based Color Evaluation of Passive Samplers for
Gases: A Review
Kanokwan Kiwfo 1,2, Kate Grudpan 1,3 , Andreas Held 2and Wolfgang Frenzel 2,*
1Center of Excellence for Innovation in Analytical Science and Technology for Biodiversity-Based Economic
and Society (I-ANALYS-T_B.BES-CMU), Chiang Mai University, Chiang Mai 50200, Thailand;
2Environmental Chemistry and Air Research, Institute of Environmental Technology, Technische Universität
3Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
*Correspondence: wolfgang.fr[email protected]
Abstract: The application of smartphone-based color evaluation of passive sampling devices for gases
has only been sparsely reported. The present review aims to compile available publications with
respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical
procedures for color detection and quantification (including calibration processes), and their application
to different target gases. The performance of the methods—whenever available—is presented regarding
the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color
evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease
of use will be outlined in view of the potential employment in education and citizen science projects. In
one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in
order to clarify the distinction of information obtained from the uptake of the passive samplers between
gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas
sensors are included in the review when applied in passive sampling configurations and evaluation is
performed with smartphone-based color evaluation. Differences in the analytical procedures employed
after the passive sampling step and prior to the detection of the colored compounds are also presented.
Keywords: passive gas sampling; diffusive sampling; ambient air measurements; immission rate;
smartphone-based color evaluation; RGB color space; colorimetric gas sensors
1. Introduction
Passive sampling of gases is a frequently applied method for the determination of
a variety of gases. The methodology and applications are the subject matter of many
reviews [
1
–
7
], innumerous original papers (a recent search in the ScifinderSearch database
revealed about 3600 hits for the combination of the terms “Passive/Diffusive sampling”,
“Gas analysis”, and “Air pollution” in the past four decades) as well as reports by gov-
ernmental institutions [8–10] and non-governmental organizations [11–13]. The attractive
features of passive gas sampling devices for gas analyses are that they do not need external
power, operate silently, are small in size, are lightweight, and are cheap to construct. They
are also readily deployable at almost any place by nonspecialists. Simultaneous gas sam-
pling in an affordable way at many sites is hence possible, providing high spatial resolution.
One limitation of many passive sampling methods can be seen in the need for considerably
long sampling times (typically days to months), providing only time-weighted integrated
values. Nonetheless, useful and frequent applications can be found in large-scale mapping,
long-term personal exposure estimates, and identification of air pollution hot spots [
14
–
19
].
However, many examples of passive sampling also exist for short-term measurements of
time-weighted exposure to gaseous contaminants applied to personal monitoring [20–22].
Atmosphere 2024,15, 451. https://doi.org/10.3390/atmos15040451 https://www.mdpi.com/journal/atmosphere
Atmosphere 2024,15, 451 2 of 21
In this context, the term passive dosimeters (rather than passive samplers) is often used to
indicate that a certain dose of contaminants is inhaled when air is aspirated.
Historically, passive gas sampling can be traced back to 1853 when the Swiss chemist
Schönbein used filter papers impregnated with potassium iodide to measure ground-level
ozone (cited in [
23
]). At the beginning of the 20th century, SO
2
was detected using the dark-
ening of a freely hanging lead acetate paper [
24
]. Visual inspection provided qualitative and,
to a certain degree (using a color scale for comparison), even semi-quantitative information.
Further early examples of passive gas sampling are the Liesegang–Glockenverfahren [
25
]
and the SO
2
cylinder (also called wick and candle) ([
26
] and references therein). In these
methods, SO
2
is trapped on solid lead peroxide, and the sulfate formed is subsequently
determined by means of gravimetry, titrimetry, or turbidimetry. All these examples rely
on sampling of the analyte gases by molecular diffusion to the absorbing surfaces. Hence,
the term passive sampling (no pump required for active air transport) or synonymously
diffusive sampling is appropriate and has been established for distinction from active
sampling since. The diffusional transport is governed by the concentration gradient be-
tween the surrounding atmosphere of the sampler and the gas-absorbing surface. Other
factors influencing the diffusion and the uptake rate of the passive sampler are ambient
air temperature and the thickness of the diffusion layer in the vicinity of the absorbing
surface, respectively. The diffusion layer thickness changes with variable air flow velocities
along the sampler, i.e., increasing air flow enhances the transfer of the gaseous compound
to the absorber and leads to a higher uptake of the target compound for a given target gas
concentration at a fixed sampling time. Since in practical applications of passive sampling,
the airflow can generally not be controlled and in outdoor measurements depends on the
wind speed, the relation between gas-phase concentrations and the uptake of the analyte
gas may not be well defined. What is actually measured in such cases is the deposition-
velocity-sensitive accumulated deposition flux, also termed the immission rate (see below),
of the target gas. The dimension of this value is deposited mass per time and area rather
than mass per volume for gas-phase concentrations. This issue was, for instance, discussed
in the context of studies about the deposition of corrosive gases on metallic surfaces [
27
].
Also, a German standard method exists determining the immission rates of gases using a
continuously recirculating absorber solution [
28
]. Obviously, the accumulated diffusion
flux (or immission rate) is connected to the gas-phase concentration, but it is not the same.
A breakthrough in passive sampling can be seen in a paper published by Palmes et al.
in 1976 [
29
]. They introduced a simple tubular sampling device with a chemical trap for
the analyte gas at the bottom of the tube. The tube-type configuration was designed in a
way that the diffusion path length becomes defined by the length of the tube. If the length
is sufficiently large (precisely the aspect, i.e., the ratio between length and inner diameter
of the tube is higher than about 5–8), wind shortening effects caused by eddies entering
the tubes become insignificant [
2
,
7
]. With the additional feature of the sampler that the
absorbing surface acts as a perfect sink for the analyte gas, i.e., the gas concentration at the
surface gets and remains zero during the sampling period, the gas-phase concentration
can be derived from Fick’s first law of diffusion [
1
,
2
,
7
]. Hence, gas-phase calibration
of the samplers for analyte quantification is not required. At about the same time as
propagating the tubular design of passive samplers, badge-type configurations with a short
distance between the open side and the analyte collecting surface were introduced [
30
–
34
].
Early applications of the badge-type samplers were personal exposure measurements in
workplace atmospheres [
1
,
33
,
34
], but later on, and until today, badge-type samplers are also
used for ambient air measurements [
35
–
37
]. The short diffusion path length of badge-type
samplers causes increased sampling rates, but wind shortening due to turbulent transfer of
the analyte gas caused by wind incursion into the open-end of the sampler is more likely
to occur. To counteract wind effects, the open side of the badge-type passive samplers is
generally either covered by a gas-permeable polymeric membrane (so that gas transport is
no longer governed by diffusion rather than permeation) or porous plugs are inserted into
the short diffusion path to act as a turbulence barrier [
2
,
4
,
5
,
38
]. In many of the published
Atmosphere 2024,15, 451 3 of 21
papers and also in commercially available passive samplers the one or the other means
has been implemented. These measures, however, require evaluating the uptake rate of
such samplers experimentally for proper quantification of target gas concentrations using
calibration with standard gases and/or comparison with established reference methods.
Colorimetric gas detectors and gas sensors have been widely applied in many fields,
such as industrial hygiene and process control, as well as ambient and indoor air quality
monitoring (e.g., [
39
–
42
]). The basic concept behind this is to measure the color change of a
sensing zone that occurs in the presence of a target analyte gas. The gas transport to the
sensing zone can occur actively by using a pump. This has been realized in the majority of
applications since it provides higher sensitivity and better precision compared to passive
sampling [
43
]. However, exposure of colorimetric gas sensors to the sample gas without
forced convection in a passive sampling mode has all the advantages mentioned above, and
a number of publications make use of these features (e.g., Chapter 4 of [43] and [44,45]).
The configurations of colorimetric gas sensors are diverse. They encompass flat sheets
of different base materials (often paper or polymer membranes), tubes filled with the
sensing material, and badges with inner support onto which the color-forming reagents
are immobilized [
39
,
40
,
42
,
43
]. In all these sensors, the gaseous compounds of interest are
absorbed or adsorbed at the collection zone, where a selective chemical reaction takes place,
leading to a colorization; in cases where the reagent is colored, it gets decolorized. The color
change and/or the change of color intensity are used as analytical information. As outlined
above, for passive samplers, the diffusional transport to the sensing zone can or cannot
occur through a defined air gap by permeation through a suitable membrane. Hence, the
distinction between colorimetric gas sensors delivering concentration information and
results about the accumulated deposition flux or immission rate can—or should—be made.
It appears worth mentioning already at this stage that in publications of passive sampling
methodologies and colorimetric gas sensors, this fact has—to the best knowledge of the
authors—never been considered (see below).
One pre-requisite for quantifying gas-phase concentrations using passive sampling is
the quantification of the collected amount of the target gas or a derivative. To this end, a
variety of procedures and detection methods have been used. A distinction can be made
between procedures where (i) determination occurs within the passive sampler, (ii) the
collected derivative is extracted after the sampling step by a suitable solvent followed
by determination in the liquid phase, and (iii) thermodesorption of volatile compounds
and their transfer to, e.g., gas chromatography [
4
,
5
,
7
]. In the two former procedures the
collected compounds/derivatives are often quantified by spectrophotometric methods. In
cases where the absorber is or contains a color-forming reagent, direct in situ measurement
can be performed, and conceptionally, this is the preferential option [
46
]. However, more
frequently, the colored compounds are extracted, followed by spectrophotometric detection
in the extract. If the collected derivative is colorless the color reaction can either be initiated
within the sampler body before or in a reaction vessel after liquid extraction.
Regarding color detection in general, and also for evaluation of passive samplers
a large variety of methodological and instrumental configurations exist. Qualitative in-
formation can be obtained by visual inspection with the naked eye. Extension of visual
detection using color cards/charts as a reference provides instrument-free semi-quantitative
measurements. Quite a number of passive samplers (often also termed passive dosime-
ters, see above) in tube and badge configurations for gas analysis—many of them being
commercially available—use this approach (e.g., [
47
–
53
]). For true quantitative detection,
spectrophotometric and, more rarely, reflectometric detection is applied either in situ or
after (liquid) extraction [9,10,46,52,53].
An attractive alternative to common optical detection in general is the application of
digital color measurements (a common term for that is digital image colorimetry, DIC).
DIC refers to a colorimetric analysis method based on digitizing images collected by some
image acquisition tools such as mobile phones, digital cameras, webcams, scanners, and
dedicated color reading devices [
54
]. Several techniques (i.e., absorbance and reflection
Atmosphere 2024,15, 451 4 of 21
measurements, fluorometric detection) of digital color measurements (often using different
terminology even when the underlying principles are the same or very similar) for analytical
chemical applications have been introduced and presented in numerous reviews [
55
–
59
].
The application of digital color imaging for analytical chemical purposes has undergone
explosive development in recent years. The main analytical applications are related to
the medical, biochemical, nutritional, and environmental fields [
55
,
56
,
59
]. Compared with
other means of digital imaging (i.e., scanners, digital cameras, webcams, color readers
with RGB sensors), smartphones are widely used as image acquisition tools in DIC due
to their ubiquitous presence, convenient use, remarkable and continuing improvement
of the camera functions, and the ability to adopt mobile applications for data evaluation
and transmission [
58
–
60
]. Not least because of these attractive features, we have limited
our review to applications of smartphone-based color evaluation of passive samplers for
gas analysis.
Color parameters generally evaluated are RGB, HSV, Greyscale color systems, and
Euclidean distance. Some smartphone applications (e.g., Photometrix, Color Grab) provide
retrieving the respective color values, but more common and flexible is to export the images
and to employ programs like ImageJ, Photoshop, GIMP, etc. for evaluation.
The use of smartphones for color detection in the evaluation of passive sampling
of gases has been presented in quite a number of papers. However, the term passive
sampling is only rarely used in the title (and often also not throughout the entire text)
of relevant publications. A closer inspection of all papers where gas sampling was per-
formed passively by molecular diffusion followed by smartphone-based color detection
and signal evaluation revealed that there are severe differences regarding the design of the
sampling devices and the way how color detection and evaluation have been performed. A
conceptual difference also exists between samplers that are suitable for gas concentration
measurements and those providing information about the accumulated deposition flux of
the gaseous compounds of interest. As mentioned already above, the former type requires
a defined and sufficiently long diffusion path or adopts turbulence barriers in front of
the absorbing layer to avoid wind-shortening effects. Within this review, we will name
passive samplers of this configuration type-1; the others with uncontrolled (air velocity
dependent) diffusion path lengths are accordingly termed type-2 passive samplers. The
design, analytical specifications, and applications of these two types will be separately
presented in the body of this paper. Figure 1provides a general scheme of the individual
steps of the methodology, starting with (i) passive sampling via (ii) color formation (or de-
colorization) reactions, (iii) smartphone photographing, (iv) color retrieval, and evaluation,
to (v) calculation of results. Differences referring to the type and geometry of the passive
samplers, illumination conditions of photographing, means of image processing, and the
kind of quantitative information eventually obtained are also implemented in Figure 1in a
somewhat generalized manner.
The aim of this review is to present for the first time a compilation of the still few
publications devoted to smartphone-based color evaluation of passive samplers for gases.
Though the underlying concept is rather simple, the instrumental and procedural details
of the published work differ in many respects. Also, various gases have been determined
using different chemical reactions for color formation or discoloration. Therefore, we
attempt to present a comprehensive description of the work performed so far with a
critical discussion of features and limitations. Since we are convinced that the methodology
inherently provides a number of very attractive features for gas analysis, the review may
also act as a stimulus for other scientists to contribute to further developments, refinements,
and areas of application.
Before coming to the title subject of this review, some remarks on terminology, dif-
ferentiation, and delimitations about passive sampling of gases and various procedures
involved in color detection of the collected compounds are presented.
Atmosphere 2024,15, 451 5 of 21
Atmosphere 2024, 15, x FOR PEER REVIEW 5 of 22
Figure 1. General scheme of the individual steps of the methodology and differences referring to
type and geometry of passive samplers, illumination conditions of photographing, means of image
processing, and the kind of quantitative information obtained.
Before coming to the title subject of this review, some remarks on terminology, dif-
ferentiation, and delimitations about passive sampling of gases and various procedures
involved in color detection of the collected compounds are presented.
2. Remarks on Terminology of Passive Sampling Devices and Conceptional
Distinction of Analytical Evaluation Procedures
This chapter appears necessary to us because the terminology relating to passive
sampling is anything but harmonized or standardized, and this may create confusion for
readers. For example, the term passive sampling is often used in publications, but severely
different sampler and gas sensor configurations and evaluation procedures are presented
or, vice versa, very similar (almost identical) configurations and procedures are referred
to using different terms. Additional confusion may arise from the fact that the term pas-
sive dosimeters [61,62] is also used instead of passive samplers (see also above). This,
however, appears quite appropriate since, with almost all passive sampling devices (in-
cluding gas sensors), time-weighted average information is achieved about the uptake
(dose) of the sampler or the sensing zone. Only in rare cases does the response of passive
sampling devices depict real-time (momentary) changes in the sample gas uptake. An-
other item that, in our opinion, deserves consideration is the way of evaluation of all pas-
sive sampling devices regarding the required chemical reactions for color formation or
discoloration involved and the optical detection techniques applied.
The adjective “passive” is used in connection with passive sampling to distinguish it
from active sampling. The latter includes (in the context of gas analysis) the supply of air
to the collection medium or a measuring system by suction with a pump or provision
using a blower. “Passive”, on the other hand, means that no intentional convection is gen-
erated to control the gas transport rather than that the gaseous analytes reach the absorber
mainly due to molecular diffusion. Convection caused by natural air movement always
exists (due to thermal gradients or wind) and will hence overlap with the diffusion process
[2]. Because of the predominantly diffusion-controlled transport to the sorbent surface,
Figure 1. General scheme of the individual steps of the methodology and differences referring to
type and geometry of passive samplers, illumination conditions of photographing, means of image
processing, and the kind of quantitative information obtained.
2. Remarks on Terminology of Passive Sampling Devices and Conceptional Distinction
of Analytical Evaluation Procedures
This chapter appears necessary to us because the terminology relating to passive
sampling is anything but harmonized or standardized, and this may create confusion for
readers. For example, the term passive sampling is often used in publications, but severely
different sampler and gas sensor configurations and evaluation procedures are presented
or, vice versa, very similar (almost identical) configurations and procedures are referred to
using different terms. Additional confusion may arise from the fact that the term passive
dosimeters [
61
,
62
] is also used instead of passive samplers (see also above). This, however,
appears quite appropriate since, with almost all passive sampling devices (including gas
sensors), time-weighted average information is achieved about the uptake (dose) of the
sampler or the sensing zone. Only in rare cases does the response of passive sampling
devices depict real-time (momentary) changes in the sample gas uptake. Another item that,
in our opinion, deserves consideration is the way of evaluation of all passive sampling
devices regarding the required chemical reactions for color formation or discoloration
involved and the optical detection techniques applied.
The adjective “passive” is used in connection with passive sampling to distinguish it
from active sampling. The latter includes (in the context of gas analysis) the supply of air to
the collection medium or a measuring system by suction with a pump or provision using a
blower. “Passive”, on the other hand, means that no intentional convection is generated to
control the gas transport rather than that the gaseous analytes reach the absorber mainly
due to molecular diffusion. Convection caused by natural air movement always exists
(due to thermal gradients or wind) and will hence overlap with the diffusion process [
2
].
Because of the predominantly diffusion-controlled transport to the sorbent surface, the
term diffuse sampling is often used synonymously with passive sampling. It is worth
mentioning that there are numerous publications in which passive/diffusive collection of
gases obviously takes place, but the respective methods are not referred to as such.
Atmosphere 2024,15, 451 6 of 21
The term sampling also requires an explanation or definition in the context of pas-
sive sampling. In the narrower sense, sampling only involves the collection of a sample
and tells nothing about the substances of interest. In passive sampling, however, it is
not the matrix gas (air, atmosphere, etc.) that is collected, but usually, a specific sub-
stance or several substances simultaneously is/are trapped and collected more or less
selectively. The term collection suggests a kind of enrichment (accumulation) of the target
component or a derivative, which is, in fact, what happens in most—but not all—cases of
passive sampling. Sometimes, conversion of the target gas in contact with the absorber
occurs, leading directly to detectable (colored) compounds. In such cases, the term gas
sensor—considering the IUPAC definition of sensors: “A chemical sensor is a device that
transforms chemical information, ranging from the concentration of a specific sample
component to total composition analysis, into an analytically useful signal” [
63
]—appears
appropriate. However, it can be stated again that the terminology is often inconsistent, and
the term “sensor” is also used for a variety of devices that involve (active and passive) sam-
pling followed by, for instance, subsequent temporal and locally separated determination
of the analyte using diverse analytical methods. Often, sensors are also defined only when
the immediate and reversible response to a target compound occurs [64].
A distinction can also be made with regard to the analytical procedure of color evalu-
ation and quantification. The three main routes are as follows: (i) A color change of the
sorptive medium occurs in contact with the target gas. To this end, the absorbing surface
itself is or contains a reagent that (preferentially) selectively forms the colored compound
or is decolorized when a colored absorbing reagent is used. This allows for direct reading
with the naked eye or evaluation by color or color intensity comparison with a scaled color
card. This is, for instance, a common practice in direct reading gas dosimeters for personal
monitoring [
57
,
58
], where time-weighted exposure information is obtained. Evaluation of
gas dosimeter tubes is typically made by length-of-stain readings [
65
,
66
]. For badge-type
geometries, a pre-printed color scale located beside the sensing zone is commonly used
for semi-quantitative concentration readings. Many colorimetric gas sensors can also be
included in this category. Often, they are directly exposed to the sample air and transport
to the sensing surface is by molecular diffusion. In all mentioned instances, qualitative and
semi-quantitative information is accessible using color cards as the reference, but quantita-
tive determination generally requires calibration with standard gases. (ii) A color-forming
reagent is added to the sampling device after the sampling period, and detection takes
place (in situ) within the passive collector. As before, qualitative and semi-quantitative
information can be achieved by visual inspection and color comparison with a reference
color card. For quantitative information, photometric or reflectometric detection is applied.
The former, however, is in many instances handicapped by geometrical restrictions of posi-
tioning the light source and detector. In order to derive the gas-phase concentration, either
calibration with standard gases is required, or in cases where the uptake can be calculated
by Fick’s first law of diffusion the determination of the amount of derivative collected is
sufficient. This is readily obtained by liquid-phase calibration of the photometric or reflec-
tometric procedure. (iii) After sampling, the target component or a derivative is extracted,
and the extract is removed from the sampler, followed by subsequent derivatization and
color detection. This is by far the most common procedure of color evaluation of passive
samplers. One advantage can be seen in the ease of using photometric detection (eventually,
the colored solutions are transferred into a cuvette). It is also possible to apply different
methods for the analysis of the extract, and repeated measurements on the same extract
can be made for control purposes. Drawbacks are the many procedural steps involved and
the loss of sensitivity due to the dilution of the collected compound in the extract.
3. Presentation of Selected Publications
In the following two Sections, an outline of publications about passive sampling
devices (including gas sensors) according to type-1 and type-2 samplers (see above) is
presented in which smartphone evaluation has been applied for color detection. We at-
Atmosphere 2024,15, 451 7 of 21
tempted to cover this subject matter comprehensively. To this end, a search in existing
databases (i.e., Google Scholar, SciFinderSearch, Web of Science, Scopus) was conducted us-
ing combinations of the search terms “smartphone”, “digital imaging”, “passive sampling”,
“dosimeters”, “color sensors”, “air analysis” and “gas analysis”. We have intentionally
limited the current presentation solely to smartphone-based color evaluation, although
other digital devices (i.e., digital cameras, scanners, webcams, dedicated RGB sensors, etc.)
have been employed for evaluation of passive samplers, dosimeters, and color sensors
and often provide similar, sometimes even improved features [
46
,
67
–
71
]. The limitation
to smartphone-based color evaluation was made since the number of existing publica-
tions would otherwise go beyond the scope of an exhaustive review. Another reason
was the fact that smartphones are presently always at-hand, are convenient to use, can
be easily connected to the internet, and provide options for the implementation of apps
for data treatment. Potentially, the evaluation of smartphone images can be readily per-
formed by non-specialists in the frame of experiments in student courses and/or citizen
science projects.
Figure 2illustrates schematically some of the individual steps of the entire procedure
presented in Figure 1. Since the instrumental configurations and procedures applied in the
respective publications that are presented below differ in many respects the figure cannot
represent all variants. It only serves as an example that encompasses the basic steps from
passive gas sampling to color evaluation.
Atmosphere 2024, 15, x FOR PEER REVIEW 7 of 22
3. Presentation of Selected Publications
In the following two Sections, an outline of publications about passive sampling de-
vices (including gas sensors) according to type-1 and type-2 samplers (see above) is pre-
sented in which smartphone evaluation has been applied for color detection. We at-
tempted to cover this subject matter comprehensively. To this end, a search in existing
databases (i.e., Google Scholar, SciFinderSearch, Web of Science, Scopus) was conducted
using combinations of the search terms “smartphone”, “digital imaging”, “passive sam-
pling”, “dosimeters”, “color sensors”, “air analysis” and “gas analysis”. We have inten-
tionally limited the current presentation solely to smartphone-based color evaluation, alt-
hough other digital devices (i.e., digital cameras, scanners, webcams, dedicated RGB sen-
sors, etc.) have been employed for evaluation of passive samplers, dosimeters, and color
sensors and often provide similar, sometimes even improved features [46,67–71]. The lim-
itation to smartphone-based color evaluation was made since the number of existing pub-
lications would otherwise go beyond the scope of an exhaustive review. Another reason
was the fact that smartphones are presently always at-hand, are convenient to use, can be
easily connected to the internet, and provide options for the implementation of apps for
data treatment. Potentially, the evaluation of smartphone images can be readily performed
by non-specialists in the frame of experiments in student courses and/or citizen science
projects.
Figure 2 illustrates schematically some of the individual steps of the entire procedure
presented in Figure 1. Since the instrumental configurations and procedures applied in
the respective publications that are presented below differ in many respects the figure
cannot represent all variants. It only serves as an example that encompasses the basic steps
from passive gas sampling to color evaluation.
Figure 2. Illustration of the individual steps of the methodology of smartphone-based color evalua-
tion of passive gas sampling (see text for further explanations).
The selected papers of the categories type-1 and type-2 are sorted by date of publica-
tion. An overview of the in-total 14 publications is given in Table 1. The respective config-
urations of the devices applied in the selected publication and the way they are exposed
to the sample air are described in the following. Emphasis will also be laid on the analyt-
ical procedures regarding the color reaction as well as the smartphone detection and color
evaluation procedures. If available, the performance of the samplers regarding analytical
specifications (working range, detection limits, selectivity) is outlined. In papers where
real sample analysis has been reported by the authors, this will be presented together with
means of validation and results achieved.
Figure 2. Illustration of the individual steps of the methodology of smartphone-based color evaluation
of passive gas sampling (see text for further explanations).
The selected papers of the categories type-1 and type-2 are sorted by date of publication.
An overview of the in-total 14 publications is given in Table 1. The respective configurations
of the devices applied in the selected publication and the way they are exposed to the
sample air are described in the following. Emphasis will also be laid on the analytical
procedures regarding the color reaction as well as the smartphone detection and color
evaluation procedures. If available, the performance of the samplers regarding analytical
specifications (working range, detection limits, selectivity) is outlined. In papers where
real sample analysis has been reported by the authors, this will be presented together with
means of validation and results achieved.
Atmosphere 2024,15, 451 8 of 21
Table 1. Compilation of publications referring to smartphone-based evaluation of passive sampler for gases/vapors. A distinction is made between Type-1 and
Type-2 samplers (see text for further information). Publications are sorted by the date of publication.
Year Analyte Gas Sorbent
Fabrication
Sampler
Geometry Reagent Detection
Condition
Photographing
Condition
Evaluation
Software
Color
System Working Range LOD Application Ref.
Type-1
2020 Ozone
pre-immobilized
coloring reagent
on sorbent
commercial
passive
samplers
(Owaga badge)
Indigo
direct detection
of fading of
blue color
photo box
Corel DRAW X5
and Matlab
software
(version R2015a)
RGB
11–109 µg m−3
(exposure time not
given)
3.3 µg m−3Suburban
environment [72]
2021 Nitrogen
dioxide
immobilized
trapping reagent
into sorbent pad
lab-made
passive
sampler (tube
type)
Griess-Saltzman
reagent
adding Griess-
Saltzman gel photo box ImageJ
(version 1.52e) RGB not given
32 µg m−3
(24-h
exposure)
No real sample
analysis [73]
2023 Nitrogen
dioxide
immobilized
trapping reagent
into sorbent pad
lab-made
passive
sampler
(Palmes tube)
Griess-Saltzman
reagent
adding pre-mix
reagents
(Griess-
Saltzman)
without a
photo box
(ambient light)
ImageJ
(version 1.53a) RGB 10–120 µg m−3
(14 days exposure)
5µg m−3
(14 days exposure)
Urban
environment [74]
Type-2
2016 Hydrogen
sulphide
pre-immobilized
coloring reagent
on sorbent
no sampler
holder
N, N-Dimethyl-p-
phenylenediamine,
and Fe (III)
direct detection
of methylene
blue product
photo box
GIMP
software
(version 2.8)
CMYK 5–50 ppm
(30 min exposure)
0.12 ppm
(30 min exposure)
Sewage
treatment plant
[75]
2016 Formaldehyde
pre-immobilized
coloring reagent
agar sorbent
no sampler
holder
4-Amino-3-
hydrazino-5-
mercapto-1,2,4-
triazole, ZnO,
KIO4
direct detection
of color change
without a
photo box
(ambient light)
Adobe
photoshop
(not given)
RGB 20–85 µg m−3
(24-h exposure)
11 µg m−3
(24-h
exposure)
Indoor air
(formaldehyde
emission flux)
[76]
2017 Mercury vapor
pre-impregnated
Corning porous
Vycor glass
reagent on
sorbent
no sampler
holder Nanogold
direct detection
of color change not given not given RGB uptake
0.06–0.6 µgnot given
Personal
sampling of
miners
[77]
2018 Mercury vapor
pre-immobilized
cuprous io-
dide/polystyrene
composite on
sorbent
no sampler
holder
Cuprous
iodide/poly-
styrene
composite
direct detection
of color change photo box ImageJ
(version 1.49 hr) RGB 61–270 µg m−3
(30 min exposure)
16 µg m−3
(30 min exposure)
No application
reported [78]
2018 VOC
pre-immobilized
reagent on
sorbent
cap of vial Polydiacetylenes
direct detection
of color change not given
Adobe photo-
shop/Android
Studio app
(not given).
RGB not given not given Identification
of VOC [79]
Atmosphere 2024,15, 451 9 of 21
Table 1. Cont.
Year Analyte Gas Sorbent
Fabrication
Sampler
Geometry Reagent Detection
Condition
Photographing
Condition
Evaluation
Software
Color
System Working Range LOD Application Ref.
2021 Hydrogen
sulphide
pre-immobilized
reagent on
sorbent
encapsulated
between two
glass plates
Arene-derivative
dye
direct detection
of color change
without a
photo box
(ambient light)
Adobe photoshop
(version CS6)
CIELAB,
RGB, HSB
and CMYK
0–1.5 ppm
(15 min
exposure)
not given No application
reported [80]
2021 Hydrogen
sulphide
pre-immobilized
coloring reagent
on the surface of
the glass
substrate
no sampler
holder
Indium oxide
nanostructure
direct detection
of color change photo box
Colorimetric
Detector
application
(not given)
Optical
darkness
ratio
not given
10 ppm
(30 sec
exposure)
No application
reported [81]
2021 Ammonia
pre-immobilized
reagent on paper
sorbent
not given Methyl orange
direct detection
of color change photo box
ColorAssist app
(version 2.1,
FTLapps)
HIS
6.0–54.0 ppb
(3 min
exposure)
2 ppb
(exposure time not
given)
Chicken farm [82]
2021
Ammonia,
formaldehyde,
hydrogen
sulfide
pre-immobilized
(screen-printing)
reagent on
polymer-coated
paper
no sampler
holder
Bromocresol
green,
fluorescent dye
(primary amine),
cupper azo
complex
direct detection
of color change photo box Time-lapse app
(not given) RGB not given not given No application
reported [83]
2023 Hydrogen
sulphide
pre-immobilized
reagent on
agarose hydrogel
The cap of a
centrifuge tube
Copper (II)-azo
complex
direct detection
of color change not given ColorAssist app
(not given) RGB 1–50 ppb
(10 min exposure)
43.34 ppb
(10 min exposure) Exhaled breath [84]
2023 Ozone
pre-immobilized
reagent on
polydimethyl
siloxane sheet
no sampler
holder o-Dianisidine
direct detection
of color change photo box ImageJ (not
given) RGB 0–200 ppb
(8 h exposure)
1.79 ppb
(8 h
exposure)
Printing store,
rubber
molding press
factory,
residential
house
[85]
Atmosphere 2024,15, 451 10 of 21
3.1. Passive Samplers of Type-1
A thorough review of all papers found in the literature search (see above) revealed
that only three publications (one of them recently submitted) fulfill the above-mentioned
criteria for this categorization. A brief outline of the content of these papers is given in
the following.
(1) A low-cost and portable method for the monitoring of daily tropospheric ozone
levels has been reported by Cerrato-Alvarez et al. [
72
] through the combination of passive
sampling and digital analysis of images taken with the camera of a smartphone. Com-
mercially available Ogawa passive samplers were employed in all measurements. The
analytical signal used was the degree of decolorization of the blue color of indigotrisul-
fonate (ITS) deposited on cellulose filters upon the reaction with ozone. Photos were taken
in a homemade photobox with controlled internal luminosity by a strip containing 14 bright
LEDs. To prevent the influence of possible inhomogeneous degree of decolorization, the
image of the whole pad was saved for digital analysis in PNG format employing CorelDraw
and MATLAB software. RGB parameters were obtained from images taken with the camera.
The red color channel was selected for quantification since it is the complementary color
of the reflected blue color of the ITS solution. Evaluation of grey-scale intensity, effective
absorbance, and Euclidean distance were tested with marginal differences regarding the
specifications achieved. The ozone concentration in ambient air was derived from Fick’s
first law of diffusion. The mass of ozone reacted during the sampling time is calculated
stoichiometrically from the equivalent amount of ITS consumed during the sampling pe-
riod, obtained from digital image analysis of the sampling pads. ITS amount deposited
on the pads and the exposure time were adjusted to fit different expected ozone levels in
ambient air. Under optimized conditions, the linear range obtained was between 11 and
109
µ
g m
−3
with a detection limit of 3.3
µ
g m
−3
. Precision and accuracy of RSD = 6.8% and
relative errors ranging from
−
14.0 to 5.7%, respectively, are stated. The practical application
of the method was tested by measuring 24-hour average levels of ozone in a suburban
environment over a period of three months. Correlation against a spectrophotometric
reference method was r = 0.77. According to the authors, the potential of the method as an
auxiliary tool to other methods for ozone determination could be demonstrated, providing
a rapid and decentralized measurement of ozone levels with adequate reliability. Another
important advantage of the presented method mentioned is that the analysis can be per-
formed by anyone without the need to be a specialized analyst. Therefore, the developed
method may help to increase community awareness and commitment to air quality issues.
(2) In a paper by Souza et al. [
73
], a passive sampling method is described for the
determination of nitrogen dioxide through the formation of a colored dye, followed by
digital image analysis of the resulting color intensity. The passive samplers were purpose-
made from conical Falcon tubes used in chemical laboratories cut to the desired size. The
gas collection was made using a mixture of triethanolamine (TEA), ethylene glycol, and
acetone as the sorbent immobilized on Whatman cellulose filters of 2.6 cm diameter. For
determination of the nitrite formed during adsorption of NO
2
in TEA, a gel containing
the Griess-Ilosvay reagent was distributed across the collecting surface with a plastic ruler,
whereafter the filters were placed in a photobox equipped with high-brightness LEDs for
illumination, and pictures were taken with a smartphone. RGB colors were evaluated
by ImageJ software. Uptake rates of the samplers were not measured and could not be
calculated on the basis of Fick’s law due to the design of the samplers using a polymeric
membrane as a turbulence barrier. In order to obtain quantitative NO
2
data, parallel
measurements have been conducted initially with on-line chemiluminescence monitors
at the same site. This permitted a correlation of the intensity of the color generated in
the passive sampling method with ambient NO
2
concentrations. Results of digital image
analysis and spectrophotometric evaluation after extraction as a reference statistically
agreed at a 95% confidence level. The authors state that the advantages of the technique
include low cost, the ready availability of components, ease of use, and sensitivity. The
achievable detection limit stated is 32
µ
g m
−3
NO
2
for a 24-h sampling period. The authors
Atmosphere 2024,15, 451 11 of 21
conclude that the method could be applied for both outside and indoor environments, in
particular for low budget laboratories. Real sample analysis has, however, not been made.
(3) A smartphone-based color evaluation procedure of tube-type passive samplers has
been exemplarily investigated for the determination of NO
2
in a paper by Shi et al. [
74
].
The purpose-made samplers were similar to Palmes’ tubes with TEA immobilized on
cellulose filters used for collection of NO
2
. Thorough optimization of various experimental
parameters affecting the color evaluation has been made. One of the specific aims of the
authors was to avoid the use of a photo-box (commonly used in most papers referring
to smartphone-based color detection) for photographing the colored objects since this
practically detracts from at-site analysis. Photographic parameters investigated were
conditions of illumination and distance between the smartphone and the colored objects
both found to be partially interrelated. In order to minimize solution handling and transfer,
the color-forming Griess-Ilosvay reagent was added directly to the adsorber pads and
smartphone photos were taken (in situ) of the colored liquids contained in the cap of
the passive sampler tube. In this context, additional parameters investigated were the
composition and volume of the reagent and the color of the caps. Smartphone photos were
saved in JPEG format, and RGB values of the colored solutions were retrieved using the
freeware ImageJ. The green color channel (the complementary color to the pink reaction
product of the nitrite assay) was used for evaluation. Calibration was performed using
liquid standards prepared and processed in the same way as the sample solutions. By taking
photos of samples and standards simultaneously, the influence of variable illumination
conditions could be eliminated.
As a result of the optimization, high sensitivity of nitrite determination (nitrite being
the product of sampling NO
2
with TEA) with a working range of 30–600 ng, a limit of
detection (LOD) of 12 ng, and good precision (<8% RSD over the entire concentration
range) was attained. For a sampling duration of 14 days and using the tube-type samplers
of the present work, the working range and LOD for atmospheric NO
2
are 5–200
µ
g m
−3
and 1.8
µ
g m
−3
, respectively. The experience gained during the principal investigation
and optimization was exemplarily put into practice in two small measurement campaigns
determining ambient NO
2
in the city of Berlin (Germany). Reasonably good agreement
was achieved with the data presented by the governmental air pollution control network
in Berlin.
3.2. Passive Samplers of Type-2
The thorough review of all papers dealing with smartphone-based evaluation of
passive samplers found in the literature search (see above) revealed that the presentations
of 11 publications can be categorized as type-2 devices. The description of the design of the
passive sampling devices (including color sensors) and the way of installation during the
sampling step evidenced that the uptake of analyte gas is indeed governed by molecular
diffusion, but no attempts were made (or not described) to obtain a controlled length of the
diffusion path. As mentioned above, this fact detracts from concentration measurements
because of the uncontrolled length of the diffusion path due to wind effects. Despite
this, the application of Fick’s law of diffusion has sometimes been falsely used for the
calculation of the sampler’s analyte uptake, and analyte gas concentrations in
µ
g m
−3
are
given rather than the appropriate value of the immission rates in
µ
g m
−2
h
−1
(see above).
In the following, the contents of the 11 publications on type-2 passive sampling devices are
presented in a condensed form.
(4) The development of a low-cost colorimetric sensor for the determination of H
2
S
using smartphone-based evaluation is reported by Pla-Tolós et al. [
75
]. A mixture of N,N-
Dimethyl-p-phenylenediamine, FeCl
3,
and glycerol has been immobilized on Whatman
filter paper circles of 3 cm diameter. In the presence of the H
2
S, methylene blue is formed,
which can be detected visually. The reaction product obtained was found to be highly stable
in this support and is free of blank signals. For quantitative estimation, smartphone imaging
(among other optical methods) has been applied. In passive sampling experiments, H
2
S
Atmosphere 2024,15, 451 12 of 21
gas was prepared in a 2 L bottle. The concentration was estimated based on the generation
of H
2
S from a known amount of Na
2
S acidified with HCl. The sensor circles are suspended
on a thread in the bottle and exposed to the gas-phase for 30 min. After exposure, the
sensor circles were washed with water to remove the excess reagent. A smartphone camera
was employed to take pictures of the sensors. Conditions of photographing, i.e., use of
a photobox or ambient light, illumination, and geometrical arrangement, are not given.
The color picker tool of GIMP was used to evaluate the color intensity of the pictures. The
CMYK (cyan, magenta, yellow, black) color-coordinate system was applied to convert the
images into numerical color values. A calibration graph was constructed as a plot of the
value of the system readout vs. H
2
S concentration. A working range of 5–50 ppm H
2
S
and a LOD of 1.12 ppm has been achieved. The proposed procedure was applied to the
determination of air samples in the vicinity of a sewage treatment plant in Comunidad
Valenciana (Spain).
(5) In a paper by Sekine et al. [76], the development of a novel colorimetric formalde-
hyde detector applied in a passive sampling configuration is reported using the built-in
camera of a mobile phone for evaluation. The colorimetric detector employs a solid phase
colorimetric reagent made from 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole, ZnO, KIO
4
,
and agar. A color change of this reagent occurs from white to purple by exposure to HCHO
gas. The colorimetric performance of the detector was first assessed by exposure of the
sensor to HCHO vapor from a droplet of HCHO aqueous solution in a closed Petri dish.
Unfortunately, the support material for the color-forming reagent, the size of the sensing
zone, and the geometrical arrangement of the sensor relative to the formaldehyde source
are not given. HCHO vapor generated moved towards the colorimetric reagent within a
headspace by molecular diffusion, and the gas molecules then reacted with the reagent. A
digital image of the detector was taken using a smartphone camera positioned 15 cm above
the desk level at ambient light (no photobox used). It was found that the calibration of the
measured color intensity with a color standard reduced the variation of the results. The
influence of different mobile phones for imaging and changing conditions of photographing
(in particular kinds of illumination) were also investigated. To quantify the color change of
the detector, the color was converted to a color value of the green channel in a RGB color
model on a personal computer. Adobe Photoshop was used for calculation. For calibration
purposes, the detector was placed in a small test chamber, and the response of the detector
to known concentrations of HCHO in air was investigated with a constant gas generation
system. The working range of the sensor for 24-h exposure is 20–85
µ
g m
−3
HCHO with a
limit of quantification of 11
µ
g m
−3
. The authors state, that this meets the requirements to
detect the indoor air quality guideline level of HCHO set by the World Health Organization.
The developed detector was also applied to classify HCHO-emitting building materials,
i.e., plywood, whose emission flux is greater than 120 µg m−2h−1.
(6) A passive sampler with smartphone evaluation for monitoring of gaseous elemental
mercury in artisanal gold mining has been presented by de Barros Santos et al. [
77
]. The
passive sampling devices were made from rods of Corning porous Vycor glass (PVG). The
rods were cut (using a diamond disc) to obtain small PVG discs of 0.1 cm in thickness and
0.6 cm in diameter. A very thin gold layer was deposited onto the surface by impregnation
of PVG discs with a HAuCl
4
solution, followed by in situ reduction of AuCl
4
to elemental
Au, using sodium borohydride as a reduction agent. The presence of nanogold was readily
detected visually by the red color appearance of the PVG/Au disc. Preliminary experiments
were conducted by placing PVG discs in the upper part of a glass vessel (resembling a
test chamber), and a drop of liquid mercury was given to the bottom of the vessel. The
red color tones changed with increasing exposure times to the Hg vapor atmosphere, and
this was used as the analytical signal. The experimental set-up of taking photos with the
smartphone (e.g., geometrical arrangement) and illumination conditions are unfortunately
not detailed in the paper. The variation of the RGB (Red, Green, Blue) color patterns of
the PVG/Au discs was plotted in histograms, and results were compared to discs (used as
control) that were not exposure to mercury vapor. RGB histograms showed that the red
Atmosphere 2024,15, 451 13 of 21
color channel is more sensitive to the amount of mercury than the green and blue channels.
To obtain quantitative data on the mercury retention, both Au and Hg in each sampler were
quantified in the lab by using ICP-MS und Direct Mercury Analysis (DMA) techniques,
respectively. In this way, it was shown that the PVG/Au sampler can detect the uptake of
Hg in the range between ~0.06 to 0.6
µ
g. Calculation of mercury vapor concentrations in air
was not intended since the aim of the work was the estimation of personal exposure. It is
worth mentioning that in the presented configuration, the results obtained refer only to the
uptake of a person’s skin or clothing rather than quantitative information about the amount
of inhaled mercury vapor. The performance of the PVG/Au sampler was evaluated in a
simple field application in an artisanal and small-scale gold mining environment in Burkina
Faso. To this end, the PVG/Au samplers were placed on the front lapels of the shirts of
miners who were present at the Au–Hg amalgam burn.
(7) Salcedo et al. [
78
] developed a device for colorimetric determination of mercury
vapor using smartphone camera-based imaging. It consists of a sensing zone based on
a cuprous iodide/polystyrene composite exhibiting a reddish color in the presence of
elemental Hg vapor. The sensing layer was prepared by mixing CuI powder with a
polymeric binder solution in tetrahydrofuran. The resulting emulsion was applied onto
a Whatman chromatography paper through a roll-coating technique using a glass rod.
The colorimetric sensing paper was then cut into small pieces and set on a glass slide for
easier handling during the experiments. The colorimetric paper sensors were placed inside
glass vials, which were then capped with a silicone septum. Different volumes of standard
Hg
0
-saturated air were injected into the vials using a syringe. This allowed the exposure
of the colorimetric paper sensors to varying concentrations of Hg vapor. Upon exposure
to Hg
0
, the color of the colorimetric sensing paper sheets changed from white to light
orange. For digital image acquisition, the colored sheets were placed in a light-tight box to
avoid the influence of varying ambient light on sensor illumination. Stable lighting was
provided by a fluorescent tube located at the upper portion of the lightbox. The photos
taken after exposure to Hg
0
were analyzed in the RGB color space using the open-source
image processing program ImageJ. Percent change in the red, green, and blue values of
the sensor before and after exposure to Hg
0
was calculated. The blue-based response
was eventually used for calibration. The linear working range of the CuI/polystyrene
composite sensors is reported to range between 61 and 270
µ
g m
−3
Hg
0
. The calculated
limit of detection was 16
µ
g m
−3
Hg
0
. Application to real sample analyses is not reported.
(8) Park et al. [
79
] have developed a smartphone-based colorimetric paper sensor for
the qualitative detection of volatile organic compounds using an array of polydiacetylenes
(PADs) as color-forming compounds. The array was formed of four different PDAs on
conventional paper using inkjet printing of the corresponding diacetylene monomers,
followed by photopolymerization. For testing vapochromism, a small volume of each
solvent was poured into a plain glass vial, and the vial was sealed by a cap and incubated
for 30 min at ambient temperature. Upon opening the cap, a punched part of the PDA
sensor array was inserted immediately into the vial, allowing exposure to the saturated
solvent vapor. After closing the cap, the time evolution of the color change (typically blue-
to-red) in the PDA array was recorded over 30 min using a smartphone camera, and the
images were analyzed using photoshop software or an Android Studio app. Unfortunately,
the conditions of photographing (e.g., camera settings, illumination) and the selection of the
region of interest of the colored spots have not been detailed. Exposure of the PDA array
to an unknown solvent promotes color changes, which are imaged. A database of color
changes (i.e., the red channel of the RGB color space and hue values) was then constructed
on the basis of different vapochromic responses of the 4 PDAs to 11 organic solvents.
To this end, a new “combinatorial” strategy (no details are given in the manuscript) was
devised, taking into account the different response behavior of the PDAs to various solvents.
Subsequently, this enabled the identification of a particular volatile organic solvent with
high accuracy.
Atmosphere 2024,15, 451 14 of 21
(9) A paper-based color sensor for sensitive and selective detection of gaseous H
2
S
has been presented by Vargas et al. [
80
]. The sensing zone contains an arene-derivative
dye embedded into a porous cellulose matrix. This paper strip containing the sensing
probe was then encapsulated between two glass plates attached to each other. In this way,
the chromatography paper was covered by the glass on both sides, producing a channel
into which the gas could easily and homogeneously penetrate by molecular diffusion. In
the stage of optimization of the procedure, exposure of the color sensor to the gas was
performed in a cuvette. Gaseous H
2
S was prepared in situ by the addition of HCl to Na
2
S
aqueous solutions within the cuvette. The sensor was fixed to one of the walls of a quartz
cuvette, keeping it out of contact with the liquid that was placed in the lower part. After
exposure, the sensors were removed, and pictures of the sensors were taken in raw image
format using the camera of a regular smartphone. Constant and uniform artificial light
(without a photobox) was used for all pictures, with a white chromatography paper strip
placed beside the sensor as a reference for the comparative analysis of the pictures and to
control for possible light fluctuations. Processing of the photos was performed using Adobe
Photoshop without applying any color or exposure corrections. The color information
for each of the sensor exposures was extracted using the major color spaces (i.e., CIELAB,
RGB, HSB, and CMYK). For an exposure time of 15 min, saturation of the color is reached
above ~1.5 ppm H
2
S, but a linear response was found between 0 and 1.5 ppm. The authors
emphasize that with the present sensor, it is possible to perform direct calibration at low
H
2
S concentrations through the color extraction of digital pictures taken with a common
smartphone, broadening the potential range of use of the disposable sensor. Unfortu-
nately, an application to real sample analysis and validation of the developed method is
not reported.
(10) A colorimetric gas sensor for determination of H
2
S was developed by
Devi et al. [
81
]. The sensor is based on indium oxide (In
2
O
3
) nanostructures, which have
been prepared by a modified sol–gel method. A sensing nanostructured film is obtained by
spray coating method onto 1 cm
×
1 cm glass plates. Exposure of H
2
S to the sensing surface
causes a color change due to the sulfurization of the top-layer of the In
2
O
3
nanostructured
film and the formation of an In
2
S
3
layer. In the experimental setup, a mobile phone was
fixed in a dark wooden cabinet and photos of the colorimetric detector were captured by
an Android mobile phone at constant illumination. To quantify the colorimetric sensing of
H
2
S gas detection, the optical darkness ratio (ODR), of the sensor has been followed by a
dedicated smartphone-based application. At room temperature the lower limit of detection
of H
2
S gas by the In
2
O
3
nanostructured film was 10 ppm for an exposure time of 30 s. The
selectivity of the sensor over NO, NO
2
, CO, N
2
, Ar, and NH
3
was high. Application to real
samples has not been done.
(11) Khachornsakkul et al. [
82
] reported a paper-based colorimetric device for the
on-site screening of ammonia gas. The detection principle is based upon a color change
from red to yellow of methyl orange immobilized on a paper substrate. After exposure to
ammonia gas for 3 min, photos were taken with a smartphone of the colored substrate in a
photobox. The color signal of the device has been measured through the hue channel of an
HSL system via the application of a smartphone. The hue values and degree from the HSL
system on the paper sensor were obtained by using the software ColorAssist installed in an
iPhone. An advantage of the hue evaluation was emphasized in that it is not relative to
the intensity and brightness of the occurred color; therefore, this channel can reduce errors
from these influences. Calibration was made by generating NH
3
gas from the evaporation
of aqueous NH
4
OH solution and placing the sensor in the headspace above this solution.
The preparation method of NH
3
gas standards was validated using an electrochemical gas
sensing instrument. The linear relationship between NH
3
concentration and the hue signal
of the sensor was from 6.0 to 54.0 ppbv with a 2 ppbv limit of detection. The applicability of
this device was demonstrated in the determination of NH
3
in a laboratory and at a chicken
farm. Since the color change of the pH-indicator is fully reversible, the recorded color
only represents the momentary uptake of ammonia gas and not, as most other passive
Atmosphere 2024,15, 451 15 of 21
samplers and color sensors, a time-weighted average. This fact has not been mentioned by
the authors and clearly limits the sensors’ applicability.
(12) A method has been developed by Engel et al. [
83
] to monitor the exposure to
different gases (viz. ammonia (NH
3
), hydrogen sulfide (H
2
S) and formaldehyde (HCHO))
in ambient air. The method is based on a visible color change of colorimetric gas sensors,
which can be evaluated by the naked eye, a stationary color reader, or the camera of a
smartphone. The sensors consist of a disposable paper tag or plastic card and gas-sensitive
materials, which have been deposited by a screen-printing process. The integration of the
gas-sensitive layers into a machine-readable pattern of a QR-like code incorporating color
reference spots provides illumination, camera-independent calibration, and quantitative
detection. For NH
3
and HCHO detection, commercially available pH-sensitive color dyes
have been employed. H
2
S is detected by an immobilized copper(II) azo dye complex. The
color change of the gas-sensitive layer due to the reaction with the target gas was character-
ized by the evaluation of RGB values taken with an in situ readout station (unfortunately, it
is not explained in the paper what this is and how it works) using the camera of an iPhone
and a commonly available time-lapse app, taking consequent consistent images every five
seconds. The determination of the color values of individual pixels was implemented with
the help of a Python script. The readout station contained in a transparent gas measurement
chamber was built in an opaque plastic box to achieve constant illumination using several
white LEDs. Information on how the sensors are fixed within the gas measurement chamber
is lacking.
In the cases of detection of NH
3
and H
2
S, the indicator reactions are reversible. There-
fore, only the momentary response to varying gas concentrations is obtained (and not the
commonly achieved time-weighted values of passive sampling devices). The color dye
4-amino-3-penten-2-one selected as a colorimetric sensing material for the detection of
formaldehyde, forms a fluorescent dye, which turns from colorless to neon yellow when it
comes in contact with the target gas. The reaction involved is also reversible, but due to the
very slow (within days) return to the colorless form, the color sensor has—according to
the author’s opinion—potential for the preparation of disposable dosimeters. None of the
presented sensors has been employed for real sample analysis.
(13) A colorimetric sensor for H
2
S detection using smartphone-based color evaluation
has been presented by Wang et al. [
84
]. The sensing zone was constructed by incorporating
copper(II) pyridine diazinonaphthol (Cu-PAN) complex into agarose hydrogel. The reaction
of H
2
S with the reagent leads to a color change from purple to yellow, which has been
used as analytical information. A small portion of the gel was pipetted into the cap of
a 10 mL centrifuge tube. For calibration, H
2
S gas was quantitatively generated by the
reaction between Na
2
S and HCl within the tube. Hence, diffusive sampling occurs from the
headspace above the liquid phase. After a sampling time of several minutes, the cap was
removed, and photos were taken of the colored gel using a smartphone camera. Conditions
of photographing, i.e., ambient light or photobox, geometrical arrangement, etc., are not
presented. The color change was read out by recording RGB values and data collected
with the help of the Color Assist app on a smartphone. Euclidean distance was applied
for quantification of the color intensity. The response of the color sensor showed good
correlation with the logarithm of H2S concentration in a wide range from below 1 ppm to
about 50 ppm for 10 min sampling time. A limit of detection of 43.34 ppb is stated. Possible
interference by various gases was tested, resulting in the high selectivity of the developed
sensor. Long-term stability was also high. The feasibility of the Cu-PAN hydrogel sensor
for the measurement of H2S levels in human exhaled breath was demonstrated.
(14) Passive sampling of ozone with colorimetric detection using o-Dianisidine as the
sorbent has been reported by Choi et al. [
85
]. The reagent was immobilized in polydimethyl-
siloxane (PDMS) sheets, which were cut to the desired size and served as a collector for
ozone. o-Dianisidine, a colorless compound, undergoes a visible color change to yellow
upon contact with O
3
. Optimization and calibration were performed by smartphone pho-
tographing of the sheets placed in a photobox illuminated with a white LED. The captured
Atmosphere 2024,15, 451 16 of 21
images were processed using ImageJ. The entire area of the sheets was selected using a
polygon selection tool, and the average red, green, blue (RGB), and greyscale intensity
were recorded using an RGB measure plugin. Exposure studies of the samplers were
conducted in a test chamber with known O
3
concentration in the range 0–200 ppb for
variable durations up to 8 h. The passive sampling sheets were calibrated by measuring
the absorption of o-Dianisidine after liquid extraction of the collecting sheets in exposure
experiments under the same conditions. Colorimetric changes were analyzed by capturing
the images obtained from smartphone photographing, and the effective absorbance of the
blue scale was shown to provide the best fit for changing O
3
concentrations. Limits of
detection and quantification of 1.79 ppb and 5.27 ppb O
3
, respectively, are stated. The
selectivity of the passive sampler was examined by exposure to several other gases po-
tentially present in indoor environments, but no interferences were found. Based on the
optimization experiments, badge-type passive samplers were constructed and fixed at the
lapel for personal exposure studies. The samplers were employed in several field tests
conducted in a printing store, a rubber molding press factory, and a residential house.
The results obtained in the printing store evidenced a significant disparity between O
3
concentrations within the room and personal O
3
exposures. The use of a smartphone app
with warning information at high O
3
exposure is mentioned in the paper, but no details
are presented. The authors conclude that the developed passive sampling methods can
increase awareness of health-threatening O3exposure among workers and occupants.
4. Conclusions, Critical Remarks, and Outlook
Passive sampling of gases and vapors is a well-established method for the determi-
nation of time-weighted average concentrations. It is widely employed in surveillance
of ambient and indoor quality and is also used for personal monitoring of exposure to
problematic air pollutants in industrial hygiene. In passive sampling, the gaseous ana-
lytes are trapped on suitable sorbents, whereby often a colored derivative is formed, the
intensity of which is used for quantitative detection. In other instances, the derivatives
are extracted, and a color reaction is initiated in a separate vessel prior to color intensity
measurements for quantification. The most common method for color detection is spec-
trophotometry, although reflectometric detection has also been used. Colorimetric gas
sensors often operate in a passive sampling mode (no pumps are used for the provision
of air to the sensing surface), so they (despite the different and inconsistent terminology)
are obviously a kind of passive sampling device with color detection for qualitative or
quantitative gas determination. In part of the present review, conceptional similarities and
distinctions are discussed.
With the advent of color imaging using digital cameras, webcams, scanners, dedicated
color readers, and smartphones, these instruments are increasingly used for the evaluation
and quantification of color and color intensities (not only) in the context of chemical analy-
sis. Smartphone-based evaluation is particularly attractive due to the ready availability of
smartphones, their convenient use, their small size, portability, the ever-increasing quality
of the camera, and dedicated apps that can be implemented for data presentation and trans-
mission. Not least because of these features (yet also to keep the number of publications
manageable), we have limited our review to smartphone-based evaluation applications for
passive samplers.
The thorough search of publications in which smartphone-based evaluation of passive
sampling devices (including colorimetric sensors for gases) is presented has finally led to
only 14 relevant papers. Determination of various gases and vapors are described, i.e., NO
2
,
O
3
, NH
3
, H
2
S, Hg vapor, formaldehyde, and toxic organic volatiles. The inspection of these
publications revealed considerable differences with respect to the design of the sampling
devices, the way they are exposed to the sample air, the color evaluation procedures of
the photos taken with a smartphone, and the calculation of results from color change or
color intensity variations. One very important aspect raised in our review is the distinction
between passive sampling devices that provide concentration information of the target
Atmosphere 2024,15, 451 17 of 21
gases (due to a defined length of the diffusion path), and those where the uptake of
the respective gas is indeed connected to gas concentration but what is really measured
is the time-weighted deposition flux (also termed immission rate, see above) since the
diffusion path varies with variable air movement in the vicinity of the samplers. This
will happen with changing wind speed in ambient air measurements or movement of
humans in personal monitoring applications. It is interesting (even surprising) that in
none of the publications, this fact has been discussed, and in most of the publications, gas
concentrations in, e.g.,
µ
g m
−3
are given rather than the appropriate value for target gas
deposition flux in mass per area and time. In our review we have made a clear separation
between these two situations by categorizing the passive sampling devices presented in the
selected publications in type-1 and type-2 and have outlined the contents of the respective
papers in two separate chapters.
Regarding the design of the passive sampling devices, both tube-type and badge-type
configurations have been used in various publications. Only in one paper, a commercially
available passive sampler has been employed, whereas in all other papers, purpose-made
samplers have been constructed. Unfortunately, details of the dimensions and way of
incorporation of the sorbent into the samplers are sometimes missing. Different procedures
of passive sampling and smartphone-based color evaluation for various target gases have
been reported in various publications. A deficiency of many papers can be seen in missing
and difficult-to-understand description (or sometimes inadequate) calibration procedures.
And even when devices have been calibrated this has been performed often in labora-
tory experiments, but how these data have been used for quantification in real sampling
situations remained unclear.
The sorbent materials and chemical reactions responsible for color development (or
in two cases of decolorization) are well described in all papers. What is not always
adequately presented in the respective publications is the experimental set-up for exposure
measurements and subsequent conditions of taking smartphone images of the colored or
decolorized zones. Photos taken with the smartphone are generally exported to a computer
and different imaging processing software (often freeware ImageJ) has been used to select
appropriate regions of interest. Only in one case the inherent capability of the smartphone
was employed. The RGB color space is most often applied for quantification of the color
response of the passive sampling devices; in some papers, CIELAB, HSB, and CMYK have
been used instead or supplementary. However, not all papers present the complete relevant
information in a way that makes it clear how the records have been made. Calibration
procedures employed in the various procedures differ considerably. They include the use of
standard gases, calculations based on color changes achieved in liquid-phase measurements
of corresponding compounds to the respective gaseous analytes, comparison with other
determination methods for the same gas applied in parallel measurements, and comparison
with prefabricated color charts. In some papers, the calibration procedures are very well
described; in others, the description is only rudimental and hard to comprehend. The
application of the developed passive sampling devices to real sample analysis is presented
in 9 of the 14 publications. The other five papers are regarded to be either only a proof of
concept or—at least in the opinion of the authors of this review—could not convincingly
demonstrate the feasibility of practicable application of gas analysis.
Considering the current features of digital color imaging in general and smartphone-
based photographing of colored objects in particular, it comes as no surprise that this
has already attracted considerable attention for analytical chemical applications. The
relatively few published works related to the evaluation of passive samplers for gases
and colorimetric gas sensors operating in a passive (diffusion-controlled) sampling mode
should not be regarded as a sign of limited advantageous features of this approach rather
than the yet missing recognition of this possibility. In our opinion, the selected papers
presented here clearly evidence significant potential for measurement of various trace gases
and vapors exceeding the ones that have been dealt with so far.
Atmosphere 2024,15, 451 18 of 21
A certainly very valuable application area of smartphone-based evaluation of passive
samplers and colorimetric gas sensors is at-site in situ color detection and quantification.
Considering the low cost and simple construction of passive sampling devices (many of
them can be self-made with low efforts from readily available materials) and the ubiquitous
presence of smartphones all over the world, it enables citizens to measure air pollutants in
their immediate environment and will probably enhance the awareness of possible risks to
health and environment [18,86].
For a couple of years, we have applied smartphone-based color evaluation of passive
sampling for the determination of NO
2
in student courses of the curriculum of the depart-
ment of environmental chemistry and air research. The feedback from the students was
very positive, and some of the students later elaborated, modified, and improved configu-
rations in the frame of Bachelor and Master Theses [
87
,
88
]. The current work of our groups
in Berlin and Chiang Mai focuses on the miniaturization of passive sampling devices in-
volving 3D-printing technology and further simplification of the color formation procedure
with ready-to-use spray reagents. An attempt has also been made to develop a dedicated
app for RGB color detection and data recording, documentation, and transmission of results
via the internet.
Additional future directions of research regarding the concept of smartphone-based
color evaluation of passive samplers should focus on (i) (further) simplification of the
analytical procedures regarding the preparation of the reactive layer of the samplers and
the post-sampling process of color formation, (ii) suitable (i.e., convenient and reliable)
calibration procedures implementing, e.g., pre-printed color scales, and (iii) application to
real-life measurements and validation of results. Finally, the distribution of information
about the potentialities of the methodology away from the sole publication in scientific
journals is another item that should be increasingly considered by researchers to motivate
the public dealing with analytical chemical problem solving and the role of healthy air for
breathing.
Author Contributions: Conceptualization, W.F.; writing—original draft preparation, K.K. and W.F.;
writing—review and editing, K.K., K.G., A.H. and W.F.; visualization, K.K. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the Alexander von Humboldt Foundation, Georg Forster
Research Fellowship of grant number [Ref 3.5—THA—1227646—GF-P], and a renewed research stay
grant number [Ref 3.5—THA/1009402].
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Acknowledgments: K. Kiwfo appreciates the support from a Georg Forster Research Fellowship of
the Alexander von Humboldt Foundation (Ref 3.5—THA—1227646—GF-P) for funding research
conducted at TU Berlin (Germany). K. Grudpan thanks the Alexander von Humboldt Foundation for
funding a renewed research stay at TU Berlin (Ref 3.5—THA/1009402).
Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Harper, M.; Purnell, C.J. Diffusive sampling—A review. Am. Ind. Hyg. Assoc. J. 1987,48, 214–218. [CrossRef]
2.
Brown, H. Monitoring the ambient environment with diffusive samplers: Theory and practical considerations. J. Environ. Monit.
2000,2, 1–9. [CrossRef]
3.
Klanova, J.; Kohoutek, J.; Hamplová, L.; Urbanová, P.; Holoubek, I. Passive air sampler as a tool for long-term air pollution
monitoring: Part 1: Performance assessment for seasonal and spatial variations. Environ. Pollut. 2006,144, 393–405. [CrossRef]
4.
Greenwood, R.; Mills, G.; Vrana, B. (Eds.) Passive sampling techniques in environmental monitoring. In Wilson & Wilson’s
Comprehensive Analytical Chemistry; Elsevier: Amsterdam, The Netherlands, 2007; Volume 48.
Atmosphere 2024,15, 451 19 of 21
5.
Kot-Wasik, A.; Zabiegała, B.; Urbanowicz, M.; Dominiak, E.; Wasik, A.; Namie´snik, J. Advances in passive sampling in
environmental studies. Anal. Chim. Acta 2007,602, 141–163. [CrossRef]
6. Seethapathy, S.; Gorecki, T.; Li, X. Passive sampling in environmental analysis. J. Chrom. A 2008,1184, 234–253. [CrossRef]
7. Pienaar, J.J.; Beukes, J.P.; van Zyl, P.G.; Lehmann, C.M.B.; Aherne, J. Passive diffusion sampling devices for monitoring ambient
air concentrations. In Comprehensive Analytical Chemistry; Forbes, P.B.C., Ed.; Elsevier: Amsterdam, The Netherlands, 2015;
Volume 70. [CrossRef]
8.
Buzica, D.; Gerboles, M.; Amantini, L. Laboratory and Field Inter-Comparison of NO
2
Diffusive Samplers; European Commission,
Institute for Environment and Sustainability, Emissions and Health Unit: Ispra, Italy, 2003. [CrossRef]
9.
Hafkenscheid, T.; Fromage-Mariette, A.; Goelen, E.; Hangartner, M.; Pfeffer, U.; Plaisance, H.; de Santis, F.; Saunders, K.; Swaans,
W.; Tang, Y.S.; et al. Review of the Application of Diffusive Samplers in the European Union for the Monitoring of Nitrogen Dioxide
in Ambient Air; European Commission Joint Research Centre Institute for Environment and Sustainability: Ispra, Italy, 2009;
ISBN 978-92-79-12052-7.
10.
US-EPA. Passive Samplers for Investigations of Air Quality: Method Description, Implementation, and Comparison to Alternative
Sampling Methods. Available online: https://clu-in.org/download/issues/vi/VI-passive-samplers-600-R-14-434.pdf (accessed
on 15 February 2024).
11.
World Meteorological Organization—Global Atmospheric Watch (GAW). Report on Passive Samplers for Atmospheric Chemistry
Measurements and Their Role in GAW, Collection(s) and Series: WMO/TD-No. 829; GAW Report-No. 122. 1998. Available
online: https://library.wmo.int/doc_num.php?explnum_id=9671 (accessed on 15 February 2024).
12.
Stevenson, K.; Bush, T.; Mooney, D. Five years of nitrogen dioxide measurements with diffusion tube samplers at over 1000 sites
in the UK. Atmos. Environ. 2001,35, 281–287. [CrossRef]
13.
Hagenbjörk-Gustafsson, A. Validation of Diffusive Samplers for Nitrogen Oxides and Applications in Various Environments.
Doctoral Dissertation, Umeå University, Umeå, Sweden, 2014.
14.
Wilson, S.M.; Serre, M.L. Use of passive samplers to measure atmospheric ammonia levels in a high-density industrial hog farm
area of eastern North Carolina. Atmos. Environ. 2007,41, 6074–6086. [CrossRef]
15.
Adon, M.; Galy-Lacaux, C.; Yoboué, V.; Delon, C.; Lacaux, J.P.; Castera, P.; Gardrat, E.; Pienaar, J.; Al Ourabi, H.; Laouali, D.; et al.
Long term measurements of sulfur dioxide, nitrogen dioxide, ammonia, nitric acid and ozone in Africa using passive samplers.
Atmos. Chem. Phys. 2010,10, 7467–7487. [CrossRef]
16.
Caballero, S.; Esclapez, R.; Galindo, N.; Mantilla, E.; Crespo, J. Use of a passive sampling network for the determination of urban
NO2spatiotemporal variations. Atmos. Environ. 2012,63, 148–155. [CrossRef]
17.
Yao, X.H.; Zhang, L. Analysis of passive-sampler monitored atmospheric ammonia at 74 sites across southern Ontario, Canada.
Biogeosciences 2013,10, 7913–7925. [CrossRef]
18.
Höhne, A.; Schulte, R.A.A.; Kulicke, M.; Huynh, T.-T.; Telgmann, M.; Frenzel, W.; Held, A. Assessing the spatial distribution of
NO
2
and influencing factors in urban areas—Passive sampling in a citizen science project in Berlin, Germany. Atmosphere 2023,
14, 360. [CrossRef]
19.
Krupa, S.V.; Legge, A.H. Passive sampling of ambient, gaseous air pollutants: An assessment from an ecological perspective.
Environ. Pollut. 2000,107, 31–45. [CrossRef]
20.
Heal, M.R.; O’Donoghue, M.A.; Agius, R.M.; Beverland, I.J. Application of passive diffusion tubes to short-term indoor and
personal exposure measurement of NO2.Environ. Int. 1999,25, 3–8. [CrossRef]
21.
Yu, C.; Morandi, M.; Weisel, C. Passive dosimeters for nitrogen dioxide in personal/indoor air sampling: A review. J. Expo. Sci.
Environ. Epidemiol. 2008,18, 441–451. [CrossRef]
22.
Tomkins, F.C.; Goldsmith, R.L. A new personal dosimeter for the monitoring of industrial pollutants. Am. Ind. Hyg. Assoc. J. 1977,
38, 371–377. [CrossRef]
23.
London, J. The observed distribution of atmospheric ozone and its variations. In Ozone in the Free Atmosphere; Whitten, R.C.,
Prasad, S.S., Eds.; Van Nostrand Reinhold Company: New York, NY, USA, 1985; pp. 11–65.
24.
McBridge, S.; Edwards, J.D. Lead Acetate Test for Hydrogen Sulfide Gas, Department of Commerce, Technologic Papers of the
Bureau of Standards, No. 41. 1914. Available online: https://nvlpubs.nist.gov/nistpubs/nbstechnologic/nbstechnologicpaperT4
1.pdf (accessed on 15 February 2024).
25. Baasch, D. Die Methoden und die Meßtechnik der quantitativen SO2-Bestimmung in der Luft. Zeitschrift für Pflanzenkrankheiten
und Pflanzenschutz 1973,80, 81–87.
26.
Thomas, F.W.; Davidson, C.M. Monitoring sulfur dioxide with lead peroxide cylinders. J. Air Pollut. Control Assoc. 1961,11, 24–27.
[CrossRef]
27. Huey, N.A. The lead dioxide estimation of sulfur dioxide pollution. J. Air Pollut. Control Assoc. 1968,18, 610. [CrossRef]
28.
Verein Deutsche Ingenieure. Determination of the Immission Rate by Means of the IRMA-Method; VDI 3794, Part 1; Beuth: Berlin,
Germany, 1982.
29.
Palmes, E.D.; Gunnison, A.F.; DiMattio, J.; Tomczyk, C. Personal sampler for nitrogen dioxide. Am. Ind. Hyg. Assoc. J. 1976,37,
570–577. [CrossRef]
30.
Reiszner, K.D.; West, P.W. Collection and determination of sulfur dioxide incorporating permeation and West-Gaeke procedure.
Environ. Sci. Technol. 1973,7, 526–532. [CrossRef]
Atmosphere 2024,15, 451 20 of 21
31.
Kring, E.V.; Lautenberger, W.J.; Baker, W.B.; Douglas, J.J.; Hoffman, R.A. A new passive colorimetric air monitoring badge system
for ammonia, sulfur dioxide and nitrogen dioxide. Am. Ind. Hyg. Assoc. J. 1980,42, 373–381. [CrossRef]
32.
Yanagisawa, Y.; Nishimura, H. A badge-type personal sampler for measurement of personal exposure to NO
2
and NO in ambient
air. Environ. Int. 1982,8, 235–242. [CrossRef]
33.
Bamberger, R.L.; Esposito, G.G.; Jacobs, B.W.; Podolak, G.E.; Mazur, J.F. A new personal sampler for organic vapors. Am. Ind.
Hyg. Assoc. J. 1978,39, 701–708. [CrossRef] [PubMed]
34.
Delcourt, J.; Sandino, J.P. Performance assessment of a passive sampler in industrial atmospheres. Int. Arch. Occup. Environ.
Health 2000,74, 49–54. [CrossRef] [PubMed]
35.
Kasper, A.; Puxbaum, H. “Badge-type” passive sampler for monitoring ambient ammonia concentrations. Fresenius’ J. Anal. Chem.
1994,350, 448–453. [CrossRef]
36.
De Santis, F.; Allegrini, I.; Fazio, M.C.; Pasella, D.; Piredda, R. Development of a passive sampling technique for the determination
of nitrogen dioxide and sulphur dioxide in ambient air. Anal. Chim. Acta 1979,346, 127–134. [CrossRef]
37.
Zhou, J.; Smith, S. Measurement of ozone concentrations in ambient air using a badge-type passive monitor. J. Air Waste Manag.
Assoc. 1997,47, 697–703. [CrossRef]
38.
West, P.W. Passive sampling of ambient and work place atmospheres by means of gas permeation. In Environmental Pollutants.
Environmental Science Research; Toribara, T.Y., Coleman, J.R., Dahneke, B.E., Feldman, I., Eds.; Springer: Boston, MA, USA, 1978;
Volume 13. [CrossRef]
39.
Zhou, X.; Lee, S.; Xu, Z.; Yoon, J. Recent progress on the development of chemosensors for gases. Chem. Rev. 2015,115, 7944–8000.
[CrossRef] [PubMed]
40.
Azzouz, A.; Vikrant, K.; Kim, K.-H.; Ballesteros, E.; Rhadfi, T.; Malik, A.K. Advances in colorimetric and optical sensing for
gaseous volatile organic compounds. TrAC Trends Anal. Chem. 2019,118, 502–516. [CrossRef]
41.
Sun, L.; Rotaru, A.; Robeyns, K.; Garcia, Y. A colorimetric sensor for the highly selective, ultra-sensitive, and rapid detection of
volatile organic compounds and hazardous gases. Ind. Eng. Chem. Res. 2021,60, 8788–8798. [CrossRef]
42.
Kawamura, K.; Miyazawa, K.; Kent, L. The past, present and future in tube- and paper-based colorimetric gas detectors. Appl.
Chem. 2021,1, 14–40. [CrossRef]
43.
Silva-Neto, H.A.; Sousa, L.R.; Coltro, W.K.T. Colorimetric paper-based analytical devices. In Paper-Based Analytical Devices for
Chemical Analysis and Diagnostics; de Araujo, W.R., Paixão, T.L.R.C., Eds.; Elsevier: Amsterdam, The Netherlands, 2022. [CrossRef]
44.
Ishiguro, G.; Kawabe, T.; Nakano, N. Development of a detection tablet for a portable NO
2
monitoring system. Anal. Sci. 2006,
22, 789–791. [CrossRef] [PubMed]
45.
Maruo, Y.Y.; Nakamura, J. Portable formaldehyde monitoring device using porous glass sensor and its applications in indoor air
quality studies. Anal. Chim. Acta 2011,702, 247–253. [CrossRef] [PubMed]
46.
Adhikari, S.; Conrad, S.C.; Held, A.; Frenzel, W. Simplified procedures for evaluation of passive samplers for nitrogen dioxide.
Talanta Open 2022,5, 100096. [CrossRef]
47. Hill, R.H.; Fraser, D.A. Passive dosimetry using detector tubes. Am. Ind. Hyg. Assoc. J. 2008,41, 721–729. [CrossRef] [PubMed]
48.
McConnaughey, P.W.; McKee, E.S.; Pritts, I.M. Passive colorimetric dosimeter tubes for ammonia, carbon monoxide, carbon
dioxide, hydrogen sulfide, nitrogen dioxide and sulfur dioxide. Am. Ind. Hyg. Assoc. J. 1985,46, 357–362. [CrossRef] [PubMed]
49.
Evergreen Medical Services, Gas Monitoring Badges. Available online: https://www.evergreenmedical.com/services/
environmental-monitoring/gas-monitoring-badges (accessed on 15 February 2024).
50.
SKC Ltd. Dorset, Passive Samplers. Available online: https://www.skcltd.com/products2/passive-samplers.html;https:
//www.skcinc.com/categories/color-dosimeter-tubes (accessed on 15 February 2024).
51.
ChemSee—Appealing Products, Inc. Toxic Gas Dosimeters. Available online: https://www.chemsee.com/commercial/toxic-
gas/available-products/dosimeters/ (accessed on 15 February 2024).
52.
Namie´snik, J.; Zabiegała, B.; Kot-Wasik, A.; Partyka, M.; Wasik, A. Passive sampling and/or extraction techniques in environmen-
tal analysis: A review. Anal. Bioanal. Chem. 2005,381, 279–301. [CrossRef] [PubMed]
53.
Cape, J.N. The use of passive diffusion tubes for measuring concentrations of nitrogen dioxide in air. Crit. Rev. Anal. Chem. 2009,
39, 289–310. [CrossRef]
54.
Fan, Y.; Li, J.; Guo, Y.; Xie, L.; Zhang, G. Digital image colorimetry on smartphone for chemical analysis: A review. Measurement
2021,171, 108829. [CrossRef]
55.
Grudpan, K.; Kolev, S.D.; Lapanantnopakhun, S.; McKelvie, I.D.; Wongwilai, W. Applications of everyday IT and communications
devices in modern analytical chemistry: A review. Talanta 2015,136, 84–94. [CrossRef]
56.
Capitán-Vallvey, L.F.; López-Ruiz, N.; Martínez-Olmos, A.; Erenas, M.M.; Palma, A.J. Recent developments in computer
vision-based analytical chemistry: A tutorial review. Anal. Chim. Acta 2015,899, 23–56. [CrossRef] [PubMed]
57.
Pohanka, M. Small camera as a handheld colorimetric tool in the analytical chemistry. Chem. Pap. 2017,71, 1553–1561. [CrossRef]
58.
Kiliç, V.; Horzum, N.; Solmaz, M.E. From sophisticated analysis to colorimetric determination: Smartphone spectrometers and
colorimetry. In Color Detection; Zeng, L.-W., Cao, S.-L., Eds.; Intech Open: London, UK, 2020. [CrossRef]
59.
Rezazadeh, M.; Seidi, S.; Lid, M.; Pedersen-Bjergaard, S.; Yamini, Y. The modern role of smartphones in analytical chemistry.
Trends Anal. Chem. 2019,118, 548–555. [CrossRef]
60.
Soares, S.; Fernandes, G.M.; Rocha, F.R.P. Smartphone-based digital images in analytical chemistry: Why, when and how to use.
TrAC Trends Anal. Chem. 2023,168, 117284. [CrossRef]
Atmosphere 2024,15, 451 21 of 21
61. Rose, V.E.; Perkins, J.L. Passive dosimetry—State of the art review. Am. Ind. Hyg. Assoc. J. 1982,43, 605–621. [CrossRef]
62.
Namiesnik, J.; Gorecki, T.; Kozlowski, E.; Torres, L.; Mathieu, J. Passive dosimeters—An approach to atmospheric pollutant
analysis. Sci. Total Environ. 1984,38, 225–258. [CrossRef]
63.
Hulanicki, A.; Glab, S.; Ingman, F. Chemical sensors: Definitions and classification. Pure Appl. Chem. 1991,63, 1247–1250.
[CrossRef]
64. Wolfbeis, O.S. Chemical sensors—Survey and trends. Fresenius’ J. Anal. Chem. 1990,337, 522–527. [CrossRef]
65. Sefton, M.V.; Kostas, A.V.; Lombardi, C. Stain length passive dosimeters. Am. Ind. Hyg. Assoc. J. 1982,43, 820–824. [CrossRef]
66.
Nash, D.G.; Leith, D. Use of passive diffusion tubes to monitor air pollutants. J. Air Waste Manag. Assoc. 2010,60, 204–209.
[CrossRef]
67.
Sen, A.; Albarella, J.D.; Carey, J.R.; Kim, P.; McNamara, W.B. Low-cost colorimetric sensor for the quantitative detection of gaseous
hydrogen sulfide. Sens. Actuators B Chem. 2008,134, 234–237. [CrossRef]
68.
Fàbrega, C.; Fernández, L.; Monereo, O.; Pons-Balagué, A.S.; Xuriguera, E.; Casals, O.; Waag, A.; Prades, J.D. Highly specific and
wide range NO2sensor with color readout. ACS Sens. 2017,2, 1612–1618. [CrossRef] [PubMed]
69.
Greenawald, L.A.; Boss, G.R.; Snyder, J.L.; Reeder, A.; Bell, S. Development of an inexpensive RGB color sensor for the detection
of hydrogen cyanide gas. ACS Sens. 2017,2, 1458–1466. [CrossRef] [PubMed]
70.
Duffy, E.; Huttunen, K.; Lahnavik, R.; Smeaton, A.F.; Morrin, A. Visualising household air pollution: Colorimetric sensor arrays
for monitoring volatile organic compounds indoors. PLoS ONE 2021,16, e0258281. [CrossRef] [PubMed]
71.
Roales, J.; Moscoso, F.G.; Vargas, A.P.; Lopes-Costa, T.; Pedrosa, J.M. Colorimetric gas detection using molecular devices and an
RGB sensor. Chemosensors 2023,11, 92. [CrossRef]
72.
Cerrato-Alvarez, M.; Frutos-Puerto, S.; Miró-Rodríguez, C.; Pinilla-Gil, E. Measurement of tropospheric ozone by digital image
analysis of indigotrisulfonate-impregnated passive sampling pads using a smartphone camera. Microchem. J. 2020,154, 104535.
[CrossRef]
73.
de França Souza, P.A.; Neto, J.L.A.; Cardoso, A.A. A simple technique based on digital images for determination of nitrogen
dioxide in ambient air. Water Air Soil Pollut. 2021,232, 72. [CrossRef] [PubMed]
74.
Shi, C.; He, X.; Kiwfo, K.; Held, A.; Frenzel, W. Optimization of smartphone-based evaluation of tube-type passive samplers
using atmospheric nitrogen dioxide determination as an example. Talanta Open 2024,submitted.
75.
Pla-Tolós, J.; Moliner-Martínez, Y.; Verdú-Andrés, J.; Casanova-Chafer, J.; Molins-Legua, C.; Campíns-Falcó, P. New optical paper
sensor for in situ measurement of hydrogen sulphide in waters and atmospheres. Talanta 2016,156–157, 79–86. [CrossRef]
76.
Sekine, Y.; Katori, R.; Tsuda, Y.; Kitahara, T. Colorimetric monitoring of formaldehyde in indoor environment using built-in
camera on mobile phone. Environ. Technol. 2016,37, 1647–1655. [CrossRef]
77.
de Barros Santos, E.; Moher, P.; Ferlin, S.; Fostier, A.H.; Mazali, I.O.; Telmer, K.; Brolo, A.K. Proof of concept for a passive sampler
for monitoring of gaseous elemental mercury in artisanal gold mining. Sci. Rep. 2017,7, 16513. [CrossRef] [PubMed]
78.
Salcedo, A.R.M.; Sevilla, F.B. Colorimetric determination of mercury vapor using smartphone camera-based imaging. Instrum.
Sci. Technol. 2018,46, 450–462. [CrossRef]
79.
Park, D.-H.; Heo, J.-M.; Jeong, W.; Yoo, Y.H.; Park, B.J.; Kim, J.-M. Smartphone-based VOC sensor using colorimetric polydi-
acetylenes. ACS Appl. Mater. Interfaces 2018,10, 5014–5021. [CrossRef] [PubMed]
80.
Vargas, A.P.; Gámez, F.; Roales, J.; Lopes-Costa, T.; Pedrosa, J.M. A paper-based ultrasensitive optical sensor for the selective
detection of H2S vapors. Chemosensors 2021,9, 40. [CrossRef]
81.
Devi, P.; Singh, J.P. Highly sensitive colorimetric gas sensor based on indium oxide nanostructures for H
2
S detection at room
temperature. IEEE Sens. J. 2021,21, 18512–18518. [CrossRef]
82.
Khachornsakkul, K.; Hung, K.-H.; Chang, J.-J.; Dungchai, W.; Chen, C.-H. A rapid and highly sensitive paper-based colorimetric
device for the on-site screening of ammonia gas. Analyst 2021,146, 2919–2927. [CrossRef] [PubMed]
83.
Engel, L.; Benito-Altamirano, I.; Tarantik, K.R.; Pannek, C.; Dold, M.; Prades, J.G.; Wöllenstein, J. Printed sensor labels for
colorimetric detection of ammonia, formaldehyde and hydrogen sulfide from the ambient air. Sens. Actuators B Chem. 2021,
330, 129281. [CrossRef]
84.
Wang, Z.; Liu, J.; Zhang, L.; Nie, W.; Liu, J.; Yang, J.; Li, Y. Copper (II)-azo complex modified hydrogel: A sensitive colorimetric
sensor for visual detection of H2S gas. Sens. Actuators B Chem. 2023,376, 132968. [CrossRef]
85.
Choi, H.; Seo, J.H.; Weon, S. Visualizing indoor ozone exposures via o-dianisidine based colorimetric passive sampler. J. Hazard.
Mater. 2023,460, 132510. [CrossRef]
86.
De Craemer, S.; Vercauteren, J.; Fierens, F.; Lefebvre, W.; Meysman, F.J.R. Using large-scale NO
2
data from citizen science for
air-quality compliance and policy support. Environ. Sci. Technol. 2020,54, 11070–11078. [CrossRef]
87.
Shi, C. Smartphone-Basierte und Kolorimetrische Auswertung von Passivsammlern für die Bestimmung von Stickstoffdioxid.
Bachelor’s Thesis, Technische Universität Berlin, Berlin, Germany, 2022.
88.
He, X. Smartphone-Basierte Auswertung von Passivsammlern für die Bestimmung von Stickstoffdioxid und Anwendung der
Methode zu Ermittlung der NO2-Belastung in Berlin. Master’s Thesis, Technische Universität Berlin, Berlin, Germany, 2024.
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