Safely managed drinking water is of utmost importance. Citizen scientists have been successful in collecting water monitoring data worldwide. However, in these projects, test strips or colorimetric assays have usually been used. The implementation of laboratory methods within a citizen science project could confer both benefits and risks regarding the data collected. We, therefore, compared citizen scientists’ data collected using either a colorimetric method or a spectrophotometric method. The citizen scientists’ data (n = 456) were evaluated by comparing these data with reference data obtained from external laboratory analyses. Citizen scientists’ colorimetric data were highly correlated and showed a moderate percentage agreement with the reference data (percentage agreement: 60%; Spearman r = 0.671). The percentage agreement was, however, poor for drinking water samples that contained larger amounts of copper when the citizen scientists used a colorimetric method. Data from the same samples obtained by the citizen scientists using a spectrophotometric laboratory method were also compared with the reference data and showed a slightly higher percentage agreement and stronger correlation with the reference data (percentage agreement: 69%; Spearman r = 0.727). The percentage agreement was consistent for samples that contained different amounts of copper when the citizen scientists used a spectrophotometric method. The data indicated that citizen science projects could improve data quality by involving citizen scientists in laboratory work.
citizen datacitizen sciencecommunity participationdrinking waterspectrophotometryThe author(s) declared that financial support was received for this work and/or its publication. The research was funded by the Federal Ministry of Education and Research (01BF 2108).section-at-acceptanceEnvironmental Citizen ScienceIntroduction
Safely managed drinking water is of utmost importance. Therefore, ensuring the availability and sustainable management of water is one of the Sustainable Development Goals agreed upon by the members of the United Nations (United Nations, 2015). One potential way to get drinking water is through a local water supplier (Umweltbundesamt, 2025). The supplier must make sure, usually through a set of standardized methods and measurements, that the distributed water is safe to drink (Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection, 2023; Enivornmental protection agency, 2025). However, it can be chemically drastically altered if it remains stagnant in the installation system for a prolonged timeframe (Kölle, 2017). For example, certain heavy metals such as copper or lead that are contained in the installation materials can be dissolved into the drinking water, potentially rendering it even more hazardous to drink (Edwards et al., 2000; Pieper et al., 2017). These changes are not usually part of the distributor’s investigations, and therefore, the extent of these changes is often not entirely clear.
Therefore, there are broad and diverse interests in drinking water measurements directly at the tap. This includes the interests of professional scientists and citizens. Citizens are individually interested in the quality of their drinking water as they use it daily. Scientists are interested in gathering relevant water chemistry data from a sufficiently large number of sample sites to make reasonable assumptions about the extent of drinking water quality changes during stagnation. Such investigations may augment the knowledge gathered from standardized model experiments (Eberle et al., 2009; Zlatanović et al., 2017). There seems to be potential in combining these interests in a citizen science project (Bonney et al., 2009). In such a project, scientists, for example, could help develop appropriate scientific research methods and optimize them for the specific use case. Citizens also bring manifold perspectives, have easy access to their drinking water, and could potentially carry out relevant analyses.
In the past, citizen scientists have been successful in gathering valuable data in a growing number of water-monitoring studies worldwide, including drinking water research (Buytaert et al., 2014; Jollymore et al., 2017; Topping and Kolok, 2021; San Llorente Capdevila et al., 2020; Thornhill et al., 2018; Brouwer et al., 2018; Jakositz et al., 2020). In these projects, citizen scientists usually used test strips or colorimetric assays rather than laboratory methods (Topping and Kolok, 2021; Ali et al., 2019). To date, reports on the involvement of citizen scientists in practical laboratory work using laboratory methods are very scarce (Buytaert et al., 2014). Applying laboratory methods in a citizen science project could help investigate parameters that are not readily detected using field tests and may also improve the quality of the data collected. This, in turn, could have positive implications for publication efforts as well (Burgess et al., 2016). At the same time, data quality and the validation of the methods applied to collect these data remain of key importance (Baker et al., 2021; Aceves-Bueno et al., 2017; Balázs et al., 2021; Loperfido et al., 2010; Stepenuck et al., 2011). This holds especially true for complex methods (Kosmala et al., 2016).
The primary aim of this study was to evaluate the quality of citizen scientists’ copper concentration data obtained using two different methods: a colorimetric method suitable for field tests and a spectrophotometric method suitable for a laboratory setting. For this, the citizen scientists’ data were compared to data obtained in an external laboratory by trained laboratory personnel using atomic absorption spectroscopy. Another aim of this study was to compare the citizen scientists’ laboratory data with their field test data obtained using the colorimetric method in a controlled manner to evaluate the potential benefits of working in a laboratory using an instrumental laboratory method. This brief report is guided by the following research question:
How well do copper concentration measurements obtained by citizen scientists using a) colorimetry and b) spectrophotometry align with reference results from external laboratory analyses?
Materials and methodsProject description
Data were collected from the citizen science project CS:iDrop (Citizen Science: Investigation of Drinking Water of and by the Public). From 2021 to 2024, CS:iDrop sought to enable citizens to sample and analyze drinking water from their own kitchen taps to investigate how certain chemical parameters can change in households during stagnation. The project was located at the Ruhr-University Bochum. Here, the project implemented a stationary laboratory for the citizen scientists throughout the project duration, referred to as the “citizen lab.”
CS:iDrop was structured into four project phases (Figure 1). The project’s laboratory and field methods were developed in the first project phase, called the “development phase” (03/2021–03/2022). To include the citizen scientists in the development and optimization of the project-specific methods, development workshops were organized (Kath et al., 2023). In the piloting phase (04/2022–07/2022), the final data collection procedure was extensively tested, and further improvements were implemented. During the main study phase (08/2022–12/2024), 32 workshop days were carried out. Up to 20 citizen scientists participated in a single workshop day. The workshop days were the data-collection events in the project. During the transfer phase (10/2024–12/2024), three workshop days were carried out in cooperation with a local high school.
Project design of CS:iDrop.
Flowchart details study phases from March 2021 to December 2024, outlining development piloting phase (from March 2021 to August 2022), main study phase (from August 2022 to October 2024), and transfer phase (from October 2024 to December 2024). It describes the workshop day activities. Periods include pre-workshop, workshop day, and post-workshop, specifying measurements taken at homes, the citizen lab, and the laboratory for different water parameters including temperature, pH, water hardness, copper, iron, and lead concentrations. Furthermore, the flowchart shows, that in the citizen lab, the colorimetry and spectrophotometry were used to determine the copper concentration in drinking water samples. In the Laboratory the copper concentration of the drinking water samples was determined by using flame AAS.The “workshop day” concept and the “citizen lab”
One to two weeks before the workshop day, participants visited the citizen lab for the first time and collected the materials they needed for the workshop day. The participants received additional information about the organizational schedule for the workshop day and about the project. They also had the opportunity to ask questions.
On the morning of the workshop day, the citizen scientists took a sample of drinking water from their kitchen tap that had stagnated in the plumbing overnight. They also performed (semi)-quantitative analyses of several water parameters, such as temperature, water pH, and dissolved iron concentration in their drinking water. For these analyses, mostly mobile colorimetric methods were used (Supplementary Figure S3; Supplementary Figure S4). While working, the citizen scientists could contact the project team via telephone, email, or an online messenger if they needed help or further instructions.
In the afternoon of the workshop day, they took their samples to the citizen lab. Here, they determined, among other parameters, the copper concentration in the water samples. For this, they added a “cuprizone” solution (bis-cyclohexanone oxalhydrazone) to their samples. Cuprizone readily reacts with copper-II ions contained in water, forming a copper–cuprizone complex that is stable under mildly alkaline conditions (Messori et al., 2007). The copper–cuprizone complex is soluble in water and has a characteristically blue color. The citizens initially evaluated the colored samples using a color comparison chart (Supplementary Figure S4). Afterward, the samples were transferred into cuvettes. The citizens then placed the cuvettes in the spectrophotometer and evaluated the same samples a second time. The citizen scientists received standardized instructions that were cooperatively developed and evaluated in development workshops (Kath et al., 2023). To ensure efficient operations at the citizen lab, the citizen scientists were tutored by up to four members of the project team. As part of the project’s quality assurance measures, some citizen scientists additionally analyzed reference samples in the citizen lab. The reference samples had a copper concentration of 0.6 mg/l (Supplementary Material M3). Furthermore, the photometers were regularly recalibrated.
In a third step, the same samples that the citizen scientists analyzed in the citizen lab were transported to an external laboratory of a project partner and analyzed there by trained laboratory personnel. In this step, atomic absorbance spectroscopy (AAS) with a flame atomizer was used to quantify the copper concentration.
Data acquisition and statistical analysis
One major aim of this study was to investigate the potential benefits of applying laboratory methods in a citizen science project with regard to the quality of the obtained scientific data. For this, the comparability of copper concentration data of citizen scientists to reference data was evaluated. In this study, the reference data were the laboratory-determined copper concentration data obtained in an external laboratory using a well-established and validated method. This study contains data from 456 datasets of drinking water samples. The project was advertised through different media (e.g., radio, newspaper, and articles on the internet). The participating citizen scientists were of different ages, came from very different professional backgrounds, and had very different employment situations (e.g., full-time, part-time, or retired) (Supplementary Figure S1; Supplementary Figure S2).
Since there were differences between the three datasets (colorimetry, spectrophotometry, and AAS) regarding the scale of measure, data were categorized for statistical analysis according to Muenich et al. (2016) or Topping and Kolok (2021). To generate suitable categories, the steps of the color comparison chart were used as mid-points of a specific concentration range (Table 1). The steps of the color comparison chart were aligned to be visually differentiable for the observers. Categorizing the data enabled systematic evaluation independent of the compared datasets. However, for this reason, only statistical comparison measures suitable for ordinal-scaled data were available. The comparability between the datasets was evaluated by carrying out two separate investigations. First, the percentage agreement was calculated (Topping and Kolok, 2021). Second, the Spearman correlation coefficient r was calculated. Both statistical analyses were performed for the comparison between the citizen scientists’ colorimetric data and the reference data and between the citizen scientists’ spectrophotometric data and the reference data (Aceves-Bueno et al., 2017; Peckenham et al., 2011; Topping and Kolok, 2021).
Measured copper concentration and the assigned categorized copper concentration.
Concentration on the color comparison chart of the colorimetric assay
Copper concentration
Categorized copper concentration
0 mg/l and 0.1 mg/l
<0.15 mg/l
1
0.2 mg/l
0.15–0.24 mg/l
2
0.3 mg/l
0.25–0.39 mg/l
3
0.5 mg/l
0.40–0.59 mg/l
4
0.7 mg/l
0.60–0.84 mg/l
5
1.0 mg/l
0.85–1.24 mg/l
6
1.5 mg/l
≥1.25 mg/l
7
Results
Using the colorimetric method, citizen scientists achieved an overall percentage agreement of 60% with the reference data (Figures 2, 3). The agreement was the highest for samples containing small amounts of copper (<0.15 mg/l). Here, the percentage agreement between the citizen scientists’ colorimetric data and the reference data was 96%. However, in samples with higher copper concentrations, the percentage agreement was rather poor and ranged from 29% to 12%. Figure 3 indicates that the citizens seemed to struggle to accurately quantify the copper concentration in this case, with the method used resulting in several underestimated results. The categorized copper concentrations analyzed by citizen scientists using the colorimetric method in the citizen lab were strongly correlated with the reference data. The r-value of this correlation is 0.671.
Percentage agreement of the citizen scientists data [(a) colorimetric data (green) and (b) spectrophotometric data (blue)] with the reference data (flame AAS data of the external laboratory) for different categorized copper concentrations (according to the reference data). Overall percentage agreement is shown with dotted lines.
Two bubble charts compare categorized copper concentrations from citizen science versus laboratory methods. Panel a uses light green bubbles for colorimetric data, while panel b uses blue bubbles for spectrophotometric data. Bubble size represents frequency of data points, with larger bubbles near lower concentration values on both axes in each chart.
Comparison of the citizens’ colorimetric data and the citizens’ spectrophotometric data with the reference data (flame AAS). The size of the bubbles illustrates the absolute count of data points for a certain categorized concentration. Perfectly agreeing results are shown in a more saturated color.
Scatterplot compares percentage agreement against copper concentration with two data series (green and blue points). Green points illustrate the citizen scientists’ colorimetric data. Blue points illustrate the citizen scientists’ spectrophotometric data. Horizontal dotted lines mark average agreements: 68.86% (blue), 60.09% (green). Values of percentage agreement for citizen scientists’ spectrophotometric data decrease from 72.00% to 50.00% as concentration decreases, and values of percentage agreement for citizen scientists’ colorimetric data decrease from from 95.56% to 12.50% as concentration decreases.
The citizen scientists’ spectrophotometric citizen lab data showed an overall percentage agreement of 69% with the reference data (Figures 2, 3). The agreement was highest for samples with a copper concentration of 0.25–0.39 mg/l according to the reference data. Here, the percentage agreement was 79%. The percentage agreement was 72% for samples with a copper concentration of <0.15 mg/l. The percentage agreement was as low as 50% for samples containing between 0.60 and 0.84 mg/l copper. The categorized copper concentrations determined by the citizen scientists in the citizen lab using the spectrophotometric method were strongly correlated with the reference data. With an r-value of 0.727, the correlation between the citizen scientists’ spectrophotometric data and the reference data was even higher than that for their colorimetric data.
Discussion
One major aim of this study was to investigate the potential benefits of working in a laboratory using an instrumental laboratory method in a citizen science project with regard to the quality of the obtained scientific data. As described above, the data generated by the citizen scientists was characterized by being highly correlated to the reference data with both methods that were used. However, the spectrophotometric laboratory data of the citizen scientists were even more comparable to the reference data than their colorimetric data. This can be explained by three key observations: (1) the percentage agreement of the citizen scientists’ spectrophotometric laboratory data with the reference data was higher than the percentage agreement of their colorimetric data with the reference data; (2) the percentage agreement of the citizen scientists’ spectrophotometric laboratory data with the reference data was more consistent for different copper concentrations compared to the citizen scientists’ colorimetric data; and (3) the correlation coefficient between the citizen scientists’ spectrophotometric laboratory data and the reference data was higher than that of the citizen scientists’ colorimetric data (Figures 2, 3).
Major differences between the citizen scientists’ colorimetric and spectrophotometric data were especially observed for drinking water samples containing higher copper concentrations. These differences might be explained by considering the influence of observer subjectivity, which may have affected the citizen scientists’ colorimetric laboratory data. However, some variances may be attributable to the challenges associated with accurately quantifying the concentration in samples when the colorimetric method is used. This becomes strongly apparent when comparing the percentage agreement for different copper concentrations (Figure 3). The data may also indicate that it was difficult for the citizen scientists to differentiate between the different colors on the color chart (Supplementary Figure S4) (Hegarty et al., 2025). In the case of the spectrophotometric method, the entire experimental setup is highly controlled. It is, therefore, far less likely that subjective factors have influenced the data in this case. Since the procedure used by the citizen scientists in the citizen lab integrated the colorimetric and spectrophotometric methods in consecutive order, sources of error than reading errors in the case of the colorimetric method are far less likely to have occurred. In such a case, the resulting error would have been observed in both datasets alike.
Furthermore, the data collected by the citizen scientists imply that it can be beneficial to use laboratory methods, such as the spectrophotometric method, instead of field tests, such as the colorimetric method. With regard to the Spearman correlation coefficients (r-value) calculated in the premises of this study, the correlation with the reference data was at least slightly increased when the citizen scientists used the spectrophotometric method to obtain the data. One must consider, however, that this description does not change the fact that, for both datasets (colorimetry and spectrophotometry), the correlations with the reference data were at least acceptable. According to a review conducted by Aceves-Bueno et al. (2017), approximately half of the analyzed citizen science studies reported that correlation coefficients exhibited moderate to strong correlations (r ≥ 0.5) between citizen science data and reference data. Both datasets showed correlations with higher r-values than the mentioned threshold value. This is also in accordance with observations from other citizen science projects showing that, with regard to the spectrophotometric data, the data quality produced by citizen scientists could be further improved by using a sensor-based method (Balázs et al., 2021). It has been shown that using sensors can reduce the impact of the observer subjectivity on the quality of the data and therefore decrease sources of error in the final data. Additionally, working in a laboratory setting such as the citizen lab controls other variables that may function as sources of error in the final measurements.
This study is also subject to several limitations. Perhaps the most important limitation is that the reference data are considered the true values in this study. These reference data are also subject to errors (Kosmala et al., 2016). Therefore, the reference data could also be imprecise and may deviate significantly from the true values. However, data from the external laboratory analyses were collected by trained laboratory personnel using a well-established and validated method (DIN 38406-7:1991-09, 1991). External standards were used to verify the stability of methods over the project’s timeframe. Interlaboratory precision and bias data for a typical AAS method indicate a relative standard deviation of 11%, with a relative error of approximately 3% (3111-Metals by Flame Atomic Absorption Spetrometry, 1989). It is, therefore, assumed that the data are sufficiently accurate to be used as reference data in this study.
There is also a case to be made that the Spearman correlation coefficient is not the most robust metric for evaluating the quality of datasets as it is a rank correlation metric. In addition, the effects arising from using categorized copper concentrations, as opposed to measured copper concentrations, on the percentage agreement and the Spearman correlation must also be taken into account. Therefore, even though a high-percentage agreement or a high-rank correlation coefficient can be interpreted on a semi-quantitative level, they do not allow for a quantitative estimation of potential differences. They might also, in some instances, lead to high correlations, as described in the Results section. A high correlation, however, does not necessarily imply high accuracy. For example, in this study, it is shown that the citizen scientists, regardless of the method that they use, can successfully identify samples with a low categorized copper concentration, achieving a very high percentage agreement with the reference data. Nevertheless, the Spearman correlation and the percentage agreement are among the most frequently used measures in citizen science projects for this purpose (Aceves-Bueno et al., 2017). Alternative agreement measures were considered but ultimately rejected. For example, weighted Kappa might have its merits, especially in studies focusing on inter-rater reliability, such as those on species identification. However, it might have overestimated agreement in our case as most deviations are within one category of each other. Bland–Altman plots were not used because of the difference in quantification limits between colorimetric and spectrophotometric data.
In CS:iDrop, citizen scientists successfully obtained laboratory data that were highly comparable to the reference data. The photometric method demonstrated a higher level of comparability with the reference method than with the colorimetric method. This does not, however, mean that the colorimetric data would be insufficient in other projects. However, one has to consider that, if available, applying a laboratory method that is thoroughly developed and optimized could improve data quality. It is also conceivable that the accuracy of citizen scientists would increase even further if they were to engage in laboratory work on a recurrent basis since participation length was found to be a significant covariate of accuracy (Aceves-Bueno et al., 2017). In the future, research could focus on the impact of long-term involvement of citizen scientists in practical laboratory work. This could potentially even pave the way for the implementation of more sophisticated laboratory methods in citizen science projects that are currently not used.
It is very important to note that during the transfer phase, the citizen lab concept—and thus also the methods reported in this study—were successfully implemented in a local high school. This demonstrated that the concept can, in principle, be used in a mobile manner. Citizen labs could, therefore, also be conceivable as on-site laboratories in large-scale projects, as long as the methods used in a project do not rely on stationary power supplies or more complex laboratory infrastructures. However, the use of citizen labs would, of course, have to be accompanied by appropriate feasibility studies in individual cases.
The involvement of citizen scientists in chemical laboratory analyses represents a rewarding challenge in many respects. For example, a venue such as the citizen lab could also serve as a place for knowledge exchange, data gathering, and socializing—and anecdotally, it indeed did. In many cases, participants shared interesting questions, offered professional insights from their individual fields of expertise, and formulated ideas to further develop the project. They were also pleased to have the opportunity to conduct the laboratory measurements themselves. Future studies could, therefore, also focus on supporting these subjective observations with appropriate objective data.
Data availability statement
The original data supporting the conclusions made in this article are included in the Supplementary Material; further inquiries can be directed to the corresponding author.
Ethics statement
Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
JK: Formal Analysis, Validation, Visualization, Writing – original draft, Data curation, Investigation, Conceptualization, Writing – review and editing, Software, Methodology. CS: Writing – review and editing, Conceptualization, Supervision, Writing – original draft. Vv: Writing – original draft, Writing – review and editing, Conceptualization. JW: Resources, Project administration, Funding acquisition, Writing – original draft, Conceptualization, Writing – review and editing, Supervision. KS: Writing – review and editing, Supervision, Writing – original draft, Funding acquisition, Resources, Conceptualization, Project administration.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1765827/full#supplementary-material
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Edited by:Steffen Fritz, International Institute for Applied Systems Analysis (IIASA), Austria
Reviewed by:Shuvojit Nath, Monash University, Australia