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PL was supported by the EU H2020 research and innovation program [grant no. 101008626] (Co-UDLabs Project), European Commission, https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-2020_en.]. 

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Reviewer Comments#

This manuscript presents original research evaluating the use of online sensor parameters (e.g.,

SAC254, NH₄-N, EC) as proxies for tracking polar organic chemicals in combined sewer

overflows (CSOs) during wet weather. The authors use data from three urban catchments of

varying sizes (S, M, L) to assess the predictive performance of these parameters, supported by

regression modeling.

The study is timely and addresses a real-world monitoring gap. The experimental design is

generally sound, the analyses are well-structured, and the conclusions are supported by the

results.

However, I recommend minor revisions before publication to enhance clarity and reproducibility.

Specific Comments:

Abstract>>

• Line 23: It would be helpful to list what the traditional methods are when introducing

online sensors.

• Line 29: Specifically, define "spectral absorption coefficient at 254 nm (SAC254 nm)".

• Line 33: The term "predict" may be too strong here. I recommend rephrasing to

"prediction of concentrations of pesticides…".

2 Materials and methods>>

Sampling Methodology (Lines 99–112)

• How was the dry weather flow baseline defined for each catchment? Was it based on

long-term averages, daily minimums, or another method?

• The dry weather baselines vary significantly between catchments (e.g., 288 m³/h in

Catchment S vs. 5040 m³/h in Catchment L). Could the authors explain how these

thresholds were selected, and if other factors like imperviousness or sewer size

influenced this choice?

• Were samples collected using a time-based or flow-paced method? A brief explanation of

the autosampler setup (e.g., sample interval, bottle volume, trigger mechanism) would

clarify data consistency.

• Sampling for rain event M.2 started 50 minutes late. How did this delay affect the dataset

and any subsequent analyses? Was this event treated differently in modeling?

• Two rain events in Catchment L (L.11 and L.12) included pre-rainfall samples, unlike

others. Why was this done, and did it affect comparability across events?

Polar Organic Chemicals (Lines 99–112)

• How were the final 20 chemicals selected from the pool identified by Furrer et al. [24]?

Were they ranked by frequency, concentration, or relevance?

• How did the authors determine the dominant source for each compound, especially for

those like DEET or benzotriazole with multiple sources?

• Why was the LogKow ≤ 5 threshold chosen? Could this exclude slightly more

hydrophobic compounds commonly found in CSOs?

• What criteria were used to classify compounds like DEET or benzotriazole as "diverse"?

Was this based on data, modeling, or literature?

2 | P a g e

• Can the authors comment on the environmental or ecotoxicological significance of the

selected chemicals for CSO impact assessment?

Section 2.4: Online Sensors

1. Why was NH₄-N only measured in the lab? Was there a reason online sensors weren’t

used (e.g., cost, maintenance)?

2. Sensor availability varies across sites. How did this affect the comparability of proxy

performance between catchments?

3. Please clarify how sensors were calibrated and maintained during field use.

4. Since turbidity and SAC254 were both available in M and L, was turbidity evaluated as a

chemical proxy?

5. How were sensor readings aligned with sample collection times? Was any interpolation

used?

6. Were data quality controls (e.g., filtering, outlier removal) applied to sensor data before

analysis?

7. Can the authors briefly comment on the cost implications of deploying and maintaining

these sensors in real-world settings?

Reviewer Comments – Results and Discussion

• The link between catchment size and dilution patterns is interesting. Can the authors

comment on how this generalizes beyond the three sites?

• Was overfitting in catchment M assessed or mitigated in model development?

• Given negative predictions and high errors, why were only linear models used? Would

log or nonlinear models help?

• Were long rain events weighted differently to avoid bias in model performance?

• Poor pesticide prediction is noted—could this be due to usage patterns, degradation, or

sensor mismatch?

• How were incomplete events (e.g., M.2) handled in the modeling?

• Could larger datasets make machine learning models more viable in the future?

• Is a 30–40% error acceptable for practical CSO monitoring or mitigation decisions?

Practical Suitability and Cost

1. Can the authors comment on the practical feasibility of deploying and maintaining these

sensors in typical municipal CSO sites (e.g., ease of access, vandalism risk,

maintenance needs)?

2. What are the approximate costs (equipment + maintenance) per sensor type, and how

might this scale across a network?

3. How much staff time or technical expertise is needed to operate these systems and

interpret the data?

4. Are the proposed regression models robust enough for routine use by utilities without

specialist data scientists?

Conclusion

3 | P a g e

• Why does catchment size impact indoor chemical behavior? Is there a size threshold

affecting this?

• Why are SAC254 nm and NH₄-N more effective in larger catchments, and what

challenges exist in smaller catchments?

• What other environmental factors could improve the prediction of road-runoff chemicals?

• Why do additional sensors not significantly improve predictions? Is it due to sensor

limitations or chemical complexity?

• How can future studies capture complex chemical behaviors like those of PPPs and

biocides?

• Could hybrid methods combining sensors and traditional monitoring improve accuracy for

Reviewer #2: Dear Laura Waldner. No.: PONE-D-25-14249

Title: Exploring online sensor parameters as proxies for organic chemicals in sewers during wet weather

This paper is the first to systematically explore the parameters of online sensors as alternative indicators for the dynamic changes of organic chemical substances in the combined sewer network during rainy days. I think the topic selection has certain technological innovation value in environmental monitoring. Through the data analysis of multi-scale catching-up areas (with 2,000 to 200,000 residents), the dynamic characteristics of chemical substances from different sources were revealed, and a prediction model framework based on sensor parameters was proposed, providing a feasible solution for reducing CSO pollution. The research design is reasonable, the data analysis method is rigorous, and the conclusion has direct reference significance for the decision-making of urban drainage management. Some important issues need to be considered and some problems to be further improved if the author try to submit other appropriate journals:

Title:

The title of this article has a clear research theme, highlighting the research background under the specific condition of the rainy season and also reflecting its research purpose. However, the title expression lacks novelty and fails to highlight the uniqueness or innovation points of the research. It is suggested to adopt the dual-element structure of "methodology - innovation point". Meanwhile, the title information is not rich enough.

Abstract:

Although this abstract mentions that the research method is to analyze the data of the three catchments, details such as the specific data collection methods and analysis methods can be appropriately mentioned to enable readers to have a clearer understanding of the research methods. The abstract does not mention the possible limitations of the research, such as the representativeness of the data and the applicable scope of the model. Moreover, appropriately mentioning the limitations in the abstract can enable readers to have a more comprehensive understanding of the reliability of the research and the potential directions for improvement.

Line 22-23 �“Currently, most overflow sites are not monitored because traditional methods are costly and time-consuming.”What are the traditional detection methods and their shortcomings? Cite the literature that supports the shortcomings of traditional detection methods.

Introduction:

Line 44-46“These discharges of amixture of wastewater and stormwater contain numerous organic chemicals [1,2] that threaten aquaticspecies.”How was the conclusion that aquatic species are threatened reached? Supplement the toxicological evidence of the threat to aquatic organisms .There is a lack of specific references.

Line 57-59“For example, a sampling interval of 3 minutes has been recommended because organic chemicals from municipal wastewater, also termed indoor chemicals, exhibit very high concen-tration fluctuations, particularly in small catchments (2,700 inhabitants) [10].”Why is it recommended that the sampling time be three minutes instead of one minute or thirty seconds? Clarify the statistical basis of the 3-minute sampling interval.

Material and Methods:

Line 108-109“For rain event M.2, sampling started 50 minutes late due to problems with the autosampler.”Will sampling delay lead to missing data or underrepresentation?It is suggested to supplement and explain: "Cubic spline interpolation was adopted to fill the data gap of the first 50 minutes. The K-S test proved that there was no significant difference between the interpolated data and the measured value distribution (p>0.05)."

Line 157-158“Programming language. Python (version 3.9.18, Python Core Team, 2023) was used for data pretreatment and analysis.”Why choose Python instead of other Programming languages? Supplement the basis for Python language selection (such as the machine learning advantages of the scikit-learn library and the specificity of the Pandas library in time series processing)

Highlights:

First of all, there is a lack of key words related to the research results: The article has drawn relevant conclusions about the correlation between different types of organic chemicals and sensor parameters as well as the prediction model through research. However, contents related to the research results such as "correlation" and "prediction model" are not reflected in the key words. It may make it difficult for readers to directly obtain the core research results information of the article when searching. Secondly, some key words can be further refined: For example, "urban drainage" is relatively broad. If it could be refined to "combined sewer system drainage", it might more accurately reflect the specific context of the research.

1. The scope of application and verification of the model need to be further clarified

The article emphasizes that the applicability of the model should be based on the premise of similar characteristics to the catchment area in this study, but does not specifically define the dimension of "feature similarity" (such as the topological structure of the pipe network, land use type, rainfall intensity, etc.).

Suggestion:Firstly, a sensitivity analysis is supplemented to explore the influence of factors such as the scale of the catchment area, population density, and rainfall patterns on the prediction error of the model. Secondly, add the test results of independent validation datasets (such as catchment areas in other cities), or evaluate the generalization ability of the model through cross-validation.

2. The reasons for the failure of prediction of pesticide chemical substances need to be explored in depth

The failure to predict pesticide concentrations was attributed to "diverse spatiotemporal patterns", but specific driving factors (such as the application cycles of different pesticides, differences in surface retention time, etc.) were not analyzed.

Suggestion:Firstly,supplement the source analysis of pesticides (such as agriculture vs. application in urban green Spaces), and explore the internal mechanism of their low correlation with sensor parameters.Secondly, it is possible to attempt to introduce time lag variables or meteorological data (such as the number of drought days before rainfall) as supplementary parameters of the model.

3. The details of the method need to be refined to ensure repeatability

The measurement frequency of sensor parameters (such as SAC at 254 nm) and the chemical substance sampling interval are not clearly specified, which may affect the reliability of temporal correlation analysis. The minimum sample size required for traditional sampling data during the model calibration phase (such as event count, time coverage) is not mentioned.

Suggestion:First, supplement the technical details of sensor and sampling synchronization (such as time alignment Methods and data interpolation strategies) in the Methods chapter. Secondly, clarify the data requirements for the calibration stage (such as at least how many rainfall events or the range of flow variation should be covered).

4. The practical application path needs to be specified

The conclusion states that "sensor monitoring can be implemented after passing the initial calibration stage", but it does not discuss the balance between calibration costs (such as traditional sampling frequency and analysis fees) and benefits.

Suggestion:First, add economic analysis to compare the long-term monitoring cost of sensors with the full-cycle cost of traditional methods. Secondly, specific strategies for optimization in the calibration stage are proposed (such as the selection of key parameters and the optimization of the sampling time window).

Other revision suggestions:

1. Chart optimization

Supplement the correlation coefficient matrix diagrams of different chemical substance categories and sensor parameters to visually display the key correlations.In the result section, add a scatter plot of the model's predicted values and measured values, supplemented by an error distribution histogram.

Supplement Table:Table 1: Minimum Data Requirement Matrix in Calibration Stage (Number of Rainfall Events × Flow Variation × Types of Pollutants)

2. Discussion extension:

To explore the potential impact of sensor maintenance (such as biofilm interference and drift correction) on the long-term stability of the model.Compare the results of this study with the application differences of SAC254 nm as a substitute parameter for micro-pollutants in sewage treatment plants.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes:  No

Reviewer #2: Yes:  Kang Mao

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Attachments

Attachment

Submitted filename: Editor Comments_v1.docx

Exploring online sensor parameters as proxies for polar organic chemicals – An innovative approach for combined sewer overflow monitoring

PONE-D-25-14249R1

Dear Dr. Waldner,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Alison Parker

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewer #2:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: The udepdated manuscript has been well revised the manuscript.

My recommendation is acceptance

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: Yes:  Kang Mao

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