PONE-D-24-05065Forecasting Air Pollution with Deep Learning with a focus on Impact of Urban Traffic on Air and Noise PollutionPLOS ONE

Dear Dr. Lameski,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Amgad Muneer

Academic Editor

PLOS ONE

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[The work presented in this article was partially funded by the Ss. Cyril and Methodius

University in Skopje, Faculty of Computer Science and Engineering. We also

acknowledge the support of NVIDIA through a donation of a Titan V GPU. E.Z., P.L.,

M.K., B.S., V.T. and M.H acknowledge the support of the CleanBreathe project. P.J.C.

acknowledges the funding by FCT/MEC through national funds and, when applicable,

co-funded by the FEDER-PT2020 partnership agreement under the project

UIDB/00308/2020.]

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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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: N/A

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Authors,

I have carefully reviewed your manuscript and would like to offer some constructive feedback to enhance its quality and clarity. This study introduces a novel approach to forecasting air pollution in Skopje by leveraging Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units, focusing on PM10 particle levels across five city locations. By analyzing historical air quality data alongside meteorological conditions, and comparing various deep learning model implementations, the research aims to enhance prediction accuracy. Additionally, it investigates the impact of urban traffic on air and noise pollution, uniquely utilizing the COVID-19 lockdown period as a case study to assess traffic's role. The findings suggest that urban traffic is not the primary contributor to air pollution, marking a significant step in understanding environmental influences in urban settings.

Below are my suggestions by line number:

65: The organization of the manuscript is not well-written at the end of the introduction.

86: Citation 20 should be replaced by the original paper of LSTM:

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

140: You wrote, "this research takes into account PM2.5 values", while in your abstract PM10 was written. Hence, which one was taken into account.

159: "The validation samples were dynamically selected, constituting 20 percent of the training data points.", it's advice to write how many samples?

160: "Hyperparameter tuning was done manually on the data for November 11, 2019, following the approaches and methods in [6]"

I checked the cited paper, and it stats that Keras Tuner library was utilized to optimze their proposed model, along with the manual selection of data for validation. The hyperparmeters tuned in that paper were learning rate, dropout rate, layers units, kernal size, num. of filters. etc. You can mention Keras Tuner as well with more details.

210: add the equation for MSE, and you should consider other evaluation metrics as well.

256: add supporting citation for the first paragraph that defines the feature selection process.

260: "we explore three popular feature selection methods: Pearson’s correlation coefficient, Chi-squared test, and K best features from Linear Regression, RandomForest Regression, and LightGBM",

263: add supporting citation for each technique (Pearson’s correlation coefficient, Chi-squared test, and K best features).

320: RMSE is used while it's not written in the line 210, if you plan to use it, then add its equation.

363: "Many studies have shown that pollution is higher during the winter months in colder conditions". Add referecnes to those studies.

The conclusion needs to add more details about the conducted experiment as it is now only summerize the limitations and future works that needs to be done.

In general, the paper needs to add more references to support its claims.

Figure 1: It is a trivial and very basic figure which doesn't reflect the methedology of the manuscript. You should redesign it using programs like draw.io or any alternative.

Increate the resolutions of your figures as I couldn't read their data.

It is advisable to avoid the first-person perspective in academic writing. Consider using passive voice constructions instead of "we" and "our."

You should add the architectures of the used techniques . i.e. RNN, LSTM, GRU, CNN etc. so novice readers can understand your paper.

I hope these comments are helpful in refining your manuscript. Your efforts to advance the understanding of fake news detection using deep learning techniques are commendable, and I look forward to the revised version of your work.

Best regards

Reviewer #2: The central idea of the article titled “Forecasting Air Pollution with Deep Learning with a focus on Impact of Urban Traffic on Air and Noise Pollution” is well-conceived and contributes valuable insights to the field of pollution. However, the paper would benefit from improved organization and clarity. Here are my suggestions:

1. The title of the article, “Forecasting Air Pollution with Deep Learning with a focus on Impact of Urban Traffic on Air and Noise Pollution,” is somewhat misleading. The term "pollution" is very general and encompasses a wide range of pollutants. To be more precise and reflective of the content, the title should specify that the study focuses on forecasting specific pollutants, such as PM2.5 and PM10. This will provide clarity to the readers about the scope of your research.

2. The introduction of the article lacks a clear structure, making it difficult to follow the main themes. At times, the author discusses works related to pollution, while at other times, the focus shifts to machine learning and IoT. To improve the flow and coherence, I suggest organizing the introduction into distinct sections. Start with an overview of the pollution problem, followed by a review of relevant works on pollution. Then, discuss the role of machine learning and IoT in addressing these issues, clearly linking each section to the overarching aim of the study.

3. The statement, "The primary innovation of our study lies in the utilization of diverse datasets obtained from local sensors to enhance prediction accuracy," suggests a novel approach. However, it is important to note that many existing works have already utilized similar methods. Therefore, this aspect of the study may not be as innovative as claimed. I recommend highlighting what specifically differentiates your approach from these existing works to clearly demonstrate its unique contributions.

4. The phrase, "An idea was put forth to forecast PM10 concentration for various time intervals by employing three distinct stepwise Multiple Linear Regression (MLR) models as proposed in [17]. In [18], the authors forecasted the concentration of a heavily polluted area," is not well expressed. It would benefit from clearer structure and coherence. For instance, consider rephrasing it to: "The study proposed forecasting PM10 concentrations for various time intervals using three distinct stepwise Multiple Linear Regression (MLR) models, as outlined in [17]. Additionally, [18] focused on forecasting the concentration of pollutants in a heavily polluted area."

5. While the authors cited the measurements gathered at each location, it is noted elsewhere in the article that meteorological variables are also included. To present the data with greater clarity, we recommend that the authors include a table that lists all the features, including the meteorological variables, along with their respective time intervals.

6. In the feature selection paragraph, the authors use the chi-squared (chi2) method for feature selection. However, since the authors are working with numerical values and the chi-squared method is designed only for categorical variables, this approach is not appropriate for the data being used.

7. The authors state that "Feature selection is a crucial step in building effective and efficient machine learning models. The process entails identifying and preserving the most pertinent features from the dataset to enhance model performance while also reducing computational complexity." However, it appears that the feature selection process described was not effectively utilized to enhance the model's performance or reduce computational complexity. More details on how the selected features improved the model and reduced complexity would be beneficial.

8. The purpose of this work is to predict the concentration of PM2.5 and PM10 for 1, 6, 12, and 24 hours ahead. However, I have concerns about the practical benefits of these short-term predictions given the methodology used. The model relies on measurements taken every 15 minutes over the last day. Therefore, if we already have detailed concentration data for each 15-minute interval from the previous day, it is unclear how beneficial it is to predict concentrations for the next 1, 6, 12, or 24 hours, as these predictions may closely mirror the short-term past data. It might be more useful to focus on predicting the concentration of these pollutants over a longer period, such as a week or a month, to provide more valuable insights for planning and intervention.

Reviewer #3: This study presents an LSTM and RNN approach to address the challenge of food supply purity and well-being. The topic is relevant and of significant interest to researchers. The contribution of the authors mainly lies in the data collection part. However, the study can benefit from the following comments:

Points for Improvement

The problem statement should be derived from the literature. I suggest citing relevant studies in the introduction to strengthen the claims of the authors.

The authors should clearly mention why LSTM and CNN are selected. What are the rationales behind selecting these models? Discussing the potential impact of the study in both the introduction and conclusion will showcase the importance of the study.

Data Description

The description, size, and dimensions of the data are not clearly described. Th

The results should be discussed rather than just presented. Additionally, it is not clear why the chosen evaluation measures are selected. The authors should explain the choice of these metrics.

The authors are advised to perform a gap analysis among recent studies. This will help in identifying the unique contributions of the current study and how it advances the field.

Real-World Applicability

Are the methods and findings applicable to real-world air pollution detection scenarios? The authors should discuss the practical implications and applicability of their approach.

Scalability

Is the approach scalable to different datasets or geographical areas? This is important to understand the broader applicability of the study’s findings.

Authors can discuss the limitations of the study and how addressing them is important.

A recent helpful study related to CNN and LSTM can be beneficial for the authors

Abid, Yawar Abbas, Jinsong Wu, Guangquan Xu, Shihui Fu, and Muhammad Waqas. "Multi-Level Deep Neural Network for Distributed Denial-of-Service Attack Detection and Classification in Software-Defined Networking Supported Internet of Things Networks." IEEE Internet of Things Journal (2024).

This will add credibility to the research.

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Reviewer #1: No

Reviewer #2: Yes: Samira Douzi

Reviewer #3: No

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PONE-D-24-05065R1Forecasting Air Pollution with Deep Learning with a focus on Impact of Urban Traffic on PM10 and Noise PollutionPLOS ONE

Dear Dr. Lameski,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 19 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Amgad Muneer

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #3: N/A

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #3: No

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for your revised submission. I have reviewed your manuscript again and you have addressed all my previous comments, but I have four minor concerns for improvement. Please address these points and submit the revised version.

Concern 1:

It is advisable to avoid the first-person perspective in academic writing. Consider using passive voice constructions instead of "we" and "our."

Concern 2:

No need to repeat the full abbreviations multiple times. Since you've already declared them once, use the abbreviations consistently throughout the text. I've noticed many of them.

Concern 3:

342:

"we chose these metrics because MSE and RMSE are robust and informative metrics for evaluating regression model".

Instead of the original sentence, you may write: "MSE and RMSE are among the most commonly used evaluation metrics for regression models, as noted in recent systematic literature reviews"

Al-Selwi, S. M., Hassan, M. F., et al. (2024). RNN-LSTM: From applications to modeling techniques and beyond—Systematic review. Journal of King Saud University-Computer and Information Sciences, 102068.

Concern 4:

282:

Avoid using contractions like "don't" in academic writing. Instead, use the full form, such as "do not," to maintain a formal tone.

I look forward to your updated submission.

Reviewer #3: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (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 #1: No

Reviewer #3: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Forecasting Air Pollution with Deep Learning with a focus on Impact of Urban Traffic on PM10 and Noise Pollution

PONE-D-24-05065R2

Dear Dr. Petre Lameski,

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 (the figures provided are blurry, please ensure to provide them with better quality before publication).

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.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

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,

Amgad Muneer

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors have addressed all the reviewers comments and paper ready for acceptance except all the figures are blurry and authors needs to provide a better quality figure before publication

Reviewers' comments: