Closing Access Streets to Schools for Vehicular Traffic—Does It Affect the Air Quality?

Round 1

Reviewer 1 Report

This paper presents the impact of vehicle traffic intensity and the closure of access roads for wheeled vehicles to selected schools in Warsaw at selected hours of the day on changes in air quality. The study primarily focuses on the common road traffic pollutant, nitrogen dioxide, while also considering the concentrations of PM10 and PM2.5, which are crucial factors in atmospheric quality. The analysis reveals a correlation between vehicle traffic intensity and air pollutant concentrations, particularly a significant relationship with nitrogen dioxide.

The research findings underscore the importance of implementing traffic management measures to improve air quality around urban schools, providing valuable insights for urban planners and policymakers. By adopting appropriate measures, such as restricting vehicle traffic, it can effectively reduce the concentration of pollutants in the air, thereby protecting the health of students and community residents.

Here are some suggestions for modifying the paper:

(1) When selecting keywords, consider adding terms related to vehicle traffic restrictions or traffic conditions around schools to enhance the paper's searchability and readability, accurately reflecting its topic and research focus.

(2) In the main body of the paper, specifically in the second chapter "Materials and Methods," provide a more detailed description of the research methods. This should include a thorough explanation of the data collection and analysis processes, including sample selection, calibration of monitoring equipment, and the timing and frequency of data collection. Additionally, explain the statistical methods and data processing techniques used to ensure that readers can understand and assess the reliability of the research results.

(3) In the third chapter, "Results," conduct a more in-depth analysis of the data and provide specific explanations and discussions. Besides describing the changes in the concentrations of nitrogen oxides, PM10, and PM2.5, further explore the relationships between these changes and factors such as vehicle traffic intensity and the implementation time of traffic restrictions. Additionally, incorporate relevant theories and prior research to delve deeper into the discussions and interpretations of the results. This will enhance the academic value and depth of the paper.

(4) In the fourth chapter, "Discussion," summarize the main findings and significance of the research, highlighting its limitations and directions for future research. Explicitly elucidating the meaning and contributions of the study will increase its impact and readability. Simultaneously, pointing out the limitations and future directions can provide valuable references and inspirations for subsequent research.

(5) The literature review may be improved by citing more relevant papers. Just list several as follows.

Equity in temporary street closures: The case of London's Covid-19 'School Streets' schemes

A Hybrid Visualization Model for Knowledge Mapping: Scientometrics, SAOM, and SAO

Overall, the research findings emphasize the importance of implementing traffic management measures to improve air quality around urban schools. This provides beneficial insights for urban planners and policymakers to adopt appropriate measures, such as restricting vehicle traffic, to effectively reduce pollutant concentrations in the air and thereby protect the health of students and community residents.

Good

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors,

I have not been able to read labels in any of your figures.

When you talk about averages in terms of pollution, I don't see anywhere how you do the calculation. You already know that concentrations are not real numbers. Therefore, the calculation tools to be used are not the usual ones. I'd like to know if you use the geometric mean or work with logarithms. I do not see this described in the article.

The same thing happens when you talk about variability. How do you analyse it? What tools do you use?

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The genesis of this work lies in conducting a comparative analysis of the concentration changes in related road traffic pollutants such as PM10, PM2.5, and NO2 before and after the introduction of restrictions on vehicle traffic near schools in Warsaw. Overall, there appears to be sufficient detail presented to support the conclusions drawn in the paper. However, one of the weaknesses identified is the presence of several speculative explanations without proper referencing or supporting evidence.

The abstract section requires reconsideration of the keywords. The current selection does not encompass all the pertinent topics.

Introduction:

Replace "[8]" with "Harrison et al. [8]".

While the introduction is comprehensive, it appears overly lengthy. It is recommended to either eliminate or melt the sixth paragraph, integrating it with the preceding paragraph to provide a concise and solid foundation.

Authors should explicitly outline why they chose locations near schools to assess the impact of vehicle traffic on air quality.

The objectives of the study, as mentioned in the last paragraph of the introduction, need reconsideration. The authors should clearly state the main questions the study aims to answer, which can then be addressed in the discussion section, making the paper more pertinent for policymakers.

Materials and Methods:

Indicate the frequency of data collection (e.g., 1-minute, 5-minute, hourly).

lines 220-221: clarify whether measurements were paused during the mentioned period.

Introduce how traffic volumes were calculated at the studied locations.

Results:

It is crucial to improve the quality of all figures, including visual representations, text, date, and axis scales.

Please verify if NO2 (Figure 9) and PM10 (Figure 8) show similar concentrations.

lines 318-325: authors should provide additional scientific support or proper referencing for their hypotheses.

Figure 12: it appears that the morning traffic rush-hour peak was reduced, but the background concentration of all pollutants remained high. Further discussion is needed.

Consider adding a section or paragraph summarizing the main factors contributing to different pollutant profiles across sites and time.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors are required to improve the paper further according to the comments, especially for 2 and 5.

Good

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors

Your graphs are actually better, but I think you are trying to show too much.

In Figures 11, 12, 14, 15, 17 you say you are showing the variability of some pollutants, but you are showing their concentrations. Variability is another mathematical concept.

You still say you are analysing air quality, but you never calculate it. You just talk about concentrations of pollutants, which is not exactly the same thing.

So your paper is a description of how pollutant concentrations change over time, but you use some mathematical terms incorrectly. You are not calculating air quality or the variability of pollutants.

Your instruments provide continuous data, aggregated into 15-minute averages. You then used Python to calculate averages and correlations. As you know, Python is a programming language. What I am asking for is the mathematical operation you performed. I mean, as you know, concentrations are not real numbers, so you cannot add them up as if they were numbers. 

You know that if you pour a 2% saline solution into a bottle of 5% saline solution, you won't necessarily get a 7% solution, right?

That is why I asked about the mathematical equations you used. It is also why I suspect that you have calculated all your averages incorrectly.

The simplest way to calculate averages with concentrations is to use the geometric mean. To calculate variability and other parameters, the easiest way is to work with their logarithms.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

This study is interesting and well-founded. The paper has been revised rigorously and I think the authors’ responses may satisfy almost all the reviewers’ comments. As such, it is the reviewer’ recommendation that this paper be considered for this journal. However, I think it is crucial to improve the quality of all figures, including visual representations, text, date, and axis scales.

Author Response

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Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

The authors have dealt with all my concerns.

Reviewer 2 Report

Dear authors 

The wording of your article has certainly improved. However, you must be aware that you have done the calculations incorrectly.

The average of 10, 15, 18 and 20 is 15.75. However, the average of 10%, 15%, 18% and 20% is 15.24%.  I think your differences are not so high, so your results must be approximate. However, your results are not perfect.

You should read:

-J. Sanchez-Balseca, A. Perez-Foguet, 2020 "Spatio-temporal air pollution modelling using a compositional approach" Heliyon 6 (e04794)