Study on the Correlation Mechanism between the Living Vegetation Volume of Urban Road Plantings and PM2.5 Concentrations
, Qianqian Sheng 1,2,3,4,5,* and Zunling Zhu 1,2,3,4,5,6,*Round 1
Reviewer 1 Report (Previous Reviewer 1)
The authors made an effort and clearly improved the work. Now, there is an introduction with the objective well defined, methods, description of the results and a discussion explaining the results obtained. The conclusions could be a bit shorter and less descriptive but I think the paper can be published in its present form.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (New Reviewer)
Authors should use mdpi template, it is very difficult to mark necessary changes without line numbers. Also, citing style needs to fit the one requested in the template.
The experiment part of the paper is designed well, the methods could be additionally clarified.
In the Results section, plant species should be written in full when first mentioned, after that it can be abbreviated.
Images need improvements, there is a different font for each of the labels and it does not look proffesional in that way.
Overall this paper has a potential to be a good article and contribute to overall knowledge in the field but authors need to do a better job in spelling, grammar and overall editing. Authors could include newer references to improve the reference quality.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report (New Reviewer)
In this article, the author takes different road greening combinations of Xianlin Avenue in Nanjing as the sample area, and sets 3-6 PM2.5 monitoring points in each sample area. Monitoring points are set at 10, 20, 30, 40, 50 and 60 m away from the subgrade to monitor the PM2.5 concentration in different areas and calculate the volume of living vegetation in different areas. The coupling relationship between the living volume of vegetation and PM2.5 concentration in different areas was evaluated by regression fitting and other methods. It was found that with the increase of the living volume of vegetation, the fitting function first increased and then decreased in a certain range. It is speculated that only when the volume of living vegetation exceeds a certain range, it will promote the reduction of PM2.5. However, in terms of the current status of the manuscript, it is not enough to be published in Sustainability. Here are some suggestions for the author to improve this manuscript.
Major:
1. The author used observation instruments to monitor PM2.5 concentration in the study area, but the quality of the data is not clear. It is suggested that the authors compare the data with observation data from local environmental monitoring stations.
2. The author focuses on the relationship between living vegetation volume and PM2.5 concentration, but only analyzes the vegetation volume in the whole S1-S8 region. Do different types of plants have different impacts on PM2.5? In addition, does the height of trees affect PM2.5 concentrations?
3. It is suggested that the author specify in the abstract which vegetation type has a greater impact on PM2.5 in as much detail as possible, instead of using S1-S8, which is not conducive to readers' understanding.
The sample size used in the article is small and may not be universal. You can refer to the literature or add samples to make the results more convincing.
4. The number statistics/area statistics of various trees in each sample area can be added to make the data analysis more quantitative rather than qualitative.
5. The annotation in Figure 3 is not detailed enough, please refer to Figure 4 for supplement.
6. PM2.5 concentration distribution is closely related to some variables, such as population density, industrial emissions and traffic intensity. If there is any difference in traffic intensity and industrial sources in these sample areas, please make a detailed analysis of the environment in the sample areas.
Minor:
1. Some Chinese appears in the text (for example, at the end of the second paragraph in Section 3.1), as well as in some figures (Figure 3).
2. The red line in Figure 3 should represent the first level standard, not the monthly average.
3. The red line in Figure 4 should represent the first level standard, not the monthly average.
4.Figure 2 is not clear enough.
5. Some figures do not indicate variable names or units (Figure 6).
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report (New Reviewer)
I would like to thank authors for accepting my suggestions. I have 2 little remarks.
Lines 120-132 needs formatting.
Line 177 and 183. formulas need numeration.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report (New Reviewer)
There are some format problems, such as line 286.
Author Response
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Author Response File: Author Response.pdf
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Given the levels of atmospheric pollution in urban areas, I think the study is interesting and very appropriate, to try to get the authorities to plant more green areas and to choose the species that can remove the most pollutants from the atmosphere.
The paper in general is well written, my main problem is with the discussion, it is not a discussion but results and methodology will have to be rewritten again to accept the article. My specific comments are in the pdf.
Comments for author File: Comments.pdf
Author Response
Response to Reviewers
First, we really appreciate editor and reviewer’s time and effort to review our original manuscript. Also, thank you all for the very detailed suggestions. All the comments and recommendations are very good and we answered all the questions and made changes accordingly.
Thank you all again and best regards.
Following are our response:
Reviewer 1:
- Given the levels of atmospheric pollution in urban areas, I think the study is interesting and very appropriate, to try to get the authorities to plant more green areas and to choose the species that can remove the most pollutants from the atmosphere.
The paper in general is well written, my main problem is with the discussion, it is not a discussion but results and methodology will have to be rewritten again to accept the article. My specific comments are in the pdf.
Response: Thanks for the recommendation. We have revised the results and discussion, please see the full manuscript, and we have also made corresponding modifications according to your specific modification suggestions in the manuscript.
Author Response File: 
Author Response.docx
Reviewer 2 Report
This study examines the correlation between the amount of living vegetation volume of urban road plantings and the PM2.5 concentration. The results showed that the PM2.5 concentration in different sample areas is significantly different. Also, the fitting curve of living vegetation volume and PM2.5 concentration in sample areas S1 and S3-S8 can explain 76.4% of the change in PM2.5 concentration, which showed a significant fit. Although the topic under study is interesting, this research needs extensive corrections:
- The contribution of this study is not well explained in the introduction section.
- Innovation and the difference with previous studies should be clearly explained.
- The theoretical channel between the living vegetation volume of urban road plantings and PM2.5 concentration is not well explained.
- The model used is not well explained.
- Policy recommendations are not provided according to the research results.
Author Response
Response to Reviewers
First, we really appreciate editor and reviewer’s time and effort to review our original manuscript. Also, thank you all for the very detailed suggestions. All the comments and recommendations are very good and we answered all the questions and made changes accordingly.
Thank you all again and best regards.
Following are our response:
Reviewer 2:
This study examines the correlation between the amount of living vegetation volume of urban road plantings and the PM2.5 concentration. The results showed that the PM2.5 concentration in different sample areas is significantly different. Also, the fitting curve of living vegetation volume and PM2.5 concentration in sample areas S1 and S3-S8 can explain 76.4% of the change in PM2.5 concentration, which showed a significant fit. Although the topic under study is interesting, this research needs extensive corrections:
- The contribution of this study is not well explained in the introduction section.
Response: Thanks for the recommendation. We have revised the contribution of this study, the contents are as follows:
The correlation between the plant community, living vegetation volume, and PM2.5 concentration in different sample areas was discussed, in order to quantify the contribution rate of three-dimensional green quantity of plants to the reduction of particulate matter. The results of this study can provide theoretical guidance for the optimal design of urban road planting and the spatial layout of green areas around urban roads with the objective of airborne particulate matter purification.
- Innovation and the difference with previous studies should be clearly explained.The theoretical channel between the living vegetation volume of urban road plantings and PM5 concentration is not well explained.
Response: Thanks for the recommendation. Many studies have claimed that plants could absorb atmospheric particles due to their special leaf surface structures and physiological and biochemical characteristics (Sheng Q.Q, 2018 and Leonard, R.J., 2016), and planting living vegetation volume is often used to describe the spatial distribution of vegetation in green space. The reduction of airborne particulate matter of composite structure of vegetation was better than the single structure of green space(Song, C et al., 2017). However, different green space structures have different three-dimensional green values, different road types and different levels of urban development, which will have different impacts on environmental particulate matter, and the differences in the correlation research results lie in the different types of green spaces are not known and need to be studied further. Therefore, further investigation is necessary to clarify the relationship between the living vegetation volume and PM2.5 concentrations.
Leonard, R.J.; McArthur, C.; Hochuli, D.F. 2016. Particulate matter deposition on roadside plants and the importance of leaf trait combinations. Urban For. Urban Green. 20, 249-253.
Song, C.; He, J.; Wu, L.; Jin, T.; Chen, X.; Li, R.; Ren, P.; Zhang, L.; Mao, H. 2017. Health burden attributable to ambient PM2.5 in China. Environ. Pollut. 223, 575-586.
- The model used is not well explained.
Response: Thanks for the recommendation.
As shown in Figure 6, through systematic analysis of the three-dimensional green quantity and particulate matter concentration in 8 sample areas, the correlation between the three-dimensional green quantity and particulate matter is preliminarily estimated. In order to further clarify the correlation between the two, SPSS software is used to conduct multiple regression analysis and establish the regression model of PM2.5 concentration in the sample area. To study whether the quadratic function can better fit the relationship between the three-dimensional green quantity and PM2.5 concentration in the 8 sample areas. According to the regression equation, both living vegetation volumes and particulate matter concentration are important indicators. The determination coefficient R2 of living vegetation volumes in sample area S1-S8 is 0.203, indicating that it can explain 20.3% of PM2.5 concentration change, but it is not significant. After the removal of sample area S2, that is, the sample area less affected by motor vehicle exhaust compared with the other 7 sample areas, the living vegetation volumes of sample areas S1, S3-S8 and PM2.5 concentration were fitted. The determination coefficient R2 was found to be 0.764, which explained 76.4% of PM2.5 concentration change, and the regression relationship reached a very significant level. It shows that the regression equation has included the main sample areas and pollutants which can show the correlation between the two. It was found that after the removal of S2 sample area, the determination coefficients R2 all achieved a good function fitting, indicating that these sample areas can be used to accurately evaluate the contribution rate and correlation of three-dimensional green amount corresponding to different green space plant configurations to the concentration of PM2.5 purification through the regression equation.
- Policy recommendations are not provided according to the research results.
Response: Thanks for the recommendation. we have revised the ms “The results of this study provided some theoretical reference and practical value for the optimization and redesign of road plantings focusing on the reduction of particle pollutants, and we also hope that the research results can provide reference for the local government's eco-environment-related policies.”
Author Response File: Author Response.docx
Reviewer 3 Report
The present study was designed to evaluate correlation between the plant community, living vegetation volume, and PM2.5 concentration in sample area. The subject of the study is interesting, however the authors did not presented the results and findings clearly.
For example, I could not see any statistical values in any figure except figure 6.
In the results and discussion also p values and statistical diffrences are missing. For example "By analyzing the PM2.5 concentrations at different monitoring points in eight sample areas, it was found that there were significant differences in PM2.5 concentrations among different sample areas."
Author Response
Response to Reviewers
First, we really appreciate editor and reviewer’s time and effort to review our original manuscript. Also, thank you all for the very detailed suggestions. All the comments and recommendations are very good and we answered all the questions and made changes accordingly.
Thank you all again and best regards.
Following are our response:
Reviewer 3:
The present study was designed to evaluate correlation between the plant community, living vegetation volume, and PM2.5 concentration in sample area. The subject of the study is interesting, however the authors did not presented the results and findings clearly.
For example, I could not see any statistical values in any figure except figure 6. In the results and discussion also p values and statistical differences are missing. For example "By analyzing the PM2.5 concentrations at different monitoring points in eight sample areas, it was found that there were significant differences in PM2.5 concentrations among different sample areas."
Response: Thanks for the recommendation. We have added the statistical values in the results and discussion. Please see page P6-12.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The authors have followed some of the reviewers' suggestions. But the discussion and conclusions remain unchanged. There is still some methodology in these two sections and it is all very descriptive without attempting to explain any of the results obtained.
Reviewer 2 Report
Considering that the article had serious flaws and I gave the authors a chance to make the corrections properly. But the corrections made are not appropriate and could not cover the article's lines. In my opinion, the article connot be published.
Reviewer 3 Report
The changes made by authors are not sufficient enough. The manuscript have serious flaws. The presentation is still not clear, many sentences are confusing.
In my opinion, One-way ANOVA and t-test that were used for comparison of mean PM2.5 concentrations at each monitoring site and significance of differences, are not enough. I would suggest authors to take help of a statistician to analyse the data properly.
