Exploring the Joint Impacts of Natural and Built Environments on PM2.5 Concentrations and Their Spatial Heterogeneity in the Context of High-Density Chinese Cities
Round 1
Reviewer 1 Report
Title: Exploring the Joint Impacts of Natural and Built Environments on PM2.5 Concentrations and Their Spatial Heterogeneity in the Context of High-Density Chinese Cities Author(s): Shanyou Duan, Qian Liu *,Dumei Jiang, Yulin Jiang, Yinzhi Lin and Ziying Gong MS Type: Article
In this study, the authors studied the impacts of the natural and built environment on air pollution in Shenzhen by using different models. The study found that meteorological factors had influence on PM2.5, as well as other natural factors can cause an increase in PM2.5 concentration. Overall, the results from this study help to understand the factors that can increase the PM2.5 concentration in the city and provide insights for future policies to control air pollution. I’m supportive of the paper and publication, but there are a few comments/questions that should be dressed to improve the clarity of the paper before publication.
General Comments
• In this study, author used the meteorological factors and other factors from different years to compare and explain the PM2.5 concentration in the city. Why not use the same year data which can be much more convincing and valuable? For examples, how representative of RH, wind speed, temperature in 2020 compare to the data in 2016?
Specific comments
Method
• Line 180-183: Why do authors use nature factor data in 2020 to explain the PM2.5 concentration in 2016? Does the data in 2020 is representative for the data in 2016?
Results
• Line 241-244: What about the RH, WS, TTI, TEM result from GWR models? Does the result similar to OLS model?
• Table 4. It seems like the top table and the bottom table are duplicated.
• Figure 2: Need caption for the left and right of Figure 2A and 2B, author should label the map for reader to know the district or the area name. Same to Figure 3.
• Line 284 -287: Why did WS has negative influence on PM2.5 concentration in the central city of the Nanshan District but it had positive influence on PM 2.5 concentration in other central cities? Can author provide more discussion?
Author Response
Responses to the reviewers’ comments on manuscript Sustainability-1384362
We thank the reviewers for their thoughtful reviews of our paper. Pondering their comments has sharpened our own thinking and helped improve this manuscript. We have made a careful revision based on these comments. Our responses to the comments are presented below. We hope that the revised paper is acceptable for publication.
Responses to Reviewer #1:
- In this study, author used the meteorological factors and other factors from different years to compare and explain the PM2.5 concentration in the city. Why not use the same year data which can be much more convincing and valuable? For examples, how representative of RH, wind speed, temperature in 2020 compare to the data in 2016? Why do authors use nature factor data in 2020 to explain the PM2.5 concentration in 2016? Does the data in 2020 is representative for the data in 2016?
Response: Thanks for pointing out the problem. As your concerns, we realized the possible issue caused by the data from different periods at the outset of our research.
The PM2.5 dataset is based on NASA Moderate Resolution Imaging Spectroradiometer (MODIS), which is only updated to 2016. This dataset is based on the grids of 1*1 km, which provides a fine-grained scale for our research. We have tried our best to contact Shenzhen Meteorological Bureau to obtain the latest meteorological data, and have successfully purchased the local meteorological dataset updated in 2020. However, this most recent meteorological dataset of Shenzhen is mainly for meteorological factors, such as wind speed and temperature, which provides limited information on PM2.5. The basic unit and precision of local dataset on PM2.5 is undesirable compared with the dataset from NASA-MODIS. On one hand, we found that a large number of existing empirical studies regarding PM2.5 published recently were utilized the 2016 dataset from NASA-MODIS. On the other hand, we made a careful comparison between these two datasets, the data from 2016 NASA-MODIS and Shenzhen Meteorological Bureau, and found that the spatial variations of PM2.5 used in our manuscript on the district-level are basically similar between these two datasets. In other words, the meteorological factors in Shenzhen have been relatively stable since 2016. Therefore, we finally chosen the NASA-MODIS data in 2016, considering its fine-grained grids and better precision.
In terms of built environment, automobile exhaust and industrial pollution are two important sources of PM2.5 pollutants in Shenzhen. Therefore, in terms of traffic, congestion and large-displacement vehicles are the most likely cause PM2.5 emissions, so we choose the traffic time index for 2016 to show the road operation in that year and the bus station density for 2015. According to the analysis, there is no obvious change in bus station in 2015 and 2016. At the same time, because the large-scale road construction period in Shenzhen has already ended, the road network density has not changed much, and there are few large-scale newly-built road facilities. So we used road data from 2018.
Based on the above reasons, we argue that our research results are still reliable even considering the issues of different years of data. The current data combination made a comprehensive consideration between data availability, data precision and the stability of a particular variable during a certain period.
- Line 241-244: What about the RH, WS, TTI, TEM result from GWR models? Does the result similar to OLS model?
Response: Thanks for your comments and questions. Based on the results of OLS model as shown in Table 2, two variables of TTI and RH are statistically insignificant in the OLS model, which are therefore excluded from the GWR model. The other two variables, WS and TEM, have statistically significant effects on PM2.5 in the OLS model, and we include these two variables in the GWR model. The spatially differentiated influences of WS and TEM on PM2.5 are shown in Figure 2, and are further discussed in Lines 282-293.
- Table 4. It seems like the top table and the bottom table are duplicated.
Response: Thanks for pointing out the problem. We have deleted the duplicated part in Table 4.
- Figure 2: Need caption for the left and right of Figure 2A and 2B, author should label the map for reader to know the district or the area name. Same to Figure 3.
Response: Thanks for your valuable suggestions. We have added the sub-captions for Figure 2. The name of each district is labeled in Figure 1. In order to make the spatial changes more clearly shown in Figure 2, we did not display the name of each district, but identified some typical locations (e.g. Shekou Peninsula, Futian CBD, Qianhai CBD and ect.) that are mentioned in the discussion part. This location-related information in both Figure 1 and Figure 2 could help improve readability of this paper.
- Line 284 -287: Why did WS has negative influence on PM2.5 concentration in the central city of the Nanshan District but it had positive influence on PM 2.5 concentration in other central cities? Can author provide more discussion?
Response: Thanks for your valuable questions. We have augmented some interpretation regarding this modeling result. Please refer to Lines 293-300 for the revisions. ‘We speculated that the relatively high FAR and building density in the central city, particularly in Nanshan and Futian CBD, obstructed air circulation. The lack of ventilation corridor weakened the role of WS in reducing the concentration of PM2.5. Contrarily, there existed a negative correlation in Shekou Peninsula of Nanshan District, probably resulting from its proximity to Shenzhen Bay. We inferred that the ventilation corridor is unblocked for the most areas of Shekou Peninsula where is adjacent to the Pearl River Estuary. Therefore, the increase of WS significantly improved the diffusion of PM2.5 pollutants, suggesting the importance of creating ventilation corridor in high-dense urban areas for better air quality.’
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The analyzes contained in the article are very extensive. In my opinion, the conclusions contain too little practical information and recommendations, especially regarding the location and density of roads.
Furthermore, I consider the sentence in lines 358 and 359 to be incorrect ("Theories believe that increasing the density of road networks is an important way to promote sustainable transportation"). Please provide a reference or delete this fragment.
Author Response
Responses to Reviewer #2:
- The analyzes contained in the article are very extensive. In my opinion, the conclusions contain too little practical information and recommendations, especially regarding the location and density of roads.
Response: Thanks for your valuable suggestions. We have Improved the conclusion part by adding further interpretation regarding FAR and road density. Please refer to Lines 343-355 and Lines 378-390 for the revisions.
- Furthermore, I consider the sentence in lines 358 and 359 to be incorrect ("Theories believe that increasing the density of road networks is an important way to promote sustainable transportation"). Please provide a reference or delete this fragment.
Response: Thanks for your careful review. We have deleted the inappropriate expressions and double checked the citations and references.
Author Response File: Author Response.pdf
Reviewer 3 Report
The study fulfills the aim and scope of this journal. In general, the structure and body of the manuscript are good.I recommend this manuscript for publication without my comment.
Author Response
Responses to Reviewer #3:
- The study fulfills the aim and scope of this journal. In general, the structure and body of the manuscript are good.I recommend this manuscript for publication without my comment.
Response: Thanks for your recommendation.
Author Response File: Author Response.pdf
Reviewer 4 Report
In this essay, Duan et al. used the ordinary least square (OLS) and geographical weighted regression (GWR) models, selected an already high-density city, and explored the effects of the natural and built environments on air pollution.
Introduction should contain more data regarding PM2.5 as in source, respiratory health effects, measuring methods, ways to counteract.
3.1. Study area and data
Gis coordonates and software used for mapping should be mentioned
Statistics were performed correct
Author Response
Responses to Reviewer #4:
- Introduction should contain more data regarding PM2.5 as in source, respiratory health effects, measuring methods, ways to counteract.
Response: Thanks for your valuable suggestion. We have double checked the references and citations. Some references regarding the influencing mechanism of PM2.5 are added in the manuscript.
- Gis coordinates and software used for mapping should be mentioned .Statistics were performed correct
Response: Thanks for your suggestion. We have added GIS coordinates’ information in Line250.
Author Response File: Author Response.pdf
