Spatiotemporal Analysis of Air Quality and Its Driving Factors in Beijing’s Main Urban Area

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I think there is a lot of merit in exploring spatiotemporal regression analysis for identifying and predicting the main sources of air pollution in urban areas. This paper makes a good contribution to the overall research in this field, particularly identifying those variables that make the most significant contributions to air quality (positive or negative). This allows some urban management/planning based suggestions for improving air quality to be made. The analysis work is based on extensive data sets and appears to have been carried out effectively. The ability to look at 'bandwidth' of variable effects is a particular strength of the paper. There is very good use of graphics and the manuscript is generally very well written.

There are a couple of typos that I have identified on the attached annotated pdf.

Other comments:

The choice of an overall AQI instead of individual pollutant concentrations could be problematic for the analysis since the AQI reflects the 'worst' sub-index pollutant at any particular time, and the identity of that pollutant might change - i.e it may be PM2.5 that determines the AQI on one day and ozone the next - this variability creates problems if you are then using the index to carry out a statistical analysis: you might not necessarily be comparing like for like. I think that the authors need to address this.

Could the district information be added to some of the maps, particularly Figure 3, where the discussion refers to different districts.

Comments on the Quality of English Language

The manuscript is well written and I have only a few suggested amendments that are contained in the annotated pdf file attached.

Author Response

Response to the Comments from Reviewer #1:

Overall comment:

I think there is a lot of merit in exploring spatiotemporal regression analysis for identifying and predicting the main sources of air pollution in urban areas. This paper makes a good contribution to the overall research in this field, particularly identifying those variables that make the most significant contributions to air quality (positive or negative). This allows some urban management/planning based suggestions for improving air quality to be made. The analysis work is based on extensive data sets and appears to have been carried out effectively. The ability to look at 'bandwidth' of variable effects is a particular strength of the paper. There is very good use of graphics and the manuscript is generally very well written.

 

Response:

Thank you for your valuable feedback and for recognizing the merit of our work. We appreciate your positive comments on our use of extensive data sets, the effectiveness of our analysis, and the clarity of our graphics. We have addressed your specific comments to improve the manuscript.

 

Comments 1:

There are a couple of typos that I have identified on the attached annotated pdf.

 

Response 1:

Thank you very much for pointing out this issue. However, I have not received the annotated PDF you uploaded. Nevertheless, we have carefully checked the content of the full text and corrected the typos. If there are still similar problems, please do not hesitate to point them out.

 

Comments 2:

The choice of an overall AQI instead of individual pollutant concentrations could be problematic for the analysis since the AQI reflects the 'worst' sub-index pollutant at any particular time, and the identity of that pollutant might change - i.e it may be PM2.5 that determines the AQI on one day and ozone the next - this variability creates problems if you are then using the index to carry out a statistical analysis: you might not necessarily be comparing like for like. I think that the authors need to address this.

 

Response 2:

We appreciate your insightful comments on the use of the overall AQI. To address this issue, we have included a detailed explanation in the manuscript that discusses the reasons for using the AQI. The AQI values are influenced by the 'worst' sub-index pollutant, but by calculating the IAQI (Individual Air Quality Index) for each pollutant gas, we are able to normalize the different pollutant gas concentrations into an air quality metric that can be uniformly compared as well as quantified, and select the maximum of all IAQIs as the AQI for the day. In contrast to compared with the analysis using individual pollutant concentrations (e.g. PM2.5, O3), the AQI as a unified quantitative air quality indicator ensures the robustness of our results. These results have now been incorporated into the revised manuscript.

 

Comments 3:

Could the district information be added to some of the maps, particularly Figure 3, where the discussion refers to different districts.

 

Response 3:

Thank you so much for your suggestion. We have added administrative divisions of Beijing's central urban area to Figure 3, making regional differences in AQI spatial distribution more intuitive.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have presented a study of how heterogeneous land cover types contribute to air quality across a diverse urban area. They further present findings on an improved metric for assessing the contributions of land cover types using the Multi-scale Geographically Weighted Regression (MGWR), which they find to be an improvement over a simpler GWR method. I agree with the authors findings that improving the ability to characterize the contributing factors to land-use type can be important in assessing future land-use types and that their findings are suitable for inclusion within the journal. I do, however, have a few suggestions for improving the quality of the manuscript prior to publication.

 

Multiple figures have hard-to-read legends in them, including:

Figure 1: the right-hand legend is nearly unreadable due to its small size.

Figure 2: the charts presented in the subsets are quite small, though perhaps these aren’t meant to be read but rather as examples?

Figure 4: the numerical values are difficult to read and would benefit from being made larger

Figure 5: The legends on the color bar are small and difficult to read.

Figure 6: The axis labels would benefit from being slightly larger

Figure 8: The legends shows different color lines, but it’s hard to link those with the dotted/dashed lines.

Figure 9: The values on the colorbar are difficult to read, as in Figure 5.

 

Lines 130-132: The authors identify that there are 35 stations in Beijing that they use and 400 stations across the country. Are all of these used in the study?

Lines 183-189: There are multiple acronyms being re-identified here. Please make sure that once the acronym is identified, you are only using the acronyms later in the paper. May be worth checking the remaining of the paper as well, but here is where it was most noticed.

Lines 210-212 and Lines 218-220: These lines are duplicates, please make sure you are appropriately referencing the formulas here.

Line 282-284: This feels like a repeat/re-word of lines 274-276. Is it needed here as well?

Line 337: This sentence “The local regression coefficient of each variable and its significant proportion” seems like it is unfinished.

Please check the figure and table callouts throughout the paper; there are some which appear to include the caption in the manuscript text in addition to the Figure or Table callout.  An example is Figure 4 on line 260.

Author Response

Response to the Comments from Reviewer #2:

Overall comment:

The authors have presented a study of how heterogeneous land cover types contribute to air quality across a diverse urban area. They further present findings on an improved metric for assessing the contributions of land cover types using the Multi-scale Geographically Weighted Regression (MGWR), which they find to be an improvement over a simpler GWR method. I agree with the authors findings that improving the ability to characterize the contributing factors to land-use type can be important in assessing future land-use types and that their findings are suitable for inclusion within the journal. I do, however, have a few suggestions for improving the quality of the manuscript prior to publication.

 

Response:

Thank you for your detailed and constructive feedback. We appreciate your positive comments on our study and its contributions to the field. We have carefully addressed your suggestions to enhance the quality of the manuscript.

 

Comments 1:

Multiple figures have hard-to-read legends in them, including:

Figure 1: the right-hand legend is nearly unreadable due to its small size.

Figure 2: the charts presented in the subsets are quite small, though perhaps these aren’t meant to be read but rather as examples?

Figure 4: the numerical values are difficult to read and would benefit from being made larger

Figure 5: The legends on the color bar are small and difficult to read.

Figure 6: The axis labels would benefit from being slightly larger.

Figure 8: The legends shows different color lines, but it’s hard to link those with the dotted/dashed lines.

Figure 9: The values on the colorbar are difficult to read, as in Figure 5.

 

Response 1:

Thank you so much for your suggestion. The issues with the following figures have been addressed as requested, as detailed below:

Figure 1: The right-hand legend has been appropriately enlarged and its clarity improved.

Figure 2: The charts shown in the subset have been enlarged and their resolution improved. The charts are displayed as examples to enrich the research framework, allowing readers to get a preliminary understanding of the research process and results when reading the technical route.

Figure 4: The entire figure has been enlarged.

Figure 5: Creating multiple subplots makes it difficult to balance readability and clarity. Therefore, the figure has been enlarged and the clarity of the legend values has been improved to enhance readability.

Figure 6: The axis labels have been appropriately enlarged.

Figure 8: The y-axis of this figure is composed of Bandwidth and Standardized Coefficient (SC), which shows the relationship between bandwidth and SC of different variables. The green solid line represents the variation of SC values for different variables, with the bubble color and size indicating the significance of the variables. The dotted/dashed lines represent the average bandwidths obtained from the GWR and MGWR models, respectively, for comparison with the optimal bandwidth of the variables.

Figure 9: Similar to Figure 5, the figure has been enlarged and the clarity of the legend values has been improved.

 

Comments 2:

Lines 130-132: The authors identify that there are 35 stations in Beijing that they use and 400 stations across the country. Are all of these used in the study?

 

Response 2:

Thank you so much for your comment. We used 35 air quality monitoring stations in Beijing and obtained data from 400 meteorological monitoring stations across China. During subsequent data processing, the meteorological station data was converted into continuous surface data through spatial interpolation, and then clipped to obtain the meteorological data for the study area.

We have supplemented this information in the manuscript as follows:

[location: Page 4 Line 130-133]

“The station monitoring data spans from 2016 to 2020, including hourly air quality monitoring data from 35 stations in Beijing and daily ground meteorological monitoring data from 400 stations across the country will be used to generate AQI and wind speed raster data for the study area, respectively.”

 

Comments 3:

Lines 183-189: There are multiple acronyms being re-identified here. Please make sure that once the acronym is identified, you are only using the acronyms later in the paper. May be worth checking the remaining of the paper as well, but here is where it was most noticed.

 

Response 3:

Thank you for highlighting this issue. We have reviewed the manuscript to ensure that acronyms are only defined once and consistently used throughout the text.

 

Comments 4:

Lines 210-212 and Lines 218-220: These lines are duplicates, please make sure you are appropriately referencing the formulas here.

 

Response 4:

Thank you for your attention to detail. We have corrected the duplicate lines and made sure that the correct formulas are referenced in the manuscript.

The corresponding modifications are as follows:

[location: Page 8 Line 218-220]

“Local Moran's I values are categorized into spatial aggregation patterns of high-high (H-H), low-low (L-L), low-high (L-H), or high-low (H-L) based on aggregation. The calculation formula is as follows:”

 

Comments 5:

Line 282-284: This feels like a repeat/re-word of lines 274-276. Is it needed here as well?

 

Response 5:

Thank you for noting this duplication. We have reviewed and revised the text to remove the duplicate information from Line 282-284 to ensure clarity and conciseness.

The corresponding modifications are as follows:

[location: Page 11 Line 286-290]

The seasonal variation of monthly average AQI values from 2016 to 2020 high-lights the difference in monthly air quality (Figure 6b). From 2016 to 2018, air quality was mainly concentrated in the range of moderate and mild pollution. From 2019 to 2020, air quality is mainly distributed between good and medium grades, showing a significant increase in the proportion of air quality improvement.

 

Comments 6:

Line 337: This sentence “The local regression coefficient of each variable and its significant proportion” seems like it is unfinished.

 

Response 6:

We apologize for the incomplete sentence. We have revised it to read, "The local regression coefficients of each variable and their significance were analyzed to        understand the global impact of each variable on urban air quality."

The corresponding modifications are as follows:

[location: Page 14 Line 340-341]

“The local regression coefficients of each variable and their significance were analyzed to understand the global impact of each variable on urban air quality (Table 3).”

 

Comments 7:

Please check the figure and table callouts throughout the paper; there are some which appear to include the caption in the manuscript text in addition to the Figure or Table callout.  An example is Figure 4 on line 260.

 

Response 7:

We have carefully reviewed the figure and table callouts throughout the manuscript to ensure they are correctly referenced and do not include captions inappropriately. The issue with Figure 4 on line 260 has been corrected.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Review comments for sustainability-3073534-peer-review-v1,

The spatiotemporal characteristics of urban air pollution and its driving factors is necessary to understand effective environmental protection and urban planning. By analyzing air quality data from main urban area of China capital Beijing (2016-2020) alongside multi-source geographic data, this study develops a comprehensive evaluation system, incorporating key driving factors. The findings enhance the understanding of air pollution dynamics and aid in refining urban planning strategies. The manuscript wrote in a scientific and fluent way, only some minor errors need to revise as follows.

Line 50, “in Table Table A1.”? Double “table”?

Line 51, “AQI Classification Standards..” delete one “.”

Line 126, “Table Table 1”?

Line260, “The diagnosis results (Figure 4. Correlation analysis matrix of indicators.” Unclear.

In figure 5, letters on ruler columns are too small to read.

Figure 6(b), five line refers to average AQI for five year?

Line 336, “4.2.2.”?

Author Response

Response to the Comments from Reviewer #3:

Overall comment:

The spatiotemporal characteristics of urban air pollution and its driving factors is necessary to understand effective environmental protection and urban planning. By analyzing air quality data from main urban area of China capital Beijing (2016-2020) alongside multi-source geographic data, this study develops a comprehensive evaluation system, incorporating key driving factors. The findings enhance the understanding of air pollution dynamics and aid in refining urban planning strategies. The manuscript wrote in a scientific and fluent way, only some minor errors need to revise as follows.

 

Response:

Thank you for your valuable comments, which helped me find the problem. In order to make the manuscript more scientific and smooth, we carefully consider your suggestions and modify the problem statement.

 

Comments 1:

Line 50, “in Table Table A1.”? Double “table”?

 

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have corrected the double “table” in line 50 to “in Table A1.”

The corresponding modifications are as follows:

[location: Page 2 Line 50]

“The classification standards for the AQI are shown in Table A1.”

 

Comments 2:

Line 51, “AQI Classification Standards.” delete one “.”

 

Response 2:

Thank you for pointing out this error. We agree with this comment and have removed the redundant table name in line 51.

The corresponding modifications are as follows:

[location: Page 2 Line 50-52]

“The classification standards for the AQI are shown in Table A1. Studying the spatiotemporal characteristics and driving factors of AQI is crucial for assessing urban air quality and mitigating air pollution[17-21].”

 

Comments 3:

Line 126, “Table Table 1”?

 

Response 3:

We appreciate your attention to detail. The double “table” in line 126 has been corrected to “Table 1.”

The corresponding modifications are as follows:

[location: Page 3 Line 123-125]

“The data used in the study primarily consists of station monitoring data, remote sensing product data, and open-access geographic big data (Table 1). Below is an introduction to the data and its preprocessing steps.”

 

Comments 4:

Line260, “The diagnosis results (Figure 4. Correlation analysis matrix of indicators.” Unclear.

 

Response 4:

Thank you so much for pointing out this. We have enlarged the entire figure and improved its clarity.

 

Comments 5:

In figure 5, letters on ruler columns are too small to read.

 

Response 5:

Thank you so much for pointing out this. Since Figure 5 consists of multiple subplots, the scale values are indeed difficult to read. Therefore, we have adjusted the figure by increasing its overall size and improving the resolution of the scale values. We believe that by appropriately scaling each subplot, the figure will meet readability requirements.

 

Comments 6:

Figure 6(b), five line refers to average AQI for five year?

 

Response 6:

Thank you so much for your comment. We apologize for our oversight in not including the legend in Figure 6(b). As you correctly noted, the five lines represent the AQI changes from 2016 to 2020. We have added the legend and increased the font size of the labels in Figure 6 to improve readability.

 

Comments 7:

Line 336, “4.2.2.”?

 

Response 7:

We acknowledge this error. The correct reference should be “4.4.2. Spatial Heterogeneity Analysis of Influencing Factors” We have made the necessary correction in line 339. In addition, we reorganize the framework in Chapter 4.4.

The corresponding modifications are as follows:

[location: Page 12 Line 304]

“4.4. Comparative Analysis of GWR and MGWR Results”

[location: Page 12 Line 313]

“4.4.1. Bandwidth Analysis Influencing Factors”

[location: Page 13 Line 339]

“4.4.2. Spatial Heterogeneity Analysis of Influencing Factors”

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Please see attachment

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Author Response

Response to the Comments from Reviewer #4:

Overall comment:

The author examines the “Spatial Distribution Characteristics of Urban Air Quality and the Spatial Heterogeneity of Driving Factors: A Case Study of Beijing.” Urban air pollution is a significant global issue, and this study addresses it by developing an advanced air quality assessment system that integrates various environmental, socio-economic, and urban layout factors. By analyzing air quality data from Beijing (2016-2020) using Geographically Weighted Regression (GWR) and Multi-scale Geographically Weighted Regression (MGWR), the research highlights the spatial variability of key factors influencing air quality, providing valuable insights for urban planning and environmental protection. The findings of the manuscript are noteworthy; however, I would like to offer some minor comments to improve the draft. Here are some suggestions for the author to enhance the manuscript:

 

Response:

Thank you for your recognition of our work in analyzing the characteristics of the factors affecting urban air quality in Beijing. Meanwhile, we have improved the manuscript in response to your specific comments.

 

Comments 1:

Title and Abstract:

• Title: Consider simplifying and making the title more specific. For example,

"Analyzing Urban Air Quality and Its Driving Factors: A Case Study of Beijing".

• Abstract: Ensure that the abstract succinctly summarizes the methods, key

findings, and their implications. Consider shortening the description of the

methodology and expanding on the key results and their significance.

 

Response 1:

Thank you for the suggestions. We have simplified the title to " Spatiotemporal Analysis of Air Quality and Its Driving Factors in Beijing's Main Urban Area". We have also revised the abstract to succinctly summarize the methods, key findings, and their implications, focusing more on the results and their significance.

The corresponding modifications are as follows:

[location: Page 1 Line 2-3]

“Spatiotemporal Analysis of Air Quality and Its Driving Factors in Beijing's Main Urban Area”

[location: Page 1 Line 8-21]

“Urban air pollution is a critical global environmental issue, necessitating an analysis of the spatiotemporal characteristics of air quality and its driving factors for effective environmental protection and urban planning. This study introduces an advanced air quality assessment system that integrates environmental, socio-economic, and urban layout factors, addressing gaps in traditional models that overlook the impact of urban spatial structures. In this paper, we utilize air quality data and 14 multi-source geographic data from the main urban areas of Beijing (2016-2020) to construct geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models for spatiotemporal analysis. Findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model provided a superior fit over GWR, capturing spatial variability more effectively. Variables such as (NDVI), economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These in-sights enhance the understanding of air pollution dynamics and aid in refining urban planning strategies.”

[location: Page 1 Line 22-23]

“Keywords: air quality; multi-source geographic data; spatiotemporal analysis; multi-scale geo-graphically weighted regression; spatial heterogeneity”

 

Comments 2:

Introduction:

• Context and Importance: Add a brief discussion on why Beijing specifically

serves as a pertinent case study for this research.

• Literature Review: Include more recent studies to provide a current context for

your research. Add some previous research results and point out their strengths,

weaknesses, and guidance for your research.

 

Response 2:

We have added a discussion on why Beijing is a pertinent case study, emphasizing its unique urban challenges and significance. Additionally, we have included more recent studies in the literature review to provide a current context and highlighted the strengths, weaknesses, and relevance of previous research to our study.

The corresponding modifications are as follows:

[location: Page 1 Line 33-42]

“As the capital of China, Beijing has a high population density and a high level of eco-nomic activity. The city has experienced rapid urbanization and a large amount of transportation and industrial activities, which have led to high concentrations of air pollutants such as particulate matter, nitrogen dioxide, and ozone. These pollutants have a significant impact on the health and quality of life of urban residents. Further-more, the Beijing government places a significant emphasis on environmental protection and has implemented a multitude of rigorous air pollution control measures, which provides this study with a distinctive perspective from which to analyze and assess the level of urban air quality, and then to explore improvement measures to combat air pollution. In conclusion, Beijing is a representative and exemplary city in the study of air pollution.”

[location: Page 2 Line 59-61]

“Recent studies have focused on smaller spatial scales, such as specific cities or their metropolitan areas, and scientists have adopted higher spatial and temporal resolutions to more finely characterize urban air quality and its drivers[25,26].”

[location: Page 2 Line 75-81]

“These studies effectively mine the specific sources of regional air pollution from both natural and socio-economic perspectives, but they also have limitations, such as neglecting differences in urban internal layouts and not recognizing the driving mechanisms of urban form, industrial structure, and other urban layout attributes on the urban air environment[34-36]. Therefore, it is crucial to select differentiated urban layout indicators on top of natural and socio-economic impacts to deepen the research on factors influencing urban air quality.”

 

Comments 3:

Methodology:

• Data Sources: Clearly state the resolution and relevance of each data source. This

section could benefit from a more concise summary, focusing on the critical

datasets.

• Model Justification: Provide a more detailed rationale for choosing the MGWR

model over other potential models.

 

Response 3:

In the “2.2. Data Sources and Pre-processing”, we provide a concise summary of the key datasets, clarifying the resolution and relevance of each data source. This is primarily done through Table 1, which presents the data sources and their descriptions. Additionally, in “1. Introduction”, we provide a detailed rationale for selecting the MGWR model, outlining its advantages over alternative potential models.

The corresponding modifications are as follows:

[location: Page 4 Line 128]

[location: Page 2-3 Line 95-102]

“Most models are limited by the collinearity among variables, which restricts their explanatory power and robustness, and fail to effectively identify scale-varying dominant influencing factors. The Multi-scale Geographically Weighted Regression (MGWR) model considers the multi-scale spatial effects between air quality and various variables, providing deeper insights into the spatial distribution of air quality and the spatial mechanisms of influencing factors. This model is particularly suited for exploring complex environmental issues and formulating precise environmental management strategies.”

 

Comments 4:

Results:

• Clarity: Make sure all figures and tables are clearly labeled and referenced in the text. Ensure that the axes and legends are readable and informative. Improve the resolution of Fig.1 and Fig.3.
• Interpretation: Expand on the interpretation of key findings. For instance, what does the U-shaped seasonal pattern signify in the context of Beijing’s urban environment?

 

Response 4:

Thank you so much for your comments and suggestions.

• Clarity: Regarding your suggestions for improving clarity, we have made the following adjustments: (1) Ensured all figures and tables are clearly labeled and properly referenced in the text. (2) Made sure all axes and legends are readable and informative. (3) Improved the resolution of the images, especially Figures 1 and 3, to make them clearer. We also adjusted Figure 3 by adding administrative division information to visually represent AQI differences between regions.
• Interpretation: The manuscript indeed lacked further explanation of the U-shaped seasonal pattern in Beijing's AQI. This pattern is significant for air pollution control, so we have provided the necessary supplementary explanation.

[location: Page 11 Line 276-281]

“The seasonal U-shaped pattern in AQI variations indicates the complex interplay of various environmental and human factors within Beijing's urban environment. This highlights the need for dynamic and season-specific approaches in regulating air quality, considering natural and anthropogenic factors that affect pollution levels throughout the year. For instance, stricter control of construction dust in spring and reducing coal use in winter are necessary measures.”

 

Comments 5:

Discussion:

• Comparison with Other Studies: Compare your findings with other relevant studies to highlight the novelty and significance of your results.
• Implications: Discuss the practical implications of your findings for urban planning and policy-making in more detail.

 

Response 5:

Thank you for your valuable comments. Comparing the results of this research with those of related studies is an effective way to demonstrate the reliability and innovativeness of our work. Therefore, we reviewed recent related studies and conducted a comparative analysis with the current research conclusions, confirming the effectiveness and advancement of the current methods. This comparative analysis has been added to Section 4.5 of the text. The original Section 4.5 has been changed to Section 4.6, where Strategies and Suggestions are explained in more detail.

The specific modifications are as follows:

[location: Page 15-16 Line 392-441]

“4.5. Research comparation

This study is highly consistent with existing relevant research findings. Tian et al. analyzed the temporal and spatial variations in air quality in Beijing from 2001 to 2017, finding a negative correlation between tree coverage and air quality, and a positive correlation between energy consumption intensity and air quality. This aligns with our findings where NDVI negatively impacts air quality and ECI positively impacts air quality, indicating that increasing tree coverage and reducing energy consumption in-tensity are effective measures to improve air quality[39]. Li et al. evaluated the effectiveness of two air quality improvement action plans in Beijing, namely the "Clean Air Action" and "Comprehensive Action," and found that under government intervention, coordinated efforts between economic growth and pollution control significantly im-proved air quality. This is consistent with our research conclusion that strict industrial emission control and optimization of energy structure can effectively improve air quality[40]. Ban et al. explored the impact of urbanization on aerosol optical depth, revealing a close correlation between land surface temperature, land use type, and aerosol concentration. This correlates with our findings on the impact of TEMP and ULI on air quality, highlighting significant effects of land use changes and temperature variations during urbanization processes[41]. Zhan et al. used a MGWR to examine the relationship between green space patterns and PM2.5 exposure, demonstrating that green spaces mitigate PM2.5 exposure risks at larger scales. Our study similarly employed the MGWR model and found that GESP have a significant positive impact on air quality, supporting the effectiveness of increasing urban green spaces as a measure to improve air quality[42].

Compared to these related studies, this research provides a more comprehensive and systematic analysis by considering multiple factors such as natural environment, socio-economic conditions, and urban spatial layout. This approach is beneficial for offering more specific guidance and references to governments and relevant departments in the practice of air pollution prevention and control. The scientific value lies in the multidimensional and multiscale perspective of this study, which reveals the key factors affecting air quality and their interaction mechanisms. This enriches the existing environmental science theories and offers significant references for future research in related fields.

 

4.5. Strategies and Suggestions

Given the comprehensive strategy for improving air quality in the study area, it is necessary to address the issue from the three key dimensions of natural environmental protection, socio-economic sustainable development, and urban spatial layout optimization.

From a natural environment perspective, emphasizing the role of WDSP in promoting the diffusion of air pollutants through rational urban planning, such as building ventilation corridors, optimizing building layouts, and enhancing air circulation capacity, is crucial. Additionally, increasing urban vegetation coverage (NDVI) not only helps regulate urban temperature and reduce the heat island effect but also directly improves air quality. In terms of socio-economic characteristics, reducing ECI and balancing the relationship between POP and economic growth (GDP) are vital. Implementing policies that promote energy efficiency and the use of renewable energy sources can significantly reduce pollution. In this process, government intervention, such as implementing strict emission standards and incentive measures, can enhance the effectiveness of policy implementation. Optimizing urban spatial layout involves addressing the impact of industrial activities (IAI) and the traffic network (TND) on air quality. By strictly controlling industrial emissions, optimizing traffic structure, encouraging public transportation use, expanding GESP, and improving GASP and HSAC, these strategies can significantly reduce air pollution sources and enhance the city's ecosystem services, creating a more livable environment. Integrating these measures provides specific guidance for urban planning and policy-making to achieve sustainable air quality improvements.”

 

Comments 6:

Conclusion:

• Summarize Key Findings: Ensure the conclusion succinctly summarizes the most critical findings and their broader implications.
• Future Work: Suggest specific areas for future research that can build on your findings.

 

Response 6:

Thank you for your valuable comments. We modified the conclusion, mainly by deleting some statements to achieve the purpose of concisely summarizing the most critical findings. We believe that future research work can build a dynamic MGWR model based on the index system we constructed and the changing rules of AQI influencing factors in time series. This can provide more detailed enlightenment for air pollution control in different cities.

The corresponding modifications are as follows:

[location: Page 16-19 Line 443-475]

“This paper analyzes the spatial and temporal distribution characteristics of AQI in the study area, and explores the relationship and spatial heterogeneity of the driving factors affecting urban air quality from three aspects: natural environment, social economy and urban spatial layout based on multi-source data. The main conclusions of the study are as follows: (1) The spatial and temporal distribution of air quality in the study area shows certain regularity. In terms of time distribution, the overall air quality shows a trend of increasing year by year, and shows a U-shaped change rule on the seasonal scale, that is, spring and winter are the high-value seasons of AQI, while summer and autumn are low. In terms of spatial distribution, the high-value aggregation area of AQI is distributed in the eastern part of Chaoyang District and Fengtai District. The low-value aggregation areas are distributed in the central and northern parts of Haidian District and the western part of Shijingshan District. (2) The fitting effect of MGWR model is better than that of GWR. The bandwidth of MGWR model can dynamically reflect the spatial effect of variables, which is suitable for mining the dominant factors affecting air quality and their spatial effect differences from multi-variate. The standardized parameter estimates obtained from the MGWR model reflect that the influence of TND and RDLS in the variables used in the study is low. Among the variables with a significant positive relationship with AQI, the degree of influence is : PREC > POP > TEMP > RD > 0.5. Among the variables showing a negative relation-ship with AQI, the degree of influence is as follows : GDP < WDSP < IAI < -0.5 < NDVI < GASP < HASC < ECI < GESP < 0. (3) Identify the intensity and spatial heterogeneity of driving factors. The strong spatial heterogeneity variables with significant positive effects on air quality are NDVI, GDP, and HSAC, and the strong spatial heterogeneity variables with significant negative effects are POP, TEMP, and RD. Therefore, in the process of urban planning, it is necessary to coordinate the relationship between population and development quality, natural environment and building environment from the perspective of regional differences, so as to improve air quality.

However, this study has certain limitations, which are reflected in the analysis of the heterogeneity of driving factors on the missing time scale. In future studies, researchers or city managers may wish to explore the spatiotemporal distribution characteristics of the AQI driving factors in the time series for an indicator level of the natural environment, socio-economy and urban layout. This allows for the construction of dynamic and specific MGWR models that will help each specific area to scientifically develop refined air quality management countermeasures.”

 

Comments 7:

Technical Corrections:

• Grammar and Syntax: Review the manuscript for grammatical errors and awkward phrasings. For instance, "has become a globally recognized environmental issue" could be simplified to "is a globally recognized environmental issue".
• Consistency: Ensure consistent use of terms and acronyms throughout the manuscript.

 

Response 7:

Thank you for carefully reviewing the grammar and syntax of the manuscript. We have reviewed the manuscript for grammatical errors and awkward phrasings, making necessary corrections. We have ensured the consistent use of terms and acronyms throughout the manuscript.

The corresponding modifications are as follows:

[location: Page 1 Line 31-33]

“Among these, urban air pollution in China is particularly severe and is a globally recognized environmental issue[6,7].”

 

Comments 8:

References:

• Update: Ensure that all references are up-to-date and relevant. Include recent

studies to strengthen the literature review.

Response 8:

We are grateful for your guidance on references. In our review of the urban air quality research literature, we focused on recently published literature with high relevance. At the same time, we also deleted some literatures with weak correlation to strengthen the timeliness and relevance of the literature review.

Author Response File: Author Response.pdf

Round 2

Reviewer 4 Report

Comments and Suggestions for Authors

The authors carefully addressed all the comments I raised, and the academic level of the revised verion met the academic requirements of the journal

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Response to the Comments from Reviewer:

Comments and Suggestions for Authors:

The authors carefully addressed all the comments I raised, and the academic level of the revised version met the academic requirements of the journal.

 

Response:

Thank you for your positive feedback. We are pleased to hear that we have addressed all the comments you raised and that the academic level of the revised version meets the requirements of the journal. Furthermore, we have made further revisions and improvements to our article based on your comments and suggestions, enhancing the overall quality of the manuscript.

 

Comments on the Quality of English Language:

Minor editing of English language required.

 

Response:

Thank you very much for pointing out this issue. We have conducted a thorough language check on the manuscript and refined the content to improve its quality and readability. The revisions were made using the track changes mode, and you can review the specific modifications in the attached document named “Revised manuscript with track changes.docx”.

Author Response File: Author Response.pdf