From the journal Environmental Science: Atmospheres Peer review history

23-Jun-2021

Dear Dr Kokkonen:

Manuscript ID: EA-ART-05-2021-000035
TITLE: The effect of urban morphological characteristics on the spatial variation of air quality in downtown Nanjing

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Dr Lin Wang
Associate Editor, Environmental Science: Atmospheres

Environmental Science: Atmospheres is accompanied by sister journals Environmental Science: Nano, Environmental Science: Processes and Impacts, and Environmental Science: Water Research; publishing high-impact work across all aspects of environmental science and engineering. Find out more at: http://rsc.li/envsci

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Reviewer 1

Overall comments:
The paper discusses relationships between ground-level PM2.5 concentrations and morphological characteristics in Nanjing, based on 3 months’ data at 31 air quality monitoring stations in Nanjing – a megacity in East China. The study covers areas of street canyons and relatively open areas, and spans the periods of before and during the COVID-19 lockdown. The main conclusion is that trees in street canyons deteriorate PM2.5 air quality due to worsening of dispersion conditions. The research topic and results are interesting. Overall, the paper is logically organized and well written, though some methods and results need further clarifications. I would recommend accepting a revised version with reference to the specific comments.

Specific comments:
1. The 31 stations are grouped as two types, i.e., street canyons and open areas. It is not clear how the authors defined the two types. Was it based on the distances to the nearest buildings?
2. The metrics adopted for PM2.5 is the monthly median of normalized one. It would be better to explain why the monthly medians are used? Note that the metrics is annual average or 24-h average for PM2.5 in China’s ambient air quality standards. Will the conclusions still hold if 24-h average PM2.5 is examined?
3. It is either not clear how the monthly medians were calculated. Theoretically, there is only one median for one site in 1 month. However, it is confusing why there are so many data points in Figures 4-7 & 9-13. Please clarify.
4. The effects of morphological characteristics on PM2.5 levels are examined in a cold season. Since meteorological conditions play critical roles in influencing physical removals of air pollutants, including deposition, it will be helpful if the authors can quantitatively or at least qualitatively discuss the effects in other seasons.
5. There are many parameters representing morphological characteristics. The authors focused on a few of them which are thought to be most relevant to PM2.5 concentrations. Though it is understandable, it would be better if the effects of other parameters, especially those proposed to be of significant relevance in other studies, can be qualitatively discussed.
6. Suggest changing “air quality” to “PM2.5 air quality” throughout the paper where the definition extension is not appropriate.
7. Lines 399-402: According to Fig. 7(a), it happened that the stations with highest fraction of trees had the shortest distances to main road. How will the results change if the two stations are removed from the regression?
8. Line 519: “… had a significant (p value < 0.001) on normalized PM2.5 (Fig. 12a and Table 2)”. Significant impact/influence?
9. Just out of my curiosity, should the height of trees be taken into consideration while concluding the effects of tree coverage on PM2.5 concentration? In many Chinese cities, especially in western China, the trees are not as high as those (7 m on average) in Nanjing.

Reviewer 2

The authors obtained the hourly PM_2.5 data from 31 monitoring stations in Nanjing during the pre-COVID period and the COVID lockdown period to study the effects of urban morphological parameters on pollution variations. They found that urban trees could greatly affect PM_2.5 concentrations via disturbing their aerodynamic effects. Generally speaking, this manuscript proposed several interesting findings, but the language and structure could be improved. Besides, still some issues should be addressed before publication and a major revision is needed. The comments are listed as below:

General comments:
1. The authors conducted regressions between normalized PM_2.5 and the four urban morphological parameters. However, in most regression results, the correlation seems very weak, i.e., the R² has a very low value. Even though the regressions are proved to be statistically significant, can such weak correlation be convincing? Furthermore, can such results be used to conclude the percentage number (e.g., the 24% increase induced by tree fractions)? The effectiveness of the correlation should be further discussed to make this part reliable.
2. The authors used PLS analysis to calculate the total variance explained by the selected urban morphological parameters and found that 59% and 73% of the variance can be explained during the pre-COVID period and the COVID lockdown period, respectively. However, these numbers are only found in a simple statement instead of a graph or table. It is suggested that the authors put the results of the PLS analysis in the form of a table to make it more straightforward for the readers.
3. The authors divided the study area around each monitoring site into 8 wind sectors. Therefore, there will be totally 31*8=248 pairs of data for regression. However, still some questions here to be addressed: 1) Do the 8 sectors share the same PM_2.5 concentration data from the monitoring site? If so, will it cause problem? 2) For the stations located in the street canyons, how is the aspect ratio calculated? Is it calculated respectively in each sector? Or do they share same value? Please make a clearer explanation to the above questions in the data part.
4. In the discussion and conclusion part, the authors mainly focused on the effects of tree fractions. However, as the manuscript is aimed at discussing the effects of urban morphological parameters, it seems that parameters like roughness height and street canyon aspect ratio should be given more importance. In other words, the authors should balance the length between trees and other parameters. Therefore, it is suggested that the authors add more discussion on other parameters.
5. The structure of the regression results part is too repeated. All of this part follows the structure of graph-discussion, which looks all the same and the point is not highlighted. It is suggested that the authors restructure the article to make the discussion of the key points more focused.

Specific comments:
1. In the regression figures, what is the purpose of coloring the tree fractions ?
2. In Table 2, all the parameters are not significant with PM_2.5 in the open areas during the COVID lockdown period? What do you think leads to this result?
3. Line 206. Why “a PLS analysis is assumed to be statistically significant” when the cross-validated R² ≥ 0.0975?
4. What is the “LOESS”? What does the LOESS fit line in Figure 3 try to express?

We want to thank the reviewers for their comments on our manuscript “The effect of urban morphological characteristics on the spatial variation of air quality in downtown Nanjing”. These changes have improved the paper. Our detailed responses are given under the numbered reviewer comments. Page and line numbers in our responses are referring to the “track changes” -version of the revised manuscript.

REVIEWER REPORT(S):
Referee: 1

Comments to the Author
Overall comments:
The paper discusses relationships between ground-level PM2.5 concentrations and morphological characteristics in Nanjing, based on 3 months’ data at 31 air quality monitoring stations in Nanjing – a megacity in East China. The study covers areas of street canyons and relatively open areas, and spans the periods of before and during the COVID-19 lockdown. The main conclusion is that trees in street canyons deteriorate PM2.5 air quality due to worsening of dispersion conditions. The research topic and results are interesting. Overall, the paper is logically organized and well written, though some methods and results need further clarifications. I would recommend accepting a revised version with reference to the specific comments.

We want to thank the reviewer for the kind statement.

Specific comments:
1. The 31 stations are grouped as two types, i.e., street canyons and open areas. It is not clear how the authors defined the two types. Was it based on the distances to the nearest buildings?

The stations in street canyon group are only the stations that are located in a “typical street canyon” (i.e. buildings directly on both sides of the street). The stations in open areas category are located e.g. next to sports field, parking lot etc., which cannot be categorized as street canyons.

The sentence describing the stations has been rephrased (P3, L120-123): “A bit more than half of the stations (N=17) are located in rather open areas (e.g., next to a sports field, large parking lot, urban park etc.) and the rest of them (N=14) are located in typical street canyons, with buildings directly on both sides of the street.”

2. The metrics adopted for PM2.5 is the monthly median of normalized one. It would be better to explain why the monthly medians are used? Note that the metrics is annual average or 24-h average for PM2.5 in China’s ambient air quality standards. Will the conclusions still hold if 24-h average PM2.5 is examined?

Yes, the conclusions would stay similar even if 24 h averages are used. However, it would increase the variability substantially and therefore makes the figures harder to read. The longer averaging period filters some of the daily variation out and increases the readability. In addition, the monthly medians were calculated for each of the wind sectors (i.e. only the wind coming from the sector included in the medians). Therefore, when using 24 h averages, the amount of data used for the averaging might not be sufficient for some of the sectors in some days.

This has now been explained in the text (P5, L187-190): “Only the hours with wind coming from the sector were included in the monthly medians for that specific sector. This is also partly the reason why monthly medians were used instead for example daily medians. With daily values, the amount of data used for the averaging might not be sufficient for some of the wind sectors.”

3. It is either not clear how the monthly medians were calculated. Theoretically, there is only one median for one site in 1 month. However, it is confusing why there are so many data points in Figures 4-7 & 9-13. Please clarify.

There were 31 stations divided into 8 different wind sectors with individually defined characteristics. This leads to 248 sectors, that were analyzed. This has been now cleared in the text (P3, L128-132): “The 500 m radius circles are divided into eight different wind sectors (width 45°) and the urban morphological characteristics are determined and the monthly medians are calculated independently for each of the wind sectors, which leads to 248 individual sectors for the analyses.”

4. The effects of morphological characteristics on PM2.5 levels are examined in a cold season. Since meteorological conditions play critical roles in influencing physical removals of air pollutants, including deposition, it will be helpful if the authors can quantitatively or at least qualitatively discuss the effects in other seasons.

Yes, this is true that seasonality could affect the results. However, the normalization of the data should minimize the effect of different meteorological conditions as stated in the text (P5, L169-171). Due to the lack of data, the seasonal effect could not be included in this study. Some discussion related to this has been added into the Discussion section (P22, L722-727):

“Also, the seasonal variation on the effect of the urban morphological characteristics in the PM2.5 air quality should be studied. The normalization of the PM2.5 concentrations used in this study should minimize the effect of varying meteorological conditions, but for example the variating leaf area index of the vegetation is assumed to affect the accumulation of pollutants and the cleaning effect of the trees.”

5. There are many parameters representing morphological characteristics. The authors focused on a few of them which are thought to be most relevant to PM2.5 concentrations. Though it is understandable, it would be better if the effects of other parameters, especially those proposed to be of significant relevance in other studies, can be qualitatively discussed.

Some discussion about the other parameters have been added to the discussion section (P19 L604-617):

“However, only 6 of the variables (complete aspect ratio, occlusivity, roughness height, zero-plane displacement height, total building volume/number of buildings, and standard deviation of height) used by Edussuriya et al. were found to vary significantly at district level62 and therefore also responsible for the spatial variation of air quality within the city. Many of these variables are the same or similar as used in this study but for example occlusivity was left out of this study since it requires very detailed data on buildings, which are not often available. The standard deviation of building height has been found to increase the vertical turbulent flux rates63 and therefore also to improve the near surface air quality36, 63. However, in this study the standard deviation of the building height was found to have an insignificant effect on PM2.5 concentrations in all the scenarios. Based on our results, the height-normalized roughness length was representing better the effect of surface roughness on the PM2.5 air quality in this case. Therefore, the standard deviation of the building height was left out of further analyses in this study.”

6. Suggest changing “air quality” to “PM2.5 air quality” throughout the paper where the definition extension is not appropriate.

This has been changed as suggested throughout the paper where relevant.

7. Lines 399-402: According to Fig. 7(a), it happened that the stations with highest fraction of trees had the shortest distances to main road. How will the results change if the two stations are removed from the regression?

The relationship stays very similar (Fig.1 of the referee response). In the Fig. 7a of the manuscript y = 1.3 + 1.8x and when the two stations are removed y = 1.3 + 1.9x (Fig. 1 of the referee response). Only the correlation coefficient is reducing due to the higher variation with lower tree fraction. The correlation coefficient with all the stations is R2 = 0.47 and with the two stations removed R2 = 0.22.


Fig. 1. Monthly medians of normalized PM2.5 concentrations for the stations in street canyons against the fraction of trees within 50 m radius of the station (f 50/trees) during the pre-COVID period. Two stations with the highest f 50/trees are removed from the figure. The coloring shows the distance to major roads (arterial roads and highways). The shaded area is showing the 95% confidence boundaries.


8. Line 519: “… had a significant (p value < 0.001) on normalized PM2.5 (Fig. 12a and Table 2)”. Significant impact/influence?

This sentence has been rephrased: “…had a significant effect (p value < 0.001) on normalized PM2.5 (Fig. 12a and Table 2).”

9. Just out of my curiosity, should the height of trees be taken into consideration while concluding the effects of tree coverage on PM2.5 concentration? In many Chinese cities, especially in western China, the trees are not as high as those (7 m on average) in Nanjing.

Tree height could play some role in the ventilation as already discussed in the Discussion section (P21, L691-694). However, this affects only the calculation of roughness length (Eq. 4) in this study, and the height of the trees is not affecting any of the other analyses. In Eq. 4, the frontal areas of the buildings are much larger than the frontal areas of the trees, so this should not have a substantial effect on the calculations. Data on tree height are often unavailable as was also in this case as stated also in the text (P5, L163). Therefore, we used only the average tree height for the Nanjing urban area available from the literature.

Referee: 2

Comments to the Author
The authors obtained the hourly PM_2.5 data from 31 monitoring stations in Nanjing during the pre-COVID period and the COVID lockdown period to study the effects of urban morphological parameters on pollution variations. They found that urban trees could greatly affect PM_2.5 concentrations via disturbing their aerodynamic effects. Generally speaking, this manuscript proposed several interesting findings, but the language and structure could be improved. Besides, still some issues should be addressed before publication and a major revision is needed. The comments are listed as below:

We want to thank the reviewer for the kind statement. The language of the manuscript has been thoroughly checked and corrected in this revision as suggested by the reviewer.

General comments:
1. The authors conducted regressions between normalized PM_2.5 and the four urban morphological parameters. However, in most regression results, the correlation seems very weak, i.e., the R² has a very low value. Even though the regressions are proved to be statistically significant, can such weak correlation be convincing? Furthermore, can such results be used to conclude the percentage number (e.g., the 24% increase induced by tree fractions)? The effectiveness of the correlation should be further discussed to make this part reliable.

Yes, it is true that the correlation is rather weak in many of the regression results. However, this is often the case with natural environments where multiple individual variables are influencing simultaneously. This can be seen clearly e.g. in Fig. 4a, where the trees have substantial effect on the scatter of the points as also been pointed out in the text when discussing the linear regression results (e.g. P10, L330-332).

Partly due to the low correlation, we included also the PLS analyses, which can handle the multiparameter problems much better. The results from both analyses are very similar, and therefore they further support each other. This has been discussed in text (P5, L199-209).

Also, some additional discussion of the weak correlation and the supporting results of the PLS analysis have been added to the Discussion section (P21, L699-704): “The correlations in the linear regression analyses were rather weak even for the statistically significant cases (from 0.029 to 0.49, Table 2). Therefore, we performed also the PLS analysis which is a statistically more robust method for multiparameter problems especially those with interrelated variables (see Section 2.3). The variable importance obtained from the PLS analysis was giving very similar results to the values from the regression analyses further supporting the results (Table 2).”

2. The authors used PLS analysis to calculate the total variance explained by the selected urban morphological parameters and found that 59% and 73% of the variance can be explained during the pre-COVID period and the COVID lockdown period, respectively. However, these numbers are only found in a simple statement instead of a graph or table. It is suggested that the authors put the results of the PLS analysis in the form of a table to make it more straightforward for the readers.

These have now been added to the Table 2 as suggested.

3. The authors divided the study area around each monitoring site into 8 wind sectors. Therefore, there will be totally 31*8=248 pairs of data for regression. However, still some questions here to be addressed: 1) Do the 8 sectors share the same PM_2.5 concentration data from the monitoring site? If so, will it cause problem? 2) For the stations located in the street canyons, how is the aspect ratio calculated? Is it calculated respectively in each sector? Or do they share same value? Please make a clearer explanation to the above questions in the data part.

1) The averages for each wind sector include only the hourly data points, when the wind is coming from that sector. This description has now been added to the text (P5, L187-188).

2) The aspect ratio is the same for all the wind directions since this does not vary with the wind direction. This explanation has now been added to the text (P8, L267-268): “The street canyon aspect ratio does not change with different wind directions, and therefore the same value of λS was used for all the wind sectors.”

4. In the discussion and conclusion part, the authors mainly focused on the effects of tree fractions. However, as the manuscript is aimed at discussing the effects of urban morphological parameters, it seems that parameters like roughness height and street canyon aspect ratio should be given more importance. In other words, the authors should balance the length between trees and other parameters. Therefore, it is suggested that the authors add more discussion on other parameters.

Since the fraction of trees was shown to be so important compared to other variables and there are controversial results in the literature, we feel that it is appropriate to give it more focus in the discussion and conclusions.

However, we have added more text related to the roughness length and some qualitative discussion about variables that were not used in this study, but have been found to be significant in others (P19, L604-617):

“However, only 6 of the variables (complete aspect ratio, occlusivity, roughness height, zero-plane displacement height, total building volume/number of buildings, and standard deviation of height) used by Edussuriya et al. were found to vary significantly at district level62 and therefore also responsible for the spatial variation of air quality within the city. Many of these variables are the same or similar as used in this study but for example occlusivity was left out of this study since it requires very detailed data on buildings, which are not often available. The standard deviation of building height has been found to increase the vertical turbulent flux rates63 and therefore also to improve the near-surface air quality36, 63. However, in this study the standard deviation of the building height was found to have an insignificant effect on PM2.5 concentrations in all the scenarios. Based on our results, the height-normalized roughness length was representing better the effect of surface roughness on the PM2.5 air quality in this case. Therefore, the standard deviation of the building height was left out of further analyses in this study.”

5. The structure of the regression results part is too repeated. All of this part follows the structure of graph-discussion, which looks all the same and the point is not highlighted. It is suggested that the authors restructure the article to make the discussion of the key points more focused.

Since we were studying many individual variables in different environments (street canyons and open areas), we think that it is clearer if the results are first discussed as differences of open and street canyon areas and compared to each other after that. Therefore, we included the PLS analysis, which allows the relative importance of the variables to be discussed easier and also in the discussion section the results of the variables are discussed with key points drawn from the results and comparing those to findings of previous studies.

We acknowledge that there are two different opinions, if the results and discussion should be included in the same section or separately. We think that in this case it is clearer to first introduce the results of individual variables and then discuss the key points using additional analyses (PLS) and the discussion section.

We have clarified this division by adding subheadings to the results section for “Linear regression analysis for the street canyons and open areas” and “Partial least squares (PLS) regression analysis”. In addition, we made small changes to the text to further highlight the comparison and different behavior of some parameters in open areas and street canyons

Specific comments:
1. In the regression figures, what is the purpose of coloring the tree fractions ?

The fraction of the trees has a large effect and the large spread in the scatters is at a large part explained by the fraction of the trees (e.g. Fig 4a) which is also discussed in the text in many places (e.g. P10, L330-332).

2. In Table 2, all the parameters are not significant with PM_2.5 in the open areas during the COVID lockdown period? What do you think leads to this result?

Most likely it is due to the reduced emissions. Therefore, there is not enough accumulation or dispersion to be statistically significant. Short description of this has been added to the Discussion section (P21, L710-713): “In addition, due to the reduced emissions, the accumulation and dispersion of pollutants were also reduced. Therefore, presumably the linear regressions in open areas during the COVID lockdown were statistically nonsignificant for all the variables studied.”

3. Line 206. Why “a PLS analysis is assumed to be statistically significant” when the cross-validated R² ≥ 0.0975?

The cross-validated R2 (i.e. Q2) is an arbitrary value generally set to 0.0975, which after the PLS analysis is assumed to be statistically significant as explained in the reference given in the text where this is discussed.

4. What is the “LOESS”? What does the LOESS fit line in Figure 3 try to express?

The LOESS fit is very widely used smooth regression fit. The reference explaining the LOESS regression is added to the figure caption. The fit line is just for clarity showing the reader where the “average” stations are in this study in terms of the aerodynamic parameters and packing densities.

18-Aug-2021

Dear Dr Kokkonen:

Manuscript ID: EA-ART-05-2021-000035.R1
TITLE: The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

Thank you for submitting your revised manuscript to Environmental Science: Atmospheres. After considering the changes you have made, I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

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Dr Lin Wang
Associate Editor, Environmental Science: Atmospheres

Environmental Science: Atmospheres is accompanied by sister journals Environmental Science: Nano, Environmental Science: Processes and Impacts, and Environmental Science: Water Research; publishing high-impact work across all aspects of environmental science and engineering. Find out more at: http://rsc.li/envsci

Reviewer 2

The authors have made a number of revisions to the manuscript and this version reads better in both structure and language. Most of the issues, including the low R2, the PLS analysis, and some of the unclear descriptions have been addressed in the revised version. The response from the authors generally replied the comments well and I think this version can meet the standard for publication.