Aerosol Characterization of Northern China and Yangtze River Delta Based on Multi-Satellite Data: Spatiotemporal Variations and Policy Implications

Round 1

Reviewer 1 Report

The comments are attched.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 1 Comments

We are very appreciated for your professional comments on our study. We have revised and replied to all your comments one by one, and the revised contents are shown in red in the response letter. We have rewritten the Abstract. Meanwhile, we made extensive English revisions to the manuscript. Anyway, thank you for your time.

 

Point 1 It is suggested to review some of the literatures that used these data for aerosol classifications e.g.:

- Bilal, M., Ali, M.A., Nichol, J.E., Bleiweiss, M.P., de Leeuw, G., Mhawish, A., Shi, Y., Mazhar, U., Mehmood, T., Kim, J. and Qiu, Z., 2022. AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA). Frontiers in Environmental Science, 10, p.981522.

- Sabetghadam, S., Alizadeh, O., Khoshsima, M. and Pierleoni, A., 2021. Aerosol properties, trends and classification of key types over the Middle East from satellite-derived atmospheric optical data. Atmospheric Environment, 246, p.118100.

- Kumar, K.R., Kang, N. and Yin, Y., 2018. Classification of key aerosol types and their frequency distributions based on satellite remote sensing data at an industrially polluted city in the Yangtze River Delta, China. International Journal of Climatology, 38(1), pp.320-336.

-Bibi, S., Alam, K., Chishtie, F. and Bibi, H., 2017. Characterization of absorbing aerosol types using ground and satellites based observations over an urban environment. Atmospheric Environment, 150, pp.126-135.

Response 1: We thank the reviewer for this constructive comment. In the revised manuscript,we review the studies done on aerosol classification using passive satellite data. Those key references have been cited in the manuscript, Please see lines 74-88.

Details: However, passive sensors can only perform simple classification of aerosols by their size based on the different optical properties of aerosols and cannot obtain information on their vertical distribution. Bibi et al[23] determined the types of absorbing aerosols by fine mode fraction, Angstrom exponent (AE), SSA as well as UVAI and studied the seasonal variation of aerosol types, confirming the feasibility of aerosol classification by CALIPSO data. Sabetghadam et al[24] used the AOD and AE values obtained from MODIS to classify aerosols, and the results showed that the Mid-Eastern region of China is dominated by mixed aerosols throughout the year. Aerosol types in Nanjing, an urban-industrial city in the YRD region of eastern China, were also studied using MODIS with OMI between 2004–2015[25]. In 2022, the aerosol generic classification using a novel satellite remote sensing approach (AEROSA) method was developed by Bilal et al to classify aerosols using AOD versus AE to provide information on the number and size of aerosols, and it was found that AEROSA not only provided nine generic aerosol categories for all observations, but also adapted to changes in location and season, whereas general approach study aerosol types do not[26].

 

Point 2 Is there any previous study that shows the CALISPO are in agreement with in-situ observation?

These are all satellite data that may have errors. It is suggested to compare the value with the AERONET data which contain the ground base observation.

The AERONET dat a should be use to show which data is more reliable.

Response 2: Thank you for the insightful questions. We are sorry that we did not make it clearer. The all AOD obtained by CALIPSO has been validated in China based on AERONET measurement stations. This MODIS AOD data was filtered from 10 km resolution level 2 daily products with QA=3 and has been validated in the China region. OMAERUV dataset has been extensively validated in the Chinese region. We enriched the references on satellite data validation in lines 211-212,225-226 and 231-232.

 

Point 3 For each region the climate and air quality of the region should be reviewed.

Response 3: Thank you for the constructive comment. We supplemented the air quality related literature for each region and summarized in section 2.1. TD has severe dusty weather, NCP has a poor environment due to economic development and geographical factors, and YRD has poor air quality due to external pollution transmission and human activities. Please, see line 172-187.

 

Point 4 It should be clarified that what is the procedure to produce these data.

Response 4: Thank you for your insightful comment. We added the aerosol extinction characteristic inversion process and aerosol type inversion principle using CALIPSO data in line 196-203.

Details: The extinction characteristic inversion based on CALIPSO is mainly divided into three steps. First, the hierarchical features in the 532 nm attenuated backscattered signal are identified using Selective Iterative Boundary Locator; second, the hierarchical types are distinguished using Scene Classification Algorithms and three-pass data; finally, the extinction characteristics of the particles are obtained using Hybrid Extinction Re-trievals Algorithm. Furthermore, CALIPSO can retrieve the aerosol type using infor-mation such as the laser-detected declination ratio, backscatter coefficient, subsurface type, and aerosol height.

 

Point 5 lt is suggest ed to use fractional value of each types (in percent )instead of the exact value.

Response 5: Thank you for your constructive comment. We have modified the contents of the Table 1. We added the percentage after the exact value.

 

Point 6 The R2 of each trend line should be added to the figure to show if the trend is statistically significant or not .

each panel should be denot ed by a), b)....

Response 6: Thank you for your constructive comment. We added R² to show whether the trend is statistically significant, while we added (a),(b),(c),(d) in Figure 7.

 

Point 7 Why the vertical distribution of extinction is important ? in should be introduced in the introduction section.

Response 7: Thank you for the insightful questions. We have added importance of vertical distribution is in the introduction section. Please, see line 48-52.

Details: Information on the vertical distribution of aerosols is important for a more complete understanding of aerosol behaviors such as emission, transport, movement, and trans-formation. Moreover, the imprecise nature of aerosol vertical information is one of the important factors contributing to the uncertainty of direct radiative forcing of aerosols

 

Point 8 lt is not clear that what is the method to classify the aerosol types. lt should be clarified.

Response 8: We thank the reviewer for this constructive comment. We have clarified the method to classify the aerosol types by CALIPSO. Please, see line 386-389.

Details: CALIPSO classifies aerosol types in the troposphere into seven subtypes by the scene classification algorithm. The algorithm uses comprehensive attenuation backscat-tering and particle depolarization measurement, surface type, layer top and founda-tion height to identify aerosol subtypes.

 

Point 9 How reliable are the SSA values of OMI over the region? These data should be evaluated or results of previous studies should be used to clarify there liability of these data.

Response 9: Thank you for the insightful questions. According to your suggestion，we supplement the papers that SSA and UVAI have been validated and widely used in China area. Please, see line 473-477.

Details: The SSA based on OMI acquisition has been validated in the Chinese region, and the results show that the agreement between OMI and AERONET data is poor in areas that are more significantly affected by human activities[66].The global and regional use of OMI UVAI based on OMI has proved the reliability of UVAI[21, 64].

Eg:

-Jääskeläinen, E., T. Manninen, J. Tamminen and M. Laine. "The aerosol index and land cover class based atmospheric correction aerosol optical depth time series 1982–2014 for the smac algorithm." Remote Sensing 9 (2017): 10.3390/rs9111095.

-Hammer, M. S., R. V. Martin, C. Li, O. Torres, M. Manning and B. L. Boys. "Insight into global trends in aerosol composition from 2005 to 2015 inferred from the omi ultraviolet aerosol index." Atmospheric Chemistry and Physics 18 (2018): 8097-8112. 10.5194/acp-18-8097-2018.

-Kang Y, Wang L L, Xin J Y, et al. 2019. Analysis of the Change Trend of Aerosol Single-Scattering Albedo in the Areas of Northern China Based on AERONET and OMI Data [J]. Climatic and Environmental Research (in Chinese), 24(5): 537-551. doi:10.3878/j.issn.1006-9585.2018.18006

 

Point 10 This is suggested to add the discussion to the previous section which is about analysis and lack in depth analysis.The previous section which is about analysis is just describing the figures and does not included analysis.It is better to merge these two section into one section.

Response 10: Thank you very much for your suggestion. Based on your suggestions, we found that the results section is indeed lacking in content, so we have integrated the previous sections 4.1 and 4.2 with the results section and further enriched the analysis.

 

Point 11 This periods also can be investigate in trend analysis section. It is better to merge these two sections as well.

Response 11: We thank the reviewer for this constructive comment. After we revisited the content of the article, we reordered some of the images. The two parts of AOD long time series analysis and the effect of pollution policy on aerosol properties are integrated.

 

Point 12 The AOD values can be over 1 in very polluted areas. Why the AOD limit is set as 1 here? ls it ormalized? if yes, it should be mentioned in the text .

Response 12: We thank the reviewer for this constructive comment. We did not normalize the data. This is the part we ignored; we plotted AOD values greater than 1 in the 0.9-1 AOD range section as well. We made a more reasonable range modification to the Figure 2,Figure 3 and Figure 8. Since the pollution is already very serious at AOD>1, we did not make a more specific delineation of the range at AOD>1.

 

Point 13 why is it important ? ls it any geographical features span on the longitude?

Response 13: We thank the reviewer for this constructive comment. Related studies have shown that the variation of aerosol properties with longitude is very important(eg:

-Mehta M , Singh N , Anshumali. Global trends of columnar and vertically distributed properties of aerosols with emphasis on dust, polluted dust and smoke - inferences from 10-year long CALIOP observations[J]. Remote Sensing of Environment, 2018, 208:120-132.

-Wang T , Chen Y , Gan Z , et al. Assessment of dominating aerosol properties and their long-term trend in the Pan-Third Pole region: A study with 10-year multi-sensor measurements[J]. Atmospheric Environment, 2020:117738.). And in our study, we also found regular conclusions about the variation of aerosol properties with the study area. We think that the variation of aerosol properties with longitude is very important. Studying the variation of aerosol properties with longitude can effectively help us to understand the vertical distribution of extinction coefficients of regional dominant types of aerosols and vertical transport of the dominating aerosols. For example, we clearly observe in the figure that the dust aerosols of TD and NCR affect the NCP and YRD regions, and the occurrence of polluted dust aerosols is always accompanied by dust aerosols and elevated smoke aerosols in the eastern part of NCR, YRD and NCR. Meanwhile, we can observe the trends of the seven aerosol types with longitude in the selected study area, for example, the dust aerosol reaches its maximum in the middle of TD and the polluted dust aerosol reaches its maximum in the middle of NCP with longitude, etc.

 

Author Response File: Author Response.docx

Reviewer 2 Report

 

Comment on “Aerosol Characterization of Northern China and Yangtze River Delta Based on CALIPSO Data” by Luan et al.

 

This manuscript studies the aerosol in Nothern China and the Yantze River Delta regions using mainly CALIPSO dataset. It is useful to understand the regional aerosol properties in those regions.

It is also interesting to learn the trend after the pollution controlling policies.

 

My major concern is that there is little or no explanation of the results. I suggest to add information on the sources of aerosol and the meteorological conditions in the introduction section and explain the results using those information. It also needs rewriting some part of the introduction section (see specific comment).

 

The title indicates that only CALIPSO is used. In fact you also used MODIS and OMI, Since the manuscript is submitted to this journal, not Atmosphere or Remote Sensing, I wonder if the title should be changed to be more relevant to sustainability. Something related to the pollution control policies would be better.

 

The paper could be accepted after minor revision.

 

Specific points

 

L12: delete a space.

 

L25: delete a space.

 

L37: sometimes, a mixture of both solid and liquid.

 

L72: It should be deep blue.

 

L92-139: This paragraph summarize some previous studies by scales. It contains both observational and modelling studies. The next paragraph points out that the limitations of the studies in this paragraph, e.g., single type of aerosols or single aerosol property, or time scale. It would be good to rewrite this paragraph. You can review the global scale first (observation, modelling, and time scale), then, regional scale (observation, modelling, and time scale) so that the readers of the paper get clearer and better understandings.

 

L140-141: Singe should be followed by a singular noun.

 

L150-157: Please add the section names. For example, we analyze …… in Section ???. Furthermore, we investigate …… in Section ???.

 

L160-176: The Google map is not on a regular grid system. It must be on some kind of map projection. The boxes on Figure 1 are not exact the longitudes and latitudes as you described.

 

L220: Miss a full stop.

 

L224: delete a full stop before [38].

 

L232: “The highest annual all AOD values in China are mainly located in on NCP”. No figure shows this.

 

L238: transported not transmitted.

 

L245: “The overall performance is MODIS>CALIPSO>OMI”. I do not understand this. What performance?

 

L259-260: “did not show this decline”. What are the reasons for this? It will be useful to add a paragraph in the introduction to give a brief review of the sources of aerosol in China and the controlling weather patterns. This is necessary to explain the results presented in this paper.

 

L278-279: “However, due to the lack of data in February 2016”. There should be a sentence or two in the dataset section to tell the readers that there is a gap in the dataset used in this paper.

 

L292-293: “ A weak upward trend was observed for TD elevated smoke- and polluted dust AOD values from 2007 to 2020.” What is the cause of this?

 

L307-337: What causes the differences? Need some explanation.

 

L351-352: “Where i–vii represent respectively clean marine、 dust 、 polluted continental/smoke 、 clean continental 、 polluted dust 、 elevated smoke, and dusty marine.” Remove some spaces and change “、” to “,”.

 

L348-424: Again, this section needs some explanation. This is the reason I suggest to add a paragraph to review the sources of aerosol in China in the introduction section.

 

L450-453: “the elevated smoke AOD is higher in northern Yunnan and Guangxi regions of China during MAM. The large amount of biomass activities in Southeast Asia produces smoke aerosols, and then the long-distance transmission of these affect the climate in the downwind region [39, 40]”. It depends on the prevail winds. Again, this highlights the importance of reviewing the sources and weather patterns in the introduction section.

 

L510: “during the heating period”. Need to describe details. When and where?

 

L534-536: “To improve air quality, China's State Council issued the Air Pollution Prevention and

Control Action Plan and the Three-Year Action Plan to Win the Blue Sky Defense War in

2013 and 2018 respectively [55, 56].“ You need to summarize the main points of the policies. What is the practice of the policies? Do you know how much reduction in the emissions since the policies put into action?

 

Figure 11: There is a overlapping in longitude in the NCP and YRD regions in Figure 1. I cannot see the overlapping in Figure 11. The label of YRT is not correct in this figure. Same question for Figures 12-13.

 

 

Author Response

Response to Reviewer 2 Comments

We are very appreciated for your professional comments on our study. We have added explanations for the differences in aerosol properties in the different study regions and have revised each section you mentioned. We have also revised the title of the article to make it more relevant to sustainability, taking into account the content of the article. The title is changed to “Aerosol Characterization of Northern China and Yangtze River Delta Based on multi-satellite Data : spatiotemporal variations and policy implications”.

 

Point 1 L12: delete a space.

L25: delete a space.

L37: sometimes, a mixture of both solid and liquid.

L72: It should be deep blue.

L220: Miss a full stop.

L224: delete a full stop before.

Response 1: We thank the reviewer for this constructive comment. We have consolidated such questions. We have fixed the above issue and checked other issues of the same type within the article.

 

Point 2 L92-139: This paragraph summarize some previous studies by scales. It contains both observational and modelling studies. The next paragraph points out that the limitations of the studies in this paragraph, e.g., single type of aerosols or single aerosol property, or time scale. It would be good to rewrite this paragraph. You can review the global scale first (observation, modelling, and time scale), then, regional scale (observation, modelling, and time scale) so that the readers of the paper get clearer and better understandings.

Response 2: We thank the reviewer for this constructive comment. We have adopted your suggestion and put the content in the text. We rewrote the global research based on CALIPSO data, and then wrote the regional scale. Then it was introduced into our research area. Please see line 102-142.

 

Point 3 L140-141: Singe should be followed by a singular noun.

L150-157: Please add the section names. For example, we analyze …… in Section ???. Furthermore, we investigate …… in Section ???.

Response 3: Thank you for the insightful questions. We have added a summary of the content of each section. Please see line 163-169.

Details:In this paper, we analyze the AOD spatial and temporal distribution characteristics of the TD,NCP, NCR and YRD in section 3.1[39]. The vertical distribution of the aerosol extinction coefficient is discussed in section 3.2 and the annual-average distribution of aerosol types in section 3.3. Furthermore, we analyzed the impact of pollution control policies developed in China in 2013 versus 2018 on aerosol properties in the study area in section 4.1 and the longitudinal variation of aerosol properties in the study area in section 4.2.

 

Point 4 L160-176: The Google map is not on a regular grid system. It must be on some kind of map projection. The boxes on Figure 1 are not exact the longitudes and latitudes as you described.

Response 4: We thank the reviewer for this constructive comment. We redrew the map and used the red boxes to draw the study area in Figure 1.

 

Point 5 L232: “The highest annual all AOD values in China are mainly located in on NCP”. No figure shows this.

Response 5: Thank you for pointing out this mistake. We have modified it. There is a misunderstanding in our expression. We again modified the sentences by Table 1 and Figure 2 to show that NCP is present with a high average annual all AOD. Please see line 260,Table 1 and Figure 2.

 

Point 6

(1)L259-260: “did not show this decline”. What are the reasons for this? It will be useful to add a paragraph in the introduction to give a brief review of the sources of aerosol in China and the controlling weather patterns. This is necessary to explain the results presented in this paper.

(2)L292-293: “ A weak upward trend was observed for TD elevated smoke- and polluted dust AOD values from 2007 to 2020.” What is the cause of this?

(3)L307-337: What causes the differences? Need some explanation.

(4)L348-424: Again, this section needs some explanation. This is the reason I suggest to add a paragraph to review the sources of aerosol in China in the introduction section.

(5)L450-453: “the elevated smoke AOD is higher in northern Yunnan and Guangxi regions of China during MAM. The large amount of biomass activities in Southeast Asia produces smoke aerosols, and then the long-distance transmission of these affect the climate in the downwind region [39, 40]”. It depends on the prevail winds. Again, this highlights the importance of reviewing the sources and weather patterns in the introduction section.

Response 6: Thank you for the insightful questions. We read these questions carefully and found that they belong to the same issue, so we have merged the questions. As you can see, we also found that there was too little analysis in the results section of the text, so we merged some of the discussion section with the results.

Details:(1): We have added the reasons for weak upward trend was observed for TD elevated smoke- and polluted dust AOD values from 2007 to 2020 in line 294-333.

(2) We have added the reasons for differences in the vertical distribution of different study areas in line 541-545.

(3) We have added the reasons for differences in the vertical distribution of different study areas in line 339-374.

(4) We added the air quality in each region in section 2.1, along with the reasons for the differences in aerosol types in the different study regions. In line 383-495.

(5) We have added the reasons for the AOD change to the text in line 301-306.

 

Point 7 L278-279: “However, due to the lack of data in February 2016”. There should be a sentence or two in the dataset section to tell the readers that there is a gap in the dataset used in this paper.

Response 7: We thank for this constructive comment. We have described the missing months of CALIPSO data in the dataset section. Please see line 218-220.

 

Point 8 L510: “during the heating period”. Need to describe details. When and where?

Response 8: Thank you for the insightful questions. We describe the main areas and periods of the heating period, as well as the main air impacts caused by the heating period. Please see line 448-452.

Details: The higher elevated smoke aerosol OF in northeast China may be related to human ac-tivities during the heating period, which is generally from October to March of the fol-lowing year in the northern China. The burning of fossil fuels in the heating period and the increase of vehicle exhaust emissions in the low temperature period will directly lead to the increase of elevated smoke aerosol.

 

Point 9 L534-536: “To improve air quality, China's State Council issued the Air Pollution Prevention and Control Action Plan and the Three-Year Action Plan to Win the Blue Sky Defense War in 2013 and 2018 respectively [55, 56].“ You need to summarize the main points of the policies. What is the practice of the policies? Do you know how much reduction in the emissions since the policies put into action?

Response 9: We thank for this constructive comment. We summarize the main implementation points of the pollution policy in 2013 and 2018, the main pollutants targeted by the policy and the summary of policy effectiveness. Please see line 503-526.

Details: In 2012 and 2013, after a large area of smog appeared in Beijing-Tianjin-Hebei, YRD, and other cities, the problem of air pollution control came to the attention of the public and government departments. In 2013, China’s State Council issued the “Action Plan for the Prevention and Control of Air Pollution”, which formally established the air pollution prevention and control model with the concentration of atmospheric par-ticulate matter as the core control target. The main points of the policy in 2013 were the requirements for provinces to effectively address the problem of particulate matter emission concentrations from coal combustion, dust, motor vehicle exhaust, restaurant fumes, and straw-burning pollution. After the implementation of the 2013 policy, the annual average PM2.5 concentrations in important cities across the country decreased by up to 35% in 2017 compared to 2013, with the annual average PM2.5 concentration in Beijing dropping from 90μg/m3 to 58μg/m3. The fine particulate matter concentra-tions in the Beijing-Tianjin-Hebei, YRD and Pearl River Delta regions decreased by about 25%, 20% and 15%, respectively. To promote continuous improvement in air quality and achieve the "13th Five-Year Plan" air quality improvement target, the State Council issued the "Three-Year Action Plan for Winning the Blue Sky Defense War" in 2018, which further consolidates and strengthens the control structure over PM2.5 and other typical compound air pollutants as the core. After three years of efforts, the na-tional and key regional air quality should be significantly improved, and the national annual average PM2.5 concentration should decrease by 20% in 2020 compared to 2017; the three key regions of Beijing, Tianjin, Hebei and the surrounding areas, the YRD, and Fenwei Plain should show decreases by 21%, 26%, and 23% respectively[67-71]. Consequently, whether long-term pollution control policies have an effect on AOD, extinction coefficient at different altitudes, and aerosol types in the TD, NCR, NCP, and YRD regions were investigated in this section.

The references are as follows:

-Wen, Bo, Fang, Xinyue, Jin, Qiang, Shan and Aidang. "Spatio-temporal variations of pm2.5 emission in china from 2005 to 2014." Chemosphere: Environmental toxicology and risk assessment 183 (2017): 429-436.

-Xs, A., A. Yz, L. A. Yu, B. Wxa, Y. A. Gang, L. A. Xin, C. Bc, T. D. Dan and B. Jwa. "Air quality benefits of achieving carbon neutrality in china." Science of The Total Environment

-Chen, Y., N. Schleicher, Y. Chen, F. Chai and S. Norra. "The influence of governmental mitigation measures on contamination characteristics of pm(2.5) in beijing." Sci Total Environ 490 (2014): 647-658. 10.1016/j.scitotenv.2014.05.049. https://www.ncbi.nlm.nih.gov/pubmed/24887192.

-Yuan, X., M. Zhang, Q. Wang, Y. Wang and J. Zuo. "Evolution analysis of environmental standards: Effectiveness on air pollutant emissions reduction." Journal of Cleaner Production 149 (2017): 511-520. 10.1016/j.jclepro.2017.02.127.

-Liu, X. J., W. Xu, E. Z. Du, A. H. Tang, Y. Zhang, Y. Y. Zhang, Z. Wen, T. X. Hao, Y. P. Pan, L. Zhang, et al. "Environmental impacts of nitrogen emissions in china and the role of policies in emission reduction." Philos Trans A Math Phys Eng Sci 378 (2020): 20190324. 10.1098/rsta.2019.0324. https://www.ncbi.nlm.nih.gov/pubmed/32981443.

 

Point 10 Figure 11: There is a overlapping in longitude in the NCP and YRD regions in Figure 1. I cannot see the overlapping in Figure 11. The label of YRT is not correct in this figure. Same question for Figures 12-13.

Response 10: We thank for this constructive comment. We are very sorry that our diagram does not clearly express what we mean. As you can see, the longitudes of NCP and YRD do overlap, but the graphs we have drawn show NCP (110.5-120.5°E) and YRD (116.5-122.5°E). For a clearer representation, we have also illustrated and added the longitude in the figure notes. Please see Figure 11-13. In the meantime, we would like to attach some references to the literature where such studies have been done(eg: -Wang T , Chen Y , Gan Z , et al. Assessment of dominating aerosol properties and their long-term trend in the Pan-Third Pole region: A study with 10-year multi-sensor measurements[J]. Atmospheric Environment, 2020:117738.).

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

As the revision has been made as suggested then it is suggested for publication.