Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Round 1

Reviewer 1 Report

Manuscript Number; sustainability-1604995

Title; Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Although the topic is of interest to the Scientific community, before considering it for publication, this paper should be improved. Authors should reconsider the main objective of the paper according to the content. They should try to synthesize and emphasize the main findings of the study and avoid long sentences. Furthermore, authors should avoid drawing risky conclusions.

 

Evaluation; Minor Revision.

Abstract

1. The analysis number and further within the manuscript and tables: Many numeric data are given with too many significant figures; 2 significant figures suffice and 3 suffice in case the first significant figure is "1".

e.g. 20.255% should be 20.3%.

 

Conclusion

1. Many paragraphs are too short. Please revise and combine them to only one paragraph in the conclusion.
2. The conclusions could be further developed, there is a lot of interesting data in the article.

Author Response

Response of

“Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Dear Reviewer,

Thank you for reviewing our paper entitled “Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”. We are grateful for the feedback of you, which has led to an improvement of our paper. We list our responses (in blue regular font). All the modifications are highlighted in red regular font in the new version of our manuscript. The basic principle and basic flow of PCA-GA-BP neural network were introduced in the supplementary material. This research was divided into four sections. The first section introduced the background of the research. The materials and methods section introduced the study area, data sources, indicators system and the principle of PCA-GA-BP neural network. The analysis results section introduced the air pollution disaster risk index and the changing trend of risk index displayed by the GIS technology from 2010 to 2019. The last section explained discussion, conclusions and the limitations of this research. Finally, we appreciate your time and efforts in processing our manuscript and we are looking forward to hearing from you in the future. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Furthermore, we carefully remodified many sentences and paragraphs. Analysis numbers in manuscript and tables have been remodified.

1. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Analysis numbers in manuscript and tables have been remodified. We carefully remodified many sentences and paragraphs. In order to make you understand and read our paper easily, we tried our effort to rebulid structure of this paper. We add clear labels and color bar explanations of the Figure and remodified the consideration.
2. Response of “Why does ozone show an increasing trend contrary to other pollutants?”: The annual average concentrations of SO₂, PM₁₀, CO and PM_2.5 of Liaoning province, Beijing, Shanghai and Guangdong Province have decreased year by year from 2010 to 2019. However, the annual average concentrations of NO₂ in each region have not decreased significantly. The annual average concentrations of O₃ in each region have increased yearly. The transport effect of air pollution, photochemical decomposition and human activities had a significant impact on increasing the concentration of O₃. Related researches conducted in other regions also showed that the concentrations of O₃ were negatively associated with the concentrations of SO₂, NO₂, PM₁₀, CO and PM_2.5.
3. Response of “Line 209, Line213, 231: The phrase was made as “sentence”. It looks awkward. Maybe numbering would help emphasize the phases”: The modifications are highlighted in red regular font in the new version of our manuscript.

Special thanks for your comments. We have tried our best to improve the manuscript according to the comments. We appreciate for your warm work earnestly and hope the revision would meet with approval. Once again, thank you very much for your comments and suggestions.

Best wishes.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Manuscript: Sustainability-1604995

Title: "Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Authors: Guoqu Deng, Hu Chen, Bo Xie, Mengtian Wang

General remarks:

This paper conducts research for developing a better air pollution disaster risk assessment indicator prediction system in 4 regions in China using four aspects.

The authors try to predict the disaster risk index using machine learning methods such as principal component analysis (PCA) combined with other methods such as genetic algorithm (GA) and back propagation (BP) neural network method. 

While the authors’ technical method sounds interesting and may have potential benefits, this paper has many underlying issues. 

First of all, this paper is very hard to follow and read. It is not because the description is not detailed enough or technical, but just too complicated and not well-organized. The sentences are often too long to digest; many times, one sentence includes multiple ideas and is made up of 6-8 lines. This reviewer had to read the paper multiple times to understand what the messages were.

Second, the key messages are not very clear. Figures should be self-exploratory. Without clear labels and color bar explanations, this is hard to achieve. Interpretation of Figure also needs to be done with careful consideration. This reviewer doesn’t think the reduced MSE simply gives the new method capability to “replace” the traditional method completely (as described in Fig. 10). The conclusion should be based on thoughtful and careful consideration. 

Third, the authors used so much technical jargon (e.g., factor loading matrix), acronyms, and inconsistent terminology. For example, the author introduces their method as a PCA-GA-BP neural network algorithm in the abstract, but this was not well explained what these actually mean. The term, “GA”, a genetic algorithm, wasn’t introduced until page 8. 

Fourth, The authors also often used inconsistent terms; sometimes used indicator, sometimes uses aspect, and sometimes used factor, which is very confusing. Considering this paper is not a technical report, the main message needs to be effectively delivered to the authors without too much detailed technical jargon. The detailed methodology can go to supplementary material.  All of these affect the readability a lot and made the reviewer lose focus. 

With the four reasons above, this reviewer strongly suggests that the authors should resubmit the manuscript after significantly improving the manuscript; it will require reconstructing the paragraphs, rewriting the sentences, and improving the figures.

Minor comments:

Figure 2. Why does ozone show an increasing trend contrary to other pollutants?

Line 209, Lin3 213, 231: The phrase was made as “sentence”. It looks awkward. Maybe numbering would help emphasize the phases.

Line 496 is missing.

Figure 8. What does each color mean?

 

Author Response

Response of

“Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Dear Reviewer,

Thank you for reviewing our paper entitled “Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”. We are grateful for the feedback of you, which has led to an improvement of our paper. We list our responses (in blue regular font). All the modifications are highlighted in red regular font in the new version of our manuscript. The basic principle and basic flow of PCA-GA-BP neural network were introduced in the supplementary material. This research was divided into four sections. The first section introduced the background of the research. The materials and methods section introduced the study area, data sources, indicators system and the principle of PCA-GA-BP neural network. The analysis results section introduced the air pollution disaster risk index and the changing trend of risk index displayed by the GIS technology from 2010 to 2019. The last section explained discussion, conclusions and the limitations of this research. Finally, we appreciate your time and efforts in processing our manuscript and we are looking forward to hearing from you in the future. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Furthermore, we carefully remodified many sentences and paragraphs. Analysis numbers in manuscript and tables have been remodified.

1. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Analysis numbers in manuscript and tables have been remodified. We carefully remodified many sentences and paragraphs. In order to make you understand and read our paper easily, we tried our effort to rebulid structure of this paper. We add clear labels and color bar explanations of the Figure and remodified the consideration.
2. Response of “Why does ozone show an increasing trend contrary to other pollutants?”: The annual average concentrations of SO₂, PM₁₀, CO and PM_2.5 of Liaoning province, Beijing, Shanghai and Guangdong Province have decreased year by year from 2010 to 2019. However, the annual average concentrations of NO₂ in each region have not decreased significantly. The annual average concentrations of O₃ in each region have increased yearly. The transport effect of air pollution, photochemical decomposition and human activities had a significant impact on increasing the concentration of O₃. Related researches conducted in other regions also showed that the concentrations of O₃ were negatively associated with the concentrations of SO₂, NO₂, PM₁₀, CO and PM_2.5.
3. Response of “Line 209, Line213, 231: The phrase was made as “sentence”. It looks awkward. Maybe numbering would help emphasize the phases”: The modifications are highlighted in red regular font in the new version of our manuscript.

Special thanks for your comments. We have tried our best to improve the manuscript according to the comments. We appreciate for your warm work earnestly and hope the revision would meet with approval. Once again, thank you very much for your comments and suggestions.

Best wishes.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Region wise Air Pollution Disaster is predicted in this paper. The following improvements can be incorporated while revising this manuscript,

1. Introduction can be improved
2. PCA-GA-BP model is proposed, to find the risk index, all the algorithms used here are well known algorithms and no new improvements over the algorithms is proposed.
3. The predicted Disaster risk index (year 2019) can be matched with the actual data of 2019.
4. The accuracy can be found.

 

 

Author Response

Response of

“Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Dear Reviewer,

Thank you for reviewing our paper entitled “Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”. We are grateful for the feedback of you, which has led to an improvement of our paper. We list our responses (in blue regular font). All the modifications are highlighted in red regular font in the new version of our manuscript. The basic principle and basic flow of PCA-GA-BP neural network were introduced in the supplementary material. This research was divided into four sections. The first section introduced the background of the research. The materials and methods section introduced the study area, data sources, indicators system and the principle of PCA-GA-BP neural network. The analysis results section introduced the air pollution disaster risk index and the changing trend of risk index displayed by the GIS technology from 2010 to 2019. The last section explained discussion, conclusions and the limitations of this research. Finally, we appreciate your time and efforts in processing our manuscript and we are looking forward to hearing from you in the future. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Furthermore, we carefully remodified many sentences and paragraphs. Analysis numbers in manuscript and tables have been remodified.

1. We reconsidered the main objective of the paper according to the content. We remodified and adjusted the findings and conclusions of this research. Analysis numbers in manuscript and tables have been remodified. We carefully remodified many sentences and paragraphs. In order to make you understand and read our paper easily, we tried our effort to rebulid structure of this paper. We add clear labels and color bar explanations of the Figure and remodified the consideration.
2. Response of “Why does ozone show an increasing trend contrary to other pollutants?”: The annual average concentrations of SO₂, PM₁₀, CO and PM_2.5 of Liaoning province, Beijing, Shanghai and Guangdong Province have decreased year by year from 2010 to 2019. However, the annual average concentrations of NO₂ in each region have not decreased significantly. The annual average concentrations of O₃ in each region have increased yearly. The transport effect of air pollution, photochemical decomposition and human activities had a significant impact on increasing the concentration of O₃. Related researches conducted in other regions also showed that the concentrations of O₃ were negatively associated with the concentrations of SO₂, NO₂, PM₁₀, CO and PM_2.5.
3. Response of “Line 209, Line213, 231: The phrase was made as “sentence”. It looks awkward. Maybe numbering would help emphasize the phases”: The modifications are highlighted in red regular font in the new version of our manuscript.

Special thanks for your comments. We have tried our best to improve the manuscript according to the comments. We appreciate for your warm work earnestly and hope the revision would meet with approval. Once again, thank you very much for your comments and suggestions.

Best wishes.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Manuscript: Sustainability-1604995

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Authors: Guoqu Deng, Hu Chen, Bo Xie, Mengtian Wang

General remarks:

The revised manuscript has been significantly improved in terms of readability. I appreciate the authors' effort on this in a very short period. 

Now, the message gets clear and relatively easy to follow with the detailed methodology in a separate technical document.

Minor comments:

In the introduction, you may want to use the future verb rather than the past, because readers haven’t looked at the rest of the paper yet. For example, in Line 89 you may want to say “The materials and methods section will introduce the study area,…….” Instead of “The materials and methods section introduced the study area”. This sounds a bit disconnected because you haven’t shown the material and method part yet, and “will” show in the next section. Consider changing verbs for the later section of the introduction. Just a minor comment.

Line 250-252: “Related researches conducted in other regions also showed that….”: what are the related researches? Add specific reference.

Line 251: Researches => research: “research” is uncountable, so you should use research. You may use “Related studies” instead.

Author Response

Response of

“Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”

Dear Reviewer,

Thank you for reviewing our paper entitled “Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions”. We are grateful for the feedback of you again. All the modifications are highlighted in red regular font in the new version of our manuscript. Special thanks for your comments. We have improved the manuscript according to the comments. We appreciate for your warm work earnestly and hope the revision would meet with approval. Once again, thank you very much for your comments and suggestions.

Best wishes.

 

Author Response File: Author Response.pdf