Regional Atmospheric CO2 Response to Ecosystem CO2 Budgets in China
Round 1
Reviewer 1 Report
Please see the attached file.
Comments for author File: 
Comments.pdf
Author Response
We are grateful for the efforts made by all the reviewers and editors to help us improve the quality of our manuscript (remotesensing-2411103). We have revised the manuscripts based on the comments. Here are our responses to all the comments. Please refer to specific lines in the manuscript.
Reviewer 1
Comments to the author
The authors have analyzed the response of regional atmospheric CO2 to regional CO2 budgets in China during the period of 2010-2017 and studied the relation of atmospheric CO2 content and CO2
budgets as well as other meteorological factors at different spatial and temporal scales. It is an interesting and useful research. However, there still exist some issues as below:
- I have noticed that the best correlation performance was obtained for a period of six months. Could it be the results of periodic variations of NEEs and CO2 emissions shown in Figure 1?
>> Indeed, both NEEs and CO2 emissions exhibit periodic variations. But to assess the correlation between the two, we analyzed the accumulated CO2 budgets for different monthly periods. It is important to note that the accumulated CO2 budgets, whether calculated for one month, six months, or even twelve months, can vary on an annual basis. Hence, we believe that the correlation's optimal performance over a six-month period cannot be attributed to the periodic variations of NEEs and CO2 emissions.
- Could the authors give more details why they calculated the wind strength as shown in equations (1) and (2)?
>> We have added the explanation why we conducted such calculations. Please see lines 111-114.
- In section 2.3 of the article, the statement "The Pearson correlation of regional atmospheric CO2 and regional CO2 budgets was conducted via Proc Corr of SAS 9.4 (SAS Institute). The correlation was deemed significant when P value was inferior to 0.05." is not clear.
>> Typically, the Pearson coefficient adequately represents the strength of correlation. However, the assessment can also be influenced by the size of the data set. In order to determine the significance of the correlation, SAS's Corr procedure calculates the corresponding P-value. However, in our analysis, we utilized a large data set for correlation purposes, and as a result, we did not include the P-values in the reported results. Consequently, we have removed the statement "The correlation was deemed significant when the P-value was less than 0.05" from the text.
Reviewer 2 Report
The article analyzes the Regional atmospheric CO2 response to ecosystem CO2 budgets in China. The topic is very important, and the origin of data justify the publication in this journal. Some points need highlighting to improve the work:
1- The article mentions the report of the IPCC (Intergovernmental Panel on Climate Change) in 2014. It is necessary to update for more recent reports.
2- it is probably best to visualize the comparison of locations (figures 1 and 2) in the form of a boxplot, since apparently there were no extreme events on the annual scale that deserve to be highlighted.
3- Figure 5 have low quality, it is very difficult for the reader to understand what is being tried to pass through it. It is necessary to transform this information into a table.
Author Response
We are grateful for the efforts made by all the reviewers and editors to help us improve the quality of our manuscript (remotesensing-2411103). We have revised the manuscripts based on the comments. Here are our responses to all the comments. Please refer to specific lines in the manuscript.
Reviewer 2
The article analyzes the Regional atmospheric CO2 response to ecosystem CO2 budgets in China. The topic is very important, and the origin of data justify the publication in this journal. Some points need highlighting to improve the work:
1- The article mentions the report of the IPCC (Intergovernmental Panel on Climate Change) in 2014. It is necessary to update for more recent reports.
>> We have updated the information by using IPCC report of 2022. Please see lines 45-48.
2- it is probably best to visualize the comparison of locations (figures 1 and 2) in the form of a boxplot, since apparently there were no extreme events on the annual scale that deserve to be highlighted.
>> In our opinion, the variations of NEEs and CO2 emissions on the annual scale were important information for the correlation analysis. But we also agree that the box-plots could facilitate the comparison between different regions. So, we have added those figures of box-plots in Appendix (Figure A3).
3- Figure 5 have low quality, it is very difficult for the reader to understand what is being tried to pass through it. It is necessary to transform this information into a table.
>> The bi-plot of RDA is the common way to present its results. We have realized that our problem is the size of Figure 5 who contained the bi-plots of 31 provinces. Thus, what we did for modification was to present only two RDA bi-plots as examples to interpreter the results and put the other bi-plots in Appendix (Figure A4) with larger pages (if it is acceptable). Please see lines 271-281 for the interpretation.
Reviewer 3 Report
Please see the attached PDF
Comments for author File: Comments.pdf
Author Response
We are grateful for the efforts made by all the reviewers and editors to help us improve the quality of our manuscript (remotesensing-2411103). We have revised the manuscripts based on the comments. Here are our responses to all the comments. Please refer to specific lines in the manuscript.
Reviewer 3
This manuscript studied the CO2 variation in different regions in China between 2010 and 2017 to mitigate global warming issues. The authors have conducted a new model for the CO2 estimation budget with collected CO2 emission measurements. Pearson correlation and weighed regression were also made among the CO2 content, budget, and other meteorological factors.
The paper also analyzed the air pollution statistical time series data in different cities. As a result, many aspects have been addressed, such as monitoring CO2 emissions in urban areas. The density of regional CO2 could not be determined for six months for most provinces in China.
This contribution is an actual case study useful for a wide range of environmental communities. Generally, the paper’s architecture is acceptable; however, it should be improved by adding more details.
The introduction is straightforward, providing background on outdoor air pollution in China. Many related works have been cited to give readers a clear idea and background. Practical methods and information were introduced in the approach section. Notably the data sources and studied areas. The result section is clear and rich in correlation graphs and maps. The conclusion must be improved.
I would suggest a few revisions:
➢ In the “2.1. Data collection” section, you should add some statics of input data (Volume, number of files, extension, etc.).
>> We have noticed the lack of such information for MEIC data and added the version information for the data. Please see line 97.
➢ In the “2.1. Data collection” A flowchart explaining the data processing should also be added.
>> The flowchart has been added. Please see Figure A1 and lines 156-157.
➢ Extending the time series data until 2023 would be better for more accuracy.
>> Indeed, extending the analysis to 2023 could get more insights to the regional CO2 cycling. Our NEE model could estimate the regional NEEs until the year of 2023. But the CO2 emission data from MEIC end for the year of 2021 while the monthly spatial CO2 concentration data from AIRS3C2M end for the year of 2017. That is why we chose the period until 2017.
➢ It would be best if you defined acronyms before using them. For instance, “NEE.”
>> The definition of the abbreviation has been added. Please see lines 79.
➢ Section “2.2. NEE estimation model construction” should be developed more.
>> The model construction has already been published, so we decided to cite the reference (10.1016/j.jag.2022.103176) for this work. In our last submission, we have added the details for the model construction, but we have confronted with the problem of plagiarism detection by MDPI system.
➢ Is there any explanation why in Figure 3, the CO2 trend increased exponentially between 2010 and 2017?
>> We felt a little confused about this comment. In our opinion, it is a linear increase of atmospheric CO2 contents probably due to the positive regional CO2 budgets shown in Figure 2.
➢ Why have you not used remote sensing data for CO2 monitoring? such OCO-2, MetOp (IASI, GOME-2),
>> Actually, we used the AIRS3C2M data for monthly atmospheric CO2. It is a remote sensing CO2 monitoring product. Thus, it can represent the density of regional CO2 since it is not the measurement for urban CO2. In addition, we have also taken the database of OCO-2 and MetOp into consideration, however they cannot meet our requirements of monthly data and whole regions of China.
➢ Can you explain why the paper focuses only on CO2, not other greenhouse gases such as CH4, CO, and O3?
>> They are two reasons. Firstly, even other greenhouse gases have much stronger greenhouse effects than CO2, but CO2 still account for the majority of greenhouse gas emissions in CO2-equivalent (more than 70%). Secondly, although MEIC data also provided the regional emissions of CO and we might be able to find the monitoring data products for atmospheric CH4, CO, and O3, but we are not able to conduct the exchange estimation model for those greenhouse gases as for CO2 at this moment. It means that we could not calculate the regional budgets for those gases. That’s why we only did the analysis for CO2.
➢ Figure 5 is not clear; can you simplify it?
>> It has been modified as it is mentioned in the response for the comments of Reviewer 2 as follows: The bi-plot of RDA is the common way to present its results. We have realized that our problem is the size of Figure 5 who contained the bi-plots of 31 provinces. Thus, what we did for modification was to present only two RDA bi-plots as examples to interpreter the results and put the other bi-plots in Appendix (Figure A4) with larger pages (if it is acceptable). Please see lines 271-281 for the interpretation.
➢ The conclusion must be improved.
>> The conclusion has been modified. Please see lines 375-385.
I suggest adding new related work that monitors air pollution using remote sensing techniques.
✓ A Stream Processing Software for Air Quality Satellite Datasets. In: Kacprzyk J., Balas V.E., Ezziyyani M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020), AI2SD 2020, Advances in Intelligent Systems and Computing, vol 1417. Springer, Cham. https://doi.org/10.1007/978-3-030-90633-7_719.4.
✓ “SAT-CEP-monitor: An air quality monitoring software architecture combining complex event processing with satellite remote sensing,” Computers & Electrical Engineering, vol. 93, p. 107257, Jul. 2021, doi: 10.1016/j.compeleceng.2021.107257.
✓ https://www.mdpi.com/2073-4433/14/4/680
>> Several of the recommended references have been added. Please see lines 513-518.
Reviewer 4 Report
Carbon peaking and carbon neutrality are two of prime strategies in China, stressing importance on how to understand the carbon cycling , response and interactions of ecosystem and anthropogenic sectors quantatively. By introducing a robust estimation model of NEE (net ecosystem of CO2 exchange) and applying analyzing method of correlation, redundancy analysis and geographically weighted regression, the quantive relations of atmospheric CO2 and its budget as well as multiple meteorological factors at different spatial and temporal perspectives were studied in this manuscript. The results also showed that CO2 budget were influencing but not dominant in changing atmosphric CO2 in a term of half year for most provinces in China.
The manuscript is reasonably written and efforts were helpful to policy-makers to design more comprehensive CO2 emission allocation strategy by evaluating both economic, ecological and meteorological factors of CO2 production, absorption and transportation with scientific model.
But more importantly, the reviewer want to encourage the authors to investigate on more detailed interactions between ecosystem CO2 budget and atmospheric CO2 distribution at a smaller spatial scale, like refining their quantitive relation of different types of ecosystem and underlying surfaces.
Specific comments:
1. Figure 6: explainations of blackdots, whiskers, upper and lower edge of boxes are needed either in titles of this figure or in the manuscript.
2. line 310: the reference ‘Zhang et al.(2022)[28]’ is wrong, correct this reference(should be [14]?)
The manuscript is reasonably written and English writing are qualified.
Author Response
Carbon peaking and carbon neutrality are two of prime strategies in China, stressing importance on how to understand the carbon cycling , response and interactions of ecosystem and anthropogenic sectors quantatively. By introducing a robust estimation model of NEE (net ecosystem of CO2 exchange) and applying analyzing method of correlation, redundancy analysis and geographically weighted regression, the quantive relations of atmospheric CO2 and its budget as well as multiple meteorological factors at different spatial and temporal perspectives were studied in this manuscript. The results also showed that CO2 budget were influencing but not dominant in changing atmosphric CO2 in a term of half year for most provinces in China.
The manuscript is reasonably written and efforts were helpful to policy-makers to design more comprehensive CO2 emission allocation strategy by evaluating both economic, ecological and meteorological factors of CO2 production, absorption and transportation with scientific model.
But more importantly, the reviewer want to encourage the authors to investigate on more detailed interactions between ecosystem CO2 budget and atmospheric CO2 distribution at a smaller spatial scale, like refining their quantitive relation of different types of ecosystem and underlying surfaces.
>> We also think that is an important step to figure out to what level the local CO2 budgets can influence the local atmospheric CO2 content. Our model could estimate the NEE of CO2 for smaller spatial scales, but it is hard to find the dataset of CO2 emissions for small scales. However, we shall make more efforts for that part in our next work.
Specific comments:
- Figure 6: explainations of blackdots, whiskers, upper and lower edge of boxes are needed either in titles of this figure or in the manuscript.
>> The information has been added. Please see 333-335.
2. line 310: the reference ‘Zhang et al.(2022)[28]’ is wrong, correct this reference(should be [14]?)
>> Yes, it is [14]. Thanks for the correction. Please see line 343.
Round 2
Reviewer 3 Report
All the suggested comments have been taken into consideration. Thus, I recommend publishing the paper in its present form.
Author Response
All the suggested comments have been taken into consideration. Thus, I recommend publishing the paper in its present form.
>> We thank very much for the help of reviewer to revise our manuscript.
