Atmospheric Impacts of COVID-19 on NO_x and VOC Levels over China Based on TROPOMI and IASI Satellite Data and Modeling

Round 1

Reviewer 1 Report

Review on the manuscript of atmosphere-12811257 titled “Atmospheric Impacts of COVID-19 on NO_x and VOC levels over China based on TROPOMI and IASI satellite data and modeling” written by T. Stavrakou et al.

The paper tried to examine the impact of lockdown due to COVID-19 on the levels of atmospheric species over China. The analysis and conclusion would be useful for scientists in the field of atmospheric modeling and remote sensing. I would recommend the manuscript for publication with a few recommendations and comments.

1. Figures: The authors mentioned the analysis regions (like NCP, HH, and YRD) or some specified area (e.g., between 28° and 38° in line 206) throughout the manuscript. However, despite the regions defined in Figure 4, it is difficult to find the analysis regions in other figures (particularly, Figs. 3, 5, and 7) because there is no information on latitude and longitude in the x and y-axis or administration border. Therefore, the authors had better provide the information on latitudes and longitudes or draw the boxes in other figures for the reader’s convenience.
2. Line 49: It would be good if the authors add the reference of “Zhang et al.: NO_x emission reduction and recovery during COVID-19 in East China, Atmosphere, 2020.”
3. Line 93: The authors need to clarify the bias correction or explain what the value represents (2.12×column – 2.12×10¹⁵ cm^-2).
4. Lines 213-236 and Fig. 5: As the author discussed, the lower ratios of modeled HCHO and CHOCHO in southern parts of China (e.g., Hubei, Hunan, Jiangxi, Fujian, and Guangdong) would be influenced significantly by the drop in anthropogenic VOC emissions as well as the underestimation of biomass burning (BB) emissions in Myanmar and Vietnam. Accordingly, in Fig. 5f, the ratio of modeled PAN over the southern part of China would also be underestimated because of the underestimation of biomass burning emissions in Myanmar and Vietnam. Of course, the biogenic sources would also be attributed to the ratios. There is something unsatisfying in the manuscript. Please, provide some more information.
5. 7 b: The modeled HCHO ratio (C₂₀₂₀/C₂₀₁₉) is quite low in spatial variability (showing a similar magnitude) over eastern China (ECN region), although the differences in the isoprene emissions between 2020 and 2019 are spatially significant. The authors need to explain the reason.

REFERENCE

Zhang, R.; Zhang, Y.; Lin, H.; Feng, X.; Fu, T.-M.; Wang, Y. NOx Emission Reduction and Recovery during COVID-19 in East China. Atmosphere 2020, 11, 433. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11040433

Comments for author File: Comments.pdf

Author Response

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Reviewer 2 Report

The extreme measures practiced in many countries during the pandemic impose huge disruptions in human activities and thus atmospheric composition. The manuscript examined the atmospheric composition change in China during February and May of 2020 with satellite products and MAGRITTEv1.1 model simulations. The topic is of great interest to the research community and the public. However, there are some major issues as described below.  

Major comments

1. The authors need to clarify the purpose of the modeling work. They did not provide any extra information by running the model in terms of detailed analysis. The modeled and satellite observed data just lay right next to each other without any further explanation or insights from the model, making the discussion an overall model evaluation. Given the analysis is based on monthly averages, I’m not convinced the model has well reproduced the observation as claimed by the authors. Also, it is better to provide a detailed base case model evaluation when using model results and ensure they compare with similar model simulations in other studies.

1. The MAGRITTEv1.1 model is a less known modeling system, and the authors did not spell out the model’s name until Section 2.2. Please specify the model name at places such as abstract and Line 65. Many details of the modeling configuration are not provided to the readers. For example, I’m confused about the temporal resolution of the simulation: are they talking about monthly average concentrations based on hourly instant model output throughout the paper compared to the monthly averaged satellite columns? How are they comparable if this is the case since satellite observations are only available once/twice daily at specific times? 

1. Please clarify the general vertical distributions of the NO2 and VOCs in the atmosphere. Are they generally within the PBL? Why is the column information from the satellite used to study the surface emission changes instead of surface observations? What related findings have been published/available on reliable sources using surface observations if this is the first study based on satellite? Are the results consistent? Please verify that this is the first study to use satellite columns as stated in multiple places, such as Line 63. Also, please deliberate on the impact of the large systematic uncertainty/error in the satellite products. How does that impact the results, especially for the percentage change/ratio, potentially blowing up the numbers? Can you quantify the uncertainty of your results?

1. How are the spatial regions selected? It seems they do not represent regions with “homogeneous” spatial patterns in almost all the compositions well. Maybe it is more natural to focus on specific metropolitan and outskirt regions? Also, please add major cities or providence lines to the maps. They are tough to relate to for people who are not very familiar with China.

Minor comments

1. The authors might want to add some brief information of HCHO and CHOCHO description before the first sentences in Lines 96 and 106.
2. Please add more details to the model configurations paragraph started from Line 151 or delete the contents instead of repeating what is shown in Table 1.
3. Please be specific when providing estimations, especially in the abstract, to avoid any misinterpretation. For example, is the -40% for observed NO2 in eastern China in the abstract and Line 170?

Author Response

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Reviewer 3 Report

The paper covers a set of simulations of  pollutant emissions in the region of China, backed up by sattellite sensor observations by TROPOMI and and IASI missions. These simulations considered many different scenarions using a well established and documented chemical transport model using a global dataset named CONFORM. The documentis well written and organized but one would expect a more extended conclusion section. While comparing the simulation results with the observation one has to taken into account on possible biases from the sensor itself and it is suggested to take a look at the discussion papers in these links https://0-doi-org.brum.beds.ac.uk/10.5194/acp-2021-309 - https://0-doi-org.brum.beds.ac.uk/10.5194/acp-2021-437  for comments on the retrievals conditions. Of course these retrievals are different from the species being probed but it can give insights on possible disagreements besides the meteorological claimed ones the paper points out. After an extension in the Conclusion section I consider the paper is ready to be published.

Author Response

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Round 2

Reviewer 1 Report

Publish as it is.

Author Response

We thank the reviewer for the positive evaluation of our manuscript.

Reviewer 2 Report

Major Comment 1:

I agree the sensitivity tests are generally useful to study specific processes. However, the model performance based on the monthly mean is not showing “broadly agreement” in most places as far as I can see. Please provide relevant modeling studies published to show the base/sensitivity model performance is acceptable/comparable. If those are rare/not available, please demonstrate your results as a reference for future studies with more detailed acknowledgment of what the model excels and where it fails, as well as suggestions/reflections for future improvement, not just saying the results are “broadly” well, which is not “true” to me and not helpful to your readers.  

Author Response

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