From the journal Environmental Science: Atmospheres Peer review history

12-Apr-2022

Dear Mr Roozitalab:

Manuscript ID: EA-ART-03-2022-000023
TITLE: Elucidating the Impacts of COVID-19 Lockdown on Air Quality and Ozone Chemical Characteristics in India

Thank you for your submission to Environmental Science: Atmospheres, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

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Associate Editor
Environmental Science: Atmospheres
Royal Society of Chemistry

************

Reviewer 1

This is an excellent study that evaluates the impacts of COVID-19 lockdown on air quality in India. The authors conduct model simulations to isolate the impacts of meteorology vs. anthropogenic emissions on air quality. Overall I find the model experiments are well designed and the results are clearly presented. Especially the section that evaluates the impacts of COVID on ozone chemistry is very interesting. I only have a few minor comments:

1. I’d suggest the authors change the colormaps of the figures that show changes, like Figures 2, 7, 8. Better to use a blue-white-red color map, so that the regions with no change are marked as white. With the jet colormap, it’s not easy to tell where emissions (or ozone or PM2.5) have increased or decreased.

2. The authors mentioned that modeled PM2.5 is biased low in 2019 and biased high in 2020, and the model biases seem to be comparable with the changes in PM2.5 (Figure 7). How would the model bias affect the interpretation of the modeling results?

3. It’s not clear to me how the TROPOMI data are used in this study. The authors show a figure of TROPOMI NO2 in Figure 1, which left me an impression they will compare modeling results with TROPOMI data, but I don’t seem to find relevant information. Maybe you can also show the changes in NO2 from TROPOMI to show if WRF-Chem is able to simulate the observed change in NO2? Also, the authors show a figure of FNR from WRF-Chem. Do they agree with what is observed from satellite observations?

4. Figure 9e: why is the figure blank? If no data points, I’d suggest not show.

Reviewer 2

The study of Roozitalab et al., investigates the impact of COVID-19 lockdown measurements in India. The authors used Chemical Transport Models (CTMs) applications to simulate various modelling scenarios aiming at disentangling the role of emissions reductions as well as meteorological conditions on several air quality indicators (i.e. O3, NOx, and PM2.5). Additionally, the authors performed an analysis of several reactions pathways relevant to O3 formation using dedicated probing tools such as the integrate reaction rate (IRR) capability.

The authors show that both emissions reduction and meteorological variability had different, non-linear, and sometimes opposite effects on the investigated pollutants.

I found the study to be very valuable and informative for the modelling community. The selection of the scenario (aiming at disentangling also the contribution of the meteorological variability) and the IRR analysis adds important values to the scientific literature on the link between COVID-19 and air quality.

The paper is generally well written, but the quality of some of the figures should be improved (mainly some specific color scale and the arrangement of the data).

I do have, however, some major comments on the analysis as currently performed/presented, as well as on the comparison with previous modelling studying related to COVID-19 and air quality (below):

<i><u>Minor Comments:</u></i>

<b>Introduction: </b>

Please replace “WRF-CHIMMERE” with “WRF-CHIMERE”.

Sometimes the author refers to “Section 0”. I think that is a typo. Please correct.

<i><u>Major Comments:</u></i>

<b>Methods: WRF-Chem modelling</b>

Additional information on the WRF-Chem modelling system are needed; and in particular:

1) The chemical initial and boundary condition were taken from WACCM outputs. Do the boundary fields directly account for the reduction in emissions as induced by lockdown measurements? And, if not, how this will affect the model’s results in regard to, e.g. O3 (being a long range transported pollutant) and particulate matter (e.g. sulphate and non-volatile organic aerosols)?
2) The study presents a detailed analysis of the reactivity of NOx and VOCs towards OH using IRR, which is very informative. However, there is little information regarding the chemical speciation applied to the anthropogenic NMVOCs. How were the VOCs compounds retrieved/lumped?
3) More details on the Secondary Organic Aerosol (SOA) module should be reported in the description section of the model.

<b>Ozone formation analysis</b>

The choice of more surrounding cells (i.e. 4x5) to characterize the different areas is sound, but I am a bit troubled with the selection of only two days (13 March and 7 April) as representative of pre-lockdown and lockdown days. I understand that the authors want to compare days with similar meteorological conditions in 2019 and 2020, but these two days are used to entirely interpret the results from the IRR analysis (more comments below). I would suggest to present those results also for longer periods, at least as additional comments in the supplementary material, by choosing the days in March until the beginning of the lockdown versus the whole month of April (for example).

<b>Model evaluation</b>

Figure 4: In seems there is little difference in NO2 concentrations between 2019 and 2020 in the model data (at least for April). I would have expected the effect of lockdown (applied to the emissions) to be more evident in the modelled NO2 concentrations during that periods (as in the observations). How this will effect model’s results? Does this reflects the model large negative bias for NO2 in 2019? More comments are needed.

Most of the results are reported in form of tables. It would be nice to produce some graphical output for this section (e.g. scatterplots/diurnals of Observed vs Modelled species).

Along the previous comment: In Figure S6 and Figure S8, I would suggest to invert the legend with the columns of the plots, so to have the model versus observation comparisons in the same plots (and have the years in the two different columns of the plots). I think it will facilitate the comparison of the results, but I will have the authors decide.

<b>Results</b>

The changes in PM2.5, O3, and NOx in Figure 6 and throughout most of the analysis are reported for daytime periods (10-17 LT). However, large NOx reductions are probably expected during rush hour peaks which will affect O3 titration (as evident from Panel b in Figure 10, and as shortly discussed in the respective section) and, possibly, also the formation the PM2.5 components. Why the authors decided to focus only on the daytime period?

Figure 7. I would apply the same color scale as in Figure 5 panel a (blue to red). It is very difficult to see the relative changes. Same for similar color scale used throughout the whole manuscript.

Model’s results indicated that primary aerosol is the dominant component with SOA contribution only to 13% to the PM2.5 mass. Is this also suggested by “measurement” data (or by relevant literature available on similar topic)?

The authors stated that major cities like Delhi experienced increased in O3 concentrations. Is this because of the reduced titration at night (which seems to be the case from by Panel b in Figure 10)? More discussion is needed.

The authors presented the effect of lockdown on both SOA and SIA constituents. How does this compare with other studies that have indicated an increase in SOA concentrations in heavily polluted areas, likely because of the increased oxidizing capacity of the atmosphere (Ciarelli et al., 2021; Le et al., 2020)?

<b>Process Analysis of ozone chemistry</b>

This is a very interesting section of the manuscript, and the results are sound. The combined analysis of the modelled scenarios and IRR analysis adds a lot of value to the study. However, I think that focusing the analysis on only two specific days might not be sufficiently representative of the investigated scenarios. I understand that the authors want to compare days with similar meteorological conditions, but selecting longer periods (as suggest in my previous comment above) might additional improve and corroborate the results of the analysis.

In Figure 7 panel b, the model shows a reduction in the reaction of NOx towards OH (in the COVID scenarios compared to BAU) and an increase in the reactions of VOCs towards OH (in the COVID scenario compared to BAU). Other studies have been applied similar IRR analysis over urban areas (Ciarelli et al., 2021). How the current study do compares with those? More comment are needed.

Did the authors also looked into the production pathways of OH? Did the photolysis of O3 (or radiation) indicated any changes between the COVID and BAU scenarios?

<b>References </b>

Ciarelli, G., Jiang, J., El Haddad, I., Bigi, A., Aksoyoglu, S., Prévôt, A.S.H., Marinoni, A., Shen, J., Yan, C., Bianchi, F., 2021. Modeling the effect of reduced traffic due to COVID-19 measures on air quality using a chemical transport model: impacts on the Po Valley and the Swiss Plateau regions. Environ. Sci.: Atmos. 1, 228–240. https://doi.org/10.1039/D1EA00036E

Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y.L., Li, G., Seinfeld, J.H., 2020. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369, 702–706. https://doi.org/10.1126/science.abb7431

We thank the reviewers for their time and valuable suggestions, which have substantially improved the manuscript. We have revised the manuscript and addressed the comments raised by the reviewers. Below, please find our responses to the reviewer’s comments. The reviewer’s comments are shown in black and our responses are shown in green.
We appreciate your time and comments and look forward to your decision.

Best Regards,
Behrooz Roozitalab, on behalf of all co-authors






Reviewer #1:

Comment: This is an excellent study that evaluates the impacts of COVID-19 lockdown on air quality in India. The authors conduct model simulations to isolate the impacts of meteorology vs. anthropogenic emissions on air quality. Overall I find the model experiments are well designed and the results are clearly presented. Especially the section that evaluates the impacts of COVID on ozone chemistry is very interesting. I only have a few minor comments:
Response: Thanks, see below for responses to your individual comments and suggestions.

Comment: I’d suggest the authors change the colormaps of the figures that show changes, like Figures 2, 7, 8. Better to use a blue-white-red color map, so that the regions with no change are marked as white. With the jet colormap, it’s not easy to tell where emissions (or ozone or PM2.5) have increased or decreased.
Response: Thanks for the suggestion. We changed the colormaps for all the difference maps in the manuscript.

Comment: The authors mentioned that modeled PM2.5 is biased low in 2019 and biased high in 2020, and the model biases seem to be comparable with the changes in PM2.5 (Figure 7). How would the model bias affect the interpretation of the modeling results?
Response: This is true that the model PM2.5 results for Delhi are biased low and high for 2019 and 2020, respectively. However, all the plots in Figure 7 are daytime modeling results. Therefore, the bias of the model should not affect the interpretation of the changes in the model since the emissions are reduced in 2020, leading to a reduction in the modeling results of PM2.5. On the other hand, the results in Figure 4, which evaluates the observed and modeled changes in Delhi, show that the model captured the observed changes in PM2.5 concentrations to a large extent. The changes between 2020 and 2019 is due to both the meteorological and emission reduction impacts. As Table S7 shows, the averaged impact of emission reduction for the Delhi region (i.e. Urban) was larger than the impact of meteorology (-15% vs -11%). So, we do not see or expect to see a different modeling result (e.g. increase in PM2.5 rather than a decrease).
Comment: It’s not clear to me how the TROPOMI data are used in this study. The authors show a figure of TROPOMI NO2 in Figure 1, which left me an impression they will compare modeling results with TROPOMI data, but I don’t seem to find relevant information. Maybe you can also show the changes in NO2 from TROPOMI to show if WRF-Chem is able to simulate the observed change in NO2? Also, the authors show a figure of FNR from WRF-Chem. Do they agree with what is observed from satellite observations?
Response: Thanks for the comment. The original purpose of Figure 1 is to show the observed changes in air quality between two years. Nevertheless, we added the difference between 2019 and 2020 for both TROPOMI and modeling results in the supplementary document and added the corresponding discussion to the manuscript.

Comment: Figure 9e: why is the figure blank? If no data points, I’d suggest not show.
Response: Thanks for the suggestion. We agree that a blank panel may be confusing for the readers. However, we believe it clearly shows that while lockdown emission reductions and following atmospheric chemistry led to some increases in ozone and secondary organic aerosols over these regions, secondary inorganic aerosols only decreased. Therefore, we think it is informative and kept that panel but added an explanation in the caption to clarity.

Reviewer #2:

Comment: The study of Roozitalab et al., investigates the impact of COVID-19 lockdown measurements in India. The authors used Chemical Transport Models (CTMs) applications to simulate various modelling scenarios aiming at disentangling the role of emissions reductions as well as meteorological conditions on several air quality indicators (i.e. O3, NOx, and PM2.5). Additionally, the authors performed an analysis of several reactions pathways relevant to O3 formation using dedicated probing tools such as the integrate reaction rate (IRR) capability.
The authors show that both emissions reduction and meteorological variability had different, non-linear, and sometimes opposite effects on the investigated pollutants.
I found the study to be very valuable and informative for the modelling community. The selection of the scenario (aiming at disentangling also the contribution of the meteorological variability) and the IRR analysis adds important values to the scientific literature on the link between COVID-19 and air quality.
The paper is generally well written, but the quality of some of the figures should be improved (mainly some specific color scale and the arrangement of the data).
I do have, however, some major comments on the analysis as currently performed/presented, as well as on the comparison with previous modelling studying related to COVID-19 and air quality (below):
Response: Thanks, see below for responses to your individual comments and suggestions.

<i><u>Minor Comments:</u></i>
<b>Introduction: </b>

Comment: Please replace “WRF-CHIMMERE” with “WRF-CHIMERE”.
Response: It is corrected now.

Comment: Sometimes the author refers to “Section 0”. I think that is a typo. Please correct.
Response: It is corrected now.

<i><u>Major Comments:</u></i>
<b>Methods: WRF-Chem modelling</b>
Comment: Additional information on the WRF-Chem modelling system are needed; and in particular:
1) The chemical initial and boundary condition were taken from WACCM outputs. Do the boundary fields directly account for the reduction in emissions as induced by lockdown measurements? And, if not, how this will affect the model’s results in regard to, e.g. O3 (being a long range transported pollutant) and particulate matter (e.g. sulphate and non-volatile organic aerosols)?
Response: That is a very good point. The operational WACCM outputs did not account for the lockdown measures. Therefore, we performed a separate experiment using another boundary condition to investigate this impact. In particular, Bouarar et al. (2021) studied the impact of global COVID-19 lockdowns on the free tropospheric ozone using different scenarios. In their study, Sim.1 refers to a control scenario and Sim. 6 refers to a scenario with surface and aircraft emissions adjusted for COVID-19 lockdown accompanied with an enhanced stratospheric denitrification in the Arctic according to Wilka et al. (2021). We performed an experiment using the results of these two simulations as the boundary condition for our model (Outputs received from authors; i.e. Ben Gaubert of NCAR). The averaged impact over the domain was less than 2 µg/m3 (ppb) for daytime PM2.5 (ozone) concentrations with negligible impact over Delhi. We have added this analysis in the main text when presenting the results in Figure 8 and added the figures in the supplementary document.

2) The study presents a detailed analysis of the reactivity of NOx and VOCs towards OH using IRR, which is very informative. However, there is little information regarding the chemical speciation applied to the anthropogenic NMVOCs. How were the VOCs compounds retrieved/lumped?
Response: An explanation of the NMVOC speciation is added to the manuscript. The CEDS emission inventory uses RETRO project speciation. Furthermore, we lump the VOCs to the available species in the model based on our personal communications with Dr. Louisa Emmons, NCAR, who developed the MOZART mechanism.

3) More details on the Secondary Organic Aerosol (SOA) module should be reported in the description section of the model.
Response: A description of SOA mechanism in the current study is added to the manuscript. This is a simplified SOA module in the model. While other Volatility Basis Set (VBS) Scheme is available in WRF-Chem, they are not coupled to the MOZART mechanism which has the IRR module.

<b>Ozone formation analysis</b>
Comment: The choice of more surrounding cells (i.e. 4x5) to characterize the different areas is sound, but I am a bit troubled with the selection of only two days (13 March and 7 April) as representative of pre-lockdown and lockdown days. I understand that the authors want to compare days with similar meteorological conditions in 2019 and 2020, but these two days are used to entirely interpret the results from the IRR analysis (more comments below). I would suggest to present those results also for longer periods, at least as additional comments in the supplementary material, by choosing the days in March until the beginning of the lockdown versus the whole month of April (for example).
Response: Thanks for the comment. First, we need to clarify a point here. Those two representative days are used only for the diurnal ozone (Figure 10) and OH reactivity analysis (Figure 11). Then, we extend our FNR and VOC sensitivity analysis for all the days in April and all the gridcells within each type of region (as was mentioned in the caption of Figure 12). It was not clear in the manuscript and we explicitly added these information.
Nevertheless, we added the similar plots for the extended periods in the supporting information as suggested by the reviewer. In particular, for the pre-lockdown period, March10-24 is considered and for the lockdown period April1-30 is considered.

<b>Model evaluation</b>
Comment: Figure 4: In seems there is little difference in NO2 concentrations between 2019 and 2020 in the model data (at least for April). I would have expected the effect of lockdown (applied to the emissions) to be more evident in the modelled NO2 concentrations during that periods (as in the observations). How this will effect model’s results? Does this reflects the model large negative bias for NO2 in 2019? More comments are needed.
Response: We acknowledge that the reduction in NO2 was expected to be more evident in the model. However, as was discussed in the supporting information and shown in Figure S22, the transportation sector is not a dominant contributor to NO2 emissions in Delhi based on CEDS_M emission inventory, which is a recent estimate. On the other hand, transportation sector is the dominant source of NO2 in HTAP v2.2 inventory. As a result, the lockdown impacts are more evident for HTAP scenario as shown below. However, since the overall statistic performance of HTAP v2.2 was poor as discussed in the supplementary document, we used the CEDS_M emission inventory as the base inventory. We added an explanation in the manuscript on this difference. Furthermore, the discussion section evaluates the impact of these two different inventories on the ozone analysis findings.

Figure 1 The difference in daytime NO2 concentration between April 2020 and 2019 in model and ground measured data over Delhi using a) CEDS_M emission inventory (i.e. Base scenario in the manuscript) and b) HTAP v2.2. emission inventory.

Comment: Most of the results are reported in form of tables. It would be nice to produce some graphical output for this section (e.g. scatterplots/diurnals of Observed vs Modelled species).
Response: Figure 3 and Figure 4 within the main text are shown for the evaluation of the model performance on capturing the observed changes in meteorological and air quality data. In addition, many figures in the supplementary document (i.e. Figure S2-S10) are provided to visualize the model performance. Moreover, we added an hourly scatterplot figure to visually evaluate the performance of the model in the supplementary document.

Comment 7: Along the previous comment: In Figure S6 and Figure S8, I would suggest to invert the legend with the columns of the plots, so to have the model versus observation comparisons in the same plots (and have the years in the two different columns of the plots). I think it will facilitate the comparison of the results, but I will have the authors decide.
Response: Thanks for the suggestion. In the current study, we are mainly interested to understand if the model is able to capture the observed changes. The results are shown in this way to convey the message that the model does capture the changes. Therefore, we decided to keep the original plots.

<b>Results</b>
Comment: The changes in PM2.5, O3, and NOx in Figure 6 and throughout most of the analysis are reported for daytime periods (10-17 LT). However, large NOx reductions are probably expected during rush hour peaks which will affect O3 titration (as evident from Panel b in Figure 10, and as shortly discussed in the respective section) and, possibly, also the formation the PM2.5 components. Why the authors decided to focus only on the daytime period?
Response: We appreciate the reviewer’s comments and we agree that less nighttime ozone titration is an important feature. And, we briefly discussed that impact in the manuscript and added a comparison with Ciarelli et al. (2021). Also, the modeled daytime concentrations include the nighttime impacts, explicitly, and reflects their impact. But since daytime ozone is of general interest in terms of air pollution, we focused mostly on daytime concentrations (and similar plots for other variables for consistency). Nevertheless, we acknowledge the importance of daily results and we have presented the corresponding figures in the supplementary document.

Comment: Figure 7. I would apply the same color scale as in Figure 5 panel a (blue to red). It is very difficult to see the relative changes. Same for similar color scale used throughout the whole manuscript.
Response: Thanks for the comment. We changed the colormaps for all the difference maps in the manuscript.

Comment: Model’s results indicated that primary aerosol is the dominant component with SOA contribution only to 13% to the PM2.5 mass. Is this also suggested by “measurement” data (or by relevant literature available on similar topic)?
Response: Thanks for the comment. To the knowledge of the authors, there are only a few recent studies focused on organic aerosols over Indian cities. The most recent study performed by Patel et al. (2021) measured the non-refractory PM1 composition in one site over Delhi. They found that SOAs contribute to about 35% of PM1 during the pre-lockdown period over that site. It supports our results that SOA are not the dominant component of PM over Delhi. Nonetheless, our results may be predicting the lower limit of SOA contribution; perhaps due to both emission inventories and corresponding VOC speciation (as mentioned in previous comments), and simplified SOA mechanism used in the current study. We have acknowledged these uncertainties in the revised version.

Comment: The authors stated that major cities like Delhi experienced increased in O3 concentrations. Is this because of the reduced titration at night (which seems to be the case from by Panel b in Figure 10)? More discussion is needed.
Response: Thanks for the comment. We rephrased the sentence since it was not accurate. Actually, the averaged daytime ozone concentrations (modeled) in Delhi during April did not increase due to the lockdown emissions as the figure and table were showing; rather, it increased for some hours and days as Figure 9 and Figure 6 suggested. We added an explanation to the manuscript.

Comment: The authors presented the effect of lockdown on both SOA and SIA constituents. How does this compare with other studies that have indicated an increase in SOA concentrations in heavily polluted areas, likely because of the increased oxidizing capacity of the atmosphere (Ciarelli et al., 2021; Le et al., 2020)?
Response: That is a good point. The potential oxidizing capacity due to increased ozone concentration could explain the slight increases in SOA concentrations in Figure 9 as suggested by Ciarelli et al. (2021) and Le et al. (2020). Regarding the SIA, our analysis showed that the absolute reductions were mainly in sulfate and ammonium since SO2 emissions were significantly reduced over the domain as Figure 2 suggests. Nevertheless, nitrate concentrations were reduced by 25% over India which can be explained in part by less NO2 emission. We have added this discussion to the manuscript

<b>Process Analysis of ozone chemistry</b>
Comment: This is a very interesting section of the manuscript, and the results are sound. The combined analysis of the modelled scenarios and IRR analysis adds a lot of value to the study. However, I think that focusing the analysis on only two specific days might not be sufficiently representative of the investigated scenarios. I understand that the authors want to compare days with similar meteorological conditions, but selecting longer periods (as suggest in my previous comment above) might additional improve and corroborate the results of the analysis.
Response: Thanks for the comment. As discussed above, we added the figures for an extended period.

Comment: In Figure 7 panel b, the model shows a reduction in the reaction of NOx towards OH (in the COVID scenarios compared to BAU) and an increase in the reactions of VOCs towards OH (in the COVID scenario compared to BAU). Other studies have been applied similar IRR analysis over urban areas (Ciarelli et al., 2021). How the current study do compares with those? More comment are needed.
Response: Thanks for the comment. The results are consistent with what Ciarelli et al. (2021) found and it is added to the manuscript.

Comment: Did the authors also looked into the production pathways of OH? Did the photolysis of O3 (or radiation) indicated any changes between the COVID and BAU scenarios?
Response: Thanks for the comment. The figure below shows three production pathways of OH (following Ciarelli et al. (2021)) using BAU and COVID scenarios during all the days in April over the urban sub-region, which indicates negligible differences.

Figure 2 OH production rate over Urban sub-region for all the days in April 2020 based on 2020BAU scenario and 2020COVID scenario. a) all the photolysis reactions (e.g. HNO3 + hv) that produce OH b) excited oxygen reaction with water c) ozone reactions with VOCs that produce OH

<b>References </b>
Comment:
Ciarelli, G., Jiang, J., El Haddad, I., Bigi, A., Aksoyoglu, S., Prévôt, A.S.H., Marinoni, A., Shen, J., Yan, C., Bianchi, F., 2021. Modeling the effect of reduced traffic due to COVID-19 measures on air quality using a chemical transport model: impacts on the Po Valley and the Swiss Plateau regions. Environ. Sci.: Atmos. 1, 228–240. https://doi.org/10.1039/D1EA00036E
Response: We have added this reference.

Comment: Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y.L., Li, G., Seinfeld, J.H., 2020. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369, 702–706. https://doi.org/10.1126/science.abb7431
Response: We have added this reference.

12-Jul-2022

Dear Mr Roozitalab:

Manuscript ID: EA-ART-03-2022-000023.R1
TITLE: Elucidating the Impacts of COVID-19 Lockdown on Air Quality and Ozone Chemical Characteristics in India

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Associate Editor
Environmental Science: Atmospheres
Royal Society of Chemistry

Reviewer 2

The authors have addressed the comments.

Reviewer 1

The authors have well addressed the reviewers' comments. Great work!