From the journal Environmental Science: Atmospheres Peer review history

26-Apr-2024

Dear Ms Agarwal:

Manuscript ID: EA-ART-12-2023-000174
TITLE: Quantifying the dominant sources influencing the 2016 particulate matter pollution episode over northern 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.

After careful evaluation of your manuscript and the reviewers’ reports, I will be pleased to accept your manuscript for publication after revisions.

Please revise your manuscript to fully address the reviewers’ comments. When you submit your revised manuscript please include a point by point response to the reviewers’ comments and highlight the changes you have made. Full details of the files you need to submit are listed at the end of this email.

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The Royal Society of Chemistry requires all submitting authors to provide their ORCID iD when they submit a revised manuscript. This is quick and easy to do as part of the revised manuscript submission process. We will publish this information with the article, and you may choose to have your ORCID record updated automatically with details of the publication.

Please also encourage your co-authors to sign up for their own ORCID account and associate it with their account on our manuscript submission system. For further information see: https://www.rsc.org/journals-books-databases/journal-authors-reviewers/processes-policies/#attribution-id

Environmental Science: Atmospheres strongly encourages authors of research articles to include an ‘Author contributions’ section in their manuscript, for publication in the final article. This should appear immediately above the ‘Conflict of interest’ and ‘Acknowledgement’ sections. I strongly recommend you use CRediT (the Contributor Roles Taxonomy, https://credit.niso.org/) for standardised contribution descriptions. All authors should have agreed to their individual contributions ahead of submission and these should accurately reflect contributions to the work. Please refer to our general author guidelines https://www.rsc.org/journals-books-databases/author-and-reviewer-hub/authors-information/responsibilities/ for more information.

I look forward to receiving your revised manuscript.

Yours sincerely,
Prof. Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

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

Reviewer 1

Manuscript Number; EA-ART-12-2023-000174
Title; Quantifying the dominant sources influencing the 2016 particulate matter pollution episode over northern India
Although the topic is of interest to the Scientific community, before considering it for publication, this paper should be improved. In this manuscript, some concerns need to be addressed to fit for publication as follows:
Evaluation; Minor Revision
1. What is difference from anthropogenic and biomass burning? Actually, biomass burning came from human-made activities. This should be including in anthropogenic emission source.
2. It is necessary to describe the number of measurements n of environmental factors applied to the analysis and about 30% of the data for the regression test should be separated for verification.
3. What is the main uncertainty of each model?
4. Surface hourly PM2.5 in this area (haze episode). Could you please specify the source apportionment in PM2.5? Any data to confirm the emission source (review literatures).
5. How to explained about long-range transportation (cross border pollution)? It should be one of the main factors to evaluated pollutants concentration.
6. The main text 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".
7. The authors should be presented the limitations of this study based on the model themselves. Future studies hope to address these limitations.

Reviewer 2

Agarwal et al. used WRF-Chem simulation to investigate the driving factors and spatiotemporal extent of an intense PM2.5 period in the Indo-Gangetic Plain in northern India during the post-monsoon season from 31 0ct - 8 Nov 2016. Their results show that the model can capture the PM2.5 and BC peaks during the second half of the simulated period, where biomass burning emission is the major PM2.5 source. Overall, the study is well-designed, and the manuscript is well-written. The results can support their conclusions. This study also aligns well with the journal. I believe this manuscript can improve our understanding of the contribution of biomass burning to regional air quality. I have a few minor comments which I hope can help the authors improve this manuscript. Please see my comments below:
1. For The right panel of Figure 3 panel, I suggest showing the percentage difference instead of the difference.
2. For Fig. 4, the “Middle” and “Lower” overlap with the ticks. I suggest putting “Upper”, “Middle”, and “Lower” on the right side of the right panel.
3. Fig. 8, please increase the font. It is too small. Also, I suggest using altitude as the right y-axis instead of pressure.

Responses to review comments

EA-ART-12-2023-000174: Quantifying the dominant sources influencing the 2016 particulate matter pollution episode over northern India
Agarwal, et al.

We are pleased to read the editor’s decision to accept our manuscript after revisions and thank the reviewers for their comments. Below, in blue text, we provide our responses on a point-by-point basis.

Responses to Reviewer #1

Although the topic is of interest to the Scientific community, before considering it for publication, this paper should be improved. In this manuscript, some concerns need to be addressed to fit for publication as follows:
Evaluation; Minor Revision

1. What is difference from anthropogenic and biomass burning? Actually, biomass burning came from human-made activities. This should be including in anthropogenic emission source.

Response: Not all biomass burning emissions are anthropogenic; biomass burning emissions also derive from wildfires. Ambiguity between a ‘natural’ or an ‘anthropogenic’ source of biomass burning arises because the satellite products used to compile biomass burning emissions inventories cannot easily assign the cause of a particular burning event. However, for the time-period and spatial domain we investigate in this work – the 2016 post-monsoon extreme particle haze event over the Indo-Gangetic Plain, the biomass burning emissions overwhelmingly derive from agricultural (i.e., anthropogenic) biomass burning. Our interest is in apportioning the contributions to the haze episode between these biomass burning emissions and other anthropogenic emissions, but we acknowledge that the terminology we used does not convey this distinction.

For the revised paper, we have added text at the end of the Introduction where we describe the aims of our work so that it now reads as follows (L84-88).
“We focus on quantifying contributions from anthropogenic and biomass burning sources to ambient PM2.5 and BC concentrations across the IGP during the 2016 pollution episode. (Throughout the remainder of the paper our use of the term ‘anthropogenic’ means anthropogenic emissions with the agricultural waste-burning sector removed.)”

We have also added a similar caveat early in the Conclusions section (L630-635):
“In this work, the WRF-Chem model with up-to-date anthropogenic and fire emissions is applied to investigate the influence and source contribution of anthropogenic, seasonal agricultural residue burning, and natural dust emissions to this episode (where anthropogenic means excluding agricultural waste burning).”

2. It is necessary to describe the number of measurements n of environmental factors applied to the analysis and about 30% of the data for the regression test should be separated for verification.

Response: We interpret that the reviewer refers here to Section 2.3 in which we describe comparisons between model and observations for some meteorological and pollutant variables. In the first paragraph of this section, we cite to Table S1 which enumerates the observation sites used in each comparison. As there were only a handful of measurement sites for each variable during that time in each of our three IGP regions (ranging between 1 and 7), it is not possible to undertake the ‘leave-some-out’ type of analysis that the reviewer mentions. An ACTM is not a statistical model but based on physical principles, so this type of statistical analysis is not relevant. WRF-Chem is a very widely used ACTM shown to perform well for simulating atmospheric pollution in a range of studies. We follow the usual practice of evaluating model values using all available observations. Indeed, we already published an extensive evaluation of the meteorological and particle pollutant output from the WRF-Chem model used in this work against a suite of ground and satellite-based observations. We cited this paper in the first paragraph of Section 2.3 of the current paper (and elsewhere). The citation details for this paper are provided in our response to the next comment.

3. What is the main uncertainty of each model?

Response: In a previous paper, we describe an extensive evaluation of the WRF-Chem model used in this work against a suite of ground and satellite-based observations (for both meteorology and particle pollution). The paper comprehensively discusses where there are biases and uncertainties in the model. We cited this earlier paper several times in the Methods section of the present manuscript. This earlier paper was under open-access peer-review at the time of submission of the current paper (Agarwal et al. (2023) https://doi.org/10.5194/egusphere-2023-1150). It is now fully published as Agarwal et al. (2024) (Evaluation of WRF-Chem simulated meteorology and aerosols over northern India during the severe pollution episode of 2016. Atmos. Chem. Phys. 24, 2239-2266. https://doi.org/10.5194/acp-24-2239-2024) and we have updated the citation details in the reference list accordingly. We published this detailed description of the model set-up and performance to avoid the need to include the material in our subsequent investigations of this pollution episode.

4. Surface hourly PM2.5 in this area (haze episode). Could you please specify the source apportionment in PM2.5? Any data to confirm the emission source (review literatures).

Response: It is not clear what the reviewer means by this comment. As the title of our paper indicates, analysis of the sources influencing this PM2.5 haze episode is the primary focus of our investigation (as far as the tools available to us allow). For example, we apportion sources of PM2.5 between anthropogenic emissions (excluding agricultural waste-burning), biomass burning emissions, and natural emissions. This apportionment is shown as daily means in Fig. 4 and spatially in Fig. 6. We also apportion different chemical components of PM2.5 to those deriving from anthropogenic emissions and those deriving from biomass-burning, as shown in Fig. 5. The emissions we use come from globally recognised and widely used sources: EDGAR v5.0 for anthropogenic emissions and FINNv2.5 for biomass-burning emissions. These data sources are already described and cited in our paper.

5. How to explained about long-range transportation (cross border pollution)? It should be one of the main factors to evaluated pollutants concentration.

Response: Our model includes 3-D boundary and initial conditions for meteorology and atmospheric composition. This means that long-range transport of pollutants into our model domain is intrinsically included in our simulations. Our model domain is purposefully larger than the Indo-Gangetic plain area on which we are focused, and the intense haze event that we investigate is overwhelmingly driven by the intense anthropogenic, biomass-burning and dust emissions from inside our model domain. (This can be shown by switching off all emissions within the model domain.) Furthermore, our interest was on the impact of emissions within the IGP and surrounding region since the anthropogenic component of these emissions is, in principle, amenable to mitigation actions in this region.

6. The main text 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".

Response: We agree that numerical values should not be presented to excessive numbers of significance, and we were actively mindful about this when writing our paper. We recognise that if all sources of uncertainty were propagated through the model, then many output values would likely need fewer significant figures than we present. However, one also needs to be aware of the different influence of accuracy and precision uncertainties: two values may have large bias uncertainties, yet much higher level of precision for the value of the difference between them. We have reviewed again the numbers of significant figures we present, and have made some changes, mainly in our supplementary material.

7. The authors should be presented the limitations of this study based on the model themselves. Future studies hope to address these limitations.

Response: We provided some discussion of the limitations of the model at the end of Section 3.1 and in our evaluation work (Agarwal et al. (2024)) . However, for our revised MS we have now added relevant text discussing some limitations of the study in a new Section 3.5 (L591-696).

“3.5 Limitations
Model simulation of this (and similar) extreme PM pollution episodes is inherently challenging because these events are driven, at least in part, by strong transient local emissions that are not captured as model input. This shortcoming is reflected by the model negative bias reported here for PM2.5 and BC during the extreme PM episode. The model uses the best estimates of emissions on average (e.g., for month of year, hour of the day, for a particular biomass burning event, etc.) but cannot capture highly spatially and temporally dynamic changes in emissions in reality: for example, episodic particle emissions during the Diwali festival celebrations, or from local rubbish burning. Further development of locally relevant temporal profiles for anthropogenic emissions is needed, with simulations performed at finer spatial resolution than the present model (12 km) to capture these crucial local features during a haze episode. As noted in Section 3.1, the model also does not currently incorporate chloride particles formed from HCl emissions from local rubbish and crop residue burning that have been observed to contribute significant PM concentration in post-monsoon Delhi. This is an area of future model development.

A further potential contributor to underestimation of surface PM in this work is a small model positive bias for surface windspeed and the influence of BC radiative properties on boundary-layer depth. On the other hand, there is evidence that the model overestimates natural dust concentrations due to both overestimation of dust uplift and underestimation of dust deposition arising from a dry bias in the model (Agarwal et al., 2024). As PM composition observations were lacking for this study period, the model’s ability to accurately represent particle compositional chemistry remains uncertain. This work analyses the modelled SOA and SIA aerosol fractions of PM2.5, but a more detailed assessment of aqueous aerosol-phase chemistry and its sensitivities to precursor gases would be helpful in characterising the intense haze episodes. Despite these acknowledged uncertainties in absolute quantification, we expect that the model provides reliable insight into the drivers of PM and its components during this episode.”

Responses to Reviewer #2

Agarwal et al. used WRF-Chem simulation to investigate the driving factors and spatiotemporal extent of an intense PM2.5 period in the Indo-Gangetic Plain in northern India during the post-monsoon season from 31 0ct - 8 Nov 2016. Their results show that the model can capture the PM2.5 and BC peaks during the second half of the simulated period, where biomass burning emission is the major PM2.5 source. Overall, the study is well-designed, and the manuscript is well-written. The results can support their conclusions. This study also aligns well with the journal. I believe this manuscript can improve our understanding of the contribution of biomass burning to regional air quality. I have a few minor comments which I hope can help the authors improve this manuscript. Please see my comments below:

1. For The right panel of Figure 3 panel, I suggest showing the percentage difference instead of the difference.

Response: The reviewer doesn’t explain why they suggest showing relative rather than absolute differences in this panel. (The panel shows the spatial pattern across the domain of the difference in AOD550nm values derived from the WRF-Chem model and the MODIS satellite product.) We believe that the absolute difference gives more useful insight into the model’s ability to capture the absolute AOD values from the satellite measurements. We note that the use of relative differences runs the risk of giving minor differences in small values the same (or greater) visual weight as large differences in larger values, when, in practical terms, it is more helpful to know where model-satellite bias is greatest. We, therefore, wish to retain the use of absolute differences in this panel in the main MS, but have added a plot of the percentage differences in the supplementary material as shown below (Fig. S6) (and provided a cross-reference to it in the caption of Fig. 3). Retaining the absolute difference panel in the main paper also ensures consistency with our previous model evaluation paper (Agarwal et al. Evaluation of WRF-Chem simulated meteorology and aerosols over northern India during the severe pollution episode of 2016. Atmos. Chem. Phys. 24, 2239-66, 2024).


2. For Fig. 4, the “Middle” and “Lower” overlap with the ticks. I suggest putting “Upper”, “Middle”, and “Lower” on the right side of the right panel.

Response: We have made the changes in the Fig. 4 in the revised MS.

3. Fig. 8, please increase the font. It is too small. Also, I suggest using altitude as the right y-axis instead of pressure.

Response: We have increased the font size in Fig. 8 in the revised MS. We also add more specific information concerning the altitude of meteorological stability parameters in the revised MS. For example, in the revised MS see Lines 544-545, 552-553 and 558.
We use pressure for the vertical axis since this is largely consistent and comparable with existing literature visualising vertical distributions of atmospheric variables (example references 9, 34, 63, 69 in our MS), including our own work (Agarwal et al. Evaluation of WRF-Chem simulated meteorology and aerosols over northern India during the severe pollution episode of 2016. Atmos. Chem. Phys. 24, 2239-66, 2024). For this reason, retained the pressure y-axis in Fig. 8 and added altitude references in the text itself.

07-May-2024

Dear Ms Agarwal:

Manuscript ID: EA-ART-12-2023-000174.R1
TITLE: Quantifying the dominant sources influencing the 2016 particulate matter pollution episode over northern 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.

After careful evaluation of your manuscript and the reviewers’ reports, I will be pleased to accept your manuscript for publication after revisions.

Please revise your manuscript to fully address the reviewers’ comments. When you submit your revised manuscript please include a point by point response to the reviewers’ comments and highlight the changes you have made. Full details of the files you need to submit are listed at the end of this email.

Please submit your revised manuscript as soon as possible using this link :

*** PLEASE NOTE: This is a two-step process. After clicking on the link, you will be directed to a webpage to confirm. ***

https://mc.manuscriptcentral.com/esatmos?link_removed

(This link goes straight to your account, without the need to log in to the system. For your account security you should not share this link with others.)

Alternatively, you can login to your account (https://mc.manuscriptcentral.com/esatmos) where you will need your case-sensitive USER ID and password.

You should submit your revised manuscript as soon as possible; please note you will receive a series of automatic reminders. If your revisions will take a significant length of time, please contact me. If I do not hear from you, I may withdraw your manuscript from consideration and you will have to resubmit. Any resubmission will receive a new submission date.

The Royal Society of Chemistry requires all submitting authors to provide their ORCID iD when they submit a revised manuscript. This is quick and easy to do as part of the revised manuscript submission process. We will publish this information with the article, and you may choose to have your ORCID record updated automatically with details of the publication.

Please also encourage your co-authors to sign up for their own ORCID account and associate it with their account on our manuscript submission system. For further information see: https://www.rsc.org/journals-books-databases/journal-authors-reviewers/processes-policies/#attribution-id

Environmental Science: Atmospheres strongly encourages authors of research articles to include an ‘Author contributions’ section in their manuscript, for publication in the final article. This should appear immediately above the ‘Conflict of interest’ and ‘Acknowledgement’ sections. I strongly recommend you use CRediT (the Contributor Roles Taxonomy, https://credit.niso.org/) for standardised contribution descriptions. All authors should have agreed to their individual contributions ahead of submission and these should accurately reflect contributions to the work. Please refer to our general author guidelines https://www.rsc.org/journals-books-databases/author-and-reviewer-hub/authors-information/responsibilities/ for more information.

I look forward to receiving your revised manuscript.

Yours sincerely,
Prof. Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

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

Reviewer 1

Accept in the current form.

Reviewer 2

Overall, the author responded to all my comments. However, I am still not fully convinced by their response to my last comments about using altitude instead of pressure for the y-axis of Figure 8. Although pressure can be used to calculate altitudes, it is still affected by meteorological conditions. Moreover, it is not straightforward for me to convert the pressure to altitude. Please consider adding altitude to the figure so readers can have an easier time understanding your results.

Referee: 2
Comments to the Author
Overall, the author responded to all my comments. However, I am still not fully convinced by their
response to my last comments about using altitude instead of pressure for the y-axis of Figure 8.
Although pressure can be used to calculate altitudes, it is still affected by meteorological conditions.
Moreover, it is not straightforward for me to convert the pressure to altitude. Please consider adding
altitude to the figure so readers can have an easier time understanding your results.
Response: We have now added the altitude axis in Figure 8 In our revised MS.

13-May-2024

Dear Ms Agarwal:

Manuscript ID: EA-ART-12-2023-000174.R2
TITLE: Quantifying the dominant sources influencing the 2016 particulate matter pollution episode over northern India

Thank you for submitting your revised manuscript to Environmental Science: Atmospheres. I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

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Reviewer 2

Thanks to the authors for addressing my comments. I think this paper is good for publishing.