From the journal Environmental Science: Atmospheres Peer review history

13-Jul-2022

Dear Dr Guttikunda:

Manuscript ID: EA-ART-03-2022-000027
TITLE: Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns

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Reviewer 1

This manuscript deals with the evolution of ambient PM2.5 in India using public data bases, along with detailed activity patterns for 1998-2020. Modelled results follow a trend similar to fuel consumption. The results are relevant for the scientific community, particularly the current challenges to curb down India's air pollution.

The manuscrip lacks a quantitative estimate of the uncertainty in the data used; it would be useful for other researchers to have such estimate. For example, a scatter plot of modeled versus measured ambient PM2.5 for some Indian cities would be wellcome (say, 2000, 2010, 2020). Likewise, some preliminary estimation on how good is the source apportionment provided by GEOS-Chem-CEDS, at least in a few cities where PM source apportionment has been published in the scientific literature. If this cannot be done because of spatial scale resolution issues, this may be pointed out as well.

Reviewer 2

In "Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns", the authors present some neat analysis on evolution of PM2.5 over India and in its major cities. Though there is not much scope to comment on the rigor and the results presented , my only concern is the originality of the study.
a) Dey and colleagues (which the authors cite https://www.mdpi.com/2072-4292/12/23/3872) inform about the evolution of PM2.5 in India over the last 2 decades, and the trends and seasonality in PM2.5 concentration that the authors show here albeit using a popular dataset are not new and is well established in literature- apart from discussing a need for this study, it may be insightful to check if the hotspots change with use of a different PM2.5 data (eg. from Dey and colleagues).
b) I understand the authors access the GBD-MAPS data to populate Table 4. The authors may additionally include the information from their APnA city studies into the table for cities under the corresponding states.

Minor checks- Correct for few grammatical errors - eg- Line 27.
PM2.5 exposure cannot ideally be 0. check the value for Lakshadweep (Table 2).

Dear Editor,

Thank you for considering our manuscript for review. Below are our responses to the 2 reviewers.

Reviewer#1
C: Comment and R: Response
________________________________________
C: This manuscript deals with the evolution of ambient PM2.5 in India using public data bases, along with detailed activity patterns for 1998-2020. Modelled results follow a trend similar to fuel consumption. The results are relevant for the scientific community, particularly the current challenges to curb down India's air pollution.
R: Thank you for the comments and suggestions. Our responses are below, along with the changes made in the revised manuscript.

C: The manuscript lacks a quantitative estimate of the uncertainty in the data used; it would be useful for other researchers to have such estimate. For example, a scatter plot of modelled versus measured ambient PM2.5 for some Indian cities would be welcome (say, 2000, 2010, 2020).
R: Thank you. The uncertainty analysis and comparative analysis between the model results and measurements is included in detail in the papers by the WUSTL team in
Hammer et al., 2020 https://pubs.acs.org/doi/full/10.1021/acs.est.0c01764
Donkelaar, et al. 2021 https://pubs.acs.org/doi/full/10.1021/acs.est.1c05309
Since we were not part of the original study, we are not able include the same uncertainty analysis in this manuscript. In this manuscript, we are only using the results from this global database to drive the pollution evolution story for India. We have included an explicit statement in the revised manuscript to this effect.
Further details on the emission databases and model results are publicly available here https://sites.wustl.edu/acag/datasets/gbd-maps/#approach

C: Likewise, some preliminary estimation on how good is the source apportionment provided by GEOS-Chem-CEDS, at least in a few cities where PM source apportionment has been published in the scientific literature. If this cannot be done because of spatial scale resolution issues, this may be pointed out as well.
R: Thank you. We agree that the resolution of the chemical transport model (GEOS-chem) and the available emission inventories have replicated the spatial and temporal patterns at the regional scale – like at the state level and the drawback of these global simulations is their inability to represent the spatial intensity within an urban airshed. A summary of results from the APnA city program covering 50 airsheds is included in the Supplementary as a table (docx and csv format). Since this table was already published here (https://doi.org/10.1016/j.aeaoa.2020.100096) we are unable to include the same in the main manuscript. This is mentioned in the revised document, along with a short note in section 3.3.


Reviewer#2
C: Comment and R: Response
________________________________________
C: In "Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns", the authors present some neat analysis on evolution of PM2.5 over India and in its major cities. Though there is not much scope to comment on the rigor and the results presented my only concern is the originality of the study.
R: Thank you for the comments and suggestions. Our responses are below, along with the changes made in the revised manuscript.
We acknowledge that reanalysis work has been presented before with data from various models, included the GBD-MAPS group which summarized their conclusions for India and the Sub-continent in their global paper. We extended the analysis by including the state level extracts and presenting how fuel consumption trends have changed over the 2 decades, which can be directly linked to the growing pollution levels. Also, the data from these extracts is made public in an easy-to-use format (csv files) for local (Indian) groups to consume, which was difficult with the formats and sizes of the original GBD-MAPS database.

a) Dey and colleagues (which the authors cite https://www.mdpi.com/2072-4292/12/23/3872) inform about the evolution of PM2.5 in India over the last 2 decades, and the trends and seasonality in PM2.5 concentration that the authors show here albeit using a popular dataset are not new and is well established in literature- apart from discussing a need for this study, it may be insightful to check if the hotspots change with use of a different PM2.5 data (eg. from Dey and colleagues).
R: Thank you for the comment. This reference is noted and referred in the document as one of the key studies which looked at the long-term changes across India. Dey et al also extended their work to include health impact analysis in subsequent publications.
https://www.mdpi.com/2072-4292/12/23/3872 refers to the methodology utilized for regressing the satellite feeds and global model reanalysis data (MERRA2) with the on the ground measurements and build the 1-km resolution dataset for all India. A comparison between this reanalysis field and the one from the GBD-MAPS systems used in the current manuscript will be a very valuable resource and it is something we have discussed with Dey et al.
Since all the known reanalysis fields (MERRA2, GBD-MAPS, and CAMS) use the same emission base (from either EDGAR or CEDS), the general spatial and temporal patterns captured by the modelling systems are similar, with some variations coming from mode the models treat their chemical mechanisms. Currently, Dey et al.’s dataset is not a public resource (https://www.saans.co.in used to be the dashboard for sharing maps and animations), which makes it difficult to conduct the comparisons. Dey et al. are waiting for permissions from the Central Pollution Control Board (co-authors on their paper) to make the database public, which will allow for a comparative study as suggested.

b) I understand the authors access the GBD-MAPS data to populate Table 4. The authors may additionally include the information from their APnA city studies into the table for cities under the corresponding states.
R: Thank you. A summary of the results from the APnA city program covering 50 airsheds is included in the Supplementary as a table (docx and csv format). Since this table was already published here (https://doi.org/10.1016/j.aeaoa.2020.100096) we are unable to include the same in the main manuscript. This is mentioned in the revised document.

C: Minor checks- Correct for few grammatical errors - eg- Line 27.
R: Thank you. This is now edited

C:PM2.5 exposure cannot ideally be 0. check the value for Lakshadweep (Table 2).
R: Thank you. This was a rounding error. These islands showed annual average concentrations less than 1.0 for the model grid. This is now edited.

22-Sep-2022

Dear Dr Guttikunda:

Manuscript ID: EA-ART-03-2022-000027.R1
TITLE: Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns

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Dr Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

Reviewer 1

The authors have answered to all issues I had raised in my review. Thus I recommend this article for publication without further revisions.