Compositional Constraints are Vital for Atmospheric PM 2.5 Source Attribution over India
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acsearthspacechem.2c00150.pdf
Description
Published version
Size
5.58 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Pai, Sidhant J
Heald, Colette L
Coe, Hugh
Brooks, James
Shephard, Mark W
Dammers, Enrico
Apte, Joshua S
Luo, Gan
Yu, Fangqun
Holmes, Christopher D
Date Issued
2022
Journal
ACS Earth and Space Chemistry
Publisher
American Chemical Society (ACS)
Citation
Pai, Sidhant J, Heald, Colette L, Coe, Hugh, Brooks, James, Shephard, Mark W et al. 2022. "Compositional Constraints are Vital for Atmospheric PM 2.5 Source Attribution over India." ACS Earth and Space Chemistry, 6 (10).
Version
Final published version
Abstract
India experiences some of the highest levels of ambient PM2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMBModified: 0.19; NMBBase: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMBModified: 0.08; NMBBase: 1.64) and ammonium simulation (NMBModified: 0.49; NMBBase: 0.90). We use this validated simulation to estimate a population-weighted annual PM2.5 exposure of 61.4 μg m-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM2.5 aerosol source attribution for health assessments and air quality management in India.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1021/ACSEARTHSPACECHEM.2C00150
