Local incomplete combustion emissions define the PM2.5 oxidative potential in Northern India.

Bhattu, Deepika; Tripathi, Sachchida Nand; Bhowmik, Himadri Sekhar; Moschos, Vaios; Lee, Chuan Ping; Rauber, Martin; Salazar, Gary; Abbaszade, Gülcin; Cui, Tianqu; Slowik, Jay G; Vats, Pawan; Mishra, Suneeti; Lalchandani, Vipul; Satish, Rangu; Rai, Pragati; Casotto, Roberto; Tobler, Anna; Kumar, Varun; Hao, Yufang; Qi, Lu; ... (2024). Local incomplete combustion emissions define the PM2.5 oxidative potential in Northern India. Nature communications, 15(3517) Nature Publishing Group 10.1038/s41467-024-47785-5

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The oxidative potential (OP) of particulate matter (PM) is a major driver of PM-associated health effects. In India, the emission sources defining PM-OP, and their local/regional nature, are yet to be established. Here, to address this gap we determine the geographical origin, sources of PM, and its OP at five Indo-Gangetic Plain sites inside and outside Delhi. Our findings reveal that although uniformly high PM concentrations are recorded across the entire region, local emission sources and formation processes dominate PM pollution. Specifically, ammonium chloride, and organic aerosols (OA) from traffic exhaust, residential heating, and oxidation of unsaturated vapors from fossil fuels are the dominant PM sources inside Delhi. Ammonium sulfate and nitrate, and secondary OA from biomass burning vapors, are produced outside Delhi. Nevertheless, PM-OP is overwhelmingly driven by OA from incomplete combustion of biomass and fossil fuels, including traffic. These findings suggest that addressing local inefficient combustion processes can effectively mitigate PM health exposure in northern India.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP)

UniBE Contributor:

Rauber, Martin, Salazar Quintero, Gary Abdiel, Szidat, Sönke

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 540 Chemistry
000 Computer science, knowledge & systems

ISSN:

2041-1723

Publisher:

Nature Publishing Group

Language:

English

Submitter:

Pubmed Import

Date Deposited:

30 Apr 2024 14:25

Last Modified:

30 Apr 2024 14:34

Publisher DOI:

10.1038/s41467-024-47785-5

PubMed ID:

38664406

BORIS DOI:

10.48350/196274

URI:

https://boris.unibe.ch/id/eprint/196274

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