Preprints
https://doi.org/10.5194/egusphere-2024-501
https://doi.org/10.5194/egusphere-2024-501
23 Feb 2024
 | 23 Feb 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Biomass burning sources control ambient particulate matter but traffic and industrial sources control VOCs and secondary pollutant formation during extreme pollution events in Delhi

Arpit Awasthi, Baerbel Sinha, Haseeb Hakkim, Sachin Mishra, Varkrishna Mummidivarapu, Gurmanjot Singh, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Vinayak Sinha, and Madhavan N. Rajeevan

Abstract. Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we source-apportioned VOCs and PM, using a high-quality recent (2022) dataset of 111 VOCs, PM2.5, and PM10 using positive matrix factorization. Contrasts between clean-monsoon and polluted-post-monsoon air, VOC source fingerprints, molecular-tracers, enabled differentiating paddy-residue burning from other biomass-burning sources, which has hitherto been impossible. Fresh paddy-residue burning and residential heating & waste-burning contributed the highest to observed PM10 (25 % & 23 %), PM2.5 (23 % & 24 %), followed by heavy-duty CNG-vehicles 15 % PM10 and 11 % PM2.5. For ambient VOCs, ozone, and SOA formation potentials, top sources were petrol-4-wheelers (20 %, 25 %, 30 %), petrol-2-wheelers (14 %, 12 %, 20 %), mixed-industrial emissions (12 %, 14 %, 15 %), solid fuel-based cooking (10 %, 10 %, 8 %) and road construction (8 %, 6 %, 9 %). Emission inventories tended to overestimate residential-biofuel emission (>2) relative to the PMF output. The major source of PM pollution was regional biomass burning, whereas traffic and industries governed VOC and secondary pollutant formation. Our novel source-apportionment method quantitatively resolved even similar biomass and fossil-fuel sources, offering insights into both VOC and PM sources affecting extreme-pollution events. It represents a notable advancement over current source apportionment approaches, and would be of great relevance for future studies in other polluted cities/regions of the world with complex source mixtures.

Arpit Awasthi, Baerbel Sinha, Haseeb Hakkim, Sachin Mishra, Varkrishna Mummidivarapu, Gurmanjot Singh, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Vinayak Sinha, and Madhavan N. Rajeevan

Status: open (extended)

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  • RC1: 'Comment on egusphere-2024-501', Anonymous Referee #1, 21 Mar 2024 reply
Arpit Awasthi, Baerbel Sinha, Haseeb Hakkim, Sachin Mishra, Varkrishna Mummidivarapu, Gurmanjot Singh, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Vinayak Sinha, and Madhavan N. Rajeevan
Arpit Awasthi, Baerbel Sinha, Haseeb Hakkim, Sachin Mishra, Varkrishna Mummidivarapu, Gurmanjot Singh, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Vinayak Sinha, and Madhavan N. Rajeevan

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Short summary
Our study uses a data set of 111 VOCs from a PTR-ToF-MS 10k, PM10 and PM2.5 in a PMF source-receptor model to resolve 11 pollution sources validated with chemical fingerprints collected at the source. Crop residue burning and heating contribute ~50 % of the PM, while traffic and industrial emissions dominate the gas-phase VOCs burden and SOA formation potential (>60 %). Non-tailpipe emissions from CNG powered commercial vehicles dominate the transport sector contribution to the PM burden.