Details

Autor(en) / Beteiligte

• Maheshwarkar, Prem
• Ralhan, Akarsh
• Sunder Raman, Ramya
• Tibrewal, Kushal
• Venkataraman, Chandra
• Dhandapani, Abisheg
• Kumar, R. Naresh
• Mukherjee, Sauryadeep
• Chatterje, Abhijit
• Rabha, Shahadev
• Saikia, Binoy K
• Bhardwaj, Ankur
• Chaudhary, Pooja
• Sinha, Baerbel
• Lokhande, Pradnya
• Phuleria, Harish C.
• Roy, Sayantee
• Imran, Mohd
• Habib, Gazala
• Azharuddin Hashmi, M.
• Qureshi, Asif
• Qadri, Adnan Mateen
• Gupta, Tarun
• Lian, Yang
• Pandithurai, G.
• Prasad, Laxmi
• Murthy, Sadashiva
• Deswal, Meena
• Laura, Jitender S.
• Chhangani, Anil Kumar
• Najar, Tanveer Ahmad
• Jehangir, Arshid

Titel

Understanding the Influence of Meteorology and Emission Sources on PM 2.5 Mass Concentrations Across India: First Results From the COALESCE Network

Ist Teil von

• Journal of geophysical research. Atmospheres, 2022-02, Vol.127 (4)

Erscheinungsjahr

2022

Quelle

Wiley-Blackwell Full Collection

Beschreibungen/Notizen

• Abstract The Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts (COALESCE) is a multi‐institutional Indian network project to better understand carbonaceous aerosol induced air quality and climate effects. This study presents time synchronized measurements of surface PM 2.5 concentrations made during 2019 at 11 COALESCE sites across India. The network median PM 2.5 concentration was 42 μg m −3 with the highest median value at Rohtak (99 μg m −3 ) and the lowest median value at Mysuru (26 μg m −3 ). The influence of six meteorological parameters on PM 2.5 were evaluated. Causality analysis suggested that temperature, surface pressure, and relative humidity were the most important factors influencing fine PM mass, on an annual as well as seasonal scale. Further, a multivariable linear regression model showed that, on an annual basis, meteorology could explain 16%–41% of PM 2.5 variability across the network. Concentration Weighted Trajectories (CWT) together with the results of causality analysis revealed common regional sources affecting PM 2.5 concentrations at multiple regional sites. Further, CWT source locations for all sites across the network correlated with the SMoG‐India emissions inventory at the 95th percentile confidence. Finally, CWT maps in conjunction with emissions inventory were used to obtain quantitative estimates of anthropogenic primary PM 2.5 sectoral shares from a mass‐meteorology‐emissions reconciliation, for all 11 pan‐India network sites. These estimates can help guide immediate source reduction and mitigation actions at the national level. Plain Language Summary Surface PM 2.5 mass causal associations with annual and seasonal meteorology during 2019 across 11 pan‐India COALESCE network locations were examined. Temperature, surface pressure and relative humidity were the most influential factors on fine PM mass concentrations. However, across the country only 16%–41% of fine PM variability was explained by meteorology on an annual basis. A fusion of trajectory ensemble methods with national emissions inventory was used for apportioning anthropogenic primary PM 2.5 at all 11 locations. Mass‐meteorology‐emissions associations helped identify priority sectors for source control across the country.