期刊
CHEMOSPHERE
卷 237, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2019.124500
关键词
PM2.5; SOC; PAHs; PMF; PCA; Health risk
资金
- Department of Science and Technology, Government of India
- DST
Deteriorating air quality with high levels of fine particulate matter (PM2.5) over National Capital Region (NCR) of India is one of the serious environmental and scientific issues. In this paper, PM2.5 samples were collected for 24 h twice or thrice a week during December 2016-December 2017 at three sites [Delhi (IG), Modinagar (MN) and Mahendragarh (HR)] over NCR to analyse the carbonaceous aerosols. Source apportionment of PM2.5 was attempted using Principal Component analysis (PCA) and Positive Matrix Factorization (PMF) based on the analysed carbonaceous fractions [Organic carbon, Elemental carbon, Secondary organic carbon (SOC)]. Organic compounds: alkanes, hopanes, steranes, polycyclic aromatic hydrocarbons (PAHs), phthalates, levoglucosan and n-alkanoic acids were analysed to distinguish the emission sources. Total Carbonaceous Aerosols (TCA) contributed significantly (similar to 26%) to PM2.5 which revealed their importance in source apportionment. Estimated SOC contributed 43.2%, 42.2% and 58.2% to OC and 5.4%, 5.3% and 7.8% to PM2.5 at IG, MN and HR sites respectively. PCA and PMF apportion five emission sources i.e., vehicular emissions (34.6%), biomass burning (26.8%), cooking emissions (15.7%), plastic and waste burning (13.5%) and secondary organic carbon (9.5%) for PM2.5. Source attributed health risk has also been calculated in terms of Lung cancer risk (LCR) associated with PAHs exposure and concluded that vehicular emissions (40.3%), biomass burning (38.1%), secondary organic carbon (12.8%) contributed higher to LCR (503.2 x 10(-5); similar to 503 cases in 1,00,000). Health risk assessment combined with source apportionment inferences signifies the immediate implementation of emissions reduction strategies with special target on transport sector and biomass burning over the NCR of India. (C) 2019 Elsevier Ltd. All rights reserved.
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