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- W3021399918 abstract "Ambient heavy fine particulate matter (PM2.5) pollution events occur frequently during winter seasons in eastern China. Investigating the evolution of source contributions during heavy pollution episodes is critical for strategies of pollution relief. In this study, a two-month field campaign was conducted in the winters of 2015 and 2016 at a regional supersite in eastern China and over one thousand hourly online measurements for twenty PM2.5 species were obtained. Hourly source apportionment for the total mass of PM2.5, and its major species (primary organic carbon, elemental carbon, nitrate, sulfate and ammonium), were then performed by Positive Matrix Factorization (PMF) and Multilinear Engine-2 (ME2) models. Three PM2.5 heavy pollution episodes including ten high concentration peaks were identified for further analysis. Results showed that ME2 performed better than PMF by fixing source profiles of secondary nitrate, secondary sulfate and sea salt, although they have dominant consistencies. Two types of pollution sources were identified from high PM2.5 mass peaks: coal combustion-oriented responsible for four peaks and secondary inorganic aerosol-oriented responsible for the remaining six peaks. Low wind speed and planetary boundary layer favorited the coal combustion-oriented peaks, but also weakened the secondary inorganic formation due to low temperature and accumulated nitric oxide. Primary emissions from coal combustion can contribute 18% to ammonium in addition to 72% from secondary inorganic aerosol. Secondary organic aerosol would contribute 20% of PM2.5 mass during the heaviest episodes. Findings in this study provide insights into the causes of heavy pollution episodes and support implementing effective control strategies to mitigate heavy pollution events in eastern China." @default.
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- W3021399918 date "2020-07-01" @default.
- W3021399918 modified "2023-10-18" @default.
- W3021399918 title "Evolution of source contributions during heavy fine particulate matter (PM2.5) pollution episodes in eastern China through online measurements" @default.
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- W3021399918 doi "https://doi.org/10.1016/j.atmosenv.2020.117569" @default.
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