Elucidating the chemical composition, sources, and health risks of fine particulate matter (PM2.5) is crucial for effectively preventing and controlling air pollution. This study collected PM2.5 samples in Linyi from November 10, 2021, to October 15, 2022, spanning the period of the 2022 Winter Olympics and Paralympics. The analysis focused on seasonal variations in the chemical composition of PM2.5, including water-soluble ions, inorganic elements, and carbonaceous aerosols. Results from the random forest model indicated that control measures during the Olympics and Paralympics reduced PM2.5 concentrations by 21.5% in Linyi. Organic matter was the dominant component of PM2.5, followed by NO3-, SO42-, and NH4+. Among secondary inorganic ions, SO42- exhibited the highest concentration in summer, while NO3- and NH4+ showed the lowest concentrations. The inorganic elements S, K, Fe, and Si had high mean annual concentrations, underscoring the need for targeted control measures for plate production, bulk coal burning, and biomass combustion in Linyi. The organic carbon (OC) to elemental carbon ratio (17.7-20.5) in Linyi was high, highlighting the importance of addressing secondary OC pollution. According to the positive matrix factorization model, coal burning, and the secondary formation processes of sulfate and nitrate were the dominant sources of PM2.5. Backward air mass trajectories revealed substantial contributions from the southeastern, local, and southwestern regions of Linyi. This suggests the need for enhanced regional joint prevention and control efforts between Linyi and neighboring cities, such as Rizhao and Jining in Shandong Province, as well as northern cities in Jiangsu Province. The highest non-carcinogenic and carcinogenic risks (CRs) were associated with As. coal burning posed significant noncarcinogenic risks and a moderate CR, contributing 41.7% and 44.0% of the total health risk, respectively. These findings are crucial for developing effective air pollution prevention and control strategies.
Keywords: Chemical composition; Health risk; PM(2.5); Random forest model; Regional transport; Source apportionment.
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