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Receptor modeling provides valuable information to help develop effective control strategies. Additionally, incorporating parametric variables into expanded receptor modeling improves the understanding of formation mechanisms and potential sources of secondary aerosol. This study was conducted in a rural township in central Taiwan, where the air pollution level was comparable with that in the urban area. Bihourly measurements were applied into an enhanced receptor modeling approach using positive matrix factorization (PMF). Eight potential sources, including oil combustion, coal combustion, secondary aerosol related, nitrate‐rich secondary aerosol, biomass burning, industry/vehicle, road dust, and SOM‐rich (dominated by secondary organic matter) secondary aerosol, were identified. SOM‐rich secondary aerosol (24%) contributed the most to PM2.5 mass, followed by biomass burning (19%) and nitrate‐rich secondary aerosol (18%). Contributions from three factors involving secondary formation features accounted for 55% of PM2.5 mass. Through the enhanced modeling approach, photo-oxidation formation, condensation and aqueous phase oxidation of volatile organic compounds, and transport of secondary nitrates from upwind urban area could be potential formation process and sources of secondary aerosol.

Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5