Ho-Tang Liao1,2, Ming-Tung Chuang1, Ping-Wen Tsai1, Charles C.-K. Chou This email address is being protected from spambots. You need JavaScript enabled to view it.1, Chang-Fu Wu This email address is being protected from spambots. You need JavaScript enabled to view it.2,3 

1 Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
2 Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan
3 Department of Public Health, National Taiwan University, Taipei 10055, Taiwan

Received: September 6, 2020
Revised: December 7, 2020
Accepted: December 8, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.200549  

Cite this article:

Liao, H.T., Chuang, M.T., Tsai, P.W., Chou, C.C.K., Wu, C.F. (2021). Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5. Aerosol Air Qual. Res. 21, 200549. https://doi.org/10.4209/aaqr.200549


  • Bihourly PM2.5 speciation data was used in an enhanced receptor modeling approach.
  • Secondary organic matter (SOM) contributed most to PM2.5 mass.
  • SOM-rich secondary aerosol was the largest one in the eight retrieved factors.
  • Condensation and aqueous phase oxidation of VOCs might be the sources of SOM.
  • Transport of secondary nitrate from upwind urban area could be an important source.


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.

Keywords: Fine particulate matter (PM2.5), Positive matrix factorization (PMF), Multilinear Engine (ME), Source apportionment, Photochemical strength

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