Chun-Sheng Huang1, Ho-Tang Liao  1, Chia-Yang Chen1,2,3, Li-Hao Young  4, Ta-Chih Hsiao  5, Tsung-I Chou1, Jyun-Min Chang1, Kuan-Lin Lai1,4, Chang-Fu Wu This email address is being protected from spambots. You need JavaScript enabled to view it.1,3 

1 Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
2 Institute of Food Safety and Health, College of Public Health, National Taiwan University, Taipei, Taiwan
3 Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
4 Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
5 Graduate Institute of Environmental Engineering, College of Engineering, National Taiwan University, Taipei, Taiwan

Received: May 25, 2023
Revised: September 27, 2023
Accepted: October 1, 2023

 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.

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Huang, C.S., Liao, H.T., Chen,C.Y., Young, L.H., Hsiao, T.C., Chou, T.I., Chang, J.M., Lai, K.L., Wu, C.F. (2023). Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression. Aerosol Air Qual. Res. 23, 230121.


  • Downweighting secondary aerosols in PMF improved linking with the primary sources.
  • A nitrate-rich secondary aerosol factor was differentiated by two-stage PMF.
  • The fusion strategy separated secondary aerosol contributions on a temporal scale.


Positive Matrix Factorization (PMF) is a commonly used receptor model for source apportionment of PM2.5. However, PMF results often retrieve an individual factor mainly composed of secondary aerosols, making it difficult to link with primary emission sources and formulate effective air pollution control strategies. To overcome this limitation, we employed a two-stage PMF modeling approach with adjustments of the species weighting, which was fused with a robust regression model to better characterize the sources of PM2.5 secondary aerosols. Additionally, organic molecular tracers were incorporated into PMF for source identification. A field campaign was conducted between May and December 2021 in Taichung, Taiwan. An improved PMF model was utilized to resolve the multiple time resolution data of 3-h online and 24-h offline measurements of PM2.5 compositions. Retrieved factors from PMF were averaged over 24-h intervals and then applied in robust regression analysis to re-apportion the contributions. Comparing with conventional PMF, downweighting the secondary aerosol-related species in the model was more effective in linking them to primary emission sources. The results from the fusion model showed that the majority of secondary aerosols (sum of secondary aerosol-related species = 2.67 µg m–3) within three hours were mainly contributed by oil combustion, while the largest contributor of secondary aerosols (1.65 µg m–3) over 24 hours was industry, highlighting the need for regulation of these two sources based on various temporal scales. The developed fusion strategy of two-stage PMF and robust regression provided refined results and can aid in the management of PM2.5.

Keywords: Secondary air pollution, Online measurements, Receptor model, Constraint

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