https://scholars.lib.ntu.edu.tw/handle/123456789/637916
標題: | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression | 作者: | Huang, Chun Sheng Liao, Ho Tang CHIA-YANG CHEN Young, Li Hao TA-CHIH HSIAO Chou, Tsung I. Chang, Jyun Min Lai, Kuan Lin CHANG-FU WU |
關鍵字: | Constraint | Online measurements | Receptor model | Secondary air pollution | 公開日期: | 1-十二月-2023 | 卷: | 23 | 期: | 12 | 來源出版物: | Aerosol and Air Quality Research | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637916 | ISSN: | 16808584 | DOI: | 10.4209/aaqr.230121 |
顯示於: | 環境工程學研究所 |
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