Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression
Abstract 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 poll...
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| Format: | Article |
| Language: | English |
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Springer
2023-10-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.230121 |
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| author | Chun-Sheng Huang Ho-Tang Liao Chia-Yang Chen Li-Hao Young Ta-Chih Hsiao Tsung-I Chou Jyun-Min Chang Kuan-Lin Lai Chang-Fu Wu |
| author_facet | Chun-Sheng Huang Ho-Tang Liao Chia-Yang Chen Li-Hao Young Ta-Chih Hsiao Tsung-I Chou Jyun-Min Chang Kuan-Lin Lai Chang-Fu Wu |
| author_sort | Chun-Sheng Huang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-ed5941bfe1514b8e9d61d350a5af580e |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-ed5941bfe1514b8e9d61d350a5af580e2025-02-09T12:23:14ZengSpringerAerosol and Air Quality Research1680-85842071-14092023-10-01231212110.4209/aaqr.230121Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust RegressionChun-Sheng Huang0Ho-Tang Liao1Chia-Yang Chen2Li-Hao Young3Ta-Chih Hsiao4Tsung-I Chou5Jyun-Min Chang6Kuan-Lin Lai7Chang-Fu Wu8Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityDepartment of Occupational Safety and Health, College of Public Health, China Medical UniversityGraduate Institute of Environmental Engineering, College of Engineering, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityInstitute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan UniversityAbstract 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.https://doi.org/10.4209/aaqr.230121Secondary air pollutionOnline measurementsReceptor modelConstraint |
| spellingShingle | Chun-Sheng Huang Ho-Tang Liao Chia-Yang Chen Li-Hao Young Ta-Chih Hsiao Tsung-I Chou Jyun-Min Chang Kuan-Lin Lai Chang-Fu Wu Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression Aerosol and Air Quality Research Secondary air pollution Online measurements Receptor model Constraint |
| title | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression |
| title_full | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression |
| title_fullStr | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression |
| title_full_unstemmed | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression |
| title_short | Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression |
| title_sort | characterizing pm2 5 secondary aerosols via a fusion strategy of two stage positive matrix factorization and robust regression |
| topic | Secondary air pollution Online measurements Receptor model Constraint |
| url | https://doi.org/10.4209/aaqr.230121 |
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