Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5
Abstract 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 conduc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-12-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.200549 |
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| author | Ho-Tang Liao Ming-Tung Chuang Ping-Wen Tsai Charles C.-K. Chou Chang-Fu Wu |
| author_facet | Ho-Tang Liao Ming-Tung Chuang Ping-Wen Tsai Charles C.-K. Chou Chang-Fu Wu |
| author_sort | Ho-Tang Liao |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f76401756acd4e2c8921bcfd0871ec24 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-f76401756acd4e2c8921bcfd0871ec242025-02-09T12:19:57ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-12-0121311410.4209/aaqr.200549Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5Ho-Tang Liao0Ming-Tung Chuang1Ping-Wen Tsai2Charles C.-K. Chou3Chang-Fu Wu4Research Center for Environmental Changes, Academia SinicaResearch Center for Environmental Changes, Academia SinicaResearch Center for Environmental Changes, Academia SinicaResearch Center for Environmental Changes, Academia SinicaInstitute of Environmental and Occupational Health Sciences, National Taiwan UniversityAbstract 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.https://doi.org/10.4209/aaqr.200549Fine particulate matter (PM2.5)Positive matrix factorization (PMF)Multilinear Engine (ME)Source apportionmentPhotochemical strength |
| spellingShingle | Ho-Tang Liao Ming-Tung Chuang Ping-Wen Tsai Charles C.-K. Chou Chang-Fu Wu Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 Aerosol and Air Quality Research Fine particulate matter (PM2.5) Positive matrix factorization (PMF) Multilinear Engine (ME) Source apportionment Photochemical strength |
| title | Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 |
| title_full | Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 |
| title_fullStr | Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 |
| title_full_unstemmed | Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 |
| title_short | Enhanced Receptor Modeling Using Expanded Equations with Parametric Variables for Secondary Components of PM2.5 |
| title_sort | enhanced receptor modeling using expanded equations with parametric variables for secondary components of pm2 5 |
| topic | Fine particulate matter (PM2.5) Positive matrix factorization (PMF) Multilinear Engine (ME) Source apportionment Photochemical strength |
| url | https://doi.org/10.4209/aaqr.200549 |
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