Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition
This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM<sub>2.5</sub>) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/8/1018 |
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| author | Chun-Sheng Huang Ho-Tang Liao Tang-Huang Lin Jung-Chi Chang Chien-Lin Lee Eric Cheuk-Wai Yip Yee-Lin Wu Chang-Fu Wu |
| author_facet | Chun-Sheng Huang Ho-Tang Liao Tang-Huang Lin Jung-Chi Chang Chien-Lin Lee Eric Cheuk-Wai Yip Yee-Lin Wu Chang-Fu Wu |
| author_sort | Chun-Sheng Huang |
| collection | DOAJ |
| description | This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM<sub>2.5</sub>) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM<sub>2.5</sub> and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation <i>R</i><sup>2</sup> > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM<sub>2.5</sub> concentrations revealed that the model performances were further improved in the high pollution season. |
| first_indexed | 2024-03-10T09:00:45Z |
| format | Article |
| id | doaj.art-c536bf5c4ee64a1999a8bc2363210d90 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T09:00:45Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-c536bf5c4ee64a1999a8bc2363210d902023-11-22T06:47:50ZengMDPI AGAtmosphere2073-44332021-08-01128101810.3390/atmos12081018Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental CompositionChun-Sheng Huang0Ho-Tang Liao1Tang-Huang Lin2Jung-Chi Chang3Chien-Lin Lee4Eric Cheuk-Wai Yip5Yee-Lin Wu6Chang-Fu Wu7Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanInstitute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanCenter for Space and Remote Sensing Research, National Central University, Taoyuan 320, TaiwanInstitute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanInstitute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanInstitute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanDepartment of Environmental Engineering, National Cheng Kung University, Tainan 701, TaiwanInstitute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, TaiwanThis study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM<sub>2.5</sub>) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM<sub>2.5</sub> and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation <i>R</i><sup>2</sup> > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM<sub>2.5</sub> concentrations revealed that the model performances were further improved in the high pollution season.https://www.mdpi.com/2073-4433/12/8/1018air pollutionelemental compositionland use regressionaerosol optical depth |
| spellingShingle | Chun-Sheng Huang Ho-Tang Liao Tang-Huang Lin Jung-Chi Chang Chien-Lin Lee Eric Cheuk-Wai Yip Yee-Lin Wu Chang-Fu Wu Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition Atmosphere air pollution elemental composition land use regression aerosol optical depth |
| title | Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition |
| title_full | Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition |
| title_fullStr | Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition |
| title_full_unstemmed | Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition |
| title_short | Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition |
| title_sort | evaluation of using satellite derived aerosol optical depth in land use regression models for fine particulate matter and its elemental composition |
| topic | air pollution elemental composition land use regression aerosol optical depth |
| url | https://www.mdpi.com/2073-4433/12/8/1018 |
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