Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

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

doi:10.3390/atmos12081018

Atmosphere (Aug 2021)

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

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

Affiliations

Chun-Sheng Huang: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan
Ho-Tang Liao: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan
Tang-Huang Lin: Center for Space and Remote Sensing Research, National Central University, Taoyuan 320, Taiwan
Jung-Chi Chang: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan
Chien-Lin Lee: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan
Eric Cheuk-Wai Yip: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan
Yee-Lin Wu: Department of Environmental Engineering, National Cheng Kung University, Tainan 701, Taiwan
Chang-Fu Wu: Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan

DOI: https://doi.org/10.3390/atmos12081018
Journal volume & issue: Vol. 12, no. 8
p. 1018

Abstract

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This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) 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 PM2.5 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 R2 > 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 PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

Published in Atmosphere

ISSN: 2073-4433 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Physics: Meteorology. Climatology
Website: http://www.mdpi.com/journal/atmosphere/

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