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

Atmosphere

Tang-Huang Lin¹  Yee-Lin Wu²  Chien-Lin Lee³  Jung-Chi Chang³  Eric Cheuk-Wai Yip³  Ho-Tang Liao³  Chang-Fu Wu³  Chun-Sheng Huang³ 

[1] Center for Space and Remote Sensing Research, National Central University, Taoyuan 320, Taiwan;Department of Environmental Engineering, National Cheng Kung University, Tainan 701, Taiwan;Institute of Environmental and Occupational Health Sciences, National Taiwan University, Room 717, No.17, Xu-Zhou Road, Taipei 100, Taiwan;

关键词: air pollution;    elemental composition;    land use regression;    aerosol optical depth;   

DOI  :  10.3390/atmos12081018

来源: DOAJ

【 摘 要 】

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 concentrations revealed that the model performances were further improved in the high pollution season.

【 授权许可】

Unknown