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 (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) Taiwan 2014. A total 12 annual LUR models developed for PM2.5 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, zinc. After applied AOD a derived-predictor, percentage, modeling, the number with leave-one-out cross-validation R2 > 0.40 significantly increased 5 9, indicating substantial benefits construction spatial prediction models. Sensitivity analyses using data stratified by revealed that model performances further improved high pollution season.