Incorporating satellite-derived data with annual and monthly land use regression models for estimating spatial distribution of air pollution

The purpose of this study was to assess the performance of annual and monthly land use regression (LUR) models for estimating the spatial distribution of NO₂ and PM_2.5 in Taiwan. Samples were collected at 73 air quality monitoring sites in 2015. Data transformation coupled with extracting principle components and satellite-derived data were integrated with LUR modeling and applied to increase PM_2.5 model performance. Results indicated that NO₂ exhibited more robust model performance compared with PM_2.5. Leave-one-out cross validation (LOOCV) R² of NO₂ annual model was 0.76 and ranged from 0.56 to 0.81 for monthly models. The LOOCV R² of PM_2.5 annual model was improved from 0.13 to 0.56 by applying principle component analysis and adding satellite data (i.e., percentage of sunshine coverage and aerosol optical depth). These approaches also improved the performance of PM_2.5 monthly models. The median LOOCV R² increased from 0.12 to 0.49.