Improving satellite aerosol optical Depth-PM 2.5 correlations using land use regression with microscale geographic predictors in a high-density urban context

Abstract

© 2018 Elsevier Ltd Estimating the spatiotemporal variability of ground-level PM2.5is essential to urban air quality management and human exposure assessments. However, it is difficult in a high-density and highly heterogeneous urban context as ground-level monitoring stations are most likely sparsely distributed. Satellite-derived Aerosol Optical Depth (AOD) observation has made it possible to overcome such difficulty due to its advantage of spatial coverage. In this study, we improve the AOD-PM2.5correlations by combining land use regression (LUR) modelling and incorporating microscale geographic predictors and atmospheric sounding indices in Hong Kong. The spatiotemporal variations of ground-level PM2.5over Hong Kong were estimated using MODerate resolution Imaging Spectroradiometer (MODIS) AOD remote sensing images for the period of 2003–2015. An extensive LUR variable database containing 294 variables was adopted to develop AOD-LUR models by seasons. Compared to the baseline models (fixed effect models include only basic weather parameters), the prediction performance of all annual and seasonal AOD-LUR fixed effect models were significantly enhanced with approximately 20–30% increases in the model adjusted R2. On top of that, a mixed effect model covers time-dependent random effects and a group of geographically and temporally weighted regression (GTWR) models were also developed to further improve the model performance. As the results, compared to the uncalibrated AOD-PM2.5spatiotemporal correlation (adjusted R2= 0.07, annual fixed effect AOD-only model), the calibrated AOD-PM2.5correlation (the GTWR piecewise model) has a significantly improved model fitting adjusted R2of 0.72 (LOOCV adjusted R2of 0.65) and thus becomes a ready reference for spatiotemporal PM2.5estimation.