Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China

Abstract

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM2.5). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction (MAIAC), provides a valuable opportunity to characterize local-scale PM2.5 at 1-km resolution. However, non-random missing AOD due to cloud/snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM2.5 measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missingness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM2.5 concentrations in 2013 and 2014 at 1km resolution with complete coverage in space and time. The daily MI models have an average R2 of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall model 10-fold cross-validation R2 (root mean square error) were 0.81 (25μg/m3) and 0.73 (18μg/m3) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy. Comparing with previous gap-filling methods, our MI method presented in this study performed better with higher coverage, higher accuracy, and the ability to fill missing PM2.5 predictions without ground PM2.5 measurements. This method can provide reliable PM2.5 predictions with complete coverage that can reduce bias in exposure assessment in air pollution and health studies.