Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China

Abstract

High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research.