Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using Machine Learning Models in China

Abstract

Numerous studies and monitoring data indicate that fine particle (PM2.5) pollution in China is still comparatively severe. Given the sparse and uneven distribution of air quality monitoring base stations established in China and the limitation of geographical conditions, inversion of aerosol optical depth by satellite remote sensing can achieve low-cost air quality monitoring in global areas. In this study, we use the machine learning algorithm XGBoost to build a prediction model to achieve nationwide average PM2.5 concentration prediction. Meanwhile, we used aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS) in a specific band, combined with a land use regression (LUR) model as predictors of surface PM2.5 concentrations in China, for the period Dec. 2019-Nov. 2021. In order to provide more accurate PM2.5 concentration prediction, the correspondence between PM2.5 and aerosol optical depth (AOD) under different seasons was studied. The coefficients of determination (R2) for different seasons are 0.86 (spring), 0.80 (summer), 0.90 (autumn), and 0.88 (winter), indicating that the fit is best for autumn and worse for summer. The study shows the potential usefulness of using the LUR model with the XGBoost algorithm for predictive assessment of PM2.5 spatial distribution.