Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

Abstract

Fine particulate matter (PM2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM2.5. Daily surface PM2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R2) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m3, and mean absolution error (MAE) of 8.42 μg/m3. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM2.5 concentrations decrease of 3 to 7 μg/m3 in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM2.5 values higher than 35 μg/m3 and the maximum reaching 95.57 μg/m3. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.