Assessing the Nonlinear Relationship between Land Cover Change and PM[sub.10] Concentration Change in China

Inhalable particulate matter (PM[sub.10] ) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM[sub.10] , which poses a threat to human health. Therefore, exploring the relationship between PM[sub.10] concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM[sub.10] monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM[sub.10] concentrations across China. The coefficient of determination (R[sup.2] ), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m[sup.3] , and 8.826 μg/m[sup.3] , respectively. Based on this, we analyzed the spatio-temporal distribution of PM[sub.10] concentrations in China from 2015 to 2021. High PM[sub.10] values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM[sub.10] concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM[sub.10] concentration change to land-cover change. The PM[sub.10] concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.