Prediction of Fine Particulate Matter Concentration near the Ground in North China from Multivariable Remote Sensing Data Based on MIV-BP Neural Network.

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants' spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM_2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM_2.5 mass concentration in North China using remote sensing products. According to our predictions, PM_2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM_2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM_2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons' instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM_2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]