Mapping PM2.5 distribution in China by fusing station measurements and satellite observation

China is currently suffering from a heavy PM
_2.5
pollution. To estimate ground-level PM
_2.5
from satellite-observed aerosol optical depth (AOD), many regional studies have been undertaken, but a few at national scale in China. Moreover, due to the wide spatial range and complex meteorological fields, the previous models' estimation accuracy of PM
_2.5
still has space to improve. In this paper, using the newly available national PM
_2.5
measurements, we develop a generalized regression neural network (GRNN) model to better describe the PM
_2.5
-AOD relationship in China. Besides, a direct average of satellite-derived PM
_2.5
can only reflect the level of PM
_2.5
pollution on some certain days when AOD data is available. To address this issue, a pixel-based merging scheme is proposed. The results suggest that the cross validation R and RMSE are 0.811 and 20.11 μg / m
³
, respectively. The results also show that our study can provide useful information for global monitoring of PM
_2.5
pollution in China.