  The integration of satellite-derived aerosol optical depth (AOD) and
station-measured PM2.5 provides a promising approach for obtaining spatial
PM2.5 data. Several spatiotemporal models, which considered spatial and
temporal heterogeneities of AOD-PM2.5 relationship, have been widely adopted
for PM2.5 estimation. However, they generally described the complex AOD-PM2.5
relationship based on a linear hypothesis. Previous machine learning models
yielded great superiorities for fitting the nonlinear AOD-PM2.5 relationship,
but seldom allowed for its spatiotemporal variations. To simultaneously
consider the nonlinearity and spatiotemporal heterogeneities of AOD-PM2.5
relationship, geographically and temporally weighted neural networks (GTWNNs)
were developed for satellite-based estimation of ground-level PM2.5 in this
study. Using satellite AOD products, NDVI data, and meteorological factors over
China as input, GTWNNs were set up with station PM2.5 measurements. Then the
spatial PM2.5 data of those locations with no ground stations could be
obtained. The proposed GTWNNs have achieved a better performance compared with
previous spatiotemporal models, i.e., daily geographically weighted regression
and geographically and temporally weighted regression. The sample-based and
site-based cross-validation R2 values of GTWNNs are 0.80 and 0.79,
respectively. On this basis, the spatial PM2.5 data with a resolution of 0.1
degree were generated in China. This study implemented the combination of
geographical law and neural networks, and improved the accuracy of
satellite-based PM2.5 estimation.
