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The integration of satellite-derived aerosol optical depth (AOD) andstation-measured PM2.5 provides a promising approach for obtaining spatialPM2.5 data. Several spatiotemporal models, which considered spatial andtemporal heterogeneities of AOD-PM2.5 relationship, have been widely adoptedfor PM2.5 estimation. However, they generally described the complex AOD-PM2.5relationship based on a linear hypothesis. Previous machine learning modelsyielded great superiorities for fitting the nonlinear AOD-PM2.5 relationship,but seldom allowed for its spatiotemporal variations. To simultaneouslyconsider the nonlinearity and spatiotemporal heterogeneities of AOD-PM2.5relationship, geographically and temporally weighted neural networks (GTWNNs)were developed for satellite-based estimation of ground-level PM2.5 in thisstudy. Using satellite AOD products, NDVI data, and meteorological factors overChina as input, GTWNNs were set up with station PM2.5 measurements. Then thespatial PM2.5 data of those locations with no ground stations could beobtained. The proposed GTWNNs have achieved a better performance compared withprevious spatiotemporal models, i.e., daily geographically weighted regressionand geographically and temporally weighted regression. The sample-based andsite-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 0.1degree were generated in China. This study implemented the combination ofgeographical law and neural networks, and improved the accuracy ofsatellite-based PM2.5 estimation.

Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5