Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression

Abstract

Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters (PM2.5). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable PM2.5 distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the PM2.5–AOD relationship. High-resolution (<1 km) PM2.5 products over the whole of China for fine exposure assessment and health research are also lacking. This study aimed to predict 750 m resolution ground-level PM2.5 in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data using a newly developed geographically neural network weighted regression (GNNWR) model. The performance evaluations demonstrated that GNNWR achieved higher prediction accuracy than the widely used methods with cross-validation and predictive R2 of 0.86 and 0.85. Satellite-derived monthly 750 m resolution PM2.5 data in China were generated with robust prediction accuracy and almost complete coverage. The PM2.5 pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 µg/m3. Nonetheless, fine-scale PM2.5 exposures at multiple administrative levels suggested that PM2.5 pollution in most urban areas needed further control, especially in southern Hebei Province. This work is the first to evaluate the potential of VIIRS IP AOD in modeling high-resolution PM2.5 over large-scale. The newly satellite-derived PM2.5 data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China.