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Satellite-Based Mapping of High-Resolution Ground-Level $PM_{2.5}$ with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression
| Content Provider | MDPI |
|---|---|
| Author | Chen, Yijun Wu, Sensen Wang, Yuanyuan Zhang, Feng Liu, Renyi Du, Zhenhong |
| Copyright Year | 2021 |
| Description | Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters $(PM_{2.5}$). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable $PM_{2.5}$ distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the $PM_{2.5}$–AOD relationship. High-resolution (<1 km) $PM_{2.5}$ 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 $PM_{2.5}$ 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 $R^{2}$ of 0.86 and 0.85. Satellite-derived monthly 750 m resolution $PM_{2.5}$ data in China were generated with robust prediction accuracy and almost complete coverage. The $PM_{2.5}$ pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 $µg/m^{3}$. Nonetheless, fine-scale $PM_{2.5}$ exposures at multiple administrative levels suggested that $PM_{2.5}$ 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 $PM_{2.5}$ over large-scale. The newly satellite-derived $PM_{2.5}$ data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China. |
| Starting Page | 1979 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13101979 |
| Journal | Remote Sensing |
| Issue Number | 10 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-19 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Pm2.5 Viirs Ip Aod High Accuracy High-resolution China Geographically Neural Network Weighted Regression |
| Content Type | Text |
| Resource Type | Article |
