High-accuracy full-coverage PM2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model

Abstract

Obtaining precise ground-level fine particulate matter (PM2.5) information is significant for human health. Spatial PM2.5 maps can be obtained by remote sensing technology, but considerable uncertainty exists when suffering from high pollution with complicated aerosol types. To address this issue, we propose using a hierarchical machine learning model to retrieve high-accuracy and daily full-coverage PM2.5 concentrations from 2014 to 2023 in China. Our hierarchical model was validated by the sample-based 10 cross-validation . Results suggest that our model performs better in terms of RMSE of 12.12 µg/m3, MAE of 8.14 µg/m3 and R2 of 0.95 than traditional model with RMSE of 18.18 µg/m3, MAE of 12.21 µg/m3 and R2 of 0.89, showing 27.49–37.41% improvements for RMSE, 21.85–39.26% improvements for MAE and 8.31–15.39% improvements for R2 at three-folds samples. On longer time scales, our model also shows better results than previous studies. Additionally, for high-pollution provinces, our model can capture PM2.5 trends more preciously than the traditional model. Under severe haze, our hierarchical model can also rightly reflect PM2.5 changes. Overall, due to the hierarchical strategy, our ML-based model can obtain daily full-coverage PM2.5 maps in China with high accuracy and can be applied for follow-up studies.

Keywords

• PM2.5
• Aerosol optical depth (AOD)
• remote sensing
• machine learning
• hierarchical model