Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)

Generating a long-term high-spatiotemporal resolution global PM2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m3 and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m3. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.