AFNO-based downscaling of global air pollution fields

Air pollution, particularly fine particulate matter (PM2.5), poses a significant risk to public health, necessitating accurate high-resolution monitoring. While global Chemical Transport Models (CTMs) like the Copernicus Atmosphere Monitoring Service (CAMS) provide continuous worldwide coverage, their coarse spatial resolution (~40 km) limits their utility for assessing local exposure relative to regional models (~10 km) that are restricted to specific domains, such as Europe. To bridge this gap, we present a novel deep learning approach for global downscaling of pollutant concentrations based on the Adaptive Fourier Neural Operator (AFNO), benchmarking its performance against a standard U-Net baseline.

We adapted the Modulated AFNO architecture for spatial super-resolution, using low-resolution CAMS Global PM2.5 and dynamic meteorological fields (wind, temperature, dew point, boundary layer height). A key innovation is integrating these inputs with high-resolution static data: orography and population density. We demonstrate that directly inputting static features into the network backbone outperforms separate spatial conditioning, effectively leveraging the Fast Fourier Transform to capture long-range dependencies while respecting local physical constraints.

The model was developed using daily forecasts from 2020 to mid-2025. Training used a sequential split into 2021–2024, preserving 2020 (COVID-19 anomalies) and 2025 as a held-out test set. The model effectively reconstructed fine-scale details and corrected global model biases. Verification against European Environment Agency observations (2020) confirmed performance comparable to high-resolution CAMS Europe regional forecasts. Crucially, the AFNO model consistently outperformed the U-Net baseline and traditional linear interpolation in spatial correlations and error rates. Finally, transferability tests in North America (AirNow data) confirmed the model generalizes effectively to unseen regions, maintaining lower errors than both the original global forecast and the baseline.