Air Quality Downscaling with Station-Guided Pseudo-Supervision
It addresses the practical need for accurate, high-resolution air quality estimates for environmental monitoring and public health, though the method is incremental (hybrid of existing techniques).
The paper tackles the challenge of downscaling coarse PM2.5 atmospheric fields to high-resolution (1 km) maps over Europe, achieving a 40x super-resolution while correcting biases. The model recovers fine-grained spatial structures and mitigates localized biases, validated through station-level evaluations.
Super-resolving coarse atmospheric fields to local PM$_{2.5}$ variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-truth observations are discrete, unaligned samples of a continuous spatial signal. To bridge this gap, we present a station-guided framework for high-resolution PM$_{2.5}$ downscaling over Europe. Taking coarse CAMS atmospheric composition fields alongside heterogeneous side information (i.e., human activity, land cover, elevation, satellite aerosol observations, and wind fields) our framework jointly super-resolves ($\times 40$, $\approx$ 1 km) and bias-corrects CAMS rasters, without relying on temporal sequence modelling. To address the challenge of densely supervising our multi-scale transformer network with sparse in-situ data, we introduce a time-agnostic propagation strategy that utilises spatial Gaussian blending of interpolated OpenAQ observations. Extensive qualitative and station-level evaluations across Europe demonstrate that our model recovers fine-grained spatial structures and effectively mitigates localised CAMS biases.