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arXiv · 2607.05292

Air Quality Downscaling with Station-Guided Pseudo-Supervision

Guorun Wang·Simone Foti·Andreas D. Demou·Leonidas Kotoulas·Theodoros Christoudias·Alexandros Koliousis·Mihalis Nicolaou·Stefanos Zafeiriou
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Abstract

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.

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Guorun Wang, Simone Foti, Andreas D. Demou, Leonidas Kotoulas, Theodoros Christoudias, Alexandros Koliousis, Mihalis Nicolaou, Stefanos Zafeiriou. 2026-07-06. Air Quality Downscaling with Station-Guided Pseudo-Supervision. https://arxiv.org/abs/2607.05292

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