Air Quality Downscaling with Station-Guided Pseudo-Supervision
2026-07-06 • Machine Learning
Machine LearningArtificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors created a method to improve coarse air pollution data (PM2.5) to show details at a much smaller scale around Europe. They use a mix of regional air data and other information like land type and weather to make more detailed and accurate pollution maps. Because the real measurements come from scattered stations, the authors developed a way to blend these sparse points to train their model better. Their approach helps correct errors and shows fine local pollution patterns without needing to analyze changes over time.
PM2.5downscalingatmospheric compositionCAMStransformer networkspatial interpolationbias correctionOpenAQGaussian blendingsatellite aerosol observations
Authors
Guorun Wang, Simone Foti, Andreas D. Demou, Leonidas Kotoulas, Theodoros Christoudias, Alexandros Koliousis, Mihalis Nicolaou, Stefanos Zafeiriou
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.