{"ID":5936966,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T16:29:05.102679088Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05292","arxiv_id":"2607.05292","title":"Air Quality Downscaling with Station-Guided Pseudo-Supervision","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.05292","url_pdf":"https://arxiv.org/pdf/2607.05292v1","authors":"[\"Guorun Wang\",\"Simone Foti\",\"Andreas D. Demou\",\"Leonidas Kotoulas\",\"Theodoros Christoudias\",\"Alexandros Koliousis\",\"Mihalis Nicolaou\",\"Stefanos Zafeiriou\"]","published":"2026-07-06T16:32:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}