[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82932-en":3,"doc-seo-82932-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82932,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Air Quality Downscaling with Station-Guided Pseudo-Supervision","Super-resolving coarse atmospheric fields into local PM2.5 variability faces a fundamental spatial-support mismatch: pixels encode regional averages while ground-truth observations are discrete, unaligned samples of a continuous signal. A station-guided framework is introduced for Europe-wide high-resolution PM2.5 downscaling. Using coarse CAMS atmospheric composition fields plus heterogeneous side information—human activity, land cover, elevation, satellite aerosol signals, and winds—the method jointly super-resolves (×40, ~1 km) and bias-corrects CAMS without temporal sequence modeling. A time-agnostic propagation strategy applies spatial Gaussian blending of interpolated OpenAQ observations to supervise a multi-scale transformer with sparse in-situ data, yielding improved fine structure recovery and reduced localized CAMS bias.","arXiv :2607 .05292v 1 [ cs .LG] 6 Jul 2026  \nAir Quality Downscaling with Station-Guided Pseudo-Supervision  \nGuorun Wang 1 , Simone Foti 1 , Andreas D. Demou2 , Leonidas Kotoulas 1 , Theodoros Christoudias2 , Alexandros Koliousis3 , Mihalis Nicolaou4 , and  \nStefanos Zafeiriou 1  \n1 Imperial College London, UK  \n2 The Cyprus Institute, Cyprus  \n3 Northeastern University London, UK  \n4 University of Cyprus, Cyprus  \nAbstract. Super-resolving coarse atmospheric fields to local PM2.5 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 PM2.5 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 (×40, ≈ 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.  \nKeywords: PM2.5 downscaling · Air-quality super-resolution · Stationguided learning · Gaussian kernel interpolation · Earth-system AI  \n1 Introduction  \nClimate change is reshaping atmospheric conditions in ways that directly influence air quality through complex interactions between meteorology, atmospheric chemistry, and human activities. As climatic conditions evolve, changes in temperature, circulation patterns, and the frequency of extreme events can affect the formation, transport, and dispersion of air pollutants, creating new challenges for air-quality management and forecasting. Air quality remains a major environmental and public health concern, affecting billions of people worldwide and contributing significantly to respiratory and cardiovascular disease burdens [17] . Accurate and timely predictions are therefore essential for supporting public health interventions, informing environmental policy, and enhancing our understanding of Earth system processes. In this context, machine-learning-based  \n2 G. Wang et al.  \nforecasting systems offer a promising pathway for improving the prediction of pollution episodes and informing public health decision-making.  \nThe rapid development of machine learning (ML) systems for scientific prediction and decision-making has ushered in a new era for environmental forecasting [37, 45, 60] . In meteorology, this progress has been particularly evident in the emergence of advanced AI-based weather forecasting systems, which have achieved remarkable performance in predicting dynamical atmospheric variables [4, 6, 7, 13, 20, 24, 26, 28, 29, 34, 35, 52, 53] . However, extending these successes from weather prediction to atmospheric-composition forecasting remains challenging. Unlike conventional weather forecasting, atmospheric-composition prediction must account not only for meteorological transport but also for emissions, chemical transformations, deposition, and human activities, making it substantially more complex and computationally demanding [5] . This challenge is especially important for air pollution, which directly affects human health and ecosystems [11, 38] .  \nTo tackle this, Aurora [5], a foundation model for the Earth system, shows a pathway for moving beyond weather forecasting towards more general Earthsystem prediction, including atmospheric composition. ML has demonstrated strong capabilities in learning the complex nonlinear relationships between meteorology, e","cbCaipC6Zwl9jAa7","https://ap.wps.com/l/cbCaipC6Zwl9jAa7","pdf",14802372,1,29,"English","en",105,"# Introduction\n## Motivation and challenges\n## Data sources and limitations","[{\"question\":\"What problem does the method address in PM2.5 downscaling?\",\"answer\":\"It addresses the mismatch between coarse model pixels representing regional averages and sparse, discrete station observations that do not align spatially with those pixels.\"},{\"question\":\"Which inputs are used to perform the downscaling?\",\"answer\":\"The framework uses coarse CAMS atmospheric composition fields together with heterogeneous side information, including human activity, land cover, elevation, satellite aerosol observations, and wind fields.\"},{\"question\":\"How does the approach handle sparse in-situ station supervision?\",\"answer\":\"It introduces a time-agnostic propagation strategy that uses spatial Gaussian blending of interpolated OpenAQ observations to densely supervise the multi-scale transformer without temporal sequence modeling.\"}]",1784184080,73,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"air-quality-downscaling-with-station-guided-pseudo-supervision","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/air-quality-downscaling-with-station-guided-pseudo-supervision/82932/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the method address in PM2.5 downscaling?","Question",{"text":74,"@type":75},"It addresses the mismatch between coarse model pixels representing regional averages and sparse, discrete station observations that do not align spatially with those pixels.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which inputs are used to perform the downscaling?",{"text":79,"@type":75},"The framework uses coarse CAMS atmospheric composition fields together with heterogeneous side information, including human activity, land cover, elevation, satellite aerosol observations, and wind fields.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the approach handle sparse in-situ station supervision?",{"text":83,"@type":75},"It introduces a time-agnostic propagation strategy that uses spatial Gaussian blending of interpolated OpenAQ observations to densely supervise the multi-scale transformer without temporal sequence 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