Spatio-temporal Bayesian Quantile Regression for High Air-Pollution Concentrations

Spatio-temporal Bayesian Quantile Regression for High Air-Pollution Concentrations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatio-temporal Bayesian Quantile Regression for High Air-Pollution Concentrations Edoardo Rosci, Jorge Castillo-Mateo, Massimo Stafoggia, Paola Michelozzi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9237170/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Exposure to high air pollution levels, especially in urban contexts, is a major risk factor for human health. Most models in literature, however, focus on the bulk of the distribution, and only few address its extremes, such as the right tail. In this work, we apply a Bayesian spatio-temporal quantile regression (QR) framework to daily air pollution data (NO 2 , PM 10 , and PM 2.5 ) in the Rome (Italy) municipality between 2011 and 2022. The model specification includes temporal, spatial and spatio-temporal predictors, and a spatial Gaussian process (GP) to adjust intercept levels and capture spatial variability between monitoring sites. Models were evaluated through temporal and spatio-temporal cross-validation (CV), and sensitivity analyses were performed. Results highlighted that the majority of variability was captured by the GP. Spatial variability was captured especially for NO 2 ; the same pollutant, however, was also the most difficult to predict in spatial CV. All pollutants showed good temporal CV results and proper in-sample calibration. Exposure surfaces for 2011 and 2022 highlighted an overall decreasing trend while preserving same extreme spots. These quantile-based exposure surfaces may support decision making and subsequent epidemiological studies. Air pollution Asymmetric Laplace distribution Bayesian modelling Exposure assessment Markov chain Monte Carlo Full Text Additional Declarations No competing interests reported. Supplementary Files EESsuppl.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers invited by journal 04 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9237170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614808012,"identity":"7f0bd341-dd30-40df-b27e-05836fcd70b8","order_by":0,"name":"Edoardo 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