Multivariate Disaggregation Modeling of Air Pollutants: A Case-Study of PM2.5, PM10 and Ozone Prediction in Portugal and Italy.
Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated level...
| Publicado en: | American Statistician Vol. 80; no. 1; pp. 109 - 135 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191630182&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191630182 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2026 vid: 80 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 191630182 10.1080/00031305.2025.2537055 ppf: 109 ppct: 26 formats: tig: atl: Multivariate Disaggregation Modeling of Air Pollutants: A Case-Study of PM2.5, PM10 and Ozone Prediction in Portugal and Italy. aug: au: Rodriguez Avellaneda, Fernando Chacón-Montalván, Erick A. Moraga, Paula affil: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia su: Italy Portugal Air pollution Particulate matter Atmospheric ozone Spatial resolution Gaussian processes Multivariate analysis Countries sug: subj: Air pollution Italy Portugal Particulate matter Atmospheric ozone Spatial resolution Gaussian processes Multivariate analysis Countries keyword: Change of support Downscaling INLA Spatial modeling SPDE Change of support Downscaling INLA Spatial modeling SPDE ab: Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM 2.5 , PM and ozone levels in Portugal and Italy, improving spatial resolution from 0.1 ° (10 km) to 0.02 ° (2 km), and revealing dependencies among pollutants. Our framework provides a robust foundation for analyzing complex pollutant interactions, offering valuable insights for decision-makers seeking to address air pollution and its impacts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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