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dc.contributor.authorRoux, Emmanuel-
dc.contributor.authorIgnotti, Eliane-
dc.contributor.authorBègue, Nelson-
dc.contributor.authorBencherif, Hassan-
dc.contributor.authorCatry, Thibault-
dc.contributor.authorDessay, Nadine-
dc.contributor.authorGracie, Renata-
dc.contributor.authorGurgel, Helen da Costa-
dc.contributor.authorHacon, Sandra de Sousa-
dc.contributor.authorMagalhães, Mônica de A. F. M.-
dc.contributor.authorMonteiro, Antônio Miguel Vieira-
dc.contributor.authorRevillion, Christophe-
dc.contributor.authorVillela, Daniel Antunes Maciel-
dc.contributor.authorXavier, Diego-
dc.contributor.authorBarcellos, Christovam-
dc.date.accessioned2021-12-04T18:42:46Z-
dc.date.available2021-12-04T18:42:46Z-
dc.date.issued2020-12-12-
dc.identifier.citationROUX, Emmanuel et al. Toward an early warning system for health issues related to particulate matter exposure in Brazil: the feasibility of using global PM2.5 concentration forecast products. Remote Sensing, v. 12, n. 24, 4074, 2020. DOI: https://doi.org/10.3390/rs12244074. Disponível em: https://www.mdpi.com/2072-4292/12/24/4074. Acesso em: 4 dez. 2021.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/42524-
dc.language.isoInglêspt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleToward an early warning system for health issues related to particulate matter exposure in Brazil : the feasibility of using global PM2.5 concentration forecast productspt_BR
dc.typeArtigopt_BR
dc.subject.keywordPrevisões de material particulado - Brasilpt_BR
dc.subject.keywordDoenças respiratórias agudas gravespt_BR
dc.subject.keywordSistema de alerta antecipadopt_BR
dc.subject.keywordSensoriamento remotopt_BR
dc.rights.license© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).pt_BR
dc.identifier.doihttps://doi.org/10.3390/rs12244074pt_BR
dc.description.abstract1: PM2.5 severely affects human health. Remotely sensed (RS) data can be used to estimate PM2.5 concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM2.5 concentration forecast products derived from models assimilating RS data have not yet been exploited to generate early alerts for respiratory problems in Brazil. We investigated the feasibility of building such an early warning system. For this, PM2.5 concentrations on a 4-day horizon forecast were provided by the Copernicus Atmosphere Monitoring Service (CAMS) and compared with the number of severe acute respiratory disease (SARD) cases. Confounding effects of the meteorological conditions were considered by selecting the best linear regression models in terms of Akaike Information Criterion (AIC), with meteorological features and their two-way interactions as explanatory variables and PM2.5 concentrations and SARD cases, taken separately, as response variables. Pearson and Spearman correlation coefficients were then computed between the residuals of the models for PM2.5 concentration and SARD cases. The results show a clear tendency to positive correlations between PM2.5 and SARD in all regions of Brazil but the South one, with Spearman’s correlation coefficient reaching 0.52 (p < 0.01). Positive significant correlations were also found in the South region by previously correcting the effects of viral infections on the SARD case dynamics. The possibility of using CAMS global PM2.5 concentration forecast products to build an early warning system for pollution-related effects on human health in Brazil was therefore established. Further investigations should be performed to determine alert threshold(s) and possibly build combined risk indicators involving other risk factors for human respiratory diseases. This is of particular interest in Brazil, where the COVID-19 pandemic and biomass burning are occurring concomitantly, to help minimize the effects of PM emissions and implement mitigation actions within populations.pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-2266-8207pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-9743-1856pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-1815-0667pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0001-9514-1751pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-0526-3531pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-0225-3696pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-4250-6742pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-8222-0992pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-6595-8274pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-6595-8274pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-3896-2083pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0001-8371-2959pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0001-5259-7732pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-1161-2753pt_BR
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