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Space-Time Relationship between Short-Term Exposure to Fine and Coarse Particles and Mortality in a Nationwide Analysis of Korea: A Bayesian Hierarchical Spatio-Temporal Model

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dc.contributor.authorKang, Dayun-
dc.contributor.authorJang, Yujin-
dc.contributor.authorChoi, Hyunho-
dc.contributor.authorHwang, Seung-sik-
dc.contributor.authorKoo, Younseo-
dc.contributor.authorChoi, Jungsoon-
dc.date.accessioned2022-07-09T14:27:53Z-
dc.date.available2022-07-09T14:27:53Z-
dc.date.created2021-05-12-
dc.date.issued2019-06-
dc.identifier.issn1661-7827-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147696-
dc.description.abstractPrevious studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air pollution and cause-specific mortality in South Korea between 2012 and 2015 using a two-stage Bayesian spatio-temporal model. We used 2012-2014 mortality and air pollution data for parameter estimation (i.e., model fitting) and 2015 data for model validation. Our results suggest that the relative risks of total, cardiovascular, and respiratory mortality were 1.028, 1.047, and 1.045, respectively, with every 10-mu g/m(3) increase in monthly PM2.5 (fine particulate matter) exposure. These findings warrant protection of populations who experience elevated ambient air pollution exposure to mitigate mortality burden in South Korea.-
dc.language영어-
dc.language.isoen-
dc.publisherMDPI-
dc.titleSpace-Time Relationship between Short-Term Exposure to Fine and Coarse Particles and Mortality in a Nationwide Analysis of Korea: A Bayesian Hierarchical Spatio-Temporal Model-
dc.typeArticle-
dc.contributor.affiliatedAuthorChoi, Jungsoon-
dc.identifier.doi10.3390/ijerph16122111-
dc.identifier.scopusid2-s2.0-85067921920-
dc.identifier.wosid000473750500045-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.16, no.12, pp.1 - 11-
dc.relation.isPartOfINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH-
dc.citation.titleINTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH-
dc.citation.volume16-
dc.citation.number12-
dc.citation.startPage1-
dc.citation.endPage11-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEnvironmental Sciences & Ecology-
dc.relation.journalResearchAreaPublic, Environmental & Occupational Health-
dc.relation.journalWebOfScienceCategoryEnvironmental Sciences-
dc.relation.journalWebOfScienceCategoryPublic, Environmental & Occupational Health-
dc.subject.keywordPlusPARTICULATE AIR-POLLUTION-
dc.subject.keywordPlusEUROPEAN CITIES-
dc.subject.keywordPlusPUBLIC-HEALTH-
dc.subject.keywordPlusUNITED-STATES-
dc.subject.keywordPlusSEOUL-
dc.subject.keywordPlusSUSCEPTIBILITY-
dc.subject.keywordPlusMISALIGNMENT-
dc.subject.keywordPlusASSOCIATION-
dc.subject.keywordPlusSEASON-
dc.subject.keywordAuthorair pollution-
dc.subject.keywordAuthormortality-
dc.subject.keywordAuthorcardiovascular disease-
dc.subject.keywordAuthorrespiratory disease-
dc.subject.keywordAuthorspatio-temporal model-
dc.subject.keywordAuthorPoisson model-
dc.subject.keywordAuthorBayesian approach-
dc.identifier.urlhttps://www.mdpi.com/1660-4601/16/12/2111-
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