Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/76624
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dc.contributor.authorSiwatt Pongpiachanen_US
dc.contributor.authorThaneeya Chetiyanukornkulen_US
dc.contributor.authorWirat Manassanitwongen_US
dc.date.accessioned2022-10-16T07:13:48Z-
dc.date.available2022-10-16T07:13:48Z-
dc.date.issued2021-09-01en_US
dc.identifier.issn25103768en_US
dc.identifier.issn2510375Xen_US
dc.identifier.other2-s2.0-85106752569en_US
dc.identifier.other10.1007/s41810-021-00105-6en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85106752569&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/76624-
dc.description.abstractSeveral empirical studies of reductions in air pollutants as social distancing and working from home (WFH) policies have sparked recommendations that the COVID-19 pandemic might have been responsible for better air quality particularly in urban area. These findings offer a compelling provocation for the scientific community to detect and investigate variations to air quality as a consequence of government enforced quarantine. In spite of countless research studies focusing on the connection between WFH policy and air pollutant levels, the majority of discussion has unfortunately ignored the central role of other potential sources (e.g. agricultural waste burnings, cooking emissions, and industrial releases) in governing air quality, or has neglected the psychological and social impacts of COVID-19. In this study, a t test was used to compare the average concentrations of PM2.5 and COVID-19-infected numbers (n) in three different periods which were n < 300 vs. n ≧ 300, n < 500 vs. n ≧ 500, and n < 700 vs. n ≧ 700. Some significant differences were observed in the groups of n < 500 vs. n ≧ 500, and n < 700 vs. n ≧ 700 indicating that the psychological and social impacts play a crucial role in restricting daily activities and thus reducing the atmospheric contents of PM2.5 in some areas. Further assessments were conducted by separating PM2.5 contents into three different periods (i.e. Period-I: day-1 ~ day-10; Period-II: day-11 ~ day-20; Period-III: day-21 ~ day-31). Some significant reductions of PM2.5 during the Period-I were detected in the eastern area of Bangkok. In addition, Pearson correlation analysis showed that hot-spot numbers appear to be a minor of importance in controlling PM2.5 levels in the ambient air of Bangkok, Thailand.en_US
dc.subjectEnvironmental Scienceen_US
dc.subjectMaterials Scienceen_US
dc.titleRelationship Between COVID-19-Infected Number and PM<inf>2.5</inf> Level in Ambient Air of Bangkok, Thailanden_US
dc.typeJournalen_US
article.title.sourcetitleAerosol Science and Engineeringen_US
article.volume5en_US
article.stream.affiliationsThailand National Institute of Development Administrationen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsEnvironment Departmenten_US
Appears in Collections:CMUL: Journal Articles

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