Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia

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dc.contributor.authorEdna Manga
dc.contributor.authorNorhashidah Awang
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:46Z
dc.date.available2024-05-10T03:25:46Z
dc.date.issued2018
dc.description.abstractRapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM10 are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM10 levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM10 concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011.
dc.identifier.citationManga, E., & Awang, N. (2018). Bayesian autoregressive spatiotemporal model of PM 10 concentrations across Peninsular Malaysia. Stochastic environmental research and risk assessment, 32, 3409-3419.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/610
dc.language.isoen
dc.publisherSpringer
dc.publisher.alternativeStochastic Environmental Research and Risk Assessment
dc.subjectAutoregressive
dc.subjectBayesian
dc.subjectForecast
dc.subjectPM10
dc.subjectPrediction
dc.subjectSpatiotemporal
dc.titleBayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia
dc.typeJournal

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