Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia
Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Rapid 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.
Description
Keywords
Autoregressive, Bayesian, Forecast, PM10, Prediction, Spatiotemporal
Citation
Manga, E., & Awang, N. (2018). Bayesian autoregressive spatiotemporal model of PM 10 concentrations across Peninsular Malaysia. Stochastic environmental research and risk assessment, 32, 3409-3419.