Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia

Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM 10 are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal d...

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Bibliographic Details
Published in:	Stochastic environmental research and risk assessment 2018-12, Vol.32 (12), p.3409-3419
Main Authors:	Manga, Edna, Awang, Norhashidah
Format:	Article
Language:	eng
Subjects:	Air pollution Aquatic Pollution Atmospheric models Autoregressive models Bayesian analysis Chemistry and Earth Sciences Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Environment Environmental monitoring Environmental policy Haze Math. Appl. in Environmental Science Original Paper Parameter estimation Particulate matter Physics Pollutants Pollution monitoring Predictions Probability Theory and Stochastic Processes Process parameters Public health Regression models Spatial distribution Statistical analysis Statistics for Engineering Structural hierarchy Vigilance Waste Water Technology Water Management Water Pollution Control
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Summary:	Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM 10 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 PM 10 levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM 10 concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM 10 in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM 10 concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM 10 forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R 2 and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.
ISSN:	1436-3240 1436-3259