Hierarchical space-time modelling of PM₁₀ pollution

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• 作者：Daniela Cocchi ; Fedele Greco ; Carlo Trivisano
• 关键词：Bayesian hierarchical models ; Dynamic linear models ; Particulate matter pollution ; Spatial models
• 刊名：Atmospheric Environment
• 出版年：2007
• 期刊代码：11_13522310
• 类别：env
• 出版时间：January 2007
• 卷：41
• 期：3
• 页码：532-542
• 文件大小：219 K

摘要

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.