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Bayesian univariate space-time hierarchical model for mapping pollutant concentrations in the municipal area of Taranto

机译：绘制塔兰托市市区污染物浓度的贝叶斯单变量时空分层模型

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摘要

An analysis of air quality data is provided for the municipal area of Taranto (Italy) characterized by high environmental risks as decreed by the Italian government in the 1990s. In the context of an agreement between Dipartimento di Scienze Statistiche-Universita degli Studi di Bari and the local regional environmental protection agency air quality, data were provided concerning six monitoring stations and covering years from 2005 to 2007. In this paper we analyze the daily concentrations of three pollutants highly relevant in such an industrial area, namely SO_2, NO_2 and PM10, with the aim of reconstructing daily pollutants concentration surfaces for the town area. Taking into account the large amount of sparse missing data and the non normality affecting pollutants' concentrations, we propose a full Bayesian separable space-time hierarchical model for each pollutant concentration series. The proposed model allows to embed missing data imputation and prediction of pollutant concentration. We critically discuss the results, highlighting advantages and disadvantages of the proposed methodology.
机译：根据意大利政府在1990年代颁布的法令，对塔兰托（意大利）市辖区的空气质量数据进行了分析，其特征是环境风险较高。根据国家统计局和巴里地方环境保护局之间的协议，提供了六个监测站的数据，涵盖了2005年至2007年的年份。在这种工业区中最重要的三种污染物，即SO_2，NO_2和PM10，旨在重建城镇地区的日常污染物浓度表面。考虑到大量稀疏的缺失数据以及影响污染物浓度的非正态性，我们针对每个污染物浓度序列提出了一个完整的贝叶斯可分时空分层模型。所提出的模型允许嵌入缺失的数据归因和污染物浓度的预测。我们批判性地讨论了结果，强调了所提出方法的优缺点。

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