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A multivariate approach to the analysis of air quality in a high environmental risk area

Authors

  • Alessio Pollice,

    Corresponding author
    1. Dipartimento di Scienze Statistiche “Carlo Cecchi” Università degli Studi di Bari, Via C. Rosalba 53, Bari 70124, Italy
    • Dipartimento di Scienze Statistiche “Carlo Cecchi” Universit& degli Studi di Bari, Via C. Rosalba 53, Bari 70124, Italy.
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  • Giovanna Jona Lasinio

    1. Dipartimento di Statistica, Probabilità e Statistiche Applicate, Università di Roma “La Sapienza”, P.le Aldo Moro 5, Roma 00185, Italy
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  • This article is published in Environmetrics as a special issue on TIES 2008: Quantitative Methods for Environmental Sustainability, edited by Sylvia R. Esterby, University of British Columbia Okanagan, Canada.

Abstract

This study analyzes air quality data in the Taranto municipal area. This is a high environmental risk region being characterized by the massive presence of industrial sites with elevated environmental impact activities. We focus on three pollutants formed by combustion processes and related to meteorological conditions, namely particulate matter, sulphur dioxide, and nitrogen dioxide. Preliminary analysis involved addressing several data problems. First of all an imputation technique was considered to cope with the large number of missing data. Missing data imputation was addressed by a leave-one-out procedure based on the recursive Bayesian estimation and prediction of spatial linear mixed effects (LME) models enriched by a time-recursive prior structure. Secondly, a unique daily weather database at the city level was obtained combining data from three stations, characterized by gaps and unreliable measurements. Spatio-temporal modeling of the multivariate normalized daily pollution data was then performed within a Bayesian hierarchical framework, including time varying weather covariates and a semi-parametric spatial covariance structure. Daily estimates of the pollutants' concentration surfaces allow us to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Copyright © 2010 John Wiley & Sons, Ltd.

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