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.
Special Issue Paper
A multivariate approach to the analysis of air quality in a high environmental risk area†
Article first published online: 27 SEP 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Special Issue: TIES 2008: Quantitative methods for environmental sustainability
Volume 21, Issue 7-8, pages 741–754, November - December 2010
How to Cite
Pollice, A. and Jona Lasinio, G. (2010), A multivariate approach to the analysis of air quality in a high environmental risk area. Environmetrics, 21: 741–754. doi: 10.1002/env.1059
- Issue published online: 23 DEC 2010
- Article first published online: 27 SEP 2010
- Manuscript Accepted: 6 MAY 2010
- Manuscript Received: 31 OCT 2008
- air quality data;
- multivariate space–time data;
- missing data imputation;
- Bayesian hierarchical modeling
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.