Abstract
AbstractThis 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.