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 90s. In the context of an agreement between Dipartimento di Scienze Statistiche - Universit`a 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 SO2, NO2 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. www.graspa.org WP n. 36
Bayesian univariate space-time hierarchical models for mapping pollutant concentrations in the municipal area of Taranto
ARIMA S;
2010-01-01
Abstract
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 90s. In the context of an agreement between Dipartimento di Scienze Statistiche - Universit`a 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 SO2, NO2 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. www.graspa.org WP n. 36I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
