Study of the space-time effects in the concentration of airborne pollutants in the Metropolitan Region of Rio de Janeiro

In this article, we present an application of models with temporal and spatial components, from the Bayesian point of view, on data pollutants collected in 16 different monitoring sites located in the Metropolitan Area of Rio de Janeiro during 1999. All the models considered here assume conditionally independent observations, with a mean specified by the sum of random temporal and spatial components and a linear function of the maximum daily temperature and indicators of the day of the week. Our aim here is to analyze distinct specifications for the components, assuming different kinds of modeling that are not usually compared. The comparison is based on the posterior predictive loss function proposed by Gelfand and Ghosh (1998). The best specifications for the spatial component were the ones which considered a geostatistical approach to its correlation function. The best specification for the temporal component was the stationary autoregressive form. The pollutant concentrations were interpolated in a grid of points in the area of higher population density at a fixed period of time for the selected model. Copyright © 2003 John Wiley & Sons, Ltd.