A Multivariate Approach for the Analysis of Spatially Correlated Environmental Data

Abstract

The formulation and the evaluation of environmental policy depend upon a general class of latent variable models known as multivariate receptor models. Estimation of the number of major pollution sources, the source composition profiles and the source contributions are the main interests in multivariate receptor modelling. Many different approaches have been proposed both when the number of sources is unknown (explorative factorial analysis) and when the number and the type of sources are known (regression models). The objective of this work is to propose a flexible approach to the multivariate receptor models that incorporates the extra variability due to the spatial dependence. The method is applied to Lombardia air pollution data.

Keywords: Covariance modelling, environmental data, latent variable models, multivariate receptor models, spatio-temporal modelling