Spatio-temporal modelling in the presence of few time points

Environmental monitoring networks are providing large amounts of spatio-temporal data. Air pollution data, as other environmental data, exhibit a spatial and a temporal correlated nature. To improve the accuracy of predictions at unmonitored locations, there is a growing need for models capturing those spatio-temporal correlations. With this work, we propose a spatio-temporal model for gaussian data collected in a few number of surveys. We assume the spatial correlation structure to be the same in all surveys. Moreover, as a consequence of the reduced number of time observations, the temporal correlations are modeled as fixed effects. A simulation study, aiming to validate the model, is conducted. The proposed model is applied to heavy metal concentration data, collected using moss biomonitors in Portugal, from three surveys which occurred between 1992 and 2002. Prediction maps of the observed variable for the most recent survey, together with the corresponding prediction variance as an uncertainty measure, are presented.