Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part II: application to monitoring monthly sulfate deposition over Europe

A spatial time-series framework is adopted for stochastic modeling of monthly averaged sulfate deposition over Europe. The sulfur concentration data used in this study were measured at the European Monitoring and Evaluation Program (EMEP) monitoring network from January 1980 to December 1988. Parametric temporal trend and residual models, associated with long-term (linear trend or annual periodicity) and short-term (seasonal) concentration variability, respectively, are first established at monitoring stations. The resulting model parameters are regionalized in space to arrive at parametric trend and residual models at any unmonitored location. Stochastic simulation is performed for prediction and modeling of joint uncertainty regarding unknown sulfur concentration levels at unmonitored spatial locations and time instants. The case study illustrates the applicability of the proposed spatial time series framework to a real-world data set.