Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part I: methodology

A geostatistical framework for joint spatiotemporal modeling of atmospheric pollution is presented. The spatiotemporal distribution of concentration levels is modeled as a joint realization of a collection of spatially correlated time series. Parametric temporal trend models, associated with long-term pollution variability are established from concentration profiles at monitoring stations. Such parameters, e.g., amplitude of seasonal variation, are then regionalized in space for determining trend models at any unmonitored location. The resulting spatiotemporal residual field, associated with short-term pollution variability, is also modeled as a collection of spatially correlated residual time series. Stochastic conditional simulation is proposed for generating alternative realizations of the concentration spatiotemporal distribution, which identify concentration measurements available at monitoring stations. Simulated realizations also reproduce the histogram of the sample data, and a model of their spatiotemporal correlation. Such alternative concentration fields can be used for risk analysis studies.