Stochastic Modeling Of Space Time Processes
An Air Pollution Problem
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This book presents an interdisciplinary approach to an air pollution problem that takes into account the physical bases that govern the processes of interest under the framework of a Bayesian spatiotemporal model. Based on this approach I have developed a physically motivated stochastic model that decomposes the ground-level pollutant concentration field in three components, namely: transport, local production, and large-scale mean trend mostly dominated by the emission rates. The model highlighted the importance of simultaneously considering different aspects of an air pollution problem. This...
This book presents an interdisciplinary approach to an air pollution problem that takes into account the physical bases that govern the processes of interest under the framework of a Bayesian spatiotemporal model. Based on this approach I have developed a physically motivated stochastic model that decomposes the ground-level pollutant concentration field in three components, namely: transport, local production, and large-scale mean trend mostly dominated by the emission rates. The model highlighted the importance of simultaneously considering different aspects of an air pollution problem. This approach is novel in the field of environmental spatial statistics and gives a different perspective on the subject. The people who will be interested in this material are environmental scientists, statisticians, and physicists who have to analyze statistically data that evolve in time and space interactively.
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