We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded by grants to DGS from NSERC and CFCAS. Metro Vancouver graciously provided us with access to all their monitoring data. Without substantial guidance from Jim Zidek, and help with software from Anne McMillan, this work would have been far more difficult to execute.
ASJC Scopus subject areas
- Ecological Modeling
- Statistics and Probability