BME analysis of spatiotemporal particulate matter distributions in North Carolina

Spatiotemporal maps of particulate matter (PM) concentrations contribute considerably to the understanding of the underlying natural processes and the adequate assessment of the PM health effects. These maps should be derived using an approach that combines rigorous mathematical formulation with sound science. To achieve such a task, the PM 10 distribution in the state of North Carolina is studied using the Bayesian maximum entropy (BME) mapping method. This method is based on a realistic representation of the spatiotemporal domain, which can integrate rigorously and efficiently various forms of physical knowledge and sources of uncertainty. BME offers a complete characterization of PM 10 concentration patterns in terms of multi-point probability distributions and allows considerable flexibility regarding the choice of the appropriate concentration estimates. The PM 10 maps show significant variability both spatially and temporally, a finding that may be associated with geographical characteristics, climatic changes, seasonal patterns, and random fluctuations. The inherently spatiotemporal nature of PM 10 variation is demonstrated by means of theoretical considerations as well as in terms of the more accurate PM 10 predictions of composite space/time analysis compared to spatial estimation. It is shown that the study of PM 10 distributions in North Carolina can be improved by properly incorporating uncertain data into the mapping process, whereas more informative estimates are generated by considering soft data at the estimation points. Uncertainty maps illustrate the significance of stochastic PM 10 characterization in space/time, and identify limitations associated with inadequate interpolation techniques. Stochastic PM 10 analysis has important applications in the optimization of monitoring networks in space and time, environmental risk assessment, health management and administration, etc.