Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles

• Published in: Journal of statistical theory and practice 2009-06, Vol.3 (2), p.407-418
• Main Authors: Choi, Jungsoon, Reich, Brian J., Fuentes, Montserrat, Davis, Jerry M.
• Format: Article
• Language: English
• Subjects: Air pollution; Bayesian inference; Linear coregionalization model; Mathematics and Statistics; Multivariate spatiotemporal processes; Probability Theory and Stochastic Processes; Speciated particulate matter; Statistical Theory and Methods; Statistics
• ISSN: 1559-8608; 1559-8616
• DOI: 10.1080/15598608.2009.10411933

Fine particulate matter (PM 2.5 ) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM 2.5 has five main components: Sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain better understanding about the spatial-temporal distribution of each component of the total PM 2.5 mass, and also to estimate how the composition of PM 2.5 changes with space and time to conduct spatial-temporal epidemiological studies of the association of these pollutants and adverse health effects. We introduce a multivariate spatial-temporal model for speciated PM 2.5 Our hierarchical framework combines different sources of data and accounts for bias and measurement error in each data source. In addition, a spatiotemporal extension of the linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We apply our framework to speciated PM 2.5 data in the United States for the year 2004.