Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles

Jungsoon Choi; Brian J Reich; Montserrat Fuentes; Jerry M Davis

doi:10.1080/15598608.2009.10411933

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Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles

Journal article

Peer reviewed

Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles

Jungsoon Choi, Brian J Reich, Montserrat Fuentes and Jerry M Davis

Journal of Statistical Theory and Practice, Vol.3(2), pp.407-418

06/01/2009

DOI: 10.1080/15598608.2009.10411933

PMID: 20407598

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Abstract

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.

Air Pollution

Linear coregionalization model

Bayesian inference

Speciated particulate matter

Multivariate spatiotemporal processes

Details

Title: Subtitle: Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles
Creators: Jungsoon Choi - Department of Statistics , North Carolina State University
Brian J Reich - Department of Statistics , North Carolina State University
Montserrat Fuentes - North Carolina State University
Jerry M Davis - Department of Marine, Earth and Atmospheric Sciences , North Carolina State University
Resource Type: Journal article
Publication Details: Journal of Statistical Theory and Practice, Vol.3(2), pp.407-418
Publisher: Taylor & Francis Group
DOI: 10.1080/15598608.2009.10411933
PMID: 20407598
ISSN: 1559-8608
eISSN: 1559-8616
Language: English
Date published: 06/01/2009
Academic Unit: Statistics and Actuarial Science; Biostatistics; Provost Office Administration
Record Identifier: 9983763482502771

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