Spatio-temporal modeling of fine particulate matter
Spatio-temporal modeling of fine particulate matter
Studies indicate that even short-term exposure to high concentrations of fine atmospheric particulate matter (PM2.5) can lead to long-term health effects. In this paper, we propose a random effects model for PM2.5 concentrations. In particular, we anticipate urban/rural differences with regard to both mean levels and variability. Hence we introduce two random effects components, one for rural or background levels and the other as a supplement for urban areas. These are specified in the form of spatio-temporal processes. Weighting these processes through a population density surface results in nonstationarity in space. We analyze daily PM2.5 concentrations in three Midwestern U.S. states for the year 2001. A fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns as well as predictions in time and space.
hierarchical model, Markov chain Monte Carlo, nonstationary spatio-temporal process, separable process
61-86
Sahu, S.K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, A.E.
19fc5f1c-23a6-4522-b1d6-fa78648e135f
Holland, D.M.
66a7f30b-2bf8-4e70-95ad-583ab9254b99
2006
Sahu, S.K.
33f1386d-6d73-4b60-a796-d626721f72bf
Gelfand, A.E.
19fc5f1c-23a6-4522-b1d6-fa78648e135f
Holland, D.M.
66a7f30b-2bf8-4e70-95ad-583ab9254b99
Sahu, S.K., Gelfand, A.E. and Holland, D.M.
(2006)
Spatio-temporal modeling of fine particulate matter.
Journal of Agricultural, Biological and Environmental Statistics, 11 (1), .
(doi:10.1198/108571106X95746).
Abstract
Studies indicate that even short-term exposure to high concentrations of fine atmospheric particulate matter (PM2.5) can lead to long-term health effects. In this paper, we propose a random effects model for PM2.5 concentrations. In particular, we anticipate urban/rural differences with regard to both mean levels and variability. Hence we introduce two random effects components, one for rural or background levels and the other as a supplement for urban areas. These are specified in the form of spatio-temporal processes. Weighting these processes through a population density surface results in nonstationarity in space. We analyze daily PM2.5 concentrations in three Midwestern U.S. states for the year 2001. A fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns as well as predictions in time and space.
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Published date: 2006
Keywords:
hierarchical model, Markov chain Monte Carlo, nonstationary spatio-temporal process, separable process
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Statistics
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Local EPrints ID: 30169
URI: http://eprints.soton.ac.uk/id/eprint/30169
PURE UUID: e269f0cc-fe8d-4056-af5e-d403dec4a557
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Date deposited: 11 May 2006
Last modified: 16 Mar 2024 03:15
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Author:
A.E. Gelfand
Author:
D.M. Holland
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