An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland

Huang, G., Lee, D. and Scott, M. (2015) An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland. Spatial and Spatio-Temporal Epidemiology, 14-15, pp. 63-74. (doi:10.1016/j.sste.2015.09.002) (PMID:26530824)

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Abstract

The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan and Huang, Mr Guowen
Authors: Huang, G., Lee, D., and Scott, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Spatial and Spatio-Temporal Epidemiology
Publisher:Elsevier
ISSN:1877-5845
ISSN (Online):1877-5853
Copyright Holders:Copyright © 2015 The Authors
First Published:First published in Spatial and Spatio-Temporal Epidemiology 14-15:63-74
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
588351A rigorous statistical framework for estimating the long-term health effects of air pollution.Duncan LeeEngineering & Physical Sciences Research Council (EPSRC)EP/J017442/1M&S - STATISTICS