Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale

Dadvand, Payam and Rushton, Stephen and Diggle, Peter J. and Goffe, Louis and Rankin, Judith and Pless-Mulloli, Tanja (2011) Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale. Atmospheric Environment, 45 (3). pp. 659-664. ISSN 1352-2310

Full text not available from this repository.

Abstract

Whilst exposure to air pollution is linked to a wide range of adverse health outcomes, assessing levels of this exposure has remained a challenge. This study reports a modeling approach for the estimation of weekly levels of ambient black smoke (BS) at residential postcodes across Northeast England (2055 km(2)) over a 12 year period (1985-1996). A two-stage modeling strategy was developed using monitoring data on BS together with a range of covariates including data on traffic, population density, industrial activity, land cover (remote sensing), and meteorology. The first stage separates the temporal trend in BS for the region as a whole from within-region spatial variation and the second stage is a linear model which predicts BS levels at all locations in the region using spatially referenced covariate data as predictors and the regional predicted temporal trend as an offset. Traffic and land cover predictors were included in the final model, which predicted 70% of the spatio-temporal variation in BS across the study region over the study period. This modeling approach appears to provide a robust way of estimating exposure to BS at an inter-urban scale. (C) 2010 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Journal or Publication Title: Atmospheric Environment
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1900/1902
Subjects:
Departments: Faculty of Health and Medicine > Medicine
ID Code: 51912
Deposited By: ep_importer_pure
Deposited On: 08 Dec 2011 13:55
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2020 07:45
URI: https://eprints.lancs.ac.uk/id/eprint/51912

Actions (login required)

View Item View Item