Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Payam Dadvand, Professor Stephen Rushton, Dr Phillip Diggle, Louis Goffe, Professor Judith Rankin, Professor Tanja Pless-Mulloli


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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 km2) 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.

Publication metadata

Author(s): Dadvand P, Ruston S, Diggle PJ, Goffe L, Rankin J, Pless-Mulloli T

Publication type: Article

Publication status: Published

Journal: Atmospheric Environment

Year: 2011

Volume: 45

Issue: 3

Pages: 659-664

Print publication date: 27/10/2010

ISSN (print): 1352-2310

ISSN (electronic): 1873-2844

Publisher: Pergamon


DOI: 10.1016/j.atmosenv.2010.10.034


Altmetrics provided by Altmetric