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

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Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale. / Dadvand, Payam; Rushton, Stephen; Diggle, Peter J.; Goffe, Louis; Rankin, Judith; Pless-Mulloli, Tanja.

In: Atmospheric Environment, Vol. 45, No. 3, 01.2011, p. 659-664.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Dadvand, P, Rushton, S, Diggle, PJ, Goffe, L, Rankin, J & Pless-Mulloli, T 2011, 'Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale', Atmospheric Environment, vol. 45, no. 3, pp. 659-664. https://doi.org/10.1016/j.atmosenv.2010.10.034

APA

Dadvand, P., Rushton, S., Diggle, P. J., Goffe, L., Rankin, J., & Pless-Mulloli, T. (2011). Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale. Atmospheric Environment, 45(3), 659-664. https://doi.org/10.1016/j.atmosenv.2010.10.034

Vancouver

Author

Dadvand, Payam ; Rushton, Stephen ; Diggle, Peter J. ; Goffe, Louis ; Rankin, Judith ; Pless-Mulloli, Tanja. / Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale. In: Atmospheric Environment. 2011 ; Vol. 45, No. 3. pp. 659-664.

Bibtex

@article{9584415a3c16419f9150e3401967f5ea,
title = "Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale",
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.",
keywords = "Spatiotemporal modeling, Exposure assessment, Geographical information system, GIS, Air pollution, Black smoke, Exposure modeling, AIR-POLLUTION, ENGLAND, HEALTH, REGION",
author = "Payam Dadvand and Stephen Rushton and Diggle, {Peter J.} and Louis Goffe and Judith Rankin and Tanja Pless-Mulloli",
year = "2011",
month = jan,
doi = "10.1016/j.atmosenv.2010.10.034",
language = "English",
volume = "45",
pages = "659--664",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",
number = "3",

}

RIS

TY - JOUR

T1 - Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale

AU - Dadvand, Payam

AU - Rushton, Stephen

AU - Diggle, Peter J.

AU - Goffe, Louis

AU - Rankin, Judith

AU - Pless-Mulloli, Tanja

PY - 2011/1

Y1 - 2011/1

N2 - 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.

AB - 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.

KW - Spatiotemporal modeling

KW - Exposure assessment

KW - Geographical information system

KW - GIS

KW - Air pollution

KW - Black smoke

KW - Exposure modeling

KW - AIR-POLLUTION

KW - ENGLAND

KW - HEALTH

KW - REGION

U2 - 10.1016/j.atmosenv.2010.10.034

DO - 10.1016/j.atmosenv.2010.10.034

M3 - Journal article

VL - 45

SP - 659

EP - 664

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

IS - 3

ER -