Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas
Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas
This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process
319-327
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Gulliver, John
1db4bc34-2799-4644-a106-dc0197845fb4
Fuller, Gary W
3c8830cc-19da-4e60-8615-8a700f3f89db
Blangiardo, Marta
410dbbc0-9c77-43f9-8edf-000bde88d013
2014
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Gulliver, John
1db4bc34-2799-4644-a106-dc0197845fb4
Fuller, Gary W
3c8830cc-19da-4e60-8615-8a700f3f89db
Blangiardo, Marta
410dbbc0-9c77-43f9-8edf-000bde88d013
Pirani, Monica, Gulliver, John, Fuller, Gary W and Blangiardo, Marta
(2014)
Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas.
Journal of Exposure Science and Environmental Epidemiology, 24 (3), .
(doi:10.1038/jes.2013.85).
Abstract
This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process
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e-pub ahead of print date: November 2013
Published date: 2014
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 373183
URI: http://eprints.soton.ac.uk/id/eprint/373183
ISSN: 1559-0631
PURE UUID: d6d1778e-80a0-4e8c-8ccb-c8d23d79fb39
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Date deposited: 09 Jan 2015 15:20
Last modified: 14 Mar 2024 18:49
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Author:
Monica Pirani
Author:
John Gulliver
Author:
Gary W Fuller
Author:
Marta Blangiardo
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