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Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches
  1. Susan Hodgson1,
  2. Mark J Nieuwenhuijsen2,
  3. Roy Colvile3,
  4. Lars Jarup1
  1. 1Small Area Health Statistics Unit (SAHSU), Imperial College London, London, UK
  2. 2Department of Epidemiology and Public Health, Imperial College London, UK
  3. 3Centre for Environmental Policy, Imperial College London, London, UK
  1. Correspondence to:
 Dr S Hodgson
 Institute of Health and Society, 4th Floor William Leech Building, The Medical School, University of Newcastle, Newcastle upon Tyne NE2 4HH, UK; susan.hodgson{at}ncl.ac.uk

Abstract

Background: The Runcorn area, north-west England, contains many pollution sources, the health effects of which have been under discussion for over 100 years. Preliminary investigations revealed an excess risk of mortality from kidney disease in people living nearest to several point sources of pollution, using distance as a proxy for exposure. Ongoing epidemiological investigations into the effect of ambient mercury exposure on dose and renal effect required a more refined assessment of exposure.

Methods: Atmospheric dispersion modelling was used to assess mercury dispersion from three mercury-emitting sources (including a large chlor alkali plant), based on knowledge of emissions, local meteorology and topography.

Results: The model was sensitive to various input parameters, with different dispersion patterns and ground-level concentrations, and therefore different exposed populations identified when different input parameters were defined. The different approaches to exposure assessment also had an impact on the epidemiological findings. The model output correlated well with weekly monitoring data collected in the local area, although the model underestimated concentrations in close proximity to the chlor alkali plant. The model identified that one point source did not contribute significantly to ground-level mercury concentrations, so that inclusion of this source when using the “distance as a proxy” approach led to significant exposure misclassification.

Conclusions: The model output indicates that assessment of ambient exposure should give consideration to the magnitude of emissions, point source characteristics, local meteorology and topography to ensure that the most appropriate exposure classification is reached. Even if dispersion modelling cannot be undertaken, these data can be used to inform and improve the distance as a proxy approach, and improve the interpretability of the epidemiological findings.

  • ADMS, Atmospheric Dispersion Modelling System
  • DETR, Department of the Environment, Transport and the Regions
  • EA, Environment Agency
  • TCA, total cloud amount

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Footnotes

  • Published Online First 19 December 2006

  • Competing interests: None.

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