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The use of GIS to evaluate traffic-related pollution
  1. D J Briggs
  1. Correspondence to:
 Professor D J Briggs
 Department of Epidemiology and Public Health, Imperial College, Norfolk Place, London W2 1PG, UK; d.briggs{at}

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GIS for exposure modelling

One of the major challenges in epidemiological research is to devise appropriate metrics and methods for exposure assessment. In the context of traffic-related air pollution, this is particularly problematic because of continuing uncertainty about the causal agents, the likelihood of important interactive and cumulative effects from different pollutants, high levels of both spatial and temporal variability in pollutant concentrations and a dearth of monitoring data. Against this background, models that can estimate at unsampled locations are clearly needed. The paper by Morgensten et al1(see page 8) in this issue presents an example of how geographic information system (GIS) techniques can be used to develop such models for urban-scale analysis, on the basis of readily available data.

The use of GIS methods for exposure modelling in this way has a relatively recent history. Outside epidemiology, the emphasis has mainly been on dispersion modelling, and a range of so-called second-generation models have been developed (eg, AERMOD, ADMS-Urban) to support air pollution management. To date, however, these models have been rather rarely used for epidemiological purposes, partly because of their demanding data requirements, and also, no doubt, because of lack of awareness, lack of understanding or distrust by this research community. By contrast, in epidemiology, the focus has been on developing GIS-based methods. Initially, these mainly involved the extraction of relatively simple distance-based metrics of exposure (eg, based on proximity to source). However, over the past 10 years, attention has turned to GIS-based pollution mapping, using interpolation techniques, such as inverse distance weighting and kriging, and what has become known (perhaps rather misleadingly) as land use regression modelling. …

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