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A GIS approach to modelling traffic related air pollution

By Susan Collins, The Netherlands Bilthoven National Institute of Public Health and Environmental Protection and London London School of Hygiene and Tropical Medicine


There is increased concern regarding the effect of traffic related pollution on public\ud heath. As the number of vehicles on the roads continues to rise, it is becoming\ud increasingly more important to identify areas where the population may be at a greater\ud risk to raised levels of pollution and areas where the implementation of policy to\ud control and monitor levels of pollution would be beneficial.\ud \ud \ud Traditionally, levels of air pollution have been established through dispersion modelling\ud or monitoring. However, for modelling traffic related pollution for large populations,\ud these methods have proved inappropriate.\ud \ud \ud Three new approaches have been developed to model traffic related air pollution and\ud are reported in this thesis. The approaches have been developed in a Geographical\ud Information System (GIS) and involve generating detailed maps of the pollution\ud surface from monitored data and information about the pollution sources. The new\ud methods are compared against the geostatistical technique kriging.\ud \ud \ud The first approach combines spatial interpolation from monitoring sites and dispersion\ud modelling, linking the dispersion model to the GIS, the second combines GIS\ud techniques for filtering data and spatial interpolation, and the third uses a combination\ud of GIS techniques for filtering and statistical techniques.\ud \ud \ud The three approaches are tested and validated by predicting levels of pollution at\ud monitoring sites not used to develop the models. It was found that the new\ud approaches provided more reliable estimates of pollution at unsampled locations than\ud kriging, with the last of these proving to be the most effective. The adjusted r2 values\ud for kriging, interpolation and dispersion, interpolation and filtering, and filtering and\ud statistics were found to be 0.44, 0.63, 0.67 and 0.82 respectively.\ud \ud \ud The approaches therefore have clear potential in the areas of air pollution management\ud and epidemiology, where the maps can be used to help identify locations where levels\ud of pollution exceed air quality standards, assess the relationship between air pollution\ud and health outcome and examine the risk of exposure to raised levels of pollution

Topics: GE
OAI identifier: oai:eprints.hud.ac.uk:4843

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