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Mapping ambient urban air pollution at the small area scale : a GIS approach

By Kirsty Louise Smallbone


Air pollution is an emotive and complex issue, affecting materials, vegetation\ud growth and human health. Given that over half the world's population live\ud within urban areas and that those areas are often highly polluted, the ability to\ud understand the patterns and magnitude of pollution at the small area (urban\ud environment) level is increasingly important. Recent research has highlighted,\ud in particular, the apparent relationship between traffic-related pollution and\ud respiratory health, while the increasing prevalence of asthma, especially\ud amongst children, has been widely attributed to exposure to traffic-related air\ud pollution. The UK government has reacted to this growing concern by\ud publishing the UK National Air Quality Strategy (DOE 1996) which forces all\ud Local Authorities in England and Wales to review air quality in their area and\ud designate any areas not expected to meet the 2005 air quality standards as Air\ud Quality Management Areas (AQMAs), though what constitutes AQMAs and\ud how to define them remains vague.\ud \ud \ud Against this background, there is a growing need to understand the patterns and\ud magnitude of urban air pollution and for improvements in pollution mapping\ud methods. This thesis aims to contribute to this knowledge. The background to\ud air pollution and related research has been examined within the first section of\ud this report. A review of sampling methods was conducted, a sampling strategy\ud devised and a number of surveys conducted to investigate both the spatial\ud nature of air pollution and, more specifically, the dispersion of pollution with\ud varying characteristics (distance to road, vehicle volume, height above ground\ud level etc). The resultant data was analysed and a number of patterns identified.\ud The ability of linear dispersion models to accurately predict air pollution was\ud also considered. A variety of models were examined, ranging from the\ud simplistic (e.g. DMRB) to the more complex (e.g. CALINE4) model. The\ud model best able to predict pollution at specific sites was then used to predict concentrations over the entire urban area which were then compared to actual\ud monitored data. The resultant analysis, indicated that the dispersion model is\ud not a good method for predicting pollution concentrations at the small area\ud level, and therefore an alternative method of mapping was investigated. Using\ud the ARC/INFO geographical information system (GIS) a regression analysis\ud approach was applied to the study area. A number of variables including\ud altitude, landuse type, traffic volume and composition etc, were examined and\ud their ability to predict air pollution tested using data on nitrogen dioxide from\ud intensive field surveys. The study area was then transformed into a grid of\ud 10m2, regression analysis was performed on each individual square and the\ud results mapped. The monitored data was then intersected with the resultant\ud map and monitored and modeled concentrations compared. Results of the\ud analysis indicated that the regression analysis could explain up to 61 per cent of\ud the variation in nitrogen dioxide concentrations and thus performed\ud significantly better than the dispersion model method. The ease of application\ud and transferability of the regression method means it has a wide range of\ud applied and academic uses that are discussed in the final section

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