A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments

Accurate, high-resolution maps of traffic-related air pollution are needed both as a basis for assessing exposures as part of epidemiological studies, and to inform urban air-quality policy and traffic management. This paper assesses the use of a GIS-based, regression mapping technique to model spat ial patterns of traffic-related air pollution. The model - developed using data from 80 passive sampler sites in Huddersfield, as part of the SAVIAH ( Small Area Variations in Air Quality and Health) project - uses data on tra ffic flows and land cover in the 300-m buffer zone around each site, and al titude of the site, as predictors of NO, concentrations. It was tested here by application in four urban areas in the UK: Huddersfield (for the year f ollowing that used for initial model development), Sheffield, Northampton, and part of London. In each case, a GIS was built in ArcInfo, integrating r elevant data on road traffic, urban land use and topography. Monitoring of NO2 was undertaken using replicate passive samplers (in London, data were o btained from surveys carried out as part of the London network). In Hudders field, Sheffield and Northampton, the model was first calibrated by compari ng modelled results with monitored NO2 concentrations at 10 randomly select ed sites; the calibrated model was then validated against data from a furth er 10-28 sites. In London, where data for only 11 sites were available, val idation was not undertaken. Results showed that the model performed well in all cases. After local calibration, the model gave estimates of mean annua l NO2 concentrations within a factor of 1.5 of the actual mean (approx. 70- 90%) of the time and within a factor of 2 between 70 and 100% of the time. r(2) values between modelled and observed concentrations are in the range o f 0.58-0.76. These results are comparable to those achieved by more sophist icated dispersion models. The model also has several advantages over disper sion modelling. It is able, for example, to provide high-resolution maps ac ross a whole urban area without the need to interpolate between receptor po ints. It also offers substantially reduced costs and processing times compa red to formal dispersion modelling. It is concluded that the model might th us be used as a means of mapping long-term air pollution concentrations eit her in support of local authority air-quality management strategies, or in epidemiological studies. (C) 2000 Elsevier Science B.V. All rights reserved .