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J Gulliver

Publications and source records attributed to J Gulliver.

2 recordsLinked to original sources

Time activity modelling of domestic exposures to radon.

Radon gas occurs naturally in the environment with a variable distribution. In some areas radon concentrates sufficiently within the built environment that it is considered as a public health risk. It is possible, successfully, to reduce radon levels in the built environment, and it has been shown that such remediation programmes can be justified in terms of the costs and benefits accruing. However, the estimated dose received by people in their homes depends on the time spent indoors. The research presented here uses data derived from time activity surveys in Northamptonshire, together with radon data from a representative home, to model potential exposures for different population sub-groups. Average home occupancy ranged from 14.8h (probable error 2.5h) for students to 17.7 (3.1) h for adults; schoolchildren spent an average of 14.9 (1.2) h at home. Over a quarter of adults, however, were in the home for 22 h on more. These differences in occupancy patterns lead to substantial differences in radon exposure. In a home with an average hourly ground floor radon concentration of 467 Bqm(-3), modelled hourly average exposures ranged from ca. 250 Bqm(-3) for students and school children, to over 340 Bqm(-3), for women based at home. Modelled exposures show a non-linear association with total time spent at home, suggesting that exposure estimates based on linear models may provide misleading estimates of health risks from radon and the potential benefits of radon remediation. Highest hourly exposures are likely to be experienced by people with highly occupancy, living in single-storey, ground floor accommodation (for example, the elderly the infirm and non-working young mothers). Since these may be least aware of radon risks, and least able to take up remediation measures, they should be specifically targeted for radon monitoring and for assistance in remediation schemes.

Activities of Daily Living↗

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 spatial 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 traffic flows and land cover in the 300-m buffer zone around each site, and altitude of the site, as predictors of NO2 concentrations. It was tested here by application in four urban areas in the UK: Huddersfield (for the year following that used for initial model development), Sheffield, Northampton, and part of London. In each case, a GIS was built in ArcInfo, integrating relevant data on road traffic, urban land use and topography. Monitoring of NO2 was undertaken using replicate passive samplers (in London, data were obtained from surveys carried out as part of the London network). In Huddersfield, Sheffield and Northampton, the model was first calibrated by comparing modelled results with monitored NO2 concentrations at 10 randomly selected sites; the calibrated model was then validated against data from a further 10-28 sites. In London, where data for only 11 sites were available, validation was not undertaken. Results showed that the model performed well in all cases. After local calibration, the model gave estimates of mean annual 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. r2 values between modelled and observed concentrations are in the range of 0.58-0.76. These results are comparable to those achieved by more sophisticated dispersion models. The model also has several advantages over dispersion modelling. It is able, for example, to provide high-resolution maps across a whole urban area without the need to interpolate between receptor points. It also offers substantially reduced costs and processing times compared to formal dispersion modelling. It is concluded that the model might thus be used as a means of mapping long-term air pollution concentrations either in support of local authority air-quality management strategies, or in epidemiological studies.

Air Pollutants↗