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Abstract
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In quantifying the benefits of air pollution reduction measures, it is desirable to compare the size of the benefits with the effects of other individual confounding factors such as smoking or passive smoking. The effect of pollution is rarely very large and in order to quantify it, exposure estimating procedures must be as accurate as possible. Dispersion models, run for hourly time intervals and controlled by measurements, are therefore used to provide estimates for specific receptor points.
RESULTS of three consecutive cross-sectional investigations in an area of Oslo characterized by heavy traffic are presented. The study was designed to provide repeated information on the effects of traffic diversion measures on the self-reporting of symptoms of reduced health of 1100 adults living in Oslo. The principal source of air pollution in Oslo is vehicular traffic. The primary pollutants of interest are nitrogen dioxide (NO2) and respirable particles (PM2.5 and PM10). The mean hourly concentration of exposure was estimated at each participant's home by means of a time-dependent finite dispersion model combined with subgrid models to describe the source contribution to the grid concentrations. The study controlled the confounding factors. Using the symptom fatigue, the study illustrates that by controlling the changes in population composition, estimated exposure-effect relationships for health symptoms allow the effect of the studied traffic measures on the population to be evaluated. Since the method is based on individual estimates of exposure to different pollutants, it allows standardizing the exposure to compare effects of different pollutants. The study offers a methodology that is useful in evaluating the benefits of measures by both being able to quantify and compare the effects of different compounds and effects on different population sub-groups.
Language: en |