Modelling Gender Specific Exposure to Air Pollution

Background and objective: Most exposure studies take into account the variation in air quality which is provided by models or by kriging of measurements. The spatial and temporal variation in population density is often ignored and exposure is usually based on adress data only. We present an integrated chain of models that enables us to estimate exposure taking into account temporally and spatially resolved information about people's location and pollutant concentrations. We focus on gender specific differences in NO2 exposure due to different time-activity patterns. Methods: We used the activity-based model Albatross to model activities and trips for all the individuals within the population in the Netherlands for 4000 population zones. Air quality was modeled with AURORA, a 3-dimensional Eulerian model, at a resolution of 3x3 km. Hourly concentration data resulting from the dispersion modeling were combined with hourly population data derived from the activity-based model to provide detailed dynamic exposure assessments. Results: Neglecting people's travel behaviour in NO2 exposure analysis will underestimate daily exposure by 4% and hourly exposure up to 30% on average. A disaggregated exposure analysis demonstrates that average exposure concentrations of men are generally higher. Differences of up to 12% occur in the morning when men perform activities at locations with higher concentrations than women. The higher exposure of men in the afternoon is explained by the fact that more men than women have a paid job (more women work part-time jobs) and the workplace exposure concentrations are higher. Differences in pollutant intake rate are more explicit when taking into account gender-specific breathing rates. Conclusion: The most interesting feature of activity-based models is their ability to retain demographic and socio-economic data of the people making trips and performing activities. In this way exposure analysis can be disaggregated by different subgroups in the population.

Publication year:2009

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