Health effects of air pollution on individuals depend on their personal exposure, but few modelling tools are available which can predict how the distribution of personal exposures within a city will change in response to policies to reduce emissions both indoors and outdoors. We describe a new probabilistic modelling framework (INDAIR-2/EXPAIR), which provides predictions of the personal exposure frequency distribution (PEFD) across a city to assess the effects of both reduced emissions from home sources and reduced roadside concentrations on population exposure. The model uses a national time activity database, which gives the percentage of each population group in different residential and non-residential micro-environments, and links this, for the home, to predictions of concentrations from a three-compartment model, and for non-residential microenvironments to empirical indoor/outdoor ratios. This paper presents modelled PEFDs for NO2 in the city of Leicester, for children, the elderly, and office workers, comparing results in different seasons and on different days of the week. While the mean NO2 population exposure was close to, or below the urban background concentration, the 95%ile of the PEFD was well above the urban background concentration. The relationship between both mean and 95%ile PEFD and urban background concentrations was strongly influenced by air exchange rate. The 24 h mean PEFD showed relative small differences between the population groups, with both removal of home sources and reductions of roadside concentrations on roads with a high traffic density having similar effects in reducing mean exposure. In contrast, the 1 h maximum of the PEFD was significantly higher for children and the elderly than for office workers, and showed a much greater response to reduced home emissions in these groups. The results demonstrate the importance of understanding the dynamics of NO2 exposure at a population level within different groups, if the benefits of policy interventions are to be accurately assessed.
Bibliographical noteFunding Information:
This work was supported by UK Department of Health . We also acknowledge the contribution of the Engineering and Physical Sciences Research Council (EPSRC) to the infrastructure of the Instrumented City Facility, which provided traffic and air pollution data, and Leicester City Council for providing input data for our analysis. We gratefully acknowledge the assistance of Matt Hill, and of two MSc students, at the University of Bradford, in developing the model parameterisation for Leicester.
- Indoor air pollution
- Nitrogen dioxide
- Personal exposure