Using a Chemistry Transport Model to Account for the Spatial Variability of Exposure Concentrations in Epidemiologic Air Pollution Studies

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] ≤ 10 μm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants. IMPLICATIONS The methodology developed in this study suggests that spatially resolved pollutant concentrations combined with demographic data may be used in a parametric way in an epidemiological time-series study and provide additional information on the spatial variability of exposure. The principal advantage of the method is that the time series of the exposure surrogates used in the regression against health outcomes are less correlated among co-pollutants. It is therefore possible to estimate statistically significant associations between each co-pollutant and the health outcome and thus distinguish the corresponding health effects.