Stochastic modeling was used to predict nitrogen dioxide and fine particles [particles collected with an upper 50% cut point of 2.5 [micro]m aerodynamic diameter (PM2.5)] levels at 1,669 addresses of the participants of two ongoing birth cohort studies conducted in Munich, Germany. Alternatively, the Gaussian multisource dispersion model IMMI[S.sup.net/em] was used to estimate the annual mean values for N[O.sub.2] and total suspended particles (TSP) for the 40 measurement sites and for all study subjects. The aim of this study was to compare the measured N[O.sub.2] and P[M.sub.2.5] levels with the levels predicted by the two modeling approaches (for the 40 measurement sites) and to compare the results of the stochastic and dispersion modeling for all study infants (1,669 sites). N[O.sub.2] and P[M.sub.2.5] concentrations obtained by the stochastic models were in the same range as the measured concentrations, whereas the N[O.sub.2] and TSP levels estimated by dispersion modeling were higher than the measured values. However, the correlation between stochastic- and dispersion-modeled concentrations was strong for both pollutants: At the 40 measurement sites, for N[O.sub.2], r = 0.83, and for PM, r = 0.79; at the 1,669 cohort sites, for N[O.sub.2], r = 0.83 and for PM, r = 0.79. Both models yield similar results regarding exposure estimate of the study cohort to traffic-related air pollution, when classified into tertiles; that is, 70% of the study subjects were classified into the same category. In conclusion, despite different assumptions and procedures used for the stochastic and dispersion modeling, both models yield similar results regarding exposure estimation of the study cohort to traffic-related air pollutants. Key words: air pollutants, dispersion modeling, GIS, stochastic modeling, traffic. doi:10.1289/ehp.7662 available via http://dx.doi.org/[Online 15 April 2005]

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Recent interest has focused on traffic-related air pollution and the potential health effects associated with exposure (Kunzli et al. 2000). The acute health effects of short-term exposures to traffic-related pollution have been widely demonstrated, but much less is known about the chronic effects of exposure. Several studies have found associations between chronic morbidity or mortality and traffic-related pollution (e.g., Brunekreef et al. 1997; Heinrich and Wichmann 2004; Hock et al. 2002a; Weiland et al. 1994; Wjst et al. 1993). On the other hand, a number of studies have found no detectable effects (Magnus et al. 1998; Wilkinson et al. 1999). Thus, the extent to which the long-term exposure to air pollution contributes to chronic health effects remains unknown. Much of the uncertainty relates to the problems of potential confounding variables and of reliable estimates of exposure to traffic-related pollution at the individual or small-area level, across large populations and cities. To date, most assessments of the health impacts of long-term exposure have involved between-city comparisons using a limited number of monitors within each city. Such between-city comparisons are subject to exposure misclassification because they rely on a small number of monitors. A recently conducted study in four European countries [SAVIAH (Small-Area Variation in Air Pollution and Health)] found important variations in the concentrations of nitrogen dioxide and sulfur dioxide on a small scale within cities (Lebret et al. 2000). Several other studies have documented important within-city variation of concentration, especially related to nearness to motorized traffic and location within the city--for example, center versus suburb (Bernard et al. 1997; Cyrys et al. 1998; Raaschou-Nielsen et al. 2000).

To overcome these problems, some studies used surrogate variables, such as distance to major road or traffic intensity (objectively determined or self-reported) (Brunekreef et al. 1997; van Vliet et al. 1997; Weiland et al. 1994; Wjst et al. 1993) to account for within-city variability in exposure. A disadvantage of t2hese exposure indicators is that they are frequently not validated, and it may therefore be unclear what the actual exposure contrast is.

A potential solution to these problems is the use of geographic information systems (GIS) in which geographic data can be either used for the development of dispersion models (Bellander et al. 2001; Pershagen et al. 1995) or combined with concentration measurements to estimate exposures for individual members of large study populations by regression (stochastic) models (Brauer et al. 2003; Briggs et al. 1997; Gehring et al. 2002).

So far, epidemiologic studies used either stochastic or dispersion modeling, but not both in parallel. Only in the international collaborative study on the risks of development of childhood asthma and other allergic diseases [TRAPCA (Traffic-Related Air Pollution on Childhood Asthma) study (Brauer et al. 2002; Gehring et al. 2002)] were both approaches (stochastic and dispersion modeling) used in parallel to predict the outdoor exposure to N[O.sub.2] and particulate matter (PM) for 1,669 study participants. For the stochastic modeling, N[O.sub.2] and particles collected with an upper 50% cut point of 2.5 [micro]m aerodynamic diameter (P[M.sub.2.5]) were measured at 40 sites spread over the city area to estimate the annual average concentrations of these pollutants. This data set offers the unique opportunity to evaluate the result of the dispersion and stochastic modeling. The aim of the study is to compare the measured levels of the two pollutants with the levels predicted by the two modeling approaches (for the 40 measurement sites) and to compare the results of the stochastic and dispersion modeling for all 1,669 study participants.