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GIS Based Estimation of Exposure to Particulate Matter and NO2 in an Urban Area: Stochastic Versus Dispersion Modeling
Josef Cyrys, Matthias Hochadel, Ulrike Gehring, Gerard Hoek, Volker Diegmann, Bert Brunekreef, and Joachim Heinrich


Stochastic modeling was used to predict nitrogen dioxide and fine particles [particles collected with an upper 50% cut point of 2.5 µm 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 IMMISnet/em was used to estimate the annual mean values for NO2 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 NO2 and PM2.5 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). NO2 and PM2.5 concentrations obtained by the stochastic models were in the same range as the measured concentrations, whereas the NO2 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 NO2, r = 0.83, and for PM, r = 0.79; at the 1,669 cohort sites, for NO2, 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.

Read paper at Environment Health Perspective

air pollutants, dispersion modeling, GIS, stochastic modeling, traffic
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