BACKGROUND: There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model. OBJECTIVES: We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models], and in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques. METHODS: We used data of 1058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEF) at the age of 8 years. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters <2.5 and <10 µm (PM2.5, PM10), and PM2.5 soot] were estimated for the current addresses of the participants by a dispersion and a LUR model, respectively. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment. RESULTS: Correlations between LUR and dispersion modeled pollution concentrations were high for NO2, PM2.5 and PM2.5 soot (R: 0.86-0.90) but low for PM10 (R: 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, with the exception of associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model. CONCLUSIONS: Predictions from LUR and dispersion models correlated very well for PM2.5, NO2 and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children.