Spatial variation and land use regression modeling of the oxidative potential of fine particles

Background: Oxidative potential (OP) has been suggested to be a more health-relevant metric than particulate matter (PM) mass. Land use regression (LUR) models can estimate long-term exposure to air pollution in epidemiological studies, but few have been developed for OP. Objectives: We aimed to characterize the spatial contrasts of two OP methods and to develop and evaluate LUR models to assess long-term exposure to the OP of PM_2.5. Methods: Three 2-week PM_2.5 samples were collected at 10 regional background, 12 urban back-ground, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OP^ESR) and dithiothreitol (OP^DTT). LUR models were developed using temporally adjusted annual averages and a range of land-use and traffic-related GIS variables. results: Street/urban background site ratio was 1.2 for OP^DTT and 1.4 for OP^ESR, whereas regional/urban background ratio was 0.8 for both. OP^ESR correlated moderately with OP^DTT (R² = 0.35). The LUR models included estimated regional background OP, local traffic, and large-scale urbanity with explained variance (R²) of 0.60 for OP^DTT and 0.67 for OP^ESR. OP^DTT and OP^ESR model predictions were moderately correlated (R² = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM_2.5 model (R² = 0.37–0.52), and highly correlated with predictions from previously published models of traffic components (R² > 0.50). Conclusion: LUR models explained a large fraction of the spatial variation of the two OP metrics. The moderate correlations among the predictions of OP^DTT, OP^ESR, and PM_2.5 models offer the potential to investigate which metric is the strongest predictor of health effects.