Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles.

NITROGEN oxide analysis; AIR pollution; ANALYSIS of variance; CONFIDENCE intervals; STATISTICAL correlation; ENVIRONMENTAL monitoring; EPIDEMIOLOGICAL research; GEOGRAPHIC information systems; OXIDATION-reduction reaction; PROBABILITY theory; REGRESSION analysis; RESEARCH funding; TRANSPORTATION; ENVIRONMENTAL exposure; PARTICULATE matter; PREDICTIVE tests; DATA analysis software; STATISTICAL models; DESCRIPTIVE statistics; ODDS ratio

BELGIUM; NETHERLANDS

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 PM2.5. METHODS: Three 2-week PM2.5 samples were collected at 10 regional background, 12 urban background, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OPESR) and dithiothreitol (OPD ). 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 OPDTT and 1.4 for OPESR, whereas regional/urban background ratio was 0.8 for both. OPESR correlated moderately with OP (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 OPDTT and 0.67 for OPESR. OPDTT and OPESR model predictions were moderately correlated (R² = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM2.5 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 OPDTT, OPESR, and PM2.5 models offer the potential to investigate which metric is the strongest predictor of health effects. [ABSTRACT FROM AUTHOR]