Article pubs.acs.org/est Modeling Exposures to the Oxidative Potential of PM10 Jeﬀ D. Yanosky,* Cathryn C. Tonne,†,‡ Sean D. Beevers,† Paul Wilkinson,† and Frank J. Kelly† Department of Public Health Sciences, A210 Penn State College of Medicine 600 Centerview Drive, Suite 2200 Hershey, Pennsylvania 17033-0855, United States *S Supporting Information ABSTRACT: Diﬀerences in the toxicity of ambient particulate matter (PM) due to varying particle composition across locations may contribute to variability in results from air pollution epidemiologic studies. Though most studies have used PM mass concentration as the exposure metric, an alternative which accounts for particle toxicity due to varying particle composition may better elucidate whether PM from speciﬁc sources is responsible for observed health eﬀects. The oxidative potential (OP) of PM < 10 μm (PM10) was measured as the rate of depletion of the antioxidant reduced glutathione (GSH) in a model of human respiratory tract lining ﬂuid. Using a database of GSH OP measures collected in greater London, U.K. from 2002 to 2006, we developed and validated a predictive spatiotemporal model of the weekly GSH OP of PM10 that included geographic predictors. Predicted levels of OP were then used in combination with those of weekly PM10 mass to estimate exposure to PM10 weighted by its OP. Using cross-validation (CV), brake and tire wear emissions of PM10 from traﬃc within 50 m and tailpipe emissions of nitrogen oxides from heavy-goods vehicles within 100 m were important predictors of GSH OP levels. Predictive accuracy of the models was high for PM10 (CV R2=0.83) but only moderate for GSH OP (CV R2 = 0.44) when comparing weekly levels; however, the GSH OP model predicted spatial trends well (spatial CV R2 = 0.73). Results suggest that PM10 emitted from traﬃc sources, speciﬁcally brake and tire wear, has a higher OP than that from other sources, and that this eﬀect is very local, occurring within 50−100 m of roadways. ■ INTRODUCTION Several recent epidemiologic studies have reported hetero- geneity in eﬀect estimates of ﬁne particles from diﬀerent sources,1−8 some of which are supported by recent toxicological studies.9−11 Though less extensive, studies also document heterogeneity in eﬀect estimates of PM10.12,13 Many studies have found increased respiratory7 and cardiac health risks from exposures to traﬃc14,15 or PM from traﬃc sources2,4,6,16−18 suggesting that traﬃc-related PM is more toxic than PM from other sources. Diﬀerential toxicity of PM from diﬀerent sources may explain some of the observed heterogeneity in results from epidemiologic studies that used PM mass concentration as the metric of exposure. Understanding the diﬀerential toxicity of PM from diﬀerent sources has been identiﬁed as a priority for environmental health research.19 These eﬀorts are complicated by the diverse physical and chemical properties of PM, many of which can be measured but whose relevance in the toxicity of PM is poorly understood.20 Oxidative potential (OP) of PM oﬀers a convenient means of integrating over these complex characteristics, providing a summary measure indicative of PM toxicity that is directly relevant to pollutant behavior in the human lung.21−24 OP can be considered a measure of the inherent capacity of PM to induce oxidative stress in the lung. Although models have been developed to predict exposure to traﬃc-related PM, to date, there are no models of exposure to the oxidizing properties of PM that can be applied in epidemiological studies. Our objectives were 2-fold: (1) To establish the feasibility of using an approach similar to land-use regression (LUR) alone, or in combination with spatiotemporal modeling, to predict the weekly average OP of PM10 in greater London, U.K.; and (2) To generate an exposure metric reﬂecting OP- weighted PM10 that could be compared to PM10 mass concentration in epidemiologic investigations. ■ MATERIALS AND METHODS Using a database of measured OP and PM10 levels, together with information about the surroundings of the monitoring sites derived from a geographic information system (GIS), we developed predictive geostatistical models to estimate weekly average location-speciﬁc levels of the OP of PM10 as well as PM10 mass concentrations from 2002 to 2006. Because our intent was to predict OP levels during the monitored time period, our analysis focused on determining spatial predictors of OP, with purely temporal predictors to be identiﬁed in a separate analysis. Air Pollutant Data. PM10 concentrations are routinely measured at many ambient air monitoring sites across greater London using Tapered Element Oscillating Microbalance (TEOM; Rupprecht & Patashnick model 1400A, New York) Received: March 15, 2012 Revised: June 20, 2012 Accepted: June 25, 2012 Published: June 25, 2012 © 2012 American Chemical Society 7612 dx.doi.org/10.1021/es3010305 | Environ. Sci. Technol. 2012, 46, 7612−7620