Environmental Science & Technology to that at other locations, and that this eﬀect is extremely local, occurring within 50 m of roadways. This suggests that “fresh” and very local PM10 brake and tire wear emissions may be more toxic with respect to OP than PM10 from other sources, perhaps due to the metal content in brake material. This suggestion is supported by our CV results, where very local (within 50 m) PM10 brake and tire wear emissions proved to be the most eﬀective predictor of spatial variation in GSH OP levels. We have demonstrated the predictive accuracy of geostatistical models of weekly GSH OP and PM10 levels within the study domain of greater London, U.K. from 2002 to 2006. For GSH OP, the results demonstrate the ability of LUR using traﬃc emissions and geographic road network data to explain spatial variability in measured GSH OP levels. In addition to spatial variability, our results also demonstrate substantial temporal variability in GSH OP and PM10, as described by the temporal smoothing terms in our models; these trends in time would not have been captured using LUR alone. Our model for GSH OP was able to explain a signiﬁcant portion of the variability in GSH OP levels (model R2 = 0.52), and CV results showed that predictive accuracy for GSH OP at left-out monitoring locations (substituting for unmeasured residential locations) was moder- ate (CV R2 = 0.44). Predictions from the GSH OP and PM10 models can be combined to deﬁne an exposure metric which quantitatively describes the inherent capacity of PM to induce oxidative stress in the lung. Use of such an exposure metric, having more relevance to impacts on the lung which in turn could drive local and systemic inﬂammation,24 has the potential to reduce exposure error in studies of PM health eﬀects and allow greater precision in health-eﬀect estimates. We observed seasonal variation in the eﬀect of the PM10 brake and tire wear emissions covariate on GSH OP levels. This variation may in part be due to seasonal changes in the vehicular emissions themselves, but is more likely due to diﬀerent meteorological trends during the seasons, or to changes in the composition of PM, perhaps as a consequence of interactions with other seasonal pollutants. The higher slopes for the PM10 brake and tire wear emissions covariate in spring and summer seasons indicate that GSH OP levels increase more sharply near roadways during these seasons than others. This may be the result of reaction with secondary pollutants that are higher during those periods of the year with increased solar radiation, such as ozone, of seasonal changes in the metal content of PM, or of increased resuspension of PM near roadways in drier, windier seasons. We also observed a negative association between NOX tailpipe emissions from heavy-goods vehicles within 100 m and GSH OP, though for this term the eﬀect size was smaller than for PM10 brake and tire wear emissions. While somewhat counterintuitive, this negative association may suggest that PM10 emissions from roadways with greater heavy-goods vehicle traﬃc exhibit diﬀerences in composition that inﬂuence GSH OP. Interestingly, this ﬁnding is consistent with locations where elemental carbon would be expected to contribute a higher proportion of PM10 mass (those with higher heavy-goods traﬃc indicated by NOX emissions) corresponding to lower GSH OP. We note that NOX tailpipe emissions are likely reﬂecting variation in PM components, rather than causing a direct eﬀect on GSH OP, because OP analyses were done on PM collected on the sampling ﬁlters; gaseous NOX would have passed through the sampling ﬁlters and therefore not aﬀected later analyses. LUR has been widely used to describe spatial variability in measured pollutant levels.36,37 Our GSH OP analysis is distinct Article from typical LUR models, however, in that we are modeling a characteristic of collected PM10 (its GSH OP in units of OP μg−1) rather than a pollutant concentration, and in that, using a generalized additive mixed model, we explicitly model temporal variation (rather than averaging it out) while accounting for autocorrelation among the within-site errors. Nonetheless, our study has several limitations. One is that only estimates of the weekly average GSH OP were available due to the irregular sampling time periods. Even though this feature of the data could induce errors resulting in too much smoothing of the estimated time trend and increased autocorrelation in model residuals, we found only little to moderate evidence of autocorrelation. We were not able to determine to what extent the varying duration of the sampling periods aﬀected the GSH OP measurements, but note that the eﬀects of GIS covariates in our model are estimated based on spatial comparisons, rather than temporal; the high spatial CV R2 (0.73) would suggest that the model describes these spatial patterns quite well. Also, despite our use of a site-speciﬁc random eﬀect, the data may not be missing completely at random and thus the spatiotemporal imbalance in our data may have induced bias in the model eﬀect estimates. Additionally, the TEOM monitor involves heating of sample ﬁlters which, could have resulted in an underestimation of GSH OP due to a loss of oxidatively active volatile species. However, GSH OP has been found to be similar when collected by TEOM compared to other methods that retain the volatile component of PM10.30 Also, GSH OP is not a direct measure of particle toxicity. Rather, it only captures the OP of components in collected PM that do not require cellular metabolism to induce oxidative stress, and so may underestimate the total oxidative stress burden of inspired PM. Additionally, geographic data on roadway locations in greater London, U.K. do not necessarily reﬂect only spatial variation in traﬃc-related air pollutant emissions, but rather may also reﬂect to some extent spatial variation in other sources which may occur at the same locations, such as tobacco smoke from sidewalks lining busy roads. However, those emissions are unlikely to be highly correlated with cumulative traﬃc levels and are likely unaﬀected by changes in the vehicle group on a given roadway. Thus, given our ﬁnding of a linear relationship between GSH OP and traﬃc emissions speciﬁc to a given emission type (brake and tire wear), and that the strength of this relationship varied by emission type, this explanation seems implausible. Given our ﬁnding of very local (within 50−100 m) spatial variation in GSH OP levels, future investigations should focus on modeling weekly or even daily wind-direction dependent traﬃc- related air pollutant emissions as a predictor of GSH OP. Such a predictor may be able to explain additional variation in GSH OP levels, and may further elucidate the link between traﬃc-related air pollutant emissions, the OP of PM, and the time-scale and magnitude of their impact on human health. Also, future research on the relationships between GSH OP and speciﬁc PM10 components related to traﬃc sources is warranted. Epidemio- logic studies attempting to assess exposures to oxidant-weighted PM should focus on capturing variation in OP on smaller spatial scales (0−100 m) than typically used for routine ambient monitoring networks. ■ ASSOCIATED CONTENT *S Supporting Information Two tables and ﬁve ﬁgures and tables referred to in the text, as well as additional details on the methods and results concerning alternate spatial statistical approaches and on the seasonal 7618 dx.doi.org/10.1021/es3010305 | Environ. Sci. Technol. 2012, 46, 7612−7620