Environmental Science & Technology monitors; data from 66 sites were available for this analysis. Hourly TEOM data were reported in units of μg m−3 and corrected for volatilization losses using a multiplier of 1.3 for 2002 and a volatilization-correction model for 2003−2006.25 OP Data. OP was measured based on the collected particles’ ability to deplete the antioxidant reduced glutathione (GSH) in an acellular model of respiratory-tract lining ﬂuid.21,22 The methods used to measure GSH OP are described in detail elsewhere.23,26,27 Brieﬂy, suspensions of PM collected on TEOM ﬁlters at a standardized concentration (50 μg mL−1) were incubated in an antioxidant solution for 4 h. Particle-free, negative (inert black-carbon particles), as well as positive (residual oil ﬂy ash) controls were run in parallel to assess auto-oxidative losses and allow for standardization between batches. The amount of GSH lost over the 4 h period relative to a particle-free control was calculated per μg PM10. Data on GSH OP were available from 34 monitoring sites across the study domain of greater London (shown in Supporting Information Figure S1), though not continuously at all sites. Valid data were available for 25% of all possible weeks. TEOM ﬁlters were changed on an irregular schedule based on ﬁlter loading, with about half of the sampling periods less than two weeks in duration (median: 15 days; 25th and 75th percentiles are 7 and 28 days, respectively (n = 841)). Thus, each ﬁlter provided an OP measurement integrated over the sampling period, but the sampling periods did not necessarily coincide in time at diﬀerent sites. To obtain weekly average OP values with concurrent start and stop days across the 34 monitoring sites, we assigned the multiday integrated OP measurements to each day of the corresponding sampling period and derived weekly average values by taking the weighted average (in proportion to days of measurement) of daily values that occurred within each calendar week, excluding weeks with less than 4 valid days. Further details on the temporal realignment of the OP data can be found in the Supporting Information. Geographic Data. The geographic coordinates of each monitoring site on the British National Grid were used in combination with data on building and traﬃc density to derive geographic covariates describing the surroundings of each monitoring location. Building density variables were provided by colleagues at the Bartlett School of Architecture, University College London, U.K. These data were derived from postcode level information from the Ordnance Survey MasterMap Topography Layer28 and Cities Revealed Topography layer.28 From these data sources, the following variables were calculated at 100, 250, 500, and 1000 m radii around the monitoring sites: (1) the “built-space” ratio (proportion of land covered with buildings or structures), (2) the artiﬁcial coverage ratio (proportion of land covered with human-made surfaces), (3) the green coverage ratio (proportion of land covered with green surfaces), (4) the water coverage ratio (proportion of land covered with inland water), and (5) the urban volumetric density (built-space ratio multiplied by the average height). Data on traﬃc density and emissions were obtained from the London Atmospheric Emissions Inventory29 for the year 2005. For each road segment in the inventory, emissions of nitrogen oxides (NOX) and PM10 from road traﬃc were calculated based on (1) the Annual Average Daily Total (AADT) traﬃc count for 2005, and (2) the mixture of vehicle types present on London roads in 11 categories. These categories were further reﬁned using data on the proportions of Euro classes within each vehicle category using data from the U.K. Department for Transport’s national stock model and, for taxis and buses, from the Greater Article London Authority. For this analysis the traﬃc emissions data were collapsed into three groups: heavy-goods vehicles, light- goods vehicles, and “other” vehicles (buses, cars, motorcycles, and taxis). This collection of road location, traﬃc density, and emissions data provided a highly spatially resolved database on (1) cumulative traﬃc levels and (2) vehicular NOX and PM10 emissions with enough spatial accuracy to reliably estimate gradients within 50 m of the monitoring site. To calculate cumulative traﬃc levels, we multiplied the AADT for each road segment by the segment length within 50, 100, and 200 m buﬀers, by vehicle group and total. For vehicular emissions of NOX and PM10, we calculated tailpipe NOX and PM10 emissions, by vehicle group and total, within 50, 100, and 200 m buﬀers. Additionally, for PM10 only, we calculated emissions from brake and tire wear within the same buﬀers. The data on traﬃc density and NOX and PM10 tailpipe emissions, as well as PM10 from brake and tire wear, were also summed, by vehicle group and for all road and nonroad sources combined, within 1 km2 grid cells to provide area-level aggregate measures. A hybrid emissions-dispersion/regression model of annual- average PM10 levels with high spatial resolution has been previously developed for use in greater London and is described in detail elsewhere.30,31 This model used emissions data from the London Atmospheric Emissions Inventory29 and incorporated near-road impacts and meteorology using ADMS-Roads; we used it to obtain annual-average PM10 levels for 2003, 2004, and 2006 on a 20 m grid over the study domain of central London. We then bilinearly interpolated these annual-average values to each prediction location, and averaged the resulting three values by location to create a spatially varying covariate that described long-term average PM10 levels with high spatial resolution. Statistical Models. GSH OP. We developed a geostatistical spatiotemporal model to predict weekly average GSH OP levels at unmeasured locations anywhere within the study domain throughout 2002−2006. To do this, we evaluated several modeling approaches of varying complexity, from simple space-time models using penalized splines and linear eﬀects of GIS covariates, to approaches which further added time-varying smooth spatial trends, nonlinear covariate eﬀects, and diﬀerent assumptions with respect to the correlation structure among within-site errors. We used cross-validation (CV) at the location level rather than the location-week level to describe the predictive accuracy of each candidate model using the following steps. First, we divided the data set into two parts by leaving out the monitoring data for a given location. Next, we ﬁtted the candidate model to the reduced data set containing data from all locations except the left- out location. We then used that ﬁtted model to predict weekly values at the left-out location, and repeated this process for each of the monitoring sites. The CV R2 was calculated as the squared Pearson correlation between the measured values at a given location and the model predictions when that location was left out of the model ﬁtting process. Because we performed CV at the location level, the presence of successive weekly data points at a given site did not inﬂuence CV results. The degree of model overﬁtting was assessed as the diﬀerence between the model R2 and the CV R2. The candidate model with the highest CV R2 was used as the ﬁnal prediction model. Because our focus was on modeling spatial variation, in instances where candidate models diﬀered only with respect to the spatial covariates included, we averaged the measurements and model predictions across time (one mean per site and year and one mean per site) to obtain and compare annual-average and spatial CV R2 values, respectively. 7613 dx.doi.org/10.1021/es3010305 | Environ. Sci. Technol. 2012, 46, 7612−7620