Environmental Science & Technology indicating that predictions from the hybrid emissions-disper- sion/regression model were properly scaled to the measure- ments. However, as distinct from the GSH OP model, removing the compound symmetric covariance structure did not substantially increase the df in the smooth function for time trend h(t) (219.7 with and 220.0 without), did not increase overﬁtting (0.2% with and without), and had no substantive eﬀect on predictive accuracy (CV R2 = 0.83 with and without). Due to this ﬁnding, we used the simpler GAM without control for correlation among within-site errors for prediction of weekly PM10 levels. Despite this, no consistent pattern of autocorrela- tion in adjacent lag periods was observed in the model residuals and autocorrelation values were generally small (r < 0.25 for lags greater than two weeks). Predictive accuracy decreased somewhat when predicting only spatial variability compared to weekly levels of PM10 (Table 1; spatial CV R2 = 0.61 among sites with at least 40 weeks of data), but increased for temporal predictions, highlighting the importance of temporal versus spatial variability for PM10 (Table 1; temporal CV R2 = 0.76). Also, predictive accuracy was high for annual averages (CV R2 = 0.67). We found very little bias in weekly model predictions (linear regression slope = 1.00 and MFB = 1.2%), and moderate precision (Table 1; RMSPE = 4.62 μg m−3 or 17% of the mean; MFE = 12.4%). Model precision improved when comparing only spatial variability (Table 1; RMSPE = 2.65 μg/m3 or 10% of the mean). The PM10 model performed well across seasons, years and in highly as well as less urban areas. Weekly predicted values across the 66 sites ranged from 10.3 to 100.8 μg/m3, whereas measured levels ranged from 8.1 to 101.3 μg m−3. The model explained about 84% of the variability in weekly PM10 levels (Supporting Information Table S1), and thus, in comparison with the CV R2, overﬁtting of the model was very well controlled for at 0.2%. M0306HED was quite inﬂuential on predicted weekly PM10 values; weekly PM10 predictions varied by 62% across the range of M0306HED relative to the mean. The maps in Supporting Information Figure S6 display the spatial and temporal variability in predicted PM10 concentrations in central London. Higher PM10 concentrations near both major and minor roadways are evident (Supporting Information Figure S6), as is the importance of temporal compared to spatial variability: Using a conventional sums of squares decomposition, the variability in predicted values at these grid points was 7% spatial and 93% temporal. GSH OP-Weighted PM10. The maps in Figure 1 show the spatial and temporal variability in ÊGSH OP in central London, U.K. The inﬂuence of PM10 brake and tire wear emissions and NOX tailpipe emissions near roadways is again evident. Again using a conventional sums of squares decomposition, the variability in weekly predictions was 19% spatial and 79% temporal, with the 2% remainder attributable to noise. Multiplying predicted GSH OP by predicted PM10 to calculate ÊGSH OP increased the spread of the distribution compared to that of PM10 alone (interquartile range is 80% of the median for ÊGSH OP vs 42% for ÊPM10), and will likely increase the exposure contrast available for epidemiologic studies, though the extent of this result will depend on the proportion of the population living near (<50−100 m) roadways. Perhaps most importantly, the correlation between ÊGSH OP and ÊPM10 is only moderate (Spearman’s r = 0.62), indicating that Article Figure 1. Maps of predicted GSH OP-weighted PM10 levels in OP m−3 in a selected area of central London, U.K. (A) Lowest week (beginning December 6, 2004; values ranged from −3.4 to 29.2 OP m−3); (B) Mean across 2002−2006 (values ranged from 3.0 to 50.4 OP m−3); (C) Highest week (beginning July 21, 2003; values ranged from 22.0 to 88.3 OP m−3). weighting by OP aﬀects the relative ranking of location-speciﬁc PM exposure estimates. ■ DISCUSSION Our results suggest that PM10 at locations with higher brake and tire wear emissions from vehicles has higher GSH OP compared 7617 dx.doi.org/10.1021/es3010305 | Environ. Sci. Technol. 2012, 46, 7612−7620