Because of the temporal sparsity of mobile measurements relative to fixed-site monitoring, it is critical to consider both the uncertainty inherent in a small sample size and the potential for temporal bias based on the days and times of day sampled. To mitigate temporal bias from lower sampling rates we imposed the following standards for all data used in this analysis: road segments were included if valid measurements were made on at least 3 days, and census blocks were only included if they integrated measurements from 3 or more road segments and included a total of 100 s or more of valid sampling data. The median cumulative sampling time of each block was 19 minutes, collected during a median of 47 unique visits over 20 days. A bootstrap resampling technique, described in SI Appendix, was used to estimate uncertainty at different sampling intensities. The characteristic sampling intensity for each study area is provided in SI Appendix (Table ). A Monte Carlo subsampling analysis of an exceptionally highly sampled area (West Oakland) shows that with moderate sampling intensity (each block visited 10-20 days) provides a 90% confidence interval around the population-weighted median concentration of approximately ± 20% for NO2 and UFP, ± 30% for BC, and ± 40% for NO, with confidence intervals that are positively skewed (SI Appendix, Table ). At higher sampling intensity (20-40 days) the 90% confidence interval is ± 20% for the medians of all pollutants. Considering the influence of sampling intensity on estimates of within-neighborhood variability, we find that even at high sampling intensity the interdecile range is overestimated in 95% of simulations, affecting both estimates of local variance and the total-population variance. Despite this effect, we find that study areas that accounted for large portions of SSD for NO, NO2, and UFP included some of the mostsampled (Downtown Oakland and Berkeley) as well as moderately sampled areas (SF Financial and Fruitvale Group, see SI Appendix Table ). Nevertheless, this finding serves as a caution against over-interpreting variation in less-sampled areas. Diurnal sampling bias metrics show that sampling of NO2, BC, and UFP was broadly representative (± 15%) of daytime conditions, while the distribution of hourly measurements resulted in a systematic low bias in NO concentrations (SI Appendix, Table ). Error tended to be in the same direction across all study areas (e.g., 12 out of 13 are biased ~10% low for BC) and thus had a small effect on the relative rankings of concentrations among study areas (SI Appendix, Fig ). Annual representativeness ratios show less consistent patterns across study areas, and the application annual adjustment factor results in minor changes in the relative rankings of NO2 and BC among study areas and increases the ranges of NO and UFP exposure distributions among high concentration study areas. Overall, these findings are robust against the effects of temporal bias. Because of significant differences in the racial and ethnic composition of each neighborhood, concentration distributions in some study areas contribute much more to specific race/ethnicities (e.g., East Oakland for the Hispanic/Latino population, West Oakland for the Black population). Vertical lines show the indicated statistics (mean, median, 10 th and 90 th percentiles) for each race/ethnicity. Corresponding relative disparity values are provided in Table .