The full complexity of multi-scale patterns of air pollution is largely unknown in most urban areas despite great advances in measurement and modeling methods over the past few decades. Regulatory monitoring sites are sparsely distributed and generally do not measure unregulated pollutants of health concern, such as black carbon (BC) and ultrafine particles (UFP). While satellite remote sensing provides nearly global spatial coverage, most conventional products are limited in resolution to 1-5 km 2 and do not include all pollutants of interest . Mechanistic models predict concentrations over broad domains but are limited by computational constraints and data gaps . Recent statistical models provide both high spatial resolution and geographic coverage, but concentration predictions reflect generalized patterns, tend to predict central tendencies better than extremes, and may miss local idiosyncrasies, especially for models with broad (e.g., national) domains . Despite the growing sophistication of these technologies, in many cases they are best suited to depict patterns of pollutants that are predominantly secondary, such as ozone and PM2.5, which tend to vary more over the regional than neighborhood scale . In contrast, finescale gradients dominate spatial variability for directly emitted (primary) pollutants like BC and for pollutants with highly localized transformation dynamics like UFP and NO . Both BC and UFP are suspected to cause distinct health impacts , but these effects-and the potential racial/ethnic and socioeconomic health disparities-will remain poorly understood until higherresolution measurements are more widespread . Mobile monitoring and low-cost sensors are increasingly used to detect fine-scale pollution gradients with applications ranging from new risk estimates for cardiovascular disease to the identification of unexpected sources of exposure disparity . Low-cost sensors are used to supplement the density of existing regulatory networks, but appropriate sensors are not available for all pollutants and operation is limited by the capacity for consistent calibration and maintenance . Mobile monitoring has been used to measure multipollutant gradients in a wide range of urban contexts . Example applications of mobile monitoring include studies of short-range multipollutant variation attributable to highway traffic , infrastructure geometry such as street canyons and near-road barriers , and a variety of specific local sources . While mobile monitoring is a flexible method, it is also labor intensive, requiring many repeated visits to collect enough localized measurements to capture the full temporal variation in conditions. Because of the large resource requirement for long-term mobile monitoring, only a small number of campaigns have measured multi-year, high-resolution patterns over multiple neighborhoods . Few have attempted comprehensive coverage of all major and residential streets of several contiguous neighborhoods . This study presents the results of 32-months of mobile monitoring along every street of 13 cities, towns, and urban districts (93 km 2 ) distributed through four counties of the San Francisco Bay Area, providing over 2,100 hours of sampling of four pollutants at ~0.01 km 2 (i.e., ~100×100m 2 ). We employ two custom-equipped Google Street View cars to repeatedly measure city block air quality, providing estimates of outdoor air pollution for a year-2010 population of ~450,000 individuals and a novel opportunity to characterize, quantify, and analyze multiscale gradients across the urban landscape. We specifically consider implications for racial and ethnic exposure disparities. We find that dense, urban neighborhoods exhibit peaks that vary by pollutant in both location and magnitude, reflecting complex interactions among diverse emission sources and urban microenvironments. Despite high-magnitude hyperlocal peaks, we find that concentration differences between sampling areas (i.e., among distinct neighborhoods and cities) cause greater average concentrations for people of color. These findings demonstrate the need to consider mitigation policies at multiple urban scales to address environmental inequity.