We create empirical maps of four air pollutants in 13 groupings of neighborhoods that constitute urban districts, cities, or towns (total population: ~450,000), ranging in land use, urbanization, traffic density, demographic composition, and historical housing policy (SI Appendix, Figure , S2, S12, Tables ). We calculate census-block scale concentrations (see Methods) to best approximate conditions at individual residences given the resolution of demographic data (median block population: 36 people). Hereon, we describe outdoor concentration as "exposure" for block residents, terminology consistent with literature linking health effects to census block estimates ; we acknowledge that individual exposure also depends on multiple other factors (e.g., diurnal activity, indoor infiltration and dynamics, and physiology). Study area maps (Figure ) reveal sharp concentration gradients (~2-5 ×) along groups of contiguous blocks, as well as marked differences in the range of concentrations within different study areas. In discussing spatial variation we term gradients among neighboring blocks (~100 m) as "hyperlocal," variation within each study area (~1 km) as "local," and variation among study areas (~10 km) as "regional." Among the four pollutants, NO shows the highest-magnitude hyperlocal peaks, with a typical ratio of 10× between a peak and local median (SI Appendix, Figure and Table ). BC, NO2 and UFP (peak ratios ). These differences demonstrate the importance of measuring multiple pollutants. Furthermore, these patterns likely differ from those of other important pollutants like fine particulate matter (PM2.5) and air toxics, both in location and degree of local and regional variation. Exposure variation reflects where residents live relative to observed local and regional gradients, although residents may also experience peaks in unpopulated areas during daily activities. We find that the weighting of population within more homogeneous residential areas mildly diminishes the importance of local variation, but both local and regional gradients contribute to the broad range of exposures across the study domain. Figure shows the full distribution of exposure levels within each study area. Comparing median exposures between the most-and-least polluted study areas, concentrations varied by a factor of 4, 5, 6, and 28 for BC, NO2, UFP, and NO, respectively, while within-neighborhood interdecile ranges showed variation up to a factor of 4 for BC, NO2, UFP, and a factor of 19 for NO. Generally, neighborhoods with higher BC and NO medians also display a wider range of exposures, while NO2 and UFP ranges remain more consistent across neighborhoods. To partition exposure variability into local and regional components, we decomposed the sum-of-squared deviation from the mean (SSD) of each resident versus the study area mean and of all study areas versus the grand mean (SI Appendix, Table ). We find that local gradients contribute the majority of exposure variation for primary pollutants (NO: 52%, BC: 63% of SSD), but the minority for NO2 and UFP (37% and 28%, respectively), two pollutants where secondary processes are a key source. A subset of study areas account for a disproportionate share of local variation: for example, the SF Financial District and East Oakland (24% of study population) account for roughly 50% of local exposure variation for NO and BC, and 40% for UFP and NO2. These study areas represent denser urban settings with a greater mix of land uses. A similar comparison of purely spatial units (SSD among blocks, SI Appendix Tables ) finds that local variability is much greater for the primary pollutants NO and BC (76% and 79% of the SSD, respectively) and moderately higher for NO2 and UFP (NO2, 54%, UFP, 46%).