Hyperlocal sensing and inverse modeling reveal community impacts of urban air pollutant emissions

Addressing urban air pollution requires identifying, quantifying, and mitigating emission sources, yet bottom-up inventories often lack fine spatiotemporal precision and rarely capture unpermitted or informally operated sources. We combine dense mobile and fixed-site measurements in a Bayesian inverse modeling framework to produce hourly, hyperlocal black carbon (BC) emission maps (150 meters by 150 meters) that reveal how and where key sources are underrepresented. In West Oakland, CA—an overburdened community near major freight infrastructure—our approach identifies underestimated and unaccounted emission sources, increasing estimated BC emissions by ∼33%. These corrections quadruple the contribution of neighborhood and port-related sources to population-weighted exposures (from 5 to 20%), disproportionately affecting low-income neighborhoods adjacent to freight corridors. Our results demonstrate that combining dense observational data with inverse modeling enables detection and quantification of overlooked emissions, supporting targeted intervention. Sensitivity analyses indicate similar performance with a substantially reduced fixed-site network (∼10 monitors; ∼1 site per square kilometer, compared with 6.7 sites per square kilometer in the full campaign), paired with time-averaged mobile maps, supporting applicability in data-limited urban settings.