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 emissions inventories often lack fine spatiotemporal precision and rarely capture unpermitted or informally operated sources. For the first time, we combine dense mobile and fixed-site air pollution measurements in a Bayesian inverse modeling framework to produce hourly, hyperlocal (150 m × 150 m ≈ 0.02 km2) black carbon emission maps that directly reveal how and where key sources may be underrepresented. In a test case in West Oakland, CA — a community with long-documented elevated diesel particulate matter exposures and proximity to major freight infrastructure — our approach identifies previously unaccounted and underestimated emission sources, increasing total estimated BC emissions by ~33%. Crucially, 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 multi-platform, dense observational data with inverse modeling enables reliable detection and quantification of overlooked emissions, supporting more precisely targeted policy intervention.