Connecting Urban Black Carbon Emissions and Measured Concentrations: A Fusion of Hyperlocal Monitoring and Bayesian Techniques

Understanding urban air pollution at fine scales is essential for pinpointing emission sources that disproportionately impact vulnerable communities. Traditional emission inventories often suffer from insufficient spatial granularity and lack observational grounding, thus hampering effective source-specific interventions.

Here, we introduce a novel application of receptor-oriented models (RMs) for the hyperlocal source apportionment of black carbon (BC). By integrating a rich dataset from both dense mobile monitoring and temporally detailed fixed-site measurements into a Bayesian inversion framework using the WRF-STILT model, we quantify BC contributions in the community of West Oakland, CA, USA from diverse urban sources including on-road vehicles (notably diesel trucks), locomotives, port cargo-handling equipment, and maritime vessels, with a high spatial resolution of 150 meters (~0.02 km²). We reveal a wide variety of uninventoried neighborhood-scale emissions sources that substantially impact this overburdened community.

Our method employs a data-driven spatiotemporal model that combines both mobile and fixed-site data within a factor analysis framework, providing robust observational constraints for Bayesian inference. The robustness of our method is particularly notable given the uncertainties in prior emissions inventories. Moreover, we demonstrate that with only 10 strategically placed stationary sensors within a 15 km² area, supplemented by time-averaged mobile measurements, reliable source apportionment can be achieved.

This study advances the methodology of RMs by providing a scalable and adaptable approach for incorporating hyperlocal measurements, providing critical insights into the effectiveness of these models in real-world urban scenarios. Future applications of the method would support observationally constrained strategies for fine-scale urban emissions tracking and community-centered air quality improvements.