Hyperlocal Sensing and Inverse Modeling Reveal Community Impacts of Urban Air Pollutant Emissions
- 1University of California, Berkeley
- 2Environmental Defense Fund
- 3University of Washington
Abstract
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 m × 150 m) 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 / km 2 , compared with 6.7 sites/km 2 in the full campaign), paired with time-averaged mobile maps, supporting applicability in data-limited urban settings.
Versions
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BibTeX
@misc{manchanda2026hyperlocal,
title = {{Hyperlocal Sensing and Inverse Modeling Reveal Community Impacts of Urban Air Pollutant Emissions}},
author = {Chirag Manchanda and Ronald Cohen and Ramon Alvarez and Tammy Thompson and Maria Harris and Alexander Turner and Julian Marshall and Robert Harley and Joshua Apte},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv-2025-zt4zh/v2},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-zt4zh/v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Hyperlocal Sensing and Inverse Modeling Reveal Community Impacts of Urban Air Pollutant Emissions
AU - Chirag Manchanda
AU - Ronald Cohen
AU - Ramon Alvarez
AU - Tammy Thompson
AU - Maria Harris
AU - Alexander Turner
AU - Julian Marshall
AU - Robert Harley
AU - Joshua Apte
PY - 2026
DA - 2026/09/10
PB - ChemRxiv
DO - 10.26434/chemrxiv-2025-zt4zh/v2
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-zt4zh/v2
AB - 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 m × 150 m) 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 / km 2 , compared with 6.7 sites/km 2 in the full campaign), paired with time-averaged mobile maps, supporting applicability in data-limited urban settings.
ER -