Hyperlocal sensing and inverse modeling reveal community impacts of urban air pollutant emissions
7 affiliations
- 1Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.
- 2Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA 94720, USA.
- 3College of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA.
- 4Environmental Defense Fund, Washington, DC 20004, USA.
- 5Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA.
- 6Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
- 7School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA.
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 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.
Versions
Related
- Habit-locked-in carbon-air-pollutant emissions dominate China's household footprints
- Using GIS, remote-sensing and linear modeling to prioritize spatially explicit action against erosion and sediment transport within Lake Victoria watershed in Kenya
- Disentangling the Drivers of Fish Community Variation in a Highly Urbanized Estuary Based on Hierarchical Modeling of Species Communities ( HMSC )
- Environmentally Relevant Concentrations of Nanomaterials Drive Photochemical Production of Reactive Species and Pollutant Degradation in Surface Waters
- Enhancing pollutant removal from contaminated soils using yield stress fluids as selective blocking agents
BibTeX
@article{manchanda2026hyperlocal,
title = {{Hyperlocal sensing and inverse modeling reveal community impacts of urban air pollutant emissions}},
author = {Chirag Manchanda and Ronald C. Cohen and Ramon A. Alvarez and Tammy M. Thompson and Maria H. Harris and Alexander J. Turner and Julian D. Marshall and Robert A. Harley and Joshua S. Apte},
journal = {Science Advances},
year = {2026},
month = oct,
doi = {10.1126/sciadv.aeb7647},
url = {https://www.science.org/doi/abs/10.1126/sciadv.aeb7647},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Hyperlocal sensing and inverse modeling reveal community impacts of urban air pollutant emissions
AU - Chirag Manchanda
AU - Ronald C. Cohen
AU - Ramon A. Alvarez
AU - Tammy M. Thompson
AU - Maria H. Harris
AU - Alexander J. Turner
AU - Julian D. Marshall
AU - Robert A. Harley
AU - Joshua S. Apte
PY - 2026
DA - 2026/10/28
JO - Science Advances
DO - 10.1126/sciadv.aeb7647
UR - https://www.science.org/doi/abs/10.1126/sciadv.aeb7647
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 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.
SN - 2375-2548
ER -