Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion.

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R

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Copyright © 2021. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.