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

Alejandro Valencia, Saravanan Arunachalam, Vlad Isakov, Brian Naess, Marc Serre

Abstract read

In one paragraph

Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Alejandro ValenciaDepartment of Environmental Sciences and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Saravanan ArunachalamInstitute for the Environment, The University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA. Electronic address: sarav@email.unc.edu.

Vlad IsakovOffice of Research and Development, U.S. EPA, Research Triangle Park, NC 27711, USA.

Brian NaessInstitute for the Environment, The University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA.

Marc SerreDepartment of Environmental Sciences and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026

$36.3M

Intramural EPA EPA999999NIEHS NIH HHS P30 ES010126

6 · The paper itself

Abstract

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

Indexed as

Air PollutantsAir PollutionBayes TheoremCarbonEnvironmental MonitoringParticulate MatterVehicle EmissionsAir PollutantsCarbonParticulate MatterVehicle EmissionsBlack CarbonCommunity-scale air quality assessmentFine-scale dispersion modelingGeospatial data fusionInverse modelingRailyard emissionsWarehouse emissions

Identifiers

PMID34171801

DOI10.1016/j.scitotenv.2021.148378

PMCPMC8457356

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.