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Open AccessArticle

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

1
Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711, USA
2
Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
3
Office of Transportation and Air Quality, U.S. EPA, Ann Arbor, MI 48105, USA
4
Jacobs Technology, Research Triangle Park, NC 27711, USA
5
U.S. EPA, Region 7, Kansas City, KS 66219, USA
6
Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(10), 610; https://doi.org/10.3390/atmos10100610
Received: 27 August 2019 / Revised: 25 September 2019 / Accepted: 8 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants in Urban Environments)
Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates. View Full-Text
Keywords: near-source; dispersion modeling; rail yard; air pollution near-source; dispersion modeling; rail yard; air pollution
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Isakov, V.; Arunachalam, S.; Baldauf, R.; Breen, M.; Deshmukh, P.; Hawkins, A.; Kimbrough, S.; Krabbe, S.; Naess, B.; Serre, M.; Valencia, A. Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization. Atmosphere 2019, 10, 610.

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