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Creating Locally-Resolved Mobile-Source Emissions Inputs for Air Quality Modeling in Support of an Exposure Study in Detroit, Michigan, USA

Bibliographic Data

ID15474283
AuthorsMichelle Snyder (0000-0003-4225-1556, University of North Carolina at Chapel Hill, corresponding author), Saravanan Arunachalam (0000-0002-6836-6944, University of North Carolina at Chapel Hill), Vlad Isakov (0000-0002-7039-3925, Environmental Protection Agency), Kevin Talgo (University of North Carolina at Chapel Hill), Brian Naess (0000-0002-1314-9870, University of North Carolina at Chapel Hill), Alejandro Valencia (0000-0002-9004-9814, University of North Carolina at Chapel Hill), Mohammad Omary (University of North Carolina at Chapel Hill), Neil Davis (0000-0001-5886-247X, University of North Carolina at Chapel Hill), Rich Cook (Environmental Protection Agency), Adel Hanna (0000-0002-7732-7568, University of North Carolina at Chapel Hill)
Year2014
Volume11
Issue12
Pages12739-12766
Publication date2014-12-09
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph111212739
PMID25501000
PMCIDPMC4276644
OpenAlexW1977544346
LanguageEN
Citations received6
References cited4

This work describes a methodology for modeling the impact of traffic-generated air pollutants in an urban area. This methodology presented here utilizes road network geometry, traffic volume, temporal allocation factors, fleet mixes, and emission factors to provide critical modeling inputs. These inputs, assembled from a variety of sources, are combined with meteorological inputs to generate link-based emissions for use in dispersion modeling to estimate pollutant concentration levels due to traffic. A case study implementing this methodology for a large health study is presented, including a sensitivity analysis of the modeling results reinforcing the importance of model inputs and identify those having greater relative impact, such as fleet mix. In addition, an example use of local measurements of fleet activity to supplement model inputs is described, and its impacts to the model outputs are discussed. We conclude that with detailed model inputs supported by local traffic measurements and meteorology, it is possible to capture the spatial and temporal patterns needed to accurately estimate exposure from traffic-related pollutants

Air pollutants · Air pollution · Air quality index · Atmospheric dispersion modeling · Geography · Meteorology · Pollutant · Sensitivity (control systems · Transport engineering · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · Engineering · Environmental Science · Vehicle emissions and performance

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Citations per year0.5
Citation span2014 - 2023 (10)
Highly citedNo

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