1321 Vehicle-Induced Turbulence Characterization for Air Quality Modeling

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Vlad Isakov, U.S. EPA, Research Triangle Park, NC; and R. Baldauf, K. Hashad, B. Yang, and M. Zhang

Traffic-related air pollution is associated with a range of health issues including respiratory and cardiovascular problems, birth defects, and cancer. The adverse health effects from exposures to traffic emissions has elevated the call for methods to reduce the concentrations of these pollutants at the local level. Models have been used to provide spatially-resolved air quality characterization within communities and to help identify potential strategies to mitigate exposures to traffic-related air pollution. Vehicle induced turbulence (VIT) has a major impact on the initial dispersion of pollutants. The VIT contribution to the overall on-road turbulence could be as high as 95% depending on traffic and meteorological conditions. Thus, accurate characterization of VIT is very important for estimating pollutant concentration in the near-road environment. To address this need, U.S. EPA conducted a field study on a major highway in Las Vegas. The experimental data include extensive, collocated measurements of traffic, turbulence and air pollutant concentrations. The study provided data to characterize VIT in the near-road environment and to evaluate VIT modeling approaches. There are various modeling techniques available to simulate VIT in Computational fluid dynamics (CFD) models. However, current methods for high-fidelity VIT simulations using CFD, which require resolving physical vehicle shapes, are often computationally expensive or prohibitive. In this study, we explore three alternative CFD approaches to model VIT: the turbulent kinetic energy (TKE) method, the force method, and the moving force method. The simulation results are evaluated against the experimental data collected in the Las Vegas field study. All three methods can model complex traffic scenarios and are computationally feasible.
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