Development of Advanced Modeling Tools for Hotpot Analysis of Transportation Emissions
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Development of Advanced Modeling Tools for Hotpot Analysis of Transportation Emissions

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    Final Report 12/31/07- 06/30/09
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    Hot-spot analysis, also known as project-level analysis, assesses impacts of transportation emissions on local air pollution of carbon monoxide (CO), air toxics and particulate matter (PM). It is required for regional transportation plans (RTP), transportation improvement programs (TIP) and transportation project development/modification by transportation conformity rules and NEPA process. Such transportation conformity studies are particularly important in non-attainment areas and locales with concentrated and heavily traveled transportation infrastructures (e.g., major transportation corridors, border crossings, congested intersections, etc.). Gaussian plume dispersion models of line sources such as CALINE4 have been widely used in quantitative hotspot analysis of CO from transportation sources and have proven successful in modeling inert gaseous pollutants such as CO with sound scientific basis satisfying empirical accuracy. However, the current hotspot analysis modeling tools used by the transportation management community are not capable of simulating the air quality impact of complex roadway network in typical urban environments and not treating explicitly the various turbulent mixing processes near roadways. Those limitations hinder the accurate assessment of environmental and health impacts of transportation emissions. In this report, we present the development of two advanced modeling tools for hotspot analysis of transportation emissions. One is a multi-link dispersion model (MLDM) based on USEPA regulatory dispersion model AERMOD. And the other is a computation fluid dynamics (CFD) model that incorporates vehicle-induced turbulence (VIT) and road-induced turbulence (RIT), named CFD-VIT-RIT. We applied the CFD-VIT-RIT model in simulating the spatial gradients of carbon monoxide near two major highways with different traffic mix and roadway configurations. The modeling results were compared to the field measurements and those from CALINE4 model, which does not account for VIT and RIT. We demonstrated that the incorporation of RIT considerably improved the modeling predictions, especially on the vertical gradients of carbon monoxide. Our study implies that roadway designs can significant influence the near-road air pollution. Thus we recommend that mitigating near-road air pollution through roadway designs be considered in the air quality and transportation management. In addition, thanks to their rigorous representation of turbulent mixing mechanisms, CFD models can become valuable tools in the roadway designs process
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