Odor dispersion from road emissions were investigated using CFD (Computational Fluid Dynamics). The Shear Stress Transport k-ω model in FLUENT CFD code was used to simulate odor dispersion around the road. The two road configurations used in the study were at-grade and fill road. Experimental data from the wind tunnel obtained in a previous study was used to validate the numerical result of the road dispersion. Five validation metrics are used to obtain an overall and quantitative evaluation of the performance of Shear Stress Transport k-ω models: the fractional bias (FB), the geometric mean bias (MG), the normalized mean square error (NMSE), the geometric variance (VG), and the fraction of predictions within a factor of two of observations (FAC2). The results of the vertical concentration profile for neutral atmospheric show reasonable performance for all five metrics. Six atmospheric stability conditions were used to evaluate the stability effect of road emission dispersion. It was found that the stability category D case of at-grade decreased the non-dimensional surface odor concentration smaller 0.78~0.93 times than those of stability category A case, and that F case decreased 0.39~0.56 times smaller than those of stability category A case. It was also found that stability category D case of filled road decreased 0.84~0.92 times the non-dimensional surface odor concentration of category A case and stability category F case decreased 0.45~0.58 times compared with stability category A case.

Air pollution dispersion from rooftop emissions around hexahedron buildings was investigated using computational fluid dynamics (referred to hereafter as CFD). The Shear Stress Transport (referred to hereafter as SST) k-ω model in FLUENT CFD code was used to simulate the flow and pollution dispersion around the hexahedron buildings. The two buildings used in the study had the dimensions of H: L: W (where H = height, L = length, and W = width) with the ratios of 1:1:1 and 1:1:2. Experimental data from the wind tunnel obtained by a previous study was used to validate the numerical result of the hexahedron building. Five validation metrics are used to obtain an overall and quantitative evaluation of the performance of SST k-ω models: the fractional bias (FB), the geometric mean bias (MG), the normalized mean square errors (NMSE), the geometric variance (VG), and the factor of 2 of the observations (FAC2). The results of vertical concentration profile and longitudinal surface concentration of the 1:1:2 building illustrate the reasonable performance for all five metrics. However, the lateral concentration profile at X = 3H (where X is the distance from the source) shows poor performance for all of the metrics with the exception of NMSE, and the lateral concentration profile at X = 10H shows poor performance for FB and MG.

The dispersion of air pollution in complex situations such as the cases of the filled road is a significant problem for the public safety and living quality. Application of computational fluid dynamics (CFD) helps to build the model calculation in order to estimate the dispersion of air pollutants. In order to assess its accuracy, this study used the Realizable k-ε model, the RNG k-ε model, and the Shear-Stress Transport k-ω turbulence model in FLUENT CFD code. The results were compared with the wind tunnel experiments. The Realizable k-ε turbulence model provided the best prediction for the surface concentration and concentration profiles of selected downwind positions of the filled road. It was found that a noise barrier, which positioned on the filled road, increases the vertical air pollution impact distance larger 1.75~1.92 times and decrease the horizontal impact distance lower 0.46~0.54 times than those of no barrier case. It was also found that two or three noise barriers increase 1.63~1.79 times the vertical air pollution impact distance. It contributes the decrease of horizontal air pollution impact distance 0.49~0.63 times compare with no barrier case.

Noise barriers along the road do not only block the traffic noise but also prevent traversing the car exhausts. These barriers may affect air pollution dispersion, leading to increase vertical mixing due to the upwind deflection of air flow caused by the noise barriers. In this study we investigated the air pollution dispersion around multi-noise barriers using commercial software FLUENT. Investigated cases were 8 cases which had from zero to three noise barriers and two emission sources. Simulated results show noise barriers increase the vertical air pollution impact distance larger 1.7~2.1 times than that of no barrier case. It was also found that noise barriers decrease the horizontal air pollution impact distance lower 0.6~0.8 times than that of no barrier case.