Abstract
Research assessing on-road emission flow patterns from motor vehicles is essential in monitoring urban air quality, since it helps to mitigate atmospheric pollution levels. To reveal the influence of vehicle induced turbulence (VIT) caused by both front- and rear-vehicles on traffic exhaust and verify the applicability of the simplified line source emission model, a Computational Fluid Dynamics (CFD) numerical simulation was used to investigate the micro-scale vehicle pollutant flow patterns. The simulation results were examined through sensitivity analysis and compared with the field measured carbon monoxide (CO) concentration. Conclusions indicate that the vehicle induced turbulence caused by the airflow blocking effect of both front- and rear-vehicles impedes the diffusion of front-vehicle traffic exhaust, compared with that of the rear vehicle. The front-vehicle isosurface with the CO mass fraction of 0.0012 extended to 6.0 m behind the vehicle, while that of the rear-vehicle extends as far as 12.7 m. But for the entire motorcade, VIT is beneficial to the diffusion of pollutants in car-following situations. Meanwhile, within the range of 9 m behind the rear of the lagging vehicle lies a vehicle induced turbulence zone. Furthermore, the influence of vehicle induced turbulence on traffic exhaust flow pattern is obvious within a range of 1 m on both sides of the vehicle body, where the concentration gradient of on-road emission is larger and contains severe mechanical turbulence. As a result, in the large concentration gradient area of the pollutant flow field, which accounts for 99.85% of the total concentration gradient, using the line source models to represent the on-road emission might introduce considerable errors due to neglecting the influence of vehicle induced turbulence. Findings of this study may shed lights on predicting emission concentrations in multiple locations by selecting appropriate on-road emission source models.
Highlights
The rapid growth vehicle ownership in China leads to increasingly serious traffic emission problem, which has become one of the main contributors of air pollution since the 1990s [1,2]
Different from most previous correlated studies, to make the numerical simulation more accurate, this paper used dynamic grid technology to solve the fluid boundary motion problem in a transient simulation, which can better simulate the shape of flow field changing with time due to boundary motion
A complex fleet situation was decomposed into the superposition of front- and rear-car following models, and the influence of vehicle induced turbulence (VIT) on one group of car-following models was studied
Summary
The rapid growth vehicle ownership in China leads to increasingly serious traffic emission problem, which has become one of the main contributors of air pollution since the 1990s [1,2]. Dense traffic and population induce heavy concentrated on-road emissions and a high pollution exposure level, imposing a threat to human health [3]. Vehicle pollutant concentration prediction is crucial in improving the air quality management level. Operational models and CFD models are two major urban pollutant dispersion assessment tools [4]. The operational models use the mathematical methods from empirical monitoring and experiments, which have been mostly applied to the prediction of emission concentrations in city scales under different circumstances, including the CALINE model, OSPM model, and so on [5,6]. Computational Fluid Dynamics (CFD) models are generally utilized in small-scale pollutant dispersion scenarios by analyzing subject-concerned fluid motion and heat transfer using computer-based numerical methods [7]. Assessing pollutant concentrations under heavy traffic in urban areas is still a challenging question [8]. Microscale simulations with CFD models have become a useful method for investigating pollutant diffusion principles [9]
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