Department of Civil, Architecture and Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States.
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, P. R. China.
Environ Sci Technol. 2020 Sep 1;54(17):10483-10492. doi: 10.1021/acs.est.0c01587. Epub 2020 Aug 12.
A parameterization of initial vertical dispersion coefficient (σ) was developed for incorporation into California line source dispersion model, version 4 (CALINE4) and AMS/EPA regulatory model (AERMOD) to better predict pollutant concentrations near roadways. The momentum wake theory of moving vehicles indicates that both vehicle-induced turbulence (VIT) and dispersion occur in the vehicle wake. Based on a literature review, it is postulated that σ near roadways can be estimated using a "wake area model" concept of effective wake area defined as the vehicle height times the wake length, vehicle density, and vehicle type. A total of 523 5-min near-roadway simultaneous measurements (2016-2018) of pollutant concentrations and meteorological and traffic information were used to evaluate the model. Two roadways with distinct fleet composition and simple road configurations were selected for monitoring. The near-roadway σ ranged from 1 to 4 m for light-duty vehicles (LDVs) and from 3 to 7 m for fleet-mix (LDVs and heavy-duty vehicles (HDVs)). The results demonstrate that the dispersion contribution from one HDV was 31 times larger than that from one LDV. Calculated pollutant dispersion using the wake area model compared favorably with measurements ( = 0.91, slope = 1.07). These results indicate that σ varies with vehicle density and HDVs. Pollutant dispersion related to the vehicle wakes can be used to correctly parameterize dispersion models and improve prediction of pollutant concentrations near roadways.
为了更好地预测道路附近的污染物浓度，我们针对加利福尼亚线源扩散模型（CALINE4）和美国环保署/环境保护局（AERMOD），开发了一种初始垂直扩散系数（σ）的参数化方法。移动车辆的动量尾流理论表明，车辆诱导的湍流（VIT）和扩散都发生在车辆尾流中。基于文献综述，我们假设可以使用“尾流区模型”的概念来估算道路附近的σ，该概念将有效尾流区定义为车辆高度乘以尾流长度、车辆密度和车辆类型。总共使用了 523 个 5 分钟的道路附近同步测量（2016-2018 年）的污染物浓度以及气象和交通信息来评估模型。选择了两个具有不同车队组成和简单道路配置的道路进行监测。轻载车辆（LDV）的道路附近σ范围为 1 到 4 米，车队混合（LDV 和重载车辆（HDV））的σ范围为 3 到 7 米。结果表明，一辆 HDV 的扩散贡献比一辆 LDV 大 31 倍。使用尾流区模型计算的污染物扩散与测量结果相当吻合（=0.91，斜率=1.07）。这些结果表明，σ随车辆密度和 HDV 而变化。与车辆尾流相关的污染物扩散可以用于正确地参数化扩散模型，并改善道路附近污染物浓度的预测。
