Hashad Khaled, Steffens Jonathan T, Baldauf Richard W, Heist David K, Deshmukh Parikshit, Zhang K Max
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA.
Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA.
Env Sci Adv. 2024 Jan 18;3:411-421.
Communities located in near-road environments experience elevated levels of traffic-related air pollution. Near-road air pollution is a major public health concern, and an environmental justice issue. Roadside green infrastructure such as trees, hedges, and bushes may help reduce pollution levels through enhanced deposition and mixing. Gaussian-based dispersion models are widely used by policymakers to evaluate mitigation strategies and develop regulatory actions. However, vegetation barriers are not included in those models, hindering air quality improvement at the community level. The main modeling challenge is the complexity of the deposition and mixing process within and downwind of the vegetation barrier. We propose a novel multi-regime Gaussian-based model that describes the parameters of the standard Gaussian equations in each regime to account for the physical mechanisms by which the vegetation barrier deposits and disperses pollutants. The four regimes include vegetation, a downwind wake, a transition, and a recovery zone. For each regime, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition, a major factor in pollutant reduction by vegetation barriers. We parameterized the multi-regime model using data generated from a fields-validated computational fluid dynamics (CFD) model, covering a wide range of vegetation properties and meteorological conditions. The proposed multi-regime Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing dispersion and deposition. The multi-regime model's normalized mean error (NME) ranged between 0.18 and 0.3, the fractional bias (FB) ranged between -0.12 and 0.09, and value ranged from 0.47 to 0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable ranges for air quality dispersion modeling. Even though the multi-regime model is parameterized for coniferous trees, our sensitivity study indicates that it can provide useful predictions for hedges/bushes vegetative barriers as well.
位于近路环境中的社区面临与交通相关的空气污染水平升高的问题。近路空气污染是一个重大的公共卫生问题，也是一个环境正义问题。路边的绿色基础设施，如树木、树篱和灌木丛，可能有助于通过增强沉降和混合来降低污染水平。基于高斯的扩散模型被政策制定者广泛用于评估缓解策略和制定监管行动。然而，植被屏障并未包含在这些模型中，这阻碍了社区层面的空气质量改善。主要的建模挑战在于植被屏障内部及其下风向的沉降和混合过程的复杂性。我们提出了一种新颖的基于多区域高斯的模型，该模型描述了每个区域中标准高斯方程的参数，以解释植被屏障沉降和扩散污染物的物理机制。这四个区域包括植被区、下风向尾流区、过渡区和恢复区。对于每个区域，我们将相关的高斯烟羽方程参数拟合为植被特性和当地风速的函数。此外，该模型还考虑了颗粒物沉降，这是植被屏障减少污染物的一个主要因素。我们使用经过现场验证的计算流体动力学（CFD）模型生成的数据对多区域模型进行了参数化，这些数据涵盖了广泛的植被特性和气象条件。我们对所提出的基于多区域高斯的模型在9种粒径和一种示踪气体上进行了评估，以评估其捕捉扩散和沉降的能力。多区域模型的归一化平均误差（NME）在0.18至0.3之间，分数偏差（FB）在 -0.12至0.09之间，并且在所有粒径和示踪气体的地面浓度方面， 值在0.47至0.75之间，这些都在空气质量扩散建模的可接受范围内。尽管多区域模型是针对针叶树进行参数化的，但我们的敏感性研究表明，它也可以为树篱/灌木丛植被屏障提供有用的预测。
