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Resolving the effect of roadside vegetation barriers as a near-road air pollution mitigation strategy
Citation:
Hashad, K., J. Steffens, R. Baldauf, D. Heist, P. Deshmukh, AND M. Zhang. Resolving the effect of roadside vegetation barriers as a near-road air pollution mitigation strategy. Environmental Science: Advances. Royal Society of Chemistry, London, Uk, 3:411-421, (2024). https://doi.org/10.1039/D3VA00220A
Impact/Purpose:
Exposures to traffic air pollution near roads has been shown to increase public health risks. In recent years, roadside vegetation has been shown to be a potential method of mitigating these exposures. This paper describes model algorithms developed to characterize air pollution dispersion and reduction by roadside vegetation. The results of this work can be used by environmental and urban planners to properly design and implement roadside vegetation projects
Description:
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 R2 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.
URLs/Downloads:
DOI: Resolving the effect of roadside vegetation barriers as a near-road air pollution mitigation strategy