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Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

机译:评估绿色基础设施影响的微观和宏观空气污染分散模型的考虑因素

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Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments. (C) 2019 Elsevier B.V. All rights reserved.
机译:城市地区的绿色基础设施(GI)可作为被动控制系统采用以减少空气污染物浓度。然而,目前的分散模型提供有限的建模选项,以评估其对环境污染物浓度的影响。本综述的范围围绕以下问题:如何在易于可用的分散模型中考虑GI,以便评估其对污染物浓度和健康风险评估的影响吗?我们在沉积方案所需的颗粒物质和气态污染物的沉积速度和数据集的参数上进行了发表的文献。我们评估了两个空间尺度的不同空气污染分散模型的限制 - 微观(即10-500米)和宏观(即5-100公里) - 考虑到GI对空气污染物浓度和暴露改变的影响。我们得出结论,表示GI的沉积方案详细介绍了复杂的资源密集型,涉及丰富的输入数据量。在分散模型中,在分散模型中的适当处理GI特征(例如空气动力学效果,空气污染物和表面粗糙度)是在不同空间尺度下在GI存在下的空气污染物浓度模拟的机理所必需的。 GI对空气污染物浓度和健康风险评估(例如,死亡率,发病率)的影响部分探讨。具有本映射模型的I树工具已被用于估计通过在Macroscale在Gi Canopies上沉积除去的每年平均空气污染物的健康结果。然而,通过在短期暴露中的GI相关变化导致的空气污染健康风险评估的研究通过污染物浓度在微尺寸和增强的大气污染物稀释通过宏观上的表面粗糙度增加,以及沉积,是罕见的。适用于耦合的分散沉积模型和对现实世界情景评估的所有物理和化学方法对健康风险评估至关重要。 (c)2019 Elsevier B.v.保留所有权利。

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