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Reduction of particulate matter concentrations by local removal in a building courtyard: Case study for the Delhi American Embassy School

机译:通过在建筑庭院中局部去除来减少颗粒物质浓度:德里美国大使馆学校的案例研究

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Exposure to particulate matter (PM) is strongly linked to human morbidity and mortality, where higher exposure entails higher all-cause daily mortality and increased long-term risk of cardiopulmonary mortality. The objective of this study is to demonstrate how and to what extent the local removal of PM2.5 can lead to reduced exposure for the children and teachers in the naturally ventilated courtyard of the American Embassy School (AES) high school building in Delhi. The study is performed by computational fluid dynamics (CFD) with the 3D steady Reynolds-averaged Navier-Stokes (RANS) equations in combination with the realizable k-epsilon turbulence model on a very high resolution grid. First, CFD validation is performed using wind-tunnel experiments of the flow pattern in and above a generic single street canyon. Next, the case study is conducted where four commercially available electrostatic precipitation (ESP) units are installed at different positions inside the courtyard and the resulting performance is evaluated. PM2.5 dispersion is modeled with an Eulerian advection-diffusion equation. It is shown that the best ESP positions yield overall volume-averaged PM2.5 concentration reductions up to 34.1% in the courtyard's corridors, demonstrating the proposed mitigation strategy to be effective. Perspectives for further reduction of the PM concentrations and the related reduction of health risks are discussed. (C) 2019 Elsevier B.V. All rights reserved.
机译:暴露于颗粒物质(PM)与人的发病率和死亡率强烈地联系起来,其中较高的暴露会导致较高的每日死亡率,并增加心肺死亡率的长期风险。本研究的目的是展示如何以及在多大程度上以及在德里美国大使馆学校(AES)高中大楼的自然通风院内的儿童和教师的暴露会导致曝光程度。该研究是由计算流体动力学(CFD)与3D稳定雷诺平均的Navier-Stokes(RAN)方程组合在非常高分辨率网格上结合可实现的K-EPSILON湍流模型。首先,使用通用单街峡谷的流动模式的风隧道实验来执行CFD验证。接下来,进行案例研究,其中四种市售的静电降水(ESP)单元安装在庭院内的不同位置,并评估所得到的性能。 PM2.5分散用欧拉普通扩散方程建模。结果表明,最佳的ESP位置在庭院走廊中产生总体体积平均PM2.5浓度降低高达34.1%,展示了拟议的缓解策略是有效的。讨论了进一步减少PM浓度和相关卫生风险的相关性的观点。 (c)2019 Elsevier B.v.保留所有权利。

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