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雨水绿色基础设施模型的流量和水质预测性能分析。

Performance analysis of a stormwater green infrastructure model for flow and water quality predictions.

机构信息

Department of Civil Engineering, Monash University, 3800, Victoria, Australia; CRC for Water Sensitive Cities, Monash University, Australia.

Department of Civil & Environmental Engineering, Faculty of Engineering, National University of Singapore, Block E1A, #07-01, 1 Engineering Drive 2, Singapore, 117576, Singapore.

出版信息

J Environ Manage. 2022 Aug 15;316:115259. doi: 10.1016/j.jenvman.2022.115259. Epub 2022 May 23.

Abstract

Nature-based solutions or Green infrastructure (GI) used for managing stormwater pollution are growing in popularity across the globe. Stormwater GI models are important tools to inform the planning of these systems (type, design, size), in the most efficient and cost-effective manner. MUSIC, an example of such a tool, uses regression and first order decay models. Studies validating MUSIC model performance are, however, scarce, hindering future model development and transferability of the model for systems operating under different design and climatic conditions. To close this gap, this paper evaluates MUSIC for a field scale bioretention system, stormwater wetland and vegetated swale operating under Singapore tropical climate. The treatment modules were able to simulate outflows and effluent pollutant concentrations reasonably well for cumulative event volumes (mostly within ±25%) and cumulative TP and TN loads (within ±30%). Outflow TSS loads were significantly under-estimated as a result of greater variability in measured TSS concentrations across events. The findings indicate that simple empirical models such as MUSIC can be transferred to different regions provided that management decisions are based on long-term modelling efforts. The modules generally simulated the outflow hydrographs and pollutographs of the different inflow and drying/wetting conditions relatively poorly.

摘要

基于自然的解决方案或绿色基础设施(GI)用于管理雨水污染在全球范围内越来越受欢迎。雨水 GI 模型是一种重要的工具,可以以最有效和最具成本效益的方式为这些系统的规划提供信息(类型、设计、规模)。MUSIC 就是这样一种工具的示例,它使用回归和一阶衰减模型。然而,验证 MUSIC 模型性能的研究很少,这阻碍了未来模型的开发以及在不同设计和气候条件下运行的模型的可转移性。为了弥补这一差距,本文评估了 MUSIC 在新加坡热带气候下运行的场地规模生物滞留系统、雨水湿地和植被洼地的应用。对于累积事件体积(大部分在±25%以内)和累积 TP 和 TN 负荷(在±30%以内),处理模块能够很好地模拟出流和出水污染物浓度。由于在不同事件中测量的 TSS 浓度变化较大,因此 TSS 出流负荷被显著低估。研究结果表明,只要管理决策基于长期建模工作,简单的经验模型(如 MUSIC)可以转移到不同的地区。这些模块通常相对较差地模拟不同进水和干湿条件下的出流水文图和污染物图。

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