Impact of bioretention cells in cities with a cold climate: modeling snow management based on a case study

IF 2.3 Q3 ENVIRONMENTAL SCIENCES Blue-Green Systems Pub Date : 2023-05-02 DOI:10.2166/bgs.2023.032
Garance Gougeon, O. Bouattour, Emma Formankova, Julien St-Laurent, Samuel Doucet, S. Dorner, Sandrine Lacroix, M. Kuller, D. Dagenais, F. Bichai
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引用次数: 1

Abstract

The performance of blue-green infrastructure (BGI) has been well documented in temperate and subtropical climates, but evidence supporting its application in cold climates, especially during snowmelt, is still scarce. To address this gap, the present study proposes a modeling method for simulating the performance of bioretention cells during snowmelt according to different spatial implementation scenarios. We used the Storm Water Management Model (SWMM) of a catchment in a medium-sized city in Quebec, Canada as a case study. Pollutants commonly found in the snow (TSS, Cr, Pb, Zn, Cl–) were included in the model using event mean concentrations (EMCs) documented in the literature. Bioretention cells performed best on industrial road sites for the entire snowmelt period. Bioretention cell performance was affected by snow management procedures applied to the roads in residential areas. Not modeling the snow cover build-up and meltdown in the simulation led to higher runoff and bioretention cell performance. Modeling results facilitated the identification of bioretention cell sites that efficiently controlled runoff during snowmelt. Such information is needed to support decision planning for BGI in cities with cold climate.
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寒冷气候城市中生物滞留细胞的影响:基于案例研究的雪管理建模
蓝绿色基础设施(BGI)在温带和亚热带气候中的性能已得到充分证明,但支持其在寒冷气候中应用的证据仍然很少,尤其是在融雪期间。为了解决这一差距,本研究提出了一种建模方法,用于根据不同的空间实施场景模拟融雪过程中生物滞留细胞的性能。我们使用加拿大魁北克省一个中等城市集水区的雨水管理模型(SWMM)作为案例研究。使用文献中记录的事件平均浓度(EMCs),将雪中常见的污染物(TSS、Cr、Pb、Zn、Cl–)包括在模型中。在整个融雪期,生物滞留细胞在工业道路上的表现最好。生物滞留细胞的性能受到应用于住宅区道路的雪管理程序的影响。模拟中没有对积雪的堆积和融化进行建模,导致了更高的径流和生物滞留池性能。建模结果有助于识别在融雪期间有效控制径流的生物滞留细胞位点。需要这些信息来支持华大基因在寒冷气候城市的决策规划。
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Blue-Green Systems
Blue-Green Systems Multiple-
CiteScore
8.70
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0.00%
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