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利用山地农村混合人工湿地处理生活污水。

Phytoremediation of domestic wastewater using a hybrid constructed wetland in mountainous rural area.

机构信息

a National Center for Research and Studies on Water and Energy (CNEREE), Cadi Ayyad University , Marrakech , Morocco.

b Laboratory of Hydrobiology , Ecotoxicology and Sanitation (LHEA, URAC 33), Faculty of Sciences Semlalia , Marrakech , Morocco.

出版信息

Int J Phytoremediation. 2018 Jan 2;20(1):75-87. doi: 10.1080/15226514.2017.1337067.

Abstract

The purpose of this study is to evaluate the efficiency of hybrid constructed wetlands (HCWs) in a rural mountainous area. The experiment was set up in small rural community named Tidili within the region of Marrakech, Morocco. The wastewater treatment plant was composed of three vertical flow constructed wetlands (VFCWs) working in parallel, followed by two parallel horizontal-subsurface flow constructed wetlands (HFCWs), with hydraulic loading rates of 0.5 and 0.75 m/m.d, respectively. The two units were planted with Phragmites australis at a density of 4 plants/m. Wastewater samples were collected at the inlet of the storage tank and at the outlet of the whole system (VFCWs, HFCWs) stages. The main removal percentages of total suspended solids (TSS), biochemical oxygen demand measured in a 5-day test (BOD), chemical oxygen demand (COD), total nitrogen, and total phosphorus were respectively 95%, 93%, 91%, 67%, and 62%. The system showed a very high capacity to remove total coliforms, fecal coliforms, and fecal streptococci (4.46, 4.31, and 4.10 Log units, respectively). Artificial neural networks (ANNs) were used to model the quality parameters (TSS, BOD, COD) and total coliforms and fecal streptococci. Based on the obtained results, the ANN model could be considered as an efficient tool to predict the studied phytoremediation performances using HCWs.

摘要

本研究旨在评估混合构造湿地(HCWs)在农村山区的效率。该实验在摩洛哥马拉喀什地区的一个名为 Tidili 的小村庄社区内进行。污水处理厂由三个垂直流构造湿地(VFCWs)并联组成,随后是两个平行的水平潜流构造湿地(HFCWs),水力负荷率分别为 0.5 和 0.75 m/m.d。两个单元均种植了密度为 4 株/m 的芦苇。在储水箱的入口和整个系统(VFCWs、HFCWs)的出口处采集废水样本。总悬浮固体(TSS)、五日生化需氧量(BOD)、化学需氧量(COD)、总氮和总磷的主要去除率分别为 95%、93%、91%、67%和 62%。该系统对总大肠菌群、粪大肠菌群和粪链球菌的去除能力非常高(分别为 4.46、4.31 和 4.10 Log 单位)。人工神经网络(ANNs)用于对质量参数(TSS、BOD、COD)和总大肠菌群及粪链球菌进行建模。根据获得的结果,ANN 模型可以被认为是使用 HCWs 预测研究植物修复性能的有效工具。

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