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生物滞留系统是一种以渗透为基础的城市雨洪管理措施,主要由过滤介质、植物和微生物等介质构成[1],以自然方式削减道路、停车场和屋面等不透水区域产生的雨水径流量及其污染物[2-3]。种植于生物滞留系统过滤介质中的植物可通过直接吸收或间接影响根际微群落的方式去除多种污染物[4]。不同植物对污染物的去除效能具有显著差异性,特别是氮素的去除[5-7],且生物滞留系统的整体效能取决于植物种类。相关研究表明,生物滞留系统中栽种植物能提高系统对污染物的净化能力[8],如陈韬等[9]研究发现丹麦草、萱草、狼尾草等3种植物能较好的吸收无机氮,且对NH4+-N的净化吸收能力优于NO3--N。但植物的选择不仅会影响系统的水力性能[10-11],还会影响系统对有机物和营养物的去除[12]。同时,植物根系越发达越能促进系统对氮磷营养物的去除[13]。因此,在生物滞留系统植物筛选中通常选用根系发达的功能植物。
植物筛选是实现生物滞留系统预期功能及长效运行的关键,植物筛选不当甚至还会产生氮素淋失现象[14-15]。采用传统的单一目标污染物去除效能的筛选方法,难以确保生物滞留系统的整体控污目标。虽然目前国内外研究筛选出大量能高效去除多种污染物的植物,但其多数是基于单一目标污染物的筛选[9, 16-17]。同时,对于地形起伏较大的山地城市而言,城市道路形成的初期径流污染程度较平原城市更严重,且污染物种类多,势必要求生物滞留系统具有多目标污染物的控制能力,而针对山地城市生物滞留系统的研究主要集中在设施的开发建设、优化设计等[18-19],缺乏对功能植物的筛选研究。因此,有必要筛选出能发挥生物滞留系统整体控污功能目标的最佳植物。筛选能实现生物滞留系统控污目标功能的最佳植物属于一个综合评估问题,目前在生物滞留系统植物的综合评估研究中尚未形成标准化和成熟的方法体系[20-21]。目前,通常采用模糊综合评价、主成分分析或多目标决策分析法对水资源进行综合评价,并形成各自的应用方法体系[22-25]。基于反向传播算法的动态神经网络(BP-DNN)技术因其具有良好的自主学习、自适应和泛化能力,已广泛用于水资源管理的综合评价中[26-27]。由于基于多目标污染物的植物筛选问题属于多目标决策分析范畴,可运用BP-DNN技术对植物除污性能进行综合评价。
本研究选取了10种重庆市本土植物构建了雨水生物滞留系统,以控制山地城市道路雨水径流,考察不同植物种类的生物滞留系统控污能力。采用BP-DNN技术构建了基于多目标污染物的评估模型,并对所有植物系统进行了综合评估以筛选出最佳植物,以期为山地城市道路雨水生物滞留系统植物筛选提供理论依据。
生物滞留系统植物筛选与综合评价
Selection and comprehensive assessment of plants in bioretention system
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摘要: 为科学筛选出生物滞留系统中能有效去除多种污染物的最佳植物,选取10种重庆市本土植物构建雨水生物滞留系统,以控制山地城市道路雨水径流污染; 并采用反向传播动态神经网络(BP-DNN)技术构建了基于多目标污染物的综合评价模型,对所有生物滞留系统进行应用评价。结果表明:植物可有效去除有机物和营养物等多种污染物,但存在差异性; 植被种类对COD的去除影响显著,其中,风车草去除效能最佳,而千屈菜最差; 植物的栽种可有效提高系统对NH4+-N的去除率,但差异性并不显著,同时,部分植物能在一定程度上提高系统对NO3--N的去除,但不稳定; 植物的栽种能有效提高系统的除氮性能,选用风车草时系统除氮性能最佳,TN去除率可达64.30%~86.82%,而植物的栽种对TSS和TP的去除无显著影响。在综合评价的10种植物中,风车草和美人蕉为生物滞留系统的最佳植物。Abstract: To scientifically screen out the best plant species for the effective removal of multiple pollutants in a bioretention system, different rain bioretention systems were built with ten species of native plant in Chongqing, China to conduct the experiments for control the runoff pollution from mountainous urban roads. Furthermore, a comprehensive assessment model based on multi-target pollutants removal was established with back propagation dynamic neural network (BP-DNN) technology, which was used to evaluate the application performance of all bioretention systems. Results showed that plants could effectively remove multi-target pollutants, such as organics and nutrients, but the performance differences occurred among them. Plant species had a significant effect on the removal efficiency of COD, of which Cyperus alternifolius L. presented the best COD removal efficiency, while Lythrum salicaria L presented the lowest one. The plant planting could significantly elevate NH4+-N removal efficiency, while slight difference occurred among different plants. To a certain degree, a part of tested plants could enhance NO3--N removal of the systems, but their removal efficiencies were unstable. In general, the plant planting could effectively improve the nitrogen removal performance of the system, of which Cyperus alternifolius L. was the best plant with the TN removal efficiency of 64.30%~86.82%. However, the plant planting had an insignificant effect on TSS and TP removal. Among ten plant species for comprehensive assessment with the model, Cyperus alternifolius L. and Canna indica L. were the best plants for removing the six typical pollutants in a bioretention system.
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Key words:
- bioretention /
- best plants /
- dynamic neural network /
- multi-target pollutants /
- comprehensive assessment
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表 1 实验植物
Table 1. Experimental plants
序号 中文名 科 拉丁学名 选用原因 1 美人蕉 美人蕉科 Canna indica L. 常用于人工湿地 2 千屈菜 千屈菜科 Lythrum salicaria L. 常用于园林景观和湿地 3 风车草 莎草科 Cyperus alternifolius L. 常用于生物滞留设施、植草沟、雨水塘与雨水湿地,具有氮磷营养物净化能力 4 梭鱼草 雨久花科 Pontederia cordata L. 常用于湿地 5 纸莎草 莎草科 Cyperus papyrus 常用于人工湿地和植草沟 6 鸢尾 鸢尾科 Iris tectorum Maxim. 常用于园林景观、生物滞留设施和植被缓冲带 7 芦竹 禾本科 Arundo donax 常用于生物滞留设施、植草沟、雨水塘与雨水湿地 8 狗牙根 禾本科 Cynodon dactylon (L.) Pers. 常用于生物滞留设施进水区,以缓解进水水流速度 9 细叶芒 禾本科 Miscanthus sinensis cv. 多年生草本植物,常用于园林景观 10 香根草 禾本科 Vetiveria zizanioides (L.) Nash 常用于生物滞留设施,具有氮磷营养物净化能力 表 2 模拟径流雨水水质
Table 2. Semi-synthetic runoff rainwater quality
污染指标 化学试剂 浓度/(mg∙L-1) TSS 底泥(粒径<0.2 mm) 320~382 COD C6H12O6+C6H5NO2 364~408 TP KH2PO4 1.1~2.1 TN NH4Cl+KNO3+C6H5NO2 8.1~19.4 NH4+-N NH4Cl 3.9~6.7 NO3--N KNO3 2.1~4.9 表 3 生物滞留系统除污效能评价标准
Table 3. Assessment standards based on the experimental results
% 污染指标 评价标准 强(1) 较强(2) 中(3) 较弱(4) 弱(5) TSS >89.88 86.00~89.88 82.12~86.00 78.25~82.12 <78.25 COD >73.57 62.58~73.57 51.60~62.58 40.61~51.60 <40.61 NH4+-N >90.80 82.31~90.80 73.81~82.31 65.31~73.81 <65.31 NO3--N >65.76 31.65~65.76 (-2.47)~31.65 (-36.58)~(-2.47) <(-36.58) TN >76.32 54.00~76.32 31.69~54.00 9.38~31.69 <9.38 TP >83.63 70.54~83.63 57.44~70.54 44.35~57.44 <44.35 -
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