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  • 余雪花,任萍萍,郑爽,程启洪.生物滞留系统植物筛选与综合评价[J].环境工程学报,2019,13(7):1634-1644.DOI:10.12030/j.cjee.201811044    [点击复制]
  • YU Xuehua,REN Pingping,ZHENG Shuang,CHENG Qihong.Selection and comprehensive assessment of plants in bioretention system[J].,2019,13(7):1634-1644.DOI:10.12030/j.cjee.201811044   [点击复制]
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生物滞留系统植物筛选与综合评价
余雪花1,任萍萍1,郑爽1,程启洪1
0
(1.重庆交通大学河海学院,重庆 400074;2.重庆交通大学,环境水利工程重庆市工程实验室,重庆 400074)
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。
关键词:  生物滞留  最佳植物  动态神经网络  多目标污染物  综合评价
DOI:10.12030/j.cjee.201811044
投稿时间:2018-11-07
基金项目:国家自然科学基金资助项目51709024;重庆市基础科学与前沿技术研究项目cstc2017jcyjAX0292;重庆市留创计划资助项目cx2017065国家自然科学基金资助项目(51709024);重庆市研究生科研创新项目(CYS17200,CYS18219);重庆市基础科学与前沿技术研究项目(cstc2017jcyjAX0292);重庆市留创计划资助项目(cx2017065)
Selection and comprehensive assessment of plants in bioretention system
YU Xuehua1,REN Pingping1,ZHENG Shuang1,CHENG Qihong1
(1.School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China;2.Engineering Laboratory of Environmental Hydraulic Engineering of Chongqing, Chongqing Jiaotong University, Chongqing 400074, China)
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.
Key words:  bioretention  best plants  dynamic neural network  multi-target pollutants  comprehensive assessment