[1]刘霖皓,程麒铭,陈垚,等.生物滞留系统去除典型重金属的影响因素识别[J].中国给水排水,2023,39(23):124-132.
　LIULin-hao,CHENGQi-ming,CHENYao,et al.Identification of Influencing Factors for Removal of Typical Heavy Metals in Bioretention System[J].China Water & Wastewater,2023,39(23):124-132.
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生物滞留系统去除典型重金属的影响因素识别

中国给水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第39卷 期数: 2023年第23期 页码: 124-132 栏目: 出版日期: 2023-12-01
Title:
Identification of Influencing Factors for Removal of Typical Heavy Metals in Bioretention System
作者:
刘霖皓1， 程麒铭1， 陈垚1,2， 袁绍春1,2， 吴琼1
（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）
Author(s):
LIU　Lin-hao1， CHENG　Qi-ming1， CHEN　Yao1，2， YUAN　Shao-chun1，2， WU　Qiong1
（1. School of River and Ocean Engineering， Chongqing Jiaotong University， Chongqing 400074， China； 2. Engineering Laboratory of Environmental Hydraulic Engineering of Chongqing Municipal Development and Reform Commission， Chongqing Jiaotong University， Chongqing 400074， China）
关键词:
生物滞留;  CART算法;  机器学习;  二叉树模型;  重金属;  设计参数;  环境变量
Keywords:
bioretention;  CART algorithm;  machine learning;  binary tree model;  heavy metal;  design parameter;  environment variable
摘要:
生物滞留系统具有雨水径流削减和污染控制双重功能，但其对重金属的去除效果易受设计参数和环境因素的影响而不稳定。基于文献数据驱动，利用CART算法构建了生物滞留系统设计参数和环境变量的二叉树机器学习模型，并对生物滞留系统去除Cu、Zn和Pb等典型重金属的影响因素进行识别。结果表明，影响Cu和Pb去除的最敏感因素为入流浓度，而影响Zn去除的最敏感因素为介质土深度。二叉树模型对3种重金属影响因素的识别准确率（p0）分别为0.86、0.80和0.74，分类性能均取得了中等以上的一致性，Cohen’s Kappa系数（Ka）分别可达到0.72、0.60和0.48。研究证实，单变量相关性分析法难以识别出生物滞留系统去除典型重金属的敏感因素，而基于文献数据驱动的机器学习方法不仅可有效挖掘出生物滞留系统中敏感因素的影响程度，还能识别出相应的阈值，可为后续优化设计和运维管理提供一定参考。
Abstract:
Bioretention system has dual functions of stormwater runoff reduction and pollution control. However, its removal performance of heavy metals is susceptible to be affected by design parameters and environmental variables. Based on the literature data, a binary tree machine learning model was constructed by CART algorithm for determining the design parameters and environmental variables of biological retention system, and the influencing factors of biological retention system for removal of Cu, Zn, Pb and other typical heavy metals were identified. The most sensitive factor affecting the removal of Cu and Pb was the inflow concentration, while the most sensitive factor affecting the removal of Zn was the depth of soil medium. The accuracy (p0) of the binary tree model for identification of the three heavy metal influencing factors was 0.86, 0.80 and 0.74, respectively, the classification consistency was above medium level, and the Cohen’s Kappa coefficient (Ka) was 0.72, 0.60 and 0.48, respectively. Univariate correlation analysis was difficult to identify the sensitive factors of bioretention system for the removal of typical heavy metals. In contrast, the machine learning method based on literature data could not only effectively mine the influence degree of sensitive factors in bioretention systems, but also identify the corresponding threshold, which could provide some reference for the subsequent optimization design and operation and maintenance management.

相似文献/References:

[1]汤元磊,韩冰冰.用于处理雨水径流的相关专利技术分析及展望[J].中国给水排水,2020,36(18):126.
　TANG Yuan-lei,HAN Bing-bing.Analysis and Prospect of Related Patent Technology of Rainfall Runoff Treatment[J].China Water & Wastewater,2020,36(23):126.
[2]李澄,何伶俊,周洁,等.生物滞留设施建设常见问题及解决方案[J].中国给水排水,2020,36(20):105.
　LI Cheng,HE Ling-jun,ZHOU Jie,et al.Common Problems and Solutions in Construction of Bioretention Facility[J].China Water & Wastewater,2020,36(23):105.
[3]张珊,李俊奇,李小静,等.生物滞留设施对城市雨水径流热污染的削减效应[J].中国给水排水,2021,37(3):116.
　ZHANG Shan,LI Jun-qi,LI Xiao-jing,et al.Reduction Effect of Bioretention on Urban Stormwater Runoff Thermal Pollution[J].China Water & Wastewater,2021,37(23):116.

更新日期/Last Update: 2023-12-01