开发数据驱动模型,以评估设计变量对人工湿地重金属去除的影响

IF 2.3 Q3 ENVIRONMENTAL SCIENCES
Jiadong Zhang, V. Prodanovic, A. Lintern, Kefeng Zhang
{"title":"开发数据驱动模型,以评估设计变量对人工湿地重金属去除的影响","authors":"Jiadong Zhang, V. Prodanovic, A. Lintern, Kefeng Zhang","doi":"10.2166/bgs.2021.024","DOIUrl":null,"url":null,"abstract":"\n Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.","PeriodicalId":9337,"journal":{"name":"Blue-Green Systems","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands\",\"authors\":\"Jiadong Zhang, V. Prodanovic, A. Lintern, Kefeng Zhang\",\"doi\":\"10.2166/bgs.2021.024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.\",\"PeriodicalId\":9337,\"journal\":{\"name\":\"Blue-Green Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2021-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Blue-Green Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2166/bgs.2021.024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Blue-Green Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/bgs.2021.024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 1

摘要

人工湿地是一种常用于城市雨水处理的绿色基础设施。先前的研究表明,各种设计特征对流出的重金属浓度有影响。在本研究中,我们开发了贝叶斯线性混合模型(BLMM)和贝叶斯线性回归模型(BLRM)来预测重金属(Cd、Cu、Pb和Zn)的流出浓度,使用流入浓度(Cin)和五个设计变量,即介质类型、人工湿地类型、水力停留时间,存在沉淀池(SedP)和湿地与集水区面积之比(ratio)。结果表明,BLMM具有更好的性能,校准时的平均Nash-Sutcliffe效率在0.51(Pb)和0.75(Cu)之间,验证时的平均纳什-萨克利夫效率在0.28(Pb)到0.71(Zn)之间。流入浓度对所有重金属的流出浓度有显著影响,而其他变量对湿地性能的影响因金属而异,例如CWT和SedP与Cd和Cu呈正相关,而介质和比率与Pb和Zn呈负相关。结果还表明,100倍校准和验证在识别关键影响因素方面是优越的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands
Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Blue-Green Systems
Blue-Green Systems Multiple-
CiteScore
8.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信