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基于云模型的饮用水源地原水重金属健康风险综合评价 被引量:2

Environmental health risk assessment of heavy metals in drinking water source based on cloud model
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摘要 利用云模型能够处理各种不确定性问题的特点,构建基于云模型的重金属健康风险评价模型.将评价标准分为六个风险等级,分别选取Fe、Mn、Cu、Cr、Cd、As和Pb作为评价因子,将构建好的风险评价模型应用于某饮用水源地原水的重金属评价.评价结果表明,该水源地的成人重金属风险值为Ⅳ级,存在一定风险;基于云模型的评价方法不仅能够确定风险等级,还能更全面地反映同级别重金属危害程度的高低;通过与基于区间数理论的评价结果对比发现,云模型通过确定度来确定最终风险等级,验证了该方法的适用性;另外该方法的可视性强,结合云图,可以较为直观地了解当前风险大小. For solving all kinds of uncertain problems, the model of health risk assessment of heavy metals was established based on cloud model. As a case study, the risk is classified into six levels, and the model established is applied for health risk assessment of Fe, Mn, Cu, Cr, Cd, As and Pb in a drinking water source. The results show that the risk of the water resource is high to IV grade. The evaluation method based on cloud model is not only able to determine the level of risk, but also reflect the difference in the same risk level of heavy metals. Compared with the methods based on Interval Number Theory, the cloud model uses the degree to determine the final risk level and the applicability of the method is verified. The cloud model visualizes the evaluation results and people can intuitively understand the current risk.
作者 吴俊伟 曾悦 杨月 陈琴 WU Jun-wei, ZENG Yue, YANG Yue, CHEN Qin (College of Environment and Resources, Fuzhou University, Fuzhou, Fujian 350116, China)
出处 《福州大学学报:自然科学版》 CAS CSCD 北大核心 2014年第2期327-332,共6页 Journal of Fuzhou University(Natural Science Edition)
基金 福建省自然科学基金资助项目(2013J01045)
关键词 重金属 健康风险评价 云模型 地表水源地 heavy metal health risk assessment cloud model dringking water sources
作者简介 通讯作者:曾悦(1973-),副教授,研究方向:资源与环境管理,E—mail:yzeng@fzu.edu.cn
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  • 1Abyaneh HZ. Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters [J].J Environ Health Sci Eng ,2014, 12:40. 被引量:1
  • 2Khalil BM, Awadallah AG, Karaman H, et al. Application of artificial neural networks for the prediction of water quality variables in the Nile Delta [J]. Water Resource Prot, 2012, 4: 388-394. 被引量:1
  • 3Cinar O, Merdun H. Application of an unsupervised artificial neural network technique to multivariant surface water quality data[ J ]. Orig Article, 2009, 24:163-173. 被引量:1
  • 4Chebud Y ,Naja GM. Waterquality monitoring using remote sensing and an artificial neural network[J]. Water Air Soil Pollut ,2012, 223: 4875 - 4887. 被引量:1
  • 5Yerel S, Ankara H. Application of multivariate statistical techniques in the assessment of water quality in Sakarya River, Turkey [ J ].J Geol Soc India,2011,78:1-5. 被引量:1
  • 6Pati S, Dash MK, Mukherjee CK, et al. Assessment of water quality using multivariate statistical techniques in the coastal region of Visakhapatnam, India [J].Environ Monit Assess,2014,186:6385- 6402. 被引量:1
  • 7Mostafaei A.Applieation of multivariate statistical methods and water quality index to evaluation of water quality in the kashkan [J]. Environ Manage,2014,53:865- 881. 被引量:1
  • 8Zare-Garizi A, Sheikh V, Sadoddin A.Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods [J ]. Int J Environ Sci Technol,2011,8 :581-592. 被引量:1
  • 9Furnass WR, Mounce SR, Boxall JB. Linking distribution system water quality issues to possible causes via hydraulic pathways [J]. Environ Model Software,2013, 40:78-87. 被引量:1
  • 10Girija TR, Mahanta C, Chandramouli V. Water quality assessment of an untreated effluent impacted urban stream: the Bharalu tributary of the Brahmaputra River, India [J]. Environ MonitAssess ,2007,130: 221-236. 被引量:1

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