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3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法

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孙金岭, 何元庆, 何则, 庞娟. 3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法[J]. 环境工程学报, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
引用本文: 孙金岭, 何元庆, 何则, 庞娟. 3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法[J]. 环境工程学报, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
shu
Sun Jinling, He Yuanqing, He Ze, Pang Juan. A fuzzy-stochastic coupled method to assess groundwater pollution risk in a landfill[J]. Chinese Journal of Environmental Engineering, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
Citation: Sun Jinling, He Yuanqing, He Ze, Pang Juan. A fuzzy-stochastic coupled method to assess groundwater pollution risk in a landfill[J]. Chinese Journal of Environmental Engineering, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
shu

3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法

  • 1.

    中国科学院寒区旱区环境与工程研究所冰冻圈科学国家重点实验室, 兰州 710030

  • 2.

    兰州理工大学经济管理学院, 兰州 710050

  • 3.

    中国科学院大学, 北京 100049

  • 基金项目:

    中国科学院知识创新群体项目(KZZD-EW-04-05-01)

    中国科学院寒区旱区环境与工程研究所冰冻圈科学国家重点实验室自主课题(SKLCS-ZZ-2012-01-02)

    国家自然科学基金创新研究群体项目(41121001,41273010)

    中国科学院国家外国专家局创新团队国际合作伙伴计划

  • 中图分类号: X820

A fuzzy-stochastic coupled method to assess groundwater pollution risk in a landfill

  • 1.

    Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Acadamy Sciences, Lanzhou 710030, China

  • 2.

    College of Economics and Management, Lanzhou University of Technology, Lanzhou 710050

  • 3.

    University of Chinese Academics of Sciences, Beijing 100049, China

  • Fund Project:

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  • 摘要: 为分析参数不确定性对填埋场渗漏风险评估结果的影响,构建了填埋场地下水污染风险评价的物理过程模型,在此基础上,分别采用模糊理论和概率理论刻画模糊不确定性参数和随机不确定性参数,同时采用基于随机理论的Monte Carlo方法模拟模糊不确定参数,最终构建了基于模糊随机耦合的填埋场地下水污染风险评价方法。采用该模型对东北某一般工业固废填埋场进行案例研究,结果表明,实测浓度在模型模拟的的浓度区间(10%~90%分位值浓度)之内。说明本模型构建的模糊-随机耦合的地下水污染风险评价模型能较准确地预测地下水中污染物实际浓度,可以用于填埋场地下水污染风险评价.风险评估结果表明,该填埋场地下水的潜在污染物为As和Mn,其中As为主要健康风险物质,其非致癌风险值超过风险可接受水平的概率为22%,致癌风险超过10-4的概率为33%,超过10-5的概率为86%,应该采取措施控制含As填埋废物中As的溶出,降低其环境风险;Mn的非致癌风险值小于风险可接受水平的概率为100%,无风险。
    Abstract: To consider the influence of the uncertainty of parameters in assessing the risk of landfill leachate leakage, a physical-process-based conception model for leachate leakage's risk to groundwater has been constructed. Based on this, the fuzzy set theory and probability theory were used to describe the fuzzy uncertainty parameters and random uncertainty parameters. In addition, the Monte Carlo simulation method based on random theory was used to simulate the fuzzy uncertainty parameters. Finally, a fuzzy-stochastic coupled method based on random theory was constructed to asses the groundwater pollution risk in a landfill.An industry solid waste landfill was selected as case study. The case study result indicates that the measured value of contaminant concentration fell in the range of the predicted 10th and 90th percentile value, which means the constructed model was accurate enough to simulate the actual concentration distribution in the groundwater around the landfill. Risk assessment showed that the potential contaminants were arsenic(As) and manganese(Mn). As was the main contaminant with noticeable toxic effects on human health. The probability that its hazard quotient exceeded the acceptable value is 22%. Furthermore, the probability that its cancer risk value exceeds the acceptable value of 10-5 and 10-4 are 33% and 86%,respectively, which means that its potential health risk was high and measurement should be taken to control the present risks. Mn has a non-carcinogenic effect and the probability that its hazard quotient exceeded the acceptable risk level is zero, which means its environmental effect can be ignored.
  • [1] 王春晓. 全球水危机及水资源的生态利用. 生态经济, 2014, 30(3): 4-7
    [2] 唐克旺, 吴玉成, 侯杰. 中国地下水资源质量评价(Ⅱ):地下水水质现状和污染分析. 水资源保护, 2006, 22(3): 1-4 Tang Kewang, Wu Yucheng, Hou Jie. Assessment of groundwater quality in China: Ⅱ. Groundwater quality and pollution analysis. Water Resource Protection, 2006, 22(3): 1-4(in Chinese)
    [3] Pizzol L., Zabeo A., Critto A., et al. Risk-based prioritization methodology for the classification of groundwater pollution sources. Science of the Total Environment, 2014, 506-507: 505-517
    [4] Dimitriou E., Karaouzas I., Sarantakos K., et al. Groundwater risk assessment at a heavily industrialised catchment and the associated impacts on a peri-urban wetland. Journal of Environmental Management, 2008, 88(3): 526-538
    [5] Chica-Olmo M., Luque-Espinar J. A., Rodriguez-Galiano V., et al. Categorical Indicator Kriging for assessing the risk of groundwater nitrate pollution: The case of Vega de Granada aquifer (SE Spain). Science of the Total Environment, 2014, 470-471: 229-239
    [6] Li Ying, Li Jinghui, Chen Shusheng, et al. Establishing indices for groundwater contamination risk assessment in the vicinity of hazardous waste landfills in China. Environmental Pollution, 2012, 165: 77-90
    [7] Wang Junjie, He Jiangtao, Chen Honghan. Assessment of groundwater contamination risk using hazard quantification, a modified DRASTIC model and groundwater value, Beijing Plain, China. Science of the Total Environment, 2012, 432: 216-226
    [8] Rodriguez-Galiano V., Mendes M. P., Garcia-Soldado M. J., et al. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of the Total Environment, 2014, 476-477: 189-206
    [9] 詹良通, 刘伟, 陈云敏, 等. 某简易垃圾填埋场渗滤液在场底天然土层迁移模拟与长期预测. 环境科学学报, 2011, 31(8): 1714-1723 Zhan Liangtong, Liu Wei, Chen Yunmin, et al. Numerical simulation and prediction of migration of leachate into natural soil strata under a simple MSW dump. Acta Scientiae Circumstantiae, 2011, 31(8): 1714-1723(in Chinese)
    [10] Slack R. J., Gronow J. R., Hall D. H., et al. Household hazardous waste disposal to landfill: Using LandSim to model leachate migration. Environmental Pollution, 2007, 146(2): 501-509
    [11] Lee Y. W., Dahab M. F., Borgardi I. Fuzzy decision making in ground water nitrate risk management. JAWRA Journal of the American Water Resources Association, 1994, 30(1): 135-147
    [12] Kumar G., Maiti J. Modeling risk based maintenance using fuzzy analytic network process. Expert Systems with Applications, 2012, 39(11): 9946-9954
    [13] Goodrich M. T., McCord J. T. Quantification of uncertainty in exposure assessments at hazardous waste sites. Ground Water, 1995,33(5): 727-732
    [14] 金菊良, 吴开亚, 李如忠. 水环境风险评价的随机模拟与三角模糊数耦合模型. 水利学报, 2008, 39(11): 1257-1261 Jin Juliang, Wu Kaiya, Li Ruzhong. Damage analysis of reinforced concrete surrounding the spiral case under the action of repeated water pressure. Journal of Hydraulic Engineering, 2008, 39(11): 1257-1261(in Chinese)
    [15] 徐亚, 朱雪梅, 刘玉强, 等. 基于随机—模糊耦合的污染场地健康风险评价及案例. 中国环境科学, 2014, 34(10): 2692-2700 Xu Ya, Zhu Xuemei, Liu Yuqiang, et al. A fuzzy-stochastic integrated model of contaminated site risk assessment model and case study. China Environmental Science, 2014, 34(10): 2692-2700(in Chinese)
    [16] 徐亚, 刘景财, 刘玉强, 等. 基于Monte Carlo方法的污染场地风险评价及不确定性研究. 环境科学学报, 2014, 34(6): 1579-1584 Xu Ya, Liu Jingcai, Liu Yuqiang, et al. Quantification of uncertainty in evaluating the health risk of a contaminated site based on Monte Carlo method. Acta Scientiae Circumstantiae, 2014, 34(6): 1579-1584(in Chinese)
    [17] 徐亚, 刘玉强, 刘景财, 等. 填埋场渗漏风险评估的三级PRA模型及案例研究. 环境科学研究, 2014, 27(4): 447-454 Xu Ya, Liu Yuqiang, Liu Jingcai, et al. 3-Level of probability risk assessment on environmental risk of landfill leakage and it's case study. Research of Environmental Sciences, 2014, 27(4): 447-454(in Chinese)
    [18] Schroeder P. R., Dozier T. S., Zappi P. A., et al. The Hydrologic Evaluation of Landfill Performance (HELP) Model: Engineering Documentation for Version 3. Washington, DC: U.S. Environmental Protection Agency Office of Research and Development, 1994
    [19] Field C. R., Nangunoori R. K. Case Study-Efficacy of the HELP Model: A myth or reality//HELP Model Workshop for Landfill Design and Evaluation Proceedings. Tampa, 1993
    [20] 陈鸿汉, 谌宏伟, 何江涛, 等. 污染场地健康风险评价的理论和方法. 地学前缘, 2006, 13(1): 216-223 Chen Honghan, Chen Hongwei, He Jiangtao, et al. Health-based risk assessment of contaminated sites: Principles and methods. Earth Science Frontiers, 2006, 13(1): 216-223(in Chinese)
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  • 收稿日期:  2015-05-05
  • 刊出日期:  2016-06-03
孙金岭, 何元庆, 何则, 庞娟. 3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法[J]. 环境工程学报, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
引用本文: 孙金岭, 何元庆, 何则, 庞娟. 3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法[J]. 环境工程学报, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
shu
Sun Jinling, He Yuanqing, He Ze, Pang Juan. A fuzzy-stochastic coupled method to assess groundwater pollution risk in a landfill[J]. Chinese Journal of Environmental Engineering, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
Citation: Sun Jinling, He Yuanqing, He Ze, Pang Juan. A fuzzy-stochastic coupled method to assess groundwater pollution risk in a landfill[J]. Chinese Journal of Environmental Engineering, 2016, 10(5): 2761-2768. doi: 10.12030/j.cjee.201412259
shu

3(in Chinese)基于随机-模糊耦合的填埋场地下水污染风险评价方法

  • 1.  中国科学院寒区旱区环境与工程研究所冰冻圈科学国家重点实验室, 兰州 710030
  • 2.  兰州理工大学经济管理学院, 兰州 710050
  • 3.  中国科学院大学, 北京 100049
  • 收稿日期:  2015-05-05
基金项目:

中国科学院知识创新群体项目(KZZD-EW-04-05-01)

中国科学院寒区旱区环境与工程研究所冰冻圈科学国家重点实验室自主课题(SKLCS-ZZ-2012-01-02)

国家自然科学基金创新研究群体项目(41121001,41273010)

中国科学院国家外国专家局创新团队国际合作伙伴计划

摘要: 为分析参数不确定性对填埋场渗漏风险评估结果的影响,构建了填埋场地下水污染风险评价的物理过程模型,在此基础上,分别采用模糊理论和概率理论刻画模糊不确定性参数和随机不确定性参数,同时采用基于随机理论的Monte Carlo方法模拟模糊不确定参数,最终构建了基于模糊随机耦合的填埋场地下水污染风险评价方法。采用该模型对东北某一般工业固废填埋场进行案例研究,结果表明,实测浓度在模型模拟的的浓度区间(10%~90%分位值浓度)之内。说明本模型构建的模糊-随机耦合的地下水污染风险评价模型能较准确地预测地下水中污染物实际浓度,可以用于填埋场地下水污染风险评价.风险评估结果表明,该填埋场地下水的潜在污染物为As和Mn,其中As为主要健康风险物质,其非致癌风险值超过风险可接受水平的概率为22%,致癌风险超过10-4的概率为33%,超过10-5的概率为86%,应该采取措施控制含As填埋废物中As的溶出,降低其环境风险;Mn的非致癌风险值小于风险可接受水平的概率为100%,无风险。

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