某钼矿尾矿库地下水污染的随机模拟

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摘要为分析参数不确定性对地下水污染数值模拟模型输出结果的影响，以某钼矿尾矿库地下水污染问题作为研究实例，选取钼离子作为模拟因子，建立该钼矿尾矿库地下水污染数值模拟模型，对输出结果进行不确定性分析.为降低替代模型的维数，运用灵敏度分析法筛选出对模拟模型输出结果影响较大的2个参数作为模型中的随机参数.为减少反复调用数值模拟模型产生的计算负荷，分别运用克里格方法和支持向量机法建立模拟模型的替代模型，并比较二者的精度，选择精度较高的替代模型完成蒙特卡罗随机模拟.最后，对随机模拟的输出结果进行统计分析与区间估计，对地下水污染超标的风险进行评价.结果表明：置信度为80%时，井1，2，3浓度值的置信区间分别为0.71~2.29，0.28~1.02，1.55~3.25mg/L.此外，结合《地下水质量标准》以及污染物浓度分布函数曲线，井1，2，3中水质达到地下水质量标准Ⅴ类的概率分别为99.7%，97.1%，99.6%.本研究可为地下水污染防治提供更科学、全面的参考依据.

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	王梓博
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关键词 ：钼矿尾矿库, 地下水污染随机模拟, 替代模型, 不确定性分析, 风险评估

Abstract：In order to analyze the effect of parameter uncertainty on the output of groundwater pollution numerical simulation model, in this paper, the groundwater pollution of amolybdenum mine tailings pond was taken as an example, the Mo²⁺ was selected as the simulation factor, the numerical simulation model of groundwater pollution of themolybdenum mine tailings pondwas established, the uncertainty analysis of the output resultswere carried out. In order to reduce the dimension of the substitution model, the sensitivity analysis method was used to filter out two parameters that have a greater impact on the output of the simulation model as random parameters in the model. In order toreduce the computational load from repeated calls to the numerical simulation model, the Kriging method and the Support Vector Machine method were used to establish asubstitutionmodel of the simulation model, and the accuracy of the two was compared, selecting a higher precisionsubstitution model to complete the Monte Carlo random simulation. Finally, the output results of the random simulation were analyzed and the interval estimation was carried out, to evaluate the risk of groundwater pollution exceeding the standard. The results show that when the confidence levelwas 80%, the confidence intervals of the concentration values of the well 1,2,3 were 0.71 to 2.29, 0.28 to 1.02, 1.55 to 3.25mg/L. In addition, combined with thestandard for groundwater quality and the contaminant concentration distribution function curves, the probability of the V class of water quality in well 1, 2, 3 to meet the standard for groundwaterqualitywas 99.7%, 97.1% and 99.6%. The study canprovide a more scientific and comprehensive reference for the prevention and control of groundwater pollution.

Key words： molybdenum mine tailings pond stochasticsimulation of groundwater pollution substitution model uncertainty analysis risk assessment

收稿日期: 2019-10-22

PACS:

X523

基金资助:国家自然科学基金资助项目（41672232，41972252）

通讯作者: 卢文喜,教授,luwenxi@jlu.edu.cn E-mail: luwenxi@jlu.edu.cn

作者简介: 王梓博(1997-),女,黑龙江哈尔滨人,吉林大学硕士研究生,主要从事地下水数值模拟方面的研究.

引用本文:

王梓博, 卢文喜, 王涵, 李久辉, 范越. 某钼矿尾矿库地下水污染的随机模拟[J]. 中国环境科学, 2020, 40(5): 2124-2131. WANG Zi-bo, LU Wen-xi, WANG Han, LI Jiu-hui, FAN Yue. Stochastic simulation of the groundwater pollution in the molybdenum mine tailings pond. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(5): 2124-2131.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2020/V40/I5/2124

[1]	Yeh H D, Chang T H, Lin Y C. Groundwater contaminant source identification by a hybrid heuristic approach[J]. Water Resources Research, 2007,43(9).
[2]	Wu C M, Yeh T C J, Zhu J, et al. Traditional analysis of aquifer tests:Comparing apples to oranges?[J]. Water Resources Research, 2005,41(9).
[3]	Goodrich M T, McCord J T. Quantification of uncertainty in exposure assessments at hazardous waste sites[J]. Groundwater, 1995,33(5):727-732.
[4]	常振波,卢文喜,辛欣,等.基于灵敏度分析和替代模型的地下水污染风险评价方法[J]. 中国环境科学, 2017,37(1):167-173. Chang Z B, Lu W X, Xin X, et al. Groundwater contamination risk assessment method based on sensitivity analysis and surrogatemodel[J]. China Environmental Science, 2017,37(1):167-173.
[5]	王涵,卢文喜,李久辉,等.地下水DNAPLs污染多相流的随机模拟及其不确定性分析[J]. 中国环境科学, 2018,38(7):2572-2579. Wang H, Lu W X, Li J H, et al. Stochastic simulation and uncertainty analysis of multi-phase flow of groundwater polluted by DNAPLs[J]. China Environmental Science, 2018,38(7):2572-2579.
[6]	李久辉,卢文喜,常振波,等.基于不确定性分析的地下水污染超标风险预警[J]. 中国环境科学, 2017,37(6):2270-2277. Li J H, Lu W X, Chang Z B, et al. Risk prediction of groundwater pollution based on uncertainty analysis[J]. China Environmental Science, 2017,37(6):2270-2277.
[7]	姚丽利,胡立堂,龚芳芳,等.北京市平原区地下水开采量反演的数值模拟方法[J]. 北京师范大学学报(自然科学版), 2017,53(4):436-442. Yao L L, Hu L T, Gong F F, et al. Numerical simulations for groundwater withdrawal inversion in Beijing plain[J]. Journal of Beijing Normal University (Natural Science), 2017,53(4):436-422.
[8]	韩林山,李向阳,严大考.浅析灵敏度分析的几种数学方法[J]. 中国水运(下半月), 2008,8(4):177-178. Han L S, Li X Y, Yan D K.A brief analysis of several mathematical methods of sensitivity analysis[J]. China Water Transport(the second half of the month), 2008,8(4):177-178.
[9]	束龙仓,王茂枚,刘瑞国,等.地下水数值模拟中的参数灵敏度分析[J]. 河海大学学报(自然科学版), 2007,35(5):491-495. Shu L C, Wang M M, Liu R G, et al. Sensitivity analysisofparametersin numerical simulation ofgroundwater[J]. Joumal of Hohai University (Natural Sciences), 2007,35(5):491-495.
[10]	董海彪.基于克里格替代模型和改进的Bayesian-MCMC方法的地下水污染源反演识别研究[D]. 长春:吉林大学, 2016. Dong H B. Identification of groundwater pollution sources based on Kriging alternative model and improved Bayesian-MCMC methods[D]. Changchun:Jilin University, 2016.
[11]	欧阳琦,卢文喜,侯泽宇,等.基于替代模型的地下水溶质运移不确定性分析[J]. 中国环境科学, 2016,36(4):1119-1124. Ou Y Q, Lu W X, Hou Z Y, et al. Uncertainty analysis of groundwater solute transport based on surrogate model[J]. China Environmental Science, 2016,36(4):1119-1124.
[12]	肖传宁,卢文喜,赵莹,等.基于径向基函数模型的优化方法在地下水污染源识别中的应用[J]. 中国环境科学, 2016,36(7):2067-2072. Xiao C N, Lu W X, Zhao Y, et al. Optimization method of identification of groundwater pollution sources based on radial basis function model[J]. China Environmental Science, 2016,36(7):2067-2072.
[13]	Zhao Y, Lu W X, Xiao C N. A Kriging surrogate model coupled in simulation-optimization approach for identifyingrelease history of groundwater sources[J]. Journal of Contaminant Hydrology, 2016,185:51-60.
[14]	李耀辉.基于Kriging模型的全局近似与仿真优化方法[D]. 武汉:华中科技大学, 2015. Li Y H. The Kriging-based global approximation and simulationoptimization methods[D]. Wuhan:Huazhong University of Science and Technology, 2015.
[15]	Xu H X, Zhu G X, Tian J W, et al. Image segmentation based on support vector machine[J]. Journal of Electronic Science and Technology, 2005,3(3):226-230.
[16]	李刚,刘志强.基于支持向量机替代模型的可靠性分析[J]. 计算力学学报, 2011,28(5):676-681. Li G, Liu Z Q. Surrogate-based reliability analysis by support vector machine[J]. Chinese Journal of Computational Mechanics, 2011,28(5):676-681.
[17]	向国齐,严志坚,黄大贵.支持向量机替代模型的遗传优化设计[J]. 电子科技大学学报, 2009,38(3):459-462. Xiang G Q, Yan Z J, Huang D G. Genetic optimization design based onsupport vector regression metamodeling[J]. Journal of University of Electronic Science and Technology ofChina, 2009,38(3):459-462.
[18]	王涵,卢文喜,李久辉,等.地下水DNAPLs污染多相流的随机模拟及其不确定性分析[J]. 中国环境科学, 2018,38(7):2572-2579. Wang H, Lu W X, Li J H, et al. Stochastic simulation and uncertainty analysis of multi-phase flow of groundwater polluted by DNAPLs[J]. China Environmental Science, 2018,38(7):2572-2579.
[19]	马雷.非均质多孔介质多尺度模型及其在地下水模拟中的应用[D]. 合肥:合肥工业大学, 2013. Ma L. Multiscale model of heterogeneous porous medium and its appliacation in groundwater modeling[D]. Hefei:Hefei University of Technology, 2013.
[20]	GB/T 14848-2017地下水质量标准[S]. GB/T 14848-2017 Standard for groundwater quality[S].