基于过程模拟的地下水污染源识别研究进展

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Abstract Groundwater is an important water resource. Compared with air and surface water, groundwater pollution has the characteristics of concealment, lag and irreversibility. After the occurrence of groundwater pollution, the information of pollution sources (quantity, location, release history) can be quickly grasped through the identification of pollution sources, which plays an important role in the prevention and control of groundwater pollution sources, the reasonable design of pollution remediation programs, and the accurate identification of pollution responsibilities. In this paper, the mathematical equation inversion method based on process simulation in groundwater pollution source identification was reviewed. Besides, the relevant literature based on CiteSpace and VOSviewer bibliometric software was analyzed to obtain the research status and hot spots in this field. Finally, the future development trend in the research of mathematical equation inversion based on process simulation was put forward.

Key words： groundwater pollution source identification methods bibliometric

Received: 04 February 2024

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X523

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	ZHENG Chuang
	DONG Jun
	ZHANG Wei-hong
	GE Yuan-bo

Cite this article:

ZHENG Chuang,DONG Jun,ZHANG Wei-hong等. Research progress of groundwater pollution source identification based on process simulation[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(9): 4999-5006.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I9/4999

[1] 席北斗,李娟,汪洋,等.京津冀地区地下水污染防治现状、问题及科技发展对策[J]. 环境科学研究, 2019,32(1):1-9. Xi B D, Li J, Wang Y, et al. Strengthening the innovation capability of groundwater science and technology to support the coordinated development of Beijing-Tianjin-Hebei region: status quo, problems and goals [J]. Research of Environmental Sciences, 2019,32(1):1-9.
[2] 王涵.基于组合启发式搜索方法的地下水DNAPLs污染源识别[D]. 长春:吉林大学, 2022. Wang H. Identification of groundwater DNAPLs contamination source based on combined heuristic search method [D] . Changchun: Jilin University, 2022.
[3] 林斯杰,齐永强,杨梦曦,等.基于PCA-SOM的北京市平谷区地下水污染溯源[J]. 环境科学研究, 2020,33(6):1337-1344. Lin S J, Qi Y Q, Yang M X, et al. Source analysis of groundwater polution in Pinggu District of Beijing using PCA-SOM [J]. Research of Environmental Sciences, 2020,33(6):1337-1344.
[4] 张涵,杜昕宇,高菲,等.联合PMF模型与稳定同位素的地下水污染溯源[J]. 环境科学, 2022,43(8):4054-4063. Zhang H, Du X Y, Gao F, et al. Groundwater pollution identification by combination of PMF model and stable isotope technology [J]. Environmental Science, 2022,43(8):4054-4063.
[5] 刘传旸,柴一荻,徐宪根,等.南方某河水质荧光指纹特征及污染溯源[J]. 光谱学与光谱分析, 2021,41(7):2142-2147. Liu C Y, Chai Y D, Xu X G, et al. Aqueous fluorescence fingerprint characteristics and discharge source identification of a river in Southern China [J].Spectroscopy and Spectral Analysis, 2021,41(7): 2142-2147.
[6] Ala N K, Domenico P A. Inverse analytical techniques applied to coincident contaminant distributions at otis air force base, massachusetts [J]. Groundwater, 1992,30(2):212-218.
[7] Sidauruk P, Cheng A H-D, Ouazar D. Ground water contaminant source and transport parameter identification by correlation coefficient [J]. Ground water, 1998,36(2):208.
[8] Moghaddam M B, Mazaheri M, Samani J M V. Inverse modeling of contaminant transport for pollution source identification in surface and groundwaters: A review [J]. Groundwater for Sustainable Development, 2021,15:100651.
[9] Atmadja J, Bagtzoglou A C. State of the art report on mathematical methods for groundwater pollution source identification [J]. Environmental Forensics, 2001,2(3):205-214.
[10] 常振波.地下水LNAPLs污染溯源辨识及源-汇关系研究——以吉林市某石油化工污染场地为例[D]. 长春:吉林大学, 2022. Chang Z B. Study on source identification and source-sink relationship of LNAPLs contamination in groundwater ——Taking a petrochemical contaminated site in Jilin City as an example [D]. Changchun: Jilin University, 2022.
[11] 张宇.基于随机统计方法的地下水污染源反演识别研究[D]. 长春:吉林大学, 2016. ZHANG Y. Groundwater pollution source identification based on random statistical methods [D]. Changchun: Jilin University, 2016.
[12] Skaggs T H, Kabala Z J. Recovering the history of a groundwater contaminant plume -method of quasi-reversibility [J]. Water Resources Research, 1995,31(11):2669-2673.
[13] Bagtzoglou A C, Atmadja J. Marching-jury backward beam equation and quasi-reversibility methods for hydrologic inversion: Application to contaminant plume spatial distribution recovery -art. 1038[J]. Water Resources Research, 2003,39(2):1038.
[14] Skaggs T H, Kabala Z J. Recovering the release history of a groundwater contaminant [J]. Water Resources Research, 1994,30(1): 71-79.
[15] Neupauer R M, Borchers B, Wilson J L. Comparison of inverse methods for reconstructing the release history of a groundwater contamination source [J]. Water Resources Research, 2000,36(9): 2469-2475.
[16] Han K, Zuo R, Ni P, et al. Application of a genetic algorithm to groundwater pollution source identification [J]. Journal of Hydrology, 2020,589:125343.
[17] Guneshwor L, Eldho T I, Vinod Kumar A. Identification of groundwater contamination sources using meshfree rpcm simulation and particle swarm optimization [J]. Water Resources Management, 2018,32(4):1517-1538.
[18] Chakraborty A, Prakash O. Identification of clandestine groundwater pollution source locations and their release flux history [J]. IOP Conference Series: Earth and Environmental Science, 2021,626: 012003.
[19] Anshuman A, Eldho T I. Estimation of contamination release histories using meshless radial point collocation method and multiverse optimization [J]. Journal of Hydroinformatics, 2023,25(3):738-754.
[20] 葛渊博,卢文喜,白玉堃,等.基于SSA-BP与SSA的地下水污染源反演识别[J]. 中国环境科学, 2022,42(11):5179-5187. Ge Y B, Lu W X, Bai Y K, et al. Inversion and identification of groundwater pollution sources based on SSA-BP and SSA [J]. China Environmental Science, 2022,42(11):5179-5187.
[21] 李超.基于和声搜索算法的地下水污染源反向识别问题的研究[D]. 北京:华北电力大学, 2013. Li C. A study of the pollution source identification problem in groundwater systems based on HS [D].Beijing: North China Electric Power University, 2013.
[22] 罗成明,卢文喜,潘紫东,等.基于替代模型和流向算法的地下水污染源反演识别[J]. 中国环境科学, 2023,43(11), 5823-5832. Luo C M, Lu W X, Pan Z D, et al. Inversion and identification of groundwater pollution sources based on surrogate model and flow direction algorithm [J]. China Environmental Science, 2023,43(11): 5823-5832.
[23] 王梓博.基于组合搜索方法的地下水DNAPLs污染溯源辨识研究[D]. 长春:吉林大学, 2022. Wang Z B. Identification of DNAPLs contamination source in groundwater based on a combined search method [D] .Changchun: Jilin University, 2022.
[24] 葛渊博.基于改进麻雀搜索算法的地下水DNAPLs污染溯源反演辨识研究[D]. 长春:吉林大学, 2023. Ge Y B. Identification of DNAPLs contamination source in groundwater based on improved sparrow search algorithm [D]. Changchun: Jilin University, 2023.
[25] 高琬玉,卢文喜,潘紫东,等.基于自适应权重粒子群优化算法的地下水污染溯源辨识[J]. 中国农村水利水电, 2022,(12):1-7,16. Gao W Y, Lu W X, Pan Z D, et al. Inverse identification of contamination sources in groundwater based on adaptive weighted particle swarm optimization algorithm [J]. China Rural Water and Hydropower, 2022,(12):1-7,16.
[26] 袁乾.基于Stacking替代模型的地下水LNAPLs污染源解析研究[D]. 长春:吉林大学, 2021. Yuan Q. Inversion identification of LNAPLs contamination source in groundwater based on Stacking surrogate model [D]. Changchun: Jilin University, 2021.
[27] 侯泽宇.地下水DNAPLs污染源反演识别及不确定性分析研究[D]. 长春:吉林大学, 2018. Hou Z Y. Source identification and uncertainty analysis of DNAPLs-contaminated groudwater [D]. Changchun: JilinUniversity, 2018.
[28] 李久辉.地下水LNAPLs污染溯源辨析[D]. 长春:吉林大学, 2021. Li Y H. Inverse identification of LNAPLs contamination source in groundwater [D] . Changchun: Jilin University, 2021.
[29] Li J, Lu W, Wang H, et al. Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique [J]. Environmental Science and Pollution Research, 2020,27(27):34107-34120.
[30] Pan Z, Lu W, Wang H, et al. Recognition of a linear source contamination based on a mixed-integer stacked chaos gate recurrent unit neural network–hybrid sparrow search algorithm [J]. Environmental Science and Pollution Research, 2022,29(22):33528-33543.
[31] 颜雪松,周正琛,胡成玉.基于贝叶斯的地下水污染源识别方法综述[J]. 华中科技大学学报(自然科学版), 2022,50(10):104-114. Yan X S, Zhou Z C, Hu C Y. Review on groundwater contamination source identification based on Bayesian method [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2022,50(10):104-114.
[32] 李雪利,罗建男,刘勇.不同建议分布MCMC算法在地下水污染源反演识别中的对比研究[J]. 中国环境科学, 2023,43(4):1646-1654. Li X L, Luo J N, Liu Y. Comparative study of different proposal distribution MCMC algorithms in groundwater pollution source identification [J]. China Environmental Science, 2023,43(4):1646-1654.
[33] 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.
[34] 顾文龙,卢文喜,张宇,等.基于贝叶斯推理与改进的MCMC方法反演地下水污染源释放历史[J]. 水利学报, 2016,47(6):772-779. Gu W L, Lu W X, Zhang Y, et al. Reconstructing the release history of groundwater contamination sources based on the Bayesian inference and improved MCMC method [J]. Journal of Hydraulic Engineering, 2016,47(6):772-779.
[35] 张双圣,强静,刘汉湖,等.基于拉丁超立方抽样的改进型多链DRAM算法求解地下水污染反问题[J]. 郑州大学学报(工学版), 2020,41(3):72-78. Zhang S S, Qiang J, Liu H H, et al. Improved multi-chain DRAM algorithm based on latin hypercube samplingfor inverse problems of underground water pollution [J]. Journal of Zhengzhou University (Engineering Science, 2020,41(3):72-78.
[36] 张双圣.基于不确定理论的地下水污染源识别及抽出-处理优化方法研究[D]. 北京:中国矿业大学, 2019. Zhang S S. Study on identification of groundwater pollution sources and optimization method of pump-treat scheme based on uncertainty theory [D]. Beijing: China University of Mining and Technology, 2019.
[37] Yukun Bai, Wenxi Lu, jiuhui Li, et al. Groundwater contamination source identification using improved differential evolution markov chain algorithm [J]. Environmental science and pollution research international, 2022,29(13):19679-19692.
[38] Xia X, Jiang S, Zhou N, et al. Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network [J]. Journal of Hydrology, 2023,617.
[39] Xu T, Zhang W, Gómez-Hernández J J, et al. Non-point contaminant source identification in an aquifer using the ensemble smoother with multiple data assimilation [J]. Journal of Hydrology, 2022,606:127405.
[40] 白玉堃,卢文喜,李久辉.卡尔曼滤波方法在地下水污染源反演中的应用[J]. 中国环境科学, 2019,39(8):3450-3456. Bai Y K, Lu W X, Li J H. Application of Kalman filter to identify the groundwater contaminant sources [J]. China Environmental Science, 2019,39(8):3450-3456.
[41] Wang Z, Lu W, Chang Z, et al. Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble kalman filter – adaptive step length ant colony optimization algorithm [J]. Journal of Hydrology, 2022,605:127352.
[42] 刘勇.基于深卷积编解码神经网络的地下水LNAPLs污染源特征识别研究[D]. 长春:吉林大学, 2023. Liu Y. Inversion identification of LNAPLs contamination source in groundwater based on deep convolutional encoder-decoder neural networks [D] .Changchun: Jilin University, 2023.
[43] 周念清,张瑞城,江思珉,等.ES-MDA算法融合ERT数据联合反演地下水污染源与含水层参数[J]. 南水北调与水利科技(中英文), 2022,20(3):478-486. Zhou N Q, Zhang R C, Jiang S M, et al. Joint inversion of contaminant source and aquifer parameters by assimilating ERT data with the ES-MDA algorithm [J]. South-to-North Water Transfers and Water Science & Technology, 2022,20(3):478-486.
[44] 罗建男,李雪利,王鹤,等.局部-全局混合自适应替代模型在地下水污染源反演识别中的应用[J]. 中国环境科学,2023,43(7):3664-3671. Luo J N, Li X L, Wang H, et al. Groundwater pollution sources inversion based on local-global hybrid adaptive surrogate model [J]. China Environmental Science, 2023,43(7):3664-3671.
[45] Wang H, Lu W, Chang Z. An iterative updating heuristic search strategy for groundwater contamination source identification based on an acpso–elm surrogate system [J]. Stochastic Environmental Research and Risk Assessment, 2021,35(10):2153-2172.
[46] An Y, Yan X, Lu W, et al. An improved bayesian approach linked to a surrogate model for identifying groundwater pollution sources [J]. Hydrogeology Journal, 2022,30(2):601-616.
[47] 葛渊博,卢文喜,王梓博,等.基于BP神经网络替代模型的地下水污染随机模拟[J]. 中国农村水利水电, 2022,(3):107-113,119. Ge Y B, Lu W X, Wang Z B, et al. Random simulation of groundwater pollution based on BP neural network substitution model [J]. China Rural Water and Hydropower, 2022,(3):107-113,119.
[48] 曹文庚,付宇,南天,等.机器学习在地下水环境背景值与污染风险评价的应用和展望[J]. 地质学报, 2023,97:2408-2424. Cao W G, Fu Y, Nan T, et al. Machine learning model in groundwater background value and pollution risk assessment:application and prospect [J]. Acta Geologica Sinica, 2023,97:2408-2424.
[49] Wang Z, Lu W, Chang Z. Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter [J]. Journal of Hydrology, 2023,625(Part A): 129965.
[50] Y W, J B, X S, et al. Sensitivity-dependent dynamic searching approach coupling multi-intelligent surrogates in homotopy mechanism for groundwater dnapl-source inversion [J]. Journal of contaminant hydrology, 2023,255:104151.
[51] Anshuman A, Eldho T I. A parallel workflow framework using encoder-decoder lstms for uncertainty quantification in contaminant source identification in groundwater [J]. Journal of Hydrology, 2023, 619:129296.
[52] Pan Z, Lu W, Bai Y. Groundwater contamination source estimation based on a refined particle filter associated with a deep residual neural network surrogate [J]. Hydrogeology Journal, 2022,30(3):881-897.
[53] 江思珉,孔维铭,吴延浩,等.基于稠密连接网络的地下水污染替代模型研究[J]. 同济大学学报(自然科学版), 2023,51(2):229-237. Jiang S M, Kong W M, Wu Y H, et al. A densenet network based surrogate model for simulating contaminant transport in groundwater systems [J]. Journal of Tongji University (Natural Science), 2023, 51(2):229-237.
[54] Mo S, Zabaras N, Shi X, et al. Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification [J]. Water Resources Research, 2019,55(5):3856-3881.
[55] Wang Z, Lu W, Chang Z, et al. A combined search method based on a deep learning combined surrogate model for groundwater dnapl contamination source identification [J]. Journal of Hydrology, 2023, 616.
[56] Wang H, Lu W, Chang Z. Simultaneous identification of groundwater contamination source and aquifer parameters with a new weighted– average wavelet variable–threshold denoising method [J]. Environmental Science and Pollution Research, 2021,28(28):38292-38307.
[57] Luo J, Ma X, Ji Y, et al. Review of machine learning-based surrogate models of groundwater contaminant modeling [J]. Environmental Research, 2023,238(Part 2):117268.
[58] Hou Z, Lao W, Wang Y, et al. Cyclic feedback updating approach and uncertainty analysis for the source identification of dnapl-contaminated aquifers [J]. Journal of Water Resources Planning & Management, 2021,147(2):1-13.
[59] 贾顺卿,卢文喜,李久辉,等.基于U-D分解卡尔曼滤波地下水污染源溯源辨识[J]. 中国环境科学, 2021,41(2):713-719. Jia S Q, Lu W X, Li J H, et al. Inversion identification of groundwater contamination source based on U-D factorization Kalman filter [J]. China Environmental Science, 2021,41(2):713-719.
[60] Ayvaz M T. A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems [J]. Journal of contaminant hydrology, 2010,117(1-4):46-59.
[61] Li J, Wu Z, Lu W, et al. Simultaneous identification of the number, location and release intensity of groundwater contamination sources based on simulation optimization and ensemble surrogate model [J]. Water Supply, 2022,22(10):7671-7689.
[62] Pan Z, Lu W, Bai Y. Groundwater contaminated source estimation based on adaptive correction iterative ensemble smoother with an auto lightgbm surrogate [J]. Journal of Hydrology, 2023,620(Part B): 129502.
[63] 张瑞城,周念清,江思珉,等.ILUES算法融合ERT数据反演污染源参数与渗透系数场[J]. 同济大学学报(自然科学版), 2022,2022,50(2): 223-230. Zhang R C, Zhou N Q, Jiang S M, et al. Estimation of contaminant sourceparameters and hydraulic conductivityfield by assimilating electricalresistance tomography data withiterative local updating ensemble smoother algorithm [J]. Journal of Tongji University (Natural Science), 2022,50(2):223-230.
[64] Li J, Wu Z, He H, et al. Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning [J]. Environmental Science and Pollution Research, 30(13):38663-38682.
[65] Li J, Wu Z, He H, et al. Application of the complementary ensemble empirical mode decomposition for the identification of simulation model parameters and groundwater contaminant sources [J]. Journal of Hydrology, 2022,612(Part C):128244