Groundwater pollution source identification using the backward beam equation method

被引:0
|
作者
Atmadja, J [1 ]
Bagtzoglou, AC [1 ]
机构
[1] Columbia Univ, Dept Civil Engn, New York, NY 10027 USA
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The reliable assessment of hazards or risks arising from water contamination problems and the design of efficient and effective techniques to mitigate them require the capability to predict the behavior of chemical contaminants in flowing water. Most attempts at quantifying contaminant transport have relied on a solution of some form of a well-known governing equation referred to as advection-dispersion-reaction equation. In this paper, the method of Backward Beam Equation (BBE) is studied and enhanced to solve the Advection-Dispersion Equation (ADE) within a contaminant source identification context. Even though the BBE has been applied successfully to parabolic problems before, it has never been applied to solving the ADE. The BBE employed in this work is capable of finding the history of a groundwater contaminant plume from measurements of its current spatial distribution. For the examples presented in this work, the method solves the heterogeneous case with better accuracy than the homogeneous case. It also backtracks the bimodal concentration plume more accurately than the unimodal one. With a modification to the method, rendering it a hybrid between a marching and a jury method, we were also able to make the computational time requirements manageable.
引用
收藏
页码:397 / 404
页数:8
相关论文
共 50 条
  • [21] Simultaneous identification of groundwater pollution source location and release concentration using Artificial Neural Network
    Chaubey, Jyoti
    Srivastava, Rajesh
    ENVIRONMENTAL FORENSICS, 2022, 23 (3-4) : 293 - 300
  • [22] Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm
    Gurarslan, Gurhan
    Karahan, Halil
    HYDROGEOLOGY JOURNAL, 2015, 23 (06) : 1109 - 1119
  • [23] An Effective Kalman Filter-Based Method for Groundwater Pollution Source Identification and Plume Morphology Characterization
    Jiang, Simin
    Fan, Jinhong
    Xia, Xuemin
    Li, Xianwen
    Zhang, Ruicheng
    WATER, 2018, 10 (08)
  • [24] Source identification by a statistical analysis of backward trajectories based on peak pollution events
    Cesari, Rita, 1600, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (55): : 1 - 4
  • [25] Source identification by a statistical analysis of backward trajectories based on peak pollution events
    Cesari, Rita
    Paradisi, Paolo
    Allegrini, Paolo
    INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION, 2014, 55 (1-4) : 94 - 103
  • [26] Identification of clandestine groundwater pollution source locations and their release flux history
    Chakraborty, A.
    Prakash, O.
    2ND INTERNATIONAL CONFERENCE ON ADVANCES IN CIVIL AND ECOLOGICAL ENGINEERING RESEARCH, 2021, 626
  • [27] Machine learning for groundwater pollution source identification and monitoring network optimization
    Yiannis N. Kontos
    Theodosios Kassandros
    Konstantinos Perifanos
    Marios Karampasis
    Konstantinos L. Katsifarakis
    Kostas Karatzas
    Neural Computing and Applications, 2022, 34 : 19515 - 19545
  • [28] Optimization Design of Groundwater Pollution Monitoring Wells and Identification of Contamination Source
    Zhang S.
    Liu H.
    Qiang J.
    Liu X.
    Zhu X.
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2019, 46 (06): : 120 - 132
  • [29] Mixed integer optimization approach to groundwater pollution source identification problems
    Zhao, Ying
    Lu, Wenxi
    Xiao, Chuanning
    ENVIRONMENTAL FORENSICS, 2016, 17 (04) : 355 - 360
  • [30] Machine learning for groundwater pollution source identification and monitoring network optimization
    Kontos, Yiannis N.
    Kassandros, Theodosios
    Perifanos, Konstantinos
    Karampasis, Marios
    Katsifarakis, Konstantinos L.
    Karatzas, Kostas
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (22): : 19515 - 19545