Characterization of Groundwater Pollution Sources with Unknown Release Time History

Abstract

Characterizations of unknown groundwater pollution sources in terms of source location, source flux release history and sources activity initiation times, from sparse observation concentration measurements are a challenging task. Optimization-based methods are often applied to solve groundwater pollution source characterization problem. These methods are effective only when the starting times of activity of the sources are precisely known, or the possible time window within which the sources activity actually start is known with a fair degree of certainty. However, in real life scenarios, the starting time of the activity of the sources is either unknown or can lie anywhere within a time window of years or decades. Absence of any prior information about the span of time window, within which the sources become active, makes existing source identification methodologies inefficient. As an alternative, an optimization-based source identification model is proposed, to simultaneously estimate source flux release history and sources activity initiation times. The method considers source flux release history and sources activity initiation times as explicit decision variables, optimally estimated by the decision model. Performance of the developed methodology is evaluated for an illustrative study area having multiple sources with different source activity initiation times, missing observation data and transient flow conditions. These evaluation results demonstrate the potential applicability of the proposed methodology and its capability to correctly estimate the unknown source flux releasing history and sources activity initiation times.

Share and Cite:

Prakash, O. and Datta, B. (2014) Characterization of Groundwater Pollution Sources with Unknown Release Time History. Journal of Water Resource and Protection, 6, 337-350. doi: 10.4236/jwarp.2014.64036.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Mahar, P.S. and Datta, B. (2000) Identification of Pollution Sources in Transient Groundwater System. Water Resource Management, 14, 209-227. http://dx.doi.org/10.1023/A:1026527901213
[2] Yeh, W.W.-G. (1986) Review of Parameter Identification Procedure in Groundwater Hydrology: The Inverse Problem. Water Resources Research, 22, 95-108. http://dx.doi.org/10.1029/WR022i002p00095
[3] Gorelick, S.M., Evans, B. and Ramson, I. (1983) Identifying Sources of Groundwater Pollution: An Optimization Approach. Water Resources Research, 19, 779-790. http://dx.doi.org/10.1029/WR019i003p00779
[4] Datta, B., Beegle, J.E., Kavvas, M.L. and Orlob, G.T. (1989) Development of an Expert-System Embedding Pattern-Recognition Techniques for Pollution Source Identification. Technical Report, Department of Civil Engineering, California University, Davis.
[5] Bagtzoglou, A.C., Dougherty, D.E. and Tompson, A.F.B. (1992) Application of Particle Methods to Reliable Identification of Groundwater Pollution Sources. Water Resources Management, 6, 15-23.
http://dx.doi.org/10.1007/BF00872184
[6] Wagner, B.J. (1992) Simultaneous Parameter Estimation and Contaminant Source Characterization for Coupled Groundwater Flow and Contaminant Transport Modeling. Journal of Hydrology, 135, 275-303.
http://dx.doi.org/10.1016/0022-1694(92)90092-A
[7] Woodbury, A.D. and Ulrych, T.J. (1996) Minimum Relative Entropy Inversion: Theory and Application to Recovering the Release History of a Groundwater Contaminant. Water Resources Research, 32, 2671-2681.
http://dx.doi.org/10.1029/95WR03818
[8] Woodbury, A.D., Sudicky, E., Ulrych, T.J. and Ludwig, R. (1998) Three Dimensional Plume Source Reconstruction Using Minimum Relative Entropy Inversion. Journal of Contaminant Hydrology, 32, 131-158.
http://dx.doi.org/10.1016/S0169-7722(97)00088-0
[9] Mahar, P.S. and Datta, B. (1997) Optimal Monitoring Network and Ground-Water Pollution Source Identification. Journal of Water Resources Planning and Management, 123, 199-207.
http://dx.doi.org/10.1061/(ASCE)0733-9496(1997)123:4(199)
[10] Mahar, P.S. and Datta, B. (2001) Optimal Identification of Ground-Water Pollution Sources and Parameter Estimation. Journal of Water Resources Planning and Management, 127, 20-29.
http://dx.doi.org/10.1061/(ASCE)0733-9496(2001)127:1(20)
[11] Sidauruk, P., Cheng, A.H.-D. and Ouazar, D. (1997) Ground Water Contaminant Source and Transport Parameter Identification by Correlation Coefficient Optimization. Ground Water, 36, 208-214.
http://dx.doi.org/10.1111/j.1745-6584.1998.tb01085.x
[12] Skaggs, T.H. and Kabala, Z.J. (1994) Recovering the Release History of a Groundwater Contaminant. Water Resources Research, 30, 71-79. http://dx.doi.org/10.1029/93WR02656
[13] Liu, C. and Ball, W.P. (1999) Application of Inverse Methods to Contaminant Source Identification from Aquitard Diffusion Profiles at Dover AFB, Delaware. Water Resources Research, 35, 1975-1985.
http://dx.doi.org/10.1029/1999WR900092
[14] Skaggs, T.H. and Kabala, Z.J. (1995) Recovering the Release History of a Groundwater Contaminant Plume: Method of Quasi-Reversibility. Water Resources Research, 31, 2669-2673.
http://dx.doi.org/10.1029/95WR02383
[15] Bagtzoglou, A.C. and Atmadja, J. (2003) Marching-Jury Backward Beam Equation and Quasi-Reversibility Methods for Hydrologic Inversion: Application to Contaminant Plume Spatial Distribution Recovery. Water Resources Research, 39, 10-14.
[16] Atmadja, J. and Bagtzoglou, A.C. (2001) Pollution Source Identification in Heterogeneous Porous Media. Water Resources Research, 37, 2113-2125. http://dx.doi.org/10.1029/2001WR000223
[17] Bagtzoglou, A.C. and Baun, S.A. (2005) Near Real-Time Atmospheric Contamination Source Identification by an Optimization-Based Inverse Method. Inverse Problems in Science and Engineering, 13, 241-259.
http://dx.doi.org/10.1080/10682760412331330163
[18] Aral, M., Guan, J. and Maslia, M. (2001) Identification of Contaminant Source Location and Release History In aquifers. Journal of Hydrologic Engineering, 6, 225-234.
http://dx.doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225)
[19] Mahinthakumar, G. and Sayeed, M. (2005) Hybrid Genetic Algorithm-Local Search Methods for Solving Groundwater Source Identification Inverse Problems. Journal of Water Resources Planning and Management, 131, 45-57.
http://dx.doi.org/10.1061/(ASCE)0733-9496(2005)131:1(45)
[20] Singh, R.M. and Datta, B. (2006) Identification of Groundwater Pollution Sources Using GA-Based Linked Simulation Optimization Model. Journal of Hydrologic Engineering, 11, 101-109.
http://dx.doi.org/10.1061/(ASCE)1084-0699(2006)11:2(101)
[21] Singh, R.M. and Datta, B. (2004) Groundwater Pollution Source Identification and Simultaneous Parameter Estimation Using Pattern Matching by Artificial Neural Network. Environmental Forensics, 5, 143-159.
http://dx.doi.org/10.1080/15275920490495873
[22] Singh, R.M. and Datta, B. (2007) Artificial Neural Network Modeling for Identification of Unknown Pollution Sources in Groundwater with Partially Missing Concentration Observation Data. Water Resources. Management, 21, 557-572.
http://dx.doi.org/10.1007/s11269-006-9029-z
[23] Singh, R.M., Datta, B. and Jain, A. (2004) Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks. Journal of Water Resource Planning and Management, 130, 506-514.
http://dx.doi.org/10.1061/(ASCE)0733-9496(2004)130:6(506)
[24] Datta, B., Chakrabarty, D. and Dhar, A. (2009) Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources. Water Resources Management, 23, 2031-2049.
[25] Datta, B., Chakrabarty, D. and Dhar, A. (2009) Simultaneous Identification of Unknown Groundwater Pollution Sources and Estimation of Aquifer Parameters. Journal of Hydrology, 376, 48-57.
[26] Datta, B., Chakrabarty, D. and Dhar, A. (2011) Identification of Unknown Groundwater Pollution Sources Using Classical Optimization with Linked Simulation. Journal of Hydro-Environment Research, 5, 25-36.
http://dx.doi.org/10.1016/j.jher.2010.08.004
[27] Azghadi, B.N.S., Kerachian, R., Lari, M.R.B. and Solouki, K. (2010) Characterizing an Unknown Pollution Source in Groundwater Resources Systems Using PSVM and PNN. Expert Systems with Applications, 37, 7154-7161.
http://dx.doi.org/10.1016/j.eswa.2010.04.019
[28] Bagtzoglou, A.C. (2003) On the Non-Locality of Reversed Time Particle Tracking Methods. Environmental Forensics, 4, 215-225. http://dx.doi.org/10.1080/713848511
[29] Ababou, R., Bagtzoglou, A.C. and Mallet, A. (2010) Anti-Diffusion and Source Identification with the RAW Scheme: A Particle-Based Censored Random Walk. Journal of Environmental Fluid Mechanics, 10, 41-76.
http://dx.doi.org/10.1007/s10652-009-9153-4
[30] Ayvaz, T.M. (2010) A Linked Simulation-Optimization Model for Solving the Unknown Groundwater Pollution Source Identification Problems. Journal of Contaminant Hydrology, 117, 46-59.
http://dx.doi.org/10.1016/j.jconhyd.2010.06.004
[31] Jha, M.K. and Datta, B. (2011) Simulated Annealing Based Simulation-Optimization Approach for Identification of Unknown Contaminant Sources in Groundwater Aquifers. Desalination and Water Treatment, 32, 79-85.
http://dx.doi.org/10.5004/dwt.2011.2681
[32] Prakash, O. and Datta, B. (2012) Sequential Optimal Monitoring Network Design and Iterative Spatial Estimation of Pollutant Concentration for Identification of Unknown Groundwater Pollution Source Locations. Environment Monitoring Assess, 185, 1-8. http://dx.doi.org/10.1007/s10661-012-2971-8
[33] Chadalavada, S., Datta, B. and Naidu, R. (2011) Optimisation Approach for Pollution Source Identification in Groundwater: An Overview. International of Environment and Waste Management, 8, 40-61.
http://dx.doi.org/10.1504/IJEWM.2011.040964
[34] Amirabdollahian M. and Datta, B. (2013) Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview. Journal of Environmental Protection, 4, 26-41.
http://dx.doi.org/10.4236/jep.2013.45A004
[35] Atmadja, J. and Bagtzoglou, A.C. (2001) State of the Art Report on Mathematical Methods to Reliable of Groundwater Pollution Source Identification. Environmental Forensics, 2, 205-214.
http://dx.doi.org/10.1006/enfo.2001.0055
[36] Bagtzoglou, A.C. and Atmadja, J. (2005) Mathematical Methods for Hydrologic Inversion: The Case of Pollution Source Identification. Chapter in Environmental Impact Assessment of Recycled Wastes on Surface and Ground Waters. In: Kassim, T.A., Ed., Engineering Modeling and Sustainability, The Handbook of Environmental Chemistry, Water Pollution Series, Vol. 3, Springer-Verlag, Heidelberg-New York, Vol. 5, Part F, 65-96.
[37] Harbaugh, A.W., Banta, E.R., Hill, M.C. and McDonald, M.G. (2000) MODFLOW-2000, the US Geological Survey Modular Ground-Water Model. US Geological Survey Open-File Report 00-92, US Geological Survey, Reston.
[38] Rushton, K.R. and Redshaw, S.C. (1979) Seepage and Groundwater Flow. Earth Surfaces Processes, 5, 339.
[39] Zheng, C. and Wang, P.P. (1999) MT3DMS: A Modular Three-Dimensional Multi-Species Transport Model for Simulation of Advection, Dispersion and Chemical Reactions of Contaminants in Groundwater Systems. US Army Engineer Research and Development Center Contract Report SERDP-99-1, Vicksburg.
[40] Domenico, P.A. and Schwartz, F.W. (1998) Physical and Chemical Hydrogeology. 2nd Edition, John Wiley & Sons, Inc., New York.
[41] Kirkpatrick, S., Gelatt, D.C. and Vecchi, P.M. (1983) Optimization by Simulated Annealing. Science Magazine, 220, 671-680. http://dx.doi.org/10.1126/science.220.4598.671
[42] Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. and Teller, E. (1953) Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21, 1087-1092. http://dx.doi.org/10.1063/1.1699114
[43] Datta, B., Prakash, O., Campbell, S. and Escalada, G. (2013) Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor. Water Resources Management, 27, 4959-4976.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.