Application of Dedicated Monitoring–Network Design for Unknown Pollutant-Source Identification Based on Dynamic Time Warping
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 11
Abstract
Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when inadequate and arbitrary concentration measurement data are initially available. The research reported in this paper focuses on estimating three main parameters that are essential for efficient and accurate characterization of groundwater pollution sources, as follows: (1) location of source, (2) its starting time of release, and (3) duration of its activity. Most of the methodologies developed so far for unknown pollutant source identification have not adequately addressed the complexities involved with estimation of starting time of release and the duration of activity. Estimation of the time gap between the first observation of contamination in the aquifer at a location and the starting time of release is important for source identification. The main complexity arises due to the fact that the spatial location and the duration of activity of a pollutant source are interrelated. Therefore, explicitly specifying one and solving for the other simplifies the source characterization problem. In the research reported in this paper, both the source location and starting time of release are treated as explicit unknowns. The developed methodology uses dynamic time warping (DTW) distance as a cost function in the linked simulation–optimization model to design a monitoring network to efficiently estimate source characteristics including the starting time of release of unknown groundwater pollutant source. Performance of the developed methodology is evaluated with data obtained from a real aquifer. The evaluation results demonstrate that pollutant source characterisation based on pollutnat concentration measurements obtained from a designed monitoring network consisting of a fraction of total observation wells available compares very well with that based on all concentration information recorded at all the 74 monitoring wells over a period of 4 years. These evaluation results demonstrate the potential use of the developed methodology for efficient identification of unknown contaminant source in an aquifer.
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Acknowledgments
The writers are indebted to Mr. Adrian Haggi of Parsons Brinkerhoff for providing groundwater quality monitoring data for the contaminated site discussed in this paper. Cooperative Research Center (CRC) for Contamination Assessment and Remediation of the Environment (CARE) funded the research reported in this paper. The writers thank CRC–CARE for making this paper possible. Thanks are also due to the reviewers who helped improve this paper.
References
Aral, M. M., Guan, J., and Maslia, M. L. (2001). “Identification of contaminant source location and release history in aquifers.” J. Hydrol. Eng., 225–234.
Atmadja, J., and Bagtzoglou, A. C. (2001). “State of the art report on mathematical methods for groundwater pollution source identification.” Environ. Forensics, 2(3), 205–214.
Bagtzoglou, A., and Atmadja, J. (2005). “Mathematical methods for hydrologic inversion: The case of pollution source identification.” The handbook of environmental chemistry, T. Kassim, ed., Vol. 3, Springer, Berlin, 65–96.
Bagtzoglou, A. C., Dougherty, D. E., and Tompson, A. F. B. (1992). “Application of particle methods to reliable identification of groundwater pollution sources.” Water Resour. Manage., 6(1), 15–23.
Bagtzoglou, A. C., Tompson, A. F. B., and Dougherty, D. E. (1991). “Probabilistic simulation for reliable solute source identification in heterogeneous porous media.” Water Resources Engineering Risk Assessment, J. Ganoulis, ed., Springer, Heidelberg, 189–201.
Chadalavada, S., Datta, B., and Naidu, R. (2011a). “Optimisation approach for pollution source identification in groundwater: An overview.” Int. J. Environ. Waste Manage., 8(1), 40–61.
Chadalavada, S., Datta, B., and Naidu, R. (2011b). “Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site.” Environ. Monit. Assess., 173(1–4), 929–940.
Datta, B. (2002). “Discussion of ‘Identification of contaminant source location and release history in aquifers’ by Mustafa M. Aral, Jiabao Guan, and Morris L. Maslia.” J. Hydrol. Eng., 399–400.
Datta, B., Chakrabarty, D., and Dhar, A. (2009a). “Optimal dynamic monitoring network design and identification of unknown groundwater pollution sources.” Water Resour. Manage., 23(10), 2031–2049.
Datta, B., Chakrabarty, D., and Dhar, A. (2009b). “Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters.” J. Hydrol., 376(1–2), 48–57.
Datta, B., Chakrabarty, D., and Dhar, A. (2011). “Identification of unknown groundwater pollution sources using classical optimization with linked simulation.” J. Hydro-Environ. Res., 5(1), 25–36.
Datta, B., and Purwar, D. K. (1992). “Optimal design of groundwater quality monitoring network incorporating uncertainties.” Proc., National Symp. on Environment, Bhabha Atomic Research Center, Mumbai, India, 129–131.
Dhar, A., and Datta, B. (2010). “Logic-based design of groundwater monitoring network for redundancy reduction.” J. Water Resour. Plann. Manage., 88–94.
Doherty, J. E., and Hunt, R. J. (2010). Approaches to highly parameterized inversion: A guide to using PEST for groundwater-model calibration, USGS, Reston, VA.
Fetter, C. W. (1994). Applied hydrogeology, Vol. 691, Prentice Hall, Upper Saddle River, NJ.
Foster, S. S. D., and Chilton, P. J. (2003). “Groundwater: The processes and global significance of aquifer degradation.” Philos. Trans. Roy. Soc. London B Biol. Sci., 358(1440), 1957–1972.
Gorelick, S. M., Evans, B., and Remson, I. (1983). “Identifying sources of groundwater pollution: An optimization approach.” Water Resour. Res., 19(3), 779–790.
Ingber, L. (1996). “Adaptive simulated annealing (ASA): Lessons learned.” Control Cybern., 25(1), 33–54.
Javandel, I., Doughty, C., and Tsang, C. F. (1984). “Groundwater transport: Handbook of mathematical models.”, American Geophysical Union, Washington, DC.
Jha, M., and Datta, B. (2013). “Three dimensional groundwater contamination source identification using adaptive simulated annealing.” J. Hydrol. Eng., 307–317.
Kollat, J. B., Reed, P. M., and Maxwel, R. M. (2011). “Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.” Water Resour. Res., 47(2), W02529.
Loaiciga, H., Charbeneau, R., Everett, L., Fogg, G., Hobbs, B., and Rouhani, S. (1992). “Review of groundwater quality monitoring network design.” J. Hydraul. Eng., 11–37.
Lu, G., Clement, T. P., Zheng, C., and Wiedemeier, T. H. (1999). “Natural attenuation of BTEX compounds: Model development and field-scale application.” Ground Water, 37(5), 707–717.
Mahar, P. S., and Datta, B. (1997). “Optimal monitoring network and ground-water-pollution source identification.” J. Water Resour. Plann. Manage., 199–207.
Mahar, P. S., and Datta, B. (2000). “Identification of pollution sources in transient groundwater systems.” Water Resour. Manage., 14(3), 209–227.
Mahar, P. S., and Datta, B. (2001). “Optimal identification of ground-water pollution sources and parameter estimation.” J. Water Resour. Plann. Manage., 20–29.
Mahinthakumar, G. K., and Sayeed, M. (2005). “Hybrid genetic algorithm–local search methods for solving groundwater source identification inverse problems.” J. Water Resour. Plann. Manage., 45–57.
Michalak, A. M., and Kitanidis, P. K. (2004). “Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling.” Water Resour. Res., 40(8), W08302.
Minsker, B. (2003). “Long-term groundwater monitoring—The state of the art.” Am. Soc. Civ. Eng. Stock, 40678.
Morris, B. L., Lawrence, A. R., Chilton, P. J., Adams, B., Caylow, R. C., and Klinck, B. A. (2003). “Groundwater and its susceptibility to degradation: A global assessment of the problems and options for management.”, Geneva.
Pinder, G., Ross, J., and Dokou, Z. (2009). “Optimal search strategy for the definition of a DNAPL source.”, U.S. Dept. of Defense, Washington, DC.
Puech, V. (2010). “Upper Macquarie groundwater model.”, SKM, Malvern, VIC, Australia.
Rabiner, L., and Juang, B. H. (1993). Fundamentals of speech recognition, Vol. 103, Prentice Hall, Upper Saddle River, NJ.
Singh, R. M., and Datta, B. (2004). “Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network.” Environ. Forensics, 5(3), 143–153.
Singh, R. M., and Datta, B. (2006). “Identification of groundwater pollution sources using GA-based linked simulation optimization model.” J. Hydrol. Eng., 101–109.
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 Resour. Manage., 21(3), 557–572.
Singh, R. M., Datta, B., and Jain, A. (2004). “Identification of unknown groundwater pollution sources using artificial neural networks.” J. Water Resour. Plann. Manage., 506–514.
Sun, A. Y., Painter, S. L., and Wittmeyer, G. W. (2006a). “A constrained robust least squares approach for contaminant release history identification.” Water Resour. Res., 42(4), W04414.
Sun, A. Y., Painter, S. L., and Wittmeyer, G. W. (2006b). “A robust approach for iterative contaminant source location and release history recovery.” J. Contam. Hydrol., 88(3–4), 181–196.
Sun, N. Z. (1994). “An introduction to inverse problems.” Chapter 2, Inverse problems in groundwater modeling, Kluwer, Rotterdam, Netherlands, 12–37.
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© 2015 American Society of Civil Engineers.
History
Received: Apr 3, 2013
Accepted: Dec 8, 2014
Published online: Apr 10, 2015
Discussion open until: Sep 10, 2015
Published in print: Nov 1, 2015
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