Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations
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One of the difficulties in accurate characterization of unknown groundwater pollution sources is the uncertainty regarding the number and the location of such sources. Only when the number of source locations is estimated with some degree of certainty that the characterization of the sources in terms of location, magnitude, and activity duration can be meaningful. A fairly good knowledge of source locations can substantially decrease the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. A methodology is developed to use a sequence of dedicated monitoring network design and implementation and to screen and identify the possible source locations. The proposed methodology utilizes a combination of spatial interpolation of concentration measurements and simulated annealing as optimization algorithm for optimal design of the monitoring network. These monitoring networks are to be designed and implemented sequentially. The sequential design is based on iterative pollutant concentration measurement information from the sequentially designed monitoring networks. The optimal monitoring network design utilizes concentration gradient information from the monitoring network at previous iteration to define the objective function. The capability of the feedback information based iterative methodology is shown to be effective in estimating the source locations when no such information is initially available. This unknown pollution source locations identification methodology should be very useful as a screening model for subsequent accurate estimation of the unknown pollution sources in terms of location, magnitude, and activity duration.
- Azghadi, B. N. S., & Kerachian, R. (2010). Locating monitoring wells in groundwater systems using embedded optimization and simulation models. Science of the Total Environment, 408(10), 2189–2198. CrossRef
- Chandalavada, S., & Datta, B. (2008). Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resource Management 22, 651–670.
- Chandalavada, S., Datta, B., & Naidu, R. (2011). Uncertainty based optimal monitoring network design for chlorinated hydrocarbon contaminated site. Environment Monitoring Assess, 173, 929–940. CrossRef
- Cieniawski, S. E., Eheart, J. W., & Ranjithan, S. (1995). Using genetic algorithm to solve a multiple objective groundwater monitoring problem. Water Resource Research, 31(2), 399–409. CrossRef
- Datta, B., & Dhiman, S. D. (1996). Chance-constrained optimal monitoring network design for pollutants in groundwater. Journal of Water Resource Planning & Management, 122(3), 180–188. CrossRef
- Dhar, A., & Datta, B. (2007). Multi-objective design of dynamic monitoring networks for detection of groundwater pollution. Journal of Water Resource Planning and Management, 133(4), 329–338. CrossRef
- Dhar, A., & Datta, B. (2010). Logic-based design of groundwater monitoring network for redundancy reduction. Journal of Water Resource Planning and Management, 136, 88. CrossRef
- Fethi, B. J., Loaiciga, H. A., & Marino, M. A. (1994). Multivariate geostatistical design of groundwater monitoring networks. Journal of Water Resource Planning and Management. ASCE, 120(4), 505–522. CrossRef
- Goffe, W. L. (1996). SIMANN: A global optimization algorithm using simulated annealing. Studied in nonlinear dynamics and econometrics. Berkeley: Berkeley Electronic Press.
- Grabow, G., Yoder, D. C., & Mote, C. R. (2000). An empirically-based sequential ground water monitoring network design procedure. Journal of American Water Resource Association, 36(3), 549–566. CrossRef
- GSLIB. (1998). Geostatistical Software Library and user’s guide, 1998 developed by Deutsch CV and Journel AG. New York: Oxford University Press.
- Hudak, P. F., Loaiciga, H. A., & Marino, M. A. (1995). Regional-scale ground water quality monitoring via integer programming. Journal of Hydrology (Amst), 164(1–4), 153–170. CrossRef
- Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983).Optimization by simulated annealing. Science, 220, 671–680.
- Kollat, J. B., Reed, P. M., & Kasprzyk, J. R. (2008). A new epsilon-dominance hierarchical bayesian optimization algorithm for large multi-objective monitoring network design problems. Advances in Water Resources, 31(5), 828–845. CrossRef
- Kollat, J. B., Reed, P. M., & Maxwell, R. (2011). Many-objective groundwater monitoring network design using bias-aware ensemble kalman filtering, evolutionary optimization, and visual analytics. Water Resource Research, 47, W02529. CrossRef
- Loaiciga, H. A. (1989). An optimization approach for groundwater quality monitoring network design. Water Resource Research, 25(8), 1771–1782. CrossRef
- Loaiciga, H. A., & Hudak, P. F. (1992). A location modelling approach for groundwater monitoring network augmentation. Water Resource Researce, 28(3), 643–649. CrossRef
- Loaiciga, H. A., & Hudak, P. F. (1993). An optimization method for monitoring network design in multilayered groundwater flow systems. Water Resource Research, 29, 2835.
- Mahar, P. S., & Datta, B. (1997). Optimal monitoring network and ground-water-pollution source identification. Journal of Water Resource Planning and Management, 123(4), 199–207. CrossRef
- Massmann, J., & Freeze, R. A. (1987). Groundwater pollution from waste management sites: the interaction between risk-based engineering design and regulatory policy. I: Methodology. Water Resource Research, 23(2), 351–367. CrossRef
- McKinney, D. C., & Loucks, D. P. (1992). Network design for predicting groundwater pollution. Water Resource Research, 28(1), 133–147. CrossRef
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M., Teller, A. H., and Teller, E., (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.
- Meyer, P. D., & Brill, E. D., Jr. (1988). A method for locating wells in a groundwater pollution monitoring network under conditions of uncertainty. Water Resource Research., 24(8), 1277–1282. CrossRef
- Meyer, P. D., Valocchi, A. J., Eheart, J. W. (1994). Monitoring network design to provide initial detection of groundwater pollution. Water Resource Research, 30, 2647
- mGstat V 0.99 (2004). A MATLAB code developed by Thomas Mejer Hansen. http://sourceforge.net/projects/mgstat/files/
- Montas, H. J., Mohtar, R. H., Hassan, A. E., & AlKhal, F. A. (2000). Heuristic space-time design of monitoring wells for pollutant plume characterization in stochastic flow fields. Journal of Contaminant Hydrology, 43(3–4), 271–301. CrossRef
- Mugunthan, P., & Shoemaker, C. A. (2004). Time varying optimization for monitoring multiple pollutants under uncertain hydrogeology. Bioremediation Journal, 8(3–4), 129–146. CrossRef
- Nunes, L. M., Cunha, M. C., & Ribeiro, L. (2004a). Groundwater monitoring network optimization with redundancy reduction. Journal of Water Resource Planning and Management, 130(1), 33–43. CrossRef
- Nunes, L. M., Cunha, M. C., & Ribeiro, L. (2004b). Optimal space–time coverage and exploration costs in groundwater monitoring networks. Environment Monitoring Assess, 93(1–3), 103–124. CrossRef
- Reed, P., & Minsker, B. S. (2004). Striking the balance: long-term groundwater monitoring design for conflicting objective. Journal of Water Resource Planning and Management, 130(2), 140–149. CrossRef
- Sreenivasulu, C., & Datta, B. (2008). Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resource Management, 22(6), 651–670. CrossRef
- Wu, J., Zheng, C., & Chien, C. C. (2005). Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. Journal of Contaminant Hydrology, 77, 41–65.
- Yeh, M. S., Lin, Y. P., & Chang, L. C. (2006). Designing an optimal multivariate Geostatistical groundwater quality monitoring network using factorial Kriging and genetic algorithm. Journal of Environmental Geology, 50, 101–121. CrossRef
- Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations
Environmental Monitoring and Assessment
Volume 185, Issue 7 , pp 5611-5626
- Cover Date
- Print ISSN
- Online ISSN
- Springer Netherlands
- Additional Links
- Optimal monitoring network
- Groundwater pollution
- Geostatistical spatial interpolation
- Pollution source locations identification
- Industry Sectors
- Author Affiliations
- 1. Discipline of Civil and Environmental Engineering, School of Engineering and Physical Sciences, James Cook University, Townsville, QLD, 4811, Australia
- 2. CRC for Contamination Assessment and Remediation of the Environment, Mawson Lakes, SA, 5095, Australia