Environmental Monitoring and Assessment

, Volume 185, Issue 7, pp 5611–5626 | Cite as

Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations



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.


Optimal monitoring network Groundwater pollution Geostatistical spatial interpolation Optimization Pollution source locations identification 



Size of the grid in the i,j direction, respectively


Maximum permissible number of monitoring wells that can be placed in the study area including the existing ones in the current design stage


Average of the measured concentration from the initial and implemented well locations


A very high value of concentration


Total number of monitoring wells already existing at the beginning of each design iteration


Decision variable that can have integer values, 0 or 1


Variable representing iteration number


Current iteration number


Grid location co-ordinates


Total number of initially available wells


Number of implemented monitoring wells


Total number of wells eliminated


Total number of wells in a field before the current iteration ITR


Initially observed pollutant concentration data from arbitrary observation wells

Cobs\( {{{M_{\mathrm{ITR}}}}}\)

Current observed pollutant concentration

\( {C_{{\mathrm{avg}_{{{M_{\mathrm{ITR}}}}}^{\mathrm{ITR}}}}} \)

Average concentration for current iteration ITR

\( {C_{{\mathrm{krig}_{i,j}^{\mathrm{ITR}}}}} \)

Kriged concentration values at all grid locations i,j for current iteration ITR

\( \mathrm{Var}_{i,j}^{\mathrm{ITR}} \)

Variance of Gaussian noise distribution at all the nodes i,j, for current iteration ITR

For all


Belongs to


  1. 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.CrossRefGoogle Scholar
  2. Chandalavada, S., & Datta, B. (2008). Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resource Management 22, 651–670.Google Scholar
  3. Chandalavada, S., Datta, B., & Naidu, R. (2011). Uncertainty based optimal monitoring network design for chlorinated hydrocarbon contaminated site. Environment Monitoring Assess, 173, 929–940.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. Dhar, A., & Datta, B. (2010). Logic-based design of groundwater monitoring network for redundancy reduction. Journal of Water Resource Planning and Management, 136, 88.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Goffe, W. L. (1996). SIMANN: A global optimization algorithm using simulated annealing. Studied in nonlinear dynamics and econometrics. Berkeley: Berkeley Electronic Press.Google Scholar
  10. 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.CrossRefGoogle Scholar
  11. GSLIB. (1998). Geostatistical Software Library and user’s guide, 1998 developed by Deutsch CV and Journel AG. New York: Oxford University Press.Google Scholar
  12. 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.CrossRefGoogle Scholar
  13. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983).Optimization by simulated annealing. Science, 220, 671–680.Google Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. Loaiciga, H. A. (1989). An optimization approach for groundwater quality monitoring network design. Water Resource Research, 25(8), 1771–1782.CrossRefGoogle Scholar
  17. Loaiciga, H. A., & Hudak, P. F. (1992). A location modelling approach for groundwater monitoring network augmentation. Water Resource Researce, 28(3), 643–649.CrossRefGoogle Scholar
  18. Loaiciga, H. A., & Hudak, P. F. (1993). An optimization method for monitoring network design in multilayered groundwater flow systems. Water Resource Research, 29, 2835.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. McKinney, D. C., & Loucks, D. P. (1992). Network design for predicting groundwater pollution. Water Resource Research, 28(1), 133–147.CrossRefGoogle Scholar
  22. 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.Google Scholar
  23. 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.CrossRefGoogle Scholar
  24. Meyer, P. D., Valocchi, A. J., Eheart, J. W. (1994). Monitoring network design to provide initial detection of groundwater pollution. Water Resource Research, 30, 2647Google Scholar
  25. mGstat V 0.99 (2004). A MATLAB code developed by Thomas Mejer Hansen. http://sourceforge.net/projects/mgstat/files/
  26. 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.CrossRefGoogle Scholar
  27. Mugunthan, P., & Shoemaker, C. A. (2004). Time varying optimization for monitoring multiple pollutants under uncertain hydrogeology. Bioremediation Journal, 8(3–4), 129–146.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.Google Scholar
  33. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Discipline of Civil and Environmental Engineering, School of Engineering and Physical SciencesJames Cook UniversityTownsvilleAustralia
  2. 2.CRC for Contamination Assessment and Remediation of the EnvironmentMawson LakesAustralia

Personalised recommendations