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

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Abstract

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.

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Abbreviations

dx,dy :

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

k :

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

ε min :

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

ε max :

A very high value of concentration

m :

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

f i,j :

Decision variable that can have integer values, 0 or 1

itr:

Variable representing iteration number

ITR:

Current iteration number

i,j :

Grid location co-ordinates

M int :

Total number of initially available wells

M imp :

Number of implemented monitoring wells

M eli :

Total number of wells eliminated

M ITR :

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

Cobs :

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

Є:

Belongs to

References

  • 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.

    Article  Google Scholar 

  • 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 

  • Chandalavada, S., Datta, B., & Naidu, R. (2011). Uncertainty based optimal monitoring network design for chlorinated hydrocarbon contaminated site. Environment Monitoring Assess, 173, 929–940.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dhar, A., & Datta, B. (2010). Logic-based design of groundwater monitoring network for redundancy reduction. Journal of Water Resource Planning and Management, 136, 88.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • GSLIB. (1998). Geostatistical Software Library and user’s guide, 1998 developed by Deutsch CV and Journel AG. New York: Oxford University Press.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983).Optimization by simulated annealing. Science, 220, 671–680.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Loaiciga, H. A. (1989). An optimization approach for groundwater quality monitoring network design. Water Resource Research, 25(8), 1771–1782.

    Article  CAS  Google Scholar 

  • Loaiciga, H. A., & Hudak, P. F. (1992). A location modelling approach for groundwater monitoring network augmentation. Water Resource Researce, 28(3), 643–649.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • McKinney, D. C., & Loucks, D. P. (1992). Network design for predicting groundwater pollution. Water Resource Research, 28(1), 133–147.

    Article  CAS  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Mugunthan, P., & Shoemaker, C. A. (2004). Time varying optimization for monitoring multiple pollutants under uncertain hydrogeology. Bioremediation Journal, 8(3–4), 129–146.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Discipline of Civil and Environmental Engineering, School of Engineering and Physical Sciences, James Cook University, Townsville, QLD, 4811, Australia

    Om Prakash & Bithin Datta

  2. CRC for Contamination Assessment and Remediation of the Environment, Mawson Lakes, SA, 5095, Australia

    Om Prakash & Bithin Datta

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  1. Om Prakash
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  2. Bithin Datta
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Correspondence to Om Prakash.

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Prakash, O., Datta, B. Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations. Environ Monit Assess 185, 5611–5626 (2013). https://doi.org/10.1007/s10661-012-2971-8

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  • DOI: https://doi.org/10.1007/s10661-012-2971-8

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