Environmental Modeling & Assessment

, Volume 18, Issue 1, pp 95–103 | Cite as

Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

  • Adam W. Pollak
  • J. Jeffrey Peirce
  • Lino J. Alvarez-VázquezEmail author
  • Miguel E. Vázquez-Méndez


A method to optimally site river water quality sensors is expanded and applied to a case study river to explore the application of mathematical siting methods to the design of river sampling networks. Fecal coliform contamination due to flooded swine waste lagoons was modeled as it moves downstream, and optimal sensor locations are located by minimizing the objective function of the optimization problem. The results of the simulations are analyzed by varying the number of allowed sampling locations and the simulated contamination event. For the case study application, the model suggests three sampling locations along the modeled river section. These three suggested sensing points did not greatly vary in location for different river flows and contamination events, indicating the robustness of the model results for this specific case study. Generally, the application of mathematical contaminant modeling is a useful and systematic approach to aid the design of river water quality monitoring networks.


Optimal location Water quality sampling Monitoring Water pollution River systems 



The authors thank the additional collaborators who developed the original contaminant model, Aurea Martínez and Miguel A. Vilar. We also thank Martha Absher, assistant dean for Education and Outreach Programs at Duke University, who provided opportunity for this project through the Pratt Research Fellowship program. The financial support provided by Projects MTM2009-07749 and MTM2012-30842 of M.E.C. (Spain) is also gratefully acknowledged.


  1. 1.
    Alvarez-Vázquez, L. J., Martínez, A., Vázquez-Méndez, M. E., & Vilar, M. A. (2006). Optimal location of sampling points for river pollution control. Mathematics & Computers in Simulation, 71, 149–160.CrossRefGoogle Scholar
  2. 2.
    Alvarez-Vázquez, L. J., Martínez, A., Vázquez-Méndez, M. E., & Vilar, M. A. (2009). An application of optimal control theory to river pollution remediation. Applied Numerical Mathematics, 59, 845–858.CrossRefGoogle Scholar
  3. 3.
    Alvarez-Vázquez, L. J., Martínez, A., Rodriguez, C., & Vázquez-Méndez, M. E. (2001). Numerical convergence for a sewage disposal problem. Applied Mathematical Modelling, 25, 1015–1024.CrossRefGoogle Scholar
  4. 4.
    Arzberger, P. (2004). Sensors for environmental observatories. Report of the NSF Sponsored Workshop.Google Scholar
  5. 5.
    Bazaraa, M. S., & Shetty, C. M. (1979). Nonlinear programming. Theory and algorithms. New York: Wiley.Google Scholar
  6. 6.
    Chang, N. B., & Makkeasorn, A. (2010). Optimal site selection of watershed hydrological monitoring stations using remote sensing and grey integer programming. Environmental Modeling & Assessment, 15, 469–486.CrossRefGoogle Scholar
  7. 7.
    Chang, N. B., Wimberly, B., & Xuan, Z. M. (2012). Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model. Journal of Environmental Monitoring, 14, 992–1005.CrossRefGoogle Scholar
  8. 8.
    Clesceri, N. L. (2004). Collaborative Large-Scale Engineering Analysis Network for Environmental Research (CLEANER): an engineering cyberinfrastructure “test bed”. Program Solicitation 03607. US National Science Foundation. Accessed 2 January 2012.
  9. 9.
    Dixon, W., Smyth, G. K., & Chiswell, B. (1996) Topologically optimum monitoring of rivers by approximation algorithms. Paper presented at the International Symposium on Environmental Chemistry and Toxicology, Sydney, 14–18 July 1996.Google Scholar
  10. 10.
    Dixon, W., Smyth, G. K., & Chiswell, B. (1999) Optimized selection of river sampling sites. Water Research, 33, 971–978.CrossRefGoogle Scholar
  11. 11.
    Dove, R. (2012). North Carolina Riverkeepers & Waterkeeper Alliance home page. Accessed 2 January 2012.
  12. 12.
    Environmental Management Commission, NC Division of Water Quality (2009). Neuse River Basinwide Water Quality Plan, Chapter 21, 417.Google Scholar
  13. 13.
    Hren, J., Childress, C. J. D., Norris, J. M., Chaney, T. H., & Myers, D. N. (1990). Regional water quality: evaluation of data for assessing conditions and trends. Environmental Science & Technology, 24, 1122–1127.CrossRefGoogle Scholar
  14. 14.
    Karamouz, M., Kerachian, R., Akhbari, M., & Hafez, .B. (2009) Design of river water quality monitoring networks: a case study. Environmental Modeling & Assessment, 14, 705–714.CrossRefGoogle Scholar
  15. 15.
    Lettenmaier, D. P., & Burges, S. J. (1977). Design of trend monitoring networks. ASCE Journal of Environmental Engineering Division, 103, 785–802.Google Scholar
  16. 16.
    National Resources Defense Council (1998). America’s animal factories: how states fail to prevent pollution from livestock waste (Chapter 17). North Carolina.Google Scholar
  17. 17.
    Ning, S. K., & Chang, N. B. (2002). Multi-objective, decision-based assessment of a water quality monitoring network in a river system. Journal of Environmental Monitoring, 4, 121–126.CrossRefGoogle Scholar
  18. 18.
    Ning, S. K., & Chang, N. B. (2004). Optimal expansion of water quality monitoring network by fuzzy optimization approach. Environmental Monitoring & Assessment, 91, 145–170.CrossRefGoogle Scholar
  19. 19.
    Ning, S. K., & Chang, N. B. (2005). Screening and sequencing analysis for the relocation of water quality monitoring network by stochastic compromise programming. Journal of the American Water Resources Association, 41, 1039–1052.CrossRefGoogle Scholar
  20. 20.
    Sanders, T. G., Ward, R. C., Loftis, J. C., Steele, T. D., Adrian, D. D., & Yevjevich, V. (1983). Design of networks for monitoring water quality. Littleton: Water Resources Publications.Google Scholar
  21. 21.
    Sharp, W. E. (1971). A topographical optimum water-sampling plan for rivers and streams. Water Resource Research, 7, 1641–1646.CrossRefGoogle Scholar
  22. 22.
    Strobl, R. O., Robillard, P. D., Shannon, R. D., Day, R. L., & McDonnell, A. J. (2006). A water quality monitoring network design methodology for the selection of critical sampling points: part I. Environmental Monitoring and Assessment, 112, 137–158.CrossRefGoogle Scholar
  23. 23.
    Ward, P. R. B. (1973). Prediction of mixing lengths for river flow gauging. ASCE Journal of the Hydraulics Division, 99, 1069–1081.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Adam W. Pollak
    • 1
  • J. Jeffrey Peirce
    • 1
  • Lino J. Alvarez-Vázquez
    • 2
    Email author
  • Miguel E. Vázquez-Méndez
    • 3
  1. 1.Department of Civil and Environmental EngineeringDuke UniversityDurhamUSA
  2. 2.Department of Applied Mathematics IIUniversity of VigoVigoSpain
  3. 3.Department of Applied MathematicsUniversity of Santiago de CompostelaLugoSpain

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