Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

  • Demetrios G. Eliades
  • Marios M. Polycarpou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7722)


In this work we present a contamination detection methodology for water distribution networks. The proposed detection method is based on chlorine sensor measurements, which are compare to certain computed upper and lower periodic bounds. The bounds are computed using randomized simulations aimed at capturing the variations in chlorine concentration due to the significant uncertainty in the water demand patterns, average nodal consumptions, roughness parameters and reaction coefficients. The proposed method is applied to a set of high-impact contamination fault scenarios using a benchmark distribution network, for which on-line chlorine concentration sensors are assumed to have been installed at certain locations following an optimization procedure. The results indicate that by using the periodic bounds computed from the randomized simulations, for the proposed benchmark, contamination events are detected within reasonable time.


Water Security Contamination Detection Water Quality Drinking Water Distribution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Critical infrastructure and key resources sector-specific plan as input to the national infrastructure protection plan. Tech. rep., US EPA & DHS (2007)Google Scholar
  2. 2.
    Guidelines for the Physical Security of Water Utilities (56-10) and Guidelines for the Physical Security of Wastewater/Stormwater Utilities (57-10). ASCE (2011)Google Scholar
  3. 3.
    Byer, D., Carlson, K.: Real time detection of intentional chemical contamination in the distribution system. Journal American Water Works Association 97(7), 12 (2005)Google Scholar
  4. 4.
    Cook, J.B., Byrne, J.F., Daamen, R.C., Edwin, A., Roehl, J.: Distribution system monitoring research at charleston water system. In: Proc. ASCE Water Distribution Systems Analysis, p. 20 (2006)Google Scholar
  5. 5.
    Eliades, D., Polycarpou, M.: A fault diagnosis and security framework for water systems. IEEE Transactions on Control Systems Technology 18(6), 1254–1265 (2010)CrossRefGoogle Scholar
  6. 6.
    European Commission: Council Directive 98/83/EC of 3 November 1998 on the quality of water intended for human consumption. Official Journal of the European Communities (December 1998)Google Scholar
  7. 7.
    European Commission: Critical infrastructure protection in the fight against terrorism (October 2004), COM(2004) 702 finalGoogle Scholar
  8. 8.
    Foran, J., Brosnan, T.: Early warning systems for hazardous biological agents in potable water. Environmental Health Perspectives 108(10), 993–995 (2000)CrossRefGoogle Scholar
  9. 9.
    Hall, J., Zaffiro, A.D., Marx, R.B., Kefauver, P.C., Krishnan, E.R., Haught, R.C., Herrmann, J.G.: On-line water quality parameters as indicators of distribution system contamination. Journal American Water Works Association 99(1), 66–77 (2007)CrossRefGoogle Scholar
  10. 10.
    Hart, D., McKenna, S.: User’s Manual For CANARY. National Security Applications Dept., Sandia National Laboratories, Albuquerque, NM, USA, 4.3.1 edn. (September 2011), ePA 600/R-08/040BGoogle Scholar
  11. 11.
    Helbling, D.E., VanBriesen, J.M.: Modeling residual chlorine response to a microbial contamination event in drinking water distribution systems. ASCE Journal of Environmental Engineering 135(10), 918–927 (2009)CrossRefGoogle Scholar
  12. 12.
    Hrudey, S., Huck, P., Payment, P., Gillham, R., Hrudey, E.: Walkerton: Lessons learned in comparison with waterborne outbreaks in the developed world. Journal of Environmental Engineering and Science 1(6), 397–407 (2002)CrossRefGoogle Scholar
  13. 13.
    Jarrett, R., Robinson, G., O’Halloran, R.: On-line monitoring of water distribution systems: Data processing and anomaly detection. In: Proc. ASCE Water Distribution Systems Analysis, Cincinnati, Ohio, p. 14 (2006)Google Scholar
  14. 14.
    Jonkergouw, P.M.R., Khu, S.T., Savic, D.: Chlorine: A possible indicator of intentional chemical and biological contamination in a water distribution network? In: Proc. of IWA Conference on Automation in Water Quality Monitoring (AutMoNet), Vienna, Austria, p. 8 (2004)Google Scholar
  15. 15.
    Klise, K.A., McKenna, S.A.: Multivariate applications for detecting anomalous water quality. In: Proc. ASCE Water Distribution Systems Analysis, p. 11 (2006)Google Scholar
  16. 16.
    Koch, M.W., McKenna, S.A.: Distributed sensor fusion in water quality event detection. ASCE Journal of Water Resources Planning and Management 137(10), 10–19 (2011)CrossRefGoogle Scholar
  17. 17.
    Krause, A., Leskovec, J., Guestrin, C., VanBriesen, J., Faloutsos, C.: Efficient sensor placement optimization for securing large water distribution networks. ASCE Journal of Water Resources Planning and Management 134(6), 516–526 (2008)CrossRefGoogle Scholar
  18. 18.
    Kroll, D., King, K.: Laboratory and flow loop validation and testing of the operational effectiveness of an on-line security platform for the water distribution system. In: Proc. ASCE Water Distribution Systems Analysis, Cincinnati, Ohio, p. 16 (2006)Google Scholar
  19. 19.
    LeVeque, R.: Nonlinear conservation laws and finite volume methods. In: Steiner, O., Gautschy, A. (eds.) Computational Methods for Astrophysical Fluid Flow, pp. 1–160. Springer, Berlin (1998)Google Scholar
  20. 20.
    McKenna, S.A., Wilson, M., Klise, K.A.: Detecting changes in water quality data. Journal American Water Works Association 100(1), 74–85 (2008)CrossRefGoogle Scholar
  21. 21.
    Murray, R., Haxton, T., Janke, R., Hart, W.E., Berry, J., Phillips, C.: Sensor Network Design for Drinking Water Contamination Warning Systems: A Compendium of Research Results and Case Studies Using the TEVA-SPOT Software. In: EPA (2010)Google Scholar
  22. 22.
    Murray, S., Ghazali, M., McBean, E.A.: Real-time water quality monitoring: Assessment of multisensor data using bayesian belief networks. ASCE Journal of Water Resources Planning and Management 138(1), 63–70 (2012)CrossRefGoogle Scholar
  23. 23.
    Ostfeld, A., Uber, J.G., Salomons, E., Berry, J.W., Hart, W.E., Phillips, C.A., Watson, J.P., Dorini, G., Jonkergouw, P., Kapelan, Z., di Pierro, F., Khu, S.T., Savic, D., Eliades, D., Polycarpou, M., Ghimire, S.R., Barkdoll, B.D., Gueli, R., Huang, J.J., McBean, E.A., James, W., Krause, A., Leskovec, J., Isovitsch, S., Xu, J., Guestrin, C., VanBriesen, J., Small, M., Fischbeck, P., Preis, A., Propato, M., Piller, O., Trachtman, G.B., Wu, Z.Y., Walski, T.: The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms. ASCE Journal of Water Resources Planning and Management 134(6), 556–568 (2008)CrossRefGoogle Scholar
  24. 24.
    Pasha, M.F.K., Lansey, K.: Effect of parameter uncertainty on water quality predictions in distribution systems-case study. IWA Journal of Hydroinformatics 12(1), 1–21 (2010)CrossRefGoogle Scholar
  25. 25.
    Polycarpou, M., Uber, J., Wang, Z., Shang, F., Brdys, M.: Feedback control of water quality. IEEE Control Systems Magazine 22(3), 68–87 (2002)CrossRefGoogle Scholar
  26. 26.
    Raciti, M., Cucurull, J., Nadjm-Tehrani, S.: Anomaly detection in water management systems. In: Lopez, J., Setola, R., Wolthusen, S.D. (eds.) Critical Information Infrastructure Protection. LNCS, vol. 7130, pp. 98–119. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  27. 27.
    Rossman, L.A.: EPANET 2 Users manual. National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH (September 2000)Google Scholar
  28. 28.
    Shang, F., Uber, J.G., Rossman, L.A.: EPANET Multi-Species Extension User’s Manual. National Risk Management Research Laboratory, Office of Research and Development, U.S. Enviromental Protection Agency, Cincinnati, OH 45268 (January 2008), EPA/600/S-07/021Google Scholar
  29. 29.
    Umberg, K.A.: Performance Evaluation of Real-time Event Detection Algorithms. Master’s thesis, University of Cincinnati (2006)Google Scholar
  30. 30.
    U.S. Government: National primary drinking water regulations - Title 40, Code of federal regulations, Part 141 - Enviromental Protection Agency (EPA) (2002)Google Scholar
  31. 31.
    Yang, Y.J., Haught, R.C., Goodrich, J.A.: Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results. Journal of Environmental Management 90(8), 2494–2506 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Demetrios G. Eliades
    • 1
  • Marios M. Polycarpou
    • 1
  1. 1.KIOS Research Center for Intelligent Systems and Networks, Department of Electrical and Computer EngineeringUniversity of CyprusNicosiaCyprus

Personalised recommendations