Advertisement

Identification of Contamination Potential Source (ICPS): A Topological Approach for the Optimal Recognition of Sensitive Nodes in a Water Distribution Network

  • Gilda Capano
  • Marco Amos Bonora
  • Manuela Carini
  • Mario MaioloEmail author
Conference paper
  • 43 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11973)

Abstract

The correct management of urban water networks have to be supported by monitoring and estimating water quality. The infrastructure maintenance status and the possibility of a prevention plan availability influence the potential risk of contamination. In this context, the Contamination Source Identification (CSI) models aim to identify the contamination source starting from the concentration values referring to the nodes. This paper proposes a methodology based on Dynamics of Network Pollution (DNP). The DNP approach, linked to the pollution matrix and the incidence matrix, allows a topological analysis on the network structure in order to identify the nodes and paths most sensitive to contamination, namely those that favor a more critical diffusion of the introduced contaminant. The procedure is proposed with the aim of optimally identifying the potential contamination points. By simulating the contamination of a synthetic network, using a bottom-up approach, an optimized procedure is defined to trace back to the chosen node as the most probable contamination source.

Keywords

Water quality Contamination sources Graph theory 

Notes

Aknowledgments

This work is partially supported by the Italian Regional Project (POR CALABRIA FESR 2014–2020): “origAMI, Original Advanced Metering Infrastructure” CUP J48C17000170006.

References

  1. 1.
    Al-Jasser, A.O.: Chlorine decay in drinking-water transmission and distribution systems: pipe service age effect. Water Res. 41(2), 387–396 (2007).  https://doi.org/10.1016/j.watres.2006.08.032CrossRefGoogle Scholar
  2. 2.
    Adedoja, O.S., Hamam, Y., Khalaf, B., Sadiku, R.: Towards development of an optimization model to identify contamination source in a water distribution network. Water 10(5), 579–606 (2018).  https://doi.org/10.3390/w10050579CrossRefGoogle Scholar
  3. 3.
    Borowski, E.J., Borwein, J.M.: The HarperCollins Dictionary of Mathematics. HarperCollins, New York (USA) (1991)Google Scholar
  4. 4.
    Dawsey, W.J., Minsker, B.S., VanBlaricum, V.L.: Bayesian belief networks to integrate monitoring evidence of water distribution system contamination. J. Water Resour. Plann. Manage. 132(4), 234–241 (2006).  https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(234)CrossRefGoogle Scholar
  5. 5.
    Di Cristo, C.D., Leopardi, A.: Pollution source identification of accidental contamination in water distribution networks. J. Water Resour. Plann. Manage. 134(2), 197–202 (2008).  https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(197)CrossRefGoogle Scholar
  6. 6.
    Digiano, F.A., Zhang, W.: Pipe section reactor to evaluate chlorine-wall reaction. J.-Am. Water Works Assoc. 7(1), 74–85 (2005)CrossRefGoogle Scholar
  7. 7.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Di Nardo, A., Di Natale, M.: A heuristic design support methodology based on graph theory for district metering of water supply networks. Eng. Optim. 43(2), 193–211 (2011).  https://doi.org/10.1080/03052151003789858CrossRefGoogle Scholar
  9. 9.
    Guan, J., Aral, M.M., Maslia, M.L., Grayman, W.M.: Identification of contaminant sources in water distribution systems using simulation-optimization method: case study. J. Water Resour. Plann. Manage. 132(4), 252–262 (2006).  https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(252)CrossRefGoogle Scholar
  10. 10.
    Harary, F.: Graph Theory. Reading. Addison-Wesley, Boston (USA) (1994)Google Scholar
  11. 11.
    Kang, D., Lansey, K.: Revisiting optimal water-distribution system design: issues and a heuristic hierarchical approach. J. Water Resour. Plann. Manage. 138(3), 208–217 (2012).  https://doi.org/10.1061/(ASCE)WR.1943-5452.0000165CrossRefGoogle Scholar
  12. 12.
    Kessler, A., Ostfeld, A., Sinai, G.: Detecting accidental contaminations in municipal water networks. J. Water Resour. Plann. Manage. 124(4), 192–198 (1998).  https://doi.org/10.1061/(ASCE)0733-9496(1998)124:4(192)CrossRefGoogle Scholar
  13. 13.
    Kim, M., Choi, C.Y., Gerba, C.P.: Source tracking of microbial intrusion in water systems using artificial neural networks. Water Res. 42(4–5), 1308–1314 (2008).  https://doi.org/10.1016/j.watres.2007.09.032CrossRefGoogle Scholar
  14. 14.
    Kim, H., Kim, S., Koo, J.: Modelling chlorine decay in a pilot scale water distribution system subjected to transient. In: Civil and Environmental Engineering, Pusan National University (Korea) and Environmental Engineering, University of Seoul (2015)  https://doi.org/10.1016/j.proeng.2015.08.89
  15. 15.
    Kim, S.H., Aral, M.M., Eun, Y., Park, J.J., Park, C.: Impact of sensor measurement error on sensor positioning in water quality monitoring networks. Stoch. Environ. Res. Risk Assess. 31(3), 743–756 (2017).  https://doi.org/10.1007/s00477-016-1210-1CrossRefGoogle Scholar
  16. 16.
    Liu, L., Zechman, E.M., Mahinthakumar, G., Ranji Ranjithan, S.: Identifying contaminant sources for water distribution systems using a hybrid method. Civil Eng. Environ. Syst. 29(2), 123–136 (2012).  https://doi.org/10.1080/10286608.2012.663360CrossRefGoogle Scholar
  17. 17.
    Maiolo, M., Carini, M., Capano, G., Pantusa, D., Iusi, M.: Trends in metering potable water. Water Pract. Technol. 14(1.1), 1–9 (2019).  https://doi.org/10.2166/wpt.2018.120CrossRefGoogle Scholar
  18. 18.
    Maiolo, M., Pantusa, D.: A methodological proposal for the evaluation of potable water use risk. Water Pract. Technol. 10(1), 152–163 (2015).  https://doi.org/10.2166/wpt.2015.017CrossRefGoogle Scholar
  19. 19.
    Maiolo, M., Pantusa, D.: Combined reuse of wastewater and desalination for the management of water systems in conditions of scarcity. Water Ecol. 4(72), 116–126 (2017).  https://doi.org/10.23968/2305-3488.2017.22.4.116-126CrossRefGoogle Scholar
  20. 20.
    Maiolo, M., Pantusa, D.: A proposal for multiple reuse of urban wastewater. J. Water Reuse Desalin. 8(4), 468–478 (2018a).  https://doi.org/10.2166/wrd.2017.144CrossRefGoogle Scholar
  21. 21.
    Maiolo, M., Pantusa, D.: Infrastructure Vulnerability Index of drinking water systems to terrorist attacks. Cogent Eng. 5(1), 1456710 (2018b).  https://doi.org/10.1080/23311916.2018.1456710CrossRefGoogle Scholar
  22. 22.
    Mostafa, N.G., Minerva, E., Halim, H.A.: Simulation of Chlorine Decay in Water Distribution Networks. Public Works Department, Faculty of Engineering, Cairo (Egypt), Using EPANET. Case Study - Sanitary and Environmental Engineering Division (2013)Google Scholar
  23. 23.
    Nagatani, T., et al.: Residual chlorine decay simulation in water distribution system. In: The International Symposium on Water Supply Technology, Yokohama (Japan) (2008)Google Scholar
  24. 24.
    Preis, A., Ostfeld, A.: Genetic algorithm for contaminant source characterization using imperfect sensors. Civil Eng. Environ. Syst. 25(1), 29–39 (2008).  https://doi.org/10.1080/10286600701695471CrossRefGoogle Scholar
  25. 25.
    Rashid, B., Rehmani, M.H.: Applications of wireless sensor networks for urban areas: a survey. J. Netw. Comput. Appl. 60, 192–219 (2016)CrossRefGoogle Scholar
  26. 26.
    Rossman, L.A., Clark, R.M., Grayman, W.M.: Modeling chlorine residuals in drinking water distribution systems. J. Environ. Eng. 120(4), 803–820 (1994)CrossRefGoogle Scholar
  27. 27.
    Rossman, L.A.: EPANET 2: Users Manual (2000)Google Scholar
  28. 28.
    Tao, T., Huang, H.D., Xin, K.L., Liu, S.M.: Identification of contamination source in water distribution network based on consumer complaints. J. Central S. Univ. Technol. 19, 1600–1609 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Environmental and Chemical EngineeringUniversity of CalabriaRendeItaly

Personalised recommendations