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Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

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Abstract

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.

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Notes

  1. Risk = Consequences × Threat × Vulnerability.

  2. We will talk further about OWA weights in order to avoid confusion with the weights assigned to the sensitivity criteria by the decision makers.

  3. «Système Informatisé du Répertoire National des Entreprises et des Établissements» Governmental database of all French public and private organisations.

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Acknowledgements

The work presented in the paper is part of the French-German collaborative research project SMart-OnlineWDN that is funded by the French National Research Agency (ANR Project: ANR-11-SECU-006) and the German Federal Ministry of Education and Research (BMBF; Project: 13N12180).

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Authors and Affiliations

  1. Joint research unit Territorial Management of Water and the Environment (GESTE) Irstea-Engees, 1 quai Koch, 67070, Strasbourg Cedex, France

    Amir Nafi & Eric Crastes

  2. School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, BC, V1V 1V7, Canada

    Rehan Sadiq

  3. Water Infrastructure Asset Management Team, Water Department, ETBX Research Unit, Irstea, 33612, Cestas, France

    Denis Gilbert & Olivier Piller

Authors
  1. Amir Nafi
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  2. Eric Crastes
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  3. Rehan Sadiq
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  4. Denis Gilbert
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  5. Olivier Piller
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Corresponding author

Correspondence to Amir Nafi.

Appendix

Appendix

The first inference engine concerns the assessment of structural vulnerability index as illustrated in the Fig. 12.

Table 10 illustrates the rule base for inference 1 in order to estimate the structural vulnerability. IF Level of protection is “P” AND Ease of installation of the injection device is “I” THEN Structural vulnerability is “SV”.

Table 10 Rule base for structural vulnerability index assessment
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The second inference engine aims at assessing the vulnerability of intrusion point linked to its environment as illustrated by Fig. 13.

Fig. 12
figure 12

Knowledge-base for the retained criteria: case of structural vulnerability index

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Table 11 illustrates the rule base for inference 2 to estimate the intrinsic vulnerability linked to the environment. IF Level of surveillance is “S” AND Ease of physical access is “A” THEN Vulnerability linked with the environment is “VE”.

Table 11 Rule base for vulnerability linked to the environment of intrusion point
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Table 12 Rule-base for the assessment of intrinsic vulnerability
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The last inference engine concerns the assessment of intrinsic vulnerability index based on the aggregation of previous indexes. The knowledge-base of inference 3 is illustrated by the Fig. 14.

Fig. 13
figure 13

Knowledge-base for the retained criteria: case of vulnerability linked to the environment

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The Table 6 illustrates the rule base for inference 3 to estimate the intrinsic vulnerability linked to the environment. IF Vulnerability linked with the environment of the intrusion site (VE) is “SE” AND Structural Vulnerability is SV” THEN Intrinsic Vulnerability is “IV” (Fig. 14).

Fig. 14
figure 14

Knowledge-base for the retained indexes: case of intrinsic vulnerability index

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Nafi, A., Crastes, E., Sadiq, R. et al. Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments. Stoch Environ Res Risk Assess 32, 527–544 (2018). https://doi.org/10.1007/s00477-017-1415-y

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