Integrating electrical and automation risks into water safety plans: A semi-quantitative framework and application to the Negrisiola drinking water supply system
Mario Cerroni, Clara Sette, Martino Passoni, Andrea Longato, Roberto ScroccaroABSTRACT
Ensuring drinking-water safety requires the consideration of hazards that, although not directly affecting water quality, may compromise treatment reliability, operational continuity, and public health protection. The integration of electrical and automation-related risks into Water Safety Plan (WSP) assessments remains methodologically underdeveloped, despite their critical role in system reliability and process control. This study proposes a semi-quantitative framework to incorporate such risks into WSP processes through structured checklists and a rule-based probability–severity scoring approach. The methodology was applied to the Negrisiola drinking water supply system in Northern Italy. Thirteen risk scenarios were assessed, resulting in two high-risk cases (R = 12), two medium-risk cases (R = 6 and R = 9), and the remaining scenarios classified as low risk. The analysis shows that risk is predominantly driven by severity rather than probability, reflecting the low-frequency but high-impact nature of infrastructure-related failures. A power outage event observed during the study period, associated with a reduction in free chlorine concentration, provides illustrative evidence consistent with the identified failure pathways. Although no causal relationship can be definitively established, this observation supports the plausibility of the proposed risk mechanisms. The results demonstrate that electrical and automation systems should be considered integral components of water safety management. The proposed framework provides a practical and reproducible tool to enhance the integration of operational risks into WSPs and support risk prioritisation. Further research should focus on strengthening empirical validation through the integration of operational and analytical datasets.