Peer review artikel

Quantitative microbial risk assessment of repairs of the drinking water distribution system


“A Quantitative Microbial Risk Assessment model was developed to assess the infection risk due to faecal contamination events after repairs of drinking water mains. Ingress was modelled per section between isolation valves; transport of pathogens and consumed dose were modelled with a hydraulic network model and stochastic drinking water demand patterns. Dose response models were then used to calculate the risk of infection. The sensitivity analysis showed that the contamination concentration is the most important parameter for the ingested dose, while the choice of dose response relation highly impacts the resulting infection risk. The time of day that valves are opened after repairs, releasing the contamination, in combination with the time that drinking water is withdrawn from the tap influences the amount of contamination consumed versus flushed away through non-exposure uses such as toilet flushes and showering. A standard QMRA approach that typically neglects the diurnal consumption pattern may underestimate the risk. The consumption volume is less important. Issuing a boil water advice and opening only one valve before “releasing” the isolation section are effective mitigation options to reduce the infection risk per event by 50% to 80%. The statutory Dutch sampling protocol requires E. coli analysis of a 100 ml sample taken the day after the repair. The modelled probability of detecting a contamination under these circumstances is approximately 25%, because the contamination rapidly leaves the drinking water distribution system through the customers’ taps, toilets and showers. If a sample is taken 1–4 hours after the repair the modelled probability of detection exceeds 80% when taken at the optimal location.”

© 2017 Elsevier B.V. All rights reserved.

(Citaat: Blokker, E.J.M., Smeets, P.W.M.H., Medema, G.J. – Quantitative microbial risk assessment of repairs of the drinking water distribution system – Microbial risk analysis 8(2018)22-31 –

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