%0 Journal Article %A Blokker, E.J.M. %A Smeets, P.W.M.H. %D 2017 %T CCWI2017: F13 'Reduction of Infection Risk by Automated Rapid Detection of Faecal Contamination of Drinking Water Distribution Systems' %U https://orda.shef.ac.uk/articles/journal_contribution/CCWI2017_F13_Reduction_of_Infection_Risk_by_Automated_Rapid_Detection_of_Faecal_Contamination_of_Drinking_Water_Distribution_Systems_/5364520 %R 10.15131/shef.data.5364520.v1 %2 https://orda.shef.ac.uk/ndownloader/files/9219130 %K CCWi2017 %K Faecal Contamination %K Infection Risk %K Sensoring %K Civil Engineering not elsewhere classified %X Water companies may perform a quantitative microbial risk assessment to verify that the produced water is safe to drink. However, in the drinking water distribution system (DWDS) a faecal contamination may occur when the integrity of the system is broken. Therefore a similar verification of water safety during distribution is desirable, however challenging due to the complexity and extent of the network. Currently verification consists of regulatory 100 ml samples for E.coli and enterococci analyses at the consumer tap. The drawbacks are that not all contaminations are detected and analysis results take 24 to 48 hours, before action can be taken. Automated analysers that take a sample and analyse it on location within a few hours are now available and can act as online sensors for faecal contamination. This allows a much faster response when a contamination is detected. In this study a hydraulic model was run with many contamination scenario’s and sensor networks to quantify the resulting exposure of consumes to pathogens. The resulting infection risk was then determined with a previously developed QMRA model for distribution systems. It was shown that a boil water notice within hours instead of days can significantly reduce the infection risk. Next to reducing infection risk, these automated analysers can help to better assess infection risk levels and enable more flexibility in network maintenance. %I The University of Sheffield