Concerns about drinking water (DW) quality contamination during water distribution raise a need for real-time monitoring and rapid contamination detection. Early warning systems (EWS) are a potential solution. The EWS consist of multiple conventional sensors that provide the real-time measurements and algorithms that allow the recognizing of contamination events from normal operating conditions. In most cases, these algorithms have been established with artificial data, while data from real and biological contamination events are limited. The goal of the study was the event detection performance of the Mahalanobis distance method in combination with on-line DW quality monitoring sensors and manual measurements of grab samples for potential DW biological contamination scenarios. In this study three contamination scenarios were simulated in a pilot-scale DW distribution system: untreated river water, groundwater and wastewater intrusion, which represent realistic contamination scenarios and imply biological contamination. Temperature, electrical conductivity (EC), total organic carbon (TOC), chlorine ion (Cl-), oxidation–reduction potential (ORP), pH sensors and turbidity measurements were used as on-line sensors and for manual measurements. Novel adenosine-triphosphate and flow cytometric measurements were used for biological water quality evaluation. The results showed contamination detection probability from 56% to 89%, where the best performance was obtained with manual measurements. The probability of false alarm was 5–6% both for on-line and manual measurements. The Mahalanobis distance method with DW quality sensors has a good potential to be applied in EWS. However, the sustainability of the on-line measurement system and/or the detection algorithm should be improved.

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