Detection of drinking water contamination event with Mahalanobis distance method, using on-line monitoring sensors and manual measurement data

Abstract

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.

Dynamic Multivariate Outlier Detection Algorithm Using Ultraviolet Visible Spectroscopy for Monitoring Surface Water Contamination With Hydrological Fluctuation in Real-Time

“…Mahalanobis distance represents the distance between test spectrum x i and residual sample spectral set X n −1 and is calculated as follows: 16 where x false¯ is the mean spectrum of matrix X n −1 by row average, 1 is the column vector with each element being 1, r is the rank of the covariance matrix C ~, λ is the singular value of the matrix, l is the number of singular values chosen, 1 ≤ l < r .…”

Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review

“…In developing new techniques for monitoring ORP, Qasem et al [134] built a remotely operated underwater vehicle containing ORP sensors, and sensors for three more parameters (electrical conductivity, dissolved oxygen, and temperature), to identify oil spills in ocean waters using Raspberry Pi 3 connected to the internet via Wi-Fi. Dejus et al [135] also used a system containing online ORP sensors and five more parameters to detect drinking water contamination events. For water quality control, Helmi, Hafiz, and Rizam [136] developed a surface water quality monitoring buoy with ORP, pH, and temperature sensors, also remotely controlled by an internet-connected computer that sends instructions to the Intel Atom N2600 board, which, in addition to the quality data, sends the physical location coordinates.…”

Experimental methods in chemical engineering: pH

“…Researchers are developing techniques to relate σ and pH and measure these two properties simultaneously for applications in water quality analyses, monitoring the health of plants, agricultural applications, and monitoring systems for body odours. [87][88][89][90][91] However, the systems generally have multiple sensors to monitor these properties. Instrumentation developed to measure conductivity is based on: induction, [87][88][89][90][91] potentiometry [92] with Pt electrodes, [92] microneedles, [93,94] gold-coated glass electrodes, [95] and local electric fields.…”

“…[87][88][89][90][91] However, the systems generally have multiple sensors to monitor these properties. Instrumentation developed to measure conductivity is based on: induction, [87][88][89][90][91] potentiometry [92] with Pt electrodes, [92] microneedles, [93,94] gold-coated glass electrodes, [95] and local electric fields. [96]…”