A real time method of contaminant classification using conventional water quality sensors

Early warning systems are often used to detect deliberate and accidental contamination events in a water source. After contamination detection, it is important to classify the type of contaminant quickly to provide support for implementation of remediation attempts. Conventional methods commonly rely on laboratory-based analysis or qualitative geometry analysis, which require long analysis time or suffer low true positive rate. This paper proposes a real time contaminant classification method, which discriminates contaminants based on quantitative analysis. The proposed method utilizes the Mahalanobis distance of feature vectors to classify the type of contaminant. The performance and robustness of the proposed method were evaluated using data from contaminant injection experiments and through an uncertainty analysis. An advantage of the proposed method is that it can classify the type of contaminant in minutes with no significant compromise on true positive rate. This will facilitate fast remediation response to contamination events in a water system.