Contaminant classification using cosine distances based on multiple conventional sensors. Issue 2 (22nd December 2014)

• Record Type: Journal Article
• Authors: Liu, Shuming; Che, Han; Smith, Kate; Chang, Tian
• Is Part Of: Environmental science. Volume 17:Issue 2(2015)
• Page Start: 343
• Page End: 350
• Publication Date: 2014-12-22
• Subjects: Environmental monitoring -- Periodicals; Biological monitoring -- Periodicals; Environmental chemistry -- Periodicals; 363.7363
• DOI: 10.1039/c4em00580e
• Languages: English
• ISSNs: 2050-7887

Abstract

Abstract : This paper proposes a new contaminant classification method to discriminate contaminants in a real time manner, independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from injection experiments and compared with a Euclidean distance-based method. Abstract : Emergent contamination events have a significant impact on water systems. After contamination detection, it is important to classify the type of contaminant quickly to provide support for remediation attempts. Conventional methods generally either rely on laboratory-based analysis, which requires a long analysis time, or on multivariable-based geometry analysis and sequence analysis, which is prone to being affected by the contaminant concentration. This paper proposes a new contaminant classification method, which discriminates contaminants in a real time manner independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory and compared with a Euclidean distance-based method. The robustness of the proposed method was evaluated using an uncertainty analysis. The results show that the proposed method performed better in identifying the type of contaminant than the Euclidean distance based method and that it could classify the type of contaminant in minutes without significantly compromising the correct classification rate (CCR).