Literature DB >> 24953418

Contamination event detection using multiple types of conventional water quality sensors in source water.

Shuming Liu1, Han Che, Kate Smith, Lei Chen.   

Abstract

Early warning systems are often used to detect deliberate and accidental contamination events in a water system. Conventional methods normally detect a contamination event by comparing the predicted and observed water quality values from one sensor. This paper proposes a new method for event detection by exploring the correlative relationships between multiple types of conventional water quality sensors. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory. Results from these experiments demonstrated the correlative responses of multiple types of sensors. It was observed that the proposed method could detect a contamination event 9 minutes after the introduction of lead nitrate solution with a concentration of 0.01 mg L(-1). The proposed method employs three parameters. Their impact on the detection performance was also analyzed. The initial analysis showed that the correlative response is contaminant-specific, which implies that it can be utilized not only for contamination detection, but also for contaminant identification.

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Year:  2014        PMID: 24953418     DOI: 10.1039/c4em00188e

Source DB:  PubMed          Journal:  Environ Sci Process Impacts        ISSN: 2050-7887            Impact factor:   4.238


  3 in total

1.  A method of detecting contamination events using multiple conventional water quality sensors.

Authors:  Shuming Liu; Han Che; Kate Smith; Chao Chen
Journal:  Environ Monit Assess       Date:  2014-12-03       Impact factor: 2.513

2.  Review of Modeling Methodologies for Managing Water Distribution Security.

Authors:  Emily Zechman Berglund; Jorge E Pesantez; Amin Rasekh; M Ehsan Shafiee; Lina Sela; Terranna Haxton
Journal:  J Water Resour Plan Manag       Date:  2020-06-13       Impact factor: 3.054

3.  Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring.

Authors:  Yingchi Mao; Hai Qi; Ping Ping; Xiaofang Li
Journal:  Sensors (Basel)       Date:  2017-12-04       Impact factor: 3.576

  3 in total

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