Near real-time detection of E. coli in reclaimed water

• Main Authors: Sherchan, Samendra, Miles, Syreeta, Ikner, Luisa, Yu, Hye-Weon, Snyder, Shane Allen, Pepper, Ian L.
• Other Authors: Nanyang Environment and Water Research Institute
• Format: Article
• Language: English
• Published: 2018
• Subjects: Water Quality; Online Sensors; DRNTU::Engineering::Environmental engineering
• Online Access: https://hdl.handle.net/10356/88440; http://hdl.handle.net/10220/45735
• Institution: Nanyang Technological University

Advanced treatment of reclaimed water prior to potable reuse normally results in the inactivation of bacterial populations, however, incremental treatment failure can result in bacteria, including pathogens, remaining viable. Therefore, potential microorganisms need to be detected in real-time to preclude potential adverse human health effects. Real-time detection of microbes presents unique problems which are dependent on the water quality of the test water, including parameters such as particulate content and turbidity, and natural organic matter content. In addition, microbes are unusual in that: (i) viability and culturability are not always synonymous; (ii) viability in water can be reduced by osmotic stress; and (iii) bacteria can invoke repair mechanisms in response to UV disinfection resulting in regrowth of bacterial populations. All these issues related to bacteria affect the efficacy of real-time detection for bacteria. Here we evaluate three different sensors suitable for specific water qualities. The sensor A is an on-line, real-time sensor that allows for the continuous monitoring of particulates (including microbial contaminants) using multi-angle-light scattering (MALS) technology. The sensor B is a microbial detection system that uses optical technique, Mie light scattering, for particle sizing and fluorescence emission for viable bacteria detection. The last sensor C was based on adenosine triphosphate (ATP) production. E. coli was used a model organism and out of all tested sensors, we found the sensor C to be the most accurate. It has a great potential as a surrogate parameter for microbial loads in test waters and be useful for process control in treatment trains.