Identification of surrogates for rapid monitoring of microbial inactivation by ozone for water reuse: A pilot-scale study.

J Hazard Mater

Environmental Simulation and Pollution Control State Key Joint Laboratory, State Environmental Protection Key Laboratory of Microorganism Application and Risk Control (SMARC), School of Environment, Tsinghua University, Beijing 100084, PR China; Beijing Laboratory for Environmental Frontier Technologies, Beijing 100084, PR China; Shenzhen Environmental Science and New Energy Technology Engineering Laboratory, Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, PR China.

Published: February 2022

The complex contaminants in reclaimed water sources and delayed feedback of microbial detection have brought tremendous challenges to disinfection process control. The identification of sensitive and online surrogates for indicating microbial inactivation efficacy is vital to evaluate and optimize the disinfection technologies and processes. This study analyzes the inactivation of microbial indicators during ozone disinfection at a pilot-scale study over 5 months. It is identified that total fluorescence (TF) intensity, ultraviolet absorbance at 254 nm (UV) and intracellular adenosine triphosphate (cATP) concentration can act as surrogates in predicting microbial inactivation by ozone. Particularly, the empirical linear correlations for log removal values (LRV) of TF, UV and cATP concentration are developed for the inactivation of four widely applied microbial indicators, namely the total coliforms, fecal coliforms, Escherichia coli (E. coli) and heterotrophic plate count (HPC) (R = 0.86-0.96). Validation analyses are further conducted to verify the robustness and effectiveness of empirical models. Notably, TF is considered as the most efficient surrogate due to its high sensitivity, accuracy and reliability, whereas cATP concentration is an efficient supplement to directly reflect total microbial counts. The study is important to provide a rapid and reliable approach for ozone disinfection efficiency evaluation and prediction.

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Source
http://dx.doi.org/10.1016/j.jhazmat.2021.127567DOI Listing

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