Literature DB >> 30833266

Early warning method for cyanobacteria toxin, taste and odor problems by the evaluation of fluorescence signals.

C Moldaenke1, Y Fang2, F Yang2, A Dahlhaus3.   

Abstract

Permanganate and ozone are often used in drinking water treatment plants for the oxidation of taste and odor compounds, toxins, and algae as well as the reduction of mussel activity. The disadvantage of an overuse of such oxidants is the potential lysis of cyanobacterial cells. Cell lysis causes taste and odor components as well as toxins to be released into the water, which results in the need for even more treatment to remove these compounds completely. Our research in the CLIENT-SIGN project investigated an innovative method to monitor the lysis of cyanobacteria cells: increases in a specific fluorescence emission spectrum of the cyanobacteria pigment phycocyanin were used as a proxy for cell lysis and other compounds (taste/odor, toxins) leaving the cells. We call this form of phycocyanin "free phycocyanin" or "unbound phycocyanin". By monitoring free phycocyanin via a relatively fast and inexpensive measurement, water utilities will be better able to optimize the dosage of pre-oxidation compounds to remove extracellular compounds while preventing the lysing of cells. Laboratory studies and a case study at Yangcheng Lake (adjacent to Lake Taihu, Yangcheng Lake Water Treatment Plant, Suzhou Industrial Park, China) are presented herein. An online surveillance system that monitors incoming raw water and the water after pre-oxidation is proposed to better cope with changing water conditions.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Chlorine; Cyanotoxins; Fluorescence; Free phycocyanin; Ozone; Taste and odor

Mesh:

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Year:  2019        PMID: 30833266     DOI: 10.1016/j.scitotenv.2019.02.271

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning.

Authors:  Xucheng Fan; Na Xue; Zhiguo Han; Chao Wang; Heer Ma; Yaoqin Lu
Journal:  J Healthc Eng       Date:  2021-09-17       Impact factor: 2.682

  1 in total

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