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  • Fluorescence probes for real-time remote cyanobacteria monitoring: A review of challenges and opportunities

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    BertonePUB7034.pdf (493.7Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Bertone, Edoardo
    Burford, Michele A
    Hamilton, David P
    Griffith University Author(s)
    Burford, Michele A.
    Hamilton, David P.
    Bertone, Edoardo
    Year published
    2018
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    Abstract
    In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as ...
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    In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as identifying the effect of environmental factors on pigment production, and in turn, the conversion to cyanobacteria abundance estimation. We argue that a comprehensive calibration approach to obtain reliable readings goes well beyond manufacturers’ recommendations, and should involve several context-specific experiments. We also believe that if such a comprehensive set of experiments is conducted, the data collected from fluorescence sensors could be used in artificial intelligence modelling approaches to reliably predict, in near real-time, the presence and abundance of different cyanobacteria species. This would have significant benefits for both drinking and recreational water management, given that cyanobacterial toxicity, and taste and odour compounds production, are species-dependent.
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    Journal Title
    Water Research
    Volume
    141
    DOI
    https://doi.org/10.1016/j.watres.2018.05.001
    Funder(s)
    ARC
    Grant identifier(s)
    DP190101848
    Copyright Statement
    © 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Other environmental sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/381440
    Collection
    • Journal articles

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