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  • Potential of Bayesian networks for adaptive management in water recycling

    Author(s)
    Beaudequin, Denise
    Harden, Fiona
    Roiko, Anne
    Mengersen, Kerrie
    Griffith University Author(s)
    Roiko, Anne H.
    Year published
    2017
    Metadata
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    Abstract
    Water recycling is an important solution to increasing water scarcity. However, universal wastewater treatment standards deter uptake of recycling schemes. Lack of data also impedes fit-for-purpose water recycling and water managers are challenged by decision making under uncertain conditions. Bayesian networks (BNs) are increasingly recognised as a valuable tool for decision making under uncertainty. In this study BNs are used to model health risks associated with wastewater irrigation of a public open space. Three BNs based on quantitative microbial risk assessment model risk reduction potential along a treatment chain and ...
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    Water recycling is an important solution to increasing water scarcity. However, universal wastewater treatment standards deter uptake of recycling schemes. Lack of data also impedes fit-for-purpose water recycling and water managers are challenged by decision making under uncertain conditions. Bayesian networks (BNs) are increasingly recognised as a valuable tool for decision making under uncertainty. In this study BNs are used to model health risks associated with wastewater irrigation of a public open space. Three BNs based on quantitative microbial risk assessment model risk reduction potential along a treatment chain and at the site of reuse. The BNs simulate multiple exposure profiles and scenarios, providing conditional probability of infection or illness, comparable with health-based targets. Study findings highlight the significant impact of post treatment risk mitigation, despite challenging conditions. BNs provide a transparent, defensible evidence base for mapping and quantifying risk pathways, comparing decision options and predicting outcomes of management policies.
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    Journal Title
    Environmental Modelling & Software
    Volume
    91
    DOI
    https://doi.org/10.1016/j.envsoft.2017.01.016
    Subject
    Environmental management
    Publication URI
    http://hdl.handle.net/10072/341989
    Collection
    • Journal articles

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