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Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model

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Abstract

Urban treated wastewater can be used as a sustainable water supply alternative; however, it contains chemical and biochemical contaminants and poses risks to the affected area. This study aims to assess the risk of treated wastewater application in four sectors: agriculture, landscape irrigation, groundwater artificial recharge, and industry, considering environmental, social, economic, and technical criteria using the novel Hybrid Bayesian Network. The risk of using 27 Iran’s wastewater treatment plants’ effluent in the four sectors were calculated and the Bayesian Networks results demonstrate that four risk models were well trained and were capable of predicting the risks with acceptable performance. Using the trained risk frameworks, the risk of applying the treated wastewater of Tabriz metropolitan wastewater treatment plant in Iran for various sectors was obtained. The results showed that the risk of using the treated wastewater in agriculture, landscape irrigation, artificial recharge, and industry was 26.9%, 17.7%, 18.7%, and 11.9%, respectively. The developed Hybrid Bayesian Network framework in this study can help water resources managers for risk-based prioritizing the effluent reuse scenarios with high performance.

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Authors and Affiliations

  1. Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

    Javad Shafiee Neyestanak & Abbas Roozbahani

Authors
  1. Javad Shafiee Neyestanak
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  2. Abbas Roozbahani
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Contributions

Javad Shafiee Neyestanak: Writing - original draft preparation, Formal analysis and investigation, Software

Abbas Roozbahani: Supervision, Conceptualization, Methodology, Supervision, Writing - Review & Editing

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Correspondence to Abbas Roozbahani.

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Shafiee Neyestanak, J., Roozbahani, A. Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model. Water Resour Manage 35, 5049–5072 (2021). https://doi.org/10.1007/s11269-021-02989-y

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  • DOI: https://doi.org/10.1007/s11269-021-02989-y

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