Potential of Bayesian networks for adaptive management in water recycling

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 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.