• Project No 4114
  • Project Name Antimicrobial resistance in advanced water treatment systems and supply networks
  • Lead Organisation Seqwater
  • Research Lead Griffith University
  • Main Researcher Stephanie Faulks
  • Completion Year 2024

Project Description

Antimicrobial resistance has been identified by the water industry as a growing concern in the delivery of wastewater treatment and water recycling schemes. Research has shown that antimicrobial resistance genes are consistently being measured in treated wastewater, however, limited scientific knowledge exists to explain this phenomenon. The implications of the presence of antimicrobial resistance in water supplies is part of a ‘one-health’ public health burden with broad regulatory relevance. The Australian Drinking Water Guidelines (ADWG) do not include a guideline relevant to antimicrobial resistance as the understanding of the water industry’s role in the ‘one-health’ burden has not yet established.

This project will characterise the occurrence and fate of antimicrobial resistant organisms and genes across an advanced water treatment plant and supply system. The supply network being investigated is comprised of secondary treated wastewater feeding into an advanced water treatment plant (microfiltration, reverse osmosis, ultraviolet advanced oxidation, and chlorine disinfection steps), released to a pipe network and ultimately discharged into a natural water storage.

This project will draw on both grey and published literature, expert opinion, and monitoring data to explore factors likely to influence the presence and persistence of antimicrobial resistance genes and the effectiveness of treatment technologies, designed to be critical control points for pathogen removal. The knowledge gained through these multiple avenues will form the basis of a Bayesian Belief Network model. It is hoped that is model can be used by stakeholders as a decision support tool to help understand and manage antimicrobial resistance in an advanced water treatment plant and supply network. The ability to incorporate uncertainty and to complement data with expert knowledge makes Bayesian Network modelling a useful tool to support environmental health risk assessment, decision-making and strategy evaluation.

This project aims to provide greater insight into the implications of the presence of antimicrobial resistance genes in water supplies to help establish the ‘one-health’ public health burden and contribute to the globally significant challenge of antimicrobial resistance.