From hazard identification to risk management. From hazard identification to risk management. This project aims to explore health risks from water- and sediment-borne bacteria to recreational users of urban rivers, using a suite of novel molecular microbiological and in-vitro assays and microbial risk assessment modelling. This project also aims to develop source tracking methods to mitigate and manage these risks. The number of bacterial-related water-borne outbreaks associated with recreationa ....From hazard identification to risk management. From hazard identification to risk management. This project aims to explore health risks from water- and sediment-borne bacteria to recreational users of urban rivers, using a suite of novel molecular microbiological and in-vitro assays and microbial risk assessment modelling. This project also aims to develop source tracking methods to mitigate and manage these risks. The number of bacterial-related water-borne outbreaks associated with recreational activities is rising, but waterway managers are under pressure to re-open these rivers for recreation. The project is expected to benefit urban communities by ensuring waterway managers make informed decisions about river recreation.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less