Harmful pathogens and compounds must be removed from wastewater before it can be discharged to the environment or used for irrigation, and many source waters need salts removed to make them potable. Microfiltration and ultrafiltration membranes remove pathogens and unwanted chemicals but as they are used, they become fouled and blocked by particulates and compounds from the water being filtered, as well as by the formation of biofilm, and by chemical interactions between solutes and the membrane materials. Although the membranes are cleaned regularly, the cleaning chemicals themselves can cause a problem. This research characterised the damage that various cleaning regimens inflict on membranes made of different materials and examined the effects of progressive and consecutive stages of membrane aging and degradation on performance. It was concluded that the type of cleaning agent affects the mechanism of membrane degradation and the severity of membrane integrity loss. More information is needed regarding the cleaning protocols and agents used in industry. Information from this research can inform a generally applicable model to predict membrane aging and decline in performance.

Sewage is delivered to wastewater treatment plants (WWTPs) where benign microbial organisms within ‘activated sludge’ vessels contribute to the removal of harmful pathogens from the sewage. The activity and pathogen-removing ability of these helpful organisms is affected by many factors including temperature, numbers of fine particles, pH, ammonia, and the time available to remove the pathogens. Regulatory authorities require at least 90% (one log removal value, LRV) of the pathogens to be removed, but as WWTP operating conditions vary, the LRVs change. This problem led to recognition of the need to develop models capable of predicting relationships between plant operating parameters (such as temperature) and pathogen removal. This research reviewed published reports and datasets, then set up and ran an experimental activated sludge pilot plant to generate data about a range of operating conditions and pathogen removals. These datasets were used to develop models which had only a ‘poor’ predictive value for clostridia but were ‘good’ for giardia and ‘very good to excellent’ for the removal of other pathogens. These models need to be extended with more operating conditions but have the potential to be used to attribute LRVs and for future integration into online real-time monitoring.

Although water utilities recognise the value of online instruments that provide real-time monitoring capability, there are problems with visualising and interpreting datasets, and with distinguishing between data resulting from real-world changes in treatment plant operating conditions, for example changed turbidity or flow, and instrument failure. There are also challenges around instrument installation and operation. This project developed tools to support data visualisation and interpretation by building a prototype visualisation platform for analysing complex online UV spectral data in conjunction with weather and lab data (see Factsheet 2 ‘Development of an online platform’). To improve differentiation between instrument failure and real-world data a Bayesian Belief Network model was developed to analyse patterns and variations within datasets. Real operational, high turbidity data was used to demonstrate that this model could accurately identify different causes for the readings which included filter ripening, backwash and other causes (see Factsheet 3 ‘Improving decision making in water plant operability through Bayesian Belief networks’). Strategies for instrument installation and operation were illustrated through case studies.

The ADWG has methods for predicting risks to water quality, but these were not developed for managing extreme climate-change driven weather events such as bushfires or floods. This research developed a risk assessment tool for managing water-related health risks associated with extreme weather events. Real-world datasets and experience of water cloudiness (turbidity), colour and blue-green algae were used to create and validate environmental models which were developed further by applying Baysian network and System Dynamics concepts. This iteration of the model was not constrained by, and did not reflect existing risk profiles, but was judged to be flexible enough to provide a realistic representation of future hazards arising from extreme weather events.

Water utilities lack the information they need to implement risk-based adaptation and planning strategies that incorporate climate change. This research addresses this problem by modelling the effects of climate change on reservoirs in three climate zones: temperate, humid tropical and Mediterranean. By integrating different modelling approaches it was concluded that increased temperatures will increase water stratification; the differences in water temperature that occur with depth. This is important because the duration and type of stratification affects the storage and release of substances from reservoir floors and this in turn affects blue-green algal blooms and water quality. The integrated modelling approach developed in this project can be applied to the management of contaminants running off the catchments and for future risk assessment. This information will also support the development of business cases for targeted catchment interventions.

Cryptosporidium, a microscopic single-cell parasite, forms an “oocyst” with a resistant outer layer analogous to an eggshell. Oocysts survive for a long time in the environment but UV in sunlight, and high temperatures that cause desiccation, kill them. If a mammal drinks water containing live oocysts, they embed in the gut wall and continue their lifecycle until eventually many more oocysts are excreted. There are 26 species of cryptosporidium but only five infect humans, and two; Cryptosporidium hominus and Cryptosporidium parvum, cause approximately 95% of all human infections. C. hominus occurs only in humans, but C. parvum is also found in cattle, sheep, and other animals. The problem is that human-infecting oocysts are excreted by animals in catchments and rain can wash live oocysts into water reservoirs. This research collated peer-reviewed published literature, and information and data collected by the water industry, then characterised the distribution of different Cryptosporidium species in Australian catchments. This led to recognition of a research need to track and predict live and dead oocyst transport during different weather events, and to model and evaluate catchment management initiatives such as excluding cattle from reservoir areas.

The ADWG 2011 lacked objective, quantifiable criteria for measuring pathogen removal from source waters. The WHO requires that health-based targets (HBTs) are used to ensure safe drinking water. HBTs can be set by making ‘Disability Adjusted Life Year’ (DALY) calculations which incorporate information about a population; the average Australian lifespan and the impact of infection on the length of a healthy life. Pathogen levels in water that correspond to micro-DALYs with minimal population-level effects are too low to be detectable by existing tests. This problem is addressed by calculating ‘log removal values’ (LRVs). Pathogens can be measured in source and treated water after the application of a defined method. When 90% of the pathogen is removed, the LRV equals 1, while 99% removal has an LRV of 2. HBTs incorporate LRVs, which are based on objective, quantifiable criteria, to reduce pathogen loads to levels that correspond to acceptable Australian micro-DALY levels. This research collated existing datasets about pathogens in source waters and the efficiency of their removal by treatment plants around Australia. From this, default pathogen levels for a range of Australian source waters and climate events, and LRVs for different treatment methodologies were obtained.

Water is disinfected to remove harmful organisms, then filtered and treated to remove contaminating pollutants. The treated water then enters distribution pipe networks which eventually terminate at customers taps. The distribution systems can be many kilometres long and if the drinking water is in the distribution system for extended periods of time, the activity of the disinfectant gradually declines. This project used a laboratory model of a representative distribution system to make changes to initial water treatment, then examined their effects on the maintenance of disinfection and water quality.

This paper discusses various water quality risk management techniques and proposes a step-by-step catchment risk assessment methodology that is compatible with the Australian Drinking Water Guidelines.

Cryptosporidium is a waterborne microscopic parasite with different forms at various stages of its lifecycle. One form, the spherical oocyst, is excreted by infected people and transported in rivers and surface waters. The problem is that it is not known if oocysts found in water are dead or are alive and infectious. This leads to an overestimation of the public health risk posed by oocysts found in source waters.

This research developed an in vitro cell culture test to differentiate between dead and infectious oocysts. The immortalised HCT-8 cell line was derived from cancerous human intestinal cells. When the cells are grown in a culture vessel, they display many characteristics of normal human gut cells. It was discovered that treating oocysts with acid to mimic stomach pH and using centrifugal force to ensure oocysts contact the HCT-8 cells, live (but not dead) oocysts react as though they are infecting a human host. The oocysts ‘hatch’ the next sporozoite form of the lifecycle, and these can be counted using a microscope. This ‘infectivity assay’ gives an improved, more accurate method for quantifying risks to human health presented by unidentified cryptosporidium oocysts in water.