The environmental conditions which cause blue-green algae (cyanobacteria) blooms vary according to location, the climate, and other attributes of aquatic ecosystems. This variety has made it difficult to develop one broadly applicable predictive model for cyanobacterial blooms. Water utilities monitor source waters to implement cyanobacterial risk management programmes but there are no standard protocols while limited information transfer between utilities has prevented the identification of management strategies that do or do not work. This research reviewed literature about early warning systems (Almuhtaram et al., 2021) and source control strategies, conducted a survey of 35 utilities in America and Canada (74%) and Australia (Kibuye et al., 2021) and evaluated selected control strategies. These different types of information were synthesised into decision trees within an overarching guidance document. It was concluded that a 3-tier framework to detect algal blooms which monitored biological activity, then confirmed the identification of cyanobacterial genes and associated metabolites gave sufficient early warning, while multi-barrier control strategies gave field-scale efficacy and enabled timely responses.

The ADWG explains policies but does not provide the specific steps and actions needed to apply risk management principles within a water treatment plant (WTP). The original ‘Guide to Drinking Water Supply Systems for the Management of Microbial Risk’ (WaterRA Project 1074) filled this gap by providing Australian-specific advice about managing and optimising common water treatment processes to achieve microbial health-based targets. Since its publication in 2015 it has become a popular reference document, and its widespread acceptance and use has prompted the production of this second edition. Included are updated technologies and regulations, and a series of auditing tools and templates for application in a variety of situations, including the identification and quantification of risk.

Click here to download the Good Practice Guide.

European carp have decimated native fish species in the Murray-Darling River. The federally funded National Carp Control Plan proposes using a carp-specific virus to kill the pest-fish, but before doing so are consulting with a broad array of environmental, conservation and other stakeholders, including the water industry. There are concerns that large amounts of dead and decaying carp near water offtakes or storages might overwhelm the capacity of water treatment plants (WTPs) to remove organic matter and taste and odour compounds, and might compromise the production of safe, palatable drinking water. In this research a series of experiments led to the conclusions that medium to high carp densities could be managed by adding a 30 minute procedure to existing WTP methods, and that there would not be an increased risk to public health.

Cyanobacteria (blue-green algae) which float in reservoirs have been studied for decades because when they bloom, the very high cell numbers cause a problem for water treatment plant (WTP) operators, who have to remove the cells, toxins, and taste and odour compounds they produce. Benthic, bottom-living cyanobacteria which also produce toxins were recently discovered in Australian reservoirs. The problem is that benthic cyanobacteria are not included in routine monitoring practices and very little is known about them. This research provided information about the incidence of benthic cyanobacteria and the toxins they produce in various catchments; identified environmental conditions that stimulate bloom formation, and investigated naturally occurring biodegradation of taste and odour compounds. It was concluded that there is a need to monitor benthic cyanobacterial mats to ascertain the risk they pose, and to obtain additional in-situ data about more benthic species, because this will support the construction of predictive models to facilitate improved management of catchment and source waters.

Disinfection is essential for removing harmful microbial pathogens and making safe drinking water but can also cause formation of disinfection by-products (DBPs), some of which pose a health risk. Thirty years of research have amassed a wealth of knowledge about the identification, formation, treatment and control, toxicology and epidemiology of DBPs in Australia. This project compiled, assessed and presented an overview of DBP-related research in Australia.

The management of blue-green algae (cyanobacteria), and the toxins and taste and odour compounds they produce, have been the focus of more than 30 years of research, but there is still a need for a suite of user-friendly tools to assess and manage aesthetic and toxin risks. This project conducted an extensive literature review about the ability of six treatment paradigms to remove MIB, geosmin, saxitoxins, microcystins and cylindrospermopsin. An empirical spreadsheet-base model was then built and used to simulate ‘whole-of-plant’ removal of cells and toxic metabolites. This model performed well when tested with two years of full-scale sampling data.

In Australia, remote and regional communities frequently manage relatively small, isolated water treatment and waste management systems which have water quality and health risks characteristic of small-scale decentralised operations. Australian water utilities have a wealth of experience in addressing these issues, and this project gathered and documented a series of case studies from Victoria, Tasmania and the Northern Territory to form a knowledge base that can be referred to in the future.

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.

Water treatment plants (WTP) take in source waters then remove 95-99% of blue-green algae (cyanobacteria) cells and the toxins they produce. During this removal process waste sludge is generated and transferred from clarifier tanks in the treatment plant to lagoons. It was thought that confinement in the sludge killed the cyanobacteria, but this research found that when algal blooms have generated very high cell numbers, viable, toxin-producing cyanobacteria are retained in the sludge and can release toxins into the clarifier supernatant. It was concluded that timely removal to lagoons will avoid problems, and it is recommended that risk assessment for recycling lagoon supernatant back to the head of the WTP should incorporate extended times of 3 to 4 weeks after the end of algal blooms, to ensure cyanobacterial cell death and toxin degradation.

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.