Raw source water contains parts of plants, blue-green algae and their toxins, and many other types of organic matter. Identifying the types and amounts of organic matter helps treatment plant operators make informed decisions about the most efficient and cost-effective methods for treating and removing unwanted substances from source waters. The problem is that many of the tests for identifying organic compounds can take hours to days to deliver results. This research developed a test that gives information immediately. It uses three commercially available fluorescent probes that each emit fluorescent light at a specific wavelength. Certain compounds within organic matter, such as proteins, “reflect” the fluorescent light, but at different wavelengths which can be detected by the probes. These patterns of “reflected” fluorescence were related to traditional tests for organic compounds. This on-line fluorescence monitoring was then trialled at real-world treatment plants. The patterns gave reliable information about broad categories of organic compounds and there was a linear correlation between dissolved organic carbon and fluorescent intensity in both raw and treated waters. This research has provided a valuable addition to the suite of tools available for producing safe, high quality drinking water.

Blue-green algae reduce water quality, especially when they produce toxins. Each algal cell can grow, reproduce all its DNA, and split into two ‘daughter’ cells, then those two ‘daughter’ cells produce four more until the numbers of algal cells bloom to extremely high numbers. High algal growth rates are associated with favourable environmental conditions (for the algae), stationary growth rates occur when the production of new cells is about the same as the number of dying cells, and if more cells die than are reproduced, the growth rate declines. The ability to predict or measure which of these three population growth rates is prevalent, and how much toxin is being produced, is information that the water industry needs to select the best methods for treating water. This project analysed the amount of DNA, and some specific sequences of DNA which correspond to the genes coding for toxins; and related the DNA analysis to actual counts of cells and measurements of toxin in water samples. This allowed the development of an improved and more informative technique for forecasting and monitoring toxic blue-green algae blooms.

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.

Blue-green algae (cyanobacteria) can bloom in marine and freshwater and cause additional problems for water utilities when they produce toxins and taste and odour compounds. This project consolidated a wealth of knowledge and experience in the management of cyanobacteria into an electronic / online international, practical, and user-friendly manual. It includes information about conducting risk assessments, developing monitoring programmes and incident management strategies, and management procedures to mitigate the risk posed by cyanotoxins.

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.

A survey of water utilities identified the top five challenges faced in daily operations, and technical, economic and literature reviews identified remote sensing strategies and technologies to address these five operational issues. The need for, and benefits of real-time monitoring which facilitates cost-effective and efficient responses to rapidly changing conditions, were common to all five challenges, which were: monitoring cyanobacteria and their metabolites, the effects of contamination and extreme climate-change driven events on water quality, the variation in climatological data over relatively small distances (need to increase focus, precision and produce ‘finer’ datasets for reservoir management), asset inspection and management and monitoring catchments for a variety of factors. This research evaluated and explained the solutions, strengths, weaknesses, and costs of products best suited for addressing each challenge.

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.

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.

Water treatment plant operators remove cyanobacteria and the toxins they produce from source waters but calculating the amount of treatment needed for effective removal is difficult, particularly in bloom conditions when cyanobacterial cell numbers and toxins change quickly. Current cell counting and toxin measurement can take hours or days to complete, and the results are not available quickly enough to help treatment plant operators respond to changing conditions. There is a need for a real-time method that gives instant results. This research examined the utility of fluorometers; probes that emit light that is ‘reflected’ back at different wavelengths by living cells and other matter in the water and is detected by the fluorometer. It was found that when only one species of cyanobacteria was present, there was a good correlation between the fluorescent signal and cell number, particularly when source waters were clear and not cloudy. Cell numbers did not relate well to levels of toxins or taste and odour compounds. When fluorometers were installed in 13 water treatment plants the correlation between cyanobacteria cell numbers and fluorometer signals was validated, and this led to the conclusion that fluorometers can give early warning of blue-green algae blooms.