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.

There are many species of blue-green algae (cyanobacteria), and each species can have a number of genotypes. Water utilities routinely monitor reservoirs and lagoons for the harmful toxin-producing species, and when they find a threshold number of cells, proceed to test for toxins. The problem is that not all genotypes of known toxin-producing species produce toxins. There is already a well-established quantitative PCR method that detects the genes responsible for making toxins, but the relationship between occurrence of these toxic genotypes and the amount of toxin in one water sample is not straightforward.

This project will create a modified version of the existing regulatory protocols for managing toxic blue-green algal blooms by adding the toxic gene qPCR test to the current tests for toxin and species identification. This modified protocol will then be used to assess samples for which there are already results from all three tests. The original management costs will be compared to the desktop analysis of hypothetical costs that would have been incurred if genotoxicity testing had been included in the protocol. Could gene testing reduce overall cyanobacteria management costs and confer operational benefits?

Several cyanobacteria species are well known for their potential to produce cyanotoxins. However, not all genotypes of known toxin producing species produce cyanotoxins and of these there is significant variation in the spatial and temporal dynamics of toxin production. The water industry currently relies of observational measurement of the presence of ‘potentially toxic species’, toxin gene and toxin presence to inform management of cyanobacteria blooms in water supply storages. Predictive tools and preventative management are limited by a lack of simple environmental predictors to predict toxin production events. Understanding the drivers for toxin production that inform risk management frameworks would be of great benefit to water supply managers and to inform alternate management options. These tools would enable better responses to bloom events and allowing for the establishment of pre-emptive measures to minimize cyanotoxin production by targeted manipulation of environmental drivers.

There is a need to have a clear and consistent approach to assessing the efficacy and performance of the growing number of algal bloom management technologies emerging on the market. Often technologies are offered by suppliers with the promise that they will solve algal bloom issues. However, there are a number of factors that, if poorly understood and addressed, can result in performance being unsatisfactory for water utility needs. This research will develop a customisable protocol for selection, deployment, and performance assessment of algal bloom management technologies.

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.

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.

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.

Blue-green algae (cyanobacteria) reduce water quality especially when they bloom and form high numbers of cells which produce toxins, and taste and odour compounds. Most cyanobacteria photosynthesise and tend to grow and float at depths which optimise their exposure to sunlight, but an increase in unexplained occurrences of taste and odour compounds in reservoirs, and a bloom of benthic (bottom-living) cyanobacteria, forced the closure of a water supply. This research examined the role that bottom-living benthic cyanobacteria play in the production of toxins or taste and odour compounds. Seven DNA-based PCR tests were developed to identify benthic species of cyanobacteria and their capacity for producing toxins. A taste and odour compound, and two toxins were found in winter and spring in an SA reservoir, whereas a different taste and odour compound and toxin assemblage were found in summer and autumn in a reservoir in NSW. These results will help water suppliers to anticipate and manage future aesthetic or toxin issues related to benthic cyanobacteria.