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.

Burkholderia pseudomallei is a bacteria that is widespread in SE Asia and northern Australia, where there are an average of 16 cases per 100,000 people. This research found that water samples collected in and around Cairns from June to September did NOT contain Burkholderia pseudomallei and led to the conclusion that the risk of infection is much lower than in Darwin or Townsville.

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.

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.

‘Microbial source tracking’ (MST) is a technique that aims to identify the animal that excreted faeces and polluted water. There are a number of ways to do this, but the problem is that no one method accurately identifies the origins of faecal pollution in environmental water samples. This research found that faeces could be stored in a freezer or a laboratory -80°C cold-store for up to a month without changing the relative numbers of the different types of bacteria in the samples of faeces. Up to seven faeces samples from different animals were mixed together and examined using 17 techniques to identify the original animals. Three of the most accurate and reliable methods used mitochondrial DNA, the analysis of a bacterial enzyme sequence (beta-glucuronidase), and specific DNA sequences form bacteria known to come from humans, horses and cows. These three types of tests were selected for inclusion in a ‘Toolbox’ from which a combination of methods will allow accurate and reliable management of faecal contaminants in source waters.

Blue-green algae (cyanobacteria) blooms decrease water quality by releasing toxins and unpalatable taste and odour compounds. The problem is that it is difficult to rapidly and accurately identify toxic cyanobacteria in drinking, recycled or recreational waters. This research developed a reliable and sensitive DNA-based PCR test which used robotic equipment to carry out the laboratory component of the test. The speed and accuracy of this diagnostic test has the potential to improve management of blooms and contribute to the maintenance of water quality.

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.

Pathogenic microscopic organisms in source waters pose a risk to public health if water treatment plants do not remove them. It was thought that sensitive PCR tests could be developed to inform decision-making about the most appropriate treatment processes, and to check the absence of pathogens from drinking water. This research focussed on four pathogen classes: cryptosporidium, microcystis, adenovirus and ammonia oxidising bacteria; and evaluated six DNA extraction kits. The Qiagen kit was most cost-effective for extracting DNA and Promega To-Taq polymerase was best for carrying out the PCR test on pathogens in real-world water samples. Other components of the PCR tests that were developed included test controls and DNA standards. A test for each class of pathogen was established and written as a ‘Standard Operating Protocol’ (SOP) which was then applied in different laboratories around Australia. Between-laboratory comparison of results showed the developed PCR tests to be highly reproducible and reliable. They can now be added to the existing suite of tools used to minimise risks to public health.