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.

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.

‘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.

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.