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 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.

‘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 pathogen, forms infectious oocysts which are removed by specific and targeted water treatments. Oocysts can only be seen by using a microscope but finding an infectious dose of 10 oocysts in a litre of water is like finding a needle in a haystack. Usually a larger volume of water, 1000mL, is filtered to recover all the oocysts into a small volume of 0.1 to 1 mL, because this is small enough to be examined under a microscope. It is scientific practice to add some dead, colour-dyed oocysts to the large volume of water. If all the coloured oocysts are counted on the filter, the recovery is 100%. From this it became clear that there was a problem with oocyst recovery. This research found that different elements reduced recovery from different types of water, for example, iron and silica reduced oocyst recovery from river or groundwaters. The approved method for quantifying the environmental occurrence of oocysts can now be modified to increase recovery, and this change improves analysis and consequent management which further reduces risks to public health.

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.