Real time monitoring and process control is a crucial component of optimal, pro-active water and wastewater management but there is a lack of information about real-world experience, operation, return on investment and the costs that are related to online monitoring. This research collated information from literature reviews, case studies, industry surveys and interviews, and concluded that more than 250 manufacturers sell over 100 water quality applications. In this context it is important to carefully define operational needs and the availability of skills, resources and budget before choosing an application. It is also important to manage and set realistic expectations and to embed online monitoring within the organisation.

New satellites and drones have the capacity to provide higher resolution images, of larger areas, more often than ever before, but how can water managers and scientists access or use this data, and what will they need to incorporate remotely sensed information into analysis, planning and other decision-making processes? This is a desk-top study to review peer-refereed literature and service providers technical information, with a focus on the remote sensing of parameters relevant to optically complex inland waters.

Recycling wastewater to a standard that makes it fit for use in irrigation is an efficient and cost-effective strategy for managing water resources, and has prompted the installation of separate pipe and tap reticulation systems in domestic housing schemes such as at Rouse Hill in Sydney. The problem is that water used to irrigate gardens and parks is not recycled to the same high (and expensive) standard as potable drinking water. This makes it important to monitor the drinking water to ensure that it is not accidently contaminated with slightly lower standard irrigation water. This research showed that an inexpensive 4-electrode sensor reliably detected changes in electroconductivity and ultra-violet fluorescence that corresponded to only 1mL of recycled water leaking into 9mL of drinking water. It is likely that a real-world installation of one sensor by the inflow pipe for the housing scheme and multiple sensors by household taps will be even more sensitive. It was concluded that R&D should continue in a larger-scale trial to monitor drinking water quality in dual reticulation systems.

One wastewater treatment (WWT) option is disinfection with ultra violet (UV) light to remove pathogens and some contaminants, but substances in treated wastewater, such as particles of solid matter, can absorb the UV radiation and reduce its disinfecting activity. This research identified the sources of substances which are not removed by standard WWT plant processes, and which impair UV disinfection. It was concluded that trade wastes are a major source of organic, non-biodegradable humic-like substances which pose more of a problem to UV disinfection than total suspended solids. A sensor-probe based method for analysing fluorescence (‘EEM-PARAFAC’) was found to be an effective way to evaluate amounts of humic-like substances in effluents, and this information will improve management of UV WWT.

Microbial pathogens are removed from source waters to make safe drinking water. Health-based targets (HBTs) refer to the quantities of pathogens that will NOT cause illness, and water treatment plants (WTPs) must ensure that the numbers of pathogens in potable water are the same or lower than the HBTs. WTP operators select the best pathogen removal treatment for a range of conditions such as season, rainfall and agricultural activity by referring to ‘sanitary surveys’. These are carried out approximately once a year by scientists driving and walking in the source water catchment, making observations, and collecting samples which are examined to look for indicators of harmful pathogens. Recent requirements to achieve HBTs, combined with technological advances, led to this project which aims to provide flexible sanitary survey guidance applicable to the broad range of Australian conditions and utilities. This Good Practice Guide reviews existing procedures, describes modern methods including aerial photography and the use of spatial databases, and describes the incorporation of sanitary surveys into ongoing source water operational monitoring in ways that support the establishment of log removal values and enable compliance with HBTs.

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.

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.

Chlorine removes harmful pathogens from water but has the disadvantage of forming disinfection by-products (DBPs) by reacting with organic matter sometimes found in water. Chloramine also disinfects, is less likely to form DBPs and is more stable, so remains active in for longer in the pipelines which distribute drinking water from the plant to the tap. The problem is that it is difficult to predict exactly how much chloramine to add; it needs to be enough to maintain disinfecting activity in the pipeline distribution system, but not so much that customers find the smell of chlorine in tap water unpleasant. Traditionally, the chemical reaction rate has been used to predict the gradual ‘decay’ of chloramine in pipelines, but this is inaccurate. This research developed a computer software statistical programme that uses ‘artificial neural network’ concepts and operations to predict the longevity of chloramine residuals in water distribution systems. This is more accurate than traditional methods.