The ADWG explains policies but does not provide the specific steps and actions needed to apply risk management principles within a water treatment plant (WTP). The original ‘Guide to Drinking Water Supply Systems for the Management of Microbial Risk’ (WaterRA Project 1074) filled this gap by providing Australian-specific advice about managing and optimising common water treatment processes to achieve microbial health-based targets. Since its publication in 2015 it has become a popular reference document, and its widespread acceptance and use has prompted the production of this second edition. Included are updated technologies and regulations, and a series of auditing tools and templates for application in a variety of situations, including the identification and quantification of risk.

Click here to download the Good Practice Guide.

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.

Water treatment plants (WTP) produce safe drinking water that does not contain harmful microscopic pathogens, but subsequent pipe-leaks or valve or hydrant malfunctions en route to the customers tap increase the risk of pathogens entering the public water supply. International studies indicate a small increase in gastroenteritis for some customers downstream of mains pipeline breaks and repairs. This research compiled Australian cases and found that health risks are probably lower than international examples for many reasons, including that sewer and drinking water pipes tend not to be laid in the same trench. After improving understanding regarding the contamination risks of renewal and water main repair activities in Australia, a control and management framework was produced in accordance with the ADWG. This gives guidance to water utilities about staff training, risk assessment and optimal documentation processes for dynamic site evaluations and repair control mechanisms.

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.