Although a cluster of customer complaints can identify specific water quality issues such as a dirty water event, it is more difficult to understand the extent of general customer satisfaction with water quality and taste. The Final Report for this project includes four printable water quality Factsheets for the general public which explain tastes and odours, fluoridation, and disinfection. The Report also includes the protocols and texts for two customer engagement techniques: Blind Taste Testing and Systematic Taste Testing; as well as a comprehensive customer survey instrument to assess perceptions of drinking water quality. These can be downloaded to electronic mobile devices and used to direct face to face customer engagement with the tastes and odours of different types of potable water. The scoresheet links to a rating spreadsheet that collates results and generates graphical representations of customer acceptance of water sample taste. These tools have been used to assess customer satisfaction with water quality at large events including National Water Quality week or Utility information days.

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.