Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir

Abstract

Understanding and predicting Taste and Odour events is as difficult as critical for drinking water treatment plants. Following a number of events in recent years, a comprehensive statistical analysis of data from Lake Tingalpa (Queensland, Australia) was conducted. Historical manual sampling data, as well as data remotely collected by a vertical profiler, were collected; regression analysis and self-organising maps were the used to determine correlations between Taste and Odour compounds and potential input variables. Results showed that the predominant Taste and Odour compound was geosmin. Although one of the main predictors was the occurrence of cyanobacteria blooms, it was noticed that the cyanobacteria species was also critical. Additionally, water temperature, reservoir volume and oxidised nitrogen availability, were key inputs determining the occurrence and magnitude of the geosmin peak events. Based on the results of the statistical analysis, a predictive regression model was developed to provide indications on the potential occurrence, and magnitude, of peaks in geosmin concentration. Additionally, it was found that the blue green algae probe of the lake's vertical profiler has the potential to be used as one of the inputs for an automated geosmin early warning system.

Analysis of the Mixing Processes in a Shallow Subtropical Reservoir and Their Effects on Dissolved Organic Matter

“…There was no obvious post-rainfall variation in the turbidity levels before decreasing the dam level. In fact, previous studies [39] showed a two-fold increase in the average raw water turbidity following the approximately 50% reduction in storage volume for Tingalpa reservoir. These results suggest that before decreasing dam levels, Tingalpa reservoir had enough volume to dilute the turbidity inflows so that no significant impact on the water quality was observed.…”

Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

“…The original capacity of 24,868 ML was reduced to 13,206 ML in 2014 because of safety reasons relating to the dam wall. A direct consequence of the dam lowering was a decline in water quality, with turbidity, nutrients and metals increasing after 2014 [6]. Between November 2015 and January 2016, geosmin levels peaked twice to above 160 ng/L, substantially higher than previous average peak concentrations of 15 ng/L [28].…”

“…Daily dam volume data were also collected. This was used to create a data-driven model to help understand peak geosmin events [6]. The model used a conceptual regression tree to provide an estimation of geosmin based on water temperature, cyanobacteria (Microcystis aeruginosa and Dolichospermum circinale only) and nitrogen [6].…”

“…This was used to create a data-driven model to help understand peak geosmin events [6]. The model used a conceptual regression tree to provide an estimation of geosmin based on water temperature, cyanobacteria (Microcystis aeruginosa and Dolichospermum circinale only) and nitrogen [6]. Another data-driven model was later developed to estimate the amount of powdered activated carbon required to remove the resulting T&O [5].…”

Data-Modelling Applications in Water System Management

“…The second key point is analyzed by Bertone and O'Halloran (2016) [2], which tried to understand and predict taste and odor events in drinking water treatment plants. Historical manual sampling data, as well as data remotely collected by a vertical profiler, were collected, and regression analysis and self-organizing maps were the used to determine correlations between taste and odor compounds and potential input variables.…”