Project Rationale
In recent decades, anthropogenic pressures and climate change have significantly worsened the water quality of many rivers and reservoirs, leading to substantial alterations in ecosystem functioning and structure. One of the consequences of these detrimental changes is the uncontrolled growth of cyanobacteria, a group of photosynthetic bacteria that can form widespread blooms and release undesired metabolites and toxins into the water. Of these, release of metabolites known as Taste and Odour compounds are becoming an increasing problem in many waterbodies.
Geosmin and 2-methylisoborneol (MIB) are two dominant Taste and Odour compounds. These metabolites affect the organoleptic characteristics of water, influencing both its taste and odour, which have a negative impact on the end users (i.e. the drinkers) perception of the quality of their drinking water. Although no health risks are associated with Taste and Odour compounds in drinking water, they can be perceived by humans at very low concentrations (5 – 10 ng/L), resulting in consumer complaints. Overall, they represent a significant challenge for drinking water companies charged with managing their concentrations in waterbodies.
Despite numerous Taste and Odour episodes described around the world, the exact causes and conditions that drive the production of Taste and Odour compounds by cyanobacteria remain unconstrained.
Project Team
Chris Williamson, Lecturer in Polar Microbiology, School of Geographical Sciences, University of Bristol, UK.
Rupert Perkins, Senior Lecturer in Marine Biosciences, School of Earth and Environmental Sciences, Cardiff University, UK.
Carmen Espinosa Angona, Postdoctoral Research Associate, School of Geographical Sciences, University of Bristol, UK
Dŵr Cymru Welsh Water, specifically the Catchment Management Team led by Sophie Straiton
Fresh-water Cyanobacteria viewed down the microscope.
Project Aim
Smart Catchment Management is a Dŵr Cymru Welsh Water funded project that aims to use a host of modeling techniques to predict the occurrence of Taste and Odour compounds in freshwater reservoirs in order to allow effective (i.e. smart) catchment management.
Our Approach
Using databases of physiochemical, biological and meteorological observations produced by Dŵr Cymru Welsh Water over the recent past, we are developing modeling pipelines capable of predicting Taste and Odour compound concentrations in case-study reservoirs across Wales. Given the complexity of the modeling task, we are employing a series of modeling approaches that range from multiple-linear-regression to machinelearning based artificial neural networks (ANNs) to produce the most robust predictive tools. We anticipate the latter will provide increased flexibility in predictive capabilities given the nonlinearity apparent in water systems.
Artificial neural network schematic
Once we’ve ascertained the best model for our aim, we will integrate it into Dŵr Cymru Welsh Water’s catchment management data system to provide Taste and Odour compound prediction across reservoirs, allowing intervention before the problem arises. Into the future, we’ll optimize our models through repeat training with newly derived field observations taken as part of the normal operation of Dŵr Cymru Welsh Water.
This project represents a great combination of microbiology, modeling and applied science in order to produce a lasting improvement in water quality. It will also advance the modeling toolkit of Microlab@Bristol that we can apply to other problems in other systems.
Watch this space for new project outputs and news….
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