Details of Grant 

EPSRC Reference:	EP/K035886/1
Title:	Developing an Event Prediction and Correction Framework for Microbial Management in Drinking Water Systems.
Principal Investigator:	Pinto, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Scottish Water
Department:	School of Engineering
Organisation:	University of Glasgow
Scheme:	First Grant - Revised 2009
Starts:	01 August 2013	Ends:	31 July 2015	Value (£):	96,583
EPSRC Research Topic Classifications:	Coastal & Waterway Engineering Water Engineering
EPSRC Industrial Sector Classifications:	Water
Related Grants:
Panel History:	Panel Date Panel Name Outcome 07 May 2013 Engineering Prioritisation Meeting 7/8 May 2013 Announced
Summary on Grant Application Form
Drinking water is teeming with microbial life. In fact, drinking water can contain anywhere from millions to hundreds of millions of microbial cells per litre, all with extremely different evolutionary histories and abilities. For example, microbes can (1) affect human health by causing diseases, (2) corrode infrastructure, and (3) also deteriorate the aesthetic quality of water. In an effort to limit these detrimental scenarios, drinking water companies invest significant amounts of labour, energy, and money towards limiting microbial presence through the use of disinfection. Though disinfection approaches have been effective in reducing the incidence of waterborne diseases, they are not 100% successful and microbial communities persist. As a result, drinking water companies also engage in microbial management by implementing rigorous sampling programs with the goal of early detection of microbial contamination events. These early detection programs are reactive in nature and can only detect a problem once it has occurred and are limited to informing strategies that try to mitigate the imminent risk posed to consumers. Further, they also typically focus only on pathogenic microorganisms and ignore all other microbial impacts (e.g. corrosion causing bacteria that deteriorate water supply pipes). These inefficiencies in microbial management can be remedied by transitioning from the existing Early Event Detection and Mitigation approach to an Event Prediction and Correction (EPC) framework in the drinking water industry. An EPC framework would enable the drinking water companies to predict the risk presented by an array of detrimental microbes (disease/corrosion/odour/taste causers) over operationally relevant time-scales and allow for the initiation of measured and proactive corrective action strategies to eliminate this risk before it is manifested. The key towards developing a robust EPC framework would be to (1) identify key locations in the drinking water system that can serve as predictive indicators, (2) quantify the temporal dynamics of these locations and how it correlates with the whole drinking water system, and (3) develop statistical models informed by microbial community data to predict contamination events. In this study, we will engage in an extensive effort to characterize the bacterial, protozoal, and fungal communities at multiple drinking water systems in Scotland. This will be followed by a long-term sampling campaign at one representative drinking water system to quantify the spatial and temporal dynamics of the drinking water microbiome. By tapping into the on-going nucleic acid revolution, we will be able to describe the drinking water microbial communities at an unprecedented level of detail. This detailed quantitative insight will be used to parameterize and shape a statistical model that describes assembly of complex microbial communities and predicts their fate in the drinking water system. This project has several anticipated benefits over a range of time-scales. In the short-term, this project will substantially improve our understanding of the drinking water microbial communities, which has been traditionally under-studied. In the medium-term, it will enable the predictive management of the drinking water systems, which will help prevent microbial contamination problems, rather than tackle them once they have occurred which can be risky, expensive, and laborious. Further, predictive models may also allow us to isolate and treat sources of microbial risk within the drinking water treatment plant, thus preventing its entry into the distribution system. Over the medium-long term, we anticipate that building a predictive microbial management capacity in the drinking water sector will enable us to beneficially manipulate the drinking water microbiome to transform the way we treat and deliver water.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.gla.ac.uk