Smart monitoring technologies for water quality and sustainable supply management

Project Description

Private water supplies (PWS) are a concern to public health, both globally and locally, as they are vulnerable to breakdown and subsequent ingress from agricultural run-off and septic tank failures, leading to potential consumption of microbial (and other) contaminants. In the UK, approximately 1% of all consumers receive their drinking water from PWS; in Scotland, this is around 7%. The responsibility for maintenance falls on the owners of the supplies, and so PWS experience greater administrative, managerial and resourcing challenges compared to larger (mains) supplies. Routine water quality testing involves measuring faecal indicator organisms (FIOs) as a proxy for the presence of pathogens, and usually takes (one to two) days. Novel monitoring technologies designed to act as early warning systems for water quality that take only hours to obtain results are therefore essential tools to allow users to take necessary precautions in a timely manner, thereby mitigating health risks. Further research into the constraints and motivations of PWS users in the uptake of novel technologies will support a more holistic understanding of the interaction between public health, water quality, and behavioural change.

The innovative thrust of this interdisciplinary project is to develop, implement and test novel monitoring technologies for water quality on drinking water from PWS in Scotland, which can subsequently influence social attitude and behaviour to drinking water safety. We envision this project combining both computational modelling with some laboratory skill development. The project will involve the student designing new monitoring system based on the available data and state of the art technologies such as sensors and Artificial Intelligence (AI) algorithms in conjunction with all project partners. The student will evaluate the existing sensor technologies for suitability to PWS application and design experimental work to further develop, improve and adapt the system and link with AI. This will require use of various machine learning/deep learning techniques and we propose also to carry out computational modelling to predict the properties of the new designs before implementation. The successful candidate will also benefit from a range of training opportunities through the Hydro Nation Scholarship Programme.

Applicants must hold/achieve a minimum of a Master’s degree (or international equivalent) in Computer/Data Science, Electronic/Chemical Engineering, Environmental/ Biological Sciences or equivalent disciplines. Applicants without a master’s qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Strong programming skills with excellent analytical skills and experimental expertise are essential. Any prior experience in water or chemical/environmental sciences is also beneficial, but not essential. A background understanding in sensors, computational tools, artificial intelligence /machine learning methods or bioinformatics would be advantageous.

Applicants are strongly advised to make an informal enquiry about the PhD to the primary supervisor well before the final submission deadline. Applicants must send a completed Hydro Nation Scholarship application form (available here https://www.hydronationscholars.scot/apply) with a Curriculum Vitae and covering letter to Dr Abdelfateh Kerrouche ([Email Address Removed] ) by the final submission deadline of 7^th January 2022.