Details of Award
NERC Reference : NE/Z503654/1
SenseH2O: a scalable, integrated systems-based approach to monitoring water quality from headwaters to river outlets
Grant Award
- Principal Investigator:
- Professor PD Hunter, University of Stirling, Biological and Environmental Sciences
- Co-Investigator:
- Professor CA Miller, University of Glasgow, School of Mathematics & Statistics
- Co-Investigator:
- Professor AN Tyler, University of Stirling, Biological and Environmental Sciences
- Co-Investigator:
- Dr E Spyrakos, University of Stirling, Biological and Environmental Sciences
- Co-Investigator:
- Dr C J Wilkie, University of Glasgow, School of Mathematics & Statistics
- Grant held at:
- University of Stirling, Biological and Environmental Sciences
- Science Area:
- None
- Overall Classification:
- Unknown
- ENRIs:
- None
- Science Topics:
- None
- Abstract:
- Freshwater ecosystems are critical to biodiversity as well as human health, wealth and wellbeing but are under substantial pressure from a range of catchment and climate stressors. Inputs of chemical nutrients from agricultural land, urban settlements, and discharges of wastewater from treatment works and sewer outflows are the most common cause of poor water quality in the UK. These issues are also being made worse by the increased occurrence of extreme weather events such as storms, floods, and droughts that increase the delivery of nutrients and organics to fresh waters during high rainfall events while acting to concentrate them during periods of low rainfall and river flow. In the UK, there has been significant public and political debate surrounding the state of our rivers and other fresh waters, with questions raised about the adequacy of current approaches to monitoring and regulation. Recent changes to the policy landscape, as well advancements in areas such as low-cost sensing, wireless communications, and artificial intelligence, now provide an opportunity to rethink approaches and embrace new monitoring technologies. However, many commercial solutions for water quality sensing are still too expensive to implement at scale (i.e., region- or nation-wide) or are too limited by their power and data telemetry requirements to enable them to be deployed in more challenging, but often the most data scarce locations. Moreover, while immense progress has been made in the development of artificial intelligence and machine learning methods for data processing and analysis - there are few examples of where these techniques have been integrated into water quality monitoring systems to improve the data provision to users. Finally, some sensor manufacturers use outdated protocols for data transfer that are not compliant with the latest cybersecurity standards, which could potentially introduce vulnerabilities into networks also used by the water industry to support critical national infrastructure. The SenseH2O project will address these challenges by targeting innovation at specific areas of the water quality monitoring lifecycle to develop a new highly integrated, 'systems-level' approach. Our overarching aim of our systems-level approach is to improve the efficacy and scalability of real-time water quality monitoring in the UK. We will achieve this by designing, developing, and demonstrating a prototype water quality monitoring system that integrates the latest in low-cost sensor technologies, adaptable solutions for off-grid power and data communications, artificial intelligence tools for data processing and analysis, and the very best practices in web-based data visualisations. Ultimately, SenseH2O will provide a vision for the future of water quality monitoring at scale in the UK that better addresses the needs of the water industry.
- NERC Reference:
- NE/Z503654/1
- Grant Stage:
- Awaiting Event/Action
- Scheme:
- Research Grants
- Grant Status:
- Active
- Programme:
- IEM
This grant award has a total value of £670,195
FDAB - Financial Details (Award breakdown by headings)
| Exception - Equipment | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|---|
| £559,520 | £33,807 | £51,644 | £5,827 | £14,776 | £3,782 | £837 |
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