Land-use and agriculture are responsible for around one quarter of all human greenhouse gas (GHG) emissions. While some of the activities that contribute to these emissions, such as deforestation, are readily observable, others are not. It is now recognised that freshwater ecosystems are active components of the global carbon cycle; rivers and lakes process the organic matter and nutrients they receive from their catchments, emit carbon dioxide (CO2) and methane to the atmosphere, sequester CO2 through aquatic primary production, and bury carbon in their sediments. Human activities such as nutrient and organic matter pollution from agriculture and urban wastewater, modification of drainage networks, and the widespread creation of new water bodies, from farm ponds to hydro-electric and water supply reservoirs, have greatly modified natural aquatic biogeochemical processes. In some inland waters, this has led to large GHG emissions to the atmosphere. However these emissions are highly variable in time and space, occur via a range of pathways, and are consequently exceptionally hard to measure on the temporal and spatial scales required. Advances in technology, including high-frequency monitoring systems, autonomous boat-mounted sensors and novel, low-cost automated systems that can be operated remotely across multiple locations, now offer the potential to capture these important but poorly understood emissions. In the GHG-Aqua project we will establish an integrated, UK-wide system for measuring aquatic GHG emissions, combining a core of highly instrumented 'Sentinel' sites with a distributed, community-run network of low-cost sensor systems deployed across UK inland waters to measure emissions from rivers, lakes, ponds, canals and reservoirs across gradients of human disturbance. A mobile instrument suite will enable detailed campaign-based assessment of vertical and spatial variations in fluxes and underlying processes. This globally unique and highly integrated measurement system will transform our capability to quantify aquatic GHG emissions from inland waters. With the support of a large community of researchers it will help to make the UK a world-leader in the field, and will facilitate future national and international scientific research to understand the role of natural and constructed waterbodies as active zones of carbon cycling, and sources and sinks for GHGs. We will work with government to include these fluxes in the UK's national emissions inventory; with the water industry to support their operational climate change mitigation targets; and with charities, agencies and others engaged in protecting and restoring freshwater environments to ensure that the climate change mitigation benefits of their activities can be captured, reported and sustained through effectively targeted investment.

The Quantum Technology Hub in Sensors and Timing, a collaboration between 7 universities, NPL, BGS and industry, will bring disruptive new capability to real world applications with high economic and societal impact to the UK. The unique properties of QT sensors will enable radical innovations in Geophysics, Health Care, Timing Applications and Navigation. Our established industry partnerships bring a focus to our research work that enable sensors to be customised to the needs of each application. The total long term economic impact could amount to ~10% of GDP. Gravity sensors can see beneath the surface of the ground to identify buried structures that result in enormous cost to construction projects ranging from rail infrastructure, or sink holes, to brownfield site developments. Similarly they can identify oil resources and magma flows. To be of practical value, gravity sensors must be able to make rapid measurements in challenging environments. Operation from airborne platforms, such as drones, will greatly reduce the cost of deployment and bring inaccessible locations within reach. Mapping brain activity in patients with dementia or schizophrenia, particularly when they are able to move around and perform tasks which stimulate brain function, will help early diagnosis and speed the development of new treatments. Existing brain imaging systems are large and unwieldy; it is particularly difficult to use them with children where a better understanding of epilepsy or brain injury would be of enormous benefit. The systems we will develop will be used initially for patients moving freely in shielded rooms but will eventually be capable of operation in less specialised environments. A new generation of QT based magnetometers, manufactured in the UK, will enable these advances. Precision timing is essential to many systems that we take for granted, including communications and radar. Ultra-precise oscillators, in a field deployable package, will enable radar systems to identify small slow-moving targets such as drones which are currently difficult to detect, bringing greater safety to airports and other sensitive locations. Our world is highly dependent on precise navigation. Although originally developed for defence, our civil infrastructure is critically reliant on GNSS. The ability to fix one's location underground, underwater, inside buildings or when satellite signals are deliberately disrupted can be greatly enhanced using QT sensing. Making Inertial Navigation Systems more robust and using novel techniques such as gravity map matching will alleviate many of these problems. In order to achieve all this, we will drive advanced physics research aimed at small, low power operation and translate it into engineered packages to bring systems of unparalleled capability within the reach of practical applications. Applied research will bring out their ability to deliver huge societal and economic benefit. By continuing to work with a cohort of industry partners, we will help establish a complete ecosystem for QT exploitation, with global reach but firmly rooted in the UK. These goals can only be met by combining the expertise of scientists and engineers across a broad spectrum of capability. The ability to engineer devices that can be deployed in challenging environments requires contributions from physics electronic engineering and materials science. The design of systems that possess the necessary characteristics for specific applications requires understanding from civil and electronic engineering, neuroscience and a wide range of stakeholders in the supply chain. The outputs from a sensor is of little value without the ability to translate raw data into actionable information: data analysis and AI skills are needed here. The research activities of the hub are designed to connect and develop these skills in a coordinated fashion such that the impact on our economy is accelerated.

Filter membranes play a critical role in providing clean drinking water, access to which is one of the most pivotal human rights. Typically, the operation of the filters has relied on manual, local monitoring of operational markers such as flow rates and contaminants' concentrations. This need for hands on expert maintenance is preventing membrane technology from reaching its full potential. To correct this, the monitoring of water filter needs to be achieved by sensors, transmitting data in real-time for centralised artificial intelligence (AI) based analysis. Such an AI driven water filter system must be scalable to meet with the global demands for clean water. There is therefore a massive global opportunity for membrane systems to benefit from being implemented as cyber-physical systems (CPS). This discipline hopping grant (DHG) will provide the PI and discipline hopper Das with an immersive information and communication technology (ICT) experience. It will enable him to bring the ICT capabilities and use of smart wireless-sensor technologies for autonomous, real-time monitoring, together with AI driven data analytics within the broader area of CPS into his home discipline relating to membrane water treatment. This will be achieved by supporting/mentoring the PI at 50% FTE for 2 years to experience ways for developing a membrane-CPS (m-CPS) based on intelligent CPS architecture, embedded with a smart wireless sensor network (WSN) for continuous real-time monitoring of the performance of a membrane-treatment unit enhanced by cloud-based AI data analytics and decision making.