The Joint Centre builds upon current Environmental Intelligence research and expertise at both the University of Exeter and Met Office. Current projects include:
CLIMAR®
Framework for quantifying and visualising the risks associated with environmental and climatic change.
Data Science and AI used to integrate data on each component to create decision-ready information.
Visualise results in a form that is accessible to a range of audiences policy makers, industry, general public.
Air Quality Digital Twin (AQDT)
The Air Quality Digital Twin (AQDT) is a collaborative project between the JCEEI and the University of Manchester to develop a prototype digital service that reliably replicates air quality and personal exposures to pollutants.
In the UK, exposure to poor air quality is one of the biggest environmental threats to public health. Clean air guidelines are currently based on ambient concentrations of pollutants, yet these do not consider the exposure experienced by individuals. In addition to the underlying hazard, the health risk due to poor air quality should also account for the individual’s personal exposure and vulnerability.
In this project, we will create an air quality digital twin demonstrator as an interactive web-based user interface. This will display estimates of personal exposure and model how it can be affected by changes to the underlying demographics or emissions profiles in an urban environment. The work will exemplify how environmental intelligence can be used to provide meaningful insight for improved decision strategies against exposure to poor air quality in the city of Manchester.
Renewing biodiversity through a people-in-nature approach (RENEW)
The RENEW project is led by a collaboration between the University of Exeter and the National Trust, and funded by NERC. The JCEEI will utilise its expertise to act as the core ‘technical engine’ of RENEW. The JCEEI team will integrate data science and AI capability within the different RENEW work streams, providing a computational ecosystem that will enable data integration and advanced data science and AI methodologies to be applied at scale.
The RENEW computational ecosystem will also act as a host for web-based dashboards, tools and apps. It will facilitate the co-development of bespoke tools and datasets together with accessible interfaces, apps and dashboards. The result will be an interactive ‘Biodiversity dashboard’ containing a suite of visualisation tools that will provide a unified route to data and information and to highlight the outputs of the analytic work within RENEW in a form that can feed directly into decision making processes and inform business and investment decisions that impact UK biodiversity, particularly those that affect land-use.
Decision support under climate uncertainty for energy security and net zero
Working with the University of Edinburgh, Imperial College London and the University of Warwick we are developing data science and AI methods to quantify uncertainty in frequency of extreme weather effects on future power systems, and for policy and planning decision support against a background of uncertain climate.
Funded by the Alan Turing Institute, this project will develop decision support methodology relating to vital societal issues of decarbonisation and adapting to effects of climate change.
Climate Resilience Demonstrator (CReDo)
Delivered through the government funded National Digital Twin programme, CReDo will develop, for the first time in the UK, a digital twin across energy, water and telecoms networks to provide a practical example of how connected data can improve climate adaptation and resilience.
The CReDo project looks specifically at the impact of flooding caused by climate change on energy, water and telecoms networks. It demonstrates how those who own and operate them can use secure information sharing, across sector boundaries, to mitigate the effect of flooding on network performance and ensure reliable service delivery to customers.
Impact of Climate Change on Agriculture: Building the next generation models to support resilient agricultural policy
We are building an integrated national crop modelling framework, using currently available models and data to allow the testing and development of new policies or management practices prior to implementation. This modelling framework will support interventions aimed at ensuring the UK’s continued food security.
Environmental Monitoring: Blending satellite and surface data
Delivering the intelligent fusion of data from satellite and in-situ surface sensors to help understand our changing planet. We are developing reproducible and interpretable methods to increase scientific understanding, build tools to help environmental measurement planning, and provide the underpinning tools for intelligent real-time monitoring.
GAPH On-line: Breathing Clean Air
Our innovative work in developing the Data Integration Model for Air Quality (DIMAQ), enabled us to give the WHO access to accurate information on population-exposures to fine particulate matter air pollution for every country, even those for which there are no recognised monitoring networks.
