Clean Air funding awards
Details of funding awards from the Clean Air programme
On Clean Air Day 2020 the National Physical Laboratory (NPL) announced their receipt of funding as part of the Clean Air programme. The principle reason for NPL’s involvement in the Clean Air programme is to bring independent metrological expertise to the diverse research activities being undertaken within the programme, and operation of the Metrology Network provides an ideal route to achieve this goal.
Open funding calls for the Clean Air programme were announced in August 2019. Following the bidding process and evaluation of the submitted bids, we are pleased to announce that the following projects will begin in early 2020.
Developing a UK Community Emission Modelling System (DUKEMS)
Awardees: UK Centre for Ecology & Hydrology (UKCEH, coordination), Ricardo, Aether, University of York, University of Manchester, University of Birmingham, King’s College London, Imperial College London, Rothamsted Research
The core objectives of the project are the delivery of a framework and tools designed to be operational long-term in supporting the atmospheric modelling community by providing a flexible, user-friendly system to deliver emission input data for modelling in a transparent, traceable and reproducible manner. The focus is not on blue-skies discovery science, but primarily on supporting and enabling science. In order to ensure delivery and oversight, as well as safeguard the engagement with the wider community, the project will convene a User Group and a Stakeholder Group, with meetings throughout the project runtime.
The project will support networking across multidisciplinary communities by co-design and engagement with the atmospheric modelling community in the UK, but as well stakeholders using model outputs (e.g. policy decision makers, regulators) and the research international community. The project team is actively involved in current and emerging projects in the SPF Clean Air, which will enable added value through utilisation of current and future data streams, including other areas, such as the SPF Digital Environment and UKRI national capability funding.
Visit the DUKEMS website.
Coupled national and local scale air quality modelling - MAQS-Health (Multi-Model Air Quality System for Health Research)
Awardees: Cambridge Environmental Research Consultants Ltd (CERC), with expertise in software development, support and application of local dispersion models (ADMS), and regional model experts from the universities of Birmingham, Edinburgh, Hertfordshire and Lancaster.
This project has been awarded in response to the call for ‘Urban Outdoor Air Quality Modelling’ to provide a high-resolution prediction capability to support personal exposure for health impacts. The system will comprise a coupled air quality modelling system spanning national to urban street scales and accounting for physical and chemical processes occurring at all relevant spatial and temporal scales. It will be flexible and modular, linking established regional chemical transport models including CMAQ, CAMx, EMEP and WRF-Chem and the local model ADMS-Local derived from the widely used urban street scale model ADMS-Urban. An important component of the overall system will be a verification module which will be used for validation of model predictions. The system will be available to the UK research community via the SPF Clean Air Framework platform and will have an open structure facilitating system development and modification. Compatibility with associated SPF projects “UK Emissions Modelling System” and “Air Quality Exposure Modelling” will be ensured through close liaison with those projects.
Air quality exposure modelling - Data Integration Model for Exposure Modelling (DIMEX-UK)
Awardees: University of Exeter, University of Manchester
This project will develop a framework in which data on concentrations of air pollution can be combined with human activity and health data. The aim of the project is to develop a modelling framework to integrate ambient and indoor concentrations with human activity to estimate personal exposures to air pollution for use in future health impact analysis and other applications. The personal exposure model developed here, for the UK, will be based on a theoretical modelling framework for estimating personal exposures stochastically with full integration of the uncertainties inherent in the process. The framework will allow these uncertainties to be propagated into final estimates of personal exposures, in a form that is suitable for further integration into assessment of health effects and the effects of interventions.