Details of Award
NERC Reference : NE/N008367/1
High resolution air quality modelling and prediction
Training Grant Award
- Lead Supervisor:
- Professor M Scott, University of Glasgow, School of Mathematics & Statistics
- Grant held at:
- University of Glasgow, School of Mathematics & Statistics
- Science Area:
- Atmospheric
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Pollution and Waste
- Science Topics:
- Environmental Planning
- Statistics & Appl. Probability
- Pollution
- Abstract:
- This is a collaborative proposal between the Scottish Environment Protection Agency and the School of Mathematics and Statistics in the field of air quality modelling and prediction. Urban Air Quality is a key national issue and the national and UK Governments are actively developing means of evaluating the effects of measures to improve Air Quality. Statistical models play an important role in describing, explaining and predicting local and regional air quality and ultimately contribute to better air quality management by local councils. Statistical models are required to provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales, and to also integrate the routine but sparse monitoring undertaken within our cities and the increasingly detailed physico-chemical models , including transport and meteorology being developed. The national air quality monitoring network provides quality assured, daily measurements, which are spatially very specific, sparse and preferentially located, this is supplemented by systems operated by local authorities such as diffusion tubes, and more recently by small scale trial studies utilising low-cost sensor systems (both static and mobile). Assessment of measures to improve air quality often requires a detailed modelling study and while there are many different air quality modelling tools, one of the key challenges concerns the integration of models with differing spatial and temporal resolution and routine monitoring data. Recent statistical developments include functional data analysis which is a statistical framework that is growing in popularity in handling big and complex data frames, such as those generated by high resolution air pollution models. In the functional data setting, the existing air quality models generate surfaces with complex characteristics, including spatial dependence and one challenge is to make use (through statistical fusion)of models with different spatial resolution (ranging from km to metre scales). The monitoring data are functional data objects but in the temporal domain with similarly complex characteristics including missingness (through equipment malfunction for example), and potentially complex temporal dependencies. The objectives of this research proposal are to: develop and apply a suite of functional data analysis tools for air quality modelling; develop and apply functional data models and tools to calibrate and ground-truth air pollution models; develop visualisation tools for the model and data outputs. This project proposal represents an excellent opportunity to combine research in Statistics with a vital contribution to a pressing and high profile Human Health issue. The immediate beneficiaries of the research include the environmental protection agencies, local councils,environmental planners, environmental health officers and academics.
- NERC Reference:
- NE/N008367/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £91,266
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - RTSG | Total - Student Stipend |
|---|---|---|
| £17,991 | £11,000 | £62,277 |
If you need further help, please read the user guide.