Matthew completed his BSc. in Mathematics and Statistics from the University of Bath in 2014 prior to joining the SAMBa CDT. As part of his degree course Matthew had a placement year, where he was a Statistical Programmer working with clinical trial data in the pharmaceutical company Roche. Outside university, Matthew enjoys watching TV and films, listening to music and attending pub quizzes.

Research project title:
Modelling air pollution using data assimilation

Supervisor(s):
Gavin Shaddick, Melina Freitag

Project description:
In order to assess the burden of disease which may be attributable to air pollution, accurate estimates of exposure are required globally. There is a need for comprehensive integration of information from remote sensing, atmospheric models and surface monitoring to facilitate estimation of concentrations in areas throughout the world. Data assimilation is a method of combining model forecast data with observational data in order to more accurately understand the state of a system. Methods vary greatly in complexity and Matt explored different methods from both a statistical and numerical analysis standpoint. Elements of a suitable method included flexibility, modularity, the ability to incorporate multiple levels of uncertainty and techniques that allowed relationships between surface monitoring, remote sensing and atmospheric models that vary spatially and allow information to be ‘borrowed’ where monitoring data may be sparse. Throughout the project, the efficacy of different methods in this setting was examined by applying them to data from the Global Burden of Disease project. Of particular interest was their scaleability with regards to use with high-dimensional data.