Application of modern statistical techniques to air quality data

This project will couple the disciplines of atmospheric science and statistical model development to create new methods for the analysis of air pollution data. Increasingly, intervention policies such as low emission zones are being used as a way in which to mitigate important air pollution impacts. The difficulty however, is that there is a major lack of robust analysis methods available to assess whether these policies actually bring about the benefits expected. Analysing air pollution data (and atmospheric composition data in general) is highly challenging due to the inherent complexity of the atmospheric physics and chemistry involved. Historically, this has made it difficult to statistically model and ’explain’ ambient concentrations of different species and their potential change in concentration due to an intervention. Recent statistical development in the area of boosting and regression trees for example, potentially provide a much-needed opportunity to more robustly model air pollution time series and better explore some of the underlying influences such as local meteorology that control air pollutant concentrations. This project will involve the collaboration of the Department of Chemistry and Mathematics at the University of York to carry out research to develop advanced statistical methods that can better model time series of air pollution data; providing a much improved potential to better quantify the actual impact of air pollution policies.

Related undergraduate subjects: Chemistry, Physics, Mathematics, Statistics

Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. Shortlisted applicants will be invited for an interview to take place at the University of York on Friday 12 May. Candidates will be asked to give a short presentation prior to their interview by an academic panel. All research students follow our innovative Doctoral Training in Chemistry (iDTC): cohort-based training to support the development of scientific, transferable and employability skills
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