Understanding and Attributing Extreme Air Pollution Events in a Changing Climate

Poor air quality is a global health concern affecting all industrialised nations and all society. It is characterised by harmful levels of particulate matter, ozone and nitrogen oxides for which chronic and acute exposure to may increase morbidity. Worldwide, ~3 million deaths are attributable to air pollution annually. In the UK, tens of thousands of premature deaths occur annually and many locations breach air quality guidance thresholds. The objective of this PhD is to develop a series of statistical models based on measured air pollutant concentrations to: 1) characterise the frequency, duration and spatial extent of extreme air pollution events; 2) investigate meteorological drivers of extreme pollution events and; 2) assess how climate change may influence the occurrence of extremes. The project will be conducted in 3 phases.

1. Characterising UK extreme air quality episodes
The UK's AURN network consists of ~200 measurement sites and is used to determine the concentration and trends of air pollutants. Hourly AURN data are used to warn the public of adverse air quality and for compliance reporting against EU Air Quality Directives. A first step in this project will be to (a) gain familiarly with the AURN network, including pollutant (PM, O3, NO2) behaviour and observed trends at different site types (e.g. urban/rural/roadside), and (b) to write code to read/process the data. A comprehensive evaluation of extreme air pollution events will be performed, exploiting sites with long records (>20 years). Using Extreme Value Analysis, statistical models will be developed to estimate the likelihood of pollutants exceeding air quality guidance thresholds. Site-wise and regional variability in the magnitude and likelihood of extremes will be investigated.

2. Meteorological drivers of extreme pollution episodes
To both accurately forecast extreme pollution events and quantify if and how the characteristics of these are changing, it is vital to understand the connection between the episodes themselves and any key meteorological drivers. We would also like to understand how the relationship between pollutants and their drivers differs between extreme and non-extreme scenarios. These questions will be addressed using extreme value covariate modelling methods, with models fitted initially on a site-wise basis. We will investigate parametric and semi-parametric covariate models for the driver-response relationship, trying where possible to construct models that can be used for short lead-time forecasting, by using lagged driver variables and/or directly modelling the serial extremal dependence in the responses.

3.Developing spatiotemporal models for UK air pollution
There is uncertainty in the physical drivers of long-term trends or cycles in extreme air pollution events. To complement work in (2), we will use extreme value latent process models, in which site-specific parameters are modelled as functions in time and/or space using latent processes to produce data-driven visualisations of the behaviour of such processes. The advantages of this approach over the regression-based model are that (i) no knowledge of, and minimal prior assumptions on the stochastic behaviour of, the drivers is needed and (ii) they naturally extend to include temporal and/or spatial dependence structures. Inclusion of dependence is important both in improving scientific understanding of the physical processes (both pollutants and drivers) and in obtaining precise and efficient parameter and uncertainty estimates from the statistical model. There is a relatively large literature on use of latent process modelling for extreme events, primarily through implementation of a Bayesian Hierarchical modelling framework. Dynamic linear models, much less well explored in an extreme value context, may also be a useful framework for capturing temporal changes. This PhD's novelty will be in the application and development of these model types for UK air pollu