|UK Students, EU Students
|£18,622 - please see advert
|23rd November 2023
|14th February 2024
This PhD project is fully funded for UK applicants; tuition fees will be covered and you will receive an annual tax free stipend set at the UKRI rate (£18,622 for 23/24).
Poor air quality is the largest environmental risk to public health, and the World Health Organization (WHO) estimates that 4.2 million premature deaths every year can be attributed to fine particulate ambient air pollution (PM2.5) . Comprehensive information is required on air quality to provide the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks, however, these may not always be able to provide the spatial and/or temporal coverage that is required. In such cases, information from ground measurements can be combined with information from other sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data.
The aim of this PhD is to develop and implement models for integrating data from multiple sources to estimate air quality, along with associated measures of uncertainty. Some traditional models can be relatively restrictive in nature and lack capabilities to deal with large datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential to work with international partners such as the WHO. The models developed will used to answer a series of substantial questions related to variation in exposure to air pollution over space and time. Specifically, the results will be used to assess the burden of disease attributable to and assess the trends in population exposures to air pollution.
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second-Class UK Honours degree, or the equivalent qualifications gained outside the UK, in Statistics, Mathematics, Data Science or Computer Science.
Please contact the lead supervisor for this project - Dr. Matthew Thomas (email@example.com) - before you apply.
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