PhD Studentship: Combining information from Multiple Sources to Estimate Exposures to Air Pollution

The University of Manchester - Earth and Environmental Sciences

Qualification Type:PhDLocation:ManchesterFunding for:UK StudentsFunding amount:Tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid.Hours:Full TimePlaced On:30th June 2025Closes:24th August 2025

Deadline: 24.08.2025

Number of positions: 1

For UK students only

This 3.5 year PhD project is fully funded for home students only. The successful candidate will receive a tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year.

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) [1]. 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.

To apply, please contact the supervisor, Dr. Matthew Thomas (matthew.l.thomas@manchester.ac.uk). Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.