Understanding pollen exposure in the UK is critical for addressing its health impacts, particularly in combination with air pollution, which contributes to a significant disease burden. This project will harness newly available high-resolution environmental datasets, including a UK-wide gridded reanalysis of air quality (2003–2019) and a complementary pollen reanalysis (2000–2025), developed by the UK Met Office under the stewardship of Co-I Lucy Neal. Together, these datasets offer an unparalleled opportunity to assess long-term exposure and its health implications.
The student will become an expert in using these reanalyses and will focus on applying them in public health contexts, especially in collaboration with the UK Health Security Agency (UKHSA), with points of contact being Dr Emma Marczylo and Dr Alex Elliot. These analyses will support the development of exposure–response frameworks, help quantify the health burden of respiratory conditions, and assess the role of extreme events such as thunderstorm asthma.
With only 11 active regulatory-grade pollen monitoring sites in the UK, which equates to approximately one site per six million people, the new reanalysis provides essential (and previously unavailable) spatial and temporal coverage for health research. Early findings have already informed a study, involving CoI Lucy Neal, published in Allergy (https://onlinelibrary.wiley.com/doi/10.1111/all.16612), demonstrating the dataset’s potential.
The project will address pressing public health questions: around 20% of the UK population suffers from allergic rhinitis, and asthma costs the NHS over £1 billion annually in England and Wales. Despite this, routine pollen data remain limited in resolution and frequency, underscoring the need for retrospective datasets to support targeted health interventions and improve forecasting systems.
This PhD will be embedded in a vibrant interdisciplinary environment at the University of Birmingham, with training provided through the Birmingham Institute for Sustainability and Climate Action (BISCA) and the Institute for Interdisciplinary Data Science and AI. The student will work at the intersection of environmental science, data analysis, and health policy, delivering research of lasting value for science, forecasting, and public health.
Figure 1: Example output from the Met Office NAME model showing the daily pollen index for the UK on the 14th June 2023. The circles represent the official UK pollen monitoring sites with the local observations overplotted on the NAME model data.
This project is a CENTA Flagship Project.
This project is suitable for CASE funding
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This project will make use of high-resolution, UK-wide reanalyses of pollen and air pollution, developed by the Met Office, to explore links between environmental exposure and public health outcomes. The student will apply statistical and machine learning techniques, including time-series analysis, spatial epidemiology, interpolation, and bias correction, to investigate exposure–response relationships across space and time.
The PhD will actively engage with the syndromic surveillance unit and toxicology department at UKHSA, to ensure the outputs from the project are aligned with the public health priorities of the UK. A particular focus will be on assessing risks from extreme events such as thunderstorm asthma, using known case studies for validation.
The project will also evaluate how these datasets can improve forecast models and inform public health interventions. The resulting tools and insights will support policy-relevant research into respiratory health and allergic disease, using novel, nationally consistent datasets for the first time.
DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.
The student will receive interdisciplinary training in data science (e.g. Python, R, machine learning), atmospheric science, and environmental epidemiology. They will attend University of Birmingham MSc modules and short courses via BISCA and the Data Science Institute, including training on ethical data use and policy engagement. The student will work closely with the UK Met Office through placements and joint supervision, gaining real-world experience in environmental modelling, public health data linkage, and policy-relevant science. The linkages with project collaborators UKHSA will further help in the student’s training.
The Met Office will act as the CASE partner, providing supervision, data access, and support for air pollution reanalysis and forecasting. UKHSA will support epidemiological applications, facilitate access to health data (where possible), and provide public health context. The student will be embedded in a collaborative, policy-relevant research environment.
This project builds upon the AIPS initiative (Artificial Intelligence for Pollen and Spore Detection). The student will benefit from the collaborations across UoB, UKHSA, the Met Office, as well as strategic input from the Birmingham Institute for Sustainability and Climate Action (BISCA) and the Institute for Interdisciplinary Data Science & AI.
Year 1: Literature review; acquire and preprocess pollen and air pollution data; evaluate modelling approaches; initial engagement with epidemiological datasets.
Year 2: Using the UK gridded reanalysis; begin exploratory health analyses; investigate bias correction and validation strategies; Met Office placement.
Year 3: Finalise analyses; publish findings; develop integration into forecast systems; assess policy relevance (e.g. thunderstorm asthma); international conference attendance and thesis preparation.
Pollen related papers from the groups of Francis Pope and Lucy Neil
For any enquiries related to this project please contact Professor Francis Pope, [email protected]
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