Description du poste
Understanding and forecasting airborne pollen is essential for improving operational environmental prediction across the UK. While daily pollen forecasts have improved substantially in recent years, one of the greatest remaining challenges is predicting the overall magnitude of each pollen season. For example, total birch pollen concentrations can vary by a factor of eight between consecutive years, driven by complex interactions between previous-season pollen production, meteorological conditions during the preceding summer, and weather during the flowering season itself. These processes remain poorly understood and are not currently represented within operational forecasting systems. This project will harness a unique UK-wide pollen reanalysis (2000–2025), developed by the UK Met Office under the stewardship of Co-I Lucy Neal, together with long-term pollen observations and meteorological datasets, to develop next-generation machine learning approaches for predicting seasonal pollen magnitude. The resulting models will improve understanding of the environmental drivers of interannual variability while providing practical tools to enhance the operational Met Office NAME pollen forecast. With only 8 active regulatory-grade pollen monitoring sites across the UK, equivalent to approximately one site per eight million people, long-term pollen observations are extremely sparse. The pollen reanalysis provides an unprecedented spatially and temporally complete dataset for developing and validating artificial intelligence methods. The student will investigate how biological memory, antecedent weather conditions and environmental variables combine to determine pollen production, developing interpretable machine learning models capable of predicting seasonal severity months in advance. This PhD will be embedded in a vibrant interdisciplinary research environment at the University of Birmingham, combining expertise in environmental science, artificial intelligence and applied mathematics. Joint supervision from Environmental Sciences, the School of Mathematics, and the UK Met Office will provide outstanding training in machine learning, environmental modelling and operational forecasting. The research will directly address a major limitation in current pollen forecasting systems, delivering new scientific understanding alongside AI tools with clear operational impact for the UK Met Office and wider environmental forecasting community. For further information on this project and details of how to apply to it please click the 'Apply' button above. Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/ Funding notes: This project is offered through the CENTA3 DLA, funded by the Natural Environment Research Council. Funding covers: annual stipend, tuition fees (at home-fee level), Research Training Support Grant. Academic requirements: at least a 2:1 at UK BSc level or a pass at UK MSc level or equivalent. International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating to moving or residing in the UK. International applicants must fulfil the international student entry requirements, including English language: https://www.birmingham.ac.uk/postgraduate/pgt/requirements-pgt/international/index.aspx . References: Neal et al, Development and verification of a taxa-specific gridded pollen modelling system for the UK. Aerobiologia, 2025. Fuertes et al, 2025. Vulnerability to Pollen‐Related Asthma Hospital Admissions in the UK Biobank: A Case‐Crossover Study. Allergy, 80(7). Mills et al (2024) Local spatiotemporal dynamics of oak pollen measured by machine learning aided optical particle counters. Science of the Total Environment. 941. González-Alonso et al (2024) Influence of Meteorological Variables and Air Pollutants on Measurements from Automatic Pollen Sampling Devices. Science of the Total Environment, 31. Mills et al, 2023. Machine learning methods for low-cost pollen monitoring–Model optimisation and interpretability. Science of the Total Environment, 903.