Project Description
Supervisors
Professor Iain Lake, School of Environmental Sciences, University of East Anglia
Dr Matthew Jones, School of Environmental Sciences, University of East Anglia
Professor Andrew Wilson, Norwich Medical School, University of East Anglia
Scientific Background
The 2025 wildfire season had destructive effects across Europe. Over 10,000 km2 burnt, an area equivalent in size to Cyprus. Wildfires produce hazardous smoke that can travel hundreds of km influencing air quality over large areas. Of particular concern is fine particulate matter (PM2.5), which penetrates deep into the lungs and enters the bloodstream, leading to asthma, lung disease, heart conditions, strokes, and pregnancy/birth complications. Some countries have studied these health impacts extensively, but this is not the case in Europe (1,2). European healthcare systems remain largely unaware and underprepared for the growing threat of wildfire smoke exposure. This project will provide vital new insights into the health effects of wildfire smoke in Europe and by generating evidence-based public health guidance and developing early warning interventions (3), this project will help society adapt to the increasing threat of wildfire smoke.
Research Methodology
We will use data from the European Centre for Medium-range Weather Forecasts (ECMWF) to isolate the contribution of fire to air pollution (PM25) levels across Europe. Two model experiments one with fires, one without will assess the fire specific contribution to PM25 beyond background sources. Using machine learning, we will derive dose-response curves for ill health and death associated with exposures to fire-related PM25. Analysis will be stratified by factors such as age, sex and occupation. Finally, you will incorporate results into the State of Wildfires report authored by members of the research team (4).
Training
This PhD provides an exciting opportunity to work with an interdisciplinary team of experts in wildfires and health from 2 UEA schools, North Carolina State University and the ECMWF. Training will be provided in the extraction of health and climate data from official sources alongside statistical and machine learning techniques to generate smoke-health dose-response curves. Broadly you will gain sought after skills in the analysis and interpretation of large datasets using statistical and machine learning techniques. The skills you will develop are highly valued by employers.
Person Specification
An enthusiastic individual with a quantitative degree. Experience of Environmental Modelling or Public Health would be advantageous but not essential.
Acceptable first degree subjects: A degree or equivalent in numerate, computational, or environmental subject areas.
Project code: LAKE_UEA_ARIES26