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Improving UK air quality forecasts during heatwaves

Poor air quality is a major global health concern affecting all industrialised nations and virtually all of society. Worldwide, ~3 million deaths are attributable to air pollution annually (World Health Organization), with ~40,000 premature deaths in the UK alone (Royal College of Physicians). Air pollution-related disease places a significant financial burden on the NHS and social care (to soon reach £billions), thus effective strategies to limit public exposure are vital. A focus of this project is on UK heatwaves – which are expected to become more frequent due to climate change and which provide meteorological conditions conducive to poor air quality.

Air quality forecasts to alert the public to upcoming pollution episodes (like weather forecasts) form a growing part of government strategy to limit population exposure ( Forecasts are available from process-based models operated by different institutions (e.g. Met Office). These models represent the physical/chemical/meteorological processes governing air pollutant behaviour but are subject to substantial bias. For instance, different forecast models can predict pollutant levels that vary by up to a factor of 3, thus undermining public confidence in forecast quality.

This project aims to improve the UK’s operational capacity to produce skilful air quality forecasts during heatwaves. The successful applicant will develop a novel statistical forecasting tool in collaboration with partners at the UK Met Office. This will involve the use of advanced statistical and machine learning approaches applied to air quality and meteorological measurements collected routinely at hundreds of UK monitoring sites. In addition to producing an air quality forecasting tool with operational capability, the project will provide novel insight into the drivers of extreme air quality episodes and areas where process models can be improved.

Applicants should hold a minimum of a UK Honours degree at 2:1 level or equivalent in Mathematics, Statistics, Chemistry, Physics, Natural or Environmental Science, or a related discipline involving Data Science. Applicants who additionally have a Masters degree, or relevant work experience, will be particularly competitive.

For further details please contact Dr Ryan Hossaini (