PhD Studentship: Advanced Bioaerosols Network Measurements and Forecasting

Bioaerosols are important for human health in indoor and outdoor environments. They are linked to various respiratory illnesses which range in severity from minor to deadly. A high percentage of the UK population has hay fever (allergic rhinitis) due to tree and grass pollen. For many it is an annoyance that can be treated with over-the-counter drugs. However, for a significant percentage of population, the symptoms are far more serious, leading to reductions in work productivity and learning outcomes. Better detection and forecasting of pollen would allow for interventions to be developed that would reduce their risk to human health.   

The current methodologies available for the detection of pollen are either expensive and / or time consuming. The UK Met Office currently has only 11 regulatory grade pollen monitoring sites, where pollen is counted daily in May to August and only weekly in March to September. The Met Office has been developing a dispersion-model based capability for forecasting pollen. However, there are still many uncertainties and areas not captured by the model, for example the considerable variability of pollen levels between years. For use in real time forecasting, it is therefore critical to adjust the model according to recent observations, for example by applying bias correction techniques to the model fields. Current pollen observations are limited both in space and in frequency of updates, which significantly impacts the ability to do real time adjustments to the forecast. Low cost, but well distributed monitoring devices with high temporal resolution would allow gaps in the observation data to be filled and subsequently used to improve pollen forecasts. 

This PhD combines two rapidly developing technologies. It will bring together distributed internet-of-things (IoT) sensor arrays in combination with artificial intelligence (AI) techniques. Fortunately for this project, pollen has well defined sizes that are distinct to the background aerosol which makes detection possible. Machine learning algorithms will be used to classify the pollen and other species of interest and generate approaches to detect them in real time. This real time detection will allow for improvements in real-time pollen forecasts. 

For further information on this project and details of how to apply to it please click on the above 'Apply' button

Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/

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