| EPSRC Reference: |
EP/Y018532/1 |
| Title: |
Artificial Intelligence for Pollen and Spore Detection, Forecasting and Human Health (AIPS) |
| Principal Investigator: |
Pope, Professor FD |
| Other Investigators: |
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| Project Partners: |
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| Department: |
Sch of Geography, Earth & Env Sciences |
| Organisation: |
University of Birmingham |
| Scheme: |
Standard Research - NR1 |
| Starts: |
02 October 2023 |
Ends: |
01 April 2025 |
Value (£): |
530,358
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Summary on Grant Application Form |
Pollen and fungal spores are important for human health in both outdoor and indoor environments. They are linked to several 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, which can lead to reductions in work productivity and learning outcomes. Indoors, fungal spores are often found in damp and cold environments. These spores can also have significant health outcomes. The cost of living crisis has led to an increase in damp and mould problems within UK homes. Better detection and forecasting of pollen and fungal spores would allow for interventions to be developed that would reduce their risk to human health.
The current methodologies available for the detection of pollen and fungal spores are either expensive or time consuming, and often both. This hugely limits their use. For example, the UK Met Office currently only has available 11 regulatory grade sites for pollen monitoring from which their pollen forecast is based upon. This equates to about one regulatory pollen monitoring station per 11 million people in the UK. Similarly, UK agencies lack cheap methodologies to detect fungal spores in both outdoor and indoor locations. A cheaper, more agile detection method would much increase the UK's capacity for the detection and forecasting of pollen and fungal spores.
This proposal combines two rapidly developing technologies. It will bring together distributed internet-of-things (IoT) sensor arrays in combination with artificial intelligence (AI) techniques. The IoT sensors measure the size distribution of the small particles that are present within the air. The sources and compositions of these particles are many and varied. Atmospheric particles include bioaerosols that are composed of fragments from the biosphere, including pollen and fundal spores. Finding these bioaerosols within the much larger populations of other atmospheric aerosols, is like finding a needle in a haystack. Fortunately for this project, pollen and fungal spores have well defined sizes that are distinct to the background aerosol which makes detection possible. AI approaches will use machine learning algorithms to classify the pollen and fungal spore species of interest and generate approaches to detect them in real time. This real time detection will allow for data-driven real-time forecasts of the pollen and spore species of interest.
The project will assess the efficacy and applicability of the new AI and IoT tools with respect to the UK's bioaerosol detection and forecasting needs. The project will widely engage with UK stakeholders who are involved with monitoring and assessment of the health impacts of bioaerosols. These stakeholders include the UK Met Office, Environment Agency, and UKHSA who are named partners on the project.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.bham.ac.uk |