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Project title

Classification and Forecasting of Bioaerosol by Machine Learning

Primary Funding Agency

Irish Research Council

Co-Funding Organisation(s)

Lead Organisation

Dublin City University

Lead Applicant

Not listed

Project Abstract

It has been shown that airborne bioaerosol (pollen, fungal spores, bacteria) concentrations impact health (hay fever and asthma) and the climate. Bioaerosols have been monitored routinely in Europe for the past few decades, however, monitoring in Ireland has only been initiated recently (2018-2019), with a preliminary network sampling this significant airborne component. Thus, we have limited data on their concentrations and species identities. Traditional methods for determining these important particles are labour intensive, based on optical analysis and have a lag associated with the outputted data. Making mitigating exposure difficult. This proposed research will solve this problem via the use of state of the art real-time instrumentation and the development of machine learning analysis techniques, as to allow automatic detection. Validation of the new method will be achieved by co-locating the traditional samplers with novel devices on the roof of Met Eireann. Followed by subsequent comparison of the two devices. This work will provide information on average pollen and fungal concentrations, the most prevalent species and the seasonality of different bioaerosol types in the Irish environment. This will provide vital information to those that suffer from hay fever or asthma potentially lessening their exposure to such allergens. Finally the collected data will be incorporated into the production of an Irish bioaerosol forecast/model (Current forecasts estimated based on data from the UK). This model will be based on observational data such as previous bioaerosol concentrations, weather conditions and inspections of local sources and will be of direct benefit to the public especially those with respiratory conditions.

Grant Approved

�122,020.00

Research Hub

Climate Change

Research Theme

4. Air Science

Start Date

01/09/2023

Initial Projected Completion Date

31/08/2027