Assessing indoor fungal spore health impacts with real-time detection technologies

Bioaerosols are ubiquitous airborne microorganisms comprised of bacteria, fungi, pollen, virus and their constituents. Fungi have been associated with negative health effects ranging in severity from allergic reactions to asthma and serious infection, where susceptible individuals are at greater risk of life-threatening health outcomes resulting from exposure. While airborne fungi are abundant indoors, their airborne concentrations and source fluxes are poorly characterized due to the low temporal resolution of traditional offline sampling methods, limiting our understanding of key emission drivers in critical microenvironments and their impacts on air quality. 

There is a critical need to better characterize background fungal aerosol concentrations across a range of indoor microenvironments to build representative emission baselines to explore exposure assessment. Here we demonstrate the utility of emerging real-time detection methods across several indoor microenvironments to characterize the concentrations of key aeroallergenic fungi at high time resolution. 

In this study, Multiparameter Bioaerosol Spectrometer (MBS) and Plair Rapid E+ UV-LIF real-time bioaerosol spectrometers were deployed in University Place, a large multifunctional public space within the University of Manchester campus, over a 4-week period.  The single particle fluorescence and morphological data from the spectrometers was leveraged via cutting edge supervised and unsupervised machine learning approaches to yield 5-minute timeseries of key bioaerosol classes to investigate the impacts of human activity on emissions. Follow up studies with an MBS also investigated emissions within a large lecture theatre over several days and a busy thoroughfare within the Manchester Museum located on campus. 

Clear trends relating to the general movements of people through the microenvironments were observed, with notable increases in fungal aerosols correlating to a maximum in footfall and occupancy. Interestingly large, rapidly decaying spikes in concentration were observed in University Place around the hour, corresponding with a high flux of people through the building as they attend lectures or use other facilities. Crucially, these characteristic emission features would not be evident from sample integrations typical of offline sampling.  

The work presented here demonstrates the utility of real-time detection approaches to assess bioaerosol impact on indoor air quality and exposure. The use of specialised supervised learning training data focused on indoor bioaerosol composition in conjunction with high resolution, multiparameter UV-LIF spectrometers provides excellent high temporal resolution datasets to interrogate bioaerosol emission mechanisms and evaluate impacts on air quality, informing mitigation strategies and regulatory controls.