Summary
This field-based study employed real-time bioaerosol spectrometry at the North Wyke Farm Platform to characterise agricultural bioaerosol emissions over spring months. Using machine learning classification (UMAP) and modelling (GAM), the authors identified dominant fungal species and quantified their dependence on meteorological conditions, particularly elevated temperatures (>15 °C) and humidity (>80 %). The findings provide baseline emission profiles to inform agricultural planning and worker health protection strategies.
Regional applicability
The study was conducted at a United Kingdom research farm (North Wyke Farm Platform), making the findings directly applicable to UK agricultural conditions and regional planning. The temperate climate context and intensive livestock systems studied align with UK farming practice, though seasonal patterns and emission profiles may vary with specific farm design and management.
Key measures
Real-time bioaerosol concentrations (particle counts by type); fungal species composition (Penicillium, Cladosporium); relationship between meteorological variables (temperature, relative humidity, trace gases) and bioaerosol emissions
Outcomes reported
The study measured real-time airborne bioaerosol concentrations, composition, and emissions from animal houses and agricultural fields using the Multiparameter Bioaerosol Spectrometer. Penicillium and Cladosporium fungi were identified as dominant bioaerosol components with temperature and humidity-dependent emission patterns.
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