Summary
This field-based study deployed advanced real-time bioaerosol monitoring at the North Wyke Farm Platform to characterise airborne microbial emissions from agricultural sources over April–May. Using machine learning approaches (UMAP classification and Generalized Additive Modelling), the authors identified animal houses and cultivated fields as primary emission sources and established that both dominant fungal taxa showed heightened activity above 15 °C and at relative humidity exceeding 80 %. The work aims to inform agricultural planning and public health policy by establishing baseline bioaerosol profiles and emission drivers in farming environments.
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; fungal composition (Penicillium and Cladosporium abundance); temperature thresholds; relative humidity thresholds; trace gas relationships
Outcomes reported
The study measured real-time airborne bioaerosol concentrations and composition at an agricultural site using the Multiparameter Bioaerosol Spectrometer, identifying Penicillium and Cladosporium as dominant fungal species. Bioaerosol emissions were characterised by source (animal houses and agricultural fields) and their relationships to meteorological conditions were quantified.
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