Real-time field measurements of bioaerosols in the agricultural environment: Concentrations, components and environmental impacts

Chen, Z., Crawford, I., Matthews, Emily, Flynn, M., Bannan, Thomas, Cardenas, LauraORCID logo, West, JonORCID logo, Coe, Hugh, Topping, D. and Gallagher, M. (2025) Real-time field measurements of bioaerosols in the agricultural environment: Concentrations, components and environmental impacts. Journal of Environmental Management, 393. p. 127033. 10.1016/j.jenvman.2025.127033
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In agricultural production, bioaerosols inevitably pose health hazards to animals and workers. Currently, there is alack of research on real-time bioaerosol concentration monitoring at agricultural sites. We conducted a real-time airborne bioaerosol measurement study using the multiparameter Bioaerosol Spectrometer (MBS) and applied a Uniform Manifold Approximation and Projection (UMAP) approach to classify bioaerosol emissions from the North Wyke Farm Platform between April and May. Penicillium and Cladosporium were the most dominant fungi. Another machine learning approach, Generalized Additive Model (GAM) was also constructed to explore the relationship with meteorological data and selected trace gases. It was found that animal houses and agricultural fields were the main sources of bioaerosols, and significant dispersion was observed downwind of these point sources. Two main bioaerosol types were Cladosporium and Penicillium, which accounted for 29.8 % and 24.1 % of the total, respectively. Cladosporium had an average concentration of 3.79 L-1 in the animal house direction, which is 2.19 L-1 higher and about 2.37 times that in the farmland direction (1.60 L-1). For Penicillium, the average concentration was 2.44 L-1 in the animal house direction, 0.93 L-1 higher and 1.61 times that in the farmland direction (1.52 L-1). And both bioaerosols are more active at temperatures above 15 ◦C and relative humidity above 80 %. These results may provide recommendations for detection and identification of bioaerosol composition and emission patterns in the agricultural environments, and emission profiles associated with animal farms to provide better understanding for agricultural regional planning and public health perspectives.


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