Characterization of the atmospheric microbiome in a semi-rural area of Central Europe using flow cytometry

Characterizing bioaerosols is important for understanding their potential impacts on the environment and public health. In this study, we developed a novel flow cytometry-based approach to determine the low nucleic acid (LNA), high nucleic acid (HNA), dead, and intact bioaerosol populations in samples collected with a wet cyclone at Payerne, Switzerland, during spring and summer 2024. We found that the average bioaerosol number concentration reached (2.47 ± 3.35)×10⁴ m^-3. The HNA and intact populations were the most abundant populations, representing the largest fraction of total bioaerosols within 65% and 97% of the samples, respectively. Our results show that the LNA can be composed of dead bioaerosols, which correlated strongly with atmospheric particulate mass. Quantitative Polymerase Chain Reaction (qPCR) and metagenomic analysis reveal significant correlations and associations (Spearman, PERMANOVA, and Mantel) between the different kingdoms analyzed, reflecting complex ecological interactions in the atmosphere among the communities. Despite this complexity, LNA was mainly associated with the archaea Nitrososphaerota and bacteria Actinomycetota, whereas HNA was enriched by fungal classes such as Pichiomycetes and Ustilaginomycetes. Pollen abundance was positively correlated with temperature and negatively correlated with relative humidity and pollution (NO_x and NO₂), as these conditions promote the formation of sub-pollen particles (pollen fragments) through osmotic (bursting) and oxidative stress. Factor analysis indicates a seasonal dynamics transition from plant-associated bioaerosols in the spring season, to other bioaerosol types to be co-emitted during summer. Overall, the integration of flow cytometry with molecular analysis provides a framework to characterize and quantify bioaerosols and provides new insights into the ecological structure, variability, and sources of the atmospheric microbiome.