Information on microorganisms in indoor air of various institutional and commercial buildings has significant value in a public health management perspective. However, there is a lack of prior research comparing indoor airborne microbiota across different categories of those buildings. We characterized indoor airborne bacteria in 10 buildings (two for each of five categories: train station, parking garage, mart, public library, and daycare center) during summer and winter. The 16S rRNA gene in the bacterial gDNA extracted from samples was quantified using quantitative real-time polymerase chain reaction and sequenced with the Illumina MiSeq™ platform for characterizing community composition. We collected information on temperature, relative humidity, CO2 concentration, and particulate matter (PM) concentrations by particle size (<1 µm, 1–2.5 µm, 2.5–10 µm) indoors. We performed a multivariate regression analysis to identify factors influencing bacterial quantity and Permutational Multivariate Analysis of Variance (PERMANOVA) to determine factors affecting cluster dissimilarity. We found that bacterial concentration was significantly (p-values < 0.05) associated with season and CO2 concentration. The PERMANOVA analyses showed the significant (p-values < 0.05) associations of bacterial cluster dissimilarity with season, building category, and CO2. Our study indicated that the season, and CO2 concentrations may be important factors associated with the indoor airborne bacterial concentration and composition. Building category and usage appeared to significantly influence the bacterial community composition but not the concentration. Our study may provide basic data on bacterial community composition and their concentration that are needed for properly managing microbial exposures in occupants or customers of the studied institutional and commercial buildings.
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Kim et al. (2024) studied this question.
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