Architectural Design and the Indoor Microbiome
We sampled 155 rooms from a building on the University of Oregon campus – Lillis Hall- shortly after its completion and opening. Lillis is a four-story building, with a combination of classrooms, offices, hallways, bathrooms, and open-area spaces, and which features a combination of natural (open window) and mechanical ventilation strategies. Different space types within the building hosted different bacterial communities; classrooms were different from hallways which were different from offices and so forth (Figure 4). Rooms which were physically connected shared more bacteria than rooms farther apart- indicating that microbes are dispersed into and our of spaces regularly (Figure 6).
Dust communities within a building cluster by space type and are strongly correlated with building centrality and human occupancy. Points represent centroids (±SE) from distance based redundancy analysis (DB-RDA). Space types hold significantly different communities (P = 0.005), though this is driven primarily by restrooms. Bacterial OTUs that have the strongest influence in sample dissimilarities are shown at the margins; numbers in parentheses indicate multiple OTUs in the same genus. Centrality (along y-axis) represents network betweenness and degree; human occupancy (along x-axis) represents annual occupied hours and human diversity. All four correlates (simple linear models as a factor of ordination axis) are significant along their respective axes (all P<0.001).
Offices in Lillis Hall show a strong distance-decay pattern. When only considering a single space type, biological similarity (y-axis; 1 – Canberra distance) decreases with connectance distance (number of intermediate space boundaries [e.g., doors] one would walk through to travel the shortest distance between any two spaces) (Mantel test; R = 0.189; P = 0.002). The same pattern was also observed at the whole-building scale (not shown; Mantel test; R = 0.112; P = 0.001).
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