Variability of indoor fungal microbiome of green and non-green low-income homes in Cincinnati, Ohio.

"Green" housing is designed to use low-impact materials, increase energy efficiency and improve occupant health. However, little is known about the indoor mycobiome of green homes. The current study is a subset of a multicenter study that aims to investigate the indoor environment of green homes and the respiratory health of asthmatic children. In the current study, the mycobiome in air, bed dust and floor dust was compared between green (study site) and non-green (control site), low-income homes in Cincinnati, Ohio. The samples were collected at baseline (within four months following renovation), and 12months after the baseline at the study site. Parallel sample collection was conducted in non-green control homes. Air samples were collected by PM2.5 samplers over 5-days. Bed and floor dust samples were vacuumed after the air sampling was completed. The DNA sample extracts were analyzed using ITS amplicon sequencing. Analysis indicated that there was no clear trend in the fungal communities between green and non-green homes. Instead, fungal community differences were greatest between sample types - air, bed, and floor. Microbial communities also changed substantially between sampling intervals in both green and non-green homes for all sample types, potentially indicating that there was very little stability in the mycobiomes. Research gaps remain regarding how indoor mycobiome fluctuates over time. Longer follow-up periods might elucidate the effect of green renovation on microbial load in buildings.

Fungal Microbiomes Associated with Green and Non-Green Building Materials.

Water-damaged buildings can lead to fungal growth and occupant health problems. Green building materials, derived from renewable sources, are increasingly utilized in construction and renovations. However, the question as to what fungi will grow on these green compared to non-green materials, after they get wet, has not been adequately studied. By determining what fungi grow on each type of material, the potential health risks can be more adequately assessed. In this study, we inoculated green and non-green pieces of ceiling tile, composite board, drywall, and flooring with indoor dust containing a complex mixture of naturally occurring fungi. The materials were saturated with water and incubated for two months in a controlled environment. The resulting fungal microbiomes were evaluated using ITS amplicon sequencing. Overall, the richness and diversity of the mycobiomes on each pair of green and non-green pieces were not significantly different. However, different genera dominated on each type of material. For example, Aspergillus spp. had the highest relative abundance on green and non-green ceiling tiles and green composite boards, but Peniophora spp. dominated the non-green composite board. In contrast, Penicillium spp. dominated green and non-green flooring samples. Green gypsum board was dominated by Phialophora spp. and Stachybotrys spp., but non-green gypsum board by Myrothecium spp. These data suggest that water-damaged green and non-green building materials can result in mycobiomes that are dominated by fungal genera whose member species pose different potentials for health risks.

Indoor air quality in green-renovated vs. non-green low-income homes of children living in a temperate region of US (Ohio).

Green eco-friendly housing includes approaches to reduce indoor air pollutant sources and to increase energy efficiency. Although sealing/tightening buildings can save energy and reduce the penetration of outdoor pollutants, an adverse outcome can be increased buildup of pollutants with indoor sources. The objective of this study was to determine the differences in the indoor air quality (IAQ) between green and non-green homes in low-income housing complexes. In one housing complex, apartments were renovated using green principles (n=28). Home visits were conducted immediately after the renovation, and subsequently at 6 months and at 12 months following the renovation. Of these homes, eight homes had pre-renovation home visits; this allowed pre- and post-renovation comparisons within the same homes. Parallel visits were conducted in non-green (control) apartments (n=14) in a nearby low-income housing complex. The IAQ assessments included PM2.5, black carbon, ultrafine particles, sulfur, total volatile organic compounds (VOCs), formaldehyde, and air exchange rate. Data were analyzed using linear mixed-effects models. None of the indoor pollutant concentrations were significantly different between green and non-green homes. However, we found differences when comparing the concentrations before and after renovation. Measured immediately after renovation, indoor black carbon concentrations were significantly lower averaging 682 ng/m(3) in post-renovation vs. 2364 ng/m(3) in pre-renovation home visits (p=0.01). In contrast, formaldehyde concentrations were significantly higher in post-renovated (0.03 ppm) than in pre-renovated homes (0.01 ppm) (p=0.004). Questionnaire data showed that opening of windows occurred less frequently in homes immediately post-renovation compared to pre-renovation; this factor likely affected the levels of indoor black carbon (from outdoor sources) and formaldehyde (from indoor sources) more than the renovation status itself. To reduce IAQ problems and potentially improve health, careful selection of indoor building materials and ensuring sufficient ventilation are important for green building designs.

Key determinants of the fungal and bacterial microbiomes in homes.

BACKGROUND: The microbiome of the home is of great interest because of its possible impact on health. Our goal was to identify some of the factors that determine the richness, evenness and diversity of the home's fungal and bacterial microbiomes. METHOD: Vacuumed settled dust from homes (n=35) in Cincinnati, OH, were analyzed by pyrosequencing to determine the fungal and bacterial relative sequence occurrence. The correlation coefficients between home environmental characteristics, including age of home, Environmental Relative Moldiness Index (ERMI) values, occupant number, relative humidity and temperature, as well as pets (dog and cat) were evaluated for their influence on fungal and bacterial communities. In addition, linear discriminant analysis (LDA) was used for identifying fungal and bacterial genera and species associated with those housing determinants found to be significant. RESULTS: The fungal richness was found to be positively correlated with age of home (p=0.002), ERMI value (p=0.003), and relative humidity (p=0.015) in the home. However, fungal evenness and diversity were only correlated with the age of home (p=0.001). Diversity and evenness (not richness) of the bacterial microbiome in the homes were associated with dog ownership. Linear discriminant analysis showed total of 39 putative fungal genera/species with significantly higher LDA scores in high ERMI homes and 47 genera/species with significantly higher LDA scores in homes with high relative humidity. When categorized according to the age of the home, a total of 67 fungal genera/species had LDA scores above the significance threshold. Dog ownership appeared to have the most influence on the bacterial microbiome, since a total of 130 bacterial genera/species had significantly higher LDA scores in homes with dogs. CONCLUSIONS: Some key determinants of the fungal and bacterial microbiome appear to be excess moisture, age of the home and dog ownership.