Date of Award
Master of Public Health (MPH)
School of Public Health
As part of a larger sampling campaign with mold inspectors across the US, samples (dust and direct mold) from 11 New York homes during pre- and post-remediation were analyzed by sequencing the interspaced transcribed region of extracted DNA and bioinformatic methods. Overarching findings from this study include: 1) pre/post- remediation fungal communities exhibit subtle differences and 2) larger degrees of fungal community differences exist between direct mold and settled dust. Mainly known as environmentally derived fungal species, Cladosporium ramotenellum and Rhodosporidium diobovatum were the two main taxa to be abundant among post-remediation samples. The thermophilic fungi, Thielavia terrestris, was higher abundances in pre-remediation samples. Subsequent analysis identified more distinct fungal communities between direct mold and settled dust (regardless of remediation status) and found that several fungal species found in the settled dust were related to fungi commonly found in the environment whereas direct mold was dominated by taxa like Aspergillus Subversicolor and Thielavia terrestris. While there were no associations between homes observed, there was a weak positive relationship when settled dust increased with distance to direct mold, indoor dust tended to have more common fungal taxa with associated outdoor settled dust. Results from the random forest classification model suggests two things that future iterations of similar work to consider: optimization may be needed to accurately classify post-remediation home, or that it certainly takes more than 6 months for indoor fungal communities to recover from these perturbations. Among all the other limitations discussed in this paper, the largest would be the fact that there is simply not enough work done in this field to confirm whether the fungal community observed for homes post-remediation reflect that of a successful remediation.
Ly, Truc T., "Fungal Community Characterization And Machine Learning Classification Of Building Mold Status Between Pre- And Post-Remediation" (2020). Public Health Theses. 1969.