Microbial growth and volatile organic compound (VOC) emissions from carpet and drywall under elevated relative humidity conditions
Bibliographic record
Abstract
Abstract Background Microbes can grow in indoor environments if moisture is available, and we need an improved understanding of how this growth contributes to emissions of microbial volatile organic compounds (mVOCs). The goal of this study was to measure how moisture levels, building material type, collection site, and microbial species composition impact microbial growth and emissions of mVOCs. We subjected two common building materials, drywall, and carpet, to treatments with varying moisture availability and measured microbial communities and mVOC emissions. Results Fungal growth occurred in samples at >75% equilibrium relative humidity (ERH) for carpet with dust and >85% ERH for inoculated painted drywall. In addition to incubated relative humidity level, dust sample collection site (adonis p =0.001) and material type (drywall, carpet, adonis p =0.001) drove fungal and bacterial species composition. Increased relative humidity was associated with decreased microbial species diversity in samples of carpet with dust (adonis p = 0.005). Abundant volatile organic compounds (VOCs) that accounted for >1% emissions were likely released from building materials and the dust itself. However, certain mVOCs were associated with microbial growth from carpet with dust such as C 10 H 16 H + (monoterpenes) and C 2 H 6 SH + (dimethyl sulfide and ethanethiol). CO 2 production from samples of carpet with dust at 95% ERH averaged 5.92 mg hr -1 kg -1 , while the average for carpet without dust at 95% ERH was 2.55 mg hr -1 kg -1 . Conclusion Microbial growth and mVOC emissions occur at lower relative humidity in carpet and floor dust compared to drywall, which has important implications for human exposure. Even under elevated relative humidity conditions, the VOC emissions profile is dominated by non-microbial VOCs, although potential mVOCs may dominate odor production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".