Assessing fungal burden in Nunavik homes across seasonal conditions: Relationship between air and surface contamination
Bibliographic record
Abstract
Indoor air contamination and fungal growth in buildings are important factors influencing indoor environmental quality. This study assessed airborne and surface-associated fungi in 60 dwellings in Nunavik across summer 2023 and winter 2024 using an activated air sampling protocol with the SASS® 3100 Dry Air Sampler (300 L/min), the first such application in a remote northern context. Airborne fungal concentrations showed strong seasonal variation. Cladosporium peaked in summer (10³ copies/m³) but was nearly absent in winter, while Penicillium / Aspergillus declined from 10⁴ to 10³ copies/m³. Water damage-associated ( C. globosum and T. viride ) and human health-relevant species ( A. fumigatus and A. versicolor ) decreased by >2 logs from summer to winter. S. chartarum remained consistent across seasons (10 1 –10 2 copies/m 3 ). Outdoor sampling confirmed contributions from Penicillium and Aspergillus , whereas water damage moulds were mostly absent outdoors, supporting their value as indoor moisture contamination markers. Surface sampling revealed diverse fungal communities across both seasons, dominated by Aspergillus, Penicillium , and Cladosporium , with the highest diversity in bathrooms. Yeasts such as Rhodotorula and Cryptococcus were persistently isolated, reflecting occupant contributions. We also demonstrated that activated air sampling is particularly valuable for detecting moulds that are not apparent through surface inspection. Overall, these findings highlight pronounced seasonal dynamics in indoor fungal loads, the influence of outdoor air and ventilation, and the importance of monitoring water damage-associated moulds as indicators of indoor contamination in northern housing for establishing baseline data for future assessments
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".