VOC injection into a house reveals large surface reservoir sizes in an indoor environment
Bibliographic record
Abstract
The total partitioning capacity of indoor surface reservoirs determines the mechanism by which humans receive nondietary exposure to organic contaminants, via inhalation, dermal uptake, and dust ingestion. And yet, this capacity is largely unknown. Surface organic films are ubiquitously present but have very low partitioning volume being only 10’s of nanometer thick, whereas other surface reservoirs such as building materials and furnishings can be permeable or porous with large surface areas at the molecular level. Here, we assess the total partitioning capacity of volatile organic compounds (VOCs) in an indoor environment from the measured kinetics of VOC surface uptake after injection of compounds with variable volatility into a well-characterized, unoccupied test house. We show that the size of the indoor surface reservoirs is very large with an octanol-equivalent average thickness on the order of micrometers, indicating that permeable/porous materials such as painted surfaces and wood are likely the major surface reservoirs in the house rather than organic surface films. Large surface reservoirs result in compounds with octanol-air partition coefficients ( K OA ) larger than 10 5 being predominantly partitioned to indoor surface reservoirs, making them hard to be removed via ventilation. This result significantly impacts our understanding of VOC fate and human exposure in indoor environments. With such a large partitioning capacity, organic contaminants will have much longer indoor residence times than previously predicted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".