How are Humans Exposed to Organic Chemicals Released to Indoor Air?
Bibliographic record
Abstract
Abstract Humans are exposed to organic chemicals released to indoor air through near-field exposure routes such as air inhalation and nondietary dust ingestion as well as far-field exposure routes such as consumption of food. Here, we explore the relative importance of near- and far-field exposure routes and its variability between chemicals, age groups, and subpopulations, by modeling aggregate human exposure to indoor-released chemicals with diverse partitioning behavior and degradability. Our model results indicate that if chemicals are assumed to be perfectly persistent, dietary and nondietary ingestion dominates human exposure to hydrophobic chemicals of relatively low volatility (with an octanol–air partition coefficient KOA > 106.5 and an octanol–water partition coefficient KOW < 1011), whereas inhalation of indoor air dominates human exposure to volatile chemicals. Other exposure routes, for example, dermal absorption and drinking water, make a relatively small contribution to human exposure. Reduced chemical persistence in environmental media and biota lowers the contribution of dietary ingestion. For most chemicals other than those with a KOA between 109 and 1012 and a KOW between 106 and 109 (e.g., polybrominated diphenyl ethers), the relative importance of near- and far-field exposure routes is primarily governed by chemical partitioning and degradability rather than age- and population-dependent human exposure factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".