Integrating Monitoring and Biomonitoring Data with Mechanistic Models to Better Estimate and Characterize Aggregate Human Exposures to Semivolatile Organic Chemicals
Bibliographic record
Abstract
Humans are exposed to many chemicals from multiple sources through various pathways. Many semivolatile organic chemicals (SVOCs) are ubiquitous in indoor environments, but the extent of exposure and relative importance of different pathways (near-field or far-field) are uncertain. Here, 37 SVOCs with measured concentrations in indoor media are used in conjunction with a mass balance indoor fate, exposure, and toxicokinetic model to 1) estimate exposures from indoor environments, 2) incorporate measured dietary (far-field) exposures, 3) evaluate modeled biological concentrations in blood and urine against previously published human biomonitoring (HBM) estimates, 4) calculate the relative importance of different exposure pathways, and 5) demonstrate the value of using models and monitoring data to estimate aggregate exposure for human health assessment. All model calculated blood and urine concentrations are within 2 orders of magnitude of HBM values, and 73% are within 1 order of magnitude. The method explicitly considers uncertainty in measured indoor concentrations and mouthing-mediated ingestion (MMI). When median measured chemical concentrations in dust and MMI are used for modeling, far-field dietary intake is determined to be the dominant contributor to the overall exposure for almost all investigated SVOCs. However, when modeling with higher (third quartile reported) measured chemical concentrations in dust and ∼ 3x higher dust ingestion rates, near-field sources result in exposures for some SVOCs that are comparable to or exceed contributions from far-field exposure pathways. The model also addresses measurement data gaps and, combined with the monitoring data, provides a method to estimate chemical emission rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".