The Effect of Workload on Biological Monitoring of Occupational Exposure to Toluene and N-Hexane: Contribution of Physiologically Based Toxicokinetic Modeling
Bibliographic record
Abstract
A physiologically based toxicokinetic model was used to examine the impact of work load on the relationship between the airborne concentrations and exposure indicator levels of two industrial solvents, toluene and n-Hexane. The authors simulated occupational exposure (8 hr/day, 5 days/week) at different concentrations, notably 20 ppm and 50 ppm, which are the current threshold limit values recommended by ACGIH for toluene and n-hexane, respectively. Different levels of physical activity, namely, rest, 25 W, and 50 W (for 12 hr followed by 12 hr at rest) were simulated to assess the impact of work load on the recommended biological exposure indices: toluene in blood prior to the last shift of the workweek, urinary o-cresol (a metabolite of toluene) at the end of the shift, and free (nonhydrolyzed) 2,5-hexanedione (a metabolite of n-hexane) at the end of the shift at the end of the workweek. In addition, urinary excretion of unchanged toluene was simulated. The predicted biological concentrations were compared with the results of both experimental studies among human volunteers and field studies among workers. The highest predicted increase with physical exercise was noted for toluene in blood (39 microg/L at 50 W vs. 14 microg/L at rest for 20 ppm, i.e., a 2.8-fold increase). The end-of-shift urinary concentrations of o-cresol and toluene were two times higher at 50 W than at rest (for 20 ppm, 0.65 vs. 0.33 mg/L for o-cresol and 43 vs. 21 microg/L for toluene). Urinary 2,5-hexanedione predicted for 50 ppm was 1.07 mg/L at 50 W and 0.92 mg/L at rest (+16%). The simulations that best describe the concentrations among workers exposed to toluene are those corresponding to 25 W or less. In conclusion, toxicokinetic modeling confirms the significant impact of work load on toluene exposure indicators, whereas only a very slight effect is noted on n-hexane kinetics. These results highlight the necessity of taking work load into account in risk assessment relative to toluene exposure.
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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 | 0.000 | 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) | 0.000 | 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".