Relationship between perfluorooctanoate and perfluorooctane sulfonate blood concentrations in the general population and routine drinking water exposure
Bibliographic record
Abstract
In regions with heavily contaminated drinking water, a significant contribution of drinking water to overall human perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) exposure has been well documented. However, the relationship of PFOA/PFOS blood concentrations in the general population to routine drinking water exposure is not well characterized. This study determined the PFOA and PFOS concentrations in 166 drinking water samples across 28 cities in China. For 13 of the studied cities, PFOA and PFOS concentrations were analyzed in 847 human blood samples which were collected in parallel with the drinking water samples. The geometric mean PFOA and PFOS concentrations in drinking water were 2.5 ± 6.2 ng/L and 0.7 ± 11.7 ng/L, and population-weighted geometric mean blood concentrations were 2.1 ± 1.2 ng/mL and 2.6 ± 1.3 ng/mL, respectively. We found a significant correlation between the PFOA concentration in drinking water and blood (r = 0.87, n = 13, p < 0.001). The total daily intake of PFOA (0.24-2.13 ng/kg/day) and PFOS (0.19-1.87 ng/kg/day) were back-calculated from the blood concentrations with a one-compartment toxicokinetic model. We estimated relative source contributions (RSCs) of drinking water to total daily intake in China of 23 ± 3% for PFOA and 12.7 ± 5.8% for PFOS. Using the mean RSCs, we derived the health advisory values of 85 ng/L for PFOA and 47 ng/L for PFOS in China.
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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 | 0.002 |
| 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 | 0.000 |
| 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".