Observation of a Commercial Fluorinated Material, the Polyfluoroalkyl Phosphoric Acid Diesters, in Human Sera, Wastewater Treatment Plant Sludge, and Paper Fibers
Bibliographic record
Abstract
Sources of human exposure to perfluorinated carboxylic acids (PFCAs) are not well-characterized. Polyfluoroalkyl phosphoric acids (PAPs) are fluorinated surfactants used in human food contact paper products. PAPs can migrate into food and food simulants, and their bioavailability and biotransformation into PFCAs has been demonstrated using a rat model. To characterize human exposure to PAP materials, we analyzed pooled human sera samples collected in 2004 and 2005 (n = 10) and 2008 (n = 10) from the midwestern United States for the 4:2 through 10:2 PAP diesters (diPAPs). The 2004 and 2005 sera samples contained 4.5 microg/L total diPAPs, with the 6:2 diPAP dominating the congener profile at 1.9 +/- 0.4 microg/L DiPAP concentrations observed in the 2004 and 2005 human sera samples were similar to those of the C8 to C11 PFCAs (0.13 +/- 0.01 to 4.2 +/- 0.3 microg/L) monitored in the same samples. 6:2 diPAP was also consistently observed in the 2008 human sera samples at a mean concentration of 0.63 +/- 0.13 microg/L As diPAPs have been shown to degrade to PFCAs in vivo, our observation of diPAPs in human sera may be a direct connection between the legacy of human PFCA contamination and PAPs commercial applications. Wastewater treatment plant (WWTP) sludge and paper fibers were analyzed for diPAPs as a proxy for human use and potential exposure to diPAPs. DiPAPs were observed in WWTP sludge at concentrations ranging from 47 +/- 22 to 200 +/- 130 ng/g, a range similar to perfluorooctane sulfonic acid (PFOS) (100 +/- 70 ng/g) and greater than the C8 to C11 PFCAs (1.6 +/- 0.6 to 0.17 +/- 0.10 ng/g) observed in the same samples. DiPAPs were observed in paper fiber extracts at concentrations ranging from 34 +/- 30 to 2200 +/- 400 ng/g. The high diPAP concentrations in WWTP sludge suggest PAP materials may be prevalent in our daily lives.
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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 | 0.001 | 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".