Phosphorus-Containing Fluorinated Organics: Polyfluoroalkyl Phosphoric Acid Diesters (diPAPs), Perfluorophosphonates (PFPAs), and Perfluorophosphinates (PFPIAs) in Residential Indoor Dust
Bibliographic record
Abstract
Indoor dust is thought to be a source of human exposure to perfluorocarboxylates (PFCAs) and perfluorosulfonates (PFSAs), but exposures to emerging organofluorine compounds, including precursors to PFCAs and PFSAs via indoor dust, remain unknown. We report an analytical method for measuring several groups of emerging phosphorus-containing fluorinated compounds, including polyfluoroalkyl phosphoric acid diesters (diPAP), perfluorophosphonates (PFPA), and perfluorophosphinates (PFPIA), as well as perfluoroethylcyclohexane sulfonate (PFECHS) in indoor dust. This method was used to analyze diPAP, PFPA, and PFPIA levels in 102 residential dust samples collected in 2007-2008 from Vancouver, Canada. The results indicated a predominant and ubiquitous presence of diPAPs (frequency of detection 100%, mean and median ΣdiPAPs 7637 and 2215 ng/g). Previously measured median concentrations of perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and fluorotelomer alcohols (FTOHs) in the same samples were 14-74 times lower than ΣdiPAP levels, i.e. 71 ng/g PFOS, 30 ng/g PFOA, and 152 ng/g ΣFTOHs. PFPAs and PFPIAs were detected in 62% and 85% of samples, respectively, at concentrations nearly 3 orders of magnitude lower than diPAPs (median 2.3 ng/g ΣPFPAs and 2.3 ng/g ΣPFPIAs). PFECHS was detected in only 8% of dust samples. To the best of our knowledge, this is the first report of these compounds in indoor dust. In this study, diPAP concentrations represented 98% ± 7% of the total measured analytes in the dust samples. Detection of diPAPs at such high concentrations in indoor dust may represent an important and as-yet unrecognized indirect source of PFCA exposure in humans, given the identified biotransformation pathways. Identifying the sources of diPAPs to the indoor environment is a priority for future research to improve air quality in households.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".