Modeling the Global Fate and Transport of Perfluorooctane Sulfonate (PFOS) and Precursor Compounds in Relation to Temporal Trends in Wildlife Exposure
Bibliographic record
Abstract
A global-scale fate and transport model was applied to investigate the historic and future trends in ambient concentrations of perfluorooctane sulfonate (PFOS) and volatile perfluorooctane sulfonyl fluoride (POSF)-based precursor compounds in the environment. First, a global emission inventory for PFOS and its precursor compounds was estimated for the period 1957-2010. We used this inventory as input to a global-scale contaminant fate model and compared modeled concentrations with field data. The main focus of the simulations was to examine how modeled concentrations of PFOS and volatile precursor compounds respond to the major production phase-out that occurred in 2000-2002. Modeled concentrations of PFOS in surface ocean waters are generally within a factor of 5 of field data and are dominated by direct emissions of this substance. In contrast, modeled concentrations of the precursor compounds considered in this study are lower than measured concentrations both before and after the production phase-out. Modeled surface ocean water concentrations of PFOS in source regions decline slowly in response to the production phase-out while concentrations in remote regions continue to increase until 2030. In contrast, modeled concentrations of precursor compounds in both the atmosphere and surface ocean water compartment in all regions respond rapidly to the production phase-out (i.e., decline quickly to much lower levels). With respect to wildlife biomonitoring data, since precursor compounds are bioavailable and degrade to PFOS in vivo, it is at least plausible that declining trends in PFOS body burdens observed in some marine organisms are attributable to this exposure pathway. The continued increases in PFOS body burdens observed in marine organisms inhabiting other regions may reflect exposure primarily to PFOS itself, present in the environment due to production and use of this compound as well as degradation of precursor compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".