High-Resolution Atmospheric Modeling of Fluorotelomer Alcohols and Perfluorocarboxylic Acids in the North American Troposphere
Bibliographic record
Abstract
A high spatial and temporal resolution atmospheric model is used to evaluate the potential contribution of fluorotelomer alcohol (FTOH) and perfluorocarboxylate (PFCA) emissions associated with the manufacture, use, and disposal of DuPont fluorotelomer-based products in North America to air concentrations of FTOH, perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) in North America and the Canadian Arctic. A bottom-up emission inventory for PFCAs and FTOHs was developed from sales and product composition data. A detailed FTOH atmospheric degradation mechanism was developed to simulate FTOH degradation to PFCAs and model atmospheric transport of PFCAs and FTOHs. Modeled PFCA yields from FTOH degradation agree with experimental smog-chamber results supporting the degradation mechanism used. Estimated PFCA and FTOH air concentrations and PFCA deposition fluxes are compared to monitoring data and previous global modeling. Predicted FTOH air concentrations are generally in agreement with available monitoring data. Overall emissions from the global fluorotelomer industry are estimated to contribute approximately 1-2% of the PFCAs in North American rainfall, consistent with previous global emissions estimates. Emission calculations and modeling results indicate that atmospheric inputs of PFCAs in North America from fluorotelomer-based products will decline by an order of magnitude in the near future as a result of current industry commitments to reduce manufacturing emissions and lower the residual fluorotelomer alcohol raw material and trace PFCA product content.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".