Exploring controls on perfluorocarboxylic acid (PFCA) gas–particle partitioning using a model with observational constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The atmospheric fate of perfluorocarboxylic acids (PFCAs) has attracted much attention in recent decades due to the role of the atmosphere in global transport of these persistent chemicals. There is a gap in our understanding of gas-particle partitioning, limited by availability of reliable atmospheric measurements, partitioning properties, and models of gas-particle interactions. The gas-particle equilibrium phase partitioning of C2-C16 PFCAs in the atmosphere were modeled here by taking account of both deprotonation and phase partitioning equilibria among air, aerosol liquid water, and particulate water-insoluble organic matter using a range of available PFCA partitioning properties. We systematically varied water and organic matter content to simulate the full range of atmospheric conditions. Except in severe organic matter pollution episodes, shorter-chain PFCAs are predicted to mainly partition between air and aqueous phase, while for PFCAs with carbon chains longer than 12, organic matter is more likely to be the dominant particle phase reservoir. The model framework underestimated the particle fraction of C2-C8 PFCAs compared with several ambient observations, with larger discrepancies observed for longer-chain PFCAs. The discrepancy could result from externally mixed dust components, non-ideality of aerosol liquid water, surfactant descriptions at phase boundaries, and missed interactions between organic matter and charged PFCA molecules. Reliable measurements of ambient PFCAs with high time resolution and the measurement of uptake parameters by particle-relevant components will be beneficial to more reliable environmental fate modeling of ambient PFCAs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it