A global atmospheric chemistry model for the fate and transport of PFCAs and their precursors
Bibliographic record
Abstract
Perfluorocarboxylic acids (PFCAs) are environmental contaminants that are highly persistent, and many are bio-accumulative and have been detected along with their atmospheric precursors far from emission sources. The overall importance of precursor emissions as an indirect source of PFCAs to the environment is uncertain. Previous studies have estimated the atmospheric source of PFCAs using models and degradation pathways of differing complexities, leading to quantitatively different results. We present results from simulations of atmospheric PFCA formation and fate using the chemical transport model GEOS-Chem. We simulate the most up-to-date chemistry available to our knowledge for the degradation of the precursors fluorotelomer alcohol (FTOH), fluorotelomer olefin (FTO), and fluorotelomer iodide (FTI), as well as the deposition and transport of the precursors, intermediates and end-products of the formation chemistry. We calculate yields of C3-C13 PFCAs formed from 4 : 2 to 12 : 2 fluorotelomer precursors and their deposition to the surface. We find that the ratio of long-chain to short-chain PFCAs increases strongly with distance from source regions. We compare our model results to remote deposition measurements and mid-latitude rainwater measurements. The model captures the observed relationship between rainwater abundance and PFCA chain length, as well as the average deposition rates at mid-latitude and Arctic sites, but underestimates the deposition of PFDoA, PFDA, and TFA at mid-latitudes and PFNA at the Devon Ice Cap. We provide estimates of cumulative PFCA deposition globally. We find that given the most recent emission inventory, the atmospheric source of PFCAs is 6-185 tonnes per year globally and 0.1-2.1 tonnes per year to the Arctic.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".