Atmospheric removal of trifluoroacetic acid by dry and wet deposition: a multi year analysis in Toronto
Bibliographic record
Abstract
Trifluoroacetic acid (TFA) is a mobile and persistent compound that is widespread in the environment. While TFA forms in the atmosphere and deposition is its primary loss process, the relative contributions of wet and dry deposition pathways require better observational constraints. Here, we present a record of TFA in total, wet, and dry deposition in Toronto, Canada, between 2018 and 2024. Samples were collected using custom-built total and automated wet deposition samplers. All total (n=103) and wet (n=98) deposition samples contained TFA, with concentrations ranging from 0.07-4.55 µg L-1 and 0.09-3.19 µg L-1, respectively. Seasonal variation showed fluxes peaking in summer months, consistent with atmospheric oxidation of precursors as a major source. Fluxes in 2020 were statistically lower than other years by a factor of 2-5, coinciding with reduced anthropogenic activity during COVID-19 lockdowns. This provides compelling evidence that TFA sources in Toronto are mainly driven by short-lived precursors. The median dry deposition flux (104 µg m-2 a-1) accounted for 0-94 % (mean 45 %) of the total TFA deposition, higher and more variable than model estimates. These results provide the first field-based quantification of TFA in dry deposition, demonstrating that it is a significant and previously underrepresented removal pathway.
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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".