Selective ultra-trace atmospheric passive sampling of gaseous perfluoroalkyl carboxylic acids
Bibliographic record
Abstract
Acidic pollutants have long been known to have detrimental impacts on remote ecosystems and subject to assessment through long-term monitoring with low cost methodologies, such as passive sampling. Atmospheric oxidation is becoming recognized as a source of novel persistent perfluorinated carboxylic acids (PFCAs) derived from volatile poly- and perfluorinated alkyl substance (PFAS) precursors. In this work, nylon substrate passive air samplers that are selective for atmospheric acids are described and verified through extensive quality assurance and control tests, including controlled chamber tests for sampling rates. The sampling rate for gaseous trifluoroacetic acid (TFA; C2) was established experimentally, with ultra-trace detection limits at parts per quadrillion by volume mixing ratios (ppqv; 10-15 mol mol-1) demonstrated for a one week sampling period. Sampling rates for the C3-C6 homologues of the PFCA family were derived from two diffusion theories of varying complexity. Proof-of-concept measurements were performed at urban, rural continental, and remote marine sites in Canada for over a year. All PFCA homologues from C2-C6 were detected above the method detection limits, with abundance decreasing with increasing chain length and from urban to rural to remote locations. Atmospheric abundance was dominated by TFA, which also showed trends consistent with known sources of precursors and atmospheric oxidation chemistry. This new PFCA-specific passive air sampling method represents a promising new option for the study of PFCA formation, transport, and fate in the atmosphere.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".