Fast analysis of short-chain and ultra-short-chain fluorinated organics in water by on-line extraction coupled to HPLC-HRMS
Bibliographic record
Abstract
A rapid on-line solid-phase extraction liquid chromatography high-resolution mass spectrometry (on-line SPE-LC-HRMS) method was developed to analyze 11 ultra-short and short-chain PFAS in surface water. Analytical optimization involved screening 7 chromatographic columns and 5 on-line SPE columns, as well as evaluating SPE loading conditions, filters, sample acidification, chromatographic mobile phases, and SPE loading mobile phases. The optimized method was then applied to 44 river water samples collected in Eastern Canada, including sites near airports with fire-training areas. Among the 11 targeted PFAS, the most frequently detected were trifluoroacetic acid (TFA, 4.6-220 ng/L), perfluorobutanoic acid (PFBA, 0.85-33 ng/L), perfluoropentanoic acid (PFPeA, 1.2-2100 ng/L), trifluoromethane sulfonic acid (TMS, 0.01-4.3 ng/L), and perfluorobutane sulfonic acid (PFBS, 0.07-450 ng/L). Levels of C3-C5 perfluoroalkyl carboxylic acids (PFCAs), C2-C4 perfluoroalkyl sulfonates (PFSAs) and n:3 polyfluoroalkyl acids (n = 2,3; n:3 acids) were significantly higher in water bodies near fire-training area sites compared with rivers in urban areas. In contrast, TFA, TMS, and 1:3 acid were not significantly elevated, likely reflecting atmospheric deposition or other diffuse sources for these compounds. Nontarget and suspect screening analysis revealed an abundance of other ultra-short and short-chain PFAS in AFFF-impacted water bodies. Perfluoroalkyl sulfonamides (FASA, C2, C3, and C5), perfluoroalkyl sulfonamide propanoic acids (FASA-PrA, C1-C2) and n:3 acids (n = 1, 4, and 5) were detected for the first time in environmental surface waters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".