Fast Analysis of Ultra-short Chain Per- and polyfluoroalkyl substances (PFAS) in River Water by Hydrophilic Interaction Liquid Chromatography Mass Spectrometry
Bibliographic record
Abstract
A fast and sensitive analytical method was developed for the determination of 11 ultra-short and short-chain PFAS in surface water. For the first time, online solid-phase extraction coupled with liquid chromatography high-resolution mass spectrometry (on-line SPE-LC-HRMS) was used to analyze these emerging PFAS. Screening of 7 chromatographic columns and 5 on-line SPE columns was performed, and experimental designs were applied to optimize SPE loading conditions. Other method parameters were tested, leading to the choice of filter (glass fiber), sample acidification, chromatographic mobile phases (25 mM ammonia acetate in water/acetonitrile), and SPE loading mobile phase (0.0125% formic acid in mQ-water). The method was validated in surface water matrix with suitable determination coefficients, detection limits (LOD range: 0.006-3.3 ng/L), accuracy (71%-130%), intraday precision (0.48%-20%), and inter-day precision (0.92%-19%). The method was applied to 44 river water samples collected in Eastern Canada, including airport sites with fire-training areas. Of the 11 ultra-short and short-chain PFAS targeted for screening, the most frequent 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 PFBS, PFBA, and PFPeA were orders of magnitude higher in rivers near fire-training area sites compared with other rivers, while TFA and TMS were not, likely reflecting atmospheric deposition sources for these two compounds. Ultrashort 1:3, 2:3 and 3:3 polyfluoroalkyl acids were also detected in environmental waters for the first time
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".