Analysis for Perfluorocarboxylic Acids/Anions in Surface Waters and Precipitation Using GC−MS and Analysis of PFOA from Large-Volume Samples
Bibliographic record
Abstract
The presence of perfluorocarboxylates (PFCAs) in the environment is of increasing concern, following the discovery of perfluoroalkyl acids (PFAs) in wildlife and human samples. Here we report a method forthe determination of (C2-C9) PFCAs by preparing the 2,4-difluoroanilides of the acids and analyzing by using GC-MS. Detector response was linear over the range 0.1 -1000 pg of each perfluoroalkyl anilide. A complete suite of PFCAs can be analyzed in an individual sample with the PFCAs detected at levels similar to or lower than those determined by other methods. For a comparison between the present method and the more common LC-MS/MS method, 10 replicates of a sewage treatment plant discharge were analyzed for perfluoro-octanoic acid (PFOA) using both methods. Results were nearly identical with low standard deviation (GC-MS 30.9 +/- 1.88 ng/L; while the LC-MS/MS 34.7 +/- 3.05 ng/L). PFCA concentrations for water samples collected from depth profiles in mid-Lake Ontario were analyzed by GC-MS with most PFCAs (C2-C8) present above the detection limit (0.5 ng/L). Major PFCAs were trifluoroacetate (TFA) (100 ng/L) and perfluorobutanoate (PFBA) (> 5 ng/L). Results for PFOA (2.5 ng/L) were in good agreement with recent analyses by LC-MS/MS. PFCAs were also detected in the precipitation samples at concentrations lower than those of the samples from the lake profiles or sewage treatment plants (STPs) effluent. Since PFOA levels may be less than the lower detection limit (<0.5 ng/L) in 1 L samples, a method for large volumes using XAD-7 resin was developed that allows detection to 0.01 ng/L. This method was applied to Lake Superior samples which produced good agreement for C6-C9 PFCAs between regular analysis (GC-MS) and the XAD-7 followed by GC-MS analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".