Application of a Quantitative Non-targeted Analysis Workflow to Characterize PFAS in Environmental Waters
Bibliographic record
Abstract
Non-targeted analysis (NTA) enables the detection of novel chemicals but is limited in providing quantitative estimates for risk-based interpretation. Quantitative NTA (qNTA) using surrogate chemicals has been previously explored, yielding metrics for qNTA performance and proof-of-principle approach comparisons across matrices. Here, we apply that work in an integrated qNTA workflow, with demonstration on surface and groundwaters impacted by historic PFAS waste. Commercially available surrogate chemicals ( n = 37) spiked into pooled matrix provided initial calibration data and were used for qNTA estimation via a naïve bounded response factor approach. A validation subset, using paired NTA and targeted analysis estimates (16 PFAS and 129 paired values), showed median accuracy within a factor of 2, an uncertainty fold-range of 12, and overall reliability of 85%. Lower than expected reliability indicates an underestimation of uncertainty, likely from suboptimal surrogate selection. The validated qNTA workflow produced concentration and uncertainty estimates for 210 individual PFAS, with legacy and emerging PFAS estimated concentrations as high as parts-per-billion. PFAS with available standards ( n = 22) contributed, on average, to 91% of the estimated sum concentrations. These findings suggest quantitative estimation for chemicals identified via NTA is valuable to ensure that exposure, hazard, and risk assessments consider the total PFAS burden for impacted watersheds.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".