A systematic review of methods for the analysis of total per- and polyfluoroalkyl substances (PFAS)
Bibliographic record
Abstract
This manuscript systematically reviews 156 peer-reviewed articles on methods for estimating total per- and polyfluoroalkyl substances (PFAS), following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Direct and indirect methods of estimating total PFAS include targeted analysis, total fluorine (TF), total organic fluorine (TOF), extractable organic fluorine (EOF), absorbable organic fluorine (AOF), and total oxidizable precursor (TOP) assay. Combustion ion chromatography (CIC) was the most utilized method (>50%), followed by particle-induced gamma-ray emission (PIGE, 9%) and high-resolution-continuum source graphite furnace molecular absorption spectrometry (HR-CS-GFMAS, 6%). Techniques like instrumental neutron activation analysis (INAA) and nuclear magnetic resonance (NMR) were less common. A geographic bias was evident, with 69% of studies from the US (33%), Sweden (12%), China (12%), and Germany (11%). Most research targeted environmental samples (water, soil, sediments), while significant data gaps were noted in South America, Africa, and atmospheric PFAS. Challenges in inter-laboratory comparisons arise from inconsistent reporting units (e.g., mg/L, μg/m 3 , %, etc.). About 75% of studies involved pre-treatment (e.g., solvent extraction, sorbents), while 25% did not. PFAS detection limit and observed concentrations varied widely, from low concentrations in water (ng/L) to higher levels in soil, biota, and products (mg/L). Limitations of total PFAS methods include contradictory results when complementary techniques are applied to the same sample, potentially leading to over- or under-estimation. Across studies, a substantial fraction of TF remains unaccounted for, highlighting the need for non-targeted screening (NTS) to identify unknown PFAS (UPFAS or UOPFAS). Bridging these gaps is critical for advancing PFAS research and environmental risk assessment. • 156 articles on total PFAS identified by systematic review following PRISMA • Total PFAS concentrations reported in varied units complicate interstudy comparisons • TF (38% of studies) most reported parameter, followed by EOF (34%), and TOPA (31%) • CIC (53% studies) most used analytical method, followed by TOPA by LC-MS/MS (31%), and PIGE (9%) • Over 10 different calculations used to estimate total PFAS concentrations and mass balance
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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 | 0.051 | 0.147 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".