Field intercomparison on the determination of volatile and semivolatile polyfluorinated compounds in air
Bibliographic record
Abstract
Environmental context. Polyfluorinated compounds are an emerging group of organic pollutants that are globally distributed in urban and natural environments. Reliable air monitoring methods are crucial to our understanding of how these pollutants are released to, and transported in, the atmosphere. This intercomparison study, involving laboratories that investigate polyfluorinated compounds in air, uncovers inconsistencies in the reported data, and highlights necessary analytical improvements for future research projects. Abstract. Fluorotelomer alcohols, perfluorinated sulfonamides or sulfonamido ethanols are volatile precursors of persistent perfluorinated acids. Published air-sampling strategies and analytical methods to determine these compounds differ. To assess performance on the determination of airborne polyfluorinated compounds, an intercomparison comprising four international research groups was established. Instrumental and analytical comparability between laboratories varied depending on the compound class. Variability in different sampling strategies was assessed for active (high-volume) v. passive samplers consisting of either semipermeable membrane devices or sorbent-impregnated polyurethane foam disks. Results from passive samplers were typically within an order of magnitude of air concentrations from averaged continuous high-volume samples. Smallest deviations (passive v. active) were observed for sorbent-impregnated polyurethane foam disk samplers with best agreement for fluorotelomer alcohols. This study reveals that it is important to be aware of several types of uncertainty or error for the determination of airborne polyfluorinated compounds and report data in this context or to take steps to minimise their impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".