Perfluoroalkyl sulfonic and carboxylic acids: A critical review of physicochemical properties, levels and patterns in waters and wastewaters, and treatment methods
Bibliographic record
Abstract
Perfluorinated acids (PFAs) are an emerging class of environmental contaminants present in various environmental and biological matrices. Two major PFA subclasses are the perfluorinated sulfonic acids (PFSAs) and carboxylic acids (PFCAs). The physicochemical properties and partitioning behavior for the linear PFA members are poorly understood and widely debated. Even less is known about the numerous branched congeners with varying perfluoroalkyl chain lengths, leading to confounding issues around attempts to constrain the properties of PFAs. Current computational methods are not adequate for reliable multimedia modeling efforts and risk assessments. These compounds are widely present in surface, ground, marine, and drinking waters at concentrations that vary from pg L(-1) to microg L(-1). Concentration gradients of up to several orders of magnitude are observed in all types of aquatic systems and reflect proximity to known industrial sources concentrated near populated regions. Some wastewaters contain PFAs at mg L(-1) to low g L(-1) levels, or up to 10 orders of magnitude higher than present in more pristine receiving waters. With the exception of trifluoroacetic acid, which is thought to have both significant natural and anthropogenic sources, all PFSAs and PFCAs are believed to arise from human activities. Filtration and sorption technologies offer the most promising existing removal methods for PFAs in aqueous waste streams, although sonochemical approaches hold promise. Additional studies need to be conducted to better define opportunities from evaporative, extractive, thermal, advanced oxidative, direct and catalyzed photochemical, reductive, and biodegradation methods. Most PFA treatment methods exhibit slow kinetic profiles, hindering their direct application in conventional low hydraulic residence time systems.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".