The significance of fluorinated compound chain length, treatment technology, and influent composition on per- and polyfluoroalkyl substances removal in worldwide wastewater treatment plants
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are deemed major conveyors and point sources of per- and polyfluoroalkyl substances (PFAS) to the environment. This statistical meta-analysis of existing literature from the past 15 years focused on the significance of treatment type for PFAS removal efficiencies and the influence of PFAS sources (domestic vs. industrial) on their removal. Different sampling events, WWTPs across the world, different treatment technologies, configurations, and processes, as well as different PFAS classes and compounds were considered. This study evaluated 13 PFAS analyzed predominantly in 161 WWTPs across the world. The statistical test results revealed that these 13 frequently detected and reported PFAS can be divided into four groups based on their behavior during wastewater treatment, namely (1) C6-10 perfluorocarboxylic acids (PFCAs), (2) C4,5,11,12 PFCAs, (3) C4,6,8 perfluoroalkane sulfonic acids (PFSAs), and (4) C10 PFSA. In this study, biological treatments such as (1) membrane bioreactors, (2) combination of two or more biological treatments, and (3) biofilm processes revealed the highest PFAS removals, although the addition of a tertiary treatment actually had a nonbeneficial effect on PFAS removal. Moreover, a strong statistical correlation was observed between industrial wastewater sources and the presence of high influent PFAS concentrations in the receiving WWTPs. This indicates that industrial sources were the main contributors of the PFAS load in the analyzed WWTPs. Integr Environ Assess Manag 2024;20:59-69. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".