Poly- and Perfluoroalkyl Substances in Municipal Wastewater Treatment Plants in the United States: Seasonal Patterns and Meta-Analysis of Long-Term Trends and Average Concentrations
Bibliographic record
Abstract
This paper presents an up-to-date meta-analysis assessing per- and polyfluoroalkyl substance (PFAS) concentrations at wastewater treatment plants (WWTPs) as well as changes over time. PFAS concentrations were compiled for WWTPs in the United States from peer-reviewed studies, technical reports, and original data. Perfluorooctanoic acid (PFOA) increased by an average of 6.0 ± 1.6 ng/L from the influents to the effluents of WWTPs, but perfluorosulfonic acid (PFOS) did not significantly change, indicating sorption to sludge is offset by biotransformation of precursor compounds. The occurrence of individual PFAS may vary temporally; for example, perfluoropentanoic acid correlated weakly with seasonal temperatures at a site in Virginia. Wastewater effluent PFOA concentrations decreased at a site in Nevada from 2012 to February 2020 but appeared to increase during the COVID-19 pandemic. Effluent PFOA also declined nationally from 1999 to 2020 by ∼13% per year. Nevertheless, the national mean PFOA concentration was 8.4 ± 0.4 ng/L in data collected from 2013 to 2020 with outliers omitted, indicating persisting low-level occurrence. This would equate to 383 ± 20 kg of PFOA per year continuing to enter the environment via WWTP effluents.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.023 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".