PFAS Experts Symposium 2: PFAS Remediation research – Evolution from past to present, current efforts, and potential futures
Bibliographic record
Abstract
Abstract Due to the diverse chemistries of per‐ and polyfluoroalkyl substances (PFAS) and their apparent recalcitrance to natural biological and abiotic transformation processes, remediation of this class of compounds in groundwater environments is much more challenging than that of other common contaminants such as chlorinated solvents, hydrocarbons, methyl tert‐butyl ether, and 1,4‐dioxane. Overall, the groundwater remediation community is faced with substantial challenges that will require both continued enhancement of existing technologies and development of new technologies and strategies to manage PFAS‐impacted sites. Fortunately, an extraordinary breadth and depth of ongoing research in PFAS remediation is funded through a variety of different agencies and organizations. This research can be organized into three main categories: (1) nondestructive approaches that remove PFAS from water and other matrices; (2) destructive technologies that break carbon–fluorine and carbon–carbon bonds to create nontoxic products; and (3) coupled systems that concentrate and then destroy PFAS. As with previous groundwater contaminants, an initial focus on ex situ PFAS treatment is now slowly evolving to include more in situ research. However, as of 2021, there are no practical groundwater remediation technologies that have been shown to destroy target PFAS (i.e., mineralize and/or create nontoxic products) in situ at full‐scale field application. While the historical goal of in situ treatment for most contaminants has been destruction, practitioners, facility owners, and regulators may need to alter their expectations and objectives for PFAS, at least in the short term, to management strategies that include treatment at receptor locations to avoid exposures and adsorption‐based attenuation strategies for some plumes. These approaches can be used as practical alternatives to PFAS destruction or to buy time until promising technologies become both commercially available and accepted by the industry. The success of any remedial effort typically depends upon meeting regulatory criteria, which in the case of PFAS, are currently in flux at the federal level and differ by orders of magnitude among state regulatory bodies. While this is understandable given the uncertainty and complexity of this issue, setting firm, consistent, and attainable regulatory standards is necessary to provide researchers and practitioners with necessary benchmarks for remediation technology development and commercialization.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".