Conceptualizing Controlling Factors for PFAS Salting Out in Groundwater Discharge Zones Along Sandy Beaches
Bibliographic record
Abstract
Understanding fate and transport processes for per- and poly-fluoroalkyl substances (PFAS) is critical for managing impacted sites. "PFAS Salting Out" in groundwater, defined herein, is an understudied process where PFAS in fresh groundwater mixes with saline groundwater near marine shorelines, which increases sorption of PFAS to aquifer solids. While sorption reduces PFAS mass discharge to marine surface water, the fraction that sorbs to beach sediments may be mobilized under future salinity changes. The objective of this study was to conceptually explore the potential for PFAS Salting Out in sandy beach environments and to perform a preliminary broad-scale characterization of sandy shoreline areas in the continental U.S. While no site-specific PFAS data were collected, our conceptual approach involved developing a multivariate regression model that assessed how tidal amplitude and freshwater submarine groundwater discharge affect the mixing of fresh and saline groundwater in sandy coastal aquifers. We then applied this model to 143 U.S. shoreline areas with sandy beaches (21% of total beaches in the USA), indirectly mapping potential salinity increases in shallow freshwater PFAS plumes as low (<10 ppt), medium (10-20 ppt), or high (>20 ppt) along groundwater flow paths before reaching the ocean. Higher potential salinity increases were observed in West Coast bays and the North Atlantic coastline, due to the combination of moderate to large tides and large fresh groundwater discharge rates, while lower increases occurred along the Gulf of Mexico and the southern Florida Atlantic coast. The salinity increases were used to estimate potential perfluorooctane sulfonic acid (PFOS) sorption in groundwater due to salting out processes. Low-category shorelines may see a 1- to 2.5-fold increase in sorption of PFOS, medium-category a 2.0- to 6.4-fold increase, and high-category a 3.8- to 25-fold increase in PFOS sorption. The analysis presented provides a first critical step in developing a large-scale approach to classify the PFAS Salting Out potential along shorelines and the limitations of the approach adopted highlights important areas for further research.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".