Uptake of Per- and Polyfluoroalkyl Substances into Concrete from Aqueous Film-Forming Foams: Experimental Investigations and Comparison to Field-Impacted Samples
Bibliographic record
Abstract
The widespread use of aqueous film-forming foams (AFFFs) in firefighting has led to significant contamination by per- and polyfluoroalkyl substances (PFAS), including in building materials like concrete. This study investigated the initial phase of PFAS contamination in concrete, focusing on factors influencing PFAS retention and penetration. Laboratory experiments assessed the uptake kinetics of PFAS into concrete over one year, revealing that PFAS penetrated beyond surface layers, as confirmed by high-resolution mass spectrometry and desorption electrospray ionization mass spectrometry imaging. PFAS mass loss into the concrete was limited, with 0.99% to 18.5% (mean 6.6%) of initial spiked PFAS being retained. Uptake behaviors were influenced by PFAS chain length and chemistry, concrete surface characteristics, as well as wetting/drying cycles, which accelerated PFAS penetration through the wick effect. Damaged concrete surfaces also showed faster PFAS penetration due to the exposed interfacial transition zones. Field-impacted concrete samples from Canada revealed some similar migration trends with lab-exposed concrete, with shorter-chain PFAS exhibiting greater mobility in the concrete matrix, though notable differences were observed between field and lab samples. These findings highlight the complex dynamics of PFAS contamination in concrete and provide insights into factors affecting PFAS penetration and retention.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".