Practical Perspectives of 1,4‐Dioxane Investigation and Remediation
Bibliographic record
Abstract
1,4‐Dioxane (dioxane) is a contaminant of emerging concern that is classified by the U.S. Environmental Protection Agency as a likely human carcinogen. Dioxane has been used as a minor or major ingredient in many applications, and is also generated as an unwanted by‐product of industrial processes associated with the manufacturing of polyethylene, nonionic surfactants, and many consumer products (cosmetics, laundry detergents, shampoos, etc.). Dioxane is also a known stabilizer of chlorinated solvents, particularly 1,1,1‐trichloroethane, and has been commonly found comingled with chlorinated solvent plumes. Dioxane plumes at chlorinated solvent sites can complicate site closure strategies, which to date have not typically focused on dioxane. Aggressive treatment technologies have greatly advanced and are clearly capable of achieving lower parts per billion cleanup criteria using ex situ advanced oxidation processes and sorption media. In situ chemical oxidation has also been demonstrated to effectively remediate dioxane and chlorinated solvents. Other in situ remedies, such as enhanced bioremediation, phytoremediation, and monitored natural attenuation, have been studied; however, their ability to achieve cleanup levels is still somewhat questionable and is limited by co‐occurring contaminants. This article summarizes and provides practical perspectives on dioxane analysis, plume stability relative to other contaminants, and the development of investigation tools and treatment technologies.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".