Remediation of NAPL Source Zones: Lessons Learned from Field Studies at Hill and Dover AFB
Bibliographic record
Abstract
Innovative remediation studies were conducted between 1994 and 2004 at sites contaminated by nonaqueous phase liquids (NAPLs) at Hill and Dover AFB, and included technologies that mobilize, solubilize, and volatilize NAPL: air sparging (AS), surfactant flushing, cosolvent flooding, and flushing with a complexing-sugar solution. The experiments proved that aggressive remedial efforts tailored to the contaminant can remove more than 90% of the NAPL-phase contaminant mass. Site-characterization methods were tested as part of these field efforts, including partitioning tracer tests, biotracer tests, and mass-flux measurements. A significant reduction in the groundwater contaminant mass flux was achieved despite incomplete removal of the source. The effectiveness of soil, groundwater, and tracer based characterization methods may be site and technology specific. Employing multiple methods can improve characterization. The studies elucidated the importance of small-scale heterogeneities on remediation effectiveness, and fomented research on enhanced-delivery methods. Most contaminant removal occurs in hydraulically accessible zones, and complete removal is limited by contaminant mass stored in inaccessible zones. These studies illustrated the importance of understanding the fluid dynamics and interfacial behavior of injected fluids on remediation design and implementation. The importance of understanding the dynamics of NAPL-mixture dissolution and removal was highlighted. The results from these studies helped researchers better understand what processes and scales are most important to include in mathematical models used for design and data analysis. Finally, the work at these sites emphasized the importance and feasibility of recycling and reusing chemical agents, and enabled the implementation and success of follow-on full-scale efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".