Geoelectrical monitoring of dense non-aqueous phase liquid (DNAPL) remediation: Numerical, experimental, and field studies
Bibliographic record
Abstract
Effective remediation of brownfield sites contaminated with hazardous industrial chemicals – specifically dense non-aqueous phase liquids (DNAPLs) like coal tar, creosote and chlorinated solvents – remains a major geoenvironmental challenge. Remedial programs can benefit from geophysical methods to non-invasively map changes in the evolving DNAPL mass in space and time. Geoelectrical techniques have long exhibited strong potential in this context, but they are yet to become common tools at DNAPL sites. This presentation summarizes our recent numerical, laboratory and field work to assess the application of various geoelectrical methods for improved monitoring of DNAPL remediation. Novel couplings between a DNAPL model and geoelectrical models (e.g., GPR, ERT, TDIP) were developed to provide valuable and cost-effective exploratory tools for assessing the performance of GPR, ERT and TDIP for monitoring DNAPL remediation in complex, field-scale environments. Laboratory tank experiments were conducted to introduce a new surface-to-horizontal borehole configuration for improved ERT mapping of DNAPL remediation. At an industrial field site, ERT was used to successfully monitor a DNAPL source zone undergoing thermal remediation. Overall, this body of work demonstrates that geoelectrical techniques are indeed promising for mapping DNAPL remediation subject to some limitations and sometimes requiring innovative means of deployment, monitoring, and inversion.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".