Monitoring carbon-based remediation of DNAPL-contaminated groundwater via spectral induced polarization
Bibliographic record
Abstract
Colloidal activated carbon (CAC) is an emerging remedial enhancement fluid that is injected into the subsurface to adsorb hazardous industrial compounds for subsequent removal. CAC-enhanced remediation relies on accurate subsurface characterization and monitoring to ensure CAC reaches intended treatment locations. The objective of this study was to assess the effectiveness of the spectral induced polarization (SIP) technique to track CAC migration within porous media and its adsorption of the chlorinated solvent, tetrachloroethylene (PCE). Dynamic column experiments were performed with cyclic injection and flow of groundwater, CAC, and PCE within porous media, and simultaneous measurements of SIP and effluent quality. Results showed an increase in both the real and imaginary conductivities of the SIP response during injection/flow of CAC within porous media. Real conductivity returned to pre-CAC levels during subsequent flushing of CAC with groundwater, which had left behind only carbon-coated soil grains; however, imaginary conductivity identified the change in polarizability due to the alterations on the grain surface. The subsequent adsorption of aqueous phase PCE did not generate a distinctive change in SIP response, mainly due to the low 50 mg/L concentrations used. Overall, this study suggests that SIP can be a valuable tool to effectively and non-invasively track the migration of injected CAC within porous media for contaminant adsorption, suggesting it can be used to enhance the implementation and management of environmental remediation programs.
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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 | 0.000 | 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) | 0.000 | 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".