Monitoring Remediation of Organic Contaminants using Electrical Resistivity and Induced Polarization Techniques
Bibliographic record
Abstract
Effective characterization and monitoring of subsurface processes associated with the remediation of sites contaminated by dense non-aqueous phase liquids (DNAPLs) remains a substantial environmental challenge. Geophysical techniques such as direct current (DC) resistivity and induced polarization (IP) offer the potential to considerably enhance our understanding of such complex phenomena occurring in the subsurface. However, despite extensive research highlighting the benefits of employing IP – in the time domain (DCIP) and/or frequency domain (spectral IP, SIP) – for contaminant investigations, more research is needed to understand the role of IP for monitoring DNAPL remediation. The goal of this thesis was to evaluate the DCIP and SIP techniques for tracking the remediation of DNAPLs. A novel combined DNAPL-DCIP model was developed, and realistic field-scale numerical simulations were performed, demonstrating that DCIP can provide valuable information on (i) subsurface lithology which controls DNAPL source zone architecture, and (ii) full-phase DNAPL mass removal over time via groundwater dissolution. Static laboratory column experiments highlighted the sensitivity of SIP to track changes in soils following the addition of remedial amendment fluids such as colloidal activated carbon (CAC) and subsequent destruction via smoldering combustion remediation. A suite of dynamic flow-through column experiments was then conducted to show the effectiveness of SIP to monitor temporal CAC progression in soils. In contrast, SIP had limited sensitivity to aqueous phase DNAPL and its adsorption to CAC. This thesis presents new work on evaluating DCIP and SIP at DNAPL-impacted sites, with a specific focus on the IP phenomena. The work contributes new knowledge to help clarify the potential role that these techniques could play in characterization and monitoring activities at DNAPL sites undergoing remediation.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".