Temporal moment-based approach to understand the dissolved-phase LNAPL recovery and associated characteristics in the porous system under dynamic groundwater table conditions
Bibliographic record
Abstract
Abstract The dissolved-phase hydrocarbon recovery can be the first step in decontaminating the soil–water system if spilled with light/dense non-aqueous phase liquid (L/D-NAPL). This study proposes a temporal moment-based approach to investigate the effectiveness of groundwater table manipulations for recovering dissolved-phase byproducts of light non-aqueous phase liquid (LNAPL) from the subsurface system. Temporal moments were computed utilizing experimentally observed and HYDRUS-simulated dissolved-phase toluene concentration data, representative of LNAPL, under stable and dynamic groundwater table fluctuation (GWTF) scenarios. Zeroth temporal moment (ZTM) showed that the hydrocarbon mass recovery varied from 1,804 to 5,190.6 mg/L × h, with the highest variation for the rapid GWTF scenario. An increase in the ZTM of hydrocarbon was observed with an increase in the rate of change of magnitude of the water table and pore velocity fluctuation as in the case of a rapid GWTF as compared to a stable GWTF case. The value of mean residence time for the stable groundwater table case was highest for the entire experimental duration, followed by slow, general, and rapid cases. Temporal moment analysis revealed that the high dissolved-phase hydrocarbon recovery could be achieved by manipulating groundwater table conditions. The present study provides a powerful technique to improve dissolved hydrocarbon remediation in mineral aquifers using hydrological restorations.
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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 | 0.001 | 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) | 0.001 | 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".