Methane soil gas gradient method for quantifying natural source zone depletion rates at petroleum contaminated sites
Bibliographic record
Abstract
This study presents a novel method that relies on the methane gradient in soil gas for estimating natural source zone depletion (NSZD) rates of light non-aqueous phase liquids (LNAPL) in the subsurface. Methane generation via methanogenesis at the LNAPL source, followed by methane oxidation in the unsaturated zone, is typically the rate-limiting degradation pathway and can, therefore, serve as a reliable indicator for NSZD rate estimation of bulk LNAPL. Considering that methanogenesis associated with natural soil respiration processes is often negligible, this method can be used to directly convert methane fluxes into NSZD rates. Unlike other methods that focus on O 2 , CO 2 or volatile organic compounds (VOCs), this approach is based on an analytical model that incorporates both diffusion and advection-driven transport of methane in soil gas. The application of this model supports the general assumption that diffusion dominates methane transport in the air-connected vadose zone, except in scenarios with high-pressure gradients (e.g., 10 Pa/m) and high soil permeability (e.g., sandy soils), where advection becomes significant relative to diffusion. Additionally, the analysis shows that the overall methane velocity in the aerobic oxidation zone, in most cases, falls within the range of 0.1-1 m/d. By multiplying this velocity by the maximum methane concentration in soil gas and the stoichiometric coefficient of the reference hydrocarbon compound (e.g., 1.14 g C8H18 /g CH4 for octane), a reliable estimate of the NSZD rate can be derived. When applied to typical soil gas concentrations, this methane gradient method yields NSZD estimates consistent with values reported in the literature, validating its use as a simplified screening approach.
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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 | 0.002 | 0.001 |
| 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".