Soil gas gradient method for estimating natural source zone depletion rates of LNAPL and specific chemicals of concern
Bibliographic record
Abstract
• A simplified soil gas method to estimate COC-specific NSZD rates is presented. • The simplified approach requires only soil gas profiles and diffusion coefficients. • The proposed approach was validated using a numerical model and BioVapor. • A screening approach based only on maximum soil gas concentration is introduced. • The results obtained are consistent with previous modeling and experimental studies. This paper presents a simplified approach for the soil gas gradient method for estimating natural source zone depletion (NSZD) rates of specific contaminants of concern (COCs) at sites contaminated by light non-aqueous phase liquids (LNAPL). Traditional approaches to quantify COC-specific NSZD rates often rely on numerical or analytical reaction-transport models that require detailed site-specific data. In contrast, the proposed method employs simple analytical solutions, making it more accessible to practitioners. Specifically, it requires only the maximum soil gas concentration, the effective diffusion coefficient, and the diffusive reaction length calculated from vertical soil gas concentration profiles. The simplified approach was validated against a reactive transport numerical model reported in the literature, showing consistent results within the same order of magnitude for BTEX NSZD rates at a gasoline spill site in South Carolina. Further validation using a larger dataset involved comparing NSZD rate estimates for benzene and total petroleum hydrocarbons (TPH) against those obtained using BioVapor, utilizing empirical soil gas data from the USEPA Petroleum Vapor Intrusion Database. Results demonstrated a strong correlation between NSZD rates and maximum soil gas concentrations, allowing the development of a rapid screening approach based only on the measured soil gas concentrations and literature values for diffusion coefficients and diffusive reaction lengths. This approach aligned well with previous modeling studies and was consistent with literature values for TPH NSZD rates. Overall, both the simplified and screening approaches offer practical, easy-to-use tools for evaluating temporal variability in natural attenuation rates, supporting baseline assessments and ongoing performance evaluations of remediation at LNAPL sites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".