DNAPL Source Depletion: 1. Predicting Rates and Timeframes
Bibliographic record
Abstract
Given the relatively rapid rate of dense nonaqueous‐phase liquid (DNAPL) ganglia depletion, source zones are generally dominated by horizontal layers of DNAPL after a release to the saturated zone. Estimating the time required to attain specific source strength reduction targets resulting from partial DNAPL source depletion is challenging due to a lack of available screening models, and because little has been done to synthesize available empirical data. Analytical and semi‐analytical models are used to study general DNAPL pool dissolution dynamics. The half‐life for the decline in DNAPL source strength (i.e., aqueous mass discharge) is demonstrated as proportional to the square root of the pool length, the thickness of the pool, and the solubility for single component DNAPLs. The through‐pool discharge is shown to be potentially significant for thin pools or in upper regions of thicker pools. An empirical analysis is used to evaluate average concentration decline rates for 13 in situ chemical oxidation (ISCO) and 16 enhanced in situ bioremediation (EISB) sites. Mean apparent decline rates, based on the time required to achieve the observed source strength reduction, are calculated for the ISCO and EISB sites (half‐lives of 0.39 year and 0.29 year, respectively). The empirical study sites are shown to have faster decline rates than for a large, complex study site where ISCO was implemented (half‐life of 2.5 years), and for a conceptual pool‐dominated trichloroethene source zone where EISB was simulated (half‐life of 2.5 years). Guidance is provided on using these findings in estimating timeframes for partial DNAPL depletion goals. © 2014 Wiley Periodicals, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".