Influence of Transient Flow on Contaminant Biodegradation
Bibliographic record
Abstract
The rate of biodegradation in contaminated aquifers depends to a large extent on dispersive mixing processes that are now generally accepted to result from spatial variations in the velocity field. It has been shown, however, that transient flow fields can also contribute to dispersive mixing. The influence of transient flow on biodegrading contaminants is particularly important since it can enhance mixing with electron acceptors, further promoting the reactive process. Using numerical simulations, the effect of transient flow on the behavior of a biodegradable contaminant is evaluated here both with respect to the development of apparently large horizontal transverse dispersion and also with respect to enhanced mixing between the substrate (electron donor) and electron acceptor. The numerical model BIO3D, which solves for advective-dispersive transport coupled with Monod-type biodegradation of substrates in the presence of an electron acceptor, was used for the simulations. The model was applied in a two-dimensional plan view mode considering a single substrate. Transient flow fields were found to yield larger apparent transverse dispersion because the longitudinal dispersivity also acts transverse to the mean flow direction. In the reactive case, the transient flow field increases substrate-oxygen mixing, which in turn enhances the overall rate of biodegradation. The results suggest that in the case of moderate changes of flow directions, a steady-state flow field can be justified, thereby avoiding the higher computational costs of a fully transient simulation. The use of a higher transverse horizontal dispersivity in a steady flow field can, under these conditions, adequately forecast plume development.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".