Modeling the impact of ethanol on the persistence of benzene in gasoline‐contaminated groundwater
Bibliographic record
Abstract
The effect of ethanol on the persistence of benzene in gasoline‐contaminated aquifers is simulated using a multicomponent reactive transport model. The conceptual model includes a residual gasoline source which is dissolving at the water table into an aquifer containing a limited amount of dissolved oxygen. The coupled processes include nonaqueous phase liquid (NAPL) source dissolution, transport of the dissolved components, and competitive aerobic biodegradation. Comparisons are made between dissolved benzene plumes from a gasoline spill and those from an otherwise equivalent spill containing 10% ethanol (gasohol). Simulations have shown that under some conditions a 10% ethanol component in gasoline can extend the travel distance of a benzene plume by up to 150% relative to that from an equivalent ethanol‐free gasoline spill. The increase occurs because ethanol preferentially consumes oxygen, which reduces the biodegradation rate of benzene. The impact is limited, however, because sufficient oxygen disperses behind the ethanol plume into the slightly retarded benzene plume. A sensitivity analysis for two common spill scenarios showed that background oxygen concentrations and benzene retardation have the most significant influence on ethanol‐induced benzene persistence. The results are highly relevant in light of the increasing use of ethanol‐enhanced fuels throughout the world and the forthcoming ban of methyl tertiary‐butyl‐ether (MTBE) in California and its probable replacement by ethanol by the end of 2002.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".