UV/H<sub>2</sub>O<sub>2</sub> Treatment of Methyl <i>tert</i>-Butyl Ether in Contaminated Waters
Bibliographic record
Abstract
Methyl tert -butyl ether (MTBE) is a pollutant often found in groundwaters contaminated by gasoline spills or from leaking underground storage tanks. The common techniques often used for the remediation of contaminated water are not very effective for MTBE. This study examines the UV/H 2 O 2 advanced oxidation technology to determine its effectiveness in the treatment of MTBE. The degradation of MTBE was found to follow pseudo- first-order kinetics, and hence the figure-of-merit electrical energy per order ( E EO ) is appropriate for estimating the electrical energy efficiency. The E EO values were found to depend on the concentrations of MTBE, H 2 O 2, and other components, such as benzene, toluene, and xylenes (BTX). This study shows that MTBE can be treated easily and effectively with the UV/H 2 O 2 process with E EO values between 0.2 and 7.5 kWh/m 3 /order, depending on the initial concentrations of MTBE and H 2 O 2 . The treatment efficiency of 10 mg L - 1 MTBE is not adversely affected by the presence of low concentrations of BTX (<2 mg L - 1 ). However, the degradation efficiency is significantly decreased at BTX levels greater than 2 mg L - 1 . A kinetic model, based on the initial rates of degradation, provides good predictions of the E EO values for a variety of conditions.
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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 | 0.000 | 0.000 |
| 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".