Evaluation of Strategies to Remediate Mixed Wastes at an Industrial Site in Brazil
Bibliographic record
Abstract
Abstract Complex mixtures of contaminants at hazardous waste sites often pose significant challenges for remediation. For example, within the largest industrial area in northeastern Brazil, one of the sites is contaminated with at least 26 chemicals, six of which are present in the part per million range: chlorobenzene (CB), 1,2‐dichlorobenzene (1,2‐DCB), 4‐nitrotoluene (4‐NT), 2,6‐dinitrotoluene (2,6‐DNT), 4‐isopropylaniline (4‐IPA), and 1,2‐dichloroethane (1,2‐DCA). Other chemicals of concern include 2,4‐dinitrotoluene (2,4‐DNT), 2‐ and 3‐nitrotoluene (NT), and 1,4‐dioxane. The objective of this study was to evaluate remediation strategies that include aerobic and anaerobic biodegradation, along with chemical reduction and oxidation. In microcosms prepared with site soil and groundwater, aerobic biodegradation of CB, 1,2‐DCB, 2‐NT, 3‐NT, and 4‐NT was demonstrated, while the dinitrotoluene isomers, 1,2‐DCA, and 1,4‐dioxane were recalcitrant. 2,6‐DNT, 2,4‐DNT, and 4‐NT were readily reduced to amino‐toluenes under anaerobic conditions by microbes with lactate serving as the electron donor or using zero valent iron. Amino‐toluenes were amenable to chemical oxidation and/or aerobic biodegradation. This suggests a sequential treatment strategy may be the most effective remediation approach, consisting of aerobic biodegradation, followed by anaerobic reduction (abiotic or biotic) and then aerobic biodegradation and/or chemical oxidation. This approach was the most effective in a continuous flow column experiment using site soil. Batch tests with mixtures of contaminants as well as groundwater exposed to chemical oxidation revealed modest to no inhibitory effects. While these mixtures may slow the rate of biodegradation, a remediation strategy that incorporates aerobic and anaerobic biodegradation is achievable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".