Natural Attenuation and Anaerobic Benzene Detoxification Processes at a Chlorobenzene-Contaminated Industrial Site Inferred from Field Investigations and Microcosm Studies
Bibliographic record
Abstract
A five-year site investigation was conducted at a former chemical plant in Nanjing, China. The main contaminants were 1,2,4-trichlorobenzene (TCB) reaching concentrations up to 7300 μg/L, dichlorobenzene (DCB) isomers, monochlorobenzene (MCB), and benzene. Over time, these contaminants naturally attenuated to below regulatory levels under anaerobic conditions. To confirm the transformation processes and to explore the mechanisms, a corresponding laboratory microcosm study was completed demonstrating that 1,2,4-TCB was dechlorinated to 1,2-DCB, 1,3-DCB, and 1,4-DCB in approximately 2%/10%/88% molar proportions. The DCB isomers were dechlorinated via MCB to benzene, and, finally, benzene was degraded under prevailing sulfate-reducing conditions. Dechlorination could not be attributed to known dechlorinators Dehalobacter or Dehalococcoides, while anaerobic benzene degradation was mediated by microbes affiliated to a Deltaproteobacterium ORM2, previously associated with this activity. Unidentified organic compounds, possibly aromatic compounds related to past on-site production processes, were fueling the dechlorination reactions in situ. The microcosm study confirmed transformation processes inferred from field data and provided needed assurance for natural attenuation. Activity-based microcosm studies are often omitted from site characterization in favor of rapid and less expensive molecular surveys. However, the value of microcosm studies for confirming transformation processes, establishing electron balances, assessing cocontaminant inhibition, and validating appropriate monitoring tools is clear. At complex sites impacted by multiple compounds with poorly characterized transformation mechanisms, activity assays provide valuable data to incorporate into the conceptual site model to most effectively inform remediation alternatives.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".