Reductive dechlorination of 1,2-dichloroethane in the presence of chloroethenes and 1,2-dichloropropane as co-contaminants
Bibliographic record
Abstract
1,2-Dichloroethane (1,2-DCA) is one of the most abundant manmade chlorinated organic contaminants in the world. Reductive dechlorination of 1,2-DCA by organohalide-respiring bacteria (OHRB) can be impacted by other chlorinated contaminants such as chloroethenes and chloropropanes that can co-exist with 1,2-DCA at contaminated sites. The aim of this study was to evaluate the effect of chloroethenes and 1,2-dichloropropane (1,2-DCP) on 1,2-DCA dechlorination using sediment cultures enriched with 1,2-DCA as the sole chlorinated compound (EA culture) or with 1,2-DCA and tetrachloroethene (PCE) (EB culture), and to model dechlorination kinetics. Both cultures contained Dehalococcoides as most predominated OHRB, and Dehalogenimonas and Geobacter as other known OHRB. In sediment-free enrichments obtained from the EA and EB cultures, dechlorination of 1,2-DCA was inhibited in the presence of the same concentrations of either PCE, vinyl chloride (VC), or 1,2-DCP; however, concurrent dechlorination of dual chlorinated compounds was achieved. In contrast, 1,2-DCA dechlorination completely ceased in the presence of cis-dichloroethene (cDCE) and only occurred after cDCE was fully dechlorinated. In turn, 1,2-DCA did not affect dechlorination of PCE, cDCE, VC, and 1,2-DCP. In sediment-free enrichments obtained from the EA culture, Dehalogenimonas 16S rRNA gene copy numbers decreased 1-3 orders of magnitude likely due to an inhibitory effect of chloroethenes. Dechlorination with and without competitive inhibition fit Michaelis-Menten kinetics and confirmed the inhibitory effect of chloroethenes and 1,2-DCP on 1,2-DCA dechlorination. This study reinforces that the type of chlorinated substrate drives the selection of specific OHRB, and indicates that removal of chloroethenes and in particular cDCE might be necessary before effective removal of 1,2-DCA at sites contaminated with mixed chlorinated solvents.
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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 | 0.001 | 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".