Biodegradation of 2,4-dicholophenoxyacetic acid using an acidogenic anaerobic sequencing batch reactor
Bibliographic record
Abstract
A bench-scale study was carried out to investigate the potential to biologically treat 2,4-dicholophenoxyacetic acid (2,4-D) contaminated wastewater in an anaerobic sequencing batch reactor (ASBR), operated in the acid-phase digestion mode. The effects of 2,4-D feed concentration (20 to 200 mg L1) and temperature (ambient and 33 °C) on biodegradation were investigated at a hydraulic retention time of 48 h and a solids retention time of 10 d, using glucose as a supplemental substrate. Following a long acclimation period of about 100 d, complete 2,4-D degradation was observed at feed concentrations of 20 and 100 mg L1. However, at a 2,4-D concentration of 200 mg L1, only 65% removal was achieved. Overall, operation at an ambient temperature resulted in a slightly better performance than that at 33 °C. An adaptation period of approximately a week was required any time the 2,4-D concentration was increased, indicating a sensitive behavior towards shock loadings. On the other hand, glucose was completely and readily degraded throughout the study. A sequential utilization pattern of glucose and 2,4-D was also observed, with degradation of both substrates following first-order kinetics. Moreover, volatile fatty acids (VFAs) were the main products of acidogenesis, accounting for 65% of the effluent soluble chemical oxygen demand (COD), with acetic acid being by far the most predominant VFA detected. Key words: anaerobic sequencing batch reactor, acidogenesis, kinetics, 2,4-D, glucose, volatile fatty acids.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".