Characterization of an organic filter medium for the biofiltration treatment of air contaminated with 1,2‐dichlorobenzene
Bibliographic record
Abstract
Abstract Laboratory experiments were conducted to determine the potential for removing 1,2‐dichlorobenzene (1,2‐DCB) in gaseous phase by biofiltration. Experiments were carried out over 8 months in a steel tank (0.45 m 3 ) using an organic filter medium composed of peat, maple wood chips, chicken manure and 1,2‐DCB‐contaminated soil. During the first 6 months, the biofilter was operated without injecting 1,2‐DCB in order to characterize the physicochemical, mechanical and microbiological properties of the filter bed. The results revealed that it is an excellent medium for both microbial development (up to 10 9 cells $\hbox{g}^{-1}_{\rm dry\ soil}$ for heterotrophic bacteria) and long‐term stability with a limited drop of pressure (30 cm of water) and no clogging. Over the final 2 months, the biofilter treated air laden with 1,2‐DCB (0.30 and 0.75 g m −3 ) and the maximum elimination capacity reached was 9 g m −3 h −1 (inlet load of 13 g m −3 h −1 ), which represented 69% efficiency. Elimination performance was strongly dependent upon inlet concentration, sorption/desorption and biodegradation phenomena occurring in the filter medium. Sorption/desorption and biodegradation mechanisms during the start‐up period were characterized using the elimination efficiency (%). At the beginning of the 1,2‐DCB injection, the microorganisms were strongly impacted and sorption/desorption phenomena prevailed. With the decrease of the inlet concentration, biodegradation progressively increased to become the most important mechanism. It was concluded that biofiltration possesses an excellent potential for treating volatile chlorinated benzene, known to be recalcitrant to biodegradation. Copyright © 2003 Society of Chemical Industry
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".