A Study of the Biofiltration of High-Loads of Toluene in Air: Carbon and Water Balances, Temperature Changes and Nitrogen Effect
Bibliographic record
Abstract
Biodegradable atmospheric pollutants, released at low to moderate concentrations, can be removed by biofiltration. In this work, a laboratory-scale compost-based biofilter has been evaluated for the removal of high levels of toluene in air (∼ 4.0 g.m−3). By applying a variable nitrogen input in the irrigation solution, it was shown that the biodegradation extent can be controlled through the nutrient supply. The maximum elimination capacity achieved was 135 g.m−3.h−1, for a N-concentration of 3.0 g of N.L−1. A quantitative analysis of the bioreaction aspects (stoichiometry, temperature) led to the determination of the water flow rates associated with the toluene oxidation. Thus, it was estimated that some 530 to 800 g of water.day−1 were lost at the bioreactor outlet, but were balanced by the irrigation system. Les polluants atmosphériques, émis à de faibles à moyennes concentrations, peuvent être éliminés par biofiltration. Dans ce travail, un biofiltre à base de compost a été testé pour l'élimination de fortes concentrations de toluène dans l'air (∼ 4.0 g.m−3). L'ajout de quantités variables d'azote, via la solution nutritive, a montré que la réaction de biodégradation peut être contrôlée par l'apport nutritionnel. La capacité d'élimination maximale obtenue était de 135 g.m−3.h−1, pour une concentration d'azote de 3.0 g of N.L−1. Une analyse quantitative des aspects réactionnels (stœchiométrie, température) a conduit à la détermination des quantités d'eau générées par l'oxydation du toluène. Ainsi, il a été estimé que quelques 530 à 800 g d'eau par jour étaient perdus par le bioréacteur, sous forme de vapeur, mais que ces pertes étaient contrebalancées par l'irrigation quotidienne du système.
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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 | 0.000 |
| 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 | 0.000 | 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 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".