Fate kinetic coefficients and correlation models for tetrabromobisphenol A (TBBPA) in membrane bioreactors and conventional activated sludge process
Bibliographic record
Abstract
Tetrabromobisphenol A (TBBPA) is a fire retardant with endocrine disruption properties, which unfortunately has been detected in trace amounts in various environmental samples. Studies have shown that TBBPA enters the aquatic environment, sediments, soils and biota predominantly through the wastewater treatment process. The dominant contributor to the fate and transport of TBBPA in wastewater is biosorption by the sludge matrix. Accordingly, the goal of this research was to determine the fate kinetics, biosorption correlation models and mechanisms of TBBPA from both conventional activated sludge (CAS) and membrane bioreactor (MBR) systems. Experiments were conducted to determine different types of partitioning and fate kinetic coefficients such as solid–liquid phase coefficient, log Kp (4.53), sludge matrix organic carbon coefficient, log Koc (6.05) and glass wall partition coefficient, Kw (0.053). The experimental values of these coefficients for TBBPA are not available in the literature for application to the models. The biosorption data were also verified and fitted to Freundlich, Langmuir, Temkin, Toth, Redlich–Peterson, Sips, Brunauer–Emmett–Teller (BET), Jovanovic and Dubinin–Radushkevich biosorption isotherm models to estimate the isotherm constants. These coefficients will allow engineering application for validation of various fate models for TBBPA.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".