Deciphering the Role of Low Superficial Gas Velocity (SGV) in Integrated Fixed-Film Activated Sludge (IFAS) System under Aniline Stress: Effects on Microbial Assembly and Electron Transfer Process
Bibliographic record
Abstract
Integrated fixed-film activated sludge (IFAS) systems provide an energy-efficient method for nitrogen removal in aniline wastewater treatment. However, the effects of low SGV on microbial community dynamics and electron transfer under aniline stress in continuous-flow IFAS systems remain insufficiently understood. Herein, we systematically evaluated IFAS performance under varying SGVs (0.15, 0.10, and 0.04 cm/s) in treating 400 mg/L aniline wastewater. Aniline removal remained consistently high (>99%) across all conditions, while the total nitrogen removal efficiency declined from 82.65% at 0.15 cm/s to 46.58% at 0.04 cm/s. Reduced SGV, a key determinant of dissolved oxygen (DO), induced metabolic stress on microbial consortia and suppressed nitrification by reducing ammonia-oxidizing bacteria (AOB) abundance and downregulating amoA and hao. Community assembly analyses revealed a shift from deterministic selection at higher SGVs to stochastic processes (ecological drift and dispersal limitation) at lower SGVs. Microbial compositional shifts were observed, with Actinobacteria (aniline degraders) enrichment at reduced SGVs. Across all conditions, biofilms demonstrated a dominant role in nitrogen removal over suspended sludge. Electron transfer adaptations exhibited a strategic microbial response, characterized by the recovery of related functional gene abundance under lower SGVs.
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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 | 0.000 | 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 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".