Phosphorus Removal from Aerobic Granular Sludge: Proliferation of Polyphosphate-Accumulating Organisms (PAOs) under Different Feeding Strategies
Bibliographic record
Abstract
Aerobic granular sludge (AGS) is known for high phosphorus removal from wastewaters, and phosphorus can be recovered from high phosphorus-containing waste sludge granules. This study aimed at determining the feeding strategy that provides the best performance in terms of the proliferation of polyphosphate-accumulating organisms (PAOs) and phosphorus removal. Using three AGS bioreactors, this study compared phosphorus removal and the proliferation dynamics of PAOs under three different feeding strategies: anaerobic slow feeding (R1), pulse feeding + anaerobic mixing (R2), and pulse feeding (R3). Results indicate that R1 and R2 achieved significantly higher phosphorus removal (97.6 ± 3% for R1 and 98.3 ± 1% for R2) than R3 (55 ± 11%). The anaerobic slow feeding procedure (R1) achieved the highest specific phosphorus release rate (SPRR) and specific phosphorus uptake rate (SPUR) as compared to the other two feeding conditions. 16S ribosomal ribonucleic acid (rRNA) gene sequencing assay of the microbial community for the three feeding strategies indicated that although the feeding strategy impacted reactor performance, it did not significantly alter the microbial community. The bacteria community composition maintained a similar degree of diversity. Proteobacteria, Bacteroidetes, and Verrucomicrobia were the dominant bacterial phyla in the system. Dominant PAOs were from the class Betaproteobacteria and the genera Paracoccus and Thauera. Glycogen-accumulating organisms were significantly inhibited while other less-known bacteria such as Wandonia and Hyphomonas were observed in all three reactors.
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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 | 0.001 | 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".