Granule size influences anammox performance through functional differentiation and microbial specialization in a UASB system
Bibliographic record
Abstract
Anammox has emerged as an energy-efficient and sustainable technology for nitrogen removal in wastewater treatment. However, the performance of anammox reactors is highly dependent on the structural and functional characteristics of granular sludge. The specific influence of granule size on microbial activity and community structure remains insufficiently understood. This study investigates the anammox performance, amino acid metabolism and microbial community dynamics in relation to granule size in a lab-scale upflow anaerobic sludge blanket (UASB) reactor. Granular sludge was classified into three size categories based on granule diameter: small (< 1 mm), medium (1–2 mm), and large (> 2 mm). Batch tests showed a positive correlation between granule size and specific anammox activity (SAA). Large granules achieved the highest SAA of 21.43 ± 2.83 mg NH 4 + -N/g VSS/day, attributed to enhanced metabolic efficiency and structural robustness. Extracellular polymeric substances (EPS) content in large granules was 5-fold higher than in smaller granules and dominated by polysaccharides, contributing to the maintenance granule integrity. Microbial analysis demonstrated size-dependent community specialization: large granules exhibited greater microbial diversity, enriched anammox bacteria Ca. Brocadia and Ca. Kenenia, and elevated expression of nitrogen metabolism genes. Amino acid profiling identified hydrophobic compounds as key mediators of microbial aggregation. Additionally, cross-feeding interactions were identified between amino acid-synthesizing and anammox bacteria which possibly contribute to enhanced community-level metabolic resilience. This study highlights granule size as a critical factor shaping microbial function and reactor efficiency, providing a foundation for targeted granule management in the development of high-performance anammox-based wastewater treatment systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".