Effect of the Carbon Source on Nitrifying in an Activated Sludge System Treating Aquaculture Wastewater
Bibliographic record
Abstract
The nitrogen in the aquaculture wastewater can have significant effects on receiving water bodies like eutrophication and ammonia toxicity to fish communities. Removing nitrogen by nitrification-denitrification can reduce the potential impact of aquaculture wastewater discharge. Nitrification is affected by different factors including dissolved oxygen, temperature, pH, alkalinity, toxicity, unionized ammonia and substrate concentration. All these parameters affect ammonia-oxidizing and nitrite-oxidizing bacterial activity. The aim of this work is to study the effect of the carbon source on nitrifying bacterial activity in an activated sludge treating aquaculture wastewater. An activated sludge (AS) system was set up and operated continuously for 180 days. The operation was divided into two phases. During Phase I a source of organic carbon (CH3COONa) with a C/N level of 2.4 (operation days 1 to 101) was fed into the system, while in Phase II a source of inorganic carbon (NaHCO3) with a C/N level of 16.2 was fed into the system (operation days: 102 to 180). The maximum NH4+-N removal efficiency was 49.7% during the Phase I, during which the NO3--N and NH3+-N concentrations were 37.1 ± 14.0 and 2.9 ± 1.1 mg/L, respectively. In Phase II, the maximum NH4+-N removal efficiency was 45% and NO3--N and NH3+-N effluent concentrations were 2.8 ± 0.3 mg/L and 210 ± 49 mg/L, respectively. Ammonia- and nitrite-oxidation decreased in Phase I from 0.231 ± 0.005 mg NH4+/gVSS min to 0.018 ± 0.004 mg NH4+-N/gVSS min and in Phase II from 0.049 ± 0.011 mgNO2--N/gVSS min to 0.010 ± 0.002 mgNO2--N/gVSS min.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".