Removal of nitrogen from wastewater using microalgae and microalgae–bacteria consortia
Bibliographic record
Abstract
Exceeding nitrogen discharge into natural water bodies can lead to eutrophication in natural aquatic environments, as well as the decline in shellfish habitat and aquatic plant life. Currently, bacterial biological treatment process is the most common process employed in wastewater treatment plants, which requires extensive oxygen. The large demand for oxygen provided by mechanical aeration is costly and can strip out volatile compounds. Microalgae are photosynthetic micro-organisms, which can be a good source of oxygen in the wastewater treatment process. The effect of using microalgae, either solo or in consortia systems along with other micro-organisms (mainly bacteria) have been studied by researchers to improve their contaminant removal efficiency. In a consortia system, microalgae generate oxygen through photosynthesis to satisfy the oxygen requirement of bacteria. Simultaneously, they also remove contaminating nutrients throughout their growth cycle. Various factors affect the performance of the consortia systems such as lighting, pH, and species of microalgae and bacteria. Since microalgae are suspended and dispersed in the media, harvesting is crucial to achieving a high-quality effluent. This paper presents an overview on nitrogen removal from wastewater using different types of systems including microalgae solo and microalgae–bacteria consortia systems. The parameters that affect system performance as well as biomass harvesting methods are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".