Microalga-Mediated Tertiary Treatment of Municipal Wastewater: Removal of Nutrients and Pathogens
Bibliographic record
Abstract
The microalgal strain Chlorella sorokiniana isolated from a waste stabilization pond was used for tertiary treatment of municipal wastewater. Three light:dark (L:D) regimes of 12:12, 16:8, and 24:0 were used for treating wastewater in microalga (A), microalga + sludge (A + S), and sludge (S) reactors. The removal of nutrients (N and P) was found to be the highest in the microalga-based reactor, with more than 80% removal of biochemical oxygen demand (BOD) and 1.2–5.6 log unit removal of pathogens. The addition of sludge improved chemical oxygen demand (COD) removal. Nitrifiers were found to be predominant in the A + S reactor. Algal biomass productivity was more than 280 mg/L/d in all the L:D regimes. The increase in light regime improved nutrient removal and biomass productivity in the algal reactor. Results of the kinetic study showed that (i) nitrifiers had more affinity for ammonium than microalga, and hence, most of the ammonia was oxidized to nitrate, (ii) microalga assimilated nitrate as the primary nitrogen source in the A + S reactor, and (iii) solubilization of particulate organic nitrogen originated from dead cells reduced the nitrogen removal efficiency. However, in the microalga-based reactor, the ammonium uptake was higher than nitrate uptake. Among pathogens, the removal of Salmonella and Shigella was better in the A + S reactor than in the other two reactors (microalga and sludge reactor). Additionally, the heterotrophic plate count was drastically reduced in the presence of microalga. No such drastic reduction was observed in the stand-alone sludge reactor. Kinetic modeling revealed that microalga–pathogen competition and pH-induced die-off were the two predominant factors for pathogen inactivation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".