Evaluating the Efficiency of Chlorella Vulgaris and Spirulina Microalgae in Wastewater Remediation Under Different Light Conditions
Bibliographic record
Abstract
Microalgae have emerged as a pivotal component of wastewater treatment paradigms, offering an environmentally friendly, sustainable, and cost-effective approach.Beyond the purification of wastewater from diverse sources, microalgae exploit these effluents as a nutrient matrix, facilitating the biosynthesis of valuable bioproducts, bioenergy, and biomaterials.The present study evaluates the pollutant remediation capabilities of Chlorella vulgaris and Spirulina in the presence of common wastewater contaminantschemical oxygen demand (COD), nitrate (NO3 -), and cadmium (Cd 2+ ).These pollutants were selected due to their prevalence in wastewater, with nitrates and COD representing primary organic and inorganic pollutants, respectively, and cadmium being recognized for its acute toxicity.Experimental setups involved two flasks, each containing a mixture of the aforementioned contaminants, with one flask exposed to sunlight and the other placed in a darkroom to simulate varied lighting conditions.The removal efficiencies of Spirulina in the sunlit flask reached 86.6% for COD, 99.1% for NO3 -, and 84.5% for Cd 2+ , while the darkroom condition yielded lower efficiencies of 54.3% for COD, 64.4% for NO3 -, and 61.8% for Cd 2+ .Conversely, Chlorella vulgaris exhibited removal efficiencies of 50.5% for COD, 52.3% for NO3 -, and 74.6% for Cd 2+ under sunlight, and 25.4%, 33.01%, and 53.3% for the respective contaminants in darkness.These findings underscore the crucial influence of sunlight and temperature on algal photosynthesis, thereby enhancing the bioremediation potential of wastewater contaminants.The study substantiates the significant role of microalgae in the reduction of contaminants, affirming their utility as an effective and economical treatment option.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".