Native microalgal-bacterial consortia from the Ecuadorian Amazon region: an alternative to domestic wastewater treatment
Bibliographic record
Abstract
In low-middle income countries (LMIC), wastewater treatment using native microalgal-bacterial consortia has emerged as a cost-effective and technologically-accessible remediation strategy. This study evaluated the effectiveness of six microalgal-bacterial consortia (MBC) from the Ecuadorian Amazon in removing organic matter and nutrients from non-sterilized domestic wastewater (NSWW) and sterilized domestic wastewater (SWW) samples. Microalgal-bacterial consortia growth, in NSWW was, on average, six times higher than in SWW. Removal rates (RR) for NH 4 + - N and PO 4 3− -P were also higher in NSWW, averaging 8.04 ± 1.07 and 6.27 ± 0.66 mg L −1 d −1 , respectively. However, the RR for NO 3 − -N did not significantly differ between SWW and NSWW, and the RR for soluble COD slightly decreased under non-sterilized conditions (NSWW). Our results also show that NSWW and SWW samples were statistically different with respect to their nutrient concentration (NH 4 + -N and PO 4 3− -P), organic matter content (total and soluble COD and BOD 5 ), and physical-chemical parameters (pH, T, and EC). The enhanced growth performance of MBC in NSWW can be plausibly attributed to differences in nutrient and organic matter composition between NSWW and SWW. Additionally, a potential synergy between the autochthonous consortia present in NSWW and the native microalgal-bacterial consortia may contribute to this efficiency, contrasting with SWW where no active autochthonous consortia were observed. Finally, we also show that MBC from different localities exhibit clear differences in their ability to remove organic matter and nutrients from NSWW and SWW. Future research should focus on elucidating the taxonomic and functional profiles of microbial communities within the consortia, paving the way for a more comprehensive understanding of their potential applications in sustainable wastewater management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".