Effects of different substrates in the mitigation of algae-induced high pH wastewaters in a pilot-scale free water surface wetland system
Bibliographic record
Abstract
Waste stabilization ponds (WSPs), as part of municipal wastewater treatment strategies, can exhibit variability in performance due to climatic conditions. Under elevated temperature and strong solar radiation, algal blooms and subsequent high pH effluents have often been observed. In this study, four substrates (gravel, peat, organic mulch, and topsoil) were evaluated for their ability to attenuate high pH effluents from a WSP. Synthetic wastewater with pH > 9.5, and low organic and nutrient loadings, was used to mimic algal-induced high pH effluents in 72 L rectangular bench-scale superficial constructed wetland configuration reactors. Peat exhibited the highest attenuation ability, where the effluent pH decreased substantially from 10.3 to 7.7, primarily due to its high organic contents. Peat also removed 53.7% of the influent total phosphorus, which could effectively limit algal growth. No statistically significant differences were discovered among gravel, topsoil, and organic mulch in terms of pH attenuation. Topsoil and organic mulch both have a relatively high alkalinity, making them ideal to maintain consistent pH levels. However, naturally high chemical oxygen demand levels in organic mulch raised concerns in the leaching of these compounds into the treated wastewater, making it less appealing for systems with low organic loading.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".