Treatment of swine wastewater with subsurface-flow constructed wetlands in Yucatán, Mexico: Influence of plant species and contact time
Bibliographic record
Abstract
This study evaluates the capability of horizontal subsurface-flow constructed wetlands (SSF CWs) for treating pretreated swine wastewater as a function of contact time (CT) and type of macrophyte under the local conditions of Yucatán, Mexico. Experiments were conducted from July 2004 to November 2005 on a swine-fattening farm. The study had three stages: first, macrophytes were located and collected; second, plants were acclimatised; and third, a pilot plant with 6 wetlands was set up, operated and evaluated. The effectiveness of the wetlands was intensively tested over 2 periods (April and November 2005). The results indicate that treatment efficiency significantly improved with increased CT for most of the analysed contaminants. The highest removal rates were recorded at a CT of 3 d and ranged between 64 and 78% for total suspended solids, 52 and 78% for COD, 57 and 74% for BOD5, 57 and 79% for total nitrogen, 63 and 75% for ammonium nitrogen, 70 and 81% for nitrate, 0 and 28% for total phosphorus and 3.3 and 4.2 log-units for total coliforms. Results also suggest that the macrophyte species used did not significantly differ on their contribution to overall treatment efficiency. However, vegetated beds slightly improved water quality when compared to unplanted systems. Horizontal SSF CWs are a suitable technology for treating swine wastewater under the local conditions of Yucatán. Contact time should be given special attention in the design of future full-scale facilities.
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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".