Feasibility of<i>Typha Latifolia</i>for High Salinity Effluent Treatment in Constructed Wetlands for Integration in Resource Management Systems
Bibliographic record
Abstract
High salinity wastewaters have limited treatment options due to the occurrence of salt inhibition in conventional biological treatments. Using recirculating marine aquaculture effluents as a case study, this work explored the use of Constructed Wetlands as a treatment option for nutrient and salt loads reduction. Three different substrates were tested for nutrient adsorption, of which expanded clay performed better. This substrate adsorbed 0.31 mg kg(-1) of NH4(+)-N and 5.60 mg kg(-1) of PO4(3-)-P and 6.9 mg kg(-1) dissolved salts after 7 days of contact. Microcosms with Typha latifolia planted in expanded clay and irrigated with aquaculture wastewater (salinity 2.4%, 7 days hydraulic retention time, for 4 weeks), were able to remove 94% NH(4+)-N (inlet 0.25 +/- 0.13 mg L(-1)), 78% NO2(-)-N (inlet 0.78 +/- 0.62 mg L(-1)), 46% NO3(-)-N (inlet 18.83 +/- 8.93 mg L(-1)) whereas PO4(3-)-P was not detected (inlet 1.41 +/- 0.21 mg L(-1)). Maximum salinity reductions of 52% were observed. Despite some growth inhibition, plants remained viable, with 94% survival rate. Daily treatment dynamics studies revealed rapid PO4(3-)-P adsorption, unbalancing the N:P ratio and possibly affecting plant development. An integrated treatment approach, coupled with biomass valorization, is suggested to provide optimal resource management possibilities.
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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".