Wastewater Treatment for Nutrients and Pathogens in a Demonstration-Scale Outdoor Constructed Wetland System
Bibliographic record
Abstract
A demonstration-scale outdoor constructed wetland (CW) wastewater treatment system was evaluated for about two years for its effectiveness in treating septic system effluents. The system included three treatment cells: an anaerobic cell, an aerated cell, and a basic oxygen furnace slag (BOFS)-based phosphorus (P) treatment cell. High removal efficiencies were achieved, with reductions of >99% for PO4-P, >83% for NH3-N, >99% for cBOD5, and >76% for COD, with influent concentrations averaging 6, 48, 63, and 143 mg L−1, respectively. Additionally, pathogens removal were high, with an over 99% decrease in E. coli and total coliform levels. The BOFS cell was critical, elevating effluent pH to 10.9 ± 1.5, which effectively inactivated pathogens. Environmental safety before discharge was ensured by CO2(g) sparging to adjust the pH and a zero-valent iron layer in the BOFS cell to control dissolved metal concentrations. Analytical techniques (FESEM-EDX, FTIR, and XANES) confirmed the formation of Ca carbonate and Ca phosphate on spent BOFS, highlighting their role in the treatment process. This study highlights the potential of integrating complementary technologies in constructed wetlands for sustainable and efficient wastewater management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".