Enhanced Removal of Common Wastewater-Derived Trace Organic Contaminants in Vertical-Flow Constructed Wetlands Amended with Fe(III)-EDTA
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Constructed wetlands (CWs) have gained scholarly attention in the last two decades as promising technologies for the attenuation of trace organic contaminants (TrOCs) from municipal wastewater effluent and combined sewer overflow discharge. Using lab-scale vertical flow constructed wetlands, we investigated amending these systems with Fe-EDTA to improve CW degradation of five representative trace organic contaminants. The study combined a 7-month monitoring campaign, 3 different hydraulic regimes, and soil extraction data to elucidate the effects of the amendment on the fate of the TrOCs within the systems. Our results indicate that Fe-EDTA contributed to the degradation of carbamazepine and sulfamethoxazole under the studied flow regimes. Iron-amended soil columns ( n = 5/9 columns fed for 7 months with synthetic domestic wastewater) removed 12 ± 19% of influent carbamazepine (the most recalcitrant TrOC included in the study), 18% higher than the control columns. Operating the columns with periods of retention and discharge further improved carbamazepine and sulfamethoxazole removal efficiency (removal increased to 49 ± 7.6% and 81 ± 9.2% of influent concentrations, respectively). The more readily degradable compounds atenolol and trimethoprim were removed with >97% efficiency in both control and amended columns, regardless of flow. This column study positively correlates Fe-EDTA with improved removal efficiencies of environmentally recalcitrant TrOCs without affecting readily degradable TrOCs.
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) | 0.000 | 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".