Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review
Bibliographic record
Abstract
Constructed wetlands (CW) are a treatment option for agricultural wastewater. Their ability to adequately function in cold climates continues to be evaluated as they are biologically active systems that depend on microbial and plant activity. In order to assess their performance and to highlight regional specific design considerations, a review of CWs in Eastern Canada and the Northeastern USA was conducted. Here, we synthesize performance data from 21 studies, in which 25 full-scale wetlands were assessed. Where possible, data were separated seasonally to evaluate the climatic effects on treatment performance. The wastewater parameters considered were five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), E. coli, fecal coliforms, total Kjeldahl nitrogen (TKN), ammonia/ammonium (NH3/NH4+-N), nitrate-nitrogen (NO3−-N), and total phosphorus (TP). Average concentration reductions were: BOD5 81%, TSS 83%, TKN 75%, NH4+-N 76%, NO3−-N 42%, and TP 64%. Average log reductions for E. coli and fecal coliforms were 1.63 and 1.93, respectively. Average first order areal rate constants (ka, m·y−1) were: BOD5 6.0 m·y−1, TSS 7.7 m·y−1, E. coli 7.0 m·y−1, fecal coliforms 9.7 m·y−1, TKN 3.1 m·y−1, NH4+-N 3.3 m·y−1, NO3−-N 2.5 m·y−1, and TP 2.9 m·y−1. In general, CWs effectively treated a variety of agricultural wastewaters, regardless of season.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".