Field assessment of nutrient removal in two constructed urban stormwater wetlands in a cold semi-arid region
Bibliographic record
Abstract
Constructed wetlands have been widely used for managing urban stormwater runoff and improving stormwater quality. However, the removal efficiency of nutrients in these systems varies significantly. To investigate the nutrient removal behaviors and the influencing factors in constructed wetlands, two wetlands (Rocky Ridge “RR” and Royal Oak “RO”) in Calgary, Alberta, Canada, were selected for field monitoring in the open-water seasons of 2018 and 2019. It was found that the annual loading removal efficiency of total nitrogen in the RR wetland was 37 % and 64 % in 2018 (dry year) and 2019 (wet year), respectively, while in the RO wetland it was 45 % and 33 %, respectively. The annual loading removal efficiency of total phosphorus in the RR wetland was 34 % and 44 % in 2018 and 2019, respectively, while in the RO wetland it was 57 % and 75 %, respectively. The annual nutrient mass removal (in mg/m 2 /yr) in the wet year was substantially (7–16 times for TN; 7–29 times for TP) larger than that in the dry year for the same wetland, mainly attributed to the significantly larger inflow volume in the wet year. Further analysis showed that large rainfall events increased nutrient concentrations in both inflow and outflow, leading to variations in removal efficiency. The outflow concentration was primarily affected by inflow in large rain events but by in-pond water in small events. The study highlights seasonal and interannual variations, emphasizing the role of environmental parameters in nutrient removal. Finally, the unique aspects of nutrient removal by cold-regions stormwater wetlands are discussed.
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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 | 0.001 | 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".