Impact of de-icing salt runoff in spring on bioretention efficiency
Bibliographic record
Abstract
Abstract We investigated the effect of de-icing salt in stormwater runoff on bioretention system hydrology and filtration of contaminants. Salt runoffs during the snow melt period were simulated in 20 mesocosms planted with 1 of 3 plant species (Cornus sericea, Juncus effusus and Iris versicolor) or left unplanted, and then watered with semi-synthetic stormwater runoffs supplemented with 4 NaCl concentrations (0, 250, 1,000 or 4,000 mg Cl/L). All bioretention mesocosms, irrespective of treatment, were efficient in reducing water volume, flow and pollution level. There was no phytotoxic effect of NaCl on plants, even at the highest NaCl concentration tested. Water volume reduction and flow rate were influenced by plant species, but salt concentration had no effect. Salt runoffs significantly increased the removal of some metals, such as Cr, Ni, Pb and Zn, but had no effect on nutrient removal. Because snowmelt laden with de-icing salt is of short duration and occurs during plant dormancy, plants in bioretention may be less affected by de-icing salt than previously thought, provided that salinity decreases rapidly to normal levels in the soil water. The long-term effects of de-icing salt and general performance of bioretention should be further studied under full-scale conditions.
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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) | 0.001 | 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".