Road salt reduces plant cover in bioretention systems within road rights-of-way
Bibliographic record
Abstract
Bioretention systems for managing urban runoff rely on healthy plants to reduce runoff and nutrient pollution via evapotranspiration and uptake. However, loss of plant cover is common and, in cold climates, potentially caused by the application of road salt. To investigate the impact of road salt on bioretention systems, we measured salt concentrations in the media and plant tissues and assessed plant cover at 19 sites in Toronto, Canada, in the field. Winter road salt was identified as the primary driver of plant cover loss: low-tolerance species accumulated excessive sodium and chloride, resulting in chlorotic and necrotic damage even under moderate salinity (median electrical conductivity (EC), 0.31-0.35 mS/cm, as measured in soil-water suspensions). Continuous EC monitoring showed no net salt buildup in any season, although salinity peaked in winter and was lower in summer. Low-tolerance species exhibited high salt ion uptake and substantial damage from legacy salt retained in the media. Although species-specific ion accumulation patterns were observed, they did not always align with species salt tolerance as described in the literature. Among the 14 species studied, Hemerocallis 'Happy Returns' (low tolerance) and Panicum virgatum (medium tolerance) significantly accumulated sodium, up to 2126 and 586 mg/kg, respectively, whereas Salvia officinalis (medium tolerance) significantly accumulated chloride (up to 20 mg/g); yet only Panicum virgatum displayed minimal damage (<5 %), while Hemerocallis 'Happy Returns' and Salvia officinalis displayed >50 % damage. These findings underscore the importance of selecting salt-tolerant species to ensure long-term bioretention performance.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".