Sodium sorption and desorption in riparian soils impacted by road salt application
Bibliographic record
Abstract
Sodium chloride (NaCl) is regularly used as road salt in cold regions to improve road safety during icy conditions. Once these ions enter the environment, Na is stored in the soils, with high concentrations of it leading to soil structure deterioration, organic matter leaching, and nutrient displacement. These impacts raise concerns about potential road salt effects in riparian soils, as these soils come into contact with direct runoff from urban areas and elevated Na levels in streams. Aiming to quantify the retention and release of Na in these ecosystems, this study evaluated Na sorption and desorption mechanisms of 18 different riparian soil types. The adsorption process followed a Langmuir isotherm within the tested concentration range (0–4800 mg Na/L) with deviation from linearity starting at ∼600 mg Na/L. The soils retained Na at the expense of other cations (Ca, Mg, and K), with maximum adsorption capacities ranging from 4000 to 13,700 mg Na/kg. Na build up in riparian soils is mostly driven by organic matter content, with clay and initial Na levels contributing to a lesser extent. Significant proportions of this adsorbed Na (>65 %) readily desorbed back into solution in the controlled experiments, illustrating the highly dynamic association of Na with soil components. These findings suggest that the first flushes following the road salt application season may mobilize previously retained Na in the field. The net effect of this behavior may be a recurring desorption and leaching of essential macronutrients from the soil.
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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".