Influence of inorganic and organic road deicing salts on the mobilization of metals within an agricultural roadside soil
Bibliographic record
Abstract
Seasonal road salt application is vital for winter road safety, but repeated use of chloride (Cl − )-rich salts (e.g., NaCl) releases high Cl − loads to nearby soils. Elevated Cl − levels can mobilize toxic trace metals from roadside soils, posing environmental risks. Although Cl − -free organic salts are recommended as alternatives, their geochemical behavior in soils under varying environmental conditions remains less well understood. This study investigated the effects of pH and temperature on metal release performance from an agricultural roadside soil exposed to different deicing chemicals (NaCl, CaCl 2 , KCl, calcium magnesium acetate [CMA], sodium acetate [NaOAc], and potassium formate [HCOOK]) under laboratory conditions. The soil was amended with Pb (13 mg/kg) and Cd (3 mg/kg), divided into six portions, incubated separately with each salt (1000 mg/L for Cl − -based salts and 1400 mg/L for formate/acetate-based salts) and subjected to alternative freshening and salting cycles under two pHs (5 and 8) and temperatures (5 and 20 °C). Changing the temperature from 5 °C to 20 °C had a minimal influence on the pH of the salt-added soil samples, except for NaOAc-treated soils, which caused the highest alkalinization (pH 6.0 to pH 8.8). This soil also released the greatest concentrations of Zn 2+ , Cu 2+ , Pb 2+ , and Cd 2+ in all four pH and temperature conditions during the first freshening stage (cumulative release ∼ 2 mg/kg), mostly driven by stable acetate-complexes; for instance, ∼70% of total aqueous Pb 2+ was mobilized as acetates at pH 8 and 20 °C, as identified through geochemical modeling. In contrast, Cl − -based salts showed limited trace metal leaching given the low Cl − input concentration (∼28 mM) used during the incubation. CMA had the least influence on soil properties, including pH, EC, Cl − , and trace metal release, while promoting Ca 2+ and Mg 2+ availability. Although all trace metal concentrations remained below regulatory limits, the findings highlight distinct Cl − -driven vs. organic ligand-driven pathways that govern metal mobility and underscore the need for salt selection strategies based on soil chemistry and seasonal factors.
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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 | 0.001 | 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".