Enhanced mercury removal from contaminated soils in cold regions via KI leaching and freeze-thaw cycle
Bibliographic record
Abstract
Mercury (Hg) contamination in seasonally frozen soils presents significant environmental and health risks, with effective remediation remaining challenging. This study explores a novel remediation approach combining potassium iodide (KI) leaching with freeze-thaw cycle to enhance Hg removal. Laboratory aging and batch experiments were used to elucidate factors controlling Hg adsorption, desorption, and mobility. Soil column experiments evaluated four strategies: (i) water leaching, (ii) KI leaching, (iii) KI saturation and water leaching, and (iv) KI saturation, freeze-thaw cycle and water leaching. Results showed that Hg adsorption was controlled by soil organic matter and clay content, while KI-induced Hg release involved complex desorption via heterogeneous diffusion. Water leaching showed negligible Hg removal (0.59 %), while KI leaching removed 78.96 % Hg and increased residual mobile Hg fractions and soil toxicity. Optimized methods (iii: 73.31 %; iv: 76.37 %) enhanced Hg removal while reducing toxicity and saving 90 % KI dosage. Crucially, the freeze-thaw-enhanced method soil exhibited significantly lower Hg mobility, reducing mobile and semi-mobile fractions by 21.00 % and 8.21 %, respectively, compared to non-freeze-thaw treatment. This enhancement stems from frozen front-driven Hg migration, where freeze-thaw cycle disrupts soil structure, releases bounded Hg, and enhances hydraulic conductivity for effective contaminant removal. Moreover, this approach eliminates the need for engineered well and pump systems required by regular chemical washing/leaching methods. Integrating KI leaching with freeze-thaw cycle offers a promising, cost-effective, and less toxic strategy for mercury remediation in seasonally frozen regions. These findings advance nature-based remediation solutions and contribute to the development of theoretical models for pollutant transport under dynamic environmental conditions. • Soil organic matter and clay content modulate Hg mobility and stability. • KI leaching effectively removes over 76 % of Hg by forming stable complexes. • Hg desorption by KI involves a complex mechanism with heterogeneous diffusion. • Freeze-thaw cycle improves Hg removal efficiency and reduces residual Hg toxicity. • Combined leaching and freeze-thaw is cost-effective for seasonally frozen regions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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 teacher head, 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".