Predicting Heavy Metal Partition Equilibrium in Soils: Roles of Soil Components and Binding Sites
Bibliographic record
Abstract
Core Ideas WHAM 7 successfully predicted heavy metal partition in various soils. Major soil and solution parameters influenced the accuracy of model predictions. Soil organic matter dominated metal binding at most acidic to neutral pH in soils. WHAM 7 predicted bidentate binding as the dominant form of metal complexes. The reactivity and bioavailability of heavy metals in soils are controlled by their binding to reactive soil components, including soil organic matter (SOM), metal (hydr)oxides, and clay minerals. In this study, we specifically investigated how soil components and SOM binding sites controlled metal partition at various chemistry conditions. We used the Windermere Humic Aqueous Model (WHAM 7) to predict the solid‐solution partition and speciation of Cd, Cu, Ni, Pb, and Zn based on compiled literature data including 98 soil samples from five continents. Based on the root‐mean‐square‐error (RMSE) values of logarithm of dissolved metal concentrations between model predictions and experimental results, WHAM 7 reasonably predicted metal partition equilibrium over a wide range of reaction conditions, with RMSE less than 0.5 for Cd and Zn, and less than 1.0 for the other three metals. Soil organic matter dominated metal binding at most acidic to neutral pH, clay minerals were significant at low pH, and iron (hydr)oxides might effectively compete with SOM for metal binding when pH was high. For all five heavy metals, WHAM 7 predicted the bidentate bindings were the dominant form of metal complexes, in which both complexes formed by two carboxylic sites and that by one carboxylic and another phenolic sites were major complexes. The formation of monodentate complexes and electrostatic outer‐sphere complexes was significant at low pH, while tridentate complexes were only significant at high pH values. The modeling results help to accurately predict the environmental behavior of heavy metals in pH 3 to 7 soil environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".