Speciation and complexation of trace metals in eastern Canadian soils
Bibliographic record
Abstract
An important task of research on trace metals in soils is to evaluate how much metals are potentially bioavailable and may cause toxic effects. In this thesis, the chemical speciation and complexation of cadmium (Cd), copper (Cu), mercury (Hg) and lead (Pb) were examined in eastern Canadian soils collected from different locations around smelters. Our goal is to be able to predict metal binding in the soil solution and on the solid phase under a wide range of field conditions. In Chapter 2, speciation of Cd, Cu and Pb in the lysimeter soil solutions was determined using an ion exchange technique (IET) involving a resin column. The IET-speciation data were used to estimate the metal-dissolved organic carbon (DOC) binding constants using the non-ideal competitive adsorption (MICA)-Donnan model, which assumed a continuous distribution of binding affinities on the DOC molecule. The published Cd and Pb speciation data in a variety of soils (Chapter 3) were also used to test the effectiveness of two speciation models, the MICA-Donnan model and WinHumicV. Both models satisfactorily predicted the concentrations of Cd2+ and Pb2+. The two chapters of metal speciation demonstrated that the NICA-Donnan model could estimate the binding strength of organic matter in soil solutions. Proton and metal complexation to the surface of soil particles (Chapters 4 and 5) was investigated using back-titration and batch adsorption procedures. It was shown that the surface binding of H+, Cd2+, Hg2+ and Pb2+ was significantly related to soil organic matter (SOM). Though the soil particle surface was covered by a mixture of organic and mineral components, a two-site distribution could be identified from the titration curves. With the parameters derived from the back-titration and adsorption data, the MICA-Donnan model reasonably predicted the surface complexation of proton and metals. Furthermore, the statistically significant relationships between the model parameters and soil organic matter supported the assumptions in this thesis: (1) Organic matter was the most important sorbent on the particle surface; (2) The MICA-Donnan model may be used to interpret the surface binding data in these soils.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".