Effects of chemical speciation on the bioaccessibility of zinc in spiked and smelter-affected soils
Bibliographic record
Abstract
Abstract Previous studies have suggested that understanding soil metal speciation, rather than relying solely on total metal content, can improve the accuracy and utility of contaminated site risk assessments. Because soil properties and reaction time can alter metal speciation, speciation should influence metal bioaccessibility. For example, under gastrointestinal conditions, it is expected that metal species will differ in bioaccessibility depending on their stability in acidic pH environments. We studied the links between metal speciation and bioaccessibility. A combination of synchrotron-based X-ray diffraction and X-ray absorption near edge structure (XANES) was used to identify the zinc (Zn) speciation in spiked and smelter-affected soils. After conducting in vitro digestion tests on the soil samples, XANES and linear combination fitting were carried out on the residual pellets to identify the species of Zn that remained after digesting the soils in the simulated gastric and duodenal fluids. The metal species that were not present in the residual pellets were inferred to have been dissolved and, thus, more bioaccessible. Sphalerite (ZnS), ZnO, and outer-sphere Zn contributed more to Zn bioaccessibility than franklinite (ZnFe2O4) and Zn incorporated into a hydroxy interlayer mineral (Zn-HIM). The bioaccessibility of Zn-aluminum layered double hydroxides (Zn-Al-LDH) was found to be inversely proportional to its residence time in soil. It was also observed that the relatively high pH of the duodenum favors metal reprecipitation and readsorption, leading to a reduction in bioaccessible metal concentration. These results imply that metal speciation mainly controls metal bioaccessibility. Environ Toxicol Chem 2019;38:448–459. © 2018 SETAC.
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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 | 0.001 |
| 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".