Formation and Immobilization of Cr(VI) Species in Long-Term Tannery Waste Contaminated Soils
Bibliographic record
Abstract
Abstract Chromium speciation in naturally contaminated soils appears more complex than spiked studies have shown. This study characterized Cr speciation (oxidation states; availability; molecular geometry) intended to highlight the genesis of immobile Cr(VI) species in long-term tannery waste-contaminated soils. In a series of samples obtained from Shuitou in China, chemical extraction methods showed that Cr(III) was dominant(>96.7% of total Cr), with Cr(VI) concentration up to 144 mg kg–1. Of the total Cr(VI) present, immobile Cr(VI) represents >90%. Synchrotron-based X-ray near-edge structure spectroscopy (XANES) showed the occurrence of Cr(VI), which was not removed by phosphate buffer extraction, confirming a significant amount of immobile Cr(VI) fractions in soils. X-ray fluorescence maps exhibited the heterogeneous distribution of Cr in soils associated with both Mn and Fe. Such a distribution suggests Cr(III) oxidation to Cr(VI) by Mn oxides and a possible immobilization of both Cr(III) and Cr(VI) onto Fe (hydr)oxides. Linear combination fitting of XANES spectra revealed that fractional weights (%) in samples were CrFeO3 (49.3–53.6), CrOOH (22.3–30.8), and CaCrO4 (13.2–25.3). Our results demonstrate that (i) Cr(VI) is immobilized in soils and (ii) mechanisms of Cr(VI) immobilization are CaCrO4 precipitation and recrystallization with Fe (hydr)oxides.
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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 | 0.000 | 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 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".