Chemical Forms of Mercury in Pyrite: Implications for Predicting Mercury Releases in Acid Mine Drainage Settings
Bibliographic record
Abstract
Pyrite (cubic FeS 2 ) is the most abundant metal sulfide in nature and also the main host mineral of toxic mercury (Hg). Release of mercury in acid mine drainage resulting from the oxidative dissolution of pyrite in coal and ore and rock resulting from mining, processing, waste management, reclamation, and large construction activities is an ongoing environmental challenge. The fate of mercury depends on its chemical forms at the point source, which in turn depends on how it occurs in pyrite. Here, we show that pyrite in coal, sedimentary rocks, and hydrothermal ore deposits can host varying structural forms of Hg which can be identified with high energy-resolution XANES (HR-XANES) spectroscopy. Nominally divalent Hg is incorporated at the Fe site in pyrite from coal and at a marcasite-type Fe site in pyrite from sedimentary rocks. Distinction of the two Hg bonding environments offers a mean to detect microscopic marcasite inclusions (orthorhombic FeS 2 ) in bulk pyrite. In epigenetic pyrite from Carlin-type Au deposit, up to 55 ± 6 at. % of the total Hg occurs as metacinnabar nanoparticles (β-HgS NP ), with the remainder being substitutional at the Fe site. Pyritic mercury from Idrija-type Hg deposit (α-HgS ore) is partly divalent and substitutional and partly reduced into elemental form (liquid). Divalent mercury ions, mercury sulfide nanoparticles, and elemental mercury released by the oxidation of pyrite in acid mine drainage settings would have different environmental pathways. Our results could find important applications for designing control strategies of mercury released to land and water in mine-impacted watersheds.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".