Removal of Arsenate in Acid Mine Drainage by a Permeable Reactive Barrier Bearing Granulated Blast Furnace Slag: Column Study
Bibliographic record
Abstract
Immobilization of arsenate in groundwater impacted by acid mine drainage was investigated using a permeable reactive barrier (PRB) column bearing granulated blast furnace slag (GBFS) to compare with iron granules which are commonly used. Sorption capacity of arsenate onto the GBFS was quite lower than iron granules in the amount of sorbed arsenate per unit surface area of sorbents (mmol/m2) at the equilibrium, Q′, in two orders of magnitude in batch tests, however, the amount of sorbed arsenate per unit amount of sorbents (mmol/kg) at the equilibrium, Q, were comparative to each other, because of much higher porosity in the GBFS. Results of column performance showed that 15 mg/L of As was decreased to be less than 0.4 mg/L for more than 18 pore volumes (pv) in the GBFS-PRB by sorption, co-precipitation and presumably formation of hydrated calcium arsenate, and less than 0.04 mg/L for more than 17 pv in the iron bearing PRB probably by co-precipitation with iron (oxyhydro)oxides. Additionally 15 mg/L of Mn2+ ions was also decreased to less than 0.3 mg/L and 0.03 mg/L, respectively, in iron bearing and the GBFS bearing PRB columns, probably caused by sorption and precipitation of oxides and carbonates. The GBFS has advantages to compensate its low reactivity with high porosity, to facilitate the industrial handling with low density, and to utilize industrial wastes for more valuable applications, emphasizing a potential of alternative reactive materials instead of iron granules in PRB for immobilization of arsenic and manganese in acid mine drainage.
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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) | 0.001 | 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) | 0.001 | 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".