Immobilization Of Arsenic In Mine Tailings Using Standard And \nNanoscale Metal Oxides
Bibliographic record
Abstract
Elevated levels of arsenic can be found in mine tailings, sediment and soil samples.Leaching of arsenic from tailings can lead to the contamination of surface and groundwater.One potentially sustainable method is to add various agents to stabilize the waste and ensure that arsenic does not leach out of the waste.In the current study, the effectiveness of various types of metal oxides as immobilizing agents was tested.Leaching tests and SSE (Selective Sequential Extraction) were performed on different mixtures of mine tailings and metal oxides, using different weight ratios, reaction times, types of oxides.The mine tailings were taken from different sites in Canada.The metal oxides used were either regular (commercial grade) or nanoscale powders.The additives evaluated were MgO, ZnO, Fe 3 O 4 , TiO 2 , CaO and Al 2 O 3 .These additives were chosen for their successful use as commercial agents in chemical decontamination.The leaching tests were done using a solution of distilled water and sulphuric acid at a pH of 3 to simulate acid rain fall on the mine tailings that could occur.The concentration of arsenic in the leachate was measured using arsenic test kits and ICP-MS instrumentation.It was found that both regular and nanoscale ZnO (zinc oxide) had the highest capacity to immobilize the arsenic present in the mine tailings, whereas the other metal oxides tested and Fe 3 O 4 (magnetite) had little or no effect.Leaching tests performed on Noranda and Golden Giant mine tailings over a 24 hour period revealed that the addition of 7.5% in weight of nanoscale ZnO caused a 99.4% to 99.7% reduction in the amount of arsenic leached into solution of distilled water at a pH of 3. A 91% to 92% reduction was observed when the additives were left to immobilize the tailings for a period of 1 month.iv SSE tests confirmed that ZnO is a very effective immobilizing agent in all of the five chemical phases and stable on a long-term basis.SSE tests showed a reduction in the amount of arsenic leached by a factor of more than 31 in the exchangeable phase, and by a factor of 4.9 in the water soluble phase, and 5.1 in the carbonate phase for Noranda mine tailings treated with 7.5% regular grade ZnO.Leaching from each of the other phases was also minimized.These results indicate the possibility of developing a sustainable remediation process for mining areas as well as other contaminated soil using ZnO.Further studies on immobilization are recommended using ZnO in conjunction with other metal oxides or compounds that would improve efficiency, reduce costs and minimize the leaching of zinc into the soil.
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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".