Characterization of Arsenic Upon Liming and Formation of Residual NAPL in the Vadose Zone
Bibliographic record
Abstract
Incorporation of lime amendments is an effective treatment to neutralize acidity, reduce soluble metals, and facilitate plant growth in mine wastes. In general, arsenic (As) solubility increases with increasing pH in As laden mine wastes. However, a number of laboratory and field studies have shown the decrease in As solubility with increase in pH. It has been hypothesized that reduction in soluble As was likely due to presence of amorphous oxides of iron, aluminum and manganese. Further, in experimental studies it has been seen that the total concentration of Fe+Al+Mn was approximately ten times higher in the mine wastes exhibiting decrease in soluble As. There was no correlation suggested with any single element. This study uses PHREEQC for geochemical modeling to investigate the factors and reaction pathways affecting changes in soluble-As concentrations upon liming acidic metalliferous mine wastes. The results indicate that a change in solubility of As upon liming is mainly affected by the presence of amorphous phases of aluminum. Iron and manganese oxides don't play a significant role. Managing contaminated sites (due to infiltration of NAPL) can be expensive, but multiphase models can be an effective tool to predict the subsurface behavior of contaminants and help reduce associated costs. One of the major deficiencies of such models is the prediction of the amount of residual non-aqueous phase liquids (NAPL). In order to accurately predict the behavior of residual NAPL, it is important to understand the formation of residual NAPL. The presence of residual NAPL in the vadose zone has been demonstrated by many researchers, but the conditions under which residual NAPL is formed are poorly understood. Traditionally permeability-saturation-pressure (k-s-p) relations have been used to demonstrate the formation of residual NAPL. Herein, we use electrical conductivity to investigate the process of formation of residual NAPL. Results from the experiments indicate formation of residual NAPL during drainage of NAPL from the system. Further, experimental studies ware undertaken to validate the model presented by Wipfler and van der Zee (2001) with experimental data set in which both oil and water pressure head are determined. Natural soil sample was used instead of Ottawa sand. Ottawa sand has a more uniform grain size than soils so this series of experiments tests the model in a more complex system. Another difference between previous studies and this experiment was the choice of NAPL. Oleic acid, which is a light NAPL was used herein instead of the dense NAPL used by Hofstee et al. (1997). Results from the experiments indicate that the model failed to predict the formation of the residual NAPL under the experimental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".