Remediation scenario of the abandoned Kettara mine site (Morocco): acid mine drainage (AMD) transport modeling
Bibliographic record
Abstract
Abstract In this study, we present the efficiency of remediation scenario to attenuate the impact of acid mine drainage (AMD) contamination in the Kettara abandoned mine site. The study focuses on the AMD groundwater contamination of the Sarhlef shists aquifer. To predict the evolution of AMD groundwater contamination in the Kettara mine site under remediation scenario, a model of groundwater flow and AMD transport was performed. Piezometric heads were measured at the dry and wet periods from eleven wells located downstream of mine wastes. To elaborate a conceptual groundwater flow model, we faced with to the heterogeneity and anisotropy of fractured Sarhlef shists aquifer. Consequently, the study focused on the use of various approaches: 1. The inverse modeling by the CMA-ES algorithm is adopted as an alternative approach to determine hydraulic parameters indirectly, and 2. the model is treated as an equivalent porous media (EPM). The groundwater flow model was carried out in steady-state and transient conditions in the dry and wet periods using the PMWIN interface. The obtained results are satisfactory and show an excellent correlation between measured and computed heads. Contaminant transport model is used to solve the advection–dispersion equation and to generate the AMD concentration by MT3D via the PMWIN interface. A sensitivity analysis of the dispersivity coefficient is carried out. The AMD transport simulation was computed during periods of 1, 5 and 10 years, and the performed model indicates that the simulated concentrations under remediation scenario are reduced 1000 times comparing to the current concentrations. The study revealed a necessary approach in addressing an environmental issue for the AMD contamination. The results of the study will be a start-up for further research work in the study area and implementing it for the prevention of AMD propagation plume.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".