Dispersed alkaline substrate passive treatment technology for highly contaminated acid mine drainage: 20 years of successful application
Bibliographic record
Abstract
• Two decades of DAS tech deliver high acidity and metal removal (>95%) • Integration of NFOL pre‑oxidation boosts Fe removal and system longevity • MgO‑DAS and BaCO₃‑DAS target divalent metals and sulfate, respectively • Alternative reagents (wood ash, by‑products) reduce costs and carbon footprint • Valorization of sludges enables circular economy recovery of critical metals Over the past two decades, Dispersed Alkaline Substrate technology (DAS) has emerged as a highly effective passive approach to treating acid mine drainage with extreme acidity and metal loading. By mixing alkaline materials - such as limestone, magnesia, barium carbonate or industrial by-products - into an inert wood-chip matrix, DAS systems maintain high porosity and neutralization capacity without rapidly clogging. This treatment technology has evolved from laboratory trials to pilot and full-scale field testing, using multi-step systems integrated with Natural Fe-Oxidizing Lagoons (NFOL) for pre-oxidation, which have demonstrated their long-term effectiveness for acidity and metal removal. The sustainability and applicability of the process has been improved by the search for new reagents (e.g. MgO for divalent metals removal, BaCO₃ for sulfate removal, wood ash or calcite-rich waste for cost reduction). Field trials in diverse regions - from the Iberian pyrite belt to South Africa, Canada, South America, Asia, Europe and Oceania - report net acid removal often exceeding 95% and near complete retention of metal(loid)s such as Al, Cu, Zn, Pb and As. Mineralogical analyses indicate that contaminants precipitate primarily as hydroxides, oxyhydroxides, and sulfates (e.g., schwertmannite, basaluminite, barite), allowing targeted valorization of metal-rich sludges. Remaining challenges include assessing long-term reagent life under variable hydrological conditions, extending full-scale use to phosphogypsum and other industrial leachates, and developing strategies for residue stabilization and resource recovery. The aim of this review is to synthesise these developments, assess current performance and identify future research needs for the advancement of passive DAS treatment technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".