Multi-contaminant removal from synthetic mine-impacted water by permeable reactive barriers under cold conditions
Bibliographic record
Abstract
Permeable reactive barriers (PRB) effectively attenuate multiple groundwater contaminants in temperate climates, but their efficacy remains uncertain in sub(Arctic) climates where cold temperatures inhibit kinetic biogeochemical reactions governing contaminant removal. This study applies bench-scale columns mimicking PRBs, containing varying proportions of zero-valent iron (ZVI), gravel, and wood chips, to treat synthetic mine-impacted water containing nitrate, arsenic, and uranium at low temperatures (5 °C) over 36 weeks. Columns were amended with sodium acetate during weeks 20–33 to stimulate microbial activity. Speciation was investigated by combining X-ray Absorption Spectroscopy and geochemical speciation modeling using PHREEQC. Arsenic removal efficiency exceeded 95 % over the experimental duration in all ZVI-bearing columns and was mostly driven by adsorption and coprecipitation with ZVI oxidation products. Nitrate removal was limited in the absence of acetate amendments but improved to ∼50 % during the amendment. Denitrification to N 2 gas was incomplete, likely due to kinetic limitations on the various nitrogen reduction reaction steps. Uranium removal was >95 % in ZVI-bearing columns before the acetate amendment and was predominantly explained by U(VI) adsorption onto Fe-(oxyhydr)oxides. However, U was remobilized during the amendment, likely due to increased aqueous complexation of U by calcium and carbonate that drove the desorption of U from Fe-(oxyhydr)oxides. These experiments show that PRB technology holds promise for multi-contaminant removal under cold conditions, while exposing ongoing challenges associated with concurrent removal of contaminants exhibiting contrasting geochemical behavior.
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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".