Treatment of Arsenic, Heavy Metals, and Acidity Using a Mixed ZVI-Compost PRB
Bibliographic record
Abstract
A 30-month performance evaluation of a pilot permeable reactive barrier (PRB) consisting of a mixture of leaf compost, zerovalent iron (ZVI), limestone, and pea gravel was conducted at a former phosphate fertilizer manufacturing facility in Charleston, SC. The PRB is designed to remove heavy metals and arsenic from groundwater by promoting microbially mediated sulfate reduction and sulfide-mineral precipitation and arsenic and heavy metal sorption. Performance monitoring showed effective treatment of As, Pb, Cd, Zn, and Ni from concentrations as high as 206 mg L(-1), 2.02 mg L(-1), 0.324 mg L(-1), 1060 mg L(-1), and 2.12 mg L(-1), respectively, entering the PRB, to average concentrations of <0.03 mg L(-1), < 0.003 mg L(-1), < 0.001 mg L(-1), < 0.23 mg L(-1), and <0.003 mg L(-1), respectively, within the PRB. Both As(III) and As(V) were effectively removed from solution with X-ray absorption near edge structure (XANES) analysis of core samples indicating the presence of As(V) in oxygen-bound form and As(III) in both oxygen- and sulfur-bound forms. XANES solid phase sulfur analysis indicated decreases in the peak amplitude of intermediate oxidized sulfur species and sulfate components with increasing distance and depth within the PRB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".