Potential for the phytoremediation of arsenic-contaminated mine tailings in Fiji
Bibliographic record
Abstract
The objectives of this study were (1) to compare the bioavailability of arsenic (As) to plants in an As-spiked agricultural soil and a naturally contaminated mine tailings, (2) to compare the theoretical ability of various chemical amendments to solubilise As in naturally contaminated mine tailings, and (3) to examine the ability of Brassica juncea (Indian mustard) plants to remove the solubilised As from the soil and tailings. The growth media used for this study included mine tailings from a gold mine in Fiji contaminated with As (683 As mg/kg) due to the presence of arsenopyrite in the mined rock, and a pasture soil from New Zealand (Manawatu sandy loam) amended with lime and/or As. Brassica juncea was grown in these substrates in a glasshouse. In a separate batch experiment, we examined the theoretical ability of several chemical extractants to solubilise As from the mine tailings. Of the tested extractants, only hydrochloric acid (HCl) and a mixture containing ammonium oxalate (NH4)2C2O4, oxalic acid, and ascorbic acid were effective in extracting As from the tailings. In the plant growth experiment, solutions of these 2 chemicals were used as soil amendments at 2 different concentrations to increase As uptake by 6-week-old, actively growing B. juncea plants. Arsenic bioavailability as a function of the growth media influenced the germination rate of B. juncea, the As concentration in the plants, and the water-soluble As concentration in the media. There was approximately a 3-fold reduction in the germination of seeds, and a 64- and 380-fold increase in As concentration in plant and soil solution, respectively, in the spiked Manawatu soil compared with the naturally contaminated Fiji mine tailings. The spiking of soil with As did not mimic naturally contaminated tailings in this experiment. The total amount of As taken up by B. juncea plants increased approximately 9 fold with the addition of the amendments. However, the phytoremediation capacity of B. juncea for As extraction in Fiji mine tailings was too low for efficient remediation even in the presence of solubilising chemicals.
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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) | 0.001 | 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".