Assessment of Gold and Mercury Losses in an Artisanal Gold Mining Site in Nigeria and Its Implication on the Local Economy and the Environment
Bibliographic record
Abstract
The objective of this work was to establish the gold and mercury losses in an artisanal mining deposit (Uke) in Nigeria to convince miners about their inefficiency and suggest changes in their gold extraction practices. Samples of feeds and tailings from five sluice box concentration processes previously ground in hammer mills below 1 mm (P80 = 0.5 mm) were systematically sampled every 15 min. for 4 h and sent for gold analyses by a fire assay and intensive cyanidation. Dry grain size analyses of primary and amalgamation tailings allowed us to find out in which size fraction gold and mercury are lost. Total mercury losses in sixteen operations were obtained by weighing mercury at the beginning and in all steps of the concentrates’ amalgamation. After analyses, the average gold grade in the feed resulted in 3.80 ± 1.52 ppm (two standard deviations). The gold recovery was 29.24 ± 13.24%, which is low due to a lack of liberation of the fine gold particles from the gangue (silicates). Finer grinding would be necessary. The mercury balance revealed that 42% of the mercury added is lost, in which 26% involves tailings and 16% evaporated. The HgLost-to-AuProduced ratio was found to be 3.35 ± 9.46, which is exceedingly high for this type of amalgamation process that should have this ratio around 1. One reason is the excessive amount of mercury in the amalgams, 76.5 ± 38.12%, when the normal is around 40%–50%. Mercury lost by evaporation in open bonfires is clearly contaminating amalgamation operators (usually children), neighbours, and the environment. The Hg-contaminated tailings and primary tailings are sold to local cyanidation plants, and this can form toxic soluble Hg(CN)2 in the process. The results of this research were brought to the attention of the miners and other stakeholders, including the regulatory agencies of the government. The % gold recovery by amalgamation was not established in this study, but if this process recovers 50 to 60% of the liberated gold particles in a concentrate and 30% of gold was recovered in the sluice boxes, then the total gold recovery should be between 15 and 20; i.e., 80 to 85% of gold mined is lost. On average, an operation produces 8.26 g of gold/month, which is split to six miners, representing USD 69/month/miner or USD 2.3/day. It was discussed with miners, authorities, and community members (in particular female miners) how to avoid exposure to mercury, how to improve gold recovery without mercury, and the health and environmental effects of this pollutant.
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 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 | 0.000 | 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 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".