Aquatic Mercury Pollution from Artisanal and Small-Scale Gold Mining in Sub-Saharan Africa: Status, Impacts, and Interventions
Bibliographic record
Abstract
Mercury (Hg) pollution remains an environmental global concern due to its non-degradable and toxic nature. Natural and anthropogenic sources of Hg adversely affect the functioning of aquatic ecosystems and biological processes. In sub-Saharan Africa (SSA), unregulated artisanal and small-scale gold mining (ASGM) contributes up to 20% of global gold production and uses 205–496 tonnes/yr of Hg. Despite being a vital economic driver for 20–30 million people, ASGM threatens the health of aquatic systems from Hg pollution, presenting a complex challenge that demands urgent interventions. This review seeks to (1) establish the current status of aquatic Hg pollution, (2) explore the environmental impacts of aquatic Hg, and (3) highlight the proposed interventions for aquatic Hg pollution in SSA. We examined publications and institutional reports between 2000 and 2023 addressing aquatic Hg pollution, impacts, and interventions in the ASGM of SSA. Results indicate a rise in aquatic Hg pollution due to the expansion and intensification of ASGM. West Africa remained the highest contributor (50.2%), followed by Central Africa (39.6%), Southern Africa (9.6%), and Eastern Africa (<1%). Contamination of freshwater ecosystems, toxicity to aquatic biota, and environmental health risks to humans were evident. Alternative Hg-free ASGM technologies, including physical, metallurgical, and pyrometallurgical, were investigated from case studies and recommended for adoption.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".