Evidence of transboundary mercury and other pollutants in the Puyango-Tumbes River basin, Ecuador–Peru
Bibliographic record
Abstract
In Portovelo in southern Ecuador, 87 gold processing centers along the Puyango-Tumbes River produce an estimated 6 tonnes of gold per annum using a combination of mercury amalgamation and/or cyanidation and processing poly-metallic ores. We analysed total Hg, Hg isotopes, total arsenic, cadmium, copper, lead and zinc in water and sediment along the Puyango in 2012-2014. The highest total mercury (THg) concentrations in sediments were found within a 40 km stretch downriver from the processing plants, with levels varying between 0.78-30.8 mg kg-1 during the dry season and 1.80-70.7 mg kg-1 during the wet season, with most concentrations above the CCME (Canadian Council of Ministers of the Environment) Probable Effect Level (PEL) of 0.5 mg kg-1. Data from mercury isotopic analyses support the conclusion that mercury use during gold processing in Portovelo is the source of Hg pollution found downstream in the Tumbes Delta in Peru, 160 km away. The majority of the water and sediment samples collected from the Puyango-Tumbes River had elevated concentrations of, arsenic, cadmium, copper, lead and zinc exceeding the CCME thresholds for the Protection of Aquatic Life. At monitoring points immediately below the processing plants, total dissolved concentrations of these metals exceeded the thresholds by 156-3567 times in surface waters and by 19-740 times in sediment. The results illustrate a significant transboundary pollution problem involving Hg and other toxic metals, amplified by the fact that the Puyango-Tumbes River is the only available water source in the semi-arid region of northern Peru.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".