Assessment of Hg pollution in stream waters and human health risk in areas impacted by mining activities in the Ecuadorian Amazon
Bibliographic record
Abstract
Illegal gold mining activities have contributed to the release and mobilization of Hg and environmental degradation in many parts of the world. This study aims to determine the concentration of Hg in five provinces of the Amazon Region of Ecuador, in addition to assessing the risk to human health of exposed populations, applying deterministic and probabilistic methods. For this purpose, 147 water samples were collected in rivers and streams crossing and/or located near mining areas. As a result, 100% of the samples analyzed exceeded the maximum permissible limit (MPL) according to the water quality criteria for the preservation of aquatic life of the Ecuadorian regulations, while 7% of the samples exceeded the MPL for drinking water. On the other hand, considering the European Environmental Quality Standard (EQS) for surface water bodies, in our study, 100% of the samples exceed the maximum permissible limit (0.07 µg/L), and with respect to the Canadian water quality guidelines, 35% of the samples exceed the permissible limit (0.001 mg/l) for drinking water, and 100% of the samples exceed the limit for life in water bodies (0.0001 mg/l). The risk assessment revealed that the probability of developing adverse health effects from exposure to Hg is below the recommended limits according to the probabilistic assessment; this is in relation to the criterion of residential and recreational use of water resources. However, it was identified that the child population doubles the acceptable systemic risk level according to the results of the deterministic assessment in the residential scenario. This information can be used by decision-makers to implement strategies to reduce Hg contamination and exposure of the population in Ecuadorian Amazonian rivers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".