Environmental pollution by heavy metals within the area influenced by the Tungurahua volcano eruption – Ecuador
Bibliographic record
Abstract
Volcanic eruptions can have long-lasting negative effects on nearby environments and communities, especially those relying on agriculture. The Tungurahua and Sangay volcanoes in Ecuador's highlands pose a significant risk to the region's agricultural economy and inhabitants. The most recent eruption of the Tungurahua volcano spanned from September 1999 to March 2016. Volcanic ash is known to contain both non-essential and essential elements for plant growth, but excessive amounts of the latter can also be toxic and disrupt physiological processes. Additionally, the use of pesticides and fertilizers introduces additional elements to agricultural soils, which can potentially transfer to edible plants and raise health concerns for consumers. Despite this, little is known about the impact of the Tungurahua volcano's latest eruption on soil deposition of micronutrient and heavy metals and their potential transfer to the food chain. To address this knowledge gap, we conducted a study to explore the impact of the Tungurahua volcano's latest eruption on soil deposition of Cd, Cr, Ni, Pb, As, Fe, Cu, Mn, and Zn, as well as the physicochemical characteristics of cultivated and uncultivated soil samples and vegetables in the cantons most affected by volcanic ash. According to our findings, the physicochemical properties, micronutrient, and heavy metal concentrations of both cultivated and uncultivated soils differ and, in some cases, exceeded the maximum limits established, which could affect the health of the soil as well as of human beings through the trophic chain. This study provides valuable information for monitoring the chemical and physical properties of soil and vegetables in areas affected by the Tungurahua volcanic activity and agriculture, aiding in understanding their impact on the environment, agriculture, and potential health risks associated with locally grown crops in rural Ecuadorian communities.
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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 | 0.001 | 0.000 |
| 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".