Survey of Heavy Metal Contamination in Water Sources in the Municipality of Torola, El Salvador, through In Situ Sorbent Extraction
Bibliographic record
Abstract
The presence of heavy metals in water resources directly affects consumer health. The quality of surface water resources in Central America is usually low due to the presence of metals and other pollutants. The lack of analytical instrumentation to perform routine monitoring of water has encouraged the development of easy tools to facilitate the determination of heavy metals in waters in remote sites. In this study, we evaluated the use of different sorbents, such as Adsorbsia As600 (titanium dioxide), Iontosorb Oxin, 8-hydroxyquinoline bearing functional groups, and Duolite GT-73, with thiol functionality, for Cd, Ni, Cu, Zn, and Al extraction. It was found that both Adsorbsia As600 and Iontosorb Oxin allowed the adsorption of all metals, and the recovery was achieved using either HCl or ethylenediaminetetraacetic sodium salt (EDTA) solutions. Hence, Adsorbsia As600 was employed for in situ sampling in the metal contamination evaluation of water samples (from 15 wells and nine storage tanks) from the municipality of Torola, Mozarán, El Salvador. The developed procedure allowed all the metals in the samples to be detected, and Ni and Al were found to be above Salvadoran guidelines for drinking water quality.
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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 | 0.000 |
| 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) | 0.000 | 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 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".