Occurrence of glyphosate in surface and drinking water sources in Cúcuta, Norte de Santander, and its removal using membrane technology
Bibliographic record
Abstract
Glyphosate is currently the herbicide with the highest use worldwide for weed control. It has been detected in different water sources, including drinking water, which could be generating potential damage to human health. In the Metropolitan Area of Cúcuta, intensive rice cultivation is predominant, and as it grows in flooded areas, the use of herbicides has greater contact with water bodies, which are used as sources of supply. Based on this, the concentration of glyphosate was quantified in five sampling points of surface and drinking water of the Pamplonita and Zulia rivers, using UV-Vis spectrophotometry, establishing that the concentration found in drinking water (216 and 204.5 µg/L) was below the maximum allowable limits of countries such as the United States, Canada, and Australia and above those of the European Union and the United Kingdom. Once the occurrence was identified, the removal capacity of glyphosate was evaluated using membrane technology through reverse osmosis (RO) and nanofiltration (NF) in a pilot plant, for which response surface optimization models were implemented, and 100% removals were obtained, with repeatability close to 1% with respect to other reported investigations, highlighting that the NF process was more efficient even though the molecular weight of glyphosate was below the limit of the membrane. In contrast, it was determined that, according to the concentrations found in the drinking water supplied to the Metropolitan Area of Cúcuta, this has a low risk according to the guidelines for drinking water quality in Canada and a moderate risk according to the World Health Organization (WHO). The conventional systems currently used for water purification are insufficient to remove traces of contaminants such as herbicides. Therefore, it is necessary to implement new technologies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".