Fitorremediación de suelos contaminados con arsénico, cobre y plomo empleando Echeveria elegans y Crassula ovata
Bibliographic record
Abstract
Inadequate management of industry-generated waste has contributed to soil contamination by heavy metals and metalloids, becoming a problem, due to its persistence in the soil for years, causing toxicological effects on plants and animals, as well as affectations on human health. For these reasons, the remediation of contaminated soils is necessary. Phytoremediation is a cost-effective and environmentally friendly alternative treatment for metal and metalloid extraction for contaminated soils. This project evaluated the process of phytoremediation (phytoextaction and phytostabilization) in a soil from a metalworking industry, contaminated with arsenic, copper and lead, using the plant species Echeveria elegans (echeveria) and Crassula ovata (jade tree). Two experiments were conducted, one in soil without the addition of nutrients and the second in soil with the addition of nutrients. The results obtained dictated that both plants had the ability to accumulate and stabilize As, Cu and Pb in soil with little or high nutrient content. Both plants were hypertolerant according to their bioconcentration factors (BCF) and translocation factors (TF), which indicate their potential to be used in a phytoremediation treatment. That is, they were effective for the technique of phytostabilization at high concentrations of these contaminants. At the end of six months of treatment, the concentrations of As, Cu and Pb were not reduced below the maximum permissible limits in industrial soil set out in NOM-147-SEMARNAT/SSA1-2004 and in the Canadian Industrial Soil Quality Guide (the Cu is not regulated in Mexico) in both experiments. However, the approximate time and velocity of removal of these contaminants was obtained, concluding that the process would be ideal when concentrations of pollutants were less than 4 or 5 times, for arsenic, 10 to 12 times for lead and up to 20 times for copper.
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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 | 0.000 | 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".