Phytoremediation—A Sustainable Solution for Reducing Heavy Metal Contamination from the Bhalaswa Landfill Site
Bibliographic record
Abstract
The physico-chemical characteristics of the Bhalaswa Landfill site were studied by analyzing soil and plants species to estimate the heavy metals contamination of the site. The aim of this study was to explore the feasibility of using phytoremediation for reducing environmental contamination of heavy metals due to landfill site. The five most abundant plants species identified in the region were: Prosopis juliflora, Riccinus communis, Calotropis procera, Chenopodium murale, and Chrysopogon zizanioides, used as the indicators for the level of contamination at the site. The samples were collected from 0 m, 500 m, 1000 m, and 2000 m distance from the landfill site to assess the variations in heavy metals contents as a function of distance from the landfill site. Bio-concentration factor revealed the result that C. zizanioides was the best accumulator for iron, C. murale for copper and cadmium, and P. juliflora for silver and therefore can be used for the phytoremediation of the contaminated site. The lowest moisture content of 3.45% in soil was found to be at the landfill site, indicating high amount of dry density and thereby increased level of gas transport. One approach in making dumping of solids wastes on this landfill sustainable with regards to heavy metals pollution could be to grow these plants and harvest them upon maturity. This could help in controlling increased rate of heavy metal pollution of soil, groundwater in this region. A conscious effort is needed now to slow down the increase of metal pollution due to the Bhalaswa Landfill for protecting human health and environmental health.
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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 | 0.001 | 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".