A Study of the Level of Heavy Metal Pollution in the Soils Near Durgapur Industrial Area, West Bengal, India
Bibliographic record
Abstract
The elevated concentrations of heavy metals in surface soils of Durgapur, an industrial hub in West Bengal, India, present a significant environmental risk, due to leaching. This study evaluated the extent of contamination and identified sources of pollution using statistical analyses and pollution indices. Soil samples were collected from 16 sites and analyzed using Atomic Absorption Spectrophotometer, with average concentrations of 10.59, 71.43, 0.07, 12.68, and 1.61 mg/kg for Nickel, Zinc, Mercury, Lead, and Cadmium, respectively. Observed concentrations of heavy metals were compared with soil pollution criteria established by Canadian Council of Ministers of Environment and the U.S. Environmental Protection Agency. According to MÜLLER’s classification, all sampled sites showed alarming levels of pollution, especially Cadmium, with an Igeo value of 2.874. The highest Potential Ecological Risk Index recorded at 445.13. Consequently, the study highlighted the necessity for implementing various in-situ and ex-situ remediation approaches, including solidification/stabilization with chemical additives, electrokinetic removal of contaminants from soils, bioremediation, and phytoremediation, to rehabilitate polluted land. Aligned with United Nations Sustainable Development Goals (UNSDGs) 3, 6, and 15, the findings highlight the urgent need for continuous and systematic monitoring of heavy metal pollution in industrial zones to mitigate potential risks in terrestrial and aquatic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".