Evaluating monsoon season heavy metal contamination in groundwater of Uttar Dinajpur District using pollution indices and Principal Component analysis
Bibliographic record
Abstract
Heavy metal contamination in groundwater poses significant environmental and public health challenges globally. This study investigates the presence and distribution of heavy metal contamination in groundwater within Uttar Dinajpur District, India, focusing on zinc (Zn), manganese (Mn), copper (Cu), iron (Fe), and lead (Pb). Groundwater samples were collected and analysed for heavy metal concentrations, and statistical analyses, including descriptive statistics, histograms, box plots, Heavy Metal Pollution Index (HPI) analysis, Metal Index (MI) analysis, and Principal Component Analysis (PCA), were conducted to assess contamination levels, spatial distribution, and potential sources. Results indicate varying levels of heavy metal contamination across the district, with manganese, iron, and lead frequently exceeding permissible limits, posing potential health risks. The PCA revealed common sources and relationships among heavy metals, aiding in understanding contamination patterns. The study underscores the importance of continuous monitoring and targeted interventions to manage heavy metal contamination in groundwater, emphasizing the need for further research to develop effective mitigation strategies. • Heavy metal contamination in groundwater was assessed during the monsoon season. • Manganese, iron, and lead concentrations frequently exceed safe limits, posing health risks. • Pollution indices and PCA identified contamination patterns and potential pollution sources. • Study underscores the need for continuous monitoring and effective mitigation strategies. • Findings offer insights relevant to regions facing similar groundwater contamination globally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".