A Comparative Study of Heavy Metal Pollution in Ambient Air and the Health Risks Assessment in Industrial, Urban and Semi-Urban Areas of West Bengal, India: An Evaluation of Carcinogenic, Non-Carcinogenic, and Additional Lifetime Cancer Cases
Bibliographic record
Abstract
Air pollution is an immense problem due to its detrimental health effects on human populations. This study investigates the distribution of particle-bound heavy metals and associated health risks in three diverse areas (Durgapur as an industrial complex, Kolkata as an urban area, and Bolpur as a semi-urban region) in West Bengal, India. Twenty-one (84 samples) sampling sites were chosen, covering industrial, traffic, residential, and sensitive zones. The respirable suspended particulate matter (RSPM) samples were collected using a portable Mini-Vol Tactical Air Sampler, and heavy metal concentrations (Cd, Cr, Mn, Ni, Pb, and As) were analyzed using ICP-OES. The non-carcinogenic and carcinogenic health risks were assessed using exposure concentration (EC), hazard quotient (HQ), hazard index (HI), and additional lifetime cancer cases. The results highlight variations in heavy metal concentrations across the regions, with industrial areas exhibiting higher levels. Principal component analysis (PCA) unveiled distinct metal co-variation patterns, reflecting sources such as industrial emissions, traffic, and natural contributors. The sum of non-carcinogenic risks (HI) of all heavy metals exceeded the US EPA’s risk limit (HI<1) in both Kolkata and Durgapur, except for Bolpur. Similarly, the sum of cancer risk in three distinct areas exceeded the USEPA limits (1.00E-06). The Monte Carlo simulation revealed the 5th and 95th percentile range of cancer risk was 9.12E-06 to 1.12E-05 in Bolpur, 3.72E-05 to 4.49E-05 in Durgapur and 2.13E-05 to 2.57E-05 in Kolkata. Kolkata had the highest additional lifetime cancer cases compared to Bolpur and Durgapur. This study provides information on the complex connections between heavy metal pollution and possible health risks in industrial, urban, and semi-urban regions.
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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 | 0.002 | 0.002 |
| 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".