Pollution Load on Indian Soil-Water Systems and Associated Health Hazards: A Review
Bibliographic record
Abstract
India’s soil-water systems provide a vital source of freshwater and sustain the drinking water supply for the world’s second-largest population. However, groundwater within the large geographical area of India continues to be affected by geogenic pollution and industrial spills. Thus, an effort has been made to review the studies performed to investigate distributions and behaviors of major pollutants of Indian soil-water systems. Furthermore, a state-of-the-art literature survey has been performed to understand the progress in wetland hydrology and in the estimation of greenhouse gas (GHG) emissions from India’s soil-water systems. The geochemically induced health issues have been presented here. Five major observations have been noted as follows: (1) the majority of aquifers in India are highly affected by multiple pollutants, i.e., arsenic (As), fluoride (F), nitrate (N), selenium (Se), uranium (U), and hydrocarbons; (2) although there is sufficient literature on laboratory and field investigations of individual pollutants’ behavior in India soil-water systems, these investigations haven’t been performed for multipollutants; (3) scant information is available on reactive solute behaviors in Indian wetland systems; (4) significant work has been done in the past to estimate GHG emissions from Indian wetlands, dams/reservoirs, forests, and crop lands, but very limited information is available on its connectivity with local and regional hydrological processes and water quality; (5) large populations are affected by serious health issues such as dental/skeletal fluorosis, malignancy, and hyperkeratosis in areas highly contaminated with multipollutants. This is the first study to present the current status of multi-pollutants’ distributions and behaviors in Indian soil-water systems and associated health hazards. The manuscript will help policy makers, geochemists, and environmental scientists to frame management and remediation plans for polluted sites in India.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".