Physicochemical and heavy metal pollution level in Hindon River ecosystem: An implication to public health risk assessment
Bibliographic record
Abstract
Abstract The study was conducted to determine heavy metal contamination and physicochemical parameters in Hindon River's water. The parameters evaluated included cadmium (Cd), copper (Cu), chromium (Cr), iron (Fe), manganese (Mn), zinc (Zn), and lead (Pb), as well as pH, electrical conductivity (EC), turbidity (TUR), total dissolved solids (TDS), total hardness (TH), total alkalinity (TA), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), chloride (Cl − ), calcium (Ca), sulfate () and nitrate (). The findings showed the distribution of metals in river water. Some of the values of Cd, Cu, Cr, Fe, Mn, Zn, and Pb in surface water were above the standards set by the Bureau of Indian Standards (BIS). The mean concentrations of physicochemical parameters in surface water sometimes exceeded the BIS standards due to untreated effluent discharge. All the Water Quality Index (WQI) values were above 100; therefore, the water was unsuitable for human consumption. The heavy metal pollution index (HPI) showed that Station 1 (S1) is the most polluted, followed by the S3 and S5. Multivariate statistical analysis showed that most heavy metals (HMs) in river water originated from artificial or anthropogenic sources, that is, human sources. The calculated hazard quotient (HQ) and hazard index (HI) showed that the oral ingestion route could be dangerous to humans (both adults and children) since the HQ and HI values were higher than the acceptable limits (HQ > 1). Dermal exposure assessment showed that the S1 was more dangerous to children's and adults' health than the S3 and S5. This trend showed that the Hindon River was polluted due to many untreated industrial effluents being discharged. The research highlights the pollution level in the Hindon River, and there is a need for proper monitoring and control to maintain the sustainability of the river ecosystem.
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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".