Suitability Evaluation of CCME-WQI and GWQI for the Modeling of Groundwater and Human Health Risk Assessment of Heavy Metals - Eastern India
Bibliographic record
Abstract
<title>Abstract</title> The present study assessed the suitability of groundwater by using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) and the Groundwater Water Quality Index (GWQI) Model. Six heavy metals viz. arsenic (As), Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), and Nickel (Ni) were investigated in the groundwater from 65 locations of Ranchi city by the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The spatial distribution of WQI was established by Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.3. The mystery of hydrogeochemical evolution in groundwater was elucidated by plotting the Piper trilinear diagram of major cations (Ca<sup>2+</sup>, Na<sup>+</sup>, Mg<sup>2+,</sup> K<sup>+</sup>) and anions (HCO<sub>3</sub><sup>-</sup>, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, F<sup>-</sup>). Significant fluctuations in the water level during PRM (7.38mbgl to 10.5 mbgl) and POM (4.3- 6.4 mbgl) season were observed in the central part of the study area. Performance evaluation of WQI models indicated that the CCMEWQI performed better than GWQI for assessing the quality index of groundwater with a comparatively higher coefficient value (R<sup>2</sup> 0.97) and less NMSE (4.34) RMSE (27.38), MAPE (0.357). The health risk of heavy metals via the oral route was investigated by calculating hazard quotient (HQ) and hazard index (HI). The HI value was observed maximum for As followed by Mn >Pb > Ni >Fe >Cu for adults and children. The spatial distribution map of HI indicated that most of the studies area are at a non-carcinogenic risk of heavy metals. The study provides immense help for water authorities and public health decision-makers to prevent the community's health risk.
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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 | 0.018 | 0.001 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".