Assessment of groundwater quality in Patna district, Bihar, India, using the Water Quality Index method (Canadian Council of Ministers of the Environment method)
Bibliographic record
Abstract
In this study, we assessed the groundwater quality in Patna district, Bihar, India, using the Water Quality Index (WQI) method, specifically the Canadian Council of Ministers of the Environment approach. Secondary data from various agencies (2004 – 2020) were analyzed to evaluate physicochemical parameters and spatial-temporal trends. Results indicated that while most samples fell within the permissible limits, samples from some locations showed elevated pH, electrical conductivity, hardness, alkalinity, chloride, and nitrate, suggesting localized contamination from natural and anthropogenic sources. Piper diagram analysis reveals Ca²⁺-Mg²⁺-HCO₃− dominance, pointing to carbonate rock dissolution, with some influence from agricultural and industrial activities. WQI classification categorized 76% of samples as fair to excellent, whereas 24% were marginal to poor. A heatmap analysis highlighted an improvement in water quality after 2012, though water from some stations remained persistently poor. Quantum geographic information system-based spatial mapping using the inverse distance weighting technique effectively visualized pollution hotspots and safe water zones. In conclusion, findings from the study underscore the need for regular monitoring, pollution control, advanced treatment methods, and sustainable groundwater management to ensure safe drinking water.
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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 | 0.004 |
| Science and technology studies | 0.001 | 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".