Evaluation of Groundwater Quality for Drinking Purpose using Different Water Quality Indices in Parts of Gautam Budh Nagar District, India
Bibliographic record
Abstract
Water Quality Index (WQI) is a mathematical value for quality of water, which includes the concentration of a number of constituents present in it. Chemical and biological measures of water quality are used to represent water quality index. Measured value of the selected parameters, ideal concentration of that parameter in pure water and standard value given by different quality control organizations are used to determine this number. In the present study, three indexing methods; Weighted Arithmetic Water Quality Index (WAWQI), Groundwater Quality Index (GWQI) and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) were used to calculate water quality index of groundwater. A hydrochemical analysis of 22 groundwater samples was conducted to evaluate the quality of groundwater for drinking purpose. Ten hydrochemical parameters were selected for computing WQIs of groundwater samples and BIS standards were used for standardization. Analytical results show that 100 % of samples have magnesium and total hardness above the acceptable limit of BIS (10500, 2012). The mean concentration of nitrate was found 196.96 ± 9.3.18 mg/L. Spatial distribution of various parameters shows that south-east part of study area has contaminated groundwater. The results of water quality index shows that drinking water quality of 82 % (WAWQI), 95 % (GWQI) and 77 % (CCME WQI) of groundwater samples were poor to unsuitable type. Water quality index map also reveals that groundwater of village Badhpura, Dhoom Manikpur, Dairy Maccha, Khera Dharampura, Bisrakh road, Duryai and Dujana of district Gautam Budh Nagar of India, was not fit for human consumption.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".