Groundwater Quality Assessment Using CCME WQI and GIS Technique for Ujjain City, India
Bibliographic record
Abstract
<p>Groundwater is a significant source of freshwater for people all around the world. About 97.2 % of the water on Earth is saline, with only 2.8 % available for usage as fresh water, of which approximately 20 % is groundwater. In India, a large portion of the populace relies on groundwater for drinking. The determination of water quality in residential, commercial, and industrialised areas is of great importance, and for this, the water quality index (WQI) is an effective tool which determines the suitability for drinking water of groundwater. The WQI is described as an index that reflects the combined impact of several water quality parameters that are analysed and accounted for while calculating the water quality index. In the present study, 54 groundwater samples were collected from the 54 wards of Ujjain city, Madhya Pradesh, India, during the summer period of 2019. The Bureau of Indian Standards (BIS, 2012) was used to assess the appropriateness of groundwater for drinking and calculate WQI. The water quality index was calculated using eight water quality parameters, including pH, turbidity, electrical conductivity (EC), total dissolved solids (TDS), alkalinity, chlorides (Cl–), hardness, and fluorides (F–). The objective of the study is to determine the class of all 54 samples using the Canadian Council of Ministers of Environment Water Quality Index (CCMEWQI) into five classes: excellent, good, fair, marginal, and poor. Also, the Geographic Information System (GIS) mapping technique was used to outline the spatial distribution trend of physiochemical properties and predominant ion concentration in groundwater. The obtained results suggest that wards 34 and 39 had the lowest CCMEWQI values of 32.873 and 32.120, respectively, which is unsatisfactory when compared to other wards. As a result, the general water quality of both wards (34 and 39) is poor and completely unfit for direct drinking. The CCMEWQI was found to be marginal in Wards 2, 3, 4, 6, 8, 9, 10, 12, 15, 19, 24, 25, 26, 35, 38, 40, 41, 42, 45, 46, 48, 49, and 53. Wards 5, 8, 11, 13, 14, 21, 22, 23, 28, 29, 30, 31,32, 33, 36, 50, 51, and 54 had fair water quality. CCMEWQI > 79 indicates that the water quality is good, as in Wards 20, 44, and 47. It is concluded from CCMEWQI that 6% of samples were found in the good category. 33% of the ground water samples were found to be in the range of fair quality. Similarly, 41% of samples were marginal, while 20% of samples were found to be poor. In the study area, groundwater is the main source of drinking water, so it must be managed effectively before its quality degrades.</p>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".