Assessment of Groundwater Quality using CCME Water Quality Index in Baramati Tehsil, Maharashtra, India
Bibliographic record
Abstract
Abstract Groundwater quality evaluation is essential in semi-arid regions where significant agricultural demands and varying hydrogeological conditions exert considerable stress on aquifer systems. To determine the suitability of groundwater for irrigation and to evaluate its quality, 15 physicochemical parameters were analyzed for samples collected from Baramati tehsil during both pre- and post-monsoon seasons of 2023. Seasonal groundwater samples were obtained from 66 sites following standard APHA protocols. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was used through Bureau of Indian Standards (BIS) guidelines. The suitability of irrigation was also tested by Sodium Adsorption Ratio (SAR), USSL classification diagrams. The CCME-WQI shows that groundwater quality shows mainly on marginal condition in both seasons, with mean values of 56.64 ± 8.28 (pre-monsoon) and 54.79 ± 7.93 (post-monsoon) respectively. A Wilcoxon signed-rank test showed a small but significant decrease in WQI values after monsoons (p < 0.05), indicating that a monsoonal recharge does not uniformly enhance groundwater quality. The SAR values for most samples were in the low to medium sodium hazard (S1-S2) classes, whereas USSL plots revealed an overwhelming predominance of high salinity (C3-C4) categories, highlighting constraints for irrigation without any management methods. Wilcox classification further showed that the majority of samples ranged from permissible to doubtful for irrigation, with limited seasonal improvement. Overall, the integrated WQI, SAR, and Wilcox evaluations indicate that groundwater in Baramati tehsil is largely marginal and locally unsuitable for unrestricted irrigation, emphasizing the need for site-specific groundwater management and continuous monitoring.
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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 | 0.003 |
| Science and technology studies | 0.000 | 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".