Assessing the Suitability of CCME WQI as a Groundwater Quality Monitoring Tool: An Environmental Ergonomics Case Analysis
Bibliographic record
Abstract
This study was conducted with the aim of protecting groundwater, which plays a crucial role in ensuring food quality in the market, preserving public health, and safeguarding the ecosystem, as many regions rely on clean natural groundwater for their population’s survival. The objective of this study was to use the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for groundwater at 12 stations in the Okhla Industrial Area, Nangloi, and Karol Bagh in the Delhi Region. CCME WQI is an effective tool for assessing groundwater quality and communicating water conditions to various users. The research methodology involved fieldwork from June to October 2020 for three different periods in the year: pre-monsoon, monsoon, and post-monsoon, to observe variations in water quality and differences in various physicochemical properties of water. The CCME WQI was applied using sixteen water quality parameters, fourteen of which were physicochemical parameters and two of which were microbiological parameters. Among the physicochemical parameters were color, odor, pH, turbidity, nitrate, total hardness, iron, chloride, fluoride, total dissolved solids, calcium, magnesium, sulfate, and alkalinity, while the microbiological parameters included the total coliform and Escherichia coli counts. Based on the results obtained from the water quality index, station A9 scored between 0 and 44, indicating the lowest water quality index due to wastewater discharges and industrial contamination. The water quality at other stations also requires attention to achieve excellent ratings. The study concludes that serious measures should be taken for proper management of the area to protect the population from hazardous diseases. The research results show that stations 1, 2, and 10 were rated as excellent, station 12 as good, stations 4, 5, and 8 as moderate, stations 3, 6, and 11 as marginal, and station 9 as the poorest in terms of water quality in the year 2020 during the pre-monsoon, monsoon, and post-monsoon periods. To improve the parameters and groundwater quality, it would be necessary to reduce the impact of industry, anthropogenic–geogenic activities, and domestic activities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".