Temporal Variation in Water Quality Assessment Using WQI Methods: A Case Study of Alhussein Water Treatment Plant in Karbala
Bibliographic record
Abstract
Surface water quality, particularly in rivers and lakes, has long been deteriorating due to various factors, including anthropogenic and natural activities.Herein, the Alhussein Water Treatment Plant is selected as a case study to assess the quality of treated water pumped to residents in Karbala City.The Weighted Arithmetic Index (WAI) method and the Canadian Council of Ministers of the Environment (CCME) method are used to calculate the Water Quality Index (WQI).The water quality and efficiency of the Alhussein Water Treatment Plant are assessed based on seven chemical and physical parameters: Total Dissolved Solids (TDS), pH, Turbidity, Sulphates (SO₄), Electrical Conductivity (EC), Chloride, and Total Hardness.Four measurement points were selected at different distances from the Alhussein Water Treatment Plant at three different times: 9 am, 1 pm, and 4 pm.Based on the current findings, regarding the measurement time in the early morning (9 am), water quality ranges from good to excellent.Generally, the water quality of the plant is acceptable and can be trusted for various uses, as the average WQI across all measurement sites is 74.This study recommends that investigating the specific sources of pollution is essential for devising targeted mitigation strategies, such as improving wastewater treatment and reducing industrial or agricultural runoff.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".