Evaluation of the surface water quality using global water quality index (WQI) models: perspective of river water pollution
Bibliographic record
Abstract
Rapid industrialization, urbanization, global warming, and climate change are compromising surface water quality across the globe. Consequently, water conservation is essential for both environmental sustainability and human survival. This study assesses the water quality of the Jamuna River in Bangladesh at five distinct sites during wet and dry seasons. It employs six global water quality indices (WQIs) and contrasts the results with Bangladesh's Environmental Quality Standard (EQS) and the Department of Environment (DoE) criteria. The WQI models used are the Weighted Arithmetic WQI (WAWQI), British Columbia WQI (BCWQI), Canadian Council of Ministers of the Environment WQI (CWQI), Assigned WQI (AWQI), Malaysian WQI (MWQI), and Oregon WQI (OWQI). Fifteen physicochemical parameters were analyzed according to each WQI model's guidelines. The findings reveal that most parameters surpass the standard permissible values. The WQI model results indicate that the average water quality across the five sites falls into the lowest category. A comparison of the WQI models suggests potential correlations between WAWQI and AWQI, as well as between MWQI and OWQI. The straightforward presentation of the WQI models indicates that while the river water requires treatment for household and drinking use, it remains suitable for irrigation. The decline in water quality is likely attributable to human activities, urbanization, municipal waste disposal, and industrial effluents. Authorities must prioritize regular monitoring and assessment of water quality to address the identified challenges. Restoring the water to an acceptable standard will become increasingly difficult without proactive measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".