Water quality index models and parameters in five different countries (Canada, USA, Australia, Malaysia and Iraq): A review
Bibliographic record
Abstract
Water is an important resource to human health, aquatic ecosystem, socioeconomic development, and food security. Therefore, in every part of the world, the presence of contaminants in natural freshwater causes serious environmental issues. Constant monitoring of water bodies using some quality parameters is required. In this study, turbidity, dissolved oxygen (DO), nitrate, fecal coliform (FC), total solids, pH, biochemical oxygen demand (BOD), temperature and phosphate were assessed. One of the most vital and effective strategies in conveying information related to water bodies is the water quality index (WQI). WQI is primarily a mathematical means of deriving a single value which shows the water quality level in certain water basins, e.g. lake, river or stream. For different regions, many WQIs have already been introduced by national and international agencies for water quality assessment considering various uses and control of contamination. This study focuses on a comprehensive review about WQI models, water quality parameters and comparison of using WQI models in five different countries, namely, Canada, USA, Australia, Malaysia and Iraq. Comparison of water quality and index models through these five countries reveals that the application and modification of WQI models can help to improve water quality. Also the selection of water quality parameters and index models is based on assessment, cost effectiveness and availability of data. Iraq compared to the other four countries used in the study needs more attention to improve water quality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".