Development of a health risk-based weighted water quality index using multivariate statistical analysis: a case study from Taichung's Dajia River Basin, Taiwan
Bibliographic record
Abstract
ABSTRACT Conventional water quality indices (WQIs), such as the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), typically assign equal weights to all parameters – a practice that may obscure the health relevance of pollutants with chronic toxicity. This study proposes a health risk-weighted variant of the CCME-WQI that integrates multivariate statistical analysis and toxicological criteria to enhance public health responsiveness. Using long-term monitoring data from the Dajia River Basin in Taichung, Taiwan (2003–2024), 30 physical and chemical parameters were analyzed using principal component analysis and factor analysis to reduce redundancy and identify key indicators. Ten core parameters were selected based on statistical contribution and health risk thresholds, including hazard quotient (HQ > 1), cancer risk (CR > 10−4), and IARC classifications. Risk-based weights were assigned accordingly. Seasonal validation showed strong agreement between the optimized and original CCME-WQI models (RMSE = 7.72; p = 0.610), while improving sensitivity to high-risk contaminants such as arsenic, lead, and cadmium, particularly for children. The proposed framework offers a scalable and resource-efficient tool, making it suitable for both centralized and decentralized water quality management contexts, while supporting health-informed monitoring and contributing to Sustainable Development Goal 6.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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".