Development of Canadian Water Quality Index (WQI) of Vidhyadhari, an Estuarine River in Eastern India
Bibliographic record
Abstract
Surface water represents one of the most utilized sources for water distribution systems globally, despite the fact that rapid urbanization and industrialization has reduced its cleanliness. As a result, the end-user's health is seriously impacted by the dirty water. Nevertheless, it is clear that many developing nations, including India, pay little regard to or care about this crucial issue. The Vidhyadhari River has become a receiver of high organic and bacteriological load of entire Kolkata City through Basanti canal. As a result, the purpose of this investigation is to evaluate how rapid urbanization and industrialization has changed the water quality of Vidhyadhari River using Canadian Water Quality Index (WQI). We have used seasonal water quality data collected at two monitoring stations (Haroa bridge and Malancha) from 2011 to 2020 to study the water quality of Vidhyadhari River. Results of the study indicated that the annual WQI value of Haroa brigde ranged between 21.62 (very bad) and 62.89 (medium) with an average of 27.29 (bad), whereas annual WQI value of Malancha station ranged between 15.44 (very bad) and 43.09 (bad) with an average of 18.77 (very bad). In comparison to downward location, the water quality of Vidhyadhari River was somehow good at upstream i.e., Haroa brigde. According to WQI, the water quality of Vidhyadhari River fall into bad to very bad category, which indicated deterioration of river water quality. Factor analysis revealed that both stations are predominated by hardness cluster (hardness, calcium, magnesium and chloride) followed by cluster of total dissolved solids (TDS), sulphate and ammonia. Sewer, excessive human activity, industrial discharges, poor sanitation, and urban runoff outflow can be extrapolated as the main causes for the deterioration of Vidhyadhari River water quality. This study emphasized the significance of implementing measurement actions, introduction of watershed characteristics and implications for developing water management strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".