Evaluation of Surface Water Quality of the Gorganrood River Basin for Different Uses with Canadian (CCME) Water Quality Index Approach
Bibliographic record
Abstract
Water quality characteristics play a crucial role in water resources management, watershed health assessment, and implementing effective management strategies. The objective of this research was to present an overall assessment of the surface water quality in the Gorganrood River Basin to be utilized for developing effective watershed management plans and programs. Various physicochemical water quality data including main anions and cations, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Sodium Absorption Ratio (SAR), pH, and total hardness recorded at 25 hydrometric stations across the basin were analyzed and assessed with the Canadian (CCME) Water Quality Index. The mean water quality index for drinking, agriculture, and industrial purposes indicated that headwaters and higher areas generally exhibited better water quality compared to the downstream areas of the basin. Geochemical processes and the introduction of various pollutants during water flow from the headwaters to the basin outlet contribute to a decline in water quality. The highest water quality was observed in the Kabudval and Shirabad stations, whereas the Baghesalian station exhibited the lowest. For drinking water use, hardness, bicarbonate, and chloride were identified as variables contributing to water quality decline in the headwaters and upstream areas. However, these areas predominantly maintained a moderate to good quality for drinking purposes. Conversely, downstream areas experienced a significant deterioration in water quality with higher pollutant levels such as total dissolved solids (TDS), sulfate, and sodium, resulting in relatively poor to poor conditions. Approximately 60% of the stations in the basin had excellent water quality for agricultural use, with no limiting factors. Only three stations near the basin's outlet exhibited relatively poor to poor water quality due to elevated chloride levels, sodium adsorption ratio (SAR), and electrical conductivity. only 28% of hydrometric stations demonstrated good water quality for industrial use. Hardness, pH, and TDS are the main variables contributing to water quality decline for industrial use in the upstream, while downstream areas are impacted by chloride and sulfate. The outcomes of this study hold significant implications for effective water resources management, watershed preservation, and natural resource conservation in the Gorganrood basin. From industry and especially health aspects, however, more detailed investigations are needed, taking into account some other important variables of water quality (including nitrate, total coliform, fecal coliform, etc.).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".