Evaluation of Shiraz wastewater treatment plant effluent quality for agricultural irrigation by Canadian Water Quality Index (CWQI)
Bibliographic record
Abstract
BACKGROUND: Using treated wastewater in agriculture irrigation could be a realistic solution for the shortage of fresh water in Iran, however, it is associated with environmental and health threats; therefore, effluent quality assessment is quite necessary before use. The present study aimed to evaluate the physicochemical and microbial quality of Shiraz wastewater treatment plant effluent for being used in agricultural irrigation. In this study, 20 physicochemical and 3 microbial parameters were measured during warm (April to September) and cold months (October to march). Using the measured parameters and the Canadian Water Quality Index, the quality of the effluent was determined in both warm and cold seasons and in all the seasons together. RESULTS: The calculated index for the physicochemical parameters in the effluent was equal (87) in warm and cold months and it was obtained as 85 for the seasons all together. When the microbial parameters were used in order to calculate the index, it declined to 67 in warm and cold seasons and 64 in all the seasons together. Also, it was found that three physicochemical parameters (TDS, EC, and NO3) and three microbial parameters (Fecal coliform, Helminthes egg, and Total coliform) had the most contribution to the reduction of the index value. CONCLUSIONS: The results showed that the physicochemical quality of Shiraz Wastewater Treatment Plant Effluent was good for irrigation in the warm, cold, and total of the two kinds of seasons. However, by applying the microbial parameter, the index value declined dramatically and the quality of the effluent was marginal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 teacher head, 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".