Analyzing Riyadh Treated Wastewater Parameters for Irrigation Suitability Through Multivariate Statistical Analysis and Water Quality Indices
Bibliographic record
Abstract
An alternative irrigation water supply that prioritizes quality standards, promotes sustainable water resource management, and uses ecologically friendly approaches is still being researched. The purpose of this study is to evaluate the thirteen physicochemical properties of Riyadh wastewater treatment plants (WWTPs) over eight years for their potential use in irrigation. Wastewater quality was assessed using the Comprehensive Water Pollution Index (CPI) and the Canadian Wastewater Quality Index (CWQI). Principal component analysis and heatmaps were also used to identify trustworthy parameters. The CWQI results, ranging from 72.95 to 95.55%, showed acceptable variations over eight years, indicating adequate quality. The CPI values varied from 0.19 to 0.77. However, the average CPI was determined to be 0.6, indicating that there had been some slight contamination throughout the study. The first and second components (PC1 and PC2) represented 32.6% of the data, revealing a dominant pattern for a better understanding of the effluent characteristics. The effluent parameters loaded onto PC1 were EC, Ca2++Mg2+, NO3, and COD, whereas NH4, DO, and turbidity were loaded onto PC2. The effluent from the Riyadh WWTPs is appropriate for irrigation, highlighting the necessity of TWW for agriculture and supporting Saudi Arabia’s Green Riyadh Initiative.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".