Assessing surface water quality in Fayoum, Egypt using an integrated WQI-GIS approach for multi-purpose reuse
Bibliographic record
Abstract
Water quality management remains a critical challenge in arid and semi-arid regions, where limited freshwater resources are increasingly stressed by anthropogenic activities and natural constraints. This study provides a summer 2024 assessment of surface water quality in Egypt’s Fayoum Governorate, emphasizing spatial variability, dominant pollution drivers, and sectoral suitability. Ten sites across agricultural drains and wastewater discharge points were analyzed for 17 physicochemical parameters. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was applied to evaluate drinking, irrigation, industrial, and ecological uses, while spatial patterns were mapped using Inverse Distance Weighting (IDW) in a Geographic Information System. The results reveal critical exceedances in salinity (TDS up to 3,420 mg/L; EC up to 6,840 μS/cm), nutrient enrichment (PO 4 3− up to 10.85 mg/L; NH 3 –N up to 10.78 mg/L), and turbidity (105 nephelometric turbidity units), mainly from untreated sewage, agricultural return flows, and limited dilution. WQI classification for drinking water showed 30% good, 50% fair, and 20% poor (<45), with S9 and S10 posing high health risks due to cumulative sewage, industrial discharges, and intensive farming runoff. For irrigation, 60% of sites were rated good, though elevated sodium, magnesium hazard, and potential salinity indicate risks of soil degradation. Industrial assessments revealed scaling (LI >0) and corrosion (RSI >8.5) in more than half the samples. Ecologically, 50% of sites recorded poor WQI (<45), reflecting eutrophication, organic load, and elevated temperatures. CCME-WQI/GIS mapping identified S9 and S10 as hotspots, concentrated near Lake Qarun where pollutant accumulation is intensified by weak hydrological flushing. The contrasting signatures of nutrient-enriched agricultural drains and salinity-dominated industrial reaches underscore the need for targeted interventions. Strengthening wastewater treatment, optimizing fertilizer use, enforcing standards, and enhancing public awareness are recommended. The integrated CCME-WQI/GIS framework offers a replicable tool for sustainable water management in arid, agriculture-dependent regions and supports progress toward Sustainable Development Goal 6.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".