Assessment of industrial pollution and water quality in the Nile River using GIS-based indices at Aswan, Egypt
Bibliographic record
Abstract
Industrial pollution represents a critical threat to freshwater ecosystems, particularly in regions where rivers serve multiple socio-economic functions. The Nile River in Aswan Governorate, southern Egypt, is a vital source of drinking water, irrigation, fisheries, and tourism, yet it is increasingly exposed to untreated industrial effluents. This study assessed the spatial extent and intensity of such pollution using geographic information system (GIS)-based spatial interpolation alongside two comprehensive indices: the weighted arithmetic water quality index (WAWQI) and the canadian water quality index (CWQI). Findings revealed distinct pollution hotspots near major industrial discharge areas, where water quality was classified as poor to very poor (WAWQI: 52.6-127.4; CWQI: 74.2-68.3). In contrast, upstream and downstream zones more distant from effluent sources exhibited good water quality (WAWQI: 24.5-38.1; CWQI: 93.69-85.52). The most degraded sites were characterized by elevated concentrations of chemical oxygen demand (13.1-59.6 mg/L), biochemical oxygen demand (7.67-36.5 mg/L), total suspended solids (4.67-13.1 mg/L), turbidity (6.01-17.1 NTU), total nitrogen (1.13-3.35 mg/L), and phenol (8.01-10.10 µg/L). These results highlight the ecological vulnerability of the Nile River in Aswan to industrial activities, with direct implications for ecosystem health and resource sustainability. They also underscore the limitations of relying solely on traditional water quality monitoring without spatial analysis, as GIS-based interpolation proved critical in detecting pollution gradients beyond sampling sites. The study supports the enforcement of Egypt's Law 48/1982 on water protection and contributes to global sustainable development goals, particularly SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water).
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".