Moroccan water resources under pressure: Challenges of groundwater quality and nitrate contamination
Bibliographic record
Abstract
Study region Country of Morocco, north-western part of Africa. Study focus In numerous regions worldwide, groundwater serves as the primary source of drinking water supply. However, the increase of industrial and agricultural activities, as well as the succession of drought periods in regions such as Morocco have led to water scarcity and the introduction of pollutants into ground and surface water. Nitrate (NO₃⁻), due to its high solubility, has become a pervasive contaminant, presenting an increasingly detrimental issue over time, particularly in arid environments such as Morocco. Hence, the goal of this work is to give an overview of groundwater and surface water quality across Morocco. Specifically, the research aims to identify aquifers suffering from nitrate contamination, determine the origin of this contamination, and assess the suitability of this groundwater for both drinking and irrigation purposes. In general, Moroccan rivers exhibit good to moderate water quality, except for localized areas downstream of domestic and industrial discharge points. Dams generally maintain excellent to moderate water quality, with only 5 % showing bad quality attributed to wastewater discharge. On the other hand, groundwater witnesses a deterioration in their quality, primarily due to elevated nitrate levels and high salinity. Nitrate contamination is prevalent in many aquifers in Morocco with the highest concentrations exceeding 100 mg/l, indicating very bad quality. This degradation stems from domestic and industrial wastewater discharge and excessive fertilizer use in irrigated areas. Additionally, nitrate contamination worsened by seawater intrusion, observed in Atlantic aquifers like Chaouia, Doukkala and Akermoud, especially within 2000 m of the sea. Furthermore, isotopic analyses of δ¹ ⁵N–NO₃ and δ¹ ⁸O–NO₃ in both the Mediterranean aquifers “BouAreg” and Atlantic aquifers “Massa” indicate that the main sources of nitrate are likely manure, sewage, and agricultural fertilizers used in irrigated areas. The denitrification phenomenon was detected in some wells of the Massa aquifer characterized by an enrichment of ¹ ⁵N and low NO₃⁻ content, however this process only affects one well in the BouAreg aquifer. This comprehensive overview might serve as a reference for decision-makers to build efficient water management strategies.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".