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EN
The needles of the Aleppo pine (Pinus halepensis) (PA) are very abundant in the forest of the National Park of Tazekka (Ta-za-Morocco) and are unexploitable. Moreover, they constitute a potential danger because they facilitate the outbreak of wildfires. To solve this problem, we have considered turning these needles into a biomaterial that could be used as raw material for different uses, such as wastewater treatment. The biomaterial of the Aleppo pine is obtained from its needles which are harvested in spring, dried, cut, crushed, and sieved. The powder obtained is analyzed before and after the extraction of essential oil. The physicochemical and spectroscopic analyses show that this biomaterial is porous, hygroscopic, slightly acidic, moderately moist, and not very conducive. Its average density in the anhydrous state is 0.6. It is rich in carbon (79.91%) and oxygen (18.91%) in the form of aromatic compounds and ketone imprints; thus, relating the presence of cellulose, pectin, lignin, and hemicellulose. Its composition in mineral elements (Na, Mg, Ca, K, Cl, S) is deficient. Gas chromatography-mass spectrometry (GC-MS) analysis of the oils extracted from the needle powder relates that it is a complex mixture of bioactive compounds such as mono-terpenoid α and β-pinene hydrocarbons. These results show that our biomaterial can be used as an adsorbent in wastewater treatment and the extracted essential oils can be used in the pharmacological, agro-food field.
EN
In the present review article, a soil from the region of Fez-Sefrou Morocco was screened for some physicochemical characteristics using the Plackett-Burman model in order to determine the most important factors that promote its fertility. Five independent variables were selected: pH, electrical conductivity, humidity, organic matter, and C/N ratio. These variables were evaluated by statistical analysis, based on their significance, the value of the coefficient of determination and the Pareto chart. The results suggest that humidity and C/N ratio have an influence with a high level of confidence, while the other three show no significant effect on the content of nutrients in the soil. The analysis of the R2 variance value also showed that the models used for prediction were large and significant factors (p less than 0.05). Pareto chart plots for each response and its characteristics provided accurate data to select well-fitting variables for further optimization.
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