Comment la composition chimique peut influencer la couleur des roches magmatiques et sédimentaires: Cas des roches du Haut Atlas de Maroc

peer reviewedIn spite of color being one of the physicochemical parameters most commonly used to characterize a rock, very limited studies have studied the correlation between the nature, chemical composition, and color of a rock. This study presents a new approach for quantitatively assessing the relationship between these three parameters for specific rocks (example of igneous and sedimentary rocks) collected from the High Atlas of Morocco. A spectrophotometer was used to measure the color of samples, and the measurements were expressed in CIE L*a*b* color system units then converted to Hex color codes. Whereas, the chemical composition of samples was carried out by X-ray fluorescence. The most abundant oxides in magmatic rock samples are SiO2, Al2O3, Fe2O3, MgO, and CaO, while K2O, Na2O, TiO2, and P2O5 are generally found in trace concentrations. Two categories of clays were studied, non-calcareous raw materials without carbonate contents ( 10%). Phosphate samples are rich in phosphorus (4.4%-17.5%) and CaO (11.2%-42.7%) with relatively low contents of SiO2 (28.5%-52.2%), Al2O3 (3.1%-17.5%), and Fe2O3 (1.1%-6.6%). Results show that the change in the content of these elements from one rock type to another may be indicative of rocks with particular characteristics that do have an impact on color. The main coloration agent of clays was iron, Fe2+, and Fe3+ ions can color clay minerals either red or green or in various shades of orange and brown. However, in marls and phosphates, the high concentration of carbonates inhibits this iron effect by affecting a* (red) and b* (yellow) color parameters, which leads to grayish materials. The same applies to magmatic rocks rich in Fe2O3 and CaO

Acidogenic digestion of organic municipal solid waste in a pilot scale reactor: Effect of waste ratio and leachate recirculation and dilution on hydrolysis and medium chain fatty acid production

The purpose of this investigation is to study the effect of 1) leachate recirculation (LR) and dilution (LD), 2) the increment of the waste ratio, and 3) co-digestion using poultry waste as co-substrate for enhancing the acidogenesis process of the organic municipal solid waste (OMSW), thereby increasing the medium chain fatty acids (MCFAs) production. The results of the experiment carried in a batch pilot reactor show that LR increases hydrolysis as well as the OMSW conversion efficiency. Meanwhile, the highest total MCFA production of around 62,000 mg/L is shown for LR with a high OMSW ratio. Thus, a high concentration of hexanoic acid is shown in LR lysimeters (5475 mg/L, 6627 mg/L, and 10,889 mg/L respectively). However, a metabolic shift toward the production of heptanoic and octanoic acids is reported for LD samples. Nonetheless, the use of poultry waste as co-substrate in the co-digestion process multiplied the concentration of the produced MCFAs

Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region

Effect of Alternating Well Water with Treated Wastewater Irrigation on Soil and Koroneiki Olive Trees

The use of treated wastewater (TWW) in irrigation has a positive impact by bringing fertilizers and organics. However, increases in the soil’s sodium adsorption ratio (SAR) creates a barrier to long-term TWW irrigation. Alternating well water with wastewater irrigation is one practical solution that could be used to address the problem. This work aims to study the effect of alternating two years of well water with two years of treated wastewater irrigation on the soil characteristics of a Koroneiki olive tree mesocosm. Urban and agri-food wastewater treated using various technologies, such as lagooning, activated sludge, multi-soil-layering, and constructed wetlands, were used for irrigation. The results showed that an increase in salinity (SAR and ESP) in soil and olive tree leaves are the main negative effects of continuous irrigation with TWW on soil and tree performance. Several chemical and biochemical parameters, such as SAR and Na+ concentration, demonstrated that alternating well water with treated wastewater irrigation can reverse these negative effects. This recovery effect occurs in a relatively short period of time, implying that such a management practice is viable. However, long-term well water application reduces soil fertility due to the leaching of organics and exchangeable ions

The Use of Artificial Neural Network and Advanced Statistics to Model Sediment Yield on a Large Scale: Example of Morocco

Morocco ranks among countries with the greatest achievements in the field of dams in Africa but is affected by the sedimentation phenomenon due to soil erosion in upstreams. The assessment of Sediment Yield (SY) and Suspended Sediment Yield (SSY) remains a challenging global issue, especially in Morocco, characterized by a great diversity of morphological, climatic, and vegetation cover. The main objective of this paper was to perform advanced statistics and artificial neural networks (ANN) in order to understand the spatial distribution of sediment yield and the factors most controlling it, including factors of the RUSLE model (Revised Universal Soil Loss Equation). In order to produce a model able to assess SY, we collected and analyzed extensive data of most variables that can be affecting SY using 42 catchments of the biggest and important dams of Morocco. Statistical analysis of the studied watersheds shows that SY is mainly related to the watershed area and the length of the drainage network.  On the other hand, the SSY is higher in watersheds where gully erosion is abundant and lower in areas with no soil horizon. The SSY is mainly related to the altitude, aridity index, sand fraction, and drainage network length. In front of the complexity of preserving this phenomenon, the ANN was applied and gave very good satisfactory results in predicting the SSY (NSE=0.93, R2=0.93)

Artificial Intelligence and Wastewater Treatment: A Global Scientific Perspective through Text Mining

The concept of using wastewater as a substitute for limited water resources and environmental protection has enabled this sector to make major technological advancements and, as a result, has given us an abundance of physical data, including chemical, biological, and microbiological information. It is easier to comprehend wastewater treatment systems after studying this data. In order to achieve this, a number of studies use machine learning (ML) algorithms as a proactive approach to solving issues and modeling the functionalities of these processing systems while utilizing the experimental data gathered. The goal of this article is to use textual analysis techniques to extract the most popular machine learning models from scientific documents in the “Web of Science” database and analyze their relevance and historical development. This will help provide a general overview and global scientific follow-up of publications dealing with the application of artificial intelligence (AI) to overcome the challenges faced in wastewater treatment technologies. The findings suggest that developed countries are the major publishers of articles on this research topic, and an analysis of the publication trend reveals an exponential rise in numbers, reflecting the scientific community’s interest in the subject. As well, the results indicate that supervised learning is popular among researchers, with the Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), Linear Regression (LR), Adaptive Neuro-Fuzzy Inference System (ANFIS), Decision Tree (DT), and Gradient Boosting (GB) being the machine learning models most frequently employed in the wastewater treatment domain. Research on optimization methods reveals that the most well-known method for calibrating models is genetic algorithms (GA). Finally, machine learning benefits wastewater treatment by enhancing data analysis accuracy and efficiency. Yet challenges arise as model training demands ample, high-quality data. Moreover, the limited interpretability of machine learning models complicates comprehension of the underlying mechanisms and decisions in wastewater treatment

Potential of medium chain fatty acids production from municipal solid waste leachate: Effect of age and external electron donors.

A large quantity of leachate is generated during municipal solid waste collection operation and in landfills due to the large amount of organic waste and high humidity. The content of medium chain fatty acids (MCFAs) in the leachate is a low cost feedstock for bio-based chemical and fuel production processes. The aim of this study is to investigate the MCFA production potential of three leachate ages through chain elongation process under uncontrolled pH batch test. Moreover, the effect of using different external electron donors (ethanol, methanol and a mixture of both) is studied. The experiment consists of characterizing the samples then adding external electron donors with a specific ratio to leachate samples under mesophilic temperature. For this investigation, also a statistical analysis is done, which shows the production of MCFAs is highly influenced by leachate age. The results indicate that the production of even-numbered acids increase from 600 to 1,000 mg/L by the end of the ethanol chain elongation experiment for young leachate. However, a higher MCFA production of more than 1,000 mg/L is achieved by using the mixture of methanol and ethanol as electron donor. Furthermore, all methanol chain elongation experiments lead to an odd-numbered production of MCFAs, such as pentanoic and heptanoic acids. These results confirm the potential improvement of MCFA production from leachate through choosing the optimal leachate age and electron donor. Overall, producing MCFAs from leachate is a good example of circular bio-economy because waste is used to produce biochemicals, which closes the material cycle

Soil Salinity Detection and Mapping in an Environment under Water Stress between 1984 and 2018 (Case of the Largest Oasis in Africa-Morocco)

Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4–5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil.. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R2) and the Nash–Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% 2, respectively, while a decrease of about 50% is observed during the periods of 1996–2000 and 2005–2018

Soil Salinity Detection and Mapping in an Environment under Water Stress between 1984 and 2018 (Case of the Largest Oasis in Africa-Morocco)

Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4–5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil.. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R2) and the Nash–Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% < 10%, and the low value of the root mean square error (RMSE) confirms its very good performance. The drought cyclicity led to the intensification of the soil salinization process and accelerated soil degradation. The standardized precipitation anomaly index (SPAI) is strongly correlated to soil salinity. The hydroclimate condition is the factor that further controls this phenomenon. An increase in salinized surfaces is observed during the periods of 1984–1996 and 2000–2005, which cover a surface of 11.50 and 24.20 km2, respectively, while a decrease of about 50% is observed during the periods of 1996–2000 and 2005–2018