Investigation of heavy metal contamination and ecological and health risks in farmland soils from southeastern phosphate plateaus of Khouribga (Morocco)

The present study was conducted in the SE area of phosphate plateaus (Khouribga) located in central Morocco. It attempted to assess the heavy metal (HM) (Cd, Cr, Cu, Pb, Zn) contamination in the farmland soils and their potential ecological hazard and non-non-carcinogenic risks using various pollution indices, magnetic susceptibility (MS), and Geographical Information System (GIS) methods. A total of 41 soil samples were collected and analyzed for pH, electrical conductivity (EC), grain-size, organic matter (OM), calcium carbonate (CaCO3), and MS and HM elements. The results showed a mean dominance order of Zn>Cr>Cu>Pb>Cd where mean concentrations of HMs, except Pb, exceeded their local background and Food and Agriculture Organization (FAO) and World Health Organization (WHO) permissible guidelines. The values of geo-accumulation index (Igeo), nemerow pollution index (PI), and pollution load index (PLI) revealed significant high level of HM contamination in soils. The MS values showed a spatial distribution pattern similar to those of HMs, attesting the ability of the MS method for mapping the contaminated soils. Agricultural and mining activities and geologic materials were the main sources of HM accumulation. According to the potential ecological risk index (RI) (195.93<RI<1092.53), the soil samples had moderate (65.85%) to high ecological (34.15%) risk. The hazard index (HI) showed that adults and children are not exposed to non-carcinogenic risk from the studied HMs, apart from two soil samples where Cd posed health risks to children compared to the other studied HMs. The statistical results revealed that soils are polluted by anthropogenic activities. Accordingly, effective agricultural practices that respect the environment, including the reduction of inputs as fertilizers, pesticides, herbicides, and fungicides should be required to guarantee the safety of cropland and the residents in the studied area. Hence, the findings from this study provided some useful information for soil pollution control and management in the study area

Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers

Text summarization based on conceptual data classification

In this paper, we present an original approach for text summarization using conceptual data classification. We show how a given text can be summarized without losing meaningful knowledge and without using any semantic or grammatical concepts. In fact, concept date classification is used to extract the most interacting sentences from the main text and ignoring the other meaningless sentences in order to generate the text summary. The approach is tested on Arabic and English texts with different sizes and different topics and the obtained results are satisfactory. The system may be incorporated with the indexers of search engines over the Internet in order to find key words and other pertinent information of the new deployed Web pages that would be stored in databases for quick search.Scopu

Déclaration d'Errachidia et lignes directrices pour le développement durable des écosystèmes oasiens

Les actes de la 1re Rencontre scientifique internationale sur les oasis, ISMO 2023, se sont concentrés sur le thème ''Quel apport de la recherche scientifique pour la sauvegarde et le développement des oasis?'' et ont eu lieu à Errachidia du 20 au 22 novembre 2023. The proceedings of the 1st International Scientific Meeting on Oases, ISMO 2023, focused on the theme ''What contribution does scientific research make to the preservation and development of oases?'' and took place in Errachidia from November 20 to 22, 2023