Polysaccharides have emerged as a promising material for hydrogel preparation due to their biocompatibility, biodegradability, and low cost. This review focuses on polysaccharide-based hydrogels’ synthesis, characterization, and applications. The various synthetic methods used to prepare polysaccharide-based hydrogels are discussed. The characterization techniques are also highlighted to evaluate the physical and chemical properties of polysaccharide-based hydrogels. Finally, the applications of SAPs in various fields are discussed, along with their potential benefits and limitations. Due to environmental concerns, this review shows a growing interest in developing bio-sourced hydrogels made from natural materials such as polysaccharides. SAPs have many beneficial properties, including good mechanical and morphological properties, thermal stability, biocompatibility, biodegradability, non-toxicity, abundance, economic viability, and good swelling ability. However, some challenges remain to be overcome, such as limiting the formulation complexity of some SAPs and establishing a general protocol for calculating their water absorption and retention capacity. Furthermore, the development of SAPs requires a multidisciplinary approach and research should focus on improving their synthesis, modification, and characterization as well as exploring their potential applications. Biocompatibility, biodegradation, and the regulatory approval pathway of SAPs should be carefully evaluated to ensure their safety and efficacy.

The purpose of this study is to evaluate the efficiency of hybrid constructed wetlands (HCWs) in a rural mountainous area. The experiment was set up in small rural community named Tidili within the region of Marrakech, Morocco. The wastewater treatment plant was composed of three vertical flow constructed wetlands (VFCWs) working in parallel, followed by two parallel horizontal-subsurface flow constructed wetlands (HFCWs), with hydraulic loading rates of 0.5 and 0.75 m³/m².d, respectively. The two units were planted with Phragmites australis at a density of 4 plants/m². Wastewater samples were collected at the inlet of the storage tank and at the outlet of the whole system (VFCWs, HFCWs) stages. The main removal percentages of total suspended solids (TSS), biochemical oxygen demand measured in a 5-day test (BOD₅), chemical oxygen demand (COD), total nitrogen, and total phosphorus were respectively 95%, 93%, 91%, 67%, and 62%. The system showed a very high capacity to remove total coliforms, fecal coliforms, and fecal streptococci (4.46, 4.31, and 4.10 Log units, respectively). Artificial neural networks (ANNs) were used to model the quality parameters (TSS, BOD₅, COD) and total coliforms and fecal streptococci. Based on the obtained results, the ANN model could be considered as an efficient tool to predict the studied phytoremediation performances using HCWs.

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.