Novel chitosan-based barrier materials for environmental containment: Synthesis, characterization, and contaminant removal capacities and mechanisms.

This study critically appraises employing chitosan as a composite with bentonite, biochar, or both materials as an alternative to conventional barrier materials. A comprehensive literature review was conducted to identify the studies reporting chitosan-bentonite composite (CBC), chitosan amended biochar (CAB), and chitosan-bentonite-biochar composite (CBBC) for effective removal of various contaminants. The study aims to review the synthesis of these composites, identify fundamental properties affecting their adsorption capacities, and examine how these properties affect or enhance the removal abilities of other materials within the composite. Notably, CBC composites have the advantage of adsorbing both cationic and anionic species, such as heavy metals and dyes, due to the cationic nature of chitosan and the anionic nature of montmorillonite, along with the increased accessible surface area due to the clay. CAB composites have the unique advantage of being low-cost sorbents with high specific surface area, affinity for a wide range of contaminants owing to the high surface area and microporosity of biochar, and abundant available functional groups from the chitosan. Limited studies have reported the utilization of CBBC composites to remove various contaminants. These composites can be prepared by combining the steps employed in preparing CBC and CAB composites. They can benefit from the favorable adsorption properties of all three materials while also satisfying the mechanical requirements of a barrier material. This study serves as a knowledge base for future research to develop novel composite barrier materials by incorporating chitosan and biochar as amendments to bentonite.

Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review.

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.