Nome da Revista: Environmental
Research
Classificação: B1
Dossiê Temático: Biotechnological approaches to
empower the pollution remediation: Environmental perspectives
Prazo: 31/01/2021
Titulação: Não informada.
Environmental biotechnology promises to provide sustainable solutions to remediate the emerging anthropogenic pollutants caused by the growing industrialization and population. To achieve this, a substantial understanding of these emerging pollutants and their environmental impact along with the exploration of environmentally feasible remediation approaches are warranted. Biotechnological mediated pollutant remediation is facing an uprising as they offer sustainable pollutant management solutions with a circular bioeconomy feasibility. Despite these advantages, practically- and economically-viable biotechnological strategies at the field level and are lacking. Thus, it is important to investigate the key techno-biological factors limiting the development of bioremediation methods and exemplify the biotechnological strategy for remediating the emerging pollutants. This special issue solicits papers to unify the cutting-edge multidisciplinary research to address these biotechnological issues but not limited to the following topics:
Metabolic impact of emerging pollutants on biological systems in the polluted environment · Elucidating the mechanisms underpinning the bio-reduction of toxic pollutants · Development of biodegradable materials to alleviate the adverse effect of recent industrialization · Demonstration of sustainable strategies towards the green synthesis of commercially important materials · Isolation of microorganisms for biodegradation and their evolutionary analyses · Genetic improvement of biological systems with enhanced biodegradability and bioresorbability · Identification of the critical metabolic node(s) in chassis strain(s) and elucidating its mechanistic role in the key biochemical degradation pathway · Uncovering the ecological roles of uncultured microbes in their natural habitats using high-throughput analysis