Abstract
Enhanced population growth, rapid industrialization, urbanization and hazardous industrial practices have resulted in the development of environmental pollution in the past few decades. Heavy metals are one of those pollutants that are related to environmental and public health concerns based on their toxicity. Effective bioremediation may be accomplished through “ex situ” and “in situ” processes, based on the type and concentration of pollutants, characteristics of the site but is not limited to cost. The recent developments in artificial neural network and microbial gene editing help to improve “in situ” bioremediation of heavy metals from the polluted sites. Multi-omics approaches are adopted for the effective removal of heavy metals by various indigenous microbes. This overview introspects two major bioremediation techniques, their principles, limitations and advantages, and the new aspects of nanobiotechnology, computational biology and DNA technology to improve the scenario.
Highlights
Environmental pollution is increasing rapidly due to urbanization and industrialization
The technique is considered cost-effective and it provides a permanent solution that is less expensive compared to other physicochemical methods and has become more prevalent in treating soils contaminated by heavy metals
The methods of “in situ” bioremediation are preferred for restoring contaminated soil and water environments (Jørgensen et al, 2007) as they involve the mechanisms of removing target pollutants from the natural environment with the help of the metabolic potential of the microbial systems without the process of excavation of contaminated samples (Fruchter & Demian, 2002)
Summary
Environmental pollution is increasing rapidly due to urbanization and industrialization. Pollution nature, depth of pollution, location and cost are some of the criteria which are taken into consideration while choosing any bioremediation technique (Frutos et al, 2012; Smith et al, 2015). Apart from these criteria, temperature, pH, nutrient and oxygen concentration determines the success of bioremediation. Heavy metal contaminated soil or aquatic system can be remediated with the help of “ex situ” and “in situ” techniques. “Ex situ” techniques are complex and have a higher cost compared to “in situ” techniques This mini-review would focus on various mechanisms of “in situ” and “ex situ” bioremediations of heavy metals by microbial systems. It will focus on the latest systems on machine learning, deep learning and artificial neural networks (ANN) for understanding the mechanisms of bioremediations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Environmental Engineering and Landscape Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
