analysis of data (Environmental biotechnology ) full eassy.
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Environmental biotechnology refers to the use of microorganisms to improve the environmental quality and so far it has focused on the development of technologies to clean up the aquatic, terrestrial and aerial environment. This article is an overview of environmental applications of biotechnology. Environmental remediation, pollution prevention, detection and monitoring with regard to the achievements are among the perspectives in the development of biotechnology. A variety of relevant topics have been chosen to illustrate each of the main areas of environmental biotechnology: wastewater treatment, soil treatment and treatment of gaseous pollutants, using microbiological processes. The distinct role of environmental biotechnology in the future would be to contribute with new solutions and directions in the remediation of polluted environments, to minimize future waste release, and to create pollution prevention alternatives.
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Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health, and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting 10 top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems.
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