Natural Attenuation of Groundwater Contaminants: New Paradigms, Technologies, and Applications

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Cleaning up the large number of groundwater contamination sites is a significant and complex environmental challenge. The environmental industry is continuously looking for remediation methods that are both effective and cost-efficient. Over the past 10 years there have been amazing, important developments in our understanding of key attenuation processes and technologies for evaluating natural attenuation processes, and a changing institutional perspective on when and where Monitored Natural Attenuation (MNA) may be applied. Despite these advances, restoring groundwater contaminated by anthropogenic sources to allow for unrestricted use continues to be a challenge. Because of a complex mix of physical, chemical, and biological constraints associated with active in-situ cleanup technologies, there has been a long standing focus on understanding natural processes that attenuate groundwater contaminant plumes.
We will build upon basic environmental science and environmental engineering principles to discover how to best implement MNA as a viable treatment for groundwater contamination plumes. Additionally we will delve into the history behind and make predictions about the new directions for this technology.
Any professional working in the environmental remediation industry will benefit from this in-depth study of MNA. We will use lectures, readings, and computational exercises to enhance our understanding and implementation of MNA. At the completion of this course, students will have updated understanding and practical tools that can be applied to all possible MNA sites.

From the lesson

Modeling Tools to Support MNA

In this series of lectures, we talk about models and how they can be used to understand MNA. We first start with two key MNA questions: How Long? (will the plume get) and How Far? (how long until the site is clean). Then a review of analytical computer models, remediation timeframe models, the more complex but more powerful numerical models, and even a discussion of the “Fourth Paradigm: Big Data”.