Brown University School of Engineering

SRP Seminar: Linda M. Abriola

Friday, October 11, 2019

12:00pm - 1:00pm

Superfund Research Program

Barus and Holley, 190

In Search of the Silver Bullet: Progress and Perspectives on Contaminated Subsurface Characterization and Restoration

Linda M. Abriola
University Professor
Director, Tufts Institute of the Environment
Tufts University 

Chlorinated solvent contamination of aquifers is a recalcitrant problem that has challenged environmental engineering professionals, regulators, and site managers for decades. When solvents are introduced to the subsurface, whether through accidental spills or leaking containment facilities, they create a persistent contaminant source to flowing groundwater, posing a long term health risk to downstream receptors. Over the past thirty years, a great deal of research has been undertaken to advance our understanding of the migration and fate of these chlorinated compounds (also known as dense nonaqueous liquids or DNAPLs) and to develop innovative methods for their destruction and/or recovery. Despite these advances, however, it is now generally accepted that no single technology will result in complete mass removal. In addition, future progress in the management of sites containing DNAPL source zones is hampered by the difficulties associated with characterizing the location and distribution of DNAPL mass, commonly termed ‘architecture’, which tends to control contaminant plume evolution and longevity.

This presentation provides an overview of interdisciplinary research designed to improve our ability to predict the migration and fate of DNAPLs in natural subsurface formations and to develop improved methodologies for site characterization and management. Numerical simulations and experimental observations are used to illustrate advances in our understanding of the hydrologic and abiotic and biotic transformation processes influencing DNAPL transport and persistence. The presentation highlights the severe challenges, posed by the presence of natural heterogeneities, to reliable predictions of subsurface system behavior. Recent research results demonstrate the potential utility of innovative statistical and machine learning methods for site characterization and risk assessment.