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PLoS One. 2015 Feb 9;10(2):e0117140. doi: 10.1371/journal.pone.0117140. eCollection 2015.

An enhanced adaptive management approach for remediation of legacy mercury in the South River.

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United States Army Engineer Research and Development Center Duty Station: U.S. Army Corps of Engineers New England District, Concord, MA 01742, United States of America.
Contractor for the United States Army Engineer Research and Development Center, Concord MA 01742, United States of America.
DuPont Corporate Remediation Group, Wilmington, DE 19805, United States of America.


Uncertainties about future conditions and the effects of chosen actions, as well as increasing resource scarcity, have been driving forces in the utilization of adaptive management strategies. However, many applications of adaptive management have been criticized for a number of shortcomings, including a limited ability to learn from actions and a lack of consideration of stakeholder objectives. To address these criticisms, we supplement existing adaptive management approaches with a decision-analytical approach that first informs the initial selection of management alternatives and then allows for periodic re-evaluation or phased implementation of management alternatives based on monitoring information and incorporation of stakeholder values. We describe the application of this enhanced adaptive management (EAM) framework to compare remedial alternatives for mercury in the South River, based on an understanding of the loading and behavior of mercury in the South River near Waynesboro, VA. The outcomes show that the ranking of remedial alternatives is influenced by uncertainty in the mercury loading model, by the relative importance placed on different criteria, and by cost estimates. The process itself demonstrates that a decision model can link project performance criteria, decision-maker preferences, environmental models, and short- and long-term monitoring information with management choices to help shape a remediation approach that provides useful information for adaptive, incremental implementation.

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