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An Enhanced Adaptive Management Approach for Remediation of Legacy Mercury in the South River

机译:改进的自适应管理方法在南河遗留汞的修复中

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摘要

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.
机译:未来状况的不确定性以及所采取措施的影响以及资源稀缺性,一直是采用自适应管理策略的驱动力。但是,自适应管理的许多应用因许多缺点而受到批评,其中包括从行动中学习的能力有限以及缺乏考虑利益相关者目标的能力。为了解决这些批评,我们用决策分析方法补充了现有的适应性管理方法,该方法首先为管理选择的初始选择提供信息,然后根据监视信息和利益相关者价值的纳入,定期对管理选择进行重新评估或分阶段实施。我们基于对弗吉尼亚州韦恩斯伯勒附近南河中汞的负载和行为的了解,描述了这种增强的适应性管理(EAM)框架在比较南河中汞的补救替代方案中的应用。结果表明,替代方案的排名受汞加载模型中的不确定性,不同标准的相对重要性以及成本估算的影响。该过程本身表明,决策模型可以将项目绩效标准,决策者的偏好,环境模型以及短期和长期监控信息与管理选择联系起来,以帮助制定补救方法,从而为适应性增量实施提供有用的信息。

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