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Key Concepts

The Triad manages hazardous waste site decision-making uncertainty through systematic planning, dynamic work strategies, and real-time measurement technologies.

The Triad approach offers a new paradigm for addressing the uncertainties associated with hazardous waste site decision-making, and the design and implementation of sampling programs intended to support site decisions. This paradigm consists of three principal components:

The Triad approach emphasizes managing decision uncertainty, rather than simply analytical uncertainty. For example, the Triad recognizes that the contribution of sampling uncertainty to decision uncertainty often dwarfs the contribution of analytical uncertainty. Given this fact, the Triad focuses on data collection methods that can increase spatial coverage for an area without sacrificing sample representativeness or driving up total sampling program costs. The Triad uses a weight of evidence approach to decision-making where appropriate, based on collaborative data sets. Collaborative data sets can contain data from a number of sources, including quantitative and screening analytical methods.

A Triad approach can be applied equally effectively to either statistical or judgmental sampling programs. The use of dynamic work strategies and real-time measurement technologies allows data collection programs and field activities to adapt or adjust to site conditions and analytical results as they are encountered. This, in turn, focuses work on site decision-making needs, and provides a mechanism for responding to unexpected site conditions or data quality problems while the work is underway. This ultimately means that with a Triad approach data collection can continue until decision quality goals have been achieved and decisions confidently made.

From a sampling program perspective, the goal of the Triad is to produce effective or decision quality data (i.e., data that are of known quality, that are defensible, and that support the decision that needs to be made) in a manner that is as cost and schedule-efficient as possible.

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