The Triad approach builds on real-world field, laboratory, and regulatory experiences to construct a second-generation data quality model based on two fundamental principles:
- Environmental matrices should be assumed heterogeneous in composition and contaminant distribution, unless shown to be otherwise.
- "Data quality" is assessed according to the ability of data to provide the data user with information that supports correct decisions.
From these basic principles emerges the need to:
- use a conceptual site model to understand contaminant distributions in environmental matrices;
- manage a full range of sampling and analytical factors that contribute variability to data results; and
- integrate systematic project planning, modern technologies, and technical expertise when generating data to guide management of contaminated sites.
The second-generation data quality model recognizes that perfectly "accurate" analytical results for tiny samples can be misleading if sampling was not dense enough to manage spatial heterogeneity. Standard fixed laboratory methods, at least as currently deployed, are generally too expensive to permit the higher sampling density needed to manage all sources of decision uncertainty. Screening analytical methods and many field-deployable methods can provide higher sampling densities at an affordable cost. In addition, these methods usually provide real-time results, allowing field work to adapt to the realities encountered. Therefore, these tools can play vital roles in developing the conceptual site model and supporting project decisions. With less expensive real-time methods available to manage sampling uncertainties and guide data collection, standard fixed lab analysis can continue to play its traditional role, but on samples selected specifically for their ability to be representative of the intended decision or to clarify ambiguities present real-time measurements.
