Definitive, Screening, and Effective Data

Environmental data may be classified according to the level of associated total error as screening data and definitive data. These two data types are also different with respect to their intended use and QC elements. The selection of the type of data to collect depends on the project DQOs and is made in the planning phase. The realm of possible data uses and methods of data collection are the defining factors in the selection. 

Definitive data are obtained with rigorous analytical methods, such as EPA-approved methods or other standard analytical methods. For the data to be definitive, either analytical or total measurement error must be determined. Definitive data, which are analyte-specific and have a high degree of confidence in analyte identity and concentration, are used for decisions that have consequences for human health and the environment, such as site closure, risk assessment, and compliance monitoring of water effluents and air emissions. Definitive data may be generated at a field (mobile) laboratory or at an off-site (fixed-base) laboratory. 

The use of screening data enables project teams to estimate an environmental condition in a rapid manner and to facilitate real-time decision-making in the field. Screening results usually determine future action at the project site, and that is why screening data, imprecise as they may be, must reliably reflect the true site conditions. Screening data may help formulate important decisions for many types of environmental projects, such as the following  

Screening data are often compared to the action levels for the purpose of making preliminary decisions during project implementation. For final decision-making, only definitive data of acceptable quality should be used for comparisons to the action levels. 

Understanding the nature of the environmental sample and the limitations it places on accuracy and precision of analysis is a critical factor in selecting what type of data to collect. Some matrices are so inherently indefinite that the analytical data produced for them even with the most accurate and precise methods cannot be called definitive, as illustrated in Example 2.4. 

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A4 Project/task organization A5 Problem definition/background 2.1 What are the Data Quality Objectives 2.2 Definitive, screening, and effective data... 

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