| Abstract: | Large quantities of data are collected and evaluated throughout every stage of the remedial process. The usability of these data is often a function of the perceived “quality” of the data, with good data considered usable and bad data considered unusable. The assessment of data quality and usability has traditionally focused on analytical and other direct measurement errors and uncertainty. However, problems with the data that are measurement-related are usually a relatively minor portion of the total error and uncertainty. Error and uncertainty are introduced throughout every aspect of any remedial process, including planning errors, measurement errors, and interpretation errors. Although each error component is important, the errors and uncertainty associated with the design, collection, and interpretation of data are often much greater than measurement-related errors. Nevertheless, there is typically a disproportionate level of effort expended addressing the minor types of measurement errors when compared to other more important error components in the remedial process. However, the key to obtaining optimum data use requires the general redirection of Data Quality Assurance (DQA) activities from measurement-related errors/uncertainty to other important planning and interpretation elements. The elements considered essential for developing an effective approach for maximizing data usability include: focused project planning activities stressing a rigorous data quality objective process and a geostatistical approach to the design of the data collection program; the development of sensible and appropriate data validation/review objectives; and the development of realistic error and uncertainty limits for measurement/analytical activities. |
