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' Project Timetable <br /> Activity Projected Start Date Anticipated Completion Date <br /> Obtain soil/water from quarry 2/4/00 Completed <br /> Prep columns 2/16/00 Completed <br /> ' Initiate column leaching tests 2/25/00 4/30/00 <br /> Submit supplemental information to 2/25/00 5/30/00 <br /> ' regulatory agencies <br /> ' 1.7 Measurement Quality Objectives <br /> Quality assurance (QA) is a management system for ensuring that all information, data, and <br /> decisions based upon the interpretation of the analytical data are technically sound and properly <br /> ' documented. Quality control (QC) is the mechanism whereby the QA system is ensured. The QA <br /> system is presented in this QAPP. The goal of the environmental data collection is to produce data <br /> capable of withstanding scientific scrutiny and of a quality appropriate for a specific task. This will <br /> allow CGRS, Inc. to fully assess the impact of past activities and target analytes, i.e., identification, <br /> quantification, and delineation of the extent. <br /> ' Data quality refers to the level of uncertainty associated with a data set. Data quality objectives <br /> (DQOs) for each task will reflect the amount of uncertainty in the data that will be acceptable to <br /> meet the goals of the program and the objectives of the task. Project and task DQOs are discussed <br /> in Section 1.8. Techniques to validate and verify the quality of the data are presented in Section 4.2. <br /> ' Quality assurance objectives differ for individual sample matrix groups and parameters by site. The <br /> QA objectives will be based on a common understanding of the intended use of the resulting data, <br /> available laboratory procedures, and available resources. Special attention must be paid to the <br /> detection limits and holding times. These limits are sometimes insufficient for the analysis of <br /> drinking water,groundwater,and/or soils. <br /> 1.8 Data Quality Objectives <br /> DQOs are qualitative and quantitative statements that specify the quality of the data required to <br /> ' support decisions made during the project. DQOs are applicable to collection activities and are <br /> based on the end use of the data being collected. DQOs will be described in detail within individual <br /> sampling and analyses plans(SAPS). <br /> ' Precision, Accuracy, Representativeness, Completeness and Comparability (PARCC) parameters <br /> are indicators of data quality. The end use of the measurement data should define the necessary <br /> PARCC parameters. Numerical precision, accuracy, and completeness goals must be established <br /> ' in each site SAP and will aid in selecting the measurement methods. <br /> 1.8.1 Data Precision and Accuracy <br /> ' Precision is a measurement of the reproducibility of a measurement under a given set of conditions. <br /> The closer the numerical values of the measurements, the more precise is the overall measurement. <br /> Precision will be stated in terms of the standard deviation for three or more measurements of the <br /> ' percent difference for two measurements, depending on the necessary precision of a particular <br /> study. Laboratory precision will be within established control limits for a particular analytical <br /> Varra Companies <br /> Quality Assurance Project Plan <br /> Page t <br />