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Geosynte& <br /> consultants <br /> 6. DATA REVIEW AND REPORTING <br /> The following section identifies the data review, verification, validation, and reporting <br /> requirements for the project used to demonstrate that the data quality is sufficient to meet <br /> the project objectives. <br /> 6.1 Review of Field Monitoring Data <br /> Data measured by field instruments will be recorded on Daily Field Reports(DFRs), field <br /> laptops or tablets, and/or on required field forms. Field data will be reviewed and verified <br /> by the Field Operations Supervisor to evaluate completeness of the field records and <br /> appropriateness of the field methods employed. <br /> 6.2 Data Verification and Validation for Laboratory Data <br /> Laboratory data generated during this planned work will be subjected to a QA/QC review <br /> performed according to Geosyntec internal data quality control procedures. This review <br /> will include a verification of data completeness, correctness, and compliance to the <br /> project requirements as well as validation of the analytical quality of the data by <br /> accepting, qualifying, or rejecting data on the basis of sound criteria using established <br /> U.S. EPA guidelines and other applicable sources. <br /> Specifically,this QA/QC review will consist of the following: <br /> • Review of data package completeness and compliance with holding times and <br /> sample preservation requirements for each method; <br /> • Review of the practical quantitation limits (PQLs)/reporting limits (RLs) and/or <br /> method detection limits (MDLs) achieved by each method to assess analytical <br /> sensitivity compared to project-specific requirements; <br /> • Evaluation of data accuracy based on surrogate recoveries; LCS/LCS duplicate <br /> (LCSD) percent recoveries; and results of any method blanks, trip blanks, field <br /> blanks, and equipment blanks reported; <br /> • Evaluation of data precision based on LCS/LCSD and field duplicate <br /> sample/primary sample result RPDs; <br /> • Assessment of other QA/QC issues documented in the data deliverables; and <br /> • Application of standard data validation qualifiers to the data. <br /> Quality Assurance Project Plan 10 30 April 2025 <br />