Laserfiche WebLink
<br /> <br /> <br />10-1 <br /> <br />Section 10 <br />Assessment and Oversight <br />Assessments conducted throughout the course of the Project will be implemented as outlined in the <br />following sections. <br />10.1 Assessments and Response Actions <br />Periodic assessments will be conducted so that data are collected according to requirements <br />presented in this QAPP. The QAO will have the primary responsibility for assessing compliance with <br />the QAPP and SAP/SOP requirements pertaining to sample collection and handling procedures, field <br />analytical procedures, and laboratory analytical procedures. In addition, the QAO is responsible for <br />assessing compliance with the QAPP, SAP, and SOPs. The QAO will review field sampling and analysis <br />procedures at the beginning of the project. Laboratory analyses will be continually assessed by <br />evaluating the results of QC samples. <br />If an assessment reveals discrepancy in the methodology used to collect data or the analytical <br />results, the QAO will discuss the discrepancy with the PM to determine if the data are accurate, the <br />cause of the discrepancy, how the discrepancy impacts data quality, and the corrective action. The <br />QAO will then follow up so that the corrective action is implemented, and data are qualified as <br />needed. <br />The QAO has the power to stop sampling and analytical work by both sampling personnel and <br />contract laboratories if the discrepancies noted are determined to be detrimental to data quality. <br />10.2 Laboratory Assessment and Oversight <br />Data quality will be evaluated based on sampling techniques and analytical QC. The laboratory is <br />NELAC-certified; therefore, formalized audits of laboratory systems will not be performed as part of <br />the Project. Informal audits of field work will be sufficient to ensure that SOPs are being followed. <br />Performance of both field and laboratory QA systems will be assessed based on results of laboratory <br />and field QC samples. A general evaluation of data quality will consider potential sources of error <br />including gross errors, systematic errors, and random errors. <br /> <br /> <br />