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2024-01-31_REVISION - M1977344 (30)
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2024-01-31_REVISION - M1977344 (30)
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Last modified
2/7/2024 8:26:25 AM
Creation date
2/6/2024 8:44:08 AM
Metadata
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Template:
DRMS Permit Index
Permit No
M1977344
IBM Index Class Name
Revision
Doc Date
1/31/2024
Doc Name Note
App 4.4 Holcim RCQ SAP
Doc Name
Adequacy Review - Preliminary
From
Holcim
To
DRMS
Type & Sequence
AM2
Email Name
TC1
MAC
Media Type
D
Archive
No
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Quality Assurance Project Plan <br />Red Creek Quarry Monitoring Program Section 4: DQOs and Criteria <br /> <br /> <br />4-3 <br /> <br />4.3 Data Representativeness <br />Representativeness is the degree to which the sample data accurately and precisely represent site <br />conditions. The representativeness criterion is best satisfied by confirming that sampling locations <br />are properly selected, a sufficient number of samples are collected, sample collection procedures <br />are appropriate and consistently followed, and analytical results meet DQOs. The monitoring <br />programs described in the SAP have been designed to provide data that are representative of the <br />sampling media and a sufficient number of samples to meet the project DQOs. Sampling procedures <br />will follow the sampling procedures in the project-specific SOPs. <br />Representativeness is evaluated during data review, verification, validation, and reconciliation efforts <br />by comparing the combination of data accuracy, precision, measurement range, and methods and <br />assessing other potential sources of bias, including sample holding times, reported results of blank <br />samples, and laboratory QA review. <br />4.4 Data Comparability <br />Comparability is the confidence with which one data set can be compared to another data set. Use of <br />standard sampling and analytical procedures will maximize comparability. For data comparability, <br />sample collection procedures will be consistently followed, the same analytical procedures will be <br />used, and the same laboratory, which is certified by NELAC, will be used to analyze the samples. <br />4.5 Data Completeness <br />Completeness is calculated by subtracting the number of rejected and unreported results from the <br />total planned results and dividing by the total number of planned results, expressed as a percentage. <br />Estimated results do not count against completeness because they are considered to be usable as <br />long as limitations are identified. Results rejected because of out-of-control analytical conditions, <br />matrix effects, broken or spilled samples, or samples that could not be analyzed for other reasons <br />are subtracted from the total planned number of results. A completeness goal of 90 percent or <br />greater is established for the Project. If a sampling event does not meet the completeness goal, the <br />data will be discussed with the program manager and a course of action will be agreed upon. Any <br />required departure from this goal will be justified and explained in the project records. <br /> <br />
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