Quality Assurance Project Plan Illinois TMDL Watershed Sampling Page 38
are consistent with the method requirements as specified in the laboratory’s SOPs and that
the QA/QC requirements for each method are met. Examples of method requirements include
verifying the calibration and data reduction procedures. However, these requirements vary by
analyte and are presented in more detail in the laboratory QA/QC Manual.
4.1.2. Data Review Requirements
The Field Manager will perform data reviews that consist of screening the field data sheets
and laboratory data sheets according to established criteria listed in this section. If the
established screening criteria are not met, an additional review of available laboratory data
(e.g., quality control checks, relevant laboratory bench sheets) may be conducted.
Investigation of the issue will be documented and the data will be discarded or flagged
appropriately, identifying the limitations of the data.
Field Data Sheet Reviews. The following criteria may be used to screen the physical
parameter measurements recorded by the field crews:
• temperature readings – check for reasonableness of values
• pH readings – check for reasonableness of values
• dissolved oxygen readings –compare concentrations to percent saturation
Laboratory Data Sheet Reviews. The following criteria will be used to screen the
analytical measurements performed by the contract laboratory:
• equipment blanks –values should be less than detection limits
• method blanks –values should be less than detection limits
• field blanks – are values less than detection limits
• review of all analytical results – check for reasonableness of values
4.1.3. Data Validation Requirements
Data validation is typically performed by someone independent of the project activity and
not associated with the organization responsible for producing the dataset. However, the data
validator needs to be familiar with both the data validation requirements and the project
objectives. A scientist/engineer not directly involved in the project administration, project
management, field or laboratory operations will conduct the data validation. There are four
requirements in the data validation process as follows:
• Inspect the data verification and review records to ensure that no oversights were made
during that process.
• Evaluate the data against the project DQOs. If data do not meet one or more of the
DQOs, the data validation process will include an investigation into causes and an
assessment of the impact of the noncompliant data on project objectives.
• Evaluate the data in the context of the project’s overall objectives.
• Communicate the data validation results to the rest of the project team.
4.2. Verification and Validation Methods (D2)
All environmental measurement data and samples collected by project staff will be subjected
to quality control prior to being entered into the project database. This is a multi-step process
where the laboratory QA/QC Manager will have primary responsibility for verifying the data
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