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Elements of a QAPP

According to EPA guidance, 24 distinct elements can be included in a QAPP, although not all elements may be necessary for all programs. Which elements you end up including in your QAPP depends on your project's goals, objectives, scope, data uses, and on the guidance you receive from your state or EPA regional quality assurance contacts. The 24 elements are grouped into four overall categories:

A. Project Management

  1. Title and Approval Page
  2. Table of Contents
  3. Distribution List
  4. Project/Task Organization
  5. Problem Identification/ Background
  6. Project/Task Description
  7. Data Quality Objectives for Measurement Data
  8. Training Requirements/Certification
  9. Documentation and Records

 

B. Measurement/Data Acquisition

  1. Sampling Process Design
  2. Sampling Methods Requirements
  3. Sample Handling and Custody Requirements
  4. Analytical Methods Requirements
  5. Quality Control Requirements
  6. Instrument/Equipment Testing, Inspection, and Maintenance Requirements
  7. Instrument Calibration and Frequency
  8. Inspection/Acceptance Requirements for Supplies
  9. Data Acquisition Requirements
  10. Data Management

 

C. Assessment and Oversight

  1. Assessment and Response Actions
  2. Reports

 

D. Data Validation and Usability

  1. Data Review, Validation, and Verification Requirements
  2. Validation and Verification Methods
  3. Reconciliation with Data Quality Objectives

 

There are several basic Quality Assurance and Quality Control concepts to understand and address in a QAPP.   The first is called the "PARCC" Parameters.  Taken together, the terms Precision, Accuracy, Representativeness, Completeness, and Comparability, comprise the major data quality indicators used to assess the quality of your data. It is essential to understand these terms and to address them in your QAPP.

It is equally important to understand the terminology of quality assurance and quality control in order to develop a QAPP. Key definitions include:

 

Precision -- the degree of agreement among repeated measurements of the same characteristic. It may be determined by calculating the standard deviation, or relative percent difference, among samples taken from the same place at the same time.

 

Accuracy -- measures how close your results are to a true or expected value and can be determined by comparing your analysis of a standard or reference sample to its actual value.

 

Representativeness -- the extent to which measurements actually represent the true environmental condition or population at the time a sample was collected.

 

Completeness --the comparison between the amount of valid, or usable, data you originally planned to collect, versus how much you collected.

 

Comparability -- the extent to which data can be compared between sample locations or periods of time within a project, or between projects.”