Data Quality Objectives (DQOs) definition

Data Quality Objectives (DQOs) means performance and acceptance criteria that clarify study objectives, define the appropriate type of data, and specify tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support decisions. Unless otherwise approved by the Secretary, the DQOs shall be prepared consistent with EPA Guidance documents; Guidance on Systematic Planning Using the Data Quality Objectives Process EPA QA/G-4, EPA/240/B-06/001, February 2006; Guidance for Developing Quality Systems for Environmental Programs EPA QA/G-1, EPA/240/R-008, November 2002; and any subsequent revisions or editions.
View Source
Data Quality Objectives (DQOs) means qualitative and quantitative statements derived from the DQO process that clarify study objectives, define the appropriate type of data, and specify tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support decisions. The planning process for ensuring environmental data are the type, quality, and quantity needed for decision making is called the DQO process.
View Source

Examples of Data Quality Objectives (DQOs) in a sentence

  • Existing data are used whenever they meet the Data Quality Objectives (DQOs) for the decision being made, or can be validated with minimal additional supporting data of higher quality.

  • This section will include identification of Data Quality Objectives (DQOs) for the new data collected under this program.

  • The Program Chemist will act as a POC on all chemistry-related issues and shall be responsible for ensuring that all Data Quality Objectives (DQOs) are met.

  • The parties also agreed during the course of negotiations to hold further discussion on potential efficiencies in the regulatory Data Quality Objectives (DQOs).

  • Data Quality Objectives (DQOs) are quantitative and qualitative statements that specify the tolerable levels of potential errors in the data (U.

  • The overall objective of this project was to deliver a high accuracy SI traceable measurement infrastructure to underpin direct measurements of NO2 amount fractions in the atmosphere to meet the Data Quality Objectives (DQOs) established by the World Meteorological Organisation Global Atmospheric Watch (WMO-▇▇▇) programme.

  • The Project Chemist will act as a POC on all chemistry-related issues and shall be responsible for ensuring that all Data Quality Objectives (DQOs) are met.

  • Data Quality Objectives (DQOs) are qualitative and quantitative statements which specify the purpose, quality, and/or quantity of the environmental data required to support management and remedial decisions at each site.

  • The Contractor shall develop project specific Data Quality Objectives (DQOs) consistent with the project’s requirements and designed to ensure data of adequate quality are collected to support project decisions.

  • Another important component of this process is for the EPA Clermont XLC Project Team to clearly define and clarify all of EPA’s technical requirements and QA terms, including EPA's use of such terms as Data Quality Objectives (DQO's) and Quality Performance Criteria.