Last edited by Shakashakar
Monday, November 16, 2020 | History

1 edition of Data quality objectives for remedial response activities found in the catalog.

Data quality objectives for remedial response activities

Data quality objectives for remedial response activities

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  • 19 Currently reading

Published by CDM Federal Programs Corp. in Annadale, Va .
Written in English

    Subjects:
  • Hazardous waste sites -- Environmental aspects -- United States.,
  • Hazardous waste sites -- United States -- Management.,
  • Hazardous wastes -- Analysis.,
  • Hazardous wastes -- Standards -- United States.,
  • Quality control.

  • Edition Notes

    Statementprepared by CDM Federal Programs Corporation.
    ContributionsUnited States. Environmental Protection Agency. Office of Emergency and Remedial Response., United States. Environmental Protection Agency. Office of Solid Waste and Emergency Response., United States. Office of Waste Programs Enforcement., CDM Federal Programs Corporation.
    Classifications
    LC ClassificationsT55.3.H3 D28 1987
    The Physical Object
    Pagination1 v. (various pagings) :
    ID Numbers
    Open LibraryOL16815673M


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Data quality objectives for remedial response activities Download PDF EPUB FB2

Data Quality Objectives For Remedial Activities (Development Process), guides the user through the process of developing data quality objectives (DQOs) for site-specific remedial activities. Remedial response activities include remedial investigations (RI).

feasibility studies (FS). remedial design (RD). and remedial action (RA). Get this from a library. Data quality objectives for remedial response activities. [CDM Federal Programs Corporation.; United States.

Environmental Protection Agency. Office of Emergency and Remedial Response.; United States. Office of Waste Programs Enforcement.;]. Data quality objectives for remedial response activities: example scenario (RI/FS activities at a site with contaminated soils and ground water) Author: CDM Federal Programs Corporation.

DATA QUALITY OBJECTIVES PROCESS FOR SUPERFUND Fact Sheet EPA Orderentitled. Policy and Program Requirements to Implement the Mandatory Quality Assurance Program, establishes mandatory quality assurance (QA) requirements for Agency environmental data collection activities.

expanded site inspection/remedial investigation; or recommend a response action such as an emergency/time-critical removal action, a non-time-critical early action, the initiation of the NPL listing process, and/or the initiation of enforcement activities. Advanced Assessment, Phase I: recommend the SEA designation for the site; orFile Size: KB.

The U.S. Environmental Protection Agency (EPA) has developed the Data Quality Objectives (DQO) Process as an important tool for project managers and planners to determine the type, quantity, and quality of data needed to support Agency decisions.

This guidance is the culmination of experiences in applying DQOs in different Program Offices at the EPA. statement is to help define environmental data quality assurance (QA) requirements, for environmental sampling conducted in the course of investigation and cleanup of releases of hazardous substances to the environment.

The agency Quality Management Plan (DEQHQQMP) outlines an overall quality management. Guidelines, the role of systematic planning in the Quality System, the steps. of the Data Quality Objectives (DQO) Process, and the benefits of. applying the DQO Process for an environmental data collection project.

Unless some form of planning is conducted prior to investing the necessary time and. Guide for Developing Data Quality Objectives for Ecological Risk Assessment at DOE Oak Ridge Operations Facilities. This document has been approved by the K Site Technical Information Office for release to the public.

Date: This report has been reproduced directly from the best available by: 2. The focus here is the development and implementation of a quality system which requires all Superfund activities to develop and operate management processes and structures for assuring that the data collected are of known quality.

The Data Quality Objective process is an effective means by which managers and technical staff may plan and design a more efficient QA plan and a more timely .