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U.S. Department of Energy Memorandum: Institutionalizing the Data Quality Objectives Process for EM's Environmental Data Collection Activities

 
   Date:    07-Sep-94


Reply to
Attn of:    EM-263 (Carter: 301-427-1677)


Subject:    Institutionalizing the Data Quality Objectives Process
			for EM's Environmental Data Collection Activities


	 To:    Distribution


			To balance Department of Energy (DOE) environmental sampling and analysis 
			costs with the need for sound environmental data that address regulatory 
			requirements and stakeholder concerns, the Department must implement 
			approaches to streamline procedures, minimize time requirements, and 
			eliminate unnecessary costs associated with current environmental sampling 
			and analysis activities.  Accordingly, it is the policy of the Office of
			Environmental Management (EM) to apply up-front planning, where practical, 
			to ensure safer, better, faster, and cheaper environmental sampling and 
			analysis programs for all EM projects and operations.  Specifically, it is 
			EM policy that the Data Quality Objectives (DQO) process be used in all 
			environmental projects where there may be a need to collect significant
			environmental data.  The DQO process has already been adapted to site 
			characterization and remediation in DOE's Streamlined Approach for 
			Environmental Restoration (SAFER) program.  In addition, the Office of 
			Waste Management is developing guidance to apply the DQO planning process 
			to efficiently define and integrate process knowledge information with 
			sampling and analysis to streamline and expedite the activities needed to 
			meet regulatory requirements and public concerns.

			The DQO Process, defined by the U.S. Environmental Protection Agency (EPA), 
			is a series of planning steps to identify and design more efficient and 
			timely data collection programs.  The DQO process relies heavily on 
			customer and supplier communication to define data requirements and 
			acceptable levels of errors in decision making before major resources are 
			expended on data collection and to assure the customer (whether internal or
			external) is satisfied with the results.  The DQO process is outlined in 
			Attachment A.  An example of how to integrate process knowledge into the 
			DQO process is described in Attachment B.

			In October 1993, the EPA issued its interim final "Guidance for Planning 
			for Data Collection in Support of Environmental Decision Making Using the 
			Data Quality Objectives Process" (EPA QA/G-4).  This document provides 
			excellent guidance on the steps of the DQO process for developing data 
			quality criteria and performance specifications for data operations.  The 
			EPA's Superfund program has tailored that guidance to its specific 
			programmatic needs with its interim final guidance on "Data Quality 
			Objectives Process for Superfund" (EPA/540/G-93/071, September 1993).  
			Attachment C contains these two EPA guidance documents.

			In your application of the DQO process, expertise of the Analytical Services 
			Division (ASD), EM-263 is available to provide technical assistance.  ASD can 
			provide consultation on specific issues and help EM programs develop 
			independent capability for planning sound sampling and analysis projects.

			ASD has been providing training and direct technical assistance to the field 
			in using the DQO process to plan, implement, and assess site characterization, 
			waste characterization, and new remedial action technology.  Implementing the 
			DQO process has successfully achieved substantial cost savings for waste 
			characterization and environmental remediation projects at Hanford, Oak Ridge, 
			and Savannah River.

			The DQO process has application for environmental sampling and analysis 
			planning EM-wide, including environmental restoration, waste management, 
			facility deactivation, decontamination and decommissioning, and technology 
			development projects.  Some potential EM applications of the DQO process 
			include:

				  *  Waste Management
						-  Characterizing waste, using process knowledge verified by minimal
						   necessary sampling and analysis data, to meet acceptance criteria
						   for treatment, storage, and disposal
						-  Designing optimized monitoring networks for groundwater and
						   surface water discharges, and air emissions

				  *  Environmental Restoration
						-  Focusing regulatory and public concerns relating to remediation
						-  Effectively identifying target analytes of concern for remedial 
						   activities
						-  Determining when remediation has met cleanup levels

				  *  Facility Transition and Management
						-  Performing characterization assessments, using existing
						   information or collecting new data, to verify facilities for 
						   acceptance into the EM program
						-  Evaluating alternative end-state conditions and planning facility 
						   deactivation in preparation for eventual decontamination and 
						   decommissioning
						-  Designing optimized short- and long-term environmental
						   surveillance and monitoring

				  *  Decontamination and Decommissioning
						-  Determining location and levels of facility contamination
						-  Determining when decontamination and decommissioning is complete

				  *  Technology Development
						-  Determining what constitutes and acceptably demonstrates success 
						   in technology development and evaluation

			For further information and assistance in implementing EM's policy for 
			institutionalizing the DQO process across environmental data collection 
			activities, please contact the Analytical Services Division, EM-263, 
			at 301-427-1677 or 301-903-7945 (voice mail).


									   Thomas P. Grumbly
									   Assistant Secretary for
									   Environmental Management

 
									ATTACHMENT A

					 Data Quality Objectives (DQO) Planning Process

		 The DQO planning process consists of seven key steps:

			   1.    State the Problem:  Stakeholders work together to define their 
					 concerns and issues based on descriptions of the site, waste 
					 stream, issue, etc., and agree on the question or problem to be 
					 studied.

			   2.    Identify the Decision:  Stakeholders design the answer or result 
					 that will answer the question or solve the problem, including the 
					 threshold level for action.

			   3.    Identify Inputs to the Decision:  Stakeholders define the 
					 measurements needed to answer the question.

			   4.    Define the Boundaries:  Stakeholders define the time and space 
					 circumstances covered by the decision.

			   5.    Develop a Decision Rule:  Technical staff and stakeholders develop
					 the formulation to obtain the needed data (quality and quantity) 
					 and to identify acceptability or confidence in the ultimate 
					 decision.

			   6.    Specify Acceptable Limits on Decision Errors:  In concert with 
					 Step 5, stakeholders define the tolerance for making incorrect 
					 decisions.

			   7.    Optimize Data Design:  Technical staff identifies the most 
					 resource effective data collection design.

		 Implementation of the DQO process forces data suppliers and data users to 
		 consider the following questions:

			   *  What decision has to be made ?

			   *  What type and quality of data are required to support the decision? 

			   *  Why are new data needed for the decision ?

			   *  How will new data be used to make the decision ?

		 The DQO planning process has several notable strengths.  It brings together 
		 the right players (stakeholders and technical staff) at the right time to gain 
		 consensus and commitment about the scope of the project.  This interaction 
		 results in a clear understanding of the problem, the actions needed to address 
		 that problem, and the level of uncertainty that is acceptable for making 
		 decisions.  Through this process, data collection and analysis are optimized 
		 so only those data needed to address the appropriate questions are collected.  
		 Similarly, SAFER represents an enhancement of the DQO planning process by 
		 combining planning with remedy selection and implementation, resulting in a
		 process for addressing all facets of an environmental restoration project.
 


 
									ATTACHMENT B

	  DECIDING WHEN TO APPLY PROCESS KNOWLEDGE OR PERFORM SAMPLING AND ANALYSIS FOR 
								 WASTE CHARACTERIZATION 
				  -- EMPLOYING THE DATA QUALITY OBJECTIVES (DQO) PROCESS

	  Efficient characterization of DOE's large number of waste streams relies on 
	  information obtained from process knowledge (PK), sampling and analysis (S&A), 
	  or from a combination of both.  PK means applying knowledge of the hazard 
	  characteristic of the waste in light of the materials or the processes used 
	  (40 CFR 262.11(a)(2)).  This would include knowledge about the origin, 
	  storage, use, and potential exposure of the waste material.  For example, if 
	  the feed stocks for making a product are known, the likely range of chemical 
	  components in the waste streams can be predicted.  This information may be 
	  sufficient to address regulatory concerns.

	  To the extent applicable, using PK to characterize waste is more cost 
	  effective than S&A.  What is needed is a logical approach to identify the 
	  sufficiency of PK to characterize a particular waste and what is the minimum 
	  amount of new S&A data necessary.  The Data Quality Objectives (DQO) planning 
	  process focuses on these specific questions.  DQO planning helps stakeholders 
	  decide what questions require characterization information and determine 
	  whether those questions can be answered by existing PK, if the PK must be 
	  supported by new S&A data, or if new data are required because PK is entirely 
	  inadequate.  The first several steps of the DQO process help determine whether 
	  existing PK is adequate to characterize the waste.  If PK is determined 
	  insufficient, the last few steps of the DQO process lay out the new data needs 
	  and optimize the S&A design.

	  Attached is a flow chart illustrating the basic framework for applying DQO 
	  planning for waste characterization.  First the issues and regulatory drivers 
	  that form the basis for the need to characterize a waste are identified.  Then 
	  the questions, possible answers, and data needs for addressing those issues 
	  and drivers are formulated.  Waste characterization issues and regulatory 
	  drivers may involve determining whether a waste stream meets treatment, 
	  storage, or disposal waste acceptance criteria.  Questions to address these 
	  issues focus on whether PK satisfactorily determines the waste content.  
	  Questions about validity of PK may include how accurate is the knowledge of 
	  the waste generation process, can the waste be traced to the point of origin, 
	  has any significant degradation of the waste occurred, has anything been added 
	  to the waste, and has there been adequate QA/QC of the PK determination.

	  Once questions and data needs are determined, relevant existing data are 
	  compiled.  Existing data include both PK or S&A information already available.  
	  Existing data may be obtained from manifests of the subject waste or waste 
	  generated from areas or processes matching the subject waste, or from 
	  knowledge about the specific process that generated the waste.  When all 
	  relevant existing information is compiled, it is evaluated to determine if it
	  is sufficient to meet the data needs established during the first several 
	  steps of the DQO process.  If existing information is sufficient, no further 
	  characterization should be required.  If not, then new data are necessary, and 
	  a S&A plan based on the determined data needs should be designed.
 



 
   Date:    07-Sep-94


Reply to
Attn of:    EM-263 (Carter: 301-427-1677)


Subject:    Institutionalizing the Data Quality Objectives Process
			for EM's Environmental Data Collection Activities


	 To:    Distribution


			To balance Department of Energy (DOE) environmental sampling and analysis 
			costs with the need for sound environmental data that address regulatory 
			requirements and stakeholder concerns, the Department must implement 
			approaches to streamline procedures, minimize time requirements, and 
			eliminate unnecessary costs associated with current environmental sampling 
			and analysis activities.  Accordingly, it is the policy of the Office of
			Environmental Management (EM) to apply up-front planning, where practical, 
			to ensure safer, better, faster, and cheaper environmental sampling and 
			analysis programs for all EM projects and operations.  Specifically, it is 
			EM policy that the Data Quality Objectives (DQO) process be used in all 
			environmental projects where there may be a need to collect significant
			environmental data.  The DQO process has already been adapted to site 
			characterization and remediation in DOE's Streamlined Approach for 
			Environmental Restoration (SAFER) program.  In addition, the Office of 
			Waste Management is developing guidance to apply the DQO planning process 
			to efficiently define and integrate process knowledge information with 
			sampling and analysis to streamline and expedite the activities needed to 
			meet regulatory requirements and public concerns.

			The DQO Process, defined by the U.S. Environmental Protection Agency (EPA), 
			is a series of planning steps to identify and design more efficient and 
			timely data collection programs.  The DQO process relies heavily on 
			customer and supplier communication to define data requirements and 
			acceptable levels of errors in decision making before major resources are 
			expended on data collection and to assure the customer (whether internal or
			external) is satisfied with the results.  The DQO process is outlined in 
			Attachment A.  An example of how to integrate process knowledge into the 
			DQO process is described in Attachment B.

			In October 1993, the EPA issued its interim final "Guidance for Planning 
			for Data Collection in Support of Environmental Decision Making Using the 
			Data Quality Objectives Process" (EPA QA/G-4).  This document provides 
			excellent guidance on the steps of the DQO process for developing data 
			quality criteria and performance specifications for data operations.  The 
			EPA's Superfund program has tailored that guidance to its specific 
			programmatic needs with its interim final guidance on "Data Quality 
			Objectives Process for Superfund" (EPA/540/G-93/071, September 1993).  
			Attachment C contains these two EPA guidance documents.

			In your application of the DQO process, expertise of the Analytical Services 
			Division (ASD), EM-263 is available to provide technical assistance.  ASD can 
			provide consultation on specific issues and help EM programs develop 
			independent capability for planning sound sampling and analysis projects.

			ASD has been providing training and direct technical assistance to the field 
			in using the DQO process to plan, implement, and assess site characterization, 
			waste characterization, and new remedial action technology.  Implementing the 
			DQO process has successfully achieved substantial cost savings for waste 
			characterization and environmental remediation projects at Hanford, Oak Ridge, 
			and Savannah River.

			The DQO process has application for environmental sampling and analysis 
			planning EM-wide, including environmental restoration, waste management, 
			facility deactivation, decontamination and decommissioning, and technology 
			development projects.  Some potential EM applications of the DQO process 
			include:

				  *  Waste Management
						-  Characterizing waste, using process knowledge verified by minimal
						   necessary sampling and analysis data, to meet acceptance criteria
						   for treatment, storage, and disposal
						-  Designing optimized monitoring networks for groundwater and
						   surface water discharges, and air emissions

				  *  Environmental Restoration
						-  Focusing regulatory and public concerns relating to remediation
						-  Effectively identifying target analytes of concern for remedial 
						   activities
						-  Determining when remediation has met cleanup levels

				  *  Facility Transition and Management
						-  Performing characterization assessments, using existing
						   information or collecting new data, to verify facilities for 
						   acceptance into the EM program
						-  Evaluating alternative end-state conditions and planning facility 
						   deactivation in preparation for eventual decontamination and 
						   decommissioning
						-  Designing optimized short- and long-term environmental
						   surveillance and monitoring

				  *  Decontamination and Decommissioning
						-  Determining location and levels of facility contamination
						-  Determining when decontamination and decommissioning is complete

				  *  Technology Development
						-  Determining what constitutes and acceptably demonstrates success 
						   in technology development and evaluation

			For further information and assistance in implementing EM's policy for 
			institutionalizing the DQO process across environmental data collection 
			activities, please contact the Analytical Services Division, EM-263, 
			at 301-427-1677 or 301-903-7945 (voice mail).


									   Thomas P. Grumbly
									   Assistant Secretary for
									   Environmental Management

 
									ATTACHMENT A

					 Data Quality Objectives (DQO) Planning Process

		 The DQO planning process consists of seven key steps:

			   1.    State the Problem:  Stakeholders work together to define their 
					 concerns and issues based on descriptions of the site, waste 
					 stream, issue, etc., and agree on the question or problem to be 
					 studied.

			   2.    Identify the Decision:  Stakeholders design the answer or result 
					 that will answer the question or solve the problem, including the 
					 threshold level for action.

			   3.    Identify Inputs to the Decision:  Stakeholders define the 
					 measurements needed to answer the question.

			   4.    Define the Boundaries:  Stakeholders define the time and space 
					 circumstances covered by the decision.

			   5.    Develop a Decision Rule:  Technical staff and stakeholders develop
					 the formulation to obtain the needed data (quality and quantity) 
					 and to identify acceptability or confidence in the ultimate 
					 decision.

			   6.    Specify Acceptable Limits on Decision Errors:  In concert with 
					 Step 5, stakeholders define the tolerance for making incorrect 
					 decisions.

			   7.    Optimize Data Design:  Technical staff identifies the most 
					 resource effective data collection design.

		 Implementation of the DQO process forces data suppliers and data users to 
		 consider the following questions:

			   *  What decision has to be made ?

			   *  What type and quality of data are required to support the decision? 

			   *  Why are new data needed for the decision ?

			   *  How will new data be used to make the decision ?

		 The DQO planning process has several notable strengths.  It brings together 
		 the right players (stakeholders and technical staff) at the right time to gain 
		 consensus and commitment about the scope of the project.  This interaction 
		 results in a clear understanding of the problem, the actions needed to address 
		 that problem, and the level of uncertainty that is acceptable for making 
		 decisions.  Through this process, data collection and analysis are optimized 
		 so only those data needed to address the appropriate questions are collected.  
		 Similarly, SAFER represents an enhancement of the DQO planning process by 
		 combining planning with remedy selection and implementation, resulting in a
		 process for addressing all facets of an environmental restoration project.
 


 
									ATTACHMENT B

	  DECIDING WHEN TO APPLY PROCESS KNOWLEDGE OR PERFORM SAMPLING AND ANALYSIS FOR 
								 WASTE CHARACTERIZATION 
				  -- EMPLOYING THE DATA QUALITY OBJECTIVES (DQO) PROCESS

	  Efficient characterization of DOE's large number of waste streams relies on 
	  information obtained from process knowledge (PK), sampling and analysis (S&A), 
	  or from a combination of both.  PK means applying knowledge of the hazard 
	  characteristic of the waste in light of the materials or the processes used 
	  (40 CFR 262.11(a)(2)).  This would include knowledge about the origin, 
	  storage, use, and potential exposure of the waste material.  For example, if 
	  the feed stocks for making a product are known, the likely range of chemical 
	  components in the waste streams can be predicted.  This information may be 
	  sufficient to address regulatory concerns.

	  To the extent applicable, using PK to characterize waste is more cost 
	  effective than S&A.  What is needed is a logical approach to identify the 
	  sufficiency of PK to characterize a particular waste and what is the minimum 
	  amount of new S&A data necessary.  The Data Quality Objectives (DQO) planning 
	  process focuses on these specific questions.  DQO planning helps stakeholders 
	  decide what questions require characterization information and determine 
	  whether those questions can be answered by existing PK, if the PK must be 
	  supported by new S&A data, or if new data are required because PK is entirely 
	  inadequate.  The first several steps of the DQO process help determine whether 
	  existing PK is adequate to characterize the waste.  If PK is determined 
	  insufficient, the last few steps of the DQO process lay out the new data needs 
	  and optimize the S&A design.

	  Attached is a flow chart illustrating the basic framework for applying DQO 
	  planning for waste characterization.  First the issues and regulatory drivers 
	  that form the basis for the need to characterize a waste are identified.  Then 
	  the questions, possible answers, and data needs for addressing those issues 
	  and drivers are formulated.  Waste characterization issues and regulatory 
	  drivers may involve determining whether a waste stream meets treatment, 
	  storage, or disposal waste acceptance criteria.  Questions to address these 
	  issues focus on whether PK satisfactorily determines the waste content.  
	  Questions about validity of PK may include how accurate is the knowledge of 
	  the waste generation process, can the waste be traced to the point of origin, 
	  has any significant degradation of the waste occurred, has anything been added 
	  to the waste, and has there been adequate QA/QC of the PK determination.

	  Once questions and data needs are determined, relevant existing data are 
	  compiled.  Existing data include both PK or S&A information already available.  
	  Existing data may be obtained from manifests of the subject waste or waste 
	  generated from areas or processes matching the subject waste, or from 
	  knowledge about the specific process that generated the waste.  When all 
	  relevant existing information is compiled, it is evaluated to determine if it
	  is sufficient to meet the data needs established during the first several 
	  steps of the DQO process.  If existing information is sufficient, no further 
	  characterization should be required.  If not, then new data are necessary, and 
	  a S&A plan based on the determined data needs should be designed.
 



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