NUREG-1576
                           EPA402-B-01-003
                          NTISPB2001-106745

                           %
                               \
                                I
      Multi-Agency Radiological
Laboratory Analytical Protocols Manual
             PLANNING
         MARLAP
    ASSESSMENT
MPLEMENTATON
             uses     NIST
 Draft for Public Comment
       August 2001

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                                  ABSTRACT

The Multi-Agency Radiological Laboratory Analytical Protocols (MARLAP) manual provides
guidance for the planning, implementation, and assessment of projects that require the laboratory
analysis of radionuclides. MARLAP's basic goal is to provide guidance and a framework for
project planners, managers, and laboratory personnel to ensure that radioanalytical laboratory
data will meet a project's or program's data requirements. To attain this goal, the manual is
intended to provide the guidance necessary for national consistency in the form of a performance-
based approach for meeting a project's data requirements. The guidance in MARLAP is designed
to help ensure the generation of radioanalytical data of known quality, appropriate for its
intended use.

MARLAP was developed by a workgroup that included representatives from the U.S. Environ-
mental Protection Agency (EPA), Department of Energy  (DOE), Department of Defense (DOD),
Nuclear Regulatory Commission (NRC), National Institute of Standards and Technology (NIST),
U.S. Geological Survey (USGS), and U.S. Food and Drug Administration (FDA). State participa-
tion in the development of the manual involved contributions from representatives from the
Commonwealth of Kentucky and the State of California.  Contractors to EPA, DOE, and NRC,
and members of the public, have been present during the  open meetings of the MARLAP
workgroup.

Examples of data collection activities that MARLAP supports include site characterization, site
cleanup and  compliance demonstration, decommissioning of nuclear facilities, remedial and
removal actions, effluent monitoring of licensed facilities, environmental site monitoring,
background studies, and waste management activities.
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                                    NOTICE

This draft manual being released for simultaneous public and peer review, and technical
comments are solicited as described below. MARLAP has not been approved for use in part or in
whole and should not be used, cited, or quoted except for the purposes of providing comments as
requested by the agencies participating in its development.

MARLAP was developed by a workgroup that included representatives from the U.S. Environ-
mental Protection Agency (EPA), Department of Energy (DOE), Department of Defense (DOD),
Nuclear Regulatory Commission (NRC), National Institute of Standards and Technology (NIST),
U.S. Geological Survey (USGS), and U.S. Food and Drug Administration (FDA). State participa-
tion in the development of the manual involved contributions from representatives from the
Commonwealth of Kentucky and the State of California. Contractors to EPA, DOE,  and NRC,
and members of the public, have been present during the open meetings of the MARLAP
workgroup.

Although Federal Government personnel are involved in the preparation of this document, the
draft manual does not yet represent the official position of any participating agency.  This review
is a necessary step in the development of a multi-agency consensus manual. References within
this manual to any specific commercial product, process, or service by trade name, trademark,
manufacturer, or otherwise does not necessarily constitute or imply its endorsement or
recommendation by the United States Government.

Members of the public  are invited and encouraged to submit comments to the following website
http://www.eml.doe.gov/marlap/. Comments may also be submitted to either:

                         U.S. Environmental Protection Agency
                     ATTN: Air and Radiation Docket, Mail Stop 6102
                        Docket Number A-2001-16, Room M1500
                                   401 M Street, SW
                                Washington, DC 20460

                                          or

                           Chief, Rules and Directives Branch
                           Division of Administrative Services
                                   Mail Stop T6D59
                          U.S. Nuclear Regulatory Commission
                              Washington, DC 20555-0001
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	Notice

All comments received will be reviewed by the entire MARLAP workgroup. Comments received
by the date published in the Federal Register Notice announcing the availability of the document
for public review will be considered. Comments received after that date will be considered if it is
practical to do so, but no assurance can be given for consideration of late comments.

Copies of the draft MARLAP manual and all comments received may be examined or copied for
a fee at the EPA Docket Room M1500, Docket Number A-2001-16, First Floor Waterside Mall,
401 M Street, SW, Washington, DC 20460; and the NRC Public Document Room, at U.S.
Nuclear Regulatory Commission, Public Document Room, Washington, DC 20555. The
document is also available through the National Technical Information Service (NTIS). The
NTIS document number is PB2001-106745, and the NTIS Sales Desk can be reached between
8:30 a.m. and 6:00 p.m. Eastern Time, Monday through Friday at 1-800-553-6847; TDD (hearing
impaired only) at (703) 487-4639.

In addition to providing comments on individual chapters and appendices, reviewers are also
requested to address the following questions while reviewing the draft manual:

   (1) Is the performance-based approach used in MARLAP for the planning, implementation,
   and assessment phases of projects technically sound, and is the approach reasonable in terms
   of ease of implementation by project managers and laboratories? Does the approach
   effectively link the three phases of a project, and is the guidance on quality control
   appropriate and supportive of a performance-based approach?

   (2) Is the guidance on laboratory operations in Part II (Chapters 10-20) technically accurate
   and useful?

   (3) Are the concepts covered under measurement statistics—specifically measurement
   uncertainty, detection and quantification capability—presented accurately and appropriately?

   (4) Is the information understandable and presented in logical sequence? How can the
   presentation of material be modified to improve the manual?

   (5) Does MARLAP provide benefits that are not currently available through other
   approaches? What are the costs associated with implementing the guidance in MARLAP in
   comparison with currently available alternatives?

Commentors are encouraged to use the website, http://www.eml.doe.gov/marlap, for their
review. The website has detailed instructions on how to submit comments and has several
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Notice	

features that should aid the review process. Commentors also may submit written comments to
either of the addresses listed on page iv of this Notice using the same general approach described
in the MARLAP website. Comments should be accompanied by supporting details, rationale, or
data. To ensure efficient and complete comment resolution, commentors are requested to
reference the page number and the line number to which the comment refers. Comments
corresponding to an entire chapter, section, or table should be referenced to the line number for
the title of the chapter (always line number 1), section, or table. Comments on footnotes should
be referenced to the line in the text where the footnote appears (footnotes do not have line
numbers). Comments on figures should be referenced to the page on which the figure appears
(figures do not have line numbers) and figure number. Comments on the entire manual should be
referenced to the title page.
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                        ACKNOWLEDGMENTS

The origin of the Multi-Agency Radiological Laboratory Analytical Protocols (MARLAP)
manual can be traced to the recognition by a number of agencies for the need to have a nationally
consistent approach to producing radioanalytical data that meet a program's or project's needs. A
multi-agency workgroup was formed with representatives from the U.S. Environmental
Protection Agency (EPA), Department of Energy (DOE), Nuclear Regulatory Commission
(NRC), Department of Defense (DOD), U.S. Geological Survey (USGS), National Institute of
Standards and Technology (NIST), and Food and Drug Administration (FDA) to develop
guidance for the planning, implementation,  and assessment of projects that require the laboratory
analysis of radionuclides. Representatives from the Commonwealth of Kentucky and the State of
California also contributed to the development of the manual.

Of particular importance to the workgroup is that the guidance needs to be both scientifically
rigorous and flexible enough to be applied to a diversity of projects and programs. The draft
MARLAP manual is the result of a cooperative effort with these goals in mind.

MARLAP would not have been possible  without the workgroup members who contributed their
time, talent, and efforts to develop this guidance document:

                               John  Griggs*, EPA, Chair

EPA:  H.Benjamin Hull                       DOE: EmileBoulos*
      Marianne  Lynch*                             Carl Gogolak
      Keith McCroan*                               Pam Greenlaw*
      Eric Reynolds                                Catherine Klusek*
      Jon Richards                                 Stan Morton*
                                                   Colin Sanderson*
                                                   Stephanie Woolf*

DOD: CPT Andrew Scott (Army)                NRC: Rateb (Boby) Abu Eid
       Ronald Swatski* (Army)                       Tin Mo
      Jan Dunker (Army Corps of Engineers)           George Powers
      Troy Blanton (Navy)
      CAPT David Farrand (Navy)             USGS: Ann Mullin*
      Dale Thomas (Air Force)

NIST: Kenneth GW. Inn*                      FDA: Edmond Baratta

* These workgroup members also served as chapter chairs.

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Acknowledgments
Special recognition is given to John Volpe, Commonwealth of Kentucky, and Penny Leinwander,
State of California, for their contributions to the development of the MARLAP manual. The
following Federal Agency contractors provided assistance in developing the MARLAP manual:

EPA:  N. Jay Bassin (Environmental Management Support, Inc.)
      Diane Dopkin (Environmental Management Support, Inc.)
      U. Hans Behling ( S. Cohen & Associates, Inc.)
      Richard Blanchard (S. Cohen & Associates, Inc.)
      Harry Chmelynski (S. Cohen & Associates, Inc.)
      Scott Hay (S. Cohen & Associates, Inc.)
      Patrick Kelly (S. Cohen & Associates, Inc.)
      Robert Litman (S. Cohen & Associates, Inc.)
      Charles (Chick) Phillips (S. Cohen & Associates, Inc.)
      William Richardson in (S. Cohen & Associates, Inc.)
      Steven Schaffer (S. Cohen & Associates, Inc.)

 DOE: David McCurdy (Duke Engineering & Services)
      John Maney (Environmental Measurements Assessments)
      Stan Blacker (MACTEC, Inc.)
      Pat Harrington (MACTEC, Inc.)
      Mike Miller (MACTEC, Inc.)
      Lisa Smith (Argonne National Laboratory)

NRC: Eric W. Abelquist (ORISE)
      Dale Condra (ORISE)

The MARLAP Workgroup was greatly aided in the development of the manual by the
contributions and support provided by the individuals listed below.

David Bottrell (DOE)           David Friedman (EPA)          Kevin Miller (DOE)
Lloyd Currie (NIST)           LCDR Lino Fragoso (Navy)     Jim Mitchell (EPA)
Mike Carter (EPA)             Richard Graham (EPA)          Colleen Petullo (EPA)
Mary Clark (EPA)             Patricia Gowland (EPA)         Steve Pia (EPA)
Ron Colle (NIST)             Larry Jensen (EPA)            Phil Reed (NRC)
Mark Doehnert (EPA)          Jim Kotton (NRC)             Cheryl Trottier (NRC)
Steve Domotor (DOE)          Ed Messer (EPA)              John Warren (EPA)
Joan Fisk (EPA)
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                                  CONTENTS

                                                                                 Page
Abstract  	in

Notice	IV

Acknowledgments	  VII

Acronyms and Abbreviations	  XLVII

1 Introduction to MARLAP 	1-1
    1.1 Overview	1-1
    1.2 Purpose of the Manual	1-2
    1.3 Use and Scope of the Manual  	1-3
    1.4 Key MARLAP Concepts and Terminology	1-4
       1.4.1  Data Life Cycle 	1-4
       1.4.2  Directed Planning Process	1-5
       1.4.3  Performance-Based Approach	1-6
       1.4.4  Analytical Process  	1-7
       1.4.5  Analytical Protocol  	1-8
       1.4.6  Analytical Method	1-8
       1.4.7  Uncertainty and Error	1-8
       1.4.8  Precision, Bias,  and Accuracy	1-10
       1.4.9  Performance Objectives: Data Quality Objectives and Measurement Quality
             Objectives  	1-11
       1.4.10 Analytical Protocol Specifications	1-12
       1.4.11 The Assessment Phase	1-13
    1.5 The MARLAP Process	1-14
    1.6 Structure of the Manual	1-15
       1.6.1  Overview of Part I	1-17
       1.6.2  Overview of Part II	1-17
       1.6.3  Overview of the Appendices 	1-19
    1.7 References  	1-20

2 Project Planning Process 	2-1
    2.1 Introduction  	2-1
    2.2 The Importance of Directed Project Planning	2-2
    2.3 Directed Project Planning Processes 	2-4
       2.3.1  A Graded Approach to Project Planning	2-4

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       2.3.2   Guidance on Directed Planning Processes	2-4
       2.3.3   Elements of Directed Planning Processes  	2-6
   2.4 The Project Planning Team 	2-7
       2.4.1   Team Representation	2-7
       2.4.2   The Radioanalytical Specialists	2-8
   2.5 Direct Planning Process and Role of the Radioanalytical Specialists	2-9
       2.5.1   Define the Problem 	2-12
       2.5.2   Identify the Decision 	2-13
          2.5.2.1 Action Level	2-13
          2.5.2.2 Scale of the Decision	2-14
          2.5.2.3 Inputs and Boundaries to the Decision  	2-15
          2.5.2.4 Data Needs	2-15
       2.5.3   Specify the Decision Rule and the Tolerable Decision Error Rates  	2-15
       2.5.4   Optimize the Strategy for Obtaining Data	2-17
          2.5.4.1 Analytical Protocol Specifications	2-18
          2.5.4.2 Measurement Quality Objectives	2-18
   2.6 Results of the Directed Planning Process  	2-19
       2.6.1   Output Required by the Radioanalytical Laboratory: The Analytical Protocol
              Specifications	2-20
       2.6.2   Chain of Custody	2-21
   2.7 Project Planning and Project Implementation and Assessment  	2-21
       2.7.1   Documenting the Planning Process	2-21
       2.7.2   Obtaining Analytical Services	2-22
       2.7.3   Selecting Analytical Protocols	2-23
       2.7.4   Assessment Plans	2-23
          2.7.4.1 Data Verification	2-24
          2.7.4.2 Data Validation 	2-24
          2.7.4.3 Data Quality Assessment	2-24
   2.8 References  	2-25

3 Key Analytical Planning Issues and Developing Analytical Protocol Specifications	3-1
   3.1 Introduction 	3-1
   3.2 Overview of the Analytical Process	3-2
   3.3 General Analytical Planning Issues	3-2
       3.3.1   Develop Analyte List	3-4
       3.3.2   Identify Concentration Ranges	3-6
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       3.3.3  Identify and Characterize Matrices of Concern	3-6
       3.3.4  Determine Relationships Between the Radionuclides of Concern  	3-7
       3.3.5  Determine Available Project Resources and Deadlines	3-8
       3.3.6  Refine Analyte List and Matrix List 	3-8
       3.3.7  Method Performance Characteristics and Measurement Quality Objectives . . . 3-9
          3.3.7.1 Develop MQOs for Select Method Performance Characteristics  	3-11
          3.3.7.2 The Role of MQOs in the Protocol Selection and Evaluation Process . . . 3-16
          3.3.7.3 The Role of MQOs in the Project's Data Evaluation Process  	3-16
       3.3.8  Determine Any Limitations on Analysis Options 	3-17
          3.3.8.1 Gamma Spectrometry	3-18
          3.3.8.2 Gross Alpha and Beta Analysis	3-19
          3.3.8.3 Radiochemical Nuclide-Specific Analysis	3-19
       3.3.9  Determine Method Availability  	3-19
       3.3.10 Determine the Type and Frequency of, and Evaluation Criteria for, Quality
          Control Samples  	3-20
       3.3.11 Determine Sample Tracking and Custody Requirements  	3-21
       3.3.12  Determine Data Reporting Requirements	3-21
   3.4 Matrix-Specific Analytical Planning Issues	3-22
       3.4.1  Solids	3-23
          3.4.1.1 Homogenization and Subsampling	3-24
          3.4.1.2 Removal of Unwanted Materials   	3-24
       3.4.2  Liquids	3-25
       3.4.3  Filters and Wipes	3-26
   3.5 Assembling the Analytical Protocol Specifications	3-26
   3.6 Level of Protocol Performance Demonstration	3-27
   3.7 Project Plan Documents	3-30
   3.8 References 	3-31

4 Project Plan Documents  	4-1
   4.1 Introduction  	4-1
   4.2 The Importance of Project Plan Documents 	4-2
   4.3 A Graded Approach to Project Plan Documents  	4-3
   4.4 Project Plan Documents  	4-4
       4.4.1  Guidance on Project Plan Documents	4-4
       4.4.2  Approaches to Project Plan Documents  	4-5
   4.5 Elements of Project Plan Documents	4-6
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       4.5.1  Content of Project Plan Documents	4-7
       4.5.2  Plan Documents Integration	4-9
       4.5.3  Plan Content for Small Projects  	4-10
   4.6 Linking the Project Plan Documents and the Project Planning Process  	4-10
       4.6.1  Planning Process Report  	4-15
       4.6.2  Data Assessment 	4-16
          4.6.2.1 Data Verification	4-16
          4.6.2.2 Data Validation 	4-16
          4.6.2.3 Data Quality Assessment	4-17
   4.7 References 	4-18

5 Obtaining Laboratory Services	5-1
   5.1 Introduction  	5-1
   5.2 Importance of Writing a Technical and Contractual Specification Document  	5-2
   5.3 Statement of Work—Technical Requirements 	5-2
       5.3.1  Analytes	5-3
       5.3.2  Matrix	5-3
       5.3.3  Measurement Quality  Objectives	5-3
       5.3.4  Unique Analytical Process Requirements  	5-4
       5.3.5  Quality Control Samples and Participation in External Performance Evaluation
             Programs  	5-4
       5.3.6  Laboratory Radiological Holding and Turnaround Times	5-5
       5.3.7  Number of Samples and Schedule  	5-5
       5.3.8  Quality System	5-6
       5.3.9  Laboratory's Proposed Methods  	5-6
   5.4    Request for Proposal—Generic Contractual Requirements	5-7
       5.4.1 Sample Management	5-7
       5.4.2  Licenses, Permits and  Environmental Regulations 	5-8
          5.4.2.1 Licenses	5-8
          5.4.2.2 Environmental and Transportation Regulations  	5-9
       5.4.3  Data Reporting and Communications 	5-9
          5.4.3.1 Data Deliverables	5-9
          5.4.3.2 Software Verification and Control	5-10
          5.4.3.3 Problem Notification and Communication  	5-10
          5.4.3.4 Status Reports  	5-11
       5.4.4  Sample Re-Analysis Requirements	5-11
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       5.4.5   Subcontracted Analyses	5-11
    5.5 Laboratory Selection and Qualification Criteria 	5-12
       5.5.1   Technical Proposal Evaluation  	5-12
          5.5.1.1 Scoring and Evaluation Scheme  	5-12
          5.5.1.2 Scoring Elements	5-13
       5.5.2   Pre-Award Proficiency Evaluation  	5-15
       5.5.3 Pre-Award Assessments and Audits	5-15
    5.6    References  	5-16
       5.6.1   Cited References  	5-16
       5.6.2   Other Sources	5-17

6 Selection and Application of an Analytical Method  	6-1
    6.1 Introduction 	6-1
    6.2 Method Definition	6-2
    6.3 Life Cycle of Method Application	6-6
    6.4 Generic Considerations for Method Development and Selection	6-10
    6.5 Project-Specific Consideration for Method Selection	6-13
       6.5.1   Matrix and Analyte Identification  	6-13
          6.5.1.1 Matrices	6-13
          6.5.1.2. Analytes and Potential Interferences  	6-15
       6.5.2 Process Knowledge	6-16
       6.5.3   Radiological Holding and Turnaround Times	6-17
       6.5.4   Unique Process Specifications	6-18
       6.5.5   Measurement Quality Objectives	6-19
          6.5.5.1 Method Uncertainty  	6-19
          6.5.5.2 Quantification Capability	6-20
          6.5.5.3 Detection Capability  	6-21
          6.5.5.4 Applicable Analyte Concentration Range	6-23
          6.5.5.5 Method Specificity	6-23
          6.5.5.6 Method Ruggedness   	6-24
          6.5.5.7 Bias Considerations	6-24
    6.6 Method Validation	6-25
       6.6.1   Laboratory's Method Validation Protocol	6-26
       6.6.2   Tiered Approach to Validation  	6-27
          6.6.2.1 Existing Methods Requiring No Additional Validation  	6-29
          6.6.2.2 Use of a Validated Method for Similar Matrices   	6-30
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          6.6.2.3 New Application of a Validated Method	6-30
          6.6.2.4 Newly Developed or Adapted Methods	6-32
       6.6.4  Method Validation Documentation	6-32
   6.7 Analyst Qualifications and Demonstrated Proficiency 	6-33
   6.8    Method Control  	6-33
   6.9    Continued Performance Assessment	6-34
   6.10   Documentation To Be Sent to the Project Manager  	6-36
   6.11   References  	6-37

7 Evaluating Methods and Laboratories	7-1
   7.1 Introduction 	7-1
   7.2 Evaluation of Proposed Analytical Methods	7-2
       7.2.1  Documentation of Required Method Performance  	7-2
          7.2.1.1 Method Validation Documentation	7-3
          7.2.1.4 Method Experience, Previous Projects, and Clients  	7-5
          7.2.1.5 Internal and External Quality Assurance Assessments 	7-5
       7.2.2  Performance Requirements of the SOW—Analytical Protocol Specifications . 7-6
          7.2.2.1 Matrix and Analyte Identification 	7-6
          7.2.2.2 Process Knowledge 	7-7
          7.2.2.3 Radiological Holding and Turnaround Times	7-7
          7.2.2.4 Unique Processing Specifications 	7-9
          7.2.2.5 Measurement Quality Objectives	7-9
          7.2.2.6 Bias Considerations	7-15
   7.3 Initial Evaluation of a Laboratory 	7-17
       7.3.1  Review of Quality System Documents 	7-17
       7.3.2  Adequacy of Facilities, Instrumentation, and Staff Levels 	7-19
       7.3.3  Review of Applicable Prior Work	7-19
       7.3.4  Review of Performance Indicators	7-20
          7.3.4.1 Review of Internal QC Results  	7-20
          7.3.4.2 External PE Program Results	7-21
          7.3.4.3 Internal and External Quality Assessment Reports	7-21
       7.3.5  Initial Audit  	7-22
   7.4 Ongoing Evaluation of the Laboratory's Performance 	7-22
       7.4.1  Quantitative Measures of Quality  	7-23
          7.4.1.1 MQO Compliance 	7-24
          7.4.1.2 Other Parameters  	7-30
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       7.4.2   Operational Aspects	7-31
          7.4.2.1 Desk Audits	7-31
          7.4.2.2 Onsite Audits	7-33
   7.5 References 	7-36

  Radiochemical Data Verification And Validation	8-1
   8.1 Introduction 	8-1
   8.2 Data Assessment Process	8-2
       8.2.1   Planning Phase of the Data Life Cycle	8-2
       8.2.2   Implementation Phase of the Data Life Cycle	8-3
          8.2.2.1  Project Objectives	8-4
          8.2.2.2  Documenting Project Activities  	8-4
          8.2.2.3  QA/QC 	8-4
       8.2.3   Assessment Phase of the Data Life Cycle 	8-5
   8.3 Validation Plan	8-7
       8.3.1   Technical and Quality Objectives of the Project	8-8
       8.3.2   Validation Tests	8-9
       8.3.3   Data Qualifiers	8-9
       8.3.4   Reporting and Documentation	8-11
   8.4 Other Essential Elements	8-11
       8.4.1   Statement of Work	8-12
       8.4.2   Verified Data Deliverables	8-12
   8.5 Data Verification and Validation Process  	8-13
       8.5.1   The Sample Handling and Analysis System 	8-14
          8.5.1.1 Sample Descriptors  	8-15
          8.5.1.2 Aliquant Size	8-16
          8.5.1.3 Dates of Sample Collection, Preparation, and Analysis  	8-16
          8.5.1.4 Preservation	8-17
          8.5.1.5 Tracking	8-18
          8.5.1.6 Traceability  	8-18
          8.5.1.7 QC Types and Linkages	8-18
          8.5.1.8 Chemical Separation (Yield)	8-19
          8.5.1.9 Self-Absorption (Residue)	8-20
          8.5.1.10 Efficiency, Calibration Curves, and Instrument Background  	8-20
          8.5.1.11 Spectrometry Resolution 	8-20
          8.5.1.12 Dilution and Correction Factors	8-21
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          8.5.1.13 Counts and Count Time (Duration)	8-22
          8.5.1.14 Result of Measurement, Uncertainty, Minimum Detectable Concentration,
              and Units  	8-22
       8.5.2   Quality Control Samples 	8-22
          8.5.2.1 Method Blank	8-24
          8.5.2.2 Laboratory Control Samples  	8-25
          8.5.2.3 Laboratory Replicates  	8-25
          8.5.2.4 Matrix Spikes and Matrix Spike Duplicates 	8-26
       8.5.3   Tests of Detection and Unusual Uncertainty	8-26
          8.5.3.1 Detection  	8-26
          8.5.3.2 Detection Capability  	8-27
          8.5.3.3 Large or Unusual Uncertainty  	8-28
       8.5.4   Final Qualification and Reporting	8-29
   8.6 Validation Report	8-30
   8.7 Other Sources of Information  	8-32

9 Data Quality Assessment	9-1
   9.1 Introduction 	9-1
   9.2 Assessment Phase  	9-2
   9.3 Graded Approach to Assessment	9-3
   9.4 The Data Quality Assessment Team  	9-4
   9.5 Data Quality Assessment Plan	9-4
   9.6 Data Quality Assessment Process  	9-6
       9.6.1   Review of Project Documents	9-8
          9.6.1.1 TheProjectDQOsandMQOs	9-8
          9.6.1.2 The DQA Plan	9-9
          9.6.1.3 Summary of the DQA Review	9-9
       9.6.2   Sample Representativeness 	9-10
          9.6.2.1 Review of the Sampling Plan	9-10
          9.6.2.2 Sampling Plan Implementation 	9-13
          9.6.2.3 Data Considerations	9-14
       9.6.3   Data Accuracy  	9-16
          9.6.3.1 Review of the Analytical Plan	9-19
          9.6.3.2 Analytical Plan Implementation	9-21
       9.6.4   Decisions and Tolerable Error Rates	9-22
          9.6.4.1 Statistical Evaluation of Data  	9-23
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          9.6.4.2 Evaluation of Decision Error Rates 	9-26
   9.7 Data Quality Assessment Report	9-27
   9.8 References  	9-29
       9.8.1  Cited Sources	9-29
       9.8.2  Other Sources	9-29

10 Field and Sampling Issues That Affect Laboratory Measurements   	10-1
   Part I: Generic Issues  	10-1
   10.1   Introduction  	10-1
       10.1.1 The Need for Establishing Channels of Communication	10-2
       10.1.2 Developing Field Documentation  	10-2
   10.2   Field Sampling Plan: Non Matrix Specific Issues	10-3
       10.2.1 Determination of Analytical Sample Size	10-3
       10.2.2 Field Equipment and Supply Needs	10-3
       10.2.3 Selection  of Sample Containers	10-4
          10.2.3.1   Container Material	10-4
          10.2.3.2   Container Opening and Closure	10-5
          10.2.3.3   Sealing Containers	10-5
          10.2.3.4   Precleaned and Extra Containers	10-5
       10.2.4 Container Label and Sample Identification Code 	10-5
       10.2.5 Field Data Documentation	10-6
       10.2.6 Field Tracking, Custody, and Shipment Forms	10-8
       10.2.7 Chain of Custody	10-9
       10.2.8 Field Quality Control	10-9
       10.2.9 Decontamination of Field Equipment   	10-10
       10.2.10 Packing and Shipping  	10-11
       10.2.11 Worker Health and Safety Plan  	10-12
          10.2.11.1  Physical Hazards 	10-13
          10.2.11.2  Biohazards	10-15
   Part II:  Matrix-Specific Issues That Impact Field Sample Collection, Processing, and
       Preservation	10-16
   10.3   Liquid Samples  	10-17
       10.3.1 Liquid Sampling Methods	10-18
       10.3.2 Liquid Sample Preparation: Filtration	10-18
          10.3.2.1   EPA Guidance for Samples/Filtration	10-19
          10.3.2.2   Filters	10-21
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       10.3.3 Field Preservation of Liquid Samples  	10-22
          10.3.3.1   Sample Acidification	10-22
          10.3.3.2   Non-Acid Preservation Techniques	10-23
       10.3.4 Liquid Samples: Special Cases  	10-26
          10.3.4.1   Radon-222 in Water	10-26
          10.3.4.1   Milk	10-27
       10.3.5 Non-aqueous Liquids and Mixtures  	10-27
    10.4   Solids  	10-29
       10.4.1 Soils	10-29
          10.4.1.1   Soil  Sample Preparation   	10-30
          10.4.1.2   Sample Ashing  	10-31
       10.4.2 Sediments  	10-31
          10.4.2.1   Initial Mixing and Transport Dispersion of Radionuclides Discharged to
                    Water	10-31
          10.4.2.2   Sediment Effect	10-32
          10.4.2.3   Sample Preparation/Preservation  	10-32
       10.4.3 Other Solids  	10-32
          10.4.3.1   Structural Materials	10-32
          10.4.3.2   Biota: Samples of Plant and Animal Products	10-33
    10.5   Air Sampling 	10-37
       10.5.1 Sampler Components  	10-37
       10.5.2 Filter Selection Based on Destructive Versus Non-destructive Analysis  ...  10-39
       10.5.3 Sample Preservation and Storage   	10-39
       10.5.4 Special Cases: Collection of Gaseous and Volatile Air Contaminants	10-40
          10.5.4.1   Radioiodines   	10-40
          10.5.4.2   Gases  	10-41
          10.5.4.3   Tritium Air Sampling	10-41
       10.5.5 Radon	10-42
          10.5.5.1   Radon Sampling Methods  	10-43
          10.5.5.2   Selecting a Radon Sampling Method Based on Data
             Quality Objectives	10-46
    10.6   Wipe Sampling for Assessing Surface Contamination 	10-47
       10.6.1 Sample Collection Methods  	10-48
          10.6.1.1   Dry Wipes 	10-48
          10.6.1.2   Wet Wipes	10-48
       10.6.2 Sample Handling	10-50
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    10.7   References 	10-50

11  Sample Receipt, Inspection, and Tracking	11-1
    11.1   Introduction 	11-1
    11.2   General Considerations  	11-3
       11.2.1 Communication Before Sample Receipt	11-3
       11.2.2 Standard Operating Procedures	11-3
       11.2.3 Laboratory License	11-4
       11.2.4 Sample Chain-of-Custody	11-5
    11.3   Sample Receipt  	11-6
       11.3.1 Package Receipt	11-6
       11.3.2 Radiological Screening  	11-7
       11.3.3 Corrective Action	11-9
    11.4   Sample Inspection  	11-9
       11.4.1 Physical Integrity of Package and Sample Containers	11-9
       11.4.2 Sample Identity Confirmation	11-10
       11.4.3 Confirmation of Field Preservation	11-11
       11.4.4 Presence of Hazardous Materials	11-11
       11.4.5 Corrective Action	11-12
    11.5   Laboratory Sample Tracking	11-12
       11.5.1 Sample Log-In	11-13
       11.5.2 Sample Tracking During Analyses	11-13
       11.5.3 Storage of Samples	11-14
    11.6   References 	11-14

12  Laboratory Sample Preparation	12-1
    12.1   Introduction 	12-1
    12.2   General Guidance for Sample Preparation	12-2
       12.2.1 Potential Sample Losses During Preparation	12-2
          12.2.1.1   Losses as Dust or Particulates 	12-2
          12.2.1.2   Losses Through Volatilization	12-3
          12.2.1.3   Losses Owing to Reactions Between Sample and Container	12-4
       12.2.2 Contamination  from Sources in the Laboratory	12-6
          12.2.2.1   Airborne Contamination  	12-6
          12.2.2.2   Contamination of Reagents  	12-7
          12.2.2.3   Contamination of Glassware/Equipment	12-7
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          12.2.2.4   Contamination of Facilities 	12-8
       12.2.3 Cleaning of Labware, Glassware, and Equipment	12-8
          12.2.3.1   Labware and Glassware	12-8
          12.2.3.2   Equipment  	12-10
       12.3.1 General Procedures  	12-14
          12.3.1.1   Exclusion of Material	12-14
          12.3.1.2   Principles of Drying Techniques	12-14
          12.3.1.3   Obtaining a Constant Weight	12-24
          12.3.1.4   Subsampling	12-26
       12.3.2 Soil/Sediment Samples  	12-30
          12.3.2.1   Soils	12-31
          12.3.2.2   Sediments	12-31
          12.3.3.1   Biological Samples  	12-32
          12.3.3.2   Food	12-32
          12.3.3.3   Vegetation  	12-32
          12.3.3.4   Bone  and Tissue	12-33
       12.3.4 Other Samples 	12-33
    12.4   Filters	12-33
    12.5   Wipe Samples 	12-34
    12.6   Liquid Samples  	12-35
       12.6.1 Conductivity	12-35
       12.6.2 Turbidity	12-36
       12.6.3 Filtration	12-36
       12.6.4 Aqueous Liquids  	12-36
       12.6.5 Nonaqueous Liquids  	12-37
       12.6.6 Mixtures	12-38
          12.6.6.1   Liquid-Liquid Mixtures	12-38
          12.6.6.2   Liquid-Solid Mixtures	12-39
    12.7   Gases  	12-39
    12.8   Bioassay	12-40
    12.9   References 	12-41
       12.9.1 Cited Sources	12-41
       12.9.2 Other Sources	12-47

13  Sample Dissolution 	13-1
    13.1   Introduction 	13-1
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    13.2   The Chemistry of Dissolution  	13-2
       13.2.1 Solubility and the Solubility Product Constant, Ksp	13-2
       13.2.2 Chemical Exchange, Decomposition, and Simple Rearrangement Reactions .  13-3
       13.2.3 Oxidation-Reduction Processes	13-4
       13.2.4 Complexation	13-5
       13.2.5 Equilibrium: Carriers and Tracers	13-6
    13.3   Fusion Techniques	13-6
       13.3.1 Alkali-Metal Hydroxide Fusions	13-10
       13.3.2 Boron Fusions  	13-11
       13.3.3 Fluoride Fusions	13-12
    13.4   Wet Ashing and Acid Dissolution Techniques 	13-13
       13.4.1 Acids and Oxidants 	13-13
       13.4.2 Acid Digestion Bombs	13-23
       13.4.3 Is it Dissolved?  	13-23
    13.5   Microwave Digestion	13-24
       13.5.1 Focused Open-Vessel Systems 	13-25
       13.5.2 Low-Pressure, Closed-Vessel Systems  	13-25
       13.5.3 High-Pressure, Closed-Vessel Systems	13-26
    13.6   Special Matrix Considerations	13-26
       13.6.1 Liquid Samples  	13-26
          13.6.1.1 Aqueous Samples	13-26
          13.6.1.2Nonaqueous Samples	13-27
       13.6.2 Solid Samples  	13-27
       13.6.3 Filters	13-27
       13.6.4 Wipe Samples  	13-28
       13.6.5 Liquid Scintillation Samples 	13-28
          13.6.5.1   Wet Oxidation  	13-28
          13.6.5.2   Dry Oxidation   	13-29
    13.7   Total Dissolution and Leaching	13-29
       13.7.1 Acid Leaching  	13-30
       13.7.2 Total Dissolution through Fusion 	13-31
       13.7.3 Acid Digestion — Fusion Combined Approach  	13-32
    13.8   Examples of Decomposition Procedures	13-32
    13.9   References  	13-33
       13.9.1 Cited References  	13-33
       13.9.2 Other Sources	13-36
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14  Separation Techniques	14-1
    14.1   Introduction  	14-1
    14.2   Oxidation/Reduction Processes	14-2
       14.2.1  Introduction 	14-2
       14.2.2  Oxidation-Reduction Reactions	14-3
       14.2.3  Common Oxidation  States	14-6
       14.2.4  Oxidation State in Solution  	14-11
       14.2.5  Common Oxidizing  and Reducing Agents  	14-12
       14.2.6  Oxidation State and Radiochemical Analysis 	14-14
    14.3   Complexation	14-19
       14.3.1  Introduction 	14-19
       14.3.2  Chelates  	14-21
       14.3.3  The Formation (Stability) Constant	14-24
       14.3.4  Complexation and Radiochemical Analysis 	14-25
          14.3.4.1   Extraction of Laboratory Samples and Ores  	14-25
          14.3.4.2  Separation by Solvent Extraction and Ion-Exchange Chromatography
                      	14-25
          14.3.4.3    Formation and Dissolution of Precipitates	14-26
          14.3.4.4   Stabilization of Ions in Solution	14-27
          14.3.4.5   Detection and Determination	14-27
    14.4   Solvent Extraction	14-27
       14.4.1  Extraction Principles 	14-27
       14.4.2  Distribution Coefficient	14-28
       14.4.3  Extraction Technique	14-30
       14.4.4  Solvent Extraction and Radiochemical Analysis	14-33
       14.4.5  Solid-Phase Extraction	14-35
          14.4.5.1   Extraction Chromatography Columns	14-36
          14.4.5.2  Extraction Membranes	14-37
       14.4.6  Advantages and Disadvantages of Solvent Extraction  	14-38
          14.4.6.1   Advantages	14-38
          14.4.6.2  Disadvantages  	14-38
    14.5   Volatilization and Distillation	14-39
       14.5.1  Introduction 	14-39
       14.5.2  Volatilization Principles  	14-40
       14.5.3  Distillation  Principles  	14-42
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       14.5.4  Separations in Radiochemical Analysis	14-43
       14.5.5  Advantages and Disadvantages of Volatilization  	14-44
          14.5.5.1   Advantages	14-44
          14.5.5.2   Disadvantages  	14-44
    14.6   Electrodeposition	14-45
       14.6.1  Electrodeposition Principles  	14-45
       14.6.2  Separation of Radionuclides  	14-46
       14.6.3  Preparation of Counting Sources	14-47
       14.6.4  Advantages and Disadvantages of Electrodeposition  	14-47
          14.6.4.1   Advantages	14-47
          14.6.4.2 Disadvantages  	14-47
    14.7   Chromatography	14-48
       14.7.1  Chromatographic Principles	14-48
       14.7.2  Gas-Liquid and Liquid-Liquid Phase Chromatography	14-49
       14.7.3  Adsorption Chromatography	14-50
       14.7.4  Ion-Exchange Chromatography	14-50
          14.7.4.1   Principles of Ion Exchange  	14-50
          14.7.4.2   Resins	14-53
       14.7.5  Affinity Chromatography	14-59
       14.7.6  Gel-Filtration Chromatography	14-59
       14.7.7  Chromatographic Laboratory Methods  	14-60
       14.7.8  Advantages and Disadvantages of Chromatographic Systems 	14-61
    14.8   Precipitation and Coprecipitation 	14-61
       14.8.1  Introduction 	14-61
       14.8.2  Solutions  	14-62
       14.8.3  Precipitation	14-64
          14.8.3.1   Solubility and the Solubility Product Constant, Ksp	14-65
          14.8.3.2   Factors Affecting Precipitation  	14-70
          14.8.3.3   Optimum Precipitation Conditions  	14-75
       14.8.4  Coprecipitation	14-76
          14.8.4.1   Coprecipitation Processes  	14-77
          14.8.4.2   Water as an Impurity 	14-81
          14.8.4.3   Postprecipitation   	14-81
          14.8.4.4   Coprecipitation Methods	14-82
       14.8.5  Colloidal Precipitates	14-86
       14.8.6  Filterability of Precipitates	14-88
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       14.8.7  Advantages and Disadvantages of Precipitation and Coprecipitation	14-90
    14.9   Carriers and Tracers	14-91
       14.9.1  Introduction 	14-91
       14.9.2  Carriers	14-91
          14.9.2.1   Isotopic Carriers	14-92
          14.9.2.2   Nonisotopic Carriers  	14-93
          14.9.2.3   Common Carriers	14-94
          14.9.2.4   Holdback Carriers  	14-98
          14.9.2.5   Yield (Recovery) of Isotopic Carriers 	14-98
       14.9.3  Tracers	14-99
          14.9.3.1   Characteristics of Tracers	14-101
          14.9.3.2   Coprecipitation	14-103
          14.9.3.3   Deposition on Nonmetallic Solids	14-103
          14.9.3.4   Radiocolloid Formation  	14-103
          14.9.3.5   Distribution (Partition) Behavior	14-105
          14.9.3.6   Vaporization	14-105
          14.9.3.7   Oxidation and Reduction	14-106
    14.10   Radiochemical Equilibrium 	14-107
       14.10.1    Basic Principles of Equilibrium	14-107
       14.10.2   Oxidation State  	14-110
       14.10.3    Hydrolysis  	14-111
       14.10.4   Polymerization	14-113
       14.10.5    Complexation	14-114
       14.10.6   Radiocolloid Interference	14-114
       14.10.7   Isotope Dilution Analysis	14-115
       14.10.8    Masking and Demasking	14-116
       14.10.9   Review of Specific Radionuclides	14-120
          14.10.9.1  Americium	14-120
          14.10.9.2  Cesium	14-125
          14.10.9.3  Cobalt	14-130
          14.10.9.4  Iodine	14-136
          14.10.9.5  Plutonium	14-144
          14.10.9.6  Radium	14-153
          14.10.9.7  Strontium	14-161
          14.10.9.8  Technetium  	14-167
          14.10.9.9  Thorium	14-173
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          14.10.9.10 Tritium	14-180
          14.10.9.11 Uranium	14-185
          14.10.9.12 Zirconium	14-196
    14.11  References 	14-204
    14.12  Selected Bibliography  	14-222
       14.12.1    Inorganic and Analytical Chemistry	14-222
       14.12.2    General Radiochemistry	14-223
       14.12.3    Radiochemical Methods of Separation  	14-223
       14.12.4    Radionuclides	14-223
       14.12.5    Separation Methods	14-225

15 Nuclear Counting Instrumentation  	15-1
    15.1   Introduction  	15-1
    15.2   Alpha Counting  	15-2
       15.2.1 Introduction  	15-2
       15.2.2 Detectors for Alpha Counting  	15-3
          15.2.2.1 lonization Chambers 	15-3
          15.2.2.2 Proportional Counters  	15-3
          15.2.2.3 Scintillation Counters  	15-4
          15.2.2.4 Liquid Scintillation Counters	15-5
          15.2.2.5 Semiconductor Detectors	15-6
    15.3   Beta Counting  	15-7
       15.3.1 Introduction  	15-7
       15.3.2 Proportional Counter	15-7
       15.3.3 Liquid Scintillation 	15-8
       15.3.4 Solid Organic Scintillators	15-9
       15.3.5 Beta Particle Counter	15-10
       15.3.6 Associated Electronic Equipment  	15-11
    15.4   Gamma Counting	15-12
       15.4.1 Introduction  	15-12
       15.4.2 Energy Efficiency Relationship	15-16
       15.4.3 Sodium Iodide Detector Assembly  	15-18
       15.4.4 High Resolution Germanium Detectors	15-19
       15.4.5 Low Background High Resolution Germanium Detectors 	15-19
       15.4.6 High Resolution Detectors for Low Energy Spectrometry 	15-20
       15.4.7 CsI(Tl) Detectors	15-20
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       15.4.8 CdZnTe Detectors 	15-20
       15.4.9 BGO Detectors	15-21
    15.5   Spectrometry Systems 	15-21
       15.5.1 Alpha/Gamma Coincidence Systems	15-21
       15.5.2 Beta/Gamma Coincidence Systems	15-21
       15.5.3 Gamma/Gamma Coincidence Systems 	15-21
       15.5.4 Photon-Electron Rejecting Alpha Li quid Scintillation Systems 	15-22
    15.6   Special Instruments  	15-22
       15.6.1 4-Ti Counter  	15-22
       15.6.2 Low-Geometry Counters	15-23
       15.6.3 Internal Gas Counters	15-23
    15.7    Spectrometers and Energy-Dependent Detectors	15-24
       15.7.1 Anti-Coincidence Counters	15-29
       15.7.2 Coincidence Counters  	15-30
    15.8   Shielding 	15-31
    15.9   Instrument Calibration	15-31
    15.10  Other Considerations	15-32
       15.10.1 Alpha	15-32
          15.10.1.1 Troubleshooting	15-32
          15.10.1.2 Calibration	15-35
          15.10.1.3 Costs	15-35
          15.10.1.4 Quality Control  	15-37
       15.10.2 Beta	15-40
          15.10.2.1 Introduction 	15-40
          15.10.2.2 Alpha Particle Interference and Beta Energy Resolution	15-41
          15.10.2.3 Liquid Scintillation Quenching 	15-42
          15.10.2.4 Beta Particle Attenuation	15-42
          15.10.2.5 Calibration	15-44
          15.10.2.6 Costs  	15-44
          15.10.2.7 Quality Control  	15-46
       15.10.3 Gamma  	15-46
          15.10.3.1 Troubleshooting	15-46
          15.10.3.2 Calibration	15-48
          15.10.3.3 Software	15-49
          15.10.3.4  Costs	15-50
          15.10.3.5  Quality Control  	15-50
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       15.10.4   Non-Nuclear Instrumentation 	15-51
          15.10.4.1 ICP-Mass Spectrometry	15-51
          15.10.4.2 Laser  	15-53
          15.10.4.3 Radionuclides Analyzed By Neutron Activation	15-54
   15.11  References  	15-55
       15.11.1   Cited References  	15-55
       15.11.2   Other Sources	15-61

Attachment 15A Field Measurements	15-63
   15A.1  Introduction 	15-63
   15A.2  Analytical Level of Measurements  	15-63
   15A.3  Documentation of Methodology  	15-65
   15A.4  Instrument Operating Conditions	15-66
   15A.5  Site Conditions/Limitations	15-66
   15A.6  Interferences	15-67
   15A.7  Calibration	15-67
   15A.8  Minimum Detectable Concentrations 	15-68
   15A.9  Precision	15-69
   15A. 10Accuracy  	15-69
   15 A. 11 Representativeness	15-69
   15A.12Completeness	15-70
   15 A. 13 Comparability	15-71
   15A. 14Reference Measurements	15-71
   15A.15Record Keeping	15-72
   15A. 16Quality Improvement	15-72
   15A. 17Management Assessment	15-73
   15 A. ISCombined Laboratory and Field Measurements 	15-73
   15A.19References  	15-73

16 Instrument Calibration and Test Source Preparation	16-1
   16.1   Introduction 	16-1
   16.2   Instrument Calibration	16-2
       16.2.1 Standards  	16-3
       16.2.2 Correspondence	16-3
       16.2.3 Homogeneity 	16-4
       16.2.4 Uncertainty	16-4
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    16.3   General Test Source Characteristics  	16-4
       16.3.1  Geometrical Arrangement	16-5
       16.3.2  Uniformity of Test Source Material	16-5
       16.3.3  Self-Absorption and Scattering 	16-6
       16.3.4  CountingPlanchets  	16-8
    16.4   Test Source Preparation and Calibration for Alpha Measurements	16-8
       16.4.1  Proportional Counters  	16-9
          16.4.1.1 Alpha Test Source Preparation  	16-9
          16.4.1.2 Proportional Counter Calibration — Alpha	16-10
       16.4.2  ZnS(Ag) Scintillation Counter	16-11
       16.4.3  Alpha Spectrometry With Semiconductor Detectors	16-12
       16.4.4  Liquid-Scintillation Spectrometer	16-13
    16.5   Characteristics of Sources for Beta Measurements	16-13
       16.5.1  Proportional Counters  	16-13
          16.5.1.1 Beta Test Source Preparation	16-14
          16.5.1.2 Proportional Counter Calibration — Beta	16-14
       16.5.2  Liquid-Scintillation Spectrometers 	16-15
          16.5.2.1 Liquid Scintillation Test Source Preparation	16-16
          16.5.2.1 Liquid-Scintillation Spectrometer Calibration	16-17
    16.6   Characteristics of Sources for Gamma-Ray Measurements	16-18
       16.6.1  Gamma Test Source Preparation	16-18
       16.6.2  Gamma Spectrometer Calibration	16-20
    16.7   Methods of Test Source Preparation 	16-20
       16.7.1  Electrodeposition	16-20
       16.7.2  Coprecipitation	16-23
       16.7.3  Evaporation 	16-25
       16.7.4  Thermal Volatilization/Sublimation  	16-26
       16.7.5  Preparing Sources to Measure Radioactive Gases	16-27
       16.7.6  Preparing Air Filters for Counting	16-29
       16.7.7  Preparing Swipes/Smears for Counting	16-29
    16.8   References  	16-30

17  Data Acquisition, Reduction,  and Reporting	17-1
    17.1   Introduction  	17-1
    17.2   Data Acquisition  	17-2
       17.2.1  Generic Counting Parameter Selection  	17-3
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          17.2.1.1 Counting Duration	17-4
          17.2.1.2 Counting Geometry 	17-5
          17.2.1.3 Software	17-5
       17.2.2  Basic Data Reduction Calculations 	17-6
    17.3   Data Reduction on Spectrometry Systems  	17-8
       17.3.1  Gamma Spectrometry  	17-8
          17.3.1.1 Peak Search or Identification	17-10
              Regions of Interest (ROI) Method	17-11
              Gaussian Function Derivative Method  	17-12
              Channel Differential Method	17-12
              Correlation Method  	17-12
          17.3.1.2 Singlet/Multiplet Peaks  	17-13
          17.3.1.3 Definition of Peak Centroid and Energy 	17-14
          17.3.1.4 Peak Width Determination	17-14
          17.3.1.5 Peak Area Determination	17-16
          17.3.1.6 Calibration Reference File	17-19
          17.3.1.7 Activity and Concentration  	17-19
          17.3.1.8    Summing Considerations	17-20
          17.3.1.9 Uncertainty Calculation	17-22
       17.3.2  Alpha Spectrometry	17-23
          17.3.2.1 Radiochemical Yield	17-27
          17.3.2.2 Uncertainty Calculation	17-27
       17.3.3  Liquid Scintillation Spectrometry 	17-28
          17.3.3.1 Overview of Liquid Scintillation Counting	17-28
          17.3.3.2 Liquid Scintillation Spectra	17-29
          17.3.3.3 Pulse Characteristics  	17-29
          17.3.3.4 Coincidence Circuitry  	17-30
          17.3.3.5 Quenching  	17-30
          17.3.3.6 Luminescence	17-30
          17.3.3.7 Test Source Vials	17-31
          17.3.3.8 Data Reduction for Liquid Scintillation Counting	17-31
    17.4   Data Reduction on Non-Spectrometry Systems	17-32
    17.5   Reporting Data	17-37
       17.5.1  Sample and Analysis Method Identification 	17-37
       17.5.2  Units and Radionuclide Identification	17-38
       17.5.3  Values, Uncertainly, and Significant Figures	17-38
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       17.5.4  Other Information to be Provided on Request	17-38
    17.6   Data Packages  	17-39
    17.7   Electronic Data Deliverables	17-39
    17.8   References  	17-40
       17.8.1  Cited References 	17-40
       17.8.2  Other Sources	17-42

18  Laboratory Quality Control  	18-1
    18.1   Introduction  	18-1
       18.1.1  Organization of Chapter	18-2
       18.1.2  Format  	18-2
    18.2   Quality Control  	18-3
    18.3   Evaluation of Performance Indicators	18-4
       18.3.1  Importance of Evaluating Performance Indicators	18-4
       18.3.2  Statistical Means of Evaluating Performance Indicators — Control Charts .. 18-5
       18.3.3  Measurement Uncertainty 	18-7
    18.4   Radiochemistry Performance Indicators 	18-9
       18.4.1  Method and Reagent Blank	18-9
       18.4.2  Laboratory Replicates  	18-13
       18.4.3  Laboratory Control  Samples, Matrix Spikes, and Matrix Spike Duplicates .  18-16
       18.4.4  Certified Reference Materials  	18-18
       18.4.5  Chemical/Tracer Yield	18-21
    18.5   Instrumentation Performance Indicators 	18-25
       18.5.1  Instrument Background Measurements	18-25
       18.5.2  Efficiency Calibrations  	18-27
       18.5.3  Spectrometry Systems  	18-31
          18.5.3.1   Energy Calibrations	18-31
          18.5.3.2   Peak Resolution and  Tailing 	18-34
       18.5.4  Gas Proportional Systems 	18-38
          18.5.4.1   Voltage Plateaus	18-38
          18.5.4.2   Self-Absorption, Backscatter, and Crosstalk  	18-39
       18.5.5  Liquid Scintillation  	18-41
          18.5.6 Summary 	18-41
    18.6   Related Concerns	18-43
       18.6.1  Detection Capability  	18-43
       18.6.2  Secular Equilibrium  	18-44
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       18.6.3 Half-Life	18-47
       18.6.4 Interferences	18-48
       18.6.5 Negative Results  	18-50
       18.6.6  Blind Samples	18-51
       18.6.7 Calibration of Apparatus Used for Weight and Volume Measurements  .... 18-54
    18.7   References  	18-55
       18.7.1 Cited Sources	18-55
       18.7.2 Other Sources	18-57
    Attachment ISA: Control Charts  	18-59
       ISA. 1 Introduction 	18-59
       18A.2 X Charts	18-59
       18A.3  Charts  	18-63
       18A.4 R Charts	18-64
       18A.5 Control  Charts for Instrument Response	18-65
       18A.6 References 	18-70
    Attachment 18B: Statistical Tests for QC Results 	18-71
       18B.1 Introduction 	18-71
       18B.2 Tests for Excess Variance in the Instrument Response	18-71
       18B.3 Instrument Background Measurements	18-78
          18B.3.1   Detection of Background Variability	18-78
          18B.3.2   Comparing a Single Observation to Preset Limits	18-80
          18B.3.3   Comparing the Results of Consecutive Measurements	18-84
       18B.4 Negative Activities	18-86
       18B.5 References 	18-86

19  Measurement Statistics 	19-1
    19.1   Overview	19-1
       19.2   Statistical  Concepts and Terms  	19-2
          19.2.1 Basic Concepts	19-2
          19.2.2 Summary of Terms	19-5
       19.3   Measurement Uncertainty 	19-7
          19.3.1 Measurement, Error, and Uncertainty  	19-7
          19.3.2 The  Measurement Process	19-8
          19.3.3 Analysis of Measurement Uncertainty	19-10
          19.3.4 Corrections for Systematic Effects	19-11
          19.3.5 Counting Uncertainty	19-11
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          19.3.6  Expanded Uncertainty  	19-11
          19.3.7  Significant Figures	19-12
          19.3.8  Reporting the Measurement Uncertainty	19-13
          19.3.9  Recommendations 	19-14
          19.3.10 Summary of Terms	19-14
       19.4   Detection and Quantification Capability	19-17
          19.4.1  Analyte Detection Decisions 	19-17
          19.4.2  The Minimum Detectable Concentration	19-19
          19.4.3  Differences between the ISO and IUPAC Definitions	19-22
          19.4.4  Other Detection Terminologies	19-23
          19.4.5  The Minimum Quantifiable Concentration	19-24
          19.4.6  Recommendations 	19-25
          19.4.7  Summary of Terms	19-25
       19.5   Procedures for Estimating Uncertainty  	19-27
          19.5.1  Identifying Sources of Uncertainty  	19-27
          19.5.2  Evaluation of Standard Uncertainties  	19-28
             19.5.2.1  Type A Evaluations	19-28
             19.5.2.2 Type B Evaluations	19-31
          19.5.3  Combined  Standard Uncertainty 	19-33
          19.5.4  The Estimated Covariance of Two Output Estimates 	19-37
          19.5.5  Nonlinear Models	19-38
             19.5.5.1  Uncertainty Propagation	19-38
             19.5.5.2 Bias	19-40
             19.5.5.3  Nominal Values  	19-42
       19.6   Radiation Measurement Uncertainty	19-43
          19.6.1  Radioactive Decay	19-43
          19.6.2  Radiation Counting  	19-44
          19.6.3  Count Rate	19-47
             19.6.3.1  Dead Time	19-48
             19.6.3.2 A Confidence Interval for the Count Rate  	19-49
          19.6.4  Instrument Background 	19-50
          19.6.5  Counting Efficiency	19-52
          19.6.6  Radionuclide Half-life	19-56
          19.6.7  Gamma Spectrometry  	19-56
          19.6.8  Balances	19-56
          19.6.9  Pipets and Other Volumetric Apparatus  	19-58
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          19.6.10 Digital Displays and Rounding  	19-62
          19.6.11 Subsampling	19-63
          19.6.12 The Standard Uncertainty for a Hypothetical Measurement	19-64
       19.7   Detection and Quantification Limits  	19-65
          19.7.1  Calculation of the Critical Value	19-65
             19.7.1.1  Normally Distributed Signals	19-66
             19.7.1.2  Poisson Counting  	19-67
             19.7.1.3  Reagent Blanks	19-70
          19.7.2  Calculation of the Minimum Detectable Concentration  	19-71
             19.7.2.1  The Minimum Detectable Net Instrument Signal	19-72
             19.7.2.2  Normally Distributed Signals	19-72
             19.7.2.3  Poisson Counting  	19-74
             19.7.2.4  The MDC   	19-75
             19.7.2.5  Regulatory Requirements 	19-76
             19.7.2.6  Testing the MDC	19-77
          19.7.3  Calculation of the Minimum Quantifiable Concentration	19-78
       19.8   References 	19-80
          19.8.1  Cited Sources	19-80
          19.8.2  Other Sources	19-84
       Attachment 19A Distributions	19-85
          19A.1  Introduction  	19-85
          19A.2  Normal Distributions	19-85
          19A.3  Log-normal Distributions	19-86
          19A.4  Chi-square Distributions  	19-87
          19A.5  T-Distributions	19-88
          19A.6  Rectangular Distributions  	19-90
          19A.7  Trapezoidal and Triangular Distributions 	19-91
          19A.8  Exponential Distributions  	19-92
          19A.9  Binomial  Distributions	19-92
          19A.10 Poisson Distributions	19-93
          19A.11 References  	19-95
       Attachment 19B  Multicomponent Analyses	19-97
          19B. 1  Matrix Equations 	19-97
          19B.2  Random Vectors and Matrices	19-98
          19B.3  Linear Least Squares 	19-99
          19B.4  General Least Squares  	19-101
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          19B.5 The Covariance Matrix for a Least-Squares Solution  	19-102
          19B.6 Critical Values 	19-103
          19B.7 Detection and Quantification Limits  	19-104
          19B.8 References  	19-104
       Attachment 19C Estimation of Coverage Factors	19-105
          19C.1 Introduction 	19-105
          19C.2 Procedure	19-105
          19C.3 Poisson Counting Uncertainty	19-106
          19C.4 References  	19-107
       Attachment 19D Low-Background Detection Limits	19-109
          19D.1 Overview 	19-109
          19D.2 Calculation of the Critical Value	19-109
             19D.2.1  Normally Distributed Signals	19-109
             19D.2.2  Poisson Counting  	19-110
          19D.3 Calculation of the Minimum Detectable Concentration  	19-123
             19D.3.1  The Minimum Detectable Net Instrument Signal	19-123
             19D.3.2  Normally Distributed Signals	19-123
             19D.3.3  Poisson Counting  	19-126
          19D.4 References  	19-132
       Attachment 19E Example Calculations  	19-135
          19E.1 Overview  	19-135
          19E.2 Sample Collection and Analysis	19-135
          19E.3 The Measurement Model 	19-136
          19E.4 The Combined Standard Uncertainty	19-138
          19E.5 The Critical Net Count	19-140
          19E.6 The Minimum Detectable Concentration	19-143
          19E.7 The Minimum Quantifiable Concentration  	19-148
       Attachment 19F  Tests for Normality 	19-149
          19F.1 Purpose	19-149
          19F.2 Normal Probability Plots  	19-149
          19F.3 Filliben's Statistic	19-151
          19F.4 References	19-155
       Attachment 19G Balance Measurement Uncertainty  	19-157
          19G.1 Purpose	19-157
          19G.2 Considerations	19-157
          19G.3 Repeatability 	19-157
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          19G.4 Environmental Factors	19-158
          19G.5 Calibration	19-159
          19G.6 Linearity	19-160
          19G.7 Air Buoyancy Corrections 	19-160
          19G.8 Combining the Components	19-164
          19G.9 References  	19-165

20 Waste Management in a Radioanalytical Laboratory  	20-1
   20.1   Introduction 	20-1
   20.2   Types of Laboratory Wastes  	20-1
   20.3   Waste Management Program	20-2
       20.3.1 Program Integration	20-3
       20.3.2 Staff Involvement	20-3
   20.4   Waste Minimization	20-4
   20.5   Waste Determinations and Characterization	20-6
   20.6   Specific Waste Management Requirements  	20-7
       20.6.1 Sample/Waste Exemptions  	20-9
       20.6.2 Storage	20-10
          20.6.2.1 Container Requirements	20-11
          20.6.2.2 Labeling Requirements  	20-11
          20.6.2.3 Time Constraints  	20-11
          20.6.2.4 Monitoring Requirements  	20-12
       20.6.3 Treatment	20-12
       20.6.4 Disposal	20-13
   20.7   Contents of a Laboratory Waste Management Plan/Certification Plan	20-14
       20.7.1 Laboratory Waste Management Plan	20-14
       20.7.2 Waste Certification Plan/Program	20-14
   20.8   Useful Web Sites	20-16
   20.9   References  	20-17
       20.9.1 Cited References 	20-17
       20.9.2 Other Sources	20-18

Glossary  	to be added follow ing public review
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                                      Appendices

Appendix A:  Directed Planning Approaches  	  A-l
   A. IDirected Planning Approaches	  A-l
   A.2Elements Common to Directed Planning Approaches	  A-l
   A.SData Quality Objectives Process 	  A-2
   A.4Observational Approach  	  A-3
   A. 5 Streamlined Approach for Environmental Restoration	  A-4
   A.6Technical Project Planning  	  A-4
   A.TExpedited Site Characterization	  A-5
   A.SValue Engineering	  A-5
   A.9Systems Engineering 	  A-6
   A. 10  Total Quality Management 	  A-7
   A. 11  Partnering	  A-7
   A. 12  References 	  A-8
       A.12.1 Data Quality Objectives	  A-8
       A. 12.2 Observational Approach  	  A-10
       A. 12.3 Streamlined Approach for Environmental Restoration (Safer)	  A-10
       A. 12.4 Technical Project Planning  	  A-l 1
       A. 12.5 Expedited Site Characterization	  A-ll
       A. 12.6 Value Engineering	  A-13
       A. 12.7 Systems Engineering 	  A-14
       A. 12.8 Total Quality Management  	  A-16
       A. 12.9 Partnering	  A-17

Appendix B:  The Data Quality Objectives Process 	B-l
   Bl.O  Introduction 	B-l
   B2.0  Overview of the DQO Process	B-2
   B3.0  The Seven Steps of the DQO Process 	B-3
       B3.1  DQO Process Step 1: State the Problem  	B-3
       B3.2  DQO Process Step 2: Identify the Decision	B-4
       B3.3  DQO Process Step 3: Identify Inputs to the Decision  	B-5
       B3.4  DQO Process Step 4: Define the Study Boundaries 	B-7
       B3.5  Outputs of DQO Process Steps 1 to 4 Lead Into Steps 5 to 7 	B-8
   B3.6  DQO Process Step 5: Develop  a Decision Rule  	B-8
   B3.7  DQO Process Step 6: Specify the Limits on Decision Errors 	B-9
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   B3.8   DQO Process Step 7: Optimize the Design for Obtaining Data  	B-12
   B3.9   References  	B-14
   Attachment B-l Decision Error Rates And The Gray Region  	B-16
       B-l.l  Introduction 	B-16
       B-1.2  The Region of Interest	B-16
       B-l.3  Measurement Uncertainty at the Action Level  	B-17
       B-1.4  The Null Hypothesis  	B-18
          Case 1: Assume The True Concentration is Over 1.0  	B-18
          Case 2: Assume The True Concentration is 0.9	B-20
       B-l.5  The Critical Region  	B-20
       B-l.6  The Gray Region  	B-21

Appendix C: Measurement Quality Objectives for Method Uncertainty And Detection and
   Quantification Capability	C-l
   C.I    Introduction 	C-l
   C.2    Hypothesis Testing	C-2
   C.3    Development of MQOs for Analytical Protocol Selection 	C-4
   C.4    The Role of the MQO for Method Uncertainty in Data Evaluation  	C-9
       C.4.1 Uncertainty Requirements at Various Concentrations  	C-9
       C.4.2 Acceptance Criteria for Quality Control Samples	C-12
   C.5    References  	C-19

Appendix D  Content of Project Plan Documents	  D-l
   Dl.O   Introduction 	  D-l
   D2.0   Group A: Project Management  	  D-3
       D2.1   Project Management (Al): Title and Approval Sheet	  D-6
       D2.2   Project Management (A2): Table  of Contents	  D-7
       D2.3   Project Management (A3): Distribution List  	  D-7
       D2.4   Project Management (A4): Project/Task Organization	  D-8
       D2.5   Project Management (A5): Problem Definition/Background 	  D-8
       D2.6   Project Management (A6): Project/Task Description  	  D-10
       D2.7   Project Management (A7): Quality Objectives and Criteria for
          Measurement Data	  D-12
          D2.7.1 Project's Quality Objectives  	  D-12
          D2.7.2 Specifying Measurement Quality Objectives	  D-13
          D2.7.3 Relation between the Project DQOs, MQOs, and QC Requirements . .  .  D-14
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      D2.8  Project Management (A8): Special Training Requirements/Certification . . .  D-14
      D2.9  Project Management (A9): Documentation and Record  	  D-14
   D3.0  Group B: Measurement/Data Acquisition	  D-16
      D3.1  Measurement/Data Acquisition (Bl): Sampling Process Design 	  D-16
      D3.2  Measurement/Data Acquisition (B2): Sampling Methods Requirements ...  D-18
      D3.3  Measurement/Data Acquisition (B3): Sample Handling and Custody
          Requirements	  D-20
      D3.4  Measurement/Data Acquisition (B4): Analytical Methods Requirements  ..  D-21
      D3.5  Measurement/Data Acquisition (B5): Quality Control Requirements  	  D-23
      D3.6  Measurement/Data Acquisition (B6): Instrument/Equipment Testing, Inspection,
             and Maintenance Requirements  	  D-24
      D3.7  Measurement/Data Acquisition (B7): Instrument Calibration and Frequency  D-25
      D3.8  Measurement/Data Acquisition (B8): Inspect!on/Acceptance Requirements for
             Supplies and Consumables  	  D-26
      D3.9  Measurement/Data Acquisition (B9): Data Acquisition Requirements for Non-
             Direct Measurement Data  	  D-27
      D3.10 Measurement/Data Acquisition (BIO): Data Management	  D-28
   D4.0  Group C: Assessment/Oversight	  D-29
      D4.1  Assessment/Oversight (Cl): Assessment and Response Actions 	  D-29
      D4.2  Assessment/Oversight (C2): Reports To Management	  D-30
   D5.0  Group D: Data Validation and Usability	  D-31
      D5.1  Data Validation and Usability (Dl): Verification and Validation
          Requirements  	  D-31
      D5.2  Data Validation and Usability (D2): Verification and Validation Methods  .  D-32
          D5.2.1 Data Verification	  D-32
          D5.2.2 Data Validation  	  D-33
      D5.3  Data Validation and Usability (D3): Reconciliation with Data Quality
          Objectives  	  D-34
   D6.0  References  	  D-35

Appendix E: Contracting Laboratory Services  	E-l
   E. 1 Introduction 	E-l
   E.2 Procurement of Services 	E-5
      E.2.1  Request for Approval of Proposed Procurement Action	E-6
      E.2.2  Types of Procurement Mechanisms	E-6
   E.3 Request for Proposals—The Solicitation   	E-8
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       E.3.1  Market Research  	E-9
       E.3.2  Length of Contract	E-10
       E.3.3  Subcontracts	E-10
   E.4Proposal Requirements  	E-l 1
       E.4.1  RFP and Contract Information	E-l 1
       E.4.2  Personnel  	E-14
       E.4.3  Instrumentation  	E-17
          E.4.3.1 Type, Number, and Age of Laboratory Instruments  	E-18
          E.4.3.2    Service Contract	E-18
       E.4.4  Narrative to Approach	E-18
          E.4.4.1 Analytical Methods or Protocols	E-18
          E.4.4.2Meeting Contract Measurement Quality Objectives  	E-19
          E.4.4.3    Data Package 	E-19
          E.4.4.4    Schedule	E-19
          E.4.4.5    Sample Storage and Disposal	E-20
       E.4.5  Quality Manual  	E-21
       E.4.6  Licenses and Accreditations	E-22
       E.4.7  Experience	E-22
          E.4.7.1    Previous or Current Contracts	E-23
          E.4.7.2    Quality of Performance 	E-23
   E.5 Proposal Evaluation and Scoring Procedures  	E-23
       E.5.1  Evaluation Committee	E-24
       E.5.2  Ground Rules — Questions	E-24
       E.5.3  Scoring/Evaluation Scheme	E-24
          E.5.3.1 Review of Technical Proposal and Quality Manual  	E-26
          E.5.3.2    Review of Laboratory Accreditation  	E-28
          E.5.3.3    Review of Experience  	E-28
       E.5.4  Pre-Award Proficiency Samples 	E-28
       E.5.5  Pre-Award Audit  	E-29
       E.5.6  Comparison of Prices	E-33
       E.5.7  Debriefing of Unsuccessful Vendors	E-34
   E.6The Award	E-34
   E.7For the Duration of the Contract  	E-35
       E.7.1  Managing a Contract 	E-35
       E.7.2  Responsibility of the Contractor 	E-36
       E.7.3  Responsibility of the Agency	E-36
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       E.7.4  Anomalies and Nonconformance	E-36
       E.7.5  Laboratory Assessment 	E-37
          E.7.5.1    Performance and Quality Control Samples 	E-37
          E.7.5.2    Laboratory Performance Evaluation Programs 	E-38
          E.7.5.3    Laboratory Evaluations Performed During the Contract Period  	E-39
   E.8 Contract Completion 	E-40
   E.9References  	E-41

Appendix F Laboratory Subsampling	F-l
   F.I Introduction  	F-l
   F.2 Basic Concepts	F-2
   F.3 Sources of Measurement Error 	F-4
       F.3.1  Sampling Bias  	F-4
       F.3.2  Fundamental Error	F-5
       F.3.3  Grouping and Segregation Error 	F-7
   F.4 Implementation of the Particulate Sampling Theory	F-10
       F.4.1  The Fundamental Variance 	F-10
       F.4.2  Scenario 1 -Natural Radioactive Minerals	F-ll
       F.4.3  Scenario 2 - Hot Particles 	F-12
       F.4.4  Scenario 3 -Particle Surface Contamination  	F-14
   F.5 Summary 	F-16
   F.6 References  	F-17

G Statistical Tables  	  G-l
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                                    List of Figures

Figure 1.1 The Data Life Cycle	1-5
Figure 1.2 Typical Components of an Analytical Process  	1-7
Figure 1.3 The MARLAP Process  	1-16

Figure 3.1 Typical components of an analytical process  	3-3
Figure 3.2 Analytical protocol specifications	3-28
Figure 3.3 Example analytical protocol specifications	3-29

Figure 6.1 Analytical process	6-3
Figure 6.2 Method application life cycle  	6-6
Figure 6.3 Expanded Figure 6.2 addressing the laboratory's method evaluation process  ....  6-7

Figure 7.1 Considerations for the initial evaluation of a laboratory	7-18

Figure 8.1 The Assessment Process  	8-6

Figure 9.1 Using physical samples to measure a characteristic of the population representatively 1
Figure 9.2 Types of sampling and analytical errors	9-18

Figure 10.1 Example of chain-of-custody record	10-10

Figure 11.1 Overview of sample receipt, inspection, and tracking	11-2

Figure 12.1 Degree of error  in laboratory  sample preparation (Scwedt, 1997)	12-1
Figure 12.2 Laboratory Sample Preparation Flowchart (for Solid Samples)	12-13

Figure 14.1 Ethylenediaminetetraacetic Acid (1)(EDTA)	14-22
Figure 14.2 Crown ethers 	14-23
Figure 14.3 The behavior of elements in  concentrated hydrochloric acid on cation-exchange
   resins  	14-57
Figure 14.4 behavior of elements in concentrated hydrochloric acid on anion-exchange resinsJ-58
Figure 14.5 The electrical double layer	14-86

Figure 15.1 Gamma-ray Interactions with Germanium	15-12

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Figure 15.2 Gamma-ray Spectra of 60Co 	15-13
Figure 15.3 Energy Spectrum of 22Na  	15-15
Figure 15.4 Efficiency vs. Gamma-ray Energy  	15-16
Figure 15.5 Standard Cryostat FtPGe Background Spectrum 	15-20
Figure 15.6 Low Background Cryostat HPGe Background Spectrum	15-20
Figure 15.7 Nal(Tl) Energy Spectrum of 137Cs  	15-26
Figure 15.8 HPGe Energy Spectrum of 137Cs	15-27
Figure 15.9 Spectrum of 210Pb, 210Bi, and 210Po	15-29
Figure 15.10 Range vs. Energy for Alpha Particles in Air  	15-35
Figure 15.11 Range vs. Energy for Beta Particles in Air and Water	15-43
Figure 15.12 Beta Detector Efficiency Curve for 131I vs. Weight  	15-43
Figure 15.13 Beta-gamma coincidence efficiency curve for 129I	15-55

Figure 17.1 Gamma-ray spectrum	17-9
Figure 17.2 Gamma-ray analysis sequence	17-11
Figure 17.3 Low-energy tailing	17-16
Figure 17.4 Photopeak baseline continuum  	17-17
Figure 17.5 Photopeak baseline continuum-step function  	17-18
Figure 17.6 Alpha spectrum  	17-23

Figure 18.1 Control chart for daily counting of a standard reference source, with limits corrected
   for decay	18-7
Figure 18.2 Three general categories of blank changes  	18-12
Figure 18.3 Failed performance indicator: replicates	18-15
Figure 18.4 Failed performance indicator: chemical yield	18-23

Figure 19.1 A symmetric distribution	19-4
Figure 19.2 An asymmetric distribution	19-4
Figure 19.3 The critical value xc  and minimum detectable value XD
   of the net state variable  	19-20
Figure 19.4 Expected fraction of atoms remaining at time t	19-44
Figure 19.5 A normal distribution  	19-85
Figure 19.6 A log-normal distribution  	19-86
Figure 19.7 Chi-square distributions	19-88
Figure 19.8 The ^-distribution with 3 degrees of freedom  	19-89
Figure 19.9 A rectangular distribution  	19-91
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Figure 19.10  A trapezoidal distribution  	19-91
Figure 19.11  An exponential distribution	19-92
Figure 19.12  Type I error rate for the Poisson-normal approximation (tB = ts)  	19-113
Figure 19.13  Type I error rates for Formula A	19-115
Figure 19.14  Type I error rates for Formula B  	19-116
Figure 19.15  Type I error rates for Formula C  	19-118
Figure 19.16  Type I error rates for the Stapleton approximation	19-119
Figure 19.17  Type I error rates for the nonrandomized exact test 	19-121
Figure 19.18  Example: Normal probability plot	19-154

Figure Bl Seven steps of the DQO process	B-2
Figure B2(a)  Decision performance goal diagram null hypothesis: the parameter exceeds the
   action level	B-l 1
Figure B2(b)  Decision performance goal diagram null hypothesis: the parameter is less than the
   action level	B-l 1
Figure B3 How Proximity to the action level determines what is an acceptable level of
   uncertainty	B-13

Figure C.I Required Analytical Standard Deviation (aReq)	C-10

Figure E-l General Sequence Initiating and Later Conducting Work with a Contract LaboratoBy-4
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                                    List of Tables

Table 2.1 Summary of the directed planning process and radioanalytical specialists
   participation	2-10

Table 3.1  Matrix-specific analytical planning issues	3-23

Table 4.1  Elements of project plan documents	4-7
Table 4.2  Crosswalk between project plan document elements and directed planning process4-ll

Table 6.1  Tiered method validation approach  	6-28

Table 7.1  Cross reference of information available for method evaluation	7-4

Table 9.1  Summary of the DQA process 	9-6

Table 10.1  Summary of sample preservation techniques	10-25

Table 11.1  Typical topics addressed in standard operating procedures related to sample receipt,
   inspection, and tracking	11-4

Table 12.1  Examples of volatile radionuclides	12-4
Table 12.2 Properties of sample container materials  	12-5
Table 12.3  Examples of dry-ashing temperatures (platinum container)	12-23

Table 13.1  Common fusion fluxes  	13-7
Table 13.2 Examples of acids used for wet ashing	13-14
Table 13.3  Standard  reduction potentials of selected half-reactions at 25 °C 	13-14

Table 14.1  Oxidation states of elements	14-9
Table 14.2 Stable oxidation states of selected elements	14-10
Table 14.3  Redox  reagents for radionuclides	14-14
Table 14.5  Radioanalytical methods employing solvent extraction	14-35
Table 14.6 Radioanalytical methods employing extraction  chromatography	14-36
Table 14.7 Elements separable by volatilization as certain species 	14-41
Table 14.8  Typical functional groups of ion-exchange resins 	14-54

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Table 14.9 Common ion-exchange resins	14-55
Table 14.10 General solubility behavior of some cations of interest	14-63
Table 14.11 Summary of methods for utilizing precipitation from
   homogeneous solution	14-74
Table 14.12 Influence of precipitation conditions on the purity of precipitates	14-76
Table 14.13 Common coprecipitating agents for radionuclides	14-83
Table 14.14 Coprecipitation behavior of plutonium and neptunium 	14-85
Table 14.15 General properties of common filter papers	14-89
Table 14.16 Atoms and mass of select radionuclides equivalent to 500 dpm  	14-91
Table 14.17 Masking agents for ions of various metals	14-117
Table 14.18 Masking agents for anions and neutral molecules  	14-119

Table 15.1 Typical percent gamma-ray efficiencies for a 55 percent high-purity germanium
   detector with various counting geometries  	15-17
Table 15.2 Nuclides for gamma-ray spectrometer calibration	15-48

Table 16.1 Nuclides for alpha calibration	16-10
Table 16.2 Nuclides for beta calibration	16-15

Table 17.1 Units for data reporting	17-38

Table 18.1 Problems leading to loss of analytical control  	18-3
Table 18.2a Certified Massic activities for natural radionuclides
   with a normal distribution of measurement results  	18-20
Table 18.2b Certified Massic activities for anthropogenic radionuclides with a Weibull
   distribution of measurement results	18-20
Table 18.2c Uncertified Massic activities	18-20
Table 18.3 Instrument background evaluation  	18-27
Table 18.4 Root cause analysis of performance check results  	18-37
Table 18.5 Instrument calibration: example frequency and performance criteria 	18-41
Table 18A.I Bias-correction factor for the experimental standard deviation	18-60

Table 19.1 Applications of the uncertainty propagation formula	19-34
Table 19.2 Density of air-free water	19-60
Table 19.3 95% confidence interval for a Poisson mean 	19-94
Table 19.4 Critical gross count (well-known blank)	19-111
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Table 19.5  Bias factor for the experimental standard deviation	19-125
Table 19.6  Estimated and true values of SD (tB = tS)	19-131
Table 19.7  Input estimates and standard uncertainties	19-139

Table 20.1  Examples of laboratory-generated wastes	20-2

Table Dl QAPP groups and elements	  D-2
Table D2 Comparison of project plan contents  	  D-3
Table D3 Content of the three elements that constitute the project description	  D-9

Table E.I  Examples of procurement options to obtain materials or services	E-7
Table E.2  SOW checklists for the agency and proposer	E-13
Table E.3  Laboratory technical supervisory personnel listed by position title and examples for
   Table suggested minimum qualifications  	E-15
Table E.4  Laboratory technical personnel listed by position title and examples for suggested
   minimum qualifications and examples of optional staff members  	E-16
Table E.5  Laboratory technical staff listed by position title and examples for suggested
   minimum qualifications	E-16
Table E.6  Example of a proposal evaluation plan  	E-26

Table G.I Quantiles of the standard normal distribution	  G-l
Table G.2 Quantiles of Student's t distribution	  G-3
Table G.3 Quantiles of chi-square	  G-5
Table G.4 Critical values for the nonrandomized exact test  	  G-7
Table G.5 Critical values of Filliben's statistic	  G-l 1
Table G.6 Summary of probability distributions	  G-12
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               ACRONYMS AND ABBREVIATIONS

   Note: Bracketed numbers following each definition represent the first chapter in which the acronym appears.
ADC	analog to digital convenor [18]
AEA	Atomic Energy Act [20]
AL 	action level [C]
ANSI  	American National Standards Institute [1]
AOAC  	Association of Official Analytical Chemists [3]
APHA	American Public Health Association [6]
APS  	analytical protocol specification [1]
ARARs	applicable or relevant and appropriate requirements (CERCLA/Superfund) [D]
ASL  	analytical support laboratory [15]
ASQC	American Society for Quality Control [2]
ASTM  	American Society for Testing and Materials [1]
AID	alpha track detector [10]

BOA	basic ordering agreement [4]

CAA	Clean Air Act [20]
CBD	Commerce Business Daily [E]
CC 	charcoal canisters [10]
CEDE	committed effective dose equivalent [2]
CERCLA ....  Comprehensive Environmental Response, Compensation, and Liability Act of
                  1980 (Superfund) [2]
CFM	cubic feet per minute [16]
CFR  	Code of Federal Regulations [20]
CL 	central line (of a control chart) [15]
CMPO  	  [octyl(phenyl)]-N,N-diisobutylcarbonylmethylphosphine oxide [14]
CMST	Characterization, Monitoring, and Sensor Technology Program (DOE) [A]
COC	chain of custody [2]
COR	contracting officer's representative [5]
cpm	counts per minute [12]
cps 	counts per second [15]
CRM	continuous radon monitor [10]
CRM	certified reference material [18]
CWA  	Clean Water Act [20]
CWLM	continuous working level monitor [10]
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Acronyms and Abbreviations
DAAP	diamylamylphosphonate [14]
DCGL	derived concentration guideline level [2]
DIN	di-isopropylnaphthalene [16]
DL  	discrimination limit [C]
DoD  	U.S. Department of Defense [1]
DOE	U.S. Department of Energy [1]
DOELAP 	DOE Lab Accreditation Program [18]
DOT	U.S. Department of Transportation [5]
DPM	disintegrations per minute [12]
DPPP  	dipentylpentylphosphonate [14]
DQA	data quality assessment [1]
DQI	data quality indicators [3]
DQO	data quality objective [1]
DTP A	di ethyl ene triamine penta-acetic  acid [10]
DVB	divinylbenzene [14]

EDD	electronic data deliverables [17]
EDTA	ethyl ene diamine tetra acetic acid [10]
EGTA	ethyleneglycol bis(2-aminoethylether)-tetraacetate [14]
EPA  	U.S. Environmental Protection Agency [1]
ERPRHVIS  . .  . Environmental Resources Program Management System (U.S. Air Force) [17]
ESC  	expedited site characterization [A]
eV	electron volts [15]

FAR	Federal Acquisition Regulations [E]
FDA	U.S. Food and Drug Administration [1]
FWFDVI	full width of a peak at half maximum [8]
FWTM	full width of a peak at tenth maximum [18]

GC  	gas chromatography [14]
GLPC	gas-liquid phase chromatography [14]
GM	Geiger-Mueller detector [11]
GUM  	Guide to the Expression of Uncertainty in Measurement [1]

HDBP	dibutylphosphoric acid [14]
HDEHP  	bis(2-ethylhexyl) phosphoric acid [16]
HDEHP  	diethylhexylphosphoric acid [14]
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                                                           Acronyms and Abbreviations
HDPE	high density polyethylene [10]
HPGe  	high-purity germanium [semiconductor] [15]
HPLC	high-pressure liquid chromatography; high-performance liquid chromatography
                  [14]
HTRW  	hazardous, toxic and radioactive waste [10]

ICP-MS  	inductively coupled plasma-mass spectroscopy [14]
IPPD	integrated product and process development [A]
ISO	International Organization for Standardization [1]
IUPAC	International Union of Pure and Applied Chemistry [1]

LAN	local area network [17]
LBGR	lower boundary of the gray region [B]
LCL  	lower control limit [18]
LCS  	laboratory control samples [3]
LDPE	low density polyethylene  [10]
LEGe  	low energy germanium [15]
LEVIS  	Laboratory Information Management System [17]
LLD  	lower limit of detection [19]
LLRW  	low-level radioactive waste [20]
LLRWPA ....  Low Level Radioactive Waste Policy Act [20]
LOMI	low oxidation-state transition-metal ion [10]
LPC  	liquid partition chromatography; liquid-phase chromatography [14]
LS	liquid scintillation [15]
LSC  	liquid scintillation counting [15]
LWL	lower warning limit [18]

MAPEP  	Mixed Analyte Performance Evaluation Program [DOE] [5]
MARSSEVI . . .  Multi-Agency Radiation Survey and Site Investigation Manual [1]
MCA  	multichannel analyzer [15]
MDA  	minimum detection analysis [15]; minimum detectable amount [7]
MDC  	minimum detectable concentration [3]
MDL	method detection limit [19]
MDC  	minimum detectable concentration [2]
MIBK	methyl isobutyl ketone [14]
MQC  	minimum quantifiable concentration [3]
MQO  	measurement quality objective [1]
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Acronyms and Abbreviations
MS  	
MSD	
MVRM	

NELAC  	
NESHAP
NIST	
NRC	
NRTP  	
NTAorNTTA
NTU	
NVLAP  	

OA	
OFHC	
OFPP  	

PARCC  	
PCB  	
PDF  	
PE	
PFA  	
PIC	
PT	
PTFE  	
PUREX  	
PVC  	

QA	
QAP	
QAPP	
QC

RCRA	
REE  	
REGe  	
RFP  	
matrix spike [8]
matrix spike duplicate [8]
method validation reference material [5]

National Environmental Laboratory Accreditation Conference [5]
National Emission Standards for Hazardous Air Pollutants [12]
National Institute of Standards and Technology [1]
U.S. Nuclear Regulatory Commission [1]
NIST Radiochemistry Intercomparison Program [ 18]
nitrilotriacetate [14]
nephelometric turbidity units [10]
National Voluntary Laboratory Accreditation Program (NIST) [5]

observational approach [A]
oxygen-free high-conductivity [15]
Office of Federal Procurement Policy [E]

precision, accuracy, representativeness, completeness, and comparability [3]
polychlorinated biphenyl [20]
probability density function [19]
performance evaluation  [5]
perfluoroalcoholoxil™ [13]
pressurized ionization chamber [15]
performance testing [5]
polytetrafluoroethylene  [12]
plutonium uranium reduction extraction [14]
polyvinyl chloride [10]

quality assurance [2]
Quality Assessment Program (DOE) [5]
quality assurance project plan [1]
quality control  [1]

Resource Conservation and Recovery Act [15]
rare earth elements [13]
reverse-electrode germanium [semiconductor] [15]
request for proposals [5]
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                                                            Acronyms and Abbreviations
RFQ  	request for quotations [E]
RMDC  	required minimum detectable concentration [8]
ROI	regions of interest [17]
RPD  	relative percent difference [7]
RPM	Remedial Project Manager [2]
RSD  	relative standard deviation [19]
RSO  	Radiation Safety Officer [11]

SA 	 spike activity [7]
SAFER	 streamlined approach for environmental restoration (DOE) [2]
SAM	 Site Assessment Manager  [2]
SAP  	 sampling and analysis plan [1]
SI  	international system of units [3]
SMO	 sample management office [2]
SOP  	 standard operating procedure [4]
SOW	 Statement of Work [1]
SQC  	 statistical quality  control [15]
SR	unspiked sample result [7]
SRM	 standard reference material [18]
SSR  	 spiked sample result [7]

TAT  	turnaround time [7]
TBP  	tributyl phosphate [14]
TC 	to contain [glassware] [18]
TCLP  	toxicity characteristic leaching procedure [13]
TD 	to deliver [glassware] [18]
TEC  	technical evaluation committee [5]
TEDE	total effective dose equivalent [2]
TES  	technical evaluation sheet (USGS) [5]
TFM	tetrafluorometoxil™ [13]
TIOA  	tri-iso-octylamine [14]
TLD  	thermoluminescent dosimeter [10]
TOPO	trioctylphosphinic oxide [14]
TPO  	Technical Project Officer [2]
TPP	technical project planning  [2]
TPU  	total propagated uncertainty [19]
TQM	Total Quality Management [A]
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Acronyms and Abbreviations
TRUEX  	trans uranium extraction [14]
TSCA	Toxic Substances Control Act [20]
TSDF  	treatment, storage, or disposal facility [20]
TTA	thenoyltrifluoroacetone [14]

UBGR  	upper bound of the gray region [7]
UCL	upper control limit [18]
USGS	United States Geological Survey [1]
UWL  	upper warning limit  [18]

V	volts [15]

WCP	waste certification plan [20]

XtGe	extended-range germanium [semiconductor] [15]
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                         1  INTRODUCTION TO MARLAP
 2     1.1    Overview

 3     Each year, hundreds of millions of dollars are spent on projects and programs that rely, to varying
 4     degrees, on radioanalytical data for decision-making. These decisions often have a significant
 5     impact on human health and the environment. Of critical importance to informed decision-
 6     making are data of known quality appropriate for its intended use. Making incorrect decisions
 7     due to data inadequacies, such as failing to remediate a radioactively contaminated site,
 8     necessitates the expenditure of additional resources, causes delays in project completions and,
 9     depending on the nature of the project, can result in the loss of public trust and confidence. The
10     Multi-Agency Radiological Laboratory Analytical Protocols (MARLAP) Manual addresses the
11     need for a nationally consistent approach to producing radioanalytical laboratory data that meet a
12     project's or program's data requirements. MARLAP provides  guidance for the planning,
13     implementation, and assessment phases of those projects that require the laboratory analysis of
14     radionuclides. The guidance provided by MARLAP is both scientifically rigorous and flexible
15     enough to be applied to a diversity of projects and programs. This guidance is intended for
16     project planners, managers, and laboratory personnel.

17     MARLAP is divided into two main parts. Part I is primarily for project planners and managers
18     and provides guidance on project planning with emphasis on analytical planning issues and
19     analytical data requirements. Part I also provides guidance on preparing  project plan documents
20     and radioanalytical statements of work (SOWs), obtaining and evaluating radioanalytical
21     laboratory services, data validation, and data  quality assessment. Part I of MARLAP covers the
22     entire life of a proj ect that requires the laboratory analysis of radionuclides from the initial
23     project planning phase to the assessment phase.

24     Part II of MARLAP is primarily for laboratory personnel and provides guidance in the relevant
25     areas of radioanalytical laboratory work. Part II offers information on the laboratory analysis of
26     radionuclides. It provides guidance on a variety of activities performed at radioanalytical
27     laboratories including sample preparation, sample dissolution, chemical  separations, instrument
28     measurements, data reduction, etc. Note that Part II of the manual is not a compilation of
29     analytical procedures. While the chapters in Part n do not contain detailed step-by-step
30     instructions of how to perform certain laboratory tasks, they do provide information on many of
31     the options available for these tasks, and discuss advantages and disadvantages of each.

32     MARLAP was developed collaboratively by the following Federal agencies: the Environmental
33     Protection Agency (EPA), the Department of Energy (DOE), the Nuclear Regulatory
34     Commission (NRC), the Department of Defense (DOD), the National Institute of Standards and

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35     Technology (NIST), the United States Geological Survey (USGS), and the Food and Drug
36     Administration (FDA). State participation in the development of MARLAP involved
37     contributions from representatives from the Commonwealth of Kentucky and the State of
38     California.

39     1.2    Purpose of the Manual

40     MARLAP's basic goal is to provide guidance and a framework for project planners,  managers,
41     and laboratory personnel to ensure that radioanalytical laboratory data will meet a project's or
42     program's data requirements and needs. To attain this goal, MARLAP provides the necessary
43     guidance for national consistency in radioanalytical work in the form of a performance-based
44     approach for meeting a project's data requirements. In general terms, a performance-based
45     approach to laboratory analytical work involves clearly defining the analytical data needs and
46     requirements of a proj ect in terms of measurable goals during the planning phase of a proj ect.
47     These project-specific analytical data needs and requirements then serve as measurement
48     performance criteria for decisions as to exactly how the laboratory analysis will be conducted
49     during the implementation phase of a project. They are used subsequently as criteria  for
50     evaluating analytical  data during the assessment phase. Therefore, through a performance-based
51     approach, MARLAP provides guidance in the planning, implementation and assessment phases
52     for those projects that require the laboratory analysis of radionuclides. The manual focuses on
53     activities performed at radioanalytical laboratories, as well as activities and issues that direct,
54     affect, or can be used to evaluate activities performed at radioanalytical laboratories.  The
55     guidance in MARLAP is intended to help ensure the generation of radioanalytical data of known
56     quality appropriate for its intended use.

57     Specific objectives of MARLAP include:

58      • Promoting a directed planning process for projects involving individuals from relevant
59        disciplines including radiochemistry;

60      • Highlighting common radioanalytical planning issues;

61      • Providing a framework and information resource for using a performance-based approach for
62        planning and conducting radioanalytical work;

63      • Providing guidance on linking project planning, implementation, and assessment;

64      • Providing guidance on obtaining and  evaluating radioanalytical laboratory services;

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                                                                        Introduction to MARLAP
65      •  Providing guidance for evaluating radioanalytical laboratory data, i.e., data verification, data
66         validation, and data quality assessment;

67      •  Promoting high quality radioanalytical laboratory work; and

68      •  Making collective knowledge and experience in radioanalytical work widely available.

69     As indicated by the list of objectives, MARLAP provides guidance to project planners, managers,
70     and laboratory personnel for a range of activities for those projects and programs that require the
71     laboratory analysis of radionuclides.

72     1.3   Use and Scope of the Manual

73     The guidance contained in MARLAP is for both governmental and private sectors. Users of
74     MARLAP include project planners, project managers, laboratory personnel, regulators, auditors,
75     inspectors, data evaluators, decision makers, and other end users of radioanalytical laboratory
76     data.

77     Since MARLAP uses a performance-based approach to laboratory measurements, the guidance
78     contained in the manual is applicable to a wide range of projects and activities that require
79     radioanalytical laboratory measurements. Examples of data collection activities that MARLAP
80     supports include:

81      •  Site characterization activities;
82      •  Site cleanup and compliance demonstration activities;
83      •  License termination activities;
84      •  Decommissioning of nuclear facilities;
85      •  Remedial and removal actions;
86      •  Effluent monitoring of licensed  facilities;
87      •  Environmental site monitoring;
88      •  Background studies;
89      •  Routine ambient monitoring; and
90      •  Waste management activities.

91     MARLAP and the Multi-Agency Radiation Survey and Site Investigation Manual (MARS SIM,
92     2000) are complementary guidance  documents in support of cleanup and decommissioning
93     activities. MARS SIM provides guidance on how to plan and carry out a study to demonstrate that

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        Introduction to MARLAP
 94      a site meets appropriate release criteria. It describes a methodology for planning, conducting,
 95      evaluating, and documenting environmental radiation surveys conducted to demonstrate
 96      compliance with cleanup criteria. MARLAP provides guidance and a framework for both project
 97      planners and laboratory personnel to ensure that radioanalytical data will meet the needs and
 98      requirements of cleanup and decommissioning activities.

 99      While MARLAP is designed to support a wide range of projects, some topics are not specifically
100      discussed in the manual. These include high-level waste, mixed waste, and medical applications
101      involving radionuclides. While they are not specifically addressed, much of MARLAP's
102      guidance may be applicable in these areas. Although the focus of the manual is to provide
103      guidance for the planning, implementation, and assessment phases of those projects that require
104      the laboratory analysis of radionuclides, much of the guidance on the planning and assessment
105      phases can be applied wherever the measurement process is conducted, for example, in the field.
106      In addition, MARLAP does not provide specific guidance on sampling design issues, sample
107      collection, field measurements, laboratory quality assurance issues, or laboratory health and
108      safety practices. However, a brief discussion of some aspects of these activities has been included
109      in the manual because of the effect these activities often have on the laboratory analytical
110      process.

in      1.4   Key MARLAP Concepts and Terminology

112      Some of the terms used in MARLAP were developed for the purpose of this manual, while
113      others are commonly used terms that have been adopted by MARLAP. Where possible, every
114      effort has been made to use terms and definitions from consensus-based organizations (e.g.,
115      International Organization for Standardization [ISO], American National Standards Institute
116      [ANSI], American Society for Testing and Materials [ASTM], International Union of Pure and
117      Applied Chemistry [IUPAC]).

118      The following sections are intended to familiarize the reader with the key terms and concepts
119      used in MARLAP. In general, each term or concept is discussed individually in each section
120      without emphasizing how these terms and concepts are linked. Section 1.5 ties these terms and
121      concepts together to provide an overview of the MARLAP process.

122      1.4.1  Data Life Cycle

123      The data life cycle (EPA, 2000) approach provides a structured means of considering the major
124      phases of projects that involve data collection activities (Figure 1.1). The three phases of the data
125      life cycle are planning, implementation, and assessment. MARLAP provides information on all

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126
127
128
129
130
131
132
133
134
135

136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155

156

157
158
159
160
three phases for two major types of
activities: those performed at radioanaly-
tical laboratories and those that direct,
affect, or evaluate activities performed at
radioanalytical  laboratories (such as
project planning, development of plan
documents, data verification and data
validation). Consequently, MARLAP
provides guidance for project planners,
managers, and laboratory personnel.

One of the specific objectives of the
MARLAP Manual is to provide
guidance on, and to emphasize the
importance of, establishing the proper
linkages among the three phases of the
data life cycle—planning, implemen-
tation and assessment—thereby resulting
in an integrated and iterative process that
accurately translates the expectations
and requirements of data users into
DATA LIFE CYCLE
PROCESS
o
c
'c
c
ro
0.
Implementation
Assessment
Directed Planning
Process
Plan Documents
Contracting Services
Sampling
Analysis
Verification
Validation
Data Quality Assessment
PROCESS OUTPUTS
Development of Data Quality Objectives and
Measurement Quality Objectives (Including Optimized
Sampling and Analytical Design)
Project Plan Documents
Including Quality Assurance Project Plan (QAPP);
Work Plan or Sampling and Analysis Plan (SAP); Data
Validation Plan; Data Quality Assessment Plan
Statement of Work (SOW)
and Other Contractual Documents
Laboratory Samples
Laboratory Analysis
(Including QC Samples)
Complete Data Package
1
Verified Data
Data Verification Report
Validated Data
Data Validation Report
1
Assessment Report
Data of Known Quality Appropriate for the Intended Use
FIGURE 1.1 — The Data Life Cycle
measurement performance criteria for data suppliers. From an analytical perspective, the
integration of the three phases of the data life cycle is critical to ensure that the analytical data
requirements defined during the planning phase serve as measurement performance criteria
during the implementation phase and subsequently as criteria for data evaluation during the
assessment phase. The proper linkages and integration of the three phases of the data life cycle
should be established during the planning phase. Without the proper linkages and integration of
the three phases, there is a significant likelihood that the analytical data will not meet a project's
data requirements, and the data may be evaluated using criteria that have little relation to their
intended use. Therefore, failure to integrate and adequately link the three phases of the data life
cycle increases the likelihood of project cost escalation or project failure.

1.4.2   Directed Planning Process

MARLAP recommends the use of a directed or systematic planning process. A directed planning
process is an approach for setting well-defined, achievable objectives and developing a cost-
effective, technically sound sampling and analysis design that balances the data user's tolerance
for uncertainly in the decision process with the resources available for obtaining data to support a
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161      decision. While MARLAP recommends and promotes the use of a directed planning process, it
162      does not recommend or endorse any particular directed planning process. However, MARLAP
163      employs many of the terms and concepts associated with the data quality objective (DQO)
164      process (ASTM D5792, EPA, 2000). This was done to ensure consistent terminology throughout
165      the manual, and also because many of the terms and concepts of this process are familiar to those
166      engaged in environmental data collection activities.

167      1.4.3  Performance-Based Approach

168      MARLAP provides the necessary guidance for using a performance-based approach to meet  a
169      project's analytical data requirements. In a performance-based approach, the project-specific
170      analytical data requirements that are determined during directed planning serve as measurement
171      performance criteria for analytical selections and decisions. The project-specific analytical data
172      requirements also are used for the initial, ongoing, and final evaluation of the laboratory's
173      performance and the laboratory's data. MARLAP provides guidance for using a performance-
174      based approach for all three phases—planning, implementation and assessment—of the data life
175      cycle for those projects that require radioanalytical laboratory data. This involves not only using a
176      performance-based approach for selecting an analytical protocol, but also using a performance-
177      based approach for other project activities, such as developing acceptance criteria for laboratory
178      quality control samples, laboratory evaluations, data verification, data validation, and data quality
179      assessment.

180      There are three major steps or processes associated with a performance-based approach.  The first
181      is clearly and accurately defining the analytical data requirements for the project. This process is
182      discussed in more detail in Section  1.4.9 of this chapter. The second involves using an organized,
183      interactive process for selecting  or developing analytical protocols to meet the specified
184      analytical data requirements and for demonstrating the protocol's ability to meet the analytical
185      data requirements. The last major activity involves using the analytical data requirements as
186      measurement performance criteria for the ongoing and final evaluation of the laboratory data,
187      which would include data verification, data validation, and data quality assessment. MARLAP
188      provides guidance in all three of these areas. Within the constraints of other factors, such as cost,
189      a performance-based approach allows for the use of any analytical protocol that meets the
190      project's analytical data requirements. For all relevant project activities, the common theme of a
191      performance-based approach is the use of project-specific analytical data requirements that are
192      developed during project planning and serve as measurement performance criteria for selections,
193      evaluations, and decision-making.
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194      1.4.4  Analytical Process

195      Most environmental data collection
196      efforts center around two major
197      processes: the sampling process and
198      the analytical process. MARLAP
199      does not provide general guidance
200      on the sampling process, except for
201      brief discussions of certain activities
202      that often affect the analytical
203      process (field processing,
204      preservation, etc.). The analytical (or
205      measurement) process is a general
206      term used by MARLAP to refer to a
207      compilation of activities starting
208      from the time a sample is collected
209      and ending with the reporting of
210      data. These activities typically
211      include field sample preparation and
212      preservation, sample receipt and
213      inspection, laboratory sample
214      preparation, sample dissolution,
215      chemical separations, preparation of
216      samples for instrument measure-
217      ments, instrument measurements,
218      data reduction, data reporting, and
219      quality control  of the process. Figure
220      1.2 illustrates the maj or components    FIGURE 1.2 — Typical Components of an Analytical Process
221      of an analytical process. It should be noted that a particular analytical process for a project may
222      not include all  of the activities listed. For example, if a project involves the analysis of tritium in
223      drinking water, then the analytical process for the project will not include sample dissolution and
224      the chemical separation of the radionuclide of concern. It is important to identify the relevant
225      activities of the analytical process for a particular project early in the planning phase. Once the
226      activities have been identified, the analytical requirements of the activities can be established,
227      which will ultimately lead to defining how the activities  will be  accomplished through the
228      selection or development of written procedures for the various activities.

Sample Tracking
i
r
Field Sample Preparation and
Preservation
i

Sample Receipt and Inspection
i

Laboratory Sample Preparation
i

Sample D ssolution
i

Chemical Separation of
Radionuclides of Concern
i

Preparation of Samples for
Instrument Measurements
i

Instrument Measurements
i

Data Reduction and Reporting
QA/QC
i
r
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229     The analytical process should not be confused with the written procedures necessary to perform
230     the associated activities of the analytical process. The analytical process (i.e., the compilation of
231     activities starting from the time a sample is collected and ending with the reporting of the data)
232     should be performed according to written procedures

233     1.4.5  Analytical Protocol

234     MARLAP uses the term "analytical protocol" to refer to a compilation of specific procedures and
235     methods that are performed in succession for a particular analytical process. For example, a
236     protocol for the analysis of drinking water samples for tritium would be comprised of the set of
237     procedures that describe the relevant activities, such as sample tracking, quality control, field
238     sample preparation and  preservation, sample receipt and inspection, laboratory sample prepara-
239     tion (if necessary), preparing the samples for counting, counting the samples, and data reduction
240     and reporting. A written procedure may cover one or more of the activities, but it is unlikely that
241     a single procedure will cover all of the activities of a given analytical process. It should be noted
242     that with a performance-based approach, there may be a number of alternative protocols that
243     might be appropriate analytical  protocols for a particular analytical process.  Selecting or develop-
244     ing an analytical protocol  requires knowledge of the particular analytical process, as well as an
245     understanding of the analytical data requirements developed during the project planning phase.

246     1.4.6  Analytical Method

247     A major component of an analytical protocol is the analytical method, which normally includes
248     written procedures for sample digestion, chemical separation (if required)  and counting. It is
249     recognized that in many instances the  analytical method may cover many of the activities of a
250     particular analytical process. Therefore attention is naturally focused on the selection or
251     development of an analytical method.  However, many analytical methods  do not address
252     activities such as field preparation and preservation, certain aspects of laboratory preparation,
253     laboratory subsampling, etc., which are often important activities within an analytical process.
254     The analytical protocol is  generally  more inclusive of the activities that make up the analytical
255     process than the analytical method.  For this reason, MARLAP focuses on the selection,
256     implementation, and assessment of  analytical protocols  that cover the entire analytical process
257     for a particular project or program.

258     1.4.7  Uncertainty and Error

259     An important aspect of sampling and measurement is uncertainty. The term "uncertainty" has
260     different shades of meaning in different contexts, but generally the word always refers to a lack


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261     of complete knowledge about something of interest. In the context of metrology (the science of
262     measurement), the more specific term "measurement uncertainty" often will be used. "Uncertain-
263     ty (of measurement)" is defined in the Guide to the Expression of Uncertainty in Measurement
264     (ISO 1995—"GUM") as a "parameter, associated with the result of a measurement, that charac-
265     terizes the dispersion of values that could reasonably be attributed to the measurand." The
266     "measurand" is the quantity being measured. MARLAP recommends the terminology and
267     methods of GUM for describing, evaluating, and reporting measurement uncertainty. The
268     uncertainty of a measured value is typically expressed as an estimated standard deviation, called
269     a "standard uncertainty" (or "one-sigma uncertainty"). The standard uncertainty of a calculated
270     result usually is obtained by propagating the standard uncertainties of a number of other
271     measured values, and in this case,  the standard uncertainty is called a "combined standard
272     uncertainty." The combined standard uncertainty may be multiplied by a specified factor called a
273     "coverage factor" (e.g., 2 or 3) to obtain an "expanded uncertainty" (a "two-sigma" or "three-
274     sigma" uncertainty), which describes an interval about the result that can be expected to contain
275     the true value with a specified high probability. MARLAP recommends that either the combined
276     standard uncertainty or an expended uncertainty be reported with every result. Chapter 19
277     discusses the terminology, notation, and methods  of GUM in more detail and provides guidance
278     for applying the concepts to radioanalytical measurements.

279     While measurement uncertainty is a parameter associated with an individual result and is
280     calculated after a measurement is performed, MARLAP uses the term "method uncertainty" to
281     refer to the predicted uncertainty of a measured value that likely would result from the analysis of
282     a sample at a specified analyte concentration. Method uncertainty is a method performance
283     characteristic much like the detection capability of a method.  Reasonable values for both
284     characteristics can be predicted for a particular method based on typical values for certain
285     parameters and on information and assumptions about the samples to be analyzed. These
286     predicted values can be used in the method selection process to identify the most appropriate
287     method based on a project's data requirements.  Chapter 3 provides MARLAP's recommenda-
288     tions for deriving analytical protocol selection criteria based on the required method uncertainty
289     and other analytical requirements.

290     When a decision maker bases a decision  on the results of measurements, the measurement
291     uncertainties affect the probability of making a wrong decision. When sampling is involved,
292     sampling statistics also contribute  to the probability of a wrong decision. Since decision errors
293     are possible, there is uncertainty in the decision-making process. MARLAP uses the terms
294     "decision uncertainty" or "uncertainty of the decision" to refer to this type of uncertainty.
295     Decision uncertainty is usually measured by the estimated probability of a decision error under
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296     specified assumptions. Appendix B discusses decision uncertainty further in the context of the
297     DQO process.

298     A concept that should not be confused with uncertainty is error. In general, error refers to
299     something that deviates from what is correct, right or true. In terms of measurements such as
300     laboratory analyses, the difference between the measured result and the actual value of the
301     measurand is the error of the measurement. Since the actual value of the measurand is generally
302     not known, the measurement error cannot be determined. Therefore, the error of a measurement
303     is primarily a theoretical concept with little practical use. However, the measurement uncertainty,
304     which provides an estimated bound for the likely size of the measurement error, is very useful
305     and plays a key role in MARLAP's  performance-based approach.

306     1.4.8  Precision, Bias, and Accuracy

307     Analytical data requirements often have been described in terms of precision and bias. Precision
308     is usually expressed as a standard deviation, which measures the dispersion of measured values
309     about their mean. It is more natural  to speak of imprecision, since larger values of the standard
310     deviation indicate less precision and greater imprecision. Bias is a persistent difference between
311     the measured result and the true value of the quantity being measured, which does not vary if the
312     measurement is repeated. If the measurement process is in statistical control, then imprecision
313     may be reduced by averaging the results of many independent measurements of the same
314     quantity. Bias is unaffected by averaging.

315     A bias in a data set may be caused by measurement errors that occur in  steps of the measurement
316     process that are not repeated, such as the determination of a half-life. Imprecision may be caused
317     by measurement errors in steps that are repeated many times, such as weighing, pipetting, and
318     radiation counting. However, distinguishing between bias and imprecision is complicated by the
319     fact that some steps in the process, such as instrument calibration or tracer preparation, are
320     repeated at frequencies less than those of other steps, and the measurement errors in seldom
321     repeated steps may affect large blocks of data. Consequently measurement errors that produce
322     apparent biases in small data sets might produce apparent imprecision in larger data sets.

323     Because the same type of measurement error may produce either bias or imprecision, depending
324     on one's point of view, the concept  of measurement uncertainty, described in Section 1.3.7, treats
325     all types of measurement error alike and combines estimates of their magnitudes into a single
326     numerical parameter (i.e., combined standard uncertainty). The concepts of imprecision and bias
327     are useful in context when a measurement process or a data set consisting of many measurement
328     results is considered. When one considers only a single measurement result, the concept of


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329     measurement uncertainty tends to be more useful than the concepts of imprecision and bias.
330     Therefore, it is probably best to consider imprecision and bias to be characteristics of the
331     measurement process or of the data set, and to consider measurement uncertainty to be an aspect
332     of each individual result.

333     Quality control samples are analyzed for the purpose of assessing imprecision and bias. Spiked
334     samples and method blanks are typically used to assess bias, and duplicates are used to assess
335     imprecision. Since a single measurement of a spike or blank cannot in principle distinguish
336     between imprecision and bias, a reliable estimate of bias requires a data set that includes many
337     such measurements.

338     Different authors have given the word accuracy different technical definitions, expressed in
339     terms of bias and imprecision. MARLAP avoids all  of these technical definitions and uses the
340     term "accuracy"  in its common, ordinary sense, which is consistent with its definition in the
341     International Vocabulary of'Basic and General Terms in Metrology(ISO, 1993). InMARLAP's
342     terminology, the result of a measurement is "accurate" if it is close to the true value of the
343     quantity being measured. Inaccurate results may be caused either by bias or imprecision in the
344     measurement process.

345     While it is recognized that the terms bias, imprecision, and accuracy are commonly used in data
346     collection activities, these terms are used somewhat sparingly in this manual. MARLAP
347     emphasizes and provides guidance in the use of measurement uncertainty as a means of
348     establishing analytical data requirements and in the evaluation of single measurement results.

349     1.4.9   Performance Objectives: Data Quality Objectives and Measurement Quality
350            Objectives

351     One of the outputs of a directed planning process is DQOs for a project or program. DQOs are
352     qualitative and quantitative statements that clarify the study objectives; define the most
353     appropriate type  of data to collect; determine the most appropriate conditions from which to
354     collect the data; and specify tolerable limits on decision error rates (ASTM D5792; EPA, 2000).
355     DQOs apply to all data collection activities associated with a project or program, including
356     sampling and analysis. In particular, DQOs should encompass the "total uncertainty" resulting
357     from all data collection activities, including analytical and sampling activities.

358     From an analytical perspective, a process of developing the analytical data requirements from the
359     DQOs of a project is essential. These analytical data requirements serve as measurement perfor-
360     mance criteria or objectives of the analytical process. MARLAP refers to these performance


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361     objectives as "measurement quality objectives" (MQOs). The MARLAP Manual provides
362     guidance on developing the MQOs from the overall project DQOs (Chapter 3). MQOs can be
363     viewed as the analytical portion of the DQOs and are therefore project-specific. MARLAP
364     provides guidance on developing MQOs during project planning for select method performance
365     characteristics, such as method uncertainty at a specified concentration; detection capability;
366     quantification capability; specificity, or the capability of the method to measure the analyte of
367     concern in the presence of interferences; range; ruggedness, etc. An MQO is a statement of a
368     performance objective  or requirement for a particular method performance characteristic. Like
369     DQOs, MQOs can be quantitative and qualitative statements. An example of a quantitative MQO
370     would be a statement of a required method uncertainty at a specified radionuclide concentration,
371     such as the action level—i.e., "a method uncertainty of 3.7 Bq/kg (0.10 pCi/g) or less is required
372     at the action level of 37 Bq/kg (1.0 pCi/g)." An example of a qualitative MQO would be a
373     statement of the required specificity of the analytical protocol—the ability to analyze for the
374     radionuclide of concern given the presence of interferences—i.e., "the protocol must be able to
375     quantify the amount of 226Ra present given high levels of 235U in the samples."

376     The MQOs serve as measurement performance criteria for the selection or development of
377     analytical protocols and for the initial evaluation of the analytical protocols. Once the analytical
378     protocols have been selected and evaluated, the MQOs serve as criteria for the ongoing and final
379     evaluation of the laboratory  data, including data verification, data validation, and data quality
380     assessment. In a performance-based approach, analytical protocols are either selected or rejected
381     for a particular project, to a large measure, based on their ability or inability to achieve the stated
382     MQOs. Once selected,  the performance of the analytical protocols is evaluated using the project-
383     specific MQOs.

384     1.4.10  Analytical Protocol Specifications

385     MARLAP uses the term "analytical protocol specifications" (APSs) to refer to the output of a
386     directed planning process that contains the project's analytical data requirements in an organized
387     concise form. In general, there will be an APS developed for each analysis type, and since most
388     projects require that a number of different analyses be performed, several APSs will normally be
389     developed for a particular project.  These specifications serve as the basis for the evaluation and
390     selection of the analytical protocols that will be used for a particular project. In accordance with a
391     performance-based approach, the APSs contains only the minimum level of specificity required
392     to meet the project's analytical data requirements without dictating exactly how the requirements
393     are to be met. At a minimum, the APSs should indicate the analyte of interest, the matrix of
394     concern, the type and frequency of quality control (QC) samples, and provide the required MQOs
395     and any specific analytical process requirements, such as chain-of-custody for sample tracking.


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396     Depending on the particular project, a number of specific analytical process requirements may be
397     included. For example, if project or process knowledge indicates that the radionuclide of interest
398     exists in a refractory form, then the APSs may require a fusion step for sample digestion.
399     However, the level of specificity in the APSs should be limited to those requirements that are
400     considered essential to meeting the project's analytical data requirements. In most instances, a
401     particular APS document would be a one-page form (see Chapter 3, Figure 3.2). For a particular
402     project, APSs would be developed for each analysis required.

403     Within the constraints of other factors, such as cost, MARLAP's performance-based approach
404     allows the use of any analytical protocol that meets the requirements contained in the APSs. The
405     requirements in the APSs, in particular the MQOs, are used for the selection and evaluation of
406     the analytical protocols. Once the analytical protocols have been selected and evaluated, the
407     APSs then serve as criteria for the ongoing and final evaluation of the laboratory data, including
408     data verification, data validation, and data quality assessment.

409     1.4.11 The Assessment Phase

410     As noted, the MARLAP Manual provides guidance for the assessment phases for those projects
411     that require the laboratory analysis of radionuclides. The guidance on the assessment phase of
412     projects focuses  on three major activities: data verification, data validation,  and data quality
413     assessment.

414     Data verification assures that laboratory conditions and operations were compliant with the
415     statement of work and any appropriate project plan documents (e.g., Quality Assurance Project
416     Plan), which may reference laboratory documents such as laboratory standard operating
417     procedures) Verification compares the material delivered by the laboratory to these requirements
418     (compliance) and checks for consistency and comparability of the data throughout the data
419     package, correctness of calculations, and completeness of the results to ensure that all necessary
420     documentation is available. The verification process produces a report identifying which
421     requirements are not met. The verification report is used to determine  payment for laboratory
422     services and to identify problems that should be investigated during data validation. Verification
423     works iteratively and interactively with  the generator (i.e., laboratory) to assure receipt of all
424     available, necessary data. Although the  verification process identifies specific problems, the
425     primary function should be to apply appropriate feedback resulting in corrective action
426     improving the analytical services before the work is completed.

427     Validation addresses the reliability of the data. The validation process begins with a review of the
428     verification report and laboratory data package to screen the areas of strength and weakness of


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429     the data set. The validator evaluates the data to determine the presence or absence of an analyte
430     and the uncertainty of the measurement process for contaminants of concern. During validation,
431     the technical reliability and the degree of confidence in reported analytical data are considered.
432     Validation "flags" (i.e., qualifiers) are applied to data that do not meet the acceptance criteria
433     established to assure data meet the needs of the project. The product of the validation process is a
434     validation report noting all data sufficiently inconsistent with the validation acceptance criteria in
435     the expert opinion of the validator.  The appropriate data validation tests should be established
436     during the project planning phase.

437     Data quality assessment (DQA), the third and final step of the assessment phase, is defined as the
438     "scientific and statistical evaluation of data to determine if data are of the right type, quality, and
439     quantity to support their intended use." DQA is more global in its purview than the previous
440     verification and validation steps. DQA, in addition to reviewing the issues raised during verifica-
441     tion and validation, may be the first opportunity to review other issues, such as field activities
442     and their impact on data quality and usability. DQA should consider the combined impact of all
443     project activities in making a data usability determination, which is documented in a DQA report.

444     1.5   The MARLAP Process

445     An overarching objective of the MARLAP Manual is to provide a framework and information
446     for the selection, development, and evaluation of analytical protocols and the resulting laboratory
447     data. The MARLAP  process is a performance-based  approach that develops APSs and uses these
448     requirements as criteria for the analytical protocol selection, development and evaluation
449     processes, and for the evaluation of the resulting laboratory data. This process, which spans the
450     three phases of the data life cycle for  a project—planning,  implementation and assessment—is
451     the basis for achieving MARLAP's basic goal of ensuring that radioanalytical data will meet a
452     project's data requirements. A brief overview of this process, which is referred to as the
453     MARLAP  process and is the focus  of Part I of the manual, is provided below.

454     The MARLAP process starts with a directed planning process. Within a directed planning
455     process, key analytical issues based on the  project's particular analytical processes are discussed
456     and resolved. The resolution of these  key analytical issues produces the APSs, which include the
457     MQOs. The APSs are documented in project plan documents (e.g., Quality Assurance Project
458     Plans,  Sampling and Analysis Plans). A SOW is then developed that contains the APSs. The
459     laboratories receiving the SOW respond with proposed analytical protocols based on the require-
460     ments of the APSs and provide evidence that the proposed protocols meet the performance
461     criteria in the APSs.  The proposed analytical  protocols are initially evaluated by the project
462     manager or designee to determine if they will meet the requirements in the APSs. If the proposed

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463     analytical protocols are accepted, the project plan documents are updated by the inclusion or
464     referencing of the actual analytical protocols to be used. During analyses, resulting sample and
465     QC data will be evaluated primarily using MQOs from the respective APSs. Once the analyses
466     are completed,  an evaluation of the data will be conducted, including data verification, data
467     validation, and  data quality assessment with the respective MQOs serving as criteria for
468     evaluation. The role of the APSs (particularly the MQOs, which make up an essential part of the
469     APSs) in the selection, development, and evaluation of the analytical protocols and the laboratory
470     data is to provide a critical link between the three phases of the data life cycle of a project. This
471     linkage helps to ensure that radioanalytical laboratory data will meet a project's data require-
472     ments, and that the data are of known quality appropriate for their intended use. The MARLAP
473     process is illustrated in Figure 1.3. Although the diagram used to represent the MARLAP Process
474     is presented in a linear fashion, it is important to note that the process is an iterative one, and
475     there can be many  variations on this stylized diagram.

476     1.6   Structure of the Manual

477     MARLAP is divided into two main parts. Part I provides guidance on implementing the
478     MARLAP process as described in Section 1.5. This part of the manual focuses  on the sequence
479     of steps involved when using a performance-based approach for projects requiring radioanalytical
480     laboratory work starting with a directed planning process and ending with DQA. Part I provides
481     the overall guidance for using a performance-based approach for all three phases of a project. A
482     more detailed overview of Part I is provided in Section 1.6.1.
483
484     Part II of the manual provides information on the laboratory analysis of radionuclides to support
485     a performance-based approach. Part II provides guidance and information on the various
486     activities performed at radioanalytical laboratories, such as sample preparation, sample
487     dissolution, chemical separations, preparing sources for counting,  nuclear counting, etc. Using
488     the overall framework provided in Part I, the material  in Part n can be used to assist project
489     planners, managers, and laboratory personnel in the selection, development, evaluation, and
490     implementation of analytical protocols for a particular project or program. A more detailed
491     overview  of Part n is provided in Section 1.6.2. In addition to Part I and Part II, MARLAP has
492     several appendices that support both Part I and Part n of the manual. An overview of the
493     appendices is provided in Section 1.6.3 of this chapter.

494     Because of the  structure and size of the manual, most  individuals will naturally focus on those
495     chapters that provide guidance in areas directly related to their work. Therefore, to help ensure
496     that key concepts are conveyed to the readers, there is some material is repeated, often in very
497     similar or even  the same language, throughout the manual.

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Introduction to MARLAP
                                                             Directed Planning Process (Chapter 2)
                                                      • Key Analytical Issues (Chapter 3)
                                                      • Development of Analytical Protocol Specifications
                                                      • Includes MQOs (Chapter 3)
                                                            Develop Plan Documents That Incorporate
                                                           Analytical Protocol Specifications (Chapter 4)
                                                              (e.g., QAPP, SAP, Data Validation Plan)
                                                                Development of SOW (Chapter 5)
                                                         • Includes Analytical Protocol Specifications (MQOs)
                                                          Laboratory Responds with Analytical Protocols
                                                                          (Chapter 6)
                                                 • Selected to Meet Requirements of Analytical Protocol Specifications
                                                 • Performance Data Provided
                    Project Manager
                                       Protocols
                                       Rejected
Initial Evaluation of Analytical Protocols and Laboratory
                       (Chapter 7)
• Review of Performance Data
• Performance Evaluation (PE) Samples/Certified Reference
   Materials (CRMs) Analyzed
• Quality Systems Audit
                                                                                   Protocols Accepted
                                                                  Update Plan Documents
                                                                      (Chapter 4)
                                                      • Include or Reference Accepted Analytical Protocols
                  Project Planning Team
                                      Corrective
                                        Actions
                                                                                  Start Analysis of Samples
         Ongoing Evaluation of Laboratory Performance
                       (Chapter 7)
     • Evaluation of QC and PE Sample Results
     • Laboratory Audits
     • Evaluation of Sample-Specific Parameters (i.e., yield)
                                                                                  Analyses Completed
                                                                     Data Evaluation and Assessment
                                                           • Data Verification (Chapter 8)
                                                           • Data Validation (Chapter 8)
                                                           • Data Quality Assessment (Chapter 9)
                                                             Planning
                                                              Phase
                                                                                                              Implementation
                                                                                                             —   Phase
                                                            Assessment
                                                               Phase
                                                            Data of Known Quality for Decision Making
                                                FIGURE 1.3 — The MARLAP Process
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                                                                          Introduction to MARLAP
498     1.6.1  Overview of Part I

499     Part I begins with Chapter 2, Project Planning Process, which provides an overview of the
500     directed planning process and discusses important analytical outputs of the planning process.
501     Chapter 3, Key Analytical Planning Issues and Developing APSs., provides an overview of key
502     analytical planning issues that need to be addressed during a directed planning process and
503     provides guidance on developing APSs, which are outputs of the planning process. These outputs
504     are incorporated into plan documents (e.g., work plans, quality assurance project plans, sampling
505     and analysis plans), which are covered in Chapter 4, Project Plan Documents. Chapter 4 provides
506     an overview of different types of project plan documents and provides guidance on the linkage
507     between project planning and project plan documents. Information from the plan documents is
508     then incorporated into a SOW, which is covered in Chapter 5, Obtaining Laboratory Services.
509     Chapter 5 provides guidance on developing a SOW that incorporates the APSs. Chapter 6,
510     Selection and Application of an Analytical Method, provides guidance on selecting or developing
511     analytical protocols that will meet the MQOs and other requirements as outlined in the APSs.
512     Chapter 7, Evaluating Protocols and Laboratories, provides guidance on the initial and ongoing
513     evaluation of analytical protocols and also provides guidance on the overall evaluation of
514     radioanalytical laboratories. Chapter 8, Radiochemical Data Verification and Validation,
515     provides an overview of the data evaluation process, provides general guidelines for data
516     verification and validation, and provides "tools" for data validation.  The last chapter of Part I,
517     Chapter 9, Data Quality Assessment, provides an overview of data quality assessment and
518     provides guidance on linking data quality assessment and the planning process.

519     Figure 1.3, the MARLAP Process, illustrates the sequence of steps that make up the framework
520     of a performance-based approach for the planning, implementation, and assessment phases of
521     projects that require the laboratory analysis of radionuclides. The primary audience for Part I is
522     project planners and managers. However, Chapter 6, Selection and Application of an Analytical
523     Method, is intended primarily for laboratory personnel. This is because, under a performance-
524     based approach, a laboratory would be able to use any analytical protocol that meets the
525     analytical requirements as defined by the APSs. Other factors, such as cost, also will play a role
526     in the selection of analytical protocols. While the primary audience for Part I is project planners
527     and managers, other groups, such as laboratory personnel, can benefit from the  guidance in Part I.

528     1.6.2  Overview of Part II

529     The chapters in Part II are intended to provide information on the laboratory analysis of
530     radionuclides. The chapters provide information on many of the options available for analytical
531     protocols, and discuss common advantages and disadvantages of each. The chapters highlight


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532     common analytical problems and ways to identify and correct them. The chapters also serve to
533     educate the reader by providing a detailed explanation of the typical activities performed at a
534     radioanalytical laboratory. Consistent with a performance-based approach, the chapters in Part II
535     do not contain detailed step-by-step instructions on how to perform certain laboratory tasks, such
536     as the digestion of a soil sample. The chapters do contain information and guidance intended to
537     assist primarily laboratory personnel in deciding on the best approach for a particular laboratory
538     task. For example, while the chapter on sample dissolution does not contain step-by-step
539     instructions on how to dissolve a soil sample, it does provide information on acid digestion,
540     fusion techniques, and microwave digestion, which is intended to help the reader select the most
541     appropriate technique or approach for a particular project.

542     The primary audience for Part II is laboratory personnel and the chapters generally contain a
543     significant amount of technical information. While the primary target audience is laboratory
544     personnel, other groups, such as project planners and managers, can benefit from the guidance in
545     Part II. Listed below are the chapters that make up Part n of the manual. It should be noted that
546     Part II of the manual does not provide specific guidance for some laboratory activities that are
547     common to all laboratories, such as laboratory quality assurance, and laboratory health and safety
548     practices. This is primarily due to the fact that these activities are not unique to radioanalytical
549     laboratories and considerable guidance in these areas already exists.

550         Chapter 10   Requirements When Collecting, Preserving, and Shipping Samples That
551                      Require Analytical Measurement
552         Chapter 11   Sample Receipt, Inspection and Tracking
553         Chapter 12   Laboratory Sample Preparation
554         Chapter 13   Sample Dissolution
555         Chapter 14   Separation Techniques
556         Chapter 15   Nuclear Counting Instrumentation
557         Chapter 16   Instrument Calibration and  Source Preparation
558         Chapter 17   Nuclear Counting and Data Reduction and Reporting
559         Chapter 18   Laboratory Quality Control
560         Chapter 19   Measurement Statistics
561         Chapter 20   Waste Disposal

562     Chapters 10 through 17 provide information on the typical components of an analytical process
563     in the order in which activities that make up an analytical process are normally performed. While
564     not providing step-by-step procedures for activities such as sample preservation, sample
565     digestion, nuclear counting, etc., the chapters do provide an overview of options available for the
566     various activities and importantly, provide information on the appropriateness of the assorted


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567     options under a variety of conditions. Chapter 18, Laboratory Quality Control, provides
568     guidance on monitoring key laboratory performance indicators as a means of determining if a
569     laboratory's measurement processes are in control. The chapter also provides information on
570     likely causes of excursions for select laboratory performance indicators, such as chemical yield,
571     instrument background, quality control samples, etc. Chapter 19, Measurement Statistics,
572     provides information on statistical principles and methods applicable to radioanalytical
573     measurements, calibrations, data interpretation, and quality control. Topics covered in the chapter
574     include detection and quantification, measurement uncertainty, and procedures for estimating
575     uncertainty.  Chapter 20,  Waste Disposal provides an overview of many of the regulations for
576     waste disposal and provides guidance for managing wastes in a radioanalytical laboratory.

577     1.6.3  Overview of the Appendices

578     MARLAP includes several appendices to both Part I and Part II of the manual to provide
579     additional guidance on specific topics. Brief descriptions of the appendices are provided below.

580      •  Appendix A, Directed Planning Approaches, provides an overview of a number of directed
581         planning processes and discusses some common elements  of the different approaches.

582      •  Appendix B, The Data Quality Objective Process, provides an expanded discussion of the
583         Data Quality Objectives Process including detailed guidance on setting up a "gray region"
584         and establishing tolerable decision error rates.

585      •  Appendix C, Measurement Quality Objectives for Method Uncertainty and Detection and
586         Quantification Capability, provides the rationale and guidance for developing MQOs for
587         select  method performance characteristics.

588      •  Appendix D, Content of Project Plan Documents, provides guidance on the appropriate
589         content of plan documents.

590      •  Appendix E, Contracting Laboratory Services, contains detailed guidance on contracting
591         laboratory services.

592      •  Appendix F, Laboratory Subsampling, provides information on improving and evaluating
593         laboratory subsampling techniques.

594      •  Appendix G, Statistical Tables, provides a compilation of statistical tables.
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595     1.7    References

596     American Society for Testing and Materials (ASTM). D5792. Standard Practice for Generation
597        of Environmental Data Related to Waste Management Activities: Development of Data
598        Quality Objectives, 1995.

599     International Organization for Standardization (ISO).  1993. International Vocabulary of Basic
600        and General Terms in Metrology. ISO, Geneva, Switzerland.

601     International Organization for Standardization (ISO).  1995. Guide to the Expression of
602        Uncertainty in Measurement. ISO, Geneva, Switzerland.

603     MARSSEVI. 2000. Multi-Agency Radiation Survey and Site Investigation Manual, Revision 1.
604        NUREG-1575 Rev 1, EPA 402-R-97-016 Revl, DOE/EH-0624 Revl. August. Available
605        from http://www.epa.gov/radiation/marssim/filesfm.htm.

606     U.S. Environmental Protection Agency (EPA). 2000.  Guidance for the Data Quality Objective
607        Process (EPA QA/G-4). EPA/600/R-96/055, Washington, DC. available from www.epa.gov/
608        qualityl/qa_docs.html.
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                        2  PROJECT PLANNING PROCESS
 2     2.1    Introduction

 3     Efficient environmental data collection activities depend on successfully identifying the type,
 4     quantity, and quality of data needed, as well as how the data will be used to support the decision
 5     making process. MARLAP recommends the use of a directed or systematic planning process.
 6     These planning processes provide a logic and framework for setting well-defined, achievable
 7     objectives and developing a cost-effective, technically sound and defensible sampling and
 8     analysis design that balances the data user's tolerance for uncertainty in the decision process and
 9     the available resources for obtaining data to support a decision. MARLAP has chosen to use the
10     term "directed planning" to emphasize that the planning process, in addition to having a
11     framework or structure (i.e., it is systematic), is focused of defining the data needed to achieve an
12     informed decision for a specific project.

13     The objective of this MARLAP chapter is to promote:

14     1.  Directed project planning as a tool for project management to identify and document the data
15        quality objectives (DQOs)—that is, qualitative and quantitative statements that define the
16        project objectives and the tolerable rate of making decision errors that will be used as the
17        basis for establishing the quality and quantity  of data needed to support the decision—and the
18        measurement quality objectives (MQOs) that define the analytical data requirements
19        appropriate for decision making;

20     2.  The involvement of technical experts in particular radioanalytical specialists in the planning
21        process;  and

22     3.  Integration of the outputs from the directed planning process into the implementation and
23        assessment phases of the project through documentation in project plan documents, the
24        analytical SOW, and the data assessment plans (e.g., for data validation, data verification, and
25        data and data quality assessment—DQA).

26     MARLAP will use the terms "DQOs" and  "MQOs," as defined above and in Chapter 1,
27     throughout this document because of their  widespread use in environmental data collection
28     activities. These concepts may be expressed by other terms, such as "decision performance
29     criteria" or "project quality objectives" for DQOs and "measurement performance criteria" or
30     "data quality requirements" for MQOs.
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31     This chapter provides an overview of the directed planning process. Additional discussion on the
32     planning process in Chapter 3, Key Analytical Planning Issues and Developing Analytical
33     Protocol Specifications, will focus on project planning from the perspective of the analytical
34     process and the development of Analytical Protocol Specifications (APSs). Section 2.2 will
35     discuss the importance of directed project planning. The approach, guidance and common
36     elements of directed planning are discussed in Section 2.3. The project planning team is
37     discussed in Section 2.4, and the role of the radioanalytical specialists is highlighted in Section
38     2.5. The results of the planning process are discussed in Section 2.6. Section 2.7 presents the next
39     steps of the planning phase of the project, which will  document the results of the planning
40     process and will link the results of the planning process to the implementation and assessment
41     phases of data collection activities.

42     The environmental data collection process consists of a series of elements: planning, developing,
43     and updating project plan documents; contracting for services; sampling; analysis; data
44     verification; data validation; and data quality assessment (see Section  1.4.7, "Data Life Cycle," of
45     Chapter 1, Introduction to MARLAP}. These elements are interrelated  (sampling and analysis
46     cannot be performed efficiently or resources allocated effectively without first identifying data
47     needs during planning). Linkage and integration of the data collection process elements are
48     essential to the success of the environmental data collection activity.

49     2.2   The Importance of Directed  Project Planning

50     A directed planning process has several notable strengths. It brings together the stakeholders (see
51     box), decision makers, and technical experts at the beginning of the project to gain commitment
52     to the project and a consensus on the nature of the problem and the desired decision. MARLAP
53     recognizes the need for a directed planning process that involves radioanalytical and other
54     technical experts as principals to ensure the decision makers' data requirements and the results
55     from  the field and radioanalytical laboratory are linked effectively. Directed planning enables
56     each participant to play a constructive role in clearly defining:

57       •  The problem that requires resolution;
58       •  What type, quantity, and quality of data the decision maker needs to resolve that problem;
59       •  Why the decision maker needs that type and quality of data;
60       •  What are the tolerable decision error rates; and
61       •  How the decision maker will use the data to make a defensible decision.
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62

63
64

65
66

67
68

69
70

71
72


73
74
75
76
77

78
79
80
81
82
83
84
85
86

87
88
89
90
                     Example of Stakeholders for a Cleanup Project

      A stakeholder is anyone with an interest in the outcome of an activity. For a cleanup
      project, some of the stakeholders could be:

       •  Federal, regional, State, and tribal environmental agencies with regulatory
         interests (e.g., NRC and EPA).

       •  States with direct interest in transportation, storage and disposition of wastes,
         and a range of other issues.

       •  City and County Governments with interest in the operations and safety at sites
         as well as economic development and site transition.

       •  Site Advisory Boards, citizens groups, licensees, special interest groups, and
         other members of the public with interest in cleanup activities at the site.
A directed planning process encourages efficient planning by providing a framework for
organizing complex issues. The process promotes timely, open, and effective communication
among the stakeholders resulting in well-conceived and documented plans. Because of the
emphasis on documentation, directed planning also provides project management with a more
efficient and consistent transfer of knowledge to new project members.

A directed planning process focuses on collection of only those data needed to address the
appropriate questions and support defensible decisions. Directed planning helps to eliminate poor
or inadequate sampling and analysis designs that require analysis of (1) too few or too many
samples, (2) samples that will not meet the needs of the project, or (3) inappropriate QC samples.
During directed planning, which is an iterative process, the sufficiency of existing data is
evaluated, and the need for additional data to fill the gaps, as well as the desired quality of the
additional data, is determined. By defining the MQOs, directed planning provides input for
obtaining appropriate radioanalytical services, which balance constraints and the required data
quality.

The time invested in preliminary planning can greatly reduce resource expenditure in the more
resource-intensive execution phase of the project. Less  overall time (and money) is expended
when early efforts are focused on defining (and documenting) the project's objectives (DQOs),
technically based, project-specific analytical data needs (MQOs and any specific analytical
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 91      process requirements), and measures of performance for the assessment phase of the data
 92      collection activity.

 93      2.3    Directed Project Planning Processes

 94      The recognition of the importance of project planning has resulted in the development of a
 95      variety of directed planning approaches. MARLAP does not endorse any one planning approach.
 96      Users of this manual are encouraged to consider the available approaches and choose a directed
 97      planning process that is appropriate to their project and agency. Appendix A, Directed Planning
 98      Approaches, provides brief descriptions of several directed planning processes.

 99      A graded approach to project planning will  be discussed in Section 2.3.1. Standards and guidance
100      on project planning are presented in Section 2.3.2. An overview of common elements of project
101      planning is discussed in Section 2.3.3. The elements of project planning will be discussed in
102      detail in Section 2.5.

103      2.3.1   A Graded Approach to Project Planning

104      The sophistication, the level of QC and oversight, and the resources applied should be approp-
105      riate to the project (i.e., a "graded approach"). Directed planning for small or less complex
106      projects follows the logic of the process but will proceed faster and involve fewer people. The
107      goal still will be to (1) plan properly to collect only the data needed to meet the objectives of the
108      project and (2) establish the measures of performance for the implementation and assessment
109      phases of the data life cycle of the proj ect.

110      2.3.2   Guidance on Directed Planning Processes

ill      The following national standards related to  directed project planning for environmental data
112      collection are available:

113       • Standard Practice (D5792) for Generation of Environmental Data Related to Waste
114         Management Activities: Development of Data Quality Objectives (American Society for
115         Testing and Materials (ASTM, 1995a), which addresses the process of development of data
116         quality objectives for the acquisition of environmental data. This standard describes the DQO
117         process in detail.

118       • Standard Provisional Guide (PS85) for Expedited Site Characterization of Hazardous Waste
119         Contaminated Sites (ASTM, 1996a), which describes the Expedited Site Characterization

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120         (ESC) process used to identify all relevant contaminant migration pathways and determine
121         the distribution, concentration and fate of the contaminants for the purpose of evaluating risk,
122         determining regulatory compliance, and designing remediation systems.

123       • Standard Guide (D5730) Site Characteristics for Environmental Purposes with Emphasis on
124         Soil, Rock, the Vadose Zone and Ground Water (ASTM, 1996b), which covers a general
125         approach to planning field investigations using the process of defining one or more
126         conceptual site models that is useful for any type of environmental reconnaissance or
127         investigation plan with a primary focus on the surface and subsurface environment.

128       • Standard Guide (D5612) Quality Planning and Field Implementation of a Water Quality
129         Measurements Program (ASTM, 1994), which defines criteria and identifies activities that
130         may be required based on the DQOs.

131       • Standard Guide (D5851) Planning and Implementing a Water Monitoring Program (ASTM,
132         1995b), which provides a procedural flowchart for planning the monitoring of point and non-
133         point sources of pollution of water resources (surface or ground water, rivers, lakes or
134         estuaries).

135      Several directed planning approaches have been implemented by the federal  sector for
136      environmental data collection activities. MARLAP does not endorse a single planning approach
137      and project planners should be cognizant of their agency's requirements for planning. The
138      following guidance is available:

139       • EPA developed the DQO Process (EPA, 2000) and has tailored DQO Process guidance for
140         specific programmatic needs of project planning under the Comprehensive Environmental
141         Response, Compensation, and Liability Act of 1980 (CERCLA/Superfund) (EPA, 1993) and
142         for site-specific remedial investigation feasibility study activities (EPA, 2000).

143       • The U. S. Army Corps of Engineers (ACE) Technical Project Planning (TPP) Process (ACE,
144         1998) was developed for technical projects planning for hazardous, toxic and radioactive
145         waste sites.

146       • DOE has developed the Streamlined Approach for Environmental Restoration (SAFER)
147         (DOE, 1993) for its environmental restoration activities.

148       • Planning guidance, including decision frameworks, for projects demonstrating compliance
149         with a dose- or risk-based regulation is available for final status radiological surveys


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150         (MARSSIM, 2000) and radiological criteria for license termination (NRC, 1998a; NRC,
151         1998b).

152     Additional information on the DQO Process (ASTM, 1995a; EPA, 2000) is presented in
153     Appendix B, The Data Quality Objectives Process.

154     2.3.3  Elements of Directed Planning Processes

155     Environmental data collection activities require planning for the use of data in decision making.
156     The various directed planning approaches, when applied to environmental data collection
157     activities, address common planning considerations. Some common elements of the planning
158     processes are:

159     1.   Define the problem: Identifying the problem(s) facing the stakeholder/customer that requires
160         attention, or the concern that requires streamlining.

161     2.   Identify the Decision: Defining the decision(s) or the alternative  actions that will address the
162         problem(s) or concern and satisfy the stakeholder/customer, and  determine if new data are
163         required to make the decision.

164     3.   Specify the Decision Rule and the Tolerable Decision Error Rates: Develop a decision rule to
165         get from the problem  or concern to the desired decision and define the limits on the decision
166         error rates that will be acceptable to the stakeholder/customer. The decision rule can take the
167         form of "if ...then..." statements for choosing among decisions or alternative actions.

168     4.   Optimize the Strategy for Obtaining Data: Determine the optimum, cost-effective way to
169         reach the decision while satisfying the desired quality of the decision. Define the quality of
170         the data that will be required for the decision by establishing specific, quantitative and
171         qualitative analytical performance measures (e.g, MQOs). Define the process and criteria to
172         evaluate the suitability of the data to support their intended use (DQA).

173     The objective of the directed project planning process for environmental data collection activities
174     is to reach consensus among the stakeholders on defining the problem, the full range of possible
175     solutions, the desired decision, the optimal data collection strategy, and performance measures
176     for implementation  and assessment phases of the project. If a cursory job is done defining the
177     problem or the desired results, the consequence will be the development of a design that may be
178     technically sound but answers the wrong question, may answer the question only after the
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179     collection of significant quantities of unnecessary data, or may collect insufficient data to answer
180     the question.

181     The key outputs of the directed planning process are DQOs: qualitative and quantitative
182     statements that define the project objectives and the tolerable decision error rates that will be
183     used as the basis for establishing the quality and quantity of data needed to support the decision.
184     The MQOs and the decisions on key analytical planning issues will provide the framework for
185     Analytical Protocol Specifications. The MQOs and the tolerable decision error rates will provide
186     the basis for the data assessment phase (data validation and DQA). The elements of project
187     planning will be discussed in detail in Section 2.5 from the perspective of the radioanalytical
188     specialists after introducing the concepts of the project planning team and radioanalytical
189     specialists in Section 2.4. Key analytical planning issues and Analytical Protocol Specifications
190     are discussed in Chapter 3, Key Analytical Planning Issues and Developing Analytical Protocol
191     Specifications.

192     2.4   The Project Planning Team

193     Participants in the project planning process will vary depending on the nature of the project, but
194     in most cases a multi-disciplinary team will be required. The project planning team should
195     consist of all the parties who have a vested interest  or can influence the outcome (stakeholders).
196     A key to successful directed planning of environmental projects is getting the data users and data
197     suppliers to work together early in the process to understand each other's needs and require-
198     ments, to agree on the desired end product, and to establish lines of communication. Equally
199     important is having integrated teams of operational and technical experts. These experts will
200     determine whether the problem has been sufficiently defined and if the desired outcomes are
201     achievable. With technical expert input early in the planning process, efforts are focused on
202     feasible solutions, and resources are not wasted pursuing unworkable solutions.

203     2.4.1   Team Representation

204     Thus, members of the project planning team may include program and project managers,
205     regulators, public representatives, project engineers, health and safety advisors, and specialists in
206     statistics, health physics, chemical analysis, radiochemical analysis, field sampling, quality
207     assurance/quality control (QA/QC), data assessment, contract and data management, field
208     operation,  and other technical specialists. The program or project manager(s) may be a Remedial
209     Project Manager (RPM), a Site Assessment Manager (SAM), or a Technical Project Officer
210     (TPO). Some systematic planning processes, such as Expedited Site Characterization,  utilize a
211     core technical team supported as needed by members of larger technical and operational teams.

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212     Throughout this document, the combined group of decision makers and technical experts is
213     referred to as the "project planning team."

214     The duration of service for the project planning team members can vary, as can the level of
215     participation required of each member during the various planning phases. While the project
216     planning team may not meet as frequently once the project objectives and the sampling and
217     analysis design have been established, a key point to recognize is that the project planning team
218     should not disband. Rather, the team or a "core group" of the team (including the project
219     manager and other key members) should continue to meet at agreed upon intervals to review the
220     project's progress and to deal with actual project conditions that require changes to the original
221     plan. The availability of a core team also provides the mechanism for the radioanalytical
222     laboratory to receive needed information to clarify questions as they arise.

223     A key concept built into directed planning approaches is the ability to revisit previous decisions
224     after the initial planning is completed  (i.e.,  during the implementation phases of the
225     environmental data collection process). Even when objectives are clearly established by the
226     project planning team and contingency planning was included in the plan development, the next
227     phases of the project may uncover new information or situations, which require alterations to the
228     data collection strategy. For example, finding significantly different levels of analytes or different
229     analytes than were anticipated based on existing information may require changes in the process.
230     To respond to unexpected events, the project planning team (or the core group) should remain
231     accessible during other phases of the data collection process to respond to  questions raised,
232     revisit and revise project requirements as necessary, and communicate the  basis for previous
233     assumptions.

234     2.4.2  The Radioanalytical Specialists

235     Depending on the size and complexity of the project, MARLAP recognizes that a number of key
236     technical experts should participate on the project planning team and be involved throughout the
237     project as needed. When the problem or concern involves radioactive analytes, it is important
238     that the radioanalytical specialist(s) are part of the project planning team, in addition to radiation
239     health and safety specialists. MARLAP recommends that the radioanalytical specialists be a part
240     of the integrated effort of the project planning team. Throughout this manual, the term
241     "radioanalytical specialists" will be used to  refer to the radioanalytical expertise needed.

242     Radioanalytical specialists may provide expertise in (1) radiochemistry and radiation/nuclide
243     measurement systems and (2) the knowledge of the chemical characteristics of the analyte of
244     concern. In particular, the radioanalytical specialist plays a key role in the  development of


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245     MQOs. The radioanalytical specialists may also provide knowledge about sample transportation
246     issues, preparation, preservation, sample size, subsampling, available analytical protocols and
247     achievable analytical data quality. If more than one person is needed, the specialists members
248     need not be from the same organization. The radioanalytical specialists need not be from the
249     contractual radioanalytical laboratory. The participation of the radioanalytical specialists is
250     critical to the success of the planning process and the effective use of resources available to the
251     project.

252     2.5   Direct Planning Process and Role of the Radioanalytical Specialists

253     The importance of technical input in a directed planning process becomes apparent when one
254     examines the common difficulties facing the radioanalytical laboratory. Without sufficient input,
255     there is often a disconnect in translating the project planning team's analytical data requirements
256     into laboratory requirements and products. Radioanalytical advice and input during planning,
257     however, help to assure that the analytical protocol(s) selected will satisfy the data requirements,
258     including consideration of time, cost and relevance to the data requirements and budget. The role
259     of the radioanalytical specialists during the early stage of the directed planning process is to focus
260     on whether the desired radionuclides can be measured and the practicality of obtaining the
261     desired analytical data. During the latter part of the process, the radioanalytical specialists can
262     provide specific direction and fine tuning for defining the analytical performance requirements
263     (MQOs) and other items of the Analytical Protocol Specifications.

264     Planning with input from radioanalytical specialists can help ensure that the data received by the
265     data users will meet the project's DQOs. Common areas that are improved with radioanalytical
266     specialists' participation in project planning include:

267       •  The correct radionuclide is measured;

268       •   MQOs are adequately established and achievable;

269       •  Consideration is given to the impact of half-life and parent/progeny factors;

270       •  The data analysis is not compromised by interferences;

271       •  Unnecessary or overly sophisticated analytical techniques are avoided in favor of analytical
272         techniques appropriate to the required level of measurement uncertainty;

273       •  Optimum radioanalytical variables, such as count time and sample volume, are considered;

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274


275


276


277

278

279
 •  Environmental background levels are considered;

 •  Chemical speciation is addressed; and

 •  Consideration is given to lab operations (e.g., turnaround time, resources).

These improvements result in an appropriate data collection design with specified MQOs and any
specific analytical process requirements to be documented in the project plan documents and
SOWs.
280      The following sections, using the common planning elements outlined in Section 2.3.3, will
281      discuss the process and results of directed planning in more detail and emphasize the input of
282      radioanalytical specialists. Table 2.1 provides a summary of (1) the information needed by the
283      project planning team, (2) how the radioanalytical specialists participate, and (3) the output or
284      product for each element of the directed planning process. It must be emphasized that a directed
285      planning process is an iterative, rather than step-wise, process. Although the process is presented
286      in discrete sections, the project planning may not progress in such an orderly fashion. The
287      planning team will more precisely define decisions and data needs as the planning progresses and
288      use new information to modify or change earlier decisions until the planning team has
289      determined the most resource effective approach to the problem. The common planning elements
290      are  used for ease of presentation and to delineate what should be covered in planning, not the
291      order of discussion.
292

293

294
295
    TABLE 2.1  Summary of the Directed Planning Process and Radioanalytical Specialists Participation
   Element
 1.  State the
    problem
Information Needed by The
  Project Planning Team
 Key stakeholders and their
 concerns.
 Facts relevant to current
 situation (e.g., site history,
 ongoing studies).
 Analytes of concern or
 analytes driving risk.
 Matrix of concern.
 Regulatory requirements and
 related issues.
 Existing data and the
 reliability of the information.
 Known sampling constraints.
 Resources and relevant
 deadlines.
  Radioanalytical Specialists
     Particip ation/Input
Evaluate existing radiological data
for use in defining the issues (e.g.,
analytes of concern).
Assure that the perceived problem
is really a concern by reviewing
the underlying data that is the
basis for the problem definition.
Consider how resource limitations
and deadlines will impact
measurement choices.
Use existing data to begin to
define the analyte of concern and
the potential range of
concentrations.
  Output/Product
Define the problem with
specificity.
Identify the primary
decision maker, the
available resources, and
constraints.
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296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
Project Planning Process

Element
2a. Identify
the
decision(s)
2b. Identify
inputs to
the
decision(s)
2c. Define the
decision
boundaries
3a. Develop a
decision
rule
3b. Specify
limits on
decision
error rates
Information Needed by The
Project Planning Team
• Analytical aspects related to
the decision.
• Possible alternative actions.
• Sequence and priority for
addressing the problem.
• All useful existing data.
• The general basis for
establishing an action level.
• Acquisition strategy options
(if new data is needed).
• Sampling or measurement
timeframe.
• Sampling areas and
boundaries.
• Subpopulations.
• Practical constraints on data
collection (season,
equipment, turnaround time,
etc.).
• Available protocols.
• Statistical parameter to be
used to describe the
parameter of interest and to
be compared to the action
level.
• The action level
(quantitative).
• The scale of decision
making.
• Potential consequences of
making wrong decisions.
• Possible range of the
parameter of interest.
• Allowable differences
between the action level and
the actual value.
• Acceptable level of decision
Radioanalytical Specialists
Particip ation/Input
• Provide focus on what analytes
need to be measured considering
analyte relationships and
background.
• Begin to address the feasibility of
different analytical protocols.
• Begin to identify the items of the
Analytical Protocol
Specifications.
• Begin to determine how sample
collection and handling will affect
MQOs.
• Review the quality and sufficiency
of the existing radiological data.
• Identify alternate analytes.
• Identify temporal trends and
spatial heterogeneity using
existing data.
• With the sampling specialists,
identify practical constraints that
impact sampling and analysis.
• Determine feasibility of obtaining
new data with current
methodology.
• Identify limitations of available
protocols.
• Potentially useful methods.
• Estimates of measurement
uncertainty and detection limits of
available analytical protocols.
• Assess variability in existing data
for decisions on hypothesis testing
or statistical decision theory.
• Evaluate whether the tolerable
decision error rates can be met
with available laboratory
protocols or the error tolerance
needs to be relaxed or new
Output/Product
• Statements that link the
defined problem to the
associated decision(s)
and alternative actions.
• Defined list of needed
new data.
• Define the characteristic
or parameter of interest
(analyte/matrix).
• Define the action level.
• Identify estimated.
concentration range for
analyte(s) of interest.
• Temporal and spatial
boundaries.
• The scale of decision.
• A logical, sequential
series of steps
("if... then") to resolve
the problem.
• Definition of the
baseline condition (null
hypothesis) and quanti-
tative estimates of
acceptable decision
error rates.
• Define the range of
possible parameter
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321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
             Element
 4.  Optimize
    the
    Strategy
    for
    Obtaining
    Data
                Information Needed by The
                  Project Planning Team
                 errors or confidence.
All outputs from all previous
elements including
parameters (analytes and
matrix) of concern, action
levels, anticipated range of
concentration, tolerable
decision error rates,
boundaries, resources and
practical constraints.
Available protocols for
sampling and analysis.
                             Radioanalytical Specialists
                                Particip ation/Input
                           methods developed.
With sampling specialists, consider
the potential combinations of
sampling and analytical methods, in
relation to:
  Sample preparation, compositing,
  subsampling.
  Available protocols.
  Method requirement by
  regulations (if any).
  Detection and quantitation
  capability.
  MQOs achievable by method,
  matrix and analyte.
  Quality control sample types,
  frequencies, and evaluation
  criteria.
  Sample volume, field processing,
  preservatives, and container
  requirements.
  Assure that the MQOs for sample
  analysis are realistic.
  Assure that the parameters for the
  Analytical Protocol Specifications
  are complete.
  Resources and time frame to
  develop and validate new
  method(s). if required.	
                                    Output/Product
                                  values where the
                                  consequence of a Type
                                  II decision error is
                                  relatively minor (gray
                                  region).	
The most resource-
effective sampling and
analysis design that
meets the established
constraints (i.e., number
of samples needed to
satisfy the DQOs and
the tolerable decision
error rates).
A method for testing the
hypothesis.
The MQOs and the
statement(s) of the
Analytical Protocol
Specifications.
The process and criteria
for data assessment.
342
2.5.1   Define the Problem
343      The first and most important step of the project planning process is a clear statement of the
344      fundamental issue to be addressed by the project. Correctly implemented, directed planning
345      ensures that a clear definition of the problem is developed before any additional resources are
346      committed. The project planning team should understand clearly the conditions or circumstances
347      that are causing the problem and the reason for making a decision (e.g., threat to human health or
348      environment).

349      Many projects present a complex interaction of technical, economic and political factors. The
350      problem definition should include a summary of the study objectives, regulatory context, funding
351      and other resources available, relevant deadlines, previous study results, and any obvious data
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352     collection design constraints. By participating in the initial stages of the project planning, the
353     radioanalytical specialists will understand the context of the facts and logic used to define the
354     problem and begin to formulate information on applicable protocols based on the projects's
355     resources (time and budget).

356     Existing data (e.g., monitoring data, radioactive materials license, emergency actions, site permit
357     files, operating records) may provide specific details about the identity, concentrations, and
358     geographic, spatial, or temporal distribution of analytes. However, these data should be examined
359     carefully. Conditions may have changed since the data were collected. For example, additional
360     waste disposal may have occurred, the contaminant may have been released or migrated, or
361     decontamination may have been performed. In some cases, a careful review of the historical data
362     by the project planning team will show that a concern is not a problem or the problem can be
363     adequately addressed using the available data.

364     2.5.2  Identify the Decision

365     The project planning team will define the decision(s) to be made (or the question the project will
366     attempt to resolve) and the inputs and boundaries to the decision. There may also be multiple
367     decision criteria that have to be met and each should be clearly defined. For example, the
368     decision may be for an individual survey area rather than the site as a whole, or a phase of the site
369     closure project (scoping, characterization, operation or final status survey) rather than the project
370     as a whole because of the different objectives and data requirements.

371     The decision should be clear and unambiguous. It may be useful to state specifically what
372     conclusions may and may not be drawn from the data. If the study is to be designed, for example,
373     to investigate whether or not a site  may be released for use by the general public, then the project
374     planning team may want to specifically exclude other possible uses for the data.

375     2.5.2.1 Action Level

376     The term "action level" is used in this document to denote the numerical value that will cause the
377     decision maker to choose one of the alternative actions. The action level may be a derived
378     concentration guideline level, background level, release criteria, regulatory decision limit, etc.
379     The action level is often associated with the type of medium, analyte and concentration limit.

380     Some action levels, such as the release criteria for license termination, are expressed in terms of
381     dose or risk. The release criterion is typically based on the total effective dose equivalent
382     (TEDE), the committed effective dose equivalent (CEDE), risk of cancer incidence (morbidity)


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383     or risk of cancer death (mortality) and generally cannot be measured directly. For example, in site
384     cleanup, a radionuclide-specific predicted concentration or surface area concentration of specific
385     nuclides that can result in a dose (TEDE or CEDE) or specific risk equal to the release criterion
386     is called the "derived concentration guideline level" (DCGL). A direct comparison can be made
387     between the project's analytical measurements and the DCGL (MARSSEVI, 2000). For drinking
388     water analysis, an example of an action level would probably be a radionuclide specific
389     concentration based on the Maximum Contaminant Level under the Safe Drinking Water Act.

390     The project planning team should also determine possible alternative actions that may be taken.
391     Consideration should also be given to the option of taking no action, as this option is frequently
392     overlooked (e.g., no technology available, too costly, relocation will create problems).

393     During these discussions of the directed planning process, the role of the radioanalytical
394     specialists is to ensure that the analytical aspects of the project have been clearly defined and
395     incorporated into the decision(s). The radioanalytical specialists focus on defining: (1) the
396     parameter (analyte/matrix) of interest; (2) what analytical information could resolve the problem;
397     and (3) the practicality of obtaining the desired field and laboratory data. Sections 3.3.1 through
398     3.3.7 of Chapter 3  discuss in more detail the analytical aspects of the decision (or question) and
399     determining the characteristic or parameter of concern. This information is incorporated into the
400     Analytical Protocol Specifications.

401     2.5.2.2 Scale of the Decision

402     The project planning team clearly should define the geographical area(s) to which the decision
403     will apply. The scale of the decision selected should be the smallest, most appropriate subset of
404     the population for which decisions will be made based on the spatial or temporal boundaries. For
405     example, at a remediation site, a survey unit is generally formed by grouping contiguous site
406     areas with a similar use history and the same classification of potential concentration of the
407     analyte of interest. The survey unit will be defined with a specified size and shape for which a
408     separate decision will be made as to whether the unit attains the site-specific reference-based
409     cleanup standard for the designated analyte of interest (MARSSEVI, 2000;  NRC,  1998c).

410     The survey unit is established to delineate areas or volumes of similar composition and history
411     for which a single decision can be made based on the statistical analysis of the data. The
412     variability in the measurement data for a survey unit is a combination of the imprecision of the
413     measurement process and the real spatial  and temporal variability of the analyte concentration. If
414     the measurement data include a background contribution, the spatial variability of the
415     background adds to the overall measurement variability.


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416     2.5.2.3 Inputs and Boundaries to the Decision

417     The project planning team determines the specific information and data required for decision
418     making. The statistical parameter (e.g., mean) that will be used in the comparison to the action
419     level should be established. Typically, the study boundaries are discussed when the project
420     planning team defines the problem. Changing conditions (e.g., weather, temperature, humidity)
421     that could impact the success of sampling or analysis or data interpretation should be considered
422     as well. The radioanalytical specialists can provide input during the determination of the
423     appropriate action level and the appropriate parameter of interest (e.g.,  mean concentration).

424     2.5.2.4 Data Needs

425     The project planning team should develop a list of the specific data (number and type) and data
426     requirements (quality). An estimate of the expected variability of the data will be needed.
427     Existing data, experience and scientific judgement can be used to establish the estimate.
428     Information on environmental background levels and variability may be needed (see Chapter 3
429     for a discussion of background). The project planning team establishes whether the existing data
430     are sufficient or whether new data are needed to resolve the problem.

431     2.5.3   Specify the Decision Rule and the Tolerable Decision Error Rates

432     A decision statement or rule is developed by combining the decisions and the alternative actions.
433     The decision rule presents the strategy or logical basis for choosing among the alternative
434     decisions, generally by use of a series of "if. ..then" statements. For a complex problem, it may be
435     helpful to develop a logic flow diagram (called a decision tree or decision framework), arraying
436     each element of the issue in its proper sequence along with the possible actions. The decision
437     rule identifies (1) the action level that will be a basis for decision and (2) the statistical parameter
438     that is to be compared to the action level.
439
440
441
Example of a Decision Rule:

If the mean concentration in the survey unit is less than the action level, then the
survey unit is in compliance with the release criterion.
442     The radioanalytical specialists play a key role in the development of alternative technical actions
443     that are realistic and quantifiable and that satisfy the programmatic and regulatory needs. The
444     results of the technical actions must be measurable: the protocols suggested will be able to detect
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445     the radionuclide of interest, (see Chapter 3, Critical Analytical Planning Issues and Developing
446     Analytical Protocol Specifications, for additional discussion on background.)

447     For each proposed alternative technical action, the radioanalytical specialists can:

448       •  Focus the project planning team on what radionuclides will need to be measured and what
449         types of analytical techniques are available;

450       •  Address whether it is feasible to obtain the necessary analytical results;

451       •  Present the technical limitations (i.e., the minimum detectable concentrations—MDCs) of
452         available measurement systems; and

453       •  Address how sample collection and handling will affect what measurement techniques can be
454         used.

455     The project planning team also assesses the potential consequences of making a wrong decision.
456     While the possibility of a  decision error can never be totally eliminated, it can be controlled. The
457     potential consequences of a decisions error are used to establish tolerable limits on the
458     probability that the data will mislead the decision maker into making an incorrect decision, (see
459     Appendix B for a discussion of hypothesis testing, action levels, and Type I and Type n decision
460     errors). The decision rule  and decision makers' limits on the decision error rates are used to
461     establish performance criteria for a data collection design.
462     In developing the tolerable decision error rate, the team needs to look at alternative measurement
463     approaches, the sources of error in field and laboratory handling of samples and analysis, factors
464     that would influence the likelihood of a Type I or Type II error, estimates of the cost of analysis,
465     and judicious use of resources. Determining realistic levels of tolerable decision error rates for
466     the decision rule will reduce or eliminate attempts by the project planning team in developing
467     and optimizing the sampling and analysis design that later will have to be re-designed to attain
468     more realistic decision error rates.

469     2.5.4  Optimize the Strategy for Obtaining Data

470     During the process of developing and optimizing the options for the sampling and analysis of
471     data, the technical team members should determine the most resource effective analytical
472     protocols and associated quality control that will meet all the requirements (desired outputs)


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473     established by the project planning team. Optimizing the data collection design generally requires
474     extensive coordination between the radioanalytical specialists and the sampling specialists.

475     Typical issues that require consideration in the development of the analysis design include the
476     number of samples required, the analytical protocol specifications, which include the MQOs
477     (e.g., a statement of the required method uncertainty) required of the analytical procedures. The
478     analytical protocol specifications, which include the MQOs, will be discussed in Sections 2.5.4.1
479     and 2.5.4.2 below. In general, the more certainty required in the DQOs, the greater the number of
480     samples or the more precise and unbiased the measurements need to be. During planning, the
481     costs and time for field and analytical procedures must be balanced against the level of certainty
482     that is needed to arrive at an acceptable  decision.

483     The radioanalytical specialists are involved in evaluating the technical options and their effect on
484     the sources of decision error, their resource requirements and the ability to meet the project's
485     objectives. The radioanalytical  specialists can identify an array of potential analytical methods,
486     which can be combined in analytical protocols to meet the defined data needs and MQOs.
487     Working with the sampling specialists, potential sampling methods are identified based on the
488     sample requirements of the potential analytical protocols and other sampling constraints. The
489     planning team specialists need to consider sources of bias and imprecision that will impact the
490     representativeness of the samples and the  accuracy of the data collected. Appropriate
491     combinations of sampling methods, analytical protocols and sampling constraints can then be
492     assessed with regard to resource effectiveness.

493     It may be useful at this point for the project planning team to perform a sensitivity analysis on the
494     input parameters that contribute to the final analytical result. The final analytical result directly
495     impacts the decision, so this sensitivity analysis will allow the project planning team to identify
496     the portions of the analytical protocols, which potentially have the most impact on the decision.
497     Once identified, these portions  of the analytical protocols can be targeted to receive a propor-
498     tionally larger share of the resources available for developing the protocols.

499     2.5.4.1 Analytical Protocol Specifications

500     Requirements of the desired analytical protocol(s) should be based on the intended use of the
501     data.  That is, project-specific critical parameters should be considered, including the type of
502     radioactivity and the nuclides of concern, the anticipated range of concentrations, the media type
503     and complexity, regulatory required methods and customer method preferences, the measurement
504     uncertainty required at some activity concentration, detection limits required, necessary chemical
505     separation, qualification or quantification  requirements, QC requirements and turnaround time


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506     needed. MQOs are a key component of the Analytical Protocol Specifications and are discussed
507     in Section 2.5.4.2. Chapter 3, Key Analytical Planning Issues and Developing Analytical
508     Protocol Specifications, contains more detailed discussion on some of the key decisions and
509     needed input to successfully optimize the sampling and analysis design and develop Analytical
510     Protocol Specifications. Chapter 6 discusses the selection of an analytical protocol from the
511     laboratory's perspective.

512     The project planning team should ensure that there are analytical methods available to provide
513     acceptable measurements. If analytical methods do not exist, the project planning team will need
514     to consider the resources needed to develop a new method, reconsider the approach for providing
515     input data, or perhaps reformulate the decision statement.

516     2.5.4.2 Measurement Quality Objectives

517     When additional data are to be obtained, the project planning process should establish measures
518     of performance for the analysis (MQOs) and evaluation of the data. Without these measures of
519     performance, data assessment is difficult and arbitrary.

520     A MQO is a statement of a performance objective or requirement for a particular method
521     performance characteristic such as the required method uncertainty at some concentration. MQOs
522     can be both quantitative and qualitative performance objectives. Quantitative and qualitative
523     MQOs are used for real-time compliance monitoring by field and lab staff and during subsequent
524     assessments and data usability determinations. Quantitative MQOs provide numerical criteria for
525     field and laboratory QC samples or procedure performance (e.g., specifications for MDC, yield,
526     efficiency, laboratory control sample precision and recovery, blank levels, lab duplicate
527     precision, collocated sample precision). Precision, bias, completeness, and sensitivity are
528     common data quality indicators for which quantitative MQOs could be  developed during the
529     planning process (ANSI/ASQC, 1994). Thus, quantitative MQOs are statements that contain
530     specific units of measure, such as: x percent recovery, x percent relative standard uncertainty, a
531     standard deviation of x Bq/L, or a MDC of x Bq/g. The specificity  of the MQOs allows specific
532     comparisons of the data to an MQO. Chapter 3 provides detailed guidance  on developing MQOs
533     for select method performance characteristics.

534     A graded approach should be taken to the selection of the MQOs. For example, from a project
535     viewpoint, it is highly practical and economical to establish MQOs on a graded basis that are in
536     concert with the anticipated range of the analytes concentration compared to the action level. For
537     example, the required method uncertainty, when the analyte concentration is much greater than
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538     the action level, can be less restrictive than when the analyte concentration approaches the action
539     level. These decisions are extremely important in the protocol selection process.

540     The MQOs for the analytical data should be documented in the project plan documents (e.g., the
541     QA Project Plan). MQOs are also the basis for the data verification and validation criteria (see
542     Appendix D, Section 2.7, for discussion of MQOs and QA Project Plans).

543     2.6   Results of the Directed Planning Process

544     By the end of the directed planning process, the project planning team has established their
545     priority of concerns, the definition of the problem, the  decision(s) or outcome to address the
546     posed problem, the inputs and boundaries to the decision(s), and the tolerable decision error
547     rates. They have also agreed on decision rules that incorporate all this information into a logic
548     statement about what must be done to  obtain the desired answer. The key output of the planning
549     process is the DQOs: qualitative and quantitative statements that clarify study objectives, define
550     the appropriate type of data, and specify the tolerable rate of making decision errors that will be
551     used as the basis for establishing the quantity and quality of data needed to support the decisions
552     and the criteria for data assessment.

553     If new data are required, then the project planning team has defined the desired analytical quality
554     of the data (MQOs). That is, the project planning team has determined the type, quantity, and
555     quality of data needed to support a decision. The directed planning process has clearly linked
556     sampling and analysis efforts to an action and a decision. This linkage allows the project
557     planning team to determine when enough data have been collected.

558     If new data are to be obtained, the project planning team has developed the most resource-
559     effective sampling and analysis design that will provide adequate data for decision making.
560     Based on the DQOs, the project planning team specifies the sampling collection design and
561     Analytical Protocol Specifications, including:

562       •  The type and quantity of samples to be collected;
563       •  Where, when, and under what conditions they should be collected;
564       •  What radionuclides are to be measured;  and
565       •  The MQOs to ensure that the analytical errors are controlled sufficiently to meet the tolerable
566         decision error rates specified in the DQOs.
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567     2.6.1  Output Required by the Radioanalytical Laboratory: The Analytical Protocol
568            Specifications

569     As a result of directed planning, the description of the DQOs for the project and the Analytical
570     Protocol Specifications, which contain the MQOs and any specific analytical process require-
571     ments for additional data will provide the radioanalytical laboratory with a clear and definitive
572     description of the desired data, as well as the purpose and use of the data. This information will
573     be provided to the project implementation team through the SOW and the project plan
574     documents. Precise statements of analytical needs may prevent the radioanalytical laboratory
575     from:

576      • Having to make a "best guess" as to what data are really required;
577      • Using the least costly or most routine protocol, which may not meet the needed data quality;
578      • Independently developing solutions for unresolved issues without direction from the project
579        planning team; and
580      • Having "moving targets" and "scope creep" that stem from ambiguous statements of work.

581     The output of the planning process, from the perspective of the radioanalytical laboratory, is the
582     Analytical Protocol Specifications. The Analytical Protocol Specifications should contain the
583     minimum level of specificity required to meet the project data requirements. In accordance with a
584     performance based measurement approach the laboratory will use this information to select or
585     develop (specific) analytical protocols that will meet the MQOs. The Analytical Protocol
586     Specifications should present the resolution  of the project planning  team on both general issues
587     and matrix-specific issues. Chapter 3, Key Analytical Planning Issues and Developing Analytical
588     Protocol Specifications., addresses some  of the common radioanalytical planning issues.

589     The Analytical Protocol Specifications should include, but not be limited to:

590      • The radionuclide(s) of concern;
591      • The media of concern with information on chemical, explosive  and other hazardous
592        components;
593      • The anticipated concentration range (estimate, maximum or detection capability);
594      • The MQOs desired for the radionuclides of concern;
595      • The sample preparation and preservation requirements (laboratory and field);
596      • The type and frequency of QC samples required of each radionuclide of concern;
597      • The sample transport, tracking and custody requirements;
598      • The required analytical turnaround time for the project and the anticipated budget for the
599        analysis; and


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600      •  The data reporting requirements.

601     2.6.2   Chain of Custody

602     Requirements for formal Chain of Custody (COC) should be specified in the Analytical Protocol
603     Specifications if required. COC procedures provide the means to trace possession and handling
604     of the sample from collection to data reporting. The data report requires a number of items, not
605     all of which can be listed here. COC will impact how the field and lab components handle the
606     sample. COC is discussed in Chapter 10 and Chapter 11.

607     2.7   Project Planning and Project Implementation and Assessment

608     A directed planning process generally is considered complete with the approval of an optimal
609     data collection design approach or when historical data are deemed sufficient to support the
610     desired decision. However to complete the process, the project planning team clearly should
611     document the results of the planning process and link DQOs and MQOs to the implementation
612     and assessment processes. The directed planning process is the first activity in the project's
613     planning phase (see Figure 1.1, "The Data Life Cycle"). The planning process outputs are key
614     inputs to the implementation and assessment processes of the data collection activities. That is,
615     the outputs of the directed planning process are the starting point for developing plan documents,
616     obtaining analytical services, selecting specific analytical protocols and assessing the data
617     collected. This section will provide an overview of the next steps of the planning phase and the
618     linkage to the implementation and assessment phases and to other chapters in MARLAP, Part I.

619     2.7.1   Documenting the Planning Process

620     A concept inherent in directed planning approaches is the establishment of a formal process to
621     document both the decisions and supporting logic established by the team during the project
622     planning process. Establishing this documentation process is not only good management practice,
623     but also tends to prevent situations where new team members recreate the past logic for activities
624     being performed upon the departure of their predecessors. As actual field conditions or other
625     situations force changes to the original plans, the documentation can then be updated through a
626     change control process to continue to maintain the technically defensible basis for the actions
627     being taken.

628     When properly documented, the directed planning process:

629      •  Provides a background narrative of the project;

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630      •  Defines the necessary input needed (nuclides, matrices, estimate of concentration range, etc.);
631      •  Defines the constraints and boundaries within which the project would have to operate;
632      •  Defines the decision rule, which states the action level that will be the basis for the decision
633         and the parameter that is to be compared to the action level;
634      •  Identifies the tolerable decision error rates;
635      •  Identifies MQOs for new analytical data; and
636      •  Identifies processes and criteria for usability of the data.

637     The results of the project planning process are also needed for the development of project plan
638     documents required for implementing the sampling and analysis activities. These project plan
639     documents may include a Quality Assurance Project Plan (QAPP), Work Plan, or Sampling and
640     Analysis Plan (SAP). The format and naming of plan documents are usually a function of the
641     authoring organization's experience, the controlling federal or state regulations, or the controlling
642     Agency. Project plan documents are discussed in Chapter 4, Project Plan Documents, and in
643     Appendix D, Content of Project Plan Documents. The project plan documents will rely on the
644     planning process outputs, including the MQOs, to describe in comprehensive detail the necessary
645     QA, QC, and other technical activities that must be implemented to ensure that the results of the
646     work performed will satisfy the stated DQOs. The project plan documents should also document
647     the processes and criteria developed for data assessment. MARLAP recommends that the
648     planning process rationale is documented and the documentation integrated with the project plan
649     documents. Documentation of the planning process can be incorporated directly in the project
650     plan documents or through citation to a separate report on the planning process.

651     2.7.2  Obtaining Analytical Services

652     If contractual laboratory services are required, the contracting office or Sample Management
653     Office (SMO) should rely on the planning process statements of required data and data quality,
654     the Analytical Protocol Specifications, to develop the Statement of Work (SOW) for the
655     laboratory. The SOW is the contractual agreement, which describes the project scope and
656     requirements (i.e., what work is to be accomplished). Contracting laboratory services is discussed
657     in Chapter 5, Obtaining Laboratory Services, and Chapter 7, Evaluating Methods and
658     Laboratories. MARLAP recommends that a SOW be developed even if a contract is not
659     involved, for example, when an agency employs one of its own labs.

660     2.7.3  Selecting Analytical Protocols

661     From an  analytical perspective, one of the most important functions of a directed planning
662     process is the identification and resolution of key analytical planning issues for a project. A key


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663      analytical planning issue may be defined as one that has the potential to be a significant contribu-
664      tor of uncertainty to the analytical process and ultimately the resulting data. Identifying key
665      analytical issues for a particular process requires a clear understanding of the analytical process.
666      It is the role of the radioanalytical specialist on the project planning team to ensure that key
667      analytical planning issues have been clearly defined and articulated and incorporated into the
668      principal decision or principal study question. Chapter 3 discusses the key analytical planning
669      issues.

670      The selection of radioanalytical protocols by the laboratory is made in response to the Analytical
671      Protocol Specifications (for each analyte/matrix) developed by the project planning team as
672      documented in the SOW. Unless required by  regulatory policy, rarely will a radioanalytical
673      method be specifically stated. A number of radioanalytical methods are available but no one
674      method provides a general solution; all have advantages and disadvantages. The selection of a
675      method is related to a broad range of consideration, including analyte and matrix characteristics,
676      technical complexity and practicality of the method, quality requirements, availability of
677      equipment, facility  and staff resources, regulatory and economic considerations, and practicality
678      and previous use of the method. Chapter 6 discusses the selection of a protocol, as well as, the
679      modification of an existing protocol to account for changes in sample substrate.

680      2.7.4  Assessment Plans

681      Concurrent with the development of MQOs and other specifications of the optimized analytical
682      design, is the development of the data assessment plans. Data assessment is difficult and
683      arbitrary when attempted at the end of the project without planning and well defined, project
684      specific criteria. The development of these plans during the project planning process should
685      ensure that the appropriate documentation will be available for assessment and that those
686      implementing and assessing data will be aware of how the data will be assessed. Assessment of
687      environmental data consists of three separate and identifiable phases: data verification, data
688      validation, and data quality assessment (DQA). Verification and validation pertain to evaluation
689      of analytical data generated by the laboratory. DQA considers all sampling, analytical, and data
690      handling details, and other historical project data when determining the usability of data in the
691      context of the decisions to be made.  The focus of verification and validation is on the analytical
692     process and a data point by data point review, while DQA considers  the entire data collection
693     process and the entire data set as it assesses data quality. Verification, validation, and DQA
694      assure the technical strengths and weaknesses of the overall project data are known, and
695      therefore, establishes the technical defensibility of the data. Assessment plan documents are
696      discussed in detail in Chapters  8 and 9.
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697     2.7.4.1 Data Verification

698     The data verification process should be defined during the project planning process and
699     documented in a data verification plan or the project plan documents (e.g., the QAPP). The
700     verification plan should specify the types of documentation needed for verification. Analytical
701     data verification assures that laboratory conditions and operations were compliant with the
702     contractual SOW and project plan (i.e., SAP or QAPP). The contract for analytical services and
703     the project plan determine the procedures the laboratory must use to produce data of acceptable
704     quality (MQOs) and the content of the analytical data package. Verification compares the
705     material delivered by the laboratory to these requirements and checks for consistency of the data
706     throughout the data package, correctness of calculations, and completeness of the results to
707     ensure all documentation is available. Compliance, exceptions, missing documentation and the
708     resulting inability to verify compliance must be recorded in the data verification report. Data
709     verification is discussed in more detail in Chapter 8, Radiological Data Verification and
710     Validation.

711     2.7.4.2 Data Validation

712     Performance objectives and criteria for data validation should be developed during the project
713     planning process and documented in a separate plan or included in the project plan documents
714     (e.g., QAPP). Guidance on Data Validation Plans is provided in Chapter 8, Radiological Data
715     Verification and Validation. After the data are collected, data validation activities will  rely on the
716     planning process statements of the MQOs to confirm whether the obtained data meet the
717     requirements of the project.

718     2.7.4.3 Data Quality Assessment

719     The DQA process evaluates whether the quality and quantity of data will support their intended
720     use. The DQA process determines whether the data meet the assumptions under which the DQOs
721     and the data collection design were developed and whether the analytical uncertainty in the data
722     will allow the decision maker to use the data to support the decision within the  tolerable decision
723     error rates established during the directed planning process. Guidance on the  DQA Process and
724     plan development is provided in Chapter 9, Data Quality Assessment. The process and criteria to
725     be used for DQA process should be developed by the project planning team and documented  in
726     the project plan documents or in a stand alone plan that is cited or appended to  the project plan
727     documents.
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728
729
730
731
732
733
Summary of Recommendations
 • MARLAP recommends the use of a directed project planning process.
 • MARLAP recommends that the radioanalytical specialists be a part of the integrated effort
   of the project planning team.
 • MARLAP recommends that the planning process rationale be documented and the
   documentation integrated with the project plan documents.
734     2.8   References

735     American National Standards Institute and the American Society for Quality Control
736        (ANSI/ASQC). 1994. Specifications and Guidelines for Quality Systems for Environmental
737        Data Collection and Environmental Technology Programs, National Standard E-4.

738     American Society for Testing and Materials (ASTM). 1994. Standard Guide for Quality
739        Planning and Field Implementation of a Water Quality Measurements Program, D5612.

740     American Society for Testing and Materials (ASTM). 1995a. Standard Practice for Generation
741        of Environmental Data Related to Waste Management Activities: Development of Data
742        Quality Objectives, D5792-95.

743     American Society for Testing and Materials (ASTM). 1995b. Standard Guide for Planning and
744        Implementing a Water Monitoring Program, D5851.

745     American Society for Testing and Materials (ASTM). 1996a. Standard Provisional Guidance for
746        Expedited Site Characterization of Hazardous Waste Contaminated Sites, PS85-96.

747     American Society for Testing and Materials (ASTM). 1996b. Standard Guide for Site
748        Characterization for Environmental Purposes with Emphasis on Soil, Rock, the Vadose Zone
749        and Ground Water, D5730-96.

750     MARSSEVL 2000. Multi-Agency Radiation Survey and Site Investigation Manual, Revision 1.
751        NUREG-1575 Rev 1, EPA 402-R-97-016 Revl, DOE/EH-0624 Revl. August. Available
752        from http://www.epa.gov/radiation/marssim/filesfm.htm.

753     U.S. Army Corps of Engineers (ACE). 1998. Technical Project Planning (TPP) Process.
754        Engineer Manual EM-200-1-2.
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755     U.S. Department of Energy (DOE). December 1993. Remedial Investigation/Feasibility Study
756        (RI/FS) Process, Elements and Techniques Guidance, Module 7 Streamlined Approach for
757        Environmental Restoration., Office of Environmental Guidance, RCRA/CERCLA Division
758        and Office of Program Support, Regulatory Compliance Division Report DOE/EH-
759        94007658.

760     U.S. Environmental Protection Agency (EPA). September 1993. Data Quality Objective Process
761        for Superfund. EPA/540/G-93/071, Washington, DC.

762     U.S. Environmental Protection Agency (EPA). 2000. Guidance for the Data Quality Objective
763        Process (EPA QA/G-4). EPA/600/R-96/055, Washington, DC. available from www.epa.gov/
764        qualityl/qa_docs.html.

765     U.S. Environmental Protection Agency (EPA). 1998. Guidance for the Quality Assurance
766        Project Plans (EPA QA/G-5). EPA/600/R-98/018, Washington, DC.

767     U.S. Environmental Protection Agency (EPA). 2000. Data Quality Objectives Process for
768        Hazardous Waste Site Investigations (Quality Assurance/G-4HW), EPA 600/R-00/007,
769        Washington, DC.

770     U.S. Nuclear Regulatory Commission (NRC). 1998a. Decision Methods for Dose Assessment to
111        Comply with Radiological Criteria for License Termination. NUREG-1549 (Draft).

772     U.S. Nuclear Regulatory Commission (NRC). 1998b. Demonstrating Compliance with the
773        Radiological Criteria for License Termination. Regulatory Guide DG-4006.

774     U.S. Nuclear Regulatory Commission (NRC). 1998c. A Nonparametric  Statistical Methodology
775        for the Design and Analysis of Final Status Decommissioning Surveys. NUREG-1505, Rev. 1.
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 i               3  KEY ANALYTICAL PLANNING ISSUES

 2           AND DEVELOPING ANALYTICAL PROTOCOL

 3                                 SPECIFICATIONS


 4     3.1   Introduction

 5     This chapter provides an overview of key analytical planning issues that should be addressed and
 6     resolved during a directed planning process (see Chapter 2).  The resolution of these issues results
 7     in the development of Analytical Protocol Specifications (APSs). A key analytical planning issue
 8     may be defined as one that has a significant effect on the selection and development of analytical
 9     protocols, or one that has the potential to be a significant contributor of uncertainty to the
10     analytical process and, ultimately, the resulting data. It should be noted that a key analytical
11     planning issue for one project may not be a key issue for another project. From an analytical
12     perspective, one of the most important functions of a directed planning process is the
13     identification and resolution of these key issues for a project.

14     In accordance with a performance-based approach, APSs only should contain the minimum level
15     of specificity required to meet the project or program data requirements and resolve the key
16     analytical planning issues. Identification and resolution of these issues should be an integral part
17     of a directed planning process, and the APSs should be an output or product of that process. This
18     chapter provides a focused examination of analytical planning issues and the development of
19     APSs.

20     In order to assist the project planning team in identifying key issues, this chapter provides a list
21     of potential key analytical planning issues. Neither the list nor discussion of these potential issues
22     is an exhaustive examination of all possible issues for a project. However, this chapter does
23     provide a framework and a broad base of information that can assist in the identification of key
24     analytical planning issues for a particular project during a directed planning process.

25     Analytical planning issues can be divided into two broad categories—those that tend to be
26     matrix-specific and those that are more general in nature. While there is certainly some overlap
27     between these two broad categories, MARLAP divides analytical planning issues along these
28     lines because of the structure and logic it provides in developing APSs. This approach involves
29     identifying key analytical planning issues from the general (non-matrix-specific) issues first and
30     then proceeding on to the matrix-specific issues. Examples of non-matrix-specific analytical
31     planning issues include sample tracking and custody issues. These general issues are discussed in
32     detail in Section 3.3. Examples of matrix-specific  issues include filtration and preservation issues
33     of water samples. Matrix-specific analytical planning issues will be discussed in detail in Section

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34     3.4. Section 3.5 provides guidance on assembling the APSs from the resolution of these issues.
35     Section 3.6 discusses defining the level of protocol performance demonstration required for a
36     particular project, and Section 3.7 discusses incorporating the APSs into the project plan
37     documents.

38     3.2   Overview of the Analytical Process

39     Identifying key analytical issues for a particular project requires a clear understanding of the
40     analytical process. The analytical process as described in Chapter 1 includes all activities, starting
41     with field sample preparation and preservation, followed by sample receipt and inspection,
42     laboratory sample preparation; sample dissolution; chemical separations; instrument measure-
43     ments, data reduction and reporting, and sample tracking and quality control of the process.
44     Figure 3.1 illustrates the analytical process. It should be noted that a particular project's ana-
45     lytical process may not include all of the activities listed above. For example, if the project's
46     analytical process involves performing gamma spectrometry on soil samples, sample dissolution
47     and chemical separation activities normally are not required. Each step of a particular analytical
48     process contains potential planning issues that may be key analytical planning issues depending
49     on the nature and data requirements of the project. Therefore, it is important to identify the
50     relevant activities of the analytical process for a particular project early in the directed planning
51     process. Once the analytical process for a particular project has been established, key analytical
52     planning issues, including both general and matrix-specific ones, can be  identified.

53     3.3   General Analytical Planning Issues

54     This section discusses a number of general analytical planning issues that are common to many
55     types of projects and are often key planning issues, depending on the nature and data
56     requirements of the project. (Section 6.5 of Chapter 6 also discusses a number of these planning
57     issues to provide context on the method selection process.) This section presents each planning
58     issue as an activity to be accomplished during a directed planning process and also identifies the
59     expected outcome of the  activity in general terms.  The resolution of these general analytical
60     planning issues, particularly those that are key planning issues for a project, provides the basic
61     framework of the APSs and, therefore, should be identified and resolved before proceeding to
62     matrix-specific planning  issues. Normally the resolution of these issues results, at a minimum, in
63     an analyte list, identified  matrices of concern, measurement quality objectives (MQOs), and
64     established frequencies and acceptance criteria for quality control (QC) samples. The resolution
65     of matrix-specific issues, particularly those that are key  issues for a project, normally provides
66     the necessary additions and modifications to the basic framework of the APSs needed to
67     complete and finalize the specifications. MARLAP recommends that any assumptions made

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               Sample Tracking
                                               Field Sample Preparation
                                                  and Preservation
                                             Sample Receipt and Inspection
                                             Laboratory Sample Preparation
                                                 Sample Dissolution
                                               Chemical Separation of
                                               Radionuclides of Concern
                                              Preparation of Samples for
                                              Instrument Measurements
                                              Instrument Measurements
                                             Data Reduction and Reporting
                                                                                     QA/QC
                    Figure 3.1 — Typical components of an analytical process
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68     during the resolution of key analytical planning issues are documented, and that these
69     assumptions are incorporated into the appropriate narrative sections of project plan documents.
70     Documenting these assumptions may help answer questions or help make decisions during the
71     implementation and assessment phases of the project.

72     3.3.1  Develop Analyte List

73     From an analytical perspective, one of the most important planning issues that should be
74     addressed very early in a directed planning process by the project planning team is the target
75     analyte list—the radionuclides of concern for the project. For many projects, data are available
76     from previous activities for this purpose. Four possible sources of information are (1) historical
77     data, (2) process knowledge, (3) previous studies, and (4) information obtained from conducting
78     a preliminary survey or characterization study. Although discussed separately in Section 3.3.3,
79     the identification and characterization of matrices of concern  is often done concurrently with the
80     development of an analyte list.

81     Historical data are one source of existing information. Many activities associated  with
82     radioactive materials have been well-documented. For example, activities licensed by the
83     Nuclear Regulatory Commission (NRC) or NRC Agreement States normally generate much
84     documentation. Chapter 3 of MARS SUV! (2000) provides guidance on obtaining and evaluating
85     historical site data.

86     Another source of existing information is process knowledge. Some sites are associated with a
87     specific activity or process that involved radioactive material, where the process was well-
88     defined and the fate of the radioactive material in the process  was known or controlled. Examples
89     include uranium and rare earth ore processing, operations at Department of Energy (DOE)
90     weapons facilities, and operations at commercial nuclear power plants. (See Section 6.5.2 of
91     Chapter 6 for additional discussion on process knowledge.)

92     A third source of existing information is previous studies. Similar projects or studies of related
93     topics can provide valuable information during a directed planning process. Previous studies may
94     provide useful information on background radiation. Many radionuclides are present in measur-
95     able quantities  in the environment. Natural background radiation is due both to primordial and
96     cosmogenic radionuclides. Anthropogenic background includes radionuclides that are ubiquitous
97     in the environment as a result of such human activities as the  atmospheric testing  of nuclear
98     weapons. Natural and anthropogenic backgrounds can be highly variable even within a given site.
99     It may be important to consider the background and its variability when choosing an action level
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100     and when establishing the MQOs. Every effort should be made to obtain as much existing infor-
101     mation as possible priorto initiating a directed planning process.

102     Sometimes there are little or no historical data that can help identify radionuclides or the
103     concentration range of potential concern, or the existing data may be of inadequate quality. In
104     these cases, it may be necessary to perform preliminary analyses to identify the radionuclides of
105     concern or their concentration range. A fourth source of information is generated by conducting a
106     preliminary survey or characterization study. The design of preliminary surveys or characteriza-
107     tion studies should be part of the project planning process. The need for fast turnaround and
108     lower costs  at this stage of the project may lead to different data quality objectives (DQOs) and
109     MQOs that are less restrictive  than those used for the primary phase of the project. However, it is
110     important that analytical requirements for the survey or study be established during the project
ill     planning process. Gross alpha, gross beta, and gamma spectrometry analyses often are used for
112     preliminary survey or characterization studies.

113     The benefits of performing these types of measurements include:

114      •  Rapid analysis and short turnaround time;
115      •  Relatively low analytical costs; and
116      •  Detecting the presence of a wide range of radionuclides in a variety of media.

117     There are also limitations on the use of these analyses. These limitations include:

118      •  No specific identification for pure alpha- or pure beta-emitting radionuclides and low-energy,
119         gamma-emitting radionuclides are generally not identified; and

120      •  Failing to identify the presence of several radionuclides (e.g., 3H and other volatile
121         radionuclides; 55Fe and other radionuclides that decay by electron capture).

122     OUTPUT: An initial list of radionuclides of potential concern including a brief narrative explain-
123     ing why each radionuclide is on the list as well as an explanation of why certain radionuclides
124     were  considered but not listed. This list may be modified as more project-specific information
125     becomes available. It is better  to include radionuclides on the initial list even if the probability
126     that they significantly contribute to the addressed concerns is small. The consequence of
127     discovering an additional radionuclide of concern late in a project generally outweighs the effort
128     of evaluating its potential during planning.
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129     3.3.2  Identify Concentration Ranges

130     Once the radionuclides of concern have been identified, the expected concentration range for
131     each radionuclide should be determined. Historical data, process knowledge, and previous
132     studies, if available, can be used to determine the expected concentration range for each analyte.
133     While most analytical protocols are applicable over a fairly large concentration range for the
134     radionuclide of concern, performance over a required concentration range can serve as an MQO
135     for the protocol selection process  and some analytical protocols may be eliminated if they cannot
136     accommodate the expected concentration range. In addition, the expected concentration ranges  of
137     all of the radionuclides of concern can provide useful information about possible chemical and
138     spectral interferences. For example, while an analytical protocol for a particular radionuclide may
139     be able to accommodate the expected concentration range for that radionuclide, the concentra-
140     tions of other radionuclides may present interference problems, thus eliminating the use of that
141     analytical protocol.

142     OUTPUT: The expected concentration range for each radionuclide of concern as well as the
143     expected concentration range of any potential chemical or radiological interference.

144     3.3.3  Identify and Characterize  Matrices of Concern

145     During a directed project planning process, the matrices of concern should be clearly identified.
146     For many projects, typical matrices  may include surface soil, subsurface soil, sediment, surface
147     water, groundwater, drinking water, air particulates, biota, structural materials, metals, etc.
148     Historical data, process knowledge, previous studies, conceptual site models, transport models,
149     and other such sources generally are used to identify matrices of concern.  It is critical to be as
150     specific as possible when identifying a matrix.

151     From an analytical perspective, information on the chemical and physical  characteristics of a
152     matrix is extremely useful. Therefore, in addition to identifying the matrices of concern, every
153     effort should be made to obtain any information available on the chemical and physical charac-
154     teristics of the matrices. This information is particularly important when determining the required
155     specificity of the analytical protocol, i.e., the ability to accommodate possible interferences. It is
156     also important to identify any possible hazards associated with the matrix, such as the presence
157     of explosive or other highly reactive chemicals. Issues related to specific matrices, such as filtra-
158     tion of water samples and removal of foreign material, are discussed in more detail  in Section 3.5
159     and in Section 6.5.1.1 of Chapter  6.
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160     OUTPUT: A list of the matrices of concern along with any information on the chemical and
161     physical characteristics of the matrices and any information on possible hazards associated with
162     them. As previously noted, the list of matrices of concern and the analyte list often are developed
163     concurrently. In some cases, one analyst list is applicable to all the matrices of concern, and in
164     other cases there are variations in the analyte lists for each matrix.

165     3.3.4  Determine Relationships Between the Radionuclides of Concern

166     Known or expected relationships among radionuclides can be used to establish "alternative"
167     radionuclides that may be easier and less costly to measure. In most cases, an "easy-to-measure"
168     radionuclide is analyzed, and the result of this analysis is used to estimate the concentration of
169     one or more radionuclides that may be difficult to measure or costly to analyze.

170     One of the best known and easiest relationships to establish is between a parent radionuclide and
171     its associated progeny. Once equilibrium conditions have been established, the concentration of
172     any member of the decay series can be used to estimate the concentration of any other member of
173     the series. For example, the thorium decay series contains 12 radionuclides. If each radionuclide
174     in this series is analyzed separately, the  analytical costs can be very high. However, if equilib-
175     rium conditions for the decay  series have been established, a single analysis using gamma spec-
176     trometry may be adequate for quantifying all of the radionuclides in the series simultaneously.

177     Similarly, process knowledge can be used to predict relationships between radionuclides. For
178     example, in a nuclear power reactor, steel may become irradiated, producing radioactive isotopes
179     of the elements present in the  steel. These isotopes often include 60Co, 63Ni,  and 55Fe. 60Co decays
180     by emission of a beta particle  and two high-energy gamma rays, which are easily measured using
181     gamma spectrometry. 63Ni also decays by emission of a beta particle but has no associated
182     gamma rays. 55Fe decays by electron capture and has several associated X-rays with very low
183     energies. Laboratory analysis of 63Ni and 55Fe typically is time-consuming and expensive.
184     However, since all three radionuclides are produced by the same mechanism from the same
185     source material, there is an expected relationship at a given time in their production cycle. Once
186     the relationship between these radionuclides has been established, the 60Co concentration can be
187     used to estimate the concentration of 63Ni and 55Fe.

188     The uncertainty in the concentration ratio between radionuclide concentrations used in the alter-
189     nate analyte approach should be included as part of the combined standard uncertainty of the
190     analytical protocol in the measurement process. Propagation of uncertainties is discussed in
191     Chapter 19.
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192     OUTPUT: A list of known radionuclide relationships (e.g., those based on parent-progeny rela-
193     tionships or previous study results) and a list of potential radionuclide relationships (i.e., based
194     on process knowledge). A preliminary study to determine the project-specific radionuclide
195     relationships may be necessary, and additional measurements may be required to confirm the
196     relationship used during the project. This information may be used to develop a revised analyte
197     list.

198     3.3.5  Determine Available Project Resources and Deadlines

199     The available project resources can have a significant impact on the selection or development of
200     analytical protocols, as well as the number and type of samples to be analyzed. In addition,
201     project deadlines, and, in particular, required analytical turnaround times (see Section 6.5.3), can
202     be important factors in the selection and development of analytical protocols for a particular
203     project. During a directed planning process, radioanalytical specialists can provide valuable
204     information on typical costs and turnaround times for various types of laboratory analyses.

205     OUTPUT: A statement of the required analytical turnaround times for the radionuclides of concern
206     and the anticipated budget for the laboratory analysis of the samples.

207     3.3.6  Refine Analyte List and Matrix List

208     As additional information about a project is collected, radionuclides may be added to or removed
209     from the analyte list. There may be one analyte list for all matrices or separate lists for each
210     matrix. Developing an analyte list is an iterative process, however. The list should become more
211     specific during the project planning process.

212     Radionuclides might be added to the analyte list when subsequent investigations indicate that
213     additional radionuclides were involved in a specific project. In some cases, radionuclides may be
214     removed from the analyte list. When the initial analyte list is compiled, there may be significant
215     uncertainty associated with the presence of specific radionuclides. These radionuclides may be
216     included on the analyte list to be conservative, even when there is only a small probability they
217     may be present. Subsequent investigations may determine if specific radionuclides are actually
218     present and need  to be considered as part of the project. For example, a research laboratory was
219     licensed for a specific level of activity from all radionuclides with atomic numbers between 2 and
220     87. Even limiting the analyte list to radionuclides with a half-life greater than six months
221     provides several dozen radionuclides. A study may be designed to identify the actual
222     radionuclides of concern through the use of historical records and limited analyses to justify
223     removing radionuclides from the analyte list.


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224     OUTPUT: A revised analyte list. Radionuclides can always be added to or removed from the
225     analyte list, but justification for adding or removing radionuclides should be included in the
226     project documentation.

227     3.3.7  Method Performance Characteristics and Measurement Quality Objectives

228     The output of a directed planning process includes DQOs for a project. DQOs apply to all data
229     collection activities associated with a project, including sampling and analysis. In particular,
230     DQOs for data collection activities describe the overall level of uncertainty that a decisionmaker
231     is willing to accept for project results. This overall level of uncertainty is made up of
232     uncertainties from sampling and  analysis activities.

233     Since DQOs apply to both sampling and analysis activities, what are needed from an analytical
234     perspective are performance objectives specifically for the analytical process of a particular
235     project. MARLAP refers to these performance objectives as measurement quality objectives. The
236     MQOs can be viewed as the analytical portion of the overall project DQOs. In a performance-
237     based approach, the MQOs are used initially for the selection and evaluation of analytical
238     protocols and are subsequently used for the ongoing and final evaluation of the analytical data.

239     In MARLAP, the development of MQOs for a project depends on the selection of an action level
240     and gray region for each analyte during the directed planning process. The term "action level" is
241     used to denote the numerical value that will cause the decisionmaker to choose one of the
242     alternative actions. The "gray region" is a set of concentrations close to the  action level, where
243     the project planning team is willing to tolerate a high decision error rate  (see Chapter 2 and
244     Appendices B and C for a more detailed discussion of action levels and gray region). MARLAP
245     recommends that an action level  and gray region be established for each analyte during the
246     directed planning process.

247     MARLAP provides guidance on  developing MQOs for select method performance characteristics
248     such as:

249       •  The method uncertainty at a specified concentration (expressed as an estimated standard
250         deviation);

251       •  The method's detection capability (expressed as the minimum detectable concentration, or
252         MDC);
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253      •  The method's quantification capability (expressed as the minimum quantifiable
254         concentration, or MQC);

255      •  The method's range, which defines the method's ability to measure the analyte of concern
256         over some specified range of concentration;

257      •  The method's specificity, which refers to the ability of the method to measure the analyte of
258         concern in the presence of interferences; and

259      •  The method's ruggedness, which refers to the relative stability of method performance for
260         small variations in method parameter values.

261     An MQO is a statement of a performance objective or requirement for a particular method per-
262     formance characteristic. An example MQO for the method uncertainty at a specified concentra-
263     tion, such as the action level, would be: "A method uncertainty of 0.01 Bq/g or less is required at
264     the action level of 0.1 Bq/g." A qualitative example of an MQO for method specificity would be
265     "The method must be able to quantify the amount of 226Ra present, given elevated levels of 235U
266     in the samples." MQOs may be quantitative or qualitative in nature.

267     The list provided in this section is not intended to be an exhaustive list of method performance
268     characteristics, and for a particular project, other method performance characteristics may be
269     important and should be addressed during the project planning process. In addition, one or more
270     of the method performance characteristics listed may not be important for a particular project.
271     From an analytical perspective, a key activity during project planning is the identification of
272     important method performance characteristics and the development of MQOs for the method
273     performance characteristics.

274     In addition to developing MQOs for method performance characteristics, MQOs may be estab-
275     lished for other parameters, such as data quality indicators (DQIs). DQIs are qualitative and
276     quantitative descriptors used in interpreting the degree of acceptability or utility of data. The
277     principal DQIs are precision, bias, representativeness, comparability, and completeness. These
278     five DQIs are also referred to by the acronym PARCC; the "A" stands for accuracy instead of
279     bias, although both indicators are included in discussions of the PARCC parameters (EPA,
280     1998). Since the distinction between imprecision and bias depends on context, and since a
281     reliable estimate of bias requires a data set that includes many measurements, MARLAP focuses
282     on developing an MQO for method uncertainty. Method uncertainty effectively combines
283     imprecision and bias into a  single parameter whose interpretation does not depend on context.
284     This approach assumes that all potential sources of bias present in the analytical process have


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285     been considered in the estimation of the measurement uncertainty and, if not, that any appre-
286     ciable bias would only be detected after a number of measurements of QC and performance
287     evaluation samples have been performed. MARLAP provides guidance on the detection of bias,
288     for example, during analytical protocol validation and evaluation (Chapters 6 and 7). However,
289     the most likely time to detect, and possibly correct, an unanticipated bias is during data quality
290     assessment (see Chapter 9).

291     While MARLAP does not provide specific guidance on developing MQOs for the DQIs, estab-
292     lishing MQOs for the DQIs may be important for some projects. EPA Guidance for Quality
293     Assurance Project Plans (EPA, 1998) contains more information on DQIs. MARLAP provides
294     guidance on developing MQOs for method performance characteristics in the next section.

295     3.3.7.1 Develop MQOs for Select Method Performance Characteristics

296     Once the important method performance characteristics for an analytical process have been iden-
297     tified, the next step is to develop MQOs for them. This section provides guidance on developing
298     MQOs for the method performance characteristics listed in the previous section. As noted, other
299     method performance characteristics may be important for a particular analytical process, and
300     MQOs should be developed for them during project planning.

301     METHOD UNCERTAINTY

302     While measurement uncertainty is a parameter associated with an individual result and is calcu-
303     lated after a measurement is performed, MARLAP uses the term "method uncertainty" to refer to
304     the predicted uncertainty of a measured value that would likely result from the analysis of a
305     sample at a specified analyte concentration. Method uncertainty is a method performance charac-
306     teristic much like the detection capability of a method. Reasonable values for both characteristics
307     can be predicted for a particular method based on typical values for certain parameters and on
308     information and assumptions about the samples to be analyzed.  These predicted values can be
309     used in the method selection process to identify the most appropriate method based on a project's
310     data requirements. Because of its importance in the selection and evaluation of analytical proto-
311     cols and its importance in the evaluation of analytical data, MARLAP recommends that the
312     method uncertainty at a specified concentration (typically the action level) always be identified
313     as an important method performance characteristic, and that an MQO be established for it for
314     each analyte.

315     The MQO for the method uncertainty at a specified concentration plays a key role in MARLAP's
316     performance-based approach. It effectively links the three phases of the data life cycle: planning,


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317     implementation, and assessment. This MQO, developed during the planning phase, is used
318     initially in the selection and validation of an analytical method for a project (Chapter 6). This
319     MQO provides  criteria for the evaluation of QC samples during the implementation phase
320     (Appendix C and Chapter 7). It also provides criteria for verification and validation during the
321     assessment phase (Chapter 8). The use of the project-specific MQOs for the method uncertainty
322     of each analyte  in the three phases of the life of a project, as opposed to arbitrary non-project-
323     specific criteria, helps to ensure the generation of radioanalytical data of known quality
324     appropriate for  its intended use.

325     The MQO for method uncertainty for an analyte at a specified concentration, normally the action
326     level, is related  to the width of the gray region. The gray region has an upper bound and a lower
327     bound. The upper bound typically is the action level. The width of the gray region is represented
328     by the symbol A. See Appendix B for information on setting up a gray region.

329     Appendix C provides the rationale and detailed guidance on the development of MQOs for
330     method uncertainty. Outlined below is MARLAP's recommended guideline for developing
331     MQOs for method uncertainty when a decision is to be made about the mean of a population
332     represented by multiple samples. Appendix C provides additional guidelines for developing
333     MQOs for method uncertainty when decisions are to be made about individual items or samples.

334     If decisions are  to be made about the mean of a sampled population, MARLAP recommends that
335     the method uncertainty (HMR) be less than or equal to the width of the gray region  divided by  10
336     for sample concentrations at the upper bound of the gray region (typically the action level). If this
337     requirement cannot be met, the project planners should require at least that the method
338     uncertainty be less than or equal to the width of the gray region divided by 3 (Appendix C).
339
340
341
342
343
344
345
                                     EXAMPLE

Suppose the action level is 0.1 Bq/g and the lower bound of the gray region is 0.02 Bq/g. If
decisions are to be made about survey units based on samples, then the required method uncer-
tainty (HMR) at 0.1 Bq/g is
                             _A_ _ 0.1 -0.02
                              10      10
= 0.008  Bq/g
If this uncertainty cannot be achieved, then a method uncertainty (HMR) as large as A / 3 =
0.027 Bq/g may be allowed if more samples are taken.
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346     In the example above, the required method uncertainty (HMR) is 0.008 Bq/g. In terms of method
347     selection, this particular MQO calls for a method that can ordinarily produce measured results
348     with expected combined standard uncertainties (la) of 0.008 Bq/g or less at sample concentra-
349     tions at the action level (0.1 Bq/g in this example). Although individual measurement uncertain-
350     ties will vary from one measured result to another, the required method uncertainty is effectively
351     a target value for the individual measurement uncertainties.

352     OUTPUT: MQOs expressed as the required method uncertainty at a specified concentration for
353     each analyte.

354     DETECTION AND QUANTIFICATION CAPABILITY

355     For a particular project, the detection capability or the quantification capability may be identified
356     as an important method performance characteristic during project planning. If the issue is
357     whether an analyte is present in an individual sample and it is therefore important that the
358     method be able to reliably distinguish small amounts of the analyte from zero, then an MQO for
359     the detection capability should be established during project planning. If the emphasis is on being
360     able to make precise measurements of the analyte concentration for comparing the mean of a
361     sampled population to the  action level, then an MQO for the quantification capability should be
362     established during project planning.

363     Detection Capability

364     When decisions are to be made about individual items or samples (e.g., drinking water samples),
365     and the lower bound of the gray region is at or near zero for the analyte of concern, the detection
366     capability of the method is an important method performance characteristic, and an MQO should
367     be developed for it. MARLAP recommends that the MQO for the detection capability be
368     expressed as a required MDC (Chapter 19).

369     Outlined below is MARLAP's recommended guideline for developing MQOs for detection
370     capability. Appendix C provides the rationale along with detailed guidance on the development
371     of MQOs for detection capability.

372     If the lower bound of the gray region is at or near zero and decisions are to be made about
373     individual items or specimens, choose an analytical method whose minimum detectable
374     concentration is no greater than the upper bound of the gray region.l
           The MDC is defined as the analyte concentration at which the probability of detection is 1 - p.
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375     Quantification Capability

376     When decisions are to be made about a sampled population and the lower bound of the gray
377     region is at or near zero for the analyte of concern, the quantification capability of the method is
378     an important method performance characteristic and an MQO should be developed for it.
379     MARLAP recommends that the MQO for the quantification capability be expressed as  a required
380     MQC (see Chapter 19).

381     Outlined below is MARLAP's recommended guideline for developing MQOs for quantification
382     capability. The MQC,  as used in the guideline, is defined as the analyte concentration at which
383     the relative standard uncertainty is 10 percent (see Chapter 19). Appendix C provides the ration-
384     ale along with detailed guidance on the development of MQOs for quantification capability.

385     // the lower bound of the gray region is at or near zero and decisions are to be made about a
386     sampled population, choose an analytical method whose minimum quantifiable concentration is
387     no greater than the upper bound of the gray region which is typically the action level.

388     If an MQO for method uncertainty has been established, then establishing an MQO for the
389     quantification capability in terms of a required MQC is somewhat redundant since an MQC is
390     defined in terms of a specified relative standard uncertainty.  However, this method performance
391     characteristic is included in MARLAP for several reasons. First, it has been included to empha-
392     size the importance of the quantification capability of a method for those instances where the
393     issue is  not whether an analyte is present or not—for example measuring 238U in soil where the
394     presence of the analyte is given—but rather how precisely the analyte can be measured. Second,
395     this method performance characteristic has been included so as to promote the MQC as an
396     important method parameter. And last, it has been included as an alternative to the overemphasis
397     on establishing required detection limits in those instances where detection (reliably distinguish-
398     ing an analyte concentration from zero) is not the key analytical question.

399     OUTPUT: If the lower bound of the gray region is at or near zero, and decisions are to be made
400     about a  sample population, MQOs expressed as MQCs should be developed for each analyte. If
401     the lower bound of the gray region is zero and decisions are to be made about individual items or
402     specimens, MQOs expressed as MDCs should be developed for each analyte.

403     RANGE

404     Depending on the expected concentration range for an analyte (Section 3.3.2), the method's
405     range may be an important method performance characteristic. Most radioanalytical methods are


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406     capable of performing over a fairly large range of activity concentrations. However, if the
407     expected concentration range is large for an analyte, the method's range should be identified as
408     an important method performance characteristic and an MQO should be developed for it. The
409     radioanalytical specialist on the project planning team will determine when the expected concen-
410     tration range of an analyte warrants the development of an MQO for the method's range. Since
411     the expected concentration range for an analyte is based on past data which may or may not be
412     accurate, the MQO for the method's range should require that the method perform over a larger
413     concentration range than the expected range. This will help prevent the selection of methods
414     which cannot accommodate the actual concentration range of the analyte.

415     OUTPUT: MQOs for the method's concentration range for each analyte.

416     SPECIFICITY

417     Depending on the chemical and physical characteristics of the matrices, as well as the concen-
418     trations of analytes and the concentrations of other chemical constituents, the method's speci-
419     ficity may be an important method performance characteristic for an analytical process. Method
420     specificity refers to the ability of the method to  measure the analyte of concern in the presence of
421     interferences. In order to determine if method specificity is an important method performance
422     characteristic, the radioanalytical specialist on the project planning team will need information on
423     expected concentration ranges of the analytes of concern and other chemical constituents in the
424     samples (Section 3.3.2), along with information on the chemical and physical characteristics of
425     the matrices (Section 3.3.3). If it is determined that method specificity is an important method
426     performance characteristic, then an MQO should be developed for it. The MQO can be qualita-
427     tive or quantitative in nature.

428     OUTPUT: MQOs for the method specificity for those analytes likely affected by interferences.

429     RUGGEDNESS

430     For a project which involves analyzing samples which are complex in terms of their chemical
431     and physical characteristics, the method's ruggedness may be an important method performance
432     characteristic. Method ruggedness refers to the relative stability of the method's performance
433     when small variations in method parameter values are made, such as a change in pH, a change in
434     amount of reagents used, etc. In order to determine if method ruggedness is an important method
435     performance characteristic, the radioanalytical specialist on the planning team needs detailed
436     information on the chemical and physical characteristics of the samples. If it is determined that
437     method ruggedness is an important method performance characteristic, then an MQO should be


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438     developed for it. The MQO may require performance data which demonstrates the method's
439     ruggedness for specified changes in select method parameters. The statistical manual of the
440     Association of Official Analytical Chemists (AOAC) and the Standard Guide for Conducting
441     Ruggedness Tests ASTM El 169 provides guidance on ruggedness testing.

442     OUTPUT: MQOs for method ruggedness for specified changes in select method parameters.

443     3.3.7.2  The Role of MQOs in the Protocol Selection and Evaluation Process

444     Once developed, the MQOs become an important part of the project's APSs and are subsequently
445     incorporated into project plan documents (Chapter 4) and into the analytical Statement of Work
446     (Chapter 5). In MARLAP, MQOs are used initially in the selection, validation, and evaluation of
447     analytical protocols (Chapters 6 and 7). In a performance-based approach, analytical protocols
448     are either accepted or rejected largely on their ability or inability to meet the project MQOs.

449     3.3.7.3  The Role of MQOs in the Project's Data Evaluation Process

450     Once the analytical protocols have been selected and implemented, the MQOs and—in
451     particular—the MQOs for method uncertainty, are used in the evaluation of the resulting
452     laboratory data relative to the project's analytical  requirements. The most important MQO for
453     data evaluation is the one for method uncertainty  at a specified concentration. It is expressed as
454     the required method uncertainty (HMR)  at some concentration, normally the action level (for this
455     discussion, it is assumed that the action level is the upper bound of the gray region). When the
456     analyte concentration of a laboratory sample is less than the action level, the combined standard
457     uncertainty of the measured result should not exceed the required method uncertainty.

458     For example, if the required method uncertainty is 0.01 Bq/g or less at an action level of 0.1
459     Bq/g, then for any measured result less than 0.1 Bq/g, the laboratory's reported combined
460     standard uncertainty should be less than or equal to 0.01 Bq/g. When the concentration is greater
461     than the action level, the combined standard uncertainty of the measured result should not exceed
462     the relative value of the required method uncertainty. If the required method standard uncertainty
463     is 0.01 Bq/g or less at an action level of 0.1 Bq/g  (10 percent of the action level),  then for any
464     measured result greater than 0.1 Bq/g,  the laboratory's reported combined standard uncertainty
465     should be no greater than 10 percent of the measured result. If an expanded uncertainty is
466     reported with each measured value, and the coverage factor is also specified,  the combined
467     standard uncertainty may be calculated and checked against the required value. The check
468     described relies on the laboratory's estimate of its measurement uncertainty. Additional checks
469     are needed to ensure that the uncertainties are not seriously underestimated.


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470
471
472

473
474
475


476

477
478
479
480
481
482

483

484
485
486
487
488
489
490


491

492
493
494
495
496
497
498
499
Appendix C provides guidance on developing criteria for QC samples based on the MQO for
method uncertainty. Specifically, Appendix C contains equations for determining warning and
control limits for QC sample results based on the project's MQO for method uncertainty.

The following example illustrates the use of the MQO for method uncertainty in evaluating QC
sample results. Chapter 8, Data Verification and Validation, provides guidance on developing
validation criteria based on the MQO for the required method uncertainty.
                                       EXAMPLE

 Suppose the upper bound of the gray region (the action level) is 0.1 Bq/g, and the required
 method uncertainty (HMR) at this concentration is 0.01 Bq/g, or 10 percent. A routine laboratory
 control sample (LCS) is prepared with an analyte concentration of 0.150 Bq/g. (For the
 purpose of this example the uncertainty in the spike concentration is assumed to be negligible.)
 The lab analyzes the LCS with a batch of samples and obtains the measured result 0.140 ±
 0.008 Bq/g, where 0.008 Bq/g is the combined standard uncertainty (la).

 Question: Is this LCS result acceptable?

 Answer: The LCS result may be acceptable if it differs from the accepted true value by no
 more than three times the required method uncertainty at that concentration. In this example
 the required method uncertainty is 10 percent at 1.50 Bq/g. So, the LCS result is required to be
 within 30 percent of 1.50 Bq/g, or in the range 0.105-0.195 Bq/g. Since 0.140 Bq/g is clearly
 in the acceptance range, the data user considers the result acceptable. Note also that the
 laboratory's reported combined standard  uncertainty is less than the required method
 uncertainty,  as expected.
3.3.8   Determine Any Limitations on Analysis Options

With the outputs of the resolution of a number of key analytical planning issues, such as a refined
analyte list, MQOs for the analyte list, known relationships between radionuclides of concern, a
list of possible alternate analytes, required analytical turnaround times, the analytical budget, etc.,
the project planning team may choose to determine the analyses to be performed for the project
and thereby limit the analysis options available to the laboratory. It should be emphasized that
determining which analyses need to be performed is not the same as indicating that a particular
analytical  protocol or analytical method has to be used. With the exception of gross alpha and
beta measurements and gamma spectrometry, MARLAP uses the term "analysis " to refer to a
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500     radionuclide/matrix combination. Examples of analyses to be performed include 3H in water, 90Sr
501     in milk, 238Pu in soil, etc. Although determining the analyses to be performed during the planning
502     process may seem inconsistent with a performance-based approach, the project planning team
503     may determine the analyses to be performed or may decide to eliminate some analyses from
504     consideration. This decision may be based on information obtained during project planning, such
505     as the absence of equilibrium between the analyte and other radionuclides in its decay chain or
506     the presence of other radionuclides known to cause spectral interferences. However, in the
507     absence of such considerations, the project planning team should allow the laboratory the flexi-
508     bility of selecting the analyses which meet the analytical requirements as contained in the Ana-
509     lytical Protocol Specifications.

510     The role of the radioanalytical specialist is critical in determining if any limitations on analytical
511     options are necessary because of the many laboratory-related issues and factors involved. For
512     example, if several of the radionuclides of concern on the target analyte list are gamma-emitters,
513     the radioanalytical specialist can determine if gamma spectrometry is an appropriate analysis
514     given the required MQOs, matrices of concern, possible spectral interferences, etc. The radio-
515     analytical specialist may determine that not only is gamma spectrometry an appropriate analysis
516     for the gamma-emitting radionuclides of concern, but since there is evidence that equilibrium
517     conditions are present, the results for gamma spectrometry can be used for other radionuclides of
518     concern in the same decay chain as the gamma-emitting radionuclides. In other instances, such as
519     the use of gamma spectrometry to quantify 226Ra in the presence of elevated levels of 235U, the
520     radioanalytical specialist may determine that gamma spectrometry is not an appropriate analysis
521     due to possible spectral interferences. The following sections provide a brief overview of some
522     analysis procedures.

523     3.3.8.1  Gamma Spectrometry

524     In general, gamma spectrometry has many advantages over other choices. It is capable of
525     identifying and quantifying a large number of radionuclides.  In comparison with other analyses, it
526     offers a fairly quick turnaround time and, since limited sample manipulation is involved, it is
527     relatively inexpensive, particularly compared to analyses which  require sample dissolution and
528     chemical separations. It also allows for the use of relatively large sample sizes, thereby reducing
529     the measurement  uncertainty associated with subsampling at the laboratory. However, given its
530     many advantages, gamma spectrometry cannot be used to analyze for all radionuclides. For
531     example, gamma  spectrometry may not be able to achieve the project's MQOs, since some or all
532     of the radionuclides of concern may not be gamma-emitters,  interfering radionuclides may
533     present problems, etc. The radioanalytical specialist on the planning team can evaluate the
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534     appropriateness of the use of gamma spectrometry for some or all of the radionuclides on the
535     analyte list or for alternate analytes.

536     3.3.8.2 Gross Alpha and Beta Analysis

537     Gross alpha and beta analysis provides information on the overall level of alpha- and beta-
538     emitting radionuclides present in a sample. The analysis has the advantage of a relatively quick
539     turnaround time and generally is inexpensive compared to other analyses. The analysis also has
540     significant limitations. It does not identify specific alpha- and beta-emitting radionuclides,  so the
541     source of the overall alpha and beta radiation is not determined by the analysis. It does not  detect
542     contribution from low-energy beta-emitting radionuclides such as 3H. The measurement uncer-
543     tainty of the analysis, particularly  for matrices other than water, tends to be larger than the meas-
544     urement uncertainty of other analyses. However, even with these limitations, gross alpha and beta
545     analysis  can be an important and appropriate analysis for a project.

546     3.3.8.3 Radiochemical Nuclide-Specific Analysis

547     In many instances, due to the project's MQOs,  the lack of an appropriate alternate analyte,  the
548     lack of equilibrium conditions, etc., radiochemical nuclide-specific analyses are required. This is
549     often true when radionuclides such as 3H, 14C, 90Sr, isotopes of Pu, "Tc, etc., are on the analyte
550     list. These analyses generally involve more manipulation of the samples than do gamma spec-
551     trometry and gross alpha and beta analysis. These analyses often require sample dissolution and
552     chemical separation of the radionuclides of concern. For liquid scintillation counting, distillation
553     is usually required for water samples, and some oxidative/combustion procedure is usually
554     required for solid samples. Because of this, these analyses generally have longer turnaround
555     times and are more expensive than other analyses.

556     Given the many analytical factors and considerations involved, the role of the radioanalytical
557     specialist is critical to determining if any limitations on analysis options are necessary.

558     OUTPUT: Any limitations on analysis options, if appropriate.

559     3.3.9  Determine Method Availability

560     After the required analyses have been determined along with the sample matrices, the required
561     MQOs, the analytical turnaround times, etc., the radioanalytical specialist should be able to
562     determine if there are analytical methods currently available to meet the project's requirements.
563     There are a number of sources of radioanalytical methods, including those published by the


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564     American Society of Testing and Materials (ASTM), Standard Methods for the Examination of
565     Water and Waste Water (APHA/AWWA,  1992), methods published in scientific journals,
566     methods published in laboratory procedure manuals, and those published by Federal and State
567     agencies.

568     If there are no known analytical methods that would meet the project's analytical requirements,
569     the project planning team must evaluate options. They may decide to reevaluate the analytical
570     data requirements,  such as the MQOs, to see if they can be changed to allow the use of existing
571     methods or increase the analytical budget and project timeline to allow for method development.

572     OUTPUT: A statement of method availability.

573     3.3.10 Determine the Type and Frequency of, and Evaluation Criteria for, Quality Control
574            Samples

575     There are three main types of laboratory QC samples—blanks, replicates, and spikes. In addition,
576     there are different types of blanks, replicates, and spikes. For example, spikes can be matrix
577     spikes, laboratory control samples, external performance evaluation samples, etc. Chapter 18
578     contains a detailed discussion of the different types of QC samples and the information they pro-
579     vide. Since the results of the three main types of QC samples often are used to evaluate different
580     aspects of the analytical process, most projects should employ all three types as part of the QC
581     process.

582     The frequency of laboratory QC sampling  for a project essentially represents a compromise
583     between the need to evaluate and control the analytical process and the resources available. In
584     addition, the nature of the project and the intended use of the data will play a role in determining
585     the frequency of QC samples required. For example, the frequency of QC samples for a project
586     involving newly developed methods for analytes in a complex matrix normally should be greater
587     than the frequency of QC samples for a project using more established methods on a simpler
588     matrix, assuming the intended use of the data is  the same for both projects. The radioanalytical
589     specialists on the project planning team play a key role in determining the type and frequency of
590     QC samples for a project.

591     In order to adequately evaluate laboratory  data, it is important that the  QC samples be clearly
592     linked to a group of project samples. Typically, this is done by analyzing QC samples  along with
593     a batch of samples and reporting the results together.
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594     In addition to determining the type and frequency of QC samples, evaluation criteria for the QC
595     sample results should be developed during the directed planning process and incorporated into
596     the project's APSs. Appendix C provides guidance on developing criteria for QC samples and
597     contains equations that calculate warning and control limits for QC sample results based on the
598     proj ecf s MQO for method uncertainty.

599     OUTPUT: List of type and frequency of QC samples required and the criteria for evaluating QC
600     sample results.

601     3.3.11  Determine Sample Tracking and Custody Requirements

602     A procedural method for sample tracking should be in place for all projects so that the proper
603     location and identification of samples is maintained throughout the life of the project. Sample
604     tracking should cover the entire process from sample collection to sample disposal. For some
605     projects, a Chain-of-custody (COC) process is needed. COC procedures are particularly
606     important in demonstrating sample control when litigation is involved. In many cases, Federal,
607     State, or local agencies may require that COC be maintained for specific samples. Chapter 10,
608     Field and Sampling Issues that affect Laboratory Measurements, provides guidance on sample
609     tracking and COC. It is important that the requirements for sample tracking be clearly established
610     during project planning.

611     OUTPUT: Project sample tracking requirements.

612     3.3.12  Determine Data Reporting  Requirements

613     The data reporting requirements should be established during project planning. This involves
614     determining not only what is to be reported but also how it is to be reported. Items that are
615     routinely reported are listed below. It should be noted that this is not a comprehensive list, and
616     some projects may require the reporting of more items while other projects may require the
617     reporting of fewer items:

618      • Field sample identification number
619      • Laboratory sample identification number
620      • Sample receipt date
621      • Analysis date
622      • Radionuclide
623      • Radionuclide concentration units
624      • Sample size (volume, mass)


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625      •  Aliquant size (volume, mass)
626      •  Radionuclide concentration at specified date
627      •  Combined standard uncertainty or expanded uncertainty (coverage factor should be indicated)
628      •  Sample-specific minimum detectable concentration
629      •  Analysis batch identification
630      •  Quality control sample results
631      •  Laboratory instrument identification
632      •  Specific analytical parameters (e.g., chemical yields, counting times, etc.)
633      •  Analytical method/procedure reference

634     It is important that the required units for reporting specific items be determined during project
635     planning. MARLAP recommends that units of the International System of Units (SI) be used
636     whenever possible. However, since regulatory compliance levels are usually quoted in traditional
637     radiation units, it may be appropriate to report in both SI and traditional units, with one being
638     placed in parenthesis. MARLAP also recommends that all measurement results be reported
639     directly as obtained, including negative values, along with the measurement uncertainty—for
640     example 2a, 3a,  etc. Additional guidance on data reporting, including a discussion of electronic
641     data deliverables, is provided in Chapter 17, Data Acquisition, Reduction, and Reporting, and in
642     Chapters, Obtaining Laboratory Services.

643     OUTPUT: Data reporting requirements for a project.

644     3.4   Matrix-Specific Analytical Planning Issues

645     This section discusses a number of matrix-specific analytical planning issues common to many
646     types of projects. For each matrix there is a discussion of several potential key analytical plan-
647     ning issues specific to that matrix. It should be noted that what may be a key  analytical planning
648     issue for one project, may not be  a key issue for another project. The list of potential matrix-
649     specific key analytical planning issues discussed in this section is summarized in Table 3.1.
650     Table 3.1 is not a comprehensive list, but rather is an overview of some common matrix-specific
651     planning issues.

652     This section is divided into  solids, liquids,  filters and wipes. While filters and wipes are solids,
653     they are discussed separately because of the unique concerns associated with them.
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654
655
656
657
658
                  TABLE 3.1 — Matrix-specific analytical planning issues
659
660
661
662
663
       MATRIX
 Solids (soil, sediment,
 structural material,
 biota, metal, etc.)
 Liquids (drinking water,
 groundwater,
 precipitation, solvents,
 oils, etc.)
 Filters and Wipes
 RECOMMENDED KEY ISSUES
Homogenization
Subsampling
Removal of unwanted material
Is filtering required?
Sample preservation
Should sample be filtered or preserved
first?
Filter material
Pore size
Sample volume or area wiped
       POTENTIAL KEY ISSUES
Container type
Container material
Sample preservation
Screening samples for health and safety
Volatile compounds
Sample identification
Cross-contamination
Sample size
Compliance with radioactive materials license
Compliance with shipping regulations
Chemical and physical form of the substrate
Sample identification
Volume of sample
Immiscible layers
Precipitation
Total dissolved solids
Reagent background
Compliance with radioactive materials license
Compliance with shipping regulations
Sample identification
Compliance with radioactive materials license
Compliance with shipping regulations
Subsampling
Background from filter material	
664
3.4.1   Solids
665      Solid samples consist of a wide variety of materials that include soil and sediment; plant and
666      animal tissue; concrete; asphalt; trash, etc. In general, most solid samples do not require preser-
667      vation (Chapter 10) but do require specific processing both in the field and in the laboratory. In
668      certain instances, some biota samples may require preservation, primarily in the form of lowered
669      temperatures, to prevent sample degradation and loss of water. Some common analytical
670      planning issues for solid samples include homogenization and Subsampling (Section 3.4.1.1) and
671      the removal of unwanted materials (Section 3.4.1.2). For certain types of biological samples,
672      removal and analysis of edible portions may be a key analytical planning issue.

673      Other issues that may represent key analytical issues for solids include container type and mate-
674      rial (Chapter 10); sample preservation (Chapter 10); sample drying—wet, dry, ashed weights and
675      ratios—(Chapter  10), screening samples for health and  safety (Chapter 11); volatile compounds
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676     (Chapter 10); sample identification (Chapters 10, 11, and 12); cross-contamination (Chapter 10);
677     sample size (Chapters 10, 11, and 12); compliance with the radioactive materials license and
678     shipping regulations (Chapter 11); and the chemical and physical form of the sample substrate
679     (Chapters 13 and 14).

680     3.4.1.1 Homogenization and Subsampling

681     For many types of analyses, a portion of the sample sent to the laboratory must be removed for
682     analysis. As with sampling in the field, this portion of the sample should be representative of the
683     entire sample. Adequate homogenization and proper subsampling techniques are critical to
684     obtaining a representative portion of the sample for analysis. Developing requirements for—and
685     measuring the adequacy of—homogenization processes and subsampling techniques can be
686     complicated for various types of solid matrices. General guidance on homogenization and sub-
687     sampling is provided in Chapter 12 and Appendix F. The input of the radioanalytical specialist as
688     a member of the project planning team is critical to developing requirements for homogenization
689     processes and subsampling techniques.

690     3.4.1.2 Removal of Unwanted Materials

691     When a solid sample is collected in the field, extraneous material may be collected along with the
692     "intended" sample. For example, when collecting a soil sample, rocks, plant matter, debris, etc.,
693     may also be collected. Unless instructed otherwise, samples received by the laboratory typically
694     are analyzed exactly as they are received. Therefore, it is important to develop requirements
695     regarding the treatment of extraneous materials. Ultimately, these guidelines should be based on
696     the project's DQOs. The requirements should clearly state what, if anything, is to be removed
697     from the sample and should indicate  what is to be done with the removed materials. The
698     guidelines should indicate where the removal process should  occur (in the field, in the laboratory
699     or at both locations) and the material to be removed should be clearly identified.

700     For soil samples, this may involve identifying rocks of a certain sieve size, plant matter, debris,
701     etc., as extraneous material to be removed, weighed, and stored at the laboratory. For sediment
702     samples, requirements for occluded water should be developed. In the case of biological samples,
703     if the entire sample is not to be analyzed, the analytical portion should be identified clearly.
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                                                                   Key Analytical Planning Issues..
704     3.4.2  Liquids

705     Liquids include aqueous liquids (e.g., surface water, groundwater, drinking water, aqueous
706     process wastes, and effluents), nonaqueous liquids (e.g., oil, solvents, organic liquid process
707     wastes), and mixtures of aqueous and nonaqueous liquids.

708     A key analytical planning issue for most liquids is whether or not filtering is required or neces-
709     sary; this is discussed in Chapter 10. The question of whether or not to filter a liquid is generally
710     defined by the fundamental analytical question (Section 3.3.3). If the question is related to total
711     exposure from  ingestion, the liquids are generally not filtered or the filters are analyzed
712     separately and the results summed. If the question is concerned with mobility of the analyte the
713     concentration in the liquid fraction becomes more important than the concentration in the sus-
714     pended solids (although some suspended solids may still be important to questions concerning
715     mobility of contamination). In many projects, all of the liquids are filtered and the question
716     becomes which filters need to be analyzed. Issues related to this  decision include where and
717     when to filter (Chapter 10); homogenization and subsampling (Chapter 10); volatile compounds
718     (Chapter 10); screening for health and safety (Chapter 11); and cross-contamination (Chapter
719     10).

720     Another key analytical planning issue involves preservation of liquid samples, which is also dis-
721     cussed in Chapter 10. Sample preservation involves decisions about the method of preservation
722     (temperature or chemical, Chapter  10), container type and material (Chapter 10), and chemical
723     composition of the sample (Chapters 13 and 14). Preservation of radionuclides in liquids is
724     generally accomplished in the same manner as  preservation of metals for chemical analysis.
725     There are of course exceptions such as for 3H and 129I.

726     A third key analytical issue results  from the first two issues and involves the decision of which
727     issue should be resolved first. Should the sample be filtered and  then preserved, or preserved first
728     and filtered later? This issue is also discussed in Chapter 10. In general, acid is used to preserve
729     liquid samples. Since acid brings many radionuclides into solution from suspended or undis-
730     solved material, filtering is generally performed in the field prior to preserving the sample with
731     acid.

732     Other analytical planning issues that may be important for a specific project include: sample
733     identification (Chapters 10, 11, and 12); volume of sample (Chapter 10); compliance with radio-
734     active materials license and shipping regulations (Chapter 11); immiscible layers (for mixtures of
735     aqueous and nonaqueous liquids, Chapter  12);  precipitation between filtration and analysis
736     (Chapter 12); total dissolved solids (Chapter 12); and reagent background (Chapter 12).


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737     3.4.3  Filters and Wipes

738     Filters include a wide variety of samples, including liquid filters, air filters for suspended
739     particulates, and air filters for specific compounds. Once the decision to filter has been made,
740     there are at least three key analytical planning issues: filter material, pore size, and volume of
741     material to be filtered.

742     The selection of filter or wipe material can be very important. The wrong filter or wipe can
743     dissolve, break, or tear, thus invalidating the sample. Chapter 10 includes a discussion of the
744     various types of filter and wipe materials. Issues influencing this decision include the volume of
745     material to be filtered, the loading expected on the filter, and the chemical composition of the
746     material to be filtered.

747     The pore size is also important when preparing to filter. Too large a pore size will fail to collect
748     all of the material that is needed, while too small a pore size may lead to clogged filters and
749     reduced sample sizes. If an evaluation is being performed of respirable-size particles being
750     released by a process, the pore size of the filter should reflect this requirement.

751     The volume of material to be filtered, or area to be wiped, is generally determined by the detec-
752     tion requirements for the project. Lower detection limits require larger samples. Larger samples
753     may, in turn, result in problems with shipping samples or analytical problems where multiple
754     filters were required to meet the requested detection limits.

755     Other analytical planning issues that may be important for a specific project include sample
756     identification (Chapters  10, 11, and 12), compliance with radioactive materials license and ship-
757     ping regulations (Chapter 11), and background contributions from filter materials (Chapter 12).

758     3.5   Assembling the Analytical Protocol Specifications

759     After key general and matrix-specific analytical planning issues have been identified and
760     resolved, the next task of the project planning team is to organize and consolidate the results of
761     this process into APSs for the project. In general, there will be an APS for each type of analysis
762     (analyte-matrix combination). At a minimum, the APS should include the analyte list, the sample
763     matrix, possible interferences, the MQOs, any limitations on analysis options, the type and
764     frequency of QC samples along with acceptance criteria, and any analytical process requirements
765     (e.g., sample tracking requirements). The analytical process requirements should be limited to
766     only those requirements which are considered essential to meeting the project's analytical data
767     requirements. For example, if the analyte of concern is known to exist in a refractory form in the

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                                                                   Key Analytical Planning Issues...
768     samples, then fusion for sample digestion may be included as an analytical process requirement.
769     However, in a performance-based approach, it is important that the level of specificity in the
770     Analytical Protocol Specifications should be limited to those requirements which are considered
111     essential to meeting the project's analytical data requirements. The APS should be a one- or
772     two-page form that summarizes the resolution of key analytical planning issues.

773     Figure 3.2 provides an example form for Analytical Protocol Specifications with references to
774     sections in this chapter as major headers on the form. Figure 3.3 provides for the purpose of an
775     example, an APS for 226Ra in soil for an information gathering project.

776     3.6   Level of Protocol Performance Demonstration

777     As discussed in Section 3.3.7.3, during project planning, the project planning team should deter-
778     mine what level of analytical performance demonstration or method validation is appropriate for
779     the project. The question to be  answered is how the analytical protocols will be evaluated. There
780     are three parts of this overall evaluation process: (1) the initial evaluation, (2) the ongoing evalu-
781     ation, and (3) the final evaluation. This section briefly discusses the initial evaluation of protocol
782     performance. Chapters 7 and 8 provide guidance on the ongoing and final evaluation of protocol
783     performance, respectively.

784     The project planning team should determine what level of initial performance demonstration is
785     required from the laboratory to demonstrate that the analytical protocols the laboratory proposes
786     to use will meet the MQOs and other requirements in the APSs.  The project planning team
787     should decide the type and amount of performance data required. For example, for the analysis of
788     3H in drinking water, the project planning team may decide  that past performance data from the
789     laboratory, such as the results of internal QC samples for the analysis of 3H in drinking water, are
790     sufficient for the initial demonstration of performance for the laboratory's analytical protocols if
791     they demonstrate the protocol's ability to meet the MQOs. If the analysis is for 238Pu in a sludge,
792     the project planning team may  decide that past performance data (if it exists) would not be
793     sufficient for the initial demonstration of performance. The  planning team may decide that
794     satisfactory results on performance evaluation samples would be required for the initial
795     demonstration of analytical protocol performance. Section 6.6 provides detailed guidance on
796     protocol performance demonstration/method validation, including a tiered approach based on the
797     project analytical needs and available resources.
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798

799
800
801
802
803
804
805
806
807
808
809
810

811
812
813
814
815
816
817
818
819
820
821
822

823
824

825
                               Analytical Protocol Specifications
Analyte List: (Section 3.3.1. 3.3.7	  Analysis Limitations: (Sections 3.3.9)	
Matrix: (Section 3.3.3)	  Possible Interferences: (Sections 3.3.3. 3.3.5)
Concentration Range: (Section 3.3.2)     Action Level (Section 3.3.8)
                                             MQOs:
 (Section 3.3.8)
  (Section 3.3.8)
(Section 3.3.8)
  (Section 3.3.8)
QC Samples
Type
(Section 3. 3. 11)
(Section 3. 3. 11)
(Section 3. 3. 11)
(Section 3. 3. 11)
Frequency
(Section 3. 3. 11)
(Section 3. 3. 11)
(Section 3. 3. 11)
(Section 3. 3. 11)
Evaluation Criteria
(Section 3.3. 8.2)
(Section 3.3. 8.2)
(Section 3.3. 8.2)
(Section 3.3. 8.2)
Analytical Process Requirements*
Activity
Field Sample Preparation and Preservation
Sample Receipt and Inspection
Laboratory Sample Preparation
Sample Dissolution
Chemical Separations
Preparing Sources for Counting
Nuclear Counting
Data Reduction and Reporting
Sample Tracking Requirements
Other
Special Requirements
(Section 3. 4)
(Section 3. 4. 12)
(Section 3. 4)
(Section 3. 4)
(Section 3. 4)
(Section 3. 4)
(Section 3. 4)
(Section 3.3. 13)
(Section 3. 3. 12)

* Consistent with a performance-based approach, analytical process requirements should be kept to a minimum,
therefore none or N/A may be appropriate for many of the activities.
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                                                                       Key Analytical Planning Issues...
826

827
828
829
830
                        Analytical Protocol Specifications (Example)
Analyte List: 226Ra
Matrix: Soil
Analysis Limitations: Must perform direct measurement of
analyte or analysis of progeny allowed if equilibrium established at
laboratory
Possible Interferences: Elevated levels of 235U
831
Concentration Range: 0.01 to 1.50Bq/g      Action Level:   0.5 Bq/g
832                                                  MQOs:
833      A method uncertainty (»„,') of 0.04 Bq/g or less at 0.5 Bq/g
QC Samples
Type
Method blank
Duplicate
Matrix Spike
Frequency
1 per batch
1 per batch
1 per batch
Evaluation Criteria
See attachment B*
See attachment B*
See attachment B*
834
835
836
837
838

839
840
841
842
843
844
845
846
847
848
849
850
851

852
Analytical Process Requirements
Activity
Field Sample Preparation and Preservation
Sample Receipt and Inspection
Laboratory Sample Preparation
Sample Dissolution
Chemical Separations
Preparing Sources for Counting
Nuclear Counting
Data Reduction and Reporting
Sample Tracking Requirements
Other
Special Requirements
None
None
None
None
None
None
None
See attachment A*
Chain-of -Custody

  Attachments A and B are not provided in this example
                  FIGURE 3.3 — Example analytical protocol specifications
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853     3.7    Project Plan Documents

854     Once the APSs have been completed, they should be incorporated into the appropriate project
855     plan documents and, ultimately, into the analytical Statement of Work. Chapters 4 and 5 provide
856     guidance on the development of project plan documents and analytical Statements of Work,
857     respectively. While the APSs are concise compilations of the analytical data requirements, the
858     appropriate plan documents should detail the rationale behind the decisions made in the develop-
859     ment of the APSs.
860

861
862
863

864
865

866
867
868

869
870

871
872

873
874

875
876
                        Summary of Recommendations

MARLAP recommends that any assumptions made during the resolution of key analytical
planning issues are documented, and that these assumptions are incorporated into the
appropriate narrative sections of project plan documents.

MARLAP recommends that an action level and gray region be established for each analyte
during the directed planning process.

MARLAP recommends that the method uncertainty at a specified concentration (typically
the action level) always be identified as an important method performance characteristic,
and that an MQO be established for it for each analyte.

MARLAP recommends that the MQO for the detection capability be expressed as a
required minimum detectable concentration.

MARLAP recommends that the MQO for the quantification capability be expressed as a
required minimum quantifiable concentration.

MARLAP recommends that units of the International System of Units (SI) be used
whenever possible.

MARLAP recommends that all measurement results be reported directly as obtained,
including negative values, along with the measurement uncertainty.
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87?     3.8    References

878     American Public Health Association, American Water Works Association, Water Environment
879        Federation (APHA/AWWA). Standard Methods for the Examination of Water and
880         Wastewater, 18th ed, 1992. American Public Health Association, Washington, DC.

881     American Society for Testing and Materials (ASTM) El 169. Standard Guide for Conducting
882        Ruggedness Test. 1989.

883     U.S. Environmental Protection Agency (EPA). 1998. Guidance for the Quality Assurance
884        Project Plans (EPA QA/G-5). EPA/600/R-98/018, Washington, DC.

885     MARSSEVI. 2000. Multi-Agency Radiation Survey and Site Investigation Manual, Revision 1.
886        NUREG-1575 Rev 1, EPA 402-R-97-016 Revl,  DOE/EH-0624 Revl. August. Available
887        from http://www.epa.gov/radiation/marssim/filesfm.htm.

888     Youden, WJ. and E.H. Steiner. 1975. Statistical Manual of the Association of Official Analytical
889        Chemists. Association of Official Analytical Chemists International, Gaithersburg, MD.
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                        4  PROJECT PLAN DOCUMENTS
 2     4.1    Introduction

 3     The project plan documents are a blueprint for how a particular project will achieve data of the
 4     type and quality needed and expected by the project planning team. In the planning documents,
 5     the data user's expectations and requirements, which are developed during the planning
 6     process—including the Analytical Protocol Specifications and measurement quality objectives
 7     (MQOs)—are documented along with the standard operating procedures (SOPs), health and
 8     safety protocols, and quality assurance/quality control (QA/QC) procedures for the field and
 9     laboratory analytical teams. The objectives of this chapter are to discuss:

10      • The importance of project plan documents;
11      • The elements of project plan documents; and
12      • The link between project planning and project plan documents, in particular the incorporation
13        of the analytical protocols.

14     The importance of project plan documents is discussed in Section 4.2. Section 4.3 discusses a
15     graded approach to project plan documents. The different types of planning documents and the
16     elements of the project plan documents are discussed in Sections 4.4 and 4.5, respectively. The
17     link between project planning and project plan documents is discussed in Section 4.6.

18     The project plan documents should be dynamic documents, used and updated over the life of the
19     project. Under a performance-based approach, the analytical protocols requirements in the project
20     plan documents initially may reflect the Analytical Protocol Specifications established by the
21     project planning team and issued in the statement of work (SOW) (or Basic Ordering Agreement
22     Task Order). When the analytical laboratory has been selected, the project plan documents should
23     be updated to reflect the actual protocols to be used. The protocols should be cited, or the SOPs
24     for the protocols should be included as appendices. (Analytical Protocol Specifications and the
25     relation to project measurement quality objectives (MQOs) have been discussed in Chapter 3 and
26     represented in Figure 3.2 and 3.3).

27     While this chapter will address the documentation of QA/QC used in project activities,
28     MARLAP is cognizant of, and fully endorses, the need for an organizational quality system and a
29     quality system, management plan, or quality manual. The development of the project plan
30     documents should be addressed in the quality system requirements documentation. The project
31     plan documents should reflect, and be consistent with, the organization's QA policies and
32     procedures. Guidance on elements of a quality system for environmental data collection activities
33     is available from several sources including ANSI/ASQC (1994) and ISO Standard 9001 (1994).

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34     The QA requirements have been developed by several Federal Agencies and consensus standard
35     organizations including the following:

36      •  10 CFR 830.120
37      •  10 CFR 50, Appendix B
38      •  ANSIN42.23-1996
39      •  ASMENQA-1-1989
40      •  DOE Order 4.14.1  on QA
41      •  EPA Order 5360.1  on  Quality Systems (1998c)
42      •  DODQA requirement MIL-Q-9858A

43     4.2   The Importance of Project Plan Documents

44     Project plan documents are important in environmental data collection activities to ensure that
45     the type and quantity of data are sufficient for the decision to be made. Project plans document
46     the decisions made during the planning process and integrate the technical operations with the
47     management and quality system practices. Project plans also:

48      •  Support data defensibility for environmental compliance;
49      •  Can be used to defend project objectives and budget; and
50      •  Are a tool for communication with stakeholders.

51     The development of project plan documents and the implementation of the project plan provide
52     the following benefits:

53      •  Full documentation for legal, regulatory, and historical use of the information;

54      •  Specification of data collection and quality control;

55      •  Documentation of analytical requirements through the incorporation of an Analytical
56         Protocol Specifications;

57      •  Implementation of planned data collection activities (through internal  and external
58         assessment and oversight activities); and

59      •  Meeting project-specific criteria (i.e., MQOs, DQOs) through data validation and usability
60         assessment.
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6i     4.3    A Graded Approach to Project Plan Documents

62     A graded approach is the process of basing the level of management controls applied to an item
63     or work on the intended use of the results and the degree of confidence needed in the quality of
64     the results (ANSI/ASQC, 1994). MARLAP recommends a graded approach to project plan
65     development because of the diversity of environmental data collection activities. This diversity in
66     the type of project and the data to be collected impacts the content and extent of the detail to be
67     presented in the plan document. The plan document development team should be flexible in their
68     application of guidance according to the nature of the work being performed and the intended use
69     of the data.

70     Under a graded approach, a mix of project-specific and site-based quality system documentation
71     may be relied upon to ensure quality. For example, the project specific plan may:
72
• Address design, work processes, and inspection; and
73      •  Incorporate by citation site-wide plans that address records management, quality
74         improvement, procurement, and assessment.

75     A comprehensive and detailed project plan is required for some data collection activities because
76     of the need for legal and scientific defensibility of the data. A comprehensive and detailed plan
77     may also be desirable when Office of Management and Budget (OMB) clearance and approval is
78     needed to carry out the project (e.g., NRC/EPA proposed Publicly Owned Treatment Works
79     Survey).

80     Other environmental data collection activities, such as basic studies or small projects, may only
81     require a discussion of the experimental process and its objectives, which is often called a project
82     narrative statement.  (Other titles used for project narrative statements are "QA narrative
83     statement" and "proposal QA plan" (EPA, 1998a). Basic studies and small projects generally are
84     of short duration or limited scope and could include proof of concept studies, exploratory
85     projects, small data collection tasks, feasibility studies, qualitative screens, or initial work to
86     explore assumptions or correlations. Although basic studies and small projects may be used to
87     acquire a better understanding of a phenomenon, they will not by themselves be used to make
88     significant decisions or establish policy. Further discussion on the content of plan documents for
89     basic studies and small projects is provided in Section 4.5.3.
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 90      4.4    Project Plan Documents

 91      The ANSI/ASQC (1994) definition for a QA Project Plan (QAPP), which is also applicable to
 92      other integrated project plan documents, is "a formal document describing in comprehensive
 93      detail the necessary QA, QC and other technical activities that must be implemented to ensure
 94      that the results of the work performed will satisfy the stated performance criteria." The project
 95      plan documents should contain this information in a clear and integrated manner so that all
 96      implementation teams can understand their role and the project objectives.

 97      Project plan documents vary in size and format and are referred to by a variety of names. The
 98      size of the project plan documents tends to reflect the issuing agency's requirements, complexity,
 99      and scope of the project activities. Some projects with multiple phases may have more than one
100      plan document. For example, separate plan documents may be developed for scoping surveys,
101      characterization, and the final status survey for the same site because of the different objectives
102      and data requirements. Available guidance on project plans will be discussed in Section 4.4.1,
103      and a general discussion of various approaches is discussed in Section 4.4.2.

104      4.4.1   Guidance on Project Plan Documents

105      National standards guidance on project plan documents is available in:

106       • ASTM Standard Practice (D5283) for Generation of Environmental Data Related to Waste
107         Management Activities: Quality Assurance and Quality Control Planning and
108         Implementation (ASTM, 1992);

109       • Standard Guide (D5612), Quality Planning and Field Implementation of a Water Quality
110         Measurements Program (ASTM, 1994); and

ill       • Standard Provisional Guide (PS85) for Expedited Site Characterization of Hazardous Waste
112         Contaminated Sites (ASTM, 1996).

113      Guidance on project plans for environmental data collection activities in the federal sector is also
114      available (EPA, 1998a; 40 CFR 300.430; NRC, 1989; and USACE,  1994 and 1997). Other
115      Federal Agency guidance may follow EPA guidance for QAPPs (EPA, 1998a).
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116      4.4.2   Approaches to Project Plan Documents

117      The approach and naming of project plan documents is usually a function of the authoring
118      organization's experience, any controlling Federal or state regulations, or the controlling Agency.
119      Project plan, work plan, QAPP, field sampling plan, sampling and analysis plan, and dynamic
120      work plan are some of the names commonly used for project plan documents. The names  can
121      however often represent different documents to different agencies, states, companies and even to
122      different people within the same organization.

123      A work plan is often the primary and integrating plan document when the data collection activity
124      is a smaller supportive component of a more comprehensive project (for example, data collection
125      activity in support of an aspect of an environmental impact statement for a large multi-year
126      project). The QAPP is often the primary document when the data collection activity is a major
127      portion of the project (for example,  data collection activity in support of an initial site
128      investigation). A National Contingency Plan (NCP) format (specified in 40 CFR 300.430) is
129      appropriate when data collection activities are in support of National Priorities List (NPL)
130      Superfund site projects. The NCP format has a sampling and analysis plan as the primary  plan
131      document. The project documentation consists of two integrated documents: a field sampling
132      plan and a QAPP. Stand-alone health and safety plans are also developed.

133      Traditional site investigations  are generally based on a phased engineering approach, which
134      collects samples based on a pre-specified grid pattern and does not provide the framework for
135      making changes in the plan in  the field. The work plan (the project plan document) for the site
136      investigation typically will specify the number of samples to be collected, the location of each
137      sample and the analyses to be performed. A newer concept is to develop a dynamic work plan
138      (the project plan document), which, rather than specifying the number of samples to be collected
139      and the location of each sample, would specify the decision making logic that will be used in the
140      field to determine where the samples will be collected,  when the sampling will stop, and what
141      analyses will be performed. Guidance on dynamic work plans is available in the Standard
142      Provisional Guide (PS85) for Expedited Site Characterization of Hazardous Waste Contaminated
143      Sites (ASTM, 1996).

144      MARLAP does not recommend a particular project plan document  approach, title or arrange-
145      ment. Federal and state agencies have different requirements for the various environmental data
146      collection activities. In certain cases there are regulatory requirements. If an organization has
147      successful experience addressing the essential content of plan documents (Section 4.5) in  a well
148      integrated, document format, it is usually unnecessary and wasteful of time and monies to change
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149      a proven approach. The project plan document should reflect, and be consistent with, the
150      organization's QA policies and procedures.

151      MARLAP recommends a primary project plan document that includes other documents by
152      citation or as appendices. The primary project plan document serves to integrate the multi-
153      disciplinary sections, other management plans, and stand alone documents into a coherent plan.
154      Appropriate management plans may include the Health and Safety Plan, Waste Management
155      Plan, Risk Analysis Plan, Community Relations Plan, or Records Management Plan. If a detailed
156      discussion of the project already  exists in another document, which is available to project
157      participants, then a brief description of site history and incorporation of the document into the
158      project plan document by reference may be appropriate.  Incorporation by citation may also be
159      appropriate when the complexity of the project requires  an extensive discussion of background
160      issues. Other  documents that should be integrated, if available, are the report on the planning
161      process, the Data Validation Plan (Chapter 8), and the DQA Plan (Chapter 9). If stand alone
162      documents are not immediately available to project participants, they  should be appended to the
163      (primary) project plan document.

164      4.5    Elements of Project Plan Documents

165      A project plan document must address a range of issues. The extent of the detail is dependent on
166      the type of project and the intended use of the results as  previously discussed in applying a
167      graded approach to plan documents (Section 4.3). For all projects, the project plan document
168      must provide  the project information and decisions developed during  the project planning
169      process. Project plan documents  should address:

170       • Theproject'sDQOsandMQOs;

171       • The sampling and analytical  protocols that will be used to achieve the project objectives;  and

172       • The assessment procedures and documentation that are sufficient to confirm that the data are
173         of the type and quality needed.

174      Content of plan documents is discussed in Section 4.5.1. The integration of project plan
175      documents is  discussed in Section 4.5.2. Special consideration of project documentation for
176      small projects is discussed in Section 4.5.3.
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111
4.5.1   Content of Project Plan Documents
178      The plan document development team should remain flexible with regards to format and should
179      focus on the appropriate content of plan documents needed to address the elements listed above.
180      The content of plan documents, regardless of the title or format, will include similar information,
181      including:
182
    The project description and objectives;
183
184
   Identification of those involved in the data collection and their responsibilities and
   authorities;
185
   Enumeration of the QC procedures to be followed;
186
   Reference to specific SOPs that will be followed for all aspects of the projects; and
187
   Health and Safety protocols.
188      The project plan document(s) should present the document elements as integrated chapters,
189      appendices, and stand alone documents, and plans should be included by citation. Table 4.1
190      provides summary information on project plan elements for three different plan documents:
191      project plans, dynamic work plans, and QAPPs as provided in ASTM and EPA guidance. The
192      table also illustrates the similarity of project plan content.
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
                         TABLE 4.1—Elements of Project Plan Documents
             Project Plan
 (ASTM D5283,1992 and ASTM D5612,
 	1994)	
 Project Management
 Identify individuals with designated res-
 ponsibility and authority to: (1) develop
 project documents; (2) select organizations
 to perform the work; (3) coordinate com-
 munications; and (4) review and assess
 final data.
 Background Information
 Reasons for data collection.
 Identify regulatory programs governing
 data collection.
    Dynamic Work Plan
    (ASTM PS 85,1996)
1.   Regulatory Framework
2.   Site Descriptions and
    History of Analyte Use and
    Discovery
3.   Analysis of Prior Data and
    Preliminary Conceptual
    Site Model	
           QAPP
         (EPA, 1998a)
                           A. Project Management
                           Al Approval Sheet
                           A2 Table of Contents
                           A3 Distribution List
                           A4 Project Organization
A5 Problem Definition and
   Background
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208
209
210
211
212
213
214
215
216
217
218
219

220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
                       Project Plan
           (ASTM D5283,1992 and ASTM D5612,
           	1994)	
Project Objectives
  Clearly define objectives of field and
  laboratory work.
  Define specific objectives for the
  sampling location.
  Describe intended use of data.
Sampling Requirements
Sample requirements are specified,
including:
  Sampling locations.
  Equipment and Procedures (SOPs).
  Sample preservation and handling .
Analytical Requirements
The analytical requirements are specified,
including:
  Analytical procedures (SOPs).
  Analyte list.
  Required method uncertainty.
  Required detection limits.
  Regulatory requirements and DQO
  specifications are considered.	
Quality Assurance and Quality Control
Requirements
  QA/QC requirements are addressed for
  both field and laboratory activities.
  Type and frequency of QC samples will
  be specified.
  Control parameters for field activities
  will be described.
  Performance criteria for laboratory
  analysis will be specified.
  Data validation criteria (for laboratory
  analysis) will be specified.
                                           Dynamic Work Plan
                                           (ASTM PS 85,1996)
Dynamic Technical Program
  Essential questions to be
  answered or specific
  objectives.
  Identify the investigation
  methods and the areas in which
  they may be applied.
  Provide clear criteria for
  determining when the project
  objectives have been met.
Field Protocols and Standard
Operating Procedures (this
section may be attached as a
separate document)

[* see footnote]
Quality Assurance and Quality
Control Plan
                                           QAPP
                                       (EPA, 1998a)
A6 Project Description.
A7 Quality Objectives and Criteria
    for Measurement Data.
A8 Special Training Require-
    ments/Certifications .
A9 Documentation and Records.
B. Measurement/Data
Acquisition
B1 Sampling Process Designs.
B2 Sampling Method
    Requirements.
B3 Sample Handling and Custody
    Requirements.
B4 Analytical Methods
    Requirements.
B5  Quality Control Requirements.
B6  Instrument/Equipment Testing
    Inspection and Maintenance
    Requirements.
B7  Instrument Calibration and
    frequency.
B8  Inspection/Acceptance
    Requirements for Supplies and
    Consumables.
B9  Data Acquisition
    Requirements for Non-direct
    Measurements.
BIO Data Management.	
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241
242
243
244
245
246
247
248
249
250
251
252

253
254
255
256
257
258
259

260

261
262
263
264
265
266
267
268

269
270
271
272
273
                     Project Plan
         (ASTM D5283,1992 and ASTM D5612,
         	1994)	
 Project Documentation
 All documents required for planning,
 implementating, and evaluating the data
 collection efforts are specified, may
 include:
   SOW, Work Plan, SAP, QAPP, H&S
   Plan, Community Relations Plan.
   Technical reports assessing data.
   Requirements for field and analytical
   records.	
                                       Dynamic Work Plan
                                        (ASTM PS 85,1996)
1.  Data Management Plan
2.  Health and Safety Plan
3.  Community Relations Plan
                                      QAPP
                                   (EPA, 1998a)
C. Assessment/Oversight
Cl Assessments and response
   Actions.
C2 Reports to Management.
D. Data Validation and Usability
D1 Data Review, Verifications
   and Validation Requirements.
   Verification and Validation
   Methods.
   Reconciliation with DQO.
                           D2
                           D3
[* The combined Dynamic Technical Program section and Field Protocols and SOPs section is the functional
equivalent of a Field Sampling and Analysis Plan.]

Appendix D provides more detailed guidance on the content of project plan documents following
the outline developed by EPA requirements (EPA, 1998b) and guidance (EPA, 1998a) for
Quality Assurance Project Plans for environmental data operations. The EPA element identifiers
(Al, A2, etc.) and element titles are used in the tables and text of this chapter for ease of cross
reference to the appropriate section in Appendix D. The EPA elements for a QAPP are used to
facilitate the presentation and do not represent  a recommendation by MARLAP on the use of a
QAPP as the project plan document format.

4.5.2  Plan Documents Integration

MARLAP strongly discourages the use of a number of stand-alone plan components of
equivalent status without integrating information and without a document being identified as a
primary document. For large project plan compilations, it is appropriate to issue stand-alone
portions of the plan that focus on certain activities such as sampling, analysis or data validation,
since it can be cumbersome for sampling and laboratory personnel to keep the entire volume(s)
of the project plan document readily available.  However, each stand-alone component should
contain consistent project information, in addition to the component specific plan information,
such as the following:

  •  A brief description of the proj ect including pertinent history;
  •  A brief discussion of the problem to be solved or the question to be answered  (DQO);
  •  An organizational chart or list of key contact persons and means of contact;
  •  The analyte(s) of interest; and
  •  The appropriate health and safety protocols and documentation requirements.
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274     In addition, a cross-referenced table is helpful in the primary document, which identifies where
275     project plan elements are located in the integrated plan document.

276     4.5.3   Plan Content for Small Projects

277     The project plan documents for small projects and basic studies (Section 4.3) generally consist of
278     three elements: the Title and Approval Sheet, the Distribution List, and a Project Narrative. The
279     Project Narrative should discuss in a concise manner the majority of issues that are normally
280     addressed in a project plan  document, such as a QAPP. A typical Project Narrative (EPA, 1998b)
281     may be a concise and brief description of:

282      •  Problem and site history (A5)
283      •  Project/taskorganization(A4)
284      •  Project tasks, including a schedule and key deliverables (A6)
285      •  Anticipated use of the data (A5, A6)
286      •  MQOs(A7)
287      •  Sampling process design requirements and description (Bl)
288      •  Sample type and sampling location requirements (B2)
289      •  Sample handling and custody requirements (B3)
290      •  Analytical protocols (B4)
291      •  QC and calibration requirements for sampling and analysis (B5, B7)
292      •  Inspection and maintenance of analytical instrumentation (B6)
293      •  Plans for peer or readiness reviews prior to data collection (C1)
294      •  Assessments to be conducted during actual operation (Cl)
295      •  Procedure for data review (D2)
296      •  Identification of any special reports on QA/QC activities, as appropriate (C2)
297      •  Reconciliation with DQOs or other objectives (D3)

298     Table 4.2 or Appendix D gives information  on what is addressed in each bullet above, using the
299     element identifier shown in parenthesis.

300     4.6   Linking the Project Plan Documents and the Project Planning Process

301     Directed planning processes (see Chapter 2 and Appendix B) yield many outputs, such as the
302     Analytical Protocol Specifications (Chapter 3), which must be captured in project plan
303     documents to ensure that data collection activities are implemented properly. MARLAP
304     recommends that the project plan documents integrate all technical and quality aspects for the life
305     cycle of the project, including planning, implementation, and assessment.

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306
307
308


309
310
311
312



313
                    TABLE 4.2-Crosswalk Between Project Plan Document Elements
                   	and Directed Planning Process	
314
315
316
 ID
  Project Plan Document
         Elements
   (QAPP, EPA QA/R-5,
          1998b)*
             Content
  Directed Planning Process Input
                                       PROJECT MANAGEMENT
Al
A2_
A3
A4
A5
A6
A7
Title and Approval Sheet
Table of Contents
Distribution List
Project/Task Organization
Problem Definition/
Background
Project/Task Description
Quality Objectives and
Criteria for Measurement
Data
Title and approval sheet.
Document control format.
Distribution list for the plan
document revisions and final
guidance.
1) Identify individuals or
organizations participating in the
project and discuss their roles and
responsibilities.
2) Provide an organizational chart
showing relationships and
communication lines.
1) State the specific problem to be
solved and decision to be made.
2) Include enough background to
provide a historical perspective.
Identify measurements, special
requirements, sampling and
analytical methods, action levels,
regulatory standards, required data
and reports, quality assessment
techniques, and schedules.
1) Identify DQOs, data use, type of
data needed, domain, matrices,
constraints, action levels, statistical
parameters, and acceptable decision
errors.

2) Establish MQOs that link
  Include the members of the project
  planning team and stakeholders.
The directed planning process:
• Identified the stakeholders, data
  users, decision makers.
• Identified the core planning team and
  the technical planning team members
  who will often be responsible for
  technical oversight.
• Will often identify the specific
  persons/organizations that will be
  responsible for project
  implementation (sampling and
  analysis).	
Project planning team:
• Documented the problem, site
  history, existing data, regulatory
  concerns, background levels and
  thresholds.
• Developed a decision statement.
Project planning team identified:
• Deadlines and other constraints that
  can impact scheduling.
• Existing and needed data inputs.
Project planning team established:
• Action levels and tolerable decision
  error rates that will be the basis for
  the decision rule.
• The optimized sampling and
  analytical design as well as quality
  criteria.
Project planning team:
• Identified the regulatory standards
  and the action level(s).
• Established the decision rule.
• Described the existing and needed
  data inputs.
• Described practical constraints and
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318
319
320
321
           ID
A8
A9
       Project Plan Document
             Elements
        (QAPP, EPA QA/R-5,
              1998b)*
Special Training
Requirements/
Certification
Documentation and Record
                                        Content
                                           analysis to the user's quality
                                           objectives.
                            Identify and discuss special
                            training/certificates required to
                            perform work.
Itemize the information and records,
which must be included in a data
report package including report
format and requirements for storage
etc.
                                    Directed Planning Process Input
                                                                     the domain.
                                                                     Established the statistical parameter
                                                                     that is compared to the action level.
                                                                     Established tolerable decision error
                                                                     rates used to choose quality criteria.
                                                                     Established quality criteria linked to
                                                                     the optimized design.
                                                                     Establish data verification, validation
                                                                     and assessment criteria and
                                                                     procedures.
                                                                     Establish APS and MQOs.	
                                  Project planning team:
                                  •  Identified training, certification,
                                    accreditation requirements for field
                                    and laboratory.
                                  •  Identified Federal and state
                                    requirements for certification for
                                    laboratories.
                                  •  Identified Federal and state
                                    requirements for activities, such as
                                    disposal of field-generated residuals.
                                                             Project planning team:
                                                             •  Indicated whether documents will be
                                                                controlled and the distribution list
                                                                incomplete.
                                                             •  Identified documents that must be
                                                                archived.
                                                             •  Specified period of time that
                                                                documents must be archived.
                                                             •  Specified procedures for error
                                                                corrections (for hard copy and
                                                                electronic files).
                                  MEASUREMENT/DATA ACQUISITION
Bl
B2
Sampling Process Designs
(Experimental Designs)
Sampling Methods
Requirements
(1) Outline the experimental design,
including sampling design and
rationale, sampling frequencies,
matrices, and measurement
parameter of interest.
(2) Identify non-standard methods
and validation process.	
Describe sampling procedures,
needed materials and facilities,
decontamination procedures, waste
handling and disposal procedures,
                                                                Project planning team established the
                                                                rationale for and details of the
                                                                sampling design.
                                                                Project planning team specified the
                                                                preliminary details of the optimized
                                                                sampling method.
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323
324
325
326
327
           ID
B3
B4
B5
B6
B7
B8
       Project Plan Document
             Elements
        (QAPP, EPA QA/R-5,
              1998b)*
Sample Handling and
Custody Requirements
Analytical Methods
Requirements
Quality Control
Requirements
Instrument/Equipment
Testing Inspection and
Maintenance Requirements
Instrument Calibration and
Frequency
Inspection/Acceptance
Requirements for Supplies
and Consumables
                                        Content
                                           and include a tabular description of
                                           sample containers, sample volumes,
                                           preservation and holding time
                                           requirements.	
Describe the provisions for sample
labeling, shipment, sample tracking
forms, procedures for transferring
and maintaining custody of samples.
Identify analytical methods and
procedures including needed
materials, waste disposal and
corrective  action procedures.
(1) Describe QC procedures and
associated acceptance criteria and
corrective actions for each sampling
and analytical technique.
(2) Define the types and frequency
of QC samples should be defined
along with the equations for
calculating QC statistics.	
1) Discuss determination of
acceptable instrumentation
performance.
2) Discuss the procedures for
periodic, preventive and corrective
maintenance.
(1) Identify tools, gauges and
instruments, and other sampling or
measurement devices that need
calibration.
(2) Describe how the calibration
should be done.
Define how and by whom the
sampling supplies and other
consumables will be accepted for
use in the project.	
                                    Directed Planning Process Input
  Project planning team described the
  regulatory situation and site history,
  which can be used to identify the
  appropriate sample tracking level.
Project planning team:
• Identified inputs to the decision
  (nuclide of interest, matrix, etc.).
• Established the allowable
  measurement uncertainty that will
  drive choice of the analytical
  protocols.
• Specified the optimized sampling
  and analytical design.	
Project planning team:
• Established the allowable
  measurement uncertainty, which will
  drive QC acceptance criteria.
• Established the optimized analytical
  protocols and desired MQOs.
  Project planning team established the
  desired MQOs, which will drive
  acceptance criteria for
  instrumentation performance.
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       Project Plan Documents
ID
B9
Bl
0
Project Plan Document
Elements
(QAPP, EPA QA/R-5,
1998b)*
Data Acquisition
Requirements (Non-direct
Measurements)
Data Management
Content
Define criteria for the use of non-
direct measurement data such as
data that come from databases or
literature.
(1) Outline of data management
scheme including path of data, use
of storage and& record keeping
system. (2) Identify all data handling
equipment and procedures that will
be used to process, compile, analyze
the data, and correct errors.
Directed Planning Process Input
Project planning team:
• Identified the types of existing data
that are needed or would be useful.
• Established the desired MQOs that
would also be applicable to archived
data.

ASSESSMENT/OVERSIGHT
Cl
C2
Assessments and Response
Actions
Reports to Management
(1) Describe the number, frequency
and type of assessments needed for
the project.
(2) For each assessment: list
participants and their authority, the
schedule, expected information,
criteria for success and unsatis-
factory conditions and those who
will receive reports and procedures
for corrective actions.
Identify the frequency, content and
distribution of reports issued to keep
management informed.
• The project planning team
established the MQOs and
developed statements of the
Analytical Protocol Specifications,
which are used in the selection of the
analytical protocols and in the
ongoing evaluation of the protocols.

DATA VALIDATION AND USABILITY
Dl
D2
Data Review, Verification
and Validation Requirements
Verification and Validation
Methods
State the criteria including specific
statistics and equations, which will
be used to accept or reject data
based on quality.
Describe the process to be used for
validating and verifying data,
including COC for data throughout
the lifetime of the project.
• Project planning team established.
• Established the MQOs for the
sample analysis, and may also have
discussed completeness and
representativeness requirements that
will be the basis of validation.
• Established the action level(s)
relevant to the project DQOs.
• Established the data validation
criteria.
Project planning team:
• Determines appropriate level of
custody.
• May develop a Validation Plan.
328
329
330
331
332
333


334
335
336
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                                                                           Project Plan Documents
337
         ID
 D3
       Project Plan Document
            Elements
       (QAPP, EPA QA/R-5,
             1998b)*
Reconciliation With Data
Quality Objectives
                                   Content
Describe how results will be
evaluated to determine if DQOs are
satisfied.
                               Directed Planning Process Input
Project planning team:
•  Defined the necessary data input
  needs.
•  Defined the constraints and
  boundaries with which the project
  would have had to comply.
•  Defined the decision rule.
•  Identified the hypothesis and
  tolerable decision error rates.
•  Defined MQOs for achieving the
  project DOOs.	
338
339

340
341
342
343
344
345
346

347

348
349
350
351
352
353
354
355
356
357

358
359
[Adapted from: EPA, 1998a]
[* EPA QAPP elements are discussed in Appendix D]

The project plan should be a dynamic document, used and updated over the life of the project.
For example, the analytical methods requirements in the project plan documents (B4) will
initially reflect the Analytical Protocol Specifications established by the project planning team
(Chapter 3) and issued in the SOW or BOA Task Order (Chapter 5). When the analytical
laboratory has been selected (Chapter 7), the project plan document should be updated to reflect
the specific analytical protocols: the actual protocols to be used, which should be included by
citation or inclusion of the SOPs as appendices.

4.6.1   Planning Process Report

MARLAP recommends the inclusion, by citation or as an appendix, of the directed planning
process report in the project plan documents. If the planning process was not documented in a
report, MARLAP recommends that a summary of the planning process addressing, for example,
the assumptions  and decisions, the established action levels, the DQO  statement,  and the
Analytical Protocol Specifications, which include the established MQOs and any specific
analytical process requirements, be included in the project plan document section on Problem
Definition/Background (A5). Additional detailed information on the analytical protocol
specifications including the MQOs will be presented in the project plan document sections on
Project/Task Description (A6), Quality Objectives and Criteria for Measurement  Data (A7), and
Analytical Methods Requirements (B4).

MARLAP views the project plan documents as the principal product of the planning process. To
illustrate how to capture and integrate the outputs of the planning process into the plan docu-
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360     ment(s), Table 4.2 presents a crosswalk of the elements of the EPA QAPP Document and outputs
361     of a directed planning process.

362     4.6.2  Data Assessment

363     Assessment (Verification, Validation and Data Quality Assessment) is the last step in the
364     project's data life cycle and precedes the use of data. Assessment, and in particular DQA, are
365     designed to evaluate the suitability of project data to answer the underlying project question or
366     the suitability of project data to support the project decision. The project planners should define
367     the assessment process in enough detail that achievement or failure to meet goals can be
368     established upon project completion. An important output of the directed planning process to be
369     captured in the project plan document is the data verification, validation and assessment criteria
370     and procedures.

371     4.6.2.1 Data Verification

372     Analytical data verification assures that laboratory conditions and operations were compliant
373     with the contractual SOW and the project plan. Verification compares the data package to these
374     requirements (contract compliance) and checks for consistency and comparability of the data
375     throughout the data package and completeness of the results to ensure all necessary documen-
376     tation is available. Performance criteria for verification should be documented in the contract and
377     in the project plan document in the sections that address Data Review, Verification, and
378     Validation Requirements (Dl), and Verification and Validation Methods (D2).

379     4.6.2.2 Data Validation

380     Validation addresses the reliability of the data. During validation, the technical reliability and the
381     degree of confidence in reported analytical data are considered. Data validation criteria and
382     procedures should be established during the planning process and captured in the project plan
383     document (and the SOW for the validation contractor). Performance criteria for data validation
384     can be documented directly in the project plan document in Data Review, Verifications, and
385     Validation Requirements (Dl) and Verifications and Validation Methods (D2) or in a separate
386     plan, which is included by citation or as an appendix in the project plan document.

387     Guidance on Data Validation Plans is provided in Chapter 8, Section 8.3. The data validation
388     plan should contain the following information:

389      •  A summary of the project, which provides sufficient detail about the project's Analytical
390         Protocol Specifications, including the MQOs;
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391       •  The set of data to be validated and whether all the raw data will be reviewed and in what
392         detail;

393       •  The necessary validation criteria and the MQOs deemed appropriate for achieving project
394         DQOs;

395       •  Specifications on what qualifiers are to be used and how final qualifiers are to be assigned;
396         and

397       •  Information on the content of the validation report.

398     4.6.2.3 Data Quality Assessment

399     Data Quality Assessment consists of a scientific and statistical evaluation of project-wide
400     knowledge to determine if the data set is of the right type, quality and quantity to support its
401     intended use. The data quality assessor integrates the data validation report, field information,
402     assessment reports and historical project data and compares the findings to the original project
403     objectives and criteria (DQOs).

404     Performance criteria for data usability for the project should be documented in the project plan
405     documents in a section on DQA or reconciliation of the data results with DQOs (D3) or in a
406     separate plan, which is included by citation or as an appendix in the project plan document.
407     Guidance on DQA Plans is provided in Section 9.5. The DQA plan should contain the following
408     information:

409       •  A summary of the project, which provides sufficient detail about the project's DQOs and
410         tolerable  decision error rates;

411       •  Identification of what issues will be addressed by the DQA;

412       •  Identification of any statistical tests that will be used to evaluate the data;

413       •  Description of how the  representativeness of the data will be evaluated (for example, review
414         the sampling strategy, the suitability of sampling devices, subsampling procedures,
415         assessment findings);

416       •  Description of how the  accuracy of the data, including potential impact of non-measurable
417         factors (for example, subsampling bias) will be considered (for example, review the


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418
419

420
421

422

423


424

425
426

427
428

429
430

431
432
433
434
435


436
   Analytical Protocol Specifications and the analytical plan, the suitability of analytical
   protocols, subsampling procedures, assessment findings);

   Description of how the MQOs will be used to determine the usability of measurement data
   (that is, did the uncertainty in the data significantly affect confidence in the decision);

   Identification of what will be included in the DQA report; and

   Identification of who will receive the report and the mechanism for its archival.
 Summary of Recommendations

  • A graded approach to project plan writing because of the diversity of environmental data
    collection activities.

  • A primary integrating project plan that includes other documents by citation or as
    appendices.

  • Project plan documents that integrate all technical and quality aspects for the life-cycle of
    the project, including planning, implementation, and assessment.

  • Inclusion, by citation or as an appendix, of the report on the directed planning process in
    the project plan documents. If the planning process was not documented in a report,
    MARLAP recommends that a summary of the planning process addressing assumptions
    and decisions, established action levels, DQO statement and established MQOs, and
    Analytical Protocol Specifications be included in the project plan documents.
4.7    References
437     American National Standards Institute and the American Society for Quality Control
438       (ANSI/ASQC). 1994. Specifications and Guidelines for Quality Systems for Environmental
439       Data Collection and Environmental Technology Programs, National Standard E-4.

440     American National Standards Institute (ANSI). 1996. Measurement and Associated Instruments
441       Quality Assurance for Radioassay Laboratories, National Standard N42.23.

442     American Society of Mechanical Engineers (ASME). 1989. Quality Assurance Program
443       Requirements for Nuclear Facilities. NQA-1, ASME, New York, New York.
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                                                                       Project Plan Documents
444     American Society of Testing and Materials (ASTM). 1992. Standard Practice for Generation of
445       Environmental Data Related to Waste Management Activities: Quality Assurance and Quality
446       Control Planning and Implementation., D 5 2 8 3.

447     American Society of Testing and Materials (ASTM). 1994. Standard Guide for Quality Planning
448       and Field Implementation of a Water Quality Measurements Program, D5612.

449     American Society of Testing and Materials (ASTM). 1996. Standard Provisional Guidance for
450       Expedited Site Characterization of Hazardous Waste Contaminated Sites, PS85.

451     Code of Federal Regulations (CFR). 1999.  10 CFR 50 Appendix B, "Quality Assurance Criteria
452       for Nuclear Power Plants and Fuel Reprocessing Plants."

453     Code of Federal Regulations (CFR). 1994.  10 CFR 830.120, "Nuclear Safety Management -
454       Quality Assurance Requirements."

455     Code of Federal Regulations (CFR). 1997. 40 CFR 300.430, "National Oil and Hazardous
456       Substance Pollution Contingency Plan - Remedial Investigation/Feasibility Study and
457       Selection of Remedy."

458     International  Organization for Standardization (ISO). 1994. Quality Systems -Modelfor Quality
459       Assurance  in Design, Development, Installation and Servicing, ISO Standard 9001.

460     U.S. Army Corps of Engineers (USAGE). 1994. Requirements for the Preparation of Sampling
461       and Analysis Plans. Engineer Manual EM 200-1-3.

462     U.S. Army Corps of Engineers (USAGE). 1997. Chemical Quality Assurance for Hazardous,
463       Toxic and Radioactive Waste Projects. Engineer Manual EM 200-1-6.

464     U.S. Department of Defense (DOD). 1963. Quality Program Requirements. Military
465       Specification MIL-Q-9858A. Washington, DC.

466     U.S. Department of Energy (DOE). 1991. Quality Assurance. DOE  Order 414.1  (Replaced DOE
467       Order 5 700. 6C), Washington, DC.

468     U.S. Environmental Protection Agency (EPA). 1998a. EPA Guidance for Quality Assurance
469       Project Plans (EPA QA/G-5). EPA/600/R-98/018, Washington, DC.
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470     U.S. Environmental Protection Agency (EPA). 1998b. EPA Requirements for Quality Assurance
471       Project Plans for Environmental Data Operations. EPA QA/R-5, External Review Draft Final,
472       Washington, DC.

473     U.S. Environmental Protection Agency (EPA). 1998c. EPA Policy and Program Requirements
474      for the Mandatory Agency-Wide Quality System. EPA Order 5360.1, Washington, DC.

475     U.S. Nuclear Regulatory Commission (NRC). 1989. Standard Format and Content of
476       Decommissioning Plans for Licensees Under 10 CFR Parts 30, 40, and 70. Regulatory Guide
477       3.65.
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                  5  OBTAINING LABORATORY SERVICES
 2      5.1    Introduction
 3
 4      This chapter provides guidance on obtaining radioanalytical laboratory services. In particular,
 5      this chapter discusses the broad items that should be considered in the development of a
 6      procurement vehicle to obtain laboratory services. Throughout this chapter, MARLAP uses the
 7      request for proposal (RFP) as an example of a procurement vehicle. Agencies and other
 8      organizations may use a variety of procurement vehicles, depending upon circumstances and
 9      policies. The RFP typically includes a statement of work (SOW), generic contractual
10      requirements, and the description of the laboratory qualification and selection process. It should
11      be noted that for some agencies or organizations, not all technical, quality, and administrative
12      aspects of a contract are specified in a SOW. Many technical, administrative, legal, and
13      regulatory items are specified in a RFP and eventually in a contract. More detailed guidance and
14      discussion for contracting issues (such as scoring proposals, etc.) can be found in Appendix E.
15      This chapter is written for contracting outside laboratory services, but the principal items and
16      information provided would be applicable to obtaining services not requiring a formal contract,
17      such as a service agreement within an Agency or organization. It should be noted that the
18      information and specifications  of a SOW may appear in many contract vehicles other than a
19      formal contract resulting from a RFP. These include purchase and work orders, as well as a task
20      order under a Basic Ordering Agreement. MARLAP recommends that technical specifications be
21      prepared in writing in a single document designated as a SOW for all radioanalytical laboratory
22      services, regardless of whether the services are to be contracted out or performed by an
23      Agency's laboratory.
24
25      Analytical Protocol Specifications (APSs) should be compiled in the SOW in order for the
26      laboratory to propose the analytical protocols that the laboratory wishes to use for the project
27      (Chapter 6). The development of APSs, which includes the measurement quality objectives
28      (MQOs), was described in detail in Chapter 3, and the incorporation of these protocols into the
29      relevant project plan documents was covered in Chapter 4. These specifications should include
30      such items as the MQOs, the type and frequency of quality control (QC) samples, the level of
31      performance demonstration needed, number and type of samples, turnaround times, and type of
32      data package.
33
34      Section 5.3 of this chapter discusses the technical requirements of a SOW, Section 5.4 provides
35      guidance on generic contractual requirements, and Section 5.5 discusses various elements of the
36      laboratory selection and qualification criteria.
37
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38      5.2   Importance of Writing a Technical and Contractual Specification
39            Document
40
41      One objective of the SOW and contractual documents is to provide the analytical requirements in
42      a concise format that will facilitate the laboratory's selection of the appropriate analytical
43      protocols. The authors of the SOW may be able to extract most, if not all, of the necessary
44      technical information from the project plan documents (Chapter 4) if they have been prepared
45      properly. If specific information is not available, the author should contact the planning team.
46      The preparation of a SOW can be viewed as a check to make sure that the project planning
47      documents contain all the information required for the selection and implementation of the
48      appropriate analytical protocols. One important aspect of writing  the SOW is that it should
49      clearly identify the project laboratory's responsibility for documentation to be provided for
50      subsequent data verification, validation, and quality assessment—these project laboratory
51      requirements should be addressed in the assessment plans developed during directed planning
52      (Chapter 2).
53
54      5.3   Statement of Work — Technical Requirements
55
56      A review of the project plan documents (Chapter 4) should result in a summary list of the
57      technical requirements needed to develop a SOW. Much of this information, including the
58      project MQOs and any unique analytical process requirements, will be contained in the APSs.
59      When possible, a project summary of sufficient detail (i.e., process knowledge) to be useful to
60      the laboratory should be included in the SOW. The Project Planning  Team is responsible for
61      identifying and resolving key analytical planning issues and for ensuring that the resolutions of
62      these issues are captured in the APSs. Consistent with a performance-based approach, the level
63      of specificity in the APSs is limited to those requirements that are essential to meeting the
64      project's analytical data requirements. In response to such project management decisions, the
65      laboratory may propose for consideration several alternative validated methods that meet the
66      MQOs under the performance-based approach (such as measurement of a decay progeny as an
67      alternate radionuclide).  Chapter 7 provides guidance on the evaluation of a laboratory and
68      analytical methods.
69
70      The SOW should specify what the laboratory needs to provide in  order to demonstrate its ability
71      to meet the technical specifications in the RFP. This should include documentation relative to the
72      method validation process to demonstrate compliance with the MQOs and information on
73      previous contracts for similar analytical work  as well as performance in performance evaluation
74      (PE) programs using the proposed method. Any specific requirements on sample delivery
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 75      (Section 5.3.7) should also be made clear to the laboratory. In addition, the requirements for the
 76      laboratory's quality system should be discussed.
 77
 78      5.3.1  Analytes
 79
 80      Each APS should state the analyte of concern. The SOW should specify all analytes of concern
 81      and, when possible, an analyte's expected chemical form and anticipated concentration range
 82      (useful information for separating high activity samples from low activity samples) and potential
 83      chemical or radiometric interferences (Chapter 3, Sections 3.3.1 and 3.3.2). In some instances,
 84      because  of process knowledge and information on the absence of equilibrium between analytes
 85      and their parents and progeny, the SOW may require the direct measurement of an analyte rather
 86      than allowing for the measurement of other radionuclides in the analyte's decay chain. In these
 87      cases, the SOW should indicate the analyses to be performed. Examples of analyses include gross
 88      alpha and beta, gamma spectrometry, and radionuclide/matrix specific combinations such as 3H
 89      in water and 238Pu in soil.
 90
 91      5.3.2  Matrix
 92
 93      Each APS should state the sample matrix to be analyzed. The sample matrix for each
 94      radionuclide or analysis type (e.g., gamma-ray spectrometry) should be listed and described in
 95      detail where necessary. The matrix categories may include surface soil, sub-surface soil,
 96      sediment, sludge, concrete, surface water, ground water, salt water, aquatic and terrestrial biota,
 97      air, air sample filters, building materials, etc. Additional information should be provided for
 98      certain matrices (e.g., the chemical form of the matrix for solid matrices) in order for the
 99      laboratory to select the appropriate sample preparation or dissolution method (Chapter 3, Section
100      3.3.3).
101
102      5.3.3  Measurement Quality Objectives
103
104      The APSs should provide the MQOs for each analyte-matrix combination. The MQOs can be
105      viewed as the analytical portion of the overall project data quality objectives (DQOs). An MQO
106      is a statement of a performance objective or requirement for a particular method performance
107      characteristic. Examples of method performance characteristics include the method's uncertainty
108      at some concentration,  detection capability, quantification capability, specificity, analyte
109      concentration range, and ruggedness. An example MQO for the method uncertainty at some
110      analyte concentration such as the action level would be, "A method uncertainty of 0.5 Bq/g is
111      required at the action level of 5.0 Bq/g" (Chapters 1, 3, and 19). The MQOs are a key part of a
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112     project's APSs. Chapter 3 provides guidance on developing MQOs for select method
113     performance characteristics.
114
115     5.3.4  Unique Analytical Process Requirements
116
117     The APS should state any unique analytical processing requirement. The SOW should give any
118     matrix-specific details necessary for the laboratory to process the sample, such as type of soil,
119     type of debris to be removed, whether or not filtering a sample at the laboratory is required,
120     processing whole fish versus edible parts, drying of soils, information on any known or suspected
121     interferences, hazards associated with the sample, etc. (Chapter 3, Section 3.4). In some cases,
122     unique analytical process requirements or instructions should be specified that further delineate
123     actions to be taken in case problems occur during sample processing. For example, the SOW may
124     require that the laboratory reprocess another aliquant of the sample by a more robust technique
125     when a chemical yield drops below a stated value.
126
127     If necessary, special instructions should be provided as to how or when the analytical results are
128     to be corrected for radioactive decay or ingrowth. In some cases, the sample collection date may
129     not be the appropriate date to use in the decay or ingrowth equations.
130
131     5.3.5  Quality Control Samples and Participation in External Performance Evaluation
132            Programs
133
134     The SOW should state the type and frequency of internal QC samples needed as well as whether
135     they are to be included on a batch or some other basis. The quality acceptance limits for all types
136     of QC samples should be stated (see Appendix E for guidance on developing acceptance limits
137     for QC samples based on the MQO for method uncertainty). In addition, the SOW should state
138     when and how the project manager or the contracting officer's representative (COR) should be
139     notified about any nonconformity. In addition, the SOW should spell out the conditions under
140     which the laboratory will have to re-analyze samples due to a nonconformance.
141
142     The evaluation of the laboratory's ability to perform the required radiochemical analyses should
143     be based on the acceptability of the method validation documentation submitted by the
144     laboratory. The evaluation should also include the laboratory's performance in various external
145     PE programs administered by government agencies or commercial radioactive source suppliers
146     that are traceable to the National Institute of Standards and Technology (NIST; additional
147     information on evaluating a laboratory's performance is provided in Chapter 7). As  such, the
148     RFP should request the laboratory's participation in a NIST-traceable PE program appropriate for
149     the analytes and matrices under consideration. In addition, the weighting factor (Appendix E)


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150     given to scoring the laboratory's performance in such a program should be provided to the
151     laboratory. Some examples of government programs include DOE's Quality Assessment
152     Program (QAP) and the Mixed Analyte Performance Evaluation Program (MAPEP) and the
153     NIST-administered National Voluntary Laboratory Accreditation Program (NVLAP)
154     Performance Testing (PT) providers.
155
156     5.3.6  Laboratory Radiological Holding and Turnaround Times
157
158     The SOW should include specifications on the required laboratory radiological holding time (i.e.,
159     the time between the date of sample collection and the date of analysis) and the sample
160     processing turnaround time (i.e., the time between the receipt of the sample at the laboratory to
161     the reporting of the analytical results). Such radiological holding and turnaround times, which are
162     usually determined by specific project requirements, are typically specified in terms of calendar
163     or working days. The SOW should state whether the laboratory may be requested to handle
164     expedited or rush samples. In some cases, time  constraints become an important aspect of sample
165     processing (e.g., in the case of radionuclides that have short half-lives). Some analyses will call
166     for specific steps that take a prescribed amount of time. Requesting an analytical protocol that
167     requires several days to complete is obviously not compatible with a 24-hour turnaround time.
168     This highlights the need for input from radioanalytical specialists during the planning process.
169
170     In some cases, the required sample-processing turnaround times are categorized according to
171     generic headings such as routine, expedited or rush, and emergency sample processing. Under
172     these circumstances, the SOW should specify the appropriate category for the samples and
173     analyses.
174
175     5.3.7  Number of Samples and Schedule
176
177     Estimating the volume of work for a laboratory is commonly considered part of the planning
178     process that precedes the initiation of a project. Thus, the SOW should estimate the anticipated
179     amount of work and should spell out the conditions under which the laboratory will have to
180     reanalyze  samples due to some non-conformance. Similarly, the estimate should allow the
181     laboratory to judge if its facility has the capacity to compete for the work. The estimate for the
182     number of samples is a starting point, and some revision to the volume of work may occur,
183     unless the laboratory sets specific limits on the number of samples to be  processed.
184
185     The SOW should indicate whether samples will be provided on a regular basis, seasonally, or on
186     some other known or unknown schedule. It should also be specified if some samples may be sent
187     by overnight carrier for immediate analysis. Holidays may be listed when samples will not be


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188      sent to the laboratory. The SOW should state if Saturday deliveries may be required.
189      Furthermore, it should specify whether samples will be sent in batches or individually, and from
190      one location or different locations.
191
192      The carrier used to ship samples to the laboratory should be experienced in the delivery of field
193      samples, provide next day and Saturday deliveries, have a package tracking system and be
194      familiar with hazardous materials shipping regulations.
195
196      5.3.8   Quality System
197
198      The RFP should require that a copy of the laboratory's Quality System documentation (such as a
199      Quality Manual), related standard operating procedures (including appropriate methods) and
200      documentation (such as a summary of the internal QC and external PE sample results) be
201      included with the proposal submittal,  as necessary. Only those radioanalytical laboratories that
202      adhere to a well-defined quality system can ensure the appropriate quality of scientifically valid
203      and defensible data. The laboratory's  Quality System (NELAC, 2000; ANSI N42.23; ISO/IEC
204      17025) for a radioanalytical laboratory should address at a minimum the following items:
205
206       •  Organization and management;
207       •  Quality system establishment, audits, essential quality controls and evaluation and data
208         verification;
209       •  Personnel (qualifications and resumes);
210       •  Physical facilities—accommodations and environment;
211       •  Equipment and reference materials;
212       •  Measurement traceability and calibration;
213       •  Test methods and standard operating procedures (methods);
214       •  Sample handling, sample acceptance policy and sample receipt;
215       •  Records;
216       •  Subcontracting analytical samples;
217       •  Outside support services and supplies; and
218       •  Complaints.
219
220      5.3.9   Laboratory's Proposed Methods
221
222      Under the performance-based approach to method selection, the laboratory will select and
223      identify a radioanalytical methods (Chapter 6) that will meet the MQOs and other performance
224      specifications of the SOW. MARLAP recommends that the laboratory submit the proposed
225      methods and required method validation documentation with the formal response. The SOW


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226      should state that the proposed methods and method validation documentation will be evaluated in
227      accordance with agency procedures by a Technical Evaluation Committee (TEC) based on
228      experience,  expertise, and professional judgement. MARLAP uses the term TEC for the group
229      that performs this function. Agencies and other organizations may use various terms and
230      procedures for this process.
231
232      The TEC should provide their findings and recommendations to the organization's contracting
233      officer for further disposition. In some cases, the organization may inform a laboratory that the
234      proposed methods were deemed inadequate, and, if appropriate, request that the laboratory
235      submit alternative methods with method validation documentation within a certain time period.
236
237      When the methods proposed by the laboratories have been deemed adequate to meet the technical
238      specifications of the SOW, the TEC may want to rank the proposed methods (and laboratories)
239      according to various factors (e.g., robustness, performance in PE programs or qualifying samples,
240      etc.) as part of the contract scoring process.
241
242      5.4   Request for Proposal—Generic Contractual Requirements
243
244      Not all quality and administration aspects of a contract are specified in a SOW. Many quality
245      (e.g., requirement for a quality system), administrative, legal, and regulatory items need to be
246      specified in a RFP and eventually in the contract. Although not inclusive, the items or categories
247      discussed in the following sections should be considered as part of the contractual requirements
248      and specifications of a RFP.
249
250      5.4.1 Sample Management
251
252      The RFP should require the laboratory to have an appropriate sample  management program that
253      includes those administrative and quality assurance aspects covering sample receipt, control,
254      storage and  disposition. The RFP should require the laboratory to have adequate facilities,
255      procedures,  and personnel in place for the following actions:
256
257       •  Receive, log-in, and store samples in a proper fashion to prevent deterioration, cross-
258         contamination, and analyte losses;
259
260       •  Verify the receipt of each sample shipment: compare shipping documentation with samples
261         actually received; notify the point of contact or designee by telephone within a prescribed
262         number  of business days and subsequently provide details in all case narratives of any
263         discrepancies in the documentation;

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264
265       •  Sign, upon receipt of the samples, the sample receipt form or, if required, chain of custody
266         (COC) form(s) submitted with each sample release. Only authorized laboratory personnel
267         should sign the forms. The signature date on the COC form, if required, is normally the
268         official sample receipt date. All sample containers should be sealed prior to their removal
269         from the site; and
270
271       •  Store unused portions of samples in such a manner that the analyses could be repeated or new
272         analyses requested, if required, for a certain specified time period following the submission
273         of an acceptable data package. Unused sample portions should be stored with the same
274         sample handling requirements that apply to samples awaiting analysis. Documentation should
275         be maintained pertaining to storage conditions and sample archival or disposal.
276
277      5.4.2  Licenses, Permits and Environmental Regulations
278
279      Various  Federal, State, and local permits, licences and certificates (accreditation) may be
280      necessary for the operation of a radioanalytical laboratory. The RFP should require the laboratory
281      to have the necessary government permits, licenses, and certificates in place before the
282      commencement of any laboratory work for an awarded contract. The following sections provide a
283      partial list of those provisions that may be necessary. Some projects may require special
284      government permits in order to conduct the work and transport and analyze related samples. For
285      these cases, the necessary regulations or permits should be cited in the RFP.
286
287      5.4.2.1 Licenses
288
289      When required, the laboratory will be responsible for maintaining a relevant Nuclear Regulatory
290      Commission (NRC) or Agreement State License to accept low-level radioactive samples for
291      analyses. In certain circumstances, the laboratory may have to meet host nation requirements if
292      operating outside the United States (e.g., military fixed or deployed laboratories located
293      overseas).
294
295      When necessary, the laboratory should submit a current copy of the laboratory's radioactive
296      materials license with their proposal. Some circumstances may require a copy of the original
297      radioactive materials license. For more complete information on license requirements, refer to
298      either the NRC or State government offices  in which the laboratory resides, or to 10 CFR 30.
299
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300      5.4.2.2 Environmental and Transportation Regulations
301
302      Performance under a contract or subcontract must be in compliance with all applicable local,
303      State, Federal, and international laws and regulations. Such consideration must not only include
304      relevant laws and regulations currently in effect, but also revisions thereto or public notice that
305      has been given that may reasonably be anticipated to be effective during the term of the contract.
306
307      The laboratory may be required to receive (and in some cases ship) samples according to
308      international, Federal, State, and local regulations. In particular, the laboratory should be aware
309      of U.S. Postal Service and Department of Transportation (DOT) hazardous materials regulations
310      applicable to the requirements specified in the SOW and aware that appropriate personnel should
311      be trained in these regulations.
312
313      5.4.3  Data Reporting and Communications
314
315      The type of information, schedules and data reports required to be delivered by the laboratory, as
316      well as the expected communications between the appropriate staff or organizations, should be
317      delineated in the RFP. The required schedule and content of the various reports, including sample
318      receipt acknowledgment, chain of custody, final  data results, data packages, QA/QC project
319      summaries, status reports, sample disposition, and invoices should be provided in the RFP. In
320      addition, the expected frequency and lines of communications should be specified.
321
322      In some cases, the RFP may request relevant information relative to the point-of-contact for
323      certain key laboratory positions such as the Laboratory Director, Project Manager, QA Officer,
324      Sample Manager, Record Keeping Supervisor, Radiation Safety or Safety Officer and
325      Contracting Officer. Contact persons should be identified along with appropriate telephone
326      numbers (office, FAX, pager), e-mail, and postal and courier addresses.
327
328      5.4.3.1 Data Deliverables
329
330      The SOW should specify what data are required for data verification, validation, and quality
331      assessment. A data package, the pages of which  should be sequentially numbered, may include a
332      project narrative, the results in a specified format including units, a data review  checklist, any
333      non-conformance memos resulting from  the work, sample receipt acknowledgment or chain of
334      custody form (if required), sample and quality control sample data, calibration verification data,
335      and standard and tracer information. In addition, the date and time of analysis, instrument
336      identification, and analyst performing the analysis should be included on the appropriate
337      paperwork.  At the inception of the project, initial calibration data may be required for the


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338      detectors used for the work. When a detector is recalibrated, or a new detector is placed in
339      service, updated calibration data should be required whenever those changes could affect the
340      analyses in question. In some cases, only the summary or final data report may be requested. In
341      these cases, the name of the data reviewer, the sample identification information, reference and
342      analysis dates, and the analytical results along with the reported measurement uncertainties
343      should be reported.
344
345      The laboratory should be informed of the acceptable formats for electronic and hard copy
346      records. The SOW should state at what intervals the data will be delivered (batch, monthly,  etc.).
347
348      5.4.3.2  Software Verification and Control
349
350      The policy for computer software verification, validation and documentation typically are
351      included in the laboratory's Quality Manual. If there are specific software verification and
352      validation requirements germane to the project, the RFP should instruct or specify such
353      requirements. ASTM E919, "Standard Specification for Software Documentation for a
354      Computerized System," describes computer program documentation that should be provided by a
355      software supplier. Other sources for software QC are ANSI ANS 10.3 "Documentation of
356      Computer Software" and IEEE Standard  1063, "IEEE Standard for Software User
357      Documentation."
358
359      5.4.3.3 Problem Notification and Communication
360
361      Communication is key to the successful management and execution of the contract. Problems,
362      schedule delays, potential overruns, etc., can be resolved quickly only if communication between
363      the laboratory and organization's representative is conducted promptly. The RFP should state
364      explicitly when, how, and in what time frame communication or notification is required by the
365      laboratory for special technical events, such as the inability to meet MQO specifications for a
366      sample or analyte, when a QC sample result is outside of an acceptance limit or some other  non-
367      conformance and when—if required by the project manager—the laboratory fails to meet its
368      internal QC specifications.
369
370      The laboratory should document and report all deviations from the method and unexpected
371      observations that may be of significance to the data reviewer or user. Such deviations should be
372      documented in the narrative section of the data package produced by the contract laboratory.
373      Each narrative should be monitored closely to assure that the laboratory is documenting
374      departures from contract requirements  or acceptable practice.
375
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376      Communication from the organization's representative to the laboratory is also important. A key
377      element in managing a contract is the timely review of the data packages provided by the
378      laboratory. Early identification of problems allows for corrective actions to improve laboratory
379      performance and, if necessary, the cessation of laboratory analyses until solutions can be
380      instituted to prevent the production of large amounts of data that are unusable. Note that some
381      sample matrices and processing methods can be problematic for even the best laboratories. Thus,
382      the organization's technical representative must be able to discern between failures due to
383      legitimate reasons and poor laboratory performance.
384
385      5.4.3.4 Status Reports
386
387      The SOW may require the laboratory to submit, on a specified frequency, sample processing
388      status reports that include such information as the sample identification number, receipt date,
389      analyses required, expected analytical completion date and report date. Depending on the
390      project's needs, a status report may include the disposition of remaining portions of samples
391      following sample processing or sample processing wastes.
392
393      5.4.4   Sample Re-Analysis Requirements
394
395      There may be circumstances when samples should be re-analyzed due to questionable analytical
396      results or suspected poor quality as reflected by the laboratory's batch QC or external PT
397      samples. Specific instructions and contractual language should be included in the RFP that
398      address such circumstances and the resultant fiscal responsibilities (Appendix E).
399
400      5.4.5   Subcontracted Analyses
401
402      MARLAP recommends that the RFP state that subcontracting will be permitted only with the
403      contracting organization's approval. In addition, contract language should be included giving the
404      contracting organization the authority to approve proposed subcontracting laboratories. For
405      continuity or for quality assurance, the contract may require one laboratory to handle the entire
406      analytical work load. However, the need may arise to subcontract work to another laboratory
407      facility if the project calls for a large number of samples requiring quick turnaround times or
408      specific methodologies that are not part of the primary laboratory's support services. The use of
409      multiple service providers adds complexity to the organization's tasks of auditing, evaluating and
410      tracking services.
411
412      Any intent to use a subcontracted laboratory should be specified in the response to the RFP or
413      specific task orders. The primary laboratory should specify which laboratory(ies) are to be used,


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414      should require that these laboratories comply with all contract or task order requirements, and
415      verify that their operations can and will provide data quality meeting or exceeding the SOW
416      requirements. Subcontract laboratories should be required to allow the contracting organization
417      full access to inspect their operations, although it should be understood that the primary
418      laboratory should maintain full responsibility for the performance of subcontract laboratories.
419
420      5.5   Laboratory Selection and Qualification Criteria
421
422      A description of the laboratory qualification and selection process should be stated in the RFP.
423      The initial stages of the evaluation process focus on the technical considerations only. Cost will
424      enter the selection process later. The organization's TEC will consider all proposals and then will
425      make an initial selection (see Figures E.6a and E.6b in Appendix E), whereby some laboratories
426      are eliminated based on the screening process. The laboratory selection process is based on
427      predetermined criteria that are related to the RFP and how a laboratory is technically able to
428      support the contract. A laboratory that is obviously not equipped to perform work according to
429      the RFP is certain to be dropped early in the selection process. In some  cases, the stated ability to
430      meet the analysis request may be verified by the organization, through pre-award audits and
431      proficiency testing as described below. Letters notifying unsuccessful bidders may be sent  at this
432      time.
433
434      5.5.1  Technical Proposal Evaluation
435
436      The RFP requires each bidding contractor laboratory to submit a technical proposal  and a copy of
437      its Quality Manual. This Quality Manual is intended to address all of the technical and general
438      laboratory requirements. As noted previously, the proposal and Quality Manual are reviewed by
439      members of the TEC who are both familiar with the proposed project and are clearly
440      knowledgeable in the field of radiochemistry and laboratory management.
441
442      5.5.1.1 Scoring and Evaluation Scheme
443
444      The RFP should include information concerning scoring of proposals or weighting factors  for
445      areas of evaluation. This helps a laboratory to understand the relative importance of specific
446      sections in a proposal and how a proposal will be evaluated or scored. This allows the laboratory
447      to focus on those areas of greater importance. If the laboratory submits a proposal that lacks
448      sufficient information to demonstrate support in a specific area, the organization can then
449      indicate how the proposal does not fulfill the need as stated in the request. Because evaluation
450      formats differ from organization to organization, laboratories may wish to contact the
451      organization for additional organization-specific details concerning this process. A technical

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452      evaluation sheet (TES) may be used in conjunction with the Proposal Evaluation Plan as outlined
453      in the next section (see Figures E.6a and E.6b in Appendix E) to list the total weight for each
454      factor and to provide a space for the evaluator' s assigned rating. In the event of a protest, the TES
455      can be used to substantiate the selection process. The TES also provides areas to record the RFP
456      number, identity of the proposer, and spaces for total score, remarks, and evaluator's signature.
457      The scoring and evaluation scheme is based on additional, more detailed, considerations which
458      are discussed briefly in the Sections E.4 and E.5 in Appendix E.
459
460      Once all proposals are accepted by the organization, the TEC scores the technical portion of the
461      proposal. MARLAP recommends that all members of the TEC have a complete technical
462      understanding of the subject matter related to the proposed work. These individuals are also
463      responsible for responding to  any challenge to the organization's selection for the award of the
464      contract. Their answers to such challenges are based on technical merit in relation to the
465      proposed work.
466
467      5.5.1.2  Scoring Elements
468
469      Although each organization may have a different scoring process to evaluate a laboratory's
470      response to a RFP, there are various broad categories or common elements that are typically
471      evaluated. For example, these may include the following:
472
473       •  Technical merit;
474       •  Adequacy and  suitability of laboratory resources and equipment;
475       •  Staff qualifications;
476       •  Related experience and record of past performance; and
477       •  Other RFP requirements.
478
479      Although each organization may score or weight these items differently, performance-based
480      contracting requires the weighting of past performance of the contractor as a significant technical
481      element. Each of these elements is considered in the following paragraphs. Outlined below are
482      the key elements that are discussed in more detail in Appendix E.
483
484      TECHNICAL MERIT
485
486      The response to the RFP should include details of the laboratory's Quality System and all the
487      analytical methods to be employed by the laboratory as well as the method validation
488      documentation (Section 6.6).  The information provided should outline or demonstrate that the
489      methods proposed are likely to be suitable and meet the APSs. The methods should be evaluated


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490      against the APSs and MQOs provided in the SOW. Chapter 7 provides guidance on the
491      evaluation of methods and laboratories. The laboratory's Quality Manual should be reviewed for
492      adequacy and completeness to ensure the required data quality.
493
494      ADEQUACY AND SUITABILITY OF LABORATORY RESOURCES AND EQUIPMENT
495
496      When requested, the laboratory will provide a listing of the available instrumentation or
497      equipment by analytical method category. In addition, the RFP may request information on the
498      available sample processing capacity and the workload for other clients during the proposed
499      contract period. The information provided should be evaluated by the TEC to determine if the
500      laboratory has the sample processing capacity to perform the work. The instrumentation and
501      equipment must be purchased, set-up,  calibrated, and on-line before award of contract. In
502      addition, the laboratory should provide information relative to the adequacy and suitability of the
503      laboratory space available for the analysis of samples.
504
505      STAFF QUALIFICATIONS
506
507      The RFP should require the identification of the technical staff and their duties, along with their
508      educational background and experience in radiochemistry, radiometrology or laboratory
509      operations. The laboratory staff that will perform the radiochemical analyses should be employed
510      and trained prior to the award of the contract. Appendix E provides guidance on staff
511      qualifications.
512
513      RELATED EXPERIENCE AND RECORD OF PAST PERFORMANCE
514
515      The RFP should require the laboratory to furnish references in relation  to its past or present work.
516      To the extent possible, this should be done with regard to contracts or projects similar in
517      composition, duration and number of samples to the proposed project. In some cases, the
518      laboratory's previous performance for the same Agency may be given special consideration.
519
520      OTHER RFP REQUIREMENTS
521
522      Within the response to the RFP, the laboratory should outline the various programs and
523      commitments (QA,  safety, waste management, etc.) as well as submit various certifications,
524      licences and permits to ensure the requirements of the RFP will be met. The reasonableness of
525      the proposed work schedule, program  and commitments should be evaluated by the TEC. In
526      addition, if accreditation is required in the RFP,  the TEC should confirm the laboratory's
527      accreditation for radioanalytical services by contacting the organization that provided the


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528      certification. If State accredited, a laboratory is typically accredited by the State in which it
529      resides. If the organization expects a laboratory to process samples from numerous States across
530      the United States, then additional accreditations for other States may be required. The TEC
531      should review and confirm the applicability and status of the licenses and permits with respect to
532      the technical scope and duration of the project.
533
534      5.5.2  Pre-Award Proficiency Evaluation
535
536      Some organizations may elect to send proficiency or PT samples (also referred to as "perfor-
537      mance evaluation" samples) to the laboratories that meet a certain  scoring criteria in order to
538      demonstrate the laboratory's analytical capability. The composition and number of samples
539      should be determined by the nature of the proposed project.  The PT sample matrix should be
540      composed of well-characterized materials. It is recommended that  site specific PT matrix
541      samples or method validation reference material (MVRM, See Chapter 6) be used when
542      available.
543
544      Each competing lab should receive an identical set of PE samples.  The RFP should specify who
545      will bear the cost of analyzing these samples as well as the scoring scheme, e.g., pass/fail or a
546      sliding scale. Any laboratory failing to submit results should be disqualified. The results should
547      be evaluated and each laboratory given a score. This allows  the organization to make a second
548      cut—after which only two or three candidate laboratories are considered.
549
550      5.5.3 Pre-Award Assessments and Audits
551
552      The RFP should indicate that the laboratories with the highest combined  scores for technical
553      proposals and proficiency samples may be given an on-site audit. A pre-award assessment or
554      audit may be performed to provide assurance that a selected laboratory is capable of fulfilling the
555      contract in accordance with the RFP (Appendix E). In other words, is the laboratory's represen-
556      tation on paper (i.e., proposal) realistic when compared to the actual facilities? To answer this
557      question, auditors should be looking to see that a candidate laboratory appears to have all the
558      required elements to meet the proposed contract's needs. Refer to Appendix E for details on the
559      pre-award assessments and audits.

562
563
564
565
566
                        Summary of Recommendations

MARLAP recommends that technical specifications be prepared in writing in a single
document designated as a SOW for all radioanalytical laboratory services, regardless of
whether the services are to be contracted out or performed by an Agency's laboratory.
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    MARLAP recommends that the laboratory submit the proposed methods and required
    method validation documentation with the formal response.

    MARLAP recommends that the RFP state that subcontracting will be permitted only with
    the contracting organization's approval.

    MARLAP recommends that all members of the TEC have a complete technical
    understanding of the subject matter related to the proposed work.
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567
568
569
570
571
572
573
574
575
576
577
578      5.6   References
579
580      5.6.1  Cited References
581
582      American National Standard Institute (ANSI) N42.23. Measurement and Associated
583         Instrumentation Quality Assurance for Radioassay Laboratories. 1996.
584
585      American National Standard Institute (ANSI) ANS 10.3. Documentation of Computer Software.
586
587      American Society for Testing and Materials (ASTM) E919. Standard Test Methods for Software
588         Documentation for a Computerized System.
589
590      U.S. Environmental Protection Agency (EPA). 1998. Guidance on Quality Assurance Project
591         Plans (EPA QA/G-5). EPA/600/R-98/018, Washington, DC. Available from www.epa.gov/
592         quality l/qs-docs/g5-final.pdf.
593
594      International Electrical and Electronics Engineers (IEEE). Standard 1063. Software User
595         Documentation.
596
597      International Standards Organization/International Electrotechnical Commission (ISO/IEC)
598         17025. General Requirements for the Competence of Testing and Calibration Laboratories.
599         December 1999, 26 pp.
600
601      National Environmental Laboratory Accreditation  Conference (NELAC). 2000. Quality Systems.
602         July. Available from: http://www.epa.gov/ttn/nelac/.
603
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604     5.6.2  Other Sources
605
606     U.S. Department of Energy (DOE).  Order 414.1-1: Implementation Guide for Use with Indepen-
607         dent and Management Assessment Requirements of 10 CFR Part 830.120 and DOE 5700.6c
608         Quality Assurance. August. Available from www.directives.doe.gov/pdfs/doe/doetext/
609         neword/414/g4141-l.
610
611     U. S. Department of Energy (DOE).  1997. Model Statement of Work for Analytical Laboratories.
612         Albuquerque Operations Office, Prepared by AGRA Earth and Environmental, Inc.,
613         Albuquerque, NM. March.
614
615     U.S. Nuclear Regulatory Commission (NRC). 1996. NRC Acquisition of Supplies and Services.
616         Directive 11.1, July 23.
617
618     U.S. Nuclear Regulatory Commission (NRC). 1994. NRC Procedures for Placement and
619         Monitoring of Work With the U. S.  Department of Energy (DOE). Directive 11.7, May 3.
620
621     Office of Federal Procurement Policy  (OFPP) 1997. Performance-Based Service Contracting
622         (PBSC) Solicitation/Contract/Task Order Review Checklist. August 8. Available from: http://
623         www.arnet.gov/Library/OFPP/PolicyDocs/pbscckls.html.
624
625
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 i               6  SELECTION AND APPLICATION OF AN

 2                            ANALYTICAL METHOD

 3     6.1    Introduction

 4     This chapter provides guidance to both the project manager and the laboratory on the selection
 5     and application of analytical method. It offers guidance to the project manager on the develop-
 6     ment of the Analytical Protocol Specifications (APSs) from the laboratory's perspective on
 7     method appropriateness and availability. It offers guidance to the laboratory on the key elements
 8     to consider when selecting an analytical method (Chapter 1, Section 1.4.5) to meet the objectives
 9     of the APSs contained in the Statement of Work (SOW). Assuming that the laboratory has
10     received a SOW, certain subsections of Section 6.5 provide guidance on how to review and
11     properly evaluate the APSs therein. However, Section 6.5 also provides guidance for the project
12     planning team on the important laboratory considerations needed to develop the Measurement
13     Quality Objectives (MQOs). Section 6.6 deals with method validation requirements and has been
14     written for both the project planners and the laboratory.

15     Because the method constitutes the major part of the analytical protocol (Chapter 1), this chapter
16     focuses on the selection of a method. However, other parts of the protocol should be evaluated
17     for consistency with the method (Figure 6.1). MARLAP recommends the performance-based
18     approach for method selection. Thus, the laboratory should be able to propose whichever method
19     meets the project's analytical data requirements (MQOs), within constraints of other factors such
20     as regulatory requirements, cost, and project deadlines. The selection of a method by the
21     laboratory is in response to the APSs (Chapter 3) that were formulated during the directed
22     planning process (Chapter 2) and documented in the SOW (Chapter 5). In most project plan
23     documents, the project manager or the project planning team has the authority and responsibility
24     for approving the methods proposed by the laboratory. The APSs will, at a minimum, document
25     the analytes, sample matrices, and the MQOs. A MQO is a statement of a performance objective
26     or requirement for a particular method performance characteristic. The MQOs can be viewed as
27     the analytical portion of the DQOs (Chapter 3).

28     Background material in Section 6.2.1 provides the reader with the subtleties of the performance-
29     based approach to method selection, contrasted with the use of prescribed methods and the
30     importance of the directed panning process and MQOs in the selection of the method. This
31     chapter does not provide a listing of existing methods with various attributes indexed to certain
32     applications. Analytical methods may be obtained from national standards bodies, government
33     laboratories and publications, and the open literature.
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34     In this chapter, method validation is defined as the demonstrated method applicability for a
35     particular project. MARLAP recommends that only methods validated for a project's application
36     be used. This recommendation should not be confused with the generic method validation that all
37     methods should undergo during method development. The laboratory should validate the method
38     to the APS requirements of a SOW for the analyte/matrix combination and provide the method
39     validation documentation to the project manager prior to the implementation of routine sample
40     processing (Section 6.6). If applicable, consideration should be given to the uncertainty of the
41     laboratory's protocol for subsampling (heterogeneity) of the received field sample when selecting
42     a method. Appendix F provides guidance on the minimization of subsampling uncertainty.

43     Section 6.3 provides an overview of the generic application of a method for a project and how a
44     laboratory meets the recommendations of the guidance provided in this and other chapters.
45     Generic considerations for the method selection process that a laboratory should evaluate are
46     provided in Section 6.4. Project-specific considerations for method selection relevant to APSs are
47     discussed in Section 6.5. Recommendations on the degree of method validation specified by the
48     project planning team are outlined in Section 6.6. Sections 6.7, 6.8, and  6.9 provide guidance on
49     analyst qualifications, method control, and continued laboratory performance assessment,
50     respectively. Section 6.10 outlines recommendations for the method  proposal and validation
51     documentation that a laboratory should send to the project manager.

52     6.2    Method Definition

53     For this chapter, a laboratory "method" includes all physical, chemical, and radiometric processes
54     conducted at a laboratory in order to provide an analytical result. These processes, depicted in
55     Figure 6.1, may include sample preparation, dissolution, chemical separation, mounting for
56     counting, nuclear instrumentation counting, and analytical calculations.  This chapter will
57     emphasize the laboratory's selection of the radioanalytical method that will be proposed in
58     response to a SOW. Each each method is assumed to address a particular analyte in a specified
59     matrix or, in some cases, a group of analytes having the same decay emission category that can
60     be identified through spectrometric means (e.g., gamma-ray spectrometry). However, it should be
61     emphasized that the project planning team should have evaluated every component of the APSs
62     for compatibility with respect to all analytes in a sample and the foreseen use of multiple
63     analytical methods by the laboratory. For example, samples containing multiple analytes must be
64     of sufficient size (volume or mass) to ensure proper  analysis and to meet detection  and quantifi-
65     cation requirements. Multiple analytes in a sample will require multiple analyses for which a
66     laboratory may use a sequential method that addresses multiple analytes or stand-alone individual
67     methods for each analyte. The analytical protocol must ensure that the samples are properly
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                                    Selection and Application of an Analytical Method
             Field Sample Preparation
                 and Preservation
                 Sample Receipt
                   and Tracking
                Laboratory Sample
                   Preparation
                Sample Dissolution
               Chemical Separation
               Sample Preparation
           for Instrument Measurement
            Instrument Measurement of
                  Radionuclides
            Analytical Calculations and
                  Data Reduction
            Data Verification, Validation
                  and Reporting
            May be Included
              These Steps Are
              Typically Considered
              to be "The Method"
                        FIGURE 6.1 — Analytical process
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 68     preserved for each analyte and sufficient sample is collected in the field to accommodate the
 69     analytical requirements.

 70     Certain aspects of a method are defined in this chapter in order to facilitate the method selection
 71     process. The following subsections describe the underlying basis of a performance-based
 72     approach to method selection and provide a functional definition related to MARLAP.

 73     Performance-Based Approach and Prescriptive Method Application

 74     MARLAP uses a performance-based approach to select a method, which is based on a
 75     demonstrated capability to meet defined project performance criteria (e.g., MQOs). With a
 76     properly implemented quality system, a validated method should produce appropriate and
 77     technically defensible results under the applicable conditions. The selection of any new method
 78     usually requires additional planning and, in some cases, may result in additional method
 79     development or validation. The selection  of a method under the performance-based approach
 80     involves numerous technical, operational, quality, and economic considerations. However, the
 81     most important consideration in the selection of a method under the performance-based approach
 82     is compliance with the required MQOs for the analytical data. These requirements should be
 83     defined in the SOW or appropriate project plan document.

 84     When developing the MQOs, the project planning team should have evaluated all processes that
 85     have a potential to affect the analytical data. Those involved in the directed planning process
 86     should understand and communicate the needs of the project. They should also understand how
 87     the sampling (field, process, system, etc.) and analytical activities will interact and the ramifica-
 88     tions that the data may have on the decisionmaking process. These interactive analysis and
 89     communication techniques should be applied in all areas where analytical data are produced. As
 90     new projects are implemented, it should not be assumed that the current methods are necessarily
 91     the most appropriate and accurate; they should be reevaluated based on project objectives. The
 92     application of a performance-based approach to method selection requires the quantitative
 93     evaluation of all aspects of the analytical process. Once the MQOs for a project have been
 94     determined and incorporated into the APSs, under the performance-based approach, the
 95     laboratory will evaluate its existing methods and propose one or more methods that meet each
 96     APS. This chapter contains guidance on how to use the APSs in the laboratory's method
 97     evaluation process.

 98     The objective of a performance-based approach to method selection is to facilitate the selection,
 99     modification, or development of a method that will reliably produce quality analytical data as
100     defined by the MQOs. Under the performance-based approach,  a laboratory, responding to a


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101      SOW, will propose a method that best satisfies the requirements of the MQO and the laboratory's
102      operations.

103      In certain instances, the requirement to use prescribed methods may be included in the SOW. The
104      term "prescribed methods" has been associated with those methods that have been selected by
105      industry for internal use or selected by a regulatory agency, such as the U.S. Environmental
106      Protection Agency (EPA), for specific programs. The methods for analyzing radionuclides in
107      drinking water prescribed by EPA (1980 ) provides an example of applying a limited number of
108      methods to a well-defined matrix.  In many companies or organizations, prescribed methods are
109      widely used. Methods that have been validated for a specific application by national standard
110      setting organizations such as the American Society for Testing and Materials (ASTM), American
111      National  Standards Institute (ANSI), American Public Health Association (APHA),  etc., may
112      also be used as prescribed methods by industry and government agencies.

113      Typically, the prescribed methods  were selected by an organization to meet specific objectives
114      for a regulation under consideration or for a program need. In most cases, the prescribed methods
115      had undergone some degree of method validation, and the responsible organization had required
116      a quality  system to demonstrate continued applicability and quality, as well as laboratory
117      proficiency.  The use of any analytical method, whether prescribed or from the performance-based
118      approach, has a life  cycle that can  be organized into the major categories of selection, validation,
119      and continued demonstrated capability and applicability. This chapter will cover in detail only
120      the first two of these categories. A discussion on ongoing laboratory evaluations is presented in
121      Chapter 7 and Appendix C.

122      A final note  should be made relative to prescribed methods and the performance-based approach
123      to method selection. The performance-based approach for method selection allows more latitude
124      in dealing with the potential diversity of matrices (such as waste-, sea-, ground- or surface water;
125      biota; air filters; waste streams; swipes; soil; sediment;  or sludge) from a variety of projects, or in
126      dealing with different levels of data quality requirements or a laboratory's analytical proficiency.
127      Even though the prescribed method approach may initially appear suitable and cost effective, it
128      does not allow a laboratory to select a method from the many possible methods that will meet the
129      MQOs.

130      Many individuals have the wrong impression that prescribed methods do not need to be validated
131      by a laboratory. However, as discussed in this chapter, all methods should be validated to some
132      level of performance for a particular project by the laboratory prior to their use. In addition,  the
133      laboratory should demonstrate continued proficiency in using the method through internal QC
134      and external performance evaluation (PE) programs (Chapter 18).


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135
6.3    Life Cycle of Method Application
136      In responding to a SOW for a given analyte/matrix combination, a laboratory may have one or
137      more methods that may be appropriate for meeting the MQOs. The final method selected from a
138      set of methods may be influenced by many other technical, operational, or quality considerations.
139      Figure 6.2 provides an overview of the life cycle of the method application. Figure 6.3 expands
140      the life cycle into a series of flow diagrams.
                                      Analyte / Matrix
                                    Process Knowledge-
                                                     Project
                                                 Management
                 Documentation of
                 Method Validation
                  & Performance
                  During Project
               Continued
              Performance
              Assessments
                                                  Analytical
                                                  Protocol
                                                Specifications
                                       Proposed
                                       Method
                                      Modification
           External
         PE Programs
                              Laboratory
                             Management
                                                        Method
                                                       Selection
                                                               Initial
                                                               Approval
                 Method
                 Control
                (Quality System)
                                                       Method
                                                      Validation
                                                      Demonstrated
                                                      Performance)
                        Analyst
                       Selection /
                       Qualification
                                                                           Available
                                                                           Methods
                                                                          Existing     Method     Method
                                                                          Methods   Development  Modification
                                                 P.
                                               Approval
                Samples
            Project   External QC
                               Approved^
                                Method
                                 FIGURE 6.2 — Method application life cycle
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                                                                  Selection and Application of an Analytical Method
            Analyte / Matrix
          Process Knowledge
            Analytical Protocol
              Specifications
Analytes -  Radionuclides w/ decay products

       - Health significance of nuclides
       - Scaling factors (alternative related analytes) for related nuclides; decay corrected and
         based on process knowledge and the uncertainty of the alternate analyte
          measurements
       - Chemical species of analyte; process knowledge or experiment
       - Analyte stability during and following sampling
           -  Preservation requirements

Matrix  - Description from process knowledge or field collection reports
       - Chemical or radioactive interferences and inherent analyte in matrix
       - Analyte Contaminant or inherent in matrix; process knowledge
       - Analyte uniformly distributed within matrix
       - Sample prep considerations
Data use - Define final form for analysis
              -  wet or dry for soil and vegetation
              -  analyte cone. / particle size distribution
              -  analyte cone. / dissolved or suspended or both
              - analyte cone. / chemical & physical species

MQO    - Define action level for each analyte / matrix
        - Define MDC or MQC
        - Define required method uncertainty at the action level
        - Define MQOs for other method performance characteristics as
         appropriate - method specificity, ruggedness and analyte concentration
         range

Method Validation Testing Protocol
        - Select Method Validation Level
               - Test at  several analyte concentration levels including zero analyte (blanks);
                  MDC  or MQC requirement
               - Include known chemical or radionuclide interferences at appropriate levels
        - Select project specific or appropriate surrogate matrix PT samples
        - Establish acceptable chemical / radiotracer yield values
        - State data testing criteria
      Method Development / Selection
              (Section 6.5)
 FIGURE 6.3 — Expanded Figure 6.2 addressing the laboratory's method evaluation process
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Selection and Application of an Analytical Method
    Method Development / Selection
            (Section 6.5)
           Method Validation
             (Section 6.6)
           Method Approval
                                                       Sample Prep / Sample Dissolution / Chemical Separation /
                                                                 Test Source / Nuclear Counting

                                                              - Measurement quality objectives
                                                              - Analyte / radionuclide of interest
                                                              - Sample volume
                                                              - Chemical / physical species
                                                              - Preservation applied in field / lab
                                                              - Chemical / radionuclide interferences
                                                              - Matrix considerations - subsampling considerations
                                                              - Method of analyte (or alternative analyte) detection
                                                              - Method complexity
                                                              - Required turnaround and radiological holding times
                                                              - Validation status of possible methods
                                                              - Availability of qualified staff
                                                              - Hazardous waste production
                                                              - Facility / benchspace and equipment availability
                                                              - Associated  costs
                                                       Review specificied method validation requirements and deterr
                                                              - Use of exisiting validated method
                                                              - Modify existing method and validate
                                                              - Develop new method and validate

                                                       Prepare method validation documentation
                                                       Laboratory proposes method and submits:
                                                              - SOP
                                                              - Method validation documentation
                                                              - Previous history of method use

                                                       Project Manager evaluates proposed method
                                                              - Technical review of proposed method
                                                              - Review of method validation documentation
                                                              - Approval signoff
               Analyst
        Selection / Qualifications
             (Section 6.7)
                                            Expanded  Figure 6.2 addressing the laboratory's method
                                                     evaluation  process
FIGURE 6.3 (continued)
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                                                             Selection and Application of an Analytical Method
              Analyst
       Selection / Qualifications
            (Section 6.7)
           Method Control
             (Section 6.8)
        Continued Performance
             Assessment
             (Section 6.9)
  Analyst selection consistent with level of method difficulty
       - Education
       - Experience & familiarity of method concepts

  Documented training in lab safety, radiation safety, chemical hygiene and waste
        management

  Documented training on selected method
       - Method review
       - Supervised hands on training

  Analyst completes profiency tests
       - Analytical results meet quality performance requirements for MQOs
Controlled Method Manual
     -  Latest revision applied
     -  Signature signoff

Instrument calibration & radiotracers - NIST traceable standards

Instrumentation quality control
     -  Balances, pipettes, volumetric glassware
     -  Daily / prior-to-use nuclear and chemistry instrumentation QC checks

Radiotracer / gravimetric yield within specified range

Internal batch QC samples

SOPs for troubleshooting "out of control" situations
     FIGURE 6.3 (continued) — Expanded Figure 6.2 addressing the laboratory's method
                                                   evaluation process
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         Selection and Application of an Analytical Method
              Continued Performance
                  Assessment
                  (Section 6.9)
                 Documentation
                  (Section 6.10)
                                   - Internal batch QC samples meet quality performance criteria
                                   - External double and single blind QC / PT samples from contracting organization and / or agency
                                    monitoring laboratory
                                   - External single blind PT samples from national PE program - traceable to NIST
                                   - Data verification and validation
                                   - Internal assessments / audits / surveillances
                                   - External assessments / audits / surveillances
                                   - Method validation records
                                   - Analyst training program
                                   - Method manual control and archiving
                                   - Software verification and validation records
                                   - Instrument calibration & QC records
                                   - Internal method batch QC sample results
                                   - Internal & external assessments
                                   - External double / single QC sample results
                                   - Corrective action reports
                                   - Analytical results - hard and electronic copy
141
             FIGURE 6.3 (continued)
                                Expanded Figure 6.2 addressing the laboratory's method
                                       evaluation process
6.4    Generic Considerations for Method Development and Selection
142      This section provides guidance on the technical, quality, and operational considerations for the
143      development of a new method or the selection of an existing radioanalytical method. Unless
144      required by a regulatory or internal policy, rarely should a method be specified in an APS or a
145      SOW. MARLAP recommends that a SOW containing the MQOs and analytical process
146      requirements be provided to the laboratory.

147      If the nature of the samples and  analytes are known in advance, and variations in a sample matrix
148      and analyte concentration are within a relatively small range, the development or selection of
149      analytical methods is easier. In most situations, however, the number of samples, sample
150      matrices, analyte interferences, chemical form of analytes, and variations among and within
151      samples may influence the selection of a method for a given analyte. A number of radioanalytical
152      methods are available, but no single method provides a general solution (all have advantages and
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153      disadvantages). The method selection process should consider not only the classical
154      radiochemical methods involving decay emission detection (alpha, beta or gamma) but also non-
155      nuclear methods, such as mass spectrometric and kinetic phosphorescence analysis.

156      In the performance-based approach to method selection, the laboratory may select and propose a
157      gross measurement (alpha, beta, or gamma) method that can be applied to analyte concentrations
158      well below the action level for the analyte, as well as an analyte specific method for analyte
159      levels exceeding a proposed "screening level" that is a fraction of the action level. For example,
160      it may be acceptable to propose a gross measurement method when its combined standard
161      uncertainty meets the method uncertainty requirement at concentration levels much below the
162      action level. A gross measurement method may be employed initially for some projects. Such an
163      approach would have to be agreed to by the laboratory and project manager. The method
164      validation, discussed in Section 6.6, should demonstrate that the gross measurement method can
165      measure the analyte of interest (directly or indirectly) at the proposed analyte concentration and
166      meet the uncertainty requirement in the presence of other radionuclides. Appendix C provides
167      guidance on how to determine the acceptable method uncertainty at an analyte concentration
168      relative to the action level.

169      In general, the development or selection of a method follows several broad considerations. These
170      include analyte and matrix characteristics, technical complexity and practicality of methods,
171      quality requirements, availability of equipment, facility and staff resources, regulatory concerns,
172      and economic considerations. Each of the broad considerations can be detailed. The following
173      list, although not inclusive, provides insight into the selection of an appropriate method. Many of
174      these categories are discussed in subsequent MARLAP Part n chapters.

175       0 Analyte/radionuclide/isotope of interest
176           o  Decay emission (particle or photon), atom detection, or chemical (photon detection)
177           o  Half-life of analyte
178           o  Decay products (progeny); principal detection method or interference
179           °  Chemical/physical forms  (e.g., gas, volatile)
180           o  Use of nondestructive or destructive sample analysis

181       0 Level of other radionuclides or chemical interference
182           °  Level of decontamination or selectivity required, e.g.,  a decontamination factor of 103for
183                an interfering nuclide (60Co) present with the analyte of interest (241Pu)
184           °  Resolution of measurement technique
185           o  Robustness of technique for handling large fluctuations in interference levels and
186             variations in a matrix
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187          ° Radionuclides inherent in background
188      0 Matrix
189          o Destructive testing
190            -   Stable elemental interferences
191            -   Difficulty in dissolution of a matrix
192            -   Difficulty in ensuring homogeneity of aliquant
193            -   Inconsistency in chemical forms and oxidation states of the analyte versus the tracer
194          °  Non-destructive testing
195            -   Heterogeneity of final sample for analysis
196            -   Self absorption of particle/photon emissions within a matrix

197      0 Degree of method complexity
198          o Level of technical ability required of analysts
199          ° Reproducibility of quality results between analysts
200          o Method applicability to sample batch processing
201          ° Extensive front-end chemical-processing technique (sample dissolution, analyte
202            concentration and purification/isolation, preparation for final form for radiometrics)
203          ° Nuclear instrumentation oriented technique (minimal chemical processing)

204      0 Required sample turnaround time
205          o Half-life of analyte
206          ° Sample preparation or chemical method processing time
207          o Nuclear instrumentation measurement/analysis time
208          ° Chemical  or sample matrix preservation time
209          o Batch processing
210          ° Degree of automation available/possible

211      0 Status of possible methods and applications
212          o Validated for the intended application
213          ° Staff qualified and trained to use  method(s)
214          o Existing QC program for method(s)
215          ° Specialized equipment, tracers, reagents, or materials available

216      0 Hazardous or Mixed waste production
217          o Older classical techniques versus new advanced chemical technologies
218          ° Availability and expense of waste disposal

219      0 Associated costs
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                                                   Selection and Application of an Analytical Method
220          o Labor, instrumentation usage, facilities, radiological waste costs
221          ° Method applicability to portable or mobile laboratory facilities
222          o Availability of service hookups
223          ° Need for facility environmental controls
224          o Need for regulatory permitting of mobile laboratory facility

225     6.5    Project-Specific Consideration for Method Selection

226     Certain parameters of the APSs (See Chapter 3 and the example in Figure 3.2) within the SOW
227     are important to the method selection process. These include the analytes, matrix type, matrix
228     characterization, analyte and matrix interferences, analyte speciation information gathered from
229     process knowledge, sample process specifications (such as radiological holding times and sample
230     processing turnaround times), and the MQOs. While these issues should be resolved during
231     project planning, they are presented here as guidance to the laboratory for their review and
232     evaluation of the technical adequacy of the SOW and to provide context for the method
233     evaluation and selection process. Many of the issues from the project planning point of view are
234     discussed in Section 3.3.

235     6.5.1  Matrix and Analyte Identification
        The first step in selecting a method is knowing what analytes and sample matrices are involved.
        The following sections discuss what important information should accompany analyte and matrix
        1 /~\ £itt+i TI /"»o+i r\-r\
236
237
238     identification.
239     6.5.1.1 Matrices

240     A detailed identification and description of the sample matrix are important aspects in the
241     selection of an analytical method to meet the MQOs. The SOW should provide the necessary
242     detailed sample matrix description, including those important matrix characteristics gathered
243     from process knowledge. The laboratory should evaluate whether the existing sample preparation
244     and dissolution steps of a method (Chapters 10 and 12 through 15) will be sufficient to meet the
245     MQOs or the method validation requirements. The matrix will also determine, to a certain extent,
246     waste handling and disposal at the laboratory. If the matrix description is too vague or generic,
247     the laboratory should contact the technical representative named in the SOW and request
248     additional information.
249
250     The laboratory should ensure that the sample matrix description in the SOW reflects what is
251     considered to be the "sample" by the project manager and the description is of sufficient detail to


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252     select the method preparation or analyte isolation steps that will meet the MQOs for the matrix.
253     The laboratory should not accept generic sample matrix descriptions such as liquids or solids. For
254     example, the differences between potable water and motor oil are obvious, but both may be
255     described as a "liquid sample." However, there may be only subtle differences between potable
256     surface water and groundwater but major differences between potable and process effluent
257     waters. The laboratory should consider how much method robustness is needed  in order to
258     address the varied amounts of possible stable elements or compounds within a non-specified
259     water matrix. Furthermore, when water from a standing pool is received in the laboratory, it may
260     contain some insoluble matter. Now the questions arise whether the sample is the entire contents
261     of the container, what remains in the container, the insoluble material, or just the water? A clay
262     will act as an ion exchange substrate, while a sand may have entirely different retention
263     properties. Both can be described as a soil or sediment, but the properties with which they retain
264     a radionuclide are substantially different; thus, the method to properly isolate a particular
265     radionuclide will vary. The laboratory should ensure that the selected method is consistent with
266     the intended sample matrix, and the analytical results convey analyte concentration related to the
267     proper matrix (i.e., Bq/L dissolved, Bq/L suspended, or Bq/L total). For such cases, the
268     laboratory should request the project manager to clarify the "matrix" or "sample" definition.

269     Matrices genetically identified as "solid" require additional clarification or information in order
270     to select  and validate a method properly. For example, sludges from a sewerage treatment facility
271     may be classified as a solid, but the suspended and aqueous portions (and possibly the dried
272     residual material) of the sample may have to be  analyzed. Normally, the radioanalyte concentra-
273     tion in soils and sediments is reported in terms of becquerels per dry weight. However, certain
274     projects may require additional sample process specifications (Section 6.5.4) related to the soil or
275     sediment matrix identification that will affect the method selection process and the reporting of
276     the data.  This may involve sectioning of core samples, specified drying temperature of the
277     sample, determining wet-to-dry weight ratio, removing organic material or detritus, homogeni-
278     zing and pulverizing, sieving and sizing samples, etc. In order to determine the average analyte
279     concentration of a sample of a given size containing radioactive particles, proper sample
280     preparation and  subsampling coupled with the applicable analytical methods are required
281     (Chapter 12 and Appendix F). For alpha-emitting radionuclides, the method selected may only be
282     suitable to analyze a few grams of soil or sediment, depending on the organic content. The
283     laboratory should identify to the project manager the typical subsample or aliquant size that is
284     used for the proposed method. If information provided to the laboratory on process knowledge
285     indicates that there may be a possibility of radioactive particles, or selected analyte adsorption
286     onto soil or sediment particles, the  laboratory should propose sample preparation and analytical
287     methods that will address these matrix characteristics. The laboratory should submit the proposed
288     methods annotated with the suspected matrix characterization issues.


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289     When selecting the methods for the analysis of flora (terrestrial vegetation, vegetables, aquatic
290     plants, algae, etc.) or fauna (terrestrial or aquatic animals) samples, the detailed information on
291     the matrix or the unique process specifications should be used by the laboratory to select or
292     validate the method, or both. The laboratory should ensure that the specific units for the
293     analytical results are consistent with the matrix identification and unique process specifications
294     stated in the SOW. Most flora and fauna results are typically reported in concentrations of wet
295     weight. However,  for dosimetric pathway analyses, some projects may want only the edible
296     portion of the sample  processed and the results to reflect this portion, e.g., fillet of sport fish,
297     meat and fluid of clams, etc. For the alpha- and beta-emitting radionuclides,  aquatic vegetation
298     normally is  analyzed in the dry form, but the analyte concentration is reported as wet weight. The
299     laboratory should ensure that the sample preparation method (Chapter 12) includes the
300     determination of the necessary wet and dry weights.

301     These considerations bear not only on the method selected but also on how the sample should be
302     collected and preserved during shipment. When possible, the laboratory should evaluate the
303     proposed sample collection and preservation methods, as well as timeliness of shipping, for
304     consistency with the available analytical methods. Discrepancies noted in the SOW for such
305     collateral areas should be brought to the attention of the project manager. For example, sediment
306     samples that have been cored to evaluate the radionuclide depth profile should have been
307     collected and treated in a fashion to retain the depth profile.  A common method is to freeze the
308     core samples in the original plastic coring sleeves and ship the samples on ice.  The SOW  should
309     define the specifics on how to treat the core samples and the method of sectioning the samples
310     (e.g., cutting the cores into the desired lengths or flash heating the sleeves with subsequent
311     sectioning).

312     The SOW should have properly delineated the proper matrix specifications required for method
313     validation. In some cases, sufficient information may have been provided to  define the
314     parameters necessary to prepare method validation reference material (MVRM) for method
315     validation purposes (Section 6.6). The laboratory should ensure that sufficient information and
316     clarity have been provided on the matrix to conduct a proper method validation.

317     6.5.1.2. Analytes and Potential Interferences

318     The SOW should describe the analytes of interest and the presence of any other chemical  and
319     radionuclide contaminants (potential  method interferences and their anticipated concentration)
320     that may be in the samples. This information should be provided in the SOW to allow the
321     laboratory's radiochemist to determine the specificity and robustness of a method that will
322     address the  multiple analytes and their interferences. The delineation of other possible interfering


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323     radionuclides is extremely important in the selection of a method to ensure that the necessary
324     decontamination factors and purification steps are considered.

325     The size of the sample needed by the laboratory will depend on the number of analytes and
326     whether the laboratory will select individual methods for each analyte or a possible "sequential"
327     analytical method, where several analytes can be isolated from the same sample and analyzed. If
328     a sample size is listed in the SOW, the laboratory should determine if there will  be sufficient
329     sample available to analyze all analytes, the associated QC samples, and any backup sample for
330     re-analyses. Other aspects, such as the presence of short-lived analytes or analytes requiring very
331     low detection limits, may complicate the determination of a proper sample size.

332     The laboratory should ensure that the method validation requirements in the SOW are consistent
333     with the analytes and matrix. The method validation protocols defined in  Section 6.6 are
334     applicable to methods for single analyte analyses or to a "sequential method" where several
335     analytes are isolated and analyzed. The laboratory should develop a well-planned protocol
336     (Section 6.6.2) for method validation that considers the method(s), analyte(s), matrix and
337     validation criteria.

338     6.5.2 Process Knowledge

339     Process knowledge typically is related to facility effluent and environmental surveillance
340     programs, facility decommissioning, and site remediation activities. Important process
341     knowledge may be found in operational history or regulatory reports associated with these
342     functions or activities. It is imperative that the laboratory review the information provided in the
343     SOW to determine whether the anticipated analyte concentration and matrix are consistent with
344     the scope of the laboratory operations. Process knowledge contained in the SOW should provide
345     sufficient detail for the laboratory to determine, quickly and decisively, whether or not to pursue
346     the work. If sufficient detail is not provided in the SOW, the laboratory should request the project
347     planning documents. Laboratories having specialized sample preparation  facilities that screen the
348     samples upon arrival can make the necessary aliquanting or dilutions to permit the processing of
349     all low-level samples in the laboratories. Laboratories that have targeted certain sectors  of the
350     nuclear industry or a particular nuclear facility may be very knowledgeable in the typical
351     chemical  and physical forms of the analytes of a given  sample matrix and may not require
352     detailed process knowledge information. However, under these circumstances, the laboratory's
353     method should be robust and rugged enough to handle the expected range of analyte concen-
354     trations, ratios of radionuclide and chemical interferences, and variations  in the sample matrix.
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355     Process knowledge may provide valuable information on the possible major matrix constituents,
356     including major analytes, chemical/physical composition, hazardous components, radiation
357     levels, and biological growth (e.g., bacteria, algae, plankton, etc.) activities. When provided, the
358     laboratory should use this information to determine if the sample collection and preservation
359     methodologies are consistent with the proposed radioanalytical method chosen. In addition, the
360     information also should be reviewed to ensure that the proposed sample transportation or
361     shipping protocols comply with regulations governing the laboratory operation.

362     Process knowledge information in the SOW may be used by the laboratory to refine method
363     selection from possible radiometric/chemical interferences, chemical properties of the analytes or
364     matrix, and hazardous components, among others. Chapter 14 describes the various generic
365     chemical processes that may be used to ensure proper decontamination or isolation of the analyte
366     from other interferences in the sample. These include ion exchange, co-precipitation, oxidation/
367     reduction, and solvent extraction among others. The process knowledge information provided in
368     the SOW should be reviewed to determine whether substantial amounts of a radionuclide that
369     normally would be used as a radiotracer will be present in the sample.  Similarly, information on
370     the levels of any stable isotope of the analyte being evaluated is equally important. Substantial
371     ambient or background amounts of either a stable isotope of the radionuclide or the radiotracer in
372     the sample may produce elevated and false chemical yield factors. In addition, substantial
373     amounts of a stable isotope of the analyte being evaluated may render certain purification
374     techniques inadequate (e.g., ion exchange or solid extractants).

375     6.5.3  Radiological Holding  and Turnaround Times

376     The SOW should contain the requirements for the analyte's radiological holding and sample
377     turnaround times. MARLAP defines radiological holding time as the time differential between
378     the date of sample collection and the date of analysis. It is important that the laboratory review
379     the specifications for radionuclides that have short half-lives (less than 30 days), because the
380     method proposed by the laboratory may depend on the required radiological holding time. For
381     very short-lived radionuclides, such as 131I or 224Ra, it is very important to  analyze the samples
382     within the first two half-lives in order to meet the MQOs conveniently. A  laboratory may have
383     several methods for the analysis of an analyte, each having a different analyte detection and
384     quantification capability. Of the possible methods available, the method selected and proposed by
385     the laboratory most likely will be dependent on the radiological holding time requirement, half-
386     life of the analyte, and the time available after sample receipt at the laboratory. When a
387     laboratory has several methods to address variations in these constraints, it is recommended that
388     the laboratory propose more than one method with a clarification that addresses the radiological
389     holding time and MQOs. In some cases, circumstances arise which require the classification of


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390     sample processing into several time-related categories (Chapter 5). For example, the determina-
391     tion of 131I in water can be achieved readily within a reasonable counting time through direct
392     gamma-ray spectrometry (no chemistry) using a Marinelli beaker counting geometry, when the
393     detection requirement is 0.4 Bq/L and the radiological holding time is short. However, when the
394     anticipated radiological holding time is in the order of weeks, then a radiochemistry method
395     using beta detection or beta-gamma coincidence counting would be more appropriate to meet the
396     detection requirement. The more sensitive method also may be used when there is insufficient
397     sample size or when the analyte has decayed to the point where the less sensitive method cannot
398     meet the required MQOs. Another example would be the analysis of 226Ra in soil, where the
399     laboratory could determine the 226Ra soil concentration through the quantification of a 226Ra
400     decay product by gamma-ray spectrometry after a certain ingrowth period, instead of direct
401     counting of the alpha particle originating from the final radiochemical product (micro-
402     precipitate) using alpha spectrometry.

403     Sample (processing) turnaround time normally means the time differential from the receipt of the
404     sample at the laboratory to the reporting of the analytical results. As such, the laboratory should
405     evaluate the SOW to ensure that the sample turnaround time, radiological holding time, data
406     reduction and reporting times, and project needs for rapid data evaluation are consistent and
407     reasonable. Method selection should take into consideration the time-related SOW requirements
408     and operational aspects. When discrepancies are found in the SOW,  the laboratory should
409     communicate with the project manager and resolve any issue. Additionally, the response to the
410     SOW should include any clarifications needed for sample turnaround time and/or radiological
411     holding time issues.

412     6.5.4   Unique Process Specifications

413     Some projects may incorporate detailed sample processing parameters, specifications, or both
414     within the SOW. Specifications for parameters related to sample preparation may include the
415     degree of radionuclide heterogeneity in the final sample matrix prepared at the laboratory, the
416     length of the sections of a soil or sediment core for processing, analysis of dry versus wet weight
417     material, partitioning of meat and fluid of bivalves for analyses, and reporting of results for
418     certain media as a dry or wet weight. Specifications related to method analysis could include
419     radionuclide chemical speciation in the sample matrix.  The laboratory must  evaluate these
420     specifications carefully, since various parameters  may affect the method proposed by the
421     laboratory. When necessary, the laboratory should request clarification of the specifications in
422     order to determine a compatible method. In addition, the laboratory should ensure that the
423     method validation process is consistent with the unique process requirements. In some cases, not
424     all special process specifications must be validated and, in other cases, site-specific materials


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425     (also referred to as MVRM) will be required for method validation. When necessary, the
426     laboratory also should request site-specific reference materials having the matrix characteristics
427     needed for proper method validation consistent with the special process requirements. It is
428     incumbent upon the laboratory to understand clearly the intent of the special process
429     specifications and how they will be addressed.

430     6.5.5  Measurement Quality Objectives

431     The specific method performance characteristics having a measurement quality objective may
432     include:

433       •  Method uncertainty at a specified analyte concentration level;
434       •  Quantification capability (minimum quantifiable concentration);
435       •  Detection capability (minimum detectable concentration);
436       •  Applicable analyte  concentration range;
437       •  Method specificity; and
438       •  Method ruggedness.

439     How each of these characteristics affect the method selection process will be discussed in detail
440     in the subsequent paragraphs.

441     6.5.5.1  Method Uncertainty

442     From the directed planning process, the required method uncertainty at a stated analyte
443     concentration should have been determined for each analyte/matrix combination. The method
444     uncertainty requirement may be linked to the width of the gray region (Appendix C). MARLAP
445     recommends that the SOW include the  specifications for the action level and the required method
446     uncertainty for the analyte concentration at the action level for each analyte/matrix. For research
447     and baseline monitoring programs, the action level and gray region concepts may not be
448     applicable. However, for these applications, the project manager should establish a concentration
449     level of interest and a required method uncertainty at that level. The laboratory should ensure that
450     this method uncertainty requirement is clearly stated in the SOW.

451     The laboratory should select a method that will satisfy the method uncertainty requirement at the
452     action level or other  required analyte level. MARLAP uses the term "method uncertainty" to
453     refer to the predicted uncertainty of a result that would be measured if a method were applied to a
454     hypothetical laboratory sample with a specified analyte concentration. The uncertainty of each
455     input quantity (method parameter) that may contribute significantly to the total uncertainty


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456     should be evaluated. For some methods, the uncertainty of an input quantity may vary by analyst
457     or spectral unfolding software. Chapter 19 provides guidance on how to calculate the combined
458     standard uncertainty of the analyte concentration, and Section 19.6.12 shows how to predict the
459     uncertainty for a hypothetical measurement. For most basic methods, uncertainty values may be
460     included for the following input quantities (parameters):

461      •  Poisson counting statistics (net count rate);
462      •  Detector efficiency, if applicable;
463      •  Chemical yield (when applicable) or tracer yield;
464      •  Sample volume/weight;
465      •  Decay/ingrowth factor; and
466      •  Radiometric interference correction factor.

467     Typically, for low-level environmental remediation or surveillance activities, only those input
468     quantities having an uncertainty greater than one percent significantly contribute to the combined
469     standard uncertainty. Other than the radiometric interference correction factor and Poisson
470     counting uncertainties, most input quantity uncertainties normally do not vary as a function of
471     analyte concentration. At analyte levels near or below the detection limit, the Poisson counting
472     uncertainty may dominate the method's uncertainty. However, at the action level or above, the
473     Poisson counting uncertainty may not dominate.

474     When appropriate, the laboratory should determine the method uncertainty over the MQO analyte
475     concentration range (Section 6.5.5.3), including the action level or other specified analyte
476     concentration. The laboratory's method validation (Section 6.6) should demonstrate or show
477     through extrapolation or inference (e.g., from  a lower or higher range of concentrations) that this
478     method uncertainty requirement can be met at the action level or specified analyte concentration
479     value. Method validation documentation should be provided in the response to the SOW.

480     6.5.5.2  Quantification Capability

481     For certain projects or programs, the project planning team may develop an MQO for the
482     quantification capability of a method. The quantification capability, expressed as the minimum
483     quantifiable concentration (MQC), is the smallest concentration of the analyte that ensures a
484     result whose relative standard deviation is not greater than a specified value, usually 10 percent.
485     Chapter 19 provides additional information on the minimum quantifiable concentration.

486     MARLAP recommends that, when required, a laboratory analyze each sample to meet the MQC
487     requirement. For example, if the MQC requirement for 89Sr is 1.0 Bq/g  (with a 10 percent relative


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488     standard deviation), the laboratory should select a method that has sufficient chemical yield
489     (Chapter 19), beta detection efficiency, low background, sample (processing) turnaround time for
490     a given sample mass, and radioactive decay to achieve a nominal measurement uncertainty of 0.1
491     Bq/g when the 89Sr concentration is 1.0 Bq/g. The same forethought that a laboratory gives to
492     estimating a method's minimum detectable concentration (MDC) for an analyte should be given
493     to the MQC requirement. The laboratory should consider the uncertainties of all input quantities
494     (detector efficiency, chemical yields, interferences, etc.), including the Poisson counting
495     uncertainty when selecting a method. This is an important consideration, because for some
496     methods, the Poisson counting uncertainty at the MQC level may contribute only 50 percent of
497     the combined  standard uncertainty. Therefore, the laboratory may have to select a method that
498     will meet the MQC requirement for a variety of circumstances, including variations in matrix
499     constituents and chemical  yields, radionuclide and chemical interferences, and radioactive decay.
500     In addition, sufficient sample size for processing may be critical to achieving the MQC
501     specification.

502     During the method validation process, the ability of the method to meet the required MQC
503     specification should be tested. The method validation acceptance criteria presented in Section 6.6
504     have been formulated to evaluate the MQC requirement at the proper analyte concentration level,
505     i.e., action level  or other specified analyte concentration.

506     Since the laboratory is to report the analyte concentration value and its measurement uncertainty
507     for each sample, the project manager or data validator easily can  evaluate the reported data to
508     determine compliance with the MQC requirement. Some projects may send performance testing
509     (PT) material spiked at the MQC level as a more in-depth verification of the compliance with this
510     requirement.

511     6.5.5.3 Detection Capability

512     For certain projects or programs, the method selected and proposed by the laboratory should be
513     capable of meeting a required MDC for the analyte/matrix combination for each sample
514     analyzed. For  certain monitoring or research projects, the analyte MDC may be the important
515     MQO to be specified in the SOW. For such projects, the MDC specification may be based on the
516     analyte concentration of interest or the state-of-the-art capability  of the employed technology or
517     method. No matter what premise is used to set the value by the project planning team, the
518     definition of, or the equation used to calculate, the analyte MDC  should be provided in the SOW
519     (Chapter 19). Furthermore, the SOW should specify how to treat appropriate blanks or the
520     detector background when calculating the MDC. The laboratory should be aware that not all
521     agencies or organizations define or calculate the MDC in the same manner. It is important for the


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522     laboratory to check that the SOW clearly defines the analyte detection requirements. In most
523     cases, it would be prudent for the laboratory to use a method that has a lower analyte MDC than
524     the SOW required MDC.

525     In some situations, a radiochemical method may not be robust or specific enough to address
526     interferences from other radionuclides in the sample. The interferences may come from the
527     incomplete isolation of the analyte of interest resulting in the detection of the decay emissions
528     from these interfering nuclides. These interferences would increase the background of the
529     measurement for the analyte of interest and, thus, increase the uncertainty of the measurement
530     background. Consequently, an aprioriMDC, since it is calculated without prior sample
531     knowledge or inclusion of the interference uncertainties, would underestimate the actual
532     detection limit for the sample under analysis. Another example of such interferences or increase
533     in an analyte's background uncertainty can be cited when using gamma-ray spectrometry to
534     determine 144Ce in the presence of 137Cs. The gamma energy usually associated with the
535     identification and quantification of 144Ce is 133.5 keV. The gamma energy for 137Cs is 661.6 keV.
536     If a high concentration of 137Cs is present in the sample, the Compton scattering from the 661.6
537     keV into the 133.5 keV region may decrease the ability to detect 144Ce by one to two orders of
538     magnitude over an a priori calculation that uses a nominal non-sample specific background
539     uncertainty. Another example can be cited for alpha-spectrometry and the determination of
540     isotopic uranium. If some interfering metal is present in unexpected quantities and carries onto
541     the final  filter mount or electrodeposited plate, a substantial decrease in the peak resolution may
542     occur (resulting in an increased width of the alpha peak).  Depending on the severity of the
543     problem, there may be overlapping alpha peaks resulting in additional interference terms that
544     should be incorporated into the MDC equation. In order to avoid subsequent analyte detection
545     issues, it is important for the laboratory to inquire whether or not the project manager has
546     considered all the constituents (analytes and interferences) present in the sample when specifying
547     a detection limit for an analyte.

548     The laboratory should include documentation in the response to the SOW that the method
549     proposed can meet the analyte's MDC requirements for the method parameters  (e.g., sample size
550     processed, chemical yield, detector efficiency, counting times, decay/ingrowth correction factors,
551     etc.). When practicable, care should be given to ensure the blank or detector background
552     uncertainty includes contributions from possible anthropogenic and natural radionuclide
553     interferences. In addition, any proposed screening method should meet the  detection limit
554     requirement in the presence of other radionuclide interferences or natural background
555     radioactivity. When appropriate or required, the laboratory should test the method's capability of
556     meeting the required MDC using MVRMs that have analytes and interferences in the expected
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557     analyte concentration range. Upon request, the project manager should arrange to provide
558     MVRMs to the laboratory.

559     6.5.5.4 Applicable Analyte Concentration Range

560     The SOW should state the action level for the analyte and the expected analyte concentration
561     range. The proposed method should provide acceptable analytical results over the expected
562     analyte concentration range for the project. Acceptable analytical results used in this context
563     means consistent method precision (at a given analyte concentration) and without significant
564     bias. The applicable analyte concentration range may be three or four orders of magnitude.
565     However, most radioanalytical methods, with proper analyte isolation and interference-decon-
566     tamination steps, will have a linear relationship between the analytical result and the analyte
567     concentration. For certain environmental monitoring or research projects, the laboratory should
568     ensure that there are no instrument or analytical blank background problems. If the background is
569     not well-defined, there may be an inordinate number of false positive and false negative results.

570     In its response to the SOW, the laboratory should include method validation documentation that
571     demonstrates the method's capability over the expected range. The laboratory's method
572     validation (Section 6.6) should demonstrate or show through extrapolation or inference (e.g.,
573     from a different range of concentrations) that the method is capable of meeting the analyte
574     concentration range requirement.

575     6.5.5.5 Method Specificity

576     The proposed method should have the necessary specificity for the analyte/matrix combination.
577     Method specificity refers to the method's capability, through the necessary decontamination or
578     separation steps, to remove interferences or to isolate the analyte of interest from the sample over
579     the expected analyte concentration range. Method specificity is applicable to both stable and
580     radioactive constituents inherent in the sample. Certain matrices, such as soil and sediments,
581     typically require selective isolation of femtogram amounts of the analyte from milligrams to
582     gram quantities of matrix material. In these circumstances, the method requires both specificity
583     and ruggedness to handle variations in the sample constituents.

584     If other radionuclide interferences are known or expected to be present, the SOW should provide
585     a list of the radionuclides and their expected concentration ranges. This information enables the
586     laboratory to select and propose a method that has the necessary specificity to meet the MQOs.
587     As an alternative, the project manager may specify in the SOW the degree of decontamination a
588     method needs for the interferences present in the samples. If the laboratory is not provided this


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589     information, method specificity cannot be addressed properly. The laboratory should ensure that
590     related information on the matrix characteristics, radiometric or chemical interferences, and
591     chemical speciation is provided to properly select a method.

592     6.5.5.6MethodRuggedness

593     Ruggedness is the ability of the method to provide accurate analytical results over a range of
594     possible sample constituents, interferences, and analyte concentrations, as well as to tolerate
595     subtle variations in the application of the method by various chemists (EPA, 1998; APHA,
596     1989). Ruggedness is somewhat qualitative (Chapter 7). Therefore, the desirable parameters of a
597     rugged method are difficult to specify quantitatively. A ruggedness test usually is conducted by
598     systematically altering the critical variables (or quantities) associated with the method and
599     observing the magnitude of the associated changes in the analytical results. ASTM El 169
600     provides generic guidance on how to conduct method ruggedness tests under short-term, high-
601     precision conditions. In many cases, a rugged method may be developed over time (typically
602     when difficulty is experienced applying an existing method to variations in the sample matrix or
603     when two analysts have  difficulty achieving the same level of analytical quality or precision).

604     A laboratory may have several methods for an analyte/matrix combination. Samples from
605     different geographical locations or having different processes may have completely different
606     characteristics. Therefore, the laboratory should select a method that is rugged enough to meet
607     the APSs in the SOW. As indicated in Section 6.6, the prospective client may send site-specific
608     MVRM samples for the  method validation process or for PT samples (Chapter7).

609     6.5.5.7 Bias Considerations

610     As discussed earlier, the proposed method should provide acceptable analytical results over the
611     expected analyte concentration range for the project. Acceptable results used in this context
612     means consistent method precision (at a given analyte concentration) and without significant
613     bias. According to ASTM (E177, E1488, D2777, D4855), "bias of a measurement process is a
614     generic  concept related to a constant or  systematic difference between a set of test results from
615     the process and an accepted reference value of the property being measured," or "the difference
616     between a population mean of the measurements or test results and the accepted reference or true
617     value."  In contrast, ASTM (D2777) defines precision as "the degree of agreement of repeated
618     measurements of the same property, expressed in terms of dispersion of test results (measure-
619     ments) about the arithmetical mean result obtained by repetitive testing of a homogeneous
620     sample  under specified conditions." MARLAP considers bias to be a persistent difference of the
621     measured result from the true value of the quantity being measured, which does not vary if the


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622     measurement is repeated. Normally, bias cannot be determined from a single result or a few
623     results (unless the bias is large) because of the analytical uncertainty component in the measure-
624     ment. Bias may be expressed as the percent deviation from a "known" analyte concentration.
625     Note that the estimated bias, like any estimated value, has an uncertainty—it is not known
626     exactly.

627     If bias is detected in the method validation process or from other QA processes, the laboratory
628     should make every effort to eliminate it when practical. Implicitly, bias should be corrected
629     before using the method for routine sample processing. However, in some cases,  the bias may be
630     very small and not affect the overall data quality. The project manager should review the method
631     validation documentation and results from internal QC  and external PE programs obtained during
632     the laboratory review process  (Chapter 7) and determine if there is a bias and its possible impact
633     on data usability.

634     6.6   Method Validation

635     For the purposes of MARLAP, method validation is the demonstration that the radioanalytical
636     method selected by the laboratory for the analysis of a particular radionuclide in a given matrix is
637     capable of providing analytical results to meet the project's MQOs and any other requirements in
638     the APS. Without reliable analytical methods, all the efforts of the project may be jeopardized.
639     Financial resources, timeliness, and public perception and confidence are at risk,  should the data
640     later be called into question. Proof that the method used is applicable to the analyte and sample
641     matrix of concern is paramount for defensibility. The project manager should ensure the methods
642     used in the analyses of the material are technically sound and legally defensible.

643     The method selected and proposed by the laboratory must be based on sound scientific principles
644     and must be demonstrated to produce repeatable results under a variety of sample variations.
645     Each step of the method should have been evaluated and tested by a qualified expert (radio-
646     analytical specialist) in order to understand the limits of each step and the overall method in
647     terms of the MQOs. These steps may involve well-known and characterized sample digestion,
648     analyte purification and decontamination steps that use ion exchange, solvent extraction,
649     precipitation and/or oxidation /reduction applications. Method validation will independently test
650     the scientific basis  of the method selected for a given analyte and sample matrix.

651     A method validation protocol  should be a basic element in the quality system employed by a
652     laboratory. A proposed method for a specific analyte should be validated in response to the
653     requirements within a SOW. Demonstration of method performance to meet the MQOs prior to
654     processing project  samples is a critical part of the MARLAP process. As a result  of internal QC

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655     and external PE programs, most laboratories normally have documentation on the general or
656     overall performance of a method. As discussed later, this information, depending on many
657     aspects, may be sufficient in meeting the method validation criteria.

658     Methods obtained from the literature, from recognized industry standards (ASTM, ANSI, APHA)
659     or government method manuals may have been validated for certain general applications by the
660     developing or issuing laboratory. However,  other laboratories would have to validate the method
661     for specific project use.

662     6.6.1   Laboratory's Method Validation Protocol

663     During the discussion on method validation, certain terms are used. These include MVRM, QC,
664     and PT materials. QC samples and programs are related to those samples or processes that are
665     used to evaluate the quality of the analytical results for the fundamental purpose of directly
666     controlling the quality of the analytical process by  initiating control mechanisms. PT materials
667     are materials prepared for use in a PE program or for validating methods. MVRM refers to  site-
668     specific materials that have the same or similar chemical  and physical properties as the proposed
669     project samples. Although the MVRM is the most  appropriate material for testing a laboratory's
670     project-specific performance, or for validating a method for a particular project, its availability
671     may be limited depending on the project manager's ability to supply such material.

672     The laboratory's method validation protocol should include the evaluation of the method for
673     project specific MQOs for an analyte or generic quality performance criteria as well as other
674     generic parameters. With a properly designed  method validation protocol, important information
675     may be ascertained from the analytical results  generated by the method validation process.

676     The parameters that should be specified, evaluated, or may be ascertained from the analytical
677     results generated by the method validation process are listed below:

678      0 Defined Method Validation Level (Table 6.1)
679      <> APSs including MQOs  for each analyte/matrix
680          ° Chemical  or physical characteristics of analyte when appropriate
681          o Action level (if applicable)
682          ° Method uncertainty at a specific concentration
683          o MDC or MQC
684          ° Bias (if applicable)
685          o Applicable analyte concentration range including  zero analyte (blanks)
686          ° Other qualitative parameters to measure the degree of method ruggedness or specificity


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687      <> Defined matrix for testing, including chemical and physical characteristics that approximate
688         project samples
689      <> Selected project-specific or appropriate alternative matrix PT samples, including known
690         chemical or radionuclide interferences at appropriate levels
691      <> Defined sample preservation
692      0 Stated additional data testing criteria (such as acceptable chemical/radiotracer yield values)

693     In order to properly demonstrate that a method will meet project MQOs, the method should be
694     evaluated over a range of analyte concentrations. The analyte concentration range of the matrix
695     spikes (covering the testing levels) used for method validation should cover the expected analyte
696     concentration range for the project (Section 6.5.5.3), with the middle of the range set near the
697     action level. At the upper end of the range, the method validation samples should be analyzed to
698     have a Poisson counting uncertainty between 1 percent (ANSI N42.23) and 3 percent (1 sigma).
699     Keeping the Poisson uncertainty <3 percent (1 sigma) will ensure the observed precision, as
700     measured by multiple samples, is not dominated by the Poisson counting uncertainty. In addition,
701     anticipated or known chemical and radionuclide interferences should be added in the appropriate
702     "interference to analyte" activity or concentration ratio. Appropriate method blanks (also
703     containing interferences when practical) should be analyzed concurrently with the matrix spikes
704     to determine analyte  interferences  or biases near the detection limit.

705     The number of samples for the method validation process varies according to the method
706     validation level needed. As proposed in Table 6.1, the number of samples may vary from 6 to 21,
707     depending on the robustness of the method validation.

708     6.6.2   Tiered Approach to Validation

709     While MARLAP recommends that as each new project is implemented, the methods used in the
710     analysis of the associated samples undergo some level of validation, it is the project manager's
711     responsibility to assess the level of method validation necessary. Although the end result of
712     method validation is to ensure that the method selected meets the MQOs for an analyte/matrix,
713     the extent of the validation process depends  on whether the laboratory should elect to develop a
714     new method or whether there is an existing validated method available that can be adapted or
715     validated for another specific project need. Therefore, MARLAP recommends that a tiered
716     approach be taken for method validation. The recommended protocols to be considered for
717     existing methods are provided in the next four sections, requiring from least to most effort: no
718     additional validation, modification of a method for a similar matrix, new application of a method,
719     and newly developed or adapted methods. Table 6.1 consolidates recommended validation
720     requirements from various government agencies and consensus organizations. The suggested


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721      levels of validation are indicative of the modification required of the method. It should be noted
722      that the method validation requirements of Table 6.1 permit the laboratory to use internal QC, PE
723      program, or site-specific MVRM samples, or permit the project manager may provide PT, PE
724      program, or site-specific MVRM samples for the laboratory to use. Sometimes, a project
725      manager may provide PT samples as part of the qualifying process. In this case, the project
726      manager should ensure consistency with the method validation requirements of Table 6.1.
727
728
729
730
731
732
733

734
736
737

738
739


740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
                       TABLE 6.1 — Tiered method validation approach
Validation
Level
A
Without
Additional
Validation
B
C
D
ASTM D2777
E
ASTM D2777
F
EPA
Equivalency
G
ASTMD2777
H
ASTMD2777
(Involves the
two testing
protocols stated
to the right)
Application
Existing
Validated
Method
Similar Matrix
Similar Matrix/
New Application
New
Application
New Application
New
Application/
newly Developed
or Adapted
Method
Newly
Developed
or
Adapted Method
Sample
Type
-
Internal QC
External PE
Internal QC
External PE
MVRM
Samples
MVRM
Samples
MVRM
Samples for
both
protocols
Acceptance
Criteria*
Method previously
validated
(by one of Validation
Levels B though H)
Measured value within
±3 uMRof known value
Measured value within
±3 UMR of known value
Each measured value
<30% of known
at 5 times MDC
Levels
(Concentrations) * *
-
3
3
Replicates
-
3
7
Three to five groups of two
samples with concentrations
within 20% of each other
3
7
Three to five groups of two
samples with concentrations
within 20% of each other
Three to five groups of two
samples with concentrations
within 20% of each other
Three to five groups of two
samples with concentrations
within 20% of each other
bracketing 5 times the MDC
# of Analyses
-
9
21
6-10
6-10
21
6-10
6-10
6-10
* Assumes that each sample is counted to have a Poisson counting uncertainty of < 3% (sigma) when the analyte concentration is
near the action level or MQC. This criterion is applied to each analysis in the method validation, not to the mean of the analyses.
UMR is the required method uncertainty at the action level or required concentration.  UMR is an absolute value for concentrations
less than the action level and a relative (%) value for concentrations greater than the action level. In the absence of a specified
value, the default of ± 3 UMR acceptance criterion is: each measured value at the action level or other specified concentration must
be within ± 30% of known value. See references for ASTM D2777.
"Concentration levels should cover the expected analyte concentration range for a project including the action level
concentration. A set of three blanks (not considered a level) should be analyzed during the method validation process.
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759     The tiered approach to method validation outlined Table 6.1 was developed to give the project
760     manager flexibility in the method validation process according to the project requirements. The
761     degree of method validation increases from the lowest (Level A) to the highest (Level H). The
762     table's acceptance criteria for the validation process for a given project are based on the MQO for
763     the method uncertainty at the action level or other stated concentration. Each of the validation
764     levels evaluates the proposed method over the expected concentration range of the analytes and
765     interferences. The acceptance criterion of having each analytical result meet the ± 3  u^ of the
766     known value ensures a high degree of confidence that a method will meet the required method
767     uncertainty (MQO) at the action level or other specified concentration. (See Appendix C for the
768     definition of the method uncertainty at the action level or other stated concentration, %£.) In
769     addition to evaluating the method uncertainty, the method should be evaluated for bias.

770     During the method validation process, the laboratory should ensure that the observed precision
771     for the samples processed is consistent with the estimated individual sample measurement uncer-
772     tainty. An evaluation should be conducted for replicate sample analyses that have the same
773     approximate relative measurement uncertainties. Samples having analyte concentrations within a
774     narrow range of one another (ASTM D2777 Youden Pairs) may be considered when their
775     relative measurement uncertainties  are approximately the same. If the estimated measurement
776     uncertainty of a given sample is much smaller than the observed method precision for the
777     replicate samples, then the laboratory may not have properly estimated the uncertainty of one of
778     the input quantities (parameters) or has omitted an input quantity in the measurement uncertainty
779     (combined standard uncertainty).

780     6.6.2.1 Existing Methods Requiring No Additional Validation

781     For completeness, it is necessary to discuss the possibility that a previously validated method of
782     choice requires no additional validation (Level A of Table 6.1) for a specific project use. As
783     noted in the table, the method has undergone some level (Level B through H) of previous
784     validation.  It may be that the samples (matrix and analyte specific) associated with a new project
785     are sufficiently similar to past samples analyzed by the same laboratory that the project manager
786     feels additional validation is unwarranted. The decision to use Level A method validation should
787     be made with caution. While the sampling scheme may be a continuation, the analytical
788     processing capabilities at the laboratory may have changed sufficiently to  merit limited method
789     validation.  Without some level of method validation, the project manager has no assurance that
790     the analytical laboratory will perform to the same standards as an extension of the earlier work.
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791     6.6.2.2 Use of a Validated Method for Similar Matrices

792     When a previously validated method is to be used in the analysis of samples that are similar to
793     the matrix and analyte for which the method was developed, MARLAP recommends that
794     validation of the method be implemented according to Level B or C of Table 6.1. These levels
795     will provide a reasonable assurance to both the laboratory and the project manager that the
796     method will meet the required MQOs associated with the project. Level B may be used if the
797     laboratory has the capability to produce internal QC samples. When the laboratory does not have
798     the capability to produce internal QC samples, the Level C validation protocol should be used.
799     However, PE programs may not provide the necessary matrices needed for the Level C validation
800     protocol.

801     Since a method inherently includes initial sample preparation, projects that have severe
802     differences in analyte heterogeneity may require a moderate change in a radiochemical method's
803     initial sample treatment. A change in the method to address the increased heterogeneity of the
804     analyte distribution within the sample may require another method validation depending on the
805     robustness of the method and the degree of analyte heterogeneity.

806     6.6.2.3 New Application of a Validated Method

807     Methods that have been validated for one application normally require another validation for a
808     different application, such as a different sample matrix.  In addition, the MQOs may change from
809     one project to another or from one sample matrix to another. The validation process for an
810     existing validated method should be reviewed to ensure applicability of the new (which can be
811     more or less restrictive) measurement quality objectives. In most cases, applying an existing
812     method for one matrix to another matrix is not recommended without  another method validation.
813     MARLAP recommends, based on the extent of the modification and the difficulty of the matrix,
814     that Levels C-F of Table 6.1 be used to validate the performance of the modified method. The
815     following paragraphs and the next section provide information on whether a validated method
816     requires a slight modification or a complete revision.

817     Validation of an existing method for a different application depends on the extent of the
818     departure from the original method application, in terms of:

819      •  Dissimilarity of matrices;
820      •  Chemical speciation of the analyte or possible other chemical interference;
821      •  Analyte, chemical or radiometric interferences;
822      •  Complete solubilization of the analyte and sample matrix; and


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823      •  Degree of analyte or sample matrix heterogeneity.

824     When the chemical species of the analyte in a sample from a new project varies from the
825     chemical species for which the method was validated, then the method will have to be altered
826     and another validation performed. An example would be when a method had been developed to
827     extract iodide via ion exchange chromatography but the new application may have I2, iodate, or
828     iodide in the sample. Another example would be the initial development of a method for Pu in
829     soil generated from liquid effluents using acid dissolution and then trying to apply the same
830     method to high-fired plutonium oxide in soil. For these two examples, if the original methods
831     were to undergo the validation process for the new application, definite deficiencies and poor
832     results would become evident.  Portions of the original method would have to be modified to
833     address the chemical speciation problems. The modified method requires validation to ensure
834     that the measurement quality objectives for the new application can be met.

835     When additional analyte, chemical, or sample matrix interferences are known to exist for a new
836     application compared to the old method application, the previously validated method should
837     undergo another validation, depending on the degree of interference and the problems anticipa-
838     ted. For example, applying a method used for the analysis of an analyte in an environmental
839     matrix containing few interfering radionuclides would typically be inappropriate for the analysis
840     of process waste waters containing many interfering radionuclides at high concentrations. In
841     essence, the degree of decontamination (degree of interference removal) or analyte purification
842     (isolation of the analyte from other radionuclides) necessary for one application may be
843     completely inadequate or inappropriate for another application (an indication of method
844     specificity).

845     Another example would be the use of a method for soil analysis employing 234Th as a radiotracer
846     for chemical yield for the isotopic analysis  of thorium when the soil also has a high concentration
847     of uranium. 234Th is an inherent decay product of 238U and will exist in the sample as a natural
848     analyte, thus creating erroneous chemical yield factors. A third example would be the application
849     of a 90Sr method developed for freshwater to seawater samples for which the amount of chemical
850     interferences  and ambient Sr levels are extensive. For these three examples, conducting the
851     validation process for the original methods for the new applications would, depending on the
852     severity of the analyte and chemical interference, illustrate method deficiencies  and the inability
853     to meet measurement quality objectives.

854     Some matrices and analytes may be solubilized easily through acid dissolution or digestion. For
855     some applications, the analyte of interest may be solubilized from the sample matrix through an
856     acid extraction process. The applicability of such methods  should be carefully chosen and, most


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857     important, the method must be validated for each application. Definite problems and
858     misapplication can be the result of using an acid extraction process when a more robust complete
859     sample dissolution is necessary.

860     6.6.2.4 Newly Developed or Adapted Methods

861     MARLAP recommends that methods under development by the laboratory or adapted from the
862     literature that have not been previously validated for a project be validated according to Levels
863     F to H of Table 6.1. These levels provide the most comprehensive testing of method perfor-
864     mance. For low-level environmental surveillance applications, it may be advantageous to use the
865     second set of requirements of Level H (each measured value must be within ±30 percent of the
866     known value at 5 times the MDC) as part of the other validation levels as well. This requirement
867     will assess the method's ability to perform at the concentration ranges more commonly associated
868     with environmental samples. When process knowledge is available or the matrix under
869     consideration is unique or site-specific, it is best to validate the method using the matrix (e.g.,
870     MVRM) under consideration. This is extremely important for process/effluent waters versus
871     laboratory deionized water and for various heavy metal radionuclides in soils or sediments when
872     compared to spiked sand or commercial topsoil. For site-specific materials containing severe
873     chemical and radionuclides interferences, many methods have been unable to properly address
874     the magnitude of interferences.

875     6.6.4  Method Validation Documentation

876     Method validation, depending on the required level of validation, can be accomplished by the
877     project manager sending PT samples to the laboratory or by the laboratory using internal or
878     external PT/QC samples. When PT  samples are sent to a laboratory to evaluate or validate the
879     laboratory's method and capabilities, the appropriate technical representative should retain all
880     records dealing with applicable method validation protocols (Section 6.6.3), PT sample
881     preparation certification, level of validation (from Table 6.1), results, and evaluations. The
882     laboratory should provide the necessary documentation to the project manager for these PT
883     samples as required by the SOW. The laboratory should request feedback from the project
884     manager as to the method performance. This information, along with the sample analytical
885     results documentation, should be retained by the laboratory for future method validation
886     documentation.

887     When the laboratory conducts its  own method validation, all records, laboratory workbooks, and
888     matrix spike data used to validate an analytical method should be retained on file and retrievable
889     for a specified length of time after the method has been discontinued.


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890     6.7    Analyst Qualifications and Demonstrated Proficiency

891     The required level of qualification of an analyst is commensurate with the degree of difficulty
892     and sophistication of the method in use. The selection of the analyst for the method application is
893     typically determined initially on experience, education and proven proficiency in similar
894     methods. Basic guidance for the minimum education and experience for radioassay laboratory
895     technicians and analysts has been provided in Appendix E and ANSI N42.23.

896     For radiochemical methods, there may be several analysts involved. At most major laboratories,
897     different individuals may be involved in the sample preparation, radiochemistry, and radiation
898     detection aspects of the method. In these cases, the entire staff involved in the method should
899     undergo method proficiency tests to demonstrate their ability to meet quality requirements and
900     performance goals. The staff involved in the initial validation of an acceptable method would be
901     considered proficient in their particular role in the method application and the results of their
902     performance should be documented in their training records.

903     Successful proficiency is established when the performance of the analyst or staff meet
904     predefined quality requirements defined in the laboratory's quality system or a SOW, as well as
905     processing goals. Parameters involved in operational processing goals are typically turnaround
906     time, chemical yields, frequency of re-analyses (percent failure rate), and frequency of errors.

907     The continued demonstrated analyst proficiency in the method is usually measured through the
908     acceptable performance in internal QC  and external PE programs associated with routine sample
909     processing.

910     6.8    Method Control

911     Method control is an inherent element of a laboratory's quality system. Simply stated, method
912     control is the ongoing process used to ensure that a validated method continues to meet the
913     expected requirements as the method is routinely used. Method control is synonymous with
914     process control in most quality  systems. For a laboratory operation, method control can be
915     achieved by the application of the following:

916      • Controlled method manual (latest revision and signature sign-off);

917      • NIST traceable calibration standards and the conduct of an instrument QC program that
918        properly evaluates the variable parameters on an appropriate frequency;
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919      • Radiotracers or chemical yields for each sample and the evaluation of the measured chemical
920        yield values to expected ranges;

921      • Internal QC and external PT samples to determine deviations from expected quality
922        performance ranges;

923      • Standard operating procedures for troubleshooting "out of control" situations; and

924      • Problem reporting, corrective action, and quality improvement process.

925     The above method control elements are typically addressed in the quality manual of the
926     laboratory or the project plan document for the project under consideration. Refer to Chapter 18
927     for additional information.

928     6.9    Continued Performance Assessment

929     The assessment of a laboratory's continued performance is covered in detail in Chapter 7.
930     However, it is important to briefly discuss certain aspects of evaluating a method's continued
931     performance from a laboratory's perspective.
932
933     In order to properly perform statistical analyses or compliance interpretation of the analytical data
934     produced from an analytical method, it is assumed that data quality does not vary  significantly.
935     Therefore, the user of the  data expects that the overall data quality will not change throughout the
936     program or project. From  a laboratory management perspective, a performance indicator system
937     should be in place that assesses and provides feedback on the quality of the routine processing.
938     The most useful and cost-effective means of assessing a method's performance is through the
939     implementation of internal QC or external performance evaluation programs or both. Of course,
940     it can be argued that method assessment through a QC or PE program evaluates the combined
941     performance of the method  and the analyst. However, statistical and inferential interpretation of
942     the QC/PE data can provide insight into whether the method is failing or whether  an analyst is
943     underperforming. Chapters 7 and 18 and Appendix C provides guidance on quality control
944     programs and the use of the internal laboratory QC or external PE data to assess the laboratory's
945     performance in  meeting performance criteria.

946     The laboratory management should use the internal QC program to detect and address
947     radioanalytical issues before the client does. Many SOWs require the use of internal QC samples
948     for every batch  of project  samples (Chapter 18). In effect, the client is essentially setting the level
949     of internal quality control  and the frequency of method performance evaluation. It should be

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950     recognized that an internal QC program evaluates method performance related to the initial
951     calibrations or internal "known values." An external NIST-traceable PE program will explain
952     method biases relative to the national standard or to the agency's PE program.

953     Some users of laboratory services have developed "monitoring" laboratory programs (ANSI
954     N42.23). For these programs, the user engages a recognized independent monitoring laboratory
955     to intersperse double- and single-blind external PT materials into batches of normal samples
956     submitted to a laboratory. The complexity and frequency of the monitoring laboratory PT
957     samples vary among programs, projects, and Federal and state agencies. An external double-blind
958     PE program conducted by a monitoring laboratory using site-specific matrices probably provides
959     the most realistic estimate of the method's or laboratory's true performance. When the
960     monitoring laboratory is traceable to NIST, either directly or through a NIST reference laboratory
961     (ANSI N42.23), the monitoring laboratory program will provide an estimate of any method bias
962     as related to the national standard.

963     Method performance can also be determined, although on a less frequent basis, through the
964     laboratory's participation in the various PE programs.  For a laboratory providing services to
965     government agencies, the participation in such programs is typically a requirement. The PE
966     programs commonly send out non site-specific PT materials  on a quarterly or semiannual basis.

967     The laboratory's performance in certain PE program is public knowledge. Such information is
968     useful to project managers in selecting a laboratory during the laboratory selection  and qualifying
969     processes. Similar to the monitoring laboratory, when  the laboratory conducting the PE program
970     is traceable to NIST, either directly or through a NIST reference laboratory (ANSI N42.23), the
971     PE program may provide an estimate of the bias as related to the national standard  as well as the
972     precision of the method, depending on the distribution of replicate samples.

973     Some projects require that all analytical results received from a laboratory undergo a data
974     verification and validation process. Chapter 8 provides more detail on these processes. When
975     properly conducted, certain aspects and parameters of the method can be assessed during the data
976     verification and validation process.

977     Internal and external audits/assessments are also key elements in a laboratory's quality system to
978     assess the continuing performance of a method (Chapter 7). The level and frequency of the audits
979     and assessments typically vary according to the magnitude and importance of the project and on
980     the performance of the laboratory. Another quality system element that is very effective is a self-
981     assessment program. A functioning and effective self-assessment program may identify
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 982
 983


 984

 985
 986
 987
 988
 989

 990
 991
 992
 993
 994
 995
 996

 997
 998
 999
1000


1001

1002

1003

1004
1005

1006
1007
1008
weaknesses or performance issues more readily and timely than formal internal and external
audits.

6.10  Documentation To Be Sent to the Project Manager

The documentation related to the life cycle of a method application is essentially the information
gathered during the use of the method. A formal method documentation program is unnecessary
since the information should be part of the quality system documentation. Documented
information available from the quality system, related to a method's development, validation, and
control, include the following:

 • Method validation protocol and results;
 • Analyst training and proficiency tests;
 • Method manual control program;
 • Instrument calibration and QC results;
 • Internal QC and external PT sample results;
 • Internal and external assessments; and
 • Corrective actions.

Data verification and validation information should be kept available and retained for those
projects requiring such processes. In addition to QA documentation, the analytical results, either
in hard copy or electronic form, should be available from the laboratory for a specified length of
time after the completion of a project.
                            Summary of Recommendations

    MARLAP recommends the performance-based approach for method selection.

    MARLAP recommends that only methods validated for a project's application be used.

    MARLAP recommends that a SOW containing the MQOs and analytical process
    requirements be provided to the laboratory.

    MARLAP recommends that the SOW include the specifications for the action level and
    the required method uncertainty for the analyte concentration at the action level for each
    analyte/matrix.
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                                                  Selection and Application of an Analytical Method
1009
1010
1011
MARLAP recommends that as each new project is implemented, the methods used in the
analysis of the associated samples undergo some level of validation.

MARLAP recommends that a tiered approach (Table 6.1) be taken for method validation.
1012      6.11  References

1013      American National Standards Institute (ANSI) N42.23. Measurement and Associated
1014         Instrumentation Quality Assurance for Radioassay Laboratories. 1996.

1015      American Public Health Association (APHA) 1989. Standard Methods for the Examination of
1016         Water and Waste Water. Washington, DC.

1017      American Society for Testing and Materials (ASTM) D 2777. Standard Practice for
1018         Determination of Precision and Bias of Applicable Test Methods of Committee D-19 on
1019         Water.

1020      American Society for Testing and Materials (ASTM) D 4855. Standard Practice for Comparing
1021         Methods.

1022      American Society for Testing and Materials (ASTM) E 177. Standard Practice for Use of the
1023         Terms Precision and Bias in ASTM Test Methods.

1024      American Society for Testing and Materials (ASTM) E 1169. Standard Guide for Conducting
1025         Ruggedness Tests.

1026      American Society for Testing and Materials (ASTM) E 1488. Standard Guide for Statistical
1027         Procedures to Use in Developing and Applying ASTM Test Methods.

1028      U.S. Environmental Protection Agency (EPA). 1980. Prescribed Procedures for Measurement of
1029         Radioactivity in Drinking Water. Environmental Monitoring and Support Laboratory,
1030         Cincinnati, OH.  EPA 600-4-80-032, August.

1031      U.S. Environmental Protection Agency (EPA). 1998. Guidance for Quality Assurance Project
1032         Plans EPA QA/G-5. EPA 600-R-98-018, February.
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           7 EVALUATING METHODS AND LABORATORIES
 2      7.1 Introduction

 3      This chapter provides guidance for the initial and ongoing evaluation of radioanalytical labora-
 4      tories and methods proposed by laboratories. Appendix E, Contracting Laboratory Services.,
 5      provides additional guidance on the initial laboratory evaluation. More details about evaluating
 6      and overseeing a laboratory's performance can be found in ASTM El691 and ASTM E548.

 7      The performance-based approach to method selection allows a laboratory the freedom to propose
 8      one or several methods for a specific analyte/matrix combination that will meet the needs of the
 9      Analytical Protocol Specifications (APSs) and measurement quality objectives (MQOs)
10      delineated in the Statement of Work (SOW). However, the laboratory should demonstrate,
11      through a method validation process, that the method is capable of producing analytical results of
12      quality that meet the needs of the SOW (Chapter 5). Guidance and recommendations on the
13      selection of an analytical method based on the performance-based approach were presented in
14      Chapter 6. Section 7.2 of this chapter provides guidance on how to evaluate the methods
15      proposed by a laboratory. Section 7.3 provides guidance on the initial evaluation of a laboratory,
16      and Section 7.4 discusses the  continual evaluation of the quantitative measures of quality and
17      operational aspects of the laboratory once sample processing has commenced.

18      Method applicability and performance compliance should be demonstrated prior to the initiation
19      of the sample analyses, as well as during the project period. A defined logical process for demon-
20      strating and documenting that the analytical  method selected meets the project's data needs and
21      requirements may involve, for example, a review of the method validation documentation, an
22      evaluation of past performance data from other proj ects (if available), the analysis of external
23      performance evaluation (PE) program results, the analysis of matrix-specific standard reference
24      materials (or method validation reference materials) sent during the initial work period and
25      throughout the project, and the final evaluation of the protocol's performance during the data
26      verification and validation process. Chapter  8, Radiochemical Data Verification and Validation.,
27      covers the final evaluation of the protocol's performance.

28      In addition to the evaluation of the analytical methods, the capability of the laboratory to meet all
29      SOW requirements needs to be reviewed and evaluated. Supporting information, such as method
30      validation documentation, safety manuals, licenses and certificates, and quality manual are typi-
31      cally submitted with the response to the Request for Proposals (RFP). A generic evaluation of the
32      laboratory operation may be conducted during the initial laboratory audit or assessment. This
33      may be an initial onsite audit. This first evaluation covers those generic SOW requirements
34      dealing with the laboratory's capability and operation,  including verification of adequate

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35      facilities, instrumentation, and staffing and staff training and qualifications. Following the first
36      audit, emphasis should be on ensuring the laboratory continues to meet the APSs through a
37      continuous or ongoing evaluation effort.

38      7.2   Evaluation of Proposed Analytical Methods

39      A laboratory may submit several methods for a particular APS contained in the SOW, but each
40      method should be evaluated separately and, if appropriate, approved by the project manager or
41      designee. The method should be evaluated to be consistent with the overall analytical process
42      that includes the proposed field sampling and preservation protocols (Chapter 1). The project
43      manager may delegate the method review process to a technical evaluation committee (TEC) that
44      has a radioanalytical specialist. MARLAP recommends that a radioanalytical specialist review
45      the methods for technical adequacy. The acceptance, especially of a new method, may be the
46      most critical aspect of the performance-based approach for method selection. Acceptance of the
47      method requires the project manager to verify that the method is scientifically sound.

48      Each step of the method should be evaluated by a radioanalytical specialist in order to understand
49      how the results are derived. These steps may involve sample digestion, analyte purification and
50      decontamination steps that use ion exchange, solvent  extraction, precipitation or oxidation/
51      reduction applications. Once these steps have been reviewed, and the method evaluation data
52      (e.g., from method validation documentation or various performance evaluation results) confirm
53      that the proposed method is acceptable, the project manager should have the confidence
54      necessary to endorse and verify the use of the method in the analysis of the routine samples.

55      As discussed in Chapter 6, the laboratory should provide method validation and analytical data
56      that demonstrates method performance. The data should show conclusively that the proposed
57      method meets the requirements as defined by the APSs. If method performance is questionable,
58      additional data may be required. For such cases, the project manager may decide to send per-
59      formance testing (PT) materials to the laboratory in order to evaluate or validate the method. The
60      preparation of the PT material used to evaluate the method should be based on sound scientific
61      principles and representative of the expected sample matrix (see Chapter 6 on method validation
62      options using site-specific materials). If there is sufficient reason to believe that the PT material
63      is an adequate substitute for the sample matrix and that the laboratory will follow the same
64      method, then the need to justify each step in the method may be drastically reduced.

65      7.2.1  Documentation of Required Method Performance

66      Certain documentation submitted by the laboratory with the proposed methods, as well as

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67      available external information on the laboratory's analytical performance, should be reviewed
68      and evaluated by the radioanalytical specialist. Table 7.1 outlines where such information can be
69      typically found by the TEC. This section will discuss various information categories that may be
70      available during the method evaluation process.

71      7.2.1.1 Method Validation Documentation

72      Chapter 6 outlines the various method validation options that can be specified by the project
73      manager. In the MARLAP process, the method validation requirements will be contained in the
74      SOW. The laboratory must submit the necessary method validation documentation consistent
75      with the SOW specification. The laboratory may choose to validate a method to a higher degree
76      of validation or to submit method validation documentation for a higher degree of validation than
77      that specified by the  SOW. The radioanalytical specialist or project manager should review the
78      documentation to ensure that validation criteria for the number of analyte concentration levels
79      and replicates meet or exceed the required validation criteria (Chapter 6,  Table 6.1). Although
80      not specified in the method validation protocol, some laboratories may include chemical and
81      analytical interferences in their method validation plan to gain a perspective on the method's
82      specificity and ruggedness. However, it should be noted that the graded approach to method
83      validation presented  in Chapter 6 does inherently increase the degree of ruggedness in terms of
84      having the method address site-specific materials which may include chemical and radionuclide
85      interferences.

86      In addition to reviewing the documentation for compliance with the method validation protocol,
87      the results of the method validation process  should be evaluated to determine if the project
88      specific MQOs will be met. The method validation may or may not have been specifically
89      conducted for the project at hand. When the method has been validated (Chapter 6, Section 6.6)
90      to the  SOW specifications (validation level and MQOs), then evaluation  of the documentation
91      can be straight forward. If the method has been previously validated for the MQOs of other
92      projects, then the laboratory should provide  a justification and calculations to show that the
93      method validation results will meet the MQOs for the new project. The TEC should verify these
94      calculations and review the assumptions and justifications for reasonableness and technical
95      correctness.

96      7.2.1.2 Internal Quality Control or External PE Program Reports

97      The documentation of internal QC and external PE program results should be reviewed relative
98      to the  MQOs. Method uncertainty and internal biases can be estimated from the information
99      available in the laboratory's internal quality control reports, summaries of batch QC results that


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                                                             Evaluating Methods and Laboratories
100     may be submitted with the RFP response and external PE program reports. The TEC should
101     review these documents and, when possible, estimate the method uncertainty and bias for various
102     analyte concentration levels. However, it is imperative that no confusion exists in terms of what
103     method produced the results: the proposed method or another method available to the laboratory.
104     This is especially important when reviewing external PE program results. It should also be noted
105     that although a laboratory may meet performance acceptance criteria for an external PE program,
106     this fact may have no bearing on whether the method will meet the MQOs of the SOW.

107     Review of the internal batch QC data can provide additional information on typical sample
108     analysis times and rates of blank contamination and sample reanalysis. This information is
109     important when comparing methods (from the same or between laboratories) in terms of APS
110     characteristics. The frequency of blank contamination would be very important to national char-
111     acterization studies (groundwater or soil analyses) for the determination of ambient analyte
112     levels. Method evaluation for these projects may weight the blank contamination rate more
113     heavily than other SOW parameters. The rate of sample re-analysis would be important to
114     projects having pending operations that are conducted based on a short sample processing turn-
115     around time (TAT). In some site remediation projects, the  contractor may remain onsite pending
116     analytical results. A delay in reporting data or not meeting a TAT due to sample re-analysis may
117     be costly. Projects of this nature may weight TAT and low sample re-analyses more heavily than
118     other SOW parameters.

119     7.2.1.4 Method Experience, Previous Projects, and Clients

120     When permitted by former clients, the laboratory  may submit information relative to the previous
121     or ongoing clients and projects for which the proposed method has been used. The TEC should
122     verify with the laboratory's clients that the laboratory has previous experience using the method.
123     When available and allowed, the information should also include the analyte(s) and interferences
124     and their applicable concentration range, matrix type, and project size in terms of the number of
125     samples per week or other time periods.  From this information, the TEC can evaluate whether or
126     not to contact the laboratory's client for further information on the operational adequacy of the
127     method. The client may offer some information on the quality of the results based on their
128     external single- or double-blind QC program, percent completion of reports,  TAT, and sample re-
129     analysis frequency. The sharing of laboratory assessment reports may be advantageous when
130     reviewing the performance of the laboratory during its employment of the method.

131     7.2.1.5 Internal and External Quality Assurance Assessments

132     When available, internal and external quality assurance assessment reports should be evaluated to


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133      determine the adequacy of the method performance based on previous projects. Problems with
134      the conduct of the method due to procedural and technical issues may be readily evident. These
135      issues may include an ineffective corrective action program creating delayed remedies to
136      problems, insufficient understanding of the method, inadequate training of staff, internal and
137      project-specific QC issues, and higher-than-expected failure rates for sample TATs and re-
138      analyses. Information in these reports may disclose problems with a particular method that are
139      not common to another proposed method. As such, the TEC may give one method a higher
140      weighting factor than another method.

141      7.2.2  Performance Requirements of the SOW—Analytical Protocol Specifications

142      Under the performance-based approach to method selection, a laboratory will propose one or
143      several analytical methods that can meet the stated APSs and MQOs in the SOW for a given
144      analyte and matrix combination. Chapters 3, 5, and 6 discuss the APSs and MQOs in detail in
145      terms of their basic description, their inclusion in a SOW, and as key considerations for
146      identifying existing validated methods or developing new methods. The purpose of this section is
147      to provide guidance on what available information should be evaluated in order to approve the
148      various proposed methods.

149      The following subsections cover key aspects of the SOW that should be addressed during the
150      method evaluation and  approval process.

151      7.2.2.1 Matrix and Analyte Identification

152      The TEC should review the method(s) proposed by the laboratory to determine if the method
153      under evaluation is applicable for the analyte/matrix combination specified in the SOW. In some
154      cases, several methods may be proposed, including gross screening methods and specific
155      radionuclide or isotopic methods having high specificity and ruggedness (Section 6.5.1.1 has
156      additional guidance). Each method should be evaluated on its own application and merit. When
157      methods are proposed by the laboratory that use alternative nuclides (such as decay products) to
158      determine the analyte of interest, the TEC should carefully review the objective or summary of
159      the method to determine if the proposed method is truly applicable for the analyte of interest
160      given the radiological holding time and MQOs (i.e., can it properly quantify the analyte of
161      interest through decay progeny measurements?). For gross screening techniques, the TEC should
162      evaluate the analyte's decay scheme to determine the underlying gross radiation category (beta,
163      alpha, X-ray, or gamma-ray emitting) and the applicability of the proposed method's radiation
164      detection methodology.
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165     Each proposed method should be evaluated to determine if the method can analyze the sample
166     matrix identified in the SOW. A method validated for water cannot be applied to soil samples
167     without modification and validation (Section 6.5). The planning team should have made—
168     through historical process knowledge, previous matrix characterization studies or common
169     experience—a determination on the uniqueness of the site-specific matrices compared to typical
170     matrices and provided guidance in the SOW as to the level of method validation. In addition, if
171     the radioanalytical specialist of the  project planing team is concerned that the physiochemical
172     form of the analyte or the sample matrix substrate may present special problems to the radio-
173     analytical process, a detailed description of the analyte and matrix should have been included in
174     the SOW. Chapters 12 and 13 discuss possible sample matrix problems and Section 6.5 provides
175     guidance on the  need for method validation. The radioanalytical specialist should carefully
176     review the summary of the method  to determine if the proposed method is applicable for the
177     sample matrix.

178     At this point, if it is determined that the proposed method(s) is not applicable and cannot meet
179     the SOW specifications, there is no need to continue the method evaluation process.

180     7.2.2.2 Process Knowledge

181     The radioanalytical specialist should review the process knowledge information and determine if
182     the proposed method is capable of addressing these issues by virtue of its specificity, ruggedness
183     and applicability. Discussions on method specificity and ruggedness  may be found on in
184     subsections on pages 7-13 and 7-15, respectively.

185     As discussed in  Section 6.5.2 and above, process knowledge is extremely important for identify-
186     ing potential  radioanalytical problems on some projects. Historical information or process
187     knowledge may  identify chemical and radionuclide interferences, expected analyte and inter-
188     fering radionuclide concentration ranges, sample analyte heterogeneity issues, and the physio-
189     chemical form of the analyte, and the sample matrix substrate. In some special cases, it may be
190     necessary to determine if the radiological holding time will be an issue if the laboratory must
191     analyze an alternative nuclide to determine supported and unsupported radionuclides (decay
192     progeny nuclides) in the matrix.

193     7.2.2.3 Radiological Holding and Turnaround Times

194     The radioanalytical specialist should review the proposed method in  light of the radiological
195     holding time, analyte's half-life and typical sample delivery options and determine if the method
196     is capable of meeting the MQOs in a reasonable counting period given the typical method param-


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197      eters (such as sample weight processed, chemical yields, radiation detection efficiency, branching
198      ratio and background, ingrowth periods for decay progeny analysis, etc.). Radiological holding
199      time is defined as the time between the sample collection and the end of the sample analysis (end
200      of final measurement), while sample processing TAT refers to the time between sample receipt at
201      the laboratory and the issuance of an analytical report. The physical (analyte's half-life) and
202      chemical (stability or preservation concerns) characteristics of the analyte, as well as biological
203      degradation for some matrices, usually will dictate the radiological holding time. Project-specific
204      schedules and practicalities related to project and laboratory processing capacities normally enter
205      into establishing TATs. If the radiological holding time appears to be a critical issue, then the
206      laboratory should submit information on the typical batch size being processed by the method.
207      This information is needed in the method evaluation and review process. Without special
208      problems (e.g., inadvertent delay of sample delivery), the laboratory should be able to meet the
209      MQOs with a good margin of error for the maj ority of the samples processed. For very short-
210      lived analytes, too large a batch size may result in the last samples in the batch having difficulty
211      in meeting the radiological holding time. For short-lived analytes, counting the sample (or final
212      processing products) longer typically is not practical because the analyte is decaying too rapidly
213      to make any gain counting the sample  longer.

214      In some cases, the laboratory may want to propose two methods for a short-lived analyte: one for
215      normal delivery and processing schedules and another method for situations when lower detec-
216      tion limits are needed. An example of such a situation is the analysis of 131I in environmental
217      media. A method with adequate detection limits for reasonable radiological holding times is
218      gamma spectrometry. Another method that can be applied for lower detection limits or longer
219      radiological holding times is radiochemical separation followed by beta-gamma coincidence
220      counting.

221      Certain projects may be concerned with the chemical speciation of the analyte in the sample. For
222      these projects, the  radiological holding time should have been specified to ensure that the chem-
223      ical species are not altered prior to processing. The project normally should specify chemical
224      preservation specifications applicable  at the time  of sample collection.

225      In the case of biological media,  sample deterioration (Chapter 10) may become a problem, and
226      biological preservatives should be added to the sample to retard degradation. However, the
227      radiological holding time should be  specified to limit problems with sample degradation. The
228      radioanalytical specialist should evaluate the method in light of the foregoing information and
229      determine its adequacy to meet the radiological holding time and the pertinent MQOs

230      A laboratory's sample (processing) TAT for a method typically is not related to the method's


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231      technical basis unless the radiological holding time and the TAT are nearly equal for a short-
232      lived analyte. However, sufficient time should be available between the completion of sample
233      analysis and the delivery of the analytical report. Meeting the radiological holding time but
234      failure to meet the TAT will not affect the quality of the analytical results but may place a
235      hardship on the project to meet schedules. The TEC should review the proposed method, the
236      radiological holding time and the TAT to determine if the method can process the samples in a
237      reasonable time period to meet the TAT. The sample delivery rate, sample batch size, level of
238      data automation and the laboratory's existing sample processing capacity will affect the
239      laboratory's ability to meet the TAT requirement.

240      7.2.2.4 Unique Processing Specifications

241      The TEC should review the proposed methods for compliance or applicability to unique sample
242      processing specifications stated in the SOW. Chapter 6 provides a limited discussion on what a
243      project may identify as unique or special sample process specifications. Examples may include
244      chemical speciation, analyte depth profiles, analyte particle size distribution, analyte hetero-
245      geneity within the sample, wet-to-dry analyte concentration ratios in biologicals, and possible
246      scaling factors between radionuclides in the sample. In  some cases, the proposed method(s) for
247      the analyte(s) may have to be evaluated with respect to  all analytes or other sample preparation
248      specifications in order to determine method applicability and adequacy.

249      7.2.2.5 Measurement Quality Objectives

250      Method  performance characteristics (Method Uncertainty, Quantification Capability, Detection
251      Capability, Applicable Analyte Concentration Range, Method Specificity, and Method
252      Ruggedness) will be discussed in the following subsections. For a particular project, MQOs
253      normally will be developed for several (but not all) of the performance characteristics discussed
254      below.

255      METHOD UNCERTAINTY

256      The SOW should specify the required method uncertainty at a stated analyte concentration (or
257      activity level) for each sample matrix and the level of method validation (Section 6.6) needed to
258      qualify the method at the stated analyte concentration.

259      MARLAP uses the term "method uncertainty" to refer to the predicted uncertainty of a result that
260      would be measured if a method were applied to a hypothetical laboratory sample with a specified
261      analyte concentration. As presented in Chapter 6 and formulated in Chapter 19, the method


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262      uncertainty of the analyte concentration for a given method is determined by mathematically
263      combining the standard uncertainties of the many input quantities (parameters), involved in the
264      entire radioanalytical process. This will involve making some assumptions and normally involve
265      using typical or worst case values for a conservative estimate of the method uncertainty. Some of
266      these input quantities, and thus the method uncertainty, vary according to analyte level or concen-
267      tration in the final measured product; others do not. In some cases, the magnitude of the method
268      uncertainty for an analyte may increase in proportion to the magnitude (concentration/activity) of
269      any interfering radionuclide present in the final measurement product. Therefore, it is imperative
270      that the TEC evaluate the laboratory's submitted documentation relative to this requirement,
271      especially the information provided on method specificity, given the historical or expected inter-
272      fering nuclides and the needed decontamination factors (chemical separation factors) to render a
273      good measurement for the analyte of interest.
274
275      In evaluating the documentation relevant to meeting the method uncertainty requirement, it is
276      important to determine if the method validation requirements stated in the SOW have been met.
277      The TEC should review the submitted method validation documentation and verify that the
278      method's performance meets the requirements of Table 6.1 (Chapter 6) for the specified valida-
279      tion level. It is important that the laboratory submit definitive documentation of method
280      validation compliance for the method uncertainty requirement.

281      The method performance documentation may include documentation or data from method
282      validation, internal or external (organization sending QC samples) QC data, external PE program
283      data,  and results of pre-qualifying laboratories by sample analyses. By evaluating the actual QC
284      and PE program performance data, it can be determined if the quoted measurement uncertainty
285      for a  reported QC sample result (calculated by the laboratory) truly reflects the method uncer-
286      tainty under routine processing of samples. The required method uncertainty can be viewed as a
287      target value for the overall average measurement uncertainty for the samples at a specified
288      analyte concentration. It is important that the precision, as calculated from repeated measure-
289      ments, is consistent with the laboratory's stated measurement uncertainty for a given sample
290      result whose analyte concentration is near the specified concentration. If the quoted measurement
291      uncertainty of a  QC or test measurement is quoted to be ± 10 percent and QC or PE program data
292      indicates a data set standard deviation of ± 20 percent, then the laboratory may not have
293      identified all possible uncertainty components or may have underestimated the magnitude of a
294      component.

295      QUANTIFICATION CAPABILITY

296      A requirement for the quantification capability of a method and the required method validation


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297      criteria may be specified in a SOW. The quantification capability, expressed as the minimum
298      quantifiable concentration (MQC), is the smallest concentration of the analyte that ensures a
299      result whose relative standard deviation is not greater than a specified value, usually 10 percent.

300      The project manager or TEC should review available documentation on the method to determine
301      if the laboratory can meet the method quantification requirement. Method validation documen-
302      tation sent by the laboratory should demonstrate explicitly, or by extrapolation, that the method,
303      using certain input quantities and their uncertainties, can meet the quantification requirement.
304      The method validation acceptance  criteria presented in Section 6.6 have been formulated to eval-
305      uate the MQC requirement at the proper analyte concentration level, i.e., action level or other
306      specified analyte concentration.

307      Some projects may send performance testing material spiked at the MQC level as a more in-
308      depth verification of the compliance with this requirement. Laboratories may also submit docu-
309      mentation for internal QC or external PE program results that cover the MQC value. The TEC
310      should evaluate the reported results to determine if the MQC requirement can be met.

311      DETECTION CAPABILITY

312      A radiochemical method's detection capability for an analyte is usually expressed in terms of
313      minimum detectable concentration (MDC) or activity (MDA). Chapter 19 provides the definition
314      and mathematical equations for the MDC1 and MDA. A MDC requirement for each analyte/
315      matrix combination may be stated in a SOW. Any proposed  method should document the basis
316      and equation for calculating the MDC. The  supporting documentation on the method should
317      contain the input quantity values that may be entered into the MDC equation to calculate the
318      detection capability under a variety of assumptions. The TEC should evaluate the assumptions
319      and parameter values for reasonableness and practicality. This evaluation is especially important
320      for recently validated methods that have a limited routine processing history. It is recommended
321      that the TEC perform an independent calculation of the method's MDC using laboratory-stated
322      typical or sample-specific parameters.

323      When the proposed method has been validated recently or previously used on similar projects,
324      sufficient data should exist that either are directly related to testing the method's detection capa-
325      bility or can be used to estimate the method's detection capability. Any data submitted that
326      document direct testing of the method's detection capability should be reviewed for appropri-
327      ateness or applicability, reasonableness, and accuracy. If method detection testing is performed, it
         'The MDC should not be confused with the concept of the critical value (Chapter 19).

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328      normally will be for one analyte concentration level or value. It should not be expected that the
329      MDC testing process included varying the magnitude of the method's many parameters over a
330      wide range.

331      The reported quantitative results of the blanks can be used to estimate the MDC to within a
332      certain degree of confidence (for most methods). At or below the MDC value, the majority of the
333      measurement uncertainty typically is due to the Poisson counting uncertainty. For well-controlled
334      methods, the uncertainties of the other method parameters (input quantities), such as sample
335      weight, detection efficiency, and chemical yield, may range up to 10 percent. Therefore, a simple
336      rule of thumb to estimate the MDC for most methods involves reviewing the measurement
337      uncertainty for the reported blank results. If the blanks were analyzed to meet the MDC
338      requirement, then the reported MDC (based on blank and sample paired observations) for most
339      methods should be between 3 and 4 times the measurement uncertainty of the blank when the
340      background counts (per measurement interval) are greater than  10. It is  more complicated to
341      estimate the MDC for methods that use low background detectors (such as alpha spectrometry)
342      having background counts less than 10 per counting interval.  The TEC  should evaluate the blank
343      data to determine the reasonableness of the quoted MDC values. These  rules of thumb can be
344      applied to actual samples when the quoted analyte concentration value is less than two times its
345      associated  combined standard uncertainty value.

346      APPLICABLE ANALYTE CONCENTRATION RANGE

347      The applicable analyte concentration range can vary substantially depending on whether the
348      project deals with process waste streams, environmental remediation or monitoring, or environ-
349      mental or waste tank characterization research. The proposed method being evaluated should
350      provide accurate results over the analyte concentration range  stated in the SOW. Acceptable
351      analytical results used in this context means consistent method uncertainty (at a given analyte
352      concentration) and without significant bias. The range may be over several decades, from a
353      minimum value (the MDC for some projects) to 100 times the action level or MQC.

354      Due to the  effects of the Poisson counting uncertainty, most methods will provide more precise
355      results at higher analyte concentration levels compared to those concentration levels near zero. At
356      concentration levels near zero, background effects will render the results less precise. If the
357      background (instrument or ambient levels of analyte in the matrix) is not well characterized,  a
358      bias may also exist. For projects or programs (environmental  characterization research) that have
359      no action level requirement, the lower portion of the required concentration range or the MDC
360      requirement may be most important. For those situations, particular emphasis should be placed
361      on evaluating method and reagent blank data (i.e., net results  that take into account inherent


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362      analyte content in the reagents or tracers) to ensure that a bias does not exist. Refer to Section
363      7.2.2.6, "Bias Considerations," on page 7-15 for additional guidance.

364      Typically, radiation detection systems are linear in signal response over a very large range of
365      count rates. However, depending on the magnitude of the chemical or radionuclide interferences
366      in the sample, the method may not produce linear results over the entire application range.
367      Therefore, it is critical that when a mixture of radionuclides is present in a sample, the method
368      must provide sufficient "analyte  selectivity/isolation or impurity decontamination" to ensure
369      valid results and "method linearity." In some cases, such as that for pure beta-emitting analytes,
370      the degree of needed decontamination from other interfering nuclides may be as much as six
371      orders of magnitude.

372      There are several sources of information available from the laboratory that should be reviewed
373      and possibly evaluated to ensure the method is capable of meeting this MQO. These include
374      method validation documentation, previous projects or experience using the method, PE program
375      results, internal and external QC sample results, and pre-qualifying test samples. When evalua-
376      ting the data, the  TEC should evaluate the method's performance as a function of analyte concen-
377      tration with and without interferences. However, this evaluation would be most valid when the
378      samples were processed to the same MQO (especially MDC or MQC), a situation that may not
379      be realistic for different projects. If the MDC requirement results in a longer counting time from
380      one project to another, there may be an impact on the method's uncertainty for a given analyte
381      concentration due to difference in the Poisson counting uncertainty. Bias typically is not affected
382      by increasing the counting time.  A graphical plot of this data would be visually helpful and may
383      be used to determine if the method uncertainty requirement would be met at the action level
384      (extrapolation may be necessary).

385      METHOD SPECIFICITY

386      Method specificity refers to the ability of the method to measure the analyte of concern in the
387      presence  of other radionuclide or chemical interferences. The need for or degree of method
388      specificity depends on the degree or magnitude of the interferences and their effect on the ability
389      to measure the analyte of interest. Gross alpha, beta, and gamma-ray methods are considered to
390      be methods of low specificity and are used when individual nuclide specificity is not possible or
391      needed. Radiochemical methods involving sample digestion, purification and decontamination
392      steps followed by alpha spectrometry, such as for 239Pu in soil, are considered methods of high
393      specificity. However, the relative degree of specificity of these nuclide specific methods depends
394      on the number of analyte isolation and interference decontamination steps. High resolution
395      gamma-ray spectrometry employing a germanium (Ge) detector is considered to have better


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396      specificity than the lower resolution sodium iodide (Nal) gamma-ray spectrometry.

397      The TEC should evaluate the proposed methods for adequacy to meet the specificity require-
398      ments stated in the SOW. As mentioned in Chapter 6, methods of low specificity, such as gross
399      radiation detection methods, may be proposed if the methods meet the MQOs. For example,
400      when a single analyte having a relatively elevated action level needs to be evaluated, such as 60Co
401      in soil at an action level of 26 Bq/kg (0.7 pCi/g), then a method with less specificity (gross
402      counting methods  for gamma-ray or beta emitting nuclides) may be sufficient to meet the MQOs.
403      For this example, a less expensive Nal gamma-ray spectrometric analysis with a lower resolution
404      capability may be more desirable compared to a more costly high resolution germanium gamma-
405      ray spectrometric analysis. If greater method specificity for a certain analyte/matrix combination
406      has been required in the SOW, then a high resolution non-destructive sample analysis method
407      (such as high resolution gamma-ray spectrometry) or a destructive sample analysis by a detailed
408      radiochemical method would be appropriate. For proposed methods of high specificity, it is
409      important that the  TEC review and evaluate the basic purification and decontamination steps of
410      the method, or the resolution of the radiation detection system,  for adequacy in relation to the
411      expected mixture of analytes and interferences.  For radiochemical  methods, the TEC may be able
412      to estimate the needed distribution/partition coefficients, extraction and solubility factors, etc., of
413      the various purification steps and compare the values against the needed decontamination factors
414      for the interfering  chemical or radionuclide interferences.

415      The adequacy of method specificity can be evaluated by the analytical results from the analysis of
416      site-specific PT materials during method validation and/or laboratory pre-qualifying tests. A
417      further discussion  on the use of these  materials is presented below.

418      METHOD RUGGEDNESS

419      Method ruggedness refers to the ability of the method to produce accurate results over wide
420      variations in sample matrix composition and chemical and radionuclide interferences, as well as
421      when steps (such as pH adjustments) in the method are varied slightly by the analyst. For some
422      projects, the matrix composition and level of analyte  or interferences may very dramatically in a
423      given project.

424      Ruggedness studies have been defined by EPA (1998). A testing protocol for method ruggedness
425      has been outlined by the American Public Health Association (APHA). Some laboratories may
426      have developed methods according to the APHA protocol for method ruggedness or are using
427      methods contained in standards methods (APHA, 1989). Documentation on any internal
428      ruggedness study may be available from the laboratory.


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429      As mentioned in Chapter 5 and 6, the use of site-specific PT materials is a means of testing the
430      ruggedness of a method for a defined project. If ruggedness and method specificity are concerns
431      due to the sample matrix of a defined project, then a variety of site-specific performance testing
432      materials should be sent to the laboratory as part of the pre-qualification process or as a method
433      validation requirement. National PE programs, such as DOE's Multiple Analyte Performance
434      Evaluation Program (MAPEP) and Quality Assessment Program (QAP), use generic PT
435      materials and may not be applicable or representative of the matrices for a defined project. The
436      results of the pre-qualifying or method validation processes using site-specific PT materials
437      should be evaluated by the TEC to determine the adequacy of the method to meet this MQO
438      parameter. If the sample matrix and analytes are fairly standard, then no other evaluation of the
439      available information may be necessary.

440      7.2.2.6 Bias Considerations

441      The method proposed by the laboratory should produce analytical results that are unbiased.
442      MARLAP considers bias to be a persistent difference of the measured result from the true value
443      of the quantity being measured, which does not vary if the measurement is repeated. Normally,
444      bias cannot be determined from a single result or a few results (unless the bias is large). Bias may
445      be expressed as the percent deviation in (or deviation from) the "known" analyte concentration.
446      Since bias is estimated by repeated measurements, there will be an uncertainty in the calculated
447      value. It is incumbent upon the project manager or TEC to evaluate the proposed methods for
448      possible bias over the applicable analyte  concentration range. A laboratory should eliminate all
449      known biases before using a method. However, there may be circumstances,  such as the
450      processing of site-specific sample matrices, that may produce some inherent bias that is difficult
451      to assess or correct in a reasonable time or economical fashion. For the methods proposed, the
452      project manager must determine  if the magnitude of the bias will significantly affect the data
453      quality.

454      A bias can be positive or negative. Methods may have a bias at all analyte concentration levels
455      due to the improper determinations of chemical yield,  detector efficiency or resolution, subtrac-
456      tion of interferences, and improper assumptions for the analyte's half-life or an emission
457      branching ratio. When reporting  an analyte concentration based on a decay progeny analysis,
458      improper ingrowth assumptions may lead to a bias.

459      It is recommended that the project manager or TEC evaluate the available data provided by the
460      laboratory or from performance evaluations for bias, based on multiple analyses covering the
461      applicable analyte concentration  range. One means of estimating a bias is through the evaluation
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462      of external PE program data.2 For proper evaluation of the PE program sample results, it is
463      essential that the PE program provider use sample preparation techniques that will produce
464      performance testing (PT) samples (or a sample distribution) having insignificant "within or
465      between" sample analyte heterogeneity and whose analyte concentrations are accurately known.

466      For the purpose of evaluating whether a laboratory method has an observable bias based on
467      multiple laboratory internal QC samples (matrix or method spikes) or external PE program
468      samples, the following equations can be used:
                                      Dj  =100
                                                         Known
                                                      Known
                                                V
(7.1)
469      where D; is the percent deviation, Xj is an individual analytical result and Y; known is the "known"
470      value for the sample analyzed. The D; should be determined for each test sample in the data set.
471      The mean percent deviation for the method for a series of analyses in the data set can be
472      estimated by the equation:

                                                                                              (7.2)
                                                    N

473      Refer to various references (ASTM D2777, NBS 1963, Taylor 1990) for applicable tests that may
474      be performed to determine if there is a statistical difference at a given significance level.

475      There may be a negative or positive bias at low analyte concentrations due to the improper
476      determination of the appropriate detector background or analytical blank value. For an individual
477      blank result, the result (net activity or concentration value) would be considered to be a
478      statistically positive value if the magnitude of its value is greater than 1.65 times the quoted
479      measurement uncertainty. An older, much more conservative approach was to consider a reported
480      value as a positive value when the magnitude of a result was greater than 3 times the measure-
481      ment uncertainty.

482      Since the measurement process is statistical in nature and involves the subtraction of an
483      appropriate background or blank which also has an uncertainty, there is a 50 percent probability
         2 In order to standardize against the national standard (NIST), an external performance evaluation program should
         be implemented by a well-qualified provider that has standardized its reference materials to NIST or is participating
         in a NIST traceability program

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484      (half of the results) that the analytical result for a blank sample will have a negative magnitude,
485      e.g., -1.5 ± 2.0. For an individual blank measurement, the measurement may be considered to be
486      problematic when the negative magnitude is greater than 2 or 3 times the measurement
487      uncertainty.

488      For most radionuclides, other than those that are naturally occurring, the major source of a
489      positive blank is from contamination, either cross-contamination from other samples or dirty
490      glassware during sample processing or from tracer impurities.  A poor estimate of the instrument
491      background or ambient analyte levels in the matrix/reagent can lead to results being too negative
492      in magnitude. A statistical test should be performed on a series of the data results to determine if
493      there is a negative bias. The relative importance of the negative bias depends on the magnitude of
494      the negative bias, magnitude of the action level and type of project.

495      7.3   Initial Evaluation of a Laboratory

496      The basic information to be considered in the initial evaluation of a laboratory has been
497      summarized according to major categories in Figure 7.1. Not all categories will be discussed in
498      detail as subsections. Some categories may be grouped and discussed under a single generic
499      subsection heading. In order to allow for flexibility, no definitive guidance or detailed acceptance
500      criteria for the parameters under discussion will be provided.

501      7.3.1  Review of Quality System Documents

502      A radiochemical laboratory providing usable analytical data should have a quality manual. A
503      review of this document by a knowledgeable evaluator can reveal a great deal about the quality
504      and acceptability of the laboratory relative to the work to be performed. A well-developed quality
505      manual contains a description of the quality system and descriptive material covering most other
506      aspects of a laboratory's operation. The standard operating procedures, method documentation,
507      list of instrumentation, and personnel resumes should be reviewed. For some projects, the project
508      manager may require the laboratory to develop a specific project quality plan, system, and
509      manual. The following items, taken from the NELAC  Quality Systems (NELAC 2000), should be
510      discussed at a minimum:

511       •  Organization and management
512       •  Quality system establishment, audits, essential quality controls and evaluation, and data
513         verification
514       •  Personnel (qualifications and resumes)
515       •  Physical facilities (accommodations and environment)

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                                        INITIAL EVALUATION OF LABORATORY
             GENERIC SPECIFICATIONS
                FOR LABORATORY
                  OPERATIONS
                                 CAPABILITY TO MEET SOW
                                     SPECIFICATIONS
              ADMINISTRATIVE / PROJECT
                   MANAGEMENT
                                    ADEQUACY OF FACILITIES,
                                  INSTRUMENTATION, AND STAFF
                                          LEVELS
                  QUALITY SYSTEM
                  DOCUMENTATION
                                   EXPERIENCE BASE FOR SOW
                                   ANALYTICAL REQUIREMENTS
                 QUALITY MANUAL

               Organization and Management
             Quality Sytem - Establishment, Audits,
               Essential Quality Controls and
               Evaluations, and Data Verification
                     Personnel
             Physical Facilities - Accomodations and
                    Environment
              Equipment and Reference Materials
            Measurement Traceabiltty and Calibration 9
               Method and Standard Operating
                     Procedures
             Sample Handling, Sample Acceptance
                Policy, and Sample Receipt
                     Records
              Subcontracting Analytical Samples
             Outside Support Services and Supplies
                     Complaints
                                   CAP ABILITY TO MEET SAMPLE
                                   PROCESSING AND REPORTING
                                       REQUIREMENTS
                                   RADIOAHALYTICAL METHODS
                                    APPLICABILITY I QUALITY
                                        METHOD REVIEW
                                       «. COMPARABILITY
                                            METHOD
                                           VALIDATION
                                         DOCUMENTATION
 DEMONSTRATED
  QUALITY ON
SIMILAR PROJECTS
516
517
518
519
520
521
522
523
        FIGURE 7.1 — Considerations for the initial evaluation of a laboratory
Equipment and reference materials
Measurement traceability and calibration
Test methods and standard operating procedures (methods)
Sample handling, sample acceptance policy and sample receipt
Records
Subcontracting analytical samples
Outside support services and supplies
Complaints
524      The laboratory evaluation should involve a review of the quality system documents for
525      completeness, thoroughness, and clarity.
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526      7.3.2  Adequacy of Facilities, Instrumentation, and Staff Levels

527      Many factors enter into a laboratory's ability to meet the analytical requirements of a SOW. The
528      resources and facilities of a laboratory may become stretched depending on the number of clients,
529      the analytical services needed, and the deadlines of the committed work activities. Some SOWs
530      may request information about the current workload of the laboratory and available facilities,
531      staff and nuclear instrumentation for the specified work scope. The resources needed will vary
532      considerably depending on the analysis  and number of samples: from minimal bench space,
533      hoods, and nuclear instrumentation for fairly simple gross analyses to maximum bench space,
534      hoods, staff, and nuclear instrumentation for low-level analyses of soil. In addition, the laboratory
535      capacity also depends on the number of samples that are routinely processed in a batch. Various
536      factors may control the batch size, including the hood  processing area, bench space,  and
537      equipment setup, available number of radiation detectors, counting time, and half-life of
538      radionuclide, among others.

539      The adequacy of the  facilities, instrumentation, and staff levels can be estimated by two general
540      mechanisms: detailed supporting information in the SOW and an initial onsite audit. Information
541      received from the prospective laboratory may provide  an estimate of the laboratory's resources,
542      but an initial  onsite audit goes verifies the actual existence and maintenance of the resources.

543      7.3.3  Review of Applicable Prior Work

544      If required in a SOW, a laboratory will provide a list of clients for whom radioanalytical  services
545      had been performed that are considered comparable in terms of work scope, DQOs, MQOs,
546      APSs, and project type. A written or oral verification of the client list should be performed. As
547      part of the verification process, the following items related to adherence to contract or project
548      requirements should  be discussed and documented:

549       •  Radionuclides analyzed;
550       •  Sample matrices  types;
551       •  Laboratory capacity (number of samples per week  or another time period);
552       •  MQO for method uncertainty, detection  and quantification capability;
553       •  Radiological holding times;
554       •  Sample turnaround times;
555       •  Corrective actions; and
556       •  Communications related to schedule, capacity, or quality issues.

557      It should be noted that under performance-based contracting, a laboratory's prior work for an


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558      agency should be considered, either as a positive or negative performance weighting factor, when
559      scoring a laboratory's performance during the technical evaluation process.

560      7.3.4   Review of Performance Indicators

561      Some laboratories compile a semiannual or annual QA report summarizing the internal QC
562      sample results for the methods used during a given time period, as well as an internal quality
563      assessment report summarizing the internal and external audit findings and corrective actions
564      taken. Although the laboratory's internal quality criteria for a given radionuclide/matrix may be
565      different from the project MQOs, the internal QC sample results can be used to gauge the
566      laboratory's performance capabilities. If these documents are available, they should be reviewed
567      for documentation of process control and pertinent quality parameters such as bias, precision,
568      unusually high number of positive blank detection, chemical recoveries, turnaround times,
569      number of recurring deficiencies or findings, and corrective action effectiveness.

570      7.3.4.1 Review of Internal QC Results

571      A quality assessment report may contain a summary of various QA-related activities, including
572      internal audits and surveillance, report of conditions adverse to quality, investigation requests,
573      corrective actions, and the results of external PE programs and internal QC samples. The content
574      and frequency of the reports normally are outlined in the laboratory's quality manual. Frequently,
575      this type of quality assessment report may be submitted with the laboratory's response to the RFP
576      without request. The TEC may want to specifically request such a report when available.

577      When the laboratory's quality system is effectively implemented, the information contained in
578      these QA reports can be used not only to gauge the quality of the analyses but also the effective-
579      ness and timeliness of such quality system activities as identifying conditions adverse to quality,
580      controlling and monitoring the radioanalytical quality using internal QC samples, and corrective
581      actions. The internal QC sample results can be used to gauge the laboratory's performance
582      capability. Results of the QC  samples for a radionuclide and sample matrix should be reviewed
583      for both the batch QC samples and single- or double-blind samples submitted by the QA officer.
584      Batch QC samples typical include laboratory control samples, method blanks, matrix spikes, and
585      duplicates. Such parameters as acceptable percent deviation for spiked samples, acceptable
586      precision as measured by duplicate sample analyses, false nuclide detection, positive blanks, and
587      compliance to internal quality requirements should be reviewed, depending on the type of QC
588      sample. The single- and double-blind samples submitted independently by the QA officer are
589      considered more operationally independent than the batch QC samples.
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590      When quality problems are observed by the reviewer, it is important to check if the laboratory's
591      quality system also has found and reported the same problem and whether an investigation or
592      corrective action has been undertaken.

593      Additional specific guidance is provided in Chapter 18 on evaluating internal QC samples to
594      meet internal laboratory QC performance criteria. It is recommended that the project managers
595      review this chapter to gain a perspective on how to use reported internal QC results to gauge a
596      laboratory's potential to meet project MQOs.

597      7.3.4.2 External PE Program Results

598      Typically, a laboratory's performance or capability to perform high quality radioanalyses can be
599      evaluated through two external PE program mechanisms. The first mechanism, which may not be
600      available for all projects, is the submittal, as an initial laboratory evaluation process, of project-
601      specific PT samples prepared by the organization or a contracted source manufacturer. When
602      previous knowledge or experience exists, well-characterized site-specific matrix samples
603      containing the nuclides of interest can be used.  This approach can use site-specific matrix
604      materials for background samples or for samples spiked with target analytes. For this evaluation
605      mechanism, and depending on the number and type of samples, the laboratory's capability to
606      meet all proposed project MQOs and quality performance specifications may be evaluated.

607      The second mechanism, available to most projects, is the laboratory's participation in
608      government or commercial PE programs for radioanalyses. Each PE program has its own
609      acceptable performance criteria related  to a laboratory's bias with  respect to the PE program's
610      "known" analyte concentration value. Acceptable performance criteria are established for each
611      nuclide/matrix combination. A PE program may also evaluate a laboratory based on a false
612      positive analyte detection criterion. Typically, the laboratory's performance data in government
613      PE programs are provided in reports available to the public.
614
615      The project manager should be aware that the acceptable performance criteria used by the PE
616      programs may be inconsistent with or more lenient than the MQOs of the project. The
617      laboratory's performance should be  evaluated in terms of the established MQOs of the project
618      rather than a PE program's acceptable performance criteria. In some cases, the laboratories could
619      be ranked as to their level of performance in these programs.

620      7.3.4.3 Internal and External Quality Assessment Reports

621      Most laboratories undergo several external and internal QA audits per year, with resultant audit


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622      reports. Typically, a summary of the findings and commitments of internal and external quality
623      audits or assessments are tracked on some type of QA database as part of the laboratory's
624      corrective action process. Access to the audit reports or database information may be limited.
625      This information is not normally requested as part of the RFP process, nor do most laboratories
626      submit such information with their response to an RFP. Therefore, obtaining previous QA audit
627      information from a laboratory outside a formal, external, onsite audit process may be limited.

628      7.3.5  Initial Audit

629      An initial assessment or audit may be performed to provide assurance that a potentially selected
630      laboratory is capable of fulfilling the project requirements in accordance with the SOW.
631      Essentially, the objectives of an initial audit are twofold. The first objective is to verify that what
632      the laboratory claims in response to the SOW or RFP, such as the various quality and safety
633      programs, are being correctly and fully implemented, and when used during the project period,
634      will ensure that stipulated requirements will be met. The second objective is to determine if the
635      laboratory has the instruments, facilities, staffing levels and other operational requirements
636      available to handle the anticipated volume of work. In other words, is the laboratory's proposal
637      realistic when compared to the actual facilities? To answer this question, auditors will be looking
638      to see whether a candidate laboratory has all the required elements to meet the project needs.

639      Detailed guidance and information on what should be evaluated in an initial audit has been
640      provided in Appendix E, Section E5.5 and Table E7. This section also contains recommendations
641      on the key items or parameters that should be reviewed during the initial audit. Depending on the
642      project, other quality or operational parameters/requirements (such as requirements related to
643      chemical speciation or subsampling at the laboratory) not covered in Appendix E should be
644      included in the initial audit plan.

645      7.4   Ongoing Evaluation of the Laboratory's Performance

646      The evaluation framework presented here is intended to be sufficiently generic to cover the
647      operations of a laboratory performing work according to a SOW as recommended in Chapter 5.
648      As described in MARLAP, MQOs are a key component of the SOW. Therefore, the sample
649      schedule,  analyses to be performed, MQOs, and other analytical requirements have been defined.
650      The methods selected by the laboratory have been demonstrated to meet the MQOs and have
651      been approved by the project manager. In addition, the laboratory and its programs should have
652      undergone an initial audit to ensure that the laboratory has met or is  capable of meeting project
653      requirements,  including sample processing capacity, sample TATs, deliverables for analytical
654      reports, etc. This would include maintaining a satisfactory quality system that includes

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655      monitoring and controlling the radioanalytical processes through an instrument and internal
656      sample QC program and the acceptable performance in an external PE program.

657      The ongoing evaluation of a laboratory's performance includes the evaluation of the method
658      applicability or the quality of the data produced, and assessing the laboratory's quality system
659      and operations through onsite or desk audits or assessments. The continued method performance
660      can be evaluated through the laboratory's internal sample QC program, a possible external QC
661      program maintained by the project manager, or an external PE program. It should be noted that
662      samples used to control  and  monitor the quality of laboratory analyses have been defined
663      according to their use. For example, batch or external QC samples are used to control as well as
664      monitor the quality of the analytical process (the process can be stopped immediately if the QC
665      sample results indicate that the process is outside appropriate SOW specifications or laboratory
666      control limits). As defined previously, PT samples are used to compare the performance of the
667      radioanalytical processing to some acceptance criteria but are not used to control the process.

668      The ongoing evaluation of the laboratory quality system and operations is accomplished through
669      a visit to the laboratory or by a desk audit (the review of records and data from the laboratory).
670      These audits or assessments  are more focused on whether the laboratory is meeting project
671      specifications rather than whether the laboratory has the capability to meet project or SOW
672      requirements.

673      Once a laboratory has initiated work on a project, the laboratory's performance should be
674      evaluated for the duration of the project. The quality of the radioanalytical measurements, as well
675      as the pertinent key operational aspects of the laboratory, should be evaluated against the
676      requirements of the MQOs and SOW. Both the quantitative and qualitative measures of
677      laboratory performance  should be evaluated on a continual basis. In addition, the operational
678      aspects of the laboratory germane to the effective implementation of the project requirements
679      should be evaluated/monitored on a continual basis.

680      7.4.1   Quantitative Measures of Quality

681      The laboratory's ongoing demonstrated ability to meet the MQOs and other APS requirements
682      can be evaluated through various quantitative measures using internal QC data and external PE
683      program QC data. From these data, quantitative tests, as outlined in Appendix C can be used to
684      measure and monitor the MQO parameters on a short-term basis. Also, the QC and PE program
685      data can be used to evaluate  the laboratory's performance, on a long-term trending basis, in
686      meeting other quality related parameters such as bias and precision, unusually high number of
687      positive blank detection, false nuclide detection, MDC or MQC adherence, radiological holding


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688      times, etc. The following subsections will discuss the use of data from these samples to evaluate
689      the laboratory's radioanalytical quality with respect to the requirements.

690      7.4. 1 . 1 MQO Compliance

691      MARLAP recommends that project specific MQOs be established and incorporated into the
692      SOW for laboratory radioanalytical services. Appendix C provides guidance on developing the
693      MQOs for method uncertainty, detection capability, and quantification capability. Establishing a
694      gray region and action level are important to the development of the MQOs. For certain research
695      programs and characterization studies, the concept of an action level may not be applicable. For
696      these studies or programs, the MDC requirement and restrictions on the frequency of false
697      positive detections may be more important. As such, the project planning team for these
698      programs should establish the basis for their own MQOs and develop tests to evaluate a
699      laboratory's performance to meet the  requirements. These tests may be different from those
700      presented below.

701      MARLAP recommends that a MQO for method uncertainty be established for each analyte/
702      matrix combination. The method uncertainty is  affected by laboratory sample preparation, sub-
703      sampling, and the analytical method. In the absence of other information, the required method
704      uncertainty (HMR) at the upper bound of the gray region (UBGR) may be defined as:


                                              UMR = ~^                                    (7.3)

705      where u^ is the method uncertainty and A is the width of the gray region (difference between the
706      upper and lower bounds of the gray region) as defined in Appendix C. In terms of the relative
707      fraction of the upper bound of the gray region (action level), (pMft is defined:
                                                                                            (74)
708      The following subsections describe methods to quantitatively monitor a laboratory's performance
709      relative to meeting this principal MQO through the use of internal or external batch QC samples.
710      In some cases, the laboratory's internal quality program may have more restrictive quality control
711      limitations for method performance compared to the proposed control limits used by the project
712      manager to monitor adherence to the MQO for method uncertainty. Evaluation of the labora-
713      tory's performance in NIST-traceable external PE programs will determine the degree of bias of
714      the laboratory's method with respect to the national standard, as opposed to the determination of


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715      the laboratory's internal bias through the use internal QC samples. The tests presented assume
716      that all known internal (related to QC values and calibrations) and external (calibration differ-
717      ences with respect to the national standard) biases have been defined and eliminated and, as such,
718      the difference between the measured result and the "expected known" value is a result of the
719      method uncertainty only.

720      USE OF INTERNAL QC SAMPLE RESULTS

721      For most projects, the SOW will specify that the laboratory incorporate internal QC samples
722      within a defined batch of samples. The QC samples may include a laboratory control sample,
723      sample duplicates, a matrix spike sample and a method or reagent blank, or both. Appendix C
724      provides examples on the use  of the following quantitative tests to measure a laboratory's
725      performance in meeting the MQO for method uncertainly.

726      Quality Performance Tests and Acceptance Criteria for Quality Control Samples

111      Laboratory Control Sample (LCS). The analyte concentration of an LCS should be high enough
728      so that the resulting Poisson counting uncertainty is small and the relative uncertainty limit cp^ is
729      appropriate with respect to the action level and the spike concentration chosen. The percent
730      deviation (%D) for the LCS analysis is defined as

                                       %D= SSR"SAxlQO%                               (75)
                                               SA                                         (   }

731      where
732         SSR   is the measured result (spiked sample result) and
733         SA    is the spike activity (or concentration) added.

734      It is assumed that the uncertainty of SA is negligible with respect to the uncertainty of SSR.
735      Refer to Appendix C for the basic assumption and limitation of this test. For long-term trending,
736      the %D results should be plotted graphically in terms of a quality control chart as described in
737      Chapter  18. The warning and control limits on %D are summarized below:
738

739
740
741
Laboratory Control Samples

   Statistic:         %D
   Warning limits:   (± 2(pm) x 100%
   Control limits:    (± 3qm) x 100%
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742
743
744
745
746
747

748
749
Duplicate Analyses. The acceptance criteria for duplicate analysis results depend on the analyte
concentration of the sample, which is estimated by the average x of the two measured results xl
and x2.

                                    ~x=^-^-                                     (7.6)
When x< UBGR, the absolute difference] xl - x2\ of the two measurements is used in the testing
protocol. For these tests, only upper warning and control limits are used, because the absolute
value]xl - x2 \ is being tested.
When x> UBGR, the acceptance criteria may be expressed in terms of the relative percent
difference (RPD) defined as
                                      RPD
                                             x 100%
                                                x
                       (7.7)
750      The requirements for duplicate analyses are summarized below.
751
752
753
754
755
756
757
758
759
760
761
762
Duplicate Analyses
If *UBGR:
Statistic: RPD
Warning limit: 2
Control limit: 4
Method Blanks. When an
However, the measured v
control uncertainty limits


ri " xi 1
83 "MR
24 u
- v 100%
x
83 (pivjR x 100%
o/i „ y i nn°/
Z4- (pMR x lUU/o
aliquant of a blank material is analyzed, the target value is zero.
alue may be either positive or negative. The applicable warning and
for blank samples are defined as:
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                                                             Evaluating Methods and Laboratories
763

764
765
766

767
768
769
770

771

772
773
774
775
776
777

778
779
780
781
783

784

785
786
Method Blanks

   Statistic:
   Warning limits:
   Control limits:
                             Measured Concentration Value
                             ±2u,
                                 MR
                             ±31
                                  ^
         Matrix Spikes. The acceptance criteria for matrix spikes are more complicated than those
         described above for the other laboratory QC samples because of the pre-existing activity that is
         inherent to the unspiked sample. The pre-existing activity (or concentration) must be measured
         and subtracted from the activity measured after spiking.
        MARLAP recommends the "Z score," defined below, as the test for matrix spikes.

                                 z=	SSR - SR - SA
                                                 max(SR, UBGR)2
                                                                                          (7.8)
        where:
            SSR  is the spiked sample result,
            SR    is the unspiked sample result,
            SA    is the spike concentration added (total activity divided by aliquant mass), and
                  max(SR,UBGR) denotes the maximum of SR and UBGR.

        The warning and control limits for Zare set at ± 2 and ± 3, respectively. It is assumed that the
        uncertainty of SA is negligible with respect to the uncertainty of SSR. For long-term trending, the
        Z results should be plotted graphically in terms of a quality control chart,  as described in Chapter
        18.
782      The requirements for matrix spikes are summarized below.
Matrix Spikes

   Statistic:         Z =

   Warning limits:   ± 2
   Control limits:    ± 3
                                         SSR-SR-SA
                                            + max(SR, UBGR)2
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787      USE OF EXTERNAL PE PROGRAM AND QC SAMPLE RESULTS

788      Information on a laboratory's performance in an external PE program or from double-blind QC
789      samples is very useful in monitoring a laboratory's ability to meet MQOs. A PE program will
790      provide a snapshot in time whereas external QC samples included with samples submitted to the
791      laboratory permit a continuous evaluation of the method's performance. When traceable to NIST,
792      the PE program will elucidate any measurement or instrument calibration biases as related to the
793      national standard. An external QC program may not have NIST traceability, and thus calibration
794      biases to the national standard would not be determined.

795      For monitoring the performance of a laboratory using external PE program and QC sample
796      results, the tests provided in the previous subsection ("Use of Internal QC Sample Results," page
797      7-25) may be used when there are sufficient data. The test equations assume that the project has
798      an MQO for method uncertainty at a specific concentration. In addition, it is assumed that the
799      Poisson counting uncertainty for the radioanalysis of these samples is minimal.

800      Results from PE Programs

801      In many SOWs, the laboratory is required to participate in a recognized PE program for the
802      nuclides and media of interest. In some cases, a certificate of participation may be needed as part
803      of response to the RFP. However, it also should be noted that although a laboratory may meet
804      performance acceptance criteria for an external PE program, this fact may have no bearing on
805      whether the method will meet the MQOs of the SOW.

806      Monitoring ongoing laboratory performance is limited due to the minimum frequency of testing
807      of the PE program, i.e., usually quarterly or semiannually. Some PE programs require multiple
808      measurements to estimate precision but most only request a single result be reported. In addition,
809      the concentration of the analyte typically never approaches an action level value and the media
810      used are not site specific. For PE program samples, when possible, the laboratory should analyze
811      a sample to reach a la Poisson counting uncertainty that is less than five percent.

812      Multiple Analyses and Results

813      When a PE program requires the analysis of multiple samples, the laboratory's measurement
814      precision  and bias (to a "known value")  at the analyte concentration may be estimated and
815      reported by the PE program provider. When only duplicates sample results are reported, then the
816      tests for laboratory control samples and duplicate analyses given in the previous section should
817      be used. The duplicate analysis test can be used as is, but the laboratory control sample test


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818      should be evaluated based on the mean of the duplicate results. By using the mean of the two
819      results, the LCS test provides a better estimate of any laboratory measurement bias with respect
820      to the PE program provider. As discussed in Appendix C, the measurement (combined standard)
821      uncertainty of each measured result value should be smaller than the required u^ or cp^.

822      Results from External QC Samples

823      The project manager may elect to establish an external QC program wherein QC samples are
824      submitted to  the laboratory with each batch of routine samples for the purpose of "controlling,"
825      rather than monitoring, the quality of the analytical processes. The types of QC samples may
826      include matrix spikes, blanks, and possibly duplicates if prepared under controlled and exacting
827      protocols. An agency may use  a qualified reference or monitoring laboratory (ANSI N42.23) to
828      prepare the performance testing materials. When available, these QC samples may be prepared
829      from site-specific materials.

830      When acceptance criteria are not met, the organization may issue a stop-work order and request
831      corrective actions and reanalysis before routine processing can resume. In order to do this, the
832      SOW must define the performance acceptance criteria and stipulate that the agency or
833      organization  has the right to stop laboratory processing when the performance requirements are
834      not met. This application is not widespread but may have merit for certain project types. For
835      example, research  or national monitoring programs may monitor groundwater for specific
836      naturally occurring radionuclides at state-of-art detection levels. For these programs, frequent
837      false positive results, due to the application of incorrect instrument background or an analytical
838      blank to the analytical result, would be unacceptable. Rather than permit a high rate of false
839      positive results to continue, the agency can use the external batch QC samples to detect problems
840      early and have the laboratory discontinue sample processing until a root cause is discovered and a
841      corrective action undertaken. Non-conformance of a single analysis to performance criteria
842      would not warrant the issuance of a stop work order unless a severe blunder has occurred.
843      Typically, a certain amount of  statistical trending of the data is in order to truly elucidate
844      deficiencies.

845      Since the number of QC samples is similar to the recommendations for the laboratory's internal
846      batch QC samples, there should be sufficient data for trending.  The statistical tests provided in
847      the section on "Use of Internal QC Sample Results," beginning on page 7-25, may be applied to
848      these QC samples.
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849      7.4.1.2 Other Parameters

850      The laboratory's performance in meeting the requirements for the other APSs that are listed in
851      the SOW should be evaluated quantitatively when possible. In some cases, the information
852      needed to perform the evaluations may be found in the final analytical results data package. For
853      certain types of evaluations, a follow-up onsite or desk audit may be needed to complete the
854      evaluation, e.g., a review of logbooks on unique processes or software algorithms and the
855      analytical data base for proper spectral resolution.

856      RADIOLOGICAL HOLDING AND TURNAROUND TIMES

857      The data packages or analytical results report should contain the sample collection (reference),
858      sample analysis, and reporting dates. From this information, the radiological  holding and sample
859      processing TATs can be calculated and compared against requirements. When a method uses a
860      decay progeny to measure the analyte of interest (222Rn to measure 226Ra), the decay of the parent
861      nuclide and ingrowth of the decay progeny are important parameters for evaluation. Unless
862      requested in the SOW, most laboratories do not report the ingrowth factor as a standard output.
863      Therefore, the information on the sample specific ingrowth factor may be available  in the data
864      reports or during audits. When required, these time related requirements will be evaluated for
865      compliance during data verification and validation.

866      CHEMICAL YIELD

867      When appropriate, the SOW may specify limits on the chemical yield for each analyte.  For
868      radionuclides, this requirement typically is related to the provision of robust or rugged methods
869      so that extreme yields become flags indicating potential problems. Wide swings in the chemical
870      yield may be indicative of method's difficulty handling matrix or radionuclide interferences. The
871      data packages or analytical results report should contain the chemical yield for each analyte
872      listed. This reported value can be compared to the SOW yield limit. When required, these
873      requirements will be evaluated for compliance during data verification and validation.

874      SPECTRAL RESOLUTION

875      Problems with spectral resolution of gamma-ray and alpha spectra cannot be evaluated through a
876      review of the analytical results report. If spectral resolution limits have been  stated in the SOW,
877      the evaluator should review and evaluate each sample spectrum against the SOW limit. Spectral
878      information may be available  in data packages when required or may be obtained during audits.
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879      During an initial audit, a preliminary evaluation of the method's SOP and review of past
880      performance data for spectral resolution should be undertaken. The TEC may want to determine
881      the baseline or typical spectral resolution for the radiation detection systems that will be used in
882      the analysis of project samples. Trends of the spectral resolution of each detection system during
883      the conduct of the project may be used to determine compliance with a spectral resolution
884      specification.

885      7.4.2   Operational Aspects

886      Once a laboratory begins providing radioanalytical services, certain operational aspects need to
887      be reviewed and evaluated periodically to determine if the laboratory is maintaining project
888      requirements or if new problems have occurred. It is also important to ensure that the laboratory
889      has been properly maintained and is operated and managed in a manner that will not create a
890      liability to any client. Many of the operational areas that were discussed in Sections 7.3.1 and
891      7.3.2 for the initial evaluation of a laboratory also  should be evaluated periodically to ensure
892      commitments are being met. The audit frequency varies according to the organization and the
893      extent of the project or contract. Desk audits can be conducted more frequently than onsite audits
894      because they require fewer resources. However, not all operational aspects may be reviewed
895      during desk audits. The operational aspects that may be considered during desk and onsite audits
896      are presented below.

897      7.4.2.1  Desk Audits

898      A desk audit is conducted as an off-site activity, usually by a technical representative of the
899      project manager. A radioanalytical specialist should review all technical aspects of the desk
900      audit, including method and calculation (data reduction) changes, method performance,
901      instrument recalibrations, corrective actions, and case narratives. The desk audit is most useful
902      when performed periodically to monitor certain activities or programs following an extensive
903      onsite laboratory audit. However, for some smaller projects, the desk audit may be the only
904      assessment mechanism used to monitor the laboratory's operations. The desk audit may be used
905      to review or monitor the following operational aspects or items:

906       <> Organization and Management
907          o  Changes in key personnel
908          o  Reassignments

909        Quality  System
910          o   Internal and external audits conducted, including laboratory certification audits


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911          o  Corrective action implementations
912          o  Quality control and performance evaluations
913             -   Instrument and batch sample QC results
914             -   External PE program results
915          o  Laboratory data verification (narrative status reports)
916          o  Additional method validation studies

917       0 Certificates, licenses, equipment, and reference materials
918          o  Standard and tracer certificates
919          o  New and updates to instrument calibrations
920          o  Instrument repairs and new instruments put into service
921          o  NRC/State radioactive materials licence updates
922          o  State or EPA drinking water certification status changes

923       <> Personnel
924          o  Updates to staff qualification/proficiency for methods
925          o  Updates to staff training files
926             -   Radiation and chemical safety
927             -   Quality assurance
928             -   Technical principles
929             -   Hands-on training records

930       <> Radioanalytical Methods and Standard Operating Procedures
931          o  Updates to methods and SOPs
932          o  Technical basis for updates
933          o  Detection limits or method uncertainty studies

934       0 Sample Receipt, Handling and Disposal
935          o  Sample receipt acknowledgment
936          o  Chain-of-custody
937          o  Sample- and waste-disposal tracking logs and manifests

938      Desk audits may also be used to review the data packages provided by the laboratory and,
939      periodically, to verify certain method results by hand calculations. In addition, verification of
940      compliance to radiological holding and turnaround times may be performed during the desk
941      audit. In the absence of a full data verification and validation program (Chapter 8), the desk audit
942      may be used to periodically evaluate the detailed instrument and data reduction reports of the
943      data packages for method adherence, technical correctness and valid application.


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944      7.4.2.2 Onsite Audits

945      The onsite laboratory audit is more comprehensive and resource intensive than a desk audit. An
946      onsite audit typically is conducted to assess, periodically and in depth, a laboratory's capability to
947      meet project requirements. Section E.5.5 of Appendix E provides guidance on the conduct of an
948      initial onsite audit during a contract award process. EPA (1997) provides limited guidance on the
949      conduct of an audit for a radiological laboratory. NELAC (2000) provides some generic guidance
950      on laboratory assessments, although not specifically for a radiological laboratory.

951      Onsite audits usually cover the operational aspects delineated in Section 7.4.2.1 and also provide
952      an opportunity to evaluate the physical conditions at the laboratory, in terms of adequacy and
953      upkeep of the facilities, and the full application or conduct of programs and resources. Informa-
954      tion sent in data packages or submitted for desk audits can be confirmed or verified during an
955      onsite audit.  Furthermore, an onsite audit permits the tracking of a sample from receipt through
956      processing to sample storage and disposition and can verify the related instrument and batch QC
957      samples specific to the sample being tracked. During an onsite audit, the auditors may have
958      interviews with the staff to gauge their technical proficiency and familiarity with methods.

959      For large projects, onsite audits may be formal in nature and have a predefined audit  plan, which
960      has been developed by a designated audit team, for a specific  project or program. The audit team
961      typically is comprised of qualified QA representatives and technical experts. MARLAP
962      recommends that the audit team include a radioanalytical specialist familiar with the  project's or
963      program's technical aspects and requirements.

964      In addition to the items in Section 7.4.2.1 ("Desk Audits"), the following items and programs
965      should be assessed during an onsite laboratory audit:

966       0 Organization and Management
967          o  Qualifications of assigned laboratory project manager
968          o  Implementation of management's policy on quality
969          o  Timeliness of addressing client complaints
970          o  Timeliness of implementing corrective actions

971       0 Physical  Facilities
972          o  Adequacy of facilities (sample receipt, processing, instrumentation and storage areas,
973             waste processing and storage, offices, etc.)
974          o  Physical conditions of facilities including laboratories, hoods, bench tops, floors, offices,
975             etc.
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 976          o Environmental controls, such as climate control (heating, ventilation, air conditioning)
 977            and electrical power regulation
 978          o Sample processing capacity
 979          o Sample storage conditions including chain-of-custody lockup areas and cross
 980            contamination control (separation of samples by project and from radioactive sources or
 981            wastes)

 982       0 Instrumentation and Equipment
 983          o Age of nuclear instrumentation and equipment
 984          o Functionality of nuclear instrumentation and equipment
 985          o Calibrations and QC logs
 986          o Maintenance and repair logs
 987          o Sample throughput capacity
 988          o Contamination control for radiation detectors
 989          o Background spectra of radiation detectors

 990       <> Methods and Standard Operating Procedures
 991          o Use of latest revisions of methods and SOPs (spot check method manuals used by
 992            technical staff)
 993          o Conformance to method application (surveillance of method implementation)
 994          o Effectiveness of administering the controlled method manual

 995       <> Certifications, Licenses and Certificates of Traceability
 996          o Ensure existence and applicability of, and conformance to, certifications and licenses
 997          o Noted citations during audits related to certifications and licenses
 998          o Ensure use of NIST-traceable materials (calibration standards)/review of vendors' report
 999            of NIST traceability

1000       <> Waste Management Practices
1001          o Adherence to waste management SOPs
1002          o Proper packaging, labeling, manifests, etc.
1003          o Sample storage and records
1004          o Training and qualification records

1005       <> Radiological Controls
1006          o Adherence to radiological  safety SOPs
1007          o Contamination control effectiveness (spill control, survey requirements and adherence,
1008            posted or restricted areas, proper ventilation, cleaning policies, etc.)


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                                                              Evaluating Methods and Laboratories
1009          o Badging and survey adherence

1010       <> Personnel
1011          o Number and technical depth of processing staff
1012          o Training files
1013          o Testing/qualifications
1014          o Personal interviews to determine familiarity of methods and safety SOPs

1015        Quality Systems
1016          o Performance indicator program (feedback from program)Quality assurance reports (QC
1017            and audits) for all laboratory processing
1018          o Ongoing method evaluations and validations
1019          o Corrective action program (effectiveness and outstanding issues for all processing; spot
1020            check for implementation of corrective actions)
1021          o Records/reports related to audits of vendors used by laboratory
1022          o Reagent control program (spot check conformance for effectiveness)
1023          o Audits of laboratories that are subcontracted
1024          o Laboratory's data verification and validation processes

1025       0 Software Verification and Validation
1026          o Spot review of key method calculation and data reduction programs that include MDC,
1027            MQC,  and measurement uncertainty; spectral unfolding routines or crosstalk factors;
1028            application of instrument background and analytical blanks; etc.
1029          o Spot verification of consistency between electronic data deliverable and data packages

1030       0 Radiological Holding and Sample Turnaround Times
1031          o Verification of compliance to radiological holding and sample TAT specifications (spot
1032            check samples and confirm paperwork)
1033
1034
1035
1036
1037
1038
                        Summary of Recommendations

MARLAP recommends that a radioanalytical specialist review the methods for technical
adequacy.

MARLAP recommends that project specific MQOs be established and incorporated into
the SOW for laboratory radioanalytical services.

MARLAP recommends that a MQO for method uncertainty be established for each
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         Evaluating Methods and Laboratories
1039
1040
1041
analyte/matrix combination.

MARLAP recommends that an audit team include a radioanalytical specialist familiar with
the project's or program's technical aspects and requirements.
1042     7.5    References

1043     American National Standards Institute (ANSI) N42.23. Measurement and Associated
1044        Instrumentation Quality Assurance for Radioassay Laboratories.

1045     American Public Health Association (APHA) 1989. Standard Methods for the Examination of
1046         Water and Waste Water. Washington, DC.

1047     American Society for Testing and Materials (ASTM) D2777. Standard Practice for
1048        Determination of Precision and Bias of Applicable Test Methods of Committee D-19 on
1049         Water.

1050     American Society for Testing and Materials (ASTM) El 77. Standard Practice for Use of the
1051         Terms Precision and Bias in ASTM Test Methods.

1052     American Society for Testing and Materials (ASTM) E548. Standard Guide for General Criteria
1053         Used for Evaluating Laboratory Competence.

1054     American Society for Testing and Materials (ASTM) E1580. Standard Guide for Surveillance of
1055        Accredited Laboratories.

1056     American Society for Testing and Materials (ASTM) El 691. Standard Guide for Evaluation and
1057        Assessment of Analytical Chemistry Laboratories.

1058     U.S. Environmental Protection Agency (EPA). 1997. Manual for the Certification of
1059        Laboratories Analyzing Drinking Water. EPA 815-B-97-001.

1060     U.S. Environmental Protection Agency (EPA). 1998. Guidance for Quality Assurance Project
1061        Plans EPA QA/G-5. EPA 600-R-98-018, February.
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1062      International Organization for Standardization (ISO) 17025. General Requirements for the
1063         Competence of Testing and Calibration Laboratories. International Organization for
1064         Standardization, Geneva, Switzerland. 1999.

1065      National Bureau of Standards (NBS). 1963. Experimental Statistics. NBS Handbook 91, National
1066         Bureau of Standards, Gaithersburg, MD.

1067      National Environmental Laboratory Accreditation Conference (NELAC) 2000. Chapter 5,
1068         Quality Systems. July. Available at: http://www.epa.gov/ttn/nelac/.

1069      Taylor, John Keenan. 1990. Statistical Techniques for Data Analysis. Lewis Publishers, Chelsea,
1070         MI.
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 i          8  RADIOCHEMICAL DATA VERIFICATION AND

 2                                      VALIDATION


 3     8.1    Introduction

 4     The goal of the data collection process is to produce credible and cost-effective data to meet the
 5     needs of a particular project. The process can be divided into several stages, as illustrated in the
 6     data life cycle (Chapter 1). This chapter is the first of two chapters that address the assessment
 7     phase of the project. Because the efficiency and success of these assessment activities are heavily
 8     dependent on the completion of the preceding steps in the data collection process, especially the
 9     initial planning activity (Chapter 2), the integration of planning and  assessment is discussed in
10     Section 8.2 prior to presenting material on data verification and validation.

11     Data verification compares the material delivered by the laboratory to the requirements in the
12     statement of work (SOW) and identifies problems, if present, that should be investigated during
13     data validation. Data validation compares the data produced with the measurement quality
14     objectives (MQOs) and any other analytical process requirements contained in the analytical
15     protocol specifications (APSs) developed in the planning process. It may not be necessary in all
16     instances to validate all project data. This chapter outlines a validation plan that specifies the data
17     deliverables and data qualifiers to be assigned that will facilitate the data quality assessment. The
18     project-specific data validation plan should establish a protocol that prioritizes the data to be
19     validated. This is to eliminate unnecessarily strict requirements that  commit scarce resources to
20     the in-depth evaluation of data points with high levels of acceptable uncertainty. For example,
21     results very much above or below an action level may not require rigorous  validation, since
22     relatively large measurement uncertainty would not affect the ultimate decision or action.
23     Planners should also identify those samples or data sets that have less rigorous standards for data
24     quality and defensibility.

25     This chapter presents suggested criteria to evaluate data and addresses the appropriate function
26     and limits of radiochemical techniques and measurements. Since calibration is more efficiently
27     evaluated as part of an audit,  this chapter does not recommend that the complete calibration-
28     support documentation be included as part of the data package. MARLAP recommends that
29     calibration be addressed in a Quality System and through an audit (Chapter 18), although
30     demonstration of calibration may be required as part of a project's deliverables. Detector
31     calibration, self absorption curves and efficiencies should be addressed as part of the evaluation
32     of laboratories during the procurement process and continued during subsequent assessments
33     (Chapter 7). Availability and  retention of calibration records are decisions that are project-
34     specific, but should be clearly identified for contract clarity and to assure project completeness

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35     (i.e., customer needs met). External sources of information, such as performance evaluation
36     sample results and internal laboratory control samples, provide useful interim information on
37     calibration status and accuracy.

38     8.2   Data Assessment Process

39     Figure 1.1 of Chapter 1 graphically depicts the three phases—planning, implementation, and
40     assessment—of the data life cycle, and the associated activities and products of each phase.
41      While these activities are addressed in separate chapters in MARLAP, it should be emphasized
42     that integration of planning, sampling, and analysis with subsequent data verification, data
43     validation, and data quality assessment (DQA) is essential.

44     This section reviews the data life cycle from the perspective of the assessment phase and focuses
45     on those issues that have the potential to impact the quality and usability of the data. Section
46     8.2.1  addresses the development of the assessment procedures during project planning. Section
47     8.2.2  considers assessment needs for documentation and a quality system  during implemen-
48     tation. Section 8.2.3 focuses on the assessment phase and addresses the interrelationship of the
49     three  assessment processes. This introduction to the data life cycle process emphasizes the
50     importance of linkages among planning, implementation, and assessment.

51     8.2.1   Planning Phase of the Data Life  Cycle

52     Directed project planning and the development of the associated DQOs, MQOs, and other
53     specifications for the project were reviewed in Chapters 2 and 3. These chapters emphasize  the
54     need for planners to thoroughly define the assessment processes (i.e, verification, validation and
55     data quality assessment) in sufficient detail that success or failure in meeting goals can be
56     determined upon project completion. MARLAP recommends that the assessment phase of a
57     project (verification, validation,  and DQA processes) be designed during the directed planning
58     process and documented in the respective  plans as part of the project plan documents. This
59     requires the project planning team to develop detailed procedures for data verification, data
60     validation, and  data quality assessment, as well as identify the actual personnel who will perform
61     assessment or the required qualifications and expertise of the assessors.

62     The development of these procedures during the directed planning process will increase the
63     likelihood that the appropriate documentation will be available for assessment, and that those
64     generating and assessing data will be aware of how the data will be assessed. A secondary
65     advantage, which assessment plans have, is that prior to their completion,  they often result in the
66     detection of design flaws (e.g., lack of proper quality control [QC] samples, lack of a field audit)

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67     that upon correction will result in the complete information necessary for the proper assessment
68     of data usability.

69     The culmination of the planning process is documentation of the outputs of the directed planning
70     process in the project plan documents. The project plan documents should capture the DQOs,
71     MQOs, and the optimized data collection design (i.e., Analytical Protocol Specifications,
72     sampling and analysis plans, and SOPs). The project plans should also include the assessment
73     plans as discussed above, and describe the field, lab, safety, and QA activities in sufficient detail
74     that the project can be implemented as designed. Chapter 4 discusses guidance for the authoring
75     and content of project plan documents.

76     If the directed planning process, its outputs (DQOs,  MQOs, optimized sampling and analysis
77     designs), and associated assumptions are not documented well in project plan documents, the
78     assessment phase will have difficulties evaluating the resulting data in terms of the project's
79     objectives.

80     8.2.2   Implementation Phase of the Data  Life Cycle

81     The project plans are executed during the implementation phase. Ideally, the plans would be
82     implemented as designed, but due to errors, misunderstandings, the uncontrolled environments
83     under which sampling is implemented, and matrix-specific issues that complicate sample
84     handling and analysis, most project plans are not implemented without some deviation.

85     Understanding the realities of implementation, the assessment process, in particular the DQA
86     process, will evaluate the project's implementation by considering: (a) if the plans were adequate
87     to meet the project's DQOs, (b) if the plans were implemented as designed, and (c) if the plans as
88     implemented were adequate to meet the project DQOs. MARLAP recommends that project
89     objectives, implementation activities and QA/QC  data be well documented in project plans,
90     reports, and records, since the success of the assessment phase is highly  dependent upon the
91     availability of such information.

92     Documentation and record keeping during the planning and implementation phase of the data life
93     cycle are essential to subsequent data verification, data validation, and data quality assessment.
94     Thorough documentation will allow for  a determination  of data quality and data usability.
95     Missing documentation can result in uncertainty,  and a lack of critical documentation (e.g.,
96     critical quality control results) can result in unusable data. The quality and usability of data can
97     not be assessed if the supporting documentation is not available.
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 98     8.2.2.1 Project Objectives

 99     The DQOs, MQOs, and other specifications, requirements, and assumptions developed during
100     the planning phase will influence the outcomes during the subsequent implementation and
101     assessment phases of the data life cycle. It is important that these objectives, specifications,
102     requirements, and assumptions are well documented and available to those implementing the
103     program so they can make informed decisions. This documentation is reviewed during the DQA
104     process (see discussion of the review of DQOs in Section 9.6.1.1, sampling plan in
105     Section 9.6.2.1, and analysis plan in Section 9.6.3.1).

106     8.2.2.2 Documenting Project Activities

107     The assessment of data in terms of sampling and analytical MQOs requires an accurate record of
108     QC sample data and compliance with specifications and requirements. If these records are
109     missing or inadequate, then compliance with APSs, including the MQOs that were identified
110     during the planning phase, will not be ascertainable and will raise questions regarding quality.

ill     Additional documentation is required to assess compliance with plans and contracts, and to
112     assess field and lab activities (e.g., compliance with SOPs) and the associated organizational
113     systems (e.g., laboratory Quality Manual). This information is gleaned from the review of field
114     and laboratory notebooks, deviation reports, chain-of-custody forms, verification reports, audit
115     reports, surveillance reports, performance evaluation sample analyses, corrective action reports
116     and reports to management that may identify deviations, contingencies, and quality problems.
117     Assessment of these types of contemporaneous records allow  for the assessment of data in the
118     context of pertinent issues that may have arisen during proj ect implementation.

119     Project records should be maintained for an agreed upon period of time, which should be
120     specified in project plan documents. Record maintenance should comply with all regulatory
121     requirements and parallel the useful life of the data for purposes of re-assessment as questions
122     arise or for purposes of secondary data uses that were not originally anticipated.

123     8.2.2.3 QA/QC

124     To ensure that the data collection activity generates data of known quality, it is essential that the
125     project plan documents specify the requirements for an appropriate quality  system that is capable
126     of implementing the quality controls and the quality assurance necessary for success.
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127     The quality system will oversee the implementation of QC samples, documentation of QC
128     sample compliance or non-compliance with MQOs, audits, surveillances, performance evaluation
129     sample analyses, corrective actions, quality improvement and reports to management. The
130     documentation generated by these quality assurance activities and their outputs during project
131     implementation will be a key basis for subsequent assessments and data usability decisions.

132     8.2.3   Assessment Phase of the Data Life Cycle

133     Assessment of environmental data currently consists of three separate and identifiable phases:
134     data verification, data validation, and DQA. Verification and validation pertain to evaluation of
135     analytical data. Verification and validation are considered as two separate processes, but as the
136     MARLAP recommended planning process is implemented, they may be combined—with the
137     verification activities constituting the bulk of the review. DQA considers all sampling, analytical,
138     and data handling details, external QA assessments, and other historical project data to determine
139     the usability of data for decision-making.

140     Figure 8.1 is a graphical depiction of the assessment phase. Although, it portrays a linear
141     progression through the various steps, and from verification and validation to data quality
142     assessment, this linear advancement is not entirely necessary. It is possible for parallel progress
143     within an assessment process (e.g., existing documents are verified while waiting for the
144     production of others) and between assessment processes (e.g., analysis of the DQOs  for data
145     quality assessment while data validation is being completed). Typically, the focus of verification
146     and validation is on the analytical process and on a data point by data point review, while data
147     quality assessment considers the entire data collection process and the entire data set as it
148     assesses data quality.

149     Analytical data verification assures laboratory conditions and operations were compliant with the
150     SOW based on project plan documents. The updated project plan documents specify the
151     analytical protocols the laboratory should use to produce data of acceptable quality and the
152     content of the analytical data package (see MARLAP Process in Chapter 1). Verification
153     compares the analytical data package delivered by the laboratory to these requirements
154     (compliance), and checks for consistency and comparability of the data throughout the data
155     package, correctness of basic calculations, data for basic calculations, and completeness of the
156     results to ensure all necessary documentation is available.  Verification can be accomplished
157     through use of a plan or simply a check list. The verification process produces a report
158     identifying which requirements are not met (i.e., exceptions qualified with an "E" to alert the
159     validator). The verification report is used to confirm laboratory compliance with the  SOW and to
160     identify problems that should be investigated during data validation. Verification works


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C^Start of Assessment^)
1
f DATA VERIFICATION 1
1
| Identification of missing 1
1 documentation I
1

a^d contract requirements
1
Identification of noncompliant
procedures
1
Identification of noncompliance
with SOW and MQOs
1
Identification of Exceptions
1
Verification Report

V 	 Y—
COMPLIANCE
(Measurable Factors)

I

f DATA VALIDATION j
1
C Review exceptions identified in
[ Verification Report
1
Determine if analytical system
was in control
(Compliance with MQOs)
1
Determine if analytical system was
applicable to sample matrix
	 i 	
and uncertainty
1
Apply Qualifiers
1
Validation Report


J ^

1
f DATA QUALITY ASSESSMENT 1
1
Review DQOs and Project Plans
1


1
Determine if data are
accurate
1

^__L_^^
C^End of Assessment^)

(^JVIAKE DECISIoi\P^)
. 	
V ~
USABILITY
(Measurable & Nonmeasurable Factors)
^ - 	 '
Focus is typically on the analytical process
& individual datum
y-^^
Focus on the entire data collection process
& the entire dataset
                                  FIGURE 8.1 — The Assessment Process
161      iteratively and interactively with the generator (i.e., laboratory) to assure receipt of all necessary
162      data. Although the verification process identifies specific problems, the primary function should
163      be to apply appropriate feedback to the lab resulting in corrective action improving the analytical
164      services before the project is completed.
165      Validation addresses the reliability of the data. The validation process begins with a review of the
166     verification report and laboratory data package to identify its areas of strength and weakness.
167     This process involves the application of qualifiers that reflect the impact of not meeting the
168     MQOs. Validation then evaluates the data to determine the presence or absence of an analyte,
169     and the uncertainty of the measurement process. During validation, the technical reliability and
170     the degree of confidence in reported analytical data are considered. The data validator should be
171     a scientist with radiochemistry experience.
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172     Validation flags (i.e., qualifiers) are applied to data that do not meet the performance acceptance
173     criteria established in the SOW and the project plan documents. The products of the validation
174     process are validated data and a validation report stating which data are acceptable, which data
175     are sufficiently inconsistent with the validation acceptance criteria in the expert opinion of the
176     validator, and a summary of the QC sample performance. The appropriate data validation tests
177     should be established during the project planning phase. The point of validation is to perform a
178     systematic check on a set of data being used to meet the project MQOs and any other analytical
179     process requirements. Documenting that such a check cannot be done is an appropriate and
180     essential validation activity. (For example,  applying numerical tests to data already determined to
181     be unreliable data are of no value.)

182     Data Quality Assessment'^ the last phase of the data collection process, and consists of a
183     scientific and statistical evaluation of project-wide knowledge to assess the usability of data sets.
184     To assess and document overall data quality and usability, the data quality assessor integrates the
185     data validation report, field information, assessment reports, and historical project data,  and
186     compares the findings to the original project DQOs. The DQA process uses the combined
187     findings of these multi-disciplinary assessments to determine data usability for the intended
188     decisions, and to generate a report documenting that usability and the causes of any deficiencies.
189     It may be useful for a validator to work with the assessor to assure the value of the validation
190     process (e.g., appropriateness of rejection decision), and to make the process more efficient.
191     DQA will be covered in Chapter 9.

192     8.3 Validation Plan

193     The validation plan should integrate the contributions and requirements of all stakeholders and
194     present this information in a clear, concise format. To achieve this goal, validation planning
195     should be part of initial planning (e.g., directed planning process) to assure that the data will be
196     validated efficiently to determine  its reliability and technical defensibility in an appropriate
197     context and to an appropriate degree.

198     The validation plan is an integral part of the project  plan documents (Chapter 4), and should be
199     included as either a section within the plan  or as a stand-alone document attached as an appendix.
200     The validation plan should be approved by  an authorized representative of the project, the
201     validation group performing the validation, and any other stakeholder whose agreement is
202     needed.

203     The information and documentation identified in the validation plans should be communicated to
204     the laboratory as part of the SOW. Integration  of validation plan specifications, contractual

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205     requirements, and validator instructions/contracts is essential to ensure data collection process
206     efficiency. Implementation of the data validation plan will ensure that proper laboratory
207     procedures are followed and data are reported in a format useful for validation and assessment,
208     and will improve cost-effectiveness of the data collection process.

209     The data validation plan should contain the following information:

210       •  Summarize the project that provides sufficient detail about the project technical and quality
211         objectives in terms of sample and analyte lists, required measurement uncertainty, and
212         required detection limit and action level on a sample/analyte-specific basis. Specify the scope
213         of validation, e.g., whether all the raw data will be reviewed and in what detail (see Section
214         8.3.1).

215       •  Specify the necessary  validation criteria, as derived from the MQOs, and performance
216         objectives deemed appropriate for achieving project objectives (see Section 8.3.2).

217       •  Direction to the validator on what qualifiers are to be used and how final qualifiers are
218         assigned (see Section  8.3.3).

219       •  Direction to the validator on the content of the validation report (see Section 8.3.4).

220     8.3.1   Technical and Quality Objectives of the Project

221     The identity of key analytes and how the sample results drive project decisions should be
222     specified in the validation plan. In addition, the plan should define the association of required
223     quality control samples with project environmental samples.

224     This section of the validation plan should specify the following:

225       •  Quality control (QC) acceptance criteria;

226       •  Level of measurement uncertainty considered unusually high and unacceptable (tests of
227         unusual uncertainty and rejection); and

228       •  Action level and MQOs for detection and quantification capability (e.g., required detection
229         and quantification limit) (tests of detection).
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230     The quality control acceptance criteria serve two purposes: (1) to establish if the analytical
231     process was in control; and (2) to determine if project requirements were met. If the analytical
232     process is in control, the assumption was that the analysis was performing within established
233     limits and indicates a reasonable match among matrix/analyte/method. Generally this means that
234     routine data quality expectations are appropriate. The tests of unusual (i.e., analysis not in
235     control) uncertainty should verify the data meet the statistical confidence limits for uncertainty
236     associated with the planning process. During validation, the uncertainty associated with sampling
237     cannot be estimated. The tests of detection determine the presence or absence of analytes.

238     8.3.2  Validation Tests

239     Validating data requires three specific decisions that will allow the validator to qualify the data.
240     The project planning team should determine:

241       •  Which QC samples should be employed and how do they relate to the environmental
242         samples?

243       •  Which validation tests are appropriate?

244       •  What validation limits should be used for the specific tests?

245     The answers to these  questions are driven by the need to know whether the data meets the MQOs
246     for the project, and the allocation of resources between planning and implementation (i.e.,
247     conservative review may be more costly than real or perceived value in the decision). This
248     section of the  validation  plan should address the following:

249       •  QC sample validation criteria;

250       •  Specific validation tests to be used; and

251       •  Statistical  confidence intervals or fixed limit intervals applied to each of the validation tests
252         and criteria based on the MQOs for the project (Appendix C).

253     8.3.3  Data Qualifiers

254     Data qualifiers are codes placed on an analytical result that alert data users to the validator's or
255     verifier's concern about the result. This section of the validation plan should outline:
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256       •  The basis for rejection or qualification of data; and
257       •  The qualification codes that will be assigned.

258     These issues are discussed in detail in Section 8.5, which provides guidance for assigning data
259     qualifiers.

260     The verification process uses a qualifier (E) to alert the validator to non-compliance, including
261     missing documentation, contract compliance, etc. This qualifier may be removed or replaced
262     during validation, based on the validator's interpretation of the effect of the non-compliance on
263     the data's integrity.

264         E  A notice to the validator that something was noncompliant.

265     The validation process uses the qualifiers listed below to identify data points that do not meet the
266     project MQOs or other analytical process requirements listed in the SOW or appropriate project
267     plan document. The assignment of the J and R qualifiers relies heavily on the judgement and
268     expertise of the reviewer and therefore, these qualifiers should be assigned as appropriate  at the
269     end of data validation.

270         U  A normal, not detected (< critical value) result.

271
272         Q  A reported combined standard uncertainty, which exceeds the project's required method
273            uncertainty.

274         J  An unusually uncertain or estimated result.

275         R  A rejected result: the problems (quantitative and/or qualitative) are so severe that the data
276            can not be used.

277     The data validator should be aware that a data qualifier or a set of qualifiers does not apply to all
278     similar data. The data validator should incorporate the project MQOs into the testing and
279     qualifying decision-making process. During the data validation process the data validator  may
280     use additional qualifiers based on QC sample results and acceptance criteria. These qualifiers
281     may be summarized as U, J, R or Q in the final validation report. The final validation reports
282     should also include a summary of QC sample performance for use by the data assessor.
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283         S  A result with a related spike result (laboratory control sample (LCS), matrix spike(MS) or
284            matrix spike duplicate MSD), which is outside the control limit for recovery (%R), S+ or
285            S- used to indicate high or low recovery.

286         P  A result with an associated replicate result that exceeds the control limit.

287         B  A result with associated blank result, which is outside the control limit, B+ or B-.

288     8.3.4  Reporting and Documentation

289     The purpose of this section is to define the format and program needs for validation reports and
290     supporting documentation. This section should include:

291      •  Documentation and records that should be included in a validation report;

292      •  Disposition requirements for records and documents from the project;

293      •  Report format, i.e., a summary table with results, uncertainties and qualifiers; and

294      •  Procedures for non-conformance reporting, which detail the means by which the laboratory
295         communicates non-conformances against the validation plan. The procedures should include
296         all instances where the analytical data requirements and validation requirements established
297         by the planning process and validation plan, respectively, cannot be met due to sample matrix
298         problems and/or unanticipated laboratory issues (loss of critical personnel or equipment).

299     Detailed information about the Validation Report is presented  in Section 8.6.

300     8.4   Other Essential Elements

301     Effective data validation is dependent on:

302      •  A SOW and project plan  documents that clearly define the data needs and the data quality
303         requirements (i.e., MQOs); and

304      •  A data package that has been verified for completeness, consistency, compliance, and
305         correctness.

306     8.4.1  Statement of Work

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307     The analytical services procurement options should be considered during the planning process.
308     The SOW should specify the QC requirements that will be evaluated by the validator (see
309     Chapter 5). The elements that should be specified include, but are not limited to:

310      •  External performance evaluation (PE) participation and acceptance criteria;
311      •  Replicate sample frequency and acceptance criteria;
312      •  LCS and acceptance criteria;
313      •  Blank requirements and acceptance criteria;
314      •  MS and MSD samples and acceptance criteria;
315      •  Uncertainty calculations; and
316      •  Sample result equations and calculations including corrections for yield, percent moisture,
317         efficiencies and blank, if applied.

318     Section 8.5.2 provides guidance on evaluating QC sample results based on the project's MQO for
319     measurement uncertainty.

320     8.4.2   Verified Data Deliverables

321     Verification compares the sample receipt information and the sample report delivered by the
322     laboratory against the SOW and produces a report that identifies those requirements that were not
323     met (called exceptions). Verification can be accomplished using a plan or checklist, which
324     doesn't necessarily need to be project-specific. Verification exceptions normally identify:

325      •  Required steps not carried out by the laboratory (i.e., correction for yield, proper signatures);

326      •  Method QC not conducted at the required frequency (i.e., blanks, duplicates); and

327      •  Method QC not meeting pre-set acceptance criteria (i.e., non-compliant laboratory control
328         sample analysis).

329     The verifier checks the data package (paper or electronic) for completeness, consistency,
330     correctness, and compliance. Completeness means all required information is present.
331     Consistency means values are the same when reported redundantly on different reports, or
332     transcribed from one report to another. Correctness means the reported results are based on
333     properly documented  and correctly applied algorithms. Compliance means the data pass
334     numerical QC tests based on parameters or limits derived from the MQOs specified in the SOW.
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335     The verifier should provide, within the verification package, checklists for contract or SOW
336     specifications, noted deficiencies related to contract compliance, noted discrepancies or obvious
337     quality related problems, and pertinent external QC results. The verification package notes the
338     deficiencies, discrepancies, and quality-related problems that could not be resolved with the
339     laboratory. The validator should take this information into consideration during the data
340     validation process.

341     8.5   Data Verification and Validation Process

342     In its most basic form, data validation focuses on the reliability of each data point. After each
343     point is evaluated, summary conclusions concerning the validity of groups of data (sets) are
344     drawn and finally, after the reliability of all data sets has been established,  an overall conclusion
345     about the quality and defensibility of a project's analytical database is reached (DQA).

346     The first step in establishing the reliability of an analytical measurement is to determine that the
347     measurement analytical process used in making the measurement is in control. That is, the
348     sample handling and analysis system is performing within an accepted operating range
349     (established by instrument manufacturer, method, or contract specifications and/or long-term
350     historical laboratory performance). After it has been determined that the measurement analytical
351     process is in control, it is necessary to demonstrate that the sample is responding as expected
352     when introduced into the measurement system.

353     The measurement process includes devices such as detectors for measuring radioactive decay and
354     balances for determining the mass of materials. The measurement process  also includes the
355     software that takes the output from the measurement device and  calculates the result as a quantity
356     of target radionuclide (activity/mass activity/volume). The measurement process performance
357     normally is specified by the SOW and appropriate project plan documents, and monitored by
358     routine laboratory quality control procedures. Laboratory performance against these requirements
359     is determined by the verification process uses these requirements to determine laboratory
360     performance.

361     When an environmental sample is analyzed, new sources of variability are encountered in
362     addition to those associated with the measurement process. These sources include laboratory
363     subsampling, sample preparation (e.g., digestion, leaching, etc.), sample matrix effects, and data
364     transcription, to list a few. These processes, taken together with the previously discussed
365     measurement process, comprise the analytical process.
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366     The performance of the analysis can be predicted based on previous experience with similar
367     materials. Analysis performance is monitored by laboratory quality control procedures specified
368     in the SOW and appropriate project plan documents. Unlike the analytical process performance,
369     the overall performance of the analysis is not amenable to assessment by the data verification
370     process. Since each sample matrix, analyte, and method set is unique, the evaluation of overall
371     analysis performance and resulting data is the role of a knowledgeable validator.

372     Using the validation plan,  which specifies QC samples, validation tests, and validation limits,
373     validation occurs in four stages:

374      •  Determine whether the sample handling and analysis system is in control (Section 8.5.1);
375      •  Determine whether quality control sample analyses meet specified MQOs (Section 8.5.2);
376      •  Apply validation tests  of detection and unusual uncertainty (Section 8.5.3); and
377      •  Determine final data qualifiers and document the results (Section 8.5.4).

378     For other  chemistry methods, identification of the analyte is also a primary decision. Except for
379     gamma spectroscopy, this  is rarely an issue in radiochemistry. For radiochemistry, the
380     laboratory's ability to reliably identify analytes do reliable identifications is best checked by
381     auditors and verified by checking the calibration check samples.

382     8.5.1   The Sample Handling and Analysis System

383     As described in earlier sections of this guidance, it is necessary to know the extent to which the
384     data delivered for validation meet the requirements of the SOW and appropriate project plan
385     documents. These documents normally specify the minimum acceptable performance of the
386     analytical process. These specifications are the basis of the tests of quality control (QC tests) that
387     establish that the sample handling and analysis system is in control at the time the analyses were
388     performed. It is also necessary to know that all  reporting requirements are complete. Normally,
389     this evaluation against the requirements is made during the data verification process. If the data
390     do not conform to the requirements, notification should be provided in the verification report.

391     The review of the verification package (and data package) by the validator determines if
392     sufficient information is provided to proceed with data validation. The outcome  of the
393     verification process is the  designation of exceptions to the quality control tests. These exceptions
394     should be flagged with a qualifier (re-evaluated by the validator), which is appended to a data or
395     report requirement that does not meet specifications to alert the validator of potential problems.
396     The validator should then  determine if sufficient reliable data are available to proceed with
397     validation. The validator should use the data requirements and criteria developed in the


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398     validation plan to determine if the quality control exceptions have an adverse impact on one or
399     more of the data points being validated.

400     Rarely, if ever, should quality control exceptions result in the decision to reject a complete data
401     set. Those types  of situations should have been detected by the laboratory during the analytical
402     process and the samples reanalyzed. The validator should not reject (assign an "R" code) single
403     data points based on a single QC test exception. Normally, only numerous QC exceptions and
404     failures in one or more of the tests of detection and uncertainty are sufficient reason to reject
405     data. The validation report should fully explain the assignment of all  qualifiers as previously
406     discussed.

407     The following paragraphs discuss some of the more important evaluations that should be applied
408     to the sample handling and analysis system. Limited guidance is provided on how the QC test
409     may impact data quality and defensibility.

410     8.5.1.1  Sample Descriptors

411     Sample descriptors include sample identification number, analytical method, analyte, and matrix,
412     among others.

413     Criteria. Each sample should have a unique identifier code that can be cross-referenced to a
414     unique field sample or an internally generated laboratory sample. This unique identifier and
415     associated sample descriptors should be included in all analytical reports to properly document
416     the sample and requested analysis (Chapters 10 and 11).

417     The matrix and other characteristics of the sample that affect method selection and performance
418     should be clearly identified. The method(s) used in sample preparation and analysis should be
419     identified.

420     If laboratory replicate analyses are reported  for a sample, they should be distinguishable by a
421     laboratory-assigned code.

422     Verification. Each of the criteria related to describing the sample should be checked for and
423     found in the analytical data package. If any of the criteria are missing, they should be flagged
424     with an "E" code.

425     Validation. Missing information will increase the uncertainty on any result reported on a
426     sample(s) and justify the assignment of a " J" code. Missing information may be inferred from


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427     other information in the data package and eliminate the added uncertainty. For example, if the
428     sample matrix is not provided, it may be inferred from:

429      •  The aliquant units are expressed in units of mass or volume;
430      •  The sample preparation method is specific for soils;
431      •  The final results are expressed in units of mass; and
432      •  The sampling report describes sampling soil.

433     The majority of related information should support the decision that the exception does not
434     increase the uncertainty of the result. If the supporting information is incomplete or conflicting,
435     the assignment of a "J" code to data points is warranted. If documentation is inadequate to
436     support the reporting of a data point, the data point should be qualified with an "R" code.

437     8.5.1.2 Aliquant Size

438     Criteria. The aliquant or sample size used for analysis should be documented so that it can be
439     checked when reviewing calculations, examining dilution factors or analyzing any data that
440     requires aliquant as an input. It is also imperative that the appropriate unit (liter, kilogram, etc.) is
441     assigned to the aliquant.

442     Verification. The criteria related to describing the sample aliquant should be checked for and
443     found in the analytical data package. If the aliquant size is missing,  it should be flagged with an
444     "E" code.

445     Validation. The missing information will increase the uncertainty on any result reported on a
446     sample(s) and justify the assignment of a " J" code.

447     8.5.1.3 Dates of Sample Collection, Preparation, and Analysis

448     Criteria. The analytical data package should report date of sampling, preparation, and analysis.
449     These data are used to calculate radiological holding times, some of which may be  specified in
450     the sampling and analysis plan.

451     There are few circumstances  where radiological holding times are significant for radionuclides.
452     The best approach to minimize the impact of holding time on analysis is to analyze the samples
453     as quickly as possible. Holding times may be applied to environmental samples that contain
454     radionuclides with short half lives. Holding times would apply to these radionuclides to prevent
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455     reporting of high measurement uncertainties and MDCs, and to detect the radionuclide, if present
456     at low concentration, before it decays to undetectable levels.

457     Verification. Each of the criteria related to sample holding time should be checked for and found
458     in the analytical data package. If any of the objectives are missing, they should be flagged with
459     an "E" code.

460     If a holding time is specified in the project plan documents or validation plan, the reported values
461     should be compared to this specification. If the holding time is exceeded, the affected criteria
462     (holding time) should be flagged with an "E" code.

463     Validation. The data points impacted by the missed holding time should be flagged with a "J"
464     code by the validator or the justification for discounting the holding time impact described in the
465     narrative section of the validation report.

466     8.5.1.4 Preservation

467     Criteria. Appropriate preservation is dependent upon analyte and matrix, and should be defined
468     in sampling and analysis documentation. Generally, preservation is applied to samples being
469     analyzed for radionuclides to prevent precipitation, adsorption to container walls, etc. The criteria
470     (required presence or absence) for this QC process should be provided in the sampling and
471     analysis  plan (see Chapter 10).

472     Verification. The criteria related to preservation should be checked for and  found in the
473     analytical data package. If any of the criteria are missing,  they should be flagged with an "E"
474     code.

475     Validation. If exceptions to the preservation criteria are noted, the validator should decide if a
476     "J" code should be  assigned to data points because the improper preservation increased the
477     overall uncertainty  in the data point(s). In some cases where improper preservation severely
478     impacts data quality or defensibility (e.g., the use of acid preservation in water samples being
479     analyzed for 14C), the validator should assign an "R" qualifier. The assessor  may elect to use the
480     data, but they have  the responsibility of addressing the data quality and defensibility in the
481     assessment report.
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482     8.5.1.5 Tracking

483     Criteria. Each analytical result should be traceable to the instrument or detector on which it was
484     counted. The requirement for this traceability normally is found in the project plan documents.
485     The analytical sequence log (or some other suitable record) should be available in the data
486     package submitted by the laboratory.

487     Verification. If any of the analytical data are not traceable to the instrument or detector, it should
488     be flagged with an "E" code.

489     Validation. The validator may factor the absence of the traceability into their evaluation of data
490     quality and usability. At most, this should result in increasing the uncertainty of the
491     determination and the possible assignment of a " J" code to the data. This would not occur
492     normally unless one or more of the detectors used in analyzing the samples was shown to be
493     unreliable. Then,  the inability to trace  a reliable detector to a sample increases the uncertainty of
494     the data point(s).

495     8.5.1.6 Traceability

496     Criteria. The traceability of standards and reference materials to be used during the analysis
497     should be specified in the sampling and analysis plan.

498     Verification. The source of the reference materials and standards should be checked for and
499     found or referenced in the analytical data package. If any of the sources are missing they should
500     be flagged with an "E" code.

501     Validation. The validator may factor the absence of the traceability into their evaluation of data
502     quality and usability. At most, this should result in increasing the uncertainty of the
503     determination and the possible assignment of a " J" code to the data. This would not occur
504     normally unless one or more of the standards used in analyzing the samples was shown to be
505     unreliable. Then,  the inability to trace  a reliable standard to a sample increases the uncertainty of
506     the data point(s).

507     8.5.1.7 QC Types and Linkages

508     Criteria. The type and quantity of QC samples should be identified and listed in the SOW, and
509     the results provided by the laboratory in a summary report. Replicates and matrix  spike results
510     should be linked to the original sample results. The approximate level of matrix spike

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511     concentrations should be specified in the SOW, but the actual levels should be reported by the
512     laboratory. The QC analyses should be traceable to the original field sample.

513     Verification. Each of the criteria related to the QC samples should be checked for and found in
514     the analytical data package. If any of the objectives are missing, they should be flagged with
515     an "E" code.

516     Validation. The validator should compare any QC sample exceptions to similar ones that
517     precede and follow the non-conforming  QC sample.  If these are in control, the validator can
518     discount the impact of the single QC sample exception on the data results (i.e., analytical
519     blunder). If a trend of failing values is found, the validator should consider if they affected a
520     group of data points to the extent that the level of uncertainty was increased. This may warrant
521     the assignment of a "J" code to the data.

522     8.5.1.8 Chemical Separation (Yield)

523     Criteria. Yield assesses the effects of the sample matrix and the chemical separation steps on the
524     analytical result and estimates the analyte loss throughout the total analytical process. Yield is
525     typically measured gravimetrically (via a carrier) or radiometrically (via a tracer). All the
526     components in the calculation of the yield should be identified in a defined sequence. These
527     specifications are found in the project plan documents.

528     Criteria for both analytical process and sample analysis may be given in the project plan
529     documents. The criteria should be based on historical data for the method and matrix. In that
530     case, yield is determined on both quality control samples and actual field samples.

531     The most important yield-related question is whether the yield has been determined accurately.
532     Typically, a yield estimate that is much greater than  100 percent cannot be accurate, but the
533     estimate may also be questionable if the  yield is far outside its historical range. Extremely low
534     yields also tend to have large measurement uncertainties, which increase the uncertainties of the
535     results. The uncertainties of factors such as the yield, counting efficiency, and aliquant volume,
536     which affect the sensitivity of the measurement, should be kept relatively small.

537     Verification. Each of the yield-related criteria pertaining to the sample should be checked for
538     and found in the analytical data package. If missing,  the data should be returned to the lab to
539     correct for yi el d.
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540     Validation. The experimentally determined yield is used to normalize the observed sample
541     results to 100% yield. Exceptions to the yield value outside the range specified in the project plan
542     documents may result in the validator assigning a "J" qualifier to otherwise acceptable data.

543     8.5.1.9 Self-Absorption (Residue)

544     Criteria. For some radiochemical analytical methods, the SOW may specify the generation of a
545     self-absorption curve, which correlates mass of sample deposited in a known geometry to
546     efficiency.

547     Verification. Each self-absorption curve called for in the SOW should be checked for and found
548     in the analytical data package. If missing, they should be flagged with an "E" code.

549     Validation. If required self-absorption curves are missing, the validator may select to qualify
550     affected data with a "J" qualifier to signify an increased level of uncertainty in the measurement
551     because of the inability to correct the measured value for self-absorption.

552     8.5.1.10 Efficiency, Calibration Curves, and Instrument Background

553     Criteria. For some methods based on decay emission counting, efficiency is reported as count
554     rate divided by disintegration rate. Methods employing radiotracers determine a sample-specific
555     effective efficiency factor that is a product of the chemical yield and the detector efficiency. This
556     criteria may be specified in the SOW. Instrument background count rate is determined for each
557     detector for each region of interest and subtracted from the sample count rate.

558     Verification. Each efficiency determination, efficiency calibration curve, and instrument
559     background called for in the project plan documents should be checked for and found in the
560     analytical data package. If missing, they should be flagged with an "E" code.

561     Validation. If required factors are missing, the validator may select to qualify affected data with
562     a "J" qualifier to signify an increased level of uncertainty in the measurement because of the
563     inability to correct the measured value for efficiency.

564     8.5.1.11 Spectrometry Resolution

565     Criteria. The measured resolution of alpha, gamma-ray, and liquid scintillation spectrometers, in
566     terms of the full width of a peak at half maximum  (FWHM), can be used to assess the adequacy
567     of instrument setup, detector selectivity, and chemical separation technique that may affect the

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568     identification and quantification of the analyte. When sufficient peak definition (i.e., sufficient
569     number of counts to provide an adequate Gaussian peak shape) has been reached for a sample,
570     the resolution of the analyte peak should be evaluated to determine if proper peak identification
571     and separation or deconvolution was made. Spectral information should be provided in the data
572     packages to accomplish this evaluation.

573     Verification. There are no established acceptance criteria, but should be provided in the package
574     or available in the audit.

575     Validation. If required calculations are missing, the validator may select to qualify affected data
576     with a "J"  qualifier to signify an increased level of uncertainty in the measurement because of the
577     inability to evaluate instrument setup and separation technique. An "R" code may be applied if
578     there is no separation.

579     8.5.1.12  Dilution and Correction Factors

580     Criteria. Samples for radiochemistry are usually not diluted, but a larger sample may be
581     digested, taking an aliquant for analysis to obtain a more representative subsample. The dilution
582     factors are normally used for tracers and carriers. Dilutions of the stock standards are prepared
583     and added  to the samples. This dilution normally affects yield calculations, laboratory control
584     samples, and matrix spikes. This data should be provided in the data package so that the final
585     calculations of all data affected by dilution factors can be recalculated and confirmed, if required.

586     Other correction factors that may be applied to the  data are dry weight correction, ashed weight
587     correction, and correction for a two-phased sample analyzed as separate phases.

588     Verification. Each dilution and correction factor affecting the sample should be checked for and
589     found in the analytical data package. If any of the factors are missing, they should be flagged
590     with an "E" code.

591     Validation. Those  results impacted by missing dilution factors should be flagged with a "J" or
592     "R" qualifier, reflecting increased uncertainty in the data point(s). "R" may be warranted if the
593     calculation cannot be confirmed due to missing data.
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594     8.5.1.13 Counts and Count Time (Duration)

595     Criteria. The count time for each sample, QC analysis, and instrument background should be
596     recorded in the data package. The ability to detect radionuclide disintegrations is directly related
597     to the count time. The longer the count time, the lower the detection limit. The project plan
598     documents should specify the MQOs, which will drive the count time for each analyte.

599     Verification. Each count time relating to the sample analysis should be checked for and found in
600     the analytical data package. If any of the objectives are missing, they should be flagged with
601     an "E" code.

602     Validation. The validator should estimate the impact of the actual count times on the ability to
603     detect the target analyte and the impact on the uncertainty of the measurement. If the MQOs are
604     met, the sample should not be qualified for count time. It should be noted that preset count
605     determination, rather than preset count time, will result in the same uncertainly for all the
606     samples. The qualifiers should be adjusted accordingly and the justification provided in the
607     validation report.

608     8.5.1.14 Result of Measurement, Uncertainty, Minimum Detectable Concentration, and Units

609     Criteria. MARLAP recommends that the result of each measurement, its expanded measurement
610     uncertainty, and the estimated sample- or analyte-specific MDC be reported for each sample in
611     the appropriate units. These values, when compared with each other,  provide information about
612     programmatic problems with the calculations, interference of other substances, and bias.  The
613     report should state the coverage  factor used if calculating expanded measurement uncertainties,
614     and the Type I and Type n error probabilities used to calculate MDCs.

615     Verification. The linkage between the result, measurement uncertainties, MDC, and the sample
616     identification should be checked. If linkage is not  evident, data should be flagged with an "E"
617     code.

618     Validation. The validator should assign data qualifiers to those data points for which they feel
619     sufficient justification exists. Each qualifier should be discussed in the validation report.

620     8.5.2  Quality Control Samples

621     Historically, data validation has  placed a strong emphasis on review of QC sample data
622     (laboratory control samples, duplicates, etc). The assumption is that if the analytical process was

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623     in control and the QC samples responded properly, then the environmental samples (field
624     samples plus the preparation sequences used to prepare the sample for analysis) would respond
625     properly. It is possible to have excellent performance on simple matrices (e.g., quality control
626     samples), but unacceptable performance on complex matrices (i.e., environmental) reported in
627     the same batch as the QC samples. Directly evaluating the environmental sample performance is
628     essential to determine measurement uncertainty and the likelihood of false positive and negative
629     detect!on of the target analyte.

630     Method blanks and laboratory control samples relate to the analytical batch (a series of similar
631     samples prepared and analyzed together as a group) quality control function. They are required
632     by most analytical service contracts, sampling and analysis plans, and project plan documents.
633     They serve a useful function as monitoring tools that track the continuing analytical process
634     during extended analytical sequences. They are the most ideal samples analyzed as part of a
635     project. Normally, their performance is compared to fixed limits derived from historical
636     performance or additionally project specific limits derived from the MQOs.

637     Laboratory duplicates and matrix spikes are quality control samples that directly monitor sample
638     system performance.  The laboratory duplicates (two equal-sized samples of the material being
639     analyzed, prepared, and analyzed separately as part of the same batch) measure the overall
640     precision of the sample measurement process beginning with laboratory sub-sampling of the field
641     sample. Matrix  spikes (a known amount of target analyte added to the environmental sample)
642     provide a direct measure of how the target analyte responds when the environmental sample is
643     prepared and measured, thereby estimating the bias introduced by the sample matrix.

644     Other QC tests can be applied to determine how the analytical process performs during the
645     analysis of environmental samples. These are yield/recovery, efficiency, self-absorption,
646     resolution, and drift. They are the same QC tests that were applied to routine QC samples (blanks
647     and laboratory control samples) in the previous discussion of the analytical process, but now are
648     applied to environmental  samples. The difference lies in how performance is measured. Fixed
649     limits based on historical  performance and/or statistics are usually the basis for evaluating the
650     results of routine QC samples.

651     The following paragraphs discuss how QC tests should be used to determine if the results for QC
652     samples meet the project MQOs. Guidance is provided on how to relate QC sample and
653     environmental sample performance to determine environmental sample data quality and
654     defensibility. Direction is also given about how  to assign data qualifiers to environmental sample
655     data based on the tests of quality control. Appendix C provides guidance on developing criteria
656     for evaluating QC sample results. Specifically, Appendix C contains equations that allow for the


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657     determination of warning and control limits for QC sample results based on the project's MQO
658     for measurement uncertainty.

659     8.5.2.1 Method Blank

660     The method blank (Section 18.4.1) is generated by carrying all reagents and added materials
661     normally used to prepare an environmental sample through the same preparation process. It
662     establishes how much, if any, of the measured analyte is contributed by the reagents and
663     equipment used in the preparation process. For an ideal system, there will be no detected
664     concentration or activity.

665     Since measured results are usually corrected for instrument and reagent background levels, it is
666     possible to obtain final results that are less than zero. A method blank result that is much less
667     than zero may indicate that the correction term is too large and therefore analyte concentrations
668     in actual samples may be underestimated.

669     Criteria. The requirement for a method blank is usually established in the SOW and appropriate
670     plan documents. The objective is to establish the target analyte concentration or activity
671     introduced by the sample preparation sequence. Method blanks are normally analyzed once per
672     analytical batch.

673     Other types of blanks, such as field blanks and trip blanks, are used to evaluate aspects of the
674     data collection effort and laboratory operations that are not directly related to the validation of
675     environmental analytical data quality or technical defensibility.  They can be important to the
676     overall data assessment effort, but are beyond the scope of this guidance (Chapter 10).

677     See Appendix C for guidance on developing criteria for evaluating blanks based on the  project's
678     MQO for method uncertainty.

679     Verification. If a method blank was required but not performed, or if the required data is
680     missing, the verifier flags the missing information with an "E" code.

681     Validation. If a blank result does not comply with the  established criteria, the associated samples
682     are flagged "B+" to indicate that the blank result is greater than the upper limit, or "B-" to
683     indicate that the blank result is less than the lower limit.
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684     8.5.2.2 Laboratory Control Samples

685     The laboratory control sample (LCS) is a QC sample of known composition or an artificial
686     sample (created by spiking a clean material similar in nature to the environmental sample), which
687     is prepared and analyzed in the sample manner as the environmental sample. In an ideal situation,
688     the LCS would give 100 percent of the concentration or activity known to be present in the
689     fortified sample or standard material. Acceptance criteria for the LCS sample are based on the
690     complexity of the matrix and the historical capability of the lab and method to recover the
691     activity. The result normally is expressed as percent recovery. The LCS recovery differs from the
692     recovery of a matrix spike in that the matrix spike is  added directly to the environmental sample
693     and the percent recovery is determined by comparing the  difference between the original and
694     spiked samples.

695     Criteria. The objective of the LCS is to measure the response of the analytical process to a QC
696     sample with a matrix similar to the environmental sample. This will  allow inferences to be drawn
697     about the reliability of the analytical process.

698     See Appendix C for guidance on developing control limits for LCS results based on the project's
699     MQO for method uncertainty.

700     Verification. If a required LCS is not analyzed, or if required information is missing, the verifier
701     flags the missing information with an "E" code.

702     Validation. When the measured result for the LCS is outside the control limits, the associated
703     samples are flagged with the "S" qualifier (S+ or S-).

704     8.5.2.3 Laboratory Replicates

705     Replicates are used to determine the precision of laboratory preparation and analytical
706     procedures. Laboratory replicates are two aliquants selected from the laboratory sample and
707     carried through preparation and analysis as part of the same batch.

708     The discussion of field replicates is beyond the scope of this chapter.

709     Criteria. The objective of replicate analyses is to measure laboratory precision based on each
710     sample matrix. The variability of the samples due to field sample heterogeneity is also reflected
711     in the replicate result. The laboratory may not be in control of the precision. Therefore, replicate
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712     results are used to evaluate reproducibility of the complete laboratory process that includes
713     subsampling, preparation, and analytical process.

714     See Appendix C for guidance on developing control limits for replicate results based on the
715     project's MQO for method uncertainty.

716     Verification. If replicate analyses are required but not performed, or if the required data is not
717     present in the report, the verifier flags the missing information with an "E" code.

718     Validation. When the replicate analysis is outside the control limit, the associated samples are
719     flagged with the "P" qualifier.

720     8.5.2.4 Matrix Spikes and Matrix Spike Duplicates

721     The matrix spike is an aliquant of a sample, fortified (spiked) with known quantities of target
722     analytes and subjected to the entire analytical procedure to establish if the method or procedure is
723     appropriate for the analysis of the particular matrix.

724     Criteria. Matrix spike samples provide information about the effect of each sample matrix on
725     the preparation and measurement methodology.  The test uncovers the possible existence of
726     recovery problems, based on either a statistical test or a specified fixed control limit.

727     See Appendix C for guidance on developing criteria for evaluating matrix spikes based on the
728     proj ecf s MQO for method uncertainty.

729     Verification. If a required matrix spike analysis was not performed, or if the required
730     information is missing, the missing information  should be flagged with an "E" code.

731     Validation. If the results of the matrix  spike analysis do not meet the established criteria,  the
732     samples should be qualified with an "S+" or "S-" indicating unacceptable spike recoveries.

733     8.5.3  Tests of Detection and Unusual Uncertainty

734     8.5.3.1 Detection

735     The purpose of a test of detection is to decide if each result for a regular sample is significantly
736     different from zero. Since most radiochemistry methods always produce a result, even if a very
737     uncertain or negative one, some notion of a non-detected but measured result may be needed for

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738     some projects. A non-detected result is generally as valid as any other measured result, but it is
739     too small relative to its measurement uncertainty to give high confidence that a positive amount
740     of analyte was actually present in the sample. Ordinarily, if the material being analyzed is
741     actually analyte-free, most results should be "non-detected."

742     For some projects, detection may not be an important issue. For example, it may be known that
743     all the samples contain a particular analyte, and the only question to be answered is whether the
744     mean concentration is less than an action level. However, all laboratories should be able to
745     perform a test of detection routinely for each analyte in each sample.

746     Criteria. An analyte is considered detected when the measured analyte concentration exceeds its
747     critical value (see Chapter 19). Both values are calculated by the laboratory performing the
748     measurement; so, the detection decision can be made at the laboratory and indicated in its report.
749     If there is no evidence of additional unquantified uncertainty in the result (e.g., lack of statistical
750     control or blank contamination),  the laboratory's decision may be taken to be final.

751     Verification. Typically, the role  of the verifier is limited to checking that required information,
752     such as the critical value, is present in the report. If information is missing, the result should be
753     flagged with an "E" code.

754     Validation. The validator examines the result of the measurement, its critical value, and other
755     information associated with the sample and the batch in which it was analyzed, including method
756     blank results in particular, to make a final determination of whether the analyte has been detected
757     with confidence. If the data indicates the analyte has been detected in both the sample and the
758     method blank, its presence in the sample may be questionable. A quantitative comparison of the
759     total amounts of analyte in the sample and method blank, which takes into account the associated
760     measurement uncertainties, may  be needed to resolve the question.

761     8.5.3.2 Detection Capability

762     Criteria. If the project requires a certain detection capability, the requirement should be
763     expressed as a required minimum detectable concentration (RMDC). The data report should
764     indicate the RMDC and the sample-specific estimate of the actual minimum detectable
765     concentration (MDC) for each analyte in each sample.

766     In some situations, it may not be  necessary or even possible for a laboratory to meet the MDC
767     requirement for all analytes in all samples. In particular, if the analyte is present and quantifiable
768     at a concentration much greater than the action level, a failure to meet a contract-required

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769     detection limit is usually not a cause for concern. A failure to meet the RMDC is more often an
770     important issue when the analyte is not detected.

771     Verification. The RMDC specified in the contract is compared to the sample-specific MDC
772     achieved by the method. The analytes that do not meet the RMDC are flagged with an "E" code.

773     Validation. If the sample-specific MDC estimate exceeds the RMDC, the data user may be
774     unable to make a decision about the sample with the required degree of certainty. A "UJ"
775     qualifier is warranted if the estimated MDC exceeds the RMDC and the analyte was not detected
776     by the analysis. A final decision about the usability of the data should be made during the data
777     assessment phase of the data collection process.

778     An assignment of "R" to the data points affected by this type of exception may be appropriate in
779     some cases, but the narrative report may classify the data as acceptable (no qualifier), "U," or " J,"
780     based on the results of the tests of detection and uncertainty. This allows the assessor to make an
781     informed judgement about the usability of the data point(s) and allows them the opportunity to
782     provide a rationale of why the data can be used in the decision process.

783     8.5.3.3 Large or Unusual Uncertainty

784     When project planners follow MARLAP's recommendations for developing MQOs, they
785     determine a required method uncertainty at a  specified analyte concentration. The required
786     method uncertainty is normally expressed in concentration units, but it may be expressed as a
787     relative method uncertainty (percent based on the upper bound of the gray region, which is
788     normally the action level). It is reasonable to  expect the laboratory's combined standard
789     uncertainty at concentrations lower than the action level to be no greater than the required
790     method uncertainty (expressed in concentration units) and to expect the laboratory's relative
791     combined standard uncertainty at concentrations above the action level to be no greater than the
792     required relative method uncertainty (expressed as a percent). Each measured result should be
793     checked against these expectations (see Appendix C).

794     Criteria. The reported combined standard uncertainty is compared to the maximum allowable
795     standard uncertainty. Either absolute (in concentration units) or relative uncertainties (expressed
796     as a percent) are used in the comparison, depending on the reported concentration. The result is
797     qualified with a "Q"  if the reported uncertainty is larger than the requirement allows.

798     Verification. The test for large uncertainty is straightforward enough to be performed during
799     either verification or validation.  If there is a contractual requirement for measurement

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800     uncertainty, the verifier should perform the test and assign the "E" qualifier to results that do not
801     meet the requirement. Note that it may sometimes happen that circumstances beyond the control
802     of the laboratory make it impossible to meet the requirement.

803     Validation. If a "Q" qualifier is assigned, the validator may consider any special circumstances
804     that tend to explain it, such as interferences, small sample sizes, or long decay times, which were
805     beyond the control of the laboratory. He or she may choose to remove the qualifier, particularly if
806     it is apparent that the original uncertainty requirement was too restrictive.

807     8.5.4  Final Qualification and Reporting

808     The final step of the  validation process is to assign and report final qualifiers for all regular
809     sample results. The basis for assignment of final qualifiers is  qualifiers and reasons from all
810     previous tests, patterns of problems in batches of samples, and validator judgement.

811     The difficult issue during final qualifier assignment is rejecting data. What follows summarizes
812     some of the issues to consider when thinking about rejecting data.

813     Rejecting a result is an unconditional statement that it is not useable for the intended purpose. A
814     result should only be rejected when the risks of using it are significant relative to the benefits of
815     using whatever information it carries. If the DQA team or users feel data is being rejected for
816     reasons that don't affect usability, they may disregard all validation conclusions. Rejected results
817     should be discarded and not used in the DQA phase of the data life cycle.

818     There are three bases on which to reject data:

819         1.  Insufficient or only incorrect data are available to make fundamental decisions about data
820            quality. For example, if correctly computed uncertainty estimates are not available, it is
821            not possible to do most of the suggested tests. If the intended use depends on a consistent,
822            high level of validation, it may be proper to reject such data.

823            The missing data should be fundamental. For example, missing certificates for standards
824            are unlikely to be fundamental if lab performance on spiked samples is acceptable. In
825            contrast, if no spiked sample data is available, it may be impossible to determine if a
826            method gives even roughly correct results, and rejection may be appropriate.

827         2.  Available data indicate that the assumptions underlying the method are not true. For
828            example, QC samples may demonstrate that the lab's  processes are out of control.
829            Method performance data may indicate that the method simply does not work for
830            particular samples. These problems should be so severe that is not possible to make
831            quantitative estimates of their effects.

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832         3.  A result is "very unusually uncertain." It is difficult to say what degree of uncertainty
833            makes a result unusable. Whenever possible, uncertain data should be rejected based on
834            multiple problems with one result, patterns in related data, and the validator's judgement,
835            not the outcome of a single test. This requires radiochemistry expertise and knowledge of
836            the intended use.

837     Based on an evaluation of the tentative qualifiers, final qualifiers are assigned to each regular
838     sample result.

839     After all necessary validation tests have been completed and a series of qualifiers assigned to
840     each data point based on the results of the tests, a final judgment to determine which, if any, final
841     qualifiers will be attached to the data should be made. The individual sample data from the
842     laboratory should retain all the qualifiers. The basic decision making process for each result is
843     always subj ect to validator judgement:

844      •  As appropriate, assign a final "R";

845      •  IP'S", "P", or "B" were assigned, determine whether the qualifiers warrant the assignment of
846         an "R";

847      •  If "R" is not assigned, but some test assigned a tentative S, P, B, Q, or J, or a pattern exists
848         that makes it appropriate, assign a final S, P, B, Q, or J and summarize QC sample
849         performance;

850      •  If a final S, B, or J was assigned, + or -, but not both, was tentatively assigned, and the
851         potential bias is not outweighed by other sources of uncertainty, make the + or - final; and

852      •  For non-R results, if any test assigned a tentative "U," make it final.

853     The final validation decision should address the fact that the broader purpose of validation is to
854     contribute to the total data collection process,  i.e., effectively translate and interpret analytical
855     results for efficient use by an assessor. This means the validator should examine the full range of
856     data available to search  for and utilize relationships among the data elements to support the
857     acceptance and use of data that falls outside method or contract specifications and data validation
858     plan guidance.

859     8.6   Validation Report

860     The final product of validation is a package that summaries the validation process and its
861     conclusions in an orderly fashion. This package should include:
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862      •  A narrative or summary table written by the validator that summarizes exceptional
863         circumstances: In particular, it should document anything that prevented executing the
864         planned validation tests. Further, the narrative should include an explicit statement explaining
865         why data has been rejected or qualified based on the findings of the validation tests and the
866         validator's judgment.

867      •  A list of validated samples that provides a cross-reference of laboratory and client sample
868         identifiers: This report should also include other identifiers useful in the context of the
869         project, such as reporting batch,  chain of custody, or other sample management system
870         sample information.

871      •  A summary of all validated results with associated uncertainty for each regular sample with
872         final qualifiers: Unless  specified in the sampling and analysis plan, non-detects are reported
873         as measured, not replaced by a detection limit or other "less than" value.

874      •  A summary of QC sample performance and the potential effect on the data both qualified and
875         not qualified.

876     Assuming the client wants additional information, the following, more detailed reports can be
877     included in the validation package. Otherwise, they are simply part of the validation process and
878     the verification contract compliance:

879      •  A detailed report of all tentative  qualifiers and associated reasons for their assignment;

880      •  QC sample reports that  document analytical process problems; and

881      •  Reports that summarize performance by method—these should support looking across related
882         analyses at values such  as yields and result ratios.

883     The data in the summary reports should be available in a computer-readable format. If no result
884     was obtained for a particular analyte, the result field should be left blank. The validation report
885     should package analytical results as  effectively as possible for application and use by the
886     individual assembling and assessing all project data.

887     The validation report should contain a discussion describing the problem(s) found during the
888     validation process. For the validation codes, the discussion summarizes the performance criteria
889     established in the validation plan. If the validation test performance criteria were changed (e.g.,
890     increased or decreased level of unusual uncertainty) because the nature of the  sample matrix or
891     analyte was different than expected, the new criteria should be explained in the report and the
892     qualifiers applied using the  new criteria. The approval of the project manager  should be obtained
893     (and documented) before the new criteria are applied. The project manager should communicate

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894     the changes to the project planning team to maintain the consensus reached and documented
895     during validation planning.

896     Well-planned and executed analytical activities can be expected to meet reasonable expectations
897     for data reliability. This means that for most data points or data sets, the results of the tests of
898     quality control, detection, and unusual uncertainty will  show that the data are of sufficient quality
899     and defensibility to be forwarded to the assessor with little or no qualification for final
900     assessment. A small number of points will be rejected because random errors in the analytical
901     process or unanticipated matrix problems resulted in massive failure of several key validation
902     tests.

903     A smaller number of data points will show conflicting results from the validation tests and
904     present the greatest challenge to the validator. The more important the decision and/or the lower
905     the required detection limit, the more common this conflict will become, and the more critical it
906     is that the data validation plan provide guidance to the validator about how to balance the
907     conflicting results. Is the ability to detect the analyte more important than the associated
908     statistical unusual uncertainly, or is the presence of the  analyte relatively definite but the unusual
909     uncertainty around the project decision point critical to major decisions?  The necessary guidance
910     should be developed during the planning phase to guide the final judgment of the validator.

9ii     8.7   Other Sources  of Information

912     American National Standards Institute (ANSI) N13.30. 1996. Performance Criteria for
913         Radiobioassay.

914     U.S. Environmental Protection Agency (EPA).  1994. Contract Laboratory Program National
915         Functional Guidelines for Inorganic Data Review. EPA-540/R-94-013 (PB94-963502).
916         February. Available from http://www.epa.gov/oerrpage/superfund/programs/clp/download/
917         fginorg.pdf.
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                         9  DATA QUALITY ASSESSMENT
 2     9.1    Introduction

 3     This chapter provides an overview of the data quality assessment (DQA) process, the third and
 4     final process of the overall data assessment phase of a project. Assessment is the last phase in the
 5     data life cycle and precedes the use of data. Assessment—in particular DQA—is intended to
 6     evaluate the suitability of project data to answer the underlying project questions or the suitability
 7     of project data to support the project decisions.  The output of this final assessment process is a
 8     determination as to whether a decision can or cannot be made within the project-specified data
 9     quality objectives (DQOs).

10     The discussions in this chapter assume that prior to the DQA process, the individual data
11     elements have been subjected to the first two assessment processes, "data verification" and "data
12     validation" (see Chapter 8, Radiochemical Data Verification and Validation). The line between
13     these three processes has been blurred for some time and varies from guidance to guidance and
14     practitioner to practitioner. Although the content of the various processes is the most critical
15     issue, a common terminology is necessary to minimize confusion and to improve communication
16     among planning team members, those who will implement the plans, and those responsible for
17     assessment. MARLAP defines these terms in Section 1.4 and discusses assessment in  Section 8.2

18     This chapter is not intended to address the detailed and specific technical  issues needed to assess
19     the  data from a specific project but rather to impart a general understanding of the DQA process
20     and its relationship to the other assessment processes, as well as of the planning and implemen-
21     tation phases of the project's data life cycle. The target audience for this chapter is the project
22     planner, project manager, or other member of the planning team who wants to acquire a general
23     understanding of the DQA process; not the statistician, engineer, or radiochemist who is seeking
24     detailed guidance for the planning or implementation of the assessment phase. Guidance on
25     specific technical issues is available (EPA, 2000; MARSSEVI, 2000; NRC, 1998).

26     This chapter emphasizes that assessment, although represented as the last phase of the project's
27     data life cycle, should be planned for during the directed planning process, and the needed
28     documentation should be provided during the implementation phase of the project.

29     Section 9.2 reviews the role of DQA in the assessment phase. Section 9.3 discusses the graded
30     approach to DQA. The role of the DQA team is discussed in  Section 9.4.  Section 9.5 describes
31     the  content of DQA plans. Section 9.6 details the activities that are involved in the DQA process.
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32     9.2   Assessment Phase

33     The assessment phase was discussed in Section 8.2. This subsection provides a brief overview of
34     the individual assessment processes, their distinctions, and how they interrelate.

35     "Data verification" generally evaluates compliance of the analytical process with project-plan
36     and other project-requirement documents, and the statement of work (SOW), and documents
37     compliance and noncompliance in a data verification report. Data verification is a separate
38     activity in addition to the checks and review done by field and laboratory personnel during
39     implementation.

40     Documentation generated during the implementation phase will be used to determine if the
41     proper procedures were employed and to determine compliance with project plan documents
42     (e.g.,  QAPP), contract-specified requirements, and measurement quality objectives (MQOs). Any
43     data associated with noncompliance will be identified as an "exception," which should elicit
44     further investigation during data validation.

45     Compliance,  exceptions,  missing documentation, and the resulting inability to verify compliance
46     should be recorded in the data verification report. Validation and DQA employ the verification
47     report as they address the usability of data in terms of the project DQOs.

48     "Data validation" qualifies the usability of each datum after interpreting the impacts of
49     exceptions identified during verification. The validation process should be well defined in a
50     validation plan that was completed during the planning phase. The validation plan, as with the
51     verification plan or checklist, can range from sections of a project plan to large  and detailed
52     stand-alone documents. Regardless of its size or format, the validation plan should address the
53     issues presented in Section 8.3.  Data validation begins with a review of project objectives and
54     requirements, the data verification report, and the identified exceptions. The data validator
55     determines if the analytical process was in statistical control (Section 8.5.1) at the time of sample
56     analysis, and whether the analytical process as implemented was appropriate for the sample
57     matrix and analytes of interest (Section 8.5.2). If the system being validated is found to be under
58     control and applicable to  the analyte and matrix, then the individual data points can be evaluated
59     in terms of detection (Section 8.5.3.1), detection capability (Section 8.5.3.2), and unusual
60     uncertainty (Section 8.5.3.3). Following these determinations, the data are assigned qualifiers
61     (Section 8.5.4) and a data validation report is completed (Section 8.6). Validated data are rejected
62     only when the impact of an exception is so significant that the datum is unreliable.
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63     While both data validation and DQA processes address usability, the processes address usability
64     from different perspectives. "Data validation" attempts to interpret the impacts of exceptions
65     identified during verification and the impact of project activities on the usability of an individual
66     datum. In contrast, "data quality assessment" considers the results of data validation while
67     evaluating the usability of the entire data set.

68     During data validation, the guidance in Chapter 8 strongly advises against the rejection of data
69     unless there is a significant argument to do so. As opposed to rejecting data, it is generally
70     preferable that data are qualified and that the data validator details the concerns in the data
71     validation report. However, there are times when data should be rejected, and the rational for the
72     rejection should be explained in the data validation report. There are times when the data
73     validator may have believed data should be rejected based on a viable concern, yet during DQA,
74     a decision could be made to employ the rejected data.

75     In summary, data validation is a transition from the compliance testing of data verification to
76     usability determinations. The results of data validation, as captured  in the qualified data and
77     validation reports, will greatly influence the decisions made during  the final assessment process,
78     data quality assessment, which is discussed in Section 9.6.

79     9.3   Graded Approach to Assessment

80     The sophistication of the assessment phase—and in particular DQA and the resources applied—
81     should be appropriate for the project (i.e., a "graded approach"). Directed planning for small or
82     less complex projects usually requires fewer resources and typically involves fewer people and
83     proceeds faster. This graded approach to plan design is also applied to the assessment phase.
84     Generally, the greater the importance of a project, the more complex a project, or the greater the
85     ramifications of an incorrect decision, the more resources will be expended on assessment in
86     general and DQA in particular.

87     It is important to note that the depth and thoroughness of a DQA will be affected by the
88     thoroughness of the preceding verification and validation processes. Quality control or statement
89     of work (SOW) compliance issues that are not identified as an "exception" during verification, or
90     qualified during validation, will result in potential error sources not being reviewed and their
91     potential impact on data quality will not be evaluated. Thus, while the graded approach to
92     assessment is a valid and necessary management tool, it is necessary to consider all assessment
93     phase processes  (data verification, data validation, and data quality  assessment) when assigning
94     resources to assessment.
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 95      9.4    The Data Quality Assessment Team

 96      The project planning team is responsible for ensuring that its decisions are scientifically sound
 97      and comply with the tolerable decision-error rates established during planning. MARLAP
 98      recommends the involvement of the data assessment specialist(s) on the project planning team
 99      during the directed planning process. This should result in a more efficient assessment plan and
100      should increase the likelihood that flaws in the design of the assessment processes will be
101      detected and corrected during planning. Chapter 2.4 noted that it is important to have an
102      integrated team of operational and technical experts. The data assessment specialist(s) who
103      participated as members of the planning team need not be the final assessors. However, using the
104      same assessors who participated in the directed planning process is advantageous, since they will
105      be aware of the complexities of the project's goals and activities.

106      The actual personnel who will perform data quality assessment, or their requisite qualifications
107      and expertise,  should be specified in the project plan documents. The project planning team
108      should choose a qualified data assessor (or team of data assessors) who is technically competent
109      to evaluate the project's activities and the impact of these activities on the quality and usability of
110      data. Multi-disciplinary projects may require a team of assessors (e.g., radiochemist, engineer,
ill      statistician) to address the diverse types of expertise needed to assess properly the representa-
112      tiveness of samples, the accuracy of data, and whether decisions can be made within the specified
113      levels of confidence. Throughout this manual, the term "assessment team" will be used to refer to
114      the assessor expertise needed.

ii5      9.5    Data  Quality Assessment Plan

116      To implement the assessment phase as designed and ensure that the usability of data are assessed
117      in terms of the project objectives, a detailed DQA plan should be completed during the planning
118      phase of the data life cycle.  This section focuses on the development of the DQA plan and its
119      relation to DQOs and MQOs.

120      The DQA plan should address the concerns and requirements of all stakeholders and present this
121      information in a clear, concise format. Documentation of these DQA specifications,
122      requirements, instructions, and procedures are essential to assure process efficiency and that
123      proper procedures are followed.  Since the  success of a DQA depends upon the prior two
124      processes of the assessment phase, it is key that the verification and validation processes  also be
125      designed and documented in respective plans  during the planning phase. Chapter 8 lists the types
126      of guidance and information that should be included in data verification and validation plans.
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127     MARLAP recommends that the DQA process should be designed during the directed planning
128     process and documented in a DQA plan. The DQA plan is an integral part of the project plan
129     documents and can be included as either a section or appendix to the project plan or as a cited
130     stand-alone document. If a stand-alone DQA plan is employed, it should be referenced by the
131     project plan and subjected to a similar approval process.

132     The DQA plan should contain the following information:

133      •  A short summary and citation to the project documentation that provides sufficient detail
134         about the project objectives (DQOs), sample and analyte lists, required detection limit, action
135         level, and level of acceptable uncertainty on a sample- or analyte-specific basis;

136      •  Specification of the necessary sampling and analytical assessment criteria (typically
137         expressed as MQOs for selected parameters such as method uncertainty) that are appropriate
138         for measuring the achievement of project objectives and constitute a basis for usability
139         decisions;

140      •  Identification of the actual assessors or the required qualifications and expertise that are
141         required for the assessment team performing the DQA (Section 9.4);

142      •  A description of the steps and procedures (including statistical tests) that will constitute the
143         DQA, from reviewing plans and implementation to authoring a DQA report;

144      •  Specification of the documentation and information to be collected during the project's
145         implementation;

146      •  A description for any project-specific notification or procedures for documenting the usability
147         or non-usability of data for the project's decision making;

148      •  A description of the content of the DQA report;

149      •  A list of recipients for the DQA report; and

150      •  Disposition and record maintenance requirements.
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151     9.6   Data Quality Assessment Process

152     MARLAP's guidance on the DQA process has the same content as other DQA guidance (ASTM
153     D6233; EPA, 2000; MARSSIM, 2000; NRC, 1998; USAGE, 1998), however, MARLAP
154     presents these issues in an order that parallels project implementation more closely. The
155     MARLAP guidance on the DQA process can be summarized as an assessment process that—
156     following the review of pertinent documents (Section 9.6.1)—answers the following questions:
157
158
159
   Are the samples representative? (Section 9.6.2)
   Are the analytical data accurate? (Section 9.6.3)
   Can a decision be made? (Section 9.6.4)
160     Each of these questions is answered first by reviewing the plan and then evaluating the
161     implementation. The process concludes with the documentation of the evaluation of the data
162     usability in a DQA Report (Section 9.7).

163     The DQA Process is more global in its purview than the previous verification and validation
164     processes. The DQA process should consider the combined impact of all project activities in
165     making a data usability determination. The DQA process, in addition to reviewing the issues
166     raised during verification and validation, may be the first opportunity to review other issues, such
167     as field activities and their impact on data quality and usability. A summary of the DQA steps
168     and their respective output is presented in Table 9.1.
169

170

171
172
                       TABLE 9.1 — Summary of the DQA process
 DQA PROCESS
                            Input
                                Output for DQA Report
1.  Review Project
   Plan Document
               The project plan document (or a cited
               stand-alone document) that addresses:
               (a) Directed Planning Process Report,
                  including DQOs, MQOs and
                  optimized Sampling and Analysis
                  Plan.
               (b) Revisions to documents in (a) and
                  problems or deficiency reports.
               (c) DQA Plan.
                                  Identification of project documents.
                                  Clear understanding by the assessment team of
                                  project's DQOs and MQOs.
                                  Clear understanding of assumptions made
                                  during the planning process.
                                  If a clear description of the DQOs does not
                                  exist, the assessment team should record the
                                  DQOs (as they were established for
                                  assessment).
173
174
2.
Are the Samples
Representative?
The project plan document (or a cited
stand-alone document) that addresses:
(a) The sampling portion of the
   Sampling and Analysis Plan.
Documentation of all assumptions as potential
limitations and, if possible, a description of
their associated ramifications.
The determination of whether the design
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            DQA PROCESS
                                  Input
                                                     Output for DQA Report
                              (b) SOPs for sampling.
                              (c) Sample handing and preservation
                                  requirements of the APS
                                                           resulted in a representative sampling of the
                                                           population of interest.
                                                           The determination of whether the sampling
                                                           locations introduced bias.
                                                           The determination of whether the sampling
                                                           equipment and their use as described in the
                                                           sampling procedures were capable of extracting
                                                           a representative set of samples from the material
                                                           of interest.
                                                           An evaluation of the necessary deviations
                                                           (documented),  as well as those deviations
                                                           resulting from misunderstanding or error, and a
                                                           determination of their impact on the representa-
                                                           tiveness of the  affected samples.
175
176
3.  Are the Data     The project plan documents (or a
   Accurate?        cited stand-alone document) which
                    address:
                    (a) The analysis portion of the
                       Sampling and Analysis Plan.
                    (b) The MQOs.
                    (c) SOPs for analysis.
                    (d) Analytical Protocol Specifications,
                       including quality control
                       requirements and MQOs.
                    (e) SOW.
                    (f) The selected analytical protocols.
                    (g) Ongoing evaluations of
                       performance.
                    (h) Data Verification and Validation
                       plans and reports.
                                                        A determination of whether the selected method
                                                        was appropriate for the intended application.
                                                        The identification of any potential sources of
                                                        inaccuracy.
                                                        An assessment of whether the sample analyses
                                                        were implemented according to the analysis
                                                        plan.
                                                        An evaluation of the impact of any deviations
                                                        from the analysis plan on the usability of the
                                                        data set.
177
178
4.
Can a Decision
be Made?
The project plan document (or a cited stand-alone
document) that addresses:
(a) The DQA plan, including the statistical tests to
   be used.
(b) The DQOs and the tolerable decision error
   rates.
 • Results of the statistical tests. If new tests were
   selected, the rationale for their selection and the
   reason for the inappropriateness of the
   statistical tests selected in the DQA plan.
 • Graphical representations of the data set and
   parameters) of interest.
 • Determination of whether the DQOs and
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                                                            tolerable decision error rates were met.
                                                            A final determination as to whether the data are
                                                            suitable for decision-making, estimation, or
                                                            answering questions within the levels of
                                                            certainty specified during planning.
179     9.6.1  Review of Project Documents

180     The first step of the DQA process is for the team to identify and become familiar with the DQOs
181     of the project and the DQA plan. Like the planning process, the steps of the DQA process are
182     iterative, but they are presented in this text in a step-wise fashion for discussion purposes.
183     Members of the assessment team may focus on different portions of the project plan documents
184     and different elements of the planning process. Some may do an in-depth review of the directed
185     planning process during this step; others will perform this task during a later step. The
186     assessment team should receive revisions to the project planning documents and should review
187     deficiency reports associated with the project. The subsections below discuss the key and
188     minimum project documents that should be reviewed.
189
190     9.6.1.1 The Project DQOs and MQOs

191     Since the usability of data is measured in terms of the project DQOs, the first step in the DQA
192     process is to acquire a thorough understanding of the DQOs. If the DQA will be performed by
193     more than one assessor, it is essential that the assessment team shares a common understanding
194     of the project DQOs and tolerable decision error rates.  The assessment team will refer to these
195     DQOs continually as they make determinations about data usability. The results of the directed
196     planning process should have been documented in the project plan documents. The project  plan
197     documents,  at a minimum, should describe the DQOs and MQOs clearly and in enough detail
198     that they are not subject to misinterpretation or debate at this last phase of the project.

199     If the DQOs and MQOs are not described properly in the project plan documents or do not
200     appear to support the project decision, or if questions arise, it may be necessary to review other
201     planning documents (such as memoranda) or to consult the project planning team or the core
202     group (Section 2.4). If a clear description of the DQOs does not exist, the assessment team
203     should record any clarifications the assessment team made to the DQO statement as part of the
204     DQA report.
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205     9.6.1.2 The DQA Plan

206     If the assessment team was not part of the directed planning process, the team should familiarize
207     itself with the DQA plan and become clear on the procedures and criteria that are to be used for
208     the DQA Process. If the assessment team was part of the planning process, but sufficient time has
209     elapsed since the conclusion of planning, the assessment team should review the DQA plan. If
210     the process is not clearly described in a DQA plan or does not appear to support the project
211     decision, or if questions arise, it may be necessary to consult the project planning team or the
212     core group. If necessary, the DQA plan should be revised. If it cannot be, any deviations from it
213     should be recorded in the DQA report.

214     During DQA, it is important for the team, including the assessors and statistician, to be able to
215     communicate accurately. Unfortunately, this communication can be complicated by the different
216     meanings assigned to common words (e.g., samples, homogeneity). The assessment team should
217     be alert to these differences during their deliberations. The assessment team will need to
218     determine the usage intended by the planning team.

219     It is important to use a directed planning process to ensure that good communications exist from
220     planning through data use. If the statistician and other experts are involved through the data life
221     cycle and commonly understood terms are employed, chances for success are increased.

222     9.6.1.3 Summary of the DQA Review

223     The review of project documents should result in:

224       • An identification and understanding of project plan documents, including any changes made
225        to them and any problems encountered with them;

226       • A clear understanding of the DQOs for the project.  If a clear description of the DQOs does not
227        exist, the assessment team should reach consensus  on the DQOs prior to commencing the
228        DQA and record the DQOs (as they were established for assessment) as part of the DQA
229        report; and

230       • A clear understanding of the terminology, procedures,  and criteria for the DQA process.
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231     9.6.2  Sample Representativeness

232     MARLAP does not provide guidance on developing sampling designs or a sampling plan. The
233     following discussion of sampling issues during a review of the DQA process is included for
234     purposes of completeness.

235     "Sampling" is the process of obtaining a portion of a population (i.e., the material of interest as
236     defined during the planning process) that can be used to characterize populations that are too
237     large or complex to be evaluated in their entirety.  The information gathered from the samples  is
238     used to make inferences whose validity reflects how closely the samples represent the properties
239     and analyte concentrations of the population. "Representativeness" is the term employed for the
240     degree to which samples properly reflect their parent populations. A "representative sample,"  as
241     defined in ASTM D6044, is "a sample collected in such a manner that it reflects one or more
242     characteristics of interest (as defined by the project objectives) of a population from which it was
243     collected" (Figure 9.1). Samples collected in the field as a group and subsamples generated as a
244     group in the laboratory (Appendix F) should reflect the population physically and chemically.  A
245     flaw in any portion of the sample collection or sample analysis design or their implementation
246     can impact the representativeness of the data and the correctness of associated decisions.
247     Representativeness is a complex issue related to analyte of interest, geographic and temporal
248     units of concern, and project objectives.

249     The  remainder of this subsection discusses the issues that should be considered in assessing the
250     representativeness of the samples: the sampling plan (Section 9.6.2.1) and its implementation
251     (Section 9.6.2.2). MARLAP recommends that all  sampling design and statistical assumptions  be
252     identified clearly in project plan documents along with the rationale for  their use.

253     9.6.2.1 Review of the Sampling Plan

254     The  sampling plan and its ability to generate representative samples are  assessed in terms of the
255     project DQOs. The assessors review the project plan with a focus on the approach to sample
256     collection, including sample preservation, shipping and subsampling in  the field and laboratory,
257     and sampling standard operating procedures (SOPs). Ideally the assessors would have been
258     involved in the planning process and would be familiar with the DQOs  and MQOs and the
259     decisions made during the selection of the sampling and analysis design. If the assessors were
260     part  of the project planning team, this review to become familiar with the project plan will go
261     quickly, and the team can focus on deviations from the plan that will introduce unanticipated
262     imprecision or bias (Section 9.6.2.2).
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                                                ANALYTICAL
                                               SUBSAMPLES
                                               n   n   n
            FIELD SAMPLES
                                                       Collectively Subsamples
                                                        represent population
                                                                              DATABASE
                          Collectively Samples
                          represent population
                                                         Database accurately represents
                                                           the measured population
                                                               characteristic
                                                POPULATION
263
    FIGURE 9.1 — Using physical samples to measure a characteristic of the population representatively.

APPROACH TO SAMPLE COLLECTION
264     Project plan documents (e.g., QAPP, SAP, Field Sampling Plan) should provide details about the
265     approach to sample collection and the logic that was employed in its development. At this stage,
266     the assessment team should evaluate whether the approach, as implemented, resulted in
267     representative samples. For example, if the approach was probabilistic, the assessment team
268     should determine if it was appropriate to assume that spatial or temporal correlation is not a
269     factor, and if all portions of the population had an equal chance of being sampled. If an
270     "authoritative" sample collection approach was employed (i.e., a person uses his knowledge to
271     choose sample locations and times), the assessment team—perhaps in consultation with the
272     appropriate experts (e.g., an engineer familiar with the waste generation process)—should
273     determine if the chosen sampling conditions do or do not result in a "worst case" or "best case."

274     The assessment team should evaluate whether the chosen sampling locations resulted in a
275     negative or positive bias, and whether the frequency and location of sample collection accounted
276     for the population heterogeneity.
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277     Optimizing the data collection activity (Section 2.5.4 and Appendix B3.8) involved a number of
278     assumptions. These assumptions are generally employed to manage a logistical, budgetary, or
279     other type of constraint, and are used instead of additional sampling or investigations. The
280     assessment team needs to understand these assumptions in order to fulfill its responsibility to
281     review and evaluate whether their continued validity based on the project's implementation. The
282     assessment team should review the bases for the assumptions made by the planning team because
283     they can result in biased samples and incorrect conclusions. For example, if samples are collected
284     from the perimeter of a lagoon to characterize the contents of the lagoon, the planning team's
285     assumption was that the waste at the lagoon perimeter has the same composition as that waste
286     located in the less-accessible center of the lagoon. In this  example,  there should be information to
287     support the assumption, such as historical data, indicating that the waste is relatively homogen-
288     ous and well-mixed. Some assumptions will be stated clearly in project plan documents. Others
289     may only come to light after a detailed review. The assessment team should review assumptions
290     for their scientific soundness and potential impact on the representativeness of the samples.

291     Ideally,  assumptions would be identified clearly in project plan documents, along with the
292     rationale for their use.  Unfortunately, this is uncommon, and in some cases, the  planners may be
293     unaware of some of the implied assumptions associated with a design choice. The assessment
294     team should document any such assumptions in the DQA report as  potential limitations and, if
295     possible, describe their associated ramifications. The assessment team  may also suggest
296     additional investigations to verify the validity of assumptions which are questionable or key to
297     the project.

298     SAMPLING SOPs

299     Standard operating procedures for sampling should be assessed for  their appropriateness and
300     scientific soundness. The assessment team should assess whether the sampling equipment and
301     their use, as described  in the sampling procedures, were capable of extracting a representative  set
302     of samples from  the material of interest.  The team also should assess whether the equipment's
303     composition was compatible with the analyte of interest. At this stage,  the assessment team
304     assumes the sampling  device was employed according to  the appropriate SOP. Section 9.6.2.2
305     discusses implementation and deviations from the protocols.

306     In summary, the  assessment team should investigate whether:

307      • The sampling device was compatible  with the material being sampled and with the analytes of
308        interest;
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309      • The sampling device accommodated all particle sizes and did not discriminate against
310        portions of the material being sampled;

311      • The sampling device resulted in contamination or loss of sample components;

312      • The sampling device allowed access to all portions of the material of interest;

313      • The sample handling, preparation, and preservation procedures maintained sample integrity;
314        and

315      • The field and laboratory subsampling procedures resulted in a subsample that accurately
316        represents the contents of the original sample.

317     These findings should be detailed in the DQA report.

318     9.6.2.2 Sampling Plan Implementation

319     The products of the planning phase are integrated project plan documents that define how the
320     planners intend the data collection process to be implemented.  At this point in the DQA process,
321     the assessment team determines whether sample collection was done according to the plan,
322     reviews any noted deviations from the protocols, identifies any additional deviations, and
323     evaluates the impact of these deviations on sample representativeness and the usability of the
324     data. The success of this review will be a function of the documentation requirements specified
325     during the planning process, and how thoroughly these requirements were met during sample
326     collection.

327     The determination as to whether the plans were implemented as written typically will be based
328     on a review of documentation generated during the implementation phase, through on-site
329     assessments, and during verification, if sampling activities (e.g., sample preservation) were
330     subjected to verification. In some instances, assessment team members may have firsthand
331     knowledge from an audit that they performed, but in general the assessment team will have to
332     rely upon documentation generated by others. The assessment team will review field notes,
333     sample forms, chain-of-custody forms, verification reports, audit reports, deviation reports,
334     corrective action documentation, QA reports, and reports to management. The assessment team
335     also may choose to interview field personnel to clarify issues or to account  for missing
336     documentation, .
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337     Due to the uncontrolled environments from which most samples are collected, the assessment
338     team expects to find some deviations even from the best-prepared plans. Those not documented
339     in the project deficiency and deviation reports should be detailed in the DQA report. The
340     assessment team should evaluate these necessary deviations, as well as those deviations resulting
341     from misunderstanding or error, and determine their impact on representativeness of the affected
342     samples. These findings also should be detailed in the DQA report.

343     In summary, the assessment team will develop findings and determinations regarding any
344     deviations from the original plan, the rationale for the deviations, and if the deviations raise
345     question of representativeness.

346     9.6.2.3 Data Considerations

347     Sample representativeness also can be evaluated in light of the resulting data. Favorable
348     comparisons of the data to existing data sets (especially those data sets collected by different
349     organizations and by different methods) offer encouraging evidence of representativeness, but
350     not absolute confirmation of sample representativeness, since both data sets could suffer from the
351     same bias and imprecision. The project plan documents should have referenced any credible and
352     applicable existing data sets identified by the planning team. Comparisons to existing data sets
353     may offer mutual support for the accuracy of each other, and when differences result they tend to
354     raise questions about both data sets. Quite often, the DQA assessors are looking for confirmatory
355     or conflicting information. How existing data sets are used during the DQA will be determined
356     by how much confidence the assessors place in them. If they are very confident in the accuracy of
357     existing data sets, then they may classify the new data as unusable if it differs from the existing
358     data. If there is little confidence in the existing data set, then the assessors may just mention in
359     the DQA report that the new data set was in agreement or not in agreement. However, if the
360     planning team has determined that additional data were needed, they probably will not have
361     sufficient confidence in the existing data set for purposes of decision-making.

362     Data comparison is an issue that could be addressed during validation to some degree, depending
363     on the validation plan. However, at this point in the DQA, comparable data sets serve a different
364     purpose. For example, the MDCs, concentration units, and the analytical methods may be the
365     same and allow for data comparison in validation. However, the assessors during DQA would
366     look for similarities and dissimilarities in reported concentrations for different areas of the
367     populations, and whether any differences might be an indication of a bias or imprecision that
368     makes the samples less representative. Temporal  and spatial plots of the data also may be helpful
369     in identifying portions of the sampled population that were over- or under-represented by the data
370     collection activity.


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371     The planning process and development of probabilistic sampling plans typically require
372     assumptions regarding average concentrations and variances. If the actual average concentrations
373     and variances are different than anticipated, it is important for the assessment team to evaluate
374     the ramifications of these differences on sample representativeness. As reported values approach
375     an action level, the greater the need for the sample collection activities to accurately represent the
376     population characteristics of interest.

377     During the evaluation of sample representativeness, as discussed in the previous subsections, the
378     assessment team has the advantage of hindsight, since they review the sample collection design
379     in light of project outcomes and can determine if the sample collection design could have been
380     optimized differently to better achieve project objectives. Findings regarding the representative-
381     ness of samples and how sampling can be optimized should be expeditiously passed to project
382     managers if additional sampling will be performed.

383     In summary, results of the evaluation of the sample representativeness are:

384       • An identification of any assumptions that present limitations and, if possible, a description of
385        their associated ramifications;

386       • A determination of whether the design resulted in a representative sampling of the population
387        of interest;

388       • A determination of whether the specified sampling locations, or alternate locations  as
389        reported, introduced bias;

390       • A determination of whether the sampling equipment and their use, as described in the
391        sampling procedures or as implemented, were capable of extracting a representative set of
392        samples from the material of interest; and

393       • An evaluation of the necessary deviations from the plan, as well as those deviations resulting
394        from misunderstanding or error, and a determination of their impact on the representativeness
395        of the affected samples.

396     The product of this step is a set of findings regarding the impact of representativeness—or the
397     lack thereof—that affects data usability. Findings and determinations regarding representative-
398     ness will impact the usability of the resulting data to varying degrees. Some findings may be so
399     significant (e.g., the wrong waste  stream was sampled) that the samples can be determined to be
400     non-representative and the associated data cannot be used; as a result, the DQA need not progress


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401     any further. Typically, findings will be subject to interpretation, and the impacts on representa-
402     tiveness will have to be evaluated in light of other DQA findings to determine the usability of
403     data.

404     9.6.3  Data Accuracy

405     The next step in the DQA process is the evaluation of the analysis process and accuracy of the
406     resulting data. The term "accuracy" describes the closeness of the result of a measurement to the
407     true value of the quantity being measured. The accuracy of results may be affected by both
408     imprecision and bias in the measurement process, and by blunders and loss of statistical control
409     (see Chapter 19, Measurement Statistics, for a discussion on measurement error).

410     Since MARLAP uses "accuracy" only as a qualitative concept, in accordance with the
411     International Vocabulary of Basic and General Terms in Metrology (ISO, 1993), the agreement
412     between measured results and true values is evaluated quantitatively in terms of the
413     "imprecision" and "bias" of the measurement process. "Imprecision" usually is expressed as a
414     standard deviation, which measures the dispersion of results about their mean. "Bias" is a
415     persistent distortion of results from the true value.

416     During the directed planning process, the project planning team should have made an attempt to
417     identify and control sources of imprecision and bias (Appendix B3.8). During DQA, the
418     assessment team should evaluate the degree of imprecision and bias and determine its impact on
419     data usability. Quality control samples are analyzed for the purpose of assessing imprecision and
420     bias. Spiked samples and method blanks typically are used to assess bias, and duplicates are used
421     to assess imprecision. Since a single measurement of a spike or blank principle cannot
422     distinguish between imprecision and bias, a reliable estimate of bias requires a data set that
423     includes many such measurements. Control charts of quality control (QC) data, such as field
424     duplicates, matrix spikes, and laboratory control samples are graphical representations  and
425     primary tools for monitoring the control of sampling and analytical methods and identifying
426     precision and bias trends  (Chapter 18, Quality Control).

427     Measurable types of bias  are identified and controlled through the application of quantitative
428     MQOs to QC samples, such as blanks, standard reference materials, performance evaluation
429     samples, calibration check standards, and spikes samples. Non-measurable forms of bias (e.g., a
430     method being implemented incorrectly, such as reagents being added in the incorrect order) are
431     usually identified and controlled by well-designed plans that specify quality assurance systems
432     that detail needed training, use of appropriate SOPs, deficiency reporting systems,  assessments,
433     and quality improvement processes.


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434     Bias in a data set may be produced by measurement errors that occur in steps of the measurement
435     process that are not repeated. Imprecision may be produced by errors that occur in steps that are
436     repeated many times. The distinction between bias and imprecision is complicated by the fact
437     that some steps,  such as instrument calibration and tracer preparation and standardization, are
438     repeated at varying frequencies. For this reason, the same source of measurement error may
439     produce an apparent bias in a small data set and apparent imprecision in a larger data set. During
440     data assessment, an operational definition of bias is needed. A bias may exist if results for
441     analytical spikes (i.e., laboratory control samples, matrix spike, matrix spike duplicate),
442     calibration checks, and performance evaluation samples associated with the data set are mostly
443     low or mostly high, if the results of method blank analyses tend to be positive, or if audits
444     uncover certain types of biased implementation of the SOPs. At times, the imprecision of small
445     data sets can incorrectly indicate a bias, while at other times, the presence of bias may be masked
446     by imprecision. Statistical methods can be applied to imprecise data sets and used to determine if
447     there are statistically significant differences between data sets or between a data set and an
448     established value. If the true value or reference value (e.g., verified concentration for a standard
449     reference material) is known, then statistics can be used to determine whether there is a bias.

450     Figure 9.2 employs targets to depict the impacts of imprecision and bias on measurement data.
451     The true value is portrayed by the bulls-eye and is 100 units (e.g., dpm, Bq, pCi/g). Ideally, all
452     measurements with the same true value would be centered on the target, and after analyzing a
453     number of samples with the  same true value, the reported data would be 100 units for each and
454     every sample. This ideal condition of precise and unbiased data is pictured in Figure 9.2(a). If the
455     analytical process is very precise but suffers from a bias, the situation could be as pictured in
456     Figure 9.2(b) in which the data are very reproducible but express a significant 70  percent
457     departure from the true value—a significant bias. The opposite  situation is depicted in Figure
458     9.2(c), where the data are not precise and every sample yields a different concentration. However,
459     as more samples are analyzed,  the effects of imprecision tend to average out, and lacking any
460     bias, the average measurement reflects the true concentration. Figure 9.2(d) depicts a situation
461     where the analytical process suffers from both imprecision and bias, and even if innumerable
462     samples with the same true value are collected and analyzed to control the impact of imprecision,
463     the bias would result in the reporting of an incorrect average concentration.
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                                       Precise
                                                                 Precise
                                                  100ppm = true
                                                  concentration
                                      Ave. = 100 = True Value
                                         i
                                         I
                                                                Biased
                                                                Ave. = 170
       True Value
       (100 ppm)
                                                                         Ave. = 170
                                       Imprecise
           Imprecise
                                                  100ppm = true
                                                  concentration
                                (c)      Unbiased
                                       Ave. = 100
                                       Ave. = 100 = True Value
            BiaFed
            Ave. = 150
                                                              True Value
                                                              (100 ppm) ]
                    i Ave. = 150
                            FIGURE 9.2 — Types of sampling and analytical errors.
464      Each target in Figure 9.2 has an associated frequency distribution curve. Frequency curves are
465      made by plotting a concentration value versus the frequency of occurrence for that concentration.
466      Statisticians employ frequency plots to display the imprecision of a sampling and analytical
467      event, and to identify the type  of distribution. The curves show that as imprecision increases the
468      curves flatten-out and there is  a greater frequency of measurements that are distant from the
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469     average value (Figures 9.2c and d). More precise measurements result in sharper curves (Figures
470     9.2a and b), with the majority of measurements relatively closer to the average value. The greater
471     the bias (Figures 9.2b  and d), the further the average of the measurements is shifted from the true
472     value. The smaller the bias (Figures 9.2a and c), the closer the average of the measurements is to
473     the true value.

474     The remainder of this  subsection focuses on the review of analytical plans (Section 9.6.3.1) and
475     their implementation (Section 9.6.3.2) as a mechanism to assess the accuracy of analytical data
476     and their suitability for supporting project decisions.

477     9.6.3.1 Revi ew of the  Analyti cal PI an

478     The analytical plan is  that portion of the project plan documentation (e.g., in QAPP or SAP) that
479     addresses the optimized  analytical design and other analytical issues (e.g., analytical protocol
480     specifications, SOPs). Its ability to generate  accurate data is assessed in  terms of the project
481     DQOs. The assessment team will refer to the DQOs and the associated MQOs as they review the
482     analytical protocol specifications to understand how the  planning team selected methods and
483     developed the analytical plan. If the assessors were part of the project planning team, this review
484     process will go quickly and the team can focus  on deviations from the plan that will introduce
485     unanticipated imprecision or bias. (The  term "analytical  plan" is not meant to indicate a separate
486     document.)

487     REVIEW OF THE MQOs, ANALYTICAL PROTOCOL SPECIFICATIONS, AND OPTIMIZED ANALYTICAL
488     DESIGN

489     The assessment team's review of the analytical  plan first should focus on the analytical protocol
490     specifications, including the MQOs, which were established by the project planning team
491     (Chapter 3). The team should understand how the analytical protocol specifications were used to
492     develop the SOW (Chapter 5) and select the method (Chapter 6). If the project and contractual
493     documentation are silent or inadequate on how  they address these key issues, the assessment
494     team may be forced to review the analytical  results in terms of the project DQOs and determine if
495     the MQOs achieved were sufficient to meet  the project's objectives.

496     As with the approach  to  sample collection, optimizing the analytical activity involved a number
497     of assumptions. Assumptions were made when  analytical issues were resolved during planning
498     and the decisions were documented in the analytical protocol specifications (Chapter 3). It is
499     important for the assessment team to be aware of these assumptions because they can result in
500     biases and incorrect conclusions. Some  assumptions will be clearly stated in the project plan


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501     documents. Others may only come to light after a detailed review. The assessment team should
502     review assumptions for their scientific soundness and potential impact on the data results.

503     Ideally, assumptions would be identified clearly in project plan documents, along with the
504     rationale for their use. Unfortunately, this  is uncommon, and in some cases, the planners may be
505     unaware of some of the implied assumptions associated with a design choice. The assessment
506     team should document any such assumptions in the DQA report as potential limitations and, if
507     possible, describe their associated ramifications. The assessment team may also suggest
508     additional investigations to verify the validity of assumptions which are questionable or key to
509     the project.

510     REVIEW OF THE ANALYTICAL PROTOCOLS

511     The analytical plan and the associated analytical protocols will be reviewed and assessed for their
512     scientific soundness, applicability to the sample matrix and the ability to generate precise and
513     unbiased data. The analytical protocols review should consider the entire analytical process, from
514     sample preparation through dissolution and separations, counting, data reduction, and reporting.
515     MARLAP, whose focus is on the analytical process, defines "analytical process" as including
516     sample handling in the field (e.g., filtration, sample preservation) to ensure that all activities that
517     could impact analyses would be considered. The assessment team should consider both sampling
518     and analytical processes in assessing data  quality—and such field activities as sample preserva-
519     tion—along with other issues that can affect representativeness (Section 9.6.2). The assessment
520     team also should review the contract evaluation (under the performance-based approach) for the
521     selection of the analytical protocols to  assure that the documentation showed that the protocol
522     could meet the analytical protocol specifications (which defines the MQOs).

523     Since the review of the analytical protocols will be performed with the advantage of hindsight
524     gained from the data verification and data validation reports, the assessment team also should
525     attempt to identify any flaws in the analytical protocols that may have resulted in noncompliance
526     with MQOs. The identification of these flaws is essential if future analyses will be required.

527     REVIEW OF VERIFICATION AND VALIDATION PLANS

528     To understand how the verification and validations processes were implemented and the degree
529     to which the assessors can rely upon their  findings, the assessors should familiarize themselves
530     with the verification and validation plans that were developed during the planning phase. A
531     review of these plans will indicate whether the issues deemed of importance to the assessors were
532     evaluated and the thoroughness of the evaluations.


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533     9.6.3.2  Analytical Plan Implementation

534     After reviewing the analytical plan, the assessment team should assess whether sample analyses
535     were implemented according to the analysis plan. Typically, the first two steps of the assessment
536     phase—data verification and data validation—have laid most of the groundwork for this
537     determination. However, the issue of whether the plan was implemented as designed needs to be
538     reviewed one final time during the DQA process. This final review is needed since new and
539     pertinent information may have been uncovered during the first steps of the DQA process.

540     The goal of this assessment of the analytical protocols and associated MQOs is to confirm that
541     the selected method was appropriate for the intended application and to identify any potential
542     sources of inaccuracy, such as:

543      • Laboratory subsampling procedures that resulted in the subsample that may not accurately
544        represent the content of the original sample;

545      • Sample dissolution methods that may not have dissolved sample components quantitatively;

546      • Separation methods whose partitioning coefficients were not applicable to the sample matrix;

547      • Unanticipated self-absorption that biased counting data;

548      • Non-selective detection systems that did not resolve interferences; or

549      • Data reduction routines that lacked needed resolution or appropriate interference corrections
550
551     The success of the assessment of the analytical plan implementation will be a function of the
552     documentation requirements specified during the planning process, and how thoroughly these
553     requirements were met during sample analysis. In some instances, assessment team members
554     may have firsthand knowledge from an audit that they performed, but in general the assessment
555     team will have to rely upon documentation generated by others.

556     In addition to verification and validation reports, the assessment team will review pertinent
557     documents such as: laboratory notebooks, instrument logs, quality control charts, internal
558     sample-tracking documentation, audit reports, deviation reports, corrective action documentation,
559     performance evaluation sample reports, QA reports,  and reports to management provided for
560     verification and validation. To clarify issues or to account for missing documentation, the
561     assessment team may choose to interview laboratory personnel.


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562     Verification and validation reports will be used to identify nonconformance, deviations, and
563     problems that occurred during the implementation of the analytical plan. The challenge during
564     DQA is to evaluate the impact of nonconformance, deviations, problems, and qualified data on
565     the usability of the overall data set and the ability of the data set to support the decision.

566     Deviations from the plan will be encountered commonly and the assessment team will evaluate
567     the impact of these deviations upon the accuracy of the analytical data. The deviations and the
568     assessment team's related findings should be detailed in the data quality assessment report.

569     The prior verification and validation processes and the prior DQA steps involving the evaluation
570     of sampling are all an attempt to define the quality of data by (1) discovering sources of bias,
571     quantifying their impact, and correcting the reported data; and (2) identifying and quantifying
572     data imprecision. The products of this step are a set of findings regarding the analytical process
573     and their impact on data usability. Some findings may be so significant (e.g., the wrong analytical
574     method was employed) that the  associated data cannot be used,  and as a result, the DQA need not
575     progress any further. Typically,  findings will be subject to interpretation and a final
576     determination as to the impacts will have to wait until the data has been subjected to evaluations
577     described in Section 9.6.4.

578     After reviewing the verification and validation reports, the outputs of the analytical data
579     evaluation are:

580      • A determination of whether the selected analytical protocols and analytical performance
581        specifications were appropriate for the intended  application;

582      • An identification of any potential sources of inaccuracy; and

583      • A determination of whether sample analyses were implemented according to the analysis plan
584        and the overall impact of any deviations on the usability of the data set.

585     9.6.4   Decisions and Tolerable Error  Rates

586     A goal of DQA is to avoid making a decision based on inaccurate data generated by analytical
587     protocols found to be out of control or on data generated from samples found to be nonrepresen-
588     tative, and to avoid making decisions based on data of unknown quality. Preferably, a decision
589     should be made with data of known quality (i.e., with data of known  accuracy from samples of
590     known representativeness) and within the degree of confidence  specified during the planning
591     phase.


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592     This section focuses on the final determination by the assessment team, who uses the information
593     taken from the previous assessment processes and statistics to make a final determination of
594     whether the data are suitable for decision-making, estimating, or answering questions within the
595     levels of certainty specified during planning.

596     9.6.4.1  Statistical Evaluation of Data

597     Statistics are used for the collection, presentation, analysis, and interpretation of data. The two
598     major branches of statistics, "descriptive statistics" and "inferential statistics," are applicable to
599     data collection activities. "Descriptive statistics" are those methods that describe populations of
600     data. For example, descriptive statistics include the mean, mode, median, variance, and
601     correlations between variables, tables, and graphs to describe a set of data.  "Inferential statistics"
602     use data taken from population samples to make estimates about the whole population
603     ("inferential estimations") and to make decisions ("hypothesis testing"). Descriptive statistics is
604     an important tool for managing and investigating data in order that their implications and
605     significance to the project goals can be understood.

606     Sampling and inferential statistics have identical goals—to use samples to make inferences about
607     a population of interest and to use sample data to make defensible decisions. This similarity is
608     the reason why planning processes, such  as those described in Chapter 2, couple sample
609     collection activities with statistical techniques to maximize the representativeness of samples, the
610     accuracy of data, and the certainty of decisions.

611     Due to the complexity of some population distributions (Appendix 19A) and the complex
612     mathematics needed to treat these distributions and associated data, it is often best to consult
613     with someone familiar with statistics to ensure that statistical issues have been addressed
614     properly. However, it is critical for the non-statistician to realize that statistics has its limitations.
615     The following statistical limitations should be considered when assessment teams and the project
616     planning team are planning the assessment phase and making decisions:

617      • Statistics are used to measure precision and, when true or reference values are known,
618        statistics can be applied to imprecise data to determine if a bias exists. Statistics do not
619        address all types of sampling or measurement bias directly.

620      • If the characteristic of interest in a  sample is  more similar to that of samples adjacent to it than
621        to samples that are further removed, the samples are deemed to be "correlated" and are not
622        independent of each other (i.e., there is a serial correlation such that samples collected close in
623        time or space have more similar concentrations than those samples further removed).


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624        Conventional parametric and non-parametric statistics require that samples be independent
625        and are not applicable to populations that have significantly correlated concentrations.

626     The statistical tests typically are chosen during the directed planning process and are documented
627     in the project plan documents (e.g., DQA plan, QAPP). However, there are occasions when the
628     conditions encountered during the implementation phase are different than anticipated (e.g., data
629     were collected without thorough planning, or data are being subjected to  an unanticipated
630     secondary data use). Under these latter conditions, the statistical tests will be chosen following
631     data collection.

632     The statistical analysis of data consists of a number of steps. The following outline of these steps
633     is typical of the analyses that a statistician would implement in support of a data quality
634     assessment.

635     CALCULATE THE BASIC STATISTICAL PARAMETERS

636     Chapter 19 has a detailed discussion of statistical issues, so a few concepts key to understanding
637     are summarized here. Statistical "parameters" are fundamental quantities that are used to describe
638     the central tendency or dispersion of the data being assessed. The mean, median, and mode are
639     examples of statistical parameters that are used to describe the central tendency,  while range,
640     variance, standard deviation, coefficient of variation, and percentiles are  statistical parameters
641     used to describe the dispersion of the data. These basic parameters are used because they offer a
642     means of understanding the data, facilitating communication and data evaluation, and generally
643     are necessary for subsequent statistical tests.

644     GRAPHICAL REPRESENTATIONS

645     Graphical representations of the data are similar to basic statistical parameters in that they are a
646     means of describing and evaluating data sets. Graphical representations of QC-sample results
647     used to evaluate project-specific control limits and warning  limits derived from the MQO criteria
648     are discussed in Appendix C. Graphical representations of field data over space or time have the
649     additional ability of offering insights,  such as identifying temporal and spatial patterns, trends,
650     and correlations. Graphical depictions are also an excellent means of communicating and
651     archiving information.
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652     REVIEW AND VERIFY TEST ASSUMPTIONS

653     Statistical tests are the mathematical structure that will be employed to evaluate the project's data
654     in terms of the project decision, question, or parameter estimate. Statistical tests are not
655     universally applicable, and their choice and suitability are based on certain assumptions. For
656     example:

657       • Some tests are suitable for "normal" distributions, while others are designed for other types of
658        distributions.

659       • Some tests assume that the data are random and independent of each other.

660       • Assumptions that underlie tests for "outliers" should be understood to ensure that hot spots or
661        the high concentrations symptomatic of skewed distributions (e.g., lognormal) are not
662        incorrectly censored.

663       • Assumptions are made regarding the types of population distributions whenever data are
664        transformed before being subjected to a test.

665       • Assumptions of test robustness need to be reviewed in light of the analyte. For example,
666        radiological data require statistical tests that can accommodate positive and negative numbers.

667     It is important that a knowledgeable person identify all assumptions that underlie the chosen
668     statistical tests, and that the data are tested to ensure that the assumptions are met.  If any of the
669     assumptions made during planning proved to be not true, the assessment team should evaluate
670     the appropriateness of the selected statistical tests. Any decision to change statistical tests should
671     be documented in the DQA report.

672     APPLYING STATISTICAL TESTS

673     The chosen statistical tests will be a function of the data properties, statistical parameter of
674     interest, and the specifics of the decision or question. For example, choice of the appropriate tests
675     will vary according to whether the data are continuous or discrete; whether the tests will be
676     single-tailed or double-tailed, whether a population is being compared to a standard or to a
677     second  population, or whether stratified sampling or simple random sampling was employed.
678     Once the statistical tests are deemed appropriate, they should be applied to the data by an
679     assessor who is familiar with statistics.  The outputs from applying the statistical tests and
680     comparisons to project DQOs are discussed in the following section.


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681     9.6.4.2 Evaluation of Decision Error Rates

682     The heterogeneity of the material being sampled and the imprecision of the sampling and
683     analytical processes generate uncertainty in the reported data and in the associated decisions and
684     answers. The project planning team, having acknowledging this decision uncertainty, will have
685     chosen "tolerable decision errors rates" during the planning process, which balanced resource
686     costs against the risk of making a wrong decision or arriving at a wrong answer. During this final
687     step of DQA process, the assessment team will use the project's tolerable levels of decision error
688     rates as a metric of success.

689     The DQA process typically corrects data for known biases and then subjects the data to the
690     appropriate statistical tests to make a decision, answer a question, or supply an estimate of a
691     parameter. The assessment team will compare statistical parameters—such as the sample mean
692     and sample variance estimates employed during the planning process—to those that were
693     actually obtained from sampling. If the distribution was different, if the mean is closer to the
694     action level, or if the variance is greater or less than estimated, one or all of these factors could
695     have an impact on the certainty of the decision. The assessment team also will review the results
696     of the statistical tests in light of missing data, outliers, and rejected data. The results of the
697     statistical tests are then evaluated in terms of the project's acceptable decision error rates. The
698     assessment team determines whether a decision could or could not be made, or why the decision
699     could not be made, within the project specified decision error rates.

700     In summary, outputs from this step  are:

701       • Generated statistical parameters;

702       • Graphical representations of the  data set and parameters of interest;

703       • If new tests were selected, the rationale for selection and the reason for the inappropriateness
704        of the statistical tests selected in the DQA plan;

705       • Results of application of the statistical tests; and

706       • A final determination as to whether the data are suitable for decision making, estimating, or
707        answering questions within the levels of certainty specified during planning.
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708     9.7    Data Quality Assessment Report

709     The DQA process concludes with the assessment team documenting the output of the statistical
710     tests and the rationale for why a decision could or could not be made, or why the decision could
711     not be made within the project specified decision error rates. The DQA report will document
712     findings and recommendations and include or reference the supporting data and information. The
713     DQA report will summarize the use of the data verification and data validation reports for data
714     sets of concern, especially if rejected for usability in the project's decision making. The report
715     also will document the answers to the three DQA questions:

716      • Are the samples representative?
717      • Are the data accurate?
718      • Can a decision be made?

719     Although there is little available guidance on the format for a DQA report, the report should
720     contain, at a minimum:

721      • An executive summary that briefly answers the three DQA questions and highlights major
722        issues, recommendations, deviations, and needed corrective actions;

723      • A summary of the project DQOs used to assess data usability, as well as  pertinent
724        documentation such as the project plan document, contracts, and SOW;

725      • A listing of those people who performed the DQA;

726      • A summary description of the DQA process, as employed, with a discussion of any deviations
727        from the DQA plan designed during the planning process (the DQA plan should be appended
728        to the report);

729      • A summary of the data verification and data validation reports that highlights significant
730        findings and a discussion of their impact on data usability (the data verification and data
731        validation reports should be appended to the DQA report);

732      • A discussion of any missing documentation or information and the impact of their absence on
733        the DQA process and the usability of the data;
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734
735
736

737
738
739
740

741
742

743
744
745
746


747

748
749
750

751
752
753

754
755

756
757

758
759
 • A thorough discussion of the three DQA questions addressing the details considered in
   Sections 9.6.2 through 9.6.4 (possible outputs to be incorporated in the report are listed at the
   conclusion of each these section);

 • A discussion of deviations, sampling, analytical and data management problems, concerns,
   action items, and suggested corrective actions (the contents of this section should be
   highlighted in the executive summary if the project is ongoing and corrections or changes are
   needed to improve the quality and usability of future data); and

 • A recommendation or decision on the usability of the data set for the project's decision
   making.

Upon completion, the DQA report should be distributed to the appropriate personnel as specified
in the DQA plan and archived along with supporting information for the period of time specified
in the project plan document. Completion of the DQA report concludes the assessment phase and
brings the data life cycle to closure.
                             Summary of Recommendations

   MARLAP recommends that the assessment phase of a project (verification, validation, and
   DQA processes) be designed during the directed planning process and documented in the
   respective plans as part of the project plan documents.

   MARLAP recommends that project objectives, implementation activities, and QA/QC data
   be well documented in project plans, reports, and records, since the success of the
   assessment phase is highly dependent upon the availability of such information.

   MARLAP recommends the involvement of the data assessment specialist(s) on the project
   planning team during the directed planning process.

   MARLAP recommends that the DQA process  should be designed during the directed
   planning process and documented in a DQA plan.

   MARLAP recommends that all sampling design and statistical assumptions be clearly
   identified in project plan documents along with the rationale for their use.	
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760     9.8   References

761     9.8.1  Cited Sources

762     American Society for Testing and Materials (ASTM) D6044. Guide for Representative Sampling
763        and Management of Waste and Contaminated Media. 1996.

764     American Society for Testing and Materials (ASTM) D6233. Standard Guide for Data
765        Assessment for Environmental Waste Management Activities. 1998.

766     International Organization for Standardization (ISO) 1993. International Vocabulary of Basic
767        and General Terms in Metrology.

768     MARSSIM. 2000. Multi-Agency Radiation Survey and Site Investigation Manual, Revision 1.
769        NUREG-1575 Rev 1, EPA 402-R-97-016 Revl, DOE/EH-0624 Revl. August. Available
770        from http://www.epa.gov/radiation/marssim/filesfm.htm.

771     U.S. Army Corps of Engineers (USAGE). 1998. Technical Project Planning (TPP) Process.
Ill        Engineer Manual EM-200-1-2.

773     U.S. Environmental Protection Agency (EPA). 2000. Guidance for the Data Quality Objective
774        Process (EPA QA/G-4). EPA/600/R-96/055, Washington, DC. available from www.epa.gov/
775        qualityl/qa_docs.html.

776     U.S. Nuclear Regulatory Commission (NRC).  1998. A Nonparametric Statistical Methodology
111        for the Design and Analysis of Final Status Decommissioning Surveys. NUREG 1505, Rev. 1.

778     9.8.2  Other Sources

779     American Society for Testing and Materials (ASTM). 1997. Standards on Environmental
780        Sampling, 2nd Edition, ASTM PCN 03-418097-38. West Conshohocken, PA.

781     American Society for Testing and Materials (ASTM) D5956. Standard Guide for Sampling
782        Strategies for Heterogeneous Wastes.

783     American Society for Testing and Materials (ASTM) D6051. Guide for Composite Sampling and
784        Field Subsampling for Environmental Waste Management A ctivities. 1996.

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785     American Society for Testing and Materials (ASTM) D6311. Standard Guide for Generation of
786        Environmental Data Related to Waste Management Activities: Selection and Optimization of
787        Sampling Design. 1998

788     American Society for Testing and Materials (ASTM) D6323. Standard Guide for Laboratory
789        Subsampling of Media Related to Waste Management Activities. 1998.

790     Taylor, John Keenan. 1987. Quality Assurance of Chemical Measurements. Lewis Publishers,
791        Chelsea MI, ISBN 0-87371 -097-5.

792     U. S. Environmental Protection Agency (EPA). 1994. Guidance for the Data Quality Objective
793        Process, EPA QA/G-4, EPA/600/R-96/055, September.

794     U. S. Environmental Protection Agency (EPA). 1997. Guidance for Quality Assurance Project
795        Plans, EPA QA/G-5, August.
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 i         10  FIELD AND SAMPLING ISSUES THAT AFFECT

 2                     LABORATORY MEASUREMENTS


 3     Part I:  Generic Issues

 4     10.1   Introduction

 5     The primary purpose of this chapter is to provide guidance on issues that affect laboratory
 6     measurements to project planners and managers tasked with developing a field sampling plan.
 7     Specifically, this chapter provides guidance on activities conducted primarily after the proper
 8     collection of the sample. Sampling design and collection are beyond the scope of MARLAP. A
 9     field sampling plan should be a comprehensive document  that provides detailed guidance for
10     collecting, preparing, preserving, shipping, tracking field samples, and recording field data. The
11     principal objective of a well-designed sampling plan is to  provide representative samples of the
12     proper size for analysis. Critical to the sampling plan are outputs of the systematic planning
13     process, which commonly define the Analytical Protocol Specifications (APS) and the
14     Measurement Quality Objectives (MQO) that must be met. While a comprehensive discussion
15     that extends to field sampling strategies is beyond the scope  of this chapter, specific aspects of
16     sample collection methods and physical preparation and preservation of samples warrant further
17     discussion because they impact the analytical process and  the data quality.

18     This chapter is divided into two main parts. Part I identifies general elements of a field sampling
19     plan and provides project planners with general guidance.  Part II provides more detailed informa-
20     tion. Matrix-specific guidance and technical data are presented for liquid, solid, airborne, and
21     surface contaminants requiring field sampling. This information will assist project planners
22     further in the development of standard operating procedures (SOPs) and training for field
23     personnel engaged in preparation and preservation of field samples.

24     The need to specify  sample collection methods, and preparation and preservation of field
25     samples, is commonly  dictated by one or more of the following:

26      • The systematic planning process that identifies the type,  quality, and quantity of data needed
27        to satisfy a decision process;

28      • The potential alteration of field samples by physical, chemical, and biological processes
29        during the time between collection and analysis;

30      • Requirements specified by the analytical laboratory pertaining to sample analysis;

31      • Requirements of analytical methods; and

32      • Requirements of regulators (e.g., Department of Transportation).

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33     10.1.1 The Need for Establishing Channels of Communication

34     Of critical importance to the effective design of a sampling plan are the input and recommen-
35     dations of members representing: (1) the field sampling team; (2) the health physics professional
36     staff; (3) the analytical laboratory; (4) statistical and data analyses; (5) quality assurance
37     personnel, and (6) end-users of data.

38     Beyond the initial input that assist the project planners in the design of the sampling plan, it is
39     equally important to maintain open channels of communication among key members of the
40     project team throughout the process. For example, the analytical laboratory should be provided
41     with contacts from the field sampling team to ensure that modifications discrepancies and
42     changes are addressed and the timely resolution of potential problems.

43     Communication among project staff, field personnel, and the laboratory offer a means to
44     coordinate activities, schedules, and sample receipt. Project planning documents generated from
45     the systematic planning process, such as APS and statements of work (SOWs), should be
46     consulted, but they cannot address all details. Additional communication likely will be necessary.
47     Communication conveys information about the number and type of samples the laboratory can
48     expect at a certain time. Documentation with special instructions regarding the samples should be
49     received before the samples arrive. This information notifies the laboratory of any health and
50     safety concerns so that laboratory personnel can implement proper contamination management
51     practices. Health and safety concerns may affect analytical procedures, sample disposition, etc.
52     The analytical laboratory should have an initial understanding about the relative number of
53     samples that will be received and the types of analyses that are expected for specific samples.
54     Furthermore, advance communications allow laboratory staff to adjust to modifications,
55     discrepancies, and changes.

56     10.1.2 Developing Field Documentation

57     The field organization must conduct its operations in such a manner as to provide reliable
58     information that meets the data quality objectives (DQOs).  To achieve this goal,  all relevant
59     procedures pertaining to sample collection and processing should be based on documented
60     standard operating procedures that include the following activities:

61      •  Developing a technical basis for defining the size of individual samples;
62      •  Selecting field equipment and instrumentation;
63      •  Using proper sample containers and preservatives;
64      •  Using consistent container labels and sample identification codes;
65      •  Documenting field sample conditions and exceptions;
66      •  Documenting sample location;
67      •  Tracking, accountability and custody, and shipment forms;

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68      • Legal accountability, such as chain-of-custody record, when required;
69      • Selecting samples for field QC program;
70      • Decontaminating equipment and avoiding sample cross-contamination;
71      • Sample packaging, shipping, and tracking; and
72      • Health and safety plan.

73     10.2  Field Sampling Plan: Non Matrix Specific Issues

74     10.2.1 Determination of Analytical Sample Size

75     When collecting environmental samples for radioanalysis, an important parameter for field
76     personnel is the mass, volume, or weight of an individual sample that must be collected. The
77     required minimum sample size is best determined through the collective input of project
78     planners, field technicians, and laboratory personnel who must consider the likely range of the
79     contaminant concentrations, the type of radiation emitted by  constituents or analytes (a, p, y),
80     field logistics, and the radioanalytical methods that are to be  employed. For samples to yield
81     useful data, it is important to have a quantitative understanding of the relationship between
82     sample size and project specific requirements.

83     10.2.2 Field Equipment and Supply Needs

84     Before starting field sampling activities, all necessary equipment  and supplies should be
85     identified, checked for proper operation and availability, and—when appropriate—pre-
86     assembled. Instrumentation and equipment needs will depend not only on the medium to be
87     sampled, but also  on the accessibility of the medium and the  physical and chemical properties of
88     radionuclide contaminants under investigation.

89     Independent of specialized field equipment and instrumentation, field sampling supplies
90     commonly include the following:

91      • Sampling devices (e.g., trowel, hand auger, soil  core sampler, submersible water pump, high
92        volume air filter, etc.);

93      • Sampling preparation equipment (e.g., weighing scales, volume measuring devices, soil
94        screening sieves, water filtering equipment, etc.);

95      • Sample preservation equipment and agents (e.g., refrigeration, ice, formaldehyde or acid
96        additives);

97      • Personnel protective gear (e.g., respiratory protective devices, protective clothing such as
98        gloves and booties, life-preservers, etc.);
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 99      • Proper writing utensils (e.g., permanent pens and markers);

100      • Field logbooks and field tracking forms;

101      • Maps, distance measuring equipment, global positioning systems, or other location-
102        determining equipment;

103      • Field sampling flags or paint;

104      • Chain-of-custody (COC) forms;

105      • Sample tags, labels, documents;

106      • Appropriately labeled sample containers;

107      • Shipment containers and packing materials that meet DOT regulations;

108      • Shipment forms;

109      • Analysis request form identifying the type of radioanalysis to be performed; and

110      • Health and Safety Plan requirements (medical kit, etc.).

ill     10.2.3  Selection of Sample Containers

112     There are several physical and chemical characteristics that must be considered when selecting a
113     suitable container for shipping and storing samples. Important characteristics include the
114     container material and its size, configuration, and method for ensuring a proper seal.

115     10.2.3.1   Container Material

116     Sample containers must provide reasonable assurance of maintaining physical integrity (i.e.,
117     against breakage, rupture, or leakage) during handling, transport, and potentially long periods of
118     storage. The most important factor to consider in container selection is the chemical
119     compatibility between container material and sample. Containers may include ordinary bottle
120     glass, borosilicate glass (such as Pyrex or Corex), plastics (e.g., high density polyethylene—
121     FIDPE), low density polyethylene, polycarbonate, polyvinyl chloride (PVC), fluorinated ethylene
122     propylene (Reflon), or polymethelpentene. For select samples, the choice of containers may
123     require metal construction or be limited to paper envelopes.
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124     10.2.3.2   Container Opening and Closure

125     Selection of a suitable container also must consider the ease with which the sample is introduced
126     into the container. For example, a wide-mouthed container will provide easier access for the
127     introduction and withdrawal of sample material and eliminate spills or the need for additional
128     tools or equipment (e.g., funnel) that may become a source of cross contamination among
129     samples.

130     Equally important is the container closure or seal. As a rule, snap-on caps should not be
131     considered for liquid samples because they do not ensure a proper seal. Even when screw caps
132     are used, it is frequently prudent to protect against vibration by securing the cap with electrical or
133     duct tape. A proper seal is important for air samples, such as radon samples. The container cap
134     material, if different from the container material, must be equally inert with regard to sample
135     constituents.

136     10.2.3.3   Sealing Containers

137     Tamper-proof seals offer an additional measure to ensure sample integrity. A simple example
138     includes placing a narrow strip of paper over a bottle cover and then affixing this to the container
139     with a wide strip of clear tape (EPA, 1987, Exhibit 5-6, example of custody seals). The paper
140     strip can be initialed and dated in the field to indicate the staff member who sealed the sample
141     and the date of the seal. Individually sealing each sample with a custody seal with the collector's
142     initials and the date the sample was sealed may be required by the project. The seal ensures legal
143     defensibility and integrity of the sample at collection. Tamper-proof seals should only be applied
144     once field processing and preservation steps are completed. Reopening this type of sealed
145     container in the field might warrant using a new container or collecting another sample.

146     10.2.3.4   Precleaned and Extra Containers

147      The reuse of sample containers is discouraged because traces of radionuclides might persist from
148     initial container use to subsequent use. The use of new containers for each collection removes
149     doubts concerning radionuclides from previous sampling. New containers might also require
150     cleaning (ASTM D5245) to remove plasticizer used in container production or to pretreat glass
151     surfaces. Retaining extra empty containers from a new lot or a special batch of precleaned and
152     treated containers offers the laboratory container blanks for use as part of quality control. Extra
153     containers are also useful for taking additional samples as needed during field collection and to
154     replace broken or leaking containers.

155     10.2.4 Container Label and Sample Identification Code

156     Each sample can only be identified over the life of a study if a form of permanent identification
157     is provided with or affixed to the container or available in sample log. The most useful form of

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158     identification utilizes a unique identifier for each sample. Such unique identification codes
159     ensure the project's ability to track individual samples. The standard operating procedure (SOP)
160     that addresses sample identification should describe the method to be used to assure that samples
161     are properly identified and controlled in a consistent manner. Containers sometimes may be pre-
162     labeled with identification numbers already in place.

163     Any identification recorded on a container or a label affixed to the container should remain with
164     the container throughout sample processing and storage. The identification information should be
165     written with a permanent marker—especially if the labels are exposed to liquids. Information can
166     be recorded directly on the container or on plastic or paper tags securely fixed to the container.
167     However, tags are more likely to become separated from containers than are properly secured
168     labels.

169     Labels, tags, and bar codes should be rugged enough so no information is lost or compromised
170     during field work, sample transport, or laboratory processing. Transparent tape can be used to
171     cover the label once it is completed. The tape protects the label, adds moisture resistance,
172     prevents tampering with the sample information, and helps secure the label to the container.

173     The project manager needs to determine if a sample number scheme may introduce bias into the
174     analysis process. That is, the lab may be aware of trends or locations from the sample
175     identification and this could influence their judgment as to the anticipated result and thereby
176     introduce actions on the part of lab personnel that they would not otherwise take. The project
177     manager needs to determine the applicability of electronic field data recorders and the issue of
178     electronic signatures for the project.

179     A unique identifier can include a code for a site, the sample location at the site,  and a series of
180     digits identifying the year and day of year (e.g., "1997-127" uses the Julian date, and "062296"
181     describes a month, day, and year). Alternatively, a series of digits can be assigned sequentially by
182     site, date, and laboratory destination. The use of compass headings and grid locations also
183     provides additional unique information (e.g., "NW fence, sampled at grid points: Al through
184     C25, 072196, soil"). With this approach, samples  arriving at a laboratory are then unique in two
185     ways. First, each sample can be discriminated from materials collected at other  sites. Second, if
186     repeat  samples are made at a single site, then subsequent samples from the same location are
187     unique only by date. Labeling of samples sequentially might not be appropriate  for all studies.
188     Bar coding may reduce transcription errors and should be evaluated for a specific project.

189     10.2.5  Field Data Documentation

190     All information pertinent to field sampling is documented in a log book or on a data form. The
191     log book should be bound and the pages numbered consecutively and forms should be page-
192     numbered and dated. Where the same information is requested routinely, preprinted log books or
193     data sheets will minimize the effort and will standardize the presentation of data. Even when

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194     standardized preprinted forms are used, all information recorded should be in indelible ink, with
195     all entry errors crossed out with a single line and initialed. The color of ink used should be
196     compatible with the need to copy that information. All entries should be dated and signed on the
197     date of entry. Initials should be legible and traceable, so that it is clear who made the entry.

198     Whenever appropriate, log or data form entries should contain—but are not limited to—the
199     following:

200      •  Identification of Project Plan  or Sampling Plan;

201      •  Location of sampling (e.g., reference to grid location, maps,  photographs, location in a
202         room);

203      •  Date and time of sample collection;

204      •  Sample medium (e.g., surface water, soil, sediment, sludge, etc.);

205      •  Suspected radionuclide constituents;

206      •  Sample-specific ID number;

207      •  Sample volume, weight, depth;

208      •  Sample type (e.g., grab, composite);

209      •  Sample preparation used (e.g., removal of extraneous matter);

210      •  Sample preservation used;

211      •  Requested analyses to be performed (e.g., gross beta/gamma, gamma spectroscopy for a
212         specific radionuclide, radiochemical analysis);

213      •  Sample destination including name and address of analytical laboratory;

214      •  Names of field persons responsible for collecting sample;

215      •  Physical and meteorological conditions at time of sample collection;

216      •  Special handling or safety precautions;
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217       •  Recommendations regarding time to date of analysis that reflect (1) the loss of radioactivity
218         due to natural decay, (2) the ingrowth and secular equilibrium of short-lived progeny, or (3)
219         the potential loss of radioactivity due to evaporation or volatility; and

220       •  Signatures or initials of appropriate field personnel. When using initials, ensure that they can
221         be uniquely identified with an individual.

222     Labels affixed to individual sample containers should contain key information that is an abstract
223     of log book data sheets. When this is not practical, a copy of individual sample data sheets may
224     be included along with the appropriately ID-labeled sample.

225     10.2.6 Field Tracking, Custody, and Shipment Forms

226     A sample tracking procedure must be in place for all projects in order that the proper location and
227     identification of samples is maintained throughout the process from collection through handling,
228     preservation, storage, transfer to laboratory, and disposal. The term "tracking," when used here,
229     connotes a tracking and accountability process that meets generally acceptable laboratory
230     practices as described by accrediting bodies, but is less stringent than a formal chain-of-custody
231     process. Tracking also develops a record of all individuals responsible for the custody and
232     transfer of the samples. Chapter 4 {Project Plan Documents) discusses the process of tracking
233     and accountability. Also, Chapter 11  (Sample Receipt, Inspection, and Tracking)  discusses the
234     laboratory  process of tracking.

235     When transferring the possession of samples, the  individuals relinquishing and the individuals
236     receiving the samples should sign, date, and note  the time on the form. A standardized form
237     should be designed for recording tracking or formal chain-of-custody information related to
238     tracking sample possession. If samples are to be split and distributed to more than one analytical
239     laboratory, multiple forms will be needed to accompany sample sets. The sample collector is
240     responsible for initiating the sample tracking record. The following information is considered
241     minimal for sample tracking:

242       •  Name of project;
243       •  Sampler's signature;
244       •  Sample ID;
245       •  Sample location
246       •  Date and time sampled;
247       •  Sample type;
248       •  Preservatives;
249       •  Number of containers;
250       •  Analysis required;
251       •  Signatures of persons relinquishing, receiving, and transporting the samples;
252       •  Signature for laboratory receipt;

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253      •  Method of shipment or carrier and air bill when shipped or shipping manifest identification
254         upon receipt; and
255      •  Comments regarding the integrity of shipping container and individual samples.

256     10.2.7 Chain of Custody

257     The legal portion of the tracking and handling process that ensures legal defensibility from
258     sample collection to data reporting has become relatively standardized and is referred to as the
259     chain-of-custody (COC) process (APHA, 1996).  Guidance is provided in "Standard Practice for
260     Sampling Chain-of-Custody Procedures" (ASTM D4840) and NIOSH (1983). The level of
261     security required to maintain an adequate chain of custody is that necessary to establish a
262     "reasonable probability" that the sample has not been tampered with. For court proceedings, the
263     requirements are established in law. COC procedures are important in demonstrating sample
264     control when litigation is involved. In many cases, Federal, State or local agencies may require
265     that COC be maintained for specific projects. COC is usually not required for samples  that are
266     generated and immediately tested within a facility or continuous (rather than discrete or
267     integrated) samples that are subject to real- or near-real-time analysis (e.g., continuous
268     screening).

269     When COC is required, the custody information is recorded on a COC form. Chain-of-custody
270     documents vary by organization. Communication between field and laboratory personnel is
271     critical to the successful use of COC. Any error made on a custody form is crossed out with a
272     single line and dated and initialed. Use of correction ink or obliteration of data is not acceptable.
273     Inform the laboratory when COC is required before the samples are received (see Section  11.2
274     for further information). The COC documents are signed by personnel who collect the  samples.
275     A chain-of-custody record accompanies the shipment and one or more copies are distributed to
276     the project coordinator or other office(s) where field and laboratory records are maintained. An
277     example of a COC form is shown in Figure 10.1. Additional information and examples of
278     custody forms are illustrated by EPA (1987) and  EPA (1994).

279     10.2.8 Field Quality Control
280
281     A project plan should have been developed to ensure that all  data are accurate and that decisions
282     based on these data are technically sound and defensible. The implementation of a project plan
283     requires quality control (QC) procedures. QC procedures, therefore, represent specific tools for
284     measuring the degree to which quality assurance  objectives are met. Field quality control
285     measures are comprehensively discussed in ASTM D5283.

286     While some types of quality control (QC) samples are used to assess analytical process, field
287     quality control samples are used to assess the actual sampling process. The type and  frequency of
288     these field QC samples must be specified by the project planning process along with being
289     included in the project planning documents and identified in the sampling plan. Definitions for

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CHAIN-OF-CUSTODY RECORD
FIELD
IDENTIFI-
CATION
NUMBER










FIELD LOCATION










DATE










TIME










Relinquished by: (Signature)
Relinquished by: (Signature)
Relinquished by: (Signature)
Received by: (Signature)
Dispatched by: (Signature)
Date
SAMPLERS (Signature)
SAMPLE MATRIX
Water










Soil










Air










SEQ.
No.










No. of
Containers










Received by: (Signature)
Relinquished by: (Signature)
Received by: (Signature)
Received by Laboratory for field analysis:
(Signature)
Time
Received for Laboratory by:
Analysis
Required










Date/Time
/
Date/Time
/
Date/Time
/
Date/Time
/
Date/Time
/
Method of Shipment:
Distribution: Orig. - Accompany Shipment
1 Copy - Survey Coordinator Field Files

                           FIGURE 10.1—Example of chain-of-custody record.

290     certain types of field QC samples can be found in ASTM D5283 and MARS SIM (2000).

291     10.2.9 Decontamination of Field Equipment

292     Sampling SOPs must describe the recommended procedure for cleaning field equipment before
293     and during the sample collection process, as well as any pretreatment of sample containers. The
294     SOPs should include the cleaning materials and solvents used, the purity of rinsing solution or
295     water, the order of washing and rinsing, associated personnel safety precautions, and the disposal
296     of cleaning agents.
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297     Detailed step-by-step procedures for the decontamination of field equipment used in the
298     sampling of low-activity soils, soil gas, sludges, surface water, and ground water are given in
299     ASTMD5608.

300     10.2.10 Packing and Shipping

301     The final responsibility of field sampling personnel is to properly prepare and package samples
302     for transport or shipment by a commercial carrier. All applicable State and Federal shipping
303     requirements, as discussed later in this section, must be followed. Samples transported over
304     shorter distances by the sampling or testing agency by way of automobile, van, or truck will
305     require less stringent packing requirements. In most instances, placing sealed sample containers
306     within cardboard boxes (or similar containers) in which individual samples are sufficiently
307     cushioned to guard against bumping, rolling, or dropping, is adequate.

308     When samples must be shipped by way of a commercial carrier or the U.S. Postal Service,
309     containers must be designed to protect samples against crushing forces, impacts, and severe
310     temperature fluctuations. Within each shipping container, the cushioning material (sawdust,
311     rubber, polystyrene, urethane foam, or material with similar resiliency) should encase each
312     sample completely.  The cushioning between the samples and walls of the shipping containers
313     should have a minimum thickness of one inch. A minimum thickness of two inches should be
314     provided on the container floor.

315     Consideration must also be given to protect samples against potentially adverse impacts of
316     temperature fluctuations. When appropriate, sample protection against freezing, thawing,
317     sublimation, evaporation, or extreme temperature variation may require that the entire interior
318     surface of the shipping container be lined with an adequate layer of insulation. In many instances,
319     the insulating material may also serve as the cushioning material.

320     When metal containers are used, the requirements for container security,  cushioning, and insula-
321     tion apply equally. For smaller volume and low-weight samples, properly lined containers
322     constructed with laminated fiberboard, plastic, or reinforced cardboard outer walls also may be
323     used.

324     When samples are shipped as liquids in glass or other breakable sample containers, additional
325     packaging precautions may have to be taken. Additional protection is obtained when sample
326     containers are shipped in nested containers, in which several smaller containers (i.e., inner
327     containers) are packed inside a second larger container (i.e., the outer pack or overpack). To
328     contain any spills of sample material within the shipping container, it is advisable either to wrap
329     individual samples or to line the shipping container with absorbent material, such as asbestos-
330     free vermiculite or pearlite.
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331     For proper packaging of liquid samples, additional guidance has been given by EPA (1987) and
332     includes the following:

333      • All sample bottles are taped closed;
334      • Each sample bottle is placed in a plastic bag and the bag is sealed;
335      • Each sample bottle may be placed in a separate metal can filled with vermiculite or other
336        packing material, then the lid may be fixed to the can with tape;
337      • The cans are placed upright in a cooler that has its drain plug taped closed, inside and out,
338        and lined with a plastic bag; and
339      • The cooler is filled with packing material—"bubble wrap" or cardboard separators may be
340        used—and closed with sealing tape.

341     Field screening measurements are made for compliance with Department of Transportation
342     regulations, 49 CFR Parts 170 through 189, as well as compliance with the laboratory's U.S.
343     NRC (10 CFR Part 71) and Agreement State license. International requirements may also apply.
344     See International Air Transport Association (IATA) Dangerous Goods Regulations for additional
345     guidance. These regulations not only set contamination and dose limits for shipping containers,
346     but also describe the types of containers and associated materials that are to be used based on the
347     total activity and quantity of materials shipped. When the samples are screened in the field with
348     survey instrumentation, the results should be provided to the laboratory. This information should
349     also  state the distance used from the probe to the packing container wall. Measurements normally
350     are made in contact or at one meter. The readings in contact are most appropriate for laboratory
351     use.  The screening measurements in the field are mainly for compliance with transportation
352     requirements and are usually in units of exposure. Laboratory license requirements are usually by
353     isotope and activity. Project planning and communication are essential to ensure that a  specific
354     set of samples can be transported, received, and analyzed safely while complying with applicable
355     rules and regulations.
356
357     The  external surface of each shipping container must be labeled clearly, contain information
358     regarding the sender and receiver, and should include the respective name and telephone number
359     of a  contact. When required, proper handling instructions and precautions should be clearly
360     marked on shipping containers. Copies of instructions, shipping manifest or container inventory,
361     chain of custody, and any other paperwork that is enclosed within a shipping  container should be
362     safeguarded by placing documents within a sealed protected envelope.

363     10.2.11 Worker Health and Safety Plan

364     In some cases, field samples will be collected where hazardous agents or site conditions might
365     pose health and safety considerations for field personnel.  These can include chemical, biological,
366     and radiological agents, as well as common industrial hazards associated with machinery, noise
367     levels, and heat stress. The health and safety plan established in the planning  process should be
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368     followed. For the Department of Defense (DOD), these plans may include imminent threats to
369     life, such as unexploded ordnance, land mines, hostile forces, chemical agents, etc.

370     10.2.11.1 Physical Hazards

371     MECHANICAL EQUIPMENT

372     Personnel working with hand-held tools (e.g., sledge hammers used for near-surface coring) or
373     power tools and equipment are subject to a variety of hazards. For example, personnel drilling
374     monitoring wells are exposed to a variety of potential mechanical hazards, including moving
375     machinery, high-pressure lines (e.g., hydraulic lines), falling objects, drilling through under-
376     ground utilities, flying machinery parts, and unsafe walking and working surfaces. The
377     consequences of accidents involving these physical hazards can range from minor to fatal injury.

378     At a minimum, workers should be required to wear protective clothing, which includes hard hats,
379     gloves, safety glasses, coveralls (as an option) and steel-toed safety shoes. Workers required to
380     climb (e.g., ladders, drilling masts) must be required to wear harnesses and lanyards and be tied
381     off throughout the process.

382     For sampling operations that require drilling, open boreholes and wells must be covered or
383     secured when unattended, including during crew breaks.

384     ELECTRICAL HAZARDS

385     Electric power often is supplied by gasoline or diesel engine generators. Working conditions may
386     be wet, and electrical shock with possibly fatal consequences may occur. In addition, it is
387     possible that drilling operations may encounter overhead or buried electrical utilities, potentially
388     resulting in exposure to very high voltages, which could be fatal or initiate fires.

389     All electrical systems used during field operations should be checked for proper grounding
390     during the initial installation. Temporary electrical power provided to the drill site shall be
391     protected by ground fault circuit interrupters.

392     NOISE HAZARDS

393     Power equipment is capable of producing sound levels in excess of 85dB(A), the eight-hour
394     threshold limit value recommended by the American Conference of Governmental Industrial
395     Hygienists (ACGIH). Exposure to noise levels in excess of 85dB(A) for long periods of time can
396     cause irreversible hearing loss. If noise levels
397     exceed 85dB(A), a controlled area must be
398     maintained at this distance with a posting at
399     each entrance to the controlled area to read:
CAUTION
NOISE HAZARD
Hearing Protection Required Beyond This Point
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400     HEAT STRESS

401     The use of protective clothing during summer months significantly increases the potential for
402     personnel to experience heat stress. Adverse effects from heat stress include heat cramps,
403     dehydration, skin rash, heat edema, heat exhaustion, heat stroke or death. When heat stress
404     conditions exist, the following ought to be available:

405      •  A cool and shaded rest area;
406      •  Regular rest breaks;
407      •  An adequate supply of drinking water; and
408      •  Cotton coveralls rather than impermeable Tyvek coveralls.

409     CHEMICAL AND RADIOLOGICAL HAZARDS

410     The health and safety plan should contain information about a site's potential radionuclides and
411     hazards that might be encountered during implementation of field sampling and survey
412     procedures. All field personnel should read the health and safety plan and acknowledge an
413     understanding of the radiological hazards associated with a site. Site specific training must be
414     provided that addresses the chemical and radiological hazards likely to be associated with a site.
415     Field procedures should include either information relating to these hazards or should reference
416     appropriate sections of the Health and Safety Plan. References related to the use  of protective
417     clothing are given in EPA (1987), DOE (1987, Appendix J), and in 29 CFR 1910, Subpart I.

418     When procuring environmental solid and liquid samples, unusual characteristics such as color,
419     suspended material, or number of phases and unusual odors should be noted and a description
420     should be provided to the on-site safety officer as well as the analytical laboratory. Additional
421     information concerning field methods for rapid screening of hazardous materials is presented in
422     EPA (1987). This source primarily addresses the appearance and presence of organic compounds
423     that might be present on occasions when one is collecting materials to detect radioactivity.
424     Checking samples for chemical or radiological hazards can be as simple as visual inspection or
425     using a hand-held radiation meter to detect radiation levels. Adjustments to laboratory
426     procedures, particularly those involving sample handling and preparation, can only be made
427     when pertinent field information is recorded and relayed to the project planner and to the
428     laboratory. In some cases, a laboratory might not have clearance to receive  certain types of
429     samples (such as explosives or chemical agents) because of their content, and it will be necessary
430     to divert these samples to an alternate laboratory.  It might be necessary to reduce the volume
431     sampled in order to meet shipping regulations if high concentrations of radioactivity are present
432     in the samples. In some cases, the activity of one radionuclide might be much higher than others
433     in the same sample. Adjustments made on the basis of the radionuclide of higher activity might
434     result in collection of too little of another radionuclide to provide adequate detection and thus
435     prevent identification of these radionuclides because of their relatively low minimum detectable
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436     concentrations. These situations should be considered during planning and documented in the
437     appropriate sampling plan document.

438     10.2.11.2  Biohazards

439      Precautions should be taken when handling unknown samples in the field. Some examples are
440     wearing gloves, coveralls or disposable garments, plastic booties, dust masks or other respiratory
441     protection. Some biohazards may be snakes, ticks, spiders, and rodents (Hanta virus). Prevention
442     of potential exposure is the goal of a safety program. The type of protective equipment in the
443     field should be discussed in the planning process and specified in the appropriate plan document.
444     Since there are many specifics that are site dependent, it is difficult to create a comprehensive
445     list. But the information is discussed to provide an awareness and starting point for additional
446     discussion.

447     PERSONNEL TRAINING AND QUALIFICATION

448     All field operations that could lead to injury for sample collectors should be performed by
449     personnel  trained to documented procedures. When sampling is conducted in radiologically
450     controlled areas (RCAs) as defined in regulatory standards (i.e., 10 CFR 20, 10 CFR 835).
451     Formal training and qualification  of field personnel may be required.

452     Training may require both classroom and practical applications in order to familiarize personnel
453     with the basic theory of radiation  and radioactivity and the basic rules for minimizing external
454     exposures through time, distance, shielding, and avoidance of internal exposure (by complying
455     with rules regarding smoking, drinking, eating, and washing of hands). Other topics to cover
456     include common routes of exposure (e.g., inhalation, ingestion, skin contact); proper use of
457     equipment and the  safe handling of samples; proper use of safety equipment such as protective
458     clothing, respirators, portable shielding, etc.

459     Guidance  for the training and qualification of workers handling radioactive material has been
460     issued by the Nuclear Regulatory  Commission (see appropriate NRC NUREGs and Regulatory
461     Guides on training  of radiation workers), Department of Energy (1994), and the Institute of
462     Nuclear Power Operations (INPO 88-010). These and other documents should be consulted for
463     the purpose of training and qualifying field personnel.

464     PERSONNEL MONITORING AND BIOASSAY SAMPLING

465     When conditions dictate the need for personnel monitoring, various methods are commonly
466     employed to assess external and internal exposure that might have resulted from the inhalation or
467     ingestion of a radionuclide.
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468     To monitor for external exposures to the whole body or extremities, thermoluminescent
469     dosimeters (TLDs) or film badges may be used to document a worker's exposure. For internal
470     exposures, assessment of dose may be based on: (1) air monitoring of the work area or the
471     worker's breathing zone; (2) in vivo bioassay (whole-body counting); or (3) in vitro bioassays
472     that normally involve urinalysis but may also include fecal analysis and nasal smears. For in vitro
473     bioassays (i.e., urine or fecal), the standard method involves a 24-hour sample collection in a
474     sealable container. Samples may be kept under refrigeration until laboratory analysis can be
475     performed to retard bacterial action. (Bioassay sample collection is normally not performed in the
476     "field.")

477     The following guidance documents may be used for personnel monitoring and the collection and
478     preservation of bioassay samples:

479      • ANSI/ANSHPSN13.30 (1996), Performance Criteria for Radiobioassay;
480      • ANSI/ANS HPS N13.14 (1994), Internal Dosimetry Programs for Tritium Exposure—
481        Minimum Requirements;
482      • ANSI/ANS HPS 13.22 (1995), Bioassay Programs for Uranium;
483      • ANSI/ANS HPS 13.42 (1997), Internal Dosimetry for Mixed Fission Activation Products;
484      • DOE Implementation Guide, Internal Dosimetry Program, G-10 CFR 835/C1—Rev.  1 Dec.
485        1994a;
486      • DOE Implementation Guide, External Dosimetry Program, G-10 CFR 835/C2—Rev. 1  Dec.
487        1994b;
488      • DOE Implementation Guide, Workplace Air Monitoring, G-10 CFR 835/E2-Rev. 1 Dec.
489        1994c;
490      • DOE Radiological Control Manual, DOE/EH-0256T,  Rev. 1, 1994d;
491      • NRC Regulatory Guide 8.9, Acceptable Concepts, Models, Equations, and Assumptions for a
492        Bioassay Program;
493      • NRC Regulatory Guide 8.11, Applications of Bioassay for Uranium;
494      • NRC Regulatory Guide 8.20, Applications of Bioassay for 125I and 131I;
495      • NRC Regulatory Guide 8.22, Bioassays at Uranium Mills;
496      • NRC Regulatory Guide 8.26, Applications of Bioassay for Fission and Activation Products;
497      • NRC Regulatory Guide 8.32, Criteria for Establishing a Tritium Bioassay Program;
498      • NCRP (1987), Use of Bioassay Procedures for Assessment of Internal Radionuclides
499        Deposition; and
500      • INPO (1988), Guidelines for Radiological Protection at Nuclear Power Stations.

501     Part II:  Matrix-Specific Issues That Impact Field Sample Collection,
502     Processing, and Preservation

503     Field processing should be planned in advance so that  all necessary materials are available during
504     field work. Preparing checklists of processing equipment, instruments, and expendable
505     materials—as exemplified in part by lists accompanying sampling procedures described by  EPA

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506     1994—helps this planning effort and serves to organize field methods. Field personnel who
507     communicate problems should prevent loss of time, effort, and improper sample collection, as
508     well as documents exactly what equipment, instruments, etc. were used.

509     The initial steps taken in the field frequently are critical to the laboratory analysis performed
510     hours, days, or even weeks after a sample is obtained. Various sample preparation steps may be
511     required before samples are packaged and shipped for laboratory analysis. The need for sample
512     processing and preservation is commonly determined by the sample matrix, the data quality
513     objectives of the analysis, the nature of the radionuclide, and the analytical method.

514     The goal of sample preservation is to maintain the integrity of the sample between the time the
515     sample is collected and the time it is analyzed, thus assuring that the analysis is performed on a
516     sample representative of the media collected. In general, the aim of sample preservation is to
517     limit biological and chemical actions that might alter the concentration or physical state of the
518     radionuclide constituents or analytes. For example, cations at very low concentrations can be lost
519     from solution (e.g., cesium can exchange with potassium in the glass container, and radio-
520     nuclides can be absorbed by algae or slime growths in sample lines or containers that remain in
521     the field for extended periods). Requirements for sample preservation should be determined
522     during project planning when analytical protocols are selected. Sample preservation in the field
523     typically follows or accompanies processing activities.

524     This section provides matrix-specific guidance that focuses on the preparation and processing of
525     field samples. In order to assist project planners in developing a sampling plan, a limited
526     discussion is also provided that describes matrix-specific methods commonly employed for the
527     collection of field samples. Guidance is presented for only the most common materials or
528     environmental media, which are generically classified as liquids, solids, and air. In some
529     instances, a solid material to be analyzed involves particulate matter suspended in a liquid or air
530     that is commonly obtained by filtration. Because filter media can affect analytical protocols, a
531     separate discussion is provided that addresses sample materials contained on filter materials,
532     including surface contamination associated with wipe samples.

533     10.3  Liquid Samples

534     Liquid samples are typically classified as aqueous, non-aqueous, and as mixtures. Aqueous
535     samples requiring analysis are likely to represent surface water, ground water, drinking water,
536     precipitation, tanks and lagoons, and runoff. Non-aqueous liquids may include a variety of
537     solvents, oils and other organic liquids. Mixtures of liquids represent a combination of aqueous
538     and non-aqueous liquids or a solid suspended in either aqueous and non-aqueous liquids.
539     Standardized water sampling procedures are described in numerous documents (APHA, 1996;
540     EPA, 1985; EPA, 1987; DOE, 1997; ASTM D3370).  Important decisions include the choice of
541     instrument or tool used to obtain the sample, the sample container material, the need for sample
542     filtration, and the use of sample preservatives.

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543      10.3.1  Liquid Sampling Methods

544      The effect of the sample collection process on the sample integrity needs to be understood and
545      managed. Two examples are dissolved gases and cross contamination. It may be necessary to
546      minimize dissolved oxygen and carbon dioxide which may cause some dissolved metals to
547      undergo reaction or precipitation.

548      Sampling is discussed in Navy Environmental Compliance Sampling and Field Testing
549      Procedures Manual, NAVSEA T0300-AZ-PRO-010. USAGE discusses sampling in Technical
550      Project Planning Guidance for Hazardous,  Toxic and Radioactive Waste (HTRW) Data Quality
551      Design, Engineer ManuaJEM-200-1-2, Appendix H, Sampling Methods, July 1995. This
552      reference has been superseded but the revision does not include sampling. The sampling
553      references listed in Appendix H are:

554       • U.S. Environmental Protection Agency (EPA). 1984. Characterization of Hazardous Waste
555         Sites—A Method Manual, Vol. U, Available Sampling Methods, Second Edition, EPA 600-
556         4-84-076.

557       • U.S. Environmental Protection Agency (EPA). 1982. Handbook for Sampling and Sample
558         Preservation of Water and Wastewater, EPA 600-4-82-029.

559       • U.S. Environmental Protection Agency (EPA). 1986. Compendium of Methods  for
560         Determination of Superfund Field Operation Methods, EPA 600-4-87/006.

561       • U.S. Environmental Protection Agency (EPA). 1987. A Compendium of Methods for
562         Determination of Superfund Field Operation Methods, EPA 540-P-87-001a, OSWER
563         Directive 9355.0-14.

564       • U.S. Department of the Interior. 1980. National Handbook of Recommended Methods for
565         Water for Water-Data Acquisition, Volume I and II.

566      10.3.2  Liquid Sample Preparation: Filtration

567      Filtration of a water sample may be a key analytical planning issue and is discussed  in Chapter 3,
568      Section 3.3.2. A decision needs to be made during project planning whether or not to filter the
569      sample in the field. Filtration of water or other liquids may be required to determine contaminant
570      concentrations in solubilized form, suspended particulates, or sediment. The method of filtration
571      will depend on the required sample volume, the amount and size of suspended parti culates, and
572      the availability of portable equipment and resources (e.g., electricity).
573
574      The potential need to filter a water sample principally depends on the source of water and the
575      objectives of the project investigation. If, for example, the source of water is drinking water "at-

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        	Field and Sampling Issues That Affect Laboratory Measurements

576     the-spigot" and the intent is to assess human internal exposure from ingestion, unfiltered tap
577     water samples are likely to be required. Conversely, filtration may be required for water taken
578     from an unlined field monitor well that is likely to contain significant amounts of particulate
579     matter. These solids are of little relevance but may interfere with radioanalytical protocols (e.g.,
580     sample absorption may occur during gross alpha or beta counting where the analytical procedure
581     involve s the simple evaporation of a water aliquant on a planchet).

582     For remote sampling sites, sample processing may be restricted to gravity filtration that requires a
583     minimum of equipment and resources. Drawing samples through filters by pressure or suction
584     that is created by syringe, vacuum pump, or aspiration are alternative options. If filter papers or
585     membranes capture materials that will be retained for analysis, they should be handled with clean
586     rubber or plastic gloves, forceps,  or other instruments to prevent sample contamination.

587     Each Federal Agency may have unique guidance to determine the need and process for filtering
588     samples. One performance-based example is that of EPA, discussed in the next section. This
589     guidance applies to either the field or laboratory filtration.

590     10.3.2.1    EPA Guidance for Samples/Filtration

591     The Special Topics Subcommittee of EPA's Science Advisory Board's Environmental
592     Engineering Committee met to examine the question of whether or not to filter ground-water
593     samples when analyzing for metals in the context of a review of the Office of Emergency and
594     Remedial  Response's (OERR) proposed guidance on field filtration of ground-water samples
595     taken from monitoring wells for metals analysis as part of a Superfund site assessment (EPA,
596     1997). The key findings of the Subcommittee were:

597      • Several factors could introduce errors in the sampling and analysis of ground water for metals
598        or metallic radionuclides. Well construction, development, sampling, and field filtering are
599        among the steps that could influence the metals measured in the ground-water samples. Field
600        filtering is often a smaller source of variability and bias compared to these other factors.
601        Therefore, the Agency should emphasize in its guidance the importance of proper well
602        construction, development, purging, and water pumping rates so that the field filtering
603        decisions can also be made accurately.

604      • Under ideal conditions, field-filtered ground-water samples should yield identical metals
605        concentrations when compared to unfiltered samples. However, under non-ideal conditions,
606        the sampling process may introduce geological materials into the sample and would require
607        field filtration. Under such conditions, filtering to remove the geological artifacts has the
608        potential of removing colloids (small particles that may have migrated as suspended materials
609        that are mobile in the aquifer). Available scientific evidence indicates that when wells have
610        been properly  constructed, developed, and purged, and when the sample has been collected
611        without stirring or agitating the aquifer  materials (turbidity less than 5 nephelometric turbidy

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612         units, NTU), then field filtering should not be necessary. For Superfund site assessments, the
613         low-flow sampling technique without filtration is the preferred sampling approach for
614         subsequent metal analysis when well construction, well maintenance, and hydrogeological
615         conditions such as flow rate allow. Under such conditions, the collected samples should be
616         representative of the dissolved and particulate metals that are mobile in ground-water
617         systems. The Agency's proposal to rely on low flow sampling and unfiltered samples is a
618         conservative approach that favors false positives over false negatives.

619      •  When the turbidity of the sample is high, the situation is different. In-line filtering provides
620         samples that retain their chemical integrity. Therefore, field filtering of properly collected
621         ground-water samples should be done when turbidity in the samples is higher than 5 NTU,
622         even after slow pumping has been utilized to obtain the sample.

623     They acknowledged, however, that differences in the way wells are installed, their packing
624     materials, and the techniques used to collect ground-water samples  can lead to variability in
625     analytical results between wells and between individual samples. Filtering a  sample can be seen
626     as a way to remove suspended particles  and some colloids that contain metals that would not
627     normally be in the  ground water if the material were not disturbed during sampling. Here a
628     colloid is defined as a particle that ranges in size from 0.003 to 10 jim (Puls  et al., 1990) or
629     particles having diameters of less than lOjim (Puls and Powell, 1992). The literature indicates
630     that colloids as large as 2 jim can be mobile in porous media (Puls and Powell., 1992), and that
631     colloid concentration can be as high as 1,000 times higher in fractured granitic systems
632     (McCarthy and Deguelde, 1993). Saar (1997) presents a review of the industry practice of
633     filtration of ground-water samples. For some sites with low hydraulic conductivity the presence
634     of an excess of colloids presents numerous monitoring challenges and field filtration might be
635     necessary.

636     The desire to disturb the aquifer as little as possible has led to the use of low-flow sampling of
637     wells—low-flow purging and sampling  occurs typically at 0.1 to 0.3 L/min (Saar, 1997). The
638     low-flow technique maximizes representativeness by (EPA, 1997):

639      •  Minimizing disturbances that might suspend geochemical materials that are not usually
640         mobile;

641      •  Minimizing disturbances that might expose new reactive sites that could result in leaching or
642         adsorption of inorganic constituents of ground water;

643      •  Minimizing exposure of the ground water to the atmosphere or negative pressures, ensuring
644         that the rate of purging and sampling does not remove ground water from the well at a rate
645         much greater than the natural ground-water influx; and
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646       •  Monitoring indicator parameters to identify when stagnant waters have been purged and the
647         optimum time for sample collection.

648     In summary, based on the ability of the low-flow sampling technique to collect representative
649     samples, EPA suggests that filtering of ground-water samples prior to metals analysis is usually
650     not required (EPA, 1997).

651     10.3.2.2   Filters

652     When filtration is required, it should be done in the field or as soon as practicable. The
653     advantages of filtering in the field are that acid preservatives can be added shortly thereafter
654     which minimizes both the adsorption of soluble contaminants and avoids the dissolution of
655     particulate matter, volume reduction, and waste reduction. Unless specific requirements dictate
656     otherwise, the removal of suspended particles is commonly achieved by filtration that removes
657     particles larger than 0.45 |im (ASTM D3977).

658     In other instances, the investigative objectives may not be restricted to water-solubilized
659     contaminants but include analysis of contaminated suspended particulate  matter. To detect the
660     presence of radionuclides that are highly insoluble, such as isotopes of uranium, thorium, and
661     plutonium, analysis of particulate matter is considered more sensitive than the filtered water
662     (EPA, 1994).

663     The fact that small particles  pass through membrane filters has been recognized for some time
664     (Kennedy et al., 1974). The arbitrary cutoff of 0.45 jim between dissolved and suspended matter
665     has gained such wide use that it is the filter size that is commonly recommended by laboratory
666     protocols. Filtering through a 0.45 jim filter may take considerable time and may require suction
667     or pressure to accomplish in a reasonable time.
668
669     It should be noted, however, that manufacturers of filters usually specify only what will not pass
670     through the filter; they make no claims concerning what actually does pass through the filter.
671     Laxen and Chandler (1982) present a comprehensive  discussion of some effects of different filter
672     types. They refer to thin (5 to 10 jim) polycarbonate filters as screen types, and thick (100 to
673     150 |im) cellulose nitrate and acetate filters as depth types. The polycarbonate-screen type clogs
674     much more rapidly. Once the filtration rate drops, particles that would normally pass through the
675     filter  are trapped  in the material already retained. Hence, the use of so-called polycarbonate-
676     screen filters, because of their increased propensity to clog, is generally not recommended.

677     In addition to the difficulty of contending with clogging, Silva and Yee (1982) report adsorption
678     of dissolved radionuclides on membrane filters. Although these drawbacks cannot be completely
679     overcome, they are still less  than the potential difficulties that arise from not filtering.
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680     Finally, good laboratory practices must be used for field sampling. The most likely sources of
681     contamination for the filters are improperly cleaned tubing and filter holders and handling the
682     filters with contaminated fingers. Tubing and holders should be thoroughly cleaned and rinsed
683     between samples and the entire system should be rinsed several times with the water to be
684     sampled. Filters should be handled with clean rubber gloves.

685     10.3.3 Field Preservation of Liquid Samples

686     Sample degradation may occur between the time of collection and analysis due to microbial
687     contaminants or chemical interactions. Although sample degradation cannot destroy or alter the
688     radiological properties of a contaminant, it can alter the radionuclide's chemical properties and
689     its potential distribution within a sample. For example, microbial processes are known to affect
690     both the chemical state and the distribution of radioelements due to oxidation-reduction
691     reactions, complexation and solubilization by metabolic compounds, bioaccumulation,
692     biomylation, and production of gaseous substances such as CO2, H2, CH4, and H2S (Francis,
693     1985; Pignolet et al., 1989).

694     10.3.3.1   Sample Acidification

695     Acidification is the method of choice for preserving most types of water samples. The principal
696     benefit of acidification is that it keeps many radionuclides in solution and minimizes their
697     potential for removal by chemical and physical adsorption or by ion exchange. The mode by
698     which a radionuclide is potentially removed from  solution is strongly affected by the radionuclide
699     and the container material. For example, studies conducted by Bernabee et al. (1980) and Milkey
700     (1954) demonstrated that the removal of metal ions from solution is dominated by physical (i.e.,
701     van der waals) adsorption. Their conclusion is based on: (1) their observation that the loss of
702     uranium, lead, and thorium ions from solution was significantly greater for containers made of
703     polyethylene when compared to borosilicate glass; and (2) the fact that while adsorption by glass
704     may potentially involve all three adsorption processes; with polyethylene plastic, there are no
705     valence-type attractive forces or ions to exchange  and only physical van der waals adsorption is
706     possible.

707     Similar observations were reported by: (1) Dyck (1968), who compared long-term adsorption of
708     silver ions by molded plastic to glass containers; (2) Jackson (1962), who showed that
709     polyethylene containers absorbed about five times as much 90Sr as glass containers at pH of about
710     seven; and (3) Martin and Hylko (1987a; 1987b),  who reported that greater than 50 percent of
711     "Tc was adsorbed by polyethylene containers from non-acidified samples.

712     For sample acidification, either nitric or hydrochloric acid is commonly added until a pH of less
713     than two. Table 7010:1 in Standard Methods for the Examination of Water and Wastewater
714     (APHA, 1995) and Method 900.0 in Prescribed Procedures for Measurement of Radioactivity in
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715     Drinking M/ater(EPA, 1980) provide additional guidance. Guidance for sample preservation by
716     acidification has been issued by Federal Agencies and others as summarized below.

717     In instances of very low activity samples where container adsorption poses a significant concern,
718     but where acidification of the sample interferes with the radioanalytical method, the choice of
719     sample container may be limited to glass or require alternative methods. For example, the use of
720     acids as a preservative is not recommended for the analysis of tritium (3H), carbon-14 (14C), or
721     radon in water, and precautions must be taken for the following reasons:

722       •  For radon, sample preservation offers no benefit and is therefore not required for analytical
723         accuracy.

724       •  The addition of acid to a sample containing 14C may result in the production of 14CO 2 and the
725         loss of radioactivity from the sample.

726       •  The adverse impact of acid on tritiated water is due to  the fact that water dissociates and
727         recombines continuously (i.e., H2O _ FT + OH" or HO  _ T+ + OH"). The tritium ion that was
728         part of the water molecule may, therefore, be exchanged for the hydrogen ion from the acid.
729         The impact of this exchange is realized as a result of distillation, which is a common method
730         for purifying water in preparation for liquid scintillation counting. When the sample is heated
731         and the distillate is collected on a cold finger, distilled tritiated water, in the presence of acid,
732         would have a reduced specific activity over the original sample.

733     Although acidification has been shown to effectively reduce the adsorption of technetium by
734     polyethylene, technetium in the TcO 4"4 state has been observed to volatilize in strong acid
735     solutions during evaporation while preparing water samples for gross beta analysis (NAS, 1960).
736     To hasten evaporation, the planchet is commonly flamed. This dilemma can be resolved by either
737     precoating planchets with a film of detergent prior to the addition of the acidified water sample
738     or by passive evaporation of the acidified water sample that avoids the higher temperature
739     associated with flaming (Blanchard et al., 1993).

740     10.3.3.2   Non-Acid Preservation Techniques

741     If a sample contains significant organics, or if contaminants under investigation react with acids
742     that interfere with the radioanalytical methods, other methods of sample preparation should be
743     considered.

744     REFRIGERATION AND FREEZING

745     The effect of refrigeration or freezing temperatures to arrest microbial activity is a fundamental
746     concept. Temperatures near the freezing mark or below not only retard or block bacterial growth
747     but arrest essentially all other metabolic activity. It should, however, be noted that most bacteria

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748     can survive even in extreme temperatures. (Indeed, if a suspension of bacterial cells is frozen
749     rapidly with no appreciable formation of ice crystals, it can be kept at temperatures as low as
750     -194° C for indefinite periods of time with little loss of viability.)

751     The choice between refrigeration and freezing is dictated by the potential impacts of ice
752     formation on sample constituents. Besides physical changes of organic constituents, the initial
753     formation of ice crystals and the exclusion of any solutes may concentrate the solutes to the point
754     of precipitation. Quick freezing methods that minimize ice crystal formation are beneficial for
755     preserving some organic constituents. Quick freezing is commonly done by packing sealed
756     samples in liquid nitrogen or dry ice. Care must be taken, however, to avoid container breakage
757     due to sample volume expansion. An air space of a least 10 percent and a container made of
758     plastic provide reasonable assurance for container integrity.

759     When refrigeration is employed, attempts should be made to avoid temperatures that could result
760     in slow freezing and the formation of ice crystals. Optimum refrigeration temperatures for sample
761     preservation at 4 ± 2° C can be achieved by packing samples in ice or freeze packs within a
762     thermally insulated leak-proof container (ASTM D3856; ASTM D3370).

763     PAPER PULP

764     Adsorption and loss of radionuclides over time to the container wall can be avoided with the
765     addition of paper pulp. Due to its adsorptive property and large surface, paper pulp has been
766     shown to remove more than 95 percent of radionuclides from solution (Bernabee et al., 1980).
767     About two grams of finely ground paper pulp are added per liter of acidified sample at time of
768     collection.  The  pH should be adjusted to one or less and vigorously shaken. The sample may be
769     stored in this condition for an extended period of time. To prepare for analysis, the pulp is
770     removed from solution by filtration and subjected to wet ashing using strong acids (Chapter 12).
771     This ashed solution is commonly added to the original filtrate to make a reconstituted sample
772     solution.
773
774     The use of paper pulp and the need for wet ashing, however, pose problems for certain
775     radioanalytical laboratory protocols and must therefore be thoroughly evaluated.

776     SULFITE

777     To prevent the loss of radioiodine from solution, sodium bisulfite (NaHSO3) or sodium
778     metabisulfite (Na2S2O5) may be used. These compounds act as  strong reducing agents and
779     prevent the volatilization of iodine. If acid is also employed to preserve samples for analysis of
780     other radionuclides, it is important to note that acid will negate the reductant's effectiveness in
781     behalf of iodine. For this reason, samples collected for iodine analyses typically are collected and
782     preserved in a separate container. It should also be noted that the reducing environment produced
783     by the sulfite preservative may render iron, uranium, and other easily reduced elements or their

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                                     Field and Sampling Issues That Affect Laboratory Measurements
784     compounds to an insoluble state. The loss of reduced insoluble radionuclides from solution will
785     have an obvious adverse impact on radioanalytic measurements that require chemical separation.
786     Chapter 14.9 has additional information on carriers and tracers.
787
OTHERS
788     Other methods that have been used to preserve liquid samples containing organics and biological
789     materials include chemical preservatives (e.g., formaldehyde and methanol) or quick freezing by
790     means of liquid nitrogen. Table 10.1 summarizes the advantages and disadvantages of these and
791     previously described preservation methods.
792

793

794
                    TABLE 10.1—Summary of sample preservation techniques.
Preservation Technique
Addition of HNO3
Addition of Hcl
Addition of Sulfite
Addition of
Formaldehyde
Cooling
(Ice at approximately 0°
C)
Freezing
(Dry Ice at approximately
-78° C)
Addition of Paper Pulp
Advantages
Reduces pH and inhibits plating of
metals on container walls.
Reduces pH and inhibits plating of
metals on container walls.
Chloride forms strong anionic
complexes with Iron and Uranium.
Forms a reducing environment to
prevent the volatilization of iodine.
Preserves organic samples.
Prevents further biological activity.
Preserves organic samples (i.e.,
water, foods).
Reduces dehydration and retains
moisture.
Reduces biological activity.
Preserves organic samples (i.e.,
water, plant, animal).
Suspends biological activity.
Provides large surface area for
adsorption of metals, thus
minimizing adsorption on container
walls.
Disadvantages
Strong oxidizer that might react with organic
compounds.
Tritium might be separated preferentially as acid
hydrogen;
1/fC might be lost as 14CO2.
Tritium will be preferentially separated as acid
hydrogen;
14C might be lost as 14CO2
Might cause corrosion of stainless steel
planchets on gross analyses.
Might produce insoluble compounds from
reduced forms of iron or uranium.
May create disposal problems.
Ice melts, requiring replacement over time.
Dry ice sublimates and requires replacement.
Requires pH to be one or less.
Requires filtration and wet ashing of paper pulp
and combining liquids to make a new solution.
795
796

797
798

799
800
801
802
803
804

805
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806     10.3.4 Liquid Samples: Special Cases
807
808     In some cases, liquid samples require special handling in order to preserve or retain a volatile or
809     gaseous radionuclide. The following are examples of specific methods used to recover or
810     preserve such samples of interest.

811     10.3.4.1   Radon-222 in Water

812     Waterborne radon is analyzed most commonly by liquid scintillation methods, although gamma
813     spectroscopy and other methods have been employed or proposed. Liquid scintillation has the
814     obvious advantage of being designed for automated sample processing and is, therefore, less
815     labor intensive or costly. A key to consistency in analytical results is the zero headspace sampling
816     protocol such as the one described below.

817     Since radon is inert and nonpolar, it diffuses through plastic more rapidly than glass. The use of
818     plastic scintillation vials, therefore, leads to significant loss of radon in water (Whittaker, 1989;
819     Hess and Beasley, 1990). For this reason, it is recommended that the water sample is collected in
820     a 23 mL glass scintillation vial, capped with a Teflon or foil-lined cap.

821     Samples are collected from a non-aerated faucet or spigot, which has been allowed to flow for
822     sufficient time so that the sample is representative of the water in the distribution system or well.
823     The time will vary depending on the source. The following zero headspace procedure will
824     minimize the loss of radon from the sample during collection:

825      •  Place sample vial in a 300-600 mL beaker or other suitable container and attach the universal
826         adapter and fill-line to spigot, and start the flow.

827      •  Fill the vial to prevent it from floating.  Then fill the beaker until the vial is submerged.

828      •  Place the tip of the fill line about two thirds of the way into the vial and fill until
829         approximately two or more vial (50-100 mL) volumes have been displaced.

830      •  Carefully remove the vial with a pair of 10-inch tweezers and cap the vial with a Teflon or
831         foil-lined cap. Invert the sample and check for air bubbles. If any bubbles are present, discard
832         the sample and repeat the sampling procedure. Record date and time the sample was collected
833         and store the sample in a cooler to prevent temperature excursions. Transport the samples to
834         the laboratory in a cooler or other suitable insulated package.

835     10.3.4.1   Milk

836     Milk commonly is viewed as the food product of greatest potential dose significance for airborne
837     releases of radionuclides. Due to the metabolic discrimination,  however, only a few radionuclides

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838     have a significant dose impact via the milk pathway, notably 90Sr, 131I, and 137Cs. Raw milk
839     should be obtained from the closest cows or goats downwind from a source.

840     To prevent milk from souring or curdling, samples should be refrigerated. Preservation of milk
841     may also be achieved through the addition of formaldehyde or methanol (DOE, 1987),
842     merthiolate, or Thimerosal (EPA,  1994). Analytical procedures for select radionuclides in milk
843     are well established and should be considered when deciding on a sample preservation method.

844     Owing to the volatility and potential loss of 131I, a known amount of Nal dissolved in water
845     should be added to the milk sample at time of collection.  The Nal not only serves as a carrier for
846     the chemical separation of radioiodine, but also provides  a quantitative tool for determining any
847     loss prior to analysis (DOE, 1990).

848     10.3.5 Non-aqueous Liquids and Mixtures

849     Non-aqueous liquids and mixtures include a wide range of organic fluids or solvents, organic
850     materials dissolved in water, oils, lubricants, etc. These liquids are not likely to represent
851     contaminated environmental media or matrices, but most likely represent waste streams that must
852     be sampled. Non-aqueous waste streams are generated as part of normal operations by nuclear
853     utilities, medical facilities, academic and research facilities, State and Federal Agencies, radio-
854     pharmaceutical manufacturers, DOE weapons complexes, mining and fuel fabrication facilities,
855     etc. Examples of these non-aqueous liquids and mixtures include waste oils and other lubricants
856     that are generated routinely from maintenance of various  types equipment associated with
857     nuclear power plant operations or the production of nuclear fuel and nuclear weapon
858     components; and organic and inorganic solvents, acids, and bases that are used in a variety of
859     medical,  research, and industrial applications.

860     In addition to the production of non-aqueous liquid wastes from routine operations by these
861     facilities, large quantities of non-aqueous liquids containing radionuclide contaminants are also
862     generated by routine facility decontamination efforts and  final decontamination associated with
863     facility decommissioning. For decontamination and decommissioning activities, a wide range of
864     processes have been developed that employ halogenated organic compounds, such as Freon,
865     chloroform, or trichloroethane. Other aggressive chemical decontamination processes involve
866     dissolution and removal of metal and oxide layers from surfaces using acid solutions (e.g.,
867     sulfuric acid, nitric acid, phosphoric acids, and oxalic acid). Chemical decontamination also may
868     use chelating agents in concentrated processes (5 to 25 percent wt. chemical in solution) and
869     dilute processes (one percent wt. or less chemicals in solution). Examples of chemical processes
870     that can be used in both concentrated and dilute forms include the low oxidation-state transition-
871     metal ion (LOMI) and LOMI-nitric permanganate, developed by Dow Chemical Company and
872     AP/Citron. The reagents used in both the concentrated and dilute processes include chelating and
873     complexing agents such as ethylene diamine tetra acetic acid (EDTA), diethylene triamine penta-
874     acetic acid (DTPA), citric acid, oxalic acid, picolinic acid, and formic acid. Chelating agents and

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875     organic acids are used in decontamination formulas because they form strong complexes with
876     actinides, lanthanides, heavy metals, and transition metals and assist in keeping these elements in
877     solution.

878     Generally, these chemical decontamination solutions, once used, are treated with ion-exchange
879     resins to extract the soluble activity. The ion-exchange decontamination solutions must,
880     nevertheless, be sampled to assess the amount of residual radioactivity.

881     The radionuclides that may be encountered with non-aqueous liquids and mixtures depend on
882     both the nature of the liquid and its usage. The following listing of radionuclides and liquids are
883     based on published data collected by NRC (1992) and the State of Illinois (Klebe 1998; IDNS
884     1993-1997):

885       •  Toluene/xylene/scintillation fluids used by research and clinical institutions: 3H, 14C, 32P, 35S,
886         45Ca, 63Ni, "Tc, 90Sr, 125I, 147Pm,  226/228Ra, 228/230/232Th, 234/235/238U, 238/239/2«Pu, 241Am.

887       •  Waste oils and lubricants from operation of motors, pumps, and other equipment: 3H, 54Mn,
888         65Zn, 60Co, 134/137Cs, 228/230/232Th.

889       •  Halogenated organic and solvents from refrigeration, degreasing, and decontamination: 3H,
890         14C, 32P, 35S, 54Mn, 58/60Co, 63Ni, 90Sr, 125/129I, 134/137Cs, 226/228Ra, 22*™32Th, 232/234/238U,
891         23
892       •  Other organic solvents from laboratory and industrial operations and cleaning: 3H, 32P, 35S,
893         45Ca, 125I, U-nat.

894       •  Inorganic and organic acids and bases from extraction processes and decontamination: 3H,
895         14C, 32P, 35S, 54Mn, 67Ga, 1251, 60Co, 137Cs, 201/202Th, and U-nat.

896     Due to the large number of potential non-aqueous liquids and the complex mixtures of
897     radionuclide contaminants that may require radiochemical analysis, a comprehensive discussion
898     of sample preparation and preservation is beyond the scope of this discussion. In most instances,
899     however, these samples are not likely to require refrigeration or chemical preservatives that
900     protect against sample degradation.

901     Some organic solvents and highly acidic or basic liquids may react with plastic containers,
902     causing brittleness or breakage. In  selecting sample containers for these non-aqueous samples, it
903     is important to assess the manufacturers product specifications, which typically provide
904     information regarding the container's resistance to chemical and physical agents. When non-
905     aqueous samples are stored for long periods of time, containers should be checked routinely.

906     10.4  Solids

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907
908     Solid samples consist of a wide variety of materials that include soil and sediment, plant and
909     animal tissue, metal, concrete, asphalt, trash, etc. In general, most solid samples do not require
910     preservation, but require specific processing in the field before transporting to the laboratory for
911     analysis. For example, soil sample field processing may require sieving in order to establish
912     sample homogeneity. These and other specific handling requirements are described below in the
913     section on each type of solid sample.

914     The most critical aspect is the collection of a sufficient amount of a representative sample. One
915     purpose of soil processing is to bring back only that sample needed for the laboratory. Unless
916     instructed otherwise, samples received by the laboratory are typically analyzed exactly as they are
917     received. This means that extraneous material should be removed at the time of sample
918     collection, if indicated in the appropriated plan document.

919     In many instances, sample moisture content at the time of collection is an important factor. Thus,
920     the weights of solid samples should be recorded at the time a sample is collected. This allows one
921     to track changes in wet weight from field to laboratory. Dry and ash weights generally are
922     determined at the laboratory.

923     Unlike liquid samples that may be introduced or removed from a container by simple pouring,
924     solid samples may require a container that is designed for easy sample placement and removal.
925     For this reason, large-mouth plastic containers with screw caps or individual boxes with sealable
926     plastic liners are commonly used. The containers also minimize the risk for breakage and sample
927     cross-contamination.

928     10.4.1 Soils
929
930     ASTM D653 (Standard Terminology Relating to Soil, Rock, and Contained Fluids) defines soil
931     as: "Sediments or other unconsolidated accumulations of solid particles produced by the physical
932     and chemical degradation of rocks, and that might or might not contain organic matter." ASTM
933     C999 provides generic guidance for soil sample preparation for the determination of
934     radionuclides. The American Society for Testing and Materials provides additional information
935     on soil and rock in the following standards:

936      •  ASTM D 4914, Section 4, Construction, Volume 4.08 Soil and Rock (I).
937      •  ASTM D 4943, Section 4, Construction, Volume 4.09 Soil and Rock (II): Geosynthetics.

938     The distribution of radionuclides in soil should be assumed to be heterogeneous. The degree of
939     heterogeneity is dictated by the radionuclide's mode of entry into the environment and soil, the
940     chemical characteristics of the radionuclide contaminant, soil composition, meteorological and
941     environmental conditions, and land use. For example, soil contamination from an airborne
942     release of a radionuclide with strong affinity for clay or other mineral constituents of soil (i.e.,

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943     high kd value) will likely exhibit a gradient with rapidly diminishing concentrations as a function
944     of soil depth. Moreover, contamination may be differentially distributed among soil particles of
945     different sizes. In most cases, because the contaminant is adsorbed at the surface of soil particles
946     and since the surface-to-volume ratio favors smaller particles, smaller soil particles will exhibit a
947     higher specific activity when compared to larger particles. If land areas include areas of farming,
948     tilling of soil will clearly impact the distribution of surface contamination.

949     10.4.1.1    Soil Sample Preparation

950     Extraneous material should be removed at the time of sample collection, if indicated in the
951     appropriate plan document. The material may have to be saved and analyzed separately,
952     depending on the project requirements and MQOs. If rocks, debris, and roots are removed from a
953     soil sample after it arrives at the laboratory, there might not be sufficient material to complete all
954     the requested analyses. A sufficient amount of sample should be collected to provide the net
955     quantity necessary for the analysis. Subsequent drying at the laboratory may remove a large
956     percentage of the sample weight that is available for analysis. Field-portable balances or scales
957     may be used to weigh samples as they are collected, further ensuring sufficient sample weights
958     are obtained. For certain types of samples, the project DQOs may require maintaining the
959     configuration of the sample, such as core samples where concentration verses depth will be
960     analyzed.

961     The project plan should address the impact of heterogeneity of radionuclide distribution in soil.
962     Some factors to consider that may impact radionuclide distribution are: determining sampling
963     depth, the need for removal of vegetative matter, rocks, and debris, and the homogenation of soil
964     particulates. For example, soil sampling depths of the top 5  cm is recommended for soils
965     contaminated by recent airborne releases (ASTM C998); soil depth to 15 cm may be appropriate
966     when exposure involves the need to monitor the root zone of food crops (MARSSEVI, 2000;
967     NRC, 1990). The need for sample field QC,  such as field splitting, should be evaluated. Some
968     types of field QC can be used to evaluate the extent of radionuclide homogeneity. In general, no
969     special preservation measures are required for soil samples; however, preliminary soil  sample
970     preparation involving drying, sieving, homogenizing, and splitting may be performed by a field
971     laboratory prior to sample shipment to the analytical laboratory.

972     If volatile elements are among other non-volatile contaminants, samples must be fractionated
973     before drying to  avoid loss of the contaminant of interest. Dried samples are homogenized by
974     mortar and pestle, jaw crusher,  ball mill, parallel plate grinder, blender, or a combination of these
975     techniques and sieved to obtain a uniform sample. Sieve sizes from 35 to 200 mesh generally are
976     recommended for wet chemistry procedures. ASTM C999 correlates various mesh sizes with
977     alternative designations, inclusive of physical dimensions expressed in inches or in the metric
978     system. In addition,  samples for chemical separations are usually ashed in a muffle furnace to
979     remove any remaining organic materials that may interfere with the procedures.
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 980      10.4.1.2   Sample Ashing

 981      Soil samples that require chemical separation for radionuclide analysis may also be ashed by the
 982      field laboratory. The use of the term "field laboratory" can cause confusion, since no one
 983      definition is possible. It is used here to define a lab that is close to the point of sample collection.
 984      In no way does it imply that there is a distinction in requirements or specifications that impact
 985      quality. For soil samples, ashing is performed in a muffle furnace to remove any organic
 986      materials that may interfere with radiochemical procedures.

 987      10.4.2  Sediments
 988
 989      Sediments of lakes, reservoirs, cooling ponds, settling basins, and flowing bodies of surface
 990      water may become contaminated as a result of direct liquid discharges, wet surface deposition, or
 991      from runoffs associated with contaminated soils. Because of various chemically and physically
 992      binding interactions with radionuclides,  sediments serve as integrating media that are important
 993      to environmental monitoring. An understanding of the behavior of radionuclides in the aquatic
 994      environment is critical to designing a sampling plan, because their behavior dictates their
 995      distribution and sampling locations. Sediment cores may be sampled, frozen, and then  sectioned.

 996      The fate of radionuclides entering surface waters and their subsequent interaction with sediment
 997      is complex due to numerous mechanisms and processes that affect the initial mixing and
 998      dispersion of radionuclides, their distribution in water, sediment, plants and animals, and their
 999      long-term retention within these compartments. Several factors must be considered to establish
1000      appropriate sediment sampling locations and depths and are discussed briefly below.

1001      10.4.2.1   Initial Mixing and Transport Dispersion of Radionuclides Discharged to Water

1002      The rapid initial mixing phase in the nearfield is dominated by the characteristics of the effluent
1003      and the outfall structure. The extent of nearfield mixing and dilution is strongly affected by the
1004      quantity of effluent relative to the receiving body of water, the level of turbulence produced by
1005      means  of the discharge momentum  (jet action), the discharge buoyancy (plume action), the
1006      outfall  configuration, and the depth and  current flow rate in the vicinity of outfall.

1007      Predictive models have been proposed for surface and submerged discharges;  single point and
1008      multi-point outfalls; deep and shallow, stagnant and flowing water; and buoyant (positive and
1009      negative) and non-buoyant effects. An understanding of the basic hydrodynamic variables that
1010      define  each of these conditions will aid in the selection of sampling locations.

1011      In the case of small and medium bodies  of surface waters, where vertical thermal stratification is
1012      the  primary factor that determines inflow and outflow dynamics, a simple one- or two-
1013      dimensional model may be appropriate as discussed in Regulatory Guide  1.113 (NRC 1977).  For
1014      large bodies of surface water where neither horizontal nor vertical homogeneity can be assumed,

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1015      more complex three-dimensional dispersion models must be applied to properly assess
1016      hydrodynamics and the distribution of radionuclides in sediment. A review of numerical
1017      hydrodynamic models for large bodies of surface waters has been presented by Johnson (1980).

1018      10.4.2.2   Sediment Effect

1019      Following initial mixing in the nearfield (i.e., outfall), subsequent transport and distribution of a
1020      dissolved radionuclide is greatly impacted if the radionuclide is absorbed strongly from solution
1021      onto sediments by processes that include ion exchange, precipitation-mineral formation,
1022      complexation-hydrolysis, and oxidation-reduction. Both suspended and less-mobile bed
1023      sediments may absorb radionuclides, but suspended sediments usually absorb more efficiently
1024      per unit weight than bed sediments (Friend et al., 1965; Parker et al., 1965).

1025      The impacts of sediment absorption in a flowing body of water are obvious: the required time for
1026      sediment absorption allows the dissolved radionuclide to move considerable distances
1027      downstream before being absorbed, and sediment absorption steadily reduces the concentration
1028      of dissolved radionuclides with the result that an activity gradient is established in downstream
1029      water, sediment, and aquatic biota. Concentration gradients are further complicated by the high
1030      mobility of suspended sediments, the slow but steady erosion of bed sediments, the mobility and
1031      transfer of the radionuclide contaminant that has entered the aquatic food web, and the various
1032      mechanisms that modify sediment adsorption and desorption.

1033      10.4.2.3   Sample Preparation/Preservation

1034      In most cases, sediment is separated from water by simple decanting, but samples also may be
1035      obtained by filtering a slurry or through passive evaporation. As noted previously, care must be
1036      taken to avoid cross contamination from sampling by decontaminating or replacing tools and also
1037      from avoiding contact between successive samples.  Suitable sample containers include glass or
1038      plastic jars with screw caps. The presence of volatile or semi-volatile organic and micro-
1039      organisms may impact the radionuclide  concentration, therefore,  samples should be kept on ice
1040      while in the field and refrigerated while awaiting radioanalysis.

1041      10.4.3  Other Solids
1042
1043      10.4.3.1   Structural Materials

1044      In some cases, a project plan requires sample analysis of structural materials such as concrete or
1045      steel. Concrete from floors, walls, sidewalks or road surfaces is typically collected by scabbling,
1046      coring, drilling, or  chiseling. Depending on the radionuclides of interest and detection methods,
1047      these sample preparations may require crushing, pulverization, and sieving.
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1048      Metal associated with structures (e.g., I-beams, rebar) or machines may be contaminated on
1049      exterior or interior surfaces or through activation may become volumetrically contaminated.
1050      Surface contamination may be assessed by swipe samples that provide a measure of removable
1051      contamination (Section 10.7) or by scraping, sandblasting, or other abrasive techniques.
1052      Volumetric contamination is frequently assessed by non-destructive field measurements that rely
1053      on gamma-emitting activation products. However, drill-shavings or pieces cut by means of a
1054      plasma arc torch may be collected for further analysis in a laboratory where they can be analyzed
1055      in a low-background environment. In general, these materials require no preservation but, based
1056      on activity/dose rate levels and sample size and weight, may require proper shielding, engineered
1057      packaging, and shipping by a licensed carrier.

1058      10.4.3.2   Biota: Samples of Plant and Animal Products

1059      The release of radionuclides to the environment from normal facility operations or as the result of
1060      an accident requires the sampling of a wide variety of terrestrial and aquatic biota. Guidance
1061      provided below is directed principally to those responsible for designing a sampling plan, who
1062      must make decisions pertaining to the type of samples that should be collected, where and how to
1063      collect the samples, and the preferred methods for sample preparation. For most biota,  sample
1064      preservation usually is achieved by icing samples in the field and refrigeration until receipt by the
1065      analytical laboratory.

1066      The specific media that fall under this general category include food, domestic animals (meat and
1067      poultry), animal products, game animals, game birds, etc.  The field sampling plan should
1068      describe the type of processing and preservation required.

1069      Samples of food and certain terrestrial animals are of greatest importance in environmental
1070      surveillance because they provide the most direct basis for assessing the radiation dose to man.
1071      The principal pathways for radionuclide contamination of food and plants are atmospheric
1072      deposition from airborne releases and crop irrigation from rivers, ponds or lakes receiving liquid
1073      effluents. Care should also be  taken not to select a sampling site that has been fertilized or has
1074      been contaminated by runoffs  from fertilized soil due to enhanced natural radioactivity content of
1075      many fertilizers (ASTM C998).

1076      To determine the dose to a population, pathway analysis may require sampling of food and biota.
1077      One example is the analysis of meat from domestic or game animals. Samples from food and
1078      biota also may be used to determine radionuclide accumulation in the  environment. For example,
1079      the analysis of growth rings from trees may indicate when a radionuclide was released  into the
1080      environment.

1081      Animal feeds also provide important data for determining radionuclide concentrations in the food
1082      chain. Foods may be categorized according to the U.S. Department of Agriculture scheme as
1083      leafy vegetables, grains, tree-grown fruits, etc., and representative samples from each group may

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1084      be selected for analysis. Guidance for procuring or preparing terrestrial samples is provided
1085      below.

1086      MEAT, PRODUCE, AND DAIRY PRODUCTS

1087      Meat, poultry, eggs, fresh produce, and other food should be procured from local farmers most
1088      likely to have been affected by a singular event. The choice of sample is dependent on the
1089      pathway. Meat samples also may be collected at a slaughter house if the origin of the animals can
1090      be documented. Local health departments may be able to assist in getting samples. Samples
1091      should be placed in sealed plastic bags and appropriately labeled and preserved by means of ice
1092      in the field and refrigeration during interim storage prior to delivery to the analytical laboratory.
1093      All food samples may be reduced to edible portions (depending on study objective) for analysis
1094      in a manner similar to that for human consumption (i.e., remove cores, bones, seeds, other
1095      nonedible parts) and weighed as received from the field (i.e., wet weight) within 24 hours. Wet
1096      weights are desired, since consumption data are generally on this basis.

1097      For sampling fresh produce, fruits, meats, and other domestic animal products, a local land-use
1098      study may be necessary to determine what crops and animals are important in the local diet and
1099      where they are produced with respect to the site. Fruit and vegetable samples should be collected
1100      near the point of maximum predicted annual ground concentration from airborne releases and
1101      from areas that may be contaminated by water into which liquid plant wastes have been
1102      discharged (e.g., irrigated crops). Local land usage should be reviewed periodically, as well as
1103      current farming and stock-feeding practices at sampling locations.

1104      ANIMAL FEED AND VEGETATION

1105      Crops raised for animal feed and vegetation consumed by grazing farm animals may be sampled.
1106      Depending upon radionuclides under investigation and their analytical sensitivities, kilogram
1107      quantities of vegetative matter may be needed. The choice of species  and sample type must be
1108      guided by factors such as  exposure pathways,  species  availability, seasonal growth patterns, soil
1109      types, and farming practices.

1110      As in all terrestrial samples, naturally occurring 40K and the uranium and thorium series
llll      contribute to the radiation observed. Deposition of such cosmic-ray-produced nuclides as 7Be and
1112      fallout from nuclear tests  also may be present. Properly selected processed items from commer-
1113      cial sources may be helpful in providing natural and anthropogenic background data.

1114      WILDLIFE

1115      Wild animals that are hunted and eaten may be of interest for potential dose estimates and
1116      therefore may require sampling. However, the data from small numbers of samples of wild
1117      animals or game birds should be viewed with caution  because of their great variation in mobility,

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1118      age, and diet. Examples of wildlife that have been used are rabbits and rodents that may feed on
1119      and live in a contaminated site.

1120      Wildlife samples can be trapped, acquired from hunters, collected after accidental road kills, or
1121      obtained by request to the appropriate state game agency. Wildlife that is relatively rare locally
1122      should not be taken as environmental samples. Since the choice of species samples may be
1123      crucial to the usefulness of the results, local ecologists and biologists should be consulted to
1124      ensure consideration of factors that affect animal radionuclide uptake and retention, such as size,
1125      age, sex, feeding locus, and food consumption. An estimate of the radionuclide intake of the
1126      animal just before its death may be provided by analyzing the stomach content, especially the
1127      rumen in deer.  However, the sample must be collected within a brief period (two to four hours)
1128      after death.

1129      AQUATIC ENVIRONMENTAL SAMPLES

1130      In addition to natural radionuclides and natural radionuclides enhanced by human activity, there
1131      are numerous man-made radionuclides that have the potential for contaminating surface and
1132      ground water. The most common of these are fission and activation products associated with
1133      reactor operation and fuel cycle facilities. Radioanalysis of aquatic samples may therefore
1134      include 54Mn, 58Co, 60Co, 65Zn, 95Zr, 90Sr, 134Cs, 137Cs, and transuranics, such as 239Pu.

1135      When surface and ground waters are contaminated, radionuclides may be transferred through a
1136      complex food web consisting of aquatic plants and animals. Aquatic plants and animals, as
1137      discussed here, are any species which derive all or substantial portions of their nourishment from
1138      the aquatic ecosystem, are part of the human food chain, and show significant accumulation of a
1139      radionuclide relative to its concentration in water. Although fish, aquatic mammals, and
1140      waterfowl provide a direct link to human exposure, lower members of the food chain also may be
1141      sampled.

1142      FLORA

1143      Aquatic biota such as algae, seaweed, and benthic organisms are indicators and concentrators of
1144      radionuclides—especially 59Fe, 60Co, 65Zn, 90Sr, and 137Cs—and can be vectors in the water-fish-
1145      human food  chain. As such, they may be sampled upstream and downstream at locations similar
1146      to those described for sediment. Because of their high water content, several kilograms (wet
1147      weight) should be collected per sample. The wet weight of the sample should be recorded.
1148      Enough of the  wet sample should be processed so that sufficient sample remains following the
1149      drying process. Both algae (obtained by filtering water or by scraping submerged substrates) and
1150      rooted aquatic  plants should be sampled.

ii5i      FISH AND SHELLFISH
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1152      For practical reasons, fish and shellfish may be purchased from local sources if the origin can be
1153      determined. Samples also can be obtained by pole fishing, netting, or electric shock devices. The
1154      sampling plan will describe the processing needed. Samples should include each of the principal
1155      edible types in local catches. Several kilograms of each fish sample are usually required; this may
1156      be one large fish, but preferably a composite of a number of small ones. Analysis of the edible
1157      portions of food fish as prepared for human consumption is of major interest. Fish may be de-
1158      boned, if specified in the sampling plan. The whole fish is analyzed if it is used for the
1159      preparation of a fish meal for consumption or if only trend indication is required. In a program
1160      where fish are the critical pathway, fish are analyzed by species; if less detail is required, several
1161      species with similar feeding habits (such as bottom feeders, insectivores, or predators) may be
1162      collected and the data grouped.

1163      In large bodies of water, samples from several locations are desirable because of the difficulty in
1164      knowing whether a fish caught at a given location had lived there for an extended period. Thus,
1165      the presence or absence of a radionuclide in a specific fish does not permit any definite
1166      conclusion concerning the presence of the radionuclide in water at that location. For some fish,
1167      more specific information concerning their usual location may be available; for example, dams,
1168      salinity gradients, and temperature gradients can be effective barriers to their movement.
1169      Information on  fish age, feeding habits, and the quality  of the aquatic environment are desirable
1170      to evaluate the significance of any findings.

1171      Shellfish, such as clams, oysters, and crabs, are collected for the same reasons as fish, but have
1172      the advantage as indicators of being relatively stationary. Their restricted mobility contributes
1173      substantially to the interpretation and application of analytical results to environmental
1174      surveillance. Edible and inedible portions of these organisms can be prepared separately.

1175      WATERFOWL

1176      Waterfowl, such as ducks and geese, may also concentrate radionuclides from their food sources
1177      in the aquatic environment and serve as important food sources to humans. The migratory
1178      patterns and feeding habits of waterfowl vary widely. Some species are bottom feeders and, as
1179      such, tend to concentrate those radionuclides associated with sediments  such as 60Co,  65Zn, and
1180      137Cs. Others feed predominantly on surface plants, insects, or fish.

1181      Whenever practical, and if time permits, waterfowl should be obtained by  hunting, but a trapping
1182      procedure may also be used. An important consideration in obtaining a sample from waterfowl is
1183      that their exterior surfaces, especially feathers, may be contaminated. It is important to avoid
1184      contaminating the "flesh" sample during handling. As with other biota samples, analyses may be
1185      limited to the edible portions and should be reported on a wet weight basis. Local game officials
1186      or aquatic ecologists may provide valuable information for choosing  the proper species.
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1187      Caution is advised in the selection of background or control locations for all biota (terrestrial and
1188      aquatic) sampled, at least for those species whose mobility and feeding habits may significantly
1189      affect the results obtained.  Since this mobility makes it difficult to establish upstream/
1190      downstream sampling locations for biota in a manner analogous to those for air, water, or plants,
1191      a sound sampling strategy may require the expert advice and direction of local ecologists, and
1192      fish and game personnel. Samples from the background locations should be from an ecosystem
1193      identical to that of those collected near the site, but unaffected by site effluents.

ii94      10.5  Air Sampling

1195      The measurement of airborne radionuclides as gases or particulates provides a means of
1196      evaluating internal exposure through the inhalation pathways. The types of airborne radioactivity
1197      that may require air sampling are normally categorized as: (1) airborne particulates; (2) noble
1198      gases; (3) volatilized halogens (principally radioiodines); and (4) tritiated water. Depending upon
1199      the source term and the objectives of the investigation, air sampling may be conducted outdoors
1200      as well as indoors on behalf of a variety of human receptors. For example,  routine outdoor air
1201      samples may be taken for large population groups living within a specified radius of a nuclear
1202      facility. On the other end of the spectrum, air samples may be taken for a single person or small
1203      group of persons exposed occupationally to a highly localized source of airborne radioactivity.

1204      The purpose of the samples being collected must, therefore, be well defined in terms of sampling
1205      location, field sampling equipment, and required sample volumes. Due to the wide range of
1206      conditions that may mandate air sampling, and the limited scope of this section, only generic
1207      topics of air sampling will be discussed.

1208      10.5.1  Sampler Components

1209      Common components of air sampling equipment include a sample collector (i.e., filter), a sample
1210      collector holder, an air mover, and a flow-rate measuring device.

1211      The sample holder should provide adequate structural support while not damaging the filter,
1212      should prevent sampled air from bypassing the filter, should facilitate changing the filter, and
1213      should facilitate decontamination. A backup support that produces negligible pressure drop
1214      should be used behind the filter to prevent filter distortion or deterioration.

1215      If rubber gaskets are used to seal the filter to the backing plate, the gasket should be in contact
1216      with the filter along the entire circumference to ensure a good fit.

1217      Air movers or vacuum systems should provide the required flow through the filter and to
1218      minimize air flow reduction due to filter loading. Consideration should be  given to the use of air
1219      movers that compensate for pressure drop. Other factors to consider should include size, power
1220      consumption, noise, durability, and maintenance requirements.

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1221      Each air sampler should be equipped with a reliable calibrated air flow measuring device with
1222      specified accuracy. To calculate the concentrations of any radionuclide in air collected, it is
1223      necessary to accurately determine the total volume of air sampled. The planning documents
1224      should state who is responsible for making volume corrections. Also, the information needed for
1225      half-life corrections for short-lived radionuclides needs to be recorded.

1226      Generally, a parameter of the air mover can be related to flow. If the mean flow during a
1227      collection period can be determined, the total volume of air sampled can be readily calculated.
1228      Accurate flow measurements and the total integrated  sample volume of air can be obtained using
1229      a mass flow meter and a totalizer. This direct technique of air flow measurement becomes
1230      impractical at remote field locations, due to cost and exposure of the flow meter to harsh
1231      environments. Other procedures for the measurement of air flow in sampling systems are
1232      reviewed by Lippmann (1989a). The equipment readings (flow rate, volume, etc.) should be
1233      recorded by the sample collector.

1234      The collection medium or filter used depends on the physical and chemical properties of the
1235      materials to be collected and counted. A variety of particulate filters (cellulose,  cellulose-
1236      asbestos, glass fiber, membrane, polypropylene, etc.)  is available. The type of filter is selected
1237      according to needs, such as high collection efficiency, particle-size selectivity, retention of alpha
1238      emitters on the filter surface, and the compatibility with radiochemical analysis. The criteria for
1239      filter selection are good collection efficiency for submicron particles at the range efface
1240      velocities used, high particle and mass loading capacity, low-flow resistance, low cost, high
1241      mechanical strength, low-background activity, compressibility, low-ash content, solubility in
1242      organic solvents, non-hygroscopicity, temperature stability, and availability in a variety of sizes
1243      and in large quantities. The manufacturer's specifications and literature should provide a source
1244      for filter collection efficiency. In the selection of a filter material, a compromise must be made
1245      among the above-cited criteria that best satisfies the sampling requirements. An excellent review
1246      of air filter material used to monitor radioactivity was published by Lockhart and Anderson
1247      (1964).  Lippmann (1989b) also provides information on the selection of filter materials for
1248      sampling aerosols by filtration. See ANSI (1999),  Annex D and Table D.I, for criteria for the
1249      selection of filters for sampling airborne radioactive particles.

1250      In order to select a filter medium with adequate collection efficiency, it may be  necessary to first
1251      determine the distribution of size of airborne particulates. Several methods, including impactors
1252      (e.g., multistage cascade impactor) and electrostatic precipitators, can be used to classify particle
1253      size. Waite and Nees (1973) and Kotrappa et al. (1974) discuss techniques for particle sizing
1254      based on the flow discharge perturbation method and the HASL cyclone, respectively. These
1255      techniques are not recommended for routine environmental surveillance of airborne parti culates,
1256      although their use for  special  studies or for the evaluation of effluent releases should  not be
1257      overlooked. Specific data on various filter materials, especially retention efficiencies, have been
1258      reported by several authors (Lockhart and Anderson,  1964; Denham, 1972; Stafford,  1973;
1259      ASTM  STP555) and additional information is available from manufacturers.

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1260      10.5.2 Filter Selection Based on Destructive Versus Non-destructive Analysis

1261      Pure cellulose papers are useful for samples to be dissolved and analyzed radiochemically, but
1262      the analytical filter papers used to filter solutions are inefficient collectors for aerosols and clog
1263      easily. Cellulose-asbestos filter papers combine fairly high efficiency, high flow rates, high
1264      mechanical strength, and low pressure drops when loaded. They are very useful for collecting
1265      large samples but present difficulties in dissolution, and their manufacture is diminishing because
1266      of the asbestos. Fiberglass filters  can function efficiently at high flow rates, but require fluoride
1267      treatment for dissolution and generally contain sufficient radioactive nuclides to complicate low-
1268      activity analysis. Polystyrene filters are efficient and capable of sustaining high air flow rates
1269      without clogging. They are readily destroyed for analysis by ignition (300° C) or by wet washing
1270      with oxidizing agents, and also are soluble in many  organic liquids. They have the disadvantage
1271      of low mechanical and tensile strength, and they must be handled carefully. Membrane filters are
1272      excellent for surface collection efficiency and can be used for direct alpha spectrometry on the
1273      filter. However, they are fragile and suffer from environmental dust loading. An alternative
1274      choice for radionuclides in the environment is the polypropylene fiber filter, Dynaweb Grade
1275      DW7301L. Filters come in two sizes: a 20.32 cm circle and a 20.32 cm x 25.40 cm rectangle.
1276      The filter is composed of a 100 percent polypropylene web that is  100 percent binderless. Three
1277      layers of this web are collated and sandwiched between two sheets of a protective DuPont Reeme
1278      (100 percent polyester) scrim.

1279      10.5.3 Sample Preservation and Storage

1280      Since paniculate air samples are generally dry samples that are chemically and physically stable,
1281      they require no preservation. However, care must be exercised to avoid loss of sample from the
1282      filter medium and the  cross contamination among individual samples. A common method is to
1283      fold filters symmetrically so that the two halves of the collection surface are in contact. Filters
1284      should be stored in individual envelopes  that have been properly labeled. Filters may also be
1285      stored in special holders that attach on the filter's edge outside of the collection surface.
1286      When background levels of 222Ra and 220Ra progeny interfere with evaluation of alpha air
1287      samples, a holdup time of several hours may be required before samples are counted. Corrections
1288      or determinations can  also be made for the contribution of radon or thoron progeny present on a
1289      filter (Setter and Coats, 1961).

1290      10.5.4 Special Cases: Collection of Gaseous and Volatile Air Contaminants

1291      Prominent radionuclides that may exist in gaseous states include noble gases, 14C as carbon
1292      dioxide or methane, 3H as water vapor, and volatilized radioiodines. (Radon is discussed in
1293      Section 10.5.5.)
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1294      10.5.4.1   Radioiodines

1295      The monitoring of airborne iodine, such as 129I and 131I, may be complicated by the probable
1296      existence of several species, including paniculate iodine or iodine bound to foreign particles,
1297      gaseous elemental iodine, and gaseous non-elemental compounds of iodine. A well-designed
1298      sampling program should be capable of distinguishing all possible iodine forms. While it may
1299      not always be necessary to differentiate between the various species, care should be taken so that
1300      no bias can result by missing one or more of the possible species. See ANSI (1999) Annex C.3,
1301      for information on collection media for radioiodine.

1302      In addition to the problems noted above,  charcoal cartridges (canisters) for the collection of
1303      radioiodine in air are subject to channeling. Hence, they  should be carefully checked before
1304      operation in the field (analogous to DOP testing of high  efficiency particulate air (HEP A) filters
1305      in situ ) or several should be mounted in  series to prevent loss of iodine. Too high a sampling rate
1306      reduces both the collection efficiency and retention time of charcoal filters, especially for the
1307      non-elemental forms of iodine (Keller et al., 1973; Bellamy, 1974). The retention of iodine in
1308      charcoal is dependent not only on charcoal volume, but also the length of the charcoal bed.
1309      Typical air flow rates for particulate sampling of 30 to 90 L/min (1 to 3 ft3/min) are normally
1310      acceptable for environmental concentrations of radioiodine. The method proposed by the
1311      Intersociety Committee (APHA, 1972) for 131I concentrations in the atmosphere involves
1312      collecting iodine in its solid and gaseous states with an "absolute" particulate filter in series with
1313      an activated charcoal cartridge followed by gamma spectrometric analysis of the filter and
1314      cartridge. The Intersociety-recommended charcoal cartridges are 5/8 in.  diameter by 1.5 in. deep
1315      containing 3 g of 12 to 30 mesh Kl-activated  charcoal. The minimum detectable level using the
1316      Intersociety method is 3.7 x 10"3 Bq/m3 (0.1 pCi/m3). Larger cartridges will improve retention,
1317      permitting longer sampling periods. A more sensitive system has been described by Baratta et al.
1318      (1968), in which concentrations as low as 0.037 Bq/m3 (0.01 pCi/mL) of air are attainable.

1319      For the short-lived radioiodines (mass numbers 132, 133, 135), environmental sampling is
1320      complicated by the need to obtain a sufficient volume for analysis, while at the same time,
1321      retrieving the sample soon enough to minimize decay (with half-lives ranging from two hours to
1322      31 hours). Short period (grab) sampling with  charcoal  cartridges is possible, with direct counting
1323      of the charcoal as soon  as possible for gamma emissions, but radon and thoron will affect
1324      detect on 1 evel s.

1325      Because of the extremely long half-life and normally low environmental concentrations, 129I
1326      determinations must usually be performed by neutron activation or mass spectrometry analysis
1327      after chemical isolation of the iodine.  For concentrations about 3 x 10"10 |iCi/mL, liquid
1328      scintillation counting can be used after solvent extraction (Gabay et al., 1974).
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1329      10.5.4.2   Gases

1330      Sampling for radioactive gases is either done by grab sample that employs an evacuated chamber
1331      or by airflow through a medium such as charcoal, water, or a variety of chemical absorbers. For
1332      example, radioactive CO2 is most commonly extracted by passing a known volume of air through
1333      columns filled with 3 M NaOH solution. After the NaOH is neutralized with sulfuric acid, the
1334      CO2 is precipitated in the form of BaCO3, which then can be analyzed in a liquid scintillation
1335      counter (NCRP, 1985).

1336      Because noble gases have no metabolic significance, and concern is principally limited to
1337      external exposure, surveillance for noble gases is commonly performed by ambient dose rate
1338      measurements. However, the noble gases xenon and krypton may be extracted from air by
1339      adsorption on activated charcoal (Scarpitta and Harley,  1990). However, depending upon the
1340      analytical method and instrumentation employed, significant interference may result from the
1341      presence of naturally occurring radioactive gases of 222Rn and 220Rn.

1342      10.5.4.3   Tritium Air Sampling

1343      In air, tritium occurs primarily in two forms: as water vapor (HTO) and as hydrogen gas (HT).
1344      Tritiated organic compounds in the vapor phase or attached to paniculate matter occur only
1345      occasionally. To measure tritium as HT or in tritiated organic, the gas phase can be oxidized,
1346      converting the tritium to  HTO before desiccation and counting. For dosimetric purposes, the
1347      fraction present as HT can usually be neglected, since the relative dose for a given activity
1348      concentration of HTO is  400 times that for HT (NCRP, 1978). However,  if HT analysis is
1349      required, it can be removed from the atmosphere by oxidation to water (HTO) using CuO/MnO2
1350      at 600° C (Pelto et al., 1975),  or with air passed over platinum alumina catalyst (Bixel and
1351      Kershner 1974). These methods also oxidize volatile tritiated organic compounds to yield
1352      tritiated water (ANSI, 1999, Annex H).

1353      A basic system for sampling HTO consists of a pump, a sample collector, and a flow-measuring
1354      or flow-recording device. Air is drawn through the collector for a measured time period at a
1355      monitored flow rate to determine the total volume of air sampled. The total amount of HTO
1356      recovered from the collector is divided by the total volume of air  sampled to  determine the
1357      average HTO-in-air concentration of the air sampled. In some sampler types, the specific activity
1358      of the water collected is measured and the air concentration is determined from the known or
1359      measured humidity. Some common collectors are cold traps, tritium-free  water, and solid
1360      desiccants, such as silica gel, DRIERITE™, or molecular sieve.

1361      Cold traps are usually made of glass and consist of cooled collection traps through which sample
1362      air flows. The trap is cooled well below the freezing point of water, usually with liquid nitrogen.
1363      The water vapor collected is then prepared for analysis, usually by liquid  scintillation  counting.
1364      Phillips and Easterly (1982) have shown that more than 95 percent HTO collection efficiency can

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1365      be obtained using a single cold trap. Often a pair of cold traps is used in series, resulting in a
1366      collection efficiency in excess of 99 percent.

1367      Gas-washing bottles (i.e., "bubblers") filled with an appropriate collecting liquid (usually tritium-
1368      free water) are used quite extensively for collecting HTO from air. HTO in the sample gas stream
1369      "dissolves" in the collecting liquid. For the effective collection rate to remain the same as the
1370      sample flow rate, the specific activity of the bubbler water must be negligible with respect to the
1371      specific activity of the water vapor. Thus, the volume of air that can be sampled is ultimately
1372      limited by the volume of water in the bubbler. However, except when sampling under conditions
1373      of very high humidity, sample loss (dryout) from the bubbler usually limits collection time rather
1374      than the attainment of specific activity equilibrium. Osborne (1973)  carried out a thorough
1375      theoretical and experimental evaluation of the HTO collection efficiency of water bubblers over a
1376      wide range of conditions.

1377      The use of silica gel as a desiccant to remove moisture from air is a common technique for
1378      extracting HTO. The advantage of using silica gel is that lower HTO-in-air concentrations can be
1379      measured, since the sample to be analyzed is not significantly diluted by an initial water volume,
1380      which occurs when a liquid-sampling sink is used. Correcting for dilution is discussed in Rosson
1381      et al. (2000).

1382      10.5.5  Radon

1383      There are three isotopes of radon in nature: 222Rn is a member of the 238U decay chain; 220Rn is a
1384      member of the 232Th decay chain; and 219Rn is a member of the 235U decay chain. Because of the
1385      small relative abundance of the parent nuclides and the short half-lives  of 220Rn (55 seconds) and
1386      219Rn (4 seconds), the term "radon" generally refers to the isotope 222Rn. Owing to  its ubiquitous
1387      presence in soils, uranium mill tailings, underground mines, etc., and the health risks to large
1388      populations and occupational groups, radon is perhaps the most studied radionuclide.

1389      Consequently,  many reports and articles have been published in the scientific literature dealing
1390      with the detection methods and health risks from radon exposures. Many of them appear in
1391      publications issued by the EPA, DOE, NCRP, NAS, and in radiation-related journals, such as the
1392      journals Health Physics and Radiation Research. Given the voluminous amount of existing
1393      information, only a brief overview of the sampling method can be presented here.

1394      10.5.5.1   Radon Sampling Methods

1395      Quantitative measurements of radon  gas and its short-lived decay products can be obtained by
1396      several techniques that are broadly categorized as grab sampling, continuous radon monitoring,
1397      and integrative sampling. Each method imposes unique requirements that should be followed
1398      carefully. The U.S. EPA Radon Measurement Proficiency (RMP) Program should  be consulted
1399      for current guidance for sample collection (EPA, 1992; EPA, 1993). Information is available on

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1400      the RMP home page at www.epa.gov/radonpro/index.htm. Working with the Radon Proficiency
1401      Program (RPP) is described in a separate handbook (EPA, 1996). A description of additional
1402      sampling methods and materials is also presented in EPA (1994) and Cohen (1989).

1403      In general, EPA's protocols specify that radon sampling and measurements be made under
1404      standardized conditions when radon and its progeny are likely to be at their highest concentra-
1405      tions and maximum equilibrium. For indoor radon measurement, this implies minimum building
1406      ventilation through restrictions on doors, windows, HVAC systems, etc. Also sampling should
1407      not take place during radical changes in weather conditions. Both high winds and rapid changes
1408      in barometric pressure can dramatically alter a building's natural ventilation rate. Although
1409      recommended measurements are likely to generate higher than actual average concentrations, the
1410      benefit of a standardized sampling condition is that it is reproducible, least variable, and
1411      moderately conservative. Brief descriptions of the basic techniques used to sample air for radon
1412      and its progeny are provided below.

1413      GRAB SAMPLING

1414      The term "grab sampling" refers to very short-term sampling. This method consists of evaluating
1415      a small volume of indoor air for either radon or radon decay product concentration. In the radon
1416      grab sampling method, a sample of air is drawn into and subsequently sealed in a flask or cell
1417      that has a zinc  sulfide phosphor coating on its interior surfaces. One surface of the cell is fitted
1418      with a clear window that is put in contact with  a photomultiplier tube to count light pulses
1419      (scintillations)  caused by alpha disintegrations from the sample interacting with the zinc sulfide
1420      coating. The number of pulses is proportional to the radon concentration in the cell. The cell is
1421      counted about four hours after filling to allow the short-lived radon decay products to reach
1422      equilibrium with the radon.  The  results are corrected to compensate for decay during the time
1423      between collection  and counting, and for decay during counting.

1424      Several  methods for performing such measurements have been developed. However, two
1425      procedures  that have been most widely used with good results are the Kusnetz procedure and the
1426      modified Tsivogiou procedure. In brief, the Kusnetz procedure (Kusnetz, 1956; ANSI, 1973)
1427      may be used to obtain results in working levels (WL) when the concentration of individual decay
1428      products is  not important. Decay products in up to 100 liters of air are collected on a filter in a
1429      five-minute sampling period. The total  alpha activity on the filter is counted any time between 40
1430      and 90 minutes after sampling is completed.  Counting can be done using a scintillation-type
1431      counter  to obtain gross alpha counts for a selected counting time. Counts from the filter are
1432      converted to disintegrations using the appropriate counter efficiency. The disintegrations from
1433      the decay products  may be converted into working levels using the appropriate "Kusnetz factor"
1434      for the counting time used.

1435      The Tsivogiou procedure may be used to determine both WL and the concentration of the
1436      individual radon decay products. Sampling is the same as in the Kusnetz procedure. However,

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1437      the filter is counted three separate times following collection. The filter is counted between 2 and
1438      5 minutes, 6 and 20 minutes, and 21 and 30 minutes after sampling is complete. Count results are
1439      interpreted by a series of equations that calculate concentrations of the three radon decay
1440      products and WL.

1441      The advantages of grab sampling are that the analysis time is relatively short, results are available
1442      within a short time, and conditions during the measurement are known to the sampler. In
1443      addition, grab sampling does not provide a long-term average and house conditions must be
1444      controlled for 12 hours prior to measurement.

1445      CONTINUOUS RADON MONITOR

1446      A continuous radon monitor (CRM) samples the ambient air by pumping air into a scintillation
1447      cell after passing it through a paniculate  filter that removes dust and radon decay products. As
1448      the radon in the air decays, the ionized radon decay  products plate out on the interior surface of
1449      the scintillation cell. As the radon decays, the alpha particles strike the coating on the inside of
1450      the cell, causing scintillations. The scintillations are detected by the photomultiplier tube in the
1451      detector, which generates electrical signals. The signals are processed  and the results are either
1452      stored in the memory of the CRM or printed on paper tape by the printer. The CRM must be
1453      calibrated in a known environment to obtain the conversion factor used to convert count to radon
1454      concentration.

1455      The CRM may be a flowthrough-cell type or a periodic-fill type. In the flowthrough-cell type, air
1456      flows continuously into and through the scintillation cell. The periodic-fill type fills the cell once
1457      during each preselected time interval, counts the scintillations, then begins the cycle again.

1458      An analogous device to the continuous radon monitor is the Continuous Working Level Monitor
1459      (CWLM). This device filters air at a low flow rate of about 0.2 to one liter per minute and
1460      measures the amount of radon decay products on the filter medium. An alpha detector, such as a
1461      diffused-junction or surface-barrier detector,  counts the alpha particles produced by the radon
1462      decay products as they decay on the  filter. The detector is normally set to detect alpha particles
1463      with energies between 2 and 8 meV. The alpha particles emitted from  the radon decay products
1464      218Po and 214Po are the significant contributors to the events that are measured by the detector.
1465      The event count is directly proportional to the number of alpha particles emitted by the radon
1466      decay products on the filter. The unit typically contains a microprocessor that stores the number
1467      of counts and elapsed time. The unit can be set to record the  total counts registered over specified
1468      time periods. The unit must be calibrated in a calibration facility to convert count rate to working
1469      level (WL) values. This may be  done initially by the manufacturer and should be done
1470      periodically thereafter by the operator.
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1471      INTEGRATING SAMPLING DEVICES

1472      By far, the most common technique for measuring radon is by means of integrating devices.
1473      Integrating devices, like the charcoal canister and the Electret-Passive Environmental Radon
1474      Monitor, are commonly employed as short-term integrating devices (two to seven days), while
1475      alpha track detectors are commonly used to provide measurements of average radon levels over
1476      periods of weeks to months.

1477      CHARCOAL CANISTERS

1478      Charcoal canisters (CC) are passive devices requiring no power to function. The passive nature
1479      of the activated charcoal allows continual adsorption and desorption of radon. During the
1480      measurement period, the adsorbed radon undergoes radioactive decay. Therefore, the technique
1481      does not uniformly integrate radon concentrations during the exposure period. As with all
1482      devices that store radon, the average concentration calculated using the mid-exposure time is
1483      subject to error if the ambient radon concentration adsorbed during the first half of the sampling
1484      period is substantially higher or lower than the average over the period. For a 2 to 7 day exposure
1485      period, the minimum detectable concentration (MDC) should be 18.5 Bq/m3 (0.5 pCi/L) or less
1486      (EPA,  1989). This detection level can normally be achieved with a counting time of up to 30
1487      minutes. This MDC should be calculated using the results of charcoal background
1488      determinations. The coefficient of variation should not exceed 10 percent (1 sigma) at radon
1489      concentrations of 148 Bq/m3  (4 pCi/L) or greater (EPA, 1989). This precision should be
1490      monitored using the results of duplicate canister analyses.  CCs can achieve an average coefficient
1491      of variation of less than five percent at concentrations of 148 Bq/m3 (4 pCi/L) or greater.

1492      ELECTRET-PASSIVE ENVIRONMENTAL RADON MONITORS

1493      Electret-passive environmental radon monitors (E-perms) require no power and function as true
1494      integrating detectors that measure the average concentration during the exposure period. E-
1495      PERMS contain a permanently charged Electret (an electrostatically charged disk of Teflon) that
1496      collects ions formed in the chamber by radiation emitted from radon decay products. When the
1497      device is exposed, radon diffuses into the chamber through filtered openings. Ions that are
1498      generated continuously by the decay of radon and radon decay products are drawn to the surface
1499      of the electret and reduce its surface voltage. The amount of voltage reduction is related directly
1500      to the average radon concentration  present during the exposure period. There are both short-term
1501      (2 to 7 days) and long-term (1 to 12 months) E-PERMS that are marketed currently. The
1502      thickness of the electret affects the usable measurement period. For a 7-day exposure period
1503      using a short-term E-PERM,  as well as for a long-term E-PERM, the MDC is about 11.1 Bq/m3
1504      (0.3 pCi/L) (EPA, 1989). The coefficient of variation should not exceed 10 percent (1 sigma) at
1505      radon concentrations of 148 Bq/m3 (4 pCi/L) or greater. This precision should be verified by
1506      using results of duplicate detector analysis.
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1507      ALPHA TRACK DETECTORS

1508      An alpha track detector (ATD) consists of a small piece of plastic or film enclosed in a container
1509      with a filter-covered opening. Radon diffuses through the filter into the container and alpha
1510      particles emitted by radon and its decay products strike the detector and produce submicroscopic
1511      damage tracks. At the end of the measurement period, the detectors are returned to a laboratory.
1512      Plastic detectors are placed in a caustic solution that accentuates the damage tracks so they can be
1513      counted using a microscope or an automated counting system. The number of tracks per unit area
1514      is correlated to the radon concentration in air, using a conversion factor derived from data
1515      generated at a calibration facility. The number of tracks produced per unit time is proportional to
1516      the radon concentration, so an ATD functions as a true integrating detector and measures the
1517      average concentration over the measurement period. The MDC and precision of an ATD system
1518      is dependent upon the tracks counted and, therefore, the area of the detector that is analyzed.
1519      With present ATDs, routine counting achieves a MDC of 6,660 Bq/m3-days (180 pCi/L-days).
1520      The coefficient of variation (precision) should be monitored using the results of duplicate
1521      detectors. The coefficient of variation should not exceed 20 percent (1 sigma) at radon
1522      concentrations of 148 Bq/m3 (4 pCi/L) or greater (EPA, 1989).

1523      10.5.5.2   Selecting a Radon Sampling Method Based on Data Quality Objectives

1524      The choice from among the sampling methods described above depends  on whether the measure-
1525      ment is intended as a quick screening measurement or as a measurement that determines average
1526      exposure. In practice, the choice of a measurement system often is dictated by availability. If
1527      alternative systems are available, the cost or duration of the measurement may become the
1528      deciding factor. Each system has its own advantages and disadvantages, and the investigator
1529      must exercise some judgment in selecting the system best suited to the DQOs of the
1530      investigation.

1531      There are, however, some general guidelines concerning standardized measurement conditions
1532      and quality assurance objectives which apply to all measurement techniques.  The following
1533      elements of quality assurance should be included in any measurement program: detector
1534      calibrations, replicate measurements, background measurements, and routine sensitivity checks.

1535      Detector calibrations are measurements made in a known radon environment, such as a
1536      calibration chamber. Detectors requiring laboratory readout, such as charcoal canisters and alpha-
1537      track detectors, should be exposed  in the calibration chamber and then analyzed. Instruments
1538      providing immediate results, such as continuous working-level monitors and  continuous radon
1539      monitors, should be operated in a chamber to establish calibration.

1540      There are two types of calibration measurements that should be made for alpha-track detectors
1541      and charcoal canisters. The first measurements determine and verify the conversion factors used
1542      to derive the concentration results.  These measurements, commonly called spiked samples, are

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1543      done at the beginning of the measurement program and periodically thereafter. The second
1544      calibration measurements monitor the accuracy of the system. These are called blind calibration
1545      measurements and consist of detectors that have been exposed in a radon calibration chamber.
1546      The detectors are not labeled as such when sent to a processing laboratory.

1547      Background measurements, or blanks, should also be conducted. Such measurements should be
1548      made using unexposed passive detectors, or should be instrument measurements conducted in
1549      very low (outdoor) radon concentration environments and separated from the operating program.
1550      Generally, these should be equivalent in frequency to the spiked samples and should also not be
1551      identified as blanks when submitted for analysis to external laboratories. In addition to these
1552      background measurements, the organization performing the measurements should calculate the
1553      minimum detectable concentration MDC for the measurement system. This MDC is based on the
1554      system's background and can restrict the ability of some measurement systems to measure low
1555      concentrations.

1556      Duplicate measurements provide an estimate of the precision of the measurement results.
1557      Duplicate measurements should be included in at least 10 percent of the samples. If enough
1558      measurements are made, the number of duplicates may be reduced, as long as enough are used to
1559      analyze the precision of the method.

1560      A quality assurance program should include a written plan for satisfying the preceding
1561      objectives. A system for monitoring the results of the four types of quality assurance
1562      measurements should also be maintained.

1563      Calibrated radon detection devices and on-site measurements can also be obtained under contract
1564      from commercial vendors who have demonstrated their proficiency in measuring radon and
1565      radon decay products, and who have had their quality assurance programs assessed by the EPA or
1566      state agencies.

1567      10.6  Wipe Sampling for Assessing Surface Contamination

1568      Surface contamination falls into two categories: fixed and loose. The wipe test (also referred to
1569      as "swipes" or "smears") is the universally accepted technique for detecting removable
1570      radioactive contamination on surfaces (Section 12.5). It is often a stipulation of radioactive
1571      materials licenses and is widely used by laboratory personnel to monitor their work areas,
1572      especially for low-energy radionuclides that are otherwise difficult to detect with hand-held
1573      survey instruments. A comprehensive history of "Use of Smears for Assessing Removable
1574      Contamination" is presented by Frame and Abelquist (1999).

1575      The U.S. Nuclear Regulatory Commission (NRC, 1981) suggests that 100 cm2 areas be wiped
1576      and lists acceptable levels for surface contamination. However, NRC neither recommends the
1577      collection device nor the manner in which to conduct such surveys, relying instead on

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1578      suggestions by the National Committee on Radiation Protection (1964) and the National Council
1579      on Radiation Protection and Measurements (1978).

1580      10.6.1  Sample Collection Methods

1581      10.6.1.1   Dry Wipes

1582      Smears for removable surface activity are obtained by wiping an area of approximately 100 cm2
1583      using a dry filter paper, such as Whatman 50 or equivalent, while applying moderate pressure. A
1584      47 mm diameter filter is typically used, although for surveys for low-energy beta emitters,
1585      smaller sizes may be more appropriate because they can be placed directly into a liquid
1586      scintillation vial for counting.  Small pieces of wipes occasionally are used for smears for tritium
1587      (Slobodine and Grandlund, 1974). A smear for removable contamination is obtained at each
1588      location of direct surface activity measurement.

1589      For surveys of small penetrations, such as cracks or anchor-bolt holes, cotton swabs are used to
1590      wipe the area of concern. Samples (smears or swabs) are placed into envelopes or other
1591      individual containers to prevent cross-contamination while awaiting analysis. Smears for alpha
1592      and medium- or high-energy beta activity can be evaluated in the field by counting them on an
1593      integrating sealer unit with appropriate detectors; the same detectors utilized for direct
1594      measurements may be used for this purpose. However, the more  common practice is to return the
1595      smears to the laboratory, where analysis can be conducted using more sensitive techniques. The
1596      most common method for analyzing wipe samples is to use a proportional counter. For very low-
1597      energy beta emissions, wipe samples are commonly analyzed by liquid scintillation counting.

1598      10.6.1.2   Wet Wipes

1599      Although dry wipes are more convenient to handle, and there are fewer chances of cross
1600      contamination, a general limitation of dry wipes is their low recovery of surface contamination.
1601      The low recovery using dry wipes is due to the higher affinity for the surface by the contaminant
1602      than for the filter paper. Several studies have shown that for maximum sensitivity, a wipe
1603      material moistened with a suitable solvent may be indicated. For example, Ho and Shearer
1604      (1992) found that alcohol-saturated swabs were 100 times more efficient at removing
1605      radioactivity than dry swabs.

1606      In another study, Kline et al. (1992) assessed the collection efficiency of wipes from various
1607      surfaces that included vinyl floor tile, plate glass, and lead foil. Two different collection devices,
1608      cotton swabs  and 2.5  cm diameter glass fiber filter disks, were evaluated under various collection
1609      conditions. Dry wipes were compared to collections made with the devices dampened with
1610      different amounts of either distilled H2O, 70 percent ethanol, or a working-strength solution of a
1611      multipurpose laboratory detergent known to be effective  for removing contaminants from
1612      laboratory glassware (Manske et al., 1990).

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         	Field and Sampling Issues That Affect Laboratory Measurements

1613      The entire area of each square was manually wiped in a circular, inwardly-moving motion with
1614      consistent force. The collection capacity of each device was estimated by wiping progressively
1615      larger areas (multiple grids) and comparing the measured amounts of radioactivity with the
1616      amounts placed on the grids.

1617      Collection efficiency varied with both the wipe method and the surface wipe. Contamination was
1618      removed most readily from unwaxed floor tile and glass; lead foil released only about one-half
1619      the radioactivity. Stainless steel, another common laboratory surface, has contamination retention
1620      properties similar to those of glass.

1621      In most cases, collection was enhanced by at least a factor of two after dampening either the
1622      swabs or filter disks with water. Dampening with ethanol or the detergent produced removals that
1623      were statistically indistinguishable from samples dampened with an equal amount of water.

1624      The filter disks had a higher collection capacity for removable contaminants than cotton swabs,
1625      nearly doubling the radioactivity removed for each doubling of surface area wiped. Variability
1626      within all methods was high, with coefficients of variation ranging from 2 to 30 percent.

1627      For the moistened wipes, wipe efficiency depended on three factors, including the polarity of the
1628      solvent, the polarity of the contaminant being measured, and the affinity of the compound for the
1629      contaminated surface. For a solvent to readily dissolve a compound (i.e., remove it from the
1630      surface), the solvent and the compound must have  similar polarities. Nonpolar solvents include
1631      ethyl acetate and petroleum ether; for polar solvents, water or methanol may be used (Cambell et
1632      al., 1993). There are other factors that influence the affinity of a compound for a surface,
1633      including porosity of the surface and available binding sites on the surface. One important factor
1634      which influences binding capacity is the type of treatment that a surface has received. When
1635      working with a surface treated with a nonpolar wax, such as that used on floor tile, a nonpolar
1636      compound will be adsorbed to the surface, which further limits recovery. In contrast, recovery
1637      from absorbent surfaces, such as lab bench paper or untreated wood, may give poor recoveries
1638      due to the porous nature of the surface.

1639      10.6.2  Sample Handling

1640      Filter paper or other materials used for wipe tests in the field should be placed in separate
1641      containers that prevent cross contamination during transport and allow for labeling of each
1642      sample. Plastic bags, paper or glassine envelopes, and disposable plastic petri dishes are
1643      containers typically used to store and transport wipe samples.  Field workers can use plastic or
1644      rubber gloves and forceps when applying the wipe material to a surface and during handling as
1645      each wipe is placed into a container. Protection of the sample wipe surface is the main concern
1646      when a wipe must be placed in a container for transport. If a scintillation vial or planchet will be
1647      used in the lab, then a field worker may put wipes directly into them.  Planchets containing loose
1648      or self-sticking wipes can also be put into self-sealing plastic bags to  separate and protect the

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1649      integrity of the sample's surface. Excessive dust and dirt can cause self adsorption or quenching,
1650      and therefore should be minimized.

1651      To maintain constant geometry in an automatic proportional counter, it is important that the wipe
1652      remain flat during counting. Additionally, material that will curl can jam the automatic counter
1653      and cause cross contamination or even destroy the instrument window. When it is necessary to do
1654      destructive analysis on the wipe, it is critical that the wipe can easily be destroyed during the
1655      sample preparation step, and that the residue not cause interference problems.

1656      When wipes are put directly into liquid scintillation cocktail, it is important that the wipe not add
1657      color or react with the cocktail. For maximum counting efficiency, as well as reproducibility, the
1658      wipe should either dissolve or become translucent in the cocktail.

1659      10.7  References

1660      American National Standards Institute/American Nuclear Society (ANSI/ANS). 1994. Internal
1661         Dosimetry Programs for Tritium Exposure - Minimum Requirements, HP S N13.14.

1662      American National Standards Institute/American Nuclear Society (ANSI/ANS). 1995. Bioassay
1663         Programs for Uranium, HPS N13.22.

1664      American National Standards Institute/American Nuclear Society (ANSI/ANS). 1996.
1665         Performance Criteria  for Radiobioassay, HPS N13.30.

1666      American National Standards Institute/American Nuclear Society (ANSI/ANS). 1997. Internal
1667         Dosimetry for Mixed Fission Activation Products, HPS N13.42.

1668      American National Standards Institute (ANSI). 1973. American National Standard for Radiation
1669         Protection in Uranium Mines. N13.8-1973.

1670      American National Standards Institute (ANSI). 1999. Sampling and Monitoring Releases of
1671         Airborne Radioactive  Substances from the Stacks and Ducts of Nuclear Facilities. HPS
1672         N13.1.

1673      American Public Health Association (APHA). 1972. Intersociety Committee for a Manual of
1674         Methods for Ambient Air Sampling and Analysis, Methods of Air Sampling and Analysis.
1675         APHA, Washington, DC.

1676      American Public Health Association (APHA). 1995. Standard Methods for the Examination of
1677          Water and Wastewater. 19th Edition, APHA, Washington, DC.
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         	Field and Sampling Issues That Affect Laboratory Measurements

1678      American Public Health Association (APHA). 1996. Standard Methods for the Examination of
1679         Water and Wastewater. 19th Edition Supplement, APHA, Washington, DC.

1680      American Society for Testing and Materials (ASTM) STP 555. Instrumentation for Monitoring
1681         Air Quality, 1974. West Conshohocken, Pennsylvania.

1682      American Society for Testing and Materials (ASTM) C998. Sampling Surface Soil for
1683         Radionuclides, 1995. West Conshohocken, Pennsylvania.

1684      American Society for Testing and Materials (ASTM) C999. Soil Sample Preparation for the
1685         Determination of Radionuclides, 1995. West Conshohocken, Pennsylvania.

1686      American Society for Testing and Materials (ASTM) D420. Site Characterization for
1687         Engineering, Design, and Construction Purposes., 1998. West Conshohocken, Pennsylvania.

1688      American Society for Testing and Materials (ASTM) D653. Terminology Relating to Soil, Rock,
1689         and Contained Fluids, 1991. West Conshohocken, Pennsylvania.

1690      American Society for Testing and Materials (ASTM) D3370, Standard Practices for Sampling
1691         Water from Closed Conduits. ASTM, West Conshohocken, Pennsylvania.

1692      American Society for Testing and Materials (ASTM) D3856. Good Laboratory Practices in
1693         Laboratories Engaged in Sampling and Analysis of Water, 1995. West Conshohocken,
1694         Pennsylvania.

1695      American Society for Testing and Materials (ASTM) D3977. Determining Sediment
1696         Concentration in Water Samples, 1991. West Conshohocken, Pennsylvania.

1697      American Society for Testing and Materials (ASTM) D4840. Sampling Chain-of-Custody
1698         Procedures, 1999. West Conshohocken, Pennsylvania.

1699      American Society for Testing and Materials (ASTM) D4914. Density of Soil and Rock in Place
1700         by the Sand Replacement Method in a Test Pit, 1999. West Conshohocken, Pennsylvania.

1701      American Society for Testing and Materials (ASTM) D4943. Shrinkage Factors of Soils by the
1702         Wax Method, 1995. West Conshohocken, Pennsylvania.

1703      American Society for Testing and Materials (ASTM) D5245. Cleaning Laboratory Glassware,
1704         Plasticware, and Equipment Used in Microbiological Analyses, 1998. West Conshohocken,
1705         Pennsylvania.
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         Field and Sampling Issues That Affect Laboratory Measurements	

1706      American Society for Testing and Materials (ASTM) D5283. Generation of Environmental Data
1707         Related to Waste Management Activities Quality Assurance and Quality Control Planning
1708         and Implementation, 1991. West Conshohocken, Pennsylvania.

1709      American Society for Testing and Materials (ASTM) D5608. Decontamination of Field
1710         Equipment Used at Low Level Radioactive Waste Sites, 1994. West Conshohocken,
1711         Pennsylvania.

1712      Army Corps of Engineers (USAGE). 1995. Technical Project Planning Guidance for Hazardous,
1713         Toxic and Radioactive Waste (HTRW) Data Quality Design. Engineer Manual EM-200-1-2,
1714         Appendix H, Sampling Methods.

1715      Baratta, E.J., G.E. Chabot, and RJ. Donlen. 1968. Collection and Determination of Iodine-131 in
1716         the  Air. Amk. Ind. Hyg. Assoc. J., 29:159.

1717      Bellamy, R.R. 1974. Elemental Iodine and Methyl Iodide Adsorption on Activated Charcoal at
1718         Low Concentrations. Nuclear Safety Volume  15, U.S. Atomic Energy Commission Technical
1719         Information Center, Oak Ridge, Tennessee.

1720      Bernabee, R. P., D. R. Percival, and D. B. Martin. 1980. Fractionation of Radionuclides in Liquid
1721         Samples from Nuclear Power Facilities. Health Physics 39:57-67.

1722      Bixel, J.C. and CJ. Kershner. 1974. A  Study of Catalytic Oxidation and Oxide Adsorption for
1723         Removal of Tritium from Air. Proceedings of the 2nd AEC Environmental Protection
1724         Conference page 261, April 16-19,  Report No. CONF-740406, WASH-1332 (74).

1725      Blanchard, R.L., R. Leiberman, W.S. Richardson in, and C.L. Wakamo. 1993. Considerations of
1726         Acidifying Water Samples for Tc-99 Analysis. Health Physics 65(2):214-215.

1727      Cambell,  J.L., C.R. Santerre, P.C. Farina, and L.A. Muse. 1993. Wipe Testing for Surface
1728         Contamination by Tritiated Compounds. Health Phys. 64:540-544.

1729      Cohen, B. S. 1989. Sampling Airborne Radioactivity. Air Sampling Instruments for Evaluation of
1730         Atmospheric Contaminants, 7th edition, American Conference of Governmental Industrial
1731         Hygienists, Cincinnati, Ohio.

1732      Dehnam,  D.H. 1972. Effectiveness of Filter Media for Surface Collection of Airborne
1733         Radioactive Particulates.  Health Physics Operational Monitoring Vol. 2, Gordon and Breach,
1734         New York.
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         	Field and Sampling Issues That Affect Laboratory Measurements

1735      Department of Energy (DOE). 1987. The Environmental Survey Manual, Appendices E, F, G, H,
1736         I, J, andK. DOE/EH-0053, Vol. 4 of 4, DOE, Office of Environmental Audit, Washington,
1737         DC.

1738      Department of Energy (DOE). 1990. EML Procedures Manual (HASL-300-Ed.27). G. de
1739         Planque Editor, Environmental Measurements Laboratory.

1740      Department of Energy (DOE). 1994a. Implementation Guide, Internal Dosimetry Program. G-10
1741         CFR835/Cl-Rev. 1.

1742      Department of Energy (DOE). 1994b. Implementation Guide, External Dosimetry Program. G-
1743         10CFR835/C2-Rev. 1.

1744      Department of Energy (DOE). 1994c. Implementation Guide, Workplace Air Monitoring. G-10
1745         CFR835/E2-Rev. 1.

1746      Department of Energy (DOE). 1994d. Radiological Control Manual. DOE/EH-0256T, Rev. 1.

1747      Department of Energy (DOE). 1997. EML Procedures Manual. HASL-300, 28th Edition,
1748         Environmental Measurements Laboratory.

1749      Department of the Interior (DOT). 1980. National Handbook of Recommended Methods for Water
1750         for Water-Data Acquisition, Volume I and II.

1751      Dyck, W. 1968. Adsorption of Silver on Borosilicate Glass. Anal. Chem. 40:454-455.

1752      Environmental Protection Agency. (1980). Prescribed Procedures for Measurement of
1753         Radioactivity in Drinking Water. EPA-600/4-80-032, EPA, Environmental Monitoring and
1754         Support Laboratory, Cincinnati, Ohio.

1755      Environmental Protection Agency (EPA). 1982. Handbook for Sampling and Sample
1756         Preservation of Water and Wastewater. EPA-600/4-82-029, EPA, Washington, DC. (PB83-
1757         124503)

1758      Environmental Protection Agency (EPA). 1984. Characterization of Hazardous Waste Sites- A
1759         Method Manual, Vol. II, Available Sampling Methods. EPA-600/4-84-076, Second Edition.

1760      Environmental Protection Agency (EPA). 1985. Sediment Sampling Quality Assurance User's
1761         Guide. EPA/600/4-85/048, Environmental Monitoring Systems Laboratory, Las Vegas, NV.
1762         (PB85-233542).
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         Field and Sampling Issues That Affect Laboratory Measurements	

1763      Environmental Protection Agency (EPA). 1986. Compendium of Methods for Determination of
1764         Superfund Field Operation Methods, EPA 600-4-87/006. Office of Emergency and Remedial
1765         Response, Washington, DC.

1766      Environmental Protection Agency (EPA). 1987. A Compendium of Superfund Field Operations
1767         Methods. EPA/540/P-87/001. Office of Emergency and Remedial Response, Washington,
1768         DC. (PB88-181557).

1769      Environmental Protection Agency (EPA). 1989. Indoor Radon and Radon Decay Product
1770         Measurement Protocols. Radon Division, Washington, DC.

1771      Environmental Protection Agency (EPA). 1992. Indoor Radon and Radon Decay Product
1772         Measurement Device Protocols. EPA 402-R-92-004, EPA, Office of Air and Radiation,
1773         Washington, DC.

1774      Environmental Protection Agency (EPA). 1993. Protocols for Radon and Radon Decay Product
1775         Measurements in Homes. EPA 402-R-92-003, EPA, Office of Air and Radiation,
1776         Washington, DC.

1777      Environmental Protection Agency (EPA). 1994. Routine Environmental Sampling Procedures
1778         Manual For Radionuclides. EPA, Office of Radiation and Indoor Air and National Air and
1779         Radiation Environmental Laboratory, Montgomery, AL.

1780      Environmental Protection Agency (EPA). 1996. Radon Proficiency Program - Handbook. EPA
1781         402-R-95-013, EPA, Office of Radiation and Indoor Air, Washington, DC.

1782      Environmental Protection Agency (EPA). 1997. To Filter or Not to Filter, That is the Question.
1783         EPA Science Advisory Board (SAB), July 11, 1997.

1784      Frame, P.W. and E.W. Ablequist. May 1999. Use of Smears for Assessing Removable
1785         Contamination. Operational Radiation Safety 67 (5):S57-S66.

1786      Francis, AJ.  1985.  Low-Level Radioactive Wastes in Subsurface Soils. Soil reclamation
1787         Processes: Microbiological Analyses and Applications, NY.

1788      Friend, A.G., A.H Story, C.R. Henderson, and K.A. Busch. 1965. Behavior of Certain
1789         Radionuclides Released into Fresh- Water Environments. U. S. Public Health Service
1790         Publication999-RH-13.

1791      Gabay, J.J., CJ.Paperiello, S. Goodyear, J.C. Daly, and J.M. Matuszek. 1974. A Method for
1792         Determining Iodine-129 in Milk and Water. Health Physics 26:89.
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         	Field and Sampling Issues That Affect Laboratory Measurements

1793      Hess, C.T. and S.M. Beasley. 1990. Setting Up a Laboratory for Radon in Water Measurments.
1794         Radon, Radium and Uranium in Drinking Water, Lewis Publishers, Chelsea, MI.

1795      Ho, S.Y. and D.R. Shearer. 1992. Radioactive Contamination in Hospitals from Nuclear
1796         Medicine Patients. Health Physics 62:462-466.

1797      Illinois Department of Nuclear Safety (IDNS). 1993. 1992 Annual Survey Report. Spingfield,
1798         Illinois.

1799      Illinois Department of Nuclear Safety (IDNS). 1994. 1993 Annual Survey Report. Spingfield,
1800         Illinois.

1801      Illinois Department of Nuclear Safety (IDNS). 1995. 1994 Annual Survey Report. Spingfield,
1802         Illinois.

1803      Illinois Department of Nuclear Safety (IDNS). 1996. 1995 Annual Survey Report. Spingfield,
1804         Illinois.

1805      Illinois Department of Nuclear Safety (IDNS). 1997. 1996 Annual Survey Report. Spingfield,
1806         Illinois.

1807      Institute of Nuclear Power Operations (INPO). 1988. Guidelines for Radiological Protection at
1808         Nuclear Power Stations. INPO 88-010, Atlanta, Georgia.

1809      Jackson, E.W.  1962. Prevention of Uptake of Strontium Ions on Glass. Nature 194:  672.

1810      Johnson, B.H.  1980. A Review of Numerical reservoir Hydrodynamic Modeling. U.S. Army
1811         Corps of Engineers, Waterways Experiment Station, Vicksburg, Mississippi.

1812      Keller, J.H., T.R. Thomas, D.T. Pence, and W.J. Maeck. 1973. An Evaluation of Materials and
1813         Techniques Used for Monitoring Airborne Radioiodine Species. Proceedings of the 12th AEC
1814         Air Cleaning Conference. U.S. Atomic Energy Commission, Washington, DC.

1815      Kennedy, V.C., G.W. Zellweger, and B.F. Jones. 1974. Filter Pore Size Effects on the Analysis
1816         of Al, Fe, Mn, and Ti in Water. Water Resources Research 10(4):785-790.

1817      Klebe, M.  1998. Illinois Department of Nuclear Safety. Correspondence of June 12, 1998 to Mr.
1818         J.C. Dehmel, SC&A,  Inc., with copies of Tables 4 and 5 from  survey questionnaires for the
1819         years of 1994 to 1997.

1820      Kline, R.C, I. Linins, E.L. Gershey. 1992. Detecting Removable Surface Contamination. Health
1821         Phys. 62:186-189.

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         Field and Sampling Issues That Affect Laboratory Measurements	

1822      Kotrappa, P., S.K. Dua, D.P. Bhanti, and P.P. Joshi. 1974. HASL Cyclone as an Instrument for
1823         Measuring Aerosol Parameters for New Lung Model. Proceedings of the 3d International
1824         Congressional Radiation Protection Association, September 9-14, 1973.

1825      Kusnetz, H.L. 1956. Radon Daughers in Mine Atmospheres - A Field Method for Determing
1826         Concentrations. Am. Ind. Hyg. Assoc. QuarterlyVol. 17.

1827      Laxen, D.P.H. and I.M. Chandler. 1982. Comparison of Filtration Techniques for Size
1828         Distribution in Freshwaters. Analytical Chemistry 54(8): 1350-1355.

1829      Lippmann, M. 1989a. Calibration of Air Sampling Instruments. Air Sampling Instruments. 7th
1830         Edition, American Conference of Governmental Industrial Hygienists, Cincinnati, OH, pp.
1831         73-100.

1832      Lippmann, M. 1989b. Sampling Aerosols by Filtration. Air Sampling Instruments. 7th Edition,
1833         American Conference of Governmental Industrial Hygienists, Cincinnati, OH, pp. 305-336.

1834      Lockhart, L., R. Patterson and W. Anderson. 1964. Characteristics of Air Filter Media Used for
1835         Monitoring Airborne Radioactivity. Naval Research Laboratory Report NRL-6054,
1836         Washington, DC.

1837      Manske, P., T. Stimpfel, and E.L. Gershey. 1990. A Less Hazardous Chromic Acid Substitute for
1838         Cleaning Glassware. J. Chew. Educ. 67:A280-A282.

1839      MARSSEVI. 2000. Multi-Agency Radiation Survey and  Site Investigation Manual, Revision 1.
1840         NUREG-1575 Rev 1, EPA 402-R-97-016 Revl, DOE/EH-0624 Revl. August. Available
1841         from http://www.epa.gov/radiation/marssim/filesfm.htm.

1842      Martin, I.E. and J.M. Hylko. 1987a. "Formation of Tc-99 in Low-Level Radioactive Waste
1843         Samples from Nuclear Plants." Radiation Protection Management, 4:6, p 67-71.

1844      Martin, I.E. and J.M. Hylko. 1987b. "Measurement of 99Tc in Low-Level Radioactive Waste
1845         from Reactors Using 99Tc as a Tracer." Applied Radiation and Isotopes, 38:6, p 447-450.

1846      McCarthy, J. F. and C. Deguelde. 1993. "Sampling and  Characterization of Colloids and
1847         Particles in Ground Water for Studying Their Role in Contaminant Transport."
1848         Environmental Analytical and Chemistry Series. Environmental Particles, Vol.  2, Lewis
1849         Publishers, Chapter 6.

1850      Milkey, R.G. 1954. Stability of Dilute Solutions of Uranium, Lead, and Thorium Ions. Anal.
1851         Chem. 26(11): 1800-1803.
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         	Field and Sampling Issues That Affect Laboratory Measurements

1852      National Academy of Sciences (NAS). 1960. The Radiochemistry of Technetium. Office of
1853         Technical Services, Washington, DC.

1854      National Committee on Radiation Protection. 1964. Safe Handling of Radioactive Materials.
1855         NCRP Report 30, Washington, DC.

1856      National Council on Radiation Protection and Measurements (NCRP). 1978. Instrumentation
1857         and Monitoring Methods for Radiation Protection. NCRP Report 57.

1858      National Council on Radiation Protection and Measurements (NCRP). 1985. A Handbook of
1859         Radioactivity Measurements Procedures. NCRP Report 81.

1860      National Council on Radiation Protection and Measurements (NCRP). 1987. Use ofBioassay
1861         Procedures for Assessment of Internal Radionuclides Deposition. NCRP Report No. 87.

1862      National Institue for Occupational Safety and Health (NIOSH). 1983. Industrial Hygiene
1863         Laboratory Quality Control-587. NIOSH, Cincinnati, Ohio.

1864      Navy Environmental Compliance Sampling and Field Testing Procedures Manual (NAVSEA).
1865         T0300-AZ-PRO-010.

1866      Nuclear Regulatory Commission (NRC). Acceptable Concepts, Models, Equations, and
1867         Assumptions for a Bioassay Program. NRC Regulatory Guide 8.9.

1868      Nuclear Regulatory Commission (NRC). Applications of Bioassay for Uranium. NRC
1869         Regulatory Guide 8.11.

1870      Nuclear Regulatory Commission (NRC). Applications of Bioassay for 1-125 and 1-131. NRC
1871         Regulatory Guide 8.20.

1872      Nuclear Regulatory Commission (NRC). Bioassays at Uranium Mills. NRC Regulatory Guide
1873         8.22.

1874      Nuclear Regulatory Commission (NRC). Applications of Bioassay for Fission and Activation
1875         Products. NRC Regulatory Guide 8.26.

1876      Nuclear Regulatory Commission (NRC). Criteria for Establishing a Tritium Bioassay Program.
1877         NRC Regulatory Guide 8.32.

1878      Nuclear Regulatory Commission (NRC). 1977. Estimating Aquatic dispersion of Effluents from
1879         A ccidental and Routine Reactor Releases for the Purpose of Implementing Appendix I. NRC
1880         Regulatory Guide 1.113.

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         Field and Sampling Issues That Affect Laboratory Measurements	

1881      Nuclear Regulatory Commission (NRC). 1981. Radiation Safety Surveys at Medical Institutions.
1882         NRC Regulatory Guide 8.23.

1883      Nuclear Regulatory Commission (NRC). 1990. Model Feasibility Study of Radioactive Pathways
1884         From Atmosphere to Surface Water. NUREG/CR-5475, Washington, DC.

1885      Nuclear Regulatory Commission (NRC). 1992. National Profile on Commercially generated
1886         Low-Level Radioactive Mixed Waste. NUREG/CR-593 8, Washington, DC.

1887      Osborne, R.V. 1973. Sampling for Tritiated Water Vapor. Proceedings of the 3d International
1888         Congress. International Radiation Protection Association, CONF-730907-P2, 1973:1428-
1889         1433.

1890      Parker, H.M., R.F. Foster, I.L. Ophel, F.L. Parker, and W.C. Reinig. 1965. North American
1891         Experience in the Release of Low-Level Waste to Rivers and Lakes. Proceedings of the Third
1892         United National International Symposium on the Peaceful Uses of Atomic Energy Vol.
1893         14:62-71.

1894      Pelto, R.H., CJ. Wierdak, and V.A. Maroni. 1975. Tritium Trapping Kinetics in Inert Gas
1895         Streams. Liquid Metals Chemistry and Tritium Control Technology Annual Report ANL-7 5-
1896         50, p. 35 (Argonne National Laboratory, Lemont, IL).

1897      Phillips, I.E. and C.E. Easterly.  1982. Cold Trapping Efficiencies for Collecting Tritiated Water
1898         Entrained in a Gaseous Stream. Rev. Sci. Instrum. 53(1).

1899      Pignolet, L., F. Auvray,  K. Fonsny, F. Capot,  and Z. Moureau. 1989. Role of Various
1900         Microorganisms on Tc Behavior in Sediments. Health Physics 57(5):791-800.

1901      Puls, R.W., J.H. Eychaner, and R.M. Powell.  1990. Colloidal-Facilitated Transport of Inorganic
1902         Contaminants in Ground Water: Part I. Sampling Considerations. EPA/600/M-90/023.
1903         (NTISPB 91-168419).

1904      Puls, R. W., and R. M. Powell. 1992. Transport of Inorganic Colloids Through Natural Aquifer
1905         Material: Implications for Contaminant Transport. Environmental Science & Technology
1906         26(3):614-621.

1907      R. Rosson, R. Jakiel, S.  Klima, B. Kahn, P.O. Fledderman. 2000. "Correcting Tritium
1908         Concentrations in Water Vapor with Silica Gel." Health Physics, 78:1, p  68-73. Also
1909         available at http://www.srs.gov/general/sci-tech/fulltext/ms9800901/ms9800901 .html.

1910      Saar, R. A.  1997. Filtration of Ground Water  Samples: A Review of Industry Practice. GWMR,
1911         Winter, 1997, 56-62.

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         	Field and Sampling Issues That Affect Laboratory Measurements

1912      Scarpitta, S.C. and N.H. Harley. 1990. Adsorption and Desorption of Noble Gases on Activated
1913         Charcoal. I. 133X Studies in a Monolayer and Packed Bed. Health Physics 59(4):383-392.

1914      Setter, L.R. and G.I. Coats. 1961. "The Determination of Airborne Radioactivity," Industrial
1915         Hygiene Journal, February, pp 64-69.

1916      Silva, RJ. and A.W. Yee. 1982. Geochemical Assessment of Nuclear  Waste Isolation: Topical
1917         Report, Testing of Methods for the Separation of Soil and Aqueous Phases. Lawrence
1918         Berkeley Laboratory, Report LBL-14696, UC-70.

1919      Slobodine, MJ. and R.W. Granlund. 1974. Exuruded Expanded Polystyrene: A Smear Material
1920         for Use in Liquid Scintillation Counting. Health Physics 27:128-129.

1921      Stafford, R.B. 1973. Comparative Evaluation of Several Glass-Fiber Filter Media. Los Alamos
1922         Scientific Laboratory, LA-5297.

1923      Waite, D. A. and W.L. Nees. 1973. A Novel Particle Sizing Technique for Health Physics
1924         Application. Battelle, Pacific Northwest Laboratories,  BNWL-SA-4658.

1925      Whittaker, E.L., J.D. Akridge,  and J. Giovino. 1989. Two Test Procedures for Radon in Drinking
1926         Water: Interlaboratory Collaborative Study. EPA 600/2-87/082, Environmental Monitoring
1927         Systems Laboratory.
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        11  SAMPLE  RECEIPT, INSPECTION, AND TRACKING
 2     11.1   Introduction

 3     This chapter provides guidance on laboratory sample receiving and screening, inspecting,
 4     documenting custody, and assigning laboratory tracking numbers. These topics are presented in a
 5     sequentially in this chapter, but they may be done in a different order. The chapter is directed
 6     primarily at laboratory personnel (as are all of the Part II chapters), although the Project Manager
 7     and field personnel need to be aware of the steps involved in sample receipt, inspection, and
 8     tracking. For the purposes of MARLAP, the "sample receipt" process includes the screening of
 9     the package and sample containers for radiological contamination. "Sample inspection" is used to
10     check the physical integrity of the package and samples, to confirm the identity of the sample, to
11     confirm field preservation (if necessary), and to record and communicate the presence of
12     hazardous materials. "Laboratory sample tracking" is a process starting with sample log-in and
13     assignment of a unique laboratory tracking number to be used to account for the sample through
14     analyses, storage, and shipment. Laboratory tracking continues the tracking that was initiated in
15     the field during sample collection.

16     Figure 11.1 presents an overview of the topics discussed in this chapter. Note that the flow
17     diagram in the field sample preparation chapter (Chapter 10, Field and Sampling Issues that
18     Affect Laboratory Measurements) leads into sample receipt. This chapter focuses on sample
19     receipt, inspection, and tracking of samples in the laboratory because these are the three modes of
20     initial control and accountability. Sample receipt and inspection activities need to be done in a
21     timely manner to allow the laboratory and field personnel to resolve any problems (e.g.,
22     insufficient material collected, lack of field preservation, etc.) with the samples received by the
23     laboratory as soon as is practical. An effective interface between field personnel and the
24     laboratory not only facilitates problem resolution but also prevents unnecessary delays in the
25     analytical process.

26     Other relevant issues, including the laboratory's license conditions and proper operating
27     procedures are also noted because these topics are linked to receipt, inspection, and tracking ities.
28     The end result of the sample receipt and inspection activities is to accept the samples as received
29     or to perform the necessary corrective action (which may include rejecting samples).

30     Health and safety information is not presented but can be found in NRC (1998a; 1998b).
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                             Field Sample Shipment
                                (see Chapter 10)
                         Shipping Manifest
                         • Number and type of samples along
                           with field sample number
                         • Field processing and preservation
                         • Analysis reqested
                     Sample returned
                           or
                       disposed of
                       according to
                     appropriate regs
  Short-term
sample storage
  or sample
 prep/analysis
  laboratory
                    Sample received in designated area
                    • Authorized user notified for radiological
                      screening of package
                    • Check for evidence of breakage or
                      leakage of exterior of package then
                      sample containers — If yes,
                      radiological screening (swipe) and
                      decontaminate
                    • License requirements
                    • Corrective action if necessary
                    • COC procedures if required
                                                                In designated rad receiving/prep area:
                                                                • Check container labels against
                                                                  shipping manifest
                                                                • Check sample content against
                                                                  shipping manifest
                                                                • Check tamper seals
                                                                • Verify preservation against shipping
                                                                  manifest
                                                                • Check preparation against shipping
                                                                  manifest
                                                                • Corrective action if necessary	
                                                                Any discrepancies in:
                                                                • Screening value
                                                                • Expected nuclide content
                                                                • Number and type of samples
                                                                will result in corrective action
           FIGURE 11.1 — Overview of sample receipt, inspection, and tracking
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31     11.2  General Considerations

32     11.2.1 Communication Before Sample Receipt

33     Before the samples are received, the laboratory should know the relative numbers of samples that
34     will be received within a specific timeframe and the types of analyses that are expected for the
35     samples. Laboratory personnel should be provided with a contact in the field and with means of
36     contacting the person (telephone, FAX, e-mail). Communication between laboratory personnel
37     and project staff in the field allows the parties to coordinate activities, schedules, and sample
38     receipt. In particular, the Project Manager should provide to the laboratory special instructions
39     regarding the samples before shipment of samples. This information serves to notify the
40     laboratory of health and safety concerns and provides details that will affect analytical
41     procedures, sample disposition, etc. For example, without this communication, a laboratory
42     might receive a partial shipment and not realize that samples are missing. Furthermore, advance
43     communications allow laboratory staff to arrange for special handling or extra space for storage
44     should the need arise.

45     Planning for the samples to be received at the laboratory starts during the development of the
46     appropriate plan document and the statement of work (SOW) and continues through the
47     communication between the project staff in the field and the laboratory. For example, the
48     laboratory could pre-label and bar-code the appropriate containers to be used in the field. This
49     process would assist in assigning appropriate sample numbers for the laboratory tracking system,
50     which starts with sample receipt. The laboratory should instruct the field staff to place the
51     shipping manifest on the inside of the cooler lid for easy access  and to include any other pertinent
52     information (field documentation, field screen information, etc.).

53     11.2.2 Standard  Operating Procedures

54     A laboratory  should have standard operating procedures (SOPs) for laboratory activities related
55     to sample receipt, inspection, and tracking. Some typical topics that might be addressed in
56     laboratory SOPs are presented in Table 11.1. For example, the laboratory should have an SOP
57     that describes what information should be included in the laboratory sample tracking system.
58     Laboratory SOPs should describe chain-of-custody procedures giving a comprehensive list of the
59     elements in the program such as  signing the appropriate custody forms, storing samples in a
60     secure area, etc. (ASTM D4840; ASTM D5172; EPA, 1995).
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61          TABLE 11.1 —Typical topics addressed in standard operating procedures related to sample receipt,
62      	inspection, and tracking	
63       Sample         •   Order and details for activities associated with receiving shipments of samples.
64       Receipt:        •   Screening methods.
65       Inspection:      •   pH measurement instructions.
                       •   Confirm sample identification.
                       •   Assign samples to laboratory information management system (LIMS).
                       •   Check physical integrity.
                       •   Identify/manage hazardous materials.
66       Tracking:       •   Ensure proper identification of samples throughout process.
                       •   Procedures to quickly determine location and status of samples within laboratory.
                       •   Maintain chain of custody and document sample handling during transfer from the field to
                          thg. !.?!?9^pry, then within the laboratory.
67       Custodian:      •   Execution^of responsibilities of the sample custodian.
68       Forms/Labels:   •   Examples of forms and labels used to maintain sample custody and document sample
        	handling in the lab.	
69      The laboratory needs to establish corrective action guidelines (Section 11.3.3) as part of every
70      SOP for those instances when a nonconformance is noted. Early recognition of a nonconfor-
71      mance will allow the Project Manager and the laboratory more options for a quick resolution.

72      11.2.3 Laboratory License

73      Laboratory facilities with a few exceptions (e.g., certain DOE National Laboratories and DOD
74      laboratories) that handle radioactive materials are required to have a radioactive materials license
75      issued by the NRC or the Agreement State in which the laboratory operates. The radioactive
76      materials license lists the radionuclides that the laboratory can possess, handle, and store. In
77      addition, the license limits the total activity of specific radionuclides that can be in the possession
78      of the laboratory at a given time.

79      The laboratory needs to have specific information from the field staff to make sure they can
80      receive samples with the particular radionuclides expected to be present in the samples and that
81      the laboratories have the proper radioactive materials license. The information needed includes
82      the results of radiological field screening measurements. Both the laboratory and the Project
83      Manager need to be aware of the type of radionuclide(s) in the samples and the total number of
84      samples to be sent to the laboratory (this should be included in the appropriate plan document
85      and SOW prior to sampling).
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 86     The laboratory is required by the license to maintain a current inventory of certain radioactive
 87     materials present in the facility. The radioactive materials license also requires the laboratory to
 88     develop and maintain a radiation protection plan (NRC, 1998b) that states how radioactive
 89     samples will be received, stored, and disposed. The laboratory will designate an authorized user
 90     (NRC, 1998b) to receive the samples. A Radiation Safety Officer (RSO) may be an authorized
 91     user but not always. NRC (1998b) gives procedures for the receipt of radioactive  samples during
 92     working hours and non-working hours; part of these procedures are as follows:

 93        During normal working hours, immediately upon receipt of any package of licensed material,
 94        each package must be visually inspected for any  signs of shipping damage such as crushed or
 95        punctured containers or signs of dampness. Any  obvious damage must be reported to the
 96        RSO immediately. Do not touch any package suspected of leaking. Request the person
 97        delivering the package to remain until monitored by the RSO.

 98        Any packages containing radioactive material that arrive between (state times, e.g., 4:30 p.m.
 99        and 7:00 a.m.  or on Saturdays or Sundays) shall be signed for by the security guard (or other
100        designated trained individual) on duty and taken immediately to the designated receiving
101        area. Security personnel (or other designated trained individual) should unlock the door, place
102        the package in the designated secured storage area and re-lock the door.

103        Since certain packages of licensed material will have detectable external radiation, they
104        should be sent immediately to a designated storage area, where they will be checked for
105        contamination and external radiation level as soon as practical. They should not be allowed to
106        remain in the receiving area any longer than necessary, as they may be a source of exposure
107        for receiving personnel.

108     11.2.4 Sample Chain-of-Custody

109     "Sample chain-of-custody" (COC) is defined  as a process whereby a sample is maintained under
110     physical possession or control during its entire life cycle, that is, from collection to disposal
ill     (ASTM D4840—see Chapter 10). The purpose of COC is to ensure the security of the sample
112     throughout the process. COC procedures dictate the documentation needed to demonstrate that
113     COC is maintained. When a sample is accepted  by the laboratory it is said to be in the physical
114     possession or control of the laboratory. ASTM D4840 says that a sample is under "custody" if it
115     is in possession or under control so as to prevent tampering or alteration of its characteristics.
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116      If the samples are transferred under COC the relinquisher and the receiver should sign the
117      appropriate parts of the COC form with the date and time of transfer. After receipt and inspection
118      the samples should be kept in a locked area or in an area with controlled access.

119      COC is not a requirement for all samples. COC is most often required when the sample data may
120      be used as legal evidence. The project plan should state whether COC will be required. The
121      paperwork received with the samples should also indicate whether COC has been maintained
122      from the time of collection and must be maintained  in the laboratory. If the laboratory has been
123      informed that COC procedures should be followed,  but it appears that appropriate COC
124      procedures have not been followed (before or after sample receipt at the laboratory) or there are
125      signs of possible sample tampering when the samples arrive, the Project Manager should be
126      contacted. The problem and resolution should be documented. Additional information on COC
127      can be  found in EPA (1985).

128      11.3  Sample Receipt

129      Laboratory sample receipt occurs when a package containing samples is accepted, the package
130      and sample containers are screened for radiological  contamination, and the physical integrity of
131      the package and samples is checked. Packages include the shipping parcel that holds the smaller
132      sample containers with the individual samples (see Section 11.3.2 on radiological screening).
133      Also note that topics and activities covered in Section 11.3 appear in a sequence but, in many
134      cases, these activities are performed simultaneously during initial receiving activities (i.e.,
135      package screening and observation of its physical integrity).

136      11.3.1  Package Receipt

137      Packages can be accepted only at a designated receiving area. Packages brought to any other
138      location by a carrier should be redirected to the appropriate receiving area. All packages labeled
139      RADIOACTIVE I, II, or in require immediate notification of the appropriate  authorized user
140      (NRC,  1998b).

141      A sample packing slip or manifest is required and must be presented at the time of receipt, and
142      the approximate activity of the shipment should be compared to a list of acceptable quantities. If
143      known, the activity of each radionuclide contained in the shipment must be reviewed relative to
144      the total amount of that radionuclide currently on site to ensure that the additional activity will
145      not exceed that authorized by the NRC or Agreement State in the laboratory's license.
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146     Screening measures described in Section 11.3.2 may indicate that the samples are more
147     radioactive than expected and that the radiation license limit may be exceeded. The laboratory
148     should take extra precautions with these samples, but the screening results should be verified.
149     The Federal, State, or local agency should be contacted immediately when verified license limits
150     are exceeded. The laboratory must respond quickly to stay in compliance with their license.

151     If the package is not accepted by the laboratory, the laboratory should follow corrective-action
152     procedures prescribed in the radiation materials license, the appropriate plan document (if this is
153     a reasonable possibility for the project), and the laboratory's SOPs.

154     11.3.2 Radiological Screening

155     In addition to ensuring compliance with the laboratory's license and verifying estimates of
156     radionuclide activity (Section 11.3.1), the radiological screening of packages during sample
157     receipt serves to identify and prevent the spread of external contamination. All packages
158     containing samples for analysis received by the laboratory should be screened for external
159     contamination and surface exposure rate. Exceptions may include known materials (types under
160     exclusion should be listed in the laboratory SOP) intended for analysis as: a) well-characterized
161     samples; b) bioassays; and c) radon and associated decay products in charcoal media. Screening
162     of packages and sample containers received in the laboratory should be conducted in accordance
163     with the laboratory's established, documented procedures and the laboratory radiation protection
164     and health and safety plan. The exterior of the package is screened first; if there is no evidence of
165     contamination or that the laboratory licence would be exceeded, the package is opened up and the
166     sample containers screened individually. These procedures should include the action level and
167     appropriate action as established by the facility. Personnel performing screening procedures
168     should be proficient in the use of portable radiation screening instruments and knowledgeable in
169     radiological contamination control procedures. Health and safety considerations are affected by
170     the suspected or known concentrations of radionuclides in a sample or the total activity of a
171     sample.

172     Radiation screening is normally conducted using Geiger-Mueller (GM) detectors, ionization
173     chambers, micro-R meters, or alpha scintillation probes, as appropriate. The laboratory should
174     refer to any information they obtained before receipt of samples or with the samples, especially
175     concerning the identity and concentration of radioactive and chemical constituents in the
176     samples. Radiological screening needs to be performed as soon as practical after receipt of the
177     package, but not later than three hours (10 CFR 20.1906) after the package is received at the
178     licensee's facility for packages received during normal working hours. For packages received
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179     outside of normal working hours, the screening must be performed no later than three hours from
180     the beginning of the next workday.

181     Monitor the exterior of a labeled package for radioactive contamination (10 CFR 20.1906). If the
182     package is small (less than 100 cm2), the whole package should be wiped. Wipes are not always
183     used, but if there is reason to believe that something has leaked, then wipes should be used. An
184     external exposure rate determination of the package is also required within three hours after the
185     package is received (or three hours from beginning of the next business day for packages
186     received outside of normal working  hours). This screening is performed to detect possible
187     violations of Department of Transportation (DOT) packaging and labeling regulations, as well as
188     to determine the possible presence of gamma- and some beta-emitting radionuclides that may
189     require special handling. Also, screening can help to avoid introducing a high-activity sample
190     into a low-activity area.

191     The Consolidated Guidance About Materials Licenses (NRC 1998b) gives the following sample
192     model for opening packages containing radioactive material:

193      •  Wear gloves to prevent hand contamination.

194      •  Visually inspect the package for any  sign of damage (e.g. crushed, punctured). If damage is
195         noted, stop and notify the RSO.

196      •  Check DOT White I, Yellow II, or Yellow HI label  or packing slip for activity of contents, so
197         shipment does not exceed license possession limits.

198      •  Monitor the external surfaces of a labeled package according to specifications in Table 8.4,
199         Section 13.14, Item 10.

200      •  Open the outer package (following supplier's directions if provided) and remove packing
201         slip. Open inner package to verify contents (compare requisition, packing slip and label on
202         the bottle or other container). Check  integrity of the final source container (e.g., inspecting
203         for breakage of seals or vials, loss of liquid, discoloration of packaging material, high count
204         rate on smear). Again check that the  shipment does not exceed license possession limits. If
205         you find anything other than expected, stop and notify the RSO.

206      •  Survey the packing material and packages for contamination before discarding. If
207         contamination is found, treat as radioactive waste. If no contamination is found, obliterate the
208         radiation labels prior to discarding in the regular trash.


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209      • Maintain records of receipt, package survey, and wipe test results.

210      • Notify the final carrier and by telephone, telegram, mailgram, or facsimile, the Administrator
211        of the appropriate NRC Regional Office listed in 10 CFR 20, Appendix D when removable
212        radioactive surface contamination exceeds the limits of 10 CFR 71.87(i); or external radiation
213        levels exceed the limits of 10 CFR 71.47.

214     11.3.3 Corrective Action

215     The laboratory's SOPs should specify corrective actions for routine and non-routine sample
216     problems, including deficiency in sample volume, leaking samples, and labeling errors. The
217     appropriate corrective action may require consulting the Project Manager and other laboratory
218     personnel. Timely response can allow for a broader range of options and minimize the impact of
219     the sample problem on the project. The laboratory should document the problem, the cause (if
220     known), the corrective action taken, and the resolution of each problem that requires corrective
221     action. The documentation should be included in the project files.

222     11.4  Sample Inspection

223     After sample receipt, the next steps are to confirm that the correct sample has been sent, to check
224     that the appropriate field preservation and processing have been performed, and to identify any
225     hazardous chemicals.

226     Documents accompanying the samples should be reviewed upon receipt of the samples at the
227     laboratory. If the proper paperwork is not present, the Project Manager should be notified. Data
228     recorded on the paperwork, such as collection dates, sample  descriptions, requested analyses, and
229     field staff personnel, should be compared to data on the sample containers and other documen-
230     tation. Any deficiencies or discrepancies should be recorded by the laboratory and reported to the
231     Project Manager. The documents can provide data useful for health and safety screening,
232     tracking, and handling/processing of critical short-lived radionuclides.

233     11.4.1 Physical Integrity of Package  and Sample Containers

234     This section discusses checking for leakage or breakage and  tampering of packages and sample
235     containers. Sample containers should be thoroughly inspected for evidence of sample leakage.
236     Leakage can result from a loose lid, sample container puncture, or container breakage. Packages
237     suspected to contain leaking sample containers should be placed in plastic bags. The authorized

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238     user or alternate authorized user must be notified immediately for assistance. If leakage has
239     occurred, appropriate radiological and chemical contamination controls should be implemented.
240     Sample materials that have leaked or spilled are normally not suitable for analysis and should be
241     properly disposed. In all cases, the laboratory's management and Project Manager should be
242     notified of leaks, breakage, spills, and the condition of sample materials that remain in the
243     original containers.

244     Containers that have leaked from a loose lid or puncture may still hold enough sample for the
245     requested analyses. The laboratory must first determine if there is sufficient sample and if this
246     material is representative of the original sample. An assessment should be made to determine the
247     quantity of sample that remains and if this material is likely to be contaminated. If the sample
248     was contaminated with the analyte of interest at the time when the container leaked, the sample is
249     normally not analyzed.  Unless appropriate information is provided in the project plan or SOW,
250     the Project Manager should determine whether or not the sample materials can be used for
251     analysis or if new samples are required to replace those lost due to leakage or contamination.

252     Packages, cooler chests, or individual sample containers may arrive at the laboratory bearing
253     custody seals. These seals provide a means to detect unauthorized tampering. When packages or
254     samples arrive with custody seals, they should be closely inspected for evidence of tampering.
255     Custody seals are made from material that cannot be removed without tearing. If a custody seal is
256     torn or absent, sample tampering may have occurred.  This evidence of possible tampering is
257     generally sufficient to preclude use of the sample for laboratory analyses. The Project Manager
258     should be notified of the condition of the custody seal to determine if new samples are needed.
259     Observations regarding the condition of the custody seals should be recorded according to the
260     laboratory's standard procedures.

261     11.4.2 Sample Identity Confirmation

262     Visual inspection is the means to confirm that the correct sample has been received. Verifying
263     the identity of a sample is a simple process where the appearance, sample container label, and
264     chain-of-custody record or shipping manifest are compared. If all three  sources of information
265     identify the same sample, then the sample is ready for the next step. If the sample label indicates
266     the sample is a liquid and the container is full of soil,  this discrepancy would indicate a
267     nonconformance. If the sample label states that there is  1,000 mL of liquid and there only appears
268     to be 200 mL in the container, there may be a nonconformance. Visual inspection can be used to:

269      •  Verify identity of samples by matching container label IDs and sample manifest IDs;
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270      •  Verify that the samples are as described by matrix and quantity;

271      •  Check the tamper seal (if used);

272      •  Verify field preparation (for example, filtering, removing extraneous material ), if indicated;
273         and

274      •  Note any changes to samples since shipping, such as a reaction with the preservative.

275     11.4.3 Confirmation of Field Preservation

276     For those liquid samples requiring acid preservation, pH measurements may be performed on all
277     or selected representative liquid samples to determine if acid has been added as a preservative.
278     The temperature of the sample may also be part of field preservation and the actual measured
279     temperature should be compared to the specified requirements in the documentation.

280     11.4.4 Presence of Hazardous Materials

281     The presence of hazardous materials in a sample typically creates the need for additional health
282     and safety precautions when handling, preparing, analyzing, and disposing samples. If there is
283     documentation on the presence of non-radiological hazardous constituents, the Project Manager
284     should notify the laboratory about the presence of these chemicals. These chemical contaminants
285     should be evaluated by the laboratory to determine the need for special precautions. The
286     laboratory can also perform preliminary sample screening for chemical contaminants using
287     screening devices such as a photoionization detector for volatile components. The presence of
288     suspected or known hazardous materials in a sample should be identified, if possible, during
289     project planning and documented in the plan document and SOW. Visual inspection can also be
290     used such as checking the color of the sample (i.e., a green-colored water sample may indicate
291     the presence of high chromium levels). The presence of suspected or known hazardous materials
292     determined in the field should be communicated to the laboratory prior to the arrival of samples
293     and noted on documentation accompanying the samples to the laboratory. If no documentation on
294     non-radiological hazardous constituents is available, the laboratory should review previous
295     experience concerning samples from the site to assess the likelihood of receiving samples with
296     chemical contaminants. The laboratory should notify the Health and Safety Officer and the
297     Project Manager about the presence of potentially hazardous chemical contaminants.
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298     11.4.5 Corrective Action

299     Visual inspection can also verify whether field sample preparation was performed as stated in
300     accompanying documentation. Samples that were not filtered in the field or that reacted with the
301     preservative to form a precipitate may represent a significant problem to the laboratory. If it
302     appears that the sample was filtered in the field (i.e., there is a corresponding filter sample for the
303     liquid sample), the liquid generally will be analyzed as originally specified. Laboratory personnel
304     should check the project plan or SOW to see if the filter and filtered materials require analyses
305     along with the filtered sample. If it appears that the sample was not  filtered in the field (i.e., there
306     is no corresponding filter, there are obviously solid particles in a liquid sample), sample
307     documentation should be reviewed to determine if a deviation from the project plan was
308     documented for the sample. It may be appropriate to filter the sample in the laboratory. The
309     Project Manager should be notified immediately to discuss possible options such as filtering the
310     sample at the laboratory or collecting additional samples.

311     One example of a corrective action for inspection is, if the pH is out of conformance, it may be
312     possible to obtain a new sample. If it is not possible or practical to obtain a new sample, it may
313     be possible to acidify the sample in the laboratory.

314     Visual inspection can serve to check certain aspects of sample collection. For example, if the
315     SOP states that a soil sample is supposed to have twigs, grass, leaves, and stones larger than a
316     certain size removed  during sample collection and some of this foreign material is still included
317     as part of the sample, this discrepancy results in a nonconformance.

318     11.5   Laboratory Sample Tracking

319     Sample tracking should be done to ensure  that analytical results are  reported for the "correct"
320     sample. A good sample tracking system helps to prevent sample mix-up. Sample tracking is a
321     process by which the location and status of a sample can be identified and documented. The
322     laboratory is responsible for sample tracking starting with receipt (at which time a unique
323     laboratory tracking number is assigned), during sample preparation, and after the performance of
324     analytical procedures until final sample disposition. The process of  sample tracking begins the
325     moment a field worker assigns an identification number (based on the information provided in
326     the appropriate plan document) and documents how materials are collected. The way samples are
327     transported from the field to the laboratory should be documented. The sample receiving
328     procedures and documentation should be consistent when applicable with 10 CFR Part 20
329     Subpart J, and the client's requirements as stated in the appropriate  plan document or statement
330     of work.

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331     11.5.1  Sample Log-In

332     Laboratory sample numbers should be assigned to each sample in accordance with the
333     laboratory's SOP on sample codes. Each sample should receive a unique tracking number by
334     which it can be logged into the laboratory tracking system, scheduled for analysis, tracked, and
335     disposed. Information to be recorded during sample log-in should include the field sample
336     identification number, laboratory sample tracking number, date and time samples were collected
337     and received, reference date for decay calculations, method of shipment, shipping numbers,
338     condition of samples, requested analyses, number and type of each sample, quality control
339     requirements, special instructions, and other information relevant to the analyzing and tracking of
340     samples at the laboratory. Laboratory sample tracking is a continuation of field sample tracking.

341     Documents generated for laboratory sample tracking must be sufficient to verify the sample
342     identity, that the sample may be reliably located, and that the right sample is analyzed for the
343     right analyte. The documentation should include sample log-in records, the analysis request form,
344     names of staff responsible for the work, when procedures are completed, and details concerning
345     sample disposal. The documentation must conform to the laboratory's SOPs.

346     During sample log-in, laboratory quality control (QC) samples may be scheduled for the analyses
347     requested. The type and frequency of QC samples should be provided by the plan document or
348     SOW and consistent with the laboratory's SOPs.

349     11.5.2 Sample Tracking During Analyses

350     At this point, samples are introduced into the laboratory's analytical processing system. The
351     information gathered during screening, along with the assigned tracking identification, passes to
352     the laboratory where specific preparation and analyses are performed. The sample may be further
353     sub-sampled. Each sub-sample, along with the original sample, requires tracking to account for
354     all materials handled and processed in the laboratory.

355     At the same time that samples are received at the laboratory, each set of samples should be
356     accompanied by documents listing requests for specific analyses. This documentation should be
357     compared to separate paperwork obtained before sample receipt. Laboratory management
358     personnel should be notified of any discrepancies. The requested analyses should be entered into
359     the laboratory's tracking system. Typically, only one sample container of sufficient volume or
360     quantity will be provided for a single or multiple set of different analyses. Each aliquant removed
361     from the original container may require tracking (and perhaps a different tracking number).
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362     Aliquants used during the analytical process can be tracked using analysis laboratory notebooks,
363     forms, or bench sheets that record laboratory tracking numbers, analyte, reference date for decay
364     correction, aliquant size, and designated quality control samples. Bench sheets are loose-leaf or
365     bound pages used to record information during laboratory work. Bench sheets are used to assist
366     in sample tracking. Each sheet is helpful for identifying and processing samples in batches that
367     include designated quality control samples. The bench sheet, along with the laboratory log book,
368     can later be used to record analytical information for use during the data review process. Bench
369     sheets can also be used to indicate that sample aliquants were in the custody of authorized
370     personnel during the analytical process.

371     After receipt, verification of sample information and requested analyses, and assignment of
372     laboratory sample tracking numbers, the requested analyses can be scheduled for performance in
373     accordance with laboratory procedures. Using this system, the laboratory can formulate a work
374     schedule,  and completion dates can be projected.

375     11.5.3 Storage of Samples

376     If samples are to be stored and analyzed at a later date, they must be placed in a secure area that
377     meets all custody requirements. Before storage, any special preservation requirements, such as
378     refrigeration or additives, should be determined.

379     The laboratory should keep records of the sample  identities and the location of the sample
380     containers. Unused sample aliquants should be returned to the storage area for final disposition.
381     In addition, for some samples, depending on the level of radioactivity or hazardous constituents
382     present, the laboratory must record when the sample was disposed and the location of the
383     disposal facility. These records are necessary to ensure compliance with the laboratory's license
384     for radioactive materials and other environmental  regulations.

385     Areas where samples are stored must be designated  and posted as radioactive materials storage
386     areas. Depending on the activity level of the samples, storage areas may require special posting.
387     If additional storage space or shielding is needed,  arrangements that are consistent with the
388     license must be made with the authorized user. See Chapter 20 on waste disposal for more
389     information.

390     11.6  References

391     American Society of Testing and Materials (ASTM) D4840.  Standard Guide for Sampling
3 92         Chain-of-Custody Procedures.

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393      American Society of Testing and Materials (ASTM) D5172. Standard Guide for Documenting
3 94         the Standard Operating Procedures Used for the Analysis of Water.

395      U.S. Environmental Protection Agency (EPA).  1985. NEIC Policies and Procedures. EPA-
396         300/9-78DDI-R, June.

397      U.S. Environmental Protection Agency (EPA).  1995. QA/G-6, Guidance for the Preparation of
398         Standard Operating Procedures (SOPS) for Quality-Related Documents.

399      U.S. Nuclear Regulatory Commission. 1998b. Consolidated Guidance About Materials Licenses,
400         Volume 7. (NRC91). NUREG 1556.
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                12  LABORATORY SAMPLE PREPARATION
 2     12.1  Introduction

 3     On first impression, sample preparation may seem the most mundane aspect of an analytical
 4     protocol. However, it is critical that the analyst realize and remember that a determination is only
 5     as good as the sample preparation that has preceded it. If an aliquant taken for analysis does not
 6     represent the original sample accurately, the results of this analysis are questionable. As a general
 7     rule, the error in sampling and the sample preparation portion of an analytical procedure is
 8     considerably higher than that in the methodology itself, as illustrated in Figure 12.1.
Sampling
Concentration,
Isolation, et

, \ /
1 \
\
SeparationX /
c. Steps \
. \


/
\ / Sample
/ Preparation
/



                  FIGURE 12.1—Degree of error in laboratory sample preparation (Scwedt, 1997)


 9     One goal of laboratory sample preparation is to provide, without sample loss, representative
10     aliquants that are free of laboratory contamination that will be used in the next steps of the
11     protocol. Samples are prepared in accordance with applicable standard operating procedures
12     (SOPs) and laboratory SOPs using information provided by field sample preparation (Chapter 10,
13     Field and Sampling Issues that Affect Laboratory Measurements)., sample screening activities,
14     and objectives given in the appropriate planning documents. The laboratory sample preparation
15     techniques presented in this chapter include the physical manipulation of the sample (heating,
16     screening, grinding, mixing, etc.) up to the point of dissolution. Steps such as adding carriers
17     and tracers, followed by wet ashing or fusion, are discussed in Chapter 13 (Sample Dissolution)
18     and Chapter 14 (Separation Techniques).
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19     This chapter presents some general guidance for sample preparation on the avoidance of sample
20     loss and of sample contamination. Owing to the physical nature of the media, sample preparation
21     for solids requires the most attention, and therefore is discussed at great length (Section 12.3).
22     General procedures for preparing solid samples (such as drying, obtaining a constant weight,
23     grinding, sieving, mixing, and subsampling) are discussed. Some sample preparation procedures
24     then are presented for typical types of solid samples (e.g., soil and sediment, biota, vegetation
25     including food, etc.). This chapter concludes with specific guidance for preparing samples of
26     filters (Section 12.4), wipes (Section 12.5), liquids (Section 12.6), gases (Section 12.7), and
27     bioassay (Section 12.8).

28     12.2  General Guidance for Sample Preparation

29     Some general considerations during sample preparation are to minimize sample losses and to
30     prevent contamination. Possible mechanisms for sample loss during preparation steps are
31     discussed in Section 12.2.1, and the contamination of samples from sources in the laboratory is
32     discussed in Section 12.2.2. Control of contamination through cleaning labware is important and
33     described in Section 12.2.3, and laboratory contamination control is discussed in Section 12.2.4.

34     12.2.1  Potential Sample Losses During Preparation

35     Materials may be lost from a sample during laboratory preparation. The following sections
36     discuss the potential types  of losses and the methods used to control them. The addition of tracers
37     or carriers (Chapter 13) is encouraged at the earliest possible point and prior to any sample
38     preparation step where there might be a loss of analyte. Such preparation steps may include
39     homogenization or sample heating. The addition of tracers or carriers prior to these steps helps to
40     account for any analyte loss during sample preparation.

41     12.2.1.1   Losses as Dust  or Particulates

42     When a sample is dry ashed, a fine residue (ash) is often formed. The small particles in the
43     residue are resuspended readily by any flow of air over the sample. Air flows are generated by
44     changes in temperature (e.g., opening the furnace while it is hot) or by passing a stream of gas
45     over the sample during heating to assist in combustion. These losses are minimized by ashing
46     samples at as low a temperature as possible, gradually increasing and decreasing the temperature
47     during the ashing process, using a slow gas-flow rate, and never opening the door of a hot
48     furnace (Section 12.3.1). If single samples are heated in a tube furnace with a flow of gas over
49     the sample, a plug of glass or quartz wool can be used to collect particulates or an absorption
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50     vessel can be used to collect volatile materials. At a minimum, all ash or finely ground samples
51     should be covered before they are moved.

52     Solid samples are often ground to a fine particle size before they are fused or wet ashed (see
53     Chapters 13 and 14 on dissolution and separation) to increase the surface area  and speed up the
54     reaction between the sample and the fluxing agent or acid. Since solid samples are frequently
55     heterogeneous, a source of error arises from the difference in hardness among  the sample
56     components. The softer materials are converted to smaller particles more rapidly than the harder
57     ones, and therefore, any loss in the form of dust during the grinding process will alter the
58     composition of the sample. The finely ground particles are also susceptible to resuspension.
59     Samples may be moistened carefully with a small amount of water before adding other reagents.
60     Reagents should be added slowly to prevent losses as spray owing to reactions between the
61     sample and the reagents.

62     12.2.1.2  Losses Through Volatilization

63     Some radionuclides are volatile under specific conditions (e.g., heat, grinding, strong oxidizers),
64     and care should be taken to identify samples requiring analysis for these radionuclides. Special
65     preparation procedures should be used to prevent the volatilization of the radionuclide of interest.

66     The loss of volatile elements during heating is minimized by heating without exceeding the
67     boiling point of the volatile compound. Ashing aids can reduce losses by converting the sample
68     into less volatile compounds. These reduce losses but can contaminate samples. During the wet
69     ashing process, losses of volatile elements can be minimized by using a reflux condenser. If the
70     solution needs to be evaporated, the reflux solution can be collected separately. Volatilization
71     losses can be prevented when reactions are carried out in a sealed tube or a closed metal or
72     Teflon™ bomb. Table 12.1 lists some commonly analyzed radioisotopes,  their volatile chemical
73     form, and the boiling point of that species at standard pressure (note that the boiling point may
74     vary based on solution, matrix, etc.).

75     Often the moisture content, and thus, the chemical composition of a solid is altered during
76     grinding and crushing (Dean, 1995). Decreases in water content are sometimes observed while
77     grinding solids containing essential water in the form of hydrates, likely as  a result of localized
78     heating. (See Section 12.3.1.2 for a discussion of the types of moisture present in solid samples.)
79     Moisture loss is also observed when samples containing occluded water are ground and crushed.
80     The process ruptures some of the cavities, and exposes the water to evaporation. More
81     commonly, the grinding process results in an increase in moisture content owing to an increase in
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 82
 83

 84

 85

 86

 87
surface area available for absorption of atmospheric water. Both of these conditions will affect
the analysis of 3H since 3H is normally present in environmental samples as 3HOH.

	TABLE 12.1 — Examples of Volatile Radionuclides	
 89
 90
 91


 92

 93

 94
 95
 96
 97
 98

 99

100
101
102
103
104
105
106
107
 Isotope
Chemical Form
Boiling Point (°C)
 Tritium — 3H

 Carbon — 14C

 Iodine —131I,129I
 Cesium — 134Cs, 135Cs,
 137Cs
 Ruthenium — 106Ru
 Technetium — "Tc
H2O
CO2 (produced from CO3"2 or
oxidation of organic material)
Cs metal
Cs oxides
(nitrates decompose to oxides)
CsCl
RuO4
RuCl3-;sH2O
Tc207
TcCl4 _
100

-78.5

185.2 (sublimes readily)
678.4
-650

1290
40
decomposes above 500
310.6
Sublimes above 300
Source: Greenwood and Earnshaw (1984).

Additional elements that volatilize under specific conditions include arsenic, antimony, tin,
polonium, lead, selenium, mercury, germanium, and boron. Osmium is volatilized as the
tetroxide under oxidizing conditions similar to those for ruthenium. Carbon, phosphorus, and
silicon may be volatilized as hydrides, and chromium is volatilized under oxidizing conditions in
the presence of chloride.

12.2.1.3   Losses Owing to Reactions Between Sample and Container

Specific elements may be lost from sample materials owing to interaction with a container. Such
losses may be significant, especially for trace analyses used in radioanalytical work.  Adsorption
reactions are discussed in Chapter 10 for glass and plastic containers. Losses owing to adsorption
may be minimized by using pretreated glassware with an established hydrated layer.  Soaking new
glassware overnight in a dilute nitric or hydrochloric acid solution will provide an adequate
hydrated  layer. Glassware that is used on a regular basis will already have established an
adequate hydrated layer. The  use of strong acids to maintain a pH less than one also  helps
minimize losses from adsorption.
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108      Reactions among analytes and other types of containers are described in Table 12.2. Leaving
109      platinum crucibles uncovered during dry ashing to heat samples will minimize reduction of
110      samples to base metals which form alloys with platinum. It is recommended that porcelain not be
ill      used for analysis of lead, uranium, and thorium because the oxides of these elements react with
112      porcelain glazes. Increasing the amount of sample for dry ashing increases the amount of ash,
113      allowing trace elements to react with the ash instead of with the container.
114
115
116
117
118

119

120
                       TABLE 12.2 — Properties of Sample Container Materials
Material	Recommended Use Properties
121
122
123
124

125
126
Borosilicate
Glass
Fused Quartz

Porcelain

Platinum
Nickel
Zirconium
Alumina
(A1203)

Polyethylene
Teflon™
General applications Transparent; good thermal properties; fragile; attacked by HF, H3PO4, and
                   alkaline solutions.
High temperature
applications
High temperature
applications
High temperature or
corrosive
applications
Molten alkali metal
hydroxide and na2o2
fusions
Peroxide fusions
Acids and alkali
melts at low
temperatures
Sample and reagent
storage
Corrosive
applications
Transparent; excellent thermal properties (up to 1100° C); fragile; more
expensive than glass; attacked by HF, H3PO4, and alkaline solutions.
Used at temperatures up to 1100° C; less expensive than quartz; difficult to
shape; attacked by HF, H3PO4, and alkaline solutions.
Virtually unaffected by acids, including HF; dissolves readily in mixtures of
HNO3 and HC1, C12 water or Br2 water; adequate resistance to H3PO4; very
expensive; forms alloys with Hg, Pb, Sn, Au, Cu, Si, Zn, Cd, As, Al, Bi, and
Fe, which may be formed under reducing conditions; permeable to H2 at red
heat, which serves as a reducing agent; may react with S, Se, Te, P, As, Sb,
B, and C to  damage container; soft and easily deformed, often alloyed with
Ir, Au, or Rh for strength. Do not use with Na2CO3 for fusion.
Suitable for use with strongly alkaline solutions.
Less expensive alternative to platinum; extremely resistant to HC1; resistant
to HNO3; resistant to 50% H2SO4 and 60% H3PO4 up to 100° C; resistant to
molten NaOH; attacked by molten nitrate and bisulfate; usually available as
Zircaloy—98% Zr, 1.5% Sn, trace Fe, Cr, and Ni.
Resistant to acids and alkali melts; rapidly attacked by bisulfate melts;
brittle, requires thick walled containers.

Resistant to many acids; attacked by 16M HNO3 and glacial acetic acid;
begins to soften and lose shape at 60° C; appreciably porous to Br2, NH3,
H2S, H2O, and HNO3 (aqueous solutions can lose ~1% volume per year
when  stored for extended periods of time).
Inert to almost all inorganic  and organic compounds except F2; porosity to
gases  is significantly less than that of polyethylene; safe to use at 250° C but
decomposes at 300° C; difficulty in shaping containers results in high cost;
low thermal conductivity (requires long periods of time to heat samples).
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127     12.2.2 Contamination from Sources in the Laboratory

128     Contamination leads to biased data that misrepresent the concentration or presence of
129     radionuclides in a specific sample. Therefore, laboratory personnel should take appropriate
130     measures to prevent the contamination of samples. Such precautions are most important when
131     multiple samples are processed together. Possible sources of contamination include:

132      • Airborne;
133      • Reagents (tracers are discussed in Chapter 13);
134      • Glassware/equipment; and
135      • Facilities.

136     The laboratory should use techniques that eliminate air particulates or the introduction of any
137     outside material (such as leaks from aerosols) into samples and that safeguard against using
138     contaminated glassware or laboratory equipment. Contamination of samples can be controlled by
139     adhering to established procedures for equipment preparation and decontamination before and
140     after each sample is prepared. Additionally, the results of blank samples (e.g., sand), which are
141     run as part of the internal quality assurance program, should be closely monitored, particularly
142     following the processing of samples with elevated activity.

143     "Cross-contamination" is the contamination  of one sample by another sample that is being
144     processed concurrently or that was processed prior to the current sample leaving a residue on the
145     equipment being used. Simply keeping samples covered whenever practical is one technique to
146     minimize  cross-contamination. Another technique is to order the processing of samples
147     beginning with the lowest contamination samples first. It is not always possible to know the
148     exact rank of samples, but historical or field  screening data may be useful.

149     Laboratory personnel should be wary of using the same equipment (gloves, tweezers for filters,
150     contamination control mats, etc.) for multiple samples. Countertops and other preparation areas
151     should be routinely monitored for contamination.

152     12.2.2.1   Airborne Contamination

153     Airborne contamination is most likely to occur when grinding or pulverizing solid samples. Very
154     small particles (-10 microns) may be produced, suspended in air, and transported in the air
155     before settling onto a surface. Other sources  of potential airborne contamination include samples
156     that already consist of very small particles, or radionuclides that decay through a gaseous
157     intermediate (i.e., 226Ra decays to 222Rn gas and eventually decays to 210Pb). Therefore, the


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158     grinding or pulverizing of solid samples or the handling of samples that could produce airborne
159     contamination should be carried out under a laboratory hood to prevent dispersal or deposition in
160     the laboratory of contaminated air particulates. These particles easily can contaminate other
161     samples stored in the area. To prevent such cross-contamination, other samples should be
162     covered or removed from the area while potential sources of airborne contamination are being
163     processed.

164     If contamination from the ambient progeny of 222Rn is a concern, this can be avoided by
165     refraining from the use  of suction filtration in chemical procedures, prefiltering of room air
166     (Lucas, 1967), and use of radon traps (Lucas,  1963; Sedlet, 1966).  The laboratory may have
167     background levels of radon progeny from its construction materials.

168     12.2.2.2   Contamination of Reagents

169     Contamination from radiochemical impurities in reagents is especially troublesome in low-level
170     work (Wang et al., 1975). Care must be taken in obtaining reagents with the lowest contamina-
171     tion possible. Owing to the ubiquitous nature  of uranium and thorium, they and their progeny are
172     frequently encountered  in analytical reagents. For example, Yamamoto et al. (1989) found
173     significant 226Ra contamination in common barium and calcium reagents. Other problematic
174     reagents include the rare earths (especially cerium salts), cesium salts which may contain 40K or
175     87Rb, and potassium salts. Precipitating agents such as tetraphenyl borates and chloroplatinates
176     may also suffer from contamination problems. In certain chemical  procedures,  it is necessary to
177     replace inert carriers of the element of interest with non-isotopic carriers when it is difficult to
178     obtain the inert carrier in a contamination-free condition. Devoe (1961) has written an extensive
179     review article on the radiochemical contamination of analytical reagents.

180     12.2.2.3   Contamination of Glassware/Equipment

181     Other general considerations in sample preparation include the  cleaning of glassware and
182     equipment (Section 12.2.3). Criteria established in  the planning documents or laboratory SOPs
183     should give guidance on proper care of glassware and equipment (i.e., scratched glassware
184     increases the  likelihood of sample contamination and losses owing to larger surface area).
185     Glassware should be routinely inspected for scratches, cracks, etc., and discarded if damaged.
186     Blanks and screening should be used to monitor for contamination of glassware.

187     Whenever possible, the use of new or disposable containers or labware is recommended. For
188     example, disposable weigh boats can be used to prevent contamination of a balance. Disposable
189     plastic centrifuge tubes are often less expensive to use than glass tubes that require cleaning after


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190     every use. If non-disposable containers or labware are used, it may be necessary to use new
191     materials for each new project to reduce the potential for contamination. Blanks can be used to
192     detect cross-contamination. Periodic rinsing with a dilute solution of nitric acid can aid in
193     maintaining clean glassware. However, Bernabee et al. (1980) could not easily remove  nuclides
194     sorbed onto the walls of plastic containers by washing with strong mineral acids. They report that
195     nuclides can be wiped from the walls, showing the importance of the physical action of a brush
196     to the cleaning process.

197     12.2.2.4   Contamination of Facilities

198     In order to avoid contamination of laboratory facilities and possible contamination of samples or
199     personnel,  good laboratory practices must be constantly followed and the laboratory must be kept
200     in clean condition. The laboratory should establish and maintain a Laboratory Contamination
201     Control Program (Section 12.2.4) to avoid contamination of facilities and to deal with it
202     expeditiously if it occurs.

203     12.2.3 Cleaning of Labware, Glassware, and Equipment

204     12.2.3.1   Labware and Glassware

205     Some labware is too  expensive to be used only once (e.g., crucibles, Teflon™ beakers, separately
206     funnels). Labware that will be used for more than one sample should be subjected to thorough
207     cleaning between uses. A typical cleaning protocol includes a detergent wash, an acid soak (HC1,
208     HNO3, or citric acid), and a rinse with deionized or distilled water. As noted in Chapter 10, the
209     use of a brush to physically scrub glassware aids in the removal of contaminates.

210     The Chemical Technician's Ready Reference Handbook (Shugar and Ballinger, 1996) offers
211     practical advice on washing and cleaning laboratory glassware:

212      •  Always clean your apparatus immediately after use. It is much easier to clean the glassware
213         before the residues become dry and hard. If dirty glassware cannot be washed immediately, it
214         should be left in water to soak.

215      •  Thoroughly rinse all soap  or other cleaning agent residue after washing glassware to prevent
216         possible contamination. If the surface is clean, the water will wet the surface uniformly; if the
217         glassware is still  soiled, the water will stand in droplets.
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218      •  Use brushes carefully and be certain that the brush has no exposed sharp metal points that can
219         scratch the glass. Scratched glassware increases the likelihood of sample contamination and
220         losses owing to larger surface areas. Moreover, scratched glassware is more easily broken,
221         especially when heated.

222     Automatic laboratory dishwashers and ultrasound or ultrasonic cleaners are also used in many
223     radiochemical laboratories. It is important to note that cleaning labware in an automatic
224     laboratory dishwasher alone may not provide adequate decontamination. Contaminated glassware
225     may need to be soaked in acid or detergent to ensure complete decontamination. Ultrasonic
226     cleaning in an immersion tank is an exceptionally thorough process that rapidly and efficiently
227     cleans the external, as well as the internal, surfaces of glassware or equipment. Ultrasonic
228     cleaners generate high-frequency sound waves and work on the principle of "cavitation," the
229     formation and collapse of submicron bubbles. These bubbles form and collapse about 25,000
230     times each second with a violent microscopic intensity which produces a scrubbing action
231     (Shugar and Ballinger, 1996). This action effectively treats every surface of the labware because
232     it is immersed in the solution  and the sound energy penetrates wherever the solution reaches.

233     The Manual for the Certification of Laboratories Analyzing Drinking Water (EPA, 1992)
234     contains a table of glassware cleaning and drying procedures for the various methods given in the
235     manual (including methods for the analysis of radionuclides in water). The suggested procedure
236     for cleaning glassware for metals analysis is to wash with detergent, rinse with tap water, soak
237     for 4 hours in 20 percent (v/v) HNO3 or dilute HNO3 (8 percent)/HCl (17 percent), rinse with
238     reagent water, then air dry. Shugar and Ballinger (1996) suggest treating acid-washed glassware
239     by soaking it in a solution containing 2 percent NaOH and 1 percent disodium ethylenediamine
240     tetraacetate for 2 hours, followed by a number of rinses with distilled water to remove metal
241     contaminants.

242     More specifically to radionuclides, in their paper discussing the simultaneous determination of
243     alpha-emitting nuclides in soil, Sill et al. (1974) examined the decontamination of certain
244     radionuclides from common labware and glassware:

245         By far the most serious source of contamination is the cell, electrode, and "O" ring used
246         in the electrodeposition step. Brief rinsing with a strong solution of hydrochloric acid
247         containing hydrofluoric acid and peroxide at room temperature was totally ineffective in
248         producing adequate decontamination. Boiling anode and cell with concentrated nitric acid
249         for 10 to  15 minutes removed virtually all of the activity resulting from the analysis of
250         samples containing less than 500 disintegrations per minute (dpm). When larger
251         quantities of activity such as the 2.5 x 104 counts per minute (cpm) used in the material


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252         studies ... had been used, a second boiling with clean acid was generally required.
253         However, boiling nitric acid precipitates polonium and other procedures have to be used
254         in its presence. When such high levels of activity have been used, a blank should be run
255         to ensure that decontamination was adequate before the system is permitted to be used in
256         the analysis of subsequent low-level samples. Prudence suggests that a separate system
257         should be reserved for low-level samples and good management exercised over the level
258         of samples permitted in the low-level system to minimize the number of blanks and full-
259         length counting times required to determine adequate decontamination.

260         ...Beakers, flasks, and centrifuge tubes in which barium sulfate has been precipitated must
261         be cleaned by some agent known to dissolve barium sulfate, such as boiling perchloric or
262         sulfuric acids or boiling alkaline DTPA [diethylenetriaminepentacetate]. This is a
263         particularly important potential source of contamination, particularly if hot solutions
264         containing freshly-precipitated barium sulfate are allowed to cool without stirring. Some
265         barium sulfate post-precipitates after cooling and adheres to the walls so tenaciously that
266         chemical removal  is required.  Obviously, the barium sulfate will contain whichever
267         actinide is present, and will not dissolve even in solutions containing hydrofluoric acid.
268         Beakers or flasks in which radionuclides have been evaporated to dryness will invariably
269         contain residual activity which generally requires a pyrosulfate fusion to clean completely
270         and reliably. Separately funnels can generally be cleaned adequately by rinsing them with
271         ethanol and water  to remove the organic solvent, and then with hydrochloric-hydrofluoric
272         acids and water to remove traces of hydrolyzed radionuclides...

273     However, one should note that current laboratory safety guidelines discourage the use  of
274     perchloric acid (Schilt, 1979).

275     12.2.3.2    Equipment

276     In order to avoid cross-contamination, grinders, sieves, mixers  and other equipment should be
277     cleaned before using them for a new sample. Cleaning equipment prior to use is only necessary if
278     the equipment has not been used for some time. The procedure can be as simple or as
279     complicated as the analytical objectives warrant as illustrated by Obenhauf et al. (website
280     reference) in the SPEX Certiprep Handbook of Sample Preparation and Handling. In  some
281     applications, simply wiping down the equipment with ethanol may suffice. Another practical
282     approach is to brush out the container, and briefly process an expendable portion of the next
283     sample and discard it.  For more thorough cleaning, one may process one or more batches of pure
284     quartz sand through the piece of solid processing equipment, and then wash it carefully.  The
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285     efficacy of the decontamination is determined by monitoring this sand for radionuclide
286     contamination.

287     An effective cleaning procedure for most grinding containers is to grind pure quartz sand
288     together with hot water and detergent, then to rinse and dry the container. This approach
289     incorporates a safety advantage in that it controls respirable airborne dusts. It is important to note
290     that grinding containers become more difficult to clean with age because of progressive pitting
291     and scratching of the grinding surface. Hardened steel containers can also rust, and therefore
292     should be dried thoroughly after cleaning and stored in a plastic bag containing a desiccating
293     agent. If rust does occur, the iron oxide coating can be removed by a warm dilute oxalic acid
294     solution or by abrasive cleaning.

295     12.2.4 Laboratory Contamination Control Program

296     The laboratory should establish a general program to prevent the contamination of samples.
297     Included in the program should be ways to detect contamination from any source during the
298     sample preparation steps if contamination of samples occurs. The laboratory contamination
299     control program should also provide the means to correct procedures to eliminate or reduce any
300     source of contamination. Some general aspects of a control program include:

301       •  Appropriate  engineering controls, such as ventilation, shielding, etc., should be in place.

302       •  The laboratory should be kept clean and good laboratory practices should be followed.
303         Personnel should be well-trained in the safe handling of radioactive materials.

304       •  Counter tops and equipment should be cleaned and decontaminated following spills of
305         liquids or dispersal of finely powdered solids. Plastic-backed absorbent benchtop coverings
306         or trays help to contain spills.

307       •  There should be an active health physics program that includes frequent monitoring of
308         facilities and personnel.

309       •  Wastes should be stored properly and not allowed to accumulate in the laboratory working
310         area. Satellite accumulation areas should be monitored.

311       •  Personnel should be mindful of the use of proper personnel protection equipment and
312         practices (e.g., habitual use of lab coats, frequent glove changes, routine hand washes).
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313      •  Operations should be segregated according to activity level. Separate equipment and facilities
314         should be used for elevated and low-level samples whenever possible.

315      •  SOPs describing decontamination and monitoring of labware, glassware, and equipment
316         should be available.

317      •  Concentrated standard stock solutions should be kept isolated from the general laboratory
318         working areas.

319     As an example, Kralian et al. (1990) have published the guidelines for effective low-level
320     contamination control.

321     12.3  Solid Samples

322     This section discusses laboratory preparation procedures for solid samples as illustrated in
323     Figure 12.2. General procedures such as exclusion of unwanted material in the sample; drying,
324     charring, and ashing of samples; obtaining a constant weight (if required);  and homogenization
325     are discussed first. Examples of preparative procedures for solid samples are then presented.
326
Solid samples may consist of a wide variety of materials, including:
327      •  Soil and sediment;
328      •  Biota (plants and animals); and
329      •  Other materials (metal, concrete, asphalt, solid waste, etc.).

330     Before a solid sample is prepared, the specific procedures given in the planning documents
331     should be reviewed. This review should result in a decision that indicates whether materials other
332     than those in the intended matrix  should be removed, discarded, or analyzed separately. Any
333     material removed from the sample should be identified, weighed,  and documented.

334     To ensure that a representative aliquant of a sample is analyzed, the sample should first be dried
335     or ashed and then blended or ground thoroughly (Appendix F). Homogenization should result in
336     a uniform distribution of analytes and particles throughout the sample. The size of the particles
337     that make up the sample will have a bearing on the representativeness of each aliquant.
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                                                                        Laboratory Sample Preparation
                   Does the Sample
                   Require Heating?
                        No  •*-
                                             Yes-
                  Refer to Planning Documents to
                 Determine What Should be Removed,
                 Discarded, Analyzed, and/or Saved for
                        Future Analyses
Yes-
       Options:
 Heat to Remove Moisture
  Heat to Char Organics
Dry Ash to Destroy Organics
                     Are Separate
                    Aliquots Needed
                      for Each
                      Analyte?
                     Prepare Separate Aliquots
                        for Each Analyte
                    FIGURE 12.2—Laboratory Sample Preparation Flowchart (for Solid Samples)
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338     12.3.1 General Procedures

339     The following sections discuss the general procedures for exclusion of material, heating solid
340     samples (drying, charring, and ashing), obtaining a constant weight, mechanical manipulation
341     grinding, sieving, and mixing), and subsampling. Not every step is done for all solid sample
342     categories (soil/sediment, biota, and other) but are presented here to illustrate the steps that could
343     be taken during preparation.

344     12.3.1.1   Exclusion of Material

345     Exclusion of Material by Size and Particles. During solid preparation, some particles may be
346     identified in the sample that are not a part of the matrix intended for analysis. Examples of such
347     particles are rocks and pebbles or fragments of glass and plastic. Depending on the specific
348     procedures given in the planning documents on the constitution of the sample taken, rocks and
349     pebbles can be removed and analyzed separately if desired. The sample should be weighed before
350     and after any material is removed. Other materials that are not a part of the required matrix can
351     also be removed and analyzed separately. If analysis of the material removed  is necessary,
352     applicable SOPs should be used to prepare the material for analysis.

353     Exclusion of Organic Material. Leaves, twigs, and grass can easily be collected inadvertently
354     along with samples  of soil or sediment. Because these are not usually intended for analysis, they
355     are often removed and stored for future analysis, if necessary. The material removed should be
356     identified, if possible, and weighed.

357     12.3.1.2   Principles of Drying Techniques

358     Applying elevated temperatures during sample preparation is a widely used technique for the
359     following reasons:

360      •  To remove moisture or evaporate liquids (raise the temperatures to 60° to 110° C).

361      •  To prepare organic material for subsequent wet ashing or fusion ("char" the material by
362         heating to medium temperature of 200° to 300° C).

363      •  To prepare the sample for subsequent determination of nonvolatile constituents (dry ash at
364         high temperature of 450° to 750° C).
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365     Once a decision is made to use elevated temperatures during sample preparation, several
366     questions should be considered:

367      • What material should be used for the sample container?

368      • What should serve as the heat source?

369      • How quickly should the temperature be raised? (Rate of stepwise temperature increase)

370      • What is the maximum temperature to which the sample should be exposed?

371      • How long should the sample be heated at the maximum temperature?

372      • How quickly should the sample be cooled afterward?

373     The following sections provide information related to these questions.

374     Note that there are times during sample preparation when samples should not be heated. For
375     example, samples to be prepared for 3H or 14C determination should not be heated. Since 3H is
376     normally present as tritiated water in environmental samples, heating will remove the 3H.
377     Similarly, 14C is usually present in environmental samples as carbonates or 14CO2 dissolved in
378     water, and heating will release 14C as a gas. Samples to be analyzed for iodine, mercury,
379     antimony, or other volatile elements should be heated only under conditions specified in the
380     planning documents. If both volatile and nonvolatile elements are determined from the same
381     sample, aliquants of the original sample should be removed for determination of the volatile
382     elements.

383     Ovens, furnaces, heat lamps, and hot plates are  the traditional means to achieve elevated
384     temperatures in the laboratory. However, more  recently,  microwave ovens have added an
385     additional tool for elevating temperature during sample preparation. Walter et al. (1997) and
386     Kingston and lassie (1988) give an overview of the diverse field of microwave-assisted sample
387     preparation. A dynamic database of research articles related to this topic can be found at the
388     SamplePrep Web™ at http://www.sampleprep.duq.edu/index.html. As microwave sample
389     preparation has developed, numerous standard methods with microwave assistance have been
390     approved by the American Society for Testing and Materials (ASTM), Association of Official
391     Analytical Chemists (AOAC), and the U.S. Environmental Protection Agency (EPA). The
392     majority of the microwave-assisted methods are for acid-dissolution (Chapter 13),  but several are
393     for drying samples.


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394     Alternatives to heating samples include drying them slowly in a vacuum desiccator, air-drying, or
395     freeze-drying. ASTM D3974 describes three methods of preparing soils, bottom sediments,
396     suspended sediments, and waterborne materials: (1) freeze-drying; (2) air-drying at room
397     temperature; and (3) accelerated air-drying.

398     Drying Samples

399     It must be determined at the start of an analytical procedure if the results are to be reported on an
400     as-received or dry-weightbasis. Most analytical results for solid samples should be reported on a
401     dry-weight basis, which  denotes material dried at a specified temperature to a constant weight or
402     corrected through a "moisture" determination made on an aliquant of the sample taken at the
403     same time as the aliquant taken for sample analysis.

404     Typically, samples are dried at temperatures of 105° to 110° C. Sometimes it is difficult to
405     obtain constant weight at these temperatures, then higher temperatures must carefully be used.
406     Alternatively, for samples that are extremely heat sensitive and decompose readily, vacuum
407     desiccation or freeze-drying techniques are applicable.

408     The presence of water in a sample is a common problem frequently facing the analyst. Water
409     may be present as a contaminant (i.e., from the atmosphere or from the solution in which the
410     substance was formed) or be bonded as a chemical compound (i.e.,  a hydrate). Regardless of its
411     origin, water plays a role in the composition of the sample. Unfortunately, especially in the case
412     of solids, water content is variable and depends upon such things as humidity, temperature, and
413     the state  of subdivision.  Therefore, the make-up of a sample may change significantly with the
414     environment and the method of handling.

415     Traditionally, chemists distinguish several ways in which water is held by a solid (Dean, 1995).

416       •  Essential water is an integral part of the molecular or crystal structure and is present in
417         stoichiometric quantities, for example, CaC2O4»2H2O.

418       •  Water of constitution is not present as such in the solid, but is formed as a product when the
419         solid undergoes decomposition, usually as a result of heating. For example, Ca(OH)2 ^ CaO
420         + H2O.

421       •  Nonessential water is retained by physical forces, is non-stoichiometric, and is not necessary
422         for the characterization of the chemical composition of the sample.
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423      •  Absorbed water is retained on the surface of solids in contact with a moist environment, and
424         therefore, is dependent upon the humidity, temperature, and surface area of the solid.

425      •  Sorbed water is encountered with many colloidal substances such as starch, charcoal, zeolite
426         minerals, and silica gel and may amount to as much as 20 percent or more of the solid.
427         Sorbed water is held as a condensed phase in the interstices or capillaries of the colloid and it
428         is greatly dependent upon temperature and humidity.

429      •  Occluded water is entrapped in microscopic pockets spaced irregularly throughout solid
430         crystals.  These cavities frequently occur naturally in minerals and rocks.

431      •  Water also may be present as a solid solution in which the water molecules are distributed
432         homogeneously throughout the solid. For example, natural glasses may contain several
433         percent moisture in this form.

434     Heat Source. There are several choices when heating to dryness. The heat source is often
435     determined by the amount of time available for drying and the potential for the sample to spatter
436     or splash during drying. When time is not a primary concern and there is little or no chance of
437     sample cross-contamination, samples are heated uncovered in a drying oven at the minimum
438     temperature  needed to remove moisture. If time is of concern, samples with high moisture
439     content can usually be dried or evaporated faster using a hot plate. Heating on a hot plate
440     significantly increases the chance of cross-contamination by spattering or splashing during
441     boiling. However, ribbed watch glasses, which cover the  sample yet still allow for evaporation,
442     can be used  to minimize cross contamination in this approach. Samples may also be placed under
443     a heat lamp.  This method reduces the risk of cross-contamination by applying heat to the surface
444     where vaporization occurs, minimizing splashing during boiling. However, the elevated
445     temperature  is difficult to measure or control, and  spattering still may be a problem when the
446     sample reaches dryness.

447     Microwave systems may also be used to dry samples. ASTM  El358 and ASTM D4643 use
448     microwave energy to dry either wood or soil to a constant weight.  In a similar fashion, AOAC
449     Methods 985.14 and 985.26 use  microwave energy to dry fat from meat or water from tomato
450     juice. Other  examples include Beary  (1988) who has compared microwave drying to
451     conventional techniques using NIST  solid standards (coal, clays, limestone, sediment) and foods
452     and food materials (rice and wheat flour) standards and Koh (1980) who discusses microwave
453     drying of biological materials.
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454     Container Material. A sample container's material composition typically poses no problem.
455     Borosilicate glass is generally recommended because it is inexpensive, transparent, reusable, and
456     has good thermal properties. Platinum, Teflon™ (polytetrafluoroethylene—PTFE), porcelain, or
457     aluminum foil containers are acceptable and may be preferable in certain situations. Polyethylene
458     and other plastics of low melting point are only useful in hot water baths or ovens where the
459     temperature is closely monitored. If polyethylene is going to be used, be aware that it is affected
460     by heat applied directly to the container. The properties of several common materials used for
461     sample containers are presented in Table 12.2 (on page 12-5). Note that the sample containers
462     commonly received from the field will be those suitable for bulk samples rather than containers
463     used during sample preparation. The plan will identify the type of container material to be used
464     for field activities for samples to be shipped to the laboratory and the type of container material
465     to be used during the various steps of sample preparation.

466     Heating Rate. The heating rate is generally not considered when removing moisture, because the
467     maximum temperature typically is very low (60° to 110° C). Samples simply are placed inside
468     the preset oven. Hot plates may be preheated to the desired temperature before heating the
469     sample or turned on and gradually heated with the sample in place.

470     Maximum Temperature. The maximum temperature used for drying samples typically is just
471     above the boiling point of water—105 ° to 110° C. Higher temperatures will not dry the samples
472     significantly faster and may result in accidents or cross-contamination due to uneven heating.
473     Lower temperatures will not reduce the chance of cross-contamination, but will significantly
474     increase the drying time. One exception to this rule occurs when the physical form of the sample
475     needs to be preserved. Many minerals and chemicals have waters of hydration that affect the
476     structure and may also affect the chemical and physical properties. Samples heated at 60° C will
477     retain the waters of hydration in most chemicals and minerals and still provide dry samples in a
478     reasonable period of time (e.g., 12 to 15 hrs.).

479     Time. The duration a sample is heated to  remove moisture depends on the size of the sample, the
480     amount of moisture in the sample, the air flow around the sample, and the temperature applied to
481     the sample. If heating the sample is to provide a constant dry weight, it is more difficult to
482     determine how long to heat the sample. One convenient approach, especially when working with
483     numerous samples, is to dry all materials overnight, or occasionally longer.  This amount of
484     heating is usually more than sufficient for drying samples for radiochemical analysis. If time is a
485     critical factor or if a quantitative assessment of the uncertainty in the sample weight is required
486     by the planning documents, the sample can be subjected to repeated cycles of drying and
487     weighing until a series of weights meet the specified requirements (Section 12.3.1.3). For
488     example, one such requirement might be to obtain three consecutive weights with a standard


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489     deviation less than 5 percent of the mean. While repeated cycles of drying and weighing can
490     provide a quantitative measure of the uncertainty in the sample weight over time, a single weight
491     after an overnight drying cycle typically provides a similar qualitative level of confidence with
492     significantly less working time. Another time-saving step is to use microwave techniques rather
493     than conventional heating sources during sample preparation (ANL/ACL, 1992; Walter et al.,
494     1997).

495     Alternatives to Heating. (1) Vacuum-desiccation. A desiccator is a glass or aluminum container
496     that is filled with a substance that absorbs water, a "desiccant." The desiccator provides a dry
497     atmosphere for objects and substances. Dried materials are stored in desiccators while cooling in
498     order to minimize the uptake of ambient moisture. The ground-glass or metal rim of the desicca-
499     tor should be greased lightly with petroleum jelly or silicone grease to improve performance.
500     Calcium sulfate, sodium hydroxide, potassium hydroxide, and silica gel are a few of the common
501     desiccants. The desiccant must be renewed frequently to keep it effective. Surface caking is a
502     signal to renew or replace the desiccant. Some desiccants contain a dye that changes color upon
503     exhaustion.

504     Vacuum desiccators are equipped with a side-arm so that they may be connected to a vacuum to
505     aid in drying. The contents of the sealed evacuated desiccator are maintained in a dry, reduced-
506     pressure atmosphere. Care must be exercised when applying a vacuum as a rapid pressure
507     reduction, for high water content samples can result in "boiling" with subsequent sample loss and
508     potential cross-contamination.

509     (2) Freeze-drying. Certain substances (i.e., biological materials, pharmaceuticals), which are
510     extremely heat sensitive and cannot be dried at atmospheric conditions, can be freeze-dried
511     (Cameron and Murgatroyd,  1996). Freeze-drying, also known as "lyophilization," is the process
512     by which substances are frozen, then subjected to high vacuum. Under these conditions ice
513     (water) sublimes and other volatile liquids are removed. The non-sublimable material is left
514     behind in a dry state.

515     To freeze-dry effectively, dilute solutions are used. In order to increase the surface area, the
516     material  is spread out on the inner surface of the container as it is frozen. Once the solution or
517     substance to be dried is frozen solid, the primary drying stage begins in which a high vacuum is
518     applied, and the ice sublimes, desorbing the free ice and some of the bound moisture. During
519     secondary drying, a prolonged drying stage, the sorbed water which was bound strongly to the
520     solids, is converted to vapor. This can be a slow process because the remaining bound water has
521     a lower pressure than the free liquid at the same temperature, making it more difficult to remove.
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522     Secondary drying actually begins during the primary drying phase, but it must be extended after
523     the total removal of free ice to achieve low levels of residual moisture.

524     Commercial freeze-drying units are self contained. Simple units consist of a vacuum pump,
525     adequate vapor traps, and a receptacle for the material to be dried. More sophisticated models
526     include refrigeration units to chill the solutions, instrumentation to designate temperature and
527     pressure, heat and cold controls, as well as vacuum-release valves. The vacuum pump should be
528     protected from water with a dry-ice trap and from  corrosive gases with  chemical gas-washing
529     towers.

530     Charring of Samples to Partially Oxidize Organic Material

531     Heating samples at a moderate temperature (200°  to 300° C) is sometimes used as a method of
532     preparing a sample for subsequent decomposition  using wet ashing or fusion techniques. Large
533     amounts of organic material can react violently or even explosively during decomposition.
534     Heating the sample to  partially oxidize—or "char"—the organic material may limit reactivity
535     during subsequent preparation.

536     Heat Source.  Heat lamps, muffle furnaces,  or hot plates may be used as a heat source for charring
537     samples. Heat lamps are often selected because they can also  be used to dry the sample before
538     charring. Once dried, the sample can be moved closer to the lamp to raise the temperature and
539     char the sample (confirmed by visual inspection). Heat lamps also reduce the potential for cross-
540     contamination by minimizing  spattering and splashing. Hot plates can be used similarly to heat
541     lamps. The sample is dried and the temperature is  raised to char the sample; however, hot plates
542     increase the probability of spattering and  splashing. Muffle furnaces can be used when the
543     charring is performed  as part of dry ashing instead of part of the drying process. In this case, the
544     muffle furnace temperature is  first raised  slowly.

545     Sample Container. The choice of sample  container depends primarily on the next step in the
546     sample preparation process. When dry ashing or fusing, the sample container will usually be a
547     platinum or porcelain  crucible. Zirconium or nickel crucibles may also  be used. If the sample will
548     be dissolved using wet ashing techniques, the container may be borosilicate glass or a platinum
549     crucible. Care should be taken to prevent ignition  of samples  in glass containers. Ignited samples
550     may burn at temperatures high enough to cause damage to the container and loss of sample.
551     Polyethylene  and Teflon™ generally are not acceptable because of the increased temperature and
552     risk of melting the container.
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553     Heating Rate. Heating rate becomes a concern when charring samples because of the increased
554     temperatures. The general rule is to raise the temperature slowly to heat the sample evenly and
555     prevent large increases in temperature within the sample, which could lead to ignition. Typically,
556     a rate of 50° to 100° C per hour is considered appropriate. Samples containing large quantities of
557     organic material may require slower heating rates.

558     Maximum Temperature. One of the primary goals of charring a sample is to oxidize the materials
559     slowly and gently. Gentle oxidation is accomplished by slowly raising the temperature close to
560     the ignition point and letting the sample smolder. Many organic compounds ignite in the range of
561     200° to 300° C (e.g., paper burns at 230° C),  so this is usually the range of temperatures where
562     charring takes place. Ignition results in rapid oxidation accompanied by large volumes of
563     released gases and potential sample loss. This reaction can raise the temperature of the sample to
564     several hundred degrees above the desired maximum and result in significant losses during off-
565     gassing. The progress of the reaction can be monitored visually by observing the volume of gas
566     or smoke released. Thin wisps of smoke are usually allowable; clouds of smoke and flames are
567     not. Visual inspection is easily accomplished when hot plates or heat lamps are used as heat
568     sources. Some muffle furnaces are fitted with  viewing windows to allow visual inspection. Never
569     open a muffle furnace just to check on the progress of a reaction. This will cause a sudden
570     change in temperature, increase the oxygen  level and possibly ignite the sample, and disrupt air
571     currents within the furnace to increase potential  sample loss.

572     Time. The duration required to char a sample depends on the sample size, the amount of organic
573     material in the sample, the ignition point of the organic material, the temperature of the sample,
574     and the oxygen supply. Samples usually are heated until smoke begins to appear and allowed to
575     remain at that temperature until no more smoke is evident. This process is repeated until the
576     temperature is increased and no more smoke appears. Charring samples may require a significant
577     amount of time and effort to complete. The  duration  may be reduced by improving the flow of air
578     to the sample or mixing HNO3 or nitrate salts  with the sample before drying. However, this
579     approach is recommended only for well-characterized samples, those previously evaluated for the
580     applicability of this technique, because nitrated organic compounds can oxidize in a violent or
581     explosive manner.

582     Dry Ashing Samples

583     The object of dry ashing is to combust all of the organic material and to prepare the sample for
584     subsequent treatment using wet ashing or fusion techniques. This procedure involves heating a
585     sample in an open dish or crucible in air, usually in a muffle furnace to control the temperature
586     and flow of air. Microwave techniques are also available for dry ashing samples.


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587     Dry ashing is used to determine ash weight as well as nonvolatile constituents. The associated
588     chemistry is very complex, with oxidizing and reducing conditions varying throughout the
589     sample and over time. During the combustion process, temperatures in the sample may reach
590     several hundred degrees above the desired temperature, particularly if there is good air flow at
591     the beginning of the ashing process (Bock,  1979). Covering samples during heating is not
592     recommended, especially when using platinum crucibles. The lack of air produces a reducing
593     atmosphere that results in reduction of metals that alloy with the crucible (Table 12.2 on page 12-
594     5). This reaction results in loss of sample and potential for contamination of subsequent samples
595     when using the same crucible.

596     Heat Source. The traditional heat sources for dry ashing are muffle furnaces or burner flames.
597     Electronic muffle furnaces are recommended for all heating of platinum crucibles because
598     burners produce  significant levels of hydrogen gas during combustion, and platinum is permeable
599     to hydrogen gas at elevated temperatures. Hydrogen gas acts as a reducing agent that can result in
600     trace metals becoming alloyed to the platinum.

601     Microwave ovens have also proved to be quick and efficient when dry ashing plant tissue
602     samples, with results comparable to conventional resistance muffle furnaces (Zhang and Dotson,
603     1998). The microwave units  are fitted with ashing blocks (a ceramic insert) which absorb
604     microwave energy and quickly heats to high temperatures. This, in combination with the
605     microwave energy absorbed  directly by the sample, allows for rapid dry ashing of most materials.
606     The units are designed for increased air flow which further accelerates combustion of the
607     samples.

608     Sample Container. Platinum, zirconium, or porcelain are usually used to form crucibles for dry
609     ashing. Nickel may also be appropriate for some applications (Table 12.2). Platinum generally is
610     recommended when available and is essentially inert and virtually unaffected by most acids.
611     Zirconium and porcelain crucibles are resistant to most acids, are more resistant to HC1, and are
612     significantly less expensive than platinum. Glass and plastic containers should not be used for
613     dry ashing because the elevated temperatures exceed the melting point of these materials.

614     Crucibles fabricated from ceramic, graphite, and platinum can be used in microwave
615     applications.  Quartz fiber crucibles can accelerate the ashing process since this material rapidly
616     cools and allows many sample types to be reweighed in 60 seconds or less after removal from the
617     microwave unit.

618     Heating Rate. Samples should be dried before dry ashing and placed in an unheated furnace;
619     then, the furnace temperature is gradually increased. The sample should be spread as thinly and


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620      evenly as possible on the bottom of the container to allow for its equal heating. To ensure even
621      heating of the sample and to minimize the chance of ignition, the temperature of the furnace is
622      raised slowly. If the sample was previously charred, a rate of approximately 100° C per hour is
623      typical. This rate is slow enough that small amounts of organic material or water can be removed
624      from the sample without violent reactions. If the sample is not charred and contains a significant
625      amount of organic material, a slower rate may be necessary to control the oxidation of organic
626      material.
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641

642
643
644

645

646

647


648

649
Maximum Temperature. The maximum temperature is determined by the sample matrix and the
volatility of the elements to be analyzed.  Generally, the temperature should be as low as possible
to reduce the loss of volatile compounds, but high enough to ensure complete combustion of the
sample. A minimum temperature of 450° C is often used to ensure complete combustion (Bock,
1979). The upper limit for dry ashing is usually determined by the sample container and the
elements being analyzed and is generally considered to be 750° C, but sample-specific conditions
may use temperatures up to 1,100° C. However, in practice, some components which are
normally considered to be nonvolatile may be lost at temperatures above 650° C (Bock, 1979).
Ashing aids may be added to samples to accelerate oxidation, prevent volatilization of specific
elements, and prevent reaction between the sample and the container. Examples include adding
nitrate before drying to assist oxidation and loosen the ash during combustion, adding sulfate to
prevent volatilization of chlorides (e.g., PbCl2, CdCl2, NaCl) by converting them to the higher
boiling sulfates, and adding alkaline earth hydroxides or carbonates to prevent losses of anions
(e.g., Cl", As"3, P"3, B). Table 12.3 lists dry ashing procedures using a platinum container material
for several  elements commonly determined by radiochemical techniques.

	TABLE 12.3 — Examples of Dry-Ashing Temperatures (Platinum Container)	
  Element  Temperature/Matrix
 Cobalt     450° to 600° C for biological material; some losses reported owing to reactions with crucible;
           increased volume of sample increases volume of ash and limits loss of sample.
 Cesium    400° to 450° C for food and biological material; CsCl and CsNO3 volatilize at temperatures above
           500° C.
 Iodine     400° to 500° C with an alkaline ashing aid to prevent volatilization; losses reported as low as 450°
           C; total volatilization >600° C.
 Lead      450° to 500° C acceptable for most samples; bone or coal (lead phosphate) may be ashed as high as
           900° C without significant losses; PbO2 reacts with silica in porcelain glaze at low temperatures;
           PbCl2 is relatively volatile and nitrate or sulfate ashing aids have been used to good effect.
 Plutonium  450° C with nitric acid ashing aid for biological material, 550° C for dust on air filters, 700° C for
           soil; high temperature leads to adsorption onto carbon particles and incomplete dissolution of ash.
 Strontium  450 ° to 550 ° C for plants, 600 ° C for meat,  700 ° C for milk and bone.	
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650
651
652
           Element  Temperature/Matrix
Technetium 725 ° to 750 ° C for plants treated with ammonia.
Thorium   750° C.
Uranium   600 ° C for coal, 750 ° C for biological material; uranium reacts with porcelain glaze resulting in
	sample losses.	
653                Source: Bock (1979).
654                (Note that reducing conditions for platinum containers are given in Table 12.2)

655      Time. The duration required to completely combust a sample depends on the size of the sample,
656     the chemical and physical form of the sample before and after ashing, and the maximum
657     temperature required to ash the sample. In many cases, it is convenient to place the sample in an
658     unheated furnace and gradually raise the temperature during the day until the maximum
659     temperature is achieved. The furnace is then left at the maximum temperature overnight (12
660     hours). The furnace is allowed to cool during the next day, and samples are removed from a cold
661     oven. This procedure helps prevent sudden changes in temperature that could cause air currents
662     which may potentially disturb the ash. An alternative is to leave the sample at maximum
663     temperature for 24 hours and let the sample cool in the oven the second night to ensure complete
664     combustion of the  sample.

665     The elapsed time for dry ashing samples can be significant (greater than 36 hours), but the actual
666     time required by laboratory personnel is minimal.

667      12.3.1.3    Obtaining a Constant Weight

668     If required, constant weight is obtained by subjecting a sample to repetitive cycles of drying and
669     weighing until a series of weights meets specified requirements. Project-specific planning
670     documents or laboratory SOPs should define the acceptance criteria. For example, in the methods
671     for Total Dissolved Solids and Total Suspended Solids, from the Standard Methods for the
672     Examination of Water and Wastewater (Greenberg et al., 1992), solids are repetitively heated for
673     an hour, then weighed until successive weighings agree within 4 percent of the mass or within
674     0.5 mg. In the  ASTM guidelines for the preparation of biological samples (ASTM D4638), an
675     accurately weighed sample (1 to 2 g ± 0.1 mg, 5 to 10 g ± 1 mg, >10 g ± 10 mg) is heated for 2
676     hours, cooled in a desiccator, and weighed. Drying is repeated at hourly intervals to attain a
677     constant weight within the same accuracy.

678     Laboratory conditions, calibration of the balance, dynamic range of the balance, drying
679     techniques, drying vessel material, sample transfer techniques, weighing technique, taring and  re-
680     taring the balance, recording techniques, data manipulation and modeling all impact the


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681     uncertainty of determining the mass of the sample. At the National Institute of Science and
682     Technology (NIST), samples are dried to constant weight when taking a sample for analysis,
683     calibrating carriers, and recovering carriers after radiochemical separations. To minimize the
684     weighing uncertainty:

685       •  Balances are calibrated annually with weights traceable to NIST.

686       •  The balance is on a vibration resistant table in a low air turbulence area and, when possible,
687         the weighing room is on grade level.

688       •  Clean plastic, glass,  or metal weighing containers are used at temperatures well below their
689         softening point.

690       •  Samples are ground to provide consistent particle size and surface area for reproducible
691         drying.

692       •  Temperature or vacuum ramp and duration are designed to provide stoichiometric
693         consistency and cooled in desiccators.

694       •  Once desiccators are opened, masses are corrected for absorption of moisture from the air.

695       •  Weighings are conducted in a temperature and humidity controlled room.

696       •  The calibrated 5 decimal place balance is double shielded from drafts with a lucite enclosure
697         box.

698       •  The operator sits in front of the balance for about 10 minutes before weighing begins to warm
699         the balance with body heat.

700       •  Temperature, pressure and relative humidity are recorded periodically to correct for air
701         buoyancy.

702       •  Disposable gloves are used to prevent transfer of finger oils to the sample container.

703       •  "Zero" or tares are periodically taken to monitor fluctuations and drift in the balance
704         operation.

705       •  The operator tries to sight the readings off the scale reproducibly.


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706      •  After a weight has been put on or taken off of the pan, the balance is allowed to settle for
707         about  1 minute before a reading is taken.

708      •  When possible, an electronic balance is used for recording the masses through a cable linked
709         to a computer. In the absence of an electronic balance, two operators are used to read and
710         record the masses.

711      •  Moisture absorption models are assessed for realistic extrapolation to the time when the
712         sample was taken from the desiccator, and uncertainties are assigned at t = 0 and for
713         differences among models.

714      •  All decimal places are carried until the final value is reported.

715      •  Replicate drying of several samples are used to validate the efficacy of the drying protocol
716         before routine use.

717     Among this list, major contributors to uncertainty are reaching stoichiometric consistency in the
718     dried sample, calibration  of the balance, fluctuation and drift in the balance operation, and curve-
719     fitting moisture absorption corrections.  These sources of measurement uncertainties should be
720     quantified by measurement, inference, or judgement, then combined in quadrature as the mass
721     uncertainty.

722     12.3.1.4   Sub sampling

723     Laboratories routinely receive larger samples than required for analysis. The challenge then
724     becomes to prepare a sample that is representative and large enough for analysis, but not so large
725     as to cause needless work in its final preparation. Generally, a raw sample first is crushed to a
726     reasonable particle size and a portion of the crushed material is taken for analysis. This step may
727     be repeated with intermittent sieving of the material until an appropriate sample size is obtained.
728     Then, this final portion is crushed to a size that minimizes sampling error and is fine enough for
729     the dissolution method (Dean 1995; Pitard, 1993).

730     French geologist Pierre Gy (1992) has developed a theory of particulate sampling, which is
731     applicable to subsampling in the laboratory. Appendix F summarizes important aspects of the
732     theory and includes applications to radiochemistry. Some of the important points to remember
733     include the following:
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734      • For most practical purposes, a subsample is guaranteed to be unbiased only if every particle
735        in the sample has the same probability of being selected for the subsample.

736      • The weight of the subsample should be many times greater than the weight of the largest
737        particle in the sample.

738      • The variance associated with subsampling may be reduced either by increasing the size of the
739        subsample or by reducing the particle sizes before subsampling.

740      • Grouping and segregation of particles tends to  increase the subsampling variance.

741      • Grouping and segregation can be reduced by increment sampling, splitting, or mixing.

742     Increment samplingis a technique in which the subsample is formed from a number of smaller
743     portions selected from the sample. A subsample formed from many small increments will
744     generally be more representative than a subsample formed from only one increment. The more
745     increments the better. An example of increment sampling is the one-dimensional "Japanese slab-
746     cake" method (Appendix F).

747     Splittingis a technique in which the sample is divided into a large number of equal-sized
748     portions and several portions are then recombined  to form the subsample. Splitting may be
749     performed by a manual  procedure, such as fractional shoveling (Appendix F), or by a mechanical
750     device, such as a riffle splitter. A riffle splitter consists of a series of chutes directed alternately to
751     opposite sides. The alternating chutes divide the sample into many portions, which are then
752     recombined into two. The riffle may be used repeatedly until the desired sample size is obtained.
753     Riffle splitters are normally used with free-flowing materials such as screened soils.

754     Another traditional method for splitting is coning and quartering (Appendix F). Gy (1992) and
755     Pitard (1993) do not recommend coning and quartering because with similar tools and effort, one
756     can do fractional shoveling, which is a more reliable method.

757     If proper techniques and tools are used and adequate care is taken, samples of the sizes typically
758     encountered in the laboratory can  be mixed  effectively. However, the effects of mixing tend to be
759     short-lived because of the constant influence of gravity. Heterogeneous material may begin to
760     segregate immediately after mixing.

761     The method and duration needed to  mix a sample adequately depends on the volume and type of
762     material to be mixed. Small volumes can be mixed by shaking for a relatively short time. Large


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763     volumes may require hours. Pitard (1993) describes dynamic and discontinuous processes for
764     mixing samples including:

765      •  Mechanical mixing of test tube samples is useful for small sample size and can be performed
766         on many samples at once. Some examples are a pipette shaker with a motor-activated,
767         rocking controlled motion; a nutator mixer with the test tubes fixed to an oscillating plate;
768         and a tube rotator where tubes are attached to a rotating plate mounted at an angle.

769      •  Mechanical mixing of closed containers by rotating about a tumbling axis. A turbula
770         mechanical mixer is an example.

771      •  Magnetic stirrers are commonly used to homogenize the contents of an open beaker.

772      •  V-blenders are used to homogenize samples from several hundred grams to kilogram size.

773      •  Stirrers coupled with propellers or paddles are used to mix large volumes of slurries or pulp.

774      •  Sheet mixing or rolling technique, in which the sample is placed on a sheet of paper, cloth, or
775         other material, and the opposite corners are held while rolling the sample (see ASTM C702
776         for aggregates).

777      •  Ball and rod mills homogenize as well as grind the sample (see ASTM C999 for soils).

778     When dealing with solid samples, it is often necessary to grind the sample to reduce the particle
779     size in order to ensure homogeneity and to facilitate attack by reagents. The SPEX CertiPrep
780     Handbook of Sample Preparation and Handling (Obenauf et al., website reference) is an
781     excellent resource for information regarding grinding and blending.

782     For hand grinding, boron carbide mortars and pestles  are recommended. For samples which can
783     be pulverized by impact at room temperature, a shatterbox, a mixer-mill, or a Wig-L-Bug™ is
784     appropriate, depending on the sample size. For brittle materials—such as wool, paper, dried
785     plants, wood, and soft rocks—which require shearing as well as impact, a hammer-cutter mill is
786     warranted. For flexible or heat-sensitive samples such as polymers, cereal grains,  and biological
787     materials, cryogenic grinding is necessary. Methods are described below:

788      •  A shatterbox spins the sample, a puck, and a ring inside a dish-shaped grinding container in a
789         tight, high-speed horizontal circle. Within two to five minutes, approximately 100 grams of
790         brittle material can be reduced to less than 200 mesh. Shatterboxes are typically used to grind


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791         soils, cement mix, rocks, slags, ceramics, and ores. They have also been used for hundreds of
792         other materials including dried marsh-grass, pharmaceuticals, fertilizers, and pesticides.
793         When used in a cryogenic atmosphere, this approach can be used to grind rubber, polymers,
794         bone, hair, and tissue.

795      •  A mixer-mill grinds samples by placing them in a container along with one or more grinding
796         elements and imparting motion to the container. The containers are usually cylindrical, and
797         the grinding  elements are ordinarily balls, but may be rods, cylinders or other shapes. As the
798         container is rolled, swung, vibrated or shaken, the inertia of the grinding elements causes
799         them to move independently into each other and against the container wall, thus, grinding the
800         sample. Mixer-mills are available for a wide-range of sample sizes. The length of time
801         necessary to  grind a sample depends on the hardness of the material and the fineness desired
802         in the final product.

803      •  The Wig-L-Bug™ is an effective laboratory mill for pulverizing and blending very small
804         samples, typically in the range of 0.1 to 1 mL.

805      •  A hammer-cutter mill utilizes high-speed revolving hammers and a serrated grinding
806         chamber lining to combine both shearing and impact. A slide at the bottom of the hopper
807         feeds small portions of the sample (up to 100 mL) into the grinding chamber. After the
808         sample is adequately pulverized, it passes through a perforated-steel screen at the bottom of
809         the grinding  chamber and is then collected. With this approach, dried plants and roots, soils,
810         coal and peat, chemicals, and soft rocks all grind quickly with little sample loss.

811      •  Many analytical samples—such as polymers, rubber, and tissues that are too flexible or
812         susceptible to degradation to be impact-ground at room temperature—can be embrittled by
813         chilling, and then pulverized. Samples can be frozen and placed in a traditional grinder,  or
814         alternatively, a freezer mill can be utilized. In a freezer mill, the grinding vial is immersed in
815         liquid nitrogen and an alternating magnetic field shuttles a steel impactor against the ends of
816         the vial to pulverize the brittle material. Researchers at Los Alamos National Laboratory
817         (LANL) developed a method of cryogenic grinding of samples to homogenize them and
818         allow the acquisition of a representative aliquant of the materials (LANL, 1996).

819     When samples agglomerate or "cake" during grinding, further particle size reduction is
820     suppressed. Caking can be caused from moisture, heat, static charge accumulation, the fusing of
821     particles under pressure, etc. When it occurs, caking is a serious challenge. There are two main
822     approaches to this problem, slurry grinding and dry grinding.
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823      •  In slurry grinding, particles are suspended in solution during grinding. Water, alcohol, or
824         other liquids are added to the sample before grinding, and have to be removed afterwards.
825         Slurry grinding is a fairly reliable way of grinding a sample to micron-sized particles, but it is
826         sloppy and time-consuming.

827      •  Dry grinding is often simpler and quicker, but requires careful matching of the technique to
828         the sample. If caking is due to moisture, as in many soils or cements, the sample should be
829         dried before grinding. Grinding aids such as lubricants, antistatic agents, abrasives, and
830         binding agents can also be used. Examples of grinding aids include dry soap or detergent (a
831         lubricant), graphite (an antistatic agent as well as a lubricant), polyvinyl alcohol, phenyl
832         acetate, propylene glycol, and aspirin. For example, propylene glycol (one drop for up to ten
833         grams of sample) is used for laboratory fine grinding of Portland cement and many minerals.

834     Grinding efficiency can be improved through intermittent screening of the material. The ground
835     sample is placed upon a wire or cloth sieve that passes particles of the desired size. The residual
836     particles are reground and this process is repeated until the entire sample passes through the
837     screen. Sieves with large openings can be used in the initial stages of sample preparation to
838     remove unwanted large rocks, sticks, etc.

839     12.3.2 Soil/Sediment Samples

840     For many studies, the majority of the solid samples will be soil/sediment samples or samples that
841     contain some soil. The definition of soil is given in Chapter 10 {Field and Sampling Issues that
842     Affect Laboratory Measurements). Size is used to distinguish between soils (consisting of sands,
843     silts, and clays) and gravels.

844     The procedures to be followed to process a raw soil sample to obtain a representative subsample
845     for analysis depend, to some extent, upon the size of the sample, the amount of processing
846     already undertaken in the field, and more importantly, the radionuclide of interest and the nature
847     of the contamination. Global fallout is relatively homogeneous in particle size and distribution in
848     the sample, and therefore, standard preparation procedures should be adequate for this
849     application. However, when sampling accidental or operational releases, the standard procedures
850     may be inadequate. Transuranic elements, especially plutonium, are notorious for being present
851     as "hot-spots" ions (Eberhardt and Gilbert, 1980; Sill, 1975) and great care must be employed so
852     that the subsample taken for analysis accurately represents the total sample. This will depend on
853     the size and the degree of homogeneity. Multiple subsampling, larger aliquants,  and multiple
854     analysis may be the only techniques available to adequately define the content of radionuclides in
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855
856
	Laboratory Sample Preparation

heterogeneous samples. Therefore, it is imperative that the analyst choose a preparation approach
appropriate to the nature of the sample.
857     12.3.2.1   Soils

858     ASTM has developed a Standard Practice for the preparation of soil samples (ASTM C999).
859     Guidance is given in this ASTM method for the preparation of a homogenous soil sample from
860     composited core samples. The soil samples are dried at 110° C until at constant weight, ground
861     and mixed in a ball mill, and processed through a U.S. Series No.  35 (500-jim or 32-mesh) sieve.
862     This method is intended to produce a homogeneous sample from which a relatively small
863     aliquant (10 g) may be drawn for radiochemical analyses.

864     A similar procedure for homogenizing soil samples is given in HASL-300 (DOE, 1997).
865     Unwanted material (e.g, vegetation, large rocks) is removed as warranted, and the sample is
866     dried. If the sample contains small rocks or pebbles, the entire soil sample is crushed to 6.35 mm,
867     or the entire sample is sieved through a 12.7-mm screen. The sample is blended, then reduced in
868     size by quartering. This subsample of soil is processed through a grinder, ball mill, sieve, or
869     pulverizer until the soil is reduced to < 1.3 mm (15 mesh equivalent).

870     Sill et al. (1974) described a procedure where they dried raw soil samples for two to three hours
871     at 120° C and then ground the cooled sample lightly in a mortar and pestle.  All rocks larger than
872     1A inch were removed. The sample was charred at 400° C for two  to three hours, cooled and
873     passed though a No. 35 U.S. standard sieve, and then blended prior to  aliquanting (10.0 g are
874     taken for the analysis).

875     12.3.2.2   Sediments

876     ASTM D3976 is a standard practice for the preparation of sediment samples for chemical
877     analysis.  This ASTM practice describes the preparation of test samples collected from streams,
878     rivers, ponds, lakes, and oceans. The procedures are applicable to the determination of volatile,
879     semivolatile,  and nonvolatile constituents of sediments. Samples are first screened to remove
880     foreign objects and then mixed by stirring. The solids are allowed to settle and the supernatant
881     liquid is decanted. To minimize stratification effects due to differential rates of settling, the
882     sample is mixed again before aliquanting for drying and analysis.
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883     12.3.3 Biota Samples

884     12.3.3.1   Biological Samples

885     ASTM D4638 is a standard guide for the preparation of biological samples for inorganic
886     chemical analysis. This ASTM guide gives procedures for the preparation of test samples of
887     plankton, mollusks, fish, and plants. The preparation techniques are applicable for the
888     determination of volatile, semivolatile, and nonvolatile inorganic  compounds in biological
889     materials. However, different preparation steps are involved for the three classes of inorganic
890     compounds. In the case of nonvolatile compounds, the first step is to remove foreign objects and
891     most of the occluded water. For large samples such as fish, samples are homogenized using a
892     tissue disrupter, blender, or equivalent, and a moisture determination is performed on a one to
893     two gram aliquant. The samples then are dried by heating in an oven, by dessication, by air
894     drying, by freeze drying, or by low-temperature drying using an infrared lamp, hot plate, or a low
895     setting on a muffle furnace. Finally, the samples are dry ashed.

896     12.3.3.2   Food

897     The International Atomic Energy Agency has provided a guidebook for the measurement of
898     radionuclides in food and the environment (IAEA, 1989). Sample preparations for milk and other
899     foods such as meat, fish, fruit, vegetables, and grains are given in this guidebook. Additionally,
900     methods are presented in HASL-300 for the preparation of milk, vegetables, composite diets,  etc.
901     These methods generally involve dry ashing. The samples first  are dried thoroughly at 125° C.
902     Then, the temperature is raised at intervals over an 8-hour period  through the critical range where
903     ignition occurs, and finally to 500° C for 16 hours. If only a portion of ash is to be used for
904     analysis,  it is ground and sieved prior to aliquanting.

905     12.3.3.3   Vegetation

906     There are several DOE site references that contain examples of sample preparation for
907     vegetation. Los Alamos National Laboratory (LANL, 1997) recently grew pinto beans, sweet
908     corn, and zucchini squash in a field experiment at a site that contained observable levels of
909     surface gross gamma radioactivity within Los Alamos Canyon. Washed  edible and nonedible
910     crop tissues (as well as the soil) were prepared for analysis for various radionuclides.
911     Brookhaven National Laboratory has also evaluated the effect of its operation on the local
912     environment. Their site environmental report (DOE, 1995) gives  sample preparation steps for
913     radionuclide analysis of vegetation and fauna (along with ambient air, soil, sewage effluent,
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     TABLE 12.4—Preliminary Ashing Temperature for
                    Food Samples
        (Method Sr-02-RC, HASL-300 [DOE, 1997])
       Material	Temperature (° C)
       Eggs	  150-250
       Meat	  Burning
       Fish	  Burning
                             175-325
                             175-325
914     surface water, and groundwater). HASL-300 also
915     describes sample preparation techniques for
916     vegetation samples for a variety of radionuclides.

917     12.3.3.4   Bone and Tissue

918     Bone and tissue samples can be dry ashed in a
919     muffle furnace (HASL-300, Fisenne, 1994;
920     Fisenne et al.,1980), wet ashed with nitric acid
921     and peroxide (Fisenne and Perry, 1978) or
922     alternately dry ashed and wet ashed with nitric
923     acid until all visible signs of carbonaceous
924     material has disappeared (Mclnroy et al., 1985).

925     12.3.4  Other Samples

926     The category "other" includes such matrices as
927     concrete,  asphalt, coal, plastic, etc. The sample
928     preparation procedures applied to soils are
929     generally applicable for the "other" category,
930     except for more aggressive grinding and blending
931     in the initial step. For example, items such as
932     plastic or rubber which are too flexible to  be
933     impact-ground at room temperature must be
934     ground cryogenically. They are embrittled by
935     chilling and then pulverized. ASTM Cl 14 describes the sample preparation steps for the
936     chemical  analysis of hydraulic cement, whereas ASTM C702 describes the sample preparation of
937     aggregate samples, and is also applicable to lime and limestone products as noted in ASTM C50.
938     Additionally, ASTM D2013 describes the preparation of coal samples for analysis.

939     12.4   Filters
Fruit (fresh)  	
Fruit (canned)	
Milk (dry)	
Milk (wet)  	
Buttermilk (dry)	
Vegetables (fresh)  . . .
Vegetables (canned) . .
Root vegetables 	
Grass  	
Flour	
Dry beans	  175-250
Fruit juices	  175-225
Grains	  225-325
Macaroni  	  225-325
Bread  	  225-325
                             175-325


                             175-225
                             175-250
                             200-325
                             225-250
                             Burning
940     Filters are used to collect analytes of interest from large volumes of liquids or gases. The exact
941     form of the filter depends on the media (e.g., air, aqueous liquid, nonaqueous liquid), the analyte
942     matrix (e.g., sediment, suspended particulates, radon gas), and the objectives of the project (e.g.,
943     volume of sample passing through the filter, flow rate through the filter, detection limits, etc. (see
944     the section on filtration in Chapter 10, Field and Sampling Issues that Affect Laboratory
945     Measurements).
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946     Filter samples from liquids usually consist of the filter with the associated solid material. For
947     samples with a large amount of sediment, the solid material may be removed from the filter and
948     analyzed as a solid. When there is a relatively small amount of solid material, the filter may be
949     considered as part of the sample for analytical purposes. When large volumes of liquid are
950     processed at high flow rates, filter cartridges often are used. Typically, the cartridge case is not
951     considered part of the sample, and laboratory sample preparation includes removing the filter
952     material and sample from the cartridge case. Any special handling instructions should be
953     included as SOPs in the planning documents.

954     Air filters may be particulate filters, which are prepared in the same manner as liquid filters, or
955     they may be cartridges of absorbent material. Filters that absorb materials are typically designed
956     for a specific analysis. For example, activated charcoal  cartridges are often used to collect
957     samples of iodine or radon. Silver zeolite cartridges may be used for noble gases such as argon,
958     krypton, or xenon.  These cartridges are often designed to be analyzed intact, so no special sample
959     preparation is needed. If the cartridges need to be disassembled for analysis, a special SOP for
960     preparing these samples is usually required.

961     Homogenization is rarely an issue when preparing filter samples. Typically, the entire filter is
962     digested and analyzed. However, obtaining a representative sample of a filter does become an
963     issue when the entire filter is not analyzed. The planning document should give the details of
964     sample preparation for portions of a filter (e.g., sample  size reduction through quartering). Steps
965     such as using tweezers for holding filters and using individual sample bags should be taken to
966     prevent the loss of material collected on the filter during handling and processing.

967     12.5  Wipe Samples

968     Wipe samples (also referred to as "swipes" or "smears") are collected to indicate the presence of
969     removable surface  contamination. The loose contamination is transferred from the surface to a
970     sample of wipe material. The wipe material can be virtually anything, but common materials
971     include Whatman filter paper and nylon membrane. The greatest challenge in preparing wipe
972     samples is homogenizing the sample to obtain a representative portion for analysis, although
973     usually the entire wipe is analyzed. Wipe samples are commonly digested prior to analysis, but
974     they can be analyzed directly through appropriate counting techniques (McFarland, 1998a,
975     1998b).

976     Many wipe samples are collected using filter paper or disc smears. In  many cases, the
977     contamination on these samples is simply assumed to be fairly evenly distributed, and the wipe
978     samples are prepared like filter samples.  Sometimes, a specific analytical procedure is anticipated

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 979      and a special wipe material is used. For example, Styrofoam generates static electricity and can
 980      attract dust particles from a relatively clean surface. Styrofoam is dissolved easily in most (if not
 981      all) commercially available liquid scintillation cocktails. These wipe samples can be very easily
 982      collected, stored, and transported in liquid scintillation vials. Have the cocktail added by the
 983      laboratory and be counted for gross alpha and gross beta activity using a liquid scintillation
 984      counter.

 985      12.6  Liquid Samples

 986      Liquid samples are commonly classified as aqueous, nonaqueous, and mixtures. Aqueous liquids
 987      are most often surface water, groundwater, drinking water, precipitation, effluent, or runoff.
 988      Nonaqueous liquids may include solvents, oils, or other organic liquids. Mixtures may be
 989      combinations of aqueous and nonaqueous liquids, but may include solid material mixed with
 990      aqueous or nonaqueous liquids or both.

 991      Preliminary sample measurements (e.g., conductivity, turbidity) may be performed to provide
 992      information about the sample and to confirm field processing (see measurement of pH to confirm
 993      field preservation in Chapter 11). These measurements are especially useful when there is no
 994      prior historical information available from the sample collection site. In addition, this
 995      information can also be helpful in the performance of certain radiochemical analyses. These
 996      preliminary measurements typically require little or no sample preparation. In many cases,  the
 997      results of preliminary measurements can be used to determine the quantity of sample to be used
 998      for a specific analysis.

 999      12.6.1 Conductivity

1000      In radiochemistry, conductivity measurements typically are used as a surrogate to estimate
1001      dissolved solids content for gross-alpha and gross-beta measurements. Because the preservation
1002      of samples with acid prevents the measurement of conductivity, the recommendation is to
1003      perform the QC checks for conductivity in the field when the original measurements are
1004      performed. If the sample is not preserved in the field, the measurement can be done in the
1005      laboratory.

1006      ASTM Dl 125 is the standard test method for determining the electrical conductivity of water.
1007      The method is used for the measurement of ionic constituents, including dissolved electrolytes in
1008      natural and treated water.
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1009      12.6.2  Turbidity

1010      The presence of dissolved or suspended solids, liquids, or gases causes turbidity in water.
1011      Measurement of turbidity provides a means to determine if removal of suspended matter is
1012      necessary in order to meet the specifications for liquid samples as given in the plan document.
1013      ASTM D1889 is the standard test method for the determination of turbidity of water and
1014      wastewater in the range from 0.05 to 40 nephelometric turbidity units (NTU). In the ASTM
1015      method, a photoelectric nephelometer is used to measure the amount of light that a sample
1016      scatters when the light is transmitted through the sample.

1017      12.6.3  Filtration

1018      The filtration of samples is based on the appropriate plan document which should also give the
1019      selection of the filter material to be used. If samples have not been filtered in the field, the
1020      laboratory can perform the filtration. Guidance on filtration of liquid samples is provided in
1021      Section 10.3.3. Filtering is normally done in the field so that preservatives can be added without
1022      promoting the dissolution of undissolved solids in the sample at the time of collection.

1023      12.6.4  Aqueous Liquids

1024      Aqueous liquids are a common matrix analyzed by laboratories, and are often referred to as water
1025      samples.  Examples of possible aqueous liquids requiring radionuclide analysis include the
1026      following:

1027       • Drinking water;
1028       • Surface water;
1029       • Ground water;
1030       • Soil pore water;
1031       • Storage tank water;
1032       • Oil production water or brine;
1033       • Trench or landfill leachate; and
1034       • Water from vegetation.

1035      For certain samples  that are not filtered, inversion is a form of homogenization. Typically, the
1036      sample is homogenized by inverting the container several times to mix the sample thoroughly. If
1037      there is some air in the container, the passage of air bubbles through the sample will create
1038      sufficient turbulence to mix the sample thoroughly with three or four inversions of the sample
1039      container. If the sample contains zero  headspace (so there is no air in the sample container), the


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1040      sample should be inverted and allowed to stay inverted for several seconds before the next
1041      inversion. Ten to twenty inversions of the sample container may be required to ensure that the
1042      sample is mixed thoroughly under zero headspace conditions. Simply shaking the container will
1043      not mix the contents as thoroughly as inverting the sample container. Mechanical shakers,
1044      mixers, or rotators may be used to homogenize aqueous samples thoroughly.

1045      Filtration and acidification performed in the field is typically the only preparation required for
1046      aqueous liquids (Chapter 10). A general  discussion concerning preparation of water samples for
1047      the measurement of radioactivity is presented in NCRP (1976). Analytical Chemistry Laboratory
1048      Sample Preparation Methods (ACL, 1992) gives a number of sample preparation methods for
1049      various materials, including water samples.

1050      ASTM gives standard test methods for the preparation of water samples for the determination of
1051      alpha and beta radioactivity (ASTM D1943 and D1890, respectively). After collecting the water
1052      sample in accordance with ASTM D3370, the sample is made radioactively homogeneous by
1053      addition of a reagent in which the radionuclides present in the sample are soluble in large
1054      concentrations. Acids,  complexing agents, or chemically similar stable carriers may be used to
1055      obtain homogeneity. The chemical nature of the radionuclides and compounds present and the
1056      subsequent steps in the method will indicate the action to be taken. Different preparation
1057      techniques for freshwater and seawater samples are illustrated in Radiochemical Analytical
1058      Procedures for Analysis of Environmental Samples (EPA,  1979) and for drinking water in EPA
1059      (1980).

1060      12.6.5  Nonaqueous Liquids

1061      Nonaqueous liquids can be substances other than water such as organic solvents, oil, or grease.
1062      Many organic solvents are widely used to clean oil, grease, and residual material from electrical
1063      and mechanical equipment. The resulting waste liquid may contain a significant amount of solid
1064      material.  It may be necessary to filter such liquids to determine (1) if the analyte  is contained in
1065      the filtrate and is soluble, or (2) if the analyte is contained in the solids and therefore is insoluble.
1066      The appropriate plan document should be reviewed to determine if filtration is necessary. ASTM
1067      C1234, Standard Test Method for Preparation of Oils and Oily Waste Samples byHigh-
1068      Pressure, High-Temperature Digestion for Trace Element Determinations., describes the
1069      preparation of homogeneous samples from nuclear processing facilities.

1070      Homogenization of nonaqueous samples is accomplished in a manner similar to that for aqueous
1071      samples. Visual inspection is typically used as a qualitative measure of homogeneity in
1072      nonaqueous samples. If a quantitative measure of mixing is desired, turbidity measurements can


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1073      be performed after a predetermined amount of mixing (e.g., every 10 inversions, every 2 minutes,
1074      etc.) until a steady level of turbidity is achieved (e.g., 1 to 10 percent variance, depending on the
1075      project objectives—see ASTM D1889, Standard Test Method for Turbidity of Water).

1076      DOE (ANL/ACL, 1995) evaluated sample preparation techniques used for the analysis of oils. In
1077      evaluating the performance of a sample preparation technique, DOE considered the following
1078      qualities to be important:

1079       •  Thorough sample decomposition;
1080       •  Retention of volatile analytes;
1081       •  Acceptable analyte recovery;
1082       •  Minimal contamination from the environment or the digestion vessel;
1083       •  Low reagent blanks; and
1084       •  Speed.

1085      One of the preparation methods involved combustion of oil under oxygen at 25 atm pressure
1086      (ASTM E926) and another used nitric acid decomposition of the oil in a sealed vessel heated
1087      with a microwave (EPA, 1990).

1088      Many nonaqueous liquids present a health hazard (e.g., carcinogenicity) or require special safety
1089      considerations (e.g., flammability). Any special handling requirements based  on health and safety
1090      considerations should be documented in the planning documents.

1091      12.6.6 Mixtures

1092      Some common examples of mixtures that may be encountered by the laboratory are water with
1093      lots of total dissolved  solids and undissolved solids or water and oil in separate layers. The
1094      following sections discuss preparation procedures for these types of mixtures.

1095      12.6.6.1    Liquid-Liquid Mixtures

1096      When aqueous and  nonaqueous liquids are combined, they usually form an immiscible mixture,
1097      such as oil and water.1 In most cases, a separately funnel helps in separating the liquids into two
         1 It is often necessary to determine which liquid is aqueous and which liquid is nonaqueous. Never assume that the
         top layer is always nonaqueous, or the bottom layer is always aqueous. The density of the bottom layer is always
         greater than the density of the top layer. Halogenated solvents (e.g., carbon tetrachloride, CC14) tend to have
                                                                                         (continued...)

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1098     samples. Each sample then is analyzed separately. If, in the rare case, both liquids must be
1099     processed together, there is greater difficulty in preparing the combined liquids for analysis.
1100     Obtaining a homogenous aliquant is a key consideration in this case. Often times, the entire
1101     sample should be analyzed. This approach avoids processing problems and yields the desired
1102     result.

1103     12.6.6.2   Liquid-Solid Mixtures

1104     Mixtures of liquids and solids are usually separated by filtering, centrifuging, or decanting, and
1105     the two phases are analyzed separately. If the mixture is an aqueous liquid and a solid, and will
1106     be analyzed as a single sample, the sample is often treated as a solid. Completely drying the
1107     sample followed by dry ashing before any attempt at wet ashing is recommended to reduce the
1108     chance of organic solids reacting with strong oxidizing acids (e.g., H2SO4, HNO3, etc.). If the
1109     mixture includes a nonaqueous liquid and a solid, it is suggested that the phases be separated by
1110     filtration and the solid rinsed thoroughly with a volatile solvent such as ethanol or methanol
1111     before continuing with the sample preparation process.

1112     In rare cases where a sample contains a mixture of aqueous liquid, nonaqueous liquid, and solid
1113     material, the sample can be separated into three different phases before analysis. The sample
1114     should be allowed to settle overnight and the liquids decanted. The liquids can then be separated
1115     in a separatory funnel without the solid material clogging the funnel. Each liquid should be
1116     filtered to remove any remaining solid material. The solid should be filtered to remove any
1117     remaining liquid and rinsed with a volatile solvent. This rinse removes any traces of organic
1118     liquids to reduce problems during subsequent dissolution activities. The three phases are then
1119     analyzed separately. If necessary, the results can be added together to obtain a single result for the
1120     mixture after the separate analyses are completed.

1121     12.7   Gases

1122     Sample preparation steps are usually not required for gas samples. Lodge (1988) gives general
1123     techniques, including any necessary sample preparation, for the sampling and storage of gases
         l(.. .continued)
         densities greater than about 1 g/mL, so they typically represent the bottom layer. Other organic liquids (e.g., diethyl
         ether, oil, etc.) tend to have densities less than 1 g/mL, so they typically represent the top layer. Mixtures of organic
         liquids may have almost any density. To test the liquids, add a drop of water to the top layer. If the drop dissolves in
         the top layer, the top layer is aqueous. If the drop settles through the top layer and dissolves in the bottom layer, the
         bottom layer is aqueous.

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1124      and vapors. The determination of the tritium content of water vapor in the atmosphere is one of
1125      the example procedures. ASTM D3442 is a standard test method for the measurement of total
1126      tritium activity in the atmosphere. Sample preparation is covered in this test method.

1127      EPA has prepared "Background Information Document: Procedures Approved for Demonstrating
1128      Compliance with 40 CFR Part 61" (EPA, 1989) for use in demonstrating compliance with the
1129      radionuclide National Emission Standards for Hazardous Air Pollutants (NESHAP). This
1130      document includes references to air sampling and sample preparation. Table 3-1 of EPA (1989)
1131      lists numerous references to radionuclide air sampling and preparation; examples include:

1132       • A Study of Airborne Radioactive Effluents from the Pharmaceutical Industry (Cehn, 1979).

1133       • "The Fraction of Material Released as Airborne Activity During Typical Radioiodinations,"
1134         (Eichling, 1983).

1135       • "Application for Renewal of Source Material License: SUB-526, Docket 40-3392," (Allied
1136         Chemical, 1982).

1137       • "Airborne Concentrations of 1-131  in a Nuclear Medicine Laboratory" (Browning et al.,
1138         1978).

1139      12.8  Bioassay

1140      Analyses of bioassay samples are necessary to monitor the health of employees involved in
1141      radiological assessment work. Normally these types of samples include urine and fecal
1142      specimens.

1143      Urine samples are typically wet ashed with nitric acid (DOE, 1997; HASL-300) or with nitric
1144      acid and peroxide (RESL, 1982). Alternatively, there are procedures which co-precipitate the
1145      target analytes in urine by phosphate precipitation (Horwitz et al., 1990; Stradling and
1146      Popplewell, 1974; Elias, 1997). Fecal samples are normally dry ashed in a muffle furnace
1147      (HASL-300), or prepared by lyophilization, "freeze drying" (Dugan and McKibbin,  1993).

1148      It is important to note that although ANSI 13.30 indicates that aliquanting a homogeneous
1149      sample to determine the activity present in the total sample is acceptable, this standard dictates
1150      that the entire sample should be prepared for analysis and the aliquant taken after the sample
1151      preparation has been completed.
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1152      12.9  References

1153      12.9.1 Cited Sources

1154      Allied Chemical UF6 Conversion Plant. 1982. "Application for Renewal of Source Material
1155         License: SUB-526, Docket 40-3392," Metropolis, Illinois.

1156      American National Standards Institute (ANSI). 1996. ANSI 13.30 Performance Criteria for
1157         Radiobioassay. Health Physics Society.

1158      Analytical Chemistry Laboratory (ACL) Procedure Compendium. 1992. Volume 2: Sample
1159         Preparation Methods. Battelle Pacific Northwest Laboratories, PNL-MA-559.

1160      ANL/ACL. 1992. Innovative Methods for Inorganic Sample Preparation. Argonne National
1161         Laboratory, April 1992.

1162      ANL/ACL. 1995. Preparation of Waste Oil for Analysis to Determine Hazardous Metals.
1163         Argonne National Laboratory, July.

1164      Association of Official Analytical Chemists International (AOAC). 1995. Official Method
1165         985.14. "Moisture in Meat and Poultry Products," in Official Methods of Analysis of AOAC
1166         International, Cuniff, P., Ed., Arlington, VA.

1167      Association of Official Analytical Chemists International (AOAC). 1995. Official Method
1168         985.26. "Solids (Total) in Processed Tomato Products", In Official Methods of Analysis of
1169         AOAC International, Cuniff, P., Ed.; Association of Official Analytical Chemists
1170         International: Arlington, VA.

1171      American Society for Testing and Materials (ASTM) C50. Standard Practice for Sampling,
1172         Inspection, Packing, and Marking of Lime and Limestone Products.

1173      American Society for Testing and Materials (ASTM) Cl 14. Standard Test Method for Chemical
1174         Analysis of Hydraulic Cement.

1175      American Society for Testing and Materials (ASTM) C702. Standard Practice for Reducing
1176         Samples of Aggregate to Testing Size, Vol 04.02.
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llll      American Society for Testing and Materials (ASTM) C999. Standard Practice for Soil Sample
1178         Preparation for the Determination of Radionuclides.

1179      American Society for Testing and Materials (ASTM) C1234. Standard Test Method for
1180         Preparation of Oils and Oily Waste Samples by High-Pressure, High-Temperature Digestion
1181         for Trace Element Determinations.

1182      American Society for Testing and Materials (ASTM) Dl 125. Standard Test Method for
1183         Determining the Electrical Conductivity of Water.

1184      American Society for Testing and Materials (ASTM) D1889. Standard Test Method for Turbidity
1185         of Water.

1186      American Society for Testing and Materials (ASTM) D1890. Standard Test Method for Beta
1187         Particle Radioactivity of Water.

1188      American Society for Testing and Materials (ASTM) D1943. Standard Test Method for Alpha
1189         Particle Radioactivity of Water.

1190      American Society for Testing and Materials (ASTM) D2013. Standard Method of Preparing
1191         Coal Samples for Analysis.

1192      American Society for Testing and Materials (ASTM) D3370. Standard Practices for Sampling
1193         Water from Closed Conduits.

1194      American Society for Testing and Materials (ASTM) D3442. Standard Test Method for Gaseous
1195         Tritium Content of the Atmosphere.

1196      American Society for Testing and Materials (ASTM) D3974. Standard Practice for Extraction of
1197         Trace Elements from Sediments.

1198      American Society for Testing and Materials (ASTM) D3975, Standard Practice for Development
1199         and Use  (Preparation) of Samples for Collaborative Testing of Methods for Analysis of
1200         Sediments.

1201      American Society for Testing and Materials (ASTM) D3976. Standard Practice for Preparation
1202         of Sediment Samples for Chemical Analysis.
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                                                                  Laboratory Sample Preparation
1203      American Society for Testing and Materials (ASTM) D4638. Standard Guide for Preparation of
1204         Biological Samples for Inorganic Chemical Analysis.

1205      American Society for Testing and Materials (ASTM) D4643. Standard Test Method for
1206         Determination of Water (Moisture) Content in Soil by the Microwave Oven Method.

1207      American Society for Testing and Materials (ASTM) E926. Standard Practices for Preparing
1208         Refuse-Derived Fuel (RDF) Samples for Analyses of Metals.

1209      American Society for Testing and Materials (ASTM) E1358. Standard Test Method for
1210         Determination of Moisture Content of Particulate Wood Fuels Using a Microwave Oven.

1211      Beary, E.S.  1988. "Comparison of Microwave Drying And Conventional Drying Techniques For
1212         Reference Materials." Anal. ChemVol. 60, pp. 742-746.

1213      Bernabee, R. P., D. R. Percival, and D. B. Martin. 1980. "Fractionation of Radionuclides in
1214         Liquid Samples from Nuclear Power Facilities." Health Physics Vol. 39, pp. 57-67.

1215      Bock, R. 1979. A Handbook of Decomposition Methods in Analytical Chemistry. International
1216         Textbook Company, Limited. T. & A. Constable Ltd., Great Britain.

1217      Browning, E.J., K. Banerjee, and W.E. Reisinger. 1978. "Airborne Concentrations of 1-131 in a
1218         Nuclear Medicine Laboratory," Journal of Nuclear Medicine, Vol. 19, pp. 1078-1081.

1219      Cameron, P., and Murgatroyd, K. 1996. Good Pharmaceutical Freeze-Drying Practice.
1220         Interpharm Press.

1221      Cehn, J.I. 1979. A Study of Airborne Radioactive Effluents from the Pharmaceutical Industry,
1222         Fianl Report, Prepared by Teknekron, Inc., for the U.S. EPA Eastern Environmental Research
1223         Facility, Montgomery, AL.

1224      Dean, J.A. 1995. Analytical Chemistry Handbook, McGraw-Hill, Inc., New York.

1225      DeVoe, J.R. 1961. Radioactive Contamination of Materials Used in Scientific Research.
1226         Publication 895, NAS-NRC.

1227      Dugan, J.P.  and T.T. McKibbin. 1993. "Preparation of Fecal Samples for Radiobioassay by
1228         Lyophilization," Radioactivity & Radiochemistry4:3, pp. 12-15.


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1229      Eberhardt, L.L., and R.O. Gilbert. 1980. "Statistics and Sampling in Transuranic Studies," in
1230         Transuranic Elements in the Environment, edited by W.C. Hanson, U.S. Department of
1231         Energy. DOE/TIC-22800.

1232      Eichling, J. 1983. "The Fraction of Material Released as Airborne Activity During Typical
1233         Radioiodinations," Proceedings of the 9th Biennial Conference of Campus Radiation Safety
1234         Officers, University of Missouri-Columbia, June 6-8, 1983.

1235      Elias, G. 1997. "A Rapid Method for the Analysis of Plutonium and Uranium in Urine Samples,"
1236         Radioactivity & RadiochemistryK:!, pp. 20-24.

1237      Fisenne, I.M. and P. Perry 1978. "The Determination of Plutonium in Tissue by Aliquat-336
1238         Extraction," Radiochem Radioanal. Letters Vol. 33, pp. 259-264.

1239      Fisenne, I.M., P. Perry and G.A. Welford. 1980. "Determination of Uranium Isotopes in Human
1240         Bone Ash," Anal. Chem. Vol. 52, pp. 777-779.

1241      Fisenne, I.M. 1994. "Lead-210 in Animal and Human Bone: A New Analytical Method," Env.
1242         Int. Vol. 20, pp. 627-632.

1243      Greenberg, A.E., L.S. Clesceri, and A.D. Eaton (Eds). 1992. Standard Methods for the
1244         Examination of Water and Wastewater. American Public Health Association.

1245      Greenwood, N. N. and A. Earnshaw. 1984. Chemistry of the Elements. Pergamon Press, Inc.
1246         Elmsford, New York.

1247      Gy, Pierre M. 1992. Sampling of Heterogeneous and Dynamic Material Systems: Theories of
1248         Heterogeneity, Sampling, and Homogenizing. Elsevier, Amsterdam, The Netherlands.

1249      Horwitz, E.P., M.L. Dietz, D.M. Nelson, JJ. LaRosa, and W.D. Fairman 1990. "Concentration
1250         and Separation of Actinides from Urine using a Supported Bifunctional Organophosphorous
1251         Extractant," Analytica Chimica Acta. Vol. 238, pp. 263-271.

1252      IAEA. 1989.  Measurement of Radionuclides in Food and the Environment—A  Guidebook.
1253         Technical Reports Series No. 295, International Atomic Energy Agency, Vienna.

1254      Kingston, H.M., and lassie, L.B. 1988. Introduction to Microwave Sample Preparation: Theory
1255         and Practice, American Chemical Society, Washington, DC.


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                                                                  Laboratory Sample Preparation
1256      Koh, T.S. 1980. AnalChewVol. 52, pp. 1978-1979.

1257      Kralian, M.A., MJ. Atkins, and S.A. Farber. 1990. "Guidelines for Effective Low-Level
1258         Contamination Control in a Combination Environmental/Radioactive Waste Analysis
1259         Facility," Radioactivity & Radiochemistry. 1:3, pp. 8-18.

1260      Lodge, J.  1988. Methods of Air Sampling and Analysis. Third Edition, CRC Press, Florida.

1261      LANL. 1996. Application of Cryogenic Grinding to Achieve Homogenization of Transuranic
1262         Waste, Atkins, W. H., LANL-13175.

1263      Los Alamos National Laboratory (LANL).  1997. Radionuclide Concentration in pinto beans,
1264         sweet corn, and zucchini squash grown in Los Alamos Canyon at Los Alamos National
1265         Laboratory, Fresquez, P. R., M. A. Mullen, L. Naranjo, and D. R. Armstrong, May 1997.

1266      Lucas, H.F., Jr. 1963. "A Fast and Accurate Survey Technique for Both Radon-222 and Radium-
1267         226,"  The Natural Radiation Environment, Proceedings of the International Symposium,
1268         William Rice University, Houston, TX, 315-319.

1269      Lucas, H.F.  1967. "A Radon Removal System for the NASA Lunar Sample Laboratory: Design
1270         and Discussion," Argonne National Laboratory Radiological Physics Division Annual
1271         Report, ANL-7360.

1272      McFarland, R.C. 1998a. "Determination of Alpha-Particle Counting Efficiency for Wipe-Test
1273         Samples," Radioactivity & Radiochemistry. 9:1, pp. 4-8.

1274      McFarland, R.C. 1998b. "Determination of Counting Efficiency for Wipe-Test Samples
1275         Containing Radionuclides that Emit High-Energy Beta Particles," Radioactivity &
1276         Radiochemistry'9:1, pp. 4-9.

1277      Mclnroy,  J.F., H.A. Boyd, B.C. Eutsler, and D. Romero. 1985. "Part IV: Preparation and
1278         Analysis of the Tissue and Bones," Health Physics, 49:4, pp. 585-621.

1279      NCRP Report No. 50. 1976. Environmental Radiation Measurements.

1280      Obenhauf, R.H., R. Bostwick, W. Fithian, M. McCann, J.D. McCormack, and  D. Selem SPEX
1281         CertiPrep Handbook of Sample Preparation and Handling, SPEX CertiPrep, Inc., Metuchen,
1282         NJ. Also, at http://www.spexcsp.com/spmain.


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         Laboratory Sample Preparation
1283      Pitard, F. F. 1993. Pierre Gy's Sampling Theory and Practice. CRC Press, Inc., Boca Raton, FL.
1284         Second Edition.

1285      RESL Analytical Chemistry Branch Procedures Manual, DOE-ID, collected and published in
1286         1982 and containing procedures beginning in 1969.

1287      Schilt, A. 1979. Perchloric Acid and Perchlorates. The G. Frederick Smith Chemical Company,
1288         Columbus, Ohio.

1289      Scwedt, G. 1997. The Essential Guide to Analytical Chemistry (Translation of the revised and
1290         updated German Second Edition. Translated by Brooks Haderlie), John Wiley & Sons,
1291         England.

1292      Sedlet, J. 1966. "Radon and Radium," in Treatise on Analytical Chemistry, Part II, Vol. IV,
1293         p219-366, edited by I.M. Kolthoff and PJ. Elving, John Wiley & Sons, Inc, New York.

1294      Shugar, G.J. and J.T. Ballinger. 1996. Chemical Technicians' Ready Reference Handbook.
1295         McGraw-Hill, New York.

1296      Sill, C.W., K.W. Puphal, and F.D. Hindman.  1974. "Simultaneous Determination of Alpha-
1297         Emitting Nuclides of Radium through Californium in Soil," Anal. Chem 46:12, pp. 1725-
1298         1737.

1299      Sill, C.W. 1975. "Some Problems in Measuring Plutonium in the Environment,"  Health Physics.
1300         Vol. 29, pp. 619-626.

1301      Stradling, G.N. and D.S. Popplewell. 1974. "Rapid Determination of Plutonium in Urine by
1302         Ultrafiltration," Int. J. Appl. Radiation Isotopes. Vol. 25, p 217.

1303      U.S. Department of Energy (DOE). 1990. EML Procedures Afam/a/(HASL-300-Ed.27),  G. de
1304         Planque, Environmental Measurements Laboratory.

1305      U.S. Department of Energy (DOE). 1995. Brookhaven National Laboratory Site Environmental
1306         Report for Calendar Year 1995, Naidu, J. R., D. E. Paquette, G. L. Schroeder, BNL
1307         December, 1996.

1308      U.S. Department of Energy. 1997 (DOE).EML Procedures Manual (HASL-300-Ed.28), Edited
1309         by N. A. Chieco, Environmental Measurements Laboratory.


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                                                                  Laboratory Sample Preparation
1310      U.S. Environmental Protection Agency (EPA). 1979. Radiochemical Analytical Procedures for
1311         Analysis of Environmental Samples; F. B. Johns, P. B. Hahn, D. J. Thome, and E. W.
1312         Bretthauer, EMSL, March 1979.

1313      U.S. Environmental Protection Agency (EPA). 1980. Prescribed Procedures for Measurement of
1314         Radioactivity in Drinking Water. H. L. Krieger and E. L. Whittaker, EPA 600-4-80-032,
1315         August 1980.

1316      U.S. Environmental Protection Agency (EPA). 1989. Background Information Document:
1317         Procedures Approved for Demonstrating Compliance with 40 CFR Part 61, Subpart I. EPA
1318         520-1-89-001, Office of Radiation Programs, October, 1989.

1319      U.S. Environmental Protection Agency (EPA). 1990. Test Methods for Evaluating Solid Waste—
1320         Physical/Chemical Methods. SW-846, Third Edition, Method 3051.

1321      U.S. Environmental Protection Agency (EPA). 1992. Manual for the Certification of
1322         Laboratories Analyzing Drinking Water: Criteria and Procedures. Fourth Edition, EPA 814-
1323         B-92-002, Office of Ground Water and Drinking Water, Cincinnati, Ohio.

1324      Walter, P., S. Chalk, and H. Kingston. 1997. "Overview of Microwave-Assisted Sample
1325         Preparation." Chapter 2, Microwave-Enhanced Chemistry., H. Kingston and S. Haswell,
1326         editors, American Chemical Society, Washington, DC.

1327      Wang, C.H., Willis, D.L., and Loveland W.D. 1975. Radiotracer Methodology in the Biological,
1328         Environmental, and Physical Sciences. Prentice-Hall,  Inc., New Jersey.

1329      Yamamoto, M., Komura, K.,  and Ueno, K. 1989. "Determination of Low-Level 226Ra in
1330         Environmental Water Samples by Alpha-Ray Spectrometry," Radiochimica Acta Vol. 46, pp.
1331         137-142.

1332      Zhang, H. and P. Dotson. 1998. The Use of Microwave Muffle Furnace for Dry Ashing Plant
1333         Tissue Samples.  CEM Corporation. Also, Commun. Soil Sci. Plant Anal. 25:9&10, pp. 1321-
1334         1327 (1994).

1335      12.9.2 Other Sources

1336      American Society for Testing and Materials (ASTM) D5245. Standard Practice for Cleaning
1337         Laboratory Glassware, Plasticware,  and Equipment Used in Microbiological Analyses.


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         Laboratory Sample Preparation
1338      American Society for Testing and Materials (ASTM) El 157. Standard Specification for
1339         Sampling and Testing of Reusable Laboratory Glassware.

1340      Delherz, R. 1957. Chemist-Analyst. Vol. 46, p. 11.

1341      Kahn, B., Shleien, B., and Weaver, C. 1972. "Environmental Experience with Radioactive
1342         Effluents From Operating Nuclear Power Plants," page 559 in Peaceful Uses of Atomic
1343         Energy, Vol. 11 (United Nations, New York). Also, Kahn, B. 1973. "Determination of
1344         Radioactive Nuclides in Water," page 1357 in Water and Water Pollution Handbook, Vol. 4
1345         Ciaccio, L. L., Ed. (M. Decker, New York).

1346      Krieger, H.L. and E.L. Whittaker. 1980. "Prescribed Procedures for Measurement of
1347         Radioactivity in Drinking Water," Environmental Monitoring and Support Laboratory,
1348         Cincinnati, OH, EPA-600/4-80-032.

1349      Laug, E.P. 1934. Ind. Eng. Chem., Anal Ed. Vol. 13, pp. 419.

1350      Shugar and Dean. 1990. The Chemist's Ready Reference Handbook, McGraw-Hill.

1351      U.S. Environmental Protection Agency. 1987. Eastern Environmental Radiation Facility
1352         Radiochemistry Procedures Manual. Compiled and edited by R. Lieberman, EPA 520-5-84-
1353         006, Office of Radiation Programs, August, 1987.
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                              13  SAMPLE DISSOLUTION
 2     13.1  Introduction

 3     The overall success of any analytical procedure depends upon many factors, including proper
 4     sample preparation, appropriate sample dissolution, and adequate separation and isolation of the
 5     target analytes. This chapter describes sample dissolution techniques and strategies. Some of the
 6     principles of dissolution are common to those of radiochemical separation that are described in
 7     the next chapter, but their importance to dissolution is reviewed in this chapter.

 8     Sample dissolution can be one of the biggest challenges facing the analytical chemist, because
 9     most samples consist mainly of unknown compounds with unknown chemistries. There are many
10     factors for the analyst to consider: What are the data quality objective requirements for bias and
11     precision to meet the data quality objectives of the program? What is the nature of the sample; is
12     it refractory or is there only surface contamination? How effective is the dissolution technique?
13     Will any analyte be lost? Will the vessel be attacked? Will any of the reagents interfere in the
14     subsequent analysis or can any excess reagent be removed? What are the safety issues involved?
15     What are the labor and material costs? How much and what type of wastes are generated? The
16     challenge  for the analyst is to balance these factors and to choose the method that is most
17     applicable to the material  to be analyzed.

18     The objective of sample dissolution is to mix a solid or nonaqueous liquid sample quantitatively
19     with water to produce an aqueous solution (homogeneous mixture),so that subsequent separation
20     and analyses may be performed. Because very few natural or organic materials are water-soluble,
21     these materials routinely require the use of acids or fusion salts to bring them into solution. These
22     reagents typically achieve dissolution through an oxidation-reduction process that leaves the
23     constituent elements in a more soluble form. Moreover, because radiochemists routinely add
24     carriers or use the technique of isotope dilution to determine certain radioisotopes, dissolution
25     helps to ensure exchange between the carrier or isotopic tracer and the element or radioisotope to
26     be determined, although additional chemical treatment might be required to ensure exchange.

27     There are  three main techniques for sample decomposition discussed in this chapter:

28      •  Fusion;
29      •  Wet ashing, acid leaching, or acid dissolution;  and
30      •  Microwave digestion.

31     Fusion and wet ashing techniques are used singly or in combination to decompose most samples
32     analyzed in radioanalytical laboratories. Generally, fusion techniques are used when a total

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33     dissolution of a difficult sample matrix is required. Leaching techniques are used to determine
34     the soluble fraction of the radionuclide of interest under specific conditions. Because recent
35     advances in microwave vessel design have allowed for the use of larger samples, microwave
36     dissolution is becoming an important tool in the radiochemistry laboratory.

37     Because of the potential for injury and explosions, it is essential that proper laboratory safety
38     procedures be in place, the appropriate safety equipment be available, a safe work space be
39     provided, and that the laboratory personnel undergo the necessary training to ensure a safe
40     working environment before any of these methods are used.

41     Aspects of proper sample preparation, such as moisture removal, oxidation of organic matter, and
42     homogenization, were discussed in Chapter 12, Laboratory Sample Preparation. Fundamental
43     separation principles and techniques, such as complexation, solvent extraction, ion exchange, and
44     co-precipitation, are reviewed in Chapter 14, Separation Techniques.

45     There are many excellent references on the topic of sample dissolution, including A Handbook of
46     Decomposition Methods in Analytical Chemistry (Bock, 1979), Analytical Chemistry Handbook
47     (Dean, 1995), Methods for Decomposition in Inorganic Analysis (Sulcek and Povondra, 1989),
48     and "A Decomposition and Dissolution of Samples: Inorganic" (Bogen, 1978).

49     13.2  The Chemistry of Dissolution

50     In order to dissolve a sample completely, each insoluble component must be converted into a
51     soluble form. Several basic chemical methods are employed to accomplish complete dissolution
52     of the sample, but usually the tracer is added to the sample. An outline of the principles of these
53     chemical methods is provided in this section, but a complete description is available in Chapter
54     14 (Chemical Separations), where the principles are applied to a broader range of topics.

55     13.2.1  Solubility and the Solubility Product Constant, Ksp

56     The solubility data of many compounds, minerals, ores, and elements are available in reference
57     manuals. Solubilities typically are expressed in grams of substance per 100 mL of solvent,
58     although other units are used sometimes. The information is more complete for some substances
59     than others, and for many substances solubility is expressed only in general terms, such as
60     "soluble," "slightly soluble," or "insoluble." Many environmental samples consist of complex
61     mixtures of elements, compounds, minerals, or ores, most of which are insoluble and must be
62     treated chemically to dissolve  completely. In some cases, the sample constituents are known to
63     the analyst, but often they are not. Solubility data might not be available even for known

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64     constituents, or the available data might be inadequate. Under these circumstances, sample
65     dissolution is not a simple case of following the solubilities of known substances. For known
66     constituents with solubility data, the solubilities indicate those that must be treated to complete
67     dissolution. This, in turn, provides a guide to the method of treatment of the sample. Given the
68     potential complexity of environmental samples, it is difficult to describe conditions for
69     dissolving all samples. Sometimes one method is used to dissolve one part of the sample while
70     another is used to dissolve the residue.

71     The solubility of many compounds in water is very low, on the order of small fractions of a
72     grams per 100 mL. Instead, the solubility is often expressed by a solubility product constant
73     (Ksp), an equilibrium constant for dissolution of the compound in water (see Section 14.8.3.1,
74     "Solubility and Solubility Product Constant"). The solubility product constant for strontium
75     carbonate, a highly insoluble salt (0.0006 g/100 mL), is the equilibrium constant for the process:

76                                   SrCO3(s) - Sr+2(aq) + CCV2(aq)
77     and is represented by:
78                                   Ksp = [Sr+2][CO3-2] = 1.6 x ID'9

79     The brackets indicate the molar concentration (moles/liter) of the respective ions dissolved in
80     water. The  very small value of the constant results from the low concentration of dissolved ions,
81     and the compound is referred to as "insoluble." Chemical treatment is necessary sometimes to
82     dissolve the components of a compound in water. In this example, strontium carbonate requires
83     the addition of an acid to solubilize Sr+2. The next section describes chemical treatment to
84     dissolve compounds.

85     13.2.2 Chemical Exchange, Decomposition, and Simple Rearrangement Reactions

86     Chemical exchange, decomposition, and simple rearrangement reactions refer to one method for
87     solubilizing components of a sample. In this chemical process, the sample is treated to convert
88     insoluble components to a soluble chemical species using chemical exchange (double displace-
89     ment), decomposition, or simple rearrangement reactions rather than oxidation-reduction
90     processes or complex formations. Some fluxes solubilize sample components using  chemical
91     exchange. Radium or strontium cations in radium or strontium carbonate (RaCO3  or SrCOj)
92     exchange the carbonate anion for the chloride ion on acid treatment with HC1 to produce the
93     soluble chlorides; the carbonic acid product decomposes to carbon dioxide and water:

94                                  RaCO3 + 2 HC1 - RaCl2 + H2CO3
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 95                                         H2CO3 - CO2 + H2O
 96      and the net reaction is as follows:
 97                                 RaCO3 + 2 HC1 - RaCl2 + CO2 + H2O

 98      Sodium pyrosulfate fusion, for example, converts zirconia (ZrO2) into zirconium sulfate
 99      [Zr(SO4)2], which is soluble in acid solution by a simple (nonoxidative) rearrangement of oxygen
100      atoms (Hahn, 1961, p. 81; Steinberg, 1960, p. 4):

101                               ZrO2 + 2 Na2S2O7 - 2 Na2SO4 + Zr(SO4)2

102      Many environmental samples contain insoluble silicates, such as aluminum silicate [Al2(SiO3)3 or
103      A12O3 • 3SiO2], which can be converted into soluble silicates by fusion with sodium carbonate:

104                        Al2(SiO3)3 + 4 Na2CO3 - 3 Na2SiO3 + 2 NaAlO2 + 4 CO2

105      Dissolution of radium from some ores depends on the exchange of anions associated with the
106      radium cation (sulfate for example) to generate a soluble compound.  Extraction with nitric acid is
107      partly based on this process, generating soluble radium nitrate.

108      13.2.3  Oxidation-Reduction Processes

109      Oxidation-reduction (redox) processes play an important role in sample dissolution because
110      solubility is highly dependent not only on the chemical form of the element, but also on oxidation
ill      state. Moreover, many radiochemical procedures require the addition of a carrier and isotope
112      tracer, and to achieve quantitative yields, there must be complete equilibration (isotopic
113      exchange) between the added isotopes and all chemical species present. Dissolution of the
114      sample in the presence of the appropriate carrier and/or tracer is one way to promote
115      equilibration by exposing all components of the analytical mixture to the same redox conditions.

116      An oxidation-reduction reaction is a reaction that redistributes electrons among the atoms,
117      molecules, or ions in the reaction. In some redox reactions, electrons actually are transferred from
118      one reacting species to another. In other redox reactions, electrons are not transferred completely
119      from one reacting species to another; the electron density  about one atom decreases, while it
120      increases about another atom. A complete discussion of oxidation and reduction is found in
121      Section 14.2, "Oxidation/Reduction Processes."

122      Many oxidizing agents used in sample dissolution convert metals to a stable oxidation state
123      displacing hydrogen from hydrochloric, nitric, sulfuric, and perchloric acids. (This redox process


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124     often is referred to in the literature as nonoxidative hydrogen replacement by an active metal, but
125     it is a redox process where the metal is oxidized to a cation, usually in its highest oxidation state,
126     and the hydrogen ion is reduced to it elemental form.) Dissolution of uranium for analysis is an
127     example of hydrogen-ion displacement to produce a soluble substance (Grindler,  1962, p. 252):

128                             U + 8 HNO3 - UO2(NO3)2 + 6 NO2 + 4 H2O

129     Prediction of the reactivity of a metal with acids is dependent on its position in the electromotive
130     force series (activity series). A discussion of the series appears in Section 13.4.1, "Acids and
131     Oxidants." In general, metals below hydrogen in the reduction series will displace hydrogen from
132     acid solution and be dissolved, while acids above the series will not. Perchloric acid offers a
133     particular advantage because very soluble perchlorate salts are formed.

134     Other important oxidizing processes depend on either oxidizing a lower, less soluble oxidation
135     state of a metal to a higher, more soluble state or oxidizing the counter anion to generate a more
136     soluble compound. Oxidation to a higher oxidation  state is common when dissolving uranium
137     samples in acids or during treatment with fluxes. The uranyl ion (UO2+2) forms soluble salts—
138     such as chloride, nitrate, and perchlorate—with anions of the common acids (Grindler, 1962, p.
139     255 and pp. 9-14).  (Complex-ion formation also plays a role in these dissolutions; see the next
140     section). Dissolution of oxides, sulfides,  or halides of technetium by alkaline hydrogen peroxide
141     converts all oxidation states to the soluble technate  salts (Cobble, 1964,p. 418):

142                           2 TcO2 + 2 NaOH + 3 H2O2 - 2 NaTcO4 + 4 H2O

143     13.2.4  Complexation

144     The formation of complex ions (see also Section 14.3, "Complexation") is important in some
145     dissolution processes and usually occurs in conjunction with treatment by an acid, but can also
146     occur during fusion. Complexation increases solubility in the dissolution mixture and helps to
147     minimize hydrolysis of the cation. The solubility of radium sulfate in concentrated sulfuric acid
148     is the result of forming a complex-ion, Ra(SO4)2"2. The ability of both hydrochloric and
149     hydrofluoric acids to act as a solubilizing agent is dependent on their abilities to form stable
150     complex ions with cations. Refractory plutonium samples are solubilized in a nitric acid-
151     hydrofluoric acid solution forming cationic fluorocomplexes such as PuF+3 (Booman and Rein,
152     1962, p. 244). Numerous stable complexes of anions from solubilizing acids  (HC1, HF, HNO3,
153     H2SO4, HC1O4) contribute to the dissolution of other radionuclides, such as americium, cobalt,
154     technetium, thorium, uranium, and zirconium (see Section 14.10, "Radiochemical Equilibrium").
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155      The process effusion with sodium carbonate to solubilize uranium samples is also based on the
156      formation of UO2(CO3)2"4 after the metal is oxidized to U+6 (Grindler, 1962, p. 256).

157      13.2.5 Equilibrium: Carriers and Tracers

158      Carriers and tracers that are required for radiochemical separation and detection procedures
159      usually are added to samples before dissolution in order to subject them to the same chemical
160      treatment as the analyte. Addition as soon as practical promotes equilibration with the analyte.
161      The dissolution process tends to bring the carrier and tracers to the same oxidation state as the
162      analyte and ensures intimate mixing of all the components in solution. Acid mixtures also create
163      a large hydrogen-ion concentration that minimizes the tendency of cations to hydrolyze and
164      subsequently form  insoluble complexes. Detailed discussions of carriers and tracers as well as
165      radiochemical equilibration are found in Section 14.9, "Carriers and Tracers" and Section 14.10,
166      "Radiochemical Equilibration." Knowledge of the behavior of carriers and tracers and of the
167      principles behind radiochemical equilibrium is very important, because the final form of the
168      analyte in solution  is crucial to understanding their behavior, not only during solubilization of the
169      sample but also in the separation and detection steps of the analysis. During each of the steps in
170      the method, the analyst should be aware of the expected oxidation states of the analyte and its
171      tendency to hydrolyze, polymerize, and form complexes and radiocolloids, and other issues
172      during each step  of the procedure. Knowledge of these processes will ensure that the analyst will
173      be able to recognize and address problems if they arise.

174      13.3  Fusion  Techniques

175      Sample decomposition through fusion is most employed often for samples that are difficult to
176      dissolve in acids such as soils, sludges, silicates, and some mineral oxides. Fusion is accomp-
177      lished by heating a salt (the flux) mixed with a small amount of sample. The mixture is heated to
178      a temperature above the melting point of the salt, and the sample is allowed to react in the molten
179      mixture. When the reaction is completed, the mixture is allowed to cool to room temperature.
180      The fused sample is then dissolved, and the analysis is continued. Any residue remaining may be
181      treated by repeating the fusion with the same salt, performing a fusion with a different salt, wet
182      ashing, or any combination of the three.

183      Decomposition of the sample matrix depends on the high temperatures required to melt a flux
184      salt and the ratio of the flux salt to the sample. For a fusion to be successful, the sample must
185      contain chemically bound oxygen as in oxides, carbonates, and silicates. Samples that contain no
186      chemically bound oxygen, such as sulfides, metals, and organics, must be oxidized before the
187      fusion process.

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188     Samples to be fused should be oven-dried to remove moisture. Charring to remove organic
189     material is not usually necessary because samples with significant amounts of organic material
190     are typically dry ashed or wet ashed before fusion. Solid samples are ground mesh size to
191     increase the surface area, allowing the fusion process to proceed more readily. The sample must
192     be thoroughly mixed with the flux in an appropriate ratio. Generally, the crucible should never be
193     more than half-filled at the outset of the fusion process. Fusions may be performed using sand or
194     oil baths on a hot plate, in a muffle furnace, or over a burner. Crucibles are made of platinum,
195     zirconium, nickel, or porcelain (Table 13.1). The choice of heat source and crucible material
196     generally depends on the salt used for the fusion.
197

198
199

200
201

202
203
204

205
206
207


208


209
210
211
212


213
214
215
216
217
218


219
220
                          TABLE 13.1 — Common fusion fluxes
Flux
(mp, °C)
Na2S2O7 (403) or
K2S207(419)
NaOH (321)
or
KOH (404)
Na2CO3 (853) or
K2CO3 (903)
Na2O2
H3BO3 (169)
Na2B4O7 (878)
Li2B4O7 (920)
or
LiBO2 (845)
NH4HF2 (125) NaF
(992)
KF (857)
or
KHF, (239)
Fusion
Temperature, °C
Up to red heat
450-600
900-1,000
600

1,000-1,200
1,000-1,100
900
Type of
Crucible
Pt, quartz,
porcelain
Ni, Ag, glassy
carbon
Ni
Pt for short
periods (use lid)
Ni; Ag, Au, Zr;
Pt (<500 °C)
Pt
Pt
Pt, graphite
Pt
Types of Sample Decomposed
For insoluble oxides and oxide-containing samples,
particularly those of Al, Be, Ta, Ti, Zr, Pu, and the
rare earths.
For silicates, oxides, phosphates, and fluorides.
For silicates and silica-containing samples (clays,
minerals, rocks, glasses), refractory oxides, quartz,
and insoluble phosphates and sulfates.
For sulfides; acid-insoluble alloys of Fe, Ni, Cr, Mo,
W, and Li; Pt alloys; Cr, Sn, and Zn minerals.
For analysis of sand, aluminum silicates, titanite,
natural aluminum oxide (corundum), and enamels.
For A12O3; ZrO2 and zirconium ores, minerals of the
rare earths, Ti, Nb, and Ta, aluminum-containing
materials; iron ores and slags.
For almost anything except metals and sulfides. The
tetraborate salt is especially good for basic oxides and
some resistant silicates. The metaborate is better
suited for dissolving acidic oxides such as silica and
TiO2 and nearly all minerals.
For the removal of silicon, the destruction of silicates
and rare earth minerals, and the analysis of oxides of
Nb, Ta, Ti, and Zr.
Source: Dean (1995) and Bock (1979).

Fusions are heated slowly and evenly to prevent ignition of the sample before the reaction with
the molten salt can begin. It is especially important to raise the temperature slowly when using a
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221     gas flame because the evolution of water and gases is a common occurrence at the beginning of
222     the fusion, and hence a source of spattering. The crucible can be covered with a lid as an added
223     precaution. Sand and oil baths provide the most even source of heat, but they are difficult to
224     maintain at very high temperatures. Muffle furnaces provide an even source of heat, but when
225     using them it is difficult to monitor the progress of the reaction and impossible to work with the
226     sample during the fusion. Burners are used often as a convenient heat source although they make
227     it difficult to heat the sample evenly.

228     The maximum temperature employed varies considerably and depends on the sample and the
229     flux. In order to minimize attack of the crucible and decomposition of the flux, excessive
230     temperatures should be avoided. Once the salt has melted, the melt is swirled gently to monitor
231     the reaction. The fusion continues until visible signs of reaction are completed (e.g., formation of
232     gases, foaming, fumes). It is frequently difficult to decide when heating should be discontinued.
233     In ideal cases, a clear melt serves to indicate the completeness of sample decomposition. In other
234     cases, it is not as obvious, and the analyst must base the heating time on past experience with the
235     sample type.

236     The melt is swirled during cooling to spread it over the inside of the crucible. Thin layers of salt
237     on the sides of the crucible often will crack and flake into small  pieces during cooling. These
238     small fragments are easier to dissolve.

239     After the sample has returned to room  temperature, the fused material is dissolved. The solvent is
240     usually warm water or a dilute acid solution, depending on the salt. For example,  dilute acid
241     typically would not be used to dissolve a carbonate fusion because of losses to spray caused by
242     release of CO2. The aqueous solution from the dissolution of the fusion melt should be examined
243     carefully for particles  of undissolved sample. If undissolved particles are present,  they should be
244     separated from solution by centrifugation or filtration, and a second fusion should be performed.

245     Several types of materials are used for crucibles, but platinum, other metals (Ni, Zr, Ag), and
246     graphite are most common. Graphite crucibles are a cost-effective alternative to metal crucibles;
247     they are disposable, which eliminates the need for cleaning  and the possibility of cross-sample
248     contamination. Graphite crucibles are chemically inert and heat-resistant, although they do
249     oxidize slowly at temperatures above 430 °C. Graphite is not recommended for extremely
250     lengthy fusions or for  reactions where the sample may be reduced. Platinum is probably the most
251     commonly used crucible material. It is virtually unaffected by any of the usual acids, including
252     hydrofluoric, and it is  attacked only by concentrated phosphoric acid at very high  temperatures,
253     and by sodium carbonate. However, it dissolves readily in mixtures of hydrochloric and nitric
254     acids (aqua regia), nitric acid containing added chlorides, or chlorine water or bromine water.


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255     Platinum offers adequate resistance toward molten alkali metal, borates, fluorides, nitrates, and
256     bisulfates. When using a platinum crucible, one should avoid using aqua regia, sodium peroxide,
257     free elements (C, P, S, Ag, Bi, Cu, Pb, Zn, Se, and Te), ammonium, chlorine and volatile
258     chlorides, sulfur dioxide, and gases with carbon content. Platinum crucibles can be cleaned in
259     boiling HC1, by hand cleaning with sea sand, or by performing a blank fusion with sodium
260     hydrogen sulfate.

261     Many kinds of salts are used for fusions. The lowest melting flux capable of reacting completely
262     with the sample is usually the optimum choice. Basic fluxes, such as the carbonates, the
263     hydroxides, and the borates, are used to attack acidic materials. Sodium or potassium nitrate may
264     be added to furnish an oxidizing agent when one is needed, as with the sulfides, certain oxides,
265     ferroalloys, and some silicate materials. The most effective alkaline oxidizing flux is sodium
266     peroxide; it is both a strong base and a powerful oxidizing agent. Because it is such a strong
267     alkali, sodium peroxide is often used even when no oxidant is required. Alternatively, acid fluxes
268     are the pyrosulfates, the acid fluorides,  and boric acids. Table  13.1 lists several types effusions,
269     examples of salts used for each type of fusion, and the melting points of the salts.

270     SULFATE FUSION is useful for the conversion of ignited oxides to sulfates, but is generally an
271     ineffective approach for silicates. Sulfate fusion is particularly useful for BeO, Fe2O3, Cr2O3,
272     MoO3, TeO2, TiO2, ZrO2, Nb2O5,  Ta2O5, PuO2, and rare earth oxides (Bock,  1979, pp. 77-82).
273     Pyrosulfate fusions are prepared routinely in the laboratory by heating a mixture of sodium or
274     potassium sulfate with a stoichiometric excess of sulfuric acid:

275                           Na2SO4 + H2SO4 - [2NaHSO4] - Na2S2O7 + H2O

276                                      Na2S2O7 - Na2SO4 + SO31

277                                             Na2SO4 etc.

278     The rate of heating is increased with time until the sulfuric acid has volatilized and a clear
279     pyrosulfate fusion is obtained. It is important to note that pyrosulfate fusions are reversible and,
280     if needed, the fusion can be cooled, additional sulfuric acid added, and the fusion repeated as
281     many times as needed to dissolve the sample.  The analyst must distinguish between insoluble
282     material that has not yet or will not dissolve, and material that has precipitated during the final
283     stages of a prolonged pyrosulfate fusion. In the latter situation the fusion must be cooled,
284     additional sulfuric acid added, and the sample refused until the precipitated material redissolves
285     and a clear melt is obtained. Otherwise, the precipitated material will be extremely difficult, if
286     not impossible, to  dissolve in subsequent steps. Platinum or quartz crucibles are recommended


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287     for this type effusion, with quartz being preferred for analysis of the platinum group metals.
288     After the melt is cooled and solidified, it should be dissolved in dilute sulfuric or hydrochloric
289     acid rather than in water to avoid hydrolysis and precipitation of Ti, Zr, etc. Niobium and
290     tantalum may precipitate even in the presence of more concentrated acid. In order to avoid
291     precipitation of Nb or Ta, concentrated sulfuric acid, tartaric acid, ammonium oxalate, hydrogen
292     peroxide, or hydrofluoric acid must be used. Mercury and the anions of volatile acids are largely
293     volatilized during these fusion procedures.

294     13.3.1 Alkali-Metal Hydroxide Fusions

295     Alkali metal  hydroxide fusions are used for silicate analysis of ash and slag; for decomposition of
296     oxides, phosphates, and fluorides (Bock, 1979, pp. 102-108); and for dissolution  of soils for
297     actinide analyses (Smith et al., 1995). Sodium hydroxide (NaOH) generally is used because of its
298     lower melting point, but potassium hydroxide (KOH) is just as effective. These fusions generally
299     are rapid, the melts are easy to dissolve in water, and the losses because of volatility are reduced
300     because of the low temperature of the melt. Nickel, silver, or glassy carbon crucibles are
301     recommended for this type of fusion. The maximum suggested temperature for nickel crucibles is
302     600 °C, but silver crucibles can be used up to 700 °C. Generally, crucibles made of platinum,
303     palladium, and their alloys should not be used with hydroxide fusions because the crucibles are
304     easily attacked in the presence of atmospheric oxygen. The weight ratio of fusion salt to sample
305     is normally 5-10:1. Typically, these fusions are carried out below red heat at 450  to 500 °C for
306     15 to 20 minutes, or sometimes at higher temperatures between 600 to 700 °C for 5 to 10
307     minutes. The solidified melt dissolves readily in water; and therefore, this step may be carried out
308     directly in the crucible, or alternatively in a nickel dish. Under no circumstances should the
309     dissolution be carried out in a glass vessel because the resulting concentrated hydroxide solution
310     attacks glass quite readily.

311     FUSION WITH SODIUM CARBONATE  (Na2CO3) is a common procedure for decomposing  silicates
312     (clays, rocks, mineral, slags, glasses, etc.), refractory oxides (magnesia, alumina,  beryllia,
313     zirconia, quartz, etc.), and insoluble phosphates and sulfates (Bogen, 1978). The fusion may
314     result in the formation of a specific compound such as sodium aluminate, or it may simply
315     convert a refractory oxide into a condition where it is soluble in hydrochloric acid—this is the
316     method of choice when silica in a silicate is to be determined, because the fusion  converts an
317     insoluble silicate into a mixture that is easily decomposed by hydrochloric acid ("M" represents a
318     metal in the equations below):

319                         MSiO3  + Na2CO3 - Na2SiO3 + MCO3 (or MO + CO2),
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320     followed by acidification to form a more soluble chloride salt,

321            Na2SiO3 + MCO3 + 4 HC1 + *H2O - H2SiO3 • *H2O + MC12 + CO2 + H2O + NaCl.
322     Carbonate fusions provide an oxidizing melt for the analysis of chromium, manganese, sulfur,
323     boron, and the platinum group metals. Organic material is destroyed, sometimes violently.
324     Na2CO3 generally is used because of its lower melting point. However, despite its higher melting
325     point and hygroscopic nature, K2CO3 is preferred for niobium and tantalum analyses because the
326     resulting potassium salts are soluble, whereas the analogous sodium salts are insoluble.

327     The required temperature and duration of the fusion depend on the nature of the sample as well
328     as particle size. In the typical carbonate fusion, 1 g of the powdered sample is mixed with 4 to 6 g
329     of sodium carbonate and heated at 900 to 1,000 °C for 10 to 30 minutes. Very refractory
330     materials may require heating at 1,200 °C for as long as 1 to 2 hours. Silica will begin to react at
331     500 °C, while barium sulfate and alumina react at temperatures above 700 °C. Notably, volatility
332     is a problem at these temperatures. Mercury and thallium are lost completely, while selenium,
333     arsenic, and iodine suffer considerable losses. Non-silicate samples should be dissolved in water,
334     while silicate samples should be treated with acid (Bock, 1979, p. 1 1 1).

335     Platinum crucibles are recommended, even though there is a 1 to 2 mg loss of platinum per
336     fusion. Attack on the crucible can be reduced significantly by covering the melt with a lid during
337     the fusion process, or virtually eliminated by working in an inert atmosphere. Moreover, nitrate is
338     often added to prevent the reduction of metals and the subsequent alloying with the platinum
339     crucibles. The platinum crucibles may be seriously attacked by samples containing high
340     concentrations of Fe2+, Fe3+, Sn4+, Pb2+, and compounds of Sb and As, because these ions are
341     reduced easily to the metallic state and then form intermetallic alloys with platinum that are not
342     easily dissolved in mineral acids. This problem is especially prevalent when fusion is carried out
343     in a gas flame. Porcelain crucibles are corroded rapidly and should be discarded after a single
344     use.

345     13.3.2 Boron Fusions

346     Fusions with boron compounds are recommended for analysis of sand, slag, aluminum silicates,
347     alumina (A12O3), iron and rare earth ores, zirconium dioxide, titanium, niobium, and tantalum.
348     Relatively large amounts of flux are required for these types effusions. The melts are quite
349     viscous and require swirling or stirring, so they should not be performed in a furnace. Platinum
350     crucibles should be used for these fusions because other materials are rapidly attacked by the
351     melt,  even though some platinum is lost in each fusion.


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352     BORIC ACID (H3BO3) can be used to fuse a number of otherwise rather inert substances such as
353     sand, aluminum silicates, titanite, natural aluminum oxide (corundum), and enamels. Boric acid
354     fusions generally require 4 to 8 times as much reagent as sample. Initially, the mixture should be
355     heated cautiously while water is being driven off, then more strongly until gas evolution is
356     completed, and then more vigorously if the sample has yet to be fully decomposed. Normally, the
357     procedure is complete within 20 to 30 minutes. The cooled and solidified melt usually is
358     dissolved in dilute acid. Additionally, boric acid has one great advantage over all other fluxes in
359     that it can be completely removed by addition of methanol and subsequent volatilization of the
360     methyl ester.

361     Because MOLTEN SODIUM TETRABORATE (Na2B4O7) dissolves so many inorganic compounds, it is
362     an important analytical tool for dissolving very resistant substances. Fusions with sodium
363     tetraborate alone are useful for A12O3, ZrO2 and zirconium ores, minerals of the rare earths,
364     titanium, niobium, and tantalum, aluminum-containing materials, and iron ores and slags (Bock,
365     1979). Relatively large amounts of borax are mixed with the  sample, and the fusion is carried out
366     at a relatively high temperature (1,000 to 1,200 °C) until the  melt becomes clear. Thallium,
367     mercury, selenium, arsenic, and the halogens are volatilized under these conditions. Boric acid
368     can be removed from the melt as previously described. By dissolving the melt in dilute
369     hydrofluoric acid, calcium, thorium, and the rare earths can be separated from titanium, niobium,
370     and tantalum as insoluble fluorides.

371     Fluxes of LITHIUM TETRABORATE (Li2B4O7) are well suited for dissolving basic oxides such as
372     alumina (SiO2) and some resistant silicates. However, lithium metaborate, LiBO2, (or a mixture
373     of meta- and tetraborate) is more basic and better suited for dissolving acidic oxides such as
374     silica or titanium dioxide, although it is capable of dissolving nearly all minerals (Dean, 1995).
375     Platinum dishes normally are used for this type of fusion,  but occasionally graphite crucibles are
376     advantageous because they can be heated rapidly by induction heating and because  they are not
377     wetted by Li2B4O7 melts. The fusion melt typically is dissolved in dilute acid, usually nitric but
378     sometimes sulfuric. When easily hydrolyzed metal ions are present, it is recommended that
379     dissolution be  carried out in the presence of EDTA or its sodium salt in  0.01 M HC1 (Bock,  1979,
380     p. 92). Moreover, when titanium is present, hydrogen peroxide can be used to help  maintain the
381     titanium in solution.

382     13.3.3 Fluoride Fusions

383     Fluoride fusions are used for the removal of silicon, the destruction of silicates and rare earth
384     minerals, and the analysis of oxides of niobium, tantalum, titanium, and zirconium. Sill et al.
385     (1974) and Sill and Sill (1995) has described a method using potassium  fluoride/potassium


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386     pyrosulfate fusion for determining alpha-emitting nuclides in soil (see Sect. 13.8). Sulcek and
387     Povondra (1989) describe the isolation of the rare earth elements (REE) and thorium from
388     silicate materials and their minerals, especially monazite, through potassium hydrofluoride
389     fusion. The silicate matrix is first degraded by evaporation with HF, then the residue is fused
390     with tenfold excess flux, and finally the melt is digested with dilute acid. The resulting fluorides
391     (REE + Th + Ca + U) are filtered off, dissolved, and further separated by chromatography.

392     Platinum crucibles are recommended for fluoride fusions. Silicon and boron are volatilized
393     during these fusion procedures, and if the temperature is high enough, some molybdenum,
394     tantalum, and niobium also are lost. Residual fluoride can be a problem for subsequent analysis
395     of many elements such as aluminum, tin, beryllium, and zirconium. This excess fluoride usually
396     is removed by evaporation with sulfuric acid.

397     13.4  Wet Ashing and Acid Dissolution Techniques

398     "Wet ashing" and "acid dissolution" are terms used to describe sample decomposition using hot,
399     concentrated acid solutions. Because many inorganic matrices such as oxides, silicates, nitrides,
400     carbides, and borides can be difficult to dissolve  completely, geological or ceramic samples can
401     be particularly challenging. Therefore,  different acids are used alone or in combination to
402     decompose specific compounds that may be present in the sample.  Few techniques will
403     completely decompose all types of samples.  Many decomposition procedures use wet ashing to
404     dissolve the major portion of the sample but leave a minor fraction as residue. Whether or not
405     this residue requires additional treatment (by wet ashing or fusion) depends on the amount of
406     residue and whether it is expected to contain the radionuclides of interest. The residue should not
407     be discarded until all of the results have been reviewed and determined to be acceptable.

408     13.4.1 Acids and Oxidants

409     Numerous acids are commonly used in wet ashing procedures. Table 13.2 lists several acids and
410     the types of compounds they generally  react with during acid dissolution. The electromotive
411     force series (Table 13.3) is a summary  of oxidation-reduction half-reactions arranged in
412     decreasing oxidation strength and is also useful in selecting reagent systems (Dean, 1995). The
413     table allows one to predict which metals will dissolve in nonoxidizing acids, such as
414     hydrochloric, hydrobromic, hydrofluoric, phosphoric, dilute sulfuric, and dilute perchloric acid
415     The dissolution process is simply a replacement of hydrogen by the metal (Dean,  1995). In
416     practice, however, what actually occurs is influenced by a number of factors, and the behavior  of
417     the metals cannot be predicted from the potentials alone.  Generally, metals below hydrogen in
418     Table 13.3 displace hydrogen and dissolve in nonoxidizing acids with the evolution of hydrogen.

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419 Notable exceptions include the very slow dissolution by hydrochloric acid of lead, cobalt, nickel,
420 cadmium, and chromium. Also, lead is insoluble in sulfuric acid because of the formation of a
421 surface film of insoluble lead sulfate.
422 TABLE 13.2 — Examples of acids used for wet ashing
423 Acid
Typical Uses
TT , _ . . . , TT_ Removal of silicon and destruction of silicates; dissolves oxides of Nb, Ta, Ti, and Zr,
424 Hydrofluoric Acid, HF ,,_,,_
and Nb, and Ta ores.
... TT , .... . , TT^, Dissolves many carbonates, oxides, hydroxides, phosphates, borates, and sulfides;
425 Hydrochloric Acid, HC1 ,-,
dissolves cement.
426 Hydrobromic Acid, HBr Distillation of bromides (e.g., As, Sb, Sn, Se).
427 Hydroiodic Acid, HI Effective reducing agent; dissolves Sn (IV) oxide and Hg (II) sulfide.
.„„ „ ,,, . . ., TT „„ Dissolves oxides, hydroxides, carbonates, and various sulfide ores; hot concentrated
428 Sulfunc Acid, H2SO4 ., .„ ... • ,
acid will oxidize most organic compounds.
429 Phosphoric Acid, H3PO4 Dissolves A12O3, chrome ores, iron oxide ores, and slag.
430 Nitric Acid, HNO3 Oxidizes many metals and alloys to soluble nitrates; organic material oxidized slowly.
_ ,, . . . , TT™~ Extremely strong oxidizer; reacts violently or explosively to oxidize organic
431 Perchloric Acid, HC1O4 . , , ,, i
compounds; attacks nearly all metals.
432 TABLE 13.3 — Standard reduction potentials of
433 selected half- reactions at 25 °C
434 Half-Reaction
435 Ag^+ + e" = Ag+ 	
436 S2O82" + 2e" = 2SO42"
437 HN3 + 3H+ + 2e" = NH4+ + N2
438 Ce4+ + e = Ce3+ 	
439 MnO4" + 4H+ + 3e" = MnO2 (c) + 2H2O 	
440 2HC1O + 2H+ + 2e" = C12 + 2H2O
44 1 2HBrO + 2H+ + 2e" = Br2 + 2H2O
442 NiO2+ 4H+ + 2e = Ni2+ + 2H2O 	
443 Bi2O4 (bismuthate) + 4H+ + 2e = 2BiO+ + 2H
444 MnO4" + 8H+ + 5e" = Mn2+ + 4H2O
445 2BrO3' + 12H+ + lOe" = Br2 + 6H2O
446 PbO2 + 4H+ + 2e" = Pb2+ + 2H2O 	
447 Cr2O72' + 14H+ + 6e" = 2Cr3+ + 7H2O 	
448 C12 + 2e" = 2C1"
449 2HNO2 + 4H+ + 4e = N2O + 3H2O 	
E° (volts)
	 1.980
1 96
1 96
	 1.72
	 1.70
1630
1604
	 1.593
2O 	 1.59
1 51
1478
	 1.468
	 1.36
1 3583
	 1.297
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                                                                Sample Dissolution
                   Half-Reaction
                                                        E° (volts)
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
MnO2 + 4H++ 2e = Mn2+ + 2H2O ..........................    1.23
O2 + 4H+ + 4e = 2H2O ...................................    1.229
C1O4- + 2H+ + 2e = ClO-3 + H2O ...........................    1.201
2IO3" + 12H+ + 10e" = I2 + 3H2O ...........................    1.195
N2O4 + 2H+ + 2e  = 2HNO3 ...............................    1.07
2ICl-2 + 2e- = 4C1' + 12 ...................................    1.07
Br2 (Iq) + 2e" = 2Bf .....................................    1.065
N2O4 + 4H+ + 4e  = 2ND + 2H2O ...........................    1.039
HNO2 + H++ e = NO + H2O ..............................    0.996
NO3- + 4H+ + 3e = NO + 2H2O ............................    0.957
NO3- + 3H+ + 2e = HNO2 + H2O ...........................    0.94
2Hg2+ + 2e- = Hg22+ ......................................    0.911
Cu2+ + T + e" = Cul ......................................    0.861
OsO4 (c) +  8H+ + 8e = Os + 4H2O .........................    0.84
Ag+ + e=Ag ..........................................    0.7991
Hg22+ + 2e  = 2Hg .......................................    0.7960
Fe3+ + e" = Fe2+ .........................................    0.771
H2SeO3 + 4H+ + 4e = Se + 3H2O ...........................    0.739
HN3+llH+ + 8e- = 2NH4+ ................................    0.695
O2 + 2H+ + 2e= H2O2 ...................................    0.695
Ag2SO4 + 2e = 2Ag + SO42' ...............................    0.654
Cu2+ + Br + e- = CuBr (c) ................................    0.654
2HgCl2 + 2e = Hg2Cl2 (c) + 2C1' ...........................    0.63
Sb2O5 + 6H+ + 4e = 2SbO+ + 3H2O .........................    0.605
H3AsO4 + 2H+ + 2e = HAsO2 + 2 H2O ......................    0.560
TeOOH+ +  3H+ + 4e" = Te + 2H2O .........................    0.559
Cu2+ + Cr + e = CuCl (c) .................................    0.559
r2 + 2e = 3r ...........................................    0.536
I2 + 2e" = 21 ...........................................    0.536
Cu+ + e = Cu ..........................................    0.53
4H2SO3 + 4H+ + 6e' = S4O62- + 6H2O ........................    0.507
Ag2CrO4 +  2e- = 2Ag + CrO42 .............................    0.449
2H2SO3 + 2H+ + 4e" = S2O32' + 3H2O ........................    0.400
UO2+ + 4H+ + e" = U4+ + 2H2O .............................    0.38
Cu2+ + 2e- = Cu .........................................    0.340
VO2+ + 2H+ + e' = V3+ + H2O ..............................    0.337
BiO+ + 2H+ + 3e = Bi + H2O ..............................    0.32
UO22+ + 4H+ + 2e = U4+ + 2H2O ...........................    0.27
Hg7Cl7 (c) + 2e" = 2Hg + 2C1"  .............................    0.2676
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                   Half-Reaction                                            E° (volts)

489                AgCl + e" = Ag + Cr  	    0.2223
490                SbO+ + 2H+ + 3e = Sb + H2O  	    0.212
491                CuCl32' + e = Cu + 3C1'	    0.178
492                SO/' + 4H+ + 2e- = H2SO3 + H2O  	    0.158
493                Sn4+ + 2e = Sn2+	    0.15
494                CuCl + e-= Cu + Cr	    0.121
495                TiO2++ 2H++ e-= Ti3++ H2O	    0.100
496                S4O62- + 2e = 2S2O32-	    0.08
497                2H+ + 2e" = H2  	    0.0000
498                Hg2I2 + 2e = 2Hg + 21-	   -0.0405
499                Pb2+ + 2e = Pb	   -0.125
500                Sn2+ + 2e = Sn	   -0.136
501                Agl + e = Ag + T	   -0.1522
502                V3+ + e = V2+	   -0.255
503                Ni2+ + 2e- = Ni  	   -0.257
504                Co2+ + 2e- = Co	   -0.277
505                PbSO4 + 2e = Pb + SO42'	   -0.3505
506                Cd2+ + 2e- = Cd	   -0.4025
507                Cr* + e' = Cr*	   -0.424
508                Fe2+ + 2e" = Fe  	   -0.44
509                H3PO3 + 2H+  + 2e" = HPH2O2 + H2O	   -0.499
510                U4+ + e=U3+	   -0.52
511                Zn2+ + 2e = Zn	   -0.7626
512                Mn2++ 2e = Mn	   -1.18
513                A13+ + 3e" = Al  	   -1.67
514                Mg2+ + 2e" = Mg	   -2.356
515                Na+ + e=Na	   -2.714
516                K+ + e=K	   -2.925
517                Li+ + e" = Li  	   -3.045
518                Source: Dean, 1995.

519      Oxidizing acids, such as nitric acid, hot concentrated sulfuric acid, or hot concentrated perchloric
520      acid, are used to dissolve metals above hydrogen. For nitric acid, the potential of the nitrate ion-
521      nitric oxide couple can be employed as a rough estimate of the solvent power. For aqua regia, the
522      presence of free chlorine ions allows  one to make predictions based upon the potential of the
523      chlorine-chloride couple, although NOC1 also plays a significant role. Some oxidizing acids
524      exhibit a passivating effect with transition elements such as chromium and pure tungsten,
525      resulting in a very slow attack because of the formation of an insoluble surface film of the oxide


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526     in the acid (Bogen, 1978). Moreover, oxides are often resistant to dissolution in oxidizing acids
527     and, in fact, dissolve much more readily in nonoxidizing acids. A common example is ferric
528     oxide, which is readily soluble in hydrochloric acid but is relatively inert in nitric acid.

529     However, insoluble oxides of the lower oxidation states of an element sometime dissolve in
530     oxidizing acids with concurrent oxidation of the element. For example, UO2 and U3O8 dissolve
531     readily in nitric acid to produce a solution of uranyl ion (UO2+2).

532     HYDROFLUORIC ACID. The most important property of HF is its ability to dissolve silica and
533     other silicates. For example:

534                                    SiO2 + 6HF - H2SiF6 + 2H2O

535     whereby the fluorosilicic acid formed dissociates into gaseous silicon tetrafluoride and hydrogen
536     fluoride upon heating:

537                                        H2SiF6-SiF4t+2HF

538     HF also exhibits pronounced complexing properties that are widely used in analytical chemistry.
539     Hydrofluoric acid prevents the formation of sparingly soluble hydrolytic products in solution,
540     especially of compounds of elements from the IVth to VIth groups of the periodic table (Sulcek
541     and Povondra, 1989). In the presence of fluoride, soluble hydrolytic products that are often
542     polymeric depolymerize to form reactive monomeric species suitable for further analytical
543     operations. Formation of colloidal solutions is avoided and the stability of solutions is increased
544     even with compounds of elements that are hydrolyzed easily in aqueous solution (e.g., Si, Sn, Ti,
545     Zr, Hf, Mb, Ta, and Pa).

546     HF also exhibits pronounced complexing properties that are widely used in analytical chemistry.
547     Hydrofluoric acid prevents the formation of sparingly soluble hydrolytic products in solution,
548     especially of compounds of elements from the IVth to VIth groups of the periodic table (Sulcek
549     and Povondra, 1989). In the presence of fluoride, soluble hydrolytic products that are often
550     polymeric depolymerize to form reactive monomeric species suitable for further analytical
551     operations. Formation of colloidal solutions is avoided and the stability of solutions is increased
552     even with compounds of elements that are easily hydrolyzed in aqueous solution (e.g., Si, Sn, Ti,
553     Zr, Hf, Nb, Ta, and Pa).

554     HF should never be used or stored in glass containers. Platinum containers are preferred, and
555     Teflon is acceptable as long as the temperature does not exceed 250 °C; the constant boiling


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556     azeotrope boils at 112 °C. HF works most effectively when used alone. Samples should be
557     ground to a fine powder to increase the surface area and moistened with water to prevent losses
558     as dust and spray when the acid is added to the sample. After the addition of HF, the sample is
559     allowed to stand overnight to dissolve the silicates. However, the reaction can be sped up by
560     heating the solution. Because it is such a strong complexing agent, excess fluoride ion can cause
561     problems with many chemical reactions. Residual fluoride is usually removed by evaporation to
562     fumes in a low-volatility acid (e.g., H2SO4, HNO3, HC1O4) or, in extreme cases, excess fluoride
563     ion can be removed by fusing the residue with K2S2O7 or by the addition of quartz  (SiO2).

564     HYDROCHLORIC ACID (HC1) is one of the most widely used acids for wet ashing samples because
565     of the wide range of compounds it reacts with and the low boiling point of the azeotrope (110
566     °C); after a period of heating in an open container, a constant boiling 6M solution  remains. HC1
567     forms strong complexes with gold (HI), titanium (HI), and mercury (II). The concentrated acid
568     will also complex iron (in), gallium (HI), indium (HI), and tin (IV). Most chloride  compounds are
569     readily soluble in water except for silver chloride, mercury  chloride, titanium chloride, and lead
570     chloride. HC1 can be oxidized to form chlorine gas by manganese dioxide, permanganate, and
571     persulfate. While HC1 dissolves many carbonates, oxides, hydroxides, phosphates, borates,
572     sulfides, and cement, it does not dissolve the following:

573      •  Most silicates or ignited oxides of Al, Be, Cr, Fe, Ti, Zr, or Th;
574      •  Oxides of Sn, Sb, Nb, or Ta;
575      •  Zr phosphate;
576      •  Sulfates of Sr, Ba, Ra, or Pb;
577      •  Alkaline earth fluorides;
578      •  Sulfides of Hg; or
579      •  Ores of Nb, Ta, U, or Th.

580     The dissolution behavior of specific actinides by hydrochloric acid is discussed by Sulcek and
581     Povondra(1989):

582         "The rate of decomposition of oxidic uranium ores depends on the U(VI)/U(IV) ratio. The so-
583         called uranium blacks with minimal contents of U(IV) are even dissolved in dilute
584         hydrochloric acid. Uraninite (UO2) requires an oxidizing mixture of hydrochloric acid with
585         hydrogen peroxide, chlorate, or nitric acid for dissolution. Uranium and thorium compounds
586         cannot be completely leached from granites by hydrochloric acid. Natural and synthetic
587         thorium dioxides are highly resistant toward hydrochloric acid and must be decomposed in a
588         pressure vessel. Binary phosphates of uranyl and divalent cations, e.g., autunite and tobernite,
589         are dissolved without difficulties. On the other hand, phosphates of thorium, tetravalent


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590         uranium, and the rare earths (monazite and xenotime) are only negligibly attacked, even with
591         the concentrated acid."

592     Arsenic (in), antimony (HI), germanium (HI), and selenium (IV) are easily volatilized in HC1
593     solutions, while mercury (II), tin (IV), and rhenium (VII) are volatilized in the latter stages of
594     evaporation. Glass is the preferred container for HC1 solutions.

595     HYDROBROMIC ACID (HBr) has no important advantages over HC1 for wet ashing samples. HBr
596     forms an azeotrope with water containing 47.6 percent w/w of HBr, boiling at 124.3 °C. HBr is
597     used to distill off volatile bromides of arsenic, antimony, tin, and selenium. HBr can also be used
598     as a complexing agent for liquid-liquid extractions of gold, titanium, and indium.

599     HYDROIODIC ACID (HI) is readily oxidized and often appears as a yellowish-brown liquid
600     because of free iodine. HI is most often used as a reducing agent during dissolutions. HI also
601     dissolves tin (IV) oxide, and complexes and dissolves mercury (II) sulfide. HI forms an azeotrope
602     with water containing 56.9 percent w/w of HI, boiling at 127  °C.

603     SULFURIC ACID (H2SO4) is another widely used acid for sample decomposition. Part of its
604     effectiveness is due to its high boiling point (about 340 °C). Oxides, hydroxides, carbonates, and
605     sulfide ores can be dissolved in H2SO4. The boiling point can be raised by the addition of sodium
606     or potassium sulfate to  improve the attack on ignited oxides, although silicates will still not
607     dissolve. H2SO4 is not appropriate when calcium is a major constituent because of the low
608     solubility of CaSO4. Other inorganic sulfates are typically soluble in water, with the notable
609     exceptions of strontium, barium, radium, and lead.

610     Dilute H2SO4 does not exhibit oxidizing properties, but the concentrated acid will oxidize many
611     elements and almost all organic compounds. Oxidation of organic compounds in H2SO4 is a slow
612     reaction with a tendency to form indestructible charred residues. Moreover, because of the high
613     boiling point of H2SO4, there is an increased risk of losses because of volatilization. Iodine can
614     be distilled quantitatively, and boron, mercury, selenium, osmium, ruthenium, and rhenium may
615     be lost to some extent. The method of choice is to oxidize the  organic substances with HNO3,
616     volatilize the nitric acid, add H2SO4 until charred, followed by HNO3 again, repeating the process
617     until the sample will not char with either HNO3 or H2SO4. Dissolution is then continued with
618     HC1O4.

619     Glass, quartz, platinum, and porcelain are resistant to H2SO4 up to the boiling point. Teflon
620     decomposes at 300 °C, below the boiling point, and, therefore, is not recommended for
621     applications involving H2SO4 that require elevated temperature.


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622     PHOSPHORIC ACID (H3PO4) seldom is used for wet ashing because the residual phosphates
623     interfere with many procedures. H3PO4 attacks glass, although glass containers are usually
624     acceptable at temperatures below 300 °C. Alumina, chromium ores, iron oxide ores, and slags
625     can be dissolved in H3PO4. The acid also has been used to dissolve silicates selectively without
626     attacking quartz.

627     NITRIC ACID (HNO3) is one of the most widely used oxidizing acids for sample decomposition.
628     Most metals and alloys are oxidized to nitrates, which are usually very soluble in water, although
629     many metals exhibit a pronounced tendency to hydrolyze in nitric acid solution. Nitric acid does
630     not attack gold, hafnium, tantalum, zirconium, and the metals of the platinum group (except
631     palladium). Aluminum, boron, chromium, gallium, indium, niobium, thorium, titanium, calcium,
632     magnesium, and iron form a layer of insoluble oxide when treated with HNO3 and are thereby
633     pacified and do not dissolve in the concentrated acid. However, calcium, magnesium, and iron
634     will dissolve in more dilute acid.

635     Complexing agents (e.g., Cl", F", citrate, tartrate) can assist HNO3 in dissolving most metals. For
636     example, Sulcek and Povondra (1989) describe the decomposition of thorium and uranium
637     dioxides in nitric acid, which is catalytically accelerated by the addition of 0.05 to 0.1 M FTP.
638     They report that a solid solution of the mixed oxides (Pu, U)O2 or PuO2 ignited at temperatures
639     below 800 °C behaves analogously.

640     Although nitric acid is a good oxidizing agent, it usually boils away before sample oxidation is
641     complete.  Oxidation of organic materials proceeds slowly and is usually accomplished by
642     repeatedly heating the solution to HNO3 fumes. Refluxing in  the concentrated acid can help
643     facilitate the treatment, but HNO3 is seldom used alone to decompose organic materials.

644     PERCHLORIC ACID (HC1O4). Hot concentrated solutions of HC1O4 act as a powerful oxidizer, but
645     dilute aqueous solutions are not oxidizing. Hot concentrated HC1O4 will attack nearly all metals
646     (except gold and platinum  group metals) and oxidize them to the highest oxidation state, except
647     for lead and manganese, which are oxidized only to the +2 oxidation state. Perchloric acid is an
648     excellent solvent for stainless steel, oxidizing the chromium and vanadium to the hexavalent and
649     pentavalent acids, respectively. Many nonmetals also will react with HC1O4. Because of the
650     violence of the oxidation reactions, HC1O4 is rarely used alone for the destruction of organic
651     materials.  H2SO4 or HNO3 are used to dilute the solution and break down easily oxidized material
652     before HC1O4 becomes an  oxidizer above 160 °C.

653     The concentrated acid is a  dangerous oxidant that can explode violently. The following are
654     examples of some reactions with HC1O4 that should never be attempted:


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655      •  Heating Bi metal and alloys with concentrated acid.

656      •  Dissolving metals (e.g., steel) in concentrated acid when gas-phase hydrogen becomes
657         heated.

658      •  Heating uranium turnings or powder in concentrated acid.

659      •  Heating finely divided  aluminum and silicon in concentrated acid.

660      •  Heating antimony or antimony (HI) compounds in HC1O4

661      •  Mixing HC1O4 with hydrazine or hydroxylamine.

662      •  Mixing HC1O4 with hypophosphates.

663      •  Mixing HC1O4 with fats, oils, greases, or waxes.

664      •  Evaporating solutions of metal salts to dryness in HC1O4.

665      •  Evaporating alcoholic filtrates after collection of KC1O4 precipitates.

666      •  Heating HC1O4 with cellulose, sugar, and polyhydroxy alcohols.

667      •  Heating HC1O4 with N-heterocyclic compounds.

668      •  Mixing HC1O4 with any dehydrating agent.

669     Perchloric acid vapor should never be allowed to come in contact with organic materials such as
670     rubber stoppers. The acid should be stored only in glass bottles. Splashed or spilled acid should
671     be diluted with water immediately and mopped up with a woolen cloth, never cotton. HC1O4
672     should only be used only in specially designed fume hoods incorporating a washdown system.

673     Acid dissolutions involving HC1O4 should only be performed by analysts experienced in working
674     with this acid. When any procedure is designed, the experimental details should be recorded
675     exactly. These records are used to develop a detailed SOP that must be followed exactly to
676     ensure the safety of the analyst (Schilt, 1979).
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677     AQUA REGIA. One part concentrated HNO3 and 3 parts concentrated HC1 (volume/volume) are
678     combined to form aqua regia:

679                                3HC1 + HNO3 - NOC1 + C12 + 2H2O

680     However, the interaction of these two acids is  much more complex than indicated by this simple
681     equation. Both the elemental chlorine and the trivalent nitrogen of the nitrosyl chloride exhibit
682     oxidizing effects,  as do other unstable products formed during the reaction of these two acids.
683     Coupled with the catalytic effect of C12 and NOC1, this mixture combines the acidity and
684     complexing power of the chloride ions. The solution is more effective if allowed to stand for 10
685     to 20 minutes after it is prepared.

686     Aqua regia dissolves sulfides, phosphates, and many metals and alloys including gold, platinum,
687     and palladium. Ammonium salts are decomposed in this acid mixture. Aqua regia volatilizes
688     osmium as the tetroxide; has little effect on rhodium, iridium, and ruthenium; and has no effect
689     on titanium. Oxidic uranium ores with uraninite and synthetic mixed oxides (U3O8) are dissolved
690     in aqua regia, with oxidation of the uranium (VI) to UO22+ ions (Sulcek and Povondra, 1989).
691     However, this dissolution procedure is insufficient for poor ores; the resistant, insoluble fraction
692     must be further attacked (e.g., by sodium peroxide or borate fusion) or by mixed-acid digestion
693     with HF, HNO3, and HC1O4.

694     Oxysalts, such as KMnO4 (potassium permanganate) and K2Cr2O7 (potassium dichromate), are
695     commonly not used to solubilize or wet ash environmental samples for radiochemical analysis
696     because of their limited ability to oxidize metals and the residue that they leave in the sample
697     mixture. These oxysalts are more commonly used to oxidize organic compounds.

698     POTASSIUM PERMANGANATE (KMnO4) is a strong oxidizer whose use is limited primarily to the
699     decomposition of organic substances and mixtures, although it oxidizes  metals such as mercury
700     to the ionic form.  Oxidation can be performed in an acid, neutral, or basic medium; near-neutral
701     or basic solutions  produce an insoluble residue of manganese dioxide (MnO2) that can be
702     removed by filtration. Oxidation in acid media leaves the manganese (II) ion in solution, which
703     might interfere with additional chemical procedures or analyses. Extreme caution must be taken
704     when using this reagent because KMnO4 reacts violently with some organic substances such as
705     acetic acid and glycerol, with some metals such as antimony and arsenic, and with common
706     laboratory reagents such as hydrochloric acid and hydrogen peroxide.

707     POTASSIUM BICHROMATE (K2Cr2O7) is a strong oxidizing agent for organic compounds but is not
708     as strong as KMnO4. K2Cr2O7 has been used to determine carbon and halogen in organic


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709     materials, but the procedure is not used extensively. K2Cr2O7 is commonly mixed with sulfuric
710     acid and heated. The chromium (HI) ion remains after sample oxidation and this might interfere
711     with other chemical procedures or analyses. K2Cr2O7  can react violently with certain organic
712     substances such as ethanol and might ignite in the presence of boron. Caution also must be
713     observed in handling this oxidizing agent because of human safety concerns, particularly with the
714     hexavalent form of chromium.

715     SODIUM BROMATE (NaBrO3) is an oxidizing agent for organic compounds but is not used for
716     metals. Unlike KMnO4 and K2Cr2O7, the bromate ion can be removed from solution after sample
717     oxidation by boiling with excess HC1 to produce water and Br2. Caution must be observed when
718     using this oxidizing agent because it can react violently with some organic and inorganic
719     substances.

720     13.4.2 Acid Digestion Bombs

721     Some materials that would not be totally dissolved by acid digestion in an open vessel on a
722     hotplate, can be completely dissolved in an acid digestion bomb. These pressure vessels hold
723     strong mineral acids or alkalies at temperatures well above normal boiling points, thereby
724     allowing one to obtain complete digestion or dissolution of samples that would react slowly or
725     incompletely at atmospheric pressure. Sample dissolution is obtained without losing volatile
726     elements and without adding contaminants from the digestion vessel. Ores, rock samples, glass
727     and other inorganic samples can be  dissolved quickly using strong mineral acids such as HF,
728     HC1, H2SO4, HNO3, or aqua regia.

729     These sealed pressure vessels are lined with Teflon, which offers resistance to cross-
730     contamination between samples and to attack by HF.  In all reactions, the bomb must never be
731     completely filled; there must be adequate vapor space above the contents. When working with
732     inorganic materials, the total volume of sample plus reagents must never exceed two-thirds of the
733     capacity of the bomb. Moreover, many organic materials can be treated satisfactorily in these
734     bombs, but critical attention must be given to the nature of the sample as well to possible
735     explosive reactions with the digestion media.

736     13.4.3 Is it Dissolved?

737     Following aggressive acid digestion and even fusion,  the analyst often must determine if the
738     sample has indeed been dissolved. This determination is first made through visual inspection for
739     particulate matter in the acid leachate or dissolved fusion melt.  If a residue is observed, this
740     residue can be physically separated  and subsequently fused or treated in an acid digestion bomb


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        Sample Dissolution
741     to determine if any analyte was left behind. Sometimes these residues are inconsequential and
742     contain no analyte of interest. In other cases, residues may consist of materials such as zircons or
743     other minerals that can contain trapped uranium, thorium, etc. Even if no particles are readily
744     observed, small undissolved particles that are invisible to the naked eye may be present.
745     Therefore, the analyst  may choose to filter the sample through a 0.22 to 0.45 jim filter, and then
746     count the filter for gross a, p, and y activity to determine if any activity has been left behind in
747     the residue. However,  this approach is applicable only for samples that contain elevated levels of
748     radioactivity. Finally, for those cases where the laboratory has decided to perform an acid
749     digestion rather than a total dissolution fusion, it is advisable to perform a total dissolution on a
750     subset of the samples and compare the results to those obtained from the acid digestion. This
751     check will help to substantiate that the acid digestion approach is adequate for the particular
752     sample matrix.

753     13.5  Microwave  Digestion

754     Microwave energy as a heat source for sample digestion was first described more than 20 years
755     ago (Abu-Samra et al., 1975). Its popularity is derived from the fact that it is faster,  cleaner, more
756     reproducible, and more accurate than traditional hot-plate digestion. However, until recently, this
757     technology has had limited application in the radiochemical  laboratory because of constraints on
758     sample size resulting from vessel pressure limitations. Because of this drawback, microwave
759     dissolution was not practical for many radiochemical procedures where larger sample sizes are
760     dictated to achieve required detection limits. However, recent advances in vessel design and
761     improved detection methods, such as ICP-MS (inductively coupled plasma-mass spectrometry)
762     and ion chromatography have eliminated this disadvantage, and microwave dissolution is
763     becoming an important tool for today's radiochemists (Smith and Yaeger, 1996;  Alvarado et al.,
764     1996). A series of articles in the journal Spectroscopy describes recent advances in microwave
765     dissolution technology (Kammin and Brandt, 1989; Grillo, 1989 and 1990; Oilman  and
766     Engelhardt, 1989; Lautenschlager, 1989; Noltner et al.,  1990), and Dean (1995) presents a
767     synopsis of current microwave theory and technology in the  Analytical Chemistry Handbook.
768     Moreover, Introduction to Microwave Sample Preparation:  Theory and Practice by Kingston
769     and lassie (1988) and Microwave-Enhanced Chemistry—Fundamentals, Sample Preparation,
770     and Applications by Kingston and Haswell (1997), are excellent resources for this topic.

771     Some example protocols for various  media are given in ASTM standards: "Standard Practice for
772     Acid-Extraction from  Sediments Using Closed Vessel Microwave Heating" (ASTM D5258)
773     describes the decomposition of soil and sediment samples for subsequent analyte extraction;
774     "Standard Practice for Sample Digestion Using Closed Vessel Microwave Heating Technique for
775     the Determination of Total Metals in Water" (ASTM D4309) addresses the decomposition of

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                                                                               Sample Dissolution
776     surface, saline, domestic, and industrial waste water samples; and "Standard Practice for
777     Microwave Digestion of Industrial Furnace Feedstreams for Trace Element Analysis" (ASTM
778     D5513) covers the multistage decomposition of samples of cement raw feed materials, waste-
779     derived fuels, and other industrial feedstreams for subsequent trace metal analysis. A method for
780     acid digestion of siliceous and organically based matrices is given in EPA (1996).

781     There are various brands and models of microwave instruments that may be satisfactory
782     depending on sample preparation considerations. The three main approaches to microwave
783     dissolution are: focused open-vessel, low-pressure closed-vessel, and high-pressure closed-
784     vessel. Each has certain advantages and disadvantages and the choice of system depends upon the
785     application.

786     13.5.1  Focused Open-Vessel Systems

787     A focused open-vessel system has no oven but consists of a magnetron to generate microwaves, a
788     waveguide to direct and focus the microwaves and a cavity to contain the sample (Grillo, 1989).
789     Because of the open-vessel design, there is no pressure buildup during processing, and reagents
790     may be added during the digestion program. These systems are quite universal in that any reagent
791     and any type of vessel (glass, Perfluoroalcoholoxil™ [PFA], or quartz) can be used.

792     The waveguide ensures that energy is directed only at the portion  of the vessel in the path of the
793     focused microwaves thereby allowing the neck of the vessel and refluxer to remain cool and
794     ensuring refluxing action. Because of this refluxing action, the system maintains all elements,
795     even selenium and mercury. The focused microwaves cause solutions to reach higher
796     temperatures faster than with conventional hotplates or block-type digesters and do so with
797     superior reproducibility.  An aspirator removes excess acid vapors and decomposition gases.
798     Depending on the system, up to 20 g of solids or 50 to 100 mL of liquids can be digested within
799     10 to 30 minutes on average.

800     13.5.2  Low-Pressure, Closed-Vessel Systems

801     These systems consist of a microwave oven equipped with a turntable, a rotor to hold the sample
802     vessels, and a pressure-control module (Grillo, 1990). The PFA vessels used with these systems
803     are limited to approximately 225 °C, and, therefore, low-boiling reagents or mixtures of reagents
804     should be used. However, waste is minimized in these systems because smaller quantities of acid
805     are required. Moreover, because little or no acid is lost during the digestion, additional portions
806     of acid may not be required and blank values are minimized. Additionally, these sealed vessels
807     are limited to 100 to 300 psi, depending on the model thereby limiting the size of organic


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808     samples utilized. However, inorganic materials such as metals, water and waste waters, minerals,
809     and most soils and sediments are easily digested without generating large amounts of gaseous by-
810     products. Typical sample sizes are on the order of 0.5 g for solids and 45 mL for waters.

811     The pressure control module regulates the digestion cycle by monitoring, controlling, and
812     dwelling at several preferred pressure levels for specified time periods in order to obtain
813     complete dissolution and precise recoveries in the minimum amount of time. As the samples are
814     irradiated, temperatures in the vessels rise thereby increasing the pressure. The pressure
815     transducer will cycle the magnetron to maintain sufficient heat to hold the samples at the
816     programmed pressure level for a preset dwell time.  The vessels are designed to vent safely in
817     case of excessive internal pressure.

818     13.5.3 High-Pressure, Closed-Vessel Systems

819     Recent advances in vessel design have produced microwave vessels capable of withstanding
820     pressures on the order of 1,500 psi (Lautenschlager, 1989), allowing for larger sample sizes on
821     the order of 1 to 2 g for soil (Smith and Yaeger, 1996) or 0.5 to 3 g for vegetation (Alvarado et
822     al., 1996) and, consequently, better detection limits. These high-pressure vessels are used to
823     digest organic and inorganic substances, such as coals, heavy oils, refractories, and ceramic
824     oxides, which cannot easily be digested with other techniques. Additionally, vessel composition
825     continues to improve. Noltner et al. (1990) have demonstrated that Tetrafluorometoxil™ (TFM)
826     vessels exhibit significantly lower  blank background values from residual contamination and
827     reuse than vessels produced with the more traditional PFA. This lower "memory" results in lower
828     detection limits, a clear advantage  for environmental laboratories.

829     13.6  Special Matrix Considerations

830     13.6.1 Liquid Samples

831     13.6.1.1 Aqueous Samples

832     Aqueous samples are usually considered to be in solution. This  may not always be true, and,
833     based on the objectives of the project, additional decomposition of aqueous samples may be
834     requested.
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835     13.6.1.2 Nonaqueous Samples

836     Most radiochemical analyses are performed in aqueous solutions. Because nonaqueous liquids
837     are incompatible with this requirement, these samples must be converted into an aqueous form.
838     In most cases, the nonaqueous liquid is simply a solvent that does not contain the radionuclide of
839     interest, and the nonaqueous solvent simply can be removed and the residue dissolved as
840     described in Sections 13.3 and 13.4.

841     Occasionally, the nonaqueous phase must be analyzed. A procedure for the decomposition of
842     petroleum products is described by Coomber (1975). There are restrictions on how many
843     nonaqueous liquids can be disposed of, even as laboratory samples. Evaporation of volatile
844     solvents may initially be an  attractive alternative, but the legal restrictions on evaporating
845     solvents into the air should be investigated before this method is implemented. Burning flam-
846     mable liquids such as oil may also initially appear attractive, but legal restrictions on incineration
847     of organic liquids may need to be considered. A liquid-liquid extraction or separation using ion
848     exchange resin may be the only alternative for transferring the radionuclide of interest into an
849     aqueous solution. Unfortunately, these methods require extensive knowledge of the sample
850     matrix and chemical form of the contaminant, which is seldom available. Often, gross
851     radioactivity measurements  using liquid scintillation counting techniques or broad spectrum
852     direct measurements such as gamma spectroscopy are the only measurements that can be
853     practically performed on nonaqueous liquids.

854     13.6.2  Solid Samples

855     Decomposition of solid samples is accomplished by applying fusion, wet ashing, leaching, or
856     combustion techniques singly or in some combination. A discussion of each of these techniques
857     is included in this chapter.

858     13.6.3  Filters

859     Air filter samples generally  have a small amount  of fine paniculate material on a relatively small
860     amount of filter media. In many cases, filters of liquid  samples also have limited amounts of
861     sample associated with the filter material. This situation may initially appear to make the sample
862     decomposition process much easier, the small amount  of sample appears to dissolve readily in a
863     simple acid dissolution. The ease with which many filters dissolve in concentrated acid does not
864     always mean that the sample has dissolved, and the fine particles are often impossible to see in
865     an acid solution. If the radionuclides of concern are known to be in the oxide form, or if the
866     chemical form of the contaminants is unknown, a simple acid dissolution will not completely


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867     dissolve the sample. In these cases, the sample may be dry ashed to destroy the filter and the
868     residue subjected to fusion or other decomposition of oxides in the sample.

869     13.6.4 Wipe Samples

870     If oxides and silicates are not present in wipe samples, acid dissolutions are generally acceptable
871     for sample decomposition. In many cases, it is not the sample but the material from which the
872     wipe is constructed that causes problems with acid dissolution. Paper wipes are decomposed
873     easily in sulfuric-nitric solutions or in perchloric nitric solutions or by combustion, and it may be
874     necessary to dry ash the sample before dissolution. If volatile isotopes are expected, precautions
875     must be taken to prevent loss when heating (see Section 14.5, "Volatilization and Distillation).
876     "Sticky" smears can be more difficult to dissolve—the glue can be especially troublesome and
877     should be watched closely if perchloric acid is used. Other materials used for wipe samples
878     should be evaluated on an individual basis to determine the best method for sample
879     decomposition. In some cases, the sample will be a problem to decompose as well. Oil and
880     grease are often collected on wipe samples from machinery, and these samples are usually dry
881     ashed before acid dissolution to remove the organic material. If large amounts of solid material
882     (i.e., soil, dust,  etc.) are collected with the wipe, it is  recommended that the sample be treated as
883     a solid (the analytical protocol specification or the project manager should be consulted before
884     removing the wipe and simply analyzing the solid sample).

885     13.6.5 Liquid  Scintillation Samples

886     Sample oxidation is used in association with liquid scintillation counting to enhance the
887     solubility of samples, decolorize samples to limit quenching, separate radionuclides, concentrate
888     the analyte from bulk material, or for a combination of these reasons.

889     13.6.5.1   Wet Oxidation

890     Wet oxidation reagents are used to liberate 14CO2,3H2O, and 35SO3 from samples containing 14C,
891     3H, and 35S, respectively, with limited success (Gibbs et al., 1978;  Peng, 1977). Nitric acid, nitric
892     acid with perchloric acid, fuming sulfuric acid with periodate and  chromic acid,  and perchloric
893     acid with hydrogen peroxide are employed. However, a consequence of using these strong
894     reagents is the production of chemiluminescence. Moreover, these reagents also  suppress
895     counting efficiency because they are strong quenching agents.
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896     13.6.5.2   Dry Oxidation

897     Dry oxidation refers to combustion of the sample in an oxygen atmosphere to yield the highest
898     oxides, e.g., H2O, CO2, SO3. Sample oxidizers are currently available for liquid scintillation
899     based upon this approach. The current system uses a continuous flow of oxygen to ensure
900     complete oxidation of the sample and to force the gaseous products through the H2O and CO2
901     collection regions and any untrapped gases to vented waste. The sample is loaded into a
902     platinum-rhodium wire basket and then is sealed into the combustion flask. Oxygen begins to
903     flow as an electric current passes through the wire basket to ignite the sample. The continuous
904     flow of O2 sweeps the gaseous combustion products into the air-cooled condenser. The collection
905     of the combustion products consists of two consecutive stages. First, the water produced in the
906     combustion process is condensed at 2 °C and collected. Second, the CO2 produced in the
907     combustion is isolated by a CO2 absorber. Each fraction is then mixed with liquid scintillation
908     cocktail and counted. This instrument is designed to give highly reproducible recoveries of 3H
909     and 14C while eliminating chemiluminescence and various quenching problems.

910     13.7  Total Dissolution and Leaching

911     Sample dissolution can be one of the biggest challenges facing the analyst because the adequacy
912     of the dissolution has direct and profound effects on the resultant data. The analyst must balance
913     numerous factors such as the nature of the sample and the analyte (e.g., is it refractory or
914     volatile?), the effects of excess reagents during subsequent analyses, the accuracy and precision
915     requirements for the data, and the costs associated with effort, materials, and waste generation.
916     Consequently, the question of total  dissolution through fusion or digestion, or through acid
917     leaching, is under constant debate, and it is important for the analyst to be  aware of the
918     limitations of both methods.

919     The MARLAP process enables one to make a decision concerning the dissolution required
920     through its process of establishing data quality objectives, analytical protocol specification, and
921     measurement quality objectives. During this process, all pertinent information is available to the
922     radioanalytical specialist who then evaluates the alternatives and assists with the decision. The
923     following discussion on acid leaching focuses on its use for the complete dissolution of the
924     analyte of interest and not for such procedures as the Environmental Protection Agency's
925     "Toxicity Characteristic Leaching Procedure" (TCLP; 40 CFR 261), which are intended to
926     determine the teachability of a chemical.
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        Sample Dissolution
927     13.7.1 Acid Leaching

928     "Acid leaching" has no accepted definition, but will be defined here as the use of nitric or
929     hydrochloric acid to put the radionuclide into solution. The acid concentration may vary up to
930     and include concentrated acid. Normally, the use of hydrofluoric acid and aqua regia are not
931     included in this definition. Sample size is usually relatively much larger than that used for fusion.
932     Although mineral acids might not totally break down all matrices, they have been shown to be
933     effective leaching solvents for metals, oxides, and salts in some samples. In some cases, leaching
934     requires fewer chemicals and less time to accomplish than complete sample dissolution. For
935     matrices amenable to leaching, multiple samples are easily processed simultaneously using a
936     hotplate or microwave system, and excess reagents can be removed through evaporation.
937     Complete dissolution of a sample is not necessary if it can be demonstrated confidently that the
938     radionuclide of interest is completely leached from the sample medium. However, if complete
939     dissolution of the analyte cannot be so demonstrated,  then it may be necessary to compare
940     leaching data with data from totally dissolved samples in order for the analyst to determine the
941     appropriate method for total analyte content of a specific set of samples. When leaching is a
942     viable option for analyte removal, as an alternative to complete dissolution, the samples can be
943     treated with strong acids to leach all or a large fraction of the radionuclides of interest from solid
944     media. It may be possible to complete the dissolution of leach residue with hot  aqua regia and
945     then followed by hot hydrofluoric acid. The use of these acids is usually used on relatively small
946     sample residues and may also be used on small samples.

947     Sill and Sill (1995) point out that:

948        "In many cases, the mono-, di-, and small tervalent elements can be leached fairly
949        completely from simple solids by boiling with concentrated hydrochloric or nitric acids.
950        However, even these elements cannot necessarily be guaranteed to be dissolved
951        completely by selective leaching. If they are included in a refractory matrix, they will not
952        be removed completely without dissolution of the matrix. If the samples have been
953        exposed to water over long periods of time, such as with sediments in a radioactive waste
954        pond, small ions such as divalent cobalt will have diffused deeply into the rock lattice
955        from which they cannot be removed without complete dissolution of the host matrix. In
956        contrast, because of its large size, ionic cesium has a marked tendency to undergo
957        isomorphous replacement in the lattice of complex silicates from which it too cannot be
958        removed completely. In some unpublished work by the present authors, 15% of the 137Cs
959        and 5% of the 60Co in some pond sediments remained in the residue after extensive
960        leaching,  and could not be removed by further boiling for two hours with either
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                                                                                Sample Dissolution
961         concentrated nitric or hydrochloric acids. The fraction remaining in the residue was
962         obviously much greater with shorter, more reasonable leaching times."

963     13.7.2 Total Dissolution through Fusion

964     There are those within the radiochemistry community who maintain that leaching techniques are
965     always inadequate. Sill and Sill (1995), longtime proponents of total dissolution, state, "Any
966     procedure that fails to obtain complete sample dissolution for whatever reasons of economy,
967     speed, sample load, or other expediency is untrustworthy at best, and will inevitably give low and
968     erratic results." They go on to support their argument:

969         "The large ter-, quadri-, and pentavalent elements are extremely hydrolytic and form
970         hydroxides, phosphates, silicates, carbides, etc., that are very insoluble and difficult to
971         dissolve in common acids, particularly if they have been heated strongly and converted to
972         refractory forms. For example, eight samples of soil taken in the vicinity of a plutonium-
973         handling facility were analyzed in the facility's own laboratory for 239Pu by their routine
974         procedure involving leaching with nitric acid in the presence of 236Pu tracer. The insoluble
975         residues were then analyzed for the same radionuclide by one of the present authors using
976         a procedure involving complete dissolution in  a potassium fluoride fusion in the presence
977         of 236Pu tracer. Four of the residues contained more 239Pu than the corresponding
978         leachates, three residues contained about half as much as the leachates, and only one
979         contained as little as 22%, largely because that sample contained relatively high activity
980         of the radionuclide (Sill, 1981). None of the water-soluble 236Pu tracer used in the original
981         leach  determination was present in any of the residues, showing that heterogeneous
982         exchange did not occur (Sill, 1975). The original results from leaching were, therefore,
983         grossly inaccurate."

984     However, there are also disadvantages and challenges associated with the fusion approach.
985     Fusions are frequently more labor intensive than the leaching approach. More often than not, it is
986     one sample at a time using a burner. Large quantities of the flux are generally required to
987     decompose most substances, often 5 tolO times the sample weight. Therefore, contamination of
988     the sample by impurities in the reagent is quite possible. Furthermore, the aqueous solutions
989     resulting from the fusions will have a very high salt content, which may lead to difficulties in
990     subsequent steps of the analysis, i.e., difficulties of entrainment, partial replacements, etc. The
991     high temperatures associated with these fusions increase the danger of loss of certain analytes by
992     volatilization. Finally, the  crucible itself is often attacked by the flux, once again leading to
993     possible contamination of the sample. The typical  sample size for fusions ranges from typically
994     one to ten grams. The analyst must consider whether a this sample is representative.


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         Sample Dissolution
 995      13.7.3  Acid Digestion — Fusion Combined Approach

 996      Clearly, the sample history, as well as the analytical protocol specifications of a study, should
 997      play a significant role in the choice of analytical method. The analyst must be certain that the
 998      chosen dissolution technique will provide adequate data for the problem at hand, whether it be
 999      through acid leaching or total dissolution. However, as a compromise, it is common practice to
1000      employ a combination of the two approaches when the majority of the material to be analyzed is
1001      acid-soluble. First, an acid leach is applied to the bulk of the  sample. Then any undecomposed
1002      residue is isolated by filtration and fused with a relatively small quantity of suitable flux. Finally,
1003      the melt is dissolved and combined with the rest of the sample.

1004      Through this approach, the total matrix is decomposed, but the problems, such as reagent
1005      quantity, and sample and fusion vessel size (commonly associated with fusions), are limited. The
1006      quantities of added salt are less; therefore, the sources of contamination or of subsequent
1007      chemical interferences are reduced. Moreover, losses because of volatility tend to be less because
1008      only a small fraction of the sample is exposed to the high temperatures associated with the fusion
1009      process.

1010      13.8   Examples of Decomposition Procedures

1011      DECOMPOSITION OF ORGANIC MATERIAL WITH SULFURIC AND NITRIC ACIDS. Add H2SO4 to the
1012      sample and heat to fumes in a Kjeldahl flask. Add concentrated HNO3 by drops to the flask,
1013      allowing the reaction to subside after each addition. Periodically heat to fuming to remove water
1014      and to keep the temperature high. When the solution is clear and colorless, the reaction is
1015      complete. Very reactive material can be left overnight in a 1:1 solution of the acids. Red or white
1016      fuming nitric acid can be used to speed up the reaction, if necessary.

1017      DECOMPOSITION OF ORGANIC MATERIAL WITH PERCHLORIC AND NITRIC ACIDS. The  acids can be
1018      added to the sample as a mixture or the sample can be treated with concentrated FDSTO3 first to
1019      destroy any highly reactive material. The solution is heated to drive off the HNO3 and to raise the
1020      temperature to 160  °C, where the HC1O4 begins to oxidize the organic material. The  reaction is
1021      generally accompanied by foaming, and HNO3 is used to cool the solution and to control the
1022      formation of foam.  The solution should be cooled immediately if any layer of material begins to
1023      separate and turn brown. HNO3 is added to the sample before it is returned to the hot plate. The
1024      transition into HC1O4 continues until the foaming is completed and dense white fumes are
1025      evolved, indicating that HC1O4 is being evaporated. The volume is reduced and the solution
1026      converted to FDSTO3 by repeated addition of FDSTO3 and evaporation to near dryness.
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                                                                                Sample Dissolution
1027      DECOMPOSITION OF A SAMPLE OF UNKNOWN COMPOSITION (Noyes and Bray, 1927/1943; Bock,
1028      1979, Appendix  1). First, destroy the organic material with perchloric and nitric acids and then
1029      perform an oxidizing dissolution with HBr and Br2. Separate the residue and oxidize it with nitric
1030      acid. Subsequently, heat to fumes with perchloric acid and HF to destroy any silicates present.
1031      Combine with the Fffir solution and distill off the bromides of arsenic, germanium, and selenium.
1032      Oxidize the residue with nitric acid, add sodium peroxide, and distill off osmium as the tetroxide.
1033      Add perchloric acid and distill off ruthenium as the tetroxide. Reduce the contents of the flask
1034      with formic acid. Separate the residue, and leach with FTP to dissolve niobium, tantalum, and
1035      tungsten. Separate the residue, and fuse with sodium carbonate to convert fluorides to carbonates;
1036      then dissolve the melt in dilute perchloric acid. Separate the residue, and treat with aqua regia to
1037      dissolve the gold group metals. Separate the residue, and treat with ammonia to dissolve silver.
1038      Separate the residue,  and fuse with K2S2O7; then dissolve the melt in water. Separate the residue,
1039      and fuse with sodium peroxide.

1040      DECOMPOSITION OF SOIL FOR ACTINIDE ANALYSIS (Sill et al., 1974; Sill and Sill, 1995). Sill has
1041      described a potassium fluoride-potassium pyrosulfate fusion technique that can be used before
1042      elemental separation  for the alpha-emitting nuclides of radium through californium. The organic
1043      matter of the soil is initially destroyed by heating the sample with nitric acid in a platinum
1044      crucible. To a 1 g sample, potassium fluoride is added and mixed well. The potassium fluoride
1045      fusion is carried out using a blast burner at approximately 900 °C. After the melt is cooled,
1046      concentrated sulfuric acid is added, and the mixture is heated to decompose the potassium
1047      fluoride cake, with the simultaneous volatilization of hydrogen fluoride and silicon tetrafluoride.
1048      After the cake is  completely transformed, anhydrous sodium sulfate is added and the pyrosulfate
1049      fusion is performed. The resultant cake is then dissolved in dilute HC1 before subsequent
1050      elemental separation.

1051      13.9   References

1052      13.9.1  Cited References

1053      Abu-Samra, A., Morris, J.S., and Koirtyohann, S.R. 1975. "Wet  Ashing of Some Biological
1054          Samples in a  Microwave Oven," Analytical Chemistry, 47:8, pp 1475-1477.

1055      Alvarado, J.S., Neal,  T.J., Smith, L.L., and Erickson, M.D. 1996. "Microwave Dissolution of
1056         Plant Tissue and the Subsequent Determination of Trace Lanthanide and Actinide Elements
1057         by Inductively Coupled Plasma-Mass Spectrometry," Analytica Chimica Acta, Vol. 322, pp.
1058          11-20.
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         Sample Dissolution
1059      American Society of Testing Materials (ASTM) D4309. "Standard Practice for Sample Digestion
1060         Using Closed Vessel Microwave Heating Technique for the Determination of Total Metals in
1061         Water," in 1994 Annual Book of ASTM Standards, Vol. 11.01, 1996.

1062      American Society of Testing Materials (ASTM) D5258. "Standard Practice for Acid-Extraction
1063         from Sediments Using Closed Vessel Microwave Heating," in 1992 Annual Book of ASTM
1064         Standards, Vol. 11.02, 1992.

1065      American Society of Testing Materials (ASTM) D5513. "Standard Practice for Microwave
1066         Digestion of Industrial Furnace Feedstreams for Trace Element Analysis," in 1994 Annual
1067         Book of ASTM Standards, Vol. 11.04, 1994.

1068      Bock, R. 1979. A Handbook of Decomposition Methods in Analytical Chemistry., Halsted Press,
1069         John Wiley and Sons, New York.

1070      Bogen, DC. 1978. "Decomposition and Dissolution of Samples: Inorganic," in Kolthoff, I.M. and
1071         Elving, P.J., Eds., Treatise on Analytical Chemistry, Part I, Vol. 5, Wiley-Interscience, New
1072         York, pp. 1-22.

1073      Booman, G.L. and Rein, I.E. 1962. "Uranium," in Treatise on Analytical Chemistry, Kolthoff,
1074         I.M. and Elving, P.J., Eds., Part II, Volume  9, John Wiley and Sons, New York, pp. 1-188.

1075      40 CFR 261, Appendix II, Method 1311, "Toxicity Characteristic Leaching Procedure (TCLP)."

1076      Cobble, J.W. 1964. "Technetium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
1077         Elving, P.J., Eds., Part II, Volume 6, John Wiley and Sons, New York, pp. 404-434.

1078      Coomber, D.I. 1975. "Separation Methods for Inorganic Species," in RadiochemicalMethods in
1079         Analysis, Coomber, D.I., Ed., Plenum Press, New York, pp. 175-218.

1080      Dean, J.  1995. Analytical Chemistry Handbook, McGraw-Hill, New York.

1081      Environmental Protection Agency (EPA). December, 1996. "Microwave Assisted Digestion of
1082         Siliceous and Organically Based Materials," in Test Methods for Evaluating Solid Waste,
1083         Physical/Chemical Methods, SW-846, Method 3052.

1084      Gibbs, J., Everett, L., and Moore, D. 1978. Sample Preparation for Liquid Scintillation
1085         Counting, Packard Instrument Co., Downers Grove, IL., pp  65-78.


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                                                                             Sample Dissolution
1086      Oilman, L.B., and Engelhardt, W.G. 1989. "Recent Advances in Microwave Sample
1087         Preparation," Spectroscopy, 4:8, pp. 4-21.

1088      Grille, A.C. 1989. "Microwave Digestion by Means of a Focused Open-Vessel System,"
1089         Spectroscopy, Vol. 4, No. 7, pp. 16-21.

1090      Grille, A.C. 1990. "Microwave Digestion Using a Closed Vessel System," Spectroscopy, Vol. 5,
1091         No. 1, pp. 14, 16, 55.

1092      Grindler, I.E. 1962. The Radiochemistry of Uranium, National Academy of Sciences-National
1093         Research Council (NAS-NS), NAS-NS 3050, Washington, DC.

1094      Hahn, R.B. 1961. "Zirconium and Hafnium," in Treatise on Analytical Chemistry, Kolthoff, I.M.
1095         and Elving, P.J., Eds., Part n, Volume 5, John Wiley and Sons, New York, pp. 61-138.

1096      Kammin, W.R., and Brandt, M.J. 1989. "The Simulation of EPA Method 3050 Using a High-
1097         Temperature and High-Pressure Microwave Bomb," Spectroscopy, 4:6, pp. 22, 24.

1098      Kingston, H.M., and lassie, L.B. 1988. Introduction to Microwave Sample Preparation: Theory
1099         and Practice, American Chemical Society, Washington, DC.

1100      Kingston, H.M., and SJ. Haswell.  1997. Microwave-Enhanced Chemistry: Fundamentals,
1101         Sample Preparation, and Applications, American Chemical Society, Washington, DC.

1102      Lautenschlager, W. 1989. "Microwave Digestion in a Closed-Vessel, High-Pressure System,"
1103         Spectroscopy, 4:9, pp. 16-21.

1104      Noltner, T., Maisenbacher, P., and  Puchelt, H. 1990. "Microwave Acid Digestion of Geological
1105         and Biological  Standard Reference Materials for Trace Element Analysis by ICP-MS,"
1106         Spectroscopy, 5:4, pp. 49-53.

1107      Noyes,  A.A.  and Bray, W.C.  1927, reprint 1943. A System of Qualitative Analysis for the Rarer
1108         Elements, MacMillan, New York.

1109      Peng, T. 1977. Sample Preparation in Liquid Scintillation Counting, Amersham Corporation,
1110         Arlington Heights, IL., pp. 48-54.
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         Sample Dissolution
1111      Schilt, A. 1979. Perchloric Acids and Perchlorates, The G. Frederick Smith Company,
1112         Columbus, Ohio.

1113      Sill, C.W., Puphal, K.W., and Hindman, F.D. 1974. "Simultaneous Determination of Alpha-
1114         Emitting Nuclides or Radium through Californium in Soil," Analytical Chemistry, 46:12, pp.
1115         1725-1737.

1116      Sill, C.W.  1975. "Some Problems in Measuring Plutonium in the Environment," Health Physics,
1117         Vol. 29, pp. 619-626.

1118      Sill, C.W.  1981. "A Critique of Current Practices in the Determination of Actinides," in
1119         Actinides in Man and Animal, Wren, M.E., Ed., RD Press, Salt Lake City, Utah, pp. 1-28.

1120      Sill, C.W. and Sill, D.S. 1995. "Sample Dissolution," Radioactivity and Radiochemistry, 6:2, pp.
1121         8-14.

1122      Smith, LL., Crain, J.S., Yaeger, J.S., Horwitz, E.P., Diamond, H., and Chiarizia, R.  1995.
1123         "Improved Separation Method for Determining Actinides in  Soil  Samples," Journal of
1124         RadioanayticalNuclear Chemistry, Articles, 194:1, pp. 151-156.

1125      Smith, L.L. and Yaeger, J.S.  1996. "High-Pressure Microwave Digestion: A Waste-Minimization
1126         Tool for the Radiochemistry Laboratory," Radioactivity and Radiochemistry, 7:2, pp. 35-38.

1127      Steinberg, E.O. 1960. The Radiochemistry of Zirconium and Hafnium, National Academy of
1128         Sciences-National Research Council (NAS-NRC), NAS-NRC 3011, Washington, DC.

1129      Sulcek, Z., and Povondra, P.  1989. Methods for Decomposition in Inorganic Analysis, CRC
1130         Press, Inc., Boca Raton, Florida.

1131      13.9.2  Other Sources

1132      Bishop, C.T., Sheehan, W.E., Gillette, R.K., and Robinson, B. 1971.  "Comparison of a Leaching
1133         Method and a Fusion Method for the Determination of Plutonium-23 8 in Soil," Proceedings
1134         of Environmental Symposium, Los Alamos Scientific Laboratory, Los Alamos, NM, U.S.
1135         Atomic Energy Commission, Document LA-4756, December, pp. 63-71.
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                                                                            Sample Dissolution
1136      Department of Energy (DOE). 1990. EML Procedures Manual, Chieco, N.A., Bogen, DC., and
1137         Knutson, E.O., Eds., HASL-300, 27th Edition, DOE Environmental Measurements
1138         Laboratory, New York.

1139      Environmental Protection Agency (EPA). 1992. Guidance for Preforming Site Inspections Under
1140         CERCLA, EPA/540-R-92-021, Office of Solid Waste and Emergency Response, Washington,
1141         DC.

1142      Environmental Protection Agency (EPA). 1997. Multi-Agency Radiation Survey and Site
1143         Investigation Manual (MARSSIM), Petullo, C.F., Chair, NUREG-1575, EPA 402-R-97-016,
1144         Washington, DC.

1145      Grimaldi, F.S. 1961. "Thorium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving,
1146         P.J., Eds., Part II, Volume 5, John Wiley and Sons, New York, pp.  142-216.

1147      Krey, P.W. and Bogen, DC. 1987. "Determination of Acid Leachable and Total Plutonium in
1148         Large Soil Samples," Journal of Radioanalytical and Nuclear Chemistry, 115:2 pp. 335-355.

1149      Smith, L.L., Markun, F., and TenKate, T. 1992. "Comparison of Acid Leachate and Fusion
1150         Methods to Determine Plutonium and Americium in Environmental Samples," Argonne
1151         National Laboratory, ANL/ACL-92/2.
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                          14  SEPARATION TECHNIQUES
 2     14.1  Introduction

 3     The methods for separation, collection, and detection of radionuclides are similar to ordinary
 4     analytical procedures and employ many of the chemical and physical principles that apply to their
 5     nonradioactive isotopes. However, some important aspects of the behavior of radionuclides are
 6     significantly different, resulting in challenges to the radiochemist to find a means for isolation of
 7     a pure sample for analysis (Friedlander et al., 1981, pp. 292-293).

 8     The contents of Chapter 14 provide in one reference document: (1) a review of the important
 9     chemical principles that constitute the foundation of radiochemical separations, (2) a survey of
10     the important separation methods used in radiochemistry with a discussion of the advantages  and
11     disadvantages of each method, and (3) an examination of the particular features of radioanalytical
12     chemistry that differentiate it from ordinary analytical chemistry. Extensive examples have been
13     employed throughout the chapter to illustrate various principles, practices, and procedures in
14     radiochemistry. Many were purposely selected from agency procedural manuals to provide
15     illustrations from familiar and available documents. Others were taken from the classical and
16     recent radiochemical literature to afford a broad, general overview of the subject.

17     The material  in this chapter is presented in three topic areas. It begins with a review of oxidation-
18     reduction processes and complex-ion formation, two subjects that constitute the principal
19     foundation of radiochemistry procedures and provide background for the topics to follow. The
20     chapter continues with a description of separation techniques commonly found in radiochemical
21     procedures: solvent extraction, volatilization and distillation, electrodeposition, chromatography,
22     and precipitation and coprecipitation. It  concludes with two subjects unique to radioanalytical
23     chemistry: carriers and tracers, and radiochemical equilibrium. This organization is designed  to
24     provide a developmental approach to the description of each topic area. Explanation of the
25     separation techniques, for example, is dependent on basic chemical principles generally known to
26     the reader, as well as the specific principles developed in the preceding sections. Descriptions of
27     carriers and tracers, and radiochemical equilibrium are contingent on an adequate knowledge of
28     preceding topics, and their explanation makes extensive use of the principles developed in these
29     sections. In all sections of Chapter 14, specific radionuclide examples are used to illustrate the
30     principles and practices involved. Practical guidance is also provided for the practicing
31     radiochemist.

32     Because the radiochemist detects atoms by their radiation, the success or failure of a radio-
33     chemical procedure often depends on the ability to separate extremely small quantities of
34     radionuclides (e.g., 10"6 to 10"12 g) that might interfere with detection of the analyte. For example,

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        Separation Techniques
35     isolation of trace quantities of a radionuclide that will not precipitate on their own with a counter-
36     ion requires judicious selection of a carrier and careful technique to produce a coprecipitate
37     containing the pure radionuclide, free of interfering ions. In detection procedures, the differences
38     in the behavior of radionuclides provide unique opportunities not available in the traditional
39     analytical chemistry of nonradioactive elements. Radionuclides can often be detected by their
40     unique radiation regardless of the chemical form of the element. There is also a time factor
41     involved, because of the short half-lives of some radionuclides. Traditional procedures involving
42     long digestions or slow filtrations cannot be  used for short-lived radionuclides, thereby requiring
43     that rapid separations be developed. Another distinction is the hazards associated with
44     radioactive materials. At very high activity levels (radiolysis), chemical effects of the radiation,
45     such as decomposition of solvents and heat effects, can affect the procedures. Equally important,
46     even  at lower activity levels, is the radiation dose (especially with gamma-emitters) that the
47     radiochemist can receive unless protected by shielding or distance. Even at levels where the
48     health concerns are minimal, special care needs to be taken to guard against laboratory and
49     equipment contamination. Moreover, the modern radiochemist should be concerned about the
50     type and quantity of the waste generated by the chemical procedures employed, because the costs
51     and difficulties associated with the disposal  of low-level and mixed radioactive waste continue to
52     rise. A review of the basic chemical principles that apply to the analysis of radionuclides is
53     presented in this chapter with an emphasis on the unique behavior of radionuclides.

54     14.2  Oxidation/Reduction Processes

55     14.2.1 Introduction

56     Oxidation and reduction (redox) processes play an important role in radioanalytical chemistry,
57     particularly from the standpoint of the dissolution, separation, and detection of analytes, tracers,
58     and carriers. Ion exchange, solvent extraction, and solid-phase extraction separation techniques,
59     for example, are highly dependent upon the oxidation state of the analytes. Moreover, most
60     radiochemical procedures involve the addition of a carrier or isotope tracer, and to achieve
61     quantitative yields, there should be complete equilibration (isotopic exchange) between the added
62     isotope(s) and all the analyte species present. The oxidation number of a radionuclide can affect
63     its (1) chemical stability in the presence of water, oxygen,  and other natural substances in
64     solution; (2) reactivity with reagents used in the radioanalytical procedure; (3) solubility in the
65     presence of other ions and molecules; and (4) behavior in the presence of carriers and tracers.
66     The oxidation numbers of radionuclides in solution and their susceptibility to change, because of
67     natural or induced redox processes, are critical, therefore, to the physical and chemical behavior
68     of radionuclides during these analytical procedures. The differences in mass number of all
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69     radionuclides of an element are so small that elements with the same oxidation number will
70     exhibit the same chemical behavior during radiochemical analysis.

71     14.2.2 Oxidation-Reduction Reactions

72     An oxidation-reduction reaction (redox reaction) is a reaction in which electrons are
73     redistributed among the atoms, molecules, or ions in the reaction. In some redox reactions,
74     electrons are actually transferred from one reacting species to another. Oxidation under these
75     conditions is defined as the loss of electron(s) by an atom or other chemical species, whereas
76     reduction is the gain of electron(s). Two examples will illustrate this type of redox reaction:

77                                        U + 3 F2 - U+6 + 6 F1

78                                      Pu+4 + Fe+2 - Pu+3 + Fe+3

79     In the first reaction, uranium (U) loses electrons, becoming a cation, and fluorine (F) gains an
80     electron, becoming an anion. In the second reaction, the reactants are already ions, but the
81     plutonium cation (Pu+4) gains electrons, becoming Pu+3, and the ferrous ion (Fe+2) loses electrons,
82     becoming Fe+3.

83     In other redox reactions, electrons are not completely transferred from one reacting species to
84     another: the electron density about an atom decreases, while it increases about another atom. The
85     change in electron density occurs as covalent bonds,  in which electrons are shared between two
86     atoms, are broken, and/or are made during a chemical reaction. In covalent bonds between two
87     atoms of different elements, one atom is more electronegative than the other atom. Electronega-
88     tivity is the ability of an atom to attract electrons in a covalent bond. One atom, therefore, attracts
89     the shared pair of electrons more effectively, causing a difference in electron density about the
90     atoms in the bond. An atom that ends up bonded to a more electronegative atom at the end of a
91     chemical reaction loses net electron density. Conversely, an  atom that ends up bonded to a less
92     electronegative atom gains net electron density. Electrons are not transferred completely to other
93     atoms, and ions are not formed because the electrons are still shared between the atoms in the
94     covalent bond. Oxidation, in this case, is defined as the loss  of electron density, and reduction is
95     defined as the gain of electron density. When carbon (C) is oxidized to carbon dioxide (CO2) by
96     oxygen (O2):

97                                            C + O2 - CO2
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 98     the electron density associated with the carbon atom decreases, and that of the oxygen atoms
 99     increases, because the electronegativity of oxygen is greater than the electronegativity of carbon.
100     In this example, carbon is oxidized and oxygen is reduced. Another example from the chemistry
101     of the preparation of gaseous uranium hexafluoride (UF6) illustrates this type of redox reaction:
102                                     3UF4 + 2C1F3 - 3UF6 + Cl
                                                                2
103      Because the order of electronegativity of the atoms increases in the order U
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                                                                          Separation Techniques
132      specific exception would be nitrogen in the cation and anion in ammonium nitrate, NH4NO3.) It
133      is important to remember that in many cases, oxidation numbers are not actual electrical charges,
134      but only a helpful bookkeeping method for following redox reactions or examining various
135      oxidation states. As we will see below, the oxidation number of atoms in isolated elements and
136      monatomic ions are actually the charge on the chemical species. The priority rules are:

137         1.   The sum of oxidation numbers of all atoms in a chemical  species adds up to equal the
138             charge on the species.  This is zero for elements and compounds because they are
139             electrically neutral species and are the total charge for a monatomic or polyatomic ion.

140         2.   The alkali metals (the IA elements, Li, Na, K, Rb, Cs, and Fr) have an oxidation number
141             of+1; the alkaline earth metals (the HA elements, Be, Mg, Ca, Sr, Ba, and Ra) have an
142             oxidation number of +2.

143         3.   Fluorine (F) has  an oxidation number of -1; hydrogen (H) has an oxidation number of +1.

144         4.   Oxygen has an oxidation number of -2.

145         5.   The halogens (the VIIA elements, F, Cl, Br, I, and At) have an oxidation number of -1.

146         6.   In binary compounds (compounds containing elements), the oxidation number of the
147             oxygen family of elements (the VIA elements, O, S, Se, Te, and Po) is -2; for the nitrogen
148             family of elements (the VA elements except Bi, N, P, As, and Sb), it is -3.

149      Applying these rules illustrates their use:

150         1.   The oxidation number of metallic uranium and molecular oxygen is 0. Applying rule one,
151             the charge on elements is 0.

152         2.   The oxidation number of Pu+4 is +4. Applying rule one again,  the charge is +4.

153         3.   The oxidation numbers of carbon and oxygen in CO2 are +4 and -2, respectively.
154             Applying rule one, the oxidation numbers of each atom must add up to the charge of 0
155             because carbon dioxide is a molecule. The next rule that applies is rule four. Therefore,
156             the oxidation number of each oxygen atom is -2. The oxidation number of carbon is
157             determined by C + 2(-2) = 0, or +4. Notice that there is no charge on carbon and oxygen
158             in carbon dioxide because the compound is molecular and does not consist of ions.
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159         4.  The oxidation numbers of calcium and hydrogen in calcium hydride (CaH2) are +2 and -1,
160             respectively. The compound is neutral, and the application of rule one requires that the
161             oxidation numbers of all atoms add up to 0. By rule two, the oxidation number of calcium
162             is +2. Applying rule one, the oxidation number of hydrogen is: 2H + 2=0, or -1. Notice
163             that in this example, the oxidation number as predicted by the rules does not agree with
164             rule three, but the number is determined by rules one and two, which take precedence
165             over rule three.

166         5.  The oxidation numbers of uranium and oxygen in the uranyl ion, UO2+2, are +6 and -2,
167             respectively. Applying rule one, the oxidation numbers of each atom must add up to the
168             charge of+2. Rule four indicates that the oxygen atoms are -2 each. Applying rule one,
169             the oxidation number of uranium is U + 2(-2) = +2, and uranium is +6. In this example,
170             the charges on uranium and oxygen are not actually +6 and -2, respectively, because the
171             polyatomic ion is held together through covalent bonds. The charge on the ion is the
172             result of a deficiency of two electrons.

173      Oxidation numbers (states) are commonly represented by zero and positive and negative
174      numbers, such as +4, -2, etc. They  are sometimes represented by Roman numerals for metals,
175      especially the oxidation numbers of atoms participating in covalent bonds of a molecule  or those
176      of polyatomic ions. Uranium in UO2+2 can be represented as U(VI) instead of U+6, or chromium
177      in Cr
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                                                                            Separation Techniques
192     Each of the transition metals has at least two stable oxidation states, except for Sc, Y, and La
193     (Group TUB), which exhibit only the +3 oxidation state. Generally, negative oxidation states are
194     not observed for these metallic elements. The large number of oxidation states exhibited by the
195     transition elements leads to an extensive, often complicated, oxidation-reduction chemistry. For
196     example, oxidation states from -1 through +7 have been observed for technetium, although the
197     +7 and +4 are most common (Anders, 1960, p. 4). In an oxidizing environment, Tc exists
198     predominantly in the heptavalent state as the pertechnetate ion, TcO^1, which is water soluble,
199     but which can yield insoluble salts with large cations. Technetium forms volatile heptoxides and
200     acid-insoluble heptasulfides. Subsequently, pertechnetate is easily lost upon evaporation of acid
201     solutions unless a reducing agent is present or the evaporation is conducted at low temperatures.
202     Technetium(VII) can be reduced to lower oxidation states by reducing agents such as bisulfite
203     (HSO34). This process proceeds through several intermediate steps, some of which are slow;
204     therefore, unless precautions are taken to maintain technetium in the appropriate oxidation state,
205     erratic results can be obtained. The +7 and +4 ions behave very differently in solution. For
206     instance, pertechnetate does not coprecipitate with ferric hydroxide, while Tc(IV) does.

207     The oxidation states of the actinide elements have been comprehensively discussed by Ahrland
208     (1986, pp. 1480-1481) and Cotton and Wilkinson (1988, pp. 985-987 and pp. 1000-1014). The
209     actinides exhibit an unusually broad range of oxidation states, of from +2 to +7 in solution.
210     Similar to the lanthanides, the most common oxidation state is  +3 for actinium (Ac), americium
211     (Am),  and curium (Cm). The +4 state is common for thorium and plutonium, whereas +5 is most
212     common for protactinium (Pa) and neptunium (Np). The most stable state for uranium is the +6
213     oxidation state.

214     In compounds of the +3  and +4  oxidation states, the elements are present as simple M+3 or M+4
215     cations; but for higher oxidation states, the most common forms in compounds and in solution
216     are the oxygenated actinyl ions,  MO2+1 and MO2+2:

217      •  M+3. The +3 oxidation state is the most stable condition for actinium, americium, and curium.
218         It is easy to  produce Pu+3. This stability is of critical importance to the radiochemistry of
219         plutonium. Many separation schemes take advantage of the fact that Pu can be selectively
220         maintained in either the +3 or +4 oxidation state. Unlike Pu and Np, U+3 is such a strong
221         reducing agent that it is difficult to keep  in solution.

222      •  M+4. The only oxidation state of thorium that is experienced in radiochemical separations is
223         +4. Pa+4, U+4, and Np+4 are stable, but they are easily oxidized by O2 In acid solutions with
224         low plutonium concentrations, Pu+4 is stable. Americium and curium can be oxidized to the
225         +4 state with strong oxidizing agents such as persulfate.


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226      •  M+5. The actinides from protactinium through americium form MO2+1 ions in solution. PuO2+1
227         can be the dominant species in solution at low concentration in natural waters that are
228         relatively free of organic material.

229      •  M+6. M+6 is the most stable oxidation state of uranium, which exist as the UO2+2 species.
230         Neptunium, plutonium, and americium also form MO2+2 ions in solution. The bond strength,
231         as well as the chemical stability toward reduction for these MO2+2 ions, decrease in the order
232         U > Np > Pu > Am.

233     Reactions that do not involve making or breaking bonds, M+3 ^ M+4 or MO2+1 ^ MO2+2, are fast
234     and reversible, while reactions that involve chemical bond formation, M+3 -> MO2+1 or
235     M+4 ^ MO2+2, are slow and irreversible.

236     Plutonium exhibits redox behavior unmatched in the periodic table. It is possible to prepare
237     solutions of plutonium ions with appreciable concentrations of four oxidation states, +3, +4, +5,
238     and +6, as Pu+3, Pu+4, PuO2+1, and PuO2+2, respectively. [Detailed discussions can be found in
239     Cleveland (1970), Seaborg and Loveland (1990), and in the Coleman (1965) monograph.]
240     According to Cleveland (1970), this polyvalent behavior occurs because of the tendency of Pu+4
241     and Pu+5 to disproportionate:

242                                3Pu+4 + 2H2O - 2Pu+3 + PuO2+2 + 4H+1

243                               3PuO2+1 + 4H+1 - Pu+3 + 2PuO2+2 + 2H2O

244     and because of the slow rates of reaction involving formation or rupture of Pu-O bonds (such as
245     PuO2+ andPuO22+) compared to the much faster reactions involving only electron transfer. The
246     distribution depends on the type and concentration of acid used for dissolution, the method of
247     solution preparation, and the initial concentration of the different oxidation states. In HC1, HNO3,
248     and HC1O4, appreciable concentrations of all four states exist in equilibrium. Seaborg and
249     Loveland (1990, p. 88) report that in 0.5 M HC1 at 25 °C, the equilibrium percentages of
250     plutonium in the various oxidation states are found to be as follows:

251
252
253
254
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Pu+3
Pu+4
Pu+5
Pu+6
27.2%
58.4%
-0.7%
13.6%

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                                                                           Separation Techniques
255
256
257
258
259
260
261

262
263
264

265

266

267
Apart from the disproportionation reactions, the oxidation state of plutonium ions in solution is
affected by its own decay radiation or external gamma and X-rays. Radiolysis products of the
solution can oxidize or reduce the plutonium, depending on the nature of the solution and the
oxidation state of plutonium. Therefore, the stated oxidation states of old plutonium solutions,
particularly old HC1O4 and H2SO4 solutions, should be viewed with suspicion. Plutonium also
tends to hydrolyze and polymerize in solution, further complicating the situation (see Section
14.10, Radiochemical Equilibrium).

Tables 14.1 and 14.2 summarize the common oxidation number(s) of some important elements
encountered in the radioanalytical chemistry of environmental samples and the common
chemical form of the oxidation state.

                       TABLE 14.1 — Oxidation states of elements(1)
Element
Am




Cs
Co

Fe


3H

I



Ni
Nb

Po
Oxidation
State(2)
+3
+4
+5

+6
+1
+2
+3
+2
+3

+1

-1
-1/3
+5
+7
+2
+3
+5
+4
Chemical
Form
Am+3
Am+4
AmO2+1

Am02+2
Cs(H20)x+1
Co(H20)6+2
Co(H20)6+3
Fe(H20)6+2
Fe(H20)6+3

3HOH and
3HOH2O+1
r1
V1
io3-'
KV1
Ni(H20)6+2
Unknown
HNb6019-7

Notes
Pink; stable; difficult to oxidize
Pink-red; unstable in acid
Pink-yellow; disproportionates in strong acid; reduced by products
of its own radiation
Rum color; stable
Colorless; x probably is 6
Pink to red; oxidation is very unfavorable in solution
Rapidly reduced to +2 by water unless acidic
Green
Pale violet; hydrolyses in solution to form yellow or brown
complexes
Exchange of tritium is extremely rapid in samples that have water
introduced.
Colorless
Brown; commonly in solutions of I"1 exposed to air
Colorless; formed in vigorously oxidized solutions
Colorless
Green
In sulfuric acid solutions of Nb2O5


268
269

270


271

272
273
274

275
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Element
Pu









Ra
Sr
Tc


Th
U




Zr


Oxidation
State*2'
+3
+4
+5



+6
+7


+2
+2
+4
+5
+7
+4
+3
+4
+5
+6

+4


Chemical
Form
Pu(H20)x+3
Pu(H20)x+4
Pu(H20)x+5
or

PuO2+5
Pu02+5
Pu05-3
or
PuO4(OH)2-3
Ra(H2O)x+2
Sr(H20)x+2
Tc03-2
TcO3-'
TcO4-'
Th(H20)8+4
U(H20)X+3
U(H20)8.9+4
U02+1
U02(l^20)5+

Zr(H20)6+4
Zr4(OH)8(H2
O^,/2
Notes
Violet; stable to air and water; easily oxidized to +4
Tan; first state formed in freshly prepared solutions; stable in 6 M
acid; disproportionates in low acidity to +3 and +6
Never observed alone; always disproportionates; most stable in low
acidity
Purple
Yellow-pink; stable but fairly easy to reduce
Green

PuO4(OH)2-3 more likely form
Colorless; behaves chemically like Sr and Ba
Colorless



Colorless; at pH>3 forms complex hydrolysis products
Red-brown; slowly oxidized by water and rapidly by air to +4
Green; stable but slowly oxidized by air to +6
Unstable but more stable at ph 2-4; disproportionates to +4 and +6
Yellow; only form stable in solution containing air; difficult to
reduce
Only at very low ion concentrations and high acidity
At typical concentrations in absence of complexing agents

276
277
278
279


280
281
282
283
284
285
(1)  Compiled from: Booman and Rein, 1962;
    Earnshaw, 1984; Grinder, 1962; Hampel,
(2)  Most common form is in bold.
Cotton and Wilkinson, 1988; Emsley, 1989; Greenwood and
1968; Katzin, 1986; Latimer, 1952; and Pauling, 1970.
286
287
288
289
290
291
292
293
294
295
296
297
               TABLE 14.2 — Stable oxidation states of selected elements
                                                                                  (1,2)
Element + 1
Titanium
Vanadium
Chromium
Manganese
Iron
Cobalt
Nickel
Strontium
Yttrium
Molybdenum
+2 +3 +4 +5 +6 +7 +8
0 0 •
o o • •
• o o •
0 • 0 0 •
• 0 0
•
o o

•
o o • • •
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298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317

318
Element
Technetium
Silver
Cesium
Barium
Lanthanides
Lead
Polonium
Radium
Actinium
Thorium
Protactinium
Uranium
Neptunium
Plutonium
Americium
Curium
+1 +2 +3 +4 +5 +6 +7 +8
o o • o o •
• o o
•
•
•
• o
0 • 0
•
•
•
0 •
0 0 0 •
o o • o o
o • o o
• 0 0 0
• 0
              (1) The stable nonzero oxidation states are indicated. The more common oxidation
                 states are indicated by solid black circles.
              (2) Data compiled from Seaborg and Loveland (1990) and the NAS-NRC
                 monographs listed in the references.
14.2.4 Oxidation State in Solution
319     For the short-lived isotopes that decay by alpha emission or spontaneous fission, high levels of
320     radioactivity cause heating and chemical effects that can alter the nature and behavior of the ions
321     in solution and produce chemical reactions not observed with longer-lived isotopes. Decompo-
322     sition of water by radiation (radiolysis) leads to H and OH free radicals and formation of H2 and
323     H2O2, among other reactive species, and higher oxidation states of plutonium and americium are
324     produced.

325     The solutions of some ions are also complicated by disproportionation, the autooxidation-
326     reduction of a chemical species in a single oxidation state to higher and lower oxidation states.
327     The processes are particularly dependent on the pH of the solution. Oxidation of iodine, uranium,
328     americium, and plutonium are all susceptible to this change in solution. The disproportionation
329     of UO2+1, for example, is represented by the chemical  equation:

330                         2 UO2+1 + 4 H+1 - U+4 + UO2+2 + 2 H2O  (K = 1.7 x 106)
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331     The magnitude of the equilibrium constant reflects the instability of the +5 oxidation state of
332     uranium in UO2+1 described in Table 14.1, and the presence of hydrogen ions reveals the
333     influence of acidity on the redox process. An increase in acidity promotes the reaction.

334     14.2.5 Common Oxidizing and Reducing Agents

335     HYDROGEN PEROXIDE. Hydrogen peroxide (H2O2) has many practical applications in the
336     laboratory. It is a very strong oxidizing agent that will spontaneously oxidize many organic
337     substances, and water samples are frequently boiled with peroxide to destroy organic compounds
338     before separation procedures. When hydrogen peroxide serves as an oxidizing reagent, each
339     oxygen atom changes its oxidation state from -1 to -2. For example, the reaction for the oxidation
340     of ferrous ion is as follows:

341                                H2O2 + 2H+1 + 2Fe+2 - 2H2O + 2Fe+3

342     Hydrogen peroxide is frequently employed to oxidize Tc+4 to the pertechnetate:

343                                   4H2O2 + Tc+4 - Tc
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                                                                            Separation Techniques
357     on the experimental conditions. For example, on reduction in acidic solutions, the permanganate
358     ion accepts five electrons, forming the manganous ion Mn+2:

359                                 MnCV1 + 5e4 + 8H+1 - Mn+2 + 4H2O

360     In neutral or basic solution, permanganate accepts 3 electrons, and forms manganese dioxide
361     (MnO2), which precipitates:

362                               MnCV1 + 3e4 + 4H+1 - MnO2 I + 2H2O

363     These oxidizing agents are discussed further in Section 12.4, "Wet Ashing and Acid Dissolution
364     Techniques."

365     NITRITE. Nitrite ion (NO^1), plays an important role in the manipulation of Pu oxidation states in
366     solution. It is capable of oxidizing Pu+3 to Pu+4 and of reducing Pu+6 to Pu+4. Because most
367     aqueous processes center around Pu+4, sodium nitrite (NaNO2) is frequently used as a valence
368     adjuster to convert all Pu to the +4 state. And because the Pu+6^ Pu+4 reaction by nitrite is slow,
369     another reducing agent, such as the ferrous ion, often is added to increase the rate of reaction.

370     PERCHLORIC ACID. The use of perchloric acid (HC1O4) as an oxidizing agent is covered in depth
371     in Section 12.4, "Wet Ashing and  Acid Dissolution Techniques."

372     METALS IONS. Generally, metals ions (Ti+3, Cr+2, Fe+2, etc.) are strong reducing agents. For
373     example, both Ti+3 and Cr+2 have been shown to reduce Pu+4 to Pu+3 rapidly in acidic media.
374     Fe+2 rapidly reduces Np+5 to Np+4 in H2SO4.

375     Ti+3 is used extensively as a reducing agent in both inorganic and organic analyses. Ti+3 is
376     obtained by reducing Ti+4, either electrolytically or with zinc. Ti+4 is the most stable and common
377     oxidation state of titanium. Compounds in the lower oxidation states (-1, 0, +2, and +3) are quite
378     readily oxidized to Ti+4 by air, water, or other reagents.

379     ASCORBIC ACID. Commonly known as vitamin C, ascorbic acid  is an important reducing agent
380     for  the radiochemist. Because the ferric ion interferes with the uptake of Am+3 in several popular
381     extraction schemes, ascorbic acid is frequently used to reduce Fe+3 to Fe+2 to remove this
382     interference. Ascorbic acid is also  used to reduce Pu+4 to Pu+3.
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383     14.2.6 Oxidation State and Radiochemical Analysis

384     Most radiochemical analyses require the radionuclide be in aqueous solution. Thus, except for
385     water samples, the first step of an analysis is the complete dissolution of the sample so that all
386     components remaining at the end of the process are in a true solution. Dissolution of many
387     samples requires vigorous conditions to release the radionuclides from its natural matrix. Strong
388     mineral acids or strong bases, which also serve as powerful oxidizing agents, are used in boiling
389     mixtures or under fusion conditions to decompose the matrix—evaporating portions of the acid
390     or base from the mixture and oxidizing the radionuclide to a common oxidation  state.  The final
391     state depends, generally, on the radionuclide, oxidizers used, and pH of the solution (see notes in
392     Table 14.1, Section 14.2.3, "Common  Oxidation States"). Even water samples might contain
393     radionuclides at various states of oxidation because of their exposure to a variety of natural
394     oxidizing conditions in the environment and the pH of the sample.

395     Once the analyte is in solution, the radioelement and  the tracers and carriers used in the
396     procedure must be in the same oxidation state to ensure the same chemical behavior (Section
397     14.10.2, "Oxidation State"). For radionuclides that can exist in multiple oxidation states, one
398     state must be achieved; for those such  as plutonium, which disproportionates, a reproducible
399     equilibrium mixture of all oxidation states can be established. Oxidizing or reducing agents are
400     added to the reaction mixture to establish the required conditions. Table 14.3 contains a  summary
401     of several chemical methods for the oxidation and reduction of select radionuclides.
402
403
404
405
406

407

408
409
410

411
412
TABLE 14.3 — Redox reagents for radionuclides(
Redox Reaction
Am+3 - AmO2+2
Am+4 - AmO2+2
AmO2+1 - AmO2+2

AmO2+2 - AmO2+1

AmO2+2 - Am+3
Am+4 - Am+3
Co+2 - Co+3

Co+3 - Co+2
Fe+2 - Fe+3


Reagent
Ag+2, Ag+/S208
03
Ce+4
03
Br ', Cl'1
Na2CO3
T1, H2O2, NO,'1, SO2
alpha radiation effects
03
02, H202
H20
02

Ce+4, MnO4-'f NCV1, NO2
Conditions

13MNH4F
HC104
Heated HNO3 or HC1O4

Heat to precipitate NaAmO2CO3; dissolve in H+1

Spontaneous
Cold HC1O4
Complexed cobalt
Rapid with evolution of H2
Faster in base; slower in neutral and acid solution; decreases
withH+1

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Redox Reaction

Fe+3 - Fe+2



r1 - 12




r1 - ro3-'

r1 - KV
KV' - 12

KV - r1
i2 - r1
Pu+3 - Pu+4





Pu+4 - Pu02+2











Pu02+1 - Pu02+2




PuO,+2 - PuO,+1
Reagent
Cr2072
H2S, H2SO3
Zn, Cd, Al, Ag amalgams
Sn+2, 1'1, Cu+1, Ti+3
NH2OH
HNO2 (NaNO2 in acid)
MnO2 in acid
6M HNO3
NaHSO3orNaHSO3in
Na2SO3; Na2S2O3
KMnO4
50% CrO3 in 9M H2SO4
NaClOinbase
NH2OH-HC1
H2C2O4
NaHSO3 in acid
SO2; NaHSO3
Br03-'
Ce+4
Cr2O7 2, KV, MnO4-'
NO,'1
N03-'
HNO2
NaBiO3
Br03-'
Ce+4
HOC1 (KC1O)
MnO4-'
03
Ag(H)
Cr2072
C12
NO3"'
Ag20
io3-
HNO3
NH2OH-HC1
r1
SO2
V+3orTi+3
r1
Conditions
HClorH2SO4
Excess removed by boiling


Boiling solution
Does not affect other halides
Well suited for lab work







(9 M H2SO4


Dilute H+1
HC1 of H2SO4 solution
Dilute H+1
HNO3
HNO3 or dilute HC1 (100°C)

HNO3
Dilute HNO3 at 85 °C
Dilute HNO3 or HCLO4
pH 4.5 at 80°C or 45% K2CO3 at 40°C
Dilute HNO3
Ce+4 or Ag+1 catalyst or dil. H2SO4/60°C
Ag+1/S2O8"' in dil. HNO3
Dilute H2SO4
Dilute H2SO4 at 80°C or dil.HClO./Cr1
Dilute HNO3 at 95 °C
43%K2CO3at75°C

Dilute; slow
Slow
pH 2; slow
Dilute H+1; slow
HC1O4; slow
pH2
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Redox Reaction





Pu02+2 - Pu+4








PuO2+1 - Pu+4

Pu+4 - Pu+3










Tc+4 - Tc(V


TcO2(hydrated) -
Tc04-'

TcCl6-2 - TcO;1




TcO4-' - Tc+4 or
TcO2(hyd)


Reagent
S02
Fe+2
V+3orU+4
HNO2
Ag
C2042
r1
Fe+2
Sn+2
H2O2
Ti+3
Cu2O
HNO2
Zn
HNO2
NH2OH-HC1
hydroquinone
H2/Pt
r1
HS03-'
NH2OH-HC1
Zn
SO2
Ti+3
ascorbic acid
u+4
H2S
HNO3
H202
O2 (air)
Ce+4

H2O2
HNO3
H202
C12
Ce+4
MnO4J
N2H4

NH2OH
ascorbic acid
Conditions
H+1
HClO4orHCl
HC1O4
dil. HNO3NaNO3
dil. HC1
75°C;RTwithdil.HCl
HNO3
HC1,HNO3, orH2SO4
HC1/HC1O4
HNO3; continues to Pu+3 in absence of Fe+3
HC1O4
45%K2CO375°C
HNO3/75°C
dil. HC1
slow
dil. HC1, slow
dil. HNO3
HC1
dil. HC1
dil. HNO3

dil. HC1
dil. HNO3
HC1, dil. H2SO4, or dil. HNO3/H2SO4
HNO3
dil. HC1O4
dil. acid











dil. H2SO4

dil. H2SO4
dil. H,S(X
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Redox Reaction

U+3 _ U+4






U+4 - U02+2













U02+1 - U02+2
UO2+2 - U+4




UO2+2 - U+3
U02+1 - U+4


Reagent
Sn+2
Zn
Cone. HC1
CIO;1
Co+3 complexes
Cr+3 and Cr+3 complexes
H2O
U02+1
U02+2
O2 (air)
Br2
BrCV1
Ce+4
cicv1
Fe+3
HC1O2
HCrCV
HNO2
HNO3
H202
02
Pu+4
PuO2+2
MnO2
Fe+3
Cr+2
Eu+2
Np+3
Ti+3
V+2 and V+3
Zn(Hg)
Cr+2
H2
Zn(Hg)
Conditions
dil. H2SO4
dil. HC1
to TcCV2
dil. HC1O4
dil. HClO4orLiClO4
dil. HClO4orLiClO4
dil. or cone. HClorH2SO4
dil. HC1O4
dil. HC1O4

catalyzed by Fe+3 or Mn+2
HC1O4
dil. HC1O4
catalyzed by Fe+2 or V+5

phenol

catalyzed by Fe+2
















434
436
437
438
439
440
441
442
443
(1)  Compiled from: Anders, 1960; Bailar et al., 1984; Bate and Leddicotte, 1961; Cobble, 1964; Coleman, 1965;
    Cotton and Wilkinson, 1988; Greenwood and Earnshaw, 1984; Hassinsky and Adloff, 1965; Kleinberg and
    Cowan, 1960; Kolthoff et al.,  1969; Latimer, 1952; Metz and Waterbury, 1962; Schulz and Penneman, 1986;
    Weigel, 1986; and Weigel et al., 1986.
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444     In some radioanalytical procedures, establishing different states at different steps in the procedure
445     is necessary to ensure the requisite chemical behavior of the analyte.

446     One method for the analysis of 129I in aqueous solutions illustrates the use of oxidation and
447     reduction chemistry to bring the radionuclide to a specific oxidation state so that it can be
448     isolated from other radionuclides and other elements (DOE, 1995, Method RP230). Iodine
449     species in the water sample are first oxidized to iodate (KV1) by sodium hypochlorite (NaCIO),
450     and then reduced to iodide (I"1) by sodium bisulfite. The iodine is finally oxidized to molecular
451     iodine (I2) and extracted from most other radionuclides and elements in solution by a nonpolar
452     organic solvent such as carbon tetrachloride (CC14) or chloroform (CHC13) (see Section 14.4,
453     "Solvent Extraction").

454     Plutonium and its tracers can be equilibrated in a reproducible mixture of oxidation states by the
455     rapid reduction of all forms of the ion to the +3 state, momentarily, with iodide ion (I"1) in acid
456     solution. Disproportionation begins immediately, but all radionuclide forms of the analyte and
457     tracer begin at the same time from the same oxidation state, and a true equilibrium mixture of the
458     radionuclide and  its tracer is achieved. All plutonium radionuclides in the same oxidation state
459     can be expected to behave the  same chemically in subsequent separation and detection
460     procedures.

461     In addition to dissolution and separation strategies,  oxidation-reduction processes are used in
462     several quantitation steps of radiochemical analyses. These processes include titration of the
463     analyte and  electrochemical  deposition on a target for counting.

464     The classical titrimetric method is not commonly employed in the quantitation of environmental
465     level samples because the concentrations of radionuclides in these samples  are typically too low
466     for detection of the endpoint of the titration, even by electrometric or spectroscopic means.
467     However, the method is used for the determination  of radionuclides in other samples containing
468     larger quantities of long-lived radionuclides. Millimole quantities of uranium and plutonium in
469     nuclear fuels have been determined by titration using methods of endpoint detection as well as
470     chemical indicators (IAEA,  1972). In one method, uranium in the +6 oxidation state is reduced to
471     +3  and +4 with Ti+3, and that in the +3 state is oxidized to +4 with air bubbles (Baetsel and
472     Demildt, 1972). The solution is then treated with a  slight excess of Ce+4 solution of known
473     concentration, which oxidizes  U+4 to U+6 (as UO2+2) while being reduced, as follows:

474                                      U+4 + 2 Ce+4 - U+6 + 2 Ce+3

475                            (U+4 + 2 Ce+4 +2 H2O  - UO2+2 + 2 Ce+3 + 4 H+1)


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476     The excess Ce+4 is back-titrated with Fe+2 solution, using ferrion as indicator for the endpoint of
477     the titration:
478                                     Fe+2 + Ce+4 - Fe+3 + Ce+3

479     Electrochemical methods are typically used in radiochemistry to reduce ions in solution, plating
480     them onto a target metal for counting. Americium ions (Am+3) from soil samples are ultimately
481     reduced from solution onto a platinum (Pt) electrode by application of an electrical current in an
482     electrolytic cell (DOE, 1990 and 1997, Method Am-01). The amount of americium on the
483     electrode is determined by alpha spectrometry.

484     In some cases, the deposition process occurs spontaneously without the necessity of an applied
485     current. Polonium (Po) and lead (Pb) spontaneously deposit from a solution of hydrochloric acid
486     (HC1) onto a nickel (Ni) disk at 85 °C (Blanchard, 1966). Alpha and beta counting are used to
487     determine 210Po and 210Pb. Wahl and Bonner (1951, pp. 460-465) contains a table of
488     electrochemical methods used for the oxidation and reduction of carrier-free tracers.

489     14.3   Complexation

490     14.3.1  Introduction

491     A complex ion is formed when a metal atom or ion bonds with one or more molecules or anions
492     through an atom capable of donating one or more electron pairs. A ligandis any molecule or ion
493     that has at least one electron pair that can be donated to the metal. The bond is called a
494     coordination bond, and a compound containing a complex ion is a coordination compound. The
495     following are several examples of the formation  of complex ions:

496                                    Th+4 + 2 NO34 - Th(NO3)2+2

497                                   Ra+2 + EDTA'4 * - Ra(EDTA)'2

498                                     U+4 + 5 CO,'2 - U(CO3)5-6

499             * EDTA-4 = Ethylenediaminetetraacetate, C1OOC)2-NH-CH2-CH2-NH-(COO-1)2

500     In a fundamental sense, every ion in solution can be considered complexed; there are no free or
501     "naked" ions. Dissolved ions are surrounded by solvent molecules. In aqueous solutions, the
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502
503

504

505
506
507

508

509
510
        complexed water molecules, referred to as the inner hydration sphere, form aquo ions that can be
        either weakly or strongly bound:

                                        Fe+2 + 6 H2O - Fe(H2O)6+2

        From an elementary standpoint, the process of complexation is simply the dynamic process of
        replacing one set of ligands, the solvent molecules, with another. The complexation of a metal
        ion in aqueous solution with a ligand, L, can be expressed as:
                          M(H2O)n+x
                                                    M(H2O)n.1Lx-y + H2O
        Successive aquo groups can be replaced by other ligand groups until the complex MLnx"ny is
        formed as follows:
511

512
513

514
515
516
517
518
519
520
521
522

523

524
525
526


527
528
                                ^L + L - M(H2O)n.2L2 + H2O, etc.
In the absence of other complexing agents, in dilute aqueous solution solvated metal ions are
simply written as M+n for simplicity.

Ligands are classified by the number of electrons they donate to the metal to form coordination
bonds to the metal. If only one atom in the ligand is bonded to the metal, it is called a unidentate
ligand (dentate is from the Latin word for teeth. It is a categorization of ligands that describe the
number of atoms with electron pairs a ligand has available for donation in complex-ion
formation; if two atoms, bidentate, and so on for tridentate, tetradentate, pentadentate, and
hexadentate.) The term coordination number is also used to indicate the number of atoms
donating electrons to the metal atom.  The coordination number is five in U(CO3)5"6, as illustrated
above. EDTA, also illustrated above,  is a hexadentate ligand, because it bonds to the metal
through the four oxygen atoms and two nitrogen atoms.

Table 14.4 lists some common ligands arranged by type.

       _ TABLE 14.4 — Common ligands _
            Ligand Type'
                                                            Examples
       Unidentate
       Bidentate
       Tridentate
                                       Water (H2O), halides (X'1), hydroxide (OH'1), ammonia (NH3)
                                       cyanide (CN1), nitrite (NO/1), thiocyanate (SCN'1), carbon
                                       monoxide (CO)
                                       Oxalate, ethylenediamine, citrate
                                       Diethylenetriamine, 1,3,5 triaminocyclohexane
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529
530
531

532
533
534
535
536
537
538
                     Ligand Type'
                                                     Examples
        Polydentate
8-hydroxyquinoline, p-diketones (acetylacetone-2-
thenoyltrifluoroacetone [TTA]), ethylenediaminetetraacetic acid
(EDTA), diethylenetriaminepentaacetic acid (DTPA),
organophosphates: (octyl(phenyl)-A^A^-diiso-butylcarbamoyl-
methylphosphine oxide [CMPO]); tributyl phosphate (TBP),
trioctylphosphinic oxide (TOPO), quaternary amines (tricaprylyl-
methylammonium chloride [Aliquat-336]), triiso-octylamine
(TIOA), tri-n-octylamine (TnOA), macrocyclic polyethers (crown
ethers such as [181-crown-6X cryptates	
    (1) Ligands are categorized by the number of electron pairs available for donation. Unidentate ligands donate
       one pair of electrons; bidentate donate two pairs, etc.

A ligand can be characterized by the nature and basicity of its ligand atom. Oxygen donors and
the fluoride ion are general complexing agents; they combine with any metal ion (cation) with a
charge of more than one. Acetates, citrates, tartrate, and p-diketones generally complex all
metals. Conversely, cyanide (CN"1), the heavy halides, sulfur donors, and—to a smaller
extent—nitrogen donors, are more selective complexing agents than the oxygen donors. These
ligands do not complex the A-metals of the periodic table; only the cations of the B-metals and
the transition metals coordinate to carbon, sulfur, nitrogen, chlorine, bromine, and iodine.

14.3.2 Chelates
540     When a multidentate ligand is bound to the metal atom or ion by two or more electron pairs,
541     forming a ring structure, it is referred to as a chelate and the multidentate ligand is called a
542     chelating agent or reagent. Chelates are organic compounds containing two, four, or six
543     carboxylic acid (RCOOH) or amine (RNH2) functional groups. A chelate is effective at a pH
544     where the acid groups are in the anionic form as carboxylates, RCOO"1, but the nitrogen is not
545     protonated so that its lone pair of electrons is free for bonding.  The chelate bonds to the metal
546     through the lone pair of electrons of these groups as bi, tetra, or hexadentate ligands, forming a
547     coordination complex with the metal. Binding through multiple sites wraps up the metal in a
548     claw-like fashion, thus the name chelate, which means claw. Practically all chelates form five- or
549     six-membered rings on coordinating with the metal. Chelates are much more stable than complex
550     compounds formed by unidentate reagents. Moreover, if multiple ring systems are formed with a
551     single metal atom or ion, stability improves. For example, ethylenediaminetetraacetic acid
552     (EDTA),  a hexadentate ligand, forms especially stable complexes with most metals. As
553     illustrated in Figure 14.1, EDTA has two donor pairs from the nitrogen atoms, and four donor
554     pairs from the oxygen atoms.
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H
H



H U u u
rlo rlo rl
II L L
. c-c c- cv
-O7 X /
:: N .
/
.. ^r - r
\-s \-s
H
1 H2
:O:
:O :
o Ho
L L II
C - C \ ••
\ / O-H
. N
\ 6-H
c - c ^ .-
H2 1

:O:
                              FIGURE 14.1 — Ethylenediaminetetraacetic Acid(1) (EDTA)
555
556
557
558
559
560
561
(1) EDTA forms very stable complexes with most metal atoms because it has two pairs of electrons available from
   the nitrogen atoms, and four pairs of electrons from the oxygen atoms. It is often used as a complexing agent in
   a basic solution. Under these conditions, the four carboxylic-acid groups ionize with the loss of a hydrogen ion
   (H+1), forming ethylenediaminetetraacetate (EDTA"4), a stronger complexing agent. EDTA is often used as a
   food additive to increase shelf life, because it combines with transition metal ions that catalyze the decompo-
   sition of food. It is also used as a water softener to remove calcium (Ca+2) and magnesium (Mg+2) ions from
   hard water.
562      Various chelating agents bind more readily to certain cations, providing the specificity for
563      separating ions by selective bonding. Usually, the complex is insoluble under the solvent
564      conditions used, allowing the collection of the complex by precipitation. Selectivity of a chelate
565      can be partially controlled by adjusting the pH of the medium to vary the net charge on its
566      functional groups. Different chelates provide specificity through the number of functional groups
567      available for bonding and the size of claw formed by the molecular structure, providing a select
568      fit for the diameter of a specific cation. The electron-donating atoms of the chelate form a ring
569      system with the metal atom when they participate in the coordination bond. In most cases,
570      chelates form much more stable complexes than unidentate ligands. For example, the complex
571      ion formed between Ni+2 and the bidentate ligand ethylenediamine (H2N-CH2-CH2-NH2, or en),
572      Ni(en)3+2, is almost 108 times more stable than the complex ion formed between the metal ion
573      and ammonia, Ni(NH3)+2.
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574
575
576
577
Another class of ligands that is becoming increasingly important to the radiochemist doing
laboratory analyses is the macrocyclic polyethers, commonly called crown ethers (Horwitz et al.,
1991 and 1992; Smith et al., 1996 and 1997). These compounds are cyclic ethers containing a
number of regularly spaced oxygen atoms. Some examples are given in Figure 14.2.
                                          O
                                              0>
                [18]-crown-6
                                   [12]-crown-4
dibenzo[ 18]-crown-6
578
579
580
581

582
583
584
585
586
587
588
589
590
591
592

593
594
595
                               FIGURE 14.2 — Crown ethers
First identified in 1967, crown ethers have been shown to form particularly stable coordination
complexes. The term, "crown ether," was suggested by the three-dimensional shape of the
molecule. In the common names of the crown ethers, the ring size is given in brackets, and the
number of oxygen atoms follows the word "crown."

Crown ethers have been shown to react rapidly and with high selectivity (Gokel, 1991; Hiraoka,
1992). This property is particularly significant when a separation requires high selectivity and
efficiency in removing low-level species from complex and concentrated matrices, a situation
frequently encountered in environmental or mixed-waste analyses. Because crown ethers are
multidentate chelating ligands, they have very high formation constants. Moreover, because the
metal ion must fit within the cavity,  crown ethers demonstrate some selectivity for metal ions
according to their size. Crown ethers can be designed to be very selective by changing the ring
size, the ring substituents, the ring number, the donor atom type, etc. For example, dibenzo-18-
crown-6 forms a strong complex with potassium; weaker complexes with sodium, cesium, and
rubidium; and no complex with lithium or ammonium, while 12-crown-4, with its smaller cavity,
specifically complexes with lithium.

Other crown ethers are selective for radionuclide ions such as radium and UO2+2. Addition of 18-
crown-6 to solutions containing NpO2+2 causes the reduction of neptunium to Np(V) as NpO2+1,
which is encircled by the ether ligand (Clark et al.,  1998).
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596     14.3.3 The Formation (Stability) Constant

597     The stability of the complex is represented by the magnitude of an equilibrium constant
598     representing its formation. The complex ion, [Th(NO3)2+2], forms in two equilibrium steps:

599                                     Th+4 + NO,4 - Th(NO3)+3

600                                  Th(NO3)+3 + NO,'1 - Th(NO3)2+2

601     The stepwise formation (stability) constants are:

                                                 [Th(NQ3)+3]
602                                       K' = [Th"][N03-']
603     and
                                         v       [Th(N03)2+2]
604
                                              [Th(N03)+3][NO,
605     The overall formation (stability) constantly.
                                                [Th(N03)2+2]
606                                       K =
                                               [Th+4][N03~1]2

607     which can be calculated from Kx and K2:

608                                           K = Kj x K2.

609     In the Ni+2 examples cited in the preceding section, the relative stabilities of the complex ions are
610     represented by the values of K; for Ni(en)3+2 it is 1018'28, and for Ni(NH3)+2 it is 108'61 (Cotton and
611     Wilkinson, 1988, p. 45).

612     Many radionuclides form stable complex ions and coordination compounds that are important to
613     the separation and determination steps in radioanalytical chemistry. Formation of a complex
614     changes the properties of the ion in several ways. For example:

615      •  Complexation of UO2+2 with carbonate to form UO2(CO3)'4 increases the solubility of the
616         uranium species in groundwater (Lindsay, 1988, p. 9.2-19).
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617      •  Th+2 forms Th(NO3)6"2 in nitric acid solution (optimally at 7 M) that is the basis for separation
618         of thorium from other actinides and thorium progeny, because they do not form anionic
619         complexes under these conditions (Hyde, 1960, p. 25).

620      •  Ra+2 form a very insoluble compound with sulfate (RaSO4) but is soluble in hot concentrated
621         sulfuric acid because of the formation of Ra(SO4)2"2 (Kirby and Salutsky, 1964, p. 9).

622     In addition, the complex ion in solution is in equilibrium with the free (hydrated) ion, and the
623     equilibrium mixture might, therefore, contain sufficient concentration of the free ion for it to be
624     available for other reactions, depending on the stability of the complex ion:

625                                     Cu+2 + 4 NH3 - Cu(NH3)4+2

626     14.3.4 Complexation and Radiochemical Analysis

627     Property changes also accompany the formation of complex ions and coordination compounds
628     from simple radionuclide ions.  These changes provide a valuable approach in radiochemistry for
629     isolating, separating, and measuring radionuclide concentrations, and are important in several
630     areas of radiochemistry.

631     14.3.4.1   Extraction of Laboratory Samples and Ores

632     Uranium ores are leached with alkaline carbonates to dissolve uranium as the UO2(CO3)2"4
633     complex ion after oxygen is used to convert U+4 to U+6 (Grindler, 1962, p. 256). Samples
634     containing refractory plutonium oxides are dissolved with the aid of a nitric acid-hydrofluoric
635     acid solution to produce the complex cation PuF+3 and similar cationic fluorocomplexes
636     (Booman and Rein,  1962, p. 244). Refractory silicates containing niobium (Nb) also yield to
637     fluoride treatment. Potassium bifluoride (KF2 J) is used as a low-temperature flux to produce a
638     fluoride complex NbFg"1 (Willard and Rulfs, 1961, p. 1046; Greenwood and Earnshaw, 1984,
639     p.  1158).

640     14.3.4.2   Separation by Solvent Extraction and Ion-Exchange Chromatography

641     Many ion-exchange separations of radionuclides are based on the formation of complex ions
642     from the metal ions  in solution or the displacement of ions bound to an exchanger by complex
643     formation. Uranium in urine samples, for example, is partly purified by forming a chlorocomplex
644     of U+4 and UO2+2 ions, UC16"2 and UC^CV1, that bind preferentially to the anion-exchange ligands
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645     in 7 M HC1. Other cations pass through the column under these conditions. Uranium is
646     subsequently eluted with 1 M HC1 (DOE, 1990 and 1997, Method U-01).

647     For separation on a larger scale—such as in an industrial setting—chelates are often used in a
648     column chromatography or filtration unit. They are immobilized by bonding to an inert matrix,
649     such as polystyrene or an alumina/silica material. A solution containing the ions to be  separated
650     is passed continuously through the column or over the filter, where the select cations are bonded
651     to the chelate as the other ions pass through. Washing the column or filter with a solution at
652     alternate pH or ionic strength will permit the elution of the bound cation.

653     Tetrapositive thorium (Th) is adsorbed more strongly by cation exchangers than most  other
654     cations (Hyde, 1960, pp. 21-23). The adsorbed thorium is separated from most other ions by
655     washing the column with mineral acids or other eluting agents. Even the tetrapositive  plutonium
656     ion, Pu+4, and the uranyl ion, UO2+2, are washed off with high concentrations of HC1 because they
657     form chlorocomplexes, PuCl6"2 and UC^CV1, respectively. Thorium is then removed by eluting
658     with a suitable complexing agent such as oxalate, which reduces the effective concentration of
659     Th+4, reversing the adsorption process. Using oxalate, Th(C2O4)4"4 forms and the anion is not
660     attracted to the cation exchanger.

661     14.3.4.3   Formation and Dissolution of Precipitates

662     A classical procedure for the separation and determination of nickel  (Ni) is the precipitation of
663     Ni+2 with dimethylglyoxime, a bidentate ligand that forms a highly selective, stable chelate
664     complex with the ion, Ni(C4H7N2O2'1)2 (DOE,  1995, Method RP300). Uranium in the  +4
665     oxidation state can also be precipitated from acidic solutions with  a chelating agent, cupferron
666     (ammonium nitrosophenylhydroxylamine, C8H5(NO)O"1NH4+1) (Grindler,  1962, p.  256). In
667     another procedure, Co+2 can be selectively precipitated from solution as K3Co(NO2)6. In this
668     procedure,  cobalt, which forms the largest number of complexes of all the metals, forms a
669     complex anion with six nitrite ligands, Co(NO2)6"3  (EPA, 1973, pp. 53-58).

670     In radiochemical separations and purification procedures, precipitates of radionuclides are
671     commonly  redissolved to release the metal ion for  further purification or determination. In the
672     determination of 90Sr, strontium (Sr+2) is separated from the bulk of the solution by direct
673     precipitation of the sulfate, SrSO4. The precipitate  is redissolved by forming a complex ion with
674     EDTA, Sr(EDTA)'2, to separate it from lanthanides and actinides (DOE, 1995, Method RP520).
675     Radium also forms a very stable complex with EDTA. Solubilization of radium, Ra+2,
676     coprecipitated with barium sulfate (BaSO4) is used in the 228Ra determination of drinking water
677     by using EDTA (EPA, 1980, pp. 49-57).


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678     14.3.4.4   Stabilization of Ions in Solution

679     In some radiochemical procedures, select radionuclides are separated from other elements and
680     other radionuclides by stabilizing the ions as complex ions, while the other substances are
681     precipitated from solution. In a procedure extensively used at Oak Ridge National Laboratory
682     (ORNL), 95Nb is determined in solutions by taking advantage of complex-ion formation to
683     stabilize the ion (Nb+5) in solution during several steps of the procedure (Kallmann, 1964,
684     pp. 343-344). The niobium sample and carrier are complexed with oxalic acid in acidic solution
685     to prevent precipitation of the carrier and to promote interchange between the carrier and 95Nb.
686     Niobium is precipitated as the pentoxide after warming the solution to destroy the oxalate ion,
687     separating it from the bulk of other ions in solution. Niobium is also separated specifically from
688     zirconium by dissolving the zirconium oxide in hydrofluoric acid.

689     14.3.4.5  Detection and Determination

690     Compleximetric titration of metal  ions with EDTA using colorimetric indicators to detect the
691     endpoint can be used for determination procedures. Uranium does not form a selective complex
692     with EDTA, but this chelate  has been used to titrate pure uranium solutions (Grindler, 1962,
693     p. 94). The soluble EDTA complex of thorium is the basis of a titrimetric determination of small
694     amounts of thorium (Hyde, 1960, p.  9).

695     Spectrometric determinations are also based on the formation of complex ions. Microgram
696     quantities of uranium are  determined by the absorbance at 415 nm (a colorimetric determination)
697     of the uranyl chelate complex with dibenzoylmethane, C6H5-CO-CH2-CO-C6H5 (Grindler, 1962,
698     pp. 271-276).

699     14.4  Solvent Extraction

700     14.4.1  Extraction Principles

701     Since the early days of the Manhattan Project, when scientists extracted uranyl nitrate into diethyl
702     ether to purify the uranium used in the first reactors, solvent extraction has been an important
703     separation technique for radiochemists. Solvent extraction, or liquid-liquid extraction, is a
704     technique used both in the laboratory and on the industrial scale. However, current laboratory
705     trends  are away from this technique, mainly because of the costs of materials and because it is
706     becoming more difficult and costly to dispose of the mixed waste generated from the large
707     volumes of solvents required. The technique also tends to be labor intensive because of the need
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708     for multiple extractions using separately funnels. Nonetheless, solvent extraction remains a
709     powerful separation technique worthy of consideration.

710     Solvent extraction refers to the process of selectively removing a solute from a liquid mixture
711     with a solvent. As a separation technique,  it is a partitioning process based on the unequal
712     distribution of the solute (A) between two immiscible solvents, usually water (aq) and an organic
713     liquid (org):

714                                              Aaq * Aorg

715     The solute can be in a solid or liquid form. The extracting solvent can be water, a water-miscible
716     solvent,  or a water-immiscible solvent; but it must be insoluble in the solvent of the liquid
717     mixture. Solutes exhibit different solubilities in various solvents. Therefore, the choice of
718     extracting solvent will depend upon the properties of solute, the liquid mixture, as well as other
719     requirements of the experimental procedure. The solvents in many applications are water and a
720     nonpolar organic liquid, such as hexane or diethyl  ether, but other solvent pairs are commonly
721     used. In  general terms, the solute to be removed along with impurities or interfering analytes to
722     be separated are already dissolved in one of the solvents (water, for example). In this  example, a
723     nonpolar organic solvent is added and the  two are thoroughly mixed, usually by shaking  in a
724     separately funnel. Shaking produces a fine dispersion of each solvent in the other that will
725     separate into two distinct layers after standing for several minutes. The more dense solvent will
726     form  as the bottom layer. Separation is achieved because the solute and accompanying impurities
727     or analytes have different solubilities in the two solvents. The solute, for example, might
728     preferentially remain in the aqueous phase, while the impurities or analyte selectively dissolve in
729     the organic phase. The impurities and analyte are extracted from the aqueous layer into the
730     organic layer.  Alternatively, the solute might be more soluble in the organic solvent and will be
731     extracted from the aqueous layer into the organic layer, leaving the impurities behind in the
732     aqueous layer.

733     14.4.2 Distribution Coefficient

734     The different solubilities of a solute in the solvent  pairs of an extraction  system are described by
735     the distribution or partition coefficient, Kd. The coefficient is an equilibrium constant that
736     represents the solubility of the solute in one solvent relative to its solubility in another solvent.
737     Once equilibrium is established, the concentration of solute in one phase has a direct  relationship
738     to the solute concentration in the other phase. This is expressed mathematically by:

739                                           Kd=[Aorg]/[Aaq]


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740     where [Aorg] and [Aaq] are the concentration of the solute in the organic and aqueous phase
741     respectively, and Kd is a constant. The concentrations are typically expressed in units of moles/kg
742     (molality) or g/g; therefore, the constant is unitless. These solubilities usually represent saturated
743     concentrations for the solute in each solvent. Because the solubilities vary with temperature, the
744     coefficient is temperature-dependent, but not by a constant factor. Wahl and Bonner (195 1, pp.
745     434-439) contains a table of solvent extraction systems for carrier-free tracers containing
746     laboratory conditions and distribution coefficients.

747     A distribution coefficient of 90 for a solute in a hexane/water system, for example, means that
748     the solute is 90 times more soluble at saturation conditions in hexane than in water, but note that
749     some of the water still contains a small amount of the solute. Solvent extraction selectively
750     dissolves the solute in one solvent, but it does not remove the solute completely from the other
751     solvent. A larger coefficient would indicate that, after extraction, more solute would be
752     distributed in hexane relative to water, but a small quantity would still be in the water. Solvent
753     extraction procedures often use repeated extractions to extract a solute quantitatively from a
754     liquid mixture.

755     The expression of the distribution law is only a very useful approximation; it is not thermo-
756     dynamically rigorous, nor does it account for situations in which the solute is involved in a
757     chemical reaction, such as dissociation  or association, in either phase. Consider, for example,
758     dimerization in the organic phase:

759                                            2Aorg*(A)2j0rg

760     where the distribution ratio, D, is an alternate form of the distribution coefficient expressed by:

761                                   D — (|_AorgJmonomer + LAorgJdimer)/|_AaqJ
762     or
763                                    D = ([Aorg]+2[(A)2i0rg])/[Aaq]

764     Because the concentration of the monomer that represents the dimeric form of the solute is twice
765     that of the concentration of the dimer:

766                                         [Aorg]dimer = 2[(A)2j0rg]

767     Substitution of ^produces:

768                                         D
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769     where K2 is the dimerization constant, K2 = [(A)2 org]/[Aorg]2. Because dimerization decreases the
770     concentration of the monomer, the species that takes part directly in the phase partition, the
771     overall distribution increases.

772     14.4.3 Extraction Technique

773     There is extensive literature on the topic of extraction technique, but only a few sources are listed
774     here. The theory of solvent extraction is covered thoroughly in Irving and Williams (1961), Lo et
775     al. (1983),  and Dean (1995). Moreover, the journal Solvent Extraction and Ion Exchange is an
776     excellent source for current advances in this field. A practical discussion on the basics of solvent
777     extraction is found in Korkisch (1969). The discussion applies to a metallic element in solution
778     as a cation extracted by a nonpolar solvent:

779         "In solvent extraction, the  element which is to be separated, contained in an aqueous solution,
780         is converted to a compound which is soluble in an organic solvent. The organic solvent must
781         be virtually immiscible with water. By shaking the aqueous solution with the organic solvent
782         (extractant) in a separating funnel, the element is extracted into the organic phase. After
783         allowing the aqueous and organic phases to separate in the funnel, the organic extract is
784         removed from contact with the aqueous layer. This single-stage batch extraction method is
785         employed when Kd is relatively large and for a simple separation it is essential that the
786         distribution coefficients of the metal ions to be separated be sufficiently different. As in the
787         case of ion exchange, the effectiveness of separation is usually expressed by means of the
788         separation factor which is given by the ratio of the distribution coefficients of two different
789         elements which were determined under identical experimental conditions. This ratio
790         determines the separability of two elements by liquid-liquid extraction. Separations can only
791         be achieved  if this ratio shows a value which is different from unity and they are clean and
792         can be  quickly and easily achieved where one of the  distribution coefficients is relatively
793         large and the other very small (high separation factor).

794         "In those extractions where the separation factor approaches unity, it is necessary to employ
795         continuous extraction or fractionation methods. With the latter techniques distribution,
796         transfer and  recombination of various fractions are performed a sufficient number of times to
797         achieve separation. In continuous extraction use is made of a continuous flow of immiscible
798         solvent through the solution or a continuous counter-current flow of both phases. In
799         continuous extraction the spent solvent is stripped and recycled by distillation, or fresh
800         solvent is  added continuously from a reservoir. Continuous counter-current extraction
801         involves a process where the two liquid phases are caused to flow counter to each other.
802         Large-scale separations are usually performed using this technique.


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803         "When employing liquid-liquid extraction techniques, one of the most important
804         considerations is the selection of a suitable organic solvent. Apart from the fact already
805         mentioned that it must be virtually immiscible with water, the solubility of the extracted
806         compound in the solvent must be high if a good separation is to be obtained. Furthermore, it
807         has to be selective, i.e., has to show the ability to extract one component of a solution in
808         preference to another. Although the selectivity of a solvent for a given component can be
809         determined from phase diagrams, it is a little-used procedure in analytical chemistry. The
810         principal difficulty is simply that too few phase diagrams exist in the literature. The result is
811         that the choice of an extractant is based on either experience or semi-empirical
812         considerations. As a rule, however, polar solvents are used for the extraction of polar
813         substances from nonpolar media, and vice versa. Certainly the interactions of solute and
814         solvent will have an effect on the selectivity of the solvent. If the solute is readily solvated by
815         a given solvent, then it will be soluble in that solvent. Hydrogen bond formation between
816         solute and solvent influences solubility and selectivity.

817         "Almost as important as the selectivity of the extractant is the recovery of the solute from the
818         organic extract. Recovery can be achieved by distillation or evaporation of the solvent,
819         provided that the solute is nonvolatile and thermally  stable. This technique is, however, less
820         frequently used than the principle of back extraction  (stripping) which involves the treatment
821         of the organic extract with an aqueous solution containing a  reagent which causes the
822         extracted solute to pass quantitatively into the aqueous layer...

823         "In solvent extraction the specific gravity of the extractant in relation to the aqueous phase is
824         important. The greater the difference  in the solvent densities, the faster will be the rate at
825         which the immiscible layers  separate. Emulsions are  more easily produced when the densities
826         of the two solvents are similar. Sometimes troublesome emulsions can be broken by
827         introducing a strong electrolyte into the system or by the addition of small  quantities of an
828         aliphatic alcohol" (Korkisch, 1969, pp. 20-22).

829     Korkisch continues:

830         "Liquid-liquid extraction  can be applied to the analysis of inorganic materials in two different
831         ways.

832            (a) Where  the element or elements to be determined are  extracted into the organic phase.

833            (b) Where  the interfering elements are removed by extraction, leaving the element or
834               elements to be determined in the aqueous phase.


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835         "Solvent extraction separations are mainly dependent for their successful operation upon the
836         distribution ratio of the species between the organic and aqueous phase and the pH and salt
837         concentration of the aqueous phase. Much of the selectivity which is achieved in liquid-liquid
838         extraction is dependent upon adequate control of the pH of the solution. The addition of
839         masking agents such as EDTA and cyanide can greatly improve selectivity, but they too are
840         dependent upon the pH of the solution to exert their full effect. In many cases complete
841         extractions and separations are obtained only in the presence of salting-out agent. An
842         example is the extraction of uranyl nitrate. In the presence of additional nitrate, the increase
843         in the concentration  of the nitrate ion in the aqueous solution shifts the equilibrium between
844         the uranyl ion and the nitrate complexes toward the formation of the latter, and this facilitates
845         a more complete extraction of the uranium into the organic solvent. At the same time, the
846         salting-out agent has another, more general, effect: as its affinity for water is large, it
847         becomes hydrated by the water molecules so that the substance to be extracted is really
848         dissolved in  a smaller amount of water, and this is the same as if the concentration in the
849         solution were increased. As a result, the distribution coefficient between the aqueous and the
850         organic phases is increased. As a rule the salting-out agent also lowers the solubility of the
851         extractant in the aqueous phase, and this is often important in separations by extraction. The
852         efficiency of the salting-out action depends upon the nature and the concentration of the
853         salting-out agent. For the same molar concentration of the salting-out agent its action
854         increases with an increase in the charge and decrease in the radius of its cation" (Korkisch,
855         1969, pp. 23-24).

856     A hydrated metal ion will always prefer the aqueous phase to the organic phase because of
857     hydrogen bonding and dipole interaction in the aqueous phase. Therefore, to get the metal ion to
858     extract, some or all of the inner hydration sphere must be removed. The resulting complex must
859     be neutrally charged and organophilic. Removal of the hydration sphere is accomplished by
860     coordination with an anion to form a neutral complex. Neutral complexes will generally be more
861     soluble in an organic phase. Larger complexing anions favor the solubility in the organic phase.

862     Extracting agents are thus  divided into three classes:  polydentate organic anions, neutral organic
863     molecules, and large organic cations. Many of the multidentate ligands discussed previously are
864     used in solvent extraction systems.

865     The radioanalytical procedure for uranium (U) and thorium (Th) employs solvent extraction to
866     separate the analytes before alpha counting (EPA, 1984, pp. U/Th-01-1-14). An aqueous solution
867     of the two is extracted with a 10 percent solution of triisooctylamine (TIOA) in para-xylene to
868     remove uranium, leaving thorium in the water (Grinder, 1962, pp. 175-180). Each solution is
869     further processed to recover the respective radionuclides for separate counting.


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870     14.4.4 Solvent Extraction and Radiochemical Analysis

871     In many purification procedures, separated solutions are used directly in further isolation steps. If
872     necessary, the substances can be collected by distillation or evaporation of the respective
873     solvents. In the uranium/thorium procedure described above, the aqueous layer containing
874     thorium is evaporated, and the thorium is redissolved in an alternate solution before it is purified
875     further. In other cases, the solution is extracted again to take up the solute in another solvent
876     before the next step in the procedure. Uranium in TIOA//>-xylene, for example, is extracted back
877     into a nitric acid solution for additional purification (EPA, 1984, pp. U/Th-01-l-U/Th-01-14).

878     In some solvent-extraction procedures, more than one extraction step is required for the
879     quantitative removal of a solute from its  original solvent. The solute is more soluble in one
880     component of the solvent pair, but not completely insoluble in the other component, so
881     successive extractions of the aqueous solution of the solute by the organic solvent will remove
882     more and more of the solute from the water until virtually none remains in the aqueous layer.
883     Extraction of uranium with TIOA//>-xylene, for example, requires two extractions before
884     quantitative removal is achieved (EPA, 1984, pp. U/Th-01-l-U/Th-01-14). The organic layers
885     containing the uranium are then combined into one solution for additional processing.

886     Solvent extraction is greatly influenced by the chemical form  (ionic or molecular) of the solute to
887     be extracted, because different forms of the solute can have different solubilities in the solvents.
888     In the uranium/thorium procedure described above, uranium is extracted from water by
889     TIOA/hydrochloric acid, but it is stripped from the amine solution when extracted with nitric
890     acid. Simply changing the anion of uranium and TIOA from chloride to nitrate significantly alters
891     the complex stability of uranium and TIOA.

892     Organic amines are sometimes converted to their cationic forms, which are much more soluble in
893     water and much less soluble in organic solvents. The amine is converted to the corresponding
894     ammonium salt by an acid,  such as hydrochloric acid:

895                                     RNH2 + HC1 - RMV'Cr1

896     Correspondingly, carboxylic acids are converted to their carboxylates that are more soluble in
897     water and less soluble in organic solvents. They are produced by treating the carboxylic acid with
898     a base, such as sodium hydroxide:

899                               RCOOH + NaOH - RCOO'W1 + H9O
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900     Multidentate organic anions that form chelates are important extracting agents. These reagents,
901     such as the p-diketonates and thenoyltrifluoroacetone (TTA) (Ahrland,  1986, pp. 1518-1521), are
902     commonly used for extracting the actinide elements. When the aqueous solution and organic
903     phase come into contact with one another, the chelating agent dissolves in the aqueous phase,
904     ionizes, and complexes the metal ion; the resulting metal chelate subsequently dissolves in the
905     organic phase.

906     A number of organophosphorus compounds are also efficient extractants because they and their
907     complexes are readily soluble in organic solvents. The actinide MO2+2 and actinide +4 ions are
908     very effectively extracted by reagents such as monobasic diethylhexylphosphoric acid (HDEHP)
909     and dibutylphosphoric acid (HDBP) (Cadieux and Reboul, 1996).

910     Among the neutral compounds, alcohols, ethers, and ketones have been commonly employed as
911     extractants. Methyl isobutyl ketone was used in one of the early large-scale processes (the Redox
912     process) to recover uranium and plutonium from irradiated fuel (Choppin et al., 1995, p. 607).
913     However, the most widely used neutral extractants are the organophosphorus compounds such as
914     TBP (tributyl phosphate). The actinide elements thorium, uranium, neptunium, and plutonium
915     easily form complexes with TBP (Choppin et al., 1995, p. 607). Salting-out agents such as HNO3
916     and A1(NO3)3 are commonly employed to increase extraction in these systems. This chemistry is
917     the basis of the Purex process used to reprocess spent nuclear fuel (Choppin et al.,  1995, pp.  608-
918     610).

919     An important addition to the Purex process is the solvent extraction procedure known as TRUEX
920     (7/ans Uranium .Extraction). This process uses the bifunctional extractant CMPO
921     ([octyl(phenyl)]-N,N-diisobutylcarbonylmethylphosphine oxide) to remove transuranium
922     elements from the waste solutions  generated in the Purex process. This type of compound
923     extracts actinides at high acidities, and can be stripped at low acidity or with complexing agents.
924     Many of the recent laboratory procedures for biological waste and environmental samples are
925     based upon this approach (see Section 14.4.5.1, "Extraction Chromatography Columns").

926     The amines, especially the tertiary and quaternary amines, are strong cationic extractants. These
927     strong bases form complexes with actinide metal cations. The extraction efficiency improves
928     when the alkyl groups have  long carbon chains, such as in trioctylamine (TnOA) or
929     triisooctylamine (TIOA). The pertechnetate ion (TcO44) is also extracted by these cationic
930     extractants (Chen, 1990).

931     Table 14.5 lists common solvent extraction procedures for some radionuclides of interest and
932     includes the examples described above.


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933
934
935

936
          TABLE 14.5 — Radioanalytical methods employing solvent extraction
                                                                                       (i)
Analyte
89/90Cj.
"Tc04-
210pb
Radium through
Californium
Actinides
Thorium
Uranium
Extraction Conditions (Reference)
From soils and sediments with dicyclohexano-18-crown-6 in trichloromethane with back
extraction with EDTA (Pimpl, 1995)
From dilute H2SO4 solutions into a 5% TnOA in xylene mixture and back extracted with NaOH
(Golchert and Sedlet, 1969; Chen, 1990); from dilute H2SO4, HNO3, and HC1 solutions into a
5% TnOA in xylene (Dale et al., 1996); from HNO3 into 30% TnOA in xylene and back
extracted with NaOH (Hirano, 1989); from dilute H2SO4 solutions into TBP (Holm et al., 1984;
Garcia-Leon, 1990); the tetraphenyl arsonium complex of Tc into chloroform (Martin and
Hylko, 1987); from K2CO3 with MEK (Paducah R-46); from alkaline nuclear-waste media with
crown ethers (Bonnesen et al., 1995)
As lead bromide from bone, food, urine, feces, blood, air, and water with Aliquat-336 (DOE,
1990 and 1997, Method Pb-01; Morse and Welford, 1971)
From soil following KF-pyrosulfate fusion and concentration by barium sulfate precipitation
with Aliquat-336 in xylene (Sill et al., 1974)
From water following concentration by ferric hydroxide precipitation and group separation by
bismuth phosphate precipitation, uranium extracted by TOPO, plutonium and neptunium
extracted by TIOA from strong HC1, and thorium separated from americium and curium by
extraction with TOPO (EPA, 1980, Method 907.0)
And other metals from TOPO (NAS-NS 3 102) and from high-molecular weight amines such as
TIOA(NAS-NS3101).
Uranium and plutonium from HC1 with TIOA (Moore, 1958)
From nitric acid wastes using the TRUEX process with CMPO (Horwitz et al., 1985 and 1987)
With various extractive scintillators followed by PERALS® spectrometry (McDowell 1986 and
1992); with HDEHP after extraction chromatography followed by PERALS® spectrometry
(Cadieux and Reboul, 1996)
From aqueous samples after ion exchange with TTA, TIOA, or Aliquat-336 (DOE, 1995,
Method RP570)
From waters with ethyl acetate and magnesium nitrate as salting-out agent (EPA, 1980, Method
908. 1); with URAEX™ followed by PERALS® spectrometry (Leyba et al., 1995)
From soil, vegetation, fecal ash, and bone ash with Alamine-336 (DOE, 1990 and 1997,
Methods Se-Ol.U-03)
937

938
939
940
941

942
943
944

945
(1) This list is representative of the methods found in the literature. It is not an exhaustive compilation, nor does it
   imply preference over methods not listed.

14.4.5 Solid-Phase Extraction
946     A technique closely related to solvent extraction is solid-phase extraction (SPE). SPE is a
947     solvent-extraction system in which one of the liquid phases is made stationary by adsorption onto
948     a solid support, usually silica, and the other liquid phase is mobile. Small columns or membranes
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949     are used in the SPE approach. Many of the same extracting agents used in solvent extraction can
950     be used in these systems. SPE is becoming widely accepted as an excellent substitute for liquid-
951     liquid extraction because it is generally faster, more efficient, and generates less waste.

952     14.4.5.1   Extraction Chromatography Columns

953     Over the past decade, extraction chromatography methods have gained wide acceptance in the
954     radiochemistry community as new extraction chromatographic resins have become commercially
955     available, such as Sr, TRU, and TEVA resins (Eichrom Industries, Inc., Darien, IL) (Dietz and
956     Horwitz, 1993; Horwitz et al., 1991, 1992, and 1993). These resins are composed of extractant
957     materials, such as CMPO and 4,4'(5')-bis(t-butylcyclohexano)-18-crown-6, absorbed onto an
958     inert polymeric support matrix. They are most frequently used in a column, rather than a batch
959     mode.
960
961
962
963
964
965
966
967

968
969
970
971
Another example of the advances in the area are use of fibrous discs impregnated with high
molecular weight chelates, selective for certain nuclides such as Cs, Sr, and Tc (Empore Discs,
3M Company, and the TEVA Disc, Eichrom Industries, Inc.). Many of the traditional methods
based upon repetitive precipitations, or solvent extraction in separately funnels, have been
replaced by this strategy. This approach allows for the specificity of liquid-liquid extraction with
the convenience of column chromatography. Numerous papers detailing the determination of
radionuclides by this technique have been published recently, and examples are cited in Table
14.6.
     TABLE 14.6 — Radioanalytical methods employing extraction chromatography
                                                                                 (i)
Analyte
Ni-59/63
Sr-89/90
Sr-90
Y-90
Tc-99
Ligand
dimethylgloxime
4,4'(5')-bis(t-butyl-cyclohexano)-18-
crown-6 in n-octanol
octyl(phenyl)-7V,yV-diisobutyl-
carbamoylmethylphosphine oxide
[CMPO] in tributyl phosphate
4,4'(5')-bis(t-butyl-cyclohexano)-18-
crown-6 in n-octanol
Aliquat-336N
Method Citations
Aqueous samples (DOE, 1997)
Biological, Environmental, and Nuclear Waste (Horwitz
et al., 1991 and 1992); Water (ASTM, D5811-95; DOE,
1995, Method RP500); Urine (Dietz and Horwitz, 1992;
Alvarez and Navarro, 1996); Milk (Jeter and Grob,
1994); Geological Materials (Pin and Bassin, 1992)
Brines (Bunzl et al., 1996)
Medical applications (Dietz and Horwitz, 1992)
Low-level radioactive waste (Banavali, 1995); Water
(Sullivan et al., 1993; DOE, 1993, Method RP550)
972


973

974
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Analyte
Pb-210
Ra-228
Rare earths
Actinides
Ligand
4,4'(5')-bis(t-butyl-cyclohexano)-18-
crown-6 in isodecanol
octyl(phenyl)-7V,yV-diisobutyl-
carbamoylmethylphosphine oxide
[CMPO] in tributyl phosphate or
diethylhexyl-phosphoric acid [HDEHP]
impregnated in Amberlite XAD-7
diamyl,amylphosphonate
octyl(phenyl)-7V,yV-diisobutyl-
carbamoylmethylphosphine oxide [C-
MPO] in tributyl phosphate and dieth
ylhexyl-phosphoric acid [HDEHP]
impregnated in Amberlite XAD-7
octyl(phenyl)-7V,yV-diisobutyl-
carbamoylmethylphosphine oxide
[CMPO] in tributyl phosphate and
4,4'(5')-bis(t-butyl-cyclohexano)-18-
crown-6 in n-octanol
octyl(phenyl)-7V,yV-diisobutyl-
carbamoylmethylphosphine oxide
[CMPO] in tributyl phosphate
diamyl,amylphosphonate
tri-n-octylphosphine oxide [TOPO] and
di(2-ethylhexyl)phosphoric acid
[HDEHP]
Method Citations
Water (DOE, 1995, Method RP280); Geological
materials (Horwitz et al., 1994; Woittiez and Kroon,
1995); complex metal ores (Gale, 1996)
Natural waters (Burnett et al., 1995); Volcanic rocks
(Chabaux, 1994)
Actinide-containing matrices (Carney, 1995)
Sequential separation of light rare earths, U, and Th in
geological materials (Pin et al., 1996)
Concomitant separation of Sr, Sm, and Nd in silicate
samples (Pin etal., 1994)
Air filters (Berne, 1995); Waters (Berne, 1995); Group-
screening (DOE, 1997, Method RP725); Urine (Horwitz
et al., 1990; Nguyen et al., 1996); Acidic media
(Horwitz, 1993; DOE, 1997); Soil and sludge (Smith et
al., 1995; Kaye et al., 1995); Environmental (Bunzl and
Kracke, 1994)
Acidic media (Horwitz et al., 1992)
Environmental and industrial samples (Testa et al.,
1995)
975
976
977
978
979
980

981
(1) This list is representative of the methods found in the literature. It is not complete, nor does it imply preference
   over methods not listed.

14.4.5.2   Extraction Membranes
982     SPE membranes have also become a popular approach to sample preparation for organic
983     compounds in aqueous samples over the past decade. As of 1995, 22 methods employing SPE
984     disks have been accepted by the U.S. Environmental Protection Agency. More recently, disks
985     have been developed for specific radionuclides, such as technetium, strontium, and radium
986     (DOE, 1990 and 1997; Orlandini, 1998; Smith et al., 1996 and 1997).
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 987      These SPE membranes significantly reduce extraction time and reagent use. Samples typically
 988      are processed through the membranes at flow rates of at least 50 milliliters per minute; a one liter
 989      sample can be processed in as little as 20 minutes. Moreover, these selective-membranes often
 990      can be counted directly, thereby condensing sample preparation and counting source preparation
 991      into a single step. Many of the hazardous reagents associated with more traditional methods are
 992      eliminated in this approach, and these membrane-based extractions use up to 90 percent less
 993      solvent than liquid-liquid extractions. The sorbent particles embedded in the membrane are
 994      extremely small and evenly distributed, thereby eliminating the problem of channeling that is
 995      associated with columns.

 996      14.4.6  Advantages and Disadvantages of Solvent Extraction

 997      14.4.6.1   Advantages

 998       • Lends itself to rapid and very selective separations that are usually highly efficient.

 999       • Partition coefficients are often approximately independent of concentration down to tracer
1000         levels and, therefore, can be applied to a wide range of concentrations.

1001       • Can usually be followed by back-extraction into aqueous solvents or, in some cases, the
1002         solution can be used directly in subsequent procedures.

1003       • Wide scope of applications—the composition of the organic phase and the nature of
1004         complexing or binding agents can be varied so that the number of practical combinations is
1005         virtually unlimited.

1006       • Can be performed with simple equipment, but can also be automated.

1007       • Column extraction is fast, very selective, generates a low volume of waste, can often be
1008         applied to samples from very acidic media, requires relatively inexpensive materials, and can
1009         often be correlated with liquid/liquid extraction.

1010      14.4.6.2   Disadvantages

1011       • Cumbersome for a large number of samples or for large samples.

1012       • Often requires toxic and/or flammable solvents.
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1013       •  Can be time consuming, especially if attainment of equilibrium is slow.

1014       •  Can require costly amounts of organic solvents and generate large volumes of organic waste.

1015       •  Can be affected by small impurities in the solvent(s).

1016       •  Multiple extractions might be required, thereby increasing time, consumption of materials,
1017          and generati on of waste.

1018       •  Formation of emulsions can interfere.

1019       •  Counter-current process can be complicated and can require complicated equipment.

1020       •  Alteration of chemical form can change, going from one phase to the other, thereby altering
1021          the distribution coefficient and effectiveness of the extraction.

1022       •  Tracer-levels of analytes can form radiocolloids that cannot be extracted, dissociate into less
1023          soluble forms,  or adsorb on the container surface or onto impurities in the system.

1024       •  Extraction columns cannot be reused.

1025      14.5  Volatilization and Distillation

1026      14.5.1 Introduction

1027      Differences in vapor  pressures of elements or their compounds can be exploited for the
1028      separation of radionuclides. Friedlander et al. (1981,  p. 300), describes the process:

1029          "The most straightforward application is the removal of radioactive rare gases from aqueous
1030          solutions or melts by sweeping an inert gas  or helium. The volatility of... compounds ... can
1031          be used to effect  separations ...by distillation ... Distillation and volatilization methods often
1032          give clean separations, provided that proper precautions are taken to avoid contamination of
1033          the distillate by spray or mechanical entrapment.  Most volatilization methods can be done
1034          without specific carriers, but some nonisotopic carrier gas might be required. Precautions are
1035          sometimes necessary to avoid loss of volatile radioactive substances during the dissolving of
1036          irradiated targets  or during irradiation itself."
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1037     Similar precautions are also advisable during the solubilization of samples containing volatile
1038     elements or compounds (Chapter 13, Sample Dissolution).

1039     14.5.2 Volatilization Principles

1040     Volatilization particularly provides a rapid and often selective method of separation for a wide
1041     range of elements (McMillan, 1975, p. 306). A list of the elements that can be separated by
1042     volatilization and their chemical form(s) upon separation are given in Table 14.7 (McMillan,
1043     1975, p. 307).

1044     McMillan continues (1975, p. 306):

1045         "While many of the volatile species are commonly encountered and a large proportion can be
1046         produced from aqueous solutions, a significant number are rarely met. The volatilization of
1047         highly reactive materials and those with high boiling points are only used in special
1048         circumstances, e.g., for very rapid separations. ... Many other volatile compounds have been
1049         used to separate the elements, including sulphides, carbonyls, stable organic complexes
1050         and fluorinated p-diketones for the lanthanides."

1051         "Separation, ... , is achieved by differentiation during the volatilization process, fractionation
1052         by transfer, and selective collection. Gaseous evolution can be controlled by making use of
1053         differences in vapor pressure with temperature, adjustment of the oxidation state of the
1054         element in solution or by alteration of the matrix, in order to change the chemical
1055         combination of the element. Once gaseous, additional separation is possible and physical
1056         processes can be adopted such as gas chromatography, zone refining, fractional distillation,
1057         electrostatic precipitation, filtration of condensed phases and low temperature trapping.
1058         Chemical methods used are mainly based on the selective trapping of interfering substances
1059         by solid or liquid reagents. The methods of preferential collection of the species sought are
1060         similar to those used in the transfer stage."Both solid and liquid samples can be used in
1061         volatilization separations (Krivan, 1986, p. 377):

1062         "With solid samples, there are several types of separation methods. The most important of
1063         them are ones in which (1) the gas forms a volatile compound with only the trace elements
1064         and not the matrix, (2) the gas forms a volatile compound with the matrix but not the trace
1065         elements, and (3) volatile compounds are formed with both the matrix and the trace elements.
1066         Different gases have been used in separation by volatilization, including inert gases N2, He,
1067         and Ar and the reactive gases H2O, O2,  H2, ... F2, and HF. The apparatus usually consists of
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                                                              Separation Techniques
TABLE 14.7 — Elements separable by volatilization as certain sp

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         Separation Techniques
1068         three parts: gas regulation and purification, oven with temperature programming and control,
1069         and condensation or adsorption with temperature regulation."

1070         "The radiotracer technique provides the best way to determine the recoveries of trace
1071         elements in the volatilization process and to optimize the separation with respect to the
1072         pertinent experimental parameters."

1073     14.5.3 Distillation Principles

1074     Distillation is the separation of a volatile component(s) of a mixture by vaporization at the
1075     boiling point of the mixture and subsequent condensation of the vapor. The vapor produced on
1076     boiling the mixture is richer in the more volatile component—the component with the higher
1077     vapor pressure (partial pressure) and correspondingly lower boiling point. The process of
1078     distillation, therefore, essentially takes advantage of the differences in the boiling points  of the
1079     constituents to separate a mixture into its components. It  is a useful separation tool  if the analyte
1080     is volatile or can be transformed into a volatile compound. Most inorganic applications of
1081     distillation involve batch distillation, whereas most organic applications require some type of
1082     fractional distillation. In a simple batch distillation, the sample solution containing  a single
1083     volatile component or components with widely separated boiling points is placed in a distillation
1084     flask, boiling is initiated, and the vapors are then continuously removed, condensed, and
1085     collected. Mixtures containing multiple volatile components require fractional distillation., which
1086     employs repeated vaporization-condensation cycles for separation, and is commonly performed
1087     in a fractionation column for that purpose. The column allows the cycles to occur in one
1088     operation, and the separated component is collected after the last condensation.

1089     Distillation has been widely used for separating organic mixtures but this approach has less
1090     applicability in inorganic analysis (Korkisch, 1969, p. 25). Korkisch states: "Nevertheless, some
1091     of the elements of interest to radiochemists can be very effectively separated by distillation as
1092     their volatile chlorides, bromides, and oxides .... these elements are germanium (Ge), selenium
1093     (Se), technetium (Tc), rhenium (Re), ruthenium (Ru), and osmium (Os) (Korkisch,  1969, p. 25;
1094     also see DOE, 1995 Method RP530). Two common analytes determined through distillation,
1095     tritium and 226Ra, by radon emanation are discussed below.

1096     Specific distillation principles are commonly found in chemistry reference and textbooks. For a
1097     theoretical discussion of distillation see Peters (1974) and Perry and Weisberger (1965, pp. 1-
1098     229). Distillation procedures are discussed for many inorganic applications in Dean (1995) and
1099     for less common radioanalytes in the NAS-NS 3108 Monograph, Application of Distillation
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                                                                             Separation Techniques
1100      Techniques to Radiochemical Separation (DeVoe, 1962), and in NAS-NS 3104 Monograph,
1101      Rapid Radiochemical Separations (Kuska and Meinke, 1961).

1102      14.5.4 Separations in Radiochemical Analysis

1103      The best known use of distillation in radiochemical analysis is in the determination of tritium
1104      (EPA, 1984, pp. H-01-1-8; DOE, 1995, pp. RP580). Water is the carrier as simple distillation is
1105      used to separate tritium from water or soil samples. For determination of tritium, the aqueous
1106      sample is treated with a small amount of sodium hydroxide (NaOH) and potassium permanganate
1107      (KMnO4), and it is then distilled. The early distillate is discarded, and a portion of the distillate is
1108      collected for tritium determination by liquid scintillation counting. The alkaline treatment
1109      prevents other radionuclides, such as radioiodine or radiocarbon, from distilling over with the
1110      tritium (3H), and the permanganate (MnO44) treatment destroys trace organic material in the
1111      sample that could cause quenching during the counting procedure.

1112      Larger samples are distilled using a round-bottom flask, while a MICRO DIST® tube can be
1113      utilized for smaller samples (DOE, 1995, Method RP580). The distillate can be added directly to
1114      a liquid scintillation cocktail  (EPA, 1980, Method 906.0), or further enriched by acid electrolysis
1115      (DOE, 1990 and 1997, Method 3H-01) or alkaline electrolysis (DOE, 1990 and 1997, Method 3H-
1116      02).

1117      Iodine (I2) is separated from aqueous samples by distillation from acidic solutions into alkaline
1118      solutions (EPA, 1973, pp. 73-76). Iodide (I"1) is  added as carrier; but nitric acid (HNO3) as part of
1119      the acid solution, oxidizes the anion to molecular iodine as the mixture is heated for distillation.

1120      One  determination of 79Se employs an optional purification step, distillation of the metal as
1121      selenous acid, H2SeO3 (DOE, 1995, Method RP530).  The solution is maintained with excess
1122      bromine (Br2) and hydrobromic acid (HBr) to hold the selenium in the oxyacid form during the
1123      distillation. Technetium can be separated from other elements, or can be separated from
1124      ruthenium, osmium, or rhenium by distillation of their oxides (Friedlander et al., 1981, p 300).
1125      Metals are sometimes distilled in their elemental form—polonium in bismuth or lead (McMillan,
1126      1975, p. 308).

1127      226Ra in solution can be determined by de-emanating its gaseous progeny 222Rn into an ionization
1128      chamber or scintillation cell.  Generally, the procedure initially involves the concentration of
1129      radium by coprecipitation with barium sulfate (BaSO4). The barium sulfate is then dissolved in
1130      an EDTA solution, transferred to a sealed bubbler, and stored to allow for the ingrowth of 222Rn.
1131      Following sufficient in-growth, the 222Rn is de-emanated by purging the solution with an inert


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         Separation Techniques
1132      gas, such as helium (He) or argon (Ar), and is transferred via a drying tube to a scintillation cell
1133      or ionization chamber. After the short-lived 222Rn progeny have reached secular equilibrium with
1134      the 222Rn (approximately four hours), the sample is counted to determine alpha activity (EPA,
1135      1980, Method 903.1; DOE, 1990 and 1997, Methods Ra-01 through Ra-07; Sedlet, 1966; Lucas,
1136      1990).

1137      When processing samples containing radon, care should be taken to guard against the inadvertent
1138      loss of the gas or contamination of the distillation apparatus. Radon can be adsorbed on, or
1139      permeate through, materials used in its handling. Diffusion through rubber and plastic tubing or
1140      through polyethylene bottles has been observed. Since radon is soluble in many organic
1141      compounds, impurities, including greases used in ground-glass connections,  can increase
1142      adsorption.

1143      14.5.5 Advantages and Disadvantages of Volatilization

1144      14.5.5.1   Advantages

1145       • Can be very selective, producing clean separations.
1146       • Very rapid, especially with high-vacuum equipment.
1147       • Can be performed from solid or liquid samples.
1148       • Most can be performed without a specific carrier gas.

1149      14.5.5.2   Disadvantages

1150       • Relatively few volatile elements or inorganic compounds are available.

1151       • Atmosphere can alter the nature of a volatile form of the tracer or surface material.

1152       • Effects of experimental parameters (carrier gas, gas flow, temperature, time, and recovery)
1153         are highly variable.
1154       • Precautions are sometimes necessary to avoid loss  of volatile radionuclide substances during
1155         subsequent procedures.

1156       • Some systems require high-temperature, complex equipment.

1157       • Contamination of distillate by carrier, spray, or mechanical entrapment is a potential problem.
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1158     14.6  Electrodeposition

1159     14.6.1 Electrodeposition Principles

1160     Radionuclides in solution as ions can be deposited (plated) by electrochemical reactions (redox
1161     reactions) onto an electrode, either by a spontaneous process (produced by a favorable electrode
1162     potential existing between the ion and electrode) or by a nonspontaneous process (requiring the
1163     application of an external voltage (potential) (Section 14.2, "Oxidation and Reduction
1164     Processes").

1165     Spontaneous electrochemical processes are described by the Nernst equation,  which relates the
1166     electrode potential of the reaction to the activity of substances participating in a reaction:
1167                                        E=E° - RT/nF ln(ap/ar)

1168     where E is the electrochemical potential, E° is the standard potential for the process, R is the
1169     ideal gas constant, T is the absolute temperature, n is the number of electrons exchanged in the
1170     redox reaction, F is Faraday's constant, and 3p and ar are the activities of the products of the
1171     reaction and the  reactants, respectively. The activity^ of ions in solution is a measure of their
1172     molar concentration (c in moles/L) under ideal conditions of infinite dilution. Expressing the
1173     activities in terms of the product of molar concentrations and activity coefficients, y (a measure
1174     of the extent the ion deviates from ideal behavior in solution; thus a=y • c, where y < 1), the
1175     Nernst equation  becomes:
1176                                      E=E° - RT/nF ln(ypcp/yrcr)

1177     For dilute solutions of electrolytes (< 10"2 molar), the activity coefficient is approximately one
1178     (y~ 1; it approaches one as the solution becomes more dilute, becoming one under ideal
1179     conditions). Then, the Nernst equation is expressed in terms of the concentrations of ions in
1180     solution, the typical form in which the equation is found in most chemistry textbooks (see also
1181     Section 14.8.3.1, "Solubility and Solubility Product Constant, Ksp," for an application of activity
1182     to the solubility product constant):
1183                                         E=E° - RT/nF ln(cp/cr)

1184      At concentrations less than 10"6M, electrodeposition may show considerable deviations from
1185      behavior of macroamounts of elements whose behavior partly depends on the nature and
1186      previous treatment of the electrode (Adolff and Guillaumont, 1993, p. 275). Inconsistent


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1187      behavior is the result of heterogeneity of the surface metal, a very important consideration when
1188      electrodepositing radionuclides at very low concentrations. The spontaneity predicted by the
1189      Nernst equation for macroconcentrations of ions in solution at controlled potential is not always
1190      observed for microconcentrations (Choppin et al.,  1995, p. 246). The activity of radionuclide ions
1191      is usually unknown at low concentrations even if the concentration is known, because the activity
1192      coefficient (y) is dependent on the behavior of the mixed electrolytic system. In addition, the
1193      concentration might not be accurately known because ions might adsorb on various surfaces,
1194      form complexes with impurities, or precipitate on the electrode, for example. (See ection
1195      14.9.3.7, "Oxidation and Reduction of Tracers," for another application of the Nernst equation.)
1196      Separation is limited partly because electrodeposition from very dilute solutions is slow, but it is
1197      also limited because it rarely leads to complete separation of one element from many others
1198      (Coomber, 1975, p. 313). Overall, the behavior of an element during an electrochemical process
1199      is determined by its electrochemical potential, which depends on the nature of the ion; its
1200      chemical form, its concentration, the general composition of the electrolyte, the current density,
1201      material and design of the electrode, and construction features of the electrochemical cell
1202      (Zolotov, 1990, pp. 94-95).

1203      Often trace elements are deposited on a solid cathode, but large separation  factors between
1204      micro- and macro-components are required. This condition is met when electrochemically active
1205      metals are the main components or when the analyzed matrix does  not contain macro-
1206      components that will separate on the cathode (Zolotov, 1990, p. 95). Deposition of heavy metals
1207      and actinides can be more difficult to control, for example, because of the decomposition of
1208      water and reactions of cations and anions at electrodes (Adolff and Guillaumont, 1993, p.  158).
1209      In some cases, deposition of matrix components can be avoided by selection of a suitable
1210      medium and composition of the electrolyte. Overall, the effectiveness of electrodeposition of
1211      trace components depends on the electrode potential, electrode material and its working surface
1212      area, duration of electrolysis, properties of the electrolyte (composition and viscosity),
1213      temperature, and mixing rate (Zolotov, 1990, pp. 95-96). Even so, published data are empirical
1214      for the most part, and  conditions for qualitative reproducible separation are determined for each
1215      case. It is difficult, therefore, to make general recommendations for selecting concentration
1216      conditions. It is advisable to estimate and account for possible effects of different electrolysis
1217      factors when developing separation or concentration methodologies (Zolotov, 1990, p. 98).

1218      14.6.2 Separation of Radionuclides

1219      Although electrodeposition is not frequently used as a radiochemical separation technique,
1220      several radionuclides [including iron (Fe) (Hahn, 1945), cadmium (Cd) (Wright, 1947),  and
1221      technetium (Tc) (Flagg, 1945)] have been isolated by electrodeposition on  a metal electrode.


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1222      Electrodeposition is, however, the standard separation technique for polonium (Po), copper (Cu),
1223      and platinum (Pt). Polonium is isolated through deposition on nickel from a strong hydrochloric
1224      acid (HC1) medium (DOE, 1990 and 1997, Method Po-01). This separation is very specific, and,
1225      therefore, can be accomplished in the presence of many other radionuclides. Electrodeposition at
1226      a mercury cathode has also been used to separate technetium from fission products and for group
1227      separation of fission products (Coomber, 1975, p. 198). Numerous metals have been deposited
1228      on thin metal films by electrolysis with a magnesium (Mg) cathode. According to Coomber,
1229      "Electrodeposition of metals can be sensitive to the presence of other substances" (Coomber,
1230      1975, p. 198). Deposition of polonium on silver (Ag) is inhibited by iron unless a reducing agent
1231      is present; and the presence of fluoride (F"1), trace amounts of rare earths, can inhibit the
1232      deposition of americium (Am). "In many cases the uncertainties of yield can be  corrected by the
1233      use of another radioisotope as an internal standard" (Coomber, 1975, p. 198).

1234      14.6.3 Preparation of Counting Sources

1235      Electrodeposition is primarily used to prepare counting sources by depositing materials uniformly
1236      in an extremely thin layer. Because of potential self-absorption effects, this approach is ideal for
1237      the preparation of alpha sources. Numerous methods have been published for the electro-
1238      deposition of the heavy metals, e.g., the Mitchell method from hydrochloric acid (Mitchell,
1239      1960), the Talvitie method from dilute ammonium sulfate [(NH4)2SO4] (Talvitie, 1972), and the
1240      Kressin method from sodium sulfate-sodium bisulfate media (Kressin, 1977).

1241      Sill and Williams (1981) and Hindman (1983, 1986) contend that coprecipitation is the preferred
1242      method for preparation of sources for alpha spectrometry and that the it should be assessed when
1243      electrodeposition is being considered. Also see Section 16.7.2, "Coprecipitation," in this manual.

1244      14.6.4 Advantages and Disadvantages of Electrodeposition

1245      14.6.4.1   Advantages

1246       • Highly selective in some cases.
1247       • Deposits material in an extremely thin uniform layer resulting in excellent spectral resolution.
1248       • One of the common methods for preparing actinides for alpha spectrometry.

1249      14.6.4.2 Disadvantages

1250       • Not applicable to many radionuclides.
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1251       • Sensitive to the presence of other substances.

1252       • For tracer-level quantities, the process is relatively slow, it seldom leads to complete
1253         separation of one element from many others, and there is usually no direct comparison of
1254         concentration in solution to deposited activity.

1255       • No further separations can be performed (see Section 16.7.2, "Coprecipitation," for methods
1256         using NdF3.).

1257      14.7 Chromatography

1258      14.7.1 Chromatographic Principles

1259      Chromatography is a separation technique that is based on the unequal distribution (partition) of
1260      substances between two immiscible phases, one moving past the other. A mixture of the
1261      substances (the analytical mixture) in the mobile phase passes over the immobile phase. Either
1262      phase can be a solid, liquid, or gas, but the alternate phase cannot be in the same physical state.
1263      The two most common phase pairs are liquid/solid and gas/liquid. Separation occurs as the
1264      components in the mixture partition between the two phases because, in a properly designed
1265      chromatographic system, the phases are chosen so that the distribution of the components
1266      between the phases is not equal.

1267      With the broad range of choices of phase materials, the number of techniques employed to
1268      establish differential distributions of components between the phases, and the various practical
1269      laboratory methods used to cause the mobile phases to pass over the immobile phases, there are
1270      many chromatographic techniques available in separation chemistry. The names of the
1271      chromatographic techniques themselves partially identify the methods or principles employed
1272      and suggest the variety of applications available using this approach to separation. They include
1273      paper chromatography, ion-exchange chromatography, adsorption chromatography, gas
1274      chromatography, high-pressure liquid chromatography, and affinity chromatography. Each aspect
1275      of chromatography used in separation chemistry will be described below, including the phases
1276      commonly employed, the principles used to establish differential distributions, and the laboratory
1277      techniques employed to run a chromatographic separation.

1278      The most common phase pairs used in chromatography are  a mobile liquid phase in contact with
1279      a solid phase. The liquid phase can be a pure liquid, such as water or an organic solvent, or it can
1280      be a  solution, such as methyl alcohol, sodium chloride in water, or hexane in toluene. The solid
1281      phase can be a continuous material such  as paper, or a fine-grained solid such as silica, powdered

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                                                                            Separation Techniques
1282      charcoal, or alumina. The fine-grained solid can also be applied to a supporting material, such as
1283      paper, plastic, or glass, to form a coat of continuous material. Alternatively, gas/liquid phase
1284      systems can consist of an inert gas, such as nitrogen (N2) or helium (He), in conjunction with a
1285      high-boiling point liquid polymer coated on the surface of a fine-grained inert material, such as
1286      firebrick. This system is called gas-liquid phase chromatography (GLPC), or simply gas
1287      chromatography(GC). In each system, both phases play a role in the separation by offering a
1288      physical or chemical characteristic that will result in differential distribution of the components
1289      of the analytical mixture being separated. Liquid/liquid phase systems are similar to gas/liquid
1290      phase systems in that one of the liquid phases is bound to an inert surface and remains stationary.
1291      These systems are often referred to as liquid partition chromatography or liquid-phase
1292      chromatography (LPC), because they are essentially liquid-liquid extraction systems with one
1293      mobile and one immobile phase (Section 14.4, "Solvent Extraction").

1294      Differential distributions are established between the separating phases by the combination of
1295      physical and chemical properties of the two phases in combination with those of the components
1296      of the analytical mixture.  The properties that are most commonly exploited by separation
1297      chromatography are solubility, adsorption, ionic interactions, complementary interactions, and
1298      selective inclusion. One or more of these properties is acting to cause the separation to occur.

1299      14.7.2  Gas-Liquid and Liquid-Liquid Phase Chromatography

1300      In gas-liquid phase chromatography,  the components of the analytical mixture are first converted
1301      to a vapor themselves and added to the flowing gas phase. They are then partitioned between the
1302      carrier gas and liquid phases primarily by solubility differences of the components in the liquid
1303      phase. As the gas/vapor mixture travels over the liquid phase, the more soluble components of
1304      the mixture spend more time in the liquid. They travel more slowly through the chromatography
1305      system and are separated from the less soluble, and therefore faster moving, components.
1306      Liquid/liquid phase chromatography  provides separation based on the same principle of
1307      solubility in the two liquid phases, but the separation is performed at ambient temperatures with
1308      the components of the analytical mixture initially dissolved in the mobile phase. Partitioning
1309      occurs between the two phases as the mobile phase passes over the stationary liquid phase.

1310      Gas chromatography has been used to concentrate tritium, and to separate krypton and xenon
1311      fission products and fission-produced halogens (Coomber, 1975, p. 189). A large number of
1312      volatile metal compounds could be separated by gas chromatography, but few have been
1313      prepared. Lanthanides and trivalent actinides have been separated on glass capillary columns
1314      using volatile double halides formed  with aluminum chloride (Coomber, 1975, p. 189).
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1315      14.7.3 Adsorption Chromatography

1316      Adsorption chromatography partitions components of a mixture by means of their different
1317      adsorption characteristics onto the surface of a solid phase and their different solubilities in a
1318      liquid phase. Adsorption phenomena are primarily based on intermolecular interactions between
1319      the chemical components on the surface of the solid and the individual components of the
1320      mixture. They include Van der Waals forces, dipole-dipole interactions, and  hydrogen bonds.
1321      Silica is a useful adsorption medium because of the ability of its silyl OH groups to hydrogen
1322      bond or form dipole-dipole interactions with molecules in the mixture. These forces compete
1323      with similar intermolecular interactions—between the liquid phase and the components of the
1324      mixture—to produce the differential distribution of the components. This process causes
1325      separation to occur as the liquid phase passes over the solid phase.

1326      Many separations have been performed via paper and thin-layer chromatography. Modified and
1327      treated papers have been used to separate the various valence states of technetium (Coomber,
1328      1975, p. 189).

1329      14.7.4 Ion-Exchange Chromatography

1330      14.7.4.1   Principles of Ion Exchange

1331      Since the discovery by Adams and Holmes (1935) that synthetic resins can have ion-exchanging
1332      properties, ion exchange has become one of the most popular, predominant, and useful tech-
1333      niques for radiochemical separations,  both with and without carriers. There are many excellent
1334      references available in the literature, e.g., Dean (1995), Dorfner (1972), Korkisch (1989), Rieman
1335      and Walton (1970), and NAS monographs (listed in References, under the author's name). The
1336      j ournal, Ion Exchange and Solvent Extraction,  reports recent advances in this field of separation.

1337      Ion-exchange methods are based on the reversible exchange of metal ions between a liquid
1338      phase, typically water, and a solid ionic phase of opposite charge, the resin. The resin competes
1339      with the ion-solvent interactions in the liquid phase, primarily ion-dipole interactions and
1340      hydrogen bonding, to produce the selective partition of ions, causing separation. The solid phase
1341      consists of an insoluble, but permeable, inert polymeric matrixthat contains  fixed charged groups
1342      (exchange sites) associated with mobile  counter-ions of opposite charge. It is these counter-ions
1343      that are exchanged for other ions in the liquid phase. Resins are either naturally occurring sub-
1344      stances, such  as zeolites (inorganic silicate polymers) or synthetic polymers.  The synthetic resins
1345      are organic polymers with groups containing the exchange sites. The exchange sites are acid or
1346      base groups (amines, phenols, and carboxylic or sulfonic acids) used over a specific pH range


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                                                                              Separation Techniques
1347     where they are in their ionic form. Typical exchange groups for cations (K+1, Ca+2, and UO2+2) are
1348     the sulfonate anion, RSO34, or the carboxylate anion, RCOO"1. The quaternary-amine cation,
1349     RNH3+1, or its derivative, is a common exchange group for anions (Cl"1, OH"1, and UO2(SO4)3"4).

1350     In a practical description of ion-exchange equilibria, the weight distribution coefficient, Kd, and
1351     the separation factor, a, are significant. The weight distribution coefficient is defined as:

1352                                    Kd = ((Vg^J / (C2 /mLsolution)

1353     where Cx is the weight of metal ion adsorbed on 1 g of the dry resin, and C2 is the weight of
1354     metal that remains in 1 mL of solution after equilibrium has been reached. The separation factor
1355     refers to the ratio of the distribution coefficients for two ions that were determined under
1356     identical experimental conditions:

1357                                   Separation factor (a) = Kd a / Kd b

1358     where a and b refer to a pair of ions. This ratio determines the separability of the two ions;
1359     separation will only be achieved if a * 1. The more that a deviates from unity, the easier it will
1360     be to obtain separation.

1361     An example of the separation process is the cation-exchange resin. It is usually prepared for
1362     separation procedures as a hydrogen salt of the exchange group. Separation occurs when an
1363     aqueous solution of another alkali-metal ion (i.e., Li+1, K+1, Rb+1, or Cs+1) comes in contact with
1364     the resin. Different ions bond selectively to the exchange group, depending on the separation
1365     conditions, displacing the counter-ion that is present in the  prepared resin as follows:

1366                               ResinS03-1 H+1 + Cs+1 - ResinS03-1Cs+1 + H+1

1367     Diffusion is an important process during ion exchange; the solute ions must penetrate the pores
1368     of the spherical resin beads to exchange with the existing ions. Equilibrium is established
1369     between each ion in the analyte solution and the exchange site on the resin. The ion least tightly
1370     bonded to the exchange site and most solvated in solution spends more time in solution. Selec-
1371     tive bonding is a factor of the size and charge of the ion, the nature of the exchange group, and
1372     the pH and ionic strength of the media. The order of strength of bonding at low  acid concentra-
1373     tions in this example is H+1 or Li+1 < Na+1 < K+1 < Rb+1 < Cs+1 (Showsmith, 1984). Under the
1374     appropriate conditions, for  example, Cs+1 will bond exclusively, or Cs+1 and Rb+1 will bond,
1375     leaving the remaining cations in solution. The process can be operated as a batch operation or via
1376     continuous-flow with the resin in an ion-exchange column. In either case, actual separation is


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         Separation Techniques
1377      achieved as the equilibrated solution elutes from the resin, leaving select ions bonded to the resin
1378      and others in solution. The ion that spends more time in solution elutes first. The ability to "hold"
1379      ionic material is the resin capacity, measured in units of mg or meq per gram of resin. Eventually,
1380      most of the exchange groups are occupied by select ions. The resin is essentially saturated, and
1381      additional cations cannot bond. In a continuous-flow process, breakthrough will then occur. At
1382      this time, added quantities of select cations (Cs+1 or Cs+1 and Rb+1 in this example) will pass
1383      through the ion-exchange column and appear in the output solution (eluate). No further separa-
1384      tion can occur after breakthrough, and the bonded ions must be remove to prepare the column for
1385      additional separation. The number of bed volumes of incoming solution (eluanf) that passes
1386      through a column resin before breakthrough occurs provides one relative measure of the treat-
1387      ment capacity of the resin under the conditions of column use. The bonded cations are displaced
1388      by adjusting the pH of the medium to change the net charge on the exchange groups. This change
1389      alters the ability of the exchange groups to attract ions, thereby replacing the bonded cations with
1390      cations that bond more strongly. More commonly, the resin is treated with a more concentrated
1391      solution of the counter-ion—H+1 in this example. Excess H+1 favors the equilibrium that produces
1392      the initial counter-ion form of the exchange group. This process that returns the column to its
1393      original form is referred to as "regeneration."

1394      Overall, selectivity of the exchange resin determines the efficiency of adsorption of the analyte
1395      from solution, the ease with which the ions can be subsequently removed from the resin, and the
1396      degree to which two different ions of like charge can be separated from each other. The
1397      equilibrium distribution of ions between the resin and solution depends on many factors, of
1398      which the most important are the nature of the exchanging ions, the resin, and the solution:

1399       • In dilute solutions, the stationary phase will show preference for ions of higher charge.

1400       • The selectivity of ion exchangers for ions increases with the increase of atomic number
1401         within the same periodic group, i.e., Li+ < Na+ < K+ < Rb+ < Cs+.

1402       • The higher the polarizability and the lower the degree of solvation (favored by low charge
1403         and large size), the more strongly an ion will be adsorbed.

1404       • Resins containing weakly acidic and weakly basic groups are highly selective towards H+ and
1405         OH" ions. Ion-exchange resins that contain groups capable of complex formation with
1406         particular ions will be more selective towards those ions.

1407       • As cross-linking is increased (see discussion of resins below), resins become more selective
1408         in their behavior towards ions of different sizes.
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                                                                             Separation Techniques
1409       • No variation in the eluent concentration will improve the separation for ions of the same
1410         charge; however, for ions of different net charges, the separation does depend on the eluent
1411         concentration.

1412      14.7.4.2   Resins

1413      The most popular ion-exchange resins are polystyrenes cross-linked through divinylbenzene
1414      (DVB). The percentage of DVB present during polymerization controls the extent of cross-
1415      linking. Manufacturers indicate the degree of cross-linking by a number following an X, which
1416      indicates the percentage of DVB used. For instance, AG 1-X8 and AG 1-X2 are 8 percent and 2
1417      percent cross-linked resins, respectively. As this percentage is increased, the ionic groups effec-
1418      lively come into closer proximity, resulting in increased selectivity. However, increases in cross-
1419      linking decrease the diffusion rate in the resin particle. Because diffusion is the rate-controlling
1420      step in column operations, intermediate cross-linking in the range of 4 to 8 percent is commonly
1421      used.

1422      Particle diameters of 0.04-0.3 mm (50-400 mesh) are commonly used, but larger particles give
1423      higher flow rates. Difficult separations can require 200-400 mesh resins. Decreasing the particle
1424      size reduces the time required for attaining equilibrium; but at the same time, it decreases flow
1425      rate. When extremely small particle sizes are used, pressure must be applied to the system to
1426      obtain acceptable flow rates (see discussion of high pressure liquid chromatography in Section
1427      14.7.7, "Chromatographic Methods").

1428      Ion-exchange resins are used in batch operations,  or more commonly, in column processes in the
1429      laboratory. Columns can be made in any size desired. The diameter of the column depends on the
1430      amount of material to be processed, and the length of the  column depends primarily on the
1431      difficulty of separations to be accomplished. Generally, the ratio of column height to diameter
1432      should be 8:1. Higher ratios lead to reduced flow rate; lower ratios might not provide effective
1433      separations.

1434      Some other factors should be considered when using ion-exchange resins:

1435       • Resins should not be allowed to dry out, especially during analysis. Rehydration of dried
1436         resins will result in cracking; these resins should  not be used.

1437       • Non-ionic and weakly ionic solutes may be absorbed (not exchanged) by the resin. These
1438         materials, if present during analysis, can alter the exchange characteristics of the resin for
1439         certain ions.
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         Separation Techniques
1440
1441
1442

1443
1444

1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458

1459
1460
1461
1462
1463

1464
1465
1466
1467
1468

1469
1470
 • Particulate matter present in the analyte solution may be filtered by the resin. This material
   will have several undesired effects, such as decreased flow rate, reduced capacity, and
   ineffective separation.

 • Organic solvents suspended in the analyte solution from previous separation steps can be
   adsorbed by the resin creating separation problems.

Ion exchangers are classified as cationic or anionic (cation exchangers or anion exchangers,
respectively), according to their affinity for negative or positive counter-ions. They are further
subdivided into strongly or weakly ionized groups. Most cation exchangers (such as Dowex-50
and Amberlite IR-100) contain free sulfonic acid groups, whereas typical anion exchangers (such
as AG 1 and Dowex-1) have quaternary amine groups with replaceable hydroxyl ions (see Table
14.8).
                         TABLE 14.8 — Typical functional groups
                                  of ion-exchange resins
Cation Exchangers
-SO3H
-COOH
-OH
-SH
Anion Exchangers
-NH2
-NHR
-NR2
-NIC
                        R=alkyl group

The sulfonate resins are known as strong acid cation (SAC) resins because the anion is derived
from a strong sulfonic acid (RSO3H). Likewise, the carboxylate resins are known as weak acid
cation (WAC) resins because the anion is derived from a weak carboxylic acid (RCOOH). R in
the formulas represents the inert matrix. The quaternary-amine cation (RNH3+1) or its derivatives,
represents the common exchange group for anions.

Several examples from the literature illustrate the use of ion-exchange chromatography for the
separation of radionuclides. Radium is separated from other alkaline-earth cations (Be+2, Mg+2,
Ca+2, Sr+2, and Ba+2) in hydrochloric solutions on sulfonated polystyrene resins (Kirby and
Salutsky, 1964, pp. 26-27), or converted to an anionic complex with citrate or EDTA and
separated on a quaternary ammonium polystyrene resin (Sedlet, 1966, p. 302).

Anion-exchange resins separate anions by an analogous process beginning with a prepared resin,
usually in the chloride form (RNH3+1C1"1), and adding a solution of ions. Anion-exchange
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                                                                            Separation Techniques
1471
1472
1473
1474
1475
1476
1477

1478
1479

1480
1481
1482
1483
1484

1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495

1496
1497
1498
1499
1500

1501
1502
chromatography is used in one step of a procedure to isolate thorium for radioanalysis by alpha
counting (EPA, 1984, pp. U/Th-01-1-14). Thorium cations (Th+4) form anionic nitrate complexes
that bind to an anion-exchange resin containing the quaternary complex, R-CH2-N(CH3)3+1. Most
metal ion impurities do not form the complex and, as cations, they do not bind to the exchanger,
but remain with the liquid phase. Once the impurities are removed, thorium itself is separated
from the resin by treatment with hydrochloric acid (HC1) that destroys the nitrate complex,
leaving thorium in its +4 state, which will not bind to the anionic exchanger.

A selection of commercially available resins commonly employed in the radiochemistry
laboratory is given in Table 14.9.

The behavior of the elements on ani on- and cation-exchange resins is effectively summarized for
several resins in Paris and Buchanan (Paris and Buchanan, 1964), Kraus and Nelson (Kraus and
Nelson, 1956), and Nelson et al. (1964). The behavior in concentrated hydrochloric acid is
illustrated for cations on cation-exchange resins in Figure 14.3 (Dorfner, 1972, p. 208) and for
cations on anion-exchange resins in Figure 14.4 (Dorfner, 1972,  p. 210).
                      TABLE 14.9 — Common ion-exchange resins
                                                                 (i)
Resin type &
nominal %
cross-link
Minimum wet
capacity
meq» ml/1
Density
(nominal)
g» ml/1
Description
Anion-exchange resins — gel type — strongly basic — quaternary ammonium functionality
Dowex, AG or
Eichrom
1-X4
Dowex, AG or
Eichrom
1-X8
1.0
1.2
0.70
0.75
Strongly basic anion exchanger with S-DVB matrix for
separation of organic acids, nucleotides, and other anions.
Molecular weight exclusion < 1400.
Strongly basic anion exchanger with S-DVB matrix for
separation of inorganic and organic anions with molecular
weight exclusion < 1000. 100-200 mesh is standard for
analytical separations.
Anion-exchange resins — gel type — intermediate basicity
Bio-Rex 5
1.1
0.70
Intermediate basic anion exchanger with primary tertiary amines
on an polyalkylene-amine matrix for separation of organic
acids.
Anion-exchange resins — gel type — weakly basic — polyamine functionality
Dowex or AG
4-X4
Amberlite
IRA-68
0.8
1.6
0.7
1.06
Weakly basic anion exchanger with tertiary amines on an
acrylic matrix. Suitable for use with high molecular weight
organic compounds.
Acrylic -DVB with unusually high capacity for large organic
molecules.
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          Separation Techniques
Resin type &
nominal %
cross-link
Minimum wet
capacity
meq» ml/1
Density
(nominal)
g* ml/1
Description
Cation-exchange resins - gel type - strongly acidic - sulfonic acid functionality
Dowex, AG or
Eichrom
SOW- X4
Dowex, AG or
Eichrom
SOW- X8
Amberlite
IR-120
1.1
1.7
1.9
0.80
0.80
1.26
Strongly acidic cation exchanger with S-DVB matrix for
separation of amino acids, nucleosides and cations. Molecular
weight exclusion is < 1400.
Strongly acidic cation exchanger with S-DVB matrix for
separation of amino acids, metal cations, and cations. Molecular
weight exclusion is < 1000. 100-200 mesh is standard for
analytical applications.
8% styrene-DVB type; high physical stability.
Selective ion-exchange resins
Duolite GT-73
Amberlite
IRA-743A
Amberlite
IRC-718
Chelex® 100
Eichrom
Diphonix®
1.3
0.6
1.0
0.4

1.30
1.05
1.14
0.65

Removal of Ag, Cd, Cu, Hg, and Pb.
Boron-specific.
Removal of transition metals.
Weakly acidic chelating resin with S-DVB matrix for heavy
metal concentration.
Chelating ion-exchange resin containing geminally substituted
diphosphonic groups chemically bonded to a styrenic -based
polymer matrix. Extraordinarily strong affinity for actinides in
the tetra- and hexavalent oxidation states from highly acidic
media.
Anion exchanger — macroreticular type — strongly basic — quaternary ammonium functionality
AGMP-1
1.0
0.70
Strongly basic macroporous anion exchanger with S-DVB
matrix for separation of some enzymes, and anions of
radionuclides.
Cation-exchange resin — macroreticular type — sulfonic acid functionality
AG MP-50
1.5
0.80
Strongly acidic macroporous cation exchanger with S-DVB
matrix for separation of cations of radionuclides and other
applications.
Microcrystalline exchanger
AMP-1
4.0

Microcrystalline ammonium molybophosphate with cation
exchange capacity of 1.2 meq/g. Selectively adsorbs larger
alkali-metal ions from smaller alkali-metal ions, particularly
cesium.
1527      (1) Dowex is the trade name for Dow resins; AG and Bio-Rex are the trade names for Bio-Rad Laboratories resins;
1528         Amberlite is the trade name of Rohm & Haas resins. MP is the acronym for macroporous resin; S-DVB is the
1529         acronym for styrene-divinylbenzene.
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                                                                  Separation Techniques
                              7
                            -, £Q   —
**S
                                    tel
                5P?
                 -M
c  Z
J1J
                    0)1 6o1
               . __,	i.
               ml 1
                   n
                                      13:
                                             fel
                                             fel
                                               *~>- v
                                               ca <\j
                                                 z
                                             M If._
                                 fel
                                                    s:\
                                                      fel
                                                         •*r
                               :  jfej
                                                     IM
                                                          -bA
                                                          fe)
                                    ^
                                                                -E
                                              -=?^
                                        ^K
                                            ^
                     FIGURE 14.3 — The behavior of elements in concentrated
                          hydrochloric acid on cation-exchange resins
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Separation Techniques
ads. ~ no exchange 0.1 < JW HCI
acfc. = some exchonge m 72 M HCI
, ads. — stronger exchange Cy »
                                          fei
                       ax
                                             M;

                                                     ~fei
                                                  L
                ^i
                                                         B)
                                                           7
                                                         -e
                                                        N ?
                                                          §'
                       -e
                     Figure 14.4 —The behavior of elements in concentrated
                          Hydrochloric acid on anion-exchange resins
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                                                                             Separation Techniques
1530      14.7.5 Affinity Chromatography

1531      Several newer types of chromatography are based on highly selective and specific attractive
1532      forces that exist between groups chemically bound to an inert solid matrix (ligands) and molecu-
1533      lar or ionic components of the analytical mixture. Affinity chromatography is an example of this
1534      separation technique, which is used in biochemistry to isolate antigenic materials, such as
1535      proteins.  The proteins are attracted to their specific antibody that is bonded to a solid matrix.
1536      These attractive forces are often called complementary interactions because they are based on a
1537      lock-and-key type of fit between the two constituents. The interaction is complementary because
1538      the two components match (fit) each other in size and electrical nature.

1539      Crown ethers bonded to solid matrices serve as ligands in a chromatographic separation of
1540      radium ions from aqueous solutions containing other cations (see Section 14.4.5.1, "Extraction
1541      Chromatography Columns"). Even other alkaline-earth cations with the same +2 charge, such as
1542      strontium (Sr+2) and barium (Ba+2), offer little interference with radium binding because the
1543      cyclic nature of the crown ether creates a ring structure with a cavity that complements the radius
1544      of the radium ion in solution. In addition, the oxygen atoms of the cyclic ether are inside the ring,
1545      allowing these electron-dense atoms to form effective ion-dipole interactions through water
1546      molecules with the radium cation. Radionuclides analyzed by this method include 89Sr/90Sr, "Tc,
1547      90Y, and 210Pb.

1548      14.7.6 Gel-Filtration Chromatography

1549      Another physical property that is used to separate molecules by a chromatographic procedure is
1550      the effective size (molecular weight) of the molecule. High molecular-weight ions can also be
1551      separated by this procedure. The method is known by several names, including gel-filtration
1552      chromatography., molecular-sieve filtration, exclusion chromatography, and gel-permeation
1553      chromatography. This technique is primarily limited to substances such as biomolecules with
1554      molecular weights greater than 10,000 Daltons. In similar types of solutions (similar solutes and
1555      similar concentrations), the molecules or ions have a similar shape and molecular weight that is
1556      approximately proportional to the hydrodynamic diameter (size) of the molecule or ion. The solid
1557      phase consists of a small-grain inert resin that contains microscopic pores in its matrix that will
1558      allow molecules  and ions up to a certain diameter, called included particles, to enter the resin.
1559      Larger particles are excluded. Of the included particles, the smaller ones spend more time in the
1560      matrices. Separation of the molecules or ions is based on the fact that those substances that are
1561      excluded are separated in a batch from the included substances, while those that are included are
1562      separated by size. The log of the molecular weight of the included molecules or ions is
1563      approximately inversely proportional  to the time the particles spend in the matrix.


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         Separation Techniques
1564      14.7.7 Chromatographic Laboratory Methods

1565      Chromatographic separations are achieved using a variety of laboratory techniques. Some are
1566      actually quite simple to perform, while others require sophisticated instrumentation. Paper
1567      chromatography employs a solid-liquid phase system that separates molecules and ions with
1568      filter paper or similar material in contact with a developing solvent. The analytical mixture in
1569      solution is spotted at the bottom of the paper and allowed to dry, leaving the analytes on the
1570      paper. The paper is suspended so that a small part of the bottom section is in a solvent, but not so
1571      deep that the dry spots enter the solvent. By capillary action, the solvent travels up the paper. As
1572      the solvent front moves up, the chromatogram is produced with the components of the mixture
1573      partitioning between the liquid phase and the paper. Thin-layer chromatography-is similar, but
1574      the paper is replaced by a thin solid phase of separately material (silica gel, alumina, cellulose,
1575      etc.) coated on an inert support, such as plastic or glass.

1576      Column chromatographycan accommodate a larger quantity of both phases and can, therefore,
1577      separate greater quantities of material by accepting larger loads or provide more separating power
1578      with an increased quantity  of solid phase. In the procedure, a solid phase is packed in a glass or
1579      metal column and a liquid phase is passed through the column under pressure supplied by gravity
1580      or low-pressure pumping action. For this reason, gravity flow (or pumping the liquid phase under
1581      pressures similar to those generated by gravity flow) is often referred to as low-pressure
1582      chromatography. The liquid phase is usually referred to as the eluentand the column is eluted
1583      with the liquid. Column chromatography is the common method used in ion-exchange chroma-
1584      tography. With column chromatography, separation depends on: (1) type of ion-exchange resin
1585      used (i.e., cationic, anionic, strong, or weak); (2) eluting solution  (its polarity affects ion
1586      solubility, ionic strength affects displacement of separating ions, and pH affects net charge of
1587      exchange groups or their degree of ionization in solution); (3) flow rate, grain size, and
1588      temperature, which affect how closely equilibrium is approached  (generally, low flow rate, small
1589      grain  size, and high temperature aid the approach to equilibrium and, therefore, increase the
1590      degree of separation); and (4) column dimensions (larger diameter increases column capacity,
1591      while increased length increases separation efficiency  by increasing distance between ion bands
1592      as they travel through the column) (Wahl and Bonner,  1951, pp. 137-139).

1593      Metal columns can withstand considerably more pressure than glass columns. High-pressure
1594      liquid chromatography(FIPLC) employs stainless steel columns and solid phases designed to
1595      withstand high pressures without collapsing. The method is noted for its rapid separation times
1596      because of relatively high flow rates under high pressures (up to 2,000 lbs/in2). For this reason,
1597      the acronym FIPLC alternatively represents high-performance liquid chromatography. FIPLC is
1598      often performed  with a liquid-partition technique between an aqueous phase and organic phase,


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                                                                              Separation Techniques
1599     but gel filtration, ion exchange, and adsorption methods are also employed. In the case of liquid-
1600     partition separations, either a stationary aqueous phase or stationary organic phase is selected.
1601     The former system is referred to as normal phase chromatography and the latter as reversed phase
1602     chromatography, a holdover from the first applications of the technique that employed a
1603     stationary aqueous phase. The aqueous phase is made stationary by adsorption onto a solid
1604     support, commonly silica gel, cellulose powder, or polyacrylamide. An organic stationary phase
1605     is made from particles of a polymer such as polyvinyl chloride or Teflon®. Reversed phase HPLC
1606     has been used to separate individual elements of the lanthanides and actinides and
1607     macroquantities of actinides (Choppin et al., 1995, p. 248).

1608     Gas/liquid phase systems are also used. During gas-liquid phase chromatography (GLPC) [or
1609     simply, gas chromatography (GC)], the gas phase flows over the liquid phase (coated onto an
1610     inert solid) as an inert carrier gas—commonly helium (He) or nitrogen (N2)—flows through the
1611     system at low pressure. The carrier gas is supplied from a tank of the stored  gas.

1612     14.7.8 Advantages and Disadvantages of Chromatographic Systems

1613     Ion-exchange chromatography is by far the predominant chromatographic method used for the
1614     separation of radionuclides. Its advantages and disadvantages  is presented exclusively in this
1615     section.

1616         Advantages                               Disadvantages
1617         •  Highly selective.                          •  May require high volume of eluent.
1618         •  Highly efficient as a preconcentration method.   •  Usually a relatively slow process, but rapid
1619         •  Works as well with carrier-free tracer quantities     selective elution processes are known.
1620            as with weighable amounts.                  •  Requires narrow pH control.
1621         •  Produces a high yield (recovery).
1622         •  Can separate radionuclides from interfering
1623            counter-ions.
1624         •  Simple process requiring simple equipment.
1625         •  Wide scope of applications.
1626         •  Can handle high volumes of sample.


1627     14.8  Precipitation and Coprecipitation

1628     14.8.1 Introduction

1629     Two of the most common and oldest methods for the separation and purification of ions in
1630     radioanalytical chemistry are precipitation and coprecipitation. Precipitation is used to isolate

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1631      and collect a specific radionuclide from other (foreign) ions in solution by forming an insoluble
1632      compound. Either the radionuclide is precipitated from solution itself, or the foreign ions are
1633      precipitated, leaving the radionuclide in solution.  Sometimes a radionuclide is present in solution
1634      at sub-micro concentrations, i.e., levels so low that the radionuclide will not form an insoluble
1635      compound upon addition of a counter-ion. In these cases, the radionuclide can often be brought
1636      down from solution by coprecipitation, associating it with an insoluble substance that precipitates
1637      from solution. This phenomenon is especially important in gravimetric analysis and
1638      radiochemistry. In gravimetric analysis, carrying down of impurities is a problem. For
1639      radiochemists, coprecipitation is a valuable tool.

1640      14.8.2 Solutions

1641      Precipitation and coprecipitation provide an analytical method that is applied to ions in solution.
1642      Solutions are simply homogeneous mixtures (a physical combination of substances), which can
1643      be solids, liquids, or gases. The components of a solution consist of a solute and a solvent. The
1644      solute is generally defined as the substance that is dissolved, and the solvent is the substance that
1645      dissolves the solute.  In an alternative definition, particularly suitable for liquid components when
1646      it is not clear what is being dissolved or doing the dissolving, the solute is the minor constituent
1647      and the solvent is the major constituent. In any event, the solute and solvent can consist of any
1648      combinations of substances, so long as they are soluble in each other. However, in this chapter,
1649      we are generally referring to aqueous solutions in which a solute is dissolved in water. The terms
1650      below further describe solutions:

1651        •  Solubility is defined as the concentration of solute in solution that exists in equilibrium with
1652          an excess of solute; it represents the maximum amount of solute that can dissolve in a given
1653          amount of the solvent. The general solubilities of many of the major compounds of concern
1654          are described in Table 14.10.

1655        •  An unsaturated solution is one in which the concentration of the solute is less than the
1656          solubility. When additional solute is added to  an unsaturated solution, it dissolves.

1657        •  A saturated solution is one that is in equilibrium  with an excess of the solute. The
1658          concentration of a saturated solution is equal to the solubility of the solute. When solute is
1659          added to the saturated solution, no more solute dissolves.

1660        •  A supersaturated solution is a solution in which the concentration of solute is temporarily
1661          greater than its solubility—an unstable condition. Therefore, when additional solute is added
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1662
1663
  to a supersaturated solution, solute comes out of solution as solid until the concentration
  decreases to that of the saturated solution.
1664

1665

1666
1667

1668
1669

1670

1671
1672
1673
1674
1675
1676
1677
1678
1679
1680

1681
1682
1683
1684
1685

1686
1687
1688

1689
1690
        TABLE 14.10 — General solubility behavior of some cations of interest
                                                                                      (i)
                                 The Common Cations

             Na+1, K+1, NH4+1, Mg+2, Ca+2, Sr+2, Ba+2, Al+3, Cr+3, Mn+2, Fe+2, Fe+3,
                Co+2, Ni+2, Cu+2, Zn+2, Ag+1, Cd+2, Sn+2, Hg2+2, Hg+2, and Pb+2

There are general rules of solubilities for the common cations found in most basic chemistry
texts (e.g., Pauling, 1970, p. 453).

                      Under the class of mainly soluble substances:

 • All nitrates (NO3") are soluble.
 • All acetates (C2H3O2") are soluble.
 • All chlorides (Cl"), bromides (Br"), and iodides (I") are soluble, except for those of silver,
   mercury, and lead. PbCl2 and PbBr2 are sparingly soluble in cold water, and more soluble
   in hot water.
 • All sulfates (SO4"2) are soluble, except those of barium, strontium, and lead. CaSO4,
   Ag2SO4, and Hg2SO4 are sparingly soluble.
 • Most salts of sodium (Na), potassium (K), and ammonium (NH4+) are soluble. Notable
   exceptions are NaSb(OH)6, K3Co(NO2)6, K2PtCl6, (NH4)2PtCL6, and (NH4)3Co(NO2)6.
                     Under the class of mainly insoluble substances:

 • All hydroxides (OH"1) are insoluble, except those of the alkali metals (Li, Na, K, Rb, and
   Cs), ammonium, and barium (Ba). Ca(OH)2 and Sr(OH)2 are sparingly soluble.
•  All normal carbonates (CO3"2) and phosphates (PO4"3) are insoluble, except those of the
   alkali metals and ammonium. Many hydrogen carbonates and phosphates are soluble, i.e.,
   Ca(HCO3)2, Ca(H2PO4)2.

•  All sulfides (S"2), except those of the alkali metals, ammonium, and the alkaline-earth
   metals (Be, Mg, Ca, Sr, Ba, and Ra), are insoluble. Both aluminum- and chromium sulfide
   are hydrolyzed by water,  resulting in the  precipitation of A1(OH)3 and Cr(OH)3.

•  Some cations, such as Ba+2, Pb+2, and Ag+1, form insoluble chromates (CrO4"2), which can
   be used as a basis for separation.
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1691
1692
1693
1694
1695
1696
1714
                                    Actinide Elements
The solubility properties of the actinide M   ions are similar to those of the tripositive
lanthanide ions, while the behavior of the actinide M+4 ions closely resembles that of Ce+4.

 •  The fluorides (F), oxalates (C2O42), hydroxides (OH), and phosphates are insoluble.
•   The nitrates, halides (except fluorides), sulfates, perchlorates (ClO^, and sulfides are all
    soluble.
1697     (1)  Solubility data for specific compounds can be found in the CRC Handbook of Chemistry and Physics (CRC,
1698         1999) and in the NAS-NS monographs.

1699     14.8.3 Precipitation

1700     Precipitation is accomplished by combining a selected ion(s) in solution with a suitable counter-
1701     ion in sufficient concentrations to exceed the solubility of the resulting compound and produce a
1702     supersaturated solution. Nucleation occurs and growth of the crystalline substance then proceeds
1703     in an orderly manner to produce the precipitate (see Section 14.8.3.1, "Solubility and the
1704     Solubility Product Constant, Ksp"). The precipitate is collected from the solvent by a physical
1705     method, such as filtration or centrifugation. A cation (such as Sr+2, for example) will precipitate
1706     from an aqueous solution in the presence of a carbonate anion, forming the insoluble compound,
1707     strontium carbonate (SrCO3), when sufficient concentrations of each ion are present in solution
1708     to exceed the solubility of SrCO3. The method is used to isolate and collect strontium from water
1709     for radioanalysis (EPA, 1984, pp. Sr-04-1-19).

1710     A precipitation process should satisfy three main requirements:

1711       •  The targeted species should be precipitated quantitatively.

1712       •  The resulting precipitate should be in a form suitable for subsequent handling; it should be
1713         easily filterable and should not creep.
• If it is used as part of a quantitative scheme, the precipitate should be pure or of known purity
  at the time of weighing for gravimetric analysis.
1715         at the time of weighing for gravimetric analysis.
1716     Precipitation processes are useful in several different kinds of laboratory operations, particularly
1717     gravimetric yield determinations—as a separation technique and for preconcentration—to
1718     eliminate interfering ions, or for coprecipitation.
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1719      14.8.3. 1   Solubility and the Solubility Product Constant, Ksp

1720      Chemists routinely face challenges in the laboratory as a result of the phenomenon of solubility.
1721      Examples include keeping a dissolved component in solution and coprecipitating a trace-level
1722      analyte from solution.

1723      Solubility equilibrium refers to the equilibrium that describes a solid dissolving in solution, such
1724      as strontium carbonate dissolving in water, for example:

1725                                   SrCO3(s) -  Sr+2(soln) + CO3'2(soln)

1726      or, alternately, a solid forming from  solution, with the carbonate precipitating:

1727                                   Sr+2(soln) + CO3'2(soln) - SrCO3(s)

1728      The solubility product constant, K^ is the equilibrium constant for the former process, a solid
1729      dissolving and forming ions in solution. Leussing explains Ksp in general terms as follows:

1730         "For an electrolyte, MJVn, which dissolves and dissociates according to the equation:

173 1                           MJVn(s) ^ MmNn(so\n) ^ mM+n (soln.) + nNm (soln.)

1732         "The equilibrium conditions exists that:

1733                                aMmNn(s) = aMmNn(soln) = ^M+n^oln) ' ^ N-m(soln.)
1734         "[The value a is the activity of the ions in solution, a measure of the molar concentration
1735         (moles/L) of an ion in solution under ideal conditions of infinite dilution.] (Also see Section
1736         14.6.1, Principles of Electrodeposition, for a discussion of activity as applied to the Nernst
1737         equation.) [This equation] results in the familiar solubility product expression since the
1738         activity of a solid under given conditions is a constant. Expressing the activities in terms of
1739         the product of molar concentrations and activity coefficients, j [a measure of the extent the
1740         ion deviates from ideal behavior in solution; thus a = y • c where y < 1], [this] equation
1741         becomes...
1742                                [AFT [tfT fM+nfN-n, = a constant = Ksp

1743         (Leussing, 1959, pp. 689-690).
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1744     For dilute solutions of electrolytes (< 10"2 molar), the activity coefficient is approximately one
1745     (y~ 1; it approaches one as the solution becomes more dilute, becoming one under the ideal
1746     conditions of infinite dilution). Then, the solubility product constant is expressed in terms of the
1747     concentrations of ions in solution, the typical form in which the equation is found in most
1748     chemistry textbooks:

1749                                          Ksp=[M+n]m [N'm]n

1750     For strontium carbonate, Ksp is defined in terms of the concentrations of Sr+2 and CO3"2:

1751                                    Ksp = [Sr+2][CO3-2] = 1.6 x 10-9

1752     In order for the carbonate to precipitate, the product of the concentration of the ions in solution
1753     representing the ions in the equilibrium expression, the common ions, must exceed the value of
1754     the Ksp. The concentration of each common ion does not have to be equal. For example, if [Sr+2]
1755     is 1 x 10"6 molar, then the carbonate ion concentration must be greater than 0.0016 molar for
1756     precipitation to occur because (1 x 10"6) x (.0016) = 1.6 x 10"9.

1757     At higher concentrations (> 10"2 molar), where the ions in solution deviate from ideal behavior,
1758     the value of the activity coefficient decreases, and the concentrations of the ions do not
1759     approximate their activities. Under these conditions, the concentrations do not reflect the
1760     behavior of the dissolution equilibrium, and the equation cannot be used for precipitation or
1761     solubility calculations. More complex estimations of activity coefficients must be made and
1762     applied to the general equation (Birkett et al.,  1988, pp. 2.6-1  to 2.6-24). Generally, radiochemi-
1763     cal separations use an excess of a precipitating agent. The exact solution concentrations do not
1764     need to be known but they should be high to ensure compete reaction. Practical radiochemical
1765     separations performed based on solubility (either Ksp or coprecipitation phenomenon) are best
1766     described by M.L.  Salutsky (1959, pp. 744-755).

1767     Analysts often need to know if a precipitate will form when two solutions are mixed. For
1768     example:

1769        "If a chemist mixes 100 mL of 0.0050 MNaCl with 200 mL of 0.020 M Pb(NO3)2, will lead
1770        chloride precipitate? The ion product, Q, must be calculated and compared to Ksp for the
1771        process:

1772                                   PbCl2(s) - Pb+2(soln) + 2Cr(soln)
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1773          After the two solutions are mixed, [Pb+2] = 1.3 x 10'2 M (0.2 L x 2.0 x 10'2 M/0.3 L), and [Cl"
1774          ] = 1.7 x 10'3 M (0.1 L x 5.0 x 10'3 M/0.3 L). The value for the ion product is calculated from
1775          the expression

1776                                Q = [Pb+2][Clf or [1.3 x 1Q-2][1.7 x 10'3]2

1777                                            Q = 3.8xlQ-8

1778          The numerical value for Ksp  is 1.6 x 10"5. Because the ion product Q is less than Ksp, no
1779          precipitate will form. Only when the ion product is greater than Ksp will a precipitate form."

1780      Conditions in the solution phase can affect solubility. For example, the solubility of an ion is
1781      lower in an aqueous solution containing a common ion, one of the ions comprising the
1782      compound, than in pure water because a precipitate will form if the Ksp is exceeded. This
1783      phenomenon is known as the common ion effect and is consistent with LeChatelier's Principle.
1784      For example, the presence of soluble sodium carbonate (Na2CO3) in solution with strontium ions
1785      can cause the precipitation of strontium carbonate, because carbonate ions from the sodium salt
1786      contribute to their overall concentration in solution and tend to reverse the solubility equilibrium
1787      of the "insoluble" strontium carbonate:

1788                                Na2CO3(s) - 2 Na+1(soln) + CCV2(soln)

1789                                  SrCO3(s) - Sr+2(soln) + CCV2(soln)

1790      Alternatively, if a complexing agent or ligand is available that can react with the cation of a
1791      precipitate, the solubility of the  compound can be markedly enhanced. An example from Section
1792      14.3.4.3, "Formation and Dissolution of Precipitates," provides an illustration of this
1793      phenomenon. In the determination of 90Sr, Sr+2 is separated from the bulk of the solution by direct
1794      precipitation of the sulfate (SrSO4). The precipitate is redissolved by forming a complex ion with
1795      EDTA,  Sr(EDTA)'2, to separate it from lanthanides and actinides (DOE, 1994, Method RP520):

1796                                   SrSO4(s) - Sr+2(soln) + SO4'2(soln)

1797                                Sr+2(soln) + EDTA'4 - Sr(EDTA)-2(soln)

1798      Additionally, many metal ions are weakly acidic and hydrolyze in solution. Hydrolysis of the
1799      ferric ion (Fe+3) a classical example of this phenomenon:
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1800                                    Fe+3 + H2O - Fe(OH)+2 + H+1

1801     When these metal ions hydrolyze, producing a less soluble complex, the solubility of the salt is a
1802     function of the pH of the solution, increasing as the pH decreases. The minimum solubility is
1803     found under acidic conditions when the concentrations of the hydrolyzed species become
1804     negligible. As demonstrated by Leussing, the solubility of a salt also depends upon the activity of
1805     the solid phase. There are a number of factors that affect the activity of the solid phase (Leussing,
1806     1959, pp.  690-692):

1807      •  Polymorphism is the existence of a chemical substance in two or more crystalline forms. For
1808         example, calcium carbonate can have several different forms; only one form of a crystal is
1809         stable at a given temperature. At ordinary pressures and temperatures, calcite with a solubility
1810         of 0.028 g/L, is the stable form. Aragonite, another common form  of calcium carbonate
1811         (CaCO3), has a solubility of 0.041g/L at these conditions. It is not necessarily calcite that
1812         precipitates when solutions of sodium carbonate and calcium nitrate are mixed. Extremely
1813         low concentrations of large cations, such as strontium, barium, or lead, promote the
1814         precipitation of aragonite over calcite (Wray and Daniels, 1957). On aging, the more soluble
1815         aragonite converts to calcite.

1816      •  Various possible hydrates of a solid have different solubilities. For instance, at 25 °C, the
1817         molar solubility of gypsum (CaSO42H2O) is 0.206 and that of anhydrite (CaSO4) is 0.271.

1818      •  The solid phase can undergo a reaction with a salt in solution.

1819      •  Particle size of a solid can  affect its solubility, because it has been demonstrated that the
1820         solubility of smaller particles is greater than that of larger particles.

1821      •  Age of a precipitate can affect solubility. For example,  Biederman and Schindler (1957) have
1822         demonstrated that the solubility of precipitated ferric hydroxide [Fe(OH)3] undergoes a four-
1823         fold decrease to a steady state after 200 hours.

1824      •  Exchange of ions at the surface of the crystal with ions in the solution can affect the solubility
1825         of a solid. This effect is a function of the amount of surface available  for exchange and is,
1826         therefore, greater for a finely divided solid.  For example, Kolthoff and Sandell (1933)
1827         observed that calcium oxalate (CaC2O4) can exchange with either sulfate or barium ions:

1828                           CaC2O4(s) + S
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                                                                              Separation Techniques
1829                           CaC2O4(s) + Ba+2(soln) - BaC2O4(s) + Ca+2(soln)

1830            The excess of common ions that appears on the right-hand side of the equations represses
1831            the solubility of calcium oxalate according to the laws of mass action.

1832     Ideally, separation of common ions from foreign ions in solution by precipitation will result in a
1833     pure solid that is easy to filter. This method should ensure the production of a precipitate to meet
1834     these criteria as closely as possible. The physical process of the formation of a precipitate is quite
1835     complex, and involves both nucleation and crystal growth. Nucleation is the formation within a
1836     supersaturated solution of the smallest particles of a precipitate (nuclei) capable of spontaneous
1837     growth. The importance of nucleation is summarized by Salutsky (1959, p. 734):

1838         "The nucleation processes govern the nature and purity of the resulting precipitates. If the
1839         precipitation is carried out in such a manner as to produce numerous nuclei, precipitation will
1840         be rapid, individual crystals will be small, filtration and washing difficult, and purity low. On
1841         the other hand, if precipitation is carried out so that only a few nuclei are formed, precipita-
1842         tion will be slower, crystals larger, filtration easier, and purity higher. Hence, control of
1843         nucleation processes is of considerable significance in analytical chemistry."

1844     Once the crystal nuclei are formed, crystal growth proceeds through diffusion of the ions to the
1845     surface of the growing crystal and deposition of those ions on the surface. This crystal growth
1846     continues until supersaturation of the precipitating material is eliminated and equilibrium
1847     solubility is attained.

1848     Thus, the goal is to produce fewer nuclei during precipitation so that the process will occur
1849     slowly, within reasonable limits, and larger crystals will be formed.  Impurities result from three
1850     mechanisms: (1) inclusion, either by isomorphous replacement (isomorphic inclusion),
1851     replacement of a common ion in the crystal structure by foreign ions of similar size and charge to
1852     form a mixed crystal, or by solid solution formation (nonisomorphic inclusion), simultaneous
1853     crystallization of two or more solids mixed together; (2) surface absorption of foreign ions; and
1854     (3) occlusion, the subsequent entrapment of adsorbed ions as the crystal grows.  Slow growth
1855     gives the isomorphous ion time to be replaced by a common ion that fits the crystal structure
1856     perfectly, producing a more stable crystal. It also promotes establishment of equilibrium
1857     conditions  for the formation of the crystal structure so that adsorbed impurities are more likely to
1858     desorb and be replaced by a common ion rather than becoming entrapped. In addition, for a given
1859     weight of the solid that is forming, a small number of large crystals  present an overall smaller
1860     surface area than a large number of small crystals. The large crystals provide less surface area for
1861     impurities to adsorb.


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1862      14.8.3.2   Factors Affecting Precipitation

1863      Several factors affect the nature and purity of the crystals formed during precipitation. A
1864      knowledge of these factors permits the selection and application of laboratory procedures that
1865      increase the effectiveness of precipitation as a technique for the separation and purification of
1866      ions, and for the formation of precipitates that are easily isolated. These factors, summarized
1867      from Berg (1963, pp. 251-284) and Salutsky (1959, pp. 736-742), include the following:

1868      •  Rate of precipitation. Formation of large, well-shaped crystals is encouraged through  slow
1869         precipitation because fewer nuclei form and they have time to grow into larger crystals to the
1870         detriment of smaller crystals present. Solubility of the larger crystals is less than that of
1871         smaller crystals because smaller crystals expose more surface area to the solution. Larger
1872         crystals also provide less surface area for the absorption of foreign ions. Slow  precipitation
1873         can be accomplished by adding a very dilute solution of the precipitant gradually, with
1874         stirring, to a medium in which the resulting precipitate initially has a moderate solubility.

1875      •  Concentration of Ions and Solubility of Solids. The rate of precipitation depends on the
1876         concentration of ions in solution and the solubility of the solids formed during the
1877         equilibrium process. A solution containing a low concentration of ions, but sufficient
1878         concentration to form a precipitate, will slow the process, resulting in larger crystal
1879         formation. At the same time, increasing the solubility of the solid, either by selecting the
1880         counter-ion for precipitation or by altering the precipitating conditions, will also slow
1881         precipitation. Many radionuclides form insoluble solids with a variety of ions, and the choice
1882         of precipitating agent will affect the solubility of the precipitate. For example, radium sulfate
1883         (RaSO4) is the most insoluble radium compound known. Radium carbonate (RaCO3) is also
1884         insoluble, but its Ksp is greater than that of radium sulfate (Kirby and Salutsky, 1964, p. 9).

1885      •  Temperature. Precipitation at higher temperature slows nucleation and crystal  growth
1886         because of the increased thermal motion of the particles in solution. Therefore, larger  crystals
1887         form, reducing the amount of adsorption and occlusion. However, most solids are more
1888         soluble at elevated temperatures, effectively reducing precipitate yield; an optimum
1889         temperature balances these opposing factors.

1890      •  Digestion. Extremely small particles, with a radius on the order of one micron, are more
1891         soluble than larger particles because of their larger surface area compared to their volume
1892         (weight). Therefore, when a precipitate is heated over time (digestion) the small crystals
1893         dissolve and larger crystals grow (Ostwaldripening). Effectively, the small crystals are
1894         recrystallized, allowing the escape of impurities (occluded ions) and growth of larger  crystals.


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1895         This process reduces the surface area for adsorption of foreign ions and, at the same time,
1896         replaces the impurities with common ions that properly "fit" the crystal lattice. Recrystal-
1897         lization perfects the crystal lattice, producing a purer precipitate (see Reprecipitation below).
1898         Digestion is used in an 131I determination to increase the purity of the lead iodide (PbI2)
1899         crystals (EPA, 1984, pp. 1-01-1-9).

1900       •  Degree of Supersaturation. A relatively high degree of supersaturation is required for
1901         spontaneous nucleation, and degree of supersaturation is the main factor in determining the
1902         physical character of a precipitate. Generally, the higher the supersaturation required, the
1903         more likely a curdy, flocculated colloid will precipitate because more nuclei form under
1904         conditions of higher supersaturation and crystal growth is faster. In contrast, the lower the
1905         supersaturation required, the more likely a crystalline precipitate will form because fewer
1906         nuclei form under these conditions and crystal  growth is slower. Most perfect crystals are
1907         formed, therefore, from supersaturated solutions that require lower ion concentrations to
1908         reach the necessary degree of supersaturation and, as a result, inhibit the rate of nucleation
1909         and crystal growth. Degree of supersaturation ultimately depends on physical  properties of
1910         the solid that affect its formation. Choice of counter-ion will determine the type of solid
1911         formed from a radionuclide, which, in turn, determines the degree of saturation required for
1912         precipitation. Many radionuclides form insoluble solids with a variety of ions, and the  choice
1913         of precipitating agent will  affect the nature of the precipitate.

1914       •  Solvent. The nature of the  solvent affects the solubility of an ionic solid (precipitate) in the
1915         solvent. The polarity of water can be reduced by the addition of other miscible solvents such
1916         as alcohols, thereby reducing the solubility of precipitates. Strontium chromate (SrCrO4) is
1917         soluble in water, but it is insoluble in a methyl alcohol (CH3OH)-water mixture and can be
1918         effectively precipitated from the solution (Berg, 1963, p. 364). In some procedures,
1919         precipitation is achieved by adding alcohol to an aqueous solution, but the dilution effect
1920         might reduce the yield because it lowers the concentration of ions in solution.

1921       •  Ion Concentration. The common-ion effect causes precipitation to occur when the
1922         concentration of ions exceeds the solubility-product constant. In some cases, however, excess
1923         presence of common ions  increases the solubility of the precipitate by decreasing the activity
1924         of the ions in solution, as they become more concentrated in solution and deviate from ideal
1925         behavior. An increase in concentration of the ions is necessary to reach the activity of ions
1926         necessary for precipitate formation.

1927       •  Stirring. Stirring the solution during precipitation increases the motion of particles in solution
1928         and decreases the localized buildup of concentration of ions by keeping the solution


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1929         thoroughly mixed. Both of these properties slow nucleation and crystal growth, thus
1930         promoting larger and purer crystals. This approach also promotes recrystallization because
1931         the smaller crystals, with their net larger surface area, are more soluble under these
1932         conditions. Virtually all radiochemical laboratories employ stirring with a magnetic stirrer
1933         during precipitation reactions.

1934      •  Complex-Ion Formation. Formation of complex ions can be used to hold back impurities
1935         from precipitating by producing a more soluble form of a solid. The classical example of this
1936         phenomenon is the precipitation of lead (Pb+2) in the presence of silver ions (Ag+1). Chloride
1937         ion (Cl"1) is the precipitating agent that produces insoluble  lead chloride (PbCl2). In an excess
1938         of the agent,  silver chloride (AgCl) is not formed because a soluble salt containing the
1939         complex ion, AgCl^1 is formed. Complex -ion formation is  also used to form precipitates (see
1940         Section 14.3, "Complexation").

1941      •  pH Effect. Altering the pH of aqueous solutions will alter the concentration of ions in the
1942         precipitation equilibrium by the common-ion effect, if the hydrogen ion (H+1) or hydroxide
1943         ion (OH"1) is common to the equilibrium. For example,  calcium oxalate (CaC2O4) can be
1944         precipitated or dissolved, depending on the pH of the solution, as follows:

1945                                       Ca+2 + C2
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                                                                             Separation Techniques
1957                                           HS'1 - H+

1958                                           Pb+2 +S'2 - PbS

1959          The pH can also influence selective formation of precipitates. Barium chromate will
I960          precipitate in the presence of strontium at pH 4 to 8, leaving strontium in solution. Sodium
1961          carbonate is added and strontium precipitates after ammonia (NH3) is added to make the
1962          solution more alkaline. This procedure is the basis for the separation of radium from
1963          strontium in the radioanalysis of strontium in drinking water (EPA,  1980, p. 63).

1964       •  Precipitation from Homogeneous Solution. Addition of a precipitating agent to a solution of
1965          ions causes a localized excess of the reagent (higher concentrations) to form in the mixture.
1966          The excess reagent is conducive to rapid formation of a large number of small crystals,
1967          producing a precipitate of imperfect crystals that contains excessive impurities. The
1968          precipitate formed under these conditions is sometimes voluminous and difficult to filter.
1969          Localized excesses can also cause precipitation of more soluble solids than the expected
1970          precipitate.

1971          These problems largely can be avoided if the solution is homogenous in all stages of
1972          precipitate formation, and if the concentration of precipitating agent is increased, as slowly as
1973          practical, to cause precipitation from the most dilute solution possible. This increase in
1974          concentration is accomplished, not by adding the precipitating agent directly to the solution,
1975          but rather by generating the agent throughout the solution, starting with a very small
1976          concentration and slowly increasing the concentration while stirring. The precipitating agent
1977          is generated indirectly as the result of a chemical change of a reagent that produces the
1978          precipitating agent internally and homogeneously throughout the solution. The degree of
1979          supersaturation is low because the concentration of precipitating agent in solution is always
1980          uniformly low enough for nucleation only. This method produces larger crystals with fewer
1981          impurities.

1982          Table 14.11 (Salutsky, 1959, p. 741) summarizes methods used for precipitate formation
1983          from homogeneous solution. Descriptions of these methods can be found in Gordon et al.
1984          (1959).

1985          Some agents are generated  by decomposition of a compound in solution. Hydrogen sulfide,
1986          for example, is produced from thioacetamide:

1987                            CH3CSNH2 + 2 H2O - CH3COO4 + H2S + NH4+1


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1988
1989
1990
1991
   Copper sulfide (CuS) coprecipitates technetium from a homogeneous medium by the
   generation of hydrogen sulfide by this method (EPA, 1973, pp. 67-72). Other agents alter the
   pH of the solution (see "pH Effect" above). Hydrolysis of urea, for example, produces
   ammonia, which raises the pH of a solution:
1992


1993
1994
1995
1996
1997
1998
1999
                             H2NCONH2 +H2O
                         CO2 + 2 NH3
2000

2001
2002
2003
2004
2005

2006
Precipitant
Hydroxide
Phosphate
Oxalate
Sulfate
Sulfide

lodate
Carbonate
Chromate
Periodate
Chloride

Arsenate
              TABLE 14.11 — Summary of methods for utilizing precipitation
             	from homogeneous solution(1)	
Reagent
Element Precipitated
Urea
Acetamide
Hexamethylenetetraamine
Metal Chelate and H2O2
Triethyl Phosphate
Trimethyl Phosphate
Metaphosphoric Acid
Urea
Dimethyl Oxalate
Diethyl Oxalate
Urea and an Oxalate
Dimethyl Sulfate
Sulfamic Acid
Potassium Methyl Sulfate
Ammonium Persulfate
Metal Chelate and Persulfate
Thiocetamide

Iodine and Chlorate
Periodate and Ethylene Diacetate
 (or B-Hydroxy Acetate)
Ce(III) and Bromate
Trichloroacetate
Urea and Dichromate
Potassium Cyanate and Dichromate
Cr(III) and Bromate
Acetamide
Silver Ammonia Complex
 and B-Hydroxyethyl Acetate
Arsenite and Nitrite
Al, Ga, Th, Fe(III), Sn, and Zr
Ti
Th
Fe(III)
ZrandHf
Zr
Zr
Mg
Th, Ca, Am, Ac, and Rare Earths
Mg, Zn, and Ca
Ca
Ba, Ca, Sr, and Pb
Ba, Pb, and Ra
Ba, Pb, and Ra
Ba
Ba
Pb, Sb, Bi, Mo, Cu, and As, Cd, Sn, Hg,
andMn
Th and Zr
ThandFe(III)

Ce(IV)
Rare Earths, Ba, and Ra
Ba and Ra
Ba, Ra
Pb
Pb
Ag

Zr
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2007

2008
2009
2010
2011

2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
Precipitant
Tetrachlorophthalate
Dimethylgloxime
8-Hydroxyquinoline
Fluoride
Reagent
Tetrachlorophthalic Acid
Urea and Metal Chelate
Urea and Metal Chelate
Fluoroboric Acid
Element Precipitated
Th
Ni
Al
La
(1) Salutsky, 1959, p. 741

 • Reprecipitation. This approach increases the purity of precipitates. During the initial
   precipitation, crystals collected contain only a small amount of foreign ions relative to the
   common ions of the crystal. When the precipitate is redissolved in pure solvent, the foreign
   ions are released into solution, producing a concentration of impurities much lower than that
   in the original precipitating solution. On reprecipitation, a small fraction of impurities is
   carried down with the precipitate, but the relative amount is much less than the original
   because their concentration in solution is less. Nevertheless, foreign ions are not eliminated
   because absorption is greater at lower, rather than at higher, concentrations. On balance,
   reprecipitation increases the purity of the crystals. Reprecipitation is used in the procedure to
   determine americium (Am) in soil (DOE, 1990 and 1997, Method Am-01). After americium
   is coprecipitated with calcium oxalate (CaC2O4), the precipitate is reprecipitated to purify the
   solid.
2024      14.8.3.3   Optimum Precipitation Conditions

2025      There is no single, fixed rule to eliminate all impurities during precipitation (as discussed in the
2026      section above), but over the years, a number of conditions have been identified from practical
2027      experience and theoretical considerations that limit these impurities (Table 14.12). Precipitations
2028      are generally carried out from dilute solutions adding the precipitant slowly with some form of
2029      agitation to a hot solution. Normally, the precipitant is then allowed to age before it is removed
2030      by filtration and washed. Reprecipitation is then commonly performed. Reprecipitation is one of
2031      the most powerful techniques available to the analyst because it increases purity, regardless of the
2032      form of the impurity.

2033      Table 14.12 highlights the optimum precipitation conditions to eliminate impurities.
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2034

2035


2036
2037
2038
2039
2040
2041
2042
2043
2044

2045

2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
       TABLE 14.12 — Influence of precipitation conditions on the purity of precipitates
                                                                                         0,2)
Condition
Dilute solutions
Slow precipitation
Prolonged digestion
High temperature
Agitation
Washing the precipitate
Reprecipitation
Form of Impurity
Mixed
Crystals
o
+
-
-
+
o
+
Surface
Adsorption
+
+
+
+
+
+
+
Occlusion and
Inclusion
+
+
+
+
+
o
+
Post-
precipitation
o
-
-
-
o
o
0
       (1) +, increased purity; -, decreased purity; o, little or no change in purity
       (2) Salutsky, 1959, p. 764

14.8.4 Coprecipitation

In many solutions, especially those of environmental samples, the concentration of the
radionuclide of interest is too low to cause precipitation, even in the presence of high
concentrations of its counter-ion, because the product of the concentrations does not exceed the
solubility product. Radium in most environmental samples, for example, is not present in
sufficient concentration to cause its very insoluble sulfate (RaSO4) to precipitate. The
radionuclide can often be brought down selectively and quantitatively from solution during
precipitation of an alternate insoluble compound by a process called coprecipitation. The
insoluble compound commonly used to coprecipitate radium isotopes in many radioanalytical
procedures is another insoluble sulfate, barium sulfate (BaSO4) (EPA, 1984, Method Ra-01;
EPA, 1980, Method 900.1). The salt is formed with barium, also a member of the alkaline earth
family of elements with chemical properties very similar to those of radium. Alternatively, a
different salt that is soluble for the radionuclide can be used to cause coprecipitation. Radium can
be coprecipitated with lanthanum fluoride, even though radium fluoride is soluble itself. For
trace amounts of some radionuclides, other isotopic forms of the element are available that can
be added to the solution to bring the total concentration of all forms of the element to the level
that will result in precipitation. For trace quantities of 90Sr, inactive strontium (85Sr), which will
not interfere with the radioanalysis of 90Sr, is added to permit the precipitation of strontium
carbonate in the presence of carbonate ions. The added ion that is present in sufficient
concentration to  cause a precipitate to form is called a carrier (Section 14.9, "Carriers and
Tracers"). Barium, lanthanum,  and stable strontium, respectively, are carriers in these examples
(DOE, 1995, Method RP5001;  DOE, 1990 and 1997, Method Sr-02; EPA,  1984, Sr-04). The
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2067     term carrier is also used to designate the insoluble compound that causes coprecipitation. Barium
2068     sulfate, lanthanum fluoride (LaF3), and strontium carbonate are sometimes referred to as the
2069     carrier in these coprecipitation procedures. See Wahl and Bonner (1951, p. 403) for additional
2070     examples of tracers and their carriers used for coprecipitation.

2071     The common definition of coprecipitation is, "the contamination of a precipitate by substances
2072     that are normally soluble under the conditions of precipitation" (Salutsky, 1959, p. 748). In a very
2073     broad sense, coprecipitation is alternately defined as the precipitation of one compound
2074     simultaneously with one or more other compounds to form mixed crystals (Berg, 1963, p. 296).
2075     Each is present in macro concentrations (i.e., sufficient concentrations to exceed the solubility
2076     product of each). As the term is used in radiochemistry, coprecipitation is the simultaneous
2077     precipitation of one compound that is normally soluble under the conditions of precipitation with
2078     one or more other compounds that form a precipitate under the same conditions. Coprecipitation
2079     of two or more rare earths as oxalates, barium and radium as sulfates, or zirconium (Zr) and
2080     hafnium (Hf) as phosphates are examples of this broader definition (Salutsky, 1959,  p. 748). By
2081     either definition, coprecipitation introduces foreign ions into a precipitate as impurities that
2082     would normally be expected to remain in solution; and precipitation techniques, described in the
2083     previous section, are normally used to maximize this effect while minimizing the introduction of
2084     true impurities. As a method to separate and collect radionuclides present in solution at very low
2085     concentration, coprecipitation is performed in a controlled process to associate the ion of choice
2086     selectively with a precipitate, while excluding other foreign ions that would interfere with the
2087     analytical procedure.

2088     14.8.4.1   Coprecipitation Processes

2089     In order to choose the best conditions to coprecipitate an ion selectively, two processes should be
2090     considered. First is precipitation itself and the appropriate techniques employed to minimize
2091     association of impurities (see Section 14.8.3). Second is coprecipitation mechanisms and the
2092     controlling factors associated with each. Three processes (described above in Section 14.8.3.1,
2093     "Solubility and the Solubility Product Constant") are responsible for coprecipitation, although
2094     the distinction between these processes is not always clear (Hermann and Suttle, 1961, p.  1369).
2095     They consist of: (1) inclusion, i.e., uptake from solution of an ion similar in size and charge to
2096     the solid forming the precipitate in order to form a mixed crystal or solid solution; (2) surface
2097     adsorption; and (3) occlusion (mechanical entrapment).

2098     Inclusion. If coprecipitation is accomplished from a homogeneous solution allowing the crystals
2099     to form slowly in an orderly manner, then inclusion contributes to the coprecipitation process.
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2100     Under these conditions, the logarithmic distribution law applies, which represents the most
2101     efficient coprecipitation method that involves mixed crystals (Salutsky, 1959, p. 750):

2102                                        log(VIf) = A log(P;/Pf)

2103     In the equation, \ is the concentration of impurity in solution at the start of crystallization and If
2104     is the concentration at the end. P represents the corresponding concentration of the primary ion in
2105     solution. Lambda, A, is the logarithmic distribution coefficient and is a constant. Values of A for
2106     some tracers distributed in solid carriers can be found in Wahl and Bonner (1951, p. 393).
2107     Lambda values greater than one represent removal of a foreign ion by inclusion during
2108     coprecipitation. The larger the value of lambda, the more effective and selective for a specific ion
2109     the process is. Lambda is also inversely proportional to the rate of precipitation. Slow
2110     precipitation, as accomplished by homogeneous precipitation, results in larger values and more
2111     efficient coprecipitation. For example, "Actinium [Ac] has been  selectively removed from
2112     solutions containing iron and aluminum [Al] through slow oxalate precipitation by the controlled
2113     hydrolysis of dimethyl  oxalate" (Hermann and Suttle, 1961, p. 1376). Also, as described in
2114     Section 14.8.3.2, "Factors Affecting Precipitation," technetium is coprecipitated with copper
2115     sulfide (CuS) carrier produced by the slow generation of hydrogen sulfide (H2S) as thioacetamide
2116     is hydrolyzed in water (EPA,  1973, pp. 67-72).

2117     Generally, A decreases  as the temperature increases; thus, coprecipitation by inclusion is favored
2118     by lower temperature.

2119     Digestion of the precipitate at elevated temperature over lengthy time periods—a process that
2120     promotes recrystallization and purer crystals—will often cause mixed crystals to form by an
2121     alternate mechanism (i.e., homogeneous distribution) that is not as efficient, but which is often as
2122     successful as logarithmic distribution.  The equilibrium distribution lawis represented by
2123     (Salutsky, 1959, p. 749):

2124                                         (I/P)ppt = D(I/P)soln.

2125     where I represents the amount of impurity and P the amount of primary substance forming the
2126     precipitate. The symbol .Dis the homogeneous distribution coefficient. Values of D greater than
2127     one represent removal of a foreign ion by inclusion during coprecipitation. Some values of D can
2128     be found in Wahl and Bonner (1951, p. 393). According to Hermann and Suttle:

2129         "Homogeneous distribution is conveniently obtained at ordinary temperatures by rapid
2130         crystallization from supersaturated solutions with vigorous stirring. Under such conditions


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2131         the precipitate first formed is very finely divided, the recrystallization of the minute crystals
2132         is rapid, and each molecule (sic) passes many times between solution and precipitate. If this
2133         process is repeated often enough, an equilibrium between solid and solution is obtained, and
2134         all the resulting crystals grow from a solution of constant composition" (Hermann and Suttle,
2135         1961, pp. 1473-1474).

2136     In either case, optimal results are obtained through inclusion when the precipitate contains an ion
2137     with chemical properties similar to those of the foreign ion, although it is not necessary for the
2138     similarity to exist in every successful coprecipitation. Barium sulfate is very successful in
2139     coprecipitating Ra+2, primarily because radium is in the same chemical family as barium, and has
2140     the same charge and a similar ionic radius. For best results, the radius of the foreign ion should
2141     be within approximately 15 percent of that of one of the common ions in the precipitate
2142     (Hermann and Suttle, 1961, p. 1479).

2143     Surface Adsorption. During surface adsorption, ions are adsorbed from solution onto the surfaces
2144     of precipitated particles. The conditions leading to surface adsorption are described by Salutsky:

2145         "The surface of a precipitate is particularly active. Ions at the surface of a crystal (unlike
2146         those within the crystal) are incompletely coordinated and, hence are free to attract other ions
2147         of opposite charge from solution" (Salutsky, 1959, p. 754).

2148     Adsorption involves a primary adsorption layer that is held very tightly, and a counter-ion layer
2149     held more loosely. Ions common to the precipitate are adsorbed most strongly at the surface to
2150     continue growth of the crystal. During precipitation of BaSO4, barium ions (Ba+2) and sulfate ions
2151     (SO4"2) are the primary ions adsorbed. If only one of the common ions remains in solution, then
2152     foreign ions of the opposite charge are adsorbed to maintain  electrical neutrality. When barium
2153     sulfate is precipitated from a solution containing excess barium ions, for example, foreign ions
2154     such as Cl"1, if present, are adsorbed after sulfate ions are depleted in the precipitation process.
2155     Foreign ions of the same charge, such as Na+1, are repelled from the surface. Surface adsorption
2156     can be controlled, therefore, by controlling the concentration of ions during precipitation or by
2157     the addition of ions to alter the concentration. A precipitate of silver chloride (AgCl) in excess
2158     Ag+1 repels 212Pb+2, but in a solution containing an equal quantity of the common silver and
2159     chloride ions, approximately 2 percent of 212Pb is adsorbed (Salutsky, 1959, pp. 754-755). In
2160     contrast, almost 86 percent of 212Pb is adsorbed if an iodide solution is added to precipitate  the
2161     silver ions  as silver iodide (Agl), thereby reducing the concentration of silver ions and making
2162     the chloride ion in excess in the solution. According to the Paneth-Fajans-Hahn adsorption rule,
2163     the ion most adsorbed will be the one that forms the least soluble compound with an ion of the
2164     precipitate. For example, barium sulfate in contact with  a solution containing excess sulfate ions


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2165     will adsorb ions of Pb > Ca > K > Na, which reflects the order of solubility of the respective
2166     sulfates: thus, PbSO4 <  CaSO4 < K2SO4 < Na2SO4 (Salutsky, 1959, p. 755).

2167     "Because adsorption is  a surface phenomenon, the larger the surface area of a precipitate, the
2168     greater the adsorption of impurities" (Salutsky, 1959, p. 755). For that reason, colloidal crystals
2169     exhibit a high degree of nonspecific adsorption. When a colloid is flocculated by the addition of
2170     an electrolyte, the electrolyte can be adsorbed as an impurity. This interference largely can be
2171     eliminated by aging the precipitate, thereby growing larger crystals and reducing the surface area.
2172     Additionally, nonvolatile impurities can be replaced on the particle by washing the colloidal
2173     precipitate with a dilute acid or ammonium salt solution. Well-formed large crystals exhibit
2174     much less adsorption, and adsorption is not a significant factor in coprecipitation with these
2175     solids. The tendency for a particular ion to be adsorbed depends on, among other factors, charge
2176     and ionic size (Berg,  1963, p. 299). Large ions with a high charge exhibit high adsorption
2177     characteristics: a high ionic charge increases the electrostatic attraction to the charged surface,
2178     and an ion with a large  radius is less hydrated by the  solution and not as attracted to the solution
2179     phase.

2180     "The amount of adsorption is also affected by prolonged standing of the precipitate in contact
2181     with the solution. The fraction adsorbed is higher for some tracer ions, while the fraction is lower
2182     for others. Recrystallization occurring during standing decreases the surface area so that the
2183     fraction  of tracer carried will decrease unless the tracer is trapped in the growing crystals ... in
2184     which case the fraction  carried may increase." (Wahl, 1951, p. 117).

2185     Adsorption also depends on the  concentration of an ion in solution (Berg, 1963, p. 299). A high
2186     concentration of impurity increases the probability of solute interaction at the solid surface and
2187     favors adsorption. Salutsky comments on the percent adsorption:

2188         "Generally, the percent adsorption is much greater at low concentrations than at high
2189         concentrations. At very high concentrations of impurity, adsorption reaches a maximum
2190         value, i.e., the adsorption is saturated" (Salutsky, 1959, pp. 755-756).

2191     Occlusion. Occlusion of an impurity within a precipitate results when the impurity is trapped
2192     mechanically by subsequent crystal layers. For that reason, occluded impurities cannot be
2193     physically removed by washing. Occlusion is more prevalent with colloidal precipitates than with
2194     large crystals because of the greater surface area of colloidal solids. Freshly prepared hydroxides
2195     and sulfides commonly  contain occluded impurities, but most of them are released upon aging of
2196     the precipitate.
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2197     Mechanical entrapment occurs particularly when the precipitating agent is added directly to a
2198     solution. Because of the localized high concentrations of precipitant, impurities are precipitated
2199     that become occluded by the subsequent precipitation of the primary substance. The speed of the
2200     precipitation process also affects the extent of occlusion. Occlusion can be reduced, therefore, by
2201     homogeneous precipitation. Coprecipitation of strontium by barium sulfate, for example, is
2202     accomplished by the homogeneous generation of sulfate by the hydrolysis of dimethyl sulfate,
2203     (CH3)2SO4 (Hermann and Suttle, 1961, p. 1480). Digestion also eliminates occluded particles as
2204     the solid is recrystallized. Considerable occlusion occurs during nucleation, and, therefore,
2205     reducing the precipitation rate by lowering the temperature and reducing the number of nuclei
2206     formed reduces the initial coprecipitation by occlusion.

2207     This type of coprecipitation is not limited to solid impurities. Sometimes the solvent and other
2208     impurities dissolved in the solvent become trapped between layers of crystals. This liquid
2209     occlusion is common in numbers of minerals such as quartz and gypsum.

2210     14.8.4.2    Water as an Impurity

2211     In addition to  other impurities, all precipitates formed from aqueous solutions contain water
2212     (Salutsky, 1959, pp. 761-763). This water might be essential water, present as an essential part of
2213     the chemical composition (e.g., MgNH4PO4 • 6H2O, Na2CO3 • H2O), or it might be nonessential
2214     water. Nonessential water can be present in the precipitate as hygroscopic water, surface water,
2215     or included water. Hygroscopic w/aterrefers to the water that a solid adsorbs from the surroun-
2216     ding atmosphere. Many colloidal precipitates are highly hygroscopic because of their large
2217     surface areas.  Moreover, water can be adsorbed to the surface of the precipitate or included
2218     within the crystal matrix, as described previously.

2219     14.8.4.3    Postprecipitation

2220     Postprecipitation results when a solution contains two ions, one that is rapidly precipitated and
2221     another that is slowly precipitated by the precipitating agent (Kolthoff et al., 1969, p.  245). The
2222     first precipitate is usually contaminated by the second one. For example, calcium oxalate is a
2223     moderately insoluble compound that can be precipitated quantitatively with time. Because the
2224     precipitation tends to be slow, the precipitate is allowed to remain in contact with the solution for
2225     some time before filtering. Magnesium oxalate is too soluble to precipitate on its own under
2226     normal conditions. As long as the solution contains a predominance of calcium ions, very little
2227     magnesium precipitates. However, as the precipitation of calcium approaches quantitative levels,
2228     the competition of calcium and magnesium ions for adsorption at the surface becomes more
2229     intense. As time progresses, the magnesium oxalate adsorbed on the surface acts as seed to


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2230     induce the post-precipitation of a second solid phase of magnesium oxalate (MgC2O4). Once
2231     precipitated, the magnesium oxalate is only slightly soluble and does not redissolve.

2232     14.8.4.4   Coprecipitation Methods

2233     Selective coprecipitation of a radionuclide with an insoluble compound is primarily
2234     accomplished by the judicious selection of the compound that forms the precipitate and the
2235     concentration of solutions used in the precipitate's formation. Using good precipitation technique
2236     minimizes the coprecipitation of impurities. The compound, then, should maximize
2237     coprecipitation of the select radionuclide while providing a well-formed solid that attracts a
2238     minimum of other foreign ions as impurities. In general, conditions that favor precipitation of a
2239     substance in macroamounts also favor the coprecipitation of the same material from tracer
2240     concentrations (i.e., too low for precipitate formation) with a foreign substance (Friedlander
2241     et al., 1981, p. 294). Wahl and Bonner provide a useful summary for coprecipitation of a tracer
2242     by a carrier:

2243         "In general a tracer is efficiently carried by an ionic precipitate if: (1) the tracer ion is
2244         isomorphously incorporated into the precipitate, or (2) the tracer ion forms a slightly soluble
2245         or slightly dissociated compound with the oppositely charged lattice ion and  if the precipitate
2246         has a large surface with charge opposite to that of the tracer ion (i.e., presence of excess of
2247         the oppositely charged lattice ion)" (Wahl and Bonner, 1951, p. 105).

2248     Considering the principles of precipitation and coprecipitation, radium is coprecipitated
2249     quantitatively with barium sulfate using excess sulfate in solution because: (1) radium forms the
2250     least soluble sulfate of the other elements in the alkaline earth family (Paneth-Fajans-Hahn
2251     adsorption rule); (2) the radium ion carries the same charge as the barium ion and is very similar
2252     in size (inclusion); and 3) an excess of sulfate preferentially creates a common-ion layer on the
2253     crystalline solid of sulfate ions that attracts barium ions and similar ions such as  radium
2254     (absorption). For example, in a procedure to determine 226Ra in water samples, radium  is
2255     coprecipitated as barium sulfate using 0.36 moles of sulfate with 0.0043 moles of barium, a large
2256     excess of sulfate (EPA,  1984, Method Ra-03).

2257     The isolation of microquantities of tracers often occurs in two steps: first the tracer is separated
2258     by coprecipitation with a carrier, and then it is separated from the carrier (Hermann and Suttle,
2259     1961, p.  1486). Use of carriers that can be easily separated from the tracer is helpful, therefore,
2260     coprecipitation by inclusion is not generally used. Coprecipitation by surface  adsorption on
2261     unspecific carriers is the most common method employed. Manganese dioxide MnO2, sulfides
2262     (MnS), and hydroxides [Mn(OH)2] are important nonspecific carries because of their high surface


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                                                                             Separation Techniques
2263
2264
2265
2266
2267

2268
2269
2270
2271
2272
2273
2274

2275
2276
2277

2278
2279
2280
2281
2282

2283
2284
2285
areas. Ferric hydroxide [Fe(OH)3] is very useful for adsorbing cations, because it forms a very
finely divided precipitate with a negative charge in excess hydroxide ion. Ferric hydroxide is
used, for example, to collect plutonium in solution after it has been isolated from tissue (DOE,
1990 and 1997, Method Pu-04). Tracers can be separated by dissolving the solid in acid and
extracting the iron in ether.

   "The amount of ion adsorbed depends on its ability to compete with other ions in solution.
   Ions capable of displacing the ions of the radioelements are referred to as holdback carriers
   (see Section 14.9.2.4, Holdback Carriers). Highly charged ions, chemical homologs, and ions
   isotopic with the radioelement are among the most efficient displacers. Thus, the addition of
   a little inactive strontium makes it possible to precipitate radiochemically pure radiobarium
   as the nitrate or chloride in the presence of radiostrontium" (Hermann and Suttle, 1961, p.
   1487)

Tables 14.13  and  14.14 provide more details about common coprecipitating  agents for
radionuclides.
            TABLE 14.13 — Common coprecipitating agents for radionuclides(1)
Radionuclide
Am
Cs
Co
Fe
I
Ni
Nb
Oxidation
State
13
+1
+2
+3
-1
+2
+5
Coprecipitate
hydroxide
iodate
fluoride, oxalate, phosphate,
hydroxide
oxalate
acetate
fluoride, sulfate
acetate
phosphomolybdate,
chloroplatinate,
bismuthnitrate,
silicomolybdate
hydroxide
potassiumcobaltnitrate
1 -nitroso-2-napthol
sulfide
hydroxide
ammoniumpyrouranate
iodide
dimethylgloxime hydroxide
hydroxide, phosphate
Carrier(2)
Am+3, Fe+3
Ce+4, Th+4, Zr+4
La+3, Ce+3, Nd+3, Bi+3
Ca+2
Am+4
La+3
U02+2
Cs+1
Co+2
Co+2
Co+2
Co+2
Fe+3
Fe+3
Pb+2, Ag+1, Pd+2
Ni+2
Nb+5
Notes






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2286
2287
2288
2289
2290
2291
2292
Radionuclide
Po
Pu
Ra
Sr
Tc
Th
U





Oxidation
State
+4
+3
+4
+6
+2
+2
+4
+7
+4
+4

+5

+6

Coprecipitate
tellurium
tellurate
selenium
dioxide
hydroxide
sulfide
fluoride
sulfate
fluoride
oxalate, iodate
phosphate
sodium uranylacetate
hydroxide
sulfate, chromate, chloride,
bromide
oxalate, phosphate
fluoride
carbonate
nitrate
chromate
sulfate
phosphate
hydroxide
hydroxide
chlorate, iodate,
perruthenate,
tetrafluoroborate
sulfide
hydroxide
fluoride
iodate
phosphate, peroxide
sulfate
oxalate
cupferron, pyrophosphate,
phosphate, iodate, sulfate,
oxalate
fluoride
phosphate
sulfate
cupferron
pyrouranate
Carrier®
Te
Pb+2
Se or Se2
Mn+4
Fe+3, Al+3, La+3
Cu+2, Bi+2, Pb+2
La+3, Nd+3, Ce+3, Ca+2
La+3(K+1)
La+3, Nd+3, Ce+3
Th+4
Zr+2, Bi+3
U02+2
Fe+3
Ba+2
Th+4, Ca+2, Ba+2
La+3
Sr+2, Ba+2, Ca+2
Sr+2, Ba+2
Ba+2
Sr+2, Ca+2, Pb+2
Sr+2
Fe+3
Tc+4, Fe+3, Mn+2
(Phenyl)4As+1
Tc+7, Re+7, Cu+2, Cd+2
Th+4, La+3, Fe+3, Zr+3,
Ac+3, Zn+2
Th+4, La+3, Nd+3, Ce+3
Th+4, Zr+3
Th+4, Bi+3
Ba+2
Ca+2
u+4
La+3, Nd+3
Zr+3
Ca+2
u+6
u+6
Notes
tellurate reduced with SnQ2


alkaline pH






neutral solution
from aqueous NH3, many
ions stay in solution as NH3
complex
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                                                                                     Separation Techniques
2293
2294
2295
2296
2297
2298

2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
Radionuclide




Zr+4
Oxidation
State





Coprecipitate
phosphate
peroxide
hydroxide
fluoride

Carrier®
U+6, Al+3
U+6
Fe+3
Th+4

Notes

Th+4, Zr+3 also coprecipitate
without carbonate


(1)  Compiled from: Anders, 1960; Booman and Rein, 1962; Cobble, 1964; EPA, 1973; 1980; 1984; DOE, 1990,
    1995, 1997; Finston and Kinsley, 1961, Grimaldi, 1961; Grindler, 1962; Hyde, 1960; Kallmann, 1961;
    Kallmann, 1964; Kirby and Salutsky, 1964; Metz and Waterbury, 1962; Sedlet, 1964; Sundermann and
    Townley, 1960; and Turekian and Bolter, 1966.
(2)  If the radionuclide itself is listed, alternate isotopic forms are sometimes used as carriers to form the precipitate.

              TABLE 14.14 — Coprecipitation behavior of plutonium and neptunium
Carrier Compound
Hydroxides
Calcium fluoride
Lanthanum fluoride
Barium sulfate
Phosphates:
Calcium phosphate
Bismuth phosphate
Zirconium phosphate
Thorium pyrophosphate
Thorium hypophosphate
U(IV) hypophosphate
Oxalates:
Lanthanum oxalate
Bismuth oxalate
Thorium oxalate
U(IV) oxalate
lodates:
Zirconium iodate
Ceric iodate
Thorium iodate
Sodium uranyl acetate
Zirconium phenylarsenate
Thorium peroxide
Bismuth arsenate
Pu(ill)
C
C
C
C

C
C
NC
NC



C
C
C
C




NC
NC


Pu(IV)
C
C
C
C

C
C
C
C
C
C

C
C
C
C

C
C
C
NC
C
C
C
Pu(VI)
C

NC
NC



NC
NC
NC
NC

NC
NC
NC
NC

NC
NC
NC
C
NC

NC
Np(IV)
C
C
C
C

C
C
C




NC

C


C
C
C
NC
C
C
C
Np(V)
C

C
NC


NC
NC












Poor
Poor


Np(VI)
C

NC
NC


NC
NC











NC
C
NC


       "C" indicates nearly quantitative coprecipitation under proper conditions; "NC" indicates that
       coprecipitation can be made less than 1-2 percent under proper conditions. [Data compiled from
       Seaborg and Katz, Korkisch (1969), and the NAS-NS 3050, 3058 and 3060 monographs.]
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          Separation Techniques
2328

2329
2330
2331
2332
2333
2334
2335
2336
2337
2338

2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
14.8.5 Colloidal Precipitates

Many precipitates exhibit colloidal properties, especially when freshly formed (Salutsky, 1959,
p. 744). The term "colloid state" refers to the dispersion of one phase that has colloidal
dimensions (less than one micrometer, but greater than one nanometer) within a second phase. A
colloidal solution is a colloid in which the second phase is a liquid (also known as a sol).
However, in radiochemistry, a colloid refers to the dispersion of solid particles in the solution
phase. The mixture is not a true solution: particles of the dispersed phase are larger than typical
ions and molecules, and can often be viewed by a light microscope. Colloidal precipitates are
usually avoided in analytical procedures because they are difficult to filter and to wash.
Moreover, the purity of the precipitate is controlled by the tremendously large surface area of the
precipitate and by the localized electrical character of the colloidal surface.

The stability of colloidal solutions and suspensions is governed by two major forces, one of
attraction between the particles (van der Waals) and one  of repulsion (electrical double layer)
(Salutsky, 1959, p.  745). This repulsive force is  a result of the adsorptive capacity of the colloidal
particles for their own ions.  For instance, when silver chloride is precipitated in the presence of
excess silver ions, the particles adsorb silver ions and become positively charged. Then counter-
ions of opposite charge (in this case, nitrate ions) tend to adsorb to the particles to form a second
electrical layer, as illustrated in Figure 14.5.
               Counter ions

               Adsorbed Layer
               (Primary Layer)

                Ions in surface
      FIGURE 14.5 — The electrical double layer: A schematic representation of adsorption of nitrate
     counter-ions onto a primary adsorbed layer of silver ions at the surface of a silver chloride crystal
                                      (Peters et al., 1974).
In a similar fashion, in the presence of a slight excess of alkali chloride, the silver chloride
particles would adsorb chloride ions and become negatively charged. Therefore, precipitates
brought down in the presence of an excess of one of the lattice ions tend to be contaminated with
ions of the opposite charge. Moreover, because all of the particles have the same charge, they
repel each other. If these repulsive forces exceed the attractive van der Waals' forces, a stable
colloid results, and the tightness with which the counter-ions are held in and with the water layer,
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                                                                               Separation Techniques
2352      or the completeness with which they cover the primary adsorbed ion layer, determines the
2353      stability of the colloid.

2354      Such adsorption of ions upon the surface of solids in solution is largely, but not entirely, based
2355      upon electrical attraction, otherwise adsorption would not be selective. Recall that there are four
2356      other factors, in addition to magnitude of charge, that affect the preferential adsorption by a
2357      colloid (see Surface Adsorption in Section 14.8.4.1, "Coprecipitation Process").

2358       •  The Paneth-Faj ans-Hahn Law dictates that when two or more types of ions are available for
2359         adsorption, the ion that forms the least soluble compound with one of the lattice ions will be
2360         adsorbed preferentially.

2361       •  The ion present in the greater concentration will be adsorbed preferentially.

2362       •  Ions with a large radius will be adsorbed more readily than ions with a smaller radius because
2363         the larger ion is less hydrated by the solution and not as attracted to the solution phase.

2364       •  The ion that is closer to the same size as the lattice ion will be adsorbed preferentially. For
2365         example, radium ions are adsorbed tightly onto barium sulfate, but not onto calcium sulfate;
2366         radium ions are close in size to barium ions, but are much larger than calcium ions.

2367      If an excess of electrolyte is added to the colloidal solution, the electrical double layer is
2368      destroyed and the particles can agglomerate to form larger particles that can settle to the bottom
2369      of the container, a process known as flocculation (or coagulation). For example, Smith et al.
2370      (1995)  used polyethylene glycol to remove colloidal  silica from a dissolved-soil solution before
2371      the addition of the sample to an ion-exchange resin. Alternatively, the process whereby
2372      coagulated particles pass back into the colloidal state is known as deflocculation., (or peptiza-
2373      tion). Special precautions should be taken during the washing of coagulated precipitates to assure
2374      that deflocculation does not occur. When coagulation is accomplished through charge
2375      neutralization, deflocculation would occur if the precipitate was washed with water. A solution
2376      containing a volatile electrolyte such as nitric acid should be used instead.

2377      There are two types of colloidal solutions (Salutsky,  1959, p. 744):

2378       •  Hydrophobic colloids show little or no attraction for water. These solutions have a low
2379         viscosity, can be easily flocculated by the addition of an appropriate electrolyte, and yield
2380         precipitates that are readily filterable.
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          Separation Techniques
2381       •  Hydrophilic colloids have a high affinity for water and are often highly viscous. They are
2382         more difficult to flocculate than hydrophobic colloids, and relatively large amounts of
2383         electrolytes are necessary to cause precipitation. The flocculate keeps water strongly adsorbed
2384         and tends to form jellylike masses that are difficult to filter.

2385      Colloidal precipitations can be a useful separation technique. Because of their great adsorption
2386      capacity, colloidal precipitates are excellent scavengers (collectors) for concentrating trace
2387      substances (Salutsky, 1959, p. 747). Unspecific carriers such as manganese dioxide, sulfides and
2388      hydrated oxides are frequently used as scavengers. For example, protactinium can be efficiently
2389      scavenged and concentrated on manganese dioxide that is precipitated by adding a manganous
2390      salt to a solution containing permanganate. Ferric hydroxide is commonly used to scavenge
2391      cations (Section 14.8.4.4, "Coprecipitation Methods"). Moreover, scavenging precipitations can
2392      sometimes be used to remove interferences. For example, a radionuclide that is capable of
2393      existing in two oxidation states can be effectively purified by precipitation in one oxidation state,
2394      followed by scavenging precipitations for impurities, while the element of interest  is in another
2395      oxidation state. A useful procedure for cerium purification involves repeated cycles of eerie
2396      iodate precipitation, reduction to Ce+3, zirconium iodate (ZrIO3) precipitation to remove
2397      impurities (with Ce+3 staying in solution), and reoxidation to Ce+4.

2398      14.8.6  Filterability of Precipitates

2399      The physical nature of a precipitate not only affects the purity of the precipitate, but also the
2400      filterability of the precipitate. Large, well-formed crystals are desirable because they tend to
2401      contain fewer impurities, and are also easier to filter and wash. Many coagulated colloidal
2402      precipitates, such as hydrous oxides or sulfides, tend to form slimy aggregates and  to clog the
2403      filter during filtration. There are several approaches that can be taken to improve the physical
2404      form of the precipitate (Salutsky, 1959, pp. 758-761):

2405       •  A trace quantity of a hydrophilic colloid can be added to produce complete and rapid
2406         flocculation. For example, gelatin has been used as a sensitizer'm the precipitation of zinc
2407         sulfide, hydrous silica, and various other hydrous oxides, as well-coagulated, filterable
2408         precipitates (Salutsky,  1959, p. 759).

2409       •  The slow precipitation techniques described in Section 14.8.3.2, "Factors Affecting
2410         Precipitation," can be used to produce good precipitates.

2411       •  Aging the precipitate can result in a precipitate more amenable to filtration. During aging,
2412         small particles with a larger solubility go into solution, and larger particles grow at  the cost of


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                                                                            Separation Techniques
2413
2414
2415
2416
2417

2418

2419

2420
2421
2422
2423
2424


2425


2426


2427


2428

2429
2430


2431


2432
   the smaller ones (see "Digestion" under Section 14.8.3.2, "Factors Affecting Precipitation").
   Ostwald ripening results in a decrease in the number of particles and, therefore, a decrease in
   surface area. The speed of aging generally increases with temperature and with the increasing
   solubility of the precipitate in the aging media. Shaking can sometimes promote aging,
   perhaps by allowing particles to come into contact and to cement together.

Table 14.15 summarizes general properties of common filters used in analytical procedures.

              TABLE 14.15 — General properties of common filter papers (1)
Whatman
Grade
Particle
Retention
(jim)
Porosity
Ash
(%)
Filter
speed
Applications
Qualitative filter papers
1
2
3
4
5
6
>11
>8
>6
> 20-25
>2.5
>3
Medium
Medium-
fine
Medium-
fine
Coarse
Fine
Fine
0.06
0.06
0.06
0.06
0.06
0.2
Medium-
fast
Medium
Medium
Fast
Slow
Slow
Medium crystalline precipitates. A general purpose
filter used in routine laboratory applications, air
pollution monitoring, and soil chemical assays.
Crystalline precipitates. More retentive and
adsorbent than Grade 1, but with increased filtering
time.
Double the thickness of Grade 1 for high precipitate
capacity and increased wet strength. Suitable for
suction filtration.
Coarse and gelatinous precipitates. Used in air
pollution monitoring and for routine cleanup of
biological fluids.
Fine crystalline precipitates. Most retentive of the
series, and is useful for clarifying cloudy
suspensions and water analysis.
Fine crystalline precipitates. Twice as fast as Grade
5. Often specified in boiler water analyses.
Quantitative - ashless
40
41
42
>8
> 20-25
>2.5
Medium
Coarse
Medium
0.010
0.010
0.010
Medium
fast
Slow
Medium crystalline precipitates: calcium oxalate,
well-digested barium. Widely used, general
purpose.
Gelatinous precipitates and coarse particles: iron
and aluminum hydroxides. Also used in quantitative
air analyses.
Highly retentive for fine particles. The laboratory
standard for critical gravimetric analysis.
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          Separation Techniques
2433


2434

2435
2436

2437

2438

2439
2440
2441

2442

2443

2444

2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
Whatman
Grade
43
44
Particle
Retention
(jim)
>16
>3
Porosity
Medium
Fine
Ash
(%)
0.010
0.010
Filter
speed
Medium-
fast
Slow
Applications
Medium crystalline precipitates. Foodstuff and soil
analyses, particle collection in air pollution
monitoring by XRF.
Fine crystalline precipitates. Somewhat thinner and
faster than Grade 42.
Quantitative - hardened low ash
50
52
54
>2.7
>7
> 20-25
Fine
Medium
Coarse
0.025
0.025
0.025
Slow
Medium
Fast
Hardened papers are designed for use in Buchner
funnels, possess great wet strength and lintless
surfaces, and will withstand scraping. Grade 50:
fine crystalline precipitates. Grade 52: crystalline
precipitates, general purpose filtration. Grade 54:
gelatinous precipitates and coarse particles.
Quantitative - hardened ashless
540
541
542
>8
> 20-25
>2.7
Medium
Coarse
Fine
0.008
0.008
0.008
Medium
Fast
Slow
Crystalline precipitates. Gravimetric analysis of
metals in acid/alkali solutions. Collecting
hydroxides after precipitation from strong alkali
solutions.
Coarse gelatinous precipitates. Used for strongly
acidic or alkaline conditions.
Fine crystalline precipitates from under demanding
acidic/alkali conditions.
(1) Fisher (2000-01)

14.8.7 Advantages and Disadvantages of Precipitation and Coprecipitation
                  Advantages
    Provides the only practical method of separation
    or concentration in some cases.
    Can be highly selective and virtually quantitative.
    High degree of concentration is possible.
    Provides a large range of scale (mg to industrial).
    Convenient, simple process.
    Carrier can be removed and procedure continued
    with tracer amounts of material (e.g., carrier iron
    separated by solvent extraction).
            Disadvantages
Can be time consuming to digest, filter, and/or
wash the precipitate.
Precipitate can be contaminated by carrying of
ions or postprecipitation.
Sarge amounts of carrier might interfere with
subsequent separation procedures.
Coprecipitating agent might contain isotopic
impurities of the analyte radionuclide.
Scavenger precipitates  are not as selective and
are more sensitive to changes in separation
procedures.
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                                                                             Separation Techniques
2455
14.9  Carriers and Tracers
2456
14.9.1  Introduction
2457
2458
2459
2460
2461
2462
2463
2464

2465
2466
2467
2468
2469
2470
2471

2472
2473
2474
2475
2476
2477

2478
Radiochemical analysis frequently requires the radiochemist to separate and determine
radionuclides that are present at extremely small quantities. The amount can be in the picomole
range or less, at concentrations in the order of 10"15 to 10"11 molar. Analysis of radionuclides
using counting techniques, such as alpha spectrometry, liquid scintillation, proportional counting,
or gamma spectrometry, allows activities of radionuclides of 10,000 disintegrations per minute
(dpm) to be determined easily,  even though the number of atoms (and mass percent) of these
materials is vanishingly small. Table 14.16 identifies the number of atoms and mass present in
several radionuclides, based on an activity of 500 dpm.

      TABLE 14.16 — Atoms and mass of select radionuclides equivalent to 500 dpm(
Radionuclide
Radium-226
Polonium-210
Lead-212
Thallium-208
Half-life
1590 y
140 d
10.6 h
3.1m
Number of Atoms
6.0x10"
1.5 x 108
4.5 x 105
2.3 x 103
Mass (g)
2.3 x 10-10
5.0 xlO'14
1.6 xlO'16
8.0 xlO'19
(1) Based on Wahl andBonner, 1951, p. 102

Considering the minute masses of these analytes and their subsequently low concentration in
solution, it is obvious why conventional techniques of analysis, such as gravimetry, spectro-
photometry, titrimetry, and electrochemistry, cannot be used for their quantitation. However, it is
not immediately obvious why these small quantities might present other analytical difficulties.
As described below, the behavior of such small quantities of materials  can be seriously affected
by macro constituents in an analytical mixture in a way that may be unexpected chemically.

14.9.2 Carriers
2479     The key to radiochemical analysis of samples with multiple radionuclides is effective separation
2480     of the different analytes. Separations are most easily accomplished when performed on a macro
2481     scale. As described above, however, the analytes are frequently at levels that challenge the
2482     analyst and the conventional methods to perform the separations. The use of a material that is
2483     different in isotopic make-up to the analyte and that raises the effective concentration of the
2484     material to the macro level is referred to as a carrier. In many cases, the carrier is a non-
2485     radioactive isotope of the analyte. Some carriers are stable isotopes of chemically similar
2486     elements.
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         Separation Techniques
2487     A distinction exists between traditional and radiochemical analyses when referring to macro
2488     amounts. Generally, carriers are present in quantities from a few tenths to several hundred
2489     milligrams of material during the progress of the radiochemical separation.

2490     14.9.2.1   Isotopic Carriers

2491     An isotopic carrier is usually a stable isotope of the analyte. Stable strontium (consisting of
2492     naturally occurring 84Sr, 86Sr, 87Sr, and 88Sr) is frequently used as the carrier in the analysis of 89Sr
2493     and 90Sr. Regardless of the stability of the isotope, the number of protons in the nucleus
2494     ultimately governs the chemical properties of the isotope. Thus, all nuclei that have 38  protons
2495     are strontium and react as strontium classically does.

2496     The purpose of adding a carrier is to raise the chemical concentration of the analyte to the point
2497     where it can be separated using conventional techniques, but for the carrier to perform  properly,
2498     it must have the same oxidation state and chemical form as the analyte. It is important then to add
2499     the carrier to the sample as early as possible in chemical process. For example, in the determina-
2500     tion of 131I in milk, the radioiodine might be present as I"1, KV1, CH3I, or I2. The analyst should
2501     assume that all states are present, and treat the  sample so that all atoms are brought to a common
2502     oxidation state and chemical form during some step in the procedure, before any separation takes
2503     place. If the final step is precipitation of Agl and the carrier is in the IC^"1 form, no precipitate
2504     will form since AgIO3 that forms when Ag+1 is added is relatively soluble compared to  Agl.
2505     Furthermore, if separations of other radioisotopes are performed before this step, there  is the
2506     possibility that quantities of the radioiodine could be trapped in the precipitate with other
2507     separated analytes. When concentrations of these materials are very  small, even small losses are
2508     significant. The carrier also functions to prevent losses of the analyte during the separation of
2509     other radionuclides or interfering macro-contaminants. This is another reason that it is  essential
2510     to add the carrier prior to any chemical treatment of the sample.

2511     The laws of equilibrium for precipitation, distillation, complexation, and oxidation-reduction will
2512     apply to the entire chemical form of analyte  in  solution, both carrier and radioisotope. If, for
2513     example, 99.995 percent of all strontium is determined to be precipitated during a radiochemical
2514     procedure, then the amount of stable strontium remaining in solution will be 0.005 percent,
2515     which means that 0.005 percent of the radiostrontium still remains in the solution as well. Losses
2516     such as this occur  during any chemical process. Frequently then, carriers are used in radiochemi-
2517     cal analyses not only to raise the chemical concentration of the element, but also to determine the
2518     yield of the process. In order to determine the exact amount of radionuclide that was originally
2519     present in the sample, the yield (sometimes called the recovery) of the radionuclide collected at
2520     the end of the procedure should be known. However, since the amount of analyte at the start of


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                                                                             Separation Techniques
2521     the procedure is the unknown, the yield should be determined by an alternate method. The mass
2522     of the radioanalyte is insignificant in comparison to the carrier, and measuring the yield of the
2523     carrier (gravimetrically, for example) will allow the calculation of the yield of the analyte.

2524     14.9.2.2  Nonisotopic Carriers

2525     Non-isotopic carriers are materials that are similar in chemical properties to the analyte being
2526     separated, but do not have the same  number of protons in their nucleus. Usually these carriers
2527     will be elements in the same family  in the periodic table. In the classical separation of radium by
2528     the Curies, the slight difference in solubility of radium chloride versus barium chloride allowed
2529     the tedious fractional crystallization of radium chloride to take place (Hampel, 1968, p. 586).
2530     When barium is present in macro-quantities and the radium in femtogram quantities, however,
2531     the two may be easily precipitated together as a sulfate.

2532     For several elements, non-isotopic carriers are chosen from a different family of elements, but
2533     they have the same ionic charge or similar crystalline morphology as the analyte.  Lanthanum and
2534     neodymium as +3 ions are frequently used as nonisotopic carriers for U(IV) and Pu(IV) in their
2535     final separation as insoluble fluorides by the process of coprecipitation (Metz and Waterbury,
2536     1962, p. 254) (see also Section 14.8, "Precipitation and Coprecipitation"). The chemical form of
2537     the uranium and plutonium is particularly important for this process; the +4 oxidation state will
2538     coprecipitate, but the +6 in the MO2+2 form, will not. Uranium(IV) is present in solution as  UO2+2
2539     and, therefore, will not be coprecipitated with lanthanum fluoride. However, it is very important
2540     to note that even though the precipitation of LaF3 may be quantitative (i.e., >99.995 percent may
2541     be precipitated), there is no measure of how much uranium will also be coprecipitated. Since
2542     uranium and plutonium are not chemically equivalent, the laws of solubility product constant for
2543     lanthanum cannot be applied to uranium. For these types of processes, separate methods should
2544     be used to determine the chemical yield of the process.

2545     For alpha counting rare earths, fluorides (such as NdF3) are frequently used to coprecipitate
2546     elements (Hindman, 1983 and 1986; Sill and Williams, 1981).

2547     Another group of non-isotopic carriers can be described as general scavengers. Substances  with
2548     high surface areas, or the ability to occlude contaminants in their floe, can be used to effect gross
2549     separation of all radionuclides from  macro quantities of interfering ions. Ferric hydroxide,
2550     manganese dioxide (MnO2) and sulfides (MnS), and hydrated oxides [Mn(OH)x]  are examples of
2551     these nonspecific carriers that have been used in many radiochemical separations to eliminate
2552     gross quantities of interfering substances.
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2553     14.9.2.3    Common Carriers

2554     Carriers for specific analytes are discussed below.

2555     Alkaline Earths

2556     STRONTIUM AND BARIUM. Carrier-free strontium (Sr2) and barium (Ba+2) will coprecipitate with
2557     ferric hydroxide [Fe(OH)3], while calcium (Ca+2) exhibits the opposite behavior and does not
2558     coprecipitate with ferric hydroxide. Lead sulfate (PbSO4) will also carry strontium and barium.

2559     Frequently, inactive strontium and barium are used as carriers for the radionuclides in order to
2560     facilitate separation from other matrix constituents and from calcium. The precipitates used most
2561     frequently in radiochemical procedures are the chromates (CrO4"2), nitrates (NC^"1), oxalates
2562     (C2O4"2), sulfates (SO4-2), and barium chloride (BaCl2).  Several different methods of separation
2563     are identified here:

2564      •  Chromate precipitation is used in the classical separation of the alkaline earths. Barium
2565         chromate (BaCrO4) is precipitated from a hot solution buffered to a pH of 4 to minimize
2566         strontium and calcium contamination of the barium precipitate. Ammonium ion (NH4+1) is
2567         then added to the solution, and strontium  chromate (SrCrO4) is precipitated.

2568      •  Barium and strontium can be separated from calcium as the nitrates. Fuming nitric acid is
2569         used to increase the nitric acid concentration to 60 percent, conditions at which barium and
2570         strontium nitrate [Ba(NO3)2 and Sr(NO3)2] precipitate and calcium does not.

2571      •  Oxalate precipitation does not separate one alkaline earth from another, but it is usually used
2572         to produce a weighable and reproducible form suitable for radioassay. The precipitation is
2573         accomplished from a basic solution with ammonium oxalate [(NH4)2C2O4].

2574      •  Barium sulfate (BaSO4) precipitation is generally not used in separation procedures. It is
2575         more common as a final step to produce a precipitate that can be readily dried, weighed, and
2576         mounted for counting. Barium is readily precipitated by slowly adding dilute sulfuric acid
2577         (H2SO4) to a hot barium solution and digesting the precipitate. For the precipitation of
2578         strontium or calcium sulfate (SrSO4 and CaSO4), a reagent such as alcohol should be added to
2579         lower the solubility, and the precipitant must be coagulated by heat.

2580      •  Insolubility of barium chloride (BaCl2) in strong hydrochloric acid solution (HC1) is the basis
2581         of the method to separate barium from calcium, strontium, and other elements. The


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2582         precipitation is performed either by adding an ether-hydrochloric acid solution or by bubbling
2583         dry hydrogen chloride gas into the aqueous solution.

2584     RADIUM. Radium (Ra) yields the same types of insoluble compounds as barium: sulfates,
2585     chromates, carbonates (CO3"2), phosphates (PO4"3), oxalates, and sulfites (SO3"2). Hence, radium
2586     coprecipitates with all barium compounds and, to a lesser extent, with most strontium and lead
2587     compounds. Barium sulfate and barium chromate are most frequently used to carry radium. Other
2588     compounds that are good carriers for radium include ferric hydroxide when precipitated at
2589     moderately high pH with sodium hydroxide (NaOH), barium chloride when precipitated from a
2590     cold mixed solvent of water and alcohol saturated with hydrochloric acid, barium iodate (BaIO3)
2591     and various insoluble phosphates, fluorides (F"1)  and oxalates (e.g., thorium phosphate
2592     [Th3(PO4)], lanthanum fluoride (LaF3), and thorium oxalate [Th(C2O4)].

2593     Rare Earths. Scandium. Yttrium, and Actinium

2594     Ferric hydroxide and calcium oxalate (CaC2O4) will coprecipitate carrier-free rare earths without
2595     difficulty.

2596     The rare earths will coprecipitate one with another in almost all of their reactions; one rare earth
2597     can always be used to coprecipitate another. The rare earth hydroxides, fluorides,  oxalates, and 8-
2598     hydroxyquinolates in ammoniacal solution are insoluble. Conversely, the rare earth hydroxides
2599     will carry a number of elements that are insoluble in basic solution; the rare earth oxalate will
2600     coprecipitate calcium; and the rare earth fluorides tend to carry barium and zirconium (Zr). In the
2601     absence of macro quantities of rare earths, actinium will carry on barium sulfate and lead sulfate
2602     (PbSO4).

2603     Lead

2604     Ferric hydroxide and aluminum hydroxide [A1(OH)3] carry lead very effectively from ammonium
2605     solutions under a variety of conditions. Lead is carried by barium or radium chloride, but not
2606     carried by barium or radium bromide (BaBr2 or RaBr2). This behavior has been used to  separate
2607     radiolead isotopes from radium  salts. Lead is also carried by barium carbonate (BaCO3), barium
2608     sulfate, radium sulfate, radium chloride, lanthanum carbonate [La2(CO)3], barium chloride, and
2609     silver chromate (Ag2CrO4). Calcium sulfate in the presence of alcohol has also been used to
2610     coprecipitate lead.
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2611     Polonium

2612     Trace quantities of polonium (Po) are carried almost quantitatively by bismuth hydroxide
2613     [Bi(OH)3] from ammoniacal solution. Ferric, lanthanum, and aluminum hydroxides have also
2614     been used as carriers for polonium in alkaline solutions. Colloidal platinum and coagulated silver
2615     hydroxide (AgOH) and ferric hydroxide sols have been used to carry polonium. Because of the
2616     high oxidation state of polonium, it is susceptible to being a contaminant in almost any
2617     precipitate. Removal of polonium by electrodeposition on nickel metal is recommended prior to
2618     final precipitation for any gross counting technique (proportional counting and liquid
2619     scintillation, for example).

2620     Actinides

2621     THORIUM. Thorium (Th) will coprecipitate with ferric, lanthanum [La(OH)3], and zirconium
2622     hydroxide [Zr(OH)4]. These hydroxide carriers are nonspecific, and therefore, will only remove
2623     thorium from a simple group of contaminants or as a group separation. The ferric hydroxide
2624     precipitation is best carried out at pH 5.5-6.

2625     Thorium will coprecipitate quantitatively with lanthanum fluoride from strongly acidic solutions,
2626     providing an effective means to remove small quantities of thorium from uranium solutions.
2627     However, the rare earths will also carry quantitatively, and zirconium and barium radioisotopes
2628     will carry unless macro quantities of these elements are added as holdback carriers (see Section
2629     14.9.2.4, "Holdback Carriers").

2630     Precipitation of thorium with barium sulfate is possible from  strongly acidic solutions containing
2631     high concentrations of alkali metal sulfates; however, this coprecipitation is nonspecific. Other
2632     actinides, lead, strontium, rare earths, bismuth, scandium (Sc), and yttrium will also carry.

2633     Coprecipitation of thorium on hydrogen hypophosphate (HPO3"2) or phosphate carriers can be
2634     performed from  rather strongly acidic solutions. Zirconium phosphate [Zr3(PO4)4] serves as a
2635     good carrier for trace levels of thorium. Moreover, thorium also will carry quantitatively on
2636     zirconium iodate from a strongly acidic solution. If coprecipitation is performed from a strongly
2637     acidic solution and the precipitate is washed with a solution containing iodate, the rare earths and
2638     actinium are eliminated. Ce+4 must be reduced to Ce+3 before  precipitation so that it does not
2639     carry.

2640     PROTACTINIUM.  Protactinium will be carried quantitatively on hydroxide, carbonate, or
2641     phosphate precipitates of tantalum (Ta), zirconium, niobium (Nb), hafnium (Hf), and titanium


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2642     (Ti). It is also carried by adsorption onto flocculent precipitates of calcium hydroxide [Ca(OH)2)]
2643     or ferric hydroxide, and it is carried by manganese dioxide, which is produced by addition of
2644     potassium permanganate (KMnO4) to a dilute nitric acid (HNO3) solution containing manganese
2645     nitrate. However, titanium and zirconium are also carried under these conditions.

2646     URANIUM. Trace concentrations of uranium can be coprecipitated with any of the common
2647     insoluble hydroxides. When coprecipitating U(VI) with hydroxides at pH 6-7, the ammonium
2648     used must be free of carbonate or some of the uranium will remain in solution as the stable
2649     anionic carbonate complex. Hydroxide precipitation is nonspecific, and many other metals will
2650     carry with the uranium.

2651     Uranium(IV) can be coprecipitated as the fluoride or phosphate [UF4 or U3(PO4)4] from relatively
2652     strong acid media; however, U(VI) phosphate [(UO2)3(PO4)2] is precipitated only from very weak
2653     acid solutions (pH 5-6) by the addition of carbonate-free ammonium. The rare earths, and other
2654     metals can also coprecipitate under these conditions.

2655     In general, U+4 should behave similarly to Pu+4 and Np+4, and should be carried by lanthanum
2656     fluoride, eerie and zirconium iodates [Ce(IO4)3 and Zr(IO4)4], cerium and thorium oxalates
2657     [Ce2(PO4)3], barium sulfate, zirconium phosphate [Ce2(PO4)3], and bismuth arsenate (BiAsO4).
2658     However, U(VI) does not carry with these agents as long as the concentration of either carrier or
2659     that of uranium is not too high.

2660     PLUTONIUM AND NEPTUNIUM. Classically, plutonium (Pu) and neptunium (Np) in their ter- and
2661     tetravalent oxidation states have been coprecipitated with lanthanum fluoride in the method most
2662     widely used for the isolation of femtograms of plutonium. However, large amounts of aluminum
2663     interfere with coprecipitation of plutonium, and other insoluble fluorides, such as the rare earths,
2664     calcium, and U+4, coprecipitate.

2665     AMERICIUM AND CURIUM. Bismuth phosphate (BiPO4), which historically has been used to
2666     precipitate plutonium, will also carry americium and curium from 0.1-0.3 M  nitric acid.
2667     Impurities such as calcium and magnesium are not carried under these conditions.

2668     Lanthanum fluoride provides a convenient carrier for Am+3 and Cm+3. A lanthanum fluoride
2669     precipitation is not totally specific, but it can provide a preliminary isolation from the bulk of the
2670     fission products and uranium. Additionally, a lanthanum fluoride precipitation can be used to
2671     separate americium from curium. Am+3 is oxidized to Am(V) in dilute acid with persulfate, and
2672     fluoride is added to precipitate Cm+3 on lanthanum fluoride.
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2673      14.9.2.4   Holdback Carriers

2674      It is often necessary to add holdback carriers to analytical mixtures to prevent unwanted
2675      radionuclides from being carried in a chemical process. Coprecipitation of a radionuclide with
2676      ferric hydroxide carries other ions in addition to the analyte, because of its tendency to adsorb
2677      other ions and occlude them in its crystal matrix. The addition of a holdback carrier, a highly-
2678      charged ion,  such as Co+3, represses counter-ion exchange and adsorption to minimize the
2679      attraction of foreign ions. The  amount of a given substance adsorbed onto a precipitate depends
2680      on its ability  to compete with other ions in solution. Therefore, ions capable of displacing the
2681      radionuclide  ions (the hold-back carrier) are added to prohibit the coprecipitation of the
2682      radionuclide. Highly charged ions, chemical homologs, and ions isotopic with the radionuclide
2683      are among the most efficient holdback carriers. Hence, the addition of inactive strontium makes
2684      it possible to precipitate radiochemically pure radiobarium as the nitrate or chloride in the
2685      presence of radiostrontium. Actinium and the rare earth elements can be separated from
2686      zirconium and radium by lanthanum fluoride coprecipitation with the addition of zirconium and
2687      barium holdback carriers. Holdback carriers are used in other processes as well. The extraction of
2688      lutetium from water employs neodymium ions (Nd+3) to avoid adsorption loses (Choppin et al.,
2689      1995, p. 262).

2690      14.9.2.5   Yield (Recovery) of Isotopic Carriers

2691      The use of an isotopic carrier to determine the chemical yield of the analyte is  a critical step in
2692      the plan of a  radiochemical analysis. The analytical method being used to determine the final
2693      amount of carrier will govern the method of separation. If a gravimetric method is to be used for
2694      the final yield determination, the precipitate must have all the characteristics that would be used
2695      for macro gravimetric analysis—easily dried,  definite stoichiometry, non-hygroscopic, and the
2696      like.

2697      Similarly, the reagent used as source of carrier at the beginning of the analysis must be of
2698      primary-standard quality to ensure that the initial mass of carrier added can be determined very
2699      accurately. For a gravimetric yield determination, the equation would be the following:

2700                     Percent Yield = (mass of carrier in final separation step) x 100%
2701                                       (mass of carrier added)

2702      It should be recognized that the element of interest is the only quantity used in this formula. For
2703      example, if strontium nitrate is used as the primary standard and strontium sulfate is the final
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                                                                             Separation Techniques
2704     precipitate, both masses should be corrected, using a gravimetric factor, so that only the mass of
2705     strontium is used in the equation in both the numerator and denominator.

2706     Other methods to determine the yield of the carrier include atomic absorption spectrometry, ultra-
2707     violet/visible spectrometry, titrimetry, and potentiometry.

2708     14.9.3 Tracers

2709     The term  tracer was classically used to  express the concentration of any pure radionuclide in
2710     solution that had a mass too small to be measured by an analytical balance (<0.0001 to
2711     0.00001 g). More recently, the definition of a tracer has become more pragmatic. The current
2712     definition of a tracer is a known quantity of a radioisotope that is added to a solution of a
2713     chemically equivalent radioisotope of unknown concentration so that the yield of the chemical
2714     separation can be monitored. In general, a tracer is not a carrier, and a carrier is not a tracer.

2715     The analysis of 241Am in an environmental sample provides an example of a radioisotope
2716     employed in a manner consistent with the recent use of the term tracer. In the analytical
2717     procedure, no stable isotope of americium exists to act as a carrier. Femtogram quantities of
2718     243Am can be produced,  however, with accurately known activities. If a known quantity of 243Am
2719     in solution is added to the unknown sample containing 241Am at the beginning of the separation
2720     procedure, and if the resulting activity of 243Am can be determined at the end of the procedure,
2721     then the yield of 241Am can be determined accurately for the process. 243Am added to the sample
2722     in this example is used as a tracer. A measurable mass of this element was not used, but a known
2723     activity was added through addition of the solution.  During the course of the radiochemical
2724     separation, lanthanides may have been used to help carry the americium through analysis.
2725     However, they are  not used to determine the yield in this example and would be considered,
2726     therefore, a non-isotopic carrier.

2727     When using a tracer in an analytical method, it is important to consider the availability of a
2728     suitable isotope, its chemical form, its behavior in the system, the amount of activity required, the
2729     form in which it should be counted, and any health hazards associated with it (McMillan, 1975,
2730     p. 298).

2731     Perhaps the most important property of the tracer is its half-life. It is preferable to select an
2732     isotope with a half-life that is long compared to the duration of the experiment. By doing so, one
2733     avoids the problems of having to handle high levels of activity at the beginning of the experiment
2734     and of having to make large decay corrections.
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2735     Purity of the tracer is of critical importance. Radionuclide and radiochemical impurities are the
2736     two principal types of impurities encountered. Radionuclide impurity refers to the presence of
2737     radionuclides other than those desired. For instance, it is very difficult to obtain 236Pu tracer that
2738     does not contain a very small quantity of 239Pu. This impurity should be taken into account when
2739     calculating the 239Pu activity levels of samples. Radiochemical impurity refers to the nuclide of
2740     interest being in an undesired chemical form. This type of impurity has its largest effects in
2741     organic tracer studies, where the presence of a tracer in the correct chemical form is essential. For
2742     example, the presence of 32P-labeled pyrophosphate in an orthophosphate tracer could lead to
2743     erroneous results in an orthophosphate tracer study.

2744     Tracer solutions can also contain other forms of radiochemical impurities. Many tracers are
2745     actinides or other isotopes that have progeny that are radioactive. Tracer solutions are purchased
2746     with known specific activities for the isotopes listed in the  solutions. However, from the time of
2747     production of the tracer, ingrowth of progeny radioisotopes occurs. 236Pu is used as a tracer for
2748     239Pu and 240Pu analysis, for example. 236Pu has a half-life of 2.9 years and decays to 232U, which
2749     has a half-life of 72 years. After solutions of 236Pu have been stored for about three years, half of
2750     the radionuclide will be converted to 232U. If the solution is then used as a tracer in a procedure
2751     for analysis of uranium and plutonium in soil, erroneously high results would be produced for the
2752     content of uranium if a gross-counting technique is used. Thus, it is important to consider
2753     chemical purification of a tracer solution prior to use to remove unwanted radioactive progeny.

2754     Tracer analysis is very dependent upon the identical behavior of the tracer and the analyte.
2755     Therefore, tracers should be added to the system as early as possible, and complete isotopic
2756     exchange should be ensured as discussed previously (see Section 14.10, "Radiochemical
2757     Equilibrium"). Obvious difficulties arise when a tracer is added to  a solid sample, especially if
2758     the sample is subdivided. Unless complete dissolution and  isotopic exchange is ensured, results
2759     should be interpreted carefully.

2760     Isotopes selected for tracer work should be capable of being easily measured. Gamma-emitting
2761     isotopes are ideal because they can easily be detected by gamma spectroscopy without being
2762     separated from other matrix constituents. Alpha- and beta-emitting tracers require separation
2763     before counting. Some common tracers are listed below:

2764       •  85Sr has a 514 KeV gamma ray that can be used to monitor the  behavior of strontium in a
2765         system, or for yield determination in a 89Sr/90Sr procedure, as long as the gamma is accounted
2766         for in the beta-counting technique.
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2767      •  99mTc with a half-life of 6.02 h and a 143 KeV gamma ray is sometimes used as a yield
2768         monitor for "Tc determinations. Samples are counted immediately to determine the chemical
2769         recovery, then the 99mTc is allowed to decay before analysis of the "Tc.

2770      •  152Eu and 145Sm are frequently used in the development of a new method to estimate the
2771         behavior of the +3 actinides and lanthanides.

2772      •  3H, 14C, 32P, and 36C1 are frequently used in biological studies. In some of these studies, the
2773         radionuclide is covalently bonded to a molecule. As a result, the chemical behavior of the
2774         radionuclide will follow that of the molecule, not the element.

2775      •  229Th is used for Th determinations, both in alpha spectroscopy and inductively coupled
2776         plasma-mass spectroscopy (ICP-MS).

2777      .  232u is commonly used as a tracer in alpha spectroscopy, whereas 236U is used for ICP-MS
2778         determinations. It should be noted that 232U decays to 228Th and therefore needs to be taken
2779         into account if determining Th isotopes in the same sample.

2780      •  242Pu and 236Pu are both used as tracers in  Pu analyses. However, 236Pu decays to 232U, which
2781         needs to be taken into account when analyzing both Pu and U in the same sample aliquant.

2782      •  243Am is employed in the analysis of 241Am and Cm by alpha spectroscopy. It is assumed that
2783         Am and Cm are displaying similar chemical behavior.

2784     14.9.3.1   Characteristics of Tracers

2785     The behavior of tracers is often different from that of elements in normal concentrations. The
2786     chemical form of a radionuclide predominant at normal concentrations, for  example, might not
2787     be the primary form at tracer concentrations. Alternatively, a shift in the equilibrium that is partly
2788     responsible for a radionuclide's chemical behavior might increase or reduce its concentration as a
2789     result of the low tracer concentration. Hydrolysis reactions are influenced particularly by changes
2790     in concentration because water is one of the species in the equilibrium. For  example, hydrolysis
2791     of the uranyl ion is represented by (Choppin et al., 1995, p.243):

2792                            m • UO2+2 + p- H2O - (UO2)m(OH) 2m-p + p- H+1
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2793     At tracer quantities, the equilibrium will shift to the left as the amount of the uranyl ion
2794     decreases. At 10"3 molar (pH 6), the uranyl ion is 50 percent polymerized; at 10"6 molar, there is
2795     negligible polymerization.

2796     Interactions of radionuclides with impurities present special problems at low concentration.
2797     Difficulties include adsorption onto impurities such as dust, silica, or colloidal or suspended
2798     material, or adsorption onto the walls of the container. Generally, 10"8 to 10"7 moles are needed to
2799     cover a container's walls; but at tracer concentrations, much less is present (Choppin et al., 1995,
2800     p. 242). Adsorption depends on (see Surface Adsorption within Section 14.8.4.1, "Coprecipita-
2801     tion Processes"):

2802       •  Concentration. A larger percentage is adsorbed at lower tracer concentrations than at higher
2803         concentrations, because a larger surface area is available compared to the amount of tracer
2804         present. Dilution with carrier decreases the amount of tracer adsorbed because the carrier is
2805         competing for adsorption, and the relative amount of tracer interacting with the walls is much
2806         less.

2807       •  Chemical State. Adsorption increases with charge on the ion.

2808       •  Nature of the Surface Material. Surfaces that have a negative charge or that contain hydroxyl
2809         groups can interact with cations through electrostatic attraction and hydrogen bonding,
2810         respectively.

2811       •  pH. Generally, adsorption decreases with a lower pH (higher hydrogen ion concentration)
2812         because the ions interact with negatively-charged surfaces, and hydrogen bonding decreases
2813         their ability to interact with metal ions.

2814     All these processes will reduce the quantity of analyte available for radiochemical  procedures
2815     and, therefore, the yield of a procedure. The amount measured by the detection process will be
2816     correspondingly lower, introducing additional error  and uncertainty that would go  undetected at
2817     normal concentrations.

2818     Adsorption can be useful, however. For example, carrier-free  yttrium (Y+3) is quantitatively
2819     adsorbed onto filter paper from basic strontium solutions at concentrations at which yttrium
2820     hydroxide, Y(OH)3, will not precipitate. Also, carrier-free niobium (Nb)  has been adsorbed on
2821     glass fiber filters for a fast  specific separation technique (Friedlander et al.,  1981, p. 296).
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2822     Specific behavior characteristics of compounds in separation techniques are further described
2823     below. Additional discussion can also be found in the respective sections found earlier in this
2824     document that describe each separation technique.

2825     14.9.3.2   Coprecipitation

2826     Often, the concentration of tracer is so low that precipitation will not occur in the presence of a
2827     counter-ion that, at normal concentrations, would produce an insoluble salt. Under these
2828     conditions, carriers are used to coprecipitate the tracer. (Coprecipitation is  described in
2829     Section 14.8)

2830     14.9.3.3   Deposition on Nonmetallic Solids

2831     Radionuclides can be deposited onto preformed ionic solids, charcoal, and ion-exchange resins
2832     (Wahl and Bonner, 1951, p. 124).  The mechanisms of adsorption onto preformed ionic solids are
2833     similar to those responsible for Coprecipitation: counter-ion exchange and isomorphous exchange
2834     (Section 14.8, "Precipitation and Coprecipitation"). Adsorption is favored by a large surface area,
2835     charge of the solid and radionuclide, solubility of compound formed between the solid and the
2836     radionuclide, and time of contact;  however, it depends, to a large extent, on whether or not the
2837     radionuclide ion can fit into the crystal lattice  of the precipitate. Similarly,  adsorption onto
2838     charcoal depends on the amount of charcoal and its surface area, time of contact, and nature of
2839     the surface, because it can be modified by the  presence of other ions or molecules.

2840     Adsorption of radionuclides, with and without carriers (Friedlander et al., 1981, p. 297), onto
2841     ion-exchange resins, followed by selective elution, has been developed into a very efficient
2842     separation technique (Wahl and Bonner, 1951, p. 145) (see Section 14.6.4, "Ion-Exchange
2843     Chromatography"). Friedlander et al.  (1981), illustrates this phenomenon:

2844         "Ion-exchange separations  generally work as well with carrier-free tracers as with weighable
2845         amounts of ionic species. A remarkable example was the original isolation of mendelevium at
2846         the level of a few atoms (p. 298)...The transuranium elements in the solution were ...
2847         separated from one another by elution ...through a cation-exchange  column" (p. 450).

2848     14.9.3.4   Radiocolloid Formation

2849     At the tracer level, a radionuclide  solution is not necessarily truly homogeneous, but can be a
2850     microparticle (colloid) of variable size or aggregation (Adolff and Guillaumont, 1993, p. 196).
2851     Carrier-free tracers can become colloidal by two mechanisms:


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         Separation Techniques
2852         1.  Sorption onto a preexisting colloidal impurity (approximately 0.001 |i to 0.5 |i), such as
2853            dust, cellulose fibers, glass fragments, organic material, and polymeric metal hydrolysis
2854            products (Choppin et al., 1995, p. 243; Adolff and Guillaumont, 1993, p. 196)

2855         2.  Polycondensation of a monomeric species consisting of aggregates of 103 to 107
2856            radioactive atoms (Adolff and Guillaumont, 1993, p. 197)

2857     The presence of radiocolloids in solution can be detected by one or more of the following
2858     characteristics of the solution, which is not typical behavior of a true solution (Adolff and
2859     Guillaumont, 1993, p. 196):

2860       •  The radionuclide can be separated from solution by a physical method such as ultrafiltration
2861         or ultracentrifugation.

2862       •  The radionuclide does not follow the laws of a true solution when a chemical gradient
2863         (diffusion, dialysis, isotopic exchange) or electrical gradient (electrophoresis, electrolysis,
2864         electrodialysis) is applied.

2865       •  Adsorption on solid surfaces and spontaneous deposition differ from those effects observed
2866         for radionuclides in true solution.

2867       •  Autoradiography reveals the formation of aggregates of radioactive atoms.

2868     Several factors affect the formation of radiocolloids (Wahl and Bonner, 1951, pp. 145-148):

2869       •  Solubility of the Tracer. The tendency of the tracer radionuclide to hydrolyze and form an
2870         insoluble species with another component of the solution favors radiocolloid formation,
2871         while the presence of ligands that form soluble complexes hinders formation; low pH tends
2872         to minimize hydrolysis of metallic radionuclides.

2873       •  Foreign Particles. The presence of foreign particles provides sites for the tracer to adsorb
2874         onto their surfaces; solutions containing ultrapure water prepared with micropore filters
2875         reduce their presence, although the preparation of water completely free of suspended
2876         particles is difficult.

2877       •  Electrolytes. Electrolytes affect the nature (species) of the tracer ions in solution (see Section
2878         14.10, Radiochemical Equilibrium)., as well as the charge on both the  radiocolloid and the
2879         foreign particle from which the colloid might have been derived.


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                                                                              Separation Techniques
2880       •  Solvent. Polar and nonpolar solvents can favor the formation of radiocolloids, depending on
2881         the specific radiocolloid itself.

2882       •  Time. The amount of radiocolloidal formation generally increases with the age of solution.

2883      14.9.3.5   Distribution (Partition) Behavior

2884      Distribution (partition) coefficients, which reflect the behavior of solutes during solvent
2885      extraction procedures (Section 14.4, "Solvent Extraction"), are virtually independent of
2886      concentration down to tracer concentrations (Friedlander et al., 1981, p. 299). Whenever the
2887      radioactive substance itself changes into a different form, however, the coefficient naturally
2888      changes, affecting the distribution between phases during extraction or any distribution
2889      phenomena, such as ion-exchange or gas-liquid chromatography (Section 14.7, "Chromatog-
2890      raphy"). Several properties of tracer solutions can alter the physical or chemical form of the
2891      radionuclide in solution and alter its distribution behavior (Wahl and Bonner, 1951, pp. 149-
2892      151):

2893       •  Radiocolloid formation might concentrate the radionuclide in the alternate phase or at the
2894         interface between the phases.

2895       •  Shift in equilibrium during complex-ion formation or hydrolysis reactions can alter the
2896         concentration of multiple radionuclide species in solution (Section 14.9.3.1, "Characteristics
2897         of Tracers").

2898      14.9.3.6   Vaporization

2899      Radioisotope concentrations that challenge the minimum detectible concentration (MDC) can be
2900      vaporized from solid surfaces or solution (Section 14.5, "Volatilization and Distillation"). Most
2901      volatilization methods of these trace quantities of radionuclides can be performed without
2902      specific carriers, but some nonisotopic carrier gas might be required (Friedlander et al., 1981,
2903      p. 300).

2904      Vaporization of these amounts of materials from solid surfaces differs from the usual process of
2905      vaporization of macroamounts of material, because the surface of the solid is usually not
2906      completely covered with the radionuclide (Wahl and Bonner, 1951, pp. 151-158). Carrier-free
2907      radionuclides  at the surface are bonded with the surface particles instead of with themselves, and
2908      the bonds broken during the process are between the solid and the radioisotope, rather than


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2909     between the radioisotope particles themselves. Additionally, the nature of the radioisotope can be
2910     altered by small trace quantities of gases such as oxygen and water present in the vacuum.
2911     Therefore, the identity of the radionuclide species vaporizing might be uncertain, and the data
2912     from the procedure can be hard to interpret. The rate of vaporization of radioisotopes also
2913     decreases with time, because the number of radioisotope particles available on the solid surface
2914     decreases with time.

2915     Radioisotopes near the MDC and macroquantities of radionuclide solutes should behave very
2916     similarly in vaporization experiments from solution, however, because both are present as a
2917     small fraction of the solution. They are, therefore, surrounded and bonded to solvent molecules
2918     rather than to other solute particles (Wahl and Bonner, 1951,  p. 156). The nature of the solvent,
2919     the pH, and the presence of electrolytes generally affect the solubility of the solute and its
2920     vaporization behavior.

2921     14.9.3.7  Oxidation and Reduction

2922     Some radionuclides exist in only one oxidation  state in solution, but others  can exist in several
2923     stable states (Tables 14.1 and 14.2). If multiple  states are possible, it might be difficult to
2924     ascertain in which state the radionuclide actually exists because the presence of trace amounts of
2925     oxidation or reduction (redox) impurities might convert the radionuclide to a state other than the
2926     one in which it was prepared (Wahl and Bonner, 1951, pp. 158-159). Excess redox reagents can
2927     often be added to the solution to convert the forms to a fixed ratio and keep the ratio constant
2928     during subsequent procedures.

2929     For a redox equilibrium such as:

2930                              PuO2+2 + 4 H+1 + Hg - Pu+4 + Hg+2 + 2 H2O

2931     the Nernst equation is used to calculate the redox potential, E, from the standard potential, E°:

2932                               E = E° - kT ln([Pu+4] [Hg+2]/[PuO2+2] [FT1]4)

2933     where k is a constant for the reaction (R/2F, containing the ideal gas constant, R, and Faraday's
2934     constant, F) and T is the absolute temperature. Water and metallic mercury  (Hg) do not appear i
2935     the equation, because their activity is one for a pure substance. Minute concentrations  of ions ir
2936     solution exhibit the same redox potential as macroquantities of ions because, E depends on the
2937     ratio of ion concentrations and not their total concentration.
 in
in
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2938     Electrolysis of some solutions is used for electrodeposition of a carrier-free metal on an electrode
2939     (Choppin et al., 1995, p. 246) or other substance, leaving the impurities in solution (Friedlander
2940     et al., 1981, p. 301). The selectivity and efficiency, characteristic of deposition of macro-
2941     quantities of ions at a controlled potential, is not observed, however, for these metals. The
2942     activity of the ion is not known, even if the concentration is, because the activity coefficient is
2943     dependent on the behavior of the mixed electrolytic system. In addition, the concentration of the
2944     metal in solution might not be known because losses may occur through adsorption or
2945     complexation with impurities. Electrolytic deposits are usually extremely thin—a property that
2946     makes them useful for counting measurements (Wahl and Bonner, 1951, p. 162).

2947     Deposition by chemical displacement is sometimes used for the separation of tracer from bulk
2948     impurities (Friedlander et al., 1981, p. 301). Polonium and lead spontaneously deposit from  a
2949     solution of hydrochloric acid onto a nickel disk at 85  °C (Blanchard, 1966). Alpha and beta
2950     counting is then used to determine 210Po and 210Pb. The same technique is frequently used in low-
2951     level analysis of transuranic elements to remove lead and polonium so that they do not interfere
2952     with the subsequent alpha analysis of the elements. Wahl and Bonner (1951, pp. 460-465)
2953     contains a helpful table (6F) of electrochemical methods used for the oxidation and reduction of
2954     carrier-free tracers.

2955     14.10  Radiochemical Equilibrium

2956     14.10.1    Basic Principles  of Equilibrium

2957     Radiochemical analysis is based on the assumption that an element reacts the same chemically,
2958     whether or not it is radioactive. This assumption is valid when the  element (analyte) and the
2959     carrier/tracer are in the same oxidation state, complex, or compound. The atomic weight of most
2960     elements is great enough that the difference in atomic weight between the radionuclide of interest
2961     and the carrier or tracer will not result in any chemical separation of the isotopes. This
2962     assumption might not be valid for the very lightest elements (e.g., H, Li, Be, and B) when mass
2963     fractionation or measuring techniques are used.

2964     Most radiochemical procedures involve the addition of one of the following:

2965      • A carrier of natural isotopic composition (i.e., the addition of stable strontium carrier to
2966        determine 89Sr/90Sr; EPA, 1980, Method 905.0).

2967      • A stable isotope tracer (i.e., enriched 180,15N, and 14C,  are frequently used in mass
2968        spectroscopy studies).

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2969      •  A radionuclide tracer (i.e., the addition of a known quantity of 236Pu tracer to determine 239Pu
2970         by alpha spectroscopy; DOE, 1990 and 1997, Method Pu-02).

2971     To achieve quantitative yields, there must be complete equilibration (isotopic exchange) between
2972     the added isotope and all the analyte species present. In the first example, isotopic exchange of
2973     the carrier with the radiostrontium is achieved and a weighable, stoichiometric compound of the
2974     carrier and radionuclide are produced. The chemical recovery from the separation technique is
2975     determined gravimetrically. Alternatively, a known quantity of an isocesiumtope is used and
2976     determined independently by mass analysis, pulse-height analysis, or another counting technique.

2977     Carriers and tracers are added as soon in the sample preparation process as possible, usually after
2978     the bulk sample is dried and homogenized, but before sample decomposition to ensure that the
2979     chemistry of the carriers or tracers is truly representative of the radioisotope  of interest. Thus,
2980     losses occurring during sample preparation steps, before decomposition, are  not quantified and
2981     might not be detected, although losses during these earlier steps are usually minimized. Having
2982     the carriers and tracers present during the sample decomposition provides an opportunity to
2983     equilibrate the carrier or tracer with the sample so that the carrier, tracer, and analyte are in the
2984     identical chemical form. While this can initially appear to be rather easy, in some cases it is
2985     extremely difficult.  The presence of multiple valence states and the formation of chemical
2986     complexes are two conditions that introduce a host of equilibration problems (Section 14.2.2,
2987     "Oxidation-Reduction Reactions"; Section 14.2.3, "Common Oxidation States"; and Section
2988     14.2.4, "Oxidation State in Solution"). Crouthamel and Heinrich (1971, pp. 5473-5474) has an
2989     excellent discussion of the intricacies and challenges associated with attaining true isotopic
2990     exchange:

2991         "Fortunately, there are many reactions which have high exchange rates. This applies even
2992         to many heterogeneous systems, as in the heterogeneous catalysis of certain electron
2993         transfer reactions. In 1920, Hevesy, using ThB (212Pb), demonstrated the  rapid  exchange
2994         between active lead nitrate and inactive lead chloride by the recrystallization of lead
2995         chloride from the homogeneously mixed salts. The ionization of these salts leads to the
2996         chemically identical lead ions, and a rapid isotopic exchange is expected. Similar
2997         reversible reactions account for the majority of the rapid exchange reactions observed at
2998         ordinary temperatures. Whenever possible, the analyst should conduct the isotope
2999         exchange reaction through a known reversible reaction in a homogeneous system. The
3000         true homogeneity of a system is not always obvious, particularly when dealing with the
3001         very low concentrations of the carrier-free isotopes. Even the usually well-behaved alkali-
3002         metal ions in carrier-free solutions will adsorb on the surfaces of their containment
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3003         vessels or on colloidal and insoluble material in the solution. This is true especially in the
3004         heavier alkali metals, rubidium and cesium. Cesium ions in aqueous solution have been
3005         observed to absorb appreciably to the walls of glass vessels when the concentrations were
3006         below 10-6g/mL"

3007     The reaction described above can be written as follows:

3008                           212Pb(NO3)2(s) + PbCl2(s) - Pb(NO3)2 + 212PbCl2

3009     Any of the following techniques may be employed to achieve both chemical and isotopic
3010     equilibration:

3011      •  Careful adding, mixing, stirring,  shaking, etc., to assure a homogeneous solution and prevent
3012         layering.
3013
3014

3015
3016
          •  Introducing the carrier or tracer in several different chemical forms or oxidation states,
             followed by oxidation or reduction to a single state.

          •  Treating the carrier or tracer and sample initially with strong oxidizing or reducing agents
             during decomposition (e.g., wet ashing or fusion).

3017      •  Carrying out repeated series of oxidation-reduction reactions.

3018      •  Requiring that, at some point during the sample decomposition, all the species be together in
3019         a clear solution.

3020     Once a true equilibration between carrier or tracer and sample occurs, the radiochemistry
3021     problem shifts from one of equilibration to that of separation from other elements, and ultimately
3022     a good recovery of the radionuclide of interest.

3023     Crouthamel  and Heinrich summarizes the introduction to equilibration (isotopic exchange)
3024     (Crouthamel and Heinrich, 1971, pp. 5475-5476):

3025         "Probably the best way to give the reader a feeling for the ways in which isotopic
3026         exchange is achieved in practice is to note some specific examples from radiochemical
3027         procedures. The elements which show strong tendencies to form radiocolloids in many
3028         instances may be stabilized almost quantitatively as a particular complex species and
3029         exchange effected. Zirconium, for example, is usually exchanged in strong nitric acid-


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3030         hydrofluoric acid solution. In this medium, virtually all the zirconium forms a ZrF62"
3031         complex. Niobium exchange is usually made in an oxalate or fluoride acid medium. The
3032         exchange of ruthenium is accomplished through its maximum oxidation state, Ru(VIII)
3033         which can be stabilized in a homogeneous solution and distilled as RuO4. Exchange may
3034         also be achieved by cycling the carrier through oxidation and reduction steps in the
3035         presence of the radioactive isotope. An iodine carrier with possible valence states of -1 to
3036         +7 is usually cycled through its full oxidation-reduction range to ensure complete
3037         exchange. In a large number of cases, isotopic exchange is not a difficult problem;
3038         however, the analyst cannot afford to relax his attention to this important step. He must
3039         consider in each analysis the possibility of both  the slow exchange of certain chemical
3040         species in homogenous solution and the possible very slow exchange in heterogeneous
3041         systems. In the latter case, this may consist simply of examining the solutions for
3042         insoluble matter and taking the necessary steps to  either dissolve or filter it and to assay
3043         for possible radioactive content."

3044     Also see the discussion of equilibration of specific radionuclides in Section 14.10.9,  "Review of
3045     Specific Radionuclides."

3046     14.10.2    Oxidation State

3047     Some radionuclides exist in solution in one oxidation  state that does not change, regardless of the
3048     kind of chemical treatment used for analysis. Cesium  (Cs), radium, strontium, tritium (3H), and
3049     thorium are in the +1, +2, +2, +1, and +4 oxidation  states, respectively, during all phases of
3050     chemical treatment. However, several radionuclides can exist in more than one state, and some
3051     are notable for their tendency to exist in multiple states simultaneously, depending on the other
3052     components present in the mixture. Among the former are cobalt, iron, iodine, and technetium,
3053     and among the latter are americium, plutonium, and uranium. To ensure identical chemical
3054     behavior during the analytical procedure, the radionuclide of interest and its carriers and/or
3055     tracers in solution must be converted to identical oxidation states. The sample mixture containing
3056     the carriers and/or tracer is treated with redox agents to convert each state initially present to the
3057     same state, or to a mixture with the same ratio of states. Table 6E in Wahl and Bonner (1951, pp.
3058     450-459) provides a list of traditional agents for the oxidation and reduction of carrier-free
3059     tracers that is a useful first guide to the selection of conditions for these radioequilibrium
3060     processes.
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3061      14.10.3    Hydrolysis

3062      All metal ions (cations) in aqueous solution interact extensively with water, and, to a greater or
3063      lesser extent, they exist as solvated cations (Katz et al., 1986, p. 1141):

3064                                      Ra+2 + x-H2O - Ra(H2O)x+2

3065      The more charged the cation, the greater is its interaction with water. Solvated cations, especially
3066      those with +4, +3, and small +2 ions, tend to act as acids by hydrolyzing in solution. Simply
3067      stated, hydrolysis is complexation where the ligand is the hydroxyl ion. To some extent, all metal
3068      cations in solution undergo hydrolysis and exist as hydrated species. The hydrolysis reaction for a
3069      metal ion is represented simply as (Choppin et al.,  1995, p.650):

3070                                M+n + m- H2O - M(OH)m+(n-m) + m- H+1

3071      Hydrolysis of the ferric ion (Fe+3) is a classical example:

3072                                    Fe+3 + H2O - Fe(OH)+2 + H+1

3073      Considering the hydrated form of the cation, hydrolysis is represented  by:

3074                                M(OH2)x+n - M(OH2)x4(OH)(n-1)+ + H+1

3075      In the latter equation, the hydrated  complex ion associated with the hydroxide ion, is known as
3076      the aquo-hydroxo species (Birkett et al., 1988, p. 2.7-3). As each equation indicates, hydrolysis
3077      increases the acidity of the solution, and the concentration of the hydrogen ion (pH) affects the
3078      position  of equilibrium. An increase  in acidity (increase in H+1 concentration; decrease in pH)
3079      shifts the position of equilibrium to the left, decreasing hydrolysis, while a decrease in acidity
3080      shifts it to the right, increasing hydrolysis. The extent of hydrolysis, therefore, depends on the pH
3081      of the solution containing the radionuclide. The extent of hydrolysis is also influenced by the
3082      radius and charge of the cation (charge/radius ratio). Generally, a high ratio increases the
3083      tendency of a cation to hydrolyze. A  ratio  that promotes hydrolysis is generally found in small
3084      cations with a charge greater than one (Be+2, for example). The thorium cation, Th+4, with a
3085      radius three times the size of the beryllium ion but a +4 charge, is hydrolyzed extensively, even at
3086      a pH of four (Baes and Mesmer, 1976, p.  158). It is not surprising, therefore, that hydrolysis is an
3087      especially important factor in the behavior of several metallic radionuclides in solution, and is
3088      observed in the transition, lanthanide, and actinide groups. For the actinide series, the +4 cations
3089      have the greatest charge/radius ratio  and undergo hydrolysis most readily. Below pH 3, the


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3090     hydrolysis of Th4+ is negligible, but at higher pH, extensive hydrolysis occurs. Uranium(IV)
3091     undergoes hydrolysis in solution at a pH above 2.9 with U(OH)3+being the predominant
3092     hydrolyzed species. Neptunium ions undergo hydrolysis in dilute acid conditions with evidence
3093     of polymer formation in acidic solutions less than 0.3 M. The hydrolysis of plutonium is the most
3094     severe, often leading to polymerization (see Section 14.10.4, "Polymerization"). In summary, the
3095     overall tendency of actinides to hydrolyze decreases in the order (Katz et al., 1986, p. 1145):

3096                                   An+4 > AnO2+2 > An+3 > AnO2+1

3097     where "An" represents the general chemical symbol for an actinide.

3098     For some cations, hydrolysis continues past the first reaction with water, increasing the number
3099     of hydroxide ions (OH"1) associated with the cation in the aquo-hydroxo species:

3100                                     U+4 + H2O - U(OH)+3 + H+1

3101                                  U(OH)+3 +H2O -  U(OH)2+2 + H+1

3102     This process can, in some cases, conclude with the precipitation of an insoluble hydroxide, such
3103     as ferric hydroxide. "Soluble hydrolysis products are especially important in systems where the
3104     cation concentrations are relatively low, and hence the range of pH relatively wide over which
3105     such species can be present and can profoundly affect the chemical behavior of the metal" (Baes
3106     and Mesmer, 1976, p.  3).

3107     Solutions containing trace concentrations of metallic radionuclides qualify as an example of
3108     these systems. The form of hydrolysis products present can control important aspects of chemical
3109     behavior such as (Baes and Mesmer, 1976, p. 3):

3110      • Adsorption of the radionuclide on surfaces, especially on mineral and  soil particles.
3111      • Tendency to coagulate colloidal particles.
3112      • Solubility of the hydroxide or metal oxide.
3113      • Extent of complex formation in solution.
3114      • Extent of extraction from solution by various reagents.
3115      • Ability to oxidize or reduce the radionuclide to another oxidation state.

3116     Thus, a knowledge of the identity and stability of radionuclide ion hydrolysis products is
3117     important in understanding or predicting the chemical behavior of trace quantities of
3118     radionuclides in solution (Baes and Mesmer, 1976, p. 3). As the equilibrium equation indicates,


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                                                                            Separation Techniques
3119     H+1 is produced as cations hydrolyze. Undesirable consequences of hydrolysis can, therefore, be
3120     minimized or eliminated by the addition of acid to the analytical mixture to reverse hydrolysis or
3121     prevent it from occurring. Numerous steps in radioanalytical procedures are performed at low pH
3122     to eliminate hydrolytic effects. It is also important to know the major and minor constituents of
3123     any sample, since hydrolysis effects are a function of pH and metal concentration. Thus,
3124     maintaining the pH of a high iron-content soil sample below pH 3.0 is important, even if iron is
3125     not the analyte.

3126     14.10.4   Polymerization

3127     The hydrolysis products of radionuclide cations described in the preceding section are
3128     monomeric—containing only one metal ion. Some of these monomers can spontaneously form
3129     polymeric metal hydroxo polymers in solution, represented by formation of the dimer (Birkett
3130     etal., 1988, p. 2.8-1):

3131                    2 M(H2O)x.1(OH)+(n-1) - [(H2O)x.2M(OH)2M(H2O)x.2]+2(n-1) + 2 H2O

3132     The polymers contain -OH-bridges between the metal ions that, under high temperature,
3133     prolonged aging, and/or high pH, can convert to -O-bridges, leading eventually to precipitation of
3134     hydrated metal oxides. Birkett et al. (1988) states that:

3135        "Formation of polymeric hydroxo species has been reported for most metals, although in
3136        some cases, the predominant species in solution is the monomer.  Some metals form only
3137        dimers or trimers, while a few form much larger, higher-molecular-weight polymeric species.

3138        "Increasing the pH of a metal ion solution, by shifting the position of hydrolysis
3139        equilibrium ..., results in an increased concentration of hydrolyzed species ..., which in turn
3140        causes increased formation of polymeric species .... Diluting a solution has two opposing
3141        effects on the formation of polymeric species:

3142            "(1)    Because dilution  of acidic solutions causes a decrease in FT1 concentration  (i.e.,
3143                   an increase in pH), it causes a shift in the hydrolyzed equilibrium toward
3144                   formation of hydrolyzed species.

3145            "(2)    On the other hand, dilution decreases the ratio of polymeric to monomeric
3146                   complexes in solution.  For metals that form both  monomeric and polymeric
3147                   complexes, this means that monomeric species predominate beyond a certain level
3148                   of dilution" (Birkett et  al., 1988,  p. 2.8-2).


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3149     Because this type of polymerization begins with hydrolysis of a cation, minimizing or
3150     eliminating polymerization can be achieved by the addition of acid to lower the pH of the
3151     analytical solution to prevent hydrolysis (Section 14.10.3, "Hydrolysis").

3152     14.10.5    Complexation

3153     Radionuclides exist as metal ions in solution, and many have a tendency to form stable complex
3154     ions with molecules or anions present as analytical reagents or impurities. The tendency to form
3155     complex ions is, to a considerable extent, an expression of the same properties that lead to
3156     hydrolysis; high positive charge on a +3 or +4 ion provides a strong driving force for the
3157     interaction with ligands (Katz et al., 1986, p.  1146) (Section 14.3, "Complexation").

3158     Complex-ion formation by a radionuclide alters its form, introducing in solution additional
3159     species of the radionuclide whose concentrations depend on the magnitude of the formation
3160     constant(s). Alternate forms have different physical and chemical properties,  and behave
3161     differently in separation techniques, such as extraction or partition chromatography. The behavior
3162     of alternate forms of radionuclides can present problems in the separation scheme that should be
3163     avoided if possible or addressed in the protocol.  Some separation schemes, however, take
3164     advantage of the behavior of alternate radionuclide species  formed by Complexation, which can
3165     alter the solubility of the radionuclides in a solvent or their  bonding to an ion-exchange resin
3166     (Section 14.3.4.2, "Separation by Solvent Extraction and Ion-Exchange Chromatography").

3167     14.10.6    Radiocolloid Interference

3168     The tendency of some radionuclides in solution, particularly tracer levels of radionuclides, to
3169     form radiocolloids, alters the physical and chemical behavior of those radionuclides (see Section
3170     14.9.3.4, "Radiocolloid Formation").  Radioanalytical  separations will not perform as expected in
3171     solutions containing radiocolloids, particularly as the solubility of the radionuclide species
3172     decreases.

3173     Solutions containing large molecules, such as polymeric metal hydrolysis products, are more
3174     likely to form radiocolloids (Choppin et al., 1995, p. 243). "If the solution is kept at sufficiently
3175     low pH and extremely free of foreign particles, sorption and radiocolloid formation are usually
3176     avoided as major problems" (Choppin et al. 1995, p. 243). If tracer levels of radionuclides are
3177     present, trace impurities become especially significant in the radiochemical procedure, and
3178     should be minimized or avoided whenever possible (Crouthamel and Heinrich, 1971, p. 5493).
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3179     Crouthamel and Heinrich provide some specific insight into radiocolloidal interference in the
3180     equilibration problem:

3181         "The transition metals tend to form radiocolloids in solution, and in these heterogeneous
3182         systems the isotopic exchange reaction between a radiocolloid and inactive carrier added to
3183         the solution is sometimes slow and, more often, incomplete. Elements which show a strong
3184         tendency to form radiocolloids, even in macro concentrations and acid solutions, are titanium,
3185         zirconium, hafnium, niobium, tantalum, thorium, and protactinium, and, to a lesser degree,
3186         the rare earths. Other metals also may form radiocolloids, but generally offer a wider choice
3187         of valence states which may be stabilized in aqueous solutions" (Crouthamel and Heinrich,
3188         1971, p. 5474).

3189     14.10.7    Isotope Dilution Analysis

3190     The basic concept of isotope dilution analysis is to measure the changes in specific activity of a
3191     substance upon its incorporation into a system containing an unknown amount of that  substance.
3192     Friedlander et al. (1981), define specific activity.

3193         "Specific activity is defined as the ratio of the number of radioactive atoms to the total
3194         number of atoms of a given element in the sample (N*/N). In many cases where only the
3195         ratios of specific activities are needed, quantities proportional to N*/N, such as activity/mole,
3196         are referred to as specific activity" (Friedlander et al., 1981, p. 432).

3197     For example, isotope dilution can be used to determine the amount of some inactive material A
3198     in a system (Wang et al., 1975). To the system containing x grams of an unknown weight of the
3199     inactive form of A, y grams of active material A* of known activity D is added. The specific
3200     activity of the added active material, Sl3 is given by:

3201                                              Sj = D/y

3202     After ensuring isotopic exchange, the mixture of A and A* is isolated, but not necessarily
3203     quantitatively, and purified. The specific activity, S2, is measured. Owing to the conservation of
3204     matter,

3205                                            S2 = D/(x + y)

3206     and by substituting for SjV for D and rearranging, the amount x of inactive A is given as
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3207                                          x = y(S1/S2-l)

3208     However, this equation is valid only if complete isotopic exchange has occurred, a task not
3209     always easy to achieve.

3210     14.10.8   Masking and Demasking

3211     Masking is the prevention of reactions that are normally expected to occur through the presence
3212     or addition of a masking reagent. Masking reactions can be represented by the general reversible
3213     equation:

3214                                         A + Ms-A-Ms

3215     where A is the normal reacting molecule or ion, and Ms is the masking agent. The decreased
3216     concentration of A at equilibrium determines the efficiency of masking. An excess of masking
3217     agent favors the completeness of masking, as expected from LeChatelier's Principle. Feigl (1936,
3218     p. 409) has described masking reagent and the masking of a reaction as follows: "... the
3219     concentration of a given ion in a solution can be so diminished by the addition of substances
3220     which unite with the ion to form complex salts that an ion product sufficient to form a precipitate
3221     or cause a color reaction is no longer obtained. Thus we speak of the masking of a reaction and
3222     call the reagent responsible for the disappearance of the ions necessary for the reaction, the
3223     masking reagent." The concepts of masking and demasking are discussed further in Perrin (1979.
3224     pp. 600-643) and in Dean (1995, pp. 2.9-2.15).

3225     Masking techniques are frequently used in analytical chemistry because they often provide
3226     convenient and efficient methods to avoid the effects of unwanted components of a system
3227     without having to separate the interferant physically. Therefore, the selectivity of many analytical
3228     techniques can be increased through masking techniques. For example, copper can be prohibited
3229     from carrying on ferric hydroxide at pH 7 by the addition of ammonium ions to complex the
3230     copper ions. Fe3+ and A13+ both interfere with the extraction of the +3 actinides and lanthanides in
3231     some systems, but Fe3+ can be easily masked through reduction with ascorbic acid, and A13+ can
3232     be masked through complexation with fluoride ion (Horwitz et al., 1993 and 1994). In another
3233     example, uranium can be isolated on a U/TEVA column (Eichrom Industries, Inc., Darien, IL)
3234     from nitric acid solutions by masking the tetravalent actinides with oxalic acid; the tetravalent
3235     actinides are complexed and pass through the column, whereas uranium is extracted (SpecNews,
3236     1993). Strontium and barium can be isolated from other metals by cation exchange from a
3237     solution of water, pyridine, acetic acid and glycolic acid. The other metals form neutral or
3238     negative complexes and pass through the cation column, while strontium and barium are retained


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3239
3240
3241
3242

3243
3244
3245

3246

3247
3248
3249
3250
3251

3252
3253

3254
3255

3256

3257
3258
3259
3260
3261
3262

3263
3264
3265
(Orlandini,  1972). Masking phenomena are present in natural systems as well. It has been
demonstrated that humic and fulvic acids can complex heavy metals such that they are no longer
bioavailable and are, therefore, not taken up by plants. Tables 14.17 and 14.18 list common
masking agents.
                  TABLE 14.17 — Masking agents for ions of various metals
                                                                                   (i)
 Metal Masking Agent
 Ag    Br, citrate, Cl", CN", I", NH3, SCN" S2 O3"2, thiourea, thioglycolic acid, diethyldithiocarbamate,
       thiosemicarbazide, bis(2-hydroxyethyl)dithiocarbamate
 Al    Acetate, acetylacetone, BF4", citrate, C2O4"2, EDTA, F", formate, 8-hydroxyquinoline-5-sulfonic acid,
       mannitol, 2,3-mercaptopropanol, OH", salicylate, sulfosalicylate, tartrate, triethanolamine, tiron
 As    Citrate, 2,3-dimercaptopropanol, NH2OHHC1, OH", S2"2, tartrate
 Au    Br, CN-, NH3, SCN", S2O3"2, thiourea
 Ba    Citrate, cyclohexanediaminetetraacetic acid, AfTV-dihydroxyethylglycine, EDTA, F", SO4"2, tartrate
 Be    Acetylacetone, citrate, EDTA, F", sulfosalicylate, tartrate
 Bi    Citrate, Cl', 2,3-dimercaptopropanol, dithizone, EDTA, I', OH", Na5P3O10, SCN', tartrate, thiosulfate,
       thiourea, triethanolamine
 Ca    BF4", citrate, 7V,yV-dihydroxyethylglycine, EDTA, F", polyphosphates, tartrate
 Cd    Citrate, CN", 2,3-dimercaptopropanol, dimercaptosuccinic acid, dithizone, EDTA, glycine, I", malonate,
       NH3, 1,10-phenanthroline, SCN', S2O3"2, tartrate
 Ce    Citrate, AfjV-dihydroxyethylglycine, EDTA, F", PO4"3, reducing agents (ascorbic acid), tartrate, tiron
 Co    Citrate, CN", diethyldithiocarbamate, 2,3-dimercaptopropanol, dimethylglyoxime, ethylenediamine, EDTA,
       F', glycine, H2O2, NH3, NO2, 1,10-phenanthroline, Na5P3O10, SCN", S2O3"2, tartrate
 Cr    Acetate, (reduction with) ascorbic acid + KI, citrate, AfjV-dihydroxyethylglycine, EDTA, F", formate,
       NaOH + H2O2, oxidation to CrO4"2,Na5P3O10, sulfosalicylate, tartrate, triethylamine, tiron
 Cu    Ascorbic acid + KI, citrate, CN", diethyldithiocarbamate, 2,3-dimercaptopropanol, ethylenediamine, EDTA
       glycine, hexacyanocobalt(III)(3-), hydrazine, I", NaH2PO2, NH2OHHC1, NH3, NO"2, 1,10-phenanthroline,
       S"2, SCN" + SO3"2, sulfosalicylate, tartrate, thioglycolic acid, thiosemicarbazide, thiocarbohydrazide,
       thiourea
 Fe    Acetylacetone, (reduction with) ascorbic acid, C2O4"2, citrate, CN" 2,3-dimercaptopropanol, EDTA, F",
       NH3, NH2OHHC1, OH", oxine 1,10-phenanthroline, 2,2'-bipyridyl, PO4"3, P2O7"4, S"2, SCN", SnCl2, S2O3"2,
       sulfamic acid, sulfosalicylate, tartrate, thioglycolic acid, thiourea, tiron, triethanolamine, trithiocarbonate
 Ga    Citrate, Cl", EDTA, OH", oxalate, sulfosalicylate, tartrate
 Ge    F", oxalate, tartrate
 Hf    See Zr
 Hg    Acetone, (reduction with) ascorbic acid, citrate, Cl", CN", 2,3-dimercaptopropan-l-ol, EDTA, formate, I",
       SCN", SO3"2, tartrate, thiosemicarbazide, thiourea, triethanolamine
 In    Cl", EDTA, F", SCN", tartrate thiourea, triethanolamine
 Ir     Citrate, CN", SCN", tartrate, thiourea
 La    Citrate, EDTA, F", oxalate, tartrate, tiron
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 VIetal  Masking Agent
 Mg

 Mn

 Vlo

 Nb
 Nd
 NH4+
 Ni

 Np
 Os
  a
 Pb
 Rare

 Re
 Rh
 Ru
 Sb
 Sc
 Se
 Sn

 Ta
 Te
 Th

 Ti

 n
 LJ
 V
Citrate, C2O4"2, cyclohexane-l,2-diaminetetraacetic acid, yV,yV-dihydroxyethylglycine, EDTA, F", glycol,
hexametaphosphate, OH", P2O7"4, triethanolamine
Citrate, CN", C2O42, 2,3-dimercaptopropanol, EDTA, F, Na5P3O10, oxidation to MnO4", P2O7"4, reduction to
Mn(II) with NH2OH HC1 or hydrazine, sulfosalicylate, tartrate, triethanolamine, triphosphate, tiron
Acetylacetone, ascorbic acid, citrate, C2O4"2, EDTA, F", H2O2, hydrazine, mannitol, Na5P3O10, NH2OHHC1,
oxidation to molybdate, SCN", tartrate, tiron, triphosphate
Citrate, C2O4 2, F, H2O2, OH', tartrate
EDTA
HCHO
Citrate, CN", A^A^-dihydroxyethylglycine, dimethylglyoxime, EDTA, F", glycine, malonate, Na5P3O10,
NH3 1,10-phenanthroline, SCN", sulfosalicylate, thioglycolic acid, triethanolamine, tartrate
F
CN-, SCN-, thiourea
H202
Acetate, (C6H5)4AsCl, citrate, 2,3-dimercaptopropanol, EDTA, I", Na5P3O10, SO4"2, S2O3"2, tartrate, tiron,
tetraphenylarsonium chloride, triethanolamine, thioglycolic acid
Acetylacetone, citrate, CN", EDTA, I", NH3, NO2, SCN", S2O3"2, tartrate, triethanol-amine
Citrate, CN", EDTA, I", NH3, NO2", SCN", S2O3'2, tartrate, urea
Reduction to Pu(IV) with sulfamic acid
C2O4"2, citrate, EDTA, F, tartrate
Earths
Oxidation to perrhenate
Citrate, tartrate, thiourea
CN", thiourea
Citrate, 2,3-dimercaptopropanol, EDTA, I", OH", oxalate, S"2, S2"2,  S2O3"2, tartrate, triethanolamine
Cyclohexane-l,2-diaminetetraacetic acid, F", tartrate
Citrate, F", I", reducing agents, S"2, SO3"2, tartrate
Citrate, C2O3"2, 2,3-dimercaptopropanol, EDTA, F", I", OH", oxidation with bromine water, PO4"3, tartrate,
triethanolamine, thioglycolic acid
Citrate, F", H2O2, OH", oxalate, tartrate
Citrate, F", I", reducing agents, S"2, sulfite, tartrate
Acetate, acetylacetone, citrate, EDTA, F", SO4"2, 4-sulfobenzenearsonic acid, sulfosalicylic acid, tartrate,
triethanolamine
Ascorbic acid, citrate, F", gluconate, H2O2, mannitol, Na5P3O10, OH", SO4"2, sulfosalicylic, acid, tartrate,
triethanolamine, tiron
Citrate, Cl", CN", EDTA, HCHO, hydrazine, NH2OHHC1, oxalate, tartrate, triethanolamine
Citrate, (NH4)2CO3, C2O4"2, EDTA, F", H2O2, hydrazine + triethanolamine, PO4"3, tartrate
(reduction with) Ascorbic acid, hydrazine, or NH2OH HC1, CN", EDTA, H2O2, mannitol, oxidation to
vanadate, triethanolamine, tiron
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                                                                                 Separation Techniques
 VIetal  Masking Agent
 W     Citrate, F~, H2O2, hydrazine, Na5P3O10, NH2OHHC1, oxalate, SCN", tartrate, tiron, triphosphate, oxidation
        to tungstate
        Cyclohexane-l,2-diaminetetraacetic acid, F"
        Citrate, CN", A^A^-dihydroxyethylglycine, 2,3-dimercaptopropanol, dithizone, EDTA, F", glycerol, glycol,
        hexacyanoferrate(II)(4-), Na5P3O10, NH3, OH", SCN", tartrate, triethanolamine
        Arsenazo, carbonate, citrate, C2O"2, cyclohexane-l,2-diaminetetraacetic acid, EDTA, F", H2O2, PO4"3, P2O7"
        4, pyrogallol, quinalizarinesulfonic acid, salicylate, SO4"2 + H2O2, sulfosalicylate, tartrate, triethanolamine
     Cnmnilefl frnm Pen-in C1979 nn 609-611 ^ and Dean (1995 nn 7)	
               TABLE 14.18 — Masking agents for anions and neutral molecules
 Anion or Neutral
 Molecule          Masking Agent
 Boric Acid
 Bf
 Br2
 BrO3"
 Chromate(VI)
 Citrate
 cr
 C12
 cio3-
 cio4-
 CN-
 EDTA
 F-
 Fe(CN)3-3
 Germanic Acid
 r
 I2
 io3-
 io4-
 MnO4-
 MoO4'2
 NO2
 Oxalate
 Phosphate
 S
 s-2
 Sulfate
 Sulfite
 so6-2
 Se and its anions
F", glycol, mannitol, tartrate, and other hydroxy acids
Hg(H)
Phenol, sulfosalicylic acid
Reduction with arsenate(III), hydrazine, sulfite, or thiosulfate
Reduction with arsenate(III), ascorbic acid, hydrazine, hydroxylamine, sulfite, or thiosulfate
Ca(II)
Hg(II), Sb(III)
Sulfite
Thiosulfate
Hydrazine, sulfite
HCHO, Hg(II), transition-metal ions
Cu(II)
Al (III), Be(II), boric acid, Fe(III), Th(IV), Ti(IV), Zr(IV)
Arsenate(III), ascorbic acid, hydrazine, hydroxylamine, thiosulfate
Glucose, glycerol, mannitol
Hg(H)
Thiosulfate
Hydrazine, sulfite, thiosulfate
Arsenate(III), hydrazine, molybdate(VI), sulfite, thiosurfate
Reduction with arsenate(III), ascorbic acid, azide, hydrazine, hydroxylamine, oxalic acid,
sulfite, or thiosulfate
Citrate, F, H2O2, oxalate, thiocyanate + Sn(II)
Co(II), sulfamic acid, sulfanilic acid, urea
Molybdate(VI), permanganate
Fe(III), tartrate
CN-, S2', suffite
Permanganate + sulfuric acid, surfur
Cr(III) + heat
HCHO, Hg(II), permanganate + sulfuric acid
Ascorbic acid, hydroxylamine,  thiosulfate	
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         Separation Techniques
3333
3334
3335
          Anion or Neutral
          Molecule        Masking Agent
          Tel"            Diaminobenzidine, sulfide, sulfite
          Tungstate        Citrate, tartrate
          Vanadate        Tartrate
3336      (1) Compiled from Perrin( 1979, p. 612) and Dean (1995)

3337     Demasking refers to any procedure that eliminates the effect of a masking agent already present
3338     in solution. There are a variety of methods for demasking, including changing the pH of the
3339     solution and physically removing, destroying, or displacing the masking agent. The stability of
3340     most metal complexes depends on pH, so simply raising or lowering the pH is frequently
3341     sufficient for demasking. Another approach to demasking involves the formation of new
3342     complexes or compounds that are more stable than the masked species. For example, boric acid
3343     commonly is used to demask the fluoride complexes of Sn4+ or Mo6+, and hydroxide is used to
3344     demask the thiocyanate complexes of Fe3+. In addition, it might be possible to destroy the
3345     masking  agent in solution through a chemical reaction (i.e., via the oxidation of EDTA in acidic
3346     solutions by permanganate or another strong oxidizing agent).

3347     14.10.9   Review of Specific Radionuclides

3348     14.10.9.1 Americium

3349     Americium is a metal of the actinide series which is produced synthetically by neutron activation
3350     of uranium or plutonium  followed by beta decay.

3351     Isotopes

3352     Twenty isotopes of americium  are known, 232Am through 248Am, including three metastable
3353     states. All isotopes are radioactive. 243Am and 241Am, alpha emitters, are the longest lived with a
3354     half-lives of 7,380 years and 432.7 years, respectively. 241Am and 243Am also undergo
3355     spontaneous fission. 242mAm has a half-life of 141 years, and the half-lives of the remaining
3356     isotopes are measured in  hours, minutes, or seconds. 241Am is the most common isotope of
3357     environmental concern.

3358     Occurrence

3359     None of the isotopes of americium occur naturally. It is produced synthetically by neutron
3360     bombardment of 238U or 239Pu followed by beta decay of the unstable intermediates. 241Am is

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                                                                             Separation Techniques
3361     found in military wastes and can be extracted from reactor wastes. Some industrial ionization
3362     sources also contain americium. Decay of 241Pu injected in the atmosphere during weapons
3363     testing contributes to the presence of 241Am.

3364     The silver metal is prepared by reduction of americium fluoride (AmF3) or americium oxide
3365     (AmO2) with active metals at high temperatures and is purified by fractional distillation, taking
3366     advantage of its exceptionally high vapor pressure compared to other transuranium elements.
3367     Kilogram quantities of 241Am are available, but only 10 to 100 g quantities of 243Am are prepared.

3368     Soft gamma emission from 241Am is used to measure the thickness of metal sheets and metal
3369     coatings, the degree of soil compaction, sediment concentration in streams, and to induce X-ray
3370     fluorescence in chemical analysis. As an alpha emitter, it is mixed with beryllium to produce a
3371     neutron source for oil-well logging and to measure water content in soils and industrial process
3372     streams. The alpha source is also used to eliminate static electricity and as an ionization source in
3373     smoke detectors.

3374     Solubility of Compounds

3375     Among the soluble salts are the nitrate, halides, sulfate, and chlorate of americium(in). The
3376     fluoride, hydroxide, and oxalate are insoluble. The phosphate and iodate are moderately soluble
3377     in acid solution. Americium(VI) is precipitated with sodium acetate to produce the hydrate,
3378     NaAmO2(C2H3O2)3-xH2O.

3379     Review of Properties

3380     The study of the properties of americium is very difficult because of the intense alpha radiation
3381     emitted by 241Am and 243Am, but some properties  are known. Americium metal is very ductile
3382     and malleable but highly reactive and unstable in  air, forming the oxide. It is considered to be a
3383     slightly more active metal than plutonium and is highly reactive combing directly with oxygen,
3384     hydrogen, and halides to form the respective compounds, AmO2, AmH3, and AX3. Alloys of
3385     americium with platinum, palladium, and iridium have been prepared by hydrogen reduction of
3386     americium oxide in the presence of the finely divided metals.

3387     Unless the transuranium elements are associated with high-level gamma emission, the principal
3388     toxicological problems associated with the radionuclides  are the result of internal exposure after
3389     inhalation or ingestion. When inhaled or ingested, they are about equally distributed between
3390     bone tissue and the liver. At high doses transuranics lead  to malignant tumors years later. In
3391     addition, large quantities of 241Am could conceivably lead to criticality problems, producing


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         Separation Techniques
3392     external radiation hazards or neutron exposure from (a,n) reactions. 241Am is also a gamma
3393     emitter.

3394     Americium is generally thought to be absorbed by all common rocks at pH values found in the
3395     environment. Complexation of Am(ni) by naturally occurring ligands, however, would be
3396     expected to strongly reduce its adsorption.

3397     Solution Chemistry

3398     Americium can exist in solution in the +3, +4, +5, and +6 oxidation states. Simple aqueous ions
3399     of Am+3 and AmO2+2 (VI oxidation state) are stable in dilute acid, but Am+3 is the predominant
3400     oxidation state. Free radicals produced by radiolysis of water by alpha particles reduce the higher
3401     states spontaneously to Am+3. The +3 oxidation state exists as Am(OH)3 in alkaline solution.
3402     Simple tetravalent americium is unstable in mineral acid solutions, disproportionating rapidly to
3403     produce Am+3 and AmO2+1 [Am(V)] in nitric and perchloric acid solutions. Conversely,
3404     dissociation of Am(OH)4 or AmO2 [both Am(IV)] in sulfuric acid solutions produces solutions
3405     containing Am+3 and AmO2+2. Stability is provided by complexation with fluoride ions and
3406     oxygen-containing ligands such as carbonate and phosphate ions. The AmO2+1 ion also
3407     disproportionates in acid solutions to yield Am+3 and AmO2+2, but the process for 241Am is so
3408     slow that radiation-induced reduction dominates. Evidence exists for the presence of Am+7 in
3409     alkaline solutions from the oxidation of AmO2+2.

3410     OXIDATION-REDUCTION BEHAVIOR. Although disproportionation reactions convert the +4 and +5
3411     oxidation states into the +3 and +6 states, radiolysis eventually converts the higher oxidation
3412     state into Am+3. Redox processes are used, however, to produce solutions of alternate oxidation
3413     states and to equilibrate the forms of americium into a common state, usually +3, but sometimes
3414     +6.

3415     The +4 state is reduced to Am+3 by iodide. In dilute, non-reducing solutions, peroxydisulfate
3416     (S2O8"2) oxidizes both the +3 and +5 states to the +6 state. Ce+4 and ozone (O3) oxidizes the +5
3417     state to +6 in perchloric acid solution. Electrolytic oxidation of Am+3  to AmO2+2 occurs in
3418     phosphoric, nitric, and perchloric acid solutions and solutions of sodium bicarbonate (Na2CO3).
3419     The latter ion is reduced to Am+3 by iodide, hydrogen peroxide, and the nitrite ion (NO/).

3420     COMPLEXATION. The +3 oxidation state  forms complexes in the following order of strength (in
3421     aqueous solution): F" > H2PO4" > SCN" > NO3" > Cl". Both americium  (+3) and (IV) form
3422     complexes with organic chelants. These  are stable in aqueous and organic solvents. Americium
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3423     (IV) however can be easily reduced unless special oxidizing conditions are maintained." The
3424     AmO2+2 ion also forms significant complex ions with nitrate, sulfate, and fluoride ions.

3425     HYDROLYSIS. The actinide elements are known for their tendency to hydrolyze and, in many
3426     cases, form insoluble polymers. In the predominant +3 oxidation state in solution, americium,
3427     with its large radius, has the least tendency of the +3 actinides to hydrolyze; yet, hydrolysis is
3428     expected to occur with some polymerization. Hydrolysis that does occur is complicated and
3429     depends on the nature of the cations present and may start at pH values as low as 0.5-1.0. In
3430     contrast, the AmO2+2, like all actinyl ions, undergoes hydrolysis to an appreciable extent. The
3431     tendency to form polymers of colloidal dimensions, however, appears to be small relative to
3432     other actinide ions in the +6 oxidation state. Precipitation occurs early on after relatively small
3433     polymeric aggregates form in solution. The strong tendency to form insoluble precipitates after a
3434     small amount of hydrolysis makes characterization of the water-soluble polymers a difficult
3435     problem.

3436     RADIOCOLLOIDS. At trace concentrations, a colloidal form of Am+2 can easily be prepared, so
3437     steps should be taken to avoid its formation during analytical procedures. At high pH ranges,
3438     colloids form from the Am(OH)3, and at lower pH ranges through adsorption of Am+3 onto
3439     foreign particles. Their formation depends on storage time, pH,  and ionic strength of the solution.

3440     Dissolution of Samples

3441     Americium is generally dissolved from irradiated reactor fuels, research compounds, and soil,
3442     vegetation, and biological samples. Spent fuel elements may be difficult to dissolve but
3443     eventually yield to digestion with hydrofluoric acid, nitric acid,  or sulfuric acid. Aqua regia is
3444     used if platinum is present, and hydrochloric acid with an oxidizing agent such as sodium
3445     chlorate. Perchloric acid, while a good solvent for uranium, reacts too vigorously. Sodium
3446     hydroxide-peroxide is a good basic solvent. Research compounds, usually salts, yield to hot
3447     concentrated nitric or sulfuric acid. Soil samples are digested with concentrated nitric acid,
3448     hydrofluoric  acid, or hydrochloric acid. Vegetation and biological samples are commonly wet
3449     ashed, and the residue is treated with nitric acid.

3450     Separation Methods

3451     The separation  of americium, particularly from other transuranics, is facilitated by the
3452     exceptional stability of Am(in) compared to the trivalent ions of other actinides, which more
3453     readily convert to higher oxidation states under conditions that americium remains trivalent.
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         Separation Techniques
3454     PRECIPITATION AND COPRECIPITATION. Coprecipitation with lanthanum fluoride (LaF3) is
3455     achieved after reduction of higher oxidation states to Am(in). Select oxidation of other
3456     transuranic elements such as neptunium and plutonium to the IV or VI oxidation  states
3457     solubilizes these radionuclides leaving americium in the insoluble form. Although coprecipita-
3458     tion with rare earths as fluorides or hydroxides from a bicarbonate solution of americium(VI), is
3459     used to purify americium, it is not as effective as ion-exchange procedures. Other coprecipitating
3460     agents for americium(in) include thorium oxalate [Th(C2O4)2], calcium oxalate (CaC2O4), ferric
3461     hydroxide [Fe(OH)3), and lanthanum potassium sulfate [LaK(SO4)2]. Americium(IV) is also
3462     coprecipitated with these reagents as well as with zirconium phosphate [Zr3(PO4)2].
3463     Americium(VI) is not coprecipitated with any of these reagents but with sodium uranyl acetate
3464     [NaUO2(C2H3O2)2].

3465     SOLVENT EXTRACTION. Organic solvents and chelating agents are available for separating
3466     americium from other radionuclides by selectively extracting either americium or the alternate
3467     radionuclide from aqueous solutions into an organic phase. Tributyl phosphate (TBP) in kerosene
3468     or thenoyltrifluoroacetone (TTA) in xylene removes most oxidation states of neptunium and
3469     plutonium from americium(in) in the presence of dilute nitric acid. The addition of sodium
3470     nitrate (6 M) tends to reverse the trend making americium more soluble in TBP than uranium,
3471     neptunium, or plutonium radionuclides. Di(2-ethylhexyl)phosphoric acid (HDEHP) in toluene is
3472     highly effective in extracting americium(ni) and is used in sample preparation for alpha
3473     spectroscopic analysis.

3474     Recently, solvent extraction chromatography has offered an efficient, easy technique for rapidly
3475     separating americium and other transuranic elements. A process using octylphenyl-N,N-
3476     diisobutyl carbamoylphoshpine oxide (CMPO) in dissolved TBP and fixed on an inert polymeric
3477     resin matrix has been used to isolate americium(in). The column is loaded with 2 M nitric acid,
3478     and americium is eluted with 4 M hydrochloric acid. It is important to note that iron, found in
3479     most environmental samples, does not effect the americium isolation if the iron is kept in the +2
3480     oxidation state. The ferric ion (Fe+3) is detrimental to the separation.

3481     ION EXCHANGE. Separation of americium can be achieved by cation-exchange chromatography.
3482     Any of its oxidation states absorb on a cation resin in dilute acid solution, but the higher
3483     oxidation states are not important in cation-exchange separations because they are unstable
3484     toward reduction to the +3  state. Generally, americium(in) is the last tripositive ion among the
3485     actinides eluted from a cation-exchange matrix, although the order may not be maintained under
3486     all conditions. Many eluting agents are available for specific separations.  Concentrated
3487     hydrochloric acid, for example, has been used for separating actinides such as americium from
3488     the lanthanides. Anion-exchange chromatography has been widely used for separating


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                                                                            Separation Techniques
3489     americium. Anionic complexes of americium(ni) form at high chloride concentrations, providing
3490     a chemical form that is easily exchanged on an anion-exchange column. The column can be
3491     eluted using dilute hydrochloric acid or a dilute hydrochloric acid/ammonium thiocyanate
3492     solution. Anion-exchange separations of americium are also realized with columns prepared with
3493     concentrated nitric acid solutions. The sequential separation of the actinides is accomplished
3494     readily using anion-exchange chromatography. Americium, plutonium, neptunium, thorium,
3495     protactinium, curium, and uranium can all be separated by the proper application of select acid or
3496     salt solutions to the column.

3497     ELECTRODEPOSITION. Americium can be electrodeposited for alpha spectrometry measurement
3498     on a highly-polished platinum cathode. The sample is dissolved in a dilute hydrochloric acid
3499     solution that has been adjusted to a pH of about six with ammonium hydroxide solution using
3500     methyl red indicator. The process runs for one hour at 1.2 amps.

3501     Methods of Analysis

3502     241Am is detected and quantified by either alpha counting or gamma spectroscopy. Trace
3503     quantities  of 241Am are analyzed by alpha counting, after separation from interfering
3504     radionuclides by solvent extraction,  coprecipitation, or ion-exchange chromatography. The
3505     isolated radionuclide is collected by coprecipitation, filtered, and mounted on a planchet or
3506     electroplated onto a platinum electrode for counting by alpha spectrometry. 243Am is added to the
3507     analytical  solution as a tracer to measure chemical recovery. 241Am in bulk soil samples can be
3508     determined by gamma spectroscopy.

3509        Compiled from: Ahrland, 1986;  Baes and Mesmer, 1976; Choppin et al., 1995; Considine
3510        and Considine,  1983; Cotton and Wilkinson, 1988; DOE, 1990 and 1997, 1995; 1997;
3511        Ehmann and Vance,  1991; Greenwood and Earnshaw, 1984; Haissinsky and Adolff, 1965;
3512        Katz et al., 1986; Lindsay, 1988; Metz and Waterbury, 1962; NEA, 1982; SCA, 2001;
3513        Penneman, 1994; Penneman and Keenan, 1960; Schulz and Penneman, 1986; Seaborg and
3514        Loveland, 1990; Horwitzetal, 1993.

3515     14.10.9.2  Cesium

3516     Cesium is  the last member of the naturally occurring alkali metals  in group IA of the periodic
3517     table with  an atomic number of 55. As such, its radiochemistry is simplified because the Group
3518     IA metals  form only +1  ions. Elemental cesium is a very soft, silver-white metallic solid in the
3519     pure state  with a melting point of only 28.5  °C. It tarnishes quickly to a golden-yellow color
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         Separation Techniques
3520     when exposed to small amounts of air. In larger amounts of air it ignites spontaneously. It is
3521     normally stored under xylente/toluene to prevent contact with air.

3522     Isotopes

3523     Cesium isotopes of mass number 112 to 148 have been identified. 133Cs is the only stable isotope.
3524     134Cs and 137Cs are the only two isotopes of significance from an environmental perspective. Both
3525     are formed from the nuclear fission process. Their half-lives are 2.06 and 30.17 years,
3526     respectively.

3527     Occurrence

3528     Cesium is widely distributed in the Earth's crust with other alkali metals. In granite and
3529     sedimentary rocks the concentration is less than 7 ppm. In seawater it is about 0.002 ppm, but in
3530     mineral springs the concentration may be greater than 9 mg/L. Cesium is found in complex
3531     minerals such as carnallite, a potassium and magnesium chloride mineral that contains small
3532     percentages of cesium compounds; lepidolite ores, a lithium aluminum silicate; and pollucite, a
3533     cesium-rich ore of the oxides of cesium, aluminum, and silicon. 137Cs is produced in nuclear
3534     fission and occurs in atmospheric debris from weapons tests and accidents. It is a very important
3535     component of radioactive fallout; and because of its moderately long half-life and high solubility,
3536     it is a major source of long-lived external gamma radiation from fallout. It accounts for 30
3537     percent of the gamma activity of fission products stored for one year, 70 percent in two years,
3538     and 100 percent after five years.

3539     Cesium metal is not produced on a commercial scale. It is isolated from its minerals, however, by
3540     acid extraction, fusion with alkaline  fluxes, or direct reduction of an ore to metallic cesium.
3541     Extraction and fusion yield a cesium salt, which is treated by oxidation-reduction processes to
3542     make the pure metal. The salt is either roasted with carbon, heated with calcium or lithium, or
3543     electrolyzed as a melt to reduce the cation to pure cesium. Special equipment should be used in
3544     these processes because of the very reactive chemical nature of the metal.

3545     Metallic cesium is used in photoelectric cells, spectrographic instruments, scintillation counters,
3546     and other optical and detecting devices, sometimes alloyed with calcium, strontium, or barium to
3547     facilitate handling. Its most recognized use is in the atomic clock that serves to define the second.
3548     Cesium has been considered as a fuel in ion-propulsion engines for deep space travel and as a
3549     heat-transfer medium for some applications. 137Cs has replaced 60Co in the treatment of cancer
3550     and has been used in industrial radiography for the control of welds. Cesium compounds are used
3551     in glass and ceramic production, as an absorbent in carbon dioxide production plants,  and in the


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                                                                            Separation Techniques
3552     preparation of density gradients for the separation of macromolecules by centrifugation. 37Cs is
3553     also used commercially as a sealed source in liquid scintillation spectrometers. The 661 keV
3554     gamma ray it emits is used to create an electron (Compton effect) distribution which allows the
3555     degree of sample quench to be determined.

3556     Solubility of Compounds

3557     Most cesium salts are very soluble in water and dilute acids. Among the salts of common anions,
3558     the notable exceptions are cesium perchlorate and periodate (CsClO4 and CsIO4). Several cesium
3559     compounds of large anions are insoluble. Examples include the following:  silicotungstate
3560     [Cs8SiW12O42], permanganate (CsMnO4), chloroplatinate (Cs2PtCl6), tetraphenylborate
3561     [CsB(C6H5)4], alum [CsAl(SO4)2], and cobaltnitrate complex [Cs3Co(NO3)6].

3562     Review of Properties

3563     Cesium is the most active and electropositive of all the metals. It forms compounds with most
3564     inorganic and organic anions; it readily forms alums with all the trivalent cations that are found
3565     in alums. The metal readily ionizes, and in ammonia solutions and it is a powerful reducing
3566     agent. When exposed to moist air, it tarnishes initially forming oxides and  a nitride and then
3567     quickly melts or bursts into flame. With water the reaction is violent. Cesium reacts vigorously
3568     with halogens and oxygen, and it is exceptional among the alkali metals in that it can form stable
3569     polyhalides such as CsI3. Reaction with oxygen forms a mixture of oxides: cesium oxide (Cs2O),
3570     cesium peroxide (Cs2O2), and cesium  superoxide (CsO2). The toxicity of cesium compounds is
3571     generally not important unless combined with another toxic ion.

3572     137Cs, introduced into the water environment as cations, is attached to soil particles and can be
3573     removed by erosion and runoff. However, soil sediment particles act as sinks for 137Cs, and the
3574     radionuclide is almost irreversible bound to mica and clay minerals in freshwater environments.
3575     It is unlikely that 137Cs will be removed from these  sediments under typical environmental
3576     conditions. Solutions of high ionic strength as occur in estuarine environments might provide
3577     sufficient exchange character to cause cesium to become mobile in the ecosphere.

3578     Solution Chemistry

3579     The cesium ion exists in only the +1 oxidation state, and its solution chemistry is not complicated
3580     by oxidation-reduction reactions. As a result, it undergoes complete, rapid  exchange with carriers
3581     in solution. The cesium ion is colorless in solution and is probably hydrated as a hexaaquo
3582     complex.


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         Separation Techniques
3583     COMPLEXATION. Cesium ions form very few complex ions in solution. The few that form are
3584     primarily with nitrogen-donor ligands or beta-diketones. Anhydrous beta-diketones are insoluble
3585     in water, but in the presence of additional coordinating agents, including water, they become
3586     soluble in hydrocarbons. One solvent-extraction procedure from aqueous solutions is based on
3587     chelation of cesium with  1,1,1 -trifluoro-3-(2 '-thenoyl)acetone (TTA) in a hydrocarbon solvents.
3588     Cesium is sandwiched between crown ligands, associated with the oxygen  atoms of the ether, in
3589     [Cs9(18-C-6)14]+9.

3590     HYDROLYSIS. With the small charge and large radius of the cesium ion, hydrolysis reactions are
3591     inconsequential.

3592     ADSORPTION. When cesium is  present in extremely low concentrations, even in the presence of 2
3593     M acid, adsorption on the walls of glass and plastic containers leads to complications for the
3594     radioanalyst. Half the activity of cesium radionuclides, for example, can be lost from acid
3595     solutions stored for one month in these containers. Experiments indicate that addition of 1 jig
3596     cesium carrier per mL of solution  is sufficient to stabilize acid solutions for six months.

3597     Dissolution of Samples

3598     Radiochemists generally dissolve  cesium samples from irradiated nuclear fuel, activated cesium
3599     salts, natural water, organic material, agriculture material, and soils. Nuclear fuel samples are
3600     generally dissolved in HC1, HNO3, HF, or a combination of these acids. Care should be taken to
3601     ensure that the sample is representative if 137Cs has been used as a burn-up  monitor. Precautions
3602     should also be taken with these samples to prevent loss of cesium because  of leaching or
3603     incomplete sample dissolution. Most cesium salts dissolve readily in water and acid solutions. In
3604     water samples, the cesium might require concentration, preferably by ion exchange, or by
3605     precipitation or coprecipitation if interfering ions are present. Organic materials are either
3606     decomposed by HNO3 or dry ashed, and the cesium is extracted with hot water or hot acid
3607     solution. Extraction and leaching procedure have been use to assess exchangeable or teachable
3608     cesium using ammonium acetate solutions or acid solutions, but soils are generally completely
3609     solubilized in HNO3, HC1, HF, H2SO4, or a mixture of these acids in order  to account for all the
3610     cesium in a soil sample.

3611     Separation Methods

3612     PRECIPITATION AND COPRECIPITATION. Cesium is separated and purified by several precipitation
3613     and coprecipitation methods using salts of large anions. Gravimetric procedures rely on
3614     precipitation to collect cesium  for weighing, and several radiochemical techniques isolate cesium


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                                                                             Separation Techniques
3615     radionuclides for counting by precipitation or coprecipitation. Cesium can be precipitated, or
3616     coprecipitated in the presence of cesium carrier, by the chlorate, cobaltinitrate, platinate, and
3617     tetraphenylborate ions. Other alkali metals interfere and should be removed before a pure
3618     insoluble compound can be collected. Cesium can be isolated from other alkali metals by
3619     precipitation as the silicotungstate. The precipitate can be dissolved in 6 M sodium hydroxide,
3620     and cesium can be further processed by other separation procedures. The tetraphenylb orate
3621     procedure first removes other interfering ions by a carbonate and hydroxide precipitation in the
3622     presence of iron, barium, lanthanum, and zirconium carriers. Cesium is subsequently precipitated
3623     by the addition of sodium tetraphenylb orate to the acidified supernatant. Alum also precipitates
3624     cesium from water samples in the presence of macro quantities of the alkali metals. Trace
3625     quantities of cesium radionuclides are precipitated using stable cesium as a carrier.

3626     ION EXCHANGE. The cesium cation is not retained by anion-exchange resins and does not form a
3627     suitable anion for anion-exchange chromatography. The process is used, however, to separate
3628     cesium from interfering ions that form anionic complexes. Cesium elutes  first in these
3629     procedures. Cesium is retained by cation-exchange resins. Because the cesium ion has the largest
3630     ionic radius and has a +1  charge, it is less hydrated than most other cations. Therefore, cesium
3631     has a small hydrated radius and can approach the  cation exchange site to form a strong
3632     electrostatic association with the ion-exchange resin. Binding of alkali metal ion to cation
3633     exchange resins follows the order: Cs+1>Rb+1>K+1>Na+1>Li+1. Cesium is generally the last alkali
3634     metal ion to elute in cation-exchange procedures.  In some procedures, the process is not
3635     quantitative after extensive elution.

3636     SOLVENT EXTRACTION. Cesium does not form many complex ions, and solvent extraction is not
3637     a common procedure for  its separation.  One solvent-extraction procedure, however, is based on
3638     chelation of cesium with  l,l,l-trifluoro-3-(2'-thenoyl)acetone (TTA) in a solvent of methyl
3639     nitrate/hydrocarbons. Cesium can also be extracted from fission product solutions with sodium
3640     tetraphenylb orate in amyl acetate. It can be stripped from the organic phase by 3 M HC1.

3641     Methods of Analysis

3642     Macroscopic quantities of cesium have  been determined by gravimetric procedures using one of
3643     the precipitating agents described above. Spectrochemical procedures for  macroscopic quantities
3644     include flame photometry, emission spectroscopy, and X-ray emission.

3645     Gamma ray spectrometry allows detection  of 134Cs, 136Cs, and 137Cs down  to very low levels. The
3646     gamma ray measured for 137Cs (661 Kev) actually is emitted from it progeny 136mBa. However,
3647     since the half-life of the barium isotope is so short (2.5 min) it is quickly equilibrated with its


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         Separation Techniques
3648     parent cesium isotope (i.e., secular equilibrium). 137Cs is used as part of a group of nuclides in a
3649     mixed radioactivity source for calibration of gamma ray spectrometers.

3650        Compiled from: Choppin et al., 1995; Considine and Considine, 1983; Cotton and
3651        Wilkinson, 1988; Emsley, 1989; EPA, 1973; EPA, 1973; EPA, 1980; Finston and Kinsley,
3652        1961; Friedlander et al., 1981; Hampel, 1968; Hassinsky and Adolff,  1965; Kallmann,  1964;
3653        Lindsay, 1988; Sittig,  1994.
3654     14.10.9.3  Cobalt

3655     Cobalt, atomic number 27, is a silvery-grey, brittle metal found in the first row of the transition
3656     elements in the periodic table, between iron and nickel. Although it is in the same family of
3657     elements as rhodium and iridium, it resembles iron and nickel in its free and combined states.

3658     Isotopes

3659     59Co is the only naturally occurring isotope of the element. The other twenty-two isotopes and
3660     their metastable states, ranging from mass numbers 50 to 67, are radioactive. Isotopes with mass
3661     numbers less than 59 decay by positron emission or electron capture. Isotopes with mass
3662     numbers greater than 59 decay by beta and gamma emission. Except for 60Co, the most important
3663     radionuclide, their half-lives range from milliseconds to days. The principle isotopes of cobalt
3664     (with their half-lives) are "Co (272 d), 58Co (71  d), and 60Co (5.27 y). Isotopes 57 and 58 can be
3665     determined by X-ray as well as gamma spectrometry. Isotope 60 is easily determined by gamma
3666     spectrometry.

3667     Occurrence and Uses

3668     The cobalt content of the crust of the earth is about 30 ppm, but the element is widely distributed
3669     in nature, found in soils, water, plants and animals, meteorites, stars, and lunar rocks. Over 200
3670     cobalt minerals are known. Commercially, the most important are the arsenides, oxides, and
3671     sulfides. Important commercial sources also include ores of iron, nickel, copper,  silver,
3672     manganese, and zinc. 60Co is produced by neutron activation of stable 59Co. 56Co and "Co are
3673     prepared by bombardment of iron or nickel with protons or deuterons.

3674     Some of the metallic cobalt is isolated from its minerals, but much of the metal is produced
3675     primarily as a byproduct of copper, nickel, or lead extraction. The processes are varied and
3676     complicated because of the similar chemical  nature of cobalt and the associated metals.


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                                                                             Separation Techniques
3677     Since ancient times cobalt ores has been used to produce the blue color in pottery, glass, and
3678     ceramics. Cobalt compounds are similarly used as artist pigments, inks, cotton dyes, and to speed
3679     the drying of paints and inks. They also serves as catalysts in the chemical industry and for
3680     oxidation of carbon monoxide in catalytic converters. One of the major uses of cobalt is the
3681     preparation of high-temperature or magnetic alloys. Jet engines and gas turbines are
3682     manufactured from metals with a high content of cobalt (up to 65 percent) alloyed with nickel,
3683     chromium, molybdenum, tungsten, and other metals.

3684     Little use if made of pure cobalt except as a source of radioactivity from 60Co. The radionuclide
3685     is used in cancer radiotherapy, as a high-energy gamma source for the radiography of metallic
3686     objects, fluids, and other solids, or as an injectable radionuclide for the measurement of flow
3687     rates in pipes.

3688     Solubility of Compounds

3689     Most simple cobalt compounds contain cobalt (U), but cobalt (U) and cobalt(UI) display varied
3690     solubilities in water. To some extent, their solubilities depend on the oxidation state of the metal.
3691     For example, all the halides of cobalt (II) are soluble but the only stable halide of cobalt (HI), the
3692     fluoride, is insoluble. The sulfates of both oxidation states are soluble in water. The acetate of
3693     cobalt (II) is soluble, but that of cobalt (HI) hydrolyses in water. The bromate, chlorate, and
3694     perchlorate of cobalt (II) are also soluble. Insoluble compounds include all the oxides of both
3695     oxidation states, cobalt (U) sulfide, cyanide, oxalate, chromate, and carbonate. The hydroxides
3696     are slightly soluble. Several thousand complex compounds of cobalt are known. Almost all are
3697     cobalt (in) complexes and many are  soluble in water.

3698     Review of Properties

3699     Metallic cobalt is less  reactive than iron and is unreactive with water or oxygen in air unless
3700     heated, although the finely divided metal is pyrophoric in air. On heating in air it forms the
3701     oxides, cobalt (II) oxide (CoO) below 200 °C and above 900 °C and cobalt (II)-cobalt (in) oxide
3702     (Co3O4) between the temperatures. It reacts with common mineral acids and slowly with
3703     hydrofluoric and phosphoric acids to form cobalt (H) salts and with sodium and ammonium
3704     hydroxides. On heating, it reacts with halogens and other nonmetals such as boron, carbon,
3705     phosphorus, arsenic, antimony, and sulfur.

3706     Cobalt exists in all oxidation states from -1 to  +4. The most common are the +2 and +3 oxidation
3707     states. The +1 state is found in  a several complex compounds, primarily the nitrosyl and carbonyl
3708     complexes and certain organic complexes. The +4 state exist in some fluoride complexes.


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         Separation Techniques
3709     Cobalt(II) is more stable in simple compounds and is not easily hydrolyzed. Few simple
3710     compounds are known for the +3 state, but cobalt is unique in the numerous stable complex
3711     compounds it forms.

3712     The toxicity of cobalt is not comparable to metals such as mercury, cadmium, or lead. Inhalation
3713     of fine metallic dust can cause irritation of the respiratory system, and cobalt salts can cause
3714     benign dermatosis. 60Co is made available in various forms, in sealed aluminum or monel
3715     cylinders for industrial applications, as wires or needles for medical treatment, and in various
3716     solid and solution forms for industry and research. Extreme care is required in handling any of
3717     these forms of cobalt because of the high-energy gamma radiation from the source.

3718     Solution Chemistry

3719     In aqueous solution and in the absence of complexing agents, cobalt (n) is the only stable
3720     oxidation state, existing in water as the pink-red hexaaquo complex ion, Co(H2O)6+2. Simple
3721     cobalt ions in the +3 oxidation state decompose water in an oxidization-reduction process that
3722     generates cobalt (II):

3723                               4 Co+3 + 2 H2O - 4 Co+2 + O2 + 4 H+1

3724     Complexation of cobalt (HI) decreases its oxidizing power and most complex ions of the +3
3725     oxidation state are stable in solution.

3726     COMPLEXATION. Several thousand complexes of cobalt have been prepared and extensively
3727     studied, including neutral structures and those containing complex cations and/or anions. Among
3728     these, the cobalt (ID) complexes are the strongest and represent one  of the largest groups of
3729     complex compounds.  The most common cobalt (IE) compounds contain six ligands bonded to
3730     the metal atom or cation (coordination number six) in an octahedral arrangement. It forms many
3731     complex ions with nitrogen-compounds such as ammonia and amines ([Co(NH3)6]Cl3) by
3732     coordinating through the nitrogen atom, and with those containing carbon (K3[Co(CN)6]), oxygen
3733     and sulfur ([Co(H2O)6]Cl3), and halides (Na3[CoF6]). Complex compounds with mixed ligands
3734     are common:  [Co(NH3)5(H2O)]Cl3 and [Co(NH3)3Cl3].

3735     The +2 oxidation state forms complexes with a coordination of four or six, and in aqueous
3736     solution, [Co(H2O)6]+2 is in equilibrium with some [Co(H2O)4]+2. In  alkaline solution Co+2
3737     precipitates as Co(OH)2, but the ion is amphoteric; and in  concentrated hydroxide solutions, the
3738     precipitate dissolves forming [Co(OH)4]"2. Many complexes of the form [Co(X)4]4 exist with
3739     monodentate anionic ligands such as Cl"1, Br"1,1"1, SCN"1, N34, and OH"1. Many aquo-halo


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                                                                            Separation Techniques
3740     complexes are known; they are various shades of red and blue. The aquo complex, [Co(H2O)6]+2,
3741     is pink.

3742     Chelate complexes are well-known and are used to extract cobalt from solutions of other ions.
3743     Acetylacetone (acac) is used, for example, in a procedure to separate cobalt from nickel. Co+2 and
3744     Ni+2 do not form chelates with the acac, Co+3 does, however, and can be easily extracted.

3745     OXIDATION-REDUCTION BEHAVIOR. Most simple cobalt +3 compounds are unstable because the
3746     +3 state is a strong oxidizing agent. It is very unstable in aqueous media, rapidly reducing to the
3747     +2 state at room temperature. The aqueous ion of cobalt(II), [Co(H2O)6]+2, can be oxidized,
3748     however, to the +3 state either by electrolysis or by ozone (O3) in cold perchloric acid (HC1O4);
3749     solutions at 0 °C have a half-life of about one week. Compounds of the cobalt(in) complex ions
3750     are formed by oxidizing the +2 ion in solution with oxygen or hydrogen peroxide (H2O2) in the
3751     presence of ligands. The cobalt(in) hexamine complex forms according to:

3752                      4 CoX2 + 4 NH4X + 20 NH3 + O2 ^ 4 [Co(NH3)6]X3 + 2 H2O

3753     HYDROLYSIS. The hydrolysis of the +2 oxidation state of cobalt is not significant in aqueous
3754     media below pH 7. At pH 7, hydrolysis of 0.001 M solution of the cation begins and is
3755     significant at a pH above 9. The hydrolysis of the +3 oxidation state is reminiscent of the
3756     hydrolysis of iron (HI), but it is not as extensive. Hydrolysis of cobalt (in) is significant at pH 5.
3757     In contrast, the hydrolysis of iron (ID) becomes significant at a pH of about 3.

3758     Dissolution of Samples

3759     Cobalt minerals, ores, metals, and alloys can be dissolved by treatment first with hydrochloric
3760     acid, followed by nitric acid.  The insoluble residue remaining after application of this process is
3761     fused with potassium pyrosulfate and sodium carbonate. In extreme cases, sodium peroxide
3762     fusion is used. Biological samples are dissolved by wet ashing, digesting with heating in a
3763     sulfuric-perchloric-nitric acid mixture.

3764     Separation Methods

3765     PRECIPITATION AND COPRECIPITATION. Cobalt can be precipitated by hydrogen sulfide (H2S),
3766     ammonium sulfide (NH4S), basic acetate (C2H3O2"1/HO"1), barium carbonate (BaCO3), zinc oxide
3767     (ZnO), potassium hydroxide and bromine (KOH/Br2), ether and hydrochloric acid [(C2H5)2O and
3768     HC1], and cupferron. Cobalt sulfide (CoS) is coprecipitated with stannic sulfide (SnS2) when
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         Separation Techniques
3769     low-solubility sulfides are precipitated in mineral acids. Care should be taken to avoid
3770     coprecipitation of zinc sulfide (ZnS).

3771     Cobalt can be separated from other metals by hydroxide precipitation using pH control to
3772     selectively precipitate metals such as chromium, zinc, uranium, aluminum, tin, iron (+3),
3773     zirconium, and titanium at low pH. Cobalt precipitates at pH 6.8, and magnesium, mercury,
3774     manganese, and silver at a pH greater than 7. Cobalt is not be separated from metals such as iron,
3775     aluminum, titanium, zirconium, thorium, copper, and nickel using ammonium hydroxide
3776     (NH4OH) solutions (aqueous ammonia), because an appreciable amount of cobalt is retained by
3777     the hydroxide precipitates of these metals produced using this precipitating agent. Various
3778     precipitating agents can be used to remove interfering ions prior to precipitating cobalt: iron by
3779     precipitating with sodium phosphate (Na3PO4) or iron, aluminum, titanium, and zirconium  with
3780     zinc oxide.

3781     The separation of cobalt from interfering ions can be achieved by the quantitative precipitation of
3782     cobalt with excess potassium nitrite (KNO2) to produce K3[Co(NO2)6] (caution — unstable to
3783     heating after standing for some time). Ignition can be used to collect the cobalt as its mixed oxide
3784     (Co3O4). Cobalt can also be precipitated with a-nitroso-p-napthol (l-nitroso-2-napthol) to
3785     separate it form interfering metals. Nickel can interfere with this precipitation, but can be
3786     removed with dimethylglyoxime. Precipitation as mercury tetracyanatocobaltate (II)
3787     (Hg[Co(SCN)4]} also is used, particularly for gravimetric analysis, and precipitation with
3788     pyridine in thiocyanate solution is a quick gravimetric product,  [Co(C5H5N)4](SCN)2.

3789     SOLVENT EXTRACTION. Various ions or chelates have been used in solvent extraction systems to
3790     isolate cobalt from other metals. Separation has been achieved by extracting either cobalt itself
3791     or, conversely, extracting contaminating ions into an organic solvent in the presence of
3792     hydrofluoric acid (HF), hydrochloric acid, and calcium chloride (HCl/CaCl2), hydrobromic acid
3793     (HBr), hydroiodic acid (HI), or ammonium thiocyanate (NH4SCN). For example, cobalt (II) has
3794     been separated from nickel (II) by extracting a hydrochloric acid solution containing calcium
3795     chloride with 2-octanol. The ion is not extracted by diethyl  ether from hydrobromic acid
3796     solutions, but it is extracted from ammonium thiocyanate solutions by oxygen-containing organic
3797     solvents in the presence of iron (HI) by  first masking the iron with citrate.

3798     Several chelate compounds have been used  to extract cobalt from aqueous solutions.
3799     Acetylacetone (acac) forms a chelate with cobalt (IE), but not cobalt (II), that is soluble in
3800     chloroform at pH 6 to 9, permitting  separation from several metals including nickel. Cobalt (II)
3801     can be oxidized to cobalt (in) with hydrogen peroxide (H2O2) prior to extraction, a-nitroso-p-
3802     napthol has also been used as a chelating agent in the separation of cobalt (in) by solvent


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                                                                            Separation Techniques
3803      extraction. Diphenylthiocarbazone (dithiozone) has been used at pH 8 to extract cobalt into
3804      carbon tetrachloride and chloroform after metals that form dithiozonates in acid solution (pH 3-
3805      4) have been removed. 8-quinolinol has been used in a similar manner at pH up to 10. Masking
3806      agents added to the system impede the extraction of iron, copper, and nickel.

3807      ION-EXCHANGE CHROMATOGRAPHY. Anion-exchange resins have been used extensively to
3808      separate cobalt from other metals. The chloro-metal complexes, prepared and added to columns
3809      in molar hydrochloric acid solutions, are eluted at varying concentrations of hydrochloric acid.
3810      Trace amounts of 59Fe, 60Co, and 65Zn and their respective carriers have been separated from
3811      neutron-irradiated biological tissue ash with a chloride system. 60Co has been eluted carrier-free
3812      from similar samples and columns prepared with hydrobromic acid. Cobalt and contaminated
3813      metals in nitric-acid systems behave in a manner similar to hydrochloric-acid systems. Cobalt
3814      (n)-cyanide and cyanate complexes have been used to separate cobalt from nickel. The basic
3815      form of quaternary amine resins (the neutral amine form) has been used in the column
3816      chromatography of cobalt. Both chloride- and nitrate-ion systems have resulted in the association
3817      of cobalt as a complex containing chloride or nitrate ligands as well as the neutral (basic)
3818      nitrogen atom of the amine resin. Resins incorporating chelates in their matrix system have been
3819      used to isolate cobalt. 8-quinolinol resins are very effective in separating cobalt from copper.

3820      ABSORBENT CHROMATOGRAPHY. Several inorganic adsorbents such as alumina, clays, and silica
3821      are used to separate cobalt. Complex ions of cobaltamines separate on alumina as well as cobalt
3822      (II) complexes of tartaric acid and dioxane. A complex of nitroso-R-salts are absorbed onto an
3823      alumina column while other metals pass through the column.  Cobalt is eluted with sulfuric acid.
3824      Cobalt dithizonates absorb on alumina from carbon tetrachloride solutions. Cobalt is eluted with
3825      acetone. The separation of cobalt from iron and copper has been achieved on aluminum
3826      hydroxide [A1(OH)3]. Clay materials, kalolinite, benotite, and montmorilloite,  separate cobalt (n)
3827      from copper (n). Copper (II) absorbs and cobalt (n) elutes with water. Silica gel and activated
3828      silica have both been used as adsorbents in cobalt chromatography.

3829      Organic adsorbents such as 8-hydroxyquinoline and dimethylgloxime have been used in cobalt-
3830      absorbent chromatographic systems. Powdered 8-hydroxyquinoline separates cobalt (II) from
3831      other cations and anions, for example, and dimethylglyoxime separates cobalt from nickel.
3832      Cobalt-cyano complexes absorb on activated charcoal, and cobalt is eluted from the column
3833      while the anionic complexes of metals such as iron, mercury, copper, and cadmium remain on
3834      the column.

3835      Numerous paper chromatograph systems employing inorganic or chelating ligands in water or
3836      organic solvents are available to separate cobalt from other metals. In one system, carrier-free


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         Separation Techniques
3837     60Co and 59Fe from an irradiated manganese target were separated with an acetone-hydrochloric
3838     solvent.

3839     ELECTRODEPOSTION. Most electroanalytical methods for cobalt are preceded by isolating the
3840     cobalt from interfering ions by precipitation or ion exchange. The electrolyte is usually an
3841     ammonia solution that produces the hexamine complex of cobalt (II), Co(NH3)6+2 in solution.
3842     Reducing agents such as hydrazine sulfate are added to prevent anodic deposits of cobalt and the
3843     oxidation of the cobalt (Il)-amine ion. Cobalt and nickel can be separated electrolytically by
3844     using an aqueous solution of pyridine with hydrazine to depolarize the platinum anode. The
3845     nickel is deposited first, and the voltage is increased to deposit cobalt.

3846     Methods of Analysis

3847     57Co, 58Co, and 60Co maybe  concentrated from solution by coprecipitation and determined by
3848     gamma-ray spectrometry. 60Co is most commonly produced by the neutron activation of 59Co, in
3849     a reactor or an accelerator. 58Co is most commonly produced from the following reaction in
3850     nuclear reactors, 58Ni(n,p)58Co, due to the presence of nickel bearing alloys which undergo
3851     corrosion and are transported through the reactor core. 58Co is the most significant contributor to
3852     the gamma ray induced radiation fields in these facilities. "Co can be produced by either of the
3853     following, 58Ni(n,d)57Co  [reactor] or 56Fe(d,n)57Co [accelerator], 57Co and 60Co are frequently
3854     used as part of a mixed radionuclide source for calibration of gamma ray spectrometers.

3855        Compiled from: Baes and Mesmer, 1976; Bate and Leddicotte, 1961; Cotton and Wilkinson,
3856        1988; Dale and Banks, 1962; EPA, 1973; Greenwood and Earnshaw, 1984; Haissinsky and
3857        Adloff, 1965; Hillebrand et al., 1980; Larsen, 1965; Latimer, 1952; Lingane, 1966.

3858     14.10.9.4 Iodine

3859     Iodine is a nonmetal, the last naturally occurring member of the halogen series, with an atomic
3860     number of 53. In the elemental form it is a diatomic molecule, I2, but it commonly exists in one
3861     of four nonzero oxidation states: -1 with metal ions or hydrogen; and +1, +5, and +7 with other
3862     nonmetals, often oxygen. Numerous inorganic and organic compounds of iodine exist, exhibiting
3863     the multiple oxidation states and wide range of physical and chemical properties of the element
3864     and its compounds. Existence of multiple oxidation states and the relative ease of changing
3865     between the -1, 0, and +5 state allows readily available methods for separation and purification of
3866     radionuclides of iodine in radiochemical procedures.
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                                                                              Separation Techniques
3867     Isotopes

3868     There are 42 known isotopes of iodine, including seven metastable states. The mass numbers
3869     range from 108 to 142. The only stable isotope is naturally occurring 127I. The half-lives of the
3870     radionuclides range from milliseconds to days with the single exception of long-lived 129I
3871     (t1/2=l-57 x 107 y). Iodine radionuclides with lower mass numbers decay primarily by electron
3872     capture. The higher mass number are, for the most part, beta emitters. The significant
3873     radionuclides are 125I (t1/2=60.Id, electron capture), 129I (beta), and 131I (t1/2=8.0d, beta).

3874     Occurrence and Uses

3875     Iodine is widely distributed, but never found in the elemental form. The average concentration in
3876     the earth's crust is about 0.3  ppm. In seawater,  iodine concentration, in the form of sodium or
3877     potassium iodide, is low (about 50 ppb), but it is concentrated in certain seaweed, especially kelp.
3878     It is also found in brackish waters from oil and salt wells. The sources are saltpeter and nitrate-
3879     bearing earth in the form of calcium iodate, well brine, and seaweed. Iodine is produced from
3880     calcium iodate by extraction of the iodate from the source with water and reduction of the iodate
3881     with sodium bisulfite to iodine. Iodine is precipitated by mixing with the original iodate liquor to
3882     cause precipitation. Iodine can also be obtained from well brine, where the iodide ion is oxidized
3883     with chlorine, and then the volatile iodine is blown out with a stream of air. Sodium or potassium
3884     iodide in seaweed is calcined to an ash with sulfuric acid, which oxidizes the iodide to iodine.
3885     Iodine from any of these processes can be purified by sublimation.

3886     Isotopes of iodine of mass >  128 may all be formed as a result of fission of uranium and
3887     plutonium. Nuclear reactors  and bomb tests are the most significant sources of these radioiso-
3888     topes with the exception of 131I. That isotope is routinely produced for use in medical imaging
3889     and diagnosis. The isotopes released from the other sources represent a short term environmental
3890     health  hazard should there be an abnormal release from reactors or if bomb testing or use were to
3891     occur.

3892     This was the case in both 1979 and 1986 when the power reactor events at Three Mile Island and
3893     Chernobyl, caused releases of radioiodines. During the former event a ban on milk distribution in
3894     the downwind corridor was enforced as a purely preventative measure.  In the latter case,  signifi-
3895     cant releases of iodines and other isotopes  caused more drastic, long term measures for food
3896     quarantine.

3897     Deposits on the surface of plants could provide a quick source of exposure if consumed directly
3898     from fruits and vegetables or indirectly from cow's milk. It would readily accumulate in the


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         Separation Techniques
3899     thyroid gland, causing a short-term exposure of concern. It represent the greatest short-term
3900     exposure after a nuclear detonation and has been released in power plant accidents. 129I, with of a
3901     half-life of more than 15 million years, represent a long-term environmental hazard. In addition
3902     to its long half-life, the environmental forms of iodine in the environment are highly soluble in
3903     groundwater and are poorly sorbed by soil components. It is not absorbed at all by granite, and
3904     studies at a salt repository indicate that 129I would be only one of few radionuclides that would
3905     reach the surface before it decayed. Therefore, research on the fate of 129I that might be released
3906     suggests that the radionuclide would be highly disseminated in the ecosystem.

3907     131I is routinely analyzed for in milk, soil and water. 129I is a low energy beta and gamma emitter,
3908     which has a very long half-life (1.47 x 107 years). The most significant concern for this isotope is
3909     in radioactive waste, and its potential for migration due to the chemistry of iodine in the
3910     environment. 131I is produced for medical purposes by neutron reaction as follows: 130Te(n,y)131Te
3911     - beta decay - 131I (half-life = 8 days).

3912     The major use of iodine, iodine radionuclides, and iodine compounds is in medical diagnosis and
3913     treatment. 123I, 125I, and 131I are use for diagnostic imaging of the thyroid gland and the kidneys.
3914     131I is used to treat hyperthyroidism and thyroid cancer. Stable iodine in the form of potassium
3915     iodide is added to commercial salt to prevent enlargement of the thyroid (goiter). Iodine in the
3916     form of the hormone thyroxine is also used for thyroid and cardiac treatment and hormone
3917     replacement therapy in iodine deficiency. Iodine radionuclides are used as a tracer in the
3918     laboratory and industry to study chemistry mechanisms and processes and to study biological
3919     activity and processes. Iodine is a bactericide and is used as an antiseptic and sterilization of
3920     drinking water. It is used as a catalyst in chemical processes and as silver iodide in film
3921     emulsions.

3922     Solubility of Compounds

3923     Molecular iodine is only very slightly soluble in water (0.33 g/L), but it is soluble in solutions of
3924     iodide ion, forming I3~l. It is appreciably soluble in organic solvents. Carbon tetrachloride (CC14)
3925     or chloroform (CHC13) are commonly used to extract iodine from aqueous solutions after
3926     alternate forms of the element, typically I"1 and IO34, are converted to I2. The solutions have a
3927     violet color in organic solvents, and iodine dimerizes to some extent in these solutions:

3928                                              2 I2 ^ I4

3929     Numerous compounds of iodine are soluble in water. All metallic iodides are soluble in water
3930     except those of silver, mercury, lead, cupurous ion, thallium, and palladium. Antimony, bismuth,


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                                                                             Separation Techniques
3931     and tin iodides require a small amount of acid to keep them in solution. Most of the iodates and
3932     periodates are insoluble. The iodates of sodium, potassium, rubidium, and the ammonium ion are
3933     soluble in water. Those of cesium, cobaltous ion, magnesium, strontium, and barium are slightly
3934     soluble in water but soluble in hot water. Most other metallic iodates are insoluble.

3935     Review of Properties

3936     Elemental iodine (I2) is a purple-black, lustrous solid at room temperature with a density of 4.9
3937     g/cm3. The brittle crystals have a slightly metallic appearance. Iodine readily sublimes and stored
3938     in a closed clear, colorless container, it produces a violet vapor with an irritating odor. Iodine has
3939     a melting point of 114 °C and a boiling point of 184 °C.

3940     The chemical reactivity of iodine is similar to the other halogens, but it is the least electro-
3941     negative member of the family of elements and the least reactive. It readily reduces to iodide, and
3942     is displaced from its iodides by the other halogens and many oxidizing agents. Iodine combines
3943     directly with most elements to form a large number of ionic and covalent compounds. The
3944     exceptions are the noble gases, carbon, nitrogen, and some noble metals.

3945     The inorganic compounds of iodine can be classified into three groups: (1) iodides, (2)
3946     interhalogen, and (3) oxides. Iodine forms iodides that range from ionic compounds such as
3947     potassium iodide (KI) to covalent compounds such as titanium tetraiodide (TiI4) and phosphorus
3948     triiodide (PI3), depending on the identity of the combining element.  More electropositive (less
3949     electronegative) metals (on the left side of the periodic table, such as alkali metals and alkaline
3950     earths) form ionic compounds. Less electropositive metals and more electronegative nonmetals
3951     tend to form covalent compounds. Interhalogen compounds include the binary halides, such as
3952     iodine chloride (IC1), iodine trichloride (IC13), and iodine pentafluoride (IF5), or contain
3953     interhalogen cations and anions, such as IC12+1, IF6+1.1+3, Clffir"1, IC\4~l, and I6"2. Oxygen
3954     compounds constitute the oxides, I2O5 and I4O9 (containing one I+3 cation and three IO34 anions),
3955     for example; the oxyacids, such as hypoiodous acid (HIO) and iodic acid (HIO3); and compounds
3956     containing oxyanions, iodates (IO3 J) and periodates (IO44) are the common ones.

3957     Organoiodides include two categories: (1) iodides and (2) iodide derivatives with iodine in a
3958     positive oxidation state because iodine is covalently bonded to another, more electronegative
3959     element. Organoiodides contain a carbon iodide bond. They are relatively dense and volatile and
3960     more  reactive than the other organohalides. They include the iodoalkanes such as ethyl iodide
3961     (C2H5I) and iodobenzene (C6H5I). Dimethyliodonium (HI) hexafluoroantimonate
3962     [(CH3)2I+3SbF6"3], a powerful methylating agent, is an example of the second category.
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         Separation Techniques
3963
3964
3965
3966
3967
3968
3969
3970

3971

3972
3973
3974
3975

3976

3977

3978
3979
3980
3981
3982
3983
3984

3985
3986

3987
3988
3989
3990
The toxicity of molecular iodine is primarily related to its vapor state and to solutions. Iodine
vapor is an eye and nasal irritant, potentially causing damage to the eyes and serious respiratory
damage. Solid iodine is not a serious problem unless confined to the skin where it causes
discoloration and eventually burns. Solutions of iodine are toxic if taken internally. The
radionuclides of iodine are radiotoxic, primarily because of their concentration in the thyroid
gland. Radiotoxicity of 129I, if released, is a concern because of its extremely long half-life. 131I,
with a half-life of eight days, is a short-term concern. The whole-body effective biological half-
lives of 129I and 131I are 140 d and 7.6 d, respectively.

Solution Chemistry

OXIDATION-REDUCTION BEHAVIOR. Iodine can exist in multiple oxidation states in solution, but
the radiochemist can control the states by selection of appropriate oxidizing and reducing agents.
In acid and alkaline solutions, the common forms of iodine are: I"1,12, and IO34. Hypoiodous acid
(HIO) and the hypoiodite ion (IO"1) can form in solution, but they rapidly disproportionate:

                             5 HIO ^ 2 I2 + KV1 + H+1 + 2 H2O

                                    3 IO4 ^ 2 I'1 + IO34

Iodine itself is not a powerful oxidizing agent, less than that of the other halogens (F2, C12, and
Br2), but its action is generally rapid. Several oxidizing and reducing agents are used to convert
iodine into desired oxidation states during radiochemical procedures. These agents are used to
promote radiochemical equilibrium between the analyte and the  carrier or tracer or to produce a
specific oxidation state before separation: I2 before extraction in an organic solvent or I"1 before
precipitation, as examples. Table 14.19 presents oxidizing and reducing agents commonly used
in radiochemical procedures:

         Table 14.19 — Common radiochemical oxidizing and reducing agents for iodine
Redox Process
r1 - 12
r1 - 103-'
i2-r
r1 - icv1
Redox Reagent
HNO2 (NaNO2 in acid)
MnO2 in acid
6 M HNO3
NaHSO3 and NaHSO4 (in acid)
Na2SO3 and Na2S2O3
Fe2(SO4)3 (in acid)
KMnO4
50%CrO3inl8NH2SO4
Notes
Does not affect other halides
Well suited for laboratory work


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                                                                             Separation Techniques
3991
3992
3993

3994
3995
3996
3997
3998
3999
4000
4001
4002

4003
4004
4005
4006
Redox Process
r1 - KV
KV1 - 12
KV1 - I2
KV - r1
i, -r1
Redox Reagent
NaClOinbase
NH2OH-HC1
NH2OH-HC1
H2C2O4inl8NH2SO4
NaHSO3 in acid
SO2 gas
NaHSO3 and (NH4)2SO3
Notes





Radiochemical exchange between I2 and I"1 in solution is complete within time of mixing and
before separation. In contrast, exchange between I2 and IO34 or KV1 in acid solution and between
IO34 and IO44 in acid or alkaline solution is slow. For radiochemical analysis of iodine,
experimental evidence indicates that the complete and rapid exchange of radioiodine with carrier
iodine can be accomplished by the addition of the latter as I"1 and subsequent oxidation to IO44 by
NaCIO in alkaline solution, addition of IO44 and reduction to I"1 with NaHSO3, or addition of one
followed by redox reactions first to one oxidation sate and then back to the original state.

COMPLEXATION. As a nonmetal, iodine is generally not the  central atom of a complex, but it can
act as a ligand to form complexes such as Sil6'2 and CoI6"3. An important characteristic of
molecular iodine is its ability to  combine with the iodide ion to form polyiodide anions. The
brown triioide is the most stable:
4007                                            I9
4008     The equilibrium constant for the reaction in aqueous solution at 25  °C is 725, so appreciable
4009     concentrations of the anion can exist in solution, and the reaction is responsible for the solubility
4010     of iodine in iodide solutions.

4011     HYDROLYSIS. Iodine hydrolyzes in water through a disproportionation reaction:

4012                                     I2 + H2O ^ H+1 + r1 + HIO

4013     Because of the low solubility of iodine in water and the small equilibrium constant (k=2.0 x
4014     10"13), hydrolysis produces negligible amounts of the products (6.4 x 10"6 M) even when the
4015     solution is saturated with iodine. Disproportionation of HIO produces a corresponding minute
4016     quantity of IO34 (see the reaction above). In contrast, in alkaline solution, I2 produces I"1 and IO"1:

4017                                    I, + 2 OH4 ^ F1 + IO'1 + H9O
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         Separation Techniques
4018     The equilibrium constant favors the products (k=30), but the actual composition of the solution is
4019     complicated by the disproportionation of IO"1 (illustrated above), giving I"1 and IO3~1. The
4020     equilibrium constant for the reaction of IO"1 with hydroxide ion is very large (IO20), and the rate
4021     of the reaction is very fast at all temperatures. Therefore, the actual products obtained by
4022     dissolving iodine in an alkaline solution are indeed I"1 and KV1,  quantitatively, and IO"1 does not
4023     exist in the solution.

4024     Dissolution of Samples

4025     Iodine compounds in rocks are often in the form of iodides that are soluble in either water or
4026     dilute nitric acid when the finely divided ores are treated with one of these agents.  Those that are
4027     insoluble under these conditions are solubilized with alkali fusion with sodium carbonate or
4028     potassium hydroxide, followed by extraction of the residue with water. Insoluble periodiates can
4029     be decomposed by cautious ignition, converting them to soluble iodides.

4030     Metals containing iodine compounds are dissolved in varying concentrations of nitric, sulfuric, or
4031     hydrochloric acids. Dissolution can often be accomplished at room temperature or might require
4032     moderation in an ice bath.

4033     Organoiodides are decomposed with a sodium peroxide, calcium oxide, or potassium hydroxide
4034     by burning in oxygen in a sealed bomb. Wet oxidation with mixtures of sulfuric  and chromic
4035     acids or with aqueous hydroxide is also used.

4036     Separation Methods

4037     PRECIPITATION.  The availability of stable iodine as a carrier and the relative ease of producing
4038     the iodide ion make precipitation a simple method of concentrating and recovering iodine
4039     radionuclides. The two common precipitating agents are silver (Ag+1) and palladium (II) (Pd+2)
4040     cations, which form silver iodide (Agl) and palladium iodide (PdI2), respectively. Silver iodide
4041     can be solubilized with a 30 percent solution of potassium iodide. Palladium precipitates iodide
4042     in the presence of chloride and bromide, allowing the separation of iodide from these halides.
4043     The precipitating agent should be free of palladium (IV), which  will precipitate chloride. If
4044     palladium (II) iodide is dried, precaution should be taken as the  solid slowly looses iodine if
4045     heated at 100 °C. lodate can be precipitated  as silver iodate,  and periodate as lead periodate.

4046     SOLVENT EXTRACTION. One solvent extraction method is commonly used to isolate iodine. After
4047     preliminary oxidation-reduction steps to insure equilibrium of all iodine in solution, molecular
4048     iodine (I2) is extracted  from aqueous solutions by a nonpolar solvent, usually carbon tetrachloride


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                                                                             Separation Techniques
4049     or chloroform. It is not uncommon to add small quantities of the oxidizing or reducing agent to
4050     the extraction solution to ensure and maintain all iodine in the molecular form. Hydroxylamine is
4051     added, for example, if iodate is the immediate precursor of iodine before extraction.

4052     ION-EXCHANGE CHROMATOGRAPHY. Both cation and anion exchange procedures are used to
4053     separate iodine from contaminants. Cation-exchange chromatography has been used to remove
4054     interfering cations. To remove 137Cs activity, an iodine sample in the iodide form is absorbed on a
4055     cation-exchange resin and eluted with ammonium sulfite  [(NH4)2SO3], to ensure maintenance of
4056     the iodide form. Cesium cations remain the resin. Bulk resin is also used, and iodide is washed
4057     free of the resin as the periodate with sodium hypochlorite (NaCIO) as the oxidizing agent.
4058     Anion-exchange resins provide absorption of the iodide ion. The halides have been separated
4059     from each other on an anion-exchange column prepared in the nitrate form by eluting with 1 M
4060     sodium nitrate. Iodide can also be separated from contaminants by addition to an anion
4061     exchanger and elution as periodate with sodium hypochlorite. The larger periodate anion is not as
4062     strongly attracted to the resin as the iodide ion. 131I separation, collection, and analysis is
4063     performed by absorbing the radionuclide on an anion-exchange resin and gamma counting it on
4064     the sealed column after eluting the contaminants.

4065     DISTILLATION. Molecular iodine  is a relatively volatile  substance. Compared to many
4066     contaminating substances, particularly metal ions in solution, its boiling point of 184 °C is very
4067     low, and the volatility of iodine provides a method for its separation from other substances. After
4068     appropriate oxidation-reductions steps to convert all forms of iodine into the molecular form,
4069     iodine is distilled from aqueous solution into  sodium hydroxide and collected by another
4070     separation process, typically solvent extraction. In hydroxide solution,  molecular iodine is
4071     converted to a mixture of iodide and hypoiodite ions and then into iodide and periodate ions, and
4072     suitable treatment is required to convert all forms into a single species  for additional procedures.

4073     Methods of Analysis

4074     Macroquantities of iodine  can be determined  gravimetrically by precipitation as silver iodide or
4075     palladium iodide. The latter substance is often used to determine the chemical recovery in
4076     radiochemical analyses.  Microquantities of 129I and 131I are coprecipitated with palladium iodide
4077     using stable iodide as a carrier and counted for quantification. 129I usually is beta counted in a
4078     liquid-scintillation system, but it  can also be determined by gamma-ray spectrometry. 131I is
4079     determined by gamma-ray emission.

4080         Compiled from: Adams, 1995; APHA, 1998; Armstrong et al., 1961; Bailar et al.,  1984;
4081         Choppin et al., 1995; Considine and Considine, 1983; Cotton and Wilkinson,  1988; DOE,


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         Separation Techniques
4082         1990 and 1997, 1997; EPA, 1973; EPA, 1980; Ehmann and Vance, 1991; Greenwood and
4083         Earnshaw, 1984; Haissinsky and Adloff, 1965; Kleinberg and Cowan, 1960; Latimer, 1952;
4084         Lindsay, 1988.

4085     14.10.9.5 Plutonium

4086     Plutonium, with an atomic number of 94 is an actinide and the second element in the transuranic
4087     series. Essentially all plutonium is an artifact, most produced by neutron bombardment of 238U
4088     followed by two sequential beta emissions, but trace quantities of plutonium compounds can be
4089     found in the natural environment. Plutonium radiochemistry is complicated by the five possible
4090     oxidation states that can exist; four can be present in solution at one time.

4091     Isotopes

4092     Plutonium has 18  isotopes with mass numbers ranging from 232 to 247, and all isotopes are
4093     radioactive. Some have a long half-life: the isotope of greatest importance, 239Pu, has a half-life
4094     of 24,110 years, but 242Pu and 244Pu have a half-lives of 376,000 and 76,000,000 years,
4095     respectively. 238Pu, 240Pu, and 241Pu have a half-lives of 87.74, 6,537, and 14.4 years, respectively.
4096     Four of these isotopes decay by alpha emission accompanied by weak gamma rays: 238Pu, 239Pu,
4097     240Pu, and 242Pu. In contrast, 241Pu decays by beta emission with weak gamma rays but its progeny
4098     is 241Am, an intense gamma emitter. 239Pu and 241Pu are fissile materials—they can be  split by
4099     both fast and  slow neutrons. 240Pu, and 242Pu are fissionable but have very small neutron fission
4100     cross-sections. 240Pu partly decays by spontaneous fission, although a small  amount of
4101     spontaneous fission occurs in most plutonium isotopes.

4102     Occurrence and Uses

4103     There are minute quantities of plutonium compounds in the natural environment as the result of
4104     thermal neutron capture and subsequent beta decay of naturally occurring 238U. All plutonium of
4105     concern is an artifact, the result of neutron bombardment of uranium in a nuclear reactor.
4106     Virtually all nuclear power-plants of all  sizes and the waste from the plants  contain plutonium
4107     because 238U is the main component of fuel used in nuclear reactors. It is also associated with the
4108     nuclear weapons industry and its waste.  Virtually all the plutonium in environmental samples is
4109     found in air samples as the results of atmospheric weapons testing. Plutonium in plant and crop
4110     samples is essentially caused by surface absorption.

4111     Plutonium is produced in nuclear reactors from 238U that absorbs neutrons emitted by the fission
4112     of 235U, which is a naturally occurring uranium isotope found with 238U. 239U is formed and emits


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4113     a beta particle to form 239Np that decays by beta emission to form 239Pu. Once started, the process
4114     is spontaneous until the uranium fuel rods become a specific uranium-plutonium mixture. The
4115     rods are dissolved in acid, and plutonium is separated primarily by solvent extraction, finally
4116     producing a concentrated plutonium solution. Pure plutonium metal can be prepared by
4117     precipitating plutonium peroxide or oxalate, igniting the precipitate to PuO2, converting the oxide
4118     to PuF3, and reducing Pu(Tfl) to the metal in an ignited mixture containing metallic calcium.

4119     Large quantities of 239Pu have been used as the fissile  agent in nuclear weapons and as a reactor
4120     fuel when mixed with uranium. It is also used to produce radioactive isotopes for research,
4121     including the study of breeder reactors, and 238Pu is used as a heat source to power instruments
4122     for space exploration and implanted heart pacemakers.

4123     Solubility of Compounds

4124     General solubility characteristics include the insolubility of the hydroxides, fluorides, iodates,
4125     phosphates, carbonates, and oxalates  of Pu(Tfl) and Pu(IV). Some of these can be dissolved in
4126     acid solution, however. The corresponding compounds of PuO2+1 and PuO2+2 are soluble, with the
4127     exception of the hydroxides. The binary compounds represented by the carbides, silicides,
4128     sulfides, and selenides are of particular interest because of their refractory nature. One of the
4129     complicating factors of plutonium chemistry is the formation of a polymeric material by
4130     hydrolysis in dilute acid or neutral solutions. The polymeric material can be a complicating factor
4131     in radiochemical procedures and be quite unyielding in attempts to destroy it.

4132     Review of Properties

4133     Plutonium metal has some unique physical properties: a large piece is warm to the touch because
4134     of the energy produced by alpha decay, and it exists in six allotropic forms below its melting
4135     point at atmospheric pressure. Each form has unusual thermal expansion characteristics that
4136     prevents the use of unalloyed plutonium metal as a reactor fuel. The delta phase, however, can be
4137     stabilized by the addition of aluminum or gallium and be used in reactors. Chemically, plutonium
4138     can exist in five oxidation states: in, IV, V, VI, and VII. The first four states  can be observed in
4139     solution, and solid compounds of all five states have been prepared. The metal is a silver-grey
4140     solid that tarnishes in air to form a yellow oxide coating. It is chemically reactive combining
4141     directly with the halogens, carbon, nitrogen, and silicon.

4142     Plutonium is a very toxic substance, but outside the body, it does not represent great danger from
4143     it low penetrating alpha emission or emission of its low intensity beta, gamma, or neutron
4144     radiation. Ingested plutonium is not readily absorbed into the body, but passes through the


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         Separation Techniques
4145     digestive tract and expelled before it can cause significant harm. Inhaled plutonium presents a
4146     significant danger. Particularly, inhalation of particles smaller than one micron would be a
4147     serious threat due to the alpha-emitting radionuclide being in direct contact with lung tissue.
4148     Plutonium would also be very dangerous if it were to enter the blood stream through an open
4149     wound, because it would concentrate in the liver and bones, leading to damage to the bone
4150     marrow and subsequent related problems. For these reasons, plutonium is handled in gloveboxes
4151     with associated precautions taken to protect the worker from direct contact with the material.
4152     When working with plutonium in any form, precautions should also be taken to prevent the
4153     accumulation of quantities of fissionable plutonium that would achieve a critical mass,
4154     particularly in solution where it is more likely to become critical than solid plutonium.

4155     Most of the plutonium in the environment is the result of weapons testing. More than 99 percent
4156     of the plutonium from these activities was released during atmospheric tests, but a small portion
4157     was also released during ground tests. An even smaller quantity is released by nuclear fuel
4158     reprocessing plants, some in the ocean, and by nuclear waste repositories. Part of the atmospheric
4159     plutonium, originally part of the weapons, settled to the earth as an insoluble oxide, locating in
4160     the bottom sediments of lakes, rivers, and oceans or becoming incorporated in sub-surface soils.
4161     The majority of environmental plutonium isotopes are the result of atmospheric nuclear bomb
4162     tests.  If the bomb material is made from uranium, the oxide is enriched to high percentages of
4163     235U, the fissile isotope. The 238U isotope does  not fission, but absorbs 1-2 neutrons during the
4164     explosion forming isotopes of 239U and 240U. These isotopes beta decay within hours to their
4165     neptunium progeny, which in turn decay to 239Pu and 240Pu. Bombs from  plutonium would yield
4166     higher fractions of 24°.241.242pu.

4167     Plutonium formed as a result of atmospheric tests is most likely to be in the form of a fine
4168     particulate oxide. If as in the case of a low altitude or underground test, there is a soil component,
4169     the plutonium will be fused with siliceous minerals. The behavior of the  soluble form of
4170     plutonium would be similar to that released from fuel reprocessing plants and from nuclear waste
4171     sites.  Like the insoluble oxide, most of the soluble form is found in sediments and soils, but a
4172     small percentage is associated with suspended particles in water. Both the soluble form of
4173     plutonium and the form suspended on particulate matter are responsible for plutonium transporta-
4174     tion in the environment. Plutonium in soil is found where the humic acid content is high. In non-
4175     humic, carbonate-rich soils, plutonium migrates downward. Migration in the former soil is slow
4176     (<0.1 cm/y) and in the latter it is  relatively fast (1-10 cm/y). In subsurface oxic soil, plutonium is
4177     relatively  mobile, transported primarily by colloids. In wet anoxic soils, most of the plutonium is
4178     quickly immobilized, although a  small fraction remains mobile. The average time plutonium
4179     remains in water is proportional to the amount of suspended material. For this reason, more than
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4180
4181

4182

4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195

4196
4197
4198
4199

4200
4201

4202

4203
4204

4205

4206

4207
90 percent of plutonium is removed from costal water, while the residence time in mid-ocean
water where particulate matter is less is much longer.

Solution Chemistry

The equilibration problems of plutonium are among the most complex encountered in
radiochemistry. Plutonium can form five oxidation states in solution, +3, +4, +5, +6, and +7. The
first four are present in solution  as Pu+3, Pu+4, PuO2+1, PuO2+2. They coexist in dilute acid
solution, and sometimes all four are present in substantial quantities. Problems of disproportiona-
tion and auto-oxidation in freshly prepared solutions also complicate the chemistry of plutonium.
The +7  state can form in alkaline solutions, and it has been suggested that the ion in solution is
PuO5"3. Plutonium ions tend to hydrolyze and form complex ions in solution. The +4 ion can
form long chain polymers that do not exhibit the usual chemical behavior of the +4 oxidation
state. Finally, the different oxidation states exhibit radically different chemical behavior. As a
result of these effects, it is possible to mix a plutonium sample with plutonium tracer, subject the
mixture to a relatively severe chemical treatment using hot acids or similar reagents,  and still
selectively recover portions of either the tracer or the sample. This characteristic explains the
challenge  in achieving reproducible radiochemical results for plutonium.

OXIDATION-REDUCTION BEHAVIOR. Numerous redox agents are available to oxidize and reduce
any of the five states of plutonium to alternate oxidation states. The following table provides a
convenient method of preparation of each state and illustrates the use of redox reagents in
plutonium chemistry:

                     Table 14.20 — Redox agents in plutonium chemistry
Oxidation State
III
IV
IV
V
VI
VII
Form
Pu+3
Pu+4
PuO2-nH2O
(polymer)
Pu02+1
Pu02+2
Pu05-3 (?)
Method of Preparation
Dissolve Pu metal in HC1 and reduce Pu+4 with NH2OH, N2H4,
SO2, or by cathodic reduction
Oxidize Pu+3 with hot HNO3; treat Pu+3 or PuO2+2 with NO/1
Heat Pu+4 in very dilute acid; peptize Pu(OH)4
Reduce PuO2+2 with stoichiometric amount of I"1 or ascorbic acid;
electrolytic reduction of PuO2+2
Oxidize Pu+4 with hot dilute HNO3 or AgO; ozonize Pu+4 in cold
dilute HNO3 with Ce+3 or Ag+1 catalyst
Oxidize PuO2+2 in alkali with O3, S2O8"2 or radiation
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         Separation Techniques
4208     Unlike uranium, the +3 oxidation state is stable enough in solution to be useful in separation
4209     chemistry. Disproportionation reactions convert Pu+4 to Pu+3 and PuO2+2 releasing H+1. The
4210     presence of acid in the solution or complexing agents represses the process. Similarly, PuO2+1
4211     disproportionates producing the same products but with the consumption of H+1. For this reason,
4212     PuO2+1 is not predominant in acid solutions. These disproportionation reactions can be involved
4213     in redox reactions by other reagents. Instead of direct oxidation or reduction, the disproportiona-
4214     tion reaction can occur first, followed by direct oxidation or reduction of one of the products.

4215     It is possible to prepare stable  aqueous solutions in which appreciable concentrations of the first
4216     four oxidation states exist simultaneously: the +3, +4, +5, and +6 states. The relative proportions
4217     of the different oxidation states depend on the acid, the acid concentration, the method of
4218     preparation of the solution, and the initial concentrations of each of the oxidation states. These
4219     relative concentrations will change over time and ultimately establish an equilibrium specific to
4220     the solution. In 0.5 M HC1 at 25 °C, for example, the equilibrium percentages  of the four
4221     oxidation states prepared from initially pure Pu+4 are +3 (27.2%), +4 (58.4%), +5 (0.7%), and +6
4222     (13.6%). Freshly prepared plutonium samples are frequently in the +4 state, while an appreciable
4223     amount of the +3 and +6 oxidation states will be present in long-standing tracer solutions.

4224     A convenient solution to this plutonium equilibration problem takes the form of a two step
4225     process:

4226       •  boil the combined sample  and tracer with a concentrated inorganic acid (e.g., HNO3) to
4227         destroy any +4 polymers that might have formed, and

4228       •  cool and dilute the solution; then rapidly (to avoid reforming polymers) treat the solution
4229         with excess iodide ion (solution turns brown or black) to momentarily reduce all of the
4230         plutonium to the +3 oxidation state.

4231     The solution will immediately start to disproportionate in the acid medium, but the plutonium
4232     will have achieved a true equilibrium starting at a certain time from one state in the solution.

4233     Alpha particles emitted by 239Pu can decompose solutions of the radionuclide by radiolysis. The
4234     radiolysis products then oxidize or reduce the plutonium, depending on the nature of the solution
4235     and the oxidation state of the element. The nature of the anion present greatly influences the rate
4236     of the redox process. For the radiochemist it is important to recognize that for old plutonium
4237     solutions, particularly those in low acidity, the oxidation labeled states are not reliable.
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4238     HYDROLYSIS AND POLYMERIZATION. Hydrolysis is most pronounced for relatively small and
4239     highly charged ions such as Pu+4, but plutonium ions in any oxidation state are more easily
4240     hydrolyzed than their larger neptunium and uranium analogues.

4241     Trivalent plutonium tends to hydrolyze more than neptunium or uranium, but the study of its
4242     hydrolysis characteristics has been hindered by precipitation, formation of Pu+4, and unknown
4243     polymerization. In strongly alkaline solutions,  Pu(OH)3 precipitates; the solubility product
4244     constant is estimated to be 2 x 10"20.

4245     Plutonium(IV) exists as  a hydrated ion in solutions that are more acidic than 0.3  M H+1. Below
4246     0.3 M, it undergoes much more extensive hydrolysis than any other plutonium species, or at
4247     lower acidities (0.1 M) if the plutonium concentration is lower. Thus, the start of hydrolysis
4248     depends on the acid/plutonium ratio as well  as the temperature and presence of other ions. On
4249     hydrolysis, only Pu(OH)+3 is important in the initial phases, but it tends to undergo irreversible
4250     polymerization, forming polymers with molecular weights as high as  1010 and chemical
4251     properties much different from the free ion.  Presence of the polymer can be detected by its bright
4252     green color. When plutonium (IV) hydroxide [Pu(OH)4] is dissolved in dilute acid, the polymer
4253     also forms. Similarly, if a solution of Pu+4 in moderately concentrated acid  is poured slowly into
4254     boiling water,  extensive polymerization occurs. The colloidal character of the polymer is
4255     manifested by its strong adsorption onto glass, silica, or small bits of paper or dirt. The chemical
4256     characteristics of the polymer, with regard to precipitation, ion-exchange, and solvent extraction,
4257     is markedly different than the chemistry of the common +4 oxidation state  of plutonium. Care
4258     should be taken in the laboratory to avoid the formation of these polymers.  For instance, these
4259     polymers can be formed by overheating solutions during evaporation. Moreover, diluting an
4260     acidic plutonium solution with water can cause polymerization because of localized areas of low
4261     acidity, even when the final concentration of the solution is too high for polymerization.
4262     Therefore, plutonium solutions should always  be diluted with acid rather than water. Polymeric
4263     plutonium can also be formed if insufficient acid is used when dissolving plutonium (IV)
4264     hydroxide.

4265     Immediately after formation, these polymers are easy to decompose by acidification with
4266     practically any concentrated inorganic acid or by oxidation. Because depolymerization is slow at
4267     room temperature and moderate acid concentrations, solutions should be made at least 6 M  and
4268     boiled to destroy the polymers. The polymer is rapidly destroyed under these conditions. Adding
4269     strong complexing agents such as fluoride, sulfate, or other strong complexing agents can
4270     increase the rate of depolymerization. However, if the polymers are allowed to "age," they can be
4271     very difficult to destroy.
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4272     The PuO2+1 ion has only a slight tendency to hydrolyze, beginning at pH 8, but study of the extent
4273     of the process is inhibited by the rapid disproportionation of hydrolyzed plutonium(V).

4274     Hydrolysis of PuO2+2 is far more extensive than expected for a large +2 ion. Hydrolysis begins at
4275     pH of about 2.7 to 3.3, giving an orange color to the solution that yields to bright yellow by pH 5.
4276     Between pH 5 and 7, dimerizatons seem to occur, and by pH 13 several forms of plutonium
4277     hydroxide have been precipitated with solubility products of approximately 2.5 x 10"25.

4278     COMPLEXATION. Plutonium ions tend to form complex ions in the following order:

4279                                    Pu+4 > Pu+3 « PuO2+2 > PuO2+1

4280     Divalent anions tend to form stronger complexes, and the order for simple anions with Pu+4 is:

4281                           carbonate > oxalate > sulfate > fluoride > nitrate >
4282                              chloride > bromide > iodide > perchlorate

4283     Complexation is preferably through  oxygen and fluorine rather than nitrogen, phosphorus, or
4284     sulfur. Plutonium also forms complexes with ligands such as phosphate, acetate, and
4285     tributylphosphate (TBP). Strong chelate complexes form with EDTA, tartrate, citrate, 2-
4286     thenoyltrifluoroacetone (TTA), acetylacetone (acac), and cupferron. Plutonium(IV) forms a
4287     strong complex with fluoride (PuF+3) that is used to solubilize plutonium oxides and keep it in
4288     the aqueous phase during extraction of other elements with organic solvents. The complex with
4289     nitrate, Pu(NO3)6"2, allows the recovery of plutonium from nuclear fuels. Carbonate and acetate
4290     complexes prevent precipitation of plutonium from solution even at relatively high pH.

4291     Dissolution of Samples

4292     Metallic plutonium dissolves in halogen acids such as hydrochloric acid, but not in nitric or
4293     concentrated sulfuric acids. The metal dissolves in hydrofluoric nitric acid mixtures. Plutonium
4294     oxide dissolves with great difficulty in usual acids when ignited. Boiling with concentrated nitric
4295     acid containing low concentrations of hydrofluoric acid  or with concentrated phosphoric acid is
4296     used. Fusion methods have also been used to dissolve the oxide as well as other compounds of
4297     plutonium. Plutonium in biological samples is readily soluble, in the case of metabolized
4298     plutonium in excreted samples, or highly refractory, in the case of fallout  samples. Most
4299     procedures for fallout or environmental samples involve treatment with hydrofluoric acid or
4300     fusion treatment with a base.
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4301     Separation Methods

4302     Extensive work has been done on methods to separate plutonium from other elements. Both
4303     laboratory and industrial procedures has received considerable treatment. The methods described
4304     below represents only a brief approach to separation of plutonium, but they indicate the nature of
4305     the chemistry employed.

4306     PRECIPITATION AND COPRECIPITATION. Macro quantities of plutonium are  readily precipitated
4307     from aqueous solution, and the methods is the basis of separating plutonium from other
4308     radionuclides in some procedures. Contamination of other metals can be a  problem, however;
4309     zirconium and ruthenium give the most trouble. Plutonium is precipitated primarily as the
4310     hydroxide, fluoride, peroxide, or oxalate. Both Pu(in) and Pu(IV) are precipitated from acid
4311     solution by potassium or ammonium hydroxide as hydrated hydroxides or hydrous oxides. On
4312     redissolving in acid, Pu (IV) tends to form the polymer,  and high concentration of acid is needed
4313     to prevent its formation. Pu(IV) peroxide is formed on the addition of hydrogen peroxide to
4314     Pu(ni), Pu(IV), Pu(V), and Pu(VI) because of the oxidizing nature of hydrogen peroxide. The
4315     procedure has been used to prepare highly pure plutonium compounds from americium and
4316     uranium.

4317     Coprecipitation of plutonium can be very specific with the  control of its oxidation states and
4318     selection of coprecipitating reagents. Lanthanum fluoride, a classical procedure for coprecipita-
4319     tion of plutonium, will bring down Pu(ni) and Pu(IV) but not Pu(VI). Only elements with similar
4320     redox and coprecipitation behavior interfere. Separation from other elements as well as
4321     concentration from large volumes with lanthanum fluoride  is also important because not many
4322     elements form acid-soluble lanthanum fluoride coprecipitates. Bismuth phosphate (BiPO4) is also
4323     used to coprecipitate Pu(in) and Pu(IV). In contrast to lanthanum fluoride and bismuth
4324     phosphate, zirconium phosphate (ZrPO4) and an organic coprecipitate, zirconium phenylarsenate
4325     [Zr(C6H5)AsO4], will coprecipitate Pu(IV) exclusively.

4326     SOLVENT EXTRACTION. A  wide variety of organic extractants have been developed to separate
4327     plutonium from other radionuclides and metals by selectively extracting them from aqueous
4328     media. The extractants, among others, include organophosphorus compounds such as phosphates
4329     (organoesters of phosphoric acid), amines and their quaternary salts, alcohols, ketones, ethers,
4330     and amides. Chelating agents such as thenoyltrifluoroacetone (TTA) and cupferron have also
4331     been used. Numerous studies have been performed on the behavior of these systems. It has been
4332     found that the performance of an extracting system is primarily related to the organic solvent in
4333     which the extractant is dissolved and the concentration of the extractant in the solvent, the nature
4334     of the aqueous medium (the acid present and its concentration (pH) and the presence of salting


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4335     agents), the temperature of the system, and the presence and nature of oxidizing agents. One
4336     common system, used extensively in the laboratory and in industrial process to extract plutonium
4337     from fission products, illustrates the use of solvent extraction to separate plutonium from
4338     uranium and other metals. The PUREX process (pjutonium uranium reduction extraction) is used
4339     in most fuel reprocessing plants to separate the radionuclides. It employs TBP, tri-/>butyl
4340     phosphate [(C4H9)3PO], in a hydrocarbon solvent, as the extractant. The uranium fuel is dissolved
4341     in nitric acid as Pu(Tfl), and plutonium is oxidized to Pu(IV) and uranium to U(VI) by oxidizing
4342     agents. Plutonium and uranium are extracted into a 30 percent TBP solution, and the organic
4343     phase is scrubbed with nitric acid solution to remove impurities. The plutonium is removed by
4344     back-extracting it as Pu(ni) with a nitric acid solution  containing a reducing agent.

4345     Solvent extraction chromatography has provided an efficient, easy technique for rapidly
4346     separating plutonium and other transuranic elements. A process using octylphenyl-N,N-
4347     diisobutyl carbamoylphoshpine oxide (CMPO) in TBP and fixed on an inert polymeric resin
4348     matrix has been used to isolate plutonium  (IV). All plutonium in the analyte is adjusted to
4349     plutonium (IV), and the column is loaded from 2 M nitric acid. Plutonium is eluted with 4 M
4350     hydrochloric acid and 0.1 M hydroquinone or 0.1 M ammonium hydrogen oxalate (NH4HC2O4).
4351     It is important to note that iron, found in most environmental samples, does not  effect the
4352     separation if the element is kept in the +2 oxidation state as ferrous ions. This is commonly
4353     achieved using ascorbic acid. The ferric ion (Fe+3) is detrimental to the separation.

4354     ION-EXCHANGE CHROMATOGRAPHY. Ion-exchange chromatography has been used extensively
4355     for the radiochemical separation of plutonium. All cationic plutonium species in non-complexing
4356     acid solutions  readily exchanges onto cation resins at low acid concentrations and desorb at high
4357     acid concentrations. Plutonium in all its oxidation states form neutral or anionic complexes with
4358     various anions, providing an alternate means for eluting the element. Various cation-exchange
4359     resins have been used with hydrochloric, nitric, perchloric, and sulfuric acids for separation of
4360     plutonium from metals including other actinides, but the most common use of plutonium cation-
4361     exchange chromatography is concentrating a dilute solution or separation from nonabsorbable
4362     impurities such as organic reagents, redox agents, for example.

4363     Anion-exchange chromatography is the primary ion-exchange method for the separation of
4364     plutonium from other metals and the separation of the plutonium oxidation states, and many
4365     procedures have been developed using this method. On a strong anion-exchange resin, for
4366     example, the higher oxidation states (IV, V, and VI) occurs at hydrochloric acid concentrations
4367     above 6 M, while desorption occurs at 2 M acid. Plutonium (in) does not absorb on the column,
4368     and plutonium (VI) absorbs from 2 to 3 M hydrochloric acid solution. Plutonium can be
4369     separated from other actinides and most other elements by absorbing the plutonium cations—


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4370     Pu(IV) and Pu (VI)—onto a strong-anion resin from 6 M hydrochloric acid, and subsequently
4371     eluting the plutonium by reducing it to plutonium ILL Anion exchange in 7 to 8 M nitric acid is
4372     also an effective method for separating plutonium. The radionuclide loads on the column as
4373     Pu(NO3)6"2 and is eluted with dilute acid or after reduction.

4374     ELECTRODEPOSTION. Separation methods based on elecrtodeposition are not common, but one
4375     method for the alpha analysis of plutonium is in use. Plutonium is electrodeposited on a stainless
4376     steel disc from an ammonium sulfate solution at  1.2 amps for one hour. The separation is used
4377     after isolating the radionuclide by extraction chromatography, and the plutonium isotopes are
4378     resolved by alpha spectroscopy.

4379     Methods of Analysis

4380     238Pu, 239Pu, 240Pu, and 241Pu are collected for analysis either by electrodepositon on a platinum or
4381     nickel disc or by microprecipitation with lanthanum fluoride (LaF3). Radionuclides of 238Pu,
4382     239Pu, and 240Pu are determined by alpha spectrometry or gas flow proportional counting. 241Pu is
4383     beta counted. 236Pu or 242Pu are used as a tracer for measuring chemical yield. They are measured
4384     by alpha spectrometry.

4385         Compiled from: Baes and Mesmer, 1976; Choppin et al., 1995; Coleman, 1965; Cotton and
4386         Wilkinson, 1988; DOE 1990, 1995, and 1997; EPA 1973 and 1980; Metz and Waterbury,
4387         1962;  Seaborg and Loveland, 1990; Weigel et al., 1986.

4388     14.10.9.6  Radium

4389     Radium, with an atomic number of 88, is the heaviest (last) member of the family of alkaline
4390     earth metals,  which, in addition, includes beryllium (Be), magnesium (Mg), calcium (Ca),
4391     strontium (Sr), and barium (Ba). It is the most basic and reactive of the series, and exists
4392     exclusively as +2 cations in compounds and solution. All isotopes are radioactive, and essentially
4393     all analyses are made by radioactive measurements.

4394     Isotopes

4395     There are 25  isotopes of radium from 205Ra to 234Ra; all are radioactive. The most important with
4396     respect to the environmental contamination are members of the 238U and 232Th naturally occurring
4397     decay series:  226Ra and 228Ra, respectively. 226Ra is the most abundant isotopic form with a half-
4398     life of 1,602 years. As a member of the 238U series, it is produced by alpha emission from 230Th.
4399     226Ra emits an alpha particle and, in turn, produces 222Rn, an inert gas that is also an alpha


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         Separation Techniques
4400     emitter. Radium generates radon at the rate of 0.1 jiL per day per gram of radium, and its
4401     radioactivity decreases at the rate of about one percent each 25 years. 228Ra, half-life of 5.77
4402     years, is produced in the 232Th decay series by emission of an alpha particle from 232Th itself.

4403     Occurrence

4404     In nature, radium is primarily associated with uranium and thorium, particularly in the uranium
4405     ores—carnotite and pitchblende, where 226Ra is in radioactive equilibrium with 238U and its other
4406     progeny. The widespread dispersal of uranium in rocks and minerals results in a considerable
4407     distribution of radium isotopes throughout nature. It is generally found in trace amounts in most
4408     materials, therefore, the radium/uranium ratio is about 1 mg radium per 3 kg uranium (1 part in 3
4409     x 106 parts uranium). This leads to a terrestrial abundance of approximately 10"6 ppm: 10"12g/g in
4410     rocks and minerals. Building materials, such as bricks and concrete blocks for example, that
4411     contain mineral products also contain radium. With leaching from soil, the  concentration is about
4412     10"13g/L in river and streams, and uptake in biological systems produces concentrations of
4413     10"14g/g in plants and 10"15g/g in animals.

4414     Uranium ores have been processed with hot mineral acids or boiling alkali carbonate to remove
4415     radium  and/or uranium. Extracted radium was usually coprecipitated with barium sulfate,
4416     converted to carbonate or sulfide, and solubilized with hydrochloric acid. Separation from
4417     barium was usually accomplished by fractional crystallization of the chlorides, bromides, or
4418     hydroxides, since barium salts are usually slightly more soluble. The free metal has been
4419     prepared by electrolysis of radium chloride solutions, using a mercury cathode. The resulting
4420     amalgam is thermally decomposed in a hydrogen atmosphere to produce the pure metal. The
4421     waste streams from these industrial operations contain radium, primarily as a coprecipitate of
4422     barium  sulfate. Since many other natural ores also contain uranium and radium, processing can
4423     result in uranium and its equilibrium progeny appearing in a product or byproduct. Apatite, a
4424     phosphate ore, is used to produce phosphoric acid, and the gypsum byproduct contains all the
4425     radium  originally present in the ore.

4426     226Ra extracted from ores has historically been used in diverse ways as a source of radioactivity.
4427     It has been mixed with a scintillator to produce luminous paint, and at one time, the most
4428     common use for its salts was radiation therapy. As a source of gamma radiation, radium activity
4429     was enhanced by sealing a radium salt in a capsule that prevented escape of the gaseous progeny,
4430     222Rn, and allowing the radon to decay into its successive progeny. Two progeny are 214Pb and
4431     214Bi, the principal emitters of gamma radiation in the source. For the most part radium has been
4432     replaced in medical technology by other sources of radioactivity, but numerous capsules
4433     containing the dry, concentrated substances still exist.


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                                                                              Separation Techniques
4434     Radium salts are used in various instruments for inspecting structures such as metal castings by
4435     gamma-ray radiography, to measure the thickness of catalyst beds in petroleum cracking units,
4436     and to continuously measure and control the thickness of metals in rolling mills. Radium is also
4437     used for the preparation of standard sources of radiation, as a source of actinium and protac-
4438     tinium, and as a source of ionizing radiation in static charge eliminators. In combination with
4439     beryllium, it is a neutron source for research, in the analysis of materials by neutron activation,
4440     and radio-logging of oil wells.

4441     Radium in the environment is the result of natural equilibration and anthropogical activity such
4442     as mining and processing operations. Radium is retained by many rock and soil minerals,
4443     particularly clay minerals, and migrates only very slowly in through these materials. The decay
4444     progeny of 226Ra, gaseous  222Rn,  is an important environmental pollutant and represents the most
4445     significant hazard from naturally occurring radium. Concentration of the alpha-emitting gas in
4446     some occupied structures contributes to the  incidence of lung cancer in humans. During the
4447     decay of 226Ra, the recoil of the parent nucleus after it emits  an alpha particle, now 222Rn, causes
4448     an increased fraction of radon to escape from its host mineral, a larger fraction than can be
4449     explained by intramineral  migration or diffusion.

4450     In groundwater, radium likely encounters dissolved sulfate and/or carbonate  anions, which could
4451     precipitate radium sulfate  or radium carbonate. Although both salts are relatively insoluble,  a
4452     sulfate concentration of 0.0001 M would  still allow an equilibrium concentration of about 0.1
4453     ppm Ra+2 to exist in solution. Thus, the insolubilities of either of these salts are not likely to
4454     prevent contamination of the environment.

4455     Radium also contaminates the environment because of past disposal practices of some proces-
4456     sing, milling, and reclamation operations. Radium process tailings have been discovered in land
4457     areas as seams or pockets  of insoluble radium compounds, such as barium radium sulfate, or
4458     unprocessed radium (uranium) ore, such as carnotite. Release of solid or liquid process streams
4459     and subsequent mixing with local soil has resulted in intimate contamination of soil particles,
4460     primarily  as Ra+2 absorbed onto clay-sized fractions. This form of absorbed radium is tightly
4461     bound to soil but can be extracted partially by hot concentrated acid solutions.

4462     Solubility of Compounds

4463     The solubility of radium compounds can usually be inferred from the solubility of the correspon-
4464     ding barium compound and the trend in the  solubilities of the corresponding alkaline earth
4465     compounds. The common water-soluble radium salts are the chloride, bromide, nitrate, and
4466     hydroxide. The fluoride, carbonate, phosphate, biphosphate  (hydrogen phosphate),  and oxalate


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         Separation Techniques
4467     are only slightly soluble. Radium sulfate is the least soluble radium compound known, insoluble
4468     in water and dilute acids, but it is soluble in concentrated sulfuric acid, forming a complex ion
4469     with sulfate anions, Ra(SO4)2"2.

4470     Radium compounds are essentially insoluble in organic solvents. In most separation procedures
4471     based on extraction, other elements, not radium, are extracted into the organic phase. Exceptions
4472     are known (see "Separation," below), and crown ethers have been  developed recently that
4473     selectively remove radium from an aqueous environment.

4474     Review of Properties

4475     Radium is highly toxic exclusively because of its radioactive emissions: gamma radiation of the
4476     element itself and beta particles emitted by some of its decay progeny. It concentrates in bones
4477     replacing calcium and causing anemia and cancerous growths. Its immediate progeny, gaseous
4478     radon, is an alpha emitter that is a health threat when inhaled.

4479     Metallic radium is brilliant white and reacts rapidly with air, forming a white  oxide and black
4480     nitride. It is an active metal that reacts with cold water to produce radium hydroxide, hydrogen,
4481     and other products. The radium ion in solution is colorless. Its compounds also are colorless
4482     when freshly prepared but  darken and decompose on standing because of the intense alpha
4483     radiation. The original color returns when the compound is recrystallized. Alpha emissions also
4484     cause all radium compounds to emit a blue glow in air when sufficient quantities are available.
4485     Radium compounds also are about 1.5 °C higher in temperature than their surroundings because
4486     of the heat released when alpha particles loose energy on absorbance by the compound. Glass
4487     containers turn  purple or brown in contact with radium compounds and eventually the glass
4488     crystallizes and becomes crazed.

4489     Like all alkaline earths, radium contains two valence electrons (7s2) and forms only +2 ions in its
4490     compounds and in solution. The ionic radius of radium in crystalline materials is 152 pm (0.152
4491     nm or 1.52 A), the largest crystalline radius of the alkaline earth cations (Ra+2 > Ba+2 > Sr+2 >
4492     Ca+2 >Mg+2 > Be+2). In contrast, the hydrated ion radius in solution is the smallest of the alkaline
4493     earth cations, 398 pm (Be+2 > Mg+2 >  Ca+2 > Sr+2 > Ba+2 > Ra+2). With the smallest charge-to-
4494     crystal-radius ratio among  the alkaline earths of 1.32 (+2/1.52), the smallest hydrated radius of
4495     radium is expected, because the ratio  represents the least attractive potential for water molecules
4496     in solution.
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                                                                             Separation Techniques
4497     Solution Chemistry

4498     Existing exclusively in one oxidation state (+2), the chemistry of radium is uncomplicated by
4499     oxidation-reduction reactions that could produce alternate states in solution. It is made even less
4500     complicated by its weak tendency to form complex ions or hydrolyze in solution. These
4501     properties are a reflection of the small charge-to-crystal-radius ratio of 1.32, described above. In
4502     general, radiochemical equilibrium is established with carriers by stirring, followed by either
4503     standing or digesting in the cold for several minutes. Adsorption of trace amounts of radium on
4504     surfaces, however, is an important consideration in its radiochemistry.

4505     COMPLEXATION. Radium, like other alkaline-earth cations, forms few complexes in acid
4506     solution. Under alkaline conditions, however, several one-to-one chelates are formed with
4507     organic ligands: among others, with EDTA, diethylenetriaminepentaacetate (DTPA),
4508     ethyleneglycol bis(2-aminoethylether)-tetraacetate (EGTA), nitrilotriacetate (NTA or NTTA),
4509     and citrate. The most stable complex ion forms with DTPA. The tendency to form complexes
4510     decreases as their crystalline size increases and their charge-crystal-radius ratio decreases. Since
4511     crystalline sizes of the cations are in the order: Ra+2 > Ba+2 > Sr+2 > Ca+2, radium has the least
4512     tendency to form complex ions,  and few significant complexes of radium with inorganic anions
4513     are known. One notable exception is observed in concentrated sulfuric acid, which dissolves
4514     highly insoluble radium sulfate (RaSO4) by forming Ra(SO4)2"2.

4515     Complex-ion chemistry is not used in most radium radiochemical procedures.  Complexing
4516     agents are primarily employed as elution agents in cation exchange, in separations from barium
4517     ions by fractional precipitation, and in titration  procedures. Alkaline citrate solutions have been
4518     used to prevent precipitation of radium in the presence of lead and barium carriers until complete
4519     isotopic exchange has been accomplished.

4520     HYDROLYSIS. Similar to their behavior complex-ion formation, alkaline earths show less and less
4521     tendency to hydrolyze with increasing size of the ions, and the tendency decreases with
4522     increasing ionic strength of the solution.  Therefore, hydrolysis of radium is an insignificant factor
4523     in their solution chemistry.

4524     ADSORPTION. The adsorption of trace amounts  of radium on surfaces is an important considera-
4525     tion in its radiochemistry. Although not as significant with radium as with some ions with higher
4526     charges,  serious losses from solution can occur under certain conditions.  Adsorption on glass is a
4527     particular problem, and adsorption on polyethylene has been reported. Adsorption gradually
4528     increases with increasing pH and depends strongly on the nature of the surface. In the extreme,
4529     up to 50  percent radium has been observed to adsorb onto glass from neutral solution in 20 days,


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         Separation Techniques
4530     and 30 percent from 0.13 M hydrochloric acid (HC1). Fortunately, adsorbed radium can be
4531     removed from glass with strong acid.

4532     The presence of insoluble impurities, such as traces of dust or silica, increases adsorption, but
4533     adsorption is negligible from very pure solutions at low pH values. Tracer radium solutions,
4534     therefore, should be free from insoluble impurities, and radium should be completely in solution
4535     before analysis. The solutions should also be maintained in at least 1 M mineral acid or contain
4536     chelating agents. Addition of barium ion as a carrier for radium will probably decrease the
4537     amount of radium adsorption. Radium residues from solubilization of samples that contain  silica
4538     or lead or barium sulfates and those that result in two or more separate solutions should be
4539     avoided since the radium might divide unequally between the fractions. Destruction of silica with
4540     HF, reduction of sulfates to sulfides with zinc dust, and subsequent dissolution of the residue
4541     with nitric acid are procedures used to avoid this problem.

4542     Dissolution  of Samples

4543     Soil, mineral, ore samples, and other inorganic  solids are  dissolved by conventional treatment
4544     with mineral acids and by fusion with sodium carbonate (Na2CO3). Hydrofluoric acid (FTP)  or
4545     potassium fluoride (KF) is used to remove silica. Up to 95 percent radium removal has been
4546     leached from some samples with hot nitric acid (F£NO3), but such simple treatment will not
4547     completely dissolve all the radium in soil, rock, and mineral samples. Biological samples are wet
4548     ashed first with mineral acids or decomposed by heating to remove organic material. The residue
4549     is taken up in mineral acids or treated to remove silica. Any dissolution method that results in
4550     two or more separate fractions should be avoided, since the adsorption characteristics of trace
4551     quantities of radium may cause it to divide between the fractions.

4552     Barium sulfate (BaSO4), often used to coprecipitate radium from solution, can be dissolved
4553     directly into alkaline EDTA solutions. Radium  can be repeatedly reprecipitated and dissolved by
4554     alternate acidification with acetic acid and dissolution with the EDTA solution.

4555     Solutions resulting from dissolution of solid samples should be made at least 1  M with mineral
4556     acid before  storage to prevent radium from absorbing onto the surface of glass containers.

4557     Separation Methods

4558     COPRECIPITATION. Radium is almost always present in solution in trace amounts, and even  the
4559     most insoluble radium compound, radium sulfate, can not be used to separate and isolate radium
4560     from solution by direct precipitation. Therefore, the cation is commonly removed from solution


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                                                                             Separation Techniques
4561     in virtually quantitative amounts by coprecipitation. Since radium forms the same types of
4562     insoluble compounds as barium: sulfates (SO4"2), chromates (CrO4"2), carbonates (CO3"2),
4563     phosphates (PO4"3), oxalates (C2O4"2), and sulfites (SO4"2), it coprecipitates with all insoluble
4564     barium compounds, and to a lesser extent with most insoluble strontium and lead compounds.
4565     Barium sulfate and barium chromate are most frequently used to carry radium during coprecipita-
4566     tion. Other compounds that are good carriers for radium include: ferric hydroxide when
4567     precipitated at moderately high pH with sodium hydroxide (NaOH) or ammonium hydroxide
4568     (NH4OH), barium chloride (BaCl2) when precipitated from a cold mixed solvent of water and
4569     alcohol saturated with hydrochloric acid, barium iodate (BaIO3), and various insoluble
4570     phosphates, fluorides (F"1), and oxalates (e.g., thorium phosphate [Th3(PO4)], lanthanum fluoride
4571     (LaF3), and thorium oxalate [Th(C2O4)]. Lead sulfate (PbSO4) can be used if a carrier-free radium
4572     preparation is required, since quantitative lead-radium separations are possible while quantitative
4573     barium-radium separations are very difficult.

4574     ION EXCHANGE.  Radium has been separated from other metals on both cation- and anion-
4575     exchange resins. Barium and other alkaline earths are separated on cation-exchange columns
4576     under acidic conditions.  In dilute hydrochloric acid solutions (3 M),  the affinity of the cation for
4577     the exchange site is dominated by  ion-dipole interactions between the water molecules  of the
4578     hydrated ion and the resin. Ions  of smaller hydrated radius (smaller charge-to-crystal-radius ratio)
4579     tend to displace ions of larger hydrated radius. The affinity series is Ra+2>Ba+2>Sr+2>Ca+2, and
4580     radium elutes last. Increasing the acid concentration to 12 M effectively reverses the order of
4581     affinity, since the strong acid tends to dehydrate the ion, and ion-resin affinity is dominated more
4582     by ionic interactions, increasing in the order of increasing crystal radius: Ca+2>Sr+2>Ba+2>Ra+2,
4583     and calcium elutes last. Radium has also been separated from tri- and tetravalent ions since these
4584     ions have a much stronger affinity for the cation-exchange resin. Radium with its +2 charge is
4585     only partially absorbed, while trivalent actinium and tetravalent thorium, for example, will be
4586     completely absorbed. Tracer quantities of radium also has been separated from alkaline earths by
4587     eluting a cation-exchange column  with chelating agents  such as lactate, citrate, and EDTA;
4588     radium typically elutes last, since it forms weaker interactions with the ligands.

4589     Anion-exchange resins have been  used to separate radium from other metal ions in solutions of
4590     chelating agents  that form anionic complexes with the cations.  The affinity for the columns
4591     decreases in the order Ca > Sr > Ba > Ra, reflecting the ability  of the metal ions to form stable
4592     complex anions with the chelating agents. The difficult separation of barium from radium has
4593     been accomplished by this procedure. Radium is also separated from metals such as uranium,
4594     polonium, bismuth, lead, and protactinium that form polychloro complex anions. Since radium
4595     does not form a chlorocomplex, it does not absorbs on the anion exchanger (carrying a  positive
4596     charge), and remains quantitatively in the effluent solution.


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         Separation Techniques
4597     Ion-exchange methods are not easily adapted for the separation of macro-scale quantities of
4598     radium, because the intense radiation degrades the synthetic resin and insoluble radium
4599     compounds usually form in the ion-exchange column.

4600     SOLVENT EXTRACTION. Radium compounds have very low solubilities in organic solvents. In
4601     most extraction procedures, other organic-soluble complexes of elements, not radium, are
4602     extracted into the non-aqueous phase, leaving radium in the water. Radium is separated from
4603     actinium, thorium, polonium, lead, bismuth, and thallium, for example, by extracting these
4604     elements as 2-thenoyltrifluoroacetone (TTA) complexes. Radium does not form the complex
4605     except at very high pH, and is not extracted. One notable exception to this generality  is the
4606     extraction of radium tetraphenylborate by nitrobenzene from an alkaline solution. The presence
4607     of EDTA inhibits formation of the tetraphenylborate, however, and radium is not extracted in the
4608     presence of EDTA either.

4609     More recent developments have  employed crown ethers to selectively extract radium as a
4610     complex ion from water samples for analysis. Radium-selective extraction membranes have also
4611     been used to isolate radium from solutions.

4612     Methods of Analysis

4613     Radium is detected and quantified by counting either alpha or gamma emissions of the
4614     radionuclide or its progeny. Gamma-ray spectroscopy can be used on macro 226Ra samples
4615     (approximately 50 g or more) without pretreatment unless 235U, even in very small quantities, is
4616     present to interfere with the measured peak. The most sensitive method for the analysis of 226Ra
4617     is de-emanation of 222Rn from the radium source, complete removal, followed by alpha counting
4618     the 222Rn and its progeny.  The procedure is lengthy and expensive, however. The radium in a
4619     liquid sample is placed in a sealed tube for a specified time to allow the ingrowth of 222Rn. The
4620     radon is collected in a scintillation cell and stored for several hours to allow for ingrowth of
4621     successive progeny products. The alpha radiation is then counted in the scintillation cell called a
4622     Lucas cell. The primary alpha emissions are from 222Rn, 218Po, and 214Po.  Complete retention of
4623     radon can also be accomplished by sealing the radium sample hermetically in a container and
4624     alpha- or gamma-counting.

4625     228Ra can also be determined directly by gamma spectroscopy, using the gamma-rays  of its
4626     progeny, 228Ac, without concern  for interference; however, a lower detection limit is obtained if
4627     the 228Ac is measured by beta counting. In the beta-counting procedure, 228Ra is separated, time is
4628     allowed for actinium ingrowth, the 228Ac is removed by solvent extraction, ion-exchange, or
4629     coprecipitation, and then measured by beta counting.


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                                                                            Separation Techniques
4630     224Ra can be determined by chemically isolating the 212Pb, which is in equilibrium with the 224Ra.
4631     After an appropriate ingrowth period, 212Pb is determined by alpha counting its progeny, 212Bi and
4632     212Po.

4633         Compiled from: Baes and Mesmer, 1976; Choppin et al.,  1995; Considine and Considine,
4634         1983; DOE, 1990 and 1997, 1997; EPA, 1984; Friedlander et al., 1981; Green and Earnshaw,
4635         1984; Hassinsky and Asloff, 1965; Kirby and Salutsky, 1964; Lindsay, 1988; Salutsky, 1997;
4636         Sedlet,  1966; Shoesmith, 1964; Sunderman and Townley, 1960; Turekian and Bolter, 1966;
4637         Vdovenko and Dubasov, 1975.

4638     14.10.9.7   Strontium

4639     Strontium,  atomic number 38, is the fourth member of the alkaline-earth metals, which includes
4640     beryllium (Be), magnesium  (Mg), calcium (Ca), strontium (Sr), barium (Ba), and radium (Ra).
4641     Like radium, it exist exclusively in the +2 oxidation state in both compounds and in solution,
4642     making its  chemistry simpler than many of the radionuclides reviewed in this  section.

4643     Isotopes

4644     Strontium exists in 29 isotopic forms, including three metastable states, ranging in mass number
4645     from 77 to  102. Natural strontium is a mixture of four stable isotopes: 84Sr, 86Sr, 87Sr, and 88Sr.
4646     The lower mass number isotopes decay by electron capture, and the isotopes with higher mass
4647     numbers are primarily beta emitters. The half-lives of most isotopes are short, measured in
4648     milliseconds, seconds, minutes, hours, or days. The exception is 90Sr, a beta emitter with a half-
4649     life of 29.1 years.

4650     Occurrence and Uses

4651     Strontium is found in nature in two main ores, celestite (SrSO4) and strontianite (SrCO3), widely
4652     distributed in small concentrations. Small amounts are found associated with calcium and barium
4653     minerals. The earth's crust contains 0.042 percent strontium, ranking twenty-first among the
4654     elements occurring in rock and making it as abundant as  chlorine and sulfur. The element ranks
4655     11th in abundance in sea water, about 8-10 ppm. The only naturally occurring radioactive isotopes
4656     of strontium are the result of spontaneous fission of uranium in rocks. Other nuclear reactions
4657     and fallout from nuclear weapons test are additional sources of fission products. 90Sr is a fission
4658     product of 235U, along with 89Sr, and short-lived isotopes, 91Sr to 102Sr. 85Sr can be produced by
4659     irradiation  of 85Rb with accelerated protons or deuterons.
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         Separation Techniques
4660     Stable strontium is produced from its ores. The sulfate ore is leached with hydrochloric acid
4661     solution to remove impurities and shaken with sodium carbonate for several hours to produce
4662     strontium carbonate. Washing this product or the carbonate ore with hot water and several
4663     reprecipitation steps produce a fine grade of strontium carbonate. The metal is produced by
4664     converting the carbonate to strontium chloride with hydrochloric acid or to  strontium oxide by
4665     heating. Strontium chloride in a melt with potassium chloride is electrolyzed or the oxide is
4666     reduced by heating with aluminum in a vacuum to distill off the metal. An alternate method
4667     electrolyzes an aqueous solution of the chloride with a mercury cathode. The resultant mercury
4668     amalgam is heated in hydrogen to drive off the mercury.

4669     The major use of strontium is in glass production for color television picture tubes. Strontium is
4670     used in producing ferrite magnets, in refining zinc, to produce hardness and durability  in alloys of
4671     tin and lead, as a deoxidizer in copper and bronze, and "getter" in electron tubes. Strontium
4672     hydroxide forms soaps and greases with numerous organic acids that are stable, resistant to
4673     oxidation and decomposition over a wide temperature range, and resistant to decomposition by
4674     water and the leaching action of hydrocarbons. The beta emission of 90Sr and its progeny, 90Y
4675     (ti/2=64 h), has found applications in industry, medicine, and research. The radionuclides are  in
4676     equilibrium in about 25 days. The radiation of 90Y is more penetrating than  that of strontium. It is
4677     used with zinc sulfide in some luminescent paints. Implants of 90Sr provide radiation therapy for
4678     the treatment of the pituitary gland and breast and nerve tissue. The radiation from strontium has
4679     been used in thickness gauges, level measurements, automatic control processes, diffusion
4680     studies of seawater, and a source of electrical power. Since 90Sr is one of the long-lived and most
4681     energetic beta emitters, it might prove to be a good source of power in space vehicles,  remote
4682     weather stations, navigational buoys, and similar long-life, remote devices.  Both  89Sr and 90Sr
4683     have been used in physical chemistry experiments and in biology as tags and tracers. 90Sr to 87Sr
4684     ratios are used in geological dating, because 87Sr is formed by decay of long-lived 87Rb.

4685     Solubility of Compounds

4686     Several simple salts of strontium are soluble in water. Among these are the  acetate, chloride,
4687     bromide, iodide, nitrate, nitrite, permanganate, sulfide, chlorate, bromate, and perchlorate.
4688     Strontium hydroxide is slightly soluble and is precipitated only from concentrated solutions.

4689     Review of Properties

4690     Strontium is a low-density (2.54 g/cm3) silver-white metal. It is as soft as lead and is malleable
4691     and ductile. Three allotropic forms exit with transition temperatures of 235  and 540  °C. Freshly
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                                                                              Separation Techniques
4692     cut strontium is silver in appearance, but it rapidly turns a yellowish color on formation of the
4693     oxide in the air. It is stored under mineral oil to prevent oxidation.

4694     The metal decomposes water, producing strontium hydroxide [Sr(OH)2] and hydrogen, and the
4695     finely divided metal ignites spontaneously in the air. The hydroxide forms strontium peroxide
4696     (SrO2) when treated with hydrogen peroxide in the cold. Strontium is a strong reducing agent and
4697     combines directly with hydrogen, halogens, oxygen, and sulfur to form, respectively, the simple
4698     binary compounds:  hydride (SrH2), halogens (SrX2), oxide (SrO), and sulfide (SrS). The metal
4699     reacts with nitrogen to form the nitride (Sr3N2) only on heating to 380 °C. It also reacts
4700     vigorously with most acids to form Sr+2 salts and hydrogen. With nitric acid the reaction is fast,
4701     producing nitrogen  dioxide. In contrast, reaction with sulfuric acid is slow because of the
4702     formation of the insoluble sulfate [Sr(SO4)2].

4703     Strontium isotopes are some of the principal constituents of radioactive fallout following
4704     detonation of nuclear weapons, and they are released in insignificant amounts during normal
4705     operations of reactors and fuel reprocessing operations. Their toxicity is higher, however, than
4706     that of other fission products, and 90Sr represent a particular hazard because of its long half-life,
4707     energetic beta emission, tendency to contaminate food, especially milk, and high retention in
4708     bone structure. Strontium in bone is difficult to eliminate and has a biological half-life of
4709     approximately eleven years (4,000 d).

4710     Strontium occurring in groundwater is primarily in the form of strontium carbonate. Its solubility
4711     under oxidizing and reducing conditions is approximately 0.001  M (0.15 g/L or 150 g/m3).

4712     Solution Chemistry

4713     Strontium exists exclusively in the +2 oxidation state in solution, so the chemistry of strontium is
4714     uncomplicated by oxidation-reduction reactions that could produce alternate states in solution.

4715     COMPLEXATION.  Strontium has little tendency to form complexes. Of the few complexing agents
4716     for strontium,  the significant agents in radiochemistry to date are EDTA, oxalate, citrate,
4717     ammoniatriacetate,  methylanine-N,N-diacetate, 8-quinolinol,  and an insoluble chelate with
4718     picrolonate. The most stable complex ion forms with EDTA. Coordination compounds of
4719     strontium are not common. These chelating agents are used primarily in ion-exchange
4720     procedures. Amine  chelates of strontium are unstable, and the  p-diketones and alcohol chelates
4721     are poorly characterized.  In contrast, cyclic crown ethers and cryptates form stronger chelates
4722     with strontium than with calcium, the stronger chelating metal with EDTA and more traditional
4723     chelating agents.  Cryptates are a macrocyclic chelate of the type, N[(CH2CH2O)2CH2CH2]3N, an


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         Separation Techniques
4724     octadentate ligand containing six oxygen atoms and two nitrogen atoms as ligand bonding sites
4725     that encapsulates the cation. It might find use in the extraction chemistry of strontium.

4726     HYDROLYSIS. The tendency of the alkaline-earth cations to hydrolyze decreases as their atomic
4727     number increases.  The tendency is greater than that of the corresponding alkali metals, but
4728     hydrolysis of potassium, for example, is insignificant. An indication of the tendency of a cation
4729     to hydrolyze  is the solubility of their hydroxides, and the solubility of the alkaline earths become
4730     more soluble with increasing atomic number. Strontium hydroxide is slightly soluble in water (8
4731     g/L at 20  °C). In comparison, the hydroxide of beryllium, the first element in the alkaline earth
4732     series, has a solubility of approximately 3 x 10"4 g/L.

4733     Dissolution of Samples

4734     Dissolution of samples for the analysis  of strontium is generally simple. Water is used to dissolve
4735     soluble compounds: acetate, bromide, chloride, iodide, chlorate, perchlorate, nitrate, nitrite, and
4736     permangenate. Hydrochloric or  nitric acid dissolves the fluoride, carbonate, oxalate, chromate,
4737     phosphate, sulfate, and oxide. Strontium in limestone, cement, soil, bone, and other biological
4738     material can be dissolved from  some samples in hot hydrochloric acid.  Insoluble silica, if present,
4739     can be filtered or centrifuged. In some cases, soil can be leached to remove strontium. As much
4740     as 99.5 percent of the strontium in some crushed soil samples has been leached with 1 M nitric
4741     acid by three extractions. Soil samples have also been suspended overnight in ammonium acetate
4742     at pH 7. If leaching is not successful, soil samples  can be dissolved by alkali fusion of the ground
4743     powder with  potassium hydroxide, nitrate, or carbonate. Strontium is taken up from the residue
4744     in nitric acid. Biological materials such as plant material  or dairy products are solubilized by
4745     ashing at 600 °C and taking up  milk residue in hot, concentrated hydrochloric acid and plant
4746     residue in aqua regia. Wet ashing can be used by treating the sample with nitric acid followed by
4747     an equal-volume mixture of nitric and perchloric acids. Human and animal bone samples are
4748     ashed at 900  °C and the residue dissolved in concentrated hydrochloric acid.

4749     Separation Methods

4750     PRECIPITATION AND COPRECIPITATION. The common insoluble salts of strontium are the fluoride,
4751     carbonate, oxalate, chromate, and sulfate. Most are suitable for radiochemical procedures, and
4752     strontium separation have the advantage of stable forms of strontium that can be used as a carrier
4753     and are readily available. Precipitation of strontium nitrate in 80 percent nitric acid has been used
4754     to separate stable strontium carrier and  90Sr from its progeny, 90Y, and other soluble nitrates
4755     (calcium,  for example). The solubility of strontium chloride in concentrated hydrochloric
4756     solution has been used to separate strontium from barium—barium chloride is insoluble in the


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4757     acid. Barium and radium (as coprecipitant) have been removed from strontium by precipitating
4758     barium as the chromate at a carefully controlled pH of 5.5. Strontium chromate will not
4759     precipitate unless the pH is raised. Strontium can also be separated from yttrium by precipitation
4760     of the much less soluble yttrium hydroxide by raising an acid solution of the cations to a pH of
4761     about 8 with ammonium hydroxide. Strontium hydroxide is slightly soluble and will not
4762     precipitate without high concentrations of hydroxide or strontium or both. Carrier-free strontium
4763     is coprecipitated with ferric hydroxide, and lead sulfate is also used.

4764     SOLVENT EXTRACTION.  The application of organic solvents for separation of strontium from
4765     other metals has not been extensive. Thenoyltrifluoroacetone (TTA) has been used to extract
4766     carrier-free strontium at a pH > 10. At pH 5,90Y is extracted with TTA from strontium, which
4767     remains in aqueous solution. 8-hydroxyquinolinol in chloroform has also been used to extract
4768     strontium. The few procedures that have been available are mainly used to separate the alkaline
4769     earths from  each other. A 1:1 mixture of ethyl alcohol and diethy ether extracts calcium from
4770     strontium.

4771     In recent years, extraction procedures have been developed based on the complexation of
4772     strontium cations with crown ethers in 1-octanol. Strontium can be extracted with these mixture
4773     from 1 M to 7 M nitric acid solutions. The most advantageous application of strontium extraction
4774     procedures has been found in extraction chromatography. An extraction resin consisting of
4775     4,4'(5')-bis(Y-butylcyclohexano)-18-crown-6 (DtBuCH18C6) in 1-octanol on an inert polymeric
4776     matrix is highly selective for strontium nitrate and will separate the cation from many other
4777     metals including calcium, barium, and yttrium.  This column is used to separate strontium from
4778     potassium, cerium, plutonium, and neptunium (K+1, Ce+4, Pu+4, Np+4, respectively). The column
4779     is prepared and loaded from  8 M nitric acid. The ions listed above are eluted with 3 M nitric acid
4780     containing oxalic acid. Strontium is eluted with 0.05 M nitric acid.

4781     ION-EXCHANGE CHROMATOGRAPHY. Ion-exchange chromatography is used to separate trace
4782     quantities of strontium, but separation of macro quantities is very time consuming. Strontium is
4783     absorbed on cation-exchange resins, and elution is  often based on the formation of a stable
4784     complex. Carrier-free strontium is separated from fission products, including barium, on a
4785     cation-exchange resin and eluted with citrate. In a similar process, strontium was also separated
4786     form other alkaline earths, magnesium, calcium, barium, and radium, eluting with ammonium
4787     lactate at pH 7 and 78 °C. Good separations were also obtained with hydrochloric solutions and
4788     ammonium  citrate. 90Sr and 90Y are separated on a cation-exchange column, eluting yttrium with
4789     ammonium  citrate  at pH 3.8  and strontium at pH 6.0. Strontium and calcium have also been
4790     separated in EDTA solutions at pH 5.3. Strontium is retained on the column, and calcium elutes
4791     as the calcium-EDTA complex. Strontium elutes with 3  M hydrochloric acid.


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4792     Not many procedures use anion-exchange chromatography for separation of strontium. 90Sr has
4793     been separated from 90Y on an anion-exchange resin pretreated with hydroxide. Strontium is
4794     eluted form the column with water, and yttrium is eluted with 1 M hydrochloric acid. The
4795     alkaline earths have been separated by anion-exchange column pretreated with dilute ammonium
4796     citrate, loading the column with the chloride form of the metals, and eluting with ammonium
4797     citrate at pH 7.5.

4798     Methods of Analysis

4799     Macroquantities of strontium are determined by gravimetric methods and atomic absorption
4800     spectrometry,  and emission spectrometry. Strontium is precipitated as strontium carbonate or
4801     sulfate in gravimetric procedures. For atomic absorption analysis, the separated sample is ashed,
4802     and the product is dissolved in hydrochloric acid. Lanthanum is added to the solution to
4803     precipitate interfering anions, phosphate, sulfate, or aluminate, that would occur in the flame.

4804     89Sr and 90Sr are determined by analysis of their beta emissions. With a short half-life of 53 d,
4805     89Sr is only found in fresh fission products.  90Sr is a beta emitter with a half-life of 27.7 y. Its
4806     progeny is 90Y, which emits beta particles with a half-life of 64.0 h, producing stable 90Zr.
4807     Neither 90Sr nor 90Y is a gamma emitter. 90Sr is determined directly from its beta emission, before
4808     90Y grows in, by beta counting immediately (three to four hours) after it is collected by
4809     precipitation. The chemical yield can be determined gravimetrically by the addition of stable
4810     strontium,  after the separation of calcium. Alternatively, 90Sr can be measured from the beta
4811     emission of 90Y while it reaches secular equilibrium (two to three weeks).  The 90Y is separated by
4812     solvent extraction and evaporated to dryness or by precipitation, then beta counted. The chemical
4813     yield of the yttrium procedure can be determined by adding stable yttrium  and determining the
4814     yttrium gravimetrically. 89Sr has a half-life of 52.7 d and is only present in fresh fission material.
4815     If it is present with 90Sr, it can be determined by the difference in activity of combined 89Sr and
4816     90Sr (combined or total strontium) and the activity of 90Sr. Total strontium  is measured by beta
4817     counting immediately after it is collected by precipitation, and 90Sr is measured by isolating 90Y
4818     after ingrowth. 85Sr can be used as a tracer for determining the chemical yield of 90Sr (determined
4819     by isolating 90Y), but its beta emission interferes with beta counting of total strontium and must
4820     be accounted for in the final activity.

4821     An alternative method for determining  89Sr  and 90Sr in the presence of each other is based on the
4822     equations for decay of strontium radionuclides and ingrowth  of 90Y. Combined strontium is
4823     collected and immediately counted to determine the total strontium. During ingrowth, the
4824     mixture is recounted, and the data from the  counts are used to determine the amount of 89Sr and
4825     90Sr in the original (fresh) mixture.


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                                                                            Separation Techniques
4826        Compiled from: Baes and Mesmer, 1976; Choppin et al., 1995; Considine and Considine,
4827        1983; CRC, 1998-99; DOE, 1990 and 1997, 1997; EPA, 1973; EPA, 1980; Greenwood and
4828        Earnshaw, 1984; Hassinsky and Adloff, 1965; Riley, 1995; Sunderman and Townley, 1960;
4829        Turekian and Bolter, 1966.

4830     14.10.9.8  Technetium

4831     Technetium, atomic number 43, was the first element to be made artificially. Techetium has no
4832     stable isotopes. Natural technetium is known to exist but only in negligibly small quantities
4833     resulting from the spontaneous fission of natural uranium. Technetium is chemically very similar
4834     to rhenium, but significant differences exist that cause them to behave quite differently under
4835     certain conditions.

4836     Isotopes

4837     Thirty-one radioisotopes and unstable isomers of technetium are known with mass numbers
4838     ranging from  86 to 113. The half-lives range from seconds to millions of years. The lower mass
4839     number isotopes decay by primarily by electron capture and the higher mass number isotopes by
4840     beta emission. The significant isotopes (with half-lives/decay modes) are 95mTc (61 d/electron
4841     capture and isomeric transition), 99mTc (6.01 h/isomeric transition by low-energy gamma), and
4842     "Tc (2.13 x 105 y/beta to stable ruthenium-99). Other, long-lived isotopes  are 97Tc (2.6 x
4843     106/electron-capture) and 98Tc (4.2 x 106 y/beta emission).

4844     Occurrence and Uses

4845     The first synthesis of technetium was through the production of 99Mo by bombardment of 98Mo
4846     with neutrons and subsequent beta decay to 99Tc. Technetium is also a major constituent of
4847     nuclear reactor fission products and has been found in very small quantities in pitchblende from
4848     the spontaneous fission of naturally occurring uranium.

4849     Technetium makes up about 6 percent of uranium fission products in nuclear power plant fuels. It
4850     is recovered from these fuels by solvent extraction and ion-exchange after  storage of the fuels for
4851     several years to allow the highly radioactive, short-lived products to decay. Technetium is
4852     recovered as ammonium pertechnate (NH4TcO4) after its solutions are acidified with
4853     hydrochloric acid, precipitated with sulfide, and the sulfide (Tc2S7) is reacted with hydrogen
4854     peroxide. Rhenium and molybdenum are also removed by extraction with  organic solvents. The
4855     metal is obtained by reduction of ammonium pertechnate with hydrogen at 600 °C.
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         Separation Techniques
4856     Potassium pertechnates (KTcO4) have been used in water (55 ppm) as corrosion inhibitors for
4857     mild carbon steel in aerated distilled water, but currently there is no significant uses of elemental
4858     technetium or its compounds, although technetium and some of its alloys are a superconductor.
4859     The corrosion protection is limited to closed systems to prevent release of the radioactive isotope.
4860     99mTc, with a half-life of only 61 days, has been used in tracer work. 99mTc is used in medical
4861     diagnosis as a radioactive tracer. As a complex, the amount of 99mTc required for gamma
4862     scanning is very small, thus, it is referred to as non-invasive scanning. It is used for cardiovascu-
4863     lar and brain studies and the diagnosis of liver, spleen, and thyroid disorders. There are more than
4864     20 99mTc compounds available commercially for diagnostic purposes. With iodine isotopes, they
4865     are the most frequently used radionuclides for diagnostics. 99mTc has also been used to determine
4866     the deadtime of counting detectors.

4867     Solubility of Compounds

4868     The nature of the compounds has not been thoroughly delineated, but ammonium pertechnate
4869     is soluble in water, and technetium heptoxide forms soluble pertechnetic acid (HTcO4) when
4870     water is added.

4871     Review of Properties

4872     Technetium is a silver-grey metal that resembles platinum in appearance. It tarnishes slowly in
4873     moist air to give the oxyacid, pertechnetic acid (HTcO4). It has a density of 11.5 g/cm3. The metal
4874     reacts with oxygen at elevated temperatures to produce the volatile oxide, technetium heptoxide.
4875     Technetium dissolves in warm bromine water, nitric acid, aqua regia, and concentrated sulfuric
4876     acid, but it is insoluble in hydrochloric and hydrofluoric acids. Technetium forms the chlorides
4877     (TcCl4 and TcCl6) and fluorides (TcF5 and TcF6) by direct combination of the metal with the
4878     respective halogen. The specific halide is obtained by selecting the proper temperature and
4879     pressure for its  formation.

4880     "Tc has a high  specific activity. As a contamination hazard, it should be handled in a glove box.

4881     The behavior of technetium in groundwater is highly dependent on its oxidation state. Under
4882     oxidizing conditions, pertechnate is the predominant species. It is very soluble and only slightly
4883     absorbed to mineral components. For those reasons, it has a relatively high dissemination
4884     potential in natural systems. Under reducing conditions, technetium precipitates as technetium
4885     dioxide (TcO2), which is very insoluble. With the production of "Tc in fission fuels and
4886     considering its long half-life, the soluble form of the radionuclide is an environmental concern
4887     wherever the fuel is reprocessed or stored. As a consequence, "Tc would be expected to be one


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                                                                             Separation Techniques
4888     of the principle contributors to a radioactive release to the environment, even from repositories
4889     with barriers that could retain the radionuclide up to 10,000 years. Studies of a salt repository
4890     indicate that "Tc is one of the few radionuclides that might reach the surface before it decays.

4891     Solution Chemistry

4892     All oxidation states between -1 and +7 can be expected for technetium, but the important ones in
4893     solution are +4 and +7. The +4 state exist primarily as the slightly soluble oxide, TcO2. It is
4894     soluble only in the presence of complexing ligands; TcCl6"2, for example, is stable in solutions
4895     with a chloride concentration greater than 1 M. The most important species in solution is the
4896     pertechnate ion [TcO^1 as Tc(VII)], which is readily soluble and easily formed from lower
4897     oxidation states with oxidizing agents such as nitric acid and hydrogen peroxide. There is no
4898     evidence of polymeric forms in solution as a result of hydrolysis of the metal ion.
4899     OXIDATION-REDUCTION BEHAVIOR. Most radioanalytical procedures for technetium are
4900     performed on the pertechnate ion, TcO44. The ion can be reduced by hydrochloric acid, the
4901     thiocyanate ion (SCN"1), organic impurities, anion-exchange resins, and some organic solvents.
4902     The product of reduction can be TcO2 [Tc(IV)], although a multiplicity of other products are
4903     expected in complexing media. Even though the +7 oxidation state is easy to reduce, the
4904     reduction process is sometimes slow. Unless precautions are taken to maintain the appropriate
4905     oxidation state, however, erratic results will be obtained during the radioanalytical procedure.
4906     Several examples illustrate the precaution. Dissolution should always be performed under
4907     strongly oxidizing conditions to ensure conversion of all states to the +7 oxidation state since
4908     complications because of slow exchange with carrier and other reagents are less likely to occur if
4909     this state is maintained. Technetium is extracted with various solvents in several radioanalytical
4910     procedures, but the method can be very inefficient because of reduction of the pertechnate ion by
4911     some organic solvents. The presence of an oxidizing agent such as hydrogen peroxide will
4912     prevent the unwanted reduction. In contrast, TcO^1 is easily lost on evaporation of acid solutions
4913     unless a reducing agent is present or evaporation is conducted at a relatively low temperature.

4914     COMPLEXATION. Technetium forms complex ions in solution with several simple inorganic
4915     ligands such as fluoride and chloride.  The +4 oxidation state is represented by the TcX6"2 ion
4916     where X = F, Cl, Br, and I. It is formed from TcO^1 by reduction to the +4 state with iodide in
4917     HX. TcF6"2 is found in HF solutions during decomposition of samples, before further oxidation.

4918     Complex ions formed between organic ligands and technetium in the +5 oxidation state are
4919     known with the general formula, TcO3XLL, where X is a halide and L is an organic ligand. the


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         Separation Techniques
4920     ligands typically bond through an oxygen or nitrogen atom. Other organic complexes of the +5
4921     state have the general formulas: TcOX2L2, TcOX/1, and TcOX5'2.

4922     Dissolution of Samples

4923     Dissolution of samples containing technetium requires two precautions: it is essential that acid
4924     solutions be heated only under reflux conditions to avoid losses by volatilization, and dissolution
4925     should be done only with strongly oxidizing conditions to ensure conversion of all lower
4926     oxidation states to Tc(VII). In addition, problems with slow carrier exchange are less likely for
4927     the VII oxidation state. Molybdenum targets are dissolved in nitric acid or aqua regia, but the
4928     excess acid interferes with many subsequent analytical steps. Dissolution in concentrated sulfuric
4929     acid followed by oxidation with hydrogen peroxide after neutralization avoids these problems of
4930     excess acid. Other technetium samples can be dissolved by fusion with sodium peroxide/sodium
4931     hydroxide (Na2O2/NaOH) fluxes.

4932     Separation Methods

4933     PRECIPITATION AND COPRECIPITATION. The various oxidation states of technetium are
4934     precipitated in different forms with different reagents. Technetium (VII) is primarily present in
4935     solution as the pertechnate anion, and macro quantities are precipitated with large cations such as
4936     thallium (Tl+1), silver (Ag+1), cesium (Cs+1), and tetraphenylarsonium [(C6H5)4As+1].  the
4937     latter ion is the most efficient if ice-bath conditions are used. Perchtechnate is coprecipitated
4938     without interference from molybdenum with these cations and perrhenate (ReO44), perchlorate
4939     (ClO^1), periodate (lO/1), and tetrafluoroborate (BF^1). The salt consisting of tetraphenylar-
4940     senium and the perrhenate froms a coprecipitate fastest, in several seconds. Technetium (VII) can
4941     be precipitated from  solution as the heptasulfide (Tc2S7) by the addition of hydrogen sulfide (or
4942     hydrogen sulfide generating compounds such as thioacetamide and sodium thiosulfate) from 4 M
4943     sulfuric acid. Since many other transition metals often associated with technetium also from
4944     insoluble compounds with sulfide, the method is primarily used to concentrate technetium.

4945     Technetium (IV) is carried by ferric hydroxide. The method can be use to separate technetium
4946     from rhenium. The precipitate is solubilized and oxidized with concentrated nitric acid, and iron
4947     is removed by precipitation with aqueous ammonia. Technetium is also coprecipitated as the
4948     hexachlorotechnate (IV) (TcCl6"2) with thallium and a,a'-dipyridylhexachlororhenate (IV).

4949     Technetium (VI) (probably as TcO4.2) is carried quantitatively by molybdenum 8-hydroxyquino-
4950     late and by silver or lead molybdate. Technetium (in) is carried quantitatively by iron or zinc
4951     hydroxide and the sulfide, hydroxide, and 8-hydroxyquinolate of molybdenum.


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4952     SOLVENT EXTRACTION. Technetium, primarily in the Tc (VII) state (pertechnetate) can be
4953     isolated by extraction with organic solvents, but the principal disadvantage of all extraction
4954     systems is the inevitable introduction of organic material that might reduce the pertechnetate
4955     anion and cause difficulties in subsequent analytical  steps. The pertechnetate ion is extracted
4956     with pyridine from a 4 M sodium hydroxide solution, but perrhenate and permanganate ions are
4957     also extracted. The anion also extracts into chloroform in the presence of the tetraphenyl-
4958     arsonium ion as tetraphenylarsonium pertechnetate. Extraction is more favorable from neutral or
4959     basic sulfate solutions than chloride solutions. Perrhenate and perchlorate are also extracted but
4960     molybdenum does not interfere. Small amounts of hydrogen peroxide in the extraction mixture to
4961     prevent reduction of pertechnetate. Technetium is back-extracted into 0.2 M perchloric acid or 12
4962     M sulfuric acid. Other organic solvents are have also been used to extract pertechnetate from acid
4963     solutions, including alcohols, ketones, and tributyl phosphate. Ketones and cyclic amines are
4964     more effective for extraction form basic solutions. Tertiary amines and quaternary ammonium
4965     salts are more effective extracting agents than alcohols, ketones, and tributyl phosphate. Back
4966     extraction is accomplished several ways, depending on the extraction system. A change in pH,
4967     displacement by another anion such as perchlorate, nitrate, or bisulfate, or addition of a nonpolar
4968     solvent to an extraction system consisting of an oxygen-containing solvent.

4969     A recent extraction method has been used successfully for extraction chromatography and
4970     extractive filtration. A column material consisting of trioctyl and tridecyl methyl ammonium
4971     chlorides impregnated in an inert apolar polymeric matrix is used to separate "Tc by loading the
4972     radionuclide as the pertechnetate ion from a 0.1 M nitric acid solution. It is stripped off the
4973     column with 12 M nitric acid. Alternatively, the extraction material is used in a filter disc, and
4974     the samples containing "Tc are filtered from water at pH 2 and rinsed with 0.01 M nitric acid.
4975     Technetium is collected on the disc.

4976     Lower oxidation states of technetium are possible. The thiocyanate complexes of technetium (V)
4977     is soluble in alcohols, ethers,  ketones, and trioctylphosphine oxide or trioctylamine hydrochloride
4978     in cyclohexane or 1,2-dichloroethane. Technetium (IV), as TcCl6"2, extracts into chloroform in
4979     the presence of high  concentrations of tetraphenylarsonium ion. Pertechnate and perrhenate are
4980     both extracted from alkaline solution by hexone (methyl isobutyl ketone), but reduction of
4981     technetium to the IV state with hydrazine or hydroxylamine results in the extraction of perrhenate
4982     only.

4983     ION-EXCHANGE CHROMATOGRAPHY. Ion-exchange chromatography is primarily performed with
4984     technetium as the pertechnate anion. Technetium does not exchange on cation resins,  so
4985     technetium is rapidly separated from other cations on these columns. In contrast, it is  strongly
4986     absorbed on strong anion exchangers and is eluted with anions that have a greater affinity for the


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         Separation Techniques
4987     resin. Technetium and molybdenum are separated using ammonium thiocyanate as the eluent. A
4988     good separation of pertechnate and molybdate has been achieved on an anion-exchange resin in
4989     the phosphate form where the molybdate is preferentially absorbed. Good separation of
4990     pertechnate and perrhenate are obtained with perchlorate as the eluent.

4991     VOLATILIZATION. The volatility of technetium heptoxide allows the co-distillation of technetium
4992     with acids. Co-distillation from perchloric acid gives good yields, but only a partial separation
4993     from rhenium is achieved. Molybdenum is also carried unless complexed by phosphoric acid.
4994     Separation from rhenium can be achieved from sulfuric acid, but yields of technetium are can be
4995     very poor because of its reduction by trace impurities in the acid. Much more reproducible results
4996     can be obtained in the presence of an oxidizing agent, but ruthenium tetroxide (RuO4) also
4997     distills under these conditions. It can be removed, however, by precipitation as ruthenium dioxide
4998     RuO2. In distillation from sulfuric acid-water mixtures, technetium distills in the low-boiling
4999     point aqueous fraction, probably as pertechnetic acid. Technetium and rhenium are separated
5000     from sulfuric-hydrochloric acid mixtures; pertechnate is reduced to non-volatile Tc (IV) and
5001     remains in the acid solution. Technetium heptoxide can be separated from molybdenum trioxide
5002     by fractional  sublimation at temperatures > 300 °C.

5003     ELECTRODEPOSITION. Technetium can be electrodeposited as its dioxide  (TcO2) from 2 M
5004     sodium hydroxide. The metal is partially separated from molybdenum and rhenium, but
5005     deposition only occurs from low technetium concentrations. Carrier-free  95Tc and 96Tc have been
5006     electrolyzed on a platinum electrode from dilute sulfuric acid. Optimum electroplating of
5007     technetium has been achieved at pH 5.5 in the presence of very dilute fluoride ion. Yields were
5008     better with a copper electrode instead of platinum—about 90 percent was collected in two hours.
5009     Yields of 98-99 percent were achieved for platinum electrodes at pH 2-5  when the plating time of
5010     up to 20 hours was used.  In 2 M sulfuric acid containing traces of fluoride, metallic technetium
5011     instead of the dioxide is deposited on the electrode.

5012     Methods of Analysis

5013     "Tc is analyzed by ICP-MS  or from its beta emission. No gamma rays are emitted by this
5014     radionuclide. For ICP-MS analysis, technetium is stripped from an extraction chromatography
5015     resin and measured by the spectral system. The results should be corrected for interference by
5016     "Ru, if present. For beta  analysis, technetium can be electrodeposited on a platinum disc and beta
5017     counted. Alternatively, it is collected by extraction-chromatography techniques. The resin from a
5018     column or the disc from a filtration system is placed in a liquid scintillation vial and counted.
5019     99mjc (ti/2=g oh), measured by gamma-ray spectrometry, can be used as a tracer for measuring the
5020     chemical yield of "Tc procedures. Beta emission from the tracer should then be subtracted from


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                                                                            Separation Techniques
5021      the total beta count when measuring "Tc. Alternatively, samples are counted immediately after
5022      isolation and concentration of technetium to determine the chemical recovery, then the 99mTc is
5023      allowed to decay before analysis of the "Tc. A widely used medical application is the technetium
5024      generator. 98Mo is neutron irradiated and chemically oxidized to "MoO42". This solution is ion
5025      exchange onto an acid-washed alumina column. After about 1.25 days, the activity of 99mTc has
5026      grown-in to its maximum concentration. The 99Tc is eluted with a 0.9% solution of NaCl, while
5027      the 99Mo remains on the column. The column may have its 99mTc removed after another 1.25
5028      days, but at a slightly smaller concentration. The 99mTc thus separated is carrier free. This process
5029      historically was referred to as "milking," and the alumina column was call the "cow."

5030         Compiled from: Anders, 1960; CRC,  1998-99; Choppin et al., 1995; Cobble, 1964;
5031         Considine and Considine, 1983; Coomber, 1975; Cotton and Wilkinson, 1988; DOE, 1990
5032         and 1997, 1995, 1997; Ehmann and Vance, 1991; Fried, 1995; Greenwood and Earnshaw,
5033         1984; Hassinsky and Adloff, 1965; Kleinberg et al., 1960; Lindsay, 1988; SCA, 2001; and
5034         Wahl and Bonner, 1951.

5035      14.10.9.9  Thorium

5036      Thorium, with an atomic number of 90, is the second member in the series of actinide elements.
5037      It is one of only three of the actinides—thorium, protactinium, and uranium—that occur in nature
5038      in quantities sufficient for practical extraction. In solution, in all minerals, and in virtually all
5039      compounds, thorium exists in the +4 oxidation state; it is the only actinide exclusively in the +4
5040      state in solution.

5041      Isotopes

5042      There are 24 isotopes of thorium ranging inclusively from 213Th to 236Th; all are radioactive.
5043      232Th, the parent nuclide in the natural decay series, represents virtually 100 percent of the
5044      thorium isotopes in nature, but there are a trace amounts of 227Th, 228Th, 230Th, 231Th, and 234Th.
5045      The remaining isotopes are artifacts. The most important environmental contaminants are 232Th
5046      and 230Th, (a member of the 238U decay series). They have half-lives of 1.41 x 1010 years and
5047      75,400 years, respectively.

5048      Occurrence and Uses

5049      Thorium is widely but sparsely dispersed in the Earth's crust. At an average concentration of
5050      approximately 10 ppm, it is over three times as abundant as uranium. In the ocean and rivers,
5051      however, its concentration is about one-thousandth that of uranium (about 10"8 g/L) because its


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         Separation Techniques
5052     compounds are much less soluble under environmental conditions. There are six minerals whose
5053     essential element is thorium; thorite (uranothorite) and thorianite are common examples. Several
5054     lanthanum and zirconium minerals are also thorium-bearing minerals; examples include
5055     monazite sand and uraninite. In each mineral, thorium is present as its oxide, thorium dioxide
5056     (ThO2). Monazite sand is the most common commercial mineral, but thorite is also a source of
5057     thorium.

5058     Thorium is extracted from its minerals with hot sulfuric acid or hot concentrated alkali,
5059     converted into thorium nitrate [Th(NO3)4] (its chief commercial compound), extracted with
5060     organic solvents (commonly kerosene containing tributylphosphate), stripped from the organic
5061     phase by alkali solutions, and crystallized as thorium nitrate or precipitated with oxalate. The
5062     metal can be produced by electodeposition from the chloride or fluoride dissolved in fused alkali
5063     halides or by thermoreduction of thorium compounds by calcium (1,000-1,200 °C). Thorium can
5064     also be produced as a by-product in the production of other valuable metals such as nickel,
5065     uranium, and zirconium, in addition to the lanthanides. Unextracted minerals or partially
5066     extracted mill tailings represent some forms of thorium contaminants found in the environment.
5067     Very insoluble forms of thorium hydroxide [Th(OH)4] are other common species found.

5068     Metallic thorium has been used as an alloy in the magnesium industry and as a deoxidant for
5069     molybdenum, iron, and other metals. Because of its high density, chemical reactivity, poor
5070     mechanical properties, and relatively high cost, it is not used as a structural material. Thorium
5071     dioxide is a highly refractory material with the highest melting point among the oxides, 3,390
5072     °C. It has been used in the production of gas mantles, to prevent crystallization of tungsten in
5073     filaments, as furnace linings, in nickel alloys to improve corrosion resistance, and as a catalyst in
5074     the conversion of methanol to formaldehyde. 232Th is a fuel in breeder reactors.  The radionuclide
5075     absorbs slow neutrons, and with the consecutive emission of two beta particles, it decays to 233U,
5076     a fissionable isotope of uranium with a half-life of 159,000 years.

5077     Solubility of Compounds

5078     Thorium exists in solution as a highly charged ion and undergoes extensive interaction with
5079     water and with many anions. Few of the compounds are water soluble; soluble thorium
5080     compounds include the nitrate [Th(NO3)4], sulfate [Th(SO4)2], chloride (ThCl4), and perchlorate
5081     [Th(ClO4)4]. Many compounds are insoluble in water and are used in the precipitation of thorium
5082     from solution, including the hydroxide [Th(OH)4], fluoride (ThF4), iodate  [Th(IO3)4], oxalate
5083     [Th(C2O4)2], phosphate [Th3(PO4)4], sulfite [Th(SO3)2], dichromate [Th(Cr2O7)2], potassium
5084     hexafluorothorionate [K2ThF6], thorium ferrocyonide (II) [ThFe(CN)6], and thorium peroxide
5085     sulfate [Th(OO)2SO4].


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                                                                             Separation Techniques
5086     The thorium ion forms many complex ions, chelates, and solvated species that are soluble in
5087     organic solvents. This property is the basis of many procedures for the separation and purification
5088     of thorium (see below). For example, certain ions, such as nitrate and sulfate, form large
5089     unsolvated complex ions with thorium that are soluble in organic solvents. Chelates of 1,3-
5090     diketones, such as acetylacetone (acac) and 2-thenoyltrifluoroacetone (TTA), form neutral
5091     molecular chelates with the thorium ion that are soluble. In addition, many neutral organic
5092     compounds have strong solvating properties for thorium, bonding to the thorium ion in much the
5093     same way water solvates the ion at low pH. Tributylphosphate (TBP), diethyl ether, methyl ethyl
5094     ketone, mesityl oxide, and monoalkyl and dialkyl phosphates are examples of such compounds.

5095     Review of Properties

5096     Thorium is the first member of the actinide series of elements that includes actinium (Ac),
5097     uranium, and the transuranium elements. Thorium is a bright, silver-white metal with a density
5098     above  11 g/cm3. It tarnishes in air, forming a dark gray oxide coating. The massive metal is
5099     stable, but in finely divided form and as a thin ribbon it is pyrophoric and forms thorium oxide
5100     (ThO2). Thorium metal dissolves in hydrochloric acid, is made  passive by nitric acid, but is not
5101     affected by alkali. It is attacked by hot water and steam to form the oxide coating and hydrogen,
5102     but its  reactions with water are complicated by the presence of oxygen. Thorium has four valence
5103     electrons (6d27s2). Under laboratory conditions, chlorides, bromides, and iodides of the bi- and
5104     trivalent state have been prepared. In aqueous solution and in most compounds, including all
5105     those found in nature, thorium exists only in the +4 oxidation state; its compounds are colorless
5106     in solution unless the anion provides a color. Thorium forms many inorganic compounds in acid
5107     solution.

5108     Solution Chemistry

5109     Because the only oxidation state of thorium in solution is the +4 state, its chemistry is not
5110     complicated by oxidation-reductions reactions that might produce alternate species in solution.
5111     With the +4  charge and corresponding charge-to-radius ratio of 4.0, however, thorium forms very
5112     stable complex ions with halides, oxygen-containing ligands, and chelating agents. Although
5113     Th+4 is large (0.99 A; 0.099 nm; 99 pm) relative to other +4 ions (Ti, Zr, Hf,  Ce) and therefore
5114     more resistant to hydrolysis, as a highly charged ion, it hydrolyzes extensively in aqueous
5115     solutions above pH 3 and tends to behave more like a colloid than a true solution.  The
5116     concentration of Th+4 is negligible under those conditions. Below pH 3, however, the
5117     uncomplexed ion is stable as the hydrated ion, Th(H2O)8 Or9+4.
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         Separation Techniques
5118     COMPLEXATION. Thorium has a strong tendency to form complex ions in solution. The presence
5119     of HF forms very stable complex ions, for example, with one, two, or three ligands:

5120                                      Th+4 + HF - ThF+3 + H+1
5121                                     ThF+3 + HF - ThF2+2 + H+1
5122                                     ThF2+2 + HF - ThF3+1 + H+1

5123     These complex ions represent the predominant species in solutions containing HF. Stable
5124     complex ions also form with oxygen-containing ligands such as nitrate, chlorate, sulfate,
5125     bisulfate, iodate, carbonate, phosphate, most carboxylate anions, and chelate anions. Some
5126     chelating agents such as salicylate, acetylacetonate (acac), theonyltrifluoroacetonate (TTA), and
5127     cupferron form complexes that are more soluble in organic solvents, This property is the basis of
5128     several radiochemical isolation methods for thorium. Through the formation of soluble complex
5129     ions, chelating agents found in some industrial wastewater or natural water samples will interfere
5130     to varying degrees with the isolation of thorium by ferric hydroxide [Fe(OH)3] coprecipitation.
5131     Alternative isolation methods should be used, such as coprecipitation from an acidic solution
5132     with an alternative reagent. Protonation of the anionic form of chelates with acid renders them
5133     useless as chelating  agents. Other complexing agents also interfere with precipitation by the
5134     formation of soluble ions. Thorium, for example, does not precipitate with oxalate in the
5135     presence of carbonate ions. A procedure for separating thorium from rare-earth ions takes
5136     advantage of the formation of a soluble thorium-EDTA complex that inhibits thorium
5137     precipitation when the rare-earth ions are precipitated with phosphate. The presence of high
5138     concentrations of other complexing agents such as phosphate, chloride, and other anions found in
5139     some samples takes  thorium into a completely exchangeable form when it is solubilized in high-
5140     concentrati on nitri c  aci d.

5141     HYDROLYSIS. Beginning at pH 3, thorium ions undergo extensive hydrolysis to form monomeric
5142     and polymeric complexes in solution, leaving little (approximately 5 x 10"6 M) Th+4in a saturated
5143     solution at pH 3. Tracer solutions containing 234Th can be added at pH 2 to allow equilibration
5144     because it is not likely to occur if part of the thorium is hydrolyzed and bound in polymeric
5145     forms.

5146     The hydrolysis process is complex, depending on the pH of the solution and its ionic strength.
5147     Several species have been proposed: three are polynuclear species, Th2(OH)2+6, Th4(OH)8+8, and
5148     Th6(OH)15+9; and two are monomeric species, Th(OH)+3 and Th(OH)2+2. The monomeric species
5149     are of minor importance except in extremely dilute solutions, but they become more important as
5150     the temperature increases. The presence  of chloride and nitrate ion diminishes hydrolysis,
5151     because the formation of corresponding complex ions markedly suppresses the process.


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                                                                             Separation Techniques
5152     Hydrolysis increases with increasing hydroxide concentration (pH), and eventually polymeriza-
5153     tion of the species begins. At a pH of about 5, irreversible hydrolysis produces an amorphous
5154     precipitate of thorium hydroxide, a polymer that might contain more than 100 thorium atoms.
5155     Just before precipitation, polymerization slows and equilibration might take weeks or months to
5156     obtain.

5157     Routine fuming of a sample containing organic material with nitric acid is recommended after
5158     addition of tracer, but before separation of thorium as a hydroxide precipitate because there is
5159     evidence for lack of exchange between added tracer and isotope already in solution. Complexing
5160     with organic substances in the initial solution or existence of thorium in solution as some
5161     polymeric ion have  been suggested as the cause.

5162     ADSORPTION. The insoluble hydroxide that forms in solution above pH 3 has a tendency to
5163     coagulate with hydrated oxides such as ferric oxide. The high charge of the thorium cation (+4),
5164     high charge-to-radius  ratio, and tendency to hydrolyze all contribute to the ability of thorium to
5165     adsorb on surfaces by ion-exchange mechanisms or chemical adsorption mechanisms. These
5166     adsorption properties greatly affect the interaction of thorium with ion-exchange resins and
5167     environmental media such as soil.

5168     Dissolution of Samples

5169     Thorium samples are ignited first to remove organic materials. Most compounds will decompose
5170     when sintered with  sodium peroxide (Na2O2), and most thorium minerals will yield to alternate
5171     sodium peroxide sintering and potassium pyrosulfate (K2S2O7) fusion. It is often necessary to
5172     recover thorium from  hydrolysis products produced by these processes. The hydrolysis products
5173     are treated with hydrofluoric acid, and thorium is recovered as the insoluble fluoride. Rock
5174     samples are often dissolved in hydrofluoric acid containing either nitric acid or perchloric acid.
5175     Monazite is dissolved by prolonged sintering or with fuming perchloric or sulfuric acid. Thorium
5176     alloys are dissolved in two steps, first with aqua regia (nitric and hydrochloric acid mixture)
5177     followed by fusion with potassium pyrosulfate. Thorium targets are dissolved in concentrated
5178     nitric acid containing hydrofluoric acid, mantles in nitric or sulfuric acid, and tungsten filaments
5179     with aqua regia or perchloric acid.

5180     Separation Methods

5181     PRECIPITATION AND COPRECIPITATION. Precipitation and coprecipitation are used to separate and
5182     collect thorium from aqueous solutions either for further treatment in an analytical scheme or for
5183     preparation of a sample for counting. Formation of insoluble salts is used to precipitate thorium


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         Separation Techniques
5184     from solution; examples include the hydroxide, peroxide, fluoride, iodate, oxalate, and
5185     phosphate, among others. Tracer quantities of thorium are commonly coprecipitated with
5186     lanthanum fluoride (LaF3), neodymium fluoride (NdF3), and cerium fluoride (CeF3) in separation
5187     schemes and to prepare samples for alpha counting. Tracer quantities are also carried with
5188     calcium oxalate [Ca(C2O4)], ferric hydroxide [Fe(OH)3], zirconium iodate (ZrIO4), zirconium
5189     phosphate (Zr3PO4), and barium sulfate (BaSO4).

5190     ION EXCHANGE. The highly charged thorium cation is strongly adsorbed onto cation exchangers
5191     and is more  difficult to elute than most other ions. Its strong adsorption property makes it
5192     possible to remove trace quantities of thorium from a large volume of solution onto small
5193     amounts of ion-exchange resin. Washing the resin with mineral acids of various concentrations
5194     separates thorium from less strongly bound cations that elute from the resin. For example, Th+4
5195     remains bonded at all hydrochloric concentrations, allowing other cations to be eluted at different
5196     concentrations of acid. Thorium is eluted by complexing agents such as citrate, lactate, fluoride,
5197     carbonate, sulfate, or oxalate that reduce the net charge of the absorbing species, causing reversal
5198     of the adsorption process.

5199     Anion exchangers are useful for separating thorium, but the contrasting behavior of thorium with
5200     the resin depends on whether hydrochloric or nitric acid is used as an eluent. In hydrochloric
5201     acid, several metal  ions,  unlike thorium, form negative complexes that can be readily removed
5202     from a thorium solution by adsorption onto the anionic exchanger. Thorium forms positively
5203     charged chlorocation complexes or neutral thorium chloride (ThCl4) in the acid and is not
5204     adsorbed by the resin at any hydrochloric acid concentration. In contrast, thorium forms anionic
5205     complexes in nitric acid  solution that adsorb onto the exchanger over a wide range of nitric acid
5206     concentrations, reaching a maximum affinity near 7 M nitric acid. Behavior in nitric acid solution
5207     is the basis for a number of important radiochemical separations of thorium from rare earths,
5208     uranium, and other elements.

5209     ELECTRODEPOSITION. Thorium separated from other actinides by chemical methods can be
5210     electrodeposited for alpha counting from a dilute solution of ammonium sulfate adjusted to a pH
5211     of 2. The hydrous oxide  of thorium is deposited in one hour on a highly polished platinum disc
5212     serving  as the cathode of an electrolytic cell. The anode is a platinum-iridium  alloy.
5213
5214     SOLVENT EXTRACTION. Many complexes and some compounds of thorium  can be extracted from
5215     aqueous solutions into a variety of organic solvents. The TTA (a-theonyltrifluoroacetone)
5216     complex of metals is widely used in radiochemistry for the separation of ions. Thorium can be
5217     separated from most alkali metal, alkaline earth, and rare earth metals after the complex is
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                                                                             Separation Techniques
5218     quantitatively extracted into benzene above pH 1. Backwashing the organic solution with dilute
5219     acid leaves the more soluble ions in benzene.

5220     Extraction of nitrates and chlorides of thorium into organic solvents from the respective acid
5221     solutions is widely used for isolation and purification of the element. One of the most common
5222     processes is the extraction of thorium nitrate from a nitric acid solution with TBP (triisobutyl-
5223     phosphate).  TBP is usually diluted with an inert solvent such as ether or kerosene to reduce the
5224     viscosity of the mixture. Dilution reduces the extraction effectiveness of the mixture, but the
5225     solubility of many contaminating ions is greatly reduced, increasing the effectiveness of the
5226     separation when the thorium is recovered by backwashing.

5227     Long-chain  amine salts have been very effective in carrying thorium in laboratory and industrial
5228     extraction process using kerosene. Complex sulfate anions of thorium  are formed in sulfuric acid
5229     that act as the counter ion to the protonated quaternary amine cation. They accompany the
5230     organic salt into the organic phase.

5231     In recent years, solvent extraction chromatography procedures have been developed to separate
5232     thorium.  These procedures use extraction chromatography resins that consist of extractant
5233     materials  such as  CMPO in TBP or DPPP (dipentylpentylphosphonate), also called DAAP
5234     (diamylamylphosphonate), absorbed onto an inert polymeric material.  They are used in a column,
5235     rather than in the traditional batch mode, and provide a rapid efficient  method of separating the
5236     radionuclide with the elimination of large volumes of organic waste.

5237     Methods of Analysis

5238     Chemical procedures are used for the analysis of macroscopic quantities of thorium in solution
5239     after it has been separated by precipitation, ion  exchange, extraction, and/or extraction chroma-
5240     tography from interfering ions. Gravimetric determination generally follows precipitation as the
5241     oxalate that is calcined to the oxide (ThO2). Numerous volumetric analyses employ EDTA as the
5242     titrant. In the most common spectrometric method of analysis, thorin, a complex organoarsenic
5243     acid forms a colored complex with thorium that is measured in the visible spectrum.

5244     Trace quantities of thorium are measured by alpha spectrometry after chemical separation from
5245     interfering radionuclides. 227Th, 228Th, 230Th, and 232Th are determined by the measurement of
5246     their respective spectral peaks (energies), using 234Th as a tracer to determine the chemical yield
5247     of the procedure. The activity of the tracer is determined by beta counting in a proportional
5248     counter. 234Th also emits gamma radiation that can be detected by gamma spectrometry; however,
5249     the peak can not be measured accurately because  of interfering peaks of other gamma-emitting


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         Separation Techniques
5250     radionuclides. 229Th is sometimes used as a tracer to determine the chemical yield of the alpha
5251     spectrometric procedure, but it produces considerable recoil that might contaminate the detector.

5252        Compiled from: Ahrland, 1986; Baes and Mesmer, 1976; Cotton, 1991; Cotton and
5253        Wilkinson, 1988; DOE, 1990 and 1997, 1997; EPA, 1980 and 1984; Greenwood,  1984;
5254        Grimaldi, 1961; Hassinsky and Adloff, 1965; Hyde, 1960; Katzin, 1986; Lindsey, 1988.

5255     14.10.9.10 Tritium

5256     Unlike the elements reviewed in this section, tritium the only radionuclide of the element
5257     hydrogen. It contains two neutrons and is represented by the symbols 3H, 3T, or simply, T. The
5258     atom contains only one valence electron so its common oxidation state, besides zero, is +1,
5259     although it can exist in the -1 state as a metal hydride.

5260     Occurrence and Uses

5261     Tritium is found wherever stable hydrogen is found, with and without the other isotopes of the
5262     element (hydrogen and deuterium)—as molecular hydrogen (HT, DT, T2), water (HTO, DTO,
5263     T2O), and inorganic and organic compounds, hydrides and hydrocarbons, respectively, for
5264     example. About 99 percent of the radionuclide in nature from any source is in the form of HTO.
5265     Natural processes account for approximately one T atom per 1018 hydrogen atoms. The source of
5266     some natural tritium is ejection form the sun, but the primary source is from bombardment of 14N
5267     with cosmic neutrons in the upper atmosphere:

5268                                       147N + > - 3H + 126C

5269     Most tritium from this source appears as HTO.

5270     Tritium is produced in laboratory and industrial  processes by nuclear reactions such as:

5271                                       \D + \D - 3J + \H

5272     For large-scale production of tritium, 6Li alloyed with magnesium or aluminum is the target of
5273     neutrons:

5274                                      63Li + > - 3J + 42He
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5275     The radionuclide is retained in the alloy until released by acid dissolution of the target. Large
5276     quantities are handled as HT or HTO. HTO is formed from HT when it is exposed to oxygen or
5277     water vapor. A convenient way to store tritium is as the hydride of uranium (UT3). It is formed by
5278     reacting the gas with finely divided uranium and is released by heating the compound above 400
5279     °C.

5280     Tritium is also produced in nuclear reactors that contain water or heavy water from the neutron
5281     bombardment of deuterium:
5282
5283                                           2D +10n - 3T

5284     Most tritium (>99%) in reactors is formed from the fission process as a ternary particle.
5285     The main use for tritium is in fission bombs to boost their yield  and in thermonuclear weapons,
5286     the hydrogen bomb. Tritium bombarded with high-energy deuterons undergoes fusion to form
5287     helium and releasing neutrons:

5288                                       \H + \H -42He + >

5289     A tremendous amount of energy is released during the nuclear reaction, much more than the
5290     energy of the bombarding particle. Fusion research on controlled thermonuclear reactions should
5291     lead to an energy source for electrical generation.

5292     Tritium absorbed on metals are a source of neutrons when bombarded with deuterons. Mixed
5293     with zinc sulfide, it produces radioluminescence that is used in luminescent paint and on watch
5294     dials. Gaseous tritium in the presence of zinc sulfide produces a small, permanent light source
5295     found in  rifle sights and exit signs. Tritium is also a good tracer since it does not emit gamma
5296     radiation. Hydrological  studies with HTO is used to trace geological water and the movement of
5297     glaciers.  It is also used as a tracer for hydrogen in chemical studies and biological research. In
5298     medicine, it is used for diagnosis and radiotreatment.

5299     Review of Properties

5300     Tritium decays with a half-life of 12.3 y by emission of a low-energy beta particle to form 3He,
5301     and no gamma radiation is released. The range of the beta particle is low, 6 mm in air and 0.005
5302     mm in water or soft tissue.
5303
5304     The physical and chemical properties of tritium are somewhat different than hydrogen or
5305     deuterium because of their mass differences (isotope effects). Tritium is approximately twice as


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         Separation Techniques
5306     heavy as deuterium and three times heavier than hydrogen, and the isotope effect can be large for
5307     mass differences of these magnitudes. In its simple molecular form, tritium exists primarily as T2
5308     or DT. The oxide form is HTO, DTO, or T2O, with higher molecular weights than water (H2O).
5309     Thus molecules of tritiated water are heavier, and any process  such as evaporation or distillation
5310     that produces a phase transition results in isotopic fractionation and enrichment of tritium in
5311     water. In a mixture of the oxides, various mixed isotopic water species are generally also present
5312     because of exchange reactions: in any mixture of H2O, D2O, and T2O, HTO and DTO are
5313     found.Molecules of HTO are more stable than H2O or HDO and  are not as easily decomposed by
5314     electrolysis, to form hydrogen or oxygen. Electrolysis of a water sample to about five percent of
5315     its original volume, therefore, concentrates tritium by retaining approximately 80 percent of the
5316     tritium from the initial volume. Reaction rates of chemical bonds containing tritium are slower
5317     because of the isotope effect than those of hydrogen. The rates can be as small as 1:64 (T:H), and
5318     these differences should be considered when interpreting tracer studies of reaction mechanisms.
5319     Chemical  isotope effects are large in some biological systems.  Some algae and bacteria
5320     selectively exchange hydrogen isotopes, and the preference is tritium over deuterium over
5321     hydrogen. Enrichment of tritium can be about 2.5.

5322     Tritium can be introduced into organic compounds by exposing T2 to the compound for a few
5323     days or weeks, irradiation of the compound and a lithium salt with neutrons (recoil labeling), or it
5324     can be selectively introduced into a molecule by chemical synthesis using a molecular tritium
5325     source such as HTO. Beta radiation causes exchange reactions between hydrogen atoms in the
5326     compound and tritium and migration of the isotope within the molecule. Phenol  (C6H5OH), for
5327     example, labeled with tritium on the oxygen atom (C6H5OT) will become C6H4TOH and
5328     C6H4TOT. When tritium samples are stored in containers made from organic polymers such as
5329     polyethylene, the container will adsorb tritium, resulting in a decrease in the concentration of
5330     tritium in the  sample. Eventually, the tritium atoms will migrate to the outer surface of the
5331     container, and tritium will be lost to the environment. Catalytic exchange  also occurs in tritiated
5332     solutions or solutions containing T2 gas. Exchange is very rapid with organic compounds when
5333     H+1 or OH"1 ions or if a hydrogen-transfer agent such as Pt or Pd is present.

5334     Tritium as HT or HTO will absorb on most metallic  surfaces. Penetration at room temperature is
5335     very slow, and the radionuclide remains close to the  surface. In the form of HTO, it can be
5336     removed with water, or by hydrogen gas in the  form  of HT. Heating aids the removal. When
5337     tritium is absorbed at elevated temperatures, it penetrates deeper into the surface. Adsorption
5338     under these conditions will result in enough penetration to cause structural damage to the metal,
5339     especially if the process  continues for extended periods. Hydrogenous material such as rubber
5340     and plastics will also absorb tritium.  It will penetrate into the material, and hydrogenous
5341     materials are readily contaminated deep into the material, and it is impossible to completely


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                                                                             Separation Techniques
5342     remove the tritium. Highly contaminated metal or plastic surfaces can release some of the loosely
5343     bound tritium immediately after exposure in a process called outgassing.

5344     Pure T2O can be prepared by oxidation of tritium gas with hot copper(II) oxide or direct
5345     combination of the gas with oxygen in the presence of an electrical spark. It is never used for
5346     chemical or biological processes because one milliliter contains 2,650 curies. The liquid is self-
5347     luminescent, undergoes rapid self-radiolysis, and considerable radiation damage is done to
5348     dissolved species. For the same reason, very few compounds of pure tritium have ever been
5349     prepared or studied.

5350     The radiotoxity of tritium is rated medium. Tritium is not a hazard outside the body. Gamma
5351     radiation is not released by its decay. The beta emission is low in energy compared to most beta
5352     emitters and readily stopped by the outer layer of skin. Only ingested tritium can be a hazard.
5353     Exposure to tritium is primarily in the form of HT gas or HTO water vapor, although T2 and T2O
5354     may be present. Only about 0.005 percent of the activity of inhaled HT gas is incorporated into
5355     lung tissue, and most is exhaled. Tritiated water vapor, however, is almost 100 percent absorbed
5356     from inhalation or ingestion. In addition, tritiated water can be absorbed through the skin or
5357     wounds. Not all gloves will prevent exposure because of the  ability of tritium to be absorbed by
5358     the gloves themselves. Tritium is found in tissue wherever hydrogen is found. The biological
5359     half-life is about ten days, but the value varies significantly, depending on exertion rates and
5360     fluid intake.

5361     Environmental tritium is formed in the gaseous and aqueous  forms, but over 99 percent of tritium
5362     from all sources is found in the environment after exchange with hydrogen in water in the form
5363     of HTO. It is widely distributed in the surface waters of the earth and makes a minor contribution
5364     to the activity of ocean water. It can also be found in laboratories and industrial sites in the form
5365     of metal hydrides, tritiated pump oil, and tritiated gases such as methane and ammonia.

5366     Separation Methods

5367     DISTILLATION. Tritium in water samples is essentially in the form of HTO. It can be removed
5368     quantitatively from aqueous mixtures by distillation to dryness, which also separate it form other
5369     radionuclides. Volatile iodine radionuclides are precipitated as silver iodide before distillation, if
5370     they are present. The aqueous solution is usually distilled, however, from a basic solution of
5371     potassium permangenate, which will oxidize radionuclides, such as iodine and carbon, and
5372     oxidize organic compounds that might interfere with subsequent procedures, liquid scintillation
5373     counting, for example. Charcoal can also be added to the distillation  mixture as an additional
5374     measure to remove organic material. Contaminating tritium in soil samples can be removed by


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         Separation Techniques
5375     distillation from similar aqueous mixtures. All tritium in soil samples might not be recovered by
5376     this method, however, if the tritium is tightly bound to the soil matrix. Tritium also can be
5377     removed by distillation of an azeotrope mixture formed with toluene or cyclohexane. In some
5378     procedures, tritium is initially separated by distillation and then concentrated (enriched) by
5379     electrolysis in an acid or base solution. Recovery of tritium from the electrolytic cell for analysis
5380     is accomplished by a subsequent distillation.

5381     DECOMPOSITION. Organically bound tritium (OBT) in vegetation, food, and tissue samples can be
5382     removed by combustion. The sample is freeze dried (lyophilized), and the water from the process
5383     is collected in cold traps for tritium analysis. The remaining solid is collected as a pellet, which is
5384     burned at 700 °C in a highly purified mixture of argon and oxygen in the presence of a copper(I)
5385     oxide (CuO) catalyst, generated on a copper screen at the temperature of the process. Water from
5386     the combustion process, containing tritium from the pellet, and water from the freeze-drying
5387     process is analyzed for tritium by liquid scintillation counting.

5388     Tritium in HTO can be reduced to TH by heating with metals, such as magnesium, zinc, or
5389     calcium, and analyzed as a gas.

5390     CONVERSION TO ORGANIC COMPOUNDS. Compounds that react readily with water to produce
5391     hydrogen derivatives can be used to isolate and recover tritium that is present in the HTO form.
5392     Organic compounds containing magnesium (Grignard reagents) with relatively low molecular-
5393     weights will react spontaneously with water and produce  a gaseous product containing hydrogen
5394     from the water. Tritium from HTO in a water sample will be included in the gaseous sample. It is
5395     collected after formation by condensation in a cold trap and vaporized into a gas tube for
5396     measurement. Grignard reagents formed from butane, acetylene, and methane can be used in this
5397     method. Tritiated butane is produced by the following chemical reaction:

5398                              C4H9MgBr + THO - C4H9T + Mg(OH)Br

5399     Inorganic compounds can also be use to produce gaseous products:

5400                           A14C3 + 3  HTO + 9 H2O - 3 CH3T + 4 A1(OH)3

5401     EXCHANGE. Methods to assess tritium in compounds take advantage of exchange reactions to
5402     collect the radionuclide in a volatile substance that can be collected in a gas tube for measure-
5403     ment. Acetone is one compound that easily exchanges tritium in an acid or base medium and is
5404     relatively volatile.
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                                                                            Separation Techniques
5405     Methods of Analysis

5406     Tritium is collected primarily as HTO along with water (H2 O) by distillation and then
5407     determined from its beta emission in a liquid scintillation system. No gamma rays are emitted.
5408     The distillation process is usually performed from a basic solution of potassium permangenate to
5409     oxidize radionuclides and organic compounds, preventing them from distilling over and
5410     subsequently interfering with counting. Charcoal can also be added to the distillation mixture as
5411     an additional measure to remove organic material. Volatile iodine radionuclides can be
5412     precipitated as silver iodide before distillation.

5413         Compiled from: Choppin et al., 1995; Cotton and Wilkinson, 1988; DOE, 1994; Duckworth,
5414         1995; Greenwood and Earnshaw, 1984; Hampel, 1968; Hassinky and Adloff,  1965; Kaplan,
5415         1995; Lindsay, 1988; Mitchell, 1961; Passo and Cook, 1994.

5416     14.10.9.11 Uranium

5417     Uranium, atomic number 92, is the last naturally occurring member of the actinide series and the
5418     precursor to the transuranic elements. Three isotopes are found in nature, and uranium was the
5419     active constituent in the salts whose study led to the discovery of radioactivity by Becquerel in
5420     1896.

5421     Isotopes

5422     There are 19 isotopes of uranium with mass numbers ranging from 222 to 242. All isotopes are
5423     radioactive with half-lives range ranging from microseconds to billions of years. 235U (0.72%)
5424     and 238U (99.27%) are naturally occurring as primordial uranium.  234U has a natural abundance of
5425     0.0055%, but is present as a part of the 238U decay natural decay chain. The 234U that was formed
5426     at the time the earth was formed has long since decayed. The half-lives of these principal
5427     isotopes of uranium are listed below.

                                          Alpha Decay     Spontaneous Fission
5428                          Isotope        Half-Life           Half-Life
5429                           234       2.46 x 105years     1.42 x 1016years
5430                           235       7.04 x 108years     9.80 x 1018years
5431                           238       4.48 x 109years     8.08 x 1015years
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         Separation Techniques
5432     These isotopes have two different decay modes. Each decay mode has its own characteristic half-
5433     life. As seen above the alpha decay mode is the most significant, since it has the shortest half-life
5434     for each of these isotopes.

5435     Another isotope of uranium of significance is 232U (half-life 69.8 years). It is used as a tracer in
5436     uranium analyses and is also an alpha emitter so it can be determined concurrently with the major
5437     uranium isotopes by alpha spectrometry.

5438      235U and artificially produced 233U are fissionable material on bombardment with slow (thermal)
5439     neutrons. Other uranium radionuclides are fissionable with fast moving neutrons, charged
5440     particles, high-energy photons, or mesons. 238U and 235U are both parents of natural radioactive
5441     decay series, the uranium series of 238U that eventually decays with alpha and beta emissions to
5442     stable 206Pb and the actinium series of 235U that decays to 207Pb.

5443     Occurrence and Uses

5444     Naturally occurring uranium is believed to be concentrated in the earth's crust with an average
5445     concentration of approximately 4 ppm. Granite rocks contains up to 8 ppm or more, and ocean
5446     water contains 0.0033 ppm. Many uranium minerals have been discovered.  Among the better
5447     known are uraninite, carnotite, adavidite, pitchblende, and coffinite. The latter two minerals are
5448     important commercial sources of uranium. It is also found in phosphate rock, lignite, and
5449     monazite sands and is commercially available from these sources. The artificial isotope, 233U, is
5450     produced from natural 232Th by absorption of slow neutrons to form 233Th, which decays by the
5451     emission of two beta particles to 233U.

5452     Uranium is extracted from uranium minerals, ores, rocks, and sands by numerous chemical
5453     extraction (leaching) processes. The extraction process is sometimes preceded by roasting the ore
5454     to improve the processing characteristic of the material. The extraction process uses either an
5455     acid/oxidant combination or sodium carbonate treatment, depending on the  nature of the ore, to
5456     convert the metal to a soluble form of the uranyl ion. Uranium is recovered  from solution by
5457     precipitating the uranate salt with  ammonia or sodium hydroxide solution. Ammonium uranate is
5458     know as yellow cake.  The uranate salt is solubilized to give a uranyl nitrate  solution that is
5459     further purified by extraction into an organic phase to separate the salt from impurities and
5460     subsequent stripping with water. It is precipitated as a highly purified nitrate salt that is used to
5461     produce other uranium compounds—uranium trioxide (UO3) by thermal processing or uranium
5462     dioxide (UO2) on reduction of the tri oxide with hydrogen. Uranium tetrafluoride (UF4) is
5463     prepared, in turn, form the dioxide by treatment with hydrogen fluoride. The metal is recovered
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                                                                            Separation Techniques
5464     by fused-salt electrolysis in molten sodium chloride-calcium chloride or reduction with more
5465     active metals such as calcium or magnesium (Ames Process) in an inert atmosphere at 1,000 °C.

5466     Early in the twentieth century, the only use of uranium was in the production of a brown-yellow
5467     tinted glass and glazes; it was a byproduct of the extraction of radium, which was used for
5468     medicinal and research purposes. Since the mid-twentieth century, the most important use of
5469     uranium is as a nuclear fuel, directly in the form of 233U and 235U, fissionable radionuclides, and
5470     in the form of 238U that can be converted to fissionable 239Pu by thermal neutrons in breeder
5471     reactors. Depleted uranium, uranium whose 235U content has been reduced to below about 0.2
5472     percent, the majority of waste from the uranium enrichment process, is used in shielded
5473     containers to transport radioactive materials, inertial guidance devices, gyro compasses,
5474     counterweights for aircraft control surfaces, ballast for missile reentry vehicles, fabrication of
5475     armor-piercing conventional weapons, and tank armor plating. Uranium metal is used as a X-ray
5476     target for production of high-energy X-rays, the nitrate salt as a photographic toner, and the
5477     acetate is used in analytical chemistry.

5478     Solubility of Compounds

5479     Only a small number of the numerous uranium compounds are soluble in water. Except for the
5480     fluorides, the halides of uranium (HI and IV) are soluble, as are the chloride and bromide of
5481     uranium (V) [UOX2] and the fluoride, chloride, and bromide of uranium (VI) [UO2X2]. Several
5482     of the uranyl (UO2+2) salts of polyatomic anions are also soluble in water: the sulfate,
5483     bicarbonate, acetate, thiocyanate, chromate, tungstate, and nitrate. The latter is one of the most
5484     water-soluble uranium compounds.

5485     Review of Properties

5486     Uranium is a dense, malleable and ductile metal that exists in three allotropic forms: alpha, stable
5487     to 688 °C where it forms the beta structure, which becomes the gamma structure at 776  °C. It is
5488     a poor conductor of electricity. The metal absorbs gases and is used to absorb tritium. Uranium
5489     metal tarnishes readily in an oxidation process when exposed to air. It burns when heated to  170
5490     °C, and the finely divided metal is pyrophoric. Uranium slowly decomposes water at room
5491     temperature, but rapidly at 100 °C. Under a flux of neutrons and other accelerated particles,
5492     atoms of uranium are displaced from their equilibrium position in its metallic lattice. With high
5493     temperatures and an accumulation of fission products, the metal deforms and swells, becoming
5494     twisted, porous, and brittle. The problem can be avoided by using some of its alloys, particularly
5495     alloys of molybdenum and aluminum.
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         Separation Techniques
5496     Uranium forms a large number of binary and ternary alloys with most metals. It also form
5497     compounds with many metals: aluminum, bismuth, cadmium, cobalt, gallium, germanium, gold,
5498     indium, iron, lead, magnesium, mercury, nickel, tin, titanium, zinc, and zirconium. Many binary
5499     compounds of the nonmetals are also known: hydrides, borides, carbides, nitrides, silicides,
5500     phosphides, halides, and oxides. Although other oxides are known, the common oxides are UO2,
5501     UO3, and U3O8. Uranium reacts with acids to form the +4 salts and hydrogen. It is very reactive
5502     as a strong reducing agent.

5503     Uranium compounds are toxic at high concentrations. The physiological  damage occurs to
5504     internal organs, especially the kidneys. The radioactivity of natural uranium radionuclides is not
5505     of great concern, although it is high for some artificial isotopes. Natural uranium in the
5506     environment is considered a relatively low hazard, however, because of its very long half-life and
5507     low toxicity at minute concentrations.

5508     Uranium in nature is almost entirely in the IV and VI oxidation states. It  occurs as the oxides,
5509     UO2 and U3O8, in the solid state. In ground water under oxic conditions it exists as UO2+2 or
5510     complexes of carbonate such as UO2(CO3)3"4. Complex formation increases its solubility under
5511     all conditions in normal groundwater and even under fairly strong reducing conditions. The
5512     amount associated with paniculate matter is small in natural oxic waters. In some waters,
5513     solubility may be limited, however, by formation of an uranyl silicate species. Uranium in
5514     general is poorly absorbed on geologic media under oxic conditions, especially at moderate and
5515     high concentrations and in the presence of high carbonate concentrations. A significant
5516     adsorption occurs at pH above about 5 or 6 because of formation of hydrolytic complexes.
5517     Reduction to the IV oxidation state would increase uptake in the environmental pH range.

5518     Solution Chemistry

5519     The radiochemistry of uranium is complicated because of the multiple oxidation states that can
5520     exist in solution and the extensive complexation and hydrolytic reactions the ions are capable of
5521     undergoing in solution. Four oxidation states are possible: +3, +4, +5, and +6; the latter two exist
5522     as oxycations: UO2+1 and UO2+2, respectively. Their stabilities vary considerably, and the +4 and
5523     +6 states are stable in  solution under certain conditions; oxidation-reduction reagents are used to
5524     form and maintain these ions in solution. Each ion has different chemical properties, and those of
5525     the +4 and +6 states have been particularly exploited to stabilize, solubilize, separate, and collect
5526     uranium. The multiple possibilities of oxidation state, complexation, and hydrolysis should  be
5527     carefully considered when planning any radiochemical procedures.
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                                                                              Separation Techniques
5528     OXIDATION-REDUCTION BEHAVIOR. The multiple oxidation states can be exploited during
5529     separation procedures by taking advantage of their different chemical properties. Thorium can be
5530     separated from uranium, for example, by oxidizing uranium in solution to the +6 oxidation state
5531     with 30 percent hydrogen peroxide (H2O2) and precipitating thorium as the hydroxide; in the +6
5532     state, uranium is not precipitated.

5533     The U+3 ion is an unstable form of uranium, produced in perchlorate or chloride solutions by
5534     reduction of UO2+2 electrochemically or with zinc amalgam. It is a powerful reducing agent, and
5535     is oxidized to U+4 by chlorine or bromine. U+3 is slowly oxidized by water with the release of
5536     hydrogen, and oxygen from air causes rapid oxidation. Aqueous solutions are red-brown and are
5537     stable for several days in 1  M hydrochloric acid, especially if kept cold; rapid oxidation  occurs in
5538     more concentrated acid solutions.

5539     The tetrapositive uranous ion, U+4, is produced by dissolving water-soluble salts of the ion in
5540     solution, dissolving uranium metal with sulfuric or phosphoric acid,  reduction of UO2+1  during its
5541     disproportionation reaction, reduction of UO2+2 by Cr+2 or Ti+3, or oxidation of U+3. The  tetraposi-
5542     tive ion is green in solution. The ion is stable, but slowly oxidizes by oxygen from air to the +6
5543     state.

5544     The UO2+1 ion (+5 state) is extremely unstable in solution and exist only as a transient species,
5545     disproportionating rapidly to U+4 and UO2+2 according to the following reaction in the absence of
5546     complicating factors (k=1.7 x 106):

5547                                2 UO+1 + 4 H+1 * UO+2 + U+4 + 2 H9O
                                         2
5548     Maximum stability is observed in the pH range 2-4 where the reaction is considerably slower.
5549     Solutions of UO2+1 are prepared by the dissolution of UC15 or reduction of UO2+2 ions
5550     electrochemically or with U+4 ions, hydrogen, or zinc amalgam.

5551     The +6 oxidation state of uranium is generally agreed to be in the form of the dioxo or uranyl ion,
5552     UO2+2. As the only oxidation state stable in contact with air, it is very stable in solution and
5553     difficult to reduce. Because of its exceptional stability, the uranyl ion plays a central role in the
5554     radiochemistry of uranium. It is prepared in solution by the dissolution of certain water-soluble
5555     salts: nitrate, halides, sulfate, acetate, and carboxylates; by dissolution of uranium +6
5556     compounds; and oxidation of lower-oxidation state ions already in solution, U+4 with nitric acid
5557     for example. Its solutions are yellow in color.
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         Separation Techniques
5558     COMPLEXATION. Uranium ions form numerous complex ions, and the solution chemistry of
5559     uranium is particularly sensitive to complexing agents present. Complex-ion chemistry is very
5560     important, therefore, to the radiochemical separation and determination of uranium.
5561     Complexation, for example, provides a method to prevent the removal of uranium ions or its
5562     contaminants from solution and can influence the stability of ions in solution.

5563     Among the oxidation states exhibited in solution, the tendency for formation of anionic
5564     complexes is:

5565                                     U+4 > UO2+2 > U+3 > UO2+1,

5566     while the order of stability of the anionic complexes is represented by:

5567       fluoride > nitrate > chloride > bromide > iodide > perchlorate > carbonate > oxalate >  sulfate.

5568     Numerous organic complexes form, including citrate, tartrate, and EDTA, especially with UO2+2.

5569     There is evidence for only a few complexes of U+3, cupferron  and chloride for example. In
5570     contrast, tetrapositive uranium, U+4, forms complexes with a wide variety of anions, and many
5571     are stable: halides—including fluoride (up to eight ligands, UF8"4)—chloride, and bromide;
5572     thiocyanate; and oxygen-donors, nitrate, sulfates, phosphates,  carbonate, perchlorate, and
5573     numerous carboxylates: acetate, oxalate, tartrate, citrate, and lactate. The low charge on UO2+1
5574     precludes the formation of very stable complexes. Fluoride (from hydrogen fluoride) is notable,
5575     however, in its ability to displace oxygen from the ion, forming UFg"1—which inhibits
5576     disproportionation—and precipitating the complex ion from aqueous solution.  The uranyl ion,
5577     UO2+2, readily forms stable complexes with a large variety of inorganic and carboxylate anions
5578     very similar to those that complex with U+4. In addition, numerous organic ligands besides
5579     carboxylates are known that contain both oxygen and nitrogen as donor atoms. Complex-ion
5580     formation must be considered, therefore, during precipitation procedures. Precipitation of
5581     uranium ions is inhibited, for example, in solutions containing carbonate, tartrate, malate, citrate,
5582     hydroxylamine, while impurities are precipitated as hydroxides, sulfides, or phosphates.
5583     Conversely, uranium is precipitated with ammonia, while other ions are kept in solution  as
5584     complexes of EDTA.

5585     HYDROLYSIS. Some uranium ions undergo extensive hydrolysis in aqueous solution. The
5586     reactions can lead to formation of polymeric products, which form precipitates under certain
5587     conditions. The tendency of the various oxidation states toward hydrolysis, a specific case of
5588     complexation, is, therefore, in the same order as that of complex-ion formation (above).


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                                                                             Separation Techniques
5589     Little data are available on the hydrolysis of U+3 ion because it is so unstable in solution.
5590     Qualitative evidence indicates, however, that hydrolysis is about that to be expected for a +3 ion
5591     of its size, that is, as a much weaker acid than most other metals ions of this charge. The U+4 ion
5592     is readily hydrolyzed in solution, but exist as  the unhydrolyzed,  hydrated ion in strongly acidic
5593     solutions. Hydrolysis begins at pH
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         Separation Techniques
5625     alkaline carbonates, hydroxides, and peroxides. Liquid biological samples may also be extracted
5626     to remove uranium, or the solid sample can be ashed by a wet or dry process and dissolved in
5627     acid solution. Wet ashing is carries out with nitric acid and completed with perchloric acid, but
5628     extreme caution should be used when using perchloric acid in the presence of organic material.

5629     Separation Methods

5630     PRECIPITATION AND COPRECIPITATION. There are a large number of reagents that will precipitate
5631     uranium over a wide pH range. The number of reagents available coupled with the two possible
5632     oxidation states of uranium in solution and the complexing properties of the ions provide many
5633     opportunities to separate uranium from other cations and the two oxidation states from each
5634     other. Precipitation can be inhibited, for example, by the presence of complexing agents that
5635     form soluble complexes. Complexes that form weak complexes with uranium and  strong
5636     complexes with other cations allow the separation of uranium by its precipitation while the
5637     complexed cations remain in solution. EDTA has been used in this manner to separate uranium
5638     form many of the transition metals and alkaline earths. In contrast, uranium forms a very strong
5639     soluble complex with carbonate, and this property has been used to keep uranium in solution
5640     while ammonium hydroxide precipitates iron, titanium, zirconium, and aluminum. In a similar
5641     manner, uranium is separated from other cations  as they are precipitated as sulfides or
5642     phosphates.  Common precipitating reagents and used for separation include: ammonium
5643     hydroxide, precipitates uranium quantitatively at pH > 4; carbonate, which will form soluble
5644     anionic complexes with uranium (VI) at pH 5 to  11 while many other metals form  insoluble
5645     hydroxides;  peroxide; oxalic acid,  completely precipitate uranium (IV) while uranium (VI) forms
5646     a soluble complex; iodide; iodate; phosphate for uranium (VI) over a wide pH range; sulfate;
5647     cupferron, precipitates uranium (IV) from an acidic solution but uranium  (VI) from a neutral
5648     solution; and 8-hydroxyquinoline, which forms a quantitatively precipitate with uranium(VI)
5649     only.

5650     Coprecipitation of uranium is accomplished with several carriers. In the absence of carbonate,  it
5651     is quantitatively coprecipitated with ferric hydroxide at pH from  5 to  8. Aluminum and calcium
5652     hydroxide are also employed to coprecipitate uranium. Uranium  (VI), however, is only partially
5653     carried by metal hydroxides in the  presence of carbonate, and the amount carried decreases as the
5654     concentration of carbonate increases. Small amounts of uranium  (VI) coprecipatate with eerie
5655     and thorium fluoride, calcium, zirconium, and aluminum phosphate, barium carbonate, thorium
5656     hexametaphosphate, magnesium oxide, and thorium peroxide. Uranium (IV) is carried on eerie
5657     sulfate, the phosphates of zirconium, bismuth, and thorium, lanthanum  and neodymium  fluoride,
5658     eerie and zirconium iodates, barium sulfate, zirconium phosphate, and bismuth arsenate.
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                                                                             Separation Techniques
5659     SOLVENT EXTRACTION. Liquid-liquid extraction is the most common method for the separation
5660     of uranium in radioanalytical procedures. Extraction provides a high-recovery, one-batch process
5661     that is more reproducible than other methods. With the development of extraction chromatog-
5662     raphy, solvent extraction  has become a very efficient process for uranium separation. Many and
5663     varied procedures are used to extract uranium from aqueous solutions, but the conditions can be
5664     summarized as: (1) composition of the aqueous phase (form of uranium, type of acid present, and
5665     presence of common cations and anions and of foreign anions); (2) nature of organic phase (type
5666     and concentration of solvent and diluent); (3) temperature; and (4) time of equilibrium.
5667     Extraction processes can  be conveniently divided into three systems: those based on (1) oxygen
5668     bonding, (2) chelate formation, and (3) extraction of anionic complexes.

5669     Oxygen-bonding systems are more specific than those based on chelate formation. The employ
5670     organic acids, ethers, ketones, esters, alcohols, organophosphates (phosphoesters), and
5671     nitroalkanes. Ethers are effective for the extraction of uranyl nitrate from nitric acid solutions.
5672     Cyclic ethers are especially effective, and salting agents such as calcium nitrate increase the
5673     effectiveness. Methyl isobutyl ketone (MIBK or hexone) also effectively extracts uranium as the
5674     nitrate complex. It has been used extensively by industry in the Redox process for extracting
5675     uranium and plutonium from nuclear fuels. Aluminum  hydroxy nitrate [A1OH(NO3)2] is an
5676     excellent salting agent for the process and the extraction efficiency is increased by the presence
5677     of the tetrapropylammonium cation [(C3H7)4N+1]. Another common system, used extensively in
5678     the laboratory and in industrial process to extract uranium and plutonium from fission products,
5679     known as the PUREX process (pjutonium uranium reduction extraction), is used in most fuel
5680     reprocessing plants to separate the radionuclides. It employs TBP, tri-/>butyl phosphate
5681     [(C4H9)3PO], in a hydrocarbon  solvent, commonly kerosene, as the extractant. The uranium fuel
5682     is dissolved in nitric acid, and uranium and plutonium are extracted into a 30 percent TBP
5683     solution, forming a neutral complex, UO2(TBP)2. The organic phase is scrubbed with nitric acid
5684     solution to remove impurities, plutonium is removed by back-extracting it as Pu(ni) with a nitric
5685     acid solution containing a reducing agent, and uranium is removed with dilute nitric acid. A
5686     complexing agent can also be used as a stripping agent. Trioctylphosphine oxide is 100,000 times
5687     more efficient in extracting uranium (VI). In both cases, nitric  acid is used both to form the
5688     uranium extracting species, uranyl nitrate, and as the salting agent.  Salting with aluminum nitrate
5689     produces a higher extraction efficiency but less specificity for uranium. Specificity depends the
5690     salt used and it concentration and the diluent concentration.

5691     Uranium is also extracted with select chelate forming agents. One of the most common systems
5692     used for uranium is cupferron in diethyl ether or chloroform. Uranium (VI) is not extracted from
5693     acidic media, so impurities soluble in the mixture under acidic conditions can be extracted first.
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         Separation Techniques
5694     Uranium (VI) can be reduced to uranium (IV) for subsequent extraction. Other chelating agents
5695     used to extract uranium include 8-hydroxyquinoline or acetylacetone in hexone or chloroform.

5696     Amines with molecular weights in the 250 to 500 range are used to extract anionic complexes of
5697     uranium (VI) from acidic solutions. The amine forms a salt in the acidic medium consisting of a
5698     ammonium cation and complex anion, (C10H21)3NH+1 UO2(NO3)"1, for example. Selectivity of the
5699     amines for uranium (VI) is in the order: tertiary > secondary > primary. An anionic extracting
5700     system use extensively in laboratories and industry consists of triisooctyl amine (TIOA) in
5701     kerosene. Uranium is stripped with sodium sulfate or sodium carbonate solution. A number of
5702     mineral and  organic acids have been used with the system: hydrochloric, sulfuric, nitric,
5703     phosphoric,  hydrofluoric, acetic oxalic, formic, and maleic acid. Stripping is accomplished with
5704     dilute acid solutions.

5705     Extraction chromatography is  a simple and relatively quick method for the separation of uranium
5706     on a highly selective, efficient column system. One separation column consist of a triamyl-
5707     phosphate [(C5HnO)3PO] and diamylamylphosphonate (DAAP) [C5H11O)2(C5H11)PO] mixture in
5708     an apolar polymeric matrix. In nitric acid, uranyl nitrate forms a complex with DAAP that is
5709     soluble in triamylphosphate. Uranium can be separated in this system from many other metal
5710     ions, including thorium and the transuranium ions plutonium, americium, and neptunium. It is
5711     eluted from the column with the addition of oxalate to the eluent. Another extraction chromatog-
5712     raphy column uses octylphenyl-N,N-diisobutyl carbamoylphoshpine oxide (CMPO) dissolved in
5713     TBP and fixed on the resin matrix for isolation of uranium in nitric acid. Elution occurs with the
5714     addition of oxalic acid to the eluent.

5715     ION-EXCHANGE CHROMATOGRAPHY. Both cation- and anion-exchange chromatography have
5716     been used to separate uranium from other metal ions. Both stable forms of uranium, uranium (IV
5717     and VI) are absorbed on cation-exchange resins. Uranium (IV) is more strongly absorbed, and
5718     separation of uranium (VI) (UO2+2) is limited. On some cation-exchange columns, the ion also
5719     tends to tail  into other ion fractions during elution. Absorption increases with temperature,
5720     however, and increasing the pH also increases absorption up to the beginning of formation of
5721     hydrolytic precipitates at pH 3.8. In strong acid solutions, uranium (VI) is weakly absorbed
5722     compared to uranium (in and IV) cations. Use of complexing agents increases specificity either
5723     by elution of uranium (VI) with common complexes-forming anions such as chloride, fluoride,
5724     nitrate, carbonate, and sulfate or by forming EDTA, oxalate, acetate, or sulfate complexes with
5725     cations in the analyte, producing a more pronounced difference in absorption of the ions on the
5726     exchange resin. A general procedure for separating uranium (VI) from other metals using the first
5727     method is to absorb uranium (VI) at pH of 1.5 to 2 and elute the metal with acetate solution.
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5728     Anion-exchange chromatography of uranium takes advantage of the stable anionic complexes
5729     formed by the various oxidation states of uranium, especially uranium (VI), with many common
5730     anions. Uranium (VI) forms both anionic or neutral complexes with acetate, chloride, fluoride,
5731     carbonate, nitrate, sulfate, and phosphate. Strong anion-exchange resins are more selective and
5732     have a greater capacity than weak exchangers whose use is more limited. Factors that affect the
5733     separations include uranium oxidation state and concentration; type of anion and concentration;
5734     presence and concentration of other metallic ions and foreign ions; temperature, resin, size,
5735     porosity, and cross-linking. The various oxidation states of uranium and other metal ions,
5736     particularly the actinides, and the effect of pH on formation of complexes and net charge of the
5737     column provide two controllable variable to control the separation process.

5738     A number of chromatographic systems are available for uranium separation on anion-exchange
5739     resins. In hydrochloric acid uranium is often absorbed and other cations are not. Uranium (VI)
5740     can be absorbed from concentrated hydrochloric acid while alkali  metals,  alkaline earths, rare
5741     earths, aluminum, yttrium, actinium, and thorium are washed off the column. In contrast,
5742     uranium, molybdenum, bismuth, tin, technetium, polonium, plutonium and many transition
5743     metals are absorbed on the column, and uranium is eluted exclusively with dilute hydrochloric
5744     acid. Various oxidation states provide another method of separation. Uranium (IV) is separated
5745     from praseodymium (IV), and thorium (IV) with 8 M hydrochloric acid. Thorium, plutonium,
5746     zirconium, neptunium, and uranium can be separated individually by absorbing all the ions
5747     except thorium from concentrated hydrochloric acid Plutonium (IE) elutes with concentrated
5748     acid, zirconium at 7.5 M, neptunium (IV) with 6 M hydrochloric acid and 5 percent
5749     hydroxylamine hydrochloride, and uranium at 0.1 M acid. Uranium (IV) can be separated from
5750     uranium (VI) because both  strongly absorb from concentrated hydrochloric acid, but they
5751     separate at 6 M acid because uranium (IV) is not absorbed at that concentration. Uranium (VI)
5752     absorbs strongly on an anion-exchange resin in dilute hydrofluoric acid, and the absorption
5753     decreases with increasing acid concentration. Nitric acid provides  an excellent method to purify
5754     uranium, because uranium is more strongly absorbed from a nitric acid/nitrate solution. More
5755     selectivity is achieved when acid concentration is low and nitrate concentrations high.
5756     Absorbance is greatest when aluminum nitrate is use as the source of nitrate. Ethyl alcohol
5757     increases absorbance significantly.

5758     ELECTRODEPOSITION. Electrochemical procedures have been used to separate  metal ions from
5759     uranium in solution by depositing them on a mercury cathode from a sulfuric acid solution, using
5760     5 amps for one hour. Uranium is deposited at a cathode from acetate, carbonate, oxalate, formate,
5761     phosphate, fluoride, and chloride solutions to produce a thin, uniform film for alpha and fission
5762     counting.  This is the primary use of electrodeposition of uranium in analytical work. In another
5763     procedure, uranium (VI) is electroplated on a platinum electrode from the basic solution adjacent


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         Separation Techniques
5764     to the cathode that exist in a slightly acidic bulk solution. The conditions of the process should be
5765     carefully controlled to obtain high yields and adherent coatings on the electrode.

5766     VOLATILIZATION. Several halides of uranium and the uranyl ion are volatile and have the
5767     potential for separation by sublimation or fractional distillation. Practically, however, their
5768     volatility is not used to separate uranium in analytical procedures because of technical problems
5769     or the high temperatures that are required for some procedures, but volatilization has been used
5770     in industrial processes. Uranium hexafluoride and uranyl hexafluoride are volatile, and the
5771     property is used to separate 235U from 238U in natural uranium isotope mixtures. Uranium tetra-
5772     chloride and hexachloride are also volatile, and uranium has been isolated from phosphate rock
5773     by heating with a mixture of chlorine and carbon monoxide at 800 °C and collecting the
5774     tetrachl oride.

5775     Methods of Analysis

5776     Macroquantities of uranium, essentially 238U, are determined by fluorimetry. During the
5777     separation and purification process, the sample is eventually fused at 625 °C in a flux mixture
5778     containing potassium carbonate, sodium carbonate, and sodium fluoride. The residue is exposed
5779     to light and its fluorescence is measured. Total uranium or individual radionuclides of uranium,
5780     234U, 235U, and 238U, can be determined from their alpha particle emissions. Uranium radionuc-
5781     lides are collected by evaporating the sample to dryness on a stainless steel  planchet, by micro-
5782     precipitation with a carrier, such as lanthanum or cerium fluoride, or electrodepositon on a
5783     platinum disc. Total alpha activity is determined with a gas-flow proportional counter or an alpha
5784     liquid scintillation system. Individual radionuclides are measured by alpha spectrometry. Alpha
5785     emissions from 232U are used as a tracer to determine chemical recovery.

5786         Compiled from: Allard et al.,  1984; Ahrland, 1986; Baes and Mesmer, 1976; Bard, 1985;
5787         Booman and Rein,  1962; Choppin et al., 1995; Considine and Considine,  1983; Cotton and
5788         Wilkinson, 1988; CRC, 1998-99; DOE,  1990, 1995, and 1997; EPA, 1973; Ehmann and
5789         Vance, 1991; Fritz and Weigel, 1995; Greenwood and Earnshaw, 1984; Grindler, 1962;
5790         Hampel, 1968; Hassinsky and Adloff, 1965; Katz et al., 1986; Katzin, 1986; SCA, 2001;
5791         Weigel, 1986.

5792     14.10.9.12 Zirconium

5793     Zirconium, atomic number 40, is  a member of the second-row transition elements. It exhibits
5794     oxidation states of+2, +3, and +4, and the +4 state is the most common in both the solid state
5795     and in solution. It is immediately above hafnium in the periodic table, and both elements have


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                                                                             Separation Techniques
5796     very similar chemical properties, more so than any other two elements in the periodic table. It is
5797     very difficult, but not impossible, to prepare a sample of zirconium without the presence of
5798     hafnium.

5799     Isotopes

5800     There are twenty-nine isotopes of zirconium, including five metastable states, with mass numbers
5801     from 81 through 104. Five are naturally occurring, 90Zr,  91Zr, 92Zr, 94Zr, and 96Zr, although the
5802     least abundant, 96Zr, is radioactive with an exceptionally long half-life of 3.56 x 1017 y. The
5803     remaining isotopes have a half-life of milliseconds to days. The lower mass number isotopes
5804     decay primarily by electron capture and the upper mass number isotopes are beta emitters. 95Zr
5805     (t1/2=64.0 d) and 97Zr (t1/2=16.9 h) are fission products and are beta emitters. 93Zr (t1/2=1.53 x 106y)
5806     is a rare fission product, and 98Zr, and "Zr are short-lived products with half-lives of 30.7 s and
5807     2.1 s, respectively. All are beta emitters.

5808     Occurrence and Uses

5809     Zirconium is one of the most abundant and widely distributed metals found in the earth's crust. It
5810     is so reactive that it is found only in the combined state, principally in two minerals, zircon,
5811     zircon orthosilicate (ZrSiO4), and baddeleyite, mostly zirconium dioxide (ZrO2). Zirkite is a
5812     commercial ore that consists of both minerals. Hafnium is a minor constituent of all zirconium
5813     minerals.

5814     In the production of zirconium metal, zirconium sands, primarily zirconium dioxide, is passed
5815     through an electrostatic separator to remove titanium minerals, a magnetic separator to remove
5816     iron, ileminite, and garnet, and a gravity separator to remove the less dense silica. The recovered
5817     zircon is heated with carbon in an arc furnace to form zirconium cyanonitride, an interstitial
5818     solution of carbon, nitrogen, and oxygen (mostly carbon) in the metal. Silicon evaporates as
5819     silicon monoxide (SiO), becoming silicon dioxide (SiO2) at the mouth of the furnace. The hot
5820     zirconium cyanonitride is treated with chlorine forming volatile zirconium tetrachloride (ZrCl4),
5821     which is purified by sublimation to remove, among other impurities, contaminating oxides. The
5822     chloride is reduced in the Kroll process, in turn, with liquid magnesium under conditions that
5823     produce a metal  sponge. The byproduct, magnesium chloride (MgCl2), is then removed by
5824     melting the chloride, draining it off, and removing its residues by vacuum distillation. The
5825     zirconium sponge is crushed, melted into bars, arc-melted in an inert atmosphere, and formed
5826     into ingots. For additional purification, the van Arkel-de Boer process removes all nitrogen and
5827     oxygen. Crude zirconium is heated to 200 °C in an  evacuated container containing a small
5828     amount of iodine to form volatile zirconium tetraiodide  (ZrI4). A tungsten filament is electrically


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         Separation Techniques
5829     heated to 1,300 °C, decomposing the iodide and depositing zirconium on the filament. The
5830     commercial grade of zirconium still contains up to three percent hafnium. To be used in nuclear
5831     reactors, however,  hafnium should be removed. Separation is usually accomplished by solvent
5832     extraction of zirconium from an aqueous solution of zirconium tetrachloride as a complex ion
5833     (phosphine oxide, for example), by ion-exchange, fractional crystallization of complex fluoride
5834     salts, distillation of complexes of zirconium tetrachloride with phosphorus pentachloride or
5835     phosphorus oxychloride, or differential reduction of the mixed tetrachlorides (zirconium
5836     tetrachloride is more easily reduced to the nonvolatile trichloride than hafnium tetrachloride.

5837     95Zr and 97Zr are fission products and are also produced by bombardment of naturally occurring
5838     94Zr and 96Zr, respectively, with thermal neutrons. Stable 90Zr is a product of the 90Sr decay chain:

5839                                   9038Sr-9°39Y + p-9040Zr + p + Y

5840     Zirconium metal and its alloys are highly corrosion resistant and withstands  streams of heated
5841     water under high pressure. These properties, along with their low cross section for thermal
5842     neutrons, make them an important material for cladding uranium fuel elements and as core armor
5843     material in nuclear reactors. It is also used for making corrosive resistant chemical equipment
5844     and surgical instruments and making superconducting magnets. Zirconium compounds are also
5845     used in the ceramics industry as refractories, glazes, and enamels, in cores for foundry molds,
5846     abrasive grits,  and  components of electrical ceramics. Crystals of zircon are cut and polished to
5847     use in jewelry  as simulated diamonds. They are also used in pyrotechnics, lamp filaments, in arc
5848     lamps, cross-linking agents for polymers, components of catalysts, as bonding agents between
5849     metal and ceramics and between ceramics and ceramics, as tanning agents, ion exchangers, and
5850     in pharmaceutical agents as deodorants and antidotes for poison ivy. 95Zr is used to follow
5851     homogenization of oil products.

5852     Solubility of Compounds

5853     The solution properties of zirconium in water are very complex, mainly because of the formation
5854     of colloids and the extensive hydrolysis and polymerization of the zirconium ion. hydrolysis and
5855     polymerization are strongly dependent on the pH of the solution, concentration of the ion, and
5856     temperature. The nitrate, chloride, bromide, iodide, perchlorate, and sulfate of zirconium are
5857     soluble in acid solution, however.
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                                                                             Separation Techniques
5858     Review of Properties

5859     Pure zirconium is a grey-white (silvery) lustrous metal with a density of 6.49 g/cm3. It exist in
5860     two allotropic forms, alpha and beta, with a transition temperature of 870 °C. The alpha form is
5861     stabilized by the common impurity oxygen. The amorphous powder is blue-black. Trace amounts
5862     of common impurities (< 1 percent), such as oxygen, nitrogen, and carbon, make the metal brittle
5863     and difficult to fabricate. The metal is not considered to be a good conductor of heat and
5864     electricity, but compared to other metals it is soft, malleable, and ductile. Zirconium forms alloys
5865     with most metals except, mercury, the alkali metals, and the alkaline earths.  It can absorb  up to
5866     ten percent oxygen and nitrogen. Zirconium is a superconductor at temperatures near absolute
5867     zero, but its superconducting properties improve when the metal is alloyed with niobium and
5868     zinc.

5869     Finely divided, dry zirconium (powder and chips) is pyrophoric and extremely hazardous. It is
5870     hard to handle and store and should be moistened for safe use. Note, however, that both wetted
5871     sponge and wet and dry stored scrap have been reported to spontaneously explode. Caution
5872     should also be observed with waste chips produced from machining and cleaning (new)
5873     zirconium surfaces. Both can be pyrophoric. In contrast,  zirconium in the bulk form is extremely
5874     resistant to corrosion at room temperature and remains bright and shiny in air. Resistance is
5875     rendered by the formation of a dense, adherent, self-sealing oxide coating. The metal in this form
5876     is resistant to acids, alkalis, and seawater. Without the coating, zirconium dissolves in warm
5877     hydrochloric and sulfuric acids slowly; dissolution is more rapid in the presence of fluoride ions.
5878     The metal is also resistant to high-pressure water streams and high-temperature steam. It also has
5879     a low cross-section to thermal neutrons and is resistant to damage from neutron radiation. These
5880     properties give pure zirconium (without hafnium) very useful as a fabrication material for nuclear
5881     reactors. Zirconium metal alone, however, is not sufficiently resistant to hot water and steam to
5882     meet the needs for use in a nuclear reactor. Alloyed with small percentages of tin, iron, nickel, or
5883     chromium (Zircalloy), however, the metal meets the standards.

5884     The coated metal is becomes reactive when heated at high temperature (> 500  °C) with
5885     nonmetals, including hydrogen, oxygen, nitrogen, carbon, and the halogens,  and forms solid
5886     solutions or compounds with many metals. It reacts slowly with hot concentrated sulfuric and
5887     hydrochloric acids, boiling phosphoric acid, and aqua regia. It is also attacked by fused potassium
5888     nitrate and potassium hydroxide, but is nonreactive with aqueous alkali solutions. It is not
5889     reactive with nitric acid. Hydrofluoric acid is the only reagent that reacts vigorously with
5890     zirconium.
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         Separation Techniques
5891     Zirconium and its compounds are considered to have a low order of toxicity. Most handling and
5892     testing indicate no level of toxicity, but some individual seem to be allergic to zirconium
5893     compounds. Inhalation of zirconium compound sprays and metallic zirconium dust have
5894     produced inflammatory affects.

5895     Very small quantities of 95Zr have been released to the environment from fuel reprocessing
5896     facilities, atmospheric testing, and the Chernobyl accident. With a half-life of 64 days, the
5897     contamination of the environment is not significant. Zirconium lost from a waste repository
5898     would be expected to move very slowly because of radiocolloidal attraction to surrounding soil
5899     particles. Hydrolysis and  polymerization renders most zirconium insoluble in natural water, but
5900     absorption to suspended particles is expected to provide some mobility in an aqueous
5901     environment.

5902     Solution Chemistry

5903     The only important oxidation state of zirconium ions in aqueous solution is +4, making it a
5904     essentially a monovalent  element. The solution chemistry of zirconium is  quite complex,
5905     nevertheless, because of the easy formation of colloids and extensive hydrolysis and
5906     polymerization reactions  that are strongly dependent on pH and ion concentration.

5907     COMPLEXATION. Zirconium ions forms complexes with numerous substances: fluoride,
5908     carbonate, borate, oxalate, and other dicarboxylic acids, among others. As a large, highly
5909     charged, spherical ion,  it  exhibits high coordination numbers. One of the important chemical
5910     properties of zirconium ions in solution is their formation of a very stable hexafluorozirconate
5911     complex, ZrF6"2. For that  reason, hydrofluoric acid (HF) is an excellent solvent for the metal and
5912     insoluble zirconium compounds. Unfortunately, the fluorocomplex interferes with most
5913     separation and determination steps, and zirconium should be expelled by fuming with sulfuric or
5914     perchloric acid before proceeding. The addition of several milliliters of concentrated FTP to a cool
5915     solution of zirconium carrier and sample will produce initial equilibration; essentially all the
5916     zirconium is present in the +4 oxidation state as a fluoride complex. Note that addition of FTP to
5917     solutions above the azeotropic boiling point of the acid (120 °C) serves no useful purpose and
5918     simply evaporates the FTP.

5919     Tartrate and citrate ions form stable complexes even in alkaline solutions, and zirconium
5920     hydroxide will not precipitate in their presence (see hydrolysis below). Oxalate forms a complex
5921     that is less stable. The ion, [Zr(C2O4)3]"2, is only stable in acid solution. On addition of base, the
5922     complex is destroyed, and zirconium hydroxide precipitates. Sulfuric acid complexes in strongly
5923     acidic solutions, forming  Zr(SO4)4"2. In concentrated HC1 solutions, ZrCl6"2 is present.


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5924     Zirconium ions form chelate complexes with many organic compounds, usually through oxygen
5925     atoms in the compounds. Typical examples are: acetylacetone (acac), EDTA, thenoyltrifluoro-
5926     acetone (TTA), salicylic acid, mandelic acid, cupferron, and 8-hydroxyquinoline.

5927     HYDROLYSIS. Although Zr+4 has a large radius and any +4 cation is extensively hydrolyzed, Zr+4
5928     appears to exist at low ion concentrations (approximately 10"4 M) and high pH (1-2 M). As the
5929     Zr+4 concentration increases and the concentration of H+1 decreases, however, hydrolysis and
5930     polymerization occurs, and one or more polymeric species is dominate in solution. Amorphous
5931     hydrous oxides are precipitated near pH 2; they are soluble in base. Because of hydrolysis,
5932     soluble salts (nitrate, sulfate, perchlorate, acetate, and halides) form acidic  solutions when they
5933     dissolve. The reaction seems to be essentially a direct conversion to the tetranuclear
5934     Zr4(OH)8(H2O)16+2 ion; there is no convincing evidence for the existence of ZrO+2, thought at one
5935     time to be present in equilibrium with numerous other hydrolysis products. It should be noted,
5936     however, that freshly prepared solutions of zirconium salts might react differently from a solution
5937     left standing for several days. Whatever the actual species in solution at any given time, the
5938     behavior of zirconium (IV) depends on the pH of the solution, temperature, anion present, and
5939     age of solution. In addition, zirconium compounds formed by precipitation from solution usually
5940     do not have a constant composition because of their ease of hydrolysis. Even under exacting
5941     conditions, it is difficult to obtain zirconium compounds of known, theoretical composition, and
5942     on aging, hydrolysis products becomes more polymeric and polydisperse.

5943     In acidic solutions, trace amounts of zirconium are strongly coprecipitated with most precipitates
5944     in the absence of complexing ions, especially F"1 and C2O4"2 that form soluble complex ions.

5945     In alkaline solutions, produced by the addition of hydroxide ions or ammonia, a white gelatinous
5946     precipitate of zirconium hydroxide forms. Since the hydroxide is not  amphoteric, it does not
5947     dissolve in excess base. The precipitate is not a true hydroxide but a hydrated oxide, ZrO2-nH2O
5948     where n represents the variable nature of the water content. Freshly prepared zirconium
5949     hydroxide is soluble in acid; but as it dries, its solubility decreases. Precipitation is inhibited by
5950     tartrate or citrate ions because Zr+4 forms complexes with these organic anions even in alkaline
5951     solutions  (see "Complexation," above).

5952     In preparing zirconium solutions, it is wise to acidify the solution with the corresponding acid to
5953     reduce hydrolysis and avoid precipitation of basic salts. During solubilization and radiochemical
5954     equilibrium with a carrier, the tendency of zirconium ions to hydrolyze and polymerize even at
5955     low pH should be kept in mind. Often, the formation of a strong complex with fluoride or TTA is
5956     necessary.
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         Separation Techniques
5957     RADIOCOLLOIDS. Radiocolloids of zirconium are adsorbed on practically any foreign matter (e.g.,
5958     dirt, glass, etc.). Their formation can cause problems with dissolution, achieving radiochemical
5959     equilibrium, and analysis. Generally, it is necessary to form a strong complex with fluoride (see
5960     caution above) or TTA.

5961     Dissolution of Samples

5962     Metallic zirconium is dissolved in hydrofluoric acid, hot aqua regia, or hot concentrated sulfuric
5963     acid. Hydrofluoric acid should be removed by fuming with sulfuric acid or perchloric acid
5964     (caution), because fluoride interferes with most separation and analytical procedures. Zirconium
5965     ores, rocks, and minerals are fused at high temperatures with sodium carbonate, potassium
5966     thiosulfate, sodium peroxide, sodium tetraborate, or potassium hydrogen fluoride (remove
5967     fluoride).  The residue is dissolved in dilute acid or water and might require filtration to collect a
5968     residue of zirconia (impure ZrO2), which is dissolved in acid. As a minor constituent of natural
5969     sample or as a result of formation by nuclear reactions, zirconium typically dissolves during
5970     dissolution of the major constituents. The tendency to polymerize under low concentrations of
5971     acid and the formation  of insoluble zirconium phosphates  should be considered in any
5972     dissolution process. The tendency of zirconium to polymerize and form radiocolloids makes it
5973     important to insure equilibrium with any carrier added. Generally, formation of strong complexes
5974     with fluoride or TTA is necessary.

5975     Separation Methods

5976     PRECIPITATION AND COPRECIPITATION. One of the most insoluble precipitating agents is
5977     ammonium hydrogen phosphate (NH4)2HPO4) in 20 percent sulfuric acid. It has the advantage
5978     that it can be dissolved by hydrofluoric acid, forming hexafluorozirconate. This complex ion also
5979     forms insoluble barium hexafluorozirconate (BaZrF6), a precipitating agent that allows the
5980     precipitation of zirconium in the presence of niobium that is soluble as the heptafluoroniobate
5981     (NbF7"2). Other precipitating agents include the iodate (from 8 M nitric acid), cupferrate, the
5982     hydroxide, peroxide, selenate, and mandelate. Cupferron is used in sulfuric or hydrochloric acid
5983     solutions. It is one of the few precipitating agents in which fluoride does not interfere, but iron
5984     and titanium, among other cations, are also precipitated. The precipitate can be heated in a
5985     furnace at 800 °C to produce zirconium  dioxide for the gravimetric determination of zirconium.
5986     The hydroxide begins to precipitate  at  pH 2 and is complete at pH 4, depending on the presence
5987     of zirconium complexes. It is not recommended unless other cations are absent, because it
5988     absorbs or coprecipitates almost all other ions. Peroxide is formed from a solution of hydrogen
5989     peroxide in acid. Selenious acid in dilute hydrochloric acid separates zirconium from some of the
5990     transition elements and thorium. Mandelic acid in hot dilute hydrochloric acid quantitatively and


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                                                                             Separation Techniques
5991     specifically precipitates zirconium (and hafnium) ions. Large amounts of titanium, tin, iron, and
5992     other ions might be partially coprecipitated, but they can be eliminated by reprecipitation.

5993     Trace quantities of zirconium can be strongly coprecipitated by most precipitates from strong
5994     acid solutions that do not contain complex-forming ions. Bismuth and eerie phosphate readily
5995     carries zirconium, and in the absence of holdback carriers, it is almost quantitatively carried by
5996     rare-earth fluorides. Ferric hydroxide and thorium iodate are also effective carriers.

5997     SOLVENT EXTRACTION. Several extractants have been used to selectively remove zirconium from
5998     aqueous solutions; most are organophosphorus compounds. Di-/>butylphosphoric acid (DBFA)
5999     (di-/7-butylphosphate) is an extractant for zirconium and niobium. It is effective in extracting
6000     tracer and macro quantities of zirconium from 1 M aqueous solutions of nitric, hydrochloric,
6001     perchloric, and sulfuric acids and in separating it from many other elements. A 0.06 M solution
6002     in di-/7-butylether containing three percent hydrogen peroxide extracts more than 95 percent
6003     zirconium but less than one percent niobium. Tin and indium were also  extracted by this mixture.
6004     Tri-/7-butylphosphate (TBP) is an excellent solvent for zirconium. It is used pure or with several
6005     nonpolar diluents, ethers, kerosene, or carbon tetrachloride. Extractability increases with acid
6006     strength. A 0.01 M solution of tri-/>octylphosphine oxide (TOPO) in cyclohexane has been use
6007     to separate zirconium form iron, molybdenum, vanadium, thorium, and  hafnium.

6008     TTA and hexone (methyl isobutyl ketone) are two nonphosphorus extractants employed for
6009     separating zirconium. TTA is highly selective. A 0.5 M solution in xylene separates zirconium
6010     from aluminum, iron, thorium, uranium, and rare earths in a 6 M hydrochloric acid solution. At
6011     tracers levels, the reagent can separate 95Zr from all other fission products. It is also used to
6012     separate zirconium from hafnium. In the analysis of zirconium in zirconium-niobium-tantalum
6013     alloys, hexone separates zirconium from an aqueous solution that is 10 M hydrochloric acid and
6014     6 M sulfuric acid. This is one of the few methods that can be use to separate zirconium from
6015     these metals.

6016     ION-EXCHANGE CHROMATOGRAPHY. Zirconium can be separated form many other cations by
6017     both cation- and anion-exchange chromatography. The technique represents the best laboratory
6018     method for separating zirconium and hafnium. Cation-exchange columns strongly absorb
6019     zirconium ions, but macro quantities of zirconium and hafnium can be purified as aqueous
6020     colloidal solutions of their hydrous oxides on an organic cation-exchange resin. Many cations are
6021     retained on the column, but zirconium and hafnium, under these conditions, are not. The
6022     recovery can  be as high as 99 percent with successive passages, but titanium and iron are not
6023     removed. Zirconium and hafnium can be separated on a sulfuric-acid column from 2 M
6024     perchloric acid. Hafnium is eluted first with 6 M hydrochloric acid. Fluoride complexes of


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         Separation Techniques
6025     zirconium and hafnium can be separated from other non-complexing cations, because the
6026     negative complex ions are not absorb and the non-complexing ions are retained. Zirconium,
6027     hafnium, and niobium are eluted from rare earths and alkaline earths on cation-exchange
6028     columns with citrate. The three elements can be then be separated by the selection of appropriate
6029     citrate buffers, but the separations are not quantitative.

6030     The formation of stable zirconium complexes is the basis of anion-exchange chromatography of
6031     the metal. Separation of zirconium and hafnium from each other and form other cations can be
6032     achieved in hydrochloric-hydrofluoric acid mixtures. Separation of zirconium from hafnium,
6033     niobium, protactinium, and thorium, respectively, is accomplished by selection of the proper
6034     eluting agent. Elution of hafnium first with 9 M hydrochloric acid separates zirconium from
6035     hafnium, for example, while elution with 0.2 M hydrochloric acid/0.01M hydrofluoric acid
6036     recovers zirconium first. Elution with 6-7 M hydrochloric acid separates zirconium from
6037     niobium, in another example.

6038     Methods of Analysis

6039     95Zr decays with a half-life of 65.5 d, emitting a beta particle accompanied by gamma-ray
6040     emission. After several half-lives, it is in transient equilibrium with its progeny, 95Nb, which has
6041     a half-life of 35.0 d and is also a beta and gamma emitter. The progeny of 95Nb is stable 95Mo.
6042     Fresh samples of 95Zr are analyzed by their gamma-ray emission.  Zirconium is collected by
6043     precipitation and filtration. The sample and filter are heated at 800 °C for one hour to decompose
6044     the filter and convert zirconium to its oxide. Zirconium dioxide (ZrO2) is collected by filtration,
6045     dried, and counted immediately.

6046        Compiled from: Baes and Mesmer, 1976; Choppin et al., 1995; Considine and Considine,
6047        1983; Cotton and Wilkinson, 1988; CRC, 1998-99; Ehmann and Vance, 1991; EPA, 1973;
6048        Greenwood and Earnshaw, 1984; Hahn, 1961; Hassinsky and Adi off, 1965; Latimer, 1952;
6049        Steinberg, 1960.

6050     14.11 References

6051     Adams, B.A. and Holmes, E.L. 1935. "Absorptive Properties of Synthetic Resins. Part I,"
6052        JournalSoc.  Chem. Ind. (London), Vol. 54T, pp. T1-T6.

6053     Adams, C. 1995. "Iodine," McGraw-Hill Multimedia Encyclopedia of Science and Technology,
6054        McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.
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                                                                           Separation Techniques
6055     Adolff, J.-P. and Guillaumont, R. 1993. Fundamentals of Radiochemistry, CRC Press, Boca
6056        Raton, Florida.

6057     Alvarez, A. and Navarro, N. 1996. "Method for Actinides and Sr-90 Determination in Urine
6058        Samples," Applied Radiation and Isotopes, Vol. 47, No. 9/10, pp. 869-873.

6059     Allard, B, Olofsson, U., and Torstenfelt, B. 1984. "Environmental Actinide Chemistry,"
6060        Inorganica Chimica Acta, Vol. 94, pp. 205-221.

6061     American Society of Testing Materials (ASTM) 1995. "Standard Test Method for Strontium-90
6062        in Water," Annual Book of ASTM Standards, Designation: D 5811-95.

6063     Ahrland,  S. 1986. "Solution Chemistry And Kinetics of Ionic Reactions," in Katz, J.J., Seaborg,
6064        G.T.,  and Morss, L.R., Eds., The Chemistry of the Actinides, Vol. 2, Chapman and Hall,
6065        London, pp. 1480-1546.

6066     American Public Health Association (APHA). 1998. Standard Methods for the Examination of
6067        Water and Wastewater, Clesceri, L.S., Greenberg, A.E., Eaton, A.D., Eds., Franson, M.A.,
6068        Mang. Ed., American Public Healthe Association, Washington, DC.

6069     Anders, E. 1960. The Radiochemistry of Technetium, National Academy of Sciences-National
6070        Research Council (NAS-NS), NAS-NS 3021, Washington, DC.

6071     Armstrong, G.W., Gill, H.H., and Rolf, R.F. 1961. "The Halogens," in Treatise on Analytical
6072        Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 7, John Wiley and Sons,
6073        New York, pp. 3 3 5 -424.

6074     Baes, C.F. and Mesmer, R.E.  1976. The Hydrolysis of Cations, John Wiley and Sons, New York.

6075     Bailar, J.C., Moeller,  T., Kleinberg, J., Guss, C.O., Castellion, M.E., and Metz, C. 1984.
6076        Chemistry, Academic Press, Orlando, Florida.

6077     Banavali, A.D., Raimondi, J.M., and McCurdy, D.E. 1995. "The Determination of
6078        Technetium-99  in Low-Level Radioactive Waste," Radioactivity and Radiochemistry, 6:3,
6079        pp. 26-35.

6080     Bard, A.J., Parsons, R., and Jordan, J. 1985. Standard Potentials in Aqueous Solution, Marcel
6081        Dekker, New York.


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         Separation Techniques
6082     Bate, L.C. and Leddicotte, G. W. 1961. The Radiochemistry of Cobalt, National Academy of
6083        Sciences-National Research Council, (NAS-NS), NAS-NS 3041, Washington, DC.

6084     Berg, E.W. 1963. Physical and Chemical Methods of Separation, McGraw-Hill, New York.

6085     Berne, A. 1995. "Use of Eichrom's TRU Resin in the Determination of Am, Pu and U in Air
6086        Filter and Water Samples," Environmental Measurements Laboratory. EML-575. December.

6087     Biederman, G. and Schindler, P. 1957. "On the Solubility Product of Precipitated Iron (HI)
6088        Hydroxide," Acta Chemica Scandinavia, Vol. 11, p. 731-740.

6089     Birkett, J.D., Bodek,  I, Glazer, A.E., Grain, C.F., Hayes, D., Lerman, A., Lindsay, D.B., Loreti,
6090        C.P., and Ong, J.H. 1988. "Description of Individual Processes," in Environmental Inorganic
6091        Chemistry, Bodek, I, Lyman, W.J., Reehl, W.F., and Rosenblatt, D.H., Eds., Pergammon,
6092        New York, pp. 2.1 -2.17-18.

6093     Blanchard, R.L. 1966. "Rapid Determination of Lead-210 and Polonium-210 in Environmental
6094        Samples by Deposition on Nickel," Analytical Chemistry, Vol. 38, p. 189-192.

6095     Blumenthal, W.B. 1995. "Zirconium," McGraw-Hill Multimedia Encyclopedia of Science and
6096        Technology, McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.

6097     Bonnesen, P.V., Presley, D.J., Haverlock, T.J., and Moyer, B.A.  1995. "Removal of Technetium
6098        from Alkaline Nuclear-Waste Media by a Solvent-Extraction Process Using Crown Ethers,"
6099        presented at conference identified as CONF-9505101-6, Oak Ridge National Laboratory, Oak
6100        Ridge, Tennessee.

6101     Booman, G.L. and Rein, I.E. 1962. "Uranium," in Treatise on Analytical Chemistry, Kolthoff,
6102        I.M. and Elving, P.J., Eds., Part II, Volume 9, John Wiley and Sons, New York, pp. 1-188.

6103     Bunzl, K. and Kracke,W.  1994. "Efficient Radiochemical Separation for the Determination of
6104        Plutonium in Environmental Samples, Using a Supported, Highly Specific Extractant,"
6105        Journal of Radioanalytical and Nuclear Chemistry, Letters, Vol. 186, No. 5, pp. 401-413.

6106     Bunzl, K., Kracke, W., and Meyer, H. 1996. "A Simple Radiochemical Determination of 90Sr in
6107        Brines," Journal of Radioanalytical and Nuclear Chemistry,  Letters, Vol. 2112, No. 2, pp.
6108        143-149.
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                                                                          Separation Techniques
6109     Burnett, W. and Yeh, C.C. 1995. "Separation of Protactinium from Geochemical Materials via
6110        Extraction Chromatography," Radioactivity and Radiochemistry, Vol. 6, No. 4, pp. 22-26.

6111     Cadieux, J.R., and Reboul, S.H. 1996. "Separation and Analysis of Actinides by Extraction
6112        Chromatography Coupled with Alpha-Particle Liquid Scintillation Spectrometry,"
6113        Radioactivity and Radiochemistry, Vol. 7, No. 2, pp. 30-34.

6114     Carney, K.P., and Cummings, D.G. 1995. "The Application of Micro-Column Solid Phase
6115        Extraction Techniques for the Analysis of Rare Earth Elements in Actinide Containing
6116        Matrices," Proceedings of the Third International Conference on Methods and Applications
6117        of Radioanalytical Chemistry in Journal of Radioanalytical and Nuclear Chemistry, Articles,
6118        Vol. 194, No. 1, pp. 41-49.

6119     Chabaux, F., Othman, D.B., and Brick, J.L. 1994. "A New Ra-Ba Chromatographic Separation
6120        and Its Application to Ra Mass-Spectrometric Measurement in Volcanic Rocks," Chemical
6121        Geology, Vol.  114, pp. 191-197. Erratum, 1994. Chemical Geology, Vol. 116, p. 301.

6122     Chemical Rubber Company (CRC). 1998-1999. Handbook of Chemistry and Physics, CRC
6123        Press, Boca Raton, FL.

6124     Chen, Q., Dahlgaard, H., Hansen, H.J.M., and Aarkrog, A. 1990. "Determination of "Tc in
6125        Environmental Samples by  Anion Exchange and Liquid-Liquid Extraction at Controlled
6126        Valency," Analytica Chimica Acta, Vol. 228, pp. 163-167.

6127     Choppin, G., Rydberg, J., Liljenzin, J.O. 1995. Radiochemistry and Nuclear Chemistry,
6128        Butterworth-Heinemann, Oxford.

6129     Clark, D.L., Keog, D.W., Palmer, P.O., Scott, B.L., and Tait, C.D. 1998. "Synthesis and
6130        Structure of the First Transuranium Crown Inclusive Complex [NpO2([18]Crown-6)ClO4,"
6131        Angew. Chem. Int. Ed., Vol. 37, No.  1/2, pp. 164-166.

6132     Cleveland, J.M. 1970. The Chemistry of Plutonium, Gordon and Breach, Science Publishers, Inc.
6133        New York.

6134     Cobble, J.W. 1964. "Technetium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6135        Elving, P.J., Eds., Part II, Volume 6, John Wiley and Sons, New York, pp. 404-434.
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6136     Coleman, G.H. 1965. The Radiochemistry of Plutonium, National Academy of Sciences-
6137        National Research Council (NAS-NS), NAS-NS 3058, Washington, DC.

6138     Considine, D.M. and Considine, G.D., Eds. 1983. Van Nostrand's Scientific Encyclopedia, Van
6139        Nostrand Reinhold, New York.

6140     Coomber, D.I. 1975. "Separation Methods for Inorganic Species," in RadiochemicalMethods in
6141        Analysis, Coomber, D.I., Ed., Plenum Press, New York, pp. 175-218.

6142     Cotton, F.A. and Wilkinson, G. 1988. Advanced Inorganic Chemistry, John Wiley and Sons,
6143        New York.

6144     Cotton, S. 1991. Lanthanides and Actinides, Oxford University Press, New York.

6145     Crouthamel, C.E. and Heinrich, R.R. 1971. "Radiochemical Separations," in Treatise on
6146        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 9, John Wiley and
6147        Sons, New York, pp. 5467-5511.

6148     Dale, C.J., Warwick, P.E., and Croudace, I.W. 1996. "An Optimized Method for Technetium-99
6149        Determination in Low-Level Waste by Extraction Into Tri-n-octylamine," Radioactivity and
6150        Radiochemistry, Vol. 7, No. 3, pp. 23-31.

6151     Dale, J.M. and Banks, C.V. 1962. "Cobalt," in Treatise on Analytical Chemistry, Kolthoff, I.M.
6152        and Elving, P. J., Eds., Part H, Volume 2, John Wiley and Sons, New York, pp. 311-376.

6153     Dean, J.A. 1995. Analytical Chemistry Handbook, McGraw-Hill, New York.

6154     DeVoe, J.R. 1962. Application of Distillation Techniques to Radiochemical Separations,
6155        National Academy of Sciences- National Research Council (NAS-NS), NAS-NS 3108,
6156        Washington, DC.

6157     Dietz, Ml, Horowitz, E.P., Nelson, D.M., and Wahlgreen, M.A. 1991. "An Improved Method
6158        for Determining 89 Sr and 90 Sr in Urine," Health Physics, Vol. 61, No. 6, pp. 871-877.

6159     Dietz, M. and Horwitz, E.P. 1992. "Improved Chemistry for the Production of Y-90 for Medical
6160        Applications," Applied Radiation and Isotopes, Vol. 43, p. 1093-1102.
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                                                                         Separation Techniques
6161      Dietz, M.L. and Horwitz, E.P. 1993. "Novel Chromatographic Materials Based on Nuclear Waste
6162         Processing Chemistry," LC-GC, The Magazine ofSepartion Science, 11:6.

6163      U.S. Department of Energy (DOE) 1990 and 1997. EML Procedures Manual, Chieco, N.A.,
6164         Bogen, DC., and Knutson, E.G., Eds., HASL-300, 27th and 28th Editions, DOE
6165         Environmental Measurements Laboratory, New York.

6166      U.S. Department of Energy (DOE) 1993. DOE Methods for Evaluating Environmental and
6167         Waste Management Samples, Goheen, S.C., McCulloch, M., Thomas, B.L., Riley, R.G.,
6168         Skiarew, D.S., Mong, G.M., and Fadeff, S.K., Eds., DOE/EM-0089T, Washington, DC.

6169      U.S. Department of Energy (DOE) 1994. Primer on Tritium Safe Handling Practices, DOE-
6170         HDBK-1079-94, Washington, DC.

6171      U.S. Department of Energy (DOE) 1995. DOE Methods for Evaluating Environmental and
6172         Waste Management Samples, Goheen, S.C., McCulloch, M., Thomas, B.L., Riley, R.G.,
6173         Skiarew, D.S., Mong, G.M., and Fadeff, S.K., Eds., DOE/EM-0089T, Rev 2, Addendum  1,
6174         Washington, DC.

6175      U.S. Department of Energy (DOE) 1997. DOE Methods for Evaluating Environmental and
6176         Waste Management Samples, Goheen, S.C., McCulloch, M., Thomas, B.L., Riley, R.G.,
6177         Skiarew, D.S., Mong, G.M., and Fadeff, S.K., Eds., DOE/EM-0089T, Rev 2, Addendum  1,
6178         Washington, DC.

6179      Duckworth, H.E. 1995. "Triton," McGraw-Hill Multimedia Encyclopedia of Science and
6180         Technology, McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.

6181      Ehmann, W.D. and Vancd, D.E. 1991. Radiochemistry and Nuclear Methods of Analysis, John
6182         Wiley, New York.

6183      U.S. Environmental Protection Agency (EPA) 1973. Procedures for Radiochemical Analysis of
6184         Nuclear Reactor Aqueous Solutions, Krieger, H.L. and Gold, S., Eds., EPA-R4-73-014, EPA
6185         National Environmental Research Center, Office of Research and Monitoring, Cincinnati,
6186         Ohio.

6187      U.S. Environmental Protection Agency (EPA) 1979. Radiochemical Analytical Procedures for
6188         Analysis of Environmental Samples, Johns, F.B., Hahn, P.B., Thome, D.J., and Bretthauer,
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6189        E.W., EMSL-LV-0539-17, Environmental Monitoring and Support Laboratory, Las Vegas,
6190        Nevada.

6191     U.S. Environmental Protection Agency (EPA) 1980. Prescribed Procedures for Measurement of
6192        Radioactivity in Drinking Water, Krieger, H.L. and Whittaker, E.L., Eds., EPA-600/4-80-
6193        032, EPA Environmental Monitoring and Support Laboratory, Cincinnati, Ohio.

6194     U.S. Environmental Protection Agency (EPA) 1984. Radiochemistry Procedures Manual,
6195        Liberman, R.E., Ed., EPA-520/5-84-006, EPA Eastern Environmental Radiation Facility,
6196        Office of Radiation Programs, Montgomery, Ala.

6197     Dorfner, K. 1972. Ion Exchangers: Properties and Applications, Ann Arbor Science Publishers,
6198        Ann Arbor, Michigan.

6199     Emsley, J. 1989. The Elements, Clarendon, Oxford.

6200     Paris, J.P. and Buchanan, RJ. 1964. USAEC Report, ANL-6811.

6201     Finston, H.L. and Kinsley, M.T. 1961. The Radiochemistry of Cesium, National Academy of
6202        Sciences-National Research Council (NAS-NS), NAS-NS 3035, Washington, DC.

6203     Fisher Catalog, 2000-01 2000.  Fisher Scientific Company, Pittsburg, PA, pp. 578, 582-583.

6204     Flagg, J.F., and Bleidner, W.E.  1945. "The Electrodeposition of Element 43 and the Standard
6205        Potential for the Reaction Ma-MaO4," Journal of Chemical Physics, Vol. 13, pp. 269-276.

6206     Feigl, F. 1936. "Organic Reagents in Inorganic Analysis," Industry and Engineering Chemistry,
6207        Analytical Edition, Vol. 8, pp. 401-410.

6208     Fried,  S. 1995. "Technetium," McGraw-Hill Multimedia Encyclopedia of Science and
6209        Technology, McGraw-Hill,  New York; Software Copyright: Online Computer Systems, Inc.

6210     Friedlander,G., Kennedy, J.W., Macias, E.S., and Miller, J.M. 1981. Nuclear and
6211        Radiochemistry, 3rd Ed., John Wiley and Sons, New York.

6212     Gale, N.H. 1996. "A New Method for Extracting and Purifying Lead from Difficult Matrices for
6213        Isotopic Analysis," Analytica ChimicaActa, Vol. 332, pp. 15-21.
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                                                                          Separation Techniques
6214     Garcia-Leon, M. 1990. "Determination and Levels of "Tc in Environmental and Biological
6215        Samples," Journal of Radioanalytical and Nuclear Chemistry, Articles, Vol. 138, No. 1, pp.
6216        171-179.

6217     Gokel, G.W. 1991. Crown Ethers and Cryptands, Monographs in Supramolecular Chemistry,
6218        The Royal Society of Chemistry, London.

6219     Golchert, N.W. and Sedlet, J.  1969. "Radiochemical Determination of Technetium-99 in
6220        Environmental Water Samples," Analytical Chemistry., Vol. 41, No. 4, pp. 669-671.

6221     Gordon, L., Salutsky, M.L., and Willard, H.H. 1959. Precipitation from Homogeneous Solution,
6222        John Wiley and Sons, New York.

6223     Greenwood, N.N. andEarnshaw, A. 1984. Chemistry of the Elements, Pergamon, Oxford.

6224     Grimaldi, F.S. 1961. "Thorium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving,
6225        P.J., Eds., Part II, Volume 5, John Wiley and Sons, New York, pp. 142-216.

6226     Grindler, J.E. 1962. The Radiochemistry of Uranium, National Academy of Sciences-National
6227        Research Council (NAS-NS), NAS-NS 3050, Washington, DC.

6228     Hahn, P.P. 1945. "Radioactive Iron Procedures-Purification, Electroplating, and Analysis,"
6229        Industry and Engineering Chemistry, Analytical Edition, Vol. 17, pp.  45-46.

6230     Hahn, R.B. 1961. "Zirconium and Hafnium," in Treatise on Analytical Chemistry, Kolthoff, I.M.
6231        and Elving, P.J., Eds., Part U, Volume 5, John Wiley and Sons, New York, pp. 61-138.

6232     Hampel, C.A., Ed. 1968. The Encyclopedia of the Chemical Elements, Reinhold, New York.

6233     Hassinsky, M. and Adi off, J.-P. 1965. Radiochemical Survey of the Elements, Elsevier, New
6234        York.

6235     Hermann, J.A. and Suttle, J.F.  1961. "Precipitation and Crystallization," in Treatise on
6236        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 3, John Wiley and
6237        Sons, New York, pp. 1367-1410.

6238     Hillebrand, W.F., Lundell, G.E.F., Bright, H.A., and Hoffman, J.I. 1980, Applied Inorganic
6239        Analysis, Krieger Publishing, New York.


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6240     Hindman, F.D. 1983. "Neodymium Fluoride Mounting for a Spectrometry Determinations of
6241        Uranium, Plutonium, and Americium," Analytical Chemistry, Vol. 55, pp. 2460-2461.

6242     Hindman, F.D. 1986. "Actinide Separations for a Spectrometry Using Neodymium Fluoride
6243        Coprecipitation," Analytical Chemistry, Vol. 58, pp. 1238-1241.

6244     Hirano, S., Matsuba, M., Kamada, H. 1989. "The Determination of "Tc in Marine Algae,"
6245        Radioisotopes, Vol.  38, pp. 186-189.

6246     Hiraoka, M. 1992. Crown Ethers and Analogous Compounds, Elsevier Science Publishers B.V.,
6247        Amsterdam.

6248     Holm, E., Rioseco, J., Garcia-Leon, M. 1984. "Determination of "Tc in Environmental
6249        Samples," Nuclear Instruments: Methods of Physics Research, Vol.  223, pp. 204-207.

6250     Horwitz,  E.P., Kalina, D.G., Diamond, H., and Vandegrift, G.F. 1985. "The TRUEX Process- A
6251        Process for the Extraction of the Transuranic Elements from Nitric Acid Wastes Utilizing
6252        Modified PUREX Solvents,"  Solvent Extraction and Ion Exchange,  Vol. 3, Nos. 1 and 2, pp.
6253        75-109.

6254     Horwitz,  E.P., Diamond, H., and Martin, K.A.  1987. "The Extraction of Selected Actinides in the
6255        (HI) (IV) and (VI) Oxidation States from Hydrochloric Acid by OfD(iB)CMPO: The
6256        TRUEX-Chloride Process," Solvent Extraction and Ion Exchange, Vo. 5, No. 3, pp. 447-470.

6257     Horwitz,  E.P., Dietz, M.L., Nelson, D.M.,  Larosa, J.J., and Fairman, W.D. 1990. "Concentration
6258        and Separation of Actinides from Urine Using a Supported Bifunctional Organophosphorus
6259        Extractant," Analytica  Chimica Acta, Vol. 238, pp. 263-271.

6260     Horwitz,  E.P., Dietz, M.L., and Fisher, D.E.  1991. "Separation and Preconcentration of
6261        Strontium from Biological, Environmental, and Nuclear Waste Samples by Extraction
6262        Chromatography Using a Crown Ether,"  Analytical Chemistry, Vol.63, pp. 522-525.

6263     Horwitz,  E. P., Dietz, M.L., and Chiarizia, J. 1992. "The Application of Novel Extraction
6264        Chromatographic Materials to the Characterization of Radioactive Waste Solutions," Journal
6265        of Radioanalytical and Nuclear Chemistry, Vol. 161, pp. 575-583.

6266     Horwitz,  E.P., Chiarizia, R., and Dietz, M.L., 1992. "A Novel Strontium-Selective Extraction
6267        Chromatographic Resin." Solvent Extraction and Ion Exchange, Vol. 10, No. 2, pp. 313-336.


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                                                                           Separation Techniques
6268     Horwitz, E.P., Chiarizia, R., Dietz, M.L., Diamond, Essling, A.M., Graczyk, D. 1992.
6269        "Separation and Preconcentration of Uranium from Acidic Media by Extraction
6270        Chromatography," Analytica Chimica Acta, Vol. 266, pp. 25-37.

6271     Horwitz, E.P., Chiarizia, R., Dietz, M.L., Diamond, H., and Nelson, D.M. 1993. "Separation and
6272        Preconcentration of Actinides from Acidic Media by Extraction Chromatography," Analytica
6273        Chimica Acta, Vol. 281, pp. 361-372.

6274     Horwitz, E.P., Dietz, M.L., Rhoads, S., Felinto, C., Gale, N.H., and Houghton, J. 1994. "A
6275        Lead-Selective Extraction Chromatographic Resin and Its Application to the Isolation of
6276        Lead from Geological Samples," Analytica Chimica Acta, Vol. 292, pp. 263-273.

6277     Hyde, E.K. 1960. The Radiochemistry of Thorium, National Academy of Sciences-National
6278        Research Council (NAS-NS), NAS-NS 3004, Washington, DC.

6279     International Atomic Energy Agency (IAEA). 1972. Analytical Chemistry of Nuclear Fuels,
6280        Vienna, Austria.

6281     Irving, H. and Williams, R.J.P. 1961. "Liquid-Liquid Extraction", in Treatise on Analytical
6282        Chemistry, Kolthoff, I.M.  and Elving, P.J., Eds., Part I, Volume 3, John Wiley and Sons,
6283        New York, pp. 1309-1364.

6284     Jeter, H.W. and Grob, B. 1994. "Determination of Radiostrontium in Milk Samples Using an
6285        Extraction Chromatography Column," Radioactivity and Radiochemistry, Vol. 5, No. 3,
6286        1994, pp. 8-17.

6287     Kallmann, S. 1961. "The Alkali Metals," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6288        Elving, P.J., Eds., Part II, Volume 1, John Wiley and Sons, New York, pp. 301-446.

6289     Kallmann, S. 1964. "Niobium and Tantalum," in  Treatise on Analytical Chemistry, Kolthoff,
6290        I.M. and Elving, P.J., Eds., Part II, Volume 6, John Wiley and Sons, New York, pp. 183-406.

6291     Kaplan, L. 1995. "Tritium"' McGraw-Hill Multimedia Encyclopedia of Science and Technology,
6292        McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.

6293     Katz, J.J.,  Seaborg, G.T., and Morss, L.R. 1986. "Summary and Comparative Aspects of the
6294        Actinide Elements," in Katz, J.J., Seaborg, G.T., and Morss, L.R., Eds., The Chemistry of the
6295        Actinides, Vol. 2, Chapman and Hall, London, pp. 1121-1195.


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         Separation Techniques
6296     Katzin, L.I. 1986. "Thorium," in Katz, J.J., Seaborg, G.T., and Morss, L.R., Eds., The Chemistry
6297        of the Actinides, Vol. 1, Chapman and Hall, London, pp. 41-101.

6298     Kaye, J.H., Strebin, R.S., and Orr, R.D. 1995. "Rapid, Quantitative Analysis of Americium,
6299        Curium and Plutonium Isotopes in Hanford Samples Using Extraction Chromatography and
6300        Precipitation Plating," Journal of Radioanalytical andNuclea Chemistry, Articles, Vol. 194,
6301        No.l, pp. 191-196.

6302     Kirby, H.W. and Salutsky, M.L. 1964. The Radiochemistry of Radium, National Academy of
6303        Sciences-National Research Council (NAS-NS), NAS-NS 3057, Washington, DC.

6304     Kleinberg, J., Argersinger, W.J., and Griswald, E. 1966. Inorganic Chemistry, DC. Heath,
6305        Boston.

6306     Kleinberg, J. and Cowan, G.A. 1960.  The Radiochemistry of Fluorine, Chlorine, Bromine,  and
6307        Iodine, National Academy of Sciences-National Research Council (NAS-NRC), NAS-NRC
6308        3005, Washington, DC.

6309     Kolthoff, I.M. and Sandell, E.B. 1933. "Exchange Adsorption and Its Influence upon the
6310        Solubility of Precipitates with Ionic Lattices in Electrolyte Solution,"7ot/r/7a7 of the American
6311        Chemical Society, Vol. 5 5, p. 2170-2171.

6312     Kolthoff, I.M., Sandell, E.B., Meehan, E.J., and Bruckenstein, S. 1969.  Quantitative Chemical
6313        Analysis, The Macmillan Company, New York.

6314     Korkisch, J. 1969. Modern Methods for the Separation  of Rarer Metals, Pergamon Press, New
6315        York.

6316     Korkisch, J. 1989. Handbook of Ion Exchange Resins: Their Applicability to Inorganic
6317        Analytical Chemistry, Vol. I and II, CRC Press, Boca Raton, FL.

6318     Kraus, K.A., and Nelson, F. 1956. "Nuclear Chemistry and the Effects of Irradiation,"
6319        Proceedings of the International Conference on Peaceful Uses of Atomic Energy, 1st Geneva,
6320        Vol. 7, p. 113-167.

6321     Kressin, I.K. 1977. "Electrodeposition of Plutonium and Americium for High-Resolution Alpha
6322        Spectrometry," Analytical Chemistry, Vol.49, No. 6, pp. 842-846.
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                                                                           Separation Techniques
6323      Krivan, V. 1986. "Application of Radoiotracers to Methodological Studies in Trace Element
6324         Analysis, " in Treatise on Analytical Chemistry, Elving, P.P., Krivan, V., and Kolthoff, E.M.,
6325         Eds., John Wiley and Sons, New York, pp. 339-417.

6326      Kuska, Y. and Meinke, W. 1961. RapidRadiochemical Separations, National Academy of
6327         Sciences-National Research Council (NAS-NS), NAS-NS 3104, Washington, DC.

6328      Larsen, E.M. 1965. Transitional Elements, W.A. Benjamin, New York.

6329      Latimer, W.M. 1952. The Oxidation States of the Elements and Their Potentials in Aqueous
6330         Solutions, Prentice-Hall, Englewood Cliffs, NJ.

6331      Leussing, D.L. 1959. "Solubility," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6332         Elving, P.J.,  Eds., Part I, Volume 1, John Wiley and Sons, New York, pp. 675-732.

6333      Leyba, J.D., Vollmar, H.S., Fjeld, R.A., Devol, T.A., Brown, D.D., and Cadieux, J.R. 1995.
6334         "Evaluation  of a Direct Extraction/Liquid Scintillation Counting Technique for the
6335         Measurement of Uranium in Water," Journal of Radioanalytical and Nuclear Chemistry,
6336         Articles, Vol. 194, No. 2, pp. 337-344.

6337      Lindsay, D.B. 1988. "Radionuclides," in Environmental Inorganic Chemistry, Bodek, I, Lyman,
6338         W.J., Reehl, W.F., and Rosenblatt, D.H., Eds., Pergammon, New York, pp. 9.1 - 9.3-3.

6339      Lingane, J.L. 1966. Analytical Chemistry of Selected Metallic Elements, Reinhold, New York.

6340      Lo, T.C., Baird,  H.I., Hanson, C., Eds. 1983. Handbook of Solvent Extraction, Wiley-
6341         Interscience, New York.

6342      Lucas, H.F., Markun, F., and Boulenger, R. 1990. "Methods for Measuring Radium Isotopes:
6343         Emanometry," in The Environmental Behavior of Radium, Vol 1, Technical Report Series
6344         No. 310, International Atomic Energy Agency, Vienna, pp.  149-172.

6345      Martin, J.E. and Hylko, J.M. 1987. "Measurement of "Tc in Low-Level Radioactive Waste from
6346         Reactors Using 99mTc as a Tracer," Applied Radiation and Isotope, Vol. 38, No.  6, pp. 447-
6347         450.
         JULY 2001                                                                      MARLAP
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         Separation Techniques
6348     Marzilli, L. and Marzilli, P. A. 1995. "Cobalt," McGraw-Hill Multimedia Encyclopedia of
6349        Science and Technology, 1994 and 1996, McGraw-Hill, New York; Software Copyright:
6350        Online Computer Systems, Inc.

6351     McDowell, WJ. 1986. Alpha Counting and Spectrometry Using Liquid Scintillation Methods.,
6352        National Academy of Sciences-National Research Council (NAS-NRC), NAS-NRC 3116,
6353        Technical Information Center, Office of Scientific and Technical Information, U.S.
6354        Department of Energy, Washington, DC.

6355     McDowell, WJ. 1992. "Photon/Electron-Rejecting Alpha Liquid Scintillation (PERALS)
6356        Spectrometry: a Review," Radioactivity & Radiochemistry, Vol. 3, No. 2, pp. 26, 28, 30, 35-
6357        36, 38-42, 44-46, 48-50, 52-54.

6358     McMillan, J.W. 1975. "The Use of Tracers in Inorganic Analysis," in Radiochemical Methods in
6359        Analysis, Coomber, D.I., Ed., Plenum Press, pp. 297-348.

6360     Metz, C.F. and Waterbury, G.R. 1962. "The Transuranium Actinide Elements," in Treatise on
6361        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 9, John Wiley
6362        and Sons, New York, pp. 189-440.

6363     Mitchell, J. 1961. "Water," in Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving, P.J.,
6364        Eds., Part U, Volume 1, John Wiley and Sons, New York, pp. 67-206.

6365     Mitchell, J. 1961. "Water," in Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving, P.J.,
6366        Eds., Part H, Volume 1, John Wiley and Sons, New York, pp. 69-206.

6367     Mitchell, R.F. 1960. "Electrodeposition of Actinide Elements at Tracer Concentrations",
6368        Analytical Chemistry, Vol. 32, pp. 326-328.

6369     Moore, F.L. 1958. "Liquid-liquid Extraction of Uranium and Plutonium from Hydrochloric Acid
6370        Solution with Tri(iso-octyl)amine," Analytical Chemistry, Vol. 30, pp. 908-911.

6371     Morse, R.S., and Welford, GA. 1971. "Dietary Intake of 210Pb," Health Physics, Vol. 21, pp. 53-
6372        55.

6373     Nelson, F., Murase, T., and Kraus, K.A. 1964. "Ion Exchange Procedures I. Cation Exchange in
6374        Concentrated HC1 and HC1O4 Solutions," Journal of Chromatography, Vol.  13, pp. 503-535.
         MARLAP                                                                      JULY 2001
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                                                                          Separation Techniques
6375      Nguyen, S.N., Miller, P.E., Wild, J.F., and Hickman, D.P. 1996. "Simultaneous Determination of
6376         237Np, 232Th, and U isotopes in Urine Samples Using Extraction Chromatography, ICP-MS
6377         and Gamma-Ray Spectroscopy," Radioactivity and Radiochemistry, Vol. 7, No. 3, pp. 16-22.

6378      Nuclear Energy Agency (NEA). 1982. "The Geochemistry of Actinides, in Geological Disposal
6379         of Radioactive Waste: Geochemical Processes, Nuclear Energy Agency, Paris, pp.49-68.

6380      Orlandini, K.A. 1972. "Selective Ion Exchange for the Isolation of Certain Alkaline Earths," U.S.
6381         Patent No. 3694369, Sept. 26.

6382      Orlandini, K.A., King, J.G., and Erickson, M.D. 1998. "The Rapid Separation and Measurement
6383         of Technetium-99," submitted to Radiochimica Acta.

6384      Paducah Gaseous Diffusion Plant 1993. "99Tc Determination in Water," Method R-46.

6385      Parsa, B.  1998. "Contribution of Short-Lived Radionuclides to Alpha-Particle Radioactivity in
6386         Drinking Water and Their Impact on the Safe Drinking Water Act Regulations,"
6387         Radioactivity and Radiochemistry, Vol. 9, pp. 41-47.

6388      Passo, CJ. and Cook, G.T. 1994. Handbook of Environmental Liquid Scintillation Spectrometry:
6389         A Compilation of Theory and Methods, Packard Instrument Company, Meriden, CT, pp. 4 -
6390         1-6.

6391      Pauling, L. 1970.  General Chemistry, Dover, New York.

6392      Penneman, R.A. 1994 and 1996. "Americium," McGraw-Hill Multimedia Encyclopedia of
6393         Science and Technology, McGraw-Hill, New York; Software Copyright: Online Computer
6394         Systems, Inc.

6395      Penneman, R.A. and Keenan, T.K. 1960. "The Radiochemistry of Americium and Curium,"
6396         National Academy of Sciences-National Research Council (NAS-NRC), NAS-NRC 3006,
6397         Washington, DC.

6398      Perrin, D.D. 1979. "Masking and Demasking in Analytical Chemistry," in Treatise on Analytical
6399         Chemistry, 2nd Ed., Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 2, John Wiley and
6400         Sons,  New York, pp. 599-643.
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6401      Perry, E.S. and Weissberger, A. 1965. "Distillation," in Technique of Organic Chemistry. Perry,
6402         E.S. and Weissberger, A., Eds., Second edition, Volume IV, Wiley-Interscience, New York.

6403      Peters, D.G., Hayes, J.M, and Hieftje, G.M. 1974. Chemical Separations and Measurements:
6404         Theory and Practice of Analytical Chemistry, W.B. Saunders Company, New York.

6405      Pimpl, M. 1995. "89Sr/90Sr-Determination in Soils and Sediments Using Crown Ethers for Ca/Sr-
6406         Separation," Journal of Radioanalytical and Nuclear Chemistry, Articles, Vol. 194, No. 2,
6407         pp. 311-318.

6408      Pin, C. and Bassin, C. 1992. "Evaluation of a Strontium-Specific Extraction Chromatographic
6409         Method for Isotopic Analysis in Geological Materials," Analytica Chimica Acta, Vol. 269,
6410         pp. 249-255.

6411      Pin, C., Briot, D., Bassin, C., and Poitasson, F. 1994. " Concomitant Separation of Strontium and
6412         Samarium-Neodymium for Isotopic Analysis in Silicate Samples, Based on Specific
6413         Extraction Chromatography," Analytica Chimica Acta, Vol. 298, pp. 209-217.

6414      Pin, C. and Zalduequi, J.F.S. 1997. "Sequential Separation of Rare-Earth Elements, Thorium and
6415         Uranium by Miniaturized Extraction Chromatography: Application to Isotopic Analyses  of
6416         Silicate Rocks," Analytica Chimica Acta, Vol. 339, pp. 79-89.

6417      Rieman, W. and Walton, H. 1970.  Ion Exchange in Analytical Chemistry, Pergamon Press, New
6418         York.

6419      Riley, R.F. 1995. "Strontium." McGraw-Hill Multimedia Encyclopedia of Science and
6420         Technology, McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.

6421      Salutsky, M.L. 1959. "Precipitates: Their Formation, Properties, and Purity," in Treatise on
6422         Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 1, John Wiley and
6423         Sons, New York, pp. 733-766.

6424      Salutsky, M.L. 1995. "Radium," McGraw-Hill Multimedia Encyclopedia of Science and
6425         Technology, 1994 and 1996, McGraw-Hill, New York; Software Copyright: Online
6426         Computer Systems, Inc.

6427      Salutsky, M.L. 1997. "Radium," in McGraw-Hill Encyclopedia of Science and Technology,
6428         Parker, S.P., Ed. in Chief, Vol. 15, McGraw-Hill, New York, pp. 177-179.


         MARLAP                                                                     JULY 2001
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                                                                          Separation Techniques
6429     S. Cohen and Associates, Inc. (SCA) 2001. Laboratory Quality Assurance Plan: Standard
6430        Operating Procedures, Volume n, S. Cohen and Associates, Inc., Montgomery, Ala.

6431     Schulz, W.W. and Penneman, R.A. 1986. "Americium," in Katz, J.J., Seaborg, G.T., and Morss,
6432        L.R., Eds., The Chemistry of the Actinides, Vol. 2, Chapman and Hall, London, pp. 887-961.

6433     Seaborg, G. T. and Loveland, W.D. 1990. The Elements Beyond Uranium., John Wiley & Sons,
6434        New York.

6435     Sedlet, J. 1966. "Radon and Radium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6436        Elving, P.J., Eds., Part II, Volume 4, John Wiley and Sons, New York, pp. 219-316.

6437     Sedlet, J. 1964. "Actinium, Astatine, Francium, Polonium, and Protactinium," in Treatise on
6438        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 6, John Wiley
6439        and Sons, New York, pp. 435-610.

6440     Showsmith, D.W. 1984. "The Behavior of Radium in Soil and Uranium Mill Tailings," AECL-
6441        7818, Whitshell Nuclear Research Establishment, Tinawa, Manitoba, Canada.

6442     Sill, C.W., Puphal, K.W., and Hindman, F.D. 1974. "Simultaneous Determination of Alpha-
6443        Emitting Nuclides of Radium Through Californium in Soil," Analytical Chemistry, Vol. 46,
6444        No. 12, pp. 1725-1737.

6445     Sill, C.W. and Williams, R.L. 1981. "Preparation of Actinides for Alpha Spectrometry without
6446        Electrodeposition," Analytical Chemistry, Vol. 53, pp. 421-415.

6447     Sittig, M. 1994 and 1996. "Cesium," McGraw-Hill Multimedia Encyclopedia of Science and
6448        Technology, McGraw-Hill, New York; Software Copyright: Online Computer Systems, Inc.

6449     Smith, LL., Crain, J.S., Yaeger, J.S., Horwitz, E.P., Diamond, H., and Chiarizia, R. 1995.
6450        "Improved Separation Method for Determining Actinides in Soil Samples," Journal of
6451        RadioanayticalNuclear Chemistry, Articles, Vol. 194, No 1, pp. 151-156.

6452     Smith, L.L., Orlandini, K.A., Alvarado, J.S., Hoffmann, K.M., Seely, DC., and Shannon, R.T.
6453        1996. "Application of Empore™ Strontium Rad Disks to the Analysis of Radiostrontium in
6454        Environmental Water Samples," Radiochimica Acta, Vol. 73, pp. 165-170.
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         Separation Techniques
6455      Smith, L.L., Alvarado, J.S., Markun, F.J., Hoffmann, K.M., Seely, DC., and Shannon, R.T. 1997.
6456         "An Evaluation of Radium-Specific, Solid-Phase Extraction Membranes," Radioactivity and
6457         Radiochemistry, Vol 8 No. 1, pp. 30-37.

6458      SpecNews 1993. "Product Overview," Vol. 2, Issue 3, Eichrom Industries, Inc., Darien, IL, p. 3.

6459      Steinberg, E.O. 1960. The Radiochemistry of Zirconium and Hafnium., National Academy of
6460         Sciences-National Research Council (NAS-NRC), NAS-NRC 3011, Washington, DC.

6461      Strebin, R. et al. ??et al.???. 1997. "Nickel-59 and Nickel-63 Determination in Aqueous
6462         Samples". DOE Methods Compendium. {SEE NOTE IN ABOVE REFERENCE}

6463      Sullivan, T.M., Nelson, D.M., and Thompson, E.G. 1993. "Monitoring for "Tc in Borehole
6464         Waters Using an Extraction Chromatographic Resin," Radioactivity and Radiochemistry, Vol
6465         4, No.2, pp. 14-18.

6466      Sunderman, D.N. and Townley, C.W. 1960. "The Radiochemistry of Barium, Calcium, and
6467         Strontium," National Academy of Sciences-National Research Council (NAS-NS), NAS-NS
6468         3010, Washington, DC.

6469      Talvitie, N.A. 1972. "Electrodeposition of Actinides for Alpha Spectrometric Determination,"
6470         Analytical Chemistry, Vol. 44, No. 2, pp. 280-283.

6471      Testa, C., Desideri, D., Meli, M.A., and Roselli, C. 1995. "New Radiochemical Procedures for
6472         Environmental Measurements and Data Quality Control," Journal of Radioanalytical and
6473         Nuclear Chemistry,  Articles, Vol. 194, No. 1, pp. 141-149.

6474      Turekian, K.K. and Bolter, E. 1966. "Strontium and Barium," in Treatise on Analytical
6475         Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 4, John Wiley and Sons,
6476         New York, pp. 153-218.

6477      Vdovenko, V.M. and Dubasov, Yu.V.  1975. Analytical Chemistry of Radium," in the series
6478         Analytical Chemistry of the Elements, Malament, D., Ed., John Wiley and Sons, New York.

6479      Wahl, A.C. and Bonner, N.A. 1951. Radioactivity Applied to Chemistry, John Wiley and Sons,
6480         New York.
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                                                                          Separation Techniques
6481      Wang, C.H., Willis, D.L., and Loveland, W.D. 1975. Radiotracer Methodology in the Biological,
6482         Environmental and Physical Sciences, Prentice-Hall, New York.

6483      Weigel, F. 1986. "Uranium," in Katz, J.J., Seaborg, G.T., and Morss, L.R., Eds., The Chemistry
6484         of the Actinides, Vol. 1, Chapman and Hall, London, pp.  169-442.

6485      Weigel, F., Katz, J.J., and Seaborg, G.T. 1986. "Plutonium," in Katz, J.J., Seaborg, G.T., and
6486         Morss, L.R., Eds., The Chemistry of the Actinides, Vol. 1, Chapman and Hall, London, pp.
6487         499-886.

6488      Weigel, F. 1995. "Plutonium," McGraw-Hill Multimedia Encyclopedia of Science and
6489         Technology, 1994 and  1996, McGraw-Hill, New York; Software Copyright: Online
6490         Computer Systems, Inc.

6491      Weigel, F. 1995. "Uranium," McGraw-Hill  Multimedia Encyclopedia of Science and
6492         Technology, 1994 and  1996, McGraw-Hill, New York; Software Copyright: Online
6493         Computer Systems, Inc.

6494      Willard, H.H. and Rulfs, C.L. 1961. "Decomposition and Dissolution of Samples: Inorganic," in
6495         Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 2,
6496         John Wiley and Sons, New York, pp.  1027-1050.

6497      Wright, B.T. 1947. "Recoil of Silver Nuceli Due to D-Capture in Cadmium," Physical Review,
6498         Vol. 71, No. 12, pp.  839-841.

6499      Woittiez, J.R.W. and Kroon, KJ. 1995. "Fast, Selective and  Sensitive Methods for the
6500         Determination of Pb-210 in Phosphogypsum and Phosphate Ore," Journal of Radioanalytical
6501         and Nuclear Chemistry, Vol. 194, No. 2, pp. 319-329.

6502      Wray, J.L. and Daniels, F.  1957. "Precipation of Calcite and  Aragonite," Journal of the American
6503         Chemical Society, Vol. 79, pp.  2031 -203 4.

6504      Zolotov, Yu.A. and Kuz'man, N.M.  1990. Preconcentration of Trace Elements, Vol. XXV of
6505         Wilson and Wilson's Comprehensive Analytical Chemistry, G. Svehla, Ed., Elsevier Science
6506         Publishers, Amsterdam.
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         Separation Techniques
6507     14.12 Selected Bibliography

6508     14.12.1    Inorganic and Analytical Chemistry

6509     Baes, C.F. and Mesmer, R.E. 1976. The Hydrolysis of Cations, John Wiley and Sons, New York.

6510     Bard, A.J., Parsons, R., and Jordan, J. 1985. Standard Potentials in Aqueous Solution, Marcel
6511        Dekker, New York.

6512     Bodek, I, Lyman, W.J., Reehl, W.F., and Rosenblatt, D.H., Eds. 1988. Environmental Inorganic
6513        Chemistry, Pergammon, New York.

6514     Cotton, F.A. and Wilkinson, G. 1988. Advanced Inorganic Chemistry, John Wiley and Sons,
6515        New York.

6516     Dean, J.A. 1995, Analytical Chemistry Handbook, McGraw-Hill, New York.

6517     Dorfner, K. 1972. Ion Exchangers: Properties and Applications, Ann Arbor Science Publishers,
6518        Ann Arbor, Michigan.

6519     Greenwood, N.N. andEarnshaw, A. 1984. Chemistry of the Elements, Pergamon, Oxford.

6520     Karger, B.L., Snyder, L.R., and Horvath, C.  1973. An Introduction to Separation Science, John
6521        Wiley and Sons, New York.

6522     Kolthoff, I.M., Sandell, E.B., Meehan, E.J., and Bruckenstein, S. 1969. Quantitative Chemical
6523        Analysis, The Macmillan  Company, New York.

6524     Latimer, W.M.  1952. The Oxidation States of the Elements and Their Potentials in Aqueous
6525        Solutions, Prentice-Hall, Englewood Cliffs, NJ.

6526     Zolotov, Yu.A. and Kuz'man, N.M. 1990. Preconcentration of Trace Elements, Vol. XXV of
6527        Wilson and Wilson's Comprehensive Analytical Chemistry, G. Svehla, Ed., Elsevier Science
6528        Publishers, Amsterdam.
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6529     14.12.2    General Radiochemistry

6530     Adolff, J.-P. and Guillaumont, R. 1993. Fundamentals of Radiochemistry., CRC Press, Boca
6531        Raton, Florida.

6532     Friedlander, G., Kennedy, J.W., Macias, E.S., and Miller, J.M. 1981. Nuclear and
6533        Radiochemistry, John Wiley and Sons, New York.

6534     Choppin, G., Rydberg, J., Liljenzin, J.O. 1995. Radiochemistry and Nuclear Chemistry.,
6535        Butterworth-Heinemann, Oxford.

6536     Coomber, D.I., Ed. 1975. RadiochemicalMethods in Analysis, Plenum Press, New York.

6537     Wahl, A.C. and Bonner, N.A. 1951, Second Printing: May, 1958. Radioactivity Applied to
6538        Chemistry., John Wiley and Sons, New York.

6539     14.12.3    Radiochemical Methods of Separation

6540     Crouthamel, C.E. and Heinrich, R.R. 1971. "Radiochemical Separations," in Treatise on
6541        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 9, John Wiley and
6542        Sons, New York, pp. 5467-5511.

6543     Dietz, M.L. and Horwitz, E.P. 1993. "Novel Chromatographic Materials Based on Nuclear Waste
6544        Processing Chemistry," LC-GC, The Magazine ofSepartion Science, Vol. 11, No. 6, pp.  424-
6545        426, 428, 430, 434, 436.

6546     Horwitz, E. P., Dietz, M.L., and Chiarizia, J. 1992. "The Application of Novel Extraction
6547        Chromatographic Materials to the Characterization of Radioactive Waste Solutions," Journal
6548        of Radioanalytical and Nuclear Chemistry, Vol. 161, pp. 575-583.

6549     14.12.4    Radionuclides

6550     Anders,  E. 1960.  The Radiochemistry of Technetium, National Academy of Sciences-National
6551        Research Council (NAS-NS), NAS-NS 3021, Washington, DC.

6552     Bate, L.C. and Leddicotte, G. W.  1961. The Radiochemistry of Cobalt, National Academy of
6553        Sciences-National Research Council, (NAS-NS), NAS-NS 3041, Washington, DC.
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6554     Booman, G.L. and Rein, I.E. 1962. "Uranium," in Treatise on Analytical Chemistry., Kolthoff,
6555        I.M. and Elving, P.J., Eds., Part II, Volume 9, John Wiley and Sons, New York, pp. 1-188.

6556     Cleveland, J.M. 1970. The Chemistry of Plutonium, Gordon and Breach Science Publishers, New
6557        York.

6558     Cobble, J.W. 1964. "Technetium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6559        Elving, P.J., Eds., Part II, Volume 6, John Wiley and Sons, New York, pp. 404-434.

6560     Coleman, G.H. 1965. The Radiochemistry of Plutonium, National Academy of Sciences-
6561        National Research Council (NAS-NS), NAS-NS 3058, Washington, DC.

6562     Finston, H.L. and Kinsley, M.T. 1961. The Radiochemistry of Cesium, National Academy of
6563        Sciences-National Research Council (NAS-NS), NAS-NS 3035, Washington, DC.

6564     Grimaldi, F.S. 1961. "Thorium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving,
6565        P.J., Eds., Part II, Volume 5, John Wiley and Sons, New York, pp. 142-216.

6566     Grindler, J.E. 1962.  The Radiochemistry of Uranium, National Academy of Sciences-National
6567        Research Council (NAS-NS), NAS-NS 3050, Washington, DC.

6568     Hahn, R.B. 1961. "Zirconium and Hafnium," in Treatise on Analytical Chemistry, Kolthoff, I.M.
6569        and Elving, P.J., Eds., Part H, Volume 5, John Wiley and Sons, New York, pp. 61-138.

6570     Hyde, E.K. 1960. The Radiochemistry of Thorium, National Academy of Sciences-National
6571        Research Council (NAS-NS), NAS-NS 3004, Washington, DC.

6572     Kallmann, S. 1961. "The Alkali Metals," in Treatise on Analytical Chemistry, Kolthoff,  I.M. and
6573        Elving, P.J., Eds., Part II, Volume 1, John Wiley and Sons, New York, pp. 301-446.

6574     Kallmann, S. 1964. "Niobium and Tantalum," Kallmann, S., in Treatise on Analytical Chemistry,
6575        Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 6, John Wiley and Sons, New York pp.
6576        183-406.

6577     Kirby, H.W. and Salutsky, M.L. 1964. The Radiochemistry of Radium, National Academy of
6578        Sciences-National Research Council (NAS-NS), NAS-NS 3057, Washington, DC.
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                                                                          Separation Techniques
6579     Kleinberg, J. and Cowan, G.A. 1960. The Radiochemistry of Fluorine, Chlorine, Bromine, and
6580        Iodine, National Academy of Sciences-National Research Council (NAS-NRC), NAS-NRC
6581        3005, Washington, DC.

6582     Metz, C.F. and Waterbury, G.R. 1962. "The Transuranium Actinide Elements," in Treatise on
6583        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 9, John Wiley
6584        and Sons, New York, pp. 189-440.

6585     Schulz, W.W. and Penneman, R.A. 1986. "Americium," in The Chemistry of the Actinides, Katz,
6586        J.J., Seaborg, G.T., and Morss, L.R., Eds.,Vol. 2, Chapman and Hall, London, pp. 887-961.

6587     Seaborg, G. T. and Loveland, W.D. 1990. The Elements Beyond Uranium, John Wiley & Sons,
6588        New York.

6589     Sunderman, D.N. and Townley, C.W. 1960. "The Radiochemistry of Barium, Calcium, and
6590        Strontium," National Academy of Sciences-National Research Council (NAS-NS), NAS-NS
6591        3010, Washington, DC.

6592     Steinberg, E.O. 1960. The Radiochemistry of Zirconium and Hafnium, National Academy of
6593        Sciences-National Research Council (NAS-NRC), NAS-NRC 3011, Washington, DC.

6594     Sedlet, J. 1966. "Radon and Radium," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6595        Elving, P.J., Eds., Part II, Volume 4, John Wiley and Sons, New York, pp. 219-316.

6596     Turekian, K.K. and Bolter, E. 1966. "Strontium and Barium," in Treatise on Analytical
6597        Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part II, Volume 4,John Wiley and Sons,
6598        New York, pp. 153-218.

6599     14.12.5    Separation Methods

6600     Berg, E.W. 1963. Physical and Chemical Methods of Separation, McGraw-Hill, New York.

6601     Hermann, J.A. and Suttle, J.F. 1961. "Precipitation and Crystallization," in  Treatise on
6602        Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume  3, John Wiley and
6603        Sons, New York, pp. 1367-1410.
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         Separation Techniques
6604     Irving, H. and Williams, R.J.P. 1961. "Liquid-Liquid Extraction", in Treatise on Analytical
6605         Chemistry., Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 3, John Wiley and Sons,
6606        New York, pp. 1309-1364.

6607     Leussing, D.L. 1959. "Solubility," in Treatise on Analytical Chemistry, Kolthoff, I.M. and
6608        Elving, P.J.,  Eds., Part I, Volume 1, John Wiley and Sons, New York, pp. 675-732.

6609     Perrin, D.D.  1979. "Masking and Demasking in Analytical Chemistry," in Treatise on Analytical
6610         Chemistry, 2nd Ed., Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 2, John Wiley and
6611         Sons, New York, pp. 599-643.

6612     Rieman, W. and Walton, H.  1970. Ion Exchange in Analytical Chemistry, Pergamon Press, New
6613        York.

6614     Salutsky, M.L. 1959. "Precipitates: Their Formation, Properties, and Purity," in Treatise on
6615        Analytical Chemistry, Kolthoff, I.M.  and Elving, P.J., Eds., Part I, Volume 1, John Wiley and
6616         Sons, New York, pp. 733-766.

6617     Willard, H.H. and Rulfs, C.L. 1961. "Decomposition and Dissolution of Samples: Inorganic," in
6618         Treatise on Analytical Chemistry, Kolthoff, I.M. and Elving, P.J., Eds., Part I, Volume 2,
6619        John Wiley and Sons, New York, pp. 1027-1050.

6620     Yu.A. and Kuz'man, N.M. 1990. Preconcentration of Trace Elements, Zolotov, Vol.  XXV of
6621         Wilson and Wilson's Comprehensive Analytical Chemistry, G. Svehla, Ed., Elsevier Science
6622        Publishers, Amsterdam.
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              15 NUCLEAR COUNTING INSTRUMENTATION
        Portions of this chapter have been extracted, with permission, from D 3648-95-Standard
        Practice for the Measurement of Radioactivity, copyright American Society for Testing and
        Materials, 100 Barr Harbor Drive, West Conshohocken, PA 19428. A copy of the complete
        standard may be purchased from ASTM (tel: 610-832-9585, fax: 610-832-9555, e-mail:
        service@astm.org, website: www.astm.org).
 7     15.1   Introduction

 8     This chapter presents descriptions of counting techniques to help the user to determine what
 9     radioanalytical measurement method(s) best suit a given need. References cited in the text
10     provide additional details of how these measurements are made. The primary focus here is on the
11     variables that ultimately affect the bias and precision of the counting data. The type of informa-
12     tion that is desired—in relation to the type of radiation to detect—will determine the type of
13     instrument and associated technique one will use to generate data. For example, samples
14     containing a single radionuclide of high purity, sufficient energy, and ample activity may only
15     require a simple detector system. In this case, the associated investigation techniques may offer
16     no complications other than those related to calibration and reproducibility. At the other extreme,
17     a sample or set of samples may require quantitative identification of many radionuclides or the
18     laboratory may need to prepare unique calibration standards. In the latter case, specialized
19     instruments are available. Typically, a radiochemical laboratory will encounter samples routinely
20     that require a level of information between the two extremes described above.

21     A typical laboratory may be equipped with the following nuclear counting instrumentation:

22      • Proportional or Geiger-Mueller detectors for alpha and beta counting;

23      • Sodium iodide or high resolution germanium detectors for gamma detection and
24        spectrometry;

25      • Solid state detectors for alpha spectrometry;

26      • Scintillation counters suitable for both alpha- or beta-emitting radionuclides; and

27      • Multichannel analyzers for alpha and gamma-ray spectrometry.

28     A basic requirement for accurate measurements is the use of high quality  standards, traceable to a
29     national standards organization (Section 15.9; ANSIN42.22, ANSIN42.23), to calibrate

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30     instrumentation. Generally, with the present availability of good standards, radiochemistry
31     laboratories rarely require instrumentation suitable for producing their own calibration standards.
32     However, it is always advisable to compare each new standard received against the previous
33     standard. The next three main sections of the chapter describe counting instrumentation for alpha
34     (Section 15.2), beta (Section  15.3), and gamma (Section 15.4) radiation. In a number of cases the
35     same instrumentation is used for radionuclides with one or more types of radiation. Note that a
36     review covering descriptions of radionuclides, types of radiation, associated principles, and
37     definitions for related terminology is given in Appendix A of this manual. The discussion next
38     turns to several specific areas to cover spectrometry (Section 15.5), special instrumentation
39     (Section 15.6), and spectrometers and energy-dependent detectors (Section 15.7). Shielding
40     (Section 15.8) to reduce detector background of nuclear counting instruments and instrument
41     calibration (Section 15.9) follows. This chapter closes with a discussion of other nuclear
42     counting instrumentation considerations (Section 15.10) including a discussion on non-nuclear
43     instrumentation  (Section 15.10.4).

44     15.2  Alpha Counting

45     15.2.1 Introduction

46     Alpha particles are relatively massive, expend their energy over short distances, and typically
47     exhibit limited penetration into neighboring materials. Alpha particles are also characterized  by
48     an intense loss of energy while passing through matter (see ICRU,  1992, for a discussion of dose
49     equivalents and linear energy transfer). This loss of energy is used to differentiate alpha
50     radioactivity from other types through the dense ionization or intense scintillation it produces.
51     This high rate of loss of energy in passing through matter, however, also makes sample
52     preparation conditions for alpha counting more stringent than is necessary for other types of
53     radiation. An example of direct alpha counting to determine total alpha activity is given in
54     ASTM C799.

55     Alpha radioactivity normally is measured by one of several types of detectors in combination
56     with suitable electronic components. The detector devices most used are ionization chambers,
57     proportional  counters, silicon semiconductor detectors, and scintillation counters. The associated
58     electronic components in all cases include high-voltage power supplies, preamplifiers, amplifiers,
59     sealers, analog-to-digital  converters, and recording devices.

60     The measured alpha-counting rate from a sample will depend on a number of variables. The most
61     important of these variables are:
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62
63
64
65
66
67
Geometry;
Source diameter;
Self-absorption;
Absorption in air and detector window;
Coincidence losses; and
Backscatter.
68     These are discussed in detail in the literature (Blanchard et al., 1960; Hallden and Fisenne, 1963),
69     and can be measured or corrected for in many cases by holding conditions constant during the
70     counting of samples and standards.

71     Alpha counters have low backgrounds and high efficiencies. Thus, outside sources of alpha
72     radiation will not impact the counting process and the instrument essentially focuses on the alpha
73     source presented by the sample. However, some counters are easily contaminated internally and
74     care should be taken to avoid contamination. Silicon detectors operated in a vacuum may become
75     contaminated due to recoil from sources (Merritt et al.,  1956). Some alpha counters  are sensitive
76     to beta radiation depending on the detector (Blanchard et al.,  1960; Hallden and Fisenne,  1963).
77     In these cases, electronic discrimination is often used to eliminate the smaller pulses due to beta
78     particles. A discussion of alpha particle attenuation can be found in Section 15.10.1.1.

79     15.2.2 Detectors for Alpha Counting

80     15.2.2.1 lonization Chambers

81     As the incident particle enters the ionization chamber, ionization occurs through the interaction
82     of the particle with the fill gas. The secondary electrons produced through these interactions are
83     accelerated toward the anode as a result of the bias applied to the system. An ion current is
84     produced at the anode as a result of the collection of the free electrons (negative ions) generated
85     through ionization interactions. The charge collected at the anode is collected across an RC
86     circuit resulting in a change in potential across a capacitor. The change in potential is thus related
87     to the charge produced from the collection of electrons produced through the ionization
88     interactions of the incident particle.

89     15.2.2.2 Proportional Counters

90     As the incident particle enters the proportional counter, ionization occurs through the interaction
91     of the particle with the fill gas. The secondary electrons produced through these interactions are
92     accelerated toward the anode as a result of the bias applied to the system. In proportional
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 93      counters, the free electrons gain sufficient kinetic energy during acceleration to produce
 94      secondary ionization as they migrate toward the anode. This effect, known as "gas multiplica-
 95      tion," is used to amplify the charge collected at the anode. Similar to ionization chambers, the
 96      charge collected at the anode is collected across an RC circuit resulting in a change in potential
 97      across a capacitor. As a result of gas multiplication, the voltage pulse produced is considerably
 98      larger than the pulse produced in an ionization chamber.  The magnitude of the voltage pulse is
 99      thus proportional to the original number of ion pairs formed by the incident particle.

100      Proportional detectors are generally constructed of stainless steel, oxygen free/high conductivity
101      (OFHC) copper, or aluminum. No additional shielding is required for alpha proportional
102      counting. The counter should be capable of accepting mounts up to 51 mm in diameter.
103      Proportional counters are available in two types, either with or without a window between the
104      sample and the counting chamber. The manufacturer's specifications for either type should
105      include performance estimates of background count rate, length and slope of the voltage plateau,
106      and efficiency of counting a  specified electrodeposited standard source, along with the type of
107      gas used in the tests. For a window flow counter, the window thickness—in milligrams per
108      square centimeter—also should be  specified. With a windowless flow counter the sample and
109      sample mount should be made of an electrical conductor in order to avoid erratic behavior due to
110      static charge buildup.

111      Typical parameters for the alpha windowless flow counter are:

112         b ackground count rate    =  10 counts/h or 2.8 x 10"3 cp s
113         length of voltage plateau  =  300V
114          slope of voltage plateau   =  1 %/l 00 V for an electrodeposited source

115      For a window flow counter, typical values are:

116         window thickness        =  0.08 to 0.5 mg/cm2
117         background count rate    =  10 counts/h or 2.8^10"3 cps
118         length of voltage plateau  =  300V
119          slope of voltage plateau   =  1 %/l 00 V for an electrodeposited source
120         efficiency               =  35 to 40 percent for an electrodeposited source

121      15.2.2.3 Scintillation Counters

122      In a scintillation counter, the alpha particle transfers energy to a scintillator, such as zinc sulfide
123      (silver activated). The transfer of energy to the scintillator results in the production of light at a


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124     wavelength characteristic to the scintillator, and with an intensity proportional to the energy
125     transmitted from the alpha particle. The scintillator medium is placed in close proximity to the
126     cathode of a multiplier phototube; light photons from the scintillator strike the photo cathode,
127     and electrons are emitted. The photoelectrons are passed through a series of dynodes resulting in
128     the multiplication of electrons at each stage of the multiplier phototube. After amplification, a
129     typical scintillation vent will give rise to 107 to 1010 electrons, which is sufficient to serve as a
130     signal charge for the scintillation event. The electrons are collected across an RC circuit, which
131     results in a change in potential across a capacitor, thus giving rise to a pulse used as the
132     electronic signal of the initial scintillation event.

133     The counter size is limited by the multiplier phototube size, a diameter of 51 mm being the most
134     common. Two types of systems may be employed. In the first, the phosphor is optically coupled
135     to the multiplier phototube and either is covered with a thin (<1 mg/cm2) opaque window or
136     enclosed in a light-proof sample changer. With the sample placed as close as possible to the
137     scintillator, efficiencies approaching 40 percent may be obtained. The second system employs a
138     bare multiplier phototube housed in a light-proof assembly. The sample is mounted in contact
139     with a disposable zinc sulfide disk and placed on  the phototube for counting. This  system gives
140     efficiencies approaching 50 percent, is associated with a slightly lower background, and less
141     chance of counter contamination.

142     A major advantage of alpha scintillation counting is that the sample need not be conducting. For
143     a 51  mm multiplier phototube with the phosphor coupled to the tube, typical values obtained are
144     a background count rate of 0.006 cps and an efficiency for an electrodeposited standard source of
145     35 to 40 percent. With a disposable phosphor mounted on the sample, typical values are a
146     background count rate of 0.003  cps and an efficiency for an electrodeposited standard source of
147     45 to 50 percent. For both systems, voltage plateau length is 150 V with a slope of 5
148     percent/100 V.

149     15.2.2.4 Liquid Scintillation Counters

150     Liquid scintillation counting of alpha emitters with a commercially available instrument
151     overcomes many of the problems inherent in other techniques (Passo and Cook  1994; Horrocks,
152     1974; DeFilippis, 1990; Friedlander et al.,  1964; Curtis et al., 1955; Matt and Ramsden, 1964;
153     Overman and Clark, 1960; Price, 1964; Flynn et al., 1971). Typical background counting rates
154     range from 0.1 to 0.2 cps. Sample preparation, after radiochemical separation is performed,
155     involves mixing the sample aliquant with a suitable liquid scintillator solution or gel phosphor
156     before counting. In this way, planchet preparation is eliminated, volatile components are retained,
157     and the completely enclosed sample cannot contaminate the counting chamber. Ideally, the


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158     sample is uniformly distributed in the scintillator so there is no self-absorption. This results in a
159     counting efficiency of almost 100 percent. Because of the high alpha energies, considerable
160     chemical quenching effects can be tolerated before counting efficiency is reduced. Coincidence
161     losses are small in liquid scintillation counting at count rates up to 2><104 cps. For samples that
162     contain both alpha and high-energy beta emitters, difficulties do arise in distinguishing between
163     the two. The problem is due primarily to the broad continuum of beta energy distribution up to
164     the maximum energy and the poor resolution of liquid scintillation spectrometers. This problem
165     is aggravated because the light yield per million electron volts of alpha particles in most liquid
166     scintillators is approximately tenfold lower than a beta particle of equivalent energy, putting the
167     pulses from alphas and high-energy betas in the same region. Correction for beta activity may be
168     made by certain mathematical, graphical or electronic techniques (see discussion of pulse shape
169     discrimination in Section 15.5.4). It is preferable to separate the alpha emitter from the bulk of
170     the beta activity by chemistry.

171     15.2.2.5 Semiconductor Detectors

172     Semiconductor detectors used for alpha counting are essentially solid-state ionization chambers.
173     The ionization of the gas in an ionization chamber by alpha particles produces electron-ion pairs,
174     while in a semiconductor detector electron-hole pairs are produced. The liberated charge is
175     collected by an electric field and amplified by a charge-sensitive amplifier. In general, ion-
176     implanted-silicon or silicon surface barrier detectors are used for alpha counting. These detectors
177     are n-type base material upon which gold is evaporated to make a contact. The semiconductor
178     material must have a high resistivity since the background is a function of the leakage current.
179     This leakage current is present in an electric field since the starting material is a semiconductor,
180     not an insulator. The leakage current of silicon diodes doubles for every 5.5 to 7.5 °C change
181     in ambient temperature. Since the preamp HV bias resistor is a noise contributor, it is necessarily
182     of high value, typically 100 megohm. With a surface barrier detector having leakage current of
183     0.5 jiA, the change in bias voltage at the detector for a 2  °C change in ambient temperature can
184     be as much as 13V. This is enough bias change to affect overall gain of the detector-preamplifier
185     by a substantial amount. The reversed bias that is applied reduces the leakage current and a
186     depletion layer of free-charge carriers is created. This layer is very thin and the leakage current is
187     extremely low; therefore, the interactions of photons with the detector will have negligible effect.
188     Since the detector shows a linear response with energy, any interactions of beta particles with the
189     detector can be eliminated by electronic discrimination. The semiconductor is of special interest
190     in alpha counting where spectrometric measurements may be made since the average energy
191     required to produce an electron-hole pair in silicon is 3.5±0.1 eV compared to the 25 to 30 eV
192     needed to produce an ion pair in  a gridded ionization chamber. Consequently, silicon detectors
193     provide much improved resolution and also normally have lower background count rates.


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194     The detector size is generally less than 25 mm in diameter since the resolution decreases and cost
195     increases with detector size. For best results, the sample should be electrodeposited to make a
196     lower mass source (Puphal and Olson, 1972). However, micro precipitation as fluorides has been
197     reported with only slight lose of resolution (Sill and Williams, 1981; Hindman, 1983). The
198     detector is operated in a vacuum chamber. Typical backgrounds range from 8* 10"5 to 2* 10"4 cps.

199     15.3  Beta Counting

200     15.3.1  Introduction

201     This section covers the general techniques used to measure the beta particle activity resulting
202     from radiochemical separations of specific nuclides or groups of nuclides. Beta radioactivity may
203     be measured by several types of instruments that provide a detector and a combined amplifier,
204     power supply, and sealer. The most widely used detectors are proportional  or Geiger-Mueller
205     counters—however, scintillation systems offer certain advantages (see discussion in Section
206     15.3.3). An example of the measurement of fission product activity by beta counting is given in
207     ASTM C799, D1890, and D3648.

208     15.3.2  Proportional Counter

209     Among the gas ionization-type detectors, the proportional type counter is preferable because of
210     the shorter resolving time and greater stability of the instrument. For preparing solid sources for
211     beta activity measurement, the sample is reduced to the minimum weight of solid material having
212     measurable beta activity by dissolution, radiochemistry, precipitation, or ion exchange tech-
213     niques. For measuring solid sources resulting from individual radiochemical separation
214     procedures, the precipitate is appropriately mounted for counting.

215     Beta particles entering the sensitive region of the detector produce ionization that is converted
216     into an electrical pulse suitable for counting. The number of pulses per unit time is directly
217     related to the disintegration rate of the sample by an overall efficiency factor. This factor
218     combines the effects of sample-to-detector geometry, sample self-shielding, backscatter,
219     absorption in air and in the detector window (if any), and detector efficiency. Because most of
220     these individual components in the overall beta-particle detection efficiency factor vary with beta
221     energy, the situation can become complex when a mixture of beta emitters  is present in the
222     sample. The overall detection efficiency factor may be empirically determined with prepared
223     standards of composition identical to those of the sample specimen, or an arbitrary efficiency
224     factor can be defined in  terms of a single standard such as cesium-137 (137Cs) or other nuclide.
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225     Gross counts can provide only a very limited amount of information and therefore should be used
226     only for screening purposes or to indicate trends.

227     15.3.3 Liquid Scintillation

228     Liquid scintillation counting (LSC) avoids many sources of error associated with counting solid
229     beta sources, such as self-absorption, back scattering, loss of activity during evaporation due to
230     volatilization or spattering, and variable detection efficiency over a wide beta-energy range. In
231     addition to the greatly improved accuracy offered by liquid scintillation counting, sample
232     preparation time and counting times are significantly shorter. Sample preparation involves only
233     adding a sample aliquant to the scintillator or gel phosphor. Because every radioactive atom is
234     essentially surrounded by detector molecules, the probability of detection is quite high even for
235     low-energy beta particles. Radionuclides having maximum beta energies of 200 keV or more are
236     detected with essentially 100 percent efficiency. Liquid scintillation can, at times, be disadvan-
237     tageous due to chemiluminescence, phosphorescence, quenching, or the typically higher
238     backgrounds.

239     The observed  count rate for a liquid scintillation sample is directly related to the beta (plus
240     conversion electron) and positron emission rate in most cases. The important exceptions are: beta
241     emitters whose maximum energy is below 200 keV, and counting systems wherein quenching
242     decreases the expected photon yield, thereby decreasing the overall detection efficiency
243     significantly below 100 percent. Low-energy beta emitters, such as tritium (3H) or carbon-14
244     (14C), can be measured accurately only when the appropriate detection efficiency factor has been
245     determined with a known amount of the same radionuclide counted under identical conditions.
246     Quenching losses are greatest at low beta energies. Quenching may be evaluated by comparison
247     to known quench standards of the same radionuclide, using the channel ratio technique, or with
248     other techniques as described in the manufacturer's instructions.

249     For measurements in which data are expressed relative to a defined standard, the individual
250     correction factors cancel whenever sample composition, sample weight, and counting
251     configuration and geometry remain constant during the standardization and tests.

252     Liquid scintillation counting systems use an organic phosphor as the primary detector. This
253     organic phosphor is combined with the sample in an appropriate solvent that achieves a uniform
254     dispersion. A second organic phosphor often is included in the liquid scintillation cocktail as a
255     wavelength shifter. The wave length shifter efficiently absorbs the photons of the primary
256     phosphor and re-emits them  at a longer wavelength more compatible with the multiplier
257     phototube. Liquid scintillation counting systems use either a single multiplier phototube or two


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258     multiplier phototubes in coincidence. The coincidence counting arrangement is less likely to
259     accept a spurious noise pulse that occurs in a single phototube, and thus provides lower
260     background. The requirement that both multiplier phototubes respond to each has a slight effect
261     on the overall detection efficiency of betas with E-max >200 keV; however, system response to
262     beta E-max <200 keV will be significant. The need to minimize detectable radioactivity in the
263     detector and its surroundings is likewise important in liquid scintillation counting. To achieve
264     this, scintillation-grade organic phosphors and solvents are prepared from low 14C materials such
265     as petroleum. The counting vials are of low potassium glass or plastic to minimize counts due to
266     potassium-40 (40K). Liquid scintillation provides a fixed geometry from a given size counting
267     vial  and liquid volume. The calibration of liquid scintillation counting detectors is given in
268     ASTM El 81. The use of an organic phosphor for liquid scintillation counting creates a mixed
269     waste. Chapter 20 of this manual addresses the proper disposal of these materials.

270     Another approach to LSC without the use of organic phosphors is Cerenkov counting. When
271     charged particles pass through a dielectric medium, such as water, and there is an exchange of
272     energy to the molecules of that medium, Cerenkov radiation is produced. This happens if the
273     charged particles are moving faster than the speed of light and the exchange of energy produces
274     electronic polarization, then when the polarized molecules return to a normal state the excess
275     energy is released as electromagnetic radiation (Kessler, 1986). Wave shifters are usually
276     employed to convert the ultraviolet Cerenkov  radiation to  the visible  range. Although Cerenkov
277     counting efficiencies are about 20 to 50 percent (Scarpitta and Fisenne, 1996) lower than when
278     organic phosphors are used, mixed waste disposal is eliminated.

279     15.3.4 Solid Organic Scintillators

280     Organic scintillators, such as p-terphenyl plus a wave shifter in a plastic monomer, are
281     polymerized to form sheet material of any desired thickness. The plastic phosphor counting
282     system (Campion et al., 1960) has its widest use as a beta  particle detector for separated,  solid
283     samples rather than for beta spectrometry applications.

284     The plastic beta  scintillator phosphor is mounted directly on the sample and is discarded after
285     counting. The phosphor-sample sandwich is placed in direct contact with the multiplier
286     phototube yielding essentially a 2-n configuration. Since the output pulse of the detector system
287     is energy dependent, the counting efficiency for a given phosphor thickness of 0.25 mm yields
288     the highest counting efficiency with the lowest background.
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289     Solid samples (precipitates from radiochemical separations) containing 3 to 5 mg/cm2 of stable
290     carrier are measured in such a system. For yttrium-90 (90Y) a solid sample of this type would
291     have a counting efficiency of 45 to 50 percent.

292     A plastic scintillator/phosphor system with a 25 mm multiplier phototube shielded with 12.7 mm
293     of lead has background in the order of 4x 10"2 cps. For very low backgrounds, about 4x 10"3 cps,
294     the multiplier phototube and sample assembly are fitted into a well-type hollow anode Geiger
295     tube operated in anti-coincidence. The entire assembly is then placed in a heavy shield.

296     The system has many advantages but reduction of background is probably most important. The
297     reduction occurs since the scintillator does not see the surrounding mechanical components of
298     the counter. The additional advantage of keeping the counter itself free from contamination by
299     enclosing the phosphor-sample sandwich is also important.

300     A note of caution is advisable at this point. Any beta particle detection system, whether internal
301     gas counters or scintillation counters, will detect alpha particles.  It is not possible to
302     electronically discriminate against all the alpha pulses.

303     If a sample is suspected of containing alpha activity, a separate alpha measurement should be
304     made to determine the alpha contribution to the beta measurement.

305     15.3.5 Beta Particle  Counter

306     The end-window Geiger-Mueller tube and the internal proportional gas-flow chambers are the
307     two most prevalent types of detectors. Other types of detectors include scintillators and solid-
308     state detectors. The material used in the construction of the detector and its surroundings should
309     contain a minimal level of detectable radioactivity. If the detector is of the window-type, the
310     window thickness may be used in calculating beta-ray attenuation; however, direct calibration of
311     the entire counting system with standards is recommended. The manufacturer should provide all
312     settings and data required for reliable and accurate operation of the instrument. Detectors
313     requiring external positioning of the test sample should include a support of low-density material
314     (aluminum or plastic), which ensures a reproducible geometry between the sample and the
315     detector. Because different sample to detector geometries are convenient for differing sample
316     activity levels, the sample support may provide several fixed positions ranging from 5 to 100 mm
317     from the detector.

318     The detection capability for both Geiger-Mueller and proportional counters is a function of the
319     background counting  rate. Massive shielding or anti-coincidence detectors and circuitry, or both,


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320     are generally used to reduce the background counting rate to increase the lower limit of detection
321     (Friedlander et al., 1964). ASTM E181 covers the procedure for the calibration of beta particle
322     counting detectors. An application of beta particle counting is given in ASTM El 005.

323     15.3.6 Associated Electronic Equipment

324     The high voltage power supply amplifier, sealer, and mechanical register normally are contained
325     in a single chassis. The power supply and amplifier sections are matched with the type of detector
326     to produce satisfactory operating characteristics and to provide sufficient range in adjustments to
327     maintain stable conditions. The sealer should have a capacity for storing and visually displaying
328     at least 9xl05 counts. The instrument should have an adjustable input sensitivity matched to that
329     of the detector, and variable high voltage power supply—an adjustable power supply and meter
330     are unnecessary for liquid scintillation systems. Counting chambers of Geiger-Mueller and
331     proportional counters contain a suitable counting gas and an electrode. Counting rates that
332     exceed 200 cps should be corrected for dead time loss when using a Geiger-Mueller tube. As the
333     applied voltage to the electrode is increased, the counting chamber exhibits responses that are
334     characteristic of a particular voltage region.  At low voltages of the  order of 100 V, there is no
335     multiplication of the ionization caused by a charged particle. At voltages approaching 1,000 V,
336     there is appreciable amplification of any ionization  within the counting chamber; however, the
337     size of the output pulse is proportional to the amount of initial ionization. When operated in this
338     voltage region, the device is known as a proportional counter. Usually, there is a region at least
339     100 V wide, known as a plateau, wherein the count rate of a standard is relatively unaffected. The
340     operating voltage for proportional counters is selected to  approximate the middle of this plateau
341     in order to maintain stable responses during small voltage shifts. The plateau region is
342     determined by counting a given source at voltage settings that differ by 25 or 50 V. The number
343     of counts  at each setting is recorded, and the resultant counts versus voltage are plotted. Voltage
344     plateau curves are to be re-measured periodically to ensure continued instrument  stability, or
345     whenever an instrument malfunction is indicated. If the voltage is increased beyond the
346     proportional region into the 1,500 to 2,000 V region, the  pulse size increases and the dependence
347     on the initial ionization intensity disappears. This is the beginning of the Geiger counting region,
348     where a single ion pair produces the same large pulse as an intense initial ionization.

349     In order to eliminate alpha particle interferences a thin absorber between the sample source and
350     the detector can be used. The absorber diameter should exceed that of the detector window. The
351     absorber should be placed against the window to minimize beta particle  scatter. Any absorber
352     that stops alpha particles will also attenuate  low energy beta particles somewhat. For example, an
353     aluminum absorber of 7 mg/cm2 will absorb 48 percent of beta particles  of 350 keV maximum
354     energy. Chemical separation of the alpha and beta particle emitters produces a higher degree of


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        Nuclear Counting Instrumentation
355     accuracy for internal detector measurements. Published information on beta particle absorption
356     (Friedlander et al., 1964) should be used as a guide for use of an absorber. In liquid scintillation
357     spectra, the alpha component appears as a peak on the beta continuum and thus provides a basis
358     for resolving the two (Bogen and Welford, 1971).

359     15.4  Gamma Counting

360     15.4.1 Introduction
361
362
363
364
365
366

367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382

383
384
385
386
387
        This section covers the non-destructive measurement of gamma-ray radioactivity. Since gamma
        radiation is a penetrating form of radiation, it can be used for non destructive measurements of
        samples of any form and geometry as long as standards of the same form are available and are
        counted in the same geometry to calibrate the detector. Because of this penetrating nature,
        attenuation, because of variations in sample density or sample thickness, although usually not
        significant, can be mathematically corrected.
        When a standard cannot be obtained in
        the matrix and density of samples being
        counted, a correction for the different
        absorption in the matrices should be
        made (Modupe et al., 1993). Photons
        interact with matter in one of three
        ways: photoelectric, where all energy is
        transferred; Compton scattering, where
        only part of the energy is transferred;
        and pair production, where the energy
        creates a positron-electron pair. When
        the positron annihilates the electron, two
        511 keV photons are emitted. Figure
        15.1 shows the relative probability of
        each of the three predominant photon
        interactions with germanium.
o
if: 1E+04
d>
o
O »6*03
c

"CD
^ IE+01
CD
§ 1E+00

S 1M1
  1E-02
IIPOc


 "v
               10        100        1000
                     Energy (keV)
                                            10000
FIGURE 15.1 Gamma-ray Interactions with Germanium
        Since different nuclides emit distinct and constant spectra of gamma radiation, the use of an
        energy discriminating system provides identification and measurement of all the components
        present in a mixture of radionuclides. General information on gamma-ray detectors and gamma
        counting is covered in the literature (Friedlander et al., 1964, and ICRU,  1994). Recent applica-
        tions of gamma counting are given in several ASTM Test Methods (ASTM C758, C759, D3649).
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388
389
390
391
392
393
394

395
396
397
398
399

400
401
402
403
404
405
406
407
408
409

410
411
412
413
414
415
416
417
418
419
420
421
422
Gamma counting is generally carried out using solid detectors since a gas-filled detector will not
provide adequate stopping power for energetic gammas. In solids such as Nal(Tl) or Csl, the
gammas interact by excitation of atoms and energy is transferred to orbital electrons and then
released as light photons when the orbits are refilled. These scintillations are easily detected and
amplified into useable electrical pulses by a multiplier phototube. The Nal(Tl) detector is the
recommended detector for gross gamma counting because of its high efficiency and room
temperature operation.

In semiconductor detectors such as Si(Li) and high-purity germanium semiconductors (HPGe),
the gamma photons produce electron-hole pairs and the electrons are collected by an applied
electrical field. A charge-sensitive preamplifier is used to detect the charge transferred and
produce a useable electrical pulse. The semiconductor detectors are widely used in gamma
spectrometry.
                                          100000
                                           10000
                                            1000 -
                                             100 -
                                              10 -.
                                                        Compton Continuum
                                                                   10
                                                                ENERGY (keV)
                                                                  Hundreds
The output pulses from the multiplier
phototube or preamplifier are directly
proportional to the amount of energy
deposited, which could either be total and
included in the photopeak, or fractional and
included in the continuum or escape peaks,
in the detector by the incident photon. The
pulses may be counted using a sealer or
analyzed by pulse height to produce a
gamma-ray spectrum.

Gamma photons interact with the detector
by three distinct processes. The photo-
electric effect results in complete absorption
of the photon energy and produces the full
energy or photopeak shown.  The Compton
effect results in a partial absorption of the
photo energy and a scattered photon of
lower energy results. The scattered photon
carries energy away and the Compton continuum results (Figure 15.2). The third interaction is
pair production, which occurs at energies above 1,022 keV and results in the conversion of the
photon to mass as an electron-positron pair. The electron and positron give up their kinetic
energy to the detector and the resulting electron joins the electron population of the detector; the
positron, however, is annihilated in combining with an electron and produces two gamma
                                                                                     20
                                                 FIGURE 15.2 Gamma-ray Spectra of 60Co
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423     photons of 511 keV each. One or both of the 511 keV photons may escape from the detector
424     without interacting and the single escape and double escape peaks result.

425     The Comptons, from a higher energy photon, always present an interference problem in the
426     counting of gamma photons and appropriate corrections should be made for this effect. Pair
427     production can also be considered as an interference since the escape peaks may have an energy
428     equal to the lower energy gamma of interest. The Compton and pair production effects  can be
429     very significant interferences and should be corrected.

430     The change of the absorption coefficient with gamma energy results in a wide variation of
431     detection efficiency. The detection efficiency falls rapidly as gamma energy increases for a fixed
432     size of detector. Two other important effects are seen as a result of the variation of the absorption
433     coefficient; firstly, low energy photons may be absorbed in massive samples as sample  thickness
434     increases, such as large bottles of water, and erroneous results may be obtained. A similar
435     absorption effect is seen in HPGe systems where the can around the detector acts  as an  absorber
436     for very low-energy gammas and the efficiency passes through a maximum usually around 100
437     keV. The second result is that for low energy gammas a thin detector may be as efficient as a
438     much thicker one since the low-energy gammas are easily stopped in the thin detector.
439     Additionally, thin detectors will have better low energy detection limits because of reduced
440     background interactions.

441     Because of this variation in efficiency and the possible interferences from other activities, gross
442     gamma counting is only reliable when used to compare standards and samples of the same
443     nuclide. The use of gross gamma monitoring systems should be avoided when possible and, in all
444     cases, proper allowance should be made for the lack of accuracy.

445     At high count rates, random sum peaking may occur. Two absorptions may occur within the
446     resolving time of the detector and electronics and are summed  and seen as one pulse. For a
447     detector of resolving time, t, and a count rate of A counts per unit time, the time window
448     available for summing is 2At (since the count summed could occur as early as t before  or as late
449     as t after the other count) and the probability of another count at any time  is simply A. Therefore,
450     the sum count rate will be 2A2t in unit time. Random summing is strongly dependent on the
451     count rate A and, if summing occurs, it can be reduced by increasing the sample to detector
452     distance. Modern electronics, both conventional analog and digital (preamplifiers, amplifiers, and
453     analog-to-digital converters) are capable of processing 100,000 cps without any significant lose
454     of resolution. This is because of the very short time constants (resolving time) these systems are
455     capable of producing. Over all detector performance can be affected by count rate because
456     reduced time constants are required which will cause some loss of resolution. When a photon


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457     interaction takes place (an event is detected), charge carriers in the form of holes and electrons
458     are produced. The electrical field produced by the detector's high voltage bias supply causes
459     these carriers to be swept toward the P and N electrodes of the detector. The time it takes the
460     carriers to travel to the electrodes is called the "charge collection time." At very high count rates
461     the detector continues collecting events but the data is not valid. If a second (or third) event takes
462     place while the first set of charge carriers are still in transit, the energy from the two events get
463     added together. Therefore, if a 2,000 keV event arrives while a 1,000 keV event is in transit, the
464     detector would "see" a single 3,000 keV event, producing a random sum peak on pulse pileup.
465     When the detector starts reporting more sum peaks than valid events, you have exceeded its
466     count rate capability. Random pulse summing or pileup can also cause peak shape and risetime
467     problems. But the real upper limit to a detector throughput is pulse summing. This problem can
468     be reduced or eliminated by either reducing the number of events the detector "sees" by moving
469     the sample further away, collimate the detector, or use a smaller, less efficient detector; the
470     smaller the detector the shorter the charge collection time, which means a higher count rate limit.
471     Peak shifts may also occur with high count rates and short time constants. Another factor that
472     will affect high count rate performance is improper setting of the amplifier pole zero. Improper
473     setting of the pole zero with either under or over shooting of input pulse will effect peak
474     resolution.
475
476
477
478
479
480
481

482
483
484
485
486
487
488
489
490
491
492
Well counters that have very high efficiencies are prone to summing since, for a given source
strength, the count rate is higher than for a
detector of lower efficiency. For moderate
and high-source strengths, the trade-off is a
poor one and the well counter is best suited
for low-level work where its high efficiency
is an important advantage.
Cascade summing may occur when nuclides
that decay by a gamma cascade are counted.
Cobalt-60 (60Co) is an example; 1,173.2 keV
and 1,332.5 keV from the same decay may
enter the detector and be absorbed, giving a
2,505.7 keV sum peak. Another example of
Cascade summing occurs when counting
sodium-22 (22Na) close to the detector (see
Figure 15.3).  Cascade summing may be
reduced and eventually eliminated by
increasing the source-to-detector distance.
10000
 1000 -
 100  -
  10 -
                      10       15
                    ENERGY (keV)
                     Hundreds
                                        20
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      FIGURE 15.3 Energy Spectrum of 22Na

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493
494
495
496

497

498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521

522
523
524
525
526
The resolution of a gamma detector is the effective limit to its utility even when complex data
reduction methods are used. A typical 76x76 mm Nal(TI) detector will give full-width half-
maximum (FWHM) of approximately 60 keV at 661.6 keV gamma energy and approximately 90
keV at 1,332.5 keV gamma energy.

15.4.2  Energy Efficiency Relationship
                                           100
                                                 HPGeWeU
                                            10
                                               n-iypeHPGe
                                               p-T^peHPGe
                                            0.1
                                                                                 35% Re
                                                                                 70% Re
                                                                                 122cc
                                                                                 320cc
                                              10
                                                          100
                                                                                    10000
Because of the rapid falloff in gamma
absorption as gamma energy rises, the
detection efficiency shows a similar effect.
Figure 15.4 shows a typical efficiency vs.
energy plot of a 70 percent FIPGe p-type, a
35 percent HPGe n-type, and HPGe well
detectors of 122 cm3 with a vespel well and
320 cm3 with a Mg well. The portion of the
curve for n-type and well detectors at low
energies shows that as the absorption
coefficient increases geometry becomes the
limiting factor. The maximum efficiency for
both co-axial detectors is well below 50
percent due to the presence of a beta
absorber, the  containment of the detector
and the geometry effect.  The p-type detector
shows significant low energy efficiency
drop off because of the absorption of
gamma rays in the detector's inactive Ge dead layer. The well detector shows excellent efficiency
below 100 keV because of the geometry effect and absence of an attenuating germanium dead
layer. The 76x76 mm Nal(TI) detector is the most widely used size. A large amount of data are
available in the open literature on both the use and results obtained with detectors of this size.
Heath (1964) has written a comprehensive review and supplied many gamma-ray spectra in both
graphical and digital form.

Other sizes of detectors may be used. However, the following should be noted: smaller detectors,
such as 38x38 mm, will give efficiencies that are low and fall off more rapidly as gamma energy
increases. Small or thin detectors are useful for the measurement of low-energy gammas since
they are less responsive to high-energy gammas and the interference from Compton effects is
reduced. This will result in a lower background.
                                                                       1000
                                                               ENERGY (keV)
                                             FIGURE 15.4 Efficiency vs. Gamma-ray Energy
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527      Larger detectors will give higher efficiencies and less falloff as gamma energy increases. Larger
528      detectors are useful for situations where the highest attainable efficiency is desired and for the
529      assembly of complete absorption detectors. The increase in efficiency is accompanied by an
530      increased background count rate and an increase in the probability of summing in the detector.
531
532
533
534
535

536
537

538
539

540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
Well detectors will give very high efficiencies, up to about 80 percent for low and moderate
energy gammas. The well detector is useful for low levels of activity and the background of a
well detector is essentially the same as that of a plain cylindrical detector of the same overall
dimensions. Summing becomes a definite problem at high activities since both random and
cascade summing result from the high efficiencies and the high geometry  of the well detector.
Detector efficiency will also vary as a function of sample geometry. Table 15.1 gives counting
efficiencies obtained with various sample geometries for a 55 percent HPGe detector.
     TABLE 15.1 Typical Percent Gamma-ray Efficiencies for a 55 Percent High-Purity
                 Germanium Detector* with Various Counting Geometries
ENERGY (keV)
60
88
122
166
279
392
514
662
835
898
1115
1173
1333
1836
FILTER
PAPER
15.6
15.2
15.1
12.0
9.3
7.2
5.4
4.7
3.9
3.1
3.0
2.6
2.3
1.7
50cm3
PLANCHET
14.6
14.2
12.6
9.6
7.4
5.5
4.2
3.6
2.9
2.4
2.3
2.0
1.8
1.3
90cm3
ALCAN
11.6
11.3
10.2
8.0
6.0
4.5
3.5
3.0
2.4
2.1
1.9
1.7
1.5
1.2
600 cm3
MARINELLI BEAKER
5.0
7.4
8.4
7.9
6.1
4.8
3.8
3.1
2.7
2.2
2.1
1.8
1.6
1.3
 Although the counting efficiencies listed above were obtained with a 55 percent (relative to a 3x3 inch Nal
detector) HPGe detector, the calculation of counting efficiencies by extrapolation for detectors with different
relative efficiencies is not possible. This is because detectors with the same relative efficiency may be of
significantly different dimensions thus producing a detector/sample solid angle very different than what was used to
prepare this table.
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560     15.4.3 Sodium Iodide Detector Assembly

561     A cylindrical 76x76 mm Nal detector is activated with about 0.1 percent thallium iodide, with or
562     without an inner sample well, optically coupled to a multiplier phototube, and hermetically
563     sealed in a light-tight container. The Nal(Tl) crystal should contain less than 5 ppm of potassium
564     and be free of other radioactive materials. In order to establish freedom from radioactive
565     materials, the manufacturer should supply a gamma spectrum of the background of the detector
566     between 0.08 and 3,000 keV. The resolution of the detector for the 662 keV gamma from 137Cs
567     decay should be less than 50 keV FWHM or less than 7 percent when measured with the source
568     in contact with the end cap.

569     The following components are required for a complete Nal(Tl) gamma-ray spectrometry system:
570      High-Voltage Power Supply
571      Preamplifier
572      Analyzer with Sealer and Timer
                               500 to 2,000 V dc regulated to 0.1 percent with a ripple of
                               not more than 0.01 percent

                               Linear amplifier system to amplify the output from the
                               multiplier phototube to a maximum output of 10 V.

                               A single-channel discrimination system will accept all or
                               any part of the output from the amplifier and pass it to the
                               sealer. Any pulses lying outside the preset limits are
                               rejected. The lower limit is usually referred to as the
                               thresholdand the difference between the two limits is the
                               window.
                                         Sample mounts and containers may consist of any
                                         reproducible geometry container that is commercially
                                         available. Other considerations are cost, ease of use,
                                         disposal, and effective containment of radioactivity for the
                                         protection of the workplace and personnel from
                                         contamination.
573
Beta Absorber
A beta absorber of 3 to 6 mm of aluminum, beryllium, or
poly(methyl methacrylate) should completely cover the
upper face of the detector to prevent betas from reaching the
detector.
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574     15.4.4 High Resolution Germanium Detectors

575     High resolution germanium detectors are produced from very high purity material, the required
576     level of impurities in the detector crystal is usually less than 109 atom/cm3. Any type of
577     germanium—either planar, co-axial or well-configuration—cannot be operated at room
578     temperature because of the large thermally induced leakage current that results. These detectors
579     should be cooled in order to reduce the thermal generation of charge carriers (thus reverse
580     leakage current) to an acceptable level. Otherwise, leakage current induced noise reduces the
581     energy resolution of the detector. The  detector is mounted in a vacuum chamber which is
582     attached to or inserted into an liquid nitrogen (LN2) dewar or an electrically powered cooler. The
583     sensitive detector surfaces are thus protected from moisture and condensation contaminants.

584     The boiling point of liquid nitrogen (77 °K) is usually taken advantage of to reduce the operating
585     temperature of the detector. Since germanium detectors can be operated at temperatures as high
586     as 130 °K, mechanical closed-cycle refrigerators can also be used. These systems can cool a
587     detector to as low as 50 °K. Therefore, with proper thermal control the detector can be cooled to
588     its optimum operating temperature. The required preamplifier is normally included as part of the
589     cryostat. In this configuration the preamplifier can also be cooled to reduce electronic noise.

590     HPGe detectors are preferred for the analysis of complex gamma-ray spectra involving many
591     nuclides and peaks. However, for samples with only a few nuclides, the complexity of an HPGe
592     system may not be cost effective. The calibration of germanium detectors is given in ASTM
593     E181.

594     15.4.5 Low Background High Resolution Germanium Detectors

595     Environmental samples requiring the lowest possible minimum detection analyses (MDAs)
596     should be counted with large high efficiency germanium detectors in  low background cryostats.
597     Most of the background from naturally occurring radionuclides such as 40K from building
598     materials, radon decay products, and cosmic rays can be reduced by proper shielding. However,
599     naturally occurring 235U, 238U, 232Th, and anthropogenic 137Cs and 60Co may be present in cryostat
600     materials. With careful selection and substitution of materials, low background gamma-ray
601     systems can be fabricated. Germanium crystal mountings and detector end caps have been
602     fabricated with magnesium to eliminate aluminum contaminated with radioactive thorium
603     isotopes.  Figures 15.5  and 15.6 show shielded background spectra obtained with 56 percent
604     germanium detectors in standard and extra low background cryostats.
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           80
           64  -
           48  -
           16
              0246
                          10  12  14 16  18 20 22 24 26
                             ENERGY (keV)
                              Hundreds
                                                        64 -
                                                        48 -
                                                        32 -
                                                        16 -
                                                           0246
                     10  12 14 16  18 20  22 24 26
                        ENERGY (keV)
                         Hundreds
         FIGURE 15.5 Standard Cryostat HPGe Background
         Spectrum
      FIGURE 15.6 Low Background Cryostat HPGe
      Background Spectrum
605     15.4.6 High Resolution Detectors for Low Energy Spectrometry

606     High resolution low gamma-ray energy detectors are available in various configurations. The
607     commonly used ones are either high purity germanium or silicon. The various detector types
608     include: planar (Ge or Si), low-energy germanium (LEGe), reverse-electrode germanium (REGe)
609     and extended-range germanium (XtGe). These detectors are equipped with beryllium entrance
610     windows to reduce attenuation. These detectors are especially useful for measuring nuclides that
611     emit gamma or X-rays from a few keV to about  150 keV.

612     15.4.7 CsI(Tl) Detectors

613     CsI(Tl) crystals have the highest light output of all known scintillators. However, because light
614     output is not well matched to the sensitivity of the photocathode of a multiplier phototube, the
615     yield for gamma rays is only 45 percent of the efficiency of Nal(Tl). With the proper electronics,
616     CsI(Tl) detectors can be used for a-particle energy discrimination.

617     15.4.8 CdZnTe Detectors

618     These gamma-ray detectors, in addition to only being produced in very small volumes, do not
619     have energy resolutions  as good as HPGe but are better than Nal(Tl).  Their greatest advantage is
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620     their ability to operate at room temperature. Because of their small size and resulting low
621     gamma-ray detection efficiency, they are useful for the analysis of very high level sources.

622     15.4.9 BGO Detectors

623     Because bismuth germanate (Bi4Ge3O12)is a high Z, high density (7.13 gem3), scintillation
624     material, it is a very efficient gamma-ray absorber. Although BGO crystals a have very good
625     peak-to-Compton ratio, their effective efficiency is only 10 to  15 percent as good as a Nal(Tl)
626     crystal. However, BGO is a relatively hard, rugged, non-hygroscopic crystal which does not
627     cleave or absorb any significant amount of the scintillation light. The crystal housing does not
628     require hermetic air-tight sealing. These crystals are useful in applications where high
629     photofraction is required.

630     15.5  Spectrometry Systems

631     This section will present a number of different type of detector systems commonly use for
632     gamma-ray spectrometry.

633     15.5.1 Alpha/Gamma Coincidence Systems

634     Alpha/Gamma Coincidence Systems have been used for the direct measurement of 224Ra and
635     226Ra. The counting technique is based upon the coincidence measurement of the characteristic
636     particle-photon emissions of these isotopes. Silver activated zinc sulfide for alpha detection is
637     combined with a Nal well for gamma-ray detection (McCurdy, 1981).

638     15.5.2 Beta/Gamma Coincidence Systems

639     Many radionuclides remain in an excited state after what may be considered beta decay. This
640     results in the emission of a gamma ray as the decay process goes to the ground state. A
641     beta/gamma coincidence system will have significantly improved lower limit of detection over a
642     beta or a gamma counting system because of its very low background. Systems have been
643     designed with both 2-rc and 4-rc geometry (McCurdy et al.,  1980).

644     15.5.3 Gamma/Gamma Coincidence Systems

645     These counting systems can provide extremely low backgrounds and are very useful for
646     analyzing those radionuclides that decay with cascading (coincident) gamma rays. The systems
647     usually consist of two large Nal(Tl) detectors with a surrounding active anti-coincidence shield

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648     of either Nal(Tl) or plastic phosphor. However, HPGe detectors have also been used in place of
649     the two large Nal(Tl) detectors. Only gamma-ray pulses that are detected in both of the primary
650     detectors at the same time (coincident) and not in the active shield are recorded. Even though
651     these systems can be large, because of the shielding requirements for two detectors and an active
652     annulus, and require complex electronics, the improvement in lower limit of detection for certain
653     radionuclides is worth the investment (Perkins,  1965; Sanderson, 1969).

654     15.5.4 Photon-Electron Rejecting Alpha Liquid Scintillation Systems

655     Another technique for the analysis of alpha emitting radionuclides combines liquid scintillation
656     counting with pulse shape discrimination to significantly reduce background counts from photo-
657     electrons produced by ambient background gamma rays and to eliminate interferences from beta
658     emitters in the sample/scintillation cocktail. Pulse shape discrimination electronically selects only
659     pulses produced by alpha particles because of their longer decay times in the scintillation
660     solution. Typical alpha peak resolutions are about 5 percent. Typical detectable activities for
661     alpha emitters such as 234U and 241Am are 0.0037 and 0.37 Bq (0.1 and 10 pCi).

662     15.6  Special Instruments

663     This section covers some radiation detection instruments and auxiliary equipment that may be
664     required for special application in the measurement of radioactivity.

665     15.6.1 4-7T Counter

666     The 4-Ti counter is a detector designed for the measurement of the absolute disintegration rate of
667     a radioactive source by counting the source under conditions that approach a geometry of 4-7t
668     steradian.  Its most prevalent use is for the absolute measurement of beta emitters. For this
669     purpose, a gas-flow proportional counter is commonly used. 4-7t counting systems consist of two
670     hemispherical  or cylindrical chambers whose walls form the cathode, and a looped wire anode in
671     each chamber. The source is mounted on a thin supporting film between the two halves, and the
672     counts recorded in each half are summed.

673     Gamma-ray and hard X-ray counters with geometries approaching 4-7t steradian can be
674     constructed from both Nal(Tl) or germanium crystals in either of two ways. A well crystal (that
675     is, a cylindrical crystal with a small axial hole covered with a second crystal) will provide nearly
676     4-Tt geometry for small sources, as will two solid crystals placed very close together with a  small
677     source between them. The counts from both crystals are summed as in the gas-flow counter. The
678     deviation from 4-7t geometry can be calculated from the physical dimensions. For absolute

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679     gamma-ray counting, the efficiency of the crystal for the gamma energy being measured and the
680     absorption in the detector end cap should be taken into account. The liquid scintillation counter is
681     also essentially a 4-rc counter for alpha and beta particles, since nearly all the radiations are
682     emitted into and interact with the detecting medium.

683     15.6.2 Low-Geometry Counters

684     This type of instrument is particularly useful for the absolute counting of alpha particles. The
685     alpha emitter, in the form of a very thin solid source, is placed at a distance from the detector
686     such that only a small fraction (<1 percent) of the alpha particles are emitted in a direction to
687     enter the counter. This solid angle is obtained from the physical measurements of the instrument.
688     The space between the source and the detector is evacuated to eliminate the loss of alpha
689     particles by absorption in air. The detector can be any counter that is 100 percent efficient for all
690     alpha particles that enter the sensitive volume—a gas-flow proportional counter with a window
691     that is thin (approximately 1 mg/cm2) compared to the range of the alpha particles or the
692     semiconductor alpha detector with a 1 mg/cm2 covering. The advantages of this instrument for
693     absolute alpha counting are that the effect of absorption of alpha particles in the source itself is
694     kept to a minimum since only particles that travel the minimum distance in the source enter the
695     detector (particles that have longer paths in the source are emitted at the wrong angle), and back-
696     scattered alpha particles (those that are emitted into the source backing and are reflected back up
697     through the source) lose sufficient energy so that they cannot enter the detector. One such
698     instrument is described in Curtis et al. (1955).

699     15.6.3 Internal Gas Counters

700     The internal gas counter is so named because the radioactive material, in the gaseous state, is
701     placed inside a counting chamber and thus becomes part of the counting gas itself. It is useful for
702     high-efficiency counting of weak beta- and X-ray emitting radionuclides. The radiations do not
703     have to penetrate a counter window or solid source before entering the sensitive volume of a
704     detector. The counter may be an ionization chamber, or it may be operated in the Geiger or
705     proportional mode. Most present-day instruments are of the  latter type, and they generally take
706     the form of a metal or metal-coated glass cylinder as a cathode with a thin anode wire running
707     coaxially through it and insulated from the cylinder ends. A wire through the wall  makes
708     electrical contact to the cathode.  The counter has a tube opening through which it may be
709     connected to a gas-handling system for filling. The purity of the gas is important for efficient and
710     reproducible counting, particularly in the proportional mode.
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111     In a modification of the internal gas counter, scintillation counting has been used. The inner walls
712     of the chamber are coated with a scintillation material and the radioactive gas is introduced. An
713     optical window is made a part of the chamber, and the counting is done by placing this window
714     on a multiplier phototube to detect the scintillations. This system is particularly useful for
715     counting radon gas with zinc sulfide as the scintillator. Additional details on internal gas
716     counting may be found in Watt and Ramsden (1964).

717     15.7   Spectrometers  and Energy-Dependent Detectors

718     The availability of energy-dependent detectors (detectors whose output signal is proportional to
719     the energy of the radiation detected) that are easy to operate and maintain and have good
720     resolution makes it possible to measure not only the total activity of a radioactive sample but the
721     energy spectrum of the nuclear radiations emitted. Nuclear spectrometry is most useful for alpha
722     particles, electromagnetic radiation (gamma and X-rays), and conversion electrons, since these
723     radiations are emitted with discrete energies. Beta spectra have more limited use since beta
724     particles are emitted from a nucleus with a continuous energy distribution up to a characteristic
725     maximum (E- max), making a spectrum containing several different beta emitters difficult to
726     resolve into its components. The advantages of spectrometric over total activity measurements of
727     radioactive sources are increased selectivity, detection limit, and accuracy because nuclide
728     identification is more  certain, interference from other  radioactive nuclides in the sample is
729     diminished or eliminated, and counter backgrounds are reduced since only a small portion of the
730     total energy region is used for each radiation.

731     The detectors for alpha spectra are gridded ion-chambers and silicon semiconductor detectors.
732     Gridded ion-chambers are no longer available commercially and should be constructed by the
733     user. A variety of semiconductor detectors  for alpha spectrometry are commercially available.
734     These detectors have essentially replaced ion-chambers, although the chambers have the
735     advantages of high efficiency (nearly 50 percent) for large-area sources.

736     Silicon alpha particle  detectors have a depletion region which is formed by applying a high
737     voltage bias.  The electric field produced collects the electron-hole pairs produced by incident
738     alpha particles. Either surface barrier or passivated ion-implanted silicon are commonly used for
739     spectrometry.

740     The principal detectors used for gamma-ray spectrometry are thallium-activated sodium iodide
741     scintillation crystals, Nal(Tl), and high purity germanium semiconductors, HPGe. HPGe
742     detectors are  available in n-type and p-type germanium. P-type germanium detectors have dead
743     layers which  produce  entrance windows from 500 to 1,000 jim thick.  On the other hand, n-type

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744     detectors have extremely thin entrance windows of about 0.3 jim. These n-type detectors when
745     housed in an end cap with a beryllium window are excellent for measuring both low energy and
746     high energy (3 to 10,000 keV) gamma rays. However, applications which require the best
747     possible energy resolution, peak shape, and efficiency for gamma-ray measurements above 80
748     keV, p-type HPGe is the detector material of choice.

749     For X-rays and very low-energy gamma rays, lithium-drifted silicon semiconductor Si(Li), planar
750     germanium, and gas-filled thin window (approximately 1 mg/cm2) proportional counters are
751     used.

752     The electronic version of Heath's (1964) Ge(Li) and Si(Li) Detector Gamma-ray Spectrum
753     Catalogue is available in two forms. The document is on the Web at http://id.inel.gov/gamma; it
754     is also available on a CD-ROM.

755     The portion of the crystal end cap through which gamma rays enter is normally thinner, or
756     constructed of a low-Z material, like beryllium or magnesium, than the rest of the package in
757     order to reduce low-energy attenuation. Sodium iodide crystals are available in a large range of
758     sizes and shapes, from thin crystals for X-ray analysis and small 25 by 25 mm cylinders to
759     hemispheres and cylinders over 300 mm in  diameter. Information on the types of crystal
760     packages and mountings is available from the manufacturers.

761     A complete Nal(Tl) detector spectrometer requires a high-voltage power supply for the phototube
762     (usually operated at 600 to 1,000 V), a preamplifier,  linear amplifier, pulse-height analyzer, and
763     output recorder. Because Nal(Tl) detectors cannot resolve gamma-ray energies that are only a
764     few keV apart, a least-squares computer program should be used to quantify a complex gamma-
765     ray spectrum.

766     Germanium and silicon detectors are junction-type semiconductor devices. With silicon
767     detectors, a sensitive region is produced by  drifting lithium under the influence of an electric
768     field at an elevated temperature (100 to 400 °C) into the crystal. The crystal then functions as a
769     solid ion chamber when a high voltage is applied. Today, germanium detectors are made with
770     very high purity material that does not require lithium drifting. In order to obtain high resolution,
771     these detectors should be operated at low temperatures to reduce thermal noise. At room
772     temperature, sufficient free electrons will be present in the crystal to obscure the measurement of
773     gamma and X-rays.  Consequently, the detectors are operated at liquid nitrogen temperatures by a
774     cryostat consisting of a metallic cold-finger immersed in a Dewar flask containing liquid nitrogen
775     or mechanically refrigerated.
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776
777
778
779
780
781

782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808

809
810
                                           30
The electronic components required to obtain spectra are similar to those for Nal(Tl) detectors,
except that because smaller pulses should be measured, high-quality electronics should be used.
A complete HPGe system includes a high-voltage bias supply for the detector, a preamplifier,
amplifier (usually charge-sensitive), pulse height analyzer, and recording device. With the
exception of extremely complex spectra, most high resolution spectra can be quantified by
simple integration of full energy gamma-ray peaks.
The resolution of gamma-ray detectors
is usually specified in terms of its
FWHM. Detector resolution, expressed
in percent, improves with increasing
energy and for Nal(Tl) detectors and is
usually determined from the  662 keV
gamma ray emitted in the decay of 137Cs.
This is shown graphically in  the
gamma-ray spectrum in Figure 15.7. For
HPGe detectors, 60Co is measured
25 cm above the detector end cap.
Quality sodium iodide crystals have
resolutions in the range of 6.5 to 7
percent for 137Cs. Detection efficiency
for the same geometry and window
thickness is a function of several
parameters and much published
information on efficiencies for various
                                           25  -
                                           20  -
                                           15 -
                                           10  -
                                            5  -
                                                               ENERGY (keV)
                                                                Hundreds
                                            FIGURE 15.7 Nal(Tl) Energy Spectrum of 137Cs
energies, detector sizes, source-to-detector distances, and other variables is available
(Crouthamel et al., 1970). The efficiency for gamma-ray detection is expressed as full energy
peak efficiency; the fraction of incident gamma rays that give a full-energy peak for a particular
source-detector configuration. For a 102 mm thick Nal(Tl) crystal, with the source on the surface
(zero distance), this fraction is approximately 0.24 for the 661.6 keV gamma ray of 137Cs and
approximately 0.14 for the 1,332.5 keV gamma ray of 60Co. The peak-to-valley or peak-to-
Compton ratio is the ratio of counts at the maximum height of the full-energy peak to the counts
at the minimum of the Compton continuum. A high ratio indicates narrow peaks, that is, good
resolution, for that particular energy.

The efficiency specification of a FIPGe detector is expressed by comparing its 60Co, 1,332.5 keV
efficiency at 25 cm with that of a 76x76 mm cylindrical Nal(Tl) detector at the same distance.
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811
812
813
814
815
816
817
818

819
820
821
822

823
824
825

826
827

828
829

830
831
832
833
Photopeaks are spread over a much
smaller energy range in germanium
than in sodium iodide, the background
under the peak is much less (Figure
15.8). This means that for small
sources of moderately energetic
gamma rays, germanium is more
sensitive than sodium iodide.

Typical specifications for a
germanium gamma-ray detector could
include but should not be limited to
the following:

   DETECTOR: The gamma-ray
   detector should consist of High-
   Purity n-type germanium.
80
60 -
40  -
20 -
                             FWHM .

                             1.5 keV
                      4         6
                   ENERGY (keV)
                     Hundreds
                                            FIGURE 15.8 HPGe Energy Spectrum of 137Cs
   SIZE: The germanium crystal
   should be at least 5.5 cm in diameter and at least 7.0 cm long.

   EFFICIENCY: The relative counting efficiency compared to a 3"x3" Nal detector at 25 cm for
   60Co (1,332 keV) should be equal to or better than 50 percent.

   RESOLUTION: The resolution (FWHM) of the detector should be equal to or better than
   2.2 keV at 1,333 keV (60Co). The resolution (FWHM) at 122 keV (57Co) of the detector
   should be equal to or better than 1.0 keV. The detector resolution at FWTM should be equal
   to or better than 2 times the FWHM.
834
835
   PEAK-TO-COMPTON RATIO: The peak-to-Compton ratio for 1,333 keV (60Co) should be equal
   to or better than 50:1.
836
837
838
839
   BACKGROUND: Low radioactivity materials should be used so that any full energy gamma-ray
   line (excluding 511 keV and 1,460 keV) present in a 1,000-minute background spectrum
   (100-2,000 keV) obtained in a graded 10 cm lead shield should not exceed 0.20 counts per
   minutes.
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840         CONTACTS: The internal detector contacts should be DC-coupled ion implants so that low
841         energy gamma-ray attenuation is avoided.

842         PREAMP: A low-noise, cooled field-effect transistor preamplifier should be used to provide
843         the detector output signals.

844         CRYOSTAT: The cryostat should be constructed of low radioactivity materials throughout and
845         should contain sufficient lead shielding in order to minimize radiation from the dewar or
846         lower portion of the cryostat.

847         END CAP: The end cap should consist of a 20 mil beryllium window with 0.5 mm aluminum
848         side walls and be no greater then 7.6 cm diameter (OD). This diameter should be maintained
849         for at least 8 cm from the end cap. Below this point the outside diameter of the end cap may
850         be increased. The top of the end cap  should be between 95  and 102 cm above the outside base
851         of the dewar.

852         TEMPERATURE: The cryostat should  contain a temperature sensing circuit to provide high
853         voltage shut down in order to prevent preamplifier damage in case of warm-up due to loss of
854         liquid nitrogen.

855     Spectra of beta particles and conversion  electrons can be obtained with sodium iodide and n-type
856     HPGe detectors. A germanium detector with a volume of 120 cm3 has an efficiency  approxi-
857     mately 20 percent that of a 76x76 mm Nal(Tl) crystal. Larger HPGe detectors are available with
858     relative efficiencies over 150 percent when compared with a 76x76 mm Nal(Tl) crystal.
859
860     Presently available germanium detectors have resolutions of 1.5 to 2.5 keV at 1,332.5 keV. The
861     method used to measure the energy resolution is described in ANSI/IEEE 325. This  greater
862     resolution makes this detector the one of choice for gamma-ray spectrometry and cancels to some
863     extent the higher efficiency available from sodium iodide. Since the pulses from a single
864     semiconductor detectors  sufficiently thick (a few centimeters) to absorb the particles completely.
865     One disadvantage of sodium iodide detectors is their relatively thick entrance windows. Other
866     semiconductor detectors  have thin beryllium entrance windows and can be used for beta
867     spectrometry.

868     Good spectra of low-energy beta particles, conversion electrons, and X-rays can be obtained with
869     a gas-flow proportional Counter provided that a linear preamplifier is used. The resolution is
870     intermediate between Nal(Tl) and HPGe. Organic scintillators, such as anthracene and
871     polystyrene polymerized with scintillating compounds, are also useful for beta spectrometry.


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872     They are packaged with a phototube in a
873     manner similar to sodium iodide
874     crystals. Liquid scintillation mixtures
875     also give beta spectra, and the output of
876     a commercial liquid scintillation counter
877     is usually fed into a multichannel pulse-
878     height analyzer to obtain a beta energy
879     spectrum (Blanchard et al., 1960). A
880     spectrum of 210Pb, 210Bi, and 210Po in
881     Figure 15.9 shows the resolution
882     obtainable by liquid scintillation
883     counting of aqueous samples in a
884     dioxane-based solution. The 210Bi  curve
885     is from a beta particle,  and the 210Po
886     peak is from  an alpha particle.  Organic
887     scintillators are preferable to sodium
888     iodide for beta spectrometry because
889     less back scattering occurs.

890     15.7.1 Anti-Coincidence Counters
o
o
    210
                             210
                                 Po
                   LOG ENERGY
  FIGURE 15.9 Spectrum of 210Pb, 210Bi, and 210Po
891     Substantial background reduction can be achieved in beta and gamma counters by surrounding or
892     covering the sample detector with another detector also sensitive to beta or gamma radiation, and
893     connecting them electronically so that any pulse appearing in both detectors at the same time is
894     canceled and not recorded as a count. This is referred to as anti-coincidence shielding, and is
895     recommended for obtaining very low backgrounds. This type of counter was used for many years
896     in directional studies of cosmic rays, and was first applied to reducing the background of beta
897     counters by Libby in his study of natural 14C. The thick metal shielding (lead, iron, or mercury)
898     ordinarily used to reduce cosmic-ray and gamma-ray background should also be present, and is
899     placed outside the anti-coincidence shielding.

900     Anti-coincidence shielding of gamma-ray detectors operates in a similar way, and is particularly
901     useful in reducing the Compton continuum background of gamma rays  (Nielson, 1972). Gamma
902     rays that undergo Compton scattering and produce a pulse in both the detector and the anti-
903     coincidence shield are canceled electronically. Ideally, only those gamma  rays that are completely
904     absorbed in the sample detector produce a count that is recorded with the total energy of the
905     gamma ray (full-energy peak). There are second-order effects that prevent complete elimination
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906     of Compton scattering, but the improvement is substantial (Perkins, 1965, and Cooper et al.,
907     1968).

908     15.7.2 Coincidence Counters

909     In coincidence counting, two or more radiation detectors are used together to measure the same
910     sample, and only those nuclear events or counts that occur simultaneously in all detectors are
911     recorded. The coincidence counting technique finds considerable application in studying
912     radioactive decay schemes; but in the measurement of radioactivity, the principal uses are for the
913     standardization of radioactive sources and for counter background reduction.

914     Coincidence counting is a very powerful method for absolute disintegration rate measurement
915     (Friedlander et al., 1964; IAEA,  1959). Both alpha and beta emitters can be standardized if their
916     decay schemes are such that p-y, y-y, p-p, a-p, or a-X-ray coincidence occur in their decay.
917     Gamma-gamma coincidence counting with the source placed between two sodium iodide
918     crystals, is an excellent method of reducing the background from Compton scattered events. Its
919     use is limited, of course, to counting nuclides that  emit two photons in cascade (which are
920     essentially simultaneous), either directly as in 60Co, by annihilation of positrons as in 65Zn, or by
921     immediate emission of a gamma ray following electron capture decay. Non-coincident pulses of
922     any energy in either one of the crystals will be canceled, including cosmic-ray photons in the
923     background and degraded or Compton scattered photons from higher energy gamma rays in the
924     sample. Thus, the method reduces interference from other gamma emitters in the  sample. When
925     two multichannel analyzers are used to record the  complete spectrum from each crystal, singly
926     and in coincidence, then the complete coincident gamma-ray spectrum can be obtained with one
927     measurement. The efficiency for coincidence counting is low since it is the product of the
928     individual efficiencies in each crystal, but the detection limit is generally improved because of
929     the large background reduction (Nielsen and Kornberg,  1965). This technique is often referred to
930     as two-parameter or multidimensional gamma-ray spectrometry.

931     Additional background improvement is  obtained if the two crystals are surrounded by a large
932     annular sodium iodide or plastic scintillation crystal connected in anti-coincidence with the two
933     inner crystals. In this case a gamma ray that gives  a pulse, but is not completely absorbed in one
934     of the two inner crystals, and also gives  a pulse in  the surrounding crystal, is canceled
935     electronically (Perkins, 1965, and Nielsen and Kornberg, 1965). This provides additional
936     reduction in the Compton scattering background. Germanium detectors may be used in place of
937     the inner sodium iodide crystals for improved resolution and sensitivities (Cooper et al., 1968).
938     An example of an assay for plutonium content using passive thermal-neutron coincidence
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939     counting is given in ASTM (C1207). Another example of passive thermal-neutron coincidence
940     counting using a moveable californium source is given in ASTM (C1316).

941     15.8  Shielding

942     The purpose of shielding is to reduce the background count rate of a measurement system.
943     Shielding reduces background by absorbing some of the components of cosmic radiation and
944     some of the radiations emitted from material in the surroundings. Ideally, the material used for
945     shielding should itself be free of any radioactive material that might contribute to the
946     background. In practice, this  is difficult to achieve as most construction materials contain at least
947     some naturally radioactive species (such as 40K, members of the uranium and thorium series,
948     etc.).  The thickness of the shielding material should be such that it will absorb most of the soft
949     components of cosmic radiation. This will reduce cosmic-ray background by approximately 25
950     percent. Cosmic-ray interactions in lead shields will produce lead X-rays that are in turn shielded
951     by cadmium and copper liners. Such a shield is referred to as a "graded shield." Six millimeters
952     of oxygen-free high-conductivity (OFHC) copper can also be used to reduce the cosmic-ray
953     produced lead X-rays without the cadmium liner. Shielding of beta- or gamma-ray detectors with
954     anti-coincidence systems can further reduce the cosmic-ray or Compton scattering background
955     for very low-level counting.

956     Detectors have a certain background counting rate from naturally occurring radionuclides and
957     cosmic radiation from the surroundings; and from the radioactivity in the detector itself. The
958     background counting rate will depend on the amounts of these types of radiation and on the
959     sensitivity of the detector to the radiations.

960     In alpha counting, low backgrounds are readily achieved  since the short range of alpha particles
961     in most materials makes effective shielding easy. Furthermore, alpha detectors are quite
962     insensitive to the electromagnetic components of cosmic and other environmental radiation.

963     The size and interior dimensions of shields constructed for gamma-ray  spectrometry or gamma
964     counting in general should be considered so that sample back scatter radiation from the shield
965     wall to the detector is minimized. In general, shield wall  should be at least 10 cm from the
966     detector. Back scatter radiation will fall off as the square of the detector to shield wall distance.

967     15.9  Instrument Calibration

968     Calibrations of instruments should be made using reference materials of known and documented
969     value and stated uncertainty.  These reference materials should be supplied by:

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970      • National Institute of Science and Technology (NIST) directly;

971      • A standard source supplier whose measurement capabilities and/or manufacturing processes
972        are periodically tested by NIST; and

973      • A user who documents derived materials with stated uncertainty and whose value has been
974        verified with analytical and measurement systems that have been periodically tested through
975        an unbroken chain of comparisons to the national physical standards.

976     Periodic testing of source manufacturers, whether they be commercial or agency suppliers or end
977     users, is most cost effectively implemented through measurement assurance programs that are
978     ultimately linked to NIST traceability (Hoppes, 1990).

979     A comprehensive discussion of germanium detector set up and calibration can be found in ANSI
980     N42.14.

981     15.10 Other Considerations

982     15.10.1 Alpha

983     15.10.1.1 Troubleshooting

984     A number of factors can influence alpha counting results. These include attenuation or self
985     absorption, detector contamination, and other radionuclide interference. Attenuation or self
986     absorption corrections need not be made if constant conditions are maintained for sample and
987     calibration standard counting. If conditions can not be held constant, then corrections will have to
988     be made in order to produce accurate results. For example, the gamma rays from 137Cs in a water
989     matrix counted in a 90 cm3 aluminum can will require a 15 percent correction. Individual
990     electrical line conditioners or uninterruptible power supplies as well as supplemental air
991     conditioning can be provided in the counting rooms to maintain electrical and environmental
992     stability. Additionally, humidity control can also provided. Temperature and humidity may be
993     recorded with a chart recorder.

994     Detector contamination can also be a problem in some cases and, therefore, detector backgrounds
995     should be periodically checked. Contaminated detectors will have higher background counts and
996     even when sample spectra are corrected for the presence of contamination the higher background
997     results in higher MDAs. Finally, some alpha counters may be sensitive to beta radiation, and
998     corrections may have to be made for this interference. For a routine operating alpha counting

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 999      system periodic instrument QC checks should be performed at some specified frequency. This
1000      would include, as appropriate, counting efficiency, background, resolution, gain, and voltage
1001      plateau.

1002      Solid state detectors used for alpha spectrometry can become contaminated by recoil. This recoil
1003      contamination, which increases the detector background, takes place when fragments from
1004      sources travel to the detector and are implanted in the detector surface by the recoil energy
1005      imparted to the nucleus of an alpha-emitting atom. The energy of the fragments may be sufficient
1006      to implant them in the detector so that they cannot be removed non-destructively. Recoil
1007      contamination can invalidate a count after only a single sample count and cause a constant need
1008      to decontaminate equipment.

1009      The application of a negative bias to the sample,  in conjunction with an absorbing layer of air, or
1010      a thin film absorber (12 jig/cm3) helps to keep recoil particles from imbedding themselves into
1011      the detector. For better resolution and where recoil contamination is of no concern, it is advisable
1012      to maintain a low pressure. Typically, systems  can pump down to under 50 jim and, by
1013      continuously running the pump, maintain that level indefinitely.

1014      Detector contamination dominated by two processes, alpha recoil and "volatilization" of
1015      polonium. Alpha recoil contamination occurs when an alpha-emitting nuclide on the source plate
1016      decays to an alpha-emitting daughter or string of progeny. Since the specific activity is inversely
1017      proportional to the half-life for a fixed number of atoms, recoil will produce the most background
1018      activity when relatively short-lived progeny are produced. However, if the half-lives in question
1019      are very short (say up to a few hours), they will decay away quickly enough to be of little concern
1020      in alpha spectrometry. Particularly serious are those cases that involve transfer of recoil progeny
1021      with half-lives from days to weeks, short enough that a reasonable amount of parent activity will
1022      produce a significant amount of recoil contamination, and long enough that decay back to normal
1023      background levels will require an inappropriately long time. In addition, the effect is chronic:
1024      similar recoil-producing samples counted in the same chamber will produce a long-term build-up
1025      of detector background which could eventually become serious.

1026      Some common examples of decay-chains that produce recoil contamination include 228Th, 229Th,
1027      and 226Ra. It is important to realize that even p-emitting nuclides ejected by alpha recoil can
1028      contribute to alpha background if they subsequently decay to alpha emitters. For example, the
1029      direct daughter of 229Th is 225Ra which decays by p-emission to the a-producing daughter 225Ac.

1030      Contamination of detectors by polonium isotopes, such as 210Po (t,/2 = 138.4 days), should occur
1031      by some other process than alpha recoil. Note that 210Po, the last radioactive member of the 238U


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1032     decay series, is the daughter of 210Bi, a beta-emitter. The transfer of polonium from a source to a
1033     silicon detector has been attributed to "aggregate" recoil and inherent volatilization of polonium
1034     at low pressure. Whatever the actual cause, it is clear that polonium activity is indeed transferred
1035     to detectors, a very serious problem with long-lived 210Po  and even worse when working with
1036     209Po (t,/2 = 102 years) as a yield tracer.

1037     Liquid Scintillation Quenching

1038     Quenching, which is probably the most prevalent interference in liquid scintillation counting, can
1039     be defined  as anything which interferes with the conversion of radionuclide decay energy to
1040     photons emitted from the sample vial, resulting in a reduction of counting efficiency. Two types
1041     of quenching may be encountered in liquid scintillation counting: optical or color quenching and
1042     chemical quenching. Color quenching results in a reduction of the scintillation intensity (as seen
1043     by the multiplier phototubes) due to absorption of the scintillation light by materials present in
1044     the scintillation solution resulting if fewer photons per quanta of particle energy and a reduction
1045     in counting efficiency. Chemical quenching results in a reduction in the scintillation intensity due
1046     to the presence of materials in the scintillation  solution that interfere with the process leading to
1047     the production of light resulting in fewer photons per quanta of particle energy and a reduction in
1048     counting efficiency. The quenching process may be illustrated as follows.

1049     Radionuclide Decay	Beta ->• Solvent & Scintillator	Light Photon ->•   Multiplier Phototube

                                               Chemical Quench                Chemical Quench

1050     One can have both types of quenching present  in a sample. Note that in chemical quenching all
1051     energy radiations are equally effected, but in color quenching not all energy radiations are equally
1052     effected. Therefore, the measured sample counts should be corrected for quenching effects so
1053     that the radioactivity in the sample can be  quantified. Typical quench corrections include
1054     Channels Ratio, External Standard and Internal Standardization.

1055     Attenuation

1056     Attenuation or self absorption corrections may be necessary for alpha counting. Attenuation
1057     corrections should be made whenever the sample matrix differs from that of the calibration
1058     standard. For example, when a gross alpha analysis is performed on an evaporated water sample
1059     of some thickness and an electroplated standard was used for the calibration. Attenuation
1060     corrections will have to be made. Alpha particle attenuation corrections will generally be
1061     necessary with a sample density thickness greater than about one mg/cm2.


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1062      Figure 15.10 shows how severe the attenuation of alpha particles is in air.
1063

1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079

1080
1081
1082
1083
1084
1085
1086

1087
15.10.1.2 Calibration

Alpha counting instrumentation should
be calibrated with the specific radionuc-
lide of interest or a radionuclide of
similar alpha energy under the same
configuration that the sample will be
counted. The standard should contain
the same solid material as the sample
and be of the same weight. If the
samples and calibration standard are not
counted under identical conditions, then
corrections will have to be made. Also,
if there is a variation in weight from
sample to sample corrections will have
to be made, typically a  calibration curve
relating sample weight  to counting
efficiency is used.
    4         6
ENERGY (MeV)
                                        FIGURE 15.10 Range vs. Energy for Alpha Particles in Air
Alpha calibration standards are available from NIST or NIST-traceable commercial vendors.
Among the radionuclides available are 230Th, 241Am, 235U, 239Pu, 228Th, 238U, and 226Ra. Other
radionuclides are also available, NIST or a commercial vendor should be contacted regarding
procurement. Sources should be prepared in the manner in which the sample will be counted.
The source may be procured as a solution and then prepared in the appropriate counting
geometry, or the source may be procured directly in the appropriate geometry, such as an
electroplated standard.

15.10.1.3 Costs
1088      There are three major types of detectors used for alpha counting. Their cost will depend on the
1089      type of information wanted and the number of detectors in the unit.

1090      Solid state silicon surface barrier detectors are used to count and distinguish alpha particles of
1091      different energies. An alpha spectrometer consists of a vacuum chamber, detector, electronics to
1092      amplify the signal, a multichannel analyzer, and some means of collecting data. A system with
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1093      eight detectors, vacuum pump, computer and the software necessary for data collection and data
1094      reduction costs approximately $50,000.

1095      A liquid scintillation counter can be used to count alpha particles and in some cases provide
1096      some information about the energy distribution, although with poorer resolution than silicon
1097      surface barrier detectors. The LSC unit is typically set up to count samples sequentially, using
1098      one detector and automatic sample changer. The price depends on the background required, and
1099      will range from $25,000 to as much as $45,000. This price includes a computer and the
1100      appropriate software.

1101      A gas-flow proportional counter is used to count samples for a gross alpha (or beta) activity. The
1102      price of a unit depends on the number of detectors, the size of each detector, and the accessories.
1103      One major accessory could be an automatic sample changer. A system with 8 to 10 small
1104      detectors (1 inch in diameter) will cost from $35,000 to more  than $60,000.

1105      There are no maintenance costs associated with an alpha spectrometer. If properly used and
1106      monitored, the system will retain its specifications for a long time. The detectors may need
1107      replacing eventually, if its resolution deteriorates or it becomes contaminated, at a cost of $500-
1108      $1,000 each.

1109      A liquid scintillation counter requires the use of an organic scintillation cocktail, which cannot be
1110      reused. The total cost of this cocktail, combined with the cost of the  sample vials, should not
1111      exceed $500 for an annual throughput of approximately 1,000 samples.

1112      The operation expense associated with the use of a gas-flow proportional counter is for the ultra-
1113      high purity P10 gas, which is necessary if stable efficiencies and low backgrounds are required.
1114      All proportional counters should have calibrated gas regulators for accurate and reproducible
1115      settings of flow rates. The flow rate should be placed with the QC information that is with the
1116      other instrument QC. For almost constant operation of a system with eight detectors, as many as
1117      24 tanks of P10 gas per year will be required, at a total cost of approximately $7,000.

1118      All of the above instruments should be in a fairly constant temperature and low humidity
1119      environment, so that air conditioning and/or heating costs need to be factored in, as needed.
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1120      15.10.1.4 Quality Control

1121      Statistical quality control (SQC) is discussed here to familiarize the reader with its application to
1122      nuclear counting instrumentation. More detailed information about SQC is provided in
1123      Chapter 19.

1124      The primary tool for statistical quality control is the control chart. A control chart is a graphical
1125      tool for monitoring the distribution of values produced by a measurement process or system. The
1126      distribution of values observed during a period when the system is in statistical control is used to
1127      set up the control chart. Subsequent values are then plotted on the chart and inspected to ensure
1128      that the system remains in  control.

1129      Typically one ore more control charts for counting efficiency and background are maintained for
1130      each counting instrument.  The instrument should be fully operational before the control charts
1131      are implemented. However, control charts should be in use before calibration of the instrument
1132      for a particular analysis to  ensure that the instrument parameters are in statistical control during
1133      the calibration.

1134      The selection of the check source for monitoring counting efficiency is critical and should be
1135      made after considering guidance in this document. The source geometry, half-life, and radiation
1136      energy are important factors.

1137      A control chart should  be based on an initial data set obtained from at least 15 measurements.
1138      Ideally, at least 10,000  counts per measurement are recommended to provide a relative counting
1139      uncertainty of no more than 1 percent. For some instruments, achieving the recommended 10,000
1140      counts may be impractical, especially for a background control chart. It may also be undesirable
1141      to place a high-activity efficiency check source in a low-background detector because of the
1142      potential for contamination.

1143      The initial measurements should represent the measurement system as it is used over time.
1144      Making the measurements over several days ensures that variability due to temperature and
1145      humidity changes is included. The source should be repositioned before each measurement to
1146      ensure that variability due  to positioning error is included.

1147      The mean and standard deviation of the counts or count rates are estimated from the initial data
1148      set. The mean is  used as the central line (CL) of the control chart. Warning limits are placed at ±2
1149      standard deviations from the central line, and control limits are placed at ±3 standard deviations
1150      from the central line.
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1151      Statistical tests of the data distribution should be performed at the time the warning and control
1152      limits are established. Tests for normality are common. It is also common to test whether the
1153      counts follow the Poisson model (Chapter 19).

1154      The central line and warning and control limits for an efficiency control chart should be adjusted
1155      for radioactive decay of the check  source unless the source is very long-lived. Either the limits or
1156      the data points may be decay-corrected. It may also be necessary to adjust the counting time for
1157      the check source measurements  if the source decays considerably during the period when the
1158      chart is in use. It is important to  note that the relative standard deviation of the measured values
1159      increases as the mean number of counts per measurement decreases.

1160      When a measured value falls within the warning limits, the measurement system is considered to
1161      be in control. If a value falls outside the control limits, the system is considered out of control.
1162      These two rules are commonly used to evaluate control charts, although stricter evaluation
1163      criteria are sometimes used. Common sense should be exercised if the data meet the objective
1164      evaluation criteria but nevertheless demonstrate patterns  or trends that might indicate developing
1165      problems. For example if a long increasing or decreasing sequence of values is observed, an
1166      investigation is probably warranted even if all of the values are between the warning  limits.

1167      Generally, if a value falls within the control limits but outside the warning limits, the system may
1168      require more attention but it is not yet considered definitely out of control. The Westgard Rules,
1169      which are recommended by ASTM El329, provide more elaborate criteria for evaluating such
1170      measurements.
                                              The Westgard Rules*

           1. Is the measurement more than 2 sigma from the mean? If not, go to Step 7.
           2. Is the measurement more than 3 sigma from the mean? If so, go to Step 8.
           3. Are the last two measurements more than 2 sigma from the mean? If so, go to Step 8.
           4. Is the range of the last two measurements more than 4 sigma? If so, go to Step 8.
           5. Are the last four measurements more than 1 sigma from the mean? If so, go to Step 8.
           6. Are the last ten measurements more on the same side of the mean? If so, go to Step 8. Otherwise, go to Step 7.
           7. Accept the measurements.  Stop.
           8. The measurements are out  of control. Stop.	
1171

1172
1173
1174
1175
1176
1177
1178
1179
1180          'Adapted from ASTM E1329.

1181      The following two sections on proportional counting and liquid scintillation counting are
1182      applicable to both alpha and beta measurements.
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1183      Proportional Counters

1184      The following should be considered when QC checks are not within limits.

1185       1. Is the standard decay corrected, correctly?

1186       2. Check log book to see what changes were made to counter and if the repairman recently
1187          changed any switch settings.

1188       3. If gas cylinder was changed recently, was system allowed to purge? Was correct gas (10P)
1189          obtained? Verify the correct regulator pressure, and ensure the gas cylinder valve is open all
1190          the way.

1191       4. If backgrounds are high, check for dirt or dust on the background planchet. Check window
1192          for contamination and replace if necessary.

1193       5. Check alpha and beta voltages.

1194       6. Check discriminator settings.

1195       7. Check voltages on nim bin power supply (±12 V, ±24 V).

1196       8. Check alpha and beta plateau voltage for drift.

1197      Liquid Scintillation Counters

1198      The following should be considered when QC checks are not within limits.

1199       1. Is the standard decay corrected, correctly?

1200       2. Has the quench value for the unquenched standard for the instrument changed? The quench
1201          value for the unquenched standard indicates the overall gain of the system. Run the
1202          autocalibration and verify the result with the historical result.

1203       3. Check for dirt or fingerprints on outside of vial.

1204       4. Check for dirt inside instrument.
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1205       5. Is sample two phase?

1206       6. Has standard dark adapted and reached temperature equilibrium?

1207       7. Check log book to see what changes were made to machine and if repairman recently
1208         changed any switch settings.

1209      15.10.2 Beta

1210      15.10.2.1 Introduction

1211      Accurate beta particle measurements will depend upon the degree and extent to which the
1212      parameters that affect the measurement process under considerations are quantified. These
1213      parameters may include:

1214       • Radiation detector used;
1215       • Material and shape of the final sample mount;
1216       • Form and thickness of final sample for analysis;
1217       • Radionuclide purity of final sample;
1218       • Final sample-to-detector distance; and
1219       • Beta particle energy.

1220      Beta particle attenuation or self absorption corrections to the detector efficiency may be
1221      necessary depending on the beta particle energy detection system and final sample form. The
1222      potential of detector contamination from sample measurements is a function of the type of
1223      detector used and the stability of the final sample composition. The inherent beta particle
1224      background  of the various detection systems should be evaluated and its contribution removed
1225      from the  sample measurement result. The beta particle measurement system should be calibrated
1226      with NIST-traceable standards and its subsequent performance held to established measurement
1227      quality requirements through the use of daily or prior-to-use quality control checks. In addition,
1228      appropriate instrument quality control should be established for background, voltage plateau,
1229      quenching, resolution and alpha-beta cross talk. Guidance on beta particle counting can be found
1230      in industry standards (ASTM D1890; D3648; E1329) and publications (NCRP Report 58; Knoll,
1231      1989; Lapp and Andrews, 1954; Price, 1989; USPHS, 1967).

1232      "Gross" alpha and beta counting of evaporated samples, wherein a multitude of alpha and beta-
1233      emitting radionuclides may exist, is typically used for screening of water samples. The
1234      application of such methods may be targeted for a specific radionuclide or a category of


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1235      radionuclides such as the naturally occurring nuclides or a specific radionuclide in a facility
1236      effluent. However, extreme caution should be applied to the interpretation and use of such results
1237      without a full specific radionuclide characterization of the water source under investigation. The
1238      type of analysis is to be considered "gross" and, in most cases and for a variety of sound
1239      technical reasons, the gross measurement result does not equal the sum of the radionuclides
1240      contained in the sample.

1241      When specific radiochemistry is performed the beta-emitting radionuclide of interest will be
1242      isolated, concentrated and converted to a desired final chemical and physical form. Under these
1243      circumstances, the beta detection system should be calibrated for the radionuclide, chemical
1244      composition of the final sample form and the range of final sample weights expected from
1245      chemical recovery.

1246      15.10.2.2 Alpha Particle Interference and Beta Energy Resolution

1247      When properly operated or under optimal counting conditions (thin final samples or low LS
1248      quenching and high beta energy), most beta particle counting systems can separate alpha and beta
1249      particle detection events. However, the degree of alpha particle detection by the beta detector
1250      under consideration should be evaluated for each radionuclide, mixture of radionuclides or
1251      specific final sample form. Beta detection systems that are considered to have beta energy
1252      spectral resolution capabilities may be less affected by samples containing alpha-particle emitting
1253      radionuclides. However, for window gas proportional counters, alpha particle energy degradation
1254      by air, detector window or sample self absorption may lead to false beta detection without proper
1255      evaluation. A typical example would be a thick final sample matrix containing a mixture of alpha
1256      and low-energy beta-emitting nuclides.

1257      Some commercial window gas proportional counting systems have a feature for simultaneous
1258      alpha and beta particle counting that uses a voltage pulse height discrimination for the separation
1259      of beta and alpha particle detection events. A common and more historical means of separating
1260      alpha and beta particle events is to count the sample on the alpha proportional counting voltage
1261      plateau followed by a count on the beta (plus alpha) proportional counting voltage plateau.  An
1262      alpha-to-beta crosstalk factor should be determine for the final sample weight and for the alpha
1263      and beta energies under consideration. The net beta count is determined by multiplying the alpha
1264      counts (from the alpha window for simultaneous counting or on the alpha counting plateau) by
1265      the alpha-to-beta cross talk factor.

1266      Window gas proportional counters typically are not used for beta spectrometers but instead
1267      record beta particle detection events giving rise a voltage pulse large than a discriminator setting.


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1268     Under most circumstances, liquid scintillation counters have sufficient energy resolution
1269     capability and electronic discrimination to fully separate beta and alpha particle detection events.
1270     However, due to the nature of the beta energy continuum of an emission process and the inherent
1271     resolution of a liquid scintillation spectrometer, identification and quantification of multiple
1272     nuclides contained in the same sample is complicated unless their beta energies are widely
1273     separated. Computer software and beta interference factors should be applied in such cases.

1274     A liquid scintillation counter is typically used for Cerenkov counting. However, the final sample
1275     solution contains no scintillator as would a full liquid scintillation-sample cocktail. Cerenkov
1276     counting, due to the nature of measurement process, will not detect alpha particles of any energy
1277     or beta particles having an average beta energy less than 260 keV. Cerenkov counting is typically
1278     applied to single nuclide evaluations or for a mixture of two nuclides that have a differential
1279     maximum beta energies greater than 700 keV (e.g., 89Sr and 90Y). Beta interference factors should
1280     be applied in such cases.

1281     15.10.2.3 Liquid Scintillation Quenching

1282     The information on liquid scintillation quenching provided in Section 15.10.1.1 is applicable for
1283     beta particle detection. The degree of quenching should be determined for each radiochemical
1284     method, radionuclide or application. An appropriate correction factor/curve should be calculated
1285     and applied to the measurement results for the samples being evaluated.  The magnitude of the
1286     quench correction may approach 50 percent in certain severe quenching  situations.

1287     Cerenkov counting is less sensitive to "quenching" than liquid scintillation counters using
1288     scintillation cocktails. Typically, the final sample solution is a result of a control radiochemical
1289     process that eliminates most sources of contamination,  chemical impurities and variability in the
1290     final sample solution.

1291     15.10.2.4 Beta Particle Attenuation

1292     Beta particle attenuation should be considered for window gas proportional, plastic scintillator
1293     and solid state detector  counting applications. Beta particle attenuation can result from the
1294     interaction of a beta particle with the air, detector window or the matrix atoms of the final
1295     sample. Beta particle air attenuation  is a function of the distance between the sample or source
1296     and the detector's particle entrance window. Under most application for beta particle counting,
1297     this factor is typically insignificant compared to the other sources of beta particle attenuation.
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                                                                   Nuclear Counting Instrumentation
1298     Figure 15.11 shows the attenuation of
1299     beta particles is in air and water.
1300     Consideration of the detector window
1301     thickness and its beta particle
1302     attenuation becomes important when
1303     evaluating low energy beta particles
1304     such as 14C. Normally, the air and
1305     detector window attenuation factors
1306     are determined as a combined beta
1307     attenuati on-effi ci ency factor that
1308     includes the sample self absorption for
1309     a given application. In most
1310     applications, a back scatter factor for
1311     the material composition (Z value) of
1312     the final sample mount is included
1313     into a combined  attenuation-
1314     backscatter-efficiency factor or, more
1315     simply, the combined detector
1316     efficiency correction factor.

1317     For the lower to intermediate beta
1318     particle energies, the combined detector
1319     effi ci ency factor i s a functi on of b eta
1320     energy, final sample mass  and mass
1321     composition. For beta particles having a
1322     maximum beta energies greater than
1323     1,500 keV, the combined detector
1324     efficiency factor is nearly constant over
1325     a final sample weight range of 0 to 5
1326     mg/cm2. A typical combined beta
1327     detector efficiency curve for 131I (606
1328     keV pmax) as Cul over a weight range of
1329     0 to 50 mg is shown for a plastic
1330     scintillator beta detector in Figure 15.12.
1331     A compl ete revi ew of the detect! on
1332     method can be found in reference
1333     (McCurdyetal., 1980).
IJiJ
1E2
1E1
1EO
1E-1
P
-2. -.
1E-3


[•-"



!^





W
^*>
,x*"


Air


atei
,--'






'^






,x-






**













X1


^

^
^


0.1 1

^


^






-



















































10
ENERGY (MeV)
FIGURE 15.11 Range vs. Energy for Beta Particles in Air and
Water
Window Gas Proportional Counter
Gross Beta Data - Cs-137 Standard
Ojion
AZ\J \
OAf\r\
.4UU
.380
^^
^ O.ooO
CD
Q O.o40
P n oon
jj U.O^U
Oonn
.oUU
OOQH
.ZOU
.260
0
V







i
\
^







\
\








"\







\
\.







^v
N







\
X









50 100 150 200 250 300 350
Weight (mg)
                                                  FIGURE 15.12 Beta Detector Efficiency Curve for 131I vs.
                                                  Weight
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         Nuclear Counting Instrumentation
1334      15.10.2.5 Calibration

1335      Beta particle counting systems should be calibrated with the specific radionuclide under
1336      investigation or a surrogate radionuclide of similar beta energy having a comparable final sample
1337      composition and configuration. However, it should be mentioned that moderate to severe
1338      calibration biases may occur depending on the severity of the departure from the chemical
1339      composition of the final sample matrix and the beta energy of a surrogate. For this reason, the use
1340      of a surrogate radionuclide is discouraged unless the availability of the radionuclide of interest is
1341      non-existent. Corrections between the surrogate and radionuclide of interest should be
1342      determined and applied to sample results. For electroplated plated samples, a correction factor
1343      needs to be determined if the plating material of the surrogate is not the same as that used for the
1344      samples.

1345      Cerenkov counting normally involves a single radionuclide calibration (single energy calibration)
1346      for the final sample solution. Typically, the final sample solution is a result of a control
1347      radiochemical process that eliminates most sources of variability for the calibration process.

1348      Aqueous beta-emitting radionuclide calibration standards and sources are available from NIST or
1349      from a NIST-traceable  commercial radioactive source manufacturers. The long-lived pure beta-
1350      emitting radionuclides available from NIST include: 3H, 14C, 63Ni, 1291,89Sr, 90Sr, "Tc, 228Ra, and
1351      241Pu. The majority of the gamma-emitting radionuclides also emit beta particles in the nuclear
1352      transformation process. Check Section 15.4 for the availability of known beta- gamma emitting
1353      radionuclides. Contact a NIST-traceable radioactive source manufacturer for the availability of
1354      other pure beta or beta/gamma-emitting radionuclides (ANSI N42.15, American National
1355      Standard Check Sources for and Verification of Liquid-Scintillation Counting Systems).

1356      Aqueous radioactive standards can be prepared in the appropriate geometry for LS or Cerenkov
1357      counting or through chemical processing precipitated or electroplated as final sample form for
1358      counting by a gas proportional, plastic or solid state beta detection system.

1359      15.10.2.6 Costs

1360      There are four principal beta detection methodologies available. Window gas proportional
1361      counting and liquid scintillation counting systems (Cerenkov counting as well) can be purchased
1362      with the option of readily available automatic sequential sample counting systems. Sample
1363      capacity is typically 100. These automatic sequential counting systems are available in the
1364      $30,000 to $50,000 range depending on options. Multiple detector window gas proportional
1365      counters having a simultaneous counting capability are available from some commercial


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                                                                   Nuclear Counting Instrumentation
1366     manufacturers. The basic unit contains four detectors but several units can be combined to give
1367     eight or 16 detector systems. The basic price for such units is in the range of $20,000 to $50,000
1368     depending on the number of detectors and options.

1369     Solid state silicon surface barrier and ion implanted Ge or Si detectors are used to perform
1370     spectral analysis of beta emitting radionuclides. A solid state beta spectrometry system consists
1371     of a vacuum chamber, solid state detector, high voltage-preamp-amplifier instrumentation
1372     modules, a multichannel analyzer (MCA) or equivalent computerized MCA using an analog-to-
1373     digital  converter and electronic data storage. Individual ion-implanted Ge detectors having an
1374     active area of 450-2,000 mm2 and a 500 |im thickness range in price between $1,300 and $3,200.
1375     Beta resolution of these detectors is typically approximately 12 keV.O

1376     A beta spectrometry system consisting of eight detectors with vacuum pump and computer would
1377     be approximately $30,000-$40,000, without background reducing shielding.  Solid state
1378     spectrometry systems for beta particle applications, unlike that for alpha particles, would be
1379     sensitive to external background from cosmic radiation, terrestrial radiation and inherent beta
1380     radioactivity in the surrounding materials.

1381     Automatic sample counting, plastic scintillator beta particle detection systems have not been
1382     commercialized for the radioassay laboratory setting. Most of these systems have been fabricated
1383     by the user from readily available components, electronic modules, multichannel analyzers and
1384     lead shielding. The cost of a single  detector system is estimated to be less than $15,000.

1385     Maintenance costs for the liquid scintillation  counters, window gas proportional counters and
1386     alpha spectrometry systems have been discussed in Section 15.10.1.3.2 for alpha counting
1387     applications. If a laboratory already has existing units for the alpha particle measurement
1388     applications, there will be no additional maintenance cost relative to their use for beta particle
1389     measurements.

1390     There is no maintenance cost associated with the operation of a plastic scintillator beta
1391     spectrometry system.

1392     Costs associated with the maintenance of the room environment for the nuclear detection
1393     equipment should be considered. Service maintenance relative to the constant voltage supply or
1394     uninterruptable power sources as well as having a dust free constant temperature and humidity
1395     environment should be considered.
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         Nuclear Counting Instrumentation
1396      15.10.2.7 Quality Control

1397      See section 15.10.1.4.

1398      15.10.3 Gamma

1399      15.10.3.1 Troubleshooting

1400      Once a gamma-ray spectrometry system has been established in accordance with the manufac-
1401      turer's or supplier's instructions, a daily count of a calibration or reference source should be
1402      performed to assure the system continues to operate properly. The three parameters that should
1403      be checked and recorded are: energy calibration (keV/channel), counting efficiency (count
1404      rate/decay rate), and gamma-ray peak resolution (FWHM). With the exception of a complete
1405      detector or electronic component failure (no pulses are detected at the preamp or multiplier
1406      phototube output), degradation of gamma-ray peak resolution will be the first indication that
1407      detector is not performing properly or that electronic noise has been introduced into the counting
1408      system by the preamplifier,  amplifier, or multichannel analyzer. Any indications that the detector
1409      efficiency is not within statistical limits of expected values should be reported, since this value
1410      will be used to convert the observed count rate to decay rate. The energy calibration should either
1411      be recorded with sample spectral data or adjusted daily to a previously established  constant
1412      value. This energy calibration should be accurately known so that nuclide identifications can be
1413      made. See page 51 for a list items to be checked if the counting system is out of specifications.

1414      Gamma-ray spectrometry systems are extremely sensitive to both electronic and environmental
1415      conditions. Temperature changes can cause spectral shifts and improper nuclide identifications
1416      because of incorrect energy calibrations. Excessive humidity in the detector preamplifer can
1417      cause high voltage arcing which results in poor peak resolution or complete system failure.
1418      Improper pole zero settings, which effects the  shape of the pulses being analyzed, can cause
1419      degradation of peak shapes  and resolution. Poorly conditioned NEVI power can introduce
1420      electronic noise which will  also result in degraded peak resolution. Routing of cables between the
1421      detector, electronics, multichannel analyzer, computers, and monitors is very important. The
1422      introduction of any spurious electronic noise into any of the components that make up the
1423      gamma-ray spectrometry system can degrade the resulting data.

1424      The need to make corrections for self-absorption in environmental samples during routine
1425      gamma-ray spectrometry cannot be overemphasized (Modupe et al., 1993). The correction to be
1426      made for the  difference in self-absorption between calibration standards and sample matrices is
1427      usually small for intermediate and high energy photons, but it is not negligible at low energies


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                                                                 Nuclear Counting Instrumentation
1428      where the photoelectric effect is the most important mode of attenuation. The photoelectric
1429      process varies approximately as Z4"5 (Z is the atomic number of the elements in the medium) so
1430      that a change in the elemental composition of a sample relative to a calibration standard can
1431      require a correction factor for detector efficiency as high as a factor of 2.
1432      The quantities |i and H are the linear attenuation coefficient and the thickness of sample,
1433      respectively. I0 and I are the intensities of the beam emerging from the sample container without
1434      and with an absorbing matrix in place. This is the traditional self-absorption equation. For
1435      complex counting geometries of homogeneous materials, an estimated average H (sample
1436      thickness) can be used.

1437      The method for self-absorption correction at various energies requires that the linear attenuation
1438      coefficient, \i, of the sample matrix be known. Knowledge of |i usually requires that the
1439      elemental composition of the matrix be determined. The tedium and time required in elemental
1440      analysis may make it impractical for routine gamma-ray analyses involving large numbers of
1441      samples. Computer programs are available to calculate |i/p for various compounds when the
1442      percent elemental  composition of the compound is known. |i/p is computed as a linear
1443      combination of the mass attenuation coefficients of the composite elements.
                                                   ^iPi   '                               (152)
1444      where P; is the percent by weight of the ith element in the compound.

1445      The gamma-ray path length, H, is equal to the thickness of sample. When performing gamma-ray
1446      transmission measurements to determine |i a path length of H is used. To determine the self-
1447      absorption correction for radioactive samples, the corrections are integrated for a path length of 0
1448      to H.

1449      When a photon beam passes through a homogenous sample of mass attenuation coefficient, |i/p,
1450      density, p, and thickness, H, the percentage beam attenuation, A, is given by
                               A =^100% = (l-e~^pp)100%                      (15.3)
                                      0
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1451
15.10.3.2 Calibration
1452
1453
1454
1455
1456
1457
1458
1459

1460
1461

1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
Most gamma-ray spectrometry systems are calibrated with either single or mixed standards in an
exact matrix and geometric form as the samples to be analyzed. However, there are computer
codes that can calculate detector efficiency from the physical dimensions of the detector and
sample counting geometry (Mitchell, 1986 and 1988, Hensley et al., 1997). Commercial
standards of single or mixed gamma-ray emitters in a matrix of known chemical composition and
density can be prepared in user supplied containers. Calibrations based upon these standards can
then be adjusted to correct for any differences in composition and density between the calibration
source and the sample (Modupe et al., 1993).

Table 15.2 lists  some gamma-ray emitting nuclides that can be used for energy and efficiency
calibration (Sanderson et al.,  1993; Browne et al., 1986).
'ABLE 15.2 Nuclides for Gamma-ray Spectrometer Calibratio
NUCLIDE
210pb
241 Am
109Cd
57Co
141Ce
139Ce
203Hg
51Cr
113Sn
85Sr
137Cs
54Mn
88y
65Zn
60Co
40K
ENERGY (KeV)
46.5
59.5
88.0
122.1
145.4
165.9
279.2
320.1
391.7
514.0
661.7
834.8
898.1, 1836.1
1115.5
1173.2, 1332.5
1460.8
HALF-LIFE
22.3 years
432.2 years
462.6 days
273 days
32.5 days
137.7 days
46.6 days
27.7 days
115.1 days
64.8 days
30.0 years
3 12.5 days
106.6 days
243. 8 days
5.27 years
1. 28 xlO9 years
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                                                                 Nuclear Counting Instrumentation
1480      15.10.3.3 Software

1481      Most laboratories are now using commercially available software for the analysis of gamma-ray
1482      spectra. These programs are easy to use and do not require the user to be an expert in gamma-ray
1483      spectrometry. An evaluation of some of these programs in 1987 indicated there were substantial
1484      differences in the abilities of the programs to resolve multiplets of unequal intensity and to
1485      analyze complex spectra (Sanderson  1988).  Another evaluation was completed in 1992 (Decker
I486      and Sanderson, 1992) since many of the programs had undergone numerous revisions and there
1487      were a few new programs available. The second evaluation indicated a substantial improvement
1488      in the deconvolution of doublets and  the results of the analysis of a Chernobyl air filter were
1489      much more consistent than when a similar filter was analyzed in the first evaluation. The six
1490      programs analyzed in 1991 include GAMMA-W from Germany, INTERGAMMA from France,
1491      OSQ/Plus from Canada, SAMP090 from Finland (supplied by Canberra Industries, USA) and
1492      OMNIGAM and GDR from the United States. Some of the features which contribute to a good
1493      program included the ability to display the spectrum as well as calculated calibration files, the
1494      ability to manually insert peaks during the fitting procedure, an extensive nuclide library and the
1495      ability to easily transfer nuclides to smaller, working libraries, an analysis report which includes
1496      the names of the  calibration files used, a peak fit report including any problems with the shape of
1497      the peaks, and identification of the peaks used in the activity calculation as well as any problems
1498      with interfering lines.

1499      In 1996 the Environmental Measurements Laboratory of the U. S. Department of Energy began a
1500      Gamma Spectra Data Evaluation program (Decker et al., 1996) whose goal was to test the ability
1501      of the present day software to accurately identify and quantify the nuclides in a complex spectra
1502      and the ability of the user to properly utilize the software. In order to do this, synthetic spectra
1503      were generated using the computer code SYNTH developed by Walt Hensley at the Pacific
1504      Northwest National Laboratory. The  spectra were then converted to a variety of formats on disk
1505      and Digital Equipment Corporation (DEC) TK 50 tape and sent to DOE laboratories and DOE
1506      contractors. A calibration spectrum, a background spectrum and three sample spectra were sent
1507      to each participant. These spectra  simulated those that would be obtained when an air filter was
1508      counted 10 cm from a 22 percent coaxial detector with a 0.5 mm beryllium window. Two of the
1509      samples contained fallout and naturally occurring nuclides with half lives greater than  thirty days.
1510      The third sample contained both short and long lived fission product nuclides. Thirty one
1511      laboratories participated using 16  different software packages. The software packages included
1512      Aptec, Vertechs GDR/P, Nuclear Data ASAP, various Ortec packages, and various Canberra
1513      packages for both the PC and the DEC MicroVax. Most of the laboratories did fairly well with
1514      the first two samples. A few laboratories reported nuclides that were not present  in the third
1515      sample and did not accurately quantify those that were. The results did not seem  to be  software


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         Nuclear Counting Instrumentation
1516      dependent but were due to the user utilizing or not utilizing available software features properly.
1517      There was quite a wide range of numbers for both the uncertainty terms and for the minimum
1518      detectable activities which seems to indicate we need a consistent way of calculating these terms
1519      to make them more meaningful.

1520      15.10.3.4  Costs

1521      Gamma-ray spectrometry systems can cost from $14,000 to well over $60,000 depending upon
1522      the choice of detector. For a 75 x 75 mm Nal(Tl) system the costs would be approximately
1523      $1,000 for the detector, $5,000 for a 10 cm graded lead shield, and $8,000 to $10,000 for a
1524      multichannel analyzer. Very large HPGe detectors will cost more than $50,000. The actual
1525      detector cost will depending upon the size of the germanium crystal, its resolution, and method of
1526      cooling. Data reduction costs (software and computer) would be an additional expense for either
1527      type of system.

1528      Nal(Tl) detector systems do not require any additional maintenance beyond what any laboratory
1529      electronic system requires. Each HPGe detector will require approximately $1,200 per year for
1530      liquid nitrogen to maintain their operating temperature. An electrical/mechanical cooler can be
1531      used in place of a liquid nitrogen cryostat but it will require 0.5 to 1.0 kW of power around the
1532      clock to operate. Both systems should be operated at constant temperature for reliable
1533      performance. This may require substantial air conditioning.

1534      15.10.3.5  Quality Control

1535      Initial data to prepare solid state gamma detector QC charts may be obtained by counting a mixed
1536      gamma point source between 20 to 30 times (Ideally, these counts should be over a period of
1537      several weeks. However, if time does not permit, the counts may be accumulated over 1 to
1538      7 days.) Two or three QC charts (depending on age of mixed gamma point source) are initially
1539      established for the mixed gamma point source and control limits are established for background.
1540      The three source charts cover the low energy (88 keV, 109Cd), the medium energy (661.6 keV,
1541      137Cs), and the high energy (1,332.5 keV, 60Co).  The source is counted until between  10,000 to
1542      40,000 counts are obtained in each photopeak.

1543      Background QC charts are established according to the procedure already listed for Proportional
1544      and liquid scintillation counters with the following exception: the background is counted and the
1545      total counts in the spectrum are obtained by summing the counts in the entire spectrum.
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                                                                  Nuclear Counting Instrumentation
1546      The resolution of the detector (FWHM) is measured each month and recorded, but it is not
1547      plotted. A NIST 60Co source is positioned 25 centimeters from the end-cap face and counted for
1548      100 minutes. The FWHM is calculated by the peak search program for the 1,173.2 keV and
1549      1,332.5 keV peaks, and recorded in the logbook.

1550      When the energy of the source QC exceeds the specified energy tolerance (for example
1551      ±0.75 keV) from its initial calibrated value, the analyzer system should be recalibrated. First
1552      determine whether a gain or zero shift has occurred. A gain shift is a nonlinear shift in channels
1553      of low and high energy peaks (i.e., 109Cd peak shifts ±1 channel and 60Co peak shifts ±3 channels).
1554      A zero shift is a linear shift in channels for both low and high energy peaks (ie., 109Cd peak shifts
1555      ±1 channel and 60Co shifts ±1 channel also). Make the appropriate adjustments to the amplifier
1556      (gain) or the Analog to Digital Converter (zero). Recalibrate the analyzer and record the slope
1557      (keV/channel) and the zero intercept in the log book. If the best fit of the recalibration curve is a
1558      nonlinear fit (quadratic), record the "Q" coefficient, keV/channel2, in the log book. Also record
1559      the  updated FWHM calibration factors,  slope, offset, and FWHM at 1,332.5 keV in the log book.

1560      The following should be considered when QC checks are not within limits.

1561       •  Is standard decay corrected to the proper date?
1562       •  Check sample positioning.
1563       •  Check for zero shift.
1564       •  Check for gain shift.
1565       •  Check full width  at half-maximum.
1566       •  Check nim bin power supply voltages (±6 V, ±12 V, ±24 V).
1567       •  Check efficiency tables.
1568       •  Check for moisture on the detector due to recently filling the dewar with liquid N2.

1569      15.10.4   Non-Nuclear Instrumentation

1570      15.10.4.1 ICP-Mass Spectrometry

1571      ICP-MS is one of the most versatile and sensitive atomic spectroscopy techniques available. It
1572      can be used to determine the concentrations of over 70 elements.  The detection limit of the
1573      technique extends down to the parts-per-billion range in soils and to the parts-per-trillion range in
1574      waters. This sensitivity makes ICP-MS an attractive complement to decay-counting techniques in
1575      the  radiochemical analysis laboratory. For very long-lived radioisotopes (those with half-lives
1576      over 10,000 years, e.g., 244Pu, "Tc, 129I), ICP-MS may be faster and more sensitive than decay
1577      counting. In addition, sample preparation for ICP-MS can avoid some of the analyte separation


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         Nuclear Counting Instrumentation
1578      and purification steps required for decay counting, providing an additional dimension of time
1579      savings. Another important feature of ICP-MS is its ability to provide isotopic distribution
1580      information (e.g., 238U vs. 235U). This information is frequently useful in determining the age
1581      and/or origin of materials. (ASTM C758, C759, C799)

1582      The isotopic discrimination capabilities of ICP-MS make possible the calibration technique
1583      known as isotope dilution. In this procedure, a sample is analyzed for one isotope after having
1584      been spiked with a different isotope of the same element (e.g., analysis of 235U might involve
1585      spiking with 233U). The spiked sample is carried through all preparation and analysis steps; in this
1586      way, any matrix or procedural effects that might influence the 235U signal will influence the 234U
1587      signal to precisely the same extent. Final quantization relies on measuring the ratio of unknown
1588      (here the 235U signal) to the known (234U) signal. Isotope dilution is a way of generating highly
1589      precise and accurate data from a mass spectrometer and has been used in the characterization of
1590      many certified reference materials.

1591      Although an ICP-MS instrument is extremely delicate, with proper care and preventive
1592      maintenance system up time should range between 80 to 95 percent. An initial investment of
1593      about $200,000 will be required to obtain a current commercial state-of-the-art system. Annual
1594      maintenance costs will run from $5,000 to $20,000 depending on the purchase of a service
1595      contract.

1596      For more sophisticated measurements, at substantially higher cost, an ICP-MS with magnetic
1597      sector, instead of quadrupole,  detection can be applied. Sector instruments are capable of
1598      resolving species of very similar mass. For example, "Tc might be resolved from a
1599      contamination of "Ru with a high-resolution mass spectrometric  detector. More typically, high
1600      resolution instruments are employed for their higher signal/noise  ratio, and therefore superior
1601      detection limits. A single-collector high-resolution ICP-MS can be purchased for roughly twice
1602      the cost of a quadrupole ICP-MS, or about $300,000. For enhanced sample throughput a
1603      multiple-collector instrument might be purchased for about $500,000. These instruments, like
1604      most analytical equipment, can be expected to require about 2 to 10 percent of their purchase
1605      costs in annual maintenance costs.

1606      Thermal ionization mass spectrometers are available at a cost of $500,000. These instruments
1607      rely not on a plasma for ionization, but rather for thermal ionization from a heated filament. They
1608      provide more precise measurements than routine quadrupole ICP-MS but require substantially
1609      more delicate operator involvement, leading to markedly reduced sample throughput.
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                                                                  Nuclear Counting Instrumentation
1610      Time-of-flight plasma mass spectrometers have just recently appeared on the market; they have
1611      not yet built up a historical record of performance that would permit reliable comparison with the
1612      ICP-MS equipment described above. Likewise, Fourier-transform mass spectrometers are still in
1613      the research phase and cannot yet be considered practical options for routine radiochemical
1614      analysis.

1615      15.10.4.2 Laser

1616      APPLICATION

1617      Lasers can be used to excite uranium (ASTM D5174) and lanthanide complexes in solution.
1618      During or following excitation, the complex relaxes to a lower energy state by emitting photons
1619      of light that can be detected. The amount of light produced is proportional to the uranium or
1620      lanthanide element concentration.

1621      The light emitted can be detected by fluorescence or phosphorescence. With fluorescence and
1622      phosphorescence, the detector is at right angles to Laser excitation. Fluorescence light is emitted
1623      simultaneous to the excitation.

1624      Phosphorescence detecting differs from fluorescence in that the light emitted is not simultaneous
1625      to the excitation. This enables the light source to be pulsed and the measurement to occur when
1626      the Laser source is off. This provides improved signal to noise over fluorescence. The light signal
1627      from organic material will decay promptly,  since they have a short relatively lifetime, and not be
1628      available to the detector which is gated off at this initial time. A pulsed nitrogen dye Laser can be
1629      used as the source. Other Lasers can also be used. Chloride ion and other ions may cause
1630      interferences and may need to be removed before measurement.

1631      Kinetic phosphorimetry measures the rate of decay of the uranium or lanthanide element
1632      complex signal. Measurements are taken at fixed time intervals. In aqueous solution, the uranium
1633      or the lanthanide element is complexed to reduce quenching and increase the lifetime of the
1634      complex.

1635      UP/DOWN TIME

1636      Some reagents may have relatively short shelf life and need to be ordered accordingly. The life of
1637      a plasma cartridge is one to three years.
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         Nuclear Counting Instrumentation
1638      COST

1639      The initial cost is about $34,500 with a computer and $53,500 with an automatic sample changer.
1640      The cost of replacing a plasma cartridge is $1,400.00. The cartridge lifetime is 1 to 3 years
1641      depending on usage.

1642      15.10.4.3 Radionuclides Analyzed By Neutron Activation

1643      TECHNETIUM-99

1644      Neutron activation analysis methods have been employed since 1972 (Foti et al. 1972a; 1972b).
1645      The method was developed and applied for the analysis of "Tc in mixed fission products (Bate,
1646      1979).

1647      The method employs chemical separation of "Tc from most fission products by a cyclohexanone
1648      extraction from a basic carbonate solution. "Tc is stripped into water by addition of CC14 to the
1649      cyclohexanone phase and then adsorbed on an anion exchange column in a concentrated form.
1650      Neutron irradiation of the isolated "Tc  could be made in the pneumatic  facility at a high flux
1651      isotope reactor (e.g., at a flux of 5xl014 ng/cm2/sec for approximately  11 seconds. Thus, after
1652      irradiation "Tc is induced to 100Tc, which, because of its 15.8 second half-life, requires an
1653      automatic process to measure its 540 and 591 keV gamma lines.

1654      The lower limit of detection of the analysis under these conditions is approximately 5 ng and
1655      samples up to 100 mL volume can be processed. The method has been applied successfully to
1656      reactor fuel solutions and off-gas traps containing 6.5xlO"4 to 240 jig "Tc/mL.

1657      IODINE-129

1658      Iodine-129 can be determined by neutron activation and subsequent measurement of the
1659      12.4 hour 130I produced by the neutron capture reaction. The method (Bate and Stokely, 1982)
1660      utilizes conventional I valence adjustments and solvent extraction to isolate the I fraction.
1661      Chemically separated 129I is adsorbed on an anion exchange resin before being loaded for
1662      irradiation. With a neutron flux of 5><1014 ng/cm2/s for 100 seconds a lower limit of detection of
1663      0.03 ng can be achieved.

1664      129I also can be determined directly by mass spectrometry (Strebin et al., 1988). The measurement
1665      limit by this technique is approximately 2 femtograms.
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                                                                 Nuclear Counting Instrumentation
1666
1667
1668
1669
1670

1671

1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682

1683
1684


1685

1686
Special counting techniques have also
been applied to the analysis of 129I.
Figure 15.13 shows an efficiency plot
using beta-gamma coincidence
counting.

URANIUM, THORIUM, AND PLUTONIUM

Neutron Activation analysis method was
employed to determine uranium in the
hydrogeochemical samples from
Savannah River Plants within the scope
of the National Uranium Resource
Evaluation Program sponsored by DOE.
Uranium was determined by cyclic
activation and delayed neutron counting
of the 235U fission products. The method
relied on the absolute activation
Beta/Gamma (Nal) Coinc.
1-131 Standard
0.450
A JAA .
0.4OO
^^ n ocn _
Q O.oOO
c
2* 0.300
O
0.250
UJ
0.200
.150
04 A A
t
•



i



X.



"— — *_




^^




A — *


^
^*


^-— .




	 •


	 •







BetaEff(c/d)
Gamma Eff (c/d)
CoincidEff(c/d)
.100
10 20 30 40 50 60
Weight (mg)
                                     FIGURE 15.13 Beta-gamma coincidence efficiency curve for 129I

techniques using the Savannah River Reactor Activation Facility.
Neutron Activation Analysis followed by delayed-neutron detection was commonly used for
determination of 235U, 239Pu, and 232Th (Echo and Turk, 1957; Hochel, 1979; Alfassi, 1990).

15.11    References

15.11.1    Cited References
1687      American National Standards Institute (ANSI) N42.14. "Calibration and Use of Germanium
1688         Spectrometers for Measurement of Gamma-Ray Emitting Radionuclides," 1991, New York.

1689      American National Standards Institute (ANSI) N42.15. "American National Standard
1690         Performance Verification of Liquid-Scintillation Systems," 1990, New York.

1691      American National Standards Institute (ANSI) N42.22. American National Standard.
1692         Traceability of Radioactive Sources to the National Institute of Standards and Technology
1693         (NIST) and Associated Instrument Quality Control.
         JUNE 2001
         NOT FOR DISTRIBUTION
                                         15-55
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         Nuclear Counting Instrumentation
1694      American National Standard Institute (ANSI) N42.23. Measurement and Associated
1695         Instrumentation Quality Assurance for Radioassay Laboratories. 1996.

1696      American National Standard Institute/Institute of Electrical and Electronics Engineers,
1697         (ANSI/IEEE) 325. Standard Test Procedures for Germanium Gamma-Ray Detectors. 1996.

1698      Alfassi, Zeev B. 1990 Use of Delayed Neutrons In Activation Analysis., Activation Analysis, Vol.
1699         I, Edit. Z. Alfassi, CRC Press,, Inc, Boca Raton, Florida.

1700      American Society for Testing and Materials (ASTM) C758. Standard Test Methods for
1701         Chemical, Mass Spectrometric, Spectrochemical, Nuclear, and Radiochemical Analysis of
1702         Nuclear-Grade Plutonium Metal.

1703      American Society for Testing and Materials (ASTM) C759. Standard Test Methods for
1704         Chemical, Mass Spectrometric, Spectrochemical, Nuclear, and Radiochemical Analysis of
1705         Nuclear-Grade Plutonium Nitrate Solutions.

1706      American Society for Testing and Materials (ASTM) C799. Standard Test Methods for
1707         Chemical, Mass Spectrometric, Spectrochemical, Nuclear, and Radiochemical Analysis of
1708         Nuclear-Grade Uranyl Nitrate Solutions.

1709      American Society for Testing and Materials (ASTM) C1207. Standard Test Method for
1710         Nondestructive Assay of Plutonium in Scrap and Waste by Passive Neutron Coincidence
llll         Counting.

1712      American Society for Testing and Materials (ASTM) C1316. Standard Test Method for
1713         Nondestructive Assay of Nuclear Material in Scrap and Waste by Passive-Active Neutron
1714         Counting Using a 252.

1715      American Society for Testing and Materials (ASTM) D1890. Standard Test Method for Beta
1716         Particle Radioactivity of Water.

llll      American Society for Testing and Materials (ASTM) D3648, Standard Practices for the
1718         Measurement of Radioactivity.

1719      American Society for Testing and Materials (ASTM) D3649. Standard Test Method for High-
1720         Resolution Gamma-Ray Spectrometry of Water.
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                                                                 Nuclear Counting Instrumentation
1721      American Society for Testing and Materials (ASTM) El 81.  Standard Test Methods for Detector
1722         Calibration and Analysis ofRadionuclides.

1723      American Society for Testing and Materials (ASTM) E1005. Standard Test Method for
1724         Application and Analysis of Radiometric Monitors for Reactor Vessel Surveillance, E706
1725         (IIIA).

1726      Bate, L. C. 1979. Determination ofTc-99 In Mixed Fission Products By Neutron Activation
1121         Analysis, Radioelements Analysis — Progress and Problems, Ed. W.S. Lyon, Ann Arbor
1728         Science Publishers Inc., p. 175-189.

1729      Bate, L. C., and Stokely, J. R. 1982. "Iodine-129 Separation and Determination By Neutron
1730         Activation Analysis," J. Radioanal. Chem., Vol. 72:1-2, p. 557-570.

1731      Blanchard, R., Kahn, B., and Birkoff, R. D. 1960. "The Preparation of Thin, Uniform
1732         Radioactive Sources by Surface Absorption and Electrodeposition," Health Physics, Vol. 2,
1733         p. 246.

1734      Bogen, D.C., and Welford, G. A. 1971. Application of Liquid Scintillation Spectrometry for
1735         Total Beta and Alpha Assay, Proceedings of the International Symposium of Rapid Methods
1736         for Measuring Radioactivity in the Environment, LAEA-SM-14/3, p. 383.

1737      Browne,E., Firestone, R. B., Shirley, V. S. 1986. Table of Radioactive Isotopes, John Wiley and
1738         Sons, Inc., New York.

1739      Campion, P. J., Taylor, J. G. V., and Merritt, J. S. 1960. "The Efficiency Tracing Technique for
1740         Eliminating Self-Absorption Errors in 4-7i Beta Counting," Int. J. Appl. Radiation and
1741         Isotopes, Vol. 8, p. 8.

1742      Cooper, J. A., Ranticelit, L. A., Perkins, R. W., Hailer, W. A., and Jackson, A. L. 1968. An Anti-
1743         Coincidence Shielded Ge(Li) Gamma-Ray Spectrometer and Its Application to Neutron
1744         Activation Analyses, Report BNWL-SA-2009, Pacific Northwest Laboratory, Richland,
1745         Wash.

1746      Crouthamel, C. E., Adams, F., and Dams, R. 1970. Applied Gamma-Ray Spectrometry,
1747         Pergamon Press, New York, N. Y., 2nd ed.
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         Nuclear Counting Instrumentation
1748      Curtis, M. L., Heyd, J. W., Olt, R. G., and Eichelberger, J. F. 1955. Nucleonics, Vol. 13, May, p.
1749         38.

1750      Decker, K. M., Sanderson, C. G. 1992.  "A Reevaluation of Commercial IBM PC Software for
1751         the Analysis of Low Level Environment Gamma-Ray Spectra," Appl. Radial Isot., Vol. 43,
1752         No. 1/2, pp.323.

1753      Decker, K. M., Sanderson, C. G., Greenlaw, P. D. 1996. Report of the Department of Energy
1754         Office of Environmental Management Gamma Spectrometry Data Validation Program,
1755         EML-586, Environmental Measurements Laboratory, New York, NY.

1756      DeFilippis, S. 1990. "Activity Analysis in Liquid Scintillation Counting," Radioactivity and
1757         Radiochemistry, Vol.1, No.4, p. 22.

1758      Echo, M. W., and Turk, E. H. 1957. Quantitative Determination of U-235 by delayed neutron
1759         counting, U. S. AEC Report PTR-143.

1760      Flynn, K. F., Glendenin, L. E., and Prodi, V.  1971. Absolute Counting of Low Energy Beta
1761         Emitters Using Liquid Scintillation Counting Techniques in Organic Scintillators and Liquid
1762         Scintillation Counting, D. L. Horrocks and Chim-Tzu Peng, eds., Academic Press, New
1763         York, p. 687.

1764      Foti,  S., Delucchi, E., and V. Akamian, V. 1972a. Determination of Picogram Amounts of
1765         Technetium-99 by Neutron Activation Analysis. Anal. Chim. Acta, 60:2, p 261-268.

1766      Foti,  S., Delucchi, E., and V. Akamian, V. 1972b. Determination of Picogram Amounts of
1767         Technetium in Environmental Samples by Neutron Activation Analysis. Anal. Chim. Acta,
1768         60:2, p 269-276.

1769      Friedlander, G., Kennedy, J. W., and Miller, J. M. 1964. Nuclear and Radiochemistry, John
1770         Wiley and Sons, New York, 2nd ed.

1771      Hallden, N. A., and Fisenne,  I. M. 1963. "Minimizing Self-Absorption in 4-n Counting," Int. J.
1772         Appl. Radiation and Isotopes, Vol.  14, p. 529.

1773      Heath, R. L. 1964. Scintillation Spectrometry Gamma-Ray Spectrum Catalog, IDO  16880, and
1774         ANCR- 1000.
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                                                                Nuclear Counting Instrumentation
1775      Hensley, W. K., McKinnon, A. D., Miley, H. S., Panisko, M. E., and Savard, R. M. 1997.
1776         SYNTH for Windows., Pacific Northwest National Laboratory, Richland, WA.

1777      Hindman, F. D. 1983. "Neodymium Fluoride Mounting for Spectrometry Determination of
1778         Uranium, Plutonium, and Americium," Anal. Chem., Vol. 55, pp. 2460-2461.

1779      Hochel, R. C. 1979. A High-Capacity Neutron Activation Facility, Radioelements Analysis —
1780         Progress and Problems, Ed. W.S. Lyon, Ann Arbor Science Publishers Inc., p. 343-348.

1781      Hoppes, D. D.,  1990. "Demonstrated Measurement Traceability for Nuclear Power
1782         Radiochemistry Departments," Radioact. Radiochem., Vol.  , Nol, p9.

1783      Horrocks, D. L. 1964. "Alpha Particle Energy Resolution in a Liquid Scintillator," Review of
1784         Scientific Instruments, Vol. 55, p. 334.

1785      Horrocks, D. L. 1970. Applications of Liquid Scintillation Counting, Academic Press.

1786      International Atomic Energy Agency (IAEA). 1959. Metrology of Radionuclides, Proceedings of
1787         a Symposium, Oct.  14-16, Vienna.

1788      ICRU. 1992. Measurement of Dose Equivalents from External Photon and Electron Radiations.
1789         Report 47.

1790      Kessler, M. 1986. Cerenkov Counting, Packard Application Bulletin, No. 7. Packard Instrument
1791         Co., Downers Grove, IL.

1792      Knoll, Glenn F. 1989. Radiation Detection and Measurement, John Wiley & Sons, New York,
1793         2nd ed.

1794      Lapp, R. E., and Andrews, H. L. 1954. Nuclear Radiation Physics, Prentice Hall, New York,
1795         Englewood Cliffs, N.J., 2nd ed.

1796      McCurdy, D. E., Mellor, R.  A., Lambdin, R. W., and McLain, Jr., M. E.  1980. "The Use of
1797         Cuprous Iodide as a Precipitation Matrix in the Radiochemical Determination of Iodine-131,"
1798         Health Physics, Vol. 38, pp. 203-213.
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         Nuclear Counting Instrumentation
1799      McCurdy, D. E. and Mellor, R. A. 1981. "The Application of Coincidence Counting Techniques
1800         to the Determination of Radium-226 and Radium-228," Analytical Chemistry, Vol. 53, pp.
1801         2212.

1802      Merritt, J. S., Taylor, J. G. V., and Campion, P. J. 1956. "Self-Absorption in Sources Prepared
1803         for 4-Ti Counting," Canadian Journal of Chemistry., Vol. 37, p. 1109.

1804      Mitchell, D. J. 1986.  Sodium Iodide Detector Ananlysis Software  (SIDAS), Sandia Report
1805         SAND86-1473, Sandia National Laboratories, Albuquerque, NM.

1806      Mitchell, D. J. 1988.  Gamma Detector Response and analysis Software (GADRAS), Sandia
1807         Report SAND88-2519, Sandia National Laboratories, Albuquerque, NM.

1808      Modupe, O. O.,Decker, K. M., Sanderson, C. G. 1993. "Determination of Self-Absorption
1809         Corrections by Computation in Routine Gamma-Ray Spectrometry for Typical
1810         Environmental Samples," Radioactivity & Radiochemistry, Vol. 4, No. 1, pp.38.

1811      Moghissi, A. A. 1971. Low Level Counting by Liquid Scintillation, Organic Scintillators and
1812         Liquid Scintillation Counting, Academic Press, New York, NY.

1813      Nielson, J. M. 1972.  Gamma Ray Spectrometry in Physical Methods of Chemistry, A.
1814         Weissberger and B. W., Rossiter, eds. Vol. I, Part 1II D, Chap. X, John Wiley and Sons, Inc.
1815         New York, N.Y.

1816      Nielsen, J. M., and Kornberg, H. A.  1965. Multidimensional Gamma-Ray Spectrometry and Its
1817         Use in Biology, Radioisotope Sample Measurement Techniques in Medicine and Biology.
1818         Proceedings of a Symposium, May 24-28,  International Atomic Energy Agency, Vienna, p. 3.

1819      Overman, R. T., and Clark, H. M. 1960. Radioisotope Techniques, McGraw-Hill Book Co., Inc.,
1820         New York, NY.

1821      Passo, Jr., C. J. and Cook, G. T. 1994. Handbook of Environmental Liquid Scintillation
1822         Spectrometry, PACKARD (Canberra) Instrument Company, Meriden, CT.

1823      Perkins, R. W. 1965. "An Anti-coincidence Shielded Multidimensional Gamma-Ray
1824         Spectrometer," Nuclear Instruments and Methods, Vol. 33 p. 71.

1825      Price, W. J. 1964. Nuclear Radiation Detection, McGraw-Hill, New York, N.Y., 2nd ed.


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                                                                 Nuclear Counting Instrumentation
1826      Puphal, K. W., and Olson, D. R. 1972. "Electrodeposition of Alpha Emitting Nuclides from a
1827         Mixed Oxalate-Chloride Electrolyte," Analytical Chemistry., Vol. 44.

1828      Scarpitta, S. C., and Fisenne, I. M. 1996. "Cerenkov Counting as a Complement to Liquid
1829         Scintillation Counting," Appl. Radial Isot. Vol. 47, No.8, pp. 795-800.

1830      Sanderson, C. G.  1969. "Determination of 226Ra and 228Th in Food, Soil, and Biological Ash by
1831         Multidimensional Coincidence Gamma-Ray Spectrometer," Health Physics Vol. 16, No. 6,
1832         pp. 747-753.

1833      Sanderson, C. G.  1988. "An Evaluation of Commercial IBM PC Software for the Analysis of
1834         Low Level Environment Gamma-Ray Spectra," Environment International, Vol. 14, pp.379.

1835      Sanderson, C. G., and Decker, K. M. 1993. "A Mixed Gamma-Ray Standard for Calibrating
1836         Germanium Well Detectors," Radioactivity & Radiochemistry, Vol. 4, No. 2, pp.36.

1837      Sill, C. W., and Williams, R. L. 1981. "Preparation of Actinides for Alpha Spectrometry without
1838         Electrodeposition," Anal. Chem., Vol. 53, pp. 412-415.

1839      Strebin, R. S.  Jr.,  Brauer, F. P., Kaye, J. H., Rapids, M. S., and Stoffels, J. J. 1988.  "Neutron
1840         Activation and Mass Spectrometric Measurement of 129I," J. Radioanal. Nucl. Chem.., Vol.
1841         127:1, p. 59-73.

1842      Watt, D. E., and Ramsden, O. 1964. High Sensitivity Counting Techniques, The MacMillan
1843         Company, New York, N. Y.

1844      15.11.2    Other Sources

1845      Bell, C. G., and Hayes, F. N. 1958. Liquid Scintillation Counting., Pergamon Press, New York.

1846      Birks, J. B. 1964. The Theory and Practice of Scintillation Counting, Pergamon Press, New
1847         York.

1848      Bransome, E., Jr., 1970. The Current Status of Liquid Scintillation Counting, (ed.), Greene and
1849         Straiton, New York.

1850      Currie, L. A. 1968. "Limits for Qualitative Detection and Quantitative Determination,"
1851         Analytical Chemistry, Vol.  40, No. 3, pp. 586-593.


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         Nuclear Counting Instrumentation
1852      Flynn, K. F., and Glendenin, L. E. 1959. "Half-Life and B-Spectrum of Rubidium-87," Physics
1853         Review, Vol. 116, p. 744.

1854      Flynn, K. F., Glendenin, E., Steinberg, E. P., and Wright, P.M. 1964. "Pulse Height-Energy
1855         Relations for Electrons and a-Particles in a Liquid Scintillator," Nuclear Instruments and
1856         Methods, Vol. 27, p. 13.

1857      Gunnick, R., Colby, L. I, and Cobble, J. W. 1959. Analytical Chemistry, Vol. 31, p. 796.

1858      Harley, J. H., Hallden, N. A., and Fisenne, I. M. 1962. "Beta Scintillation Counting with Thin
1859         Plastic Phosphors," Nucleonics, NUCLA, Vol. 20, p. 59.

1860      ICRP 1994. Gamma-Ray Spectrometryin the Environment, Report 53.

1861      Jarrett, Allen A. Statistical Methods Used in the Measurement of Radioactivity with Some Useful
1862         Graphs and Nomographs, AECU 262.

1863      Katz and Penfelt. 1952. Reviews, Modern Physics, Vol.  24, p. 28.

1864      Lawson  and Cork. 1940. Physics Review, Vol. 57, p. 982.

1865      Mitchell, R. F. 1960. "Electrodeposition of Actinide Elements at Tracer Concentrations,"
1866         Analytical Chemistry, Vol. 32, pp. 326-328.

1867      Pate, B.  D., and Yaffe, L. 1955. "Disintegration Rate Determination by 4-rc Counting," Canadian
1868         Journal of Chemistry, Vol. 33, pp. 15, 610, 929,  and 1656.

1869      Sill, C. W., and Olson, D. G. 1956. "Sources and Prevention of Recoil Contamination of Solid-
1870         State Alpha Detector," Analytical Chemistry, Vol. 42, p. 1956.

1871      U.S. Environmental Protection Agency (EPA). 1972. Environmental Radioactivity Surveillance
1872         Guide, ORP/SID 72-2.

1873      U. S. Government Printing Office (GPO). 1952. Tables for the Analyses ofB Spectra, National
1874         Bureau of Standards Applied Mathematics Series Reports No. 13, Washington, DC.
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         	Nuclear Counting Instrumentation

1875             ATTACHMENT  15A  FIELD MEASUREMENTS


1876      15 A.I    Introduction

1877      The scope of environmental remediation work taking place across the country in the public and
1878      private sector has led to a need to improve the speed and cost-effectiveness of measurements for
1879      characterizing contaminant levels at sites and assessing the results of cleanup efforts. In
1880      particular, the time for decisions that are required during soil excavation and waste segregation
1881      should be kept short to avoid delays that tend to increase labor costs. Thus, the time it takes to
1882      collect, prepare and analyze samples can be a limiting factor. To this end, one can use mobile
1883      laboratories at the field site to reduce sample handling and transit times. However, even with
1884      these, the sheer volume of samples can overwhelm processing and analytical capacity. Therefore,
1885      measurements performed directly in the field (in situ) that do not require the collection and
1886      processing of a sample are an attractive alternative. Fundamentally,  a field measurement gives
1887      the concentration of a contaminant at the same place where one might otherwise have collected a
1888      sample. In effect, the instrument is brought to the sample rather than the sample to the
1889      instrument. Frequently, the field measurement can be performed within minutes with a result
1890      obtained in what is essentially "real time."

i89i      15A.2    Analytical Level of  Measurements

1892      Over the years, field measurements have formed an important component of standard
1893      radiological surveys. Typically, these measurements  have comprised scans for gross levels of
1894      alpha or beta/gamma radiation. These types of measurements, particularly where judgment is
1895      used to evaluate a change in an instrument or audible signal, are semi-quantitative in nature and
1896      therefore would be designated at analytical level 1 under the EPA classification system used in
1897      the past or Analytical Support Laboratory(ASL) level A of the American National Standards
1898      Institute (ANSI). These levels reflect the fact that the measurement is intended for screening
1899      purposes.

1900      However, field measurements can be performed at a  higher analytical level. For example, an
1901      exposure rate measurement using a pressurized ionization chamber (PIC) is definitive for
1902      assessing the external dose rate from penetrating (gamma) radiation. In this situation, the PIC
1903      provides a direct reading of the  desired measurement quantity at the actual point of interest.
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1904      Another example of a field measurement technique that has been used successfully since the
1905      1960s is in situ gamma-ray spectrometry (ICRU, 1994). This technique provides radionuclide-
1906      specific information. In its simplest application, a spectrometer could be used to identify
1907      characteristic peaks in the energy spectrum that would point to the presence of a particular
1908      radionuclide at the measurement location.  On a semi-quantitative basis, in situ spectrometry
1909      could serve as screening technique where the relative count rates—in particular spectrum
1910      peaks—are compared among measurement locations. At a higher analytical level, an appropriate
1911      calibration can be performed so that a spectrometer could be used to determine the radionuclide
1912      concentration in the media under study.  Since this represents a contaminant-specific
1913      measurement where particular QA/QC checks can be made, it would be classified traditionally at
1914      the data quality objectives (DQO) analytical  level 2, or ASL B.

1915      Despite a number of successful applications  of in situ spectrometry over the years, issues have
1916      arisen regarding the level of data quality that is obtained with this or any other field measurement
1917      technique  for the purposes of demonstrating  RCRA, CERCLA, and other regulatory compliance.
1918      In the past, field measurements by definition have not been considered to possess the quality
1919      control that needs to be established  at a DQO analytical level of 4 (analogous to ASL D) in the
1920      laboratory. However, the distinction between screening level and higher level measurements is
1921      based on factors relating to data quality, which should be demonstrable. In principle, the rigorous
1922      QA/QC protocols and documentation required for analytical level 4, using EPA Contract
1923      Laboratory Program (CLP) procedures, or ANSI ASL D, could be applied to radionuclide-
1924      specific field measurements. Using  field techniques at a higher analytical level is also in keeping
1925      with the latest EPA proposals for performance based measurement systems.

1926      Typically, a projection of cost or time savings using a novel field method leads to its substitution
1927      for a more standard sampling/laboratory analysis method. In doing so, the intended applications
1928      of the field measurement method need to be  established clearly. Using the DQO process, the
1929      requisite analytical level can be determined for the data that are to be collected. This  analytical
1930      level should then be demonstrated through an objective judgement process whereby the data
1931      quality indicators are critically examined. Included would be those that arise when applying the
1932      DQO process (the "PARCC" parameters: precision, accuracy, representativeness, completeness,
1933      and comparability). Other related indicators or elements which can be broken out separately and
1934      which need to be addressed include documentation, instrument operating conditions, site
1935      conditions, interferences, limitations, calibration procedures, minimum detectable concentrations,
1936      reference measurements, record keeping, quality improvement, and management assessment.  The
1937      following  sections will provide some discussion on each of these elements as they apply to field
1938      measurement data quality level. Although  the discussion is based on experiences with in situ
1939      spectrometry, the elements would generally apply to other field measurement techniques as well.


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1940      It would be expected that a demonstration of the data quality level of a field technique be
1941      performed in concert with regulatory bodies and stakeholders to obtain acceptance.

1942      15A.3   Documentation of Methodology

1943      A field measurement technique, like its counterpart in the laboratory, requires thorough
1944      documentation including the description of apparatus and materials, specification of personnel
1945      training/qualification level, listing of quality control checks, review of safety considerations, and
1946      issuance of non-conformance reports when necessary.

1947      Training materials, equipment manuals, reference texts, articles from technical journals, and
1948      laboratory reports are all potential sources of background information for describing a method. It
1949      would be expected that information be extracted from these sources and a comprehensive report
1950      issued that provides the necessary background and specifics for a particular site and application.
1951      This would essentially take the form of a written procedure. For multiple applications across a
1952      site, further detail may have to be provided in project-specific plans, as the conditions under
1953      which a technique is used may vary among areas. The guidance and recommendations given by
1954      standards groups can also form a key part of documentation. Adherence to these standard
1955      procedures allows one to proceed with some confidence in the measurements process. It is
1956      expected that standards groups will increasingly devote their efforts to field measurements
1957      techniques in the future.

1958      Individuals who will be working with the instruments and data collected need to be  qualified.
1959      Educational backgrounds and necessary experience should be determined and appropriate
I960      training given for each area of work. Training and procedure manuals need to track revisions that
1961      invariably result as measurement programs progress.

1962      Quality systems documents would include a general site-wide quality plan with specific factors
1963      like performance tests, pre- and post-operational checks, frequency of calibrations, and replicate
1964      measurements addressed in a separate method-specific quality systems section or document.
1965      Quality systems includes documenting procurement specifications for apparatus and control of
1966      materials and services such as calibration sources. Also, the turnaround time for field
1967      measurements may be important to specify not only for cost and schedule control but for limiting
1968      the time lag between measurements under changing environmental conditions.

1969      Unforseen measurement conditions and unusual equipment malfunctions will lead to situations
1970      where doubt is cast on the validity of a field measurement. Tracking these failures will help to
1971      elucidate the problems over time and a provide a basis for corrective actions and modifications to

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1972     the procedures for future measurements. Situations where obvious bad data is collected despite
1973     the fulfillment of QC elements will require the writing and issuance of a non-conformance report
1974     with subsequent root cause analysis.

1975     15A.4    Instrument Operating Conditions

1976     Specification of instrument operating conditions is fundamental in the field as in the laboratory.
1977     These would include power and cooling requirements as well as an acceptable range in
1978     temperature  and humidity conditions. The physical set-up of the instrument, such as a
1979     reproducible sample-detector geometry, also should be specified. For laboratory radioactivity
1980     counting systems, it is generally a planchet, can, bottle or similar small volume where the
1981     distribution of activity within the sample volume is assumed to be homogeneous. For a field
1982     measurement, the sample is in a form such as an area of ground, a storage drum, or wall.
1983     Distances and orientation to the measured area or object need to be specified and held within
1984     control limits.

1985     For a field measurement, the distribution of activity within the volume of measurement should be
1986     considered, since one does not usually have the luxury of mechanical blending as in the case of a
1987     laboratory sample. The field of view of the detector with respect to lateral and depth
1988     displacement within the volume under measurement needs to be established. For large volume
1989     sources, this generally means determining the response of the instrument across all angles or
1990     radiation incidence, not just the front face. While one cannot necessarily control the distribution
1991     of a contaminant, the instrument response needs to be established so that the integrated signal
1992     that it measures can be converted into a meaningful average result over the volume measured and
1993     the sensitivity to non-homogeneous activity distributions determined.

1994     15A.5    Site Conditions/Limitations

1995     Preparing a site for a field measurement is analogous to preparing a sample for  analysis.
1996     Procedures need to be followed that will assure that the measurement will yield a valid result.
1997     This might include removing obstructions and accounting for topography and ground cover such
1998     as vegetation or surface water. The radiation absorption properties of the type of soil where
1999     measurements are made may have to be determined beforehand depending upon the energy and
2000     type of radiation being measured.

2001     A significant element which should be addressed in performing field measurements is changes in
2002     the "sample," i.e., changes in the field conditions at the measurement point. For example,
2003     measurements at the same location several days apart may be not be comparable if soil moisture

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2004     conditions have changed. Precipitation events would increase soil moisture, while hot, dry
2005     conditions would lead to a decrease in soil moisture.

2006     Depending upon the instrumentation and the physical basis of the measurement, the effects of
2007     such variables as air and soil temperature, humidity, air pressure or related meteorological
2008     parameters may have to be taken into account.

2009     Limitations should be specified for a field measurement technique. They could include site
2010     conditions such as the water content of soil, the degree and type of ground cover, the size of an
2011     area, and the estimated depth of contamination. The radionuclide mix and the concentration level
2012     could also be limiting factors.

2013     15 A.6    Interferences

2014     The effects of interferences need to be assessed for proper QC in a field measurement. As
2015     compared to a laboratory setting where there is generally a controlled environment, adverse
2016     instrument effects may result from extraneous signals or electronic noise that could be produced
2017     by power line or other electromagnetic interference. Interferences in a measurement could also
2018     result from personnel—whether instrument operators or other workers—who enter into a
2019     measurement area and attenuate the measured radiation.

2020     Whereas a laboratory counting system may employ a shield to block out background radiation, a
2021     field measurement system is exposed to ambient radiation. If significant direct or scattered
2022     (shine) radiation is present from extraneous sources, collimation or shadow shielding may be
2023     necessary. In high radiation fields, the effects of ionization in electronic components may present
2024     a problem. In this case the sensor assembly could be kept at the measurement point with the
2025     signal processing and other electronics kept at a distance.

2026     As in the case of laboratory analysis, attention should be given to the mix of radionuclides that
2027     may be present. Interferences can result from the inability to resolve the primary energies emitted
2028     by the nuclides or because there is a high amount of secondary (scattered) radiation present.

2029     15A.7    Calibration

2030     Calibration requires that the instrument response to  a known level of measured substance be
2031     determined. This generally takes the form of measuring standard reference materials or samples
2032     spiked with known quantities.
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2033     Direct calibrations using standards or spikes are usually applied to laboratory-based counting
2034     systems since only small quantities are needed for the sample volumes used. For field
2035     measurements, direct calibrations using large volume sources with a known concentration can be
2036     performed as well, although this is generally impractical and potentially expensive. In place of
2037     this, a field calibration factor for a particular source geometry and matrix composition can be
2038     derived using a two-step process. This entails determining the response to incident radiation
2039     (fluence) as a function of energy and angle (by experimental and/or theoretical means) and then
2040     calculating the fluence at the point of measurement from a given source geometry and matrix.
2041     Two-step calibration methods sometimes are applied to laboratory sample counting geometries as
2042     well.

2043     Although a two-step process may be used for field calibrations, traceability still can exist insofar
2044     as certified point or other sources can be used in the calibration process. The calibration factor
2045     may actually represent an integrated response to a collection of sources or a single source at
2046     many different positions. In the case of a spectrometer,  calibration points will need to be spaced
2047     out across the energy range of interest. Depending upon saturation effects, the calibration may
2048     also have to extend across  a range in concentrations to assess the effects of signal  processing
2049     dead time and pulse pile-up.

2050     15A.8   Minimum Detectable Concentrations

2051     Standard to any high quality measurement technique and integral to the DQO process is an a
2052     priori estimate of the detection limits of the measurement system. This needs to be done for a
2053     field measurement technique, although it may be necessary to first obtain preliminary readings in
2054     the area where measurements are to be performed.  For example, the minimum detectable
2055     concentration (MDC) for a particular radionuclide will  be affected by the continuum of scattered
2056     radiation present in a spectrum from other radionuclides in the soil or from sources of scattered
2057     radiation outside the area under investigation.

2058     In some situations the sensitivity for a given count time can actually be higher for a field
2059     measurement as compared to a laboratory-based sample measurement, thus producing a lower
2060     MDC. This will result when the field detector gives a higher count rate per unit concentration
2061     because there is a far larger sample being analyzed.
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2062     15A.9   Precision

2063     The precision of field measurements is determined with replicate measurements as in the case of
2064     laboratory measurements. To avoid potential changes in field conditions, replicate measurements
2065     can be performed sequentially with minimum time lag.

2066     In many cases, a field measurement is a non-destructive technique. Thus, replicate measurements
2067     are easily performed. Using the results from a successive set of measurements (5 to 10), a
2068     standard  deviation about the mean can be calculated. This can then be compared to the counting
2069     error for  a single measurement that is based on Poisson statistics to assess precision.

2070     Rather than perform many replicate measurements at one point, it can be more instructive to
2071     perform two or more measurements at several different points. The reproducibility can thus be
2072     judged for a variety of site conditions.

2073     15A. 10  Accuracy

2074     Estimates of accuracy for a field measurement can be obtained through uncertainty propagation
2075     just as in the laboratory. Factors to consider include potential bias due to uncertainties in the
2076     calibration source, variations in the assumed sample/detector geometry, uncertainties in the
2077     sample matrix composition, environmental conditions, as well as the statistical counting error.

2078     Overall system accuracy can be checked with comparisons to other techniques, or to results from
2079     an independent organization using the same technique.

2080     15 A. 11  Representativeness

2081     Representativeness refers to the degree to which a measurement reflects the condition at a
2082     location or whether a group of measurements reflects the conditions in a particular area.
2083     Generally, one desires that measurements (or samples) provide a value of a radionuclide
2084     concentration that in turn yields the best dose estimate (and thus risk) to a member of a critical
2085     group for a particular scenario. In order to achieve representativeness, a number of samples or
2086     measurements in a given area would be required in order to achieve a given confidence level or
2087     power using a statistical test.

2088     Representativeness is affected by the heterogeneity of the contaminants in the media under
2089     investigation. Perhaps more than any other factor, field and laboratory measurements may differ
2090     at any particular measurement location due to the effects of heterogeneity. Heterogeneity can

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2091      exist in both the lateral and depth distribution of a contaminant and can take the form of changes
2092      in concentration across various distances: a centimeter or less, as would result from hot particles;
2093      meters, as might occur from dumping and localized spills; and tens or hundreds of meters, as
2094      from up-wind airborne sources. Survey designs incorporate techniques and sample/measurement
2095      densities to accommodate these variations. The number of measurements and the standard
2096      deviation about the mean are fundamental parameters to judge whether the mean concentration
2097      that is measured is within a certain confidence limit. These parameters can be used to compute
2098      the t statistic or applied to other statistical tests.

2099      Where variations in concentration occur on a scale of tens of meters or more, it can be expected
2100      that either field measurements or soil sampling will give similar results. It is where the variations
2101      on the scale of a few meters or less occur that agreement between any particular pair of field
2102      measurement and soil sample results might suffer. However, if the mean concentration in an area
2103      should be determined, a sufficient number of measurements or samples can ultimately yield the
2104      same average result, regardless of where the measurements or samples are taken within the area
2105      under investigation.

2106      Depending  upon the objectives of a measurement program, a field method could inherently have
2107      an advantage over discrete sampling. If the viewing area of a field instrument is significantly
2108      larger than the  area of a soil sample, a set of field measurement results would tend to show a
2109      smaller standard deviation as compared to a set of soil sample data in a heterogeneous area. The
2110      mean obtained for a given number of measurements would then be more representative of the
2111      true mean. A wide measurement area represented by a field method could also be consistent with
2112      the assumptions of a dose model which averages over a large area.

2ii3      15A.12  Completeness

2114      Measurement losses can occur in the field just as sample losses can occur in the lab. They result
2115      from equipment failure, improper measurement procedures, or environmental factors beyond the
2116      control of operators. Survey designs should incorporate allowances for  sample losses by
2117      specifying the collection of more than just the minimum number of samples needed to support a
2118      decision.

2119      There is somewhat of an advantage for a field technique in that QC checks can be performed at
2120      the time of the  measurement. Problems can then be immediately identified and the data rejected
2121      on the spot. Another measurement can then be performed in place of the lost measurement.
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2122     15A. 13  Comparability

2123     Comparability is a critical factor that readily establishes the validity of a field technique. It can be
2124     established by performing a study in which field measurement results are compared to those
2125     given by an independent technique, such as sampling and laboratory analysis. In some situations,
2126     it may be possible to compare two different field techniques.

2127     In performing a direct comparison study, it is important to establish that the two techniques are
2128     measuring the same thing. For instance, a technique that measures a contaminant concentration in
2129     the surface soil may compare poorly to one that is integrating down to greater depths. This
2130     situation would result where there  is a non-uniform concentration depth profile of the
2131     contaminant. Where comparisons are made to soil samples, core depths can be adjusted to better
2132     match the effective viewing depth  of the field measurement. The lateral distribution of the
2133     contaminant concentration across the ground could also be a factor. In this situation, compositing
2134     samples may be required to yield a better average with which to compare a field technique.

2135     Other factors to consider for data comparability include the soil moisture and stone content of
2136     soil. Where contaminant concentrations are determined with a field technique, the value is based
2137     on the wet weight of the soil in contrast to laboratory analysis which is performed on a dry
2138     weight basis. Corrections to one of the data sets therefore need to be applied. Similarly, one
2139     should consider the effects of soil sample preparation where large stones are screened out. The
2140     concentration which is then determined is based on the activity associated with the finer particle
2141     content of the soil. If there is little  or no activity in the  coarser fraction,  a concentration for a soil
2142     sample would be higher than that given by a field technique which  has averaged in the stone
2143     content.

2144     In place of comparing single field measurement points to single or  composited samples, one can
2145     instead compare the averages of sets of field measurements to sets of soil samples over a
2146     particular size area. This would be useful to establish comparability where there is a known
2147     heterogenous distribution of the contaminant and the techniques under comparison are measuring
2148     very different areas of soil.

2149     15A. 14  Reference Measurements

2150     An important QC practice in the laboratory involves the regular analysis of reference materials to
2151     confirm system calibration and performance. In practice, an analogous check can be performed
2152     for measurements in the field. A reference measurement location at a site can be designated as a
2153     field quality control station where routine, perhaps daily, measurements of the contaminants of

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2154     concern are performed. QC charts can be kept which show the results of these measurements and
2155     control limits can be specified accordingly. Unusual trends can then be identified early and
2156     corrective actions taken before unusable data is generated. Measurements at a station such as this
2157     also serve to demonstrate the effects of environmental variables such as temperature and
2158     humidity.

2159     To further qualify a field station, intensive sampling can be performed with laboratory analyses
2160     to determine contaminant concentrations. In this situation, relatively homogeneous conditions
2161     (soil type, contaminant concentration) would make the comparison more favorable and help to
2162     trace any bias between measurement methods that might be observed.

2163     In addition to reference materials, the analysis of blanks is a regular feature of laboratory-based
2164     counting systems. This establishes that contamination of equipment and materials has not
2165     occurred. Similar contamination can occur to field instrumentation such as wind blown soil
2166     particles in crevices, encrusted mud on the underside of equipment, or soil plugs in tripod legs.
2167     For a field measurement technique,  it may be possible to check self-contamination by performing
2168     measurements in a  background area where the contaminant in the soil is essentially zero. If the
2169     contaminant is present in background, such as 137Cs from nuclear weapons fallout, an offsite area
2170     at least can serve to establish a regional baseline measurement. As a standard measure of
2171     precaution, routine scanning of equipment can be performed with friskers, especially after work
2172     in highly contaminated areas.

2173     15A.15  Record Keeping

2174     Field personnel need to use log sheets or books to record necessary information about the site
2175     conditions, measurement parameters, and data storage. In place of chain of custody forms for
2176     samples, analogous records may be required for data printouts or electronic files of results
2177     (spectral data) obtained in the field as they pass through different levels in the organization (data
2178     entry, data analysis, validation, etc.).

2179     Maintenance logs or files on specific pieces of equipment need to be kept. Factory repairs or in-
2180     house replacement  of components should be noted as any changes to an instrument are likely to
2181     require recalibration. Equipment and component failures should also be tracked.

2182     15A.16  Quality Improvement

2183     Operating experience generally leads to fuller knowledge of instrument performance and
2184     characteristics as well  as better recognition of precursors to problems. Based on control chart

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2185     records, observations and correlations with other factors associated with a measurement,
2186     breakdown and repair logs, and the information contained in any non-conformance report,
2187     procedures can be modified to improve data recovery and usability. In time, the net effect of
2188     changes incorporated in standard operating procedures will lead to improvements in performance
2189     tests. Along with the identification of limiting factors and the development of solutions, it may
2190     be possible to justify raising the analytical level of the measurement based on the quality control
2191     indicators.

2192     15A.17  Management Assessment

2193     In addition to the quality control elements in place when a field technique is demonstrated
2194     initially, systems need to be in place to insure that data quality is maintained in subsequent
2195     measurements once the technique is used routinely. Deployment of a field methodology on a
2196     broader scope generally entails use by non-experts, i.e., individuals not associated with the
2197     development or implementation of an instrument. For this reason, internal assessments may be
2198     needed in the form of independent oversight (audits). The data verification and validation process
2199     can be used to insure the fulfillment of QC checks. Ultimately, data may have to be reviewed and
2200     approved by individuals who have expertise with the measurement system.

2201     15A. 18  Combined Laboratory and Field Measurements

2202     Laboratory and field measurement techniques are not mutually exclusive. They can frequently be
2203     used in concert to achieve better and more cost-effective radiological surveys. A likely
2204     combination would be reliance on field methods which are faster with the laboratory method
2205     serving as  a QC check. Appropriate ratios in the number of field to lab measurements would have
2206     to be established based on  expert judgement and by reviewing the data quality objectives. The
2207     ratio could vary from area to area within a site depending upon the situation and the presence of
2208     complicating factors.

2209     15A. 19  References

2210     International Commission on Radiation Units and Measurements (ICRU), 1994. Gamma-Ray
2211        Spectrometry in the Environment, Report 53, Bethesda, MD.
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 i      16  INSTRUMENT CALIBRATION AND TEST SOURCE

 2                                   PREPARATION


 3     16.1   Introduction

 4     This chapter provides guidance on the important functions of radiation detection instrument
 5     calibration and test source preparation. In this chapter, the term "test source" will be used to
 6     describe the radioactive material prepared to be introduced into a measurement instrument, and
 7     "laboratory sample" will be used to identify the material collected for analysis. Thus, a test
 8     source is prepared from laboratory sample material for the purpose of determining its radioactive
 9     constituents. "Calibration source" is used to indicate that the prepared source is for the purpose
10     of calibrating instruments.

11     The continuing validity of calibrations should be checked on a periodic basis (Chapter 18,
12     Laboratory Quality Control) as specified in a laboratory's quality assurance manual. This is
13     usually done by counting a check source or some secondary standard in an instrument and
14     comparing the results to those previously obtained when the instrument was known to be in
15     calibration. The frequency and other aspects of calibrations and verifications may be specified in
16     project planning documents (Chapter 4, Project Plan Documents) and in analytical statements of
17     work (Chapter 5, Obtaining Laboratory Services).

18     Test sources may be prepared by destructive or nondestructive techniques. A destructive analysis
19     is performed when the original laboratory sample material is altered by ashing or dissolution,
20     which often is followed by chemical separations. Chemical  separation usually is necessary when
21     analyzing for specific alpha- or beta-particle emitters. Nondestructive analyses can be  used when
22     the laboratory sample is to be analyzed by gamma spectrometry or for gross analyses where the
23     laboratory sample is only dried and counted directly.

24     The requirements placed upon test source preparation are dictated primarily by the type and
25     energy of the radioactivity to be measured (alpha, beta, or gamma), the radiation detector
26     employed, and—to some degree—whether the measurement is simply a gross radioactivity
27     measurement or if specific radionuclide identification is required. The nature of the laboratory
28     sample material also will have an effect on the test source preparation. These are referred to as
29     "matrix effects"  and can be caused by both the chemical and physical characteristics of the
30     laboratory sample. When matrix effects are encountered, one is faced with the choice of altering
31     the analysis methodology for that laboratory sample or possibly flagging the result to indicate a
32     high degree of uncertainty.
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33     The significant characteristics affecting the bias and precision of radioactivity measurements will
34     be discussed in relation to each type of radioactivity. This includes counting efficiency, which
35     can be affected by the characteristics of the test source as well as those of the radiation detector
36     and the geometry of the source relative to the detector. Also, methods used to prepare radioactive
37     test sources for measurement from chemically separated (isolated) radionuclides will be
38     described.

39     A number of methods and techniques employed to separate and purify radionuclides contained in
40     laboratory samples, particularly in environmental samples, are described in Chapter 14 (Separa-
41     tion Techniques), and sample dissolution is discussed in Chapter 13 (Sample Dissolution).
42     Instruments that will be used to analyze the test sources prepared as outlined in this chapter are
43     described in Chapter 15 (Nuclear Counting Instrumentation). In the case  of gross (non-nuclide
44     specific) and nondestructive measurements, chemical separation and purification procedures
45     often are not required. However, to accomplish these measurements, the test source still must be
46     prepared (mounted) in such a manner that the associated radioactivity can be quantified in a
47     reproducible and unbiased manner.

48     16.2   Instrument Calibration

49     Instrument calibrations generally are performed for the purpose of establishing the counting
50     efficiency of an instrument. The counting efficiency establishes the number of disintegrations
51     registered in the detector and electronics of a counting instrument compared to the number
52     emitted by the source. Counting efficiencies are specific to the radionuclide (or energy), the
53     geometrical relationship between the source and detector,  and a number of characteristics of the
54     source material, especially those that affect absorption and scattering of the radiation. It is
55     common practice to have several different calibrations on a given detector in order to accommo-
56     date a number of radionuclides, source-to-detector distances, and counting containers that a
57     laboratory will be required to employ in order to meet project requirements for detection
58     sensitivity, specificity, and the variety of media encountered.

59     In cases where the efficiency of the detector varies with energy, it is necessary to perform the
60     calibration at a number of energies and establish an efficiency curve that covers the range of
61     energies to be encountered. Some radiation detection instruments require other types of
62     calibrations. These will be discussed under specific instrument calibrations. Generic issues which
63     govern the conduct of calibrations will be discussed below and instrument and test source
64     specific considerations will be provided in the appropriate sections in this chapter.
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65      16.2.1 Standards

66      Instrument calibration should be performed as needed with only National Institute of Science and
67      Technology (NIST) traceable standards (ANSI N42.23). Calibrations of instruments shall be
68      made using certified reference materials of known and documented value and stated uncertainty.
69      These certified reference materials shall be supplied by:

70       •  NIST directly;

71       •  A standard source supplier whose measurement capabilities or manufacturing processes are
72         periodically tested by NIST; or

73       •  A standard source supplier who documents derived materials with stated uncertainty, and
74         whose value has been verified with analytical and measurement systems that have been tested
75         periodically through an unbroken chain of comparisons to the national standards.

76      16.2.2 Correspondence

77      To assure that the instrument calibration is unbiased, calibration sources must be prepared and
78      counted in a manner that assures that they are virtually identical to the test sources in all respects
79      that could affect the counting efficiency determination (ANSI N42.23). The geometry, including
80      the size and shape of the calibration source and counting container (beaker, planchet, vial, etc.)
81      and source-to-detector distance and alignment, must be controlled. Backscatter, scattering, and
82      self-absorption present during test source counting must be duplicated in the calibration process.
83      The density of the calibration source material should be consistent with that of the test sources.

84      When possible, counting efficiency calibrations should be performed using the radionuclide,
85      whose activity is to be determined in test sources. This may not be possible when the radionuc-
86      lide is not available as a standard reference material or when gross analyses are performed. When
87      the actual radionuclide is not available, a surrogate radionuclide may be selected that has the
88      same type of particle or photon emission (a, p, or y) and a proximate energy. When calibrating an
89      instrument in this manner, corrections must be made for any differences between the decay
90      schemes of the two nuclides.

91      If any factor can vary throughout the test sources, calibrations must be performed which simulate
92      this variability over the range expected to be encountered during test source counting. An
93      example is the necessity to develop a self-absorption curve for alpha  or beta counting to account
94      for the changing overall counting efficiency due to absorption in the variable source thickness.


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 95      16.2.3 Homogeneity

 96      The calibration source must be prepared in a manner that assures that the material is uniformly
 97      distributed throughout its volume. Any deviation from this requirement can result in a calibration
 98      that is biased and contributes to the overall uncertainty of the laboratory results.

 99      Liquid calibration sources are more likely to be homogeneous than are solids, particularly those
100      where reference material has been added to a solid material—soil,  for example. In order to
101      minimize the overall uncertainty associated with calibration, care should be taken to assure the
102      reference material is thoroughly mixed into the calibration source and distributed uniformly
103      throughout its volume.

104      16.2.4 Uncertainty

105      The total uncertainty of calibration is affected directly by the uncertainty associated with the
106      activity of the reference material used in the calibration source. Furthermore, the uncertainties
107      related to the reproducibility of the counting geometry and the non-homogeneity of the
108      calibration source must be considered. Since the uncertainty associated with these factors is
109      difficult to quantify, it should be minimized.

110      The uncertainty associated with calibration can be reduced by the accumulation of as many
ill      counts as practical during the calibration process. The two controllable factors for achieving this
112      are the amount  of activity in the calibration source and the counting time allocated for the
113      calibration. As  a general rule, at least 10,000 counts should be accumulated during the counting
114      of the calibration source. This may not always be practical when the activity of the calibration
115      source must be  limited for reasons listed below.

116      The activity of  calibration sources should be limited to an amount that will not lead to significant
117      dead-time losses and random summing in the instrument being calibrated. Unaccounted for,
118      dead-time losses and random summing could lead to an efficiency determination that is biased
119      and artificially low.  In addition, one must be aware of the potential for detector contamination,
120      this is particularly true for semiconductor detectors used for alpha spectrometry.

121      16.3  General Test Source Characteristics

122      The goal of test source preparation is to achieve maximum detection capability while introducing
123      minimum bias and uncertainty into the measurement. To realize this goal, test sources must be
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124     prepared in a consistent manner relative to the geometry, disposition of test source material, and
125     the source container.

126     16.3.1 Geometrical Arrangement

127     The geometry of a test source must be suitable for the counting instrument and—particularly—it
128     must be reproducible. The radioactivity associated with test sources is measured in geometries
129     that have been standardized by measuring the instrument response to a known quantity of
130     radioactivity in the identical geometry as the calibration source, to the extent possible. Thus, for
131     this standardization to be accurate over time, the test source geometry must remain constant from
132     source to source and with respect to that of the calibration source. This requirement is necessary
133     for performing  quantitative and unbiased measurements of all types of radioactivity and for all
134     types of measurement instruments.

135     16.3.2 Uniformity of Test Source Material

136     Test source uniformity is related to the physical nature of the source material. Uniformity of test
137     source material relative to its thickness, density (which can be influenced by water content), and
138     homogeneity is important. Nonuniformity can result from a variation in the thickness of the test
139     source material over its cross sectional area. If test sources are  deposited in a nonuniform
140     manner, absorption characteristics will vary from source to source and acceptable reproducibility
141     may not be achieved.

142     Variation in test source thickness or density can have a particularly large effect in the
143     measurement of alpha-particle activity and, because of their smaller mass and charge, a lesser
144     effect in the measurement of beta-particle activity. Alpha and beta test sources, once prepared,
145     often are stored in a desiccator to maintain a constant moisture content. Test source uniformity is
146     relevant to gamma-ray measurements, not because of the absorption of gamma-rays, but because
147     nonuniformity (non-homogeneity) in the distribution of activity throughout a large source
148     changes the effective detection efficiency. For example, if the gamma-ray emitting radionuclides
149     are concentrated in the portion of the test source container nearest the  detector, the counting
150     efficiency will be greater than if the radionuclides were uniformly distributed throughout the test
151     source. Thus, test source uniformity can have a large influence on the counting efficiency by
152     which the activity is detected and measured. Measurements of nonuniform sources are not
153     reproducible; thus, radioactive sources of all types must be homogeneous.
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154     16.3.3 Self-Absorption and Scattering

155     Absorption and scattering within the source material are less important when measuring gamma
156     rays than when analyzing for charged particles. Particulate activity emitted in a source can be
157     scattered by elastic and inelastic collisions with nuclei of the source material, degrading the
158     energy of the particle (self-scatter) or—if sufficiently thick—the particle may be absorbed totally
159     by the source (self-absorption). A scattering/self-absorption factor can be used, however, to
160     correct the measured activity to that of an infinitely thin source. For beta counting, this factor is
161     proportional to (1 - e'^/jix, where |i is the linear absorption coefficient for beta particles in the
162     test source material and x is the source thickness (Friedlander and Kennedy, 1955, p. 278).

163     Because of the much smaller mass of beta particles, scattering is more pronounced in sources
164     emitting beta particles than in those  emitting alpha particles. Depending on counter geometry,
165     measured beta activity can first increase  as the source thickness increases, because of the
166     scattering of electrons out of the source plane and into the detector (Friedlander and Kennedy,
167     1955, pp. 276-278). At greater thicknesses, self-absorption begins to predominate, and the
168     activity eventually approaches a constant value. When this occurs, the source is said to be
169     "infinitely thick." Counting a source at infinite thickness refers to a measurement  made with a
170     source thickness such that further increasing the amount of material added would  have no effect
171     on the count rate. The minimum source thickness required for this type of measurement clearly is
172     not more than the maximum range R of the particle in the source material, and is often estimated
173     to be 0.75R (Friedlander and Kennedy, 1955, p. 278).

174     To assure that scattering does not lead to bias in test source  results, it is important that standard
175     sources prepared for determination of counting efficiency and self-absorption corrections are
176     prepared identically in all aspects that affect  absorption to test sources whose activities are to be
177     assayed.

178     Self-absorption increases with the density of the source material and with the size and charge of
179     the emitted particle. Thus, source thickness is of greater concern for measuring alpha particles
180     than for beta-particle emissions and  has even less importance in measuring gamma rays, except
181     for low energy x- or gamma rays.  Thus, test sources prepared for alpha-particle measurements
182     must be very thin and uniform for maximum detection capability and reproducibility.

183     The moisture content of the source material will affect the density of the source and the
184     absorption characteristics of the source. A change in source moisture content will  alter the
185     density  and affect the reproducibility of the measurement. Thus,  the amount of moisture within
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186     the test source should be controlled. The following procedures often are followed in order to
187     maintain a low and constant moisture content of test sources to be counted.

188       •  Test sources prepared by coprecipitation are dried by washing the precipitate first with ethyl
189         alcohol and then with acetone while in the filtering apparatus. Suction to the filter apparatus
190         is continued until the test source is dry. The filter with test source is removed from the
191         filtering apparatus, mounted on a planchet, and stored in a desiccator prior to counting.

192       •  Electroplated test sources are dried by heating on a hot plate, in an oven, or under a heat
193         lamp, and then stored in a desiccator until cool and ready to count.

194       •  Laboratory samples analyzed nondestructively are usually dried prior to measurement in
195         order to control moisture content and help ensure that test source characteristics are
196         reproducible. Laboratory samples, such as soil, biota, vegetation, etc., are usually dried in an
197         oven. Test sources not counted immediately, including those for gross alpha and beta
198         measurements, as well as for gamma-ray spectroscopy, should be desiccated to maintain a
199         constant moisture content.

200       •  Evaporated test sources also are stored in a desiccator, after flaming, to maintain a constant
201         moisture content.

202     Another concern in measuring both alpha and beta particles from deposited test sources is back-
203     scattering: the scattering of particles from the source-mount back through the test source material
204     and into the sensitive part of the detector. Back-scattered beta particles have degraded energies,
205     but can have the apparent effect of increasing the counting efficiency. This may seem to have the
206     desired effect of improving the overall counting efficiency; however, the percent of back-
207     scattered beta particles from the test source must remain constant and be identical to that of the
208     standard source. The magnitude of backscatter is dependent on the beta-particle energy and the
209     thickness, density, and atomic number of the backing material (Faires and Boswell, 1981, p. 220-
210     222). Thus, to reduce the effect of backscatter on beta-particle measurements, the test source
211     often is mounted on a thin, low Z (atomic number), low density material, as for example
212     aluminum foil or thin organic films (Blanchard et al., 1960). For very precise measurements, a
213     conducting metal film is vaporized onto the organic film so  that any electrical charge build up
214     due to the emission of charged particles can be eliminated.

215     As with absorption, backscatter increases with the thickness of the scattering material up to a
216     saturation level, beyond which it remains constant. The saturation level is reached at a thickness
217     that is about one-third the maximum range of the scattered particle (Faires and Boswell, 1981, p.


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218     221). Therefore, due to the dependency of backscatter on atomic number and thickness, the
219     backing used for the standard source must be identical to that used for the test source mount. For
220     example, if the presence of HC1 in the test source requires changing from an aluminum planchet
221     to platinum, a platinum backing must also be used in counting the standard source.

222     16.3.4 Counting Planchets

223     A wide variety of planchets made of platinum, nickel, aluminum, and stainless steel can be
224     obtained in various sizes. It is normally not of great importance which type is used as long as
225     several factors are considered (PHS, 1967, p. 20). Some factors that should be considered in
226     selecting a planchet are:

227      •  Chemical reactivity. The metal planchet must be inert to the chemicals in the test source, as
228         corrosion of the planchet surface radically alters test source absorption and geometry
229         characteristics.

230      •  Radioactivity. The metal comprising the planchet should contain minimal radioactivity and,
231         although this is generally not a serious problem, the planchet background shall be measured.

232      •  Size. Two-inch planchets (assuming the detector is at least that large) are often preferred for
233         gross alpha^eta counting to expedite and simplify the evaporation of liquid samples and
234         provide a greater surface area for solid samples, while 1-inch planchets are generally used for
235         alpha spectrometry test samples.

236      •  Cost. Platinum planchets should not be used if stainless-steel ones are adequate for the
237         purpose.

238     It is usually impractical to reuse planchets, and it is generally not recommended. Except for those
239     made of platinum, planchets are inexpensive, and it is not cost effective to clean the planchets
240     and insure they are not contaminated from the prior test source. Platinum planchets are quite
241     expensive and usually can be cleaned effectively in acid and recounted prior to reuse to insure
242     that they are not contaminated.

243     16.4  Test Source Preparation and Calibration for Alpha Measurements

244     Several types of instruments are used for counting alpha particles (Chapter 15,  Nuclear Counting
245     Instrumentation).  Each type of instrument has characteristics that affect preparation and
246     mounting of sources. Similarly, these characteristics also affect the calibration of the instrument.

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247     This section discusses the attributes of commonly used instruments and their effects on test
248     source and standard source preparation.

249     16.4.1 Proportional Counters

250     Proportional counters (Section 15.2.2.1) often are used to measure alpha particles, particularly
251     when gross analyses are desired. Proportional counters may be "internal," where the test source is
252     placed into the detector or "windowed," where a thin window covers a part of the detector and
253     separates the source from the detector.

254     16.4.1.1    Alpha Test Source Preparation

255     Test sources for proportional counters are usually prepared by electrodeposition, coprecipitation,
256     or evaporation, as described below in Section 16.7.6. For internal counters, since the source is
257     placed within the detector, care must be exercised in test source preparation to avoid the
258     inclusion of chemicals which may react with the detector materials. Likewise, any spillage of test
259     source material can result in contamination of the detector.

260     The absorption of alpha particles in the source material (self-absorption) is quite important when
261     using proportional counters, or other ionization counters,  and must be addressed when preparing
262     a test source for counting. Self-absorption is primarily a function of source thickness (ts) and the
263     range (Rs) of the alpha particles in the source material. For a uniformly thick source, the fraction
264     of alpha particles absorbed by the source increases proportionately to 1/2^, when ts < R, (NCRP,
265     1978, pp. 104-105). Thus, to approach absolute counting in either 2n or 4n counting geometries,
266     test sources should be prepared as thinly and uniformly as possible.

267     Another method sometimes used for alpha-emitting test sources in ionization counters is to
268     perform  the count at infinite thickness (Section 16.3.3). The count rate of a test source at infinite
269     thickness usually is related to the count rate of a standard source prepared and measured in the
270     exactly the same manner.

271     Backscatter from alpha sources increases with the atomic number of the backing or source
272     material and with decreasing alpha energy (NAS/NRC, 1962, p. 115). Scattering of alpha
273     particles from the source material itself is not a significant problem, and scattering from the
274     source backing has only a small affect for very thin sources (NCRP, 1978, p. 107).  When
275     stainless-steel planchets are used, the increase in a count rate because of alpha backscatter is only
276     about 2 percent (PHS, 1967, p. 19).
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277      16.4.1.2   Proportional Counter Calibration — Alpha
278
279
280
281

282
283
284

285
286
287

288
289
290
291
292
293

294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
Calibration sources prepared for calibrating counters for a specific nuclide measurement shall
contain a radionuclide of similar alpha energy and be measured under identical conditions as the
test sources to be measured (ASTM D3648). A variety of radionuclides have been recommended
for calibrating for gross alpha analyses (Table 16.1).

                       TABLE 16.1— Nuclides for  alpha calibration
Purpose
Specific Nuclide and Gross Alpha
Gross Alpha
Gross Alpha
Gross Alpha
Nuclide
239Pu, 241Am, 210Po, 228Th, 226Ra, 233U,
235U,andUmt
241 Am
241 Am, 237Np, and Umt
241Am,239Pu,230Th,andUmt
Reference
ASTM D3648
EPA, 1980
ASTM D 1943
APHA (1995), Method 7110
To the extent possible, standard sources should be prepared in a manner identical to the method
used for test source mounting. The counting efficiency (e) is then determined by counting the
standard source for a sufficient time to accumulate approximately 10,000 counts and dividing the
derived counts per second (cps) by the a emission rate of source in disintegrations per second
(dps).
                                           dps
In cases where finite test source thicknesses are unavoidable, alpha-source counts can be adjusted
to account for self-absorption (PHS, 1967, p.  19). This requires that a self-absorption curve be
prepared in order to determine the change in counting efficiency as a function of source thickness
or mass.  Standard sources containing a known amount of the radionuclide of interest are prepared
in varying thicknesses (mass) and counted.  Absorption curves for gross alpha-particle measure-
ments most often are constructed using reference material containing one of the nuclides listed
above. The absorption curve is constructed by counting planchets containing varying mass of
material but with constant added radioactivity. A curve is generated by plotting the efficiency at a
given source thickness divided by the efficiency at "zero" thickness versus source mass (mg) or
density thickness in jig/cm2 or mg/cm2 (NCRP, 1978, p.  105). Thus, the efficiency relative to the
"zero thickness" efficiency can be read directly from this curve for any measured test source
thickness. Test sources prepared for gross measurement are counted in the exact geometry as
those used to prepare the absorption curve.  The material forming the matrix for the self-
absorption standard source should, when possible, be identical to that expected in the test sources
to be analyzed. Based on the test source mass or density thickness  in units of |ig/cm2 or mg/cm2,
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309     the correction factor determined from the absorption curve is applied to the test source count,
310     yielding the count rate equivalent to an infinitely thin source.

311     Most modern proportional counters are capable of simultaneous alpha and beta counting. This is
312     accomplished by identifying the two types of particles based on their pulse height. Those pulses
313     whose heights exceed an experimentally established discriminator level are registered as alpha
314     counts and those falling below this level are recorded as beta counts. Some fraction (usually less
315     than 10 percent for a weightless source) of the alpha particles is recorded as betas, even  for
316     nearly weightless test sources. This fraction increases as the thickness (mass) of the source
317     increases. A much smaller (often insignificant) fraction of the beta interactions are registered as
318     alphas. This misclassification of alpha and beta counts is referred to as "crosstalk."

319     For simultaneous alpha and beta counting, corrections must be  made to the beta count rate to
320     remove the portion contributed by alpha particles. Since the fraction of alpha counts occurring in
321     the beta channel is a function of the source mass, a crosstalk curve relating the fraction of alpha
322     particles counted as beta to source mass must be developed. This can be accomplished
323     concurrently with the self-absorption calibration if the radionuclide selected is an alpha emitter
324     only—no beta particles. This is done by recording the beta counts from the alpha self-absorption
325     determination at all source weights and plotting the fraction (beta counts/alpha + beta counts) as
326     a function of source  mass (Section 17.4.1). Beta count rates then can be corrected for the
327     influence of the alpha particles at all source thicknesses.

328     16.4.2 ZnS(Ag) Scintillation Counter

329     This type of counter is discussed in Section 15.2.2.3. Because the alpha particle must be emitted
330     from the source and  interact with the screen, as it does with the ionization chamber of an internal
331     proportional counter, the previous description concerning self-absorption and scatter of alpha
332     particles during analysis in an internal proportional counter may be applied to counting alpha
333     particles with a ZnS(Ag) scintillation counter. Additional advantages of this counting
334     arrangement are the very low backgrounds that are achievable and the small potential for
335     permanently contaminating the counter, because the zinc sulfide screens can be replaced.

336     A source mount shaped like a washer, with one side enclosed with a transparent ZnS(Ag) screen,
337     is an arrangement often used. The test source to be counted is placed in the hole of the "washer,"
338     in contact with the ZnS(Ag) screen. The other side of the test source mount is sealed, generally
339     with wide transparent tape, securing the test source within the source mount. The test source is
340     then placed on an appropriately sized photomultiplier tube and  counted. Because of the
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341     availability of large photomultiplier tubes, sources up to 5 inches in diameter can be prepared for
342     measurement (PHS, 1967, p. 26).

343     The considerations related to alpha calibrations, discussed above under proportional counters,
344     apply equally to scintillation counter calibration.

345     16.4.3 Alpha Spectrometry With Semiconductor Detectors

346     Semiconductor detectors for alpha particle counting are discussed in Section  15.2.2.5. Alpha-
347     energy spectra of very high resolution are attainable with semiconductor detectors if the prepared
348     test source is essentially weightless, < 1 |ig/mm2 (Helpers, 1986, pp.  143-145). As the thickness
349     of the test source increases, the spectral energy is degraded due to self-absorption, which
350     broadens the peak and forms a "tail" on the lower-energy side (Chapter 17). The alpha-energy
351     spectral degradation will increase, as the source thickness increases,  raising the possibility of
352     overlapping peaks with a loss of spectrum integrity. Thus, it is of utmost importance to prepare
353     very thin and uniform alpha test sources for spectrometry. This may be accomplished by
354     electrodeposition or coprecipitation (ASTM, D3084), if reagents are controlled so that only small
355     (milligram) quantities of precipitate are recovered (Sections 16.6.1 and 16.7.2). For example, in
356     the coprecipitation of actinide test sources for spectral analysis, source thicknesses of 0.4 to 1
357     |ig/mm2 (0.04-0.1  mg/cm2) are routinely achieved, which is quite adequate for producing well-
358     defined alpha spectral peaks (EPA, 1984a).

359     Semiconductor detectors used for alpha spectrometry require both efficiency and energy
360     calibrations. Calibration sources, traceable to NIST, often are prepared with multiple
361     radionuclides so they may be used for both types of calibration (ASTM D3084). Sources
362     containing 234U, 238U, 239Pu, and 241Am have been used for this purpose. When mixed-nuclide
363     calibration sources are used, the average counting efficiency is often calculated using the
364     efficiencies of the individual radionuclides. Some alpha spectrometry analysis programs calculate
365     an average efficiency where the individual radionuclide efficiency is weighted by the uncertainty
366     in its determination. Other radionuclide combinations may be used, but in addition to the
367     requirement for traceability for the disintegration value, the energies of the radionuclides  must be
368     known with a high degree of certainty.

369     Calibration sources may be prepared by either electrodeposition or coprecipitation. Due to their
370     durability and stability, electrodeposited calibration sources are often chosen. It is important that
371     the area of deposition be consistent with that of test sources to be counted and that there are no
372     significant impurities present (ASTM D3084). See the additional discussion on alpha
373     spectrometer calibration in Section 17.3.2.


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374     16.4.4 Liquid-Scintillation Spectrometer

375     With proper scintillators, liquid scintillation can be used to measure alpha-particle emitters
376     (Passo and Cook, 1994) (Section 15.2.2.4). Although the relatively high background of liquid
377     scintillation counting restricts the sensitivity relative to other counting techniques, e.g., internal
378     proportional counting or the use of ZnS(Ag) screens, the ease of source preparation and the
379     nearly 100 percent counting efficiency are advantages often exploited (Hemingway,  1975, p.
380     146). The separation of alpha- and beta-particle counts attained in the spectrometer can be
381     enhanced by proper scintillator choice. Ultima Gold AB™ was designed  specifically to maximize
382     alpha/beta  separation in aqueous solutions and, in other studies, poor alpha/beta separation has
383     been overcome by making the standard cocktail 20 percent in naphthalene (Passo and Cook,
384     1994, pp. 3-11 to 3-12). It is believed that naphthalene improves the alpha/beta separation by
385     acting as an intermediate in the energy transfer process between the solvent and the fluor
386     (McDowell, 1986).

387     EPA's (1978) recommended procedure for measuring 222Rn in water uses liquid scintillation
388     counting. The protocol  is based on the solubility of radon in a number of scintillators. To
389     measure radon in air, the radon is first adsorbed onto activated charcoal  and then mixed with an
390     appropriate scintillator  and counted (EPA,  1987; Passo and Cook, 1994, pp. 8-5 to 8-10).
391     Utilizing the high solubility of 222Rn in organic solvents, concentrations  of 222Rn in air have  been
392     determined by bubbling air through the scintillator in a scintillation vial  (Amano et al., 1985).
393     Concentration of 222Rn, determined by liquid scintillation, also can be used in the measurement of
394     its parent, 226Ra.

395     Some actinides (U and  Th) and transuranics (Np, Pu, Am, and Cm) have been measured by  a
396     procedure that involves "Extraction Scintillation Techniques" (Passo and Cook, 1994, pp. 6-1 to
397     6-2 and 13-1 to 13-6). An extraction agent, e.g., bis(2-ethylhexyl) phosphoric acid (HDEHP), is
398     mixed either with a toluene or a di-isopropylnaphthalene (DIN) based cocktail. The alpha
399     emitter, in the aqueous  laboratory sample, is extracted into the scintillation mixture and counted
400     by liquid scintillation. The discussion in Section 16.5.2.1 can be applied to both alpha and beta
401     particles.

402     16.5  Characteristics of Sources for Beta Measurements

403     16.5.1 Proportional Counters

404     Beta decay generally is accompanied by gamma-ray emission; the latter  normally is much easier
405     to identify  and quantify. Beta-particle counting typically is more difficult, due to the additional

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406     source preparation and associated complications resulting from the effects of backscatter,
407     scattering, and absorption in the source material (NAS/NRC, 1962, p. 118-119). Beta particles
408     are not emitted monoenergetically and may result in additional difficulty in quantitative
409     measurements.

410     Beta counting in ionization-type counters often is used after chemical separations are performed
411     to isolate the beta-emitting radionuclide of interest from other radionuclides. Beta measurements
412     are performed on chemically isolated pure beta emitters (beta decay not accompanied by a
413     gamma-ray) and also in cases when increased sensitivities are required to meet detection limits,
414     such as, 89Sr, 90Sr, "Tc,  131I, 134Cs, and 137Cs (EPA, 1980). The proportional counter often is used
415     for measuring these beta-particle emitters. Test sources measured in a proportional counter are
416     usually prepared by electrodeposition, coprecipitation, or evaporation, as described below in
417     Section 16.7 (Blanchard et al., 1960).  The comments on chemical reactivity of source contained
418     materials and contamination given in Section 16.3.1, apply here.

419     16.5.1.1    Beta Test Source Preparation

420     Although it remains  a consideration, self-absorption of beta particles is not as pronounced as
421     with alpha particles,  because the charge and mass of beta particles are significantly smaller.
422     Scattering, and particularly backscatter from the source mount, is much more pronounced for
423     beta counting than for alpha counting  (Blanchard et al., 1957). To reduce scatter, plastic
424     mountings are often  used to mount sources for beta counting (EPA, 1980). The effects resulting
425     from self-absorption and scattering can be minimized by preparing test sources in a standardized
426     constant thickness, or using a correction factor based on an empirical calibration curve for
427     different thicknesses (Friedlander and Kennedy, 1955, pp. 276-277; Tsoulfanidis, 1983, pp. 133-
428     134). (Section 16.3.3.)

429     For sufficiently thick sources, the beta particles emitted from the source reach a limit, and the
430     count rate becomes independent of the source thickness.

431     16.5.1.2    Proportional Counter Calibration — Beta

432     As in other calibrations, proportional counters used for beta-particle analysis shall be calibrated
433     with NIST traceable standards in a manner that is totally consistent with the counting of test
434     sources. When possible, the radionuclide to be quantified should be used as the calibration
435     source. For gross beta analysis, the radionuclides presented in Table 16.2 have been
436     recommended for calibration sources.
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437
438
439
440
441
442

443
444
445

446

447
448
449
450
451
452
453
454

455
456
457
458
459
460
461
462
463
464

465
466
467
468
469
                       TABLE 16.2 — Nuclides for beta calibration
Purpose
Gross Beta
Gross Beta
Gross Beta
Gross Beta
Nuclide
137Cs
137Cs
137Cs
137Cs and 90Sr-90Y
Reference
ASTM D3648
EPA, 1980
ASTM D 1890
APHA (1995), Method 71 10
If test sources of varying mass are to be counted for beta activity determination, a self-absorption
curve must be prepared. The method used is identical to that described under alpha calibration
for proportional counters, except that a beta-emitting reference material is used instead of alpha.

16.5.2 Liquid-Scintillation Spectrometers

When beta measurements are required, especially those involving pure beta emitters of low
energy, they are often performed in a liquid scintillation spectrometer, because self-absorption
and backscatter are eliminated and counting efficiencies are relatively high (Herpers, 1986, pp.
133-135). Although it is the preferred instrument to measure low-energy, pure beta-emitting
radionuclides, e.g., 3H,  14C, and 35S, it is a well-established procedure for measuring numerous
other beta-emitting radionuclides,  including 45Ca, 65Zn, 141Ce, 60Co, 84Sr, 55Fe, 87Rb, 147Pm, and
36C1 (Hemingway, 1975, pp.  145-146). The liquid scintillation spectrometer, applied to beta-
particle measurements, is described in detail in Section 15.3.3.

Tritium is the radionuclide most often measured by liquid scintillation counting (DOE, 1997;
EPA 1979; Lieberman and Moghissi, 1970, p. 319). The primary step in preparing water samples
for counting is distillation in the presence of an oxidizing agent, such as KMnO4, to separate the
tritium labeled water from dissolved solids, including interfering radionuclides, and any organic
material that may be present. An aliquant of the distillate  is then mixed with a liquid scintillator
and counted in a liquid scintillation spectrometer. To measure tritium in samples of other
matrices, the water in the sample can be removed and collected by distillation as an azeotrope
with, for example, /7-hexane  or cyclohexane (Moghissi, 1981; EPA, 1979). An aliquant of the
water collected is then mixed with a liquid scintillator and counted, as described above for water
samples.

Tritium can be concentrated  in a sample of water if lower detection limits are required. The
concentration process, electrolysis, uses the isotopic effect caused by the large mass difference
(three times) between JH and 3H (DOE, 1997; EPA, 1984a). Tritium becomes enriched as
electrolysis continues. Generally, 50 mL of the laboratory sample is placed in an electrolysis cell
and a current of about three amps  applied. Electrolysis is  continued until the volume reaches
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470     about 5 mL. More sample can be added to the cell during the electrolysis, if greater sensitivity is
471     necessary for the measurement. The concentrated laboratory sample is then distilled in the
472     presence of an oxidizing agent, such as KMnO4, and treated like a water sample (see above).

473     16.5.2.1   Liquid Scintillation Test Source Preparation

474     The preparation of a laboratory sample for a liquid-scintillation spectrometer usually is relatively
475     simple and fast. The radionuclide to be measured is isolated in a solution, which is then
476     introduced into and thoroughly mixed with one of a variety of ready-to-use commercially
477     available liquid scintillators. This mixture is often referred to as a scintillation "cocktail." The
478     liquid scintillator is an emulsion system, usually consisting of an aromatic solvent containing the
479     appropriate scintillator mixed with a detergent (NCRP, 1978, pp. 168-169). If a sample is
480     insoluble in the scintillator, it can be ground to a fine powder, stirred into the scintillator until a
481     homogeneous mixture is formed, and solidified with a gelling agent (Friedlander et al., 1981, p.
482     303).

483     Because much or our ecosystem consists of materials composed of carbon and hydrogen, the
484     measurement of 3H and 14C levels in biological materials is important. Water, for 3H analysis, can
485     be recovered efficiently from all types of environmental and biological samples by azeotropic
486     distillation. The laboratory sample is distilled with a hydrocarbon, such as benzene or
487     cyclohexane, which is compatible with the liquid scintillation process (Moghissi et al., 1973;
488     Moghissi, 1981). The distillate is mixed with the proper scintillator and counted in a liquid
489     scintillation counter. Tritium has been successfully measured by this technique in such samples
490     as animal and human tissues, soil, hay, grass, urine, and milk.

491     Environmental  and biological samples also can be analyzed for total 3H (that contained in both
492     the water and fibrous fractions) by quantitatively combusting the laboratory sample, collecting
493     the water formed, and analyzing it by liquid scintillation  spectrometry (DOE, 1997). In another
494     case, both 3H and 14C can be measured simultaneously (EPA, 1984b). The laboratory sample first
495     is freeze-dried to remove and collect the water fraction. The tritium in the water is measured
496     directly by liquid scintillation spectrometry. The fibrous (freeze-dried) material is combusted and
497     the H2O and CO2 are collected. As before, the 3H in the water is measured directly by liquid
498     scintillation spectrometry, while the 14C is first converted to benzene or captured as CO2 and then
499     counted by liquid scintillation spectrometry.

500     A primary problem with measurements using a liquid-scintillation spectrometer is "quenching."
501     Quenching occurs when the production of light is inhibited or the light signal is partially
502     absorbed during the light transfer process by a substance in the  liquid. The two basic types are


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503     chemical and color quenching. Some of the stronger chemical quenchers are alkyl bromides,
504     iodides, nitrates, mercaptains, and ketones (NCRP, 1978, p. 46). Color quenching involves the
505     reduction of light transmission through the solution to the cathode of the phototube by the
506     absorption of the light photons. The two techniques most often used to correct for quenching
507     involve the use of internal or external standards.

508     Chemiluminescence, the production of light by a chemical reaction, can be troublesome in liquid-
509     scintillation counting. However, the duration of chemiluminescence is generally short, and a wait
510     of a few minutes after mixing the reagents will allow the effect to dissipate before counting
511     starts. Similarly, phosphorescence, the emission of light from certain chemicals caused by
512     exposure to light, will cease a short time after being placed in the dark. This is referred to as
513     "dark adapted" (Faires and Boswell,  1981, p. 182).

514     16.5.2.1   Liquid-Scintillation Spectrometer Calibration

515     When the quenching of a group of test sources is predictable, e.g., distilled drinking water (EPA,
516     1980; ASTM D4107), a counting efficiency is determined for the group  by placing a known
517     quantity of reference material in the source  medium and scintillation solution under identical
518     conditions (vials and volumes) as the sample medium.

519     Except for test sources with very predictable amounts of quenching, it may be necessary to
520     determine a counting efficiency for each laboratory sample. Two methods of determining
521     counting efficiency are available: internal standardization and external standardization (NCRP,
522     1978).

523     Internal standardization for quench correction is by the method of standard additions. This
524     involves the counting of two aliquants of a sample, one being the sample and the other is an
525     identical aliquant that has been spiked with a known amount of the radionuclide being
526     determined. The degree of quench can then be determined from the spiked aliquant and applied
527     to the unspiked sample (DOE, 1995). This method does not require a curve for correction but
528     decreases throughput because two test source counts are required. For these reasons, the use of an
529     external standard is the more widely used technique to correct for quenching (Horrocks, 1973).

530     One external standard method is also called the "external-standard channels-ratio" (Baillie, 1960;
531     Higashimura et al., 1962). In this method, a series of vials is prepared containing a known
532     amount of reference material and varying amounts of the medium being evaluated. Windows in
533     the energy spectrum are set for a high- and low-energy region. The vials are counted and the
534     ratios of low-to-high count rates are recorded for each quenched source.  A quench curve is then


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535     prepared by plotting the ratios of low-to-high energies as a function of counting efficiency. The
536     efficiency of an unknown test source can then be determined from its low-to-high energy ratio
537     during counting.

538     The second external-standard method employs an external gamma-ray source that generates
539     Compton electrons in the scintillation solution. Count rates from the external source are
540     determined for a set of sources whose efficiency is known from the internal-standard method. A
541     quench curve is then prepared by plotting the external count rate vs. counting efficiency.

542     The external-standard methods should not be generalized beyond use for the media conditions
543     under which they were prepared.

544     16.6  Characteristics of Sources for Gamma-Ray Measurements

545     Backscatter and self-absorption, which must be addressed when measuring alpha and beta
546     emissions, cause less uncertainty in the measurement of most gamma-ray emitters. This is
547     because the penetrating nature of gamma rays is totally different from that of particles. For thick
548     samples or high-Z matrices, a detection-efficiency correction is necessary for low-energy photons
549     (especially below 200 keV) due to the self-absorption of photons in the sample. There is,
550     however, some backscatter of gamma-rays from the shield surrounding the detector, which
551     produces a small peak at about 200 keV (NAS/NRC, 1962, p. 32).

552     16.6.1  Gamma Test Source Preparation

553     No significant precautions usually are required in preparing test sources for gamma-ray
554     spectrometry, as long as the test source is homogenous  and positioned reproducibly relative to
555     the detector. Although source properties (e.g., density and moisture content) are not as important
556     in gamma-ray spectrometry as in alpha or beta measurements, test source preparation for gamma
557     measurements may still include drying and ashing to control moisture content and to reduce the
558     test source size. Homogeneity of the test source can be  attained by thoroughly mixing laboratory
559     samples that have been ashed (many combustible matrices not containing volatile radionuclides
560     are ashed), by grinding and mixing solids (e.g., soils and sediments), or by finely chopping and
561     mixing fresh vegetation.  Also, calibrations are generally conducted using standard sources with
562     identical counting geometries and the same or similar matrices  as the test source for analysis.

563     Important considerations in preparing test sources for gamma-ray spectrometry are geometry
564     (shape), size,  and homogeneity (uniformity) of the source. Test sources can be in any
565     reproducible shape or size, but the radionuclides must be uniformly distributed throughout. A

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566     counting container that allows the source to surround the detector, thus maximizing the
567     geometrical efficiency, is referred to as the "Marinelli" or "reentrant" beaker (Hill et al., 1950). It
568     consists of a cylindrical sample container with an inverted well in the bottom of the beaker that
569     fits over the detector.

570     Counting efficiencies are determined by measuring a known quantity of the radionuclide(s) of
571     interest in the same matrix and source-detector configuration as the sources requiring analysis
572     (NCRP, 1978, pp. 243-244; ASTM, D3649). This eliminates any effect that might be caused by
573     differences in test and calibration source characteristics, e.g., density, moisture content, shape,
574     and size. Efficiency curves may be prepared for a detector by measuring a variety of standardized
575     sources having different photopeak energies under identical conditions as the unknown
576     (Coomber, 1975, p. 18; ANSI, 1991).

577     Two important advantages of gamma-ray spectrometry are the ability to measure more than one
578     radionuclide simultaneously and the  elimination or reduction of the necessity for chemical
579     dissolution and radionuclide separations (nondestructive analysis). Source configurations for
580     nondestructive analyses generally are selected to optimize counting efficiency. Examples are
581     (PHS, 1967, p. 78):

582      •  Marinelli beakers of various volumes to measure liquid sources, as water, milk, and food
583         samples blended to a slurry;

584      •  Cylindrical plastic containers of various volumes, such as the 400 mL "cottage-cheese
585         container" frequently used for containing solid sources;

586      •  Planchets of various diameters to measure precipitates, air filters, etc.; and

587      •  Aluminum cans of a standardized volume into which solid sources  can be compressed, and
588         sealed, if desired, to retain radon.

589     If greater counting efficiency is required, the test source size can be reduced, allowing a greater
590     amount of the  laboratory sample to be counted and in a more favorable geometry. Examples of
591     such processes are:

592      •  Reducing the volume of water samples by evaporation;

593      •  Reducing the volume of water samples by co-precipitating the desired radionuclides;
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594      • Reducing the size of vegetation samples by compression into a large pellet or by ashing, if
595        volatile radionuclides are not of interest; and

596      • Reducing the size of filter samples by compressing the sample into a reduced standard
597        volume or by ashing, if volatile radionuclides are not of interest.

598     16.6.2 Gamma Spectrometer Calibration

599     Most gamma-ray spectrometry systems are calibrated with either single or mixed standards in an
600     exact matrix and geometric form as the samples to be analyzed. However, there are computer
601     codes that can calculate detector efficiency from the physical dimensions of the detector and
602     sample counting geometry (Mitchell, 1986; Hensley et al., 1997).

603     Commercial standards of single or mixed gamma-ray emitters in a matrix of known chemical
604     composition and density can be prepared in user-supplied containers. Calibrations based upon
605     these standards can then be adjusted to correct for any differences in composition and density
606     between the calibration source and the test source (Modupe et al., 1993).

607     MARLAP recommends that calibration data for gamma spectroscopy calibration be obtained
608     from the National Nuclear Data Center at Brookhaven National Laboratory (http://www.nndc.
609     bnl.gov/nndc/nudat/). Calibration data are readily available for common radionuclides, including
610     210Pb, 241Am, 109Cd, "Co, 141Ce, 139Ce, 203Hg, 51Cr, 113Sn, 85Sr, 137Cs, 54Mn, 88Y, 65Zn, 60Co, and 40K.
611     For more information on gamma spectrometry calibration see ANSI 42.14. (Also see Section
612     17.3.1.6 on gamma calibration.)

613     16.7  Methods of Test Source Preparation

614     16.7.1 Electrodeposition

615     High-resolution spectroscopy requires a very thin, uniform, flat, and nearly weightless source
616     mount. Ideally, the source plate to determine alpha activity by a spectrometer would be a flat
617     plate coated with a monolayer of radioactive atoms and with no foreign material above the layer
618     to attenuate the alpha radiation (Kressin, 1977). The electrodeposition of radionuclides on a
619     suitable metallic surface from an aqueous solution often can produce thin and uniform test
620     sources that approach these ideal conditions. Thus, this technique is very appropriate for
621     preparing sources of alpha emitters, especially the actinides, which  include uranium, plutonium,
622     thorium, americium, and neptunium (ASTM, D3865; DOE, 1997; EPA, 1979).
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623     There are a number of electrolytic cell designs used to electrodeposit radionuclides. The cathode,
624     on which the radionuclide deposits is often a thin metal foil or disc, such as platinum or stainless
625     steel, or a metal-coated plastic film (Blanchard et al.,  1960). The stirring rod, often made of
626     platinum, can also serve as the anode of the cell. Deposition of actinides for alpha spectrometry
627     also has been performed on disposable cells constructed form 20 mL polyethylene scintillation
628     vials and highly polished stainless steel planchets (Talvite, 1972). Disposal prevents cross
629     contamination. The composition of the electrolyte and the parameters applied in the electro-
630     deposition process, such as applied voltage, amperage, current density, and deposition time, are
631     dependent upon the chemical properties of the element, especially its reduction potential, and
632     foreign material that might be present. Thus, "Each element requires optimization of its own
633     procedure" (Adloff and Guillaumont, 1993, p. 158). Deposition time varies from 10 minutes to
634     two hours.
635
636
637
638
639
640
641
642
643

644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
Actinides and similar elements are extremely hydrolytic and can deposit on the glass cell wall or
anode or precipitate during deposition (Puphal et al., 1983). Electrodeposition typically is
performed, therefore, in electrolytic solutions at low pH (~2) to prevent hydrolysis or
precipitation. The solution may contain complexing agents (such as fluoride) and chelates (such
as EDTA) to minimize the effect of interfering ions, commonly encountered in biological and
environmental samples (Puphal and Olsen, 1972). The procedure of Kressin (1972), however,
illustrates the admonition of Adloff and Guillaumont cited above: citrate and fluoride, a chelate
and complexing agent, respectively, each interferes with the electrodeposition of plutonium and
americium  in his process.

Electrodeposition is applicable to more than 30 radionuclides. The main advantage of
electrodeposited sources over those from other methods of preparation is their extremely thin,
uniform deposit of a radionuclide on a plate, which permits high resolution spectroscopy;
however, the yield is often not quantitative (Adloff and Guillaumont, 1993, p. 158). Thus, the
yield must be monitored with the inclusion of a known quantity of an isotope, which is deposited
simultaneously with the analyte. Radioactive sources of the following elements have been
prepared successfully by electrodeposition (DOE, 1997; Blanchard et al., 1960; Johnston et al.,
1991.)
Actinium
Americium
Antimony
Bismuth
Cadmium
Cobalt
Copper
Curium
Gold
Hafnium
Indium
Iron
Lead
Neptunium
Nickel
Plutonium
Polonium
Promethium
Protactinium
Radium
Rhenium
Ruthenium
Selenium
Silver
Strontium
Tellurium
Thallium
Thorium
Tin
Uranium
Yttrium
Zinc
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660     Particularly important to environmental analysis is a procedure by which virtually all alpha-
661     emitting nuclides—radium through californium—can be determined in soil in any combination
662     on a single sample with few interferences using electrodeposition to prepare the source (Sill et
663     al., 1974).

664     Although sources of radioactive isotopes of these elements have been prepared by electro-
665     deposition, it might not be the preferred technique in some of the examples cited. For various
666     reasons, other methods of test source preparation may be superior: yields can be low, the
667     presence of other metals sometime interferes, the quality of deposition might be poor (flaking),
668     the recovery can be low, the spectral resolution might be poor, and some procedures require
669     rather elaborate equipment, are expensive, and are time consuming, thus labor intensive (Sill and
670     Williams, 1981; Hindman, 1986). Interference will be caused by  several factors: (1) "Any
671     element present in the separated fraction that is able to be electrodeposited will be present on the
672     metal disc;" (2) "Incomplete separation of rare earth elements or incomplete wet ashing for the
673     removal of organic material will decrease the efficiency of the electrodeposition and may result
674     in a thick deposit unsuitable for a-spectrometry measurement;" and (3) "Samples containing
675     more than 20 jig of U are unsuitable for measurement by a spectrometry due to the thickness of
676     the deposit" (DOE, 1997, p. 4.5-270). When stainless-steel planchets cannot be used, because of
677     the corrosive nature of the electrolyte, and platinum is required, the method can be quite
678     expensive and time consuming, since recycling of the expensive electrode material requires
679     thorough cleaning to prevent cross contamination.

680     Test sources of actinides are often prepared by electrodeposition with yields of 90 percent and
681     higher (DOE, 1997; EPA, 1979; Sill et al., 1974; Puphal and Olsen, 1972; Kressin,  1977; Talvite,
682     1972; Mitchell, 1960; Shinohara and Kohno, 1989, pp. 41-45). In addition, 54Mn sources have
683     been successfully prepared by the electrodeposition from mixed-solvent electrolytes onto
684     stainless steel planchets (Sahoo and Kannan, 1997, pp.  185-190).

685     If the redox couple between the metal cathode and the radionuclide to be deposited is positive,
686     the radionuclide will deposit spontaneously. That is, it will deposit quantitatively without using
687     any applied potential. Generally, a metal planchet is simply suspended in the solution that is
688     stirred with a glass stirring rod for a few hours (Blanchard, 1966; DOE, 1997). An example of
689     such a spontaneous reaction between polonium and nickel is given below.

690                                     Po+4 + 2 Ni ^ Po + 2 Ni+2     E° = 0.98 Volt

691     Polonium also will deposit quantitatively on silver planchets. 210Po is an important naturally
692     occurring radionuclide that is often included in environmental studies. Spontaneous deposition


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693     onto either nickel or silver is the preferred technique for preparing 210Po sources for
694     measurement.

695     A similar technique, called internal electrolysis, is preformed by selecting electrodes that have a
696     large difference in potential. A conventional electrolytic cell containing an acid solution of the
697     radionuclide to be deposited may be used. A magnesium (E° = +2.37 volts) strip, for example, is
698     inserted into the electrolyte and connected by an external circuit to the inert metal cathode
699     (planchet), usually platinum. A spontaneous current flows and deposition on the cathode will
700     occur. The conditions at the inert cathode are exactly the same as if an external voltage were
701     applied; however, longer electrolysis times are necessary to achieve quantitative recoveries. Very
702     thin and uniform sources of 106Ru,  110Ag, 203Hg, 60Co, 114In, 51Cr, 198Au, and 59Fe were prepared by
703     this technique, with greater than 96 percent recovery in all cases (Blanchard et al.,  1957, pp.  46-
704     54; Van der Eijk et al., 1973).

705     16.7.2  Coprecipitation

706     Coprecipitation (Section 13.8) has been employed to mount sources for alpha spectrometry.
707     Some radiochemists prefer the method to electrodeposition, maintaining that, "The procedure is
708     faster and more reliable than those involving electrodeposition and gives consistently higher
709     yields" (Sill and Williams, 1981). Hindman (1986) asserts that the method is "more rapid, more
710     economical, and more efficient" ... "and yields good decontamination factors, high recoveries,
711     and excellent resolution of the a spectra for uranium, plutonium, americium, and thorium."

712     Although sources prepared by Coprecipitation are thicker than those prepared by electrodepo-
713     sition, sufficiently thin sources, even for alpha spectrometry, can be prepared by controlling the
714     amount of precipitate formed.  Sources thinner than 0.5 jig/mm2 can be prepared of the actinides
715     by Coprecipitation (EPA, 1984a). Thicker sources lead to poor resolution of the spectra
716     (Hindman, 1983) and sources produced by any technique that  are greater than 10 jig/mm2 lead to
717     attenuation of alpha particles (Adolff and Guiallaumont, 1993, p. 161).

718     After separations are completed, a slurried precipitate is poured quantitatively through a filtering
719     apparatus collecting  the precipitate on a small (e.g., 25 mm dia.) filter. Vacuum filtration often is
720     used to speed the operation. With suction applied, the precipitate typically is washed with water,
721     then ethyl alcohol, and finally with acetone to dry the precipitate. The filter is removed from the
722     filtering apparatus and mounted on a metal planchet, commonly with double-stick tape, and
723     stored in a desiccator to await counting. Any 222Rn progeny that collects on the filter during the
724     filtration process will decay in a short period of time and not affect the measurement. Samples of
725     the following radionuclides have been prepared for quantitative analysis by Coprecipitation:


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726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750

751
752
753
754
755

756
757
758
759
760
761
762
763
764
765
Radionuclide
32p
51Cr
89/90Sr
90y
131j
137Cs
147Pm
2ioBi
226Ra
Th
Th
U
Np
Pu
Am
Cm
Th
Np
Pu
Am
Cm
U
a EPA (1984)
b EPA (1980)
Carrier
MgNH4PO4
BaCrO4
SrC03
Y2(C204)3
PdI2
Cs2PtCl6
Nd2(C204)3
BiOCl
BaSO4
Ce(I04)4
LaF3
LaF3 (NdF3)
LaF3
LaF3(NdF3)
LaF3(NdF3)
LaF3
Ce(OH)2
Ce(OH)2
Ce(OH)2
Ce(OH)2
Ce(OH)2
UF3
c DOE (1997)
d Hindman (1983)
References
a
a
a,b,c
a,b,c
a,b,c
b
a
a
b
d
a,b
a,b,(f)
b
a,b,d,(f)
a,b,d,(f)
b
e
e
e
e
e
e
e Sill (1981)
f Hindman (1986)
It should be emphasized that precipitated sources must be thoroughly dry before measurement,
otherwise, self-absorption and scattering will change with time as water evaporates. Also,
sources are often covered with a thin film, such as Mylar™ or Formvar™, to avoid sample loss and
contamination of counting equipment. Care must be taken to avoid excessive handling of the
source that can change the physical nature of the co-precipitate, producing an uneven thickness.

Another precipitation technique has been applied to preparing radioactive sources.  Source
preparation by precipitation can be conducted in a desiccator fitted with a valve to allow first the
evacuation of the desiccator and then the admission of a precipitating gas, such as ammonia
(NH3) or hydrogen sulfide (H2S) (Blanchard et al., 1957, pp. 26-31; Van der Eijk et al.,  1973). A
carrier is added to the sample and a know quantity is pipetted onto a planchet. The planchet
containing the test source solution is placed in the desiccator and exposed to a precipitating gas
for one to  two hours. This period of time allows settling to occur. The test source is removed
from the desiccator and evaporated beneath a heat lamp. Using an  A1C13 carrier in an ammonia
atmosphere, Yoshida et al. (1977)  prepared uniformly deposited radioactive sources of 59Fe, 60Co,
95Nb, 103Ru, and 198Au by this technique.
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766     16.7.3 Evaporation

767     When a high degree uniformity of the deposit is not a requirement for the measurement, sources
768     can be prepared by simple evaporation under a heat lamp (Bleuler and Goldsmith, 1952). This
769     procedure is easy, fast, and adequate for many type measurements. Water samples for gross alpha
770     and beta screening measurements are often prepared by this method (EPA, 1984a; EPA, 1980).
771     An aliquant of the water laboratory sample is evaporated on a hot plate until only a few milliliters
772     remain. The concentrated solution that remains is then transferred quantitatively with a pipette to
773     a tared stainless-steel planchet, usually 2-inch diameter, and evaporated to dryness under a heat
774     lamp. The planchet, with the evaporated test source,  is then flamed over a burner until dull red to
775     reduce the amount of solids present and to convert the matrix to an oxide. (Insoluble hydroxides,
776     which are often bulky and gelatinous, are prime candidates for ashing, as the oxide formed is
777     much firmer, more uniform, and better defined.) The test source is cooled, weighed, and counted
778     for alpha and beta particles in a proportional counter. Planchets containing evaporated solids
779     cannot be flamed if volatile radionuclides are to be measured.

780     Most of the solids in an evaporated source deposit in a ring around the edge. Techniques to
781     improve uniformity include the addition of a wetting agent, such as tetraethylene glycol or a 5
782     percent insulin solution (Shinohara and Kohno, 1989), freeze drying the sample, or precipitation
783     and settling of the active material prior to evaporation (Friedlander et al.,  1981, p. 305; Van der
784     Eijk and Zehner, 1977). The wetting agent is pipetted onto the  spot to be covered by the test
785     source, then removed with the pipette. That remaining can be dried under a heat lamp. A known
786     quantity of the laboratory sample is then pipetted onto the spot and dried under a heat lamp.
787     Additional portions of the sample may be added and evaporated.

788     Sample spreading on the planchet, as it is heated, can result in depositing test source material on
789     the planchet walls or in the flow of the liquid over the edge of a flat, lipless planchet.  Such
790     spreading can be controlled or restricted by  outlining the desired source area with a wax pencil.
791     Metal planchets often are constructed with a small lip around their circumference that retains the
792     test source on the planchet. All sources prepared by evaporation should be flamed to a dull-red
793     color, cooled, and stored in a desiccator until counted, unless they contain volatile radionuclides,
794     in which case simply store the evaporated test source in a desiccator.

795     Source spreading during evaporation has been restricted by electrospraying a silica gel
796     suspension onto a thin film to produce a circular pad. The radioactive source solution is dropped
797     onto the circle and evaporated to dryness (Chen et al., 1989).
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798     EPA's (1980) prescribed Method 900.0 for measuring gross alpha and beta radioactivity in
799     drinking water suggests that the sample aliquant be limited to what will produce 5 mg/cm2 of
800     solids on the planchet. Thus, for a 2-inch planchet (20 cm2), an aliquant containing 100 mg of
801     non-volatile dissolved solids is the recommended maximum test source mass.

802     After a radionuclide in solution has been purified by chemical techniques, i.e., impurities
803     removed, the solution can be transferred to a planchet and evaporated to dryness, as described
804     above. Evaporation of a laboratory sample after purification is used by the EPA to measure 228Ac
805     in the analysis for 228Ra (EPA,  1984a), and sources of thorium, isolated from marine carbonates,
806     have been prepared by evaporation for measurement by alpha spectrometry (Blanchard et al.,
807     1967). Measured count rates of identified radionuclides, for which absorption curves have been
808     prepared, can be adjusted for self absorption in evaporated test sources.

809     In the case of all dry sources, steps should be taken to prevent solids from exiting the planchet,
810     which will affect the measurement and, in time, contaminate the detector. Sources consisting of
811     loose, dry material, or with a tendency to flake, should be covered with thin plastic or
812     immobilized by evaporating a few drops of a lucite-acetone solution on the solid deposit (PHS,
813     1967, p. 21).

814     16.7.4  Thermal Volatilization/Sublimation

815     Vacuum thermal volatilization or sublimation are often used when very thin and uniform sources
816     are required (Blanchard et al., 1957, p. 7-9 and Friedlander and Kennedy, 1955, p. 122). The
817     disadvantages of this technique are that it is time consuming and the recoveries  are often less
818     than 50 percent (NAS/NRC 1962, pp. 126-127).

819     The apparatus used to perform this procedure consists of a demountable vacuum chamber that
820     contains either a ribbon filament, often with a shallow trough, or a crucible. The collector plate is
821     usually mounted less than an inch away. The source solution is first evaporated  onto the filament.
822     As the required temperature of the filament is reached, the trough in the filament tends to
823     collimate the sublimed material onto the collecting plate, increasing the recovery of the sample.

824     Pate and Yaffe (1956) designed a system for volatilizing radionuclides from a crucible heated
825     with electrical resistance wire.  Their design resulted in nearly 100 percent yields on thin
826     collecting films, and made it possible to prepare thin and uniform sources containing a known
827     aliquant of a stock solution (NAS/NRC 1962, p. 127).
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828     For very thin sources, it is necessary either to swing the collector plate away or have it covered
829     during initial heating in order to burn off impurities at low temperatures without volatilizing
830     them onto the source mount. Separation from contaminants can be accomplished at the time of
831     source preparation by considering differences in vapor pressure and carefully controlling the
832     temperature (Coomber 1975, p. 306). The temperature at which a radionuclide will volatilize
833     depends on the compound in which it exists, e.g., as a hydride, oxide, or halide. Sources have
834     been prepared by thermal volatilization/sublimation for radioisotopes of manganese, chromium,
835     cobalt, rhodium, arsenic, silver, ruthenium, technetium, and many others (Blanchard et al., 1957,
836     p. 9; Coomber 1975, pp. 306-308). See Section 13.5, Volatilization and Distillation, for further
837     discussion of this topic with examples.

838     A technique called vacuum evaporation has been used to prepare thin, uniform radioactive
839     sources (Van der Eijk,  1973). Radioactive substances are volatilized by heating a solution in an
840     oven under reduced pressure. Yields, usually rather low, can be improved by using a collimating
841     oven.

842     16.7.5 Preparing Sources to Measure Radioactive Gases

843     Gaseous radionuclides  most often measured include tritium, both as a vapor (3HOH) and in the
844     elemental form (3H-H), 14C, as CO2,  and the noble gases, 37Ar, 41Ar, 85Kr, 131mXe, and 133Xe.

845     Tritiated water vapor is often collected by condensation from a known volume of air (EPA
846     1984b). The air is drawn first through a filter to remove all particulates and then through a cold
847     trap  submerged in a dry ice/alcohol bath. A measured aliquant of the water collected is analyzed
848     by liquid scintillation spectrometry (EPA, 1984b). Tritiated water vapor is sometimes collected
849     by pulling air through a trap containing silica gel (SC&A, 1994). After collection, the water is
850     distilled from the silica gel, collected, and counted in a liquid scintillation  spectrometer.

851     Gaseous products of oxidation or combustion can be trapped in a suitable media, such as water
852     for 3H, ethanolamine for 14C, peroxide for 35S, and then analyzed by liquid scintillation
853     spectrometry (NCRP, 1978, p. 211).  For this method, it is very important to de-aerate the liquid
854     prior to introducing  the gas, and the temperature must be carefully controlled since gas
855     solubilities are temperature dependent (NCRP, 1978, p. 210), generally inversely proportional to
856     the temperature.

857     Although not as common nor convenient as liquid scintillation spectrometry, a gaseous
858     radionuclide can be  measured in an internal proportional counter as a component of the counter-
859     filling gaseous mixture, usually argon, methane, or an argon-methane mixture (Friedlander and


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860     Kennedy 1955, p. 274; NAS/NRC 1962, p. 128; Bleuler and Goldsmith 1952). For example,
861     tritiated water can be reduced to hydrogen gas (3H2) by passing water vapor over a bed of hot
862     zinc, and sodium  carbonate can be converted to carbon dioxide (14CO2) by the action of an acid
863     (NCRP, 1978, p. 211). These gases then can be mixed with a counting gas and introduced into
864     the proportional-counter chamber. The major disadvantage of this technique is that it requires a
865     gas handling system.

866     Concentrations of radioactive noble gases in the effluents of some nuclear facilities are
867     sufficiently high that source preparation simply involves filling an evacuated vessel with the
868     gaseous sample or flushing the vessel sufficiently to insure a 100 percent exchange (EPA, 1984b,
869     pp. 19-20). The counting geometries (efficiencies) of the collection vessels can be determined,
870     allowing the collected test sources to be measured directly in the vessels by gamma-ray
871     spectrometry.

872     For environmental samples collected downwind of a nuclear facility, concentrating the nuclides
873     in the gaseous sample is nearly always required prior to measurement. One example is a system,
874     called the "Penn State Noble Gas Monitor," which was designed to measure low concentrations
875     of radioactive noble gases (Jabs and Jester, 1976; Jester and Hepburn, 1977). Samples of
876     environmental air are compressed in SCUBA (high pressure) bottles to 3,000 psig, providing a
877     sample volume of 2.3 m3. The inlet air to the compressor passes through a scrubbing train that
878     contains particulate filters and activated charcoal to remove radioiodine. The noble-gas
879     measurement system consists of a spherical 14.69 L, high-pressure, stainless steel vessel with a
880     reentrant well in its base to permit insertion of a Ge detector connected to a spectrometry system.
881     The vessel is surrounded with 2 inches of lead shielding.

882     There may be occasions when radioiodine is discharged into the atmosphere in several chemical
883     forms. A molecular species filtering system, described by EPA (1990), collects four primary
884     species of iodine on separate cartridges so that they can be measured individually. Air is pulled
885     first through a particulate filter and then through the cartridges placed in series. The normal  order
886     of the four cartridges in the filtering system is as follows: (1) cadmium iodide media (CdI2) for I2
887     retention, (2) 4-iodophenol (I •  C6H4- OH) on alumina for HOI retention, (3) silver-salt (AgX)
888     loaded zeolite or impregnated charcoal for organic iodine retention, and (4) charcoal for a
889     breakthrough monitor. Air, at a calibrated flow, is passed through the system at a
890     rate of one to two cubic feet per minute (cfm). When the sample-collection period is complete,
891     the cartridges are  separated, and the activities of each are measured separately by direct counting
892     of the individual cartridges using gamma-ray spectrometry.
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893     16.7.6 Preparing Air Filters for Counting

894     Air filters containing particulates may be counted directly by a proportional or scintillation
895     detector. Minimal source preparation is normally required for directly counted filters. Some
896     project plans may require that the mass of the particulates on filters be determined. If so required,
897     the filters are weighed on receipt and the net particulate mass calculated by subtracting the mass
898     of an average filter mass or, if pre-weighed, the beginning filter mass.

899     Actual preparation may be limited to a reduction of the size of the filter and placing it in the
900     appropriate counting container, e.g., a planchet. If the filter is of the correct size and shape to fit
901     directly in a counting container, no preparation may be required. Since particulate matter is
902     deposited on the surface of the filter medium, care must be exercised in handling, particularly
903     during size reduction, so that particulate material is not removed.

904     Because potentially contaminated material is relatively easily removed from a filter surface,
905     caution is necessary to avoid  contamination of detectors. If a filter is to be gamma counted it can
906     remain in the envelope or plastic bag in which it is received for counting. The filter may be
907     placed in such an enclosure if not received in that manner. The size of the filter may be reduced
908     by simply folding the filter to a standard size for gamma counting.

909     When specific alpha- and beta-emitting nuclide analyses are required (e.g., Pu, U, Th, Am, Sr),
910     the filter media along with the particulate material are usually ashed or dissolved and processed
911     as any digestate by the procedure used in the laboratory.

912     16.7.7 Preparing Swipes/Smears for Counting

913     Swipes are collected to determine the level of removable surface contamination. They are
914     normally taken on a filter paper or fabric pad by rubbing it over a predetermined surface area,
915     nominally 100 cm2. Swipes are routinely counted directly in a proportional counter for alpha and
916     beta activity determination. The size of the swipe is selected to allow it to be placed in a
917     standard-size planchet for counting. If elevated beta radioactivity is identified, a swipe may be
918     gamma counted to determine the contributing radionuclide.  Elevated alpha activity may require
919     isotopic analyses for identification.

920     The precaution relative to detector contamination given above for air filters applies to swipes. All
921     swipes should be treated as if they are contaminated until proven otherwise.  In some cases swipes
922     may be wetted with water or alcohol prior to collection of the sample. Wet swipes shall be
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923      allowed to air dry prior to counting in order to avoid the reduction of particles reaching the
924      detector due to absorption in the liquid remaining on the swipe.

925      16.8   References

926      Adolf, J.P. and Guillaumont, R.  1993. Fundamentals of Radiochemistry, CRC Press, Boca
927         Raton, Florida.

928      Amano, H., Kasal, A., and Matsunaga, T. 1985. "Simultaneous Measurement of Radon and its
929         Progeny in Cave Air by Liquid Scintillation Techniques and Alpha Spectrometry," Health
930         Phys.  49:3, pp. 509-511.

931      American National Standards Institute (ANSI) N42.14. "Calibration and Use of Germanium
932         Spectrometers for Measurement of Gamma-Ray Emitting Radionuclides," 1991, New York.

933      American National Standard Institute (ANSI) N42.23. "Measurement and Associated
934         Instrumentation Quality Assurance for Radioassay Laboratories." 1996.

935      American Society for Testing and Materials (ASTM).  1998. 1998 Annual Book ofASTM
936         Standards, Volume 11.02, D1890, D1943, D3084, D3648, D3649, D3865, D4107, and
937         D4962.

938      American Public Health Association (APHA). 1995. Standard Methods for the Examination of
939         Water and Wastewater, 19th Edition, pp. 7-1 to 7-40.

940      Baillie, L. A. 1960. "Determination of Liquid Scintillation Counting Efficiency by Pulse Height
941         Shift," Int. J. Ppl. Radiat. Isot, .8:1.

942      Blanchard, R.L., Kahn, B., and Birkhoff, R.D. 1957. "The Preparation of Thin, Uniform  Sources
943         for a Beta-Ray Spectrometer," Oak Ridge National Laboratory Report, ORNL-2419,  Oak
944         Ridge, TN.

945      Blanchard, R.L., Kahn, B., and Birkhoff, R.D. 1960. "The Preparation of Thin, Uniform,
946         Radioactive Sources by Surface Adsorption and Electrodeposition," Health Phys., Vol. 2,
947         pp. 246-255.

948      Blanchard, R.L. 1966. "Rapid Determination of Lead-210 and Polonium-210 in Environmental
949         Samples by Deposition on Nickel," Analytical Chemistry, Vol. 38, p. 189.

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                                               Instrument Calibration and Test Source Preparation
950     Blanchard, R.L., Cheng, M.H., and Potratz, H.A. 1967. "Uranium and Thorium Series
951        Disequilibria in Recent and Fossil Marine Molluscan Shells," J. Geophys, Vol. 72, pp. 4745-
952        4757.

953     Bleuler, E. and Goldsmith, GJ. 1952. Experimental Nucleonics, Rinehart & Company, New
954        York, pp 38-39.

955     Chen, Q.J., Nielson, S.P., and Aarkrog, A. 1989. "Preparation of Thin Alpha Sources by
956        Electrospraying for Efficiency Calibration Purposes," Radioanal. Nucl. Chem. 135:2, 117-
957        123.

958     Coomber, D.I. 1975. RadiochemicalMethods in Analysis, Plenum Press, New York.

959     U.S. Department of Energy (DOE).  1997. EML Procedures Manual, Chieco, Nancy A., Ed.,
960        HASL-300, 28th Edition, DOE Environmental Measurements Laboratory, New York.

961     U.S. Environmental Protection Agency (EPA). 1978. Radon In Water Sampling Program,
962        Eastern Environmental Radiation Facility., EPA/EERF-Manual-78-1.

963     U. S. Department of Energy (DOE).  1995. DOE Methods for Evaluating Environmental and
964        Waste Management Samples, Goheen, S.C. et al. (ed.), DOE/EM- 0089T.

965     U.S. Environmental Protection Agency (EPA). 1979. Radiochemical Analytical Procedures for
966        Analysis of Environmental Samples, Johns, F.B., Hahn, P.B., Thome, D.J., and Bretthauer,
967        E.W., EMSL-LV-0539-17, Environmental Monitoring and Support Laboratory, Las Vegas,
968        Nevada.

969     U.S. Environmental Protection Agency (EPA). 1980. Prescribed Procedures for Measurement of
970        Radioactivity in Drinking Water, Krieger, H.L. and Whittaker, E.L., Eds.,  EPA 600-4-80-032,
971        EPA Environmental Monitoring and Support Laboratory, Cincinnati, Ohio.

972     U.S. Environmental Protection Agency (EPA). 1984a. Radiochemistry Procedures Manual,
973        Lieberman, R.E., Ed., EPA 520-5-84-006, EPA Eastern Environmental Radiation Facility,
974        Office of Radiation Programs, Montgomery, Ala.

975     U.S. Environmental Protection Agency (EPA). 1984b. An Airborne Radioactive Effluent Study at
976        the Savannah River Plant, EPA 520-5-84-012, Office of Radiation Programs, Eastern
977        Environmental Protection Agency, Montgomery, Alabama.


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         Instrument Calibration and Test Source Preparation
 978      U.S. Environmental Protection Agency (EPA). 1987. Interim Protocols for Screening and
 979         Follow Up Radon and Radon Decay Product Measurements, Office of Radiation Programs,
 980         Washington, DC., EPA 520-1-86-014-1.

 981      U.S. Environmental Protection Agency (EPA). 1990. Standard Operating Procedures - Field
 982         Operations and Emergency Activities, Office of Radiation and Indoor Air, National Air and
 983         Radiation Environmental Laboratory, Montgomery, Alabama.

 984      Faires, R.A. and Boswell, GJ. 1981. Radioisotope Laboratory Techniques, Butterworth & Co.
 985         Publishers, Ltd., London.

 986      Friedlander, G. and Kennedy, J.W. 1955. Nuclear and Radiochemistry, 1st Ed., John Wiley &
 987         Sons, New York, pp. 274-282.

 988      Friedlander, G., Kennedy, J.W., Macias, E.S., and Miller, J.M. 1981. Nuclear and
 989         Radiochemistry, 3rd Ed., John Wiley and Sons, New York.

 990      Hemingway, J.D. 1975. "Measurement Techniques and Instrumentation," International Review
 991         of Chemistry - Radiochemistry, Inorganic Chemistry, Series Two,  Volume 8, ed. A.G.
 992         Maddock, University Park Press, Baltimore.

 993      Hensley, W.K., McKinnon, A.D., Miley, H.S., Panisko, M.E., and Savard, R.M. 1997. SYNTH
 994         for Windows, Pacific Northwest National Laboratory, Richland, WA.

 995      Herpers, U.  1986. "Radiation Detection and Measurement," in Treatise on Analytical Chemistry,
 996         Elving, P.J., Krivan, V.,  and Kolthoff, I.M., Eds., Part I, 2nd Edition, Volume 14, John Wiley
 997         and Sons, New York, pp. 123-192.

 998      Higashimura, T., Yamada, O., Nohara, N., and Shicei, T. 1962. "External standard method for
 999         the determination of the  efficiency in liquid scintillation counting," Int. J. Appl. Radiat. Isot.
1000         13, p. 308.

1001      Hill, R. F., Hine, G. F., and Marinelli, L. D.  1950. "The Quantitative Determination of Gamma-
1002         Ray Radiation in Biological Research," Am. Jour. Roentg, 63, pp. 160-169.

1003      Hindman, F.D. 1983. "Neodymium Fluoride Mounting for  Alpha Spectrometric Determination
1004         of Uranium, Plutonium,  and Americium," Anal. Chem. Vol. 55,  pp. 2460-2461.
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                                                 Instrument Calibration and Test Source Preparation
1005      Hindman, F.D. 1986. "Actinide Separations for a Spectrometry Using Neodymium Fluoride
1006         Coprecipatation," Analytical Chemistry, Vol. 58, pp. 1238-1241.

1007      Horrocks, D.L. 1973. "Measuring Tritium With Liquid Scintillator Systems," in Tritium,
1008         Moghissi, A.A. and Carter, M.W., Eds., Messenger Graphics, Publishers, Las Vegas, NV,
1009         pp.150-151.

1010      Jabs, R.H and Jester, W.A.  1976. "Development  of Environmental Monitoring System for
1011         Detection of Radioactive Gases," Nuclear Tech. Vol. 30, 24-32.

1012      Jester, W.A. and Hepburn, F.J. 1977. "A Ge(Li) System for the Monitoring of Low Level
1013         Radioactive Gases," Trans. ANS, Vol.  26, 121.

1014      Johnston, P.N., Moroney, J.R. and Burns,  P.A. 1991. "Preparation of Radionuclide "Sources" for
1015         Coincident High-Resolution Spectrometry with Low-Energy  Photons and Electrons or Alpha
1016         Particles," Appl. Radial Isot. No. 3, 245-249.

1017      Kressin, IK. 1977. "Electrodeposition of Plutonium and Americium for High Resolution a
1018         Spectrometry," Analytical Chemistry, 49:6, pp.842-845.

1019      Lieberman, R. and Moghissi, A.A. 1970. "Low-Level counting by Liquid Scintillation, JJ.
1020         Application of Emulsions in Tritium Counting," Inter. J. Appl.  Rad. Isotopes, Vol. 21, p. 319.

1021      McDowell, W.J. 1986. "Alpha Counting and Spectrometry Using Liquid Scintillation Methods,"
1022         Nuclear Science Series on Radiochemical Techniques.

1023      Mitchell, D. J. 1988. Gamma Detector Response and Analysis Software (GADRAS), Sandia
1024         Report SAND88-2519,  Sandia National Laboratories, Albuquerque, NM.

1025      Mitchell, R.F. 1960. "Electrodeposition of Actinide Elements at  Tracer Concentration,"
1026         Analytical Chemistry, 32:3, pp. 326-328.

1027      Modupe, O.O., Decker, K.M., and Sanderson, C.G. 1993. Determination of Self-Absorption
1028         Corrections by Computation in Routine Gamma-Ray Spectrometry for Typical
1029         Environmental samples, Radioactivity & Radochemistry, 4:1, pp. 38.

1030      Moghissi, A.A. 1981. "Application of Cyclohexane in Separation of Water From Biological and
1031         Environmental Samples," Health Phys., Vol.  41, p. 413.


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         Instrument Calibration and Test Source Preparation
1032      Moghissi, A.A., Bretthauer, E.W., and Compton, E.H. 1973. "Separation of Water From
1033         Biological and Environmental Samples for Tritium Analysis," Anal. Chem., Vol. 45, 1565-
1034         1566.

1035      National Academy of Sciences-National Research Council (NAS/NRC). 1962. Detection and
1036         Measurement of Nuclear Radiation, O'Kelley, G.D., NAS-NS 3105, Washington, DC.

1037      National Council on Radiation Protection and Measurement (NCRP). 1978. A Handbook of
1038         Radioactivity Measurements Procedures, Report No. 58, Washington, DC.

1039      Passo, CJ. and Cook, G.T. 1994. Handbook of Environmental Liquid Scintillation Spectrometry
1040         — A Compilation of Theory and Methods, Packard, 800 Research Parkway, Meriden,
1041         Connecticut.

1042      Pate, B.D. and Yaffe, L. 1956. "Disintegration-Rate determination by 4n Counting, Pt. IV. Self-
1043         Absorption Correction: General Method and Application to Ni63 p- Radiation," Can. J. Chem.
1044         Vol. 34, p. 265.

1045      U.S. Public Health Service (PHS). 1967. Common Laboratory Instruments for Measurement of
1046         Radioactivity, Environmental Health Series-Radiological Health, Interlaboratory Technical
1047         Advisory Committee, Report No. 2, Rockville, Maryland, pp. 19-27, 78.

1048      Puphal, K.W. and Olsen, D.R. 1972. "Electrodeposition of Alpha-Emitting Nuclides form a
1049         Mixed Oxalate-Chloride Electrolyte," Analytical Chemistry, Vol. 44, no. 2, pp. 284-289.

1050      Puphal, K.W., Filer, T.D., and McNabb, GJ. 1983. "Electrodeposition of Actinides in a Mixed
1051         Oxalate-Chloride Electrolyte," Analytical Chemistry, Vol. 56, No. 1, pp. 114-116.

1052      Sahoo, P. And Kannan, S.E. 1997. "Preparation of an Electrodeposited Source of 54Mn,"
1053         RadiochimicaActa76:4,pp. 185-190.

1054      S. Cohen & Associates (SC&A).  1994. Routine Environmental Sampling Procedures Manual
1055         For Radionuclides, Prepared for the U. S.  Environmental Protection Agency, Office of
1056         Radiation and Indoor Air, under Contract No. 68D20155, Work Assignment No. 2-25.

1057      Shinohara, N. And Kohno, N. 1989. "Rapid Preparation of High-Resolution Sources for Alpha-
1058         Ray Spectrometry of Actinides in Spend Fuel," Appl. Radiat. Isot. 40:1, pp. 41-45.
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                                                Instrument Calibration and Test Source Preparation
1059      Sill, C. W., Puphal, K.W., and Hindman, F.D. 1974. "Simultaneous Determination of Alpha-
1060         Emitting Nuclides of Radium through Californium in Soil," Analytical Chemistry, 46:12, pp.
1061         1725-1737.

1062      Sill, C.W. and Williams, R.L. 1981. "Preparation of Actinides for a Spectrometry without
1063         Electrodeposition," Analytical Chemistry., 53:3, pp. 412-415.

1064      Talvite, N.A. 1972. "Electrodeposition of Actinides for Alpha Spectrometric Determination,"
1065         Analytical Chemistry, Vol. 44, No. 2, pp. 280-283.

1066      Tsoulfanidis, N. 1983. Measurement and Detection of Radiation, McGraw-Hill Book Company,
1067         New York.

1068      Van der Eijk, W., Oldenhof, W., and Zehner, W.  1973. "Preparation of Thin Sources, A
1069         Review," Nucl. Instr. AndMeth. Vol.  112, 343-351.

1070      Van der Eijk, W. and Zehner, W. 1977. "Preparation of Thin Sources for Absolute Beta
1071         Counting," Radiochimica Acta Vol. 24, 205-210.

1072      Yoshida, M., Miyahara, H. and Tamaki, W. 1977. "A Source Preparation for 4?ip-Counting With
1073         an Aluminum Compound," Intern. J. Appl. Rad. Isotopes Vol. 28, 633-640.
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                 17 DATA ACQUISITION, REDUCTION, AND REPORTING
 2      17.1  Introduction

 3      This chapter provides information and guidance, primarily for laboratory personnel, on data
 4      acquisition, reduction, and reporting. Its intent is to provide an understanding of the many
 5      operational parameters which should be addressed in order that the data developed and reported
 6      are compliant with project planning documents (Chapter 4), considered valid (Chapter 8), and
 7      usable for their intended purposes (Chapter 9). These processes are all linked and each is
 8      dependent upon the results of its predecessor. The material presented is intended to provide an
 9      overview of the processes which are required in all radiochemistry laboratories, but are by no
10      means performed in the same way in all laboratories.

11      In this chapter, data acquisition refers to the results produced by the radiation detection process,
12      often referred to as counting. This chapter will provide guidance for laboratory personnel on
13      selecting and applying the operational parameters related to instrumentation and the determina-
14      tion of the radioactivity contained in the test source.1 Parameters that are applicable to counting
15      for essentially all radiation detection instrumentation are discussed in Section 17.2 and those that
16      are specific to a given type of instrumentation are covered in the appropriate section describing
17      that instrument. A detailed description of the instrumentation discussed in this chapter was
18      provided in Chapter 15.

19      Once test sources have been prepared (Chapter 16) and counted using laboratory measurement
20      instruments (Chapter  15), the basic information  generated by the instrument should be reduced
21      (processed) to produce data which can be reviewed, verified, validated, and interpreted in light of
22      and in accordance with project planning documents and analytical statements of work (SOWs)
23      (Chapter 7). Data reduction is primarily mathematical in nature while data reporting involves the
24      presentation of the results of the data acquisition and reduction processes and nonmathematical
25      information necessary to interpret the data (e.g., sample identification and method of analysis).

26      Data reduction may be as simple as  a division of the counts by the counting time, the sample
27      aliquant weight or volume, and the counter efficiency, thereby producing the radionuclide
28      concentration. On the other hand, it may also require more complicated processing such as the
29      fitting of an analytical function, or the unfolding of a differential spectrum (Tsoulfanidis, 1983,
        1 The term "test source" will be used to describe the radioactive material prepared to be introduced into a measure-
        ment instrument and "laboratory sample" will be used to identify the material collected for analysis. Thus, a test
        source is prepared from laboratory sample material for the purpose of determining its radioactive constituents.
        "Calibration source" is used to indicate that the prepared source is for the purpose of calibrating instruments.

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30     p. 327). In any case, the reduction process should continue by calculating the combined standard
31     uncertainty (Chapter 19).

32     The output of some laboratory instruments is highly simplistic and consists only of the number of
33     nuclear decay events recorded by the detector in the time interval allocated for the measurement.
34     An example of this might be a gas-proportional counter whose only output is an electronic sealer
35     and the available data consists of total counts or counts per minute. On the other extreme, some
36     laboratory counting instruments with computer components produce outputs consisting of
37     radionuclide concentration, uncertainty, and other information (see Chapter 19). Examples of
38     these types of data reducing instruments are alpha- and gamma-spectrometry and liquid-
39     scintillation systems.

40     ANSI N42.23 contains an outline of a minimal data report. Most project-specific planning
41     documents (Chapter 4) and/or analytical SOWs (Chapter 5) require that the radiochemical data
42     produced by laboratories be submitted in a specific format and form (i.e. electronic or hard copy,
43     or both). In some cases, the requirements are minimal and may consist of a data report which
44     gives only the sample identifier information, accompanied by the radionuclide concentration and
45     its associated uncertainty. Many projects require much more supporting information, primarily to
46     assist in the data validation (Chapter 8) process. Support material can include information on
47     calibration, background determination, sample processing, sample receipt, quality control sample
48     performance, raw-counting data, and chain-of-custody records.

49     This chapter gives an overview of data acquisition, reduction, and reporting in radiochemical
50     laboratories. The material presented is intended to be descriptive rather than prescriptive, since
51     these processes vary greatly between laboratories; depending upon the equipment, personnel,
52     project requirements, and the methods and analyses being performed.

53     17.2  Data Acquisition

54     Data acquisition refers to the process of collecting the basic information produced by nuclear
55     counting instruments. These data may be produced in hard copy or electronic format, or visually
56     displayed for the operator to record. As previously stated, this can be simply the number of
57     counts detected by the instrument within the allotted counting time or as conclusive  as the
58     identification of the radionuclides contained in the sample along with their concentrations and
59     associated uncertainties.

60     Following generation, data requiring further processing may be electronically or manually
61     transferred to the next to the next data-reduction step. Electronic transfer should be employed as

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62     often as possible to avoid the inherent errors associated with manual transfer. On the other hand,
63     the next step in the data reduction process may be performed manually, i.e., with a calculator.

64     The reliability of the data generated also depends upon the proper operation of the instrumenta-
65     tion and the associated data reduction programs. Data quality further depends upon the correct
66     input of associated information by laboratory personnel.

67     17.2.1 Generic Counting Parameter Selection

68     Instrument operators have choices, provided by instrument manufacturers, in the setup and
69     operation of nuclear counting instruments. These selections can affect the quality and
70     applicability of the data. Some selections can be made on a one-time basis and left unadjusted for
71     the processing of all samples and others require the operator to reevaluate the settings, possibly
72     for each test source counted. In some cases adjustments can be made following counting during
73     the processing of the derived information. Some adjustments can only be made before counting
74     or by extending the counting time. In making the proper selection, there are some overall
75     considerations relative to the project requirements, as specified in project planning documents
76     (Chapter 4) or in the analytical SOW (Chapter 5). Other operator decisions depend on the nature
77     of the test source itself. Caution should be exercised when changing operational parameters so
78     that the calibrations (counting efficiency, energy, self absorption, etc.) performed on the
79     instrument remain valid. For example, changing the source container or holder may  affect the
80     counting efficiency and/or background. Determining the appropriate operating conditions
81     requires that the operator have a thorough understanding of the counting process and the
82     instruments and their operation for the production of valid and useable data.  In addition, the
83     operator should be cognizant of the measurement quality objectives (MQOs) that have been
84     established.

85     Some of the factors that affect operational parameter selection are related to  project  requirements.
86     Planning documents and the analytical SOW may specify the limits on measurement uncertainty
87     and detection capability. In order to achieve compliance with the limits, instrument operating
88     parameter adjustment may be required for some or all the samples received.  The number of
89     samples received during a time period may make it mandatory for adjustments to be made in
90     order to meet these requirements while complying with project defined turn-around-times.

91     Factors that may affect the selection of operational parameters include:

92      •  Project and External
93          - project requirements for uncertainty, detection capability, and quantification capability


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 94          - laboratory backlog and contract turn-around times

 95      •  Sample Characteristics
 96          - expected sample radionuclide concentration
 97          - interfering radionuclides
 98          - interfering stable constituents (e.g. liquid scintillation counting quenching)
 99          - amount of sample available
100          - physical characteristics of the test source (e.g. density)
101          - half-life of the radionuclide of interest

102      •  Analytical Process
103          - chemical separation process leading to counting source generation (Chapter 14)

104      •  Instrumentation
105          - instrument adjustments available and their limits
106          - conditions and limits of an instrument's calibration
107          - time availability of instruments
108          - counting efficiency
109          - calibration geometries available

110     Taking into consideration the above, the operator has control over and should select certain
ill     parameters for all radiation measurements. The selection of the basic parameters should be
112     carefully planned in advance to assure that the project requirements are met. The laboratory's
113     selection of parameters during the planning process may require alteration as the process of
114     sample analysis is actually taking place due to unavoidable changes in the samples and sample
115     characteristics throughout the duration of the study.

116     17.2.1.1 Counting Duration

117     For the Poisson counting model, the uncertainty associated with a given count determination is
118     proportional to the square root of the total number of counts accumulated (Chapter 19). The total
119     counts accumulated during counting are proportional to the activity of the source and the length
120     of the counting time. Counting duration is a controllable factor that allows one to achieve a given
121     level of counting uncertainty. The operator should then select a duration which is sufficient to
122     meet project objectives for detection capability and uncertainty. The length of time allotted for
123     determination of the instrument background will also affect the uncertainty associated with the
124     measurement (Chapter 19). Thus, when preparing an analytical protocol to meet the requirements
125     of a project, as expressed in the project planning documents, the laboratory will establish the


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126     counting durations of both sample and background accordingly. An alternative to selecting a
127     counting duration, available on many instruments, is to count until a preset number of counts is
128     obtained.

129     17.2.1.2 Counting Geometry

130     The counting efficiency of a radiation detector depends on the geometry of the source and
131     detector arrangement, e.g., the solid angle subtended at the detector by the source. A given
132     radiation detector may have the counting efficiency established for several geometries. The
133     geometry selected among those available may depend upon the amount of sample available, the
134     detection capability required for the analysis, the radionuclide concentration in the sample, the
135     dictates of the radioanalytical method, the physical characteristics of the sample, the nature and
136     energy of the decay process, and the characteristics of the detector.
137
138     The choices to be made relative to geometry selection are usually the type of test source
139     container, the source mounting, and the detector to source distance. Choices are to be made
140     among those for which the detector has an established efficiency calibration.

141     17.2.1.3 Software

142     The use of properly developed and documented computer software programs for data acquisition
143     and reduction can lead to an enhancement in the quality of laboratory data. Guidance on software
144     documentation can be found in EPA (1995). Caution should be exercised in the selection and use
145     of undocumented programs and those which may not have been tested in laboratories performing
146     analyses similar to those for which MARLAP has been developed. For example, a spectral
147     analysis program may accurately identify and quantify the radionuclides in test sources
148     containing higher levels of radioactivity (which produce spectra with well defined peaks, easily
149     distinguishable from background)  but may be inaccurate for samples with environmental levels.

150     When selecting software, a thorough review of the data reduction algorithms should be
151     performed. The user  should not blindly accept the notion that all software performs the
152     calculations in an appropriate manner without this review. When evaluating software, it is often
153     helpful to review the software manual, particularly in regard to the algorithms used in the
154     calculations. While it may not be necessary that the user understand in detail all the calculations
155     performed by highly  complex software programs, the user should understand the overall scheme
156     of analysis and reduction in order to assure data meet quality objectives and reporting
157     requirements. This understanding is also beneficial in assuring that user defined parameters are
158     properly selected.


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159     The output of some instruments is very basic, primarily counting data, i.e., counts or counts per
160     second. These data should be manipulated by external systems to convert them to the form
161     required by planning documents. The external system which performs the calculations may be a
162     calculator or a computer with the appropriate software to reduce the data to usable terms. In
163     either case, additional information relative to the processing of the sample should be input along
164     with the counting data (counting time, total counts, and background counts). This information
165     may include laboratory sample number, collection date, sample mass or volume, instrument
166     counting efficiency, and  chemical yield.

167     For computer (processor) based systems, some of this information is generated and processed
168     internally and the remainder is manually entered or electronically transferred from the Laboratory
169     Information Management System (LDVIS) or some other adjunct system where it has previously
170     been stored. It is becoming increasingly common for much or all of this adjunct information to be
171     transferred to the counting instrument by reading a bar code affixed to the test source to be
172     counted. In this manner,  the information which has previously been entered into a LEVIS is
173     electronically transferred to the counting instrument. For hand calculations, these data are simply
174     entered into the calculations.

175     17.2.2 Basic Data Reduction Calculations

176     The equations used for data reduction depend on the analytical methods used. The following
177     equations are provided as examples to illustrate the basic principles involved in data reduction.

178     Following counting, the radionuclide concentration may be calculated:

                                                  r
                                       n  _ 	UNet	
                                        c	77                              (17.1)
                                            e- V-  Y-Kc-e   l

179     where:
180        Rc    =  radionuclide concentration at time of collection (Bq/L or Bq/g)
181         Cnet   =  net count rate (cps)
182        E      =  counter efficiency for the radionuclide (cps/dps)
183         V     =  volume or mass of sample analyzed (L or g)
184         Y     =  chemical yield  (when appropriate)
185        e     =  base of natural  logarithm
186        k     =  the radioactive  decay constant for the radionuclide (s~\ miir1, or d"1)
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187         ^      =  time lapse from sample collections to beginning of source count (units consistent
188                   with A,)
189         Kc     =  the correction for decay during counting and is:

190     where:
191         tc =  actual clock time (real time) of counting (units consistent with A)

192     This calculates the radionuclide concentration at the time of sample collection2. It compensates
193     for the fact that short-lived radionuclides may experience significant reduction in activity during
194     counting, when the counting duration is a significant fraction of the half-life. For long-lived
195     radionuclides, the term Kc approaches unity and can be ignored. The efficiency used in this
196     equation may be obtained from the specific radionuclide whose concentration, Rc, is to be
197     determined or it may be obtained from an efficiency curve which plots counter efficiency vs.
198     energy. In the latter case, the abundance,  Ee, of the particle or photon being counted should be
199     considered. This is required because the energy dependent efficiency, e^ is developed in terms of
200     the fraction of particles or photons detected divided by the number emitted at that energy. Thus,
201     if the radionuclide emission being determined during the counting of a test source has an
202     abundance less than 100 percent, an adjustment should be made to Equation 17. 1, as shown in
203     Equation 17.3:

                                                   r
                                      n _ _ UNet _
                                       c -- 77                             (17.3)
                                              £
                                               e
204     Most modern instrument systems contain preprogrammed software to perform data manipula-
205     tions that convert basic counting information to a form which can be compared to the project data
206     quality objectives, or at least to begin or promote this process. Certain sample-specific
207     information should be manually entered or transferred to the system electronically in order to
208     perform the necessary calculations.
        2 For radionuclides with short half-lives detected at or near detection limits, it may be more appropriate to calculate
        the concentration at the time of counting.

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209     17.3  Data Reduction on Spectrometry Systems

210     Software is available for resolving alpha, gamma, and liquid scintillation spectra and for
211     performing the attendant functions such as calibration, energy alignment, background acquisition
212     and subtraction, and quality control functions.

213     Spectroscopic analysis for alpha particles and gamma-rays is performed to identify and quantify
214     radionuclides in samples. Since these emissions occur at discrete energies, spectrometry is useful
215     for these purposes and can be applied to the analysis of a wide range of s radionuclides. Energy
216     spectra are produced when a detector absorbs a particle or photon and produces a signal that is
217     proportional to the energy absorbed. The resulting signal is digitized by an analog-to-digital
218     converter and processed by a multichannel analyzer. A differential spectrum is produced, where
219     the number of events within an incremental energy, AE, is recorded on the jaxis and the energy
220     is represented on the * axis (Tsoulfanidis, 1983, p. 327). In this way, radionuclides can be
221     identified by the characteristic energies of their emissions and quantified because the area under
222     the full energy peak is proportional to the emission rate (activity) of the source being analyzed.

223     The spectra for alpha and gamma emitters are quite different, due to the differences in the way
224     these two types of radiation interact with matter in transferring their energy to the detector
225     material. The process of resolving the spectra into its contributing components is referred to as
226     spectral analysis (NCRP 1978,  p. 159) and unfolding (Tsoulfanidis, 1983, p. 342). Computer
227     programs for analyzing alpha and gamma spectra are available from several sources (Decker and
228     Sandderson, 1992). A method of performance testing of gamma analysis software is given in
229     ANSIN42.14.

230     17.3.1  Gamma Spectrometry

231     Gamma spectrometry on environmental samples requires the use of gamma spectral analysis
232     software for any reasonable degree  of accuracy and detection capability. This is due to the
233     potentially large number of photopeaks to resolve, the low level of radioactivity in most
234     environmental samples, and the relatively low detection limits and stringent quality control
235     requirements of most project-specific planning documents. Spectral analysis by manual
236     techniques is only practical when the number of radionuclides is limited and the contributing
237     isotopes are predictable. An example is the analysis of milk samples for gamma-emitting
238     radionuclides, where the milk production process in the cow restricts the number of radionuclides
239     in the milk product (Hagee et al., 1960, p. 36; USPHS, 1967,  pp. 1-51).
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240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257

258
259
Gamma-rays interact with matter in
three ways, namely, by photoelectric
effect, Compton scattering, and pair
production (Tsoulfanidis, 1983, pp.
141-148). These interactions within a
gamma detector, usually a high-purity
germanium or sodium iodide (see
Chapter 16), result in varying amounts
of the gamma-ray energy being
absorbed. Only  one of these inter-
actions, the photoelectric effect, results
in the total energy being absorbed in a
single interaction. The photopeak,
shown in Figure 17.1, due to a
photoelectric interaction in the detector,
results from the processing of the
detector signal through the linear
circuitry and the multichannel analyzer.
                                                                                   Full-Energy_
                                                                                   Photopeaks "~
                                                                        Photopeak
                                                                        Baseline
                                                            Channel Number and Gamma-Ray Energy
                                                     FIGURE 17.1 — Gamma-ray spectrum
This photopeak has a basic Gaussian shape (Gilmore and Hemmingway, 1995, p. 163) and may
be described by (Quittner, 1972, p.20):
                                                                                           (17.4)
260
261
262
263

264
265

266
267

268
269
where:
   A  =
   x  =
   p  =
                  the peak amplitude
                  the channel number
                  the peak centroid
        (The width of the peak is related to the full-width at half-maximum (FWHM) of the detector, F,
        where F = 2.355 a. The area under the peak is N= 1.064 A F.)

        As can be seen in Figure  17.1, the photopeak (PI) may be displaced upward by its position on the
        Compton continuum from other, higher-energy gamma-rays (P2) and background radiation.

        The photopeak is the key element in gamma-ray spectrometry in that its location on the energy
        axis provides a means for radionuclide identification, and the area under the peak is proportional
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270     to the number of gamma-ray events comprising the photopeak. This becomes the basis for
271     radionuclide identification and quantification.

272     The fundamental purposes of gamma-ray computer-based spectral analysis programs are to
273     identify the photopeaks in a spectrum and to measure the true area under the photopeaks. It
274     should do this in the presence  of natural background, a potentially large number of sometimes
275     overlapping photopeaks, and a great number of Compton-scattering events.  Once these initial
276     tasks have been performed, the computer program uses this information to determine the
277     radionuclide mix that contributed the complex spectrum and the individual concentrations in the
278     sample being analyzed.

279     Most computer programs for gamma-spectral analysis are provided by equipment manufacturers,
280     although some are supplied by independent providers. There are significant differences in the
281     structure of the programs. However, they all perform similar functions which are given below
282     and illustrated in Figure 17.2.

283     17.3.1.1 Peak Search or Identification

284     There are two basic methods of gamma spectral analysis. The first method is to allow the
285     analysis software to determine the existence of the peaks and their energy. The second method is
286     often referred to as a "library directed" search, where the operator identifies the peak energy
287     locations, e.g., regions of interest, to be searched for discernable peaks. The latter method may be
288     more sensitive (Gilmore and Hemmingway, 1995, p. 165) but, taken alone, will fail to identify
289     and report unspecified radionuclides. If the confirmation of the existence of a particular
290     radionuclide is required, the second method should be employed. Most software programs allow
291     either approach to be activated and used for each analysis.

292     A most important function performed by an analysis program is the  identification of true
293     photopeaks. In the programs available, this is achieved in one of the four ways discussed below.

294     Many spectral analysis programs allow the operator to select among two or more of the four
295     methods for peak identification. Selection of the most accurate and sensitive method depends on
296     the radionuclides present in the source, detection capability requirements for individual
297     radionuclides, the number of radionuclides present, the nature of the background spectrum, the
298     degree to which the radionuclide mix can be predicted, and the activities of the isotopes. The
299     selection of a particular peak search method can be determined by experience with similar
300     sample types  and past performance, particularly on performance evaluation (known) samples.
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Acquire Sample
Spectrum


Peak Search and
Identify

Assign

Define

Energy

Width
                                                     Peak
                                                    Library
                                                     Energy
                                                 Calibration Curve
                                                     Width
                                                 Calibration Curve
                                           Multiple!
                                                    Resolve
Radioisotope
Identification
A .



I



^

Operation Info.
Sample ID
Batch ID



Calculate
Uncertainty


I,.
h Pen


Corrections for:
Random Summing
Live Time
Decay during Count
Decay to Collection Tim


ort

Concentration
A
e


Operator Input.
Reporting Units
Sample Volume
                              FIGURE 17.2 — Gamma-ray analysis sequence

301     REGIONS OF INTEREST (ROI) METHOD

302     This is the simplest form of peak identification, but can only be used when the radionuclides
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303     present in the sample are known and when the analysis system has been compensated for gain
304     drift. ROI analysis involves the establishment of predetermined energy regions, at least one for
305     each radionuclide present. Once the spectrum has been acquired, the number of counts in each
306     region is summed after subtracting the photopeak baseline (Figure 17.1). This method of spectral
307     analysis is more applicable to alpha rather than gamma spectrometry.

308     GAUSSIAN FUNCTION DERIVATIVE METHOD

309     As previously stated, the photopeak has a basic Gaussian shape; in reality it is a histogram with a
310     Gaussian-like shape. The most widely used peak identification technique was proposed by
311     Mariscotti (Mariscotti 1967, p. 309) and uses the Gaussian function derivative to assess the
312     presence of a photopeak. For most low-level radioactivity, this peak search method may provide
313     the best peak detection capability with the fewest false peak identifications or omissions of true
314     peaks (Gilmore and Hemmingway, 1995,  p. 20).

315     CHANNEL DIFFERENTIAL METHOD

316     This method searches for a number of channels where the counts are significantly greater than the
317     preceding channels, and then looks for the expected decrease in counts corresponding to the
318     backside of the prospective photopeak. This method works relatively well  for large, well-defined
319     peaks, but is limited for poorly defined peaks with counts barely above the background baseline
320     of the peak (Gilmore and Hemmingway, 1995, p. 163).

321     CORRELATION METHOD

322     In this method, a search function is scanned across the spectrum. Each channel count, over the
323     width of the search function, is multiplied by the corresponding value of the search function. The
324     sum of these products is then made a point on a correlation spectrum. A correction  for the
325     baseline contribution leaves only positive counts within a photopeak. Although the  scan function
326     is normally Gaussian in form, other forms may be applied (Gilmore and Hemmingway, 1995,
327     p. 164).

328     Spectral analysis programs usually have some user selected peak acceptance criteria. The
329     acceptance criteria may be based on peak shape, width uncertainty, or the number of standard
330     deviations above the background to be subtracted. Care is required in selection of the values for
331     these acceptance criteria. If the values are too high, valid photopeaks remain undetected. If the
332     values selected are too low, radionuclides may be reported which are not present in the samples.
333     Knowledge of the sample origin and experience with using the analysis program  on similar


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334     samples to those being processed is useful in establishing values for these user-selected
335     parameters. Peak searches may be standard or directed (Canberra, 1994). In a standard search, all
336     peaks identified are assigned to a library contained radionuclide. In a directed search, the user
337     specifies the energies and radionuclides over which the search is performed. If reporting of a
338     specific radionuclide is required, the directed search is appropriate; however, some radionuclides
339     could go unreported if only a directed search is performed.

340     17.3.1.2 Singlet/Multiplet Peaks

341     A peak is referred to as a singlet or multiplet according to whether it is composed of a single
342     photopeak or multiple photopeaks, respectively. Deconvolution is the term given to the process
343     of resolving a multiplet into its components (Gilmore and Hemmingway, 1995, p. 172). The
344     ability of a spectral analysis program to perform this function may well be the deciding point for
345     its selection. It is particularly important if the laboratory has analyses in which one of the critical
346     radionuclides has only one gamma-ray whose energy is very near to that of another radionuclide
347     expected to be present in all or most samples.

348     There are three primary ways that programs deal with the problem of resolving multiplets. The
349     first method is a deconvolution algorithm which is based  on the peak-shape being the composite
350     of multiplet Gaussian  distributions. The second method uses the gamma-ray library to anticipate
351     where peaks occur within a multiplet. The disadvantage of the first is in dealing with small ill-
352     defined peaks and the  second cannot, of course, resolve peaks not included in the library. The
353     third method, peak stripping, again depends on defining all radionuclides whose gamma-rays
354     contribute to the multiplet. In peak stripping, one of the interfering gamma-ray's contribution is
355     subtracted from the multiplet area by using another of its  gamma-rays to estimate the peak shape
356     and size in the multiplet area. The remaining peak is, presumably, that of the interfered
357     radionuclide which can then be identified and quantified.  This method requires that one of the
358     interfering radionuclides have a second gamma emission  which identifies and tentatively, for the
359     purpose of removing its contribution, quantifies it.

360     In some cases, the uncertainty of multiplet deconvolution can be avoided by selecting photopeaks
361     from gamma-rays which are not interfered with, even though they may have lower abundances.
362     The increase in uncertainty  due to the lower number of accumulated counts may well overcome
363     the uncertainty of deconvolution (Gilmore and Hemmingway, 1995, p. 174).
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364     17.3.1.3 Definition of Peak Centroid and Energy

365     Once a peak has been detected, the centroid of the peak will be defined, since it will rarely be
366     located at exactly a whole channel number. The centroid will be used to represent the gamma-ray
367     energy and should be calculated to the fraction of a channel. An algorithm is used to calculate the
368     centroid value may be expressed as (Gilmore and Hemmingway, 1995, p. 167):
                                                      ,.
                                         Centroid =      '                                  (17.5)
369     where:
370         Cjis the count in the /h channel.

371     In order to assign a gamma-ray energy value to the peak centroid channel position, the analysis
372     program refers to a previously established energy calibration file. The detector's response to the
373     full range of gamma energies should be established by counting a source(s) having a number of
374     well-defined gamma-rays over the range of energies emitted by the radionuclides in the
375     calibration source. This calibration source is most often a "mixed-nuclide source," which also
376     has certified emission rates so that it may also be used for an efficiency calibration. The mixed-
377     nuclide source is counted on the detector, being sure to accumulate sufficient counts in the peaks
378     to obtain good statistical precision, and an energy-versus-channel relationship is established. The
379     operator will be required to provide information on the peaks to be used and their exact energies.

380     With modern spectrometry systems, the relationship between energy and channel number is
381     nearly linear. Both linear and quadratic fits have been included in available spectral analysis
382     programs.

383     17.3.1.4 Peak  Width Determination

384     In order to calculate the area under the peak, an estimate of the peak width is required, unless the
385     analysis program is operating in the region-of-interest mode. The width of a photopeak is
386     normally quoted in terms of its FWHM. For a discussion of peak width (resolution) and the
387     factors affecting it, see Chapter 15.

388     There are several ways to determine the peak boundary. These are:

389     (1)    A Gaussian shape is assumed and some number of standard deviations (2 or 3) are
390            allowed on each side of the peak centroid.


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391     (2)    A standard width for each peak, based on its energy, is used.

392     (3)    A five-point moving average is used to determine a minimum on each side of the peak,
393            which is set as the peak limits.

394     Each method has strengths and weaknesses, but all struggle with ill-defined (small number of
395     counts) peaks.  Once the peak limits are defined, determining the area under the peak is
396     accomplished by summing the counts per channel for the channels contained in the peak and
397     subtracting the baseline (see Figure 17.1).

398     The determination of FWHM requires an assumption of peak shape and, as has previously been
399     stated, the acceptance of a Gaussian function is the norm for gamma spectrometry. In addition,
400     the peak width increases with the energy of the gamma-ray, so some function should be defined
401     for the analysis program to determine the width based on the energy of the peak. This
402     relationship, in practice, is found to be nearly linear (Gilmore and Hemmingway, 1995, p.  133)
403     and described by:
                                            w=a + bE                                    (17.6)

404     where:
405         w     =   width of the peak
406         E     =   the energy
407         a, b   =   empirical constants

408     For spectra developed by high-purity germanium semiconductors (HPGe) and alpha solid state
409     detectors, it is more appropriate to assume a peak shape which is a modification of the Gaussian
410     function to allow for the low energy tailing observed in these spectra. This type of tailing is
411     illustrated in Figure 17.3. Some spectroscopy programs have algorithms to fit peaks with lower
412     energy tailing.

413     When the "tailing" peak fit option is selected, the software algorithm for peak fitting changes
414     from the pure Gaussian form to a dual fit. The channels in the peak not affected by the tailing are
415     included in the Gaussian fit (Equation 17.7), and those that are affected by tailing are modified
416     according to Equation 17.8, below:
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                                  FIGURE 17.3 — Low-energy tailing
417
418
419
420
421
422
423
where:
   x
   A
   PC
   AC
                             XX) =
                                     Ae
                                     Ae
                                               x>Pc-AC
                                            ,   x< Pc - AC
                                                                  (17.7)

                                                                  (17.8)
the channel number
the peak amplitude
the peak centroid
the tailing factor (the distance from the centriod to the point where the tailing
joins the Gaussian peak)
the width of the Gaussian peak (~ 2.355 x FWHM)
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424

425
426
427
428
429
430
431
432
433
17.3.1.5 Peak Area Determination

For single peaks sitting on a Compton continuum, two methods of peak area determination are
available. The simpler method is the addition (integration) of the number of counts per channel in
each of the channels considered to be within the peak limits, and subtracting the natural
background and Compton contribution to those same channels (Baedecker, 1971; Loska, 1988).
However, this is rarely simple since the photopeak is usually offset by a baseline continuum
whose contribution is not easily determined. While the background may be subtracted by the
spectrometry program, the Compton continuum will be estimated by the software and then
subtracted. This estimation is often based on the number of counts per channel in those channels
immediately above and below the photopeak region as shown in Figure 17.4.
                                                CHANNEL NUMBER
434
                     FIGURE 17.4 — Photopeak baseline continuum

The baseline contribution is then estimated as:
                                                                                       (17.10)
435     where:
436        B  =  the number of counts attributed to the baseline
437        N  =  number of channels in the peak
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438
439
440
441
   n  =  the number of baseline channels considered on each side of the peak for calculating
          BL and BH
   BL  =  the sum of the number of counts in the baseline region on the low-energy side
   BH =  the sum of the number of counts in the baseline region on the high-energy side
442     In practice, the baseline continuum appears to have a step beneath the peak (Gilmore and
443     Hemmingway, 1995, p. 114), as illustrated in Figure 17.5. This type of function is estimated by:
                                        N
444
445
446
447
448
449
450
                                    V   BH ~ BT
                                    L +_^	L
                                                  nG
                                                                                        (17.11)
where:
    G
    N
    n
sum of counts in the baseline region on the low-energy side
sum of counts in the baseline region on the high-energy side
counts per channel in channel j
gross counts in the peak
number of channels in the peak
number of channels in each of the two baseline regions
                                               CHANNEL NUMBER
                            FIGURE 17.5 — Photopeak baseline continuum-step
                                               function
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451     The second peak area determination method is the least-squares method, which fits a theoretical
452     peak shape plus background shape to the channels surrounding the peak (Kruse and Spettel,
453     1982; Helmer et al., 1983). Background is often subtracted prior to the fitting process (Loska and
454     Ptasinski, 1994).

455     17.3.1.6 Calibration Reference File

456     Three types of calibrations are required for gamma spectral analysis, namely those for efficiency,
457     energy, and FWHM. Efficiency and energy calibrations require a source whose gamma-ray
458     emission rate is known and referenced to a national standard, and whose gamma-ray energy lines
459     are well known. "Mixed radionuclide" reference material, containing eight or more gamma lines,
460     is available for performing these spectral calibrations. The operator is required to enter the
461     pertinent information, usually listed in the calibration source certificate, into the file prior to
462     performing the calibrations. The information generally consists of:

463       •  Radionuclide name;
464       •  Certified activity and units;
465       •  Uncertainty in activity;
466       •  Reference date and time;
467       •  Gamma energies and branching ratios; and
468       •  Half-life.

469     Once calibration files are established, the calibrations are performed according to methods
470     specific to individual software and as described  in manufacturers manuals (also see Chapter 16).

471     17.3.1.7 Activity and Concentration

472     In order to convert the counts under a photopeak to activity, an efficiency calibration should be
473     performed on the detector. Since the efficiency varies with energy, the detector should be
474     calibrated over the range of energies to be used and a calibration curve developed for the
475     detector. In constructing an efficiency calibration curve, only calibration sources with singlet
476     peaks and well-known abundances should be selected. The efficiency, at a specific energy, is
477     simply the number of counts determined in a photopeak of known energy divided by the number
478     of gamma-rays emitted by the source in the  same time period, or:


                                               * = -                                        (17.12)
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479     where:
480         s   =  efficiency in cps/yps
481         Cr =  cps in the photopeak
482         D  =  gamma emission rate of source in dps

483     The efficiency versus energy curve developed in most gamma software packages is in the form of
484     a polynomial. One such form is:

                                              n
                                                  .-[\nE]i                                (17.13)
485     where:
486         E   =  full peak efficiency
487         bj  =  coefficient as determined by calculation
488         E  =  the energy of the photopeak

489     The efficiency curve for high-purity germanium detectors shows two distinctive slopes. The
490     polynomial fit in some analysis programs allows for a dual fit, i.e., a separate fit is made to the
491     two portions of the curve.

492     This efficiency curve is maintained in the calibration file of the spectral analysis program to be
493     applied to each analysis. An efficiency curve should be maintained for each test-source geometry
494     to be used for the calibrated detector.

495     To  obtain the activity in the test source, the net counts (background subtracted) in the photopeak,
496     as determined by the software through the process described above, is divided by the geometry-
497     specific efficiency. The activity units are converted to those selected by the operator and
498     corrected for decay to the time of collection. Based on sample-aliquant size/volume information
499     supplied by the operator, sample concentration is calculated and reported.

500     17.3.1.8   Summing Considerations

501     Summing refers to the summing of the energy of two or more gamma-rays when they interact
502     with the detector within the resolving time of the spectrometer's electronics. There are two types
503     of summing: (1) random summing, where two unrelated gamma-rays are detected at the same
504     time, and (2) true coincidence summing, is due to the simultaneous emission of gamma-rays by a
505     radionuclide and their subsequent detection by the gamma detector.


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506     Random summing, sometimes referred to as pile-up, is due to gamma-rays, from different
507     radionuclides, being detected almost simultaneously. If two gamma-rays arrive at the detector
508     within the resolving time of the amplifier and both have a photoelectric interaction, instead of
509     having a count in both full-energy peaks a count will occur somewhere else in the spectrum equal
510     to the sum of the two energies. Random  summing can also occur with other than photoelectric
511     interactions, e.g., photoelectric with Compton and Compton with Compton. Since this occurs
512     randomly in nature, the probability of random summing increases with the square of the total
513     count rate. Random summing can be reduced by the use of pile-up rejection circuitry which
514     examines the pulse shape of detector signals and rejects those which are distorted by  summing
515     (Gilmore and Hemmingway, 1995). However, even with pile-up rejection random summing will
516     still be present. A mathematical correction for random summing is given by:

                                           AT = Ae2Rl                                    (17.14)

517     where:
518        AT =  the true peak area (counts)
519        A  =  the observed peak area (counts)
520        R  =  the mean (total) count rate (cps)
521        T   =  the resolving time of the electronics (|is)

522     If unknown, the resolving time can be estimated by a method similar to that described in Gilmore
523     (1995).

524     True coincidence summing is a source of error when a source contains nuclides which emit
525     gamma-rays nearly simultaneously. Coincidence summing is geometry dependent and increases
526     as the source is positioned closer to the detector. Thus, the use of multi-gamma-ray calibration
527     sources for close geometry efficiency calibrations must be done with caution. True coincidence
528     summing also increases with detector volume and is very prevalent in a well detector. The use of
529     a detector with a thin entry window opens the possibility of coincidence summing with X-rays.
530     Since coincidence summing is independent of count rate, it is a mistake to assume that the
531     measurement of environmental media is immune from errors caused by this phenomena.

532     As is the case with random summing, true coincidence summing results in the loss of counts
533     from photopeaks and a corresponding loss in efficiency. The use of single gamma-ray emitting
534     radionuclides is recommended, to the extent possible, for developing calibration curves for
535     detectors at close geometries. In practice, even when the efficiencies are determined in this
536     manner, errors in analyzing for nuclides  emitting more than one gamma-ray still exist. When a
537     multi-emitting gamma-ray source is to be measured with minimum bias, it may be necessary  to


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538     perform an efficiency calibration with the specific radionuclide to be measured in the specific
539     geometry desired.

540     In theory it is possible to mathematically correct for true coincidence summing; however, for
541     complicated decay schemes, the task is daunting (Gilmore and Hemmingway, 1995).  Some data
542     have been published which give correction factors for coincidence summing for a number of
543     radionuclides (Debertin and Helmer, 1988). Unfortunately they only apply to the particular
544     detector and geometries for which they were developed.

545     17.3.1.9 Uncertainty Calculation

546     The various components of uncertainty in the determination of the source activity should be
547     propagated to obtain the combined standard uncertainty.  The sources of uncertainty in the gamma
548     spectral analysis include those associated with the determination of the net peak area, which
549     includes the standard uncertainties of the gross counts, the background counts, and any
550     interference from other gamma radionuclides present; the uncertainty associated with the
551     unfolding of multiplets; the detector efficiency,  which includes uncertainties of the net peak area,
552     the calibration source emission rate, and decay correction factor; and uncertainty in the
553     determination of the sample volume or mass.
                                           /  2    2    2    2                              /i -7 i r\
                                         = Jt/p + uv+ uf  + Ur,                              (17.15)
                                            Vp T UF -r u^

554     where:
555         uc  =  the combined standard uncertainty
556         Up  =  the component of combined standard uncertainty due to the net peak area
557               determination
558         uv  =  the uncertainty component for the volume or mass determination
559         UK  =  the uncertainty component for the efficiency determination
560         % =  the uncertainly component for the unfolding routine for multiplets

561     Each of these factors may have a number of components of uncertainties included, for example,
562     the net peak uncertainty:
                                      up
                                         = J UG + UB + UE + uI                              (17.16)
563     where:
564         UG =  the uncertainty component for the gross counts in the peak
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565         UB =  the uncertainty component for the baseline subtraction
566         UE =  the uncertainty component for the background peak subtraction
567         Uj =  the uncertainty component for the coincidence summing correction

568     The calculations of combined standard uncertainty typically are performed by the spectrometry
569     software for an alpha-spectrometry analysis. It should be noted that not every available software
570     package will incorporate all the listed uncertainty contributions listed.

571     17.3.2 Alpha Spectrometry

572     This section deals with alpha spectrum reduction as applied to semiconductor detectors, since it
573     is likely that this is the type of detector that will be employed for environmental analyses.

574     Since the range of alpha particles is a few centimeters in air and their energy is significantly
575     degraded in passing through a few millimeters of air, alpha spectrometry is conducted in a partial
576     vacuum and on extremely thin sources prepared by electrodeposition or coprecipitation (see
577     Chapter 16).

578     The number of full energy peaks is usually not large, three to four, in an alpha spectra and they
579     are normally well separated in energy. This, coupled with the fact that the test source subjected to
580     counting has gone through a chemical separation (Chapter 14), makes the radionuclide identifica-
581     tion relatively simple when compared to gamma spectrometry. However, it is still of great benefit
582     to have alpha spectrometry software to identify s radionuclides, subtract background, perform
583     calibrations and energy alignments, determine radiochemical yields,  and perform and track
584     quality control functions. In production laboratories where hundreds of alpha spectra may be
585     generated each week, it is almost imperative that alpha spectra are resolved by properly designed
586     computer  software. An alpha spectrum produced by a semiconductor detector by the counting of
587     a thin source containing 234U, 238U, 239Pu, and 241Am is shown in Figure 17.6.
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588     The spectrum demonstrated contains
589     four peaks which are distorted from
590     their basic Gaussian shape because
591     each of the isotopes emits more than
592     one alpha particle whose energies
593     are within the resolving power of the
594     detector and electronics. The
595     FWHM of the peaks shown is
596     approximately 30 keV. Of particular
597     note is the fact that the peaks are
598     essentially sitting on the baseline.

599      Spectral analysis programs usually
600     have routines for the identification
601     of full-energy peaks. However, in
602     the case of alpha spectrometry,
603     because the locations of peaks in the
604     spectrum are known and the peaks
605     may contain a small number of
606     counts, an ROI-type of analysis is usually performed. However, peak fitting programs are
607     available and may be beneficial when overlapping of peaks is possible. The algorithms used for
608     peak fitting of alpha spectra should take into account the low energy tailing present in most alpha
609     sources (Equation 17.8). The algorithms which account for tailing are modified Gaussian
610     functions and require a peak shape calibration where a number of well-defined singlet peaks
611     covering the full energy range are acquired. The calibration program then calculates the tail
612     parameter values (see discussion entailing in Section 17.3.1.4, "Gamma Spectrometry").

613     Alpha peaks are normally sitting on the baseline (no background continuum) and display
614     minimal overlapping for well-prepared sources. For a given analysis (Pu, U, Am, Th, and etc.),
615     ROIs are established for all energies of the alpha emissions in the source being counted and the
616     count rate in a given ROI represents the emission rate of the alpha whose energy falls within that
617     ROI.
                                     6000
                                Energy (keV)
    FIGURE 17.6 — Alpha spectrum
618     Given these qualifications, the spectral analysis software performs essentially the same functions
619     as that for gamma analysis, described above. The programs may also perform system control
620     function, e.g., maintaining vacuum in the chambers. Databases related to procedures, chemical
621     tracers, and efficiency and energy calibration standards are normally maintained for calculational,
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622      documentation, and quality control purposes. The general analysis sequence for alpha
623      spectrometry will be briefly discussed below.

624      An efficiency calibration is not an absolute necessity if a standard/reference material is used for a
625      tracer in each sample and an accurate determination of the yield is not required. In some cases,
626      the laboratory  may perform an energy and efficiency calibration for an alpha spectrometry
627      analysis. This requires the operator to establish a calibration certificate file for the program to
628      reference. It should refer to this file for both energy and efficiency calibrations. Calibration
629      sources are necessary for performing the required calibrations, and the appropriate certificate
630      information should be entered into the certificate files in order to perform the calibrations and to
631      analyze test sources. This information should be supplied with calibration sources. Calibration
632      sources , consisting of three to four radionuclides, are available in the form of plated discs from
633      several commercial suppliers.

634      Information typically required by the analysis program consists of the following:

635       •  Radionuclide
636       •  Activity
637       •  Assay date
638       •  Half-life
639       •  Energy
640       •  Energy uncertainty
641       •  Emission probability per event
642       •  Emission rate uncertainty
643       •  Activity units

644      This information should be entered for each of the radionuclides included in the calibration
645      source. Once the library file has been established, an energy calibration can be performed as
646      directed by the software program.

647      The efficiency for alpha particles varies only slightly with energy, within the range of alpha
648      energies usually encountered. While the calibration source may contain several certified
649      radionuclides, during an efficiency calibration, the mean efficiency for the full-energy peaks may
650      be calculated and used as the alpha efficiency for a given detector (Chapter 16).

651      Once the alpha spectrometry system has been calibrated and a spectrum of a test source acquired,
652      either a peak search is performed to identify alpha peaks or, if operating in a ROI mode, the
653      counts in the ROI are determined. ROIs to be used for a given analysis are established prior to the


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654     spectrum acquisition by selecting an analysis protocol where the radionuclides and their alpha
655     energies are preestablished.

656     In the ROI mode, the counts accumulated during the preset counting duration in each of the
657     designated regions are corrected for background contribution and, in some cases, for reagent
658     blank activity. If a tracer has been added to the test source, the counts in the tracer ROI are
659     summed, background corrected, and the effective efficiency (yield times counting efficiency)
660     determined using certificate information previously entered by the operator and/or from a
661     protocol file. The yield, if required, is then computed by the use of an efficiency which has been
662     previously determined during an efficiency calibration process. The radionuclide concentration is
663     then calculated by3:
                                                   CR
                                                   ..
                                                   V- e
                                                                                           (17.17)
664     where:
665         RC =   radionuclide concentration of the radionuclide at time of collection (Bq/L or Bq/g)
666         CR=   net count rate in the designated ROI for the radionuclide (cps)
667         £e  '=   effective efficiency (s •  Y) for the tracer (cps/dps)
668         V  =   volume or mass of sample analyzed (L or g)
669         e  =   base of natural logarithm
670         'kj  =   the radioactive decay constant for the radionuclide (s~\ miir1, or d"1)
671         fj  =   time lapse from sample collection to beginning of source count (units consistent
672                with ^)

673     Following the spectrum acquisition process, spectral analysis programs may either automatically
674     process the data and present the results, or they may store the spectral data and await interaction
675     from the operator for processing. In either case, post-acquisition review of the analysis results is
676     recommended. This review may include the following items:

677       •  Assure that the alpha peaks fall within the ROIs;
678       •  Confirm the absence of unexpected peaks (contamination);
679       •  Verify that there are no interfering peaks;
680       •  Confirm that peak centroids are within requirements (energy alignment);
681       •  Verify that all requirements are met with regard to FWHM and chemical yield; and
          For certain alpha-emitting radionuclides,  Ra for example, a decay-correcting term is needed.

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682      •  Check units and sample aliquant information.

683     The FWHM of a given peak may depend greatly on the source preparation. However, since an
684     ROI-type of peak search is normally used, and the limits of the peak determined by the setting of
685     the ROI rather than some algorithm, the peak width definition is not significantly affected by
686     reasonable peak broadening. As a precautionary measure, the above review of each test-source
687     spectrum assures that the peaks appear within the ROIs. Alpha spectrometry analysis software
688     allows for the adjustment of the ROIs to account for peak broadening and slight displacement. A
689     review of the FWHM of the alpha peaks, as calculated by the software, will also reveal peak
690     broadening due to matrix effects and poor test-source preparation.

691     17.3.2.1 Radiochemical Yield

692     Alpha spectrometry test sources are usually prepared by radiochemical separation and the
693     chemical recovery may be less than 100%. Therefore, a radiochemical tracer, which is an isotope
694     of the radioactive species for which the analysis is being performed, may be added to the sample
695     prior to preparation and radioanalysis. The tracer is normally a certified standard solution whose
696     recovered activity is determined during the alpha spectrometric analysis in the same manner as
697     the activities of the isotopes for which the analysis is being performed. The radiochemical yield
698     is then calculated by the spectral analysis program according to:
                                              Y=                                         (17.18)

699     where:
700         Y  =  radiochemical yield
701         AR =  calculated activity recovered
702         As =  certified activity added (decay corrected to time of counting)

703     The calculation of the chemical yield is normally performed by the alpha spectrometry analysis
704     software using operator input information relative to the alpha energy and abundance, activity,
705     uncertainty,  and date of certification of the radiochemical tracer.

706     For some types of radionuclide analyses, no suitable alpha-emitting radionuclide may be
707     available for use as a chemical yield tracer. In this case, the chemical yield may be determined by
708     some other method, such as beta counting, and the resulting yield value provided to the alpha
709     analysis program  so the source activity may be calculated from the alpha spectrometry data.
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710     When a reference material is used for the chemical tracer, the effective efficiency is measured for
711     each test source. If the chemical yield is to be reported, an independent measure of the counting
712     efficiency should be made.

713     17.3.2.2 Uncertainty Calculation
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
        The calculation of the combined standard uncertainty for alpha spectrometry is similar to that for
        gamma-ray spectrometry as reported in Section 17.3.1.8 above. One additional source of
        uncertainty which should be taken into account for alpha spectrometry is that associated with the
        determination of radiochemical yield.  Since a tracer is added to the sample and the yield
        determined by a counting process, the uncertainty involved in this analysis should be accounted
        for in the total uncertainty.  The uncertainty of the yield determination involves that associated
        with the net count of the tracer, the counting efficiency, and that of the emission rate of the tracer
        material. The  combined standard uncertainty of the radionuclide concentration, RC , is given by
        either
        or
where:

    s
    Y

    V
    e
                                    N
                                                          V2
                                                                                           (17.19)
                          \
                                        u2(V)
                                          V2
                                                                 u2(Y) ^ 2u(i
                                                                  Y2
                                                                                           (17.20)
                   =  net count rate in the designated ROI for the radionuclide (cps)
                   =  the alpha counting efficiency
                   =  the chemical yield
                   =  effective efficiency (s • Y) for the tracer (cps/dps)
                   =  volume or mass of sample analyzed (L or g)
                   =  base of natural logarithm
                   =  the radioactive decay constant for the radionuclide (s"1, min"1, or d"1)
                   =  time lapse from sample collection to beginning of source count (units consistent
                      with ^)
                   denotes the standard uncertainty of a quantity
                   denotes the covariance of two quantities
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736     The two uncertainty equations are equivalent. However, when the yield is determined using an
737     alpha-emitting tracer, Equation 17.19 generally is easier to implement.

738     17.3.3 Liquid Scintillation Spectrometry

739     17.3.3.1 Overview of Liquid Scintillation Counting

740     All modern counters are computer controlled for data acquisition, spectral unfolding, data
741     reduction, sample changer control, external quench correction, and performing the multifarious
742     other functions associated with liquid scintillation counting.

743     Liquid scintillation has traditionally found its primary use in the analysis of low-energy beta
744     emitters, such as 3H and 14C. In spite of the complicating factors of high background and
745     quenching (Chapter 15), procedures for other beta- and alpha-emitting isotopes have been
746     developed over the years (Holm, 1984; Harvey,  1970).

747     Liquid scintillation has also been applied to the simultaneous analysis of alpha and beta emitters
748     in environmental media (Leyba, 1992). Discrimination between alpha and beta radiation is based
749     on differences in the fluorescence decay pulses. Pulse height is  proportional to particle energy,
750     and high counting efficiency results from 4n  (4-pi) geometry and the absence of test-source self-
751     attenuation (McDowell and McDowell, 1993). Because of these characteristics, liquid
752     scintillation counting can be utilized as an alternative to gas proportional counting (Section 17.4)
753     and alpha semiconductor counting (Section 17.3.2).

754     17.3.3.2 Liquid Scintillation Spectra

755     The amount of light produced by alpha and beta particles in a liquid scintillation cocktail is
756     proportional to the particle  energy. Beta  spectra  convey the energy continuum from zero to their
757     maximum energy. Alpha liquid scintillation spectra are similar  in shape to those obtained by
758     semiconductor spectroscopy, but with greatly decreased resolution. Because alpha particles are
759     only about one-tenth as efficient as beta particles in producing scintillation light pulses, there is
760     an overlap of alpha and beta spectra (Passo and Kessler,  1992; McDowell and McDowell, 1993).

761     Gamma radiation interactions within the scintillation cocktail depend on energy and path length,
762     with lower energy gamma rays being more efficient in transferring their energy. Gamma events
763     are recorded in the same energy range as alpha and beta particles; therefore, discrimination
764     between alpha, beta, and gamma radiation based solely on scintillation spectra is not possible
765     (Passo and Kessler 1992; McDowell and McDowell, 1993).


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        Data Acquisition, Reduction, and Reporting
766      17.3.3.3 Pulse Characteristics

767      Excited triplet and singlet energy states are formed by the fluor molecules when ionizing
768      radiation interacts with the scintillation cocktail. The excited singlet states dissipate their energy
769      very rapidly and produce short lifetime decay pulses, whereas triplet states lose their energy more
770      slowly, resulting in longer lifetime pulses. Because alpha particles have a higher linear energy
771      transfer than gamma or beta radiation, they produce a higher ratio of triplet to singlet excitation
772      states and therefore have a longer pulse duration. Differences in the decay time and shape of the
773      decay pulse are the basis for discriminating of alpha particles from  beta and gamma radiation in
774      liquid scintillation counting (Passo and Kessler 1992; Passo and Cook 1994).

775      17.3.3.4 Coincidence Circuitry

776      Most modern liquid scintillation counters employ two photomultiplier tubes  180 degrees apart
777      for the detection of pulses. The light produced when ionizing radiation in the test source interacts
778      with the scintillation cocktail is emitted in all directions. A sample  event should therefore
779      produce electronic pulses in both photomultiplier tubes simultaneously, or in coincidence.

780      Electronic noise pulses are produced randomly by the photomultiplier tubes, but the probability
781      that both tubes will produce noise pulses simultaneously is very low. An electronic gate can be
782      set to allow only pulses that are in coincidence to be registered. The rejection of random pulses
783      keeps background counts produced by electronic noise to a minimum.

784      17.3.3.5 Quenching

785      Chemical quenching reduces the amount of energy transferred to the fluor molecules. Halogens,
786      water, solvents, and oxygen are common agents that cause a decrease in the counting efficiency.

787      Color quenching is caused by impurities not removed during test-source preparation or by carrier
788      compounds such as iron chloride. Photons emitted from the fluor molecules are absorbed,
789      reducing the amount of light reaching the photomultiplier tubes.

790      Quenching causes a shift in the scintillation spectrum to lower energies and a reduction in the
791      number of counts. Quenching has a minimal  impact on alpha counting, but significantly increases
792      as the energy of the beta particle decreases.

793      The most common method for monitoring quench is through the analysis of the Compton
794      spectrum. After the test source is loaded into the counter, it is irradiated by an external gamma


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                                                         Data Acquisition, Reduction, and Reporting
795     emitting source located in the instrument. The test-source spectrum is collected and compared
796     with factory or user-generated quench standards stored in the instrument library. Both color and
797     chemical quenching cause a shift to lower energies, but the color quench broadens the spectrum
798     as well. The efficiency of the test source is extrapolated and applied to normalize the test-source
799     count rate.

800     17.3.3.6 Luminescence

801     Photoluminescence is produced by ultraviolet light from the environment reacting with the
802     scintillation cocktail. The effect can be minimized by dark adapting the test sources prior to
803     counting.

804     Chemiluminescence is produced by reactions between the scintillation cocktail and chemicals
805     introduced from the test-source preparation. To minimize this effect, oxidizers and alkaline
806     conditions should be avoided.

807     Both photoluminescence and chemiluminescence cause random scintillation events. At low
808     levels, the coincidence gate should reject most of their contribution. However, at very high
809     levels, the probability increases that two events may pass through the gate. Manufacturers use a
810     method of spectral stripping to correct for the false counts, but it is best to avoid the conditions
811     that create the problem.

812     17.3.3.7 Test Source Vials

813     Glass test-source vials contain naturally occurring impurities such as potassium-40, thorium,  and
814     uranium. Their contribution appears at the lower energy portion of the spectrum. Plastic vials
815     have a lower background, but they should be compatible with the liquid scintillation cocktail
816     being used. Teflon vials are also available from most manufacturers.

817     17.3.3.8 Data Reduction for Liquid Scintillation Counting

818     Liquid scintillation counters normally provide minimal data reduction in their output. Basic data
819     include the counting duration, count rate in one or more selected windows, and the date and time
820     of counting initiation. A blank source (background) is normally counted with each counting batch
821     and the output will provide the count rate of the blank to be  subtracted from each test source.

822     The counting efficiency will also be provided by the output information. Its form of presentation
823     in the output will depend on the calibration/counting (quench correction) method for determining


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        Data Acquisition, Reduction, and Reporting
824     counter efficiency4. If the internal (standards addition) method is used the data generated by the
825     counter must be further manipulated in order to develop the counting efficiencies for each test
826     source. When using the external-standards method (quench curve), the scintillation spectrometer
827     will apply the quench corrected efficiency and give the test sample disintegration rate by applying
828     the corrected efficiency.

829     The radionuclide or gross concentration is provided by the following equation:
830     where:

831         CG =  the gross counting rate (source + background) (cps)
832         CB=  the counting rate of the blank (cps)
833         zq  =  the radionuclide quench corrected counting efficiency (c/d)
834         Ac =  radionuclide or gross concentration (Bq/L or Bq/kg)
835         V  =  the volume or mass analyzed (L or kg)

836     17.4  Data Reduction on Non-Spectrometry Systems

837     Proportional counters are primarily used for counting of test sources for alpha and beta emitters.
838     Proportional counters may have entry windows for allowance of the emitted radiation into the
839     active portion of the detector or they may be windowless. These instruments are described in
840     Chapter 15. They are used for the  determination of specific radionuclides, following chemical
841     separation to isolate the radionuclide, and for nonspecific (gross) analyses (Chapter 16). Counters
842     are equipped to count alpha and beta simultaneously in a given source and report the activity of
843     both.

844     The basic information obtained from a determination in a proportional counter is the number of
845     counts recorded in the detector within the allotted counting duration. However, modern
846     proportional counters take the data reduction process to the point of finality, i.e., producing the
847     test-source concentration and associated counting uncertainty, providing automatic instrument
848     background subtraction, and correcting for source self-absorption and alpha/beta crosstalk.
        4 For a discussion of liquid scintillation efficiency determination, see MARLAP Chapter 16, Section 16.5.2.1.

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                                                         Data Acquisition, Reduction, and Reporting
849     The instruments may also have protocols for developing the correction factors for self-absorption
850     and for crosstalk. In addition, they should have the capacity to track and evaluate the periodic
851     quality control checks (check source and background) performed on the instrument.

852     The basic equation used to calculate test-source concentrations is:

                                                C - C
                                            A = —	                                    (17.22)
853     where:
854         A  =   the activity of the radionuclide or gross activity (Bq)
855         CG =   the gross counting rate (source + background) (cps)
856         CB =   the instrument background counting rate (cps)
857         E  =   the gross or radionuclide counting efficiency (c/d)

858     And the radionuclide or gross concentration is provided by the following equation:


                                           Ac=   G£VB                                   (17.23)


859     where:
860         Ac =   radionuclide or gross concentration (Bq/L or Bq/kg)
861         V  =   the volume or mass analyzed (L or kg)

862     The associated combined standard uncertainty is given by:


                                                                                           (17.24)
                                  ^
£
 2
863     The above simple equations apply to counting either pure alpha or beta emitters and when no
864     correction for self-absorption is necessary (weightless sources). Modifications should be made in
865     the activity and concentration calculations when both alpha and beta particles are emitted by the
866     source, and when absorption and scattering within the source cause a reduction in the effective
867     efficiency.
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        Data Acquisition, Reduction, and Reporting
868     Self-absorption factors are applied for sources where the internal attenuation of the alpha or beta
869     particle is sufficient to affect the overall efficiency (Chapter 16). Commercially available
870     proportional counters have a protocol for developing the self-absorption correction factors. These
871     protocols process the data generated by counting a series of alpha calibration sources and a series
872     of beta calibration sources, which both have varying masses of material, from "zero" to the
873     maximum to be encountered in test sources (Chapter 16). The instrument is programmed to then
874     fit the data to a mathematical function so the counting efficiency correction factor can be applied
875     at any test-source mass within the range covered by the calibration source masses. A cubic
876     polynomial is one option used for both alpha and beta counting efficiencies. A cubic polynomial
877     has the form
                                                                                          (17.25)
878     where:
879         m     =  is the residual mass of the test source
880         £m     =  the counting efficiency at mass m
881         at     =  constants determined by the data fit

882     The combined standard uncertainty of £m is given by
                    t/2(a0) + £ m2lu\a) + 2^ £ m1+Ju(apa) + (al + 2a2w + 3a3w2)2 u\m)  (17.26)
883     When the identities of the alpha or beta emitting radionuclides are unknown, an additional
884     component of uncertainty is needed to account for the dependence of the counting efficiency (and
885     self-absorption) on the unknown particle energy.

886     Another option that is often used for the beta counting efficiency is an exponential curve, which
887     has the form
                                           £   =£   e  am                                   (1727}
                                           m   zero                                       V1 ' -^')

888     where:
889         m    =   is the residual mass of the test source
890         £m    =   the counting efficiency at mass m
891         szero   =   the "zero" mass counting efficiency
892         a     =   constant determined by the data fit

893     Then the combined standard uncertainty of £m is:


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                                                          Data Acquisition, Reduction, and Reporting
                      t/c(8j = e -™a2u\ni) + t/2(8zero) + /n V(a) - 2/73 t/(8zero, a)              (17.28)

894     Again, an additional uncertainty component may be needed when the identity of the beta-emitting
895     radionuclide is unknown.

896     Crosstalk, sometimes called "spill over," refers to the misclassification of alpha- and beta-
897     produced counts in a proportional counter which is designed to count both particles
898     simultaneously. It occurs when counts produced by alpha interactions in the detector are
899     registered as beta counts and vice versa. In order to accurately record the alpha and beta activities
900     of sources containing radionuclides emitting both particles, corrections must should be made for
901     crosstalk.

902     The number of alpha interactions registered as beta counts will increase as the source self-
903     absorption increases. The opposite is true for beta crosstalk, in that the number of beta
904     interactions falsely designated as alpha counts decreases with source self-absorption. Thus,
905     crosstalk correction factors vary with test-source mass and should be developed for the range of
906     test-source masses to be encountered. Commercially available proportional counters have
907     established programs to assist in the establishment of alpha and beta crosstalk factors. The
908     algorithms to correct for crosstalk are presented below.

909     The alpha in beta crosstalk, Xa, is defined as:
910     The respective counts in the alpha channel (a) and those in the beta channel (p) counts are
911     measured with a pure alpha-emitting source. Likewise, the beta in alpha crosstalk, Xp, is:


                                             X' =                                          (1730>
912     The respective alpha (a) and beta (p) count rates are measured with a pure beta-emitting source.

913     The relationship between Xa and Xp is given by:


                                        a = a
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        Data Acquisition, Reduction, and Reporting
                                       P = P, - P,Xp + a,Xa                               (17.32)

914     Equation 17.3 1 states that the recorded alpha count rate, a, consists of the actual alpha count rate,
915     c^ (the total alpha count rate in both the alpha and beta channels due to only alpha interactions),
916     minus those alpha interactions recorded in the beta channel, plus those beta counts recorded in
917     the alpha channel. Equation 17.32 states the equivalent of Equation 17.3 1 for beta counts.
918     Solving the equations simultaneously for ad and pd gives:

                                             a-X(a + p)
                                        ad=  ,                                            (17.33)
                                                ~   ~
                                         R         «
                                         P)(! - Xp)2
                                                   ~~
                                                 " Xo " Xp
                                                                                          (17.36)
                                                                                          V   •   /
920     Since crosstalk factors vary with radionuclide, additional uncertainty components may be needed
921     when the identities of the alpha and beta emitting radionuclides are unknown.

922     Processors execute many other functions for instruments which do not perform spectrometry.
923     These instruments include proportional counters, scintillation detectors, ionization chambers, and
924     special instruments (Chapter 15). The functions performed by processors may include instrument
925     control (sample change, gas flow control, etc.) and the calculations necessary to convert the basic
926     counting information to final form data or to some intermediate step.
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                                                         Data Acquisition, Reduction, and Reporting
927     Data reduction functions which may be performed for scintillation detectors, ionization
928     chambers, and special instruments include the following:

929      •  Background determination and subtraction;
930      •  Conversion of total counts to counts per second;
931      •  Calculate activity using calibration data;
932      •  Calculate concentration using activity and operator input data;
933      •  Perform efficiency calibrations;
934      •  Calculate counting and total uncertainty;
935      •  Cross talk determination and correction;
936      •  Self-absorption corrections;
937      •  Radioactive decay corrections; and
938      •  Quality control (QC) functions (efficiency and background verification).

939     The output of manual  systems usually requires further reduction to render it usable. The
940     information generated by processor-based systems may also need further processing.

941     These additional calculations may be performed using a calculator or by a computer using
942     general or custom software programs. The data  may be electronically transferred to the
943     processing computer by a local area network (LAN) or on a computer disk. In some cases the
944     processing software may be part of the LEVIS.

945     17.5  Reporting Data

946     Quality assurance planning documents will give the level of data reporting required. This level
947     will vary from simply confirming the presence or absence of an analyte to a complete reporting
948     of all measurements, calibration data, documentation of the performance of laboratory processes,
949     provision of certain instrument counting reports, and QC sample results and analysis. Another
950     way of viewing this is as a tiered approach where preliminary studies or site surveys may only
951     require a minimum of data reporting, while a final site survey may require a detailed reporting of
952     the results. The necessary elements for data reporting are connected to the purpose for which the
953     data will be used (data quality objectives).

954     MARLAP recommendations for data reporting  are that the reported value of a measurement
955     result:  (1) be reported directly as obtained, with appropriate units, even if they are negative
956     values, (2) be expressed in an appropriate number of significant figures, and (3) include an
957     unambiguous statement of the uncertainty. The  appropriate number of significant figures is
958     determined by the magnitude of uncertainty in the reported value. Each reported measurement

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        Data Acquisition, Reduction, and Reporting
959     result should include the value and an estimate of the uncertainty (expanded uncertainty) (ANSI
960     42.23).

961     17.5.1 Sample and Analysis Method Identification

962     Sample data are normally reported by sample number, including both the field (project) and
963     laboratory assigned identifiers. In addition, the submitting laboratory should be identified as well
964     as the analysis method (ANSI 42.23, p. 38). Other information which can assist in the review and
965     interpretation of the data may be requested. This could include sample collection date (decay
966     correction reference date), analysis date, chain-of-custody (COC) number, and site or project
967     name.
968
17.5.2  Units and Radionuclide Identification
969     The individual radionuclides should be identified or, for gross analyses, the category, e.g., gross
970     alpha/beta, should be reported. Reporting units are likely specified by project planning
971     documents. If not specified, when possible, International System of Units (SI) units are preferred.
972     However, since regulatory compliance levels are usually quoted in traditional radiation units, it
973     may be appropriate to report in both SI and traditional units with one being placed within a
974     parenthesis. Both the SI and non-Si units are shown in Table 17.1 for common matrices.

                                TABLE — 17.1 Units For Data Reporting
Matrix
Airborne Particulates and Gas
Liquids
Solids
Surfaces
In Non-Si Units
pCi nT3
pCi L-1
pCikg-'orpCig-1
dpm/100cm2
In SI Units
BqnT3
BqL~'
Bqkg-1
Bq / 100 cm2
Conversion Factor From
Non-Si to SI Units
3.70 x 10~2
3.70 x 10~2
3.70x 10-2or37
1.67 x 1Q-2
975     17.5.3 Values, Uncertainty, and Significant Figures

976     The value, as measured, including zero and negative numbers, and the measurement uncertainty
977     (either expanded uncertainty or the combined standard uncertainty) should be reported in the
978     same units (Chapter 19). In general, environmental radiation measurements seldom warrant more
979     than two or three significant figures for the reported value, and one or two significant figures for
980     the uncertainty. As recommended in Chapter 19, Section 19.3.6, the measurement uncertainty
981     should be rounded to two significant figures, and both the value and uncertainty reported to the
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                                                         Data Acquisition, Reduction, and Reporting
 982      resulting number of decimal places. For example, a value of 0.8961 pCi/L with an associated
 983      measurement uncertainty of 0.0234 should be reported as 0.896 ± 0.023 pCi/L. The MDC should
 984      be reported to two significant figures (ANSI 42.23, p38). It should be noted that truncation
 985      should only occur in reporting the final results (Section 18.3.6).

 986      17.5.4 Other Information to be Provided on Request

 987      Information which should be documented and retained for provision, if requested, includes
 988      (ANSI 42.23, p38):

 989       • Total weight or volume of the sample submitted and analyzed;

 990       • Identification and documentation of specific analysis processes and analyst;

 991       • Specific analytical parameters,  i.e., chemical yields, counting times, decay factors, efficiency
 992         of detectors used;

 993       • Date, time, and place of sampling;

 994       • Sample receipt information; and

 995       • QC data demonstrating the quality of the measurement.

 996      17.6  Data Packages

 997      Project planning documents (Chapter 4) and analytical statements of work (Chapter 5) will
 998      usually define the requirements of the final data submittal. Many projects will specify a data
 999      package which contains not only the data reports described in the preceding section, but other
1000      supporting information to further describe, document, and define the analytical process. These
1001      additional requirements may be instituted to provide a basis for data verification/validation
1002      (Chapter 8), the purpose of which is to confirm that the data meet project quality objectives
1003      (Chapter 2). Material which may be required as part of a data package is discussed in Chapter 5.

1004      17.7  Electronic Data Deliverables

1005      Many project planning documents and SOWs require that laboratory data be delivered in
1006      electronic format, commonly called electronic data deliverables (EDD). This allows the data to
1007      be directly entered into a project database or, in some cases, into validation programs, and avoids

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1008      transcription errors. There is no universal format for presenting EDDs, so the laboratory may be
1009      required to produce them in various formats. While the record structure of the EDD may vary in
1010      terms of the length and order of the fields, it is likely that the following are examples that may be
1011      requested:

1012       •  Field Sample Number
1013       •  Laboratory Sample Number
1014       •  Sample Collection or Reference Date
1015       •  Sample Receipt Date
1016       •  Analysis Date
1017       •  Result Identifier (sample or type of QC sample)
1018       •  Radionuclide
1019       •  Result
1020       •  Results Units
1021       •  Measurement Uncertainty
1022       •  Sample Aliquant Size
1023       •  Aliquant Size Units
1024       •  Minimum Detectable Concentration
1025       •  Minimum Quantifiable Concentration (MQC)
1026
1027      More information on EDDs may be found at the following websites:

1028         More information on EDDs may be found at the websites listed here. The U.S. Department of
1029         Energy EDD may be found at: (http://www.em.doe.gov/namp/pitimp.html) or (http://www.
1030         em.doe.gov/namp/deemmeet.html). Another EDD that is more general has been developed. It
1031         is called the  General Electronic Data Deliverable (GEDD) and may be found at the website:
1032         (http://ersmo.inel.gov/edd/gedd.html#Entity Relationship Diagram). The EPA Environmental
1033         Data Registry may be found at: (http://www.epa.gov/edr/). U.S. Air Force Environmental
1034         Resources Program Management System (ERPREVIS) website: (http://www.afcee.brooks.af
1035         mil/ms/msc_irp.htm) also provides useful information on environmental databases and
1036         EDDs.

1037      EDDs may be transmitted by direct electronic transfer, e-mail, or by diskette.
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                                                        Data Acquisition, Reduction, and Reporting
1038      17.8  References

1039      17.8.1 Cited References

1040      American National Standards Institute (ANSI) N42.14. "Calibration and Use of Germanium
1041         Spectrometers for Measurement of Gamma-Ray Emitting Radionuclides," 1991, New York.

1042      American National Standard Institute (ANSI) N42.23. Measurement and Associated
1043         Instrumentation Quality Assurance for Radioassay Laboratories. 1996.

1044      Baedecker, P. A. 1971. Digital Methods of Photopeak Integration in Activation Analysis,
1045         Analytical Chemistry, 43:405.

1046      Canberra Nuclear. 1994. VMS Spectroscopy Applications Package User's Manual, Canberra
1047         Industries, Inc.

1048      Decker, K. M., Sanderson, C. G. 1992. A Reevaluation of Commercial IBM PC Software for the
1049         Analysis of Low Level Environmental Gamma-Ray Spectra, Appl. Fadiat. Isot, Vol. 43, No.
1050         !/2, p. 323.

1051      U.S. Environmental Protection Agency (EPA). 1995. Good Automated Laboratory Practices.
1052         Directive 2185, Office of Information Resources Management, Research Triangle Park, NC.
1053         http://www.epa.gov/irmpoli8/irm_galp/index.html

1054      Gilmore, G. and Hemmingway, J. D. 1995. Practical Gamma-Ray Spectrometry, John Wiley,
1055         Chichester.

1056      Hagee, G. R., Karches, G. J., and Goldin, A. S. 1960. Determination of 1-131, Cs-137, and Ba-
1057         140 in Fluid Milk by Gamma Spectroscopy, Talanta, Vol 5,, p. 36.

1058      Helmer, R. G.  and McCullough, C. M. 1983. Gauss VII, a Computer Program for the Analysis of
1059         Gamma-Ray Spectra From Ge Semiconductor Spectrometers, Nucl. Instr. andMeth., 206:
1060         Loska, L., 477.

1061      Kruse, H. and  Spettel, B. 1982.  A Combined Set of Automatic and Interactive Programs for
1062         INAA, J. Radioanalytical Chemistry, 70: 427.
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         Data Acquisition, Reduction, and Reporting
1063      Leyba, J. D. 1992. Gross alpha/beta determination by liquid scintillation counting. Westinghouse
1064         Savannah River Company, WSRC-TR-92-079.

1065      Loska, L. 1988. A Modification of the Total Peak Area Method for Gamma Ray Spectra, Appl.
1066         Radial Isot, 39: 475.

1067      Loska, L. and Ptasinski, J.  1994. A Simple Method for Peak-Area Determination of Multiplets,
1068         Radioactivity & Radiochemistry, Vol. 5, No. 4, p. 26.

1069      Mariscotti, M. A. 1967. A method for automatic identification of peaks in the presence of
1070         background and its application to spectrum analysis,  Nuclear Instruments and Methods, 50:
1071         309-320.

1072      McDowell, J. And McDowell, B. L. 1993. "The Growth of a Radioanalytical Method: Alpha
1073         Liquid Scintillation Spectrometry." In Liquid Scintillation Specrometry 1992, ed. Noakes, J.
1074         E., Schoenhofer, F. And Polach, H. A. Tucson: Radiocarbon, 1993.

1075      National Council on Radiation Protection and Measurements (NCRP). 1978. A Handbook of
1076         Radioactivity Measurements Procedures, Report No. 58, p. 159.

1077      Passo, C. J. And Kessler, M. 1992. The Essentials of Alpha/Beta Discrimination.

1078      Passo, C. J. and Cook, G. T. 1994. Handbook of Environmental Liquid Spectrometry, Meriden,
1079         CT.

1080      Tsoulfanidis, N.  1983. Measurement and Detection of Radiation, McGraw-Hill, New York.

1081      U.S. Public Health Service (USPHS). 1967. Radioassay  Procedures for Environmental Samples,
1082         Publication No.  999-RH-27.

1083      17.8.2 Other Sources

1084      A Handbook of Radioactivity Measurements Procedures, Report 58 (1984), National Council on
1085         Radiation Protection and Measurements, Washington, DC.

1086      American National Standard Calibration and Usage of Alpha/Beta Proportional Counters,
1087         ANSI/42.25-1997, IEEE, New York.
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                                                        Data Acquisition, Reduction, and Reporting
1088      Browne, E. and Firestone, R. B. 1986. Table of Radioactive Isotopes, Wiley, New York.

1089      Calibration and Use of Germanium Spectrometers for the Measurement of Gamma-ray Emission
1090         Rates for Radionuclides, ANSI/42.14-1991, IEEE, New York.

1091      Debertin, K. and Helmer, R. G. 1988. Gamma- and X-ray Spectrometry with Semiconductor
1092         Detectors., North Holland, Amsterdam.

1093      Dewberry, A. 1997. Measurement of Uranium  Total Alpha-Particle Activity by Selective
1094         Extraction and Photon/Electron-Rejecting Liquid Scintillation (PERALS) Spectrometry,
1095         Radioactivity and Radiochemistry, Vol. 8, No. 2.

1096      Escobar, G, Tome, F. V., and Lozano, J. C. 1999. Extractive Procedure for Radium-226
1097         Determination in Aqueous Samples By Liquid Scintillation Counting, Radioactivity and
1098         Radiochemistry, Vol 10, No. 1.

1099      Galloway, R. B. 1993. Correction for Sample Thickness in Activity Determination by Gamma-
1100         Ray Spectrometry, Radioactivity & Radiochemistry, Vol. 4, No. 3, p. 32.

1101      Harbottle, G. 1993. A Marinelli Beaker Modified for Easier Mathematical Modeling for Self-
1102         Absorption in Environmental Radioactivity Measurements, Radioactivity & Radiochemistry,
1103         Vol. 4, No.3, p. 20.

1104      Harvey, B. R. And Sutton, G.  A. 1970. Liquid scintillation counting of nickel-63. Intl. J. Appl.
1105         Rad. Isot. 21: 519-523.

1106      Holm E.,  et. al. 1984. Determination of Tc-99 in environmental water samples. Nucl. Instrum.
1107         Phys.  Res. 23: 204-207.

1108      IAEA.  1991.  X-ray and Gamma-ray standards for Detector Calibration, IAEA-TECDOC-619,
1109         IAEA, Vienna.

1110      American National Standard Institute/Institute of Electrical and Electronics Engineers,
1111         (ANSI/IEEE) 325. Standard Test Procedures for Germanium Gamma-Ray Detectors. 1996.

1112      Killian, E. W., Koeppen, L.  D., and Fermec, D. A. 1994. Quality-Assurance Techniques used
1113         with Automated Analysis of Gamma-Ray Spectra, Radioactivity & Radiochemistry, Vol. 5,
1114         No. 4, p. 34.


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         Data Acquisition, Reduction, and Reporting
1115      Kocher, D. C. 1981. Radioactive Decay Tables, U. S. Department of Energy Report DOC/TIC -
1116         11029.

1117      Knoll, G.F. 1989. Radiation Detection and Measurement, 2nd edn., John Wiley, New York.

1118      Koskelo, M. I, Burnett, W. C., and Cable, P. H. 1996. An Advanced Analysis Program for
1119         Alpha-Particle Spectrometry, Radioactivity & Radiochemistry, Vol. 7, No. 1, p. 18.

1120      Nuclear Data Sheets, Academic Press, Orlando.

1121      Oxford Instruments Inc. 1995. LB4100- W - Low Background System,  Version 1.10.

1122      Quittner, P. 1972. Gamma-Ray Spectrometry, Adam Hilger LTD, London.

1123      Shirley, V. S. and Lederer, C. M. 1978. Table of Isotopes, 7th edition, Wiley Interscience, New
1124         York.

1125      Standard Practices for the Measurement of Radioactivity, ANSI/D 3648 - 1995, New York.

1126      Standard Test for High-Resolution Gamma-Ray Spectrometry of Water, ANSI/D 3 649 - 1991,
1127         New York.

1128      Standard Test Methods for Detector Calibration and Analysis of Radionuclides, ANSI/E 181-
1129         1993, New York.

1130      U.S. Environmental Protection Agency (EPA).  1980. Upgrading Environmental Radiation Data-
1131         Health Society Committee Report HPSR-1 (1980), Watson, I.E., Chairman, EPA 520-1-80-
1132         012, Office of Radiation Programs, Washington, DC.

1133      Yule, H. P. 1995. Could Your Gamma-Ray Spectrum Analysis Reports Survive an Audit?,
1134         Radioactivity & Radiochemistry, Vol. 6, No. 4, p. 4.
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                    18  LABORATORY QUALITY CONTROL
 2      18.1  Introduction

 3      This chapter addresses internal laboratory quality control (QC), the purpose of which is to
 4      monitor performance, identify problems, and initiate corrective action. If project requests are
 5      more stringent than typical laboratory QC needs, the project manager and the laboratory should
 6      confer to see whether the laboratory can accommodate the tightened QC requirements. Labora-
 7      tory data should be produced in a quality system1 that incorporates planning, implementing, and
 8      internal assessment of the work performed by the laboratory, including QC. While this chapter
 9      focuses on laboratory QC, MARLAP fully endorses the need for a laboratory quality system and
10      a Quality Manual that delineates the quality assurance (QA) policies and QC practices of the
11      laboratory. General requirements for testing laboratories can be found in ISO/IEC 17025.

12      The chapter's  purpose is to provide guidance to laboratory staff on those activities and profes-
13      sional practices a radioanalytical laboratory should undertake to produce data of known quality.
14      This chapter also shows how to use statistical techniques to monitor specific measures of the
15      analytical process to indicate the level of control of the analytical process within the laboratory.
16      These measures are called "performance indicators," and the statistical techniques involve the
17      use of control  charts. Monitoring performance indicators through control charts enables the
18      identification of trends. The  laboratory can then address analytical problems and help improve
19      the analytical process. Section 18.3.2 and Attachment ISA at the end of this chapter provide
20      examples of several types of charts. The use of statistical techniques is the preferred method for
21      implementing quality control in the laboratory (Attachment 18B). The chapter also identifies
22      specific performance indicators, the principles that govern their use, indications and underlying
23      causes of excursions, statistical means of evaluating performance indicators, and examples of
24      root-cause evaluations.

25      The control of the analytical process in the laboratory is distinct from meeting the typical
26      analytical needs of a specific project. This chapter addresses the former, to the extent that QC
27      provides quantitative estimates of analysis and measurement controls that can be used to
28      determine compliance with project objectives.
       JA quality system is a structured and documented management framework that describes the policies, objectives,
       principles, organizational authority, responsibilities, accountability, and implementation plan of an organization for
       ensuring quality in its work processes, products (items), and services. The quality system provides for planning,
       implementing, and assessing the work performed by the organization and for carrying out required quality assurance
       and quality control (ANSI/ASQC E4, 1994).

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29     18.1.1 Organization of Chapter

30     Chapter 18 has five major sections in addition to this introduction. Section 18.2 provides a
31     general overview of QC and its application in the laboratory setting. Section 18.3 discusses the
32     importance of evaluating performance indicators and provides statistical means for their evalua-
33     tion. Sections  18.4 and 18.5 identify primary radiochemistry and instrumentation performance
34     indicators, respectively, and discuss each in detail. Section 18.6 discusses other aspects of the
35     analytical process that require scrutiny but are not formally considered performance indicators.

36     18.1.2 Format

37     The chapter is presented in a different format than the preceding chapters in order to highlight the
38     performance indicators and to give examples. For each performance indicator, general guidance
39     is provided in the format shown below.

40     Issue: Defines and summarizes the performance indicator
41
42     Discussion: Identifies those matters important to the performance indicator, including:

43       •  What is the performance indicator and how does it work?

44       •  Why is the performance indicator important, and what is its impact on the quality of the
45         measurement?

46       •  What is the relationship of the performance indicator and the combined standard uncertainty
47         derived for the analytical method?

48       •  What are the acceptable limits of the performance indicator?

49       •  What are the key assumptions underlying the performance indicator?

50       •  What limits and cautions are associated with the assumptions made?

51       •  How sensitive is the quality of the measurement to the assumptions made?

52       •  What is the appropriate frequency for assessing this performance indicator?

53     Excursions: "Excursions" are departures from the expected condition. This section addresses the

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54     likely types of excursions encountered during laboratory analysis and explains what each may
55     indicate. This section also discusses the potential reasons for these excursions and the
56     implications for the analytical results.

57     Examples: Where appropriate, this section provides typical examples of excursions, potential
58     reasons for excursions, and additional information.

59     18.2  Quality Control

60     Quality control includes all technical activities that measure the attributes and performance of a
61     process, item, or service against defined standards to verify that they meet the stated require-
62     ments established by the customer. It also includes operational techniques and activities that are
63     used to fulfill requirements for quality (ANSI/ASQC E4, 1994).

64     QC may not always detect blunders. Good laboratory practices, in addition to adherence to
65     standard operating procedures (SOPs), are part of the overall QA/QC aspects needed to check the
66     laboratory's performance. To monitor and control quality, laboratories use performance indica-
67     tors, which are instrument-  or protocol-related parameters that are routinely monitored to assess
68     the laboratory's estimate of measurement uncertainty, precision, bias, etc. Initially, these
69     parameters are used to maintain or demonstrate control over the analytical process. The
70     performance indicators should be tracked by appropriate personnel. If the performance indicator
71     control limits are exceeded, management  should be informed and corrective action should be
72     initiated.

73     Table 18.1 lists some of the potential causes for radioanalytical control excursions. By no means
74     is the list complete, and the reader should be aware  of additional potential causes of excursions
75     that are presented in the rest of this chapter and the  other chapters. Many problems are complex
76     and have multiple components that could  complicate the search for causes of protocol or instru-
77     ment related excursions. A  metrologist or radiochemist should be consulted to identify and
78     remedy any analytical problems.
79
80
81
82

83
84
r
Radiochemical
Processing
Laboratory blunder
Processing
difficulty
r ART F 18.1 —
Source
Preparation
Laboratory
blunder
Poor mounting
Poor Dialing
'rohlems leading to loss of analytical control
Instrument Related
Laboratory blunder
Electronic malfunction
• preamplifier
• power supply
• guard
Other
Laboratory
blunder
Data transcription
error
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Radiochemical
Processing
Questionable
reagent purity

Low tracer/carrier
recovery

Excessive
tracer/carrier
recovery

Inaccurate
aliquanting of
tracer/carrier

Sample aliquanting
inaccuracy
Cross-
contamination

Inadequate
dissolution of
sample
Complex matrix

Sample
heterogenity




Source
Preparation
Improper
geometry

Incorrect thin
plastic film
thickness

Improper
plating on the
planchet

Excessive
source mass

Uncorrected
self absorption
Quenching

Recoil
contamination










Instrument Related
analog to digital converter (ADC)
gain
high voltage
discriminator
pole zero
shape constant

Improper source or sample geometry

Poor counting statistics

Poor detector resolution

Detector contamination

Inappropriate/out-of-date efficiency, background or
calibration factor

Background shift

Incorrect nuclear transformation data or other constants

Variable memory effects
Peak/calibration shift

Counting gas
• pressure too high, too low, or variable
• gas impurity
Loss of vacuum/coolant
Temperature and humidity fluctuation
Measurement uroblem
Other
Incorrect units

Calculation error

Software
limitation

Computer
problem

Loss of electrical
power

Electrical power
fluctuations
Mislabeling

Loss of sample

Insufficient
sample
information
Data processing
problem

Interfering
radionuclides



 85
 86

 87
 89
 90
 91

 92
 93
 94

 95
 96

 97
 98

 99
100
101

102

103
104
105
18.3   Evaluation of Performance Indicators
106      18.3.1  Importance of Evaluating Performance Indicators

107      As stated previously, performance indicators are measures of the analytical process that the
108      laboratory monitors as part of its routine QC program. Performance indicators demonstrate
109      whether the analytical process is performing as planned, when it has exhibited a statistical
110      anomaly that requires investigation, and when a system has failed. Accordingly, monitoring
111      performance indicators using established statistical techniques provides the laboratory with an
112      effective tool for self assessment that allows the identification of trends or conditions that, while
113      still within the established bounds of acceptability, are drifting or trending out of control. These
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114     conditions can be addressed prospectively, allowing the laboratory to maintain analytical control.
115     Additionally, this process allows the development of a data base regarding a protocol's or
116     system's behavior over time or under a specified set of conditions.

117     18.3.2 Statistical Means of Evaluating Performance Indicators — Control Charts

118     The primary tool for statistical quality control is the control chart (see Attachment ISA). The
119     theory that underlies a control chart is statistical hypothesis testing (see Appendix C). The
120     implementation of a control chart makes the theory transparent to  the average user and reduces
121     the process of statistical inference to answering simple questions,  such as, "Is the measured
122     parameter greater than the upper control limit?" or "Is the measured parameter in the warning
123     region?"

124     In theory, to test whether a parameter 6 is  above or below a certain value 60, a test statistic is
125     defined and its distribution  is determined under the assumption that 6 = 60 (the null hypothesis).
126     The value of the statistic is  calculated and compared to critical values to test the assumption. In
127     practice, a control chart is designed so that a non-statistician can perform these tests easily by
128     comparing the measured value of the parameter to control limits and warning limits.

129     Most control charts  do not implement hypothesis  tests in  a rigorous manner that allows decision
130     error rates to be precisely determined. The charts  are intended to be simple and practical tools for
131     use even in situations where the assumptions needed for a rigorous test are not verifiable.

132     Every control chart has control limits, which define the acceptable range of the monitored
133     variable. Many charts have  both upper and lower  limits. However, when changes in only one
134     direction are of concern, only one limit is necessary. Most control  charts have a central line, or
135     reference line, which is an estimate of the expected value of the monitored variable. Many
136     control charts also have warning limits, which lie between the central line and the control limits.

137     By definition, control limits are action limits. A single measured value that falls outside these
138     limits requires that one stop the measurement process, investigate  the problem, and if necessary
139     take corrective action. The warning limits are optional but recommended, since they help one to
140     identify and investigate possible problems before  control  limits are exceeded.

141     Types of Control Charts:  Control charts based on grouped observations often are more power-
142     ful tools for detecting shifts of the monitored variable than charts based on individual observa-
143     tions. Average charts, or X charts, are used to monitor the arithmetic means  of measured values
144     obtained in "rational subgroups," which are subgroups of equal size chosen to ensure that the


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145      measurement variability within each subgroup is likely to represent only the inherent variability
146      of the measurement process produced by non-assignable causes (see Attachment ISA). When an
147      X chart is used, a range chart, or R chart, is generally used in tandem to monitor within-group
148      variability. (The range of a set of values is the difference between the largest value and the
149      smallest.)

150      A control chart for individual values (X chart or I chart) is used when it is impractical to obtain
151      measured values in the groups needed for an X chart. In this case, a moving range chart (MR
152      chart) is often used as well to monitor variability. The moving range chart is an R chart based on
153      the absolute differences between consecutive measured values.

154      A control chart may or may not be based on a particular type of data distribution. Most control
155      charts use limits derived from the normal distribution but are intended to be used for data with
156      almost any distribution (ISO 8258). However, when data obtained from radiation counters are
157      monitored, the Poisson distribution may often be assumed. The standard types of control charts
158      for Poisson data in industrial applications are called "c charts" (for total counts) and "u charts"
159      (for count rates). A third type of Poisson control chart, which is a variant of the u chart, is
160      frequently used to monitor radiation counter efficiency. When the data distribution is Poisson,
161      separate charts for monitoring the value of the parameter and its variability are generally
162      unnecessary because the mean and variance of a Poisson distribution are equal.

163      The following documents provide more guidance on the use of control charts:

164       • ASTMD6299. Standard Practice for Applying Statistical Quality Assurance Techniques to
165         Evaluate Analytical Measurement System Performance.

166       • ASTM E882. Standard Guide for Accountability and Quality Control in the Chemical
167         Analysis Laboratory. ANSI/ISO/ASQC A3 534-2. Statistics- Vocabulary and Symbols-
168         Statistical Quality Control.

169       • ISO 7870. Control Charts - General Guide and Introduction.

170       • ISO 7873. Control Charts for Arithmetic Average with Warning Limits.

Ill       • ISO 7966. Acceptance Control Charts.

172       • ISO 8258. Shewhart Control Charts.
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173
174

175
176
177
178
179
180
181
182
183
184
185

186
187
188
189
190
 • American Society for Testing and Materials (ASTM) MNL 7, Manual on Presentation of
   Data and Control Chart Analysis ASTM Manual Series, 6th Edition, 1990.

Figure 18.1 illustrates a typical control chart using counting data of a standard reference material
(with limits corrected for decay) showing the statistical nature of the chart.  The applicability of
control chart techniques is based on the assumption that laboratory data approximate a normal
distribution like that shown on the left of the vertical axis in the figure. The counting data plotted
graphically represent the test results on the vertical axis and the scale order or time sequence in
which the measurements were obtained on the horizontal axis. The mean of the measurements  is
represented by the central line (CL), and the limits of dispersion in terms of standard deviation
are represented by the upper and lower warning and control limits (UWL, UCL, LWL, LCL). The
warning limits are usually 2 standard deviations from the mean and the control limits are 3
standard deviations from the mean.
                                                                             UCL
                                                                                      x+30
                                                                                      x+20
                                                                                       (0
                                                                                       4M>
                                                                                       c
                                                                                       3
                                                                                       O
                                                                                       o
                                                                                      x-20
                                                                                      x-3o
                                        Time
  FIGURE 18.1 — Control chart for daily counting of a standard reference source, with limits corrected for
            decay. Statistical nature of chart is illustrated on the left by the Gaussian curve.
18.3.3 Measurement Uncertainty

Issue: Since laboratory radioactivity measurements always involve uncertainty, every measured
result is uncertain to some degree. If the measurement uncertainties are large relative to the
tolerances needed for decision making, the data may not be useful for their intended purpose. A
discussion of measurement uncertainty is contained in Chapter 19, and the terms used in this
section are defined in that chapter and in the Glossary.
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191     Discussion: In order to determine the significance of a sample result, all reported values should
192     be accompanied by the laboratory's best estimate of the uncertainty associated with the result.
193     The "combined standard uncertainty" (one-sigma uncertainty) is obtained by propagating the
194     uncertainties of all the input quantities that contribute to the calculation of the derived value
195     (Chapter 19).

196     The combined standard uncertainty is used to indicate the statistical confidence in interpreting
197     the performance indicator's ability to assess analytical quality. The estimated statistical confi-
198     dence level that is usually associated with 1 combined standard uncertainty is about 68 percent,
199     the confidence level for 2 combined standard uncertainties is  about 95 percent, and the confi-
200     dence level for 3 combined standard uncertainties is about 99 percent. It is important that the
201     combined standard uncertainty be a fair estimate because it will indicate when the analytical
202     process could be approaching the limits of statistical control and corrective actions should be
203     initiated. A performance indicator exceeding ±2 combined standard uncertainty limits from the
204     indicator's historical mean value may indicate that corrective action should be considered, and a
205     performance indicator exceeding ±3 combined standard uncertainty limits from the indicator's
206     historical mean value may indicate that an investigation must be conducted and corrective action
207     may be necessary. Because statistical confidence never reaches 100 percent, it probably would be
208     prudent to confirm the measurement for the performance indicator when it exceeds ±2 combined
209     standard uncertainty limits. If the performance indicator value for repeat measurements do not
210     exceed ±2 combined standard uncertainty limits, one may conclude that the first measurement
211     was a statistically allowable event. However, if the excursion is repeated, appropriate investiga-
212     tive actions should be considered.

213     Most of the significant sources of uncertainty in radiochemical data are known to a laboratory
214     and can be estimated. These include uncertainties associated with sample and background count-
215     ing, radiochemical yield determination, efficiency calibration, and blank assessment. Other less
216     easily defined but significant sources of uncertainty include those associated with self-absorption
217     and quench correction, sample density correction, sample geometry variation, gamma photopeak
218     area determination, determination of sample volume or weight, and dead time correction.

219     The uncertainty of a measured value is controllable, within certain limits, by decreasing the
220     uncertainty associated with some input parameters. For samples containing low levels of radio-
221     activity, a large component of the combined standard uncertainty may be associated with the
222     instrumental assessment (counting) of the sample aliquant, i.e., the standard uncertainty of the net
223     count (gross sample count minus background count). Increasing the total net count accumulated,
224     or decreasing the uncertainty of the instrument background, or both, will decrease the counting
225     uncertainty.  Changes that may be made to decrease the counting uncertainty include increasing


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226     the counting time for the sample or background, increasing the sample aliquant size (unless the
227     sample geometry, quench, or self-absorption factors offset the gain in total radioactivity counted),
228     using a more efficient geometry or detector, using an instrument with a lower background, and
229     reanalyzing the sample to obtain a greater radiochemical yield. It also may be possible to
230     concentrate the sample, which has the equivalent effect of increasing the sample aliquant size.

231     18.4  Radiochemistry Performance Indicators

232     Section 18.3 discussed how to evaluate radiochemistry performance indicators using statistically
233     based control chart techniques. Any of the indicators below (blanks, replicates, laboratory control
234     samples, matrix spikes, certified reference material, or tracer yield) can be evaluated using the
235     control chart techniques. Analysts can observe individual Z score values to identify loss of
236     control. Control charts will assist laboratory personnel in identifying the quality trends and
237     excursions of any performance indicator.
238
239     18.4.1 Method and Reagent Blank

240     Issue: A method blank is a sample of a matrix as similar as practical to the associated samples
241     that is free from the analytes (radionuclides) of interest to the extent possible. The method blank
242     is processed simultaneously with, and under the same conditions as, samples through all steps of
243     the analytical procedures. A reagent blank consists of the analytical reagent(s) in the procedure
244     without the target analyte or sample matrix, introduced into the analytical procedure at the
245     appropriate point and carried through all subsequent steps to determine the contribution of the
246     reagents and of the involved analytical steps.

247     Blank samples are used to determine whether any radionuclide contamination is introduced by
248     the measurement process. They assist in the control of any contamination introduced by the
249     laboratory. Ideally, no target analytes should be present in the blank at detectable  concentrations.
250     If that is not possible (e.g., for naturally occurring radionuclides), those radionuclides should be
251     extremely well-characterized and tracked. Control charts can be used to track these radionuclide
252     levels in blanks. Using A'charts, the laboratory can establish a program that evaluates the levels
253     and trends of radionuclides in the different laboratory blanks. The techniques for  establishing
254     such a control chart program are described in Attachment ISA.

255     Discussion: The method blank is assumed to be representative of all samples in the batch with
256     respect to the matrix and contamination assessment. When practical, it consists of the same or
257     equivalent medium as the analytical samples, such as  a deionized water blank for aqueous
258     samples. Soil blanks are often prepared using "clean sand," commercially available fine-grained

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259     or beach sand whose inherent concentrations of target radionuclides are small and have been
260     characterized sufficiently by the laboratory to allow its use as a blank. This approach may not be
261     appropriate for very low-level analyses. Powdered, natural-matrix Standard Reference Materials
262     (SRMs) are commercially available from National Institute of Standards and Technology (NIST)
263     and also may be suitable (Section 18.4.5). However, due to the natural variability of soils, each
264     choice of method blank medium must be evaluated by the laboratory prior to use. The results of
265     method blanks are not used to correct sample activities but only to monitor for contamination.

266     Reagent blanks are matrix-independent and assess any contamination only from the reagents and
267     lab-ware. They are used to correct sample activities for the contribution of naturally occurring
268     radionuclides in the reagents, and used like method blanks,  to check for unexpected contamina-
269     tion. When reagent blank results are used to correct sample activities, it is important that the
270     blank results be carefully monitored using control charts.

271     It is common practice for some laboratories to add the reagents into a volume of deionized water
272     equal to the sample volume, while other laboratories simply add the required reagents to an
273     empty container and process it as an analytical sample. In either case, it should be noted that the
274     reagent blank is not monitoring the entire analytical process. The fundamental issue for each
275     laboratory is  to decide on the appropriate reagent blank necessary to obtain the needed informa-
276     tion on the measurement system. Considerable variability exists among laboratories in the use
277     and preparation of reagent blanks.

278     In general,  the reagent blank's concentration of analyte is expected to be small compared to that
279     of the sample. However, for some low-activity environmental samples this may not be the case,
280     and the correction becomes increasingly important as the concentration of the analyte in the
281     sample approaches background concentrations. In these cases, care should be taken to accurately
282     quantify the levels of radionuclides in the reagent blanks.

283     It is important to minimize radionuclide concentrations in the blanks and bring these levels under
284     control. This is usually achieved through careful selection of reagents, maintaining laboratory
285     and counting areas free from contamination, and by segregating high and low activity samples.
286     Thorough documentation of all blank values is essential to allow for the application of statistical
287     tests to evaluate potentially anomalous values and delineate their extent.

288     Ideally, the analyte concentration in a method or reagent blank should be as close to zero as
289     possible, and replicate measurement of the blanks should be consistent within counting statistics.
290     Acceptance criteria for blank results should be established and applied to all data, and should
291     include warning and control limits (Section 18.3.2, "Statistical Means of Evaluating Performance


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292     Indicators — Control Charts"). Blank values require scrutiny as part of the data evaluation and
293     validation process for each analytical batch. Should restocking of reagents or other wholesale
294     laboratory changes occur during a project, the method and reagent blanks prepared under the new
295     conditions should be re-evaluated to ensure that they continue to be within established criteria.

296     An example of a numerical performance indicator for a method blank or a reagent blank used to
297     monitor for unexpected contamination is


                                             Zelank =
298     where x denotes the measured blank activity and uc(x) denotes its combined standard uncertainty.
299     Warning limits for ^lank are ±2 and control limits are ±3. As mentioned earlier, if a reagent blank
300     is used to blank-correct sample results, the blank results should be evaluated using control charts.

301     Typically, one method blank and/or reagent blank is analyzed with each batch or grouping of
302     analytical samples regardless of batch size. Situations may occur where more frequent blanks are
303     required to ensure that analytical conditions are stable, particularly when analyzing high and low
304     concentration samples in the same analytical batch, or when instruments, reagents, or analytical
305     method are suspect.

306     In general, corrective actions include procurement control of reagents, good laboratory cleaning
307     practices, sample segregation according to anticipated concentrations, and instrument-related
308     concerns, as discussed in this section. Good laboratory cleaning protocols should incorporate the
309     evaluation of method and reagent blank performance to indicate if current practices are adequate.
310     Instrument background data indicate  a system's stability, and can be used to pinpoint the source
311     of contamination, as can routine contamination (removable and fixed) surveys  of laboratory and
312     counting areas that are performed by  the organization's health physics or radiation safely
313     personnel.

314     Excursion: Blank changes can be grouped into three general categories: rapid changes, gradual
315     increase or decrease, and highly variable changes. These are represented in Figure 18.2 and
316     described below.

317         Rapid Changes: A sudden change in a blank value indicates the existence  of a condition
318         requiring immediate attention.  Sudden changes often are caused by the introduction of a
319         contaminant from high concentration samples, impure reagents, or contaminated sample
320         preparation areas. Laboratory cleaning practices and new or recently restocked reagents


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321
322
323
324
325
326
327
328
329
330
331
332
333
should be checked. When a sudden, significant increase in the blank occurs in conjunction
with the introduction of new reagents through restocking or other changes, the causes should
be investigated and if the reagent is contaminated, the reagent contributing the activity should
be discarded and replaced. Particular attention should be paid to the samples counted directly
prior to the contaminated blank, since small amounts of residues from these samples can
contaminate the detector and have large effects on subsequent results when analyzing
samples at or near environmental background. It may be necessary to take swipe or smear
samples of questionable areas to identify the contaminant's source followed by a thorough
cleaning or decontamination of all affected areas. Additionally, method or reagent blank
values that are suddenly depressed should be investigated and may indicate other problems,
including instrument malfunction like a loss of counting gas, incomplete chemical separation
during the chemical preparation, or the failure to add necessary reagents. These other prob-
lems may be reflected in other areas, such as instrument performance checks or tracer yields.
                                     BLANK CONTAMINATION
334

335
336
337
338
339

340
341
342

343
344

345
346
347
348
   RAPID CHANGES

 CROSS
 CONTAMINATION
  - Residual contamination
    from high concentration
    samples

 PROCEDURE FAILURE -
 INCOMPLETE
 SEPARATION

 INSTRUMENT
 INSTABILITY

 INTRODUCTION OF
 CONTAMINATED
 REAGENT
GRADUAL CHANGES

BUILDUP OF
CONTAMINATION
 -  Glassware/Laboratory
    areas require thorough
    cleaning

SUSPECTED REAGENTS

INAPPROPRIATE
PROCEDURES

INSTABILITY OF
CHEMICAL YIELD
MONITOR

INSTRUMENT DRIFT &
DETERIORATION
 HIGH VARIABILITY

PROCEDURE FAILURE

INSTRUMENT
INSTABILITY

IMPROPER
SEGREGATION OF HIGH
& LOW ACTIVITY
SAMPLES
             FIGURE 18.2 — Three general categories of blank changes
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349         Gradual Changes: Gradually increasing blank values indicate the need to inspect all sample
350         preparation and counting areas for sources of residual contamination. Often housekeeping or
351         routine contamination control details such as cleaning glassware or instrument counting
352         chambers are sufficient to bring blank values under control. Alternatively, gradually decreas-
353         ing blank values warrant scrutiny with respect to proper instrument settings and procedural
354         related problems like a lack of tracer/sample exchange, failure of chemical separation reac-
355         tions, or the addition of all necessary reagents.  The importance of documenting method and
356         reagent blank values in this regard cannot be overemphasized, since data evaluation and
357         trending analyses are impossible without complete records.

358         High Variability: Because method blank values are expected to be near zero, the degree of
359         variability they exhibit should reflect the statistical variation inherent in radiometric
360         determinations near these levels. Large variations  in blank values typically indicate problems
361         related to instruments or sample processing, as discussed in the two previous sections.

362     18.4.2 Laboratory Replicates

363     Issue: A laboratory replicate is two or more aliquants  taken at the first subsampling event,
364     normally after homogenization. In the event that there is no subsampling (when the method calls
365     for using the entire sample) replicate analysis typically involves counting the prepared sample
366     twice. The results of laboratory replicates are used to evaluate the precision of the measurement
367     process. Note that counting a sample twice only assesses the instrument portion of the measure-
368     ment process.

369     Precision is a measure of agreement among replicate measurements of the same property under
370     prescribed similar conditions. Precision is a fundamental aspect of the analytical process and
371     should be evaluated routinely as part of the laboratory's quality system. Evaluation typically is
372     performed using multiple analysis of the same sample (blanks, spikes, blinds, reference
373     materials, performance evaluation samples, etc.), in whole or part, and evaluating the analyses
374     relative to a statistically based criterion. The range of  sample types requires that the sample
375     matrix's effects on the precision be captured and evaluated by the laboratory's routine quality
376     control practices. The reproducibility of analytical  results should be evaluated by replicates to
377     establish this uncertainty component.

378     Discussion: The purpose for measuring precision is to determine whether the laboratory can
379     execute an analytical method consistently and obtain results of acceptable variability. Analytical
380     samples cover a range of physical forms or matrices, from homogeneous samples like finished
381     drinking water to complex soils or heterogeneous wastes, and each matrix has the potential to

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382     affect a protocol' s precision.

383     In general, precision for aqueous samples tends to be less affected by sample heterogeneity than
384     other media because if the sample's constituents are dissolved the sample is essentially homo-
385     geneous. This facilitates dividing the samples into equivalents fractions or aliquants. Multi-phase
386     and high-solid-content samples that are heterogeneous are more problematic.

387     The acceptance criterion for precision should be related to the combined standard uncertainties of
388     the measured results. The uncertainty of a result may depend on many factors (e.g., dissolved
389     solids in water or particle sizes of soil), but such factors should affect the acceptance criterion
390     only through their effect on the standard uncertainty.

391     As an alternative to sample duplicates, a matrix spike duplicate is sometimes used as an indicator
392     of the analytical precision, as discussed in Section 18.4.3. A matrix spike duplicate is treated in
393     the same manner as an unspiked replicate: both samples (original and duplicate) are processed
394     identically to the other samples in the batch, and each aliquant is treated as an individual sample.

395     If the sample has multiple phases, the phases should be separated for individual analysis. For
396     heterogenous materials, multiple analyses should be used, or the combined standard uncertainty
397     of the results should be increased, to account for subsampling error (Appendix F).  A typical
398     frequency for replicate analyses is a minimum  of one per analytical batch, regardless of batch
399     size. Batch is defined as samples of similar matrix type with associated QC samples analyzed
400     under the sample conditions at approximately the same time.

401     All analytical batches should be evaluated with respect to precision, whether by using replicates
402     or matrix spike duplicates. This is done typically by the use of an acceptance criterion that
403     derives a statistic that quantifies the difference between two values obtained by analyzing the
404     same sample. Limits are then placed on the criterion, and data for any batch in excess of the
405     criterion require investigation  and corrective action as appropriate. An  example of a numerical
406     performance indicator for laboratory replicates is

                                                    xi ~ xi
407     where xl and x2 denote the two measured activity concentrations and tfc(X) and Ue(x2) denote their
408     respective combined standard uncertainties. Warning limits for ZRep are ±2 and control limits
409     are ±3 .
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410     Excursions: A regularly scheduled evaluation of precision with respect to the acceptance
411     criterion should be an integral part of the laboratory quality system. Careful attention should be
412     paid to the nature and anticipated analyte concentrations of all samples processed by the
413     laboratory. Prospective identification of samples where precision is expected to be problematic
414     often can address difficulties in this area. The choice of appropriate analytical method and analyst
415     training are also important. An analyst needs to be familiar with specific steps in the procedure
416     that provide an indication of incomplete processing.

417     Precision exhibits a range of values and depends in part on sample matrix and activity, assuming
418     correct execution of the analytical method. Small changes, positive and negative, are expected
419     and should be captured in the acceptance criterion's range. It is also sensitive to sample hetero-
420     geneity or errors in processing, such as incomplete chemical separation or sample dissolution,
421     and lack of tracer or carrier equilibration. When performance indicators for precision are outside
422     acceptance criteria, the laboratory should determine the reasons why and implement corrective
423     actions.
424     Certain samples will exhibit higher variability because of their matrix, or the proximity of their
425     analyte concentration to ambient background, as discussed previously. Consideration should be
426     given to cases where a matrix requires the development and implementation of a specific accep-
427     tance criterion. The main causes for lack of precision (Figure 18.3) can be grouped as follows:
428
429
430
431
432
433
434
435
436
437
438
439
440
  Laboratory subsampling — subsampling techniques produced two dissimilar aliquants from
  one sample, and the original and duplicate are not the same. An analyst should be careful to
  ensure that the sample is thoroughly homogenized before subsampling.
                                          DECREASE IN PRECISION
                    I
PROCEDURE PROBLEM
 •  Incomplete separation
 •  Improper processing
 •  Inappropriate tracer/carrier
 •  Inadequate analyst training
 •  Wrong reagent concentration
 •  Wrong ambient laboratory
   conditions
 •  Reagent/labware change
INSTRUMENT FAILURE
 •   Counting instability

LABORATORY
SUBSAMPLING
    Replicates not equivalent
                                                                           1
MATRIX PROBLEM
 •  Matrix incompatible
 •  Excessive heterogeneity

PROCEDURE PROBLEM
 •  Analyst training required
 •  Procedure requires
   modification
                FIGURE 18.3 — Failed performance indicator: replicates.
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441      •  Matrix - Sample constituents interfere with preparation chemistry, e.g., coprecipitation of
442         interfering non-analyte radionuclides from sample or excessive dissolved solids.

443      •  Counting statistics - Sample activity is so low that small statistical variations in background
444         cause disproportionate responses.

445      •  Contamination - Intermittent contamination from measurements system, glassware, etc.,
446         produces anomalous data for the original sample, but not the duplicate/replicate.

447      •  Other - Failed chemical process, failed instrumentation, training, failed lab environment,
448         failed procurement control.

449     18.4.3 Laboratory Control Samples, Matrix Spikes, and Matrix Spike Duplicates

450     Issue: A laboratory control sample (LCS) is a QC sample of known composition (reference
451     material) or an artificial sample, created by fortifying a clean material similar in nature to the
452     environmental sample. The LCS is prepared and analyzed in the same manner as the environ-
453     mental sample. A matrix spike (MS) is an aliquant of a sample prepared by adding a known
454     quantity of target analytes to a specified amount of sample and subjected to the entire analytical
455     procedure to establish if the method or procedure is appropriate for the analysis of the particular
456     matrix. A matrix spike duplicate (MSD) is a second replicate matrix spike prepared in the lab-
457     oratory and analyzed to evaluate the precision of the measurement process.

458     An important performance indicator is the ability to ensure that the analytical methods employed
459     obtain data that are representative of the true activity in a sample, i.e., produce data that are
460     accurate. The routine analysis of spiked samples provide data for an evaluation of the labora-
461     tory' s reported measurement uncertainty and allow for the determination of bias, if one exists.
462     Evaluation is typically performed using prepared samples consisting of media equivalent to a
463     routine analytical sample with a known, measurable amount of the analyte of interest. Upon
464     completion of the analysis, the results are compared to the known or accepted value, and the
465     agreement is evaluated using a predetermined criterion. The range of sample types assayed in a
466     laboratory may require that spikes are prepared using several sample media. Use of matrix spiked
467     samples will reflect the analytical method's ability to make accurate quantitative determinations
468     in the presence of the matrix.

469     Discussion: As stated previously, analytical samples cover a range of physical forms or matrices,
470     and each matrix can change a method's expected bias. Tracking sets of LCS and matrix spike
471     results can give laboratory personnel an indication of the magnitude of bias. Care must be taken


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472     when analyzing site specific matrix spike results because these matrices may be very complex
473     and subject to large variability. In general, aqueous samples tends to be less affected than other
474     media like soils or heterogeneous materials. However, multi-phase fluids, high solid content, and
475     brackish or saline waters may be more problematic.

476     The analyst should carefully consider the spiking levels for laboratory control samples and matrix
477     spikes. Spikes and LCSs may be prepared near the lower limits of detection to test the methods
478     performance on clean or slightly contaminated samples.  Conversely, matrix spikes and LCSs
479     may be spiked at high levels for groups of highly contaminated samples. The laboratory should
480     try to spike at or near the action level or level of interest for the project.

481     Possible numerical performance indicators  for laboratory control samples and matrix spikes are

                                         r-,         X ~ d
{'
                                                                                              (3)
                                                  x - XQ - d
                                     ZMS =    2      2    =5=                             (4)
                                               (x) + uc(x0) + uc(d)
482     where xis the measured value of the spiked sample, dis the spike concentration added, x0 is the
483     measured concentration of the unspiked sample, and uc2(x), u2(d), and U^(XQ) are the squares of
484     the respective standard uncertainties. The warning limits for either of these indicators are ±2 and
485     the control limits are ±3 .

486     Excursions: Excursions in the LCSs and MSs can be used to identify various out of control
487     situations. The advantage to the LCS is that the sample matrix is always the same so matrix
488     effects should not be a factor in evaluating excursions. A rapid and one-time excursion in the
489     LCS usually indicates that a mistake was made in the procedure. A rapid change with continued
490     occurrences suggest that something occurred that is out of the ordinary, such as a new analyst
491     performing the procedure or a new standard solution or new reagents being used. If an LCS
492     shows elevated concentrations, analysts should check for contamination sources or poorly
493     prepared spiking solutions. Slow changes showing a trend usually indicate degradation or
494     contamination of equipment or reagents and may be indicative of bias and should be investigated.

495     Excursions of MSs can be difficult to interpret if the matrix changes from batch to batch.
496     However, an excursion may indicate that the method is not appropriate for a particular matrix. If


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497     the MS shows lower than expected concentrations, the analyst should check for poor techniques
498     or expired or poorly prepared reagents and spiking solutions.

499     Elevated or depressed results for site-specific MSs need to be interpreted with the results from
500     LCSs. If both the LCS and site-specific MS results are elevated or depressed then the cause is
501     usually internal to the laboratory. If only the site-specific MS is depressed or elevated, the cause
502     usually is due to the matrix.

503     18.4.4 Certified Reference Materials

504     Issue: Certified reference materials (CRMs) are well-characterized, stable, homogeneous
505     materials with physical or chemical properties determined within specified uncertainty limits.
506     Laboratories that analyze CRMs can compare their performance to the certified concentration
507     and uncertainty levels. CRMs are used for the calibration of an apparatus or the assessment of a
508     measurement method.

509     Discussion: Metrology organizations issue CRMs in various matrices with critically evaluated
510     concentration values for the radionuclide constituents. A CRM issued by NIST or under license
511     from NIST is called a "standard reference material" (SRM). The usefulness of a reference
512     material depends on the characterization of the radionuclide source, activity levels, and their
513     estimated uncertainties.

514     CRMs can be used as internal laboratory QC samples to evaluate the ability of analytical methods
515     to handle the matrix. CRMs need not be known to the analyst but can be introduced into the
516     analytical stream as a blind. Comparison of analytical results of CRMs to their certified values
517     provides linkage to the national scale of measurements and a measure of method accuracy.

518     The planning that goes into the preparation of a CRM involves the selection of analytical
519     techniques that have adequate sensitivity and precision for specific analyses. It has become
520     increasingly important to have available well-characterized CRMs of a natural "matrix" type,
521     which may be used in laboratory tests of measurements of environmental radioactivity. Such
522     materials may be used in the evaluation of competing analytical methods, and also in the
523     cross-comparison of interlaboratory data—both at the national level and the international level.

524     The Ionizing Radiation Division of NIST has constructed several SRMs for radiation
525     measurements. These are included in the 4350 series and  can be ordered through NIST. One
526     widely used SRM is the natural matrix ocean sediment (4357). The radionuclides in the NIST
527     natural matrix SRMs are not spiked into the matrix but are incorporated through natural


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528     processes to present the analyst with the combination of species that may be faced on a routine
529     basis. The SRM 4357 has two sediment sources: the Chesapeake Bay (benign) and the Irish Sea
530     ("hot").

531     The NIST natural matrix SRM project has certified actinides, fission and activation radionuclides
532     in soils, freshwater lake and river sediments, human tissues, and ocean sediment, and is working
533     on additional unique matrices:  ashed bone, ocean shellfish, and Rocky Flats Soil-H

534     A numerical performance indicator for the analysis of a CRM is essentially the same as that for a
535     laboratory control sample. An example is
                                         7    _      x - d
                                          CRM    ,        =                                 (s.\
                                                 r^r\    27T                                 i?)
                                                \juc(x) + uc(d)
536     where xis the measured value, dis the certified value, and uc\x) and uc\d) are the squares of the
537     respective combined standard uncertainties. Warning limits for ZCRM are ±2 and control limits
538     are ±3.
539     Excursions: Excursions in the CRM results can be used to identify various out-of-control
540     situations. The advantage of the CRM is that the sample matrix is always the same, and the levels
541     of analytes are known to a high degree,  so uncertainties in matrix effects and radionuclide
542     content should not be a factor in evaluating excursions. A rapid and one-time excursion in  the
543     SRM usually indicates that a mistake was made in the procedure. A rapid change with continued
544     occurrences suggest that something occurred that is out of the ordinary, such as a new analyst
545     performing the procedure or the use of a new batch of calibration solutions or reagents. Slow
546     changes showing a trend usually indicate degradation or contamination of equipment or reagents.

547     If a CRM result shows elevated concentrations, analysts should check for contamination sources
548     or poor instrument calibration. If the results show decreased concentrations, the analyst should
549     check for poor techniques or expired or poorly prepared reagents and solutions.

550     CRM results may indicate a bias in the measurement process. Tracking the performance of
551     several consecutive CRM measurements will show if the method or the laboratory consistently
552     obtains high or low results. If the results are consistently higher or lower than the certified  values,
553     they should be evaluated for a statistical difference, e.g., ^-tested. When the test indicates a
554     statistical difference, a bias is indicated and the laboratory should investigate the cause of the bias
555     and correct or characterize it.

556     Example: The NIST ocean sediment SRM 4357 offers a good example of a material for


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557      evaluating a laboratory performance using a specific analytical method. The blended sediment
558      sample has been analyzed by a number of laboratories, and 10 radionuclides have certified
559      activity values (Lin et al., 2001). The six "natural" radionuclides concentrations tended to have
560      normal distributions (Table 18.2a), while the four "man-made" radionuclides tended to have
561      Weibull distributions (Table  18.2b). There are also 11 other radionuclides where the activity
562      concentrations are not certified at this time but may be at some future time (Table 18.2c).
563
564

565

566
567
568
569
570
571

572
573

574

575
576
577
578
579

580
581
582

583

584
585
586
587
588
589
            TABLE 18.2a — Certified Massic activities for natural radionuclides
                   with a normal distribution of measurement results
Radionuclide
40K
226Ra
228Ra
228Th
230Th
232Th
Mean ± 2sm
(mBqg1)
225 ±5
12.7 ±0.4
13.3 ±0.8
12.1 ±0.3
12.0 ±0.5
13.0 ±0.3
Tolerance Limit
(2.5 to 97.5%)
(mBqg1)
190-259
10.3-15.0
9.2-17.4
9.7-14.6
9.6-14.4
11.6-14.3
Number of
Assays
31
21
20
40
18
18
Half-Life ± Is
(In years)
(1.277 ±0.008) x 109
1600 ± 7
5.75 ±0.03
1.9131 ±0.0009
75380 ± 300
d.405± 0.006) x 1010
         Table 18.2b — Certified Massic activities for anthropogenic radionuclides
                   with a Weibull distribution of measurement results
Radionuclide
90Sr
137Cs
238Pu
239pu
+ 240Pu
Mean ± 2sm
(mBqg1)
4.4 ±0.3
12.7 ±0.2
2.29 ±0.05
10.4 ±0.2
Tolerance Limit
(2.5 to 97.5%)
(mBqg1)
2.1-8.4
10.8-15.9
1.96-2.98
9.3-13.2
Number of
Assays
49
76
65
84
Half-Life ± Is
(In years)
28.87 ±0.04
30.07 ±0.03
87.7 ±0.3
24110 ±30
6564 ±11
Table 18.2c — Uncertified Massic activities. Radionuclides for which there are insufficient data
   or for which discrepant data sets were obtained. Uncertainties are not provided because
                        no meaningful estimates could be made.

Radionuclide
129T
155Eu
210p0
210pb
212pb
214Bi

Mean
(mBq g-1)
0.009
1.4
14
24
14
15


Range of Reported
Results (mBq g1)
0.006
1.2
12
14
13
9
-0.012
-1.5
-15
-35
-14
-20

Number of
Assays
6
2
5
19
5
5
Half-Life ± Is
(In years unless listed
as minutes, hours, or
days)
(1.57 ±0.04) x 107
4.68 ±0.05
138.376 ± 0.002 d
22.3 ±0.2
10.64 ± 0.01 h
19.9 ± 0.4m
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590
591
592
593
594
595
596

597

Radionuclide

234U
235U
237Np
238U
241Am

Mean
(mBq g-1)

12
0.6
0.007
12
10

Range of Reported
Results (mBq g1)

9-15
0.1-1.4
0.004-0.009
7-16
7-18

Number of
Assays

68
63
9
76
97
Half-Life ± Is
(In years unless listed
as minutes, hours, or
days)
(2.45 ± 0.02) x 105
(7.038 ± 0.006) x 108
(2.14 ±0.01) x 106
(4.468 ± 0.003) x 109
432.7 ±0.6
   SRM 4357. Data for these radionuclides are provided for information only. The Massic activities are not
   certified at this time, but may be certified in the future if additional data become available.

18.4.5  Chemical/Tracer Yield
598     Issue: Some methods require that radionuclides should be separated chemically from their
599     sample matrix and purified before measurement. During chemical processing, some of the
600     analyte radionuclide will be lost due to sample spillage, evaporation, incomplete chemical
601     reactions (i.e., precipitation or extraction), etc., as discussed in Chapter 12. While these losses
602     may correlate with a group of samples of similar chemical composition or from the same
603     sampling area, they can be sample specific. For quantitative analysis, it is necessary to correct
604     observed instrument responses for these losses for each analytical sample. Corrections are made
605     using compounds that are stable (carriers) or radioactive (tracers). An inappropriate method for
606     determining chemical yield may result in an analytical bias.

607     Discussion: Most alpha- and beta-emitting radionuclides require chemical separation prior to
608     measurement, in part because of the short effective range of the radiation.

609     CARRIERS. Since it is impossible to determine exactly how much of the analyte is lost during
610     processing, and because the physical mass of the radionuclide is too small to measure gravi-
611     metrically, a compound is  added to the sample at the start of the chemical processing, and is
612     carried through the analytical process and assayed. The added compound typically is stable and
613     exhibits the same chemical properties as the analyte and therefore "carries" the analyte
614     radionuclide—for example, stable barium that carries radium isotopes, or stable yttrium that
615     carries 90Y. These added compounds are called "carriers" and are added in sufficient quantity to
616     allow gravimetric assay upon completion of the analysis. The ratio of the carrier recovered to the
617     amount added is the chemical recovery, or yield. Because the carrier and analyte exhibit similar
618     chemical behavior, the chemical yield of both should be equal, i.e., if 85 percent of the stable
619     barium is recovered, then it follows that the observed instrument response represents 85 percent
620     of the radium present in the sample.
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621     TRACERS. For radionuclides above atomic number 83, stable isotopes do not exist, and a different
622     approach is taken to determine the analyte's yield. For these radionuclides, an isotope other that
623     those being measured is added to the sample in the same manner as described above, e.g., 232U
624     used as a tracer for isotopic uranium (234U, 235U, and 238U), 236U, or 242Pu used as a tracer for
625     isotopic plutonium (238Pu, 239Pu,  and 240Pu).

626     This approach to chemical yield  determination is based on the following assumptions regarding
627     the carrier/tracer:

628       •  It exhibits similar chemical behavior as the analyte under the protocol's conditions.

629       •  The energy emission of the tracer and progeny should not interfere with the resolution of the
630         analytes of interest.

631       •  It is chemically and physically equilibrated with the sample before losses of either occur.

632       •  Indigenous concentrations of carrier or tracer are insignificant, or are well known and can be
633         quantified and corrected for during subsequent data analysis.

634       •  The chemical form of carrier or tracer precipitates are consistent with what was used during
635         the material's preparation and standardization.

636     Care should be taken during the  analytical procedure to ensure that these assumptions are valid.
637     Different conditions, such as a lack of equilibrium between the tracer and sample analyte, can
638     result in inaccurate data. If there is indigenous tracer or carrier in the sample, this quantity should
639     be known so that the appropriate correction can be made for its contribution to the chemical
640     yield. In some cases, this will prevent the procedure's use, as described below. As stated
641     previously, the quantity of tracer or carrier added to the sample should overwhelm its indigenous
642     concentration, which cannot be determined for samples with unknown tracer or carrier content. A
643     separate analysis for trace elements or interfering radionuclides could provide information to
644     estimate the uncertainty contributed by the sample's indigenous tracer or carrier.

645     It should be noted that some analytical methods exclude direct assessment of the procedure's
646     chemical recovery for each sample analysis, e.g., Procedure 908.1 for Total Uranium in Drinking
647      Water (EPA, 1980b). In such cases, chemical recovery is typically addressed by analyzing a
648     group of prepared standards by the same protocol and the results are analyzed statistically to
649     derive a chemical recovery factor. The recovery factor is applied to routine samples based on the
650     assumption that the standards used for its derivation are representative of routine samples. This


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651     approach precludes the empirical assessment of a sample specific chemical recovery, and would
652     probably require scrutiny and periodic verification.

653     Acceptance limits for chemical/tracer yields should be specified in the laboratory's Quality
654     Manual. While it is customary to establish lower limits for chemical yield, upper limits may also
655     be necessary since excessive yields indicate a loss of analytical control. All limits developed by
656     the laboratory should be either statistically based or based on historical data, and should include
657     warning and control limits. The inherent differences among sample matrices generally require the
658     use of matrix specific criteria, i.e., finished drinking water limits may differ from limits for high
659     solid content waters, sandy soils or heterogeneous media.  Irrespective of medium, where
660     practical, the chemical yield and its uncertainty should be  determined, recorded and tracked for
661     each radiochemical measurement.

662     Excursions: There are several possible reasons for the yield to be outside of the acceptance
663     limits. These are summarized in Figure 18.4 and discussed below.
664
665
666
667
668
669

670
671
672
673
674

CHEMICAL YIELD
EXCURSIONS

1


1
  EXCESSIVE YIELDS
                   I
       LOW YIELDS
INTERFERENCE
  -  Containment
     Radionuclide
  -  Indigenous carrier in
     sample

 CHANGED
 CALIBRATION
  -  Source thickness
  -  Source diameter
  -  Source-detector distance
                                         I
PROCEDURE FAILURE
   - Reagent problem
   - Not following procedure
   - Incompatible matrix/
     interference
   - Instrument failure
   - Incomplete separation
HIGHLY VARIABLE YIELDS
                                             I
• NEW MATRIX/
   INTERFERENCE
 -  Reagent concentration

• NOT FOLLOWING
   PROCEDURE

• CONTROL OF VARIABLE
 - Temperature
 - Concentration
 - Time
 - Technique
675
            FIGURE 18.4 — Failed performance indicator: chemical yield
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676         EXCESSIVE YIELDS: A chemical yield significantly greater than 100 percent indicates a
677         problem. Typical causes of excessive chemical yields are provided below:

678          • Interference. The sample may contain an interfering radionuclide that cannot be
679            distinguished from the tracer and therefore biases the tracer response; the sample may
680            contain an indigenous concentration of the tracer or carrier used; or large amounts of
681            another stable element are present.

682          • Counting. Changes in instrument calibration factor or other factors that affect counting,
683            e.g., source thickness, diameter, source-detector distance or change in chemical form of
684            final sample precipitate.

685          • Instrument failure.

686         Low YIELDS: A very low yield usually indicates a procedural failure caused by incomplete or
687         unsuccessful chemical separation, matrix interference, missing reagents, or the exclusion of a
688         key element in the sample processing. A significantly lower yield will increase the overall
689         measurement uncertainty and degrade the procedure's effective detection capability unless
690         the counting time is appropriately extended, which may be impractical or even ineffective in
691         many cases. Furthermore, measurement of the recovered carrier or tracer becomes
692         increasingly more adversely affected by background, stable element, water absorption, and
693         other corrections as the yield decreases. Fixed lower limits for yields often are established
694         and should be specific to analytical procedures and sample matrices. Setting an upper limit is
695         recommended for the acceptable relative uncertainty in a yield measurement.

696         HIGHLY VARIABLE YIELDS: High variability in procedural temperature, concentration, time,
697         reagent concentration, or laboratory technique can have dramatic effects on yield. Highly
698         variable yields indicate a lack  of procedural control  and should be investigated and corrected.
699         A simple step such as heating  samples on a hotplate can lead to variability in yield because
700         the hotplate surface is thermally uneven. Samples can be dried and reconstituted several
701         times during the course of the  preparation protocol,  and samples may require different
702         amounts of heat or water, which introduces additional variability. When highly variable
703         chemical yields are observed, a careful examination of the analytical procedure's application
704         is recommended to determine  critical variables and the controls needed to re-establish
705         adequate management over yields.
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706     18.5  Instrumentation Performance Indicators

707     Radiometric and non-radiometric instruments are used currently to quantify radionuclides in a
708     variety of environmental matrices, and quality control measures are necessary to ensure proper
709     instrument performance. This section presents radiometric instrument performance measures that
710     indicate a measurement system is in control. For detailed information on instrument concepts and
711     specific techniques, see Chapters 15 and 16 as well as ASTM standard practices (e.g., D3648, for
712     the Measurement of Radioactivity). The specific quality control procedures to be followed
713     depend on the measurement equipment. Sufficient checks are needed to demonstrate that the
714     measurement equipment is properly calibrated, the appropriate background has been recorded,
715     and that all system components are functioning properly. QC measures for instrumentation
716     should include at a minimum: (1) instrument background measurements, (2) instrument
717     calibration with reference standards, and (3) periodic instrument performance checks subsequent
718     to the calibration. Acceptable control limits should be specified in the laboratory Quality Manual.

719     18.5.1 Instrument Background Measurements

720     Issue: In general, radionuclide detection covers more than 17 orders of magnitude of sample
721     activity, from irradiated material that produces high radiation fields to environmental samples.
722     All radiation detection instruments have a background response even in the absence of a sample
723     or radionuclide source. To determine the instrument's response to the radioactivity contributed
724     by the sample alone (net), the instrument background response is subtracted from the sample-
725     plus-background response (gross). For discussions on possible contamination, refer to Section
726     18.4.1. Background corrections become more critical when the instrument net response is small
727     relative to the background. Careful control of contamination and routine monitoring of
728     instrument background are therefore integral parts of a control program. Inappropriate
729     background correction results in analytical error and will increase the uncertainty of data
730     interpretation.

731     Discussion: Every radionuclide detector produces a signal response in the absence of a sample or
732     radionuclide source. These signals are produced by electronic dark current, cosmic radiation,
733     impurities in the instrument construction materials, crosstalk between the detector's alpha and
734     beta channels, sources in the general vicinity of the detector, and residual contamination from
735     previous counting episodes. The majority of these contributors to instrument background produce
736     a fairly constant count rate, given sufficient measurement time (i.e., dark current, cosmic
737     radiation, construction material impurities). For other sources, instrument backgrounds vary as a
738     function of time (i.e., from decay or ingrowth of residual contamination or as radon levels
739     fluctuate throughout the day and season). For low-level measurements, it is imperative that the

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740     background be maintained as low as feasible. Active or passive detector shielding, removing or
741     adequately shielding radioactive sources in the vicinity of the detector, and good laboratory
742     practices to prevent residual contamination are necessary to maintain low instrument background.

743     The instrument's background should be determined in the absence of a radionuclide source.The
744     instrument background should be well characterized. The instrument background is an important
745     factor in determining the ability to achieve a specific minimum detectable concentration (MDC).
746     Control limits for the background should be specified in the laboratory's Quality Manual, as
747     appropriate. The background population considered in the statistical calculations should cover a
748     sufficient period of time to detect gradual shifts in the measurement system's background
749     contamination or detector instability. Additionally, backgrounds should be determined in such a
750     way that they mimic actual sample measurement conditions as closely as possible, i.e., using
751     appropriate sample containers, geometries, and counting times.

752     Background measurements  should be made on a regular basis and monitored using control
753     charts. For instruments with well established background performance records and a low
754     probability of detector contamination, this frequency may be modified by the laboratory. For
755     mass  spectrometry and kinetic phosphorimetry analysis,  background measurements should be
756     performed  on a real time basis. See ASTM E181,  ANSI N42.12, and NELAC (2000) Quality
757     Systems Appendix D for more information on the  suggested frequency of background
758     measurement.

759     Excursions: Variations in instrument backgrounds may indicate instrument malfunction. Variations
760     may take the form of rapid increase or decrease in background, slow increase or decrease in back-
761     grounds, and highly variable or erratic backgrounds. These variations can result in the measurement
762     system's reduced precision and decreased detection capability. Rapid or significant increases in
763     background measurements may be due to instrument  or blank contamination, insufficient shielding with
764     relocation of nearby radionuclide sources, or large scale equipment malfunction (e.g., a broken window
765     on a gas proportional system).

766     Instrument background data should be evaluated for trends, which is facilitated by regular
767     observation of control charts. A slowly changing background could alert laboratory personnel to
768     a potentially serious instrument failure. A sufficient number of data points (Chapter 15) taken
769     over time should be included in any trend analysis. Slowly changing instrument backgrounds
770     could be caused by low counting-gas flow rates, small incremental instrument contamination, or
771     electronic drift or noise.

772     When the instrument background is more variable than expected, the reliability of measurements
773     becomes questionable, resulting in loss of confidence and increased uncertainty. This indicates a

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774
775
776

777
778

779
780
781
782
783
784
785
786
787
788
789


790

791
792
793
794
795
796
797

798
799
800
801
802
803
804
805

806
807
loss of control over the measurement environment, or limitations of the data handling software.
The root cause of the variability should be identified and corrected to re-establish statistical
control over the instrument background. Table 18.3 presents reasons for changing backgrounds.

	TABLE 18.3 — Instrument background evaluation	
                       Instrument Background Failed Performance Indicator
 Rapid Change in Background
 Electronic failure
 Detector failure
 Loss of coolant/vacuum
 Instrument contamination
 Counting gas changes
 Temperature/humidity fluctuation
 Laboratory contamination
 External sources
 Insufficient shielding
 Personnel with nuclear medicine dose
 Slow Change in Background
Instrument contamination
Electronic drift
Low counting gas flow rate
Excessively Variable Background
Sources being moved
Radon fluctuation
Insufficient shielding
Insufficient counting statistics
Interfering radionuclides
Poor peak deconvolution
Intermittent electrical short
Failing electronics
18.5.2 Efficiency Calibrations

Issue: This section discusses selected aspects of instrument calibration that are pertinent to
laboratory quality control. A more in-depth, technical discussion is provided in Chapter 16. The
number of events (counts) recorded by a detector is converted to activity (actual radionuclide
transformations) by empirically determining this relationship with NIST-traceable radionuclide
sources when available. This relationship is expressed in the system's efficiency calibration. A
separate efficiency is determined for each detector-source combination and is typically energy or
radionuclide specific.

Detector efficiency is critical for  converting the detector's response to activity. As discussed
above, routine performance checks can evaluate several aspects simultaneously (sample
geometry, matrix, etc.) and provide a means to demonstrate that the system's operational
parameters are within acceptable limits. These are typically included in the assessment of the
analytical method's bias and are specified in terms of percent recovery based on the source's
known disintegration rate. Performance checks for measurement efficiency are usually
determined statistically based on  repeated measurements with a specific check source. Detection
of a shift in measurement efficiency should be investigated.

The frequency of performance checks for efficiency calibrations is instrument specific. The
frequency of these checks is often based on a standardized time scale or a percentage of the total
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808     number of analyses performed using that method.

809     Performance checks for instrument efficiency typically are performed on a day-of-use basis. The
810     level of activity in the check source should be sufficient to allow the accumulation of enough
811     counts in a short time so that daily performance checks do not impose an unnecessary burden on
812     the laboratory. However, the source strength for spectrometry systems should be such that
813     instrument dead time is not significant and gain shifts do not occur (ANSI 42.23). For detectors
814     that are used infrequently, it may be necessary to perform a check before and after each set of
815     measurements.

816     Control charts provide a useful tool for documenting and evaluating performance checks for
817     efficiency calibrations, and should be established and maintained for the intrinsic efficiency of
818     each detector. There are several methods available for evaluating performance using control
819     charts (see Attachment ISA).

820     Discussion: Most radiation detectors do not record all of the nuclear transformations that occur
821     in samples undergoing measurement, i.e., they are not one hundred percent efficient. This occurs
822     for several reasons, and the prominent reasons are discussed briefly below.

823       •  Intrinsic or absolute efficiency2 - In the absence of all other factors, a  detector will only
824         record a fraction of the emissions to which it is exposed due to its composition and other
825         material-related aspects. Intrinsic efficiency is a measure of the probability that a count will
826         be recorded when a particle or photon of ionizing radiation is incident on a detector (ANSI
827         Nl.l).

828       •  Geometry - The spatial arrangement of sample, shielding, and detection equipment, including
829         the  solid angle subtended by the detector and sample configuration,  largely determines what
830         fraction of the emissions from the source actually reach the detector (ANSI N15.37).
831         Geometry includes the source's distance from the detector and its spatial distribution within
832         the counting container relative to the detector and shielding components.

833       •  Absorption - Radiation emitted by the sample can be absorbed by the sample itself (self
          Efficiency measures the fraction of emitted photons or particles that are actually detected. It is affected by the
         shape, size, and composition of the detector as well as by the sample-to-detector geometry. There are two ways that
         efficiency can be expressed: "Absolute efficiency" is the fraction of all the photons or particles emitted by the
         source that are actually detected, and "intrinsic efficiency" is the ratio of photons or particles detected to the number
         that actually fall on the detector.

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834         absorption), as well as other materials placed between the source and the detector, i.e.,
835         sample container, detector housing and shielding (NCRP 58).

836       •  Backscatter - Radiation emitted by the sample can hit the sample container and scatter into
837         the detector.

838     The detector response is a composite of these factors.

839     Each radiation detector should be calibrated to determine the relationship between the observed
840     count rate of the detector and the disintegration rate of the source being assayed. This
841     relationship is called the efficiency calibration—typically expressed in counts per second/
842     disintegration per second, or cps/dps—and is an integral part of the measurement protocol. For
843     alpha spectrometry systems, the efficiency of detection is energy-independent. Efficiencies for
844     gamma spectrometry are energy dependent, and an efficiency calibration typically covers a range
845     for a specific counting geometry, e.g., 50 to 1,800 kilo electron volts (keV).

846     Once this relationship is established, it should be checked at regular intervals using what is called
847     a performance or calibration check.  The performance check does not seek to reestablish the
848     detector's efficiency but simply demonstrates that the relationship is within acceptance limits.
849     When  designed properly, an efficiency performance check evaluates the intrinsic efficiency,
850     geometry and absorption in a single measurement. Accordingly, it takes the form of a single
851     value that incorporates all effects for a target radionuclide and a specific detector-sample
852     configuration. Detectors that are energy dependent and measure radionuclides with multiple
853     energies, such as photon or alpha spectrometers, should have performance checks at several
854     energies throughout the measurement range. For these detectors, the performance check can
855     simultaneously address the system's efficiency, energy calibration and resolution using a single
856     source. An internal pulser can be used to check the electronics.

857     Because the performance check's purpose is to demonstrate that the system's efficiency remains
858     constant, the source's absolute disintegration rate need not be known, provided its purity can be
859     established, its half-life is known, and its activity is sufficient to provide  adequate precision.
860     Accordingly, it is not necessary to use a NIST-traceable check source for this purpose. Check
861     sources that are non-NIST-traceable can meet the precision objectives of the performance check
862     and they are less expensive.

863     Excursions: Changes in the efficiency of a detector can only be corrected by determining the
864     root cause of the problem  and repeating the efficiency calibration. Gradual changes in geometry
865     usually indicate a problem with the technique of sample mounting or preparation. A visual


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866     inspection of the prepared sample is often helpful in eliminating sample geometry as a source of
867     the problem. For example, a precipitated sample counted on a gas proportional counter has an
868     expected appearance, i.e., a circle of precipitate centered on the planchet and often covered with
869     thin plastic film. If the prepared sample does not have the correct appearance, there could be a
870     problem with the geometry, self-absorption, and backscatter. This can sometimes be corrected by
871     preparing the sample a second time, inspecting it and presenting it for counting a second time.
872     Re-training personnel responsible for the error may also be indicated. Because samples that have
873     been improperly prepared for counting can result in contamination of or physical damage to the
874     detector, it is strongly recommended that every sample be visually inspected prior to counting.
875     Significant changes in geometry caused by modifications to the source preparation method can
876     only be corrected by recalibrating the detector. Examples of modifications to source preparation
877     methods are (1) using a new filter so that the geometry of the test source is different than the
878     geometry used for calibration, and (2) replacing the containers used for gamma spectrometry with
879     containers that have a different wall thickness or are made from different materials.

880     Changes in intrinsic efficiency generally result from a physical change to the detector and often
881     result in rapid changes in efficiency. In many cases, changes that affect the intrinsic efficiency  of
882     a detector render it inoperable. These are specific to a detector type and are listed below:

883      •  HPGe, Ge(Li), and surface barrier detectors - Real or apparent changes in intrinsic efficiency
884         caused by vacuum leaks or failure of field effect transistor.

885      •  Thin window detectors (gas proportional counters, low-energy photon) - Changes in
886         measurement efficiency are typically associated with damage to the detector window.

887      •  Gas proportional systems - Problems with efficiency related to the quality or flow of
888         counting gas.

889      •  Anti-coincidence systems with guard detectors - Electrical problems with the anti-
890         coincidence circuits that may produce apparent changes in efficiency.

891      •  Scintillation detectors - Gradual changes in efficiency are associated with the scintillator or
892         the photomultiplier tube. For example, Nal(Tl) crystals may gradually turn yellow over time
893         resulting in a lower intrinsic efficiency, and liquid scintillation counters may have residue
894         gradually build up on the  surface of the photomultiplier tube affecting the detection of
895         photons by the tube.
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896     18.5.3 Spectrometry Systems

897     18.5.3.1    Energy Calibrations

898     Issue: This section discusses selected aspects of instrument calibration that are pertinent to
899     laboratory quality control. A more in depth, technical discussion is provided in Chapter 16. All
900     radiation measurements are energy dependent to a certain extent. However, spectrometric
901     techniques such as gamma and alpha spectrometry identify radionuclides based on the energy of
902     the detected radiations. For these techniques a correct energy calibration is critical to accurately
903     identify radionuclides. Problems with energy calibration may result in misidentification of peaks.

904     Discussion: Spectrometry systems should be calibrated so that each channel number is correlated
905     with a specific energy. To identify radionuclides correctly, this energy calibration needs to be
906     established initially and verified at regular intervals. The energy calibration is established by
907     determining the channel number of the centroid of several peaks of known energy over the
908     applicable energy range. Typically, a minimum of three peaks is used, and commercially
909     available sources contain nine or ten photopeaks. The relationship between energy and channel
910     number can be determined by a least squares fit. To account for non-linearity, a second or third
911     order fit may be used. However, these require more points to define the curve. For example, a
912     first order calibration requires at least two points, while a second order calibration requires a
913     minimum of three  points. The end points of the curve define a range of applicability over which
914     the calibration is valid, and peaks identified outside the curve's range should be used carefully.
915     The uncertainty associated with the curve should be available at any point along the calibration
916     curve.

917     Quality control checks for energy calibration may be combined with checks for efficiency cali-
918     bration and resolution. Radiations emitted over the range of energy of interest are measured, and
919     two or more peaks are used to demonstrate that the energy calibration falls within acceptable
920     limits. Check sources may consist of a single radionuclide (e.g., 137Cs or 60Co) or a mixture of
921     radionuclides (e.g., mixed gamma). Because only the location of the peak is of concern, there is
922     no requirement that the check source be  calibrated or certified, except for ensuring that it does
923     contain the radionuclide(s) of interest at  a specified level of purity.

924     The energy calibration is determined when the system is initially set up by adjusting the gain of
925     the amplifier, analog-to-digital conversion (ADC) gain, and zero. Criteria that indicate when
926     readjustment is required because of gradual and abrupt changes in the energy versus channel
927     calibration should be established as an integral part of the system's operating procedure.  These
928     changes usually are monitored by the measurement system's software,  and the user specifies the

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929     allowable difference between that the system's response and the radionuclide's known energy.
930     The tolerable difference often relates to the instrument's resolution. For example, a high resolu-
931     tion instrument such as an intrinsic germanium detector typically will have acceptable limits on
932     the order of a few keV, while a low resolution instrument such as a Nal(Tl) detector typically
933     will have acceptable limits on the order of several tens of keV.

934     Spectra also can be analyzed by identifying each peak manually. With manual identification, the
935     acceptable limits for the energy calibration are determined for each spectrum based on the pro-
936     fessional judgment of the person analyzing the spectrum.

937     The frequency of QC checks for energy calibrations can be related to the expected resolution of
938     the instrument, the electronic stability of the equipment, or the frequency needs of QC
939     measurements for efficiency calibration or resolution. These are specified typically in the
940     laboratory's Quality Manual or other typical project-related documentation. Examples for three
941     detector types are provided below and in Table 18.5.

942      •  HPGe and Ge(Li) Photon Detectors. Energy calibrations are typically verified using a check
943         source on a day of use basis. Every sample spectrum should include verification of the energy
944         calibration as part of the data review process, when possible. Under extreme  conditions  (e.g.,
945         in situ measurements in bad weather), it may be necessary to perform checks at the beginning
946         and end of each measurement period or day the instrument is used.

947      •  Surface Barrier Alpha Spectrometry Detectors. The energy calibration is often performed
948         using an alpha source when the instrument is setup initially and when a detector has been
949         serviced or replaced. Electronic pulsers can be used for daily  checks on energy calibration.
950         Most alpha spectra include a chemical yield tracer with a peak of known energy that can be
951         used to verify the energy calibration during data review. Alpha spectrometers have a lower
952         resolution than germanium detectors, and newer spectrometers are sufficiently stable to  allow
953         weekly or monthly performance checks. The frequency of performance checks should be
954         based on the number and frequency of measurements and historical information on the
955         stability of the instrument.

956      •  Low-Resolution Nal(Tl) Detectors. These typically are less stable than HPGe detectors and
957         may require more frequent quality control checks, depending on the conditions under which
958         they are used.

959     For all detectors where energy calibrations are performed daily, plotting the channel numbers of
960     peak centroids can be useful for identifying trends and determining the need for adjusting the


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961     system. Changes in peak location may result in mis-identification of radionuclides. When this is
962     observed, all spectra obtained since the last acceptable energy calibration check should be
963     reviewed. If there is sufficient information within the spectrum to determine the acceptability of
964     the energy calibration, no further action may be required for that spectrum. If the spectrum con-
965     tains too few peaks of known energy, reanalysis should be initiated.

966     Gradual changes in peak location are not unexpected and the rate of these gradual changes can be
967     used to establish the appropriate frequency of energy calibration checks. The acceptable limits  on
968     peak location established during the initial system setup may be used to indicate when the energy
969     calibration needs to be readjusted.

970     Excursions: Changes in the energy calibration can be the result of many factors including power
971     surges, power spikes, changes in the quality of the electrical supply, variations in ambient condi-
972     tions (e.g., temperature,  humidity), physical  shock to the detector or associated electronics, and
973     electronic malfunction.

974     Rapid  changes in energy calibration are usually caused by power surges, power spikes, or physi-
975     cal shocks to the system. Corrective actions  typically involve recalibrating the system and repeat-
976     ing the analysis. If changes result due to loss of cryostat vacuum, the instrument may need to be
977     returned to the manufacturer to be refurbished or replaced.

978     Gradual changes in the energy calibration are usually the result of a variable or poorly condi-
979     tioned power source, changes in the ambient conditions, or electronic malfunction. Corrective
980     actions generally begin with identifying the root cause of the problem. Gradual changes that
981     begin following relocation of the instrument are more likely to be caused by the power source or
982     the ambient conditions. Installing a line conditioner, surge protector, and uninterrupted power
983     supply is recommended  to address problems related to the system's electrical power source.
984     Problems with low humidity can be corrected through the use of a humidifier in dry climates or
985     cold weather; conversely, high or variable humidity may require the use of a dehumidifier. Prob-
986     lems associated with fluctuations in temperature may require significant changes to the heating
987     and cooling system for the room or building containing the instrument in order to stabilize the
988     temperature. Gradual changes that occur following physical shocks to the system or following  a
989     rapid change in peak location with an unidentified cause are more likely to be the result of prob-
990     lems with the electronic equipment.  In most cases the amplifier is the source of these problems,
991     but the analog-to-digital converter, pre-amplifier,  power supply voltages, and multi-channel (or
992     single-channel) analyzer may also cause this type  of problem. However, they could also be the
993     result of crystal or detector failure. Systematic switching out of components and discussions with
994     the instrument manufacturer will often help  to identify which component may be the source of

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 995      the trouble. It may be especially difficult to identify the source of problems with new instruments
 996      in a new facility.

 997      18.5.3.2  Peak Resolution and Tailing

 998      Issue: The shape of the full energy peak is important for identifying radionuclides and quantify-
 999      ing their activity with spectrometry or spectrometry systems. Poor peak resolution and peak
1000      tailing may result in larger measurement uncertainty. If consistent problems with peak resolution
1001      are persistent, then an  analytical bias most likely exists. Many factors will affect peak resolution
1002      and these are discussed below.

1003      Discussion: Detectors with good resolution permit the identification of peaks which are close in
1004      energy.  When a monoenergetic source of radiation is measured with a semiconductor, scintilla-
1005      tion, or  proportional spectrometer, the observed pulse heights have  a Gaussian distribution
1006      around the most probable value (Friedlander et al., 1981). The energy resolution is usually
1007      expressed in terms of the full width at half maximum (FWHM) or the full width at tenth maxi-
1008      mum (FWTM).

1009      In a semiconductor detector, fluctuations in output pulse height result from the sharing of energy
1010      between ionization processes and lattice excitation (Friedlander, et al., 1981). The number of
1011      charge pairs created by radiation of a given energy will fluctuate statistically. This fluctuation
1012      occurs because the energy causes lattice vibrations in the semiconductor as well as the formation
1013      of charge pairs. This sharing of energy causes a variation in the number of charge pairs created
1014      and gives rise to the width of a measured peak. The magnitude of the statistical fluctuation is pro-
1015      portional to the energy of the radiation. There is also a variation in the number of charge pairs
1016      collected by a detector. This variation is accounted for by the Fano factor. Because several poorly
1017      understood factors degrade resolution in a semiconductor detector, an empirical value of the
1018      Fano factor should be used.

1019      In a scintillation detector, the statistical fluctuations in output pulse heights arise from several
1020      sources. The conversion of energy of ionizing radiation into photons in the scintillator, the elec-
1021      tronic emission at the photocathode, and the electron multiplication at each dynode are all subject
1022      to statistical variations. Note that the distance of the sample to the detector also impacts the
1023      resolution.

1024      In a proportional counter, the spread in pulse heights for monoenergetic rays absorbed in the
1025      counter volume arises from statistical fluctuations in the number of ion pairs formed and the gas
1026      amplification factor (Friedlander, et al.,  1981). If the gas gain is made sufficiently large, the


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1027      fluctuations in the number of ion pairs determine the resolution.

1028      The FWHM is typically used as a measure of resolution, while the FWTM is used as a measure
1029      of tailing for the full energy peak. For Gaussian peaks with standard deviation a, the FWHM is
1030      equal to 2.35a. The resolution of a detector is the ratio of the FWHM to the most probable peak
1031      height. The sources of fluctuations that contribute to the standard deviation are dependent on the
1032      type of detector.

1033      Resolution affects the ability to identify individual peaks in two ways (Gilmore and Heming-
1034      way, 1995). First, it determines how close together two peaks may occur in energy and still be
1035      resolved into the two components. Second, for gamma spectrometry, when a peak of small mag-
1036      nitude sits on the Compton continuum of other peaks, its ability to be detected can depend on its
1037      signal-to-noise ratio. With good resolution, the available counts are distributed in fewer channels,
1038      thus those counts will be more easily identified as a peak by the spectrometry analysis software.
1039      If resolution degrades significantly the efficiency may be in error. This is especially true when the
1040      spectrum analysis involves the region of interest (ROI) concept. When the calibration is per-
1041      formed, the full energy peak may fit within the defined ROI limits, whereas the resolution
1042      degraded peak may have counts which fall outside them. Thus, the detector efficiency will be
1043      effectively decreased and inconsistent with the previously determined efficiency.

1044      Tailing is another observable feature of the peak shape. Tailing is an increased  number of counts
1045      in the channels on either side of the full energy peak. Tailing affects the FWTM more than the
1046      FWHM, so the ratio of FWTM to FWHM can be used as a measure of tailing. For a Gaussian
1047      distribution the ratio of FWTM to FWHM is 1.823. For most germanium detectors this ratio
1048      should not exceed 2.0. Tailing may be caused by imperfect or incomplete charge collection in
1049      some regions of the detector, escape of secondary electrons from the active region of the detector,
1050      electronic noise in the amplification and processing circuitry, loss of vacuum and escape of
1051      bremsstrahlung from the active region of the detector. Tailing may also result from the source's
1052      self-absorption for alpha emitting radionuclides.

1053      The resolution (FWHM) is routinely calculated for gamma and alpha spectrometry peaks by the
1054      spectrum analysis software and can be monitored by observing the FWHM calculated for the
1055      check sources routinely counted. Resolution monitoring and charting is normally an integral part
1056      of a measurement quality system. Acceptance parameters may be established for resolution and
1057      incorporated in the analysis software. For alpha spectrometry, where radionuclide tracers are used
1058      for  chemical yield determination, the FWHM can be monitored for each analysis, if desired.
1059      Some projects may specify FWHM limits for internal tracer peaks on each sample run.
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1060     The shape of the peak is important for quantifying the activity, and resolution is important for
1061     identifying peaks in a spectrum. The shape of the peak is also important for monitoring the per-
1062     formance of a detector. Germanium detectors have very good resolution on the order of 1 per-
1063     cent. The FWHM at specific energies is provided by the manufacturer. The FWHM should be
1064     established at several energies throughout the range being measured because the FWHM is
1065     directly proportional to the energy. These energies are usually the same as those used for check-
1066     ing the energy calibration and the efficiency calibration. Control limits for FWHM and the ratio
1067     of FWTM to FWHM may be developed based on statistics using multiple measurements
1068     collected over time.

1069     The resolution of an alpha spectrum is dominated typically by self-absorption in the source. This
1070     is indicated by low energy tailing and elevated FWTM and FWHM. Most surface barrier detec-
1071     tors are capable of resolutions on the order of 30-40 keV for monoenergetic nuclides and 80-100
1072     keV for unresolved  multiplets. Acceptance of sample resolution is usually monitored by visual
1073     inspection of individual spectra. For well-prepared samples, the FWHM of the  alpha peaks may
1074     be expected to be from 30 to 80 keV.

1075     The resolution of scintillation detectors is not as good as the resolution of semiconductor detec-
1076     tors, but peak shape and tailing are just as important for analyzing samples. The FWHM should
1077     be established at several energies throughout the range being measured because the FWHM is
1078     inversely proportional to the energy. These energies are usually the same as those used for check-
1079     ing the energy calibration and the efficiency calibration. Control limits for FWHM and the ratio
1080     of FWTM to FWHM may be developed based on statistics using multiple measurements
1081     collected over time.

1082     Proportional counters are not used as spectrometers in many laboratories, so it is not necessary to
1083     perform checks for resolution and peak shape.

1084     Performance checks for resolution and tailing should be performed for all instruments used as
1085     spectrometers. These measurements are usually combined with the performance checks for
1086     energy calibration and efficiency calibration. Quality control activities should include visual
1087     inspection of all spectra to evaluate peak shape and tailing.

1088     Control charts for FWHM and the ratio of FWTM to FWHM can be developed and used to mon-
1089     itor the performance of any detector used as a spectrometer. Because the concern is when the
1090     resolution degrades (i.e., the FWHM increases) or tailing becomes a problem (i.e., the ratio of
1091     FWTM to FWHM increases), control limits are necessary. Limits can be developed based on
1092     historical performance  for a specific type of detector. Control charts offer a convenient method


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                                                                         Laboratory Quality Control
1093
1094
1095

1096
1097
1098
1099
1100
1101
1102
1103
1104

1105
1106
1107
1108
1109
1110
1111

1112

1113

1114
1115
for monitoring the results of the performance checks. As mentioned previously, the concern is
associated with an increase in the FWHM or the ratio of FWTM to FWHM. This means that only
an upper control limit or tolerance limit is required for the chart.

Excursions: Changes to the FWHM are associated with malfunctioning or misadjusted elec-
tronics, excessive noise or interference, or detector or source problems. Electronics problems
include changes in the high voltage applied to the detector, noise (including cable noise and high
voltage breakdown), and electronic drift. Electronics problems may be caused by changes in the
high voltage, improper adjustment of the pole zero or baseline restorer, or drift of the amplifier
gain or zero during acquisition. Source problems are usually only associated with alpha spectra
and result in excessive self-absorption resulting in low-energy tailing. This can result in counts
being identified with an incorrect peak. Problems that are not electronic or source related imply
that the detector is malfunctioning.

Changes to the  ratio of FWTM to FWFDVI indicate problems associated with tailing. Tailing can
occur on the high- or low-energy side of the peak. High-energy tailing indicates electronics prob-
lems that may be caused by excessive activity in the sample, incorrect adjustment of the pole zero
or pile-up rejector, or drift of the amplifier gain or zero while acquiring the spectrum. Low-
energy tailing indicates an electronic or a source problem—a possible corrective action is to
check to see if the vacuum is set properly. Table 18.4 lists common problems, the implied root
cause of the problem, and possible corrective actions.

             TABLE 18.4 — Root cause analysis of performance check results
1116

1117
1118
Observed Problem
Efficiency changed
Peak centroid moved
FWHM changed
FWTM: FWHM
changed
Implied Root Cause
Unknown
Electronics degradation
Geometry changed
Poor source
Software application
Gain changed
Offset shifted
Electronics problem
Electronics problem
Possible Corrective
Actions
Ensure the correct check source was used
Check to ensure the efficiency was evaluated using the
correct geometry
Ensure high voltage is set properly
Pulser check of electronics
Check amplifier gain
Check conversion gain
Check stability of amplifier for gain
Check zero offset
Check digital offset
Check stability of amplifier for gain
shifts or drifting
shifts or drifting
Ensure high voltage is set properly
Detector problem
Ensure high voltage is set properly
Detector problem
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         Laboratory Quality Control
1119
1120
1121
1122
1123
1124

1125
1126
1127


1128


1129
1130
1131


1132
1133
1134
1135
1136
Observed Problem

No peak or broad
peaks
Low-energy tailing
High-energy tailing
Spectra shifted
uniformly
Spectra stretched or
compressed
Implied Root Cause
Source problem
Electronics problem
Electronics problem
Source problem
Electronics problem
Source problem
(too much activity)
Offset shifted
Gain changed
Possible Corrective Actions
Repeat sample preparation and recount
Reanalyze sample
Check with weightless (plated) source
Ensure that high voltage is correct
Detector problem
Ensure that high voltage is correct
Check pole zero adjustment
Check baseline restorer
Check stability of amplifier for gain shifts or drifting
Check for loss of vacuum
Repeat sample preparation and recount
Reanalyze the sample
Check pole zero adjustment
Check pile-up rejector
Check stability of amplifier for gain shifts or drifting
Reduce volume of sample analyzed
Increase distance between the source and detector
Check zero offset
Check digital offset
Check amplifier for zero drift
Check amplifier gain
Check conversion gain
Check amplifier for sain shifts
18.5.4 Gas Proportional Systems

18.5.4.1   Voltage Plateaus

Issue: The accuracy of the results produced by a gas proportional system can be affected if the
system is not operated with its detector high voltage adjusted, such that it is on a stable portion of
the operating plateau.

Discussion:  The operating portion of a detector plateau is determined by counting an appropriate
source at increasing increments (e.g., 50 volts) of detector high voltage. For detectors which will
be used to conduct analyses for both alpha- and beta-emitting radionuclides, this should be done
with both an alpha and beta source. The sources used should be similar in both geometry and
energy to that of the samples to be counted in the detector.
1137      A plot of the source count rate (ordinate) versus high voltage (abscissa) rises from the baseline to
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                                                                          Laboratory Quality Control
1138     a relatively flat plateau region, and then rises rapidly into the discharge region for both the alpha
1139     and beta determinations. From the plateau, the operating voltage is selected or verified. The oper-
1140     ating potential is usually selected in the middle of the plateau. It remains advisable to assure that
1141     the operating point is as far as practical above the plateau knees, and in any case not less than 50
1142     to 100 volts. Operation of the counter at the upper end of the plateau is not recommended and
1143     can result in the generation of spurious discharge counts. Modern high-voltage supplies, oper-
1144     ating properly, experience little actual potential variance. The detector response should be
1145     checked after repairs and after a change of gas. The detector plateau should again be determined
1146     and plotted (voltage vs.  count rate) after repairs,  particularly to the detector unit.

1147     The historical tracking of the establishment and maintenance of this operating parameter is
1148     recommended; it aids in determining the probable cause of quality  control failure and the identi-
1149     fication of long-term instrument deterioration. Items to be recorded include date/time, instrument
1150     detector designation, source number, check source response at the operating point, and pertinent
1151     instrument parameters, such as lower level discriminator setting, alpha discriminator setting,
1152     length of the plateau, operating high voltage setting, etc.

1153     Excursions: Voltage changes of short- or long-term duration will affect reliability of a propor-
1154     tional counter. If the potential is lowered sufficiently, there is a  danger of operating below the
1155     plateau knee which,  in effect, reduces the  efficiency and would  bias the results of any sample
1156     count low. Should the voltage applied to the proportional detector be driven up to a point where
1157     the slope of the plateau is sufficiently great enough to increase the efficiency of the detector,
1158     sample counts may be biased high. A transient voltage increase  of great enough magnitude could
1159     introduce spurious counts.

1160     Shifts in the operating voltage along the plateau or length of the plateau could also result from
1161     long-term detector deterioration or electronic drift or failure.

1162     18.5.4.2   Self-Absorption, Backscatter, and Crosstalk

1163     Issue:  The accuracy of alpha and beta activity  determinations in samples with discernable solids
1164     in a gas proportional system depends in large part on the determination and maintenance of self-
1165     absorption and crosstalk curves.

1166     Discussion: Samples counted for alpha and beta activity in a gas proportional system are typi-
1167     cally prepared as inorganic salts, e.g., nitrates,  carbonates, oxides, sulfates, or oxalates, and
1168     contain on the order of tens to hundreds of milligrams of solids  when counted, which result in
1169     absorption and scattering of the particles in the sample material  and mounting planchet (Chapter


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1170     16). Thus, for gas proportional systems, the detection efficiency for a given sample depends on
1171     the self-absorption occurring within each sample volume/mass. To establish the correction factor,
1172     a calibration curve is generated using a series of standards consisting of an increasing amount of
1173     solids and known amounts of radionuclide. The relative efficiency for each calibration source is
1174     plotted against the amount of solids, and these data are used to determine a sample's efficiency as
1175     a function of sample weight. The diameter and the composition of the sample planchette, not just
1176     the weight, should be identical with what was used for routine samples. This allows calculation
1177     of the corrected amount of activity regardless of the sample mass (mass/efficiency curves).

1178     The counting of alpha and beta particles  simultaneously in a proportional counter requires that an
1179     electronic discriminator be adjusted, such that pulses of heights below that represented by the
1180     discriminator are registered as  betas, and those of greater heights are counted as alphas. Crosstalk
1181     occurs when alpha particles are counted in the beta channel or betas are registered as alphas. For
1182     electroplated sources, crosstalk may be as low 1 percent for betas in the alpha channel and 3
1183     percent for alphas in the beta channel. However, this relationship is energy dependent, and care
1184     should be taken to identify samples that differ significantly from the sources used to establish the
1185     crosstalk ratio. For example, 90Sr/90Y (Emax 2.28 meV) is typically used as a beta source for
1186     instrument calibration. However, samples containing natural uranium in equilibrium with its
1187     progeny produce beta emissions that are  considerably more energetic from the 3.28 MeV Emax
1188     betas of 214Bi. The crosstalk ratio established with 90Sr will be inadequate for such samples.

1189     As the amount of solids in the  sample increases, the alpha into beta crosstalk increases, due to the
1190     degradation of the alpha particle energy by interaction with sample material. Similarly, the beta
1191     into alpha crosstalk decreases.  Thus, crosstalk should be evaluated as a function of sample
1192     weight to correct the observed  relative alpha and beta counts. This is normally determined in
1193     conjunction with the self-absorption curve. To check these parameters, test samples should be
1194     prepared at the low and high ends of the  calibration curve, and the limit of their acceptability
1195     should be better than 1 percent (one sigma). These checks should be performed annually at a
1196     minimum, following detector replacement or significant repair. The historical tracking of the
1197     establishment and maintenance of these operating parameters is recommended. This aids in
1198     determining the probable cause of quality control failure and the identification of long-term
1199     instrument deterioration. In addition, items to be recorded include date/time, instrument detector
1200     designation, source number, operating point, and pertinent instrument parameters, such as lower
1201     level discriminator setting, alpha discriminator setting, etc.

1202     Excursions: Any change in the detector-source geometry or adsorption characteristics between
1203     the source and detector,  can affect the self-absorption and crosstalk correction factors. For
1204     example, the replacement of a  detector window with one whose density thickness is different


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                                                                        Laboratory Quality Control
1205
1206

1207

1208
1209
1210
1211

1212

1213
1214
1215
1216
1217
1218

1219
1220
1221
1222
1223
from the original window can necessitate the reestablishment of these parameters. Electronic drift
of the alpha discriminator can also affect the crosstalk ratios.

18.5.5  Liquid Scintillation

Issue: A liquid scintillation counter is essentially a spectrometer that utilizes a multi channel
analyzer to differentiate alpha or beta emission energies. These samples are subject to interferen-
ces from a variety of sources for which corrections should be made to produce useful data. A
detailed discussion of liquid scintillation counting is provided in Chapter 15.

18.5.6  Summary

Table 18.5 provides some example calibration needs, performance frequency, and performance
criteria, listed by detector type. Individual laboratories may be more or less stringent. These items
are just presented as examples for consideration in this section. The table is presented mainly for
the reader to establish their own criteria and is not intended to be a  set of minimum requirements.
For additional sources of information, see the calibration frequencies for several detector systems
given in ASTME181 and ANSI N42.12.

    TABLE 18.5 — Instrument calibration: example frequency and performance criteria
1224
1225
Example
Calibration Needs
Measurement Parameters
Performance Frequency
Performance Criteria
Gas Proportional System
Initial calibration
Background
counting
Plateau checks as applicable
Crosstalk or sensitivity as
applicable
Counting efficiency to
calculate activity in sample
Weight of solids, when mass
loading is applicable, to
calculate sample activity
Count detector background
using contamination-free clean
planchet
After repairs or major
maintenance on control of
system is re-established
After repairs or major
maintenance on control of
system is re-established
Upon incorporation of new or
changes protocols

One per week or batch when
the system is in use
Plot voltage versus counting
activity to estimate proper
operating voltages for both
alpha and beta
Crosstalk of alpha in beta:
less than 10%; Crosstalk or
sensitivity of beta in alphas:
less than 1%
Counting uncertainty <1%;
<3% uncertainty (2s) over
calibration range
Establish a curve for
efficiency versus mass
loading; <3% uncertainty
(2s) over calibration range
Establish a background
count rate value for total
alpha and beta, with
N>1000
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Laboratory Quality Control
Example
Calibration Needs
Counter control or
control standard
Measurement Parameters
Use a source of appropriate
energies
Performance Frequency
One per day when the system
is in use
Performance Criteria
Control limits: three sigma
or ± 3%, whichever is
greater
Gamma Spectrometry
Initial calibration
Background
Counter control or
control standard
Detector energy calibration
Counting efficiency matrix-
and geometry-specific
Counter detector background
to establish background level
Multi energy source covering
the general energy calibration
range
After repairs or major
maintenance if control of
system cannot be re-
established

Minimum of every week or
after analytical run, whichever
is longer
One per week or after
analytical run, whichever is
longer
Covers energy range of
desired nuclides; resolution
should be sufficient to
separate gamma-ray lines of
interest from background
peaks and other interfering
lines
Span energy range of
nuclide of interest

Control limits: three sigma
or ± 3%, whichever is
greater
Alpha Spectrometry
Initial calibration
Background
Counter control or
control standard
Energy calibration
Counting efficiency matrix-
and geometry-specific
Counter detector background
to establish background level
At least two isotopes
Monitor peak location,
resolution and efficiency
(where counting efficiency is
an analytical requirement).
After repairs or major
maintenance if control of
system cannot be re-
established

Minimum of every other week
or after analytical run,
whichever is longer
One per week or after
analytical run, whichever is
longer
No specific criteria, pending
on total channel and range
of energy spectrum of
desired nuclides
Span energy range of
nuclide of interest

Control limits: three sigma
or ± 3%, whichever is
greater
Liquid Scintillation
Initial Calibration
Calibration
Dark blank to check
photomultiplier tube
External (instrumental)
calibration
After mechanical or electronic
repairs
After repairs or major
maintenance if control of
system cannot be re-
established
Check against
manufacturer's
specifications
Check against
manufacturer's
specifications
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                                                                        Laboratory Quality Control
Example
Calibration Needs
Method Calibration
(Determining
quenching)
Background
Counter control or
control standard
Batch-approach
calibration
(Alternative
aroroach)
Measurement Parameters
Quench curve (at least five
points)
Internal standard
Counter detector background

Minimum two matrix-matched
standards and blanks
Performance Frequency
If matrix or cocktail changes
Add to each sample type
One per day or analytical
batch when the system is in
use
One per day or batch when
system is in use
One per batch
Performance Criteria



Control limits: three sigma
or ± 3%, whichever is
greater
Counting efficiency control
limits: three sigma or ± 5%,
whichever is greater
1241
1242
1243

1244
1245
1246

1247
1248
1249
1250
1251
Sources: ASTME181; ANSIN42.12.
1252
18.5.7  Non-Nuclear Instrumentation
1253      Radioactivity and radionuclide measurement techniques also employ the use of non-nuclear
1254      instrumentation such as mass spectrometry, fluorimetry, phosphorimetry, and fission tract.
1255      Although these instruments are not covered in MARLAP, analysts can apply many of the
1256      laboratory QC techniques discussed in Sections 18.3, 18.4, and 18.6 because they are basic to any
1257      laboratory method. A quality program using statistically based control charts of the performance
1258      indicators will identify out of control situations, assist in improving laboratory performance and
1259      aid in identifying the causes of trends and biases for any laboratory method. Analysts also need to
1260      consider detection capabilities, radionuclide secular equilibrium, half-life, interferences, and
1261      blind samples when using non-nuclear instrumentation.

1262      18.6  Related Concerns

1263      18.6.1  Detection Capability

1264      Issue: The detection capability of an analytical procedure is its ability to distinguish small
1265      amounts of analyte from zero (Chapter 19). The detection capability of a procedure can be
1266      estimated nominally and will depend on many factors.

1267      Discussion: In radioanalysis, the most commonly used measure of detection capability is the
1268      minimum detectable concentration (Chapter 19). The MDC is defined as the smallest concentra-
1269      tion of an analyte that has a specified probability of detection, typically 95 percent. The MDC is
1270      usually estimated as a nominal scoping performance measure of an analytical procedure, but a
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         Laboratory Quality Control
1271      sample-specific version is reported routinely by many laboratories.

1272      Detection capability is affected by many factors, including counting times, instrument back-
1273      ground levels, aliquant volume, yield, decay times, and interferences. The nominal MDC is
1274      presumably based on conservative assumptions about these factors, but measurement conditions
1275      vary. The sample-specific MDC is calculated using the actual measured values of all these
1276      factors. A high MDC by itself does not indicate that a sample result is invalid or that it cannot be
1277      used for its intended purpose. However, if an analysis fails to detect the analyte of interest and
1278      the sample-specific MDC is greater than a detection limit required by contract or other
1279      agreement, it may be necessary to reanalyze the sample in a way that reduces the MDC. Such
1280      decisions should be made case-by-case, since it is not always cost-effective or even possible to
1281      reanalyze a sample, or it may not be feasible to achieve the desired MDC.

1282      Excursions: A high sample-specific MDC  can be caused by many factors, including:

1283       • Small sample aliquant;
1284       • Low chemical/tracer yield;
1285       • Short counting times;
1286       • Long decay/short ingrowth time;
1287       • High background or blank value; and
1288       • Low counting efficiency or sample self-attenuation.

1289      18.6.2  Secular Equilibrium

1290      Issue: It is sometimes necessary to ensure that target radionuclides are in secular equilibrium
1291      with their progeny, or to establish and correct for disequilibrium conditions. This is particularly
1292      applicable for protocols that involve the chemical separation  of long-lived radionuclides from
1293      their progeny. This is also applicable for nondestructive assays like gamma spectrometry where
1294      photon emission from progeny is used to determine the concentration of the non-gamma ray
1295      emitting parent.

1296      Discussion:  Some radionuclides that have long physical half-lives decay to species whose half-
1297      lives are  shorter by several orders of magnitude. Following chemical separation of the parent, the
1298      progeny can "grow in" within a time frame relevant to analysis and provide measurable radio-
1299      active disintegration which should be considered in the analytical method. The condition where
1300      the parent and progeny radionuclide are equal in activity is called "secular equilibrium." An
1301      example  is 226Ra, a common, naturally occurring radionuclide in the uranium series with a half-
1302      life of about 1,600 years. 226Ra is found in water and soil, typically in secular equilibrium with a


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                                                                        Laboratory Quality Control
1303      series of shorter-lived radionuclides that begins with the 3.8-day-half-life 222Ra and ends with
1304      stable lead. As soon as 226Ra is chemically separated from its progeny in an analytical procedure
1305      via coprecipitation with barium sulfate, its progeny begin to reaccumulate. The progeny exhibit a
1306      variety of alpha, beta and gamma emissions, some of which will be detected when the precipitate
1307      is counted.  The activity due to the ingrowth of radon progeny should be considered when evalua-
1308      ting the counting data (Kirby,  1954). If counting is performed soon after chemical separation,
1309      secular equilibrium will be substantially incomplete and a sample-specific correction factor
1310      should be calculated and applied. In some cases, it may be necessary to derive correction factors
1311      for radioactive ingrowth and decay  during the time the sample is counting. These factors are
1312      radionuclide specific, and should be evaluated for each analytical method.

1313      Secular equilibrium concerns  also apply to non destructive assays, particularly for uranium and
1314      thorium series radionuclides. Important radionuclides in these series (e.g., 238U and 232Th) have
1315      photon emissions that are weak or otherwise difficult to measure, while their shorter-lived
1316      primary, secondary or tertiary progeny are easily measured. This allows for the parents to be
1317      quantified indirectly, i.e., their concentration is determined by measuring their progeny and
1318      accounting for the amount of parent-progeny equilibrium. The amount of parent-progeny secular
1319      equilibrium is fundamental to these analyses, and data should be scrutinized to insure that the
1320      amount is valid.

1321      When several radionuclides from one decay chain are measured in a sample, observed activity
1322      ratios can be compared to those predicted by decay and ingrowth calculations, the history of the
1323      sample and other information. For example, undisturbed soil typically contains natural uranium
1324      with approximately equal activities of 238U and 234U, while water samples often have very
1325      different 238U/234U ratio. Data  from  ores or materials involved in processing that could disrupt
1326      naturally occurring relationships require close  attention in this regard.

1327      All calculational protocols (electronic and manual) should be evaluated to determine if there is
1328      bias with respect to correction factors related to equilibrium concerns. This includes a check of
1329      all constants used to derive such correction factors, as well as the use of input data that unam-
1330      biguously state the time of all  pertinent events (chemical  separation and sample counting). The
1331      analyst should ensure that samples requiring progeny ingrowth are held for sufficient time before
1332      counting to establish secular equilibrium. Limits for minimum ingrowth and maximum decay
1333      times should be established for all analytical methods where they are pertinent. For ingrowth, the
1334      limits should reflect the minimum time required to ensure that the radionuclide(s) of interest has
1335      accumulated sufficiently to not adversely affect the detection limit or uncertainty. Conversely, the
1336      time for radioactive decay of the radionuclides of interest should be limited such that the decay
1337      factor does not elevate the MDC or adversely affect the measurement uncertainty. These will


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         Laboratory Quality Control
1338     vary depending on the radionuclide(s) and analytical method.

1339     Excursions: Samples where equilibrium is incorrectly assumed or calculated will produce data
1340     that do not represent the true sample concentrations. It is difficult to detect errors in equilibrium
1341     assumptions or calculations. Frequently, it takes anomalous or unanticipated results to identify
1342     these errors. In these cases, analysts need to know the sample history or characteristics before
1343     equilibrium errors can be identified and corrected. Some samples may not be amenable to
1344     nondestructive assays because their equilibrium status cannot be determined; in such cases, other
1345     analytical methods are indicated.

1346     Examples:

1347         Isotopic Distribution - Natural, Enriched and Depleted  Uranium: Isotopic distribution is
1348         particularly important with respect to uranium, an element that is ubiquitous in nature in soils
1349         and also a contaminant in many site cleanups.  The three predominant uranium isotopes of
1350         interest are 238U, 234U, and 235U, which constitute 99.2745, 0.0055, and 0.72 atom percent,
1351         respectively, of "natural" uranium3, i.e., uranium as found in nature (General Electric, 1984).
1352         However, human activities related to uranium  typically involve changing the ratio of natural
1353         uranium by separating the more readily fissionable 235U from natural uranium to produce
1354         material "enriched" in 235U, for use in fuel cycle and nuclear weapons related activities.
1355         Typical 235U enrichments range from 2 percent for reactor fuels to greater than 90 percent 235U
1356         for weapons. The enrichment process also produces material that is "depleted"  in 235U, i.e.,
1357         the uranium from which the 235U was taken.4 While the 235U concentrations of depleted
1358         uranium are reduced relative to natural ores, they still can be measured by several assay
1359         techniques. This gives rise to uranium with three distinct distributions of 238U, 235U, and 234U,
1360         referred to as "natural," "enriched," and "depleted" uranium. Because 238U, 235U, and 234U are
1361         alpha emitters with considerably different physical half-lives and specific activity, a measure-
1362         ment of a sample's total uranium alpha activity cannot be used to quantify the sample's
1363         isotopic composition or uranium mass without knowing if the uranium is natural or has been
1364         enriched or depleted in 235U. However, if this information is known, measurement and
1365         distribution of the sample's uranium alpha activity can be used to infer values for a sample's
1366         uranium mass and for the activities of the isotopes 238U, 235U, and 234U. This ratio can be
1367         determined directly or empirically using mass  or alpha spectrometry, techniques which are
         3 The "natural abundance" of 235U of 0.72 atom percent is a commonly accepted average. Actual values from
         specific ore samples vary.

         4 Enriched and depleted refer primarily to 235U.

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1368         time and cost intensive, but which provide the material's definitive isotopic distribution. It is
1369         often practical to perform mass or alpha spectrometry on representative samples from a site to
1370         establish the material's isotopic distribution, assuming all samples from a given area are
1371         comparable in this respect. Once established, this ratio can be applied to measurements of
1372         uranium alpha activity to derive activity concentrations for 238U, 234U, and 235U data.

1373     18.6.3 Half-Life

1374     Issue: Radionuclides with short half-lives relative to the time frame of the analysis may decay
1375     significantly from the time of sample collection or chemical separation to counting. In some
1376     cases, this decay will cause the ingrowth of other short-lived radionuclides. In both instances,
1377     sample-specific factors should be applied to correct the sample's observed counting/disintegra-
1378     tion rate. Also, determination of half-life could indicate sample purity. If radioactive impurities
1379     are not appropriately corrected, analytical errors will occur. Consecutive counting of the sample
1380     may confirm the radionuclide impurity by analyzing the decay rate between counting events.

1381     Discussion: When assaying for short-lived radionuclides, data should be corrected for decay over
1382     the time period between sample collection and counting. For example, operating power reactors
1383     routinely assay environmental samples for 131I, a fission product with about an eight-day half-life.
1384     Samples may be counted for several days up to two weeks, during which time their 131I concen-
1385     tration is decreasing via radioactive decay. Using the eight-day half-life, the counting data should
1386     be decay-corrected to the time of collection in the field. If desired,  environmental samples can be
1387     decay-corrected to a time other than sample collection.

1388     Half-life considerations also apply to radionuclide ingrowth. Certain radionuclides are assayed by
1389     an initial chemical separation which begins a period over which their direct progeny are allowed
1390     to come to secular equilibrium; this is followed by chemical separation, purification and counting
1391     of the progeny. After counting, the degree of the progeny's ingrowth is calculated, based on the
1392     radionuclides' half-lives and the elapsed time between separation and counting. Allowance
1393     should also be made for the progeny's decay from separation to counting and for decay that
1394     occurred while counting, if applicable. Two examples are the beta emitting radionuclides 228Ra
1395     and 90Sr: they are quantified by measuring the direct progeny of each, 228Ac and 90Y, respectively.
1396     For airborne concentrations of 222Rn,  sample collection and analytical methods should incorpor-
1397     ate concerns related to the short-lived progeny of other radon species, such as 220Rn. Other half-
1398     life related considerations  apply to alpha spectrometry when assaying samples for uranium and
1399     thorium chain radionuclides. Samples that have been allowed to sit for several weeks may
1400     accumulate short-lived radionuclides that have alpha emissions whose energies are in close
1401     proximity to target radionuclides. These can interfere with quantitative analyses of the target


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1402     radionuclides. Chemical yield tracers used in alpha spectrometry, such as 234Th and 232U, can
1403     cause this effect due to their short-lived progeny and all chemical yield tracers should be
1404     scrutinized for this potential prior to their use in analytical methods. Radionuclide specific limits
1405     for minimum ingrowth and maximum decay times should be established for all analytical
1406     methods where they are pertinent. These should be based on limiting the adverse effect of such
1407     calculations on the detection limit and measurement uncertainty. All analytical methods
1408     involving computational corrections for radioactive decay of the target species should be
1409     evaluated relative to half-life and secular equilibrium related concerns. This evaluation should be
1410     incorporated in the routine data review process that is performed on all analytical results.

1411     A good source for radionuclide half-lives and other nuclear data can be found at the Brookhaven
1412     National Laboratory's National Nuclear Data Center (http://www.nndc.bnl.gov/nndc/nudat/).
1413     Using this data source will ensure consistency within and among laboratories, and will provide
1414     analysts with the current values.

1415     Excursions: Samples that are assayed by "non destructive" techniques like gamma spectrometry
1416     may provide indications of potential complications due to half-life related considerations.
1417     Because the assay provides information on photon emitting radionuclides in the sample, the
1418     analyst can develop appropriate corrections  for half-life related phenomena. However, non-
1419     spectrometric techniques like gas flow proportional counting are essentially gross counting
1420     procedures that record all events without any indication of their origin. Therefore, these data
1421     should be evaluated to ensure they are free from half-life related considerations.

1422     Samples with short-lived radionuclide concentrations at or near environmental background will
1423     experience elevated detection limits and increased measurement uncertainty if there is excessive
1424     elapsed time between sample collection and counting. Because there is an additional correction
1425     factor in the algorithms for these samples (decay factor), they are more susceptible to
1426     measurement uncertainty than longer-lived radionuclides.

1427     18.6.4 Interferences

1428     Issue: Chemical or radionuclide interferences can produce erroneous results or increased
1429     measurement uncertainty.

1430     Discussion: Analytical samples, particularly environmental samples, are often chemically
1431     complex.  This complexity may include chemical constituents or other physical aspects that
1432     interfere with an analytical method to the point that they require modification of the method.
1433     Examples of modifications include limiting the size of the sample aliquant, quantifying


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1434      interfering compounds through other analyses (radiometric and non-radiometric) and changing
1435      time periods to allow adequate ingrowth of target radionuclides or decay of interferences.

1436      A common example is groundwater or well water that contains high concentrations of salts or
1437      dissolved solids, so that screening for gross alpha activity produces erratic or anomalous results.
1438      For such samples, it may be necessary to limit the aliquant volume with the resulting increase in
1439      detection limit and measurement uncertainty. There is a concentration at which this procedure
1440      cannot overcome the interferences and should not be used.

1441      Samples that contain natural concentrations of stable or unstable compounds that an analytical
1442      procedure adds to the sample for a specific purpose (carrier or tracer) may also be problematic
1443      because the sample's concentration interferes with the analysis. Because barium is used as a
1444      carrier, water samples that contain high concentration of barium may provide inaccurate carrier
1445      yields when screened for alpha-emitting radium isotopes. Quantifying the sample's barium
1446      content prospectively via a non-radiometric technique (e.g., atomic absorption) would be
1447      required to correct for this interference. With respect to unstable  compounds, two examples are
1448      provided. The first involves the radiochemical procedure for determining 228Ra in drinking water
1449      that separates radium via coprecipitation with barium sulfate. The precipitate is allowed to come
1450      to equilibrium with its direct progeny 228Ac, which is separated via co-precipitation with yttrium
1451      oxalate, purified, mounted and counted. The yttrium precipitate also carries 90Y, the direct
1452      progeny of 90Sr, a fission product often found in environmental samples as a result of
1453      atmospheric weapons testing and nuclear fuel cycle activities. Samples assayed for 228Ra may
1454      contain measurable amounts of 90Sr that require corrections based on differences in half-life
1455      (228Ac with a 6-hour half-life versus  90Y with a half-life of about 64 hours) or other parameters.
1456      The second example involves alpha  spectrometry procedures that use tracers to determine
1457      chemical yield. For example, 234Th is used as a chemical yield tracer for isotopic thorium
1458      analyses. The approach assumes that the sample's inherent concentration of the tracer
1459      radionuclide is insignificant such that it will not interfere with the tracer's ability to accurately
1460      represent the sample's chemical recovery. Samples that contain measurable amounts of these
1461      radionuclides may produce excessive interference and may not be amenable to this procedure.

1462      Alpha spectra should be checked for radionuclide interferences, e.g. look for 238U peak in a Pu
1463      spectra. If the 238U peak is present, 234U might be an interference in the 239Pu and 240Pu
1464      determinations. Data can be corrected or the sample may require reanalysis.

1465      Each analytical method should be evaluated with  respect to interferences, when its use is
1466      proposed or at least prior to their implementation  in the laboratory. Such evaluations can be
1467      based on available information and,  if properly documented, can serve as the basis for developing


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1468     the range of applicability, which becomes an integral part of the protocol. Evaluating
1469     performance indicators aids in the identification of samples that have interferences. All
1470     performance criteria would be protocol specific, and have clearly established acceptance ranges
1471     that incorporate the potential interferences discussed above.

1472     Excursions: Interfering elements can affect measurement results in several ways. For example,
1473     large amounts of non-analyte elements may overload ion exchange resins, affecting the resin's
1474     ability to collect all of the analyte. In addition, spiking elements, already in the sample prior to
1475     preparation, may cause matrix spike results to exceed acceptance limits.

1476     Carrier/tracer yields exhibiting gradual changes that appear to be correlated with a batch or group
1477     of samples from the same sampling location may indicate potentially interfering conditions. A
1478     significant decrease in the carrier/tracer recovery may indicate that the analytical method is not
1479     functioning as planned. Yields that are significantly low or in excess of 100 percent may be
1480     caused by competing reactions within the sample matrix, or by the presence of inherent
1481     concentrations of carrier/tracer within the sample.

1482     For screening analyses, e.g., gross alpha or beta, large changes in counting efficiencies or erratic
1483     counting data can reflect the presence of salts. Samples of this type are hydroscopic, and continue
1484     to gain weight following preparation in planchettes as they absorb moisture from the air. These
1485     changes could be detected by reweighing the planchettes directly prior to counting. These
i486     samples can be converted to oxides by carefully holding them over the  open flame of a laboratory
1487     burner; however, this will cause losses of volatile radionuclides, predominantly 210Po and 137Cs,
1488     which have alpha and beta emissions, respectively. An alternative approach is to thoroughly dry
1489     each planchette,  record the weight and count it immediately, followed by a post-counting
1490     weighing to ensure that the weight  did not change significantly over the measurement period.
1491     This approach may not be practical for all laboratories.

1492     18.6.5 Negative Results

1493     Issue: When an instrument background measurement is subtracted from a measurement of a low-
1494     activity sample, it is possible to obtain a net activity value less than zero.

1495     Discussion: Many factors influence the evaluation of negative results. The simplest case occurs
1496     when the background measurement is unbiased and both the gross counts and background counts
1497     are high enough  that the distribution of the net count rate is approximately normal. In this case,
1498     normal statistics can be used to  determine whether a negative result indicates a problem. For
1499     example, if a sample contains zero  activity, there is a very small probability of obtaining a net


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1500      count rate more than two-and-a-half or three standard deviations below zero Since the combined
1501      standard uncertainty is an estimate of the standard deviation, a result that is less than zero by
1502      more than three times its combined standard uncertainty should be investigated. In fact, if a blank
1503      sample is analyzed using an unbiased measurement process, negative results can be  expected
1504      about 50 percent of the time. As long as the magnitudes of negative values are comparable to the
1505      estimated measurement uncertainties and there is no discernible negative bias in a set of
1506      measurements, negative results should be accepted as legitimate data and their uncertainty should
1507      be assessed. On the other hand, if a sample activity value is far below zero, there may be a reason
1508      to investigate the result. A large percentage of negative results may also indicate a problem, even
1509      if all of the results are near zero. When instrument backgrounds are extremely low, statistics
1510      based on a normal distribution may not be appropriate (Chapter 19).

1511      A preponderance of results that are negative, even if they are close to zero, indicates either a
1512      systematic error or correlations between the results. If the results are measured independently, a
1513      pattern of negative results indicates a bias, which requires investigation.

1514      Excursions: Negative results occur routinely when samples with low levels of activity are
1515      analyzed, but a result should seldom be more than a few standard deviations below zero. Possible
1516      causes for extremely negative results or for an excessive number of negative values  include:

1517       • Instrument failure (low sample counts or high blank counts);
1518       • Positive bias in the background or reagent blank measurement;
1519       • Overestimation of interferences;
1520       • Data transcription error; or
1521       • Calculation error.

1522      18.6.6  Blind Samples

1523      Issue: The performance of the analytical method should be assessed independently on a regular
1524      basis. This assessment is achieved through  the use of blind samples that provide an objective
1525      means of evaluating the laboratory's performance for specific analytes and matrices. Blind
1526      samples can be internal or external, and either single  or double.  External blind PE samples are
1527      used for QA purposes and also can provide information that is useful to laboratory QC.

1528      Discussion: A blind sample is a sample whose concentration is  not known to the analyst, and
1529      whose purpose is to assess analytical performance. Regardless of their nature, blind  samples are
1530      effective only when their contents are unknown to the analysts. The preparation of all blind and
1531      other performance assessment samples is usually designated as a QA function. The QA staff


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1532      functions independently from personnel responsible for sample processing and analysis. Blind
1533      samples consist of a matrix routinely processed by the laboratory that contains a known amount
1534      of one or more analytes (radionuclides). A blind also may take the form of a replicate sample that
1535      is submitted for analysis such that its composition and origin are unknown to the analyst. These
1536      can be split samples (if run in the same batch) or spiked samples, and are prepared and submitted
1537      by an independent group either within the organization (internal), or from an independent
1538      organization (external). Performance on blind samples should be an integral part of the labora-
1539      tory's quality system, which includes routine evaluation of them against specific  performance
1540      criteria. For example, analysis of blind samples should be evaluated for relevant performance
1541      indicators. Data that fall outside an acceptance criterion may indicate loss of control in sample
1542      chemical processing, radiometric determination (counting) or other aspects of the analytical
1543      process. The ability to prepare blind samples depends fundamentally on the ability to obtain the
1544      appropriate combination of matrix with a radionuclide of a well-known concentration, ideally
1545      traceable to NIST or other appropriate certifying body. Also important are the expertise and
1546      experience of the preparer of the blind samples, proven and verified methodologies used for the
1547      blind samples, and detailed documentation. The use of blind  samples assumes that their physical,
1548      chemical and radiological nature are compatible with the analytical methods employed at the
1549      laboratory.

1550      When the analyst is aware that the sample is a blind sample but does not know the concentration,
1551      these samples are called single blinds. In the case of replicates , the analyst is not aware that two
1552      samples are the same; for spiked samples, the analyst may know what analytes the blind sample
1553      contains, but not the analyte's concentration. Single blinds and other internal samples of this type
1554      are generally prepared by  an organization's QA personnel that are independent of the samples'
1555      analyses. External single blind samples are  available and can be obtained from several sources.

1556      A double blind sample is the same as a single blind except that it is submitted for analysis as a
1557      routine sample. The sample should be identical in appearance to a routine sample, and the analyst
1558      is not forewarned of the analytes in the sample. In general, a double blind is thought to be a more
1559      rigorous indication of the  laboratory's performance, since analysts and other laboratory personnel
1560      may take special precautions when analyzing known PT samples, in anticipation  of the greater
1561      scrutiny associated with such samples.  This should not happen with double blind samples, since
1562      there should be no way to distinguish them  from routine samples. However, true  double blind
1563      samples are difficult to prepare.

1564         INTERNAL BLIND SAMPLES. Internal blind samples are prepared by the laboratory's QA
1565         personnel. Internal blind samples assess several aspects of the analytical process. They allow
1566         the laboratory to demonstrate that it can successfully process routine samples for a specific


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1567          analysis; in other words, they get a measured result within accepted limits. They provide an
1568          auditable, empirical record against specific quality performance criteria. They also demons-
1569          trate the efficacy of analytical methods and areas in need of adjustment. Double blind
1570          samples can pose logistical problems. It may be difficult to prepare internal double blind
1571          samples and submit them to the laboratory for analysis successfully disguised as routine
1572          samples. Evaluation criteria should be established to identify when conditions are out of
1573          acceptance limits.

1574          EXTERNAL BLIND SAMPLES. External blind samples are those prepared by an organization
1575          outside that laboratory. This may be helpful with respect to ensuring that the analyte
1576          concentrations are truly unknown to the analyst; external blinds may offer a greater variety of
1577          matrices and analytes than can easily be produced within the laboratory and augment the
1578          laboratory's internal quality control program. Alternatively, if external blinds are not
1579          appropriate to the laboratory's programs, they will be of limited utility.

1580          If differences between observed and known values typically arise, these should be
1581          investigated thoroughly, as they indicate areas where important details of the analytical
1582          process may have been overlooked. Often a laboratory's observed values  agree with the
1583          known value within acceptable tolerances, but are biased high or low. Careful documentation
1584          of the laboratory's performance in this regard can assist in characterizing  the fluctuations of a
1585          measurement system or analytical method. Like other performance indicators, large or sudden
1586          changes in bias require scrutiny.

1587      Blind samples should be an integral part of the laboratory's quality control program and they
1588      should be processed according to a predetermined schedule. Important sources of external blind
1589      samples include the NIST Radiochemistry Intercomparison Program (NRIP), National Voluntary
1590      Accreditation Program (NVLAP/EPA), Food and Drug Administration, DOE Lab Accreditation
1591      Program (DOELAP), Quality Assessment Program (DOE QAP), and Multi-Analyte Performance
1592      Evaluation Program (DOE MAPEP).

1593      Excursions: The excursions typically encountered with analytical methods for specific
1594      parameters (carrier/tracer recovery, lack of precision, elevated backgrounds, etc.) apply to blind
1595      samples as well. Additionally, instances where the analysis of external blinds produces values
1596      that do not agree with the known values, may indicate that instrument calibrations or other
1597      correction factors require reevaluation. Problems revealed by the analysis of blind blank samples
1598      can indicate a problem (e.g., bias, blunder) within the laboratory, or conditions where the current
1599      protocol is inadequate. Excursions discovered while analyzing samples from  external PE
1600      programs should be addressed.


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1601      18.6.7 Calibration of Apparatus Used for Weight and Volume Measurements

1602      Issue: Fundamental to all quantitative analysis is the use of the proper weights and volumes.
1603      Analysts should perform careful gravimetric and volumetric measurements (especially in the
1604      preparation of calibration solutions, test sources, and reagents) in order to achieve the desired
1605      levels of precision and bias in each analytical method. Therefore, laboratory balances and
1606      volumetric glassware and equipment should be calibrated and checked periodically to maintain
1607      the desired method performance levels. This section discusses the calibrations of laboratory
1608      balances and volumetric glassware and equipment.

1609      Discussion: Laboratory balances should be periodically calibrated and checked. Most balances
1610      are typically calibrated and certified by the manufacturer once a year. These calibrations are
1611      performed to achieve the manufacturer's specified tolerances for each balance. A calibration
1612      certificate is supplied to the laboratory. In addition to this yearly calibration, daily calibration
1613      checks should be performed by the laboratory. Some laboratories check the balances once a day
1614      or at the time of each use. Any balance failing the daily calibration check should be taken out of
1615      service. Ordinarily, ASTM E617 Class 1 or 2 weights are used to perform the daily calibration
1616      check, depending on application. Over time, daily wear and tear on the weights can affect
1617      calibration, so it is a good idea to get them periodically re-certified or to purchase new weights.

1618      Volumetric glassware and equipment, especially those used in the preparation of instrument
1619      calibration solutions and laboratory control samples,  should be calibrated to the desired level of
1620      accuracy. Calibration can either be performed by the  manufacturer of the equipment or by
1621      laboratory personnel. Calibration certificates for volumetric pipets and flasks are provided by the
1622      manufacturer at the time of purchase.  Borosilicate and pyrex volumetric glassware will hold its
1623      calibration indefinitely provided that it is not  exposed to hydrofluoric acid, hot phosphoric acid
1624      or strong alkalis, and that it is not heated above  150 °C when drying. Any glass volumetric pipet
1625      with a damaged tip should be discarded or re-calibrated. The manufacturer of volumetric
1626      automatic pipetting equipment calibrates the equipment and provides a certificate at the time of
1627      purchase. The re-calibration of automatic equipment should be performed annually and can be
1628      performed by the manufacturer, calibration specialty  companies, or in-house laboratory
1629      personnel. Outside calibration services should provide a calibration certificate.

1630      Laboratory personnel can calibrate and check volumetric apparatus using procedures like those
1631      specified in ASTM E542. Typically calibrations use volumes of water and are gravimetrically
1632      based. Volumes are corrected for temperature and atmospheric pressure and require thoroughly
1633      cleaned glassware, standard procedures for setting and reading the water meniscus, and accurate
1634      balances and thermometers.


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1635      Volumetric glassware is calibrated either "to contain" (TC) or "to deliver" (TD). Glassware
1636      designated as "to contain" requires the complete emptying of the vessel to yield the specified
1637      volume. "To deliver" glassware does not require complete emptying. Specified volumes for this
1638      type of apparatus do not include the residual left from surface adhesion and capillary action. TD
1639      glassware will perform with accuracy only when the inner surface is so scrupulously clean that
1640      the water wets it immediately and forms a uniform film when emptying.

1641      18.7   References

1642      18.7.1  Cited Sources

1643      American National Standards Institute/International Standards Organization/American Society
1644         for Quality Control (ANSI/ISO/ASQC) A3 534-2. Statistics- Vocabulary and Symbols-
1645         Statistical Quality Control.

1646      American National Standards Institute/American Society for Quality Control (ANSI/ASQC) E4.
1647          1994. Specifications and Guidelines for Quality Systems for Environmental Data Collection
1648         and Environmental Technology Programs.

1649      American National Standards Institute (ANSI) Nl. 1. American Nuclear Standard Glossary of
1650          Terms in Nuclear Science and Technology, 1976.

1651      American National Standards Institute (ANSI) N15.37. Guide to the Automation of
1652         Nondestructive Assay Systems for Nuclear Material Control. 1981.

1653      American National Standards Institute (ANSI) N42.12. American National Standard. Calibration
1654         and Usage of Thallium-Activated Sodium Iodide Detector Systems for Assay of
1655         Radionuclides.

1656      American National Standard Institute (ANSI) N42.23. Measurement and Associated
1657         Instrumentation Quality Assurance for Radioassay Laboratories. 1996.
1658
1659      American Society for Testing and Materials (ASTM) D3648, Standard Practices for the
1660         Measurement of Radioactivity., 1995.

1661      American Society for Testing and Materials (ASTM) D6299, Standard Practice for Applying
1662         Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System
1663         Performance, 2000

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1664      American Society for Testing and Materials (ASTM) E542, Standard Practice for Calibration of
1665         Laboratory Volumetric Apparatus, 2000.

1666      American Society for Testing and Materials (ASTM) E617, Standard Specification for
1667         Laboratory Weights And Precision Mass Standards, 1997.

1668      American Society for Testing and Materials (ASTM) El 81, Standard Test Methods for Detector
1669      Calibration and Analysis ofRadionuclides.

1670      American Society for Testing and Materials (ASTM) E882, Standard Guide for Accountability
1671         and Quality Control in the Chemical Analysis Laboratory.

1672      American Society for Testing and Materials (ASTM) MNL 7, Manual on Presentation of Data
1673         and Control Chart Analysis ASTM Manual Series, 6th Edition, 1990.

1674      Friedlander, G., Kennedy, J.W., Macias, E.S., and Miller, J.N. 1981. Nuclear and
1675         Radiochemistry. 3rd Edition, John Wiley and Sons, New York.

1676      General Electric Company. 1984. Chart of the Nuclides, Thirteenth Edition.

1677      Gilmore,  G. and Hemingway, J.D. 1995. Practical Gamma-Ray Spectrometry. Wiley, Chi Chester,
1678         England.

1679      International Standards Organization (ISO) 5725-1. Accuracy (Trueness and Precision) of
1680         Measurement Methods and Results—Part 1: General Principles and Definitions.

1681      International Standards Organization (ISO) 7870.  Control Charts - General Guide and
1682         Introduction.

1683      International Standards Organization (ISO) 7873.  Control Charts for Arithmetic Average With
1684         Warning Limits.

1685      International Standards Organization (ISO) 7966. Acceptance Control Charts.

1686      International Standards Organization (ISO) 8258. Shewhart Control Charts.

1687      International Standards Organization/International Electrotechnical Commission (ISO/IEC)
1688         17025. General Requirements for the Competence of Testing and Calibration Laboratories.


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1689         December 1999, 26 pp.

1690      Kirby, H.W. 1954. Decay and Growth Tables for the Naturally Occurring Radioactive Series.
1691         Anal. Chew. 26:6, p. 1063-1071.

1692      Lin, Z., K. G. W. Inn, and J. J. Fiilben. 2001. An alternative statistical approach for
1693         interlaboratory comparison data evaluation. Journal of Radioanalytical and Nuclear
1694         Chemistry, 248:1, 163-173.

1695      National Council on Radiation Protection and Measurements (NCRP) 58: A Handbook of
1696         Radioactivity Measurement Procedures, Second Edition. Bethesda, MD. February 1985.
1697         (Supersedes First Edition, November 1978.)

1698      National Environmental Laboratory Accreditation Conference (NELAC). 2000. Quality Systems
1699         Appendix D, Essential Quality Control Requirements. Revision 14. June 29. Available at
1700         http://www.epa.gov/ttn/nelac/2000standards.html.

1701      National Bureau of Standards (NBS). 1964. Handbook of Mathematical Functions. M.
1702         Abramowitz and Stegun, I, Editors.

1703      U.S. Environmental Protection Agency (EPA). 1977. Handbook for Analytical Quality Control
1704         in Radioanalytical Laboratories. EPA-600-7-77-088.

1705      U.S. Environmental Protection Agency (EPA). 1980. Prescribed Procedures for Measurement of
1706         Radioactivity in Drinking Water—Procedure 904.0, Determination of Radium-228 in
1707         Drinking Water. EPA 600-4-80-032.

1708      U.S. Environmental Protection Agency (EPA). 1980. Prescribed Procedures for Measurement of
1709         Radioactivity in Drinking Water—Procedure 908.1 for Total Uranium in Drinking Water.
1710         EPA 600-4-80-032.

1711      18.7.2 Other Sources

1712      American National Standards Institute (ANSI) N42.22. American National Standard.
1713         Traceability of Radioactive Sources to the National Institute of Standards and Technology
1714         (NIST) and Associated Instrument Quality Control.

1715      Chase, G.D. and Rabinowitz, J.L. 1969. Principles of Radioisotope Methodology. 3rd Edition,


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         Laboratory Quality Control
1716         Burgess Publishing Co., Minneapolis, MN.

1717      Kanipe, L.G. 1977. Handbook for Analytical Quality Control in Radioanalytical Laboratories.
1718         EPA-600/7-77-088.

1719      U.S. Environmental Protection Agency (EPA). 1995. Guidance for the Preparation of Standard
1720         Operating Procedures (SOPs) for Quality-related Documents. QA/G-6. EPA 600-R-96-027.
1721         Available at http://www.epa.gov/oerrpage/superfund/programs/clp/download/epaqag6.pdf.

1722      Zeigler, L.H. and Hunt, H.M. 1977. Quality Control for Environmental Measurements Using
1723         Gamma-Ray SpectrometryEPK 600-7-77-144.
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                                                                         Laboratory Quality Control
1724                               Attachment ISA: Control Charts

1725      18A.1  Introduction

1726      This attachment provides statistical details to augment Section 18.3.2. The term "statistical
1727      quality control" refers to QC based on statistical principles. Generally, statistical QC in the
1728      laboratory applies the principles of hypothesis testing, with varying degrees of rigor, to make
1729      inferences about a measurement system or process. The primary tool for statistical QC is the
1730      control chart.

1731      The most important purpose for statistical QC in the laboratory is to ensure that measurement
1732      uncertainties are properly estimated. The uncertainty estimate that accompanies a measured value
1733      may be misleading unless the measurement process is in a state of statistical control. Statistical
1734      control implies that the distribution of measured results is stable and predictable. It exists when
1735      all the observed variability in the process is the result of random causes that are inherent in the
1736      process. The existence of variability due to "assignable" causes, including instrumental and
1737      procedural failures and human blunders, which are not inherent in the process, implies that the
1738      process is unpredictable and hence "out of control."

1739      Statistical QC  procedures are designed to detect variability due to assignable causes. When such
1740      variability is detected, specific corrective action is required to determine the cause and bring the
1741      measurement process back into a state of statistical control. Laboratory QC procedures should be
1742      strict enough to detect variations in the measurement system that could have a significant impact
1743      on measurement uncertainties.

1744      Statistical QC  also may be used in the laboratory to monitor method performance parameters,
1745      such as chemical yield, to ensure that the measurement system is performing as expected. How-
1746      ever, the need  for corrective action in the case of a low yield may not be as urgent as in the case
1747      of a malfunctioning radiation counter, since the latter is much more likely to cause underestima-
1748      tion of measurement uncertainties.

1749      The following sections describe the various types of control charts introduced in Section 18.3.2,
1750      including the A'chart, X chart, R chart, and variants of the c chart and u chart for Poisson data.

1751      18A.2  XCharts

1752      Procedure 18.1, shown below, may be used to determine the central line, control limits,  and
1753      warning limits for an Xchart. Ideally, the data distribution should be approximately normal,

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         Laboratory Quality Control
1754      although the A'chart is often used with other types of distributions. (The data may be tested for
1755      normality using the procedure described in Attachment 19F.)

1756      In order to use Procedure 18.1, an unbiased estimate of the standard deviation of the measured
1757      values X^X2, ..., Xn is required. Although the experimental variance s2 of the data is an unbiased
1758      estimate of the true variance a2, taking the square root of s2 generates a bias . The experimental
1759      standard deviation s is given by the equation
1760
                                        s =
                                           \
                                     n-l T=
                                                                                             (1)
1761
1762
1763
1764
1765
1766
If the data are (approximately) normally distributed, s should then be divided by the value of c4
shown in Table 18A-1 below for the number of degrees of freedom v = n - 1. Thus, a is esti-
mated by si c4. The factor c4 is equal to
                                           C4 =
                                                       n- 1
where F denotes the gamma function (NBS 1964 ), but it is well approximated by c4
large n the value of c4 is approximately 1.
                                                                                     (2)
                      -. For
               TABLE 18A-1 — Bias-correction factor for the experimental standard deviation
v = n- 1
1
2
3
4
5
6
7
8
9
10
C4
0.79788
0.88623
0.92132
0.93999
0.95153
0.95937
0.96503
0.96931
0.97266
0.97535
v
11
12
13
14
15
16
17
18
19
20
C4
0.97756
0.97941
0.98097
0.98232
0.98348
0.98451
0.98541
0.98621
0.98693
0.98758
v
21
22
23
24
25
26
27
28
29
30
C4
0.98817
0.98870
0.98919
0.98964
0.99005
0.99043
0.99079
0.99111
0.99142
0.99170
v
31
32
33
34
35
36
37
38
39
40
C4
0.99197
0.99222
0.99245
0.99268
0.99288
0.99308
0.99327
0.99344
0.99361
0.99377
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                                                                       Laboratory Quality Control
1767      An alternative method of estimating the standard deviation is based on the average value of the
1768      moving range (ASTM D6299, ASTM E882). The moving range (MR) is the absolute value of
1769      the difference between consecutive measured values Xt and X1+l. If the data are normally distrib-
1770      uted, the expected value of the moving  range is


                                             — « 1.128 a                                    (3)


1771      which may be estimated by
                                               -.   n-l
                                              n - 1 j=
1772      So, a is estimated by MR /1.128. The moving-range estimate of a may be preferred because it is
1773      less sensitive to outliers in the data. Furthermore, when consecutive values of Xt are correlated, as
1774      for example when a trend is present, the moving-range estimate may produce narrower control
1775      limits, which will tend to lead  to earlier corrective action.
1776      Procedure 18.1 (A'chart). Determine the central line, control limits, and warning limits for an X
llll      chart based on a series of n independent measurements, which produce the measured values
1778      X^ X2, ..., Xn, during a period when the measurement process is in a state  of statistical control.
1779      At least 2 measurements wustbe used. Ideally, at least 20 measurements should be used.

1780      Procedure:
1781        1.   Calculate the sum T."=1X..
1782        2.   Calculate the arithmetic mean A'using the formula
1783                                            x=
                                                   n 1=1
1784        3.   Calculate an unbiased estimatea of the standard deviation (e.g., si c4 or MR/1.128).
1785        4.   Define the central line, control limits, and warning limits as follows:

                                   -^
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         Laboratory Quality Control
1786
1787
1788
1789


1790
1791
1792


1793
1794


1795

1796
1797

1798

1799
         If n is less than 20, a higher rate of false warnings and failures may occur because of the
         increased uncertainties of the estimates X and a. So, fewer than 20 measured values should be
         used only if 20 values cannot be obtained; and the limits should be recalculated when 20 values
         become available.
                                               EXAMPLE
          Problem: Suppose a series of 20 observations of a parameter yield the following normally
          distributed values.
            1,118.9  1,110.5  1,118.3  1,091.0  1,099.8  1,113.7   1,114.4  1,075.1  1,112.
            1,120.5  1,104.0  1,125.7  1,117.6  1,097.6  1,099.8   1,102.3  1,119.9  1,107.

          Determine the central line and warning and control limits for future measurements.
                                                                              1,103.7
                                                                              1,114.9
1800
          Solution:
          Step 1     Calculate£X. = 22,168.3 .
          Step 2

          Step 3
Step 4
           Calculate the mean ^=22,168.3/20 = 1,108.415

           Calculate the experimental standard deviation
                                       s =
                                          \
                                               1
                                                    20
                                              i~ H08.415)2 = 12.044
           which is based on v = 19 degrees of freedom. Find c4 = 0.98693 for v = 19 in
           Table 18.1 (or estimate c.
                                               4/I'
                                                    =2L = 0.9870), and calculate
                                                 - 3   77
                                                       12.044
                                                               = 12.2037
                                                      0.98693
                                                   H

                     Define the central line, control limits, and warning limits as follows:

                                          CL = 1,108.415
                                        UCL = 1,108.415 + 3(12.2037) = 1,145.0
                                        LCL = 1,108.415 - 3(12.2037) = 1,071.8
                                       UWL = 1,108.415 + 2(12.2037) = 1,132.8
                                        LWL = 1,108.415 - 2(12.2037) = 1,084.0
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                                                                       Laboratory Quality Control
1801
18A.3  A'Charts
1802      When subgroup averages are plotted on a control chart, Steps 1 and 2 of Procedure 18.1 may be
1803      used to determine the arithmetic mean X and the standard deviation a of a prior set of data
1804      X^Xj., ..., Xn. If .£ denotes the size of the subgroup, the central line, control limits, and warning
1805      limits for the subgroup average are calculated using the formulas
1806
1807
1808


1809
1810
1811

1812
1813

1814

1815
If n is less than about 20, a higher rate of false warnings and failures may occur because of the
increased uncertainties of the estimates A'and a. For this reason fewer than 20 measured values
should be used only if 20 values cannot be obtained.
                                      EXAMPLE
 Problem: Use the data from the preceding example to determine warning and control limits
 for subgroup averages when the subgroup size is k= 5.
 Solution:
 Step 1      Calculate£X. = 22,168.3  .

 Step 2      Calculate the mean ~X= 22,168.3 720 = 1,108.415

 Step 3      Calculate the experimental standard deviation
                                       s =
                                          \
                                               1
                                                   20
                                                - 1108.415)2 = 12.044
            which is based on v = 19 degrees of freedom. Find c4 = 0.98693 for v = 19 in
            Table 18.1 (or estimate c, ~
                      V            4
                                                  ^ = — = 0.9870), and calculate
                                                  3   77         '
                                             -   S    12 044
                                             CT = _! = J£^±L = 12.2037
                                                  c4   0.98693
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1816
         Laboratory Quality Control
          Step 4     Define the central line, control limits, and warning limits as follows:
                                            = 1,108.415
                                      LCL^= 1,108.415 - 3(12.2037) / fi = 1,092.0
                                            = 1,108.415 + 3(12.2037)/^/5 = 1,124.8
                                            = 1,108.415 - 2(12.2037) / fi = 1,097.5
                                            = 1,108.415 + 2(12.2037) / fi = 1,119.3
1817
         18A.4 R Charts
1818      The range of a set of values is the difference between the largest value and the smallest. Plotting
1819      ranges on a range chart or R chart is used to monitor within group variability because R charts
1820      detect changes in variability more easily. Duplicate measurements for any radiochemistry indi-
1821      cator are made and the difference between the duplicates are used to construct the central line
1822      (the mean range), and the control and warning limits in a similar fashion as in the X chart.
1823      Procedure 18.2 may be used to determine the parameters of the R chart.
1824      Procedure 18.2 (R chart). Determine the central line and control limits for a R chart based on a
1825      series of n independent sets of duplicate measurements, which produce the values R^ R2, ... ,Rn,
1826      during a period when the measurement process is in a state of statistical control.
1827      Procedure:
1828         1.   Calculate the range, Rh of each pair of duplicate measurements,
1829

1830
           2.   Calculate the mean range, R, using the formula
1831
                                                    n 1=1
1832
           3.    Calculate the upper control limit as UCL = 3.267 R.
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                                                                       Laboratory Quality Control
1833      This approach may also be used for the moving range of a series of individual results.
1834
1835
1836
1837
1838

1839
1840
1841

1842
1843
1844
1845

1846
1847

1848
1849
1850


1851

1852

1853
1854
1855
The factor 3.267 is called "Z?4" in references on statistical quality control. The value of D4 is
smaller when the range of a larger group is monitored. When the group size is at least seven,
there is also a factor called D3, which may be used to calculate a lower control limit for the range.
Values for D3 and D4 are tabulated in Manual on Presentation of Data and Control Chart
Analysis (ASTM MNL7), as well as many other references.
EXAMPLE
Problem: Suppose a series of 20 duplicate
pairs of values.

(0
(0
(0
(0
Determine
501,0.491)
510,0.488)
523,0.516)
506, 0.508)
(0.490,
(0.505,
(0.500,
(0.485,
0.490)
0.500)
0.512)
0.503)
(0
(0
(0
(0
observations of a parameter yield the following
479, 0.482)
475, 0.493)
513,0.503)
484, 0.487)
(o.
(o.
(o.
(o.
520
500
512
512
the central line and upper control limit for the range
,0.512)
,0.515)
, 0.497)
, 0.495)
of future
(0.500, 0
(0.498, 0
(0.502, 0
(0.509, 0
pairs of
490)
501)
500)
500)

measurements.
Solution:
Stepl




Step 2
Step 3
Calculate the range









C.cu.a.e,
0.010
0.022
0.007
0.002
he mean
Calculate the upper
of each of the 20 pairs .
0.000
0.005
0.012
0.018
range R






0.003
0.018
0.010
0.003
-. 20
1 V^ z? -
20£f '"
control limit: UCL = 3
0.
0.
0.
0.
008
015
015
017
0.189 _
20
.267

0.010
0.003
0.002
0.009
0.00945

# = (3.267)(0.00945)

= 0.0309
ISA.5  Control Charts for Instrument Response

A radioactive check source should be used to monitor the efficiency of every radiation counting
instrument. MARLAP recommends that the activity and count time for the source be chosen to
give no more than 1 percent Poisson counting uncertainty (ANSI N42.23). In other words, at
         JULY 2001
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         Laboratory Quality Control
1856      least 10,000 counts should be obtained in each measurement of the source.

1857      There may be cases when placing a high-activity source in a detector is undesirable, and
1858      obtaining  10,000 counts is therefore impractical. The instrument response may not have a
1859      Poisson distribution. In this case, if the check source is long-lived, an A'or A'chart based on
1860      replicate measurements should be set up. For example, an XorX chart is the appropriate
1861      efficiency chart for a high-purity germanium detector when the area of a specific photopeak is
1862      monitored, since the calculated size of the photopeak may have significant sources of uncertainty
1863      in addition to counting uncertainty. An A'or X chart may be used even if the response is truly
1864      Poisson, since the Poisson distribution in this case is approximated well by a normal distribution,
1865      but slightly better warning and control limits are obtained by using the unique properties of the
1866      Poisson distribution.

1867      Standard guidance documents recommend two types of control charts for Poisson data. A "c
1868      chart" typically is used in industrial quality control to monitor the number of manufacturing
1869      defects per item. A "u chart" is used to monitor the number of defects per unit "area of
1870      opportunity," when the area of opportunity may vary. Thus, the values plotted on a c chart are
1871      counts and those plotted on a u chart are count rates. The same two types of charts may be
1872      adapted for monitoring counts and count rates produced by a radioactive check source. When a u
1873      chart is used, the "area of opportunity" equals the product of the  count time and the source decay
1874      factor. In radiation laboratories a variant of the u chart is more often used when the count time
1875      remains fixed but the decay factor changes during the time when the  chart is in use.

1876      Before using control limits derived from the Poisson model, one should use Procedure El,
1877      described  in Section 18B.2 of Attachment 18B, to confirm experimentally that the Poisson
1878      approximation is adequate and that any excess variance is relatively small at the expected count
1879      rate. Factors such as source position that may vary during routine QC measurements should be
1880      varied to the same degree during the experiment.

1881      Calculation of warning and control limits using the Poisson model requires only a precise meas-
1882      urement of the source  at a time when the instrument is operating properly, preferably near the
1883      time of calibration. The precision can  be improved either by counting the source longer or by
1884      averaging several measurements. In principle both approaches should provide equally good esti-
1885      mates of the count rate; however, an advantage of the latter approach is that it can provide the
1886      data needed to detect excess variance  (using Procedure El).

1887      Procedures 18.2 and 18.3, listed below, may be used to determine warning and control limits for
1888      measurements of a radioactive check source when the total count follows the Poisson model.
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                                                                       Laboratory Quality Control
1889      Procedure 18.2 should be used only when the expected count in each measurement is the same,
1890      for example when the source is long-lived and all count durations are equal. Procedure 18.3,
1891      which implements an alternative to the u chart, may be used in all other cases.
1892      Procedure 18.2 (Control chart for Poisson efficiency check data with constant mean) A
1893      check source is counted n times on an instrument, producing the measured counts 7%, N2, ..., Nn.
1894      (Ideally, n is at least 20.) Determine control limits and warning limits for future measurements of
1895      the source count on the same instrument.

1896      Procedure:
1897         1.    Estimate the central line by
                                                    i   n
                                                    n 1=1  '

1898             and the standard deviation by
1899             NOTE: The estimate s is biased, but the bias is negligible for the large number of counts typically
1900             obtained from a check source.

1901        2.   Define the control limits and warning limits (in counts) as follows:

                                   UCL = CL + 3s      UWL  = CL+2s
                                   LCL = CL - 3s      LWL  = CL - 2s
1903      If n is less than 20, a higher rate of false warnings and failures may occur because of the
1904      uncertainty in the estimate of the mean. So, fewer than 20 measurements should be used only if
1905      20 measured values are not available.
1906      Procedure 18.3 (Control chart for Poisson efficiency check data with variable mean) A
1907      check source is counted n times (n > 1) on an instrument, producing the measured counts 7Vl3 N2,
1908      ..., Nn. (It is assumed that the background level is negligible when compared to the source count
1909      rate.) Let tt denote the duration of the /h measurement and dt the decay factor (for example,
1910      exp(-^(At + 0.5 ^))). Determine control limits and warning limits for a future measurement of the
1911      source count on the same instrument when the counting period is Tand the decay factor is D.

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         Laboratory Quality Control
1912      Procedure:
1913        1.   Compute the sums^"=1AA. and Y."=lt1d1.
1914        2.   Estimate the mean decay-corrected count rate by

1915
  3.    Estimate the central line by
                                               CL = rTD
1916
       and the standard deviation s by
1917
1918
  4.   Define the control limits and warning limits as follows:
                          UCL = CL + 3s
                          LCL = CL - 3s
UWL = CL + 2s
LWL = CL - 2s
1919
1920


1921

1922
1923
1924

1925
1926


1927
1928
1929
If ^t.d.<20 TD, a higher rate of false warnings and failures may occur because of increased
uncertainty in the estimate of the count rate r.
                                      EXAMPLE

 Problem: A source containing 90Sr and 90Y in equilibrium is used for efficiency checks on a
 proportional counter. Near the time of calibration, a series of twenty 600-s measurements are
 made. The observed counts are as follows:

     12,262  12,561  12,606  12,381  12,394  12,518  12,399  12,556  12,565  12,444
     12,432  12,723  12,514  12,389  12,383  12,492  12,521  12,619  12,397  12,562

 Assume all twenty measurements are made approximately at time 0, so the ten decay factors dt
 are all equal to 1. Use Procedure 18.3 to calculate lower and upper control limits for a 600-s
 measurement of the same source at a time exactly 1 year later.
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                                                                      Laboratory Quality Control
1930
1931


1932


1933
1934
 Solution:
 Step 1      Compute the sumsEAA. = 249,718 and Et.d. = 12,000.
                         EN.
 Step 2
 Step3
           Calculate r =
                               249,718
                         Et.d.   12,000
= 20.80983.
           The decay time for the final measurement is 1 y = 31,557,600 s. The
           corresponding decay factor is D= 0.976055. The count time is  T= 600 s. So,
           compute
                            CL = (20.80983)(600)(0.976055) = 12,187
           and
                                      s = ^/12,187 =  110.39

Step 4      The control limits and warning limits are

                               UCL = 12,187  + 3 x 110.39 = 12,518
                               LCL = 12,187  - 3 x 110.39 = 11,856
                              UWL = 12,187  + 2 x 110.39 = 12,408
                               LWL = 12,187  -2x 110.39 = 11,966
1935
1936
1937
1938
1939
1940
If substantial excess (non-Poisson) variance is present in the data, the simple Poisson charts
described above should not be used. The c chart may be replaced by an Xchart or X chart, but a
new type of chart is needed to replace the u chart. To determine warning and control limits  for
this chart, one must determine the relative excess variance of the data ^2. A value of ^2 may be
assumed or it may be estimated using procedures described in Attachment 18B. Then Procedure
18.3 may be replaced by the Procedure 18.4, shown below.
1941      Procedure 18.4 (Control chart for Poisson efficiency check data with excess variance)  A
1942      check source is counted n times on an instrument, producing the measured counts Nly N2, ..., Nn.
1943      Let tj denote the duration of the /h measurement and dt the decay factor. Let the data follow an
1944      approximately Poisson distribution with relative excess variance ^2. Determine control limits and
1945      warning limits for a future measurement of the source count on the same instrument when the
1946      counting period is Tand the decay factor is D.

1947      Procedure:
1948        1.   Compute the sums ^"=1N. and E"= l t.d..
1949        2.   Estimate the mean decay-corrected count rater by
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         Laboratory Quality Control
                                /v  7 ™ 1    0 7 7*^       1
                                r =	    where     rn =
                                    „                        U
                                   y;	i	
1950        3.   Estimate the central line by
                                               CL = fTD

1951            and the standard deviation s by
1=1
1952        4.   Define the control limits and warning limits as follows:

                                  UCL = CL + 3s      UWL = CL + 2s
                                  LCL = CL - 3s      LWL = CL - 2s
1954      18A.6 References

1955      American National Standard Institute (ANSI) N42.23. Measurement and Associated Instru-
1956         mentation Quality Assurance for Radioassay Laboratories. 1996.
1957
1958      American Society for Testing and Materials (ASTM) D6299, Standard Practice for Applying
1959         Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System
1960         Performance, 2000

1961      American Society for Testing and Materials (ASTM) E882, Standard Guide for Accountability
1962         and Quality Control in the Chemical Analysis Laboratory.

1963      American Society for Testing and Materials (ASTM) MNL 7, Manual on Presentation of Data
1964         and Control Chart Analysis ASTM Manual Series, 6th Edition, 1990.

1965      National Bureau of Standards (NBS). 1964. Handbook of Mathematical Functions. M.
1966         Abramowitz and Stegun, I, Editors.
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                                                                       Laboratory Quality Control
196?                     Attachment 18B:  Statistical Tests for QC Results
1968      18B.1  Introduction

1969      Attachment ISA describes several types of control charts that may be used for statistical quality
1970      control in the laboratory. This attachment describes additional statistical methods that may be
1971      used, where appropriate, to test the performance of measurement results from blank, replicate,
1972      LCS, spikes, CRM, yield-monitor, background, efficiency, calibration, or peak resolution results,
1973      with special emphasis on instrumentation results.

1974      18B.2  Tests for Excess Variance in the Instrument Response

1975      As noted in Chapter 19, the counting uncertainty given by the Poisson approximation does not
1976      describe the total variability in a counting measurement. A number of factors may generate a
1977      small excess component of variance. When a large number of counts are obtained in the meas-
1978      urement, the relative magnitude of the Poisson variance is small; so, the excess component may
1979      dominate.

1980      Regardless of whether replication or the Poisson approximation is used to estimate counting
1981      uncertainties, MARLAP recommends that a series of check source measurements be made on
1982      each instrument periodically to test for excess variance. Procedure El, which is presented below,
1983      may be used to evaluate the measurement results. To check the stability of the instrument itself,
1984      one should perform the measurements while holding constant any controllable factors, such as
1985      source position, that might increase the variance. To check the variance when such factors are not
1986      constant, one may use Procedure  El but vary the factors randomly for each measurement.

1987      Assume n measurements of the source produce the counts 7%, N2, . . . , Nn. If the expected count
1988      for each measurement is at least 20, so that the Poisson distribution is approximated by a normal
1989      distribution, and if the average decay-corrected count rate r is determined with adequate
1990      precision, then the quantity
1991      where tt and dt are the count time and source decay factor for the /h measurement, respectively,
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1992      should be distributed approximately as chi-square with n - 1 degrees of freedom.5 The precision
1993      of the estimate r should be adequate for the test as long as the expected count for each measure-
1994      ment is at least 20. Since a check source is involved, the expected count is usually much greater
1995      than 20.
1996      Procedure El. Determine whether a series of measurements of a check source provide evidence
1997      of variance in excess of the Poisson counting variance. Let Nt denote the count observed in the /h
1998      measurement. Let wt = ttd^ where tt denotes the count time and dt denotes the source decay factor
1999      (if relevant). If all the values wt are equal, one may use wt=\  instead for all i. It is assumed either
2000      that the background count rate is negligible or that the decay factors are all nearly equal, so that
2001      the expected count in each measurement is proportional to w}6 The procedure tests the null
2002      hypothesis that the total measurement variance is the Poisson  counting variance.

2003      Procedure:
2004         1 .  Choose the significance level a.
2005         2.  Calculate the sums T?j=lN. and ^"=lw..
2006         3 .  Estimate the mean decay-corrected count rate by
                                                       En
                                                       i=\Wi


2007         4.  Calculate the chi-square statistic as follows:
                                                                                                   (2)
                                                                                                   ^ '
                                            r = -2^\—-r\  wi
                                                                                                   (3)

2008         5.  Determine the quantile y^^a(n - 1) (see Table G. 1 in Appendix G). Reject the null
           If r denotes the true mean decay-corrected count rate, then under the null hypothesis each measured count rate
          Nt / tidi is approximately normal with mean rand variance rl ttdt, and the least-squares estimator for r is
          r = E7V,/ 'Et1dr So, the sum E(A^./ ttdt - r)2 / (rl ttd) is approximately chi-square with n- I degrees of freedom.
          If r is determined accurately, the true mean count rate r may be replaced in the formula by its estimated value r to
          obtain the formulathat_appears in the text. If all the products tjdj are equal, they cancel out of the sum, which
          becomes £(7V. - TV)2 /TV, as described by Evans (1955), Goldin (1984), and Knoll (1989).

          6 The expected gross count for the /h measurement equals RB tt + rwt, where r is the mean net count rate at time 0.
          The expected count is proportional to w, if RB = 0, or if all the decay factors are equal so that tt« w,.

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2009            hypothesis if and only if the calculated value of ^2 is greater than^a(/7 - 1). In this case
2010            conclude that the variance is greater than predicted by the Poisson model.
2011

2012
2013

2014
2015

2016
2017

2018
2019

2020
2021

2022
2023
2024
2025
2026
2027
                                       EXAMPLE

 Problem: A long-lived source is counted n = 20 times in a gross radiation detector and the
 duration of each measurement is 300 s. The following total counts are measured:

      11,189   11,105  11,183  10,910  10,998  11,137  11,144  10,751  11,128  11,037
      11,205   11,040  11,257  11,176  10,976  10,998  11,023  11,199  11,078  11,149

 Are these data consistent with the assumption that the measurement variance is no greater than
 predicted by the Poisson model? Use 5 percent as the significance level.
 Solution:
 Step 1     The significance level is specified to be a = 0.05.
 Step 2


 Step 3

 Step 4
Since the source is long-lived and all the count times are equal, let wt=\ for
each i. Calculate EN. = 221,683 and Ew. = 20.
                   1      '           1
Calculate the mean count rate r = 221,683 / 20 = 11,084.15.

Calculate the chi-square statistic
                              2
                                =
                                  r 1=1
                   5
                   w.
                                                         l
                                                               20
                                            11,084.15 7~i
                                                           . - 11,084.15)2 = 24.87
 StepS
The number of degrees of freedom is 20 - 1 = 19. According to Table G.I, the
0.95-quantile for a chi-square distribution with 19 degrees of freedom is 30.14.
Since 24.87 < 30.14, do not reject the null hypothesis. The data are consistent
with the assumption of Poisson counting statistics at the 5 percent significance
level.
A two-sided version of Procedure El may also be used to test whether the measurement variance
is either greater than or less than predicted by the Poisson model. Step 5 must be changed so that
the null hypothesis is rejected if the value of the test statistic ^2 does not lie between the two
quantiles £/2(n - 1) and i^a/2(n - 1).
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2028     A chi-square test may require many measurements or long count times to detect a small excess
2029     variance component. When all measurements have the same expected count |i, the detection limit
2030     for the relative excess variance, or its minimum detectable value, is equal to
                                                                                              (4)
2031     where p is the specified probability of a type n error (failure to detect) (Currie 1972). Note that
2032     since L,D represents a relative variance, its square root L,D represents a relative standard deviation.
2033
2034
2035
2036
2037
2038


2039
2040
2041
2042
2043
EXAMPLE: A long-lived source is counted 20 times, and each
duration. The average of the measured counts is 10,816. If a = p
detectable value of the relative excess variance is estimated by
^i 1 [ %.95(19) J 1 ( 30.14 \
^ 10,816
which corresponds to <
percent.
i relative stand
10,816^ 10.12 'J
ard deviation ^D = ^1.829 x
measurement has the same
= 0.05 , the minimum
1-978 _loonvlo-4
10,816
10^4 = 0.01352, or about 1.35
If (1) the relative excess variance in a measurement is not affected by count time, (2) a fixed total
count time is available, and (3) all measurements have the same expected count (e.g., when all
count times are equal and the source is long-lived), then it is possible to determine the number of
measurements that minimizes ^D (Currie 1972). The optimal number is the number nthat
minimizes the quantity
                                       F(ri) = n
                                                                                    (5)
2044     The solution may be found by computing F(ri) for n = 2, 3, 4, ..., until the computed value
2045     begins to increase. When a = p = 0.05, the optimal number of measurements is n = 15, although
2046     the improvement as n increases from 6 to 15 is slight. If n is increased further, the detection limit
2047     ^ worsens unless the total count time is also increased.

2048     A chi-square test may also be used to test whether the total source measurement variance consists
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                                                                         Laboratory Quality Control
2049     of a Poisson component and a specified excess component (Currie 1972). Procedure E2,
2050     described below, implements this test. If the specified component is zero, Procedure E2 is
2051     equivalent to El.
2052     Procedure E2. Determine whether a series of measurements of a check source provide evidence
2053     that the measurement variance is greater than the Poisson component plus a specified excess
2054     component. (Refer to the notation used in Procedure El.) Let ^2 denote the value of the relative
2055     excess variance under the null hypothesis H0.

2056     Procedure:
2057         1.  Choose the significance level a.

2058         2.  Calculate the sums Yfi= jTV. and Yfi= lwp where 7Vl3 N2, ..., Nn are the measured values.

2059         3.  Estimate the mean decay-corrected count rate r in two steps by

                           E"  N.                      "      N.      i"     w.
                      r»-^       and       '=£-	'—JZ-	>—             (6)
                           E"1=lWl                     -i 1 + r0w£2/  1=1 1 +r0^2

2060            (If wl = w2 = — = wn or ^2 = 0, then  r = rQ.)

2061         4.  Calculate the chi-square statistic as follows:7

                                                "(NJw.-f)2
                                           5C2 = E-	^                                  (7)
                                               1=1  rl w. + r t,

2062         5.  Determine the quantile ^ _a(n - 1) (see Table G.  1). Reject the null hypothesis if and only
2063            if the calculated value of ^2 is greater than ^ _a(n - 1). In this case conclude that the
2064            relative excess variance is greater than ^2.


2065     Procedure E2, like El, can easily be converted to a two-sided test by changing Step 5.
         7 In Currie (1972), the variance of TV, is estimated by TV, + £2TV,2. The estimated variance used here is calculated by
         pooling the counting data to reduce any small bias caused by the correlation between TV, and TV, + ^2 TV, .

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2066
2067
2068
2069
2070

2071
2072
The excess component may be estimated by solving Equations 18.6 and 18.7 for the value of ^
that gives ^2 = n - 1. An iterative computer algorithm, such as bisection, which repeatedly tries
values of £ and  computes ^2 can be used.8 An approximate confidence interval for the relative
excess variance may similarly be found by solving for values of ^ which give j^ = %n±J\j2(n ~ 1
where y is the desired confidence coefficient (Currie,  1972).

Ifwl = w2 = --- = wn, the iterative algorithm is unnecessary. In this case the value of £ may be
estimated directly using the formula
                                                                                               (8)
2073     or by £ = 0 if the preceding formula gives a negative result. Similarly, the approximate lower
2074     confidence limit is given by the formula
                                ,2   = J_
                                'lower  — -
                                      N2
                                                                                     (9)
2075     and the approximate upper confidence limit is given by
                               t2    =J_
                               Cupper   — ~
                                      N2
                                                  (AA. - N)2 - N
                        (10)
2076

2077
2078

2079
2080
                                       EXAMPLE

 Problem: A long-lived efficiency check source is counted once a day for 20 days, and each
 measurement has the same duration. Suppose the measured counts (N) are:

      14,454  15,140  15,242  14,728  14,756  15,040  14,768  15,128  15,150  14,872
      14,845  15,511  15,032  14,746  14,731  14,982  15,047  15,272  14,765  15,143
           Newton's method, which converges more rapidly, can also be used, but its use is more practical if one replaces r
         by r0 in the denominator of each term of Equation 18.7.
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2081
2082

2083
2084
2085

2086
2087
2088
2089
2090
2091
2092
          Use these data to estimate c; and determine a 95 percent two-sided confidence interval for its
          value.
          Solution: Since the source is long-lived and all the measurements have the same duration,
          wl = w2 = ---= w20 and Equations 18.8 through 18.10 may be used. So, calculate
              A. = 299,352 and 7?= 299,352/20 = 14,967.6. Then the value of £ is estimated as
                       $ =
                           14,967.6
                                             £ (Nj - 14,967.6)2 - 14,967.6 = 0.014463
          The 95 percent confidence limits are calculated as follows:
                            lower
                                                   20
                                                               -N
                                     1
                                  14,
                                = 0.0096334
                                             32.852 ^t
                                                         1, - 14.
                                                                      - 14,967.6
                            upper
                                  N\
                                       Xo025(20 - 1) '=
                                                               -N
\


                                = 0.022846
                                                    20
                                             8.9065
                                                       (N, - 14,
                                                                      - 14,967.6
         For most practical purposes the excess variance may be considered negligible in a counting
         measurement if the total count AAis less than 1 /lOc;2, since, in this case, the excess variance
         increases the standard deviation of the measured count by less then 5 percent. Similarly, the
         counting variance may be considered negligible if N> 10 / d;2.
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2093

2094


2095



2096


2097



2098


2099


2100
2101
2102
2103
2104


2105


2106
2107
2108
2109
 EXAMPLE: Suppose N= 1,000 counts observed in a measurement and £ has been estimated
 to be 0.01. Then N= 1 /10^2. The standard uncertainty of Ms evaluated as
                  u(N) = JN+Z,2N2 = ^1,000
                                            1.05/AA
 If N= 100,000, then N= 10 / ^ and
So, i/(7V)
    u(N) =
N< 1,000, and i/(7V)
                                           = ^1,100,000 « 1
                                             N> 100,000.
18B.3  Instrument Background Measurements

This section presents statistical tests related to measurements of instrument background levels.
The tests are intended for single-channel detectors but may be applied to multichannel systems if
wide spectral regions are integrated. Tests are described for comparing background levels to
preset limits, for detecting changes in background levels between measurements, and for
detecting the presence of variability in excess of that predicted by the Poisson model.

18B.3.1   Detection of Background Variability

The chi-square test (Procedure El) used to detect excess variance in measurements of a check
source may be adapted for background measurements. Procedure Bl implements a chi-square test
for backgrounds. This test is one-sided, although Step 6 can be modified to implement a two-
sided test.
2110     Procedure Bl. Determine whether a series of measurements of an instrument's background
2111     provide evidence of variance in excess of the Poisson counting variance. Let Nt denote the count
2112     observed in the /h measurement, and let tt denote the count time.

2113     Procedure:
2114       1.   Determine the significance level a.
2115       2.   Calculate the sums T,"=1N. and ^"=lt..
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                                                                        Laboratory Quality Control
2116
 3.    Estimate the mean background count rate by

                                                                                             (11)
2117
2118

2119
      Let tmin be the smallest value of tt. If rtmin > 20, go to Step 5. Otherwise, discard all
      measured values N} for which rt.<20. Impossible, restart the test at Step 2; if not, stop.
      Calculate the chi-square statistic as follows:

                                          N
                                                                                             (12)
                                       /             	
2120       6.   Determine the quantile %\ -a(# ~ 1) (see Table G. 1 in Appendix G). Reject the null
2121            hypothesis if and only if the calculated value of ^2 is greater than Xi-a(° - 1). In this case,
2122            conclude that the instrument background does not follow the Poisson model.
2123

2124
2125
2126
2127
2128

2129
2130

2131
2132

2133

2134

2135
                                     EXAMPLE

Problem: Twenty overnight background measurements are performed on a proportional
counter. The duration of each measurement is 60,000 s, and the following alpha counts are
measured:
                      14  23  23   25   28  22  19  26   20  27
                      30  21  34   32   24  27  25  19   19  25

Are these data consistent with the assumption that the measurement variance is attributable to
Poisson counting statistics? Use 5 percent as the significance level.
Solution:
Step 1      The significance level is specified to be a = 0.05.

Step 2      Calculate EN, = 483 and £ tt = 20 x 60,000 = 1,200,000.

Step 3      Calculate the mean count rate r = 483 /1,200,000 = 0.0004025.

Step 4      Since t . = 60,000, ft .  =24.15. Since 24.15 > 20, go to Step 5.
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2136
2137
Step 5      Calculate the chi-square statistic

                                        1      ™(   N.              Y
                                   	Y  	— - 0.0004025  60,000 = 18.49
                                   0.0004025 £? \, 60,000            )

Step 6      The number of degrees of freedom is 20 -  1 = 19. According to Table G.I, the
           0.95-quantile for a chi-square distribution with 19 degrees of freedom is 30.14.
           Since 18.49 < 30.14, do not reject the null hypothesis. The data are consistent with
           the Poisson model.
2138     All the background tests described below are based on the assumption of Poisson counting
2139     statistics. If Procedure Bl indicates the Poisson assumption is invalid, each test requires
2140     modification or replacement. In most cases, unless the observed background counts are very low,
2141     standard statistical tests for normally distributed data may be used instead (e.g., NBS, 1963;
2142     EPA, 1998).

2143     18B.3.2    Comparing a Single Observation to Preset Limits

2144     High background levels on an instrument degrade detection capabilities and may indicate the
2145     presence of contamination. Unusually low levels on certain types of instruments may indicate
2146     instrument failure. When these issues are of concern, one or both of the two statistical tests
2147     described below may be performed to determine whether the true background level is outside of
2148     its desired range.

2149     The result of the background measurement in counts is assumed  to have a Poisson distribution. In
2150     both of the following tests, f denotes the count time, and r denotes the preset lower or upper limit
2151     for the true mean background count rate RB. Given an observed count NB, Procedure B2
2152     determines whether RB > r and B3 determines whether RB < r.

2153     Procedure B2 should be used when ris an upper limit and B3 should be used when ris a lower
2154     limit. Thus, the background level is assumed to be within its acceptable limits unless there is
2155     statistical evidence to the contrary. The alternative approach, which changes the burden of proof,
2156     may be used if /tis large enough.

2157     If rt is extremely large (e.g., if rt > 2,500), there is probably no justification for a statistical test.
2158     Instead, the observed count rate may be compared directly to r.
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2159     Procedure B2. Determine whether the mean background count rate RB is greater than r. Test the
2160     null hypothesis H0: RB < r against the alternative hypothesis H^ RB > r.

2161     Procedure:
2162        1.   Choose the significance level a.

2163        2.   If NB < rt, conclude that there is insufficient evidence to reject the null hypothesis, and
2164            stop.  Otherwise, if rt < 20, go to Step 6. If rt > 20, go to Step 3 .

2165        3.   Calculate
                                                 0.5 + NR - rt
                                                                                               (14)
2177
2178
2179
2180
2181
2166        4.   Determine zl_a, the (1 -a)-quantile of the standard normal distribution (see Table G.I in
2167            Appendix G).

2168        5.   Reject the null hypothesis if and only if Z> zl_a. Stop.

2169                NOTE: If the background count time t is always the same, a fixed upper control limit may be
2170                calculated using the formula

2171                                        UCL = round(r? + zl _Jrt)

2172                where round denotes the function that rounds its argument to the nearest integer. Then Steps
2173                3-5 are effectively performed by comparing the observed value NB to UCL.
                            2
2174        6.   Determine ^(ITVg), the a-quantile of the chi-square distribution with 2NB degrees of
2175            freedom (see Table G.I in Appendix G), and calculate Q = 0.5

2176        7.   Reject the null hypothesis if and only if Q> rt.
                                      EXAMPLE

Problem: To ensure adequate detection capabilities, a laboratory establishes an upper limit of
0.02 cps for beta backgrounds on a proportional counter. A 6,000-s background measurement
is performed, during which 125 beta counts are observed. Determine whether this
measurement result gives 95 percent confidence that the background is greater than 0.02 cps.
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2182

2183

2184

2185

2186

2187




2188

2189
2190
2191
2192

2193

2194

2195

2196

2197
Solution:   The values of the variables are NB= 125, t= 6,000 and r= 0.02.

Step 1      The significance level a is 1 - 0.95 = 0.05.

Step 2      Since NB > rt= 120 and rt > 20, go to Step 3.

           Calculate Z= (0.5 + 125 -  120)//120 = 0.5021.

           Table G.I shows that z095 = 1.645.
Step3

Step 4

StepS
           Since 0.5021 < 1.645, do not reject the null hypothesis. There is insufficient
           evidence to conclude that the beta background exceeds 0.02 cps.
                                     EXAMPLE

Problem: The same laboratory establishes an upper limit of 0.002 cps for alpha backgrounds
on the same counter. A 6,000-s background measurement is performed, during which 19 alpha
counts are observed. Determine whether this measurement result gives 95 percent confidence
that the background is greater than 0.002 cps.
Solution:   The values of the variables are NB= 19, t= 6,000 and r= 0.002.

Step 1      The significance level a is 1 - 0.95 = 0.05.

Step 2      Since NB > rt= 12 and rt< 20, go to Step 6.

Step 6      Table G.I shows that %.05(38) = 24-88- So, Q= 0.5 • 24.88 = 12.44.

Step 7      Since 12.44 > 12, reject the null hypothesis. The data give 95 percent confidence
           that the alpha background is greater than 0.002 cps.
2198     Procedure B3. Determine whether the mean background count rate RB is less than r. Test the
2199     null hypothesis H0: RB > r against the alternative hypothesis H^ RB < r.

2200     Procedure:
2201        1.   Choose the significance level a.

2202        2.   If NB > rt, conclude that there is insufficient evidence to reject the null hypothesis, and
2203            stop. Otherwise, if rt < 20, go to Step 6. If rt > 20, go to Step 3.
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                                                                         Laboratory Quality Control
2204
 3.   Calculate
                                            z=
                                                0.5 + ND - rt
                                                                                    (15)
2205
2206

2207

2208
2209
2210


2211

2212
2213

2214
      Determine zl_a, the (1 - a)-quantile of the standard normal distribution (see Table G.I in
      Appendix G).

      Reject the null hypothesis if and only if Z< -zl_a. Stop.

         NOTE: If the background count time t is always the same, a lower control limit may be calculated
         using the formula
                                 LCL = round(rf - zl _0\
         Steps 3-5 are then effectively performed by comparing NB to LCL.

 6.   Determine ^ ^a(2NB + 2), the (1 - a)-quantile of the chi-square distribution with 2NB+ 2
      degrees of freedom (see Table G.I), and calculate Q = 0.5 il_<£lNB + 2).

 7.   Rej ect the null hypothesis if and only if Q < rt.
2215

2216
2217
2218
2219

2220

2221

2222

2223

2224

2225
                                      EXAMPLE

Problem: A laboratory establishes a lower limit of 0.01 cps for beta backgrounds on a
proportional counter. A 6,000-s background measurement is performed, during which 50 beta
counts are observed. Determine whether this measurement result gives 95 percent confidence
that the background is less than 0.01 cps.
Solution:  The values of the variables are NB= 50, t= 6,000 and r= 0.01.

Step 1     The significance level a is 1  -  0.95 = 0.05.

Step 2     Since NB < rt= 60 and rt > 20, go to Step 3.

Step 3     Calculate Z= (0.5 + 50  - 60)7^/60 = -1.226.

Step 4     Table G. 1 shows that z0 95 = 1.645.

Step 5     Since -1.226 > -1.645,  do not reject the null hypothesis.
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2226     18B.3.3   Comparing the Results of Consecutive Measurements

2227     If consecutive measurements of the background level on an instrument give significantly differ-
2228     ent values, one should be concerned about the accuracy of any laboratory sample measurements
2229     made between the two background measurements. If the background has increased, the labora-
2230     tory sample activities may have been overestimated. If the background has decreased, the activi-
2231     ties may have been underestimated.

2232     Let A\ and N2 denote the counts observed in two independent background measurements on the
2233     same instrument, and assume they represent Poisson distributions with unknown means. Let ^
2234     and t2 denote the corresponding count times. The following two procedures may be used to
2235     determine whether the difference between the two observed values is significantly larger than
2236     would be expected on the basis of the Poisson model. Procedure B4 determines whether the
2237     second value is significantly greater than the first. Procedure B5 determines whether there is a
2238     significant difference between the two values.
2239
2240
         Procedure B4. Determine whether the second mean background count rate R2 is higher than the
         first Rl . Test the null hypothesis H0: Rl > R2 against the alternative hypothesis H^ ^ < R2.
2241     Procedure:
2242        1.    Choose the significance level a.
2243
2244
2245            Step 6.
           2.   If 7% / fj > N2 1 12, conclude that there is insufficient evidence to reject the null hypothesis,
                and stop. Otherwise, if 7% > 20 and N2 > 20, go to Step 3. If 7% < 20 or N2 < 20, go to
2246       3.   Calculate
                                     Z =
                                           N2   A;
N^N2
  t,L
                                                                                            (16)
2247       4.   Determine z1_a, the (1  - a) -quantile of the standard normal distribution.

2248       5.   Rej ect the null hypothesis if and only if Z> zl _ a. Stop.

2249       6.   Let p=t1/(t1 + t2) and q=t2/(t1 + £,).  If N^
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                                                                         Laboratory Quality Control
                                         TV
                                                                                              (17)
2250
      If 7% > A^, calculate S more efficiently using the formula
                                                                                              (18)
2251
 7.   Reject the null hypothesis if and only if S < a.
2252

2253
2254
2255
2256
2257

2258

2259

2260

2261
2262
                                      EXAMPLE

Problem: A 60,000-s background measurement is performed on an alpha spectrometer and
15 total counts are observed in a particular region of interest. After a test source is counted, a
6,000-s background measurement is performed and 3 counts are observed. Assuming Poisson
counting statistics, is the second measured count rate (0.0005 cps) significantly higher than the
first (0.00025 cps) at the 5 percent significance level?
Solution:   The variables are /% = 15, ^ = 60,000, 7V2 = 3, and t2 = 6,000.

Step 1      The significance level a is specified to be 0.05.

Step 2      Since /% / ^ = 0.00025 < 0.0005 = 7V2 / 12, /% < 20, and N2 < 20, go to Step 6.
Step 6
Step 7

                               =an
                          66,000   11         66,000
                           = —. Since TVj > N2, calculate S using the second
                     formula.
                        S= 1 -      i^-l
                                               17/\11
                                                                     +/18\/10\18MO

                                                                       \18/\H/  \H
                                    = 1 -0.7788 =0.2212.
Since S > a, there is not enough evidence to reject the null hypothesis. The second
measured count rate is not significantly higher than the first.
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2263     Procedure B5. Determine whether the mean background count rates are different. Test the null
2264     hypothesis H0: Rl = R2 against the alternative hypothesis H^ Rl * R2.

2265     Procedure:
2266        1.   Choose the significance level a.

2267        2.   If 7% / fj = N21t2, conclude that there is insufficient evidence to reject the null hypothesis,
2268            and stop. Otherwise, if 7% < 20 or N2 < 20, go to Step 6. If 7% > 20 and N2 > 20, go to
2269            Step 3.

2270        3.   Calculate Zusing Equation 18.17.

2271        4.   Determine zl_a/2, the  (1 -  a/2)-quantile of the standard normal distribution.

2272        5.   Reject the null hypothesis if and only if |Z| > zl _ /2. Stop.

2273        6.   If TVj / fj < N2112, use Procedure B4 with significance level a / 2 to determine whether
2274            Ri N21t2, use Procedure B4 with significance level a / 2 and with the
2275            observations reversed to determine whether R2 < Rl.


2276     18B.4  Negative Activities

2277     When the measured count rate for a test source is less than that of the corresponding instrument
2278     background, giving a negative value for the source activity, Procedure B4 may be used to deter-
2279     mine whether the difference between the two count rates is significantly more than should be
2280     expected on the basis of the Poisson model and the assumption that the source is a blank. (Let A\
2281     and fj be the source count and counting time and let N2 and t2 be the background count and count-
2282     ing time.). If a significant difference is found, it may indicate that the background measurement
2283     was biased, the true background is variable or non-Poisson, or the instrument is unstable.

2284     18B.5  References

2285     Currie, Lloyd A. 1972. The Limit of Precision in Nuclear and Analytical Chemistry.  Nuclear
2286         Instruments and Methods 100(3): 387-395.

2287     Environmental Protection Agency (EPA).  1998. Guidance for Data Quality Assessment:
2288         Practical Methods for Data Analysis. EPA QA/G-9, QA97 Version. EPA/600/R-96/084,

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2289        EPA, Quality Assurance Division, Washington, DC.

2290     Evans, Robley D. 1955. The Atomic Nucleus. McGraw-Hill, New York, NY.

2291     Goldin, Abraham S. 1984. Evaluation of Internal Control Measurements in Radioassay. Health
2292        Physics 47(3): 361-374.

2293     Knoll, Glenn F. 1989. Radiation Detection and Measurement., 2nd ed. John Wiley and Sons, New
2294        York, NY.

2295     National Bureau of Standards (NBS). 1963. Experimental Statistics. NBS Handbook 91, National
2296        Bureau of Standards, Gaithersburg, MD.
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                         19  MEASUREMENT STATISTICS
 2     19.1  Overview

 3     This chapter discusses statistical principles and methods applicable to radioanalytical measure-
 4     ments, calibrations, data interpretation, and quality control.

 5     Laboratory measurements always involve uncertainty, which must be considered when analytical
 6     results are used as part of a basis for making decisions. Every measured value obtained by a
 7     radioanalytical procedure should be accompanied by an explicit uncertainty estimate. One
 8     purpose of this chapter is to give users of radioanalytical data an understanding of the causes of
 9     measurement uncertainly and of the meaning of uncertainty statements in laboratory reports. The
10     chapter also describes procedures which laboratory personnel use to estimate uncertainties.

11     The uncertainty associated with laboratory measurements is only a part of the total uncertainty
12     that a data user must consider. Field sampling introduces other types of uncertainty, which are
13     beyond the scope of this chapter.

14     Environmental radioactivity measurements may involve material containing very small amounts
15     of the radionuclide of interest. Measurement uncertainty often makes it difficult to distinguish
16     such small amounts from zero. An important performance characteristic of an analytical proce-
17     dure is therefore its detection capability, which is usually expressed as the smallest concentration
18     of analyte that can be reliably distinguished from zero. Effective project planning requires
19     knowledge of the detection capabilities of the analytical procedures which will be or could be
20     used. This chapter explains the performance measure, called the "minimum detectable concentra-
21     tion," or in certain cases the "minimum detectable amount," that is used to describe radio-
22     analytical detection capabilities, as well as some proper and improper uses for it. The chapter
23     also gives laboratory personnel methods for calculating the minimum detectable concentration.

24     Project planners also need to know the quantification capability of an analytical procedure, or its
25     capability for precise measurement. The quantification capability is expressed as the smallest
26     concentration of analyte that can be measured with a specified relative standard  deviation. This
27     chapter explains a performance measure called the "minimum quantifiable concentration," which
28     may be used to describe quantification capabilities.

29     The material in the chapter is arranged so that general information is presented first and the more
30     technical information intended primarily  for laboratory personnel is presented last. The general
31     discussion in Sections 19.2 through 19.4  requires little previous knowledge of statistics on the
32     part of the reader and involves  no mathematical formulas. Section 19.2 in particular may be

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33     skipped by those familiar with basic statistical concepts. The technical discussion in Sections
34     19.5 through 19.7 requires an understanding of basic algebra and at least some familiarity with
35     the fundamental concepts of probability and statistics. Attachments 19B-G are intended for tech-
36     nical specialists with stronger mathematical backgrounds. The footnotes also contain information
37     which may be skipped by most readers.
38     19.2  Statistical Concepts and Terms

39     19.2.1 Basic Concepts

40     Every laboratory measurement involves a measurement error. Methods for analyzing measure-
41     ment error are generally based on the theory of random variables. A random variable may be
42     thought of as the numerical outcome of an experiment, such as a laboratory measurement, which
43     produces varying results when repeated. In this document a random variable will most often be
44     the result of a measurement. Random variables will usually be denoted by upper-case letters.

45     Of primary importance in almost any discussion of a random variable is its distribution. The
46     distribution of a random variable X describes the possible values of X and their probabilities.
47     Although the word "distribution" has a precise meaning in probability theory, the term will be
48     used loosely in this document. Attachment 19A describes several types of distributions, including
49     the following:

50         •   Normal (Gaussian) distributions
51         •   Log-normal distributions
52         •   Chi-square distributions
53         •   Student's ^-distributions
54         •   Rectangular, or uniform, distributions
55         •   Trapezoidal distributions
56         •   Exponential distributions
57         •   Binomial distributions
58         •   Poisson distributions

59     Normal distributions are particularly important because they appear often in measurement
60     processes. The other types listed are also important in this chapter, but only the exponential,
61     binomial, and Poisson distributions are described in the text.
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62     The distribution of X is uniquely determined by its distribution function., defined by F(x) =
63     Pr[A" < x], where Pr[X < x] denotes the probability that X is less than or equal to x. If there is a
64     function fix) such that the probability of any event a < X < b is equal to \baf(x) dx (i.e., the area
65     under the curve y =f(x) between x = a and x = b), then X is a continuous random variable andyjx)
66     is & probability density function (pdf) forX. When X is continuous, the pdf uniquely describes its
67     distribution. A plot of the pdf is the most often used graphical illustration of the distribution (e.g.,
68     see Figures 19.1 and 19.2), because the height of the graph over a point x indicates the probabil-
69     ity that the value of X will be near x.

70     Two useful numerical characteristics of the distribution of a random variable are its mean and
71     variance. The mean is also called the expectation or the expected value and may be denoted by
72     \ix or E(X). The mean of a distribution is conceptually similar to the center of mass of a physical
73     object. It is essentially a weighted average of all the possible values of X., where the weight of a
74     value is determined by its probability. The variance of X, denoted by o|, Var(JQ, or V(X), is a
75     measure of the variability of X, or the dispersion of its values, and is defined as the expected
76     value of (X -
77     The standard deviation ofX, denoted by cx is defined as the positive square root of the variance.
78     Although the variance appears often in statistical formulas, the standard deviation is a more intui-
79     tive measure of dispersion. If X represents a physical quantity, then cx has the same physical
80     dimensions as X. The variance o|, on the other hand, has the dimensions of X squared.

81     Any numerical characteristic of a distribution, such as the mean or standard deviation, may also
82     be thought of as a characteristic of the random variables having that distribution.

83     The mean and standard deviation of a distribution may be estimated from a random sample of
84     observations of the distribution. The estimates calculated from observed values are sometimes
85     called the sample mean and sample standard deviation. Since the word "sample" here denotes a
86     statistical sample of observations, not a physical sample in the laboratory, metrologists often use
87     the terms arithmetic mean, or average, and experimental standard deviation to avoid confusion.

88     The mean is only one measure of the center of a distribution.  Two others are the median and the
89     mode. The median of X is a value x05 that splits the range of X into upper and  lower portions
90     which are equally likely, or,  more correctly, a value x0 5 such that the probability that X < x05 and
91     the probability that X > x05 are both at least 0.5. The mode of X is its most likely value. Figure
92     19.1 shows the probability density function of a symmetric distribution, whose mean, median,
93     and mode coincide, and Figure 19.2 shows the pdf of an asymmetric distribution, whose mean,
94     median, and mode are distinct.
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                          \i = Mean = Median = Mode
                          o = Standard deviation
                                FIGURE 19.1 — A symmetric distribution
                                          Mode
                                           ,- - Median
                                            - - • Mean
          \i = Mean
          o = Standard deviation
                               FIGURE 19.2 — An asymmetric distribution
 95      For some distributions, the median or mode may not be unique. If there is a unique mode, the dis-
 96      tribution is called unimodal; otherwise, it is called multimodal.

 97      The median of X is also called a quantile of order 0.5, or a 0.5-quantile. In general, ifp is a num-
 98      ber between 0 and 1, a/>-quantile of Xis a number xp such that the probability that X< xp is at
 99      mostp and the probability thatX < xp is at least/?. A/?-quantile is often called a \00pthpercentile.

100      Sometimes the standard deviation of a nonnegative quantity is more meaningful when expressed
101      as a fraction of the mean.  The coefficient of variation, or CV, is defined for this reason as the
102      standard deviation divided by the mean. The coefficient of variation is a dimensionless number,
103      which may be converted to a percentage. The term "relative standard deviation," or RSD, is also
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104     used. The term "relative variance" is sometimes used to mean the square of the relative standard
105     deviation.

106     The results of two analytical measurements may be correlated when they have measurement
107     errors in common. This happens, for example, if laboratory samples are analyzed using the same
108     instrument without repeating the instrument calibration. Any error in the calibration parameters
109     affects all results obtained from the instrument. This type of association between two quantities X
110     and Y is measured by their covariance, which is denoted by GXJ or Cov(X,Y). The covariance of X
111     and 7 is defined as the expected value of the product (X - |ix) (7 - |i7).

112     Covariance, like variance, is somewhat nonintuitive because  of its physical dimensions. Further-
113     more, a large value for the covariance of two variables X and 7 does not necessarily indicate a
114     strong correlation between them. A measure of correlation must take into account not only the
115     covariance oxr, but also the standard deviations GX and o7.  The correlation coefficient, denoted
116     by pxj, is therefore defined  as GXJ divided by the product of GX and o7. It is a dimensionless
117     number between -1 and +1. The quantities X and 7 are said to be strongly correlated when the
118     absolute value of their correlation coefficient is close to 1.

119     Statistical formulas are generally simpler when expressed in terms of variances and covariances,
120     but the results of statistical analyses of data are more easily understood when presented in terms
121     of standard deviations and correlation coefficients.

122     The lack of a correlation between two quantities X and 7 is not a sufficient condition to guarantee
123     that two valuesX^O and g(Y) calculated from them will also be uncorrelated. A stronger condi-
124     tion called independence is required. For most practical purposes, to say that two quantities are
125     "independent" is to say that their random components are completely unrelated. To be more
126     rigorous, X and 7 are independent if and only if Pr[Jf e / and  7 e J] = Pr[Jf e / ] • Pr[7 e J] for
127     any intervals / and Jin the real line, where the symbol e  denotes set membership.

128     When the value of a random variable Xis used to estimate  the value of an unknown parameter/?,
129     then Xis called an estimator for p. The bias of X is the difference between the mean \\.x and the
130     actual value/?. If the bias is  zero, then X is said to be unbiased,  otherwise, Xis biased.

131     19.2.2 Summary of Terms

132     arithmetic mean: The term "arithmetic mean" denotes the estimate of the expectation of a distri-
133     bution calculated by dividing the sum of a set of observed values by the number of values. It is
134     also called the "average."


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135     bias: If X is an estimator for a parameter/?, then the bias of X is \ix - p.

136     coefficient of variation: The coefficient of variation of a nonnegative distribution is the ratio of
137     its standard deviation to its mean.

138     correlated: Two random variables are correlated if their covariance is nonzero.

139     correlation coefficient: The correlation coefficient of two random variables is equal to their
140     covariance divided by the product of their standard deviations.

141     covariance: The covariance of two random variables X and 7, denoted by Cov(X,Y) or GX7, is a
142     measure of the association between them, and is defined as E[(X- |ix)(7 - |i7)].

143     distribution: The distribution of a random variable is a mathematical description of its possible
144     values and their  probabilities. The distribution is uniquely determined by its distribution function.

145     distribution function: The distribution function, or cumulative distribution function, of a ran-
146     dom variable X is the function F defined by F(x) = Pr[X < x].

147     estimator: A random variable whose value is used to estimate an unknown parameter/? is called
148     an estimator for p.

149     expectation: The expectation of a random variable X, denoted by E(X) or \ix, is a measure of the
150     center of its distribution and is defined as a probability- weighted average of the possible numer-
151     ical values.

152     expected value: See expectation.

153     independent: A collection of random variables X^ X2, ...,Xnis independent if PrjJ^ e 7l3 X2 e 72,
154     ...,Xne !„] = PrjJfj e /! ] • Pr[X, e 72] • • • Pr[Xn e 7J for all intervals 4 12, ...,!„ in the real line.
155     mean: See expectation.

156     median: A median of a distribution is any number that splits the range of possible values into
157     two equally likely portions, or, to be more rigorous, a 0.5-quantile.

158     mode: The mode of a distribution is its most probable value.
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159      percentile: A lOOp  percentile of Xis the same as a/>-quantile of X.

160      probability density function (pdf): ^probability density function for a random variable X is a
161      function f(x) such that the probability of any event a < X < b is equal to the value of the integral
162      J*XX) dx- The pdf, when it exists, equals the derivative of the distribution function.

163      quantile: Kp-quantile of a random variable Xis any value xp such that the probability that X< xp
164      is at most/? and the probability that X
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185     of the measurement and should therefore be considered a random variable. The difference
186     between the measured result and the actual value of the measurand is the error of the measure-
187     ment, which is also a random variable.

188     Measurement error may be caused by random effects or systematic effects in the measurement
189     process. Random effects cause the measured result to vary randomly when the measurement is
190     repeated. Systematic effects cause the result to tend to differ from the value of the measurand by
191     a constant absolute or relative amount, or to vary in a nonrandom manner. Generally, both
192     random and systematic effects are present in a measurement process.

193     A measurement error produced by a random  effect is a random error, and an error produced by a
194     systematic effect is a systematic error. The distinction between random and systematic errors
195     depends on the specification of the measurement process, since a random error in one measure-
196     ment process may appear systematic in another. For example, a random error in the measurement
197     of the concentration of a radioactive standard solution may be systematic from the point of view
198     of a laboratory that purchases the solution and uses it to calibrate instruments.

199     Measurement errors may also be spurious errors, such as those caused by human blunders and
200     instrument malfunctions. Blunders and other spurious errors are not taken into account in the
201     statistical evaluation of measurement uncertainty. They should be avoided, if possible, by the use
202     of good laboratory practices, or at least detected and corrected by appropriate quality assurance
203     and quality control activities.

204     The error of a measurement is primarily  a theoretical concept, because its value is unknowable.
205     The uncertainty of a measurement, however, is a concept with practical uses. According to the
206     GUM, the term "uncertainty of measurement" denotes a "parameter, associated with the result of
207     a measurement, that characterizes the dispersion of the values that could reasonably be attributed
208     to the measurand." The uncertainty of a measured value thus gives a bound for the likely size of
209     the  measurement error. In practice, there is seldom a need to refer to the error of a measurement,
210     but an estimate of the uncertainty is required for every measured result.

211     19.3.2 The Measurement Process

212     The first step in defining a measurement process is to define the measurand clearly. The specifi-
213     cation of the measurand is always ambiguous to some extent, but it should be as clear as neces-
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214      sary for the intended purpose of the data.1 For example, when measuring the concentration of a
215      radionuclide in a laboratory sample, it is generally necessary to specify the concentration as of a
216      certain date and time and whether the entire sample or only a certain fraction is of interest. For
217      very accurate work, it may be necessary to specify other conditions, such as temperature (e.g.,
218      concentration per unit volume of liquid at 20°C).

219      Often the measurand is not measured directly but instead an estimate is calculated from the meas-
220      ured values of other input quantities, which have a known mathematical relationship to the
221      measurand. For example, input quantities in a measurement of radioactivity may include the
222      gross count, instrument background count, counting efficiency, and test portion size. The second
223      step in defining the measurement process is therefore to determine the mathematical model for
224      the relationship between the measurand Y and measurable input quantities Xt on which its value
225      depends. The relationship may be a simple functional relationship, expressible as 7 =
226     J(Xl,X2,... Jffj), or it may happen that Y is most conveniently expressed as the simultaneous
227      solution of a set of equations.

228      The mathematical model for a radioactivity measurement often has the general form

                   Y_ (Gross Instrument Signal) - (Blank  Signal + Estimated Interferences)
                                                   Sensitivity

230      Each of the quantities shown here may actually be a more complicated expression. For example,
231      the sensitivity (the  ratio of the net signal to the concentration) may be the product of factors such
232      as the mass of the test portion, the chemical yield, and the instrument counting efficiency.

233      When the measurement is performed, a value xt is estimated for each input quantity, Xt, and an
234      estimated value y of the measurand is calculated using the relationship^ =f[xl,x2,.. .,%).2 Since
235      there is an uncertainty in each input estimate, xt, there is also an uncertainty in the output
236      estimate, y. In order to obtain a complete estimate of the uncertainty ofy, all input quantities that
237      could have a potentially significant effect ony should be included in the model.
          1 Because of the unavoidable ambiguity in the specification of the measurand, one should, to be precise, speak of
         "a value" of the measurand and not "the value."

          2 In accordance with the GUM, an uppercase Roman letter is used here to denote both the input or output quantity
         and the random variable associated with its measurement, while a lowercase letter is used for the estimated value of
         the quantity. For simplicity, in most of the later examples this convention will be abandoned.  Only one symbol will
         be used for the quantity, the random variable, and the estimated value of the quantity.

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238     19.3.3 Analysis of Measurement Uncertainty

239     Determining the uncertainty of the output estimate y requires that the uncertainties of all the input
240     estimates xt be determined and expressed in comparable forms. The uncertainty of xt is expressed
241     in the form of a standard deviation, called the standard uncertainty and denoted by w(x,), or in the
242     form of a variance, denoted by w2(x;), which is the square of the standard uncertainty. A standard
243     uncertainty is sometimes informally called a "one-sigma" uncertainty. The ratio u(x,) I xt is called
244     the relative standard uncertainty of xt. If the input estimates are potentially correlated, covariance
245     estimates u(xt,x^) must also be determined. The covariance u(xt,Xj) is  often recorded and presented
246     in the form of an estimated correlation coefficient, r(xt,Xj), which is defined as the quotient
247     ufaXj) I u(x^)u(x^). The standard uncertainties and estimated covariances are combined to obtain
248     the combined standard uncertainty of_y, denoted by uc(y). (The term  "total propagated uncertain-
249     ty," or TPU, has been used for the same concept; however, MARLAP recommends the ISO
250     terminology.) The square of the combined standard uncertainty, denoted by u2c(y\ is called the
251     combined variance.

252     The process of combining the standard uncertainties of the input estimates xt to obtain the com-
253     bined standard uncertainty of the output estimate^ is called "uncertainty propagation." Mathe-
254     matical  methods for propagating uncertainty and for evaluating the standard uncertainties of the
255     input estimates are described in Section 19.5.

256     Methods for evaluating the standard uncertainties u(x,) are classified as either Type A or Type B.
257     A Type A evaluation of a standard uncertainty u(x,) may be performed by making a series of inde-
258     pendent measurements of the quantity xt and calculating the arithmetic mean and experimental
259     standard deviation of the mean. The arithmetic mean is used as the input estimate xt and the
260     experimental standard deviation of the mean is used as the standard uncertainty u(x^). There are
261     other Type A methods, but all are based on repeated measurements. Any evaluation of standard
262     uncertainty that is not a Type A evaluation is a Type B evaluation.

263     Sometimes a Type B evaluation of uncertainty involves making a best guess based on all avail-
264     able information and professional judgment. Laboratory workers may be reluctant to make this
265     kind of evaluation, but it is better to make an informed guess about an uncertainty component
266     than to ignore it completely.

267     A standard uncertainty u(x,) may be called a "Type A" or "Type B" standard uncertainty, depend-
268     ing on its method of evaluation, but no distinction is made between the two types for the
269     purposes of uncertainty propagation.
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270     19.3.4 Corrections for Systematic Effects

271     When a systematic effect in the measurement process has been identified and quantified, a quan-
272     tity should be included in the mathematical measurement model to correct for it. The quantity,
273     called a correction (additive) or correction factor (multiplicative), will have an uncertainty which
274     should be evaluated and propagated.

275     Whenever a previously unrecognized systematic effect is detected, the effect should be investi-
276     gated and either eliminated procedurally or corrected mathematically.

277     19.3.5 Counting Uncertainty

278     The counting uncertainty of a radiation measurement (historically called "counting error") is the
279     component of uncertainty caused by the random nature of radioactive decay and radiation count-
280     ing. Radioactive decay is inherently random in the sense that two atoms of a radionuclide will
281     generally decay at different times, even if they are identical in every discernible way. Radiation
282     counting is also inherently random unless the efficiency of the counting instrument is 100%.

283     In many cases the counting uncertainty in a single gross radiation counting measurement can be
284     estimated by the square root of the observed counts. The Poisson counting model, which is the
285     mathematical basis for this rule, is discussed in Section 19.6. Note that the use of this approxi-
286     mation is a Type B evaluation of uncertainty.

287     Historically many radiochemistry laboratories reported only the counting uncertainties of their
288     measured results. MARLAP recommends that a laboratory consider all possible sources of meas-
289     urement uncertainty and evaluate and propagate the uncertainties for all sources believed to be
290     potentially significant in the final result.

291     19.3.6 Expanded Uncertainty

292     The laboratory  may report the combined standard uncertainty, uc(y), or it may multiply uc(y) by a
293     factor k, called  a coverage factor, to produce an expanded uncertainty, denoted by U, such that
294     the interval from;; -  Utoy+ C/has a specified high probability/? of containing the value of the
295     measurand. The specified probability, p, is called the level of confidence or the coverage proba-
296     bility and is generally only an approximation of the true probability of coverage.

297     When the distribution of the measured result is approximately normal, the coverage factor is
298     often chosen to be k = 2 for a coverage probability of approximately 95%. An expanded uncer-


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299     tainty calculated with k = 2 or 3 is sometimes informally called a "two-sigma" or "three-sigma"
300     uncertainty. In general, if the desired coverage probability is y and the combined standard uncer-
301     tainty is determined accurately, the coverage factor for a normally distributed result is k = z(1+y)/2,
302     which can be found in a table of quantiles of the standard normal distribution (see Table G.I in
303     Appendix G).

304     The GUM recommends the use of coverage factors in the range 2-3 when the combined standard
305     uncertainty is determined accurately. Attachment 19C describes a more general  procedure for
306     calculating the coverage factor kp that gives a desired coverage probability/? when there is sub-
307     stantial uncertainty in the estimate of uc(y).

308     19.3.7 Significant Figures

309     The number of significant figures that should be reported for the result of a measurement
310     depends on the uncertainty of the result. A common convention is to round the uncertainty
311     (standard uncertainty or expanded uncertainty) to either one or two significant figures and to
312     report both the measured value and the uncertainty to the resulting number of decimal places
313     (ISO 1995, Bevington 1992, EPA 1980). MARLAP recommends this convention and suggests
314     that uncertainties be rounded to two figures. The following examples demonstrate the application
315     of the rule.
316

317
318
319

320

321

322

323

324


325
326
327
328
329
                                      EXAMPLES
MEASURED
VALUE
00
0.8961
0.8961
0.8961
0.8961
0.8961
EXPANDED
UNCERTAINTY
U = kufy)
0.0234
0.2342
2.3419
23.4194
234.1944
REPORTED
RESULT
0.896 ± 0.023
0.90 ± 0.23
0.9 ±2.3
1±23
0±230
Only final results should be rounded in this manner. Intermediate results in a series of calculation
steps should be carried through all steps with additional figures to prevent unnecessary roundoff
errors. Additional figures are also recommended when the data are stored electronically. Round-
ing should be performed only when the result is reported. (See Section 19.6.10 for a discussion of
the measurement uncertainty associated with rounding.)
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330      19.3.8 Reporting the Measurement Uncertainty

331      When a measured value y is reported, its uncertainty should always be stated. The laboratory may
332      report either the combined standard uncertainty uc(y) or the expanded uncertainty U.

333      The measured value y and its expanded uncertainty f/may be reported in the format^ ± U or
334     y +- U.

335      The plus-minus format may be used to report an expanded uncertainty, but it generally should be
336      avoided when reporting a standard uncertainty, because readers are likely to interpret it as a con-
337      fidence interval. A commonly used shorthand format for reporting a result with its standard
338      uncertainty places the one or two digits of the standard uncertainty in parentheses immediately
339      after the corresponding final digits of the rounded result. For example, if the rounded result of the
340      measurement is 1.92 and the standard uncertainty is 0.14, the result and uncertainty may be
341      shown together as 1.92(14). One may also report the standard uncertainty explicitly.

342      Since laboratories may calculate uncertainties using different methods and report them using
343      different coverage factors, it is a bad practice to report an uncertainty without explaining what it
344      represents. Any analytical report, even one consisting of only a table of results, should state
345      whether the uncertainty is the combined standard uncertainty or an expanded uncertainty, and in
346      the latter case it should also state the coverage factor used and the approximate coverage prob-
347      ability. A complete report should also describe the methods used to calculate the uncertainties.

348      The uncertainties for environmental radioactivity measurements should be reported in the same
349      units as the results. Relative uncertainties (i.e., uncertainties expressed as percentages) may also
350      be reported, but the reporting of relative uncertainties alone is not recommended when the
351      measured value may be zero, because the relative uncertainty in this case is undefined. A partic-
352      ularly bad practice, sometimes implemented in software, is to compute the relative uncertainty
353      first and multiply it by the measured value to obtain the absolute uncertainty. When the measured
354      value is zero, the uncertainty is reported incorrectly as zero. Reporting of relative uncertainties
355      without absolute uncertainties for measurements of spiked samples or standards generally
356      presents no problems, because the probability of a negative or zero result is negligible.

357      It is possible to calculate radioanalytical results that are less than zero, although negative radio-
358      activity is physically impossible. Laboratories sometimes choose not to report negative results or
359      results that are near zero. Such censoring of results is not recommended. All results, whether
360     positive, negative, or zero, should be reported as obtained, together with their uncertainties.
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361     The preceding statement must be qualified, because a measured value y may be so far below zero
362     that it indicates a possible blunder, procedural failure, or other quality control problem. Usually,
363     if y + 3 uc(y) < 0, the result should be considered invalid, although the accuracy of the uncertainty
364     estimate uc(y) must be considered, especially in cases where only few counts are observed during
365     the measurement and counting uncertainty is the dominant component of uc(y). (See Chapter 18,
366     Laboratory Quality Control, and Attachment 19C of this chapter.)

367     19.3.9  Recommendations
368

369
370
371

372
373

374
375
376

377
378
379

380
381

382
383


384

385
 MARLAP makes the following recommendations.

    •   All radioanalytical laboratories should adopt the terminology and methods of the Guide
        to the Expression of Uncertainty in Measurement (ISO 1995) for evaluating and
        reporting measurement uncertainty.

    •   Each measured value should be reported with either its combined standard uncertainty
        or its expanded uncertainty.

    •   The reported measurement uncertainties should be clearly explained. In particular, the
        coverage factor and approximate coverage probability should be stated whenever an
        expanded uncertainty is reported.

    •   A laboratory should consider all possible sources of measurement uncertainty and
        evaluate and propagate the uncertainties for all sources believed to be potentially
        significant in the final result.

    •   Each uncertainty should be rounded to two significant figures, and the measured value
        should be rounded to  the same number of decimal places as its uncertainty.

    •   All results, whether positive, negative, or zero, should be reported as obtained, together
        with their uncertainties.
19.3.10 Summary of Terms

blunder: mistake made by a person performing a measurement.
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386     combined standard uncertainty: standard uncertainty of an output estimate calculated by
387     combining the standard uncertainties of the input estimates. The combined standard uncertainty
388     of y is denoted by uc(y).

389     combined variance: the square of the combined standard uncertainty. The combined variance of
390     y is denoted by u2c(y).

391     counting error:  See counting uncertainty. MARLAP uses the term "counting uncertainty" to
392     maintain a clear distinction between the concepts of measurement error and uncertainty.

393     counting uncertainty: component of measurement uncertainty caused by the random nature of
394     radioactive decay and radiation counting.

395     coverage factor: value k multiplied by the combined standard uncertainty uc(y) to give the
396     expanded uncertainty U.

397     coverage probability: approximate probability that the reported interval will contain the value of
398     the measurand.

399     error (of measurement): difference between a measured result and the value of the measurand
400     (cf uncertainty of measurement).

401     expanded uncertainty: product U of the combined standard uncertainty of a measured value y
402     and a coverage factor k chosen so  that the interval from_y -  Utoy+ C/has a desired high proba-
403     bility of containing the value of the measurand Y.

404     GUM: abbreviation used in this chapter for the Guide to the Expression  of Uncertainty in
405     Measurement (ISO 1995).

406     input estimate: measured value of an input quantity.

407     input quantity: any of the quantities in a mathematical measurement model whose values are
408     measured and used to calculate the value of another quantity, called the output quantity.

409     level of confidence: See coverage probability.

410     measurand: quantity subject to measurement.
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411     output estimate: calculated value of an output quantity.

412     output quantity: the quantity in a mathematical measurement model whose value is calculated
413     from the measured values of other quantities in the model.

414     random effect: any effect in a measurement process which causes the measured result to vary
415     randomly when the measurement is repeated.

416     random error: a measurement error which varies randomly when the measurement is repeated
417     — caused by random effects.

418     relative standard uncertainty: the ratio of the  standard uncertainty of a measured result to the
419     result itself. The relative standard uncertainty of x may be denoted by ur(x).

420     sigma (o): The term "sigma" is sometimes used informally to mean "standard uncertainty," and
421     "&-sigma" is used to mean an expanded uncertainty calculated using the coverage factor k. The
422     symbol o and the term "sigma" are more properly used to denote a true standard deviation.

423     spurious error: a measurement error caused by a human blunder, instrument malfunction, or
424     other unexpected or abnormal event

425     standard uncertainty: uncertainty of a measured value expressed as a standard deviation —
426     often called a "1-sigma" uncertainty. The standard uncertainty of x is  denoted by u(x).

427     systematic effect: any effect in a measurement process which does not vary randomly when the
428     measurement is repeated.

429     systematic error: a measurement error which does not vary randomly when the measurement is
430     repeated — caused by systematic effects.

431     total propagated uncertainty (TPU): See combined standard uncertainty, which is the
432     preferred term.

433     Type A evaluation: experimental evaluation of a standard uncertainty or covariance using
434     repeated measurements.

435     Type B evaluation: evaluation of a standard uncertainty or covariance by a method that is not a
436     Type A method.

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437     uncertainty (of measurement): "parameter, associated with the result of a measurement, that
438     characterizes the dispersion of the values that could reasonably be attributed to the measurand"
439     (ISO 1993a).

440     uncertainty propagation: mathematical technique for combining the standard uncertainties of
441     the input estimates for a mathematical model to obtain the combined standard uncertainty of the
442     output estimate.
443     19.4  Detection and Quantification Capability

444     19.4.1  Analyte Detection Decisions

445     An obvious question to be answered following the analysis of a laboratory sample is: "Does the
446     sample contain a positive amount of the analyte?" Uncertainty in the measured value often makes
447     the question difficult to answer. There are different methods for making a detection decision, but
448     the methods most often used in radiochemistry involve the principles of statistical hypothesis
449     testing.

450     Hypothesis testing has been used for analyte detection in radiochemistry since at least 1962. Two
451     influential early publications on the subject were Altshuler and Pasternack 1963 and Currie 1968.
452     Other important but perhaps less well-known documents were Nicholson 1963 and 1966. Most
453     approaches to the detection problem have been similar in principle, but there has been inadequate
454     standardization of terminology and methodology. However, there has been recent progress. In
455     1995 the International Union of Pure and Applied Chemistry (IUPAC) published "Nomenclature
456     in Evaluation of Analytical Methods Including Detection and Quantification Capabilities"
457     (IUPAC 1995), which recommends a uniform approach to defining various performance char-
458     acteristics of any chemical measurement process, including detection and quantification limits;
459     and in 1997 the International Organization for Standardization (ISO) issued  the first part of ISO
460     11843 "Capability of Detection," a two-part standard which deals with issues of detection in an
461     even more general context of measurement (ISO 1997). Part  1 of ISO 11843 includes terms and
462     definitions. Part 2, which is not available at the time of this writing, will deal with methodology.
463     Although members of the IUPAC and ISO working groups collaborated during the development
464     of their guidelines, substantial differences between the final documents remain. MARLAP
465     follows both the ISO and IUPAC guidelines where they agree but prefers the definitions of ISO
466     11843-1 for the  critical value and minimum detectable value, relating them to the terminology
467     and methodology already familiar to most radiochemists.
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468     In July 2000, ISO also published the first three parts of ISO 11929 "Determination of the Detec-
469     tion Limit and Decision Threshold for Ionizing Radiation Measurements" (ISO 2000a-c). Unfor-
470     tunately, ISO 11929 is not completely consistent with either the earlier ISO standard or the
471     IUPAC recommendations.

472     In the terminology of ISO 11843-1, the analyte concentration of a laboratory sample is the state
473     variable, denoted by Z, which represents the state of the material being analyzed. Blank material
474     is said to be in the basic state. The state variable cannot be observed directly, but it is related to
475     an observable response variable, denoted by 7, through a calibration function F, the mathemat-
476     ical relationship being written as Y = F(Z). In radiochemistry  the response variable Y is most
477     often an instrument signal, such as the number of counts observed. The difference between the
478     state variable Z and its value in the basic state is called the net state variable, which is denoted
479     by X. In radiochemistry there generally is no difference between the state variable  and the net
480     state variable, because the basic state is represented by material whose analyte  concentration is
481     zero. (In principle the basic state might correspond to a positive concentration, but MARLAP
482     does not address this scenario.)

483     A detection decision requires a choice between two hypotheses about the material being ana-
484     lyzed. The first hypothesis is the "null hypothesis" H0: The analyte concentration of the material
485     is no greater than that of the blank (i.e., the material is in the basic state). The second hypothesis
486     is the "alternative hypothesis" H^ The analyte concentration of the material is greater than that of
487     the blank. The choice between the two hypotheses is based on the observed value of the response
488     variable Y. The value of Y must exceed a certain threshold value to justify rejection of the null
489     hypothesis. This threshold is called the critical value of the response variable and is denoted
490     by yc. The  calculation of yc requires the choice of a significance level for the test. The signifi-
491     cance level is the probability a that the null hypothesis will be rejected in a situation where it is
492     in fact true (i.e., a "type I error," or "false positive"). The significance level a is usually chosen to
493     be 0.05. This means that when a blank sample is analyzed, there is a 5% probability of incor-
494     rectly deciding that the analyte is present. A smaller value of  a makes  type I errors less likely, but
495     also makes type U errors ("false negatives") more likely when the laboratory sample concentra-
496     tion is near the blank concentration.

497     The term "blank" here may mean any of several types of blanks, including instrument blanks (or
498     backgrounds) and reagent blanks. The blank is chosen to provide an estimate of the mean signal
499     produced by an actual sample that contains none of the analyte, whether the signal is produced by
500     the instrument background, contaminated reagents, or other causes.
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501     The inverse F~l of the calibration function is sometimes called the evaluation function (IUPAC
502     1995). The evaluation function, which gives the value of the net concentration in terms of the
503     response variable, is closely related to the mathematical model described in Section 19.3.2.

504     The critical value of the analyte concentration xc, according to the ISO definition, is the value
505     obtained by applying the evaluation function F~l to the critical value of the response variable ^
506     Thus, xc = F~l(yc~). In radiochemistry this formula typically involves  division by the counting
507     efficiency, test portion size, chemical yield, decay factor, and possibly other factors. In ANSI
508     N42.23, the same value xc is called the decision level concentration., or DLC (ANSI 1996b).

509     According to ISO 11843-1, a detection decision involves the critical  value of the response
510     variable, or gross instrument signal, which, in a radioactivity measurement, is typically a total
511     count or count rate. However, it has become standard practice in radioanalysis to use instead the
512     critical value of the net instrument signal, which is calculated  from the gross signal by subtract-
513     ing the estimated blank value and any interferences. This practice is consistent with the recom-
514     mendations of IUPAC  (1995), where the critical value of the net instrument signal S is denoted
515     by Sc. In principle, either approach should lead to the same detection decision.

516     Since the term "critical value" alone is ambiguous, one should specify the variable to which the
517     term refers.  For example, one may discuss the critical (value of the) analyte concentration, the
518     critical (value of the) net count, or the critical (value of the) gross count.

519     Section 19.7.1 and Section 19D.2 of Attachment 19D provide more information on the calcula-
520     tion of critical values.

521     19.4.2 The Minimum Detectable Concentration

522     The minimum detectable concentration is the concentration of analyte that must be present in a
523     laboratory sample to give a specified probability 1 - P of detection. Then p is the probability of
524     failing to reject the null hypothesis when it is false (i.e., a "type II error," or "false negative").
525     The minimum detectable concentration is often abbreviated as MDC. In the ISO terminology the
526     MDC is called the minimum detectable value of the net state variable,  denoted by XD, which is
527     defined as the smallest (true) value of the net state variable that gives a specified high probability
528     1 - P that the value of the response variable will exceed its critical value, thus leading one to
529     conclude correctly that the material analyzed is not in the basic state (i.e., the material is not
530     blank). The relationship between the critical value and the minimum detectable value of the net
531     state variable is shown in Figure 19.3.
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                                         0      x c       x D
                  FIGURE 19.3 — The critical value xc and minimum detectable value XD
                                         of the net state variable
532     Sections 19.7.2 and 19D.3 provide more information about the calculation of the minimum
533     detectable concentration.

534     When the quantity being measured is the total amount of analyte in an item and not an analyte
535     concentration, the minimum detectable value is sometimes called the minimum detectable
536     amount, which may be abbreviated as MDA. This chapter focuses on the MDC, but with few
537     changes the guidance is also applicable to the MDA.

538     While project planners and laboratories have some flexibility in choosing the significance level a
539     used for detection decisions, the MDC is usually calculated with a = P = 0.05. The use of stan-
540     dard values for a and P allows meaningful  comparison of analytical procedures.

541     The MDC concept has generated controversy among radiochemists for years and has frequently
542     been misinterpreted and misapplied. The term must be carefully and precisely defined to prevent
543     confusion.  The MDC is by definition the true concentration of analyte required to give a speci-
544     fied high probability that the measured response will be greater than the critical value. Thus, the
545     common practice of comparing a measured concentration to the MDC to make a detection
546     decision is  not defensible.

547     There are still disagreements about the  proper uses of the MDC concept. Some define the MDC
548     strictly as an estimate of the nominal detection capability of a measurement process. Those in
549     this camp consider it invalid to compute an MDC for each measurement using sample-specific
550     information such as test portion size, chemical yield, and decay factors (e.g., ANSI N42.23). The
551     opposing view is that the "sample-specific" MDC is a useful measure of the detection capability
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552     of the measurement process, not just in theory, but as it actually performed. The sample-specific
553     MDC may be used, for example, to determine whether an analysis that has failed to detect the
554     analyte of interest should be repeated because it did not have the required or promised detection
555     capability.

556     Neither version of the MDC can legitimately be used as a threshold value for a detection deci-
557     sion. The definition of the MDC presupposes that an appropriate detection threshold (i.e., the
558     critical value) has already been defined.

559     Many experts strongly discourage the reporting of a sample-specific MDC because of its limited
560     usefulness and the likelihood of its misuse. Nevertheless, this practice has become firmly estab-
561     lished  at many laboratories and is expected by many users of radioanalytical data. Furthermore,
562     NUREG/CR-4007 states plainly that "the critical (decision) level and detection limit [MDC]
563     really do vary with the nature of the sample" and that "proper assessment of these quantities
564     demands relevant information on each sample, unless the variations among samples (e.g., inter-
565     ference levels) are quite trivial" (NRC 1984).

566     Since a sample-specific MDC is calculated from measured values of input quantities such as the
567     chemical yield, counting efficiency, test portion size, and background level, the MDC estimate
568     has a combined standard uncertainty, which in principle can be obtained by uncertainty propa-
569     gation.

570     In the calculation of a sample-specific MDC, the treatment of any randomly varying but precisely
571     measured quantities, such as the chemical yield, is important and may not be identical at all lab-
572     oratories. The most common approach to this calculation uses the measured value and ignores
573     the variability of the quantity. For example, if the chemical yield routinely varies between 0.85
574     and 0.95, but for a particular analysis the yield happens to be 0.928, the MDC for that analysis
575     would be calculated using the value 0.928 with no consideration of the typical range of yields. A
576     consequence of this approach is that the MDC varies randomly when the measurement is
577     repeated under similar conditions; or, in other words, the sample-specific MDC with this
578     approach is a random variable. The nominal  MDC for the measurement process is a constant —
579     not a random variable.

580     If sample-specific MDCs are reported, it must be clear that no measured value  should ever be
581     compared to an MDC to make a detection decision. In certain cases it may be valid to compare
582     the sample-specific MDC to a required detection limit to determine whether the laboratory has
583     met contractual or regulatory requirements (remembering to consider the uncertainty of the MDC
584     estimate), and in general it may be informative to both laboratory personnel and data users to


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585     compare sample-specific MDCs to nominal estimates, but other valid uses for the sample-
586     specific MDC are rare.

587     19.4.3 Differences between the ISO and IUPAC Definitions

588     The ISO and IUPAC guidance documents give different definitions for some of the terms listed
589     above and promote somewhat different concepts. In general, the IUPAC approach is to define the
590     "critical value" and "minimum detectable value" separately for the signal and concentration
591     domains. A detection decision may be made in either domain, but the outcome of the decision
592     may depend on which domain is chosen. With the ISO approach the outcome does not depend on
593     the domain. Either domain may be chosen, but in effect all detection decisions are made in the
594     signal domain.

595     The IUPAC and ISO approaches to detection in the signal domain, although expressed differ-
596     ently, are effectively equivalent. (IUPAC bases detection decisions on the net signal S, whereas
597     ISO bases detection decisions on the gross signal 7.) The more important differences are in the
598     concentration domain (X). For example, according to IUPAC, the critical analyte concentration
599     xc is determined from the distribution of the measured concentration X, taking into account its
600     overall measurement uncertainty. According to ISO, xc is simply a function of yc, the critical
601     value of the response variable. Since xc is related toyc in the same way that Xis related to 7, it
602     makes no difference whether detection decisions are based on Xor 7— the outcome is the same.

603     The IUPAC guidance defines the minimum detectable concentration XD as the smallest concentra-
604     tion that gives a specified high probability of obtaining a measured concentration greater than xc,
605     which is inconsistent with the ISO guidance because of the differing definitions ofxc.

606     One consequence of the IUPAC definitions is that the measurement variances of sensitivity fac-
607     tors such as the test portion size, counting efficiency, and chemical yield increase the values of xc
608     and XD because they increase the variance of X. According to the ISO definitions, these variances
609     do not increase the values of xc and XD,  although they generate uncertainties in the estimates of xc
610     and XD. In principle, the ISO definitions imply that variability in the true values of these  sensitiv-
611     ity factors does increase XD, although the draft implementation guidance in ISO 11843-2 appar-
612     ently does not deal with the issue.

613     As stated above, MARLAP adopts the ISO definitions but also follows the IUPAC guidance
614     where it does not contradict the definitions of ISO 11843-1. The draft implementation guidance
615     in ISO 11843-2 appears not to be designed for typical radioanalytical measurement  processes.
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616     19.4.4 Other Detection Terminologies

617     Another term frequently used for a measure of detection capability is the "lower limit of detec-
618     tion," or LLD (Altshuler 1963, EPA 1980, NRC 1984). Unfortunately this term has been used
619     with more than one meaning. In Upgrading Environmental Radiation Data (EPA 1980), the LLD
620     is defined as a measure of the detection capability of an instrument and is expressed as an activ-
621     ity. However, the Nuclear Regulatory Commission defines the LLD to be identical to the MDC
622     when a = P = 0.05 (see, for example, NUREG/CR-4007). It is thus a measure of the detection
623     capability of a measurement process and is expressed as an activity concentration.

624     The term "detection limit" is often used as a synonym for "MDC" or for "minimum detectable
625     value" of any other measured quantity.

626     Many other terms have been used to describe detection capabilities of measurement procedures.
627     Most of them will not be listed here, but one term deserves attention because of the possibility of
628     its confusion with the MDC. The method detection limit, or MDL, is a measure of detection
629     capability used routinely in the context of analyzing samples for chemical contaminants.

630     The term "method detection limit" is defined in the Code of Federal Regulations. In Title 40
631     CFR Part  136, Appendix B, the following definition appears:

632            The method detection limit (MDL) is defined as the minimum concentration of a
633            substance that can be measured and reported with 99% confidence that the analyte
634            concentration is greater than zero and is determined from analysis of a sample in a
635            given matrix containing the analyte.

636     The definition is later clarified somewhat by a statement that the MDL "is used to judge the sig-
637     nificance of a single measurement of a future sample." Thus, the MDL serves as a critical value;
638     however, it is also used as a measure of detection capability, like an MDC. Note that, in
639     MARLAP's usage,  the "method detection limit" is not truly a detection limit.

640     The similarity between the abbreviations MDC and MDL tends to produce confusion. The term
641     "method detection limit" is seldom used in the context of radioanalysis except when the analyt-
642     ical method is one that is commonly used to measure stable elements (e.g., ICP/MS methods), or
643     when the term is misused by those who are more familiar with the terminology of hazardous
644     chemical analysis. The confusion is made worse by the fact that "MDL" is sometimes interpreted
645     by radiochemists as an abbreviation for nonstandard terms such as  "minimum detectable level"
646     and "minimum detectable limit," the use of which MARLAP strongly discourages.


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647     19.4.5 The Minimum Quantifiable Concentration

648     The minimum quantifiable concentration., or the minimum quantifiable value of the analyte con-
649     centration, is defined as the concentration of analyte in a laboratory sample at which the measure-
650     ment process gives results with a specified relative standard deviation.3 A relative standard devi-
651     ation of 10% is usually specified, although other values are possible (see for example MARLAP
652     Appendix C). Since ISO 11843 addresses detection capability but not quantification capability,
653     MARLAP follows IUPAC guidance in defining "minimum quantifiable value" (IUPAC 1995).
654     IUPAC defines both the minimum quantifiable instrument signal and the minimum quantifiable
655     concentration, although MARLAP considers only the latter. In this document the minimum quan-
656     tifiable concentration will be abbreviated as MQC and denoted in equations by XQ.

657     The term "quantification limit" may be used as a synonym for "minimum quantifiable concentra-
658     tion" or for "minimum quantifiable value" of any other measured quantity.

659     Section 19.7.3 provides more information about the calculation of the minimum quantifiable
660     concentration.

661     Historically much attention has been given to the detection capabilities of radioanalytical meas-
662     urement processes, but less attention has been given to quantification capabilities, although for
663     some analytical projects, quantification capability may be  a more relevant issue. For example,
664     suppose the purpose of a project is to determine whether the 226Ra concentration in soil from a
665     site is below an action level. Since 226Ra occurs naturally in almost any type of soil, the analyte
666     may be assumed to be present in every sample, making detection decisions irrelevant. The MDC
667     of the measurement process obviously should be  less than the action level, but  a more important
668     question is whether the MQC is less than the action level (see also Chapter 3 and Appendix C).
          3 The MQC is defined in terms of the relative standard deviation of the estimator — not the relative standard
        uncertainty of the measured result. The standard uncertainty is generally an estimate of the standard deviation.

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669     19.4.6 Recommendations
670
671
672
673
674
MARLAP makes the following recommendation.

   •   A measurement result should not be compared to the minimum detectable concentra-
       tion to make an analyte detection decision. A detection decision may be made by
       comparing the gross signal, net signal, or measured analyte concentration to its
       corresponding critical value.
675     19.4.7 Summary of Terms

676     basic state: in radiochemistry, the chemical composition of blank material.

677     critical level: See critical value.

678     critical value: in the context of analyte detection, the minimum value of the response variable
679     (or the measured analyte concentration) required to give confidence that a positive amount of
680     analyte is present in the material analyzed.

681     decision level: See critical value.

682     detection limit:  See minimum detectable value.

683     false negative: See  type I decision error. This chapter avoids the terms "false negative" and
684     "false positive," because they may be confusing in some contexts.

685     false positive: See type II decision error.

686     lower limit of detection (LLD): (1) "the smallest concentration of radioactive material in a
687     sample that will yield a net count, above the measurement process (MP) blank, that will be
688     detected with at least 95% probability with no greater than a 5% probability of falsely concluding
689     that a blank observation represents a 'real' signal" (NRC 1984); (2) "an estimated detection limit
690     that is related to the characteristics of the counting instrument" (EPA 1980).

691     method detection limit (MDL): "the minimum concentration of a substance that can be meas-
692     ured and reported with 99% confidence that the analyte concentration is greater than zero ...
693     determined from analysis of a sample in a given matrix containing the analyte" (40 CFR 136,
694     Appendix B).

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695     minimum detectable amount (MDA): the minimum detectable value of the total amount of
696     analyte in the sample being analyzed.

697     minimum detectable concentration (MDC): the minimum detectable value of the analyte con-
698     centration in a laboratory sample.

699     minimum detectable value: the smallest value of the net state variable (amount or concentration
700     of analyte) that ensures a specified high probability  1 - P of detection.

701     minimum quantifiable concentration (MQC): the minimum quantifiable value of the analyte
702     concentration in a laboratory sample.

703     minimum quantifiable value: the smallest value of the net state variable (analyte amount or
704     concentration) that ensures the relative standard deviation of the measurement is not greater than
705     a specified value, usually 10%.

706     net state variable (X): the difference between the state variable Z and its value in the basic state
707     —in radiochemistry, usually equal to Z, because the value of Z in the basic state is zero.

708     quantification limit:  See minimum quantifiable value.

709     response variable (Y): the variable that gives the observable result of a measurement—in radio-
710     chemistry, typically a  gross count  or count  rate.

711     significance level (a): in a hypothesis test, the probability of a type I decision error.

712     state variable (Z): the quantity that describes the state of the material analyzed—in radiochem-
713     istry, usually the analyte activity concentration.

714     type I decision error: in a hypothesis test, the error made by rejecting the null hypothesis when
715     it is true (a "false positive").

716     type II decision error: in a hypothesis test, the error made  by failing to reject the null hypothesis
717     when it is false (a "false negative").
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718     19.5  Procedures for Estimating Uncertainty

719     The steps for evaluating and reporting the uncertainty of a radioactivity measurement may be
720     summarized as follows (adapted from Chapter 8 of the GUM):

721        1.   Identify the measurand Y and all the input quantities Xt for the mathematical model.
722            Include all quantities whose variability or uncertainty could have a potentially significant
723            effect on the result. Express the mathematical relationship Y =f(Xl,X2,... ,XN) between the
724            measurand and the input quantities.

725        2.   Determine an estimate xt of the value of each input quantity Xt (an "input estimate," as
726            defined in Sections 19.3.2 and 19.3.9).

727        3.   Evaluate the standard uncertainty M(X;) for each input estimate xt, using either a Type A or
728            Type B method of evaluation (see Section 19.5.2).

729        4.   Evaluate the covariances u(xt,x^) for all pairs of input estimates with potentially
730            significant correlations.

731        5.   Calculate the estimate^ of the measurand from the relationship y =f(xl,x2,...,%), where/
732            is the function determined in Step 1.

733        6.   Determine the combined standard uncertainty uc(y) of the estimate^ (see  Section 19.5.3).

734        7.   Multiply uc(y) by a coverage factor k to obtain the expanded uncertainty U such that the
735            interval \y - U,y+ U] can be expected to contain the value of the measurand with a
736            specified probability (see Section 19.3.6 and Attachment 19C).

737        8.   Report the result as_y ± C/with the unit of measure, and, at a minimum, state the coverage
738            factor used to compute C/and the estimated coverage probability.

739     19.5.1 Identifying Sources of Uncertainty

740     The procedure for assessing the uncertainty of a measurement begins with listing all conceivable
741     sources of uncertainty in the measurement process. Even if a mathematical model has been iden-
742     tified, further thought may lead to the inclusion of more quantities in the model. Some sources of
743     uncertainty will be more significant than others, but all should be listed.
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744     After all conceivable sources of uncertainty are listed, they should be categorized as either poten-
745     tially significant or negligible. Each uncertainty that is potentially significant should be evaluated
746     quantitatively. In particular, counting uncertainty, pipetting and weighing uncertainties, and
747     uncertainties in standard concentrations should always be evaluated. Other possible causes of
748     uncertainty include source geometry and placement, variable instrument backgrounds and effi-
749     ciencies, time measurements used in decay and ingrowth calculations, instrument dead-time
750     corrections, approximation errors in simplified mathematical models, impurities in reagents, and
751     uncertainties in the published values for half-lives and radiation emission probabilities.

752     19.5.2 Evaluation of Standard Uncertainties

753     Calculating the combined standard uncertainty of an output estimate y =f(xl,x2, . . . ,%) requires
754     the estimation of the standard uncertainty of each input estimate xt. As stated earlier, methods for
755     evaluating standard uncertainties are classified as either "Type A" or "Type B." A Type A eval-
756     uation of an uncertainty uses a series of measurements to estimate the standard deviation empiri-
757     cally. Any other method of evaluating an uncertainly is a Type B method.

758     19.5.2.1  Type A Evaluations

759     Suppose Xt is an input quantity in the mathematical model. If a series of n independent observa-
760     tions of Xt are made under the same measurement conditions, yielding the results X. VX. 2, ...,X. n,
761     the appropriate value for the input estimate xt is the arithmetic mean, or average, X., defined as
                                                n k=i


762     The experimental variance of the observed values is defined as


                                                                                            (19.2)
                                             n -   k=i


763     and the experimental standard deviation,  s(Xt^, is the square root of s\Xih). The experimental
764     standard deviation of the mean, s(Xt), is obtained by dividing s(Xt k) by Jn .
                                                                                            (19.3)
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765     The experimental standard deviation of the mean is also commonly called the "standard error of
766     the mean."

767     The Type A standard uncertainty of the input estimate xt = X. is defined to be the experimental
768     standard deviation of the mean. Combining the preceding formulas gives the following equation
769     for the standard uncertainty of xt:
                                  u(x) =
                                        \
                                 n(n -
                                                                                (19.4)
770     When the input estimate xi and standard uncertainty u(x^) are evaluated as described above, the
771     number of degrees of freedom for the evaluation is equal to n - 1, or one less than the number of
772     independent measurements of the quantity Xt. In general, the number of degrees of freedom for a
773     statistical determination of a set of quantities equals the number of independent observations
774     minus the number of quantities estimated. The number of degrees of freedom for each evaluation
775     of standard uncertainty is needed to implement the procedure for calculating coverage factors
776     described in Attachment 19C.

777     In some cases there may be accumulated data for a measurement system, such as a balance or
778     pipet, which can be used in a Type A evaluation of uncertainty for future measurements,
779     assuming the measurement process remains in control. In fact, the use of recent historical data is
780     advisable in such cases, because it enlarges the pool of data available for uncertainty evaluation
781     and increases the number of degrees of freedom. This type of uncertainty evaluation can be
782     linked closely to the measurement system's routine quality control.
783

784
785

786


787
EXAMPLE: Ten independent measurements of a quantity^ are made, yielding the values

                       12.132   12.139  12.128   12.133   12.132
                       12.135   12.130  12.129   12.134   12.136

The estimated value xt is the arithmetic mean of the values Xik.
                         c = x. = - £ X., =  12L328 = 12.1328
                         '     '  n£i  a      10
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788
789
         The standard uncertainty of xt is
                              u(x) = s(X) =
                                                        10
                                             10(10-1) k=

                                             L12888x 1Q-6   =0.0011
790
791
792
793
        If Xt and Xj are two input quantities and estimates of their values are correlated, a Type A evalua-
        tion of covariance may be performed by making n independent pairs of simultaneous observa-
        tions of Xt andXj and calculating the experimental covariance of the means. If the observed pairs
        are (X. VX. j), (X. 2,X. 2),..., (X. n,X. n), the experimental covariance of the values is
                                               E
                                                              k ~ X)
                      (19.5)
794
        and the experimental covariance of the means X. and X. is

                                         __    s(X ,,X ,)
                                                  ^  >-*  ^
                                                     n
                                                                                         (19.6)
795     So, the Type A covariance of the input estimates xt = X. and Xj = X is
                                                                                         (19.7)
796     An evaluation of variances and covariances of parameters determined by the method of least
797     squares may also be a Type A evaluation (see Attachment 19B).
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                                                                          Measurement Statistics
798

799
800

801
802
803
804

805
806

807
808
809
810
19.5.2.2 Type B Evaluations

There are many ways to perform Type B evaluations of standard uncertainty. This section
describes some common Type B evaluations but is not meant to be exhaustive.

One example of a Type B method already given is the estimation of counting uncertainty using
the square root of the observed counts. If the observed count is «, when the Poisson counting
model is used, the standard uncertainty of n may be evaluated as u(n) = \Jn. When n may be very
small or even zero, MARLAP recommends the use  of the equation u(n) = 
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        Measurement Statistics
821     If the value of x is believed to lie between a lower bound a_ and an upper bound a+, but values
822     near these two bounds are considered less likely than those near the midpoint, then a symmetric
823     trapezoidal distribution may be used to obtain the input estimate and its standard uncertainty (see
824     Section 19A.7 in Attachment 19A). If the ratio of the width of the trapezoid at its top to the width
825     at its base is P, where 0 < P < 1, then the input estimate is the midpoint x = (a_ + a+) 1 2, and its
826     standard uncertainty is
                                        / \
                                      u(x) =
                                                     A
                                                        1 +P2
                                                                                   (19.9)
827
828


829
830
831

832
833
834
835


836
837


838
839
840
841
As P approaches zero, the trapezoidal distribution becomes triangular. As P approaches one, the
trapezoidal distribution becomes rectangular.
 EXAMPLE: Extreme bounds for a quantity Xare estimated to be 34.3 and 34.5, with values
 between 34.35 and 34.45 considered most likely. Using the trapezoidal distribution with
 a_ = 34.3, a+ = 34.5, and P = (34.45 - 34.35) / (34.5 - 34.3) = 0.5, one obtains the input esti-
 mate x = 34.4 and the standard uncertainty u(x) =
                                               34.5 -34.3
                                                                 "\
1 +0.52
        = 0.046.
When the estimate of an input quantity is taken from an external source, such as a book or a
calibration certificate, which states the uncertainty as a multiple of the standard deviation s, the
standard uncertainty is obtained by dividing the stated uncertainty by the stated multiplier of s.
 EXAMPLE: The uncertainty for a measured concentration x is stated to be 0.015 Bq g"1 and the
 stated multiplier is 2. So, the standard uncertainty of x is u(x) = 0.015 12 = 0.0075 Bq g"1.
If the estimate is provided by a source which gives a bound c for the error such that the interval
from x - c to x + c contains the true value with 100y% confidence (0 < y < 1) but no other infor-
mation about the distribution is given, the measured result may be assumed to have a normal
distribution, and the standard uncertainty may therefore be evaluated as
                                           u(x) =
                                                                                  (19.10)
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842     The value of z(1+y)/2 may be found in a table of quantiles of the standard normal distribution (see
843     Table G. 1 in Appendix G).
844
845
846
EXAMPLE: The activity concentration x of a commercial standard solution is stated to lie
within the interval 4530 ± 64 Bq g"1 with 95% confidence. The standard uncertainty may
therefore be evaluated as u(x) = 64 / z0975 = 64 /1.96 = 33 Bq g"1.
847     19.5.3 Combined Standard Uncertainty

848     Consider the mathematical model Y =f(Xl,X2, . . . ,XN). Ifx^ x2, . . . , % are measured values of the
849     input quantities Xt and_y =fixl,x2, ...,%) is the calculated value of the measurand 7, the variance
850     of y is estimated using the following formula.


                                                   N-l  N   2   2
                                                        y;  JLJLM(X.JX.)
                                                     -J  / -J  -,   -,    \ i>  ;/
                                                   ,-=i;=,-+i a*,,  a*,                        (1911)

                              The  Uncertainty Propagation Formula

851     Here w2(x,) denotes the estimated variance of xt, or the square of its standard uncertainty; u(xt,x^)
852     denotes the estimated covariance of xt and x,.; 3y / 3x; (or a// ax,) denotes the partial derivative of
853     7 with respect to Xt evaluated at the measured values xl3 x2, . . . , xw;  and wc(y) denotes the com-
854     bined standard uncertainty of y. The partial derivatives dy I dxi are called sensitivity coefficients.

855     The preceding formula, called the "law of propagation  of uncertainty" in the GUM, will be called
856     the "uncertainty propagation formula" in this document.

857     If the input estimates xl3 x2, ...,XN are uncorrelated, the uncertainty propagation formula reduces
858     to
                                              N
859     Equation 19.12 is only valid when the input estimates are uncorrelated. Although this case occurs
860     frequently in practice, there are notable exceptions. When input estimates are obtained using the
861     same measuring devices or the same standard solutions, or when they are calculated from the
862     same data, there is a potential for correlation. For example, instrument calibration parameters
863     determined by least-squares analysis may be strongly correlated. Fortunately, the method of least

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                     TABLE 19.1 — Applications of the uncertainty propagation formula
          SUMS AND
          DIFFERENCES
If a and b are constants, then
                 uc(ax±by)=a2u2(x) + b2u2(y)±2ab • u(x,y)
          PRODUCTS
If x and>- are measured values, then
                   uc (xy) = u 2(x)y 2 +x2u 2(y) + 2xy • u(x,y)
When x andy are nonzero, the formula may be rewritten as
                    2,  .    2  i( u2(x)   u2(y)  2u(x,y)\
                   uc(xy) = x y  \ —^- + —^ + —±^LL|
          QUOTIENTS
If x andy are measured values, then
                   „*(*}=«&
                                                                        2x• u(x,y)
                                                    y)    y~     y^        y
                             When x is nonzero, the variance formula may be rewritten as
                                                        y    x
          EXPONENTIALS
If a is a constant, then
                          Mc2(em)=«2e2mM2W
If n is a positive integral constant, then
                          uc(x") = n2x2n~2u2(x)
If Dispositive, then
                         y2u2(x)  ,,  ,2 2/ x  2y(\nx)u(x.y)'
                         ±i	v > -i- /^In-v-^z^ (v) +
                                          uc(x
          LOGARITHMS
If a is a constant and ax is positive, then
         1n   ,   U2(X)       ,      2,,      -     U2(X)      U2(X)
        uc (in ax) = —Y.     and    uc (Iog10 ax) =	^— »	±L-
                  x2                        (InlO)2*2  5.302 • x'
864      squares provides covariance estimates with almost no additional effort (see Attachment 19B). In
865      general, ignoring correlations between the input estimates may lead to overestimation or under-
866      estimation of the combined standard uncertainty.

867      Table 19.1 shows how to propagate uncertainties in some common cases.

868      The product of | dy I dxt  and the standard uncertainty u(x^) is called the component of the
869      combined standard uncertainty uc(y) generated by the standard uncertainty of xt, and may be
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870     denoted by u,(y). When all the input estimates are uncorrelated, the combined standard uncer-
871     tainty may be written in terms of its components as follows.
                                                                                       (19.13)
                                               2 = 1
872     Since u2c(y) is the sum of the squares of the components u,(y), the combined standard uncertainty
873     tends to be determined primarily by its largest components.
874

875
876
877
878
879
880


881

882
883
884
885
886
887
888

889

890


891
                                     EXAMPLE

Problem: A 6000-s gross alpha measurement is performed on a test source prepared by evap-
orating water on a stainless steel planchet. The measurement produces 120 alpha counts. The
preceding background measurement on the instrument had a duration of 6000 s and produced
42 alpha counts. The estimated alpha counting efficiency is 0.223 with a standard uncertainty
of 0.015. The sample volume analyzed is 0.05000 L, with a standard uncertainty of 0.00019 L.
The alpha emission rate per unit volume is described by the mathematical  model
                                 A =
                                     Nslts-NBltB
where
    1B
    £
    V
is the source count (Ns = 120)
is the background count (NB = 42)
is the source count time (ts = 6000)
is the background count time (tB = 6000)
is the counting efficiency (e = 0.223)
is the volume analyzed (V= 0.0500)
What is the output estimated and what is its combined standard uncertainty, uc(A)l
Solution: First compute the output estimate A (alphas per second per liter).
         A =
120/6000 - 42/6000
  (0.223)(0.05000)
                                                             1.17
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892
893
894
895
896
897
898
899

900

901
902
903
904
 Then compute the combined standard uncertainty uc(A). The only uncertainties included in the
 model will be those associated with the counts Ns and NB, the efficiency £, and the volume V.
 There is no reason to suspect correlations between the measured values; so, the uncertainty
 propagation formula becomes
                                                                 dA\2
                                                                 	
                                                                 an
                                            dN,
 The partial derivatives are evaluated as follows:

    dA  _  1          dA  _  -1          QA
    dNn   t~eV        dNR   tBzV         de
                = 0.0149477
                          = -0.0149477
             = -5.22834
                                                                              Nslts-NBltB
= -23.3184
 The Poisson model is used for the standard uncertainties of the counts Ns and NB. So,

                       u2(Ns) = NS=120   and    u\NB) = NB = 42

 Recall from the statement of the problem that w(e) = 0.015 and u(V) = 0.00019. When the
 values of all these expressions are substituted into the uncertainty propagation formula, the
 combined variance is u2c(A) = 0.0424; so, the combined standard uncertainty is uc(A) =
 J0.0424 «  0.21.
905
906

907
It is helpful to remember certain special forms of the uncertainty propagation formula. For
example, if the values xl3 x2, ...,xn and zl3 z2, ..., zm are uncorrelated and nonzero, the combined
standard uncertainty of y =
may be calculated from the formula
                   - -,,2
                                                                                        (19.14)
                                       f[x x   x )
908     As another example, suppose y = —" 2'"" " , where/is some specified function of xl3 x2, ...x
                                        ZlZ2'"Zm
909     all the zt are nonzero, and all the input estimates are uncorrelated. Then
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910
911
912
913
914
915
916
917
/
n (\*\ — + v +
) u2(zS
-i- ... -i- /i r\ i r\
U°V) 2 2 2 } 2 2 ' ' 2 Vy-LJ>
Z\ Z1 Zm \ Z\ Z1 Zm t
Equation 19.15 is particularly useful in radioanalysis, where flx^ x2,. . .,*„) might be a net count
rate and z^z2- • -zm might be the product of the test portion size, chemical yield, counting effi-
ciency, decay factor, and other sensitivity factors.
EXAMPLE: Consider the preceding gross-alpha example. Equation 19.15 implies the following
equation for the combined variance of A.
1/2(4\ c S S B B+/l2\u(£) + u (/)
c 22 I 2 2 I
OF \ O Y /
u\Ns)lt2s+u\NB)lt2B A2(u2(e) u\V)\
82F2
Then, since u2(Ns) = Ns and u\NB) = NB,
1,2(/t\ S S B B + A 2 \ U
1 82 F2 )
c(} W ( e2 F2 )
918     19.5.4  The Estimated Covariance of Two Output Estimates

919     Measured values obtained from two measurement processes may be correlated if some of the
920     same input estimates are used to calculate output estimates in both models. If the two measured
921     values are to be used as input quantities in a third model, their covariance must be estimated.

922     Suppose the combined set of input quantities in two mathematical models consists of X±, X2,
923     XN. Then the models can be expressed as Y =f(Xl,X2,... ,XN) and Z = g(Xl:,X2,... ,X^, where each
924     of the measurands may actually depend on only a subset of the combined list of input quantities.
925     If the input estimates are xlyx2, ..., % and the output estimates are y =f(xl,x2,..., %) and z =
926     g(xi,*2, • • • >%)>tne covariance of y and z is estimated by
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                                               N  N  n   n
                                           x _ v-> yX qy  cz
                                      u\y->z) ~ / -/ / -/ ~r—r—M(-"-,-3•"•;•)                             (19.16)
                                               2 = 17 = i ox.  dx.      J


927      Since z/(y,.y) = u2c(y), the preceding equation may be considered a generalization of the uncertainty
928      propagation formula.4

929      19.5.5 Nonlinear Models

930      19.5.5.1  Uncertainty Propagation

931      The uncertainty propagation formula tends to give better variance estimates when the function/
932      is linear, because the formula is derived from a linear approximation of/(i.e., a first-order Taylor
933      polynomial). Generally, obtaining a reliable  estimate of u2c(y) using the uncertainty propagation
934      formula requires (at least) that whenever/is nonlinear in one of the input quantities Xt, the rela-
935      tive uncertainty of the input estimate xt must be small.5 In radiochemistry this rule applies, for
936      example, to the uncertainty of an instrument calibration factor, chemical yield, or test portion
937      size.

938      If all the input estimates xt are uncorrelated and distributed symmetrically about their means, a
939      better approximation of u2c(y) may be made by including higher-order terms in the uncertainty
940      propagation formula, as shown below.
           4 The uncertainty propagation formula may also be generalized using the matrix notation of Attachment 19B. If
          =f(x), where x and y are column vectors and/is a vector-valued function, then
         This formula describes how the variances and covariances of the vector components of y are related to the variances
         and covariances of the vector components of x. Whenj has only one component, the formula here is equivalent to
         the uncertainty propagation formula.

           5 The uncertainty propagation formula also provides finite estimates of variance in cases where, strictly speaking,
         the true variance is infinite or undefined. For example, if x has a normal or Poisson distribution, the variance of 1 / x
         is undefined, although the formula provides a finite estimate of it. On the other hand, if the relative standard uncer-
         tainty of x is small, the combined variance u](l I x) will almost always be consistent with observation, making the
         estimate useful in practice.

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                      N
                                                            a*.
                                                                                        (19.17)
941     See also Section 5.1.2 of the GUM.
942
943
944

945

946
947


948


949



950



951

952


953


954
EXAMPLE: Suppose x and_y are independent estimates of input quantities X and 7, respec-
tively. Then the combined variance of the product/* = xy according to the (first-order)
uncertainty propagation formula is
For example, suppose x = 5, with u(x) = 0.5, and_y =10, with u(y) = 1. Then/? = 50, and the
first-order formula gives the combined standard uncertainty
                            uc(p) =

When higher-order terms are included,
                                                   = 7.07
       U2(p) =y2u\x) + x2u2(y) + 0 • u\x) + -u\x)u2(y) + -u2(y)u\x) + 0 • z/4(y)
            _ ,,2,. 2f
            = y u (x) +x u  (y) + u  (x)u  (y)
With numbers,
                         uc(p) = V/1020.52 + 5212 + 0.5212 = 7.09

The combined variance of the quotient q = x I y according to the first-order formula is

                                2, N   u 2(x) ,   2 u 2(y)
                               uc(q)= —r^ + tf  —^
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        Measurement Statistics
955
956
957
958
959
960


961



962
 Using the same values for x and_y again, q = 0.5 and the first-order formula gives
                                   05^
                                   102
                                                     102
         = 0.0707
 When the higher-order terms are included,
 With numbers,
                       dx
                                   J_
                                   y
                      dq_ _ _ x
                      dy   y2

                       d2g  _
                      dxdy
                 a!i = 0
                 dx2
                 82q _ 2x
                 dy2  y3
                  83q  _  2
            dx3

            83q  _ _6x
            dy3    y4
             83q
                                             dxdy2   y3    8ydx:
                                                                 = 0
                                                  11    i
                                                     - ^_^_
                                                       9
                                                     y
                                                      y)\y
J_
"2
                         i r  J  2,,  2,,    i  4x2
                           \   +0 \u2(y)u\x)+ \-  —
                        y  )     )            (2( y
                           UA     4, s
                         -—II11 oo
                                                        y
    0.52
    To2
                              1+3
+ 0.52—  1 +8—  =0.0726
                                             102
               102
19.5.5.2 Bias
963      If/is nonlinear, its nonlinearity may also tend to bias the output estimate^. The bias may be esti-
964      mated, if necessary, by the formula
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                                                                           Measurement Statistics
                          Bias(y) « -
                                                          u(x  x)
                                                              J
                                                                            (19.18)
965     which, in practice, is equivalent to
                                    »=i
                                                E
                                                            ax. ax.
                                                                                         (19.19)
966
967


968
969
970
971
972
973
974
975
976
977
978
979
980
This bias is usually negligible in comparison to the combined standard uncertainty ufy) if the
relative standard uncertainty of each input estimate is small.
 EXAMPLE: If x is an estimate of a positive quantity^7, the bias ofy = 1 /x as an estimate of
 1 / Xmay be approximated using Equation 19.19. Since y is a function of only one variable,
 the partial derivatives of y are the same as ordinary derivatives. The first derivative is dyldx =
 -x'
and the second derivative is cfy/dx2 = 2x~3. So the bias due to nonlinearity can be esti-
 mated as Bias(y) ~ (1 /2)(2x~3)w2(x) = w2(x)/x3. The combined variance of^ given by the
 uncertainty propagation formula is u2c(y) = (-x ~2)2w2(x) = w2(x)/x4. So, the ratio of the bias to
 the combined standard uncertainty can be estimated as (u 2(x) / x3) / (w(x) / x2) = u(x) / x,
 which is approximately the same as the relative standard uncertainty of x. Therefore, the size
 of the relative standard uncertainty gives an indication of the practical significance of the bias.
 EXAMPLE: If x andy are uncorrelated estimates of quantities X and 7, respectively, the bias of
 the product z = xy as an estimate of XY is given approximately by
                           Bias(z)«-
                                              &L
                                              dy2
u2fy)
 which equals zero, since 32z/ dx2 = 32z I dy 2 = 0
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         Measurement Statistics
981
982
983
 984



 985
 986
 987
 988


 989



 990


 991
          EXAMPLE: If tis an estimate of the decay time Tfor a radionuclide whose decay constant is X
          (assumed to have negligible uncertainty), the bias of the estimated decay factor d= e^J is given
          approximately by
         and the relative bias is J^u\f) 12. For example, suppose the radionuclide is 228Ac, which has a
         half-life of tll2 = 6.15 h, and the decay time has a standard uncertainty of u(f) = 2 h. Then the
         decay constant X equals In 2 / 6.15 = 0.112707 h"1. The bias equation above implies that the
         relative bias of the decay factor d due to the uncertainty of t is approximately

                                        -(0.112707)2(2)2 = 0.025


         or 2.5%. Note that the relative bias of dis small if u2(t) I tl/2 is small.
        19.5.5.3 Nominal Values
 992      Sometimes an input estimate xt is a nominal value and not the result of a measurement. This may
 993      be true for example when an analyst uses a pipet to dispense a predetermined amount of tracer
 994      into a sample. In this case the input estimate xt is the predetermined volume. Since xt never
 995      varies, its variance is zero, but the volume of liquid dispensed varies each time the measurement
 996      is repeated. So, the final result does have a variance component associated with the pipet. If the
 997      tracer is used to measure the yield for a chemical  separation, the value xt appears as a factor in the
 998      denominator of a mathematical expression, but the variable factor in that expression is actually
 999      the count rate produced by the tracer, which appears in the numerator. The variance of this count
1000      rate is increased by the variability of the tracer volume. The first-order uncertainty propagation
1001      formula gives the same result for the uncertainty of the yield regardless of whether the nominal
1002      value or the true value is assumed to be variable, but the higher-order formula may not.

1003      When nominal values appear in the calculation, one must also be careful when applying the bias
1004      formula. For example, the quotient x/y may by biased if y is the result of a measurement, but it
1005      is not inherently biased if y is a nominal value.
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                                                                           Measurement Statistics
1006


1007


1008
1009
1010
1011
1012


1013
1014
1015


1016
1017
1018
1019
1020
1021
1022
1023
EXAMPLE: Suppose the measurement model is
                                      X =
                                          Y-B
                                            a
where 7 is the gross signal, B is the blank signal, and a is the nominal value for a randomly
varying sensitivity factor^, whose true value is always unknown. Suppose 7 can be written in
the form 7 = xA + b + £7; where x is the true value of the measurand; b is the true blank level;
and s7 denotes the measurement error of 7 If all the measured (and nominal) values are
unbiased (i.e., if E(A) = a, E(B) = b, and E(eY) = 0), then the mean of X is given by
                       E(X) =
                                                    0) - 6
                                             a
                                                            a
So, X is an unbiased estimator for x. If one treats a as a random variable, this chapter' s bias-
approximation formula gives the incorrect value Xu2(a) I a2 for the bias of X.

Assume A, B, and s7 are uncorrelated. Then the variance of 7 is the sum of two components
GE and x2o^, which may be estimated by w2(s7) and X2u2(a), respectively, where u\d) is
actually an estimate of the variance of A. The combined variance of X is given by
                                            u\B) _ w2(er) +X2u2(a) + u\B)
                                            - --- - -
                                          a
                                                               a
The expression on the right may be obtained from the first-order uncertainty propagation
formula even if one incorrectly treats a as a random variable and A as a constant, so that
u2(Y) = w2(s7). If the higher-order approximation is used, the same expression is obtained only
if one correctly treats a as the constant and A as the random variable.
1024      19.6   Radiation Measurement Uncertainty

1025      19.6.1  Radioactive Decay

1026      Although it is impossible to know when an unstable nucleus will decay, it is possible to calculate
1027      the probability of decay during a specified time interval. The lifetime of the nucleus has an
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         Measurement Statistics
1028      exponential distribution., which is a model for the life of any object whose expected remaining
1029      life does not change with age.

1030      The exponential distribution is described by one parameter X, which measures the expected firac-
1031      tional decay rate. This parameter X is called the decay constant and equals In 2 / tll2, or approx-
1032      imately 0.693 / tl/2, where tll2 is the half-life of the radionuclide (sometimes denoted by Tl/2). The
1033      half-life is the same as the median of the exponential distribution.

1034      The probability that an atom will survive until time t without decaying is equal to e~u. Thus the
1035      probability of survival decreases exponentially with time. Consequently, when a large number of
1036      atoms of the same radionuclide are considered, the expected number of surviving atoms also
1037      decreases exponentially with time, as shown in Figure 19.4.
                       o.oo
                       FIGURE 19.4 — Expected fraction of atoms remaining at time t
1038      Since the probability that an atom survives until time t is equal to e~^, it follows that the
1039      probability of decay during this time is 1 - e~^.

1040      19.6.2 Radiation Counting

1041      Undoubtedly the best-known rule of radiation measurement statistics is the fact that the counting
1042      uncertainty for a gross radioactivity measurement can be evaluated as the square root of the
1043      observed counts. The square-root rule is useful, because it permits the estimation of a potentially
1044      significant uncertainty component without replicate measurements. Although the rule is usually
1045      valid as an approximation, for reasons which are discussed below, there are limits to its applica-
1046      bility. It is also important to remember that the counting uncertainty is only one component of the
1047      total measurement uncertainty.
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                                                                              Measurement Statistics
1048     When a source containing a radionuclide is placed in a detector, the probability that a particular
1049     atom of the radionuclide will produce a count is the product of three factors: the probability of
1050     decay (nuclear transformation), the probability of emission of the radiation being measured, and
1051     the probability of detection. According to the exponential decay model, the probability of decay
1052     is equal to 1 - e~^, where X is the decay constant and t is the counting time. The probability of
1053     radiation emission, denoted here by F, is a characteristic of the radionuclide. The probability of
1054     detection is the same as the counting efficiency £. Then the probability that an atom will generate
1055     a count isp = (1 - e~^)Fz.

1056     If the source initially contains n atoms of the radionuclide, the instrument is stable, and its back-
1057     ground is negligible, the number of observed counts N has a binomial distribution withparame-
1058     ters n and p. In general, if an experiment has only two possible outcomes, which may be called
1059     "success" and "failure," and the probability of success isp, then the number of successes
1060     observed when the experiment is repeated in n independent trials has a binomial distribution with
1061     parameters n and p.

1062     Actually the probability/? is a random variable, because the counting efficiency for an instrument
1063     and source can vary for a number of reasons, such as source placement, dead time, and other
1064     instrument characteristics. These variations generate measurement uncertainty, but their effects
1065     are not included in the "counting uncertainty." The counting uncertainty is the standard deviation
1066     of the theoretical distribution of counts  observed in a fixed time period when the efficiency is
1067     held constant. Thus, the actual variability observed in repeated measurements of a single radio-
1068     active source may be greater than the theoretical counting uncertainty.

1069     The mean  and variance of the binomial  distribution are np and np(\ - p), respectively.  In radia-
1070     tion counting, the value of p is usually small enough that the factor 1 - p in the variance can be
1071     ignored. When this is true, the binomial distribution can be approximated by a Poisson distri-
1072     button with mean |i = np. The variance of a Poisson distribution equals the mean; so, both can be
1073     estimated by the same measured result N, and the standard deviation can be estimated by
           6 In the rare cases when the Poisson counting model is inadequate and the binomial model is required, if the
         instrument background level is negligible, the standard deviation of the source count Ns can be estimated by
         J(l -p)Ns. If a Poisson background is measured for time tB and NB counts are observed, the standard deviation of
         Ns should be estimated instead by
         These two expressions are appropriate only when the source counts are generated by a single radionuclide or by one
         radionuclide plus the instrument background.

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         Measurement Statistics
1074     When |i is large, \[N is an excellent estimator for the standard deviation, but the estimate may be
1075     poor when |i is small. For example, if |i = 100, the coefficient of variation of \fN is only about
1076     5% and its bias is negligible. If |i = 10, the coefficient of variation is more than 16% and there is
1077     a negative bias of more than 1%. If ji = 1, the coefficient of variation is more than 63% and the
1078     negative bias is more than 22%. Furthermore, when |i is small, it is possible to observe zero
1079     counts, so that ^N = 0. MARLAP recommends that JN be replaced by ^N + 1 when extremely
1080     low counts are possible (see also Attachment 19C).7

1081     A sum of independent Poisson quantities also has a Poisson distribution. So, when the Poisson
1082     approximation is valid for all the sources of counts in a counting measurement, the total count
1083     obeys Poisson counting statistics as well.

1084     If a short-lived radionuclide (large X) is counted in a high-efficiency detector (large e), the prob-
1085     ability/* that an atom placed in the detector will produce a count may be so large that the Poisson
1086     approximation is invalid. In this case the Poisson approximation overestimates the counting
1087     uncertainty, but it is important to consider that the statistical model described thus far represents
1088     only the process of counting. In most cases previous steps in the measurement process decrease
1089     the probability that one of the atoms of interest initially present  in the test portion will  produce a
1090     count. If a correction for decay before counting is performed,  the decay factor must be included
1091     in p. If the measured activity of a (single) decay product is used to estimate the activity of a
1092     parent, p must include both ingrowth and decay factors. If a chemical extraction is performed, the
1093     recovery factor must be considered. When these factors are included, the Poisson counting model
1094     is usually valid. Note, however, that these factors must be measured and their standard uncertain-
1095     ties evaluated and propagated, increasing the  total measurement uncertainty even further.8

1096     Both the binomial and Poisson models may be invalid if one atom can produce more than one
1097     count during the measurement. This situation occurs when the activity of a parent is estimated
1098     from the total count produced by a series of short-lived progeny (Lucas and Woodward 1964,
1099     Colle and Kishorel997). For example, when 222Rn is measured by counting the emissions of its
           7 The negative bias of fif is largely eliminated if one replaces it by JN + 0.25. MARLAP recommends the
         estimator JN + 1 although it is positively biased.

           8 It is possible to evaluate the uncertainties associated with the decay and ingrowth of a small number of short-
         lived atoms before counting using the binomial model, but under the stated conditions, the assumption of Poisson
         counting statistics simplifies the calculation. A more complete evaluation of uncertainty may be necessary if the
         same source is counted more than once.

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1100      progeny, an atom of 222Rn may produce several counts as it decays through the short-lived series
1101      218Po, 214Pb, 214Bi, and 214Po, to the longer-lived 210Pb.

1102      Both counting models may also be invalid if the total dead time of the measurement is significant
1103      (see Section 19.6.3.1).

1104      Instrument background measurements are usually assumed to follow the Poisson model. This
1105      assumption is reasonable if the background counts are produced by low levels of relatively long-
1106      lived radionuclides. However, the true background may vary between measurements (e.g.,
1107      cosmic background). Furthermore, the measured background may include spurious instrument-
1108      generated counts, which do not follow a Poisson distribution. Generally, the variance of the
1109      observed background is somewhat greater than the Poisson counting variance, although it may be
1110      less for certain types of instruments, such as those that use parallel coincidence counters to com-
1111      pensate for background instability (Currie et al.  1998). Departures from the Poisson model may
1112      be detected using the chi-square test described in Section  18B.2 of Attachment  18B; however,
1113      deviations from the model over short time periods may be small and difficult to measure.

1114      19.6.3  Count Rate

1115      Suppose a radiation counting measurement of duration t is made for the purpose of estimating a
1116      mean count rate R, assumed to be constant, and the result  of the measurement N (in counts) has a
1117      distribution that is approximately Poisson with mean Rt. If tis known precisely, the best estimate
1118      of R given a single observation N = n is the measured count rate r = nl t, and the best estimate of
1119      the variance of the measured rate is u\r) = n 112 = r 11. Under the Poisson assumption, even if
1120      repeated measurements are made, the best estimates of r and its variance are obtained by pooling
1121      the counts and count times and using the same formulas.

1122      In fact the count time t is known imperfectly; so, a more complete estimate of the variance of r is

                                         it \   n   n2  if \
                                        u(r} = -  + -u(t}                               (19-2°)


1123      The uncertainty of t may be ignored if u(t) / t<^l / Jn, that is, if the relative standard uncertainly
1124      of t is much less than 1 over the square root of the count.
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         Measurement Statistics
1125
1126
1127
1128
1 1 ?Q


1130

EXAMPLE: A source is counted for t = 100 s, where t has standard uncertainty u(f)
n = 96l
rate r is
is



counts observed. When u(f) is ignored, the combined standard uncertainty
uc(r) = 
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                                                                             Measurement Statistics
1145      behavior of an actual counting system tends to fall between the two extremes. At low count rates,
1146      however, both models give essentially the same predictions.

1147      At low count rates the observed count rate nit may be corrected for dead time by dividing by the
1148      factor \-mlt. Many counting instruments perform the correction automatically by extending
1149      the real time t of the measurement to achieve a desired live time tL. Since tL = t- m, the correct-
1150      ed count rate is simply n I tL. When the dead time rate for the measurement is low, the variance
1151      of the corrected count rate may be estimated as n I tL . Thus, the Poisson model remains adequate
1152      if the "count time" is equated with the live time. When the dead time rate is high (above 20%),
1153      the same estimate may not be adequate (NCRP 1985). In this case the measurement should be
1154      repeated, if possible, in a manner that reduces the dead time rate.

1155      Dead time effects may be evaluated  experimentally to confirm that they do not invalidate the
1156      Poisson model at the count rates expected for typical measurements. The chi-square test
1157      described in Section 18B.2 of Attachment 18B can be used for this purpose.

1158      19.6.3.2  A Confidence Interval for the Count Rate

1159      When the Poisson counting model is valid, lower and upper confidence limits for the mean count
1160      rate R given an observation of n counts in time t may be calculated as follows:10
1161      Here y is the desired confidence coefficient, or the minimum probability of coverage, and j^(ti)
1162      denotes the/>-quantile of the chi-square distribution with n degrees of freedom (see Table G.3 in
1163      Appendix G). If n = 0, the chi-square distribution -£(n) is degenerate. For our purposes
1164      should be considered to be 0.
           10 The chi-square distribution is a special case of a gamma distribution, whose relationship to the Poisson distribu-
         tion is described by Hoel et al. (1971) and Stapleton (1995). This relationship is the basis for the two formulas in
         Equation 19.21. The relationship is such that if X is chi-square with In degrees of freedom and 7 is Poisson with
         mean u, then Pr[Jf < 2u] = Pr[7 > n].

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         Measurement Statistics
1165
1166
1167
1168
1169
1170
1171

1172
1173
1174
1175
1176
1177
1178
1179

1180
1181
1182
1183
1184
1185
1186
 EXAMPLE: Suppose 10 counts are observed during a 600-second instrument background
 measurement. Then the 95% confidence limits for the background count rate are
                                 R,
                                  lower
                                R
                               Xo.Q25(2°) _ 9.59078
                                (2)(600)     1200

                               fa.97s(22) _ 36.7807
                               (2)(600) "   1200
                          = 0.00799 cps
                          = 0.03065 cps
 EXAMPLE: Suppose 0 counts are observed during a 600-second measurement. Then the 95%
 confidence limits for the count rate are
P
R,
                                   ,
                                   lower
                                X0.02S(°)
                                -
                                (2)(600)
                         „
                         R
                                                   n
                                                 = 0 Cps
                                                   7.3778
                                   upper
                                         (2)(600)    1200
                                                            nnnA1-
                                                          = 0.00615 cps
19.6.4  Instrument Background

As noted above, single-channel background measurements are usually assumed to follow the
Poisson model, although there may be effects which increase the variance beyond what the model
predicts. For example, the cosmic radiation and other natural sources of instrument background
may vary between measurements, the composition of source holders and containers may vary, the
instrument may become contaminated by sources, or the instrument may be unstable. For certain
types of instruments, the Poisson model may overestimate the background variance (Currie et al.
1998). If the background does not closely follow the Poisson model, its variance should be esti-
mated by repeated measurements.

The "instrument background," or "instrument blank," is usually measured with source holders or
containers in place, since the presence of the container may affect the count rate. In many cases,
perhaps most, it is not feasible to use the same container during both the background and test
source measurements, but nearly identical containers should be used. Variations in container
composition may affect the background count rate. If test sources contain enough mass to atten-
uate background radiation, then it is best to use a similar amount of blank material during the
background measurement.
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1187      If repeated measurements demonstrate that the background level is stable, then the average x of
1188      the results of many similar measurements performed over a period of time may give the best
1189      estimate of the background.  In this case, if all measurements have the same duration, the experi-
1190      mental standard deviation of the mean s(x) is also a good estimate of the measurement uncer-
1191      tainty. Given the Poisson assumption, the best estimate of the uncertainty is still the Poisson
1192      estimate, which equals the square root of the summed counts, divided by the number of measure-
1193      ments, but the experimental  standard deviation may be used when the Poisson assumption is
1194      false.

1195      If the background drifts or varies nonrandomly over time (i.e., is nonstationary), it is important to
1196      minimize the consequences  of the drift by performing frequent blank measurements.

1197      If the background variance includes a small non-Poisson component, that component can be esti-
1198      mated from historical background data and added to the calculated Poisson component. A chi-
1199      square statistic may be used to detect and quantify non-Poisson background variance (Currie
1200      1972; see also Section 18B.3 of Attachment 18B), but chi-square provides an unbiased estimate
1201      of the additional variance only if the background remains stationary while the data are being
1202      collected. If the observed background counts, in order, are 7Vl3 N2,  ...,Nn and the corresponding
1203      counting intervals are ^, t2, ...,tn, then the quantity
 2=   1
^!/J
                            H-l
                                                                                          (19.22)
1204      may be used to estimate the non-Poisson variance of a net count rate due to background even if
1205      the background is not stationary. The distribution of ^ is not simple, and ^ may even assume
1206      negative values, which are clearly unrealistic. So, if this estimator is used, it should be calculated
1207      for several data sets and for more than one instrument, if possible, to give an indication of its
1208      reliability. Although replicate measurements are involved, this type of evaluation of uncertainty
1209      should be considered a Type B method.

1210      If background and test source measurements are performed under different conditions, the back-
1211      ground measurement may be biased. Such a bias may occur, for example, if test sources are
1212      counted in containers or on planchets which are not present during background measurements. A
1213      situation  of this kind should be avoided if possible.
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1214      When instrument background levels are low or when count times are short, it is possible that too
1215      few counts will be observed to provide an accurate estimate of the measurement uncertainty.
1216      Attachment 19C  describes a method for choosing an appropriate coverage factor when only few
1217      counts are observed.

1218      19.6.5  Counting Efficiency

1219      The counting efficiency for a measurement of radioactivity may depend on many factors, includ-
1220      ing source geometry, placement, composition, density, activity, radiation type and energy, and
1221      other instrument-specific factors. The estimated efficiency is sometimes calculated explicitly as a
1222      function of such variables (in gamma spectrometry, for example). In other cases a single meas-
1223      ured value is used (e.g., alpha spectrometry). If an efficiency function is used, the uncertainties of
1224      the input estimates, including those for both calibration parameters and sample-specific quanti-
1225      ties, must be propagated to obtain the combined standard uncertainly of the estimated efficiency.
1226      Calibration parameters tend to be correlated; so, estimated covariances must also be included. If
1227      a single value is used instead of a function, the standard uncertainty of the value is determined
1228      when the value is measured.
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243

EXAMPLE

Several sources with the same geometry are prepared and used to calibrate a radiation counter.
One blank
count rate,
efficiency.
C
M
n
NB
ts
IB
Ns,,
R,
R
£
measurement is made. Each source is counted once to obtain an estimate of the
the estimates are averaged, and the average is used to calculate the counting
The sources are long-lived and all source count times are equal. Let
= concentration of standard solution (C = 1500, u(C) = 20 Bq g"1)
= mean mass of solution added to each source (0.09980 g, added by a 0. 1-mL
= number of sources (15)
= blank count (90)
= source count time (300 s)
= blank count time (6000 s)
= gross count observed during the measurement of the /'th source
= gross count rate observed in the /th source measurement
= arithmetic mean of the gross count rates, Rt
= estimated counting efficiency



pipet)








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                                                                   Measurement Statistics
 Then the following equations may be used to calculate the mean efficiency and its standard
 uncertainty:
                             n(n -
                          £ =
                                CM
s\R)+NBlt2B +
C2M2

( C2
u\M)\
M2 1
                       11(8) =
 The source-to-source variability of the mass Mis not explicitly evaluated, because it is
 included in the observed variability of the count rates, Rt. So, the standard uncertainty u(M)
 represents only the uncertainty of the mean mass added by the pipet. This uncertainty arises
 from uncertainty in the capacity of the pipet, the density of the solution, temperature effects,
 and the analyst's technique. Assume for this example that u(M) is 0.00050 g (about 0.5%).

 Note that the uncertainty of the blank  count, NB, is negligible in this example and could have
 been ignored. It was included only for completeness.
 Assume the observed source counts,
           15,708
           15,924
           16,120
15,946
15,844
15,902
15,953
16,020
16,211
are as follows:

   16,012
   15,877
   16,181
          16,066
          16,061
          15,984
 Then the observed gross count rates, Rt, are:
           52.360
           53.080
           53.733
53.153
52.813
53.007
53.177
53.400
54.037
53.373
52.923
53.937
             53.553
             53.537
             53.280
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1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277

1278
1279
1280
1281
1282
 The average of the gross count rates is calculated as follows.
                                             15
                                                   =53.2909
 The experimental variance of R is
S2(R) =
                                                 15
                                          (R - 53.2909)2 = 0.012876
                                            '
 Then the estimated counting efficiency is

                                53.2909 -90/6000
                                 (1500)(0.09980)
                                     £ =
                             = 0.355884
 and the standard uncertainty of £ is given by
          11(8) =
0.012876 + 90 / 60002 + „ ^
(1500)2(0.09980)2
«ei2( 202 ^ O.OOOS2^
( 15002 0.099802J
In fact, the standard uncertainty of £ calculated in the preceding example may be incomplete. The
true counting efficiency may vary from source to source because of variations in geometry, posi-
tion, and other influence quantities not explicitly included in the model. So, the standard uncer-
tainty of £ should include not only the standard uncertainty of the estimated mean, as calculated
in the example, but also a second component of uncertainty due to variations of the true effi-
ciency during subsequent measurements. The second component may be written as £2(p2, where (p
is an estimate of the coefficient of variation of the true efficiency. Then the standard uncertainty
of £ equals the square root of the sum of the squares of the two components.

In the example above, the experimental variance of the count rates, s2(Ri), might be used to esti-
mate (p2. Procedure E2, which is described in Section 18B.2 of Attachment 18B, is a step-by-step
procedure for estimating such "excess" variance in a series of measurements. However, if the
procedure were applied to the series of measurements made in the example, the estimated vari-
ance might be inflated by errors in the pipetting of the  standard solution. The resulting estimate
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1283      would therefore tend to be an upper bound. A lower bound for the excess variance could be esti-
1284      mated by making replicate measurements of only one source, thus eliminating the effects of
1285      pipetting errors but also unfortunately eliminating the effects of variable source geometry. A
1286      better approach is to weigh the amount of standard solution added to each source, use the results,
1287      Mt, to calculate 15 individual estimates of the counting efficiency, £,, and estimate the excess
1288      variance of the values £,,

1289      Variations in counting efficiency due to source placement should be reduced as much as possible
1290      through the use of positioning devices that ensure a source with a given geometry is always
1291      placed in the same location relative to the detector. If such devices are not used, variations in
1292      source position may significantly increase the measurement uncertainty.

1293      Calibrating an instrument under conditions different from the conditions under which test sources
1294      are counted may lead to large uncertainties in the sample activity measurements. Source geome-
1295      try in particular tends to be an important factor for many types of radiation counting instruments.
1296      Generally, calibration sources should be prepared with the sizes and shapes of test sources and
1297      counted in the same positions, although in some cases it may be possible to calculate correction
1298      factors which allow one calibration to be used for different geometries. When correction factors
1299      are used, their uncertainties should be evaluated and propagated.

1300      If the efficiency £ is calculated from  a model that includes one of the quantities X{ appearing else-
1301      where in the sample activity model, there is a correlation between the measured values of £
1302      and Xj, which should not be ignored. It is often simpler to include the  entire expression for £ in
1303      the expression for the  laboratory sample activity before applying the uncertainty propagation
1304      formula.
1305
1306
1307
1308
1309
1310
1311
1312
EXAMPLE: Suppose the counting efficiency for a measurement is modeled by the equation
£ = A exp(-BMs), where A and B are calibration parameters and Ms is the source mass; and
suppose the chemical yield 7 is modeled byMs/Mc, where Mc is the expected mass at 100%
recovery. Then the estimated values of the counting efficiency and the yield are correlated,
because both are calculated from the same measured value of the source mass. When the com-
bined standard uncertainty of the sample activity is calculated, the covariance w(£,F) may be
included in the uncertainty propagation formula, or the variables £ and 7 in the model may be
replaced by the expressions A exp(-BMs) and Ms I Mc, respectively.
1313      In some cases the estimated value of the counting efficiency has no effect on the output estimate
1314      of laboratory sample activity. This happens often in alpha spectrometry, for example, when iso-
1315      topic tracers are used. The efficiency estimate is needed to obtain an estimate of the yield of the

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1316      chemistry procedure, but the efficiency usually cancels out of the mathematical model for the
1317      laboratory sample activity and its uncertainty is not propagated when determining the combined
1318      standard uncertainty of the activity estimate.

1319      19.6.6 Radionuclide Half-life

1320      The component of combined standard uncertainty associated with the half-life of a radionuclide
1321      is often negligible in measurements performed by typical radioanalytical laboratories, since the
1322      half-lives of most radionuclides of interest have been measured very accurately and in many
1323      cases decay times are short relative to the half-life (so that the  sensitivity coefficient is small).
1324      However, this uncertainty component is also one of the most easily obtained components, since
1325      radionuclide half-lives and their standard uncertainties are evaluated and published by the
1326      National Nuclear Data Center (NNDC) at Brookhaven National Laboratory. The data may be
1327      obtained from the NNDC website (www.nndc.bnl.doe.gov).

1328      19.6.7 Gamma Spectrometry

1329      There are a number of sources of measurement uncertainty in gamma spectrometry, including:

1330         •   Poisson counting uncertainty
1331         •   Compton baseline determination
1332         •   Background peak subtraction
1333         •   Multiplets and interference corrections
1334         •   Peak-fitting model errors
1335         •   Efficiency calibration model error
1336         •   Summing
1337         •   Density correction factors
1338         •   Dead time

1339      See Chapter 17 for further discussion of measurement models  and uncertainty analysis for
1340      gamma spectrometry.

1341      19.6.8 Balances

1342      The uncertainty of a balance measurement tends to be small, even negligible, when the balance is
1343      used properly and the mass being measured is much larger than the balance's readability. How-
1344      ever, the uncertainty may also be difficult to evaluate unless the balance is well maintained and
1345      operated in a controlled environment that protects it from external influences. In particular, drafts


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1346     or sudden changes in pressure, temperature or humidity (e.g., opening doors or dishwashers) may
1347     produce spurious errors.

1348     The uncertainty of the result of a balance measurement generally has components associated with
1349     balance calibration, linearity, repeatability, day-to-day variability due to environmental factors,
1350     and air buoyancy. Other sources of uncertainty may include leveling errors and off-center errors,
1351     which should be controlled. Static electrical charges may also have an effect. For some materials,
1352     gain or loss of mass before or after weighing (e.g., by absorption or evaporation of water) may be
1353     significant. Attachment 19G of this chapter describes several of these uncertainty components in
1354     more detail.

1355     Balance manufacturers provide specifications for repeatability and linearity, which are usually of
1356     the same order of magnitude as the balance's readability, but tests of repeatability and linearity
1357     should also be included in the routine quality control for the balance.

1358     Repeatability is expressed as a standard deviation and is typically assumed to be independent of
1359     the load. It represents the variability of the result of zeroing the balance, loading and centering a
1360     mass on the pan, and reading the final balance indication.

1361     The linearity tolerance of a balance,  aL, should be specified by the manufacturer as the maximum
1362     deviation of the balance indication from  the value that would be obtained by linear interpolation
1363     between the calibration points. Different methods may be used to convert this tolerance to a
1364     standard uncertainty, depending on the form the linearity error is assumed to take.  One method,
1365     which is recommended by the Eurachem/CITAC Guide: Quantifying Uncertainty in Analytical
1366     Measurement, is to treat the tolerance, aL, as the half-width of a rectangular distribution and
1367     divide aL by ^3 to obtain the standard uncertainty (Eurachem 2000). Another method, suggested
1368     in Attachment 19G of this chapter, is to treat aL as the amplitude of a sinusoidal function. This
1369     model requires that aL be divided by ^2 to obtain the standard uncertainty. The latter method is
1370     used below.

1371     Procedures for evaluating the relative standard uncertainties due to calibration and environmental
1372     factors and for calculating the buoyancy  correction factor and its standard uncertainty are
1373     described in Attachment  19G.

1374     A typical mass measurement in the laboratory involves separate measurements of a gross mass
1375     and a tare mass. The net mass, m, is  determined by subtracting the balance indication for the tare
1376     mass, /Tare,  from the indication for the gross mass, TQ^, and multiplying the difference, /Net, by
1377     the buoyancy correction factor, B. That is,


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         Measurement Statistics
                                                                                          (19.23)



1378      The standard uncertainty of m is given by

                                               q>) + <* + ^r) +  X0)                  (19.24)
1379      where
1380         m     is the buoyancy-corrected net mass
1381         7Net    is the net balance indication (/G,.OSS - /Tare)
1382         /Tare    is the balance indication for the tare mass
1383         /Q^   is the balance indication for the gross mass
1384         B     is the buoyancy correction factor

1385      Attachment 19G describes uncertainty equations for use in other circumstances.

1386      19.6.9 Pipets and Other Volumetric Apparatus

1387      Generally, a pipet or volumetric flask is used not to measure an existing volume of liquid, but to
1388      obtain a volume of a predetermined nominal size. The nominal value is treated as if it were a
1389      measured value, although it is known before the "measurement." The true volume is the variable
1390      quantity. Since a volumetric "measurement" of this type cannot be repeated, pipets and flasks are
1391      good examples of measurement systems for which historical data are important for Type A eval-
1392      uations of standard uncertainty.

1393      The density of a liquid depends on its temperature. For this reason, when a volume is being
1394      measured, one should determine whether the volume of interest is the volume at the current room
1395      temperature, the long-term mean room temperature, or some other temperature, such as 20°C.
1396      One should also determine whether the effect of temperature is significant for the measurement.
1397      Often it  is not, but in some cases a correction for thermal expansion may be necessary.

1398      The standard uncertainty for a volumetric measurement includes components associated with the
1399      capacity of the measuring device, temperature effects, repeatability, and the analyst's bias in
1400      using the device (e.g., reading a meniscus).

1401      The capacity of a volumetric pipet or flask (at 20°C) is generally specified with a tolerance a,
1402      which may be assumed to represent the half-width of a triangular distribution (e.g., see ASTM
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                                                                            Measurement Statistics
1403      1994 and ASTM 1995). Assuming a triangular distribution, one evaluates the uncertainty com-
1404      ponent of the volume associated with the capacity as a I ^6.

1405      The relative standard uncertainty due to temperature variations is typically a Type B standard
1406      uncertainty, which may be derived from a temperature range, T± 57, and the liquid's coefficient
1407      of thermal expansion, P, at the center of the range. Assuming a rectangular distribution for the
1408      temperature with half-width 57, the relative standard uncertainty component due to temperature
1409      variations is 10157Y/3.

1410      The nominal capacity of any volumetric glassware is usually specified at 20°C. If the glassware
1411      is used at a different temperature, the capacity is slightly different. Temperature effects on the
1412      capacity are generally very small  (much smaller than the effects on the density of the liquid) and
1413      for this reason one may usually ignore them. The relationship between the capacity and the
1414      temperature is given approximately by

                                        Fr=F20(l+a(r-20))                              (19.25)

1415      where
1416         T     is the temperature (°C)
1417         VT     is the capacity at temperature T
1418         F20    is the capacity at 20°C
1419         a      is the glassware's coefficient of thermal cubical expansion ("CT1)

1420      The value of a for ASTM Type I, Class A, borosilicate glassware is approximately 0.00001 °C"1;
1421      so, the capacity increases by only about 0.001% for each degree Celsius of temperature increase.

1422      An analyst may calibrate a pipet gravimetrically using an analytical balance. The balance, to be
1423      useful, must provide better accuracy than the pipet. In particular, the balance's repeatability and
1424      linearity tolerance should be small relative to the tolerances for the pipet. The calibration pro-
1425      vides an estimate  of the pipet's capacity, the standard uncertainty of the capacity, and the var-
1426      lability to be expected during use. The procedure involves dispensing a series of n pipet volumes
1427      of a specified liquid into a container and weighing the container and zeroing the balance after
1428      each volume is added. Usually the container must have a small mouth to reduce evaporation. The
1429      temperature of the room, the liquid, and the apparatus involved should be  specified, equilibrated,
1430      and controlled during the experiment.

1431      The procedure produces a  set of balance indications, /,, which are averaged to obtain the arith-
1432      metic mean /. To obtain the estimated mean pipet volume, v, the mean balance indication, /, is


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         Measurement Statistics
1433
1434
1435
1436
1437
1438
1439
1440
multiplied by a factor Z, which equals the quotient of the buoyancy correction factor divided by
the density of the liquid at room temperature. A correction factor for thermal expansion of the
pipet may also be included, if necessary.
                                v=/Z
                                 where
z =
                                                         1 "
                                                         PM - PA,M
                     (19.26)
and where
   pM    is the density of the liquid
   pAM   is the density of the air at the time the liquid is weighed
   pc    is the density of the calibration mass standard for the balance
   pA:C   is the density of the air at the time of the balance calibration
1441      The calibration is most often performed using water.

1442      ASTM E542, "Standard Practice for Calibration of Laboratory Volumetric Apparatus," provides
1443      additional information about the procedure, including tables of values of Z for various conditions
1444      (ASTM 2000). Table 19.2, which is taken from ASTM E542, shows the density of air-free water
1445      at various temperatures. Attachment 19G of this chapter describes an equation to calculate the
1446      density of air as a function of temperature, pressure, and humidity.

                                  TABLE 19.2 — Density of air-free water
Temperature, °C
15
16
17
18
19
20
21
22
23
24
25
Density, g/cm3
0.999098
0.998941
0.998773
0.998593
0.998403
0.998202
0.997990
0.997768
0.997536
0.997294
0.997043
Temperature, °C
26
27
28
29
30
31
32
33
34
35
Density, g/cm3
0.996782
0.996511
0.996232
0.995943
0.995645
0.995339
0.995024
0.994701
0.994369
0.994030

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1447      The volume, v, estimated by the calibration may be substituted for the pipet's nominal capacity
1448      when the pipet is used later in an analytical measurement. The uncertainty of v as an estimate of
1449      the mean volume may be calculated as follows.
                            u(IZ) =
                                   A
                                     z^
                                          n
                                                                V(Z)
                                                                                          (19.27)
  2     2    p2sr2
 9cal + 9Env + -^T-
1450      where (pCal and (pEnv denote the relative standard uncertainties of mass measurements associated
1451      with balance calibration and environmental factors, respectively (see Section 19.6.8). Note that
1452      the uncertainty of the buoyancy correction factor has been ignored here and the standard uncer-
1453      tainty of Z has been equated with the component due to thermal expansion of the liquid, which is
1454      assumed to be dominant. Also note that the correlation between Z and / induced by temperature
1455      effects on both the liquid density and the balance sensitivity is unknown and has been ignored.

1456      The uncertainty of v as a predictor of the true volume that will be dispensed during a subsequent
1457      measurement includes additional components for repeatability and temperature variability.
                                                                                          (19.28)
1458      Note that if a different analyst performs the measurement, there may be an additional uncertainty
1459      component associated with the difference in individual techniques.

1460      If the mean volume is within specified tolerances, a slightly simpler approach is possible. The
1461      pipet's nominal capacity may be used as the volume v and the tolerance a may be used in a Type
1462      B evaluation of standard uncertainty. In this case, the standard uncertainty of v is evaluated as
1463      shown below.
                                       A
                                                           2o2?T^2
                                                         V  P 87
                                                                                          (19.29)
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1464      The experimental procedure outlined above may also be adapted for other volume measuring
1465      devices, including flasks and graduated cylinders.

1466      The manufacturers of certain types of automatic pipetting devices (e.g., Eppendorf® pipettors)
1467      provide specifications for bias and imprecision. For these devices the manufacturer's specifica-
1468      tions for bias and imprecision may be assumed. In this case the Type B standard uncertainty of a
1469      pipetted volume v is
1483
1484
                                             a2    2   v2B25r2                            no-jrvv
                                             — +s  +—	                            (19.30)
1470      where a is the manufacturer's stated bias tolerance, assumed to represent the half-width of a tri-
1471      angular distribution, and s is the stated standard deviation. This approach has the advantage of
1472      simplicity; however, since many analysts may not achieve the same accuracy as the manufac-
1473      turer, the standard uncertainty given by Equation 19.30 may be unrealistic.

1474      19.6.10 Digital Displays and Rounding

1475      If a measuring device, such as an analytical balance, has a digital display with resolution 5, the
1476      standard uncertainty of a measured value is at least 5 / 2/3. This uncertainty component exists
1477      even if the instrument is completely stable.

1478      A similar Type B method may be used to evaluate the standard uncertainty due to computer
1479      roundoff error. When a value x is rounded to the nearest multiple of 10", the component of uncer-
1480      tainty generated by roundoff error is 10" / 2/3. When rounding is performed properly and x is
1481      printed with an adequate number of figures, this component of uncertainty should be negligible
1482      in comparison to the total uncertainty of x.
EXAMPLE: The readability of a digital balance is 0.1 mg. Therefore, the minimum standard
uncertainty of a measured mass is 0.1 / 2/3 = 0.029 mg.
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1485

1486

1487
1488



1489



1490

1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501

1502
1503
1504
1505
1506
1507
1508
1509
1510

1511
1512
1513
 EXAMPLE: A computer printout shows the result x of a measurement as

                               3.40E+01  +-  9.2E-02

 where the expanded uncertainty is calculated using a coverage factor of 2. The measured value
 is rounded to the nearest multiple of 0.1. So, the standard uncertainty of x is
                          «(*) =
                                ^
0.092
                                               0.1
  2  )    (
                  = 0.054.
19.6.11  Subsampling

Appendix F of this manual discusses laboratory subsampling. The subsampling of heterogeneous
materials for laboratory analysis increases the variability of the measurement result and thus adds
a component of measurement uncertainty, which is usually difficult to quantify without replicate
measurements. Appendix F summarizes important aspects of the statistical theory of particulate
sampling and applies the theory to subsampling in the radiation laboratory (see also Gy 1992 and
Pitard 1993). The mathematical estimates obtained using the theory often require unproven
assumptions about the material analyzed and rough estimates of unmeasurable parameters. How-
ever, in some cases the theory can be used to suggest how subsampling errors may be affected by
either changing the subsample size or grinding the material before subsampling. Of course, the
total measurement uncertainty, including components contributed by subsampling, may always
be evaluated by repeated subsampling and analysis.

If subsampling is not repeated, its effects may be represented in the  mathematical measurement
model by including an input quantity Fs whose value is the ratio of the analyte concentration of
the subsample to that of the total sample. This ratio, which will be called the subsampling factor
(a MARLAP term), appears in the model as a divisor of the net instrument signal and thus is
similar to the chemical yield, counting efficiency, and other sensitivity factors. The value of Fs is
estimated as 1, but the value has a standard uncertainty which increases the combined standard
uncertainty of the result. (Since its value is always 1, the factor Fs is an example of a "nominal
value," as discussed in Section 19.5.5.) The uncertainty of Fs also increases the MDC and the
MQC.

Although the component of uncertainty caused by the subsampling of heterogeneous solid matter
may be difficult to estimate,  it should not be ignored, since it may be relatively large and in some
cases may even dominate all other components. One may use previous experience with similar
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1514      materials to estimate the uncertainty, possibly with the aid of the information and methods pre-
1515      sented in Appendix F. By default, if "hot particles" are not suspected, and if reasonable precau-
1516      tions are taken to homogenize (mix) the material and to obtain a sufficient number of particles in
1517      an unbiased subsample, one may simply assume a nominal relative standard uncertainty compo-
1518      nent of 5% for solid materials.

1519      19.6.12  The Standard Uncertainty for a Hypothetical Measurement

1520      MARLAP's recommended method selection criteria in Chapter 3 require that a laboratory esti-
1521      mate the standard uncertainty for the measured concentration of a hypothetical laboratory sample
1522      with a specified concentration (i.e., the "method uncertainty," as defined by MARLAP). To
1523      estimate the combined standard uncertainty of the measured concentration, one must obtain esti-
1524      mates for all the  input quantities and their standard uncertainties. All quantities except the gross
1525      instrument signal may be measured and the standard uncertainties evaluated by routine Type A
1526      and Type B methods. Alternatively, the values and their standard uncertainties may be deter-
1527      mined from historical  data. The estimate of the gross signal and its standard uncertainty must be
1528      obtained by other means, since the laboratory sample is only hypothetical. The predicted value of
1529      the gross count Ns is calculated by rearranging the equation or equations in the model and solving
1530      for Ns. The standard uncertainty of the measured value may then be evaluated either from theory
1531      (e.g., Poisson counting statistics), historical data, or experimentation.
1532
1 533
_L ,J J J
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
EXAMPLE:

where
X
Ns
NB

tB
tD
Ms
Y
£
X
Suppose the mathematical model for a radioactivity measurement is
T/" iJ iJ D D

is the activity concentration (Bq kg"1)
is the test source count
is the blank count
is the source count time (s)
is the blank count time (s)
is the decay time (s)
is the size of the test portion (kg)
is the chemical yield
is the counting efficiency
is the decay constant (s"1)
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1545
1546
1547
1548
1549
1550



1551



With specified values for the concentration X, test portion size Ms, blank count NB, count
times ts, tB, and tD, efficiency £, and yield 7,
the source count Ns can be
dieted value is Ns = ts(XMsYe exp(-Xfe + ts / 2) ) + NB 1 tB).
measured value, its estimated variance according to Poisson
When this
statistics is
predicted. The pre-
value is treated like a
^(NS) = NS. So,
assuming negligible uncertainties in the times ts, tB, and tD, the uncertainty propagation for-
mula gives the combined variance of the output estimate X as
2fkT \ 2 2fkT \ 2
n2(Y\ - S B + Y2
M2Y2£2e~2HtD+ts/2)

(XMsY£e D+ s +NB/tB)/
A/r2V2 2 ~2^Sr\X=0] = a
                                        (19.31)
1560      where Pr[S > Sc \ X= 0] denotes the probability that the observed net signal S exceeds its critical
1561      value Sc when the true analyte concentration Xis zero, and a denotes the significance level, or
1562      the specified probability of a type I error. When the signal assumes only discrete values (e.g.,
1563      numbers of counts), there may be no value Sc that satisfies Equation 19.31 exactly. The critical
1564      value in this case is defined as the smallest value Sc such that Pr[^ > Sc \ X = 0] < a.
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1565      Determining a value of Sc which satisfies the definition requires knowledge of the distribution of
1566      the net signal S under the assumption that the analyte concentration is zero (the null hypothesis).
1567      The measured net signal may be written as S =  Y - B, where Y denotes the measured gross
1568      signal and B denotes the estimated value of the gross signal under the null hypothesis H0. In the
1569      absence of interferences, the value of B is usually estimated by measuring one or more blanks
1570      using the same procedure used to measure the test sample, and the distribution of Y under H0 is
1571      determined from that of B. In other cases, however, the value of B includes estimated baseline
1572      and other interferences that are present only during the measurement of the sample and cannot be
1573      determined from the blank.

1574      Since Sc, not_yc, has traditionally been used for analyte detection decisions in radioanalysis, the
1575      following presentation focuses primarily on Sc.  However, conversion of either of these values to
1576      the other is simple, becauseyc = Sc + B.

1577      19.7.1.1 Normally Distributed Signals

1578      If the distribution of the net signal S under H0 is approximately normal with a well-known
1579      standard deviation o0, the critical value of S is

                                             SC = Zl-a°0                                    (19.32)
1580      where z1_a denotes the (1 - a)-quantile of the standard normal distribution. Table G.I in Appen-
1581      dix G shows that zl_a~ 1.645 when a = 0.05. Attachment 19D describes the calculation of Sc
1582      when the standard deviation is not well-known.

1583      The blank signal B and its standard deviation OB may be estimated by replicate blank measure-
1584      ments, but at least 20 measurements are generally needed to ensure that the experimental stan-
1585      dard deviation SB is an accurate estimate of CB. (If fewer than 20 measurements are made, see
1586      Attachment 19D.) Given OB, the standard deviation o0 of the net signal S under the null hypothe-
1587      sis is given equal to
                                           G0 - G5A
                                   (19.33)
n
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1588      19.7.1.2 Poisson Counting

1589      Radionuclide analyses typically involve radiation counting measurements. Although radiation
1590      counting data never follow the Poisson model exactly, the model may be a useful approximation
1591      in some situations, especially those where the mean count is extremely low and the observed
1592      count therefore does not follow a normal distribution. At somewhat higher count levels, features
1593      from both models are often used, since the Poisson distribution may be approximated by a
1594      normal distribution. In this case, the Poisson model allows one to estimate o0 without replication,
1595      because one blank measurement provides an estimate of OB.

1596      When a test source is analyzed in a radiation counting measurement, either the gross count or the
1597      gross count rate may be considered the instrument signal Y. In this section, it is assumed that the
1598      instrument signal is the gross count. Therefore,
                                Y = NS             B = \-^+Rl\ts                       (19.34)
                                                       V IB


1599      and the net instrument signal is the net count, defined as


                                                                                         (19.35)
                                                V 1B     I
1600      where
1601         Ns     is the gross count (source count)
1602         NB     is the blank count
1603         Rj     is the estimated count rate due to interferences
1604         ts     is the count time  for the test source
1605         tB     is the count time  for the blank

1606      The net signal is always assumed to have zero mean.

1607      THE POISSON-NORMAL APPROXIMATION

1608      When Poisson counting statistics are assumed (possibly with additional variance components)
1609      and the instrument background remains stable at a level where the Poisson distribution is approx-
1610      imately normal, the critical net count is given approximately by the equation
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                               ^C ~ Zl-atS
                                         .
                                           R+R
                                                 I
                                       (19.36)
1611      where RB denotes the (true) mean count rate of the blank, Rt denotes the mean interference count
1612      rate, ^ denotes non-Poisson variance in the blank (count rate) correction (see Section 19.6.4),
1613      and c2(Rf) denotes the variance of the estimator for R{. When there are no interferences and no
1614      non-Poisson blank variance, this equation becomes

                                                                                          (19.37)
1615      The preceding formula is equivalent to "Currie's equation" Lc = 2.33 J\iB when tB = ts,a = 0.05,
1616      and the symbols Lc and \IB are identified with Sc and RBts, respectively (Currie 1968).

1617      In Equation 19.37, RB denotes the true mean blank count rate, which can only be estimated. In
1618      practice, one must substitute an estimated value RB for RB, as shown in the following equation.
                                                                                          (19.38)
1619      Equation 19.38 resembles Equation 19.37 (Currie's equation) but involves the estimated count
1620      rate RB, which varies with repeated measurements. The value of RB is usually estimated from the
1621      same blank value NB used to calculate the net instrument signal. (See Attachment 19D for other
1622      possible estimators.)
                                              ~   NB
                                             RB = —                                     (19.39)
1623      The resulting formula, shown below, is equivalent to equations published by several authors
1624      (Currie 1968, Lochamy 1976, Strom and Stansbury 1992, ANSI 1996a).
                                           l-a
                                             ^
                                                                                          (19.40)
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1625      If a = 0.05 and tB = ts, Equation 19.40 leads to the well-known expression 2.33 JNB for the
1626      critical net count.
1627      When the blank count is high (e.g., 100 or more), Equation 19.40 works well. At lower blank
1628      levels, it can produce a high rate of type I errors. For example, if the true mean blank count is
1629      0.693, there is a 25% chance of observing 0 blank counts and a positive number of test source
1630      counts in paired measurements of equal duration. In this case, a critical value calculated by Equa-
1631      tion 19.40 produces type I errors more than 25% of the time regardless of the chosen significance
1632      level a. Attachment 19D describes several expressions for Sc that have been proposed for use in
1633      situations where the mean blank count is less than 100.
1634

1635
1636
1637

1638
1639
                                      EXAMPLE

Problem: A 6000-s blank measurement is performed on a proportional counter and 108 beta
counts are observed. A test source is to be counted for 3000 s. Estimate the critical value of the
net count when a = 0.05.
Solution:
                                        "1-a,
                                 N
                            = 1.645
                                             \
                                               108
3000

6000;
1 +
3000
6000,
                                      = 14.8 counts.
1640

1641
1642
                                      EXAMPLE

Problem: Repeat the same problem assuming the blank correction, expressed as a count rate,
has a non-Poisson uncertainty component of ^g = 0.001 cps (see Section 19.6.4).
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1643
1644
1645
 Solution:
                                                      ,2,2
                              = 1.645
                                     \
                                                        (0001)2(6000)2
                              = 15.6 counts.
 So, 15.6 may be a slightly more realistic value for the critical net count.
1646      19.7.1.3 Reagent Blanks

1647      Equation 19.40 is derived with the assumption that a detection decision is based on counts
1648      obtained from a single radiation counter. When laboratory samples are analyzed in batches, it is
1649      common to analyze a single reagent blank per batch, so that the measurement conditions for the
1650      blank may differ somewhat from those of the samples. In particular, the counts for the laboratory
1651      samples and the blank may be measured using different instruments. If detection in a laboratory
1652      sample is defined relative to a reagent blank counted on a different instrument, Equation 19.40 is
1653      inappropriate. Even if a single instrument is used, the presence of positive amounts of analyte in
1654      the reagents probably invalidates the Poisson assumption. In principle, B should be estimated by
1655      converting the total analyte activity of the reagent blank Z^ to an estimated gross  count on the
1656      instrument used to  measure the laboratory sample. Thus,
1657
1658
1659
1660

1661

1662
where
   F
                                                                                           (19.41)
is the calibration function for the laboratory sample measurement, whose parameters
include the instrument background, counting efficiency, chemical yield, and any
estimated interferences
   ZRB    is the estimated total activity of the reagent blank

Then the net count is S = Y - B, whose critical value is
1663
                                                                                           (19.42)
where
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                                                                            Measurement Statistics
1664          o2( YQ) is the variance of the gross count Y in the test source measurement when all of the
1665                analyte in the source is derived from reagents
              ^  /*,                                /*,
1666          ^B  is the variance of the estimator B
1667      If Poisson counting statistics are assumed, then o2(70) may be estimated by B (assuming B > 0),
1668      but estimating c\B) still requires a more complicated expression, which may be based on uncer-
1669      tainty propagation or replication.  The variance of B may be difficult to estimate if positive blank
1670      values are caused not by the presence of the analyte in reagents but by contaminated glassware or
1671      instruments, which may represent a loss of statistical control of the analytical process.

1672      19.7.2 Calculation of the Minimum Detectable Concentration

1673      The minimum detectable concentration (MDC) is defined as the concentration of analyte XD that
1674      must be present in a laboratory sample to give a probability 1 - P of obtaining a measured
1675      response greater than its critical value,  leading one to conclude correctly that the analyte concen-
1676      tration is positive. In other words, the MDC is the analyte concentration at which the type II error
1677      rate is p.

1678      The MDC may also be defined as the analyte concentration XD that satisfies the relation

                                        Pr[S
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         Measurement Statistics
1691      The MDC is also commonly used in radiochemistry to describe the detection capability of the
1692      analytical process as implemented in a particular instance. In this case, the need for conservative
1693      choices is reduced. Instead, the measured values of the variable quantities may be used. How-
1694      ever, since the measured values have uncertainties, their uncertainties contribute to a combined
1695      standard uncertainty in the calculated value ofxD. For purposes of regulatory compliance, an
1696      uncertainty interval or conservative upper bound for XD may still be needed (see NRC 1984).

1697      19.7.2. 1  The Minimum Detectable Net Instrument Signal

1698      The traditional method for calculating the MDC involves first calculating the minimum detect-
1699      able value of the net instrument signal and then converting the result to a concentration using the
1700      mathematical measurement model. The minimum detectable value of the net instrument signal,
1701      denoted by SD, is defined as the mean value of the net signal that gives a specified probability
1702      1 - p of yielding an observed signal greater than its  critical value Sc. Thus,

                                                   = SD]=P                               (19.44)
1703      where S denotes the true mean net signal.

1704      19.7.2.2  Normally Distributed Signals

1705      If the net signal S is normally distributed and its estimated standard deviation q> under H0 is well-
1706      known, the critical value of S is

                                             SC=Zl-a°0                                    (19.45)

1707      as previously noted. Then, the minimum detectable net signal SD is determined implicitly by the
1708      equation
                                     SD = Sc+zl_J(S\S = SJ                            (19.46)


1709      where o2(S  S = SD) denotes the variance of the measured signal S when the true mean signal S
1710      equals SD. If the function c2(S S = SD) is constant, Equation 19.46 gives the value of SD immedi-
1711      ately, but typically o2(S | S = SD) is an increasing function of SD.
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1712     If the function G2(-S' | S = SD) has a simple form, it may be possible to transform Equation 19.46
1713     by algebraic manipulation into an explicit formula for SD. For example, the variance of S often
1714     has the form
                                          c\S)=aS2 + bS + c                                (19.47)
1715     where S denotes the true mean net signal and the constants a, b, and c do not depend on S (see
1716     Section 19.7.2.3, "Poisson Counting"). In this case, the minimum detectable net signal is given
1717     approximately by
                                /                  _ \
                                        2                   2    2
                                                                                              (19.48)
                                                                                              \      i
1718     where/p= 1  -
2
c 2
+ zi-P\
Z2^
>2 2
c 4 — c -r.
1719
1720
1721     below
       r          f

If Equation 19.46 cannot be transformed algebraically, an iterative procedure, such as fixed-point
iteration, may be used to solve the equation for SD. An outline of fixed-point iteration is shown
U ~1 „„, 11
1722           1.   Set SD = Sc + zj _p/o2(^ | S = Sc)

1723           2    repeat

1724           3.     Seth = SD

1725           4.     Set SD = SC-

1726           5.

1727
           until \SD- h\is sufficiently small

      6.    output the solution SD
1728     In many cases, one iteration of the loop (Lines 2-5) provides an adequate approximation of SD. In
1729     almost all cases, repeated iteration produces an increasing sequence of approximations
           11 Fixed-point iteration, or functional iteration, is the term for a general technique for solving an equation of the
         formx=J(x). The iteration produces a sequence x0, xbx2,..., where xn+l =j[xn). Under certain conditions, the
         sequence converges to a fixed point off, where fix) = x. Newton's Method for finding a zero of a function g(x) is
         one example of the technique.

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1730      converging upward to the solution; so, the stopping condition at Line 5 may be replaced by
1731      "until SD < h" to obtain full machine precision in the result.

1732      19.7.2.3 Poisson Counting

1733      If Sc is calculated using the Poisson model and the blank is measured with a sufficiently large
1734      number of counts, and if a = P, the minimum detectable net signal SD is given by the following
1735      simple equation.12
                                             SD = z?_p+2Sc                                    (19.49)


1736      In the special case when ts = tB and a = P = 0.05, Equation 19.49 becomes

                                             SD = 2.7l+2Sc                                    (19.50)

1737      In the general case, SD is  determined from Equation 19.48 using the following values for a, b,
1738      and c.
1739                                  a = 0     b=\     c = Rntv
                                                             D o
1740      The resulting formula for SD is
    I c
1+^1
1741     As previously noted, counting data never follow the Poisson model exactly. Variable factors such
1742     as counting efficiency, and source geometry and placement tend to increase a, while interferences
1743     and background instability tend to increase c. For example, if the counting efficiency has a 2%
           12 Some references use the value 3 instead of Zj_p in this formula. A straightforward derivation gives the value
          Zj_p, which is approximately 2.71 when (3 = 0.05, but replacing this value by -In (3 (approximately 3 when (3 = 0.05)
          accounts for the fact that when the mean count is low, a Poisson distribution is only imperfectly approximated by a
          normal distribution. The value - In (3 is the exact value of SD when the mean blank count rate is zero, because in this
          case Sc = 0, and Pr[S = 0] < (3 if and only if S > - In (3. Note also that the equation in the text is valid only if a = (3.

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1744     coefficient of variation and background instability contributes a non-Poisson standard deviation
1745     of 0.001 cps to the blank correction, then one might use Equation 19.48 with the values
1746                        a = (0.02)2    b = l     c=RRt
1747     19.7.2.4  TheMDC

1748     Traditionally the minimum detectable net signal SD has been converted directly to the minimum
1749     detectable concentration XD using the same measurement model used to convert an observed
1750     value of the signal S to a concentration f . In a typical model, the net count is divided by the
1751     sensitivity A, which is the product of factors such as the count time, test portion size, counting
1752     efficiency, chemical yield, and decay factor. The sensitivity may also include the subsampling
1753     factor, denoted by Fs, which was defined in Section 19.6. 1 1 as the ratio of the analyte concentra-
1754     tion of a subsample to that of the original sample. This factor is always estimated to be 1 and is
1755     included only for its contribution to the measurement uncertainty.

1756     If the sensitivity does not vary substantially from measurement to measurement, the MDC is
1757     given by
                                                    SD
                                               XD =                                          (19.52)
1758     If the variance of A is not negligible, it increases the value ofxD. Recall that when the variance of
1759     the net count S has the form c\S) = aS2 + bS + c, the minimum detectable net instrument signal
1760     may be approximated by Equation 19.48. If the sensitivity is normally distributed, the effect of its
1761     variance on the detection limit may be accounted for (approximately) by increasing the value of
1762     the constant a in Equation 19.48 by an amount equal to (p^(l + a), where q>A denotes the relative
1763     standard deviation of A13 For example, in the Poisson-counting scenario, where the value of a
1764     would otherwise be zero, a becomes <$A. Then the MDC is given by
           13 The word "approximately" is used here because the signal is only approximately normal when its conditional
         distribution depends on the sensitivity in the manner described.

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                    XD =
                     S,
                                          i-
                                            .
                                             *
                                                                                          (19.53)
1765
where /= 1 -
                            anda=
1766      Often the distribution of A may not be well-known or may not be approximately normal. In this
1767      case, one may replace A in the formula by a somewhat low value, such as the p-quantile ap of its
1768      distribution, and ignore its variance. Thus, assuming Poisson counting statistics, one may use
1769      Equation 19.53 with a = 0 and A = ap. Alternatively, if the subsampling error is thought to be
1770      approximately normal, one may increase a by (psamp(l + aX where (pgamp denotes the relative sub-
1771      sampling variance, and ignore the subsampling error when estimating the quantile ap (the
1772      approach used in Attachment 19E). If (pgamp i§ negligible, the MDC may be obtained directly from
1773      the minimum detectable net count SD using the following formula.
                                                  S
                                                    D
                                                   aa
                                                                                          (19.53)
1774      When a "sample-specific" MDC is calculated, the measured value of the sensitivity A may be
1775      substituted for A in the equation for XD and the variance of A may be ignored. Then, if the sub-
1776      sampling variance (psamp is also negligible, the MDC is estimated by
                                                  S
                                                    D
                                                   A
                                                                                          (19.54)
1777      However, it should be remembered that the resulting value for the MDC has an uncertainty gen-
1778      erated by the measurement uncertainties of the input estimates from which it is calculated. It may
1779      also be variable because of the variability of the true sensitivity factors (e.g., chemical yield).

1780      19.7.2.5 Regulatory Requirements

1781      More conservative (higher) estimates of the MDC may be obtained by following the recommen-
1782      dations of NUREG/CR-4007, in which formulas for MDC (LLD) include estimated bounds for
1783      relative systematic error in the blank determination  (AB) and the  sensitivity (&A~). The critical net
1784      count Sc is increased by AB^, and the minimum detectable net count SD is increased by 2 ARB.
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1785      The MDC is then calculated by dividing SD by the sensitivity and multiplying the result by
1786       1 + &A. The approach of NUREG/CR-4007, which deals with detection limits, differs fundamen-
1787      tally from that of the GUM, which considers only measurement uncertainty. The NUREG's
1788      conservative approach treats random errors and systematic errors differently to ensure that the
1789      MDC for a measurement process is unlikely to be consistently underestimated, which is an
1790      important consideration if the laboratory is required by regulation or contract to achieve a speci-
1791      fiedMDC.

1792      19.7.2.6  Testing the MDC

1793      To ensure that the MDC has been estimated properly, one may test the estimate experimentally
1794      by analyzing n identical control samples spiked with  an analyte concentration equal to XD. If the
1795      MDC has been determined properly (the null hypothesis), the probability of failing to detect the
1796      analyte in each control  sample is  at most p. Then the  number of nondetectable results in the
1797      experiment may be assumed to have a binomial distribution with parameters n and p. If A: non-
1798      detectable results are actually  obtained, one calculates the cumulative binomial probability


                         ^ = E(W)  P'O-Pr7   or  l-£Hp/'(l-pr-/                (19.55)
                             j=k\JJ                     j=0\J)

1799      and rejects the null hypothesis ifP is smaller than the chosen  significance level for the test
1800      (which may differ from the  significance level for the  analyte detection test).

1801      To make the test realistic, one should ensure that the  physical and chemical characteristics of the
1802      control samples, including potential interferences, are representative of laboratory samples
1803      encountered in practice.
1804
1805
1806
1807
                                       EXAMPLE

Problem: Assume XD is estimated with p = 0.05. As a check, 10 control samples spiked with
concentration XD are analyzed and 3 of the 10 produce nondetectable results. Does XD appear to
have been underestimated (at the 2% level of significance)?
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1808
1809
1810
Solution: The variables are n = 10, p = 0.05, and k=3. Calculate the P-value
                                  (0.05y(0.95)10^ = 1-0.9885 = 0.01 15
Since P < 0.02, reject the null hypothesis and conclude that the MDC was underestimated.
1811      19.7.3 Calculation of the Minimum Quantifiable Concentration

1812      The minimum quantifiable concentration (MQC), or the minimum quantifiable value of the con-
1813      centration, was defined in Section 19.4.5 as the analyte concentration in a laboratory sample that
1814      gives measured results with a specified relative standard deviation 1 / kQ, where kQ is usually
1815      chosen to be 10.

1816      Calculation of the MQC requires that one be able to estimate the standard deviation for the result
1817      of a hypothetical measurement performed on a laboratory sample with a specified analyte con-
1818      centration. Section 19.6.12 discusses the procedure for calculating the standard deviation for such
1819      a hypothetical  measurement.

1820      The MQC is defined symbolically as the value XQ that satisfies the relation

                                                         Zj                               (19.56)
1821      where c2(X \ X= XQ) denotes the variance of the estimator X when the true concentration X
1822      equals XQ. If the function c2(X \ X= XQ) has a simple form, it may be possible to solve Equation
1823      19.56 for XQ using only algebraic manipulation. Otherwise, fixed-point iteration, which was
1824      introduced in Section 19.7.2, may be used. The use of fixed-point iteration for this purpose is
1825      shown below.
1826            1.   Set xe =

1827            2   repeat

1828            3.
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                                                                             Measurement Statistics
1829

1830

1831
             Setxe=
      5.   until XQ - h\ is sufficiently small

      6.   output the solution XQ
1832     The sequence of values generated by the algorithm typically converges upward to the solution.

1833     When Poisson counting statistics are assumed, possibly with excess variance components, and
1834     the mathematical model for the analyte concentration is X = SIAFS, where S is the net count, A
1835     denotes the overall sensitivity of the measurement, and Fs is the subsampling factor, Equation
1836     19.56 may be solved for XQ to obtain the formula
1837
1838
1839
1840
1841
1842
1843

1844

1845

1846
where
    9s,
                  X/-1 =
                   ^   2 A I.
      amp
                           o
                     1 +
                        \
                                                                                            (19.57)
is the count time for the test source
is the count time for the blank
is the mean blank count rate
is the non-Poisson variance component of the blank count rate correction
is the mean interference count rate
is the standard deviation of the measured interference count rate
is the relative variance of the measured sensitivity, A
is the relative subsampling variance
is equal to 1 - k2Q ((p2, + (p2,   )
1847
1848
1849

1850
1851
1852
1853
If the true sensitivity^ may vary, then a conservative value, such as the 0.05-quantile a005,
should be substituted for^4 in the formula. Note that  0. If IQ < 0, the MQC is defined to be
infinite, because there is no concentration at which the relative standard deviation of X fails to
exceed 1 / kQ. In particular, if the relative standard deviation of the measured sensitivity A or the
subsampling standard deviation (pSamp exceeds 1 / kQ, then IQ < 0 and the MQC is infinite.
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1854      More generally, if the variance of the measured concentration X can be expressed in the form
1855      G\X) = aX2 + bX+ c, where a, b, and c do not depend on X, then the MQC is given by the
1856      formula
                                  2(l-k2Qa}
                                                                                        (19.58)
1857      For example, if pure Poisson counting statistics are assumed and there are no interferences, then
1858      a = 
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                                                                           Measurement Statistics
1875      American Society for Testing and Materials (ASTM). 1995. Standard Specification for Glass
1876         Volumetric (Transfer) Pipets, E 969. ASTM, West Conshohocken, PA.

1877      American Society for Testing and Materials (ASTM). 2000. Standard Practice for Calibration of
1878         Laboratory Volumetric Glassware., E 542. ASTM, West Conshohocken, PA.

1879      Bevington, Philip R., and D. Keith Robinson. 1992. Data Reduction and Error Analysis for the
1880         Physical Sciences, 2nd ed. McGraw-Hill, New York, NY.

1881      Brodsky, Allen. 1992. Exact Calculation of Probabilities of False Positives and False Negatives
1882         for Low Background Counting. Health Physics 63(2): 198-204.

1883      Burden, Richard L., and J. Douglas Faires. 1993. Numerical Analysis, 5th ed. PWS Publishing
1884         Company, Boston, MA.

1885      CFR 136. 1999. U.S. Environmental Protection Agency. "Guidelines Establishing Test
1886         Procedures for the Analysis of Pollutants."

1887      Colle, R., and Raj Kishore. 1997. An Update on the NIST Radon-in-water Standard Generator:
1888         Its Performance Efficacy and Long-term Stability. Nuclear Instruments and Methods in
1889         Physics Research A 391: 511-528.

1890      Currie, Lloyd A. 1968. Limits for Qualitative Detection and Quantitative Determination:
1891         Application to Radiochemistry. Analytical Chemistry 40(3): 586-593.

1892      Currie, Lloyd A. 1972. The Limit of Precision in Nuclear and Analytical Chemistry. Nuclear
1893         Instruments and Methods 100(3): 387-395.

1894      Currie, L.A. 1997. Detection: International Update, and Some Emerging Di-lemmas Involving
1895         Calibration, the Blank, and Multiple Detection Decisions. Chemometrics and Intelligent
1896         Laboratory Systems 37: 151-181.

1897      Currie, L.A., E.M. Eijgenhuijsen, and G.A. Klouda. 1998. On the Validity of the Poisson
1898         Hypothesis for Low-Level Counting: Investigation of the Distributional Characteristics of
1899         Background Radiation with the NIST Individual Pulse Counting System. Radiocarbon 40(1):
1900         113-127.
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1901      Environmental Protection Agency (EPA). 1980. Upgrading Environmental Radiation Data,
1902         Health Physics Society Committee Report HPSR-1, EPA, 520/1-80-012, EPA, Office of
1903         Radiation Programs, Washington, DC.

1904      Environmental Protection Agency (EPA). 1998. Guidance for Data Quality Assessment:
1905         Practical Methods for Data Analysis. EPA QA/G-9, QA97 Version. EPA/600/R-96/084,
1906         EPA, Quality Assurance Division, Washington, DC.

1907      Eurachem. 2000. Eurachem/CITAC Guide: Quantifying Uncertainty in Analytical Measurement.,
1908         2nd ed. Eurachem.

1909      Filliben, James J. 1975. The Probability Plot Correlation Coefficient Test for Normality.
1910         Technometrics 17(1): 111-117.

1911      Friedlander, Gerhart, et al. 1981. Nuclear andRadiochemistry 3rd ed. John Wiley and Sons, New
1912         York, NY.

1913      Fuller, Wayne A. 1987. Measurement Error Models. John Wiley and Sons, New York, NY.

1914      Gy, Pierre M. 1992. Sampling of Heterogeneous and Dynamic Material Systems: Theories of
1915         Heterogeneity, Sampling and Homogenizing. Elsevier Science Publishers, Amsterdam, The
1916         Netherlands.

1917      Hoel, Paul G., Sidney C. Port, and Charles J. Stone. 1971. Introduction to Probability Theory.
1918         Houghton-Mifflin, Boston, MA.

1919      International Organization for Standardization (ISO). 1993a. International Vocabulary of Basic
1920         and General Terms in Metrology. ISO, Geneva, Switzerland.

1921      International Organization for Standardization (ISO). 1993b. Statistics - Vocabulary and
1922         Symbols - Part 1: Probability and General Statistical Terms. ISO 3534-1. ISO, Geneva,
1923         Switzerland.

1924      International Organization for Standardization (ISO). 1995. Guide to the Expression of
1925         Uncertainty in Measurement. ISO, Geneva, Switzerland.

1926      International Organization for Standardization (ISO). 1997. Capability of Detection - Part 1:
1927         Terms and Definitions. ISO 11843-1. ISO, Geneva, Switzerland.


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1928      International Organization for Standardization (ISO). 2000a. Determination of the Detection
1929         Limit and Decision Threshold for Ionizing Radiation Measurements - Part 1: Fundamentals
1930         and Application to Counting Measurements without the Influence of Sample Treatment. ISO
1931         11929-1. ISO, Geneva, Switzerland.

1932      International Organization for Standardization (ISO). 2000b. Determination of the Detection
1933         Limit and Decision Threshold for Ionizing Radiation Measurements - Part 2: Fundamentals
1934         and Application to Counting Measurements with the Influence of Sample Treatment. ISO
1935         11929-2. ISO, Geneva, Switzerland.

1936      International Organization for Standardization (ISO). 2000c. Determination of the Detection
1937         Limit and Decision Threshold for Ionizing Radiation Measurements - Part 3: Fundamentals
1938         and Application to Counting Measurements by High-resolution Gamma Spectrometry,
1939         without the Influence of Sample Treatment. ISO 11929-2. ISO, Geneva, Switzerland.

1940      International Union of Pure and Applied Chemistry (IUPAC). 1995. Nomenclature in Evaluation
1941         of Analytical Methods Including Detection and Quantification Capabilities. Pure and Applied
1942         Chemistry 67(10): 1699-1723.

1943      Knoll, Glenn F. 1989. Radiation Detection and Measurement, 2nd ed. John Wiley and Sons, New
1944         York, NY.

1945      Lawson, Charles L., and Richard J. Hanson. 1974. Solving Least Squares Problems. Prentice-
1946         Hall, Englewood Cliffs, NJ.

1947      Lochamy, Joseph C.  1976. The Minimum Detectable Activity Concept. NBS Report No. NBS-
1948         SP456, National Bureau of Standards, Gaithersburg, MD.

1949      Lucas, H.F., Jr., and D.A. Woodward. 1964. Journal of Applied Physics 35: 452.

1950      Marquardt, D.W. 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters.
1951         Journal of the Society for Industrial and Applied Mathematics 11(2): 431-441.

1952      National Bureau of Standards (NBS). 1963. Experimental Statistics. NBS Handbook 91, National
1953         Bureau of Standards, Gaithersburg, MD.

1954      National Bureau of Standards (NBS). 1964. Handbook of Mathematical Functions. Applied
1955         Mathematics Series 55, National Bureau of Standards,  Gaithersburg, MD.


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         Measurement Statistics
1956      National Council on Radiation Protection and Measurements (NCRP). 1985. Handbook of
1957         Radioactivity Measurement Procedures. NCRP Report 58, 2nd ed., NCRP, Bethesda, MD.

1958      Nicholson, W.L. 1963. Fixed Time Estimation of Counting Rates with Background Corrections.
1959         AEC Research and Development Report HW-76279.

1960      Nicholson, W.L. 1966. Statistics of Net-counting-rate Estimation with Dominant Background
1961         Corrections. Nucleonics 24(8): 118-121.

1962      Nuclear Regulatory Commission (NRC). 1984. Lower Limit of Detection: Definition and
1963         Elaboration of a Proposed Position for Radiological Effluent and Environmental
1964         Measurements. NUREG/CR-4007. NRC, Washington, DC.

1965      Pitard, Francis F. 1993. Pierre Gy 's Sampling Theory and Sampling Practice: Heterogeneity,
1966         Sampling Correctness, and Statistical Process Control., 2nd ed. CRC Press, Boca Raton, FL.

1967      Press, William H., et al. 1992. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed.
1968         Cambridge University Press, New York, NY.

1969      Stapleton, James H. 1995. Linear Statistical Models.  John Wiley and Sons, New York, NY.

1970      Stapleton, James H. 1999. Personal correspondence. Department of Statistics and Probability,
1971         Michigan State University.

1972      Strom, Daniel J., and Paul S. Stansbury. 1992. Minimum Detectable Activity When Background
1973         Is Counted Longer than the Sample. Health Physics 63(3): 360-361.

1974      Turner, James E. 1995. Atoms, Radiation, and Radiation Protection, 2nd ed. John Wiley and
1975         Sons, New York, NY.

1976      19.8.2 Other Sources

1977      American Chemical Society (ACS). 1988. Detection in Analytical Chemistry: Importance,
1978         Theory, andPractice. ACS Symposium Series 361, ACS, Washington, DC.

1979      National Institute of Standards and Technology (NIST). 1994. Guidelines for Evaluating and
1980         Expressing the Uncertainty of NIST Measurement Results. NIST Technical Note 1297, NIST,
1981         Gaithersburg, MD.


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1982                                     ATTACHMENT 19A
1983                                         Distributions

1984     19A.1  Introduction

1985     This attachment briefly describes the probability distributions used in Chapter 19.

1986     Distributions may be classified according to their mathematical properties. Distributions in the
1987     same class or family are described by the  same mathematical formulas. The formulas involve
1988     numerical parameters which distinguish one member of the class from another.

1989     Two important kinds of distributions are the normal and log-normal, which are observed often in
1990     nature. Other types of distributions important in radioanalysis include the rectangular, binomial,
1991     Poisson, Student's t, chi-square,  and exponential distributions. Poisson distributions in particular
1992     are important in radiation counting measurements and are described in Section 19.6.2.

1993     19A.2  Normal Distributions

1994     Many quantities encountered in nature and in the laboratory have distributions which can be
1995     described by the "bell curve." This type of distribution, called a normal, or Gaussian, distribu-
1996     tion, is usually a reasonably good model for the result of a radioanalytical measurement. A num-
1997     ber of commonly used methods for evaluating data sets depend on their having an approximately
1998     normal distribution. The probability density function (pdf) for a normal distribution is shown in
1999     Figure 19.5.
                           -3-2-10       1        2       3
                                   FIGURE 19.5 — A normal distribution


2000     A normal distribution is uniquely specified by its mean |i and variance a2. The normal distribu-
2001     tion with mean 0 and variance 1 is called the standard normal distribution. If Xis normally dis-
2002     tributed with mean |i and variance a2, then (X -  |i) / a has the standard normal distribution.

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2003     The sum of a large number of independent random variables has an approximately normal distri-
2004     bution, even if the individual variables themselves are not normally distributed, so long as the
2005     variance of each term is much smaller than the variance of the sum.14 This is one reason why the
2006     normal distribution occurs often in nature. When a quantity is the result of additive processes
2007     involving many small random variations, the quantity tends to be normally distributed. It is also
2008     true that many other distributions, such as the binomial, Poisson,  Student's t, and chi-square, can
2009     be approximated by normal distributions under certain conditions.

2010     The mean value of a normal distribution is also its mode, or most likely value, which corresponds
2011     to the location of the peak of the curve shown in Figure 19.5. Since the distribution is symmetric
2012     about this point, the mean is also the median, or the value that splits the range into equally likely
2013     portions.

2014     The value of a normally distributed quantity will be within one standard deviation of the mean
2015     about 68% of the time. It will be within two standard deviations about 95% of the time and
2016     within three standard deviations more than 99% of the time. It is important to remember that
2017     these percentages apply only to normal distributions.

2018     19A.3 Log-normal Distributions

2019     The concentration of a contaminant in the environment may not be normally distributed. Instead
2020     it often tends to be log-normally distributed, as shown in Figure 19.6.

                         A*)
                            0          M  n u.
                                            &
                                  FIGURE 19.6 — A log-normal distribution
           14 The number of quantities required to obtain a sum that is approximately normal depends on the distribution of
         the quantities. If the distribution is already symmetric and mound-shaped like the bell curve, the number may be
         rather small. Other distributions such as the log-normal distribution, which is asymmetric, may require a much larger
         number.
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2021     By definition, a quantity X has a log-normal distribution if the logarithm of X is normally distrib-
2022     uted. The product of a large number of independent positive random variables with similar var-
2023     iances is approximately log-normal, because the logarithm of the product is a sum of independent
2024     random variables, and the sum is approximately normal. The concentration of a contaminant in
2025     the environment tends to be log-normal because it is the result of processes of concentration and
2026     dilution, which are multiplicative.

2027     The distribution  of a log-normal quantity X can be uniquely specified by the mean |ilnX and
2028     variance olnX of In X, but more commonly used descriptors are the geometric mean |ig =
2029     exp(|ilnX) and the geometric standard deviation og = exp(olnjr). The geometric mean and geomet-
2030     ric standard deviation are defined so that,  if & is a positive number, the probability that X will fall
2031     between \ig I o* and  \igcg is the same as the probability that InX, which is normally distributed,
2032     will fall between \iinX - kclnX and \iinX + kclnX. For example, the value of X will be between
2033     \ng I c2g and \ng
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         Measurement Statistics
                                        5           10           15           20
                                  FIGURE 19.7 — Chi-square distributions
2049     Chi-square distributions are used frequently in hypothesis testing, especially for tests of hypothe-
2050     ses about the variances of normally distributed data. Chi-square distributions also appear in least-
2051     squares analysis (see Attachment 19B).

2052     A sum of independent chi-square random variables is also chi-square. Specifically, if X and 7 are
2053     independent chi-square random variables with vl and v2 degrees of freedom, respectively, then
2054     X+ 7 has a chi-square distribution with vl + v2 degrees of freedom.

2055     The mean of a chi-square distribution equals the number of degrees of freedom v, and the vari-
2056     ance equals 2v. The mode equals zero if v < 2 and equals v - 2 otherwise. The median does not
2057     have a simple formula.

2058     19A.5 T-Distributions

2059     If Zis standard normal, Xis chi-square with v degrees of freedom, and Z and X are independent,
2060     then Z / ^X/v has a Student's t-distribution with v degrees of freedom. A ^-distribution is sym-
2061     metric and mound-shaped like a normal distribution and includes both positive and negative
2062     values. Figure 19.8 shows the pdf for a ^-distribution with 3 degrees of freedom. A dotted stan-
2063     dard normal curve is also shown for comparison.
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                                                                  Normal
   -4-3
FIGURE 19.8
                                        -2-10     1      2     3     4
                                        The ^-distribution with 3 degrees of freedom
2064     When v is large, the ^-distribution is virtually identical to the standard normal distribution.

2065     The median and mode of a ^-distribution are both zero. The mean is also zero if v > 1 but is
2066     undefined for v = 1 . The variance equals v / (v - 2) if v > 2 and is undefined otherwise.

2067     T-distributions are often used in tests of hypotheses about the means of normally distributed data
2068     and are important in statistical quality control, /"-distributions are also used in the procedure
2069     described in Attachment 19C for calculating measurement coverage factors.

2070     If X±, X2, ...,Xn are independent and normally distributed with the same mean ji and the same
2071     variance, then the quantity
2072     where X is the arithmetic mean and sx is the experimental standard deviation, has a ^-distribution
2073     with n - 1 degrees of freedom.

2074     IfX^ X2, ...,Xn, Yare independent and normally distributed with the same mean and variance,
2075     then the quantity
                                                   Y-X
                                                      + II n
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2076     where X is the arithmetic mean of the Xi and sx is the experimental standard deviation, has a t-
2077     distribution with n - 1 degrees of freedom.

2078     If Z is standard normal, X is chi-square with v degrees of freedom, Z and X are independent, and
2079     5 is a constant, then (Z + 5) / \/XI v  has the non-central t-distribution with v degrees of freedom
2080     and non-centrality parameter 5. When the (central) ^-distribution is used to test the null hypothe-
2081     sis  that two normal distributions have the same mean,  a non-central ^-distribution describes the
2082     distribution of the test statistic if the null hypothesis is false. For example, if X^ X2, ...,Xn, Y are
2083     independent and normally distributed with the same variance a2, and X^ X2, ...,Xn have the same
2084     mean \ix, then the statistic
2085
                                                         l/n
2086     where X is the arithmetic mean of the Xi and sx is the experimental standard deviation, has a t-
2087     distribution with n - 1 degrees of freedom if \ix = \IY, but it has a non-central ^-distribution with
2088     non-centrality parameter

2089                                           6 =	Y   X

2090     if \ix * |I7.

2091     The non-central ^-distribution is useful in the theory of detection limits and appears in Section
2092     19D.3.2 of Attachment 19D.

2093     19A.6 Rectangular Distributions

2094     If Xonly assumes values between a_ and a+ and all such values are equally likely, the distribution
2095     of Xis called a rectangular distribution, or a uniform distribution (see Figure 19.9).
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                                 FIGURE 19.9 — A rectangular distribution
2096     The mean and median of the rectangular distribution equal the midrange (a_ + a+) 12, and the
2097     standard deviation is (a+ - a_) I 2/3. The rectangular distribution is multimodal.

2098     Rectangular distributions are frequently used for Type B evaluations of standard uncertainty (see
2099     Sections 19.5.2.2 and 19.6.10).

2100     19A.7 Trapezoidal and Triangular Distributions

2101     Another type of bounded distribution used for Type B evaluations of standard uncertainty is a
2102     trapezoidal distribution, which is described in Section 19.5.2.2. If Xhas a trapezoidal distribu-
2103     tion, it only assumes values between two numbers a_ and a+, but values near the midrange
2104     (a_ + a+) 12 are more likely than those near the extremes. The pdf for a symmetric trapezoidal
2105     distribution is shown in Figure 19.10. Asymmetric trapezoidal distributions are not considered
2106     here.
                                                    2a
                                 FIGURE 19.10 — A trapezoidal distribution
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2107
2108
2109
2110
2111

2112

2113
2114
2115
2116
2117
2118
2119
2120
2121

2122
The mean and median of this distribution are both equal to the midrange. If the width of the trap-
ezoid at its base is la and the width at the top is 2«p, where 0 < p < 1, then the standard deviation
is ay (1 + p2) / 6 . As P approaches 0, the trapezoidal distribution approaches a triangular distri-
bution, whose standard deviation is a/, the number of successes
2126     has a binomial distribution with parameters n and p. Important facts about the binomial distribu-
2127     tion include the following:
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2128         •   The distribution is discrete; its only possible values are 0, 1,2, ..., n.

2129         •   The mean of the distribution is np.

2130         •   The variance is np(\ - /?).

2131         •   If n is large and/? is not close to 0 or 1, the distribution is well approximated by a normal
2132            distribution.

2133     If Xis binomial with parameters n and/?, then for k = 0, 1,2, ..., n, the probability that X = k is
2134     given by the equati on

                                                ( n\
                                     Vr[X=k\=\   \p\l-pT                              (19.61)
                                                (k)

2135     19A.10 Poisson Distributions

2136     As explained in  Section 19.6.2, the Poisson distribution arises naturally as an approximation to
2137     the binomial distribution when n is large and/? is small. Even if n is not large, the variance of the
2138     binomial distribution can be approximated using the Poisson model if/? is small. Other important
2139     facts about a Poisson distribution include the following:

2140         •   The distribution is discrete; its only possible values are the nonnegative integers
2141            0,1,2,....

2142         •   The mean and variance of the distribution are equal.

2143         •   If the mean is large, the distribution is well approximated by a normal distribution.

2144         •   A sum of independent Poisson random variables is also Poisson.

2145     If Xhas a Poisson distribution with mean |i, then for any nonnegative integer w, the probability
2146     that X=n is given by
                                                 ri\=e-»                                    (19.62)

2147     The Poisson distribution is related to the chi-square distribution, since
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                         TABLE 19.3 — 95% confidence interval for a Poisson mean
n
0
1
2
3
4
5
1 2
f^lower ~~ "Xo.CCSV^W)
0.000
0.025
0.242
0.619
1.090
1.623
Hupper= 7^.975 (2«+ 2)
3.689
5.572
7.225
8.767
10.242
11.668
                 Pr[X < n] = Pr[x2(2« + 2) > 2|i]     and    Pr[X > n] = Pr[x2(2«) < 2|i]
                                                                                          (19.63)
2148
2149
2150
2151
2152
2153
2154
         where %2(v) denotes a chi-square random variable with v degrees of freedom. This fact allows one
         to use quantiles of a chi-square distribution to construct a confidence interval for ji based on a
         single observation X= n. Table 19.3 lists 95% two-sided confidence intervals for ji some small
         values of n. For larger values of w, the quantiles ^(2n) and ^(2n + 2) may be approximated
         using the Wilson-Hilferty formula (NBS 1964):
                                                       A
                                                          9v
                                                                                          (19.64)
         As noted above, when the mean ji is large, the Poisson distribution may be approximated by a
         normal distribution. Specifically,
                                     Pr[X 20.
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2157      19 A. 11 References

2158      National Bureau of Standards (NBS). 1964. Handbook of Mathematical Functions. Applied
2159         Mathematics Series 55, National Bureau of Standards, Gaithersburg, MD.
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2160                                     ATTACHMENT 19B
2161                                  Multicomponent Analyses


2162     19B.1  Matrix Equations

2163     A multicomponent mathematical model may require the simultaneous solution of a system of
2164     equations formulated in terms of vector and matrix operations, which are implemented in soft-
2165     ware. For example, one procedure for radiostrontium analysis involves the precipitation of stron-
2166     tium from a sample, followed by multiple beta measurements of the precipitate over a period of
2167     time. Both 89Sr and 90Sr are beta emitters, and 90Sr decays to 90Y, another beta emitter. The half-
2168     life of 90Y is short enough (64 h) that significant ingrowth occurs over a period of several days,
2169     allowing the activities of 89Sr and 90Sr to be determined from the changing count rate.

2170     The net beta count _y, for a measurement of duration tt at time A^ after precipitation has an
2171     expected value given by
                                                                                          (19.66)

2172     where
2173        xl     is the 89Sr activity in the precipitate
2174        x2     is the 90Sr activity in the precipitate
2175        atl    is a function of tt, A^, and the 89Sr counting efficiency and half-life
2176        aa    is a function of tt, A^, and the 90Sr and 90Y counting efficiencies and half-lives

2177     If m measurements are performed, Equation 19.66 is repeated for each measurement, giving a
2178     system of m equations. After replacing E(y,) by the measured value yt, one can rewrite the
2179     equations as approximations in the form
                                       /7V   +  /7  V   ~  V
                                       UHA1     U12 2     -^ 1
                                                                                          (19.67)
2180     or in matrix form as Ax ~ y. Ifm > 2, the system of equations can be solved simultaneously for xl
2181     and x2. If there are exactly two measurements (m = 2), the system can be solved easily without
2182     matrix operations, but if additional measurements  are made (m > 2), a least-squares solution,

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2183     which typically involves matrix algebra, is required. The use of matrix algebra can make uncer-
2184     tainty propagation more tedious.

2185     19B.2 Random Vectors and Matrices

2186     Uncertainty propagation in matrix equations is best described in terms of random vectors and
2187     random matrices. A useful exposition of matrix theory in this manual is impractical; so, some
2188     familiarity with the basic concepts must be assumed. These basic concepts will be extended to
2189     incorporate randomness.

2190     A random vector is a vector whose components are random variables. Similarly, a random
2191     matrix is a matrix whose components are random variables.

2192     Vectors are usually denoted by bold lower-case letters and matrices by bold upper-case letters.
2193     The /'th component of a vector v is denoted by vi. The if1 component of a matrix A is usually
2194     denoted by atj. The transpose of a matrix A will be denoted here by A'. If A is square and
2195     invertible, the inverse is denoted by A'1. The length of a vector v is denoted by \\v\\.

2196     The expected value of a random vector x is defined as the vector E(x) whose /th component
2197     is E(xi). The expected value of a random matrix Fis similarly defined as the matrix E(Y) whose
2198     /yth component is E(yt).  The covariance matrix of a column vector x and a column vector y is
2199     defined by
                                   Cov(*,j) = 4* - E(x))(y - E(y))f]                         (19.68)


2200     The covariance matrix  of a random column vector x (or the variance-covariance matrix) is
2201     defined by
                                           V(x) = Cov(x,x)                                 (19.69)
2202     The covariance matrix gets its name from the fact that the if* component of CGV(JC,J) equals the
2203     covariance Cov(xi,yj).16 When* and y are vectors of measured values, the estimated covariance
2204     matrices will be denoted here by «(*,j) and u2(x).
           16 In the literature, one often sees the covariance matrix for x and y denoted by S^ and the variance-covariance
         matrix for x denoted by Ex.

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2205     19B.3  Linear Least Squares

2206     Assume y^y2, •••-,ym are independent, normally distributed measured results and F(y,) = o^ for
2207     each /'. Let xl3 x2, ...,xn denote unknown quantities on which the^ depend and whose values one
2208     needs to determine. Assume the means E(y,) are related to the quantities Xj by the following
2209     system of equations.
                              anxi
                                                             =  E(y2)
                                                                                          (19.70)
2210     For example the^ might be measured beta counts of a sample and the Xj could represent the
221 1     unknown activities of 89Sr and 90Sr in the sample at the time of collection.

2212     The linear system 19.70 can be represented using matrix notation as

                                             Ax=E(y)                                    (19.71)

2213     Typically E(y) is unknown and must be replaced in Equation 19.71  by the measured vector y, but
2214     there may be no vector A: for which Ax exactly equals y. So, it is necessary to find an approximate
2215     solution x such thatAv; is close to y in some sense. The components of the difference Ax - y are
2216     called residuals,  and when Ax is close to y, the residuals should be  small. If o, = 1 for all /', the
2217     method of least squares finds a vector x that minimizes the sum of the squares of the residuals
2218     SSRES = || Ax - y  \\2 . If o * 1  for some /', then both sides of equation /' should be divided by o,
2219     before applying the least-squares method. So, if W denotes the m x  m diagonal matrix whose /'th
2220     diagonal element is 1 / cr), then SSRES = (Ax-y)'W(Ax-y). In practice, the standard devia-
2221     tions G; are usually  replaced by the standard uncertainties w(
2222     A least-squares solution always exists. If rank^4 < w, there may be more than one solution, but
2223     this case only occurs if the measurement process is inadequate even in principle for determining
2224     the unknown quantities. So, in practice rank^4 = n. (The rank of A is the number of linearly
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2225     independent columns or rows.) Under this assumption the unique least-squares solution is given
2226     by Equation 19.72.17
                                          x = (A'WAYlA'Wy                                 (19.72)
2227     When quantities such as the test portion size Fand chemical yield Y can be factored out of the
2228     matrix A, it is generally better to do so. The presence of such variables increases the variance of
2229     the least-squares solution Jc, making critical values unnecessarily large when they are calculated
2230     as described in Section 19B.6. When quantities such as Fand Y are factored out, the components
2231     of the least-squares solution x must be divided by the missing factors to obtain activity concen-
2232     trations, and the uncertainties in the factors must be propagated.

2233     Approximating the standard deviations o, in the weight matrix Wby the standard uncertainties
2234     w(y;) may bias the least-squares solution slightly ifyt and w(y;) are correlated, which happens, for
2235     example, whenjA is a measured count and z/(y,) is the Poisson counting uncertainty calculated
2236     from a single measurement. This bias can be virtually eliminated by using the initial least-squares
2237     solution to refine the values of the standard uncertainties and then repeating the least-squares
2238     procedure using the refined estimates.

2239     The solution i: is a random vector, because it is a function of the random vector y. The covariance
2240     matrix for Jc is
                                           u2(jc) = (A'WA)-1                                  (19.73)
2241      The diagonal elements of this matrix are the variances of the components of Jc, and the off-
2242      diagonal elements are the covariances. This expression for the covariance matrix is complete
2243      only when there are no uncertainties in the coefficient matrix A. A more general formula for the
2244      covariance matrix is presented in Section  19B.5.

2245      In some cases, the variance of each yt may be unknown, although all components of y are
2246      believed to have the same variance. When this is true, the solution Jc may be computed by

                                            jc = (A'A^A'y                                   (19.74)
           17 For some least-squares problems, a direct calculation of the solution x using Equation 19.72 can be computa-
         tionally unstable. Singular value decomposition of the matrix A gives a more stable method for obtaining x but is
         beyond the scope of this document. The S VD method also allows one to find a least-squares solution (not unique)
         when rank A < n. See Lawson 1974 or Press et al. 1992 for more details.

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                                                                            Measurement Statistics
2247     and the variance of the components^, may be estimated by


                                                      ~-^-                                (19.75)
                                                   m-n
2248     (The use of Equation 19.75 is a Type A evaluation of uncertainty with m-n degrees of
2249     freedom.) When this equation is used, the covariance matrix for x is
                                                                                          (19.76)

2250     19B.4  General Least Squares

225 1     The general least-squares problem arises when there is a set of measured values _yl3 y2, ...,ym,
2252     whose expected values are functions of an w-dimensional vector* of unknown quantities, as
2253     indicated by the following  system of equations.

                                            fl(x)=E(yl)
                                            f2(x)=E(y2)
                                                 .                                         (19.77)

                                           /„(*)= *W

2254     The system of equations can be written in matrix form as/(jc) = E(y) The method of least squares
2255     finds a vector A: that minimizes the sum of the squares of the residuals
                           SSRES = £       _     = (fW -y}'W(f(x) - y)                  (19.78)
2256     When f(x) can be written as Ax for some matrix A, the problem is linear least squares, whose
2257     solution was presented in the preceding section. When the functions/ are nonlinear but differen-
2258     liable, the solution can be obtained by iterative approximation methods. The most commonly
2259     used algorithm for nonlinear least squares is the Levenberg-Marquardt algorithm (Press et al.
2260     1992). Whatever algorithm is used, it should compute the covariance matrix u2(x), described in
2261     the next section. For more details on nonlinear least-squares problems, see Marquardt 1963,
2262     Press et al. 1992, or Bevington 1992.


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2263     19B.5  The Covariance Matrix for a Least-Squares Solution

2264     Letv4 = dfl dx denote the m x n matrix whose if1 component is dft I dxj.18 Then the covariance
2265     matrix  for the least-squares solution x is approximately equal to (A'WA)~l.

2266     It often happens that the function/depends on variables other than jc, whose values, like the
2267     components of y, are measured before the least-squares method is applied. In the strontium
2268     analysis described at the beginning of this attachment, the measured counting efficiencies for
2269     89Sr, 90Sr, and 90Y are good examples. Measurement uncertainties in these variables contribute to
2270     the uncertainties in the solution A:, although the least-squares covariance matrix (A'WAy1
2271     accounts only for uncertainties in the measurement of y. Better estimates of the variances and
2272     covariances of the components of A; require that the expression for  the covariance matrix be
2273     expanded.

2274     Let the additional measured quantities be written as a vector z with components zl3 z2, ..., z,, and
2275     write f(x;z) to indicate that/depends on both x and z. Assume the  components of z are measured
2276     independently of y, and the covariance matrix u\z) is known. If the method of least squares is
2277     applied to find the unique solution x that minimizes SSRES, and if the uncertainties in the com-
2278     ponents z{ are small,  the covariance matrix for the solution is


                                                                  '                       (19.79)
2279     where dx I dz denotes the n x r matrix whose ifh component is dxi I dz.. The/h column of dx I dz
2280     may be calculated using the formula

                            dx     i
                           — = (A'WA)-i\ ^-w\y-J($;z))-A'W^-\                  (19.80)


2281     If the uncertainties in the components z; are not small, another method of solution may be needed
2282     (e.g., see Fuller 1987).

2283     When the least-squares problem is linear, they* column of dfl dz is given by the formula
           18
            The matrix/1 is the Jacobian matrix of the component functions/b/2, ...,fm.
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                                                                            Measurement Statistics
                                              8f   8A
2284     and the ifh component is given by

                                           %i   Y- da* ^
                                           -T  = E -^xk                                  (19.82)
                                           dzj   * = i 3z7

2285     When the problem is nonlinear, the components dft I dzj are calculated by other means.

2286     19B.6 Critical Values

2287     The general approach to the determination of critical values even in the case of nonlinear least
2288     squares is conceptually no different from that outlined in Section 19.7. 1 . The standard uncer-
2289     tainty of a signal or response variable is determined under the null hypothesis H0 and then multi-
2290     plied by an appropriate factor, such as the normal quantile zl_a. The response variable for a
2291     component Xj may be taken to be the corresponding component x. of the least-squares solution
2292     vector. Let x* denote the value of the vector x under H0. It will be assumed here that x* = 0, but
2293     note that the null hypothesis must give values not only to Xj but to all the components of jc,
2294     because the value of one component generally affects the measurement uncertainties of the other
2295     components of the solution vector. Generally, for this purpose one must use the measured values
2296     of all the components of x except xp although these values  may not be known accurately.

2297     To determine the critical value, first calculate the vector y* =f(x*), which is the expected value of
2298     y under H0. If the least-squares problem is linear, then y* =Ax*. Next calculate the diagonal
2299     weight matrix W, whose /'th diagonal element is the inverse 1 / u2(y,) of the estimated variance of
2300     yt under the null hypothesis. For example, if the problem is the strontium problem described in
2301     Section 19B. 1, in which yt denotes a net count, then u2(y,) might be the counting variance given
2302     by

                               u2(y) = anx*  +  or.2x2* + RBj t\\+-i-\                       (19.83)
2303     where RB t is the blank count rate and tB^ is the corresponding count time. Finally, evaluate the
2304     covariance matrix C for the solution of the least-squares problem f(x) = y* , as described in
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2305     Section 19B.5. (The solution vector x here equals x* because of the method by which y* was
2306     constructed.) Then the critical value of the/h component x  is zl _ aJc~., where zl _ a is the
2307     (1 - a)-quantile of the standard normal distribution.

2308     19B.7  Detection and Quantification Limits

2309     Computing the minimum detectable value of a component Xj requires one to find the value d such
2310     that d = zl _ a/F(0) + z\ - ^V(d), where Vfy) denotes the variance of the estimator x. as a func-
2311     tion of the true value Xj. The value of V(x^) is they* diagonal element of the covariance matrix C
2312     determined under the assumption that the true value of the/h component is Xj. Solving for d pre-
23 13     cisely generally requires  an iterative algorithm, which generates a sequence of values converging
2314     to d. Given that V(x^) and its derivative can be calculated, the equation may be solved by Newton-
2315     Raphson iteration. A simpler version of fixed-point iteration, which does not involve the deriv-
2316     ative, may also be used. The use of fixed-point iteration for this purpose is described in Section
2317     19.7.

2318     The problem of determining the minimum quantifiable value of a concentration estimated by the
2319     least-squares methods is  similar to that of finding the minimum detectable value and generally
2320     requires an iterative algorithm (e.g., see Section  19.7).

2321     19B.8  References

2322     Bevington, Philip R., and D. Keith Robinson.  1992. Data Reduction and Error Analysis for the
2323        Physical Sciences, 2nd ed. McGraw-Hill, New York, NY.

2324     Fuller,  Wayne A. 1987. Measurement Error Models. John Wiley and Sons, New York, NY.

2325     Lawson, Charles L., and  Richard J. Hanson. 1974. Solving Least Squares Problems. Prentice-
2326        Hall, Englewood Cliffs, NJ.

2327     Marquardt, D.W. 1963. An Algorithm for Least-Squares Estimation  of Nonlinear Parameters.
2328        Journal of the Society for Industrial and Applied Mathematics 11(2): 431-441.

2329     Press, William H., et al.  1992. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed.
2330        Cambridge University Press, New York, NY.
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2331                                     ATTACHMENT 19C
2332                                Estimation of Coverage Factors


2333      19C.1  Introduction

2334      Although it is common for laboratories to use a fixed coverage factor such as 2 or 3 when deter-
2335      mining an expanded uncertainty for a measured value, the true coverage probability for the resul-
2336      ting interval may be lower than expected if the standard uncertainties of the input estimates are
2337      determined from evaluations with too few degrees of freedom. This attachment summarizes a
2338      general method presented in Annex G of the GUM for determining appropriate coverage factors
2339      in these circumstances (ISO 1995). Section 19C.3 applies the method to Poisson counting
2340      uncertainties.

2341      19C.2  Procedure

2342      Assume the mathematical model for a measurement is Y =f[Xl,X2,... ,XN\ the input estimates
2343      Xj, x2, ..., % are independent, and the output estimate isy =J(xl,x2,...,%). Also assume that the
2344      combined standard uncertainty of y is not dominated by one component determined from a Type
2345      A evaluation with only a few degrees of freedom or from a Type B evaluation based on a distri-
2346      bution very different from a normal distribution. Then the distribution of the output estimate^
2347      should be approximately normal, and the following procedure may be used to obtain a coverage
2348      factor kp for the expanded uncertainty of y that gives a desired coverage probability/?.

2349      First compute the effective degrees of freedom veff of the measurement using the Welch-
2350      Satterthwaite formula
                                          v
                                           6ff   "  «fa)                                  (19.84)

                                               1 ~ 1    z

2351      Here w,(y) = \dy I dxt\ u(x^) is the component of the combined standard uncertainty generated by
2352      u(x,). If w(x;) is evaluated by a Type A method, then v, is the number of degrees of freedom for
2353      that evaluation. If w(x;) is evaluated instead by a Type B method, then v, is defined to be
  _ 1  u\x)  _ i

V'   2 rtfu(x\\   2
                                                              -2

                                                                                         (19.85)
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          Measurement Statistics
2354      where A.u(x^) is the estimated standard deviation of the standard uncertainty u(x^). Estimation of
2355      Aw(x;) often requires professional judgment.

2356      In some cases, one may consider the value of Aw(x;) for a Type B standard uncertainty to be zero
2357      or negligible, as for example when evaluating the uncertainty associated with rounding a number
2358      (Section 19.6.10). In such cases, one may assume v; = °°; so, the /th term of the sum appearing in
2359      the denominator of the Welch-Satterthwaite formula vanishes.

2360      The coverage factor kp is defined to be the (1 +p) 12-quantile t(l+p)i2(yeS) of a /-distribution with
2361      veff degrees of freedom.19 Since the calculated value of veff will generally not be an integer, it must
2362      be truncated to an integer, or else an interpolated /-factor should be used. That is, if
2363      n < veff < n + 1, then use either kp = /(i+/,)/2(veff) or

                            kP = (n + l ~ veff) '(i +,)/2(w) + Kff - ») '(i +P)/2(n + !)                  (19.86)


2364      The expanded uncertainty Up = kpuc(y) is estimated to have a coverage probability approximately
2365      equal top.

2366      19C.3 Poisson  Counting Uncertainty

2367      As stated in Section 19.5.2.2, the standard uncertainty in the number of counts n observed during
2368      a radiation measurement may often be estimated by u(n) = Jn, according to the Poisson counting
2369      model. This method of evaluating the standard uncertainty is a  Type B method;  so, the effective
2370      degrees of freedom v for the evaluation should be determined from A.u(n). The standard deviation
2371      of Jn is always less than 0.65.20 If n is greater than about  10, the standard deviation of Jn is
            19 The GUM uses the notation tp(v) to denote the (1 + p) /2-quantile of a/-distribution with v degrees of freedom
          (ISO 1995), but the same notation in most statistical literature denotes the^-quantile (e.g., ISO 1993). MARLAP
          follows the latter convention.

            20 Taking the square root of a Poisson random variable is a common variance-stabilizing transformation, as
          described in Chapter 20 of Experimental Statistics (NBS 1963). The stated (slightly conservative) upper bound for
          the standard deviation of \fn is based on calculations performed at the EPA's National Air and Radiation Environ-
          mental Laboratory, although the same approximate value may be determined by inspecting Figure 20-2 of NBS
          1963. The precise calculation maximizes a functional) whose value is the variance of the square root of a Poisson
          random variable with mean I. The first derivative of/is positive, decreasing, and convex between 1 = 0 and the
          location of the maximum of the function at I = 1.31895; so, Newton's Method converges to the solutionfrom
          below. The maximum value of/is found to be (0.642256)2.

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                                                                            Measurement Statistics
2372     approximately equal to 0.5, and, in this case, Equation 19.85 gives the estimate v ~ In. For
2373     smaller values of w, the same approximation is inadequate.

2374     MARLAP recommends that the standard uncertainty u(n) and degrees of freedom v for a Poisson
2375     measured value n be estimated by

                                  u(n) = Jn       and      v = In                         (19.87)
2376     or, if very low counts are possible, by

                              u(n) = V^TTT       and       v = 2(n + 1)                     (19.88)


2377     If the expected count is greater than about 10, these formulas tend to give a coverage probability
2378     near the desired probability p.  When the expected count is small, the coverage probability tends
2379     to be greater than p.

2380     Although the estimate u(n) = ^n + 1  may be derived by the Bayesian approach to counting statis-
2381     tics assuming a flat prior distribution for the mean count (Friedlander et al. 1981), the recom-
2382     mended expressions for u(n) and v in Equation 19.88 have been chosen for the purely practical
2383     reason that they are simple and seem to give satisfactory results. When the count is low, the
2384     assumptions underlying the Welch-Satterthwaite formula are usually violated, because the com-
2385     bined standard uncertainty is dominated by counting uncertainty, and the distribution of the count
2386     is not normal. However, even  in this case, if the formula is used, the recommended expressions
2387     for u(n) and v tend to give conservative results.

2388     19C.4  References

2389     Friedlander, Gerhart, et al. 1981. Nuclear and Radiochemistry 3rd ed. John Wiley and Sons, New
2390        York, NY.

2391     International Organization for Standardization (ISO).  1993. Statistics - Vocabulary and Sym-
2392        bols - Part 1: Probability  and General Statistical Terms. ISO 3534-1 . ISO, Geneva,
2393        Switzerland.

2394     International Organization for Standardization (ISO).  1995. Guide to the Expression ofUncer-
2395        tainty in Measurement . ISO, Geneva, Switzerland.


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2396      National Bureau of Standards (NBS). 1963. Experimental Statistics. NBS Handbook 91, National
2397         Bureau of Standards, Gaithersburg, MD.
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2398                                     ATTACHMENT 19D
2399                             Low-Background Detection Limits


2400     19D.1  Overview

2401     This attachment describes methods for determining critical values and minimum detectable con-
2402     centrations (MDCs) when the standard deviation of the blank signal is not known precisely,
2403     which occurs for example when the blank is measured by low-background Poisson counting or
2404     when the standard deviation is estimated from a small number of replicate measurements.

2405     19D.2  Calculation of the Critical Value

2406     The critical value of the net signal Sc was defined earlier by the relation

                                        Pr[S>Sc X=0]=a                               (19.89)


2407     When the signal assumes only discrete values (e.g., numbers of counts), there may be no value Sc
2408     that satisfies Equation 19.89 exactly.  The critical value in this case is defined as the smallest
2409     value Sc such that Pr[S > Sc X = 0] < a.

2410     19D.2.1 Normally Distributed Signals

2411     If the distribution of the net signal  S under H0 is approximately normal with a well-known stan-
2412     dard deviation, o0, the critical value of S is

                                            SC=Zl-a?0                                    (19.90)
2413     where z1_a denotes the (1 - a)-quantile of the standard normal distribution. Typically the stan-
2414     dard deviation o0 is not well-known and must therefore be replaced by an estimate, 60. If 60 is
2415     determined by a statistical evaluation with v degrees of freedom, the multiplier zl _ a should be
2416     replaced by ^_a(v), the (1 - a)-quantile of the ^-distribution with v degrees of freedom (cf. Type
2417     A evaluation of standard uncertainty in Section 19.5.2.1). Thus,

                                           ^c = 'i-a(v)°o                                  (19.91)
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2418     Table G.2 in Appendix G lists values of ^_a(v). In general, ^_a(v) is greater than z1_a, but the two
2419     values are approximately equal if v is large.
                /*,
2420     When  B is estimated by the average of n replicate blank measurements (assuming no interfer-
2421     ences), the standard deviation 60 of the net signal  S under the null hypothesis may be estimated
2422     from the experimental standard deviation of the measured blank values, SB. Specifically,
                                           G0 =
                                                    1+1
                                                       n
                                                                                           (19.92)
2423
         The number of degrees of freedom, v, in this case equals n - 1; so, the critical value of S is
                                                                                           (19.93)
                                                             n
2424     19D.2.2  Poisson Counting

2425     It is assumed here, as in Section 19.7, that the instrument is a radiation counter and the instru-
2426     ment signal is the gross count. Therefore,
                                   = N
                                                                                           (19.94)
2427     and the net instrument signal is the net count, defined as
2428
2429
2430
243 1
2432
2433
         where
            Ns
            N
                                                                                           (19.95)
                   is the gross count (source count)
                   is the blank count
                   is the estimated count rate due to interferences
                   is the count time for the test source
                   is the count time for the blank
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2434     If the mean blank count rate, RB, is well-known and there are no interferences, then according to
2435     the Poisson model, the critical gross count, yc, equals the smallest nonnegative integer n such that
                                                  k\
     > 1 -a
                                                                                           (19.96)
2436     Then Sc, the critical net count, equals^ - NBts I tB. Table 19.4 shows critical gross counts for
2437     a = 0.05 for small values ofRBts (adapted from NRC 1984).21 To use the table, one calculates the
2438     value ofRBts, finds the appropriate line in the table, and compares the observed gross count Ns to
2439     the value ofyc read from the table. The analyte is considered detected if and only ifNs>yc.
2440     When RBts is greater than about 20, yc may be approximated by
                                                                                           (19.97)
2441     where zl_a denotes the (1 - a)-quantile of the standard normal distribution, and \x] denotes the
2442     largest integer not greater than x.

                           TABLE 19.4 — Critical gross count (well-known blank)
RB*S
0.000-0.051
0.051-0.355
0.355-0.818
0.818-1.366
1.366-1.970
1.970-2.613
2.613-3.285
3.285-3.981
3.981-4.695
4.695-5.425
Jc
0
1
2
3
4
5
6
7
8
9
RB*S
5.425-6.169
6.169-6.924
6.924-7.690
7.690-8.464
8.464-9.246
9.246-10.036
10.036-10.832
10.832-11.634
11.634-12.442
12.442-13.255
Jc
10
11
12
13
14
15
16
17
18
19
RB*S
13.255-14.072
14.072-14.894
14.894-15.719
15.719-16.549
16.549-17.382
17.382-18.219
18.219-19.058
19.058-19.901
19.901-20.746
20.746-21.594
Jc
20
21
22
23
24
25
26
27
28
29
           21 The breaks in the table occur at RBts = 0.5 XQ 05 (2>c) and 0.5 Xo os (2yc + 2).
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2443     When the blank count rate RB is low, which is often true for alpha counting, measuring its value
2444     with good relative precision tends to be difficult, especially if the instrument background tends to
2445     drift.  However, a conservative bound, such as a 1 - a upper confidence limit, may be used if one
2446     wishes to limit type I error rates and is willing to tolerate the resulting higher detection limits.
2447     More commonly used methods for calculating the critical value are described below.

2448     THE POISSON-NORMAL APPROXIMATION

2449     As stated in Section 19.7.1.2, when Poisson counting statistics are assumed (possibly with
2450     additional variance components) and the instrument background remains stable between meas-
2451     urements at a level where the Poisson distribution is approximately normal, the critical net count
2452     is given approximately by the equation
                                     Z\-atS
                                           \
RV + RT   Rf>    9    0  ~
      / .   5  . C2 .  _2/^^                       (19.98)
   t
                                                s
2453     where RB denotes the (true) mean count rate of the blank, Rj denotes the mean interference count
2454     rate, ^ denotes non-Poisson variance in the blank (count rate) correction, and o2^) denotes the
2455     variance of the estimator for Rj. When there are no interferences and no non-Poisson blank
2456     variance, this equation becomes
                                               ^
                                                                                             (19.99)
2457     Low mean blank levels cause the Poisson distribution to deviate from the normal model. Figure
2458     19.12 shows the effects of these deviations on the type I error rates for the Poisson-normal
2459     approximation when tB = ts and a = 0.05. The graph has discontinuities because of the discrete
2460     nature of the Poisson distribution, but the type I error rate is approximately correct (equal to 0.05)
2461     when the mean blank count is 10 or more.22
            ! Probabilities on the curve are calculated using the equation
         where \a denotes the (true) mean blank count. Terms of the infinite sum are accumulated until the cumulative
         Poisson probability, e ~M£"=0 u! / /'!, approaches 1. The calculated values agree with those listed in Table 1 of

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                          P
                        0.20-

                        0.15-

                        0.10-

                        0.05-

                        0.00
                            0           5           10           15          20   E(NB)

              FIGURE 19.12 — Type I error rate for the Poisson-normal approximation (tB = ts)
2462
2463
2464
2465
2466
2467
2468
2469
2470
In Equation 19.99, RB denotes the true mean blank count rate, which can only be estimated. In
practice, one must substitute an estimated value, RB, as shown in the following equation.
                                             l\
                                                                                         (19.100)
The most frequently used expressions for Sc may be derived from Equation 19.100 using an
estimator RB that equals a weighted average of the measured blank count rate NBI tB and the
measured source count rate Ns I ts. A weighted average of both measured rates may be used here
to estimate the true blank level for the purpose of the hypothesis test, because, under the null
hypothesis of zero net source activity, both measured rates are unbiased estimates of the true
blank count rate. Given nonnegative weights ws and WB such that ws + WB = 1, the mean blank
count rate is estimated by

                               4=ws-T+w5-T                              (19.101)
         Brodsky 1992. The discontinuities occur at \a = 1? 12.332 for k = 1, 2, 3, ....

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2471      This estimate RB is always unbiased under the null hypothesis of zero net activity and no inter-
2472      ferences, but the choice of weights affects the variance of the estimator. (When interferences are
2473      present, this weighted average is inappropriate.)23

2474      This attachment will use the notation Sc, which is nonstandard, to denote any version of the
2475      critical value that depends on the gross signal Ns (or  7).

2476      It is often convenient to eliminate Ns from the expression for Sc (e.g., when calculating the
2477      MDC). When the same measured value ofNB is used to calculate both the critical value Sr and
                        /*,                                                                      '—
2478      the net signal S, elimination ofNs from Equation 19.100 produces the following formula for an
2479      alternative critical value Sr24
Zl-aWS
                                  1 +-
                                           •1-a
                                               \
                                                   l-aWS
1 +JL   +N^\  1  +-i
                                                                     (19.102)
2480      It is not generally true that Sc = Sc unless ws = 0, but either critical value may be used to imple-
2481      ment the same test for analyte detection, because  S > Sc if and only if S > Sc.

2482      If there is additional non-Poisson variance associated with the blank correction, an extra term
2483      may be included under the radical (e.g., ^t^, where ^ is as in Equation 19.98), although at very
2484      low background levels the Poisson variance tends to  dominate this excess component.

2485      FORMULA A

2486      The most commonly used approach for calculating Sc is given by Formula A (shown below).
           23 The common practice of using the same Poisson measurement data to calculate both the net signal S and its
          critical value tends to produce a correlation between the two variables. This correlation does not exist when the
          critical value is determined by a statistical evaluation of normally distributed data as described earlier in the
          attachment.
           24 The critical value Sc may be written as a function ftS) of the observed net signal S and the blank count NB.
          Then S exceeds Sc if and only if it exceeds the fixed point off, which is the value Sc where J(SC) = Sc. The fixed
          point is a function ofNB but not ofNs.

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                                      *c = -
                                             Formula A
                                                                                           (19.103)
2487
2488

2489
2490

2491
2492
2493
2494
If a = 0.05 and tB = ts, Formula A leads to the well-known expression 2.33 JWB for the critical net
count (e.g., see Currie 1968).

Formula A may be derived from the standard approximation by using the blank measurement
alone to estimate the true blank count rate — i.e., by using the weights ws = 0 and WB = 1.

As noted in Section 19.7.1.2, when the blank count is high (e.g., 100 or more), Formula A works
well, but at lower blank levels, it can produce a high rate of type I errors. Figure 19.13 shows
type I error rates for Formula A as a function of the mean blank count for count time ratios
tR/tv=\ and 5 when a = 0.05
25
                        0.00
                             0           5           10          15           20    E(NB)

                              FIGURE 19.13 — Type I error rates for Formula A
            ' Probabilities on the two curves are calculated using the equation
                                         = 1 -e
                                                                   k\
         where yc(n) = n(ts/tB) + 1.645Jn (ts I tB) (1 + ts I tB) and \i denotes the mean blank count. The same equation with
         different expressions for>-c(w) is used to calculate the type I error rates shown in Figures 19.14-17.
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2495
FORMULAE
2496     Another published formula for the critical value is (equivalent to) the following (Nicholson
2497     1966).
                                            \-a
                                                                                        (19.104)
2498     The critical value calculated by Equation 19.104 equals zl_a times the combined standard uncer-
2499     tainty of the net count. This fact is the basis for the original derivation of the formula, but the
2500     formula may also be derived from Equation 19.100 using the weights ws = tB I (ts + tB) and WB =
2501     ts I (ts + tB) to estimate RB. When Ns is eliminated from Equation 19.104, one obtains Formula B
2502     (below), which is equivalent to the equation for the critical value given in Atoms, Radiation, and
2503     Radiation Protection (Turner 1995).
                                   •l-a



H
2 /
Zl -a AT 1S 1 !

4 + sr
H '5 V
Formula B

^s 1

IB)

                                                                                        (19.105)
2504     Type I error rates for Formula B are shown in Figure 19.14.
                          P
                        0.15-


                        0.10-
                        0.05	
                                                                    = 5t<,
                                        5           10          15          20   E(NB)

                             FIGURE 19.14 — Type I error rates for Formula B
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2505     Formula B appears natural and intuitive when it is derived in terms of the combined standard
2506     uncertainty of the net count, and it gives excellent results when tB = ts and the pure Poisson
2507     model is valid. However, when the formula is derived using the weights ws and WB, as described
2508     above, the expression seems much less natural, because the weights clearly are not optimal when
2509     tB* ts. Notice that when tB > ts, the type I error rate tends to be less than a.

2510     FORMULA C

2511     If the pure Poisson model is valid, then under the null hypothesis, the weights ws=tsl (ts + tB)
2512     and WB = tB I (ts + tg) provide the minimum-variance unbiased estimator RB for the mean blank
2513     count rate and lead to the following formula for the critical net count (Nicholson 1963,  1966).26
l\

                                                          t
                                                           B
2514     Elimination of Ns from Equation 19.106 produces Formula C, shown below.
                                   2tB     ^  4tj        tB{    tB)                    (19.107)

                                            Formula C

2515     Formula C is equivalent to the equation for the "decision threshold" given in Table 1 of ISO
2516     11929-1 (ISO 2000a) for the case of fixed-time counting. Figure 19.15 shows type I error rates
2517     for Formulae.
           26 The approach here is conceptually similar to that of a two-sample /-test, which employs a pooled estimate of
         variance in the comparison of two normal populations.

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                            0           5           10          15          20   E(NB)

                             FIGURE 19.15 — Type I error rates for Formula C
2518     If the blank correction involves additional non-Poisson variance, an extra term may be included
2519     under the radical in Formula C; however, the weights ws and WB used to derive the formula are
2520     not necessarily optimal in this case. (See ISO 2000b for another approach.)

2521     Note that Formulas B and C are equivalent when tB = ts, because both assign equal weights to the
2522     blank measurement and the source measurement. In this case, both formulas are also equivalent
2523     to the formula given by Altshuler and Pasternack (1963).

2524     THE STAPLETON APPROXIMATION
2525
2526
2527
2528
When the mean counts are low and tB * ts, another approximation formula for Sc appears to out-
perform all of the approximations described above. For small values of the constant d, the
statistic
                              Z = 2
                                      Ns+d
                                      \
                                                           \

                                                                               (19.108)
2529     which involves variance-stabilizing transformations of the Poisson counts Ns and NB, has a distri-
2530     bution that is approximately standard normal under the null hypothesis (Stapleton 1999). So, the
2531     critical value of Zis zl_(V the (1 - a)-quantile of the standard normal distribution. From these
2532     facts one may derive the following expression for the critical net count as a function ofNB.
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                                                                           Measurement Statistics
Sc = d\-*--l\ + ^
      (*B    )    4
                                                     •l-a.
                                                                                        (19.109)
                                  The  Stapleton  Approximation
2533     When a = 0.05, the value d= 0.4 appears to be a near-optimal choice. Then for tB = ts, the
2534     Stapleton approximation gives the equation
                Sc= 1.35 + 2.33
                                                          0.4
              (19.110)
2535     Figure 19.16 shows the type I error rates for the Stapleton approximation when a = 0.05 and
2536     d= 0.4. This approximation gives type I error rates almost identical to those of Formulas B and C
2537     when tB = ts, but it has an advantage when tB* ts.
                          P
                        0.15-

                        0.10-
                        0.05-----;.
                        0.00
                                               = 5t<,
                            0            5           10          15          20   E(NB)

                    FIGURE 19.16 — Type I error rates for the Stapleton approximation
2538     When a * 0.05, the value d= zl_a14.112 appears to give good results (4.112 = z095 / 0.4).

2539     When the blank correction involves a small non-Poisson variance component, a term (Z^tj) may
2540     be included under the radical in Equation 19.109 to account for it.
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2541
          THE EXACT TEST
2542      Poisson counting statistics also permit an "exact" test for analyte detection, whose type I error
2543      rate is guaranteed to be no greater than the chosen value of a, although it may be less. A random-
2544      ized version of the test can provide a type I error rate exactly equal to a (Nicholson 1963), but
2545      only the nonrandomized version will be considered here, since its outcome is always based solely
2546      on the data and not on a random number generator. The test is implemented by rejecting H0 if and
2547      only if the following inequality is true.27
2548      Nicholson presents the test as a comparison of the gross count Ns to a critical value. The critical
2549      value yc is the smallest nonnegative integer n such that28
                                                                      *l-a
                                                                                              (19.112)
2550      The same (nonrandomized) test is implemented by calculating a critical gross count yc equal to
2551      the smallest nonnegative integer n such that
            27 The left-hand side of the inequality is a cumulative binomial probability (see Attachment 19A). It also equals
          where Ix(a,b) denotes the incomplete beta function (NBS 1964, Press et al. 1992).

           28 To implement the randomized test, calculate the critical value yc, and, if Ns > yc, reject H0, as in the non-
          randomized test. If Ns = yc, calculate a rejection probability P by subtracting 1 - a from the sum on the left-hand
          side of the inequality (with n = Ns) and dividing the difference by the summation's last term
Then reject H0 with probability P.

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2552     Then the critical net count Sc equals yc - NB (ts I tB). (Note that Inequality 19.113 is intended for
2553     use when NB is small.) Table G.4 in Appendix G lists critical values ^ for a = 0.01 and 0.05 and
2554     for integral values of the count time ratio tB I ts ranging from 1 to 5.

2555     Figure 19.17 shows the type I error rates for the nonrandomized exact test. (The type I error rate
2556     for the randomized version of the test equals 0.05 everywhere.)
                          P
                       0.15 H
                        0.10-
                        0.05--
                        0.00
                            0           5           10          15           20    E(NB)

                    FIGURE 19.17 — Type I error rates for the nonrandomized exact test
2557

2558
2559
2560

2561
2562
2563
                                      EXAMPLE

Problem: A 6000-s blank measurement is performed on a proportional counter and 108 beta
counts are observed. A test source is to be counted for 3000 s. Estimate the critical value of the
net count when a = 0.05.
Solution: Formula A gives the result
                                      = 1.645
                                             \
                                     108
                                      = 14.8 counts.
Formula B is not recommended.
3000

6000;
1 +
3000
6000,
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2564
2565
2566
2567
2568
2569

2570


2571
 Formula C gives the result
                          2t
zl
-a
^
*lVs tS
2 -° /
(i + 's)
V 'SJ
                       _1.6452(3000)+1615
                            2(6000)
                         15.5 counts.
                                      1.6452(3000)2   1ft_|3000
                                      	 + 10o  	
                                        4(6000^
6000
 The Stapleton approximation (with d= 0.4) gives the result
= 0.4 M222-1
     ^ 6000
= 15.6 counts.
                                                              (108 +0.4)
                                                                 3000
                                                                 6000,
               1 +
3000

6000;
 The exact test gives the result yc = 70 counts (the entry in Table G.4 for a = 0.05, tBl ts = 2,
 and NB = 108), which implies that

                         SC = 1Q- (108)(3000 / 6000) = 16 counts.
COMPARISONS
2572     Although Formula A gives the highest type I error rates of all the formulas described above in the
2573     pure Poisson counting scenario, it is the formula that can be adapted most easily for dealing with
2574     interferences. It can also be modified to reduce the very high type I error rates at low blank levels
2575     (by adding 1 or 2 to the number of blank counts NB under the radical). Formula B cannot be
2576     recommended. When the pure Poisson model is valid, Formula C gives better results than either
2577     A or B, but the Stapleton approximation appears to give the most predictable type I error rates of
2578     all.  Nicholson's exact test is the most complicated of the tests and requires either software or
2579     lookup tables to be practical, but it is the only one of the tests whose type I error rate is guaran-
2580     teed not to exceed the chosen significance level. Achieving the chosen significance level exactly
2581     appears to require the randomized version of Nicholson's test.
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2582     19D.3 Calculation of the Minimum Detectable Concentration

2583     The minimum detectable concentration, or MDC, was defined earlier as the concentration of
2584     analyte, XD, that must be present in a laboratory sample to give a probability 1  - P of obtaining a
2585     measured response greater than its critical value. Equivalently, the MDC is defined as the analyte
2586     concentration XD that satisfies the relation
                                                    = xD\=$                              (19.114)

2587     where the expression Prf^ < Sc \ X = XD] may be read as "the probability that the net signal S
2588     does not exceed its critical value Sc when the true concentration Xis equal to XD"

2589     19D.3.1  The Minimum Detectable Net Instrument Signal

2590     The MDC may be estimated by calculating the minimum detectable value of the net instrument
2591     signal, SD, and converting the result to a concentration. The minimum detectable value of the net
2592     instrument signal is defined as the mean value of the net signal that gives a specified probability
2593     1 - P of yielding an observed signal greater than its critical value Sc.  Thus,

                                                    = ^]=P                              (19.115)
2594     where S denotes the true mean net signal.

2595     19D.3.2 Normally Distributed Signals

2596     If the net signal S is normally distributed and its estimated standard deviation 60 under H0 is
2597     determined from a statistical evaluation with v degrees of freedom (e.g., n = v + 1 replicate blank
2598     measurements), then the critical value of S is

                                           SC  = tl-a(V^0                                  (19.116)

         ___                     /v
2599     Then, if the variance of S is constant at all concentrations, the minimum detectable value of the
2600     signal is given by
                                            ^ = 5o,p,vGo                                   (19.117)
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2601     where 5^p v denotes the non-centrality parameter of the non-central ^-distribution with v degrees
2602     of freedom. The parameter 5^p v is such that

                                         'p'(vAv) = 'i-a(v)                                (19.H8)
2603     where ^(^5^) denotes the p-quantile of the non-central ^-distribution. The non-centrality
2604     parameter 5^p v may be approximated by
2605     which is based on an approximation for the non-central t distribution function (NBS 1964).
2606     When a = P = 0.05 and v > 4, the non-centrality parameter is also approximated adequately by
2607     f0-95(v) x 8v / (4v + 1) (Currie 1997).

2608     Conceptually the standard deviation 60 used to calculate the critical value Sc is only an estimate
2609     and therefore can be considered a random variable. If it were the true standard deviation, the cor-
2610     rect multiplier used to calculate Sc would be zl _ a, not tl _ a(v). However, the standard deviation
2611     used to calculate SD is, conceptually at least, the true standard deviation o0, even if its value is not
2612     known exactly. The true standard deviation may be estimated by 60, but since the estimator 60 is
2613     biased,  a correction factor should be used for v  less than about 20. An unbiased estimator for o0 is
2614     G0/c4, where
                                               Wv + i\ r
                                                       2
                                                       -                                 (19.120)
                                                       v
2615     and where F denotes the gamma function (NBS 1964). The gamma function is easily computed
2616     in software (Press et al. 1992), but c4 is also approximated well by 4v / (4v + 1), and values of c4
2617     are commonly tabulated in references for statistical quality control (whence the notation c4 is
2618     borrowed). Then SD is estimated by

                                            c    s   6o
                                            ^ =  5o,Bv—                                  (19.121)
                                                  *p,
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                     TABLE 19.5 — Bias factor for the experimental standard deviation
V
1
2
3
4
5
6
7
8
9
10
C4
0.79788
0.88623
0.92132
0.93999
0.95153
0.95937
0.96503
0.96931
0.97266
0.97535
V
11
12
13
14
15
16
17
18
19
20
C4
0.97756
0.97941
0.98097
0.98232
0.98348
0.98451
0.98541
0.98621
0.98693
0.98758
V
21
22
23
24
25
26
27
28
29
30
C4
0.98817
0.98870
0.98919
0.98964
0.99005
0.99043
0.99079
0.99111
0.99142
0.99170
V
31
32
33
34
35
36
37
38
39
40
C4
0.99197
0.99222
0.99245
0.99268
0.99288
0.99308
0.99327
0.99344
0.99361
0.99377
2619     which is approximately 2 tOS5(v)60, or 2SC, when a = P = 0.05 and v > 4. Values of c4 for v = 1 to
2620     40 are listed in Table 19.5.
2621     Lower and upper confidence limits for SD may be calculated using the equations
2622
2623

2624
2625
                     , lower  "o,p,v
                                                 and
                                               Cupper = 5o,P,v
              (19.122)
where ^(v) denotes the/?-quantile of the chi-square distribution with v degrees of freedom and y
denotes the desired confidence coefficient (see Table G.3 in Appendix G).
                 /*,
If the variance of S is not constant but increases with the mean signal S, the minimum detectable
net signal is determined implicitly by the equation
                                            S
                                             D
                                             JD>
                                                                                         (19.123)
                                                JD
2626     where CD denotes the standard deviation of S when S = SD. An iterative algorithm, such as the
2627     one shown below, may be needed to solve the equation for SD.
2628
          Set GO =
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         Measurement Statistics
2629
2630
2631
2632
2633
2634
2635
2636
2.
3.
4.
5.
6.
7.
8.
9.
Se, SD
repeat
Set c
Find
= WVK

/ __2/ o 1 o o \
D ~ \ \ 1 ~ jD-'
the value of 5 such that fy(v, 5) = ^ _a(v) o0 / CD
Set h = SD
Sett
until \l
output
1D = dcD
>D - h \ is sufficiently small
the solution SD
2637     The value of the non-centrality parameter 5 in Step 5 may be approximated by


                                                                 t' = t ,re(v) —          (19.124)
i-LUz.
   4V;
                                                       2v
2638     When G0 is determined by any means other than a statistical evaluation, SD must be calculated
2639     differently.

2640     19D.3.3 Poisson Counting

2641     Another equation for SD, which was described in Section 19.7.2.2, is

                                                       77^n)                           (19.125)
2642     where Sc = zl_ ao0 and c2(S \S = SD) denotes the variance of the measured signal S when the true
2643     mean signal S equals SD. This equation is the basis for formulas that are commonly used for SD
2644     when the Poisson-normal approximation is assumed. Regardless of whether the signal follows
2645     the pure Poisson model or has non-Poisson variance, the function o2(^ | S = SD) can often be
2646     expressed in the form
                                       c\S) = aS2 + bS + c                              (19.126)
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2647     where S denotes the true mean net signal and the constants a, b, and c do not depend on S. In this
2648     case, the minimum detectable net signal is given approximately by
                                                  bS
                                                                 aS
              (19.127)
                        9
         where /„ = 1 - zl_»a.
2649

2650     Equation 19.125 is often used even when Sc is calculated using one of the formulas presented
2651     above for low-background Poisson counting, with RB tB substituted for the blank count NB, but in
2652     this case SD may be underestimated because of the fact that the calculated value of Sc varies from
2653     measurement to measurement. One option for obtaining a more conservative estimate of SD is to
2654     substitute a conservative value of Sc, which will be denoted here by [Sc]. For Poisson counting,
2655     one method of obtaining [Sc] is to use the value of Sc calculated from the largest blank count NB
2656     likely to be observed, given the assumed mean blank count rate RB (e.g., use Table 19.4 with RB tB
2657     replacing RB ts and NB replacing_yc in the column headings). To calculate SD, one may substitute
2658     [Sc] for Sc in Equation 19.127.

2659     Note that [Sc] is not used to make detection decisions. It is used only to calculate SD.

2660     For example, suppose a = P = 0.05, the assumed mean blank count rate is RB = 8 x icr4 cps, and
2661     the blank count time is tB = 6000 s. Then RBtB = 4.8 counts. Using Table 19.4, one finds 4.8 in
2662     the first column between 4.695  and 5.425,  and reads the value 9 from the second column. So, 9 is
2663     the largest value ofNB likely to be observed when measuring a blank. Now, if Stapleton's
2664     approximation is used to calculate Sc when making a detection decision, the value of [Sc] used
2665     to calculate SD is given by the following equation.
2666
                 [Sc\ = 0.4
^-1
                                      1.6452
                                                     + 1.645
                                                            ^
(9+0.4)-!
        tn
1+-£|
              (19.128)
2667     So, if ts = tB, then [Sc] = 8.48 counts. lfRBtB (4.8 counts) were used as the blank count instead,
2668     [Sc] would be only 6.66 counts.
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2669
         Measurement Statistics
         PURE POISSON COUNTING
2670     When the pure Poisson model is assumed and Formula A is used for the critical value, if the
2671     critical value, Sc, is determined from a sufficiently large total number of counts and if a = P, the
2672     minimum detectable net signal SD is given by the following simple equation.
                                          SD=Zl-&+2S<
                                                       C
                                                                                        (19.129)
2673
2674
2675
         More generally, if Formula A or C is used to calculate the critical net count Sc, then SD may be
         determined from Equation 19. 127 using the following values for a, b, and c.
2676     The resulting formula for SD is
                          SD = SC
2
1 P
2
+ZIA
zi%+ +p,d.^
4 + c +
sn j
                                                                                        (19.130)
2677     As previously noted, counting data never follow the Poisson model exactly. Variable factors such
2678     as source geometry and placement, counting efficiency, and subsampling variance tend to
2679     increase a, while interferences and background instability tend to increase c.

2680     THE STAPLETON APPROXIMATION

2681     When the Stapleton approximation is used for Sc, the minimum detectable net count SD may be
2682     calculated using Equation 19.130, but when the Poisson model is valid, a better estimate is given
2683     by the formula
                                                                                        (19.131)
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2684     Equation 19.131 also gives a better approximation of SD even when Formula C is used for the
2685     critical value as long as the ratio of count times tB I ts is not too far from 1 (see Table 19.6). It is
2686     recommended by ISO 11929-1 (ISO 2000a) in a slightly different but equivalent form.

2687     When a = P = 0.05 and tB = ts, the preceding equation becomes

                                       SD = 5.41 + 4.65jR^Ts                              (19.132)


2688     The Stapleton approximations for Sc and SD give very predictable type I and type II errors when
2689     the only measurement variance is Poisson.

2690     When the Poisson model is incomplete because of excess relative variance (a > 0), one can use
2691     Equation 19.127 with appropriate values for a, b, and c. However, a somewhat better estimate of
2692     SD can be obtained. The calculation is more involved.
                                              2a/2
2693     where
                                       -2aV + W^_4oV_^                     (19m)
2694            a' = 1 -
2695
                          2
                         -1-f
1 + —
                                  2
                                           t*
2696            c' = Rf. + -^	L£  !+_£
                            ts}
                        1 + —
2697     PRECISE CALCULATION OF SD

2698     When the Poisson model is valid, the mean blank count rate RB and the analyte detection criteria
2699     completely determine SD. So, in principle, a computer program can be written to calculate SD
2700     precisely. The calculation is most easily described when the critical net count is expressed in
2701     terms of NB but not Ns (e.g., Sc as defined by Formulas A-C, the Stapleton approximation, and
2702     the exact test). Then, at any specified value S of the mean net signal, the power of the detection
2703     test can be computed using the expression:
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         Measurement Statistics
                                                          BBB^
                     Power = 1 - ^(-RB(ts + tB}-S) E ^~  E                       (19-134)
2704     where yc(n) denotes the value of yc (or Sc + NB ts I tB) when NB = n. Terms of the infinite sum
2705     must be accumulated only until the cumulative Poisson probability, e   B BY."m=0(RBtB)m /ml,
2706     approaches 1. Given a software procedure to compute Equation 19.134, the value of SD may be
2707     determined using an iterative algorithm, such as Newton's method or bisection, which calculates
2708     the power at various trial values of S until the correct value is found where the power equals
2709     1 - p (e.g. see Burden and Faires  1993).

2710     A procedure of the type described above generated the true values of SD for Table 19.6, which
2711     shows both the estimated and true values of SD obtained when Formulas A and C and the
2712     Stapleton approximation are used for the critical value. The estimated values of SD in this table
2713     are based on values of Sc calculated using the true mean net count, not the upper bound [NB]. The
2714     use of [NB] would produce larger estimates.

2715     PRECISE CALCULATION OF XD

2716     Suppose the analyte concentration X is calculated by dividing the net signal S by the sensitivity A,
2717     where A varies considerably or there is considerable subsampling variance, but the signal is
2718     otherwise adequately described by the Poisson model. If one can assume that A has a particular
2719     distribution, such as a rectangular or triangular distribution, then it is possible to calculate XD pre-
2720     cisely in software, although the mathematics is less straightforward than that needed to calculate
2721     SD in the preceding section.  At any specified concentration x, the detection power equals

                                                oo  (R  t }"  M")J
                              Power = l-RBts + (Mx + 8)x) -Pfc+l,RBts + (^ - 8)x)
2724     where P(-, •) denotes the incomplete gamma function. Other combinations of the incomplete
2725     gamma function appear when different polygonal distributions are assumed (e.g., triangular).

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                                                                           Measurement Statistics
                           TABLE 19.6 — Estimated and true values of SD (tB = ts)
Mean Blank
Count
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Formula A
Estimated True
2.706
7.358
9.285
10.764
12.010
13.109
14.101
15.015
15.864
16.663
17.418
18.136
18.822
19.480
20.113
20.724
21.315
21.888
22.444
22.985
23.511
2.996
8.351
10.344
11.793
13.021
14.091
15.076
16.028
16.945
17.804
18.595
19.324
20.002
20.642
21.257
21.854
22.438
23.010
23.569
24.116
24.649
Formula C
Estimated True
7.083
9.660
11.355
12.719
13.894
14.942
15.897
16.780
17.605
18.383
19.120
19.823
20.496
21.142
21.764
22.366
22.948
23.513
24.062
24.596
25.116
6.296
10.095
12.010
13.551
14.826
15.930
16.902
17.785
18.614
19.406
20.170
20.903
21.602
22.267
22.900
23.506
24.091
24.657
25.206
25.738
26.252
Stapleton
Estimated True
5.411
10.063
11.991
13.469
14.716
15.814
16.807
17.720
18.570
19.368
20.123
20.841
21.527
22.185
22.819
23.430
24.020
24.593
25.149
25.690
26.217
6.296
10.095
12.010
13.551
14.826
15.930
16.902
17.785
18.614
19.406
20.170
20.903
21.602
22.267
22.900
23.506
24.091
24.657
25.206
25.738
26.252
2726     A precise power calculation of this type was performed to evaluate the results derived in the
2727     example in Attachment 19E assuming an approximately normal distribution for the subsampling
2728     error. The assumption of a normal distribution is nonsensical unless the relative standard devia-
2729     tion of A is small (because^ is positive), and in the latter case, the assumption of a triangular
2730     distribution, or even a rectangular distribution, gives approximately the same result.
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         Measurement Statistics
2731      19D.4  References

2732      Altshuler, Bernard, and Bernard Pasternack. 1963. Statistical Measures of the Lower Limit of
2733         Detection of a Radioactivity Counter. Health Physics 9: 293-298.

2734      Brodsky, Allen. 1992. Exact Calculation of Probabilities of False Positives and False Negatives
2735         for Low Background Counting. Health Physics 63(2):  198-204.

2736      Burden, Richard L., and J. Douglas Faires. 1993. Numerical Analysis, 5th ed. PWS Publishing
2737         Company, Boston, MA.

2738      Currie, Lloyd A. 1968. Limits for Qualitative Detection and Quantitative Determination:
2739         Application to Radiochemistry. Analytical Chemistry 40(3): 586-593.

2740      Currie, L.A.  1997. Detection: International Update, and Some Emerging Di-lemmas Involving
2741         Calibration, the Blank, and Multiple Detection Decisions. Chemometrics and Intelligent
2742         Laboratory Systems 37: 151-181.

2743      International Organization for Standardization (ISO). 1997. Capability of Detection - Part 1:
2744         Terms and Definitions. ISO 11843-1. ISO, Geneva, Switzerland.

2745      International Organization for Standardization (ISO). 2000a. Determination of the Detection
2746         Limit and Decision Threshold for Ionizing Radiation Measurements - Part 1: Fundamentals
2747         and Application to Counting Measurements without the Influence of Sample Treatment. ISO
2748         11929-1. ISO, Geneva, Switzerland.

2749      International Organization for Standardization (ISO). 2000b. Determination of the Detection
2750         Limit and Decision Threshold for Ionizing Radiation Measurements - Part 2: Fundamentals
2751         and Application to Counting Measurements with the Influence of Sample Treatment. ISO
2752         11929-2. ISO, Geneva, Switzerland.

2753      International Union of Pure and Applied Chemistry (IUPAC). 1995. Nomenclature in Evaluation
2754         of Analytical Methods Including Detection and Quantification Capabilities. Pure and Applied
2755         Chemistry 67(10): 1699-1723.

2756      Lochamy, Joseph C. 1976. The Minimum Detectable Activity Concept. NBS Report No. NBS-
2757         SP456, National Bureau of Standards, Gaithersburg, MD.
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                                                                         Measurement Statistics
2758     National Bureau of Standards (NBS). 1964. Handbook of Mathematical Functions. Applied
2759        Mathematics Series 55, National Bureau of Standards, Gaithersburg, MD.

2760     Nicholson, W.L. 1963. Fixed Time Estimation of Counting Rates with Background Corrections.
2761        AEC Research and Development Report HW-76279.

2762     Nicholson, W.L. 1966. Statistics of Net-counting-rate Estimation with Dominant Background
2763        Corrections. Nucleonics 24(8): 118-121.

2764     Nuclear Regulatory Commission (NRC). 1984. Lower Limit of Detection: Definition and
2765        Elaboration of a Proposed Position for Radiological Effluent and Environmental
2766        Measurements. NUREG/CR-4007. NRC, Washington, DC.

2767     Press, William H., et al. 1992. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed.
2768        Cambridge University Press, New York, NY.

2769     Stapleton, James H. 1999. Personal correspondence. Department of Statistics and Probability,
2770        Michigan State University.

2771     Strom, Daniel J., and Paul S. Stansbury. 1992. Minimum Detectable Activity When Background
2772        Is Counted Longer than the Sample. Health Physics 63(3): 360-361.

2773     Turner, James E. 1995. Atoms, Radiation, and Radiation Protection., 2nd ed. John Wiley and
2774        Sons, New York, NY.
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2775                                     ATTACHMENT 19E
2776                                     Example Calculations


2777     19E.1  Overview

2778     The following example shows how to calculate the combined standard uncertainty, critical net
2779     signal, minimum detectable concentration (MDC), and minimum quantifiable concentration
2780     (MQC) for a typical radioanalytical measurement.

2781     19E.2  Sample Collection and Analysis

2782     A soil sample is analyzed for 239/240pu and 238Pu by alpha spectrometry.

2783        •   The sample is collected on July 10, 1999, at 11:17 am EDT, and shipped to a laboratory
2784            for analysis.

2785        •   The entire laboratory sample is dried, weighed, and ground to a maximum particle size of
2786            0.2 mm. The dry weight is approximately 2 kg.

2787        •   The prepared sample is homogenized, and a test portion is removed by increments. The
2788            documented procedure requires a test portion of approximately 0.5 g.

2789        •   The test portion is weighed and the mass is found to be 0.5017 g. The standard
2790            uncertainty of the mass, including contributions from repeatability, linearity, day-to-day
2791            variability, and the balance calibration, is estimated to be 2.2 x 10~4 g.

2792        •   A 1-mL aliquant of 242Pu tracer is added to the test portion. The concentration of the
2793            tracer solution has previously been measured as 0.0705 Bq ml/1 with a standard
2794            uncertainty of 0.0020 Bq ml/1 on June 30,  1999, at 11:00 am CDT. The aliquant is
2795            dispensed by a pipet, whose dispensed volume has a combined standard uncertainty
2796            previously determined to be 0.0057 mL.

2797        •   After fusion, dissolution, chemical purification, and coprecipitation, a test source on a
2798            stainless steel planchet is prepared for counting in an alpha spectrometer.

2799        •   The efficiency of the spectrometer for the chosen geometry, which is assumed to be con-
2800            stant over the range of alpha energies of interest, has previously been measured as 0.2805
2801            with a standard uncertainty of 0.0045.
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         Measurement Statistics
2802         •   A blank source is counted in the spectrometer for 60,000 s. The blank consists of a filter
2803            mounted on a planchet in the same geometry as the test source. In the 242Pu region of
2804            interest, 2 counts are measured; and in the 238Pu region of interest, 0 counts are measured.
2805            Historical data for this and similar spectrometers at the laboratory indicate that the back-
2806            ground is stable between measurements.

2807         •   The test source is placed in the spectrometer and counted for 60,000 s, beginning on
2808            August 24, 1999, at 4:47 pm CDT. In the 242Pu region of interest, 967 counts are meas-
2809            ured; and in the 238Pu region of interest, 75 counts are measured.

2810         •   It is assumed that there is no detectable plutonium in the reagents; however, a method
2811            blank is analyzed simultaneously using a different spectrometer to check for contamina-
2812            tion of reagents and glassware.)

2813     In this example the measurand will be the mean activity concentration,  or massic activity, of
2814     238Pu in the 2-kg sample (dry weight) at the time of collection.

2815     19E.3 The Measurement Model

2816     The following notation will be used:

2817         Ms    is the mass  of the test portion (0.5017 g)
2818         T      is the tracer activity concentration (0.1205 Bq ml/1)
2819         Vt      is the tracer aliquant volume (1 mL)
2820         tB      is the blank count time (60,000 s)
2821         ts      is the count time for the test source (60,000 s)
2822         Ns     is the total count in a region of interest when the source is counted (238Pu or 242Pu)
2823         NB     is the count in a region of interest when the blank is counted (238Pu or 242Pu)
2824         R      is the fraction of alphas with measured energy in the region  of interest (238Pu or 242Pu)
2825         D      is the decay-correction factor (238Pu or 242Pu)
2826         £      is the alpha counting efficiency
2827         Y      is the plutonium chemical yield fraction
2828         Fs     is the subsampling factor (estimated as 1.00 with a Type B standard uncertainty of
2829                0.05)
2830         X      is the 238Pu activity concentration in the dried laboratory sample, decay-corrected to
2831                the time of collection

2832     Subscripts will be used to distinguish between quantities associated with particular regions of
2833     interest (238Pu or 242Pu).

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                                                                             Measurement Statistics
2834     The decay-correction factor for either isotope is calculated as follows:

                                                       ..    ~  S
2835                                         D = e ^ID ^—^ -
2836     where X is the decay constant (s"1) and tD is the time between collection and the start of the
2837     counting measurement (3,91 1,400 s). Since \ts is small for both isotopes in this example, D may
2838     be approximated accurately by
2839

                         238       242
2840     The half-lives of 238Pu and 242Pu are 87.75 y and 375,800 y, respectively. So,
2841                  £>,,, = exp 	—	  3,911,400 + ^^    = 0.9990
                        238      ( 87.75 -365.25 -86,400 (               2  JJ

2842     and £>242 = 1.000.

2843     Dead time is negligible in this example; so, no distinction is made between the real time and the
2844     live time. If the real time were greater than the live time, the correction for decay during the
2845     counting period would be based on the real time.

2846     The fraction of alphas of each isotope actually measured in the nominal region of interest is esti-
2847     mated to lie between 0.96 and 1.00. A rectangular distribution is assumed, with center at 0.98
2848     and half-width equal to 0.02. Then the Type B standard uncertainties of R23S and R242 are


2849                                  «(&>«) = ^949) = -^ = 0.01155
                                         ZW    v  2.0,2.'    r-
                                                        V3

2850     The chemical yield of plutonium is calculated using the model

                                             l-T? 0,10 / '?  -L V D o/)o / I-r.
                                              - * c
2851                                      Y =   S'
                                                 TVtzR242D242
2852     Then the following model is used to estimate the measurand.
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         Measurement Statistics
2853
2854     When numerical values are inserted,

                           967 / 60,000 - 2 / 60,000
                      7 =
                                  = 0.82990
                     X =
                          0.0705 •  1 • 0.2805 • 0.98 • 1

                                    75/60,000 -0/60,000
                          0.5017 • 0.82990 • 0.2805 • 0.98 • 0.9990 • 1.00

                       (or 10.932 Bq kg"1)
                                                  = 0.010932 Bqg
                                                                                   -i
2855     19E.4  The Combined Standard Uncertainty

2856     The efficiency £ effectively cancels out of the equation for X, because it is multiplied by the yield
2857     7 and also appears as a factor in the denominator of the expression for 7 (see also Section
2858     19.6.5). Therefore, the uncertainty of £ has no effect on the uncertainty of X. When using the
2859     uncertainty propagation formula to calculate the combined  standard uncertainty of X, one might
2860     include a covariance term for w(7,£) to account for the relationship between the measured values
2861     of 7 and £, but it is simpler to treat 7£ as one variable. Application of the uncertainty propagation
2862     formula (Section 19.5.3) to the equations above then gives  the following:
2863
WE) =
                                  2) /tS+U
                                               242
                                )
                                   1  2.  2,    2,
                                  T Vt R242D2A
                                            242
                                                        (7£)2
U2(p>   U\V)    u\R242)
                                               T2
                  R,
                                                                                   242
2864
              u'(X) =
                                                         M
                                                                          u\R
                                                                               238^
                                                          Rr
                                                                              38
2865     All other input estimates are assumed to be uncorrelated.

2866     Note that u2(Fs) is the subsampling variance associated with taking a small test portion
2867     (0.5017 g) from a much larger sample (2 kg). A default value is used here for this variance
2868     component. However, Appendix F provides more information about subsampling errors and
2869     methods for estimating their variances.
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                                                                            Measurement Statistics
2870     Since extremely low counts are possible, each Poisson counting variance in this example will be
2871     estimated by the number of observed counts plus one (see Section 19.5.2.2 and Section 19C.3 of
2872     Attachment 19C). So, for example, ^(TVg 238) equals one, not zero.

2873     Table 19.7 summarizes the input estimates and their standard uncertainties.

                         TABLE  19.7 — Input estimates and standard uncertainties
INPUT
QUANTITY
Ms
T
v,
ts
ts
^,238
^,242
Ng, 238
NS,242
^ 238; ^242
8
Fs
^238
Dw
INPUT
ESTIMATE
0.5017
0.0705
1.0000
60,000
60,000
0
2
75
967
0.98
0.2805
1.00
0.9990
1.0000
STANDARD
UNCERTAINTY
2.2 x 10~4
0.0020
0.0057
Negligible
Negligible
1
1.73
8.72
31.1
0.01155
0.0045
0.05
Negligible
Negligible
MEASUREMENT
UNIT
g
BqmL '
mL
s
s
counts
counts
counts
counts
none
none
none
none
none
TYPE OF
EVALUATION
Combined
Combined
Combined
B
B
B
B
B
B
B
Combined
B
B
B
2874     Other possible sources of uncertainty in alpha spectrometry measurements include the following:

2875         •   uncertainties in half-lives and decay times
2876         •   spillover and baseline interferences caused by poor peak resolution
2877         •   incomplete equilibration of tracer and analyte before chemical separation
2878         •   changing instrument background
2879         •   dependence of counting efficiency on alpha energy

2880     These uncertainties are evaluated as negligible in this example. Uncertainties associated with
2881     half-lives and decay times are negligible, because the decay times in the example are much
2882     shorter than the half-lives; but in practice one should confirm that any other uncertainties are
2883     small enough to be neglected.
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         Measurement Statistics
2884     When numerical values are inserted into the formulas

                  -  968 760.000^ 3/60.000*
                                                         2805)2
                     0.07052- 12-0.982- I2                        0.0705      I        0.98

                  = 0.0001094007 = 0.010462

2886     and

2887          u 2(X) =          76 ' 60'00°2 + l ' 60>00°2
                      0.50172 • (0.82990 • 0.2805)2 • 0.982 • 0.99902
                              .
                                          0.50172     0.829902 • 0.28052     0.982     l.OO2

                    = 2.1926 x 10"6 = 0.00148082

2888     So, uc(X) = 0.00148 Bq g"1 or 1 .48 Bq kg"1. If the concentration is to be reported with an expan-
2889     ded uncertainty calculated from the combined standard uncertainty uc(X) and a coverage factor
2890     k = 2, the result should appear (in SI units) as 10.9 ± 3.0 Bq kg"1 (dry weight).

2891     19E.5  The Critical Net Count

2892     Chapter 19 discusses several methods for estimating the critical net count Sc. In this example, the
2893     observed blank count is  zero; so, the mean blank count is obviously very low, and nonnormal
2894     Poisson counting statistics may be assumed. Sections 19E.5.1 through 19E.5.4 below show how
2895     to apply the formulas discussed in Section 19D.2.2 for Poisson counting measurements,
2896     assuming a significance level of a = 0.05.

2897     19E.5.1 Formula A

2898     Formula A is not recommended when the blank count is extremely low, as in this example. How-
2899     ever, if Formula A is used, it gives the following estimate of the critical value of the net count.
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2900
2904




2905





2906
                                                                            Measurement Statistics
                                            •l-a.
                                                    '
                                                         ~
                                                        J.  l
                                           = 1.645V(0)(1)(2)
                                           = 0 counts




2901     Since the net count 75 exceeds the critical net count 0, the analyte 238Pu is considered "detected."



2902     19E.5.2 Formulae




2903     Using Formula C, one obtains
2
— + T
0, l-a
2tB ^
- L6452m + i<
2
= 2.71 counts
72 f2 f ( f\
^•1 n ' 2.71, the analyte is considered detected.



19E.5.3 The Stapleton Approximation




Using the  Stapleton approximation, the critical net count is calculated as follows.
                      Sr = 0.4  — - 1
                                         •l-a
                                                        •l-a,
                                                           \
                                                      VS,238
    ^ I     ^ 1
0.4)—  1 + —

    tD\     tD
                = 0.4(0)
                                   i
                                                    . _

                                         (2)+ 1.645^(0 +0.4)(1)(2)
                         = 2.82 counts
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         Measurement Statistics
2907     Since 75 > 2.82, the analyte is considered detected.

2908     19E.5.4  Exact Test

2909     When the exact test is used, the critical value of the source count NSt23S is the smallest nonnega-
2910     tive integer j/c such that
                                                                                           (19.144)
2911     First the right-hand side is calculated:
2912
t \ ^B 238 + 1
—   '
I 73 /
                                              =(0.95)(2)0 + 1 = 1.90
2913
2914


2915

2916
2917
2918
2919
2920


2921
2922
2923

2924
2925
Then, terms of the sum on the left-hand side are accumulated until the total is at least 1.90. The
iteration stops at k = 4, when the sum reaches 1.9375 (illustrated below).
k
0
1
2
3
4
kth Term
1
0.5
0.25
0.125
0.0625
Sum
1
1.5
1.75
1.875
1.9375
Thus, the critical value of the total count is_yc = 4, which may also be found in Table G.4 in
Appendix G. Since the observed count 7V5j238 =  75 exceeds the critical count, one concludes that
the sample contains a positive amount of 238Pu.

The critical net count Sc in this case is also 4, because the blank count is zero. Note that this
value of Sc is the most conservative of the critical values calculated in this example.
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2926     19E.6 The Minimum Detectable Concentration

2927     Assume the specified probability of a type n error at the minimum detectable concentration is
2928     p = 0.05. The following describes a conservative approach to the estimation of the nominal
2929     MDC for the analytical process.

2930     Let RB denote the mean blank count rate for the 238Pu region of interest. Suppose a total of 21
2931     counts are accumulated in the 238Pu region of interest during ten 60,000-s blank measurements.
2932     The estimated blank count rate is then


2933                                    RK = ——— = 3.5xlO~5cps
                                         B   600,000

2934     This estimate has a moderately large relative standard uncertainty (approximately 22%), but
2935     detection decisions are based on the results of shorter measurements (60,000 s, not 600,000 s),
2936     which will vary even more. So, a conservative upper bound [NB] will be used for the blank count,
2937     as suggested in Section 19D.3.2 of Attachment  19D. A method for calculating the critical gross
2938     count can be  adapted to calculate the largest value of the blank count that is likely to be observed
2939     given the assumption of a mean blank count rate of 3.5 x 10~5 cps. For the current problem, Table
2940     19.4 will be used, withRBtB replacing RB ts and [NB] replacing^ in the column headings. Since
2941     the value of RB tB is 2.1, which lies between 1.970 and 2.613, Table 19.4 shows that the required
2942     value is [NB]  = 5. Therefore, one expects the number of blank counts observed in 60,000 s (tB~) to
2943     be no greater than 5. So, the MDC will be calculated here using a critical value [Sc] based on the
2944     assumption of a blank count [NB] = 5.

2945     The overall sensitivity for the measurement process is  the product^ = tsMsYeR23SD23S. Since the
2946     most variable factor in this product by far is the chemical yield 7, a conservative lower bound for
2947     A may be found by estimating the p-quantile (5th percentile) of 7 and multiplying it by estimated
2948     values of the  other factors. Assume that historical data show that the 5th percentile of 7 is approx-
2949     imately 0.60. Then with the measured efficiency 0.2805, nominal test portion mass 0.5 g, and
2950     estimated values for the ROI fraction 0.98 and decay factor 0.999, the 5th percentile of A is esti-
2951     mated as
2952                    ap = a005 = (60,000)(0.60)(0.2805)(0.5)(0.98)(0.999) = 4943 g s

2953     The approximation formulas given in the chapter will be used and the results will be compared to
2954     the results obtained from  a precise power calculation using the value ap for the sensitivity and
2955     with the assumptions that the mean blank count rate is RB = 3.5 x 10~5 cps and that the subsamp-
2956     ling error is approximately normal.

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         Measurement Statistics
2957     The following values, which appear in several formulas, are calculated first.
2958
                           c=RBts  1 +—  =(3.5 x 1(T5) (60,000) (1 + 1) = 4.2 counts
                                                      oo
                          'p = 1 - *i -pq>,p = 1 - (1.645)2(0.05)2 = 0.993236
                         /c = (0.993236)(4.2) = 4.172 counts
                          p
2959
         19E.6.1 Formula A
2960     Assuming the net signal is approximately normal at the MDC, the value of the MDC may be
2961     approximated by
                         D
                                                                                        rj
2963     where [Sc] denotes the critical net count calculated using [NB] as the blank count and (pSamp
2964     denotes the subsampling variance, which also equals u\F^). When Formula A is used,  [Sc] is
2965
PC]=*l-a.
                                  [NB]—  1 +—  = 1.645^/(5)(1)(1 + 1) = 5.201 counts
2966
         and the minimum detectable concentration is
                                   1-
                                    ^
               (4943) (0.993236)             2
             = 0.0024 Bqg-1  or  2.4Bqkg^
                                                       ^
                                                          L645"
                                                                  5.201 +(0.05)2(5.201)2+4.172
2968     If the calculation is repeated with RBtB = 2.1 substituted for [NB] = 5 as the blank count used to
2969     calculate the critical value, the resulting value of XD is 1.9 Bq kg"1. A precise power calculation
2970     shows that the actual value of XD is 2.1 Bq kg"1.
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2971
2973
2974
2975
2976

2977

2978
                                                                           Measurement Statistics
         19E.6.2 Formulae
2972     Using Formula C, one obtains
                               [Sc} =
2
Zl-aTS
2tB
1.6452
2
HZl-a
^
;i) + 1.<
Zl a** tS( tS\
4t2B [*^tB(l J
515 L6452(l)2 + 10
^ 4
                                   = 6.727 counts
         Then the minimum detectable concentration is
          XD =
                       1
                                 6.727 + L645  + 1.645,
               (4943)(0.993236)            2
             = 0.0028 Bqg^1   or   2.8Bqkg^
                                                                + 6.727 +(0.05)2(6.727)2+4.172
         If the critical value is calculated using RB tB = 2.l instead of [NB] = 5, the resulting value of XD is
         2.3 Bq kg"1. A precise power calculation gives the value XD = 2.5 Bq kg"1.

         19E.6.3 The Stapleton Approximation

         When the Stapleton approximation is used, the critical net count is
                     [V]=0.4-1
                                                         "1 -a,
                                                            \
                                                                           ~       ~
                                                              ([NB] +0.4)^  1+^
                                    i
                          = 0.4(0)
                          = 6.758 counts
                                                     , _
                                          (2)+ 1.645^(5 +0.4)(1)(2)
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         Measurement Statistics
2979     Then the minimum detectable concentration may be approximated by
2980
2981
2982
2984

2985

2986


2987



2988

2989
          XD =
               Vp
              1
                                 6.758
                                         1.645:
      (4943)(0.993236) ^           2
    = 0.0028 Bqg"1  or  2.8Bqkg~1
                                      + 1.645
                                                      \
                  1.645^
                          6.758 +(0.05)2(6.758)2+4.172
WhenRBtB is substituted for [NB] in the calculation of the critical value, the resulting value of XD
is 2.4 Bq kg"1.
2983      Alternatively, the longer calculation given in Section 19D.3.3 of Attachment 19D may be used.
                      XD =
                           an
where
                       A
                          1 + _i = 5.2244
       c' =
                   i     i
                  7
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                                                                           Measurement Statistics
2992     19E.6.4 Exact Test

2993     When the exact test for detection is used, the critical gross count [yc] equals the smallest nonneg-
2994     ative integer n such that
2995
2996     The right-hand side of the inequality is found as follows

2997                                 RHS = (1 -0.05)(1 + 1)5 + 1 =60.

2998     The value of the left-hand side exceeds 60.8 when n equals 12
2999


3000

3001
5  2     5   4
                                                      5   4096
                                                               = «>.92
Therefore,
3002     So,
                                                            \
                [yc] = 12 counts    and     [Sc] = [yc] - [NB]— = 7 counts
3003
3004
                XD =
                     Vp
                             1
            (4943) (0.993236)
          = 0.0029 Bqg"1  or  2.9Bqkg'1

The result of the precise calculation is XD = 2.8 Bq kg"1.
2
4
6452
2
+ [Sc] +
+ 1.645
2 9
(Psampl^c] + *$C
1.645 „ ,_
4
\
05)2(7)2+4.172
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         Measurement Statistics
3005      19E.7 The Minimum Quantifiable Concentration

3006      For the purpose of this example, the MQC is defined to be the analyte concentration XQ at which
3007      the relative standard deviation of the measured result is 1 / kQ, where kQ = 10. Calculation of XQ
3008      requires knowledge of the relative standard deviation of the measured sensitivity when the true
3009      sensitivity is A = a005. Assume for this example that the relative standard deviation is 
102
^)
                      = 0.042 Bqg"1   or  42Bqkg
                                                 -i
3013      The MQC is substantially increased by the measurement variance of the sensitivity A and the
3014      subsampling variance. Without them the minimum quantifiable concentration would be only
3015      21 Bq kg"1. Note also that if either the relative standard deviation of A or the subsampling stan-
3016      dard deviation were 0.1 or more, the MQC would be infinite.
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3017                                     ATTACHMENT 19F
sois                                      Tests for Normality


3019     19F.1  Purpose

3020     Many common statistical hypothesis tests are based on the assumption that data are normally dis-
3021     tributed. Normality is often assumed by default, but, since  some tests may not perform well with
3022     data that are not normal, it is often important to check the validity of the assumption. Performing
3023     a test for normality cannot prove that data are normally distributed, but it may produce strong
3024     evidence that they are not.

3025     There are a number of tests for normality. Each test requires a random sample 7l3 72, ..., Yn from
3026     the distribution being checked. Whatever test is used, it is a good idea to plot the data for visual
3027     inspection. The normal probability plot described in Section 19F.2 is useful for this purpose.

3028     One of the most powerful tests for normality is the Shapiro-Wilk test, but it is difficult to imple-
3029     ment manually. EPA QA/G-9 recommends the Shapiro-Wilk test when the sample size n is less
3030     than 50, and either Filliben's statistic or the studentized range test when n > 50 (EPA 1998). In
3031     fact, if software for the Shapiro-Wilk test is not available, then Filliben's statistic may be used in
3032     all cases for which critical values are available. Instructions for computing and using Filliben's
3033     statistic are given in Section 19F.3.

3034     19F.2  Normal Probability Plots

3035     A normal probability plot is a graph of the observed quantiles of a data set against the correspon-
3036     ding quantiles of a standard normal distribution. If the data are normally distributed and the data
3037     set is large enough (more than about 10 values), the plotted points should lie approximately on a
3038     straight line. A preliminary decision  about the distribution of the data may be based on inspection
3039     of the graph. Normal probability plots may be produced manually, although software is generally
3040     needed to make plots of large data sets feasible.

3041     Manual construction of a normal probability plot is easier when pre-printed normal probability
3042     paper is available (see Figure 19.18 at the end of this attachment).

3043     To plot a set of data on normal probability paper, perform the following steps (EPA 1998).

3044        1.   Arrange the data in ascending order:

3045                                        7(1) * 7(2) * - * 7(||)

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3046
3047

3048
3049

3050

3051

3052
3053

3054
3055

3056

3057

3058
3059
3060


3061

3062

3063

3064

3065
3066
3067
  2.   Label the vertical axis to encompass all values between 7(1) (the minimum) and Y(n) (the
       maximum).

  3.   For each /' compute the cumulative frequency Ft of the value 7(!), which is defined as the
       number of values in the data set that are less than or equal to Y(f>. (Note that Ft > /'.)

  4.   Compute the horizontal coordinate Xt = Ft /(«+!)* 100% for each /'.

  5.   Plot each ordered pair (Xt, 7(0) at the appropriate location on the grid.

To plot a set of data on ordinary graph paper, perform Steps 1-3 above followed by Steps 4'-6'
below.

  4'.   For each /', determine the quantile X. = ZF/,+I)  of the standard normal distribution (see
       for example Table G. 1).

  5'.   Label the horizontal axis to encompass all values between Xl and Xn.

  6'.   Plot each ordered pair (Xt, 7(0).

The latter version of the procedure can be adapted to construct probability plots for other types of
distributions. Only Step 4' must change, since Xt is required to be a quantile of the appropriate
distribution.
                                       EXAMPLE
 Problem: Given the data set
                    123  122  124  118  118  122  121  117  125  119
 construct a normal probability plot using normal probability paper.
 Solution:
 Step 1      Sort the 10 values:
 Step 2
             117  118  118  119  121  122  122  123  124  125

Label the vertical axis to encompass the values from 117 to 125.
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                                                                              Measurement Statistics
3068

3069
(
(
>tep 3 For each /' compute the cumulative frequency Ft of 7(!) (see the table below).
>tep 4 For each i compute Xi . = (Fi .1 1 1) x 100% and plot (Xp 7(0) .
/
7(0
Fi
x>
1
117
1
9.1%
2
118
o
6
27.3%
3
118
o
5
27.3%
4
119
4
36.4%
5
121
5
45.5%
6
122
7
63.6%
7
122
7
63.6%
8
123
8
72.7%
9
124
9
81.8%
10
125
10
90.9%
The results are shown as a normal probability plot in Figure 19.18.
3070     19F.3 Filliben's Statistic

3071     Filliben's statistic is derived from the concept of the normal probability plot and is often called
3072     the "normal probability plot correlation coefficient." The use of the statistic makes the
3073     interpretation of the probability plot less subjective, although a visual inspection of the plot is
3074     still recommended. The procedure for calculating and using the statistic is given below (Filliben
3075     1975).

3076        1.   Choose the significance level a.

3077        2.   Arrange the data in ascending order.
3078
                                     < v   < ... < v
                                     -'~    -
3079
3080
3081
     Compute the quantities Y and S as follows.
4.    For / = 1,2, ...,«, compute
                                                            A
                                                               2=1
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         Measurement Statistics
3082



3083
3084


3085


3086
                   mt =
1 -0.51/B,
(7 -0.3 175)
0.51/B,
0.365),
/ = 1
7 = 2,3,.

i = n
      and letM, be the wrquantile of the standard normal distribution zm . (Table G.I in
      Appendix G may be interpolated to obtain approximate values of tnese quantiles.)
 5.   Compute cn = JE"=1M. .

 6.   Compute Filliben's statistic r (the normal probability plot correlation coefficient).
3087
                                              r =
3088       7.   Determine a critical value from Table G.5. If r is less than the critical value, conclude that
3089            the data are not normally distributed.
3090

3091

3092

3093

3094
3095

3096
                                      EXAMPLE
Problem: Determine whether the values
                  123  122  124  118  118  122   121   117  125   119
appear to come from a normal distribution. Use the significance level 0.05.
Solution:
Step 1     The significance level is specified to be a = 0.05.

Step 2     Sort the 10 values:

                       117  118  118  119  121   122   122  123  124  125
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                                                                        Measurement Statistics
3097
3098
3099
3100
3101
Step 3 Compute 7 = — £7 = 120.9 and
^ ^ 10 '
Step 4 For each / compute mt and M. = zm (see the table below). (The quantiles Mt in this
example have been computed without using Table G.I.)
Step 5 Con
Step 6 Con
Step 7 Tab!
0.97
/
Y
n
k

1
o 117
(, 0.06697
f. -1.499

ipute cn
= fi]°=1M* = /L515 = 2.752.
OwM. 22.37 _nn,n
cnS (2.752)(8.301)
e G.5 shows that the critical value for n = 10 and a = 0.05 is 0
9 > 0.917, the data appear to be normally distributed.
2
118
0.1623
-0.9849

3
118
0.2588
-0.6470

4
119
0.3553
-0.3711

5
121
0.4518
-0.1212

678
122 122 123
0.5482 0.6447 0.7412
0.1212 0.3711 0.6470

.917. Since
9 10
124 125
0.8377 0.9330
0.9849 1.499

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Measurement Statistics
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3102      19F.4 References

3103      Environmental Protection Agency (EPA). 1998. Guidance for Data Quality Assessment:
3104         Practical Methods for Data Analysis. EPA Q A/G-9, Q A97 Version. EP A/600/R-96/084,
3105         EPA, Quality Assurance Division, Washington, DC.

3106      Filliben, James J. 1975. The Probability Plot Correlation Coefficient Test for Normality.
3107         Technometrics 17(1): 111-117.
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3108                                     ATTACHMENT 19G
3109                              Balance Measurement Uncertainty


3110     19G.1 Purpose

3111     This attachment describes methods that may be used to evaluate balance measurement uncer-
3112     tainty. The relative standard uncertainty of a measurement made with a laboratory balance tends
3113     to be small if the balance is used properly, and it may even be considered negligible when com-
3114     pared to other uncertainties associated with radioanalysis (e.g., see Section 19.6.11, "Subsamp-
3115     ling")- However, one needs to know the performance limits of any measuring instrument. For
3116     example, the measurement uncertainty may actually be relatively large if a balance is used to
3117     weigh a mass that is too small for it. Establishing reasonable acceptance criteria for balance qual-
3118     ity control also requires an understanding of the sources of the measurement uncertainly.

3119     19G.2 Considerations

3120     Regardless of the methods used to evaluate balance measurement uncertainty, the results may be
3121     misleading unless the balance is well maintained and protected from external influences, such as
3122     drafts and sudden changes in pressure,  temperature and humidity.

3123     The appropriate method for evaluating the standard uncertainty of a mass measured using a bal-
3124     ance depends on the type of balance, including its principles of calibration and operation, but the
3125     uncertainty of the measured result generally has components associated with balance sensitivity,
3126     linearity, repeatability, and air buoyancy. Typically, the component associated with sensitivity
3127     includes the uncertainty of calibration and may include variability caused by changing environ-
3128     mental conditions, such as temperature. Other sources of uncertainty may include leveling errors
3129     and off-center errors, which should be controlled. Static electrical charges may also have an
3130     effect. Changes in mass (e.g., by absorption or evaporation of water) may be very significant for
3131     some materials.

3132     19G.3 Repeatability

3133     The repeatability of a balance is expressed as a standard deviation and is usually assumed to be
3134     independent of the load. It represents the variability of the result of zeroing the balance, loading a
3135     mass on the  pan, and reading the indication.

3136     Balance manufacturers provide specifications for repeatability, but a test of repeatability should
3137     also be part of the routine quality control for the balance (see ASTM 1993). The simplest pro-
3138     cedure for evaluating repeatability is to make a series of replicate measurements of a mass

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         Measurement Statistics
3139      standard under "repeatability conditions." Repeatability conditions require one balance, one
3140      observer, one measurement location, and repetition during a short time period. For each
3141      measurement, one must zero the balance, load the mass standard, and read the balance indication.

3142      A nested experimental design can also be used to evaluate both the repeatability and the day-to-
3143      day variability due to environmental factors. In this procedure, one makes a series of replicate
3144      measurements with the same mass standard each day for a number of days. Ideally one should
3145      use a mass near the capacity of the balance to obtain the most reliable estimate of day-to-day var-
3146      lability. The repeatability standard deviation is then estimated by
Sr = <
                                                   K   J
                                                         (x  -x}2                          (19.150)
                                                         \*-ki   *k>                           ^      '
                                          K(J-\
3147     where
3148         sr      is the estimated repeatability standard deviation
3149         J      is the number of repetitions per day
3150         K      is the number of days
3151         xkj     is they* result obtained on the A* day
3152         xk     is the average of all the results on the ^h day

3153     The repeatability standard deviation determined by this method is a Type A standard uncertainty
3154     with K (J -  1) degrees of freedom.

3155     19G.4 Environmental Factors

3156     Given the experimental data from the preceding section, one may estimate the variability due to
3157     environmental factors (day-to-day variability)  as follows.29
                                     5Env
              K

              k = l
3158     where
3159         slnv    is the estimated variance due to environmental factors
3160         x      is the grand average of all the data (the average of the xk)
           29 An F-test may be used to test for the presence of variance due to environmental factors. If this variance is zero,
         then the quantity Js? I s?, where a? denotes the experimental variance of the averages x., may be assumed to have
         an F-distribution with K- I numerator degrees of freedom and K(J - 1) denominator degrees of freedom.

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3161
3 162
3163
                                                                             Measurement Statistics
         If ^Lv is found to be positive, then sEw is estimated by its square root; otherwise, sEw is assumed
         to be zero. One estimates the relative component of standard uncertainty of a measured mass due
         to environmental factors by

                                            (PEnv = T-                                  (19.152)
3 164     where Mcheck is the mass of the standard used in the experiment.

3165     19G.5 Calibration
3166
3167

3168
3169
3170
3 171
3172
3173

3174
3175
3176
3 177
3178
3179
3180
3181

3 182
3183
         The uncertainty of calibration includes components associated with the mass standard or stan-
         dards, repeatability, and variability due to environmental factors.

         When a precision mass standard is used for calibration, the standard uncertainty of its mass is
         generally negligible. However, the uncertainty may be evaluated if necessary from the specified
         mass tolerance. For example, a  100-g ASTM Class-1  mass standard has a tolerance of 0.00025 g,
         which may be assumed to represent the half-width of a triangular distribution centered at zero
         (ASTM 1991). The standard uncertainty may be found by dividing this tolerance by ^6  and is
         approximately 0.00010 g, or 1.0 x  10~6 when expressed in relative terms.

         The total relative standard uncertainty of a measured mass due to calibration may be estimated as
         follows.
                                     
-------
         Measurement Statistics
3184     19G.6 Linearity

3185     The linearity of a balance should be specified by the manufacturer as a tolerance, aL, which repre-
3186     sents the maximum deviation of the balance indication from the value that would be obtained by
3187     linear interpolation between the calibration points. Routine quality control should ensure that the
3188     linearity remains within acceptable limits.

3189     The Eurachem/CITAC Guide: Quantifying Uncertainty in Analytical Measurement recommends
3190     that the linearity tolerance aL be treated as the half-width of a rectangular distribution and that aL
3191     therefore be divided by i/3 to obtain the standard uncertainty (Eurachem 2000). However, since
3192     the linearity error is likely to vary as a sinusoidal function of the load, the divisor y/2 may be
3193     more appropriate. So, the standard uncertainty due to linearity for a simple mass measurement
3194     may be evaluated as aL/^2. Whether one uses  /3  or the more conservative value \[2 depends
3195     partly on how conservative one believes the estimate of aL to be.

3196     19G.7 Air Buoyancy Corrections

3197     Air buoyancy corrections have not often been performed in radiochemistry laboratories, but they
3198     are necessary for a realistic estimate  of the standard uncertainty of a mass measurement,
3199     especially when the material being weighed has a low density. Failure to correct for air buoyancy
3200     when weighing water, for example, introduces a relative error of approximately - 0.1%, which
3201     may be much larger than the standard uncertainty of the uncorrected mass (e.g., when weighing a
3202     gram or more of an aqueous solution on a typical four-place analytical balance).

3203     When a buoyancy correction factor is used, the true mass is estimated as follows.

                                            m=I^B                                    (19.154)

3204     where
                                              1 - pAr/ pr
                                         B =     ^C7                                 (19.155)
                                              I-PA,M!PM
3205     and
3206         m     is the corrected value for  the mass of the material being weighed
3207         7Net    is the net balance indication
3208         B     is the buoyancy correction factor
3209         pM    is the density of the material being weighed
3210         pAM   is the density of the air at the time the material is weighed


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3211
3212
                                                                             Measurement Statistics
             pc    is the density of the calibration mass standard
             pA:C   is the density of the air at the time of calibration
3213     The standard uncertainty of B may be obtained as follows.
u\B) PA,C
B2

9 9
( V
Pc
1
PA^PA
PM _l
, p^M
pi
2

                                                                                          (19.156)
3214
3215
3216

3217
3218
3219
3220
3221
3222
3223
3224
3225

3226
3227
3228
3229
         Evaluation of this uncertainty requires estimates of pM, pc, p^^ and p^,c as well as their standard
         uncertainties and covariances. The covariance z/(pAC, pc) is usually zero or negligible, and
         u($AM, pM) also is usually negligible if the material being weighed is a solid.

         The density of air at any time (p^) depends on temperature, pressure, and humidity, as shown in
         the following equation.
                       p  -p
                        A    °
                                             P-(0.3783)(RH/100%)(PV  )
                                             - 1 - >± - ll^
                                                          760
                                                                                          (19.157)
         where
             pA
             p0
             T
             P
             RH
            P
              Vap
                   is the density of air
                   is the density of dry air at 0°C and 760 torr (mm of Hg)
                   is the temperature (°C)
                   is the barometric pressure (torr)
                   is the relative humidity (%)
                   is the vapor pressure (torr) of water at temperature T
         The vapor pressure, PV3p, is a nonlinear function of T, but it can be approximated by a linear
         function in the range of temperatures typically encountered in the laboratory. When this approxi-
         mation is made, the resulting equation for the air density (g ml/1) may be written as follows.
                                           aP-(KH)(bT-c)
                                               273.15 +r
         where

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3230
3231
3232
         Measurement Statistics
       a  =  4.64746 x 1(T4
       b  =  2.5211151 x i(r6
       c  =  2.0590571 x i(r5
3233     Then the standard uncertainty of pA is given by
                                                273.15 +T
                                                                                          (19.159)
3234
3235
3236
3237

3238
3239
The density of the calibration weight (pc) and of the solid or liquid material being weighed (pM)
also depend on temperature somewhat, but these temperature effects can usually be safely
ignored when calculating the uncertainty of the buoyancy correction factor, since temperature
affects the density of air much more than the density of a solid or liquid.

The effect of pressure on the density of the material being weighed can also usually be neglected.
For most practical purposes, the compressibility of a solid or liquid  can be considered to be zero.
3240
3241
3242
3243
3244
3245
3246
3247
3248
EXAMPLE

Suppose the density of the weighed material, pM, is 0.5 g ml/1 with a tolerance of 0.2 g ml/1,
assumed to represent the half-width of a triangular distribution. The density of the calibration
mass standard, pc, is 7.850 g ml/1 with a tolerance of 0.025 g ml/1. Instead of measuring tem-
perature, pressure and humidity at the time of each measurement, the laboratory assumes the
following nominal values and tolerances:
Temperature 22.5 ±4 °C
Pressure 750 ±20 ton-
Relative humidity 50 ±20 %


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                                                                 Measurement Statistics
 Then
   PA,C
4,M
                   273.15 +T
             _ (4.64746 x I(r4)(750) - (50)((2.5211151 x !Q-b)(22.5) - 2.0590571
                                           273.15 +22.5
                          -3 _T -1
             = 1.1728x KT'gmL
 If each of the tolerances for T, P, and RH represents the half- width of a rectangular
 distribution, then
^(7) = ^. = -^,     u\P) = ^- = ^-,    and     M2(RH) =
                 4    16
                 — =
                 33
                                                          202   400
 So, the standard uncertainties of pAiC and pAM are
  U(PA.O> = M(
                                      pA)2u2(T) + (bT- c)2w2(RH)
                                     273.15 +T
                  _ ^a2(400/3)+ (6(50) + 1.1728 x !Q-3)2(16/3) + (b(22.5) - c)2(400/3)
                                              273.15 +22.5
                           -5 „ ™T -1
                  = 2.1 x 10"5gmL

 Then the buoyancy correction factor is
                   B =
              1 "P^c/Pc _ 1 - 1.1728x !Q-3/7.85
              I-PAM/PM    1 - 1.1728 x 1Q"3 / 0.5
                                                 = 1.00220
 The tolerances for the densities pc and pM are the half- widths of triangular distributions; so,

                                 °-252              2      0.22
                2/  x
              u (pr) =
                                            A
                                          and     u
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         Measurement Statistics
3260
3261
3262
3263
3264
3265
3266
3267
The covariances u(pAf, pc) and ii(pAM, pM) are zero in this example. So, the standard uncer-
tainty of B is
                  u(B) = B
                                '^c)/Pic + w2(Pc)/Pc
                          \
                       = 1.00220
                        (Pc
        .-I)2
r-l)2
(2.1 x l(T5)2
(1.1728x 10~3)2
[ 7.85
1 1.1728x 1Q~3
0.252/6
7.852
il2

(2.1 x l(T5)2 +0.22/6
(1.1728x 10~3)2 0.52
( °5 if
I 1.1728x 1Q~3 J
             = 3.87x 1Q-4

Thus, the buoyancy correction factor increases the result of the measurement by 0.22% and
generates an uncertainty component of approximately 0.04%. Note that this uncertainty
component is very small and would generally be considered negligible in the final result of a
radiochemistry measurement, but it may represent a significant fraction of the uncertainty of
the mass measurement.
3268     19G.8 Combining the Components

3269     When the balance is used to measure the mass, m, of an object placed on the pan, the mass is
3270     given by m = IB, and its standard uncertainty by
3271
3272
3273
3274
3275
3276
3277
3278
where
m
I
B

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                                                                           Measurement Statistics
3279     Often the balance is used to weigh material in a container. The balance is zeroed with the empty
3280     container on the pan and the container is then filled and weighed without being removed from the
3281     pan. In this case the linearity uncertainty component is counted twice, because the linearity error
3282     is assumed to vary between the two loads. (This assumption tends to be conservative when small
3283     masses are weighed.) Although the buoyancy factor for the tare and gross measurements may be
3284     different because of the different densities of the container and the material inside it, the only
3285     value of B that is used is the buoyancy factor for the material being weighed.

3286     In a third scenario, the empty container is weighed, removed from the pan, and then filled with
3287     material. The balance is zeroed again, and the filled container is weighed. Finally, the net mass is
3288     determined by subtracting the mass of the empty container from the total mass of the container
3289     and material. In this case both the linearity and repeatability components of uncertainty must be
3290     counted twice, because two distinct measurements are made. So, the corrected net mass and its
3291     standard uncertainty are
                                   22
                                                                                        (19.161)
                                    IT2  ,  2     2  x    2  0
                                    VNetOPcal + 
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 i      20 WASTE MANAGEMENT IN A RADIOANALYTICAL

 2                                   LABORATORY


 3     20.1  Introduction

 4     This chapter presents information on the management of radioactive waste generated during
 5     analytical processes. Federal, state, and local laws stringently regulate radioactive waste and
 6     impose severe consequences for violations. Management of waste in compliance with such
 7     regulations is, therefore, critical to the laboratory's sustained operation. Many—but not all—
 8     applicable regulations are addressed in this Chapter. A laboratory waste management plan that
 9     details procedures for the management of radioactive waste should be implemented before
10     radioactive materials are accepted for processing.

11     The following sections provide background information on managing radioactive waste  and
12     identifies issues that should be considered when preparing a laboratory-waste management plan.
13     Sections 20.2 through 20.5 of this chapter provide general guidance for managing waste in a
14     radioanalytical laboratory. Descriptions of the types of wastes that may be produced in a
15     radioanalytical laboratory are provided in Section 20.2. Section 20.3 reviews various approaches
16     that have been used to achieve effective lab oratory-waste management programs. Waste
17     avoidance and waste minimization programs are discussed in Section 20.4. Waste determination
18     and characterization are briefly reviewed in Section 20.5.  Some of the specific regulatory
19     requirements that apply to laboratory waste management are provided in Section 20.6. A
20     proposed outline for a waste management  plan is provided in Section 20.7, and Section 20.8
21     suggests a number of useful web resources related to the management of laboratory waste.

22     20.2  Types of Laboratory Wastes

23     The types of wastes generated and the waste management issues the laboratory may face are
24     determined by the analytical processes used in the laboratory and the characteristics of the
25     samples  analyzed. A laboratory that performs only one or two analytical processes may produce
26     only a few waste streams, whereas a multi-service laboratory that performs a variety of processes
27     may produce many waste streams. Waste streams produced by radioanalytical procedures can
28     include radioactive and non-radioactive wastes. A laboratory waste stream is defined as  all
29     wastes that are produced by a given analytical process. Table 20.1 provides a list of wastes that
30     may be generated by a laboratory.
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                          TABLE 20.1 — Examples of Laboratory-Generated Wastes
Waste
Dry solid waste
Aqueous waste
Organic solvent waste (used solvents,
analytical processes)
Acidic wastes
Waste Oil
Sample
Sample residue
Reagent chemicals
Sanitary waste
Sludge waste
Sharps
Example of Laboratory Generation
(Not Inclusive)
Gloves, glassware, pipette tips, plastic vials generated through
analytical processes
Solutions from analytical processes (filtrates, supernates, liquid
scintillation fluid)
Used solvents, de-greasers in cleaning operations, liquid
scintillation fluid
Solutions from analytical processes (filtrates, supernates)
Used oil from vacuum pumps
Unused sample from analytical process
Processed sample residue from analytical processes (precipitate,
filters, planchets)
Unused, expired, or surplus reagent chemicals
Sewage
Water treatment
Analytical processes (gas chromatography)
Various metal wastes/Radioactive sources | Laboratory equipment
Biohazardous waste
Toxic Substances Control Act (TSCA) waste
Radioactive waste
Resource Conservation and Recovery Act
(RCRA) hazardous waste
Mixed waste
Fecal, urine, blood-borne pathogen waste, animal carcasses, body
parts, tissues generated from bioassay, tissue or other biological
analyses
Analytical processes on polychlorinated bi-phenyls (PCB),
asbestos, chlorinated dioxin/furans
Analytical processes, radioactive standards, radioactive solutions,
dry waste, aqueous waste
Analytical processes generating characteristic and listed waste as
defined per 40 CFR 261 (Used solvents, reagent chemicals, acidic
waste, etc.)
Analytical processes generating any combination of RCRA waste
and radioactive wastes or TSCA waste and radioactive wastes
31     20.3  Waste Management Program

32     One source of guidance in assisting the laboratory in developing a waste management plan is
33     Profile and Management Options for EPA Laboratory Generated Mixed Waste (EPA, 1996).
34     This report reviews various approaches that have been taken to achieve effective laboratory waste
35     management programs. Much of the EPA report provides a review of articles and books that
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                                               Waste Management in a Radioanalytical Laboratory
36     detail the experiences of labs that manage radioactive wastes. This section draws significantly
37     from that report.

38     20.3.1 Program Integration

39     Successful waste management programs integrate important components, such as administrative,
40     regulatory requirements, training, record keeping, treatment, waste minimization, and prevention.
41     Individual management options, taken in isolation, may not be as effective as a more comprehen-
42     sive approach to waste management (EPA, 1996). Reviewing all aspects of waste management in
43     the laboratory should reveal the interactions among the component areas, providing insights that
44     allow improvements to the program as a whole without creating unknown negative effects.

45     20.3.2 Staff Involvement

46     All levels of management, scientists, and technicians should be actively involved in developing
47     and implementing the waste management program since each brings a valuable and unique
48     perspective to the waste management issue. Upper management must be committed to
49     maintaining a current and effective waste management plan because of the significant costs of
50     waste management and because of the serious civil and criminal penalties associated with non-
51     compliance. Program and project managers bring insight regarding issues, such as returning
52     samples to a site, waste management cost recovery, and data quality objectives. These managers
53     are also familiar with a full range of waste management alternatives. Laboratory environmental,
54     safety, and health personnel are essential to the process since they typically interface with
55     regulators to ensure that waste management practices are fully compliant. The input from
56     laboratory supervisors, scientists, and technicians is necessary because they generate waste at the
57     bench level and have first-hand process knowledge of how various waste streams are produced.
58     These individuals also have to implement the waste management plan on a daily basis and can
59     provide valuable feedback on improving the waste management system.

60     Waste generation planning is essential to proper waste management. Waste life cycle manage-
61     ment is a concept within the U.S. Department of Energy (DOE) Order 435.1  to reduce the
62     amount of radioactive waste generated. Waste life cycle is described as the life of a waste from
63     generation through storage, treatment, transportation, and disposal. For waste generated from a
64     new project or activity, consideration of the waste begins in the planning stage of the project or
65     activity.
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66     20.4  Waste Minimization

67     Waste avoidance actively reduces the amount of waste to be managed and is a critical part of a
68     waste management plan. An integrated approach to laboratory waste management necessarily
69     implies pollution prevention. The term pollution prevention has served as an all-encompassing
70     term for any technique, process, or procedure that minimizes waste. Broadly defined, pollution
71     prevention refers to activities that keep pollutants from being created in any media (i.e., control
72     pollution at the source). There are many strong benefits to pollution prevention including safety,
73     waste minimization, efficiency, regulatory compliance, reduction in liability, and cost reduction.
74     Pollution prevention techniques are a critical component of prudent laboratory practices and have
75     been incorporated into many laboratory waste management procedures (EPA, 1996).

76     Management options that address waste avoidance will result in the most substantial cost
77     savings. Two of the primary areas to review when seeking to minimize laboratory waste are the
78     processes and definitions that the laboratory uses to identify and categorize waste. A laboratory
79     may define and manage various categories of wastes and may develop a hierarchy of waste
80     streams similar to the  one described in Table 20.1. Properly categorizing waste at the point of
81     production will help to ensure health, safety, and regulatory compliance. This process also will
82     help to avoid unnecessary, costly, and inappropriate treatment,  storage, and disposal. However,
83     proper categorization  of waste streams can be difficult, requiring knowledge of the chemical and
84     radiological characteristics of the wastes, the production process, and a thorough understanding
85     of all-applicable regulations and regulatory guidance. Waste management regulations were
86     written primarily to regulate industrial production facilities and commercial storage, treatment,
87     and disposal facilities; their application to laboratories may not be readily apparent. The
88     laboratory waste management plan should require that each waste stream be identified prior to
89     production, so that waste minimization steps may be taken and production of unknown wastes
90     avoided.

91     The processes and definitions that a laboratory uses to determine that a waste is radioactive or
92     non-radioactive have a great influence on the amount of radioactive waste that a laboratory must
93     manage. The regulations offer little or no guidance for establishing that a waste is non-
94     radioactive, therefore  it may be up to the laboratory to make this determination. Laboratory
95     management should develop clear guidelines to make this determination. The guidelines must
96     comply with requirements specified by the agency that issues the laboratory's license for
97     radioactive materials since waste considered non-radioactive in one state may be considered
98     radioactive in another.
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 99      Once the waste has been properly categorized (either through 10 CFR Part 61 or DOE O 435.1),
100      the laboratory can prioritize the review of waste streams for elimination, reduction, or
101      modification. A waste stream schematic or flow diagram that lists waste stream characteristics
102      and management pathways can be a useful tool in reviewing waste stream management. Various
103      management options that have been used to achieve waste stream minimization include the
104      following:

105      REGULATORY. Some wastes may be exempted from regulations because of the production
106      process, level of contaminants, volume of waste produced, or management option chosen. For
107      example, some hazardous wastes may be disposed in an industrial wastewater discharge if their
108      contaminants are below established regulatory levels and if the discharge is regulated under the
109      Clean Water Act. Also, a hazardous waste generator that produces less than 100 kg of waste in a
110      month may be considered a conditionally exempt small quantity generator and thus be exempt
111      from many of the requirements of RCRA (40 CFR 261.5). Some radioactive waste may be
112      managed as not-radioactive if the total level of radioactivity is below an exempt or de minimis
113      level,  or if the activity for specific radionuclides is below established levels (10 CFR 61
114      20.2005). For certain licensees, radioactive wastes are released into the environment as gaseous
115      and liquid effluents in accordance with 10 CFR Part 61 20.2001(a)(3) and specific license
116      conditions.

117      METHOD SELECTION. The analytical method selected for the analysis of radioactive material
118      determines the type and volume of waste generated. When two methods will achieve the required
119      measurement quality objectives of the project, the laboratory may select the method that
120      produces the most easily managed waste (see Chapter 6, Selection and Application of an
121      Analytical Method).

122      PRODUCT SUBSTITUTION. In an analytical method, it may be possible to replace a hazardous
123      reagent with a non-hazardous reagent and still meet all health, safety, and data quality objectives.
124      In addition, substituting a short-lived radionuclide for a long-lived radionuclide may ultimately
125      result in a reduction of radioactive waste.

126      SAMPLE VOLUME COLLECTED. Excess sample material should not be collected. Personnel should
127      only collect enough sample material for the planned analysis and any reserve needed for re-
128      analysis or potential future use. Reserve volume should be minimized with up-front planning.

129      SAMPLE/REAGENT VOLUME. It may be possible to reduce the amount of sample and/or reagents
130      used in a method. It may also be possible to convert a method to a micro-scale method that uses
131      significantly less  sample and reagents than the original method.


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132      REAGENT PROCUREMENT CONTROLS. Often, the quantities of chemicals purchased by a
133      laboratory are determined by the price discounts available on larger quantities, instead of by the
134      amount of chemical required. The real cost of chemicals should be recognized as the initial
135      purchase price plus any disposal costs (lifetime costs). It should be noted that disposal costs of
136      excess chemicals can easily exceed the initial purchase costs. Procurement procedures for
137      hazardous material should be implemented to determine if a non-hazardous substitute is
138      available. Rotating chemical stock (first in, first out) may help avoid expiration of the chemical
139      shelf life.

140      RE-USE OF MATERIALS. Some materials may be recovered from the analytical process and  re-
141      used in subsequent analyses. For example, distillation of certain used organic solvents may purify
142      them sufficiently for reuse.

143      DECAY IN STORAGE. Since the level of radioactivity decreases with time, it may be possible to
144      store a short-lived radionuclide until the natural-decay process reduces the radioactivity to a level
145      at which the waste can be considered non-radioactive for waste management purposes.
146      Laboratory management should be aware that RCRA storage limitations might impact the
147      feasibility of this option.

148      WASTE STREAM SEGREGATION. Segregating wastes by the appropriate category allows them to
149      be managed by the most cost-effective option. Combining highly regulated waste streams with
150      less stringently regulated waste streams usually requires the total waste stream to meet the most
151      stringent waste management requirements. For example:

152       •  Non-hazardous waste mixed with hazardous waste must be managed as hazardous waste.
153       •  Non-radioactive waste mixed with radioactive waste must be managed as radioactive waste.
154       •  Hazardous waste mixed with radioactive waste must be managed in compliance with the
155         requirements of the Atomic Energy Act (AEA), RCRA, and TSCA.

156      20.5   Waste  Determinations and Characterization

157      Laboratory wastes should be properly characterized to assure compliance with applicable federal,
158      state, and local regulations, and to determine appropriate means of disposal. Waste container
159      contents should be adequately characterized during waste generation and packaging. Characteri-
160      zations should address the type of material and the physical and chemical characteristics of the
161      waste. Minimum waste characterization criteria may be specified for the radioactive waste
162      generated (DOE M 43 5.1 -1, Ch. IV, Sec. I and NRC criteria specified in 10 CFR Part 61 for
163      commercial low-level radioactive waste sites).

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164      Three basic methods of characterization are denoted here: (a) process knowledge; (b) chemical
165      characterization through laboratory analysis; and (c) activities. Factual process knowledge (e.g.,
166      from a process waste assessment) influences the amount of sampling required to correctly
167      characterize waste.

168      A generic laboratory waste management plan should be established to describe the waste life
169      cycle. This plan should focus on characterizing each waste stream and establishing a waste
170      stream profile, so that the waste stream can be properly managed. The profiled waste stream may
171      only require a periodic partial characterization, based on the profile and regulatory status.

172      20.6  Specific Waste Management Requirements

173      This section provides general guidance on the storage, treatment, and disposal of radioactive
174      waste generated within a laboratory. It should not be used as definitive guidance for managing
175      radioactive waste. Laboratory managers are encouraged to review the complete regulatory
176      requirements in developing a waste management plan to fit the compliance and operational needs
177      of the laboratory. Laboratory managers may choose to have an environmental compliance
178      specialist assist with developing the waste management plan since waste management
179      requirements can be complex and contradictory.

180      Radioactive waste is regulated under AEA, administered by the Nuclear Regulatory Commission
181      (NRC). Thirty states are NRC Agreement States and have the authority and the regulatory
182      programs in place to regulate radioactive materials management in accordance with 10 CFR Part
183      61. Some wastes may also be regulated under RCRA , TSCA, or both, administered by EPA.
184      Most states  have been granted authority to administer the mixed waste rules under RCRA.
185      Although many of the state hazardous waste laws are very similar to the federal RCRA
186      regulations, important differences may exist. This chapter focuses only on the federal
187      requirements, therefore, to ensure compliance with all applicable regulations, laboratory
188      management is strongly encouraged to review state  and local regulations when developing a
189      waste management plan. Wastes that are regulated as radioactive under AEA and as hazardous
190      under RCRA or TSCA are termed "mixed wastes."  Laboratories that generate mixed waste must
191      satisfy both NRC, which regulates the radioactive component, and EPA, which regulates the
192      hazardous component. Mixed waste management is a difficult responsibility, due to the complex
193      regulatory framework and the lack of approved treatment and disposal options for these wastes.
194      Other laws,  such as the Clean Water Act (CWA) and the Clean Air  Act (CAA), are not
195      summarized in this chapter. However, they may also have some impact on the management of
196      radioactive waste.
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197     Federal regulatory requirements for waste management are found in Title 10 of the Code of
198     Federal Regulations (10 CFR) and Title 40 of the Code of Federal Regulations (40 CFR). The
199     following Federal citations address specific areas that regulate the management of waste
200     generated by a laboratory.

201     NRC REQUIREMENTS FOR RADIOACTIVE WASTE. Title 10 CFR 20, Standards for Protection
202     Against Radiation, and 10 CFR 61, Licensing Requirements for Land Disposal of Radioactive
203     Waste, address issues that may apply to management of radioactive waste in the laboratory.

204     LICENSE. Each laboratory that handles radioactive materials must be licensed by NRC, a NRC
205     Agreement State, or be operating under a site-wide license held by DOE. Radioactive materials
206     license issued by NRC or an Agreement State may provide additional requirements that affect the
207     management of waste. DOE-owned laboratories might be required to comply with DOE orders
208     that regulate the management of radioactive wastes (such as O 435.1 or 5820.2a).

209     DOE REQUIREMENTS FOR RADIOACTIVE WASTE. Any generator of DOE radioactive waste and
210     radioactive recyclable materials shall have a Waste Certification Plan (WCP). This plan provides
211     assurance that appropriate sections of the acceptance criteria of the waste and applicable RCRA
212     waste analysis requirements are met (DOE Order 5820.2A, Radioactive Waste Management).
213     The radioactive waste generator requirements are to ensure the development, review, approval,
214     and implementation of a program for waste generation planning, characterization, certification,
215     and transfer. This program shall address characterization of waste, preparation of waste for
216     transfer, certification that waste meets the receiving facility's radioactive waste acceptance
217     requirements, and transfer of waste (DOE M 435.1-1).

218     RCRA REQUIREMENTS FOR HAZARDOUS WASTE. Laboratories that generate hazardous waste
219     must meet detailed and specific requirements for the storage, treatment, and disposal of that
220     waste. Some of the regulatory requirements vary with the total amount of hazardous waste
221     generated each month, thus it is important that the laboratory understand how to properly
222     categorize its operation (small quantity exempt generator, small quantity generator, or large
223     quantity generator). Generator status is a regulatory issue that may vary among states. RCRA
224     regulations for generators found in 40 CFR 260-262, Hazardous Waste Management System:
225     General, list requirements in the following sections:

226      • 40 CFR 261,  Identification and Listing of Hazardous Waste, describes what is, and what is
227        not, hazardous waste  and how to determine if a waste is considered hazardous under RCRA.
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228      • 40 CFR 262, Standards Applicable to Generators of Hazardous Waste, establishes
229        management requirements for generators of hazardous waste.

230      • 40 CFR 262.34, Accumulation Time, provides specific time and volume limitations on the
231        storage of hazardous waste.

232      • 40 CFR 262.40, Recordkeeping and Reporting, lists requirements a generator must meet in
233        documenting and reporting hazardous waste management activities.

234     TSCA REQUIREMENTS FOR PCB WASTE. The primary TSCA regulations that normally would
235     apply to an analytical laboratory relate to PCB waste. Laboratory waste containing PCBs at
236     concentrations of 50 ppm or greater, or are derived from PCB waste samples with concentrations
237     of 50 ppm or greater, are considered PCBs and are subject to the following regulations:

238      • 40 CFR 761.60, Disposal Requirements, describes requirements for the disposal of PCB
239        waste.

240      • 40 CFR 761.61, Poly chlorinated Biphenyls (PCBs) Manufacturing, Processing, Distribution
241        in Commerce, and Use Prohibitions, establishes prohibitions of, and requirements for, the
242        manufacture, processing, distribution in commerce, use, disposal, storage, and marking of
243        PCBs and PCB items.

244      • 40 CFR 761.65, Storage and Disposal, describes time limits for storage and storage
245        requirements of PCB waste.

246      • 40 CFR 761.64, Disposal of Wastes Generated as a Result of Research and Development
247        Activities ... and Chemical Analysis of PCBs, provides regulatory exclusion for  some PCB
248        analytical samples.

249     20.6.1  Sample/Waste Exemptions

250     Laboratory samples and  certain mixed wastes may be exempted or excluded from certain
251     regulatory provisions. Management should evaluate those regulations to determine  if they affect
252     their waste management practices. Three examples are provided below.

253     RCRA ANALYTICAL SAMPLE/TREAT ABILITY SAMPLE EXCLUSIONS. Under 40 CFR  261.4(d), a
254     sample of solid waste or a sample of water, soil, or air, which is collected for the sole purpose of
255     testing  to determine its characteristics or composition, is not subject to certain RCRA regulations


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256     if the laboratory is meeting the conditions specified in 40 CFR 261.4. Similarly, samples
257     undergoing treatability studies, and the laboratory or testing facility conducting such treatability
258     studies, are not subject to certain portions of RCRA [40 CFR 261.4(e)]. However, once a
259     material can no longer be considered a sample, it becomes waste and is subject to RCRA
260     requirements.

261     POLYCHLORINATED BiPHENYL (PCB) SAMPLE EXCLUSION. Portions of samples used in a
262     chemical extraction and analysis method for PCBs, and extracted for purposes of determining the
263     presence of PCBs or concentration of PCBs, are unregulated for PCB disposal (40 CFR 761.64).
264     All other PCB wastes from laboratory operations must be disposed in accordance with 40 CFR
265     761.61. Radioactive PCB waste may be exempt from the one year time limit for storage if the
266     waste is managed in accordance with all other applicable federal, state, and local laws and
267     regulations for the management of radioactive material (40 CFR 761.65).

268     MIXED WASTE EXEMPTION. Since August 1991, EPA has maintained a special policy on the
269     enforcement of the storage prohibition of RCRA mixed waste, which applies to generators that
270     are storing mixed wastes for which no viable treatment technology or disposal capacity exists.
271     The policy explains that EPA considers violation of the RCRA storage prohibition in section
272     3004(j) of RCRA to be a relatively low priority item among the Agency's potential civil
273     enforcement actions, as long as the wastes are stored in accordance with a RCRA permit or
274     interim status or in an environmentally sound manner. This policy, which only applies to certain
275     wastes, has been extended to October 2001. However, the policy does not apply to DOE
276     facilities.

277     20.6.2  Storage

278     Regulatory requirements for the storage of radioactive, hazardous, or PCB waste vary by the type
279     of waste, and typically address the waste storage area, type of acceptable waste containers, length
280     of time the waste may be stored, marking the storage  area and the containers, and waste
281     monitoring. Significant civil and criminal penalties exist for storing waste improperly or for a
282     longer time period than allowed. The following sections summarize some of these requirements.
283     However, laboratory management is encouraged to review the regulations in depth so they may
284     develop a waste management plan that meets the compliance and operational needs of the
285     laboratory.

286     In the case of DOE analytical contract laboratories, low-level radioactive waste that has an
287     identified path to disposal shall not be stored longer than one year prior to disposal, except for
288     the purpose of radioactive decay. Low-level waste that does not have an identified path to


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289     disposal shall be characterized as necessary to meet the data quality objectives and minimum
290     characterization requirements to ensure safe storage and to facilitate disposal (DOE M 435.1-1).

291     20.6.2.1 Container Requirements

292     RADIOACTIVE WASTE. NRC has container requirements for low-level waste. Refer to 10 CFR
293     Part 61 for Class B and C requirements. For disposal, NRC requires the use of a high integrity
294     container approved by NRC.

295     RCRA HAZARDOUS WASTE. 40 CFR 265.170-177 provides requirements for the use and
296     management of containers storing hazardous waste. In summary, this  section requires that
297     containers be in good condition, be compatible with the waste stored,  be closed at all times
298     except when adding or removing waste, and be inspected weekly, in the case of 90-day
299     accumulation areas, for signs of corrosion or leakage.

300     PCB WASTE. 40 CFR 761.65 details TSCA requirements for the storage of PCB waste, including
301     the physical constraints of the storage area and the type of containers acceptable for storing liquid
302     and non-liquid PCB wastes. Laboratory PCB waste and samples returned to the sample collector
303     or submitted to a disposal facility when sample use is terminated may be exempt from the storage
304     requirements of 40 CFR 761.65.

305     20.6.2.2 Labeling Requirements

306     RADIOACTIVE WASTE. Radioactive waste storage areas should be posted with signs and labeled
307     in accordance with 10 CFR 20.1901  -1906, Precautionary Procedures. This section specifies
308     requirements for caution signs, labeling, signals, controls, and the storage of licensed material in
309     unrestricted areas.

310     RCRA HAZARDOUS WASTE. Hazardous waste containers must be labeled with the words
311     "Hazardous Waste" and, in the case of a 90-day accumulation area, the date upon which the
312     waste accumulation began 40 CFR 262.34(a)(4)(c)(ii).

313     PCB WASTE. 40 CFR 761.40 and 761.45 provides requirements for marking and labeling PCB
314     containers and the PCB storage area  (40 CFR 761.50).

315     20.6.2.3 Time Constraints

316     RADIOACTIVE WASTE. NRC regulations in Title 10 of the Code of Federal Regulations do not
317     specifically establish a maximum amount of time that one may store radioactive waste. A
318     facility's NRC or Agreement State radioactive materials license may address this issue.
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319     RCRA-HAZARDOUS WASTE. A generator may store hazardous waste up to 90 days, 180 days, or
320     270 days depending on its status as defined by the regulations or the distance the generator is
321     from the disposal facility (40 CFR 262.34). A generator may accumulate as much as 55 gallons
322     of hazardous waste or one quart of acutely hazardous waste in containers at or near the point of
323     generation where wastes initially accumulate, which is under the control of the operator of the
324     process generating the waste (40 CFR 262.34). The storage time clock (90, 180, or 270 days)
325     does not begin until the waste volume reaches 55 gallons (or one quart, in the case of acutely
326     hazardous waste), or whenever waste is stored in a 90-day accumulation area.

327     PCB WASTE. Radioactive PCB waste may be exempt from the one-year time limit for PCB
328     storage if the waste is managed in accordance with all other applicable federal, state, and local
329     laws and regulations for the management of radioactive material (40 CFR 761.65). According to
330     40 CFR 761.65(a)10, certain PCB waste containers may be exempt from 40 CFR 761.65 if the
331     containers are disposed within 30 days.

332     20.6.2.4 Monitoring Requirements

333     RADIOACTIVE WASTE.  Radioactive waste storage areas should be surveyed and personnel should
334     be monitored in accordance with 10 CFR 20.1901-1906, Precautionary Procedures. These
335     sections specify the requirements for surveys, personnel monitoring, and storage of licensed
336     material in unrestricted areas. 10 CFR 20.1101 and 10 CFR 20.1201 address permissible doses,
337     levels, and concentrations of airborne radiation that would  apply to radioactive waste storage
338     areas.

339     RCRA FlAZARDOUS WASTE. The owner or operator of a hazardous waste storage area must
340     inspect areas in which containers are stored, at least weekly, looking for leaks and  deterioration
341     caused by corrosion or other factors (40 CFR 265.174). 40  CFR 262.34 address requirements for
342     Prevention and Preparedness, Contingency Plans, and Emergency Procedures that may apply to a
343     laboratory that stores RCRA waste.

344     PCB WASTE. All PCB  containers in storage shall be checked for leaks at least once every 30 days
345     [40CFR761.65(c)(5)].

346     20.6.3 Treatment

347     Radioactive and mixed waste may require treatment to meet one or more objectives prior to final
348     disposal. Treatment involves the physical or chemical processes that result in a waste form that is
349     acceptable for disposal or further treatment. Treatment objectives include: (1) producing a waste
350     form acceptable for land disposal; (2) volume/mobility reduction through possible  solidification
351     or sizing; (3) producing a waste more amenable for further treatment; or (4) separating radio-
352     active components from RCRA or TSCA components. Another treatment objective is to convert
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353      a radioactive RCRA regulated waste to a radioactive non-RCRA waste. Special permits may be
354      required from regulatory agencies prior to the treatment of waste.

355      Radioactive wastes may require treatment to meet the waste characteristics provided in 10 CFR
356      61.56. The following types of treatment have been used to meet those requirements:

357       • Non-solid radioactive waste may be treated with various solidification agents (such as
358         cement, asphalt, or polymers) to immobilize waste or sludge not otherwise acceptable for
359         disposal. Low-level radioactive waste (LLRW) may be absorbed onto a porous material, such
360         as silica, vermiculite, or organic materials to reduce the liquid volume.

361       • Dry radioactive waste may be treated with compaction or super-compaction to reduce the
362         waste volume.

363       • Some radioactive waste items may be decontaminated for unrestricted release by removal of
364         surface radioactivity through chemical or physical means. The residue from the
365         decontamination of a surface may require disposal as a radioactive waste.

366       • The relatively short half-lives of some radionuclides warrant storing the waste for a period of
367         time. Once the levels of radioactivity are undetectable or below an accepted de minimis level,
368         the waste may be disposed as a non-radioactive waste or in accordance with license
369         conditions.

370      20.6.4  Disposal

371      The disposal of radioactive waste is regulated by NRC in accordance with 10 CFR 20.2001,
372      which requires that waste be disposed at a licensed LLRW site. Radioactive waste that is mixed
373      with waste regulated under RCRA or TSCA is also subject to disposal requirements of the
374      respective regulations. Mixed waste must go to a facility that is licensed under both of the
375      appropriate laws. For example, radioactive RCRA waste cannot go to a RCRA landfill that is not
376      licensed under the Low Level Radioactive Waste Policy Act (LLRWPA), nor can it be disposed
377      at a LLRW site that is not licensed under RCRA.

378      In some cases, radioactive material may be disposed in a sanitary-sewage system if the
379      requirements of 10  CFR 20.2003 are met. This section provides specific limits on the quantity of
380      radionuclides that can be discharged into a sewage system. Discharges into a sewage system may
381      also be regulated by the Clean Water Act. For example, media used for liquid scintillation
382      counting, containing tritium (3H) or carbon-14 (14C) in concentration of 0.05 microcuries per
383      gram or less may be disposed as if it were not radioactive. Also, animal tissue containing 3H or
384      14C at levels less than or equal to 0.05 microcuries per gram (1,850 Bq/g) may be disposed
385      without regard to radioactivity (10 CFR 20.2005).
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386     The DOE also regulates the disposal of radioactive waste. Under DOE M 435.1-1, all radioactive
387     waste generators must have a waste certification program to ensure that the waste acceptance
388     criteria for the radioactive disposal facility are met. An outline of a waste certification plan is
389     contained in the following section.

390     20.7  Contents of a Laboratory Waste Management Plan/Certification Plan

391     20.7.1  Laboratory Waste Management Plan

392     A laboratory waste management plan will describe the waste generated by the analytical
393     laboratory. Each section of the plan is usually divided into two separate entities B one addressing
394     the needs of the laboratory analyst and the  second addressing the needs of the waste management
395     personnel. An outline of a generic plan follows:

396         1.  Recyclable Wastes
397         2.  Sanitary Wastes/Industrial Wastes
398         3.  Radioactive Wastes
399         4.  Hazardous and Mixed Wastes
400             • Satellite Accumulation Area operations
401             • 90-day Accumulation Area operations

402     Within each section, the laboratory should delineate the types of waste that fall into  each
403     category. Also, within the section for laboratory analysts, the disposal of the waste should be
404     clearly defined (e.g., paper in recyclable waste bin, unknown waste to environmental and/or
405     waste personnel). The waste management section should describe the process used by the waste
406     management personnel to dispose of the waste.

407     20.7.2  Waste Certification Plan/Program

408     The general  outline for waste certification plans described below was taken from DOE M 435.1-
409     1 Ch. IV, Sec. J (1-3):

410     CERTIFICATION REQUIREMENTS. The waste certification program shall designate the officials
411     who have the authority to certify and release waste for shipment and to specify the documen-
412     tation required for waste generation, characterization, shipment, and certification. The program
413     shall provide requirements for auditing, retrieving and storing required documentation, including
414     records retention.

415     CERTIFICATION BEFORE TRANSFER. Low-level waste shall be certified as meeting waste
416     acceptance requirements before it is transferred to the facility receiving the waste.
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417     MAINTAINING CERTIFICATION. Low-level waste that has been certified as meeting the waste
418     acceptance requirements for transfer to a storage, treatment, or disposal facility shall be managed
419     in a manner that maintains its certification status.

420     A general outline for a laboratory waste certification plan follows:

421         1.  FACILITY NAME AND LOCATION. Provide the name and the physical location of the
422           facility.

423        2.  ORGANIZATION. Describe the organizational structure for the facility's operation, quality
424           assurance program, and waste management program.

425        3.  CONTENTS OF WASTE CERTIFICATION PLAN. Provide a detailed Table of Contents,
426           including list of tables, figures, and appendices as appropriate.

427        4.  FACILITY RECYCLABLE AND WASTE MINIMIZATION STRATEGY. Identify the wastes and
428           waste streams the facility has targeted for recycling and waste minimization (i.e., source
429           reduction through product replacement).

430        5.  DUTIES AND RESPONSIBILITIES OF MANAGEMENT AND WASTE MANAGEMENT
431           PERSONNEL. Provide a description of the positions at the laboratory, including primary
432           and secondary responsibilities and line of reporting.

43 3        6.  QUALIFICATION REQUIREMENT s AND TRAINING OF WASTE MANAGEMENT PERSONNEL .
434           Describe the training and qualification program implemented for the environmental and
435           waste personnel. No specialized certifications (e.g., certified hazardous materials
436           manager, professional engineer) is needed unless specified by the job description or
437           standard operation procedures.

43 8        7.  QUALIFICATIONS OF PROCEDURES AND EQUIPMENT USED IN WASTE MANAGEMENT .
439           Describe all equipment used in the waste management processes and procedures.

440        8.  RECYCLABLE MATERIAL AND WASTE SEGREGATION CONTROL. Describe the process of
441           segregating various types of waste streams, especially in regards to radioactive and non-
442           radioactive wastes.

443        9.  PACKAGING, HANDLING AND STORAGE CONTROL. Describe the process of packaging,
444           handling, and storing waste at the facility. This would include drum inspections, cipher-
445           locked storage, etc. The disposal of the supernates is a third example of a waste stream.
446           These supernates may be disposed in a sewage system, but the pH must be above 2 or
447           below  12 to allow the supernate solutions to be exempt from RCRA regulations.
448           Elementary neutralization is allowed in the laboratory under RCRA, but state regulations

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449            may require registration of the laboratory as an elementary neutralization unit before
450            neutralization and disposal take place.

45i     20.8  Useful Web Sites

452     Listed below are useful federal web sites relevant to the management of laboratory waste. Due to
453     the nature of the Internet, these addresses may change in the future.

454     Federal and State Government Regulation and Program References
455        http://www.epa.gov/docs/epacfr40/fmd-aid.info/state/

456     Environmental Laws and Regulations, Full Text (U.S. Code)
457     More than a dozen major statutes or laws form the legal basis for the programs of the
458        Environmental Protection Agency (EPA). The full text of these laws and the U.S. Code
459        Citation for each environmental law can be accessed through the following address.
460        http://www.epa.gov/epahome/lawreg.htm

461     Environmental Regulations in Federal Register
462     Full text of all Federal Register documents issued by EPA, as well as selected documents issued
463        by other Departments and Agencies. Notices, meetings, proposed rules, and regulations are
464        divided into twelve topical categories for easy access (e.g., air, water, pesticides, toxics, and
465        waste).
466        http://www.epa.gov/fedrgstr/

467     State and Federal Agency Contact List for Mixed Waste Regulations
468        http://www.epa.gov/rpdwebOO/mixed-waste/mw_pg6e.htm

469     States and Territories Where EPA Regulates Mixed Waste
470        http://www.epa. gov/rpdwebOO/mixed-waste/mw_pg6a. htm

471     States and Territories With EPA Authorization to Regulate Mixed Waste
472        http://www.epa.gov/rpdwebOO/mixed-waste/mw_pg6b.htm

473     State Solid and Hazardous Waste Web Sites
474        http://www.epa.gov/epaoswer/osw/stateweb.htm

475     RCRA State Authorization, By State and Program Element
476        http://www.epa.gov/epaoswer/hazwaste/state/index.htm

477     NRC Agreement States
478        http ://www.hsrd. ornl .gov/nrc/asframe.htm
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479     DOE Mixed Waste Policies
480        http://www.directives.doe.gov/

481     EPA Mixed Waste Home Page
482        http://www.epa.gov/rpdwebOO/mixed-waste/index.html

483     Mixed Waste Glossary
484        http://www.epa.gov/radiati on/mixed-waste/mw_pg5.htm#AEA

485     Guidance on the Definition and Identification of Commercial Mixed Low Level Radioactive and
486        Hazardous Waste
487        http://www.epa.gov/rpdwebOO/mixed-waste/mw_pg25.htm

488     Current Mixed Waste Treatment, Storage, or Disposal Facilities (TSDFs)
489        http://www.epa. gov/rpdwebOO/mixed-waste/mw_pgl la.htm

490     NRC/EPA Draft Storage Guidance
491        http://www.epa.gov/radiati on/mixed-waste/mw_pg27.htm

492     Mixed Waste Shipping and Transportation
493        http://www.epa. gov/rpdwebOO/mixed-waste/mw_pglO. htm

494     Mixed Waste Pollution Prevention
495        http://www.epa. gov/rpdwebOO/mixed-waste/mw_pg23 .htm

496     Pollution Prevention, EPA Home Page
497        http://www.epa.gov/epahome/p2pgram.htm

498     Radioactive Waste Disposal
499        http://www.nrc.gov/NRC/radwaste.htm

500     20.9   References

501     20.9.1  Cited References

502     U.S. Department of Energy (DOE).  Order O 435.1: Radioactive Waste Management. July 1,
503        1999. http://www.directives.doe.gov/pdfs/doe/doetext/neword/435/o4351 .html.

504     U. S. Department of Energy (DOE).  M 43 5.1 -1. Radioactive Waste Management Manual. Office
505        of Environmental Management. July 9, 1999. http://www.directives.doe.gov/pdfs/doe/
506        doetext/neword/43 5/m43 51-1. html.
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507     U.S. Environmental Protection Agency (EPA). 1996. Profile and Management Options for EPA
508        Laboratory Generated Mixed Waste, EPA 402-R-96-015, August.

509     20.9.2 Other Sources

510     Lewandowski, Joseph J., Alan A. Moghissi. 1995. "Management of Mixed Waste at a Teaching,
511        Research, and Health Care Facility," Proceedings of the 3d Biennial Symposium of Mixed
512         Waste, Baltimore, MD, August.

513     Linens, Ilona, Robert C. Klein, Edward L. Gershey.  1991. "Management of Mixed Waste from
514        Biomedical Research," Health Physics, 61:3, pp. 421-426.

515     Lorenzen, William A. 1995. Operational Aspects of Harvard University's Waste Management
516        Program, pp. 415-420, August.

517     Methe, Brian M. 1993. "Managing Radioactively Contaminated Infectious Waste at a Large
518        Biomedical Facility," Health Physics, 64:2, pp. 187-191.

519     McCamey, R.B. 1995. "Building a Mixed-Waste Prevention Program at Comanche Peak,"
520        Radwaste Magazine, May, pp. 21-28.

521     National Research Council. 1995. Prudent Practices in the Laboratory; Handling and Disposal
522        of Chemicals, National Academy Press, Washington, DC.

523     Party, E. and E.L. Gershey. 1989. "Recommendations for Radioactive Waste Reduction in
524        Biomedical/Academic Institutions," Health Physics, 56:4, pp. 571-572.

525     Reinhardt, Peter A. (editor), Leonard K. Leigh, Peter C. Ashbrook. 1996. Pollution Prevention
526        and Waste Minimization in Laboratories, Boca Raton Press.

527     Ring, Joseph, William Lorenzen, Frank Osborne, Jacob Shapiro. 1995.  Bio-Medical Radioactive
528         Waste Management, July 19.

529     Todisco, L.R. and L.R. Smith. 1995. "A Manufacturer's Perspective on Low-Level Mixed Waste
530        Treatment, Storage, and Disposal," E.I. DuPont and Company, Inc., NEN Products,
531        Proceedings of the 3d Biennial Symposium of Mixed Waste, Baltimore, MD, August.

532     U.S. Nuclear Regulatory Commission/U.S. Environmental Protection Agency. 1995. Low-Level
533        Mixed Waste Storage Guidance, Federal Register 60:40204-40211, August 7.
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                             GLOSSARY

                The glossary will be prepared following public review
      ( ) Indicates the section in which the term is first used in the MARLAP document

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        APPENDIX A:   DIRECTED PLANNING APPROACHES
 2     A.I   Directed Planning Approaches

 3     There are a number of approaches being used for directed planning of environmental operations.
 4     Some of these approaches were designed specifically for data collection activities; others are
 5     applications of more general planning philosophies. Many variations to these approaches have
 6     been made for specific applications. The following are some of the approaches being used:

 7      •  Data Quality Objectives (DQO);
 8      •  Observational Approach (OA);
 9      •  Streamlined Approach for Environmental Restoration (SAFER);
10      •  Technical Project Planning (TPP);
11      •  Expedited Site Characterization (ESC);
12      •  Value Engineering;
13      •  Systems Engineering;
14      •  Total Quality Management (TQM); and
15      •  Partnering.

16     Employing any of these approaches assures that sufficient planning is carried out to define a
17     problem adequately, determine its importance, and develop an approach to solutions prior to
18     spending resources.

19     This appendix discusses some elements that are common to direct planning processes
20     (Section A.2) and provides in  Sections A.3 through A. 11 very brief descriptions of the planning
21     approaches listed above. References are listed at the end of the appendix on each of the
22     approaches to provide sources of more detailed information.

23     Several directed planning approaches have been implemented by the Federal sector for
24     environmental data collection  activities. Project planners should be cognizant of agency
25     requirements for planning. MARLAP does not endorse any one planning approach. Users of this
26     manual are encouraged to consider all the available approaches and choose a directed planning
27     process that is appropriate to their project and agency.

28     A.2   Elements Common to Directed Planning Approaches

29     To achieve the outcomes desired from directed planning, all of these approaches address the
30     following essential elements:


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31         1.  Defining the problem or need. Identifying the problem(s) facing the stakeholder/customer
32            that requires attention, or the concern that requires streamlining.

33         2.  Establishing the optimum result. Defining the decision, response, product, or result that
34            will address the problem or concern and satisfy the stakeholder/customer.

35         3.  Defining the strategy and determining the quality of the solution. Laying out a decision
36            rule or framework, roadmap, or wiring diagram to get from the problem or concern to the
37            desired decision or product and defining the quality of the decision, response, product, or
38            result that will be acceptable to the stakeholder/customer by establishing specific,
39            quantitative, and qualitative performance measures (e.g., acceptable error in decisions,
40            defects in product, false positive responses).

41         4.  Optimizing the design. Determining what is the optimum, cost-effective way to reach the
42            decision or create the product while satisfying the desired quality of the decision or
43            product.

44     To most problem solvers, these four elements stem from the basic tenets  of the scientific method:
45     "Principles and procedures for the systematic pursuit of knowledge involving the recognition and
46     formulation of a problem, the collection of data through observation and  experiment, and the
47     formulation and testing of hypotheses" (Webster's  Dictionary).

48     Each approach requires that a team of customers, stakeholders, and decision makers defines the
49     problem or concern; a team of technical staffer line operators have the specific knowledge and
50     expertise to define and then provide  the desired product; and both groups work together to
51     understand each  other's needs and requirements and to agree on the product to be produced. The
52     approaches represent slightly different creative efforts in the problem-solving process. All are
53     intended to facilitate the achievement of optimum results at the lowest cost, generally using team
54     work and effective communication to succeed.

55     A.3   Data Quality Objectives Process

56     The Data Quality Objectives (DQO) process was created by the U. S. Environmental Protection
57     Agency's Quality Assurance Management Staff (QAMS) to promote effective communications
58     between decision makers, technical staff, and stakeholders on defining and planning the
59     remediation of environmental problems.

60     The DQO process consists of seven basic  steps:

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61         1.  State the problem
62         2.  Identify the decision
63         3.  Identify inputs to the decision
64         4.  Define the study boundaries
65         5.  Develop a decision rule
66         6.  Specify limits on decision errors
67         7.  Optimize the design

68     Applying the DQO steps requires effective communication between the parties who have the
69     problem and the parties who must provide the solution. Additional information about the DQO
70     Process is provided in Appendix B to this manual.

71     A.4   Observational Approach

72     The Observational Approach (OA) emphasizes determining what to do next by evaluating
73     existing information and iterating between collecting new data and taking further action. The
74     name "observational approach" is derived from observing parameters during implementation.
75     OA was  developed by Karl Terzaghi (Peck, 1969) for geological applications. In mining
76     operations, there may be substantial uncertainty in the location of valuable geological formations.
77     Information on soil and mineral composition would help to identify such formations. Application
78     of O A utilizes the sampling information on soil and mineral composition to direct the digging
79     locations. OA should be encouraged in situations where uncertainty is large, the vision of what is
80     expected or required is poor, and the cost of obtaining more certainty is very high.

81     The philosophy of OA when applied to waste site remediation is that remedial action can be
82     initiated  without fully characterizing the nature and extent of contamination. The approach
83     provides a logical decision framework through which planning, design, and implementation of
84     remedial actions can proceed with increased confidence. OA incorporates the concepts of data
85     sufficiency, identification of reasonable deviations, preparation of contingency plans, observation
86     of the systems for deviations, and implementation of the contingency plans. Determinations of
87     performance measures and the quality of new data are done as the steps are implemented.

88     The iterative steps of site characterization, developing and refining a site conceptual model, and
89     identifying uncertainties in the conceptual model are  similar to traditional approaches. The
90     concept of addressing uncertainties as reasonable deviations is unique to OA and offers a
91     qualitative description of data sufficiency for  proceeding with site remediation.
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        Directed Planning Approaches
 92      A.5   Streamlined Approach for Environmental Restoration

 93      The Streamlined Approach for Environmental Restoration (SAFER) is an integration of the DQO
 94      process and OA developed by the U. S. Department of Energy (DOE). The planning and
 95      assessment steps of SAFER are the DQO process. The implementation steps of SAFER are the
 96      Observational Approach. The approach emphasizing team work between decision makers and
 97      technical staff reduces uncertainty with new data collection and manages remaining uncertainty
 98      with contingency plans. The labels in each SAFER step are slightly different from the DQO and
 99      OA steps, but the basic logic is the same. The SAFER Planning steps are:

100       • Develop a conceptual model;
101       • Develop remedial objectives and general response actions;
102       • Identify priority problem(s);
103       • Identify reasonable deviations and possible contingencies;
104       • Pursue limited field studies to focus and expedite scoping;
105       • Develop the decision rule;
106       • Establish acceptable conditions and acceptable uncertainty for achieving objective;  and
107       • Design the work plan.

108      A.6   Technical  Project Planning

109      Technical Project Planning (TPP) (formerly Data Quality Design),  developed by the U. S. Army
110      Corps of Engineers, is intended for developing data collection programs and defining data quality
ill      objectives for hazardous, toxic, and radioactive waste sites (HTRW). This systematic process
112      (USAGE, 1998) entails a four-phase planning approach in which a planning team—comprised of
113      decision makers, data users, and data providers—identifies the data needed to support specific
114      project decisions and develops a data collection program to obtain those data. In Phase  I, an
115      overall site strategy and a detailed project strategy are identified. The data user's data needs,
116      including the level of acceptable data quality, are defined in Phase II. Phase in entails activities
117      to develop sampling and analysis options for the data needed. During phase IV, the TPP team
118      finalizes a data collection program that best meets the decision makers' short- and long-term
119      needs within all project and site constraints. The technical personnel complete Phase IV by
120      preparing detailed project objectives and data quality objectives, finalizing the scope of work,
121      and preparing a detailed cost estimate for the data collection program. The TPP process uses a
122      multi-disciplinary team of decision makers,  data users, and data implementors focused  on site
123      closeout.
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124      A.7   Expedited Site Characterization

125      Expedited Site Characterization (ESC) was developed to support DOE's Office of Science and
126      Technology's Characterization, Monitoring, and Sensor Technology (CMST) program
127      (Burton, 1993). The ESC process has been developed by American Society for Testing and
128      Materials (ASTM) as a provisional standard for rapid field-based characterization of soil and
129      groundwater (ASTM, 1996). The process is also known as QUICKSITE and "expedited site
130      conversion." ESC is based on a core multi-disciplinary team of scientists participating throughout
131      the processes of planning, field implementation, data integration, and report writing. ESC
132      requires clearly defined objectives and data quality requirements that satisfy the needs of the ESC
133      client, the regulatory authority, and the stakeholders. The technical team uses real-time field
134      techniques, including sophisticated geophysical and environmental sampling methods and an on-
135      site analytical laboratory, to collect environmental information. Onsite computer support allows
136      the expert team to analyze data each day and decide where to focus data collection the next day.
137      Within a framework of an approved dynamic work plan, ESC relies on the judgment of the
138      technical team as the primary means for selecting the type and location of measurements and
139      samples throughout the ESC process. The technical team uses on-site data reduction, integration
140      and interpretation, and on-site decision making to optimize the field investigations.

141      Traditional site investigations generally are based on a phased engineering approach that collects
142      samples based on a pre-specified grid pattern and does not provide the framework for making
143      changes in direction in the field. A dynamic work plan (Robatt, 1997; Robatt et al., 1998)
144      relies—in part—on an adaptive sampling and analysis program. Rather than specify the sample
145      analyses to be performed, the number of samples to be collected and the location of each sample,
146      dynamic work plans specify the decision making logic that will be used in the field to determine
147      where the samples will be collected, when the  sampling will stop, and what analyses will be
148      performed. Adaptive sampling and analysis programs change or adapt based on the analytical
149      results produced in the field (Robatt, 1998; Johnson, 1993a,b).

150      A.8   Value Engineering

151      Value methodology was developed by Lawrence D. Miles in the late 1940s. He used a function-
152      based process ("functional analysis") to produce goods with greater production and operational
153      efficiency. Value methodology has evolved and, depending on the specific application, is often
154      referred to as "value engineering," "value analysis," "value planning," or "value management."
155      In the mid-1960s value engineering was adopted by three Federal organizations: the Navy Bureau
156      of Shipyards and Docks, the U. S. Army Corp  of Engineers, and the U. S. Bureau of Reclama-
157      tion. In the 1990s, Public Law 104-106  (1996) and OMB Circulars A-131 (1993) and A-ll

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158     (1997) set out the requirements for the use of value engineering, as appropriate, to reduce
159     nonessential procurement and program costs.

160     Value Engineering is a systematic and organized decision-making process to eliminate, without
161     impairing essential functions, anything that increases acquisition, operation, or support costs. The
162     techniques used analyze the functions of the program, project, system, equipment, facilities,
163     services, or supplies to determine "best value," or the best relationship between worth and cost.

164     The method generates, examines, and refines creative alternatives that would produce a product
165     or a process that consistently performs the required basic function at the lowest life-cycle cost
166     and is consistent with required performance, reliability, quality, and safety.

167     A standard job plan is used to guide the process. The six phases of the value engineering job plan
168     are:

169      •  Information;
170      •  Speculation (or creative);
171      •  Evaluation (or analysis);
172      •  Evolution (or  development);
173      •  Presentation (or reporting); and
174      •  Implementation (or execution).

175     Value engineering can be used alone  or with other management tools, such as TQM and
176     Integrated Product and Process Development (IPPD).

177     A.9   Systems Engineering

178     Systems Engineering brings together  a group of multi-disciplinary team members in a structured
179     analysis of project needs, system requirements and specifications, and a least-cost strategy for
180     obtaining the desired results. Systems engineering is a logical sequence of activities and
181     decisions that transforms an operational need into a preferred system  configuration and a
182     description of system performance parameters. Problem and success criteria are defined through
183     requirements analysis, functional analysis, and systems analysis and control. Alternative
184     solutions, evaluation of alternatives, selection of the best life-cycle balanced solution, and the
185     description of the solution through the design package are accomplished through synthesis and
186     systems analysis and control.

187     The systems engineering process involves iterative application of a series of steps:

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188      • Mission analysis or requirements understanding;
189      • Functional analysis and allocation;
190      • Requirements analysis;
191      • Synthesis; and
192      • System analysis and control.

193     A. 10  Total Quality Management

194     Total Quality Management (TQM) is a customer-based management philosophy for continuously
195     improving the quality of products (or how work is performed) in order to meet customer
196     expectations of quality and to measure and produce results aligned with strategic objectives.
197     TQM grew out of two systems developed by Walter Shewhart of Bell Laboratories in the 1920s.
198     Statistical process control was used to measure variance in production systems and to monitor
199     consistency and diagnose problems in work processes. The "Plan-Do-Check-Act"  cycle applied a
200     systematic approach to improving work processes. The work of Deming and others in Japan
201     following World War II expanded the quality philosophy beyond production and inspection to all
202     functions within an organization and defined quality as "fit for customer use."

203     TQM has been defined as "the application of quantitative methods and the knowledge of people
204     to assess and improve  (a) materials and services supplied to the organizations, (b)  all significant
205     processes within the organization, and (c) meeting the needs of the end-user, now  and in the
206     future" (Houston and Dockstader, 1997). The goal of TQM is to enhance effectiveness of
207     providing services or products.  This is achieved through an objective, disciplined approach to
208     making changes in processes that affect performance. Process improvement focuses on
209     preventing problems rather than fixing them after they occur. TQM involves everyone in an
210     organization in controlling and  continuously improving how work is done.

211     A. 11  Partnering

212     Partnering is intended  to bring together parties that ordinarily might have differing or competing
213     interests to create a synergistic effect on an outcome each views as desirable. Partnering is a team
214     building and relationship enhancing technique that seeks to identify and communicate the needs,
215     expectations, and strengths of the participants. Partnering combines the talents of the
216     participating organizations in order to develop actions that promote their common goals and
217     objectives. In the synergistic environment of partnering, creative solutions to problems can be
218     developed. Like TQM, partnering enfranchises all stakeholders (team members) in the decision
219     process and holds them accountable for the end results. Each team member (customer, manage-
220     ment, employee) agrees to share the risks and benefits associated with the enterprise. Like the

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221     other approaches, partnering places a premium on open and clear communication among
222     stakeholders to define the problem and the solution, and to decide upon a course of action.

223     A. 12  References

224     A.12.1 Data Quality Objectives

225     Guidance:

226     American Society for Testing and Materials (ASTM). 1995. Standard Practice for Generation of
221        Environmental Data Related to Waste Management Activities: Development of Data Quality
228        Objectives, D5792-95.

229     U.S. Environmental Protection Agency (EPA). 2000. Guidance for the Data Quality Objective
230        Process (EPA QA/G-4). EPA/600/R-96/055, Washington, DC. available from www.epa.gov/
231        quality l/qa_docs.html.

232     U. S. Environmental Protection Agency (EPA). 1993. Data Quality Objectives Process for
233        Superfund. EPA/540/G-93/071 (Interim Final Guidance). Office of Emergency and Remedial
234        Response. OSWER Directive 9355.9-01. September.

235     Papers:

236     Blacker, S. M. 1993. "The Data Quality Objective Process—What It Is and Why It Was
237        Created." Proceedings of the Twentieth Annual National Energy and Environmental Quality
238        Division Conference of the American Society for Quality Control.

239     Blacker, S. and D. Goodman. 1994a. "Risk-Based Decision Making An Integrated Approach for
240        Efficient Site Cleanup." Environmental Science & Technology, 28(11): 466A-470A.

241     Blacker, S. and D. Goodman. 1994b. "Risk-Based Decision Making Case Study: Application at a
242        Superfund Cleanup." Environmental Science & Technology, 28(11): 471A-477A.

243     Blacker, S. M. and P. A. Harrington. 1994. "Use of Process Knowledge and Sampling and
244        Analysis in Characterizing FFC Act Waste - Applying the Data Quality Objective (DQO)
245        Process to Find Solutions." Proceedings of the Twenty First Annual National Energy and
246        Environmental Quality Division Conference, American Society for Quality Control.
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247     Blacker, S. M. and J. Maney. 1993. "The System DQO Planning Process." Environmental
248        Testing and Analysis. July/August.

249     Blacker, S. M., J. D. Goodman and J. M. Clark. 1994. "Applying DQOs to the Hanford Tank-
250        Waste Remediation." Environmental Testing and Analysis, 3(4): 38.

251     Blacker, S., D. Neptune, B. Fairless and R. Ryti.  1990. "Applying Total Quality Principles to
252        Superfund Planning." Proceedings of the 17th Annual National Energy Division Conference,
253        American Society For Quality Control.

254     Carter, M. and D. Bottrell. 1994. "Report on the Status of Implementing Site-Specific
255        Environmental Data Collection Project Planning at the Department of Energy's (DOE) Office
256        of Environmental  Restoration and Waste Management (EM)." Proceedings of the Waste
257        Management '94 Conference. Vol 2. pp.  1379-1383.

258     Goodman, D. and S. Blacker. 1997. "Site Cleanup: An integrated Approach for Project
259        Optimization to Minimize Cost and Control Risk." In: The Encyclopedia of Environmental
260        Remediation. John Wiley & Sons, New York, NY.

261     Michael, D. I. 1992. "Planning Ahead to Get the Quality of RI Data Needed for Remedy
262        Selection: Applying the DQO Process to Superfund Remedial Investigations." Proceedings of
263        the Air and Waste Management Association 85th Annual Meeting.

264     Michael, D. I. and E. A. Brown. 1992. "Planning Tools that Enhance Remedial Decision
265        Making." Proceedings of the Nineteenth Annual  Energy and Environmental Quality Division
266        Conference for the American Society for Quality Control.

267     Neptune, M. D. and S. M. Blacker. 1990. "Applying Total Quality Principles to Superfund
268        Planning: Part I: Upfront Planning in Superfund." Proceedings of the 17th Annual National
269        Energy Division Conference, American Society for Quality Control.

270     Neptune, D., E. P. Brantly, M. J. Messner and D. I. Michael. 1990. "Quantitative Decision-
271        Making in Superfund: A Data Quality Objectives Case Study." Hazardous Material Control,
272        3:18-27.

273     Ryti, R. T. and D. Neptune. 1991. "Planning Issues for Superfund Site Remediation." Hazardous
274        Materials Control, 4:47-53.
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275     A.12.2 Observational Approach

276     Papers:

111     Brown, S. M. 1990. "Application of the Observational Method to Groundwater Remediation."
278        Proceedings of HAZMAT'90, Atlantic City, NJ.

279     Ferguson, R. D., G. L. Valet, and F. J. Hood. 1992. Application of the Observational Approach,
280         Weldon Springs Case Study.

281     Mark, D. L. et al. 1989. "Application of the Observational Method to an Operable Unit
282        Feasibility Study - A Case Study." Proceedings of Superfund'89, Hazardous Material Control
283        Research Institute, Silver Springs, MD, pp. 436-442.

284     Myers, R. S. and Gianti, S. J. 1989. "The Observational Approach for Site Remediation at
285        Federal Facilities." Proceedings of Superfund'89, Washington, D.C.

286     Peck, R. B.  1969. "Ninth Rankine Lecture, Advantages and Limitations of the Observational
287        Method in Applied Soil Mechanics." Geotechnique, 19, No. 2, pp. 171-187.

288     Smyth, J. D. and R. D. Quinn. 1991. "The Observational Approach in Environmental
289        Restoration." Proceedings of the ASCE National Conference of Environmental Engineering,
290        Reno, NV.

291     Smyth, J. D., J. P. Amaya and M. S. Peffers. 1992. "DOE Developments: Observational
292        Approach Implementation at DOE Facilities." Federal Facilities Environmental Journal,
293        Autumn, pp. 345-355.

294     Smyth, J. D., J. P. Kolman, and M. S. Peffers. 1992. "Observational Approach Implementation
295        Guidance: Year-End Report." Pacific Northwest Laboratory Report PNL-7999.

296     A. 12.3 Streamlined Approach for Environmental Restoration (Safer)

297     Guidance:

298     U. S. Department of Energy (DOE). 1993. Remedial Investigation/Feasibility Study  (RI/FS)
299        Process, Elements and Techniques Guidance, Module 7 Streamlined Approach for
300        Environmental Restoration, Office of Environmental Guidance, RCRA/CERCLA Division


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301         and Office of Program Support, Regulatory Compliance Division Report DOE/EH-
302         94007658.

303      Papers:

304      Bottrell, D. 1993. "DOE's Development and Application of Planning to Meet Environmental
305         Restoration and Waste Management Data Needs." Proceedings of the Twentieth Annual
306         National Energy & Environmental Quality Division Conference, American Society for
307         Quality Control.

308      Dailey, R., D. Lillian and D. Smith. 1992. "Streamlined Approach for Environmental Restoration
309         (SAFER): An Overview." Proceedings of the 1992 Waste Management and Environmental
310         Sciences Conference.

311      Gianti, S., R. Dailey, K. Hull and J. Smyth. 1993. "The Streamlined Approach For
312         Environmental Restoration." Proceedings of Waste Management '93. Vol 1. pp. 585-587.

313      Smyth, J. D. and J. P. Amaya. 1994. Streamlined Approach for Environmental Restoration
314         (SAFER): Development, Implementation and Lessons Learned. Pacific Northwest Laboratory
315         Report PNL-9421/UC-402, Richland, WA.

316      A.12.4 Technical Project Planning

317      Guidance:

318      U. S. Army Corps of Engineers (USAGE). 1995. Technical Project Planning Guidance for
319         Hazardous, Toxic and Radioactive Waste (HTRW) Data Quality Design. Engineer Manual
320         EM-200-1-2 (superceded by EM-200-1-2, 1998).

321      U. S. Army Corps of Engineers (USAGE). 1998. Technical Project Planning Process. Engineer
322         Manual EM-200-1-2.

323      A.12.5 Expedited Site Characterization

324      Guidance:

325      American Society for Testing and Materials (ASTM). 1996. Standard Provisional Guide for
326         Expedited Site Characterization of Hazardous Waste Contaminated Sites. D585-96.


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327     Papers:

328     Bottrell, D. 1993. "DOE's Development and Application of Planning Processes to Meet
329        Environmental Restoration and Waste Management Data Needs." Twentieth Annual National
330        Energy and Environmental Quality Division Conference.

331     Burton, J. C., et al. 1993. "Expedited Site  Characterization: A Rapid Cost-Effective Process for
332        Preremedial Site Characterization." Proceeding of Superfund XIV, Vol. n, Hazardous
333        Materials Research and Control Institute, Greenbelt, MD, pp. 809-826.

334     Burton, J. C. 1994. "Expedited Site Characterization for Remedial Investigations at Federal
335        Facilities." Proceedings Federal Environmental Restoration in and Waste Minimization n
336        Conference, Vol. II, pp. 1407-1415.

337     Johnson, R. 1993a "Adaptive Sampling Program Support for Expedited Site Characterization."
338        ER'93 Environmental Remediation Conference Proceedings.

339     Johnson, R. 1993b. "Adaptive Sampling Program Support for the Unlined Chromic Acid Pit,
340        Chemical Waste Landfill, Sandia National Laboratory, Albuquerque, New Mexico." ANL-
341        EAD/TM-2.

342     Robatt, A. 1997. "A Guideline for Dynamic Work Plans and Field Analytics: The Keys to Cost
343        Effective Site Cleanup." Tufts University Center for Field Analytical Studies and Technology
344        and U.S. EPA, Region 1, Hazardous Waste Division.

345     Robatt, A. 1998. "A Dynamic Site Investigation: Adaptive Sampling and Analysis Program for
346        Operable Unit 1 at Hanscom Air Force Base, Bedford, Massachusetts." Tufts University
347        Center for Field Analytical Studies and Technology and U.S. EPA, Region 1, Office of Site
348        Remediation and Restoration, Boston, MA.

349     Robbat, A., S. Smarason, and Y. Gankin.  1998. "Dynamic Work Plans and Field Analytics, The
350        Key to Cost-Effective Hazardous Waste Site Investigations," Field Analytical Chemistry and
351        Technology 2(5):253-65.

352     Starke, T. P., C. Purdy, H. Belencan, D. Ferguson and J. C. Burton. 1995. "Expedited Site
353        Characterization at the Pantex Plant." Proceedings of the ER'95 Conference.
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354     A. 12.6 Value Engineering

355     Guidance:

356     The February 1996 Amendment to the Office of Federal Procurement Policy Act (41 U.S.C. 401
357        et. seq.) (Public Law 104-106, Sec 4306 amended this.)

358     Federal Acquisitions Regulations. FAR, Part 48, Value Engineering.  September 19, 1992.

359     Federal Acquisitions Regulations. FAR, Part 52.248-1,-2,-3, Value Engineering Solicitation
360        Provisions and Contract Clauses. January 31, 1989.

361     National Defense Authorization Act for Fiscal Year 1996. PL 104-106, Law Requiring Value
362        Engineering in Executive Agencies. February 10, 1996.

363     Office of Management and Budget (OMB). 1993. OMB Circular A-131, Value Engineering.

364     Office of Management and Budget (OMB). 1997. OMB Circular A-11, Preparation and
365        Submission of Budget Estimates.

366     U. S. Army. Value Engineering. Army Regulation AR 5-4, Chapter 4  (Reference only).

367     U. S. Army Corps of Engineers (USAGE). Engineer Regulation. ER  5-1-11.

368     U. S. Department of Energy. 1997. Value Management. Good Practice Guide (GPG-FM-011).

369     U. S. Department of the Interior (Dol).  1995. Departmental Manual,  Management Systems and
370        Procedures, Part 369, Value Engineering, Chapter 1, General Criteria and Policy. May 18,
371        1995.

372     Books:

373     Fallen, C. 1990. Value Analysis. The Miles Value Foundation, 2nd Edition.

374     Kauffman, J. J. 1985. Value Engineering for the Practitioner. North Carolina State University,
375        Raleigh, NC.
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376     Miles, L. D. 1989. Techniques of Value Analysis and Engineering. McGraw-Hill Book
377        Company, New York, NY.

378     Mudge, A. E.  1989. Value Engineering, A Systematic Approach. J. Pohl Associates.

379     Parker, D.  199x. Value Engineering Theory. The Miles Value Foundation.

380     Papers:

381     Al-yousefi, A. 1996. "Total Value Management (TVM): A VE-TQM Integration." Proceedings
382        of the 1996 SAVE Conference,  Society of American Value Engineers.

383     Blumstein, G. 1996. "FAST Diagramming: A Technique to Facilitate Design Alternatives."
384        Proceedings of the 1996 SAVE  Conference, Society of American Value Engineers.

385     Maynor, D. 1996. Value Engineering for Radiation Hazards Remediation at the Fernald OU1,
386        Ohio. U. S. DOE, Ohio Field Office.

387     Morrel, C. 1996. Value Engineering for Radiation Hazards Remediation at Fernald OU4,  Ohio.
388        U.S. DOE Reclamation Technical Service Center.

389     Wixson, J. R.  1987. "Improving Product Development with Value Analysis/Value Engineering:
390        A Total Management Tool." Proceedings of the Society of American Value Engineers, Vol.
391        22, pp.51-66.

392     A. 12.7 Systems Engineering

393     Guidance:

394     Electronic Industries Alliance (EIA). 1994. Systems Engineering. Standard EIA/IS-632.

395     Electronic Industries Alliance (EIA). 1997. Upgrade IS-632, Process for Engineering a System.
396        EIA/SP-3537 Part 1: Process Characteristics and EIA/SP-4028 Part 2: Implementation
397        Guidance.

398     International Electrical and Electronics Engineers (IEEE). 1994. Standard for Application and
399        Management of the Systems Engineering Process. P1220.
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400     U. S. Department of Defense (DoD). 1992. Systems Engineering. MIL-STD-499B.

401     U. S. Department of Energy (DOE). 1996. Project Execution and Engineering Management
402        Planning. Good Practice Guide GPG-FM-010.

403     Books:

404     Boardman, J. 1990. Systems Engineering: An Introduction. Prentice Hall, New York, NY.

405     Chestnut, H. 1967. System Engineering Methods. John Wiley & Sons, New York, NY.

406     Churchman, C. W. 1968. The Systems Approach. Dell Publishing Co., Inc., New York, NY.

407     Eisner, H. 1998.  Computer-Aided Systems Engineering (CASE). Prentice-Hall, Englewood Cliffs,
408        NJ.

409     Goode, H. H. 1957. Systems Engineering: An Introduction to the Design of Large-Scale Systems.
410        McGraw-Hill, New York, NY.

411     Machol,  R. E.  1965. Systems Engineering Handbook. McGraw-Hill, New York, NY.

412     Smith, D. B. 1974. Systems Engineering and Management. Addison-Wesley Publ. Co., Reading,
413        MA.

414     Wymore, A. W.  1976. Systems Engineering Methodology for Interdisciplinary Teams. John
415        Wiley & Sons, New York, NY.

416     Papers:

417     Bensoussan, A. 1982. "Analysis and Optimization of Systems." Proceedings of the Fifth
418        International Conference on Analysis and Optimization of Systems, Versailles, France.
419        December 14-17, 1982.

420     David, H.T. and  S. Yoo. 1993. "Where Next? Adaptive Measurement Site  Selection for Area
421        Remediation." In: Environmental Statistics, Assessment and Forecasting (Richard Cathern,
422        Ed.). Lewis Publishers, MI.
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423      Ljunggren M. and J Sundberg. 199x. "A Systems Engineering Approach to National Solid Waste
424         Management — Case Study, Sweden." In: Proceedings 12th Int. Conference on Solid Waste
425         Management. November 17-20, 1996.

426      Pacific Northwest Laboratory. 1995. A Systems Engineering Analysis to Examine the Economic
427         Impact for Treatment of Tritiated Water in the Hanford K-Basin. Report No. PNL-S A-24970.
428         Richland, WA.

429      A. 12.8 Total Quality Management

430      Guidance:

431      U. S. Department of the Army. 1992. The Leadership for Total Army Quality Concept Plan.

432      U. S. Department of Energy (DOE).  1993. Total Quality Management Implementation
433         Guidelines. DOE/HR-0066.

434      U. S. Office of Personnel Management (OPM), Federal Quality Institute. 1990. Federal Total
435         Quality Management Handbook, How to Get Started, Booklet 1: Implementing Total Quality
436         Management, U. S. Government Printing Office.

437      Books:

438      Berk, J. and S. Berk. 1993. Total Quality Management: Implementing Continuous Improvement.
439         Sterling Publishing Co. Inc., New York, NY.

440      Carr, D. K. and I. D. Liftman. 1993. Excellence in Government. Coopers and Lybrand, Arlington,
441         VA.

442      Dobyns, L. and C. Crawford-Mason. 1994. Thinking about Quality: Progress, Wisdom and the
443         Deming Philosophy. Times Books, New York, NY.

444      Harrington, H. J. 1991. Business Process Improvement. McGraw-Hill, New York, NY.

445      Koehler, J. W. and J. M. Pankowski. 1996. Quality Government. Designing, Developing and
446         Implementing TQM. St. Lucie Press, Delray Beach, FL.
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447     Rao, A., et al. 1996. Total Quality Management: A Cross Functional Perspective. John Wiley &
448        Sons, New York, NY.

449     Walton, M. 1990. Deming Management at Work. Putnam., New York, NY.

450     Papers:

451     Blacker, S. 1990. "Applying Total Quality Concepts to Environmental Data Operations."
452        Proceedings of the Eight International Conference of the International Society for Quality
453        Control.

454     Breisch, R.E.  1996. "Are You Listening." Quality Progress, pp. 59-62.

455     Houston, A. and Dockstader, S. L. 1997. Total Quality Leadership: A Primer. Department of the
456        Navy, Total Quality Leadership Office Publication Number 97-02.

457     Kidder, P. J. and B. Ryan. 1996. "How the Deming Philosophy Transformed the Department of
458        the Navy." National Productivity Review 15(3).

459     A.12.9 Partnering

460     Guidance:

461     U. S. Department of the Army. 1993. Engineering and Design Quality Management, Appendix B
462        Partnering. ER-1110-1-12.

463     Books:

464     Hrebniak, L. 1994. We Force in Management: How to Build and Sustain Cooperation. Free
465        Press, New York, NY.

466     Maurer, R. 1992. Caught in the Middle: A Leadership Guide for Partnership in the Workplace.
467        Productivity Press,  Portland, OR.

468     Poirier, C. C.  1994. Business Partnering for Continuous Improvement: How to Forge Enduring
469        Alliances Among Employees, Suppliers,  and Customers. Berrett-Koehler, New York, NY.

470     Papers:


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471     Brown, T. L. 1993. "Is there Power in Partnering?" Industry Week, 242(9): 13.

472     Covey, S. R. 1993. "Win-Win Partnerships." Executive Excellence, 10(11): 6-7.

473     Chem-Nuclear Systems, Inc. (CNSI). 1996.  Community Partnering Plan: Pennsylvania Low-
474        Level Radioactive Waste Disposal Facility. S80-PL-021, Revision 0. Commonwealth of
475        Pennsylvania, Department of Environmental Protection, Bureau of Radiation.

476     Mosley, D. and C. C. Moore. 1994. "TQM and Partnering: An Assessment of Two Major Change
477        Strategies." PMNETwork, 18(9): 22-26.

478     Sanders, S. R. and M. M. Moore. 1992. "Perceptions on Partnering in the Public Sector." Project
479        Management Journal, 23(4): 13-19.

480     Simmons, J.  1989. "Partnering Pulls Everything Together." Journal for Quality & Participation.,
481        12:12-16.

482     U. S. Army Corps of Engineers (USAGE). 1996. "U.S. Corps of Engineers Adopts Partnering."
483        National  Academy of Public Administration Foundation, Washington, DC.
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 i         APPENDIX B:  THE DATA QUALITY OBJECTIVES

 2                                       PROCESS


 3     Bl.O Introduction

 4     MARLAP's objective in this appendix is to provide information about the basic framework of
 5     the DQO process (ASTM 5792; EPA, 2000; NRC, 1998; MARSSIM, 1997). The DQO planning
 6     process empowers both data users and data suppliers to take control and resolve issues in a
 7     stepwise fashion. It brings together at the right time all key players from the data user and data
 8     supplier constituencies and enables each participant to play a constructive role in clearly
 9     defining:

10      • The problem that requires resolution;
11      • What type, quantity, and quality of data the decision maker needs to resolve that problem;
12      • Why the decision maker needs that type and quality of data;
13      • How much risk of making a wrong decision is acceptable; and
14      • How the decision maker will use the data to make a defensible decision.

15     The DQO Process provides a logic for setting well-defined, achievable  objectives and developing
16     a cost-effective, technically sound sampling and analysis design. It balances the data user's
17     tolerance for uncertainty with the available resources for obtaining data. The number of visible
18     and successful applications of the DQO process has proven its value to  the environmental
19     community. The DQO process is adaptable depending on the complexity of the project and the
20     input from the decision makers. Some users have combined DQO planning with remedy
21     selection for restoration projects (e.g., DOE's SAFER—see Appendix A.5). Other users have
22     integrated the project scoping meetings with the DQO Process. Much of the information that is
23     developed  during the DQO process is useful for the development of the project plan documents
24     (Chapter 4) and the implementation of the data validation process (Chapter 8) and the data
25     quality assessment (DQA) process (Chapter 9).

26     Since its inception, the term "data quality objectives" has been adopted by many organizations,
27     and the definition has been adapted and modified (ee box on next page). Throughout this
28     document,  MARLAP uses EPA's (2000) definition of DQOs: "Qualitative and quantitative
29     statements derived from the DQO process that clarify study objectives,  define the appropriate
30     type of data, and specify the tolerable levels of potential decision errors that will be used as the
31     basis for establishing the quality and quantity of data needed to support decisions."
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32
33
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35
36
37
38
39
40
41
42
43
44
                          Definitions of Data Quality Objectives
     (1) Statements on the level of uncertainty that a decision maker is willing to accept in
        the results derived from environmental  data (ASTM 5283; EPA, 1986).
     (2) Qualitative and quantitative statements  derived from the DQO process that clarify
        study objectives, define the appropriate type of data, and specify the 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 (EPA, 2000).
     (3) Qualitative and quantitative statements  derived from the DQO process describing
        the decision rules and the uncertainties of the decision(s) within the context of the
        problem(s) (ASTM D5792).
     (4) The qualitative and quantitative statements that specify the quality of the data
        required to support decisions for any process requiring radiochemical  analysis
        (radioassay) (ANSI 42.23).
45

46
47
48
49
50
51
52
53

54
55
56
57
58
59
60
61
62
63
64
B2.0  Overview of the DQO Process

The DQO process (Figure Bl) consists of seven steps (EPA, 2000). In general, the first four steps
of the DQO Process require the project planning team to define the problem and qualitatively
determine required data quality. Once these steps have been addressed adequately, the last three
steps of the process establish quantitative performance measures for the decision and the data.
The last step of the process involves
developing the data collection design based on
the DQOs, which is dependent on a clear
understanding of the first six steps.
Although the DQO process is described as a
sequence of steps, it is inherently iterative. The
output from each step influences the choices
that will be made in subsequent steps. For
instance, a decision rule cannot be created
without first knowing the problem and desired
decision. Similarly, optimization of the
sampling and analysis design generally cannot
occur unless it is clear what is being optimized
—the results of the preceding steps. Often the
outputs of one step will trigger the need to


Stepl: State die Problem
I
Step 2: Identify die Decision
1
Step 3: Identify Inputs to die Decision
t
Step 4: Define die Study Boundaries
1
Step 5: Develop a Decision Rule
I
Step ft Specify Limits on Decision Errors


Step 7:  Optimize die Design for Obtaining Data
 Figure Bl—Seven steps of the DQO process.
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65     rethink or address issues that were not evaluated thoroughly in prior steps. These iterations lead
66     to a more focused sampling and analysis design for resolving the defined problem. The first six
67     steps should be completed before the sampling and analysis design is developed, and every step
68     should be completed before data collection begins. The DQO process is considered complete
69     with the approval of an optimal design for sampling and analysis to support a decision or when
70     available historical data are sufficient to support a decision.

71     In practice, project planning teams often do a cursory job on the first four steps, wanting to get
72     into technical design issues immediately. Without carefully defining the problem and the desired
73     result, the project planning team may develop a design that is technically sound but answers the
74     wrong question, or answers the questions only after the collection of significant quantities of
75     unnecessary data. Time  spent on the first four steps is time well spent. Extra effort must be given
76     to assure that Steps 1 to 4 are adequately addressed.

77     When applying the DQO process, or any planning approach, it is important to document the
78     outputs of each step to assure that all participants understand and approve the interim products,
79     and that they have a clear record of their progress. It is sometimes useful to circulate an approval
80     copy with signature page to ensure agreement of the stakeholders.

si     B3.0 The  Seven Steps of the DQO Process

82     Each step of the DQO process will be discussed in the following sections. Not all items will be
83     applicable to every project. The project planning team should apply the concepts that are
84     appropriate to the problem.

85     B3.1   DQO Process Step 1: State the Problem

86     The first step is to define the problem clearly. The members of the project planning team present
87     their concerns, identify regulatory issues and threshold levels, and review the site history. The
88     project planning team should develop a concise description of the problem. Some elements to
89     include in the description might be the study objectives, regulatory context, groups who have an
90     interest in the study, funding and other resources available, previous study results, and any
91     obvious sampling design constraints. The more facts, perceptions and concerns of the key
92     stakeholders—including important social, economic, or political issues—that are identified
93     during this step, the better the chances are that the issues driving the decisions and actions will be
94     identified.
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 95      The primary decision maker should be identified. The resources and relevant deadlines to address
 96      the problem are also defined at this time. If possible, a "site conceptual model" should be
 97      developed. This will help structure and package the diverse facts into an understandable picture
 98      of what the various issues are and how those issues can be focused into a specific problem.
 99      The expected outputs of Step  1 are:

100       •  A conceptual model that packages all the existing  information into an understandable picture
101          of the problem;

102       •  A list of the project planning team members and identification of the decision maker;

103       •  A concise description of the problem; and

104       •  A summary of available resources and relevant deadlines for the study.

105      B3.2   DQO Process Step 2: Identify the Decision

106      During Step 2 of the DQO Process, the project planning team defines what decision must be
107      made or what question the project will attempt to resolve. The decision (or question) could be
108      simple, like whether a particular discharge is or is not in compliance,  or the decision could be
109      complex, such as determining if observed adverse health is being caused by a non-point source
110      discharge. Linking the problem and the decision focuses the project planning team  on seeking
111      only that information essential for decision making, saving valuable resources (time and money).

112      The result may be a comprehensive decision for a straightforward problem, or a sequence of
113      decisions for a complex problem. For complex problems with multiple concerns, these concerns
114      should be prioritized in order of importance. Often a complex concern is associated with a series
115      of decisions that need to be made. Once these decisions have been identified, they should be
116      sequenced in a logical order so the answer to one decision provides input in answering the next
117      decision. It may be helpful to develop a logic flow diagram (decision framework), arraying each
118      element of the issue in its proper sequence along with its associated decision that requires an
119      answer.

120      The term "action level" is used in this document to denote the numerical value that will cause the
121      decision maker to choose one of the alternative actions. The action level may be a derived
122      concentration guideline level, background level,  release criteria, regulatory decision limit, etc.
123      The action level is often associated with the type of media, analyte and concentration limit. Some
124      action levels, such as the release criteria for license termination, are expressed in terms of dose or


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125      risk. The release criterion typically is based on the total effective dose equivalent (TEDE), the
126      committed effective dose equivalent (CEDE), risk of cancer incidence (morbidity) or risk of
127      cancer death (mortality) and generally can not be measured directly. A radionuclide-specific
128      predicted concentration or surface area concentration of specific nuclides that can result in a dose
129      (TEDE or CEDE) or specific risk equal to the release criterion is called the "derived concentra-
130      tion guideline level"  (DCGL). A direct comparison can be made between the project's analytical
131      measurements and the DCGL (MARS SIM, 1997).

132      The project planning team should define the possible actions that may be taken to solve the
133      problem. Consideration should be given to the option of taking no action. A decision statement
134      can then be developed by combining the decisions and the alternative actions. The decision rule
135      and the related hypothesis test will be more fully developed in the DQO process  at Steps 5 and 6.

136      By defining the problem and its associated decision clearly, the project planning team has also
137      begun to define the inputs and boundaries (DQO process  Steps 3 and 4). At the end of Step 2, the
138      project planning team has:

139       •  Identified the principal decisions or questions;

140       •  Defined alternative actions that could be taken to solve the problem based on possible
141          answers to the principal decisions and questions;

142       •  Combined the principal decisions and questions and the alternative actions into decision
143          statements that expresses a choice among alternative actions; and

144       •  Organized multiple decisions.

145      B3.3   DQO Process Step 3: Identify Inputs to the Decision

146      During Step 3, the  project planning team makes a formal  list of the specific information required
147      for decision making.  The project planning team should determine what information is needed and
148      how it can be acquired. The project planning team should specify if new measurements are
149      required for the listed data requirements. The data required are based on outcomes of discussion
150      during the previous two steps. The project planning team  should define the basis for setting the
151      action level. Depending on the level of detail of the discussion during the previous steps, then
152      efforts associated with  Step 3 may be primarily to capture that information. If the first two steps
153      have not defined the  inputs with enough specificity, then  those inputs should be defined here.
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154     However, before going further, the output should be reviewed to assure that the problem, the
155     decision steps and the input are compatible in complete agreement.

156     An important activity during Step 3 is to determine if the existing data or information, when
157     compared with the desired information, has significant gaps. If no gaps exist, then the existing
158     data or information may be sufficient to resolve the problem and make the decision. (Although
159     there may be no gaps in the data, the data may not have enough statistical power to resolve the
160     action level. See Step 6 for more discussion.) In order to optimize the use of resources, the
161     project planning team should maximize the use of historical information. If new data are
162     required, then this step establishes what new data (inputs) are needed. The specific environmental
163     variable or characteristic to be measured should be identified.  The DQO Process clearly links
164     sampling and analysis efforts to an action and a decision. This linkage allows the project
165     planning team to determine when enough data have been collected.

166     If the project planning team determines that collection of additional data is needed, the analytical
167     laboratory acquisition strategy options should be considered at this stage. Identifying suitable
168     contracting options should be based on the scope, schedule, and budget of the project, and the
169     capability and availability of laboratory resources during the life of the project, and other
170     technical considerations of the project. If an ongoing contract with a laboratory is in place, it is
171     advisable to involve them with the radioanalytical specialists as early as possible.

172     The project planning team should ensure that there are analytical protocols available to provide
173     acceptable measurements. If analytical methods do not exist, the project planning team will need
174     to consider the resources needed to develop a new method, reconsider the approach for providing
175     input data, or perhaps reformulate the decision statement.

176     The expected outputs of Step 3 are:

177      •  A list of information needed for decision making;
178      •  Determination of whether data exists and are sufficient to resolve the problem;
179      •  Determination of what new data, if any, are required;
180      •  Defined the characteristics that define the population and domain of interest;
181      •  Defined the basis for the action level;
182      •  Confirmation that appropriate analytical protocols exist to provide the necessary data; and
183      •  A review of the planning output to assure the problem, decision and inputs are fully linked.
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184     B3.4  DQO Process Step 4: Define the Study Boundaries

185     In Step 4, the project planning team should define clearly the geographic area within which the
186     decisions will apply. The project planning team specifies the spatial and temporal boundaries
187     covered by the decision statement. The spatial boundaries define the physical aspects to be
188     studied in terms of geographic area, media, and any appropriate subpopulations (e.g., an entire
189     plant, entire river basin, one discharge, metropolitan air, emissions from a power plant). When
190     appropriate, divide the population into strata that have relatively homogeneous characteristics.
191     The temporal boundaries describe the time frame the study data will represent (e.g., possible
192     exposure to local  residents over a 30-year period) and when samples should be taken (e.g.,
193     instantaneous samples, hourly samples, annual average based on monthly samples, samples after
194     rain events). Changing conditions that could impact the success of sampling and analysis and
195     interpretation need to be considered. These factors include weather, temperature, humidity, or
196     amount of sunlight and wind.

197     The scale of decision is also defined during this step. The scale of decision selected should be the
198     smallest, most appropriate subset of the population for which decisions will be made based on
199     the spatial or temporal  boundaries. During Step 4, the project planning team also should identify
200     practical constraints on sampling and analysis that could interfere with full implementation of the
201     data collection design.  These include time, personnel, equipment, and seasonal or meteorological
202     conditions when sampling is not possible or may bias the data.

203     In practice, the study boundaries are discussed when the decision makers agree on the problem
204     and its associated decision. For instance, a land area that may be contaminated or a collection of
205     waste containers would be identified as part of the problem and decision definition in Steps 1 and
206     2. The boundaries also would be considered when determining inputs to the decision in Step 3. If
207     the study boundaries had not been addressed before Step 4 or if new issues were raised during
208     Step 4, then Steps 1, 2, and 3 should be revisited to determine how Step 4 results are now
209     influencing the three previous steps.

210     The outputs of Step 4 are:

211       •  A detailed description of the spatial and temporal boundaries of the problem; and
212       •  Any practical  constraints that may interfere with the sampling and analysis activities.
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213     B3.5   Outputs of DQO Process Steps 1 to 4 Lead Into Steps 5 to 7

214     At this stage in the DQO process, the project planning team has defined with a substantial degree
215     of detail the problem, its associated decision, and the inputs and boundaries for addressing that
216     problem. The project planning team knows whether it needs new data to fill specific gaps and
217     what that data should be. The remaining three steps are highly technical and lead to the selection
218     of the sampling and analysis design. Even when new data is not required (i.e., a data collection
219     design is not needed), the project planning team should continue with Steps 5 and 6 of the DQO
220     Process. By establishing the formal decision rule and the quantitative estimates of tolerable
221     decision error rates, the  project planning team is assured that consensus has been reached on the
222     actions to be taken and information to establish criteria for DQA process.

223     It is important to emphasize that every effort must be made to assure that Steps 1 to 4 are
224     adequately addressed. If the necessary time is taken in addressing carefully the first four steps
225     and assuring consensus among the project planning team, then the three remaining steps are less
226     difficult.

227     B3.6  DQO Process Step  5: Develop a Decision Rule

228     In Step 5, the project planning team determines the appropriate statistical parameter that
229     characterizes the population, specifies the action level, and integrates previous DQO process
230     outputs into a single "if..., then  ..." statement (called a "decision rule") that describes a logical
231     basis for choosing among alternative actions. (The statistical parameters are discussed in more
232     detail in Chapter 19, Measurement Statistics.)

233     The four main elements  to the decision rule are:

234     1.  THE PARAMETER OF  INTEREST. A descriptive measure (e.g., mean, median, or proportion) that
235         specifies the characteristic or attribute that the decision maker would like to know and that
236         the data will estimate. The characteristics that define the population and domain of interest
237         was established in Step 3.

238     2.  THE SCALE OF DECISION MAKING.  The smallest, most  appropriate subset for which decisions
239         will be made. The scale of decision making was previously defined in Step 4.

240     3.  THE ACTION LEVEL.  A threshold value of the parameter of interest that provides the criterion
241         for choosing among  alternatives. Action levels may be based on regulatory standards or they
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242         may be derived from site- and analyte-specific criteria such as dose or risk analysis. The basis
243         for the action level was determined in Step 3.

244     4.  THE ALTERNATIVE ACTIONS. The actions the decision maker would take, depending on the
245         "true value" of the parameter of interest. The alternative actions were determined in Step 2.

246     The decision rule is a logical, sequential set of steps to be taken to resolve the problem. For
247     example, "If one or more conditions exits then take action 1, otherwise take action 2."

248     The outputs of Step 5 are:

249       •  The action level;
250       •  The statistical parameter of interest; and
251       •  An "if..., then  ..." statement that defines the conditions that would cause the decision maker
252         to choose among alternative courses of action.

253     B3.7  DQO  Process Step 6: Specify the Limits on Decision Errors

254     In Step  6 of the DQO process, the project planning team assesses the potential consequences of
255     making a wrong decision and establishes a tolerable level for making a decision error. The
256     project planning team defines the types of decision errors (Type I and n) and the tolerable limits
257     on the decision error rates. In general,  a Type I error is deciding against the default assumption
258     (the null hypothesis) when it is actually true; a Type II error is not deciding against the null
259     hypothesis when it is actually false (see Attachment Bl and Appendix C for detailed
260     discussions). The limits on the decision errors will be used to establish measurement
261     performance criteria for the data collection design.

262     Traditionally, the principles of statistical hypothesis testing (see Chapter 19) have been used to
263     determine tolerable levels of decision error rates. Other approaches applying decision theory have
264     been applied (Bottrell, et al., 1996a,b). Based on an understanding of the possible consequences
265     of making a wrong decision in taking alternative actions, the project planning team chooses the
266     null hypotheses and judges what decision error rates are tolerable for making a Type I or Type n
267     decision error.

268     The project planning team also specifies a range of possible values where the consequences of
269     decision errors are relatively minor (the gray region). Specifying a gray region is necessary
270     because variability in the population and imprecision in the measurement system combine to
271     produce variability in the data such that the decision may be "too close to call" when the true

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272     value is very near the action level. The gray region establishes the minimum distance from the
273     action level where it is most important that the project planning team control Type II errors. (For
274     additional information on the gray region, hypothesis testing, and decision errors, see EPA
275     (2000), NRC (1998), and Chapter 19, Measurement Statistics.)

276     The tolerable decision  error rates are used to establish performance goals for the data collection
277     design. Overall variability in the result can be attributed to several sources, including sample
278     location, collection, and handling; laboratory handling and analysis; and data handling and
279     analysis. In many environmental cases, sampling is a much larger source of uncertainty than
280     laboratory analyses. The goal is to develop a sampling and analysis design that reduces the
281     chance of making a wrong decision. The greater certainty demanded by the decision makers, the
282     more comprehensive and expensive the data collection process is likely to be. In this step, the
283     project planning team has to come to an agreement on how to determine acceptable analytical
284     uncertainty and how good the overall data results are required to be. The team has to reach a
285     consensus on the trade-off between the cost of more information and the increased certainty in
286     the resulting decision.

287     Often the project planning team does not feel comfortable with the concepts and terminology of
288     hypothesis testing (Type I and Type II errors, gray zone, critical region, tolerable decision error
289     rates). As a result the project planning team may have difficulty (or want to skip) this step of the
290     directed planning process. If these steps are skipped or insufficiently addressed, it is more likely
291     that the data will not be of the quality needed for the project. Attachment Bl is provided to give
292     some additional guidance on these concepts. MARLAP recommends that for each radionuclide
293     of concern an action level, gray region and limits on decision error rates be established during a
294     directed planning process.

295     Figure B2 summarizes the outputs of the decisions made by the project planning team  in a
296     Decision Performance  Goal Diagram (EPA, 2000).  The horizontal axis represents the (unknown)
297     true value of the parameter being estimated. The vertical axis represents the decision maker's
298     desired probability of concluding that the parameter exceeds an action limit. The "gray region"
299     (bounded on one side by the action level) defines an area where the consequences of decision
300     error are relatively minor (in other words, it defines how big a divergence from the action level
301     we wish to distinguish). The gray region is related to the  desired precision of the measurements.
302     The height of the indicated straight lines to the right and left of the gray region depict the
303     decision maker's tolerance for Type I and Type II errors.
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  1
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                                          100  125   150   175  200
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           Figure B2(a)—Decision performance goal diagram null
             hypothesis: the parameter exceeds the action level.
                     Figure B2(b)—Decision performance goal diagram null
                      hypothesis: the parameter is less than the action level.
304      For purposes of this example, the default assumption (null hypothesis) was established as the
305      measured concentration exceeded the action level (Figure B2a). The Type I error (5 percent at
306      true concentration between 100 and 150; Ipercent at >150 units) making a decision NOT to take
307      action to solve an environmental problem (e.g., remediate) when that action was in fact required
308      (e.g.,  analyte concentrations are really above an action level). The Type n error (5 percent at true
309      concentrations <25 units; 10 percent between 25  and  75 units) is understood  as taking an action
310      when in fact that action is not required (e.g., analyte concentrations are really below the action
311      level).
312      In Figure B2(b), the default assumption  (null hypothesis) was established as the measured
313      concentration is less than the action level. The Type I error (5 percent at true concentrations <25
314      units;  10 percent between 25 and 100 units) is understood as taking an action when in fact that
315      action is NOT required (e.g., analyte concentrations are really below the action level). The Type
316      II error (10 percent at true concentration between 100 and 150; 5 percent at >150 units) is
317      understood as making a decision not to take action to solve an environmental problem (e.g.,
318      remediate) when that  action was in fact  required (e.g., analyte concentrations are really above an
319      action level).

320      The output of Step 6 is:

321          •    The project planning team's quantitative measure of tolerable decision  error rates based
322              on consideration of proj ect resources.
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323     B3.8  DQO Process Step 7: Optimize the Design for Obtaining Data

324     By the start of Step 7, the project planning team has established their priority of concerns, the
325     definition of the problem, the decision or outcome to address the posed problem, the inputs and
326     boundaries, and the tolerable decision error rates. They have also agreed on decision rules that
327     incorporate all this information into a logic statement about what action to take in response to the
328     decision. During Step 7, the hard decisions are made between the planning team's desire to have
329     measurements with greater certainty and the reality of the associated resource needs (time, cost,
330     etc.) for obtaining that certainty.

331     During Step 7, the project planning team optimize the sampling and analytical design and
332     established the measurement quality objectives (MQOs) so the resulting data will meet all the
333     established constraints in the most resource-effective manner. The goal is to determine the most
334     efficient design (combination of sample type, sample number and analytical procedures) to meet
335     all the constraints established in the previous steps. Once the technical specialists and the rest of
336     the project planning team come to agreement about the sampling and analysis design, the
337     operational details and theoretical assumptions of the selected design should be documented.

338     If a proposed design cannot be developed to meet the limits on decision error rates within budget
339     or other constraints, then the project planning team will have to consider relaxing the error
340     tolerance, adjusting the width of the gray region, redefining the scale of decision, or committing
341     more funding. There is always a trade off between quality, cost and time. The project planning
342     team will need to develop a consensus on how to balance resources and data quality. If the
343     proposed design requires analysis using analytical protocols not readily available, the project
344     planning team must consider the resources (time and cost) required to develop and validate a
345     method, generate method detection limits relevant to media of concern, and  develop appropriate
346     QA/QC procedures and criteria (Chapter 6, Selection and Application of an Analytical Method).

347     If the project entails a preliminary investigation of a site or material for which little is known, the
348     planners may choose to employ MQOs and requirements that typically are achieved by the
349     selected sampling and analytical procedures. At this early point in the project, the lack of detailed
350     knowledge of the site or material may postpone the need for the extra cost of more expensive
351     sampling and analytical procedures and large numbers of samples, until more site or material
352     knowledge is acquired. The less-demanding MQOs, however, should be adequate to further
353     define the site or material. For situations when the measured values are distant from an action
354     level the MQO-compliant data could also be sufficient to support the project decision.
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355     The planning of data collection activities is typically undertaken to determine if a characteristic
356     of an area or item does or does not exist above an action level. Since the area of interest (popula-
357     tion) is usually too large to be submitted to analyses, in its entirety, these data collection activities
358     generally include sampling. If sampling is done correctly, the field sample or set of field samples
359     will represent the characteristics of interest and, if analyzed properly, the information gleaned
360     from the samples can be used to make decisions about the larger area. However,  if errors occur
361     during implementation of the project, the samples and associated data may not accurately reflect
362     the material from which the samples were collected and incorrect decisions could be made.

363     The planning team attempts to anticipate, quantify, and minimize the uncertainty in  decisions
364     resulting from imprecision, bias, and blunders—or in other words, attempts to manage uncer-
365     tainty by managing its sources. The effort expended in managing uncertainty is project dependent
366     and depends upon what constitutes an acceptable level of decision uncertainty and the proximity
367     of the  data to a decision point. For example, Figure B3(a) presents a  situation where the data
368     have significant variability. Yet the variability of the data does not materially add to the
369     uncertainty of the decision since the  measurements are so far removed from the action level.
370     More resources could be expended to control the variability. However, the additional expenditure
371     would be unnecessary, since they would not alter the decision or measurably increase confidence
372     in the decision.
373     In contrast, Figure B3(b) depicts data with
374     relatively little variability, yet this level of
375     variability is significant since the measured
376     data are adjacent to the action level, which
377     results in increased uncertainty in the
378     decision. Depending upon the consequences
379     of an incorrect decision, it may be advisable
380     to expend more resources with  the intention
381     of increasing confidence in the  decision.

382     The output of Step 7 is:

383       •  The most resource-effective design for
384         sampling and analysis that will obtain
385         the  specific amount and quality of data
386         needed to resolve the problem within
387         the  defined constraints; and
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     (a)
                                   Action
                                   Level
                       I Mean
                      Concentration
     (b)
                                 j   Action
                                 1   Level
                           Mean
                                                                         Concentration
Figure B3 — How Proximity to the action level determines
      what is an acceptable level of uncertainty.
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        The Data Quality Objectives Process
388      • Detailed plans and criteria for data assessment.

389     B3.9  References

390     American National Standards Institute (ANSI) N42.23. American National Standard
391        Measurement and Associated Instrument Quality Assurance for Radioassay Laboratories.
392        1996.

393     American Society for Testing and Materials (ASTM) D5283. Standard Practice for Generation
394        of Environmental Data Related to Waste Management Activities: Quality Assurance and
3 95        Quality Control Planning and Implementation. 1992.

396     American Society for Testing and Materials (ASTM) D5792. Standard Practice for Generation
397        of Environmental Data Related to Waste Management Activities: Development of Data
398        Quality Objectives, 1995.

399     American Society for Testing and Materials (ASTM) D6051. Standard Guide for Composite
400        Sampling and Field Subsampling for Environmental Waste Management A ctivities.  1996.

401     Bottrell, D., S. Blacker and D. Goodman. 1996a. "Application of Decision Theory Methods to
402        the Data Quality Objectives Process." In, Proceedings of the Computing in Environmental
403        Resource Management Conference, Air and Waste Management Association.

404     Bottrell, D., N. Wentworth, S. Blacker and D. Goodman. 1996b. "Improvements to Specifying
405        Limits on Decision Errors in the Data Quality Objectives Process." In, Proceedings of the
406        Computing in Environmental Resource Management Conference, Air and Waste
407        Management Association.

408     MARSSEVI. 1997. Multi-Agency Radiation Survey and Site Investigation Manual. NUREG-
409        1575, EPA 402-R-97-016.
410     U.S. Environmental Protection Agency (EPA). 2000. Guidance for the Data Quality Objective
411        Process (EPA QA/G-4). EPA/600/R-96/055, Washington, DC. available from www.epa.gov/
412        qualityl/qa_docs.html.

413     U.S. Environmental Protection Agency (EPA). 1986. Development of Data Quality Objectives,
414        Description of Stages I and II. Washington, DC.
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                                                            The Data Quality Objectives Process
415      U.S. Nuclear Regulatory Commission (NRC). 1998. A Nonparametric Statistical Methodology
416         for the Design and Analysis of Final Status Decommissioning Surveys. NUREG-1505, Rev.
417         1.
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418

419
              ATTACHMENT B-l  DECISION ERROR RATES
                              AND THE GRAY REGION
420
        B-l.l Introduction
421      This attachment is provided to present some additional discussion on decision error rates and the
422      gray region. The project planning team will need to specify a range of possible values where the
423      consequences of decision errors are relatively minor—the "gray region." Specifying a gray region
424      is necessary because variability in the population and imprecision in the measurement system
425      combine to produce variability in the data such that the decision may be "too close to call" when
426      the true value is very near the action level. The gray region  establishes the minimum distance
427      from the action level, where it is most important that the project planning team control Type II
428      errors.

429      B-l.2 The Region of Interest

430      The first step in constructing the
431      gray region is setting the range of
432      concentrations that is a region of
433      interest (a range of possible values).
434      Usually there is an  action level (such
435      as the derived concentration guide-
436      line level, a regulatory limit) that
437      should not be exceeded. If the
438      project planning team wants a
439      method to measure sample concen-
440      trations around this level, they would
441      not select one that worked at concen-
442      trations at 10 to 100 times the action
443      level, nor would they select one that
444      worked from zero to half the action level. They would want a method that worked well around
445      the action level—perhaps from 0.1 to 10 times the action level, or from one-half to two times the
446      action level. For the purpose of the example in this attachment, the action level is  1.0 and the
447      project planning team selected a region of interest that is zero to twice the action level (0-2), as
448      shown on the x-axis in Figure B-l.l.




































































>
y
&






Actic
*
f








n Ee^









el



0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1
Concentration











8 2
                                                            FIGURE B-l.l
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                                                         Decision Error Rates and the Gray Region
449

450
451
452
453
454
455

456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472

473
474
475
476
477
478
479

480
481
482
B-1.3 Measurement Uncertainty at the Action Level

The action level marks the concentration level that the project planning team must be able to
distinguish. The project planning team wants to be able to tell if the measured concentration is
above or below the action level. Does this mean that the project planning team needs to be able
to distinguish 0.9999 times the action level from 1.0001 times the action level? Sometimes, but
not usually. This is fortunate, because current measurement techniques are probably not good
enough to distinguish that small a difference in concentrations.
How close to the action level can
the project planning team plan to
measure? For this example, we will
assume that the standard uncertainty
(1 sigma, a)  of the measured
concentration is 10 percent of the
action level.  With that kind of
measurement "precision," can the
project planning team tell the
difference between a sample with
0.9 times the action level from one
right at the action level? Not
always. Figure B-1.2 shows the
distribution of the concentration that
is measured  (assuming a normal distribution). This means that about 16 percent of the time, the
measured concentration (in the shaded area) will appear to be 0.9 times the action level or less,
even though the true concentration is exactly equal to the action level.
Concentra tion
FIGURE B-1.2
Similarly, about 16 percent of the
time, the measured concentration
will appear to be at or above the
action level (as shown in the shaded
area in Figure B-1.3), even though
the true concentration is only 0.9
times the action level.

The problem is, when there is only
the measurement result  to go by, the
project planning team cannot tell the
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        Decision Error Rates and the Gray Region
483
484


485

486
487
488
489
490
491
492

493
494
495
496
497
difference with confidence. If the measured concentration is 0.9, it is more likely that the true
concentration is 0.9 than it is 1.0, but there remains a chance that it is really 1.0.

B-1.4 The Null Hypothesis
If the measured concentration is 0.95,
it is equally likely that the true
concentration is 0.9 as it is 1.0 (see
Figure B-1.4). How does the project
planning team decide what is the true
concentration? The project planning
team starts by asking:

"Which mistake is worse: (1) saying
the true concentration is 0.9 when it
is 1.0 or more? or (2) saying the true
concentration is 1.0 when it is 0.9 or
less?"
                 0.95
\ \
0.2   0.4   0.6    0.8    1    1.2   1.4   1.6   1.8

                Conce ntra tion
               FIGURE B-1.4
498     What does the project planning team mean by "worse"? The project planning team really does
499     not want to make a mistake that is likely to remain undiscovered or will be difficult or expensive
500     to correct.
501
Case 1: Assume The True Concentration is Over 1.0
502     If a true concentration of 1.0 or more is over a regulatory limit, the project planning team will not
503     want to make mistake (1) above. If the project planning team decides the true concentration is
504     less than 1.0, the project planning team is not likely to look at the sample again. That would
505     mean that the mistake would probably not be discovered until much later, if at all. On the other
506     hand, if the project planning team decides that the true concentration is over 1.0 when it really is
507     not, the project planning team will discover the mistake while they are trying to figure out how to
508     "correct" the high reading. So the project planning team will make a rule: Assume the true
509     concentration is over 1.0 unless they are really sure it is under. This is the default assumption, the
510     "null hypothesis."

511     How sure does the project planning team need to be? For this example, we will assume that the
512     project planning team would like to be 95 percent sure. To be 95 percent sure, they would have to
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                                                           Decision Error Rates and the Gray Region
513
514
515
516
517
518
519
520
521
522
523

524
525
526
527
528
529
        stay with their assumption that the
        true concentration is over 1.0 unless
        the measured concentration is 0.84
        or less (Figure B-1.5). The project
        planning team knows that this will
        only happen about 5 percent of the
        time when the true concentration is
        really 1.0. That is, the measurement
        has to be less than 0.84 to be 95
        percent sure the true concentration
        is less than 1.0.
                                                                  FIGURE B-1.5
But what if the true concentration is
0.9 or less—mistake (2) above?
Under the new rule (default assumption or null hypothesis), how often will the project planning
team say that the true concentration is over 1.0 when it is really only 0.84? As seen in Figure B-
1.6, there is only a 50-50 chance of making the right decision when the true concentration really
is 0.84. That is the price of being sure they are not over the action level.
530
53 1
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
        How low does the true concentration
        have to be in order to have a pretty
        good chance of deciding that the
        true concentrati on i s b el ow the
        limit? To be 95 percent sure, the
        true concentration needs to be twice
        as far below the action level as the
        decision point, namely at about 0.68.
        That is, the project planning team
        will need a concentration of 0.68 or
        less to be 95 percent sure that they
        will be able to decide the true
        concentration is less than 1.0 (see
        the unshaded portion in Figure B-
        1.7). In other words, it is only when the true concentration is 0.68 or less that the project planning
        team can be pretty sure that they will decide the true concentration is less than 1 .0. (Note how
        similar this looks to an MDC in reverse.)
                                          0.2
                                               0.4
                                                    0.6
                                                         0.8    1    1.2

                                                          Concentra tion
                                                                             1.6
                                                         FIGURE B-1.6
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        Decision Error Rates and the Gray Region
547
548

549
550
551
552
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554
555
556
557
558
559
560
561
562
563

564

565
566
567
568
569
570
571
572
573
574
575
576
577
578

579
580
                                          0.2
                                               0.4
                                                   0.6
                                                                       1.4
                                                                            1.6
Case 2: Assume The True
Concentration is 0.9

As stated previously, the mistake
that is most serious determines the
null hypothesis. Suppose that the
project planning team determined
that it is worse to decide that the true
concentration is over 1.0 when it is
0.9 (than it is to decide it is 0.9
when it is 1.0). Then, the default
assumption (the null hypothesis)
would be that the true concentration
is 0.9, unless the measured
concentration is large enough to convince the planning team otherwise. Only when the measured
concentration reaches 1.06 does the planning team decide the true concentration is over 1.0
(Figure B-1.8). The team will have to have a true concentration of 1.22 or more to be 95 percent
sure that they will be able to decide the true concentration is over 1.0.
B-1.5 The Critical Region
                                                        0.8    1    1.2
                                                         Concentration
                                                         FIGURE B-1.7
                                              0.4
                                                   0.6
                                                        0.8    1    1.2
                                                          Concontrstion
                                                                       1.4
                                                                            1.6
                                                                                 1.8
The mistake that is "worse" defines
the null hypothesis and also defines
a "Type I" error. The probability of a
Type I error happening is called the
"Type I error rate," and is denoted
by alpha (a). Under the original null
hypothesis (Case 1: Assume the true
concentration is over 1.0), a Type I
error would be deciding that the
concentration was less than 1.0
when it really was not. In general, a
Type I error is deciding against the null hypothesis when it is actually true. (A Type I error is also
called a "false positive." This can be confusing when the null hypothesis appears to be a
"positive" statement. Therefore, MARLAP uses the neutral terminology.)

The "less serious" mistake is called a Type n error, and the probability of it happening is the
"Type n error rate," denoted by beta (p). Under the original null hypothesis that the concentration
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                                                          Decision Error Rates and the Gray Region
581     was 1.0 or more, a Type II error would be deciding that the concentration was more than 1.0
582     when it really was not. In general, a Type II error is not deciding against the null hypothesis when
583     it is actually false.

584     In both Case 1 and Case 2, the probability of both Type I errors and Type n errors were set to 5
585     percent. The probabilities were calculated at multiples of the standard deviation, assuming a
586     normal distribution. This will not always be the case. However, the probability of a Type I error
587     is always calculated as the probability that the project planning team will decide to reject the null
588     hypothesis when it is actually true. This is simple enough, as long as there is a clear boundary for
589     the parameter of interest.

590     The parameter of interest in both Case 1 and Case 2 was the true concentration. The true
591     concentration had a limit of 1.0. Therefore, all the project planning team had to do was calculate
592     the probability that they would get a measured concentration that would cause them to decide
593     that the true concentration was less than 1.0, even though  it was equal to  1.0. In the example, the
594     project planning team actually started with the probability (5 percent) and worked out the critical
595     value. The "critical value" (or decision point) is the measured value that divides the measurement
596     results into two different sets: (1) those values that will cause us to reject the null hypothesis and
597     (2) those values that will cause us to leave the null hypothesis as the default. Set (1) is  called the
598     "critical region."

599     The Type I and Type n error rates, a and p, often are both set at 5 percent. This is only by
600     tradition. They do not have to be equal. Neither error rate  needs to be set at 5 percent. The way
601     the project planning team should set the value is by examining the consequences of making a
602     Type I or a Type II error. What consequences will happen as a result of making each type of
603     error? This is a little different than the criterion that was used to define the null hypothesis. It
604     may be that in some circumstances, a Type II error is riskier than a Type I error. In that case,
605     consider making a bigger than p

606     B-1.6 The Gray Region

607     In the previous  sections (B-l. 1 to B-l.4) the project planning team:

608       •  Set the region of interest for the measured concentrations between zero and about twice the
609         action level;

610       •  Assumed that the true concentration exceeds 1.0, unless they measure "significantly" below
611         that, the default assumption (null hypothesis);

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        Decision Error Rates and the Gray Region
612       •  Defined "significantly below" to mean a concentration that would be observed less than 5
613         percent of the time, when the true concentration is actually 1.0. To describe their uncertainty,
614         the project planning team used the normal distribution, with a relative standard deviation of
615         10 percent at the action level, as a model;

616       •  Developed an operational decision rule: If the measured concentration is less than 0.84, then
617         decide the true concentration is less than 1.0. Otherwise, decide there is not enough reason to
618         change the default assumption  (null hypothesis); and

619       •  Found using this operational decision rule that they were pretty sure (95 percent) of deciding
620         that the true concentration is less than  1.0 only when the true concentration is actually 0.68  or
621         less.

622     If the true concentration is between 0.68 and  1.0, all the project planning team really can say is
623     that the probability of deciding that the true concentration is less than 1.0 will be between 5
624     percent (when the true concentration is 1.0) and 95 percent (when the true concentration  is 0.68).
625     Conversely, when the true concentration is in this range, the probability of deciding that the true
626     concentration is not less than 1.0 (i.e., the  probability of a Type n error) will be between  5
627     percent (when the true concentration is 0.68)  and 95 percent (when the true concentration is just
628     under 1.0). This range of concentrations is called the "gray region."

629     When the null hypothesis is that the true concentration exceeds the action level (1.0), the gray
630     region is bounded from above by the action level. This is where a is set. It is bounded from
631     below at the concentration where p is set.  There is some flexibility in setting the lower boundary
632     of the gray region (LBGR). If the project planning team specifies a concentration, they can
633     calculate  the probability p. If they specify  p, they can calculate the value of the true concentration
634     that will be correctly detected as being below 1.0 with probability 1-p.

635     In our example, the project planning team found that they needed the true concentration to be
636     0.68 or less to be at least 95 percent sure that they will correctly decide (by observing a measured
637     value of 0.84 or less) that the true concentration is less than 1.0. If the project planning team
638     doesn't like that, the project planning team can find that a true concentration of 0.71 will be
639     correctly  detected 90  percent of the time (also by observing a measured value of 0.84 or less).
640     The critical value, or  decision point, is determined by a, not p.

641     If the project planning team decides to raise the LBGR (i.e., narrow the gray region) the Type n
642     error rate at the LBGR goes up. If they lower the LBGR (i.e., widen the gray region) the Type n


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                                                          Decision Error Rates and the Gray Region
643
644

645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662

663
664
665
666
667

668
669
670
671
672
673
                                                          0.92
error rate at the LBGR goes down. Nothing substantive is really happening. The project planning
team is merely specifying the ability to detect that the null hypothesis is false.

If the project planning team wants to make a substantive change, they need to change the
probability that an error is made. That is, they need to change the uncertainty (standard deviation)
of the measurements. Suppose the relative standard deviation of the measurements at the action
level is 5 percent instead of 10 percent. Then the value of the true concentration that will be
correctly detected to be below the action level (by observing a measured value of 0.92 or less) 95
percent of the time,  is 0.84. Cutting
the  standard deviation of the
measurement in half has cut the
(absolute) width of the gray region
in half, but left the width of the gray
region in standard deviations
unchanged. Previously, with a = 10
percent, the width of the gray region
was 1.0 - 0.68 = 0.32 = 3.2 (0.10) =
3.2a. As Figure B-1.9 illustrates,
with a = 5 percent, the width of the
gray region is 1.0-0.84 = 0.16 = 3.2
(0.05) = 3.2a.
                                                        o.e    1    1.2
                                                          Conce ntration
                                                         FIGURE B-1.9
What is important is the width of the gray region in standard deviations; not the width of the gray
region in concentration. In order to achieve the same specified Type n error rate at the LBGR, the
action level and the LBGR must be separated by the same number of standard deviations. The
width of the gray region (action level minus LBGR) will be denoted by delta (A), the "shift." A/a
is how many standard deviations wide the gray region is.  A/a is called the "relative shift."

If the gray region is less than one standard deviation wide, the Type n error rate may be high at
the LBGR. The only way to improve the situation would be to decrease the standard deviation
(i.e., increase the relative shift, A/a). This can be done by employing a more precise measurement
method  or by averaging several  measurements. When the width of the gray region is larger than
about three standard deviations (i.e., A/a exceeds 3), it is overkill. It may be possible to use a
simpler, less expensive measurement method or take fewer samples.
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 i                                    APPENDIX C

 2                MEASUREMENT QUALITY OBJECTIVES

 3                   FOR METHOD UNCERTAINTY AND

 4        DETECTION AND QUANTIFICATION CAPABILITY


 5      C.I   Introduction

 6      This appendix expands on issues related to measurement quality objectives (MQOs) for several
 7      method performance characteristics which are introduced in Chapter 3, Key Analytical Planning
 8      Issues and Developing Analytical Protocol Specifications. Specifically, this appendix provides
 9      the rationale and guidance for establishing project-specific MQOs for the following method per-
10      formance characteristics: method uncertainty, detection capability and quantification capability.
11      In addition,  it provides guidance in the development of these MQOs for use in the method selec-
12      tion process and guidance in the evaluation of laboratory data based on the MQOs. Section C.2 is
13      a brief overview of statistical hypothesis testing as it is commonly used in a directed planning
14      process, such as the Data Quality Objectives (DQO) Process (EPA 2000). More information on
15      this subject  is provided in Chapter 2, Directed Planning Process and Appendix B, The Data
16      Quality Objectives Process. Section C.3 derives MARLAP's recommended criteria for establish-
17      ing project-specific MQOs for method uncertainty, detection capability, and quantification capa-
18      bility. These criteria for method selection will  meet the requirements of a statistically based
19      decision-making process. Section C.4 derives MARLAP's recommended criteria for evaluation
20      of the results of quality control  analyses by project managers and data reviewers (see also Chap-
21      ter 8, Radiochemical Data Verification and Validation).

22      It is assumed that the reader is familiar with the concepts of measurement uncertainty, detection
23      capability, and quantification capability, and with terms such as "standard uncertainty," "mini-
24      mum detectable concentration," and "minimum quantifiable concentration," which are intro-
25      duced in Chapter 1, Introduction to MARLAP, and discussed in more detail in Chapter 19,
26      Measurement Statistics. MARLAP also uses the term "method uncertainty" to refer to the pre-
27      dieted uncertainty of the result that would be measured if the method were applied to a hypo-
28      thetical laboratory sample with a specified analyte concentration. The method uncertainty is a
29      characteristic of the analytical method and the measurement process.
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       MQOs For Method Uncertainty and Detection and Quantification Capability	

so     C.2   Hypothesis Testing

31     Within the framework of a directed planning process, one considers an action level, denoted here
32     by AL, which is the contaminant concentration in either a population (e.g., a survey unit) or an
33     individual item (e.g., a laboratory sample) that should not be exceeded. Statistical hypothesis
34     testing is used to decide whether the actual contaminant concentration Xis greater than AL. For
35     more information on this topic, see EPA QA/G-4, MARSSIM, NUREG-1505 (EPA 2000,
36     MARSSIM 2000, NRC 1998), or Appendix B of this manual.

37     In hypothesis testing, one formulates two hypotheses about the value of X, and evaluates the
38     measurement data to choose which hypothesis to accept and which to reject.1 The two hypotheses
39     are called the null hypothesis H0 and the alternative hypothesis Hj. They are mutually exclusive
40     and together describe all possible values of Asunder consideration. So, in any given  situation, one
41     and only one of the hypotheses must be true. The null hypothesis is presumed true unless the data
42     provide evidence to the contrary. Thus the choice of the null hypothesis determines the burden of
43     proof in the test.

44     Most often, if the action level is not zero, one assumes it has been exceeded unless the measure-
45     ment results provide evidence to the contrary.  In this case, the null hypothesis is H0: X> AL and
46     the alternative hypothesis is H^ X< AL. If one instead chooses to assume the action level has not
47     been exceeded unless there is evidence to the contrary, then the null hypothesis is FL; X< AL
48     and the alternative hypothesis is H^ X> AL. The latter approach is the only reasonable one if
49     AL = 0, because it is virtually impossible to obtain statistical evidence that an analyte concentra-
50     tion is exactly zero.

51     In any hypothesis test, there are two possible types of decision errors. A Type /error occurs if the
52     null hypothesis is rejected when it is, in fact, true. A Type //error occurs if the null hypothesis is
53     not rejected when it is false.2 Since there is always measurement uncertainty, one cannot elimi-
54     nate the possibility of decision errors. So instead, one specifies the maximum Type I decision
55     error rate a that is allowable when the contaminant  concentration is at or above the action
         1 In hypothesis testing, to "accept" the null hypothesis only means not to reject it, and for this reason many
        statisticians avoid the word "accept" in this context. A decision not to reject the null hypothesis does not imply the
        null hypothesis has been shown to be true.

         2 The terms "false positive" and "false negative" are synonyms for "Type I error" and "Type II error,"
        respectively. However, MARLAP deliberately avoids these terms here, because they may be confusing when the
        null hypothesis is an apparently "positive" statement, such as X> AL.

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56     level AL. This maximum usually occurs when the concentration is exactly equal to AL. The most
57     commonly used value of a is 0.05, or 5%. One also chooses another concentration DL (the "dis-
58     crimination limit") that one wishes to be able to distinguish reliably from the action level. One
59     specifies the maximum Type II decision error rate p that is allowable when the contaminant con-
60     centration equals DL, or, equivalently, the "power" 1 - p of the statistical test at X= DL. The
61     gray region is then defined as the interval between the two concentrations AL and DL.

62     The gray region is a set of concentrations close to the action level, where one is willing to tol-
63     erate a Type n decision error rate that is higher than p. For concentrations above the upper bound
64     of the gray region or below the lower bound, the decision error rate is no greater than the speci-
65     fied value (either a or p as appropriate). Ideally, the gray region should be narrow, but in practice,
66     its width is determined by balancing the costs involved, including the cost of measurements and
67     the estimated cost of a Type n error, possibly using prior information about the project and the
68     parameter being measured.

69     If H0 is X> AL (presumed contaminated), then the upper bound of the gray region is AL and the
70     lower bound is DL. If H0 is x < AL (presumed uncontaminated), then the lower bound of the gray
71     region is AL and the upper bound is DL. Since no assumption is made here about which form of
72     the null hypothesis is being used, the lower and upper bounds of the gray region will be denoted
73     by LBGR and UBGR, respectively, and not by AL and DL. The width of the gray region
74     (UBGR - LBGR) is denoted by A and called the shift or the required minimum detectable
75     difference in concentration (EPA 2000, MARSSEVI 2000, NRC 1998). See Appendix B, The
76     Data Quality Objectives  Process, for graphical  illustrations of these concepts.

77     Chapter 3  of MARLAP recommends that for each radionuclide of concern, an action level, gray
78     region, and limits on decision error rates be established during a directed planning process.
79     Section C.3 presents guidance on the development of MQOs for the selection and development
80     of analytical protocols. Two possible scenarios  are considered. In the first scenario, the parameter
81     of interest is the mean analyte  concentration for a sampled population. The question to be
82     answered is whether the population mean is above or below the action level. In the second
83     scenario a decision is to be made about individual items or specimens, and not about population
84     parameters. This is the typical  scenario in bioassay, for example.  Some projects may involve both
85     scenarios. For example, project planners may want to know whether the mean analyte concentra-
86     tion in a survey unit is above an action level, but they may also be concerned about individual
87     samples with high analyte concentrations.
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 88      C.3   Development of MQOs for Analytical Protocol Selection

 89      This section derives MARLAP's recommendations for establishing MQOs for the analytical
 90      protocol selection and development process. Guidance is provided for establishing project-
 91      specific MQOs for method uncertainty, detection capability, and quantification capability. Once
 92      selected, these MQOs are used in the initial, ongoing, and final evaluations of the protocols.
 93      MARLAP considers two scenarios and develops MQOs for each.

 94      SCENARIO I: A Decision Is to  Be Made about the Mean of a Sampled Population

 95      In this scenario the total variance  of the data a2 is the sum of two components

 96                                            a2 = ^ + 0$

 97      where a^is the average analytical method variance (M= "method") and a^is the variance of the
 98      sampled population. The sampling standard deviation a5 may be affected by the spatial and tem-
 99      poral distribution of the analyte, the extent of the survey unit, the physical sample  sizes, and the
100      sample collection procedures. The analytical standard deviation aMis affected by laboratory
101      sample preparation, subsampling, and analysis procedures. The value of aMmay be estimated by
102      the combined standard uncertainty of a measured value for a sample whose concentration equals
103      the hypothesized population mean concentration (see Chapter 19, Measurement Statistics).

104      The ratio A / a, called the "relative shift," determines the number of samples required to achieve
105      the desired decision error rates a and p. The target value for this ratio should be between 1 and 3,
106      as explained in MARSSIM and NUREG-1505 (MARSSIM 2000, NRC 1998). Ideally, to keep
107      the required number of samples low, one prefers that A / a ~ 3. The cost in number of samples
108      rises rapidly as the ratio A / a falls below 1, but there is little benefit from increasing the ratio
109      much above 3.

110      Generally, it is easier to control aMthan a5. If a5 is known (approximately), a target value for aM
111      can be determined. For example, if a5 < A / 3, then a value of aM no greater than JA2 / 9 - a^
112      ensures that a < A / 3, as desired.  If a5 > A / 3, the requirement that the total a be less than A / 3
113      cannot be met regardless of aM. In the latter case, it is sufficient to make aM negligible in com-
114      parison to a5.

115      Often one needs a method for choosing aMin the absence of specific information about a5. In this
116      situation, MARLAP recommends the requirement aM < A / 10 by default. The recommendation is
117      justified below.

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118      Since it is desirable to have a < A / 3, this condition is adopted as a primary requirement.
119      Assume for the moment that a5 is large. Then aM should be made negligible by comparison.
120      Generally, aM is considered negligible if it is no greater than about a5 / 3. When this condition is
121      met, further reduction of aMhas little effect on a and therefore is usually not cost-effective. So,
122      the inequality aM < a5/ 3 is adopted as a second requirement.

123      Algebraic manipulation of the equation a2 = a^+ a^ and the required inequality aM <  a5/ 3 gives

124                                                   a
                                                    •/To

125      The inequalities a < A / 3 and aM < a / JlQ together imply the requirement

126                                                   A
127      or approximately
128
129      The required upper bound for the standard deviation aM will be denoted by <5MR. MARLAP
130      recommends
131                                                   A
                                               GM?~ 10

132      by default as a requirement in Scenario I when a5 is unknown. This upper bound was derived
133      from the assumption that a5 was large, but it also ensures that the primary requirement a < A / 3
134      will be met if a5is small. When the analytical standard deviation aMis less than GMK, the primary
135      requirement will be met unless the sampling variance a| is so large that a^ is negligible by com-
136      parison, in which case little benefit can be obtained from further reduction of aM.

137      The recommended value of <5MR is based on the assumption that any known bias in the measure-
138      ment process has been corrected and that any remaining bias is much smaller than the shift, A,
139      when a concentration near the gray region is measured.

140      Achieving an analytical  standard deviation aMless than the recommended limit, A /  10, may be
141      difficult in some situations, particularly when the shift, A, is only a fraction of UBGR. When the
142      recommended requirement for aMis too costly to meet, project planners may allow a^to be


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143      larger, especially if a5 is believed to be small or if it is not costly to analyze the additional
144      samples required because of the larger overall data variance (a^+ a|). In this case, project
145      planners may choose om to be as large as A / 3 or any calculated value that allows the data
146      quality objectives to be met at an acceptable cost.

147      The true standard deviation, aM, is a theoretical quantity and is never known exactly, but the lab-
148      oratory may estimate its value using the methods described in Chapter 19, and Section 19.6.13 in
149      particular. The laboratory's estimate of aM will be denoted here by uMand called the "method
150      uncertainty." The method uncertainty, when estimated by uncertainty propagation, is the
151      predicted value of the combined standard uncertainty ("one-sigma" uncertainty) of the analytical
152      result for a laboratory sample whose concentration equals UBGR. Note that the term "method
153      uncertainty" and the symbol UM actually apply not only to the method but to the entire
154      measurement process.

155      In theory, the value om is intended to be an upper bound for the true standard deviation of the
156      measurement process, aM, which is unknown. In practice, <5MR is actually used as an upper bound
157      for the method uncertainty, t/M, which may be calculated. Therefore, the value of om will be
158      called the "required method uncertainty" and denoted by um. As noted in Chapter 3, MARLAP
159      recommends that project planners specify an MQO  for the method uncertainty, expressed in
160      terms of UMR> for each analyte and matrix.

161      The MQO for method uncertainty is expressed above in terms of the required standard deviation
162      of the measurement process for a laboratory sample whose analyte concentration is at or above
163      the upper bound of the gray region, UBGR. In principle  the same MQO may be expressed as a
164      requirement that the minimum quantifiable concentration (MQC) be less than or equal to UBGR.
165      Chapter 19 defines the MQC as the analyte concentration at which the relative standard deviation
166      of the measured value (i.e., the relative method uncertainty) is 1 / kQ, where kQ is some specified
167      positive value. The value of kQ in this case should be specified as kQ = UBGR / um. In fact, if the
168      lower bound of the gray region is zero, then one  obtains kQ = 10, which is the value most com-
169      monly used to define the MQC in other contexts. In practice the requirement  for method uncer-
170      tainty should only be expressed in terms of the MQC when kQ = 10, since to define the MQC
171      with any other value of kQ may lead to confusion.
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172
173
174
175
176
177
EXAMPLE: Suppose the action level is 1 Bq/kg and the lower bound of the gray region is 0.6
Bq/kg. If decisions are to be made about survey units based on samples, then the required
method uncertainty at 1  Bq/kg is
                                     MR
                                     MR
                                              = °-04
If this uncertainty cannot be achieved, then an uncertainty as large as A / 3 =0.13 Bq/kg may
be allowed if a5 is small or if more samples are taken per survey unit.
178      A common practice in the past has been to select an analytical method based on the minimum
179      detectable concentration (MDC), which is defined in Chapter 19, Measurement Statistics. For
180      example, the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSEVI 2000)
181      says:

182             During survey design, it is generally considered good practice to select a measure-
183             ment system with an MDC between 10-50% of the DCGL [action level].

184      Such guidance implicitly recognizes that for cases when the decision to be made concerns the
185      mean of a population that is represented by multiple laboratory samples, criteria based on the
186      MDC may not be sufficient and a  somewhat more stringent requirement is needed. It is inter-
187      esting to note that the requirement that the MDC (about 3 times aM) be 10-50% of the action
188      level is tantamount to requiring that aMbe 0.03 to 0.17 times the action level — i.e. the relative
189      standard deviation should be approximately 10% at the action level. Thus, the requirement is
190      more naturally expressed in terms of the MQC.

191      SCENARIO II: Decisions Are to Be Made about  Individual Items

192      In this scenario, the total variance of the data equals the analytical variance, a^. Consequently the
193      data distribution in most instances should  be approximately normal. The decision in this case
194      may be made by comparing the measured  concentration, x, plus or minus  a multiple of its com-
195      bined standard uncertainty to the action level, AL. The combined standard uncertainty, uc(x), is
196      assumed to be an estimate of the true standard deviation of the measurement process as applied
197      to the item being measured; so, the multiplier of uc(x) equals zl_ a, the (1 - a)-quantile of the stan-
198      dard normal distribution (see Appendix G, Statistical Tables).
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199     Alternatively, if AL is zero, so that any detectable amount of analyte is of concern, the decision
200     may involve comparing xto the critical value of the concentration, xc, as defined in Chapter 19,
201     Measurement Statistics.

202     Case II-l: Suppose the null hypothesis is x > AL, so that the action level, AL, equals the upper
203     bound of the gray region, UBGR. Given the analytical variance a^, only a measured result that is
204     less than about UBGR - zl_fl<5M will be judged to be clearly less than the action level. Then the
205     desired power of the test 1 - p is achieved at the lower bound of the gray region only if LBGR <
206     UBGR - zl_fl<5M- Z[_paM. Algebraic manipulation transforms this requirement to

                                         UBGR- LBGR       A
                                            Zl-a+Zl-B      Zl-a+Zl
207     Case II-2: Suppose the null hypothesis is x < AL, so that the action level, AL, equals the lower
208     bound of the gray region, LBGR. Then only a measured result that is greater than about LBGR +
209     z1_0!aM will be judged to be clearly greater than the action level. Then the desired power of the
210     test 1 - p is achieved at the upper bound of the gray region only if UBGR > LBGR + zl_aGM +
211     ZI-^OM- Algebraic manipulation transforms this requirement to

                                         UBGR- LBGR       A
                                            7+7        7+7
                                             1-a   1-p       1-a    1-p
212     So, in either case, we have the requirement:

                                                     A
                                                  7  +7
                                                  Z-   Z
213     Therefore, MARLAP recommends the use of
                                           MR
                                                     7   +7
                                                     1-a   1-p
214     as an MQO for method uncertainty when decisions are to be made about individual items (i.e.,
215     laboratory samples) and not about population parameters.


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216     If both a and p are at least 0.05,  one may use the value UMR = 0.3A.

217     If LBGR = 0, then A = UBGR and om = A / (zl_a + Zj_p) implies

                                                   UBGR
218     This requirement is essentially equivalent to requiring that the MDC not exceed UBGR. Thus,
219     when LBGR = 0, the MQO may be expressed in terms of the detection capability of the analytical
220     method.

221     Note that when AL = LBGR = 0, the MQO for detection capability may be derived directly in
222     terms of the MDC, since the MDC is defined as the analyte concentration at which the proba-
223     bility of detection is 1 - p when the detection criterion is such that the probability of false detec-
224     tion in a sample with zero analyte concentration is at most a.
225
226
227
228
EXAMPLE: Suppose the action level is 1 Bq/L, the lower bound of the gray region is 0.5
Bq/L, a = 0.05, and p = 0.10. If decisions are to be made about individual items, then the
required method uncertainty at 1  Bq/L is

                        A         1-0.5          0.5          -
                                                ^0.90   I- 645 + 1.282
229     C.4   The Role of the MQO for Method Uncertainty in Data Evaluation

230     This section provides guidance and equations for determining warning and control limits for QC
231     sample results based on the project-specific MQO for method uncertainty. In the MARLAP
232     Process as described in Chapter 1, these warning and control limits are used in the ongoing eval-
233     uation of protocol performance (see Chapter 7, Evaluating Protocols and Laboratories) and in
234     the evaluation of the laboratory data (see Chapter 8, Radiochemical Data Verification and
235     Validation).

236     C.4.1 Uncertainty Requirements at Various Concentrations

237     When project planners follow MARLAP's recommendations for establishing MQOs for method
238     uncertainty for method selection and development, the maximum allowable standard deviation,

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239     aMft at the upper bound of the gray region (UBGR) is specified. During subsequent data evalua-
240     tion, the standard deviation at any concentration less than UBGR should be at most aMff, and the
241     relative standard deviation at any concentration greater than UBGR should be at most
242     <5MR I UBGR, which will be denoted here by q>MK. Note that, since the true standard deviation can
243     never be known exactly, in practice the requirement is expressed in terms of the required method
244     uncertainty, UMK, to which the combined standard uncertainty of each result may be compared.
245
246
247
248


249
250
251
252
253
254
         EXAMPLE: Consider the preceding example, in which AL = UBGR = 1 Bq/L, LBGR =
         0.5 Bq/L, and UMR= 0.17 Bq/L. In this case the combined standard uncertainty for any meas-
         ured result ^should be at most 0.17 Bq/L if x< 1 Bq/L, and the relative combined standard
         uncertainty should be at most 0.17 / 1, or 17%, if x> 1 Bq/L.
        In Scenario I, where decisions are made about the mean of a population based on multiple physi-
        cal samples (e.g., from a survey unit), if the default value <5MR = A /10 is assumed for the required
        method uncertainty, then the required bound for the analytical standard deviation as a function of
        concentration is as shown in Figure C. 1 below. The figure shows that the bound, aReq, is constant
        at all concentrations, x, below UBGR, and aReq increases with ^rwhen ^ris above UBGR. So,
        aReq
, when x < UBGR and aReq = x •
                                                / UBGR when x> UBGR.
                               5
                               T3
                               
                               w
                               C3
                               a>
                                0  LBGR     UBGR

                                          True Concentration (x)
                      FIGURE C.I — Required Analytical Standard Deviation (
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        	MQOs For Method Uncertainty and Detection and Quantification Capability

255     These requirements can be relaxed somewhat for samples with very high analyte concentrations
256     as long as the project's requirements for decision uncertainty are met. However, MARLAP does
257     not provide specific guidance to address this issue for Scenario I.

258     In Scenario II, where decisions are made about individual physical samples, it is possible to
259     widen the required bounds for the standard deviation at any concentration outside the gray
260     region. For example, suppose the upper bound of the gray region (UBGR) is at the action level
261     (AL), the lower bound (LBGR) is set at some concentration below UBGR, and the decision error
262     probabilities a and p are specified. Then the project planners require the probability of a Type I
263     error not to exceed a when the true concentration is at or above UBGR, and they require the
264     probability of a Type II error not to exceed p when the true concentration is at or below LBGR.
265     The decision rule is based on the combined standard uncertainty of the measurement result: any
266     sample whose measured concentration, X, exceeds AL minus z1_a times the  combined standard
267     uncertainty, uc(x), is assumed to exceed the action level. So, assuming uc(x) is an adequate esti-
268     mate of the analytical standard deviation, the planners' objectives are met if
                                       UBGR - x  . f    T orM,
                                       	,  if x < LBGR
                                        Zl-a+Zl-P
                                       jr-LBGR   .,.    TTD™,
                                       	,  if x > UBGR
                                        Zl-a+Zl-p
                                       Zl-a+Zl-P
                                                -,  if LBGR  1.0Bq/L
                                                                1.0 Bq/L
So, ifx = 0, the requirement is uc(x) < 1 / 2.927 = 0.34 Bq/L, and, ifx= 2, the requirement is
uc(x) < (2 - 0.5) / 2.927 = 0.51 Bq/L, which is approximately 26% in relative terms.
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275     C.4.2 Acceptance Criteria for Quality Control Samples

276     The next issue to be addressed is how to set warning and control limits for quality control (QC)
277     sample results. These limits will be used by project data assessors to determine whether the lab-
278     oratory appears to be meeting MQOs. Presumably the lab has stricter internal QC requirements
279     (see Chapter 18, Laboratory Quality Control).

280     The development of acceptance criteria for QC samples will be illustrated with an example.
281     Assume the upper bound of the gray region (UBGR) is  5 Bq/kg (soil) and the lower bound of the
282     gray region (LBGR) is 1.5 Bq/kg. The width of the gray region is A = 5 - 1.5 = 3.5 Bq/kg.
283     Project planners, following MARLAP's guidance, choose the required method uncertainty at 5
284     Bq/kg (UBGR) to be
                                          ^V = |p 0-35 Bq/kg


285     or 7%. So, the maximum standard uncertainty at analyte concentrations less than 5 Bq/kg should
286     be um = 0.35 Bq/kg, and the maximum relative standard uncertainty at concentrations greater
287     than 5 Bq/kg should be (pm = 0.07, or 7%.

288     Although it is possible to relax these uncertainty criteria for samples with very high analyte con-
289     centrations, MARLAP recommends that the original criteria be used to develop acceptance limits
290     for the results of QC sample analyses.

291     C.4.2. 1  Laboratory Control Samples

292     It is assumed that the concentration of a laboratory control sample (LCS) is high enough that the
293     relative uncertainty limit (pMK = 0.07 is appropriate. The percent deviation for the LCS analysis is
294     defined as
                                              SSR " SA
                                        %D=
                                                 SA
295     where
296            SSR  is the measured result (spiked sample result) and
297            SA   is the spike activity (or concentration) added.

298     It is assumed that the uncertainty of SA is negligible; so, the maximum allowable relative stan-
299     dard deviation of %Z?is the same as that of the measured result itself, or q>MK x  100%. Then the 2-


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300
301
302

303


304


305

306
307
sigma warning limits for %D are ± 2(pMff x 100% and the 3-sigma control limits are
± 3(pMff x  100%. (In situations where (pMK is very small, the uncertainty of SA should not be
ignored.)

The requirements for LCSs are summarized below.
 Laboratory Control Samples
    Statistic:
%D=
                           SSR
                              SA
    Warning limits:   ±2^x100%
    Control limits:    ± 3qm x 100%
308

309

310
311


312


313

314

315

316

317
                                     EXAMPLE
 (UBGR = 5 Bq/kg, um = 0.35 Bq/kg, qm = 0.07.)
 Suppose an LCS is prepared with a concentration of SA = 10 Bq/kg and the result of the
 analysis is 11.61 Bq/kg with a combined standard uncertainty of 0.75 Bq/kg. Then

                                     10
                                             100% = 16.1%
 The warning limits in this case are
 and the control limits are
                                        100% = ± 14%
                                      x 100% = ±21%
 So, the calculated value of %D is above the upper warning limit but below the control limit.
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318     C.4.2.2 Duplicate Analyses

319     Acceptance criteria for duplicate analysis results depend on the sample concentration, which is
320     estimated by the average x of the two measured results xl and x2.
                                               x=
321     When x < UBGR, the warning limit for the absolute difference | xl - x2 \ is

                                           2t/  v/2-2.83 t/
                                              AffiV         MR

322     and the control limit is

                                           3 um UBGR, the acceptance criteria may be expressed in terms of the relative percent
325     difference (RPD), which is defined as

                                                \x - x I
                                         RPD =   '_  2  x 100%
                                                   x


326     The warning limit for RPD is

                                    2m  t/2 x 100% ~ 2.83 m  x 100%
                                     1 sVlK *                ' sVlK

327     and the control limit is

                                    3cp  ,_, x 100%
                                     1 sVlK *                ' sVlK


328     The requirements for duplicate analyses are summarized below.
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329

330
331
332
333

334

335

336
337


338

339

340
341

342
343

344


345


346


347
Duplicate Analyses

If* 5 Bq/kg, the acceptance criteria are expressed in terms of RPD.
                         RPD = 1?-^—Hi! x 100% = 37.84%
                                   11.1
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348


349


350
351
 The warning and control limits for RPD are

                       Warning limit = 2.83
                        Control limit = 4.24
                       0.07 x 100% = 19.81%
                       0.07 x 100% = 29.68%
 In this case, the value of RPD is above the control limit. (Also note that the relative standard
 uncertainties are larger than the 7% required for concentrations above 5 Bq/kg.)
352
C.4.2.3  Method Blanks
353     Case 1. If an aliquant of blank material is analyzed, or if a nominal aliquant size is used in the
354     data reduction, the measured blank result is an activity concentration. The target value is zero,
355     but the measured value may be either positive or negative. So, the 2-sigma warning limits are
356     ± 2uMR and the 3-sigma control limits are ± 3 UMK.

357     Case 2. If no blank material is involved (only reagents, tracers, etc., are used), the measured
358     result may be a total activity, not a concentration. In this case the method uncertainty limit UMK
359     should be multiplied by the nominal  or typical aliquant size, Ms. Then the 2-sigma warning limits
360     are ± 2 uMRMs and the 3-sigma control limits are ± 3 uMRMs.

361     The requirements for method blanks are summarized below.
362

363
364
365
366

367
368
369
370
 Method Blanks

 Concentration:
     Statistic:
     Warning limits:
     Control limits:

 Total Activity:
     Statistic:
     Warning limits:
     Control limits:
Measured concentration
±2u.
    -MR
    'MR
Measured total activity
± 2 umMs
± 3 uMRMs
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371

372

373

374

375
376

377
378

379

380

381
382
383
384

385

386
387
388
389

390
391

392
                                      EXAMPLE

 (UBGR = 5 Bq/kg, um= 0.35 Bq/kg, 9^= 0.07)

 Suppose a method blank is analyzed and the result of the measurement is

        x= 0.00020 Bq with combined standard uncertainty uc(x) = 0.00010 Bq

 Assuming the nominal aliquant mass is 1.0 g, or Ms= 0.001 kg, the result is evaluated by
 comparing xto the warning and control limits:

                               ± 2 uMRMs = ± 0.00070 Bq
                               ± 3 uMRMs = ± 0.00105 Bq

 In this case xis within the warning limits.
C.4.2.4  Matrix Spikes

The acceptance criteria for matrix spikes are more complicated than those described above for
laboratory control samples because of pre-existing activity in the unspiked sample, which must
be measured and subtracted from the activity measured after spiking. The percent deviation for a
matrix spike is defined as
%D =
SSR  SR  SA
      SA
                                                    100%
where
       SSR  is the spiked sample result
       SR    is the unspiked sample result
       SA    is the spike concentration added (total activity divided by aliquant mass).

However, warning and control limits for %D depend on the measured values; so, %Z?is not a
good statistic to use for matrix spikes. Instead we define a "Zscore"
                           Z =
                                      SSR - SR - SA
                                                   max(SR, UBGR)2
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        MQOs For Method Uncertainty and Detection and Quantification Capability
393
394
395


396


397


398
399


400

401

402

403


404

405

406

407

408


409
410
411
where "max(^r, y)" denotes the maximum of xand y. Then warning and control limits for Zare set
at ± 2 and ± 3, respectively. (It is assumed again that the uncertainty of SA is negligible.)
The requirements for matrix spikes are summarized below.
Matrix Spikes

   Statistic:
                     Z =
                                SSR - SR - SA
                                     max(SR, UBGR)2
    Warning limits:   ± 2
    Control limits:    ± 3
                                     EXAMPLE

 (UBGR = 5 Bq/kg, um = 0.35 Bq/kg, qm = 0.07)

 Suppose a matrix spike is analyzed. The result of the original (unspiked) analysis is

                 SR = 3.5 with combined standard uncertainty t/c(SR) = 0.29

 the spike concentration added is

                SA = 10.1 with combined standard uncertainty t/c(SA) = 0.31

 and the result of the analysis of the spiked sample is

               SSR =11.2 with combined standard uncertainty t/c(SSR) = 0.55

 Since SR is less than UBGR (5), max(SR, UBGR) = UBGR = 5. So,

                          SSR-SR-SA     11.2 -3.5 - 10.1    _ _.
                       cp^SSR2 + UBGR2    0.07^/1 1.22 + 52

 So, Zis less than the lower warning limit (-2) but slightly greater than the lower control limit
 (-3).
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        	MQOs For Method Uncertainty and Detection and Quantification Capability

412     C.5   References

413     Environmental Protection Agency (EPA). 2000. Guidance for the Data Quality Objectives
414        (DQO) Process. EPA QA/G-4. EPA/600/R-96/055, EPA, Quality Staff, Washington, DC.

415     MARSSEVL 2000. Multi-agency Radiation Survey and Site Investigation Manual (MARSSIM)
416        Rev. 1. NUREG-1575, Nuclear Regulatory Commission, Washington, DC. EPA 402-R-97-
417        016, Environmental Protection Agency, Washington, DC.

418     Nuclear Regulatory Commission (NRC). 1998. A Nonparametric Statistical Methodology for the
419        Design and Analysis of Final Status Decommissioning Surveys. NUREG-1505. NRC,
420        Washington, DC.
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              APPENDIX D CONTENT OF PROJECT PLAN DOCUMENTS
 2     Dl.O  Introduction

 3     Project plan documents were discussed in Chapter 4, Project Plan Documents. This appendix
 4     will discuss appropriate content of plan documents. The content of project plan documents,
 5     regardless of the document title or format, will include similar information, including the project
 6     description and objectives, identification of those involved in the project activities and their
 7     responsibilities and authorities, enumeration of the quality control (QC) procedures to be
 8     followed, reference to specific standard operating procedures (SOPs) that will be followed for all
 9     aspects of the projects, and Health and Safety protocols.

10     The discussion of project plan document content in this appendix will rely on EPA's guidance on
11     elements for a QA project plan (QAPP). MARLAP selected EPA's QAPP as a model for content
12     of a project plan document since it is closely associated with the data quality objective (DQO)
13     planning process and because other plan documents lack widely accepted guidance regarding
14     content. MARLAP hopes that presentation of a project plan document in one of the most
15     commonly used plan formats will facilitate plan writing by those less familiar with the task,
16     provide a framework for reviewing plan documents, and aid in tracking projects.

17     The discussion of plan content in sections D2 to D5  follows the outline developed by EPA
18     requirements (EPA, 1998b) and guidance (EPA,  1998a) for QAPPs for environmental data
19     operations. The QAPP elements are presented in four major sections (Table  Dl) that are referred
20     to as "groups":

21      • Proj ect Management;
22      • Measurement/Data Acquisition;
23      • Assessment/Oversight; and
24      • Data Validation and Usability.

25     There are many formats that can be used to present the project plan elements. MARLAP does not
26     recommend any particular plan format over another. The project planning team should focus on
27     the appropriate content of plan  documents needed to address the necessary quality assurance
28     (QA), QC, and other technical activities that must be implemented to ensure that the results of
29     the work performed will satisfy the stated performance criteria. Table D2 provides a crosswalk
30     between the table of contents of two example project plan documents—a QAPP and a work
31     plan—and EPA's (1998a) project plan document elements.
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                               TABLE Dl—QAPP Groups and Elementsab
GROUP
A Project Management
B Measurement/Data
Acquisition
C Assessment/Oversight
D Data Validation and
Usability
ID
Al
A2
A3
A4
A5
A6
A7
A8
A9
Bl
B2
B3
B4
B5
B6
B7
B8
B9
BIO
Cl
C2
Dl
D2
D3
ELEMENT
Title and Approval Sheet
Table of Contents
Distribution List
Project/Task Organization
Problem Definition/Background
Project/Task Description
Quality Objectives and Criteria for
Measurement Data
Special Training Requirements/Certifications
Documentation and Record
Sampling Process Design
Sample Methods Requirements
Sample Handling and Custody Requirements
Analytical Methods Requirements
QC Requirements
Instrument/Equipment Testing, Inspection and
Maintenance Requirements
Instrument Calibrations and Frequency
Inspection/ Acceptance Requirements for
Supplies and Consumables
Data Acquisition Requirements (Non-direct
Measurements)
Data Management
Assessments and Response Actions
Reports to Management
Verification and Validation Requirements
Verification and Validation Methods
Reconciliation with Data Quality Objectives
APPENDIX
SECTION
D2.1
D2.2
D2.3
D2.4
D2.5
D2.6
D2.7
D2.8
D2.9
D3.1
D3.2
D3.3
D3.4
D3.5
D3.6
D3.7
D3.8
D3.9
D3.10
D4.1
D4.2
D5.1
D5.2
D5.3
MARLAP
CHAPTER
NA
NA
NA
2
2
2
2,3
7
7, 17
NA
NA
11
6
18
15
18
NA
2
17
7
9
8
8
9
33

34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55

56
57
58
59

60
61
62
63


64
65
66
(a) Based on EPA, 1998a.
(b) MARLAP recommends a graded approach to project plan documents. All elements may not be applicable,
   especially for a small project. See Chapter 4, Section 4.3, "A Graded Approach to Project Plan Documents"
   and Section 4.5.3, "Plan Content for Small Projects."


This appendix also will discuss how the project plan document is linked to the outputs of the
project planning process. Directed project planning is discussed in Chapter 2, Project Planning
Process. The discussion of project plan documents in this appendix will use the DQO process
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                                                              Content of Project Plan Documents
 67      (EPA, 1994) as a model for directed planning (see Appendix B, The Data Quality Objectives
 68      Process). References will be made in this appendix to the steps of the DQO process, where
 69      appropriate, to illustrate the linkage between the direct planning process and plan documents.

 70      It should be noted that although the project plan documents will address both sampling and
 71      analysis, MARLAP does not provide guidance on sampling design issues or sample collection.
 72      Discussion in D3.1, "Sample Process Design," and D3.2, "Sample Methods Requirements," are
 73      provided for completeness and consistency.
 74
 79
 80
 81
 82
 83
 84
 85

 86
 87
 88
 89
 90

 91
 92
 93
 94

 95
 96
 97
 98
 99
100

101
102
D2.0  Group A: Project Management
 75      This group consists of nine elements that address project management issues such as organiza-
 76      tion of the plan itself, management systems, and a description of project goals, participants and
 77      activities. These elements ensure that the project goals are clearly stated, the approach to be used
 78      is understood, and the project planning decisions are documented.
                   TABLE D2—Comparison of Project Plan Contents
                I. Example QAPPa using EPA Guidance11 and EPA QAPP Elements0
QA PROJECT PLAN FOR RADIOLOGICAL
MONITORING TABLE OF CONTENTS
Title Page
Approval Sheet
Distribution List
1.0 Table of Contents
2.0 Project Description
2.1 Site History
2.2 Project Objectives and Requirements
2.3 DQOs
3.0 Project Organization and Responsibility
4.0 QA Objectives for Measurement Data (Precision,
Accuracy, Representativeness, Comparability,
Completeness)
5.0 Sampling Procedures, including QC [Cited Field
Sampling and Analysis Plan]
6.0 Sample Custody
6.1 Sample
6.2 Sample Identification
6.3 COC Procedures
7.0 Calibration Procedures and Frequency (Field and
Laboratory)
EPA G-5 QA PROJECT PLAN ELEMENTS
Al Title and Approval Sheet
A3 Distribution List
A2 Table of Contents
A5 Problem Definition/Background
A6 Project/Task Description
A4 Project/Task Organization
A7 Quality Objectives and Criteria for Measurement
Data
B 1 Sampling Process Designs
B2 Sampling Methods Requirements
B3 Sample Handling and Custody Requirements
B7 Instrument Calibration and Frequency
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QA PROJECT PLAN FOR RADIOLOGICAL
MONITORING TABLE OF CONTENTS
8.0 Analytical Procedures
8.1 Background
8.2 Specific Analytical Procedures
8.3 Test Methods
8.4 Control of Testing
8.5 Limits of Detection
9.0 Data Reduction, Validation and Reporting and
Record
10.0 Internal QC Checks
11.0 Performance and Systems Audits
11.1 Systems Audits
11.2 Surveillance
11.3 Performance Audits
11.4 Resolution of Discrepancies
11.5 Review of Contractor Procedures
12.0 Preventive Maintenance
13.0 Specific Routine Procedures to Assess Data
Precision, Accuracy, Completeness
14.0 Corrective Action
15.0 QA Report to Management
16.0 References




EPA G-5 QA PROJECT PLAN ELEMENTS
B4 Analytical Methods Requirements
B6 Instrument/Equipment Testing, Inspection, and
Maintenance Requirements
BIO Data Management
Dl Data review, Validation, and Verification
Requirements
A9 Documentation and Records
B5 Quality Control Requirements
Cl Assessment and Response Actions
B6 Instrument/Equipment Testing, Inspection, and
Maintenance Requirements
D3 Reconciliation with DQOs

C2 Response to Management

A8 Special Training Requirements/Certification
B8 Inspection/ Acceptance Requirements for Supplies
and Consumables
B9 Data Acquisition Requirement for Non-direct
Measurements
D2 Verification and Validation Methods
                   II. Example Work Pland and EPA QA/G-5 QAPP Elements0
Work Plan Table of Contents
Cover Letter
Title Page (including Document Number, Prepared
by/Prepared for Identification)
Approvals
Table of Contents
EPA QAPP Elements
A3 Distribution List
Al Title and Approval Sheet
Al Title and Approval Sheet
A2 Table of Contents
1 Introduction/Background
Site and Regulatory Background
A5 Problem Definition/Background
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                                                   Content of Project Plan Documents
Work Plan Table of Contents
Project Scope and Purpose
Project Organization and Management
Data Quality Objectives and Approach
Environmental Setting
Sampling Site Selection, Locations and
Identification
EPA QAPP Elements
A6 Project/Task Description
A4 Project/Task Organization
A7 Quality Objectives and Criteria for Measurement Data
A5 Problem Definition/Background
B 1 Sampling Process Design
2 Sampling and Analysis Plan
Objective
QA Objectives for Field Measurements, Laboratory
Measurements (including Calibration Procedures
and Frequency)
Sample Collection Procedures
Sample Identification, Handling and Transport
Sample Analysis
Sample Tracking and Records
Data Reduction, Validation and Reporting
Internal QC Checks
B 1 Sampling Process Design
A7 Quality Objectives and Criteria for Measurement Data
B7 Instrument Calibrations and Frequency
B2 Sample Methods Requirements
B3 Sample Handling and Custody Requirements
B4 Analytical Methods Requirements
BIO Data Management
Dl Data Review, Verification, and Validation
Requirements
D2 Verification and Validation Methods
B5 QC Requirements
3 QA Project Plan
QA Training and Awareness
Performance and Systems Audits
Preventive Maintenance
Quality Improvement
QA Reports to Management
Purchase Items and Service Control
4 Data and Records Management Plan
Objectives
Data Management
Document Control
Records Management System
Administrative Records
5 Data Interpretation Plan
Approach for Data Evaluation
Data Interpretation and Comparisons
6 Risk Analysis Plan
7 Hoaltli ctnrt ftstfotv Plan

Cl Assessments and Response Actions
B6 Instrument/Equipment Testing, Inspection, and
Maintenance Requirements
B6 Instrument/Equipment Testing, Inspection, and
Maintenance Requirements
C2 Reports to Management
B8 Inspection/ Acceptance Requirements for Supplies and
Consumables
A9 Documentation and Record
BIO Data Management
D3 Reconciliation with DQOs



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        Content of Project Plan Documents
Work Plan Table of Contents


EPA QAPP Elements
B9
A8
Data Acquisition Requirements (Non-direct
Measurements)
Special Training Requirements/Certifications
168

169
170      (a) Plan elements adapted from DOE, 1997.
171      (b) EPA, 1980.
172      (c) EPA, 1998a
173      (d) Plan elements adapted from DOE, 1996.

174      D2.1   Project Management (Al): Title and Approval Sheet

175      The project title sheet should:

176       • Clearly identify the project in an unambiguous manner;

177       • Include references to organizational identifiers such as project numbers (when appropriate);

178       • Clearly label and distinguish between draft and approved versions;

179       • Include the date of issuance of drafts or final approved version;

180       • Include revision or version numbers;

181       • Indicate if the document represents only a portion of the QAPP (e.g., Volume 1 of 4
182         Volumes);

183       • Include names of the organization(s) preparing the plan document and, if different, for whom
184         the plan was prepared; and

185       • Identify clearly on the title page if the document is a controlled copy and subjected to no-
186         copying requirements.  If so, indicate the document control number.

187      QAPPs should be reviewed on an established schedule. QAPPs should be kept current and
188      revised when necessary. Documented approval, as an amendment to the QAPP, should  be
189      obtained for modifications to the QAPP.

190      The approval sheet documents that the QAPP has been reviewed and approved prior to
191      implementation. The approval sheet should consist of the name, title, organization, signature and
192      signature date for:


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193      •  The proj ect manager or other person with overall responsibility for the proj ect;

194      •  The QA manager or other person with overall responsibility for the quality of the project
195         outputs;

196      •  The project managers or QA managers for all organizations (e.g., sampling organization,
197         laboratories, data validators) implementing project activities; and

198      •  The representative of any oversight or regulatory organization.

199     The project manager or other person with overall responsibility for the project should require an
200     approved QA program, management plan, or quality manual that supports all technical
201     operations, including data collection and assessment activities.

202     D2.2   Project Management (A2): Table of Contents

203     The table of contents should:

204      •  List all sections and subsections of the document, references, glossaries, acronyms and
205         abbreviations, appendices (including sections and subsections) and the associated page
206         numbers;

207      •  List all attachments and the associated page numbers;

208      •  List all tables and associated page numbers;

209      •  List all figures  and diagrams and associated page numbers; and

210      •  List titles of other volumes, if the QAPP consists of more than one volume.

211     A document control format is useful in maintaining reference to the latest version of the planned
212     document,  especially when only portions of a document have been copied and are being used to
213     implement or discuss project activities.

214     D2.3   Project Management (A3): Distribution List

215     The distribution list should identify all individuals, along with their titles and organizations, who
216     will receive copies and revisions of the approved QAPP and subsequent revisions. Listed


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217     individuals should include, at a minimum, all managers and QA personnel responsible for the
218     implementation and quality of the data collection activities. The project planning team or the core
219     group (see Chapter 2, Section 2.4) should be included on the document distribution list.

220     D2.4  Project Management (A4): Project/Task Organization

221     This QAPP element should:

222      •  Identify the individuals and/or organizations participating in the project, as well as contact
223         information (address, telephone number, fax number, e-mail). The stakeholders, data users,
224         decision makers,  and technical planning team members, and the person or organization that
225         will be responsible for project implementation, are identified during the directed planning
226         process (Appendix B, The DQO Process, Steps 1 and 7).

227      •  Discuss the roles and responsibilities of the individuals and/or organizations that participate
228         in the data collection, including the roles and responsibilities of the data users, decision
229         makers, and QA manager.

230      •  Include an organizational chart clearly showing the relationship, lines of authority and
231         communication, and mechanisms for information exchange among all project participants.

232     Complex projects may require more than one organizational chart to properly describe the
233     relationships among participants. At times, to clearly detail an organizations responsibilities and
234     communications, a general inter-organizational chart with  primary contacts, responsibilities, and
235     communications may need to be accompanied by secondary charts that describe intra-
236     organizational contacts, responsibilities, and lines of communication.

237     One of the keys to successful projects is communication. The QAPP should identify the  point of
238     contact for resolving field and laboratory problems. The QAPP may also summarize the  points of
239     contact for dissemination of data to managers, users and the public.

240     D2.5  Project Management (A5): Problem Definition/Background

241     The "Problem Definition/Background" element (A5) and the subsequent elements "Project/Task
242     Description" (A6)  and "Quality Objectives and Criteria" (A7) constitute the project description.
243     Separating the project description into three elements focuses and encourages the plan authors to
244     address all key issues (identification of problem to be solved, description of site history,
245     description of tasks and the quality objectives and data-acceptance criteria), some of which can


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246     be overlooked if a larger, less-focused section is written. Table D3 provides bulleted components
247     for these three elements. This section and sections D2.6 and D2.7 provide a more detailed
248     discussion of these elements.
249
250
251
252

253
254
255
256
257
258
259
260
261
262
    TABLE D3—Content of the Three Elements that Constitute the Project Description
         Problem                   Project/Task                   Objectives
  Definition/Background             Description                   and Criteria
           (A5)                          (A6)                          (A7)
263
264
265
266
267
268

269
    Serves as an Introduction
    Identifies the "problem
    to be solved" or the
    "question to be
    answered"
    Identifies the regulatory,
    legal or "informational
    needs" drivers
    Presents the historical
    perspective
Describes measurements
Identifies regulatory
standards and action levels
Identifies special
personnel, procedural and
equipment requirements
Summarizes assessment
tools
Details schedule and
milestones
Identifies record and report
requirements
   Quality Objectives
  Problem definition/Site
  history
  Data inputs
  Population boundaries
  Tolerable decision error
  rates

Criteria for Measurement
          Data
  Measurement quality
  objectives (MQOs; such as
  the measurement uncer-
  tainty at some concentra-
  tion; the detection capa-
  bility; the quantification
  capability; the range; the
  specificity; and the
  ruggedness of the method)
The Problem Definition/Background element provides a discussion of the problem and pertinent
background so that the implementation team can understand the context of the project. This
section does not discuss the details of project activities, which are described in a subsequent
project management element. Much of the information needed for this element was collected and
discussed during Step 1 of the DQO process (Appendix B3.1). The decision statement was
developed during Step 2 of the DQO process.

The "Problem Definition/Background" element should:
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270       •  Serve as an introduction to the project;

271       •  Identify the "problem to be solved" or the "question to be answered" upon successful
272         completion of the project—the decision rule (Appendix B3.6);

273       •  Discuss the assumptions, limitations, and scope of the project;

274       •  Identify the regulatory, legal, or "informational needs" drivers that are the underlying reasons
275         for the project;

276       •  Describe the context of the project so that it can be put into a historical perspective. This
277         section may include a description and maps of a facility or site, its location, its use, site
278         topography, geology and hydrogeology, past data collection activities, historical data
279         including analytes and concentrations, past and present regulatory status, past releases,
280         seriousness and potential risk of any release, site maps, and utilities; and

281       •  If the data collection activity is in support of a technology evaluation, include a discussion of
282         the purpose of the demonstrations, how the technology works, operating conditions, required
283         utilities, effluents and waste by-products and residues, past and expected efficiencies and
284         multi-media mass-balances by analyte and matrix.

285     D2.6  Project Management (A6): Project/Task Description

286     This element of the QAPP provides a discussion of the project and underlying tasks for the
287     implementation teams. It should provide a description of the work to be performed to resolve the
288     problem or answer the question, including the following information:

289       •  A description of the measurements and the associated QA/QC procedures that are to be made
290         during the course of the project. DQO Step 3 describes existing and needed data inputs, while
291         Step 7  yields the optimized sampling and analytical designs as well as quality criteria.
292         -  Identification of the analytes of interest.
293         -  A summary (preferably a table) of samples type (e.g., grab, spatial or temporal
294            composite), number of samples,  analyte or analyte class (e.g., "Tc, transuranic, gamma
295            emitters) and analytical protocol  specifications or method.

296       •  A discussion of applicable regulatory standards or action levels to which measurements will
297         be compared. Identify any applicable regulatory standard (e.g., gross alpha drinking water
298         maximum contamination limit), or applicable  or relevant and appropriate requirements


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299         (ARARs) that will be used as a metric or action level during decision-making. The DQO Step
300         6 details action levels and tolerable decision errors that will be the basis for decisions.

301       •  Identify any special requirements required to implement project tasks.
302         -  Identify any special training (e.g., hazardous waste site health and safety training (29 CFR
303            1910.120), radiation safety training).
304         -  Identify any special protective clothing and sampling equipment.
305         -  Identify any boundary conditions (e.g., only sample after a rainfall of more than 1 inch).
306         -  Specify any special document format, chain-of-custody, or archival procedures.
307         -  Identify any special sample handling (e.g., freezing of tissue samples), instrumentation, or
308            non-routine analytical protocols that are required to achieve specified performance
309            criteria (e.g., very low detection limits) (see also Chapter 3, Critical Analytical Planning
310            Issues and Developing Analytical Protocol Specifications).

311       •  Summarize the assessment tools that will be employed to determine whether measurement
312         data complied with performance criteria and are suitable to support decision-making. Include
313         a schedule of the assessment events. Assessment tools include performance evaluations,
314         program technical reviews, surveillance, technical and systems audits, and verification and
315         validation. Briefly outline:
316         -  A first tier of reviews (e.g., when field or lab personnel check each other's notes or
317            calculations).
318         -  Reviews of the work, notes and calculations of subordinates by the supervisor (e.g.,
319            review and sign all notebook entries).
320         -  The percentage of data subject to review by internal QA staff.
321         -  Data verification and validation to be performed by an independent party and the
322            guidelines or plan to be used.
323         -  Assessment of proj ect activities to be conducted by personnel independent of proj ect
324            activities (e.g., performance evaluation samples, surveillance, audits).
325         -  Assessment of how results of the proj ect will be reconciled with the proj ect DQOs ("data
326            quality assessment").

327       •  Supply a schedule that includes start and completion dates for tasks and a list of completion
328         dates for important milestones. Dates can be calendric, or as number of days following
329         approval of the QAPP, or number of days following commencement of field operations.
330         DQO Steps 1 and 4 identify deadlines and other constraints that can impact scheduling.

331       •  Identify the records and reports that will be required. This should be a brief but complete
332         listing of necessary reports and records (e.g., field and lab notebooks, sample logbooks,


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333         spectra, sample tracking records, laboratory information system print-outs, QA reports,
334         corrective action reports).

335      •  Identify whether the original documents are required or if photocopies are sufficient. More
336         detailed information will be presented in "Documentation and Records" (A9) and "Data
337         Management" (BIO).

338     D2.7  Project Management (A7): Quality Objectives and Criteria for Measurement Data

339     This element addresses two closely related but different issues, quality objectives for the project
340     and criteria used to evaluate the quality of measurement data. The element summarizes outputs
341     from all steps of the DQO process. A fundamental principle underlying plan documents is that
342     requirements for the data quality must be specified by the project planning team and documented.
343     By clearly stating the intended use of the data and specifying qualitative and quantitative criteria
344     for system performance, a critical  link between the needs of the project planning team and the
345     performance  requirements to be placed on the laboratory data is established. (See Chapter 3 for a
346     discussion of MQOs.)

347     D2.7.1 Project's Quality Objectives

348     The project's quality objectives or data quality objectives (DQOs) are qualitative and quantitative
349     statements that:

350      •  Clarify the intended use of the data (e.g., data will be used to determine if lagoon sediment
351         contains 232Th at concentrations greater than or equal to the action level);

352      •  Define the type and quantity of data per matrix needed to support the decision (e.g., 232Th
353         concentrations  in 300 composite sediments samples each composite consisting of 10 samples
354         randomly collected from a 100 m2 sampling grid adjacent to the point of discharge);

355      •  Identify the conditions under which the data should be collected (e.g., sediment samples
356         collected  from  the top 6 cm of sediment within a 100 m radius of the point of discharge into
357         lagoon #1, following de-watering of the lagoon and prior to sediment removal); and

358      •  Specify tolerable limits on the probability of making a decision error due to uncertainty in the
359         data and any associated action levels (e.g.,  95 percent confidence that the true concentration
360         is actually below the action level).
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361     Authors of project plan documents are often encouraged to condense the DQO outputs in a
362     summary statement. This approach can have value as long as critical information is not lost in the
363     summary process and the original information is cited and available for all project participants.
364     The following is an example of a DQO summary statement:

365         "The purpose of this project is to determine, to within a lateral distance of 10 m, the extent of
366         232Th in soil along a pipeline at concentrations at or above 1,145 mBq/g, with a false positive
367         rate less than or equal to 5 percent; and to define within 1 m the vertical extent of measured
368         232Th concentrations greater than 7,400 mBq/g."

369     D2.7.2 Specifying Measurement Quality Objectives

370     Measurement quality objectives (MQOs) or measurements performance criteria are essential to
371     the success of a project since they establish the necessary quality of the data. The quality of data
372     can vary as a result of the occurrence and magnitude of three different types of errors (Taylor,
373     1990).

374      •  BLUNDERS—mistakes that occur on  occasion and produce erroneous results (e.g., mis-
375         labeling or transcription errors);

376      •  SYSTEMATIC ERRORS—mistakes that are always the same sign and magnitude and produce
377         bias (i.e., they are constant no matter how many measurements are made); and

378      •  RANDOM ERRORS—mistakes that vary in sign and magnitude and are unpredictable on an
379         individual  basis (i.e., random differences between repetitive readings) but will average out if
380         enough measurements are taken.

381     The frequent occurrence of these types of errors is the reason why data quality is subject to
382     question, why  there is uncertainty when using data to make decisions and why measurement
383     performance criteria are necessary.

384     During the DQO process, project DQOs are used to establish the MQOs. An MQO is a statement
385     of a performance objective or requirement for a particular method performance characteristic.
386     Examples of method performance characteristics include the measurement uncertainty at some
387     concentration; the detection capability; the quantification capability; the range; the specificity;
388     and the ruggedness of the method. MQOs for the project should be identified and described
389     within this element of the QAPP. MARLAP provides guidance for developing MQOs for select
390     method performance characteristics in Chapter 3 and Appendix C.


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391     D2.7.3 Relation between the Project DQOs, MQOs, and QC Requirements

392     The ultimate goal of all data collection operations is the collection of appropriately accurate data.
393     Appropriately accurate data are data for which errors caused by imprecision and bias are
394     controlled such that it is suitable for use in the context outlined by the DQOs (i.e., the overall
395     error is less than that specified in the acceptable decision error). During the optimization of
396     design in the planning process, DQO-specified decision error rates are translated into MQOs with
397     the intention of monitoring, detecting, quantifying and controlling imprecision and analytical
398     bias. During optimization, precautions are also incorporated into the design with the intention of
399     preventing blunders and types of non-measurable bias not susceptible to measurement by QC
400     samples.

401     The MQOs provide acceptance or rejection criteria for the quality control samples whose types
402     and frequency are discussed in the Quality Control Requirements element (B5) (Appendix C).
403     QC samples and the project's associated MQOs are key—but not the sole mechanisms—for
404     monitoring the achievement of DQOs.

405     In summary, translating acceptable decision error rates into  a design that will produce data of
406     appropriate precision and bias is often a complex undertaking.  The team must consider the
407     synergistic and antagonistic interactions of the different options for managing errors and
408     uncertainty. Accurate data require not only control of imprecision, but must also control the
409     various forms of bias.

410     D2.8  Project Management (A8): Special Training Requirements/Certification

411     All project personnel should be qualified and experienced in their assigned task(s). The purpose
412     of this element is to add additional information regarding special training requirements and how
413     they will be managed during implementation of the project.  This element should:

414      •  Identify and describe any mandated or specialized training or certifications that are required;
415      •  Indicate if training records or certificates are included in the QAPP as attachments;
416      •  Explain how training will be implemented and certifications obtained; and
417      •  Identify how training documentation and certification records will be maintained.

418     D2.9  Project Management (A9): Documentation and Record

419     This element of the QAPP will identify which records are critical to the project, from data
420     generation in the field to final use. It should include what information needs to be contained in


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421     these records and reports, the formats of the records and reports, and a brief description of
422     document control procedures. The following are suggested records and content:

423      •  SAMPLE COLLECTION RECORDS should include sampling procedures, the names of the persons
424         conducting the activity, sample number, sample collection points, maps and diagrams,
425         equipment/protocol used, climatic conditions, and unusual observations. Bound field
426         notebooks, pre-printed forms, or computerized notebooks can serve as the recording media.
427         Bound field notebooks are generally used to record raw data and make references to
428         prescribed procedures, changes in planned activities and implementation of corrective
429         actions. Preferably, notebooks will contain pre-numbered pages with date and signature lines
430         and entries will be made in ink. Field  QC issues such as field, trip, and equipment rinsate
431         blanks, co-located samples, field-spiked samples, and sample preservation should be
432         documented. Telephone logbooks and air bill records should be maintained.

433      •  SAMPLE TRACKING RECORDS document the progression of samples as they travel from the
434         original sampling location to the laboratory and finally to their disposal or archival. These
435         records should contain sample identification, the project name, signatures of the sample
436         collector, the laboratory custodian and other custodians, and the date and time of receipt. The
437         records should document any sample anomalies. If chain-of-custody (COC) is required for
438         the project, the procedures and requirements should be outlined (Chapter 11, Sample Receipt,
439         Inspection and Tracking).

440      •  ANALYTICAL QC issues that should be documented include standard traceability, and
441         frequency and results of QC samples,  such as, method and instrument blanks, spiked
442         samples, replicates, calibration check  standards and detection limit studies.

443      •  ANALYTICAL RECORDS should include standard operating procedures for sample receipt,
444         preparation, analysis and report generation. Data report formats and the level of supporting
445         information is determined by data use and data assessment needs.

446      •  PROJECT ASSESSMENT RECORDS should include audit check lists and reports, performance
447         evaluation (PE) sample results, data verification and validation reports, corrective action
448         reports. The project may want to maintain copies of the laboratory proposal package, pre-
449         award documentation, initial precision and bias test of the analytical protocol and any
450         corrective action reports.
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451     The QAPP should indicate who is responsible for creating, tracking, and maintaining these
452     records and when records can be discarded, as well as any special requirements for computer,
453     microfiche, and paper records.

454     D3.0  Group B: Measurement/Data Acquisition

455     The Measurement/Data Acquisition group consists of 10 elements that address the actual data
456     collection activities related to sampling, sample handling, sample analysis and the generation of
457     data reports. Although these issues may have been previously considered by project management
458     elements, the project management section of the QAPP dealt with the overall perspective. The
459     measurement/data section contains the details covering design and implementation to ensure that
460     appropriate protocols are employed and documented. This section also addresses quality control
461     activities that will be performed during each phase of data collection from sampling to data
462     reporting.

463     D3.1   Measurement/Data Acquisition (Bl): Sampling Process Design

464     This element of the QAPP describes the finalized sampling design that will be used to collect
465     samples in support of project objectives. The design should describe the matrices to be sampled,
466     where the samples will be taken, the number of samples to be taken, and the sampling frequency.
467     A map of the sampling locations should be included to provide unequivocal sample location
468     determination and documentation.

469     If a separate sampling and analysis plan or a field sampling and analysis plan has been
470     developed, it can be included by citation or as an appendix. This element will not address the
471     details of standard operating procedures for sample collection, which will be covered in
472     subsequent elements. This element will describe the sampling design and the underlying logic, so
473     that implementation teams can understand the rationale behind and better implement the
474     sampling effort. Understanding the rationale for the decisions will help if plans have to be
475     modified due to conditions in the field. DQO Step 7 establishes the rationale for and the  details
476     of the sampling design.

477     This element should restate the outputs of the planning process and any other considerations and
478     assumptions that impacted the design of the sampling plan, such as:

479      • The number of samples,  including QC samples, sample locations and schedule, and rationale
480        for the number and location of samples;
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481       •  A brief discussion of how the sampling design will facilitate the achievement of project
482         objectives;

483       •  A discussion of the population boundaries (temporal and spatial) and any accessibility
484         limitations;

485       •  A description of how the sampling design accommodates potential problems caused by the
486         physical properties of the material being sampled (e.g., large particle size), the characteristic
487         of concern (e.g., potential losses due to the volatility of tritium) or heterogeneity;

488       •  A discussion of the overarching approach to sampling design (e.g., worse case or best case
489         sampling versus average value) and assumptions made in selecting this approach (e.g., an
490         assumption that the darkened soil adjacent to the leaking tank would present a worse case
491         estimate of soil contamination);

492       •  A listing of guidance and references that were relied upon when designing the sampling plan;

493       •  Identification of the characteristics of interest (e.g.,"Tc activity), associated statistical
494         parameters (e.g., mean, standard deviations, 99th percentile), and acceptable false error rates
495         (e-g-, false negative rate of less than 5%);

496       •  Identification of relevant action level and how data will be compared to the action level
497         (Appendix B3.2);

498       •  A discussion of the anticipated range of the characteristic of interest and assumed temporal
499         and spatial variations (heterogeneity), anticipated variance, anticipated sources and
500         magnitude of error (e.g., heterogeneity of material being sampled, sampling imprecision,
501         analytical  imprecision), anticipated mean values and distribution of measurements and the
502         basis (e.g., historical data, similar processes or sites) for any associated assumptions;

503       •  If any level of bias is assumed, what is the assumed magnitude and the basis of the
504         assumption (e.g., historical data, typical analytical bias for matrix type);

505       •  It is usually assumed that the magnitude of measurements made at individual  sampling
506         locations are independent of each other (e.g., no correlation of concentration with location).
507         Geostatistical approaches may be more appropriate if measurements are significantly
508         correlated with locations (e.g., serial-correlation, auto-correlation) since serial-correlation can
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509        bias estimates of variance and invalidate traditional probabilistic techniques such as
510        hypothesis testing; and

511      • A discussion of the rationale for choosing non-routine sampling protocols and why these non-
512        routine protocols are expected to produce acceptable precision and bias.

513     D3.2   Measurement/Data Acquisition (B2): Sampling Methods Requirements

514     This element of the QAPP describes the detailed sampling procedures that will be employed
515     during the project. The preliminary details of sampling methods to be employed were established
516     during Step 7 of the DQO process. The selected sampling procedures should be appropriate to (1)
517     ensure that a representative sample is collected, (2) avoid the introduction of contamination
518     during collection, and  (3) properly preserve the sample to meet project objectives. Written SOPs
519     should be included as attachments to the QAPP. This element and the appendices or other
520     documents that it references should in total contain all the project specific details needed to
521     successfully implement the sampling effort as planned. If documents to be cited in the QAPP are
522     not readily available to all project participants,  they must be incorporated as appendices. All
523     sampling personnel should sign that they have read the sampling procedures and the health and
524     safety procedures.

525     Correct sampling procedures and equipment used in  conjunction with a correct sampling design
526     should result in a collection of samples that in total will represent the population of interest. A
527     detailed discussion of sampling procedures, equipment and design are beyond the scope of
528     MARLAP. In general, the selected procedures must be designed to  ensure that the equipment is
529     used properly and that the collected  samples represent the individual sampling unit from which
530     samples are collected.  The sampling equipment should be chemically and physically compatible
531     with the analyte of concern as well as the sample matrix.  The sampling design should facilitate
532     access to individual sampling units,  result in an appropriate mass/volume of sample such that it
533     meets or exceeds minimum  analytical sample sizes, accommodates short-range heterogeneity
534     (i.e., does not preclude large particle sizes or lose small particles) and reduce or prevent loss of
535     volatile components, if appropriate.

536     This element of the QAPP should:

537      • Identify the sampling methods to be used for each matrix, including the method number if a
538        standardized method. If methods are to be implemented differently than specified by the
539        standard method or if the standard method offers alternatives for implementation, the
540        differences and alternatives should be specified;


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541      •  Identify the performance requirements of the sampling method. If the sampling method of
542         choice is unlikely to be able to achieve the level of performance demanded by the project
543         DQO, the project planning team should be notified;

544      •  Identify the required field  QC samples (e.g., trip blank, co-located duplicate);

545      •  Identify any sample equipment preparation (e.g., sharpening of cutting edges, degreasing and
546         cleaning) or site preparation (e.g., removal of overburden, establishing dust-free work space
547         for filtering) for each method;

548      •  Identify and preferably generate a list of equipment and supplies needed. For example, the
549         sampling devices, decontamination equipment, sampling containers, consumables (e.g., paper
550         towels), chain-of-custody  seals and forms, shipping materials (e.g., bubble-pack, tape), safety
551         equipment and paper work (e.g., pens, field books);

552      •  Identify and detail logistical procedures for deployment, sample shipment and demobili-
553         zation. If a mobile lab will be used, explain its role and the procedures for sample flow to the
554         mobile lab and data flow to the data-user;

555      •  Identify, preferably in a tabular form, sample container types, sizes, preservatives, and
556         holding times;

557      •  Identify procedures that address and correct problems encountered in the field (variances and
558         nonconformance to the established sampling  procedures);

559      •  Identify for each sampling method, decontamination procedures and the procedures for
560         disposing of contaminated equipment and used-decontamination chemicals and waters;

561      •  Identify the disposal procedures for waste residuals generated during the sampling process
562         (e.g., purged well waters, drilling dregs) for each method; and

563      •  Identify oversight procedures (e.g., audits, supervisor review) that ensure that sampling
564         procedures are implemented properly. The person responsible for implementing corrective
565         actions should be identified.
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566     D3.3   Measurement/Data Acquisition (B3): Sample Handling and Custody Requirements

567     This element of the QAPP details how sample integrity will be maintained and how the sample
568     history and its custody will be documented ensuring that (1) samples are collected, transferred,
569     stored, and analyzed by authorized personnel, (2) the physical, chemical and legal integrity of
570     samples is maintained, and (3) an accurate written record of the history  of custody is maintained.
571     DQO  Step 1 describes the regulatory situation which can be used to identify the appropriate level
572     of sample tracking. The QAPP should state whether COC is required. Sample handling, tracking
573     and COC requirements are discussed in detail in Chapter 11, Sample Receipt and Tracking.

574     In the QAPP, the following elements should be documented:

575      • INTEGRITY OF SAMPLE CONTAINERS: Describe records to be maintained on the integrity of
576        sample container and shipping container seals upon receipt. Describe records to be
577        maintained if specially prepared or pre-cleaned containers are required.

578      • SECURITY: If wells are being sampled, whether the wellheads were locked or unlocked should
579        be noted. Security of remote sampling sites or automatic samplers not maintained in locked
580        cages should be discussed.

581      • SAMPLE IDENTIFICATION: The assignment of sample numbers and the labeling of sample
582        containers is explained. If samples are to be assigned coded sample identifications (IDs) to
583        preclude the possibility of bias during analysis, the sample code is one of the few items that
584        will not be included in the QAPP, since the lab will receive a copy. The code and sample  ID
585        assignment process will have to be described in a separate document, which is made available
586        to the field team and the data validators. An example of a sample label should be included in
587        the QAPP.

588      • TRACKING OR CUSTODY IN THE FIELD: Procedures for sample tracking or  custody while in the
589        field and during sample shipment should be described. When COC is required, a copy of the
590        COC form and directions for completion should be included. A list of all  materials needed
591        for tracking or custody procedures should be provided (e.g., bound notebooks, shipping
592        containers, shipping labels, tape, custody seals, COC forms).

593      • SAMPLE PRESERVATION: Sample preservation procedures, if desired, should be clearly
594        described. Preservation of radiological samples is discussed in Chapter 10, Requirements
595         When Collecting, Preserving, and Shipping Samples That Require Analytical Measurement.
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596      •  TRACKING OR CUSTODY IN THE LABORATORY: A decision must be made as to whether the
597         laboratory in general is considered a secure area such that further security is not required once
598         the sample is officially received by the laboratory or whether internal tracking or custody
599         procedures will be required as the samples are handled by different personnel within the lab.
600         The laboratory's sample receipt SOP, laboratory security procedures, and—if needed—
601         internal tracking or custody procedures should be described.

602      •  SPECIAL REQUIREMENT: Any special requirements, such as shipping of flammable or toxic
603         samples, or requirements for verification of sample preservation upon sample receipt by the
604         laboratory should be clearly described.

605      •  ARCHIVAL: Document the rationale for the request to archive samples, extracts, and
606         digestates. Describe how samples, extracts, and digestates will be archived. Identify how long
607         samples, extracts, digestates, reports, and supporting documentation must be maintained.

608     D3.4  Measurement/Data Acquisition (B4): Analytical Methods Requirements

609     This element of the QAPP should identify the Analytical Protocol Specifications (APSs)
610     including the MQOs that were employed by the laboratory to select the analytical protocols. (See
611     Chapter 3 for guidance on developing APs.)  This element integrates decisions from three DQO
612     steps: Step 3 which identified the analyte of interest and needed inputs to the decision, Step 6
613     which identifies the allowable uncertainty, and Step 7 which identifies the optimized analytical
614     design. Input from all three steps drive the choice of analytical protocols. The discussion of the
615     selected analytical protocols should address:  subsampling, sample preparation, sample clean-up,
616     radiochemical separations, the measurement system, confirmatory analyses and pertinent data
617     calculation and reporting issues. A tabular summary of the analytical protocol by matrix type can
618     facilitate reference for both the plan document development team and the laboratory analytical
619     team.

620     This element of the QAPP should clearly describe the expected sample matrices (e.g.,
621     groundwater with no sediments, soils with no rocks larger than 2 cm in diameter) and what
622     should be done or who should be contacted if sample matrices are different than expected.
623     Subsampling is a key link in the analytical process which is often overlooked during planning
624     leaving important decisions to laboratory staff, this element should specify appropriate
625     subsampling procedures.

626     This QAPP element should:
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627       •  Identify the laboratories supplying analytical support. If more than one laboratory will be
628         used, detail the analyses supplied by each laboratory;

629       •  Identify analyses to be performed in the field using portable equipment or by a mobile lab;

630       •  Identify the sample preparation techniques. Non-routine preparatory protocols, such as novel
631         radiochemical separations, should be described in detail and documented in an SOP including
632         pertinent literature citations and the results of validations studies and other performance data,
633         when they exist;

634       •  Identify the analytical protocols to be used. The protocol documentation should describe all
635         necessary steps including the necessary reagents, apparatus and equipment, standards
636         preparation, calibration, sample introduction, data calculation, quality control, interferences,
637         and waste disposal;

638       •  If the selected analytical protocols have not been demonstrated for the intended application,
639         the QAPP should include information about the intended procedure, how it will be validated,
640         and what criteria must be met before it is accepted for the project's application (Chapter 6,
641         Selection and Application of an Analytical Protocol);

642       •  If potential analytical protocols were not identified during the project planning process and
643         existing analytical protocols can not meet the MQOs, an analytical protocol will have to be
644         developed and validated (Chapter 6, Section 6.5, "Method Validation").  If this issue was not
645         identified by the project planning team, the project planning team must be contacted because
646         the original project objectives and the associated MQOs may have to be  revisited and
647         changed (Appendix B);

648       •  If both high concentration and low concentration samples are expected, discuss how the two
649         sample types will be identified and handled in a manner that will prevent cross-contamination
650         or other analytical problems;

651       •  Discuss reporting requirements (e.g., suitable data acquisition and print-outs or electronic
652         data archival that will capture all necessary information), the proper units (dry weight versus
653         wet weight), the method to be employed to report the final result and its  uncertainty, and
654         reporting package format requirements; and

655       •  Identify oversight procedures  (e.g., QC samples, audits, supervisor review) for ensuring that
656         analytical procedures are implemented properly and procedures for correcting problems


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657         encountered in the laboratory. The person responsible for implementing corrective actions in
658         the lab should be identified.

659     The project plan document should be a dynamic document, used and updated over the life of the
660     project as information becomes available or changes. For example, under a performance based
661     approach, the analytical protocols requirements in the project plan documents should initially
662     reflect the Analytical Protocol Specifications established by the project planning team and issued
663     in the statement of work (or task order). When the analytical laboratory has been selected
664     (Appendix E, Contracting Analytical Services) the project plan document should be updated to
665     reflect the identification of the selected laboratory and the analytical protocols, that is, the actual
666     analytical protocols to be used should be included by citation or inclusion of the SOPs as
667     appendices.

668     D3.5   Measurement/Data Acquisition (B5): Quality Control Requirements

669     This element of the QAPP should include enough detail that the use and evaluation of QC
670     sample results and corrective actions will be performed as planned and support project activities.
671     The QC acceptance limits and the required  corrective actions for non-conformances should be
672     described. DQO Step 7 identified the optimized analytical design and the desired MQOs which
673     will help determine the QC acceptance criteria. Refer to Chapter 19.8.1 for information on
674     control charts and Chapter 18, Quality Assurance and Quality Control, for a detailed discussion
675     of radioassay QC and quality indicators. A  discussion of QC requirements in the QAPP should
676     include the following information:

677      •  A list of all QC sample types by matrix;

678      •  The frequency of QC sample collection or  analysis, preferably a tabular listing;

679      •  A list of QC sample acceptance criteria or warning limits and control limits;

680      •  Procedures for documenting QC  sample results;

681      •  Equations and calculations used to evaluate QC sample results and to determine measurement
682         performance acceptability;

683      •  Actions to be taken if QC samples fail to meet the acceptance criteria; and

684      •  Identification of the appropriate responsible person to whom QC reports  should be sent.


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685     Acceptance criteria for QC samples should be based on the project MQOs, in particular the MQO
686     for measurement uncertainty at some concentration. Appendix C provides guidance on
687     developing acceptance criteria for QC samples based on the project's MQO for the method's
688     measurement uncertainty at some concentration, typically the action level.

689     D3.6   Measurement/Data Acquisition (B6): Instrument/Equipment Testing, Inspection,
690            and Maintenance Requirements

691     The QAPP should include a discussion of testing, inspection and maintenance requirements that
692     will be followed to ensure that equipment and instrumentation will be in working order during
693     implementation of project activities. An instrument or testing equipment will be deemed to be in
694     working order if it is maintained according to protocol  and it has been inspected and tested and
695     meets acceptance criteria.

696     This element of the QAPP should:

697      • Discuss the maintenance policy for all essential instrumentation and equipment, what it
698        involves, its frequency, whether it is performed by internal staff or if it is a contracted service,
699        and whether an inventory of spare parts is maintained;

700      • Describe the inspection protocols for instrumentation and equipment. This ranges from the
701        routine inspections (i.e, gases, nebulizers, syringes  and tubing) prior to instrument or
702        equipment use and more detailed inspections employed while troubleshooting an instrument
703        or equipment problem. Mandatory inspection hold points, beyond which work may not
704        proceed, should be identified; and

705      • Address the frequency and details of equipment and instrument testing. This may involve the
706        weighing of volumes to test automatic diluters or pipets, the use of a standard weight prior to
707        weighing sample aliquots to the use of standards to test sophisticated instrumentation. If
708        standards (e.g., National Institute of Standards and  Technology [NIST] standard reference
709        material [SRM]) are used during testing,  the type, source and uncertainty of standard should
710        be identified.

711     There is not always a clear distinction between the testing component of this element and the
712     previous element addressing the use of QC samples to determine whether  an instrument is within
713     control. In any case, it is important to  describe in either of these elements of the QAPP, all
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714     procedures that are deemed important to determining whether an instrument/equipment is in
715     working order and within control.

716     D3.7  Measurement/Data Acquisition (B7): Instrument Calibration and Frequency

717     This element of the QAPP details the calibration procedures including standards, frequencies,
718     evaluation, corrective action measures and documentation. Summary tables may be used to
719     complement the more detailed discussions in the text. The following issues should be addressed
720     in this element:

721      •  Identify all tools, gauges, sampling devices, instruments, and test equipment that require
722         calibration to maintain acceptable performance;

723      •  Describe the calibration procedures in enough detail in this element or by citation to readily
724         available references so that the calibration can be performed as intended;

725      •  Identify reference equipment (e.g., NIST thermometers) and standards,  their sources, and how
726         they are traceable to national standards. Where national standards are not available, describe
727         the procedures used to document the acceptability of the calibration standard used;

728      •  Identify the frequency of calibration and any conditions (e.g., failed continuing calibration
729         standard, power failure) that may be cause for unscheduled calibration;

730      •  Identify the procedure and the acceptance criteria (i.e., in control) to be used to evaluate the
731         calibration data;

732      •  Identify the corrective actions to be taken if the calibration is not in control. When calibration
733         is out of control,  describe the evaluations to be made to determine the validity and
734         acceptability of measurements performed since the last calibration; and

735      •  Identify how calibration data will be documented, archived and traceable to the correct
736         instrument/equipment.

737     See Chapter 16, Instrument Calibration and Test Source Preparation, for a discussion of
738     radiochemical instrument calibration.
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739     D3.8  Measurement/Data Acquisition (B8): Inspection/Acceptance Requirements for
740            Supplies and Consumables

741     This element of the QAPP deals with inspecting and accepting all supplies and consumables that
742     may directly or indirectly affect the quality of the data. For some projects, this information may
743     be provided by citation to a chemical safety and hygiene plan. The contents of this element
744     should contain enough supportive information that the project and the data will be sufficient to
745     undergo solicited and unsolicited reviews. The following detail should be included in this
746     element, so the inspection process can be accurately implemented:

747      •  Identify and document all supplies and consumables (e.g., acids, solvents, preservatives,
748         containers, reagents, standards) that have the potential of directly or indirectly impacting the
749         quality of the data collection activity;

750      •  Identify the significant criteria that should be used when choosing supplies and consumables
751         (e-g-, grade, purity, activity, concentration, certification);

752      •  Describe the inspection and acceptance procedures that will be used for supplies or
753         consumables, including who is responsible for inspection, the timing of inspections and the
754         acceptance and rejection criteria. This description should be complete enough to allow
755         replication of the inspection process. Standards for receiving radiological packages are
756         provided in 10 CFR 20 Section 20.1906 "Procedures for Receiving and Opening Packages"
757         or an Agreement State equivalent;

758      •  Describe the procedures for checking the accuracy of newly purchased standards, other than
759         SRMs, by comparison to other standards purchased from other sources;

760      •  Identify any special handling and storage (e.g., refrigerated, in the dark, separate from high
761         concentration standards, lead shielding) conditions that must be maintained;

762      •  Describe the method of labeling, dating and tracking supplies and consumables and the
763         disposal method for when their useful  life has expired; and

764      •  Describe the procedures and indicate by job function who is responsible for documenting the
765         inspection process and the status of inventories.
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766     D3.9  Measurement/Data Acquisition (B9): Data Acquisition Requirements for Non-Direct
767            Measurement Data

768     This element of the QAPP addresses the use of existing data. Non-direct measurement data is
769     defined as existing data that is independent of the data generated by the current project's
770     sampling and analytical activities. Non-direct data may be of the same type (e.g., mBq/g of 232Th
771     in soil) that will complement the data being collected during the project. Other non-direct data
772     may be of a different type such as weather information from the National Weather Service, or
773     geological and hydrogeological data from the U.S. Geological Survey.
774
775     To achieve project objectives it is important that the data obtained from non-direct sources be
776     subjected to scrutiny prior to acceptance and use. Use of existing data is discussed during Step 1
777     and 3 of the DQO process. If existing data of the same type is to be used to achieve project
778     objectives, it has to be evaluated in terms of its ability to comply with MQOs established in DQO
779     Step 7. The limitations on the use of non-direct measurements should be established by the
780     project planning team.

781     This element should:

782      • Identify the type and source of all non-direct data that will be needed to achieve the project
783        objectives;

784      • State whether the same  quality criteria and QC sample criteria will be applied to the non-
785        direct measurement data. If the same criteria cannot be applied, then identify criteria that will
786        be acceptable for the non-direct data but at the same time won't bias or significantly add to
787        the uncertainty of decisions for the project;

788      • Identify whether the data will support qualitative decisions (e.g., rain occurred on the third
789        day of sampling) or if the  data will be used quantitatively (e.g., used to calculate a mean
790        concentration that will be  compared to an action level);

791      • Identify whether enough information exists to evaluate the quality of the non-direct data (e.g.,
792        spike and collocated sample data, minimum detectable concentrations, reported measurement
793        uncertainties); and

794      • If the non-direct data are to be combined with project-collected data, identify the criteria that
795        will be used to determine if the non-direct data are comparable (e.g., sampled the same
796        population, same protocol).


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797     D3.10 Measurement/Data Acquisition (BIO): Data Management

798     This element of the QAPP should present an overview of the data management process from the
799     receipt of raw data to data storage. The overview should address all interim steps, such as, data
800     transformations, transmittals, calculations, verifications, validations and data quality assess-
801     ments. The procedures should address how internal checks for errors are made. Laboratories
802     should follow accepted data management practices (EPA, 1995). Applicable SOPs should be
803     included as attachments to the QAPP. (See Chapter 17, Data Generation, Reduction and
804     Reporting for a discussion of radiochemical data generation and reduction.)

805     The discussion of data management should address the following issues:

806      •  DAT A RECORDING: The process of the initial data recording steps (e.g., field notebooks,
807         instrument printouts, electronic data storage of alpha and gamma spectra) should be
808         described. Examples of unique forms or procedures should be described. Describe the
809         procedures to be used to record final results (e.g., negative counts) and the uncertainty.

810      •  CONVERSIONS AND TRANSFORMATIONS: All data conversions (e.g., dry weight to wet weight),
811         transformations (conversion to logs to facilitate data analysis) and calculation of statistical
812         parameters (e.g., uncertainties) should be described, including equations and the rationale for
813         the conversions, transformations and calculations. Computer manipulation of data should  be
814         specified (e.g., software package, macros).

815      •  DATA TRANSMITTALS: Data transmittals occur when data are sent to another location or
816         person or when it is converted to another format (incorporated into a spreadsheet) or media
817         (hardcopy reports keyed into a computer database). All transmittals and associated QA/QC
818         steps taken to minimize transcription errors should be described in enough detail to ensure
819         their proper implementation.

820      •  DATA REDUCTIONS: Identify and explain the reasons for data reductions. Data reduction is the
821         process of changing the number of data items by arithmetic or statistical calculations,
822         standard curves, or concentration factors. A laboratory information management system may
823         use a dilution factor or concentration factor to change raw data. These changes often are
824         irreversible and in the process the original data are lost.
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825      • DATA VERIFICATION, VALIDATION AND ASSESSMENTS : Since these assessment issues are
826        discussed in a subsequent element of the QAPP (D2), only an overview should be provided
827        identify the timing and frequency of these assessments.

828      • DATA TRACKING, STORAGE AND RETRIEVAL: Describe the system for tracking and compiling
829        data as samples are being analyzed, how data are stored, and the mechanism for retrieving
830        data (e.g., from archived back-up tapes or disks).

831      • SECURITY: Describe procedures for data and computer security.

832     D4.0  Group C: Assessment/Oversight

833     The elements of this group are intended to assess progress during the project, facilitate corrective
834     actions in a timely manner (Section D4.1), and provide reports to management (Section D4.2). It
835     should be stressed that early detection of problems and weaknesses—before project commence-
836     ment or soon thereafter—and initiation of corrective actions are important for a project's success.
837     The focus of the elements in this group is the implementation of the project as defined in the
838     QAPP. This group is different from the subsequent group, data validation and usability, which
839     will assesses project data after the data collection activity is complete.

840     D4.1   Assessment/Oversight (Cl): Assessment and Response Actions

841     The QAPP authors have a range of assessment choices that can be employed to evaluate on-going
842     project activities, which include surveillance, peer review, systems reviews, technical systems
843     audits (of field and laboratory operations), and performance evaluations. A detailed discussion of
844     laboratory evaluation is presented in Chapter 7, Evaluating Radiological Laboratories. It is
845     important to schedule assessments in a timely manner. An assessment has less value if its
846     findings become available after completion of the activity. The goal is to uncover problems and
847     weaknesses before project commencement or soon thereafter and initiate corrective actions so the
848     project is a success.

849     This element of the QAPP should:

850      • Identify all assessments by type, frequency and schedule;
851      • Identify the personnel who will implement the assessments;
852      • Identify the criteria, documents, and plans upon which assessments will base their review;
853      • Describe the format of assessment reports;
854      • Identify the time frame for providing the corrective action plan; and

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855      •  Identify who is responsible for approving corrective actions and ensuring that they are
856         implemented.

857     D4.2   Assessment/Oversight (C2): Reports To Management

858     Reports to management are a mechanism for focusing management's attention on project quality
859     and on proj ect issues that may require the management's level of authority. To be effective
860     reports to management and management's review and response must be timely. The benefit of
861     these status reports is the opportunity to alert management of data quality problems, propose
862     viable solutions and procure additional resources.

863     At the end of the project, a final project report which includes the documentation of the DQA
864     findings should be prepared (Chapter 9, Data Quality Assessment). It may also be beneficial for
865     future planning efforts for the project planning team to provide a summary of the "lesson
866     learned" during the project, such as key issues not addressed during planning and discovered in
867     implementation or assessment, specialist expertise needed on the planning team, experience with
868     implementing performance-based analytical protocol selection.

869     This element of the QAPP should address the following issues:

870      •  Identify the various proj ect reports that will be sent to management;

871      •  Identify non-project reports that may discuss issues pertinent to the project (e.g., backlog
872         reports);

873      •  Identify QA reports that provide documentary evidence of quality (e.g., results of independent
874         performance testing,  routine QC monitoring of system performance);

875      •  Identify the content of "reports to management" (e.g., project status, deviations from the
876         QAPP and approved amendments, results of assessments, problems, suggested corrective
877         actions, status on past corrective actions);

878      •  Identify the frequency and schedule for reports to management;

879      •  Identify the organization or personnel who are responsible for authoring reports; and

880      •  Identify the management personnel who will receive and act upon the assessment reports.
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881     D5.0 Group D: Data Validation and Usability

882     This group of elements ensures that individual data elements conform to the project specific
883     criteria. This section of the QAPP discusses data verification, data validation and data quality
884     assessment (DQA), three processes employed to accept, reject or qualify data in an objective and
885     consistent manner. Although there is good agreement as to the range of issues that the three
886     elements, in total, should address, within the environmental community there are significant
887     differences as to how verification, validation and DQA are defined. The discussion of this group
888     of elements will use the definitions which are defined Chapter 8, Radiochemical Data
889      Verification and Validation.

890     D5.1   Data Validation and Usability (Dl): Verification and Validation Requirements

891     This element of the QAPP addresses requirements for both data verification and data validation.
892     The purpose of this element is to clearly state the criteria for deciding the degree to which each
893     data item and the data set as a whole has met the quality specifications described in the
894     "Measurement/Data Acquisition" section of the QAPP. The strength of the conclusions that can
895     be drawn from the data is directly related to compliance with and deviations from the sampling
896     and analytical design.  The requirements can be presented in tabular or narrative form.

897     Verification procedures and criteria should be established prior to the data evaluation.
898     Requirements for data verification include the following criteria:

899      •  Criteria for determining if specified protocols were employed (e.g., compliance with essential
900         procedural steps);

901      •  Criteria for determining if methods were in control  (e.g., QC acceptance criteria);

902      •  Criteria for determining if a data report is complete (e.g., list of critical components that
903         constitute the report);

904      •  Criteria for determining if the analysis was performed according to the QAPP and the SOW;

905      •  Criteria and codes used to qualify data; and

906      •  Criteria for summarizing and reporting the results of verification.
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907     A discussion of verification can be found in Chapter 8, Radiochemical Data Verification and
908      Validation.

909     Data validation should be performed by an organization independent of the group that generated
910     the data to provide an unbiased evaluation. Validation procedures and criteria should be
911     established prior to the data evaluation. Requirements for data validation include the following:

912      •  An approved list of well-defined MQOs and the action level(s) relevant to the project DQOs;

913      •  Criteria for assigning qualifiers based on the approved list of MQOs;

914      •  Criteria for identifying situations when the data validator's best professional judgement can
915         be employed and when a strict protocol must be followed; and

916      •  Criteria for summarizing and reporting the results of validation.

917     A discussion of verification can be found in Chapter 8, Radiochemical Data Verification and
918      Validation.

919     D5.2   Data Validation and Usability (D2): Verification and Validation Methods

920     D5.2.1 Data Verification

921     Data verification or compliance with the SOW is concerned with: complete, consistent,
922     compliant and comparable data. Since the data verification report documents whether laboratory
923     conditions and operations were compliant with the SOW, the report is often used to determine
924     payment for laboratory services. Chapter 5, Obtaining Laboratory Services, discusses the need to
925     prepare a SOW for all radioanalytical laboratory work regardless of whether the work is
926     contracted out or performed in-house.

927     This element of the QAPP should address the following issues to ensure that data verification
928     will focus on the correct issues:

929      •  Identify the documents (e.g., other QAPP sections, SOW, contracts, standard methods) that
930         describe the deliverables and criteria that will be used to evaluate compliance;
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931       •  Identify the performance indicators that will be evaluated (e.g., yield, matrix spikes,
932         replicates). See Chapter 18, Laboratory Quality Control, for a discussion of radiochemistry
933         performance indicators;

934       •  Identify the criteria that will be used to determine "in-control" and "not-in-control"
935         conditions;

936       •  Identify who will perform data verification;

937       •  Describe the contents of the verification report (e.g., a summary of the verification process as
938         applied; required project activities not performed  or not on schedule or not according to
939         required frequency; procedures that were performed but did not meet acceptance criteria;
940         affected samples; exceptions); and

941       •  Identify who will receive verification reports and the mechanism for its archival.

942     D5.2.2 Data Validation

943     Chapter 8, Radiochemical Data Verification and Validation, discusses radiochemical data
944     validation in detail. MARLAP recommends that a data validation plan document be included as
945     an appendix to the QAPP. The data validation report will serve as the major input to the process
946     that evaluates  the reliability of measurement data.

947     This element of the QAPP  should address the following issues:

948       •  Describe the deliverables, measurement performance criteria and acceptance criteria that will
949         be used to evaluate data validity;

950       •  Identify who will perform data validation;

951       •  Describe the contents of the validation report (e.g., a summary of the validation process as
952         applied; summary of exceptional circumstances; list of validated samples, summary of
953         validated results); and

954       •  Identify who will receive validation reports and the mechanism for its archival.
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955     D5.3  Data Validation and Usability (D3):  Reconciliation with Data Quality Objectives

956     This element of the QAPP describes how project data will be evaluated to determine its usability
957     in decision-making. This evaluation is referred to as the "data quality assessment." DQA is the
958     process that scientifically and statistically evaluates project-wide knowledge in terms of the
959     project objectives to assess the usability of data. DQA should be ongoing and integrated into the
960     project data collection activities. On project diagrams and data life cycles, it is often shown as the
961     last phase of the data collection activity. However, like any assessment process, DQA should be
962     considered throughout the data collection activity to ensure usable data. EPA guidance (EPA,
963     1996) provides a detailed discussion of that part of the DQA process that addresses statistical
964     manipulation of the data. In addition to statistical considerations, the DQA process integrates and
965     considers information from the validation report, assessment reports, the field, the conceptual
966     model and historical data to arrive at its conclusions regarding data usability. DQA is discussed
967     in Chapter 9, Data Quality Assessment.

968     The DQA considers the impact of a myriad of data collection activities in addition to measure-
969     ment activities. This element of the QAPP should direct those performing the DQA to:

970      •  Review the QAPP and DQOs;
971      •  Review the validation report;
972      •  Review reports to management;
973      •  Review identified field, sampling,  sample handling, analytical and data management
974         problems associated with project activities;
975      •  Review all corrective actions; and
976      •  Review all assessment reports and findings (e.g., surveillances, audits, performance
977         evaluations, peer reviews, management and technical system reviews).

978     In addition to the above, this element of the QAPP should address the following issues:

979      •  Identify who will perform the DQA;
980      •  Identify what issues will be addressed by the DQA;
981      •  Identify any statistical tests that will be used to evaluate the data (e.g., tests for normality);
982      •  Describe how MQOs will be used to determine the usability of measurement data (i.e., did
983         the measurement uncertainty in the data significantly affect confidence in the decision?);
984      •  Describe how the representativeness of the data will be evaluated (e.g., review the sampling
985         strategy, the suitability of sampling devices, subsampling procedures, assessment findings);
986      •  Describe how the potential impact of non-measurable factors will be considered;
987      •  Identify what will be included in the DQA report; and


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 988       •  Identify who will receive the report and the mechanism for its archival.

 989      D6.0 References

 990      American National Standards Institute and the American Society for Quality Control (ANSI/
 991         ASQC). 1994. Specifications and Guidelines for Quality Systems for Environmental Data
 992         Collection and Environmental Technology Programs, National Standard E-4.

 993      Taylor, J. K. 1990. Quality Assurance of Chemical Measurements. Lewis, Chelsea, Michigan.

 994      U. S. Department of Energy (DOE). 1996.  Project Plan for the Background Soils Project for the
 995         Paducah Gaseous Diffusion Plant, Paducah, Kentucky. Report DOE/OR/07-1414&D2. May.

 996      U.S. Department of Energy (DOE). 1997.  Quality Assurance Project Plan for Radiological
 997         Monitoring at the U.S. DOE Paducah Gaseous Diffusion Plant, Paducah, Kentucky.
 998         February.

 999      U.S. Environmental Protection Agency (EPA). 1980. Interim Guidelines and Specifications for
1000         Preparing Quality Assurance Project Plans., QAMS-005/80. Office of Monitoring Systems
1001         and Quality Assurance, Washington, DC.

1002      U.S. Environmental Protection Agency (EPA). 1994. Guidance for the Data Quality Objective
1003         Process ( EPA QA/G-4). EPA/600/R-96/055, EPA, Washington, DC.

1004      U.S. Environmental Protection Agency (EPA). 1995. Good Automated Laboratory Practices.
1005         Report 2185, EPA, Washington, DC.

1006      U.S. Environmental Protection Agency (EPA). 1996. Guidance for Data Quality Assessment:
1007         Practical Methods for Data Analysis. EPA QA/G-9, EPA/600/R-96/084, EPA, Washington,
1008         DC.

1009      U.S. Environmental Protection Agency (EPA). 1998a.  EPA Guidance for Quality Assurance
1010         Project Plans (EPA QA/G-5). EPA/600/R-98/018,  EPA, Washington, DC.

1011      U.S. Environmental Protection Agency (EPA). 1998b.  EPA Requirements for Quality Assurance
1012         Project Plans for Environmental Data Operations. EPA QA/R-5, External Review Draft
1013         Final, EPA, Washington, DC.
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 i            APPENDIX E: CONTRACTING LABORATORY

 2                                         SERVICES


 3     E.I   Introduction

 4     This appendix provides general guidance on Federal contracting and contracting terminology as
 5     used for negotiated procurements. Federal Agencies, and laboratories doing business with them,
 6     must follow applicable provisions of the Federal Acquisition Regulations (FAR) and Agency-
 7     specific supplements. The examples provided in this appendix are based primarily on procedures
 8     followed by the U.S. Geological Survey (USGS).

 9     This appendix addresses selecting a laboratory to establish services that supplement an Agency's
10     in-house activities through the contracting of additional outside support. This appendix offers a
11     number of principles that may be used when selecting a service  provider, establishing a
12     contractual agreement, and later working with a contract laboratory. These principles may also be
13     applied to contractors that are located outside of the United States. In such cases, legal counsel
14     will need to review and advise an Agency concerning pertinent issues related to international
15     contracts.

16     This appendix also covers laboratory audits that are part of a final selection process and other
17     activities that take place until the contract is concluded. Chapter 5 supports this appendix with a
18     general description on how to obtain laboratory services.  Chapter 7 complements this appendix
19     by considering information related to laboratory evaluations that are conducted throughout the
20     term of a project—whether or not this work is specifically covered by a contract.

21     Obtaining support for laboratory analyses is already a practice that is familiar to a number of
22     Federal and State Agencies. The following discussion will apply:

23      •  Agency - a Federal or State government office or department, (or potentially any other public
24         or private institution) that offers a solicitation or other mechanism to obtain outside services;

25      •  Proposer - a contracting firm or commercial facility that submits a proposal related to
26         providing services; and

27      •  Contractor - a firm that is awarded the contract and is engaged in providing analytical
28         services.
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29     Furthermore, the size and complexity of some agency projects will clearly exceed the extent of
30     the information presented here. In its present form, this appendix serves to touch on many of the
31     issues and considerations that are common to all projects, be they large or small.

32     MARLAP draws attention to another dimension of the overall contracting process by considering
33     how the Data Quality Objectives (DQOs) and Measurement Quality Objectives (MQOs) are
34     incorporated into every stage of a project—as described earlier in greater detail (Chapters 2 and
35     3). In this regard, an Agency's Project Managers and staff are given an opportunity to consider
36     options with some foresight and to examine  the larger picture, which concerns planning short- or
37     long-term projects that utilize a contractor's  services. As services are acquired, and later as work
38     is performed, the specific concepts and goals outlined by the DQOs and MQOs will be revisited.
39     This becomes an iterative process that offers the possibility to further define objectives as work is
40     conducted. Whenever the DQOs or MQOs are changed, the contract should be modified to
41     reflect the new specifications. Employing the MQOs and tracking the contractor's progress
42     provides a means by which Project Managers and contract-laboratory technical staff can return
43     and review the project at any point during the contract period.  This allows for repeated
44     evaluations to further optimize a project's goals and, if anticipated in the contract's language,
45     perhaps even provides for the option to revise or redirect the way performance-based work is
46     conducted.

47     The Office of Federal Procurement Policy (OFPP, 1997) has developed  a Performance-Based
48     Service Contracting review checklist to be used as a guide in developing a performance-based
49     solicitation. The checklist contains minimum required elements that should be present for a
50     contract to be considered performance-based. Performance-Based Service Contracting focuses on
51     three elements:  a performance work  statement; a quality assurance project plan (QAPP); and
52     appropriate incentives, if applicable. The performance work statement defines the requirements
53     in terms of the objective and measurable outputs. The performance work statement should
54     answer five basic questions: what, when, where, how many, and how well. The work statement
55     should structure and clearly define the requirements, performance standards, acceptable quality
56     levels, methods of surveillance, incentives if applicable and evaluation criteria. A market survey
57     should be conducted so that the marketplace and other stakeholders are provided the opportunity
58     to comment on draft performance requirements and standards, the proposed QA project plan, and
59     performance incentives, if applicable.

60     A number of benefits arise from establishing a formal working relationship between an Agency
61     and a contractor. For example:
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62       •  A contract is a legal document that clearly defines activities and expectations for the benefit
63         of both parties engaged in the contractual relationship.

64       •  The process of drafting language to cover legal considerations may well include contributions
65         from legal staff. Legal guidance may be obtained as needed at any time during the planning
66         stages or later when a contract is in place. However, the core of a contractor's proposal, and
67         eventually the  contract itself, provide the foundation of technical work that is required to
68         complete a project or attain an ongoing program goal. In this regard, aside from legal issues
69         that are an integral part of every contract, this appendix's principal focus is on the
70         laboratory process or technical work-related content of the contract.

71       •  The statement  of work (SOW) first appears as part of the Agency's request for proposal
72         (RFP) and later is essentially incorporated into the proposal by the proposer when responding
73         to the RFP. When work is underway, the SOW becomes a working document that both the
74         Agency and contractor refer to during the life of the contract.

75       •  Legal challenges concerning project results (i.e., laboratory data) may arise during the
76         contract period. The language in a contract should offer sufficient detail to provide the means
77         to circumvent potential or anticipated problems. For example, attention to deliveries of
78         samples to the laboratory on weekends and holidays or data reporting requirements that are
79         designed to support the proper presentation of data in a legal proceeding are important
80         aspects of many Federal- and State-funded contracts.

81     Overall, this appendix incorporates a sequence that includes both a planning and a selection
82     process. Figure E-l illustrates a series of general steps from planning before a contract is even in
83     place to the ultimate termination of the contract. An Agency first determines a need as part of
84     planning, and along the way advertises this need to solicit proposals from outside service
85     providers who operate analytical laboratory facilities. Planning future work, advertising for, and
86     later selecting services from proposals submitted to an Agency takes time—perhaps six or more
87     months pass before a laboratory is selected, a contract is in place, and  analytical work begins.
88     The total working  duration of a contract, for example, might cover services for a brief time
89     (weeks or months) and in other cases, many contracts may run for a preset one-year period or for
90     a more extended period of three to five years with optional renewal periods during that time.

91     The MARLAP user will find that planning employs a thought process much like that used to
92     prepare an RFP. In general, one starts with questions that define a project's needs. Further, by
93     developing Analytical Protocol Specifications (APSs) which include specific MQOs, one enters
94     an iterative process such that—at various times—data quality is checked in relation to work


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Planning
i
r
Procurement
Process
i
r
Request for
Proposal
i
r

|

I

I

Contractor Proposal |
i
r
Review
Evaluate
Score
Audit
i
r
Award
i
r
Contractor Servici
i
-^
r

1

|

BS |
•
•
Contract Completion |
« Includes SOW
Section E.2
^ Reflects SOW
Section E.3
Section E.4
Section E.5
Section E.6
Section E.7
Periodic Evaluations
Work According to SOW
Section E.8
                 FIGURE E.I — General Sequence Initiating and Later
                    Conducting Work with a Contract Laboratory
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 95      performed both in-house and by the outside service provider. Overall, planning results in the
 96      development of a project plan document (e.g., QAPP). During planning, a Project Manager and
 97      the Agency staff can consider both routine and special analytical services that may be required to
 98      provide data of definable quality. The SOW serves to integrate all technical and quality aspects
 99      of the project, and to define how specific quality-assurance and quality-control activities are
100      implemented during the time course of a contract. Also, at an early stage in planning, the Agency
101      may choose to assemble a team to serve as the Technical Evaluation Committee (TEC; Section
102      E.5.1). The main role of the TEC is in selecting the contract laboratory by reviewing proposals
103      and by auditing laboratory facilities. The TEC is discussed later in this appendix,  however, the
104      key issue here concerns the benefit to establishing this committee early on, even to the point of
105      including  TEC members in the initial planning activities. The result is a better informed
106      evaluation committee and a team of individuals that can help make adjustments when the
107      directed planning process warrants an iterative evaluation of the way work is performed under
108      the contract. Overall, planning initiates the process that characterizes the nature of the contracting
109      process to follow.

no      E.2   Procurement of Services

111      Recognizing that the procurement process differs from Agency to Agency, the following
112      guidance provides a general overview to highlight considerations that may already be part of—or
113      be incorporated into—the current practice. First, the request for specific analytical services can
114      be viewed as a key product of both the Agency's mission and the directed planning process. As
115      Agency staff ask questions, list key considerations to address during the work, and in turn define
116      objectives, they also eliminate unnecessary options to help focus on the most suitable contracting
117      options that satisfy the APSs. Thereafter, the scope of the work, schedule, manpower constraints,
118      availability of in-house engineering resources, and other technical considerations  all  enter into
119      estimating and defining a need for project support. This approach refines the objectives and
120      establishes needs that may be advertised in a solicitation for outside services. The resulting work
121      or project plan should clearly articulate what is typically known but not limited to the following:

122       •  Site conditions;
123       •  Analytes of interest;
124       •  Matrices of concern;
125       •  How samples are to be collected and handled;
126       •  Custody requirements;
127       •  Data needs and APSs, including the MQOs;
128       •  Stipulated analytical methods, if required
129       •  Applicable regulations; and

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130      • Data reporting.

131     All of this defines the scope of work, such that the Agency can initiate a formal request for
132     proposals or arrange for an analysis request as part of a less formal procurement.

133     E.2.1  Request for Approval of Proposed Procurement Action

134     If required within an Agency, a request is processed using forms and related paperwork to
135     document information typically including, but not limited to, the following:

136      • Identification of product or service to be procured;
137      • Title of program or project;
138      • Description of product or service;
139      • Relationship of product or service to overall program or project;
140      • Funding year, projected contract life, amounts, etc.;
141      • Name and phone number of Project Officer(s);
142      • Signature of Project Officer and date
143      • Name and phone number of Contracting Officer; and
144      • Signature of Contracting Officer and date.

145     An Agency may also be required to collect or track information for an RFP with regard to:

146      • New procurements: type of contract, grant, agreement, proposal, etc. Continuing
147        procurements: pre-negotiated options, modifications, justification for non-competitive
148        procurement, etc.
149      • Source information: small business or other  set aside, minority business, women-owned
150        business, etc.

151     In addition to the information listed above, Agency-specific forms used to initiate a procurement
152     request may also provide a place to indicate Agency approval with names,  signature lines, and
153     date  spaces for completion by officials in the office responsible for procurement and contracts.
154     An Agency administrator or director above the level of the office of procurement may also sign
155     this form indicating Agency approval.
156

157
158
E.2.2  Types of Procurement Mechanisms

Table E.I lists many of the procurement options available to the Project Manager. Each option
offers a solution to a specific need. For example, a purchase order is typically appropriate for


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159
160
161
162

163
164
165
166

167


168
169
170

171
172
173
174
175
176
177
178
179
180
181
182

183
184
tasks with a somewhat limited scope and thus is perhaps most useful when samples are to be
processed on a one-time basis. In some cases where only one or a limited number of vendors can
fulfill the needs of the project, e.g., low-level tritium analysis by helium ingrowth within a
specified time period, a sole source solicitation is commonly used.

     TABLE E.I— Examples of Procurement Options to Obtain Materials or Services.
Procurement Mechanism
Purchase order
Sole source solicitation
Request for Quotation (RFQ)
Request for Proposal (RFP)
Modification to an existing contract
or delivery order
Basic Ordering Agreement (BOA)
Example of Specific Use or Application
In-house process handled through purchasing staff; limited to small needs
without a formal request or used in conjunction with a solicitation
(competitive process) and a limited amount of funding; commonly used to
purchase equipment and supplies, but may be used for processing samples.
In specific instances, a single or a limited number of service providers are
able to offer specific services.
Formal, main process for establishing contracts — generally addresses a
major, long-term need for contractor support; this is a competitive process
based mainly on cost.
Formal, main process for establishing contracts — generally addresses a
major, long-term need for contractor support; this is a competitive process
based mainly on technical capability.
This approach meets a need that is consistent with the type of contract that
is in place, e.g., Agency amends contract to add a method for sample
processing that is similar to work already covered.
Work is arranged with a pre-approved laboratory as described in
Section E.2.2.
The process leading to a formal contract provides a more comprehensive view of nearly every
aspect of the work that an Agency expects from a contractor. The formal process includes three
types of procurement: Request for Quotation (RFQ), Request for Proposal (RFP), and the Basic
Ordering Agreement (BOA). The RFQ solicits bidders to provide a quotation for laboratory
services that have been detailed in the solicitation. The specifications may include the technical,
administrative, and contractual requirements for a project. For the RFQ, the contract typically is
awarded to the lowest bidder that can fulfill the contract specifications without regard to the
quality of the service. What appears to be a good price may not entail the use of the best or most
appropriate method or technology. There may be significant advantages in seeking to acquire
high-technology services as a primary focus in advance of, or along with, concerns pertaining to
price.

For an RFP, there is considerably more work for the Agency and the laboratory. The laboratory
must submit a formal proposal addressing all key elements of the solicitation that include how,
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185     why, what, when ,where and by whom the services are to be performed. The TEC or Contracting
186     Officer must review all proposals, rank them according to a scoring system and finally assess the
187     cost effectiveness of the proposals before making the final award.

188     The BOA provides a process that serves to pre-approved service providers. This includes a
189     preliminary advertisement for a particular type of work, such as radioanalytical services. The
190     Agency then selects and approves a number of candidates that respond to the advertisement.
191     With this approach, the Agency assembles a potential list of approved laboratories that are
192     contacted as needed to support specific needs. The Agency may choose to simply write a task
193     order (defining a specific scope of work) with a specific pre-approved laboratory, or the Agency
194     may initiate a competitive bidding process for the task order between several or all members on
195     the list of pre-approved laboratories. Once chosen, the laboratory may be guided by a combined
196     Statement of Work or Task Order that is issued by the Agency.

197     Mechanisms that permit an Agency to obtain analyses for a limited number of samples—without
198     an established contractual relationship with a specific contractor—may simply be necessitated by
199     the small number of samples, time constraints where specific analyses are not part  of an existing
200     contract, limitations related to funding, or other consideration. The formal business and legal
201     requirements of a long-term relationship warrant a stronger contractual foundation for work
202     conducted in a timely fashion, on larger numbers of samples, and over specified periods of time.
203     The contracts described above, with the exception of a BOA, are considered "requirement"
204     contracts and requires the group initiating the solicitation to use only the contracted laboratory,
205     without exception, for the contract period to perform the sample analyses.

206     E.3   Request for Proposals—The Solicitation

207     To appreciate the full extent of a competitive process leading to a formal working relationship—
208     between an Agency and a contractor—the primary example used hereafter is the solicitation and
209     selection process that starts with the issuance of a RFP, as shown in Figure E-l.

210     Federal announcements of certain RFPs can be found in the Commerce Business Daily (CBD).
211     The CBD primarily provides a synopsis or brief description of the type of work the Agency is
212     interested in purchasing. States and local governments also solicit proposals and announce the
213     availability of work in USABID (a compilation of solicitations from hundreds of city, county,
214     and state agencies). Internet sites that offer access to the CBD (http://cbdnet.access.gpo.gov/) and
215     USABID listings can be located through electronic searches using Web Browser software. Once
216     a site is located, the information can be viewed through public access or commercial Internet-
217     based services. In other cases, a State or Federal Agency may maintain a mailing list with names

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218     and addresses for potentially interested parties. This might include contractors that previously
219     supported the Agency or others who have volunteered information for the mailing list.

220     Once the RFP, State advertisement, or other form of solicitation is publicized, interested parties
221     can contact the appropriate Agency to obtain all the specific information relevant to completing a
222     candidate laboratory's contract proposal. For the present discussion, this information is contained
223     in the text of the RFP document. The RFP may be accompanied by a cover letter stating an
224     invitation to applicants and general information related to the content of a proposal and specific
225     indication for the types of sections or sub-sections the proposal will contain. For example, a
226     proposal divided into three sections technical proposal, representations and certifications, and
227     price proposal allows the Agency to separate pricing from technical information. In this way, the
228     Agency considers each candidate first on technical merits before the price of services enters the
229     selection process.

230     The Agency's RFP is designed to provide a complete description of the proposed work. For
231     example, a RFP should inform all candidate laboratories (i.e., proposers) of the estimated number
232     of samples that are anticipated for processing under the contract. The description of work in the
233     RFP as described in the SOW serves to indicate the types of radionuclide analyses required for
234     the stated sample types and the number of samples to undergo similar or different processing
235     protocols. The estimate also has a bearing on cost and other specific project details as described
236     in the SOW. Additional information provided with the RFP serves to instruct the proposer
237     regarding other technical requirements (APSs), the required number of copies of each section of
238     the proposal, proposal deadline, address where proposals are to be sent, and other general
239     concerns or specifications relevant to the solicitation.

240     The cover letter may indicate how each proposer will be notified if its proposal is dropped from
241     the competitive range of candidates during the selection process. The letter may also include
242     precautionary notes concerning whom to contact or not contact at  the Agency regarding the
243     potential contract during the competitive process. Finally, if particular sources are encouraged to
244     apply (e.g., minority or small business), this information will be mentioned in the Agency's
245     invitation to apply.

246     E.3.1  Market Research

247     The Office of Federal Procurement Policy (OFPP, 1997) recommends that the marketplace and
248     other stakeholders be provided the opportunity to comment on draft performance requirements
249     and standards. This practice allows for feedback from those people working in the technical
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250     community so that their comments may be incorporated into the final RFP and the potential
251     offerers can develop intelligent proposals.

252     E.3.2  Length of Contract

253     The time and resources involved in writing and awarding a major contract generally make it
254     impractical and cost ineffective to award contracts for less than one or more years. While
255     contracts running for shorter terms are sometimes established, single or multiple year terms are
256     commonly used to provide the necessary services for some Federal or State programs.
257     Monitoring programs are likely to go long periods  of time with renewals or RFPs that continue
258     the work into the future. Elsewhere, relatively large projects conducting radiation survey and site
259     investigations may require a contract process that,  for the most part, estimates the time services
260     will be needed to finish work through to the completion of a final status survey. In this case, the
261     contract may specify any length of time, but also include the option to renew the contract for a
262     period of time to bring the project to a close. The relationship between the length of a contract
263     and the type of project can be part of the structured planning process that seeks to anticipate
264     every facet of a proj ect from start to finish.

265     Multi-year contracts are typically initiated with an  award for the first year followed by an
266     additional number of one-year options. In this way, a five-year contract is awarded for 1 year
267     with four one-year option periods to complete the contract's full term. Problems that arise during
268     any year may result in an Agency review of the MQOs or an examination of the current working
269     relationship that may result in the Agency's decision to not extend the contract into the next
270     option year.

271     E.3.3  Subcontracts

272     For continuity or for quality assurance (QA), the contract may require one laboratory to handle
273     the entire analytical work load. However, subcontracting work with the support of an additional
274     laboratory facility may arise if the project plan calls for a large number of samples requiring
275     quick turnaround times and specific methodologies that are not part of the primary laboratory's
276     support services. A proposer may choose to list a number of subcontractors in the proposal. The
277     listing may or may not include other laboratories with whom the proposer has an existing or prior
278     working relationship. The choice of subcontracting firms may be limited during the proposal
279     process. There may be many qualified service providers to meet specific  project needs. However,
280     once work is under way, using a limited number of laboratories that qualify for this secondary
281     role helps maintain greater control of quality and thus the consistency of data coming from more
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282     than a single laboratory alone. Furthermore, the contractor may prefer working with a specific
283     subcontractor, but this arrangement is subject to Agency approval.

284     The use of multiple service providers adds complexity to the Agency's tasks of auditing,
285     evaluating, and tracking services. The contractor and their subcontractor(s) are held to the same
286     terms and conditions of the contract. The prime contractor is held responsible for the
287     performance of its subcontract laboratories. In some instances, certain legal considerations
288     related to chain of custody, data quality and reporting, or other concern may limit an Agency's
289     options and thus restrict the number of laboratories that are part of any one contract.

290     E.4   Proposal Requirements

291     The Agency's RFP will state requirements that each proposer is to cover in its proposal. The
292     proposal document itself becomes first the obj ect of evaluation and is a reflection of how the
293     contract and the SOW are structured. Whether one works with a formal contract or a simpler
294     analysis request, the Agency and contractor need to agree to all factors concerning the specific
295     analytical work. Where written agreements are established, the language should be specific to
296     avoid disputes. Clear communication and complete documentation are critical to a project's
297     success. For example, the Agency's staff asks questions  of itself during the planning process to
298     create and later advertise a clearly stated need in the RFP. The contractor then composes a
299     proposal that documents relevant details concerning their laboratory's administrative and
300     technical personnel, training programs, instrumentation,  previous project experience, etc.
301     Overall, the proposer should make an effort to address every element presented in the RFP. The
302     proposer should be as clear and complete as possible to ensure a fair and proper evaluation
303     during the Agency's selection process.

304     The planning process will reveal numerous factors related to technical requirements necessary to
305     tailor a contract to specific project needs. The following sections may be reviewed by Agency
306     staff (radiochemist or TEC) during planning to determine if additional needs are required beyond
307     those listed in this manual. Agency personnel should consider carefully the need to include every
308     necessary detail to make a concise RFP. The proposer can read the same sections to anticipate the
309     types of issues that are likely to appear in an RFP and that may be addressed in a proposal.

310     E.4.1  RFP and Contract Information

311     There are two basic areas an Agency can consider when  assembling information to include in an
312     RFP.  The proposer is expected to respond with information  for each area in its proposal. The first
313     area includes a listing of General Laboratory Requirements and Activities. The second area,

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314     Technical Components to Laboratory Functions., complements the first, but typically includes
315     more detailed information.

316     1)  General Laboratory Requirements
317      •  Personnel;
318      •  Facilities;
319      •  Meeting Contract Data Quality Requirements;
320      •  Schedule;
321      •  Quality Manual;
322      •  Data Deliverables Including Electronic Format;
323      •  Licenses and Certifications; and
324      •  Experience: Previous and Current Contracts; Quality of Performance.

325     2)  Technical Components to Laboratory Functions
326      •  Standard Operating Procedures;
327      •  Instrumentation
328      •  Training
329      •  Performance Evaluation Programs; and
330      •  Quality System.

331     The laboratory requirements and technical components indicated above are addressed in this
332     appendix. Beyond this, there are additional elements that may be required to appear with detailed
333     descriptions in an RFP and later in a formal proposal. One significant portion of the RFP, and a
334     key element appearing later in the contract itself, is the SOW. This is the third area a proposer is
335     to address, and information in a SOW may vary depending on the nature of the work.

336     The Agency will provide specifications in the RFP regarding the work the contractor will
337     perform. This initiates an interaction between a proposer and the Agency and further leads to two
338     distinct areas of contractor-Agency activity. The first concerns development and submitting of
339     proposals stating how the laboratory work will be conducted to meet specific Agency needs. The
340     second concerns Agency evaluations of the laboratory's work according to contract specifications
341     (Section E.5) and the SOW. Once the contract is awarded, a contractor is bound to perform the
342     work as proposed.

343     Specific sections of each contract cover exactly what is expected of the contractor and its
344     analytical facilities to fulfill the terms and conditions of the contract. The SOW describes the
345     required tasks and deliverables, and presents technical details regarding how tasks are to be
346     executed.  A well written SOW provides technical information and guidance that directs the


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347      contractor to a practice that is technically qualified, meets all relevant regulatory requirements,
348      and appropriately coordinates all work activities. A sample checklist for key information that
349      may be in a SOW is presented in Table E.2. Note that not all topics in the list are appropriate for
350      each project, and in some cases, only a subset is required. The list may also be considered in
351      relation to less formal working relationships (e.g., purchase order), as well as tasks covered in
352      formal contracts.
353
354
355
356
357
358
359
360
361
362
363

364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379

380
381
382
383
384
385

386
387
388
389
390
               TABLE E.2 — SOW Checklists for the Agency and Proposer
SAMPLE HISTORY
	   General background on the problem
	   Site conditions
	   Regulatory background
	   Sample origin
	   Analytes and interferences (chemical forms and estimated concentration range)
	   Safety issues
	   Data use
    	   Regulatory compliance
    	   Litigation

ANALYSIS RELATED
	   Number of samples
	   Matrix
	   Container type and volume
	   Receiving and storage requirements
	   Special handling considerations
	   Custody requirements
	   Preservation requirements, if any
	   Analytes of interest (specific isotopes or nuclide)
	   Measurement Quality Objectives
	   Proposed method (if appropriate) and method validation documentation
	   Regulatory reporting time requirement (if applicable)
	   Analysis time requirements (time issues related to half-lives)
	   QC requirements (frequency, type, and acceptance criteria)
	   Waste disposal issues during processing
	   Licenses and accreditation

OVERSIGHT
	   Quality manual
	   Required Performance  Evaluation Program participation
	   Criteria for (blind) QC
	   Site visit/data assessment
	   Audit (if any)

REPORTING REQUIREMENTS
	   Report results as gross, isotopic....
	   Reporting units
	   Reporting basis (dry weight,....)
	   How to report measurement uncertainties
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391
392
393
394
395

396
397
398
399
400
401

402
403
404
405
406
407

408
409
        Reporting Minimum Detectable Concentration and Minimum Quantifiable Concentration
        Report contents desired and information for electronic data transfer
        Turn-around time requirements
        Electronic deliverables
        Data report format and outline
 NOTIFICATION
 	   Exceeding predetermined Maximum Concentration Levels - when applicable
 	   Batch QC failures or other issues
 	   Failure to meet analysis or turnaround times
 	   Violations related to radioactive material license
 	   Change of primary staff associated with contract work

 SCHEDULE
 	   Expected date of delivery
 	   Method of delivery of samples
 	   Determine schedule (on batch basis)
 	   Method to report and resolve anomalies and nonconformance in data to the client
 	   Return of samples and disposition of waste

 CONTACT
 	   Name, address, phone number of responsible parties
410
E.4.2  Personnel
411      The education, working knowledge, and experience of the individuals that supervise operations,
412      conduct analyses, operate laboratory instruments, process data, and create the deliverables is of
413      key importance to the operation of a laboratory. The Agency is essentially asking: Who is
414      sufficiently qualified to meet the proposed project's needs? (The answer to this question may
415      come from an Agency's guidance or other specific requirements generated by the structured
416      planning process.) The laboratory staff that will perform the analyses should be employed,
417      trained, and qualified prior to the award of the contract.

418      In response to the RFP, the proposer should include a listing of staff members capable of
419      managing, receiving, logging, preparing, and processing samples; providing reports in the format
420      specified by the project; preparing data packages with documentation to support the results;
421      maintaining the chain of custody; and other key work activities. The laboratory should list the
422      administrative personnel and appoint a technical person to be a point of contact for the proposed
423      work. This person should fully understand the project's requirements and be reasonably available
424      to respond to every project need. A proposal should include the educational background and a
425      brief resume for all key personnel. The level of training for each technician should be included.
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426      Tables E.3 and E.4 are examples that briefly summarize the suggested minimum experience,
427      education, and training for the listed positions. Note, some Agency-specific requirements may
428      exceed the suggested qualifications and this issue should be explored further during the planning
429      process. The goal here is to provide basic guidance with examples that the MARLAP user can
430      employ as a starting point during planning. Once specific requirements are established, this
431      information will appear in the RFP.

432      Table E.3 provides a listing for the types of laboratory technical supervisory personnel that are
433      likely to manage every aspect of a laboratory's work. Each position title is given a brief
434      description of responsibilities, along with the minimum level of education and experience. Table
435      E.4 presents descriptions for staff members that may be considered optional personnel or, in
436      some cases, represent necessary support that is provided by personnel with other position titles.
437      Table E.5 indicates the minimum education and experience for laboratory technical staff
438      members. In some cases, specific training may add to or be substituted for the listed education or
439      experience requirement. Training may come in a number of forms, such as instrument-specific
440      classes offered by a manufacturer, to operational or safety programs given by outside trainers or
441      the laboratory's own staff.
442
443
444
445
446
447
448
449

450
451
452
453

454
455
456
457
 TABLE E.3 — Laboratory Technical Supervisory Personnel Listed by Position Title and
                     Examples for Suggested Minimum Qualifications.
   All personnel are responsible to perform their work to meet all terms and conditions of the contract.
                                Technical Supervisory Personnel
        Position Title
     and Responsibilities
          Education
            Experience
Radiochemical Laboratory
Supervisor, Director, or Manager.

Responsible for all technical
efforts of the radiochemical
laboratory.
Minimum of Bachelor's degree in
any scientific/engineering discip-
line, with training in radiochemis-
try, radiation detection instrumen-
tation, statistics, and QA.
Minimum of three years of radioanalyti-
cal laboratory experience, including at
least one year in a supervisory position.
Training in laboratory safety, including
radiation safety.
Quality Assurance Officer

Responsible for overseeing the
quality assurance aspects of the
data and reporting directly to
upper management.	
Minimum of Bachelor's degree in
any scientific/engineering discip-
line, with training in physics,
chemistry, and statistics.
Minimum of three years of laboratory
experience, including at least one year of
applied experience with QA principles
and practices in an analytical laboratory
or commensurate training in QA
principles.	
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458
459
460
461
462
463

464
465
466
467

468

469
470
471
472
473
 TABLE E.4 — Laboratory Technical Personnel Listed by Position Title and Examples for
	Suggested Minimum Qualifications and Examples of Optional Staff Members	
                                  Optional Technical Personnel
           Position Title
         and Responsibilities
          Education
         Experience
Systems Manager

Responsible for the management and
quality control of all computing systems;
generating, updating, and quality control
for deliverables.
Minimum of Bachelor's degree
with intermediate courses in
programming, information
management, database manage-
ment systems, or systems
requirements analysis.
Minimum of three years
experience in data or systems
management of programming,
including one year experience
with the software being utilized
for data management and
generation of deliverables.
Programmer Analyst

Responsible for the installation, opera-
tion, and maintenance of software and
programs, generating, updating, and
quality of controlling analytical databases
and automated deliverables.
Minimum of Bachelor's degree
with intermediate courses in
programming, information
management, information systems,
or systems requirements analysis.
Minimum of two years
experience in systems or
applications programming,
including one year experience
with the software being utilized
for data management and
generation of deliverables.
474
475
476
477
478
479
480
   TABLE E.5 — Laboratory Technical Staff Listed by Position Title and Examples for
                             Suggested Minimum Qualifications
   All personnel are responsible to perform their work to meet all terms and conditions of the contract.
Technical Staff
Position Title
Gamma
Spectrometrist
Alpha
Spectrometrist
Education
• Minimum of Bachelor's degree in chemistry or
any physical scientific/engineering discipline.
• Training courses in gamma spectrometry.
Minimum of Bachelor's degree in chemistry or
any physical scientific/engineering discipline.
• Training courses in alpha spectrometry.
Experience
• Minimum two years experience in
spectrometric data interpretation.
• Formal training or one year experience with
spectral analysis software used to analyze
data.
Formal training or one year experience with
spectral analysis software used to analyze
data.
481
482
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Position Title
Radiochemist
Counting Room
Technician
Laboratory
Technician
Education
Minimum of Bachelor's degree in chemistry or
any physical scientific/engineering discipline. In
lieu of the educational requirement, two years of
additional, equivalent radioanalytical experience
may be substituted.
Minimum of Bachelor's degree in chemistry or
any scientific/engineering discipline.
Minimum of high school diploma and a college
level course in general chemistry or
equivalent — or college degree in another
scientific discipline (e.g., biology, geology, etc.)
Experience
Minimum of two years experience with
chemistry laboratory procedures, with at least
one year of radiochemistry in conjunction
with the educational qualifications, including
(for example): 1) Operation and maintenance
of radioactivity counting equipment; 2)
Alpha/gamma spectrometric data interpreta-
tion; 3) Radiochemistry analytical procedures;
and 4) Sample preparation for radioactivity
analysis.
Minimum of one year experience in a
radioanalytical laboratory.
Minimum of one year experience in a
radioanalytical laboratory.
483
484
485
486
487
488
E.4.3  Instrumentation
489     A proposer's laboratory must have in place and in good working order the types and required
490     number of instruments necessary to perform the work advertised by the Agency. Specific factors
491     are noted in the RFP, such as:  an estimate for the number of samples, length of the contract, and
492     expected turnaround times which influence the types of equipment needed to support the
493     contract.

494     Analytical work can be viewed as a function of current technology. Changes may occur from
495     time to time, especially in relation to scientific advancements in equipment, software, etc.
496     Instrumentation represents the mechanical interface between prepared samples and the  data
497     generated in the laboratory. The capacity to process larger and larger numbers of samples while
498     sustaining the desired level of analytical sensitivity and  accuracy is ultimately a function of the
499     laboratory's equipment, and the knowledge and experience of the individuals who operate and
500     maintain the instruments. Additional support for the laboratory's on-line activities or the state of
501     readiness to maintain a constant or an elevated peak work load comes in the form of back-up
502     instruments that are available at all times. Information concerning service contracts that provide
503     repairs or replacement when equipment fails to perform is important to meeting contract
504     obligations. Demonstrating that this support will be in place for the duration of the contract is a
505     key element for the proposer to clearly describe in a proposal.
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506     E.4.3.1    Type, Number, and Age of Laboratory Instruments

507     A description of the types of instruments at a laboratory is an important component of the
508     proposal. The number of each type of instrument available for the proposed work should be
509     indicated in the proposal. This includes various counters, detectors, or other systems used for
510     radioanalytical work. A complete description for each instrument might include the age or
511     acquisition date. This information may be accompanied by a brief description indicating the level
512     of service an instrument provides at its present location.

513     E.4.3.2    Service Contract

514     The types and numbers of service contracts may vary depending on the service provider. Newly
515     purchased instruments will be covered by a manufacturer's warranty. Other equipment used
516     beyond the initial warranty period may either be supported by extensions to the manufacturer's
517     warranties or by other commercial services that cover individual instrument or many instruments
518     under a site-wide service contract. Whatever type of support is in place, the contractor will  need
519     to state how having or not having such service contracts affects the laboratory's ability to meet
520     the  terms of the contract and the potential impact related to the SOW.

521     E.4.4  Narrative to Approach

522     A proposal can "speak" to the Agency's evaluation team by providing a logical and clearly
523     written narrative of how the proposer will attend to every detail listed in the RFP. This approach
524     conveys key information in a readable format to relate a proposer's understanding, experience,
525     and working knowledge of the anticipated work. In this way,  the text also illustrates how various
526     components of the proposal work together to contribute to a unified view of the laboratory
527     functions given the proposed work load as described in the RFP and as detailed in the SOW. The
528     next four sections provide examples of proposal topics for which the proposer may apply a
529     narrative format to address how the laboratory is qualified to  do the proposed work.

530     E.4.4.1    Analytical Methods or Protocols

531      The proposer should list all proposed methods they plan to use.  The proposal should also furnish
532     all required method validation documentation to gain approval for  use. When addressing use of
533     methods, the proposer can describe how a method exhibits the best performance and also offer
534     specific solutions to meet the Agency's needs.
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535     E.4.4.2    Meeting Contract Measurement Quality Objectives

536     The Agency's planning process started with a review of questions and issues concerned with
537     generating specific project APSs/MQOs. Stating how a proposer intends to meet the APSs/
538     MQOs data quality requirements adds an important section to the proposal. This allows the
539     competing laboratories to demonstrate that they understand the requirements of the contract and
540     their individual approaches to fulfilling these requirements. Further evidence in support of the
541     proposer's preparations to meet or exceed the Agency's data quality needs is generally covered in
542     a contract laboratory's Quality Manual (Section E.4.5).

543     E.4.4.3    Data Package

544     The proposer responds to the RFP by stating how data will be processed under the contract. A
545     narrative describing the use of personnel, equipment, and facilities illustrates every step in
546     obtaining, recording, storing, formatting, documenting and reporting sample information and
547     analytical results. The specific information related to all these activities and the required
548     information as specified by the SOW is gathered into a data package. For example, a standard
549     data package includes a case narrative, the results (in the format specified by the Agency), a
550     contractor data review checklist, any non-conformance memos resulting from the work, Agency
551     and contractor-internal chains of custody, sample and quality control (QC)  sample data (this
552     includes a results listing,  calculation file, data file list, and the counting data) and continuing
553     calibration data, and standard and tracer source-trace information, when applicable. At the
554     inception  of a project, initial calibration data are provided for detectors used for the work. If a
555     detector is re-calibrated, or a new detector is placed in service, initial calibration data are
556     provided whenever those changes apply to the analyses in question.

557     Specific data from the data package may be further formatted in reports, including electronic
558     formats, as the required deliverables which the contractor will send to the Agency. The delivery
559     of this information is also specified according to a set schedule.

560     E.4.4.4    Schedule

561     The RFP will provide information that allows the proposer to design a schedule that is tailored to
562     the Agency's need. For example, samples that are part of routine monitoring will arrive at the
563     laboratory and the appropriate schedule reflects a cycle of activity from sample preparation to
564     delivering a data package to the Agency. This type of schedule is  repeatedly applied to each set
565     of samples. Other projects, surveys, or studies may follow a time  line of events from start to
566     completion, with  distinct sets of samples and unique needs that arise at specific points in time.


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567     The proposer will initially outline a schedule that may utilize some cycling of activities at various
568     stages of the work, but overall the nature of the work may change from stage to stage. The
569     schedule in this case will reflect how the contractor expects to meet certain unique milestones on
570     specific calendar dates.

571     Some projects will have certain requirements to process samples according to a graded
572     processing schedule. The SOW should provide the requirements for the radiological holding time
573     and sample processing turnaround time. Radiological holding time refers to the time required to
574     process the sample—the time differential from the sample receipt date to the final sample matrix
575     counting date. The sample processing turnaround time normally means the time differential from
576     the receipt of the sample at the laboratory (receipt date) to the reporting of the analytical results
577     to the Agency (analytical report date). As such, the turnaround time includes the radiological
578     holding time, the time to generate the analytical results, and the time to report the results to the
579     Agency.

580     Typically, three general time-related categories are stated: routine, expedited, and rush. Routine
581     processing is normally a 30-day turnaround time, whereas expedited processing may have a
582     turnaround time greater than five days but less than 30 days. Rush sample processing may have a
583     radiological holding time of less than five days. For short-lived nuclides, the RFP should state the
584     required radiological holding time, wherein the quantification of the analyte in the sample must
585     be complete within a certain time period. The reporting of such results may be the standard 30-
586     day turnaround time requirement. The Agency should be reasonable and technically correct in
587     developing the required radiological holding and turnaround times.

588     The RFP should specify a schedule of liquidated or compensatory damages that should be
589     imposed when the laboratory is non-compliant relative to technical requirements, radiological
590     holding times, or turnaround times.

591     E.4.4.5    Sample Storage and Disposal

592     The RFP should specify the length of time the  contractor must store samples after results are
593     reported. In addition, it should  state who is economically and physically responsible for the
594     disposal of the samples. The laboratory should describe how the samples will be stored for the
595     specified length of time and how it plans to dispose of the samples in accordance with local,
596     State and Federal  regulations.
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597     E.4.5  Quality Manual

598     Only those radiochemistry laboratories that adhere to well-defined quality assurance procedures
599     —pertaining to data validation, internal and external laboratory analytical checks, instrument
600     precision and accuracy, personnel training,  and setting routine laboratory guidelines—can insure
601     the highest quality of scientifically valid and defensible data. In routine practice, a laboratory
602     prepares a written description of its quality  manual that addresses, at a minimum, the following
603     items:

604      •  Organization and Management
605      •  Quality System Establishment, Audits, Essential Quality Controls and Evaluation and Data
606         Verification;
607      •  Personnel (Qualifications and Resumes);
608      •  Physical Facilities - Accommodations and Environment;
609      •  Equipment and Reference Materials;
610      •  Measurement Traceability and Calibration;
611      •  Test Methods and Standard Operating Procedures (Methods);
612      •  Sample Handling, Sample Acceptance Policy and Sample Receipt;
613      •  Records;
614      •  Subcontracting Analytical Samples;
615      •  Outside Support Services and Supplies; and
616      •  Complaints.

617     The quality manual may be a separately prepared document that may incorporate or reference
618     already available and approved laboratory standard operating procedures (SOPs). This manual
619     provides sufficient detail to demonstrate that the contractor's measurements and data are
620     appropriate to meet the MQOs and satisfy the terms and conditions of the contract. The manual
621     should clearly state the objective of the SOP, how the SOP will be executed, and which
622     performance standards will be used to evaluate the data. Work-related requirements based on
623     quality assurance are also an integral part of the SOW.

624     When a proposal is submitted for review, the contracting laboratory generally sends along a
625     current copy of its quality manual. Additional details pertaining to the content of a quality
626     manual can be found in NELAC (2000), ASQC (1995), EPA (1993, 1994, 1997a), ISO/IEC
627     (17025), and MARS SIM (2000).
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628     E.4.6  Licenses and Accreditations

629     All laboratories must have appropriate licenses from the U.S. Nuclear Regulatory Commission
630     (NRC) or other jurisdictions (Agreement State, host nation, etc.) to receive, possess, use, transfer,
631     or dispose of radioactive materials (i.e., those licensable as indicated in 10 CFR 30.70, Schedule
632     A—Exempt concentrations). A license number and current copy of a laboratory's licenses are
633     typically requested with paperwork that one submits to obtain radionuclide materials—for
634     example, when ordering and arranging to use laboratory standards. Overall, a laboratory's license
635     permits work with certain radionuclides  and limits to the quantity of each radionuclide at the
636     laboratory. A proposer's license should allow for new work with the types and anticipated
637     amounts of radionuclides as specified in an RFP. Part of the licensing requirement ensures that
638     the laboratory maintains a functioning radiation safety program and  properly trains its personnel
639     in the use and disposal of radioactive materials. For more complete information on license
640     requirements, refer to either the NRC, the appropriate State office, or 10 CFR 30.

641     The laboratory may need to be certified for radioassays by the State  in which the lab resides.
642     The RFP should request a copy of the current standing certification(s) to be submitted with the
643     proposal. If the Agency expects a laboratory to process samples from numerous States across the
644     United States, then additional certifications for other States may or will be required.  To request
645     that a proposer arrange for certification in multiple  States prior to submitting a proposal may be
646     viewed as placing an unfair burden on a  candidate laboratory who as yet to learn if it will be
647     awarded a contract. Additional fees, for each State certification, potentially add to a proposer's
648     cost to simply present a proposal. In such cases, an  Agency may indicate that additional
649     certification(s)—above that already held for the laboratory's State of residence—may be required
650     once the contract is awarded and just prior to initiating the work.

651     E.4.7  Experience

652     The contractor, viewed as a single entity made of all its  staff members, may have an extensive
653     work history as is exemplified through the number and types of projects and contracts that were
654     previously or are currently supported by  its laboratory services.  This experience is potentially an
655     important testimonial to the kind of work the contractor is presently able to handle with a high
656     degree of competence. The Agency's evaluation team will review this information relative to the
657     need(s) stated in the RFP. The more applicable the track record, the  stronger a case the proposer
658     has when competing for the award.
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659     E.4.7.1    Previous or Current Contracts

660     In direct relation to the preceding section, the proposer's staff should respond directly to the RFP
661     when asked to provide a list of contracts previously awarded and those they are presently
662     fulfilling. Of primary importance, the list should contain contracts that are similar to the one
663     under consideration (i.e., similar work load and technical requirements), with the following
664     information:

665      •  Name of the company or Agency awarding the contract;
666      •  Address;
667      •  Phone number;
668      •  Name of contact person; and
669      •  Scope of contract.

670     E.4.7.2    Quality of Performance

671     The Agency's TEC (Section E.5.1) is likely to check a laboratory's results for its participation in
672     a proficiency program which is sponsored by one of several Federal agencies. For example, the
673     U.S. Department of Energy (DOE), and National Institute of Standards and Technology (NIST)
674     offer proficiency programs. Records for the laboratory's  results may be reviewed to cover a
675     number of years. This review indicates quality and consistency in relation to the types of samples
676     that the Federal Agency  sends to each laboratory. Thus, at designated times during each year, a
677     laboratory will receive, process, and later report findings for proficiency program samples. This
678     routine is also required for certification by an Agency, such as the U.S. Environmental Protection
679     Agency (EPA) for drinking water analysis.  In this case, to obtain or maintain a certification, the
680     laboratory must pass (i.e., successfully analyze) on the basis of a specific number of the total
681     samples.

682     E.5   Proposal Evaluation and  Scoring Procedures

683     The initial stages of the evaluation process  separate technical considerations from cost. Cost will
684     enter the selection process later on. The Agency's TEC will consider all proposals and then make
685     a first cut (Table E.6 and Section E.5.3 below),  whereby some proposals are eliminated based on
686     the screening process. This selection from among the candidates is based on predetermined
687     criteria that are related to the original MQOs and how a proposer's laboratory is technically able
688     to support the contract. A lab that is obviously unequipped to perform work according to the
689     SOW is certain to be dropped early in the selection process. In some cases, the stated ability to
690     meet the analysis request should be verified by the Agency, through pre-award audits and

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691     proficiency testing, as described below. Letters notifying unsuccessful bidders may be sent at this
692     time. For information concerning a proposer's response to this letter, see Section E.5.7.

693     E.5.1  Evaluation Committee

694     The Agency personnel initially involved in establishing a new contract and starting the selection
695     process include the Contract Officer (administrative, non-technical) and Contracting Officer's
696     Representative (technical staff person). Once all proposals are accepted by the Agency, a team of
697     technical staff members score the technical portion of the proposal. The team is lead by a
698     chairperson who oversees the activities of this TEC. It is recommended that all members of the
699     TEC have a technical background relevant to the subject matter of the contract.

700     One approach to evaluation includes sending copies of all proposals to each member of the
701     committee for individual scoring (Table E.6). The Agency, after an appropriate length of time,
702     may conduct a meeting or conference call to discuss the scores and reach a unified decision.
703     Using this approach, each proposal is given a numerical score and these are listed in descending
704     order. A "break-point" in the scores is chosen. All candidates above this point are accepted for a
705     continuation of the selection process. Those below the break point may be notified at this point in
706     time. Note that evaluations performed by some agencies may follow variations on this scoring
707     and decision process.

708     The TEC must have a complete technical understanding of the subject matter related to the
709     proposed work and the contract that is awarded at the end of the selection process. These
710     individuals are also responsible for responding to any challenge to the Agency's decision to
711     award the contract. Their answers to such challenges are based on technical merit in relation to
712     the proposed work (Section E.5.7).

713     E.5.2  Ground Rules — Questions

714     The Agency's solicitation should clearly state if and when questions from an individual proposer
715     will be allowed during the selection process. Information furnished in the Agency's response is
716     simultaneously sent to all competing laboratories.

717     E.5.3  Scoring/Evaluating Scheme

718     The Agency should prepare an RFP that includes information concerning scoring of proposals or
719     weights for areas of evaluation. This helps a proposer to understand the relative importance of
720     specific sections in a proposal and how a proposal will be scored. In this case, the method of


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721     evaluation and the scoring of specific topic areas is outlined in the solicitation. If this information
722     is not listed in the solicitation and because evaluation formats differ Agency to Agency,
723     proposers may wish to contact the Agency for additional Agency-specific details concerning this
724     process.

725     An Agency may indicate the relative weight an evaluation area holds with regard to the proposed
726     work for two principle reasons. First, the request is focused to meet a need for a specific type of
727     work for a given study, project, or program. This initially allows a proposer to concentrate on
728     areas of greatest importance. Second, if the contractor submits a proposal that lacks sufficient
729     information to demonstrate support in a specific area, the Agency can then indicate how the
730     proposal does not fulfill the need as stated in the request.

731     Listed below is an example of some factors and weights that an Agency might establish before an
732     RFP is distributed:

733              Description                                                       Weight
734               Factor I ... .Technical Merit  	25
735               Factor n . . .Proposer's Past Performance	25
736               Factor HI. . .Understanding of the Requirements	15
737               Factor IV  . .Adequacy and Suitability of Laboratory Equipment and
                                 Resources	15
738               Factor V . . .Academic Qualifications and Experience of Personnel . . 10
739               Factor VI  . .Proposer's Related Experience  	10

740     The format presented above assigns relative weights for each factor—with greater weight given
741     to more important elements of the proposal. Technical merit (Factor I) includes technical merit,
742     method validation and the ability to meet the MQOs, etc. Factor II includes how well the
743     proposer performed in previous projects or related studies. A proposer's understanding (Factor
744     IE) is demonstrated by the laboratory's programs, commitments as well as certifications, licenses,
745     etc., to ensure the requirements of the RFQ will be met. Adequacy and suitability (Factor IV) is
746     generally an indication that the laboratory is presently situated to accept samples and conduct the
747     work as proposed. Factor V focuses on topics covered previously in Section E.4.2 while the
748     proposer's experience (Factor VI) is considered in Section E.4.7.

749     An Agency may use a Technical Evaluation Sheet—in conjunction with the Proposal Evaluation
750     Plan as outlined in the next section (Table E.6)—to list the total weight for each factor and to
751     provide a space for the evaluator's assigned rating. The evaluation sheet also provides areas to
752     record the RFP number, identity of the proposer, and spaces for total score, remarks, and


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753      evaluator's signature. The scoring and evaluation scheme is based on additional, more detailed,
754      considerations which are briefly discussed in the next three sections (E.5.3.1 to E.5.3.3)

755      E.5.3.1     Review of Technical Proposal and Quality Manual

756      Each bidding-contractor laboratory will be asked to submit a technical proposal and a copy of its
757      Quality Manual. This document is intended to address all of the technical and general laboratory
758      requirements. The proposal and Quality Manual are reviewed by members of the TEC who are
759      both familiar with the proposed project and are clearly knowledgeable in the field of
760      radiochemistry.

761      Table E.6 is an example of a Proposal Evaluation Plan (based on information from the U.S.
762      Geological Survey). This type of evaluation can be applied to proposals as they are considered by
763      the TEC.
764

765

766
767
768
769

770
771

772

773

774
775

776
777

778
779
780

781
                   TABLE E.6 — Example of a Proposal Evaluation Plan
                                    Proposal Evaluation

Objective: To ensure impartial, equitable, and comprehensive evaluation of proposals from contractors desiring
to accomplish the work as outlined in the Request for Proposals and to assure selection of the contractor whose
proposal, as submitted, offers optimum satisfaction of the government's objective with the best composite blend
of performance, schedules, and cost.

Basic Philosophy. To obtain the best possible technical effort which satisfies all the requirements of the
procurement at the lowest overall cost to the government.

                                  Evaluation  Procedures

1.  Distribute proposals and evaluation instructions to Evaluation Committee.

2.  Evaluation of proposals individually by each TEC member. Numerical values are recorded with a concise
    narrative justification for each rating.

3.  The entire committee by group discussion prepares a consensus score for each proposal. Unanimity is
    attempted, but if not achieved, the Chairperson shall decide the score to be given.

4.  A Contract Evaluation Sheet listing the individual score of each TEC member for each proposal and the
    consensus score for the proposal is prepared by the Chairperson. The proposals are then ranked in
    descending order.

5.  The Chairperson next prepares an Evaluation Report which includes a Contract Evaluation Sheet, the rating
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     sheets of each evaluator, a narrative discussion of the strong and weak points of each proposal, and a list of
     questions which must be clarified at negotiation. This summary shall be forwarded to the Contracting
     Officer.

 6.  If required, technical clarification sessions are held with acceptable proposers.

 7.  Analysis and evaluation of the cost proposal will be made by the Contracting Officer for all proposals
     deemed technically acceptable. The Chairperson of the TEC will perform a quantitative and qualitative
     analysis on the cost proposals or those firms with whom cost negotiations will be conducted.

                                         Evaluation  Criteria

 The criteria to be used in the evaluation of this proposal are selected before the RFP is issued. In accordance with
 the established Agency policy, TEC members prepare an average or consensus score for each proposal on the
 basis of these criteria and only on these criteria.

 A guideline for your numerical rating and rating sheets with assigned weights for each criteria are outlined next
 under Technical Evaluation Guidelines for Numerical Rating.

                     Technical Evaluation Guidelines for Numerical Rating

 1.  Each item of the evaluation criteria will be based on a rating of 0 to 10 points. Therefore, each evaluator will
     score each item using the following guidelines:

     a.   Above normal: 9 to 10 points (a quote element which has a high probability of exceeding the expressed
         RFP requirements).

     b.   Normal. 6 to 8 points (a quote element which, in all probability, will meet the minimum requirements
         established in the RFP and Scope of Work).

     c.   Below normal. 3 to 5 points (a quote element which may fail to meet the stated minimum requirements,
         but which is of such a nature that it has correction potential).

     d.   Unacceptable: 0 to 2 points (a quote element which cannot be expected to met the stated minimum
         requirements and is of such a nature that drastic revision is necessary for correction).

 2.  Points will be awarded to each element based on the evaluation of the quote in terms of the questions asked.

 3.  The evaluator shall make no determination on his or her own as to the relative importance of various items
     of the criteria. The evaluator must apply a 0 to 10 point concept to each item without regard to his or her
     own opinion concerning one item being of greater  significance than another. Each item is given a
     predetermined weight factor in the Evaluation Plan when the RFP is issued and these weight factors must be
     used in the evaluation.
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812     E.5.3.2    Review of Laboratory Accreditation

813     A copy of the current accreditation(s) should be submitted with the proposal. The Agency should
814     confirm the laboratory's accreditation by contacting the Federal or State Agency that provided
815     the accreditation. In some cases, a public listing or code number is provided. Confirming that a
816     specific code number belongs to a given laboratory will require contacting the Agency that issued
817     the code.

818     E.5.3.3    Review of Experience

819     The laboratory should furnish references in relation to its past or present work (Section E.4.7.1).
820     To the extent possible, this should be done with regard to contracts or projects similar in
821     composition and size to the proposed project. One or more members of the TEC are responsible
822     for developing a list of pertinent questions and then contacting each reference listed by the
823     proposer. The answers obtained from each reference are recorded  for use later in the evaluation
824     process. In some cases, the laboratory's previous performance for the same Agency should be
825     given special consideration.

826     E.5.4  Pre-Award Proficiency Samples

827     Some agencies may elect to send proficiency or performance testing (PT) samples to the
828     laboratories that meet a certain scoring criteria in order to demonstrate the laboratory's analytical
829     capability. The composition and number of samples should be determined by the nature of the
830     proposed project. The PT sample matrix should be composed of well-characterized materials. It
831     is recommended that site-specific PT matrix samples or method validation reference material
832     (MVRM; Chapter 6) be used when available. The matrix of which the PT sample is composed
833     must be well characterized and known to the Agency staff who supply the sample to the
834     candidate laboratory. For example, if an Agency is concerned with drinking water samples, then
835     the Agency's laboratory may use its own source of tap water as a base for making PT samples.
836     This water, with or without additives, may be supplied for this purpose.

837     Each competing lab should receive an identical set of PT samples. The RFP should specify who
838     will bear the cost of analyzing these samples, as well as the scoring scheme, (e.g., pass/fail) or a
839     sliding scale. Any lab failing to submit results should be automatically disqualified. The results
840     should be evaluated and each lab given a score. This allows the Agency to narrow the selection
841     further—after which only two or three candidate laboratories are considered.
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842     At this point, two additional selection phases remain. A visit to each candidate's facilities comes
843     next (Section E.5.5) and thereafter, once all technical considerations are reviewed, the cost of the
844     contractor's service is examined last (Section E.5.6).

845     E.5.5  Pre-Award Audit

846     A pre-award audit, which may be an initial audit, is often performed to provide assurance that a
847     selected laboratory is capable  of performing the required analyses in accordance with the SOW.
848     In other words, is the laboratory's representation (proposal) realistic when compared to the
849     actual facilities? To answer this question, auditors will be looking to see that a candidate
850     laboratory appears to have all  the required elements to meet the proposed contract's needs. In
851     some cases, it may be appropriate to conduct both a pre-award audit, followed by an evaluation
852     after the work begins (see Section E.6.7 for information on ongoing laboratory evaluations).

853     The two or three labs with the highest combined scores (for technical proposals and proficiency
854     samples) may be given an on-site audit.

855     The pre-award audit is a key evaluating factor that is employed before the evaluation committee
856     makes a final selection. Many Federal agencies, including DOE, EPA, and USGS, have
857     developed forms for this purpose. Some of the key items to  observe during an audit include:

858      •  Sample Security - Will the integrity of samples be maintained for chain of custody? If
859         possible, examine the facility's current or past chain-of-custody practice.

860      •  Methods - Are copies of SOP's available to every analyst? In some cases, one may check
861         equations used to identify  and quantitate the radionuclides of interest. Additional concerns
862         include the potential for interferences, total propagated uncertainty, decision levels, and
863         minimum detectable concentrations.

864      •  Method Validation Documentation - Verify the method validation documentation provided
865         in the response to the RFP. Have there been any  QA/QC issues related to the methods? Are
866         the identified staff (provided in the RFP) qualified to perform the methods?

867      •  Adherence to SOPs - This may include looking to see that sample preparation, chemical
868         analysis, and radiometric procedures are performed according to the appropriate SOP.

869      •  Internal QC - Check the files and records.
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870       •  External QC/PT samples - Check files and records pertaining to third-party programs.

871       •  Training - Check training logs. Examine analysts' credentials, qualifications, and proficiency
872         examination results.

873       •  Instrumentation - Check logs. Are instruments well maintained, is there much down time, are
874         types and numbers listed in technical proposal correct? Look for QC chart documentation.

875       •  Instrumentation - Calibration records. Do past and current calibration records indicate that
876         the laboratory's instruments are capable of providing data consistent with project needs?
877         Look at instrumentation characteristics, including resolution, detection efficiency, typical
878         detection limits, etc. Are NIST-traceable materials used for detector calibration and chemical
879         yield determinations?

880       •  Personnel - Talk with and observe analysts. Verbal interaction with laboratory staff during an
881         audit helps auditors to locate the information and likewise provide evidence for the
882         knowledge and understanding of persons who conduct work in the candidate laboratory.

883       •  Log-In - Is this area well-organized to reduce the possibility of sample mix-ups?

884       •  Tracking-Is there a system of tracking samples through the lab?

885     Information about each laboratory may be gathered in various ways. One option available to the
886     Agency is to provide each candidate laboratory with a list of questions or an outline for
887     information that will be collected during the  audit (Table E.7). The Agency's initial contact with
888     the laboratory can include a packet with information about the audit and questions that the
889     laboratory must address prior to the Agency's on-site visit. For example, from the checklist
890     presented in Table E.7, one can see the laboratory will be asked about equipment. In advance of
891     the audit, laboratory personnel can create a listing of all equipment or instruments that will be
892     used to support the contract. Table E.7 also indicates information to be recorded by the auditors
893     during the visit. The audit record includes the Agency's on-site observations, along with the
894     laboratory's prepared responses.
895
896
897
898
899
900
TABLE
E.7 — Sample Checklist for Information
Recorded During a Pre-Award Laboratory Audit
Laboratory:
Date:
Auditors:
1.
2.
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 A. Review packet that was sent to laboratory for completion:
     1. Laboratory Supervisor
     2. Laboratory Director
     3. Current Staff
     4. Is the laboratory responsible for all analyses? If not, what other laboratory(s) is (are) responsible?
     5. Agency responsible for [drinking water] program in the State.
     6. Does the laboratory perform analyses of environmental samples around nuclear power facilities, or
     from hospitals, colleges, universities, or other radionuclide users?
     7. Agency responsible for sample collections in item 6.

 B. Laboratory Facilities:
     1. Check all items in the laboratory packet.
     2. Comments
     3. Is there a Hot Laboratory or a designated area for samples from a nuclear power facility that would
     represent a nuclear accident or incident? Is this documented in the SOP or QA Manual?

 C. Laboratory Equipment and Supplies:
     1. Check all items on the laboratory packet. Includes analytical balances, pH meters, etc.
     2. Comments
     3. Radiation counting instruments:
        a. Thin window gas-flow proportional counters
        b. Windowless gas-flow proportional counters
        c. Liquid scintillation counter
        d. Alpha scintillation counter
        e. Radon gas-counting system
        f.  Alpha spectrometer
        g. Gamma spectrometer systems:
             1. Ge (HPGe) detectors
            2. Nal detectors
            3. Multichannel analyzer(s)

 D. Analytical Methodology:
     1. Check all items on the laboratory packet.
     2. Comments

 E. Sample  Collection, Handling, and Preservation:
     1. Check all items on the laboratory packet.
     2. Comments

 F. Quality Assurance Section:
     1. Examine laboratory SOP
        a. Comments

     2. Examine laboratory's Quality Manual
        a. Comments
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     3. Performance Evaluation Studies (Blind)
         a. Comments and results

     4. Maintenance records on counting instruments and analytical balances.
         a. Comments and results

     5. Calibration data
         a. Gamma Spectrometer system
             1. Calibration source
             2. Sufficient energy range
             3. Calibration frequency
             4. Control charts
                 a. Full Peak Efficiency
                 b. Resolution
                 c. Background

         b. Alpha/Beta counters
             1. Calibration source
             2. Calibration frequency
             3. Control charts
                 a. Alpha
                 b. Beta
                 c. Background

         c. Radon counters
             1. Calibration source
             2. Frequency of radon cell background checks

         d. Liquid Scintillation Analyzer
             1. Calibration sources
             2. Calibration frequency
             3. Control charts
                 a. H-3
                 b. C-14
                 c. Background
                 d. Quench

     6. Absorption and Efficiency curves:
         a. Alpha absorption curve
         b. Beta absorption curve
         c. Ra-226 efficiency determination
         d. Ra-228 efficiency determination
         e. Sr-89, Sr-90, and Y-90 efficiency determinations
         f.  Uranium efficiency determination
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 978
 979
 980
 981
 982
 983
 984
 985

 986
 987
 988
 989

 990
 991
 992
 993
 994
    7. Laboratory QC Samples
       a. Spikes
       b. Replicates/duplicates
       c. Blanks
       d. Cross check samples
       e. Frequency of analysis
       f. Contingency actions if control samples are out of specification
       g. Frequency of analysis

E. Records and Data Reporting
    1. Typical data package
    2. Electronic data deliverable format
    3. Final data report

H. Software Verification and Validation
    1. Instrumentation and Equipment Control and Calibrations
    2. Analytical Procedure Calculations/Data Reduction
    3. Record Keeping/Laboratory/Laboratory Information Management System/Sample Tracking
    4. Quality Assurance Related — QC sample program/instrument QC	
 995     E.5.6   Comparison of Prices

 996     To this point, the selection process focuses on technical issues related to conducting work under
 997     the proposed contract. Keeping this separate from cost considerations simplifies the process and
 998     helps to sustain reviewer objectivity. Once the scoring of labs is final, the price of analyses may
 999     be reviewed and compared. Prices are now considered along with inspection results. This part of
1000     the process is best performed by technical personnel, including members of the TEC who work
1001     in either a laboratory or the field setting, and who possess the knowledge to recognize a price that
1002     is reasonable for a given type of analysis. Various scenarios may apply where prices differ:

1003       •  Candidates are dropped generally if their proposed prices are extreme.

1004       •  Laboratories that score well—aside from their prices that may still be on the high side—are
1005         given an opportunity to rebid with a best and final cost. This lets laboratories know they have
1006         entered the final stage of the selection process.

1007     A final ranking is based on the technical evaluation, including the proficiency examination and
1008     audit if conducted, and the best-and-final prices submitted by each laboratory.

1009     While there is no way to determine how evaluations may be conducted in the future, some extra
1010     consideration may be given to proposals that offer greater technical capabilities (i.e., those that
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1011      house state-of-the-art or high-tech analytical services) as opposed to fulfilling the minimum
1012      requirements of the RFP.

1013      E.5.7  Debriefing of Unsuccessful Vendors

1014      At an appropriate time in the selection process, all unsuccessful bidders are sent a letter outlining
1015      the reasons that they were not awarded the contract. As noted previously, the RFP should be very
1016      explicit in illustrating what a proposal should contain and which areas carry more or less weight
1017      with regard to the Agency's evaluation. If so, the Agency is able to provide a written response to
1018      specifically identify areas of the proposal where the contractor lacks the appropriate services or is
1019      apparently unable to present a sufficiently strong case documenting an ability to do the work.
1020      Also, as stated previously, the proposer must present as clear a case as possible and write into the
1021      proposal all relevant information. A simple deletion of key information will put a capable
1022      proposer out of the running in spite of the experience, support, and services they are able to
1023      render an Agency.

1024      If a contractor wishes an individual debriefing, the Agency can arrange to have the TEC meet
1025      with the contractor's representatives. This meeting allows for an informal exchange to further
1026      explore issues to the satisfaction of the proposer. This exchange may offer  the Agency an
1027      opportunity to restate and further clarify the expected minimum qualifications that are required  of
1028      the proposer.

1029      A more formal approach contesting the Agency's decision follows  after a protest is lodged by the
1030      contractor. In this case, the Agency's TEC and the contractor's representatives are accompanied
1031      by legal council for both sides.

1032      E.6   The Award

1033      The selection process ends when the Agency personnel designate which contractor will receive
1034      the award. Several steps follow in advance of formally presenting the award.  This essentially
1035      includes in-house processing, a review by the Agency's legal department, and a final review by
1036      the contract staff. These activities verify that the entire selection process  was followed properly
1037      and that the contract's paperwork is correct. The Agency's contracts office then signs  the proper
1038      documents and the paperwork is sent to the contractor. The contract becomes effective as of the
1039      date when the government's contracting officer signs.
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1040      E.7   For the Duration of the Contract

1041      After the award is made, the Agency enters into a working relationship with the contract
1042      laboratory and work begins. Over the period of the contract, the Agency will send samples,
1043      receive deliverables, and periodically check the laboratory's performance. The work according to
1044      the SOW and the activities associated with performance checks and laboratory evaluations are
1045      topics covered beginning with the next section. Furthermore, as data are delivered to the Agency,
1046      invoices will be sent by the contractor to the Agency. The Agency will process the invoices in
1047      steps: that receipt of data is initially confirmed, the results are appropriate (i.e., valid), and finally
1048      that the invoice is paid. This activity may occur routinely as invoices arrive—weekly, monthly, or
1049      at some other time interval throughout the course of a contract.

1050      Keep in mind that the structured planning process is iterative in nature and may come into play at
1051      any point during a contract period. For example, Federal or State laboratories engaging contract-
1052      support services may be involved in routine monitoring of numerous sampling sites. For sets of
1053      samples that are repeatedly taken from a common location over the course of years, only the
1054      discovery of unique results or change in performance-based methods may instigate an iteration
1055      and a review of the MQOs. For other types of projects, such as a location undergoing a
1056      MARSSEVI-site survey, the project plan may change as preliminary survey work enters a period
1057      of discovery—e.g., during a scoping or characterization survey (MARSSEVI, 2000). Even during
1058      a final  status survey, discovery of some previously unknown source of radioactive contamination
1059      may force one to restate not only the problem, but to reconsider every  step in the planning
1060      process. Modification of a contract may be necessary to address these  circumstances.

1061      E.7.1  Managing a Contract

1062      Communication is key to the successful management and execution of the contract. Problems,
1063      schedule, delays, potential overruns, etc., can only be resolved quickly if communications
1064      between the laboratory and Agency are conducted promptly.

1065      A key element in managing a contract is the timely verification (assessment) of the data packages
1066      provided by the laboratory. Early identification of problems allows for corrective actions to
1067      improve laboratory performance and, if necessary, the cessation of laboratory analyses until
1068      solutions can be instituted to prevent the production of large amounts  of data which are unusable.
1069      Note that some sample matrices and processing methods can be problematic for even the best
1070      laboratories. Thus the contract manager must be able to discern between failures due to
1071      legitimate reasons and poor laboratory performance.
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1072      E.7.2  Responsibility of the Contractor

1073      First and foremost, the responsibility of the laboratory is to meet the performance criteria of the
1074      contract. If the SOW is appropriately written, this provides guidance necessary to ensure the data
1075      produced will meet the project planning goals and be of definable quality. Likewise, the
1076      laboratory must communicate anticipated or unforeseen problems as soon as possible. Again, this
1077      could easily occur with complex, unusual, or problematic sample matrices. Communication is
1078      vital to make sure that matrix interferences are recognized as early as possible, and that
1079      subsequent analyses are planned accordingly.

1080      The laboratory's managers must plan the analysis—that is, have supplies, facilities, staff, and
1081      instruments  available as needed—and schedule the analysis to meet the Agency's due date. In the
1082      latter case, a brief buffer period might be included for unanticipated problems and delays, thus
1083      allowing the laboratory the opportunity to take appropriate corrective action on problems
1084      encountered during an analysis.

1085      E.7.3  Responsibility of the Agency

1086      During the period of the contract, the Agency is responsible for employing external quality
1087      assurance oversight.  Thus the performance of the laboratory should be monitored continually to
1088      insure the Agency is  receiving compliant results. Just because a laboratory produces acceptable
1089      results at the beginning of its performance on a contract does not necessarily mean that it will
1090      continue to do so throughout the entire contract period. For example, the quality of the data can
1091      degenerate at times when an unusually heavy workload is encountered by an environmental
1092      laboratory. One way  to monitor this performance is to review the results of internal and external
1093      quality assurance programs. This may in part take the form of site visits (including onsite audits),
1094      inclusion of QC samples, evaluation of performance in Performance Evaluations or
1095      intercomparison programs, desk audits, and data assessments.

1096      E.7.4  Anomalies and Nonconformance

1097      The contractor must document and report all  deviations from the method and unexpected
1098      observations that may be of significance to the data user. Such deviations should be documented
1099      in the narrative section of the data package produced by the contract laboratory. Each narrative
1100      should be monitored closely to assure that the laboratory is documenting departures from
1101      contract requirements or acceptable practice.  The Agency's reviewer should assure that the
1102      reason(s) given for the departures are clearly  explained and are credible. The repeated reporting
1103      of the same deviation may be an indication of internal laboratory problems.


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1104      E.7.5  Laboratory Assessment

1105      As work under a contract progresses over time, there are two principle means to assess a
1106      laboratory's performance: by having the laboratory process quality control samples (Section
1107      E.7.5.1 and E.7.5.2), and by Agency personnel visiting the laboratory to conduct on-site
1108      evaluations (Section E.7.5.3).

1109      E.7.5.1    Performance and Quality Control Samples

1110      A laboratory's performance is checked in one of several ways, including the use of Agency QC
1111      samples, the laboratory's QC samples, laboratory participation in a performance evaluation
1112      program, Agency certification program, and through Agency audits, which may include an on-
1113      site visit.

1114      There are several approaches to determining that an analysis is accurate and that the data reflect a
1115      true result. One check on each analysis comes from the laboratory's own QC measures. The
1116      contractor will routinely run standards, prepared spiked samples, and blanks, along with the
1117      samples submitted by the Agency. Calibrations are also performed and a laboratory technician is
1118      expected to record information to document instrument performance.

1119      Another avenue for QC comes with measures taken by the Agency, including the incorporation
1120      of a number of double-blind samples, with each batch of samples sent to the contract laboratory.
1121      The preparation of double-blind samples for matrices other than water is difficult. A sample
1122      designated as a blind sample is one that the contractor knows is submitted by the Agency for QC
1123      purposes. A double-blind sample is  presented to the laboratory as if it were just another sample
1124      with no indication that this is for QC purposes. In the former case, the samples may be labeled in
1125      such a manner that the laboratory recognizes these as QC samples. In the latter case, unless the
1126      Agency takes steps to use very similar containers and labeling as that for the field samples, the
1127      laboratory may recognize the double-blind samples for what they are. This in effect compromises
1128      the  use of a double-blind sample. In each case, the Agency knows the level or amount of each
1129      radionuclide in the blind sample.

1130      When the analysis for a set of samples is complete and data are sent to the Agency, the Agency in
1131      turn checks the results for the QC samples and then performs data validation. In the case of
1132      characterization studies, one may continue to check results for QC samples,  but data validation
1133      packages may not be required. If the double-blind results are not within reasonable limits, the
1134      Agency will need to examine how these specific data may indicate a problem. In the meantime,
1135      work on subsequent sample sets cannot go forward until the problem is resolved. Some or all


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1136      samples in the questionable batch may need to be reanalyzed depending on the findings for the
1137      QC samples. This is a case where storage of samples by the laboratory—e.g., from three to six
1138      months after analyses are performed—allows the Agency to back track and designate specific
1139      samples for further or repeated analyses. The  one exception to going back and doing additional
1140      analyses arises for samples containing radionuclides with short half lives. This type of sample
1141      requires a more immediate assessment to allow for repeated analyses, if needed.

1142      Where data validation is required, the Agency will  routinely look at results for the QC samples
1143      that are added to the sample sets collected in the field. An additional QC measure includes a
1144      routine examination—for example, on a monthly or quarterly basis—of the laboratory's results
1145      for their own internal QC samples. This includes laboratory samples prepared as spikes,
1146      duplicates, and blanks that are also run along  with the Agency samples.

1147      The Agency can also schedule times to monitor a contractor laboratory's participation in a
1148      performance evaluation program—for example, those supported by the DOE, EPA, NIST, or
1149      NRC. Each laboratory, including the Agency's own facilities, are expected to participate in such
1150      programs. The Agency will also check to see  if a laboratory's accreditation (if required) is current
1151      and this is something that should be maintained along with participation in a Federally sponsored
1152      performance evaluation program. In general, the States accredit laboratories within their
1153      jurisdiction.

1154      E.7.5.2   Laboratory Performance Evaluation Programs

1155      Participating in a collaborative interlaboratory testing program  (such as the PT programs
1156      mentioned in E.5.4) is the best way for a laboratory to demonstrate or an Agency to evaluate a
1157      laboratory's measurement quality in comparison to other laboratories or to performance
1158      acceptance criteria. Furthermore, because MARLAP promotes  consistency among radiochemistry
1159      laboratories, it is scientifically, programmatically, and economically advantageous to embrace the
1160      concept of a common basis for radioanalytical measurements—a measurement quality system
1161      that is ultimately linked to the national physical standards. ANSI N42.23, Measurement and
1162      Associated Instrument Quality Assurance for  Radioassay Laboratories., defines a system in
1163      which the quality and traceability of service laboratory measurements to the national standards
1164      can be demonstrated through reference (and monitoring) laboratories. The service (in this case
1165      the contracted) laboratory shall analyze NIST traceable reference performance testing materials
1166      to examine the bias and precision of an analytical methodology or an analyst. Traceable reference
1167      material, a sample of known analyte concentration, is prepared  from NIST Standard Reference
1168      Material or derived reference material supplied by a NIST traceable radioactive source
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1169      manufacturer (ANSI N42.22). Demonstration of measurement performance and traceability shall
1170      be conducted at an appropriate frequency.

1171      E.7.5.3    Laboratory Evaluations Performed During the Contract Period

1172      An audit before awarding a contract emphasizes an examination of availability of instruments,
1173      facilities, and the potential to handle the anticipated volume of work. This also includes
1174      recognizing that the proper personnel are in place to support the contract. After the award, a
1175      laboratory evaluation will place additional weight on how instruments and personnel are
1176      functioning on a daily basis. Thus, logbooks, charts, or other documentation that are produced as
1177      the work progresses are now examined. This type of evaluation during the contract period uses an
1178      approach that differs from the pre-award audit (Section E.5.5). The format and documentation for
1179      an on-site audit may differ from Agency to Agency. An Agency may wish to examine the EPA
1180      forms (EPA, 1997b) and either adopt these or modify them to accommodate radionuclide work
1181      that includes sample matrices other than water or additional nuclides not presently listed.

1182      There are two types of evaluations or audits that can be performed during the life of a contract.
1183      The first involves Agency personnel that visit the contractor's  facilities. The second approach
1184      includes activities conducted by Agency personnel without visiting the laboratory.

1185      In the former case, Agency personnel examine documentation  at the laboratory, including each
1186      instrument's logbook which is used to record background values,  or to ensure that QC charts are
1187      current. During this type of evaluation, the Agency and contractor personnel have an opportunity
1188      to communicate face-to-face, which is a benefit to both parties when clarification or additional
1189      detail is needed. For example, this audit's goal essentially is to check the capability  of the
1190      laboratory to perform the ongoing work according to the contract work. In this case, an auditor
1191      may request to see one or more data packages, and then follow the information described in each
1192      package—including such items as sample tracking and documentation concerning sample
1193      preparation and analysis—to verify that the laboratory is now accomplishing the work as
1194      described by the SOW and in conformance with the Quality Manual.

1195      In the latter case, one conducts what might be called a desk audit,  where Agency personnel
1196      review the contract and examine records or documentation that have come in as part of the
1197      project's deliverables. For the most part, the Agency should constantly be monitoring activities
1198      under the contract, and in this  sense, a desk audit is a daily activity without a formal process
1199      being applied at any specific point in time. However, depending on the Agency's practice, if on-
1200      site visits are not made, then a desk audit becomes the only means to track activities under the
1201      contract. One approach to a desk audit is thus a periodic review—for example, every 6 or 12


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1202      months—of QC records to track the laboratory's performance over that period of time. This
1203      allows the Agency to determine if there are deviations, shifts, or other trends that appear over
1204      time.

1205      Each evaluation presents an additional opportunity to monitor various laboratory parameters,
1206      such as turnaround time. This is most important in cases when samples contain radionuclides
1207      having short half lives. During an on-site evaluation, the Agency is able to determine if
1208      additional emphasis is required to tighten the time frame between sample receipt and analysis.
1209      The personal interaction between Agency and laboratory permits a constructive dialog and
1210      facilitates an understanding of the possible means to increase or maintain the efficiency when
1211      processing and analyzing samples at the contractor's facility.

1212      E.8    Contract Completion

1213      There are several general areas  of concern at the close of a contract that may be addressed
1214      differently depending on the Agency or nature of the project under a given contract. For example,
1215      Agency personnel who monitor contracts will review invoices to be certain that work is complete
1216      and that the corresponding results are considered acceptable. Once such monitoring activity
1217      provides  the proper verification that the work is complete, then the Agency's financial office
1218      processes all related bills and makes final payment for the work.

1219      The laboratory should send in final deliverables, including routine submissions of raw data or
1220      records, as is the practice under the contract. Also, when applicable, Agency-owned equipment
1221      shared with the laboratory during the contract period will be returned. The disposition of samples
1222      still in storage at the contractor's facility and additional records or other raw data must be
1223      decided and specified. The Agency may wish to receive all or part of these items—otherwise,
1224      disposal of sample materials and documents held by the contractor must be arranged.

1225      In some cases, work under the contract may create conditions where more time is necessary to
1226      process samples that remain or  to process additional work that arises during the latter part of the
1227      contract period. Depending on the Agency,  funding, nature of the project, or other factor, the
1228      contract may be extended for a  period of time, which may vary  from weeks to months.
1229      Otherwise, once the contract comes to a close, the work ceases.
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                                                                 Contracting Laboratory Services
1230      E.9   References

1231      American National Standards Institute (ANSI). 1995. American National Standard—Traceability
1232         of Radioactive Sources to the National Institute of Standards and Technology (NIST) and
1233         Associated Instrument Quality Control. N42.22.

1234      American National Standards Institute (ANSI). 1996. American National Standard—
1235         Measurement and Associated Instrument Quality Assurance for Radioassay Laboratories.
1236         N42.23.

1237      American Society for Quality Control (ASQC). 1995. Specifications and Guidelines for Quality
1238         Systems for Environmental Data Collection and Environmental Technology Programs.
1239         ANSI/ASQC E4-1994, ASQC, Milwaukee, Wisconsin.

1240      U.S. Environmental Protection Agency (EPA). 1993. Quality Assurance for Superfund
1241         Environmental Data Collection Activities.  Publication 9200.2-16FS, EPA, Office of Solid
1242         Waste and Emergency Response, Washington, DC.

1243      U.S. Environmental Protection Agency (EPA). 1994. EPA Requirements for Quality Assurance
1244         Project Plans for Environmental Data Operations. EPA QA/R-5, EPA, Draft Interim Final,
1245         Quality Assurance Management Staff, Washington, DC.

1246      U.S. Environmental Protection Agency (EPA). 1997a. EPA Guidance for Quality Assurance
1247         Project Plans. Final, EPA QA/G-5, EPA, Office of Research and Development, Washington,
1248         DC.

1249      U.S. Environmental Protection Agency (EPA). 1997b. Manual for the Certification of
1250         Laboratories Analyzing Drinking Water. EPA 815-B-97-001.

1251      International Organization for Standardization (ISO/IEC) 17025. General Requirements for the
1252         Competence of Testing and Calibration Laboratories, International Organization for
1253         Standardization, Geneva, Switzerland. 1999.

1254      MARSSEVI. 2000. Multi-Agency Radiation Survey and Site Investigation Manual, Revision 1.
1255         NUREG-1575 Rev 1, EPA 402-R-97-016 Revl, DOE/EH-0624 Revl. August. Available
1256         from http://www.epa.gov/radiation/marssim/filesfm.htm.
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         Contracting Laboratory Services
1257      NELAC 2000. National Environmental Laboratory Accreditation Conference, Quality Systems,
1258         July 2000.

1259      Office of Federal Procurement Policy (OFPP). 1997. Performance-Based Service Contracting
1260         (PBSC) Solicitation/Contract/Task Order Review Checklist. August 8.
1261         http://www.arnet.gov/Library/OFPP/PolicyDocs/pbscckls.html.

1262      U.S. Code of Federal Regulations, Title 10, Part 30, Rules of General Applicability to Domestic
1263         Licensing of Byproduct Material.
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1264      APPENDIX E: CONTRACTING LABORATORY SERVICES  	E-l
1265         E.I    Introduction 	E-l
1266         E.2    Procurement of Services 	E-5
1267            E.2.1  Request for Approval of Proposed Procurement Action	E-6
1268            E.2.2  Types of Procurement Mechanisms	E-6
1269         E.3    Request for Proposals—The Solicitation   	E-8
1270            E.3.1  Market Research  	E-9
1271            E.3.2  Length of Contract	E-10
1272            E.3.3  Subcontracts	E-10
1273         E.4    Proposal Requirements  	E-l 1
1274            E.4.1  RFP and Contract Information	E-l 1
1275            E.4.2  Personnel  	E-14
1276            E.4.3  Instrumentation  	E-17
1277                E.4.3.1    Type,  Number, and Age of Laboratory Instruments  	E-18
1278                E.4.3.2    Service Contract	E-18
1279            E.4.4  Narrative to Approach	E-18
1280                E.4.4.1    Analytical Methods or Protocols	E-18
1281                E.4.4.2    Meeting Contract Measurement Quality Objectives  	E-19
1282                E.4.4.3    Data Package 	E-19
1283                E.4.4.4    Schedule	E-19
1284                E.4.4.5    Sample Storage and Disposal	E-20
1285            E.4.5  Quality Manual  	E-21
1286            E.4.6  Licenses and  Accreditations	E-22
1287            E.4.7  Experience	E-22
1288                E.4.7.1    Previous or Current Contracts	E-23
1289                E.4.7.2    Quality of Performance  	E-23
1290         E.5    Proposal Evaluation and Scoring Procedures  	E-23
1291            E.5.1  Evaluation Committee	E-24
1292            E.5.2  Ground Rules — Questions	E-24
1293            E.5.3  Scoring/Evaluation Scheme	E-24
1294                E.5.3.1    Review of Technical Proposal and Quality Manual  	E-26
1295                E.5.3.2    Review of Laboratory Accreditation 	E-28
1296                E.5.3.3    Review of Experience 	E-28
1297            E.5.4  Pre-Award Proficiency Samples  	E-28
1298            E.5.5  Pre-Award Audit  	E-29
1299            E.5.6  Comparison of Prices	E-33
1300            E.5.7  Debriefing of Unsuccessful Vendors	E-34
1301         E.6    The Award	E-34
1302         E.7    For the Duration  of the Contract  	E-35
1303            E.7.1  Managing a Contract 	E-35
1304            E.7.2  Responsibility of the Contractor  	E-36

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1305            E.7.3  Responsibility of the Agency	E-36
1306            E.7.4  Anomalies and Nonconformance	E-36
1307            E.7.5  Laboratory Assessment 	E-37
1308                E.7.5.1    Performance and Quality Control Samples  	E-37
1309                E.7.5.2    Laboratory Performance Evaluation Programs  	E-38
1310                E.7.5.3    Laboratory Evaluations Performed During the Contract Period 	E-39
1311         E.8    Contract Completion  	E-40
1312         E.9    References 	E-41
1313      Table E.I— Examples of Procurement Options to Obtain Materials or Services	E-7
1314      Table E.2 — SOW Checklists for the Agency and Proposer	E-13
1315      Table E.3 — Laboratory Technical Supervisory Personnel Listed by Position Title and Examples
1316         for Suggested Minimum Qualifications	E-15
1317      Table E.4 — Laboratory Technical Personnel Listed by Position Title and Examples for
1318         Suggested Minimum Qualifications and Examples of Optional Staff Members	E-16
1319      Table E.5 — Laboratory Technical Staff Listed by Position Title and Examples for Suggested
1320         Minimum Qualifications 	E-16
1321      Table E.6 — Example of a Proposal Evaluation Plan	E-26
1322      Figure E-l — General Sequence Initiating and Later Conducting Work with a Contract
1323         Laboratory 	E-4

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                       APPENDIX F  LABORATORY SUBSAMPLING
 2     F.I   Introduction

 3     In most cases a sample that arrives at the laboratory cannot be analyzed in its entirety. Usually
 4     only a small subsample is taken for analysis, and the analyte concentration of the subsample is
 5     assumed to be approximately equal to that of the sample itself. Obviously a subsample cannot be
 6     perfectly representative of a heterogeneous sample. Improper subsampling may introduce a
 7     significant bias into the analytical process. Even when done properly, subsampling increases the
 8     variability of the measured result. There are simple methods for controlling the bias, but
 9     estimating and controlling the random variability is less straightforward.

10     French geologist Pierre Gy has developed a theory of particulate sampling for applications in
11     mining exploration and development (Gy, 1992), and his work has been promoted in the United
12     States by Francis Pitard (Pitard, 1993). The basic concept of the theory is that the variability in
13     the analyte concentration of a laboratory sample depends on the mass of the sample and the
14     distribution of particle types and sizes in the material sampled. The parti culate sampling theory
15     developed by Gy is applicable to the sampling of soils and radioactive waste (EPA 1992a,
16     1992b). In this appendix, the theory is applied in qualitative and quantitative approaches to the
17     subsampling of parti culate solids in the radiation laboratory.

18     There are many examples of the use of Gy's theory in the mining industry (Assibey-Bonsu 1996;
19     Stephens and Chapman, 1993; Bilonick, 1990; Borgman et al., 1996), and a computer program
20     has been developed for its implementation (Minkkinen, 1989). The theory has recently been
21     adapted for use in environmental science.  To date, most environmental applications have been in
22     laboratory and field sampling for hazardous chemicals in Superfund cleanups (Borgman et al.,
23     1994; Shefsky 1997), and there  are several applications of the theory that involve mixed
24     radioactive and hazardous wastes (Tamura, 1976).

25     In principle, particulate sampling theory applies to materials of any type, since even gases and
26     liquids are composed of particles (molecules). However, sampling large numbers of randomly
27     distributed molecules in a fluid  presents few statistical difficulties; so, the theory is more often
28     applied to particulate solids.

29     One of the most likely applications of Gy's theory in the radiation laboratory is the subsampling
30     of soils. Natural soils are complex mixtures of different particle types, shapes, densities, and
31     sizes. Soil particles range from fine clays at less than 4 jim diameter to coarse sand that ranges
32     over 2 mm in diameter, spanning about 4 orders of magnitude. Contaminants may be absorbed or
33     chemically combined into the soil matrix, adsorbed onto the surfaces of particles, or may occur in

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34     discrete particles that are not bound to the soil matrix. Contaminant particles in soil can vary in
35     size from fine airborne deposits of less than 1 jim diameter to relatively large pellets. These
36     factors and others, including radionuclide half-lives, significantly affect the sampling problem.

37     F.2   Basic Concepts

38     This appendix applies Gy's sampling theory to subsampling. To avoid confusion, the terms "lot"
39     and "sample" will be used here instead of "sample" and "subsample," respectively. There may be
40     several subsampling stages at the laboratory, and all of the stages must be considered. At any
41     stage of sampling, the lot is the collection of particles from which a portion is to be taken, and
42     the sample is the portion taken to represent the lot.

43     In Gy's theory, the chemical or physical component whose proportion in a lot is of interest is
44     called the critical component. In the context of radiochemistry, the critical component may be a
45     radionuclide, but, if the chemical form of the radionuclide is known, it may be more useful to
46     consider the critical component to be a chemical compound. Certain applications of Gy's theory
47     require knowledge of the density, so the physical form of the compound may also be important.
48     In the limited context of this appendix, however, the critical component will be identified with
49     the analyte, which is usually a radionuclide.

50     The proportion of critical component by mass in a lot, sample, or particle is called the critical
51     content. In the context of radiochemistry, the critical content is directly related to the  activity
52     concentration, or massic activity, of the analyte, but it is expressed as a dimensionless number
53     between 0 and 1. Many of the mathematical formulas used in Gy's sampling theory are equally
54     valid if the critical content is replaced everywhere by analyte concentration. All the formulas in
55     this appendix will be expressed in terms of analyte concentration, not critical content.

56     The sampling error of a sample S is defined, for our purposes, as the relative error in the analyte
57     concentration of the sample, or (zs - zz) / ZL, where zs is the analyte concentration of the sample
58     and ZL is the analyte concentration of the lot. If the sample is the entire lot, the sampling error is
59     zero by definition.

60     A lot may be heterogeneous with respect to many characteristics, including particle size, density,
61     and analyte concentration. Of these,  analyte concentration is most important for the purposes of
62     this appendix. A lot may be considered perfectly homogeneous when all particles have the same
63     concentration of analyte.
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64     The term "heterogeneity" is commonly used with more than one meaning. Gy attempts to clarify
65     the concepts by distinguishing between two types of heterogeneity. The constitution hetero-
66     geneityof a lot is determined by variations among the particles without regard to their locations
67     in the lot. It is an intrinsic property of the lot itself, which cannot be changed without altering
68     individual particles. The distribution heterogeneity of a lot depends not only on the variations
69     among particles but also on their spatial distribution.1 Thus, the distribution heterogeneity may
70     change, for example, when the material is shaken or mixed. In Gy's theory, both constitution
71     heterogeneity and distribution heterogeneity are quantitative terms, which are defined
72     mathematically.

73     Heterogeneity is also sometimes described as either "random" or "nonrandom" (ASTM D5956).
74     Random heterogeneity is exhibited by well-mixed material, in which dissimilar particles are
75     randomly distributed. Nonrandom heterogeneity occurs when particles are not randomly
76     distributed, but instead are  stratified. There is a natural tendency for a randomly heterogeneous
77     lot to become more stratified when shaken, bounced, or stirred. The same material may exhibit
78     both random and nonrandom heterogeneity at different times in its history.2

79     In MARLAP's terminology, the representativeness of a sample denotes the closeness of the
80     analyte concentration of the sample to the analyte concentration of the lot. A sample is
81     representative if its analyte concentration is close to the concentration of the lot, just as a
82     measured result is accurate if its value is close  to the value of the measurand. Representativeness
83     may be affected by bias and imprecision in the sampling process, just as accuracy may be
84     affected by bias and imprecision in the measurement process.3

85     The concept of representativeness is related to  the question of heterogeneity. If a lot is completely
86     homogeneous, then any sample is perfectly representative of the lot, regardless of the sampling
87     strategy, but as the degree of heterogeneity increases, it becomes more difficult to select a
88     representative sample.
           :ASTM D5956 uses the terms "compositional heterogeneity" and "distributional heterogeneity."
           2A state of random heterogeneity exists when the distribution heterogeneity is zero. A state of
       nonrandom heterogeneity exists when the distribution heterogeneity is positive.
           3The term "representativeness" is also like "accuracy" inasmuch as it is used with different
       meanings by different people. The definition provided here is MARLAP's definition.

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 89     F.3   Sources of Measurement Error

 90     The total variance of the result of a measurement is the sum of the variances of a series of error
 91     components, including errors produced in the field and in the laboratory. Errors in the laboratory
 92     may be divided into those associated with sampling and those associated with sample preparation
 93     and analysis.

 94     Note that the practical significance of any error, including sampling error, depends on its
 95     magnitude relative to the other errors. If a crude analytical procedure is used or if there is a
 96     relatively large counting uncertainty, the sampling error may be relatively unimportant. In other
 97     cases the sampling error may dominate. If the standard uncertainty from either source is less than
 98     about one-third of the standard uncertainty from the other, the smaller uncertainty component
 99     contributes little to the combined standard uncertainty.

100     This appendix focuses only on sampling errors, which include the following:

101      •  Sampling bias;
102      •  The fundamental error; and
103      •  Grouping and segregation errors.

104     The following sections define the three types of sampling errors and present methods for
105     controlling or quantifying them. (See Chapter 19,  Measurement Statistics, for a more general
106     discussion of laboratory  measurement errors.)

107     F.3.1   Sampling Bias

108     Sampling bias is often related to distribution heterogeneity. When there is a correlation between
109     the physical properties of a particle and its location in the lot, care is required to avoid taking a
110     biased sample. For example, if the analyte is primarily concentrated at the bottom of the lot, the
ill     analyte concentration of a sample taken from the top will be biased low. Situations like this may
112     occur frequently in environmental radiochemical analysis, since non-natural radioactive materials
113     often tend to be concentrated in the smallest particles, which tend to settle to the bottom of the
114     container.

115     Sampling bias can be controlled by the use of "correct" sampling procedures. A sampling
116     procedure is called "correct" if every particle in the lot has the same probability of being selected
117     for the sample. As a practical rule, a  sample is guaranteed to be unbiased only if the  sampling
118     procedure is correct.

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119
120
RULE 1: A sample is guaranteed to be unbiased only if every particle in the lot has the same
probability of selection.
121      The preceding rule is not being followed, for example, if particles on the bottom or in recesses of
122      the container are never selected.

123      Actually the rule stated above is only approximately true.4 It is invalid if the sample consists of
124      only a few particles, or if only a few particles in the lot contain most of the mass. Therefore, a
125      second practical rule of sampling is that the sample must be many times larger (by weight) than
126      the largest particle of the lot.
127
RULE 2: The sample must be many times larger than the largest particle of the lot.
128     Grouping of particles should also be minimized. If the particles form clumps, the effective
129     number of particles in the lot is actually the number of clumps.

130     F.3.2  Fundamental Error

131     When a sample is taken, the existence of constitution heterogeneity in a lot leads to an
132     unavoidable sampling error, called the fundamental error. Its variance, called the fundamental
133     variance, is a property of the lot and the size of the sample. It represents the smallest sampling
134     variance that can be achieved without altering individual particles or taking a larger sample. The
135     fundamental variance  is not affected by homogenizing, or mixing, and exists even when the
136     sampling procedure is correct. It cannot be eliminated, but it can be reduced either by increasing
137     the size of the sample  or by reducing the particle sizes before sampling (e.g., by grinding).
138
139
140
RULE 3: The fundamental variance may be reduced by:
   •   Taking a larger sample
   •   Reducing the particle sizes before sampling
141      This theoretical minimum sampling variance is only achieved in practice when the lot is in a state
142      of pure random heterogeneity (and the sampling is performed correctly). If there is nonrandom
            4A sample is unbiased if E(ZS I Ms) = ZL, where Zs is the total analyte activity in the sample, Ms is
        the sample mass, ZL is the analyte activity concentration of the lot, and EQ denotes expected value.
        Equal selection probabilities guarantee only that E(Z^ I E(M^ = ZL.

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143      heterogeneity at the time of sampling, the total sampling variance will be larger than the
144      fundamental variance.

145      Either method for reducing the fundamental variance may be difficult or costly to implement in
146      some situations. When large objects or consolidated materials are contained in the lot, particle
147      size reduction for every lot may be unrealistically expensive. Not all materials are amenable to
148      particle size reduction (e.g., steel). If available, knowledge of the expected contamination types
149      and distributions may be used to reduce the need for particle size reduction. For example, it may
150      be known that large objects in the lot are relatively free of analyte. If so, then such objects might
151      be removed or analyzed separately using different methods, depending on the project objectives.

152      When particle size reduction is required and trace levels of contamination are expected in the lot,
153      complete decontamination of grinding or milling equipment is required to avoid the possibility of
154      cross-sample contamination. The equipment should be constructed of non-contaminating
155      materials that are compatible with the chemical components of the lot. Glass, ceramic and
156      stainless steel are typical materials. Particle size reducers, such as ball mills and ceramic plate
157      grinders, require dried samples and thorough decontamination. Mechanical splitters may be
158      difficult to  decontaminate. A grinding blank may be analyzed to check for contamination of the
159      grinding equipment.

160      Contamination from airborne sources (e.g., stack releases or incinerator emissions), leaching
161      (e.g.,  stored mill tailings), or from weathering of contaminated surfaces tends to be dispersed and
162      deposited as many fine particles. In these cases, as long as the particles of the matrix are small
163      relative to the sample size (Rule 2), grinding the material is unlikely to make dramatic
164      differences in the fundamental variance, but the variance tends to be small because of the large
165      number of contaminant particles.

166      If the lot contains only a few contaminant particles, all of which are very small, the fundamental
167      variance may remain large even after extensive grinding. However, the analytical procedure may
168      be amenable to modifications that permit larger samples to be processed. For example,
169      dissolution of a large solid sample may be followed by subsampling of the solution to obtain the
170      amount needed for further analysis. Since liquid solutions tend to be more easily homogenized
171      than solids, subsampling from the solution contributes little to the total sampling error.

172      If neither reducing the particle size nor increasing the sample size is feasible,  more innovative
173      analytical techniques may have to be considered.
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174     F.3.3  Grouping and Segregation Error

175     Since the analyte is often more closely associated with particles having certain characteristics
176     (e.g., small or dense), it may become concentrated in one portion of the lot or in clumps spread
177     throughout the lot. Such effects tend to increase distribution heterogeneity.

178     The existence of distribution heterogeneity leads to a sampling error called the grouping and
179     segregation error. The grouping and segregation variance is not as easily quantified as the
180     fundamental variance, but there are methods for reducing its magnitude.

181     Although the traditional approach to reducing the grouping and segregation error is mixing, or
182     homogenizing, the material, Gy and Pitard warn that homogenizing heterogeneous materials is
183     often difficult, especially if a large quantity is involved. Using improper methods, such as
184     stirring, may actually tend to increase segregation, and, even if a degree of homogeneity is
185     achieved, it is likely to be short-lived, because of the constant influence of gravity. Agitation of
186     particulate matter during transport and handling also tends to produce segregation of particles by
187     size, shape,  and density. During these processes, the denser, smaller, and rounder particles tend to
188     settle to the bottom of the container, while less  dense, larger, and flatter particles tend to rise to
189     the top.
190
191
192
193
RULE 4: The effects of homogenizing heterogeneous solid material tend to be short-lived
because of the constant influence of gravity. Denser, smaller, and rounder particles tend to
settle to the bottom of a container, while less dense, larger, and flatter particles tend to rise to
the top.
194     As an alternative to homogenizing, Gy and Pitard recommend sampling procedures to reduce not
195     the distribution heterogeneity itself, but its effects on the grouping and segregation error. Gy
196     classifies sampling procedures into two categories: (1) increment sampling, and (2) splitting.
197     Increment sampling involves extracting a number of small portions, called increments., from the
198     lot, which are combined to form the sample. Splitting involves dividing the lot into a large
199     number of approximately equal-sized portions and recombining these portions into a smaller
200     number of potential samples. One of the potential samples is then randomly chosen as the actual
201     sample.

202     A sample composed of many increments will generally be more representative than a sample
203     composed of a single increment. For example, if a 25 g sample is required, it is better to take five
204     5 g increments, selected from different locations in the sample, than to take a single 25 g
205     increment.

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206
207


208
209
210
211
212
213
214

215
216
217
218
219
220
221
222
223
 RULE 5: A sample composed of many increments taken from different locations in the lot is
 usually more representative than a sample composed of a single increment.
The variance reduction achievable by increment sampling depends on the distribution
heterogeneity of the lot. If the lot is in a state of pure random heterogeneity, increment sampling
provides no benefit. On the other hand, if the lot is highly stratified, the standard deviation of the
analyte concentration of a small composite sample formed from n independent increments may
be smaller by a factor of 1 / \fn than the standard deviation for a sample composed of a single
increment.5 Variance reductions intermediate between these two extremes are most likely in
practice.

Figures  F.I and F.2 illustrate what Gy calls "increment delimitation error" and "increment
extraction error," respectively. One method for extracting increments is the one-dimensional
"Japanese slab-cake" method (Gy  1992, Pitard 1993). First, the material in the lot is spread out
into an elongated pile with roughly constant width and height. Then a scoop or spatula is used to
delimit and extract evenly spaced cross-sections from the pile. A flat-bottomed scoop should be
used for this purpose to avoid leaving particles at the bottom of the pile. Ideally it should also
have vertical sides, as shown in Figure F.3, although such scoops may not be commercially
available. If a spatula is used, its width must be much larger than the largest particles to be
sampled, since particles will tend to fall off the edges (see Figure F.2).
                    FIGURE F.I — Incorrect increment delimitation using a round scoop
            5This statement assumes the stratification is such that a single large increment is likely to have no
        more constitution heterogeneity than any of the n smaller increment.
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                       FIGURE F.2 — Incorrect increment extraction using a spatula
                 FIGURE F.3 — Correct increment delimitation using a rectangular scoop
224     Splitting may be performed correctly by mechanical splitters, such as riffle splitters and sectorial
225     splitters, or it may be performed manually by "fractional shoveling" (or "fractional scooping" in
226     the laboratory). Fractional shoveling involves removing small portions of equal size from the lot
227     and depositing them into two or more empty containers (or piles), cycling through the containers
228     in order, and repeating the process until all the material has been deposited. When this process is
229     complete,  one container is chosen at random to be the sample.

230     The traditional "coning and quartering" method for splitting, although correct, is not recommen-
231     ded because it produces a subsample from too few increments. With this method, the material is
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232     mixed by forming it into a cone, adding a fraction of the sample at a time to the apex of the cone.
233     After the entire sample is mixed in this way, the cone is flattened into a circular layer. Next the
234     circular layer of material is divided into quarters and two opposite quarters are discarded. This
235     process may be repeated until a suitable sample size is obtained (Shugar and Dean, 1990).

236     Homogenization may also be achieved with some types of grinding equipment, such as a ring-
237     and-puckmill.

238     According to Gy, small  quantities of solid material, up to a few kilograms, can be homogenized
239     effectively in the laboratory. He recommends the use of a jar-shaker for this purpose and states
240     that immediately after the lot is shaken, the sample may be taken directly from the jar using a
241     spatula (Gy, 1992). Although Pitard recognizes the possibility of homogenizing small lots in the
242     laboratory using a mechanical mixer that rotates and tumbles a closed container, he also  states
243     that homogenizing heterogeneous materials is often  "wishful thinking" and recommends the one-
244     dimensional Japanese slab-cake procedure instead (Pitard,  1993, §14.4.3).

245     F.4    Implementation of the Particulate Sampling Theory

246     DISCLAIMER: Gy 's theory is currently the best-known and most completely developed theory of
247     particulate sampling, but the problem is a difficult one, and the mathematical approaches
248     offered may not give satisfactory results for all purposes. Quantitative estimates of the
249     fundamental variance are often crude. Conservative assumptions are sometimes needed to
250     permit mathematical solutions of the equations, leading to upper bounds for the fundamental
25 1     variance which may be significantly overestimated. It appears that the theory has not been
252     applied previously to sampling for radiochemical analysis, and no data are available to
253     demonstrate the limits of its applicability. Until such data are available, MARLAP recommends
254     the theory only for crude estimation.

255     F.4.1  The Fundamental Variance
                                                                                   2
256     Gy's sampling theory leads to the following equation for the fundamental variance aFE (Gy 1992,
257     Pitard 1993):
258     Here
259        Ms is the mass of the sample (g)
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260         ML is the mass of the lot (g)
261         N is the number of particles in the lot
262         zt  is the analyte concentration of the/h particle
263         ZL is the analyte concentration of the lot
264         mi is the mass of the/h particle (g)

265     Equation F. 1 is usually of only theoretical interest because it involves quantities whose values
266     cannot be determined in practice; however, it is the most general formula for the fundamental
267     variance and serves as a starting point for the development of more useful approximation
268     formulas, which are derived using known or assumed properties of the lot.

269     F.4.2  Scenario 1 - Natural Radioactive Minerals

270     Gy has derived a practical formula for the fundamental variance based on the following
271     assumptions (Gy, 1992):

272       •  The analyte concentration (actually the critical content) of a particle does not depend on its
273         size. More precisely, if the lot is divided into fractions according to particle size and density,
274         the analyte concentration of each fraction is a function of particle density but not size.

275       •  The distribution of particle sizes is unrelated to density. That is, if the lot is divided into
276         fractions by density, each fraction has approximately the same distribution of particle
277         diameters.

278     The first of these assumptions is often violated when environmental samples are analyzed for
279     non-natural radionuclides, because in these cases, the analyte concentration of a particle tends to
280     be inversely related to its size The second assumption may also be violated when non-natural
281     materials are involved. However, when natural materials are analyzed for naturally occurring
282     radionuclides, both assumptions may be valid.

283     Under the two stated assumptions, the fundamental standard deviation a^ is related to the mass
284     of the lot ML, the mass of the sample Ms,  and the maximum particle diameter dby the equation
                                                                                             (F.2)
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285     where ^is a constant of proportionality.6 The "maximum" diameter (/is defined as the length of
286     the edge of a square mesh that retains no more than a specified fraction of oversize by mass.
287     Thus, it is notthe size of the largest particle in the lot. Gy has found it most convenient to let dbe
288     the size of a square mesh that retains only 5% oversize, and his definition will be assumed here.
289     According to Gy, this value of (/also tends to be the approximate size of the largest particles that
290     are easily identifiable by sight.

291     When Ms is much smaller than ML, which is often the case, the fundamental standard deviation is
292     given more simply by
                                                   \
                                                                                             (F.3)
293     This formula implies that, to reduce the fundamental standard deviation by half, one may either
294     increase the sample size M5by a factor of 4 or reduce the maximum particle size dby a factor of
295     0.52/3 = 0.63.7

296     F.4.3  Scenario 2 - Hot Particles

297     As noted, the assumptions of Scenario 1 are often violated when environmental media are
298     analyzed for non-natural radionuclides, because there is usually a correlation between particle
299     size and radionuclide concentration. However, another approximation formula (not due to Gy)
300     may be used if the analyte occurs only in a minuscule fraction of the particles (i.e., "hot
301     particles").

302     It is assumed that:

303       •  The maximum analyte concentration of a particle zmax is known;
304       •  Every particle in the lot has concentration 0 or zmax (approximately); and
305       •  The high-activity particles make up a small fraction of the lot both by number and by mass.
            6Gy (1992) and Pitard (1993) provide more information about the constant k. MARLAP presents
        only a brief summary of Scenario 1 because of the difficulty of estimating k.
            7Equation F.3 also may be understood to say that the fundamental standard deviation is inversely
        proportional to the square root of the number of particles in the sample.

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306
        Under these assumptions the fundamental standard deviation a^ is described by the equation8
( 1 - 1
\ Zmax8//4
                                              M   M
                                                s
                                                      L
                                                            2z
307     where
308         Ms is the sample mass (g)
309         ML is the mass of the lot (g)
310         8// is the average density of a high-activity particle (g / cm3)
311         dH is the maximum diameter of a high-activity particle, defined as in Scenario 1
312         k  is a constant of proportionality.

313     The proportionality constant k depends on the distribution of sizes of the high-activity particles
314     but is most likely to lie between 0.5 and I.9

315     When Ms is much smaller than ML, Equation F.4 reduces to
                                                 ^
                                                   Zmax5//^//
                                                    2zLMs
                                                                                                 5)
316     If all the high-activity particles have approximately the same mass and the sample mass is much
317     smaller than the mass of the lot, then Equation F.5 may be rewritten in the simple form
                                             JFE
                                                    MSnL
                                                                                              (F.6)
            8 A more complete formula is OFE = f — - — 1 """""  ZL f Zm° Zl S/^//^ + &GkGdG]   , where 8G, 1G, and
                                          LI M«  MJ  2^« V  ZL                 )\
         dG describe the zero-activity particles. Equation F.4 is obtained when zmax is much greater than ZL,
         which happens when the mass of high-activity material is very small.
            9The constant k equals the square root of Gy's "size distribution factor" g. Gy recommends the
         value ^=0.25 by default for most uncalibrated materials of interest in the mining industry, but no
         assumption is made here that the same default value is appropriate for hot particles. If all the particles
         have the same size, g=\.
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        Laboratory Subsampling
318     where nL is the number of hot particles in the lot. Equation F.6 can also be derived from the fact
319     that the number of hot particles in a small sample can be modeled by a Poisson distribution,
320     whose mean and variance are equal (Chapter 19, Measurement Statistics). The fundamental
321     standard  deviation equals the coefficient of variation of the Poisson distribution, which is large
322     when the mean is small.
323

324
325
326
327
328


329

330

331
332

333
334


335
336
337
338
339
340
341
                                     EXAMPLE 1

 A 1-kg lot of soil contains approximately 1 Bq/g of 240Pu occurring as hot particles of
 relatively pure plutonium dioxide (240PuO2, density 8^= 11.4 g/cm3, specific activity
 zmax = 7.44 x 109 Bq/g) with "maximum" diameter dH= 1CT3 cm (10 jim). Assume the
 distribution of particle sizes is such that k ~ 0.5. What is the fundamental standard deviation
 for a 1-gram sample?
 According to Equation F.5,
                         aFE = 0.5
                                    (7.44xl09)(ll.4)(l(T3J3
3.3
 Thus, the fundamental standard deviation is about 330%, indicating that a 1 g sample probably
 is inadequate.

 If all the hot particles had the same size, then k would equal 1 and the fundamental standard
 deviation would be about 650%.
When the presence of a small number of hot particles makes it impossible to reduce the
fundamental standard deviation to an acceptable value by ordinary means (grinding the material
or increasing the sample size), then more innovative methods may be required. For example, the
entire lot may be spread into a thin layer and an autoradiograph made to locate the hot particles.
Then, if necessary, a biased sample containing essentially all of the hot particles may be taken
and analyzed, and the measured result corrected for sample size to obtain the average analyte
concentration of the lot.
342
F.4.4  Scenario 3 - Particle Surface Contamination
343     A third approximation formula may be used if the contaminant occurs in tiny particles, or even
344     molecules, which adhere randomlyto the surfaces of larger host particles of the matrix and
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345     cannot be selected without their hosts. In this case, the total mass of the contaminant particles is
346     assumed to be negligible. If the contaminant particles are also extremely numerous, so that many
347     of them adhere to a typical host particle, then the analyte concentration of a particle tends to be
348     inversely proportional to its diameter. In this case the fundamental variance depends primarily on
349     the characteristics of the host particles.10

350     Under the stated assumptions, the fundamental standard deviation a^ for typical soils is given by


                                                                                            (F.7)
                                                 Ws   MLi

351     where
352         Ms is the sample mass (g)
353         ML is the mass of the lot (g)
354         k  is a constant of proportionality
355         5  is the average particle density (g/cm3)
356         d  is the "maximum"  particle diameter (cm), as defined for Scenario 1

357     The factor ^may vary from lot to lot but is always less than 1  and is usually less than 0.5.

358     When the sample mass is small, Equation F.7 reduces to
                                                    2MS
                                                                                             (F.8)
359     The fundamental standard deviation a^ calculated using Equation F.8 is never greater than
360     Jdd3 /2MS, which is the square root of the ratio of the "maximum" particle mass 5 d312 to the
361     mass of the sample Ms. So, as long as the sample is much heavier than the heaviest particle in
362     the lot, the fundamental variance in Scenario 3 tends to be small. As in Scenario 1, reducing the
363     fundamental standard by half requires either increasing the sample mass M5by a factor of 4 or
364     reducing the particle diameter by a factor of 0.63. However, note that grinding may cause the
            10The formula for OFE given here describes the variability of the total surface area in a sample. A
        more complete expression includes a term for the variability of the analyte concentration per unit area,
        but this term is negligible if the number of contaminant particles is sufficiently numerous.

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365     assumptions underlying Equation F.8 to be violated if the contaminant is not redistributed onto
366     the newly created particle surfaces.
367

368
369
370
371

372
373

374

375
376

377

378
379
380

381
382

383
384

385
386
387

388
389
                                     EXAMPLE 2

 Suppose a 1-kg lot of soil contains 90Sr, which is expected to adhere randomly to the surfaces
 of the particles. The maximum particle diameter dis found to be approximately 0.2 cm. If
 nothing more is known about the distribution of particles sizes, what is the maximum
 fundamental standard deviation for a 1-g sample?

 Assuming the density of the soil particles is 8 = 2.675 g/cm3, Equation F.8 with k=\ gives the
 solution
                                  (2-675X0.2)3  =
                           r LJ  \

 Note that since ^is usually less than 0.5, the fundamental standard deviation is more likely to
 be less than 5%.
F.5   Summary

Results derived from particulate sampling theory provide sampling protocols that help to control
sampling errors, including sampling bias, fundamental error, and grouping and segregation
errors. Some of the important conclusions are listed below.

 •  For most practical purposes, a sample is guaranteed to be unbiased only if all particles in the
    lot have the same probability of selection.

 •  The sample mass should be many times greater than the heaviest particle in the lot, and
    clumping of particles should be minimized.

 •  The fundamental variance, which is considered to be the minimum achievable sampling
    variance, may be reduced by increasing the size of the sample or reducing the particle sizes
    before sampling.

 •  Grouping and segregation of particles, which occur because of the particles' differing
    physical characteristics and the influence of gravity, tend to increase the sampling variance.
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390      • Grouping and segregation errors can be reduced by increment sampling or by splitting. The
391        more increments, the better.

392      • Correct sampling requires proper tools and procedures.

393      • Small quantities of particulate material can be homogenized effectively in the laboratory
394        using mechanical mixers that rotate and tumble a closed container, but the effects of mixing
395        tend to be short-lived.

396      • Estimation of the fundamental variance requires either knowledge or assumptions about the
397        characteristics of the material being analyzed. Quantitative estimates may be crude.

398     F.6    References

399     American Society for Testing and Materials (ASTM). D5633. "Standard Practice for Sampling
400        with a Scoop." 1994.

401     American Society for Testing and Materials (ASTM). D5956. "Standard Guide for Sampling
402        Strategies for Heterogeneous Wastes." 1996.

403     Assibey-Bonsu, W. 1996. "Summary of present knowledge on the representative sampling of ore
404        in the mining industry." Journal of The South African Institute of Mining and Metallurgy
405        96(6): 289-293.

406     Bilonick, Richard A. 1990. "Gy's parti culate material sampling theory." ASTM Special
407        Technical Publication n 1097. p75-92.

408     Borgman, L.; Anderson-Sprecher, R.; GerowK.; and Flatman, G. 1994. "Cost-effective selection
409        of a sampling plan for spatially distributed hazardous waste."

410     Borgman, L. E.; Kern, J. W.; Anderson-Sprecher R.; Flatman, G.  T. 1996. "The sampling theory
411        of Pierre Gy: Comparisons, implementation,  and applications  for environmental sampling."
412        Principles of Environmental Sampling. 2nd ed.

413     Gy, Pierre M. 1992.  Sampling of Heterogeneous and Dynamic Material Systems: Theories of
414        Heterogeneity, Sampling, and Homogenizing. Elsevier, Amsterdam, The  Netherlands.
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        Laboratory Subsampling
415     Environmental Protection Agency (EPA). 1992a. Preparation of Soil Sampling Protocols:
416        Sampling Techniques and Strategies. Office of Research and Development. EPA/600/R-
417        92/128, Washington, DC.

418     Environmental Protection Agency (EPA). 1992b. CharacterizingHeterogenous Wastes. EPA
419        Office of Research and Development, EPA/600/R-92/033. U.S. Department of Energy,
420        Office of Technology Development.

421     Minkkinen, P. 1989. "A computer program for solving sampling problems." Chemometrics and
422        Intelligent Laboratory Systems (Vol. #): 189-194.

423     Pitard, Francis. 1993. Pierre Gy's Sampling Theory and Sampling Practice: Heterogeneity,
424        Sampling Correctness, and Statistical Process Control, 2nd ed., CRC Press, Boca Raton, FL.

425     Shefsky, S. 1997. "Sample handling strategies for accurate lead-in-soil measurements in the field
426        and laboratory." International Symposium of Field Screening Methods for Hazardous Wastes
427        and Toxic Chemicals, Las Vegas, NV.

428     Shugar and Dean. 1990. The Chemist's Ready Reference Handbook. McGraw-Hill, New York,
429        NY.

430     Stephens, A.J.; Chapman, G. J. 1993. Optimisation of sampling procedures at the Fimiston Open
431        Pit, Kalgoorie" Conference Series - Australasian Institute of Mining and Metallurgy n 5,
432        p!85-194.

433     Tamura, T. 1976. "Physical and Chemical Characteristics of Plutonium, in Existing
434        Contaminated Soils and Sediments." Proceedings of the Symposium on Transuranic Nuclides
435        in the Environment, IAEA Publication ST1/PUB/410, Vienna.
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                APPENDIX G  STATISTICAL TABLES
                  TABLE G.I — Quantiles of the standard normal distribution
 3
 4
 5
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
p
0.51
0.52
0.53
0.54
0.55
0.56
0.57
0.58
0.59
0.60
0.61
0.62
0.63
0.64
0.65
0.66
0.67
0.68
0.69
0.70
0.71
0.72
0.73
0.74
0.75
1-P
0.49
0.48
0.47
0.46
0.45
0.44
0.43
0.42
0.41
0.40
0.39
0.38
0.37
0.36
0.35
0.34
0.33
0.32
0.31
0.30
0.29
0.28
0.27
0.26
0.25
*J>
0.02507
0.05015
0.07527
0.1004
0.1257
0.1510
0.1764
0.2019
0.2275
0.2533
0.2793
0.3055
0.3319
0.3585
0.3853
0.4125
0.4399
0.4677
0.4959
0.5244
0.5534
0.5828
0.6128
0.6433
0.6745
P
0.76
0.77
0.78
0.79
0.80
0.81
0.82
0.83
0.84
0.85
0.86
0.87
0.88
0.89
0.90
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1.00
1-p
0.24
0.23
0.22
0.21
0.20
0.19
0.18
0.17
0.16
0.15
0.14
0.13
0.12
0.11
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
*J>
0.7063
0.7388
0.7722
0.8064
0.8416
0.8779
0.9154
0.9542
0.9945
1.036
1.080
1.126
1.175
1.227
1.282
1.341
1.405
1.476
1.555
1.645
1.751
1.881
2.054
2.326
CO
Note:
       = -z
                     (Continued on next page)
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       Appendix G
31

32
TABLE G.I (Continued) — Quantiles of the standard normal distribution

                        (Critical Values)
33
34
35
36
37
38
39
40
41
42
43
p
0.90
0.95
0.975
0.99
0.995
0.9975
0.999
0.9995
0.99975
0.9999
1-p
0.10
0.05
0.025
0.01
0.005
0.0025
0.001
0.0005
0.00025
0.0001
^
1.282
1.645
1.960
2.326
2.576
2.807
3.090
3.291
3.481
3.719
44
45
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                                                                             Appendix G
46

47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
TABLE G.2 — Quantiles of Student's t distribution
Degrees
of
Freedom
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
p = 0.90
1-/7 = 0.10
tp= 3.078
1.886
1.638
1.533
1.476
1.440
1.415
1.397
1.383
1.372
1.363
1.356
1.350
1.345
1.341
1.337
1.333
1.330
1.328
1.325
1.323
1.321
1.319
1.318
1.316
1.315
1.314
1.313
1.311
1.310
0.95
0.05
6.314
2.920
2.353
2.132
2.015
1.943
1.895
1.860
1.833
1.812
1.796
1.782
1.771
1.761
1.753
1.746
1.740
1.734
1.729
1.725
1.721
1.717
1.714
1.711
1.708
1.706
1.703
1.701
1.699
1.697
0.975
0.025
12.706
4.303
3.182
2.776
2.571
2.447
2.365
2.306
2.262
2.228
2.201
2.179
2.160
2.145
2.131
2.120
2.110
2.101
2.093
2.086
2.080
2.074
2.069
2.064
2.060
2.056
2.052
2.048
2.045
2.042
0.98
0.02
15.895
4.849
3.482
2.999
2.757
2.612
2.517
2.449
2.398
2.359
2.328
2.303
2.282
2.264
2.249
2.235
2.224
2.214
2.205
2.197
2.189
2.183
2.177
2.172
2.167
2.162
2.158
2.154
2.150
2.147
0.99
0.01
31.821
6.965
4.541
3.747
3.365
3.143
2.998
2.896
2.821
2.764
2.718
2.681
2.650
2.624
2.602
2.583
2.567
2.552
2.539
2.528
2.518
2.508
2.500
2.492
2.485
2.479
2.473
2.467
2.462
2.457
0.995
0.005
63.657
9.925
5.841
4.604
4.032
3.707
3.499
3.355
3.250
3.169
3.106
3.055
3.012
2.977
2.947
2.921
2.898
2.878
2.861
2.845
2.831
2.819
2.807
2.797
2.787
2.779
2.771
2.763
2.756
2.750
0.9975
0.0025
127.321
14.089
7.453
5.598
4.773
4.317
4.029
3.833
3.690
3.581
3.497
3.428
3.372
3.326
3.286
3.252
3.222
3.197
3.174
3.153
3.135
3.119
3.104
3.091
3.078
3.067
3.057
3.047
3.038
3.030
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        Appendix G
 80

 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
TABLE G.2 (Continued) — Quantiles of Student's t distribution
Degrees
of
Freedom
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
60
70
80
90
100
200
300
400
500
CO
p = 0.90
1-/7 = 0.10
1.309
1.309
1.308
1.307
1.306
1.306
1.305
1.304
1.304
1.303
1.303
1.302
1.302
1.301
1.301
1.300
1.300
1.299
1.299
1.299
1.296
1.294
1.292
1.291
1.290
1.286
1.284
1.284
1.283
1.282
0.95
0.05
1.696
1.694
1.692
1.691
1.690
1.688
1.687
1.686
1.685
1.684
1.683
1.682
1.681
1.680
1.679
1.679
1.678
1.677
1.677
1.676
1.671
1.667
1.664
1.662
1.660
1.653
1.650
1.649
1.648
1.645
0.975
0.025
2.040
2.037
2.035
2.032
2.030
2.028
2.026
2.024
2.023
2.021
2.020
2.018
2.017
2.015
2.014
2.013
2.012
2.011
2.010
2.009
2.000
1.994
1.990
1.987
1.984
1.972
1.968
1.966
1.965
1.960
0.98
0.02
2.144
2.141
2.138
2.136
2.133
2.131
2.129
2.127
2.125
2.123
2.121
2.120
2.118
2.116
2.115
2.114
2.112
2.111
2.110
2.109
2.099
2.093
2.088
2.084
2.081
2.067
2.063
2.060
2.059
2.054
0.99
0.01
2.453
2.449
2.445
2.441
2.438
2.434
2.431
2.429
2.426
2.423
2.421
2.418
2.416
2.414
2.412
2.410
2.408
2.407
2.405
2.403
2.390
2.381
2.374
2.368
2.364
2.345
2.339
2.336
2.334
2.326
0.995
0.005
2.744
2.738
2.733
2.728
2.724
2.719
2.715
2.712
2.708
2.704
2.701
2.698
2.695
2.692
2.690
2.687
2.685
2.682
2.680
2.678
2.660
2.648
2.639
2.632
2.626
2.601
2.592
2.588
2.586
2.576
0.9975
0.0025
3.022
3.015
3.008
3.002
2.996
2.990
2.985
2.980
2.976
2.971
2.967
2.963
2.959
2.956
2.952
2.949
2.946
2.943
2.940
2.937
2.915
2.899
2.887
2.878
2.871
2.839
2.828
2.823
2.820
2.807
        MARLAP
        DO NOT CITE OR QUOTE
                          G-4
                 JULY 2001
DRAFT FOR PUBLIC COMMENT

-------
                                                                      Appendix G
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-------
Appendix G
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-------
                                                                              Appendix G
114
TABLE G.4 — Critical values for the nonrandomized exact test

NB
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
a = 0.01
1
6
9
11
13
14
16
18
19
21
23
24
26
27
28
30
31
33
34
35
37
38
40
41
42
44
45
46
48
49
50
51
2
4
5
6
7
8
9
10
11
12
13
14
14
15
16
17
17
18
19
20
20
21
22
23
23
24
25
25
26
27
27
28
3
3
4
5
5
6
7
8
8
9
9
10
10
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
4
2
3
4
5
5
6
6
7
7
8
8
8
9
9
10
10
11
11
11
12
12
13
13
13
14
14
15
15
15
16
16
5
2
3
3
4
4
5
5
6
6
7
7
7
8
8
8
9
9
9
10
10
10
11
11
11
12
12
12
13
13
13
13
a = 0.05
1
4
6
8
9
11
12
14
15
17
18
19
21
22
23
25
26
27
29
30
31
32
34
35
36
37
39
40
41
42
44
45
2
2
3
4
5
6
7
8
8
9
10
11
11
12
13
14
14
15
16
16
17
18
18
19
19
20
21
21
22
23
23
24
3
2
3
3
4
4
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
12
13
13
14
14
15
15
16
16
16
17
4
1
2
3
3
4
4
5
5
5
6
6
7
7
7
8
8
8
9
9
9
10
10
11
11
11
12
12
12
13
13
13
5
1
2
2
3
3
3
4
4
5
5
5
6
6
6
6
7
7
7
8
8
8
9
9
9
9
10
10
10
10
11
11
        JULY 2001
        DRAFT FOR PUBLIC COMMENT
                          G-7
             MARLAP
DO NOT CITE OR QUOTE

-------
        Appendix G
147
TABLE G.4 (Continued) — Critical values for the nonrandomized exact test

NB
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
a = 0.01
1
53
54
55
57
58
59
60
62
63
64
65
67
68
69
70
72
73
74
75
77
78
79
80
82
83
84
85
86
88
89
2
29
29
30
31
32
32
33
33
34
35
35
36
37
37
38
39
39
40
41
41
42
43
43
44
45
45
46
46
47
48
3
21
21
22
22
22
23
23
24
24
25
25
26
26
27
27
27
28
28
29
29
30
30
31
31
31
32
32
33
33
34
4
16
17
17
17
18
18
19
19
19
20
20
20
21
21
21
22
22
22
23
23
23
24
24
24
25
25
25
26
26
26
5
14
14
14
15
15
15
16
16
16
16
17
17
17
18
18
18
18
19
19
19
20
20
20
20
21
21
21
22
22
22
a = 0.05
1
46
47
48
50
51
52
53
54
56
57
58
59
60
61
63
64
65
66
67
68
70
71
72
73
74
75
77
78
79
80
2
25
25
26
26
27
28
28
29
30
30
31
31
32
33
33
34
34
35
36
36
37
37
38
39
39
40
40
41
42
42
3
17
18
18
19
19
19
20
20
21
21
22
22
22
23
23
24
24
24
25
25
26
26
26
27
27
28
28
29
29
29
4
14
14
14
15
15
15
16
16
16
17
17
17
17
18
18
18
19
19
19
20
20
20
21
21
21
22
22
22
23
23
5
11
12
12
12
12
13
13
13
13
14
14
14
14
15
15
15
16
16
16
16
17
17
17
17
18
18
18
18
19
19
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
        MARLAP
        DO NOT CITE OR QUOTE
                               G-8
                 JULY 2001
DRAFT FOR PUBLIC COMMENT

-------
                                                                             Appendix G
179
TABLE G.4 (Continued) — Critical values for the nonrandomized exact test

NB
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
a = 0.01
1
90
91
92
94
95
96
97
98
100
101
102
103
104
106
107
108
109
110
112
113
114
115
116
118
119
120
121
122
123
125
2
48
49
50
50
51
51
52
53
53
54
55
55
56
56
57
58
58
59
59
60
61
61
62
63
63
64
64
65
66
66
3
34
34
35
35
36
36
37
37
37
38
38
39
39
40
40
40
41
41
42
42
43
43
43
44
44
45
45
45
46
46
4
27
27
27
28
28
28
29
29
29
30
30
30
31
31
31
32
32
32
33
33
33
34
34
34
35
35
35
36
36
36
5
22
23
23
23
23
24
24
24
25
25
25
25
26
26
26
26
27
27
27
27
28
28
28
28
29
29
29
30
30
30
a = 0.05
1
81
82
83
85
86
87
88
89
90
91
93
94
95
96
97
98
99
100
102
103
104
105
106
107
108
110
111
112
113
114
2
43
43
44
45
45
46
46
47
47
48
49
49
50
50
51
52
52
53
53
54
54
55
56
56
57
57
58
58
59
60
3
30
30
31
31
31
32
32
33
33
33
34
34
35
35
35
36
36
37
37
37
38
38
38
39
39
40
40
40
41
41
4
23
23
24
24
24
25
25
25
26
26
26
26
27
27
27
28
28
28
29
29
29
30
30
30
30
31
31
31
32
32
5
19
19
20
20
20
20
21
21
21
21
22
22
22
22
23
23
23
23
24
24
24
24
25
25
25
25
26
26
26
26
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
        JULY 2001
        DRAFT FOR PUBLIC COMMENT
                               G-9
            MARLAP
DO NOT CITE OR QUOTE

-------
        Appendix G
211
TABLE G.4 (Continued) — Critical values for the nonrandomized exact test

NB
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
a = 0.01
1
126
127
128
129
130
132
133
134
135
136
137
139
140
141
142
143
144
146
147
148
149
150
151
152
154
155
156
157
158
159
2
67
67
68
69
69
70
70
71
72
72
73
73
74
75
75
76
76
77
78
78
79
79
80
81
81
82
82
83
84
84
3
47
47
48
48
48
49
49
50
50
50
51
51
52
52
52
53
53
54
54
55
55
55
56
56
57
57
57
58
58
59
4
37
37
37
37
38
38
38
39
39
39
40
40
40
41
41
41
42
42
42
43
43
43
43
44
44
44
45
45
45
46
5
30
31
31
31
31
32
32
32
32
33
33
33
33
34
34
34
34
35
35
35
35
36
36
36
36
37
37
37
37
38
a = 0.05
1
115
116
117
118
120
121
122
123
124
125
126
127
129
130
131
132
133
134
135
136
137
139
140
141
142
143
144
145
146
147
2
60
61
61
62
62
63
64
64
65
65
66
66
67
68
68
69
69
70
70
71
72
72
73
73
74
74
75
76
76
77
3
42
42
42
43
43
44
44
44
45
45
46
46
46
47
47
47
48
48
49
49
49
50
50
51
51
51
52
52
52
53
4
32
33
33
33
33
34
34
34
35
35
35
35
36
36
36
37
37
37
38
38
38
38
39
39
39
40
40
40
40
41
5
26
27
27
27
27
28
28
28
28
29
29
29
29
30
30
30
30
31
31
31
31
32
32
32
32
32
33
33
33
33
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
        MARLAP
        DO NOT CITE OR QUOTE
                              G-10
                 JULY 2001
DRAFT FOR PUBLIC COMMENT

-------
                                   TABLE G.5 — Critical values of Filliben's statistic
>
244


245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272


32
Cd
r
o
o
0
§
w
H





9

1—1







a
o
o
H
O
H
W
O
o >
h-J ^
W T3
n
O
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Significance Level (a)
0.005 0.01 0.025 0.05
0.867 0.869 0.872 0.879
0.813 0.822 0.845 0.868
0.803 0.822 0.855 0.879
0.818 0.835 0.868 0.890
0.828 0.847 0.876 0.899
0.841 0.859 0.886 0.905
0.851 0.868 0.893 0.912
0.860 0.876 0.900 0.917
0.868 0.883 0.906 0.922
0.875 0.889 0.912 0.926
0.882 0.895 0.917 0.931
0.888 0.901 0.921 0.934
0.894 0.907 0.925 0.937
0.899 0.912 0.928 0.940
0.903 0.916 0.931 0.942
0.907 0.919 0.934 0.945
0.909 0.923 0.937 0.947
0.912 0.925 0.939 0.950
0.914 0.928 0.942 0.952
0.918 0.930 0.944 0.954
0.922 0.933 0.947 0.955
0.926 0.936 0.949 0.957
0.928 0.937 0.950 0.958
0.930 0.939 0.952 0.959
0.932 0.941 0.953 0.960
0.934 0.943 0.955 0.962
0.937 0.945 0.956 0.962
0.938 0.947 0.957 0.964
n
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
55
60
65
70
75
80
85
90
95
100
Significance Level (a)
0.005 0.01 0.025 0.05
0.939 0.948 0.958 0.965
0.939 0.949 0.959 0.966
0.940 0.950 0.960 0.967
0.941 0.951 0.960 0.967
0.943 0.952 0.961 0.968
0.945 0.953 0.962 0.968
0.947 0.955 0.962 0.969
0.948 0.956 0.964 0.970
0.949 0.957 0.965 0.971
0.949 0.958 0.966 0.972
0.950 0.958 0.967 0.973
0.951 0.959 0.967 0.973
0.953 0.959 0.967 0.973
0.954 0.960 0.968 0.974
0.955 0.961 0.969 0.974
0.956 0.962 0.969 0.974
0.956 0.963 0.970 0.975
0.957 0.963 0.970 0.975
0.957 0.964 0.971 0.977
0.959 0.965 0.972 0.978
0.962 0.967 0.974 0.980
0.965 0.970 0.976 0.981
0.967 0.972 0.977 0.982
0.969 0.974 0.978 0.983
0.971 0.975 0.979 0.984
0.973 0.976 0.980 0.985
0.974 0.977 0.981 0.985
0.976 0.978 0.982 0.985
0.977 0.979 0.983 0.986
0.979 0.981 0.984 0.987

-------
273



274



275





276





277
278
279





280





281





282





283



284

285

286
o  S
o  £
              o



              o

              o

              o
                                                  TABLE G.6 — Summary of probability distributions
              Q
              to
              H


              O
Cd
r
KH
o
o  ^
O  c





11
                     F(x) denotes the gamma function.  F(l/2) = 0t, F(l) = 1, and F(x + 1) = x • Y(x) for x> 0.

                     * If p = I, the mode is N.  Otherwise, if Np + p is an integer and p>0, both Np + p and Np + p - I are modes.

                     t If X is a positive integer, both X and X - 1 are modes.
Distribution
Binomial
Poisson
Rectangular
Trapezoidal
Normal
Log-Normal
Student's?
Exponential
Chi-Square
Parameters
N,p
»
a , a+
at - a
2
u, a
V.
v
>
V
Values

k 0123

XE [a ,at]
x E [a ,a ]


XE (-00,00)
«.*->


„,»,,
«[*-,
Probability Function
(?),•(.-,*-
Xe +
k\
I
at-a_
x - a
I
at - x
a + a
Y^ ~ flB
2 P
a + a +
x ^ a p
a + a
2n R2si' " 2 "^
1
aV2S
exp(-ln(jc/u

-fr-"2""2
g)2/2(lnag)2)
*(lnagV^
L((v+l)/2)f
L(v/2)^ I
2V(v + D/2
VJ
,,-^
xv/2 - lg -M
2V/2 L(v/2)
Mode
[Np+p\*
w
Not unique
Not unique
H
v--*""
0
0
J 0, v < 2
[ v-2, v>2
Mean
A/p
^
a +a+
2
a + a+
2
"
v'"-'-
0 (v>l)
1
I
V
Standard Deviation
^/NP(^—pj
f.
at - a
if
.-. [77?
2 \ 6
a

/ 20na )2 flna )2
u ye 8 - e g
\ V
\| v - 2
1
I
^

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