January2009

NUREG-1575,Supp.1

EPA402-R-09-001

DOE/HS-0004

Final
NUREG-1575, Supp. 1 

EPA 402-R-09-001 

DOE/HS-0004 

 

Multi-Agency Radiation Survey 

and Assessment of Materials and 

Equipment Manual 

(MARSAME) 

Department of Defense 

Department of Energy 

Environmental Protection Agency 

Nuclear Regulatory Commission 

January 2009
 MARSAME 

DISCLAIMER 

This supplement was developed by four agencies of the United States Government. Neither the 

United States Government nor any agency or branch thereof, or any of their employees, makes 

any warranty, expressed or implied, or assumes any legal liability of responsibility for any third 

party’s use, or the results of such use, of any information, apparatus, product or process disclosed 

in this supplement, or represents that its use by such third party would not infringe on privately 

owned rights. 

References within this supplement to any specific commercial product, process, or service by 

trade name, trademark, or manufacturer does not constitute an endorsement or recommendation 

by the United States Government. 

NUREG-1575, Supp.1 ii January 2009 
ABSTRACT 

 

The Multi-Agency Radiation Survey and Assessment of Materials and Equipment manual 

(MARSAME) is a supplement to the Multi-Agency Radiation Survey and Site Investigation 

Manual (MARSSIM) providing information on planning, conducting, evaluating, and 

documenting radiological disposition surveys for the assessment of materials and equipment. 

MARSAME is a multi-agency consensus document that was developed collaboratively by four 

Federal agencies having authority and control over radioactive materials: Department of Defense 

(DOD), Department of Energy (DOE), Environmental Protection Agency (EPA), and Nuclear 

Regulatory Commission (NRC). The objective of MARSAME is to provide a multi-agency 

approach for planning, performing, and assessing disposition surveys of materials and 

equipment, while at the same time encouraging an effective use of resources. 

January 2009 iii NUREG-1575, Supp. 1 

MARSAME 

CONTENTS 

Disclaimer....................................................................................................................................... ii 

Abstract.......................................................................................................................................... iii 

Acknowledgements...................................................................................................................... xxi

Acronyms and Abbreviations .................................................................................................... xxiii

Symbols, Nomenclature, and Notations.................................................................................... xxvii

Conversion Factors ................................................................................................................... xxxii

Roadmap ..................................................................................................................................RM-1 

Introduction to MARSAME .............................................................................................RM-1 

The Goal of the Roadmap.................................................................................................RM-1 

Initial Assessment.............................................................................................................RM-2 

Categorization...................................................................................................................RM-2 

Standardized Survey Designs ...........................................................................................RM-2 

Develop a Decision Rule ..................................................................................................RM-2 

Survey Design...................................................................................................................RM-3 

Measurement Quality Objectives .....................................................................................RM-4 

Implement the Survey Design ..........................................................................................RM-5 

Evaluate the Results..........................................................................................................RM-5 

Summary...........................................................................................................................RM-5 

1 Introduction and Overview................................................................................................... 1-1 

1.1 Purpose and Scope of MARSAME............................................................................. 1-1 

1.2 Understanding Key MARSAME Terminology .......................................................... 1-3 

1.3 Use of MARSAME..................................................................................................... 1-5 

1.4 Overview of MARSAME ........................................................................................... 1-6 

1.4.1 Planning Phase.............................................................................................. 1-9 

1.4.2 Implementation Phase................................................................................. 1-10 

1.4.3 Assessment Phase ....................................................................................... 1-11 

1.4.4 Decision-Making Phase .............................................................................. 1-12 

1.5 Organization of MARSAME .................................................................................... 1-12 

1.6 Similarities and Differences Between MARSSIM and MARSAME ....................... 1-14 

2 Initial Assessment of Materials and Equipment ................................................................... 2-1 

2.1 Introduction................................................................................................................. 2-1 

2.2 Categorize the M&E as Impacted or Non-Impacted................................................... 2-1 

2.2.1 Perform a Visual Inspection ......................................................................... 2-3 

2.2.2 Collect and Review Additional Historical Records...................................... 2-4 

2.2.3 Assess Process Knowledge........................................................................... 2-5 

2.2.4 Perform Sentinel Measurements................................................................... 2-7 

2.2.5 Decide Whether M&E are Impacted ............................................................ 2-8 

2.3 Design and Implement Preliminary Surveys............................................................... 2-9 

2.4 Describe the M&E .................................................................................................... 2-11 

2.4.1 Describe the Physical Attributes of the M&E ............................................ 2-11 

2.4.1.1 Describe the Physical Dimensions of the M&E.......................... 2-12 

January 2009 v NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

2.4.1.2 Describe the Complexity of the M&E ........................................ 2-13 

2.4.1.3 Describe the Accessibility of the M&E ...................................... 2-14 

2.4.1.4 Describe the Inherent Value of the M&E ................................... 2-14 

2.4.2 Describe the Radiological Attributes of the M&E...................................... 2-15 

2.4.2.1 Identify the Radionuclides of Potential Concern ....................... 2-16 

2.4.2.2 Describe the Radionuclide Concentrations or Radioactivity 

Associated with the M&E .......................................................... 2-17 

2.4.2.3 Describe the Distribution of Radioactivity................................. 2-17 

2.4.2.4 Describe the Location of Radioactivity...................................... 2-17 

2.4.3 Finalize the Description of the M&E.......................................................... 2-18 

2.5 Select a Disposition Option....................................................................................... 2-19 

2.6 Document the Results of the Initial Assessment....................................................... 2-19 

2.6.1 Document a Standardized Initial Assessment............................................. 2-20 

2.6.2 Document a Conceptual Model .................................................................. 2-21 

3 Identify Inputs to the Decision ............................................................................................. 3-1 

3.1 Introduction................................................................................................................. 3-1 

3.2 Select Radionuclides or Radiations of Concern.......................................................... 3-3 

3.3 Identify Action Levels ................................................................................................ 3-3 

3.3.1 Identify Sources of Action Levels ................................................................ 3-6 

3.3.2 Finalize Selection of Action Levels.............................................................. 3-7 

3.3.3 Modify Action Levels When Multiple Radionuclides are Present............... 3-7 

3.3.3.1 Modify Action Levels for Non-Radionuclide-Specific Measurement 

Methods......................................................................................... 3-8 

3.3.3.2 Modify Action Levels for Non-Radionuclide-Specific 

Measurements of Decay-Series Radionuclides............................. 3-9 

3.3.3.3 Modify Action Levels for Radionuclide-Specific Measurement 

Methods....................................................................................... 3-11 

3.3.4 Evaluate Interface With Exposure Pathway Models .................................. 3-13 

3.4 Describe the Parameter of Interest ............................................................................ 3-14 

3.5 Identify Alternative Actions...................................................................................... 3-14 

3.6 Identify Survey Units ................................................................................................ 3-15 

3.6.1 Define Initial Survey Unit Boundaries ....................................................... 3-17 

3.6.2 Modify Initial Survey Unit Boundaries ...................................................... 3-18 

3.7 Develop a Decision Rule........................................................................................... 3-18 

3.8 Develop Inputs for Selection of Provisional Measurement Methods ....................... 3-19 

3.8.1 Measurement Method Uncertainty ............................................................. 3-21 

3.8.2 Detection Capability ................................................................................... 3-21 

3.8.3 Quantification Capability............................................................................ 3-22 

3.8.4 Range .......................................................................................................... 3-22 

3.8.5 Specificity................................................................................................... 3-22 

3.8.6 Ruggedness ................................................................................................. 3-23 

3.9 Identify Reference Materials..................................................................................... 3-24 

3.10 Evaluate an Existing Survey Design......................................................................... 3-25 

 4 Develop a Survey Design............................................................................................ 4-1 

4.1 Introduction................................................................................................................. 4-1 

4.2 Making Decisions Using Statistics ............................................................................. 4-1 

NUREG-1575, Supp.1 vi January 2009 
MARSAME Contents (Continued) 

4.2.1 Null Hypothesis ............................................................................................ 4-2 

4.2.2 Discrimination Limit .................................................................................... 4-2 

4.2.3 Scenario A .................................................................................................... 4-4 

4.2.4 Scenario B..................................................................................................... 4-4 

4.2.5 Specify Limits on Decision Errors ............................................................... 4-5 

4.2.6 Develop an Operational Decision Rule ........................................................ 4-5 

4.3 Classify the Materials and Equipment ........................................................................ 4-6 

4.3.1 Class 1........................................................................................................... 4-6 

4.3.2 Class 2........................................................................................................... 4-7 

4.3.3 Class 3........................................................................................................... 4-7 

4.3.4 Other Classification Considerations ............................................................. 4-7 

4.4 Design the Disposition Survey.................................................................................... 4-8 

4.4.1 Scan-Only Survey Designs ........................................................................... 4-9 

4.4.1.1 Class 1 Scan-Only Surveys ......................................................... 4-13 

4.4.1.2 Class 2 Scan-Only Surveys ......................................................... 4-13 

4.4.1.3 Class 3 Scan-Only Surveys ......................................................... 4-14 

4.4.2 In Situ Survey Designs ............................................................................... 4-15 

4.4.2.1 Class 1 In situ Surveys ................................................................ 4-15 

4.4.2.2 Class 2 In situ Surveys ................................................................ 4-16 

4.4.2.3 Class 3 In situ Surveys ................................................................ 4-16 

4.4.3 MARSSIM-Type Survey Designs .............................................................. 4-16 

4.4.3.1 Class 1 MARSSIM-Type Surveys .............................................. 4-17 

4.4.3.2 Class 2 MARSSIM-Type Surveys .............................................. 4-18 

4.4.3.3 Class 3 MARSSIM-Type Surveys .............................................. 4-18 

4.4.4 Method-Based Survey Designs................................................................... 4-18 

4.4.5 Optimize the Disposition Survey Design ................................................... 4-19 

4.5 Document the Disposition Survey Design ................................................................ 4-20 

4.5.1 Routine Surveys and Standard Operating Procedures ................................ 4-21 

4.5.1.1 SOP Process ................................................................................ 4-22 

4.5.1.2 General Format for Disposition Survey SOPs ............................ 4-22 

4.5.2 Case-Specific Applications......................................................................... 4-24 

5 Implement the Survey Design .............................................................................................. 5-1 

5.1 Introduction................................................................................................................. 5-1 

5.2 Ensure Protection of Health and Safety ...................................................................... 5-1 

5.3 Consider Issues for Handling M&E............................................................................ 5-3 

5.3.1 Prepare M&E for Survey .............................................................................. 5-4 

5.3.2 Provide Access.............................................................................................. 5-5 

5.3.3 Transport the M&E....................................................................................... 5-6 

5.4 Segregate the M&E..................................................................................................... 5-6 

5.5 Set Measurement Quality Objectives.......................................................................... 5-7 

5.6 Determine Measurement Uncertainty ......................................................................... 5-9 

5.7 Determine Measurement Detectability ..................................................................... 5-10 

5.8 Determine Measurement Quantifiability................................................................... 5-12 

5.9 Select a Measurement Technique and Instrumentation Combination....................... 5-13 

5.9.1 Select a Measurement Technique ............................................................... 5-14 

5.9.1.1 Scanning Techniques .................................................................. 5-14 

January 2009 vii NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

5.9.1.2 In Situ Measurements.................................................................. 5-14 

5.9.1.3 Sampling ..................................................................................... 5-15 

5.9.1.4 Smears......................................................................................... 5-15 

5.9.2 Select Instrumentation ................................................................................ 5-16 

5.9.2.1 Hand-Held Instruments ............................................................... 5-16 

5.9.2.2 Volumetric Counters (Drum, Box, Barrel, 4-π Counters)........... 5-16 

5.9.2.3 Conveyorized Survey Monitoring Systems ................................ 5-16 

5.9.2.4 In Situ Gamma Spectroscopy...................................................... 5-17 

5.9.2.5 Portal Monitors............................................................................ 5-17 

5.9.2.6 Laboratory Analysis.................................................................... 5-17 

5.9.3 Select a Measurement Method.................................................................... 5-17 

5.9.4 Measurement Performance Indicators ........................................................ 5-23 

5.9.4.1 Blanks.......................................................................................... 5-23 

5.9.4.2 Replicate Measurements ............................................................. 5-23 

5.9.4.3 Spikes and Standards................................................................... 5-24 

5.9.5 Instrument Performance Indicators............................................................. 5-24 

5.9.5.1 Performance Tests....................................................................... 5-24 

5.9.5.2 Functional Tests .......................................................................... 5-24 

5.9.5.3 Instrument Background............................................................... 5-25 

5.9.5.4 Efficiency Calibrations................................................................ 5-25 

5.9.5.5 Energy Calibrations (Spectrometry Systems) ............................. 5-25 

5.9.5.6 Peak Resolution and Tailing (Spectrometry Systems)................ 5-25 

5.9.5.7 Voltage Plateaus (Gas Proportional Systems)............................. 5-25 

5.9.5.8 Self Absorption, Backscatter, and Crosstalk............................... 5-26 

5.10 Report the Results ..................................................................................................... 5-26 

6 Evaluate the Survey Results ................................................................................................. 6-1 

6.1 Introduction ............................................................................................................. 6-1 

6.2 Conduct Data Quality Assessment.............................................................................. 6-1 

6.2.1 Review the Data Quality Objectives and Survey Design ............................. 6-1 

6.2.2 Conduct a Preliminary Data Review ............................................................ 6-3 

6.2.2.1 Review Quality Assurance and Quality Control Reports ............. 6-3 

6.2.2.2 Perform a Graphical Data Review ................................................ 6-4 

6.2.2.3 Calculate Basic Statistical Quantities............................................ 6-5 

6.2.3 Select the Statistical Tests ............................................................................ 6-7 

6.2.3.1 Scan-Only Surveys........................................................................ 6-7 

6.2.3.2 In Situ Surveys .............................................................................. 6-8 

6.2.3.3 MARSSIM-Type Survey Designs................................................. 6-8 

6.2.4 Verify the Assumptions of the Tests ............................................................ 6-9 

6.2.5 Draw Conclusions from the Data ............................................................... 6-11 

6.3 Compare Results to the UBGR ................................................................................. 6-12 

6.4 Compare Results Using an Upper Confidence Limit................................................ 6-12 

6.4.1 Calculate the Upper Confidence Limit ....................................................... 6-13 

6.4.2 Upper Confidence Limit Example: Class 1 Concrete Rubble .................... 6-14 

6.5 Conduct the Sign Test ............................................................................................... 6-16 

6.5.1 Apply the Sign Test to Scenario A ............................................................. 6-17 

6.5.2 Apply the Sign Test to Scenario B ............................................................. 6-17 

NUREG-1575, Supp.1 viii January 2009 
MARSAME Contents (Continued) 

6.5.3 Sign Test Example: Class 1 Copper Pipes.................................................. 6-17 

6.6 Conduct the Wilcoxon Rank Sum Test..................................................................... 6-19 

6.6.1 Apply the WRS Test to Scenario A............................................................ 6-19 

6.6.2 Apply the WRS Test to Scenario B ............................................................ 6-19 

6.6.3 WRS Test Scenario A Example: Class 2 Metal Ductwork......................... 6-20 

6.6.4 WRS Test Scenario B Example: Class 2 Metal Ductwork......................... 6-21 

6.7 Conduct the Quantile Test......................................................................................... 6-23 

6.8 Evaluate the Results: The Decision .......................................................................... 6-23 

6.8.1 Compare Results to the UBGR................................................................... 6-26 

6.8.2 Compare Results Using an Upper Confidence Limit ................................. 6-26 

6.8.3 Compare Results for MARSSIM-Type Surveys ........................................ 6-27 

6.9 Investigate Causes for Survey Unit Failures............................................................. 6-27 

6.10 Document the Disposition Survey Results................................................................ 6-28 

7 Statistical Basis for MARSAME Surveys ............................................................................ 7-1 

7.1 Overview of Statistical Survey Design and Hypothesis Testing ................................ 7-6 

7.2 Statistical Decision-Making........................................................................................ 7-9 

7.2.1 Null Hypothesis ............................................................................................ 7-9 

7.2.2 Discrimination Limit .................................................................................. 7-10 

7.2.3 Scenario A .................................................................................................. 7-12 

7.2.4 Scenario B................................................................................................... 7-13 

7.2.5 Specify Limits on Decision Errors ............................................................. 7-13 

7.2.6 Develop an Operational Decision Rule ...................................................... 7-15 

7.3 Set Measurement Quality Objectives........................................................................ 7-16 

7.3.1 Determine the Required Measurement Method Uncertainty at the UBGR 7-18 

7.3.1.1 Scan-Only Survey Designs ......................................................... 7-18 

7.3.1.2 In Situ Survey Designs................................................................ 7-18 

7.3.1.3 MARSSIM-Type Survey Designs............................................... 7-19 

7.3.2 Determine the Required Measurement Method Uncertainty at Concentrations 

Other Than the UBGR ................................................................................ 7-20 

7.4 Determine Measurement Uncertainty ....................................................................... 7-22 

7.4.1 Use Standard Terminology ......................................................................... 7-23 

7.4.2 Consider Sources of Uncertainty................................................................ 7-24 

7.4.3 Recommendations for Uncertainty Calculation and Reporting.................. 7-26 

7.5 Determine Measurement Detectability ..................................................................... 7-27 

7.5.1 Calculate the Critical Value........................................................................ 7-28 

7.5.2 Calculate the Minimum Detectable Value of the Net Instrument Signal or 

Count........................................................................................................... 7-29 

7.5.3 Calculate the Minimum Detectable Concentration..................................... 7-31 

7.5.4 Summary of Measurement Detectability .................................................... 7-32 

7.5.5 Measurement Detectability Recommendations .......................................... 7-34 

7.6 Determine Measurement Quantifiability................................................................... 7-34 

7.6.1 Calculate the MQC ..................................................................................... 7-35 

7.6.2 Summary of Measurement Quantifiability ................................................. 7-36 

7.7 Establish a Required Measurement Method Uncertainty.......................................... 7-37 

7.7.1 Developing a Requirement for Measurement Method Uncertainty for 

MARSSIM-Type Surveys .......................................................................... 7-38 

January 2009 ix NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

NUREG-1575, Supp.1 x January 2009 

7.7.2 Developing a Requirement for Measurement Method Uncertainty When 

Decisions are to be Made About Individual Items ..................................... 7-40 

7.8 Calculate the Combined Standard Uncertainty of a Measurement ........................... 7-42 

7.8.1 Procedures for Evaluating Uncertainty....................................................... 7-42 

7.8.1.1 Identify the Measurand, Y, and all the Input Quantities, Xi, for the 

Mathematical Model ................................................................... 7-42 

7.8.1.2 Determine an Estimate, xi, of the Value of Each Input Quantity, 

Xi ....................................................................................................................................................7-42 

7.8.1.3 Evaluate the Standard Uncertainty, u(xi), for Each Input Estimate, 

xi, Using a Type A Method, a Type B Method, or a Combination of 

Both............................................................................................. 7-42 

7.8.1.4 Evaluate the Covariances, u(xi,xj), for all Pairs of Input Estimates 

with Potentially Significant Correlations.................................... 7-45 

7.8.1.5 Calculate the Estimate, y, of the Measurand from the Relationship 

y = f(x1,x2,...,xN) .......................................................................... 7-45 

7.8.1.6 Determine the Combined Standard Uncertainty, uc(y), of the 

Estimate, y ................................................................................... 7-45 

7.8.1.7 Optionally Multiply uc(y) by a Coverage Factor k to Obtain the 

Expanded Uncertainty, U............................................................ 7-46 

7.8.1.8 Report the Result as y ± U with the Unit of Measurement ......... 7-47 

7.8.2 Examples of Some Parameters that Contribute to Uncertainty .................. 7-47 

7.8.2.1 Instrument Background............................................................... 7-47 

7.8.2.2 Counting Efficiency .................................................................... 7-48 

7.8.2.3 Digital Displays and Rounding ................................................... 7-51 

7.8.3 Example Uncertainty Calculation............................................................... 7-52 

7.8.3.1 Model Equation and Sensitivity Coefficients ............................. 7-52 

7.8.3.2 Uncertainty Components............................................................. 7-53 

7.8.3.3 Uncertainty Budget ..................................................................... 7-56 

7.8.3.4 Reported Result........................................................................... 7-56 

7.9 Calculate the Minimum Detectable Concentration................................................... 7-57 

7.9.1 Critical Value.............................................................................................. 7-57 

7.9.2 Minimum Detectable Concentration........................................................... 7-58 

7.9.3 Calculation of the Critical Value ................................................................ 7-59 

7.9.4 Calculation of the Minimum Detectable Value of the Net Instrument 

Signal .......................................................................................................... 7-60 

7.9.5 Calculation of the Minimum Detectable Concentration............................. 7-61 

7.10 Calculate the Minimum Quantifiable Concentration ................................................ 7-63 

7.11 Calculate Scan MDCs ............................................................................................... 7-66 

7.11.1 Calculate the Relative Fluence Rate to Exposure Rate (FRER)................. 7-67 

7.11.2 Calculate the Probability of Interaction...................................................... 7-67 

7.11.3 Calculate the Relative Detector Response.................................................. 7-69 

7.11.4 Relationship Between Detector Response and Exposure Rate................... 7-69 

7.11.5 Relationship Between Detector Response and Radionuclide 

Concentration.............................................................................................. 7-70 

7.11.6 Calculation of Scan Minimum Detectable Count Rates ............................. 7-72 

7.11.7 Calculate the Scan Minimum Detectable Concentration............................ 7-73 
MARSAME Contents (Continued) 

8 Illustrative Examples ............................................................................................................ 8-1 

8.1 Introduction................................................................................................................. 8-1 

8.2 Mineral Processing Facility Concrete Rubble............................................................. 8-1 

8.2.1 Description.................................................................................................... 8-2 

8.2.2 Objectives ..................................................................................................... 8-2 

8.2.3 Initial Assessment of the M&E..................................................................... 8-2 

8.2.3.1 Categorize the M&E as Impacted or Non-Impacted..................... 8-2 

8.2.3.2 Describe the M&E......................................................................... 8-3 

8.2.3.3 Design and Implement Preliminary Surveys................................. 8-4 

8.2.3.4 Select a Disposition Option........................................................... 8-7 

8.2.3.5 Document the Results of the Initial Assessment........................... 8-8 

8.2.4 Develop a Decision Rule .............................................................................. 8-8 

8.2.4.1 Select Radionuclides or Radiations of Concern............................ 8-8 

8.2.4.2 Identify Action Levels................................................................... 8-8 

8.2.4.3 Modify the Action Levels to Account for Multiple 

Radionuclides.............................................................................. 8-10 

8.2.4.4 Describe the Parameter of Interest .............................................. 8-11 

8.2.4.5 Identify Alternative Actions........................................................ 8-12 

8.2.4.6 Identify Survey Units .................................................................. 8-12 

8.2.4.7 Define the Decision Rules........................................................... 8-12 

8.2.4.8 Develop Inputs for Selection of Provisional Measurement 

Methods....................................................................................... 8-13 

8.2.4.9 Identify Reference Materials....................................................... 8-14 

8.2.5 Develop a Survey Design ........................................................................... 8-14 

8.2.5.1 Classify the M&E........................................................................ 8-14 

8.2.5.2 Design the Scanning Survey ....................................................... 8-15 

8.2.5.3 Design the Sample Collection Survey......................................... 8-15 

8.2.5.4 Develop an Operational Decision Rule....................................... 8-16 

8.2.5.5 Document the Survey Design...................................................... 8-16 

8.2.6 Implement the Survey Design .................................................................... 8-16 

8.2.6.1 Ensure Protection of Health and Safety ...................................... 8-16 

8.2.6.2 Consider Issues for Handling the M&E...................................... 8-18 

8.2.6.3 Segregate the M&E..................................................................... 8-18 

8.2.6.4 Set Measurement Quality Objectives.......................................... 8-18 

8.2.6.5 Determine Measurement Uncertainty for the Scan MDC........... 8-18 

8.2.6.6 Determine Measurement Uncertainty for Concrete Samples...... 8-22 

8.2.6.7 Collect Survey Data .................................................................... 8-23 

8.2.7 Evaluate the Survey Results ....................................................................... 8-24 

8.2.7.1 Conduct a Data Quality Assessment........................................... 8-24 

8.2.7.2 Conduct the Wilcoxon Rank Sum Test....................................... 8-24 

8.2.8 Evaluate the Results: The Decision ............................................................ 8-25 

 8.3 Mineral Processing Facility Rented Equipment Baseline Survey............................. 8-26 

8.3.1 Description.................................................................................................. 8-26 

8.3.2 Objectives ................................................................................................... 8-26 

8.3.3 Initial Assessment of the M&E................................................................... 8-26 

8.3.3.1 Categorize the M&E as Impacted or Non-Impacted................... 8-26 

January 2009 xi NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

8.3.3.2 Describe the M&E....................................................................... 8-28 

8.3.3.3 Design and Implement Preliminary Surveys............................... 8-30 

8.3.3.4 Select a Disposition Option......................................................... 8-30 

8.3.3.5 Document the Results of the Initial Assessment......................... 8-30 

8.3.4 Develop a Decision Rule ............................................................................ 8-30 

8.3.4.1 Select Radionuclides or Radiations of Concern.......................... 8-30 

8.3.4.2 Identify Action Levels................................................................. 8-31 

8.3.4.3 Describe the Parameter of Interest .............................................. 8-31 

8.3.4.4 Identify Alternative Actions........................................................ 8-31 

8.3.4.5 Develop a Decision Rule............................................................. 8-31 

8.3.4.6 Identify Survey Units .................................................................. 8-31 

8.3.4.7 Develop Inputs for Selection of Provisional Measurement 

Methods....................................................................................... 8-31 

8.3.4.8 Reference Materials .................................................................... 8-32 

8.3.5 Develop a Survey Design ........................................................................... 8-33 

8.3.5.1 Select a Null Hypothesis ............................................................. 8-33 

8.3.5.2 Set the Discrimination Limit....................................................... 8-33 

8.3.5.3 Specify the Limits on Decision Errors........................................ 8-33 

8.3.5.4 Select a Measurement Technique................................................ 8-34 

8.3.5.5 Finalize Selection of Radiations to be Measured........................ 8-35 

8.3.5.6 Develop an Operational Decision Rule....................................... 8-35 

8.3.5.7 Classify the M&E........................................................................ 8-35 

8.3.5.8 Select a Measurement Method.................................................... 8-35 

8.3.5.9 Optimize the Disposition Survey Design.................................... 8-37 

8.3.5.10 Document the Disposition Survey Design .................................. 8-37 

8.3.6 Implement the Survey Design .................................................................... 8-37 

8.3.6.1 Ensure Protection of Health and Safety ...................................... 8-37 

8.3.6.2 Consider Issues for Handling M&E............................................ 8-37 

8.3.6.3 Segregate the M&E..................................................................... 8-38 

8.3.6.4 Determine the Measurement Detectability for the Scan Survey. 8-38 

8.3.6.5 Determine the Measurement Detectability for the Investigation 

Survey ......................................................................................... 8-38 

8.3.6.6 Determine Measurement Uncertainty for the Investigation Survey 

MDC............................................................................................ 8-39 

8.3.6.7 Perform Quality Control Measurements ..................................... 8-41 

8.3.6.8 Collect Survey Data .................................................................... 8-41 

8.3.7 Evaluate the Survey Results ....................................................................... 8-41 

8.3.7.1 Conduct a Data Quality Assessment........................................... 8-41 

8.3.7.2 Conduct a Preliminary Data Review........................................... 8-41 

8.3.7.3 Conduct the Statistical Tests ....................................................... 8-41 

8.3.8 Evaluate the Results: The Decision ............................................................ 8-42 

8.4 Mineral Processing Facility Rented Equipment Disposition Survey........................ 8-42 

8.4.1 Description.................................................................................................. 8-42 

8.4.2 Objectives ................................................................................................... 8-42 

8.4.3 Initial Assessment of the M&E................................................................... 8-42 

8.4.3.1 Categorize the M&E as Impacted or Non-Impacted................... 8-42 

NUREG-1575, Supp.1 xii January 2009 
MARSAME Contents (Continued) 

8.4.3.2 Describe the M&E....................................................................... 8-43 

8.4.3.3 Select a Disposition Option......................................................... 8-44 

8.4.3.4 Document the Results of the Initial Assessment......................... 8-44 

8.4.4 Develop a Decision Rule ............................................................................ 8-44 

8.4.4.1 Identify Action Levels................................................................. 8-44 

8.4.4.2 Evaluate an Existing Survey Design........................................... 8-44 

8.4.5 Develop a Survey Design ........................................................................... 8-44 

8.4.5.1 Select the Null Hypothesis.......................................................... 8-44 

8.4.5.2 Set the Discrimination Limit....................................................... 8-45 

8.4.5.3 Specify Limits on Decision Errors.............................................. 8-45 

8.4.5.4 Classify the M&E........................................................................ 8-45 

8.4.5.5 Optimize the Existing Survey Design......................................... 8-45 

8.4.5.6 Document the Disposition Survey Design .................................. 8-46 

8.4.6 Implement the Survey Design .................................................................... 8-46 

8.4.7 Evaluate the Survey Results ....................................................................... 8-46 

8.4.7.1 Conduct a Data Quality Assessment........................................... 8-46 

8.4.7.2 Conduct the Statistical Tests ....................................................... 8-47 

8.4.8 Evaluate the Results: The Decision ............................................................ 8-47 

Appendix A Statistical Tables and Procedures........................................................................ A-1 

 A.1 Normal Distribution ................................................................................................... A-1 

 A.2 Sample Sizes for Statistical Tests .............................................................................. A-2 

 A.3 Critical Values for the Sign Test................................................................................ A-4 

 A.4 Critical Values for the WRS Test .............................................................................. A-6 

 A.5 Critical Values for the Quantile Test ....................................................................... A-10 

Appendix B Sources of Background Radioactivity .................................................................B-1 

B.1 Introduction.................................................................................................................B-1 

B.2 Environmental Radioactivity ......................................................................................B-1 

B.2.1 Terrestrial Radioactivity ..................................................................................B-2 

B.2.2 Anthropogenic Radioactive Materials .............................................................B-3 

B.2.3 Cosmic Radiation and Cosmogenic Radionuclides .........................................B-4 

B.3 Inherent Radioactivity.................................................................................................B-4 

B.4 Instrument Background...............................................................................................B-5 

B.5 Technologically Enhanced Naturally Occurring Radioactive Material......................B-5 

B.6 Orphan Sources ...........................................................................................................B-6 

Appendix C Examples of Common Radionuclides..................................................................C-1 

Appendix D Instrumentation and Measurement Techniques .................................................. D-1 

D.1 Introduction................................................................................................................ D-1 

 D.2 General Detection Instrumentation ............................................................................ D-1 

D.2.1 Gas-Filled Detectors ....................................................................................... D-1 

D.2.1.1 Ionization Chamber Detectors......................................................... D-2 

D.2.1.2 Gas-Flow Proportional Detectors.................................................... D-2 

D.2.1.3 Geiger-Mueller Detectors................................................................ D-3 

D.2.2 Scintillation Detectors..................................................................................... D-4 

D.2.2.1 Zinc Sulfide Scintillation Detectors ................................................ D-4 

D.2.2.2 Sodium Iodide Scintillation Detectors ............................................ D-4 

January 2009 xiii NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

D.2.2.3 Cesium Iodide Scintillation Detectors............................................. D-4 

D.2.2.4 Plastic Scintillation Detectors ......................................................... D-5 

D.2.3 Solid State Detectors....................................................................................... D-5 

D.3 Counting Electronics.................................................................................................. D-5 

D.4 Hand-Held Instruments .............................................................................................. D-6 

D.4.1 Instruments...................................................................................................... D-6 

D.4.2 Temporal Issues .............................................................................................. D-6 

D.4.3 Spatial Issues................................................................................................... D-7 

D.4.4 Radiation Types .............................................................................................. D-7 

D.4.5 Range .............................................................................................................. D-8 

D.4.6 Scale................................................................................................................ D-8 

D.4.7 Ruggedness ..................................................................................................... D-8 

D.5 Volumetric Counters (Drum, Box, Barrel, Four-Pi Counters)................................... D-9 

D.5.1 Instruments...................................................................................................... D-9 

D.5.2 Temporal Issues ............................................................................................ D-10 

D.5.3 Spatial Issues................................................................................................. D-10 

D.5.4 Radiation Types ............................................................................................ D-10 

D.5.5 Range ............................................................................................................ D-10 

D.5.6 Scale.............................................................................................................. D-11 

D.5.7 Ruggedness ................................................................................................... D-11 

D.6 Conveyorized Survey Monitoring Systems ............................................................. D-11 

D.6.1 Instruments.................................................................................................... D-11 

D.6.2 Temporal Issues ............................................................................................ D-12 

D.6.3 Spatial Issues................................................................................................. D-12 

D.6.4 Radiation Types ............................................................................................ D-13 

D.6.5 Range ............................................................................................................ D-13 

D.6.6 Scale.............................................................................................................. D-13 

D.6.7 Ruggedness ................................................................................................... D-13 

D.7 In Situ Gamma Spectroscopy................................................................................... D-14 

D.7.1 Instruments.................................................................................................... D-14 

D.7.2 Temporal Issues ............................................................................................ D-14 

D.7.3 Spatial Issues................................................................................................. D-15 

D.7.4 Radiation Types ............................................................................................ D-15 

D.7.5 Range ............................................................................................................ D-15 

D.7.6 Scale.............................................................................................................. D-15 

D.7.7 Ruggedness ................................................................................................... D-16 

D.8 Hand-Held Radionuclide Identifiers ........................................................................ D-16 

D.8.1 Instruments.................................................................................................... D-16 

D.8.2 Temporal Issues ............................................................................................ D-16 

D.8.3 Spatial Issues................................................................................................. D-16 

D.8.4 Radiation Types ............................................................................................ D-17 

D.8.5 Range ............................................................................................................ D-17 

D.8.6 Scale.............................................................................................................. D-17 

D.8.7 Ruggedness ................................................................................................... D-17 

D.9 Portal Monitors ........................................................................................................ D-17

D.9.1 Instruments.................................................................................................... D-17 

NUREG-1575, Supp.1 xiv January 2009 
MARSAME Contents (Continued) 

D.9.2 Temporal Issues ............................................................................................ D-18 

D.9.3 Spatial Issues................................................................................................. D-18 

D.9.4 Radiation Types ............................................................................................ D-19 

D.9.5 Range ............................................................................................................ D-19 

D.9.6 Scale.............................................................................................................. D-19 

D.9.7 Ruggedness ................................................................................................... D-19 

 D.10 Sample with Laboratory Analysis............................................................................ D-19 

D.10.1 Instruments ................................................................................................ D-20 

D.10.1.1 Instruments for the Detection of Alpha Radiation ........................ D-20 

D.10.1.2 Instruments for the Detection of Beta Radiation........................... D-21 

D.10.1.3 Instruments for the Detection of Gamma or X-Radiation............. D-21 

D.10.2 Temporal Issues ......................................................................................... D-21 

D.10.3 Spatial Issues ............................................................................................. D-22 

D.10.3.1 Alpha Spectroscopy with Multi-Channel Analyzer ................... D-22 

D.10.3.2 Gas-Flow Proportional Counter .................................................... D-22 

D.10.3.3 Liquid Scintillation Spectrometer ................................................. D-23 

D.10.3.4 Low-Resolution Alpha Spectroscopy............................................ D-23 

D.10.3.5 High-Purity Germanium Detector with Multi-Channel 

Analyzer ........................................................................................ D-23 

D.10.3.6 Sodium Iodide Detector with Multi-Channel Analyzer ................ D-23 

D.10.3.7 Alpha Scintillation Detectors ........................................................ D-23 

D.10.4 Radiation Types ......................................................................................... D-24 

D.10.5 Range ......................................................................................................... D-24 

D.10.6 Scale........................................................................................................... D-25 

D.10.7 Ruggedness ................................................................................................ D-26 

Appendix E Disposition Criteria..............................................................................................E-1 

E.1 Department of Energy.................................................................................................E-1 

E.1.1 10 CFR 835 (Non-Exhaustive Excerpts) ......................................................E-1 

E.1.1.1 § 835.405 Receipt of Packages Containing Radioactive Material E-1 

E.1.1.2 § 835.605 Labeling Items and Containers.....................................E-2 

E.1.1.3 § 835.606 Exceptions to Labeling Requirements .........................E-2 

E.1.1.4 § 835.1101 Control of Material and Equipment ...........................E-2 

E.1.1.5 § 835.1102 Control of Areas.........................................................E-3 

E.1.2 Appendix D to 10 CFR 835 – Surface Contamination Values.....................E-3 

E.1.3 DOE Guidance and Similar Documents .......................................................E-5 

E.2 International Organizations.........................................................................................E-7 

E.2.1 International Atomic Energy Agency (IAEA)..............................................E-7 

E.2.2 European Commission..................................................................................E-7 

E.3 Nuclear Regulatory Commission ................................................................................E-8 

E.3.1 § 20.2003 Disposal by Release into Sanitary Sewerage...............................E-8 

E.3.2 § 20.2005 Disposal of Specific Wastes ........................................................E-8 

E.3.3 § 35.92 Decay-in-Storage .............................................................................E-9 

E.3.4 § 35.315 Safety Precautions .........................................................................E-9 

E.3.5 § 36.57 Radiation Surveys............................................................................E-9 

E.3.6 Appendix A to Part 40−Criteria Relating to the Operation of Uranium Mills 

and the Disposition of Tailings or Wastes Produced by the Extraction or 

January 2009 xv NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

Concentration of Source Material from Ores Processed Primarily for Their 

Source Material Content ...............................................................................E-9 

E.3.7 § 71.4 Definitions .........................................................................................E-9 

E.3.8 § 71.14 Exemption for Low-Level Materials .............................................E-11 

E.3.9 § 110.22 General License for the Export of Source Material.....................E-12 

E.3.10 § 110.23 General License for the Export of Byproduct Material ...............E-12 

E.3.11 Policies and Practices .................................................................................E-13 

E.3.12 Issues Related to International Trade .........................................................E-14 

References................................................................................................................................ Ref-1 

Glossary ....................................................................................................................................GL-1 

NUREG-1575, Supp.1 xvi January 2009 
MARSAME Contents (Continued) 

LIST OF FIGURES 

Roadmap Figure 1 Overview of MARSAME Process.........................................................RM-6 

Roadmap Figure 2 The Data Life Cycle Applied to Disposition Surveys ...........................RM-7 

Roadmap Figure 3 The Categorization Process as Part of Initial Assessment.....................RM-8 

Roadmap Figure 4 Assessing Adequacy of Information for Designing ..............................RM-9 

Roadmap Figure 5 Assessing the Applicability of Existing SOPs.....................................RM-10 

Roadmap Figure 6 Identify Inputs to the Decision ............................................................RM-11 

Roadmap Figure 7 Identify Action Levels.........................................................................RM-12 

Roadmap Figure 8 Developing Survey Unit Boundaries (Apply to All Impacted M&E for each 

set of Action Levels Identified in Section 3.3)....................................RM-13 

Roadmap Figure 9 Flow Diagram for Developing a Disposition Survey Design..............RM-14 

Roadmap Figure 10 Flow Diagram for Identifying the Number of Data Points for a MARSSIMType Disposition Survey .....................................................................RM-15 

Roadmap Figure 11 Flow Diagram for Identifying Data Needs for Assessment of Potential 

Areas of Elevated Activity in Class 1 Survey Units for MARSSIM-Type 

Disposition Surveys.............................................................................RM-16 

Roadmap Figure 12 Implementation of Disposition Surveys ..............................................RM-17 

Roadmap Figure 13 Assess the Results of the Disposition Survey......................................RM-18 

Roadmap Figure 14 Interpretation of Survey Results for Scan-Only and In Situ Surveys..RM-19 

Roadmap Figure 15 Interpretation of Survey Results for MARSSIM-Type Surveys .........RM-20 

Figure 1.1 The Data Life Cycle Applied to Disposition Surveys ......................................... 1-8 

Figure 2.1 The Categorization Process as Part of Initial Assessment................................... 2-2 

Figure 2.2 Assessing Adequacy of Information for Designing Disposition Surveys ......... 2-10 

Figure 2.3 Documentation of the Initial Assessment .......................................................... 2-20 

Figure 3.1 Identifying Inputs to the Decision........................................................................ 3-2 

Figure 3.2 Identifying Action Levels .................................................................................... 3-4 

Figure 3.3 Developing Survey Unit Boundaries ................................................................. 3-16 

Figure 4.1 Relative Shift, Δ/σ, Comparison for Scenario A: σ is Large, but the LargeΔ

Results in a Large Δ/σ and Fewer Samples ......................................................... 4-3 

Figure 4.2 Relative Shift, Δ/σ, Comparison for Scenario A: σ is Small, but the Small Δ

Results in a Small Δ/σ and More Samples........................................................... 4-3 

Figure 4.3 Illustration of Scenario A..................................................................................... 4-4 

Figure 4.4 Illustration of Scenario B..................................................................................... 4-4 

Figure 4.5 Flow Diagram for a Disposition Survey Design................................................ 4-10 

Figure 4.6 Flow Diagram for Identifying the Number of Data Points for a MARSSIM-Type 

Disposition Survey............................................................................................. 4-11 

Figure 4.7 Flow Diagram for Identifying Data Needs for Assessment of Potential Areas of 

Elevated Activity in Class 1 Survey Units for MARSSIM-Type Disposition 

Surveys............................................................................................................... 4-12 

Figure 4.8 Relationship Between the Relative Shift and the Amount of M&E to be 

Scanned.............................................................................................................. 4-13 

Figure 5.1 Implementation of Disposition Surveys............................................................... 5-2 

January 2009 xvii NUREG-1575, Supp. 1 
Contents (Continued) MARSAME 

Figure 5.2 The Critical Value (Sc) and the Minimum Detectable Value (SD) of the Net 

Instrument Signal (or Count) ............................................................................. 5-11 

Figure 6.1 The Assessment Phase of the Data Life Cycle .................................................... 6-2 

Figure 6.2 Frequency Plot of Concrete Rubble Data .......................................................... 6-15 

Figure 6.3 Screen Capture of Output from ProUCL Software for the Sample Data Set..... 6-16 

Figure 6.4 Interpretation of Survey Results for Scan-Only and In Situ Surveys ................ 6-24 

Figure 6.5 Statistical Interpretation of Results for MARSSIM-Type Surveys ................... 6-25 

Figure 7.1 Relative Shift, Δ/σ, Comparison for Scenario A: σ is Large, but the LargeΔ

Results in a LargeΔ/σ and Fewer Samples ....................................................... 7-11 

Figure 7.2 Relative Shift, Δ/σ, Comparison for Scenario A: σ is Small, but the Small Δ

Results in a Small Δ/σ and More Samples......................................................... 7-11 

Figure 7.3 Illustration of Scenario A................................................................................... 7-12 

Figure 7.4 Illustration of Scenario B................................................................................... 7-13 

Figure 7.5 Example of the Required Measurement Uncertainty at Concentrations other than 

the UBGR. In this Example the UBGR Equals the Action Level ..................... 7-21 

Figure 7.6 The Critical Value of the Net Instrument Signal (SC) and the Minimum Detectable 

Net Signal (SD) ................................................................................................... 7-31 

Figure 7.7 Relationship Between the Critical Value of the Net Count, the Minimum 

Detectable Net Counts and the MDC................................................................. 7-33 

Figure 7.8 Probability of Detection as a Function of Net Count (Lower X-Axis) and 

Concentration (Upper X-Axis) .......................................................................... 7-33 

Figure 7.9 Relationships Among the Critical Value, the MDC, the MQC, and the Probability 

of Exceeding the Critical Value......................................................................... 7-37 

Figure 8.1 Frequency Plot of Illustrative Example Data..................................................... 8-25 

Figure 8.2 Cumulative Frequency Plot of Illustrative Example Data ................................. 8-25 

Figure 8.3 Front Loader....................................................................................................... 8-27 

Figure D.1 Example Volumetric Counter (Thermo 2005).................................................... D-9 

Figure D.2 Example Conveyorized Survey Monitoring System (Laurus 2001)................. D-12 

Figure D.3 Example Portal Monitor (Canberra 2005b) ...................................................... D-18 

NUREG-1575, Supp.1 xviii January 2009 
MARSAME Contents (Continued) 

January 2009 xix NUREG-1575, Supp. 1 

LIST OF TABLES 

Table 1.1 The Data Life Cycle Used to Support Disposition Survey Design...................... 1-7 

Table 1.2 Similarities Between MARSSIM and MARSAME........................................... 1-15 

Table 1.3 Differences Between MARSSIM and MARSAME .......................................... 1-16 

Table 2.1 Physical Attributes Used to Describe M&E ...................................................... 2-12 

Table 2.2 Radiological Attributes Used to Describe M&E ............................................... 2-15 

Table 3.1 Example Detector Efficiency Calculation (232Th in Complete Equilibrium with its 

Decay Products) Using a Gas Proportional Detector......................................... 3-11 

Table 3.2 Example Alternative Actions............................................................................. 3-15 

Table 3.3 Examples of Consensus Standards for Evaluating Ruggedness ........................ 3-24 

Table 5.1 Potential Applications for Instrumentation and Measurement Technique 

Combinations ..................................................................................................... 5-18 

Table 5.2 Survey Unit Size and Quantity Restrictions for Instrumentation and Measurement 

Technique Combinations ................................................................................... 5-18 

Table 5.3 Advantages and Disadvantages of Instrumentation and Measurement Technique 

Combinations ..................................................................................................... 5-20 

Table 6.1 Issues and Assumptions Underlying the Evaluation Method ............................ 6-10 

Table 6.2 Summary of Evaluation Methods and Statistical Tests ..................................... 6-11 

Table 6.3 Sign Test Example Data..................................................................................... 6-18 

Table 6.4 Scenario A WRS Test Example Data ................................................................ 6-21 

Table 6.5 Scenario B WRS Test Example Data................................................................. 6-22 

Table 7.1 Notation for DQOs and MQOs............................................................................ 7-2 

Table 7.2 Notation for Uncertainty Calculations................................................................. 7-3 

Table 7.3 Notation for MDC Calculations........................................................................... 7-4 

Table 7.4 Notation for MQC Calculations........................................................................... 7-5 

Table 7.5 Recommended Approaches for Calculating the Critical Value of the Net 

Instrument Signal (Count), SC ............................................................................ 7-28 

Table 7.6 Recommended Approaches for Calculating the Minimum Detectable Net 

Instrument Signal or Count................................................................................ 7-30 

Table 7.7 Uncertainty Budget for the Efficiency Example................................................ 7-56 

Table 7.8 Calculation of Detector Response to Natural Uranium ..................................... 7-68 

Table 7.9 Calculation of Detector Response for Natural Thorium.................................... 7-69 

Table 7.10 Detector Response to Natural Uranium ............................................................. 7-71 

Table 7.11 Detector Response to Natural Thorium ............................................................. 7-71 

Table 7.12 Scan MDCs for FIDLER ................................................................................... 7-74 

Table 8.1 Physical Attributes of the Concrete Rubble......................................................... 8-4 

Table 8.2 Radiological Attributes of the Concrete Rubble.................................................. 8-5 

Table 8.3 Preliminary Alpha Spectrometry Results for Uranium Series Radionuclides..... 8-6 

Table 8.4 Preliminary Alpha Spectrometry Results for Thorium Series Radionuclides ..... 8-6 

Table 8.5 Preliminary Gamma Spectroscopy Results for Uranium Series Radionuclides .. 8-7 

Table 8.6 Preliminary Gamma Spectroscopy Results for Thorium Series Radionuclides .. 8-7 

Table 8.7 Radionuclide-Specific Action Levels .................................................................. 8-9 

Table 8.8 Calculation of the Gross Gamma Action Level................................................. 8-11 
Contents (Continued) MARSAME 

Table 8.9 Job Safety Analysis for Surveying Concrete Rubble......................................... 8-17 

Table 8.10 Radionuclide-Specific Required Relative Measurement Method Uncertainties8-23 

Table 8.11 Sentinel Measurement Results........................................................................... 8-27 

Table 8.12 Physical Attributes Used to Describe the Front Loader..................................... 8-28 

Table 8.13 Radiological Attributes Used to Describe the Front Loader.............................. 8-29 

Table 8.14 Potential Discrimination Limits......................................................................... 8-33 

Table 8.15 Detector Efficiency for the Mineral Processing Facility (232Th in Complete 

Equilibrium with its Progeny) using a Gas Proportional Detector .................... 8-36 

Table 8.16 Sentinel Measurement Results........................................................................... 8-43 

Table A.1 Cumulative Normal Distribution Function Φ(z)................................................. A-1 

Table A.2a Sample Sizes for Sign Test ................................................................................. A-2 

Table A.2b Sample Sizes for Wilcoxon Rank Sum Test....................................................... A-3 

Table A.3 Critical Values for the Sign Test Statistic S+ ..................................................... A-4 

Table A.4 Critical Values for the WRS Test ....................................................................... A-6 

Table A.5a Values of r and k for the Quantile Test When α Is Approximately 0.01.......... A-10 

Table A.5b Values of r and k for the Quantile Test When α Is Approximately 0.025........ A-11 

Table A.5c Values of r and k for the Quantile Test When α Is Approximately 0.05.......... A-12 

Table A.5d Values of r and k for the Quantile Test When α Is Approximately 0.10.......... A-13 

Table B.1 Typical Average Concentration Ranges of Terrestrial Radionuclides ................B-2 

Table C.1 Examples of Common Radionuclides at Selected Types of Facilities ................C-1 

Table D.1 Potential Applications for Common Hand-Held Instruments............................. D-7 

Table D.2 Typical Preparation and Counting Times ......................................................... D-22 

Table D.3 Radiation Applications for Laboratory Instruments and Methods ................... D-24 

Table D.4 Typical Energy Ranges and Maximum Activities............................................ D-25 

Table D.5 Typical Liquid and Solid Sample Sizes............................................................ D-25 

Table E.1 Surface Contamination Values1 in dpm/100 cm2 as Reported in Appendix D to 

10 CFR 835..........................................................................................................E-4 

Table E.2 Figure IV-1, from DOE Order 5400.5, as Supplemented in November, 1995 

Memorandum: Surface Activity Guidelines – Allowable Total Residual Surface 

Activity (dpm/100 cm2

) .......................................................................................E-6 

Table E.3 Summary of NRC Disposition Criteria from Current Practices for the Release of 

Materials and Equipment ...................................................................................E-14 

NUREG-1575, Supp.1 xx January 2009 
MARSAME 

ACKNOWLEDGEMENTS 

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) and the MultiAgency Radiation Survey and Assessment of Materials and Equipment manual (MARSAME) 

supplement came about as a result of individuals—at the management level—within the 

Environmental Protection Agency (EPA), Nuclear Regulatory Commission (NRC), Department 

of Energy (DOE), and Department of Defense (DOD) who recognized the necessity for a 

standardized guidance document for investigating radioactively contaminated sites. The creation 

of MARSSIM and MARSAME was facilitated by the cooperation of subject matter specialists 

from these agencies with management’s support and a willingness to work smoothly together 

toward reaching the common goal of creating a workable and user-friendly guidance manual. 

Special appreciation is extended to Robert A. Meck of the NRC and Anthony Wolbarst of EPA 

for developing the concept of a multi-agency workgroup and bringing together representatives 

from the participating agencies. 

MARSAME could not have been possible without the technical workgroup members who 

contributed their time, talent, and efforts to develop this consensus guidance document: 

CAPT Colleen F. Petullo, U.S. Public Health Service, EPA, Chair 

DOD David P. Alberth (Army) 

Dennis Chambers, CHP (Army, Retired) 

Gerald Falo, Ph.D., CHP (Army) 

Steven Doremus, Ph.D. (Navy) 

CAPT Vincent DeInnocentiis (Navy) 

Ramachandra Bhat, Ph.D., CHP (Air Force) 

Lt Col Craig Bias, Ph.D., CHP (Air Force) 

Lt Col Daniel Caputo, Ph.D. (Air Force Reserve) 

EPA Kathryn Snead 

Nidal Azzam 

Lindsey Bender 

Vicki Lloyd 

Eugene Jablonowski 

DOE W. Alexander Williams, Ph.D. 

Emile Boulos 

Harold T. Peterson, Jr., CHP (Retired) 

Amanda Anderson 

Wayne Glines, CHP 

NRC Robert A. Meck, Ph.D. 

George E. Powers, Ph.D. 

Joseph DeCicco, CHP 

Anthony Huffert, CHP 

DHS Carl V. Gogolak, Ph.D. (Retired) 

Special mention is extended to the Federal Agency contractors for their assistance in developing 

the MARSAME supplement: 

 Scott Hay (Cabrera Services, Inc.) 

 Carl V. Gogolak (Environmental Management Support, Inc.) 

 Nicholas Berliner (Cabrera Services, Inc.) 

 Robert Coleman (Oak Ridge National Laboratory) 

 Deborah Schneider (S. Cohen & Associates, Inc.) 

 Kerri Wachter (S. Cohen & Associates, Inc.) 

A special thank you is extended to Mary Clark (EPA), Schatzi Fitz-James (EPA), Paul Giardina 

(EPA), Bonnie Gitlin (EPA), Sally Hamlin (EPA), David Kappelman (EPA), Sophie Kastner 

January 2009 xxi NUREG-1575, Supp. 1 
Acknowledgements (Continued) MARSAME 

(EPA), Joseph LaFornara (EPA), Juan Reyes (EPA), Colby Stanton (EPA), Dennisses Valdes 

(EPA), Jean-Claude Dehmel (NRC), MAJ David Pugh, CHP (Air Force), Brian Renaghan (Air 

Force), Andrew Wallo III (DOE), Ethel Jacob (DHS), Kevin Miller (DHS), Peter Shebell (DHS), 

Jenny Goodman (NJ Bureau of Environmental Radiation), Nancy Stanley (NJ Bureau of 

Environmental Radiation), and Eric Abelquist (Oak Ridge Institute for Science and Education). 

The Workgroup would also like to thank EPA’s Science Advisory Board Radiation Advisory 

Committee for their consultations and peer review supporting development of the MARSAME 

supplement: 

Chair

Bernd Kahn, Ph.D., Georgia Institute of Technology 

Jill Lipoti, Ph.D., New Jersey Department of Environmental Protection (Past Chair) 

Members

Thomas B. Borak, Ph.D., Colorado State University 

Antone L. Brooks, Ph.D., Washington State University Tri-Cities 

Faith G. Davis, Ph.D., University of Illinois at Chicago 

Brian Dodd, Ph.D., Consultant 

Shirley A. Fry, Ph.D., Consultant 

William C. Griffith, Ph.D., University of Washington 

Jonathan M. Links, Ph.D., Johns Hopkins University 

Bruce A. Napier, Pacific Northwest National Laboratory 

Daniel O. Stram, Ph.D., University of Southern California 

Richard J. Vetter, Ph.D., Mayo Clinic 

SAB Consultants

Bruce W. Church, BWC Enterprises, Inc. 

Kenneth Duvall, Environmental Scientist/Consultant 

Janet A. Johnson, Ph.D., Consultant 

Paul J. Merges, Ph.D., Environment & Radiation Specialists, Inc. 

Science Advisory Board Staff

K. Jack Kooyoomjian, Ph.D., Designated Federal Officer, EPA 

The Workgroup acknowledges the interest of the NRC’s Advisory Committee on Nuclear Waste 

and Materials in the development of MARSAME.

NUREG-1575, Supp.1 xxii January 2009 
MARSAME 

ACRONYMS AND ABBREVIATIONS 

AL action level 

ALARA as low as reasonably achievable 

ANSI American National Standards Institute 

ASTM American Society for Testing and Materials 

BKGD background 

CERCLA Comprehensive Environmental Response Compensation and Liability Act 

CFR Code of Federal Regulations 

cpm counts per minute 

cps counts per second 

CSM conceptual site model 

CSU combined standard uncertainty 

CZT cadmium zinc telluride 

DAC derived air concentration 

DCGL derived concentration guideline level 

DL discrimination limit 

DOD Department of Defense 

DOE Department of Energy 

DOT Department of Transportation 

dpm disintegrations per minute 

DQA data quality assessment 

DQO data quality objective 

EMC elevated measurement comparison 

EPA Environmental Protection Agency 

EPRI Electric Power Research Institute 

EU European Union 

EZ exclusion zone 

FIDLER field instrument for the detection of low-energy radiation 

FRER fluence rate to exposure rate 

GM Geiger Mueller 

HASP health and safety plan 

HEU high-enriched uranium 

HPGe high-purity germanium 

HPS Health Physics Society 

HSA Historical Site Assessment 

HPSR Health Physics Society Report 

HWP hazard work permit 

IA initial assessment 

IAEA International Atomic Energy Agency 

IEEE Institute of Electrical & Electronics Engineers 

ISGS in situ gamma spectroscopy 

ISO International Organization for Standardization 

January 2009 xxiii NUREG-1575, Supp. 1 
Acronyms and Abbreviations (Continued) MARSAME 

JSA job safety analysis 

LBGR lower bound of the gray region 

LEU low-enriched uranium 

LSA low specific activity 

LSC liquid scintillation cocktail 

M&E materials and equipment 

MARLAP Multi-Agency Radiological Laboratory Analytical Protocols manual 

MARSAME Multi-Agency Radiation Survey and Assessment of Materials and Equipment 

manual 

MARSSIM Multi-Agency Radiation Survey and Site Investigation Manual 

MCA multi-channel analyzer 

MDC minimum detectable concentration 

MDCR minimum detectable count rate 

MDCRsurveyor MDCR by a less than ideal surveyor 

MDER minimum detectable exposure rate 

MQC minimum quantifiable concentration 

MQO measurement quality objective 

NARM naturally occurring and accelerator-produced radioactive material 

NCRP National Council on Radiation Protection and Measurements 

NIST National Institute of Science and Technology 

NJBER New Jersey Bureau of Environmental Radiation 

NORM naturally occurring radioactive material 

NRC Nuclear Regulatory Commission 

NUREG Nuclear Regulatory Commission technical report prepared by NRC staff 

NUREG/CR Nuclear Regulatory Commission technical report prepared by NRC contractor 

ORISE Oak Ridge Institute for Science and Education 

OSHA Occupational Safety and Health Administration 

OSWER EPA Office of Solid Waste and Emergency Response 

PCB polychlorinated biphenyl 

pH hydrogen ion concentration (acidity or basicity) 

PIC pressurized ion chamber 

PPE personal protective equipment 

PVC polyvinylchloride 

QA quality assurance 

QAPP quality assurance project plan 

QC quality control 

RCA radiological control area 

RCRA Resource Conservation and Recovery Act 

RCSU relative combined standard uncertainty 

RDR relative detector response 

RESRAD RESidual RADioactivity computer code (exposure pathway model) 

ROC radionuclide of concern 

RTG Radioisotopic Thermoelectric Generator 

NUREG-1575, Supp.1 xxiv January 2009 
MARSAME Acronyms and Abbreviations (Continued) 

RWP radiation work permit 

SCO surface-contaminated object 

SI International System of Units (Système International d'Unités) 

SOP standard operating procedure 

TEDE total effective dose equivalent 

TENORM technologically enhanced naturally occurring radioactive material 

TRU transuranic 

UBGR upper bound of the gray region 

UCL upper confidence limit 

UMTRCA Uranium Mill Tailings Radiation Control Act 

UNSCEAR United Nations Scientific Committee on the Effects of Atomic Radiation 

USEPA United States Environmental Protection Agency 

U.S. United States 

WRS Wilcoxon Rank Sum 

January 2009 xxv NUREG-1575, Supp. 1 

MARSAME 

SYMBOLS, NOMENCLATURE, AND NOTATIONS 

< less than 

> greater than 

≤ less than or equal to 

≥ greater than or equal to 

° degrees (angle or temperature) 

% percent 

1−β statistical power of a hypothesis test 

α Type I decision-error rate 

αQ quantile test (αQ = α/2) 

a half-width of a rectangular or triangular probability distribution 

A area 

A overall sensitivity of a measurement 

Ac actinium (isotope listed: 228Ac) 

ALi action level value an individual radionuclide (i = 1, 2, ..., n) 

ALmeas,mod modified action level for the radionuclide being measured when it is used as a 

surrogate for other radionuclide(s) 

ALmeas action level for the radionuclide being measured 

ALinfer action level for the inferred radionuclide (in surrogate measurements) 

Am americium (isotope listed: 241Am) 

β Type II decision-error rate 

b background count rate 

bi the average number of counts in the background interval (scanning) 

Be beryllium (isotope listed: 7Be) 

Bi bismuth (isotopes listed: 210Bi, 212Bi, 214Bi) 

Bq becquerel 

C carbon (isotope listed: 14C) 

C radionuclide concentration or activity 

Ci curie 

Ci concentration value an individual radionuclide (i = 1, 2, ..., n) 

ci sensitivity coefficient 

ciμ(xi) component of the uncertainty in y due to xi

Cinfer/Cmeas ratio of amount of the inferred radionuclide to that of the measured surrogate 

radionuclide 

°C degrees Celsius 

cm centimeter 

cm2

 square centimeter 

cm3

 cubic centimeter 

Cd cadmium (isotope listed: 109Cd) 

Co cobalt (isotopes listed: 57Co, 60Co) 

Cs cesium (isotope listed: 137Cs) 

CsI(Tl) cesium iodide (thallium activated) 

Δ shift (width of the gray region, UBGR–LBGR) 

January 2009 xxvii NUREG-1575, Supp. 1 
Symbols, Nomenclature, and Notations (Continued) MARSAME 

Δ/σ relative shift 

d parameter in the Stapleton Equation for the critical net signal 

d′ detectability index (scanning) 

εi instrument efficiency 

εs surface efficiency for surveyed media 

eV electron-volt 

Eγ energy of a gamma photon of concern in kiloelectron-volts (keV) 

Ei energy of a photon of interest 

°F degrees Fahrenheit 

fi relative fraction of activity contributed by radionuclide i to the total 

ft foot (feet) 

ft3 cubic foot (feet) 

Fe iron (isotope listed: 55Fe) 

g gram 

GBq gigabecquerel (1×109 becquerels) 

GGAL gross gamma action level 

h hour 

H hydrogen (isotope listed: 3H [tritium]) 

H0 null hypothesis 

H1 alternative hypothesis 

i observation time interval length (scanning) 

I iodine (isotopes listed: 123I, 125I, 131I) 

in inch 

Ir iridium (isotope listed: 192Ir) 

k coverage factor for the expanded uncertainty, U

K potassium (isotope listed: 40K) 

kBq kilobecquerel (1×103 becquerels) 

keV kiloelectron-volt (1×103 electron-volts) 

kg kilogram 

kQ multiple of the standard deviation defining yQ, usually chosen to be 10 

L grid size spacing 

L liter 

lb pound 

μ micro (10−6

) 

μ theoretical mean of a population distribution 

(μen /ρ)air mass energy absorption coefficient in air centimeters squared per gram (cm2/g) 

μR microroentgen (1×10−6

 roentgen) 

m number of reference measurements (WRS test or Quantile test) 

m meter 

m2

 square meter 

MeV megaelectron-volt (1×106

 electron-volt) 

mrem millirem (1×10−3

 rem) 

NUREG-1575, Supp.1 xxviii January 2009 
MARSAME Symbols, Nomenclature, and Notations (Continued 

mSv milliseivert (1×10−3 Sv) 

n number of survey unit measurements (WRS test or Quantile test) 

N sample size, i.e. number of data points (or samples) for the Sign test 

nEA survey unit area divided by the maximum area corresponding to the area factor, 

which yields the number of measurements needed so the scan MDC is adequate 

Na sodium (isotope listed: 22Na) 

NaI(Tl) sodium iodide (thallium activated) 

Ni nickel (isotope listed: 63Ni) 

Np neptunium (isotope listed: 237Np) 

ξBB non-Poisson variance component of the background count rate correction 

p coverage probability for expanded uncertainty, also used for efficiency of a less 

than ideal surveyor (scanning) 

P probability of interaction between radiation and a detector 

Pa protactinium (isotopes listed: 234Pa, 234mPa) 

PA probe area 

Pb lead (isotopes listed: 212Pb, 214Pb) 

PC personal computer 

pCi picocurie (1×10−12 curies) 

Pm promethium (isotope listed: 147Pm) 

Po polonium (isotopes listed: 210Po, 212Po, 214Po, 216Po) 

Pu plutonium (isotopes listed: 238P, 239Pu, 240Pu, 241Pu) 

q critical value for statistical tests (Table A.3, Table A.4) 

ρ density 

ρ(Xi,Xj) correlation coefficient for two input quantities, Xi and Xj

R ratio 

R roentgen (exposure rate) 

Ra radium (isotopes listed: 224Ra, 226Ra, 228Ra) 

RBB mean background count rate 

RI mean interference count rate 

Rn radon (isotopes listed: 220Rn, 222Rn) 

r(xi,xj) correlation coefficient for two input estimates, xi and xj

σ theoretical total standard deviation of the population distribution being sampled 

σM theoretical measurement standard deviation of the population distribution being 

sampled, estimated by the combined standard uncertainty of the measurement 

σM

2

 theoretical measurement variance of the population distribution being sampled 

σMR required measurement method standard deviation (upper limit) 

σs theoretical sampling standard deviation of the population distribution being 

sampled 

σS

2

 theoretical sampling variance of the population distribution being sampled 

)(RI

) σ standard deviation of the measured interference count rate 

σ ( y Y y = Q ) variance of the estimator y given the true concentration Y equals yQ 

σ(Xi, Xj) covariance for two input quantities, Xi and Xj

January 2009 xxix NUREG-1575, Supp. 1 
Symbols, Nomenclature, and Notations (Continued) MARSAME 

S+ Sign test statistic 

s(x) sample standard deviation of the input estimate, xi 

SC critical value of the net instrument signal 

SD mean value of the net signal that gives a specified probability, 1−β, of yielding an 

observed signal greater than its critical value SC 

si minimum detectable number of net source counts in the observation interval 

(scanning) 

si,surveyor minimum detectable number of net source counts in the observation interval by a 

less than ideal surveyor (scanning) 

Sr strontium (isotope listed: 90Sr) 

Sv seivert 

Tc techicium (isotopes listed: 99Tc, 99mTc) 

Th thorium (isotopes listed: 228Th: 230Th, 232Th, 234Th) 

Tl thalium (isotopes listed: 201Tl, 208Tl) 

tBB count time for the background 

tS count time for the source 

U expanded uncertainty 

U uranium (isotopes listed: 234U, 235U, 238U) 

u(xi) standard uncertainty of the input estimate, xi 

u(xi)/ | xi | relative standard uncertainty of xi

u(xi,xj) covariance of two input estimates, xi and xj

uc(y) combined standard uncertainty of y 

uc(y)/y relative combined standard uncertainty of the output quantity for a particular 

measurement 

uc

2

(y) combined variance of y

ui(y) component of the combined standard uncertainty, uc(y), generated by the standard 

uncertainty of the input estimate xi, u(xi), multiplied by the sensitivity coefficient, 

ci

uM measurement method uncertainty 

uMR required measurement method uncertainty 

φMR required relative measurement method uncertainty 

2

A φ )

 relative variance of the measured sensitivity 

φ(xi) relative standard uncertainty of a nonzero input estimate, xi, for a particular 

measurement. φ(xi) = u(xi)/xi

Φ(z) cumulative normal distribution function 

Wr sum of the ranks of the (adjusted) reference measurements (WRS test) 

Ws sum of the ranks of the (adjusted) sample measurements (WRS test) 

WS weighted instrument sensitivity 

x estimate of the input quantity, X

Xi an input quantity 

xC the critical value of the response variable, x

xQ minimum quantifiable value of the response variable, x

y year 

NUREG-1575, Supp.1 xxx January 2009 
MARSAME Symbols, Nomenclature, and Notations (Continued 

y estimate of the output quantity for a particular measurement, Y

Y output quantity, measurand 

yC critical value of the concentration 

yD minimum detectable concentration (MDC) 

yQ minimum quantifiable concentration (MQC) 

yd yard 

yd3 cubic yard 

Z atomic number 

z1-α (1 − α)-quantile of the standard normal distribution 

z1-β (1 − β)-quantile of the standard normal distribution 

ZnS(Ag) zinc sulfide (silver activated) 

January 2009 xxxi NUREG-1575, Supp. 1 
MARSAME

CONVERSION FACTORS 

To Convert 

From To 

Multiply 

“From” 

Quantity By

To Convert 

From To Multiply By

acre hectare 0.405 meter (m) inch 39.4 

sq. meter (m2) 4,050 mile 0.000621 

sq. feet (ft2) 43,600 sq. meter (m2) acre 0.000247 

becquerel (Bq) curie (Ci) 2.7×10−11 hectare 0.0001 

dps 1 sq. feet (ft2) 10.8 

pCi 27 sq. mile 3.86×10−7

Bq/kg pCi/g 0.027 m3 liter 1,000 

Bq/m2 dpm/100 cm2 0.60 mrem mSv 0.01 

Bq/m3 Bq/L 0.001 mrem/y mSv/y 0.01 

pCi/L 0.027 mSv mrem 100 

centimeter (cm) inch 0.394 mSv/y mrem/y 100 

Ci Bq 3.70×1010 ounce (oz) liter (L) 0.0296 

pCi 1×1012 pCi Bq 0.037 

dps dpm 60 dpm 2.22 

pCi 27 pCi/g Bq/kg 37 

dpm dps 0.0167 pCi/L Bq/m3 37 

pCi 0.451 rad Gy 0.01 

dpm/100 cm2 Bq/m2 1.67 rem mrem 1,000 

gray (Gy) rad 100 mSv 10 

hectare acre 2.47 Sv 0.01 

liter (L) cm3 1000 seivert (Sv) mrem 100,000 

m3 0.001 mSv 1,000 

ounce (fluid) 33.8 rem 100 

NUREG-1575, Supp.1 xxxii January 2009 
MARSAME Roadmap 

ROADMAP 

Introduction to MARSAME 

The Multi-Agency Radiation Survey and Assessment of Materials and Equipment manual 

(MARSAME) is a supplement to the Multi-Agency Radiation Survey and Site Investigation 

Manual (MARSSIM 2002). MARSAME provides technical information on approaches for 

planning, implementing, assessing, and documenting surveys to determine proper disposition of 

materials and equipment (M&E). 

The technical information in MARSAME is based on the data life cycle, similar to MARSSIM. 

Survey planning is based on the data quality objectives (DQO) process and is discussed in 

MARSAME Chapters 2, 3, and 4. Implementation of the survey design is described in 

MARSAME Chapter 5, with discussions on selection of instruments and measurement 

techniques as well as handling and segregating the M&E. MARSAME also includes the concept 

of measurement quality objectives (MQOs) for selecting and evaluating instruments and 

measurement techniques from the Multi-Agency Radiological Laboratory Analytical Protocols

manual (MARLAP 2004). Assessment of the survey results uses data quality assessment (DQA) 

and the application of statistical tests as described in MARSAME Chapter 6. In addition to the 

first six chapters, which present the MARSAME process, the MARSAME manual contains the 

statistical basis for the DQOs, MQOs, and survey designs (Chapter 7) and illustrative examples 

of the information and process presented in MARSAME (Chapter 8). 

The scope of MARSSIM was limited to surfaces soils and building surfaces. The scope of 

MARSAME is M&E potentially affected by radioactivity, including metals, concrete, tools, 

equipment, piping, conduit, furniture and dispersible bulk materials such as trash, rubble, roofing 

materials, and sludge. The wide variety of M&E requires additional flexibility in the survey 

process, and this flexibility is incorporated into MARSAME. 

The Goal of the Roadmap 

The increased flexibility of MARSAME comes with increased complexity. The goal of the 

roadmap is to assist the MARSAME user in negotiating the information in MARSAME and 

determining where important decisions need to be made on a project-specific basis, as 

summarized in Roadmap Figure 1. Roadmap Figure 2 provides additional detail and illustrates 

how the data life cycle is applied to disposition surveys. (Shaded blocks within the figures depict 

significant decisions or milestones.) 

This roadmap is not designed to be a stand-alone document, but to be used as a quick reference 

to MARSAME for users already familiar with the process of planning, implementing, and 

assessing surveys. Roadmap users will find flowcharts summarizing major decision points in the 

survey process combined with references to sections in MARSAME with more detailed 

information. The roadmap assumes a familiarity with MARSAME terminology. Section 1.2 of 

MARSAME discusses key terminology, and a complete set of definitions is provided in the 

glossary. 

January 2009 RM-1 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Initial Assessment 

The initial assessment (IA) is the first step in the investigation of M&E, similar to the historical 

site assessment (HSA) in MARSSIM. The purpose of the IA is to collect and evaluate 

information about the M&E to support a categorization decision and support potential disposition 

of the M&E (e.g., release or interdiction). Project managers are encouraged to use the IA to 

evaluate M&E for other hazards (e.g., lead, PCBs, asbestos) that could increase the complexity 

of the disposition survey design or pose potential risks to workers during subsequent survey 

activities (Section 5.2), or to human health and the environment following subsequent disposition 

of the M&E. 

Categorization 

MARSAME uses the term categorization to describe the decision of whether M&E are impacted 

or non-impacted. Non-impacted is a term that applies to M&E where there is no reasonable 

potential to contain radionuclide concentration(s) or radioactivity above background. Impacted is 

a term that applies to M&E that are not classified as non-impacted. Roadmap Figure 3 shows the 

categorization process as part of the IA. 

Standardized Survey Designs 

Most operating radiological facilities maintain standard operating procedures (SOPs) as part of a 

quality system. In many cases these SOPs include instructions for conducting disposition 

surveys. The first step in evaluating an existing SOP is to determine whether there is adequate 

information available to design a disposition survey. If the existing information is inadequate to 

design a disposition survey, it is inadequate for determining if an existing survey design is 

adequate either. Roadmap Figure 4 addresses assessing the adequacy of existing information for 

designing disposition surveys. Roadmap Figure 5 shows how implementing an existing SOP that 

is applicable to the M&E being investigated takes the user from MARSAME Chapter 2 to 

MARSAME Chapter 6. If a project-specific survey design needs to be developed, Roadmap 

Figure 5 directs the user to the information in MARSAME Chapter 3. 

In some cases, it may be possible to modify the M&E to match the assumptions used to develop 

the existing SOP, or modify the existing SOP to address the M&E being investigated. M&E may 

be modified by changing the physical attributes described in Table 2.1 or the radiological 

attributes described in Table 2.2. Modifications to the SOP are most often associated with MQOs 

such as the measurement detectability (Section 5.7) or measurement quantifiability (Section 5.8). 

Modifying the MQOs may result in small changes such as an increased count time (e.g., to 

account for an increase in measurement uncertainty or a decrease in counting efficiency) or 

larger changes such as selecting a different instrument (e.g., a gas-proportional detector instead 

of a Geiger-Mueller detector) or a different measurement technique (e.g., in situ measurements 

instead of scan measurements). Information on evaluating an existing survey design to determine 

if it will meet the DQOs for the M&E being investigated is provided in Section 3.10. 

Develop a Decision Rule 

MARSAME Chapter 3 focuses on developing a decision rule by identifying inputs to the 

decision (see Roadmap Figure 6, which depicts the various inputs to the decision). A decision 

NUREG-1575, Supp. 1 RM-2 January 2009
MARSAME Roadmap 

rule is a theoretical “if...then...” statement that defines how the decision maker would choose 

among alternative actions. There are three parts to a decision rule: 

• An action level that causes a decision maker to choose between the alternative actions (see 

Roadmap Figure 7 and Section 3.3), 

• A parameter of interest that is important for making decisions about the target population 

(see Section 3.4), and 

• Alternative actions that could result from the decision (Section 3.5). 

Other inputs to the decision that are discussed in MARSAME Chapter 3 include selecting 

radionuclides or radiations of concern (Section 3.2), developing survey unit boundaries (see 

Roadmap Figure 8 and Section 3.6), inputs for selecting provisional measurement methods 

(Section 3.8), and identifying reference materials if necessary (Section 3.9). 

Survey Design 

Once a decision rule has been developed, a disposition survey can be designed for the impacted 

M&E being investigated. The disposition survey incorporates all of the available information to 

determine the quality and quantity of data required to support a disposition decision. Roadmap 

Figure 9 shows how a disposition survey design is developed. 

MARSAME, like MARSSIM, provides information on using a null hypothesis that radionuclide 

concentrations or activity levels associated with the M&E exceed the action level (i.e., Scenario 

A). MARSAME also incorporates additional technical information from NUREG-1505 (NRC 

1998a) and MARLAP for designing surveys using Scenario B where the null hypothesis is that 

the radionuclide concentrations or activity levels are less than the action level. The assignment of 

values to the lower bound of the gray region (LBGR) and upper bound of the gray region 

(UBGR), specification of decision error rates, and classification are all similar to information 

provided in MARSSIM. 

MARSAME provides information on four types of survey designs: 

 

• Scan-only survey designs (Section 4.4.1), 

• In situ survey designs (Section 4.4.2), 

• Survey designs that combine scans and static measurements (MARSSIM-type surveys, 

Section 4.4.3), and 

• Method-based survey designs (Section 4.4.4). 

A method-based survey design is a special type of scan-only, in situ, or MARSSIM-type survey 

design that incorporates a required measurement method or combination of measurement 

technique and instrumentation, so Roadmap Figure 9 only depicts the first three. It will still need 

to address all of the required components, such as number, type, location, and sensitivity of 

measurements. 

Scan-only survey designs use scanning techniques to measure the M&E. In general, scan-only 

survey designs may be applied to all types of M&E, from small individual items to large 

January 2009 RM-3 NUREG-1575, Supp. 1 
Roadmap MARSAME 

quantities of materials to large, complex machines. Scan-only surveys range from hand-held 

instruments moving over the M&E to conveyorized systems that move the M&E past the 

detectors. Scan-only survey designs often require the least amount of resources to design and 

implement, and are easy to incorporate into SOPs or project-specific survey designs. In many 

cases it is not necessary to document the results of individual scanning measurements because it 

is easy to identify results that exceed some threshold corresponding to the action level. With the 

real-time feedback available during Class 1 scan-only surveys, the user can implement a “clean 

as you go” practice by segregating M&E that exceed the threshold for additional investigation. 

Drawbacks to scan-only surveys include increased measurement uncertainty because of 

variations in scan speed and source to detector distance making it difficult to detect or quantify 

radionuclides with action levels close to zero or background. 

In situ surveys are characterized by limited numbers of static measurements with long count 

times (relative to scan-only surveys) to measure the M&E. In situ surveys generally are 

applicable to situations where scan-only surveys are determined to be unacceptable. For 

example, variations in source-to-detector distance, scan speed, and surface efficiency that are 

commonly associated with scanning measurements can often be effectively controlled using an 

in situ survey design. There are a wide variety of in situ measurement techniques available 

including box counters, portal monitors, in situ gamma spectroscopy systems, and direct 

measurements with hand-held instruments. The primary difference between an in situ survey and 

a MARSSIM-type survey is that an in situ survey measures 10–100% of an item (using one or 

several measurements) to determine the average radionuclide concentration for that item. A 

MARSSIM-type survey uses a statistically based number of measurements (that generally do not 

measure 10% of the item or group of items being surveyed) to calculate an average radionuclide 

concentration for that item or group of items. 

MARSSIM-type survey designs combine a statistically based number of static measurements or 

samples (Roadmap Figure 10) to determine average radionuclide concentrations with scanning to 

identify localized areas of elevated activity (Roadmap Figure 11). MARSSIM-type surveys are 

designed using the information in MARSSIM. The process of identifying survey unit sizes, 

laying out systematic or random measurement grids, and calculating project- and item-specific 

area factors requires significantly greater effort during planning and implementation than either 

scan-only or in situ survey designs. In general, MARSSIM-type surveys of M&E are only 

performed on large, complicated M&E with a high inherent value after scan-only and in situ 

survey designs have been considered and rejected as inappropriate or unacceptable. 

Measurement Quality Objectives 

Measurement quality objectives (MQOs) are characteristics of a measurement method required 

to meet the objectives of the survey. Additional information on MQOs can be found in 

MARSAME Section 3.8, Section 5.5, and Section 7.3 as well as MARLAP Chapter 3. MQOs are 

an important concept that was not presented in MARSSIM, and should be an important factor 

when evaluating existing survey designs and SOPs for applicability to surveying M&E. 

MQOs for a project include, but are not limited to— 

NUREG-1575, Supp. 1 RM-4 January 2009
MARSAME Roadmap 

• The measurement method uncertainty at a specified concentration expressed as a standard 

deviation (Sections 3.8.1, 5.5, and 7.4); 

• The measurement method’s detection capability expressed as the minimum detectable 

concentration (MDC; see Sections 3.8.2, 5.7, and 7.5); 

• The measurement method’s quantification capability expressed as the minimum quantifiable 

concentration (MQC; see Sections 3.8.3, 5.8, 7.6, and 7.7); 

• The measurement method’s range, which defines the measurement method’s ability to 

measure the radionuclide or radiation of concern over some specified range of concentration 

or activity (see Section 3.8.4 and Appendix D); 

• The measurement method’s specificity, which refers to the ability of the measurement 

method to measure the radionuclide or radiation of concern in the presence of interferences 

(Section 3.8.5); and 

• The measurement method’s ruggedness, which refers to the relative stability of measurement 

method performance for small variations in measurement method parameter values (see 

Section 3.8.6 and Appendix D). 

Implement the Survey Design 

The implementation phase of the data life cycle is when the activities described in the survey 

design are performed. Roadmap Figure 12 illustrates the process for implementing disposition 

surveys. 

MARSAME, like MARSSIM, does not provide prescriptive guidance for implementing survey 

designs. Chapter 5 presents topics to be considered while implementing disposition surveys. This 

approach allows MARSAME users flexibility to use either existing or new and innovative 

techniques that meet the survey objectives. 

Evaluate the Results 

The assessment phase of the data life cycle involves evaluating the results of the survey as shown 

in Roadmap Figure 13. DQA is used to evaluate the survey results. DQA is a scientific and 

statistical evaluation that determines whether data are the type, quality, and quantity to support 

their intended use. When individual measurement results are not recorded, as allowed in some 

scan-only survey designs, the preliminary data review will be brief and based primarily on the 

results of quality control (QC) measurements. To increase the flexibility and general 

applicability of MARSAME, several evaluation methods have been incorporated in addition to 

the Sign test and Wilcoxon Rank Sum (WRS) test used in MARSSIM. Roadmap Figure 14 

presents information on interpreting survey results for scan-only and in situ surveys. Roadmap 

Figure 15 presents information on interpreting survey results for MARSSIM-type surveys. 

Summary 

The roadmap presents a summary of the data life cycle as it applies to disposition surveys in 

MARSAME and identifies where information on important topics are located in MARSAME. 

Flow charts are provided to summarize major steps in the survey design process, again citing 

appropriate references in MARSAME. 

January 2009 RM-5 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Categorization

Initial Assessment

Preliminary Surveys

Decision Rule

Design Disposition Survey

Disposition Survey

Verification 

& 

Validation

Evaluate Results

Decision

Roadmap Figure 1. Overview of MARSAME Process 

NUREG-1575, Supp. 1 RM-6 January 2009
MARSAME Roadmap 

Are the M&E Impacted? 

(Section 2.2)

Design and Implement 

Preliminary Surveys

(Section 2.3)

Describe the M&E

(Section 2.4)

Develop Decision Rule(s)

(Chapter 3)

Develop a Survey Design

(Chapter 4)

Implement the Survey Design

(Chapter 5)

Evaluate the Survey Results

(Chapter 6)

Make a Disposition Decision

(Section 6.8)

Yes

Document Non-Impacted 

Decision, If Necessary 

(Section 2.2.5)

No

No

No

Yes

Is There 

An Existing Survey 

Design? 

(Section 2.6)

Is the 

Existing Survey Design 

Applicable to the M&E? 

(Section 3.10)

Yes

Finalize Radionuclides of Concern

Select Action Levels

Define Parameter of Interest

Define Survey Unit Boundaries

Develop Measurement Quality Objectives

Identify Alternative Actions

Are

Preliminary Surveys 

Needed to Describe 

The M&E?

Yes

No

Define the Null Hypothesis

Specify Limits on Decision Errors

Select Appropriate 

Disposition Options

(Section 2.5)

DECIDE ASSESS IMPLEMENT PLAN

Does the 

Survey Design Meet the 

DQOs? 

(Section 6.2.1)

No

Yes

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

Roadmap Figure 2. The Data Life Cycle Applied to Disposition Surveys 

January 2009 RM-7 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Review Existing 

Relevant Information

Is the 

Existing Information 

Adequate to Categorize 

the M&E?

Perform a 

Visual Inspection 

(Section 2.2.1)

Review 

Historical Records 

(Section 2.2.2)

Assess Process 

Knowledge 

(Section 2.2.3)

Are Sentinel 

Measurements 

Applicable?

Plan and Perform 

Sentinel 

Measurements 

(Section 2.2.4)

Are the M&E 

Impacted?

Is 

Documentation 

of the Non-Impacted 

Decision 

Necessary?

No Further Action

Proceed to 

Preliminary Survey 

(Roadmap Figure 4)

No

Yes

No

Yes

Yes

No

No

Yes

Prepare Documentation of 

the Non-Impacted Decision 

(Section 2.2.5)

No Further Action

NOTE: Shaded diamonds 

represent important decision 

points.

Roadmap Figure 3. The Categorization Process as Part of Initial Assessment 

NUREG-1575, Supp. 1 RM-8 January 2009
MARSAME Roadmap 

Is the Existing

Information Adequate

to Design a Disposition

Survey?

Is the Existing

Information Adequate to Select a 

Disposition Option?

Identify Data Gaps 

(Section 2.3)

Design and Implement 

Preliminary Surveys

(Section 2.3)

Describe the M&E 

(Section 2.4)

Proceed to Roadmap

Figure 5

Yes

No

Yes

No

Select a Disposition Option

(Section 2.5)

From Roadmap Figure 3

NOTE: Shaded diamonds 

represent important decision 

points.

Roadmap Figure 4. Assessing Adequacy of Information for Designing 

January 2009 RM-9 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Is an 

Applicable SOP Available 

for this M&E?

Develop a Conceptual Model 

and Document the IA 

(Section 2.6.2)

Proceed to Roadmap Figure 6

Implement and Document the 

Results of the Survey as 

Described in the SOP 

(Section 2.6.1)

Yes No

Proceed to Roadmap Figure 13

From Roadmap Figure 4

NOTE: Shaded box represents important milestone.

Roadmap Figure 5. Assessing the Applicability of Existing SOPs 

NUREG-1575, Supp. 1 RM-10 January 2009
MARSAME Roadmap 

Select Radionuclides or 

Radiations of Concern 

(Section 3.2)

Identify Action Levels 

(Section 3.3)

Go to 

Roadmap 

Figure 7

Describe the Parameter of Interest 

(Section 3.4)

Return from 

Roadmap 

Figure 7

Identify Alternative Actions 

(Section 3.5)

Identify Survey Units

(Section 3.6)

Go to 

Roadmap 

Figure 8

Return from 

Roadmap 

Figure 8

Develop a Decision Rule

(Section 3.7)

Develop Inputs for Selection of 

Provisional Measurement Methods

(Section 3.8)

Identify Reference Materials

(Section 3.9)

Is there an Existing 

Survey Design?

Do the M&E Meet the 

Survey Requirements?

Yes

Proceed to 

Roadmap 

Figure 9

No

Implement and Document Results 

as Described in the Survey

(Section 3.10)

Proceed to 

Roadmap 

Figure 13

Yes

From Roadmap Figure 5

NOTE: Shaded boxes 

represent important 

milestones.

No

Roadmap Figure 6. Identify Inputs to the Decision 

January 2009 RM-11 NUREG-1575, Supp. 1 
Roadmap MARSAME 

From Roadmap Figure 6

Identify Applicable 

Regulatory Limits 

(Dose-, Risk-, Activity-, 

or Method Based)

Identify Applicable 

Requirements 

(e.g., ANSI N13.12)

Identify Applicable 

Administrative Limits 

(e.g., Waste Acceptance 

Criteria)

Identify Applicable DOT 

Requirements for 

Shipping M&E

Convert Potential Action Levels 

into Measurement Units

Finalize Selection of Action Level(s)

Are There Multiple 

Radionuclides? 

RadionuclideSpecific Measurements?

Modify AL Using 

Equation 3-1 

(Gross Activity AL)

Surrogate 

Measurements 

Available?

Will Surrogates 

Infer Multiple 

Radionuclides?

Apply ALARA, as Appropriate

Document Selection of 

Action Level(s)

Modify AL Using 

Equation 3-4

Modify AL Using 

Equation 3-3

Evaluate Survey Results Using 

Equation 3-2 (Unity Rule) No

Yes No

Yes

No

Yes

No

Yes

Return to Roadmap Figure 6

NOTE: Shaded boxes 

represent important 

milestones.

NOTE: Information on ALARA 

can be found in 10CFR20, 10CFR 

835, DOE 1993, ICRP 1989, 

NCRP 1993, NRC 1977, NRC 

1982, NRC 1993, NRC 2002, and 

PNL 1988.

Roadmap Figure 7. Identify Action Levels 

NUREG-1575, Supp. 1 RM-12 January 2009
MARSAME Roadmap 

Roadmap Figure 8. Developing Survey Unit Boundaries (Apply to all Impacted M&E for 

each set of Action Levels Identified in Section 3.3) 

January 2009 RM-13 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Assign Values to the 

LBGR and UBGR

Specify Limits on 

Decision Errors

Classify the M&E

Select a Type of 

Disposition Survey

Scenario A (Section 7.2.3)

Scenario B (Section 7.2.4)

Action Level (Section 3.3)

Discrimination Limit (Section 4.2.2)

Type I Error (Section 4.2.5)

Type II Error (Section 4.2.5)

Consequences of Decision Errors

and 

Class 1 (Section 4.3.1)

Class 2 (Section 4.3.2)

Class 3 (Section 4.3.3)

MARSSIM-Type

(Section 4.4.3)

Scan-Only 

(Section 4.4.1)

In Situ (Section 4.4.2)

Develop an Operation 

Decision Rule

Statement of the Statistical 

Hypothesis Test (Section 4.2.6)

Go to Roadmap 

Figure 10

Return from 

Roadmap 

Figure 10

Select a Null Hypothesis

Determine % of M&E 

to be Scanned

Determine % of M&E and 

Locations to be Measured

Select M&E and Locations 

to be Scanned

Select M&E and Locations 

to be Measured

Select Measurement and 

Scan Locations

Optimize the Survey Design

Document the Disposition 

Survey Design

(Section 4.5)

From Roadmap Figure 6

Proceed to Roadmap Figure 12

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

NOTE: A method-based survey 

(Section 4.4.4) is a special case of 

either scan-only, in situ, or 

MARSSIM-type, and so will follow 

one of the three paths show on this 

figure.

Roadmap Figure 9. Flow Diagram for Developing a Disposition Survey Design 

NUREG-1575, Supp. 1 RM-14 January 2009
MARSAME Roadmap 

Is the Radioactivity 

Present in Bkgd?

Estimate , the Variability in 

the Level of Radioactivity

Calculate the Relative Shift /

Estimate ’s, the Variabilities in 

the Reference Area and 

Survey Unit Activities

Calculate the Relative Shift /

Is /

Between

1 and 3?

Adjust LBGR Adjust LBGR

Obtain Number of Data Points 

for the Sign Test, N, from Table 

for each Survey Unit

Obtain Number of Data Points 

for WRS Test, N/2, from Table 

for each Survey Unit and 

Reference Area

Prepare Summary of Data 

Points for M&E being 

Investigated

No Yes

Yes Yes

No No

Do the Number of Data 

Points Need to be Adjusted 

for Class 1 M&E?

Yes

No

Return to 

Roadmap Figure 9

Go to Roadmap 

Figure 11

Return from 

Roadmap Figure 11

From Roadmap Figure 9

Is /

Between

1 and 3?

NOTE: Shaded boxes 

represent important 

milestones.

Roadmap Figure 10. Flow Diagram for Identifying the Number of Data Points for a 

MARSSIM-Type Disposition Survey

January 2009 RM-15 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Establish DQOs for Areas with the 

Potential for Exceeding 

Als and Acceptable Risk for 

Missing Such Areas

Identify Number of Data Points 

needed Based on Statistical Tests, n

Calculate the Area, A, Bounded by 

Sample Location, n

Determine Acceptable 

Concentrations in Various 

Individual Smaller Areas within a 

Survey Unit (i.e., Use Area Factors)

Determine the Acceptable 

Concentration Corresponding to the 

Calculated Area, A (i.e., Area 

Factor x Action Level)

Determine the Required Scan MDC 

to Identify the Acceptable 

Concentration in an Area, A

Evaluate MDCs for Available 

Instrumentation

Calculate Area Factor that 

Corresponds to the Actual Scan 

MDC (scan MDC/AL)

Determine the Maximum 

Area, A’, that Corresponds 

to the Area Factor

Recalculate Number of 

Data Points Needed

(nEA = Survey Unit Area/A’)

Determine Grid Size Spacing, L

No Additional Sampling Points 

are Necessary for Potential 

Elevated Locations

Is the Scan MDC for Available 

Instrumentation Less than the 

Required Scan MDC?

No

Yes

From Roadmap Figure 10

Return to Roadmap 

Figure 10

NOTE: Shaded boxes 

represent important 

milestones.

NOTE: “VOLUME” or 

“MASS” replaces “AREA” in 

this flow diagram as 

appropriate for a specific 

survey design, and scan MDC 

is discussed in MARSSIM 

Section 5.5.2.4

Roadmap Figure 11. Flow Diagram for Identifying Data Needs for Assessment of Potential 

Areas of Elevated Activity in Class 1 Survey Units for MARSSIM-Type Disposition 

Surveys 

NUREG-1575, Supp. 1 RM-16 January 2009
MARSAME Roadmap 

From Roadmap Figure 9

Ensure Protection of 

Health and Safety

(Section 5.2)

Handle M&E

(Section 5.3)

Prepare M&E for Survey (Section 5.3.1)

Provide Access to M&E (Section 5.3.2)

Transport the M&E (Section 5.3.3)

Do M&E Need 

Segregation?

Segregate the M&E

(Section 5.4) Yes

Set Measurement 

Quality Objectives

(Section 5.5-5.8)

Select a Measurement 

Technique and 

Instrumentation Combination

(Section 5.9)

Set Quality 

Control Requirements

(Section 5.10)

Perform the Survey & 

Report the Results

(Section 5.11)

Proceed to Roadmap Figure 13

No

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

Roadmap Figure 12. Implementation of Disposition Surveys 

January 2009 RM-17 NUREG-1575, Supp. 1 
Roadmap MARSAME 

From Roadmap Figure 12

Conduct Data Quality 

Assessment

(Section 6.2)

Proceed to Roadmap 

Figure 14

Is the Survey Design 

Scan-Only or in Situ? Yes

Evaluate the Results and 

Make a Decision

(Section 6.8)

Document the Disposition 

Survey Results

(Section 6.10)

Proceed to Roadmap 

Figure 15 No

Return from Roadmap 

Figure 14

Return from Roadmap 

Figure 15

No Further Action

NOTE: Shaded box 

represents important 

milestone.

Roadmap Figure 13. Assess the Results of the Disposition Survey 

NUREG-1575, Supp. 1 RM-18 January 2009
MARSAME Roadmap 

No

Is the

AL Equal to Zero 

or Background?

Requires Scenario B

LBGR = AL

Scan MDC UBGR

All

Results < Sc

from the

MDC?

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

Disposition

Decision Based

on Mean of a Sampled 

Population?

Disposition 

Decision Based on 

Individual

Items?

Consult a 

Statistician

Individual Results Must 

be Recorded

Recording Individual 

Scan Results 

Not Required

All

Results < Sc

from the

UBGR?

UCL UBGR?

M&E Meet the 

Disposition Criterion

No

Yes

Yes

Yes

Yes

No

Yes

Yes

No

No

Return to Roadmap 

Figure 13

From Roadmap Figure 13

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

No

Return to Roadmap 

Figure 13

Return to Roadmap 

Figure 13

Roadmap Figure 14. Interpretation of Survey Results for Scan-Only and 

In Situ Surveys 

January 2009 RM-19 NUREG-1575, Supp. 1 
Roadmap MARSAME 

Radionuclide

of Concern Present in 

Background?

Perform the 

Quantile Test

( Q = /2)

RadionuclideSpecific 

Measurements?

S+ > q?

(Xi - LBGR)

Wr > q?

(Xi + AL)

S+ > q?

(UBGR - Xi)

Yes

Yes No

No

Perform the 

Sign test

Perform the 

Sign Test

Perform the 

WRS Test

Perform the 

WRS Test

Ws > q?

(Yi - LBGR)

q or

more of the r

Largest Values 

from the Survey 

Unit?

No Yes Yes No

Yes Yes

Yes

Yes

M&E Meet the 

Disposition Criterion

No

No

M&E Do Not Meet the 

Disposition Criterion 

(Section 6.9)

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

Yes

No

No

Return to 

Roadmap 

Figure 13

From Roadmap Figure 13

NOTE: An elevated 

measurement comparison 

also needs to be performed 

for MARSSIM-type surveys.

Return to 

Roadmap 

Figure 13

Scenario A? Scenario B?

Roadmap Figure 15. Interpretation of Survey Results for MARSSIM-Type Surveys

NUREG-1575, Supp. 1 RM-20 January 2009
MARSAME Introduction and Overview 

1 INTRODUCTION AND OVERVIEW 

1.1 Purpose and Scope of MARSAME 

Large quantities of materials and equipment (M&E) potentially affected by radioactivity are 

present throughout the United States. The potential for residual radioactivity can come from use 

of source, byproduct, and special nuclear materials as well as naturally occurring radioactive 

material (NORM), naturally occurring and accelerator-produced radioactive materials (NARM) 

and technologically enhanced naturally occurring radioactive material (TENORM). This M&E 

may be commercial, research, education, or defense related. The M&E might be— 

• Used or stored at sites and facilities licensed to handle radioactivity, 

• Commercial products purposely containing radionuclides (e.g., smoke detectors), 

• Commercial products incidentally containing radionuclides (e.g., phosphate fertilizers), or 

• Associated with NARM and TENORM. 

The owners of M&E potentially affected by radioactivity need to determine acceptable 

disposition options for M&E currently under their control. Industries or facilities sensitive to the 

presence of radioactivity need to evaluate the acceptability of M&E coming under their control. 

Regulatory agencies need to distinguish items in general commerce that are inherently 

radioactive from illicit trafficking of radioactive M&E. 

This Multi-Agency Radiation Survey and Assessment of Materials and Equipment manual 

(MARSAME) is a supplement to the Multi-Agency Radiation Survey and Site Investigation 

Manual (MARSSIM). Like MARSSIM, MARSAME is a joint effort by the Department of 

Defense (DOD), Department of Energy (DOE), Environmental Protection Agency (EPA), and 

Nuclear Regulatory Commission (NRC). Information on MARSSIM can be found on the World 

Wide Web (MARSSIM 2002). MARSAME also incorporates information for measuring 

radioactivity from the Multi-Agency Radiological Laboratory Analytical Protocols manual 

(MARLAP 2004). MARSAME provides information on surveys where radiological control of 

M&E could be initiated, maintained, removed, or transferred (i.e., an M&E disposition) to 

another responsible party. In addition, MARSAME discusses the need for a graded approach to 

surveying M&E. 

MARSAME provides technical information on approaches for planning, implementing, 

assessing, and documenting surveys to determine proper disposition of M&E. Release (including 

clearance) and interdiction are types of disposition options in MARSAME. Detailed descriptions 

of these disposition options are provided in Chapter 2. 

Examples of M&E include metals, concrete, tools, equipment, piping, conduit, furniture, and 

dispersible bulk materials such as trash, rubble, roofing materials, and sludge. Liquids, gases, 

and solids stored in containers (e.g., drums of liquid, pressurized gas cylinders, containerized 

soil) are also included in the scope of this document. 

January 2009 1-1 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

Radionuclides or radioactivity on workers or members of the public are outside the scope of the 

document, as are liquid and gaseous effluent releases and real property (e.g., fixed buildings and 

structures, surface and subsurface soil remaining in place). 

The purpose of this supplement is to provide information for the design and implementation of 

technically defensible surveys for disposition of M&E. MARSAME provides information on 

selecting and properly applying disposition survey strategies and selecting measurement 

methods. The data quality objectives (DQO) process is used for selecting the best disposition 

survey design based on the selected disposition option, action level, description of the M&E 

(e.g., size, accessibility, component materials), and description of the radioactivity (e.g., 

radionuclides, types of radiation, surficial versus volumetric activity). Detailed information on 

the DQO process can be found in EPA QA/G-4 (EPA 2006a), MARSSIM Appendix D, and 

MARLAP Appendix B. 

This supplement describes a number of different approaches for performing technically 

defensible disposition surveys and provides information for optimizing survey designs. However, 

MARSAME does not represent the only acceptable approach to radiologically evaluate M&E. 

MARSAME describes a graded approach that the signatory agencies find acceptable and useful 

for most situations. The signatory agencies recognize that alternative approaches or modification 

of the MARSAME procedures may be appropriate or necessary for some situations. Nothing in 

MARSAME should be construed to prohibit the use of other appropriate procedures. 

Disposition surveys may be performed as a single event or as part of a routine process. Single 

event disposition surveys are usually performed once in association with a specific project. 

Surveying a backhoe at the completion of a decommissioning project is one example of a single 

event disposition survey. Routine process disposition surveys are usually associated with 

ongoing tasks where similar surveys are performed repeatedly. One example of a routine process 

disposition survey would be a radiological survey of tools prior to removal from a controlled 

area at a nuclear facility. Both single event and routine process types of surveys are included in 

the scope of MARSAME. 

The guidance in MARSAME is designed to incorporate existing survey methods whenever 

possible, while at the same time allowing the use of new and innovative survey techniques when 

appropriate. The use of previously established and accepted standard operating procedures 

(SOPs) as part of a standardized initial assessment (IA) is described in Section 2.6.1. The use of 

SOPs that document approved methods for performing disposition surveys along with assessing 

the results of these surveys can reduce the effort required to develop new survey designs, since 

the survey design effort was applied when the SOP was developed. MARSAME also allows 

consideration of innovative survey techniques through the modification of existing SOPs or 

development of new survey designs as described in Chapters 3, 4, and 5. Prior to 

implementation, existing SOPs should be evaluated to ensure they meet the survey design 

objectives. 

MARSAME assumes the user has some historical knowledge of the M&E being investigated. 

The historical information is gathered during the initial assessment (IA) to determine acceptable 

disposition options (Chapter 2). The characteristics, history of prior use, and inherent 

radioactivity of the M&E are important when determining the appropriate disposition options. 

NUREG-1575, Supp. 1 1-2 January 2009 
MARSAME Introduction and Overview 

The historical information is termed “process knowledge.” The role of process knowledge 

(discussed in Chapter 2) is important in providing information on the nature and amount of 

radioactivity that might be expected on, or incorporated in, the M&E being investigated. If no 

historical information is available, information on the current status of the M&E can be 

determined using preliminary surveys (i.e., scoping, characterization, remedial action support) 

prior to designing a disposition survey. 

The recommendations in this supplement may be applied to a broad range of regulations, 

including dose-, risk-, or radionuclide concentration-based regulations. The translation of a 

regulatory dose- or risk-based limit to a corresponding concentration level is not addressed in 

MARSAME. The terms dose, risk, and dose- or risk-based regulation are used throughout the 

supplement, but these terms are not intended to limit the applicability of this supplement. 

MARSAME can be applied to activity concentrations (e.g., Bq/m2) without associated dose or 

risk values. MARSAME does not address the regulatory status of the M&E (e.g., NRC exempted 

or excluded materials). 

MARSAME uses the word “should” as a recommendation. This is not to be interpreted as a 

requirement. The user need not assume that every recommendation in this supplement will be 

taken literally and applied to every project. Rather, it is expected the survey documentation will 

address how the recommendations will be applied on a project-specific basis. 

1.2 Understanding Key MARSAME Terminology 

In order to understand the information in MARSAME, the user should first become familiar with 

the scope of this supplement, the terminology, and the concepts in this document. As a 

supplement to MARSSIM, MARSAME uses terms generally consistent with MARSSIM. Some 

additional terms were developed for MARSAME, while other commonly used terms were 

adopted from other sources. This section explains some of the terms used in this supplement. 

The terms impacted, non-impacted, and graded approach are defined in MARSSIM. These 

terms are used consistently in MARSSIM and MARSAME. Unlike MARSSIM, which applies to 

land, structures, or buildings, MARSAME applies to M&E. The action taken may initiate, 

maintain, remove, or transfer radiological controls associated with the M&E. The decision to 

take action may be largely based on the results of a radiological survey designed to evaluate the 

disposition of the M&E, either through release or interdiction. Therefore, the terms release 

criterion, derived concentration guideline level (DCGL), and final status survey used in 

MARSSIM are replaced by the more generic terms disposition criterion, action level, and 

disposition survey, respectively, in MARSAME. 

Disposition is the future use, fate, or final location for something (e.g., recycle, reuse, disposal). 

Disposition options range from release to interdiction: 

• Release is a reduction in the level of radiological control, or a transfer of control to another 

party. Release includes clearance. Examples of release (other than clearance) include recycle, 

reuse, disposal as waste, or transfer of control of radioactive M&E from one authorized user 

to another. 

January 2009 1-3 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

• Interdiction is an increase in the level of radiological control or a decision not to accept 

control from another party. Examples of interdiction include identification of radioactive 

material that results in the initiation of radiological controls or identification of unauthorized 

movement of radioactive material. 

Categorization is the act of determining whether M&E are impacted or non-impacted. This is a 

departure from MARSSIM where this decision was referred to as classification. This change was 

made to emphasize the difference between the decision of whether a survey is needed (i.e., 

impacted or non-impacted) and the determination of the appropriate level of survey effort (i.e., 

classification). 

Classification is the act or result of separating impacted M&E or survey units into one of three 

designated classes: Class 1, Class 2, or Class 3. Classification is the process of determining the 

appropriate level of survey effort based on estimates of activity levels and comparison to action 

levels, where the activity estimates are provided by historical information, process knowledge, 

and preliminary surveys. 

Measurable radioactivity is radioactivity that can be quantified using known or predicted 

relationships developed from historical information, process knowledge or preliminary 

measurements as long as the relationships are developed, verified, and validated as specified in 

the DQOs and measurement quality objectives (MQOs). Measurability is of primary importance 

in MARSAME. 

Surficial radioactive material is radioactive material distributed on any of the surfaces of a solid 

object. Surficial radioactive material may be removable (by non-destructive means such as 

casual contact, wiping, brushing, or washing) or fixed. Surfaces may either be accessible or 

difficult-to-measure. Changes to the surface (e.g., paint, dirt, oxidation) may affect the 

measurability and the physical condition of surficial radioactive material. 

Survey unit for M&E is the specific lot, amount, or piece of M&E on which measurements are 

made to support a disposition decision concerning the same specific lot, amount, or piece of 

M&E. The survey unit defines the spatial boundaries for the disposition decision and a separate 

decision is made for each survey unit, similar to MARSSIM. The survey unit boundaries also 

define the population for the parameter of interest. 

Volumetric radioactive material is radioactive material that is distributed throughout or within 

the material or equipment being measured, as opposed to a surficial distribution. Volumetric 

radioactive material may be homogeneously (e.g., uniformly activated metal) or heterogeneously 

(e.g., activated reinforced concrete) distributed throughout the M&E. Volumetric radioactive 

material may be distributed throughout the M&E being measured or distributed in layers. Layers 

of volumetric radioactive material may start at the surface (e.g., porous surfaces penetrated by 

radioactive material) or under a layer of other material (e.g., activated rebar inside a concrete 

wall). By definition all radioactive liquids and gases in containers and all bulk quantities of 

radioactive material when measured as a whole are volumetric radioactive material. 

NUREG-1575, Supp. 1 1-4 January 2009 
MARSAME Introduction and Overview 

The concept of whether radioactivity is measurable is the major factor in demonstrating 

compliance with an action level. MARSAME does not provide an exact definition for the 

transition between surficial and volumetric radioactive material. Rather, the assumptions used to 

quantify the radioactivity need to be clearly defined and identified so they can be compared to 

the DQOs and MQOs. Individual action levels may specify applicability to surficial or 

volumetric radioactivity. In these cases, the definition of surficial and volumetric radioactivity 

should be specified as part of the definition of the action level.1

Accessible area is an area that can be easily reached or obtained. In many cases an area must be 

physically accessible to perform a measurement. However, radioactivity may be measurable 

even if M&E are not physically accessible (e.g., energetic gamma rays may be quantified even 

after passing through a layer of shielding). 

Difficult-to-measure radioactivity is radioactivity that is not measurable until the M&E to be 

surveyed is prepared. Preparation of M&E may be relatively simple (e.g., cleaning) or more 

complicated (e.g., disassembly or complete destruction). Given sufficient resources, all 

radioactivity can be made measurable; however, it is recognized that increased survey costs can 

outweigh the benefit of some dispositions. 

Initial assessment (IA) is an investigation to collect existing information describing M&E and is 

similar to the Historical Site Assessment (HSA) described in MARSSIM. The IA provides initial 

categorization of M&E as impacted or non-impacted. In addition to the HSA activities described 

in MARSSIM, the IA may lead to grouping or segregating M&E with similar characteristics as 

well as designing and implementing preliminary surveys. The IA also identifies the expected 

disposition of the M&E (e.g., clearance, radiological control, recycle, reuse, disposal). The 

results of the IA provide most, if not all, information needed to design a disposition survey for 

impacted M&E. A graded approach is used to determine the level of effort applied during the IA. 

Sentinel measurement is a biased measurement performed at a key location to provide 

information specific to the objectives of the IA (see Section 2.2.4). Sentinel measurements 

cannot be used as the only source of information to support a decision that M&E are nonimpacted. The objective of performing sentinel measurements as part of the IA is to gather 

additional information to support a decision regarding further action, verify assumptions based 

on process knowledge, provide additional support to a finding of impacted or non-impacted for 

M&E, and to distinguish illicit or inadvertent transport of radioactive materials from items in 

general commerce that are inherently radioactive (e.g., fertilizers, phosphates, sand-blasting grit). 

1.3 Use of MARSAME 

MARSAME provides technical information describing a framework for planning, implementing, 

and assessing radiological surveys of M&E. MARSAME does not establish or supersede any 

regulatory or license requirements. Federal and State regulatory agencies may have requirements 

or guidance that differs from what is presented in MARSAME and may be implemented as 

 

1

 This idea is consistent with the definition of a surface soil sample provided in the MARSSIM Glossary. A surface 

soil sample is a sample that reflects the modeling assumptions used to develop the DCGL for surface soil activity. 

The example in MARSSIM references 40 CFR 192, which defines surface soil as the first 15 cm of soil. 

January 2009 1-5 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

appropriate. Consequently, persons planning, implementing, and assessing disposition surveys 

should also obtain appropriate regulatory approval for the procedures that are in use to maintain 

regulatory compliance. 

Potential users of this supplement are Federal, State, and local government officials having 

authority for control of radioactive M&E, their contractors, and other parties such as 

organizations with licensed authority to possess and use radioactive materials. This supplement 

to MARSSIM is intended for a technical audience having knowledge of radiation health physics 

and an understanding of statistics as well as experience with the practical applications of 

radiation protection. Understanding and applying the recommendations in this supplement 

requires knowledge of instrumentation and measurement methodologies as well as expertise in 

planning, approving, and implementing radiological surveys. Certain situations and projects may 

require consultation with more experienced or specialized personnel (e.g., a statistician). 

MARSAME users with less professional experience than described above should still be able to 

apply the majority of guidance found in this supplement although obtaining technical support is 

recommended. The wide range of topics and subjects covered by MARSAME emphasizes the 

need for a well rounded planning team as described in the first step of the DQO process. While it 

may be difficult to identify a single person with all the required technical experience to design an 

appropriate survey, it is easier to assemble a small group of experts with the required range of 

knowledge. Consultation with the responsible regulatory agency is critical for the success of all 

disposition surveys, and even more so for MARSAME users with less professional experience. 

In addition, MARSAME provides information in Appendix B, Appendix C, and Appendix D that 

may be useful to users with less professional experience. 

MARSAME recommends that a graded approach be applied to the disposition of M&E. Nonimpacted M&E are removed from further consideration early in the process through 

categorization. Impacted M&E are classified based on the level of residual radioactivity so that a 

higher level of scrutiny can be applied to M&E with the highest potential for residual 

radioactivity. Finally, MARSAME includes practical considerations such as inherent value of the 

M&E and handling the M&E when evaluating options for disposition. The combination of these 

considerations results in a graded approach where an appropriate level of survey effort is applied 

to M&E to minimize the impacts of any decision errors. 

1.4 Overview of MARSAME 

The data life cycle is the foundation for the design, implementation, and assessment of surveys 

for disposition of M&E in this supplement. However, before commencing survey planning the 

user must select an appropriate disposition option. Multiple disposition options may exist. 

Consider all of the various disposition options and develop the most appropriate option for a 

given situation. Survey designs may then be planned using the DQO process, which is often 

iterative. The DQO process iterations may take place at different times during the disposition 

process, for example during the IA as well as during the disposition survey. The different survey 

designs are compared and the most resource-effective design that meets the survey objectives is 

selected for implementation. Following implementation of the selected survey design, the results 

NUREG-1575, Supp. 1 1-6 January 2009 
MARSAME Introduction and Overview 

are evaluated using data quality assessment (DQA). A technically defensible decision regarding 

disposition of the M&E can then be made. 

Whenever practical, MARSAME recommends designing disposition surveys where one hundred 

percent of the M&E are measurable. This means that all radioactivity associated with the M&E 

has been measured and quantified (e.g., 100% scan with conventional instruments, measurement 

with a box counter, or measurement using in situ gamma spectroscopy), a known or accepted 

relationship was used to estimate concentrations for difficult to measure radionuclides using 

surrogate measurements,2

 or that a known or accepted relationship allows quantification of 

radioactivity in areas that were not measured. MARSAME employs the use of a graded approach 

to determine if a 100% measurable survey is practical and to ensure that a sensible, 

commensurate balance is achieved between resource expenditures and risk reduction. 

MARSAME uses the data life cycle to design disposition surveys. The data life cycle is 

described in MARSSIM Section 2.3, and consists of four phases: 

• Planning phase (MARSAME Chapters 2, 3, and 4; MARSSIM Chapters 3, 4, and 5), 

• Implementation phase (MARSAME Chapter 5; MARSSIM Chapters 6 and 7), 

• Assessment phase (MARSAME Chapter 6; MARSSIM Chapter 8), and 

• Decision-making phase (MARSAME Chapter 6; MARSSIM Chapter 8). 

A brief description of each of the phases and how they apply to the disposition survey design 

process is provided in the following sections. Table 1.1 provides a simplified overview of the 

principal steps in designing a disposition survey and illustrates how the data life cycle can be 

used in an iterative fashion within the survey process. Figure 1.1 illustrates how the data life 

cycle is applied to disposition surveys. 

Table 1.1 The Data Life Cycle Used to Support Disposition Survey Design 

Disposition Survey 

Design Process Data Life Cycle MARSAME Processes 

Categorization Categorization 

Data Life Cycle 

Plan 

Implement 

Assess 

Decide 

Provides information on collecting and 

assessing existing data (Section 2.2) 

Preliminary Surveys Preliminary 

Survey Data Life 

Cycle 

Plan 

Implement 

Assess 

Decide 

Discusses the purpose (i.e., filling data 

gaps) and general approach to performing 

preliminary surveys (Section 2.3) 

Disposition Survey Disposition Survey 

Data Life Cycle 

Plan 

Implement 

Assess 

Decide 

Provides detailed information for planning 

(Chapters 3 and 4), implementing (Chapter 

5), and assessing (Chapter 6) disposition 

surveys 

 

2

 The MARSSIM term “surrogate measurement” as used here is consistent with the MARLAP term “alternate 

radionuclide.” 

January 2009 1-7 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

Are the M&E Impacted? 

(Section 2.2)

Design and Implement 

Preliminary Surveys

(Section 2.3)

Describe the M&E

(Section 2.4)

Develop Decision Rule(s)

(Chapter 3)

Develop a Survey Design

(Chapter 4)

Implement the Survey Design

(Chapter 5)

Evaluate the Survey Results

(Chapter 6)

Make a Disposition Decision

(Section 6.8)

Yes

Document Non-Impacted 

Decision, If Necessary 

(Section 2.2.5)

No

No

No

Yes

Is There 

An Existing Survey 

Design? 

(Section 2.6)

Is the 

Existing Survey Design 

Applicable to the M&E? 

(Section 3.10)

Yes

Finalize Radionuclides of Concern

Select Action Levels

Define Parameter of Interest

Define Survey Unit Boundaries

Develop Measurement Quality Objectives

Identify Alternative Actions

Are

Preliminary Surveys 

Needed to Describe 

The M&E?

Yes

No

Define the Null Hypothesis

Specify Limits on Decision Errors

Select Appropriate 

Disposition Options

(Section 2.5)

DECIDE ASSESS IMPLEMENT PLAN

Does the 

Survey Design Meet the 

DQOs? 

(Section 6.2.1)

No

Yes

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

Figure 1.1 The Data Life Cycle Applied to Disposition Surveys

NUREG-1575, Supp. 1 1-8 January 2009 
MARSAME Introduction and Overview 

1.4.1 Planning Phase 

The planning phase is where the survey design is developed and documented using the DQO 

process. The survey design documents the decision rule as well as the number, type, and location 

of measurements required to support the disposition decision. Soliciting input from regulatory 

agencies early in the planning phase helps ensure the disposition survey results will meet 

regulatory needs. 

MARSAME processes begin with the historical evaluation of the M&E being investigated. This 

IA usually combines a review of process knowledge and historical records with a visual 

inspection of the M&E. The results of the IA are used to develop a conceptual model describing 

the physical characteristics of the M&E and providing information on the radioactivity 

potentially associated with the M&E. The physical description of the M&E should include 

information on the size, shape, complexity (e.g., can it be broken down or combined with other 

M&E), accessibility (e.g., can the surveyor physically access areas of concern to perform 

measurements), and inherent value (i.e., resources associated with reuse, recycle, repair, 

remediation, replacement, and disposal). Information on radioactivity should include the 

radionuclides of potential concern, the expected levels of radioactivity, the distribution of 

radioactivity (e.g., uniform or not), and the location of the radioactivity (i.e., surface or volume). 

The IA may also include data collection in the form of sentinel measurements. The results of 

sentinel measurements can be used as the basis to reject assumptions based on process 

knowledge. However, sentinel measurements alone cannot be used to justify the categorization 

of M&E as non-impacted (see Section 2.2.4 for information on sentinel measurements). 

There are two decisions associated with the IA. The first decision, called categorization, is 

whether or not the M&E are impacted. Non-impacted M&E do not require additional 

investigation, but may require documentation of the justification for the non-impacted decision. 

The second decision is to select an appropriate disposition option for impacted M&E at the end 

of the IA to provide direction for designing a disposition survey. Additional information may be 

required before a disposition survey can be designed. Preliminary surveys (e.g., scoping, 

characterization, and remedial action support surveys) may be performed as part of the IA to 

collect this additional information. 

For single event surveys, the IA should focus on collecting the information necessary to develop 

a technically defensible disposition survey design. Information necessary to design a disposition 

survey includes a description of the M&E and the radioactivity potentially associated with the 

M&E. The results of the IA are carried forward and used to develop the survey design, which is 

usually documented in a project-specific work plan. 

For routine process surveys, the IA should lead to an existing survey design from a standard 

operating procedure (SOP), if applicable, or develop a new survey design for documentation in 

an SOP. The SOP should clearly state the assumptions used to develop the survey design, along 

with a description of the M&E and radioactivity covered by the SOP. The selection process is 

based on evaluating the M&E to determine if the survey design in a specific SOP is applicable. 

Documentation of individual survey results may not be required as long as there are records 

January 2009 1-9 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

showing that the SOP was approved, the instruments were working properly, and the personnel 

performing the survey were properly trained. Development of SOPs is usually accomplished 

using the same processes as those used to develop single event surveys. There may be regulatory 

or site-specific guidance that specifies documentation requirements for SOPs. Information on 

developing SOPs can be found in EPA QA/G-6 (EPA 2001). 

Following the IA, it is necessary to develop a decision rule for the disposition of M&E being 

investigated. The decision rule is an “if...then...” statement consisting of three parts: 

• Action level(s), 

• Parameter of interest, and 

• Alternative actions. 

An example of a decision rule might be “If the average surficial activity concentration is less 

than a level specified by the regulator, then the M&E can be cleared, otherwise the M&E are not 

cleared.” The parameter of interest is closely related to the description of the M&E, the 

description of the radioactivity, and the survey unit boundaries. The action level is influenced by 

the selection of a disposition option. The selected disposition option defines two alternative 

actions. A decision rule should be developed for each decision to be made concerning the M&E. 

For example, if the action level is stated in terms of total activity, generally only one decision 

rule is required. If, on the other hand, the action level provides limits for fixed, removable, and 

maximum levels of radioactivity, e.g., DOE Order 5400.5, Figure IV-1 (DOE 1993), then a 

decision rule is required to evaluate each action level. The measurement performance 

requirements, or MQOs, are also evaluated when developing a decision rule to ensure that an 

acceptable measurement technique is available to support the proposed survey design. 

Once the decision rule(s) have been established, a survey design is developed. The survey design 

specifies the number and quality of measurements required to support a disposition decision 

recorded in the decision rule. MARSAME recommends applying a graded approach to designing 

disposition surveys (Section 4.4). The survey design, definitions of decision errors, and burden 

of proof are determined by the selection of a null hypothesis (Section 4.2). 

The survey design should be documented in a quality document (e.g., QA Survey Plan, SOP) 

that has been reviewed and accepted by the appropriate authority (e.g., technical expert, 

management, or regulator). Survey designs that are often repeated may be documented in SOPs 

along with supporting records on instrument performance and personnel training. Other types of 

disposition surveys are usually documented in a project-specific work plan and survey results are 

presented in a disposition survey report (Sections 2.5 and 4.5). If the selected survey design is 

not technically or economically practical, the planning team can investigate additional 

disposition options if necessary (Sections 2.4 and 4.4). 

1.4.2 Implementation Phase 

To ensure flexibility and encourage the use of optimal measurement techniques for a specific 

project, MARSAME does not provide detailed information on specific implementation 

techniques. However, detailed descriptions of several measurement techniques are provided 

NUREG-1575, Supp. 1 1-10 January 2009 
MARSAME Introduction and Overview 

(Chapter 5 and Appendix D). These descriptions serve as a template for information required to 

evaluate different measurement techniques. It is important to remember that the survey design is 

usually linked to a specific option for disposition of the M&E (Chapters 3 and 4). 

During implementation, the descriptions of measurement techniques are compared to the MQOs 

defined during survey planning. A measurement method (i.e., combination of a measurement 

technique with an instrument; see Section 5.9) is selected based on its ability to meet the MQOs. 

The number and type of measurements specified in the documented survey design are performed 

at the locations specified in the survey design. If a measurement method is specified in the 

survey design, that method should generally be used during implementation. If the specified 

measurement method cannot be performed (e.g., the instrument is unavailable or the 

measurement method does not meet the MQOs), another measurement method should be 

selected based on the MQOs. The selection of the replacement measurement method should be 

documented in the survey design and survey report. 

An action level may be established for implementing disposition surveys to support disposition 

decisions about individual objects or measurement locations. If this action level is in the same 

units as measurements performed in the field, then the surveyor can make final disposition 

decisions by directly comparing the measurement results to the action level as the measurements 

are performed. This may allow the surveyor to perform remediation as required and implement a 

“clean as you go” component to the survey design (Section 6.9). Clean as you go surveys may 

reduce the amount of M&E requiring additional consideration following completion of the 

disposition survey. This clean as you go approach to surveys is only applicable for Class 1 

surveys (i.e., radionuclide concentrations or radiation levels exceed the action level and 100% of 

M&E are measured) where there is high confidence in the quality and accuracy of detection 

decisions. 

Quality control (QC) data are collected and analyzed during implementation to provide an 

estimate of the uncertainty associated with the survey results. QC measurements are technical 

activities performed to measure the attributes and performance of a survey. A well-designed QC 

program increases efficiency and provides for early detection of problems. This can save time 

and money by averting rework and enables the user to make decisions more expeditiously 

(EPA 2002c). 

1.4.3 Assessment Phase 

The assessment phase begins with verification and validation of the survey results. Data 

verification is used to ensure the requirements documented in the survey design were 

implemented as prescribed. Data validation ensures the results of the data collection activities 

support the objectives of the survey (i.e., DQOs), or permit a determination that these objectives 

should be modified (MARSSIM Section 9.3 and MARSSIM Appendix N). 

DQA determines if the collected data are of the right type, quality, and quantity to support their 

intended use. DQA helps complete the data life cycle by providing the assessment needed to 

determine that the planning objectives are achieved. DQA is described in detail in EPA QA/ 

G-9R (EPA 2006b), MARSSIM Section 8.2, and MARSSIM Appendix E. 

January 2009 1-11 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

The preliminary data review is performed to learn about the structure of the data (e.g., 

identifying patterns, relationships, or potential anomalies). Graphical techniques are used to help 

visualize the data. Calculation of basic statistical quantities is used to help describe the 

distribution of data. 

The survey data are evaluated using a statistical test. A test statistic is calculated and compared 

to a critical value. The critical value divides the potential values of the test statistic into two 

regions. The critical region includes values for the test statistic where the null hypothesis is 

rejected. The null hypothesis is not rejected for values of the test statistic outside the critical 

region. Keep in mind that a statistical test could be as simple as comparing survey results directly 

to the critical value to ensure no radiation is detected, or may involve using a more complex 

statistical evaluation such as the Wilcoxon Rank Sum (WRS) test. 

In some cases the assessment phase may be performed during survey implementation. For 

example, the “clean as you go” approach described in Section 6.9 requires that field data be 

assessed and a decision made concerning the M&E being measured. The M&E are “cleaned,” or 

remediated, as necessary and another disposition survey performed. 

1.4.4 Decision-Making Phase 

Following the assessment phase, a decision is made regarding the disposition of the M&E. The 

decision rule defines the final decision. The statistical test or data comparison determines 

whether the parameter of interest exceeds the action level. Based on the outcome, a decision can 

be made regarding the alternative actions. If multiple decision rules are defined for a single 

disposition survey (e.g., a MARSSIM-type survey where the average activity is evaluated using a 

statistical test and small areas of elevated activity are evaluated using the elevated measurement 

comparison) any one decision that the action level has been exceeded should result in additional 

investigation. 

In some cases the decision making phase may be performed during survey implementation. For 

example, the “clean as you go” approach described in Section 6.9 requires that field data be 

assessed and a decision made concerning the M&E being measured. The M&E are “cleaned,” or 

remediated, as necessary and another disposition survey is performed. 

1.5 Organization of MARSAME 

The planning, implementation, and assessment of disposition surveys in MARSAME are based 

on the data life cycle. Each chapter in MARSAME provides information for specific steps in the 

process. The planning phase is discussed in Chapters 2, 3, and 4. The implementation phase is 

discussed in Chapter 5, and Chapter 6 discusses the assessment phase and decision-making 

phase. 

Chapter 2 focuses on the IA. Information is provided on categorizing whether the M&E are 

impacted or non-impacted using existing data and sentinel measurements in Section 2.2. 

Information on designing and implementing preliminary surveys to provide the information 

needed to design a disposition survey is provided in Section 2.3. Discussions on describing the 

NUREG-1575, Supp. 1 1-12 January 2009 
MARSAME Introduction and Overview 

M&E being surveyed are provided in Section 2.4. The selection of a disposition option and 

development of a conceptual model are discussed in Section 2.5. Information pertaining to 

documenting the results of the IA is provided in Section 2.6. 

Chapter 3 provides information on developing a decision rule and discusses other inputs needed 

to design a disposition survey. Section 3.2 addresses selecting the radionuclides or radiations of 

concern which must be established before forming a decision rule. There are three parts to a 

decision rule– 

• Action level(s), discussed in Section 3.3, 

• Parameter of interest, discussed in Section 3.4, and 

• Alternative actions, discussed in Section 3.5. 

Section 3.7 brings these three components together to develop decision rule(s) that are used to 

design the disposition survey in Chapter 4. Survey units are discussed in Section 3.6, and inputs 

for selecting measurement methods are presented in Section 3.8. Section 3.9 identifies reference 

materials that can be used to estimate background radionuclide concentrations or radiation levels. 

The process for evaluating an existing survey design is described in Section 3.10. 

Chapter 4 completes the planning phase with the development of a survey design. This chapter 

discusses the selection of a null hypothesis and setting tolerable limits on decision errors (Section 

4.2), determines the level of survey effort for the disposition survey (Section 4.3), and 

determines the type, number, and location of measurements to support a disposition decision 

(Section 4.4). Information pertaining to disposition survey design documentation is provided in 

Section 4.5. The processes in Chapter 4 result in a documented survey design. 

The implementation processes in Chapter 5 focus on selection of an appropriate measurement 

technique. Recommendations are provided on issues related to health and safety that may impact 

the implementation of disposition surveys (Section 5.2). Chapter 5 also provides information on 

process control and handling of potentially radioactive M&E (Section 5.3). The use of 

segregation to help improve the efficiency of measurements and detectability of radioactivity, 

and as a tool to limit the uncertainty is described in Section 5.4. Sections 5.5 through 5.8 discuss 

the establishment of measurement uncertainty, measurement detectability, and measurement 

quantifiability as MQOs to validate the measurement method’s ability to meet the established 

performance objectives. Information is provided on several measurement techniques (Section 

5.9) that can be used for comparison to the MQOs developed in Chapter 3. These descriptions 

can also be used during the planning phase to specify a measurement technique in the survey 

design. Recommendations related to QC are also provided to ensure that survey instruments are 

functioning properly, and the data meet defined performance limits specified during planning 

(Section 5.10). Information related to collecting and documenting survey data is discussed in 

Section 5.11. 

Chapter 6 provides methods for the assessment and decision-making phases. Recommendations 

are provided for performing the preliminary data review, calculating statistical quantities, and 

preparing graphic representations that will assist the user in exploring the data (Section 6.2). 

Disposition decisions about individual items may be based on individual measurement results by 

January 2009 1-13 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

comparing data to the upper bound of the gray region (Section 6.3). Information is also provided 

for calculating the upper confidence limit (Section 6.4). Details on performing recommended 

statistical tests are also included (Sections 6.5 through 6.7). This chapter also describes how to 

make a disposition decision based on the survey results (Section 6.8), what to do if the selected 

disposition option is not accepted (Section 6.9), and the documentation to support the decision 

(Section 6.10). 

Chapter 7 discusses the general concepts of statistical survey design and hypothesis testing. 

Detailed discussions and calculations of MQOs, measurement uncertainty, minimum detectable 

concentrations (MDCs), and minimum quantifiable concentrations (MQCs) are provided in this 

chapter. Details and examples of each topic are provided. A detailed example of a scan MDC 

calculation is provided that is used to support the illustrative examples in Chapter 8. 

Chapter 8 provides detailed illustrative examples implementing specific concepts found 

throughout MARSAME. The illustrative examples cover a range of material, equipment, 

radionuclides, and disposition options. Sections of these illustrative examples are used to 

illustrate specific concepts throughout the supplement. 

MARSAME contains several appendices to provide additional information on specific topics. 

Appendix A provides copies of statistical tables needed to implement the information in 

MARSAME. Appendix B lists sources of environmental radiation such as natural background 

and fallout. A list of potential radionuclides grouped by industry or type of facility is provided in 

Appendix C. Appendix D provides detailed information on specific measurement systems unique 

to disposition surveys. Appendix E lists and describes some of the potential sources of action 

levels applicable to decisions regarding disposition of M&E. 

1.6 Similarities and Differences Between MARSSIM and MARSAME 

During the 1990s, there was a concerted effort to improve the planning, implementation, 

evaluation, and documentation of building surface and surface soil final radiological surveys for 

demonstrating compliance with standards. This effort included the preparation of NUREG-1505 

(NRC 1998a) and NUREG-1507 (NRC 1998b) by the NRC and culminated in 1997 with the 

issuance of MARSSIM (MARSSIM 2002). MARSSIM was a joint effort by DOD, DOE, EPA, 

and NRC to develop a multi-agency approach for planning, performing, and assessing the ability 

of surveys to meet dose- or risk-based standards while at the same time encouraging effective 

use of resources. MARSSIM provided recommendations for developing appropriate final status 

survey designs using the DQO process to ensure survey results were of sufficient quality and 

quantity to support a final decision. MARSSIM (MARSSIM 2002), NUREG-1505 (NRC 1998a), 

and NUREG-1507 (NRC 1998b) replaced the previous approach for such surveys contained in 

NUREG/CR-5849 (NRC 1992). 

This MARSAME supplement expands the scope of MARSSIM methods and processes to 

provide technical information supporting the disposition decision for M&E, specifically the 

design and implementation of disposition surveys, to ensure the disposition decision is 

technically defensible and optimized for efficiency. MARSSIM addressed the disposition of real 

property (e.g., buildings and land) where the only disposition options were unrestricted release, 

restricted release, or maintaining radiological controls. MARSAME addresses the disposition of 

NUREG-1575, Supp. 1 1-14 January 2009 
MARSAME Introduction and Overview 

non-real property (e.g., M&E) and includes additional options for future use including recycle or 

disposal as radioactive waste (see Section 2.5). Increasing radiological controls and interdiction 

are also included as potential disposition options. While several, or all, disposition alternatives 

may be acceptable for a specific project, optimizing the disposition survey design based on the 

selected disposition alternative is described in MARSAME. 

MARSAME as a supplement to MARSSIM expands the scope of technically sound 

measurement processes and methods to include M&E. Table 1.2 summarizes the major 

similarities between MARSSIM and MARSAME, which result from application of a graded 

approach to support a technically defensible decision regarding disposition. Table 1.3

summarizes the major differences between MARSSIM and MARSAME, which result from the 

change from real to non-real property. 

Table 1.2 Similarities Between MARSSIM and MARSAME 

Parameter MARSSIM MARSAME 

Graded Approach Used to place greater survey effort on 

areas that have, or had, the highest 

potential for residual radioactivity. 

Used to place greater survey effort on 

M&E that have, or had, the highest 

potential for residual radioactivity. 

Data Quality 

Objectives (DQO) 

Process 

Used to design technically defensible 

surveys to support decisions on 

disposition of real property. 

Used to design technically defensible 

surveys to support decisions on 

disposition of non-real property (e.g., 

M&E). 

Data Quality 

Assessment (DQA) 

Used to evaluate survey results and 

support a decision of whether to release 

real property. 

Used to evaluate survey results and 

support a disposition decision for nonreal property. 

Process Knowledge Used during the Historical Site 

Assessment to support the 

determination of whether an area is 

impacted and provide information for 

designing subsequent surveys. 

Used during the Initial Assessment to 

support the determination of whether 

M&E are impacted and provide 

information for designing subsequent 

surveys. 

Classification Determines the level of survey effort 

based on the potential amount of 

residual radioactivity present. 

Determines the level of survey effort 

based on the potential amount of 

residual radioactivity present. 

Flexibility MARSSIM allows and encourages 

flexibility in the design and 

implementation of final status surveys 

for application to diverse site 

conditions. 

MARSAME allows and encourages 

flexibility in the design and 

implementation of disposition surveys 

for application to diverse M&E. 

Statistics Used to develop a technically defensible 

survey design. 

Used to develop a technically 

defensible survey design. 

Scale of Decision 

Making 

A separate release decision is made for 

every survey unit. 

A separate release decision is made for 

every survey unit. 

Inherent Radioactivity Inherent radioactivity is site-specific 

and generally cannot be separated from 

ambient radiation. 

Inherent radioactivity is specific to the 

M&E being investigated. Segregation 

of M&E based on inherent 

radioactivity can be used to reduce 

measurement variability. 

January 2009 1-15 NUREG-1575, Supp. 1 
Introduction and Overview MARSAME

Table 1.3 Differences Between MARSSIM and MARSAME 

Parameter MARSSIM MARSAME 

Scope Surface soil and building surface 

surveys (i.e., real property). 

Materials and equipment (i.e., non-real 

property). 

Disposition Options Restricted or unrestricted release, or 

fail to release. 

Release survey (maintain, remove, or 

transfer radiological control; clearance 

for reuse, recycling, or disposal), 

or 

Interdiction survey (increase in the level 

of radiological control or a decision not 

to accept control from another party). 

Categorization Included as part of classification in 

MARSSIM. 

Separates the decision to survey from 

determining level of survey effort. 

Application of the 

Graded Approach 

Classification and survey unit size 

result in varying levels of survey 

effort. 

Multiple disposition options result in 

varying levels of survey effort. 

Sentinel Measurements Not described in MARSSIM. Allows use of sentinel measurements 

during IA to check validity of certain 

process knowledge assumptions. 

Documentation of 

Survey Designs 

Assumes project-specific survey 

designs will be developed for 

individual sites. 

In addition to project-specific survey 

design, allows SOPs for categories of 

M&E to provide standard approach to 

disposition surveys. 

Preliminary Surveys Scoping and characterization surveys 

regularly used to obtain information 

needed to design a final status survey.

Scoping and characterization surveys 

rarely used to obtain information needed 

to design a disposition survey. Historical 

information obtained during the IA is 

generally sufficient to design a 

disposition survey. If not, preliminary 

surveys may be used to provide the 

necessary information. 

Ambient Radiation Ambient radiation is site-specific and 

generally cannot be separated from 

inherent radioactivity. 

Ambient radiation is selected based on 

location where disposition surveys are 

performed, and can be separated from 

inherent radioactivity. 

Interdiction Not addressed in MARSSIM. Surveys may be performed to identify 

radioactive material resulting in an 

increase in the level of radiological 

control or deciding not to accept control 

from another party. For example, 

identifying radioactive materials and 

initiating radiological controls, or 

identifying unauthorized movement of 

radioactive material. 

NUREG-1575, Supp. 1 1-16 January 2009 
MARSAME Introduction and Overview 

Table 1.3 Differences Between MARSSIM and MARSAME (Continued) 

Parameter MARSSIM MARSAME 

Null Hypothesis MARSSIM recommends using the 

null hypothesis: “The activity in the 

survey unit exceeds the action level 

(Scenario A).” 

MARSSIM allows using the null 

hypothesis: “The activity in the 

survey unit is indistinguishable from 

background (Scenario B) with 

information from NUREG-1505 

(NRC 1998a).” 

User selects the appropriate null 

hypothesis: 

“The activity in the survey unit exceeds 

the action level (Scenario A).” 

or 

“The activity in the survey unit is 

indistinguishable from background 

(Scenario B).” 

Scan-Only Surveys Not addressed in MARSSIM M&E may be dispositioned based on the 

results of scan-only surveys provided 

the scan measurements meet the MQOs 

for the survey. 

In Situ Surveys Not addressed in MARSSIM M&E may be dispositioned based on the 

results of in situ surveys provided the in 

situ measurements meet the MQOs for 

the survey. 

January 2009 1-17 NUREG-1575, Supp. 1 
 
MARSAME Initial Assessment of Materials and Equipment 

2 INITIAL ASSESSMENT OF MATERIALS AND EQUIPMENT 

2.1 Introduction 

The initial assessment (IA) is the first step in the investigation of materials and equipment 

(M&E), similar to the historical site assessment (HSA) described in the Multi-Agency Radiation 

Survey and Site Investigation Manual (MARSSIM 2002). The purpose of the IA is to collect and 

evaluate information about the M&E in order to determine if it is impacted or non-impacted (i.e., 

categorization). During the IA process, additional information is collected to identify and support 

potential disposition of impacted M&E (e.g., clearance, increased radiological controls, 

remediation, or disposal). Project managers are encouraged to use the IA to evaluate M&E for 

other hazards (e.g., lead, PCBs, asbestos) that could increase the complexity of the disposition 

survey design or pose potential risks to workers during subsequent survey activities (Section 5.2) 

or to human health or the environment following subsequent disposition of the M&E. 

There are five major activities associated with the performance of the IA: 

• Categorize the M&E as impacted or non-impacted based on visual inspection, historical 

records, process knowledge, and results of sentinel measurements (Section 2.2). 

• Design and implement preliminary surveys to adequately describe the M&E and address data 

gaps based on a preliminary description of the M&E (Section 2.3). 

• Describe the physical and radiological attributes of the M&E (Section 2.4). 

• Select appropriate disposition option(s) and define alternative actions applicable to impacted 

M&E (Section 2.5). 

• Document the results of the IA through the use of a standard operating procedure (SOP) or 

development of a conceptual model (Section 2.6). 

For M&E that have been categorized as impacted, an existing survey design in the form of an 

SOP may be available for investigating the radiological status of the M&E. If an applicable SOP 

is available, the instructions in the SOP should be followed for implementing and assessing the 

results of the survey. The information on performing preliminary surveys (Section 2.3) can be 

used to determine whether an SOP is applicable to the M&E being investigated. The information 

on describing the M&E (Section 2.4) can be used to determine if preliminary surveys are 

necessary. The information on selecting a disposition option (Section 2.5) and documenting the 

results of the IA (Section 2.6) can be used for project-specific applications, or for developing a 

new SOP. 

2.2 Categorize the M&E as Impacted or Non-Impacted 

The first decision made when investigating M&E is whether they are impacted or non-impacted. 

M&E with no reasonable potential for containing radioactivity in excess of natural background, 

fallout levels, or inherent levels of radioactivity are non-impacted. Impacted M&E have a 

reasonable potential to contain radionuclide concentration(s) or radioactivity above background. 

The decision of whether M&E are impacted or non-impacted is primarily based on existing 

information. Figure 2.1 depicts the categorization process. 

January 2009 2-1 NUREG-1575, Supp. 1 
Initial Assessment of Materials and Equipment MARSAME

Review Existing 

Relevant Information

Is the 

Existing Information 

Adequate to Categorize 

the M&E?

Perform a 

Visual Inspection 

(Section 2.2.1)

Review 

Historical Records 

(Section 2.2.2)

Assess Process 

Knowledge 

(Section 2.2.3)

Are Sentinel 

Measurements 

Applicable?

Plan and Perform 

Sentinel 

Measurements 

(Section 2.2.4)

Are the M&E 

Impacted?

Is 

Documentation 

of the Non-Impacted 

Decision 

Necessary?

No Further Action

Proceed to 

Preliminary Survey 

(Figure 2.2)

No

Yes

No

Yes

Yes

No

No

Yes

Prepare Documentation of 

the Non-Impacted Decision 

(Section 2.2.5)

No Further Action

NOTE: Shaded diamonds 

represent important decision 

points.

Figure 2.1 The Categorization Process as Part of Initial Assessment 

NUREG-1575, Supp. 1 2-2 January 2009 
MARSAME Initial Assessment of Materials and Equipment 

If adequate information is readily available to support a categorization decision, the decision 

maker should decide if the M&E are impacted or non-impacted. A complex single unit or group 

of M&E may be divided into portions that are impacted and portions that are non-impacted. This 

is illustrated in the front loader example described in Section 8.3, where the bucket and tires may 

be impacted while the engine and cab interior are non-impacted. If additional information is 

required to support the categorization decision, visual inspection (Section 2.2.1), collection and 

review of historical records (Section 2.2.2), and assessment of process knowledge (Section 2.2.3) 

are the most common sources of additional existing information. Assumptions may be made 

regarding the use and interpretation of existing information. Data collection activities may be 

performed during the IA to specifically address questions about these assumptions. These data 

collection activities are called sentinel measurements and are discussed in Section 2.2.4. 

Additional investigation is required to make technically defensible disposition decisions 

regarding impacted M&E. All impacted M&E must receive some level of additional 

investigation, even if the expected disposition is disposal as radioactive waste. For example, 

M&E shipped for disposal as radioactive waste must meet waste acceptance criteria at the 

disposal facility as well as Department of Transportation (DOT) requirements for transporting 

radioactive material. The results of any additional investigation must clearly demonstrate 

compliance with any applicable requirements, and be appropriately documented. Non-impacted 

M&E do not receive any additional radiological investigation. 

2.2.1 Perform a Visual Inspection 

The purpose of the visual inspection is to identify and document the physical characteristics of 

the M&E (e.g., size, kind of material, shape, condition) when this description is not readily 

available to support a categorization decision. The visual inspection may be performed during a 

site visit, or by reviewing photographs or videos of the M&E. Photographs and video also 

provide a means for documenting the results of the visual inspection. The visual inspection 

corresponds to the Site Reconnaissance presented in Section 3.5 of MARSSIM. Information will 

be used to support the following activities: 

• Developing survey unit boundaries (Section 3.6). 

• Defining the parameter of interest during the development of a decision rule for impacted 

M&E (Section 3.4). 

• Verifying the requirements of an SOP are met before performing a routine survey (Section 

4.5.1). 

• Evaluating any health and safety concerns (Section 5.2). 

• Developing handling protocols for implementation of the disposition survey (Section 5.3 and 

5.4). 

Prior to performing a visual inspection, the surveyor should review what is known about the 

M&E. If little or no information is available describing potential hazards associated with the 

M&E, care should be exercised in performing a visual inspection. Screening measurements for 

radiation, chemical, and other hazards, along with the use of personal protective equipment (e.g., 

gloves, coveralls, respirators), may be necessary depending on available information. Situations 

with known or expected risks (i.e., M&E that are radiologically or chemically impacted) may 

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require preparation of a study plan or SOP anticipating activities to be performed and identifying 

specific information to be collected. Casual visual inspections of M&E with an unknown history 

are not recommended. Detailed visual inspections (e.g., disassembly of potentially impacted 

equipment to examine interior surfaces) should not be performed without proper precautions and 

are more appropriately performed by preliminary surveys (Section 2.3). 

While the primary objective for performing a visual inspection is to collect information used to 

design a disposition survey, the information can be used for other purposes. Evaluation of health 

and safety concerns (Section 5.2) and development of handling protocols for implementation of 

the disposition survey (Section 5.3) are two examples where visual inspection information would 

be used. 

2.2.2 Collect and Review Additional Historical Records 

When information on the identity, concentration, and distribution of radioactivity are not readily 

available to support a categorization decision, historical records may provide this specific 

information. Information on the physical characteristics of the M&E (e.g., size, shape, condition) 

and the characteristics of the radioactivity (e.g., radionuclides of concern, expected 

concentrations) will be used to select a disposition option in Section 2.5 and describe initial 

survey unit boundaries in Section 3.6.1. The historical information is then used to define the 

action level, parameter of interest, and alternative actions during the development of a decision 

rule for impacted M&E (Section 3.7, EPA 2006a). 

Types of historical records that provide useful information are described in MARSSIM Section 

3.4.1, and may include— 

• A facility or site radioactive materials license; 

• Permits or other documents that authorize use of radioactive materials; 

• Other permits and environmental program files; 

• Operating records (e.g., previous surveys, waste disposal records, effluent releases); 

• Corporate contract files (e.g., purchasing records, shipping records); 

• A site or facility description (e.g., locations of M&E, site photographs); and 

• Inspection reports, incident analyses, and compliance histories maintained by currently and 

formerly involved regulatory agencies. 

Another source of historical information is interviews with current or previous employees. 

Interviews may be conducted early in the data collecting process or close to the end of the IA. 

Interviews conducted early in the IA cover general topics, and information gathered is used to 

guide subsequent data collection activities. Interviews conducted late in the IA allow the 

investigator to direct the investigation to specific areas that require additional information or 

clarification. 

Once the historical records have been collected, they should be reviewed to identify information 

that supports the categorization decision. Historical information used to support the 

categorization decision should be evaluated using the data quality assessment (DQA) process 

(EPA 2006b). In particular, historical information should be examined carefully because— 

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MARSAME Initial Assessment of Materials and Equipment 

• Previous data collection efforts may not be compatible with IA objectives, 

• Previous data collection efforts may not be extensive enough to fully describe the M&E 

being investigated, 

• Measurement techniques or protocols may not be known or compatible with IA objectives, or 

• Conditions may have changed since the data were collected 

Additional information on evaluating data can be found in the following documents– 

• The Environmental Survey Manual Appendix A - Criteria for Data Evaluation (DOE 1987) 

• Upgrading Environmental Radiation Data, Health Physics Committee Report HPSR-1 (EPA 

1980) 

• Guidance for Data Usability in Risk Assessment, Part A (EPA 1992a) 

• Guidance for Data Usability in Risk Assessment, Part B (EPA 1992b) 

Historical records describing impacted M&E may include additional information that can be 

used to support additional activities during the disposition process. For example, historical 

records may provide descriptions of the M&E that are sufficient to design a disposition survey 

(Chapter 4). On the other hand, the historical records can be used to identify data gaps that are 

addressed by performing preliminary surveys (Section 2.3). 

2.2.3 Assess Process Knowledge 

The characteristics, history of prior use, and inherent radioactivity are critical for evaluating the 

impacted status of M&E. This information is termed process knowledge. Process knowledge is 

obtained through a review of the operations conducted in facilities or areas where M&E may 

have been located and the processes where M&E were involved when this information is not 

readily available to support a categorization decision. This information is used to evaluate 

whether M&E—such as structural steel, ventilation ductwork, or process piping—had been in 

direct contact with radioactive materials or had been activated, which would lead to a decision 

the M&E are impacted. Descriptions of the physical attributes of the M&E (Section 2.4.1) and 

radiological attributes of the M&E (Section 2.4.2) can be obtained from process knowledge. In 

addition, process knowledge supports the selection of a disposition option (Section 2.5). The 

disposition option is then used to identify sources of action levels, a parameter of interest, and 

alternative actions during the development of a decision rule for impacted M&E (Section 3.7 of 

this supplement and EPA 2006a). 

Process knowledge is obtained by researching the M&E and understanding the origin, use, and 

potential disposition. The level of detail required from process knowledge is project specific. The 

description of M&E could be simple, such as a set of hand tools being removed from a controlled 

area where the radiological conditions are well known. At the other extreme is a complex 

situation that requires knowledge of the manufacturing process, investigations of multiple 

processes that could impact the radiological conditions associated with the M&E, and 

understanding of recycle and reuse options that include movement of radionuclides through the 

environment. Sections 2.4.1 and 2.4.2 describe types of information that may be obtained from 

process knowledge and are necessary to support the development of a disposition survey. 

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In some cases, process knowledge of the equipment being investigated can be used to support 

categorization decisions. Consider a pump used to circulate demineralized make-up water. 

Maintenance records do not show the presence of radioactivity and operating records indicate no 

events where the pump could have been used with radioactivity. Radiological samples of the 

demineralized make-up water do not show the presence of radioactivity. Based on this process 

knowledge, the interior of the pump is categorized as non-impacted. 

Historical records (Section 2.2.2) are one source of process knowledge. Historical records, 

including interviews, provide site- and project-specific information on historical use and 

radiological processes that may affect the M&E. Engineering and chemistry books and journals 

provide information on the origins (e.g., manufacturing) and potential disposition of the M&E. 

Industry documents and company records are also potential sources of process knowledge. Other 

sources of information on M&E should be considered during the IA, indicating how, where, and 

when the M&E were used in areas where they potentially could have been affected by 

radionuclides or activation. These sources of information include— 

• Purchasing records showing when M&E were obtained, 

• Maintenance records showing where and how they were used, 

• Operating logs for systems that utilized or could have affected the M&E, 

• Disposal records showing survey results for similar types of M&E indicating types, and 

Locations of radionuclides or radioactivity. 

In some instances, process knowledge may not be available for the M&E being considered for 

release. For example, consider an outdoor material staging area for a nuclear facility where 

various pieces of surplus equipment and metal have accumulated over the years. The origin of 

these M&E is unknown. In this case, it is particularly important that preliminary surveys be 

performed on the M&E to determine if excess radioactivity is present and to finalize the list of 

radionuclides of concern. 

Techniques used to protect equipment or prevent radioactivity from entering difficult-to-measure 

areas or penetrating porous surfaces can be used to support categorization decisions. Consider 

the following examples of protection and prevention techniques: 

• Plan and coordinate all work to minimize exposure of equipment, tools, and vehicles to 

radioactivity. 

• Evaluate materials, tools, and equipment for ease of decontamination and disassembly (that 

may be required for decontamination or release) prior to use. 

• Use prefilters or have a separate source of outside air on the intake for internal combustion 

equipment subject to airborne radionuclides or radioactivity. 

• Use a filtered inlet for high volume air handling equipment such as blowers, compressors, 

etc., to minimize the potential for internal contamination due to build up of low-level 

radioactivity. 

• Do not bring electrically driven mobile equipment into controlled areas. 

• Use protective sheathing/covers, strippable coatings, or protective caps to minimize the 

potential for surficial radionuclides or radioactivity. 

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MARSAME Initial Assessment of Materials and Equipment 

• Cover and protect all openings on equipment, tools, or vehicles that may permit radioactivity 

to enter difficult-to-access or difficult-to-clean areas. 

• Select technologies that minimize radiological airborne emissions, secondary wastes, and 

tool or equipment damage. 

2.2.4 Perform Sentinel Measurements 

Sentinel measurements are biased measurements performed at key locations to provide 

information specific to the objectives of the IA. The objective of performing sentinel 

measurements as part of the IA is to gather sufficient information to support a decision regarding 

further action (e.g., categorization). Sentinel measurements may also be used to verify 

assumptions based on existing information or obtain information on the current status of the 

M&E. Sentinel measurements are not a risk assessment, scoping survey, or study of the full 

extent of radionuclides or radioactivity associated with the M&E. 

Sentinel measurements alone cannot be used to show that M&E are non-impacted. Positive 

results are definitive for determining that M&E are impacted. However, negative results provide 

only part of the evidence required for determining that the M&E are non-impacted. Since 

radioactivity in difficult-to-measure areas cannot be measured directly without accessing the area 

(e.g., disassembling equipment), sentinel measurements performed at access points to difficultto-measure areas could be used to indicate that it is unlikely that radioactivity entered that area. 

For example, smears with elevated radioactivity, collected inside ductwork, can provide 

information to support categorization of the ventilation system as impacted. Because sentinel 

measurements are usually associated with difficult-to-measure areas, they are not generally 

applicable to dispersible bulk materials. 

If protection and prevention techniques (described in Section 2.2.3) were applied to M&E used 

around radioactive material, sentinel measurements can be used in connection with process 

knowledge to support a decision of whether difficult-to-measure areas were impacted. For 

example, if prefilters are used to capture particulate airborne radioactivity of a specific size 

before the particulates enter difficult-to-measure areas, sentinel measurements can be made on 

the prefilters. 

Sentinel measurement methods may involve any of the measurement techniques discussed in 

Section 5.9.1 combined with the instruments discussed in Section 5.9.2. Advantages and 

disadvantages of different combinations of measurement techniques and instrumentation are 

listed in Table 5.5 and discussed in Section 5.9.3. The selection of a measurement method for 

sentinel measurements should be made based on project-specific considerations using the DQO 

process. 

It should be noted that access points are often modified to limit personnel radiation exposure to 

difficult-to-measure areas after use (e.g., capped, sealed, cleaned). Care should be taken to avoid 

performing sentinel measurements at modified access points to reduce the probability of making 

an incorrect decision about the status of the M&E. QA and QC should be considered during 

planning for collection of sentinel measurements. The measurement and subsequent evaluation 

of the results should be consistent with the assumptions used to define sentinel measurements. 

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2.2.5 Decide Whether M&E are Impacted 

Once there is adequate information to support a categorization decision, the decision maker 

needs to decide whether the M&E are impacted or non-impacted. The categorization decision is 

based on four sources of information: visual inspection, historical records review, process 

knowledge, and the results of sentinel measurements.1

 If the results for any part of the 

categorization process indicate a reasonable potential for radionuclide concentrations or 

radioactivity above background, the decision is the M&E are impacted. For example, if the 

visual inspection, historical records, and process knowledge all indicate the M&E are nonimpacted but the sentinel measurements indicate impacted, the M&E are impacted. Similarly, if 

the visual inspection and sentinel measurements indicate the M&E are non-impacted but the 

historical records and process knowledge indicate the M&E are impacted, the M&E are 

impacted. An important point is that sentinel measurements alone cannot be used to support a 

decision in declaring M&E as non-impacted. 

In most cases, the categorization decision is obvious based on the available information. In cases 

where the decision is not obvious, the consequences of making a decision error usually result in a 

determination that the M&E are impacted. For example, the consequence of incorrectly 

categorizing M&E as impacted when they are not impacted includes performing a radiological 

survey. However, the consequence of incorrectly categorizing M&E as non-impacted when they 

are impacted could result in inadvertent exposure for members of the public, lack of confidence 

in other radiological decisions, and potential violation of regulatory requirements. The 

consequences of incorrectly categorizing M&E are also discussed in Section 4.3.4. 

Collectively, this information should be used to develop survey strategies targeting different 

types of materials in recognition that a single survey method or procedure may not necessarily fit 

the technical requirements of all materials, given their diverse properties. For example, one 

procedure may be used to address only the routine releases of tools and equipment. On the other 

hand, a separate procedure may be developed to address infrequent releases of large amounts of 

bulk materials, such as concrete rubble. The approach suggested here is one of compartmentalizing the release activities into manageable and common functional elements with each one being 

optimized in the context of facility operations as to its effectiveness, while demonstrating 

compliance with applicable regulations. The development of standardized survey procedures for 

infrequent releases necessitates that the MARSAME user utilize processes in the remainder of 

this chapter and then move to Section 3.10 for evaluating and implementing standard operating 

procedures (SOPs). 

If there is insufficient information available to design a disposition survey following 

categorization, preliminary surveys may be performed to obtain additional information 

describing the physical and radiological characteristics of the M&E (Section 2.4). These 

preliminary surveys facilitate the development of an effective and efficient disposition survey 

design. 

If there are questions concerning the level of documentation for the categorization decision, 

consult the cognizant regulatory authority. The decision maker should consider the degree to 

 

1

 Sentinel measurements are not required to support a categorization decision. 

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MARSAME Initial Assessment of Materials and Equipment 

which documentation of the M&E categorization decision is necessary for M&E that are 

categorized as non-impacted, since no additional investigation is required. In most cases it is not 

necessary to document decisions that M&E are impacted since this decision will be documented 

later in the disposition process (e.g., documentation of the IA results in Section 2.6, 

documentation of the survey design in Section 4.5, and documentation of the disposition survey 

results in Section 6.6). 

2.3 Design and Implement Preliminary Surveys 

If there is insufficient information available to design a disposition survey following 

categorization, it may be necessary to perform preliminary surveys to obtain the required 

information. Preliminary surveys of M&E correspond to scoping and characterization surveys 

described in MARSSIM Sections 5.2 and 5.3. 

Following a decision that the M&E being investigated are impacted, the decision maker should 

determine if an applicable standardized survey design is available, usually in the form of an SOP. 

If an SOP is available and applicable to the M&E being investigated, the instructions in the SOP 

should be implemented and the results of the survey evaluated as specified in the SOP (see 

Figure 2.2 and Section 2.6.1). 

It may be necessary to evaluate the quantity and quality of data describing the M&E to determine 

if the existing data are adequate for implementing an existing SOP or developing a disposition 

survey design. If the data are adequate, no additional data collection is required. On the other 

hand, if there are data gaps that need to be addressed prior to completing a disposition survey 

design, preliminary surveys can be used to obtain the necessary data. 

The purpose of performing preliminary surveys is to obtain information describing the physical 

and radiological characteristics of the M&E. The ultimate goal is to minimize heterogeneity in 

the subset of M&E being surveyed. Minimizing heterogeneity helps to control the measurement 

uncertainties (Section 5.6), and may be helpful in selecting a disposition option (Section 2.5). For 

example, if a subset of the M&E is identified as difficult-to-measure while the majority of the 

M&E is relatively easy to measure and is considered for release, minimizing heterogeneity of all 

the M&E by segregating the difficult-to-measure subset for potential disposal may simplify 

measurements and be cost-effective. See Section 5.4 for information on segregation of M&E to 

minimize heterogeneity during implementation of the disposition survey design. 

In general, preliminary surveys are designed using professional judgment to address specific 

questions concerning the existing data. Once a data gap has been identified, a survey is designed 

and implemented to obtain the information required to fill that data gap. The results of the survey 

are evaluated to ensure the data gap has been adequately addressed and the results are 

documented. In some cases these surveys will be large and complicated, with written survey 

designs reviewed by stakeholders prior to implementation. In other cases, these will be simple 

surveys that quickly provide some small piece of information required to proceed with the 

disposition survey design. By necessity, there is no single approach that will address all types of 

preliminary surveys. However, the DQO process can be applied to successfully design a 

preliminary survey (EPA 2006a). 

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Is the Existing

Information Adequate

to Design a Disposition

Survey?

Is the Existing

Information Adequate to Select a 

Disposition Option?

Identify Data Gaps 

(Section 2.3)

Design and Implement 

Preliminary Surveys

(Section 2.3)

Describe the M&E 

(Section 2.4)

Proceed to

Figure 2.3

Yes

No

Yes

No

Select a Disposition Option

(Section 2.5)

From Figure 2.1

NOTE: Shaded diamonds 

represent important decision 

points.

Figure 2.2 Assessing Adequacy of Information for Designing Disposition Surveys 

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MARSAME Initial Assessment of Materials and Equipment 

The first step in designing a preliminary survey is to identify the data gaps to be addressed. 

Section 2.4.1 and Section 2.4.2 discuss the minimum information required to describe the M&E 

and design a disposition survey. Any of the required information that is not available or is not of 

sufficient quality represents a data gap. In addition, there may be project-specific information 

needed to complete the disposition survey design that could also represent potential data gaps. In 

order to complete the list of potential data gaps, it is recommended that the planning team work 

through the entire disposition survey planning process (Chapters 3 and 4). Whenever a data gap 

is identified, the planning team should make reasonably conservative assumptions or proceed 

with multiple survey designs based on a reasonable range of values to fill the data gap. 

Identifying a complete list of data gaps will help ensure the necessary additional information can 

be collected effectively and efficiently, with minimal waste of limited resources. If a separate 

preliminary survey is designed and implemented for every data gap as it is identified, there is an 

increased possibility of duplication of effort and increased demands on limited resources. As 

with all data collection activities, QA and QC should be considered during planning and 

evaluated during assessment of the results. 

MARSAME uses an iterative planning process for designing surveys. Changes in the available 

information may result in multiple iterations of individual steps. Iteration may be necessary at 

any time that an assumption used to design a survey is shown to be false. For example, if a 

historical record is found that changes the description of the M&E from beta-gamma emitting 

radionuclides to include alpha emitting radionuclides, it is necessary to consider additional or 

different measurement techniques to account for the alpha radiation. 

2.4 Describe the M&E 

The M&E being investigated must be described with regards to its physical and radiological 

attributes in order to establish the information necessary to design a survey approach that can 

adequately measure the M&E. This description is intended to ensure that residual radioactivity 

associated with the M&E will not be missed by the disposition survey, the M&E is left in a 

usable condition, and that any data collected meet the objectives of the disposition survey. 

2.4.1 Describe the Physical Attributes of the M&E 

A description of the physical characteristics defining the investigated M&E is required to help 

the user develop a disposition survey design. The preliminary physical description is usually 

developed using some combination of the techniques presented in Section 2.2 (i.e., visual 

inspection, historical records, and process knowledge). The physical description of the M&E is 

used to help define survey unit boundaries (Section 3.6.1) and develop a decision rule (Section 

3.7), which has a direct impact on the disposition survey design. 

Table 2.1 lists the four attributes that should be addressed when describing the physical 

characteristics of the M&E being investigated (dimensions, complexity, accessibility, and 

inherent value). Questions related to the evaluation of the attributes are provided, along with a 

list of minimum information expected to be provided by the IA. The planning team should 

consider designing and implementing preliminary surveys (Section 2.3) to verify existing 

information and investigate data gaps identified during the initial steps of the IA. 2

 

2

 The development of a planning team is discussed in MARSSIM Section 3.2. 

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Table 2.1 Physical Attributes Used to Describe M&E 

Attribute Minimum Information Questions for Consideration 

Dimensions Size (Total Mass) 

Shape (Total Surface Area) 

Are there issues with size and shape that affect 

how the M&E should be handled? 

Complexity M&E may require segregation to 

design a technically defensible 

disposition survey. 

M&E may be combined into 

similar groups and still allow a 

technically defensible disposition 

survey. 

Are there situations where segregation (e.g., 

disassembly) could affect the usefulness of the 

M&E? 

Are there situations where segregation (e.g., 

disassembly) could result in the release of 

radioactivity or hazardous chemicals to nonimpacted areas? 

Are there situations where engineering controls are 

required to prevent the release of radioactivity or 

hazardous chemicals to non-impacted areas? 

Are there component materials that are inherently 

radioactive or regulated for their chemical 

properties?3

Are there multiple component materials in the 

M&E? 

Accessibility Identification of impacted, 

difficult-to-measure areas for 

performing conventional handheld 

measurements. 

Known or potential relationships 

among radionuclide 

concentrations or radioactivity in 

accessible and difficult-tomeasure areas. 

Are there issues with size or shape that limit 

accessibility (e.g., bottom of a large, bulky 

object)? 

Are there porous surfaces that could allow 

permeation of radioactivity? 

Are there seams, ruptures, or corroded areas where 

radioactivity could penetrate to difficult-tomeasure areas? 

Inherent 

Value 

The inherent value of the M&E 

being investigated. 

Can the M&E be reused or recycled? 

Can the M&E be repaired or remediated? 

What are the replacement and disposal costs? 

2.4.1.1 Describe the Physical Dimensions of the M&E 

It is important to understand the dimensions of the M&E being investigated in order to define the 

scale of decision making (Section 3.6 on identifying survey unit boundaries), support evaluation 

of measurement techniques (Sections 3.8 and 5.9), and identify any handling issues that may 

need to be addressed (Section 5.3). The dimensions generally are defined as the size and shape of 

the M&E being investigated. The size is primarily related to the scale of decision-making and 

may be defined as the length, width, and depth of an item, or as the quantity of M&E. Quantity 

may be expressed in terms of a number (e.g., 25 pumps) or a volume (e.g., 200 cubic yards of 

 

3

 For example, materials regulated under the Resource Conservation and Recovery Act (40 CFR 261) or the Toxic 

Substances Control Act (40 CFR 700-766). 

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concrete rubble), and may be related to the mass of the M&E. An estimate of the total mass of 

the M&E should be provided. The shape of the M&E is primarily related to the evaluation of 

measurement techniques. The description of shape should consider surface conditions (e.g., clean 

or dirty, rough or smooth, curved or flat) that affect the surface efficiency for radiation 

instruments. An estimate of the total surface area of the M&E should be provided when the 

radionuclides of concern are, or could be, surficial. 

2.4.1.2 Describe the Complexity of the M&E 

The complexity of the M&E also affects the disposition survey design. Complexity refers to the 

number and types of components that make up the M&E, as well as the ability to segregate or 

combine the M&E into similar groups. M&E consisting of a single component is a simple case. 

Consider the situation where several hundred feet of pipe are being investigated and the entire 

pipe is made from steel. 

A complex situation occurs when the M&E consist of a variety of component materials. 

Consider the same amount of pipe, but some pipe is steel, some is copper, and some is lined with 

rubber, lead, or PVC. Some types of process equipment (e.g., pipe originating from mineral 

processing industries) are internally lined with rubber, lead, or PVC. The presence of such liners 

can complicate the initial categorization, as well as subsequent characterization and survey of 

such equipment. The presence of lead can complicate the final disposition of process equipment 

(e.g., recycling as ferrous steel or disposal in landfills). 

Equipment once used in process plants or systems should be checked for the presence of 

internally deposited sediment, sludge, oil, grease, water, and presence of process chemicals and 

reagents. The presence of such residues may require the implementation of special worker health 

and safety measures, procedures to collect and properly dispose of such hazardous material, and 

may restrict possible disposition options. 

Complexity also comes from the ability to break down or combine the M&E into similar groups. 

A steel I-beam represents a simple case, where there is one material that can be cut into the 

desired lengths. Dispersible bulk materials represent a situation that is slightly more complex, 

especially when different types of materials have been combined. One example is a pile of scrap 

metal, where the metal can be segregated by material (e.g., aluminum versus steel) or type (e.g., 

sheet metal versus pipe versus I-beams). 

Equipment tends to be more complex, because it often contains a variety of components that can 

generally be broken down by disassembling the equipment. Consider the case of a power tool 

consisting of a casing, an electric motor, and controls. There are different types of metal, plastic, 

and possibly glass or ceramics that make up the item, but disassembly into the individual 

components may render the tool unusable and may expose component materials that are 

inherently radioactive or hazardous. Disassembly of certain items could also result in the release 

of radioactivity or hazardous chemicals to non-impacted areas, and may require engineering 

controls to prevent such releases. The disposition survey design often increases in complexity as 

the equipment increases in size and complexity. However, complex M&E may also allow the 

user to segregate impacted from non-impacted items or components. This segregation may 

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reduce the amount of M&E requiring additional investigation. One example is a front loader 

used to move piles of potentially radioactive material at a decommissioning or cleanup site. The 

bucket and tires of the front loader may be identified as impacted while the engine and cab are 

identified as non-impacted, depending on the controls in place while the equipment was being 

used. However, there may be cases where an adequate survey design cannot be developed based 

on decisions made earlier in the planning process. In these cases, it may be necessary to revisit 

some of the decisions made earlier, for example, re-evaluating the cost to benefit analysis. 

2.4.1.3 Describe the Accessibility of the M&E 

Accessibility is the next attribute to consider when describing the M&E being investigated. 

Accessibility has a direct impact on measurability, so it is a critical issue for making technically 

defensible disposition decisions. Areas (including surfaces and individual items) are either 

accessible or difficult-to-measure. Accessible areas are areas where radioactivity can be 

measured, and the results of the measurement meet the DQOs and measurement quality 

objectives (MQOs) defined for the survey. During the IA it is necessary to distinguish areas that 

are accessible from areas that may be difficult to measure. 

The determination of whether an area is physically accessible, for purposes of the IA, should be 

based on whether a measurement could be performed using a conventional hand-held radiation 

instrument such as a sodium iodide (NaI[Tl]) detector, or Geiger-Mueller (GM) pancake probe. 

If difficult-to-measure areas are identified and these areas are categorized as impacted, the IA 

should attempt to identify if there are any known or potential relationships among radionuclide 

concentrations or radioactivity in accessible areas and radionuclide concentrations or 

radioactivity in difficult-to-measure areas. This information will be evaluated in Section 3.3.3 for 

the potential to use surrogate measurements as a method of estimating radionuclide 

concentrations or radioactivity in difficult-to-measure areas. 

The potential for permeation and penetration of radioactivity should also be discussed as part of 

accessibility. Permeation describes the spread of radioactivity throughout a material and is 

usually associated with porous materials or surfaces (e.g., wood, concrete, unglazed ceramic). 

Certain chemical and physical forms can increase the permeation rate (e.g., liquids permeate 

faster than solids; small particles permeate faster than large particles). Penetration describes 

infiltrating into difficult-to-measure areas, and is generally associated with radioactivity entering 

through access points, seams, or ruptures. Corrosion of surfaces may also result in penetration of 

radioactivity into difficult-to-measure areas. 

2.4.1.4 Describe the Inherent Value of the M&E 

A part of describing M&E that is often overlooked during the IA is determining the inherent 

value of the materials or equipment being considered for release. Estimates of the value of 

materials and equipment should include the replacement cost, condition (i.e., can the materials or 

equipment be reused or recycled), and disposal cost. Replacement costs may consider increased 

productivity due to upgrades to existing facilities and equipment, decontamination costs for 

existing and new items, and the ultimate disposal of the replacements. Condition of the materials 

and equipment may include maintenance and repair costs to start or keep the items operational, 

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MARSAME Initial Assessment of Materials and Equipment 

as well as costs to decontaminate and release the items from radiological controls. Disposal costs 

may include shipping and handling of potentially hazardous material. The limited capacity of 

existing radiological waste disposal facilities may need to be considered along with the monetary 

cost of disposal. 

2.4.2 Describe the Radiological Attributes of the M&E 

A description of the radioactivity potentially associated with M&E being investigated is required 

to design a disposition survey. The review of historical documents (Section 2.2.2) and process 

knowledge (Section 2.2.3) are the primary sources of information on radioactivity associated 

with M&E. Sentinel measurements (Section 2.2.4) and preliminary surveys (Section 2.3) may 

also provide information, such as types of radiations and identity of radionuclides. The 

information describing the radioactivity is used to support a decision of whether the M&E are 

impacted and supports the development of a disposition survey for impacted M&E. The 

description of the radioactivity is divided into four attributes: radionuclides, activity, distribution, 

and location. 

Table 2.2 lists the four attributes to be addressed when describing radioactivity potentially 

associated with the M&E being investigated. Questions related to the evaluation of the attributes 

are provided, along with a list of minimum information expected to be provided by the IA. The 

planning team should consider designing and implementing preliminary surveys (Section 2.3) to 

obtain information that is not provided by the IA. 

Table 2.2 Radiological Attributes Used to Describe M&E 

Attribute Minimum Information Questions for Consideration 

Radionuclides List of radionuclides of potential concern, 

including major radiations and energies. 

What were the potential sources and 

mechanisms for the radioactivity to 

come into contact with the M&E? 

Activity List of expected radionuclide concentrations or 

radioactivity (e.g., average, range, variance) 

associated with the M&E 

List of known and potential relationships among 

radionuclide activities (e.g., activation and 

corrosion products, fission products, natural 

decay series). 

What is the basis for the expected 

radionuclide concentrations or 

radioactivity? 

What is the basis for the known and 

potential relationships (e.g., process 

knowledge of similar sources, 

measurements of equilibrium 

conditions)? 

Distribution List of areas where the radioactivity is uniformly 

distributed. 

List of areas where the distribution of 

radioactivity is clustered. 

List of areas where the distribution is unknown. 

Can the M&E be divided into sections 

where the distribution of radioactivity 

is uniform? 

Are there areas where small areas of 

elevated activity are a concern? 

Location State whether the radioactivity is surficial, 

volumetric, or a combination of both. 

State whether surficial radioactivity is fixed or 

removable. 

Is the volumetric activity uniformly 

distributed, is there a gradient, or is the 

activity random or clustered? 

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Initial Assessment of Materials and Equipment MARSAME

2.4.2.1 Identify the Radionuclides of Potential Concern 

Identification of the radionuclides of potential concern is a critical step in making disposition 

decisions. At a minimum, the planning team should review the information available from 

Section 2.2 to identify the radionuclides of potential concern. The quality and completeness of 

the existing information should be evaluated. Information on known or expected relationships 

among radionuclides of potential concern should be identified and evaluated for applicability to 

current conditions. If necessary, a study to identify a complete list of radionuclides of potential 

concern and determine relationships among radionuclides may be initiated before designing the 

disposition survey. 

A list of radionuclides of potential concern should be developed based on existing data. The list 

should consider all potential sources of radioactivity, but only include radionuclides that are 

actually of concern for the M&E being investigated. 

The list is designed to help focus the disposition decision. The list of radionuclides of potential 

concern should include the major types of radiation (e.g., alpha, beta, photon) and their 

corresponding energies. A discussion of the sources of radionuclides of potential concern, and 

their chemical and physical form should also be included, if possible. 

Include a description of how the M&E became impacted if it is known. For example, it is 

important to document whether the potential radioactivity resulted from deposition of airborne 

particulate material, or from placing the M&E in an area of neutron flux that resulted in 

activation. All potential mechanisms for radioactivity that is associated with the M&E should be 

described. 

The description of potential radioactivity from the IA may also identify known or suspected 

relationships among radionuclides (e.g., equilibrium conditions for natural decay series, relative 

activities of fission products or activation products based on process knowledge). Additional 

investigations (e.g., preliminary surveys) may be performed to verify the presence of 

radionuclides of potential concern and provide estimates of the activity relationships among 

radionuclides. These investigations may include field measurements and sample collection with 

laboratory analysis. 

The identification of radionuclides of potential concern may impact other decisions made during 

development of a disposition survey design. Since the sources of action levels are radionuclide or 

radiation-specific, the identification of radionuclides of potential concern directly affects the 

selection of an appropriate action level. The planning team should consider the impact of the list 

of radionuclides of potential concern on other decisions (e.g., selection of measurement 

techniques or instruments) as well as the impact of other decisions on the action levels when 

considering potential sources of action levels. For example, the identification of available 

measurement techniques (Section 3.8) is also directly related to the radionuclides of potential 

concern. The determination of surficial or volumetric radioactivity (Section 2.4.2.4) may be 

based on the energy and penetrating power of the radiation emissions, which would be indirectly 

related to the radionuclides of potential concern. Caution must be used in evaluating radionuclide 

concentrations or radioactivity for M&E with high levels of inherent background radioactivity. 

NUREG-1575, Supp. 1 2-16 January 2009 
MARSAME Initial Assessment of Materials and Equipment 

2.4.2.2 Describe the Radionuclide Concentrations or Radioactivity Associated with the M&E 

A description of expected radionuclide concentrations or radioactivity is also important for 

supporting disposition decisions for M&E. Radionuclide concentrations or radioactivity in 

excess of background (see Section 3.9 and Appendix B) support a finding that the M&E are 

impacted. Historical records (Section 2.2.2) and process knowledge (Section 2.2.3) are sources 

of information on radionuclide activities associated with M&E. In addition, sentinel 

measurements (Section 2.2.4) can provide information on radionuclide concentrations or 

radioactivity. A description of the expected radionuclide concentrations or radioactivity should 

be developed for each of the radionuclides of potential concern. At a minimum, the average 

expected activity should be provided. Some assumption regarding the expected activity will be 

required in order to design a disposition survey using the guidance in Chapter 4. If no 

assumption can be made, a preliminary survey should be performed. If possible, information on 

the expected range and uncertainty (σ, as described in Sections 3.8.1 and 5.6) of the activity 

should be provided. The description of the expected activity should include the units, an estimate 

of uncertainty in the values, and a summary of how the data were obtained (e.g., purpose of data 

collection efforts, actual measurements, instrument used, count time, or process knowledge). 

Any known or suspected relationships among concentrations for individual radionuclides should 

be included in the description. For example, there is an expected relationship among fission 

products from a nuclear reactor because of the common source of the radionuclides (i.e., nuclear 

fission). Similarly, there is an expected relationship for activation and corrosion products. 

Members of the natural decay series (i.e., thorium series, uranium series, actinium series; see 

Appendix B) are also expected to have a relationship for activities based on equilibrium 

conditions. 

2.4.2.3 Describe the Distribution of Radioactivity 

The distribution of radioactivity is primarily concerned with whether the activity is clustered or 

more uniformly distributed throughout the item. A uniform distribution of activity has little 

spatial variability, so the radionuclide concentrations or levels of radioactivity are fairly constant. 

A clustered distribution of activity has high spatial variability, and small areas of elevated 

activity are present as well as areas with little or no activity above background. The expected 

distribution of radioactivity could include areas with uniform radionuclide concentrations or 

levels of radioactivity and areas where the radionuclide concentrations or radioactivity is nonuniform. For example, airborne deposition could have produced a uniform distribution of 

radioactivity on horizontal exterior surfaces, while penetration through seams and access points 

could result in clustered radioactivity on interior surfaces. In addition, the interior surfaces could 

have a uniform distribution of radioactivity over localized areas (e.g., areas around a vent or 

cooling fan). Concentrations of radionuclides on M&E can change over time due to in-growth, 

decay, or diffusion. 

2.4.2.4 Describe the Location of Radioactivity 

The location of radioactivity is primarily concerned with whether the activity is located on the 

surface or distributed throughout the volume of the M&E. Surficial radioactivity is restricted to 

the surface of the M&E and is further described as removable, fixed, or some combination of 

January 2009 2-17 NUREG-1575, Supp. 1 
Initial Assessment of Materials and Equipment MARSAME

these two. Removable (or non-fixed) radioactive material is radioactive material that can be 

readily removed from a surface by wiping with an absorbent material. Fixed radioactive material 

is not readily removed from a surface by wiping. Surficial radioactivity is generally associated 

with non-permeable solid M&E. Volumetric radioactivity is not restricted to the surface of the 

M&E and is usually associated with permeable materials, surfaces, or activation by neutrons or 

other particles. 

The question of surficial versus volumetric radioactivity is a complicated issue that may or may 

not have a significant impact on the disposition survey design. The description of the location of 

radioactivity used to design the survey may be independent of where the radioactivity is 

physically located. For example, consider two different methods for surveying 60Co activity 

concentrations distributed on the surface of several thousand small bolts. First, the bolts may be 

surveyed in a container using in situ gamma spectroscopy assuming the radioactivity is 

volumetrically distributed.4

 If the same bolts are surveyed individually using a conveyorized 

survey monitor the conceptual model may describe the 60Co as surficial radioactivity. 

In some cases, the location of the residual radioactivity may be well known. For example, 

surface deposition of radioactivity on a non-porous material (e.g., smooth stainless steel) will not 

penetrate into the material to a significant extent under most conditions, so the residual 

radioactivity could be identified as surficial. Activated materials and bulk quantities of materials 

usually have volumetric residual radioactivity, although surficial radioactivity may also be 

present. On the other hand, the actual location of the residual radioactivity may be less well 

known or unknown. 

Process knowledge is the primary source of information on the location of residual radioactivity. 

The planning team should review the information from Section 2.2.3 to determine the expected 

location of residual radioactivity and the level of knowledge (i.e., well known, less well known, 

unknown) associated with the information. 

When the location of the residual radioactivity is well known, the planning team should proceed 

with a survey design based on the appropriate assumption, surficial or volumetric. When the 

location is less well known or unknown, the planning team may choose to proceed with multiple 

survey designs to determine the possible effect the location of the residual radioactivity may 

have on the design of the disposition survey. 

2.4.3 Finalize the Description of the M&E 

A final description of the M&E should be prepared following implementation of any preliminary 

surveys. The description of the M&E should consider the information in Table 2.1 and Table 2.2 

and provide sufficient information to design the disposition survey. 

 

4

 This example does not imply that any measurement technique should be applied to every situation. The 

information in Section 3.8 should be used to develop the measurement quality objectives (MQOs) for a project. The 

MQOs can be used to evaluate measurement techniques against the action levels and select the techniques best 

suited for a specific application. 

NUREG-1575, Supp. 1 2-18 January 2009 
MARSAME Initial Assessment of Materials and Equipment 

2.5 Select a Disposition Option 

The disposition of the materials and equipment will be a key factor in designing the disposition 

survey. MARSAME broadly considers two types of disposition decisions: release and 

interdiction. Release surveys are used to determine whether radiological controls can be reduced, 

removed, maintained at the current level, or transferred to another qualified user. Interdiction 

surveys are used to initiate radiological control, or to decide current radiological controls are 

adequate. 

Examples of potential disposition options for release of impacted M&E include— 

1. Reuse in a controlled environment. 

2. Reuse without radiological controls (i.e., clearance). 

3. Recycle for use in a controlled environment (i.e., authorized disposition). 

4. Recycle without radiological controls. 

5. Disposal as industrial or municipal waste. 

6. Disposal as low-level radioactive waste. 

7. Disposal as high-level radioactive waste. 

8. Disposal as transuranic (TRU) waste. 

9. Maintain current radiological controls. 

Examples of potential disposition options for interdiction of impacted M&E include— 

1. Remove M&E from general commerce and initiate radiological controls. 

2. Decide to accept M&E for a specific application. 

3. Decide not to accept M&E for a specific application. 

4. Continue unrestricted use of M&E (no action). 

The selection of a disposition option should be based on the information available at the end of 

the IA. The disposition option (e.g., reuse, recycle, disposal, initiation of control, or refusal) 

defines the action level (Section 3.3). The expected radionuclide concentrations or levels of 

radioactivity associated with the M&E (Section 2.4.2) are compared to the action level to 

determine whether the M&E will be controlled or uncontrolled following the disposition survey. 

The disposition option also defines the alternative actions for the decision rule to be developed in 

Section 3.6. Different disposition options may be applied to separate parts of equipment. If so, 

implementation of the different dispositions implies the necessity for total or partial disassembly. 

For example, it may be possible to remove a bucket from a backhoe for disposal and allow reuse 

of the rest of the equipment. 

2.6 Document the Results of the Initial Assessment 

The results of the IA should be documented to the extent necessary to support the decisions 

made. The level of documentation required will depend on the amount of information collected, 

the quantity of M&E covered by the IA, the type of assessment (e.g., standardized or projectspecific), and, as applicable, administrative and regulatory requirements. Two options for 

documenting the assessment results are the Standardized IA and the conceptual model as 

described in the following sections. Figure 2.3 illustrates the documentation of the IA. 

January 2009 2-19 NUREG-1575, Supp. 1 
Initial Assessment of Materials and Equipment MARSAME

Is an 

Applicable SOP Available 

for this M&E?

Develop a Conceptual Model 

and Document the IA 

(Section 2.6.2)

Proceed to Figure 3.1

Implement and Document the 

Results of the Survey as 

Described in the SOP 

(Section 2.6.1)

Yes No

Proceed to Figure 6.1

From Figure 2.2

NOTE: Shaded box represents important milestone.

Figure 2.3 Documentation of the Initial Assessment 

2.6.1 Document a Standardized Initial Assessment 

A standardized IA is a set of instructions or questions that are used to perform the IA. These 

instructions are usually documented in an SOP. The SOP should be developed, reviewed, and 

documented in accordance with an approved quality system. Information on developing and 

documenting a functional quality system can be found in EPA QA/G-1 (EPA 2002c). Guidance 

on developing SOPs as part of a quality system can be found in EPA QA/G-6 (EPA 2001). 

A standardized IA is generally associated with facilities or processes that regularly evaluate 

similar types of M&E. The release of small tools and personal items from an operating nuclear 

plant is one example of such a process. Another example, this time describing an interdiction 

process, would be evaluating truckloads of scrap metal entering a recycle facility. SOPs may be 

developed to describe repeated routine disposition surveys of similar M&E for both situations. 

NUREG-1575, Supp. 1 2-20 January 2009 
MARSAME Initial Assessment of Materials and Equipment 

The documentation of the IA results is described in the SOP. The documentation should be 

sufficient to demonstrate that trained personnel using an approved SOP evaluated all potentially 

impacted M&E. For a standardized IA, all these records are maintained but may not be directly 

associated with the IA. Individual records for each item evaluated by an IA are not required. 

The SOP should clearly describe its scope and the applicable types of M&E. This information 

may be useful for determining whether the M&E are impacted as well as whether the SOP can be 

used to evaluate the M&E. For example, if the SOP is applicable to all M&E used for a certain 

process or within a certain part of a facility, this defines what M&E can be considered impacted 

by that process. 

The SOP should also describe the M&E that were used to develop the instructions. The 

description of the M&E being investigated (Sections 2.2 and 2.3) should be compared to the 

assumptions used to develop the instructions to determine if the SOP is appropriate. For 

example, it may be appropriate to apply an SOP developed for scrap metal to evaluate hand 

tools, since both are made from metal and may have similar surface radioactivity. Alternatively, 

it may not be appropriate to use an SOP developed for scrap metal to evaluate dry active waste or 

concrete rubble, since they may have volumetric activity and different surface efficiencies. At a 

minimum, the rationale for applying the SOP to M&E other than specified in the SOP should be 

documented. 

The SOP should include the training requirements for personnel implementing the SOP. 

Personnel performing the IA should be familiar with the SOP being implemented, as well as the 

potential disposition options implied or explicitly stated in the SOP. 

Additional documentation may be needed when the SOP is applied to situations other than those 

considered during development of the SOP. The purpose of the additional documentation is to 

determine whether the SOP may be applicable to a wider range of M&E. This documentation 

will help provide technical support for modifying the SOP. If incorrect decisions are made 

concerning the determination of whether M&E are impacted, or inappropriate recommendations 

are made for disposition options, it may be necessary to modify the SOP to reduce the number of 

decision errors. The additional documentation will help identify the source of the decision errors 

and help provide technical support for modifying or revising the SOP. 

2.6.2 Document a Conceptual Model 

If a standardized IA approach is not available for the M&E being investigated, the results of the 

IA should be documented in a conceptual model. If the information in MARSAME is being used 

to develop a standardized survey design (e.g., a new SOP), the information on developing a 

conceptual model applies. 

The conceptual model is applied in case-by-case situations and decisions. The conceptual model 

describes the M&E and radioactivity expected to be present for the project. The definition of 

impacted and non-impacted as it applies specifically to the project should be included in the 

conceptual model. The conceptual model describes the processes involving radioactive materials, 

as well as how the radioactivity could become associated with the M&E. 

January 2009 2-21 NUREG-1575, Supp. 1 
Initial Assessment of Materials and Equipment MARSAME

The description of the M&E documents the results of the IA investigation. At a minimum the 

conceptual model should include a description of the physical attributes of the M&E (see Section 

2.4.1 and Table 2.1), the radiological attributes of the M&E (see Section 2.4.2 and Table 2.2), 

and a list of the applicable disposition options (Section 2.5). In addition, the conceptual model 

helps identify data gaps and develop potential collection strategies for filling data gaps. 

The conceptual model will serve as the basis for the information and assumptions used to 

develop the disposition survey design in Chapter 4. In many cases the information in the 

conceptual model will be included in either the survey design documentation or in the 

documentation of the results of the disposition survey. The structure and content of the 

conceptual model should be based primarily on the future uses of the data. 

The planning team should review the information on radionuclides of potential concern provided 

by the IA for consistency with the conceptual model. If the data appear incomplete or the quality 

of the data is not adequate for the disposition survey being designed, the planning team may 

decide that additional information needs to be collected using preliminary surveys before 

proceeding with the survey design. 

NUREG-1575, Supp. 1 2-22 January 2009 
MARSAME Identify Inputs to the Decision 

3 IDENTIFY INPUTS TO THE DECISION 

3.1 Introduction 

This chapter identifies sources of information needed to evaluate the disposition option, or 

options, selected during the initial assessment (IA). During implementation of an existing 

standard operating procedure (SOP), this information would have been considered during 

development of the SOP. This chapter discusses factors affecting the selection of survey units, 

provides guidance on defining spatial and temporal boundaries, and examines practical 

constraints on collecting data. Figure 3.1 depicts the process of identifying the inputs to the 

decision. The expected output from this chapter is a decision rule, or multiple decision rules. A 

decision rule is a theoretical “if...then...” statement that defines how the decision maker would 

choose among alternative actions if the true state of nature could be known with certainty (EPA 

2006a). There are three parts to a decision rule (Section 3.7): 

• An action level that causes a decision-maker to choose between the alternative actions 

(Section 3.3), 

• A parameter of interest that is important for making decisions about the target population 

(Section 3.4), and 

• Alternative actions that could result from the decision (Section 3.5). 

Other inputs to the decision discussed in this chapter include selecting radionuclides or radiations 

of concern (Section 3.2), developing survey unit boundaries (Section 3.6), inputs for selecting 

provisional measurement methods (Section 3.8), and identifying reference material (Section 3.9). 

Also discussed in this chapter is the evaluation of an existing survey design to determine if it will 

meet the data quality objectives (DQOs; Section 3.10). 

This chapter provides guidance on performing Step 3, Step 4, and Step 5 of the DQO process 

(EPA 2006a) for designing a disposition survey. These steps build on the IA where members of 

the planning team were identified and M&E under investigation were identified as impacted 

(non-impacted M&E do not require additional investigation). A disposition option was selected 

(Section 2.5) and documented (Section 2.6). 

It is important to remember the DQO process is an iterative process. This means new information 

can be incorporated into the planning process and outputs from previous steps can be modified to 

incorporate the new information. For example, if no measurement methods are identified in 

Section 3.8 that meet the data requirements for a specific disposition option, the planning team 

may return to Section 2.5 to select a different disposition option. Alternatively, the selection of 

an action level or survey unit boundary may be affected by the available measurement 

techniques. The issues associated with surficial vs. volumetric radioactivity (see Section 2.4.2) 

affect the kinds of information (i.e., action level, survey unit identification, and measurement 

techniques) as well as the definition of study boundaries (i.e., target population, spatial 

boundaries, practical constraints on collecting data, subpopulation for which separate decisions 

will be made). 

January 2009 3-1 NUREG-1575, Supp. 1 
Identify Inputs to the Decision MARSAME

Select Radionuclides or 

Radiations of Concern 

(Section 3.2)

Identify Action Levels 

(Section 3.3)

Go to 

Figure 3.2

Describe the Parameter of Interest 

(Section 3.4)

Return from 

Figure 3.2

Identify Alternative Actions 

(Section 3.5)

Identify Survey Units

(Section 3.6)

Go to 

Figure 3.3

Return from 

Figure 3.3

Develop a Decision Rule

(Section 3.7)

Develop Inputs for Selection of 

Provisional Measurement Methods

(Section 3.8)

Identify Reference Materials

(Section 3.9)

Is there an Existing 

Survey Design?

Do the M&E Meet the 

Survey Requirements?

Yes

Proceed to 

Figure 4.5 No

Implement and Document Results 

as Described in the Survey

(Section 3.10)

Proceed to 

Figure 6.1

Yes

From Figure 2.3

NOTE: Shaded boxes 

represent important 

milestones.

No

Figure 3.1 Identifying Inputs to the Decision 

NUREG-1575, Supp. 1 3-2 January 2009 
MARSAME Identify Inputs to the Decision 

At the end of this chapter, the planning team should have the information required to design the 

disposition survey and know whether appropriate measurement techniques are available. Spatial 

and temporal boundaries will be identified, along with any practical constraints on data 

collection activities. Examples of practical constraints on data collection include time, budget, 

personnel, or equipment. For example, a box counter is selected to perform measurements for 

clearance of items from a radiologically controlled area. Assume a five-minute count time is 

required to achieve the survey objectives, and another minute is required to swap items in the 

detector. This means that ten measurements can be performed each hour. If more than 240 items 

require clearance each day, this measurement method would be impractical since a single box 

counter cannot clear all of the M&E. The decision rule(s) developed at the end of this chapter 

will be used to develop survey designs in Chapter 4. 

3.2 Select Radionuclides or Radiations of Concern 

A list of radionuclides of potential concern was developed in Section 2.4.2.1 as part of the 

description of radiological attributes associated with the M&E. Before a decision rule can be 

developed or a disposition survey designed, a final list of radionuclides or radiations to be 

measured must be prepared. 

The selection of radionuclides or radiations of concern is linked to several inputs to the decision. 

For example, the identification of an action level (Section 3.3) may determine if the survey 

results need to be radionuclide-specific, forcing the planning team to identify individual 

radionuclides of concern. On the other hand, the selection of a non-radionuclide specific 

measurement method may allow the selection of a radiation of concern (i.e., alpha [α], beta [β], 

gamma [γ], x-ray, or neutron radiation) without ever finalizing a list of radionuclides of concern. 

Finalizing the list of radionuclides or radiations of concern is an example of the iterative nature 

of the survey design process. The planning team is expected to evaluate different survey 

techniques and measurement methods. Evaluating these different survey techniques and 

measurement methods will require the planning team to return to the list of radionuclides of 

potential concern and go through the selection of radionuclides or radiations of concern. 

Actually, the final selection of radionuclides or radiations of concern may not occur until the 

disposition survey is optimized; the last step (Step 7) of the DQO process (Section 4.4.5). 

3.3 Identify Action Levels 

The action level is the numerical value or values that cause a decision maker to choose one of the 

alternative actions. The radionuclides of concern and disposition options selected at the 

completion of the IA define the alternative actions for the disposition survey. 

Figure 3.2 shows the process for selecting action levels. As shown , the iterative nature of the 

DQO process may result in changes to the action levels or disposition options based on other 

factors (e.g., availability of appropriate measurement techniques, measurability, surficial vs. 

volumetric activity). The planning team should consider the effect of action levels on other steps 

in the survey design process, as well as any effects these other steps might have on the action 

levels. 

January 2009 3-3 NUREG-1575, Supp. 1 
Identify Inputs to the Decision MARSAME

From Figure 3.1

Identify Applicable 

Regulatory Limits 

(Dose-, Risk-, Activity-, 

or Method-Based)

Identify Applicable 

Requirements 

(e.g., ANSI N13.12)

Identify Applicable 

Administrative Limits 

(e.g., Waste Acceptance 

Criteria)

Identify Applicable DOT 

Requirements for 

Shipping M&E

Convert Potential Action Levels 

into Measurement Units

Finalize Selection of Action Level(s)

Are There Multiple 

Radionuclides? 

RadionuclideSpecific Measurements?

Modify AL Using 

Equation 3-1 

(Gross Activity AL)

Surrogate 

Measurements 

Available?

Will Surrogates 

Infer Multiple 

Radionuclides?

Apply ALARA, as Appropriate

Document Selection of 

Action Level(s)

Modify AL Using 

Equation 3-4

Modify AL Using 

Equation 3-3

Evaluate Survey Results Using 

Equation 3-2 (Unity Rule) No

Yes No

Yes

No

Yes

No

Yes

Return to Figure 3.1

NOTE: Shaded boxes 

represent important 

milestones.

NOTE: Information on ALARA 

can be found in 10CFR20, 10CFR 

835, DOE 1993, ICRP 1989, 

NCRP 1993, NRC 1977, NRC 

1982, NRC 1993, NRC 2002, and 

PNL 1988.

Figure 3.2 Identifying Action Levels 

(Apply to Each Disposition Option Selected in Section 2.5) 

NUREG-1575, Supp. 1 3-4 January 2009 
MARSAME Identify Inputs to the Decision 

Action levels are radionuclide- or radiation-specific and in units of concentration or activity (e.g., 

Bq/kg of 137Cs, Bq/m2

 of alpha radiation, Bq of 60Co). Action levels may be provided, derived 

from dose- or risk-based standards, or converted into more convenient units for a specific 

measurement technique.1

 

More than one action level may be required to demonstrate compliance with a specific standard. 

For example, DOE Order 5400.5 Figure IV-1 (DOE 1993) provides limits for average total 

surface activity, maximum total surface activity, and maximum removable surface activity (see 

Appendix E). All three limits must be achieved to demonstrate compliance for disposition of the 

M&E. Sometimes multiple regulatory requirements may apply, for example transportation 

regulations combined with waste acceptance criteria and health protection standards. 

Action levels may be established based on total activity or incremental activity levels relative to 

background. Examples of incremental action levels include activity levels based on dose or risk 

above background, or interdiction at some quantity above background. For these types of action 

levels it is important to establish a representative reference material (Section 3.9) for 

comparison. 

Action levels may explicitly or implicitly require the use of a specific measurement technique 

(Section 5.9.1) or instrument (Section 5.9.2). These “method-based” requirements should be 

considered not only during survey design and implementation, but also during selection of 

disposition options and action levels. For example, U.S. Department of Transportation (DOT) 

regulations include package dose rate limits (49 CFR 173.441) as well as removable external 

radioactive contamination limits (49 CFR 173.443 and 177.843). Dose rate limits on external 

surfaces at one meter from package surfaces imply the use of in situ or direct measurements, 

although the selection of specific instrumentation is not specified. Measurements of removable 

contamination explicitly require the use of smears, but the procedure for collecting and analyzing 

the smear is not specified. The NRC regulations for transportation of radioactive packages (10 

CFR 71) replicate DOT regulations and define limits for “surface contaminated objects,” 

including fixed and non-fixed surface radionuclides (Appendix E.3.7). 

At this point, it is important to identify action levels appropriate for the disposition survey. If 

multiple action levels are identified, the planning team may decide to continue with the 

development of multiple survey designs that will be evaluated in Section 4.4. The decision maker 

and the planning team will need to evaluate the action levels and select the action level that best 

meets the DQOs developed for the survey. The selected action levels are used to develop 

decision rules in Section 3.7. Alternatively, the planning team may decide to revisit the selection 

of disposition options from the IA to further limit the scope of the disposition survey and 

eliminate some of the action levels. In either case, the selection of action levels will be finalized 

in Section 4.4 with the development of a disposition survey design. Information supporting the 

selection of an action level(s) is discussed in Sections 3.3.1 through 3.3.4. 

 

1

 Correctly converting action levels to counts or counts per minute (cpm) using the appropriate calibration may 

provide a useful comparison for real-time evaluation of field measurement results as long as field results (e.g., cpm) 

are converted to and recorded in the same radiological units as the action levels. 

January 2009 3-5 NUREG-1575, Supp. 1 
Identify Inputs to the Decision MARSAME

3.3.1 Identify Sources of Action Levels 

There are many potential sources of action levels available for use in developing disposition 

surveys. An action level may be based on— 

• Dose- or risk-based regulatory standard (i.e., disposition criterion), 

• Waste acceptance criteria at a disposal site, 

• Regulatory threshold standard (e.g., indistinguishable from background or no detectable 

radioactivity), 

• DOT regulations for shipping radioactive M&E, 

• Activity-based standard, 

• As low as reasonably achievable (ALARA) considerations, 

• Administrative limits, or 

• Limitations on technology (performance criteria for an analytical method). 

Appendix E provides information on some of the federal sources of action levels that can be 

applied to M&E. The list of sources for action levels is not exhaustive, but is intended to provide 

examples of different types of action levels that are referred to throughout this supplement. 

National and International organizations have published recommendations for action levels (e.g., 

NCRP 2002, ANSI 1999). These recommendations may be a useful source of action levels if 

approved by the appropriate authorities. 

As previously stated, in many cases the action levels will be dictated by the disposition option 

selected during the IA. For example, the action levels for M&E being considered for clearance 

may be a regulatory standard, whereas the action levels for M&E being considered for disposal 

as radioactive waste will often use the waste acceptance criteria for a disposal site. 

Multiple sources of action levels may be identified for a single disposition option. Waste 

acceptance criteria can be evaluated for several potential burial sites. 

In addition, a single source of action levels could be acceptable for more than one disposition 

option. Dose- and risk-based regulatory standards can be applied to both release and recycle 

scenarios, as well as for surficial or volumetric radioactivity. On the other hand, activity-based 

standards may have limited applicability, such as DOE Order 5400.5 (DOE 1993) Figure IV-1 

that only applies to release of M&E with surficial radioactivity. 

The identification of sources for action levels may affect other decisions made during 

development of a disposition survey design. Identification of survey units and spatial boundaries 

for a survey are often directly linked to the action levels. In addition, the expected levels of 

residual radioactivity identified during the IA (Section 2.6) will often suggest which disposition 

options are feasible. 

At a minimum the planning team should identify at least one source of action levels applicable to 

the disposition option(s) selected during the IA. Any information related to the action levels that 

may affect other decisions should also be listed. A partial list of information that may be 

available from sources of action levels includes— 

NUREG-1575, Supp. 1 3-6 January 2009 
MARSAME Identify Inputs to the Decision 

• Radionuclides of concern or types of radiation, 

• Assumptions regarding surficial (fixed and removable) or volumetric residual radioactivity, 

• Area or volume over which the residual radioactivity can be averaged, 

• Assumptions about potential disposition of the M&E (e.g., exposure scenarios, reuse vs. 

recycle), and 

• Conversions from dose or risk to activity or concentration (e.g., modeling and modeling 

assumptions). 

3.3.2 Finalize Selection of Action Levels 

In cases where more than one source of action levels is identified, it is necessary to select an 

action level as the basis for the disposition survey design. Generally, the source that provides the 

most restrictive action levels (i.e., the most protective of human health and the environment) will 

be appropriate for designing the disposition survey. If the planning team cannot determine which 

action levels are most restrictive, multiple survey designs should be developed and the selection 

of action levels will be determined by the selection of the most effective survey design (Section 

4.4). 

The expected location of residual radioactivity is an important factor in the selection of 

appropriate action levels. Some sources of action levels are only applicable for surficial 

radioactivity (e.g., Figure IV-1 of DOE 1993, DOT regulation 49 CFR 173.433). Other sources 

of action levels (e.g., ANSI 1999) or dose assessments for deriving action levels (e.g., NRC 

2003a) make assumptions about whether the residual radioactivity is surficial or volumetric, or a 

combination of both. Section 2.4.2.4 discusses the location of radioactivity associated with the 

M&E. 

While the location of residual radioactivity is important in determining the most restrictive action 

levels, other physical and radiological characteristics should also be considered. The final 

selection of action levels should be supported by the description of the M&E provided by the IA 

(Section 2.6). 

3.3.3 Modify Action Levels When Multiple Radionuclides are Present 

The implementation of action levels should be considered when evaluating whether they will be 

applied to a specific survey unit or project. Section 3.3.1 discusses potential sources for action 

levels, and Section 3.2 discusses the approach for selecting the radionuclides of concern. 

Calculating the relative ratios among multiple radionuclides and determining the state of 

equilibrium for decay series radionuclides is discussed in MARSSIM Section 4.3. This section 

describes how individual action levels can be combined and applied when more than one 

radionuclide is present. 

Action levels are often provided for types of radioactivity or groups of radionuclides. For 

example, DOE Order 5400.5 Figure IV-1 (DOE 1993) provides surface activity action levels for 

four groups of radionuclides (Appendix E). For the simple case in which the activity is entirely 

attributable to one radionuclide, the action levels for that radionuclide are used for comparison to 

January 2009 3-7 NUREG-1575, Supp. 1 
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survey data. In these examples, the disposition survey data may be obtained from direct 

measurements of activity, scanning with data logging, conveyorized survey monitor surveys, or 

other appropriate methods. 

Dose- or risk-based action levels may be radionuclide-specific. Each radionuclide-specific action 

level corresponds to the chosen disposition criterion (e.g., regulatory limit in terms of dose or 

risk). For example, ANSI 1999 provides surface and volumetric activity action levels for 

individual radionuclides. When multiple radionuclides are present at concentrations equal to the 

action levels, the total dose or risk for all radionuclides would exceed the disposition criterion. In 

these cases it is possible to modify the action levels based on relationships between the 

radionuclides of concern and still demonstrate compliance with the disposition criterion. 

The method used to modify the action levels depends on the radionuclides of concern and the 

selected measurement method. If the measurement method reports total activity for a type of 

radiation (e.g., gross α, β, or γ assays) the method is non-radionuclide specific and the guidance 

in Section 3.3.3.1 should be applied. If the measurement reports activity for individual 

radionuclides (e.g., gamma spectroscopy, alpha spectrometry) the method is radionuclide 

specific and the guidance in Section 3.3.3.3 should be applied. 

3.3.3.1 Modify Action Levels for Non-Radionuclide-Specific Measurement Methods 

For situations in which there are radionuclide-specific action levels and multiple radionuclides 

are present, a gross activity action level can be developed. Gross activity action levels are also 

discussed in Section 4.3.4 of MARSSIM. This approach enables field measurement of gross 

activity (using static direct measurements or scans), rather than determination of individual 

radionuclide activity, for comparison to the action levels. The gross activity action level for 

M&E with multiple radionuclides is calculated as follows: 

1. Determine the relative fraction (f) of the total activity contributed by the radionuclide.2

2. Obtain the action level for each radionuclide present. 

3. Substitute the values of f and action levels in the following equation. 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

++

=

n

n

AL

f ... AL

f

AL

f

2

2

1

1

1 Gross ALActivity (3-1) 

Where: 

fi = relative fraction of total activity contributed by radionuclide i (i = 1, 2,..., n) 

ALi = action level for radionuclide i

 

2

 The determination of relative fractions may be based on process knowledge, empirical data, or a combination of 

both. It may be difficult or impractical to determine the relative fractions contributed by all radionuclides of concern. 

The alternatives are to analyze each radionuclide independently, or use conservative assumptions to determine the 

relative fractions. Additional guidance is provided in MARSSIM Section 4.3. 

NUREG-1575, Supp. 1 3-8 January 2009 
MARSAME Identify Inputs to the Decision 

Example 1: Assume that 40% of the total radioactivity was contributed by a radionuclide 

with an action level of 1.4 Bq/cm2

 (8,400 dpm/100 cm2). An additional 40% of the total 

radioactivity was contributed by a radionuclide with an action level of 0.28 Bq/cm2 (1,700 

dpm/100 cm2), and the final 20% of the radioactivity was contributed by a radionuclide with 

an action level of 0.14 Bq/cm2

 (840 dpm/100 cm2). Using Equation 3-1: 

)cm100/dpm900,1(Bq/cm32.0

14.0

20.0

28.0

40.0

4.1

40.0

1 ALActivity Gross 2 2 =

⎟

⎠

⎞ ⎜

⎝

⎛ ++

=

Equation 3-1 may not be appropriate for survey units with radioactivity from multiple 

radionuclides having unknown or highly variable concentrations of radionuclides. In these 

situations, the best approach may be to select the most restrictive surface activity action level 

from the mixture of radionuclides present.3

 If the mixture contains radionuclides that cannot be 

measured using field survey equipment, such as 3

H or 55Fe, laboratory analyses of M&E samples 

may be necessary. 

3.3.3.2 Modify Action Levels for Non-Radionuclide-Specific Measurements of Decay-Series 

Radionuclides 

Demonstrating compliance with surface activity action levels for radionuclides of a decay series 

(e.g., radium, thorium, uranium) that emit both alpha and beta radiation may be demonstrated by 

assessing alpha, beta, or both radiations. However, relying on the use of alpha surface activity 

measurements often proves problematic because of the highly variable level of alpha attenuation 

by rough, porous, uneven, and dusty surfaces. Beta measurements typically provide a more 

accurate assessment of thorium and uranium (and their decay products) on most building 

surfaces because surface conditions cause significantly less attenuation of beta particles than 

alpha particles. Beta measurements, therefore, may provide a more accurate determination of 

surface activity than alpha measurements. 

The relationship of beta and alpha emissions from decay chains or various enrichments of 

uranium should be considered when determining the surface activity for comparison with the 

action level value(s). When the initial member of a decay series has a long half-life, the 

radioactivity associated with the subsequent members of the series will increase at a rate 

determined by the individual half-lives until all members of the decay series are present at 

activity levels equal to the activity of the initial member. This condition is known as secular 

equilibrium. Pages 4-6 and 4-7 in MARSSIM also provide a discussion on secular equilibrium. 

The difficulty with radionuclides that are part of a natural decay series is that time must pass for 

a sufficient number of half-lives of the longest-lived decay product that intervenes between a 

radionuclide and the initial member of a decay series in order to establish secular equilibrium. In 

the case of 232Th, the time to establish secular equilibrium is almost 40 years. This is because 

232Th decays into 228Ra, which has a half-life of 5.75 years. In the case of 238U, the time to 

establish secular equilibrium is approximately 2 million years. This is because 234U has a half-

 

3

 In Example 1, the most conservative action level is 0.14 Bq/cm2. 

January 2009 3-9 NUREG-1575, Supp. 1 
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life of approximately 250,000 years. 226Ra, another member of the 238U decay series, presents 

special problems. 226Ra decays into 222Rn, which is a noble gas that can escape the matrix and 

disrupt equilibrium. It is important to remember the reason for determining relationships between 

radionuclides. If the relationships are known or can be estimated,4

 the costs and amount of time 

required for performing measurements can be significantly reduced. The alternative to 

determining the relationships between radionuclides is performing radionuclide-specific 

measurements for each radionuclide of concern. 

Example 2: the radionuclide of concern is 232Th, and all of the decay products are in secular 

equilibrium. Assume that a gas proportional detector will be used for surface activity 

measurements. The detector’s efficiency is dependent upon the radionuclide mixture 

measured and the calibration source area. Guidance from the International Organization for 

Standardization (ISO 1988) states: 

“The dimensions of the calibration source should be sufficient to cover the 

window of the instrument detector. Where, in extreme cases, sources of such 

dimensions are not available, sequential measurements with smaller distributed 

sources of at least 100 cm2 active area shall be carried out. These measurements 

shall cover the whole window area or at least representative fractions of it and 

shall result in an average value for the instrument efficiency.” 

The concentration of 232Th is inferred from a measurement that includes the initial member of 

the decay series and all of its decay products. The efficiency of such measurements, relative to 

each decay of 232Th, can be greater than 100%. The efficiency, relative to each decay of 232Th, 

is calculated by weighting the individual efficiencies from each of the radionuclides present 

(Table 3.1). 

It is important to recognize that if the action level for 232Th includes the entire 232Th decay series, 

the total efficiency for 232Th must account for all of the radiations in the decay series. The total 

weighted efficiency calculated in Table 3.1 may be used to modify action levels for nonradionuclide specific measurements using a gas proportional counter to measure thorium series 

radionuclides. The total weighted efficiency can be substituted into an equation (e.g., MARSSIM 

Equations 6-1, 6-2, 6-3, or 6-4) to convert the action level (e.g., activity units) into measurement 

units (e.g., counts or cpm). The modified action level can then be compared directly to the 

measurement results for a real time assessment of the data. 

 

4

 There are risks and tradeoffs associated with using estimated values. The planning team should compare the 

consequences of potential decision errors with the resources required to improve the quality of existing data to 

determine the appropriate approach for a specific project. 

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Table 3.1 Example Detector Efficiency Calculation (232Th in Complete Equilibrium with its 

Decay Products) Using a Gas Proportional Detector 

Radionuclide

Energy* 

(keV) Fraction

Instrument 

Efficiency

Surface 

Efficiency

Weighted 

Efficiency

232Th 4.00 MeV alpha 1 0.40 0.25 0.1 

228Ra 7.2 keV beta 1 0 0 0 

228Ac 377 keV beta 1 0.54 0.50 0.27 

228Th 5.40 MeV alpha 1 0.40 0.25 0.1 

224Ra 5.67 MeV alpha 1 0.40 0.25 0.1 

220Rn 6.29 MeV alpha 1 0.40 0.25 0.1 

216Po 6.78 MeV alpha 1 0.40 0.25 0.1 

212Pb 102 keV beta 1 0.40 0.25 0.1 

212Bi 769 keV beta 0.64 0.66 0.50 0.211 

212Bi 6.05 MeV alpha 0.36 0.40 0.25 0.036 

212Po 8.78 MeV alpha 0.64 0.40 0.25 0.064 

208Tl 557 keV beta 0.36 0.58 0.50 0.104 

Total efficiency = 1.29 *

Alpha energies are weighted averages based on relative abundance of major particle emissions totaling at least 90% 

of the total emissions. Beta energies are average energies. Source: Japanese Atomic Energy Research Institute data 

from NRC Radiological Toolbox Version 1.0.0 (NRC 2003b). Table adapted from NUREG-1761 Table 4.3 (NRC 

2002a). 

3.3.3.3 Modify Action Levels for Radionuclide-Specific Measurement Methods 

In many cases action levels correspond to a disposition criterion (e.g., a regulatory limit) in terms 

of dose or risk. When multiple radionuclides are present at concentrations equal to the action 

levels, the total dose or risk for all radionuclides would exceed a dose- or risk-based disposition 

criterion. In this case, the individual action levels would need to be adjusted to account for the 

presence of multiple radionuclides contributing to the total dose or risk. The surrogate 

measurements discussed in this section describe adjusting action levels to account for multiple 

radionuclides when radionuclide-specific analyses of media samples or radionuclide–specific in 

situ measurements (e.g., in situ gamma spectroscopy) are performed. The use of surrogate 

measurements is also described in Section 4.3.2 of MARSSIM. Other methods used to account 

for the presence of multiple radionuclides include the use of the unity rule (MARSSIM Section 

4.3.3) and development of a gross activity action level to adjust the individual radionuclide 

action levels (see Section 3.3.3.1 and MARSSIM Section 4.3.4). 

The unity rule is satisfied when radionuclide mixtures yield a combined fractional concentration 

limit that is less than or equal to one. The unity rule can be described by Equation 3-2: 

1

2

2

1

1 ≤+++

n

n

AL

C ... AL

C

AL

C (3-2) 

Where: 

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Ci = concentration or activity value for each individual radionuclide (i = 1, 2, ..., n)

5

ALi = action level value for each individual radionuclide (i = 1, 2, ..., n) 

For the disposition of M&E that contain multiple radionuclides, it may be possible to measure 

just one of the radionuclides and still demonstrate compliance for all of the radionuclides present 

in the M&E through the use of surrogate measurements. In the use of surrogates, it is often 

difficult to establish a “consistent” ratio between two or more radionuclides. Rather than follow 

prescriptive guidance on acceptable levels of variability for the surrogate ratio, the planning team 

should review the data collected to establish the ratio (e.g., from preliminary surveys or process 

knowledge) and account for the variability as a measurement quality objective (MQO) during 

selection of a measurement method (see Sections 3.8, 5.5, and 7.3). The action levels must then 

be modified to account for the fact that one radionuclide is being used to account for the 

presence of one or more other radionuclides. 

Action levels for the measured radionuclide are modified (ALmeas,mod) to account for a single 

inferred radionuclide (e.g., inferring 55Fe based on the presence of 60Co) using Equation 3-3 

(modified from Equation 6.2 in Abelquist 2001): 

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

meas er

meas

er

er

meas meas

ALAL

C

C

AL AL AL

inf

inf

inf

mod, )( (3-3) 

Where: 

ALmeas,mod = modified action level for the radionuclide being measured 

ALmeas = action level for the radionuclide being measured 

ALinfer = action level for the inferred radionuclide (i.e., not measured) 

Cinfer/Cmeas = surrogate ratio of the inferred to the measured radionuclide 

 

When the measured radionuclide will be used as a surrogate for more than one radionuclide, 

ALmeas,mod can be calculated using Equation 3-4 (MARSSIM Equation I-14): 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+++

=

n

n

meas,

AL

R ... ALR

AL

R

AL

AL

3

3

2

2

1

mod 1

1 (3-4) 

Where: 

AL1 = action level for the measured radionuclide by itself 

AL2 = action level for the second radionuclide (or first radionuclide being inferred) 

that is being inferred by the measured radionuclide 

R2 = ratio of concentration of the second radionuclide to that of the measured 

radionuclide 

 

5

 C (radionuclide concentration) must be in the same units as the action level. If the action level is provided in 

activity units, C will also be in units of activity. 

NUREG-1575, Supp. 1 3-12 January 2009 
MARSAME Identify Inputs to the Decision 

AL3 = action level for the third radionuclide (or second radionuclide being inferred) 

that is being inferred by the measured radionuclide 

R3 = ratio of concentration of the third radionuclide to that of the measured 

radionuclide 

ALn = action level for subsequent radionuclides being inferred by the measured 

radionuclide 

Rn = ratio of concentration of subsequent radionuclides to that of the measured 

radionuclide 

Recall that the benefit of using surrogates is the avoidance of costly laboratory-based analytical 

methods to provide estimates of activity for individual radionuclides of concern. Surrogates often 

emit γ-rays, which enable the use of noninvasive and nondestructive methods. However, α- and 

β-emitting radionuclides can also be used as surrogates, depending on the objectives of the 

survey and project-specific information. The surrogates come in two forms: (1) surrogates by 

virtue of a decay series, and (2) surrogates by virtue of association. Surrogates that are part of a 

decay series are discussed in Section 3.3.3.2. Radionuclides that are not part of a decay series 

have the potential to be surrogates when they are produced by the same nuclear process (usually 

fission or activation) and have similar chemical properties and release mechanisms. However, 

this type of surrogate needs special attention because there must be a consistent ratio between the 

measured radionuclide and surrogate, which is not always easy to demonstrate. For example, in 

the case of nuclear power reactors, 60Co can be used as a surrogate of 55Fe and 63Ni because both 

are activation-corrosion products with similar chemical properties. Similarly, 137Cs can be used 

as a surrogate for the β-emitting 90Sr because both are fission products and generally are found in 

soluble cationic forms. While 137Cs has been suggested as a possible surrogate for 99Tc, it must 

be noted that 99Tc has different chemical properties and, in nuclear power reactors, it has 

different release mechanisms. Additional information is available on surrogates and establishing 

ratios (MARSSIM 2002, NRC 2000, and EPRI 2003). 

3.3.4 Evaluate Interface With Exposure Pathway Models 

Disposition criteria may be provided in units that cannot be measured directly, for example total 

effective dose equivalent (TEDE) or lifetime risk of cancer incidence. These criteria are usually 

converted into action levels with concentration or activity units. This conversion is typically 

accomplished using exposure pathway models, such as RESRAD-Recycle for metals (DOE 

2005). While the selection and application of these models is outside the scope of MARSAME, 

the assumptions used to develop action levels should be considered during development of a 

disposition survey design. 

Alternatively, disposition criteria may be provided in units more easily measured. In general, 

there are assumptions used in the development of these types of action levels. It is the 

responsibility of the authority issuing the action levels to ensure regulatory involvement in their 

development and to document and make assumptions available to users. 

The assumptions used to design the disposition survey (Section 4.4) need to match the 

assumptions used to develop the action levels. Examples of parameters that could affect 

disposition survey designs and could be inputs to exposure pathway models include— 

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• Volume, mass, or surface area of M&E; 

• Accessibility; 

• Physical and chemical characteristics of radionuclides or radiations of concern (types of 

emissions, energies, half-lives, known or expected relationships); 

• Distribution of radioactivity (uniform or variable); 

• Location of radioactivity (surficial or volumetric); and 

• Fixed or removable radioactivity, or some combination of both. 

3.4 Describe the Parameter of Interest 

The parameter of interest is the population parameter (e.g., mean, median, percentile, or total 

amount) that the planning team considers to be important for making decisions about the target 

population (EPA 2006a). The target population is the collection of all possible measurement 

results that could be used to support a disposition decision concerning the M&E being 

investigated. The target population is defined by the selection of survey unit boundaries (see 

Section 3.6), since a separate disposition decision will be made for each survey unit. 

The parameter of interest may be specified as part of the action level. For example, DOE Order 

5400.5 Figure IV-1 (DOE 1993) lists action levels (i.e., surface concentration limits in dpm per 

100 cm2), parameters of interest (i.e., mean and maximum values), and target populations (i.e., 

1 m2 for average concentration and 100 cm2 for maximum and removable limits). 

Alternatively, the planning team may need to select the parameter of interest based on projectspecific needs and considerations. The most common parameter used in decision-making is the 

mean because the mean is frequently used to model random exposure to environmental 

contamination (EPA 2006a). The more complex the parameter of interest, the more complex will 

be the decision rule (Section 3.7) and accompanying survey design. 

3.5 Identify Alternative Actions 

Before decision rules can be developed, the planning team needs to identify the alternative 

actions based on the disposition options identified in Section 2.5. Alternative actions are the 

possible actions that may be taken for disposition of M&E, including an alternative that requires 

no action. Table 3.2 lists examples of alternative actions for disposition options provided in 

Section 2.5. 

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Table 3.2 Example Alternative Actions 

Disposition Option Alternative Actions

Reuse without radiological controls 

Release for reuse Reuse with radiological controls 

Maintain current level of radiological control and do not reuse (no action) 

Recycle without radiological controls 

Release for recycle Recycle with radiological controls 

Maintain current level of radiological control and do not recycle (no action) 

Dispose of M&E as municipal or industrial waste 

Dispose of M&E as low-level radioactive waste 

Dispose of M&E as high-level radioactive waste 

Dispose of M&E as transuranic (TRU) waste 

Release for disposal 

Maintain current level of radiological control without disposal (no action) 

Remove M&E from general commerce and initiate radiological controls 

Decide to use or accept M&E for a specific application 

Decide not to use or accept M&E for a specific application 

Interdiction 

Continue unrestricted use of M&E (no action) 

3.6 Identify Survey Units 

To make a decision concerning the disposition of M&E it is necessary to describe the total 

collection of M&E being investigated and define what segment of the total will be considered for 

individual decisions. In other words, the planning team must specify the amount of M&E for 

which a separate disposition decision will be made. When the M&E consist of discrete items 

surveyed individually (e.g., hand tools) this task is simple. However, disposition decisions are 

often required for more complex situations (e.g., bulk dispersible materials, excavation 

equipment). 

Survey unit boundaries should be clearly defined in order to know exactly what amount of M&E 

is covered by a single decision. This clear and unambiguous definition will make data 

interpretation more straightforward. 

An M&E survey unit is the specific lot, amount, or piece of M&E on which measurements are 

made to support a disposition decision concerning that specific lot, amount, or piece of M&E. 

The purpose of this section is to identify the information that will be used to define the survey 

unit boundaries. The expected output from this section is the identification of survey unit 

boundaries that will be used to develop the decision rule in Section 3.7. Figure 3.3 shows the 

process used to develop survey unit boundaries. 

January 2009 3-15 NUREG-1575, Supp. 1 
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Figure 3.3 Developing Survey Unit Boundaries 

(Apply to All Impacted M&E for Each Set of Action Levels Identified in Section 3.3) 

NUREG-1575, Supp. 1 3-16 January 2009 
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Survey unit boundaries are affected by many variables associated with the action level, physical 

properties of the M&E, characteristics of the radionuclides of concern, and available 

measurement techniques. Variables affecting the definition of survey units include— 

• Action Level (Section 3.3) 

o Assumptions used to develop the action level (e.g., surficial [fixed or removable] or 

volumetric, Section 3.3.1) 

o Modeling assumptions used to convert from dose or risk to concentration or activity 

(Section 3.3.4) 

• Physical Properties of the M&E (Section 2.4.1) 

o Dimensions (i.e., size, shape, surface area) 

o Complexity (i.e., number and type of components) 

o Accessibility (i.e., measurability) 

o Inherent value 

• Radiological Attributes of the M&E (Section 2.4.2) 

o Radionuclides of concern (e.g., major radiations and energies, half-life) 

o Expected activity levels (e.g., average, range, variance, known or potential relationships) 

o Distribution (i.e., uniform or non-uniform) 

o Location (i.e., surficial [fixed or removable] or volumetric) 

• Available Measurement Methods (Section 3.8, Section 5.9) 

o Measurement quality objectives (Section 3.8, Section 5.5, Section 7.3) 

o Measurement performance characteristics (Section 5.5) 

3.6.1 Define Initial Survey Unit Boundaries 

Initial survey unit boundaries should be developed based on one primary factor and modified, as 

needed, using additional variables. MARSAME recommends using the assumptions used to 

develop the action levels as the primary factor used to develop survey unit boundaries. The 

modifying variables will usually be specific to a measurement technique, or determined by the 

M&E being investigated.6

In many cases the action levels will define the survey unit boundaries. For example, DOE Order 

5400.5 Figure IV-1 (DOE 1993) provides action levels for surface activity. The survey unit 

boundaries are restricted to the surface of the M&E being investigated. Alternatively, NUREG1640 (NRC 2003a) provides modeling assumptions used to develop the action levels for different 

materials. Radionuclide-specific action levels are provided for separate materials (e.g., ferrous 

metals, concrete) for both surficial and volumetric radioactivity. In addition, each action level 

lists the limiting exposure scenario. For example, exposure scenarios for concrete (NRC 2003a) 

include— 

 

6

 This approach differs from guidance found in MARSSIM Section 4.6. While MARSSIM also uses the assumptions 

used to develop the action levels (i.e., derived concentration guideline levels [DCGLs] in MARSSIM) as the primary 

factor in developing survey unit boundaries, the modifications are different. MARSSIM guidance allows increasing 

and decreasing survey unit size based on classification. In MARSSIM, Class 1 survey units generally are smaller 

than the area assumed in the exposure pathway model, while MARSSIM allows Class 3 survey units to be larger in 

area. Additional modifications to survey unit boundaries in MARSSIM can be made based on site-specific variables 

(e.g., room size, topography). 

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• Worker processing concrete rubble at a satellite facility, 

• Truck driver hauling concrete rubble, 

• Worker building a road using recycled concrete, 

• Driver on a road built using recycled concrete, 

• Worker handling concrete rubble at an industrial landfill, 

• Worker handling concrete rubble at a municipal landfill, 

• Individual drinking groundwater contaminated with leachate from an industrial landfill, and 

• Individual drinking groundwater contaminated with leachate from a municipal landfill. 

Each exposure scenario assumes different conditions that help define survey unit boundaries. For 

example, a truck driver hauling concrete rubble would be exposed to one truckload of concrete 

rubble, so the survey unit boundaries would be defined by a truckload of concrete rubble (i.e., 

2×104

 kg [22 tons] or 8.3 m3; NRC 2003a). 

3.6.2 Modify Initial Survey Unit Boundaries 

Modifications to survey unit boundaries are expected based on practical constraints for data 

collection activities. In most cases smaller survey units will be acceptable, since a reduction in 

size would not result in an increased dose or risk. Increasing the size of the survey unit may 

result in increased dose or risk, and therefore requires approval of the planning team and 

stakeholders. 

Constraints on collecting data are often associated with specific measurement techniques, which 

could affect the survey unit boundaries. For example, using in situ gamma spectroscopy may 

restrict survey unit sizes based on the field of view of the detector, the penetrating power of the 

gamma energies being measured, or the assumptions used to develop the instrument efficiency. 

Alternatively, using a box counter or portal monitor may restrict survey unit sizes based on what 

will fit inside or through the detector. Information on measurement parameters affecting 

disposition survey design is provided in Section 3.8. Section 5.9 and Appendix D provide 

detailed information on specific measurement methods. 

The M&E being investigated may also cause modifications to survey unit boundaries. These 

modifications are often associated with physical characteristics (e.g., size, shape). Identification 

of actual survey units as part of the final disposition survey design is discussed in Chapter 4. 

3.7 Develop a Decision Rule 

In order to design a disposition survey, the user should define a decision rule describing the 

conditions for selecting between alternative actions. The planning team should assume that ideal 

data are available and there is no uncertainty in the decision making process. The available data 

are integrated into an “if...then...” statement, which is the theoretical decision rule.7

 

7

 This is called a theoretical decision rule because it is stated in terms of the true value for the parameter of interest, 

even though in reality this value cannot be known. An operational decision rule that is based on an estimate of the 

target population parameter of interest will be incorporated as part of the final disposition survey design selected and 

documented in Chapter 4. 

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MARSAME Identify Inputs to the Decision 

The theoretical decision rule is constructed by combining the action level (Section 3.3) and the 

parameter of interest (Section 3.4) with the alternative actions (Section 3.5) in an “if...then...” 

statement. 

For example: 

Hypothetically, if the mean concentration of 226Ra in 20,000 kg (8.3 m3, one 

truckload) of concrete rubble is less than the clearance action level of 0.34 Bq/g 

for volumetric radioactivity, then the concrete rubble can be cleared, otherwise 

radiological control of the concrete will continue. 

It may be necessary to develop more than one decision rule. For example, if more than one 

action level is selected in Section 3.3, a separate decision rule needs to be developed for each 

action level. In addition, selection of multiple disposition options in Section 2.5 (e.g., release and 

disposal as low-level radioactive waste) may result in multiple alternative actions requiring 

multiple decisions and multiple decision rules. For example, 

Hypothetically, if the mean concentration of 226Ra in 20,000 kg (8.3 m3, one 

truckload) of concrete rubble is less than the clearance action level of 0.34 Bq/g 

for volumetric radioactivity, then the concrete rubble can be cleared, otherwise 

the concrete will be considered for disposal as low-level radioactive waste. If the 

concrete rubble meets the waste acceptance criteria for the low-level radioactive 

waste disposal facility (e.g., mean and total activity levels, chemical and physical 

form, toxicity) the concrete will be packaged and transported for disposal, 

otherwise radiological control of the concrete will continue. 

3.8 Develop Inputs for Selection of Provisional Measurement Methods 

The identification and evaluation of provisional measurement methods is an important step in 

developing a disposition survey design. A measurement method is the combination of 

instrumentation (e.g., GM detector, NaI[Tl] scintillation detector, gamma spectrometer) with a 

measurement technique (i.e., scan, in situ, sample collection). The selection of a measurement 

method is discussed in more detail in Section 5.9. The availability of measurement methods and 

the amount of resources required to implement specific measurement methods is an important 

factor in selecting between different survey designs, or in reducing the number of options to be 

considered when developing potential disposition survey designs. 

There are two potential results of this evaluation of provisional measurement methods. First, the 

evaluation may identify specific measurement methods that will be included in the final 

documentation of the selected disposition survey design (see Section 4.5). For example, scanning 

100% of a piece of equipment using a 2-inch by 2-inch NaI(Tl) detector at a specified height 

above the surface using a specified scan speed may be identified as the measurement method. 

Second, the evaluation may identify characteristics of a measurement method required to meet 

the objectives of a survey. These characteristics are called measurement quality objectives 

(MQOs). Section 5.5 and Section 7.3 provide additional information on MQOs applied to 

disposition surveys. 

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Examples of MQOs are described in the following sections. A list of minimum MQOs required 

for a survey can be developed and documented in the final disposition survey design (see Section 

4.5). The selection of a measurement technique that meets the MQOs is accomplished during 

implementation of the survey design. 

This section focuses on measurability. Most of the variables that need to be considered for the 

identification of measurement techniques have been discussed earlier in this chapter. The 

identification of measurement methods is directly or indirectly related to— 

• Identification of radionuclides of concern, 

• Location of residual radioactivity, 

• Application of action levels, 

• Physical properties of the M&E, 

• Distribution of residual radioactivity, 

• Expected levels of residual radioactivity, 

• Relationships between radionuclide activities, 

• Equilibrium status of natural decay series, and 

• Background radioactivity. 

Measurable radioactivity is radioactivity that can be quantified and meets the DQOs and MQOs 

established for the survey. Radioactivity that is quantified using known or predicted relationships 

developed from process knowledge or preliminary measurements is considered measurable as 

long as the relationships are developed and verified as specified in the DQOs and MQOs. The 

Multi-Agency Radiological Laboratory Analytical Protocols manual (MARLAP 2004)8

 lists 

method performance characteristics that should be considered when establishing MQOs for a 

project. This list is not intended to be exhaustive: 

• The method uncertainty at a specified concentration (expressed as a standard deviation); 

• The method’s detection capability (expressed as the minimum detectable concentration, or 

MDC); 

• The method’s quantification capability (expressed as the minimum quantifiable 

concentration, or MQC); 

• The method’s range, which defines the method’s ability to measure the radionuclide of 

concern over some specified range of concentration; 

• The method’s specificity, which refers to the ability of the method to measure the 

radionuclide of concern in the presence of interferences; and 

• The method’s ruggedness, which refers to the relative stability of method performance for 

small variations in method parameter values. 

Project-specific method performance characteristics should be developed as necessary and may 

or may not include the characteristics listed here. Once lists of performance characteristics that 

affect measurability have been identified, the planning team should develop MQOs describing 

 

8

 MARLAP was developed for selecting laboratory protocols. Applying the framework and performance-based 

approach for planning and conducting radiological work from MARLAP to the selection of field measurement 

techniques is an expansion of the original scope and purpose of MARLAP. 

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MARSAME Identify Inputs to the Decision 

the project-specific objectives for potential measurement techniques. Potential measurement 

techniques should be evaluated against the MQOs to determine if they are capable of meeting the 

objectives for measurability. 

3.8.1 Measurement Method Uncertainty 

The required measurement method uncertainty is perhaps the most important MQO to be 

established during the planning process. Section 4.2 discusses the rationale involved in setting 

the required measurement method uncertainty and developing statistical hypothesis tests for the 

implementation of disposition decision rules using measurement data. Section 5.5 discusses the 

application of MQOs, including the measurement method uncertainty, to disposition surveys for 

M&E. Section 7.3 discusses procedures for determining the required measurement method 

uncertainty and whether or not it has been achieved. 

MARLAP uses the term method uncertainty to refer to the predicted uncertainty of a measured 

value that would likely result from the performance of a measurement at a specified 

concentration, typically the action level. Reasonable values for method uncertainty can be 

predicted for a particular measurement technique based on typical values for specific parameters 

(e.g., count time, efficiency) and process knowledge for the M&E being investigated (see 

Sections 5.5 and 7.3). The MQO for measurement method uncertainty is related to the width of 

the gray region (Section 4.2.2). The required measurement method uncertainty is directly related 

to the MDC and the MQC discussed below. 

The distinction between imprecision and bias as a data quality indicator depends on context. 

Additional information on data quality indicators can be found in MARSSIM Appendix N and 

EPA QA/G-5 (EPA 2002a). A reliable estimate of bias requires a data set that includes many 

measurements, so MARSAME and MARLAP focus on developing an MQO for measurement 

method uncertainty. Measurement method uncertainty effectively combines imprecision and bias 

into a single parameter whose interpretation does not depend on context. This approach assumes 

that all potential sources of bias present in the measurement process have been considered in the 

estimation of the measurement uncertainty and, if not, that any appreciable bias would only be 

detected after a number of measurements of quality control (QC) and performance evaluation 

samples have been performed (see the QC discussion in Section 5.10). MARLAP Appendix C 

provides examples on developing MQOs for measurement method uncertainty of laboratory 

measurement techniques. 

3.8.2 Detection Capability 

The MDC (see Sections 5.7 and 7.5) is recommended as the MQO for defining the detection 

capability, and is an appropriate MQO when decisions are to be made based on a single 

measurement as to whether excess radioactivity is present or not. The MDC must not exceed the 

action level if the MDC is to be used as a decision parameter. Chapter 5 provides guidance on 

implementation of the selected measurement technique, including calculation of the MDC. 

Additional information on calculating the MDC can be found in MARSSIM (Section 6.7, 

examples in Appendix H) and MARLAP (Chapter 19, Appendix C). 

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3.8.3 Quantification Capability 

When the average of several measurements will be compared to a disposition criterion, an MQO 

more stringent than the MDC is required. The MQC (see Sections 5.8 and 7.6) is recommended 

as the parameter for defining the measurement capability for making quantitative comparisons of 

averages to a limit. An MQO for the required measurement method uncertainty (Section 5.6) is 

related to an MQO for the quantification capability because an MQC is defined as the 

concentration at which a specified relative standard uncertainty is achieved. MARLAP presents 

three reasons why it is important to consider this measurement method performance 

characteristic: 

1. To emphasize the importance of the quantification capability of a measurement technique for 

instances when the issue is not whether a radionuclide is present or not (e.g., measuring 238U 

in soil where the activity is inherent) but rather how precisely the radionuclide can be 

measured, 

2. To promote the MQC as an important measurement method performance characteristic for 

comparison of measurement techniques, and 

3. To provide an alternative to the overemphasis on establishing required MDCs in instances 

where detection (i.e., reliably distinguishing a radionuclide concentration from zero) is not 

the key analytical issue. 

The MQC must not exceed the action level if the MQC is to be used as a decision parameter.

Chapter 5 provides guidance on implementation of the selected measurement technique, 

including calculation of the MQC. Section 5.8 discusses issues related to measurement 

quantifiability. Section 7.6 provides information on the statistical basis of the MQC calculation 

including example calculations. Additional information on calculating the MQC can be found in 

MARLAP Chapter 19, with examples in MARLAP Appendix C. 

3.8.4 Range 

The expected concentration range for a radionuclide of concern (see Section 2.4.2) may be an 

important measurement method performance characteristic. Most radiation measurement 

techniques are capable of measuring over a wide range of radionuclide concentrations. However, 

if the expected concentration range is large, the range should be identified as an important 

measurement method performance characteristic and an MQO should be developed. The MQO 

for the acceptable range should be a conservative estimate. This will help prevent the selection of 

measurement techniques that cannot accommodate the actual concentration range. 

3.8.5 Specificity 

Specificity is the ability of the measurement method to measure the radionuclide of concern in 

the presence of interferences. To determine if specificity is an important measurement method 

performance characteristic, the planning team will need information on expected concentration 

ranges for the radionuclides of concern and other chemical and radionuclide constituents, along 

with chemical and physical attributes of the M&E being investigated (see Section 2.4). The 

importance of specificity depends on— 

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• The chemical and physical characteristics of the M&E being investigated, 

• The chemical and physical characteristics of the residual radioactivity, and 

• The expected concentration range for the radionuclides of concern. 

If potential interferences are identified (e.g., inherent radioactivity, similar radiations), an MQO 

should be established for specificity. 

If inherent radioactivity is associated with the M&E being investigated, a method that measures 

total activity may not be acceptable. Consider concrete, which contains measurable levels of 

naturally occurring radioactivity and emits radiation in the form of alpha particles, beta particles, 

and photons. If the action level for the radionuclide of concern is close to background (e.g., 

within a factor of 3) gross measurement methods may not meet the survey objectives. 

Performing gross alpha measurements using a gas proportional detector may not provide an 

acceptable MDC or MQC for plutonium isotopes, where a more specific measurement method 

such as alpha spectrometry following radiochemical separation would be acceptable. 

Radionuclides have similar radiations if they emit radiations of the same type (i.e., alpha, beta, 

photon) with similar energies. For example, both 226Ra and 235U emit a gamma ray with energy 

of approximately 186 keV. Gamma spectroscopy may not be able to resolve mixtures of these 

two radionuclides, which are both associated with naturally occurring radioactivity. More 

specific methods involving ingrowth of 226Ra decay products or chemical separation prior to 

measurement can be used to accurately quantify the radionuclides. 

Documented measurement methods should include information on specificity. MARSSIM Table 

7.2 lists examples of references providing laboratory measurement methods. NUREG-1506 

(NRC 1995) provides generic information on field measurement techniques, but most field 

measurement methods are documented in proprietary SOPs. If specificity is identified as an 

important issue for a project, consultation with an expert in radiometrics or radiochemistry is 

recommended. 

3.8.6 Ruggedness 

For a project that involves field measurements that are performed in hostile, hazardous, or 

variable environments, or laboratory measurements that are complex in terms of chemical and 

physical characteristics, the measurement method’s ruggedness may be an important method 

performance characteristic. Ruggedness refers to the relative stability of the measurement 

technique’s performance when small variations in method parameter values are made. For field 

measurements the changes may include temperature, humidity, or atmospheric pressure. For 

laboratory measurements, a change in pH or the quantity of a reagent may be important. In order 

to determine if ruggedness is an important measurement method performance characteristic, the 

planning team needs detailed information on the chemical and physical characteristics of the 

M&E being investigated and operating parameters for the radiation instruments used by the 

measurement technique. Information on the chemical and physical characteristics of the M&E is 

available as outputs from the IA. Information on the operating parameters for specific 

instruments should be available from the instrument manufacturer. Generic information for 

radiation detector operating parameters may be found in consensus standards. A limited list of 

examples of consensus standards is provided in Table 3.3. 

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Table 3.3 Examples of Consensus Standards for Evaluating Ruggedness 

Standard Number Title

ANSI N42.12-1994 American National Standard Calibration and Usage of Thallium-Activated 

Sodium Iodide Detector Systems for Assay of Radionuclides 

ANSI N42.17A-2003 American National Standard Performance Specifications for Health Physics 

Instrumentation – Portable Instrumentation for Use in Normal Environmental 

Conditions 

ANSI N42.17C-1989 American National Standard Performance Specifications for Health Physics 

Instrumentation – Portable Instrumentation for Use in Extreme 

Environmental Conditions 

ANSI N42.34-2003 American National Standard Performance Criteria for Hand-held Instruments 

for the Detection and Identification of Radionuclides 

IEEE 309-1999/ 

ANSI N42.3-1999 

Institute of Electrical and Electronics Engineers, Inc. Standard Test 

Procedures and Bases for Geiger Mueller Counters 

ASTM E1169-2002 Standard Guide for Conducting Ruggedness Tests 

If it is determined that measurement method ruggedness is an important performance 

characteristic, an MQO should be developed. The MQO may require performance data that 

demonstrate the measurement technique’s ruggedness for specified changes in select 

measurement method parameters. Alternatively, the MQO could list the acceptable ranges for 

select measurement method parameters and monitor the parameters as part of the QC program 

for the project (Section 5.10). For example, sodium iodide detectors are required to perform 

within 15% of the calibrated response between 0 and 40 °C (32 and 104 °F, respectively) (ANSI 

1994). The disposition survey design may call for a work stoppage at temperatures outside this 

range, or an increase in the frequency of QC measurements at temperatures outside this range. 

3.9 Identify Reference Materials 

Action levels may be developed that are related to background radioactivity, either based on an 

incremental dose or risk above background, as an administrative limit based on background, or 

as a limit on technology (e.g., minimum detectable concentration). For situations where the 

action levels are incremental above background, reference materials should be identified to 

provide an estimate of background. MARSSIM Section 4.5 provides guidance on determining 

when a reference material is required. 

Reference materials are used to develop an estimate of the distribution of background 

radioactivity that can be compared to the measurements performed in a comparable survey unit. 

The reference material is selected to provide information on the level of radioactivity that would 

be present if the M&E being investigated had not been radiologically impacted. 

Whenever possible, reference data should be obtained by performing a survey of the M&E 

before it comes in contact with radiological materials. The M&E can then be surveyed prior to 

leaving the area to determine the level of residual radioactivity. This works especially well for 

decommissioning or cleanup applications where M&E are brought into a radiologically 

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MARSAME Identify Inputs to the Decision 

controlled area for a limited time and a specific application. Unfortunately, there are numerous 

situations where pre-contact surveys are not possible. 

If the M&E cannot be used as its own reference material, it is necessary to identify reference 

material that is representative of the M&E being investigated. Non-impacted M&E that closely 

resembles the impacted M&E being investigated (i.e., similar chemical, physical, and 

radiological characteristics) will generally be acceptable as reference material. For example, if 

the conceptual model shows that only surficial activity is expected, the impacted surface may be 

removed and the non-impacted volume used as the reference material. When similar materials 

are not available, the best match available should be used as reference material. It may be 

necessary to evaluate more than one source of reference material before an acceptable match is 

identified. It may be important to perform reference material surveys in areas of low ambient 

background. Consider M&E consisting of individual objects that are small relative to the size of 

the detector used to perform the measurements. When each object receives a separate 

measurement, the ambient background may have a larger impact on the measurement than the 

background contributed by the M&E itself. 

As shown in Table B.1 in Appendix B, background radionuclide concentrations for materials can 

vary significantly. For example, concentrations for thorium series radionuclides in concrete can 

range from 15 to 120 Bq/kg (Eicholz 1980), so it is important to identify an appropriate reference 

material. 

The planning team should understand that background is variable. Ambient background can 

change with location and over time. It may be possible to simply move the M&E being 

investigated to an area with a lower ambient background to improve the detection capability of a 

measurement method. Local conditions (e.g., temperature, barometric pressure, precipitation) 

can cause variations in ambient background as discussed in NUREG-1501 (NRC 1994). 

NUREG-1505 (NRC 1998a) Chapter 13 provides information on accounting for variability in 

background. 

The planning team should evaluate the process knowledge from the IA and use professional 

judgment to identify M&E that require reference materials, and identify potential reference 

materials to support the disposition survey. 

3.10 Evaluate an Existing Survey Design 

It is not necessary to develop a new survey design for all M&E being investigated. Existing 

survey designs are often available for routine or repetitive applications. If an existing survey 

design is identified, the planning team or decision maker should evaluate the applicability of the 

existing design to the current investigation. 

Standardized survey designs for operating facilities are often documented in the form of standard 

operating procedures (SOPs, see Section 4.5.1). In other cases, existing survey designs may have 

been developed for similar projects. A description of the M&E that can be measured should be 

included in each existing SOP or survey design. If the description matches the M&E being 

investigated, the existing SOP or survey design can be used to perform the disposition survey. If 

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the description of the M&E is incomplete or vague, or the M&E do not match the description, a 

more detailed evaluation may be performed to determine the acceptability of the existing survey 

design. 

Personnel familiar with the existing survey design and the proposed application should perform 

the detailed evaluation of an existing survey design. All supporting documentation used to 

develop the existing survey design should be available for the evaluator(s), not just the SOP or 

survey design being reviewed. 

The detailed evaluation should determine whether the M&E are measurable using the existing 

survey design. If the M&E are measurable, the existing survey design can be used. Detailed 

evaluations should include a review of each step in the survey development process, including– 

• Selection of a disposition option (Section 2.5), 

• Identification of action levels (Section 3.3), 

• Specification of the population parameter of interest (Section 3.4), 

• Development of survey unit boundaries (Section 3.6), 

• Selection of measurement methods (Section 3.8 and Section 5.9), 

• Identification of alternative actions (Section 3.5), and 

• Development of a decision rule (Section 3.7 and Section 4.2.6). 

The results of the evaluation should be documented. The documentation may require a 

modification to the existing survey design. For example, the description of M&E that can (or 

cannot) be measured using a specific SOP may be expanded for M&E that are routinely or 

repeatedly surveyed. Alternatively, the documentation may consist of a notation in a survey log 

(including a name, title, and date) for unique items. 

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4 DEVELOP A SURVEY DESIGN 

4.1 Introduction 

Once a decision rule has been developed, a disposition survey can be designed for the impacted 

materials and equipment (M&E) being investigated. The disposition survey incorporates all of 

the available information to determine the quantity and quality of data required to support a 

disposition decision. This chapter provides information on selecting the type, number, and 

location of measurements required to support a decision regarding the disposition of the M&E. 

Facilities or installations can use the process in this chapter and following chapters to develop a 

standard operating procedure (SOP) so multiple surveys can be performed for similar M&E to 

avoid costly and time-consuming development of redundant survey designs. The evaluation of 

existing SOPs for usability is discussed in Section 3.10. The output from this chapter is a 

documented disposition survey design that integrates measurement, data collection, and data 

analysis techniques. 

The information in this chapter builds on the information collected and decisions made in 

Chapters 2 and 3. The disposition option selected in Section 2.5 and the action levels (ALs) 

identified in Section 3.3 are incorporated into the decision rules developed in Section 3.7. A 

decision rule is the basis for the disposition survey design. If multiple survey designs address the 

same decision rule and meet the data quality objectives (DQOs), the decision-maker needs to 

determine the most effective design for that decision rule. If none of the survey designs meet the 

DQOs for a specific decision rule, it may be necessary to reconsider decisions made earlier in the 

survey design process and adjust the DQOs.1 If there are multiple decision rules (e.g., one for 

total radioactivity and one for removable radioactivity) more than one survey design may need to 

be developed to meet all of the DQOs for the project or a single survey design may be developed 

to incorporate all of the decision rules. 

The complexity of a survey design generally reflects the complexity of the statistics used to 

interpret the results (Chapter 6). Survey designs range from simple (e.g., scan 100% of the M&E 

for surface radioactivity at a specified AL) to complex (e.g., develop a MARSSIM-type survey 

design). Simple survey designs typically require few resources for planning, but may require 

significant resources to implement. Complex survey designs typically require more resources 

during planning, with fewer resources required during implementation. If the planning and 

implementation portions of the data life cycle are performed correctly, the assessment and 

decision making stages should require few resources. This chapter provides information on 

statistical decision-making and how it is used during development of survey designs. 

4.2 Making Decisions Using Statistics 

In Section 3.6, the planning team assumed the levels and distribution of radioactivity associated 

with the M&E were known with no uncertainty. A theoretical decision rule was developed using 

this assumption to help focus the attention of the planning team on how they would make 

 

1

 Refer to Section 2.3 for information on performing preliminary surveys to help ensure at least one survey design 

will meet the DQOs. 

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decisions. In this chapter the planning team accounts for uncertainty in decisions when ideal data 

are not available by establishing a statistical test to implement the decision rule. Decisions 

regarding the disposition of M&E are based on data with uncertainties. Through the use of 

statistics, the disposition survey design attempts to control the probability of making a decision 

error because of these uncertainties. MARSSIM Section 2.3 provides additional discussions on 

the use of statistics for making decisions based on environmental data. These steps are discussed 

briefly below and in further detail in Section 7.1. MARSAME recommends the planning team 

complete the following steps: 

• Select a null hypothesis (Section 4.2.1), 

• Choose a discrimination limit (Section 4.2.2), 

• Define Type I and Type II decision errors (Section 4.2.5), 

• Set a tolerable Type I decision error rate at the action level (Section 4.2.5), and 

• Set a tolerable Type II decision error rate at the discrimination limit (Section 4.2.5). 

4.2.1 Null Hypothesis 

In hypothesis testing, two assertions about the actual level of radioactivity associated with the 

M&E are formulated. The two assertions are called the null hypothesis (H0) and the alternative 

hypothesis (H1). H0 and H1 together describe all possible radionuclide concentrations or levels of 

radioactivity under consideration. The survey data are evaluated to choose which hypothesis to 

reject or not reject, and by implication which to accept.2 In any given situation, one and only one 

of the hypotheses must be true. The null hypothesis is assumed to be true within the established 

tolerance for making decision errors (Section 4.2.5). Thus, the choice of the null hypothesis also 

determines the burden of proof for the test. 

4.2.2 Discrimination Limit 

Action levels were defined in Section 3.3 based on the selected disposition option and applicable 

regulatory requirements. The planning team also chooses another radionuclide concentration or 

level of radioactivity that can be reliably distinguished from the action level by performing 

measurements (i.e., direct measurements, scans, in situ measurements, samples and laboratory 

analyses). This radionuclide concentration or level of radioactivity is called the discrimination 

limit (DL). An example where the discrimination limit is defined is provided in Section 8.4.5. 

The gray region is defined as the interval between the action level and the discrimination limit 

(Figures 4.1, 4.2, and 4.3 provide visual descriptions of the gray region). The width of the gray 

region is called the shift and denoted as Δ. The objective of the disposition survey is to decide 

whether the concentration of radioactivity is more characteristic of the DL or of the AL, i.e., 

whether action should be taken, or if action is not necessary. The width of the gray region 

expressed as a multiple of the measurement standard deviation, σ, is called the relative shift, Δσ. 

Survey effort will increase as the relative shift decreases. 

 

2

 In hypothesis testing, to “accept” the null hypothesis only means not to reject it. For this reason many statisticians 

avoid the word “accept.” A decision not to reject the null hypothesis does not imply the null hypothesis has been 

shown to be true. 

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In Figure 4.1, it may be seen that a large σ can be tolerated because Δ is large enough that the 

resulting relative shift Δ/σ is large. In Figure 4.2, even though σ is small, either more accuracy or 

more samples are needed because Δ/σ is also small. 

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 910

Concentration

Frequency of Occurrence

Action 

Level

Mean Δ

σ

Figure 4.1 Relative Shift, Δ/σ, Comparison for Scenario A: 

σ is Large, but the Large Δ Results in a LargeΔ/σ and Fewer Samples 

0

0.3

0.6

0.9

1.2

1.5

1.8

012345 7 10

Concentration

Frequency of Occurrence

Action 

Level

Mean Δ

σ

Figure 4.2 Relative Shift, Δ/σ, Comparison for Scenario A: 

σ is Small, but the Small Δ Results in a Small Δ/σ and More Samples 

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4.2.3 Scenario A 

The null hypothesis for Scenario A specifies that the radionuclide concentration or level of 

radioactivity associated with the M&E is equal to or exceeds the action level. For Scenario A 

(H0: X > AL), the upper bound of the gray region (UBGR) is equal to the AL and the lower 

bound of the gray region (LBGR) is equal to the DL. As a general rule for applying Scenario A, 

the DL should be set no higher than the expected radionuclide concentration associated with the 

M&E. The DL and the AL should be reported in the same units. Figure 4.3 illustrates Scenario 

A. LBGR UBGR

Δ

4.2.4 Scenario B 

The null hypothesis for Scenario B specifies the radionuclide concentration or level of 

radioactivity associated with the M&E is less than or equal to the action level. For Scenario B 

(H0: X < AL), the UBGR is equal to the DL and the LBGR is equal to the AL. The DL defines 

how hard the surveyor needs to look, and is determined through negotiations with the regulator.3

In some cases the DL will be set equal to a regulatory limit (e.g., 10 CFR 36.57 and DOE 1993). 

The DL and the AL should be reported in the same units. Figure 4.4 illustrates Scenario B. This 

description of Scenario B is based on information in MARLAP and is fundamentally different 

from the description of Scenario B in NUREG-1505 (NRC 1998a). 

 

3

 In some cases, setting the discrimination limit may include negotiations with stakeholders. 

Figure 4.3 Illustration of Scenario A 

Figure 4.4 Illustration of Scenario B 

Discrimination Limit Action Level

Activity Level

Gray Region

Δ

Discrimination Limit Action Level

Activity Level

LBGR UBGR

Δ

Action Level Discrimination Limit

Activity Level

Gray Region

Action Level Discrimination Limit

Activity Level

NUREG-1575, Supp. 1 4-4 January 2009 
MARSAME Develop A Survey Design 

In NUREG-1505 (NRC 1998a) the gray region is defined to be below the AL in both Scenario A 

and Scenario B. In MARSAME and MARLAP the gray region is defined to be above the AL in 

Scenario B. The difference lies in how the action level is defined. 

4.2.5 Specify Limits on Decision Errors 

There are two possible types of decision errors: 

• Type I error: rejecting the null hypothesis when it is true, and 

• Type II error: failing to reject the null hypothesis when it is false. 

Because there is always uncertainty associated with the survey results, the possibility of decision 

errors cannot be eliminated. So instead, the planning team specifies the maximum Type I 

decision error rate (α) that is allowable when the radionuclide concentration or level of 

radioactivity is at or above the action level. This maximum usually occurs when the true 

radionuclide concentration or level of radioactivity is exactly equal to the action level. The 

planning team also specifies the maximum Type II decision error rate (β) that is allowable when 

the radionuclide concentration or level of radioactivity equals the discrimination limit. 

Equivalently, the planning team can set the “power” (1−β) when the radionuclide concentration 

or level of radioactivity equals the discrimination limit. See MARSSIM Appendix D, Section 

D.6 for a more detailed description of error rates and statistical power. 

It is important to clearly define the scenario (i.e., A or B) and the decision errors for the survey 

being designed. Once the decision errors have been defined, the planning team should determine 

the consequences of making each type of decision error. For example, incorrectly deciding the 

activity is less than the action level may result in increased health and ecological risks. 

Incorrectly deciding the activity is above the action level when it is actually below may result in 

increased economic and social risks. The consequences of making decision errors are project 

specific. 

4.2.6 Develop an Operational Decision Rule 

The theoretical decision rule developed in Section 3.6 was based on the assumption that the true 

radionuclide concentrations in the M&E were known. Because the disposition decision will be 

made based on measurement results and not the true but unknown concentration, an operational 

decision rule needs to be developed to replace this theoretical decision rule. The operational 

decision rule is a statement of the statistical hypothesis test, which is based on comparing some 

function of the measurement results to some critical value. The theoretical decision rule is 

developed during Step 5 of the DQO process (Chapter 3), while the operational decision rule is 

developed as part of Step 6 and Step 7 of the DQO process. For example, a theoretical decision 

rule might be “if the results of any measurement identify surface radioactivity in excess of 

background, the front loader will be refused access to the site; if no surface radioactivity in 

excess of background is detected, the front loader will be granted access to the site.” The related 

operational decision rule might be “any result that exceeds the critical value associated with the 

MDC set at the discrimination limit will result in rejection of the null hypothesis, and the front 

loader will not be allowed on the site” (see more examples in Chapter 7). 

January 2009 4-5 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

4.3 Classify the Materials and Equipment 

Classification is used to determine the level of survey effort for the disposition survey. The level 

of survey effort is linked to the potential to exceed the action level(s) (i.e., classification), and is 

a graded approach to survey design. Impacted M&E with the highest potential to exceed the 

action level(s) (i.e., Class 1) receive the greatest effort for the disposition survey, while M&E 

with a lower potential to exceed the action level(s) (i.e., Class 2 or Class 3) require less survey 

effort. Classification in MARSAME is analogous to classification in MARSSIM. The planning 

team needs to remember that classification is based on estimated radionuclide concentrations or 

radioactivity relative to the AL. 

There are tradeoffs (costs and benefits) associated with classification based on estimated4 or 

known radionuclide concentrations or levels of radioactivity relative to the action levels. This 

means that some knowledge of radionuclide concentrations is required before M&E can be 

classified. Known radionuclide concentrations or levels of radioactivity may be available from 

historical data identified during the initial assessment (IA; see Section 2.2), or performance of 

preliminary surveys (Section 2.3). Estimates of radionuclide concentrations can be developed 

based on historical data or process knowledge (Section 2.2). In the absence of information on the 

radionuclide concentrations, the default assumption is that all impacted M&E are Class 1. 

Because classification of impacted M&E is based in part on an action level, classification cannot 

be performed until potential action levels have been identified (Section 3.3). For projects where 

multiple potential action levels have been identified, classification and selection of an 

appropriate action level may be an iterative process used to reduce the number of survey options. 

Alternatively, multiple survey designs can be developed to address all potential action levels. In 

the final step of the DQO process the most resource efficient survey design that meets the survey 

objectives is selected (Section 4.4.4). 

4.3.1 Class 1 

Class 1 M&E are impacted M&E that have, or had, the following: (1) highest potential for, or 

known, radionuclide concentration(s) or radioactivity about the action level(s); (2) highest 

potential for small areas of elevated radionuclide concentration(s) or radioactivity; and (3) 

insufficient evidence to support reclassification as Class 2 M&E or Class 3 M&E. Such potential 

may be based on historical information and process knowledge, while known radionuclide 

concentration(s) or radioactivity may be based on preliminary surveys. This class of M&E might 

consist of processing equipment, components, or bulk materials that may have been affected by a 

liquid or airborne release, including, for example, inadvertent effects from spills. 

Class 1 M&E are those that may have been in direct contact with radioactive materials during 

operations or may have become activated and are likely to exceed the action level. Additionally, 

M&E that have been cleaned to remove residual radioactivity above the action level generally 

are considered to be Class 1. An exception to Class 1 classification may be considered if there 

 

4

 There are risks and tradeoffs associated with using estimated values. The planning team should compare the 

consequences of potential decision errors with the resources required to improve the quality of existing data to 

determine the appropriate approach for a specific project. 

NUREG-1575, Supp. 1 4-6 January 2009 
MARSAME Develop A Survey Design 

are no difficult-to-measure areas and any residual radioactivity is readily removable using 

cleaning techniques. Examples of such methods may include vacuuming, wipe downs, or 

chemical etching that quantitatively remove sufficient amounts of radionuclides such that 

surficial activity levels would be less than the release criteria. Documented process knowledge of 

cleaning methods directly applicable to the particular M&E should be provided to justify this 

exception. 

4.3.2 Class 2 

Class 2 M&E are impacted M&E that have, or had, (1) low potential for radionuclide 

concentration(s) or radioactivity above the action level(s); and (2) little or no potential for small 

areas of elevated radionuclide concentration(s) or radioactivity. Such potential may be based on 

historical information, process knowledge, or preliminary surveys. This class of materials might 

consist of electrical panels, water pipe, conduit, ventilation ductwork, structural steel, and other 

materials that might have come in contact with radioactive materials. Radionuclide 

concentration(s) and radioactivity above the action level, including small areas of elevated 

radionuclide concentration(s) or radioactivity, are not expected in Class 2 M&E. 

4.3.3 Class 3 

Class 3 M&E are impacted M&E that have, or had, (1) little, or no, potential for radionuclide 

concentration(s) or radioactivity above background; and (2) insufficient evidence to support 

categorization as non-impacted. Radionuclide concentration(s) and radioactivity above a 

specified small fraction of the UBGR are not expected in Class 3 M&E. The specified fraction 

should be developed by the planning team using a graded approach and approved by the 

regulatory authority. 

4.3.4 Other Classification Considerations 

The planning team should review any historical data used to provide information on radionuclide 

concentrations or radioactivity and evaluate whether or not the data meet the objectives of the 

disposition survey, as illustrated in the following examples. Representativeness (see MARSSIM 

Appendix N) is a key data quality indicator when evaluating historical data. Ideally, the IA 

should provide information on the radionuclides of potential concern, expected radionuclide 

concentrations or radioactivity, distribution of radioactivity, and locations where radioactivity is 

expected (e.g., surficial or volumetric, see Section 2.4.3). In addition, the data should meet the 

criteria for measurability (e.g., MQC) or detectability (e.g., MDC) established for the project (see 

Sections 3.8 and 5.5). Historical data that do not meet the objectives of the disposition survey 

may still be used to provide estimates for radionuclide concentrations or levels of radioactivity. 

The results of the IA may provide estimated radionuclide concentrations or levels of 

radioactivity based on process knowledge, historical data, sentinel measurements, or preliminary 

surveys. In some cases, a survey is performed to develop adequate estimates for levels and 

variability of radionuclide concentrations or radioactivity. Again, the planning team should 

evaluate the data used to develop the estimated radionuclide concentrations or levels of 

radioactivity. In general, estimated data will have a higher associated uncertainty than known 

data that meet the objectives of the project. The planning team should keep this in mind when 

January 2009 4-7 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

developing estimates for radionuclide concentrations or radioactivity to be used in classifying 

M&E. 

If the action level is defined in terms of average activity, the average radionuclide concentration 

or radioactivity should be compared to the action level to determine the appropriate 

classification. Similar comparisons should be developed for action levels provided in terms of 

maximum activity or total activity. For example, DOE Order 5400.5 (DOE 1993) provides three 

surface activity action levels for each group of radionuclides: average total surface activity, 

maximum total surface activity, and maximum removable surface activity. These action levels 

must be evaluated prior to disposition of the M&E. Classification would be determined by 

comparing the average total surface activity, maximum total surface activity, and maximum 

removable surface activity (or appropriate conservative estimates) to the corresponding action 

level. The overall classification would be determined by the most restrictive case. If the 

maximum total surface activity indicates the M&E is Class 1, while the maximum removable 

surface activity indicates the M&E is Class 3, the M&E should be classified as Class 1. 

The improper classification of M&E has serious implications, particularly when it leads to the 

release of material with residual radioactivity in excess of the AL. For example, if material were 

mistakenly thought to have a very low potential for having residual radioactivity, the material 

will be subjected to a survey with lesser scrutiny. This misclassification might result in releasing 

material that should not be released. The opposing possibility (i.e., when M&E is misclassified 

as impacted when it is non-impacted) involves the stakeholders expending potentially substantial 

resources involved in unnecessarily surveying non-impacted M&E. 

4.4 Design the Disposition Survey 

MARSAME recommends design of disposition surveys that measure 100% of the M&E being 

investigated whenever practical. This includes survey designs where all of the M&E are 

physically measured. Survey designs where physical measurements are performed for less than 

100% of the M&E may be acceptable if the radioactivity is measurable. Measurable radioactivity 

is radioactivity that can be quantified and meets the DQOs and MQOs established for the survey. 

Radioactivity that is quantified using known or predicted relationships developed from process 

knowledge, historical data, sentinel measurements, or preliminary measurements is considered 

measurable as long as the relationships are developed and verified as specified in the DQOs and 

MQOs. An example of such a relationship could be the immobile progeny of the measured 

radionuclides. 

Survey designs that measure 100% of the M&E being investigated reduce the uncertainty in the 

final decision. Because 100% of the M&E are measured, for practical purposes spatial variability 

can be ignored. Attention should be given to ensure that all impacted surfaces are measured in 

100% scan surveys. Surveys that use known or predicted relationships to estimate radionuclide 

concentrations or levels of radioactivity need to account for the contribution of spatial variability 

to total uncertainty. 

To make the best use of limited resources, MARSAME places the greatest level of survey effort 

on M&E that have, or had, the greatest potential for residual radioactivity (i.e., Class 1). This is 

NUREG-1575, Supp. 1 4-8 January 2009 
MARSAME Develop A Survey Design 

referred to as a graded approach. As noted in Section 1.3, survey designs that measure 100% of 

the M&E are often neither practical nor cost-effective, and could drive the user to dispose of any 

material that is potentially impacted without considering the benefits of reuse or recycle. The use 

of a graded approach to ensure that a sensible, commensurate balance is achieved between cost 

and risk reduction should always be incorporated into MARSAME survey designs. The 

following sections describe the four basic disposition-survey designs: 

• Scan-only survey designs (Section 4.4.1), 

• In situ survey designs (Section 4.4.2), 

• Survey designs that combine scans and static measurements (MARSSIM-type surveys, 

Section 4.4.3), and 

• Method-based survey designs (Section 4.4.4). 

Figures 4.5, 4.6, and 4.7 illustrate the process of designing a disposition survey. Classification 

can be used to provide a graded survey approach to individual survey designs. Information on 

adjusting the level of survey effort based on classification is provided for each type of survey 

design. Each survey design can include a variety of survey techniques (Section 5.9). 

4.4.1 Scan-Only Survey Designs 

Scan-only survey designs use scanning techniques to measure the M&E. The detector is moving 

at a constant speed relative to the M&E being surveyed while maintaining a constant distance 

relative to the M&E. In general, scan-only survey designs may be applied to all types of M&E, 

from small individual items to large quantities of materials to large, complex machines. Scan 

techniques include hand-held instruments that are moved over the M&E, as well as systems that 

move the M&E past stationary detectors (e.g., conveyor systems). For example, a scan-only 

survey may involve the use of a Geiger-Mueller (GM) pancake detector to measure potential 

surface radioactivity on hand tools. Alternatively, a scan-only survey could involve the use of a 

conveyorized system that measures large quantities of M&E (e.g., bulk material or laundry). 

Scan-only surveys generally are applicable to all types of disposition surveys. 

Scan-only survey designs often require the least amount of resources to design and implement, 

and are easy to incorporate into SOPs or project-specific survey designs. In many cases it is not 

necessary to document the results of individual scanning measurements because it is easy to 

identify results that exceed some threshold corresponding to the action level. With the real-time 

feedback available during Class 1 scan-only surveys, the user can implement a “clean as you go” 

practice by segregating M&E that exceed the threshold for additional investigation. Drawbacks 

to scan-only surveys include increased measurement uncertainty because of variations in scan 

speed and source to detector distance making it difficult to detect or quantify radionuclides with 

action levels close to zero or background. 

Scan-only surveys are characterized by large numbers of measurements with relatively short 

count times. Measurement uncertainty should account for variations in source-to-detector 

distance, scan speed, and surface efficiency that are commonly associated with scanning 

measurements. 

January 2009 4-9 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

Assign Values to the 

LBGR and UBGR

Specify Limits on 

Decision Errors

Classify the M&E

Select a Type of 

Disposition Survey

Scenario A (Section 4.2.3)

Scenario B (Section 4.2.4)

Action Level (Section 3.3)

Discrimination Limit (Section 4.2.2)

Type I Error (Section 4.2.5)

Type II Error (Section 4.2.5)

Consequences of Decision Errors

and 

Class 1 (Section 4.3.1)

Class 2 (Section 4.3.2)

Class 3 (Section 4.3.3)

MARSSIM-Type

(Section 4.4.3)

Scan-Only 

(Section 4.4.1)

In Situ (Section 4.4.2)

Develop an Operation 

Decision Rule

Statement of the Statistical 

Hypothesis Test (Section 4.2.6)

Go to Figure 4.6

Return from

Figure 4.6

Select a Null Hypothesis

Determine % of M&E 

to be Scanned

Determine % of M&E and 

Locations to be Measured

Select M&E and Locations 

to be Scanned

Select M&E and Locations 

to be Measured

Select Measurement and 

Scan Locations

Optimize the Survey Design

Document the Disposition 

Survey Design

(Section 4.5)

From Figure 3.1

Proceed to Figure 5.1

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

NOTE: A method-based survey 

(Section 4.4.4) is a special case of 

either scan-only, in situ, or 

MARSSIM-type, and so will follow 

one of the three paths show on this 

figure.

Figure 4.5 Flow Diagram for a Disposition Survey Design

NUREG-1575, Supp. 1 4-10 January 2009 
MARSAME Develop A Survey Design 

Is the Radioactivity 

Present in Bkgd?

Estimate , the Variability in 

the Level of Radioactivity

Calculate the Relative Shift /

Estimate ’s, the Variabilities in 

the Reference Area and 

Survey Unit Activities

Calculate the Relative Shift /

Is /

Between

1 and 3?

Adjust LBGR Adjust LBGR

Obtain Number of Data Points 

for the Sign Test, N, from Table 

for each Survey Unit

Obtain Number of Data Points 

for WRS Test, N/2, from Table 

for each Survey Unit and 

Reference Area

Prepare Summary of Data 

Points for M&E being 

Investigated

No Yes

Yes Yes

No No

Do the Number of Data 

Points Need to be Adjusted 

for Class 1 M&E?

Yes

No

Return to 

Figure 4.5

Go to Figure 4.7

Return from 

Figure 4.7

From Figure 4.5

Is /

Between

1 and 3?

NOTE: Shaded boxes 

represent important 

milestones.

Figure 4.6 Flow Diagram for Identifying the Number of Data Points for a MARSSIM-Type 

Disposition Survey 

January 2009 4-11 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

Establish DQOs for Areas with the 

Potential for Exceeding 

Als and Acceptable Risk for 

Missing Such Areas

Identify Number of Data Points 

needed Based on Statistical Tests, n

Calculate the Area, A, Bounded by 

Sample Location, n

Determine Acceptable 

Concentrations in Various 

Individual Smaller Areas within a 

Survey Unit (i.e., Use Area Factors)

Determine the Acceptable 

Concentration Corresponding to the 

Calculated Area, A (i.e., Area 

Factor x Action Level)

Determine the Required Scan MDC 

to Identify the Acceptable 

Concentration in an Area, A

Evaluate MDCs for Available 

Instrumentation

Calculate Area Factor that 

Corresponds to the Actual Scan 

MDC (scan MDC/AL)

Determine the Maximum 

Area, A’, that Corresponds 

to the Area Factor

Recalculate Number of 

Data Points Needed

(nEA = Survey Unit Area/A’)

Determine Grid Size Spacing, L

No Additional Sampling Points 

are Necessary for Potential 

Elevated Locations

Is the Scan MDC for Available 

Instrumentation Less than the 

Required Scan MDC?

No

Yes

From Figure 4.6

Return to 

Figure 4.6

NOTE: Shaded boxes 

represent important 

milestones.

NOTE: “VOLUME” or 

“MASS” replaces “AREA” in 

this flow diagram as 

appropriate for a specific 

survey design, and scan MDC 

is discussed in MARSSIM 

Section 5.5.2.4

Figure 4.7 Flow Diagram for Identifying Data Needs for Assessment of Potential Areas of 

Elevated Activity in Class 1 Survey Units for MARSSIM-Type Disposition Surveys 

NUREG-1575, Supp. 1 4-12 January 2009 
MARSAME Develop A Survey Design 

Evaluation of scan-only survey data depends on whether or not individual measurement results 

are recorded (Section 6.2.5). The decision of whether to record individual measurement results 

will impact the selection of instrumentation (Section 5.9) and survey documentation requirements (see Sections 4.5, 5.11, and 6.9), and may impact handling of the M&E (Section 5.3). 

4.4.1.1 Class 1 Scan-Only Surveys 

Class 1 scan-only surveys require that physical measurements be performed for 100% of the 

M&E being investigated. For individual items this may require scanning both sides of flat items 

(e.g., sheet metal, boards) and changing the surveyor’s grip on the item to ensure all areas are 

surveyed (e.g., handles). For conveyor systems this may require flipping or rotating the M&E 

and performing additional measurements. Conveyor systems can also be designed with detectors 

surrounding the M&E (e.g., above and below a conveyor belt) to provide 100% measurability. 

4.4.1.2 Class 2 Scan-Only Surveys 

Class 2 scan-only surveys use information about the M&E to reduce the total area surveyed 

using a graded approach. The amount of the M&E surveyed is calculated based on the relative 

shift (i.e., Δ/σ). The percent of the M&E to be surveyed is 10%, or the result using Equation 4-1, 

whichever is larger: 

%100

10

10

Scan% ×

⎟

⎠

⎞ ⎜

⎝

⎛ Δ −

= σ (4-1) 

The amount of M&E to be scanned should be rounded up to the next 10 percent, and at least 10% 

of the M&E must be surveyed. For example, if the percent scan is 51%, then 60% of the M&E 

will be surveyed. This means that between 10 to 100% of Class 2 M&E would be measured 

during the disposition survey. Figure 4.8 shows the relationship between the relative shift and the 

amount of M&E to be scanned. 

The scanned percentages 

need to represent spatially 

uniform coverage of the 

survey unit and coincide 

with the conceptual model 

for the M&E. Consider 

spatially uniform coverage 

when scanning 30% of a 

desk and 30% of a bucket 

of bolts. For the desk 

example, 30% coverage 

during scanning may be 

derived from performing 

scans on the top surface, 

the legs, inside the drawers, 

etc., so that essentially 30% 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12

Relative Shift (Δ/σ)

Percent M&E to be Scanned

Figure 4.8 Relationship Between the Relative Shift and the 

Amount of M&E to be Scanned 

January 2009 4-13 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

of each surface is scanned, yielding 30% total coverage of the entire desk. For the bucket of bolts 

example, 30% scanning coverage means laying out all the bolts and scanning 30% of them as 

well as 30% of the bucket itself. Alternatively, if the conceptual model for the desk showed a 

higher potential for contamination on the top, bottoms of legs, and drawer handles, 100% of 

these areas could be scanned with smaller amounts of the areas with a lower potential for 

radioactivity scanned to provide a total of 30% coverage for the entire desk. The graded 

approach should be applied to all aspects of the survey design. 

The selection of M&E to survey as part of a Class 2 survey is project specific and is determined 

based on what is known about the M&E. For example, if all of the M&E is accessible and is 

expected to have uniform radionuclide concentrations or levels of radioactivity, the M&E to be 

surveyed should be selected randomly. However, there may be areas that are difficult-to-access 

with the instrumentation selected to perform the survey. If there is a known and accepted 

relationship between radionuclides in difficult-to-access areas and radionuclides in accessible 

areas, the Class 2 measurements may be biased to only accessible areas (i.e., representative of 

measurements in difficult-to-access areas). 

If elevated radionuclide concentrations or levels of radioactivity are restricted to areas that can be 

readily identified (e.g., discolored areas, corners, cracks, access points) the Class 2 

measurements may be designed to concentrate on these biased areas. The Class 2 survey design 

should include a combination of biased and random areas to check assumptions used to support 

the survey design. 

The selection of M&E to survey may also depend on the physical characteristics of the M&E. 

For example, surveying 40% of the inside of a railroad car would be different from surveying 

40% of a pile of rubblized concrete. Section 5.3 provides information on handling M&E and 

determining what will be measured during implementation of the survey design. 

4.4.1.3 Class 3 Scan-Only Surveys 

Class 3 scan-only survey designs are identical to Class 2 scan-only survey designs. The planning 

team may decide that some Class 3 scan-only disposition surveys require that less than 10% of 

the M&E will be measured. The decision to design a survey requiring less than 10% of the M&E 

to be measured should be based on the total uncertainty associated with the disposition decision. 

The determination of total uncertainty should be based on process knowledge, historical data, 

and the results of preliminary and disposition surveys. 

In addition, some Class 3 scan-only survey designs may be based solely on biased 

measurements. In other words, random measurement locations are not required for Class 3 scanonly survey designs. However, if biased measurements are reasonable, they should be performed, 

keeping in mind that Class 3 M&E have very little or no potential for exceeding the AL. 

NUREG-1575, Supp. 1 4-14 January 2009 
MARSAME Develop A Survey Design 

4.4.2 In Situ Survey Designs 

In situ survey designs use static measurements to measure 100% of an item. The detector and the 

item being measured are held in a fixed geometry5

 for a specified count time to meet the MQOs. 

There are a wide variety of in situ measurement techniques available. Examples include box 

counters, portal monitors, and in situ gamma spectrometry systems, as well as direct 

measurements with hand-held instruments (e.g., NaI(Tl), ZnS, GM pancake, and portable gas 

proportional detectors). In situ surveys generally are applied to situations where scan-only 

surveys are determined to be unacceptable. For example, variations in source-to-detector 

distance, scan speed, and surface efficiency that are commonly associated with scanning 

measurements can often be effectively controlled using an in situ survey design. 

In situ surveys are characterized by limited numbers of measurements with long count times 

(relative to scan-only surveys). Measurement uncertainty will incorporate spatial uncertainty 

because of the source geometry assumed in the calibration. Thus, special attention needs to be 

made to the assumptions made in the calibration of in situ systems. Potential deviations from 

these assumptions need to be propagated through the calibration equation to assess the total 

measurement uncertainty (see Sections 5.6 and 7.4). Count times are determined by the MQOs 

rather than the time constant of the measurement system. In situ measurements provide a 100% 

measurement for some portion of the M&E being investigated. The M&E may be an individual 

item or piece of equipment, or some fraction of a large quantity of material determined by the 

solid angle coverage of the detector. 

In situ surveys may consist of a single measurement, or a series of measurements. Single 

measurement surveys typically are performed on individual items or relatively small batches of 

M&E. A series of in situ measurements may be used to evaluate larger quantities of M&E. In 

some cases, a series of in situ measurements may be performed of a single item or batch of M&E 

to provide several estimates of the radionuclide concentrations from different angles. The 

primary difference between an in situ survey and a MARSSIM-type survey is that an in situ 

survey measures 100% of an item (using one or several measurements) to determine the average 

radionuclide concentration for that item. A MARSSIM-type survey uses a statistically based 

number of measurements (that generally do not measure 100% of the item or group of items 

being surveyed) to calculate an average radionuclide concentration for that item or group of 

items. 

4.4.2.1 Class 1 In situ Surveys 

Class 1 in situ surveys require that physical measurements be performed for 100% of the M&E 

being investigated. Placing an item inside a 4-π measurement system, performing a series of 

measurements with overlapping fields of view that incorporate all of the M&E, or rotating the 

M&E within the field of view of the detector so 100% of the M&E are measured are examples 

where 100% of the M&E are measured. 

 

5

 There are situations where the levels of radioactivity for M&E being measured are expected to be inhomogeneous. 

Certain measurement systems can rotate the M&E during a measurement to provide an estimate of the average 

activity. For the purposes of this section, these are considered fixed geometries. Additional discussion on the 

limitations of these systems is provided in Chapter 5. 

January 2009 4-15 NUREG-1575, Supp.1 
Develop A Survey Design MARSAME

4.4.2.2 Class 2 In situ Surveys 

Class 2 in situ surveys use information about the M&E to reduce the total area surveyed using a 

graded approach. The amount of the M&E surveyed is calculated based on the relative shift (i.e., 

Δ/σ). The percent of the M&E to be surveyed is 10% or the result using Equation 4-2, whichever 

is larger: 

%100

10

10

% Coverage Angle Solid%or Measured ×

⎟

⎠

⎞ ⎜

⎝

⎛ Δ −

= σ (4-2) 

The fraction of the M&E or the solid angle coverage of the M&E to be surveyed should be 

rounded up to the next 10 percent. If the % coverage is 51%, then 60% of the M&E will be 

surveyed. This means that 10 to 100% of Class 2 M&E would be measured during the 

disposition survey. Figure 4.8, on page 4-13, shows the relationship between the relative shift 

and the amount of M&E to be surveyed. 

The selection of M&E to survey as part of a Class 2 survey is project specific and is determined 

based on what is known about the M&E. For example, if all of the M&E is accessible and is 

expected to have uniform radionuclide concentrations or levels of radioactivity, the M&E to be 

surveyed should be selected randomly. However, there may be areas that are difficult-to-access 

with the instrumentation selected to perform the survey. If there is a known and accepted 

relationship between radionuclides in difficult-to-access areas and radionuclides in accessible 

areas, the Class 2 measurements may be biased to only accessible areas (i.e., representative of 

measurements in difficult-to-access areas). If elevated radionuclide concentrations or levels of 

radioactivity are restricted to areas that can be readily identified (e.g., discolored areas, corners, 

cracks, access points) the Class 2 measurements may be designed to concentrate on these biased 

areas. The Class 2 survey design should include a combination of biased and random areas to 

check assumptions used to support the survey design. 

4.4.2.3 Class 3 In situ Surveys 

Class 3 in situ survey designs are identical to Class 2 in situ survey designs. The planning team 

may decide that some Class 3 in situ disposition surveys require that less than 10% of the M&E 

will be measured. The decision to design a survey requiring less than 10% of the M&E to be 

measured should be based on the total uncertainty associated with the decision based on process 

knowledge, historical data, and the results of preliminary and disposition surveys. 

4.4.3 MARSSIM-Type Survey Designs 

MARSSIM-type survey designs combine a statistically based number of static measurements to 

determine average radionuclide concentrations or radioactivity levels with scanning to identify 

areas of elevated radionuclide concentrations or radioactivity for specified quantities of M&E 

(i.e., survey units). Identifying survey unit sizes, laying out systematic measurement grids, and 

calculating project- and item-specific area factors requires a significant effort. Section 5.3 

discusses considerations for handling M&E, including locating measurements. The planning 

team should consider that MARSSIM-type survey designs might be more complex and require 

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MARSAME Develop A Survey Design 

more resources than scan-only or in situ survey designs that meet the DQOs. Information on 

designing MARSSIM-type surveys is found in MARSSIM Section 5.5. In general, MARSSIMtype surveys of M&E are only performed on large, complicated M&E with a high inherent value 

after scan-only and in-situ surveys have been considered and rejected. 

4.4.3.1 Class 1 MARSSIM-Type Surveys 

Class 1 MARSSIM-type surveys calculate the required number of measurements in each survey 

unit based on the shift (i.e., Δ), the variability in the radionuclide concentrations or levels of 

radioactivity (i.e., σ), and the Type I and Type II decision error rates (i.e., α and β). The number 

of measurements per survey unit is adjusted to account for small areas of elevated activity using 

the information in MARSSIM Section 5.5.2.4. In addition, scan measurements are required for 

100% of the M&E being investigated. 

The development of survey unit boundaries is discussed in Section 3.6. The quantity of M&E in 

each survey unit should be determined based on the modeling assumptions used to develop the 

action levels. 

The variability in the radionuclide concentrations in each survey unit can be estimated using the 

standard deviation of preliminary measurements or the uncertainties from individual 

measurements, whichever is larger. Whenever practical, preliminary data should be used to 

provide estimates of variability. As a last resort when preliminary data are not available, 

MARSSIM states that assuming a coefficient of variation on the order of 30% may be reasonable 

(MARSSIM Section 5.5.2.2, Page 5-26). This 30% is used as a starting point for the DQO 

process, and should be adjusted iteratively during the development of a final survey design. For 

M&E, MARSAME recommends using a higher percentage value. 

Area factors are specified in a regulation or other guidance, or developed based on the changes in 

dose or risk associated with changing the area (or volume) of activity to be less than the entire 

survey unit. For example, DOE Order 5400.5, Figure IV-1 (DOE 1993) allows use of an area 

factor of up to 3.0 for total surficial radioactivity for all radionuclides, NUREG-1640 (NRC 

2003a) is only concerned with average activity and total inventory of radioactivity, which 

implies that within the survey unit relatively high localized concentrations of radioactivity could 

exist. This implication does not mean that a large part of the survey unit may be used to 

intentionally “dilute” high concentrations of radioactivity. Rather, in the course of normal 

processing there is a non-prescriptive flexibility allowed for inhomogeneity of radionuclide 

concentrations. Nevertheless, mixing different classes of M&E (Class 1, 2, and 3) is not allowed. 

The physical characteristics of the M&E combined with potential future exposures based on the 

selected disposition option mean that area factors (and possibly exposure pathway dose or risk 

models) need to be developed for each project. In the absence of regulation-specific area factors, 

assuming an area factor of 1.0 for all radionuclides would be the most conservative approach. 

Depending on the basis of the action level, an area factor may or may not be applicable. 

MARSSIM uses completely different scenarios for real property to develop area factors in 

contrast to those scenarios used for M&E in NUREG-1640 (NRC 2003a). Area factors may be 

derived on a project-specific basis using project-specific scenarios. 

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If the radioactivity being measured is present in background, Table 5.3 in MARSSIM provides 

the number of measurements required in each survey unit as well as in each reference area. 

MARSSIM Section 5.5.2.2 and NUREG-1505 (NRC 1998a) Sections 9.4 and 9.5 provide 

information on calculating the number of required measurements when the radioactivity being 

measured is present in background. 

If the radioactivity being measured is not present in background, Table 5.5 in MARSSIM 

provides the number of measurements required in each survey unit. MARSSIM Section 5.5.2.3 

and NUREG-1505 (NRC 1998a) Sections 9.2 and 9.3 provide information on calculating the 

number of required measurements when the radioactivity being measured is not present in 

background. For convenience, statistical sample size and critical value tables for the Sign and 

Wilcoxon Rank Sum (WRS) tests taken from MARSSIM Appendix I are given in MARSAME 

Appendix A. In addition, Appendix A contains a table of critical values for the Quantile test, 

taken from NUREG-1505. 

 

Whenever area factors other than 1.0 are used to design the disposition survey, a systematic grid 

should be used to determine measurement locations. The systematic grid determines the largest 

area that could be missed by the measurements which is used to determine the required scan 

MDC. Section 5.3 provides information on handling M&E, including setting up systematic grids. 

4.4.3.2 Class 2 MARSSIM-Type Surveys 

Class 2 MARSSIM-type surveys are similar to Class 1 MARSSIM-type surveys. The numbers of 

measurements in each survey unit are determined in the same manner, although the expected 

radionuclide concentrations or levels of radioactivity and the decision error rates may change. 

Unlike MARSSIM, the survey unit size remains the same and does not change based on 

classification. The portion of the survey unit where scan surveys are required is reduced to 

between 10 and 100%. The information in Section 4.4.1.2 for Class 2 scan-only surveys should 

be used to determine the areas to be scanned. This recommendation is provided for M&E only, 

and is not intended to update the guidance in MARSSIM for surface soils and building surfaces. 

4.4.3.3 Class 3 MARSSIM-Type Surveys 

Class 3 MARSSIM-type surveys are similar to Class 1 MARSSIM-type surveys. The numbers of 

measurements in each survey unit are determined the same way, although the expected 

radionuclide concentrations or levels of radioactivity and the decision error rates may change. 

Unlike MARSSIM, the survey unit size does not change based on classification. The portion of 

the survey unit where scan surveys are required is reduced to less than 10% and is based on 

professional judgment. The information in Section 4.4.1 for scan-only surveys should be used to 

determine the areas to be scanned. This recommendation is provided for M&E only, and is not 

intended to update the guidance in MARSSIM for surface soils and building surfaces. 

4.4.4 Method-Based Survey Designs 

The action level selected in Section 3.3 may implicitly or explicitly require using a specific 

measurement technique (Section 5.9.1) or instrument (Section 5.9.2). A survey design that is 

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MARSAME Develop A Survey Design 

based on a required measurement method, or combination of measurement technique and 

instrumentation, is called a “method-based” survey design. 

A method-based survey design is a scan-only, in situ, or MARSSIM-type survey design that 

incorporates the required measurement method. The survey design will still need to address all of 

the required components, such as number, type, location, and sensitivity of measurements. 

Survey components that are not specified as part of the required measurement method should be 

identified and addressed using the DQO process. 

4.4.5 Optimize the Disposition Survey Design 

The disposition survey design process described in this supplement could result in the 

development of multiple potential disposition survey designs. For example, consider the case 

when simultaneous compliance with more than one action level is required (e.g., DOE 1993). In 

other cases the decision resulting from one survey may lead to the requirement of another survey, 

such as failure to demonstrate compliance with the disposition criterion for release resulting in a 

survey to comply with radioactive waste acceptance criteria. Multiple survey designs could result 

from selection of multiple potential disposition options, action levels, survey techniques, 

measurement systems, decision rules, or some combination of these factors. Before the planning 

team can proceed, all of the potential disposition survey designs need to be reviewed to select a 

final disposition survey design. 

The final step in the DQO process (“Develop the Detailed Plan for Obtaining Data,” Step 7) is 

designed to produce the most resource-efficient survey design that is expected to meet the 

DQOs. It may be necessary to revisit previous steps in the DQO process and work through this 

step more than once. 

There are five activities included in this step: 

1. Review existing data (e.g., historical data, sentinel measurement results, preliminary survey 

results). Use existing data to support the data collection design. If no existing data are 

available, consider performing preliminary surveys to acquire estimates of variability to 

determine numbers of measurements. Evaluate potential problems regarding detection limits 

or interferences. If new data will be combined with existing data, determine if there are data 

gaps that need to be filled or deficiencies that can be mitigated prior to implementing the 

disposition survey design. 

2. Evaluate operational decision rules. The theoretical decision rules developed in Section 3.6 

were based on the assumption that the true radionuclide concentrations or radioactivity 

present in the M&E were known. Operational decision rules based on the statistical tests 

(Chapter 6) should replace the theoretical decision rule (see Sections 3.5 and 4.2.6). Review 

the parameter of interest (e.g., maximum measured value, mean or median radionuclide 

concentration) and the possible statistical tests that could be applied to the data to evaluate 

the operational decision rules. 

3. Develop general data collection design alternatives. Sections 4.4.1, 4.4.2, and 4.4.3 provide 

information on general data collection design alternatives applicable to disposition surveys. 

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Consider individual instruments and measurements techniques (Section 5.9) combined with 

general data collection designs to develop alternative survey approaches. 

4. Calculate the number of measurements or amount of M&E to be surveyed. Sections 4.4.1, 

4.4.2, and 4.4.3 provide general information on determining the level of survey effort for the 

general data collection design alternatives based on classification. Determine the estimated 

resources required for each of the alternative survey approached. 

5. Select the most resource-effective survey design. Evaluate each of the survey approaches 

based on the required resources and the ability to meet the DQO and MQO constraints within 

the tolerable decision error limits. The survey design that provides the best balance between 

cost and meeting survey objectives while considering the non-technical economic and health 

factors imposed on the project is usually the most resource-effective. The statistical concept 

of a power curve (MARSSIM Appendix I.9) is extremely useful in investigating the 

performance of alternative survey designs. 

If none of the alternative survey designs meet the survey objectives within the tolerable decision 

error limits while considering the budget or other constraints, then the planning team will need to 

relax one or more of the constraints. Examples include— 

• Increasing the budget for implementing the survey; 

• Using exposure pathway modeling to develop site-specific action levels; 

• Increasing the decision error rates, not forgetting to consider the consequences associated 

with making an incorrect decision; 

• Increasing the width of the gray region for Scenario A surveys by decreasing the average 

activity associated with the M&E which may require remediation, or negotiating a higher 

UBGR for Scenario B which may require additional reference area investigations; 

• Relaxing other project constraints (e.g., schedule); 

• Changing the boundaries—it may be possible to reduce measurement costs by changing or 

eliminating survey units that will require different decisions; 

• Segregating the M&E based on physical or radiological attributes (Section 5.4); 

• Evaluating alternative measurement techniques with lower detection limits or lower survey 

costs; 

• Adjusting the list of radionuclides or radiations of concern (Section 3.2); and 

• Considering other disposition options that will result in higher action levels. 

4.5 Document the Disposition Survey Design 

Documentation of the disposition survey design should provide a complete record of the selected 

survey design. The documentation should include all assumptions used to develop the survey 

design, a detailed description of the M&E being investigated, along with the DQOs and MQOs 

for the survey (e.g., MQC, MDC, count time). The regulatory basis for the disposition criterion 

and calculations showing the derivation of action levels should also be provided. Sufficient data 

and information should be provided to enable an independent re-creation and evaluation of the 

disposition survey design. The documentation should provide information on the following 

topics: 

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MARSAME Develop A Survey Design 

• Information on who developed, reviewed, and approved the survey design, as well as training 

and qualification requirements for such individuals, should be included, along with any 

requirements for who can implement the survey design. 

• Information on what M&E were considered when developing the survey design along with a 

description of M&E to which the survey design applies. 

• Information on when the survey design was developed along with when the survey design 

will be implemented including restrictions on time of day, time of year, and count times 

when applicable. 

• Information on where the survey design can be applied (including restrictions on local 

background levels) along with measurement locations including fraction of M&E to be 

surveyed and locations of direct measurements or samples or methods for selecting locations 

during implementation, 

• Information on why a survey should be performed including justification for impacted and 

non-impacted decisions and assignment of classifications, 

• Information on how the survey will be performed including measurement techniques and 

instruments along with instructions for segregating and handling the M&E during the survey. 

There are two methods for documenting surveys described in the following sections, based on 

the type of project— 

• Routine or repetitive surveys, and 

• Case-specific applications. 

4.5.1 Routine Surveys and Standard Operating Procedures 

Routine (or repetitive) surveys are disposition surveys that are routinely performed on M&E 

entering or leaving an operating facility. Examples of routine surveys include– 

• Clearance of tools from radiological control areas at a radiation facility, 

• Preparation of low-level radioactive waste for disposal, and 

• Interdiction of scrap metal entering a recycling facility. 

Documenting routine survey designs, for example as SOPs, can be consistent with MARSAME 

recommendations. SOPs detail the work processes that are conducted or followed within an 

organization and document the way activities are performed. SOPs that also meet the DQOs for 

the disposition survey can be used to document routine survey designs. The development and use 

of SOPs facilitates consistent conformance to technical and quality system requirements. They 

promote quality through consistent implementation of a process within an organization, even if 

there are temporary or permanent personnel changes. The benefits of a valid SOP are reduced 

work effort combined with improved data comparability, credibility, and legal defensibility 

(EPA 2001). Additional guidance on developing SOPs, including example SOPs, is provided in 

EPA QA/G-6 (EPA 2001). 

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4.5.1.1 SOP Process 

The organization developing the SOP should have a procedure in place for determining what 

procedures or processes need to be documented. SOPs documenting these procedures or 

processes should be written by individuals knowledgeable with the activity and the 

organization’s internal structure. For disposition survey designs, a team approach to writing 

SOPs is often used. This allows input from subject-matter experts with information critical to the 

survey process, and promotes acceptance of the SOP once it is completed. 

SOPs should be concise and provide step-by-step instructions in an easy-to-read format. They 

should provide sufficient detail so that a technician with limited experience, but with a basic 

understanding of the process, can successfully implement the survey design when unsupervised. 

Disposition survey SOPs should be reviewed and validated by one or more individuals with 

appropriate training and experience in performing surveys of M&E before they are implemented. 

It may be helpful to have the draft SOP field tested by someone not directly involved in the 

development of the SOP. The review process for disposition surveys should include a regulatory 

review and appropriate stakeholder involvement. 

SOPs need to remain current. SOPs should be updated and re-approved whenever survey 

procedures are changed. SOPs should be systematically reviewed on a periodic basis to ensure 

that the policies and procedures remain current and appropriate. 

Many disposition survey activities use checklists or forms to document completed tasks (e.g., 

daily instrument checks). Any checklists or forms included as part of the disposition survey 

should be referenced at the points in the procedure where they are used and attached to the SOP. 

Remember that the checklist or form is not the SOP, but a part of the SOP. 

The organization should have a system for developing, reviewing, approving, controlling, and 

tracking documents. This process is usually documented in the Quality Management Plan. 

4.5.1.2 General Format for Disposition Survey SOPs 

In general, disposition survey SOPs consist of five elements: 

• Title Page, 

• Table of Contents, 

• Procedures, 

• Quality Assurance and Quality Control, and 

• References. 

The title page should include a title that clearly identifies the activity, an identification number, 

date of issue or revision, and the name of the organization to which the SOP applies. The 

signatures and signature dates of individuals who prepared and approved the SOP also should be 

included. 

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MARSAME Develop A Survey Design 

The table of contents lists the major section headings and the pages where the information is 

located. This provides a quick reference for locating the desired information and identifies 

changes or revisions made to individual sections. 

The procedures are specific to the disposition survey design and may include some or all of the 

following topics: 

• Scope and applicability. This section should provide a detailed description of the M&E to 

which the SOP can be applied. In addition, it is often important to clearly identify M&E to 

which the SOP does not apply. 

• Summary of method. This section briefly describes the overall survey design, identifies the 

disposition option, lists the action levels, and provides their regulatory basis. The details on 

the development of the action levels based on the disposition criterion in the regulations is 

generally referenced or included as an attachment. 

• Definitions. This section identifies and defines any acronyms, abbreviations, or specialized 

terms used in the SOP. 

• Health and safety warnings. This section indicates operations that could result in personal 

injury, loss of life, or uncontrolled release to the environment. Explanations of what could 

happen if the procedure is not followed or if it is followed incorrectly should appear here as 

well at the critical steps in the procedure. 

• Cautions. This section identifies activities that could result in equipment damage, 

degradation of data, or possible invalidation of results. Explanations of what could happen if 

the procedure is not followed or if it is followed incorrectly should appear here as well as the 

critical steps in the procedure. 

• Interferences. This section describes any component of the process that may interfere with 

the final decision regarding disposition of the M&E. 

• Personnel qualifications. This section lists the minimum experience required for individuals 

implementing the SOP. Any required certifications or training courses should be listed. For 

many routine surveys the training records of the personnel implementing the survey design 

are used to document compliance with the SOP. 

• Equipment and supplies. This section lists and specifies the equipment, materials, reagents, 

and standards required to implement the SOP. At a minimum, this section must identify the 

model number and manufacturer of instruments that will be used to perform the survey. 

• Roles and responsibilities of project personnel. This section identifies the decision-maker for 

the project as well as identifying who is responsible for performing specific tasks. An 

organizational chart documenting the chain of command and reporting authority (including 

quality control, health and safety, and any subcontractors) is a useful tool for showing 

potential interactions between project team members. 

• Procedure. This section provides all pertinent steps, in order, and materials needed to 

implement the survey design. This section should include— 

o Instrument or method calibration and standardization (generally requires a check of the 

instrument calibration date and lists the appropriate MQOs such as MQC or MDC and 

references the details for these processes), 

o Type, number, and location of measurements, 

o Data acquisition, calculations, and data reduction requirements, 

o Troubleshooting, and 

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Develop A Survey Design MARSAME

o Computer hardware and software. 

• Data and records management. This section describes the forms to fill out, reports to be 

written, and data and record storage information. At a minimum routine survey records 

should identify the personnel performing measurements and the instruments used to perform 

the measurements (i.e., model and serial number for all components of the measurement 

system). These records should show that the personnel performing the survey were properly 

trained and the instruments used to collect the data were calibrated and operating properly. 

This section should clearly state whether individual measurement results will be recorded, 

because this information is not always required. 

The QA/QC section describes the activities required to demonstrate the successful performance 

of the disposition survey. For many organizations the QC activities for individual instruments are 

provided in separate SOPs describing the proper use of that instrument, so the daily checks of the 

instruments are included by reference. The QA/QC section should identify QC requirements for 

the disposition survey such as blanks, replicates, splits, spikes, and performance evaluation 

checks. The frequency for each QC measurement should be listed along with a discussion of the 

rationale for decisions. Specific criteria should be provided for evaluating each type of QC 

measurement, as well as actions required when the results exceed the QC limits. The procedures 

for reporting and documenting the results of QC measurements should be listed in the QA/QC 

section. Section 5.10 provides additional information on QC for disposition surveys. 

The reference section should list all documents or SOPs that interface with the routine survey 

SOP. Full references (including SOP versions and dates) should be provided. Published literature 

and instrument manuals that are not readily available should be attached. 

4.5.2 Case-Specific Applications 

There are M&E that may require a disposition survey that are not covered by routine surveys. 

These are collectively referred to as case-specific applications. Case-specific applications include 

project-specific applications such as decommissioning or cleanup surveys, as well as unique 

applications involving one-time disposition of special equipment from a facility. 

Ideally, documentation of case-specific survey designs involves a comparable level of effort 

associated with routine surveys. This is obviously the case for large decommissioning or cleanup 

projects where survey designs are documented as SOPs using a process analogous to routine 

surveys. The major differences are seen in the requirements for approval and maintenance of 

SOPs, which generally are less for decommissioning or cleanup projects compared to operating 

facilities. Disposition survey designs that will be applied during decommissioning or cleanup 

activities typically are documented as part of the survey design. However, a survey design needs 

to provide all of the information supporting the development of the disposition survey design, 

where SOPs typically focus on one aspect of the survey design or implementation. Historical 

information, process knowledge, description of the M&E, and assumptions used in the 

disposition survey design need to be included and not referenced. 

The assumptions used to develop survey designs for routine surveys cannot be applied to all 

M&E, so situations will arise where a disposition survey design needs to be developed for 

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MARSAME Develop A Survey Design 

special items or unique applications. These types of surveys are often associated with M&E that 

have a high inherent value (e.g., large quantities of valuable materials, unique or very expensive 

equipment) to offset the resources required to develop a unique disposition survey design. These 

special survey designs need to be inclusive, providing all of the information supporting the 

development of the disposition survey design. Detailed discussions should be provided for all 

parts of the survey design, including selection of a disposition option, selection and development 

of action levels, development of MQOs and selection of instruments, and QA/QC requirements 

for individual measurement systems as well as for the entire disposition survey. 

For most applications the disposition survey design is expected to be documented as a standalone survey plan or as a series of SOPs. However, the planning team may determine that the 

survey design documentation can be combined with the results of the survey into a single 

document. At a minimum, instructions on the type, number, and location of measurements 

should be documented to provide instructions to the technicians performing the survey. 

Documenting the entire disposition decision process in a single document is most appropriate for 

unique applications where there is sufficient historical information or survey precedent such that 

there is little uncertainty associated with the development of a survey design. The benefit of 

documenting all of the survey decisions (e.g., design, implementation, and assessment) in one 

document is the savings in resources to develop multiple documents. The risk associated with not 

documenting the survey design process until after implementation is that the assessment will 

identify some problems with the survey design requiring additional data collection which could 

impact project costs and schedule. 

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MARSAME Implement The Survey Design 

5 IMPLEMENT THE SURVEY DESIGN 

5.1 Introduction 

This chapter discusses the implementation phase of the data life cycle and focuses on controlling 

measurement uncertainty and associated MQOs. The information in this chapter describes 

approaches for safely implementing the final disposition survey design developed in Chapter 4, 

methods for controlling uncertainty, and techniques to determine whether the measurement 

results achieve the survey objectives. Figure 5.1 illustrates the implementation phase of the data 

life cycle. 

Similar to MARSSIM, MARSAME excludes specific recommendations for implementing 

disposition surveys. Instead, MARSAME provides recommendations and information to assist 

the user in selecting measurement techniques for implementing the survey design. This approach 

encourages consideration of innovative measurement techniques and emphasizes the flexibility 

of the information in MARSAME. 

Implementation begins with health and safety considerations for the disposition survey (Section 

5.2). Section 5.3 provides information on handling M&E, while Section 5.4 discusses 

segregating M&E based on physical and radiological attributes. Section 5.5 continues the 

discussion of measurement quality objectives (MQOs) from Chapters 3 and 4. Measurement 

uncertainty (Section 5.6), detectability (Section 5.7), and quantifiability (Section 5.8), are three 

MQOs that are described in greater detail. Combining an instrument with a measurement 

technique to ensure the MQOs are achieved is discussed in Section 5.9. Section 5.10 provides 

information on quality control (QC), and information on data reporting is provided in 

Section 5.11. 

5.2 Ensure Protection of Health and Safety 

Health and safety is emphasized as an issue potentially affecting the implementation of 

MARSAME disposition surveys. The focus of minimizing hazards is shifted away from 

environmental hazards (e.g., confined spaces, unstable surfaces, heat and cold stress) and 

towards scenarios where health and safety issues may affect how a disposition survey is designed 

and performed. Work areas and procedures that present potential safety hazards must be 

identified and evaluated to warn personnel of potential hazards. Personnel must be trained with 

regard to potential physical and chemical safety hazards (e.g., inhalation, adsorption, ingestion, 

injection/puncturing) and the potential for injury (e.g., slips, trips, falls, burns). 

A job safety analysis (JSA) should be performed prior to implementing a disposition survey. The 

JSA offers an organized approach to the task of locating problem areas for material handling 

safety (OSHA 2002). The JSA should be used to identify hazards and provide inputs for drafting 

a health and safety plan (HASP). The HASP will address the potential hazards associated with 

M&E handling and movement and should be prepared concurrently with the survey design. The 

HASP identifies methods to minimize the threats posed by the potential hazards. The information 

in the HASP may influence the selection of a measurement technique and disposition survey 

procedures. Radiation work permits (RWPs) may be established to control access to 

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From Figure 4.1

Ensure Protection of 

Health and Safety

(Section 5.2)

Handle M&E

(Section 5.3)

Prepare M&E for Survey (Section 5.3.1)

Provide Access to M&E (Section 5.3.2)

Transport the M&E (Section 5.3.3)

Do M&E Need 

Segregation?

Segregate the M&E

(Section 5.4) Yes

Set Measurement 

Quality Objectives

(Section 5.5-5.8)

Select a Measurement 

Technique and 

Instrumentation Combination

(Section 5.9)

Set Quality 

Control Requirements

(Section 5.10)

Perform the Survey & 

Report the Results

(Section 5.11)

Proceed to Figure 6.1

No

NOTE: Shaded boxes 

represent important decisions 

(diamonds) or milestones 

(rectangles).

Figure 5.1 Implementation of Disposition Surveys 

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radiologically controlled areas. RWPs contain requirements from the JSA, such as dosimetry and 

personal protective equipment (PPE), as well as survey maps illustrating predicted dose rates and 

related radiological concerns (e.g., removable or airborne radioactivity). Hazard work permits 

(HWPs) may be used in place of RWPs at sites with primarily physical or chemical hazards. The 

mineral processing facility concrete rubble example presented in Chapter 8 (see Table 8.9) 

provides an example of a JSA. 

The JSA systematically carries out the basic strategy of accident prevention through the 

recognition, evaluation, and control of hazards associated with a given job as well as the 

determination of the safest, most efficient method of performing that job. This process creates a 

framework for deciding among engineering controls, administrative controls, and PPE for the 

purpose of controlling or correcting unsafe conditions (Hatch 1978). Examples of these controls 

include— 

• Engineering controls, which are physical changes in processes or machinery (e.g., installing 

guards to restrict access to moving parts during operation), storage configuration (e.g., using 

shelves in place of piles or stacks); 

• Administrative controls, which are changes in work practices and organization (e.g., 

restricted areas where it is not safe to eat, drink, smoke, etc.) including the placement of 

signs to warn personnel of hazards; and 

• Personal protective equipment, which are clothing or devices worn by employees to protect 

against hazards (e.g., gloves, respirator, full-body suits). 

Correction measures may incorporate principles of all of the controls listed above. The preferred 

method of control is through engineering controls, followed by administrative controls, and then 

personal protective equipment. 

Proper handling procedures for hazardous M&E are documented in site-specific health and 

safety plans. Compliance with all control requirements is mandatory to maintain a safe working 

environment. Personnel must regard control requirements as a framework to facilitate health and 

safety, while still taking responsibility for their own well being. Being wary of safety hazards 

remains an individual responsibility and personnel must be aware of their surroundings at all 

times in work areas. 

5.3 Consider Issues for Handling M&E 

Materials and equipment handling is addressed in this document as a process control issue. M&E 

handling requirements are determined by the final integrated survey design (Section 4.4) and the 

combination of instrumentation and measurement technique used to perform the survey (Section 

5.9). M&E may also require handling to more closely match the assumptions used to develop 

instrument calibrations used to determine measurement uncertainty (Section 5.6), measurement 

detectability (see Section 5.7), and measurement quantifiability (Section 5.8). Typically, M&E 

will be handled to— 

• Prepare a measurement grid or arrange M&E to perform a survey, 

• Provide access for performing measurements, and 

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• Transport the M&E to a different location. 

5.3.1 Prepare M&E for Survey 

Depending on the survey design, or assumptions used to develop the survey design, it may be 

necessary to prepare the M&E for survey. The amount of preparation required is determined by 

the DQOs and MQOs, and ranges from identifying measurement locations to adjusting the 

physical characteristics of the M&E (e.g., disassembly, segregation, physical arrangement). 

The performance of a MARSSIM-type survey requires determining the location where the 

measurements are to be performed. The DQOs will determine the level of effort required to 

identify, mark, and record measurement locations. 

Identifying measurement locations can be problematic because MARSSIM-type surveys 

recommend samples to be located either randomly (Class 3) or on a systematic grid (Class 1 and 

Class 2). Class 2 and Class 3 scan-only and in situ surveys do not require 100% of the M&E to 

be measured, so a method of identifying which portions will be measured is required. 

Bulk materials or M&E consisting of many small, regularly shaped objects can be spread out in a 

uniform layer, and a two-dimensional grid can be superimposed on the surface to identify 

measurement locations. However, it is virtually impossible to identify random or systematic 

locations on M&E that consist of relatively few, large, irregularly shaped objects. The reason is 

that it is virtually impossible to establish a reference grid for these M&E. It is important to note 

that the objective for random locations is to allow every portion of the survey unit the same 

opportunity to be measured. Alternatively, the objective of systematic locations is to distribute 

the measurement locations equally. It is only necessary to establish a reference grid to 

sufficiently identify the measurement locations to meet the survey objectives. 

One way to approximate a reference grid for locating measurements is to establish a grid in the 

area where the survey will be performed. The M&E to be surveyed are laid out in a single layer 

within the grid. The grid can then be used to identify measurement locations. Another option for 

locating measurements involves superimposing a grid on top of the M&E. A net could be laid 

over the M&E to be surveyed, ropes could be laid over the M&E to form a grid, or lights on a 

grid could be directed onto the M&E to approximate a grid and identify measurement locations. 

If measurement locations cannot be identified with a grid, there may be no alternative but to 

perform biased measurements. Measurements would be performed preferentially in locations 

more likely to contain radionuclides or radioactivity, based on the results of the initial 

assessment (IA) (Section 2.5). This process involves professional judgment and may result in 

overestimating the average radionuclide concentration or level of radioactivity. In all cases, it is 

important to document the criteria used for identifying measurement locations and to document 

that these criteria were followed. 

Marking measurement locations, once they have been identified, should be done in a way that 

will not interfere with the measurement. For example, using paint to mark the location of an 

alpha measurement could end up masking the presence of alpha activity. Using arrows, marking 

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borders, or using an alternate method for marking locations (e.g., encircling with chalk) should 

be considered for these types of situations. 

Recording measurement locations may be required as part of the survey objectives if the 

measurements may need to be repeated. For example, a large piece of equipment is surveyed 

prior to use on a decommissioning or cleanup project. If the exact same locations will be 

surveyed at the completion of the project, it will be necessary to record the measurement 

locations. Permanent or semi-permanent markings can be used to identify the measurement 

locations. Video or photographic records of measurement locations can also be used to return to 

a specific measurement location. 

5.3.2 Provide Access 

Large pieces of equipment may require special handling considerations. Large, mobile 

equipment (e.g., front loader, bulldozer, or crane) typically requires a specially trained operator. 

The operator may need to be available during the disposition survey to provide access to all areas 

requiring survey (e.g., move the equipment to provide access to the bottom of tires or treads). 

Other large items may require special equipment (e.g., a crane or lift) to provide access to all 

areas requiring survey. Special health and safety issues (Section 5.2) may be required to ensure 

protection of survey personnel from physical hazards (e.g., personnel or items falling from 

heights, or large items dropping on personnel or equipment). It may be necessary to partially or 

totally disassemble large pieces of equipment to provide access and ensure measurability. 

Piles of M&E may involve special handling precautions. Piles of dispersible M&E (e.g., 

excavated soil or concrete rubble) may need to be rearranged to match the assumptions used to 

develop the instrument efficiency. For example, a conical pile of excavated soil may need to be 

flattened to a uniform thickness to ensure measurability. If the M&E consists of or contains a 

significant amount of dust, precautions against generating an airborne radiation hazard may be 

necessary. Because many dust control systems use liquids to prevent the dust from becoming 

airborne, it may be necessary to account for dust control impacts on measurability of the M&E. 

For example, adding water to control dust will make it more difficult to measure alpha 

radioactivity. Piles of scrap may also present other health and safety concerns along with issues 

related to measurability. Sharp edges, pinch points, and unstable piles are examples of handling 

problems that may need to be addressed. 

Small pieces of M&E may be surveyed individually or combined into groups for survey. Care 

should be taken when combining items to prevent mixing impacted and non-impacted items, or 

mixing items with different physical or radiological attributes (see Section 2.2 and Section 5.4). 

The moving of materials at a given site may require labeling as a quality control measure to 

ensure M&E movement is tracked and documented. Labeling will help avoid the commingling of 

impacted and non-impacted materials, and facilitate the staging and storage of impacted and nonimpacted M&E in appropriate areas. 

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5.3.3 Transport the M&E 

Identification of impacted and non-impacted areas within a facility will assist in selecting areas 

for storing, staging, and surveying impacted M&E. In general, impacted M&E should be stored, 

staged, and surveyed in impacted areas. Care should be taken when moving or handling impacted 

M&E to prevent the spread of radionuclides to non-impacted areas. M&E in areas with airborne 

radioactivity issues should be moved to protect the personnel conducting surveys and reduce the 

possibility of contaminating survey instruments. 

Disposition surveys can be performed with the M&E in place, or the M&E can be moved to 

another location. For example, work areas with high levels of radioactivity may make it difficult 

or resource intensive to meet the MQOs for measurement detectability (Sections 5.7 and 7.5) or 

quantifiability (Sections 5.8 and 7.6). Moving the M&E to areas with lower levels of 

radioactivity will help reduce radiation exposure for personnel conducting surveys and facilitate 

meeting the survey objectives. 

5.4 Segregate the M&E 

The purpose of segregation is to separate M&E based on the estimated total measurement 

uncertainty, ease of handling, and disposition options. Segregation is based on the physical and 

radiological attributes determined during the IA (Chapter 2), not only on radionuclide 

concentrations or radiation levels (i.e., classification). 

In general, segregation based on measurement uncertainty should consider the physical and 

radiological attributes that affect efficiency (i.e., geometry and fluence rate). M&E with simple 

geometries, such as drums (cylinder) and flat surfaces (plane), should be separated from M&E 

with complex geometries. Fluence rate is affected by location of the radioactivity (i.e., surficial 

or volumetric) as well as surface effects (e.g., rough or smooth), density of the M&E, and type 

and energy of radiation. High fluence rates are associated with surface radioactivity with high 

energy on flat smooth surfaces made from materials with high atomic number (due to increased 

backscatter). Volumetric activity, shielded surfaces, alpha or low energy or beta radiations, 

irregular shapes, or rough surfaces can cause lower fluence rates. All of these factors should be 

considered when segregating M&E. 

Segregation of M&E should be performed conservatively. This means that the user should 

separate M&E when they are not obviously similar. It is always possible to combine M&E but it 

is not always practical or possible, to separate M&E once they have been combined. For 

example, consider a facility where all the waste materials (e.g., paper, wood, metal, broken 

equipment) are combined into a single “trash pile.” When the planning team considers different 

measurement methods and disposition options, they identify an innovative measurement method 

that only applies to non-ferrous scrap metal. This would allow for recycling of these materials 

with significant cost recovery as opposed to disposal. If the cost of re-segregating the M&E is 

not offset by the value of recycling these materials, it may not be practical to segregate the nonferrous metals. 

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It is important to note that segregation does not require physical separation. Consider a generic 

large box geometry, such as an empty shipping container or railroad car. The large, flat sides 

could be considered separate survey units from the corners. Therefore, separate surveys would be 

designed for the corners and the sides even though the entire railroad car would remain intact 

throughout implementation of the disposition survey. Alternatively (or additionally), obvious 

flaws, corrosion areas, or damaged areas could be segregated from the areas in good condition. 

Even if the entire object is eventually surveyed using a single in situ measurement (e.g., in situ 

gamma spectroscopy) it is important to segregate the M&E (at least conceptually) so an adequate 

evaluation of alternate measurement methods can be performed (Section 5.9). 

Handling of M&E during disposition surveys should also be considered during segregation 

(Section 5.3). Physical characteristics of the M&E should be considered when segregating based 

on handling requirements. Small, light items are easier to move and gain access to all surfaces 

than large, massive items. M&E that will require preparation (e.g., disassembly, crushing, 

chopping) prior to survey should be segregated from M&E that can be surveyed in their present 

form. Disposition options should also be considered when segregating M&E. M&E that can be 

reused or recycled should be segregated from M&E that is being considered for disposal. 

Selection of disposition options is discussed in Section 2.4. 

5.5 Set Measurement Quality Objectives 

A number of terms with specific statistical meanings are used in this and subsequent sections. 

These terms are defined in Chapter 7. The concept of Measurement Quality Objectives (MQOs) 

and in particular the required measurement method uncertainty is introduced in Section 3.8.1. 

These ideas are discussed in greater detail in the Multi-Agency Radiological Laboratory 

Analytical Protocols manual (MARLAP 2004) Chapter 3 and Appendix C. While MARLAP is 

focused on radioanalytical procedures, these concepts are applicable on a much broader scale and 

will be used in MARSAME to guide the selection of measurement methods for disposition 

surveys for materials and equipment. 

Section 4.2 discusses the DQO process for developing statistical hypothesis tests for the 

implementation of disposition decision rules using measurement data. These concepts are further 

developed in Chapter 7. This includes formulating the null and alternative hypotheses, defining 

the gray region using the action level and discrimination limit, and setting the desired limits on 

potential Type I and Type II decision error probabilities that a decision-maker is willing to 

accept for project results. Decision errors are possible, at least in part, because measurement 

results have uncertainties. Because DQOs apply to both sampling and measurement activities, 

method performance characteristics specifically for the measurement process of a particular 

project are needed from a measurement perspective. These method performance characteristics 

(Section 3.8) are the measurement quality objectives (MQOs). 

DQOs define the performance criteria that limit the probabilities of making decision errors by— 

• Considering the purpose of collecting the data, 

• Defining the appropriate type of data needed, and 

• Specifying tolerable probabilities of making decision errors. 

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DQOs apply to both sampling and measurement activities. MQOs can be viewed as the 

measurement portion of the overall project DQOs (Section 3.8). MQOs are— 

• The part of the project DQOs that apply to the measured result and its associated uncertainty, 

• Statements of measurement performance objectives or requirements for a particular 

measurement method performance characteristic (e.g., measurement method uncertainty and 

detection capability), 

• Used initially for the selection and evaluation of measurement methods, and 

• Used subsequently for the ongoing and final evaluation of the measurement data. 

Measurement method uncertainty refers to the predicted uncertainty of a measured value that 

would be calculated if the method were applied to a hypothetical sample with a specified 

concentration. Measurement method uncertainty is a characteristic of the measurement method 

and the measurement process. Measurement uncertainty, as opposed to sampling uncertainty, is a 

characteristic of an individual measurement. 

The true measurement method standard deviation, σM, is a theoretical quantity and is never 

known exactly, but it may be estimated using the methods described in Section 7.4. The 

estimated value of σM will be denoted here by σM and called the “measurement method 

uncertainty.” The measurement method uncertainty, when estimated by uncertainty propagation, 

is the predicted value of the combined standard uncertainty (“one-sigma” uncertainty) of the 

measurement for material with concentration equal to the upper bound of the gray region 

(UBGR). Note that the term “measurement method uncertainty” and the symbol uM actually 

apply not just to the measurement method but also to the entire measurement process: it should 

include uncertainties in how the measurement method is actually implemented. This definition of 

measurement method uncertainty is independent of the null hypothesis and applies to both 

Scenario A and Scenario B. 

The true standard deviation of the measurement method, σM, is unknown, but the required 

measurement method uncertainty, σMR, is intended to be an upper bound for σM. In practice, σM is 

actually used as an upper bound for the method uncertainty, σM, which is an estimate of σM. 

Therefore, the estimated value of σMR will be called the “required measurement method 

uncertainty” and denoted by uMR. Note that when referring to a theoretical population standard 

deviation, the symbol σ is used. Estimates of the value of σ in specific cases are denoted by the 

symbol u, for uncertainty. An uncertainty is not a standard deviation because its evaluation 

involves concepts from metrology as well as statistics, however, in many cases it is treated 

mathematically as if it were a standard deviation. 

The principal MQOs in any project will be defined by the required measurement method 

uncertainty, uMR, at and below the UBGR and the relative required measurement method 

uncertainty, φMR, at and above the UBGR: 

UBGR

uMR φ MR = (5-1) 

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Section 7.7 provides the rationale and guidance for establishing project-specific MQOs for 

controlling uM. 

Note: When making decisions about individual measurement results, uMR usually should be about 

0.3Δ, and when making decisions about the mean of several measurement results, uMR usually 

should be about 0.1Δ, where Δ is the width of the gray region, Δ = UBGR – LBGR. These rules 

of thumb require certain assumptions as discussed in Chapter 7.

This check of measurement quality against the required measurement method uncertainty relies 

on having realistic estimates of the measurement uncertainty. Often reported measurement 

uncertainties are underestimated, particularly if they are confined to the estimated Poisson 

counting uncertainty (Section 7.8). Tables of results are sometimes presented with a column 

listing simply “±” without indicating how these numbers were obtained. Often it is found that 

they simply represent the square root of the number of counts obtained during the measurement. 

The method for calculating measurement uncertainty, approved by both the International 

Organization for Standardization (ISO) and the National Institute of Standards and Technology 

(NIST) is discussed in the next section. 

5.6 Determine Measurement Uncertainty 

This section discusses the evaluation and reporting of measurement uncertainty. Measurements 

always involve uncertainty, which must be considered when measurement results are used as part 

of a basis for making decisions. Every measured and reported result should be accompanied by 

an explicit uncertainty estimate. One purpose of this section is to give users of data an 

understanding of the causes of measurement uncertainty and of the meaning of uncertainty 

statements; another is to describe procedures that can be used to estimate uncertainties. Much of 

this material is derived from MARLAP Chapter 19. 

In 1980, the Environmental Protection Agency published a report entitled “Upgrading 

Environmental Radiation Data,” which was produced by an ad hoc committee of the Health 

Physics Society (EPA 1980). Two of the recommendations of this report were that– 

1. Every reported measurement result (x) should include an estimate of its overall uncertainty 

(ux) that is based on as nearly a complete assessment as possible, and 

2. The uncertainty assessment should include every significant source of inaccuracy in the 

result.

The concept of traceability is also defined in terms of uncertainty. Traceability is defined as the 

“property of the result of a measurement or the value of a standard whereby it can be related to 

stated references, usually national or international standards, through an unbroken chain of 

comparisons all having stated uncertainties” (ISO 1996). Thus, to realistically make the claim 

that a measurement result is “traceable” to a standard, there must be a chain of comparisons 

(each measurement having its own associated uncertainty) connecting the result of the 

measurement to that standard. 

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This section considers only the measurement standard deviation, σM. Reducing sampling 

standard deviation, σS, by segregating M&E was discussed in Section 5.4. The sampling standard 

deviation is often larger than the measurement standard deviation. Although this statement may 

be true in some cases, this is not an argument for failing to perform a full evaluation of the 

measurement uncertainty, uM, to evaluate σM . A realistic estimate of the measurement 

uncertainty is one of the most useful data quality indicators for a result (Section 3.8). 

Although the need for reporting uncertainty has been recognized, often it consists of only the 

estimated component due to Poisson counting statistics. This is done because it is easier than a 

full uncertainty analysis, but it can be misleading because it is at best only a lower bound on the 

uncertainty and may lead to incorrect decisions based on overconfidence in the measurement. 

Software is available to perform the mathematical operations for uncertainty evaluation and 

propagation, eliminating much of the difficulty in implementing the mathematics of uncertainty 

calculations. There are several examples of such software (McCroan 2006, GUM Workbench 

2006, Kragten 1994, Vetter 2006). 

The methods, terms, and symbols recommended by MARSAME for evaluating and expressing 

measurement uncertainty are described in the Guide to the Expression of Uncertainty in 

Measurement, or GUM, which was published by ISO (ISO 1995). The ISO methodology is 

summarized in the NIST Technical Note TN-1297 (NIST 1994). The details of applying this 

methodology are given in Section 7.4 and 7.8. 

5.7 Determine Measurement Detectability 

This section is a summary of issues related to measurement detection capabilities. Much of this 

material is derived from the MARLAP Chapter 20. More detail may be found in Section 7.9. 

Radioactivity measurements may involve material with very small amounts of the radionuclide 

of interest. Measurement uncertainty often makes it difficult to distinguish such small amounts 

from zero. Therefore, an important MQO of a measurement process is its detection capability, 

which is usually expressed as the smallest concentration of radioactivity that can be reliably 

distinguished from zero. Effective project planning requires knowledge of the detection 

capabilities of the measurement method that will be or could be used. This section explains an 

MQO called the minimum detectable concentration (MDC) and describes radioactivity detection 

capabilities, as well as methods for calculating it. 

The method most often used to make a detection decision about radiation or radioactivity 

involves the principles of statistical hypothesis testing. It is a specific example of a Scenario B 

hypothesis testing procedure described in Section 7.2.4. To “detect” the radiation or radioactivity 

requires a decision on the basis of the measurement data that the radioactivity is present. The 

detection decision involves a choice between the null hypothesis (H0): There is no radiation or 

radioactivity present (above background), and the alternative hypothesis (H1): There is radiation 

or radioactivity present (above background). Making the choice between these hypotheses 

requires the calculation of a critical value. If the measurement result exceeds this critical value, 

the null hypothesis is rejected and the decision is that radiation or radioactivity is present. If the 

null hypothesis is rejected when it is true, a Type I decision error is made. In this case, a sample 

with no additional radiation or radioactivity above background is deemed to actually contain 

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such. The rate at which this decision error occurs is denoted by α. The critical value depends 

directly on the value of α. The planning team has to make a choice about the establishment of the 

acceptable rate for mischaracterizing a background count for a real detection count, i.e., establish 

a Type I error rate, α, for mistakenly deciding a background measurement is really a detection of 

additional radiation or radioactivity. 

Radioactivity measurements are often recorded as counts or count rates. Radiation exposure 

measurements are often expressed in different terms, e.g., ionization current. The term 

“instrument signal” is used in the following so that all types of measurement are included.1 

The relationship between the critical value of the net instrument signal (or count), SC, and the 

minimum detectable net instrument signal, SD, is shown in Figure 5.2. More detail on the 

calculation of the minimum detectable value of the net instrument signal (or count), SD, is given 

in Section 7.9. The net instrument signal obtained for a blank sample will usually be distributed 

around zero as shown. Occasionally, a net instrument signal above SC may be obtained by 

chance. The probability that this happens is controlled by the value of α, the Type I decision 

error rate, shown as the lightly shaded area in Figure 5.2. Smaller values of α result in larger 

values of SC and vice versa. The minimum detectable value of the net instrument signal SD is that 

value of the mean net instrument signal that results in a detection decision with probability 1 − β. 

That is, there is only a probability β, the Type II decision error rate shown as the more darkly 

shaded area in Figure 5.2, of yielding an observed instrument signal less than SC. Smaller values 

of β result in larger values of SD and vice versa. The planning team has to decide what an 

acceptable value of β should be, i.e. when additional radiation or radioactivity is present, at what 

rate is it acceptable to mistakenly attribute the measurement result to only background. Note that 

SD depends on the values of both α and β. 

Figure 5.2 The Critical Value (SC) and the Minimum Detectable Value (SD) of the Net Instrument 

Signal (or Count) 

The MDC is usually obtained from the minimum detectable value of the net instrument signal (or 

count), SD. The MDC is by definition an estimate of the true concentration of the radiation or 

radioactivity required to give a specified high probability that the measured response will be 

 

1

 “Net instrument signal,” is used here as a general term, because many radiation-detection instruments may have 

output other than “counts” (e.g., current for ionization chambers). In cases where the instrument output is in counts, 

the term “net counts” can be substituted for the term “net instrument signal.” 

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greater than SC. The common practice of comparing a measured concentration to the MDC, 

instead of SC, to make a detection decision is incorrect. 

To calculate the MDC, the minimum detectable value of the net instrument signal, SD, must first 

be converted to the detectable value of the net instrument signal rate (often a count rate), SD/tS

(s−1), where tS is the duration of the measurement in seconds. This in turn must be divided by the 

instrument efficiency, ε (s−1

/Bq) to get the minimum detectable activity, yD. Finally, the 

minimum detectable activity can be divided by the sample volume or mass to obtain the MDC. 

At each stage in this process, additional uncertainty may be introduced by the uncertainties in 

time, efficiency, volume, mass, etc. Prudently conservative values of these factors should be used 

so that the desired detection power, 1 − β, at the MDC is maintained. Another approach would be 

to recognize that yD itself has an uncertainty which can be calculated using the methods of 

Section 7.8. Thus, any input quantity that is used to convert from SD to yD that has significant 

uncertainty, can be incorporated to assess the overall uncertainty in the MDC. 

MARSAME recommends that when a detection decision is required, it generally should be made 

by comparing the net instrument signal to its corresponding critical value. Expressions for SC and 

SD should be chosen that are appropriate for the structure and statistics of the measurement 

process. An appropriate background should be used to predict the instrument signal produced 

when there is no radioactivity present in the sample. The MDC should be used only as a MQO 

for the measurement method. To make a detection decision, a measurement result should be 

compared to SC and never to the MDC. Finally, additional discussion of the calculation of the 

MDCs is given in Section 7.9. 

5.8 Determine Measurement Quantifiability 

This section discusses issues related to measurement quantifiability. Much of this material is 

derived from the MARLAP Chapter 20. 

Action levels are frequently stated in terms of a quantity or concentration of radioactivity, rather 

than in simply in terms of whether radioactivity is detected. In these cases, project planners may 

need to know the quantification capability of a measurement method, or its capability for precise 

measurement. The quantification capability is expressed as the smallest concentration of 

radiation or radioactivity that can be measured with a specified relative measurement standard 

deviation. This section explains an MQO called the minimum quantifiable concentration (MQC), 

which may be used to describe quantification capabilities. 

The MQC, yQ, is defined as the concentration at which the measurement process gives results 

with a specified relative standard deviation, 1/kQ, where kQ is usually chosen to be 10 for 

comparability. Thus, the MQC is generally the concentration at which the relative measurement 

uncertainty is 10%. 

Historically much attention has been given to the detection capabilities of radiation and 

radioactivity measurement processes, but less attention has been given to quantification 

capabilities. For some projects, quantification capability may be a more relevant issue. For 

example, suppose the purpose of a project is to determine whether the 226Ra concentration on 

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material at a site is below an action level. Because 226Ra can be found in almost any type of 

naturally occurring material, it may be assumed to be present in every sample, making detection 

decisions unnecessary. The MDC of the measurement process obviously should be less than the 

action level, but a more important question is whether the MQC is less than the action level. 

A common practice in the past has been to select a measurement method based on the MDC, 

which is defined in Section 5.7 and Section 7.5. For example, MARSSIM says: 

During survey design, it is generally considered good practice to select a 

measurement system with an MDC between 10-50% of the DCGL [action level]. 

Such guidance implicitly recognizes that for cases when the decision to be made concerns the 

mean of a population that is represented by multiple measurements, criteria based on the MDC 

may not be sufficient and a somewhat more stringent requirement is needed. The requirement 

that the MDC (approximately 3–5 times σM) be 10% to 50% of the action level is tantamount to 

requiring that σM be 0.02 to 0.17 times the action level. In other words, the relative measurement 

standard deviation should be approximately 10% at the action level. However, the concentration 

at which the relative measurement standard deviation is 10% of the MQC when kQ assumes its 

conventional value of 10. Thus, a requirement that is often stated in terms of the MDC may be 

more naturally expressed in terms of the MQC (e.g., by saying that the MQC should not exceed 

the action level). Further details on calculating the MQC can be found in Section 7.10. 

5.9 Select a Measurement Technique and Instrumentation Combination 

The combination of a measurement technique with instrumentation, or measurement method, is 

selected to implement a disposition survey design based on the ability to meet the MQOs (see 

Sections 3.3.2 and 5.5). Note that measurement techniques are separate from survey designs. The 

relationship between the two is explained in Sections 5.9.1.1, 5.9.1.2, and 5.9.1.3. A realistic 

determination of the measurement method uncertainty (Section 5.6) is critical to demonstrating a 

method meets the MQOs. Other considerations when selecting a measurement method include— 

• Health and safety concerns (Section 5.2), 

• M&E handling issues (Section 5.3), 

• Segregation (Section 5.4), 

• Measurement detectability (Section 5.7), and 

• Measurement quantifiability (Section 5.8). 

The measurement techniques discussed in Section 5.9.1 all can be classified as scanning 

measurements (constant motion involved in the surveying procedure) or fixed measurements 

(surveying discrete locations without motion). Fixed measurements consist of in situ 

measurements (the detection instrument moves to the M&E or measures the M&E in its 

entirety), and sampling (removing part of the M&E for separate analysis). 

Instrumentation for performing radiological measurements is varied and constantly being 

improved. Section 5.9.2 provides an overview of some commonly used types of instruments and 

how they might be applied to disposition surveys. The purpose of the discussions on 

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instrumentation is not to provide an exhaustive list of acceptable instruments, but to provide 

examples of how instrumentation and measurement techniques can be combined to meet the 

survey objectives. Additional information on instrumentation is found in Appendix D. 

Section 5.9.3 provides information on selecting a combination of measurement technique and 

instrumentation to provide a measurement method. It is necessary that the selected measurement 

method meet the MQOs established during survey design (Section 3.8). Selection of 

instrumentation can be an iterative process. The appropriate MQO (e.g., MDC, MQC) may not 

be attainable with some measurement methods. In some cases selection of a different instrument 

may be all that is necessary, while in other cases a different measurement technique or an 

entirely different measurement method will need to be considered. 

5.9.1 Select a Measurement Technique 

A measurement technique describes how a measurement is performed. The detector can be 

moved relative to the M&E (i.e., scanning), used to perform static measurements of the M&E in 

place (i.e., in situ or direct measurements), or some representative portion of the M&E can be 

taken to a different location for analysis (i.e., sampling). These three measurement techniques are 

described in Sections 5.9.1.1, 5.9.1.2, and 5.9.1.3, respectively. Smears are a type of sampling, 

where a portion of the removable radioactivity is collected (Section 5.9.1.4). 

5.9.1.1 Scanning Techniques 

Scanning techniques generally consist of moving portable radiation detectors at a specified 

distance above the physical surface of a survey unit at some specified speed to meet the MQOs. 

Alternatively, the M&E can be moved past a stationary instrument at a specified distance and 

speed (e.g., conveyorized systems or certain portal monitors). Scanning techniques can be used 

alone to demonstrate compliance with a disposition criterion (i.e., scan-only surveys, Section 

4.4.1), or combined with sampling in a MARSSIM-type survey design (Section 4.4.3). Scanning 

is used in MARSSIM-type surveys to locate radiation anomalies by searching for variations in 

readings, indicating gross radioactivity levels that may require further investigation or action. 

Scanning techniques can more readily provide thorough coverage of a given survey unit and are 

often relatively quick and inexpensive to perform. Scanning often represents the simplest and 

most practical approach for performing MARSAME disposition surveys. 

Maintaining the specified distance and speed during scanning can be difficult, especially with 

hand-held instruments and irregularly shaped M&E. Variations in source-to-detector distance 

and scan speed can result in increased total measurement method uncertainty. Determining a 

calibration function for situations other than surficial radionuclides uniformly distributed on a 

plane can be complicated, and may also contribute to the total measurement method uncertainty. 

5.9.1.2 In Situ Measurements 

In situ measurements are taken by placing the instrument in a fixed position at a specified 

distance2

 from the surface of a given survey unit of M&E and taking a discrete measurement for 

 

2

Measurements at several distances may be needed. Near-surface or surface measurements provide the best 

indication of the size of the area of elevated radionuclide concentrations or radioactivity, and are useful for model 

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a pre-determined time interval. Single in situ measurements can be performed on individual 

objects or groups of M&E. Multiple in situ measurements can be combined to provide several 

different views of the same object, or used to provide measurements for a specified fraction of 

the M&E. In situ measurements can also be performed at random or systematic locations, 

combined with scanning measurements, in a MARSSIM-type survey design. In situ 

measurements are used generally to provide an estimate of the average radionuclide 

concentration or level of radioactivity over a certain area or volume defined by the calibration 

function. 

Determining a calibration function for situations other than radionuclides uniformly distributed 

on a plane or through a regularly shaped volume (e.g., a disk or cylinder) can be complicated and 

may contribute to the total measurement method uncertainty. In situ techniques are not typically 

used to identify small areas or volumes of elevated radionuclide concentration or activity. 

5.9.1.3 Sampling 

Sampling consists of removing a portion of the M&E for separate laboratory analysis. This 

measurement technique, when combined with laboratory analysis, surpasses the detection 

capabilities of measurement techniques that may be implemented with the M&E left in place. 

This facilitates the analysis of complicated radioisotope mixtures, difficult-to-measure 

radionuclides, and extremely low concentrations of residual radioactivity. Sampling is used to 

provide an estimate of the average radionuclide concentration or level of radioactivity for a 

specified area or volume. The sample locations may be located using a random or systematic 

grid, depending on the objectives of the survey. Sampling is typically combined with scanning in 

a MARSSIM-type survey design, where sampling is used to evaluate the average concentration 

or activity and scanning is used to identify small areas or volumes with elevated radionuclide 

concentrations or radioactivity. Sampling may also be used to validate data collected using other 

measurement techniques. 

Sampling (combined with laboratory analysis) typically requires the most time for data 

generation of all the surveying techniques discussed in this chapter and is often the most 

expensive. Sampling is not an effective technique for identifying small areas or volumes of 

elevated radionuclide concentrations or levels of radioactivity. 

5.9.1.4 Smears 

Smears are used to provide an estimate of removable surface radioactivity. Smears are also 

referred to as smear tests, swipes, or wipes. Smears are a type of sample where a filter paper or 

other substance is used to wipe a specified area of a surface. The filter paper or other substance is 

then tested for the presence of radioactivity. 

Individual smear results collected by hand usually have a high uncertainty because the fraction of 

surface radioactivity transferred to the smear is unknown and variable and the surface area 

 implementation. Gamma measurements at one meter provide a good estimate of potential direct external exposure 

(MARSSIM 2002). 

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covered by the smear is variable. In addition, the results may vary with time due to 

environmental factors or interactions of surface activity with the surface itself. Action levels for 

removable activity based on smear measurements may include assumptions about the fraction of 

surface radioactivity transferred to a single smear or specify a surface area to be smeared. For 

example, DOT surface contamination guidelines assume that 10% of the surface radioactivity is 

transferred to a single smear. Also, DOE Order 5400.5 Figure IV-1 (DOE 1993) provides 

instructions for using smears to measure removable radioactivity. These instructions specify 

wiping an area of 100 cm2 with a dry filter or soft absorbent paper while applying moderate 

pressure. The instructions also discuss how to account for minor variations from the procedure. 

Determining a collection or removal fraction for smears can be complicated. The uncertainty and 

variability in the removal fraction estimate and surface area smeared can result in increased total 

measurement method uncertainty. Using a template or cutout with a known area can help control 

the variability in the area covered by a smear. Using a tool that applies consistent pressure while 

collecting smears can reduce the variability in the fraction of radioactivity removed. 

Implementing a protocol for preparing surfaces and sorting materials prior to survey can reduce 

variability in surface textures and conditions resulting in lower variability in smear collection 

conditions. 

5.9.2 Select Instrumentation 

This section briefly describes the typical types of instrumentation that may be used to conduct 

MARSAME disposition surveys. More detailed information relevant to each type of instrument 

and measurement method is provided in Appendix D. 

5.9.2.1 Hand-Held Instruments 

Hand-held instruments typically are composed of a detection probe (utilizing a single detector) 

and an electronic instrument to provide power to the detector and to interpret data from the 

detector to provide a measurement display. They may be used to perform scanning surveys or in 

situ measurements. Hand-held measurements also allow the user the flexibility to constantly vary 

the source-to-detector geometry for obtaining data from difficult-to-measure areas. 

5.9.2.2 Volumetric Counters (Drum, Box, Barrel, 4-π Counters) 

Box counting systems typically consist of a counting chamber, an array of detectors configured 

to provide 4-π counting geometry, and microprocessor-controlled electronics that allow 

programming of system parameters and data-logging. Volumetric counters are used to perform in 

situ measurements on entire pieces of small M&E. 

5.9.2.3 Conveyorized Survey Monitoring Systems 

Conveyorized survey monitoring systems automate the routine scanning of M&E. Conveyorized 

survey monitoring systems typically perform scanning surveys by moving M&E through a 

detector array on a conveyor belt. Conveyorized survey monitoring systems may be utilized to 

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take in situ measurements by halting the conveyor and continuing the measurement to improve 

the detection efficiency. 

5.9.2.4 In Situ Gamma Spectroscopy 

Some in situ gamma spectroscopy (ISGS) systems consist of a small hand-held unit that 

incorporates the detector and counting electronics into a single package. Other ISGS systems 

consist of a semiconductor detector, a cryostat, a multi-channel analyzer (MCA) electronics 

package that provides amplification and analysis of the energy pulse heights, and a computer 

system for data collection and analysis. ISGS systems typically are applied to perform in situ 

measurements, but they may be incorporated into innovative detection equipment set-ups to 

perform scanning surveys. 

5.9.2.5 Portal Monitors 

Portal monitors utilize a fixed detector array through which M&E are passed to typically perform 

scanning surveys (objects may also remain stationary within the detector array to perform in situ 

measurements). Portal monitors typically are used to perform scanning surveys of vehicles.3 In 

situ measurements may be utilized with portal monitors by taking motionless measurements to 

improve the detection efficiency. 

5.9.2.6 Laboratory Analysis 

Laboratory analysis consists of analyzing a portion or sample of the M&E. The laboratory will 

generally have recommendations or requirements concerning the amount and types of samples 

that can be analyzed for radionuclides or radiations. Communications should be established 

between the field team collecting the samples and the laboratory analyzing the samples. More 

information on sampling is provided in Section 5.9.1.3. Laboratory analyses can be developed 

for any radionuclide with any material, given sufficient resources. Laboratory analyses typically 

require more time to complete than field analyses. The laboratory may be located onsite or 

offsite. The quality of laboratory data typically is greater than data collected in the field because 

the laboratory is better able to control sources of measurement method uncertainty. The planning 

team should consider the resources available for laboratory analysis (e.g., time, money), the 

sample collection requirements or recommendations, and the requirements for data quality (e.g., 

MDC, MQC) during discussions with the laboratory. 

5.9.3 Select a Measurement Method 

Table 5.1 and Table 5.2 illustrate the potential applications and associated size restrictions for 

combinations of the instrument and measurement techniques discussed in Sections 5.9.1 and 

5.9.2, respectively. Sampling followed by laboratory analysis is not included in these tables, but 

is considered “GOOD” for all applications. Please note the following qualifiers: 

 

3

 Specialized vehicle monitors are available that monitor rates of change in ambient background to account for 

differences in vehicles being scanned to improve measurement detectability. 

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GOOD The measurement technique is well-suited for performing this application 

FAIR The measurement technique can adequately perform this application 

POOR The measurement technique is poorly suited for performing this application 

NA The measurement technique cannot perform this application 

Few A relatively small number, usually three or less 

Many A relatively large number, usually more than three 

Table 5.1 illustrates that most measurement techniques can be applied to almost any M&E and 

type of radioactivity. The quantity of M&E to be surveyed becomes a major factor for the 

selection of measurement instruments and techniques described in this chapter. Hand-held 

measurements and techniques generally are the most efficient technique for surveying small 

quantities of M&E. 

Table 5.1 Potential Applications for Instrumentation and Measurement Technique Combinations 

Radiation 

Type 

Hand-Held 

Instruments

Volumetric 

Counters 

Portal 

Monitors 

In Situ Gamma 

Spectroscopy 

Conveyorized Survey 

Monitoring Systems 

In Situ Measurements 

Alpha FAIR FAIR POOR NA FAIR 

Beta GOOD FAIR FAIR NA GOOD 

Photon GOOD GOOD GOOD GOOD GOOD

Neutron GOOD FAIR GOOD NA GOOD 

Scanning Surveys 

Alpha POOR NA POOR NA POOR 

Beta GOOD NA FAIR NA FAIR 

Photon GOOD NA GOOD GOOD GOOD 

Neutron FAIR NA FAIR NA FAIR 

Table 5.2 Survey Unit Size and Quantity Restrictions for Instrumentation and Measurement 

Technique Combinations 

Size of 

Items 

Number 

of Survey 

Units or 

Items

Hand-Held 

Instruments

Volumetric 

Counters 

Portal 

Monitors 

In Situ 

Gamma 

Spectroscopy 

Conveyorized 

Survey 

Monitoring 

Systems 

In Situ Measurements 

Few GOOD NA FAIR GOOD POOR 

> 10 m3

Many POOR NA FAIR GOOD POOR 

Few GOOD FAIR FAIR GOOD FAIR 

1 to 10 m3

Many POOR FAIR FAIR GOOD FAIR 

Few GOOD GOOD POOR GOOD GOOD 

< 1 m3

Many FAIR GOOD POOR GOOD GOOD 

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Table 5.2 Survey Unit Size and Quantity Restrictions for Instrumentation and Measurement 

Technique Combinations (Continued) 

Size of 

Items 

Number 

of Survey 

Units or 

Items

Hand-Held 

Instruments

Volumetric 

Counters 

Portal 

Monitors 

In Situ 

Gamma 

Spectroscopy 

Conveyorized 

Survey 

Monitoring 

Systems 

Scanning Surveys 

Few GOOD NA GOOD FAIR POOR 

> 10 m3

Many FAIR NA GOOD FAIR POOR 

Few GOOD NA FAIR FAIR FAIR 

1 to 10 m3

Many FAIR NA FAIR FAIR FAIR 

Few GOOD NA POOR FAIR GOOD 

< 1 m3

Many GOOD NA POOR FAIR GOOD 

Facilities that conduct routine surveys on substantial quantities of specific types of M&E may 

benefit financially from investing in measurement instruments and techniques that require less 

manual labor to conduct disposition surveys. For example, it will require significantly more time 

for a health physics technician to survey a toolbox of tools and equipment used in a 

radiologically controlled area using hand-held surveying techniques and instruments than the 

time to complete the surveying using a box counting system. Use of such automated systems will 

also reduce the potential for ergonomic injuries, and attendant costs, associated with routine, 

repetitive surveys performed using hand-held instruments. 

Hand-held surveying remains the more economical choice for a small quantity of tools and 

toolboxes, but as the quantity of tools and toolboxes increases, the cost of a box counting system 

becomes an increasingly worthwhile investment to reduce manual labor costs associated with 

surveying. Note that some M&E have no survey design options that are described as “GOOD” in 

these two tables (e.g., a large quantity of M&E impacted with residual alpha radioactivity with 

survey unit sizes greater than 10 m3). The planning team should revisit earlier DQO selections to 

see if a different approach is more acceptable (e.g., review selection of disposition options in 

Section 2.4). 

Each type of measurement technique has associated advantages and disadvantages, some of 

which are summarized in Table 5.3. All the measurement techniques described in this table 

include source-to-detector geometry and sampling variability as common disadvantages. 

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Table 5.3 Advantages and Disadvantages of Instrumentation and Measurement Technique 

Combinations 

Instrument 

Measurement 

Technique Advantages Disadvantages 

Hand-Held 

Instruments 

In Situ • Generally allows flexibility in media 

to be measured 

• Detection equipment is usually 

portable 

• Detectors are available to efficiently 

measure alpha, beta, gamma, x-ray, 

and neutron radiation 

• Generally acceptable for performing 

measurements in difficult-tomeasure areas 

• Measurement equipment is 

relatively low cost 

• May provide a good option for small 

quantities of M&E 

• Requires a relatively large 

amount of manual labor as a 

surveying technique; may 

make surveying large 

quantities of M&E laborintensive 

• Detector windows may be 

fragile 

• Most do not provide nuclide 

identification 

Hand-Held 

Instruments 

Scanning • Generally allows flexibility in media 

to be measured 

• Detection equipment is usually 

portable 

• Detectors are available to efficiently 

measure beta, gamma, x-ray, and 

neutron radiation 

• Generally good for performing 

measurements in difficult-tomeasure areas 

• Measurement equipment is 

relatively low cost 

• May provide a good option for small 

quantities of M&E 

• Requires a relatively large 

amount of manual labor as a 

surveying technique; may 

make surveying large 

quantities of M&E laborintensive 

• Detector windows may be 

fragile 

• Most do not provide nuclide 

identification 

• Incorporates more potential 

sources of uncertainty than 

most instrument and 

measurement technique 

combinations 

• Potential ergonomic injuries 

and attendant costs 

associated with repetitive 

surveys. 

Hand-Held 

Instruments 

Smear • Only measurement technique for 

assessing removable radioactivity 

• Removable radioactivity can be 

transferred and assessed in a low 

background counting area. 

• Instrument background may 

not be sufficiently low. 

• Detectors with counting 

sensitive region larger than 

the smear surface area may 

require counting adjustments 

to account for inherent 

backgrounds associated with 

other media located under the 

detector sensitive region. 

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Table 5.3 Advantages and Disadvantages of Instrumentation and Measurement Technique 

Combinations (Continued) 

Instrument 

Measurement 

Technique Advantages Disadvantages 

Volumetric 

Counters 

In situ • Able to measure small items 

• Designs are available to efficiently 

measure gamma, x-ray, and alpha 

radiation 

• Requires relatively small amount of 

labor 

• May be cost-effective for measuring 

large quantities of M&E 

• May not be suited for 

measuring radioactivity in 

difficult-to-measure areas 

• Size of instrumentation 

may discourage portability 

Portal 

Monitors 

In situ • Able to measure large objects 

• Designs are available to efficiently 

measure gamma, x-ray, and neutron 

radiation 

• Requires relatively small amount of 

labor 

• May be cost-effective for measuring 

large quantities of M&E 

• Not ideal for measuring 

alpha or beta radioactivity 

• May not be ideal for 

measuring radioactivity in 

difficult-to-measure areas 

• Size of detection 

equipment may discourage 

portability 

Portal 

Monitors 

Scanning • Able to measure large objects 

• Efficient designs available for 

gamma, x-ray, and neutron radiation 

• Residence times generally are short 

• May not require objects to remain 

stationary during counting 

• Requires relatively small amount of 

labor 

• May be cost-effective for measuring 

large quantities of M&E 

• Not ideal for measuring 

alpha or beta radioactivity 

• Source geometry is an 

important consideration 

• May not be ideal for 

measuring radioactivity in 

difficult-to-measure areas 

• Size of detection 

equipment may discourage 

portability 

In Situ 

Gamma 

Spectroscopy 

(ISGS) 

In situ • Provides quantitative measurements 

with flexible calibration 

• Generally requires a moderate 

amount of labor 

• May be cost-effective for measuring 

large quantities of M&E 

• Instrumentation may be 

expensive and difficult to 

set up and maintain 

• May require liquid 

nitrogen supply (with 

ISGS semiconductor 

systems) 

• Size of detection 

equipment may discourage 

portability 

In Situ 

Gamma 

Spectroscopy 

(ISGS) 

Scanning • Provides quantitative measurements 

with flexible calibration 

• Generally requires a moderate 

amount of labor 

• May be cost-effective for measuring 

large quantities of M&E 

• Instrumentation may be 

expensive and difficult to 

set up and maintain 

• May require liquid 

nitrogen supply (with 

ISGS semiconductor 

systems) 

• Size of detection 

equipment may discourage 

portability 

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Table 5.3 Advantages and Disadvantages of Instrumentation and Measurement Technique 

Combinations (Continued) 

Instrument 

Measurement 

Technique Advantages Disadvantages 

Conveyorized 

Survey 

Monitoring 

Systems 

In situ • Requires relatively small amount of 

labor after initial set up 

• May be cost-effective for measuring 

large quantities of M&E 

• Instrumentation may be 

expensive and difficult to 

set up and maintain 

• May not be ideal for 

assessing radioactivity in 

difficult-to-measure areas 

• Size of detection 

equipment may discourage 

portability 

• Typically does not provide 

nuclide identification 

Conveyorized 

Survey 

Monitoring 

Systems 

Scanning • Requires relatively small amount of 

labor after initial set up 

• May be cost-effective for measuring 

large quantities of M&E 

• Instrumentation may be 

expensive and difficult to 

set up and maintain 

• May not be ideal for 

assessing radioactivity in 

difficult-to-measure areas 

• Size of detection 

equipment may discourage 

portability 

• Typically does not provide 

nuclide identification 

Laboratory 

Analysis 

Sampling • Generally provides the lowest 

MDCs and MQCs, even for 

difficult-to-measure radionuclides 

• Allows positive identification of 

radionuclides without gammas 

• Most costly and timeconsuming measurement 

technique 

• May incur increased 

overhead costs while 

personnel are waiting for 

analytical results 

• Great care must be taken 

to ensure samples are 

representative 

• Detector windows may be 

fragile 

Laboratory 

Analysis 

Smear • Only measurement technique for 

assessing removable radioactivity 

• Removable radioactivity can be 

transferred and assessed in a low 

background counting area. 

• Instrument background 

may not be sufficiently 

low. 

• Detectors with counting 

sensitive region larger than 

the smear surface area may 

require counting 

adjustments to account for 

inherent backgrounds 

associated with other 

media located under the 

detector sensitive region. 

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5.9.4 Measurement Performance Indicators 

Measurement performance indicators are used to evaluate the performance of the measurement 

method. These indicators describe how the measurement method is performing to ensure the 

survey results are of sufficient quality to meet the survey objectives. 

5.9.4.1 Blanks 

Blanks are measurements of materials with little or no radioactivity and none of the 

radionuclide(s) of concern present, and performed to determine whether the measurement 

process introduces any increase in instrument signal rate that could impact the measurement 

method detection capability. Blanks should be representative of all measurements performed 

using a specific method (i.e., combination of instrumentation and measurement technique). 

When practical, the blank should consist of the same or equivalent material(s) as the M&E being 

surveyed. 

Blanks typically are performed before and after a series of measurements to demonstrate the 

measurement method was performing adequately throughout the survey. At a minimum, blanks 

should be performed at the beginning and end of each shift. When large quantities of data are 

collected (e.g., scanning measurements) or there is an increased potential for radionuclide 

contamination of the instrument (e.g., removable or airborne radionuclides), blanks may be 

performed more frequently. In general, a blank should be collected whenever enough 

measurements have been performed such that it is not practical to repeat those measurements if a 

problem is identified. 

A sudden change in a blank result indicates a condition requiring immediate attention. Sudden 

changes are caused by the introduction of a radionuclide, a change in ambient background, or 

instrument instability. Gradual changes in blank values indicate a need to inspect all survey areas 

for sources of radionuclides or radioactivity. Gradual build up of removable radionuclides over 

time or instrument drift and deterioration can result in slowly increasing blank values. High 

variability in blank values can result from instrument instability or improper classification (i.e., 

high activity and low activity M&E combined into a single survey unit. It is important to correct 

any problems with blanks to ensure measurement detectability (see Sections 5.7 and 7.5) is not 

compromised. 

5.9.4.2 Replicate Measurements 

Replicate measurements are two or more measurements performed on the same M&E, and 

performed primarily to provide an estimate of precision for the measurement method. The 

reproducibility of measurement results should be evaluated by replicates to establish this 

component of measurement uncertainty (see Sections 5.6 and 7.4). 

Replicates typically are performed at specified intervals during a survey (e.g., 5% of all 

measurements or once per day), and should be used to evaluate each batch of data used to 

support a disposition decision (e.g., one replicate per survey unit). For single measurement 

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surveys or scan-only surveys where decisions are made based on every measurement, typically 

5% of all measurements are replicated. 

Precision exhibits a range of values and depends in part on the material being measured and the 

activity level. Small changes in precision are expected, and the acceptable range of variability 

should be established prior to initiating data collection activities. The main causes for lack of 

precision include problems with repeating measurements on irregularly shaped M&E, the 

material being measured, counting statistics when the activity levels are low, and instrument 

contamination. 

5.9.4.3 Spikes and Standards 

Spikes and standards are materials with known composition and radioactivity, used to evaluate 

bias in the measurement method, and typically performed periodically during a survey (e.g., 5% 

of all measurements or once per day). When spikes and standards are available, they should be 

used to evaluate each batch of data used to support a disposition decision (i.e., at least one spike 

or standard per survey unit). 

M&E cover a broad range of physical forms and materials that can change a measurement 

method’s expected bias. Tracking results of measurements with known activity can provide an 

indication of the magnitude of bias. In general, activity levels near the action levels (or 

discrimination limits in Scenario B) will provide adequate information on the performance of the 

measurement system. 

5.9.5 Instrument Performance Indicators 

Instrument performance indicators provide information on how an instrument is performing. 

Evaluation of these indicators provides information on the operation of the instruments. 

5.9.5.1 Performance Tests 

Performance tests should be performed periodically and after maintenance to ensure that the 

instruments continue to meet performance requirements for measurements. An example of a 

performance test is a test for response time. Performance requirements should be met as 

specified in the applicable sections of ANSI N323A (ANSI 1997), ANSI N42.17A (ANSI 

2003b), and ANSI N42.17C (ANSI 1989). These tests may be conducted as part of the 

calibration procedure. 

5.9.5.2 Functional Tests 

Functional tests should be performed prior to initial use of an instrument. These functional tests 

should include— 

• General condition, 

• Battery condition, 

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• Verification of current calibration (i.e., check to see that the date due for calibration has not 

passed), 

• Source and background response checks (and other tests as applicable to the instrument), and 

• Constancy check. 

The effects of environmental conditions (temperature, humidity, etc.) and interfering radiation on 

an instrument should be established prior to use. The performance of functional tests should be 

appropriately documented. This may be as simple as a checklist on a survey sheet, or may 

include more detailed statistical evaluation such as a chi-square test. 

5.9.5.3 Instrument Background 

All radiation detection instruments have a background response, even in the absence of a sample 

or radiation source (Section 3.4.2). Inappropriate background correction will result in 

measurement error and increase the uncertainty of data interpretation. 

5.9.5.4 Efficiency Calibrations 

Detector efficiency is critical for converting the instrument response to activity (MARSAME 

Section 7.8.2.2, MARSSIM Section 6.5.4, MARLAP Chapter 16). Routine performance checks 

may be used to demonstrate the system’s operational parameters are within acceptable limits, and 

these measurements typically are included in the assessment of bias. The system’s operational 

parameters may be tracked using control charts. 

5.9.5.5 Energy Calibrations (Spectrometry Systems) 

Spectrometry systems identify radionuclides based on the energy of the detected radiations. A 

correct energy calibration is critical to accurately identify radionuclides. An incorrect energy 

calibration may result in misidentification of peaks, or failure to identify radionuclides present in 

the M&E being investigated. 

5.9.5.6 Peak Resolution and Tailing (Spectrometry Systems) 

The shape of the full energy peak is important for identifying radionuclides and quantifying their 

activity with spectrometry systems. Poor peak resolution and peak tailing may result in larger 

measurement uncertainty, or in failure to identify the presence of peaks based on shape. 

Consistent problems with peak resolution indicate the presence of an analytical bias. 

5.9.5.7 Voltage Plateaus (Gas Proportional Systems) 

The accuracy of results using 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. 

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5.9.5.8 Self Absorption, Backscatter, and Crosstalk 

Alpha and beta measurement results can be affected by the M&E through self-absorption and 

backscatter. Measurement systems simultaneously detecting alpha and beta particles using an 

electronic discriminator (e.g., gas flow proportional detectors) can be affected by crosstalk (i.e., 

identification of alpha particles as beta particles and vice versa). Accurate differentiation 

between alpha and beta activity depends on the assessment and maintenance of information on 

self-absorption and crosstalk. 

5.10 Report the Results 

Once the instruments have been checked to ensure proper operation, the data should be collected 

in a manner consistent with the survey design. Any field changes and deviations from survey 

design should be documented and described in sufficient detail to enable an independent 

recreation and evaluation at some future time. 

The reported measurements should comprise raw data that includes background radioactivity 

(i.e., gross measurement data). Electronic instruments with data logging capabilities should be 

used when applicable. Electronic data should be exported and backed up periodically to 

minimize the chance of losing data and the need for re-surveying. 

Use of a measurement identification system should be considered. If required by the objectives 

of the survey, the identification system should be developed and used such that each 

measurement is assigned and labeled with a unique (preferably sequential) identifying number, 

the collection date and time, the measurement location, and any applicable comments. 

While MARSAME does not make specific recommendations with regard to approved media 

formats for storing documentation, some users of MARSAME (e.g., private industry nuclear 

power plants) may be required to retain documentation in media formats prescribed by State and 

Federal rules of evidence. Similarly, State and Federal rules of evidence may specify retention 

periods for documentation that exceed internal facility requirements. Compliance with State and 

Federal rules of evidence is intrinsic to maintaining legally defensible records for insurance and 

litigation-related purposes. 

Projects at large, complex facilities often occur over relatively long time frames (e.g., years or 

decades). In many cases the project is divided into smaller sub-projects that are performed as 

resources and information become available. Retention of records, data compatibility, data 

accessibility, and transfer of data between sub-projects should be considered during the 

performance of individual surveys. 

Documentation of the survey measurements should provide a complete and unambiguous record 

of the data collected. Documentation should also include descriptions of variability and other 

conditions pertaining to the M&E that may have affected the measurement capabilities of the 

survey procedure, and photographs where applicable. The documentation itself should be clear, 

legible, retained, retrievable, and to the level of detail required. 

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Negative results (net activity below zero) can be obtained when an instrument background is 

subtracted from the measurement of a low activity sample. In the case where the activity is close 

to zero, the measurement uncertainty will result in a distribution of results where approximately 

one-half are less than zero and one-half are greater than zero. As long as the magnitude of 

negative values is comparable to the estimated measurement uncertainties and there is no 

discernible negative bias, negative results should be accepted as legitimate estimates of 

radionuclide concentrations or levels of radioactivity associated with the M&E. A preponderance 

of negative results, even if they are close to zero may indicate a bias or systematic error. 

The inclusion of the information described above is important in creating comprehensive 

documentation to make disposition surveys technically and legally defensible. The collection of 

all necessary data prepares the MARSAME user to assess the results of the disposition survey, 

which is discussed in Chapter 6.

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January 2009 6-1 NUREG-1575, Supp. 1 

6 EVALUATE THE SURVEY RESULTS 

6.1 Introduction 

The assessment phase of the data life cycle involves the interpretation of survey results. 

Interpretation of survey results is very straightforward when all of the data are below or all of the 

data are above the action level, and the correct decision regarding disposition of the M&E is 

obvious. In these cases very little data interpretation is required. However, formal statistical tests 

provide a valuable tool when the survey results are neither clearly above nor entirely below the 

action level. In either case, statistical tests always can be used to support the survey design in 

helping to ensure the quantity and quality of data meet the data quality objectives (DQOs) and 

measurement quality objectives (MQOs). Figure 6.1 illustrates the assessment phase of the data 

life cycle. 

 

6.2 Conduct Data Quality Assessment 

Data quality assessment (DQA) is a scientific and statistical evaluation that determines whether 

data are the right type, quality, and quantity to support their intended use (EPA 2006b). There are 

five steps in the DQA process: 

1. Review the DQOs and survey design. 

2. Conduct a preliminary data review. 

3. Select the statistical test. 

4. Verify the assumptions of the statistical test. 

5. Draw conclusions from the data. 

The effort applied to DQA should be consistent with the graded approach used to develop the 

survey design. More information on DQA can be found in Data Quality Assessment: A User’s 

Guide (EPA QA/G-9R, EPA 2006b) and Data Quality Assessment: Statistical Tools for 

Practitioners (EPA QA/G-9S, EPA 2006c). Data should be verified and validated as described in 

the quality assurance project plan (QAPP). Guidance on data verification and validation can be 

found in MARSSIM Section 9.3 and MARLAP Chapter 8. Guidance on developing a QAPP is 

available in EPA QA/G-5 (EPA 2002a) and MARLAP Chapter 4. 

6.2.1 Review the Data Quality Objectives and Survey Design 

The first step in the DQA process is a review of the DQO outputs used to develop the survey 

design to ensure they are still applicable. The review of the DQOs and survey design should also 

include the MQOs (e.g., measurement uncertainty, detectability, quantifiability). For example, if 

the data show the measurement uncertainty exceeds the estimate used to design the survey, the 

DQOs and MQOs should be revisited. 

The survey design should be reviewed for consistency with the DQOs. For example, the review 

should verify that the appropriate number or amount of measurements were performed in the 

correct locations and were analyzed using measurement methods with adequate sensitivity. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-2 January 2009 

Figure 6.1 The Assessment Phase of the Data Life Cycle 

In cases where the survey did not involve taking discrete measurements or samples (i.e., scanonly, conveyor systems, or in situ surveys), it is imperative that the minimum detectable 

concentrations (MDCs) be calculated realistically and they truly reflect at least 95% probability 

that concentrations at or about the MDC were detected. Clearly, MDCs must be capable of 

detecting radionuclide concentrations or levels of radioactivity at or below the upper bound of 

the gray region (UBGR). When detection decisions are made for individual items (i.e., Scenario 

B) the MDC should be less than or equal to the UBGR. 

The minimum quantifiable concentration (MQC) is defined as the radionuclide concentration or 

level of radioactivity at which the measurement method gives results with a specified relative 

standard deviation 1/kQ, where kQ is usually chosen to be 10 (see Section 5.8, MARLAP Section 

From Figure 5.1

Conduct Data Quality 

Assessment

(Section 6.2)

Proceed to Figure 6.4 Is the Survey Design 

Scan-Only or in Situ? Yes

Evaluate the Results and 

Make a Decision

(Section 6.8)

Document the Disposition 

Survey Results

(Section 6.10)

No Proceed to Figure 6.5

Return from Figure 6.4 Return from Figure 6.5

No Further Action

NOTE: Shaded box 

represents important 

milestone.
MARSAME Evaluate the Survey Results 

January 2009 6-3 NUREG-1575, Supp. 1 

19.4.5, MARLAP Section 19.7.3). MARSAME recommends that the MQC should be no larger 

than the upper bound of the gray region (UBGR) when making quantitative comparisons of the 

mean survey data to the action level (i.e., Scenario A). This is an expression of the fact that the 

MQC, unlike the MDC used for a simple detection decision, addresses the relative uncertainty of 

the data value obtained. If the objective of the disposition survey is to quantify radionuclide 

concentrations near the UBGR, the MQC should be no larger than the UBGR.1

For MARSSIM-type surveys (Section 4.4.3) it is important to collect sufficient data to support a 

disposition decision. This is particularly important in cases where the radionuclide 

concentrations are near the action level. This can be done prospectively during survey design to 

test the efficacy of a proposed survey design (see Chapter 4), or retrospectively during 

interpretation of survey results to demonstrate the objectives of the survey design have been 

achieved. The procedure for generating power curves for the Sign test and the Wilcoxon Rank 

Sum test are provided in Appendix I of MARSSIM. Note that the accuracy of a prospective 

power curve depends on estimates of data variability and the planned number of measurements. 

After the data are analyzed, the sample standard deviation provides an estimate of data 

variability and the actual number of valid measurements are known, and these two parameters 

are used to generate a retrospective power curve (see MARSSIM Appendix I). The consequence 

of inadequate power is an increased Type II decision error rate. For Scenario A, this means M&E 

that actually meet the release criteria have a higher probability of being incorrectly determined 

not to meet the release criterion. For Scenario B, this means M&E that actually do not meet the 

release criterion have a higher probability of being incorrectly determined to meet the release 

criterion. 

6.2.2 Conduct a Preliminary Data Review 

A preliminary data review is performed to learn more about the structure of the data by 

identifying patterns, relationships, or potential anomalies. The preliminary data review includes 

reviewing quality assurance (QA) and quality control (QC) reports, performing a graphical data 

review, and calculating basic statistical quantities. 

6.2.2.1 Review Quality Assurance and Quality Control Reports 

Quality assurance reports describing data collection and reporting processes provide valuable 

information about potential problems with or anomalies in the data. EPA QA/G-9R (EPA 2006b) 

recommends a review of (1) data validation reports that document the data collection, handling, 

analysis, reduction, and reporting procedures; (2) QC reports from laboratories or field stations 

that document measurement system performance including data from blanks, replicates, spikes, 

standards, and certified reference materials, or other internal QC measures; and (3) technical 

systems reviews, performance evaluation audits, and audits of data quality including data from 

performance evaluation measurements. EPA QA/G-9R (EPA 2006b) also suggests paying 

particular attention to information that can be used to check assumptions made during survey 

design using the DQO process, especially any anomalies in recorded data, missing values, 

deviations from SOPs, or the use of nonstandard data collection methods (e.g., new, emerging, or 

“cutting edge” technology). Verification of instrument calibrations and review of MQOs are 

 

1

 The UBGR is either the action level for Scenario A or the discrimination limit for Scenario B (see Section 4.2). 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-4 January 2009 

particularly important to disposition surveys. Periodic measurements must be made to ensure the 

measurement systems remain within acceptable calibration and control limits. 

Quality control measurements are performed during implementation of the survey design to 

monitor performance of the measurement methods, identify problems, and initiate corrective 

actions when necessary. The evaluation of QC measurements used to control measurement 

methods is distinct from the evaluation from survey results. MARLAP Section 18.3 (“Evaluation 

of Performance Indicators”), Attachment 18A (“Control Charts”), and Attachment 18B 

(“Statistical Tests for QC Results”) provide information on the evaluation of quality control 

measurements. 

Reviewing QA and QC reports is the only preliminary data review performed for surveys where 

individual measurements are not recorded (e.g., scan-only surveys with hand-held instruments). 

This increases the importance of the QA and QC reports and should be considered during survey 

planning to ensure data quality is adequate to meet the survey objectives. 

6.2.2.2 Perform a Graphical Data Review 

Preparing and evaluating graphs and other visual depictions of the data may identify trends in the 

data that go unnoticed using purely numerical methods. The graphical data review may include 

posting plots, frequency plots, quantile plots, or other methods for visually interpreting data. 

General guidance on performing a graphical data review and exploratory data analysis is 

provided in EPA QA/G-9R (EPA 2006b) and by the National Institute of Science and 

Technology (NIST 2006). A graphical data review cannot be performed unless the measurement 

results are recorded. Surveys where recording individual measurement results is not required 

(e.g., scan-only surveys with hand-held instruments) do not receive a graphical data review. 

A posting plot is simply a map of the survey unit with the data values entered at the measurement 

locations. This type of plot potentially reveals heterogeneities in the data, especially possible 

clusters of elevated radionuclide concentrations. For a reference material survey a posting plot 

can reveal spatial trends in background data that might affect the results of the statistical tests. 

If the posting plot reveals systematic spatial trends in the M&E, the cause of the trends should be 

investigated. In some cases the trends could be attributable to residual radioactivity, but they may 

also be caused by inhomogeneities in the ambient background in the area the survey is 

performed. EPA QA/G-9S (EPA 2006c) provides additional diagnostic tools for examining 

spatial trends. The role of a posting plot for a conveyorized system would be a time series 

display of the data showing any trends between adjacent batches of M&E conveyed past the 

detector. 

The geometric configuration of most M&E survey units composed of a few large irregularly 

shaped pieces of M&E is transitory. The arrangement of tools and piles of scrap metal, for 

example, changed as volumes of material were moved, or even as individual pieces were handled 

during the survey (Section 5.3). In these cases some identifying marks, numbers, or bar-code 

labels should have been used to identify and track where measurements were made, at least until 

it is determined that the M&E meet the disposition criteria. Such marking and labeling need not 

be permanent, but may be made with materials such as chalk or removable labels. 
MARSAME Evaluate the Survey Results 

January 2009 6-5 NUREG-1575, Supp. 1 

A frequency plot, or histogram, is a useful tool for examining the general shape of a distribution. 

This plot is a bar chart of the number of data points within a certain range of values. A frequency 

plot reveals any obvious departures from symmetry, such as skewness or bimodality (two peaks), 

in the data distributions for the M&E or reference material. 

The presence of two peaks in the M&E data set frequency plot may indicate the presence of 

small areas of elevated activity. In some cases it may be possible to identify an appropriate 

background distribution within the M&E data set. This type of data interpretation generally 

depends on site-specific considerations and should only be pursued after consultation with the 

responsible regulatory agency. 

The presence of two peaks in the M&E or reference material frequency plots may also indicate a 

mixture of materials with different intrinsic radiation backgrounds. The greater variability in the 

data caused by the presence of such a mixture reduces the power of the statistical tests. These 

situations should be avoided whenever possible through segregation of M&E (see Section 5.4) 

and carefully matching the reference materials to the M&E being surveyed. 

When data are obtained from scan-only surveys incorporating data loggers, large quantities of 

data are usually recorded. In essence, 100% of Class 1 M&E are measured. While the survey 

coverage may be less than 100% for Class 2 and Class 3 M&E, the number of data points is still 

likely to be large. As long as there was no bias in the selection of areas that were scanned, the 

frequency plot will be close to the population distribution of radioactivity levels in the M&E. 

The mean and standard deviation calculated from these logged values should be very close to the 

corresponding population values. 

For conveyorized survey monitors, the data may be interpreted batch-by-batch as it is scanned. In 

this case, the data treatment would be most similar to a single in situ measurement used to 

evaluate all of the M&E. If, on the other hand, the data were logged continuously the data 

treatment would be similar to a scan-only survey using data loggers. 

6.2.2.3 Calculate Basic Statistical Quantities 

Radiological survey data are usually obtained in units (e.g., counts per unit time) that have no 

intrinsic meaning relative to the action levels. For comparison of survey data to action levels, 

survey data from laboratory and field analyses are converted into action level units. MARSSIM 

Section 6.6 provides guidance on data conversion. Any uncertainty associated with data 

conversion should be included in the estimate of measurement uncertainty (Section 5.6). For 

surveys where individual results are not recorded (e.g., scan-only surveys with hand-held 

instruments) the uncertainty is associated with converting the action level into the units provided 

by the instrument in the field. Because individual results are not recorded, no statistical quantities 

can be calculated. 

Basic statistical quantities that should be calculated for the sample data set include the mean, 

standard deviation, and the median. Other statistical quantities may be calculated based on the 

survey objectives. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-6 January 2009 

The means of the two data sets can be compared to provide a preliminary indication of the 

survey unit status. 2 The difference is 0.44, with the M&E being investigated having a higher 

mean concentration. If the mean for the M&E exceeds the mean for the reference material by 

more than the action level, the M&E clearly do not meet the disposition criterion. On the other 

hand, if the difference between the largest M&E measurement (13.6 for this example) and the 

smallest reference material measurement (3.6 for this example) is below the action level, the 

M&E will pass the Wilcoxon Rank Sum (WRS) test (Section 6.6), but will have to meet other 

criteria as well. 

The value of the sample standard deviation is especially important. If the standard deviation is 

too large compared to what was assumed for variability during development of the survey 

design, this may indicate an insufficient number of samples were collected to achieve the desired 

power for the statistical test. As previously mentioned, inadequate power can lead to an increase 

in the Type II decision error rate. 

The median is the middle value of the data set when the number of data points is odd or the mean 

of the two middle values when the number of data points is even. A large difference between the 

mean and the median indicate a potential skew in the data. This would also be evident in a 

histogram of the data. 

Examining other statistical quantities such as the maximum, minimum, and range may provide 

additional useful information. When there are 30 or fewer data points, range values greater than 

4 or 5 standard deviations would be unusual. 

 

2

 Note the use of significant digits in this example. Because all of the numbers in the text are interim values in 

calculating the difference between two means, they are not rounded. If the mean and standard deviation values were 

to be reported as results they would be rounded to two significant digits because the original data is a mixture of 

numbers with two and three significant digits. If the data were rounded after each calculation, the difference in the 

rounded means appears to be 0.4 (i.e., 8.9 minus 8.5), but the actual difference is 0.44 based on the un-rounded 

means (i.e., 8.89 minus 8.45). This is an example of how rounding numbers too early in the process can result in 

additional uncertainty. 

Example 2: For the example M&E data set the minimum is 3.4 and the maximum is 13.6. 

The range is 13.6 − 3.4 = 10.2. The range is equal to 3.1 standard deviations (i.e., 10.2/3.3). 

Thus, the range for this example data set is not unusually large. The range may be greater for 

larger data sets. 

Example 1: Suppose the following 10 measurement results are obtained from a disposition 

survey: 

9.1, 10.7, 13.6, 3.4, 13.3, 7.9, 4.5, 7.7, 8.3, 10.4 

The mean of the data (μ) is 8.89 and the standard deviation (σ) is 3.3231. 

The next 10 measurement results are from an appropriate matching reference material: 

6.2, 13.8, 15.2, 9.3, 6.7, 4.9, 7.1, 3.6, 8.8, 8.9 

The mean of the reference data (μ) is 8.45 and the standard deviation (σ) is 3.6713. 
MARSAME Evaluate the Survey Results 

January 2009 6-7 NUREG-1575, Supp. 1 

6.2.3 Select the Statistical Tests 

In most cases the selection of a statistical test is determined by the survey design used to collect 

the data. The most appropriate procedure for summarizing and analyzing the data is chosen 

based on the preliminary data review. If the preliminary data review indicates that the 

assumptions used to develop the survey design are valid, the statistical tests and evaluation 

methods determined should then be applied. If the assumptions used to develop the survey 

design are determined to be invalid, it may be necessary to consult a statistician to determine the 

most appropriate statistical test for evaluating the survey results. 

6.2.3.1 Scan-Only Surveys 

Scan-only surveys generate large amounts of data. Class 1 surveys measure all of the M&E. 

When less than 100 percent of the M&E are measured (i.e., Class 2 or Class 3 surveys) the areas 

that are measured are assumed representative of the areas that are not measured. This assumption 

should be checked during the preliminary data review (Section 6.2.2). The radionuclide 

concentrations or radioactivity in the areas that are not measured can be inferred based on the 

measurement results in the areas that are measured. Data indicating this inference may not be 

reasonable should result in re-evaluation of the survey design. For example, suppose the survey 

design specifies that 137Cs is the radionuclide of concern and scanning 50% of the M&E is 

appropriate based on the expected distribution of radionuclide concentrations, expected levels of 

radioactivity, and the beta-gamma emissions from the radionuclide of concern. If additional 

historical data is found showing 239Pu is also a radionuclide of concern, the survey design should 

be re-evaluated based on the presence of an alpha emitting radionuclide as well. 

If disposition decisions will be made for individual items or based on individual measurement 

results, all of the results should be compared to the action level. Comparison to the action level 

based on a detection decision or measurement (Section 5.7) is discussed in Section 6.3. 

Individual measurement results can be recorded for scan-only surveys. The benefit of logging 

individual measurement results is the ability to statistically evaluate the data (e.g., calculate a 

mean and an upper confidence limit). If disposition decisions will be made based on the mean of 

logged data, an upper confidence limit for the mean is calculated and compared to the UBGR. 

This means that compliance with the disposition criterion can be demonstrated for the entire 

survey unit, even if some of the results exceed the UBGR. Evaluations using the upper 

confidence limit are discussed in Section 6.4. When less than 100% of the M&E are measured 

(i.e., Class 2 and Class 3 surveys), the total uncertainty includes both spatial and measurement 

uncertainty. Measuring 100% of the M&E (i.e., Class 1 survey) accounts for spatial variability, 

but there is still an uncertainty component resulting from variability in the measurement process. 

Conveyorized systems that continually log the survey results also generate large amounts of data. 

An upper confidence limit for the mean can be used for the evaluation of data from these types of 

systems (see Section 6.4) in the same manner as logged scan data. Conveyorized systems that 

operate in a batch mode are essentially treated as single in situ measurements of small batches of 

M&E. The results generated by these types of systems are evaluated as a series of comparisons 

to the UBGR; using detection decisions based on the MDC (Section 6.3). 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-8 January 2009 

6.2.3.2 In Situ Surveys 

In situ surveys may consist of a series of isolated measurements covering all or part of the M&E, 

a series of measurements with overlapping fields of view incorporating all (Class 1) or a portion 

(Class 2 or Class 3) of the M&E, or a single measurement incorporating all of the M&E (Section 

4.4.2). 

Similar to scan-only surveys, if disposition decisions will be made for individual items or based 

on individual measurement results, all of the results should be compared to the action level. 

Comparison to the action level based on a detection decision (Section 5.7) is discussed in Section 

6.3. Unlike scan-only surveys, in situ surveys are likely based on a limited number of data points. 

To perform in situ measurements, assumptions were made about the distribution of radioactivity 

within the volume of M&E being measured. These assumptions are inherent in the calibration of 

in situ measurement systems and the validity of these assumptions determines the 

appropriateness of the measurement. It is important to account for uncertainty in these 

assumptions when calculating the MDC and to evaluate these assumptions using QC 

measurements performed during the survey. If there is uncertainty about the true MDC or critical 

value, use conservative values for the efficiency as described in Section 7.5.2. 

6.2.3.3 MARSSIM-Type Survey Designs 

MARSSIM-type survey designs generally are used when instrumentation for scan-only or in situ 

measurement surveys do not provide sufficient sensitivity (e.g., the MDC is greater than the 

UBGR). A statistically based number of measurements is used to provide an estimate of the 

mean activity in each survey unit, and scanning is used to identify small areas of elevated 

activity between sample locations. 

The number of measurements is determined by the statistical test. In most cases the statistical 

tests used in MARSSIM are appropriate for Scenario A. The criteria for choosing between the 

Sign test and the WRS test are described in MARSSIM Section 8.2.3. In general, when the 

radionuclide is not present in background (or its background concentration is negligible 

compared to the action level) and radionuclide-specific measurements are made, the Sign test 

(Section 6.5) is used. Otherwise, the WRS (Section 6.6) test should be used. The Sign test is 

designed to detect whether there is radioactivity in the M&E above the action level. The WRS 

test is used to compare measurements of the M&E to measurements performed on the reference 

material. 

When Scenario B is used, the statistical tests described in NUREG-1505 (NRC 1998a) generally 

are used. The Sign test and the WRS test are still used, but the application of the test is adjusted 

to account for the difference in the null hypothesis. When using Scenario B, there is a potential 

for the WRS test to miss non-uniform radioactivity (i.e., slightly elevated radionuclide 

concentrations or levels of radioactivity over a portion of the survey unit). Randomization of the 

M&E through mixing or homogenization can eliminate this possibility. If randomization is not 

practical, the Quantile test (Section 6.7) should be used to evaluate survey units when the WRS 

test fails to reject the null hypothesis. 
MARSAME Evaluate the Survey Results 

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The results of scanning measurements performed as part of a MARSSIM-type survey are 

evaluated using the elevated measurement comparison (EMC). The EMC is simply a comparison 

to an action level (see Section 6.3). The action level used for the EMC is the action level for 

small areas of elevated activity. If there is no action level for elevated activity, the scanning 

results are compared to the action level for the mean activity in the survey unit. Additional 

information on the EMC is available in MARSSIM Section 8.5.1 and NUREG-1505 Chapter 8 

(NRC 1998a). 

6.2.4 Verify the Assumptions of the Tests 

An evaluation to determine the data are consistent with the underlying assumptions of the 

statistical tests helps to validate the use of a particular test. One may also determine that certain 

departures from these assumptions are acceptable when given the actual data and other 

information about the project. The nonparametric tests described in this chapter assume that the 

data from the M&E or the reference material consist of independent measurements from each 

distribution. The primary issue associated with the evaluation of scan-only and single in situ 

measurement survey data is the MDC or MQC as discussed in Section 6.2.1. 

Asymmetry in the data can be identified using a histogram or a Quantile plot. Information on 

histograms and Quantile plots is provided in MARSSIM Appendix I and NUREG-1505 Section 

4.2.2 (NRC 1998a). As discussed in Section 6.2.2.3, data transformations can sometimes be used 

to minimize the effects of asymmetry. 

One of the primary advantages to using the nonparametric tests is that they involve fewer 

assumptions about the data than their parametric counterparts. If parametric tests are used (e.g., 

Student’s t test) any additional assumptions made in using these tests should be verified (e.g., 

testing for normality). These issues are discussed in detail in EPA QA/G-9S (EPA 2006c). 

One of the more important assumptions made in the survey design is that the number of 

measurements is sufficient to achieve the DQOs set for the Type I (α) and Type II (β) decision 

error rates. Verification of the power of the statistical tests (1-β) may be of particular interest. 

Methods for assessing power are discussed in Appendix I.9 of MARSSIM. If there is not 

reasonable assurance the DQOs have been achieved, additional investigations including 

repeating the survey may be needed. The planning team can develop survey designs cautiously to 

avoid unnecessary and potentially costly decision errors by— 

• Estimating the potential data variability conservatively, 

• Taking more measurements than suggested by the DQO process, and 

• Estimating the MDCs conservatively. 

In the absence of other data, each of these estimates could be multiplied by a safety factor of 1.2 

(i.e., increase the estimate by 20%). Examples of assumptions and possible methods for 

evaluating and verifying these assumptions are summarized in Table 6.1. 
Evaluate the Survey Results MARSAME 

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Table 6.1 Issues and Assumptions Underlying the Evaluation Method 

Evaluation Method Issue Verification Method Survey Type 

Compare single 

measurements to a limit 

(Section 6.3) 

Verify the MDC and 

Measurement Uncertainty 

Review the MDC 

Review QA/QC Reports 

Review IA and DQOs 

Scan-Only 

In situ 

Compare an upper 

confidence limit for the 

mean to a limit 

(Section 6.4) 

Verify the MQC and 

Measurement Uncertainty 

Review the Measurement 

Uncertainty 

Review QA/QC Reports 

Review IA and DQOs 

Scan Only 

In situ 

Statistical Tests 

(Sections 6.5, 6.6, 6.7) 

Verify the Assumptions of the 

Statistical Test (e.g., spatial 

independence, symmetry, data 

variance, power) 

Preliminary Data Review 

(e.g., posting plot, 

histogram, summary 

statistics, power curve) 

MARSSIMType Survey 

Verification of scan-only and in situ survey results focuses on the estimates of the MDC and 

MQC values used to design the survey. If the assumptions used to estimate these values are 

incorrect, the survey design may be invalid. 

The first step in evaluating the MDC and MQC is to review the assumptions used to develop 

these values. In general, the key assumptions are made in determining the source and detector 

efficiencies. QA and QC reports should be reviewed to evaluate measurement performance (e.g., 

scan speed, source geometry, distance from M&E to the detector, non-uniform response of large 

area detectors). The description of the M&E from the IA should be compared to the assumptions 

used to develop the efficiency. 

In some cases it may be possible to compare the survey results of multiple measurement 

techniques. For example, if there are multiple radiations associated with the M&E it may be 

possible to compare gamma measurement results to alpha or beta measurement results to verify 

the survey results. Direct measurements may provide more quantitative results for areas of 

elevated activity identified during scan-only surveys. 

It may be possible to use an entirely different survey method to provide information to support 

verification of assumptions used to design a survey. For example, smears or surface scrapings 

can be used to verify the presence of radionuclides or radioactivity on the surface.3

 

In situ measurements or sample collection and analysis may be used to verify the results of scanonly survey designs. Care must be taken to ensure comparability of survey methods before 

evaluating the results to avoid generating conflicting results. For example, consider an in situ 

survey used to demonstrate the mean activity is less than the action level. A scan-only survey 

method is used to verify the results and identifies an area of elevated activity. This discrepancy 

in results warrants additional investigation of the small area of elevated activity. The additional 

investigation should determine if the activity in this area actually causes the mean activity to 

exceed the disposition criterion. 

 

3

 This smear procedure does not rule out additional volumetric activity. 
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6.2.5 Draw Conclusions from the Data 

The types of measurements performed on M&E are— 

• Scans, 

• In situ or direct measurements at discrete locations, and 

• Samples collected at discrete locations. 

Specific details for conducting the Sign test and the WRS test are provided in Sections 6.5 and 

6.6, respectively. When the data clearly show that the M&E meets or exceeds the disposition 

criterion, the result is often obvious without performing the formal statistical analysis. This is the 

expected outcome for Class 2 and Class 3 surveys. Table 6.2 summarizes examples of 

circumstances leading to specific conclusions based on a simple examination of the data. 

Table 6.2 Summary of Evaluation Methods and Statistical Tests 

Evaluation Method or 

Statistical Test Survey Result Conclusion 

All measurements less than the critical value 

corresponding to the MDC (e.g., does not 

exceed alarm set point) 

M&E meet the disposition 

criterion 

Comparison to a Limit (AL=0) 

− Scenario B only 

− Results may or may not be 

recorded 

− Scan-only or In situ surveys 

Any measurement exceeds the critical value 

corresponding to the MDC 

M&E do not meet the 

disposition criterion 

All measurements less than the critical value 

corresponding to the UBGR 

M&E meet the disposition 

criterion 

Comparison to a Limit (AL≠0) 

− Scenario A or B 

− Results not recorded 

− Scan-only or In situ surveys 

Any measurement exceeds the critical value 

corresponding to the UBGR 

M&E do not meet the 

disposition criterion 

Upper confidence limit less than UBGR M&E meet the disposition 

criterion 

Comparison to Upper 

Confidence Limit 

− Scenario A or B 

− Results must be recorded 

− Scan-only or In situ surveys 

Upper confidence limit greater than UBGR M&E do not meet the 

disposition criterion 

All measurements less than the action level M&E meet the disposition 

criterion 

Mean greater then the action level M&E do not meet the 

disposition criterion 

Sign Test 

− Radionuclide not in 

background 

− Nuclide-specific 

measurements 

− Scenario A or B 

− MARSSIM-type surveys 

Any measurement greater than the action level 

and the mean less than the action level 

Conduct Sign test (and 

elevated measurement 

comparison, if necessary) 

Difference between maximum survey unit 

measurement and minimum reference area 

measurement is less than the UBGR 

M&E meet the disposition 

criterion 

Difference of survey unit mean and reference 

area mean is greater than the action level 

M&E do not meet the 

disposition criterion 

Wilcoxon Rank Sum Test 

− Radionuclide in background 

− Nuclide non-specific 

measurements 

− Scenario A or B 

− MARSSIM-type surveys Difference between any survey unit 

measurement and any reference area 

measurement greater than the action level or 

the difference of survey unit mean and 

reference area mean is less than the action 

level 

Conduct WRS test (and 

elevated measurement 

comparison, if necessary) 
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Table 6.3 Summary of Evaluation Methods and Statistical Tests (Continued) 

Evaluation Method or 

Statistical Test Survey Result Conclusion 

Difference between maximum survey unit 

measurement and minimum reference area 

measurement is less than the UBGR 

M&E meet the disposition 

criterion 

Difference of survey unit mean and reference 

area mean is greater than the action level 

M&E do not meet the 

disposition criterion 

Quantile Test 

− Test for non-uniform 

radioactivity 

− Combine with WRS test 

− Scenario B only 

− MARSSIM-type surveys Difference between any survey unit 

measurement and any reference area 

measurement greater than the action level or 

the difference of survey unit mean and 

reference area mean is less than the action 

level 

Conduct Quantile test (and 

elevated measurement 

comparison, if necessary) 

6.3 Compare Results to the UBGR 

When disposition decisions will be made about individual items, or decisions will be based on 

individual measurement results, each result (plus or minus a multiple of its combined standard 

uncertainty) will be compared to the action level (see MARLAP Appendix C.4). In practice, this 

means that any result that exceeds the critical value (SC, see Section 5.7 and Section 7.5.1) when 

the minimum detectable level (SD, see Section 5.7 and Section 7.5.2) equals the UBGR provides 

evidence that the result exceeds the UBGR. 

For Scenario A, if all the results are less than the action level, then the mean and the maximum 

activity must also be below the action level. Thus, the radionuclide concentrations or levels of 

radioactivity associated with the M&E demonstrate compliance with the disposition criterion. 

For Scenario B when the action level is not zero or background, all of the results must be below 

the critical value corresponding to the MDC set equal to the UBGR. If the action level is zero or 

background, Scenario B must be used and any indication of the presence of radionuclide 

concentrations or radioactivity above background (i.e., above the discrimination level) would 

result in rejecting the null hypothesis. For this situation, any measurement result exceeding the 

critical value corresponding to the required MDC indicates the potential presence of 

radionuclides or radioactivity above background. This applies to single in situ measurements as 

well as series of in situ measurements. 

If there is an action level based on small areas of elevated activity or the maximum allowable 

value, the individual results can be compared directly to the action level. This applies primarily 

to the evaluation of scanning results for MARSSIM-type surveys (i.e., the EMC), but may be 

applied to scan-only survey data as well. 

6.4 Compare Results Using an Upper Confidence Limit 

The use of the upper confidence limit (UCL) can apply to both Scenario A and B for scan-only 

or in situ surveys where individual results are recorded. When disposition decisions are made 

about the estimated mean of a sampled population, the assessment of the survey results is 

accomplished by comparing a UCL for the mean to the UBGR. For scan-only surveys where 
MARSAME Evaluate the Survey Results 

January 2009 6-13 NUREG-1575, Supp. 1 

there are a large number of data points, a simple comparison of the mean activity to the UBGR 

may be sufficient. 

If individual scan-only survey results are recorded, a non-parametric confidence interval can be 

used to evaluate the results of the disposition survey. Similarly, a confidence interval can be used 

to evaluate a series of in situ measurements with overlapping fields of view. A one-tailed version 

of Chebyshev’s inequality or software (e.g., EPA’s ProUCL software) can be used to evaluate 

the probability of exceeding the UBGR (i.e., using a UCL). The use of a UCL applies to both 

Scenario A (where the UBGR equals the action level) and Scenario B (where the UBGR equals 

the discrimination limit).4

6.4.1 Calculate the Upper Confidence Limit 

Chebyshev’s inequality calculates the probability that the absolute value of the difference of the 

true but unknown mean of the population and a random number from the data set is at least a 

specified value. That is, given a specified positive number (n), a mean (μ), and a random number 

from the data set (r), then the probability that |μ-r| is greater than or equal to n is equal to α. In 

addition, a one-tailed version of the inequality can be used to calculate a UCL for a data set that 

is independent of the data distribution (i.e., there is no requirement to verify the data are from a 

normal, lognormal, or any other specified kind of distribution) by letting the inequality equal the 

UCL, as described in the following steps: 

1. Calculate the mean (μ) and standard deviation (σ) of the number of results (n) in the data set. 

2. For Scenario A, retrieve the Type I error rate (α ) used to design the survey. 

3. Using Chebyshev’s inequality, calculate the maximum UCL using equation 6-1: 

 n n

2 2

UCL σ

α

σ = μ + −

 (6-1) 

4. For Scenario B, substitute the Type II error rate (β) used to design the survey for α in 

Equation 6-1. 

5. If the maximum UCL is less than the UBGR, the survey demonstrates compliance with the 

disposition criterion (i.e., reject the null hypothesis for Scenario A or fail to reject the null 

hypothesis for Scenario B). 

Chebyshev’s inequality must be used with caution when there are very few points in the data set. 

This is because the population mean and standard deviation in the Chebyshev formula are being 

estimated by the sample mean and sample standard deviation. In a small data set from a highly 

skewed distribution, the sample mean and sample standard deviation may be underestimated if 

the high concentration but low probability portion of the distribution is not captured in the 

sample data set. EPA has issued guidance on calculating UCLs for exposure point concentrations 

(EPA 2002b).5

 Software for implementing EPA’s guidance is available (EPA 2006d). 

 

4

 In the case of Scenario B, if the action level is zero and the radionuclide of concern does not appear in background, 

any positive radionuclide-specific detection would result in a rejection of the null hypothesis that there is zero 

activity. 

5 In MARSAME, “exposure point concentration” is used to mean a conservative estimate of the mean radionuclide 

concentration(s) in or on M&E. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-14 January 2009 

6.4.2 Upper Confidence Limit Example: Class 1 Concrete Rubble 

This example illustrates the survey design for concrete rubble using 3 inch × 3 inch NaI(Tl) 

detectors mounted on a conveyorized survey system to measure 137Cs. A pile of concrete rubble 

was loaded on the conveyor and passed beneath the detectors at a pre-determined speed. Each 

one-second count recorded by a detector corresponds to approximately 9,800 cm3 of concrete 

rubble (i.e., a 5-cm thick disk with a 50-cm diameter). The following information was used to 

design the survey: 

• The selected disposition option was clearance, using Scenario A with the null hypothesis that 

the residual radioactivity exceeds the action level. 

• The IA indicated the concrete was potentially volumetrically contaminated prior to being 

converted to rubble. 

• The concrete rubble had a maximum particle dimension of less than 0.5 cm. 

• The average background count rate was estimated to be 38,000 cpm based on preliminary 

surveys of non-impacted concrete, and was used for the LBGR. 

• The action level was set at 20,000 cpm above the average background count rate, so the 

UBGR was set at 58,000 cpm. 

• The estimated standard deviation of background count rate is 2,500 cpm based on 

preliminary survey data. 

• The Type I decision error rate was set at 0.10, or 10%. 

The survey consisted of 9,616 independent, one-second measurements that were recorded using a 

data logger. The mean count rate for the survey was 39,252 cpm, with a standard deviation (σ) of 

5,465 cpm. The standard deviation of the mean, σN was calculated using the following equation: 

cpm cpm

N N 55.79,616

5,465 = = = σ

σ (6-2) 

As noted earlier, with such a large data set, one can expect that the sample mean and standard 

deviation should be fairly close to their population values. The minimum count rate was 30,080 

cpm, and the maximum count rate was 72,805 cpm. Note that although the mean concentration is 

well below the action level, there are data points that exceed the action level. Thus, a test against 

an UCL for the mean is warranted. Figure 6.2 shows a frequency plot of the survey results. 
MARSAME Evaluate the Survey Results 

January 2009 6-15 NUREG-1575, Supp. 1 

0

200

400

600

800

1000

1200

1400

303234363840424446485052545658606264666870

Gross Gamma Activity (cpm x 1000)

Frequency

Figure 6.2 Frequency Plot of Concrete Rubble Data 

If the sample size were small, however, the upper part of the bimodal distribution could be 

missed and the Chebyshev UCL could be underestimated. In this case, with a sample size of 

9,616, the UCL was calculated using Equation 6-1 in Section 6.4.1. 

( )

( )( )

( )

( ) 39,474 cpm 9,616

5,465

0.10 9,616

5,465 UCL 39,252

2 2

= + + = (6-3) 

The UCL of 39,474 cpm is much less than the action level of 58,000 cpm. The null hypothesis 

that the level of radioactivity exceeds the disposition criterion is rejected. 

The EPA ProUCL software was also applied to these data and the results are shown in Figure 

6.3. The software has failed to find a good fit to the data for normal, lognormal or gamma 

distributions, which is hardly surprising given the bimodal nature of the data. The 

recommendation is that either a Student’s t or a modified Student’s t 95% UCL be used. These 

are both listed as about 39,343. These are lower than the 90% Chebyshev UCL of 39,474 used 

above, but that would not change the conclusion. A 95% Chebyshev UCL calculated according 

to Section 6.4.1 would have been 39,574. Note that the 95% Chebyshev UCL calculated by 

ProUCL, rounded to the nearest count, is slightly different, 39,495, because of the way that the 

sample mean and standard deviation are estimated before entering them in the Chebyshev 

formula. The ProUCL User’s Manual can be consulted for details. However, with the number of 

data points at hand, there is little difference among any of the methods for computing an UCL. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-16 January 2009 

Figure 6.3 Screen Capture of Output from ProUCL Software for the Sample Data Set 

6.5 Conduct the Sign Test 

The Sign test is used to compare the measurement results from each survey unit with the 

applicable disposition criterion. The Sign test can be applied to either Scenario A or Scenario B. 

The Sign test should only be used if the radionuclide being measured is not present in 
MARSAME Evaluate the Survey Results 

January 2009 6-17 NUREG-1575, Supp. 1 

background or if the radionuclide being measured is present at such a small fraction of the action 

level as to be considered insignificant. Otherwise, the WRS test described in Section 6.6 should 

be applied. Additional information on the Sign test can be found in Section 8.3 of MARSSIM 

and Chapter 5 of NUREG-1505 (NRC 1998a). 

6.5.1 Apply the Sign Test to Scenario A 

The Sign test is applied to Scenario A by counting the number of measurements from each 

survey unit that are less than the action level (i.e., UBGR). Each result is subtracted from the 

action level (AL – Xi), and the number of positive values is summed. The result is the test 

statistic S+. Discard any measurement that is exactly equal to the action level and reduce the 

sample size, N, by the number of such measurements. The value of S+ is compared to the critical 

values in A.3. If S+ is greater than the critical value (q) in the table, the null hypothesis is 

rejected. 

6.5.2 Apply the Sign Test to Scenario B 

The Sign test is applied to Scenario B in a manner similar to that used for Scenario A. However, 

for Scenario B the action level (i.e., LBGR) is subtracted from each result (Xi – AL), and the 

number of positive values is summed. The result is the test statistic S+. Discard any 

measurement that is exactly equal to the action level and reduce the sample size, N, by the 

number of such measurements. The value of S+ is compared to the critical values in Table A.3. 

If S+ is greater than the critical value (q) in the table, the null hypothesis is rejected. 

6.5.3 Sign Test Example: Class 1 Copper Pipes 

This example illustrates the disposition survey design for copper pipe sections using a gas-flow 

proportional counter to measure 239Pu. Because the alpha background on the copper material is 

essentially zero, it was decided the Sign test would be used to determine whether the material 

meets the disposition criterion. The sample size was determined using the DQO Process and 

inputs such as the disposition option, action level, expected standard deviation of the 

measurement results, and the acceptable probability of making Type I and Type II decision 

errors. 

The following inputs were used to develop the survey design– 

• The selected disposition option was clearance. 

• The survey was designed using Scenario A, with the null hypothesis that the residual 

radioactivity exceeds the action level. 

• The IA indicated that the inside surfaces of the pipes potentially came in contact with liquids 

containing 239Pu, but the outside surfaces were non-impacted. 

• The gross activity action level was 100 dpm/100 cm2

. When converted to cpm the gross 

activity action level was 10 cpm (i.e., total efficiency = 0.10 counts per disintegration). 

• The LBGR (i.e., the DL) was set at the expected activity level on the copper pipe sections 

(i.e., 5 net cpm, the same as the gross mean for an alpha background of 0). 

• The standard deviation for the measurements was estimated at 2 cpm. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-18 January 2009 

• The relative shift was calculated as (10−5)/2 = 2.5. 

• The Type I and Type II decision error rates were both set at 0.05. 

Table A.2a shows the number of measurements estimated to be needed for the Sign test, N, is 15 

(α=0.05, β=0.05, and Δ/σ=2.5). Therefore, 15 surface activity measurements were randomly 

collected from the inside surfaces of the copper pipe sections. Survey results are shown in 

Table 6.3. 

Table 6.3 Sign Test Example Data 

Surface Concentration 

(cpm/100 cm2) 

Surface Concentration 

(dpm/100 cm2) < Action Level? 

4 40 Yes 

3 30 Yes 

11 110 No 

1 10 Yes 

1 10 Yes 

4 40 Yes 

6 60 Yes 

3 30 Yes 

9 90 Yes 

6 60 Yes 

14 140 No 

1 10 Yes 

4 40 Yes 

10 100 No 

2 20 Yes 

Number of measurements less than the action level (S+) = 12 

The surface activity values in Table 6.3 are determined by dividing the measured cpm by the 

total efficiency (0.10). No probe area correction is necessary. The mean count rate is 5 cpm, 

compared to the estimate of 5 cpm used for the LBGR, and the median is 4 cpm. The standard 

deviation is 4 cpm, which is higher than the value of 2 used to develop the survey design.6

 Thus, 

the power of the test is lower than planned. With the actual value of the relative shift 

(10−5)/4=1.2, 23 measurements should be collected. 

With the 15 measurements collected, the actual Type II decision error rate is between 0.10 and 

0.25 (the closest entries in Appendix A, Table A.2a are for α=0.05, β=0.10, and Δ/σ=1.2 with 

N=18, and α=0.05, β=0.25, and Δ/σ=1.2 with N=12). Three measurements exceed the action 

level. The portion of the material associated with these measurements merits further 

investigation using the elevated measurement comparison described in MARSSIM Section 8.5.1. 

 

6

 Values are reported to one significant figure based on the data in Table 6.3. Interim calculations generally carry 

extra figures, so rounding to the appropriate number of significant figures only occurs for the final calculation. 

Rounding results too soon in the calculation may result in unnecessarily deleting individual results (i.e., when the 

result is exactly equal to the UBGR) resulting in lower statistical power. 
MARSAME Evaluate the Survey Results 

January 2009 6-19 NUREG-1575, Supp. 1 

The value of S+, 12, was compared to the appropriate critical value, q, in Appendix A, Table 

A.3. In this case, for N=15 and α=0.05, the critical value is 11. Because S+ exceeds q, reject the 

null hypothesis that the survey unit exceeds the action level. In this case, the slight loss of power 

attributable to underestimating the standard deviation did not affect the result. Pending the 

outcome of the investigation of the three elevated measurements, this survey unit has satisfied 

the disposition criteria established for clearance. 

6.6 Conduct the Wilcoxon Rank Sum Test 

The WRS test is used to compare each material survey unit with an appropriately chosen 

reference material. Each reference material should be selected on the basis of its similarity to the 

survey unit material, as discussed in Section 3.9. The WRS test can be applied to either Scenario 

A or Scenario B. Further information on the WRS test can be found in Section 8.4 of MARSSIM 

and Chapter 6 of NUREG- 1505 (NRC1998a). 

6.6.1 Apply the WRS Test to Scenario A 

The WRS test is applied to Scenario A as outlined in the following steps and further illustrated 

by the example in Section 6.6.2. 

1. Obtain the adjusted reference material measurements, Zi, by adding the action level to each 

reference material measurement, Xi. Zi = Xi+ AL. 

2. The m adjusted reference sample measurements, Zi, from the reference material and the n

sample measurements, Yi, from the survey unit are pooled and ranked in order of increasing 

size from 1 to N, where N = m + n. 

3. If several measurements are tied (i.e., have the same value), they are all assigned the mean 

rank of that group of tied measurements. 

4. If there are t “less than” values, they are all given the mean of the ranks from 1 to t. 

Therefore, they are all assigned the rank t(t +1)/(2 t) = (t +1)/2, which is the mean of the first 

t integers. If there is more than one MDC,7

 all observations below the largest MDC should be 

treated as “less than” values. If more than 40% of the data from either the reference material 

or the survey unit are reported as less than detectable, the WRS test cannot be used. 

5. The sum of all the ranks, which is the sum of the first N positive integers, is N(N+1)/2, which 

equals Wr added to Ws. Thus, one needs only to sum the ranks of the either the adjusted 

reference measurements (Wr) or the sum of the ranks of the sample measurements (Ws). 

6. Compare Wr with the critical value (q) given in Table A.4 for the appropriate values of n, m, 

and α. If Wr is greater than the tabulated value for q, reject the hypothesis that the survey unit 

exceeds the disposition criterion. 

6.6.2 Apply the WRS Test to Scenario B 

The WRS test is applied to Scenario B as outlined in the following steps: 

 

7

 Examples of situations where there could be more than one MDC include using multiple laboratories to perform 

sample analyses and using different instruments with different backgrounds and different efficiencies to perform 

measurements. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-20 January 2009 

1. Obtain the adjusted survey unit measurements, Zi, by subtracting the LBGR from each survey 

unit measurement, Yi. Zi = Yi − LBGR. 

2. The n adjusted survey unit measurements, Zi, and the m reference material measurements, Xi, 

are pooled and ranked in order of increasing size from 1 to N, where N = m + n. 

3. If several measurements are tied (i.e., have the same value), they are all assigned the mean 

rank of that group of tied measurements. 

4. If there are t “less than” values, they are all given the mean of the ranks from 1 to t. 

Therefore, they are all assigned the rank t(t +1)/(2 t) = (t +1)/2, which is the mean of the first 

t integers. If there is more than one MDC, all observations below the largest MDC should be 

treated as “less than” values. If more than 40% of the data from either the reference material 

or the survey unit are reported as less than detectable, the WRS test cannot be used. 

5. Sum the ranks of the adjusted measurements from the survey unit, Ws. The sum of all the 

ranks, which is the sum of the first N positive integers, is N(N+1)/2, which equals Wr added 

to Ws. Thus, one needs only to sum the ranks of the either the adjusted reference 

measurements (Wr) or the sum of the ranks of the sample measurements (Ws). 

6. Compare Ws with the critical value (q) given in Table A.4 for the appropriate values of n, m, 

and α. (Note that when using this table for Scenario B, the roles of m and n are reversed. If 

the Quantile test is being used in addition to the WRS test, then α/2should be used rather 

than α.) If Ws is greater than the tabulated value for q, reject the hypothesis that the difference 

in the median concentration between the survey unit and the reference area is less than the 

LBGR. 

6.6.3 WRS Test Scenario A Example: Class 2 Metal Ductwork 

This example illustrates the use of the WRS test for releasing Class 2 metal ductwork. Assume 

that a gas-flow proportional detector was used to make gross (non-radionuclide-specific) surface 

activity measurements. 

The DQOs from this survey unit include α = 0.05 and β = 0.05, and the action level converted to 

units of gross cpm is 2,300 cpm, which is the UBGR. In this case, the WRS test is used because 

the estimated background level (2,100 cpm) was large compared to the action level. The 

estimated standard deviation of the measurements, σ, is 375 cpm. The estimated added activity 

level is 800 cpm; the LBGR is set at this value, and represents the DL. The relative shift is 

calculated as Δ/σ, which is (action level – LBGR)/σ, which equals 4. 

The sample size needed for the WRS test can be found in Table A.2b for these DQOs. The result 

is nine measurements in each survey unit and nine in each reference material α = 0.05, and β = 

0.05, and Δ/σ = 4). The ductwork was laid flat onto a prepared grid, and the 9 measurements 

needed in the survey unit were made using a random-start triangular grid pattern. For the 

reference materials, the measurement locations were chosen randomly on a suitable batch of 

material. Table 6.4 lists the gross count rate data obtained. 
MARSAME Evaluate the Survey Results 

January 2009 6-21 NUREG-1575, Supp. 1 

Table 6.4 Scenario A WRS Test Example Data 

Data 

(cpm) Area 

Adjusted 

Data Ranks 

Reference Material 

Ranks 

2180 R 4480 15 15 

2398 R 4698 16 16 

2779 R 5079 18 18 

1427 R 3727 10 10 

2738 R 5038 17 17 

2024 R 4324 13 13 

1561 R 3861 11 11 

1991 R 4291 12 12 

2073 R 4373 14 14 

2039 S 2039 3 0 

3061 S 3061 8 0 

3243 S 3243 9 0 

2456 S 2456 7 0 

2115 S 2115 4 0 

1874 S 1874 2 0 

1703 S 1703 1 0 

2388 S 2388 6 0 

2159 S 2159 5 0 

 Sum = 171 126 

In the “Area” column, the code “R” denotes a reference material measurement and “S” denotes a 

survey unit measurement. The adjusted data were obtained by adding the action level to the 

reference material measurements (see Section 6.6.1, Step 1). The ranks of the data range from 1 

to 18, because there are a total of 9+9 measurements (see Section 6.6.1, Step 2). Note that the 

sum of all of the ranks is still 18(18+1)/2 = 171. Checking this value with the formula in Step 5 

of Section 6.6.1 is recommended to guard against errors in the rankings. 

The total of the ranks belonging to the reference material measurements is 126. This is compared 

with the entry for the critical value of 104 in Table A.4 for α = 0.05, with n = 9 and m = 9. 

Because the sum of the reference material ranks is greater than the critical value, the null 

hypothesis (i.e., that the mean survey unit concentration exceeds the action level) is rejected, and 

the ductwork is released. 

This conclusion can be reached quickly by noting the difference between the largest survey unit 

measurement (3,243 cpm) and the smallest reference area measurement (1,427 cpm). This 

difference (3,243 – 1,427 = 1,816 cpm) is less than the action level of 2,300 cpm. Because the 

largest possible difference is less than the action level, the mean difference must also be less than 

the action level. 

6.6.4 WRS Test Scenario B Example: Class 2 Metal Ductwork 

This example illustrates the use of the Scenario B WRS test for releasing Class 2 metal 

ductwork, using the same data as in Section 6.6.3. The null hypothesis for Scenario B is that 

there is no detectable radioactivity above background. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-22 January 2009 

In this case, the action level is set at no radioactivity detectable above the estimated background 

level (2,100 cpm). The LBGR is equal to the action level, and is set to zero. The regulator 

specified that the survey be able to detect an average excess of even 1,500 cpm being released. 

This value is the DL. The UBGR is set equal to the DL (i.e., 1,500 cpm), with β = 0.025. The 

owner of the ductwork felt that there was very little if any radioactivity above background 

present, and was willing to set α = 0.20. The estimated standard deviation of the measurements, 

σ, was 375 cpm. The relative shift is Δ/σ = (UBGR – LBGR)/σ = (1,500 − 0)/375 = 4. 

The sample size needed for the WRS test can be found in Table A.2b. The result is 9 

measurements in each survey unit and 9 in each reference material α/2 = 0.10, and β = 0.025, and 

Δ/σ = 4. The data were obtained as in Section 6.6.3. Table 6.4 (on the previous page) lists the 

gross count rate data obtained. These data were reanalyzed using Scenario B and the results are 

shown in Table 6.5. 

Table 6.5 Scenario B WRS Test Example Data 

Data 

(cpm) Area Adjusted Data Ranks Survey Unit Ranks 

2180 R 2180 11 0 

2398 R 2398 13 0 

2779 R 2779 16 0 

1427 R 1427 1 0 

2738 R 2738 15 0 

2024 R 2024 6 0 

1561 R 1561 2 0 

1991 R 1991 5 0 

2073 R 2073 8 0 

2039 S 2039 7 7 

3061 S 3061 17 17 

3243 S 3243 18 18 

2456 S 2456 14 14 

2115 S 2115 9 9 

1874 S 1874 4 4 

1703 S 1703 3 3 

2388 S 2388 12 12 

2159 S 2159 10 10 

 Sum = 171 94 

In the “Area” column, the code “R” denotes a reference material measurement and “S” denotes a 

survey unit measurement. The adjusted data would be obtained by subtracting the LBGR from 

the survey unit measurements (see Section 6.6.2, Step 1), but because the LBGR is zero, no 

adjustment is needed. The ranks of the adjusted data range from 1 to 18, because there are a total 

of 9+9 measurements (see Section 6.6.2, Step 2). Note that the sum of all of the ranks is still 

18(18+1)/2 = 171. Checking this value with the formula in Step 5 of Section 6.6.2 is 

recommended to guard against errors in the rankings. The total of the ranks belonging to the 

survey unit measurements is 94. This is compared with the entry for the critical value of 100 in 

Table A.4 for α = 0.10, with n = 9 and m = 9. Because the sum of the reference material ranks is 

less than the critical value, the null hypothesis (i.e., that there is no detectable radioactivity above 

background) is not rejected, and the ductwork may be released if the Quantile test is passed. 
MARSAME Evaluate the Survey Results 

January 2009 6-23 NUREG-1575, Supp. 1 

6.7 Conduct the Quantile Test 

The Quantile test was developed to detect differences between the surveyed M&E and the 

reference material that consist of a shift to higher values in only a fraction of the surveyed M&E. 

The Quantile test is only performed when Scenario B is used, and only if the null hypothesis is 

not rejected for the WRS test. Using the Quantile test, in tandem with the WRS test, results in 

higher power to identify M&E that do not meet the disposition criterion than either test by itself. 

Apply the Quantile test as follows: 

1. Calculate αQ (αQ = α/2). 

2. Obtain the adjusted survey unit measurements, Zi, by subtracting the LBGR from each survey 

unit measurement, Yi. Zi = Yi - LBGR. 

3. The n adjusted survey unit measurements, Zi, and the m reference material measurements, Xi, 

are pooled and ranked in order of increasing size from 1 to N, where N = m + n. 

4. If several measurements are tied (i.e., have the same value), they are all assigned the mean 

rank of that group of tied measurements. 

5. Look up the values for r and q in Table A.5 based on the number of measurements in the 

survey unit (n), the number of measurements in the reference area (m), and αQ. The 

operational decision described in the next step is made using the values for r and q. 

6. If q or more of the r largest measurements in the combined ranked data set are from the 

survey unit, the null hypothesis is rejected. 

This form of the Quantile test gives only approximate results, Because Table A.5 provides a 

limited number of combinations of n, m, and αQ. It is recommended that several combinations of 

n, m, and αQ be considered when interpreting the results of the Quantile test. Sections 7.2 and 7.3 

of NUREG-1505 (NRC 1998a) provide additional guidance on interpreting the results of the 

Quantile test. 

As an example, the Quantile test can be applied to the Class 2 Metal Ductwork example of 

section 6.6.4. Using n = 9, m = 9, and αQ = 0.10, the nearest entry in Table A.5d has for r = 3 

q = 3 with αQ = 0.105 when n = 10 and m = 10. This means that all three of the highest 

measurement would have to be from the survey unit in order to reject the null hypothesis. From 

Table 6.5, one can see that the two largest measurements are from the survey unit, but the third 

largest is from the reference area. Because the ductwork has passed both the WRS and the 

Quantile test in the Scenario B example, one would conclude that it could be released from 

radiological controls. 

6.8 Evaluate the Results: The Decision 

Once the data and results of the tests are obtained, the specific steps required to make a 

disposition decision depends on the procedures approved by the regulator. The following 

considerations are suggested for the interpretation of the test results with respect to the 

disposition criteria. Note that the tests need not be performed in any particular order. 

The interpretation of results from the data evaluation or statistical test is the decision to reject or 

not to reject the null hypothesis. For some of the survey designs the decision is straightforward, 

while for other designs the interpretation is more complex. Figures 6.4 and 6.4 summarize the 

interpretation of results. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-24 January 2009 

Figure 6.4 Interpretation of Survey Results for Scan-Only and In Situ Surveys 

No

Is the

AL Equal to Zero 

or Background?

Requires Scenario B

LBGR = AL

Scan MDC UBGR

All

Results < Sc

from the

MDC?

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

Disposition

Decision Based

on Mean of a Sampled 

Population?

Disposition 

Decision Based on 

Individual

Items?

Consult a 

Statistician

Individual Results Must 

be Recorded

Recording Individual 

Scan Results 

Not Required

All

Results < Sc

from the

UBGR?

UCL UBGR?

M&E Meet the 

Disposition Criterion

No

Yes

Yes

Yes

Yes

No

Yes

Yes

No

No

Return to Figure 6.1

From Figure 6.1

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

No

Return to Figure 6.1 Return to Figure 6.1
MARSAME Evaluate the Survey Results 

January 2009 6-25 NUREG-1575, Supp. 1 

Figure 6.5 Statistical Interpretation of Results for MARSSIM-Type Surveys 

Radionuclide

of Concern Present in 

Background?

Perform the 

Quantile Test

( Q = /2)

RadionuclideSpecific 

Measurements?

S+ > q?

(Xi - LBGR)

Wr > q?

(Xi + AL)

S+ > q?

(UBGR - Xi)

Yes

Yes No

No

Perform the 

Sign test

Perform the 

Sign Test

Perform the 

WRS Test

Perform the 

WRS Test

Ws > q?

(Yi - LBGR)

q or

more of the r

Largest Values 

from the Survey 

Unit?

No Yes Yes No

Yes Yes

Yes

Yes

M&E Meet the 

Disposition Criterion

No

No

M&E Do Not Meet the 

Disposition Criterion 

(Section 6.9)

M&E Do Not Meet the 

Disposition Criterion

(Section 6.9)

Yes

No

No

Return to

Figure 6.1

From Figure 6.1

NOTE: An elevated 

measurement comparison 

also needs to be performed 

for MARSSIM-type surveys.

Return to 

Figure 6.1

Scenario A? Scenario B?
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-26 January 2009 

6.8.1 Compare Results to the UBGR 

The process for interpreting results compared to the UBGR depends on the action level used to 

develop the survey design. Refer to Table 6.1 for issues and assumptions underlying this 

evaluation method. 

If the action level is zero or background, Scenario B must be used: 

• Compare every measurement result to the critical value corresponding to the required scan 

MDC. 

• If all results are below the critical value, the M&E demonstrate compliance with the 

disposition criterion. 

• Any results that exceed the critical value provide evidence of radionuclide concentrations or 

radioactivity levels exceeding the disposition criteria, so the M&E do not demonstrate 

compliance with the release criterion. 

If the action level is not zero or background— 

• Compare every measurement result to the critical value corresponding to the UBGR. 

• If all results are below the critical value, the M&E demonstrate compliance with the 

disposition criterion. 

• Any results that exceed the critical value provide evidence of radionuclide concentrations or 

radioactivity levels exceeding the disposition criteria, so the M&E do not demonstrate 

compliance with the release criterion. 

Scan-only results are usually available as the data are collected. This real-time availability of 

results allows the surveyor to make decisions as the data are collected. M&E that exceed the 

action level can be identified and segregated during implementation of the survey. This “clean as 

you go” approach to surveys is only applicable for Class 1 surveys where there is high 

confidence in the quality and accuracy of detection decisions around the UBGR. Extensive 

documentation of the measurement process, previous applications of the process to the same or 

similar M&E, and verification of MDCs and MQCs is generally necessary to implement a “clean 

as you go” survey design. 

6.8.2 Compare Results Using an Upper Confidence Limit 

When decisions are made based on the mean of a sampled population, the survey results should 

be evaluated by comparison to a UCL (refer to Table 6.1 for issues and assumptions underlying 

this evaluation method): 

• Compare every measurement result to the critical value corresponding to the UBGR. 

• If all results are below the critical value, the M&E demonstrate compliance with the 

disposition criterion. 

• If any results are above the critical value, calculate the UCL (Section 6.4.1). 

• If the UCL is less than the UBGR, the M&E demonstrate compliance with the disposition 

criterion. 
MARSAME Evaluate the Survey Results 

January 2009 6-27 NUREG-1575, Supp. 1 

• If the UCL exceeds the UBGR, the M&E do not demonstrate compliance with the disposition 

criterion. 

• Investigate measurements exceeding the UBGR. 

• Results above the UBGR trigger a reevaluation of classification as Class 2. 

• Results above the MDC trigger a reevaluation of classification as Class 3. 

6.8.3 Compare Results for MARSSIM-Type Surveys 

The process for evaluating MARSSIM-type survey results is more complicated. This process is 

explained in more detail in MARSSIM Section 8.5 (refer to Table 6.1 for issues and assumptions 

underlying this evaluation method): 

• Calculate the test statistics (see Section 6.5.1, 6.6.1, 6.6.2, and 6.7). 

• Look up the critical value in the appropriate statistical table in Appendix A. 

• Evaluate the results of the statistical test as described in Figures 6.3 and 6.4. 

• Evaluate individual results using the elevated measurement comparison (EMC). 

• M&E must pass the statistical test and the EMC (if applicable) to demonstrate compliance. 

If the null hypothesis is rejected under Scenario A, there is sufficient evidence to show the 

median radionuclide concentrations or radiation levels are below the disposition criterion. Under 

Scenario B, failing to reject the null hypothesis means there is insufficient evidence to overturn 

the initial assumption the M&E demonstrate compliance with the disposition criterion. 

If the null hypothesis is rejected under Scenario B, additional investigations are required to 

determine the final disposition of the M&E (see Section 6.8.2). Failure to reject the null 

hypothesis under Scenario A also requires additional investigations. 

6.9 Investigate Causes for Survey Unit Failures 

When M&E fail to demonstrate compliance with the disposition criterion, the first step is to 

review and confirm the data that led to the decision. Once this is done, the DQO process can be 

used to evaluate potential problem areas leading to failure. 

If the level of radioactivity on or in some Class 1 M&E exceeds the UBGR, the simplest solution 

might be to segregate those items for a different disposition decision. The concept of “clean as 

you go” for Class 1 M&E was discussed in Section 6.8.1 where individual objects or sample 

locations were identified during implementation of the survey design. A simple modification to 

this approach is to physically segregate the objects exceeding the action level as they are 

identified, or after reanalysis shows the cleaning was not effective. The segregated M&E can 

then be evaluated for a different disposition option (e.g., reuse, disposal). 

Sometimes activity in excess of background can be removed from the M&E, or remediated, 

followed by re-evaluation or re-survey of the M&E. This approach may include evaluation of 

alternatives for remediation and a remedial action support survey prior to performing another 

final disposition survey. 
Evaluate the Survey Results MARSAME 

NUREG-1575, Supp. 1 6-28 January 2009 

If the radionuclides of concern have short half-lives, storage of the M&E until the radionuclides 

have decayed to acceptable levels, or “decay in place,” may be an option. The planning team 

should consider the intrinsic value of the M&E along with storage and disposal costs when 

considering this option. When multiple radionuclides are present with significantly different halflives (e.g., order of magnitude) radionuclide-specific measurements may be required to fully 

evaluate the acceptability of this option. 

In other cases, a different disposition option (e.g., reuse, disposal) may be selected. If such a 

situation were encountered in evaluating Class 2 or Class 3 M&E, the classification would be 

questioned and the M&E would be reclassified and surveyed as Class 1 M&E. This may also 

bring other classification decisions into question. 

As a general rule, it may be useful to anticipate possible modes of failure. These can be 

formulated as the problem to be solved using the DQO Process. Once the problem has been 

stated, the decision concerning the failing survey unit can be developed into a decision rule. For 

example, decide whether to attempt to remove the radioactivity or simply segregate certain types 

of M&E for low-level waste disposal. Next, determine the additional data, if any, needed to 

document that a survey unit where pieces with elevated measurements have been removed or 

areas of added activity removed demonstrates compliance with the disposition criterion. 

Alternatives to resolving the decision rule should be developed for each type of M&E that may 

fail the surveys. These alternatives can be evaluated against the DQOs, and a disposition survey 

design that meets the objectives of the project can be selected. 

6.10 Document the Disposition Survey Results 

Documentation of survey results is an important part of the disposition survey process. The form 

of this documentation can vary greatly depending on the survey objectives and regulatory or 

administrative requirements. Documentation of disposition survey results should be considered 

during survey design to ensure adequate records are provided during implementation. Generally, 

survey documentation requirements are provided as part of the documented survey design. 

Documented items may include— 

• A description of the final disposition, such as disposal in a landfill, return to manufacture for 

refurbishment, sold as salvage, recycled as ferrous metal, etc.; 

• A release statement to the transport carrier and recipient of the material indicating that the 

M&E described in the bill of laden meet(s) applicable state and federal regulations; and 

• Results of QC measurements made during the conduct of release surveys and confirmation of 

compliance with facility SOPs and action levels. 

In both routine and non-routine surveys, the documentation should comply with all applicable 

regulatory requirements. Development of survey documentation should allow for any necessary 

or required reviews. 

If the disposition survey is a routine survey, then the survey will be documented as specified in 

the SOP. For example, routine surveys performed to clear M&E from a facility may require 

documentation that the instruments were calibrated and functioning properly and that trained 
MARSAME Evaluate the Survey Results 

January 2009 6-29 NUREG-1575, Supp. 1 

personnel were on duty to perform the surveys. Quality assurance reviews and audits would be 

performed periodically (typically under a separate SOP) to document that the clearance surveys 

were being performed properly and that no M&E were cleared without first being surveyed. 

These records would document that properly trained personnel had adequately surveyed all M&E 

leaving the facility using properly functioning instruments. Documentation of individual 

measurement results may not be required or necessary. 

If the survey is not routine, significantly more documentation may be required. This 

documentation should provide a complete and unambiguous record of the radiological status of 

the M&E relative to the selected action levels. In addition, sufficient data and information should 

be provided to enable an independent evaluation of the survey results, including repeating 

measurements at some future time Additional information on documentation is provided in 

Section 2.5, Section 3.6, Section 4.5, MARSSIM Sections 3.8 and 8.6, and MARSSIM 

Chapter 5. 
 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-1 NUREG-1575, Supp. 1 

7 STATISTICAL BASIS FOR MARSAME SURVEYS 

The statistically rigorous quantitative application of measurement quality objectives (MQOs) 

plays a central role in the MARSAME process. MQOs did not appear explicitly in Multi-Agency 

Radiation Survey and Site Investigation Manual (MARSSIM 2002), but were subsequently 

developed for radioanalytical chemistry measurements as part of the Multi-Agency Radiological 

Laboratory Analytical Protocols (MARLAP) manual. However, these concepts apply equally 

well to field measurements of radiation and radioactivity. The MARSAME process incorporates 

these ideas and extends them to these measurements. 

A major development since the publication of MARSSIM was the publication of the Guide to the 

Expression of Uncertainty in Measurement, or “GUM” (ISO 1995). The procedures described in 

this document have become a de facto standard for estimating the uncertainty associated with 

measurements of any type. The GUM methodology is essential for the assessment of 

measurement uncertainty, but was not previously treated in MARSSIM. 

Data quality objectives (DQO) form the backbone of the MARSAME process, and are discussed 

in detail in Chapters 2 and 3. A number of terms with specific statistical meanings are used in 

this and subsequent sections. The concept of measurement quality objectives (MQOs) and in 

particular the required measurement method uncertainty was introduced in Section 3.8. These 

ideas are discussed in greater detail in MARLAP Chapter 3 and Appendix C. While MARLAP is 

focused on radioanalytical procedures, these concepts are applicable on a much broader scale and 

are used in MARSAME in Sections 5.5 through 5.8 to guide the selection of measurement 

methods for disposition surveys for materials and equipment. 

In Section 7.1 the general concepts of statistical survey design and hypothesis testing are 

discussed, with more detail in Section 7.2. In Sections 7.3, 7.4, 7.5, and 7.6, calculation of 

measurement quality objectives (particularly the required method uncertainty), measurement 

uncertainty, minimum detectable concentrations (MDCs) and minimum quantifiable 

concentrations (MQCs), respectively, are introduced. Further details and examples of these 

topics for the interested reader are then given in Sections 7.7, 7.8, 7.9, and 7.10. This advanced 

material is optional on initial reading, and may be referred to later as needed. Section 7.11 shows 

a detailed calculation of a scan MDC, which is used in Chapter 8. This process was described 

and used in MARSSIM, but a systematic example was constructed for M&E. These calculations 

are detailed, and are also optional on first reading. 

In developing the results in this chapter, a number of new and sometimes only subtly different 

definitions and symbols are used. For the convenience of the reader, many of these are 

summarized in the tables below. Table 7.1 provides a summary of notation used for DQOs and 

MQOs, used primarily in Sections 7.1 and 7.2. Table 7.2 contains notation used for setting 

MQOs for required method uncertainties (Sections 7.3 and 7.7) and in uncertainty calculations 

(Sections 7.4 and 7.8). MDC calculations (Sections 7.5 and 7.9) and MQC calculations (Sections 

7.6 and 7.10) use the notation added in Table 7.3 and Table 7.4, respectively. Symbols may not 

have an entry for both formula or reference and type. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-2 January 2009 

Table 7.1 Notation for DQOs and MQOs 

Symbol Definition Formula or reference Type 

α Probability of a Type I 

decision error 

Chosen during DQO 

process 

β The probability of a 

Type II decision error 

Chosen during DQO 

process 

Δ Width of the gray 

region (UBGR-LBGR) Chosen during DQO process 

φMR

Required relative 

method uncertainty 

above the UBGR 

uMR /UBGR Chosen during DQO 

process 

SC

The critical value of 

the net instrument 

signal (e.g., net count) 

Calculation of SC requires the choice of a 

significance level for the test. The 

significance level is a specified upper bound 

for the probability, α, of a Type I error. The 

significance level is usually chosen to be 

0.05. 

If a measured value 

exceeds the critical 

value, a decision is made 

that radiation or 

radioactivity has been 

detected 

S

ˆ net signal Experimental 

σ

The total standard 

deviation of the data (σS

2

 + σM

2

)

1⁄2 Theoretical population 

parameter 

σN

The standard deviation 

of the mean of N 

independent 

measurements 

σN = σ/√N

σS

Standard deviation due 

to sampling 

Theoretical population 

parameter 

σM

Standard deviation of 

the measurement 

method 

Theoretical population 

parameter 

σMR

Required method 

standard deviation at 

and below the UBGR 

Upper bound to the value of σM

Theoretical population 

parameter 

uMR

Required method 

uncertainty at and 

below the UBGR 

Upper bound to the value of uM

Chosen during DQO 

process 

2

c u (y) Combined variance of 

y Uncertainty propagation Calculated 

uc(y) Combined standard 

uncertainty of y Uncertainty propagation Calculated 

z1–α

(z1–β) 

1–α (or 1–β) quantile 

of a standard normal 

distribution function

Table of standard normal distribution Theoretical 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-3 NUREG-1575, Supp. 1 

Table 7.2 Notation for Uncertainty Calculations 

Symbol Definition Formula or reference Type 

a 

Half-width of a bounded 

probability distribution 

Type B evaluation of 

uncertainty Estimated 

ci Sensitivity coefficient ∂f / ∂xi, the partial derivative of 

f with respect to xi

Evaluated at the measured 

values x1,x2,...,xN

f(x1, x2,...,xN) 

The calculated value of the 

output quantity from 

measurable input quantities 

for a particular measurement

y = f(x1, x2,...,xN) Experimental 

 f(X1, 

X2,...,XN)

Model equation expressing 

the mathematical 

relationship, between the 

measurand, Y and the input 

quantities Xi

Y=f(X1, X2,...,XN) Theoretical 

k Coverage factor for 

expanded uncertainty 

Numerical factor used as a 

multiplier of the combined 

standard uncertainty in order to 

obtain an expanded uncertainty

Chosen during DQO 

process 

p Coverage probability for 

expanded uncertainty 

Probability that the interval 

surrounding the result of a 

measurement determined by 

the expanded uncertainty will 

contain the value of the 

measurand 

Chosen during DQO 

process 

r(xi,xj) Correlation coefficient for 

two input estimates, xi and xj

u(xi,xj) / (u(xi) u(xj)) Experimental 

s(xi) Sample standard deviation 

of the input estimate xi

2

,

1

1 () ( ) ( 1)

n

i ik i

k

s x xx n == − − ∑ Experimental 

u(xi) Type B standard uncertainty 

of the input estimate xi

Estimated 

ui(y) 

Component of the combined 

standard uncertainty uc(y) 

generated by the standard 

uncertainty of the input 

estimate xi, u(xi) 

ui(y) = ci u(xi) Estimated 

uc(y) Combined standard 

uncertainty of y Uncertainty propagation Calculated 

2

c u (y) Combined variance of y Uncertainty propagation Calculated 

U Expanded uncertainty 

“Defining an interval about the 

result of a measurement that 

may be expected to encompass 

a large fraction of values that 

could reasonably be attributed 

to the measurand” (GUM) 

Calculated 

u(xi,xj) Covariance of two input 

estimates, xi and xj

Experimental 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-4 January 2009 

Table 7.2 Notation for Uncertainty Calculations (Continued) 

Symbol Definition Formula or reference Type 

uc(y)/y

Relative combined standard 

uncertainty of the output 

quantity for a particular 

measurement 

Experimental 

u (xi)/xi

Relative standard 

uncertainty of a nonzero 

input estimate xi for a 

particular measurement 

Experimental 

w1,w2,...,wN

Input quantities appearing in 

the numerator of y = f(x1, 

x2,...,xN) 

See “z1,z2,...,zN” below 

X1, X2,...,XN Measurable input quantities Theoretical 

1 2 ,,, N x x x K

Estimates of the measurable 

input quantities for a 

particular measurement 

 Experimental 

Y The output quantity or 

measurand Theoretical 

y 

Estimate of the output 

quantity for a particular 

measurement 

 Experimental 

z1,z2,...,zN

Input quantities appearing in 

the denominator of y = f(x1, 

x2,...,xN)

N = n+m Experimental 

Table 7.3 Notation for MDC Calculations 

Symbol Definition Formula or reference Type 

NB Background count Experimental 

NS Gross sample count Experimental 

tS

Count time for the test source or 

sample Experimental 

tB Count time for the background Experimental 

RB Mean count rate of the blank 

B

B

B t

N R =

d 

Parameter in the Stapleton 

equation for the critical value of 

the net instrument signal 

Usually has the value 0.4 

ε Efficiency Calibration Experimental or 

Theoretical 

F Calibration function X= F(Y) 

F−1 Evaluation function 

Y =F−1

( X ), closely related to the 

mathematical model 

Y=f(X1, X2,...,XN)
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-5 NUREG-1575, Supp. 1 

Table 7.3 Notation for MDC Calculations (Continued) 

Symbol Definition Formula or reference Type 

SC

Critical value of the net 

instrument signal 

Net instrument signal is 

calculated from the gross signal 

by subtracting the estimated 

background and any 

interferences 

SD

Minimum detectable value of 

the net instrument signal 

Net instrument signal that gives 

a specified probability, 1−β, of 

yielding an observed signal 

greater than its critical value SC

X Observable response variable, 

measurable signal Experimental 

xC

The critical value of the 

response variable 

Calculation of yC requires the 

choice of a significance level for 

the test. The significance level is 

a specified upper bound for the 

probability, α, of a Type I error. 

The significance level is usually 

chosen to be 0.05. 

If a measured value 

exceeds the critical value, 

a decision is made that 

radiation or radioactivity 

has been detected 

Y State variable, measurand Uncertainty propagation 

yC

Critical value of the 

concentration yC = F−1

 (xC) 

yD

Minimum detectable 

concentration (MDC) 

D

D

S

y

ε =

ΔB| Relative systematic error in the 

background determination Experimental 

ΔA| Relative systematic error in the 

sensitivity Experimental 

Table 7.4 Notation for MQC Calculations 

Symbol Definition Formula or reference Type 

 kQ

Multiple of the standard 

deviation defining yQ,

usually chosen to be 10 

2

(| ) Q

Q

Q

yY y k

y

σ = = Chosen during 

DQO process 

2

(| ) Q σ yY y =

The variance of y given the 

true concentration Y equals 

yQ

 Theoretical 

 yQ

Minimum quantifiable 

concentration (MQC) 

The concentration at which the 

measurement process gives results with a 

specified relative standard deviation 1/kQ, 

where kQ is usually chosen to be 10 

Theoretical 

RI

Mean interference count 

rate Experimental 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-6 January 2009 

Table 7.4 Notation for MQC Calculations (Continued) 

Symbol Definition Formula or 

reference Type 

( ) RI

) σ

Standard deviation of the measured interference count 

rate Experimental

2 φεˆ Relative variance of the measured efficiency, εˆ Experimental

7.1 Overview of Statistical Survey Design and Hypothesis Testing 

Designing a MARSAME survey involves the following key statistical parameters: 

(1) The uncertainty in the measurement method. The measurement method uncertainty can be 

affected by changes to the measurement method, such as changing counting times, or 

performing repeated measurements. Generally, the measurement method uncertainty is 

characterized by its standard deviation, σM. This value may be a constant, meaning that all 

measurements will have the same standard deviation. Alternatively, this value may vary with 

the level of radionuclide concentration or radioactivity, such that the standard deviation 

increases with increasing radionuclide concentration or radioactivity. 

(2) The uncertainty in the distribution of radionuclide concentrations or radioactivity in the 

population of materials and equipment (M&E) to be measured. This variation of radionuclide 

concentrations or radioactivity in space and time can be characterized by the sampling 

standard deviation, σS. 

(3) The number of samples, N, from the population of radionuclide concentrations or 

radioactivity that comprises the survey unit. 

(4) The null (H0) and alternative (H1) hypotheses to be examined. The symbol Δ represents the 

detectable difference between the null hypothesis concentration value (the action level, or 

AL), and the alternative hypothesis concentration value (the discrimination limit, or DL). The 

range of concentrations between the AL and the DL is referred to as the gray region. 

(5) The values of α and β that quantify acceptable limits for Type I and Type II decision errors, 

respectively. A Type I decision error occurs when the null hypothesis is rejected when it is 

actually true. A Type II decision error occurs when the null hypothesis is not rejected but 

should have been rejected. The value of 1–β is termed the power, or the ability of the 

statistical test to reject the null hypothesis, when appropriate. For a specific survey design, 

the power (1–β) of the survey can be compared at different values of α, since the power is 

the probability of rejecting the null hypothesis at a given value of α.

Note: Designing a survey involves collecting a number of measurements, N, that will yield the 

desired α and power (1-β), given a detectable difference Δ, the σM for the measurement method 

selected and the σS for the distribution of radionuclide concentrations or radioactivity in the 

population of materials and equipment (M&E) to be measured. The relationships between these 

parameters are complex and interrelated. The choice or determination of one parameter affects 

the choice or determination of the other parameters. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-7 NUREG-1575, Supp. 1 

When a single measurement is taken, the variance of that measurement will equal: 

σ

2

 = σ

2

M + σ

2

S (7-1) 

In some cases, the distribution of radionuclide concentrations or radioactivity in the population 

of M&E to be measured and thus σS may not be important to a MARSAME survey, e.g., in cases 

where there is no sampling variability. It then becomes important how the measurement method 

uncertainty changes when repeated measurements of the same sampling unit are taken. It may be 

reasonable to assume that the mean of N independent measurements of the same sampling unit 

will have a standard deviation: 

σN = σM/√N (7-2) 

When variability in the distribution of radionuclide concentrations or radioactivity in the 

population of M&E to be measured occurs over time and space, then σS is not equal to zero, and 

must be included in the MARSAME survey design. The variance of the mean of a random 

sample of N measurements will fall in a range between 

σN

2

 = [σ

2

M + σ

2

S]/N (7-3)

and 

σN

2

 = σ

2

M + σ

2

S/N (7-4) 

Equation 7.3 corresponds to measurement method uncertainties that are completely uncorrelated, 

and equation 7.4 corresponds to measurement method uncertainties that are completely 

correlated, due to common parameters with the same uncertainty. Generally, as more 

measurements are taken, the contribution of the sampling variance, σ

2

S, to the overall variance of 

the mean tends to disappear, whereas some or all of the measurement method variance, σ

2

M, may 

remain. The special case where 100% of the M&E is measured may be regarded as the limit 

when N approaches infinity. Some or all of the measurement method variance may still remain. 

Once σ is estimated, the power (1–β) of a study will depend upon: 

1. The Type I decision error rate (α), 

2. The size of the gray region (Δ), and 

3. The number of measurements made (N). 

The gray region Δ is the range of radionuclide concentrations or quantities between the DL and 

the AL. In other words, differences between the DL and the AL less than Δ will be detected with 

power less than the required 1–β and therefore are uncertain, or “gray.” If the AL is defined as 

the upper bound of the gray region (UBGR), then the lower bound of the gray region (LBGR) is 

the DL, and is determined by subtracting Δ from the AL. 

All of these factors are interdependent. Generally, the process begins with a known AL, and a 

DL based on process knowledge. With an estimate of σ, an appropriate number of 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-8 January 2009 

measurements, N, is found to fulfill the desired limits on decision error rates α and β. If any of 

these are changed, it will affect the others. 

In MARSAME, the null and alternative hypotheses concern the true difference in the M&E 

between containing radionuclide concentrations or radioactivity in excess of the AL above the 

appropriate background reference M&E.1

 Scenario A uses a null hypothesis that assumes the 

radionuclide concentration or radioactivity associated with the M&E exceeds the AL. Scenario A 

is sometimes referred to as “presumed not to comply” or “presumed not clean.” Scenario B uses 

a null hypothesis that assumes the radionuclide concentration or radioactivity associated with the 

M&E is less than or equal to the AL. Scenario B is sometimes referred to as “indistinguishable 

from background” (when the AL is zero) or “presumed clean.” 

Note: Under Scenario A, the M&E are only deemed suitable for release if the null hypothesis is 

rejected, whereas under Scenario B, the M&E are suitable for release only if the null hypothesis 

is not rejected. 

For example, under Scenario A, if the true, but unknown, value of the radionuclide concentration 

or radioactivity in excess of background is less than or equal to the DL, then the hypothesis test 

upon which the survey is designed will have power 1−β to reject the null hypothesis that the true, 

but unknown, value is greater than or equal to the AL at Type I error rate α. Under Scenario B, if 

the true, but unknown, value of radionuclide concentration or radioactivity in excess of 

background is greater than the DL (AL + Δ), then the hypothesis test upon which the survey is 

designed will once again have power 1−β to reject this null hypothesis at Type I error rate α. 

 For a given α and 1−β, Δ depends on σ, so it is important that the measurement method (and 

sampling fraction, where appropriate) selected is sensitive enough to provide a small enough σ, 

in order that Δ meets survey design requirements for the DL. This ensures that the DL is not set 

too low in Scenario A or too high in Scenario B. For normally distributed measurements. 

Δ/σ = (z1−β + z1−α) (7-5) 

Segregation according to likely radionuclide concentrations or radioactivity or a measurement 

method with a longer counting time may improve σ and therefore Δ. Hypothesis testing (i.e., 

accepting or rejecting the null hypothesis) consists of comparing an estimate of the radionuclide 

concentration or radioactivity to a “critical value,” SC. The result indicates whether the observed 

estimate is consistent with the null value for a given Type I error rate α, after taking account of 

the uncertainty σ of the measurement. For Scenario A, the critical value is 

SC = AL − z1−α σ (7-6) 

And for Scenario B the critical value is 

SC = AL + z1-ασ (7-7) 

 

1

 Note that the radionuclides of concern may not be contained in the background reference M&E. If radionuclide 

specific measurements are made, background reference data will be unnecessary. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-9 NUREG-1575, Supp. 1 

Where z1−α is the 1−α quantile of the standard normal distribution. In situations where the 

distribution of the estimate may not be normally distributed, more specialized statistical analysis 

may be needed. By definition, the power 1−β is the probability as computed under the alternate 

hypothesis of rejecting the null hypothesis, or that the probability that the observed estimate is 

less than the critical value SC for Scenario A, and greater than SC for Scenario B. 

7.2 Statistical Decision-Making 

In Section 4.2, MARSAME recommends the planning team complete the following steps: 

• Select a null hypothesis, 

• Choose a discrimination limit, 

• Define Type I and Type II decision errors, 

• Set a tolerable Type I decision error rate at the action level, and 

• Set a tolerable Type II decision error rate at the discrimination limit. 

7.2.1 Null Hypothesis 

In hypothesis testing, two assertions about the actual level of radioactivity associated with the 

M&E are formulated. The two assertions are called the null hypothesis (H0) and the alternative 

hypothesis (H1). H0 and H1 together describe all possible radionuclide concentrations or levels of 

radioactivity under consideration. The survey data are evaluated to choose which hypothesis to 

reject or not reject, and by implication which to accept.2 In any given situation, one and only one 

of the hypotheses must be true. The null hypothesis is assumed to be true within the established 

tolerance for making decision errors (Section 7.2.5). Thus, the choice of the null hypothesis also 

determines the burden of proof for the test. 

If the action level (AL) is not zero, the planning team generally assumes the radionuclide 

concentration or level of radioactivity (X) exceeds the action level unless the survey results 

provide evidence to the contrary. In other words, surveys are designed to provide sufficient 

evidence to disprove H0. In this case, the null hypothesis is that the radionuclide concentration or 

level of radioactivity is greater than or equal to the action level (i.e., H0: X ≥ AL). The alternative 

hypothesis is the radionuclide concentration or level of radioactivity is less than the action level 

(i.e., H1: X < AL). MARSSIM and NUREG-1505 (NRC 1998a) describe this as Scenario A, and 

the burden of proof falls on the owner of the M&E. Scenario A is sometimes referred to as 

“presumed not to comply” or “presumed not clean.” 

On the other hand, the planning team may choose to assume the action level has not been 

exceeded unless the survey results provide evidence to the contrary. The null hypothesis 

becomes H0: X ≤ AL, and the alternative hypothesis is H1: X > AL. MARSSIM and NUREG1505 (NRC 1998a) describe this as Scenario B, and the burden of proof falls on the regulator. 

Scenario B is sometimes referred to as “indistinguishable from background” or “presumed 

 

2

 In hypothesis testing, to “accept” the null hypothesis only means not to reject it. For this reason many statisticians 

avoid the word “accept.” A decision not to reject the null hypothesis does not imply the null hypothesis has been 

shown to be true. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-10 January 2009 

clean.” This is the only practical approach when the action level is equal to zero (above 

background); because it is technically impossible to obtain statistical evidence that the 

radionuclide concentration or level of radioactivity is exactly zero. However, Scenario B can be 

applied to situations other than “indistinguishable from background.” The example in Section 8.4 

uses Scenario B to support an interdiction decision. 

7.2.2 Discrimination Limit 

Action levels were defined in Section 3.3 based on the selected disposition option and applicable 

regulatory requirements. The planning team also chooses another radionuclide concentration or 

level of radioactivity that can be reliably distinguished from the action level by performing 

measurements (i.e., direct measurements, scans, in situ measurements, samples and laboratory 

analyses). This radionuclide concentration or level of radioactivity is called the discrimination 

limit (DL). An example where the discrimination limit is defined is provided in Section 

8.4.5.The gray region is defined as the interval between the action level and the discrimination 

limit (Figures 7.1, 7.2, 7.3, and 7.4 provide visual descriptions of the gray region). The width of 

the gray region is called the shift and denoted as Δ. The objective of the disposition survey is to 

decide whether the concentration of radioactivity is more characteristic of the DL or of the AL, 

i.e., whether action should be taken, or if action is not necessary. Figures 7.1 and 7.2 show 

examples that would fall under Scenario A (discussed in Section 7.2.3). In Figure 7.1 (top) the 

difference in concentration between the AL and the DL (i.e., Δ) is large; but the variability in the 

measured concentration (i.e., σ ) is also large. In Figure 7.2 (bottom) the difference in 

concentration between the AL and the DL (i.e., Δ) is relatively small. However, the variability in 

the measured concentration (i.e., σ ) is also smaller. Figures 7.1 and 7.2 illustrate that 

determining the level of survey effort depends not just on the width of the gray region, but also 

in the ratio of that width to the expected variability of the data. This ratio, Δ/σ, is called the 

relative shift in MARSSIM. In situations where Δ/σ is small, i.e., less than 1, it may be 

impracticable to achieve the required accuracy of measurements or the number of samples to 

meet the Type I error rate in the DQOs. Section 4.4.4 presents options for relaxing project 

constraints to optimize the survey design in such cases. In Figure 7.1, Δ/σ is greater than 4; while 

in Figure 7.2, Δ/σ is approximately 1. 

As discussed in MARSSIM, generally, the larger Δ/σ, the easier the survey effort. When Δ/σ is 

greater than three, the survey effort will be minimal, and any effort to increase it by either 

widening the gray region or reducing the measurement variability usually would not be 

worthwhile. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-11 NUREG-1575, Supp. 1 

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10

Concentration

Frequency of Occurrence

Action 

Level

Mean Δ

σ

Figure 7.1 Relative Shift, Δ/σ, Comparison for Scenario A: 

σ is Large, but the LargeΔ Results in a LargeΔ/σ and Fewer Samples 

0

0.3

0.6

0.9

1.2

1.5

1.8

0 1 2 3 4 5 7 10

Concentration

Frequency of Occurrence

Action 

Level

Mean Δ

σ

Figure 7.2 Relative Shift, Δ/σ, Comparison for Scenario A: 

σ is Small, but the Small Δ Results in a Small Δ/σ and More Samples 

On the other hand, when Δ/σ is less than one, the survey effort will become substantial, and any 

effort to increase it by either widening the gray region or reducing the measurement variability 

will be worthwhile. The measurement variability is thus just as important as the width of the gray 

region when designing disposition surveys. In MARSSIM surveys, the total variability had two 

components: sampling and analytical. For some MARSAME surveys this will also be the case. 

However, in many MARSAME surveys the sampling variability will be of less importance, 

either because 100% of the survey unit is being measured, or because disposition decisions are 

being made on the basis of single measurements on single items or single locations. In such 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-12 January 2009 

cases, the required measurement method uncertainty discussed in Section 3.8.1 will be of 

paramount importance in the survey planning. The details for determining the required 

measurement method uncertainty and how to determine if it is being met are discussed in detail 

in Section 7.7. 

Depending on the survey design, the combination of action levels, expected radionuclide 

concentrations or levels of radioactivity, instrument sensitivity, and local radiation background 

contribute to defining the width of the gray region. Reducing the radionuclide concentrations or 

levels of radioactivity known or assumed to be associated with the M&E can affect the selection 

of a discrimination limit, so remediation costs may need to be considered. Increasing the 

sensitivity of a measurement method to reduce the measurement method uncertainty generally 

involves increased instrument costs or increased counting times. 

The lower bound of the gray region is denoted by LBGR and the upper bound of the gray region 

is denoted by UBGR. The association of either the UBGR or the LBGR with the DL or AL will 

depend on the scenario selected (see Sections 7.2.3 and 7.2.4). The width of the gray region 

(UBGR – LBGR) is denoted by “Δ” and is called the “shift” or the “required minimum 

detectable difference” in activity or concentration (MARSSIM Section 5.5.2 and Section D.6, 

MARLAP Section C.2, NRC 1998a, and EPA 2006a,). 

7.2.3 Scenario A 

The null hypothesis for Scenario A specifies that the radionuclide concentration or level of 

radioactivity associated with the M&E is equal to or exceeds the action level. For Scenario A 

(H0: X ≥ AL), the UBGR is equal to the AL and the LBGR is equal to the DL. As a general rule 

for applying Scenario A, the DL should be set no higher than the expected radionuclide 

concentration associated with the M&E. The DL and the AL should be reported in the same 

units. Figure 7.3 illustrates Scenario A. Note that the Type I (α) and Type II (β) error rates need 

not be equal. This is discussed further in Section 7.2.5, and an example can be seen in Section 

7.5.2. 

Figure 7.3 Illustration of Scenario A 

Action

Level

Critical

Value

Gray

Area

Discrim ination

Lim it

α β

Scenario A

(H 0: Net Activity ≥ Action Level)

Δ LBGR UBGR

Action

Level

Critical

Value

Gray

Area

Discrim ination

Lim it

α β

Scenario A

(H 0: Net Activity ≥ Action Level)

Δ LBGR UBGR
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-13 NUREG-1575, Supp. 1 

7.2.4 Scenario B 

The null hypothesis for Scenario B specifies the radionuclide concentration or level of 

radioactivity associated with the M&E is less than or equal to the action level. For Scenario B 

(H0: X < AL), the UBGR is equal to the DL and the LBGR is equal to the AL. For example, if 

the AL=0 (sometimes called indistinguishable from background), then the LBGR will be zero. 

The DL defines how hard the surveyor needs to look, and is determined through negotiations 

with the regulator.3 In some cases, the DL will be set equal to a regulatory limit (e.g., 10 CFR 

36.57 and DOE 1993). The DL and the AL should be reported in the same units. Figure 7.4 

illustrates Scenario B. As above, note that the Type I (α) and Type II (β) error rates need not be 

equal. This is discussed further in Section 7.2.5, and an example can be seen in Section 7.5.2. 

 Figure 7.4 Illustration of Scenario B 

This description of Scenario B is based on information in MARLAP and is fundamentally 

different from the description of Scenario B in NUREG-1505 (NRC 1998a). 

In NUREG-1505 (NRC 1998a) the gray region is defined as being below the AL in both 

Scenario A and Scenario B. In MARSAME and MARLAP the gray region is defined as being 

above the AL in Scenario B. The difference lies in how the action level is defined. 

7.2.5 Specify Limits on Decision Errors 

There are two possible types of decision errors: 

• Type I error: rejecting the null hypothesis when it is true. 

• Type II error: failing to reject the null hypothesis when it is false. 

 

3

 In some cases setting the discrimination limit may include negotiations with stakeholders. 

Action

Level

Critical

Value

Gray

Area

Discrimination

Limit Zero

β α

Scenario B

(H 0: Net Activity < Action Level)

Δ LBGR UBGR

Action

Level

Critical

Value

Gray

Area

Discrimination

Limit Zero

β α

Scenario B

(H 0: Net Activity < Action Level)

Δ LBGR UBGR
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-14 January 2009 

Because there is always uncertainty associated with the survey results, the possibility of decision 

errors cannot be eliminated. So instead, the planning team specifies the maximum Type I 

decision error rate (α) that is allowable when the radionuclide concentration or level of 

radioactivity is at or above the action level. This maximum usually occurs when the true 

radionuclide concentration or level of radioactivity is exactly equal to the action level. The 

planning team also specifies the maximum Type II decision error rate (β) that is allowable when 

the radionuclide concentration or level of radioactivity equals the discrimination limit. 

Equivalently, the planning team can set the “power” (1−β) when the radionuclide concentration 

or level of radioactivity equals the discrimination limit. See MARSSIM Appendix D, Section 

D.6, for a more detailed description of error rates and statistical power. 

The definition of decision errors depends on the selection of the null hypothesis. For Scenario A 

the null hypothesis is the radionuclide concentration or level of radioactivity exceeds the action 

level. A Type I error for Scenario A occurs when the decision maker decides the radionuclide 

concentration or level of radioactivity is below the action level when it is actually above the 

action level (i.e., mistakenly decides the M&E are clean when they are actually not clean). A 

Type II error for Scenario A occurs when the decision maker decides the radionuclide 

concentration or level of radioactivity is above the action level when it is actually below the 

action level (i.e., mistakenly decides the M&E are not clean when they are actually clean). 

For Scenario B, the null hypothesis is that the radionuclide concentration or level of radioactivity 

is less than or equal to the action level. A Type I error for Scenario B occurs when the decision 

maker decides the radionuclide concentration or level of radioactivity is above the action level 

when it is actually below the action level (i.e., mistakenly decides the M&E are not clean when 

they are actually clean). A Type II error for Scenario B occurs when the decision maker decides 

the radionuclide concentration or level of radioactivity is below the action level when it is 

actually above the action level (i.e., mistakenly decides the M&E are clean when they are 

actually not clean). It is important to clearly define the scenario (i.e., A or B) and the decision 

errors for the survey being designed. 

Once the decision errors have been defined, the planning team should determine the 

consequences of making each type of decision error. This process should be revisited as more 

information is obtained. For example, incorrectly deciding the activity is less than the action 

level may result in increased health and ecological risks. Incorrectly deciding the activity is 

above the action level when it is actually below may result in increased economic and social 

risks. The consequences of making decision errors are specific to the actual situation at a 

particular site and could vary significantly from one site to another, reflecting the major concerns 

of the various stakeholders. 

Once the consequences of making both types of decision errors have been identified, acceptable 

decision error rates can be assigned for both Type I and Type II decision errors. Historically, a 

decision error rate of 0.05, or 5%, often has been acceptable for decision errors. However, 

assigning the same tolerable decision error rate to all projects does not account for the 

differences in consequences of making decision errors. This becomes evident with M&E where 

there are wide ranges of disposition options generating a wide range of consequences. For 

example, a Type I decision error for Scenario A could have different consequences for a 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-15 NUREG-1575, Supp. 1 

clearance decision compared to a low-level radioactive waste disposal decision. Not all 

consequences of decision errors are the same, and it is unlikely that applying a fixed value to all 

decision error rates will result in reasonable survey designs resulting in comparable decisions. 

Project-specific decision error rates should be selected based on the project-specific 

consequences of making decision errors. 

7.2.6 Develop an Operational Decision Rule 

The theoretical decision rule developed in Section 3.7 was based on the assumption that the true 

radioactivity concentrations or radiation levels associated with the M&E were known. Since the 

disposition decision will be made based on measurement results and not the true but unknown 

concentration level, an operational decision rule needs to be developed to replace the theoretical 

decision rule. The operational decision rule is a statement of the statistical hypothesis test, which 

is based on comparing some function of the measurement results to some critical value. The 

theoretical decision rule is developed during Step 5 of the DQO Process (Chapter 3), while the 

operational decision rule is developed as part of Step 6 and Step 7 of the DQO Process. For 

example, a theoretical decision rule might be “if the results of any measurement identify surface 

radioactivity in excess of background, the front loader will be refused access to the site; if no 

surface radioactivity in excess of background is detected, the front loader will be granted access 

to the site.” The related operational decision rule might be “any result that exceeds the critical 

value associated with the MDC, set at the discrimination limit, will result in rejection of the null 

hypothesis and the front loader will not be allowed on the site” (see more examples in Chapter 

8). 

Chapter 6 provides guidance on using statistical tests to evaluate data collected during the 

disposition survey to support a disposition decision. The planning team should evaluate the 

statistical tests and possible operational decision rules and select one that best matches the intent 

of the theoretical decision rule with the statistical assumptions. Each operational decision rule 

will have a different formula for determining the number of measurements or fraction of M&E to 

be measured to meet the DQOs. 

Developing an operational decision rule incorporates all relevant information available 

concerning the M&E (Section 2.4.3), selected instrumentation and measurement technique 

(Section 5.9), selected statistical tests (Section 6.2.3), and any constraints on collecting data 

identified by the planning team. The operational decision rule will need to specify a 

measurement technique (e.g., scan-only, in situ, sample collection and analysis) and a statistical 

test. Examples of statistical tests include comparison to the UBGR (Section 6.3), comparison to 

an upper confidence interval (Section 6.4), the Sign test (Section 6.5), the Wilcoxon Rank Sum 

test (Section 6.6), and the Quantile test (Section 6.7). At this point in the survey design process it 

is not necessary to select a specific instrument to perform the measurements. However, selection 

of a measurement technique will assist the planning team in identifying the appropriate statistical 

test. For example, if a scan-only measurement method is selected it is not appropriate to select 

the Wilcoxon Rank Sum test to determine the number of measurements. However, if no scanonly or in situ measurement methods are available that meet the measurement quality objectives 

(MQOs), a MARSSIM-type survey (which combines scan and static measurements, see Section 

4.4.3) should be developed. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-16 January 2009 

The planning team uses the combination of the selected instrumentation and measurement 

technique (see Section 5.9) with a data evaluation method (see Section 6.2.5) to establish an 

operational decision rule. Then, from the operational decision rule, the planning team can 

determine the number of measurements or the fraction of the M&E that needs to be measured 

during the disposition survey. There is no formal structure for stating an operational decision 

rule. The structure of the operational decision rule is generally defined in terms that meet the 

needs of a particular project. An operational decision rule can be simple or complex. A simple 

example could be “If 100% of the surfaces of hand tools are surveyed using a scan-only 

technique that meets the DQOs, and none of the results exceed the action level for release, then 

the tools can be released.” The statistical test for this simple example is a comparison of the 

mean to the action level; however, since all of the values are below the action level, the mean 

value must also be below the action level. Therefore, it is not necessary to perform the actual 

statistical test. This represents a conservative approach to data interpretation that may not always 

be appropriate. More complex operational decision rules can– 

• Account for different types of measurements and multiple radionuclides of concern, 

• Specify critical values and test statistics for the statistical tests, and 

• Incorporate multiple decisions (e.g., average and maximum values, fixed and removable 

radioactivity) depending on the project. 

7.3 Set Measurement Quality Objectives 

Section 4.2 briefly discussed the DQO process for developing statistical hypothesis tests for the 

implementation of disposition decision rules using measurement data. This included formulating 

the null and alternative hypotheses, defining the gray region using the action level and 

discrimination limit, and setting the desired limits on potential Type I and Type II decision error 

probabilities that a decision maker is willing to accept for project results. Decision errors are 

possible, at least in part, because measurement results have uncertainties. The effect of these 

uncertainties is expressed in the size of the relative shift, Δ/σ, introduced in Section 7.2.2. The 

overall uncertainty, σ, has components that may be due to sampling variability in radioactivity 

concentration, σS, but also because of uncertainty in the measurement method, σM. Because 

DQOs apply to both sampling and measurement activities, what are needed from a measurement 

perspective are method performance characteristics specifically for the measurement process of a 

particular project. These method performance characteristics (see Section 3.8) are the 

measurement quality objectives (MQOs). 

DQOs define the performance criteria that limit the probabilities of making decision errors by– 

• Considering the purpose of collecting the data, 

• Defining the appropriate type of data needed, and 

• Specifying tolerable probabilities of making decision errors. 

DQOs apply to both sampling and measurement activities. 

MQOs can be viewed as the measurement portion of the overall project DQOs (see Section 3.8). 

MQOs are: 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-17 NUREG-1575, Supp. 1 

• The part of the project DQOs that apply to the measured result and its associated uncertainty. 

• Statements of measurement performance objectives or requirements for a particular 

measurement method performance characteristic, for example, measurement method 

uncertainty and detection capability. 

• Used initially for the selection and evaluation of measurement methods. 

• Subsequently used for the ongoing and final evaluation of the measurement data. 

A number of MQOs were introduced in Section 3.8, but for survey planning the single most 

important MQO is the required measurement method uncertainty, uMR. Other MQOs, such as 

range, ruggedness, and specificity, if not controlled, will lead to increased measurement 

uncertainty. In this sense, the required measurement method uncertainty encompasses many of 

the effects of other MQO parameters that could impact decision making. MDCs and MQCs are 

closely related to the measurement uncertainty, have a long history of use for comparing the 

appropriateness of competing measurement techniques, and can contribute much to survey 

planning. These concepts are developed further in the later sections of this chapter (Sections 7.5 

and 7.6). However, essentially the same information can be conveyed by specifying the required 

measurement method uncertainty, which is a more general concept. Thus, in this section and the 

next, it is this MQO that will be emphasized. 

Measurement method uncertainty refers to the predicted uncertainty of a measured value that 

would be calculated if the method were applied to a hypothetical sample with a specified 

radioactivity concentration or radiation level. Measurement method uncertainty is a characteristic 

of the measurement method and the measurement process. Measurement uncertainty, as opposed 

to sampling uncertainty, is a characteristic of an individual measurement. 

The true measurement method standard deviation, σM, is a theoretical quantity and is never 

known exactly, but it may be estimated using the methods described in Section 7.4. The estimate 

of σM will be denoted here by uM and called the “measurement method uncertainty.” The 

measurement method uncertainty, when estimated by uncertainty propagation, is the predicted 

value of the combined standard uncertainty (CSU, or “one-sigma” uncertainty) of the 

measurement for material with concentration equal to the UBGR. Note that the term 

“measurement method uncertainty” and the symbol uM actually apply not just to the measurement 

method but also to the entire measurement process, that is, it should include uncertainties in how 

the measurement method is actually implemented. This definition of measurement method 

uncertainty is independent of the null hypothesis and applies to both Scenario A and Scenario B. 

The true standard deviation of the measurement method, σM, is unknown, but the required 

measurement method uncertainty, σMR, is intended to be an upper bound for σM. In practice, σMR is 

actually used as an upper bound for the method uncertainty, uM, which is an estimate of σM. 

Therefore, the value of σMR will be called the “required measurement method uncertainty” and 

denoted by uMR. 

The principal MQOs in any project will be defined by the required measurement method 

uncertainty, uMR, at and below the UBGR and the relative required measurement method 

uncertainty, φMR, at and above the UBGR, φMR= uMR/UBGR. See Section 7.3.2 for further 

discussion. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-18 January 2009 

When making decisions about individual measurement results uMR should ideally be 0.3Δ, and 

when making decisions about the mean of several measurement results uMR should ideally be 

0.1Δ, where Δ is the width of the gray region, Δ = UBGR – LBGR. 

7.3.1 Determine the Required Measurement Method Uncertainty at the UBGR 

This section provides the rationale and guidance for establishing project-specific MQOs for 

controlling σM. This control is achieved by establishing a desired maximum measurement method 

uncertainty, uMR, at the upper boundary of the gray region. This control also will assist in both the 

measurement method selection process and in the evaluation of measurement data. Approaches 

applicable to several situations are detailed below. 

Four basic survey designs were described in Chapter 4: scan-only, in situ, MARSSIM-type, and 

method-based. The relative shift, Δ/σ, is important in determining the level of survey effort 

required in the first three survey designs. For a given width of the gray region, Δ, the relative 

shift, Δ/σ, can only be controlled by controlling σ. The overall standard deviation of the 

measurement results,σ, may have both a measurement component, σM, and a sampling 

component, σS. Segregation and classification may help in controlling σS (Sections 4.3 and 5.4). 

7.3.1.1 Scan-Only Survey Designs 

For 100% scan-only surveys, the decision uncertainty associated with σS is essentially eliminated 

because the entire survey unit is measured. In class 2 survey units, the scan coverage can vary 

from 10% to nearly 100% depending on the value of Δ/σ. This is a reflection of the fact that for a 

fixed measurement variability, σM, smaller values of Δ/σ imply larger sampling variability. 

Larger sampling variability demands higher scan coverage to reduce the decision uncertainty. 

That is, more of the survey unit must be measured to lower the standard deviation of the mean. In 

such cases, it will be desirable to reduce σM until it is negligible in comparison to σS. σM can be 

considered negligible if it is no greater than σS /3. Therefore, MARSAME recommends the 

requirement uMR ≤ σS /3. 

7.3.1.2 In Situ Survey Designs 

For in situ survey designs, either the entire survey unit, or a large portion of it (e.g., greater than 

10%), is covered with a single measurement. Thus, sampling variability will tend to be averaged 

out. When decisions are to be made by comparing such single measurements to an action level, 

the total variance of the data equals the measurement variance, 2 σ M , and the data distribution in 

most instances should be approximately normal. In these cases the DQOs will be met if 

11 11

UBGR-LBGR

MR u

zz zz −α − −− β αβ

Δ

≤ =

+ +

 (7-8) 

where z1–α, is the (1 – α)-quantile of the standard normal distribution and z1–β, is the (1 – β)-

quantile of the standard normal distribution. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-19 NUREG-1575, Supp. 1 

If α = β = 0.05, then 

0.95 0.95 1.645 1.645 3.29 Mr u

z z

Δ ΔΔ ≤ = = ∼ 0.3 Δ

+ +

(7-9) 

Therefore, MARSAME recommends the requirement uMR ≤ 0.3∆. The details are discussed in 

Section 7.7.2. 

For the special case where the LBGR = 0, then ∆ = UBGR and σMR = ∆ / (z1–α + z1–β) implies 

0.95 0.95 1.645 1.645 3.29 MR

UBGR UBGR UBGR u UBGR

z z

≤ = = ∼ 0.3 

+ +

(7-10)

This is equivalent to requiring that the MDC (see Section 7.9.2) be less than the action level. The 

MDC is defined as the concentration at which the probability of detection is 1 – β and the 

probability of false detection in a sample with zero concentration is at most α. 

Example 1: Suppose the action level is 10,000 Bq/m2 and the lower bound of the gray region is 

5,000 Bq/m2, α = 0.05, and β = 0.10. If decisions are to be made about individual items, then the 

required measurement method uncertainty at 10,000 Bq/m2 is 

22 2

2

1 1 0.95 0.90

10,000 Bq/m -5,000 Bq/m 5,000 Bq/m 1,700 Bq/m

1.645 1.282 MR u

zz zz − − α β

Δ == = = ++ +

7.3.1.3 MARSSIM-Type Survey Designs 

When a decision is to be made about the mean of a sampled population, generally the average of 

a set of measurements on a survey unit is compared to the disposition criterion. For MARSSIMtype designs, the ratio ∆/σ, called the “relative shift,” determines the number of measurements 

required to achieve the desired decision error rates α and β. The target range for this ratio should 

be between 1 and 3, as explained in MARSSIM (MARSSIM 2002) and NUREG-1505 (NRC 

1998a). Ideally, to keep the required number of measurements low, the DQOs are aimed at 

establishing ∆/σ ≈ 3. The cost in number of measurements rises rapidly as the ratio ∆/σfalls 

below 1, but there is little benefit from increasing the ratio much above 3. One of the main 

objectives in optimizing survey design is to achieve a relative shift, Δ/σ, of at least one and 

ideally three. Values of Δ/σ greater than three, while desirable, should not be pursued at 

additional cost. If ∆/σ is 3 and σM is negligible in comparison to σS, then σM will be ∆/10. The 

details are discussed in Section 7.7.1. 

Therefore, MARSAME recommends the requirement uMR ≤ ∆ / 10 by default when decisions are 

being made about the mean of a sampled population. If the LBGR is zero, this is equivalent to 

requiring that the MQC be less than the UBGR (Section 7.7.1). 
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NUREG-1575, Supp. 1 7-20 January 2009 

Example 2: Suppose the action level is 10,000 Bq/m2 and the lower bound of the gray region is 

2,000 Bq/m2. If decisions are to be made about survey units based on measurements at several 

locations, then the required measurement method uncertainty (uMR) at 10,000 Bq/m2 is 

10,000 2,000 2 800 Bq/m 10 10 μ MRΔ − == =

Example 3: Suppose the action level is 10,000 Bq/m2, but this time assume the lower bound of 

the gray region is 0 Bq/m2. In this case the required method measurement uncertainty, uMR, at 

10,000 Bq/m2 is 

2 1,000 /

10

10,000 0

10 MR = Bq m − = Δ

μ =

The recommended values of uMR are based on the assumption that any known bias in the 

measurement process has been corrected and that any remaining bias is well less than 10% of the 

shift, ∆, when a concentration near the gray region is measured. 

Achieving a required measurement method uncertainty uMR less than the recommended limits 

may be difficult in some situations. When the recommended requirement for uMR is too difficult 

to meet, project planners may allow uMR to be larger. In this case, project planners may choose 

uMR to be as large as ∆/3 or any calculated value that allows the data quality objectives to be met 

at an acceptable effort. Two situations that may make this possible are if σS is believed to be less 

than ∆/10 or if it is not difficult to make the additional measurements required by the larger 

overall data variance ( 2 2 σ M + σ S ). 

Example 4: Suppose the uncertainty in Example 2 of uMR = 800 Bq/m2 cannot be achieved 

because of the variability in instrument efficiency with surface roughness. A required 

measurement method uncertainty, uMR, as large as ∆ / 3 ≈ 2,700 Bq/m2 may be possible if σS is 

small or if more measurements are taken per survey unit. 

7.3.2 Determine the Required Measurement Method Uncertainty at Concentrations 

Other Than the UBGR 

The most important MQO for data evaluation is the one for measurement method uncertainty at a 

specified concentration. This MQO is expressed as the required measurement method uncertainty 

(uMR) at the UBGR. However, to properly evaluate the data usability of measurement results at 

concentrations other than the UBGR, the implications of this requirement must be extended both 

above and below the UBGR. 

When the concentration is less than or equal to the UBGR, the combined standard uncertainty 

(CSU), uc, of a measured result should not exceed the required measurement method uncertainty, 

uMR, specified at the UBGR. When the concentration is greater than the UBGR, the relative 

combined standard uncertainty (RCSU), φMR, of a measured result should not exceed the required 

relative measurement method uncertainty at the UBGR. 
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January 2009 7-21 NUREG-1575, Supp. 1 

φMR = uMR/UBGR (7.11) 

This is illustrated in Example 5 and Figure 7.5. 

Example 5: Suppose the action level is 10,000 Bq/m2 and the discrimination limit is 3,000. 

Scenario A is used, so the UBGR = AL = 10,000 Bq/m2 and the LBGR = DL = 3,000 Bq/m2. 

Thus the width of the gray region, Δ= 10,000 – 3,000 = 7,000. If decisions are to be made about 

individual items, α = 0.05, and β = 0.05, then the required measurement uncertainty at 10,000 

Bq/m2 is 22 2

2

1 1 0.95 0.95

10,000 Bq/m -3,000 Bq/m 7,000 Bq/m 2,000 Bq/m

1.645 1.645 MR u

zz zz − − α β

Δ == = ≈ ++ +

The required measurement method uncertainty, uMR, is 2,000 Bq/m2 at 10,000 Bq/m2. Thus, for 

any measured result less than 10,000 Bq/m2, the reported CSU, uc, should be less than or equal to 

2,000 Bq/m2. For example, a reported result of 4,500 Bq/m2 with 

a CSU of 1,900 Bq/m2 would meet the requirement. A reported result of 7,700 Bq/m2 with a 

CSU 2,500 Bq/m2 would not meet the requirement. 

The required relative measurement method uncertainty (φMR) is 2,000 Bq/m2 / 10,000 Bq/m2 = 

20% at 10,000 Bq/m2. Thus, for any measured result greater than 10,000 Bq/m2, the reported 

RCSU should be less than or equal to 20%. For example, a reported result of 14,500 Bq/m2 with 

a CSU of 2,900 Bq/m2 would meet the requirement because 2,900/14,500 = 20%. A reported 

result of 18,000 Bq/m2 with a CSU 4,500 Bq/cm2 would not meet the requirement because 

4,500/18,000 = 25%. 

Figure 7.5 Example of the Required Measurement Uncertainty at Concentrations other than the 

UBGR. In this Example the UBGR Equals the Action Level 

uMR = 2000 Bq/m 2

Action Level, 10000 Bq/m 2

Above the action level, the bound on 

the relative uncertainty is constant 

and equal to φMR = uMR/AL = 

. 2,000/10,000 = 20%.

0

5000

10000

15000

20000

25000

30000

0 5000 10000 15000 20000 25000 30000

0

5000

10000

15000

20000

25000

30000

0 5000 1000 15000 20000 25000 30000

0

5000

10000

15000

20000

25000

30000

0 5000 10000 15000 20000 25000 30000

.

Below the action level, the bound 

on the absolute uncertainty is 

constant and equal to uMR = 2,000 

Bq/m2

.

The required method uncertainty, uMR = 

2,000 Bq/m2

, is specified at the Action 

Level, 10,000 Bq/m

Measured Concentration (Bq/m

2

2

)

True Concentration (Bq/m2)
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NUREG-1575, Supp. 1 7-22 January 2009 

7.4 Determine Measurement Uncertainty 

Checking the measurement quality against the required measurement method uncertainty relies 

on having realistic estimates of the measurement uncertainty. Often reported measurement 

uncertainties are underestimated, particularly if they are confined to the estimated Poisson 

counting uncertainty (Section 7.8). Tables of results are sometimes presented with a column 

listing “±” without indicating how these numbers were obtained. Often, the “±” represents the 

square root of the number of counts obtained during the measurement. The method for 

evaluation calculation and reporting of measurement uncertainty, approved by both the 

International Organization for Standardization (ISO) and the National Institute of Standards and 

Technology (NIST) is discussed in this section. Further details of the method are given in 

Section 7.8. 

Measurements always involve uncertainty, which must be considered when measurement results 

are used as part of a basis for making decisions. Every measured and reported result should be 

accompanied by an explicit uncertainty estimate. One purpose of this section is to give users of 

data an understanding of the causes of measurement uncertainty and of the meaning of 

uncertainty statements; another is to describe procedures that can be used to estimate 

uncertainties. Much of this material is derived from MARLAP Chapter 19. 

In 1980, the Environmental Protection Agency published a report entitled Upgrading 

Environmental Radiation Data, which was produced by an ad hoc committee of the Health 

Physics Society (EPA 1980). Two of the recommendations of this report were that: 

1. Every reported measurement result (x) should include an estimate of its overall uncertainty 

(ux) that is based on as nearly a complete an assessment as possible. 

2. The uncertainty assessment should include every significant source of inaccuracy in the 

result.

The concept of traceability is also defined in terms of uncertainty. Traceability is defined as the 

“property of the result of a measurement or the value of a standard whereby it can be related to 

stated references, usually national or international standards, through an unbroken chain of 

comparisons all having stated uncertainties” (ISO 1996). Thus, to realistically make the claim 

that a measurement result is “traceable” to a standard, there must be a chain of comparisons 

(each measurement having its own associated uncertainty) connecting the result of the 

measurement to that standard. 

This section considers only measurement variability, σM. Reducing sampling variability, σS, by 

segregating M&E was discussed in Section 5.4. Sampling variability due to field sampling 

uncertainties is often larger than measurement uncertainties. Although this statement may be true 

in some cases, this is not an argument for failing to perform a full evaluation of the measurement 

uncertainty. A realistic estimate of the measurement uncertainty is one of the most useful data 

quality indicators for a result (Section 3.8). 

Although the need for reporting uncertainty has sometimes been recognized, often it consists of 

only the estimated component due to Poisson counting statistics. The component of uncertainty 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-23 NUREG-1575, Supp. 1 

resulting from the random nature of radioactive decay is only one component of measurement 

method uncertainty. If only this component of uncertainty is accounted for, rather than 

performing a full uncertainty analysis, the result will be misleading because it is at best only a 

lower bound of the uncertainty and may lead to incorrect decisions based on overconfidence in 

the measurement. Software is available to perform the mathematical operations for uncertainty 

evaluation and propagation, eliminating much of the difficulty in implementing the mathematics 

of uncertainty calculations. There are several examples of such software (McCroan 2006, GUM 

Workbench 2006, Kragten 1994, and Vetter 2006). 

7.4.1 Use Standard Terminology 

The methods, terms, and symbols recommended by MARSAME for evaluating and expressing 

measurement uncertainty are described in the GUM (ISO 1995). The ISO methodology is 

summarized in the NIST Technical Note TN-1297 (NIST 1994). 

The result of a measurement is generally used to estimate some particular quantity called the 

measurand. The difference between the measured result and the actual value of the measurand is 

the error of the measurement. Both the measured result and the error may vary with each 

repetition of the measurement, while the value of the measurand (the true value) remains fixed. 

The error of a measurement is unknowable, because one cannot know the error without knowing 

the true value of the quantity being measured (the measurand). For this reason, the error is 

primarily a theoretical concept. However, the uncertainty of a measurement is a concept with 

practical uses. According to the GUM and NIST Technical Note 1297, the term “uncertainty of 

measurement” denotes the values that could reasonably be attributed to the measurand. In 

practice, there is seldom a need to refer to the error of a measurement, but an uncertainty should 

be stated for every measured result. 

The first step in defining a measurement process is to define the measurand clearly. The 

specification of the measurand is always ambiguous to some extent, but it should be as clear as 

necessary for the intended purpose of the data. For example, when measuring the activity of a 

radionuclide on a surface, it is generally necessary to specify the activity, the date and time, what 

area of the surface was measured, and where. 

Often the measurand is not measured directly but instead an estimate is calculated from the 

measured values of other input quantities, which have a known mathematical relationship to the 

measurand. For example, input quantities in a measurement of radioactivity may include the 

gross count, blank or background count, counting efficiency, and area measured. The 

mathematical model measurement process specifies the relationship between the output quantity, 

Y, and measurable input quantities, X1,X2,...XN, on which its value depends: Y = f (X1,X2,...XN).

The mathematical model for a radioactivity measurement may have the simple form: 

(Gross Instrument Signal) - (Blank Signal ) Measurement = 

Efficiency (7-12)

Each of the quantities shown here may actually be a more complicated expression. For example, 

the efficiency may be the product of factors such as surveyor efficiency, surface roughness 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-24 January 2009 

efficiency correction, and the instrument counting efficiency. Interferences may be due to 

ambient background or other radionuclides that have interactions with the detector in a manner 

that contributes spuriously to the gross instrument signal. 

When a measurement is performed, a specific value xi is estimated for each input quantity, Xi, 

and an estimated value, y, of the measurand is calculated using the relationship y = f(x1, x2,...,xN). 

Since there is an uncertainty in each input estimate, xi, there is also an uncertainty in the output

estimate, y. Determining the uncertainty of the output estimate y requires that the uncertainties of 

all the input estimates xi be determined and expressed in comparable forms. The uncertainty of xi

is expressed in the form of an estimated standard deviation, called the standard uncertainty and 

denoted by u(xi). The ratio u(xi) / |Xi| is called the relative standard uncertainty of xi, where |Xi| is 

the absolute value of xi. 

The partial derivatives, ∂f / ∂xi, are called sensitivity coefficients, and are usually denoted by ci. 

The ci measure how much f changes when xi changes. The standard uncertainties are combined 

with sensitivity coefficients to obtain the component of the uncertainty in y due to xi, ( ) i i cu x . 

The square of the CSU, denoted by 2

uc (y), is called the combined variance. It is obtained using 

the formula for the propagation of uncertainty:4

 

2

2 2 22

1 1

() ( ) ( )

N N

c i ii

i i i

f u y u x cu x = = x

⎛ ⎞ ∂ = = ⎜ ⎟ ∂⎝ ⎠ ∑ ∑ (7-13)

The square root of the combined variance is the CSU of y, denoted by uc(y). Further details of 

this process are given in Section 7.8.1. 

7.4.2 Consider Sources of Uncertainty 

The following sources of uncertainty should be considered: 

• The random nature of radioactive decay (e.g., counting statistics), 

• Instrument calibration (e.g., counting efficiency), 

• Variable instrument backgrounds, 

• Variable counting efficiency (e.g., due to the instrument or to source geometry and 

placement), and 

• Interferences, such as crosstalk and spillover. 

Other sources of uncertainty could include: 

• Temperature and pressure. 

• Volume and mass measurements, 

• Determination of counting time and correction for dead time, 

 

4 If the input estimates are potentially correlated, covariance estimates u(xi,xj) must also be determined. The 

covariance u(xi,xj) is often recorded and presented in the form of an estimated correlation coefficient, r(xi,xj), which 

is defined as the quotient u(xi,xj) / u(xi)u(xj). See Section 7.8. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-25 NUREG-1575, Supp. 1 

• Time measurements used in decay and ingrowth calculations, 

• Approximation errors in simplified mathematical models, and 

• Published values for half-lives and radiation emission probabilities. 

•

There are a number of sources of measurement uncertainty in gamma-ray spectroscopy, 

including: 

• Poisson counting uncertainty, 

• Compton baseline determination, 

• Background peak subtraction, 

• Multiplets and interference corrections, 

• Peak-fitting model errors, 

• Efficiency calibration model error, 

• Summing, 

• Density-correction factors, and

• Dead time.

Additional discussion of some major sources of uncertainty may be found in Section 7.8.2.2. 

The following example may appear complex, but all but the most casual users will use software 

to perform these calculations. Some possibilities are listed after the example. A complete 

example is worked out to here to illustrate the underlying principles. 

Example 6: Consider a simple measurement of a sample. The activity will be calculated from 

( /)( /) Nt Nt SS BB y

ε

− =

Where: 

 y = sample activity (Bq) 

ε = counting efficiency 0.4176 (s-1/Bq) 

 NS = gross count observed during the measurement of the source, (11578) 

 tS = source count time (300 s) 

 NB = observed background count (87) 

 tB = background count time (6,000 s) 

The CSU of ε is given by uC (ε) = 0.005802. This is shown in Example 2 in Section 7.8.2.2. 

Assume the radionuclide is long-lived; so, no decay corrections are needed. The uncertainties of 

the count times are also assumed to be negligible. The standard uncertainties in NS and NB will be 

estimated as and N N S B using the Poisson assumption. 

Then, ( /)( /) (11578 / 300) (87 / 6000) 92.316

0.4179

Nt Nt SS BB y

ε

− − == =

2

2 2 22

1 1

() ( ) ( )

N N

c i ii

i i i

f u y u x cu x = = x

⎛ ⎞ ∂ = = ⎜ ⎟ ∂⎝ ⎠ ∑ ∑
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NUREG-1575, Supp. 1 7-26 January 2009 

2 2

2 2

2

2

( /)( /) ( /)( /)

() ()

( /)( /)

( )

SS BB SS BB

S B

S B

SS BB

Nt Nt Nt Nt

uN uN

N N

Nt Nt

u

ε ε

ε ε

ε

⎛ ⎞⎛ ⎞ − − ∂ ∂ ⎜ ⎟⎜ ⎟

= +

∂ ∂ ⎝ ⎠ ⎝ ⎠

⎛ ⎞ − ∂⎜ ⎟

+

⎝ ⎠ ∂

2 2 2

22 2

2

1/ (( / ) ( / )) 1/ ( ) ( ) () S SS BB B

S B

t Nt Nt t uN uN u ε

εε ε

⎛ ⎞ ⎛ ⎞ − ⎛ ⎞ − − =+ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

2 2 2

2 2 2

2

1/ 300 1/ 6000 (11578 / 300) (87 / 6000) 11578 87 0.005802

0.4176 0.4176 0.4176

⎛⎞ ⎛ ⎞ − −+ ⎛ ⎞

= ++ ⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎝⎠ ⎝ ⎠ ⎝ ⎠

= 0.73768 + 0.00001+ 1.64745 = 2.38515. 

Note that these calculations show which input quantities are contributing the most to the 

combined variance. NS contributes 0.73768/2.38515 ∼ 31%. NB contributes virtually nothing. The 

uncertainty in the efficiency contributes 1.64745/2.38515 ∼ 69%. An analysis such as this is 

called an uncertainty budget, and quickly points out where improvements in the measurement 

may be made. 

Taking the square root of the combined variance we find uc(y) = 1.54439. Usually the CSU is 

rounded to two significant figures and the result is rounded to match the same number of decimal 

places. So the result would be reported as 92.3 Bq with a CSU of 1.5 Bq. 

Note that if the uncertainty in the efficiency had been neglected, the CSU would have been 

underestimated as 0.86 Bq, and would have been attributed entirely to the uncertainty in the 

sample counts. This illustrates the importance of including all significant sources of uncertainty 

in the calculations. Many of these calculations can be done using computer software programs 

mentioned earlier. 

A much more detailed and involved example is given in Section 7.8.3. 

Again, it should be noted that software (e.g., McCroan 2006, GUM Workbench 2006, Kragten 

1994, Vetter 2006) is available to perform the partial derivatives, insert the proper mean and 

standard uncertainty for each input, and perform the algebra for uncertainty evaluation and 

propagation. This eliminates much of the tedium in implementing the uncertainty calculations, 

and frees the analyst to carefully examine the model equation to be sure that significant sources 

of uncertainty are not omitted. 

7.4.3 Recommendations for Uncertainty Calculation and Reporting 

• Use the terminology and methods of the GUM (ISO 1995) for evaluating and reporting 

measurement uncertainty. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-27 NUREG-1575, Supp. 1 

• Follow QC procedures that ensure the measurement process remains in a state of statistical 

control, which is a prerequisite for uncertainty evaluation. 

• Account for possible blunders or other spurious errors. Spurious errors indicate a loss of 

statistical control of the process and are not part of the uncertainty analysis described above. 

• Report each measured value with either its CSU (or its expanded uncertainty, see Section 

7.8.1.7). 

• Reported measurement uncertainties should be clearly explained. (In particular, when an 

expanded uncertainty is reported, the coverage factor should be stated and the basis for the 

coverage probability should also be given, see Section 7.8.1.7). 

• Consider all possible sources of measurement uncertainty and evaluate and propagate the 

uncertainties from all sources believed to be potentially significant in the final result. 

• Each uncertainty should be rounded to either one or two significant figures and the measured 

value should be rounded to the same number of decimal places as its uncertainty. 

• Results should be reported as obtained together with their uncertainties (whether positive, 

negative, or zero). 

7.5 Determine Measurement Detectability 

This section summarizes issues related to measurement detection capabilities. Much of this 

material is derived from MARLAP Chapter 20. More detail may be found in see Section 7.9. 

Environmental radioactivity measurements may involve material with very small amounts of the 

radionuclide of interest. Measurement uncertainty often makes it difficult to distinguish such 

small amounts from zero. Therefore, an important MQO of a measurement process is its 

detection capability, which is usually expressed as the smallest concentration of radioactivity that 

can be reliably distinguished from zero. Effective project planning requires knowledge of the 

detection capabilities of the measurement method that will be or could be used. This section 

explains an MQO called the minimum detectable concentration (MDC) and describes 

radioactivity detection capabilities, as well as methods for calculating it. 

The method most often used to make a detection decision about radiation or radioactivity 

involves the principles of statistical hypothesis testing. It is a specific example of a Scenario B 

hypothesis testing procedure described in Section 7.2.4. To “detect” the radiation or radioactivity 

requires a decision on the basis of the measurement data that the radioactivity is present. The 

detection decision involves a choice between the null hypothesis (H0): There is no radiation or 

radioactivity present (above background), and the alternative hypothesis (H1): There is radiation 

or radioactivity present (above background). In this context, a Type I error is to conclude that 

radiation or radioactivity is present when it actually is not, and a Type II error is to conclude that 

radiation or radioactivity is not present when it actually is.5

 Making the choice between these 

hypotheses requires the calculation of a critical value. If the measurement result exceeds this 

critical value, the null hypothesis is rejected and the decision is that radiation or radioactivity is 

present. 

 

5 Note that in any given situation only one of the two types of decision error is possible. If the sample does not 

contain radioactivity, a Type I error is possible. If the sample does contain radioactivity, a Type II error is possible.
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-28 January 2009 

7.5.1 Calculate the Critical Value 

The critical value defines the lowest value of the net instrument signal6

 (count) that is too large 

to be compatible with the premise that there is no radioactivity present. It has become standard 

practice to make the detection decision by comparing the net instrument count to its critical 

value, SC. The net count is calculated from the gross count by subtracting the estimated 

background and any interferences.7

 

The mean value of the net instrument count typically is positive when there is radioactivity 

present (i.e., above background). The gross count must be corrected by subtracting an estimate of 

the count produced under background conditions. See Section 7.8.2 for more information on 

instrument background. 

Table 7.5 lists some formulas that are commonly used to calculate the critical value, SC, together 

with the major assumptions made in deriving them. Note that the Stapleton formulas given in 

rows 3 through 5 especially are appropriate when the total background is less than 100 counts. 

These formulas depend on NB (background count), tB (background count time), tS ( sample count 

time), and z1-α (the (1 − α)-quantile of the standard normal distribution). The value of α

determines the sensitivity of the test. It is the probability that a detection decision is made when 

no radioactivity above background is actually present. 

More detail on the calculation of critical values is given in Section 7.9.3. Software (Strom 1999) 

is available for calculating SC using the equations recommended here, among others. 

Table 7.5 Recommended Approaches for Calculating the Critical Value of the Net 

Instrument Signal (Count), SC

8

Critical Value Equation Assumptions 

Background 

Count 

1 1 1 S S

C B

B B

t t Sz N

t t −α

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

Poisson > 100 

2 2.33 C B S N =

Poisson 

α = 0.05 

tB = tS

> 100 

 

6

 “Net instrument signal,” is used here as a general term, because many radiation-detection instruments may have 

output other than “counts” (e.g., current for ionization chambers). In cases where the instrument output is in counts, 

the term “net counts” can be substituted for the term “net instrument signal.” 

7

 “Interference” is the presence of other radiation or radioactivity or electronic signals that hinder the ability to 

analyze for the radiation or radioactivity of interest.

8 These particular expressions for the critical value of the net instrument signal (in this case the net count) depend 

for their validity on the assumption of Poisson counting statistics. If the variance of the blank signal is affected by 

interferences, or background instability, then the Equation 20.7 of MARLAP may be more appropriate.

 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-29 NUREG-1575, Supp. 1 

Critical Value Equation Assumptions 

Background 

Count 

3 ( ) 2

1

1 11 1

4

S S SS

C B

B B BB

t z t tt Sd z Nd

t t tt

α

α

−

−

⎛⎞ ⎛⎞ ⎛⎞

=× − + × + + + + ⎜⎟ ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ⎝⎠

Stapleton

tB ≠ tS

< 100 

4 ( ) 2 1.645 0.4 1 1 1.645 0.4 1

4

S S SS

C B

B B BB

t t tt S N

t t tt

⎛⎞ ⎛⎞ ⎛⎞

= × −+ ×+ + + + ⎜⎟ ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ⎝⎠

Stapleton 

tB ≠ tS

α = 0.05 

d = 0.4 

< 100 

5 = 1.35 + 2.33 + 0.4 C NB S

Stapleton

tB = tS

α = 0.05 

d = 0.4 

< 100 

d = the critical value of the net instrument signal parameter in the Stapleton Equation

Example 7: A 600-second background measurement is performed on a proportional counter and 

108 beta counts are observed. A sample is to be counted for 300 s. Estimate the critical value of 

the net instrument signal (i.e., net count) when α = 0.05. 

1 1 S S

C B

B B

t t Sz N

t t −α

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

300 s 300 s 1.645 108 1 14.8 net counts

600 s 600 s C S ⎛ ⎞⎛ ⎞

= × += ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠

Therefore, if 15 or more net counts are observed, the decision will be made that the sample 

contains radioactivity above background. Values of SC should be rounded up when necessary to 

make sure that the specified Type I error probability, α, is not exceeded.

 

7.5.2 Calculate the Minimum Detectable Value of the Net Instrument Signal or Count 

Table 7.6 lists some formulas that are commonly used to calculate the minimum detectable net 

count, SD, together with the major assumptions made in deriving them. SD, is defined as the mean 

value of the net instrument signal or count that gives a specified probability, 1 − β, of yielding an 

observed net instrument signal or count greater than its critical value SC. Therefore, SC must be 

calculated before SD. Note specifically that the Stapleton formulas given in rows 4 and 5 are 

especially appropriate when the total background is less than 100 counts. Generally, the 

Stapleton methods may be used for both high and low total background counts as they agree well 

with the more traditional methods when the background counts are over 100. The simpler, more 

familiar formulas have been included for completeness. 

It is important that the assumptions used to calculate SD are consistent with those that were used 

to calculate SC. The equations for SD depend on the same variables as SC, namely NB, tB, and tS. 

Notice that neither α nor z1-α appears explicitly, rather they enter the calculation through SC. 

However, β now enters the calculation of SD through Z1 − β. The value of β, like α, is usually 

chosen to be 0.05 or is assumed to be 0.05 by default if no value is specified. 
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NUREG-1575, Supp. 1 7-30 January 2009 

Table 7.6 Recommended Approaches for Calculating the Minimum Detectable Net 

Instrument Signal or Count9

Minimum Detectable Net Signal Equation Assumptions 

Background 

Count 

1 

2 2

1 1

1 1

2 4

S S

DC C B

B B

z z t t SS z SN

t t

β β

β

− −

−

⎛ ⎞

=+ + ++ + ⎜ ⎟ ⎝ ⎠

Poisson 

tB ≠ tS > 100 

2 2

1 2 D C Sz S = + −β

Poisson 

tB ≠ tS

α = β

> 100 

3 2.71 2 2.71 2(2.33 ) 2.71 4.66 D C BB SS N N =+=+ =+

Poisson 

α = β = 0.05 

tB = tS

> 100 

4 

2

1 1

1 1

( ) 1( ) 1 4

S SS

D B

B BB

z z t tt S zz N

t tt

α β

α β

− −

− −

+ ⎛⎞ ⎛⎞

= ++ + + ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠

Stapleton < 100 

5 5.41 4.65 D B S N = +

Stapleton 

α = β = 0.05 

tB = tS

< 100 

Example 8 A 600-second background measurement on a proportional counter produces 108 beta 

counts and a source is to be counted for 300 s. Assume the background measurement gives the 

available estimate of the true mean background count rate and use the value 0.05 for Type I and 

Type II error probabilities. From section 7.5.1, Example 7, the critical net count, SC, equals 14.8, 

so 2 2

1 2 1.645 2 (14.8) 32.3 net counts. D C Sz S =+= + = −β Values of SD should be rounded up when 

necessary to make sure that the specified Type II error probability, β, is not exceeded. 

The relationship between the critical value of the net instrument signal (or count), SC, and the 

minimum detectable net instrument signal (or count), SD, is shown in Figure 7.6. Figure 7.6 

illustrates a case where alpha is greater than beta. The net instrument signal (or count) obtained 

for a blank sample will usually be distributed around zero as shown. Occasionally, a net count 

rate above SC may be obtained by chance. The probability that this happens is controlled by the 

value of α, shown as the lightly shaded area in Figure 7.6. Smaller values of α result in larger 

values of SC and vice versa. The minimum detectable value of the net instrument signal (or 

count) SD is that value of the mean net instrument signal (or count) that results in a detection 

decision with probability 1−β. That is, there is only a probability equal to β, shown as the more 

 

9 These expressions for the critical value of the net count depend for their validity on the assumption of Poisson 

counting statistics. If the variance of the blank signal is affected by interferences, or background instability, then 

Equation 20.7 of MARLAP may be more appropriate. “Interference” is the presence of other radiation or 

radioactivity or electronic signals that hinder the ability to analyze for the radiation or radioactivity of interest. 

 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-31 NUREG-1575, Supp. 1 

darkly shaded area in Figure 7.6, of yielding an observed count less than SC. Smaller values of β

result in larger values of SD and vice versa. 

Figure 7.6 The Critical Value of the Net Instrument Signal (SC) and the Minimum 

Detectable Net Signal (SD) 

More information detail on the calculation of the minimum detectable value of the net instrument 

signal, SD, is given in Section 7.9. 

7.5.3 Calculate the Minimum Detectable Concentration 

The MDC is usually obtained from the minimum detectable value of the net instrument signal (or 

count), SD. The MDC is by definition an estimate of the true concentration of the radiation or 

radioactivity required to give a specified high probability that the measured response will be 

greater than the critical value. The common practice of comparing a measured concentration to 

the MDC, instead of to the SC, to make a detection decision is incorrect. To calculate the MDC, 

the minimum detectable value of the net signal (or count), SD, must first be converted to the 

detectable value of the net instrument signal per unit time (or count rate), SD / tS(s

-1). This in turn 

must be divided by the counting efficiency, ε (s

−1

)/Bq to get the minimum detectable activity, yD. 

Finally, the minimum detectable activity can be divided by the sample volume or mass to obtain 

the MDC. At each stage in this process, additional uncertainty may be introduced by the 

uncertainties in time, efficiency, volume, mass, etc. Thus, prudently conservative values of these 

factors should be used so that the desired detection power, 1−β, at the MDC is maintained. 

Another approach would be to recognize that yD itself has an uncertainty which can be calculated 

using the methods of Section 7.4. Thus any input quantity that is used to convert from SD to yD

that has significant uncertainty can be incorporated to assess the overall uncertainty in the MDC. 

Additional discussion of the calculation of the MDCs is given in Section 7.9.5. 

Example 9: Continuing Example 8, 32.3 net counts. D S = 

Assuming negligible uncertainty in the count time, the net count rate is 

SD/ tS = 32.3/300 = 0.1077 1 ( ) s

− . 

The mean efficiency from Example 6 in Section 7.4.2 was 0.4176 1 ( )/(Bq) s

− with a CSU of uC 

(ε) = 0.005802. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-32 January 2009 

In Example 8, the value 0.05 was specified for both Type I and Type II error probabilities. So the 

specified power was 1−β = 1 − 0.05 = 0.95. 

Assume a normal distribution for ε, to obtain a 95% probability of detection for the MDC. To 

account for the variability in the efficiency, the value used for ε should be the 5th percentile, i.e., 

0.4176 – 1.645(0.005802) = 0.4081. 

Thus, the minimum detectable activity, / 0.1077 / 0.4081 0.2639 D s

D

S t

y

ε == = Bq. 

Using the mean value of the efficiency would potentially underestimate the minimum detectable 

activity as / 0.1077 / 0.4176 0.2578 D s

D

S t

y

ε == = Bq. 

These values for Dy would then be divided by the mass or volume of the sample to yield the 

MDC. 

7.5.4 Summary of Measurement Detectability 

The concepts surrounding the MDC and the critical value are illustrated in Figure 7.7, using 

familiar formulae for SC and SD discussed above, assuming a background count of NB = 100 with 

α = β = 0.5. In this case, the equation in row 2 of Table 7.5 was used to obtain SC = 23.3, and the 

corresponding equation in row 3 of Table 7.6 to obtain SD = 49.3. The use of these equations 

implies α = β = 0.05 and tB = tS. It is important to note that traditionally the values α = β = 0.05 

are used for MDC calculations, so that the MDCs for different methods are comparable. 

However, when developing a standard operating procedure for a survey, other values for α and β

may be more appropriate. A case where this typically occurs is in the calculation of scan MDCs 

(Section 7.11.6) where α may be much greater than β, because the consequences associated with 

misidentifying a background area as elevated are much lower than the consequences associated 

with missing a true elevated area. 

Note, the upper abscissa scale is in concentration and the lower abscissa scale is in net count. 

These are related by the efficiency at the point where the MDC corresponds to the minimum 

detectable net instrument signal (or count), SD. Each of the curves illustrates the distribution of 

mean net counts (or concentration) that may exist for a measurement. The width of these curves 

represents the variation due to counting statistics. The variability due to other factors is 

associated with uncertainty in ε. Changes in the relationship between the lower and the upper 

scales result from changes in ε. This illustrates the importance of choosing realistic, or even 

conservative, values of ε. Note that the probability of making a detection decision (which is 

proportional to the area of each curve to the right of SC) depends on the concentration, increasing 

from 5% at background to 95% at the MDC, passing through 50% at SC. This is perhaps more 

clearly shown in Figure 7.8, which plots the probability of making a detection decision as a 

function of net instrument signal, count, or concentration. 

Figure 7.8 shows that for concentrations corresponding to net counts between 0 and SC the 

probability of a non-detect is greater than 50%. For concentrations corresponding to net counts 

between SC and SD the probability of detection is greater than 50%, but less than 95%. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-33 NUREG-1575, Supp. 1 

Concentrations above the MDC (with net counts greater than SD) are highly likely to be detected, 

but will have relative standard uncertainties that are somewhat large. 

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

-50 -25 0 25 50 75 100

Net Counts 

SC SD

Concentration = net count rate/efficiency

0 MDC

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

-50 -25 0 25 50 75 100

Net Counts 

SC SD

Concentration = net count rate/efficiency

0 MDC

Figure 7.7 Relationship Between the Critical Value of the Net Count, the 

Minimum Detectable Net Counts and the MDC 

 

Figure 7.8 Probability of Detection as a Function of Net Count (Lower X-Axis) and 

Concentration (Upper X-Axis) 

Net Count

Probability of Detection

Gray Region

1-β

α

Δ

0 SD

•0

•0.1

•0.2

•0.3

•0.4

•0.5

•0.6

•0.7

•0.8

•0.9

•1

S 50% C

0 5%

S 95% D

Probability

Measure

> Sc

Net 

Count

S 50% C

0 5%

S 95% D

Probability

Measure

> Sc

Net 

Count

SC

Concentration MDC

Upper x-axis in units of 

concentration, lower xaxis in units of net 

counts 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-34 January 2009 

7.5.5 Measurement Detectability Recommendations 

• When a detection decision is required, generally it should be made by comparing the net 

instrument signal (or count) to its corresponding critical value. 

• Expressions from Tables 7.5 and 7.6 for SC and SD should be chosen to match the 

assumptions and background for the measurement method. 

• An appropriate background should be used to predict the instrument signal produced when 

there is no radioactivity present in the sample. 

• The Minimum Detectable Concentration (MDC) should be used only as a MQO for the 

measurement method. To make a detection decision, a measurement result should be 

compared the critical value and never to the MDC. 

• The validity of the Poisson approximation for the measurement process should be confirmed 

using the methods described in MARLAP Chapter 20 before using an expression for the 

critical value that is based on Poisson statistics. When the Poisson approximation is 

inappropriate for determining the critical value, estimating σ by the sample standard 

deviation of replicated background measurements is preferable to using the square root of the 

number of counts. 

• Consider all significant sources of variance in the instrument signal (or other response 

variable) when calculating the critical value, SC, and minimum detectable value, SD. 

• Report each measurement result and its uncertainty as obtained even if the result is less than 

zero. Never report a result as “less than MDC” or “less than SC.”

• The MDC should not be used for projects where the issue is a quantitative comparison of 

measurements to a limit rather than just a detection decision made for a single measurement. 

For these projects, the minimum quantifiable concentration is a more relevant MQO for the 

measurement process (see Section 7.6). 

7.6 Determine Measurement Quantifiability 

This section discusses issues related to measurement quantifiability. Much of this material is 

derived from the MARLAP Chapter 20. Further details and an additional example are given in 

Section 7.10. 

Action levels are frequently stated in terms of a quantity or concentration of radioactivity, rather 

than in terms of detection. In these cases, project planners may need to know the quantification 

capability of a measurement method, or its capability for precise measurement. The 

quantification capability is expressed as the smallest concentration of radiation or radioactivity 

that can be measured with a specified relative standard deviation. This section explains an MQO 

called the minimum quantifiable concentration (MQC), which may be used to describe 

quantification capabilities. 

The MQC of the concentration, yQ, is defined as the concentration at which the measurement 

process gives results with a specified relative standard deviation 1/kQ where kQ is usually chosen 

to be 10 for comparability. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-35 NUREG-1575, Supp. 1 

Historically much attention has been given to the detection capabilities of radiation and 

radioactivity measurement processes, but less attention has been given to quantification 

capabilities. For some projects, quantification capability may be a more relevant issue. For 

example, suppose the purpose of a project is to determine whether the 226Ra concentration on 

material at a site is below an action level. Since 226Ra can be found in almost any type of 

naturally occurring material, it may be assumed to be present in every sample, making detection 

decisions unnecessary. The MDC of the measurement process obviously should be less than the 

action level, but a more important question is whether the MQC is less than the action level. 

A common practice in the past has been to select a measurement method based on the minimum 

detectable concentration (MDC), which is defined in Section 7.5. For example, MARSSIM 

(2002) says: 

During survey design, it is generally considered good practice to select a measurement 

system with an MDC between 10-50% of the DCGL [action level]. 

Such guidance implicitly recognizes that for cases when the decision to be made concerns the 

mean of a population that is represented by multiple measurements, criteria based on the MDC 

may not be sufficient and a somewhat more stringent requirement is needed. The requirement 

that the MDC (approximately 3-5 times σM) be 10% to 50% of the action level is tantamount to 

requiring that σM be 0.02 to 0.17 times the action level – in other words, the relative standard 

deviation should be approximately 10% at the action level. However, the concentration at which 

the relative standard deviation is 10% is the MQC when kQ assumes its conventional value of 

10. Thus, a requirement that is often stated in terms of the MDC may be more naturally 

expressed in terms of the MQC, e.g., by saying that the MQC should not exceed the action level. 

7.6.1 Calculate the MQC 

The minimum quantifiable concentration, when there are no interferences, can be calculated 

from: 

2 22

ˆ

22 2

ˆ

4(1 ) 11 1

2 (1 )

Q Q S S

Q B

S Q Q BB

k k t t y N tk k tt

ε

ε

φ

ε φ

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞

= ++ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

 (7-14) 

Where: 

tS = count time for the source, s 

tB = count time for the background, s

NB = background count 

2 φεˆ = relative variance of the measured efficiency, εˆ (see Section 7.8.2.2) 

kQ = relative percent standard deviation at the MQC, usually assumes a conventional 

value of 10 for purposes of comparison among methods 

If 2

Qk 2 φεˆ ≥ 1, this equation has no solution. 
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NUREG-1575, Supp. 1 7-36 January 2009 

Example 10: Continuing Example 9, tS = 300, tB = 600, NB = 108, 2 φεˆ = (0.005802/0.4176)2 = 

0.0001932, and kQ = 10. So, 

2 22

ˆ

22 2

ˆ

4(1 ) 11 1

2 (1 )

Q Q S S

Q B

S Q Q BB

k k t t y N tk k tt

ε

ε

φ

ε φ

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞

= ++ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

100 4(1 100(0.0001932)) 300 300 1 1 108 1

2(300)(0.4176)(1 100(0.0001932)) 100 600 600

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞ = ++ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

= 1.239 Bq. This value for Qy would then be divided by the mass or volume of the sample to 

yield the MQC. 

The next example is given to verify that the equation for yQ does indeed produce a value with a 

relative uncertainty of 10%. It also provides an opportunity to give another illustration of the 

methodology for the calculation of measurement uncertainty developed in Sections 7.4 and 7.8. 

Additional information on the calculation of MQCs is given in Section 7.10. 

Example 11: The calculations of Example 10 can be verified by calculating the uncertainty of a 

measurement made at the MQC. The expected number of counts for a sample at the MQC 

counted for 300 s: 

1 ( / ) (1.239 Bq)(300 s)(0.4176) (108 s )(300 / 600) 209, N yt N t t S QS B S B ε − =+ = + =

rounded to the nearest whole number. 

The model equation is the same as was used in Example 6, Section 7.4.2: 

( /)( /) Nt Nt SS BB y

ε

− = , so the equation for the CSU is the same: 

2 2 2

22 2 2

2

1/ (( / ) ( / )) 1/ () ( ) ( ) () S SS BB B

c SB

t Nt Nt t uy uN uN u ε

εε ε

⎛ ⎞ ⎛ ⎞ − ⎛ ⎞ − − =+ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

22 2

2

2

1/ 300 1/ 600 (209 / 300) (108/ 600) (209) (108) (0.005802) 0.4176 0.4176 0.4176

⎛⎞ ⎛ ⎞ ⎛ ⎞ − −+

=++ ⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎝⎠ ⎝ ⎠ ⎝ ⎠

23 4 2 1.332 10 1.72 10 2.95 10 1.534 10 −− − − = ×+×+×= ×

2 ( ) 1.534 10 0.124 c u y − = ×= . Thus, the relative uncertainty at the MQC is 0.124/1.239 = 

0.09995. This means, apart from some small difference due to rounding, the relative 

measurement uncertainty at yQ is 10%, as should be the case for the MQC. 

7.6.2 Summary of Measurement Quantifiability 

Figure 7.9 is a modification of Figure 7.8, illustrating the relationships between the critical value, 

the MDC, the MQC and the probability of exceeding the critical value. As can be seen, the issue 

of detection is almost moot at the MQC. The probability of detection is near 100%. However, the 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-37 NUREG-1575, Supp. 1 

MQC specifies a concentration with a defined relative standard uncertainty, making comparisons 

between measurements or comparisons between measurements and regulatory criteria 

meaningful. 

Figure 7.9 Relationships Among the Critical Value, the MDC, the MQC, and the 

Probability of Exceeding the Critical Value 

Three x-axis scales are shown in Figure 7.9 for net count, concentration, and multiple of 

measurement uncertainty. This figure emphasizes, for example, that the minimum detectable net 

count, SD, corresponds to the MDC, but has different units. It also shows that the MQC is by 

convention 10 times the measurement uncertainty at that concentration. The critical value of the 

net count, SC, has no corresponding common term in concentration units. This is because 

detection decisions are usually made on the basis of the net counts (instrument reading). These 

are inherently qualitative “yes or no” decisions. The relationship between SC and SD and the 

multiple of the uncertainty varies according to which set of assumptions are used and which 

equations in Table 7.5 and Table 7.6 are appropriate to those assumptions. Therefore, an 

approximate range is shown for these quantities on the multiple of uncertainty axis. 

7.7 Establish a Required Measurement Method Uncertainty 

This section provides the rationale and guidance for establishing project-specific MQOs for 

controlling σM and expands on the material in Section 7.3. Control of σM is achieved by 

establishing a desired maximum measurement method uncertainty at the upper boundary of the 

gray region. This control also will assist in both the measurement method selection process and 

in the evaluation of measurement data. Approaches applicable to several situations are detailed 

below. 

0.05

1

0 SD

Gray Region

0.5

0.95

Net Count: SC

Multiple of the uncertainty: 0 1.5–2.5 3–5 10

Concentration: 0 N/A MDC MQC

N/A

1

Probability of Detection

Gray Region
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-38 January 2009 

7.7.1 Developing a Requirement for Measurement Method Uncertainty for MARSSIMType Surveys 

When, as in MARSSIM-type surveys, a decision is to be made about the mean of a sampled 

population, generally the average of a set of measurements on a survey unit is compared to the 

disposition criterion. 

The total variance of the data, σ

2

, is the sum of two components 

2 2 2 σ = σ M +σ S (7-15) 

Where: 

2 σ M = measurement method variance (M for measurement) 

2 σ S = variance of the radionuclide concentration or activity concentration in the 

sampled population (S for sampling) 

The spatial and temporal distribution of the concentration (i.e., the variation of the true but 

unknown concentrations from place to place and from time to time), the extent of the survey unit, 

the physical sizes of the measured material, and the choice of measurement locations may affect 

the sampling standard deviation, σS. The measurement standard deviation, σM, is affected by the 

measurement methods. The value of σM is estimated in MARSAME by the CSU of a measured 

value for a measurement of material whose concentration equals the hypothesized population 

mean concentration. The calculation of measurement uncertainties is covered in Sections 7.4 and 

7.8. 

Four cases are considered below where target values for σM can be suggested depending on what 

is known about σS. Cases 1 and 2 treat the desired overall objective of keeping ∆/σ ≈ 3 or higher. 

When this is not possible, Cases 3 and 4 treat the less desirable alternative of attempting to 

prevent ∆/σ from going lower than 1. If ∆/σ < 1 then a large number of measurements will be 

required to meet the Type I and II decision error rates specified in the DQO process. If σ » ∆, it 

may be necessary to re-evaluate the error rates specified in the DQO process. 

Case 1: σS is known relative to ∆ / 3

Generally, it is easier to control σM than σS. If σS is known (approximately), a target value for σM

can be determined. 

Case 1a: σS ≤ ∆/3 

If σS ≤ ∆/3, then a value of σM no greater than 2 2 ( / 9) Δ −σ S ensures that σ ≤ ∆ / 3, 

because we have 22 2 2 2 22 ( /9 ) /9 σσσ σ σ = + ≤ Δ − + =Δ MS S S , as desired. 

Case 1b: σS > ∆/3 

If σS > ∆/3, the requirement that the total σ be less than ∆/3 cannot be met regardless of 

σM. In this case, it is sufficient to make σM negligible in comparison to σS. Generally, σM 

can be considered negligible in comparison to σS if σM < σS/3. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-39 NUREG-1575, Supp. 1 

Case 2: σS is not known relative to ∆/3

Often one needs a method for choosing σM in the absence of specific information about σS. Since 

it is desirable to have σ ≤ ∆/3, this condition is adopted as a primary requirement. Assume for the 

moment that σS is large. Then σM should be made negligible by comparison. As mentioned 

above, σM can be considered negligible if it is no greater than σS/3. When this condition is met, 

further reduction of σM has little effect on σ and therefore is usually not cost-effective. So, σM ≤

σS/3 is adopted as a secondary requirement. 

Starting with the definition 2 2 2 σ =σ M + σ S and substituting the secondary requirement σM ≤ σS/3 

we get 22 2 2 9 10 σ ≥+ = σσ σ M M M , thus

10

σ

σ M ≤ (7-16) 

Substituting the primary requirement that ∆/σ ≥ 3 (i.e., σ≤ ∆ / 3) we get / 3

10 10 M

σ

σ

Δ ≤ ≤ , thus

3 10

Δ

σ M ≤

 (7-17) 

Or approximately

10

Δ

σ M ≤

 (7-18) 

The required upper bound for the standard deviation σM will be denoted by σMR. MARSAME 

recommends the equation

10

Δ

σ MR = (7-19) 

by default as a requirement when σS is unknown and a decision is to be made about the mean of a 

sampled population. 

This upper bound was derived from the assumption that σS was large, but it also ensures that the 

primary requirement σ ≤ ∆ / 3 (i.e., ∆ /σ ≥ 3) will be met if σS is small. When the measurement 

standard deviation σM is less than σMR, the primary requirement will be met unless the sampling 

variance, 2 σ S , is so large that 2 σ M is negligible by comparison, in which case little benefit can be 

obtained from further reduction of σM. 
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NUREG-1575, Supp. 1 7-40 January 2009 

It may be that the primary requirement that ∆/σ be at least 3 is not achievable. Suppose that the 

primary requirement is relaxed to achieving ∆/σ at least 1 (i.e., σ ≤ ∆). This leads to 

consideration of– 

Case 3: σS is known relative to ∆

As in Case 1, it is generally easier to control σM than σS. If σS is known (approximately), a target 

value for σM can be determined. 

Case 3a: σS ≤ ∆ 

If σS ≤ ∆, then a value of σM no greater than 2 2 Δ −σ S ensures that σ ≤ ∆, because we 

have 2 2 2 22 2 2 ( ) σσσ σ σ = + ≤ Δ − + =Δ MS S S as desired. 

Case 3b: σS > ∆ 

If σS > ∆, the requirement that the total σ be less than ∆ cannot be met regardless of σM. In 

this case, it is sufficient to make σM negligible in comparison to σS. Generally, σM can be 

considered negligible if it σM < σS/3. 

Case 4: σS is not known relative to ∆ 

Suppose σ ≤ ∆ is adopted as the primary requirement. As in Case 2, if σS is large, then σM should 

be made negligible by comparison. As mentioned above, σM can be considered negligible if it is 

no greater than σS/3. When this condition is met, further reduction of σM has little effect on σ and 

therefore is usually not cost-effective. So, σM ≤ σS/3 is adopted as a secondary requirement. 

Starting with the definition 2 2 2 σ =σ M +σ S and substituting the secondary requirement σM ≤ σS/ 3 

we get 22 2 2 9 10 σ ≥+ = σσ σ M M M , thus–

10

σ

σ M ≤ (7-20) 

Substituting the primary requirement that ∆/σ ≥ 1 (i.e., σ ≤ ∆) we get 10 10 M

σ

σ

Δ ≤ ≤ , thus–

10 3

Δ ≈ Δ

σ M ≤ (7-21) 

 

7.7.2 Developing a Requirement for Measurement Method Uncertainty When Decisions 

are to be Made About Individual Items 

When decisions are to be made about individual items, the total variance of the data equals the 

measurement variance, 2 σ M , and the data distribution in most instances should be approximately 

normal. The decision in this case may be made by comparing the measured concentration, x, plus 

or minus a multiple of its CSU, to the action level. The CSU, uc(x), is assumed to be an estimate 

of the true standard deviation of the measurement process as applied to the item being measured; 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-41 NUREG-1575, Supp. 1 

so, the multiplier of uc(x) equals z1–α, the (1–α)-quantile of the standard normal distribution (see 

MARLAP appendix C). 

Alternatively, if AL = 0, so that any detectable amount of radioactivity is of concern, the 

decision may involve comparing the net instrument signal (e.g., count rate) to the critical value 

of the net instrument signal, SC, as defined in Section 7.5.1. 

Two cases are considered below where target values for σM can be suggested depending on what 

is known about the width of the gray region and the desired Type I and Type II decision error 

rates. Case 5 is for Scenario A, and Case 6 is for Scenario B. 

Case 5: Suppose the null hypothesis is X ≥ AL (see Scenario A in Chapter 4), so that the action 

level is the upper bound of the gray region. Given the measurement variance 2 σ M , only a 

measured result that is less than (UBGR – z1–α σM) will be judged to be clearly less than the 

action level. Then the desired power of the test 1–β is achieved at the lower bound of the gray 

region only if the LBGR ≤ UBGR – z1–ασM – z1–βσM. Algebraic manipulation transforms this 

requirement to 

α β α β

σ

− − − + −

Δ = +

≤

1 1 1 1

UBGR -LBGR

z z z z

M (7-22) 

Case 6: Suppose the null hypothesis is X ≤ AL (see Scenario B in Chapter 4), so that the action 

level is the lower bound of the gray region. In this case, only a measured result that is greater 

than LBGR + z1–ασM will be judged to be clearly greater than the action level. The desired power 

of the test 1 – β is achieved at the upper bound of the gray region only if the UBGR ≥ LBGR + 

z1–ασM + z1–βσM. Algebraic manipulation transforms this requirement to: 

α β α β

σ

− − − + −

Δ = +

≤

1 1 1 1

UBGR -LBGR

z z z z

M (7-23) 

So, in either Scenario A or Scenario B, the requirement remains that: 

α β

σ

− + −

Δ ≤

1 1 z z

M (7-24) 

Therefore, MARSAME uses the equation: 

α β

σ

− + −

Δ = =

1 1 z z

uMR MR (7-25) 

as an MQO for method uncertainty when decisions are to be made about individual items or 

locations and not about population parameters. 

If α =β = 0.05, one may use the value uMR = 0.3∆. Other combinations of α and β may lead to a 

similar result, but the relationship is nonlinear (depending on the standard normal distribution 

function) so one cannot simply apply a proportionality factor. Equation 7-25 must be used, 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-42 January 2009 

The recommended value of uMR is based on the assumption that any known bias in the 

measurement process has been corrected and that any remaining bias is well less than a third of 

the method uncertainty. 

7.8 Calculate the Combined Standard Uncertainty of a Measurement 

This section expands upon the material in Section 7.4. Calculations of combined standard 

uncertainties (CSUs) can be complex, and typically would be carried out using a software 

package. For the purpose of illustration and clarity, fully worked out examples are included in 

this section. 

7.8.1 Procedures for Evaluating Uncertainty 

The usual eight steps for evaluating and reporting the uncertainty of a measurement are 

summarized in the following subsections (adapted from Chapter 8 of GUM): 

7.8.1.1 Identify the Measurand, Y, and all the Input Quantities, Xi, for the Mathematical Model 

Include all quantities whose variability or uncertainty could have a potentially significant effect 

on the result. Express the mathematical relationship, Y = f (X1, X2,...,XN), between the measurand 

and the input quantities. 

The procedure for assessing the uncertainty of a measurement begins with listing all significant 

sources of uncertainty in the measurement process. A good place to begin is with the input 

quantities’ mathematical model Y = f (X1, X2,...,XN). When an effect in the measurement process 

that is not explicitly represented by an input quantity has been identified and quantified, an 

additional quantity should be included in the mathematical measurement model to correct for it. 

The quantity, called a correction (additive with a nominal value of zero) or correction factor

(multiplicative with a nominal value of one), will have an uncertainty that should also be 

evaluated and propagated. Each uncertainty that is potentially significant should be evaluated 

quantitatively. 

7.8.1.2 Determine an Estimate, xi, of the Value of Each Input Quantity, Xi 

This involves simply determining for the particular measurement at hand, the specific value, xi, 

that should be substituted for the input quantity Xi in the mathematical relationship, Y = f(X1,

X2,...,XN). 

7.8.1.3 Evaluate the Standard Uncertainty, u(xi), for Each Input Estimate, xi, Using a Type A 

Method, a Type B Method, or a Combination of Both 

Methods for evaluating standard uncertainties are classified as either “Type A” or “Type B” 

(NIST 1994). Both types of uncertainty need to be taken into consideration. A Type A evaluation 

of an uncertainty uses a series of measurements to estimate the standard deviation empirically. 

Any other method of evaluating an uncertainty is a Type B method. A Type B evaluation of 

standard uncertainty is usually based on scientific judgment using all the relevant information 

available, which may include: 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-43 NUREG-1575, Supp. 1 

• Previous measurement data, 

• Experience with, or general knowledge of, the behavior and property of relevant materials 

and instruments, 

• Manufacturer’s specifications, 

• Data provided in calibration and other reports, and 

• Uncertainties assigned to reference data taken from handbooks. 

The Type A standard uncertainty of the input estimate xi is defined to be the experimental 

standard deviation of the mean:

2

,

1

1 ( ) ( ) ( )/ ( 1)

n

i ik i i

k

ux x x sx n

n n =

= −= − ∑ (7-26) 

Example 12: Type A uncertainty calculation using Equation 7-26: 

Ten independent one-minute measurements of the counts from a check source Xi were made with 

a digital survey meter, yielding the values: 

12,148, 12,067, 12,207, 12,232, 12,284, 12,129, 11,862, 11,955, 12,044, and 12,150. 

The estimated value xi is the arithmetic mean of the values Xi,k. 

,

1

1 121078 12107.8

10

n

i i ik

k

xX x

n =

= == ∑

The standard uncertainty of xi is 

10

2 2

, ,

1 1

1 1 ( ) ( ) ( 12107.8) ( 1) 10(10 1)

n

i ik i ik

k k

ux x x x

n n = =

= −= − − − ∑ ∑

= 16628.84 = 128.95

There are other Type A methods, but all are based on repeated measurements. 

Any evaluation of standard uncertainty that is not a Type A evaluation is a Type B evaluation. 

Sometimes a Type B evaluation of uncertainty involves making a best guess based on all 

available information and professional judgment. Despite the reluctance to make this kind of 

evaluation, it is almost always better to make an informed guess about an uncertainty component 

than to ignore it completely. 

There are many ways to perform Type B evaluations of standard uncertainty. One example of a 

Type B method is the estimation of counting uncertainty using the square root of the observed 

counts. If the observed count is N, when the Poisson approximation is used, the standard 

uncertainty of N may be evaluated as u(N) = N . 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-44 January 2009 

Example 13: The standard uncertainty of the first value in Example 12, (12,148 counts), could 

be estimated as 12148 110.218 = counts. When N may be very small or even zero, the equation 

u(N) = N +1 may be preferable. 

Another Type B evaluation of an uncertainty u(x) consists of estimating an upper bound, a, for 

the magnitude of the error of x based on professional judgment and the best available 

information. If nothing else is known about the distribution of the measured result, then after a is 

estimated, the standard uncertainty may be calculated using the equation

3

( ) a

u x = (7-27) 

which is the standard deviation of a random variable uniformly distributed over the interval 

(x - a, x + a). The variable a is called the half-width of the interval. 

Example 14: Suppose in Example 12, all that was given was the observed range of the data from 

an analog survey meter dial (i.e., from 11,862 to 12,284), a difference of 422. If it was assumed 

that the data came from a uniform distribution across this range, then the average is 

(11,862+12,284)/2 = 12,073 

the half-width is 211, and an estimate of the standard uncertainty would be 

211 ( ) 121.821 3

u x = =

Given the same information on the range, if values near the middle of the range were considered 

more likely than those near the endpoint, a triangular distribution may be more appropriate. The 

standard uncertainty for a triangular distribution is calculated using the equation 

( ) 6

a

u x = (7-28) 

which represents the standard deviation of a random variable with a triangular distribution over 

the interval (x - a, x + a). Given the same information on the range, if values near the middle of 

the range were considered more likely than those near the endpoints, a triangular distribution 

may be more appropriate. The mean would be the same as above, 12,073. However the standard 

uncertainty then be calculated using the equation

 211 ( ) 86.14 6 6a

u x == = (7-29) 

Example 15: As in Example 14, all that is given was the observed range of the data from an 

analog survey meter dial, i.e., from 11,862 to 12,284, a difference of 422. If it was assumed that 

the data came from a triangular distribution across this range, then the average is 

(11,862+12,284)/2 = 12,073, the half-width is 211, and an estimate of the standard uncertainty 

would be 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-45 NUREG-1575, Supp. 1 

 211 ( ) 86.14 6 6a

u x == =

When the estimate of an input quantity is taken from an external source, such as a book or a 

calibration certificate, the stated standard uncertainty can be used. 

7.8.1.4 Evaluate the Covariances, u(xi,xj), for all Pairs of Input Estimates with Potentially 

Significant Correlations 

A Type A evaluation of the covariance of the input estimates xi = and xj = is

( )( ) ( 1)

1 ( , ) ,

1

, j k j

n

k

i j i k i x x x x

n n

u x x − − − = ∑= (7-30) 

An evaluation of variances and covariances of quantities determined by the method of least 

squares may also be a Type A evaluation. Evaluation of the covariance of two input estimates, xi

and xj, whose uncertainties are evaluated by Type B methods may require expert judgment. In 

such cases it may be simpler to estimate the correlation coefficient, 

 r(xi,xj) = [u(xi,xj) /u(xi)⋅u(xj)] (7-31) 

first and then multiply it by the standard uncertainties, u(xi) and u(xj) to obtain the covariance, 

u(xi,xj). 

A covariance calculation is demonstrated in Example 16 in Section 7.8.2.2. 

7.8.1.5 Calculate the Estimate, y, of the Measurand from the Relationship y = f(x1,x2,...,xN)

This involves simply substituting, for the particular measurement at hand, the specific values of 

xi for the input quantity Xi into the mathematical relationship, Y = f(X1,X2,...,XN), and calculating 

the result y = f(x1,x2,...,xN). 

7.8.1.6 Determine the Combined Standard Uncertainty, uc(y), of the Estimate, y

The CSU of y is obtained using the following formula: 

( ) ( ) 2 ( , )

1

1 1

2

2

1

2

i j

N

i

N

j i i j

i

N

i i

c u x x

x

f

x

f u x

x

f u y ∑ ∑ ∑

−

= = + = ∂

∂

∂

∂

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂ = (7-32) 

Here, u

2

(xi) denotes the estimated variance of xi, or the square of its standard uncertainty; u(xi,xj) 

denotes the estimated covariance of xi and xj; ∂f/∂xi (or ∂y/∂xi) denotes the partial derivative of f 

with respect to xi evaluated at the measured values x1,x2,...,xN; and 2

uc (y) denotes the combined 

variance of y, whose positive square root, uc(y), is the CSU of y. The partial derivatives, ∂f/∂xi, 

are called sensitivity coefficients, usually denoted ci. The sensitivity coefficient measures how 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-46 January 2009 

much f changes when xi changes. Equation 7-32 is called the “law of propagation of uncertainty” 

in GUM (ISO 1995).

If the input estimates x1,x2,...,xN are uncorrelated, the uncertainty propagation formula reduces to

( ) ( ) 2

2

1

2

i

N

i i

c u x

x

f u y ∑ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂ = =

 (7-33) 

Suppose the values x1,x2,...,xN are composed of two groups w1,w2,...,wn and z1,z2,...,zm with 

N=n+m. If all the variables, w and z, are uncorrelated and nonzero, the CSU of y = 

1 2

1 2

n

m

ww w

zz z

K

K may be calculated from the formula:

2 2 22 2 2

2 2 1 2 12

2 2 222 2

1 2 12

( ) ( ) () () () () ( ) n m

c

n m

uw uw uz uz uw uz

uy y

ww wzz z

⎛ ⎞

= + ++ + + ++ ⎜ ⎟ ⎝ ⎠K K (7-34) 

The symbols z1,z2,...,zm have been introduced simply to differentiate those values appearing in 

the denominator of the model equation from the w1,w2,...,wn appearing in the numerator. 

If y = 1 2

1 2

(, ,, ) n

m

f ww w

zz z

K

K , where f is some specified function of w1,w2,...,wn, all the zi are nonzero, 

and all the input estimates are uncorrelated. Then:

2 2 2 2

2 2 1 2 1 2

22 2

12 1 2

( ( , , , )) ( ) () () ( ) cn m

c

m m

u fww w u z uz uz

uy y

zz z z z z

⎛ ⎞

= + + ++ ⎜ ⎟ ⎝ ⎠

K K K (7-35) 

An alternative to uncertainty propagation is the use of computerized Monte Carlo methods to 

propagate not the uncertainties of input estimates but their distributions. Given assumed 

distributions for the input estimates, the method provides an approximate distribution for the 

output estimate, from which the CSU or an uncertainty interval may be derived. 

7.8.1.7 Optionally Multiply uc(y) by a Coverage Factor k to Obtain the Expanded Uncertainty 

U 

The interval [y - U, y + U], constructed using the expanded uncertainty U = k·uc(y), can be 

expected to contain the value of the measurand with a specified probability, p. The specified 

probability, p, is called the “level of confidence” or the “coverage probability” and generally is 

only an approximation of the true probability of coverage. When the distribution of the measured 

result is approximately normal, the coverage factor often is chosen to be k = 2 for a coverage 

probability of approximately 95%. An expanded uncertainty calculated with k = 2 or 3 is 

sometimes informally called a “two-sigma” or “three-sigma” uncertainty, respectively. The 

GUM recommends the use of coverage factors in the range of 2 to 3 when the CSU represents a 

good estimate of the true standard deviation. Attachment 19D of MARLAP describes a more 

general procedure for calculating the coverage factor that gives a desired coverage probability p 

when there is substantial uncertainty in the value of uc(y). 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-47 NUREG-1575, Supp. 1 

7.8.1.8 Report the Result as y ± U with the Unit of Measurement 

At a minimum, state the coverage factor used to compute U and the estimated coverage 

probability. Alternatively, report the result, y, and its CSU, uc(y), with the unit of measurement. 

The number of significant figures that should be reported for the result of a measurement 

depends on the uncertainty of the result. A common convention, recommended by MARLAP, is 

to round the uncertainty (standard uncertainty or expanded uncertainty) to two significant figures 

and to report both the measured value and the uncertainty to the same number of decimal places. 

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 round-off 

errors. Additional figures are also recommended when the data are stored electronically. 

Rounding should be performed only when the result is reported. Many of the values in the 

examples given in MARSAME carry more significant digits so that the calculations can be 

reasonably reproduced by the reader. All results, whether positive, negative, or zero, should be 

reported as obtained, together with their uncertainties. 

A measured value y of a quantity Y that is known to be positive may be so far below zero that it 

indicates a possible blunder, procedural failure, or other quality control problem. Usually, if 

y + 3uc(y) < 0, the result may be invalid. For example, if y = −10 and uc(y) = 1, this would imply 

that Y is negative with high probability, which is known to be impossible. However, if y = −1 and 

uc(y) = 1, the expanded uncertainty covers positive values with reasonable probability. The 

accuracy of the uncertainty estimate uc(y) must be considered in evaluating such results, 

especially in cases where only few counts are observed during the measurement and counting 

uncertainty is the dominant component of uc(y). (See MARLAP Chapter 18 and Attachment 

19D). 

7.8.2 Examples of Some Parameters that Contribute to Uncertainty 

The sources of uncertainty described in the following sections, drawn from MARLAP Section 

19.5, should be considered. 

7.8.2.1 Instrument Background 

Single-channel background measurements are usually assumed to follow the Poisson model, in 

which the uncertainty in the number of counts obtained, N, is given by N . There may be effects 

that increase the variance beyond what the model predicts. For example, cosmic radiation and 

other natural sources of instrument background may vary between measurements, the instrument 

may become contaminated, or the instrument may simply be unstable. Generally, the variance of 

the observed background is somewhat greater than the Poisson counting variance, although 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 estimated by repeated measurements. 

The “instrument background,” or “instrument blank,” is usually measured under the same 

conditions that will be encountered in the field. Ambient background sources should be 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-48 January 2009 

minimized, and kept constant during the measurements of M&E. Periodic checks should be 

made to ensure that the instrument has not picked up additional radioactivity from the M&E 

during the measurements. If the background drifts or varies non-randomly over time (i.e., is nonstationary), it is important to minimize the consequences of the drift by performing frequent 

background measurements. 

If repeated measurements demonstrate that the background level is stable, then the average, x , 

the results of n similar measurements performed over a period of time may give the best estimate 

of the background. In this case, if all measurements have the same duration, the experimental 

standard deviation of the mean,s x( ) , is also a good estimate of the measurement uncertainty. 

Given the Poisson assumption, the best estimate of the uncertainty is still the Poisson estimate, 

which equals the square root of the summed counts, divided by the number of measurements, 

nx x

n n = , but the experimental standard deviation may be used when the Poisson 

assumption is invalid. It is always wise to compare the value of s x( ) to the value of the Poisson 

uncertainty when possible to identify any discrepancies. 

7.8.2.2 Counting Efficiency 

The counting efficiency for a measurement of radioactivity (usually defined as the detection 

probability for a particle or photon of interest emitted by the source) may depend on many 

factors, including source geometry, placement, composition, density, activity, radiation type and 

energy and other instrument-specific factors. The estimated efficiency is sometimes calculated 

explicitly as a function of such variables (in gamma-ray spectroscopy, for example). In other 

cases a single measured value is used (e.g., alpha-particle spectrometry). If an efficiency function 

is used, the uncertainties of the input estimates, including those for both calibration parameters 

and sample-specific quantities, must be propagated to obtain the CSU of the estimated 

efficiency. Calibration parameters tend to be correlated; so, estimated covariances must also be 

included. If a single value is used instead of a function, the standard uncertainty of the value is 

determined when the value is measured. An example of the calculation of the uncertainty in 

counting efficiency is given in Example 16. 

Example 16: A radiation counter is calibrated, taking steps to ensure that the geometry of the 

source position, orientation of the source, pressure, temperature, relative humidity, and other 

factors that could contribute to uncertainty are controlled, as described below:

The standard source is counted 15 times on the instrument for 300 s. 

The radionuclide is long-lived; so, no decay corrections are needed. The uncertainties of the 

count times are assumed to be negligible. 

Within the range of linearity of the instrument, the mathematical model for the calibration is: 

,

1

1 ( /)( /) n Si S B B

i s

N t Nt

n a

ε =

− = ∑ (7-36) 

Where: 

ε = counting efficiency 

 n = number times the source is counted (15) 

 NS,i = gross count observed during the i

th measurement of the source 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-49 NUREG-1575, Supp. 1 

 tS = source count time (300 s) 

 NB = observed background count (87) 

 tB = background count time (6,000 s) 

 aS = activity of the standard source (150.0 Bq). (The standard uncertainty of the 

source, 2.0 Bq, was given in the certificate for the source.) 

The CSU of ε can be evaluated using Equation 7-36. For the purpose of uncertainty evaluation, it 

is convenient to rewrite the model as: 

as

R

ε =

Where: 

∑=

=

n

i

Ri

n

R

1

1 and , ( /)( /) Ri Si S B B = N t Nt − , i = 1,2,...,n 

The values Ri and their average, R , are estimates of the count rate produced by the standard, 

while R /aS is an estimate of the count rate produced by 1 Bq of activity. The standard 

uncertainty of R can be evaluated experimentally from the 15 repeated measurements: 

22 2

1

1 () () ( ) ( 1)

n

i

i

uR sR R R

n n =

== − − ∑ . Since only one background measurement was made, the 

input estimates Ri are correlated with each other. The uncertainty of NB, ( ) 87 B u N = , using a 

Type B evaluation based on an assumption of a Poisson distribution for the number of 

background counts. 

The covariance between Ri and Rj, for i ≠ j, may be estimated as 

2 2

22 6

2 2

1 1 87 ( ) ( , ) ( ) ( ) 2 10 6000

j i B

ij B B

B B BB B

R R u N uR R u N u N

N N tt t

− ∂ ∂ − − = = = = ≅×

∂ ∂

However, the correlation is negligible here because the uncertainty of the background count, NB, 

is much smaller than the uncertainty of each source count, NS,i. So, the correlation of the input 

estimates Ri will be approximated as zero (i.e., treated as if they were uncorrelated), and the 

correlation terms dropped from Equation 7-32. This means the evaluation used to calculate the 

CSU of ε can proceed using equation 7-33. 

2

2 2

1

() ( )

N

c i

i i

f uy ux = x

⎛ ⎞ ∂ = ⎜ ⎟ ∂⎝ ⎠ ∑ , so since

as

R

ε = , 

2 2

2 2

2 2 22 2

2

() () 1 () ( ) ( ) ( ) ( ) s s

c ss

s ss

R R

a a R u uR ua uR ua

R a aa

ε

⎛⎞ ⎛⎞ ⎜⎟ ⎜⎟ ∂ ∂ ⎛ ⎞ ⎛ ⎞ − = + =+ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ∂ ∂ ⎝ ⎠ ⎝⎠ ⎝ ⎠

2 2

2

2 2

( ) ( )s

s s

u R u a

a a

ε

⎛ ⎞⎛ ⎞

= + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠

. Therefore, 

2 2

2

2 2

( ) ( ) ( ) S

c

S S

u R u a

u

a a

ε ε = +
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-50 January 2009 

Assume the following data were obtained for the 15 separate counts of the calibration source. 

Count Number, i Gross count, NS,i Ri (s−1)

1 18,375 61.236 

2 18,644 61.236 

3 18,954 61.236 

4 19,249 64.149 

5 19,011 63.356 

6 18,936 63.106 

7 18,537 61.776 

8 18,733 62.429 

9 18,812 62.692 

10 18,546 61.806 

11 18,810 62.686 

12 19,273 64.229 

13 18,893 62.962 

14 18,803 62.662 

15 18,280 60.919 

Average, R (s−1) 62.6202 

Experimental standard deviation, s(Ri) (s−1) 0.9483 

Experimental standard deviation of the mean, s( R ) (s−1) 0.2449 

Then the estimated counting efficiency is: 

1 62.6202 s 0.4176

150.0 Bq s

R

a

ε

−

== =

And the CSU of ε is given by 

12 2

2

2 2

(0.2449 s ) (2.0 Bq) ( ) 0.4176 0.005802 (150.0 Bq) (150.0 Bq) c u ε

−

= +× =

Which may be rounded to 0.0058. 

The true counting efficiency may vary because of variations in geometry, position and other 

influence quantities not explicitly included in the model. These sources of uncertainty may not 

be controlled as they were in the above example. If this is the case, the standard uncertainty of ε

should include not only the standard uncertainty of the estimated mean, as calculated in the 

example, but also another component of uncertainty due to variations of the true efficiency 

during subsequent measurements. The additional component may be written as εφ , where φ is 

the coefficient of variation (i.e., the standard deviation divided by the mean) of the true 

efficiency. Then the total uncertainty of ε is obtained by squaring the original uncertainty 

estimate, adding ε

2

φ2, and taking the square root of the sum. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-51 NUREG-1575, Supp. 1 

2 2

2 2

2 2

( ) ( ) ( ) S

c

S S

u R u a

u

a a

ε ε φ ⎛ ⎞

=+ + ⎜ ⎟ ⎝ ⎠

 (7-37) 

In the example above, the experimental variance of the count rates, Ri, may be used to 

estimateφ . Section 18B.2 of Attachment 18B of MARLAP describes an approach for estimating 

such “excess” variance in a series of measurements. 

Variations in counting efficiency due to source placement should be reduced as much as possible 

through the use of positioning devices that ensure a source with a given geometry is always 

placed in the same location relative to the detector. If such devices are not used, variations in 

source position may significantly increase the measurement uncertainty. 

Calibrating an instrument under conditions different from the conditions under which M&E 

sources are counted may lead to large uncertainties in the activity measurements. Source 

geometry in particular tends to be an important factor for many types of radiation counters. If 

correction factors are used, their uncertainties should be evaluated and propagated, as mentioned 

in Section 7.8.1.1. 

7.8.2.3 Digital Displays and Rounding 

If a measuring device has a digital display with readability10 δ, the standard uncertainty of a 

measured value is at leastδ / 2 3 , which is the variance of a random variable uniformly 

distributed over the interval (x – δ/2, x + δ/2). Note that this is the same result as given by 

equation 7-24 with a = δ/2. This uncertainty component exists even if the instrument is 

completely stable. 

A similar Type B method may be used to evaluate the standard uncertainty due to computer 

round-off error. When a value x is rounded to the nearest multiple of 10n

 (where n is an integer), 

the component of uncertainty generated by round-off error is 10 /(2 3) n . This component of 

uncertainty should be kept small in comparison to the total uncertainty of x by performing 

rounding properly and printing with an adequate number of figures. In a long calculation 

involving mixed operations, carry as many digits as possible through the entire set of 

calculations and then round the final result appropriately as described in MARLAP Section 

19.3.7 (MARLAP 2004). 

Example 17: The readability of a digital survey dose rate meter is 1 nGy/h. Therefore, the 

minimum standard uncertainty of a measured absorbed dose rate is 1/ 2 3 = 0.29 nGy/h. 

 

10 Readability is the smallest difference that can still be read on a display. For instruments with an analog indicating 

device, the readability is equal to the smallest fraction of a scale interval that can still be estimated with reasonable 

reliability or which can be determined by an auxiliary device. For instruments with a numeric indicator (digital 

display), the readability is equal to one digital step. 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-52 January 2009 

Example 18: Suppose the results for Ri in Example 16 had been rounded to the nearest whole 

number before the analysis. Then the average would be computed as 62.6 instead of 62.6202 and 

the standard deviation would be computed as 0.9103 instead of 0.9483. This demonstrates the 

effect that rounding intermediate results can have on subsequent calculations. If this rounding to 

the nearest positive integer had already occurred prior to receiving the data, and the original data 

were no longer available, a correction for it could be made when estimating the CSU of Ri. The 

component of uncertainty generated by round-off error is 1/(2 3) : 

2

2 1 ( ) 0.9103 0.9549. 2 3 i u R ⎛ ⎞

= += ⎜ ⎟ ⎝ ⎠

7.8.3 Example Uncertainty Calculation 

To illustrate how the uncertainty calculations are performed in practice, the following example is 

given based on that of Lewis et al. (2005). The calculation will be that of the CSU in the 

calibration of a surface contamination monitor. 

7.8.3.1 Model Equation and Sensitivity Coefficients 

Surface contamination monitors are calibrated in terms of their response to known rates of 

radioactive emissions. In practice this is achieved by using large-area, planar sources that have a 

defined area and whose emission rates have been determined in a traceable manner. The 

calibration is usually determined in terms of response per emission rate per unit area. In this 

example, the source is positioned with its active face parallel to and at a distance of 3 mm from 

the face of the detector. The monitor detector area (50 cm2

) is smaller than the area of the 

calibration source, which is a 10 cm × 10 cm layer of 14C on a thick aluminum substrate. The 

monitor has an analog display and has a means to set the detector voltage. 

The efficiency, ε, is defined by: 

( )

( ) M Bf f ff V d u bs

E

A

ε − ×××× = (7-38) 

Where: 

M = observed monitor reading, s−1

B = background reading, s−1

E = emission rate of the calibration source, s−1

A = area of the active portion of the calibration source, cm2

fV = plateau voltage factor 

fd = source-detector separation factor 

fu = source uniformity factor 

fbs = backscatter factor 

The sensitivity coefficients of Equation 7-38 are given by: 

(/) ( ) AE f f f f V d u bs M M B

∂ε ε

= ×××× =

∂ − (7-39)
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-53 NUREG-1575, Supp. 1 

(/) ( ) AE f f f f V d u bs B M B

∂ − ε ε

=− × × × × =

∂ − (7-40) 

2 ( )( / ) M B AE f f f f V d u bs E E

∂ − ε ε

=− − × × × × =

∂ (7-41) 

( )(1/ ) MB E f f f f V d u bs A A

∂ε ε

= − ×××× =

∂ (7-42) 

( )( / ) d u bs

V V

M B AE f f f f f

∂ε ε

= − ××× =

∂ (7-43) 

( )( / ) V u bs

d d

M B AE f f f f f

∂ε ε

= − ××× =

∂ (7-44) 

( )( / ) V d bs

u u

M B AE f f f f f

∂ε ε

= − ××× =

∂ (7-45) 

( )( / ) Vdu

bs bs

M B AE f f f f f

∂ε ε

= − ×××=

∂ (7-46) 

Under normal conditions, the factors fV, fd, fu and fbs are each assumed to have a value of one. If 

the uncertainties are to be calculated in relative terms, the uncertainty equation becomes (see 

Equation 7-34): 

222 2 2 2 2 2 222

V d u bs C M BEA ffff

V d u bs

M B

MB M MB B E A f f f f

σ σ σσσ σσσσ

ε

⎛ ⎞ ⎛⎞ ⎛⎞ ⎛ ⎞ ⎛ ⎞⎛ ⎞⎛ ⎞ ⎛ ⎞⎛ ⎞⎛ ⎞⎛ ⎞ ⎜ ⎟ = + +++ + + + ⎜⎟ ⎜⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟⎜ ⎟ ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟ ⎝ ⎠ ⎝⎠ ⎝⎠ − − ⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠⎝ ⎠

 (7-47) 

If the relative uncertainties are all expressed as percentages, ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

i

x

x

i σ

, where xi is an input quantity, 

then the CSU will be a percentage. The relative sensitivity coefficients, ci, are the terms 

multiplying each relative uncertainty term ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

i

x

x

i σ

 in Equation 7-47. This approach produces 

relative sensitivity coefficients of unity for the last 6 terms. 

7.8.3.2 Uncertainty Components 

Monitor Reading of Source, M (Type A)

Several techniques can be used to determine the mean observed monitor reading, M, and its 

uncertainty. Assume a snap-shot technique is used whereby six successive, but randomly timed, 

readings are recorded, giving 350, 400, 400, 325, 350, 350 s−1

. The mean and standard deviation 
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NUREG-1575, Supp. 1 7-54 January 2009 

of the mean becomes 362.5 ± 12.5 s−1. This equates to a percentage uncertainty in M of 3.45% 

and the relative sensitivity coefficient from Equation 7-47, ( )

M

M − B , is 362.5/(362.5 – 32.5), 

which is equal to 1.10. The distribution is assumed to be normal. 

Monitor Reading of Background, B (Type B)

In this case, an eye-averaging technique was used whereby the highest and lowest count rates 

were recorded over a given period of time. These count rates were 40 and 25 s−1

 respectively, 

giving a mean value of 32.5 s−1

. This value is assumed to have a rectangular distribution with a 

half-width of 7.5 s−1

, and an uncertainty of 7.5/ 3 4.330 = , equating to a percentage uncertainty 

of 4.330 / 32.5 0.1332 = or 13%. The relative sensitivity coefficient from Equation 7-47, 

( )

B

M − B , is 32.5/(362.5 - 32.5), which gives a value of 0.098. 

Emission Rate of Calibration Source, E (Type B)

The emission rate of the source and its uncertainty were provided on the calibration certificate by 

the laboratory that calibrated the source using a windowless proportional counter. The statement 

on the certificate was: 

“The measured value of the emission rate is E = 2,732±13 s−1

” 

The reported uncertainty is based on a standard uncertainty multiplied by a coverage factor of 

k = 2, which provides a level of confidence of approximately 95%. The standard uncertainty on E

is therefore 13/2 = 6.5 s−1

 or 0.24%. Unless the certificate provides information to the contrary, it 

is assumed that the uncertainty has a normal distribution. 

Source Area, A (Type B)

In the absence of an uncertainty statement by the manufacturer, the only information available is 

the product drawing that shows the active area dimensions to be 10 cm × 10 cm. On the 

assumption that the outer bounds of the length, L, and the width, W, are 9.9 and 10.1 cm, the 

uncertainty of the linear dimensions may be taken to be a rectangular distribution with a halfwidth of 0.1 cm. 

L = 10 and u(L) = 0.1/ 3 0.0577 = . W = 10 and u(W) = 0.1/ 3 0.0577 = . Because A = LW, we 

get 2 2 22 22 2 2 u A u LW L u W W u L ( ) ( ) ( ) ( ) 2(10) (0.0577) 0.665858 ==+ = = , therefore 

u A( ) 0.816 = cm2

 or 0.816%. 

Plateau Voltage Factor, fV (Type B)

This applies only to those instruments where voltage adjustments are possible. If the setting is 

not checked and/or adjusted between calibrations, then this has no effect. Changing the plateau 

voltage without performing a recalibration is not recommended. If, however, the user is allowed 

to do this, the setting may not be returned to exactly that used during the calibration. In this 

particular example, the slope of the response curve in this region is taken to be 10% / 50 v. It is 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-55 NUREG-1575, Supp. 1 

assumed that an operator is more likely to set the voltage nearer to the optimum than the 

extremes and that ± 50 v represents the range at the 100% confidence level. Accordingly, a 

triangular distribution is assumed with a half-width of 50 v, equating to an uncertainty for the 

voltage of 50 / 6 20.4124 = and an uncertainty for the voltage factor of 20.4124(10%)/ 50 = 

4.0825%. 

Source-Detector Separation Factor, fd (Type B)

This effect arises from the uncertainty in mounting the calibration source exactly 3 mm from the 

detector face. Experimental evidence has shown that, for the particular 14C source at 3 mm 

source-detector separation, the change in response was 2.6% per mm. It is assumed that the 

deviation from the nominal 3-mm separation is no greater than 1 mm but that all values are 

equally probable between 2 and 4 mm, a rectangular distribution. The uncertainty in the 

separation is thus1/ 3 0.5774 = . The uncertainty of the separation factor is thus 0.5774 mm × 

2.6% / mm, equal to 1.5011%. 

Non-Uniformity of Calibration Source, fu (Type B)

Large area sources may have a non-uniform activity distribution across their surfaces. For the 

14C source, the uniformity is assumed to be better than ± 10%. This is based on comparing 10 

cm2 sections of the source. For a typical monitor with a detector area of 50 cm2

 and a calibration 

source area of 100 cm2

, a worst-case condition could be that the area under the detector has an 

activity per unit area that is 10% greater than the mean value for the whole source. (The outer 

area correspondingly will be 10% less than mean value.) Assuming a rectangular distribution, 

this represents an uncertainty of 10 / 3 5.774% = for the source non-uniformity factor. 

Backscatter Factor, fbs (Type B)

Variations in backscatter effects arise from factors such as the nature of the surface on which the 

calibration source is resting and the proximity to scattering surfaces such as walls. This effect 

can be quite marked for photon emitters, but for 14C on aluminum substrates the effect is 

negligible. 

7.8.3.3 Uncertainty Budget 

An important part of the uncertainty analysis is to determine which factors are contributing the 

most to the overall uncertainty. 

To do this, each component of uncertainty ui(y)=ci ui(xi) is squared to give its component of 

variance (ui(y))2

. These are totaled to obtain the total variance, 69.07. Finally, the ratio of each 

component of variance to the total is computed. The relative sensitivity coefficients, ci, are the 

terms multiplying each relative uncertainty term ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

i

x

x

i σ

 in Equation 7-47. 
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NUREG-1575, Supp. 1 7-56 January 2009 

The last column of the uncertainty budget (Table 7.7) shows that the major source of uncertainty 

is due to source non-uniformity (48%) followed by the voltage factor (24%) and the reading of 

the source (21%). Thus, to decrease the overall uncertainty, attention should be paid to those 

factors first. 

Table 7.7 Uncertainty Budget for the Efficiency Example 

Source of 

Uncertainty Type 

Probability 

Distribution 

Relative 

Sensitivity 

Coeffient,ci 

ui(xi) 

(%)

ui(y)= 

ci ui(xi) 

(%) (ui(y))2

(ui(y))2/Total 

Standard deviation of 

mean of M A Normal 1.10 3.45 3.80 14.44 0.21 

Standard deviation of 

mean of B B Rectangular 0.098 13.32 1.31 1.72 0.02 

Standard uncertainty 

of calibration source 

emission rate, E

B Normal 1.0 0.24 0.24 0.06 0.00 

Half -width of source 

length, L and width 

W on the area A

B 

Product of 2 

independent 

rectangular 

1.0 0.816 0.816 0.666 0.01 

Half -width of voltage 

factor, fV

B Triangular 1.0 4.08 4.08 16.65 0.24 

Half -width of sourcedetector separation 

factor, fd

B Rectangular 1.0 1.50 1.50 2.25 0.03 

Half-width of 

calibration source 

non- uniformity 

factor, fu

B Rectangular 1.0 5.77 5.77 33.29 0.48 

Uncertainty of 

backscatter factor, fbs

B n.a. 1.0 0.0 0.0 0.00 0.00 

Combined standard 

uncertainty Normal — — 

8.31 

=

69.07

Total= 

69.07 0.99 

Expanded uncertainty 

(k=2) Normal — — 2⋅8.31= 

16.6 — — 

7.8.3.4 Reported Result 

Using the formula above, the calibration factor in terms of emission rate becomes: 

( ) ( )

( ) (362.5 32.5) 1 1 1 1

2732

100

MB f f f f V d u bs

E

A

ε

−×××× − ×××× = = = 12.1 (counts ×s−1

)/(s−1 × cm−2) (7-48)
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-57 NUREG-1575, Supp. 1 

The CSU is (12.1)×(0.0831) = 1.0056. The reported expanded uncertainty will be 2.0, based on a 

standard uncertainty of 1.0 multiplied by a coverage factor of k = 2, which provides a level of 

confidence of approximately 95%. 

7.9 Calculate the Minimum Detectable Concentration 

This section is intended to expand on the material in Section 7.5. It contains more statistical 

detail and more complex examples. This advanced material may be deferred on a first reading of 

MARSAME. 

7.9.1 Critical Value 

In the terminology of ISO 11843-1 (1997), the measured concentration is the state variable, 

denoted by Y, which represents the state of the material being analyzed. The state variable 

usually cannot be observed directly, but it is related to an observable response variable, denoted 

by X, through a calibration function F, the mathematical relationship being written as X = F(Y). 

The response variable X is most often an instrument signal, such as the number of counts 

observed. The inverse, Y = F−1

( X ) of the calibration function is sometimes called the evaluation 

function. The evaluation function, which gives the value of the net concentration in terms of the 

response variable, is closely related to the mathematical model 1 2 (, ,, ) Y fX X X = K N described 

in Section 7.8.1.1. 

Either the null or the alternative hypothesis is chosen on the basis of the observed value of the 

response variable, X. The value of X must exceed a certain threshold value to justify rejection of 

the null hypothesis and acceptance of the alternative hypothesis. This threshold is called the 

critical value of the response variable and is denoted by xC. 

The calculation of xC requires the choice of a significance level for the test. The significance 

level is a specified upper bound for the probability, α, of a Type I error. The significance level is 

usually chosen to be 0.05. This means that when there is no radiation or radioactivity present 

(above background), there should be at most a 5% probability of incorrectly deciding that it is 

present. 

The critical value of the concentration, yC, is defined as the value obtained by applying the 

evaluation function, F−1

, to the critical value of the response variable, xC. Thus, yC = F−1 (xC). 

When x is the gross instrument signal, this formula typically involves subtraction of the 

background signal and division by the counting efficiency, and possibly other factors.

A detection decision can be made by comparing the observed gross instrument signal to its 

critical value, xC, as indicated above. However, it has become standard practice to make the 

decision by comparing the net instrument signal to its critical value, SC. The net signal is 

calculated from the gross signal by subtracting the estimated blank value and any interference. 

The critical value of the net instrument signal, SC, is calculated from the critical gross signal, xC, 

by subtracting the same correction terms; so, in principle, either approach should lead to the 

same detection decision. 
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NUREG-1575, Supp. 1 7-58 January 2009 

Because the term “critical value” alone is ambiguous, one should specify the variable to which 

the term refers. For example, one may discuss the critical (value of the) radionuclide 

concentration, the critical (value of the) net signal, or the critical (value of the) gross signal. In 

this document, the signal is usually a count, and the critical value generally refers to the net 

count. 

The response variable is typically an instrument signal, whose mean value generally is positive 

even when there is radioactivity present (i.e., above background). The gross signal must be 

corrected by subtracting an estimate of the signal produced under those conditions. See Section 

7.8.2.1 (Instrument Background). 

7.9.2 Minimum Detectable Concentration 

The minimum detectable concentration (MDC) is the minimum concentration of radiation or 

radioactivity that must be present in a sample to give a specified power, 1 − β. It may also be 

defined as: 

• The minimum radiation or radioactivity concentration that must be present to give a 

specified probability, 1 − β, of detecting the radiation or radioactivity; or 

• The minimum radiation or radioactivity concentration that must be present to give a 

specified probability, 1 − β, of measuring a response greater than the critical value, leading 

one to conclude correctly that there is radiation or radioactivity present. 

The power of any hypothesis test is defined as the probability that the test will reject the null 

hypothesis when it is false, i.e., the correct decision. Therefore, if the probability of a Type II 

error is denoted by β, the power is 1 − β. In the context of radiation or radioactivity detection, the 

power of the test is the probability of correctly detecting the radiation or radioactivity 

(concluding that the radiation or radioactivity is present), which happens whenever the response 

variable exceeds its critical value. The power depends on the concentration of the radiation or 

radioactivity and other conditions of measurement; so, one often speaks of the “power function” 

or “power curve.” Note that the power of a test for radiation or radioactivity detection generally 

is an increasing function of the radiation or radioactivity concentration (i.e., the greater the 

radiation or radioactivity concentration, the higher the probability of detecting it). 

In the context of MDC calculations, the value of β that appears in the definition, like α, is usually 

chosen to be 0.05 or is assumed to be 0.05 by default if no value is specified. The minimum 

detectable concentration is denoted in mathematical expressions by yD. The MDC is usually 

obtained from the minimum detectable value of the net instrument signal, SD. SD, is defined as 

the mean value of the net signal that gives a specified probability, 1 − β, of yielding an observed 

signal greater than its critical value SC. The relationship between the critical value of the net 

instrument signal, SC, and the minimum detectable net signal, SD, is shown in Figure 7.6 in 

Section 7.5.2. 

The term MDC must be carefully and precisely defined to prevent confusion. The MDC is by 

definition an estimate of the true concentration of the radiation or radioactivity required to give a 

specified high probability that the measured response will be greater than the critical value. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-59 NUREG-1575, Supp. 1 

The common practice of comparing a measured concentration to the MDC, instead of to the SC, 

to make a detection decision is incorrect. If this procedure were used, then there would be only a 

a 50% chance of deciding that radioactivity was present when the concentration was actually at 

the MDC. This is in direct contradiction to the definition of MDC. See MARLAP Appendix B, 

Attachment B1 for a further discussion of this issue. 

Since the MDC is calculated from measured values of input quantities such as the counting 

efficiency and background level, the MDC estimate has a CSU, which in principle can be 

obtained by uncertainty propagation. To avoid confusion, it may be useful to remember that a 

detection decision is usually made by comparing the instrument response to the critical value, 

and that the critical value generally does not even have the units of radiation or radioactivity 

concentration. 

7.9.3 Calculation of the Critical Value 

If the net signal is a count, then in many circumstances the uncertainty in the count can be 

estimated by a Type B evaluation using the fact that for a Poisson distribution with mean NB, the 

variance is also NB. Thus, the uncertainty in the background count is estimated as NB and the 

critical value is often an expression involving NB . 

The most commonly used approach for calculating the critical value of the net instrument signal, 

SC, is given by the following equation.11

1 1 S S

C B

B B

t t Sz N

t t −α

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

 (7-49) 

Where: 

NB = background count 

tS = count time for the sample 

tB = count time for the background 

z1-α = (1 − α)-quantile of the standard normal distribution 

Example 19: A 6,000-second background measurement is performed on a proportional counter 

and 108 beta counts are observed. A sample is to be counted for 3,000 s. Estimate the critical 

value of the net count when α = 0.05. 

1 1 S S

C B

B B

t t Sz N

t t −α

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

 

11 This expression for the critical net count depends for its validity on the assumption of Poisson counting statistics. 

If the variance of the blank signal is affected by interferences, or background instability, then Equation 20.7 of 

MARLAP may be more appropriate. Interference is the presence of other radiation or radioactivity or electronic 

signals that hinder the ability to analyze for the radiation or radioactivity of interest.
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NUREG-1575, Supp. 1 7-60 January 2009 

14.8 net counts

6,000 s

3,000 s 1

6,000 s

3,000 s 1.645 108 = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ SC = ×

If α = 0.05 and tB = tS, Equation 7-49 leads to the well-known expression 2.33 NB for the 

critical net count (Currie, 1968). 

When the background count is high (e.g., 100 or more), Equation 7-49 works well, but at lower 

background levels it can produce a high rate of Type I errors. Because this is a Scenario B 

hypothesis test, this means that too often a decision will be made that there is radiation or 

radioactivity present when it actually is not. 

When the mean background counts are low and tB ≠ tS, another approximation formula for SC

appears to out-perform all of the other approximations reviewed in MARLAP, namely the 

Stapleton approximation:

( )

2

1

1 11 1

4

S S SS

C B

B B BB

t z t tt Sd z Nd

t t tt

α

α

−

−

⎛⎞ ⎛⎞ ⎛⎞

=× − + × + + + + ⎜⎟ ⎜⎟ ⎜⎟ ⎝⎠ ⎝⎠ ⎝⎠

 (7-50) 

When α = 0.05, setting the parameter d = 0.4 yields the best results. When, in addition, tB = tS, 

the Stapleton approximation gives the equation 

= 1.35 + 2.33 + 0.4 C NB S (7-51) 

7.9.4 Calculation of the Minimum Detectable Value of the Net Instrument Signal 

The traditional method for calculating the MDC involves three steps: first calculating critical 

value of the net instrument signal, then calculating the minimum detectable value of the net 

instrument signal and finally converting the result to a concentration using the mathematical 

measurement model. 

The minimum detectable value of the net instrument signal, denoted by SD, is defined as the 

mean value of the net signal that gives a specified probability, 1 − β, of yielding an observed 

signal greater than its critical value, SC. 

Note: The MDC may be estimated by calculating the minimum detectable value of the net 

instrument signal, SD, and converting the result to a concentration. 

Counting data rarely, if ever, follow the Poisson model exactly, but the model can be used to 

calculate SD if the variance of the background signal is approximately Poisson and a conservative 

value of the efficiency constant, ε, is used to convert SD to yD. The equation below shows how to 

calculate SD using the Poisson model. 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + + + + + −

−

−

B

S

D C C B S t

t S R t

z

z

z

S S 1

2 4

2

1

1

2

1 β

β

β (7-52) 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-61 NUREG-1575, Supp. 1 

Where: 

SC = critical value 

RB = mean count rate of the blank, 

B

B

B t

N R =

NB = background count 

tS = count time for the test source 

tB = count time for the background

z1-β = (1 − β)-quantile of the standard normal distribution 

When Equation 7-49 is appropriate for the critical net count, and α = β, this expression for SD

simplifies to C z 2S 2

1−β + . If in addition, α = β = 0.05 and tB = tS then 

2.71 2 2.71 2(2.33 ) 2.71 4.66 D C BB SS N N =+=+ =+ (7-53) 

Example 20: A 6,000-s background measurement on a proportional counter produces 108 beta 

counts and a source is to be counted for 3,000 s. Assume the background measurement gives the 

available estimate of the true mean background count rate, RB and use the value 0.05 for Type I 

and Type II error probabilities. From Section 7.9.3, Example 19, the critical net count, SC, equals 

14.8, so 2 2

1 2 1.645 2 (14.8) 32.3 net counts. D C Sz S =+= + = −β

When the Stapleton approximation (Equation 7-51) is used for SC, the minimum detectable net 

count SD may be calculated using the Equation 7-53, but when the Poisson model is assumed, a 

better estimate is given by the equation: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ + + ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

+ = − −

− −

B

S

B S

B

S

D t

t

z z R t

t

z z t S 1 ( ) 1

4

( )

1 1

2

1 1

α β

α β (7-54) 

This equation is the same as that recommended by ISO 11929-1 (ISO 2000) in a slightly 

different form. 

When α = β = 0.05 and tB = tS, the preceding equation becomes: 

D B S S = 5.41+ 4.65 R t (7-55) 

Consult MARLAP Chapter 20 for a discussion of the calculation of SD and yD when both Poisson 

counting statistics and other sources of variance are considered. 

7.9.5 Calculation of the Minimum Detectable Concentration 

The MDC is often used to compare different measurement procedures against specified 

requirements. The calculation of the nominal MDC is complicated by the fact that some input 

quantities in the mathematical model, such as interferences, counting efficiency, and instrument 

background may vary significantly from measurement to measurement. Because of these 

variable quantities, determining the value of the radiation or radioactivity concentration that 
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NUREG-1575, Supp. 1 7-62 January 2009 

corresponds to the minimum detectable value of the net instrument signal, SD, may be difficult in 

practice. One common approach to this problem is to make conservative choices for the values of 

the variable quantities, which tend to increase the value of the MDC. 

The mean net signal, S, is usually directly proportional to Y, the true radiation or radioactivity 

concentration present. Hence, there is a efficiency constant, ε, such that S = εY. The constant ε is 

typically the mean value of the product of factors such as the source count time, decay-correction 

factor, and counting efficiency. Therefore, the value of the minimum detectable concentration, 

yD, is

D

D

S

y

ε = (7-56) 

The preceding equation is only true if all sources of variability are accounted for when 

determining the distribution of the net signal, S

ˆ . Note that ensuring the MDC is not 

underestimated also requires that the value of ε not be overestimated. 

Using any of the equations in Section 7.5.2 to calculate SD is only appropriate if a conservative 

value of the efficiency constant, ε, is used when converting SD to the MDC. 

Example 21: Consider a scenario where tB = 6,000 s, tS = 3,000 s, and RB ≈ 0.018 s-1. Let the 

measurement model be ( /) S BS B

S

N Nt t Y

t ε

− =

Where: 

 Y = activity of the radionuclide in the sample and 

ε = counting efficiency 2 (counts per second)/(Bq/cm )

Assume the source count time, tS, has negligible variability, the counting efficiency has mean 

0.42 and a 10% relative CSU, and from Example 20, SD = 32.3 net counts. 

The mean minimum detectable concentration is 32.3 2 0.0256 Bq/cm . (3000)(0.42)

D

D

S

S

y

t ε == =

Adjusting for the 10% variability in the counting efficiency, the uncertainty is (0.10)×(0.42) = 

0.042. Assuming that the efficiency is normally distributed, the lower 5th percentile for ε is 

(0.42)−(1.645)(0.042) = 0.35, where −1.645 is the 5th percentile of a standard normal 

distribution. Therefore, a conservative estimate of the efficiency constant is ε = 0.35 and a 

conservative estimate of the minimum detectable concentration is: 

32.3 2 0.0308 Bq/cm . (3000)(0.35)

D

D

S

S

y

t ε == =

An alternative procedure could be to recognize that because of the uncertainties in the input 

estimates entered into the measurement model to convert from SD to Y, that the MDC is actually 

a random variable. Then the methods for propagation of uncertainty given in Section 7.8 can be 

applied. Using the same assumptions as above, we would find that yD = 0.0256 ± 0.0051 with 

95% confidence based on a coverage factor of 2. Therefore the 95% upper confidence level for 

yD would be 0.0307 Bq. 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-63 NUREG-1575, Supp. 1 

More conservative (higher) estimates of the MDC may be obtained by following NRC 

recommendations (NRC 1984), in which formulas for the MDC include estimated bounds for 

relative systematic error in the background determination ( ΔB| ) and the sensitivity ( ΔA| ). The 

critical net count SC is increased by ΔB| NB (tS/tB), and the minimum detectable net count SD is 

increased by 2 ΔB| NB (tS/tB). Next, the MDC is calculated by dividing SD by the efficiency and 

multiplying the result by 1+ ΔA| . The conservative approach presented in NRC 1984 treats 

random errors and systematic errors differently to ensure that the MDC for a measurement 

process is unlikely to be consistently underestimated, which is an important consideration if it is 

required by regulation or contract to achieve a specified MDC. 

7.10 Calculate the Minimum Quantifiable Concentration 

This section is intended to expand on the material in Section 7.6. It contains more statistical 

detail and more complex examples. This advanced material may be deferred on a first reading of 

MARSAME. 

Calculation of the MQC requires that one be able to estimate the standard deviation for the result 

of a hypothetical measurement performed on a sample with a specified radionuclide 

concentration. The MQC is defined symbolically as the value Qy that satisfies the relation: 

2

(| ) QQ Q y = = k yY y σ (7-57) 

Where the specified relative standard deviation of Qy is 1/ Qk (usually chosen to be 10% so that 

Qk = 10). 2

(| ) Q σ yY y = is the variance of the estimator y given the true concentration Y equals 

Qy . If the function 2

(| ) Q σ yY y = has a simple form, it may be possible to solve the above 

equation for Qy using only algebraic manipulation. Otherwise, fixed-point iteration, or other 

more general approaches, may be used, as discussed in MARLAP Section 20.4.3. 

When Poisson counting statistics are assumed, and the mathematical model for the radionuclide 

concentration is 

 Y = S /ε (7.58) 

Where: 

S = net count 

 tS = count time for the source 

S / tS = net count rate 

ε = efficiency of the measurement 

Then Equation 7-57 may be solved for Qy to obtain: 
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NUREG-1575, Supp. 1 7-64 January 2009 

2 22

ˆ 2 2

22 2

ˆ

4(1 ) 1 1 1 () 2 (1 )

Q Q S

Q BS IS I S

SQ Q B

k k t

y Rt Rt R t

tk k t

ε

ε

φ

σ

ε φ

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞

= ++ + + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

) (7-59) 

Where: 

tS = count time for the source, s 

tB = count time for the background, s

RB = mean background count rate, s

−1

RI = mean interference count rate, s

−1

( ) RI

) σ = standard deviation of the measured interference count rate, s

−1

, and

2 φεˆ = relative variance of the measured efficiency, εˆ

If the efficiency ε may vary, then a conservative value, such as the 0.05-quantile ε0.05, should be 

substituted forε in the formula. Note that 2 φεˆ denotes only the relative variance of εˆ due to 

subsampling and measurement error; it does not include any variance of the efficiency ε itself 

(see discussion in Section 7.8). 

Note that Equation 7-59 defines the MQC only if 2 2 1 Q ˆ k − φε > 0. If 2 2 1 Q ˆ k − φε ≤ 0, the MQC is 

infinite, because there is no concentration at which the relative standard deviation of y fails to 

exceed 1 / Qk . In particular, if the relative standard deviation of the measured efficiency εˆ

exceeds 1 / Qk , then 2 2 1 Q ˆ k − φε < 0 and the MQC is infinite. 

If there are no interferences, Equation 7-59 simplifies to:

2 22

ˆ

22 2

ˆ

4(1 ) 11 1

2 (1 )

Q Q S

Q BS

SQ Q B

k k t y Rt tk k t

ε

ε

φ

ε φ

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞

= ++ + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

 (7-60) 

Example 22: Consider the scenario of Example 21, where tB = 6,000 s, tS = 3,000 s, and 

RB ≈ 0.018 s-1. Suppose the measurement model is ( /) S BS B

S

N Nt t Y

t ε

− =

Where: 

 Y = specific activity of the radionuclide in the sample 

ε = counting efficiency (cps/Bq)/(Bq/cm2) 

Assume the source count time, tS, has negligible variability, the counting efficiency has a mean 

of 0.42 and a 5% relative CSU, and SD = 32.3 net counts. 

SD / tS =32.3/3000 is the net count rate and the counting efficiency, ε, is 0.42. 

The mean minimum detectable concentration is 32.3 2 0.0256 Bq/cm . (3000)(0.42)

D

D

S

S

y

t ε == =

Also assume: 

Qk = 10 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-65 NUREG-1575, Supp. 1 

φεˆ = 0.05 

2 φεˆ = 0.052 

22 2 1 1 100 (0.05 ) 0.75 − =− × = kQφεˆ

There are no interferences so that Equation 7-60 can be used. 

Note that if the counting efficiency had a mean of 0.42 and a 10% relative standard uncertainty 

as in Example 11, then 22 2 1 1 100 (0.10 ) 0 Q ˆ k − =− × = φε and the MQC would be infinite. Therefore it 

was necessary to change the procedure for evaluating the efficiency in this example so that the 

relative CSU could be reduced. In this example it is assumed to be 5%. 

The MQC can be calculated as: 

2 22

ˆ

22 2

ˆ

4(1 ) 11 1 0

2 (1 )

Q Q S

Q BS

SQ Q B

k k t y Rt tk k t

ε

ε

φ

ε φ

⎛ ⎞ − ⎛ ⎞ ⎛ ⎞

= ++ + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ − ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

100 4 (0.75) (3000 s) 1 1 1 (0.018 s )(3000 s) 1 0 2 (3000)(0.42)(0.75) 100 (6000 s) Qy −

⎛ ⎞ ⎛ ⎞ ⎛ ⎞

= ++ + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

= 0.151 Bq/cm2 

As a check, yQ can be calculated in a different way. If yQ is the MQC and kQ = 10, then the 

relative CSU of a measurement of concentration yQ is 10%. The procedure described in Section 

7.4 can be used to predict the CSU of a measurement made on a hypothetical sample whose 

concentration is exactly yQ = 0.151 Bq/cm2. 

The measurement model is ( /) S BS B

S

N Nt t Y

t ε

− = . 

Recall from Section 7.8.1.6 that if y = 1 2

1 2

(, , , ) n

m

f xx x

zz z

K

K , where f is some specified function of 

x1,x2,...,xn, all the zi are nonzero, and all the input estimates are uncorrelated that the CSU may 

be calculated using Equation 7-35: 

2 2 2 2

2 2 1 2 1 2

22 2

12 1 2

( ( , , , )) ( ) () () ( ) cn m

c

m m

u fxx x u z uz uz

uy y

zz z z z z

⎛ ⎞

= + + ++ ⎜ ⎟ ⎝ ⎠

K K K

Substituting 

y = Y, 

1 2 (, , , ) n f xx x K =( , ,,) S BSB f NNtt = ( / )/ N Nt t t S BS B S − , 

1z = ε, and 

2

( ( / )/ ) c S BS B S u N Nt t t − = 

2 2 ( / ) (( / ) / ) c S S c BS B S u N t u Nt t t + = 

2 22

2

( )(/) ( ) c S SB c B

S

uN t t uN

t

+

 = 

2 2 2 2

2

(/) S B SB

S

N N tt

t

+

= 

2 2

2

(/) S BS B

S

N Nt t

t

+
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-66 January 2009 

Results in: 

2 2 2

2 2

22 2

( /) ( ) ( ) S BS B

c

S

N Nt t u uY Y

t

ε

ε ε

+ ⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

 or 

2 2 2

2

22 2

( /) ( ) ( ) S BS B

c

S

N Nt t u uY Y

t

ε

ε ε

+ ⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

Inserting the values 

Y= Qy = 0.151 Bq/cm2

tB = 6,000 s 

tS = 3,000 s 

ε = 0.42 2 (counts per second)/(Bq/cm )

(0.018 s )(3,000 s) 108 1 = = = −

B B B N R t and 

1 (0.151 Bq)(3000 s)(0.42) (0.018 s )(3,000 s) 244.26 N xt Rt S QS BB ε − = += + =

yields 

2 2

22 2

2 2

244.26 (108)(3,000) /(6,000) ( ) (0.151) (0.05 ) 0.0151 Bq/cm (3000) (0.42) c u Y +

= +=

Thus, the uncertainty at yQ = 0.151 is 0.0151 and the relative uncertainty is 0.1, so yQ is verified 

to be the MQC. 

As above in this example, we adjust for the (now) 5% relative CSU in the counting efficiency. 

The uncertainty is (0.05) × (0.42) = 0.02142. Assuming that the efficiency is normally 

distributed, the lower 5th percentile is (0.42) - (1.645)(0.021) = 0.385. Therefore a conservative 

estimate of the efficiency is ε = 0.385 and a conservative estimate of the minimum detectable 

concentration is: 2 (0.151)(0.42) 0.165 Bq/cm . 0.385 Qy = =

7.11 Calculate Scan MDCs 

The methodology used to determine the scan MDC is based on NUREG-1507 (NRC 1998b). 

This procedure is quite complex as it requires, among other skills, a familiarity with radiation 

transport calculations for its implementation. The information developed here will be used in the 

example in Section 8.2, “Mineral Processing Facility Concrete Rubble.” However, the details 

given in this section are not essential to understanding the example. 

The radionuclides of concern are the members of the natural uranium and thorium series. The 

instrument used is a “Field Instrument for the Detection of Low Energy Radiation” (FIDLER). 

The approach used would be similar for other instruments and radionuclides. 

The approach to determine scan MDCs includes: 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-67 NUREG-1575, Supp. 1 

• Calculate the fluence rate relative to the exposure rate (FRER) for the range of energies of 

interest (Section 7.11.1). 

• Calculate the probability of interaction (P) between the radiation of interest and the detector 

(Section 7.11.2). 

• Calculate the relative detector response (RDR) for each of the energies of interest (Section 

7.11.3). 

• Determine the relationship between the detector’s net count rate to net exposure rate in 

cpm/μR/h, Section 7.11.4). 

• Determine the relationship between the detector response and the radionuclide concentration 

(Section 7.11.5). 

• Obtain the minimum detectable count rate (MDCR) for the ideal observer, for a given level 

of performance, by postulating detector background and a scan rate or observation interval 

(Section 7.11.6). 

• Relate the MDCR for the ideal observer to a radionuclide concentration (in Bq/kg) to 

calculate the scan MDC (Section 7.11.7). 

7.11.1 Calculate the Relative Fluence Rate to Exposure Rate (FRER) 

For particular gamma energies, the relationship of NaI scintillation detector count rate 

and exposure rate may be determined analytically (in cpm/μR/h). The approach is to 

determine the gamma fluence rate necessary to yield a fixed exposure rate (μR/h) as a 

function of gamma energy. The fluence rate, following NUREG-1507 (NRC 1998b), is 

directly proportional to the exposure rate and inversely proportional to the incident 

photon energy and mass energy absorption coefficient: 

1 1 (FRER) ( /) en air

Fluence Rate X

Eγ μ ρ ∝ & (7-61) 

Where: 

X& = exposure rate (set equal to 1 μR/hr for these calculations) 

Eγ = energy of the gamma photon of concern (keV) 

(μen /ρ)air = mass energy absorption coefficient in air at the gamma photon energy of 

concern (cm2

/g) 

The mass energy absorption coefficients in air are presented in Table 7.8 (natural uranium) and 

Table 7.9 (natural thorium) along with the calculated fluence rates (up to a constant of 

proportionality, since only the ratios of these values are used in subsequent calculations). Note 

that while the mass energy absorption coefficients in air, (μen/ρ)air, are tabulated values (NIST 

1996), the selected energies are determined by the calculation of the detector response based on 

radionuclide concentration (Section 7.11.5). 

7.11.2 Calculate the Probability of Interaction 

Assuming that the primary gamma interaction producing the detector response occurs through 

the end of the detector (i.e., through the beryllium window of the detector, as opposed to the 

sides), the probability of interaction (P) for a gamma may be calculated using Equation 7-52: 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-68 January 2009 

2 3 NaI NaI ( / ) ( )( ) (0.117 cm /g)(0.16 cm)(3.67 g/ cm ) P 1 1 0.066 at 400 keV x e e − μρ ρ − =− =− = (7-62) 

Where: 

P = probability of interaction (unitless) 

(μ/ρ)NaI = mass attenuation coefficient of FIDLER NaI crystal at the energy of interest 

(e.g., 0.117 cm2

/g at 400 keV) 

x = thickness of the thin edge of the FIDLER NaI crystal (0.16 cm) 

ρ = density of the NaI crystal (3.67 g/cm3) 

The mass attenuation coefficients for the NaI crystal and the calculated probabilities for each of 

the energies of interest are presented in Table 7.8 (natural uranium) and Table 7.9 (natural 

thorium). The mass attenuation coefficients for NaI were calculated using the XCOM program 

(NIST 1998). 

Table 7.8 Calculation of Detector Response to Natural Uranium 

Energy 

(keV) 

(μen/ρ)air

(cm2/g) 

FRER 

(Section 

7.11.1) 

(μ/ρ)NaI

cm2/g 

P 

(Section 

7.11.2) 

RDR 

(Section 

7.11.3) 

cpm per 

μR/h 

(Section 

7.11.4) 

15 1.334 0.04998 47.4 1.000 0.04998 28,374 

20 0.5389 0.09278 21.8 1.000 0.09278 52,678 

30 0.1537 0.2169 7.36 0.9867 0.2140 121,498 

40 0.06833 0.3659 18.8 1.000 0.3659 207,725 

50 0.04098 0.4880 10.5 0.9979 0.4870 276,511 

60 0.03041 0.5481 6.45 0.9773 0.5356 304,123 

80 0.02407 0.5193 3.00 0.8282 0.4301 244,204 

100 0.02325 0.4301 1.67 0.6249 0.2688 152,606 

150 0.02496 0.2671 0.611 0.3015 0.08052 45,717 

200 0.02672 0.1871 0.328 0.1752 0.03278 18,613 

300 0.02872 0.1161 0.166 0.09288 0.01078 6,120 

400 0.02949 0.08477 0.117 0.06640 0.005629 3,196 

500 0.02966 0.06743 0.0950 0.05426 0.003659 2,077 

600 0.02953 0.05644 0.0822 0.04712 0.002660 1,510 

662 0.02931 0.05154 0.0766 0.04398 0.002267 1,287 

800 0.02882 0.04337 0.0675 0.03886 0.001685 957 

1,000 0.02789 0.03586 0.0588 0.03394 0.001217 691 

1,500 0.02547 0.02617 0.0470 0.02722 0.0007125 405 

2,000 0.02345 0.02132 0.0415 0.02407 0.0005133 291 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-69 NUREG-1575, Supp. 1 

Table 7.9 Calculation of Detector Response for Natural Thorium 

Energy 

(keV) 

(μen/ρ)air

(cm2/g) 

FRER 

(Section 

7.11.1) 

(μ/ρ)NaI

cm2/g 

P 

(Section 

7.11.2) 

RDR 

(Section 

7.11.3) 

cpm per 

μR/h 

(Section 

7.11.4) 

40 0.06833 0.3659 18.8 1.000 0.3659 207,725 

60 0.03041 0.5481 6.45 0.9773 0.5356 304,123 

80 0.02407 0.5193 3.00 0.8282 0.4301 244,204 

100 0.02325 0.4301 1.67 0.6249 0.2688 152,606 

150 0.02496 0.2671 0.611 0.3015 0.08052 45,717 

200 0.02672 0.1871 0.328 0.1752 0.03278 18,613 

300 0.02872 0.1161 0.166 0.09288 0.01078 6,120 

400 0.02949 0.08477 0.117 0.06640 0.005629 3,196 

500 0.02966 0.06743 0.0950 0.05426 0.003659 2,077 

600 0.02953 0.05644 0.0822 0.04712 0.002660 1,510 

662 0.02931 0.05154 0.0766 0.04398 0.002267 1,287 

800 0.02882 0.04337 0.0675 0.03886 0.001685 957 

1,000 0.02789 0.03586 0.0588 0.03394 0.001217 691 

1,500 0.02547 0.02617 0.0470 0.02722 0.0007125 405 

2,000 0.02343 0.02134 0.0415 0.02407 0.0005137 292 

3,000 0.02057 0.01620 0.0368 0.02138 0.0003464 197 

7.11.3 Calculate the Relative Detector Response 

The relative detector response (RDR) for each of the energies of interest is determined by 

multiplying the FRER by P. The results are presented in Table 7.8 (natural uranium) and Table 

7.9 (natural thorium). 

7.11.4 Relationship Between Detector Response and Exposure Rate 

Using the same methodology described in Sections 7.11.1 through 7.11.3, FRER, P, and RDR 

are calculated at the 137Cs energy of 662 keV and are also presented in Table 7.8 and Table 7.9. 

The manufacturer of the FIDLER NaI detector provides an estimated response of the crystal in a 

known radiation field, which is 1,287 cpm per μR/h at the 137Cs energy of 662 keV. The 

response at 662 keV can be used to determine the response at all other energies of interest using 

Equation 7-63: 

Cs

E

E 137

i

i RDR

RDR

R/h

1,287 cpm

μR/h

cpm ×⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = μ

 (7-63) 

Where: 

Ei = energy of the photon of interest (keV) 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-70 January 2009 

Ei μR/h

cpm = response of the detector for energies of interest, Table 7.8 and Table 7.9 

Ei RDR = RDR at the energy of interest, Table 7.8 and Table 7.9 

Cs RDR137 = RDR for 137Cs, Table 7.8 and Table 7.9

The responses in cpm per μR/h for each of the decay energies of interest are presented in Tables 

7.8 and 7.9. 

7.11.5 Relationship Between Detector Response and Radionuclide Concentration 

The minimum detectable exposure rate is used to determine the MDC by modeling a specific 

impacted area. The relationship between the detector response (in cpm) and the radionuclide 

concentration (in Bq/kg) uses a computer gamma dose modeling code to model the presence of a 

normalized 1 Bq/kg total activity source term for natural uranium and natural thorium. The 

following assumptions from NUREG-1507 (NRC 1998b) were used to generate the computer 

gamma dose modeling runs: 

• Impacted media is concrete, 

• Density of concrete is 2.3 g/cm3, 

• Activity is uniformly distributed into a layer of crushed concrete 15 cm thick, 

• Measurement points are 10 cm above the concrete surface, 

• Areas of elevated activity are circular with an area of 0.25 m2 and a radius of 28 cm, 

• 0.051 cm beryllium shield simulates the window of the FIDLER detector, and 

• Normalized 1 Bq/kg source term decayed for 50 years to allow ingrowth of decay progeny. 

The weighted cpm per μR/h response (weighted instrument sensitivity [WSi]) for each decay 

energy is calculated by multiplying the μR/h at 1 Bq/kg (exposure rate with buildup, Ri) by the 

cpm per μR/h and dividing by the total μR/h (at 1 Bq/kg) for all decay energies of interest 

(Equation 7-64): 

(cpm per R / h) i

i

T

R WS

R

× μ = (7-64) 

Where: 

WSi = weighted instrument sensitivity (cpm per μR/h) 

Ri = exposure rate with buildup (μR/h) 

RT = Total exposure rate with buildup (μR/h) 

Calculate the percent of FIDLER response for each of the decay energies of interest by dividing 

WSi by the total weighted cpm per μR/h and multiplying by 100 percent (Equation 7-62): 

100% Percent of FIDLER response i

T

WS

W

× = (7-65)

Where: 

WT = Total WSi weighted instrument sensitivity (cpm per μR/h) 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-71 NUREG-1575, Supp. 1 

The exposure rates for each of the decay energies of interest are presented in Table 7.10 

(assuming natural uranium for the source term) and Table 7.11 (assuming natural thorium for the 

source term). 

Table 7.10 Detector Response to Natural Uranium 

Energy 

keV 

Ri

(μR/h) 

(Section 7.11.5) 

cpm per μR/h 

(Section 7.11.4) 

WSi

(cpm per μR/h) 

(Section 7.11.5) 

Percent of 

FIDLER 

Response 

(Section 7.11.5) 

15 4.473×10−10 28,374 0 0.00% 

20 3.597×10−12 52,678 0 0.00% 

30 2.623×10−07 121,498 226 0.504% 

40 1.299×10−10 207,725 0 0.00% 

50 1.052×10−07 276,511 206 0.460% 

60 5.065×10−06 304,123 10903 24.3% 

80 1.518×10−06 244,204 2625 5.86% 

100 2.309×10−05 152,606 24938 55.7% 

150 5.138×10−06 45,717 1663 3.71% 

200 2.881×10−05 18,613 3796 8.48% 

300 2.237×10−07 6,120 10 0.0216% 

400 2.434×10−07 3,196 6 0.0123% 

500 4.208×10−07 2,077 6 0.0138% 

600 2.048×10−06 1,510 22 0.0489% 

800 1.478×10−05 957 100 0.224% 

1,000 5.759×10−05 691 282 0.629% 

1,500 1.695×10−06 405 5 0.0108% 

2,000 2.841×10−07 291 1 0.00131% 

Total 1.413×10−04 44,923 100% 

Table 7.11 Detector Response to Natural Thorium 

Energy 

keV 

Ri

(μR/h) 

(Section 7.11.5) 

cpm per μR/h 

(Section 7.11.4) 

WSi

(cpm per μR/h) 

(Section 7.11.5) 

Percent of 

FIDLER 

Response 

(Section 7.11.5) 

40 1.299×10−06 207,725 10 0.266% 

60 1.816×10−06 304,123 21 0.544% 

80 1.989×10−04 244,204 1855 47.8% 

100 5.027×10−05 152,606 293 7.55% 

150 5.862×10−05 45,717 102 2.64% 

200 1.135×10−03 18,613 807 20.8% 

300 8.922×10−04 6,120 209 5.37% 
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-72 January 2009 

Table 7.11 Detector Response to Natural Thorium (Continued) 

Energy 

keV 

Ri

(μR/h) 

(Section 7.11.5) 

cpm per μR/h 

(Section 7.11.4) 

WSi

(cpm per μR/h) 

(Section 7.11.5) 

Percent of 

FIDLER 

Response 

(Section 7.11.5) 

400 1.105×10−04 3,196 13 0.348% 

500 8.146×10−04 2,077 65 1.67% 

600 2.218×10−03 1,510 128 3.30% 

800 2.892×10−03 957 106 2.72% 

1,000 6.443×10−03 691 170 4.38% 

1,500 2.062×10−03 405 32 0.821% 

2,000 5.822×10−05 292 1 0.0167% 

3,000 9.249×10−03 197 69 1.79% 

Total 2.619×10−02 3881 100% 

7.11.6 Calculation of Scan Minimum Detectable Count Rates 

In the computer gamma dose modeling, an impacted area with a radius of 28 cm or 

approximately 0.25 m was assumed. Using a scan speed of 0.25 m/s provides an observation 

interval of one second. 

A typical background exposure rate is 10 μR/h. Using a conversion factor based upon field 

measurements of 1,287 cpm per μR/h for 137Cs (see 7.11.4) results in an estimated background 

count rate of 12,870 cpm. Converting this value from cpm to counts per second (cps) using 

Equation 7-66 results in a background of 214.5 cps. 

1 min 1,287 cpm 1 min (cpm) (sec) 10 R/h 1sec 214.5 cps 60 sec 1 R/h 60 sec

b i μ

μ

× × = × × ×= (7-66) 

Where: 

b = background count rate (12,870 cpm)

i = observation interval length (1 s) 

The MDCR is calculated using the methodology in NUREG-1507 (NRC 1998b) shown in 

Equations 7-67 and 7-68: 

' 1.38 214.5 20.21 counts i i s db = =× = (7-67) 

, 

' 1.38 214.5 28.58 counts

0.5

i

i surveyor

d b

s

p

× == =

MDCR 60 / 20.21 60 /1 1, 212 cpm =× = × = s i i ( ) ( ) (7-68) 
MARSAME Statistical Basis For MARSAME Surveys 

January 2009 7-73 NUREG-1575, Supp. 1 

MDCR 60 / 28.58 60 /1 1,715 cpm surveyor i surveyor = × =× = s i , ( ) ( )

Where: 

bi = average number of counts in the background interval (214.5 counts) 

i = observation interval length (one second) 

p = efficiency of a less than ideal surveyor, range of 0.5 to 0.75 from 

NUREG-1507 (NRC 1998b); a value 0.5 was chosen as a conservative 

value 

d' = detectability index from Table 6.1 of NUREG-1507 (NRC 1998b); a 

value of 1.38 was selected, which represents a true positive detection 

rate of 95% and a false positive detection rate of 60%12

si = minimum detectable number of net source counts in the observation 

interval (counts) 

si,surveyor = minimum detectable number of net source counts in the observation 

interval by a less than ideal surveyor 

MDCR = minimum detectable count rate (cpm) 

MDCRsurveyor = MDCR by a less than ideal surveyor (cpm) 

7.11.7 Calculate the Scan Minimum Detectable Concentration 

The scan minimum detectable concentration (MDC) can be calculated from the minimum 

detectable exposure rate (MDER). The MDER can be calculated using the previously calculated 

total weighted instrument sensitivities (WSi), in cpm per μR/h, for natural uranium and natural 

thorium as shown in Equations 7-69 and 7-70: 

MDCR

MDER surveyor

WT

= (7-69) 

MDER Scan MDC

T

C

R

= × (7-70) 

Where: 

MDER = MDER for the “ith” source term, by a less than ideal surveyor, (μR/h) 

MDCRsurveyor = MDCR rate by a less than ideal surveyor (cpm), from Section 7.11.6 

WT = Total weighted instrument sensitivity (cpm per μR/h, Table 7.10 and 

Table 7.11) 

RT = Total exposure rate with buildup (μR/h, Table 7.10 and Table 7.11) 

C = concentration of source term (set at 1 Bq/kg in Section 7.11.5) 

 

12 A Type I error, misidentifying a background area as elevated will have the consequence that a longer reading will 

be needed to verify the initial decision. This will happen with probability α. A Type II error, missing a true elevated 

area, may lead to incorrectly exceeding the limit for the chosen disposition option. This will happen with probability 

β. Since in this instance the consequences of a Type I error are often considered much lower than the consequences 

associated with a Type II error. Thus, α may be set higher than β. Setting both very low could result in slow 

scanning speeds and operator fatigue.
Statistical Basis For MARSAME Surveys MARSAME

NUREG-1575, Supp. 1 7-74 January 2009 

Scan MDC = minimum detectable concentration (Bq/kg) 

The Scan MDCs for the FIDLER were calculated using Equations 7-69 and 7-70 and the 

instrument response information from Table 7.10 (assuming natural uranium as the source term) 

and Table 7.11 (assuming natural thorium as the source term). The scan MDCs for natural 

uranium and natural thorium using a FIDLER are listed in Table 7.12. 

Table 7.12 Scan MDCs for FIDLER 

Source 

Term 

MDCRsurveyor

(cpm) 

Section 7.11.6 

WT

(cpm per 

μR/h) 

Section 

7.11.5 

MDER 

(μR/h) 

Section 

7.11.7 

RT

(μR/h) 

Section 

7.11.5 

C 

(Bq/kg) 

Section 

7.11.5 

Scan MDC 

(Bq/kg) 

Section 

7.11.7 

Natural 

Uranium 1,715 44,786 0.03829 1.413×10-04 1 271 ≈ 300 

Natural 

Thorium 1,715 3,881 0.4419 2.619×10-02 1 16.9 ≈ 20

The scan MDCs of approximately 300 Bq/kg for uranium and 20 Bq/kg for thorium are both less 

than their respective action levels of 38,000 and 330 Bq/kg, respectively. 
MARSAME Illustrative Examples 

8 ILLUSTRATIVE EXAMPLES 

8.1 Introduction 

This chapter presents illustrative examples providing examples of applications of the information 

in the Multi-Agency Radiation Survey and Assessment of Materials and Equipment manual 

(MARSAME) supplement to the Multi-Agency Radiation Survey and Site Investigation Manual

(MARSSIM). The purpose of these illustrative examples is to illustrate applications of the 

information in conditions that are frequently encountered and cover a broad range of situations. 

The general format for each illustrative example mirrors as closely as possible the information 

presented in MARSAME. References to information, tables, figures, and equations from Chapter 

2 through Chapter 6 are provided throughout the illustrative examples. 

MARSAME contains both procedural as well as informative sections. The illustrative examples 

provide a practical use of the MARSAME process and, as such, generally apply only the 

procedural sections. In addition, much of the information in MARSAME is designed to be 

applied iteratively. In some illustrative examples, the information is applied in a different 

sequence than it is presented in MARSAME because of this iterative nature. 

Section 8.2 provides an example of a disposition survey for a large quantity of bulk material at a 

mineral processing facility. This example establishes gross activity action levels based on 

normalized effective dose equivalents. These action levels are applied with multiple decision 

rules using a MARSSIM-type survey design to collect scan survey data as well as systematic and 

judgmental samples for laboratory analysis. 

Section 8.3 and Section 8.4 are based on the same mineral processing facility that serves as the 

basis for Section 8.2. Section 8.3 provides an example of an interdiction survey for rented heavy 

equipment that is designed to establish a “baseline” estimate of the residual radioactivity 

associated with a front loader before it is brought into a radiological control area (RCA) for the 

impacted bulk material. This baseline survey establishes zero net activity as the lower bound of 

the gray region (LBGR) and applies MARSAME processes to a Scenario B survey design. 

Section 8.4 demonstrates the clearance of the same rented front loader that was brought on to the 

site in Section 8.3. Section 8.4 describes a Scenario A clearance survey based on the same 

surface activity action levels to clear the front loader. Sections that contain redundant 

information are presented in Section 8.3 only and are omitted from Section 8.4. 

8.2 Mineral Processing Facility Concrete Rubble 

This illustrative example is provided for information purposes only and presents a theoretical 

application of MARSAME guidance. This illustrative example discusses the process of 

designing and implementing a MARSSIM-type disposition survey design for a large quantity of 

bulk material at a mineral processing facility. This example includes discussions on most of the 

guidance provided in MARSAME, including establishing gross activity action levels based on 

normalized effective dose equivalents. Calculations of uncertainties associated with scanning 

measurements are included. The MARSSIM-type survey design includes scanning, systematic 

January 2009 8-1 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

samples, and judgmental samples to support a disposition decision. The text is provided to 

illustrate the application of MARSAME guidance, and should not be considered an example 

survey plan. The amount of discussion provided in this example is based on the complexity of 

the problem and the relative difficulty expected from applying or interpreting specific portions of 

MARSAME guidance. The amount of discussion for this example is not related to, and should 

not be used as an estimate of, the level of effort associated with planning, implementing, or 

assessing an actual disposition survey. 

8.2.1 Description 

An abandoned mineral processing facility is being redeveloped for commercial/industrial use. 

The facility processed mineral ores for various metals for over 30 years and was abandoned more 

than 10 years ago. The processing equipment and existing stockpiles of ore were transferred to 

another facility when site renovations began. The receiving facility discovered radioactivity 

levels in excess of background on exterior portions of processing equipment using hand-held 

Geiger-Mueller (GM) “pancake” detectors. 

Prior to discovery of the radioactivity on the processing equipment, the concrete floors had been 

removed from the processing buildings and stockpiled on-site. Note that if the buildings were 

still intact, they could be surveyed using a MARSSIM survey. An investigation is performed to 

trace the source of the radioactivity to the appropriate portion(s) of the mineral processing 

facility. 

8.2.2 Objectives 

The objective is to make an appropriate disposition decision regarding the concrete rubble from 

the impacted portions of the mineral processing facility. It is anticipated that leaks of potentially 

radioactive processing liquids could have occurred throughout the operating lifetime of the 

facility. Airborne radioactive concrete dust may have been released during demolition activities, 

which could have exposed construction personnel and contacted components of the demolition 

equipment. 

8.2.3 Initial Assessment of the M&E 

8.2.3.1 Categorize the M&E as Impacted or Non-Impacted 

As part of the initial assessment (IA), it is necessary to determine whether the concrete rubble is 

impacted or not. A visual inspection of the concrete rubble was performed. Historical records 

from the facility concerning sources of ore, ore processing techniques, waste disposal practices, 

industrial accidents, as well as building and equipment repairs, modifications, and upgrades were 

reviewed. Interviews with key facility personnel were also performed. In addition, research into 

mineral processing techniques and radionuclide content of raw ores was performed to obtain 

additional process knowledge. 

Process knowledge indicated the facility processed ilmenite ore (iron titanium oxide, FeTiO3) 

and produced titanium dioxide. A sentinel measurement of a small amount of ilmenite ore 

NUREG-1575, Supp. 1 8-2 January 2009 
MARSAME Illustrative Examples 

remaining at the site was analyzed by alpha spectrometry and found to contain elevated levels of 

natural uranium and thorium. Additional measurements performed on the radioactive processing 

equipment reported concentrations of uranium and thorium greater than expected from 

background. 

Site history indicates that the general layout of the process was unchanged over the lifetime of 

the facility, and it is likely that spills occurred repeatedly in discrete locations. Processing liquids 

and slurries were considered hazardous because of their low pH; radioactivity was not 

considered an issue. Limited information regarding site history and operations was obtained 

through interviews with former employees and review of historical documentation. Former 

employees stated that spills and leaks of process liquids and slurries occurred periodically in 

several areas of the processing plant; these represent the only potential source of radioactivity in 

the plant. Fluid spills were quickly corrected by neutralizing the acid to protect employees and 

equipment. Spills frequently resulted from seal failure within the various pumps in use at the 

processing operation. 

Results from the visual inspection indicated there was a reasonable potential for radioactivity 

from plant activities to be associated with the concrete rubble. Several chunks of concrete rubble 

are obviously discolored from plant operations, indicating possible locations of spills. The 

facility floor consisted of reinforced concrete on a gravel base mat. Portions of the rubble contain 

possible evidence of staining. The rubble still contains rebar which, for operational reasons, must 

be segregated and treated as a separate waste stream. 

The concrete rubble is considered to be impacted due to the discovery of residual radioactivity 

on exterior portions of the processing equipment, historical records that acidic process fluids may 

have spilled on the concrete floor, and process knowledge that the acidic process fluids were 

mixed with raw ore containing elevated levels of naturally occurring radioactive material 

(NORM) from the uranium and thorium radioactive decay series. The results of the sentinel 

measurement performed on the raw ore support the categorization as impacted. 

8.2.3.2 Describe the M&E 

Table 8.1 lists the physical attributes of the concrete rubble. No data gaps associated with the 

physical attributes were identified. 

Table 8.2 lists the known radiological attributes associated with the concrete rubble, as well as 

data gaps showing where additional information is required to design a disposition survey. As 

presented, the existing information is not adequate to design a disposition survey. Preliminary 

surveys were designed and implemented to address the data gaps identified in Table 8.2. The 

results of the preliminary surveys were used to modify the conceptual site model by filling some 

of the data gaps. 

January 2009 8-3 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

NUREG-1575, Supp. 1 8-4 January 2009 

Table 8.1 Physical Attributes of the Concrete Rubble 

Attribute Description 

Dimensions Total Mass 

400 ft × 100 ft × 1 ft ≈ 40,000 ft3

40,000 ft3 × 0.0283 m3/ft3 ≈ 1,132 m3 

The approximate density of crushed concrete is 2.3 × 106 g/m3

1,132 m3 × 2.3 × 106 g/m3 = 2.60 × 109 g = 2.60 × 106 kg 

Shape 

The concrete has been broken into chunks less than one meter in the largest dimension. 

The concrete is stored in three piles. Each pile is approximately 1.5 m high, 6 m wide, 

and 40 m long. 

Complexity Rebar used to reinforce the floor is present in the concrete rubble. The rebar will be 

segregated and removed, and treated as a separate waste stream. 

Accessibility The concrete rubble may require further reduction in size to ensure measurability. 

Inherent Value The concrete represents inherent value for several potential disposition options. Crushed 

concrete serves many useful purposes, including recyclable use as roadbed material. 

This option presents potential cost savings over using virgin materials in place of 

recycled concrete and a reuse scenario that avoids the relatively high cost for disposal. 

The radionuclides of potential concern are the uranium (238U) and thorium (232Th) natural 

radioactive decay series. Based on process knowledge, radionuclide concentrations in the raw ore 

average between 750 and 1,100 Bq/kg for members of the uranium series, and between 200 and 

400 Bq/kg for members of the thorium series. Following processing, some 238U and 232Th decay 

products may not have been in equilibrium with the parents. The amount of time since the plant 

ceased operations (i.e., 10 years) indicates there is a potential for the thorium series radionuclides 

to have re-established secular equilibrium. Preliminary survey measurements are required to 

determine the equilibrium status of the uranium and thorium series radionuclides. 

8.2.3.3 Design and Implement Preliminary Surveys 

Limited scanning of concrete rubble was performed using a GM detector. The purpose of the 

scanning was to determine how the radioactivity associated with the concrete was distributed. 

The scanning survey also included additional visual inspection of the concrete. 

Intermittent staining within the concrete rubble and scanning surfaces of concrete chunks 

demonstrates that the radioactivity was heterogeneously deposited on the processing building 

floor. Higher levels of radioactivity were found in areas where spills occurred historically (i.e., 

discolored concrete). The staining did not appear to have penetrated more than one-quarter inch 

into the concrete when the floor was intact. Prior to demolition, the presence of cracks and other 

structural irregularities in the concrete floor provided preferential pathways for activity to 

penetrate to greater depths. This resulted in some variance in activity with depth of the original 

concrete floor. 
MARSAME Illustrative Examples 

Table 8.2 Radiological Attributes of the Concrete Rubble 

Attribute Description Data Gaps 

Uranium Series 

Radionuclides 

Principal 

Emission 

Particle 

Emission 

Energy 

(MeV) 

238U 

234Th 

234mPa 

234Pa 

234U 

230Th 

226Ra 

222Rn 

218Po 

214Pb 

214Bi 

214Po 

210Pb 

210Bi 

210Po 

Alpha 

Beta 

Beta/Gamma 

Beta 

Alpha 

Alpha 

Alpha/Gamma

Alpha 

Alpha 

Beta/Gamma 

Beta/Gamma 

Alpha 

Beta 

Beta 

Alpha 

4.20 

0.1886 

2.28/1.001 

0.224 

4.77 

4.688 

4.78/0.186 

5.49 

6.00 

0.67/0.352 

1.54/0.609 

7.687 

0.016 

1.161 

5.305 

Thorium Series 

Radionuclides 

Principal 

Emission 

Particle 

Emission 

Energy 

(MeV) 

Radionuclides 

232Th 

228Ra 

228Ac 

228Th 

224Ra 

220Rn 

216Po 

212Pb 

212Bi 

212Po (64%) 

208Tl (36%) 

Alpha 

Beta 

Beta/Gamma 

Alpha 

Alpha 

Alpha 

Alpha 

Beta/Gamma 

Alpha/Beta 

Alpha 

Beta 

4.01 

0.0389 

1.17/0.911 

5.42 

5.686 

6.288 

6.78 

0.334/0.238 

6.05/2.246 

8.785 

1.80 

The radioactivity is likely to have come 

in contact with the M&E through spills 

of process fluids and dumping of solid 

tailings on the concrete floor. 

Equilibrium status of the decay series is 

unknown, although sufficient time has 

elapsed since site closure for the 

thorium series to have re-established 

secular equilibrium. 

Activity Activity levels range from background 

(approximately 40 Bq/kg) to 4,000 Bq/kg from 

isolated portions of the concrete rubble where 

spills occurred. 

The expected range of activity is an 

estimate. Nature and extent of activity 

needs to be investigated to provide 

better estimates of average and 

maximum activity. Better estimates of 

background are needed. 

Distribution The radioactivity is heterogeneously 

distributed throughout the mass of concrete 

rubble. 

No data gaps were identified. The 

current distribution is not a concern 

because the concrete will be crushed to 

2–3 cm size prior to survey. 

Location The concrete rubble is considered a 

volumetrically impacted mass. The residual 

radioactivity that is present is a combination of 

fixed and removable. 

The distribution of radioactivity with 

depth may provide useful information 

for selecting measurement methods 

because it can impact the total 

measurement efficiency. 

January 2009 8-5 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Samples were collected from the crushed concrete from the processing mill floor to determine 

concentrations of residual radioactivity using alpha spectrometry and gamma spectroscopy. 

Concrete samples were collected from four biased locations, including two areas of elevated 

gross activity within the concrete rubble with GM readings as high as 250 cpm and visible 

staining (Samples 1 and 2), and two samples with readings consistent with the average readings 

observed during scanning (40 to 45 cpm) (Samples 3 and 4). Process knowledge and limited 

historical site information indicates that radiological materials were never used or stored within 

the on-site administrative building. Reference Samples 1 and 2 were collected from the concrete 

floor of the onsite administrative building to provide information on background activities in 

non-impacted concrete for the uranium and thorium decay series for the conceptual model. The 

six samples were sent to a radioanalytical laboratory for analysis, and the results of the analyses 

are provided in Tables 8.3 through 8.6. 

Table 8.3 Preliminary Alpha Spectrometry Results for Uranium Series Radionuclides 

Sample ID 234U CSU1 MDC2 235U CSU1 MDC2 238U CSU1 MDC2

Sample 1 7,000 ± 2,100 1,900 340 ± 1,900 1,600 7,600 ± 2,400 1,900 

Sample 2 7,200 ± 2,300 1,900 320 ± 1,700 1,600 7,000 ± 2,100 1,900 

Sample 3 21 ± 7.4 3.7 0.74 ± 1.9 0.74 21 ± 7.0 3.7 

Sample 4 25 ± 8.1 3.7 0.74 ± 3.0 0.74 21 ± 7.0 3.7 

Reference 

Sample 1 19 ± 5.2 3.7 0.37 ± 0.74 0.74 20 ± 5.6 3.7 

Reference 

Sample 2 13 ± 3.7 3.7 0.37 ± 0.74 0.74 11 ± 3.3 3.7 

All units in Bq/kg 

1 CSU is the combined standard uncertainty of the measurement result reported by the analytical laboratory. 2

 MDC is the minimum detectable concentration reported by the analytical laboratory. 

Table 8.4 Preliminary Alpha Spectrometry Results for Thorium Series 

Radionuclides 

Sample ID 232Th CSU1 MDC2 228Th CSU1 MDC2

Sample 1 1,400 ± 110 110 1,300 ± 150 110 

Sample 2 1,200 ± 130 110 1,500 ± 190 110 

Sample 3 21 ± 1.5 1.1 23 ± 1.5 1.1 

Sample 4 26 ± 1.1 1.1 24 ± 1.1 1.1 

Reference Sample 1 21 ± 1.1 1.1 22 ± 1.1 1.1 

Reference Sample 2 23 ± 1.1 1.1 23 ± 1.1 1.1 

All units in Bq/kg 

1 CSU is the combined standard uncertainty of the measurement result reported by the 

analytical laboratory. 2 MDC is the minimum detectable concentration reported by the analytical laboratory. 

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MARSAME Illustrative Examples 

Table 8.5 Preliminary Gamma Spectroscopy Results for Uranium Series Radionuclides 

Sample 

ID 

214Bi CSU1 MDC2 214Pb CSU1 MDC2 226Ra CSU1 MDC2

Sample 1 93 ± 920 1,400 530 ± 780 1,300 47 ± 1,100 1,500 

Sample 2 740 ± 1,000 1,300 1,000 ± 870 1,200 192 ± 1,200 1,400 

Sample 3 21 ± 1.1 3.6 21 ± 1.1 6.3 64 ± 9.6 16 

Sample 4 22 ± 1.1 4.1 23 ± 1.1 7.0 68 ± 8.5 19 

Reference 

Sample 1 17 ± 1.1 3.1 17 ± 1.1 7.0 36 ± 6.3 18 

Reference 

Sample 2 20 ± 1.1 3.4 20 ± 1.1 5.6 52 ± 7.1 17 

All units in Bq/kg 

1 CSU is the combined standard uncertainty of the measurement result reported by the analytical laboratory. 2

 MDC is the minimum detectable concentration reported by the analytical laboratory. 

Table 8.6 Preliminary Gamma Spectroscopy Results 

for Thorium Series Radionuclides 

Sample ID 228Ac CSU1 MDC2

Sample 1 1,600 ± 180 52 

Sample 2 1,400 ± 130 41 

Sample 3 14 ± 2.6 4.4 

Sample 4 21 ± 3.1 6.3 

Reference 

Sample 1 15 ± 3.3 5.9 

Reference 

Sample 2 16 ± 3.4 3.4 

All units in Bq/kg 

1 CSU is the combined standard uncertainty of the measurement 

result reported by the analytical laboratory. 2 MDC is the minimum detectable concentration reported by the 

analytical laboratory. 

Note the results provided in Tables 8.3 through 8.6 are from actual samples collected from a real 

site. However, the sample results included as part of this illustrative example were selected to 

provide specific information supporting the application of MARSAME guidance and represent a 

portion of the total amount of information available. The number and type of samples collected 

as part of preliminary survey should be determined using the DQO Process as discussed in 

Section 2.3. 

8.2.3.4 Select a Disposition Option 

The preferred disposition of the concrete rubble is clearance. It is expected that the concrete will 

be reused as roadbed or disposed of in a municipal landfill. If the activity levels exceed the 

project action levels, then the concrete may need to be disposed of as discrete naturally occurring 

or accelerator-produced (NARM) waste. If the activity is below the alternate action levels, the 

concrete may either be reused or disposed of as diffuse NARM waste. 

January 2009 8-7 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.2.3.5 Document the Results of the Initial Assessment 

The results of the IA were documented in a letter report. The purpose of the letter report was to 

document the categorization decision and all supporting information. The letter report was 

reviewed and finalized by the facility owner. Detailed results of the IA will be included in the 

final documentation of the survey design. 

8.2.4 Develop a Decision Rule 

Following completion of the IA, additional information was needed to develop the disposition 

survey design. 

8.2.4.1 Select Radionuclides or Radiations of Concern 

The list of radionuclides of concern was finalized based on the preliminary survey results. 

Uranium-238, 234U, and 226Ra are the radionuclides of concern for the uranium natural decay 

series. The alpha spectrometry results indicate that 238U and 234U are in equilibrium (i.e., have 

equal concentrations). Because alpha spectrometry for uranium isotopes provides results for both 

238U and 234U, both isotopes (and their decay products with half-lives less than six months) will 

be kept as radionuclides of concern. There is no indication of enrichment or depletion of uranium 

as a result of site activities based on the uranium alpha spectrometry results listed in Table 8.3. 

Radium-226 decay products, including 210Pb, are assumed to be out of secular equilibrium with 

the other uranium series radionuclides (e.g., 238U and 234U) because process knowledge shows 

the chemical processing at the plant would separate uranium from radium. Bismuth-214 and 

214Pb can be used as beta or gamma emission surrogates for 226Ra, because the decay products of 

226Ra should be in secular equilibrium with one another. However, a 21-day ingrowth period may 

be required to confirm this assumption. The planning team determined an ingrowth study was 

not required for this project following discussions with the regulators. 

Thorium-232 is the radionuclide of concern for the thorium natural decay series. Based on the 

alpha spectrometry and gamma spectroscopy results shown in Table 8.3, all members of the 

thorium natural decay series are in secular equilibrium. Actinium-228 emits gamma rays that are 

easy to quantify using gamma spectroscopy, and can be used as a surrogate for the members of 

the thorium series. 

8.2.4.2 Identify Action Levels 

For the purposes of this illustrative example, an action level of 0.01 mSv/y was selected based on 

discussions with the planning team. Using information provided in NUREG-1640 (NRC 2003), 

the action levels were converted into concentration units based on clearance as the disposition 

option. Incorporating the concrete rubble into roadbed material would provide the highest 

potential doses following clearance. The mean values from NUREG-1640 (NRC 2003), 

Table I1.13 (“Normalized effective dose equivalents from all pathways: Driving on road [μSv/y 

per Bq/g]”), are the basis for the action levels. 

NUREG-1575, Supp. 1 8-8 January 2009 
MARSAME Illustrative Examples 

Radionuclide of concern 238U 234U 232Th 226Ra

Mass-based EDE mean values 

(μSv/y per Bq/g) 0.26 8.2 × 10−4 30 22 

The action levels from Table I1.13, NUREG-1640 (NRC 2003) are expressed in units of μSv/y 

per Bq/g, but the preliminary survey measurement results are in Bq/kg. To make a direct 

comparison, the action levels were converted to units of Bq/kg. Note that a hypothetical dose for 

clearance was selected only for the purpose of showing example calculations for this illustrative 

example. Clearance criteria should be provided by the regulator for actual applications of this 

guidance. The action levels were converted to concentrations by inverting the action levels and 

multiplying by the hypothetical dose limit (i.e., the inverted action levels in units of Bq/g per 

μSv/y are multiplied by 0.01 mSv/y, 1,000 g/kg, and 1,000 μSv/mSv providing action levels in 

Bq/kg). Table 8.7 lists the action levels in concentration units of Bq/kg. 

Table 8.7 Radionuclide-Specific Action Levels 

Radionuclide 

Mass-Based EDE Mean Values 

(Bq/g per μSv/y) 

Action Level 

(Bq/kg) 

238U 000,38101mSv/y 01.0

μSv/y 0.26

Bq/g 1 6 × =×× 38,000 

234U 000,000,12101mSv/y 01.0

μSv/y 10 8.2

Bq/g 1 6

4- × =××

×

12,000,000 

232Th 330101mSv/y 01.0

μSv/y 10 3.0

Bq/g 1 6

1 × =××

×

330 

226Ra 450101mSv/y 01.0

μSv/y 10 2.2

Bq/g 1 6

1 × =××

×

450 

The unity rule (Equation 8-1) is used to account for the individual radionuclide action levels. The 

unity rule is satisfied when the summed analyses of each radionuclide against its respective 

action level yields a value less than one: 

 Rule Unity The 1...

2

2

1

1 ≤++=

n

n

AL

C

AL

C

AL

C (8-1) 

Where: 

C = concentration of each individual radionuclide (1, 2, ... n) 

AL = action level value for each individual radionuclide (1, 2, ... n) 

Equation 8-1 is used to calculate the sum of fractions for each of the preliminary survey results: 

 Rule Unity The 1

Ra

Ra

Th

Th

U

U

U

U

226

226

232

232

234

234

238

238

≤+++=

AL

C

AL

C

AL

C

AL

C

January 2009 8-9 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Sample 1 7,600 Bq/kg 7,000 Bq/kg 1, 400 Bq/kg 47 Bq/kg

38,000 Bq/kg 12,000,000 Bq/kg 330 Bq/kg 450 Bq/kg

= + ++ = 4.5 

Sample 2 6,900 Bq/kg 7, 200 Bq/g 1, 230 Bq/kg 192 Bq/kg

38,000 Bq/kg 12,000,000 Bq/kg 330 Bq/kg 450 Bq/g

= + ++ = 4.2 

Sample 3 21 Bq/kg 21 Bq/kg 21 Bq/kg 64 Bq/kg

38,000 Bq/kg 12,000,000 Bq/kg 330 Bq/kg 450 Bq/g

= + ++ = 0.21 

Sample 4 21 Bq/kg 25 Bq/kg 26 Bq/kg 68 Bq/kg

38,000 Bq/kg 12,000,000 Bq/kg 330 Bq/kg 450 Bq/g

= + ++ = 0.23 

The results of the calculations for Samples 1 and 2 exceed a sum of fractions of 1.0, and indicate 

the presence of small volumes of concrete with elevated activity. Note that the reported MDCs 

for gamma spectroscopy for 226Ra in Samples 1 and 2 would not meet the MQOs for clearance 

(i.e., the MDC exceeds the action level). However, the radionuclide concentrations in these two 

samples clearly exceed the action level. Therefore, the quality of these results is acceptable to 

support the disposition survey design. 

The results of the calculations for Samples 3 and 4 indicate that, on average, the concrete rubble 

is expected to have radionuclide concentrations below the action levels. Therefore, the average 

activity in the concrete rubble is expected to be below the action level. Large blocks containing 

elevated levels of radioactivity may be visually identified via staining, verified with a GM 

detector, and segregated prior to removal of the rebar. 

8.2.4.3 Modify the Action Levels to Account for Multiple Radionuclides 

Radionuclide-specific action levels need to be combined into a single gross gamma action level 

for evaluating the field instrument for detection of low-energy radiation (FIDLER) scan 

measurements. The information in Section 3.3.3.1 requires an estimate of the relative fraction of 

the total activity contributed by each radionuclide. A consistent relationship between 238U and 

232Th concentrations is not expected based on the IA, because different ore bodies could contain 

different ratios of these radionuclides. Rather than develop a preliminary survey attempting to 

develop this relationship, a conservative approach was adopted for this project. 

Assuming the entire radioactivity detected by the FIDLER results from the presence of the most 

restrictive radionuclide will provide the most conservative gross gamma action level. The ratios 

of exposure rate to radionuclide concentration (μR/h per Bq/kg) and instrument response to 

exposure rate (cpm per μR/h) were developed in Section 7.11 during development of the scan 

MDC for both 238U and 232Th. These ratios can be used to calculate the count rate above 

background associated with a radionuclide activity equal to the action level as shown in 

Equation 8-2. 

NUREG-1575, Supp. 1 8-10 January 2009 
MARSAME Illustrative Examples 

R/h cpm

Bq/kg R/h

GG AL AL

μ

μ

⎛ ⎞ ⎛ ⎞

=× × ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ (8-2) 

Where: 

GGAL = gross gamma action level (cpm) 

AL = action level value for each individual radionuclide (Bq/kg) 

Equation 8-2 was used to calculate a gross gamma count rate above background for the FIDLER 

assuming each radionuclide of concern was present at a concentration equal to the action level. 

The gross gamma count rates were divided by two to account for uncertainty associated with the 

detector efficiency calculation and added to the background count rate. The result is a gross 

gamma action level for the FIDLER to identify locations with unexpectedly high gamma activity 

that could result in doses near the action level of 0.01 mSv/y. The results of the calculations are 

shown in Table 8.8. The 232Th gross gamma action level of 30,000 cpm is more conservative 

than the 238U gross gamma action level of 140,000 cpm, so 30,000 cpm was selected as the gross 

gamma action level. 

Table 8.8 Calculation of the Gross Gamma Action Level 

Action Level 

(Bq/kg) 

μR/h per 

Bq/kg 

cpm per 

μR/h 

Gross 

Gamma 

Count Rate 

(cpm) 

Adjusted Gross 

Gamma Count 

Rate (cpm) 

Background 

Count Rate 

(cpm) 

Gross Gamma 

Action Level 

(cpm) 

238U 

38,000 1.413×10−4 45,593 244,807 122,404 12,870 140,000 

232Th 

330 2.619×10−2 3,923 33,905 16,953 12,870 30,000 

FIDLER readings that exceed the 232Th gross gamma action level indicate locations where 

radionuclide concentrations could result in doses exceeding the 0.01 mSv/y used for this 

illustrative example if all of the activity results from 232Th. 

Because 232Th has decay products in secular equilibrium that can be used to estimate the 232Th 

activity, gamma spectroscopy can be used to quantify 232Th concentrations. FIDLER readings 

that exceed 140,000 cpm identify locations where radionuclide concentrations could result in 

doses exceeding 0.01 mSv/y if all of the activity results from 238U. Alpha spectrometry is 

required to quantify 238U concentrations. 

8.2.4.4 Describe the Parameter of Interest 

Because the disposition option is stated in terms of dose, the parameter of interest is the mean 

radionuclide concentration. The target population is all of the possible measurement results that 

could be obtained within a survey unit. This means the target population will be defined by the 

survey unit boundaries (Section 8.2.4.6) and the selected measurement method (Section 8.2.4.8). 

January 2009 8-11 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.2.4.5 Identify Alternative Actions 

The alternative actions identify the results of decisions based on the measurement results. If the 

radionuclide concentrations do not result in a dose that exceeds the action level, the material is 

cleared. If the dose exceeds the action level, materials exceeding the action level will be 

segregated and investigated for disposal as NARM waste. 

8.2.4.6 Identify Survey Units 

Survey unit boundaries are based primarily on the modeling assumptions used to develop the 

action levels. The volume of concrete used to model exposures for building a road is 83 m3 

(NUREG-1640 [NRC 2003] Volume 2, Appendix B, Tables B-8 and B-11). Each survey unit 

will consist of approximately 80 m3 of crushed concrete (approximately 25 m × 22 m × 0.15 m). 

The volume of concrete poured to create the floor of the processing mill was approximately 

1,100 m3

. Crushing the concrete and removing the rebar is expected to result in approximately a 

25% increase in volume due to air gaps, for a total volume of 1,400 m3 of crushed concrete. 

Using these calculations, there will therefore be a total of 18 survey units plus one reference area. 

The concrete rubble can be spread into a relatively uniform layer approximately 15 cm thick and 

scanned. This adapts an approach used in MARSSIM to survey the top 15 cm of surface soil as a 

two-dimensional object. 

8.2.4.7 Define the Decision Rules 

MARSSIM-type surveys are designed to evaluate the average radionuclide concentration in a 

survey unit using samples or direct measurements, as well as small areas of elevated activity 

using scans. Small areas of elevated activity receive additional investigation. Because there are 

multiple action levels and multiple decisions to be made, there are multiple decision rules for the 

disposition survey. The first two decision rules address how small areas of elevated activity are 

identified by scans and what investigations will be performed. The third decision rule evaluates 

the results of the investigations of small areas of elevated activity. The fourth decision rule 

evaluates the average activity in each survey unit. 

1. If any FIDLER scanning measurement result exceeds the gross gamma action level of 30,000 

cpm (see Section 8.2.5.4), a biased sample will be collected for laboratory analysis by 

gamma spectroscopy, otherwise no biased samples will be collected. 

2. If any FIDLER scanning measurement exceeds 140,000 cpm, the biased sample collected for 

gamma spectroscopy analysis will also be analyzed by alpha spectrometry for uranium and 

thorium isotopes, otherwise the concrete will be held awaiting the results of the gamma 

spectroscopy analysis. 

3. If the results from a biased sample result in a sum of fractions for 238U, 234U, 226Ra, and 232Th 

exceeding 1.0, the concrete will be segregated and investigated for disposal as NARM waste. 

Otherwise, the survey unit will be evaluated based on the WRS test results for the samples 

taken over a systematic grid. 

NUREG-1575, Supp. 1 8-12 January 2009 
MARSAME Illustrative Examples 

4. If the mean sum of fractions in a survey unit exceeds 1.0, the concrete will be segregated and 

investigated for disposal as NARM waste. Otherwise, the WRS test will be performed to 

support the final disposition decision for that survey unit. 

8.2.4.8 Develop Inputs for Selection of Provisional Measurement Methods 

The selected measurement method will be required, at a minimum, to detect radionuclide 

concentrations at or below the action levels in Table 8.8 (page 8-11). The survey planners 

considered each of the possible measurement techniques (Section 5.9.1). 

Scan-only techniques have the ability to detect surface activity at concentrations below the action 

levels. In situ measurement techniques are also expected to have the ability to measure 

radionuclide concentrations at the action levels. However, uncertainties associated with the 

efficiency for both techniques will be large. In order to reduce these uncertainties to a level 

where the radionuclide concentrations are measurable, the concrete would need to be pulverized 

and mixed rather than just crushed to 2–3 cm size. Because the cost of processing the concrete 

this way would be a major cost associated with the disposition survey, a MARSSIM-type survey 

design was selected for the disposition survey. No method-based survey designs were identified 

that matched the description of the M&E, so no method-based survey designs were considered. 

Concrete samples will be analyzed in a laboratory using alpha spectrometry for uranium isotopes 

(i.e., 234U and 238U) as well as gamma spectroscopy for other radionuclides of concern (i.e., 214Bi, 

214Pb, and 228Ac). Sample sizes must be sufficient to allow quantification of radionuclide 

concentrations at the action levels. By convention, the MQC for each radionuclide of concern is 

selected so the measurement method uncertainty at concentrations equal to the action levels in 

Table 8.7 is 10%. Alternatively, the samples can be sealed in airtight containers for at least 

twenty-one days to allow secular equilibrium to be reestablished prior to analysis by gamma 

spectroscopy so decay products can be used as surrogate radionuclides. 

Due to the rough, irregular shape of the concrete rubble, alpha radiation is attenuated easily and 

is difficult to measure. Beta and gamma measurements typically provide a more accurate 

assessment of thorium and uranium activity on most building surfaces because surface conditions 

cause significantly less attenuation of beta and gamma particles than alpha particles. For this 

reason, scanning will be performed using instruments that detect beta or gamma radiation. 

Surface scans, using a 12.7-cm by 0.16-cm FIDLER sodium iodide (NaI[Tl]) scintillation 

detector, are used to scan for gamma emissions. The approximate detection sensitivity of the 

FIDLER is 300 Bq/kg for natural uranium and 20 Bq/kg for natural thorium when activity is 

present at the surface. The FIDLER is a large detector and can detect gammas from a greater 

height above the crushed concrete than alpha or beta detection equipment, making it a more 

practical choice for surveying large volumes of material. The selection of the FIDLER over more 

conventional NaI(Tl) detectors (e.g., a three-inch by three-inch gamma scintillation detector) is 

primarily based on the FIDLER’s ability to detect low-energy gamma radiation, which comprises 

the majority of the gamma radiation from the radionuclides of concern. 

January 2009 8-13 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.2.4.9 Identify Reference Materials 

Concrete from the administrative building contains non-impacted materials, as established by the 

process knowledge discussed in Section 8.2.3.1. The reference material measurements will be 

performed on the floor in the administrative building. The geometry of the floor is similar 

enough to the concrete rubble (after crushing to 2–3 cm size and arrangement into a 15-cm thick 

layer) that modifications to the building are not required. 

8.2.5 Develop a Survey Design 

The concrete rubble from the mineral processing facility is surveyed for clearance using a 

MARSSIM-type disposition survey. The survey includes scanning to identify small areas of 

elevated activity combined with collection and analysis of samples to evaluate the average 

activity in the concrete rubble. 

Scenario A is used to design the survey, because decisions will be made based on average 

radionuclide concentrations and radioactivity levels in each survey unit. The null hypothesis is 

that the radionuclide concentrations in the concrete rubble will result in a dose that exceeds 

0.01 mSv/y. There are two decisions for MARSSIM-type surveys. The first decision is based on 

the average radionuclide concentrations in the survey unit, and the second decision is based on 

the scanning survey results and subsequent biased sample results from flagged locations. The 

same null hypothesis applies to both decisions. 

A Type I decision error would occur if the decision-maker decided the activity levels in the 

concrete rubble were below the action level when they actually exceeded the action level. The 

consequence of making this decision error could result in increased doses to members of the 

public and failing to identify small areas of elevated radionuclide concentrations. The members 

of the planning team agreed to a Type I decision error rate of 5% based on the consequence of 

making this decision error. This Type I error rate applies to both the scanning portion of the 

survey design as well as sampling on a systematic grid. 

A Type II decision error would occur if the decision-maker decided the activity levels in the 

concrete rubble exceeded the action level when they were actually below the action level. The 

consequence of making this decision error could result in increased disposal costs. The members 

of the planning team agreed to a Type II decision error rate of 10% based on the consequence of 

making this decision error for sampling. However, during scanning the consequence of making 

this decision error is the need to perform additional investigation., As such, a Type II decision 

error rate of 40% is selected for the scanning surveys. 

8.2.5.1 Classify the M&E 

All of the concrete rubble from the floor of the processing facility has the potential to exceed one 

or more of the action levels. The concrete rubble is classified as Class 1 M&E. 

NUREG-1575, Supp. 1 8-14 January 2009 
MARSAME Illustrative Examples 

8.2.5.2 Design the Scanning Survey 

 

The concrete must be crushed prior to performing the scanning survey to reduce the size of 

individual particles to less than 2–3 cm in diameter. This provides a uniform matrix of material 

ensuring a representative sample can be collected, and also allows the rebar to be removed. The 

crushed concrete is distributed in a layer approximately 15 cm thick, and surveyed using a 

FIDLER at a height of 10 cm above the surface. The scan speed is 0.25 m/s, which is consistent 

with the scan MDC calculations. One hundred percent of the concrete rubble is scanned with 

readings in excess of 30,000 cpm flagged for additional investigation. The additional 

investigations include collection and analysis of samples using gamma spectroscopy to quantify 

activity levels for the radionuclides of concern. Samples collected from locations with readings 

in excess of 140,000 cpm are also analyzed for uranium and thorium isotopes by alpha 

spectrometry. 

8.2.5.3 Design the Sample Collection Survey 

The concrete rubble is divided into survey units and a statistically based number of samples are 

collected from each survey unit. Because multiple radionuclides are present, the unity rule is 

used to evaluate the sample results. Because the radionuclides are present in background, the 

Wilcoxon Rank Sum (WRS) test is used to evaluate the survey results. 

The upper bound of the gray region (UBGR) is set equal to the action level, which is a sum of 

fractions of 1.0 above background. The lower bound of the gray region (LBGR) is set equal to 

the expected sum of fractions based on results from the preliminary survey. The expected 

average activity in the concrete rubble is close to background, even though isolated areas have 

results more than four times the action level. An LBGR value of 0.15 is selected, which is 

consistent with results reported in Tables 8.3 through 8.6 for the two randomly selected samples 

(i.e., samples 3 and 4). Because the values are not corrected for background, this value is 

considered conservative. The shift (UBGR – LBGR) is 0.85. 

The variability in the activity levels for the concrete rubble, σS, is not well defined. To be 

conservative, the variability in the results should be large for results near the LBGR. A value of 

0.15 was selected for the variability. This value is equal to the LBGR, and represents 100% 

variability in results that are at or near background. The relative shift equals 5.6 (0.85 divided by 

0.15 and rounded down). Because relative shifts greater than 4.0 do not result in significantly 

smaller numbers of samples, a relative shift of 4.0 was used to determine the number of samples 

and also help to ensure adequate statistical power. 

Table A.2b (Appendix A) lists the number of samples required for each survey unit and reference 

area for use with the WRS test. Seven samples are required for each survey unit and reference 

area using a relative shift of 4.0, Type I decision error rate of five percent, and Type II decision 

error rate of 10 percent. The radionuclide or radioactivity concentrations derived from the dosebased action level are based on an average radionuclide concentration or level of radioactivity 

over the entire survey unit. No adjustments need to be made to the number of measurements to 

account for the scan MDC, because the scan MDC is less than the action level for both 238U and 

232Th. 

January 2009 8-15 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Seven samples of approximately 1,000 g of concrete rubble are collected from each survey unit. 

This mass corresponds to a cylinder with a diameter of approximately 6 cm (2.5 in) to a depth of 

15 cm (6 in). This disposition survey design will be applied to all of the concrete rubble, 

including the concrete segregated based on visual inspection and elevated scanning results with a 

GM detector during the preliminary surveys (Section 8.2.3.3). 

8.2.5.4 Develop an Operational Decision Rule 

The action level is stated in terms of incremental dose above background. In a MARSSIM 

survey, there are requirements for both sample measurements and scanning results. Samples will 

be collected from non-impacted concrete to represent background radionuclide concentrations. 

The WRS test will be used to evaluate the survey results. If the test statistic for the WRS test is 

less than or equal to 65 (n = m = 7, α = 0.05), decide that the dose from that survey unit exceeds 

0.01 mSv/y and the concrete will not be cleared. 

For the scanning results, if any FIDLER measurement exceeds 30,000 cpm, collect a biased 

concrete sample at the location of the elevated measurement for analysis by gamma 

spectroscopy. If any FIDLER measurement exceeds 140,000 cpm, analyze the biased concrete 

sample by alpha spectrometry as well. If the sum of fractions for any biased sample exceeds 1.0, 

decide that the dose from that survey unit exceeds the 0.01 mSv/y used for this illustrative 

example and the concrete will not be cleared. 

8.2.5.5 Document the Survey Design 

The final survey design was documented in a detailed work plan. The work plan provided the 

results of the IA, as well as all of the assumptions used to develop the survey design. The DQOs 

and MQOs for the survey design were also included. 

The draft work plan was submitted to the planning team for review. Comments were received, 

and responses to comments developed and approved. The approved responses to comments were 

incorporated into a final work plan documenting the disposition survey design. 

8.2.6 Implement the Survey Design 

8.2.6.1 Ensure Protection of Health and Safety 

A job safety analysis (JSA) was performed based on the tasks defined in the work plan 

documenting the disposition survey design. Table 8.9 shows the results of the JSA. Potential 

health and safety hazards identified by the JSA are addressed in a site-specific health and safety 

plan. No hazards associated with the concrete rubble will notably affect how the disposition 

survey is implemented. 

NUREG-1575, Supp. 1 8-16 January 2009 
MARSAME Illustrative Examples 

Table 8.9 Job Safety Analysis for Surveying Concrete Rubble 

Sequence of Basic Job Steps Potential Hazards Recommended Action or Procedure 

1. Dividing rubble into 

manageable survey units 

Use of front end loader 

by untrained personnel

Ensure equipment operators are adequately trained 

 Personnel in area could 

be struck by heavy 

equipment 

Area workers must maintain eye contact with 

equipment operators 

 Reflective vests will be worn to improve visibility 

 Exposure to silica Use of a real-time dust monitor will document dust 

levels. Respiratory protection will be used if dust 

levels exceed established action levels (dependent on 

silica content of concrete) 

 Lower back strain 

from lifting 

Proper lifting techniques will be used 

 Loads will be sized so as not to create unreasonable 

weights for manual lifting 

Exposure to 

radiological 

contamination 

PPE including booties, Tyveks, and gloves will be 

used 

2. Establish exclusion zone for 

survey area 

None anticipated 

3. Use hand-held survey 

instruments to perform survey 

measurements on the crushed 

concrete 

Unstable footing may 

result in slips, trips, or 

falls 

Spread out rubble in a way to minimize tripping 

hazards by creating clear rows between rows of 

concrete 

4. Physical handling of larger 

pieces of concrete debris to 

expose underside for gamma 

surveying 

Rough surfaces may 

cut and scrape skin on 

hands 

Wear a set of work gloves to protect hands when 

handling concrete pieces 

5. Entering Exclusion Zone (EZ) 

to perform survey 

Tripping Maintain good housekeeping in survey area 

 Exposure to 

radiological 

contamination 

PPE including booties, Tyveks, and gloves will be 

used 

 Spread of radiological 

contamination outside 

EZ 

Establish step-off area outside of EZ 

6.Moving contaminated or clean 

material to appropriate disposal 

containers 

Use of front end loader 

by untrained personnel

Ensure equipment operators are adequately trained 

 Lower back strain 

from lifting 

Proper lifting techniques will be used. Loads will be 

sized so as not to create unreasonable weights for 

manual lifting 

 Exposure to 

radiological 

contamination 

PPE including booties, Tyveks, and gloves will be 

used 

 Exposure to silica Use of a real-time dust monitor will document dust 

levels. Respiratory protection will be used if dust 

levels exceed established action levels (dependent on 

silica content of concrete) 

January 2009 8-17 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.2.6.2 Consider Issues for Handling the M&E 

The concrete rubble must be crushed to a uniform size of less than one inch to implement the 

disposition survey design and meet the MQOs. The crushing process will generate dust 

potentially containing radioactive material. Controls to limit dust generation were implemented 

during concrete crushing activities. Equipment involved in handling the concrete during crushing 

activities (e.g., front loader, crusher, rebar separator, conveyor belts, dump trucks) is categorized 

as impacted and will require a disposition survey before the equipment can be released. Surveys 

of the front loader are discussed in Sections 8.3 and 8.4. 

8.2.6.3 Segregate the M&E 

Concrete rubble with visible stains and pitting on the floor surface is segregated as having higher 

activity concentrations. Stained and unstained concrete were grouped into separate survey units. 

Following segregation, the concrete was crushed to 2–3-cm diameter pieces, and the rebar was 

removed. 

8.2.6.4 Set Measurement Quality Objectives 

The two most important MQOs for this survey design are the required measurement method 

uncertainty, uMR, for the scan MDCs for the FIDLER measurements (Section 8.2.6.5) and the 

concrete samples collected on the systematic grid (Section 8.2.6.6). Other MQOs were 

established during the development of the survey design to support selection of measurement 

methods. These included setting the MQC for each radionuclide of concern so the relative 

measurement method uncertainty, φMR, at concentrations equal to the action levels in Table 8.7 is 

10% (Section 8.2.4.8) and calculating the scan MDCs for the FIDLER (Section 7.11). 

8.2.6.5 Determine Measurement Uncertainty for the Scan MDC 

This section describes the calculation of the uncertainty for the scan MDC measurements 

performed as part of this survey using the FIDLER. An upper bound for an expanded uncertainty 

for the scan MDC calculation is derived to reduce the probability that the scan MDC has been 

underestimated. The result is used as the investigation level for evaluating the results of the scan 

survey. 

The uncertainty calculations presented in this section may be performed using commercially 

available statistical software (Section 5.6). Detailed solutions for this illustrative example are 

provided below. 

The scan MDCs for the FIDLER measurements, y, are calculated in Section 7.11. The scan MDC 

for natural uranium, yU, is approximately 400 Bq/kg. The scan MDC for natural thorium, yTh, is 

approximately 25 Bq/kg. Both scan MDCs are less than their respective action levels of 38,000 

and 330 Bq/kg. The values used to calculate the scan MDCs for the FIDLER measurements are: 

NUREG-1575, Supp. 1 8-18 January 2009 
MARSAME Illustrative Examples 

bi = average number of counts in the background interval (214.5 counts). Here bi is 

assumed to have a triangular distribution with a half-width of 30% or 64 counts, 

so the mean value of bi is 215 and ( ) == 266/64 i bu . 

i = observation interval length (one second). Here i is assumed to have a triangular 

distribution with a half-width of 0.5, so the mean value of i = 1.0 and 

u i( ) 0.5 / 6 0.2 = = . 

p = efficiency of a less than ideal surveyor, range of 0.5 to 0.75 from NUREG-1507 

(NRC 1998b); a value 0.5 was chosen as a conservative value. Here p is assumed 

to have a rectangular distribution with a half-width of 0.125, so the mean value of 

p = 0.625 and u p( ) 0.125 / 3 0.072 = = . 

d' = detectability index from Table 6.1 of NUREG-1507 (NRC 1998b); a value of 

1.90 was selected and treated as a constant. 

WT = total weighted instrument sensitivity (cpm per μR/h) 

= 44,923 for natural uranium from Table 7.10 and 

 = 3,881 for natural thorium from Table 7.11. 

RT = total exposure rate with buildup (μR/h) 

 = 1.413×10-4 for natural uranium from Table 7.10 and 

 = 2.619×10-2 for natural thorium from Table 7.11. 

C = concentration of source term (set at 1 Bq/kg and treated as a constant). 

y = Scan MDC (in Bq/kg) introduced here for simplicity of notation. 

Because we are assuming there are no correlations among the input variables, the combined 

standard uncertainty of y can be calculated using Equation 7.33 from Section 7.8.1.6: 

2

2 2

1 1

( ) ( ) ( )

N N

c i

i i i

y u y u x cu x = = x

⎛ ⎞ ∂ = = ⎜ ⎟ ∂⎝ ⎠ ∑ ∑ 2 2

i i

The concentration of the source term, C, and the detectability index, d', are treated as constants 

with no associated uncertainty, so this expands to: 

2 2 2 2 2

2 2 22 2 ( ) ( ) () ( ) ( ) ( ) c i T

i T

y yy y y u y ub ui u p uR uW b ip R W

⎛ ⎞ ∂ ∂∂ ∂ ∂ ⎛ ⎞ ⎛ ⎞ ⎛⎞ ⎛ ⎞

= ++ + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜⎟ ⎜ ⎟ ⎝ ⎠ ∂ ∂∂ ∂ ∂ ⎝ ⎠ ⎝ ⎠ ⎝⎠ ⎝ ⎠

2

T

T

The sensitivity coefficients, , are calculated as follows: 2

i c

( ) ( )

60 '

60 ' 1 1

2 2

i

T T

i i TT i

Cd b

y C iW R p

b b iW R p b

⎛ ⎞

∂ ⎜ ⎟ ⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ == = ∂ ∂ ⎝ ⎠ i

d y

b

January 2009 8-19 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

( ) 3/2 ( )

60 '

60 ' 1 1

2 2

i

T T i

T T

Cd b

y y iW R p Cd b

p p iW R p

⎛ ⎞

∂ ⎜ ⎟ ⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ = = − = −

⎝ ⎠ ∂ ∂ p

2

60 '

60 '

i

T T i

T T

Cd b

y y iW R p Cd b

i i iWR p

⎛ ⎞

∂ ⎜ ⎟

⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ = = − ⎝ ⎠ ∂ ∂ i = −

2

60 '

60 '

i

T T i

T T T T

Cd b

y y iW R p Cd b

R R RT iW R p

⎛ ⎞

∂ ⎜ ⎟ ⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ = = − ∂ ∂ ⎝ ⎠

= −

2

60 '

60 '

i

T T i

T T T T

Cd b

y y iW R p Cd b

W W iW R p

⎛ ⎞

∂ ⎜ ⎟ ⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ = = − ∂ ∂ ⎝ ⎠ WT

= −

Therefore, 

2 2 2 2 2

2 2 22 2

2 2 2 2 2

2

2

2

( ) ( ) () ( ) ( ) ( ) 2 2

( ) () ( ) () ()

2 2

26 0.2

2(215) 1

c i T

i T

i T T

i T T

y yy y y u y ub ui u p u R uW b i pR W

u b ui u p u R uW

y b i pR W

y

⎛ ⎞ ⎛ ⎞ −− − − ⎛ ⎞ ⎛⎞ ⎛⎞

= ++ + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜⎟ ⎜⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝⎠ ⎝⎠

⎡ ⎤ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎛ ⎞

= ++ + + ⎢ ⎥ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠ ⎣ ⎦

⎛ ⎞ ⎛ = + ⎜ ⎟ ⎝ ⎠

2

T

T

2 2 2 2

0.072 () () . 2(0.625

T T

T T

u R uW

R W

⎡ ⎤ ⎞ ⎛ ⎞ ⎛ ⎞⎛ ⎞ ⎢ ⎥ ⎜ ⎟ + ++ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠ ⎣ ⎦

The most notable sources of uncertainty associated with WT and RT are the modeling assumptions 

for the source-to-detector separation distance during scanning and the depth distribution of the 

radioactivity in the crushed concrete. To calculate uncertainties, the same basic modeling 

assumptions as those for the MDC calculations were applied, though with variations to both the 

source-to-detector separation distance during scanning and the distribution of the radioactivity in 

the crushed concrete. While the MDC calculation assumes a source-to-detector distance of 10 cm 

and that the activity is uniformly distributed within a cylindrical volume of crushed concrete 15 

cm thick with a radius of 28 cm, several other calculations were made using source-to-detector 

separation distances during scanning of 8, 10, and 12 cm, and by varying the distribution of the 

radioactivity in the crushed concrete from uniform to uniformly distributed within both the top 

and bottom 7.5 cm of the cylindrical volume of crushed concrete, to assess the potential 

variability in the MDC. In each calculation the total activity was the same, only the distribution 

with depth was changed. The extreme cases were for a source-to-detector distance of 8 cm with 

NUREG-1575, Supp. 1 8-20 January 2009 
MARSAME Illustrative Examples 

the activity uniformly distributed within the top 7.5 cm of the concrete versus a source-todetector distance of 12 cm with the activity uniformly distributed within the bottom 7.5 cm of the 

concrete. While more extreme conditions might be imagined, the foregoing were considered to 

represent reasonable bounds on the source-to-detector distance and the activity distribution with 

depth. The other assumptions used in the calculations were the same as used in Section 7.11. 

Therefore, there are three values each to describe the distribution of the possible values of WT

and RT : The estimated mean value calculated for a uniform distribution of radioactivity in the 15 

cm of concrete surveyed at 10 cm above; an estimated lower bound calculated for a uniform 

distribution of radioactivity in the bottom 7.5 cm of concrete surveyed at 12 cm above; and an 

estimated upper bound calculated for a uniform distribution of radioactivity in the top 7.5 cm of 

concrete surveyed at 8 cm above. 

The values for WT and RT at the extremes considered were not equally distant from the mean, i.e., 

their distribution was not symmetric. However the GUM suggests that in the absence of more 

information the simplest approximation is a symmetric rectangular distribution of the same total 

width. With this approximation, u(WT) = 6673 and u(RT) = 4.638×10-5 for natural uranium and 

u(WT) = 539 and u(RT) = 7.315×10-3 for natural thorium. 

Using the information for natural uranium in Equation 7-34 we find: 

 2 2 2 2 2

2 2

2 2 2 2 2 5

2

4

26 0.2 0.072 () () ( ) 2(215) 1 2(0.625

26 0.2 0.072 4.638 10 6673 (238) 2(215) 1 2(0.625 1.413 10 44,923

T T

cU U

T T

uR uW

uy y R W

−

−

⎡ ⎤ ⎛⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎛ ⎞

= ++ + + ⎢ ⎥ ⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎢ ⎥ ⎝⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠ ⎣ ⎦

⎡⎛⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ × ⎛ ⎞

= ++ + + ⎢⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎢⎝⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ × ⎝ ⎠ ⎣

2 10,013 (Bq/kg) .

⎤

⎥

⎥

⎦

=

So, taking the square root of the variance and rounding the result, ( ) 100 Bq/kg c U u y = .

Therefore the FIDLER Scan MDC for natural uranium, yU, is 400 Bq/kg with an expanded 

uncertainty of 200 Bq/kg, using a coverage factor of 2 and an estimated coverage probability of 

95%. The upper bound of the Scan MDC using this interval is 600 Bq/kg. 

Similarly substituting the information for natural thorium into Equation 7-34 we find: 

2 2 2 2 2

2 2

2 2 2 2 2 3

2

2

26 0.2 0.072 () () ( ) 2(215) 1 2(0.625

26 0.2 0.072 7.315 10 539 (15) 2(215) 1 2(0.625 2.619 10 3,881

T T

c Th Th

T T

uR uW

uy y R W

−

−

⎡ ⎤ ⎛⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎛ ⎞

= ++ + + ⎢ ⎥ ⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎢ ⎥ ⎝⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠ ⎣ ⎦

⎡ ⎤ ⎛⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ × ⎛ ⎞

= ++ + + ⎢⎜⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎢⎝⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ × ⎝ ⎠ ⎣

2 32 (Bq/kg) .

⎥

⎥

⎦

=

January 2009 8-21 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

So, taking the square root of the variance and rounding the result, ( ) 6 Bq/kg c Th u y = .

Therefore the FIDLER Scan MDC for natural thorium, yTh, is 25 Bq/kg with an expanded 

uncertainty of 12 Bq/kg, using a coverage factor of 2 and an estimated coverage probability of 

95%. The upper bound of the Scan MDC using this interval is 37 Bq/kg. 

The upper bound of the scan MDCs of approximately 600 Bq/kg for natural uranium and 37 

Bq/kg for natural thorium are both less than their respective action levels of 38,000 and 330 

Bq/kg. Therefore, the FIDLER is an acceptable instrument for performing the scan 

measurements. 

8.2.6.6 Determine Measurement Uncertainty for Concrete Samples 

The primary measurement quality objective is the required measurement method uncertainty at 

the action level. MARSAME recommends uMR ≤ Δ/10 by default when decisions are being made 

about the mean of a sampled population. 

For this illustrative example, 238 234 232 226

238 234 232 226

U U Th Ra

UUTh Ra

the Unity Rule, 1

CC C C

AL AL AL AL

+ + + ≤ , is used to 

compare the sum of the ratios of the radionuclide concentrations to their respective action 

levels.1

 Because the results of the survey are used to calculate a sum of fractions, the action level 

is normalized to 1. The required measurement method uncertainty at this action level is Δ/10 = 

(UBGR – LBGR)/10. Because the LBGR was chosen to be 0.15, then uMR ≤ Δ/10 = (UBGR – 

LBGR)/10 = (1.0 – 0.15)/10 = 0.085. 

Therefore, we require that: 

 

238 234 232 226 238 234 232 226

238 234 232 226 238 234 232 226

U U Th Ra U U Th Ra

UUTh Ra UUTh Ra

0.085 when 1.0. c

CC C C C CC C

u

AL AL AL AL AL AL AL AL

⎛ ⎞ ⎛ ⎞ ⎜ ⎟ ++ + ≤ ⎜ ⎟ ++ + =

⎝ ⎠ ⎝ ⎠

Clearly, if each of the four terms in the sum is constrained to a fourth of its limit, the unity rule 

will be satisfied. 

If the concentrations of the radionuclides of concern are independent, then: 

( ) ( ) ( ) ( ) ( )2

2 2 2 2

2

085.0

25.0 25.0 25.0 25.0

25.0 25.0 25.0 25.0

226

226

232

232

234

234

238

238

226

226

232

232

234

234

238

238

≤ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

=

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ + +

Ra

Ra

Th

Th

U

U

U

U

Ra

Ra

Th

Th

U

U

U

U

MR

AL

Cu

AL

Cu

AL

Cu

AL

Cu

AL

C

AL

C

AL

C

AL

C

u

If the required relative measurement method uncertainty is the same for each radionuclide, 

therefore, the required relative method uncertainty for each individual radionuclide is: 

 

1

 MARSSIM Section 4.3.3 and MARSSIM Appendix I.11 provide information on applying the unity rule. 

NUREG-1575, Supp. 1 8-22 January 2009 
MARSAME Illustrative Examples 

( ) ( ) ( ) ( ) ( ) ( )2

2 2 2 2 2

0425.0

4

085.0

25.0 25.0 25.0 25.0 226

226

232

232

234

234

238

238

≤ = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

Ra

Ra

Th

Th

U

U

U

U

AL

Cu

AL

Cu

AL

Cu

AL

Cu

The required relative measurement method uncertainties for each radionuclide are provided in 

Table 8.10. 

Table 8.10 Radionuclide-Specific Required Relative Measurement Method Uncertainties 

Radionuclide 

Modified Action Level 

(Bq/kg) 

Required Relative 

Measurement Method 

Uncertainty, φMR

238U 38,000 / 4 9500 = 4.25% 

234U 12,000,000 / 4 3,000,000 = 4.25% 

232Th 330 / 4 82.5 = 4.25% 

226Ra 450/4 = 112.5 4.25% 

The required measurement method uncertainty for each radionuclide was provided to the 

analytical laboratory. The analytical laboratory used this information to specify sample volumes 

required to ensure this MQO was achieved. 

8.2.6.7 Collect Survey Data 

As the concrete is removed from the crusher, it is placed in a wooden frame (measuring 8 m by 

10 m by 15 cm) on a concrete pad. The wooden frame’s volume (12 m3

) corresponds to the 

volume associated with each sample from the survey design (i.e., 83 m3

 divided by 7 samples). 

Therefore, 7 batches of concrete equal 1 survey unit. One sample is collected from the center of 

the concrete rubble residing in the wooden form for each batch of crushed concrete. One hundred 

percent of the surface is scanned to identify locations with count rates greater than 30,000 cpm to 

investigate for areas of elevated activity and establish biased sampling points. A sample is 

collected at each location exceeding 30,000 cpm. 

If no scan results exceed 30,000 cpm, the concrete is removed from the form and placed in the 

non-impacted concrete staging area awaiting laboratory analysis of the samples. If the scan 

survey identifies areas exceeding 30,000 cpm, the concrete is transferred to a holding container 

to control access to the concrete until the laboratory analyses are completed. A total of 126 

batches of concrete are scanned (7 batches for each of the 18 survey units). Seventeen batches of 

concrete are segregated as potentially containing elevated levels of radioactivity based on the 

scan survey results, and one additional sample is collected from each batch as part of the 

investigation. No areas exceeding 100,000 cpm are identified during implementation of the 

disposition survey. 

Five additional samples are collected from random locations on the floor of the administrative 

building to provide a total of seven reference area samples. The results of the two samples 

collected from the administrative building during the preliminary surveys are reviewed and 

determined to be of adequate quality for the disposition survey. 

January 2009 8-23 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

All of the concrete samples collected during implementation of the disposition survey are sent to 

a laboratory for analysis by gamma spectroscopy and alpha spectrometry for uranium isotopes. 

Thorium-232 is quantified based on the 228Ac gamma spectroscopy results. Radium-226 is 

quantified based on the 214Bi gamma spectroscopy results. A total of 150 samples are analyzed, 

including seven samples from the reference area. The 17 biased-sample locations identified by 

the scan survey were analyzed by gamma spectroscopy. 

Performance checks of the FIDLER were made at the beginning and end of collection activities 

for each survey unit. These performance checks included a blank measurement in an area away 

from potential sources of radioactivity and a source check. Control charts were constructed to 

monitor the performance of the FIDLER throughout the survey. One FIDLER was dropped while 

performing a scan survey and the window was damaged. The instrument was removed from 

service and all scan measurements were repeated using a replacement FIDLER for that survey 

unit. No quality related problems were identified during the performance of the scan surveys. 

The offsite laboratory provided the results of the laboratory analyses. The quality control 

measurements specified in the work plan were performed. All of the QC results were within the 

limits specified in the work plan. No quality related issues were identified during the 

performance of the sampling surveys. 

8.2.7 Evaluate the Survey Results 

8.2.7.1 Conduct a Data Quality Assessment 

The disposition survey design for the concrete rubble is verified as having been executed very 

closely to the survey design, with the appropriate number of measurements collected for each of 

the survey units. 

The quality control sample results from the laboratory are reviewed and the data are deemed 

acceptable. An exploratory data analysis of the entire data set is performed to gain an 

understanding of the structure of the data. 

The sum of fractions for each sample is calculated using the results for 238U, 234U, 232Th (228Ac), 

and 226Ra (214Bi) and the radionuclide specific action levels. Only two samples result in sums of 

fractions greater than 1.0 without correcting for background. Both of these samples came from 

batches that were segregated prior to crushing based on visual evidence of staining within the 

concrete rubble; these were also the two locations with the highest scan survey results. A 

frequency plot (Figure 8.1) and normal cumulative frequency plot (Figure 8.2) were constructed 

to provide visual representations of the data. 

8.2.7.2 Conduct the Wilcoxon Rank Sum Test 

The Wilcoxon Rank Sum test was used to compare the reference area data to the survey unit 

data. In each case the test statistic exceeded the critical value of 65, so the null hypothesis was 

NUREG-1575, Supp. 1 8-24 January 2009 
MARSAME Illustrative Examples 

rejected for all 17 survey units. It was concluded that the average activity in all the crushed 

concrete exceeds background by less than a sum of fractions of 1.0. 

0

10

20

30

40

50

60

70

80

90

0.1 0.3 0.5 0.7 0.9 1.1 1.3

Sum of Fractions

Frequency

Figure 8.1 Frequency Plot of Illustrative Example Data 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

-3 -2 -1 0 1 2 3

Normal Cumulative Frequency

Sum of Fractions

Figure 8.2 Cumulative Frequency Plot of Illustrative Example Data 

January 2009 8-25 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.2.8 Evaluate the Results: The Decision 

In every survey unit, including those with stained concrete, the test statistic for the WRS test 

exceeded the critical value in Table A.4 in Appendix A. The null hypothesis that the mean sum 

of fractions in the survey unit exceeds 1.0 is rejected. Even though the standard deviation of the 

survey unit results (0.287) exceeded the variability used to design the survey (i.e., 0.15), it did 

not significantly impact the ability to make a decision about the concrete rubble. Based on the 

results of the disposition survey, all the crushed concrete can be cleared. 

8.3 Mineral Processing Facility Rented Equipment Baseline Survey 

This illustrative example is provided for information purposes only and presents a theoretical 

application of MARSAME guidance. This example describes a scan-only interdiction survey 

using Scenario B with an action level of no detectable radioactivity. The text is provided to 

illustrate the application of MARSAME guidance, and should not be considered an example 

survey plan. The amount of discussion provided in this example is based on the complexity of 

the problem and the relative difficulty expected from applying or interpreting specific portions of 

MARSAME guidance. The amount of discussion for this example is not related to, and should 

not be used as an estimate of, the level of effort associated with planning, implementing, or 

assessing an actual disposition survey. 

8.3.1 Description 

Heavy equipment is required to move the piles of concrete rubble at the mineral processing 

facility discussed in Section 8.2. A front loader is rented to assist with the work. The radiological 

history of the rented front loader is unknown. 

8.3.2 Objectives 

The objective is to apply interdiction controls to prevent the introduction of offsite radioactive 

materials to the mineral processing facility. In addition, surveying the front loader before it 

enters the site may provide reference area data for use in clearing the front loader at the end of 

the project (Section 8.4). The scope of this illustrative example is limited to a rented front loader 

being brought to the site for on-site transport of impacted concrete rubble. 

8.3.3 Initial Assessment of the M&E 

8.3.3.1 Categorize the M&E as Impacted or Non-Impacted 

The material to be assessed is a rented front loader (Figure 8.3). A review of the existing 

information shows it is not adequate to categorize the front loader (see Figure 2.1 in Chapter 2). 

A visual inspection of the front loader as it is delivered to the site shows the equipment has been 

used, but there are no notable quantities of soil. No detailed historical records pertaining to the 

usage history of the front loader are available for review, other than that available from the rental 

company pertaining to the types of sites where heavy equipment is rented and used. Natural 

radionuclides are present in or commingled with soil, sediment, rubble, debris, and water. Heavy 

NUREG-1575, Supp. 1 8-26 January 2009 
MARSAME Illustrative Examples 

equipment is in direct contact with natural uranium and thorium during operations. Because there 

is a possibility the M&E may contain radionuclide concentrations or radioactivity exceeding the 

background at the mineral processing facility, the front loader is categorized as impacted. 

Figure 8.3 Front Loader 

Sentinel measurements were performed to provide information on whether the difficult-tomeasure portions of the front loader, specifically the engine, were impacted by activities 

conducted prior to arrival at the site. The existing air filter was removed and a sentinel 

measurement of the used air filter was performed to determine if any radioactivity was associated 

with the air filter. A smear sample was taken from the air intake beyond the air filter to test for 

removable radioactivity. A second smear sample was taken inside the exhaust pipe to test for 

removable radioactivity exiting the difficult-to-measure engine areas. Measurements were 

performed using a hand-held gas proportional detector with an effective probe area of 100 cm2

, a 

detection limit less than 1,000 dpm per 100 cm2

 (Section 8.3.5.2), and counting for 1 minute. 

Smear measurements were made using a dual phosphor detector with a detection limit less than 

1,000 dpm per 100 cm2

 (Section 8.3.5.2), and counting for 2 minutes. The results of the sentinel 

measurements are shown in Table 8.11. 

Table 8.11 Sentinel Measurement Results 

Reference Material 

Counts (Before Use, NB)B

Sample Counts 

(After Use, NS) 

Net Count 

(NS – NB) B

Critical Value of the 

Net Instrument 

Sample Signal (SC, Table 7.5)

Description α β α β α β α β

Air Filter 2 154 5 156 3 11 4.96 28.1 

Air Intake 

Smear 0 66 1 74 1 8 2.82 18.9 

Exhaust 

Smear 0 66 0 68 0 2 2.82 18.9 

January 2009 8-27 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

The sentinel measurement results are below the critical value of the net instrument signal, so no 

radioactivity was detected by the sentinel measurements. As long as the results of the interdiction 

survey do not detect any radioactivity, the decision will be that the difficult-to-measure areas of 

the front loader are non-impacted. 

8.3.3.2 Describe the M&E 

The information available after categorizing the front loader is not adequate to select a 

disposition option (see Figure 2.2 in Chapter 2). The data gaps for the front loader are associated 

with describing the physical and radiological attributes of the front loader. The scoping survey 

design includes scanning external and easily measurable areas of the front loader that have the 

highest potential to contact radioactive materials. 

A description of the physical attributes of the front loader is listed in Table 8.12 (per Table 2.1). 

The front loader is a large, complicated piece of machinery. It incorporates four wheels that are 

50 centimeters (cm) (1 feet [ft], 8 inches [in]) wide and 150 cm (5 ft) tall, a wheelbase of 345 cm 

(11 ft, 4 in), an additional section of 246 cm (8 ft, 1 in) behind the rear wheels for the engine 

housing, and a height of 363 cm (11 ft, 9 in) to the top of the operator cab. 

Table 8.12 Physical Attributes Used to Describe the Front Loader 

Attribute Description

Dimensions Size: Total Mass ≈25,490 kg (56,196 lbs) 

Shape: Total Surface Area ≈180 m2

Complexity The front loader is composed of multiple materials. Most external components are 

painted steel. However, the tires are rubber, the cab is comprised of large sections of 

glass, hydraulic fluid hoses are composed of high-pressure silicon, and the joints are 

coated with grease. 

Disassembly would ideally be avoided for the considerable time and expense it adds to 

performing disposition surveys on the equipment. 

Options for surveying interior surfaces include surveying of the engine air filters and 

interior surfaces of the exhaust plumbing to determine whether it is likely radioactive 

materials have spread into the engine.

Accessibility The inside corners of the bucket and portions of each tire and wheel are difficult to 

measure using conventional hand-held measurements, even with a relatively small 

hand-held GM detector. The large height of the front loader, the underside of the front 

loader, and the varying orientation of surfaces associated with the equipment represent 

a scenario that makes accessibility difficult. 

There are only a few porous surfaces that allow permeation of radioactivity, such as the 

grease used on external hinges and joints. 

Air inlets, grease used on external hinges and joints, and air vents in the external panels 

represent areas where radioactivity could penetrate to difficult-to-measure areas. 

Inherent Value The front loader can be decontaminated, reused, or recycled. The costs associated with 

either replacing impacted portions of the front loader, or disposing of the front loader 

and replacing it, are very high. As long as only exterior surfaces of the front loader 

become impacted, the cost of decontamination to allow unrestricted release and reuse 

elsewhere will probably not be substantial. 

NUREG-1575, Supp. 1 8-28 January 2009 
MARSAME Illustrative Examples 

The front loader uses a 320-cm wide (10 ft, 6 in), 4.7-m3) capacity bucket (6 yd3). The overall 

length with the bucket is 914 cm (30 ft, 0 in). 

The surface area was estimated by dividing the front loader into components with regular 

geometric shapes and rounding to the nearest square meter. For example, the tires were modeled 

as cylinders and the cab was modeled as a box. The bucket has a surface area of 13.5 m2

, which 

is applied to the inside and outside surfaces for a total of 27 m2

. The exterior surfaces of the body 

have a surface area of approximately 76 m2

. The tires have a surface area of 24 m2, and the 

inside of the cab is estimated at 16 m2

. Because the surfaces are not actually regular geometric 

shapes, a contingency factor of 25% (35 m2

) was used to account for irregular surfaces, hoses, 

etc. This contingency factor was based on professional judgment and approved through 

discussions with the regulators. The rounded total surface area is 180 m2

. 

The front loader is composed of multiple materials. Most external components are painted steel. 

However, the tires are rubber, the cab is comprised of large sections of glass, hydraulic fluid 

hoses are composed of high-pressure silicon, and the joints are coated with grease. The front 

loader is deemed accessible, as the areas most likely to contain radioactivity are all accessible 

(though some portions of the front loader are more accessible than others) for conducting 

measurements with hand-held instruments. Internal areas of the front loader are inaccessible 

without disassembly. 

The radiological attributes of the front loader are listed in Table 8.13 (per Table 2.2). 

Radionuclides of potential concern include any radionuclides that may be present. Members of 

the uranium and thorium radioactive decay series are used as a preliminary list of radionuclides 

because these are the radionuclides of concern for the site (Appendix C lists types of sites where 

uranium and thorium series radionuclides may be present). These are the radionuclides that are 

known to be present at the mineral processing facility. Radioactivity associated with the front 

loader is anticipated to be present at near-background concentrations. Materials may have built 

up in specific locations on the front loader (e.g., joints with external grease, tires, corners of the 

bucket) resulting in small areas of elevated radioactivity. The distribution of radioactive material 

is expected to be concentrated on the underside and lower edges of the front loader. Horizontal 

surfaces also present areas for the potential deposition of airborne radioactivity (angled and 

vertical surfaces also present areas for the potential deposition of airborne radioactivity but 

deposition of radioactivity is less likely in these areas due to surface orientation). 

Table 8.13 Radiological Attributes Used to Describe the Front Loader 

Attribute Description 

Radionuclides Radionuclides of potential concern are any radionuclides that can be identified. The 

uranium and thorium series radionuclides are used as a preliminary list, because these 

are the radionuclides of concern for the mineral processing facility. 

Activity Radionuclide concentrations are expected to be close to background or zero. 

Distribution Radioactivity is expected to be associated with materials that have come in contact 

with the front loader. These materials will likely build up in specific locations 

resulting in small areas of elevated activity that can be visually identified. 

Location Radioactivity associated with the front loader is expected to be surficial and 

removable. 

January 2009 8-29 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Given the unknown use history of the front loader, professional judgment and process knowledge 

are used to develop a likely scenario for the potential distribution of radioactivity. Radioactivity 

associated with the front loader is expected to be surficial only. Because the radioactivity is 

expected to be associated with materials from the site, the radioactivity is also expected to be 

removable. 

Process knowledge does not provide a likely scenario for activation or other method for 

volumetrically impacting the front loader. 

8.3.3.3 Design and Implement Preliminary Surveys 

A Geiger-Mueller (GM) meter is used to collect initial scanning survey data to help address data 

gaps on the bucket and tires (i.e., external and easily measurable areas of the front loader that 

have the highest potential for residual radioactivity). The maximum reading from the bucket was 

80 counts per minute (cpm), and the maximum reading from the tires was 65 cpm. A collimated 

in situ gamma spectrum made of the front loader showed no gamma lines other than those 

associated with natural uranium, potassium, and thorium. Although one might expect some trace 

amounts of 137Cs from atmospheric fallout, there was not enough to show up in the spectrum. 

A non-impacted section of steel I-beam approximately one foot long (which resembles the 

majority of the surfaces of the front loader) is used as a reference material to establish the GM’s 

background count rate. Scanning measurements are collected from flat surfaces, edges, and 

inside corners of the I-beam; count rates of 30 to 35 cpm are observed. Daily quality control 

checks were performed to ensure the instruments were operating properly. 

8.3.3.4 Select a Disposition Option 

The disposition options for the front loader are to accept it for use at the mineral processing 

facility following an interdiction survey, or to return it to the rental company. 

8.3.3.5 Document the Results of the Initial Assessment 

 

The results of the IA were documented in a letter report to the project manager. The decision to 

categorize the front loader as impacted was included in the report, along with the descriptions of 

the physical and radiological attributes of the front loader. The letter report described the scoping 

survey and listed the results of the measurements. 

8.3.4 Develop a Decision Rule 

Following completion of the IA, additional information needed to develop the disposition survey 

design is collected. 

8.3.4.1 Select Radionuclides or Radiations of Concern 

The initial assessment indicates that natural uranium and natural thorium are the radionuclides of 

potential concern. 

NUREG-1575, Supp. 1 8-30 January 2009 
MARSAME Illustrative Examples 

8.3.4.2 Identify Action Levels 

The action level selected for the interdiction survey is no detectable surface radioactivity above 

background. Because there are multiple radionuclides to be evaluated during the interdiction 

survey, additional discussion of action levels may be necessary. 

8.3.4.3 Describe the Parameter of Interest 

The parameter of interest for an interdiction survey with an action level of no detectable activity 

is the level of radioactivity above background reported for each measurement. Any measurement 

that detects the presence of radioactivity above background indicates the action level has been 

exceeded. 

8.3.4.4 Identify Alternative Actions 

The alternative actions are determined by the disposition option. If the front loader is refused 

access to the site, it will be returned to the rental company. If the front loader is granted access to 

the site, it will be used to transport concrete rubble. 

8.3.4.5 Develop a Decision Rule 

The decision rule incorporates the action level, parameter of interest, and alternative actions into 

an “if...then” statement. 

If the results of any measurement identify surface radioactivity in excess of background, then the 

front loader will be refused access to the site. If no surface radioactivity in excess of background 

is detected, then the front loader will be granted access to the site. 

8.3.4.6 Identify Survey Units 

A survey unit is defined as the quantity of M&E for which a separate disposition decision will be 

made. The front loader is the survey unit. The decision rule will be applied by comparing 

individual measurement results to the critical value for detection. All measurements must be 

below the critical value (i.e., no surface radioactivity in excess of background detected) in order 

to accept the front loader. 

8.3.4.7 Develop Inputs for Selection of Provisional Measurement Methods 

The selection of a measurement method depends on the list of radionuclides or radiations of 

concern and will affect the survey unit boundaries. Establishing performance characteristics for 

the measurement method (i.e., measurement quality objectives [MQOs]) will help ensure the 

measurement results are adequate to support the disposition decision. Three provisional 

measurement methods were identified by the planning team for consideration; scan-only, in situ, 

or a combination of both methods in a MARSSIM-type survey design. No method-based survey 

designs were identified that matched the description of the M&E, so no method-based survey 

designs were considered. 

January 2009 8-31 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Detection Capability 

 

Because the action level is stated in terms of detection capability, the detection capability is 

critical in selecting an acceptable measurement method. The detection capability is defined as the 

minimum detectable concentration (MDC). The survey design will need to specify how hard to 

look (i.e., select an appropriate discrimination limit) before the MQO for detection capability can 

be established. The MDC for the selected measurement method must be less than or equal to the 

discrimination limit. 

Measurement Method Uncertainty 

The measurement method uncertainty is also important in selecting a measurement method. The 

MQO for detection capability will determine the acceptability of a measurement method, but it 

will also include information on the measurement method uncertainty. The measurement method 

uncertainty at background concentrations is used to calculate the MDC, as well as the critical 

value for the detection decision. 

Range 

The selected measurement method must be able to detect radionuclide concentrations or 

radioactivity at the discrimination limit. However, the measurement method must also be able to 

operate and quantify radionuclide concentrations or radioactivity at levels equal to those 

identified in the M&E at the site. 

Specificity 

The requirement for specificity will be tied to the list of radionuclides and radiations of concern. 

If radionuclide specific measurements are required, the measurement method must be able to 

identify radioactivity associated with specific radionuclides. If radionuclide specific 

measurements are not required, methods that measure gross activity may be acceptable. 

Ruggedness 

Ruggedness is not expected to be a major concern for selecting a measurement method. Because 

only surficial radioactivity is expected, in situ measurements of front loader surfaces will be used 

to collect data for comparison to the action levels. The selected measurement method must be 

able to perform these surface measurements in the field where the front loader is located. The 

environmental conditions will depend on the site location (e.g. northeast versus southwest) and 

the time of the year (e.g., winter versus summer). 

8.3.4.8 Reference Materials 

The majority of the surfaces on the front loader are metal (e.g., steel), although there are several 

rubber surfaces as well (e.g., tires, hoses). The small steel I-beam used to estimate background 

during the preliminary surveys will be used as the reference materials for the disposition survey. 

There is no inherent radioactivity from the uranium or thorium decay series expected in steel or 

NUREG-1575, Supp. 1 8-32 January 2009 
MARSAME Illustrative Examples 

rubber, so the selection of the reference material is not expected to result in any bias during 

interpretation of the results. 

8.3.5 Develop a Survey Design 

 

8.3.5.1 Select a Null Hypothesis 

The hypotheses being tested are: 

• Null Hypothesis: The front loader contains no detectable radionuclide concentrations or 

radioactivity above background levels (i.e., indistinguishable from background). 

• Alternative Hypothesis: The front loader contains detectable radionuclide concentrations or 

radioactivity above background levels. 

MARSAME processes require the use of Scenario B when the action level is zero, which is the 

case for indistinguishable from background. 

8.3.5.2 Set the Discrimination Limit 

The discrimination limit is the radionuclide concentration or level of radioactivity that can be 

reliably distinguished from the action level by performing measurements. Under Scenario B, the 

discrimination limit determines how hard the surveyor needs to look to determine there is no 

detectable radioactivity. 

Acceptable surface activity levels derived from the relevant regulatory agency were selected as 

the discrimination limits for radionuclides of potential concern. Table 8.14 lists the potential 

discrimination limits based on the preliminary list of radionuclides of concern. 

Table 8.14 Potential Discrimination Limits 

Radionuclide of Potential Concern Natural U NaturalTh

Average (dpm/100 cm2) 5,000 1,000 

Maximum (dpm/100 cm2) 15,000 3,000 

Based on the preliminary selection of radionuclides of potential concern, the discrimination 

limits for natural thorium represent the limiting case. 

8.3.5.3 Specify the Limits on Decision Errors 

A Type I decision error occurs when the null hypothesis is rejected when it is true. For this 

survey, a Type I decision error would be refusing to allow the front loader onto the site even 

though there is no radioactivity present that exceeds background. The consequences of this 

decision error may include unnecessarily returning the front loader and taking additional time to 

locate a replacement, or possibly deciding to decontaminate the front loader prior to use on the 

site. During scanning, the consequence of making a Type I decision error is the need to perform 

an investigation to determine the reason for the elevated reading, A Type I decision error rate of 

25% is selected for the scanning survey to balance the potential of additional rental costs for the 

January 2009 8-33 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

front loader while additional investigations are performed and evaluated against the additional 

time required to scan at slower speeds to achieve this DQO. 

A Type II decision error occurs when the null hypothesis is not rejected when it is false. For this 

survey, a Type II decision error would be allowing the front loader to be used on the site when 

there is radioactivity above background. The consequences of a Type II decision error may 

include introducing additional radionuclides on to the site and slightly increased exposures to 

workers. It may also make it difficult to clear the front loader and return it to the rental company 

when the work is complete. For this reason a Type II decision error rate of 5% is selected for the 

scanning. 

8.3.5.4 Select a Measurement Technique 

At this point in the survey design process, the planning team decides to evaluate each of the three 

provisional measurement methods from Section 8.3.4.7 to determine what might be feasible for 

surveying the front loader. Final selection of a measurement technique will help determine the 

final survey design and decide between the multiple options currently available for the survey. 

A scan-only survey approach requires that the measurement method be capable of detecting 

radioactivity at the discrimination limit. Any results exceeding the critical value would provide 

evidence of radioactivity levels exceeding background. There would be no need to record 

individual measurement results, because every result would be compared to the critical value. 

The calculation of the total efficiency is expected to be a major source of measurement method 

uncertainty. Additional measurements or assumptions are required to select a source term as the 

basis for the efficiency calculations. Scanning can be performed for alpha, beta, gamma, or some 

combination of the types of radiation. The amount of the front loader requiring scanning (i.e. 10 

to 100%) would be determined by the classification. It is unknown if any scan-only measurement 

methods are available that meet the MQOs. 

In situ survey approaches also require that the measurement method be capable of detecting 

radioactivity at the discrimination limit. In situ techniques allow identification of specific 

radionuclides, if necessary. The major source of measurement method uncertainty will likely be 

the model used to calculate the efficiency. Additional measurements or assumptions are required 

to select a source term as the basis for the efficiency calculations. The amount of the front loader 

requiring measurement (i.e., 10 to 100%) would be determined by the classification. The final 

number of measurements will be linked to the field of view of the detector. For example, a 

detector with a 1-m2

 field of view would require more than 180 measurements to measure 100% 

of the external surfaces of the front loader. An instrument such as the GM detector used during 

the scoping survey with a field of view of less than 100 cm2

 would require thousands of 

measurements to measure the minimum 10% of the front loader. 

A MARSSIM-type approach would use a combination of direct measurements or samples with 

scanning to support a disposition decision. Sampling could damage the front loader, so direct 

measurements would be preferred. Locating measurements on the surface of the front loader will 

be problematic. Similar to scan-only and in situ designs, the scanning and direct measurements 

should be capable of detecting radioactivity at the discrimination limit. The MARSSIM-type 

survey design would require the most resources to implement. 

NUREG-1575, Supp. 1 8-34 January 2009 
MARSAME Illustrative Examples 

Based on the evaluation of measurement techniques, a scan-only survey design is the preferred 

approach. Assumptions about the radionuclides of concern will need to be established and the 

availability of scan-only measurement methods needs to be verified. 

8.3.5.5 Finalize Selection of Radiations to be Measured 

Scan-only measurement methods are available for alpha, beta, and gamma radiations. The higher 

background associated with scanning for gamma radiation makes it unlikely that the 

measurement method could detect radioactivity at the discrimination limit. Alpha particles are 

attenuated more than beta particles, increasing the uncertainty caused by variations in source to 

detector distance. Scan-only measurement methods for beta radiation should provide the 

optimum survey design. However, the lower detection limits associated with alpha 

measurements may be required to meet the detection capability MQO. Any radioactivity in 

excess of background is assumed to result from natural thorium, which is the limiting 

radionuclide. 

8.3.5.6 Develop an Operational Decision Rule 

A scan-only survey will be performed for beta (and possibly alpha) radiation. Any result that 

exceeds the critical value associated with the MDC set at the discrimination limit will result in 

rejection of the null hypothesis, and the front loader will not be allowed on the site. Additional 

constraints on data collection activities include that the front loader be clean and dry when the 

measurements are performed. 

8.3.5.7 Classify the M&E 

The expected levels of radioactivity are background (see Table 8.13). No radioactivity in excess 

of background is expected, so the front loader is classified as Class 3. 

8.3.5.8 Select a Measurement Method 

The planning team decided to verify the availability of an acceptable measurement method prior 

to finalizing the survey design. The GM detector used to perform the preliminary survey is 

evaluated first. The expected range of radioactivity based on the reference material and 

preliminary survey data is approximately 35 cpm (i.e., background) to 80 cpm. 

Based on the scanning survey data collected using the GM detector during the preliminary 

surveys, the anticipated Scan MDC of the GM detector may not be capable of detecting 

radioactivity at the discrimination limit of 1000 dpm/100 cm2

 (see Table 8.14). 

An alpha-beta gas proportional detector utilizing a larger effective probe area will help achieve a 

lower scan MDC. The maximum reading for measurements from the bucket is 250 cpm; and the 

maximum reading from the tires is 220 cpm. Measurements collected from flat surfaces, edges, 

and inside corners of the reference material I-beam provide count rates between 180 and 190 

cpm. The maximum background count rate is converted to scan MDC using NUREG-1761 

(NRC 2002a) Equations 4-3 and 4-4. 

January 2009 8-35 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

bds counts i = ′ i =×= 7.63.832.2

cpm isMDCR i 201

2

60 7.6 60

=×=×=

2 100/220

29.15.0

201 dpm cm

p

MDCR MDCScan

si

= × == εε

Where: 

bi = average number of background counts in the observation interval 

2(250/60) = 8.3 counts) 

i = the interval length (2 s) based on a scan speed of 5 cm/s 

p = efficiency of a less than ideal surveyor, range of 0.5 to 0.75 from 

NUREG-1507 (NRC 1998b); a value 0.5 was chosen as a conservative 

value 

d' = detectability index from Table 6.1 of NUREG-1507 (NRC 1998b); a 

value of 2.32 was selected, which represents a true positive detection rate 

of 95% and a false positive detection rate of 25% 

si = minimum detectable number of net source counts in the observation 

interval (counts) 

MDCR = minimum detectable count rate (cpm) 

εiεs = weighted total alpha-beta efficiency for natural thorium in equilibrium 

with its progeny on the surveyed media (1.29, see Table 8.15) 

The scan MDC for activity is now below 1,000 dpm/ 100 cm2

 and is good enough to detect 

radioactivity at the 232Th discrimination limit. 

Table 8.15 Detector Efficiency for the Mineral Processing Facility (232Th in Complete 

Equilibrium with its Progeny) using a Gas Proportional Detector 

Radionuclide 

Average Energy

(keV) Fraction 

Instrument 

Efficiency 

Surface 

Efficiency 

Weighted 

Efficiency 

232Th alpha 1 0.40 0.25 0.1 

228Ra 7.2 keV beta 1 0 0 0 

228Ac 377 keV beta 1 0.54 0.50 0.27 

228Th alpha 1 0.40 0.25 0.1 

224Ra alpha 1 0.40 0.25 0.1 

220Rn alpha 1 0.40 0.25 0.1 

216Po alpha 1 0.40 0.25 0.1 

212Pb 102 keV beta 1 0.40 0.25 0.1 

212Bi 770 keV beta 0.64 0.66 0.50 0.211 

212Bi alpha 0.36 0.40 0.25 0.036 

212Po alpha 0.64 0.40 0.25 0.064 

208Tl 557 keV beta 0.36 0.58 0.50 0.104 

Total efficiency = 1.29 

From NUREG-1761 (NRC 2002a), Table 4.3 

NUREG-1575, Supp. 1 8-36 January 2009 
MARSAME Illustrative Examples 

8.3.5.9 Optimize the Disposition Survey Design 

A scan-only interdiction survey will be performed of the exterior surfaces of the front loader. 

Because the front loader is Class 3, approximately 10% of the external surface area will be 

surveyed. Professional judgment will be used to select the locations for the scans in the locations 

with the highest potential for radioactivity (i.e., the bucket, tires, and floor of the cab). 

Approximately 50% of each of these areas will be surveyed, for a total of approximately 18 m2

(7 m2

 of the bucket, 10 m2 of the tires, and 1 m2 of the cab floor). Experienced technicians will 

be used to perform the surveys. The scan speed will be 5 cm per second, so the scan should take 

approximately one man-hour to complete. The scans will be performed using a 100 cm2

 active 

probe area alpha-beta gas-proportional detector. 

If while scanning, an area is perceived to exceed background (i.e., exceeds the scan MDC), the 

surveyor will suspend the scan survey and perform an investigation survey consisting of a oneminute measurement to verify the result of the scan measurement. The one-minute time interval 

was chosen to meet the DQOs and MQOs for this measurement. If the results of the one-minute 

verification measurement exceed the critical value calculated in 8.3.6.5, the radioactivity at that 

location exceeds background and should be recorded on a log sheet. The location of any oneminute verification measurement that exceeds the critical value will be clearly marked. 

Quality control (QC) measurements will be performed prior to the start of the survey and at the 

completion of the survey. These QC measurements will demonstrate that the instruments were 

working properly while the survey was being performed. In addition, approximately 5% of the 

survey will be repeated using a different surveyor to confirm the results of the initial survey. 

8.3.5.10 Document the Disposition Survey Design 

The interdiction survey design was documented in a letter report to the project manager. The 

results of the IA were also included in this letter report. 

8.3.6 Implement the Survey Design 

8.3.6.1 Ensure Protection of Health and Safety 

Protection of health and safety was performed as part of the survey implementation, but is not 

included in this illustrative example (see Section 8.2.6.1 for an example Job Safety Analysis.) 

8.3.6.2 Consider Issues for Handling M&E 

Because only a portion of the front loader needs to be accessed to implement the survey design, 

the front loader does not need to be moved to provide access to additional areas during the 

survey (e.g., bottom of tires, underside of bucket). Areas included in the survey do not need to be 

marked, outside of the small area that will be re-surveyed as part of the QC checks and locations 

of measurements exceeding the critical value. The front loader will not be parked adjacent to 

areas known to contain radionuclide concentrations or radioactivity in excess of background 

(e.g., piles of concrete rubble) while the survey is performed. 

January 2009 8-37 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.3.6.3 Segregate the M&E 

No segregation of the front loader is required to implement the survey design. 

8.3.6.4 Determine the Measurement Detectability for the Scan Survey 

Section 8.3.4.7 established the MQO for the measurement detectability. The scan MDC must be 

less than or equal to the discrimination limit. 

8.3.6.5 Determine the Measurement Detectability for the Investigation Survey 

As indicated in Section 8.3.5.9, an investigation survey will be performed for any result that 

exceeds the scan survey investigation level (i.e. scan MDC). Both Type I and Type II errors that 

might occur during the investigation survey are equally undesirable. The consequence of 

incorrectly alleging that the front loader contains radioactivity in excess of background (Type I 

error) may raise unnecessary regulatory concerns. On the other hand, accepting a front loader 

that has radioactivity detectable above facility background (Type II error) may make it difficult 

to clear when the work is finished. Thus it is desirable to initially set α = β = 0.01. The critical 

value for the one-minute measurement may be calculated from the equation in line 1 of Table 

7.5: 

1 1 2.326 2 250 2.326 500 52 net counts S S

C B

B B

t t Sz N

t t −α

⎛ ⎞

= += ×= = ⎜ ⎟ ⎝ ⎠

, 

Where: 

SC = the critical value 

NBB = the mean background count (250 counts) 

tS = the count time for the test source (one minute) 

tB = the count time for the background (one minute) B

z1-α = the (1 − α)-quantile of the standard normal distribution (2.326 when α =0.01). 

The minimum detectable net count can be calculated from the equation in line 1 of Table 7.6: 

( )

2 2

1 1

1

2 2

1

2 4

2.326 2.326 52 2.326 52 250 2 109 net counts, 2 4

S S

D C C B

B B

z z t t SS z SN

t t

β β

β

− −

−

⎛ ⎞

=+ + ++ + ⎜ ⎟ ⎝ ⎠

=+ + ++ =

Where: 

z1-β = the (1 − β)-quantile of the standard normal distribution (2.326 when β =0.01) 

SD = the minimum detectable value of the net instrument signal (discrimination limit, 7 

cpm) 

The MDC can be calculated from Equation 4-1 in NUREG-1761 (NRC 2002a): 

NUREG-1575, Supp. 1 8-38 January 2009 
MARSAME Illustrative Examples 

( )

( )

2 detection limit 109 109 MDC 84.5 dpm cm total efficiency sample size Probe Area 100 1.29 1.29

100 100

D

i s

S

ε ε

= == = = ×

× ×

/100 

of natural thorium. 

8.3.6.6 Determine Measurement Uncertainty for the Investigation Survey MDC 

MDC Probe Area ( ) 100

D

i s

S

ε ε

=

×

Assuming a negligible uncertainty in the probe area, the combined standard uncertainty of the 

MDC is (see Equation 7-33): 

2 2

2 2 MDC MDC (MDC) ( ) ( ) c D

D i s

u u S

S

2

i s u ε ε

ε ε

⎛ ⎞ ∂ ∂⎛ ⎞

= + ⎜ ⎟ ⎜ ⎟ ∂ ∂ ⎝ ⎠ ⎝ ⎠

. 

Note that εiεs is treated as a single input variable because it is the weighted total alpha-beta 

efficiency for natural thorium in equilibrium with its progeny on the surveyed media. 

Because the MDC is of the form of a ratio of products, Equation 7-34 may be used: 

2 2

2 2

2 22

( ) ( ) (MDC) MDC D i s

c

D is

u S u

u

S

ε ε

ε ε

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

( )

( ) ( ) ( )

2 2

1 1

1

2 2

2 2

1 1

2 2

1

2 4

2.326 2.326 2.326 2

2 4

2.326 2.326 1 2.326 1 2

2 4

2.326 2.326 2.326 2 2.326 2.326 2 2

2 4

S S

D C C B

B B

C C B

S S S S

B B B

B B B B

B B

z z t t S S z SN

t t

S S N

t t t t z N z N N

t t t t

N N

β β

β

α α

− −

−

− −

⎛ ⎞

=+ + ++ + ⎜ ⎟ ⎝ ⎠

=+ + ++

⎛ ⎞ ⎛ ⎞ ⎛ ⎞

= ++ + + + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

= ++ + + NB

+

Where the formula for SC and the values of the constants have been inserted. The uncertainties in 

the times are assumed to be negligible, so these have also been treated as constants. Thus, the 

uncertainty in SD will be due entirely to the uncertainty in the background count: 

2

2 2 () ( ) D

D B

B

S uS uN

N

⎛ ⎞ ∂ = ⎜ ⎟ ∂⎝ ⎠

The sensitivity coefficient for SD at NB = 250 is B

January 2009 8-39 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

( ) ( ) ( )

( ) ( ) ( ) ( )

( )

2 2

2 2

2

2.326 2.326 2.326 2 2.326 2.326 2 2

2 4

2.326 2.326 2.326 2.326 2 2 2.326 2 2 4

2.326 2.326

2.326 2 4

0 2.326

2

B B B

D

B B

B B

B

B B B

B

N N N

S

N N

N N N

NN N

N

⎛ ⎞ ⎛ ⎞ ⎜ ⎟ ∂ ++ + + ⎜ ⎟ ⎛ ⎞ ∂ ⎝ ⎠ ⎜ ⎟ = ∂ ∂ ⎝ ⎠

⎝ ⎠

⎛ ⎞ ⎛ ⎞ ∂ ∂+ + ⎜ ⎟ ⎛ ⎞ ∂ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠⎝ ⎠ = ++

∂∂ ∂ ⎝ ⎠

∂ + ⎛ ⎞

= ++ ⎜ ⎟

⎜ ⎟ ⎝ ⎠

( ) ( )

( ) ( )

( ) ( ) 2

2

2.326 2 2 2.326 2 2

4

2.326 2.326 2 2

4

B B B B

B

B B

N N N N

N

N N

⎡ ⎤ ⎛ ⎞ ⎡ ⎛ ⎞⎤ ⎢ + ⎜ ⎟⎥⎢∂+ + ⎜ ⎟⎥ ⎢ ⎥ ⎝ ⎠ ⎢ ⎝ ⎠⎥ ⎢ ⎥ ⎛ ⎞ ⎢ ∂ ⎥ ∂+ + ⎜ ⎟ ⎢ ⎥ ⎝ ⎠ ⎣ ⎦ ⎣ ⎦

( )

( )

( )

( )

0.5815 3.289 2 0.5815 3.289 2 1.6447 2.326

0.5815 3.289 2

1.6447 1.163 2

1.6447

0.5815 3.289 2

2.447 0.104 0.208

23.5

B B B B

B B B B

B

B B B

N N N N

N N N N

N

N N N

⎡ ⎤ ∂+ + ⎡∂+ + ⎤ ⎛ ⎞ ⎢ ⎥

= + ⎢ ⎥ ⎜ ⎟ ⎜ ⎟ ∂+ + ⎢ ∂ ⎥ ⎝ ⎠ ⎢ ⎥⎣ ⎦ ⎣ ⎦

⎛ ⎞ ⎜ ⎟ +

⎝ ⎠ = +

+ +

=+ =

Suppose the spatial variability in NB can be described by a triangular distribution with a mean of 

250 and a half-width of 50, then, 

B

u(NB) = B 50 / 6 = 20.4 

and 

( ) ( ) (0.208)(20.4) 4.2 D

D B

B

S uS uN

N

⎛ ⎞ ∂ = = ⎜ ⎟ ∂⎝ ⎠

=

A complete analysis of the uncertainty in εiεs, the weighted total alpha-beta efficiency for natural 

thorium in equilibrium with its progeny on the surveyed media involves propagation of 

uncertainty through all of the input quantities in Table 8.15. The uncertainty in the weighted total 

alpha-beta efficiency is 

( ) 0.5 / 6 0.2 i s u ε ε = = 0 . 

 

Putting this information together into Equation 7-34 for the combined total variance of the MDC 

we have: 

NUREG-1575, Supp. 1 8-40 January 2009 
MARSAME Illustrative Examples 

( )

2 2

2 2

2 22

2 2

2

2 2

( ) ( ) (MDC) MDC

4.2 0.20 84.5

109 1.29

7,140 0.000148 .024

172.4

D i s

c

D is

u S u

u

S

ε ε

ε ε

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

= +

=

So the estimated combined standard uncertainty in the MDC is (MDC) 13.1 c u = . 

8.3.6.7 Perform Quality Control Measurements 

The required QC measurements are performed as described in the survey design. 

8.3.6.8 Collect Survey Data 

Data from the survey of the front loader is collected consistent with the survey design and 

provides a complete record of the data collected. Thirty-seven locations were flagged during the 

survey for investigations using one-minute measurements. None of the one-minute measurement 

results exceeded the critical value. 

8.3.7 Evaluate the Survey Results 

8.3.7.1 Conduct a Data Quality Assessment 

The surveying procedure utilized for the front loader was verified as having been executed very 

closely to the survey design, with the appropriate survey coverage. The results of the QC 

measurements demonstrated that the instruments were working properly and a different surveyor 

could duplicate the results of the survey. Control charts used to check the performance of the 

survey instruments did not identify any potential problems with the instruments. 

8.3.7.2 Conduct a Preliminary Data Review 

The preliminary data review for this baseline survey does not yield identifying patterns, 

relationships, or potential anomalies. The locations of the additional investigations appear to be 

randomly located based on visual inspection of the front loader. 

8.3.7.3 Conduct the Statistical Tests 

The statistical test selected for this scanning survey is direct comparison to the critical level. If 

all the results are below the critical level associated with the discrimination limit, there is no 

detectable radioactivity above background. All of the scanning results that exceeded the critical 

value were subjected to additional investigation. All of the results of the additional investigations 

were below the critical value. 

January 2009 8-41 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.3.8 Evaluate the Results: The Decision 

Based on the results of the baseline survey, the front loader is determined to have no detectable 

radioactivity above background and is therefore allowed to enter the site. 

8.4 Mineral Processing Facility Rented Equipment Disposition Survey 

This illustrative example is provided for information purposes only and presents a theoretical 

application of MARSAME guidance. This example describes a scan-only disposition survey 

using Scenario A. Because this example uses the same M&E and the same survey design used in 

Section 8.3, it points out the similarities and differences between interdiction and release 

surveys. The examples in Sections 8.3 and 8.4 also point out the similarities and differences 

between surveys designed using Scenario A and surveys designed using Scenario B. The text is 

provided to illustrate the application of MARSAME guidance, and should not be considered an 

example survey plan. The amount of discussion provided in this example is based on the 

complexity of the problem and the relative difficulty expected from applying or interpreting 

specific portions of MARSAME guidance. The amount of discussion for this example is not 

related to, and should not be used as an estimate of, the level of effort associated with planning, 

implementing, or assessing an actual disposition survey. 

8.4.1 Description 

The radiological surveys at the mineral processing facility described in Section 8.2 have been 

completed. The front loader that was brought on site to assist with handling the concrete rubble 

(Section 8.3) is no longer being used. The front loader must be cleared before it can be returned 

to the rental company. 

8.4.2 Objectives 

The objective is to demonstrate the front loader can be cleared. The scope of this illustrative 

example is limited to the rented front loader used for the on-site transport of impacted concrete 

rubble. 

An interdiction survey was performed to demonstrate there was no detectable radioactivity above 

background associated with the front loader when it entered the site. This illustrative example 

provides a comparison between interdiction and clearance surveys performed on the same piece 

of equipment. 

8.4.3 Initial Assessment of the M&E 

8.4.3.1 Categorize the M&E as Impacted or Non-Impacted 

The existing information is adequate to categorize the front loader. The front loader was used to 

transport concrete rubble containing radionuclides with concentrations exceeding background. 

The front loader is impacted. Following use, the front loader was steam cleaned to remove loose 

dirt and grease (together with any associated radioactivity) for acceptance by the rental company. 

NUREG-1575, Supp. 1 8-42 January 2009 
MARSAME Illustrative Examples 

Sentinel measurements were performed to provide information on whether the difficult-tomeasure portions of the front loader, specifically the engine, were impacted by site activities. In 

addition to sentinel measurements, dust control measures were used to minimize the potential for 

airborne radioactivity from soil particulates throughout the project. Air monitoring of the work 

zone and the breathing zone of the front loader operator was performed throughout the project to 

estimate inhalation exposure for project workers. 

A new air filter was installed at the beginning of the project and a single measurement of 

radioactivity associated with the air filter was performed prior to use to provide an estimate for 

background. Following completion of soil handling activities the air filter was removed and 

stored for 72 hours to allow for decay of short-lived radon decay products. A sentinel 

measurement of the used air filter was performed following storage to determine if any 

radioactivity was associated with the air filter after being used at the site. A smear sample was 

taken from the air intake beyond the air filter to determine if there was any removable 

radioactivity. Measurements were performed using a hand-held gas proportional detector with an 

effective probe area of 100 cm2

, a detection limit less than 1,000 dpm per 100 cm2 (see Section 

8.3.5.2), and counting for 1 minute. Smear measurements were made using dual phosphor 

detector with a detection limit less than 1,000 dpm per 100 cm2

 (see Section 8.3.5.2), and 

counting for 2 minutes. The results of the sentinel measurements are shown in Table 8.16. 

Table 8.16 Sentinel Measurement Results 

Reference Material 

Counts (Before Use, NB)B

Sample Counts 

(After Use, NS) 

Net Count 

(NS – NB) B

Critical Net Signal 

Sample (SC, Table 7.5) 

Description α β α β α β α β

Air Filter 2 145 4 168 2 23 4.96 28.1 

Air Intake 

Smear 0 66 1 79 1 13 2.82 18.9 

The engineering controls minimized the potential for airborne contamination. The work zone and 

breathing zone air monitoring results reported no detectable radioactivity with detection limits 

below the acceptable derived air concentrations (DACs). The sentinel measurement results are 

below the critical net signal, so no radioactivity was detected by the sentinel measurements. The 

combination of engineering controls, air monitoring measurements, and sentinel measurements 

support categorization of the difficult-to-measure portions of the front loader as non-impacted. 

8.4.3.2 Describe the M&E 

The description of the physical attributes associated with the front loader has not changed (see 

Table 8.7). The uranium series and thorium series radionuclides listed in Table 8.2 are the 

radionuclides of potential concern for the front loader. The existing information is adequate to 

select a disposition option, and there are no data gaps. 

January 2009 8-43 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

8.4.3.3 Select a Disposition Option 

The preferred disposition option for the front loader is clearance. The existing interdiction survey 

design used to allow the front loader access to the site will be evaluated for applicability as a 

clearance survey (Section 8.4.4.2). 

8.4.3.4 Document the Results of the Initial Assessment 

The decision to categorize the front loader as impacted will be documented with the results of the 

survey. The planning team determined that no other documentation is necessary. 

8.4.4 Develop a Decision Rule 

8.4.4.1 Identify Action Levels 

The action level selected for the interdiction survey was no detectable surface radioactivity 

above background. The action levels in this case are the limits shown in Table 8.13 The limiting 

value is 1000 dpm/100 cm2

 for natural thorium. 

8.4.4.2 Evaluate an Existing Survey Design 

Because the same front loader is being surveyed, the measurement method is still adequate. The 

scan MDC of 132 dpm/100 cm2

 for natural thorium is well below the action level. There were no 

problems identified during the interdiction survey that would prevent using the measurement 

method for a clearance survey. The population parameter of interest and the survey unit 

boundaries are linked to the measurement method (see Sections 8.3.4.3 and 8.3.4.6). 

The alternative actions are different for the clearance survey. If the front loader is cleared, it will 

be returned to the rental company. If the front loader is not cleared, it will remain on site. This 

results in a change to the decision rule. If the results of any measurement identify surface 

radioactivity in excess of background, the front loader will remain on site and radiological 

controls will remain in place. If no surface radioactivity in excess of 1,000 dpm/100 cm2

 over 

background is detected, the front loader will be cleared and returned to the rental company. 

8.4.5 Develop a Survey Design 

8.4.5.1 Select the Null Hypothesis 

Scenario A is being used, so the hypotheses being tested are: 

• Null Hypothesis: The front loader contains detectable radionuclide concentrations or 

radioactivity equal to or in excess of 1,000 dpm/100 cm2

 above background levels 

• Alternative Hypothesis: The front loader contains radionuclide concentrations or 

radioactivity less than 1,000 dpm/100 cm2

 above background levels. 

NUREG-1575, Supp. 1 8-44 January 2009 
MARSAME Illustrative Examples 

8.4.5.2 Set the Discrimination Limit 

The discrimination limit is the radionuclide concentration or level of radioactivity that can be 

reliably distinguished from the action level by performing measurements. Under Scenario A, the 

discrimination limit should represent a prudently conservative estimate of any amount of natural 

thorium that may be present on the front loader in excess of background. 

8.4.5.3 Specify Limits on Decision Errors 

A Type I decision error occurs when the null hypothesis is rejected when it is true. For this 

survey, a Type I decision error would be clearing the front loader when there is radioactivity 

detectable more than 1,000 dpm/100 cm2

 above background. The consequence of a Type I 

decision error may include releasing radionuclides from the site and increased exposures to 

members of the public. The existing survey design specifies a Type I decision error rate of 5% 

for scanning measurements for this decision error. 

A Type II decision error occurs when the null hypothesis is not rejected when it is false. For this 

survey, a Type II decision error would be refusing to clear the front loader even though the 

radioactivity present exceeds background by less than 1,000 dpm/100 cm2

. The consequence of 

this decision error may include the need to perform an investigation to determine the reason for 

the elevated reading, unnecessarily remediating the front loader, incurring additional costs for 

extra rental time, or even purchasing the front loader and disposing of it as low-level radioactive 

waste. The existing survey design specifies a Type II decision error rate of 25% for the scanning 

measurements for this decision error. Note that the definitions of Type I and Type II decision 

errors are reversed compared to the existing survey design from Section 8.3. 

8.4.5.4 Classify the M&E 

The potential for radioactivity exceeding background has increased because the front loader is 

known to have contacted concrete rubble containing radionuclides at concentrations that exceed 

background. This increased potential for radioactivity exceeding background results in a higher 

classification for portions of the front loader for the clearance survey. The inside of the bucket is 

now classified as Class 1. The remaining external surfaces are considered Class 3 so professional 

judgment can still be used to determine where surveys will be performed. 

8.4.5.5 Optimize the Existing Survey Design 

The front loader will be scanned with an alpha-beta gas proportional detector. Experienced 

technicians will perform the surveys. If while scanning, an area is perceived to exceed 

background, a one-minute measurement will be performed at that location to verify the scan 

results. If the results of the one-minute count exceed 1,000 dpm/100 cm2

 above background the 

front loader will require further remediation before it can be released. The results of all oneminute verification counts will be recorded on a log sheet. The location of any one-minute count 

that exceeds the critical value will be clearly marked. 

January 2009 8-45 NUREG-1575, Supp. 1 
Illustrative Examples MARSAME 

Based on the classification of the inside of the bucket as Class 1, 100% of the inside of the 

bucket will be surveyed. In addition, 25% of the outside surface of the bucket will be surveyed, 

concentrating on the bottom where the bucket frequently came in contact with the concrete 

rubble. Similar to the interdiction survey, 50% of the tires and the floor of the cab will be 

surveyed. In addition, 10% of the bottom and 5% the top (i.e., horizontal surfaces) will be 

included in the clearance survey. Areas to be scanned will be biased to locations where residual 

dirt or grease is visible. The increased surface area to be scanned is expected to increase the scan 

time to approximately three man-hours. Based on professional judgment, four times as many 

investigations are expected for the clearance survey, or approximately 150 one-minute 

measurements. The additional investigations are expected to require an additional three manhours. 

Implementation of this survey design will likely identify locations on the front loader bucket 

with radioactivity levels exceeding 1,000 dpm/100 cm2

 above background. To minimize these 

occurrences, the front loader will be steam cleaned and dried prior to implementing the survey 

design. Locations on the bucket (which is a Class 1 survey unit) where the additional 

measurement exceeds the action level will be delineated using scanning techniques, scrubbed 

clean to remove any surface radioactivity, and re-surveyed (i.e., clean-as-you-go). Locations with 

radioactivity exceeding 1,000 dpm/100 cm2

 above background are not expected anywhere else 

on the front loader. 

8.4.5.6 Document the Disposition Survey Design 

The modified survey design was documented in a letter report to the project manager. The letter 

report included the results of the categorization decision (Section 8.4.3.1). 

8.4.6 Implement the Survey Design 

The front loader was positioned on a concrete pad during steam cleaning operations. The water 

was collected and containerized for survey prior to release. The bucket was lifted off the ground 

and supported with wooden beams to provide access to the bottom of the bucket. 

The survey was implemented as described in the survey design. The beta background in the area 

underneath the bucket was higher than expected (i.e., 350 cpm instead of the 250 cpm used to 

design the survey). The bucket was lifted higher off the ground (i.e., 1.5 meters instead of 15 cm) 

and the scan survey was repeated with a lower background. The survey results were documented 

in a letter report to the project manager. 

8.4.7 Evaluate the Survey Results 

8.4.7.1 Conduct a Data Quality Assessment 

The surveying procedure utilized for the front loader was verified as having been executed very 

closely to the survey design. The surveys included the appropriate scan coverage and number of 

additional investigations. The preliminary data review for this baseline survey does not yield 

identifying patterns, relationships, or potential anomalies. Control charts documenting the results 

NUREG-1575, Supp. 1 8-46 January 2009 
MARSAME Illustrative Examples 

of quantitative QC checks and performance checks indicate the DQOs have been achieved for 

this clearance survey. 

8.4.7.2 Conduct the Statistical Tests 

The statistical test selected for this scanning survey is direct comparison to the action level of 

1,000 dpm/100 cm2

 above background. If all of the measurement results are below the action 

level, the average natural thorium above background cannot exceed 1,000 dpm/100 cm2

 above 

background. 

At 83 locations the scan MDC of 132 dpm/100 cm2

 above background appeared to be exceeded. 

However, none of the one-minute follow up counts at those locations exceeded 500 dpm/100 cm2

above background. 

8.4.8 Evaluate the Results: The Decision 

Based on the results of the disposition survey, the front loader is determined to have no 

radioactivity above the action level and so can be cleared. 

January 2009 8-47 NUREG-1575, Supp. 1 
 
MARSAME Appendix A 

January 2009 A-1 NUREG-1575, Supp. 1 

A. STATISTICAL TABLES AND PROCEDURES 

A.1 Normal Distribution 

Table A.1 Cumulative Normal Distribution Function Φ(z) 

z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

0.00 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359 

0.10 0.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5674 0.5714 0.5753 

0.20 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141 

0.30 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517 

0.40 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879 

0.50 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224 

0.60 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549 

0.70 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852 

0.80 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133 

0.90 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.6315 0.8340 0.8365 0.8389 

1.00 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621 

1.10 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830 

1.20 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015 

1.30 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177 

1.40 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319 

1.50 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441 

1.60 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545 

1.70 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633 

1.80 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706 

1.90 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767 

2.00 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817 

2.10 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857 

2.20 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890 

2.30 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916 

2.40 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936 

2.50 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952 

2.60 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964 

2.70 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974 

2.80 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981 

2.90 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986 

3.00 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990 

3.10 0.9990 0.9991 0.9991 0.9991 0.9992 0.9992 0.9992 0.9992 0.9993 0.9993 

3.20 0.9993 0.9993 0.9994 0.9994 0.9994 0.9994 0.9994 0.9995 0.9995 0.9995 

3.30 0.9995 0.9995 0.9995 0.9996 0.9996 0.9996 0.9996 0.9996 0.9996 0.9997 

3.40 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9998 

Negative values of z can be obtained from the relationship Φ(- z) () =1− Φ z
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-2 January 2009 

A.2 Sample Sizes for Statistical Tests 

Table A.2a Sample Sizes for Sign Test 

(Number of measurements to be performed in each survey unit) 

(α,β) or (β,α) 

0.01 0.01 0.01 0.01 0.01 0.025 0.025 0.025 0.025 0.05 0.05 0.05 0.1 0.1 0.25

Δ/σ 0.01 0.025 0.05 0.1 0.25 0.025 0.05 0.1 0.25 0.05 0.1 0.25 0.1 0.25 0.25

0.1 4,095 3,476 2,984 2,463 1,704 2,907 2,459 1,989 1,313 2,048 1,620 1,018 1,244 725 345

0.2 1,035 879 754 623 431 735 622 503 333 518 410 258 315 184 88 

0.3 468 398 341 282 195 333 281 227 150 234 185 117 143 83 40 

0.4 270 230 197 162 113 192 162 131 87 136 107 68 82 48 23 

0.5 178 152 130 107 75 126 107 87 58 89 71 45 54 33 16 

0.6 129 110 94 77 54 92 77 63 42 65 52 33 40 23 11 

0.7 99 83 72 59 41 70 59 48 33 50 40 26 30 18 9 

0.8 80 68 58 48 34 57 48 39 26 40 32 21 24 15 8 

0.9 66 57 48 40 28 47 40 33 22 34 27 17 21 12 6 

1.0 57 48 41 34 24 40 34 28 18 29 23 15 18 11 5 

1.1 50 42 36 30 21 35 30 24 17 26 21 14 16 10 5 

1.2 45 38 33 27 20 32 27 22 15 23 18 12 15 9 5 

1.3 41 35 30 26 17 29 24 21 14 21 17 11 14 8 4 

1.4 38 33 28 23 16 27 23 18 12 20 16 10 12 8 4 

1.5 35 30 27 22 15 26 22 17 12 18 15 10 11 8 4 

1.6 34 29 24 21 15 24 21 17 11 17 14 9 11 6 4 

1.7 33 28 24 20 14 23 20 16 11 17 14 9 10 6 4 

1.8 32 27 23 20 14 22 20 16 11 16 12 9 10 6 4 

1.9 30 26 22 18 14 22 18 15 10 16 12 9 10 6 4 

2.0 29 26 22 18 12 21 18 15 10 15 12 8 10 6 3 

2.5 28 23 21 17 12 20 17 14 10 15 11 8 9 5 3 

3.0 27 23 20 17 12 20 17 14 9 14 11 8 9 5 3 
MARSAME Appendix A 

January 2009 A-3 NUREG-1575, Supp. 1 

Table A.2b Sample Sizes for Wilcoxon Rank Sum Test 

(Number of measurements to be performed on the reference material and for each survey unit) 

(α,β) or (β,α)

0.01 0.01 0.01 0.01 0.01 0.025 0.025 0.025 0.025 0.05 0.05 0.05 0.1 0.1 0.25

Δ/σ 0.01 0.025 0.05 0.1 0.25 0.025 0.05 0.1 0.25 0.05 0.1 0.25 0.1 0.25 0.25

0.1 5,452 4,627 3,972 3,278 2,268 3,870 3,273 2,646 1,748 2,726 2,157 1,355 1,655 964 459

0.2 1,370 1,163 998 824 570 973 823 665 440 685 542 341 416 243 116

0.3 614 521 448 370 256 436 369 298 197 307 243 153 187 109 52 

0.4 350 297 255 211 146 248 210 170 112 175 139 87 106 62 30 

0.5 227 193 166 137 95 162 137 111 73 114 90 57 69 41 20 

0.6 161 137 117 97 67 114 97 78 52 81 64 40 49 29 14 

0.7 121 103 88 73 51 86 73 59 39 61 48 30 37 22 11 

0.8 95 81 69 57 40 68 57 46 31 48 38 24 29 17 8 

0.9 77 66 56 47 32 55 46 38 25 39 31 20 24 14 7 

1.0 64 55 47 39 27 46 39 32 21 32 26 16 20 12 6 

1.1 55 47 40 33 23 39 33 27 18 28 22 14 17 10 5 

1.2 48 41 35 29 20 34 29 24 16 24 19 12 15 9 4 

1.3 43 36 31 26 18 30 26 21 14 22 17 11 13 8 4 

1.4 38 32 28 23 16 27 23 19 13 19 15 10 12 7 4 

1.5 35 30 25 21 15 25 21 17 11 18 14 9 11 7 3 

1.6 32 27 23 19 14 23 19 16 11 16 13 8 10 6 3 

1.7 30 25 22 18 13 21 18 15 10 15 12 8 9 6 3 

1.8 28 24 20 17 12 20 17 14 9 14 11 7 9 5 3 

1.9 26 22 19 16 11 19 16 13 9 13 11 7 8 5 3 

2.0 25 21 18 15 11 18 15 12 8 13 10 7 8 5 3 

2.25 22 19 16 14 10 16 14 11 8 11 9 6 7 4 2 

2.5 21 18 15 13 9 15 13 10 7 11 9 6 7 4 2 

2.75 20 17 15 12 9 14 12 10 7 10 8 5 6 4 2 

3.0 19 16 14 12 8 14 12 10 6 10 8 5 6 4 2 

3.5 18 16 13 11 8 13 11 9 6 9 8 5 6 4 2 

4.0 18 15 13 11 8 13 11 9 6 9 7 5 6 4 2 
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-4 January 2009 

A.3 Critical Values for the Sign Test 

Table A.3 Critical Values for the Sign Test Statistic, S+

Alpha

N 0.005 0.01 0.025 0.05 0.1 0.2 0.3 0.4 0.5

4 4 4 4 4 3 3 3 2 2 

5 5 5 5 4 4 3 3 3 2 

6 6 6 5 5 5 4 4 3 3 

7 7 6 6 6 5 5 4 4 3 

8 7 7 7 6 6 5 5 4 4 

9 8 8 7 7 6 6 5 5 4 

10 9 9 8 8 7 6 6 5 5 

11 10 9 9 8 8 7 6 6 5 

12 10 10 9 9 8 7 7 6 6 

13 11 11 10 9 9 8 7 7 6 

14 12 11 11 10 9 9 8 7 7 

15 12 12 11 11 10 9 9 8 7 

16 13 13 12 11 11 10 9 9 8 

17 14 13 12 12 11 10 10 9 8 

18 14 14 13 12 12 11 10 10 9 

19 15 14 14 13 12 11 11 10 9 

20 16 15 14 14 13 12 11 11 10 

21 16 16 15 14 13 12 12 11 10 

22 17 16 16 15 14 13 12 12 11 

23 18 17 16 15 15 14 13 12 11 

24 18 18 17 16 15 14 13 13 12 

25 19 18 17 17 16 15 14 13 12 

26 19 19 18 17 16 15 14 14 13 

27 20 19 19 18 17 16 15 14 13 

28 21 20 19 18 17 16 15 15 14 

29 21 21 20 19 18 17 16 15 14 

30 22 21 20 19 19 17 16 16 15 
MARSAME Appendix A 

January 2009 A-5 NUREG-1575, Supp. 1 

Table A.3 Critical Values for the Sign Test Statistic, S+ (continued) 

Alpha

N 0.005 0.01 0.025 0.05 0.1 0.2 0.3 0.4 0.5

31 23 22 21 20 19 18 17 16 15 

32 23 23 22 21 20 18 17 17 16 

33 24 23 22 21 20 19 18 17 16 

34 24 24 23 22 21 19 19 18 17 

35 25 24 23 22 21 20 19 18 17 

36 26 25 24 23 22 21 20 19 18 

37 26 26 24 23 22 21 20 19 18 

38 27 26 25 24 23 22 21 20 19 

39 27 27 26 25 23 22 21 20 19 

40 28 27 26 25 24 23 22 21 20 

41 29 28 27 26 25 23 22 21 20 

42 29 28 27 26 25 24 23 22 21 

43 30 29 28 27 26 24 23 22 21 

44 30 30 28 27 26 25 24 23 22 

45 31 30 29 28 27 25 24 23 22 

46 32 31 30 29 27 26 25 24 23 

47 32 31 30 29 28 26 25 24 23 

48 33 32 31 30 28 27 26 25 24 

49 33 33 31 30 29 27 26 25 24 

50 34 33 32 31 30 28 27 26 25 

For N greater than 50, the table (critical) value can be calculated from: 

 N N z

2 2

1−α + (A-1) 

where: 

z1−α = (1−α) percentile of a standard normal distribution (page A-9)
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-6 January 2009 

A.4 Critical Values for the WRS Test 

The parameter, m, is the number of reference area samples and the parameter, n, is the number of 

survey unit samples. When using this table under Scenario A, m is the number of reference area 

samples and n is the number of survey unit samples. When using this table for Scenario B, the 

roles of m and n in this table are reversed. 

Table A.4 Critical Values for the WRS Test 

 n 

m α 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

0.001 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 

0.005 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 40 42 

0.01 7 9 11 13 15 17 19 21 23 25 27 28 30 32 34 36 38 39 41 

0.025 7 9 11 13 15 17 18 20 22 23 25 27 29 31 33 34 36 38 40 

0.05 7 9 11 12 14 16 17 19 21 23 24 26 27 29 31 33 34 36 38 

2 

0.1 7 8 10 11 13 15 16 18 19 21 22 24 26 27 29 30 32 33 35 

0.001 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 56 59 62 65 

0.005 12 15 18 21 24 27 30 32 35 38 40 43 46 48 51 54 57 59 62 

0.01 12 15 18 21 24 26 29 31 34 37 39 42 45 47 50 52 55 58 60 

0.025 12 15 18 20 22 25 27 30 32 35 37 40 42 45 47 50 52 55 57 

0.05 12 14 17 19 21 24 26 28 31 33 36 38 40 43 45 47 50 52 54 

3 

0.1 11 13 16 18 20 22 24 27 29 31 33 35 37 40 42 44 46 48 50 

0.001 18 22 26 30 34 38 42 46 49 53 57 60 64 68 71 75 78 82 86 

0.005 18 22 26 30 33 37 40 44 47 51 54 58 61 64 68 71 75 78 81 

0.01 18 22 26 29 32 36 39 42 46 49 52 56 59 62 66 69 72 76 79 

0.025 18 22 25 28 31 34 37 41 44 47 50 53 56 59 62 66 69 72 75 

0.05 18 21 24 27 30 33 36 39 42 45 48 51 54 57 59 62 65 68 71 

4 

0.1 17 20 22 25 28 31 34 36 39 42 45 48 50 53 56 59 61 64 67 

0.001 25 30 35 40 45 50 54 58 63 67 72 76 81 85 89 94 98 102 107 

0.005 25 30 35 39 43 48 52 56 60 64 68 72 77 81 85 89 93 97 101 

0.01 25 30 34 38 42 46 50 54 58 62 66 70 74 78 82 86 90 94 98 

0.025 25 29 33 37 41 44 48 52 56 60 63 67 71 75 79 82 86 90 94 

0.05 24 28 32 35 39 43 46 50 53 57 61 64 68 71 75 79 82 86 89 

5 

0.1 23 27 30 34 37 41 44 47 51 54 57 61 64 67 71 74 77 81 84 

0.001 33 39 45 51 57 63 67 72 77 82 88 93 98 103 108 113 118 123 128 

0.005 33 39 44 49 54 59 64 69 74 79 83 88 93 98 103 107 112 117 122 

0.01 33 39 43 48 53 58 62 67 72 77 81 86 91 95 100 104 109 114 118 

0.025 33 37 42 47 51 56 60 64 69 73 78 82 87 91 95 100 104 109 113 

0.05 32 36 41 45 49 54 58 62 66 70 75 79 83 87 91 96 100 104 108 

6 

0.1 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87 91 94 98 102 
MARSAME Appendix A 

January 2009 A-7 NUREG-1575, Supp. 1 

Table A.4 Critical Values for the WRS Test (continued) 

 n 

m α 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

0.001 42 49 56 63 69 75 81 87 92 98 104 110 116 122 128 133 139 145 151

0.005 42 49 55 61 66 72 77 83 88 94 99 105 110 116 121 127 132 138 143

0.01 42 48 54 59 65 70 76 81 86 92 97 102 108 113 118 123 129 134 139

0.025 42 47 52 57 63 68 73 78 83 88 93 98 103 108 113 118 123 128 133

0.05 41 46 51 56 61 65 70 75 80 85 90 94 99 104 109 113 118 123 128

7 

0.1 40 44 49 54 58 63 67 72 76 81 85 90 94 99 103 108 112 117 121

0.001 52 60 68 75 82 89 95 102 109 115 122 128 135 141 148 154 161 167 174

0.005 52 60 66 73 79 85 92 98 104 110 116 122 129 135 141 147 153 159 165

0.01 52 59 65 71 77 84 90 96 102 108 114 120 125 131 137 143 149 155 161

0.025 51 57 63 69 75 81 86 92 98 104 109 115 121 126 132 137 143 149 154

0.05 50 56 62 67 73 78 84 89 95 100 105 111 116 122 127 132 138 143 148

8 

0.1 49 54 60 65 70 75 80 85 91 96 101 106 111 116 121 126 131 136 141

0.001 63 72 81 88 96 104 111 118 126 133 140 147 155 162 169 176 183 190 198

0.005 63 71 79 86 93 100 107 114 121 127 134 141 148 155 161 168 175 182 188

0.01 63 70 77 84 91 98 105 111 118 125 131 138 144 151 157 164 170 177 184

0.025 62 69 76 82 88 95 101 108 114 120 126 133 139 145 151 158 164 170 176

0.05 61 67 74 80 86 92 98 104 110 116 122 128 134 140 146 152 158 164 170

9 

0.1 60 66 71 77 83 89 94 100 106 112 117 123 129 134 140 145 151 157 162

0.001 75 85 94 103 111 119 128 136 144 152 160 167 175 183 191 199 207 215 222

0.005 75 84 92 100 108 115 123 131 138 146 153 160 168 175 183 190 197 205 212

0.01 75 83 91 98 106 113 121 128 135 142 150 157 164 171 178 186 193 200 207

0.025 74 81 89 96 103 110 117 124 131 138 145 151 158 165 172 179 186 192 199

0.05 73 80 87 93 100 107 114 120 127 133 140 147 153 160 166 173 179 186 192

10 

0.1 71 78 84 91 97 103 110 116 122 128 135 141 147 153 160 166 172 178 184

0.001 88 99 109 118 127 136 145 154 163 171 180 188 197 206 214 223 231 240 248

0.005 88 98 107 115 124 132 140 148 157 165 173 181 189 197 205 213 221 229 237

0.01 88 97 105 113 122 130 138 146 153 161 169 177 185 193 200 208 216 224 232

0.025 87 95 103 111 118 126 134 141 149 156 164 171 179 186 194 201 208 216 223

0.05 86 93 101 108 115 123 130 137 144 152 159 166 173 180 187 195 202 209 216

11 

0.1 84 91 98 105 112 119 126 133 139 146 153 160 167 173 180 187 194 201 207

0.001 102 114 125 135 145 154 164 173 183 192 202 210 220 230 238 247 256 266 275

0.005 102 112 122 131 140 149 158 167 176 185 194 202 211 220 228 237 246 254 263

0.01 102 111 120 129 138 147 156 164 173 181 190 198 207 215 223 232 240 249 257

0.025 100 109 118 126 135 143 151 159 168 176 184 192 200 208 216 224 232 240 248

0.05 99 108 116 124 132 140 147 155 165 171 179 186 194 202 209 217 225 233 240

12 

0.1 97 105 113 120 128 135 143 150 158 165 172 180 187 194 202 209 216 224 231

0.001 117 130 141 152 163 173 183 193 203 213 223 233 243 253 263 273 282 292 302

0.005 117 128 139 148 158 168 177 187 196 206 215 225 234 243 253 262 271 280 290

0.01 116 127 137 146 156 165 174 184 193 202 211 220 229 238 247 256 265 274 283

0.025 115 125 134 143 152 161 170 179 187 196 205 214 222 231 239 248 257 265 274

0.05 114 123 132 140 149 157 166 174 183 191 199 208 216 224 233 241 249 257 266

13 

0.1 112 120 129 137 145 153 161 169 177 185 193 201 209 217 224 232 240 248 256
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-8 January 2009 

Table A.4 Critical Values for the WRS Test (continued) 

 n 

m α 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

0.001 133 147 159 171 182 193 204 215 225 236 247 257 268 278 289 299 310 320 330

0.005 133 145 156 167 177 187 198 208 218 228 238 248 258 268 278 288 298 307 317

0.01 132 144 154 164 175 185 194 204 214 224 234 243 253 263 272 282 291 301 311

0.025 131 141 151 161 171 180 190 199 208 218 227 236 245 255 264 273 282 292 301

0.05 129 139 149 158 167 176 185 194 203 212 221 230 239 248 257 265 274 283 292

14 

0.1 128 136 145 154 163 171 180 189 197 206 214 223 231 240 248 257 265 273 282

0.001 150 165 178 190 202 212 225 237 248 260 271 282 293 304 316 327 338 349 360

0.005 150 162 174 186 197 208 219 230 240 251 262 272 283 293 304 314 325 335 346

0.01 149 161 172 183 194 205 215 226 236 247 257 267 278 288 298 308 319 329 339

0.025 148 159 169 180 190 200 210 220 230 240 250 260 270 280 289 299 309 319 329

0.05 146 157 167 176 186 196 206 215 225 234 244 253 263 272 282 291 301 310 319

15 

0.1 144 154 163 172 182 191 200 209 218 227 236 246 255 264 273 282 291 300 309

0.001 168 184 197 210 223 236 248 260 272 284 296 308 320 332 343 355 367 379 390

0.005 168 181 194 206 218 229 241 252 264 275 286 298 309 320 331 342 353 365 376

0.01 167 180 192 203 215 226 237 248 259 270 281 292 303 314 325 336 347 357 368

0.025 166 177 188 200 210 221 232 242 253 264 274 284 295 305 316 326 337 347 357

0.05 164 175 185 196 206 217 227 237 247 257 267 278 288 298 308 318 328 338 348

16 

0.1 162 172 182 192 202 211 221 231 241 250 260 269 279 289 298 308 317 327 336

0.001 187 203 218 232 245 258 271 284 297 310 322 335 347 360 372 384 397 409 422

0.005 187 201 214 227 239 252 264 276 288 300 312 324 336 347 359 371 383 394 406

0.01 186 199 212 224 236 248 260 272 284 295 307 318 330 341 353 364 376 387 399

0.025 184 197 209 220 232 243 254 266 277 288 299 310 321 332 343 354 365 376 387

0.05 183 194 205 217 228 238 249 260 271 282 292 303 313 324 335 345 356 366 377

17 

0.1 180 191 202 212 223 233 243 253 264 274 284 294 305 315 325 335 345 355 365

0.001 207 224 239 254 268 282 296 309 323 336 349 362 376 389 402 415 428 441 454

0.005 207 222 236 249 262 275 288 301 313 326 339 351 364 376 388 401 413 425 438

0.01 206 220 233 246 259 272 284 296 309 321 333 345 357 370 382 394 406 418 430

0.025 204 217 230 242 254 266 278 290 302 313 325 337 348 360 372 383 395 406 418

0.05 202 215 226 238 250 261 273 284 295 307 318 329 340 352 363 374 385 396 407

18 

0.1 200 211 222 233 244 255 266 277 288 299 309 320 331 342 352 363 374 384 395

0.001 228 246 262 277 292 307 321 335 350 364 377 391 405 419 433 446 460 473 487

0.005 227 243 258 272 286 300 313 327 340 353 366 379 392 405 419 431 444 457 470

0.01 226 242 256 269 283 296 309 322 335 348 361 373 386 399 411 424 437 449 462

0.025 225 239 252 265 278 290 303 315 327 340 352 364 377 389 401 413 425 437 450

0.05 223 236 248 261 273 285 297 309 321 333 345 356 368 380 392 403 415 427 439

19 

0.1 220 232 244 256 267 279 290 302 313 325 336 347 358 370 381 392 403 415 426

0.001 250 269 286 302 317 333 348 363 377 392 407 421 435 450 464 479 493 507 521

0.005 249 266 281 296 311 325 339 353 367 381 395 409 422 436 450 463 477 490 504

0.01 248 264 279 293 307 321 335 349 362 376 389 402 416 429 442 456 469 482 495

0.025 247 261 275 289 302 315 329 341 354 367 380 393 406 419 431 444 457 470 482

0.05 245 258 271 284 297 310 322 335 347 360 372 385 397 409 422 434 446 459 471

20 

0.1 242 254 267 279 291 303 315 327 339 351 363 375 387 399 410 422 434 446 458
MARSAME Appendix A 

January 2009 A-9 NUREG-1575, Supp. 1 

Reject the null hypothesis if the test statistic (Wr) is greater than the table (critical) value. 

For n or m greater than 20 with few or no ties, the table (critical) value can be calculated from: 

12

( 1)

2

( 1) + +

+

+ + = nm n m

z

m n m Critical Value (A-2)

If there are ties, the critical value can be calculated from: 

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

∑

+ + +

− + + + − + + = =

g

j

j j

n m n m

t t

n m

nm

z

m n m Critical Value

1

2

( )( 1)

( 1) ( 1) 2 12

( 1) (A-3)

Where: 

g = number of groups of tied measurements 

tj = number of tied measurements in the j

th group 

z = (1-α) percentile of a standard normal distribution (see list below) 

α z 

0.001 3.090 

0.005 2.575 

0.01 2.326 

0.025 1.960 

0.05 1.645 

0.1 1.282 

Other values for z can be obtained from Table A.1.
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-10 January 2009 

A.5 Critical Values for the Quantile Test 

Tables A.5a–d contain values of the parameters r and k needed for the Quantile test calculated by 

Gilbert and Simpson (Gilbert 1992) for certain combinations of m (the number of measurements 

in the reference area) and n (the number of measurements in the survey unit). The value of α

listed is that obtained from simulation studies. 

Table A.5a Values of r and k for the Quantile Test When α Is Approximately 0.01 

 Number of Survey Unit Measurements, n

m 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 

5 r,k 11,11 13,13 16,16 19,19 22,22 25,25 28,28 r,k

α 0.008 0.015 0.014 0.013 0.013 0.013 0.012 α

10 6,6 7,7 9,9 11,11 13,13 14,14 16,16 18,18 19,19 21,21 23,23 25,25 26,26 28,28 30,30 

 0.005 0.013 0.012 0.011 0.01 0.014 0.013 0.012 0.015 0.014 0.013 0.012 0.015 0.014 0.013 

15 3,3 7,6 6,6 7,7 8,8 10,10 11,11 12,12 13,13 15,15 16,16 17,17 18,18 19,19 21,21 22,22 23,23 24,24 26,26 27,27

 0.009 0.007 0.008 0.011 0.014 0.009 0.011 0.013 0.014 0.011 0.012 0.013 0.014 0.015 0.012 0.013 0.014 0.015 0.013 0.013

20 6,4 4,4 5,5 6,6 7,7 8,8 9,9 10,10 11,11 12,12 13,13 14,14 15,15 16,16 17,17 18,18 19,19 19,19 20,20 21,21

 0.005 0.008 0.009 0.01 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.012 0.012 0.012 0.012 0.012 0.012 0.015 0.015 0.015

25 4,3 7,5 4,4 5,5 6,6 7,7 8,8 9,9 9,9 10,10 11,11 12,12 12,12 13,13 14,14 15,15 16,16 16,16 17,17 18,18

 0.009 0.012 0.015 0.013 0.011 0.01 0.009 0.009 0.014 0.012 0.011 0.011 0.015 0.014 0.013 0.012 0.011 0.014 0.014 0.013

30 4,3 3,3 4,4 5,5 6,6 6,6 7,7 8,8 8,8 9,9 10,10 10,10 11,11 1211 12,12 13,13 14,14 14,14 15,15 15,15

 0.006 0.012 0.009 0.007 0.006 0.012 0.01 0.008 0.013 0.011 0.009 0.013 0.011 0.014 0.013 0.012 0.011 0.014 0.012 0.015

35 2,2 3,3 4,4 4,4 5,5 6,6 6,6 7,7 7,7 8,8 9,9 9,9 10,10 10,10 11,11 11,11 12,12 13,13 13,13 14,14

 0.013 0.008 0.006 0.014 0.01 0.007 0.012 0.009 0.014 0.011 0.009 0.013 0.01 0.014 0.011 0.015 0.012 0.011 0.013 0.012

40 2,2 3,3 7,5 4,4 5,5 5,5 6,6 6,6 7,7 7,7 8,8 8,8 9,9 9,9 10,10 10,10 11,11 11,11 12,12 12,12

 0.01 0.006 0.013 0.01 0.006 0.012 0.008 0.013 0.009 0.013 0.01 0.014 0.011 0.014 0.011 0.014 0.012 0.014 0.012 0.014

45 2,2 6,4 3,3 4,4 4,4 5,5 5,5 6,6 6,6 7,7 7,7 8,8 8,8 9,9 9,9 10,10 10,10 10,10 11,11 11,11

 0.008 0.008 0.013 0.007 0.014 0.008 0.014 0.009 0.013 0.009 0.013 0.009 0.012 0.009 0.012 0.009 0.012 0.015 0.012 0.014

50 4,3 3,3 4,4 4,4 5,5 5,5 5,5 6,6 6,6 7,7 7,7 8,8 8,8 8,8 9,9 9,9 10,10 10,10 10,10

 0.013 0.01 0.005 0.01 0.006 0.01 0.015 0.009 0.013 0.009 0.012 0.009 0.011 0.014 0.011 0.013 0.01 0.012 0.015

55 4,3 3,3 7,5 4,4 4,4 5,5 5,5 6,6 6,6 6,6 7,7 7,7 8,8 8,8 8,8 9,9 9,9 9,9 10,10

 0.01 0.008 0.013 0.008 0.014 0.007 0.011 0.007 0.01 0.014 0.009 0.012 0.008 0.01 0.013 0.009 0.012 0.014 0.011 

60 4,3 3,3 3,3 4,4 4,4 5,5 5,5 5,5 6,6 6,6 6,6 7,7 7,7 7,7 8,8 8,8 8,8 9,9 9,9 

 0.008 0.007 0.014 0.006 0.011 0.006 0.009 0.013 0.007 0.01 0.014 0.009 0.011 0.014 0.01 0.012 0.015 0.01 0.013

65 4,3 3,3 3,3 6,5 4,4 4,4 5,5 5,5 5,5 6,6 6,6 6,6 7,7 7,7 7,7 8,8 8,8 8,8 9,9 

 0.007 0.006 0.012 0.006 0.009 0.013 0.007 0.01 0.014 0.008 0.011 0.014 0.009 0.011 0.014 0.009 0.011 0.014 0.01

70 2,2 6,4 3,3 7,5 4,4 4,4 5,5 5,5 5,5 5,5 6,6 6,6 6,6 7,7 7,7 7,7 8,8 8,8 8,8 

 0.014 0.008 0.01 0.013 0.007 0.011 0.005 0.008 0.011 0.015 0.008 0.011 0.014 0.009 0.011 0.013 0.009 0.011 0.013 

75 2,2 4,3 3,3 3,3 4,4 4,4 4,4 5,5 5,5 5,5 6,6 6,6 6,6 6,6 7,7 7,7 7,7 8,8. 8,8 

 0.013 0.014 0.008 0.014 0.006 0.009 0.013 0.006 0.009 0.012 0.007 0.009 0.011 0.014 0.009 0.011 0.013 0.008 0.01

80 2,2 4,3 3,3 3,3 6,5 4,4 4,4 5,5 5,5 5,5 5,5 6,6 6,6 6,6 6,6 7,7 7,7 7,7 7,7 

 0.011 0.012 0.007 0.012 0.006 0.008 0.011 0.005 0.007 0.01 0.013 0.007 0.009 0.012 0.014 0.009 0.01 0.013 0.015

85 2,2 4,3 3,3 3,3 7,5 4,4 4,4 4,4 5,5 5,5 5,5 5,5 6,6 6,6 6,6 6,6 7,7 7,7 7,7 

 0.01 0.01 0.006 0.011 0.013 0.006 0.009 0.013 0.006 0.008 0.011 0.014 0.008 0.01 0.012 0.014 0.008 0.01 0.012

90 4,3 3,3 3,3 3,3 4,4 4,4 4,4 5,5 5,5 5,5 5,5 5,5 6,6 6,6 6,6 6,6 7,7 7,7 

 0.009 0.005 0.009 0.014 0.005 0.008 0.011 0.005 0.007 0.009 0.012 0.015 0.008 0.01 0.012 0.014 0.008 0.019

95 4,3 6,4 3,3 3,3 6,5 4,4 4,4 4,4 5,5 5,5 5,5 5,5 6,6 6,6 6,6 6,6 6,6 7,7 

 0.008 0.008 0.008 0.013 0.005 0.007 0.01 0.013 0.006 0.008 0.01 0.013 0.007 0.008 0.01 0.012 0.014 0.008 

100 r,k 4,3 4,3 3,3 3,3 7,5 4,4 4,4 4,4 4,4 5,5 5,5 5,5 5,5 6,6 6,6 6,6 6,6 6,6 

α 0.007 0.014 0.007 0.011 0.013 0.006 0.008 0.011 0.015 0.007 0.009 0.011 0.013 0.007 0.008 0.01 0.012 0.014
MARSAME Appendix A 

January 2009 A-11 NUREG-1575, Supp. 1 

Table A.5b Values of r and k for the Quantile Test When α Is Approximately 0.025

Number of Survey Unit Measurements, n

m 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

5 r,k 9,9 12,12 15,l5 17,17 20,20 22,22 25,25 r,k

α 0.03 0.024 0.021 0.026 0.024 0.028 0.025 α

10 7,6 6,6 8,8 9,9 11,11 12,12 14,14 17,17 18,18 20,20 21,21 23,23 24,24 26,26 27,27 

 0.029 0.028 0.022 0.029 0.024 0.029 0.025 0.025 0.029 0.026 0.029 0.026 0.029 0.026 0.029 

15 11,5 6,5 5,5 6,6 7,7 8,8 9,9 10,10 11,11 13,13 15,15 14,14 16,16 17,17 18,18 19,19 21,21 21,21 22,22 23,23

 0.03 0.023 0.021 0.024 0.026 0.027 0.028 0.029 0.03 0.022 0.023 0.023 0.024 0.025 0.025 0.026 0.021 0.027 0.027 0.027

20 8,4 3,3 4,4 5,5 6,6 7,7 12,11 13,12 9,9 10,10 11,11 12,12 13,13 13,13 14,14 15,15 16 16 17,17 17,17 18,18

 0.023 0.03 0.026 0.024 0.022 0.02 0.021 0.024 0.028 0.026 0.024 0.023 0.022 0.029 0.027 0.026 0.025 0.024 0.029 0.028

25 2,2 8,5 6,5 7,6 5,5 6,6 10,9 7,7 8,8 13,12 9,9 10,10 11,11 11,1l 12,12 13,13 13,13 14,14 15,15 15,15

 0.023 0.027 0.021 6.023 0.025 0.02 0.026 0.027 0.023 0.027 0.027 0.024 0.022 0.028 0.025 0.823 0.628 0.025 0.023 0.028

30 6,3 6,4 9,6 4,4 7,6 5,5 9,8 6,6 7,7 12,11 8,8 9,9 9,9 10,10 10,10 11,11 11,11 12,12 13,13 13,13

 0.026 0.026 0.026 0.021 0.029 0.026 0.024 0.029 0.023 0.021 0.025 0.021 0.027 0.023 0.029 0.025 0.03 0.026 0.023 0.027

35 7,3 4,3 3,3 6,5 4,4 10,8 5,5 9,8 6,6 7,7 7,7 8,8 8,8 9,9 9,9 10,10 10,10 11,11 11,11 12,12

 0.03 0.03 0.023 0.02 0.026 0.022 0.027 0.024 0.027 0.02 0.027 0.021 0.027 0.022 0.027 0.022 0.027 0.022 0.027 0.023

40 3,2 4,3 8,5 11,7 6,5 4,4 10,8 5,5 9,8 6,6 10,9 7,7 12,11 8,8 8,8 9,9 9,9 10,10 10,10 11,11

 0.029 0.022 0.028 0.025 0.028 0.03 0.026 0.027 0.023 0.026 0.028 0.024 0.02 0.023 0.029 0.022 0.027 0.021 0.026 0.021

45 3,2 8,4 6,4 3,3 8,6 4,4 7,6 5,5 5,5 9,8 6,6 10,9 7,7 7,7 8,8 8,8 8,8 9,9 9,9 10,10

 0.023 0.029 0.036 0.026 0.021 0.023 0.025 0.02 0.028 0.023 0.024 0.026 0.022 0.027 0.02 0.025 0.03 0.023 0.027 0.02l

50 2,2 6,4 3,3 11,7 6,5 4,4 7,6 5,5 5,5 9,8 6,6 6,6 7,7 7,7 12,11 8,8 8,8 13,12 9,9 

 0.025 0.022 0.021 0.077 6.026 0.026 0.028 0.021 0.028 0.022 0.023 0.029 0.02 0.025 0.02 0.022 0.026 0.027 0.023

55 2,2 4,3 8,5 3,3 8,6 4,4 4,4 10,8 5,5 5,5 9,8 6,6 6,6 10,9 7,7 7,7 12,11 8,8 8,8 

 0.022 0.029 0.028 0.028 0.021 0.02 0.029 0.021 0.022 0.028 0.022 0.092 0.028 0.029 0.023 0.027 0.023 0.023 0.027

60 14,5 4,3 8,5 3,3 11,7 6,5 4,4 7,6 10,8 5,5 5,5 9,8 6,6 6,6 10,9 7,7 7,7 7,7 8,8 

 0.022 0.024 0.021 0.023 0.029 0.024 0.023 0.023 0.024 0.023 0.029 0.022 0.022 0.027 0.027 0.021 0.025 0.03 0.021

65 6,3 7,4 6,4 10,6 3,3 8,6 6,5 4,4 7,6 10,8 5,5 5,5 9,8 6,6 6,6 10,9 7,7 7,7 7,7 

 0.028 0.021 0.025 0.025 0.029 0.021 0.029 0.026 0.026 0.026 0.023 0.029 0.022 0.021 0.026 0.026 0.020 0.024 0.028

70 6,3 2,2 6,4 8,5 3,3 13,8 6,5 4,4 4,4 7,6 10,8 5,5 5,5 9,8 6,6 6,6 6,6 10,9 7,7 

 0.024 0.029 0.021 0.028 0.025 0.026 0.023 0.022 0.028 0.028 0.027 0.024 0.029 0.022 0.021 0.025 0.029 0.03 0.022

75 11,4 2,2 4,3 8,5 3,3 9,6 8,6 6,5 4,4 7,6 7,6 10,8 5,5 5,5 9,8 6,6 6,6 6,6 10,9

 0.022 0.026 0.028 0.022 0.022 0.028 0.021 0.027 0.024 0.023 0.03 0.029 0.024 0.029 0.021 0.021 0.024 0.028 0.028

80 7,3 2,2 4,3 6,4 10,6 3,3 13,8 6,5 4,4 4,4 7,6 10,8 5,5 5,5 5,5 9,8 6,6 6,6 6,6 

 0.028 0.024 0.024 0.028 0.024 0.027 0.027 0.023 0.02 0.026 0.024 0.023 0.07 0.025 0.029 0.021 0.02 0.024 0.027

85 3,2 2,2 4,3 6,4 8,5 3,3 9,6 8,6 6,5 4,4 4,4 7,6 10,8 5,5 5,5 5,5 9,8 6,6 6,6 

 0.029 0.021 0.021 0.023 0.028 0.023 0.03 0.02 0.026 0.022 0.028 0.026 0.024 0.021 0.025 0.029 0.021 0.02 0.023

90 5,3 11,5 9,5 8,5 3,3 3,3 13,8 6,5 6,5 4,4 4,4 7,6 10,8 5,5 5,5 5,5 9,8 9,8 

 0.02 0.027 0.023 0.023 0.021 0.028 0.028 0.022 0.029 0.024 0.029 0.028 0.026 0.022 0.025 0.03 0.021 0.025

95 10,4 2,2 4,3 6,4 10,6 3,3 11,7 8,6 6,5 4,4 4,4 7,6 7,6 10,8 5,5 5,5 5,5 9,8 

 0.029 0.029 0.028 0.029 0.023 0.025 0.026 0.02 0.025 0.021 0.026 0.024 0.029 0.027 0.022 0.026 0.03 0.021

100 r,k 6,3 2,2 4,3 6,4 8,5 3,3 3,3 13,8 6,5 6,5 4,4 4,4 7,6 10,8 10,8 5,5 5,5 5,5 

α 0.029 0.027 0.025 0.025 0.028 0.022 0.029 0.028 0.022 0.028 0.023 0.027 0.025 0.022 0.028 0.022 0.026 0.03
Appendix A MARSAME 

NUREG-1575, Supp. 1 A-12 January 2009 

Table A.5c Values of r and k for the Quantile Test When α Is Approximately 0.05 

Number of Survey Unit Measurements, n

m 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

5 r,k 8,8 10,10 13 13 15 15 17,17 19,19 21,21 r,k

α 0.051 0.057 0.043 0.048 0.051 0.054 0.056 α

10 4,4 5,5 14,12 8,8 9,9 10,10 12,12 13,13 14,14 15,15 17,17 18,18 19,19 20,20 21,21 23,23 

 0.043 0.057 0.045 0.046 0.052 0.058 0.046 0.05 0.054 0.057 0.049 0.052 0.055 0.057 0.059 0.053 

15 2,2 3,3 4,4 5,5 6,6 7,7 8,8 9,9 9,9 10,10 11,11 12,12 13,13 14,14 15,15 16,16 16,16 17,17 18,18 19,19

 0.053 0.052 0.05 0.048 0.046 0.045 0.052 0.043 0.06 0.057 0.055 0.054 0.052 0.051 0.05 0.049 0.058 0.057 0.056 0.055

20 9,4 8,5 6,5 4,4 5,5 9,8 6,6 7,7 8,8 8,8 9,9 10,10 10,10 11,11 12,12 12,12 13,13 14,14 14,14 15,15

 0.04 0.056 0.04 0.053 0.043 0.052 0.056 48 0.043 0.057 0.051 0.046 0.057 0.052 0.048 0.057 0.053 0.049 0.057 0.054

25 6,3 6,4 3,3 6,5 4,4 5,5 5,5 6,6 11,10 7,7 8,8 8,8 9,9 9,9 10,10 11,11 11,11 11,11 12,12 12,12

 0.041 0.043 0.046 0.052 0.055 0.041 0.059 0.046 0.042 0.05 0.042 0.053 0.045 0.055 0.048 0.042 0.05 0.058 0.052 0.06

30 3,2 2,2 10,6 3,3 11,8 4,4 8,7 5,5 6,6 6,6 7,7 7,7 8,8 8,8 9,9 9,9 9,9 10,10 10,10 11,11

 0.047 0.058 0.052 0.058 0.045 0.056 0.044 0.054 0.04 0.053 0.041 0.052 0.042 0.051 0.042 0.05 0.059 0.049 0.057 0.049

35 8,3 2,2 6,4 3,3 6,5 4,4 4,4 8,7 5,5 9,8 6,6 6,6 7,7 7,7 8,8 8,8 8,8 9,9 9,9 10,10

 0.046 0.045 0.058 0.043 0.041 0.04 0.057 0.043 0.051 0.052 0.047 0.057 0.043 0.053 0.041 0.049 0.057 0.046 0.053 0.044

40 4,2 5,3 4,3 10,6 3,3 6,5 4,4 4,4 8,7 5,5 9,8 6,6 6,6 11,10 7,7 7,7 8,8 8,8 8,8 9,9 

 0.055 0.048 0.057 0.059 0.053 0.048 0.043 0.058 0.042 0.048 0.047 0.042 0.051 0.042 0.045 0.053 0.041 0.048 0.055 0.043

45 4,2 9,4 2,2 8,5 3,3 8,6 6,5 4,4 4,4 8,7 5,5 5,5 9,8 6,6 6,6 11,10 7,7 7,7 8,8 8,8 

 0.045 0.047 0.059 0.052 0.042 0.041 0.054 0.045 0.058 0.041 0.046 0.057 0.056 0.047 0.055 0.046 0.047 0.054 0.041 0.047

50 6,3 2,2 6,4 12,7 3,3 8,6 6,5 4,4 4,4 8,7 5,5 5,5 9,8 6,6 6,6 6,6 7,7 7,7 7,7 

 0.051 0.05 0.051 0.05 0.049 0.049 0.059 0.047 0.059 0.041 0.045 0.054 0.051 0.043 0.05 0.058 0.041 0.048 0.054

55 3,2 2,2 4,3 8,5 3,3 5,4 6,5 9,7 4,4 4,4 8,7 5,5 5,5 9,8 6,6 6,6 6,6 11,10 7,7 

 0.059 0.043 0.056 0.058 0.041 0.041 0.046 0.042 0.048 0.059 0.04 0.043 0.052 0.048 0.04 0.047 0.054 0.043 0.043

60 3,2 5,3 4,3 6,4 3,3 3,3 8,6 6,5 9,7 4,4 4,4 13,10 5,5 5,5 5,5 9,8 6,6 6,6 6,6 

 0.052 0.052 0.046 0.059 0.035 0.047 0.043 51 0.046 0.049 0.059 0.052 0.042 0.05 0.058 0.054 0.044 0.05 0.056

65 .3,2 5,3 2,2 6,4 10,6 3,3 3,3 6,5 6,5 4,4 4,4 4,4 13,10 5,5 5,5 5,5 9,8 6,6 6,6 

 0.045 0.043 0.053 0.048 0.05 0.04 0.052 0.041 0.055 0.042 0.05 0.06 0.052 0.041 0.048 0.055 0.051 0.041 0.047

70 8,3 9,4 2,2 4,3 8,5 5,4 3,3 3,3 6,5 6,5 4,4 4,4 4,4 13,10 5,5 5,5 5,5 9,8 9,8 

 0.057 0.048 0.047 0.055 0.05 0.041 0.046 0.057 0.045 0.058 0.043 0.051 0.06 0.051 0.041 0.047 0.054 0.048 0.057

75 8,3 6,3 2,2 4,3 6,4 10,6 3,3 3,3 8,6 6,5 9,7 4,4 4,4 5,5 13,10 8,7 5,5 5,5 5,5 

 0.049 0.056 0.043 0.047 0.054 0.053 0.04 0.051 0.044 0.049 0.041 0.044 0.052 0.06 0.051 0.047 0.046 0.052 0.058

80 4,2 6,3 5,3 2,2 6,4 8,5 5,4 3,3 3,3 6,5 6,5 9,7 4,4 4,4 7,6 13,10 8 7 5,5 5,5 

 0.059 0.048 0.053 0.055 0.046 0.055 0.041 0.045 0.055 0.041 0.052 0.043 0.045 0.053 0.058 0.051 0.046 0.045 0.051

85 4,2 3,2 5,3 2,2 4,3 4,3 10,6 5,4 3,3 3,3 6,5 6,5 9,7 4,4 4,4 7,6 10,8 8,7 5,5 

 0.054 0.058 0.047 0.05 0.054 0.048 0.056 0.049 0.049 0.059 0.044 0.055 0.046 0.046 0.053 0.059 0.06 0.045 0.044

90 3,2 5,3 2,2 6,4 6,4 8,5 5,4 3,3 3,3 8,6 6,5 6,5 4,4 4,4 4,4 7,6 10,8 8,7 

 0.053 0.041 0.046 0.059 0.051 0.058 0.042 0.044 0.053 0.045 0.047 0.058 0.04l 0.047 0.054 0.059 0.06 0.041

95 3,2 9,4 2,2 2,2 4,3 8,5 10,6 5,4 3,3 3,3 6,5 6,5 9,7 4,4 4,4 4,4 7,6 10,8

 0.048 0.048 0.042 0.056 0.059 0.05 0.058 0.048 0.048 0.056 0.041 0.05 0.040 0.042 0.048 0.054 0.59 0.059

100 r,k 3,2 6,3 5,3 2,2 4,3 6,4 10,6 5,4 3,3 3,3 3,3 6,5 6,5 9,7 4,4 4,4 4,4 7,6 

α 0.044 0.057 0.054 0.052 0.053 0.056 0.049 0.043 0.043 0.051 0.059 0.044 0.053 0.042 0.043 0.049 0.055 0.059
MARSAME Appendix A 

January 2009 A-13 NUREG-1575, Supp. 1 

Table A.5d Values of r and k for the Quantile Test When α Is Approximately 0.10 

Number of Survey Unit Measurements, n

m 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

5 r,k 7,7 8,8 10,10 12,12 14,14 15,15 17,17 r,k

α 0.083 0.116 0.109 0.104 0.1 0.117 0.112 α

10 3,3 4,4 5,5 6,6 7,7 8,8 9,9 10,10 11,11 12,12 13,13 14,14 15,15 16,16 1712 18,18 

 0.105 0.108 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 0.109 

15 9,4 10,6 3,3 4,4 5,5 5,5 6,6 7,7 7,7 8,8 9,9 9,9 10,10 11,11 11,11 12,12 13,13 13,13 14,14 15,15

 0.098 0.106 0.112 0.093 0.081 0.117 0.102 0.092 0.118 0.106 0.098 0.118 0.109 0.101 0.118 0.11 0.104 0.118 0.111 0.106

20 3,2 2,2 5,4 3,3 4,4 4,4 5,5 10,9 6,6 7,7 7,7 8,8 8,8 9,9 9,9 10,10 10,11 11,11 11,11 12,12

 0.091 0.103 0.093 0.115 0.085 0.119 0.093 0.084 0.099 0.083 0.102 0.088 0.105 0.092 0.107 0.095 0.108 0.098 0.11 0.1 

25 4,2 7,4 8,5 3,3 3,3 4,4 4,4 8,7 5,5 10,9 6,6 6,6 7,7 7,7 8,8 8,8 8,8 9,9 9,9 10,10

 0.119 0.084 0.112 0.08 0.117 0.08 0.107 0.108 0.101 0.088 0.096 0.114 0.093 0.108 0.091 0.104 0.117 0.1 0.112 0.098

30 4,2. 5,3 2,2 14,8 3,3 3,3 9,7 4,4 8,7 5,5 5,5 6,6 6,6 6,6 7,7 7,7 7,7 8,8 8,8 8,8 

 0.089 0.089 0.106 0.111 0.088 0.119 0.116 0.1 0.093 0.088 0.106 0.08 0.095 0.11 0.087 0.1 0.113 0.092 0.103 0.115

35 5,2 3,2 2,2 6,4 5,4 3,3 3,3 9,7 4,4 4,4 8,7 5,5 5,5 6,6 6,6 6,6 6,6 7,7 7,7 7,7 

 0.109 0.119 0.086 0.12 0.091 0.093 0.12 0.112 0.094 0.114 0.107 0.094 0.11 0.081 0.094 0.107 0.12 0.094 0.105 0.116

40 5,2 3,2 5,3 2,2 12,7 5,4 3,3 6,5 9,7 4,4 4,4 8,7 5,5 5,5 5,5 6,6 6,6 6,6 6,6 7,7 

 0.087 0.098 0.119 0.107 0.109 0.102 0.097 0.100 0.109 0.09 0.107 0.097 0.086 0.099 0.112 0.082 0.093 0.104 0.116 0.089

45 6,2 3,2 5,3 2,2 6,4 7,5 5,4 3,3 6,5 9,7 4,4 4,4 4,4 8,7 5,5 5,5 5,5 6,6 6,6 6,6 

 0.103 0.082 0.094 0.091 0.115 0.086 0.112 0.1 0.101 0.107 0.087 0.102 0.117 0.107 0.091 0.103 0.115 0.083 0.093 0.103

50 7,3 9,4 7,4 2,2 10,6 5,4 3,3 3,3 6,5 9,7 4,4 4,4 4,4 8,7 5,5 5,5 5,5 5,5 6,6 

 0.083 0.115 0.097 0.108 0.112 0.09 0.084 0.103 0.102 0.105 0.084 0.098 0.112 0.099 0.084 0.95 0.105 0.116 0.083

55 4,2 3,2 5,3 2,2 6,4 14,8 5,4 3,3 3,3 6,5 9,7 4,4 4,4 4,4 4,4 8,7 5,5 5,5 5,5 

 0.109 0.114 0.114 0.095 0.112 0.111 0.098 0.088 0.104 0.103 0.104 0.082 0.095 0.107 0.12 0.107 0.088 0.098 0.108

60 4,2 3,2 5,3 2,2 2,2 8,5 5,4 5,4 3,3 3,3 6,5 9,7 4,4 4,4 4,4 4,4 8,7 5,5 5,5 

 0.095 0.1 0.097 0.084 0.109 0.119 0.082 0.105 0.091 0.106 0.103 0.102 0.081 0.092 0.103 0.115 0.1 0.083 0.092

65 4,2 3,2 5,3 7,4 2,2 6,4 12,7 5,4 5,4 3,3 3,3 6,5 9,7 7,6 4,4 4,4 4,4 8,7 8,7 

 0.084 0.089 0.082 0.090 0.097 0.11 0.113 0.089 0.111 0.093 0.108 0.104 0.101 0.084 0.09 0.1 0.11 0.094 0.107

70 5,2 7,3 9,4 5,3 2,2 2,2 8,5 7,5 5,4 3,3 3,3 3,3 6,5 9,7 7,6 4,4 4,4 4,4 4,4 

 0.115 0.101 0.106 0.112 0.088 0.109 0.114 0.081 0.096 0.083 0.096 0.109 0.104 0.191 0.082 0.088 0.097 0.107 0.117

75 5,2 7,3 3,2 5,3 7,4 2,2 2,2 10,6 5,4 5,4 3,3 3,3 3,3 6,5 9,7 7,6 4,4 4,4 4,4 

 103 0.088 0.111 0.098 0.101 0.099 0.119 0.117 0.083 0.102 0.085 0.098 0.11 0.105 0.1 0.081 0.086 0.095 0.104

80 5,2 4,2 3,2 5,3 7,4 2,2 2,2 8,5 14,8 5,4 5,4 3,3 3,3 3,3 6,5 6,5 9,7 4,4 4,4 

 0.093 0.116 0.101 0.086 0.086 0.09! 0.109 0.111 0.11 0.089 0.107 0.088 0.099 0.111 0.105 0.12 0.116 0.084 0.093

85 5,2 4,2 3,2 9,4 5,3 2,2 2,2 2,2 10,6 7,5 5,4 5,4 3,3 3,3 3,3 6,5 6,5 9,7 4,4 

 0.084 0.106 0.092 117 0.111 0.083 0.101 0.118 0.112 0.084 0.094 0.111 0.09 0.101 0.112 0.105 0.119 0.114 0.083

90 4,2 3,2 3,2 5,3 7,4 2,2 2,2 8,5 12,7 5,4 5,4 3,3 3,3 3,3 3,3 6,5 6,5 9,7 

 0.097 0.085 0.119 0.099 0.095 0.093 0.109 0.108 0.114 0.083 0.099 0.082 0.092 0.102 0.113 0.105 0.119 0.113

95 4,2 7,3 3,2 5,3 7,4 2,2 2,2 2,2 10,6 14,8 5,4 5,4 3,3 3,3 3,3 3,3 6,5 6,5 

 0.089 100 0.11 0.089 0.084 0.086 0.102 0.117 0.08 0.117 0.088 0.103 0.084 0.094 0.103 0.113 0.106 0.118

100 r,k 4,2 7,3 3,2 5,3 5,3 2,2 2,2 2,2 6,4 12,7 7,5 5,4 5,4 3,3 3,3 3,3 3,3 6,5 

α 0.082 0.09 0.102 0.08 0.109 0.08 0.095 0.11 0.118 0.109 0.086 0.093 0.08 0.086 0.095 0.104 0.114 0.106
 
MARSAME Appendix B 

B. SOURCES OF BACKGROUND RADIOACTIVITY 

B.1 Introduction 

Background radioactivity can complicate the disposition decision for M&E. Background 

radioactivity may be the result of environmental radioactivity, inherent radioactivity, instrument 

noise, or some combination of the three. Special consideration is given to issues associated with 

technologically enhanced naturally occurring radioactive materials (TENORM) and orphan 

sources as contributors to background. The planning team should consider these potential 

sources of background activity and determine what effect, if any, they may have on the design of 

the disposition survey. 

Information on background radioactivity can be obtained from many sources, including: 

• The Nuclear Regulatory Commission (NRC) provides information concerning background 

radioactivity in Background as a Residual Radioactivity Criterion for Decommissioning

NUREG-1501 (NRC 1994). 

• The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 

has published a report on Sources and Effects of Ionizing Radiation (UNSCEAR 2000) and 

provides a searchable version of the report on the World Wide Web at www.unscear.org. 

• The National Council on Radiation Protection and Measurements (NCRP) has published 

reports on Exposure of the Population in the United States and Canada from Natural 

Background Radiation, NCRP Report No. 94 (NCRP 1988a) and Radiation Exposure of the 

U.S. Population from Consumer Products and Miscellaneous Sources, NCRP Report No. 95 

(NCRP 1988b). 

B.2 Environmental Radioactivity 

Environmental radioactivity is radioactivity from the environment where the M&E is located. 

There are three sources contributing to environmental radioactivity; terrestrial (Section B.2.1), 

manmade (Section B.2.2), and cosmic and cosmogenic (Section B.2.3). Although background 

radiation is present everywhere, the component radionuclide concentrations and distributions are 

not constant. Certain materials have higher concentrations of background radiation, and varying 

environmental and physical conditions can result in accumulations of background radiation. 

Information on environmental radioactivity is usually available from historic measurements 

identified during the initial assessment (IA). 

If high levels of environmental radioactivity interfere with the disposition decision (e.g., action 

level less than environmental background, variability in environmental radioactivity determines 

level of survey effort), the planning team may consider moving the M&E being investigated to a 

location with less environmental radioactivity (see Sections 3.3.1.3 and 5.3). If the level of 

environmental radioactivity is unknown, it may be necessary to collect data during a preliminary 

survey (see Section 2.3) to provide this information. 

January 2009 B-1 NUREG-1575, Supp. 1 
Appendix B MARSAME 

B.2.1 Terrestrial Radioactivity 

The naturally occurring forms of radioactive elements incorporated into the Earth during its 

formation that is still present are referred to as “terrestrial radionuclides.” The most significant 

terrestrial radionuclides include the uranium and thorium decay series, potassium-40 and 

rubidium-87. Virtually all materials found in nature contain some concentration of terrestrial 

radionuclides. Table B.1 lists average and typical ranges of concentrations of terrestrial 

radionuclides. Although the ranges in the table are typical, larger variations exist in certain areas 

(e.g., Colorado). 

Bulk materials containing elevated concentrations of terrestrial radionuclides as well as 

equipment used to handle or process these materials should be identified during the IA even if 

these materials and equipment were not impacted by site activities. 

Radon is an element that occurs as a gas in nature. Isotopes of radon are members of both the 

uranium and thorium natural decay series. These radon isotopes decay to produce additional 

radioactive isotopes, which are collectively called radon progeny. 

Table B.1 Typical Average Concentration Ranges of Terrestrial Radionuclides

Material

Radium-226 

(Bq/kg)a

Uranium-238 

(Bq/kg)a

Thorium-232

(Bq/kg)a

Potassium-40 

(Bq/kg)a

Soil, U.S. 40 (8-160)b 35 (4-140)b 35 (4-130)b 370 (100-700)b

Phosphate Fertilizer 200c - 100,000d 200-1,500b 20b -- 

Concrete (19-89)e (19-89)f (15-120)f (260-1,100)f

Concrete Block (41-780)e (41-780)f (37-81)f (290-1,100)f

Brick (4-180)e (4-180)f (1-140)f (7-1,200)f

Coal Tar 

Fly Ash-Bottom Ash 

(100-300)e

200e

(100-300)b 

200b

-- 

200b

-- 

-- 

Coal, U.S. -- 18 (1-540)g 21 (2-320)g 52 (1-710)g

Tile -- (550-810)h 650h -- 

Porcelain, Glazed -- (180-37,000)h, i -- 

Ceramic, Glazedb (79-1,200)h, i

a To convert Bq/kg to pCi/g, multiply by 0.027. 

b UNSCEAR, Sources and Effects of Ionizing Radiation (UNSCEAR 2000). 

c U.S. Environmental Protection Agency (EPA), 2000. Evaluation of EPA’s Guidelines for Technologically 

Enhanced Naturally Occurring Radioactive Materials (TENORM). 

d National Academy of Sciences (NAS). 1999. Evaluation of Guidelines for Technologically Enhanced 

Naturally Occurring Radioactive Materials (TENORM), Committee on Evaluation of EPA Guidelines for 

Exposure to Naturally Occurring Radioactive Materials Board on Radiation Effects Research Commission 

on Life Sciences National Research Council, National Academy Press, p. 72. 

e 226Ra is assumed to be in secular equilibrium with 238U. 

f Eicholz G.G., Clarke F.J., and Kahn, B., 1980. Radiation Exposure From Building Materials, in “Natural 

Radiation Environment III,” U.S. Department of Energy CONF-780422. 

g Beck H.L., Gogolak C.V., Miller K.M., and Lowder W.M., 1980. Perturbations on the Natural Radiation 

Environment Due to the Utilization of Coal as an Energy Source, in “Natural Radiation Environment III,” 

U.S. Department of Energy CONF-780422. 

h Hobbs T.G., 2000. Radioactivity Measurements on Glazed Ceramic Surfaces, J. Res. Natl. Inst. Stand. 

Technol. 105, 275-283. 

i Values reported as total radioactivity without identification of specific radionuclides. 

NUREG-1575, Supp. 1 B-2 January 2009 
MARSAME Appendix B 

Radon emissions vary significantly over time based on a wide variety of factors. For example, 

relatively small changes in the relative pressure between the source material and the atmosphere 

(indoor or outdoor) can result in large changes in radon concentrations in the air. Soil moisture 

content also has an affect on the radon emanation rate. 

Radon progeny tend to become fixed to solid particles in the air. These particles can become 

attached to surfaces as a result of electrostatic charge or gravitational settling. Air flow through 

ventilation ducts can produce an electrostatic charge that will attract these particles. A decrease 

in atmospheric pressure often precedes a rainstorm, which increases the radon emanation rate. 

Immediately prior to an electrical storm, an electrostatic charge can build up on equipment 

resulting in elevated radiation levels from radon progeny. Rainfall acts to scavenge these 

particles from the air, potentially resulting in elevated dose rates and surface activities during and 

immediately following rainfall. 

Pb-210 is a decay product of 222Rn and 238U. The 22-year half-life provides opportunities for 

buildup 210Pb and progeny in sediments and low-lying areas. As mentioned previously, rain acts 

to scavenge radon progeny from the air. Areas where rain collects and concentrates can result in 

elevated levels of 210Pb and progeny over time. In addition, lead is easily oxidized and can 

become fixed to surfaces through corrosion processes. Rust or oxide films on equipment can be 

indicators of locations with a potential for elevated background radioactivity. 

B.2.2 Anthropogenic Radioactive Materials 

Nuclear weapons testing and nuclear power reactors have produced large quantities of 

radionuclides through the fissioning of uranium and other heavy elements and the activation of 

various elements. Examples of anthropogenic radionuclides that could be in the environment are 

137Cs, 90Sr, and various isotopes of plutonium. 

Prior to the 1963 Limited Test Ban Treaty, fallout from atmospheric nuclear tests distributed 

large quantities of anthropogenic radionuclides around the globe. Following the 1963 treaty most 

nuclear weapons tests were conducted underground, although China and France continued 

atmospheric testing of nuclear weapons into the late 1970s. In 1996 a Comprehensive Test Ban 

Treaty was negotiated with the help of the United Nations. The Comprehensive Test Ban Treaty 

has not been ratified by China or the United States and was broken by Pakistan and India in 

1998. However, worldwide fallout concentrations have been declining since the mid 1960s. 

In 1964 a Department of Defense weather satellite containing a radiation source failed to achieve 

orbit. The Space Nuclear Auxiliary Power (SNAP) 9-A Radioisotopic Thermoelectric Generator 

(RTG) burned up on re-entry and dispersed the nuclear inventory (primarily plutonium-238) into 

the atmosphere. Incidents involving Soviet satellites with radioisotopes or nuclear reactors 

occurred in 1969, 1973, 1978, and 1983. In April 1986 there was a non-nuclear steam explosion 

and fire at the number four reactor at the nuclear power plant in Chernobyl in north-central 

Ukraine. Large quantities of radioactive material were released into the environment as a result 

of the catastrophe. The radionuclides from these incidents have been inhomogenously deposited 

around the world. 

January 2009 B-3 NUREG-1575, Supp. 1 
Appendix B MARSAME 

Isolated pockets with elevated concentrations of anthropogenic radionuclides can still be found. 

For example, ventilation systems that were installed prior to 1963 collected fallout radionuclides. 

If these systems are still in use and the ducts have not been thoroughly cleaned, there is a 

potential for elevated background radiation. Another potential source of elevated background 

radiation from anthropogenic radionuclides is wood ash. Trees filter and store some airborne 

pollutants, including 137Cs from fallout. When the wood is burned the 137Cs is concentrated in the 

wood ash. Materials or equipment associated with the ash could have elevated levels of 

background radiation. 

B.2.3 Cosmic Radiation and Cosmogenic Radionuclides 

Cosmic radiation consists of highly energetic particles that are believed to originate from 

phenomena such as solar flares and supernova explosions. The Earth’s atmosphere serves as a 

shield for these particles, although on rare occasions a solar flare is strong enough to produce a 

significant radiation dose in the lower reaches of the atmosphere. 

Cosmic radiation is also responsible for the production of radioactive elements called 

cosmogenic radionuclides. These radionuclides are produced from collisions between the highly 

energetic cosmic radiation with stable elements in the atmosphere. Cosmogenic radionuclides 

include 3H, 7Be, 14C, and 22Na. Background concentrations of cosmic radiation and cosmogenic 

radionuclides generally do not impact disposition surveys. 

B.3 Inherent Radioactivity 

Inherent radioactivity, or intrinsic radioactivity, is radioactivity that is an integral part of the 

M&E being investigated. For example, concrete is made from materials that contain terrestrial 

radionuclides and is inherently radioactive. Some equipment is constructed from radioactive 

components, such as electron tubes or night vision goggles containing thorium components. 

Information on inherent radioactivity is usually obtained from process knowledge or historical 

measurements identified during the IA. Manufacturers of equipment that incorporates radioactive 

components can usually provide the radionuclide and the activity incorporated into the 

equipment. Information on radionuclides and activity levels for other types of equipment or bulk 

materials that are inherently radioactive is usually more generic. Table B.1 lists ranges of 

terrestrial radionuclide concentrations in some common materials (e.g., concrete, soil, brick). 

The wide range of radionuclide concentrations observed in these materials prevents establishing 

any general rules of thumb, so it is usually necessary to obtain project-specific information. For 

release scenarios, it is strongly recommended that all M&E be surveyed before it enters a 

controlled area. This provides project-specific information on inherent radioactivity and 

minimizes complications when designing the disposition survey. For interdiction scenarios, it is 

important to understand the types of M&E being investigated and the potential for inherent 

radioactivity. It may be necessary to establish an administrative action level that defines the 

upper end of acceptable inherent radioactivity for different types of M&E (see Section 3.2). 

NUREG-1575, Supp. 1 B-4 January 2009 
MARSAME Appendix B 

B.4 Instrument Background 

Instrument background is a combination of radioactivity in the constituent materials of the 

detector, ancillary equipment, and shielding, and electronic noise contributing to the instrument 

response. Instruments designed to measure low levels of radioactivity generally are constructed 

from materials with very low levels of inherent radioactivity to minimize instrument background. 

The electronics in radiation instruments are also designed to minimize the signal-to-noise ratio, 

also reducing instrument background. Instrument background becomes the primary contributor 

to background only for radionuclide-specific measurements for radionuclides not contributing to 

environmental or inherent background (e.g., 60Co in bulk soil measured by gamma 

spectroscopy). Note that radiation from M&E can interact with instrument shielding to produce 

secondary effects that may contribute to instrument background (e.g., Compton backscatter, 

generation of secondary photons and characteristic x rays, photoelectric absorption). Additional 

information on instrument background is available in Chapter 20 of Radiation Detection and 

Measurement (Knoll 1999). 

B.5 Technologically Enhanced Naturally Occurring Radioactive Material 

Technologically Enhanced Naturally Occurring Radioactive Material (TENORM) is any 

naturally occurring material not subject to regulation under the Atomic Energy Act whose 

radionuclide concentrations or potential for human exposure have been increased above levels 

encountered in the natural state by human activities (NAS 1999). Some industrial processes 

involving natural resources concentrate naturally occurring radionuclides, producing TENORM. 

Much TENORM contains only trace amounts of radioactivity and is part of our everyday 

landscape. Some TENORM, however, contains very high concentrations of radionuclides. The 

majority of radionuclides in TENORM are found in the uranium and thorium natural decay 

series. Potassium-40 is also associated with TENORM. Radium and radon typically are 

measured as indicators for TENORM in the environment. TENORM is found in many industrial 

waste streams (e.g., scrap metal, sludges, slags, fluids) and is being discovered in industries 

traditionally not thought of as being affected by radiation. Examples of products and processes 

affected by TENORM include: 

• Uranium overburden and mine spoils, 

• Phosphate industrial wastes, 

• Phosphate fertilizers and potash, 

• Coal ash, slag, cinders, 

• Oil and gas production scale and sludge, 

• Sludge and other waste materials from treatment of drinking water and waste water, 

• Metal mining and processing waste, 

• Geothermal energy production waste, 

• Paper and pulp, 

• Scrap metal recycling, 

• Slag from industrial processes (metal and non-metal), 

• Abrasive mineral sands, and 

• Cement production. 

January 2009 B-5 NUREG-1575, Supp. 1 
Appendix B MARSAME 

Radon and radon progeny are concerns when dealing with TENORM. Radon-222 is a decay 

product of 238U. The 3.8-day half-life means that 222Rn is capable of migrating through several 

decimeters of soil or building materials and reaching the atmosphere before it decays. The 

radioactive progeny of unsupported 222Rn have short half-lives (e.g., 27 minutes for 214Pb) and 

usually decay to background levels within a few hours. 220Rn, which has a 55-second half-life, is 

a decay product of 232Th. The short half-life limits the mobility of 220Rn since it decays before it 

can migrate to the atmosphere. However, 232Th activity that is located on or very near the surface 

can produce significant quantities of 220Rn in the air. The radioactive progeny of unsupported 

220Rn can result in elevated levels of surface radioactivity for materials and equipment used or 

stored in these areas. The 10.6-hour half-life of 212Pb means that this surface radioactivity could 

take a week or longer to decay to background levels. 

B.6 Orphan Sources 

Radiation sources are found in certain types of specialized industrial devices, such as those used 

for measuring the moisture content of soil and for measuring density or thickness of materials. 

Usually, a small quantity of the radioactive material is sealed in a metal casing and enclosed in a 

housing that prevents the escape of radiation. These sources present no health risk from 

radioactivity as long as the sources remain sealed, the housing remains intact, and the devices are 

handled and used properly. 

If equipment containing a sealed source is disposed of improperly or sent for recycling as scrap 

metal, the sealed source may be “lost” and end up in a metal recycling facility or in the 

possession of someone who is not licensed to handle the source. Specially licensed sources bear 

identifying markings that can be used to trace these sources to their original owners. However, 

some sources do not have these markings or the markings become obliterated. In these cases, the 

sources are referred to as “orphan sources” because no known owner can be identified. They are 

one of the most frequently encountered sources of radioactivity in shipments received by scrap 

metal facilities. 

Scrap yards and disposal sites attempt to detect orphan sources and other contaminated metals by 

screening incoming materials with sensitive radiation detectors before they can enter the 

processing stream and cause contamination. Housings that make the sources safe also make 

detection difficult. Further, if the source is buried in a load of steel, the steel acts as further 

shielding and thus these sources may elude detection. Consequently, there is always a potential 

for sources to become mixed within and impact scrap metal. 

NUREG-1575, Supp. 1 B-6 January 2009 
MARSAME Appendix C 

C. EXAMPLES OF COMMON RADIONUCLIDES 

Table C.1 Examples of Common Radionuclides at Selected Types of Facilities 

Facility Type Common Radionuclides 

Accelerator/Cyclotron 

22Na 

Activation products (e.g., 60Co) 

Aircraft Manufacturing and Maintenance Facility 

3

H (dials and gauges) 

Magnesium-thorium alloys 

Nickel-thorium alloys 

147Pm (lighted dials and gauges) 

226Ra and progeny (radium dials) 

Depleted uranium 

Cement Production Facility Thorium series radionuclides 

Uranium series radionuclides 

Ceramic Manufacturing Facility Thorium series radionuclides 

Uranium series radionuclides 

Fertilizer Plant 

40K 

Uranium series radionuclides 

Fuel Fabrication Facility 

99Tc (reprocessing only) 

Enriched uranium 

Transuranics (e.g., 237Np, 239Pu) (reprocessing only) 

Gaseous Diffusion Plant 

99Tc 

Enriched uranium 

Transuranics (e.g., 237Np, 239Pu) 

Medical Imaging and Therapy Facility 

60Co 

90Sr 

99mTc 

131I 

137Cs 

192Ir 

201Tl 

226Ra and progeny 

Depleted uranium collimators 

Metal Foundry 

40K 

60Co 

137Cs 

Thorium series radionuclides 

Uranium series radionuclides 

Munitions and Armament Manufacturing and 

Testing Facility 

3

H (fire control devices) 

226Ra and progeny 

Depleted uranium 

January 2009 C-1 NUREG-1575, Supp. 1 
Appendix C MARSAME 

Table C.1 Examples of Common Radionuclides at Selected Types of Facilities (continued) 

Facility Type Common Radionuclides 

Nuclear Medicine Laboratory 

or Pharmaceutical Laboratory 

99mTc 

131I 

137Cs 

192Ir 

201Tl 

226Ra and progeny 

Nuclear Power Reactor 

Activation products (e.g., 55Fe, 60Co, 63Ni) 

Fission products (e.g., 90Sr, 137Cs) 

Transuranics (e.g., 237Np, 239Pu) 

Oil and Gas 226Ra and progeny 

Optical Glass Facility Thorium series radionuclides 

Uranium series radionuclides 

Paint and Pigment Manufacturing Facility Thorium series radionuclides 

Uranium series radionuclides 

Paper and Pulp Facility Thorium series radionuclides 

Uranium series radionuclides 

Radium Dial Painting 226Ra and progeny 

Rare Earth Facility 

40K 

Thorium series radionuclides 

Uranium series radionuclides 

R&D Facility with Broad Scope License 

3

H 

14C 

Research Laboratory 

3

H 

14C 

22Na 

24Na 

32P

57Co 

63Ni 

123I 

125I 

Scrap Metal Recycling Facility 

60Co 

90Sr 

137Cs 

226Ra and progeny 

Thorium series radionuclides 

Uranium series radionuclides 

NUREG-1575, Supp. 1 C-2 January 2009 
MARSAME Appendix C 

Table C.1 Examples of Common Radionuclides at Selected Types of Facilities (continued) 

Facility Type Common Radionuclides 

Sealed Source Facility 

60Co 

90Sr 

137Cs 

241Am 

Transuranic Facility 

241Am 

238, 239, 240, 241Pu 

Uranium Mill 

238U 

230Th 

226Ra and progeny 

Thorium series radionuclides 

Uranium series radionuclides 

Waste Water Treatment Facility Thorium series radionuclides 

Uranium series radionuclides 

Widely Distributed General Commerce 

3

H (exit signs) 

40K (naturally-occurring) 

57Co (lead paint analyzer) 

60Co (radiography source) 

63Ni (chemical agent detectors) 

109Cd (lead paint analyzer) 

137Cs (soil moisture density gauge, liquid level 

gauge) 

192Ir (radiography source) 

226Ra (watch dials) 

241Am (AmBe soil moisture density gauge, 

 smoke detectors) 

Orphan sources 

January 2009 C-3 NUREG-1575, Supp. 1 
 
MARSAME Appendix D

D. INSTRUMENTATION AND MEASUREMENT TECHNIQUES 

D.1 Introduction 

This appendix provides information on various field and laboratory equipment used to measure 

radiation levels and radioactive material concentrations. The descriptions provide information 

pertaining to the general types of available radiation detectors and the ways in which those 

detectors are utilized for various circumstances. Similar information may be referenced from 

MARSSIM Appendix H, “Description of Field Survey and Laboratory Analysis Equipment” 

(MARSSIM 2002), and NUREG-1761 Appendix B, “Advanced/Specialized Information” (NRC 

2002). The information in this appendix is specifically designed to assist the user in selecting the 

appropriate radiological instrumentation and measurement technique during the implementation 

phase of the data life cycle (Chapter 5). 

The following topics will be discussed for each instrumentation and measurement technique 

combination: 

• Instruments – A description of the equipment and the typical detection instrumentation it 

employs;

• Temporal Issues – A synopsis of time constraints that may be encountered through use of 

the measurement technique;

• Spatial Issues – Limitations associated with the size and portability of the instrumentation as 

well as general difficulties that may arise pertaining to source-to-detector geometry; 

• Radiation Types – Applicability of the measurement technique for different types of 

ionizing radiation; 

• Range – The associated energy ranges for the applicable types of ionizing radiation; 

• Scale – Typical sizes for the M&E applicable to the measurement technique; and 

• Ruggedness – A summary of the durability of the instrumentation (note that this is 

frequently limited by the detector employed by the instrumentation; e.g., an instrument 

utilizing a plastic scintillator is inherently more durable than an instrument utilizing a sodium 

iodide crystal); suitable temperature ranges for proper operation of the instrumentation and 

measurement technique have been provided where applicable.

D.2 General Detection Instrumentation 

This section summarizes the most common detector types used for the detection of ionizing 

radiation in the field. This will include many of the detector types incorporated into the 

measurement methods that are described in later sections of this chapter. 

D.2.1 Gas-Filled Detectors 

Gas-filled detectors are the most commonly used radiation detectors and include gas- ionization 

chamber detectors, gas-flow proportional detectors, and Geiger-Muller (GM) detectors. These 

detectors can be designed to detect alpha, beta, photon, and neutron radiation. They generally 

consist of a wire passing through the center of a gas-filled chamber with metal walls, which can 

be penetrated by photons and high-energy beta particles. Some chambers are fitted with Mylar 

January 2009 D-1 NUREG-1575, Supp. 1 
Appendix D MARSAME 

windows to allow penetration by alpha and low-energy beta radiation. A voltage source is 

connected to the detector with the positive terminal connected to the wire and the negative 

terminal connected to the chamber casing to generate an electric field, with the wire serving as 

the anode, and the chamber casing serving as the cathode. Radiation ionizes the gas as it enters 

the chamber, creating free electrons and positively charged ions. The number of electrons and 

positively charged ions created is related to the properties of the incident radiation type (alpha 

particles produce many ion pairs in a short distance, beta particles produce fewer ion pairs due to 

their smaller size, and photons produce relatively few ion pairs as they are uncharged and 

interact with the gas significantly less than alpha and beta radiation). The anode attracts the free 

electrons while the cathode attracts the positively charged ions. The reactions among these ions 

and free electrons with either the anode or cathode produce disruptions in the electric field. The 

voltage applied to the chamber can be separated into different voltage ranges that distinguish the 

types of gas-filled detectors described below. The different types of gas-filled detectors are 

described in ascending order of applied voltage. 

D.2.1.1

D.2.1.2

 

Ionization Chamber Detectors 

Ionization chamber detectors consist of a gas-filled chamber operated at the lowest voltage range 

of all gas-filled detectors.1 Ionization detectors utilize enough voltage to provide the ions with 

sufficient velocity to reach the anode or cathode. The signal pulse heights produced in ionization 

chamber detectors is small and can be discerned by the external circuit to differentiate among 

different types of radiation. These detectors provide true measurement data of energy deposited 

proportional to the charge produced in air, unlike gas-flow proportional and GM detectors which 

are detection devices. These detectors generally are designed to collect cumulative beta and 

photon radiation without amplification and many have a beta shield to help distinguish among 

these radiation types. These properties make ionization detectors excellent choices for measuring 

exposure rates from photon emission radiation in roentgens. These detectors can be deployed for 

an established period of time to collect data in a passive manner for disposition surveys. 

Ionization chamber detectors may assist in collecting measurements in inaccessible areas due to 

their availability in small sizes. 

Another form of the ionization chamber detector is the pressurized ion chamber (PIC). As with 

other ionization chamber detectors, the PIC may be applied for M&E disposition surveys when a 

exposure-based action level is used. The added benefit of using PICs is that they can provide 

more accurate dose measurements because they compensate for the various levels of photon 

energies as opposed to other exposure rate meters (e.g., micro-rem meter), which are calibrated 

to a 137Cs source. PICs can be used to cross-calibrate other exposure rate detectors applicable for 

surveying M&E, allowing the user to compensate for different energy levels and reduce or 

eliminate the uncertainty of underestimating or overestimating the exposure rate measurements. 

Gas-Flow Proportional Detectors 

The voltage applied in gas-flow proportional detectors is the next range higher than ionization 

chamber detectors, and is sufficient to create ions with enough kinetic energy to create new ion 

1

 At voltages below the ionization chamber voltage range, ions will recombine before they can reach either the 

cathode or anode and do not produce a discernable disruption to the electric field. 

NUREG-1575, Supp. 1 D-2 January 2009 
MARSAME Appendix D

pairs, called secondary ions. The quantity of secondary ions increases proportionally with the 

applied voltage, in what is known as the gas amplification factor. The signal pulse heights 

produced can be discerned by the external circuit to differentiate among different types of 

radiation. Gas-flow proportional detectors generally are used to detect alpha and beta radiation. 

Systems also detect photon radiation, but the detection efficiency for photon emissions is 

considerably lower than the relative efficiencies for alpha and beta activity. Physical probe areas 

for these types of detectors vary in size from approximately 100 cm2

 up to 600 cm2. The detector 

cavity in these instruments is filled with P-10 gas which is an argon-methane mixture (90% 

argon and 10% methane). Ionizing radiation enters this gas-filled cavity through an aluminized 

Mylar window. Additional Mylar shielding may be used to block alpha radiation; a lower voltage 

setting may be used to detect pure alpha activity (NRC 1998b). 

D.2.1.3

 

Geiger-Mueller Detectors 

GM detectors operate in the voltage range above the proportional range and the limited 

proportional range.2

 This range is characterized by extensive gas amplification that results in 

what is referred to as an “avalanche” of ion and electron production. This mass production of 

electrons spreads throughout the entire chamber, which precludes the ability to distinguish 

among different kinds of radiation because all of the signals produced are the same size. GM 

detectors are most commonly used for the detection of beta activity, though they may also detect 

both alpha and photon radiation. GM detectors have relatively short response and dead times and 

are sensitive enough to broad detectable energy ranges for alpha, beta, photon, and neutron 

emissions (though they cannot distinguish which type of radiation produces input signals) to 

allow them to be used for surveying M&E with minimal process knowledge.3

GM detectors are commonly divided into three classes: “pancake”, “end-window”, and “sidewall” detectors. GM pancake detectors (commonly referred to as “friskers”) have wide diameter, 

thin mica windows (approximately 15 cm2 window area) that are large enough to allow them to 

be used to survey many types of M&E. Although GM pancake detectors are referenced beta and 

gamma detectors, the user should consider that their beta detection efficiency far exceeds their 

gamma detection efficiency. The end-window detector uses a smaller, thin mica window and is 

designed to allow beta and most alpha particles to enter the detector unimpeded for concurrent 

alpha and beta detection. The side-wall detector is designed to discriminate between beta and 

gamma radiation, and features a door that can be slid or rotated closed to shield the detector from 

beta emissions for the sole detection of photons. These detectors require calibration to detect for 

beta and gamma radiation separately. Energy-compensated GM detectors may also be crosscalibrated for assessment of exposure rates. 

2

 The limited proportional range produces secondary ion pairs but does not produce reactions helpful for radiation 

detection, because the gas amplification factor is no longer constant. 3

 GM detectors may be designed and calibrated to detect alpha, beta, photon, and neutron radiation, though they are 

much better-suited for the detection of charged particles (i.e., alpha and beta particles) than neutral particles (i.e., 

photons and neutrons). 

January 2009 D-3 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.2.2 Scintillation Detectors 

Scintillation detectors (sometimes referred to as “scintillators”) consist of scintillation media that 

emits a light “output” called a scintillation pulse when it interacts with ionizing radiation. 

Scintillators emit low-energy photons (usually in the visible light range) when struck by highenergy charged particles; interactions with external photons cause scintillators to emit charged 

particles internally, which in turn interact with the crystal to emit low-energy photons. In either 

case, the visible light emitted (i.e., the low-energy photons) are converted into electrical signals 

by photomultiplier tubes and recorded by a digital readout device. The amount of light emitted is 

generally proportional to the amount of energy deposited, allowing for energy discrimination and 

quantification of source radionuclides in some applications. 

D.2.2.1

D.2.2.2

D.2.2.3

Zinc Sulfide Scintillation Detectors 

Zinc sulfide detector crystals are only available as a polycrystalline powder that are arranged in a 

thin layer of silver-activated zinc sulfide (ZnS(Ag)) as a coating or suspended within a layer of 

plastic scintillation material. The use of these thin layers makes them inherently dispositioned for 

the detection of high linear energy transfer (LET) radiation (radiation associated with alpha 

particles or other heavy ions). These detectors use an aluminized Mylar window to prevent 

ambient light from activating the photomultiplier tube (Knoll 1999). The light pulses produced 

by the scintillation crystals are amplified by a photomultiplier tube, converted to electrical 

signals, and counted on a digital scaler/ratemeter. Low LET radiations (particularly beta 

emissions) are detected at much lower detection efficiencies than alpha emissions and pulse 

characteristics may be used to discriminate beta detections from alpha detections. 

Sodium Iodide Scintillation Detectors 

Sodium iodide detectors are well-suited for detection of photon radiation. Energy-compensated 

sodium iodide detectors may also be cross-calibrated for assessment of exposure rates. Unlike 

ZnS(Ag), sodium iodide crystals can be grown relatively large and machined into varying shapes 

and sizes. Sodium iodide crystals are activated with trace amounts of thallium (hence the 

abbreviation NaI(Tl)), the key ingredient to the crystal’s excellent light yield (Knoll, 1999). 

These instruments most often have upper- and lower-energy discriminator circuits and when 

used correctly as a single-channel analyzer, can provide information on the photon energy and 

identify the source radionuclides. Sodium iodide detectors can be used with handheld 

instruments or large stationary radiation monitors. 

Cesium Iodide Scintillation Detectors 

Cesium iodide detectors generally are similar to sodium iodide detectors. Like NaI(Tl), cesium 

iodide may be activated with thallium (CsI(Tl)) or sodium (CsI(Na)). Cesium iodide is more 

resistant to shock and vibration damage than NaI, and when cut into thin sheets it features 

malleable properties allowing it to be bent into various shapes. CsI(Tl) has variable decay times 

for various exciting particles, allowing it to help differentiate among different types of ionizing 

radiation. A disadvantage of CsI scintillation detectors is due to the fact that the scintillation 

emission wavelengths for CsI are longer than those produced by sodium iodide crystals; because 

NUREG-1575, Supp. 1 D-4 January 2009 
MARSAME Appendix D

almost all photomultiplier tubes are designed for NaI, there are optical incompatibilities that 

result in decreased intrinsic efficiencies for CsI detectors. Additionally, CsI scintillation 

detectors feature relatively long response and decay times for luminescent states in response to 

ionizing radiation (Knoll 1999). 

D.2.2.4

 

Plastic Scintillation Detectors 

Plastic scintillators are composed of organic scintillation material that is dissolved in a solvent 

and subsequently hardened into a solid plastic. Modifications to the material and specific 

packaging allow plastic scintillators to be used for detecting alpha, beta, photon, or neutron 

radiation. While plastic scintillators lack the energy resolution of sodium iodide and some other 

gamma scintillation detector types, their relatively low cost and ease of manufacturing into 

almost any desired shape and size enables them to offer versatile solutions to atypical radiation 

detection needs (Knoll 1999). 

D.2.3 Solid State Detectors 

Solid state detection is based on ionization reactions within detector crystals composed of an 

electron-rich (n-type or electron conductor) sector and an electron-deficient (p-type or hole 

conductor) sector. Reverse-bias voltage is applied to the detector crystal; forming a central 

region absent of free charge (this is termed the depleted region). When a particle enters this 

region, it interacts with the crystal structure to form hole-electron pairs. These holes and 

electrons are swept out of the depletion region to the positive and negative electrodes by the 

electric field, and the magnitude of the resultant pulse in the external circuit is directly 

proportional to the energy lost by the ionizing radiation in the depleted region. 

Solid state detection systems typically employ silicon or germanium crystals4 and utilize 

semiconductor technology (i.e., a substance whose electrical conductivity falls between that of a 

metal and that of an insulator, and whose conductivity increases with decreasing temperature and 

with the presence of impurities). Semiconductor detectors are cooled to extreme temperatures to 

utilize the crystal material’s insulating properties to prevent thermal generation of noise. The use 

of semiconductor technology can achieve energy resolutions, spatial resolutions, and signal-tonoise ratios superior to those of scintillation detection systems. 

D.3 Counting Electronics 

Instrumentation requires a device to accumulate and record the input signals from the detector 

over a fixed period of time. These devices are usually electronic, and utilize scalers or ratemeters to display results representing the number of interaction events (between the detector and 

radionuclide emissions) within a period of time (e.g., counts per minute). A scaler represents the 

total number of interactions within a fixed period of time, while a rate-meter provides 

information that varies based on a short-term average of the rate of interactions. 

4

 Solid state detection systems may also utilize crystals composed of sodium iodide, cesium iodide, or cadmium zinc 

telluride in non-semiconductor applications. 

January 2009 D-5 NUREG-1575, Supp. 1 
Appendix D MARSAME 

Scalers represent the simpler of these two counting approaches, because they record a single 

count each time an input signal is received from the detector. Scaling circuits typically are 

designed with scalers to allow the input signals to be cut by factors of 10, 100, or 1,000 to allow 

the input signals to be counted directly by electromechanical registers when counting areas with 

elevated radioactivity. Scalers generally are used when taking in situ measurements and are used 

to determine average activities. 

Contemporary rate-meters utilize analog-to-digital converters to sample the pulse amplitude of 

the input signal received from the detector and convert it to a series of digital values. These 

digital values may then be manipulated using digital filters (or shapers) to average or “smooth” 

the data displayed. The counting-averaging technique used by rate-meters may be more helpful 

than scalers in identifying elevated activity. When using scalers in performing scanning surveys 

to locate areas of elevated activity, small areas of elevated activity may appear as very quick 

“blips” that are difficult to discern, while rate-meters continue to display heightened count rates 

once the detector has moved past the elevated activity, and display “ramped up” count rates 

immediately preceding the elevated activity as well. Rate-meters have the inherent limitation in 

that the use of their counting electronics varies the signals displayed by the meter because they 

represent a short-term average of the event rate. It is conceivable that very small areas of 

elevated activity (e.g., particle) might have their true activity concentrations “diluted” by the 

averaging of rate-meter counting electronics. 

D.4 Hand-Held Instruments 

This section discusses hand-held instruments, which may be used for in situ measurements or 

scanning surveys. 

D.4.1 Instruments 

In situ measurements with hand-held instruments typically are conducted using the detector types 

described in Section D.2. These typically are composed of a detection probe (utilizing a single 

detector) and an electronic instrument to provide power to the detector and to interpret data from 

the detector to provide a measurement display. 

The most common types of hand-held detector probes are GM detectors, ZnS(Ag) alpha/beta 

scintillation detectors, and NaI(Tl) photon scintillation detectors. There are instances of gas-flow 

proportional detectors as hand-held instruments, though these are not as common because these 

detectors operate using a continuous flow of P-10 gas, and the accessories associated with the 

gas (e.g., compressed gas cylinders, gauges, tubing) make them less portable for use in the field. 

D.4.2 Temporal Issues 

Hand-held instruments generally have short, simple equipment set-ups requiring minimal time, 

often less than ten minutes. In situ measurement count times typically range from 30 seconds to 

two minutes. Longer count times may be utilized to increase resolution and provide lower 

minimum detectable limits. Typical scanning speeds are approximately 2.5 centimeters per 

second. Slower scanning speeds will aid in providing lower minimum detectable concentrations. 

NUREG-1575, Supp. 1 D-6 January 2009 
MARSAME Appendix D

D.4.3 Spatial Issues 

Detectors of hand-held instruments typically are small and portable, having little trouble fitting 

into and measuring most M&E. Spatial limitations are usually based on the physical size of the 

probe itself. The user must be wary of curved or irregular surfaces of M&E being surveyed. 

Detector probes generally have flat faces and incongruities between the face of the detector and 

the M&E being surveyed have an associated uncertainty. ZnS scintillation and gas-flow 

proportional detectors are known to have variations in efficiency of up to 10% across the face of 

the detector. Therefore, the calibration source used should have an area at least the size of the 

active probe area. 

D.4.4 Radiation Types 

Assortments of hand-held instruments are available for the detection of alpha, beta, photon, and 

neutron radiations. Table D.1 illustrates the potential applications for the most common types of 

hand-held instruments. 

Table D.1 Potential Applications for Common Hand-Held Instruments 

Detectable Energy Range

Alpha Beta Photon Neutron

Low End 

Boundary

High End 

Boundary

Ionization chamber detectors NA FAIR GOOD NA 40-60 keV 1.3-3 MeV

Gas-flow proportional 

detectors GOOD GOOD POOR POOR 5-50 keV 8-9 MeV

Geiger-Muller detectors FAIR GOOD POOR POOR 30-60 keV 1-2 MeV

ZnS(Ag) scintillation 

detectors GOOD POOR NA NA 30-50 keV 8-9 MeV

NaI(Tl) scintillation detectors NA POOR GOOD NA 40-60 keV 1.3-3 MeV

NaI(Tl) scintillation detectors 

(thin detector, thin window) NA FAIR GOOD NA 10 keV 60-200 keV

CsI(Tl) scintillation detectors NA POOR GOOD NA 40-60 keV 1.3-3 MeV

Plastic scintillation detectors NA FAIR GOOD NA 40-60 keV 1.3-3 MeV

BF3 proportional detectors5 NA NA NA GOOD 0.025 eV 100 MeV

3

He proportional detectors5 NA NA POOR GOOD 0.025 eV 100 MeV

Notes: 

GOOD The instrument is well-suited for detecting this type of radiation. 

FAIR The instrument can adequately detect this type of radiation. 

POOR The instrument may be poorly suited for detecting this type of radiation. 

NA The instrument cannot detect this type of radiation. 

 

 

5

 The use of moderators enables the detection of high-energy fast neutrons. Either BF3 or 3He gas proportional 

detectors may be used for the detection of fast neutrons, but 3

He are much more efficient in performing this function. 

BF3 detectors discriminate against gamma radiation more effectively than 3

He detectors. 

January 2009 D-7 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.4.5 Range 

The ranges of detectable energy using hand-held instruments are dependent upon the type of 

instrument selected and type of radiation. Some typical detectable energy ranges for common 

hand-held instruments are listed above in Table D.1. More detailed information pertaining to the 

ranges of detectable energy using hand-held instruments are available in the European 

Commission for Nuclear Safety and the Environment Report 17624 (EC 1998). 

D.4.6 Scale 

There is no definitive limit to the size of an object to be surveyed using hand-held instruments. 

Hand-held instruments may generally be used to survey M&E of any size; constraints are only 

placed by the practical sizing of M&E related to the sensitive area of the probe. Limitations may 

also be derived from the physical size of the detector probes used for surveying. The largest 

hand-held detector probes feature effective detection surface areas of approximately 175 to 200 

cm2

. Detection probes larger than this may be of limited use with hand-held instruments. 

D.4.7 Ruggedness 

All varieties of hand-held instruments discussed here typically are calibrated for use in 

temperatures with lower ranges from -30 ° to -20 °C and upper ranges from 50 ° to 60 °C. The 

durability of a hand-held instrument depends largely upon the detection media (crystals, such as 

sodium iodide and germanium crystals are fragile and vulnerable to mechanical and thermal 

shock) and the presence of a Mylar (or similar material) window: 

• Ionization chamber detectors – Ionization chamber detectors are susceptible to physical 

damage and may provide inaccurate data (including false positives) if exposed to 

mechanical shock.

• Gas-flow proportional detectors – Detection gas used with gas-flow proportional detectors 

may leak from seals such that these detectors are usually operated in the continuous gas flow 

mode; the use of flow meter gauges to continuously monitor the gas flow rate is 

recommended along with frequent quality control checks to ensure the detector still meets 

the required sensitivity; gas-flow proportional detectors may also use fragile Mylar windows 

to contain the detection gases, which renders the detectors vulnerable to puncturing and 

mechanical shock.

• Geiger-Muller detectors – GM tubes typically use fragile Mylar windows to contain the 

detection gases; the presence of a Mylar window renders the detector vulnerable to 

puncturing and mechanical shock. 

• ZnS(Ag) scintillation detectors – Zinc sulfide is utilized as thin-layer polycrystalline 

powder in detectors and are noted for being vulnerable to mechanical shock; zinc sulfide 

detectors may use fragile Mylar windows, in which case the detector is vulnerable to 

puncturing and mechanical shock. 

• NaI(Tl) scintillation detectors – Sodium iodide crystals are relatively fragile and can be 

damaged through mechanical shock; sodium iodide is also highly hydroscopic such that the 

crystals must remain environmentally sealed within the detector housing.

NUREG-1575, Supp. 1 D-8 January 2009 
MARSAME Appendix D

• Plastic Scintillation Detectors – Plastic scintillators typically are robust and resistant to 

damage from mechanical and thermal shock.

D.5 Volumetric Counters (Drum, Box, Barrel, Four-Pi Counters) 

The term “box counter” is a generic description for a radiation measurement system that 

typically involves large area, four-pi (4-π) radiation detectors and includes the following industry 

nomenclature: tool counters, active waste monitors, surface activity measurement systems, and 

bag/barrel/drum monitors. Box counting systems are most frequently used for conducting in situ 

surveys of M&E that is utilized in radiologically controlled areas. These devices are best-suited 

for performing gross activity screening measurements on Class 2 and Class 3 M&E (NRC 2002). 

Typical items to be surveyed using box counters are hand tools, small pieces of debris, bags of 

trash, and waste barrels. Larger variations of box counting systems can count objects up to a few 

cubic meters in size. Because of potential problems with self-shielding, materials may need to be 

opened or partially disassembled prior to placing into a box counting system. 

D.5.1 Instruments 

Box counting systems typically consist of a counting chamber, an array of detectors configured 

to provide a 4-π counting geometry, and microprocessor-controlled electronics that allow 

programming of system parameters and data-logging. Systems typically survey materials for 

photon radiation and usually incorporate a shielded counting chamber and scintillation detectors 

(plastic scintillators or sodium iodide scintillation detectors). These systems most commonly 

utilize four or six detectors, which are situated on the top, bottom, and sides of the shielded 

counting chamber (Figure D.1). Some systems monitor M&E for beta activity, using a basic 

design similar to photon radiation detection systems, but utilizing gas-flow proportional 

counters. In rare cases, neutron detection 

has been used for criticality controls and 

counter-proliferation screening. 

Box counting systems for alpha activity 

feature a substantial departure in design 

from beta/gamma detection systems. 

Alpha activity systems do not require 

heavy shielding to filter out ambient 

sources of radiation. These devices 

utilize air filters to remove dust and 

particulates from air introduced into 

counting chambers that incorporate 

airtight seals. Filtered air introduced into 

the counting chamber interacts with any 

surface alpha activity associated with the 

M&E. 

Each alpha interaction with a surrounding 

air molecule produces an ion pair. These Figure D-1 Example Volumetric Counter (Thermo 2005) 

January 2009 D-9 NUREG-1575, Supp. 1 
Appendix D MARSAME 

ion pairs are produced in proportion to the alpha activity per unit path length. This air (i.e., the 

ion pairs in the air) is then counted using an ion detector for quantification of the specific 

activity. The specific activity of the air in the counting chamber provides a total surface activity 

quantification for the M&E (BIL 2005). 

D.5.2 Temporal Issues 

Typically, box counting systems require approximately one to 100 seconds to conduct a 

measurement (Thermo 2005). The count times are dependent on a number of factors to include 

required measurement sensitivity and background count rates with accompanying subtraction 

algorithms. The count times for box counting typically are considered relatively short for most 

disposition surveys. 

D.5.3 Spatial Issues 

Because box counters typically average activity over the volume or mass of the M&E, the spatial 

distribution of radioactivity may be a significant limitation on the use of this measurement 

technique. The design of box counting systems is not suited to the identification of localized 

elevated areas, and therefore may not be the ideal choice when the disposition criteria is not 

based on average or total activity. 

Some systems incorporate a turntable inside the counting chamber to improve measurement of 

difficult-to-measure areas or for heterogeneously distributed radioactivity. When practical, 

performing counts on objects in two different orientations (i.e., by rotating the M&E 90 or 180 

degrees and performing a subsequent count) will yield more thorough and defensible data. 

Proper use of box counters includes segregating the M&E to be surveyed and promoting accurate 

measurements through uniform placement of items to be surveyed in the counting chamber. For 

example, a single wrench placed on its side in a box counter has different geometric implications 

from a tool of similar size standing up inside the counting chamber. Counting jigs for sources 

and M&E to be surveyed are frequently employed to facilitate consistent, ideal counting 

positions between the M&E and the counting chamber detector array. 

D.5.4 Radiation Types 

Box counting systems are intrinsically best-suited for the detection of moderate- to high- energy 

photon radiation. As described in Section D.5.1, specific systems may be designed for the 

detection of low-energy photon, beta, alpha, and in some cases neutron radiation. For proper 

calibration and utilization of box counters, it is often necessary to establish the radiation types 

and anticipated energy ranges prior to measurement. 

D.5.5 Range 

Photon radiation can typically be measured within a detectable energy range of 40 to 60 keV up 

to 1.3 to 3 MeV. For example, typical box counters positioned at radiological control area exit 

points are configured to alarm at a set point of 5,000 dpm total activity. The precise count time is 

adjusted automatically by setting the predetermined count rate to limit the error. Measurement 

NUREG-1575, Supp. 1 D-10 January 2009 
MARSAME Appendix D

times will range from 5 to 45 seconds in order to complete counts of this kind, depending on 

current background conditions (Thermo 2005). Lower detection capabilities are achievable by 

increasing count times or incorporating background reduction methodologies. 

D.5.6 Scale 

Size limitations pertaining to the M&E to be surveyed are inherently linked to the physical size 

of the counting chamber. Smaller box counting systems have a counting chamber of less than 

0.028 cubic meters (approximately one cubic foot) and are often used for tools and other 

frequently used small items. The maximum size of box counters is typically driven by the 

logistics of managing the M&E to be measured, and this volume is commonly limited to a 55-

gallon waste drum. Some box counting systems allow counts to be performed on oversized items 

protruding from the counting chamber with the door open. 

D.5.7 Ruggedness 

Many volumetric counter models feature stainless steel construction with plastic scintillation 

detectors and windowless designs, which translates to a rugged instrument that is resistant to 

mechanical shock. 

D.6 Conveyorized Survey Monitoring Systems 

Conveyorized survey monitoring systems automate the routine scanning of M&E. Conveyorized 

survey monitoring systems have been designed to measure materials such as soil, clothing 

(laundry monitors), copper chop (small pieces of copper), rubble, and debris. Systems range 

from small monitoring systems comprised of a single belt that passes materials through a 

detector array, to elaborate multi-belt systems capable of measuring and segregating material 

while removing extraneously large items. The latter type comprises systems that are known as 

segmented gate systems. These automated scanning systems segregate materials by activity by 

directing material that exceeds an established activity level onto a separate conveyor. Simpler 

conveyorized survey monitoring systems typically feature an alarm/shut-down feature that halts 

the conveyor motor and allows for manual removal of materials that have exceeded the 

established activity level. 

D.6.1 Instruments 

A typical conveyorized survey monitoring system consists of a motorized conveyor belt that 

passes materials through an array of detectors, supporting measurement electronics, and an 

automated data-logging system (Figure D.2). Systems typically survey materials for photon 

radiation and usually incorporate scintillation detectors (plastic scintillators or sodium iodide 

scintillation detectors) or high-purity germanium detectors. Scintillation detector arrays are often 

chosen for gross gamma activity screening. Conveyorized survey monitoring systems designed 

to detect radionuclide mixtures with a high degree of process knowledge work best using plastic 

scintillators, while systems categorizing material mixtures where the radionuclide concentrations 

are variable are better-suited to the use of sodium iodide scintillation detectors. Conveyorized 

survey monitoring systems designed for material mixtures where the radionuclide concentrations 

January 2009 D-11 NUREG-1575, Supp. 1 
Appendix D MARSAME 

are unknown may be suitable for more expensive and maintenance-intensive high-purity 

germanium detector arrays, which will allow for quantitative measurement of complex photon 

energy spectra. An alternative method for screening materials for different photon energy regions 

of interest is to incorporate sodium iodide detector arrays with crystals of varying thickness to 

target multiple photon energies. Systems may also be fitted with gas flow proportional counters 

for the detection of alpha and beta emissions. Laundry conveyorized survey monitoring systems 

typically are designed for the detection of alpha and beta radiation, as the nature of clothes 

allows the survey media to be compressed, allowing the detector arrays to be close to or in 

contact with the survey media. 

Figure D-2 Example Conveyorized Survey Monitoring System (Laurus 2001) 

D.6.2 Temporal Issues 

Typically, conveyorized survey monitoring systems require approximately one to six seconds to 

count a given field of detection (Novelec 2001a). Systems are designed to provide belt speeds 

ranging from 0.75 meters up to 10 meters (2.5 to 33 feet) per minute to accommodate the 

necessary response time for detection instrumentation (Thermo 2008; Eberline 2004). This yields 

processing times of 15 to 45 metric tons (16 to 50 tons) of material per hour for soil or 

construction demolition-type material conveyorized survey monitoring systems (NRC 2002). 

D.6.3 Spatial Issues 

The M&E that typically are surveyed by conveyorized survey monitoring systems may contain 

difficult-to-measure areas. Most systems employ the detector arrays in a staggered, off-set 

configuration, which allows the sensitive areas of the detectors to overlap with respect to the 

direction of movement. This off-set configuration helps to eliminate blind spots (i.e., locations 

where activity may be present but cannot be detected because the radiation cannot reach the 

detectors). Some systems are designed specifically for materials of relatively small particles of 

uniform size (e.g., soil), while others have been designed to accommodate heterogeneous 

materials like rubble and debris. 

NUREG-1575, Supp. 1 D-12 January 2009 
MARSAME Appendix D

The data logging system accepts the signal pulses from the detector systems and stores the pulse 

data in counting scalers. The recorded values are continuously compared with pre-set alarm 

values corresponding to the selected action level(s). The detectors incorporate integral amplifiers 

which are routed to a PC containing multi-channel scaler hardware. The multi-channel scaler 

hardware allows data to be collected in a series of short, discrete scaler channels known as “time 

bins”. The count time for each time bin is selected as a function of the speed of the conveyor 

belt. The time bin length is frequently set up to be half the length of “dwell time,” which is the 

time the material aliquot to be surveyed spends within the detection field (Miller 2000). 

The approach cited in the paragraph above ensures that activity present within the survey unit 

will be in full view of the detector for one complete time bin. Data collection is optimized by 

performing the measurement when the activity is concentrated (i.e., within an area of elevated 

activity) as well as when the activity is approximately homogenously distributed within a given 

material aliquot. 

D.6.4 Radiation Types 

Conveyorized survey monitoring systems generally are best-suited for the detection of photon 

radiation. Specific systems may be tailored for the detection of beta emissions of moderate 

energy and even alpha radiation by employing gas flow proportional counter detector arrays. 

D.6.5 Range 

Photon radiation can typically be measured with a detectable energy range from 50 keV up to 2 

MeV. Conveyorized survey monitoring systems equipped to measure alpha and beta emissions 

can typically measure from 100 keV up to 6 MeV. 

D.6.6 Scale 

Most conveyorized survey monitoring systems are designed for soils or laundry, both of which 

are compressible media. Applicable sample/material heights range from 2 cm to 30 cm (Fuji 

2008, Canberra 2008). 

D.6.7 Ruggedness 

Conveyorized survey monitoring systems have typical operating ranges from −20 °C to 50 °C. 

Conveyorized survey monitoring systems are often constructed from steel and with plastic 

scintillation detectors and windowless designs, which makes them generally resistant to damage 

from extraneous pieces of debris during scanning. Mechanical shock is not a typical concern for 

conveyorized survey monitoring systems because there is little need for moving these systems. 

For this reason conveyorized survey monitoring systems are seldom transported from one 

location to another. 

January 2009 D-13 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.7 In Situ Gamma Spectroscopy 

In situ gamma spectroscopy (ISGS) systems combine the peak resolution capabilities of 

laboratory methods with instrumentation that is portable and rugged enough to be used in field 

conditions. These solid state systems can perform quantitative, multi-channel analysis of gammaemitting isotopes in both solid and liquid media over areas as large as 100 m2

, enabling 

spectrographic analysis of M&E that assists the user in identifying constituent radionuclides and 

differentiating them from background radiation. ISGS system measurements can also provide 

thorough coverage within broad survey areas, minimizing the risk of failing to detect isolated 

areas of elevated radioactivity that could potentially be missed when collecting discrete samples. 

D.7.1 Instruments 

ISGS systems consist of a semiconductor detector, a cryostat, a multi-channel analyzer (MCA) 

electronics package that provides amplification and analysis of the energy pulse heights, and a 

computer system for data collection and analysis. Semiconductor detection systems typically 

employ a cryostat and a Dewar filled with liquid nitrogen (−196 °C). The cryostat transmits the 

cold temperature of the liquid nitrogen to the detector crystal, creating the extreme cold 

environment necessary for correct operation of the high-resolution semiconductor diode. ISGS 

systems may have electronic coolers as well. 

ISGS systems use detectors referred to as N- and P-type detectors. N-type detectors contain 

small amounts of elements with five electrons in their outer electron shell (e.g., phosphorus, 

arsenic) within the germanium crystal (the inclusion of these elements within the germanium 

crystal is called “doping”). These result in free, unbonded electrons in the crystalline structure, 

providing a small negative current. P-type detectors utilize elements with less than four electrons 

in their outer electron shell (e.g., lithium, boron, gallium) are also used in doping to create 

electron holes, providing a small positive current. Use of these two varieties of doped germanium 

crystals provide different detection properties described below in Section D.7.5. 

D.7.2 Temporal Issues 

Setup for ISGS semiconductor systems may require one full day. The systems often require one 

hour to set up physically, six to eight hours for the semiconductor to reach the appropriate 

temperature operating range after the addition of liquid nitrogen, and quality control 

measurements may require another hour.6

 Count times using ISGS semiconductor systems tend 

to be longer than those associated with simpler detector systems for conducting static 

measurements, though this may be offset by enlarging the field-of-view. A measurement time of 

several minutes is common, depending on the intensity of the targeted gamma energies and the 

presence of attenuating materials. 

Count times can be shortened by reducing the distance between the area being surveyed and the 

detector to improve the gamma incidence efficiency or by using a larger detector. Each option 

will ultimately help the detection system see more gamma radiation in a shorter time. Yet either 

 

6

 It is important not to move the apparatus prematurely, as failure to allow the ISGS system to cool and equilibrate to 

its proper operating temperatures as may cause damage to the semiconductor detector. 

NUREG-1575, Supp. 1 D-14 January 2009 
MARSAME Appendix D

approach creates greater uncertainty associated with the source-to-detector geometry. A slight 

placement error (e.g., a 0.5-cm placement error) will result in significantly higher quantification 

error at a distance of one centimeter than at a distance of 10 centimeters. Additionally, this 

technique for decreasing count times promotes an effect called cascade summing, a phenomena 

affecting detection of gamma radiation from radionuclides that emit multiple gamma photons in 

a single decay event (e.g., 60Co, which yields gamma particles of 1.17 and 1.33 MeV). If both 

incident gammas deposit their energy in a relatively short period of time (i.e., when compared to 

the detector response time and/or the resolving time for the associated electronics), limitations of 

the detection system may prevent these individual photons from being distinguished (Knoll 

1999). 

D.7.3 Spatial Issues 

ISGS semiconductor systems require calibration for their intended use. While ISGS 

semiconductor systems can be calibrated using traditional prepared radioactive sources, some 

ISGS systems have software that enables the user to calculate efficiencies by entering parameters 

such as elemental composition, density, stand-off distance, and physical dimensions. Supplied 

geometry templates assist in generating calibration curves that can be applied to multiple 

collected spectra. The high resolution of these systems coupled with advanced electronic controls 

for system parameters allows them to overcome issues related to source-to-detector geometry 

and produce quantitative concentrations of multiple radionuclides in a variety of media (e.g., 

soil, water, air filters). Because ISGS systems integrate all radioactivity within their field-ofview, lead shielding and collimation may be required to “focus” the field-of-view on a specified 

target for some applications. 

D.7.4 Radiation Types 

ISGS systems can accurately identify and quantify only photon-emitting radionuclides. 

D.7.5 Range 

ISGS systems can identify and quantify low-energy gamma emitters (50 keV with P-type 

detectors, 10 keV with N-type detectors) and high-energy gamma emitters (ISGS systems can be 

configured to detect gamma emissions upwards of 2.0 MeV). Specially designed germanium 

detectors that exhibit very little deterioration in resolution as a function of count rate use N-type 

detectors or planar crystals with a very thin beryllium window for the measurement of photons in 

the energy range 5 to 80 keV. 

D.7.6 Scale 

These systems therefore offer functional quantitative abilities to analyze small objects (e.g., 

samples) for radionuclides. They can also effectively detect radioactivity over areas as large as 

100 m2

 or more (Canberra 2005a). With the use of an appropriate Dewar, the detector may be 

used in a vertical orientation to determine gamma isotope concentrations in the ground surface 

and shallow subsurface. 

January 2009 D-15 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.7.7 Ruggedness 

ISGS semiconductor systems are fragile, because the extremely low temperatures utilized by the 

cryostat render portions of the system brittle and susceptible to damage if not handled with care. 

Some ISGS systems are constructed of more rugged materials and their durability is comparable 

to most hand-held instruments. 

D.8 Hand-Held Radionuclide Identifiers 

Hand-held radionuclide identifiers represent a relatively new addition to the radiation detection 

market, merging the portability of hand-held instruments with some of the analytical capabilities 

of ISGS systems. Hand-held radionuclide identifiers also feature data logging and storage 

capabilities (including user-definable radionuclide libraries) and the ability to transfer data to 

external devices. These devices are most commonly used for nuclear non-proliferation, where 

immediate isotope identification is more critical than low-activity detection sensitivity. Design 

parameters for hand-held radionuclide identifiers required by ANSI N42.34 (ANSI 2003) are 

user-friendly controls and intuitive menu structuring for routine modes of operation, enabling 

users without health physics backgrounds (e.g., emergency response personnel) to complete 

basic exposure rate or radionuclide identification surveys. These units also feature restricted 

“expert” survey modes of operation to collect activity concentration data for more advanced 

applications, including disposition surveys. 

D.8.1 Instruments 

Hand-held radionuclide identifiers consist of two general types: integrated systems and modular 

systems. The integrated systems have the detector and electronics contained in a single package; 

modular systems separate the detector from the electronics. These spectrometers employ small 

scintillators, typically NaI(Tl) or CsI(Tl), or room temperature solid semiconductors, such as 

cadmium zinc telluride (CZT), linked to multi-channel analyzers and internal radionuclide 

libraries to enable gamma-emitting radionuclide identification. 

D.8.2 Temporal Issues 

Hand-held radionuclide identifiers require minimal time to set up.7

 Depending upon the 

conditions in which data is being collected (i.e., climatic, environmental, the presence of sources 

of radiological interference), it may require seconds to several minutes for the unit to stabilize 

the input signals from the field of radiation and properly identify the radionuclides. 

D.8.3 Spatial Issues 

Detectors of hand-held radionuclide identifiers typically are small and portable. Spatial 

limitations are usually based on the physical size of the probe itself, and whether the probe is 

coupled internally within the casing or externally via an extension cord. 

 

7

 The use of multi-point calibrations may add an estimated one to two hours to the time required for instrument set 

up. 

NUREG-1575, Supp. 1 D-16 January 2009 
MARSAME Appendix D

D.8.4 Radiation Types 

Hand-held radionuclide identifiers are most commonly used for the detection of photon 

radiation, although many devices have capabilities for detecting neutron and beta emissions (the 

detection of neutron radiation requires a different probe from the photon radiation probe). 

D.8.5 Range 

Photon radiation can typically be measured within a detectable energy range of 10 to 30 keV up 

to 2.5 to 3 MeV. Neutron radiation can typically be measured within a detectable energy range of 

0.02 eV up to 100 MeV. 

D.8.6 Scale 

There is no definitive limit to the size of an object to be surveyed using hand-held radionuclide 

identifiers. Hand-held radionuclide identifiers may generally be used to survey M&E of any size; 

practical constraints are only imposed by the size of M&E related to the sensitive area of the 

probe. 

D.8.7 Ruggedness 

All varieties of hand-held radionuclide identifiers discussed here typically are calibrated for use 

in temperatures from −20 °C to 50 °C and feature seals or gaskets to prevent water ingress from 

rain, condensing moisture, or high humidity. Most hand-held radionuclide identifiers have a 

limited resistance to shock, though the durability of an instrument depends largely upon the 

detection media (e.g., NaI(Tl) crystals are fragile and vulnerable to mechanical and thermal 

shock). 

D.9 Portal Monitors 

Portal monitors screen access points to controlled areas, and are designed for detecting 

radioactivity above background. These systems are used for interdiction-type surveys, and 

generally do not provide radionuclide identification. Portal monitors are primarily designed to 

monitor activity on vehicles. 

Historically, portal monitors have been used to detect radioactive materials at entrance points to 

scrap metal facilities and solid waste landfills, and radiological control area exit points within 

nuclear facilities to screen for the inadvertent disposal of radionuclides. The proximity of other 

items to be surveyed containing high concentrations of activity may influence the variability of 

the instrument background, because portal monitors survey activity by detecting small variations 

in ambient radiation (NRC 2002). 

D.9.1 Instruments 

Portal monitors can easily be arranged in various geometries that maximize their efficiencies. 

Most national and international standards, for example ANSI 42.35 (ANSI 2004) require both 

January 2009 D-17 NUREG-1575, Supp. 1 
Appendix D MARSAME 

gamma- and neutron-detecting capabilities, but gamma-only versions are available. Portal 

monitors typically use large-area polyvinyl toluene scintillators (a form of plastic scintillators) to 

detect photon radiation and 3

He proportional tubes to detect neutrons.8 Individual detectors may 

be cylindrical or flat. The detectors are usually arranged to form a detection field between two 

detectors, and items to be surveyed pass through the detection field (i.e., between the detectors) 

as shown in Figure D.3. 

The system usually consists of one 

or more detector array(s), an 

occupancy sensor, a control 

a monitoring PC. The control box 

and monitoring PC store and anal

alarm and occupancy data, store and 

analyze all gamma and neutron 

survey data, and may even send data 

through an integrated internet 

connection. The monitoring PC also 

manages software that operates 

multiple arrangements of detector 

arrays as well as third party 

instruments. For example, security 

cameras can take high-resolution 

images of objects that exceed a 

radiation screening level (Novelec 

2001b). 

box, and 

yze 

 

Figure D-3 Example Portal Monitor (Canberra 2005b) 

D.9.2 Temporal Issues 

Count or integration times are very short, typically just a few seconds (NRC 2002). Set-up time 

in the field is variable, because temporary systems may require two hours to one half-day to set 

up, while permanent systems may require one week to install. For vehicular portal monitor 

systems, objects may typically pass through the field of detection at speeds of 8 to 9.5 kilometers 

per hour (Canberra 2005b). Most systems use speed correction algorithms to minimize the 

effects of variations in dwell time (i.e., the time a given area to be surveyed spends within the 

detection field). 

D.9.3 Spatial Issues 

There are a large number of factors that affect portal monitor performance. The isotopic content 

of a radioactive material can determine the ease of detection. For example, high-enriched 

uranium (HEU) is easier to detect in a gamma portal than low-enriched uranium (LEU) or 

natural uranium because of the larger gamma emission rate from 235U. 

8

 Neutron detectors use materials that detect thermal neutrons, which may be fast neutrons that are thermalized for 

detection through the use of moderators. 

NUREG-1575, Supp. 1 D-18 January 2009 
MARSAME Appendix D

The chemical composition of a material is also important; background levels of radioactivity 

must also be considered. Neutron portals are an effective method for detecting plutonium in 

areas with large gamma backgrounds. The surface area and size of the detectors and distance 

between the detectors all affect the geometry and response of the system. In a large area system 

set-up, the closer together the detector arrays are, the better the geometric efficiencies are going 

to be. Finally, for each system there is a maximum passage speed through the portal that gives a 

counting time necessary to meet the required detection sensitivity. 

D.9.4 Radiation Types 

Portal monitors typically detect gamma radiation and can also be equipped to detect neutron 

radiation. Gamma portals often use integrated metal detectors to provide an indication of 

suspicious metal containers that could be used to shield radioactive materials. If the gamma 

radiation is not shielded adequately, the detector’s alarm will sound. Portal monitors can detect 

radioactive material even if it is shielded with a material with a high atomic number, like lead. 

D.9.5 Range 

Photon radiation can typically be measured within a detectable energy range of 60 keV up to 2.6 

MeV. Neutron radiation can typically be measured within a detectable energy range of 0.025 eV 

up to 100 MeV. Required detection sensitivities for gamma and neutron sources are described in 

ANSI 42.35, Table 3 (ANSI 2004). Portal monitors provide gross counts and cannot compute 

quantitative measurements (e.g., activity per unit mass). 

D.9.6 Scale 

Most systems are designed to monitor items ranging in size from bicycles and other small 

vehicles to tractor trailers, railroad cars, and even passenger airplanes (Canberra 2005b). The 

width of the detection field (i.e., space between the detector arrays) can usually be modified. 

D.9.7 Ruggedness 

Portal monitors have typical operating ranges from −20 ° to 55 °C, and some systems may be 

functional in temperatures as low as −40 °C according to ANSI 42.35 (ANSI 2004). Portal 

monitors are usually designed with weatherproofing to withstand prolonged outdoor use and 

exposure to the elements. 

D.10 Sample with Laboratory Analysis 

Laboratory analysis allows for more controlled conditions and more complex, less rugged 

instruments to provide lower detections limits and greater delineation among radionuclides than 

any measurement method that may be utilized in a field setting. For this reason, laboratory 

analyses are often applied as quality assurance measures to validate sample data collected using 

field equipment. 

January 2009 D-19 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.10.1 Instruments 

This section provides a brief overview of instruments used for radiological analyses in a 

laboratory setting. For additional detail on these instruments, please refer to the accompanying 

section references in MARLAP. 

D.10.1.1

 

Instruments for the Detection of Alpha Radiation 

• Alpha Spectroscopy with Multi-Channel Analyzer – This system consists of an alpha 

detector housed in an evacuated counting chamber, a bias supply, amplifier, analog-to-digital 

converter, multi-channel analyzer, and computer. Samples are placed at a fixed distance from 

the solid state partially implanted silica for analysis, and the multi-channel analyzer yields an 

energy spectrum that can be used to both identify and quantify the radionuclides. The overall 

properties of the instrumentation allow for excellent peak resolution, although this technique 

often requires a complex chemical separation to obtain the best results. 

• Gas-Flow Proportional Counter – The system consists of a gas-flow detector, supporting 

electronics, and an optional guard detector for reducing the background count rate. A thin 

window can be placed between the gas-flow detector and sample to protect the detector from 

contamination, or the sample can be placed directly into the detector. This system does not 

typically provide data useful for identifying radionuclides unless it is preceded by nuclidespecific chemical separations. 

• Liquid Scintillation Spectrometry – Typically, samples will be subjected to chemical 

separations and the resulting materials placed in a vial with a scintillation cocktail. When the 

alpha particle energy is absorbed by the cocktail, light pulses are emitted, which are detected 

by photomultiplier tubes. One pulse of light is emitted for each alpha particle absorbed. The 

intensity of light emitted is related to the energy of the alpha. This system can provide data 

useful for identifying radionuclides if the system is coupled to a multi-channel analyzer. 

• Low-Resolution Alpha Spectrometry – The system consists of a small sample chamber, 

mechanical pump, two-inch diameter silicon detector, multi-channel analyzer, readout 

module, and a computer. Unlike alpha spectroscopy with multi-channel analyzer, this method 

allows the technician to load samples for analysis without drying because the presence of 

moisture generally has negligible effects on the results. This method is therefore estimated to 

substantially reduce the time for analysis. However, the low resolution may limit the ability 

to identify individual radionuclides in a sample containing multiple radionuclides and thus 

may limit the applicability of this method (Meyer 1995). 

• Alpha Scintillation Detector – This system is used primarily for the quantification of 226Ra 

by the emanation and detection of 222Rn gas. The system consists of a bubbler system with 

gas transfer apparatus, a vacuum flask lined with scintillating material called a Lucas Cell,9

 a 

photomultiplier tube, bias supply, and a scaler to record the count data. 

9

 One end of a Lucas cell is covered with a transparent window for coupling to a photomultiplier tube and the 

remaining inside walls are coated with zinc sulfide. 

NUREG-1575, Supp. 1 D-20 January 2009 
MARSAME Appendix D

D.10.1.2

D.10.1.3

Instruments for the Detection of Beta Radiation 

• Gas-Flow Proportional Counter – The system consists of a gas-flow detector, supporting 

electronics, and an optional guard detector for reducing the background count rate. A thin 

window can be placed between the gas-flow detector and sample to protect the detector from 

non-fixed activity, or the sample can be placed directly into the detector. This technique does 

not provide data useful for identifying individual radionuclides unless it is preceded by 

nuclide-specific chemical separations. 

• Liquid Scintillation Spectrometry – Typically, samples will be subjected to chemical 

separations and the resulting materials placed in a vial with a scintillation cocktail. When the 

beta particle energy is absorbed by the cocktail, light pulses are emitted, which are detected 

by photomultiplier tubes. One pulse of light is emitted for each beta particle absorbed. The 

intensity of light emitted is related to the energy of the beta. This system can provide data 

useful for identifying radionuclides if the system is coupled to a multi-channel analyzer. This 

system must be allowed to darken (i.e., equilibrate to a dark environment) prior to 

measurement. 

Instruments for the Detection of Gamma or X-Radiation 

• High-Purity Germanium Detector with Multi-Channel Analyzer – This system consists 

of a germanium detector connected to a cryostat (either mechanical or a Dewar of liquid 

nitrogen), high voltage power supply, spectroscopy grade amplifier, analog to digital 

converter, and a multi-channel analyzer. This system has high resolution for peak energies 

and is capable of identifying and quantifying individual gamma peaks in complex spectra. It 

is particularly useful when a sample may contain multiple gamma-emitting radionuclides and 

it is necessary to both identify and quantify all nuclides present. 

• Sodium Iodide Detector with Multi-Channel Analyzer – This system consists of a sodium 

iodide detector, a high voltage power supply, an amplifier, an analog to digital converter, and 

a multi-channel analyzer. This system has relatively poor energy resolution and is not 

effective for identifying and quantifying individual gamma peaks in complex spectra. It is 

most useful when only a small number of gamma-emitting nuclides are present or when a 

gross-gamma measurement is adequate. 

D.10.2 Temporal Issues 

Laboratory analysis is usually controlled by the turnaround time involved in preparing and 

accurately measuring the collected samples. The sample matrix impacts the preparation time, 

because soils and bulk chemicals typically require more extensive preparation than liquids or 

smears. Table D.2 describes the typical preparation and counting times associated with the 

various analytical instruments and methods described in Section D.10.1. Additional issues that 

may result in extended time for sample preparation and analysis are described in MARLAP. 

January 2009 D-21 NUREG-1575, Supp. 1 
Appendix D MARSAME 

Table D.2 Typical Preparation and Counting Times 

Typical Preparation Time Typical Counting Time

Alpha Spectroscopy with MultiChannel Analyzer 1 to 7 days 100 to 1,000 minutes

Gas-Flow Proportional Counter Hours to days 10 to 1,000 minutes

Liquid Scintillation Spectrometer Minutes,10

hours to 2 days11 >60 to 300 minutes

Low-Resolution Alpha 

Spectroscopy Minutes (DOE, 1995) 10 to 1,000 minutes

High-Purity Germanium (HPGe) 

Detector with Multi-Channel 

Analyzer

Minutes to 1 day 10 to 1,000 minutes

Sodium Iodide (NaI) Detector with 

Multi-Channel Analyzer Minutes to 1 day 1 to 1,000 minutes

Alpha Scintillation Detector 1 to 4 days; 

4 to 28 days12 10 to 200 minutes

D.10.3 Spatial Issues 

This section addresses issues related to detector-M&E geometry and provides information on the 

range of impacts resulting from dissenting geometries between the calibration source and the 

measured sample. Other topics may include detector dimensions and problems positioning 

instruments. 

D.10.3.1

D.10.3.2

 

Alpha Spectroscopy with Multi-Channel Analyzer 

Sample geometry (lateral positioning on a detector shelf) in some detectors may be a small 

source of additional uncertainty. Uncertainty in the preparation of the actual calibration standards 

as well as the applicability of the calibration standards to the sample analysis should also be 

considered. 

Gas-Flow Proportional Counter 

Even deposition of sample material on the planchette is critical to the analytical process. In some 

analyses, ringed planchettes may aid in the even deposition of sample material. An uneven 

deposition may result in an incorrect mass-attenuation correction as well as introducing a 

position-dependent bias to the analysis. The latter situation arises from the fact that gas-flow 

proportional counters are not radially symmetric, so rotation of an unevenly deposited sample by 

45° may drastically change the instrument response. 

10 Minimal preparation times are possible if the sample does not require concentration prior to being added to the 

liquid scintillation cocktail vial. 

11 Longer preparation times are necessary for speciation of low-energy beta emitters. 12 Longer count times represent the necessary time for in-growth of 222Rn for 226Ra analyses. 

NUREG-1575, Supp. 1 D-22 January 2009 
MARSAME Appendix D

D.10.3.3

D.10.3.4

D.10.3.5

D.10.3.6

D.10.3.7

 

Liquid Scintillation Spectrometer 

For gross counting, samples (e.g., smears and filters) can be placed directly into a liquid 

scintillation counter (LSC) vial with liquid scintillation cocktail, and counted with no 

preparation. There are samples with more complicated matrices that require chemical separation 

prior to being placed and counted in LSC vials. Calibration sources are also kept and counted in 

these vials, so the geometry of the source and the sample compared to the detector generally are 

similar. 

Low-Resolution Alpha Spectroscopy 

Sample geometry (lateral positioning on a detector shelf) in some detectors may be a small 

source of additional uncertainty. Uncertainty in the preparation of the actual calibration standards 

as well as the applicability of the calibration standards to the sample analysis should be 

considered. 

High-Purity Germanium Detector with Multi-Channel Analyzer 

Geometry considerations are most important for spectroscopic gamma analyses. Sample 

positioning on the detector may significantly affect the analytical results, depending on the size 

and shape of the germanium crystal. Moreover, the instrument is calibrated with a source that 

should be the same physical size, shape, and weight as the samples to be analyzed.13 Discrepancies between the volume or density of the sample and the source introduce additional uncertainty 

to the analytical results. 

Sample homogeneity is a critical factor in gamma spectroscopy analyses, particularly with 

relatively large samples. For example, sediment settling during the course of analysis of a turbid 

aqueous sample will result in a high bias from any activity contained in the solid fraction. 

Likewise, the positioning of areas containing elevated activity in a solid sample will create a bias 

in the overall sample activity (the activity will be disproportionately high if the particle is located 

at the bottom of the sample, and the activity will be disproportionately low if it is located at the 

top of the sample). 

Sodium Iodide Detector with Multi-Channel Analyzer 

The spatial considerations for NaI detectors are the same as those listed above for high-purity 

germanium detectors. 

Alpha Scintillation Detectors 

Accurate sample analysis depends heavily on the complete dissolution of the 226Ra or other 

radionuclides of interest in the bubbler solution. Adequate sample preparation will help ensure 

that spatial issues do not influence results, as the apparatus itself minimizes any other potential 

geometry-related sources of error or uncertainty. 

13 Some software packages allow a single calibration geometry to be modeled to assimilate the properties of other 

geometries. 

January 2009 D-23 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.10.4 Radiation Types 

Table D.3 describes the types of radiation that each laboratory instrument and method can 

measure. 

Table D.3 Radiation Applications for Laboratory Instruments and Methods 

Alpha Beta Photon Neutron

Differentiate 

Radiation 

Types

Identify Specific 

Radionuclides

Alpha Spectrometry with a 

Multi-Channel Analyzer GOOD NA NA NA NA GOOD

Gas-Flow Proportional Counter GOOD GOOD POOR NA FAIR POOR

Liquid Scintillation 

Spectrometer POOR GOOD14 POOR NA FAIR FAIR

Low-Resolution Alpha 

Spectroscopy GOOD NA NA NA NA FAIR15

High-Purity Germanium 

Detector with Multi-Channel 

Analyzer

NA NA GOOD NA NA GOOD

Sodium Iodide Detector with 

Multi-Channel Analyzer NA NA GOOD NA NA FAIR

Alpha Scintillation Detector GOOD NA NA NA NA FAIR

Notes: 

GOOD The instrumentation and measurement technique is well-suited for this application 

FAIR The instrumentation and measurement technique can adequately perform this application 

POOR The instrumentation and measurement technique may be poorly suited for this application 

NA The instrumentation and measurement technique cannot perform this application 

D.10.5 Range 

All of the instrumentation discussed here has physical limitations as to the amount of activity 

that can be analyzed. This limitation arises primarily from the ability of the detector to recover 

after an ionizing event, and the speed with which the component electronics can process the data. 

Typically, a count rate on the order of 106 counts per second taxes the physical limitations of 

most detectors. Other practical considerations, (such as the potential to impact the detector with 

non-fixed activity) often override the physical limitations of the counting system. 

There are energy range limitations as well. For example: window proportional counters are poor 

choices for very low energy beta emitters; some gamma spectrometers have poor efficiencies at 

low energies; and some systems are not calibrated for high-energy gammas. Table D.4 describes 

the energy range that each instrument and method can be used to determine, and the maximum 

activity per sample that the method can be used to count.16

 

14 This system is designed for the detection of low-energy beta particles. 

15 The low resolution may limit the ability to identify individual radionuclides in a sample containing multiple 

radionuclides. 

16 David Burns, Paragon Analytics, Inc., private communication with Nick Berliner, Cabrera Services, Inc., March 

2005. 

NUREG-1575, Supp. 1 D-24 January 2009 
MARSAME Appendix D

Table D.4 Typical Energy Ranges and Maximum Activities 

Energy Range Maximum Activity

Alpha Spectrometry with MultiChannel Analyzer 3 to 8 MeV <10 Bq (<270 pCi)

Gas-Flow Proportional Counter 3 to 8 MeV (α) 

100 to 2,000 keV (β) 35 Bq (946 pCi)

Liquid Scintillation Spectrometer

>3 MeV 

15 to 2,500 keV (β); 

>1.5 MeV (β)

17

100,000 Bq (2.7 μCi)

Low-Resolution Alpha Spectrometry 3 to 8 MeV (α) <10 Bq (<270 pCi)

High-Purity Germanium (HPGe) 

Detector with Multi-Channel Analyzer

50 to >2,000 keV (P-type 

detector); 

5 to 80 keV (N-type detector)

370 Bq (10,000 pCi)

Sodium Iodide (NaI) Detector with 

Multi-Channel Analyzer >80 to 2,000 keV 370 Bq (10,000 pCi)

Alpha Scintillation Detector All α emission energies <10 Bq (<270 pCi)

D.10.6 Scale 

There is no minimum sample size required for a given analysis. Smaller sample sizes will 

necessarily result in elevated detection limits. Minimum sample sizes (e.g., 0.1 gram) may be 

specified in order to ensure that the sample is reasonably representative given the degree of 

homogenization achieved in the laboratory. Typical liquid and solid sample sizes are noted in 

Table D.5. 

Table D.5 Typical Liquid and Solid Sample Sizes 

Typical Liquid 

Sample Size

Typical Solid 

Sample Size

Alpha Spectrometry with MultiChannel Analyzer 1 liter 2 grams; 50 grams18

Gas-Flow Proportional Counter 1 liter 2 grams

Liquid Scintillation Spectrometer <10 milliliters; 1 liter19 <0.5 grams; 500 grams

Low-Resolution Alpha Spectrometry 1 liter 2 grams; 50 grams17

High-Purity Germanium (HPGe) 

Detector with Multi-Channel Analyzer 4 liters 1 kilogram

Sodium Iodide (NaI) Detector with 

Multi-Channel Analyzer 4 liters 1 kilogram

Alpha Scintillation Detector 1 liter 2 grams

 

17 Very high-energy beta emitters may be counted using liquid scintillation equipment without liquid scintillation 

cocktails by the use of the Cerenkov light pulse emitted as high energy charged particles move through water or 

similar substances. 

18 The use of sample digestion processes allows the processing of larger sample masses. 19 Direct depositing of sample material into the scintillation cocktail limits the sample size to the smaller sample 

sizes noted; prepared analyses may use substantially larger sample quantities as noted (this applies to both liquid and 

solid sample matrices). 

January 2009 D-25 NUREG-1575, Supp. 1 
Appendix D MARSAME 

D.10.7 Ruggedness 

Ruggedness does not hold relevance to laboratory analyses, because they are performed in a 

controlled environment that precludes the instrumentation from being exposed to conditions 

requiring durability. 

NUREG-1575, Supp. 1 D-26 January 2009 
MARSAME Appendix E 

E. DISPOSITION CRITERIA 

E.1 Department of Energy 

Disposition criteria specified by DOE regulations and orders are found in the Code of Federal 

Regulations, Title 10 (especially 10 CFR 835, Occupational Radiation Protection) and in 

applicable DOE Orders (especially DOE Order 5400.5, Radiation Protection of the Public and 

the Environment). The DOE regulations and orders govern the conduct of DOE employees and 

contractors in the operation of DOE facilities and in the disposition of real property (e.g., 

buildings and land) and non-real property (“personal property” such as materials, equipment, 

materials in containers, clothing, etc.). The DOE Order requirements are applicable to DOE 

activities only and are enforceable as contractual provisions in most DOE contracts. DOE rules 

are enforceable under 10 CFR Part 820. The following list of DOE requirements is not 

exhaustive. In addition, a listing of some non-mandatory guidance documents is also provided. 

E.1.1 10 CFR 835 (Non-Exhaustive Excerpts) 

E.1.1.1 § 835.405 Receipt of Packages Containing Radioactive Material 

(a) If packages containing quantities of radioactive material in excess of a Type A quantity (as 

defined at 10 CFR 71.4) are expected to be received from radioactive material transportation, 

arrangements shall be made to either: 

(1) Take possession of the package when the carrier offers it for delivery; or 

(2) Receive notification as soon as practicable after arrival of the package at the carrier’s 

terminal and to take possession of the package expeditiously after receiving such 

notification. 

(b) Upon receipt from radioactive material transportation, external surfaces of packages known 

to contain radioactive material shall be monitored if the package: 

(1) Is labeled with a Radioactive White I, Yellow II, or Yellow III label (as specified at 

49 CFR 172.403 and 172.436–440); or 

(2) Has been transported as low specific activity material (as defined at 10 CFR 71.4) on 

an exclusive use vehicle (as defined at 10 CFR 71.4); or 

(3) Has evidence of degradation, such as packages that are crushed, wet, or damaged. 

(c) The monitoring required by paragraph (b) of this section shall include: 

(1) Measurements of removable contamination levels, unless the package contains only 

special form (as defined at 10 CFR 71.4) or gaseous radioactive material; and 

(2) Measurements of the radiation levels, unless the package contains less than a Type A 

quantity (as defined at 10 CFR 71.4) of radioactive material. 

(d) The monitoring required by paragraph (b) of this section shall be completed as soon as 

practicable following receipt of the package, but not later than 8 hours after the beginning of the 

working day following receipt of the package. 

January 2009 E-1 NUREG-1575, Supp. 1 
Appendix E MARSAME 

E.1.1.2 § 835.605 Labeling Items and Containers 

Except as provided at § 835.606, each item or container of radioactive material shall bear a 

durable, clearly visible label bearing the standard radiation warning trefoil and the words 

“Caution, Radioactive Material” or “Danger, Radioactive Material.” The label shall also provide 

sufficient information to permit individuals handling, using, or working in the vicinity of the 

items or containers to take precautions to avoid or control exposures. 

E.1.1.3 § 835.606 Exceptions to Labeling Requirements 

(a) Items and containers may be excepted from the radioactive material labeling requirements of 

§ 835.605 when: 

(1) Used, handled, or stored in areas posted and controlled in accordance with this 

subpart and sufficient information is provided to permit individuals to take 

precautions to avoid or control exposures; or 

(2) The quantity of radioactive material is less than one tenth of the values 

specified in appendix E of this part; or 

(3) Packaged, labeled, and marked in accordance with the regulations of the 

Department of Transportation or DOE Orders governing radioactive material 

transportation; or 

(4) Inaccessible, or accessible only to individuals authorized to handle or use 

them, or to work in the vicinity; or 

(5) Installed in manufacturing, process, or other equipment, such as reactor 

components, piping, and tanks; or 

(6) The radioactive material consists solely of nuclear weapons or their 

components. 

(b) Radioactive material labels applied to sealed radioactive sources may be excepted from the 

color specifications of § 835.601(a). 

E.1.1.4 § 835.1101 Control of Material and Equipment 

(a) Except as provided in paragraphs (b) and (c) of this section, material and equipment in 

contamination areas, high contamination areas, and airborne radioactivity areas shall not be 

released to a controlled area if: 

(1) Removable surface contamination levels on accessible surfaces exceed the 

removable surface contamination values specified in appendix D of this part; 

or 

(2) Prior use suggests that the removable surface contamination levels on 

inaccessible surfaces are likely to exceed the removable surface contamination 

values specified in Appendix D of this part. 

(b) Material and equipment exceeding the removable surface contamination values specified in 

Appendix D of this part may be conditionally released for movement on-site from one 

radiological area for immediate placement in another radiological area only if appropriate 

monitoring is performed and appropriate controls for the movement are established and 

exercised. 

NUREG-1575, Supp. 1 E-2 January 2009 
MARSAME Appendix E 

(c) Material and equipment with fixed contamination levels that exceed the total contamination 

values specified in Appendix D of this part may be released for use in controlled areas outside of 

radiological areas only under the following conditions: 

(1) Removable surface contamination levels are below the removable surface 

contamination values specified in Appendix D of this part; and (2) The material or 

equipment is routinely monitored and clearly marked or labeled to alert personnel of 

the contaminated status. 

E.1.1.5 § 835.1102 Control of Areas 

(a) Appropriate controls shall be maintained and verified which prevent the inadvertent transfer 

of removable contamination to locations outside of radiological areas under normal operating 

conditions. 

(b) Any area in which contamination levels exceed the values specified in appendix D of this part 

shall be controlled in a manner commensurate with the physical and chemical characteristics 

of the contaminant, the radionuclides present, and the fixed and removable surface 

contamination levels. 

(c) Areas accessible to individuals where the measured total surface contamination levels exceed, 

but the removable surface contamination levels are less than, corresponding surface 

contamination values specified in Appendix D of this part, shall be controlled as follows 

when located outside of radiological areas: 

(1) The area shall be routinely monitored to ensure the removable surface contamination 

level remains below the removable surface contamination values specified in 

Appendix D of this part; and 

(2) The area shall be conspicuously marked to warn individuals of the contaminated 

status. 

(d) Individuals exiting contamination, high contamination, or airborne radioactivity areas shall 

be monitored, as appropriate, for the presence of surface contamination. 

(e) Protective clothing shall be required for entry to areas in which removable contamination 

exists at levels exceeding the removable surface contamination values specified in Appendix 

D of this part. 

E.1.2 Appendix D to 10 CFR 835 – Surface Contamination Values 

The data presented in Appendix D are to be used in identifying the need for posting of 

contamination and high contamination areas in accordance with § 835.603(e) and (f) and 

identifying the need for surface contamination monitoring and control in accordance with §§ 

835.1101 and 835.1102. 

January 2009 E-3 NUREG-1575, Supp. 1 
Appendix E MARSAME 

Table E.1 Surface Contamination Values1 in dpm/100 cm2 as Reported in Appendix D to 10 CFR 835 

Radionuclide Removable2,4

Total (Fixed+ 

Removable)2,3

U-nat, U-235, U-238, and associated decay products 1,0007 5,0007

Transuranics, Ra-226, Ra-228, Th-230, Th-228, Pa-231, Ac-227, I125, I-129 20 500 

Th-nat, Th-232, Sr-90, Ra-223, Ra-224, U-232, I-126, I-131, I-133 200 1,000 

Beta-gamma emitters (nuclides with decay modes other than alpha 

emission or spontaneous fission) except Sr-90 and others noted 

above5

1,000 5,000 

Tritium and tritiated compounds6 10,000 N/A 1 The values in this appendix, with the exception noted in footnote 5, apply to radioactive contamination deposited on, but not 

incorporated into the interior or matrix of, the contaminated item. Where surface contamination by both alpha-and beta-gammaemitting nuclides exists, the limits established for alpha-and beta-gamma-emitting nuclides apply independently. 2 As used in this table, dpm (disintegrations per minute) means the rate of emission by radioactive material as determined by 

correcting the counts per minute observed by an appropriate detector for background, efficiency, and geometric factors associated 

with the instrumentation. 

3 The levels may be averaged over one square meter provided the maximum surface activity in any area of 100 cm2

 is less than 

three times the value specified. For purposes of averaging, any square meter of surface shall be considered to be above the 

surface contamination value if: (1) From measurements of a representative number of sections it is determined that the average 

contamination level exceeds the applicable value; or (2) it is determined that the sum of the activity of all isolated spots or

particles in any 100 cm2 area exceeds three times the applicable value. 4 The amount of removable radioactive material per 100 cm2

 of surface area should be determined by swiping the area with dry 

filter or soft absorbent paper, applying moderate pressure, and then assessing the amount of radioactive material on the swipe 

with an appropriate instrument of known efficiency. (Note: The use of dry material may not be appropriate for tritium.) When 

removable contamination on objects of surface area less than 100 cm2

 is determined, the activity per unit area shall be based on 

the actual area and the entire surface shall be wiped. It is not necessary to use swiping techniques to measure removable 

contamination levels if direct scan surveys indicate that the total residual surface contamination levels are within the limits for 

removable contamination. 

5 This category of radionuclides includes mixed fission products, including the Sr-90 which is present in them. It does not apply

to Sr-90 which has been separated from the other fission products or mixtures where the Sr-90 has been enriched. 6 Tritium contamination may diffuse into the volume or matrix of materials. Evaluation of surface contamination shall consider 

the extent to which such contamination may migrate to the surface in order to ensure the surface contamination value provided in 

this appendix is not exceeded. Once this contamination migrates to the surface, it may be removable, not fixed; therefore, a 

“Total” value does not apply. 

7 (alpha) 

DOE Order 5400.5 (Non-exhaustive Excerpts) from Chapter II

5. Release of Property Having Residual Radioactive Material 

(a) Release of Real Property. Release of real property (land and structures) shall be in accordance 

with the guidelines and requirements for residual radioactive material presented in Chapter 

IV. These guidelines and requirements apply to both DOE-owned facilities and to private 

properties that are being prepared by DOE for release. Real properties owned by DOE that 

are being sold to the public are subject to the requirements of Section 120(h) of the 

Comprehensive Environmental Response Compensation and Liability Act (CERCLA), as 

amended, concerning hazardous substances, and to any other applicable Federal, State, and 

local requirements. The requirements of 40 CFR Part 192 are applicable to properties 

remediated by DOE under Title I of the Uranium Mill Tailings Radiation Control Act 

(UMTRCA). 

NUREG-1575, Supp. 1 E-4 January 2009 
MARSAME Appendix E 

(b) Release of Personal Property. Personal property, which potentially could be contaminated, 

may be released for unrestricted use if the results of a survey with appropriate instruments 

indicate that the property is less than the contamination limits presented in Figure IV-1. 

(c) Release of Materials and Equipment. 

(1) Surface Contamination Levels. Prior to being released, property shall be surveyed to 

determine whether both removable and total surface contamination (Including 

contamination present on and under any coating) are in compliance with the levels 

given in Figure IV-1 and that the contamination has been subjected to the ALARA 

process. 

(2) Potential for Contamination. Property shall be considered to be potentially 

contaminated if it has been used or stored in radiation areas that could contain 

unconfined radioactive material or that are exposed to beams of particles capable of 

causing activation (neutrons, protons, etc.). 

(3) Surveys. Surfaces of potentially contaminated property shall be surveyed using 

instruments and techniques appropriate for detecting the limits stated in Figure IV-1. 

(4) Inaccessible Areas. Where potentially contaminated surfaces are not accessible for 

measurement (as in some pipes, drains, and ductwork), such property may be released 

after case-by-case evaluation and documentation based on both the history of its use 

and available measurements demonstrate that the unsurveyable surfaces are likely to 

be within the limits given in Figure IV-1. 

(5) Records. The records of released property shall include: 

(a) A description or identification of the property; 

(b) The date of the last radiation survey; 

(c) The identity of the organization and the individual who performed the monitoring 

operation; 

(d) The type and identification number of monitoring instruments; 

(e) The results of the monitoring operation; and 

(f) The identity of the recipient of the released material. 

(6) Volume Contamination. No guidance is currently available for release of material that 

has been contaminated in depth, such as activated material or smelted contaminated 

metals (e.g., radioactivity per unit volume or per unit mass). Such materials may be 

released if criteria and survey techniques are approved by EH-1. 

E.1.3 DOE Guidance and Similar Documents 

The following discussion summarizes DOE policy, practice, and guidance for the disposition of 

personal property, including materials and equipment from several DOE guidance documents. 

“Application of DOE 5400.5 requirements for release and control of property containing residual 

radioactive material,” a guidance memorandum dated November 17, 1995. This guidance 

memorandum explains the procedures through which authorized limits can be approved for the 

disposition of waste materials to sanitary waste landfills. It also discusses the disposition criteria 

for certain radionuclides. Finally, it delegates some responsibilities for the approval of release of 

volumetrically contaminated materials to DOE field office managers when specified conditions 

are met. 

January 2009 E-5 NUREG-1575, Supp. 1 
Appendix E MARSAME 

Table E.2 Figure IV-1, from DOE Order 5400.5, as Supplemented in November, 1995 

Memorandum: Surface Activity Guidelines – Allowable Total Residual Surface Activity 

(dpm/100 cm2

)

1

Radionuclides2 Average3,4 Maximum4,5 Removable4,6

Group 1 - Transuranics, I-125, I-129, Ac-227, Ra -

226, Ra-228, Th-228, Th-230, Pa-231

100 300 20 

Group 2 - Th-natural, Sr-90, I-126, I-131, I-133, 

Ra-223, Ra-224, U-232, Th-232

1,000 3,000 200 

Group 3 - U-natural, U-235, U-238, and associated 

decay products, alpha emitters

5,000 15,000 1,000 

Group 4 - Beta-gamma emitters (radionuclides with 

decay modes other than alpha emission or 

spontaneous fission) except Sr-90 and others noted 

above7

5,000 15,000 1,000 

Tritium (applicable to surface and subsurface)8 N/A N/A 10,000 1 As used in this table, dpm (disintegrations per minute) means the rate of emission by radioactive material as determined by 

correcting the counts per minute measured by an appropriate detector for background, efficiency, and geometric factors 

associated with the instrumentation. 

2 Where surface contamination by both alpha- and beta-gamma-emitting radionuclides exists, the limits established for alpha- and 

beta-gamma-emitting radionuclides should apply independently. 

3 Measurements of average contamination should not be averaged over an area of more than 1 m2

. For objects of less surface area, 

the average should be derived for each such object. 4 The average and maximum dose rates associated with surface contamination resulting from beta-gamma emitters should not 

exceed 0.2 mrad/h and 1.0 mrad/h, respectively, at 1 cm. 

5 The maximum contamination level applies to an area of not more than 100 cm2

. 6 The amount of removable material per 100 cm2

 of surface area should be determined by wiping an area of that size with dry 

filter or soft absorbent paper, applying moderate pressure, and measuring the amount of radioactive material on the wiping with

an appropriate instrument of known efficiency. When removable contamination on objects of surface area less than 100 cm2 is 

determined, the activity per unit area should be based on the actual area and the entire surface should be wiped. It is not necessary 

to use wiping techniques to measure removable contamination levels if direct scan surveys indicate that the total residual surface 

contamination levels are within the limits for removable contamination. 

7 This category of radionuclides includes mixed fission products, including the Sr-90 which is present in them. It does not apply

to Sr-90 which has been separated from the other fission products or mixtures where the Sr-90 has been enriched. 8 Property recently exposed or decontaminated, [sic] should have measurements (smears) at regular time intervals to ensure that 

there is not a build-up of contamination over time. Because tritium typically penetrates material it contacts, the surface guidelines 

in group 4 are not applicable to tritium. The Department has reviewed the analysis conducted by the DOE Tritium Surface 

Contamination Limits Committee ("Recommended Tritium Surface Contamination Release Guides," February 1991), and has 

assessed potential doses associated with the release of property containing residual tritium. The Department recommends the use

of the stated guideline as an interim value for removable tritium. Measurements demonstrating compliance of the removable 

fraction of tritium on surfaces with this guideline are acceptable to ensure that non-removable fractions and residual tritium in 

mass will not cause exposures that exceed DOE dose limits and constraints. 

“Control and Release of Property with Residual Radioactive Material for use with DOE Order 

5400.5, Radiation Protection of the Public and the Environment,” DOE G 441.1-XX, a draft 

guidance document approved for interim use and issued on May 1, 2002. This guidance 

document contains detailed discussions of the disposition approaches for real and personal 

property, as well as summaries of DOE’s policies regarding the disposition or release of 

property. 

“Cross-Cut Guidance on Environmental Requirements for DOE Real Property Transfers 

(Update),” DOE/EH-413/97-12, originally issued October, 1997, revised March, 2005. This 

NUREG-1575, Supp. 1 E-6 January 2009 
MARSAME Appendix E 

guidance document contains a summary of various environmental requirements for the release or 

transfer of real property. 

“Managing the Release of Surplus and Scrap Materials,” January 19, 2001, from DOE Secretary 

Richardson to all DOE elements. This memorandum provides direction as well as guidance 

regarding the release of property from DOE radiological control. It also restricts the release of 

metal from radiological areas for recycle until certain steps are taken by DOE. 

E.2 International Organizations 

In general, each country establishes its own disposition criteria for materials and equipment. 

These national disposition criteria may be consistent with guidance promulgated by multinational organizations, such as the International Atomic Energy Agency (IAEA) or the European 

Commission (EC). One example of widely accepted regulations is the “Advisory Material for the 

IAEA Regulations for the Safe Transport of Radioactive Material SAFETY GUIDE No. TS-G1.1 (ST-2).” The references listed below provide the detailed information on guidance from the 

IAEA and the EC. URLs are provided for internet access of this information. Disposition criteria 

from specific nations should be obtained from those nations. 

E.2.1 International Atomic Energy Agency (IAEA) 

Advisory Material for the IAEA Regulations for the Safe Transport of Radioactive Material 

SAFETY GUIDE No. TS-G-1.1 (ST-2): 

http://www-pub.iaea.org/MTCD/publications/PDF/Pub1109_scr.pdf. 

Planning and Preparing for Emergency Response to Transport Accidents Involving Radioactive 

Material, SAFETY GUIDE No. TS-G-1.2 (ST-3)

http://www-pub.iaea.org/MTCD/publications/PDF/Pub1119_scr.pdf. 

Application of the Concepts of Exclusion, Exemption and Clearance SAFETY GUIDE No. RSG-1.7: http://www-pub.iaea.org/MTCD/publications/PDF/Pub1202_web.pdf. 

E.2.2 European Commission 

The publication list for radiation protection may be found on the EC website at: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication_en.htm. Contact 

information for most of the authorities in the European Union may be found in Annex 3, in the 

last pages of publication 139, “A review of consumer products containing radioactive substances 

in the European Union,” which can be found at: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication/doc/139_en.pdf. 

Radiation protection publications pertaining to disposition criteria for materials and equipment 

include: 

134: Evaluation of the application of the concepts of exemption and clearance for 

practices according to title III of Council Directive 96/29/Euratom of 13 May 1996 in EU 

Member States, Volume 1, Main Report: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication/doc/134_en.pdf. 

January 2009 E-7 NUREG-1575, Supp. 1 
Appendix E MARSAME 

122: Practical Use of the Concepts of Clearance and Exemption Part I: Guidance on 

General Clearance Levels for Practices: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication/doc/122_part1_en.

pdf. 

122: Practical Use of the Concepts of Clearance and Exemption Part II: Application of 

the Concepts of Exemption and Clearance to Natural Radiation Sources: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication/doc/122_part2_en.

pdf. 

114: Definition of Clearance Levels for the Release of Radioactively Contaminated 

Buildings and Building Rubble: 

http://europa.eu.int/comm/energy/nuclear/radioprotection/publication/doc/114_en.pdf. 

European legislation related to the transport of radioactive materials (database): 

http://europa.eu.int/comm/energy/nuclear/transport/legislation_en.htm. 

E.3 Nuclear Regulatory Commission 

Disposition criteria specified by NRC regulations are found in the Code of Federal Regulations, 

Title 10 (10 CFR). NRC regulations in 10 CFR are structured in Parts, which apply to respective 

areas of applicability. For example, 10 CFR Part 20 addresses “Standards for Protection against 

Radiation.” The regulatory citations below indicate the specific Part by the number to the left of 

the decimal point, for example, §20.2003 is in 10 CFR Part 20, and 2003 indicates a specific 

portion. In this appendix only the radiological component of those criteria pertaining to 

quantitative measurement attributes are listed; there are almost always additional regulatory 

requirements. “Disposition criteria” refers to the quantitative radiological portion of the complete 

criteria. In some circumstances, disposition criteria are not addressed in the regulations, and 

these cases are handled by existing policy and practices. A list of NRC disposition criteria, which 

is not necessarily exhaustive, follows. 

E.3.1 § 20.2003 Disposal by Release into Sanitary Sewerage. 

(2) The quantity of licensed or other radioactive material that the licensee releases into the sewer 

in 1 month divided by the average monthly volume of water released into the sewer by the 

licensee does not exceed the concentration listed in table 3 of appendix B to part 20; and 

(4) The total quantity of licensed and other radioactive material that the licensee releases into the 

sanitary sewerage system in a year does not exceed 5 curies (185 GBq) of hydrogen-3, 1 

curie (37 GBq) of carbon-14, and 1 curie (37 GBq) of all other radioactive materials 

combined. 

E.3.2 § 20.2005 Disposal of Specific Wastes 

(a) A licensee may dispose of the following licensed material as if it were not radioactive 

(1) 0.05 microcurie (1.85 kBq), or less, of hydrogen-3 or carbon-14 per gram of medium 

used for liquid scintillation counting; and 

(2) 0.05 microcurie (1.85 kBq), or less, of hydrogen-3 or carbon-14 per gram of animal 

tissue, averaged over the weight of the entire animal. 

NUREG-1575, Supp. 1 E-8 January 2009 
MARSAME Appendix E 

E.3.3 § 35.92 Decay-in-Storage 

(a) A licensee may hold byproduct material with a physical half-life of less than 120 days for 

decay-in-storage before disposal without regard to its radioactivity if it– 

(1) Monitors byproduct material at the surface before disposal and determines that its 

radioactivity cannot be distinguished from the background radiation level with an 

appropriate radiation detection survey meter set on its most sensitive scale and with 

no interposed shielding. 

E.3.4 § 35.315 Safety Precautions 

(4) Either monitor material and items removed from the patient's or the human research subject's 

room to determine that their radioactivity cannot be distinguished from the natural 

background radiation level with a radiation detection survey instrument set on its most 

sensitive scale and with no interposed shielding, or handle the material and items as 

radioactive waste. 

E.3.5 § 36.57 Radiation Surveys 

(e) Before releasing resins for unrestricted use, they must be monitored before release in an area 

with a background level less than 0.5 microsievert (0.05 millirem) per hour. The resins may 

be released only if the survey does not detect radiation levels above background radiation 

levels. The survey meter used must be capable of detecting radiation levels of 0.5 

microsievert (0.05 millirem) per hour. 

E.3.6 Appendix A to Part 40−Criteria Relating to the Operation of Uranium Mills and the 

Disposition of Tailings or Wastes Produced by the Extraction or Concentration of 

Source Material from Ores Processed Primarily for Their Source Material Content 

(6) The design requirements in this criterion for longevity and control of radon releases apply to 

any portion of a licensed and/or disposal site unless such portion contains a concentration of 

radium in land, averaged over areas of 100 square meters, which, as a result of byproduct 

material, does not exceed the background level by more than: (i) 5 picocuries per gram 

(pCi/g) of radium-226, or, in the case of thorium byproduct material, radium-228, averaged 

over the first 15 centimeters (cm) below the surface, and (ii) 15 pCi/g of radium-226, or, in 

the case of thorium byproduct material, radium-228, averaged over 15-cm thick layers more 

than 15 cm below the surface. 

E.3.7 § 71.4 Definitions 

The following terms are as defined here for the purpose of this part. To ensure compatibility with 

international transportation standards, all limits in this part are given in terms of dual units: The 

International System of Units (SI) followed or preceded by U.S. standard or customary units. 

The U.S. customary units are not exact equivalents but are rounded to a convenient value, 

providing a functionally equivalent unit. For the purpose of this part, either unit may be used. 

January 2009 E-9 NUREG-1575, Supp. 1 
Appendix E MARSAME 

A1 means the maximum activity of special form radioactive material permitted in a Type A 

package. This value is either listed in Appendix A, Table A-1, of this part, or may be derived in 

accordance with the procedures prescribed in Appendix A of this part. 

A2 means the maximum activity of radioactive material, other than special form material, LSA, 

and SCO material, permitted in a Type A package. This value is either listed in Appendix A, 

Table A-1, of this part, or may be derived in accordance with the procedures prescribed in 

Appendix A of this part. 

Low Specific Activity (LSA) material means radioactive material with limited specific activity 

which is nonfissile or is excepted under §71.15, and which satisfies the descriptions and limits 

set forth below. Shielding materials surrounding the LSA material may not be considered in 

determining the estimated average specific activity of the package contents. LSA material must 

be in one of three groups: 

(1) LSA−I 

(i) Uranium and thorium ores, concentrates of uranium and thorium ores, and other ores 

containing naturally occurring radioactive radionuclides which are not intended to be 

processed for the use of these radionuclides; 

(ii) Solid unirradiated natural uranium or depleted uranium or natural thorium or their 

solid or liquid compounds or mixtures; 

(iii) Radioactive material for which the A2 value is unlimited; or 

(iv) Other radioactive material in which the activity is distributed throughout and the 

estimated average specific activity does not exceed 30 times the value for exempt 

material activity concentration determined in accordance with Appendix A. 

(2) LSA−II 

(i) Water with tritium concentration up to 0.8 TBq/L (20.0 Ci/L); or 

(ii) Other material in which the activity is distributed throughout and the average specific 

activity does not exceed 10−4

A2/g for solids and gases, and 10−5A2/g for liquids. 

(3) LSA−III. Solids (e.g., consolidated wastes, activated materials), excluding powders, that 

satisfy the requirements of § 71.77, in which: 

(i) The radioactive material is distributed throughout a solid or a collection of solid 

objects, or is essentially uniformly distributed in a solid compact binding agent (such 

as concrete, bitumen, ceramic, etc.); 

(ii) The radioactive material is relatively insoluble, or it is intrinsically contained in a 

relatively insoluble material, so that even under loss of packaging, the loss of 

radioactive material per package by leaching, when placed in water for 7 days, would 

not exceed 0.1 A2; and 

(iii) The estimated average specific activity of the solid does not exceed 2×10−3A2/g. 

Low toxicity alpha emitters means natural uranium, depleted uranium, natural thorium; uranium235, uranium-238, thorium-232, thorium-228 or thorium-230 when contained in ores or physical 

or chemical concentrates or tailings; or alpha emitters with a half-life of less than 10 days. 

NUREG-1575, Supp. 1 E-10 January 2009 
MARSAME Appendix E 

Surface Contaminated Object (SCO) means a solid object that is not itself classed as radioactive 

material, but which has radioactive material distributed on any of its surfaces. SCO must be in 

one of two groups with surface activity not exceeding the following limit: 

(1) SCO-I: A solid object on which: 

(i) The non-fixed contamination on the accessible surface averaged over 300 cm2 (or the 

area of the surface if less than 300 cm2

) does not exceed 4 Bq/cm2 (104

microcurie/cm2) for beta and gamma and low toxicity alpha emitters, or 0.4 Bq/cm2 

(10−5 microcurie/cm2) for all other alpha emitters; 

(ii) The fixed contamination on the accessible surface averaged over 300 cm2 (or the area 

of the surface if less than 300 cm2) does not exceed 4×104 Bq/cm2 (1.0 

microcurie/cm2

) for beta and gamma and low toxicity alpha emitters, or 4×103

Bq/cm2 (0.1 microcurie/cm2) for all other alpha emitters; and 

(iii) The non-fixed contamination plus the fixed contamination on the inaccessible 

surface averaged over 300 cm2 (or the area of the surface if less than 300 cm2) does 

not exceed 4×104

 Bq/cm2 (1 microcurie/cm2) for beta and gamma and low toxicity 

alpha emitters, or 4×103 Bq/cm2 (0.1 microcurie/cm2) for all other alpha emitters. 

(2) SCO-II: A solid object on which the limits for SCO-I are exceeded and on which: 

(i) The nonfixed contamination on the accessible surface averaged over 300 cm2 (or the 

area of the surface if less than 300 cm2) does not exceed 400 Bq/cm2 (102

microcurie/cm2

) for beta and gamma and low toxicity alpha emitters or 40 Bq/cm2

(103 microcurie/cm2) for all other alpha emitters; 

(ii) The fixed contamination on the accessible surface averaged over 300 cm2 (or the area 

of the surface if less than 300 cm2) does not exceed 8×105 Bq/cm2 (20 

microcuries/cm2) for beta and gamma and low toxicity alpha emitters, or 8×104

Bq/cm2

 (2 microcuries/cm2) for all other alpha emitters; and 

(iii) The non-fixed contamination plus the fixed contamination on the inaccessible 

surface averaged over 300 cm2

 (or the area of the surface if less than 300 cm2) does 

not exceed 8×105 Bq/cm2 (20 microcuries/cm2) for beta and gamma and low toxicity 

alpha emitters, or 8×104

 Bq/cm2 (2 microcuries/cm2) for all other alpha emitters. 

E.3.8 § 71.14 Exemption for Low-Level Materials 

(a) A licensee is exempt from all the requirements of this part with respect to shipment or 

carriage of the following low-level materials: 

(1) Natural material and ores containing naturally occurring radionuclides that are not 

intended to be processed for use of these radionuclides, provided the activity 

concentration of the material does not exceed 10 times the values specified in 

Appendix A, Table A-2, of this part. 

(2) Materials for which the activity concentration is not greater than the activity 

concentration values specified in Appendix A, Table A-2 of this part, or for which the 

consignment activity is not greater than the limit for an exempt consignment found in 

Appendix A, Table A-2, of this part. 

(b) A licensee is exempt from all the requirements of this part, other than §§ 71.5 and 71.88, with 

respect to shipment or carriage of the following packages, provided the packages do not 

January 2009 E-11 NUREG-1575, Supp. 1 
Appendix E MARSAME 

contain any fissile material, or the material is exempt from classification as fissile material 

under § 71.15: 

(1) A package that contains no more than a Type A quantity of radioactive material; 

(2) A package transported within the United States that contains no more than 0.74 TBq 

(20 Ci) of special form plutonium-244; or 

(3) The package contains only LSA or SCO radioactive material, provided-- 

(i) That the LSA or SCO material has an external radiation dose of less than or equal 

to 10 mSv/h (1 rem/h), at a distance of 3 m from the unshielded material; or 

(ii) That the package contains only LSA-I or SCO-I material. 

E.3.9 § 110.22 General License for the Export of Source Material 

(3) Th-227, Th-228, U-230, and U-232 when contained in a device, or a source for use in a 

device, in quantities of less than 100 millicuries of alpha activity (3.12 micrograms Th-227, 

122 micrograms Th-228, 3.7 micrograms U-230, 4.7 milligrams U-232) per device or source. 

E.3.10 § 110.23 General License for the Export of Byproduct Material 

(2) Actinium-225 and -227, americium-241 and -242m, californium-248, -249, -250, -251, -252, 

-253, and -254, curium-240, -241, -242, -243, -244, -245, -246 and -247, einsteinium-252, -

253, -254 and -255, fermium-257, gadolinium-148, mendelevium-258, neptunium-235 and -

237, polonium-210, and radium-223 must be contained in a device, or a source for use in a 

device, in quantities of less than 100 millicurie of alpha activity (see Sec. 110.2 for specific 

activity) per device or source, unless the export is to a country listed in Sec. 110.30. Exports 

of americium and neptunium are subject to the reporting requirements listed in paragraph (b) 

of this section. 

(3) For americium-241, exports must not exceed one curie (308 milligrams) per shipment or 100 

curies (30.8 grams) per year to any country listed in Sec. 110.29, and must be contained in 

industrial process control equipment or petroleum exploration equipment in quantities not to 

exceed 20 curies (6.16 grams) per device or 200 curies (61.6 grams) per year to any one 

country. 

(5) For polonium-210, the material must be contained in static eliminators and may not exceed 

100 curies (22 grams) per individual shipment. 

(6) For tritium in any dispersed form, except for recovery or recycle purposes (e.g., luminescent 

light sources and paint, accelerator targets, calibration standards, labeled compounds), 

exports must not exceed the quantity of 10 curies (1.03 milligrams) or less per item, not to 

exceed 1,000 curies (103 milligrams) per shipment or 10,000 curies (1.03 grams) per year to 

any one country. Exports of tritium to the countries listed in Sec. 110.30 must not exceed the 

quantity of 40 curies (4.12 milligrams) or less per item, not to exceed 1,000 curies (103 

milligrams) per shipment or 10,000 curies (1.03 grams) per year to any one country, and 

exports of tritium in luminescent safety devices installed in aircraft must not exceed a 

quantity of 40 curies (4.12 milligrams) or less per light source. 

NUREG-1575, Supp. 1 E-12 January 2009 
MARSAME Appendix E 

E.3.11 Policies and Practices 

Disposition criteria for the release of materials and equipment that are not specified in NRC 

regulations are determined by the current policies and practices. NRC’s current approaches for 

making decisions on disposition of solid materials is different for materials licensees, i.e., 

industrial, research, and medical facilities, and for reactors, which include power, test, and 

research reactors. These are summarized in Table E-3, and discussed in more detail below. 

For non-reactor licensees—materials licensees—licensee requests for release of solid material 

will continue to be evaluated using the nuclide concentration tables in Regulatory Guide 1.86 

and its equivalent, Fuel Cycle Policy and Guidance Directive FC 83-23. Many materials 

licensees obtain approval, as a license condition, to routinely use these guidelines. For residual 

radioactivity within the volume of solid materials (for example, within a concrete or soil matrix), 

non-reactor licensee requests for release of solid material may continue to be approved under a 

disposal request (10 CFR 20.2002); a license termination plan; decommissioning plan review; or 

other specific license amendment. In verifying that the dose from such release is maintained 

ALARA and below the limits of our regulations in 10 Part 20, approval of a release is possible. 

The disposition of materials with volumetrically distributed radioactivity from materials 

licensees is considered on a case-by-case basis with a reference of an annual individual dose 

criterion of a “few mrem per year (a few 0.01 mSv/a).” 

Non-reactor licensees, that is, materials licensees, and reactor licensees have essentially the same 

detection level criteria for surface activity. But for materials licensees, radioactivity below these 

detection level criteria is allowed—detectable radioactivity is not allowed at any level for reactor 

licensees. 

For reactor licensees, licensees may release of solid material using the “no detectable” policy of 

NRC’s Inspection and Enforcement Circular 81-07 and Information Notices 85-92 and 88-22. 

For reactors, the policy is that released material can have no detectable licensed radioactivity. 

The levels of detection are specified by each reactor licensee’s procedures and are frequently 

consistent with a now discontinued Regulatory Guide issued in 1974. In practice, these detection 

levels for radioactivity on surfaces are: 5/6 Bq /cm2

 (5,000 dpm/100 cm2) total β-γ and 1/6

Bq/cm2

 (1,000 dpm/100 cm2) removable β-γ. Non-detection at these levels of detectability was 

considered to result in potential doses to an individual significantly less than 5 mrem/y (<<0.05 

mSv/a) from any non-detectable radioactivity that could remain on surfaces. 

Detection levels for α-emitting radioactivity are specified as 1/60 Bq/cm2 (100 dpm/100 cm2) 

total and 1/300 Bq/cm2 (20 dpm/100 cm2) for removable α-emitting radioactivity. For volumetric 

radioactivity from reactors, the detection levels are from guidance written in the late 1970s and 

specifies β-γ concentrations in the general range of 3–4 Bq/kg (81–108 pCi/kg). 

January 2009 E-13 NUREG-1575, Supp. 1 
Appendix E MARSAME 

Table E.3 Summary of NRC Disposition Criteria from Current Practices for the Release of 

Materials and Equipment 

Surficial Radioactivity Volumetric Radioactivity 

β-γ: Non-detectable [MDC 5/6 

Bq/cm2; 1/6 Bq/cm2 removable]

β-γ: Non-detectable [MDC in 

General range of ≈ 3-4 Bq/kg]

Reactor Licenses 

α: Non-detectable [MDC 1/60 

Bq/cm2

; 1/300 Bq/cm2 

removable]

α: Non-detectable [MDC not 

indicated]

β-γ: 5/6 Bq/cm2; 1/6 Bq/cm2 

removable1

β-γ: Case-by-case [Reference to a 

few 0.01 mSv in a year]

Materials Licenses 

α: 1/60 Bq/cm2

; 1/300 Bq/cm2 

removable2

α: Case-by-case [Reference to a 

few 0.01 mSv in a year] 1 Except Sr-90, I-126, I-131, and I-133, where 1/6 Bq/cm2 and 1/30 Bq/ cm2

 removable applies; and except I-125, 

and I-129 where 1/60 Bq/cm2

 and 1/300 Bq/cm2 removable applies. 2 Except natural U, U-235, U-238, and associated decay products where 5/6 Bq/cm2

 and 1/6 Bq/cm2 removable 

applies; and except transuranics, Ra-226, Ra-228, Th-230, Th-228, Pa-231, and Ac-227, where 1/60 Bq/cm2

 and 

1/300 Bq/cm2

 removable applies. 

E.3.12 Issues Related to International Trade 

With regard to issues relating to international trade of solid materials released from facilities, 

NRC’s regulations contain requirements for export and import of material and could be 

considered in handling materials that meet established international clearance criteria and, at the 

same time, do not meet the guidelines for NRC licensees. Among other things, these regulations 

require that “the proposed import does not constitute an unreasonable risk to the public health 

and safety.” 

NUREG-1575, Supp. 1 E-14 January 2009 
MARSAME References 

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Thomas, Pacific Northwest Laboratory. 

U.S. Nuclear Regulatory Commission (NRC) 2002a. Radiological Surveys for Controlling 

Release of Solid Materials. NUREG-1761. Office of Nuclear Regulatory Research, 

Washington, DC. 

U.S. Nuclear Regulatory Commission (NRC) 2002b. Information Relevant to Ensuring that 

Occupational Radiation Exposures at Uranium Mills Will Be As Low As Is Reasonably 

Achievable. Regulatory Guide 8.31. Washington, DC. 

U.S. Nuclear Regulatory Commission (NRC) 2003a. Radiological Assessments for Clearance 

of Materials from Nuclear Facilities. NUREG-1640. Office of Nuclear Regulatory 

Research, Washington, DC. 

January 2009 Ref-7 NUREG-1575, Supp. 1 
References MARSAME 

U.S. Nuclear Regulatory Commission (NRC) 2003b. Radiological Toolbox Version 1.0.0. 

Eckerman, K.F. and Sjoreen, A.L. http://www.nrc.gov/aboutnrc/regulatory/research/radiological-toolbox.html (accessed September 1, 2008). 

Occupational Safety and Health Administration (OSHA) 2002. Job Hazard Analysis. OSHA 

Publication 3071. http://www.osha.gov/Publications/osha3071.pdf (accessed September 

1, 2008). 

Pacific Northwest Laboratory (PNL) 1988. Health Physics Manual of Good Practices for 

Reducing Radiation Exposure to Levels that are As Low As Reasonably Achievable 

(ALARA). Munson, L.H., et al. PNL-6577, UC-610, , Richland, Washington. 

http://www.pnl.gov/bayesian/refs/ALARAMAN88.PDF (accessed September 1, 2008). 

Srom, D.J. Counting Statistics Utility for Comparing Eight Decision Rules. 

http://www.pnl.gov/bayesian/strom/strompub.htm (accessed September 1, 2008). 

Thermo 2005. “SAM11 Small articles and tools monitor for low level gamma measurement” 

Thermo Fisher Scientific, Inc. 

http://www.thermo.com/eThermo/CMA/PDFs/Product/productPDF_23747.pdf (accessed 

September 1, 2008). 

Thermo 2008. “FHT 3031 CCM Contamination Monitor.” Thermo Fisher Scientific, Inc.. 

http://www.thermo.com/com/cda/product/detail/0,1055,15815,00.html (accessed 

September 1, 2008). 

United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2000. 

Sources and Effects of Ionizing Radiation. Report to the General Assembly, ISBN 92-1-

142238-8. http://www.unscear.org/unscear/en/publications.html (accessed September 1, 

2008). 

Vetter, T.W. 2006. Quantifying Measurement Uncertainty in Analytical Chemistry – A Simplified 

Practical Approach. http://www.cstl.nist.gov/acd/839.03/Uncertainty.pdf (accessed 

September 1, 2008). 

NUREG-1575, Supp. 1 Ref-8 January 2009 
 Glossary 

GLOSSARY 

Accessible Area is an area that can be easily reached or obtained. In many cases an area must be 

physically accessible to perform a measurement. However, radioactivity may be measurable 

even if an area is not physically accessible. See in this glossary measurable radioactivity. 

Action Level is the numerical value that causes a decision-maker to choose one of the alternative 

actions. In the context of MARSAME, the numerical value is the radionuclide concentration or 

level of radioactivity corresponding to the disposition criterion, and the alternative actions are 

determined by the selection of a disposition option. 

Alternative Action is the choice between two mutually exclusive possibilities. See in this 

glossary decision rule. 

Ambient Radiation is radiation that is currently present in the surrounding area. Ambient 

radiation may include natural background, intrinsic radiation from surrounding materials, 

intrinsic radiation from the item(s) being measured, contamination, or radiation from nearby 

machines (e.g., x-ray machines when operating) depending on the local conditions. Ambient 

radiation changes with season, time, location, weather, and other environmental conditions. 

Background Radiation (as defined in Nuclear Regulatory Commission regulations) is radiation 

from cosmic sources; naturally occurring radioactive material including radon (except as a decay 

product of source or special nuclear material); and global fallout as it exists in the environment 

from the testing of nuclear explosive devices or from past nuclear accidents such as Chernobyl 

that contribute to background radiation and are not under the control of the licensee. 

“Background radiation” does not include radiation from source, byproduct or special nuclear 

materials regulated by the Nuclear Regulatory Commission (10 CFR 20.1003). See in this 

glossary distinguishable from background. 

Biased Measurements are measurements performed at locations selected using professional 

judgment based on unusual appearance, location relative to known contamination areas, high 

potential for residual radioactivity, and general supplemental information. Biased measurements 

are not included in the statistical evaluation of survey unit data because they violate the 

assumption of randomly selected, independent measurements. Instead, biased measurement 

results are individually compared to the action levels. Biased measurements are also called 

judgment measurements (MARSSIM 2002). 

Calibration Function is the function that relates the net instrument signal to activity (e.g., 

relates counts to disintegrations or radiations). 

Categorization is the act of determining whether M&E are impacted or non-impacted. This is a 

departure from MARSSIM where this decision was included in the definition of classification. 

Class 1 M&E are impacted M&E that have, or had, the following: (1) highest potential for, or 

known, radionuclide concentration(s) or radioactivity above the action level(s); (2) highest 

potential for small areas of elevated radionuclide concentration(s) or radioactivity; and (3) 

insufficient evidence to support reclassification as Class 2 or Class 3. Such potential may be 

based on historical information and process knowledge, while known radionuclide 

January 2009 GL-1 NUREG-1575, Supp. 1 
Glossary MARSAME 

concentration(s) or radioactivity may be based on preliminary surveys. See in this glossary Class 

2, Class 3, classification, and impacted. 

Class 2 M&E are impacted M&E that have, or had, the following: (1) low potential for 

radionuclide concentration(s) or radioactivity above the action level(s); and (2) little or no 

potential for small areas of elevated radionuclide concentration(s) or radioactivity. Such potential 

may be based on historical information, process knowledge, and preliminary surveys. See in this 

glossary Class 1, Class 3, classification, and impacted. 

Class 3 M&E are impacted M&E that have, or had, the following: (1) little or no potential for 

radionuclide concentrations(s) or radioactivity above background; and (2) insufficient evidence 

to support categorization as non-impacted. See in this glossary Class 1, Class 2, classification, 

impacted, and non-impacted. 

Classification is the act or result of separating impacted M&E or survey units into one of three 

designated classes: Class 1, Class 2, or Class 3. Classification is the process of determining the 

appropriate level of survey effort based on estimates of activity levels and comparison to action 

levels, where the activity estimates are provided by historical information, process knowledge, 

and preliminary surveys. See in this glossary Class 1, Class 2, Class 3, and impacted. 

Clearance is the removal of radiological regulatory controls from materials and equipment. 

Clearance is a subset of release. See in this glossary release, restricted release, and unrestricted 

release. 

Combined Standard Uncertainty is the standard uncertainty of an output estimate calculated 

by combining the standard uncertainties of the input estimates. The combined standard 

uncertainty of y is denoted by uc(y). See also in this glossary expanded uncertainty, input 

estimate, measurement method uncertainty, output estimate, and standard uncertainty. 

Combined Variance is the square of the combined standard uncertainty. The combined variance 

of y is denoted by [uc(y)]2. See in this glossary combined standard uncertainty. 

Concentration is activity per unit mass or volume (e.g., Bq/kg, pCi/g, or Bq/m3) or activity per 

unit area (e.g., Bq/m2

 or dpm/100 cm2). 

Conceptual Model is an idealized model or map of a component or area to be surveyed and the 

associated radionuclides or radioactivity expected to be present, and is intended to aid in 

describing or designing the survey. The initial conceptual model is based on the results of the 

initial assessment. Additional data is used to update the conceptual model iteratively throughout 

the development, implementation, and assessment of the disposition survey. See in this glossary 

initial assessment. 

Coverage Factor (k) is the value multiplied by the combined standard uncertainty uc(y) to give 

the expanded uncertainty, U. See in this glossary combined standard uncertainty and expanded 

uncertainty. 

Coverage Probability is the approximate probability that the reported uncertainty interval will 

contain the value of the measurand. See in this glossary measurand. 

NUREG-1575, Supp. 1 GL-2 January 2009 
 Glossary 

Critical Value in the context of radiation detection is the minimum measured value (e.g., of the 

instrument signal or the radionuclide concentration) required to give a specified probability that a 

positive (non-zero) amount of radioactivity is present in the material being measured. The critical 

value is the same as the critical level or decision level in publications by Currie (Currie 1968 and 

NRC 1984). 

Data Life Cycle is the process of planning the survey, implementing the survey plan, and 

assessing the survey results prior to making a decision (MARSSIM 2002). 

Data Quality Objectives (DQOs) are qualitative and quantitative statements derived from the 

DQO process that clarify [the survey] technical and quality objectives, define the appropriate 

type of data, and specify tolerable levels of potential decision errors that will be used as the basis 

for establishing the quality and quantity of data needed to support decisions (MARSSIM 2002). 

Data Quality Objectives Process is a systematic strategic planning tool based on the scientific 

method that identifies and defines the type, quality, and quantity of data needed to satisfy a 

specific use (MARSSIM 2002). See also in this glossary data quality objectives. 

Data Quality Assessment (DQA) is a scientific and statistical evaluation that determines 

whether data are the right type, quality and quantity to support their intended use (EPA 2006b). 

Decision Rule is an “if...then” statement consisting of three parts: action level(s), parameter of 

interest, and alternative actions. A theoretical decision rule is developed early in the planning 

process assuming ideal data are available to support a disposition decision (see Chapter 3). An 

operational decision rule is developed based on the measurements that will be performed as part 

of the final disposition survey (see Chapter 4). 

Detection Capability is a generic term describing the capability of a measurement process to 

distinguish small amounts of radioactivity from zero. It may be expressed in terms of the 

minimum detectable concentration. See in this glossary minimum detectable concentration. 

Difficult-to-Measure Radioactivity is radioactivity that is not measurable until the M&E to be 

surveyed is prepared. Preparation of M&E may be relatively simple (e.g., cleaning) or more 

complicated (e.g., disassembly or complete destruction). Given sufficient resources, all 

radioactivity can be made measurable; however, it is recognized that increased survey costs can 

outweigh the benefit of some dispositions. 

Discrimination Limit is the level of radioactivity selected by the members of the planning team 

that can be reliably distinguished from the action level. The lower bound of the gray region 

(LBGR) for Scenario A and the upper bound of the gray region (UBGR) for Scenario B are 

examples of discrimination limits. See also in this glossary lower bound of the gray region, 

upper bound of the gray region, Scenario A, and Scenario B. 

Disposition is the future use, fate, or final location for something (e.g., recycle, reuse, disposal). 

Disposition Decision is the selection among alternative actions to determine acceptable future 

use, fate, or final location for something (e.g., recycle, reuse, disposal).. 

Disposition Survey is a radiological survey designed to collect information to support a 

disposition decision. 

January 2009 GL-3 NUREG-1575, Supp. 1 
Glossary MARSAME 

Distinguishable from Background is the radionuclide concentration or radioactivity that is 

statistically different from the background level of that radionuclide concentration or 

radioactivity in similar M&E. See in this glossary background radiation, measurable 

radioactivity, minimum detectable concentration, measurement quality objectives. 

Energy Resolution is the quantifiable ability of a measurement method to distinguish between 

radiations with different energies. 

Environmental Radioactivity is radioactivity from the environment where the M&E are 

located. Environmental radioactivity includes background radiation as well as inherent 

radioactivity and radioactivity from nearby sources. 

Evaluation Function is a mathematical expression that allows the user to compare options and 

draw a conclusion or calculate a result. 

Expanded Uncertainty is the product, U, of the combined standard uncertainty of a measured 

value, y, and a coverage factor, k, chosen so that the interval from y – U to y + U has a desired 

high probability of containing the value of the measurand. See in this glossary combined 

standard uncertainty, coverage factor, and measurand. 

Fluence is the number of photons or particles passing through a cross-sectional area. The 

international standard (SI) unit for fluence is m−2. 

Frequency Plot is a chart plotting the number of data points against their measured values. 

Graded Approach is the process of basing the level of application of managerial controls 

applied to an item or work according to the intended use of the results and the degree of 

confidence needed in the quality of the results. See in this glossary data quality objectives 

process. 

Gray Region is the range of radionuclide concentrations or quantities between the 

discrimination limit and the action level, where the consequence of making a decision error is 

relatively minor. See in this glossary action level, discrimination limit, lower bound of the gray 

region, and upper bound of the gray region. 

Impacted is a term applied to M&E that are not classified as non-impacted. M&E with a 

reasonable potential to contain radionuclide concentration(s) or radioactivity above background 

are considered impacted (10 CFR 50.2). See in this glossary background radiation and nonimpacted. 

Inherent Radioactivity is radioactivity resulting from radionuclides that are an essential 

constituent of the material being measured (e.g., 40K in fertilizer containing potassium). 

Initial Assessment (IA) is an investigation to collect existing information describing materials 

and equipment and is similar to the historical site assessment (HSA) described in MARSSIM. 

Input Quantity is any of the quantities in a mathematical measurement model whose values are 

measured and used to calculate the value of another quantity, called the output variable. 

Instrument Efficiency is the ratio between the instrument net reading and the surface emission 

rate of a source under given geometrical conditions (ISO 1988). For a given instrument, the 

NUREG-1575, Supp. 1 GL-4 January 2009 
 Glossary 

instrument efficiency depends on the energy of the radiations emitted by the source. See in this 

glossary source efficiency and total efficiency. 

Interdiction is an increase in the level of radiological control or a decision not to accept control 

from another party. Examples of interdiction include identification of radioactive material that 

results in the initiation of radiological controls or identification of unauthorized movement of 

radioactive material. 

Interdiction Survey is the collection of data to support an interdiction decision regarding M&E. 

In general, interdiction surveys are used to accept or refuse to accept control of M&E that are 

potentially radioactive. The goal of an interdiction survey often is to detect radioactive M&E that 

should be controlled. In some cases, an interdiction survey may result in the impoundment of 

radioactive M&E that represent an unacceptable risk to human health or the environment. 

Interference is the presence of other radiation or radioactivity, chemicals, background noise, 

instrument noise, or other factors that hinders the ability to analyze for the radiation or 

radioactivity of interest. 

Intrinsic Radioactivity See in this glossary inherent radioactivity.

Lower Bound of the Gray Region (LBGR) is the radionuclide concentration or level of 

radioactivity that corresponds with the lowest value in the range where the consequence of 

decision errors is relatively minor. For Scenario A, the LBGR corresponds to the discrimination 

limit. For Scenario B, the LBGR corresponds to the action level. See in this glossary action level, 

discrimination limit, gray region, Scenario A, and Scenario B. 

Mathematical Model is the general characterization of a process, object, or concept in terms of 

mathematics, which enables the relatively simple manipulation of variables to be accomplished 

in order to determine how the process, object, or concept would behave in different situations. 

Materials and Equipment (M&E) are items considered for disposition that include metals, 

concrete, dispersible bulk materials, tools, equipment, piping, conduit, furniture, solids, liquids, 

and gases in containers, etc. M&E are considered non-real property distinguishable from 

buildings and land, which are considered real property. See in this glossary disposition and nonreal property. 

Measurand is a particular quantity subject to measurement (ISO 1996). 

Measurement Method Uncertainty See in this glossary method uncertainty. 

Measurement Quality Objectives (MQOs) are a statement of a performance objective or 

requirement for a particular method performance characteristic (MARLAP 2004). 

Measurement Standard Deviation See in this glossary standard deviation (of measurement). 

Measurement Uncertainty See in this glossary uncertainty (of measurement). 

Measurable Radioactivity is radioactivity that can be quantified using known or predicted 

relationships developed from historical information, process knowledge, or preliminary 

measurements as long as the relationships are developed, verified, and validated as specified in 

the data quality objectives (DQOs) and measurement quality objectives (MQOs). 

January 2009 GL-5 NUREG-1575, Supp. 1 
Glossary MARSAME 

Median is the middle value of the data set when the number of data points is odd, or the average 

of the two middle values when the number of data points is even. 

Method Uncertainty, uM, is the predicted uncertainty of the measured value that would be 

calculated if the method were applied to a hypothetical sample with specified concentration. 

Minimum Detectable Activity (MDA) is the minimum detectable value of activity for a 

measurement. See in this glossary minimum detectable value. 

Minimum Detectable Concentration (MDC) is the minimum detectable value of the 

radionuclide or radioactivity concentration for a measurement. See in this glossary minimum 

detectable value. 

Minimum Detectable Value is an estimate of the smallest true value of the measurand that 

ensures a specified high probability, 1 – β, of detection. This definition presupposes that an 

appropriate detection criterion has been specified (e.g., critical value). See in this glossary 

measurand and critical value. 

Minimum Quantifiable Concentration (MQC) is the smallest value of the concentration that 

ensures the relative standard deviation of a measurement of M&E with that concentration does 

not exceed a specified value, usually 10%. 

Non-impacted is a term applied to M&E where there is no reasonable potential to contain 

radionuclide concentration(s) or radioactivity above background (10 CFR 50.2). See in this 

glossary background radiation and impacted. 

Non-Real Property is property that is not real property. See in this glossary real property and

materials and equipment (M&E). 

Null Hypothesis, or baseline condition, is a tentative assumption about the true, but unknown, 

radionuclide concentration or level of radioactivity that can be retained or rejected based on the 

available evidence. When hypothesis testing is applied to disposition decisions, the data are used 

to select between a presumed baseline condition (the null hypothesis) and an alternate condition 

(the alternative hypothesis). The null hypothesis is retained until evidence demonstrates with a 

previously specified probability that the baseline condition is false. 

Output Quantity is the quantity in a mathematical measurement model whose value is 

calculated from the measured values of other quantities in the model. See in this glossary input 

quantity. 

Planning Team is the group of people who perform the DQO process. Members may include the 

decision-maker (senior manager), site manager, representatives of other data users, senior 

program and technical staff, someone with statistical expertise, and a quality assurance and 

quality control advisor (such as a QA manager) (EPA 2000a). 

Posting Plot is a map of the survey unit with the data values entered at the measurement 

locations. This type of plot potentially reveals heterogeneities in the data, especially possible 

patches of elevated radioactivity. 

NUREG-1575, Supp. 1 GL-6 January 2009 
 Glossary 

Preliminary Survey is any survey performed prior to the disposition survey in MARSAME, and 

is generally performed to provide information required to support the design of the disposition 

survey. See also in this glossary disposition survey. 

Process Knowledge is information concerning the characteristics, history of prior use, and 

inherent radioactivity of the materials and equipment being considered for release. Process 

knowledge is obtained through a review of the operations conducted in facilities or areas where 

materials and equipment may have been located and the processes where the materials and 

equipment were involved. 

Radioactive Materials consist of any material, equipment or system component determined or 

suspected to contain radionuclides in excess of inherent radioactivity. Radioactive material 

includes activated material, sealed and unsealed sources, and substances that emit radiation. See 

in this glossary inherent radioactivity. 

Radiological Controls are any means, method or activity (including engineered or 

administrative) designed to protect personnel or the environment from exposure to a radiological 

risk. 

Radionuclides or Radiations of Concern are radionuclides or radiations that are present at a 

concentration or activity that may pose an unacceptable risk to human health or the environment. 

In MARSAME, the term radionuclides or radiations of concern is used to describe the 

radionuclides or radiations that are actually measured during the disposition survey. See also in 

this glossary radionuclides or radiations of potential concern and disposition survey. 

Radionuclides or Radiations of Potential Concern are radionuclides or radiations that are 

identified during the initial assessment as potentially being associated with the M&E being 

investigated. See also in this glossary initial assessment and radionuclides or radiations of 

concern. 

Ratemeter is an instrument that indicates the counting rate of an electronic counter. In the 

context of radiological measurements, a ratemeter displays the counting rate from a radiation 

detector. The averaging time for calculating the rate is determined by the time constant of the 

meter. See in this glossary scaler. 

Real Property, in the MARSAME context, means developed or undeveloped land, fixed 

buildings and structures, or surface and subsurface soil remaining in place. Real property is 

outside the scope of MARSAME. See in this glossary materials and equipment (M&E) and nonreal property. 

Recycle is beneficial reuse of constituent materials incorporated within the M&E. A hammer that 

is melted down as scrap metal so the component metals can be reused is an example of recycle. 

Reference Material is material of similar physical, radiological, and chemical characteristics as 

the M&E considered for disposition. Reference material provides information on the level of 

radioactivity that would be present if the M&E being investigated had not been radiologically 

impacted. See in this glossary impacted. 

January 2009 GL-7 NUREG-1575, Supp. 1 
Glossary MARSAME 

Relative Standard Uncertainty is the ratio of the standard uncertainty of a measured result to 

the result itself. The relative standard uncertainty of x may be denoted by ur(x). See in this 

glossary standard uncertainty. 

Release is a reduction in the level of radiological control, or a transfer of control to another 

party. Release includes clearance. Examples of release (other than clearance) include recycle, 

reuse, disposal as waste, or transfer of control of radioactive M&E from one authorized user to 

another. See also in this glossary reuse, recycle, restricted release, and clearance. 

Release Survey is a type of disposition survey designed to collect information to support a 

release decision. See also in this glossary disposition survey and release. 

Restricted Release is a reduction in the level of radiological control, or transfer of control to 

another party, where restrictions are placed on how the released items will be used or transferred. 

Maintaining a tool crib in a radiologically controlled area restricts reuse of those tools to that 

radiologically controlled area, and tools returned to the tool crib represent a restricted release of 

those tools. See also in this glossary reuse, recycle, release, and clearance. 

Reuse is the continued use of M&E for their original purpose(s). An example of reuse is a 

hammer that continues to be used as a hammer. 

Ruggedness is the relative stability of a measurement technique’s performance when small 

variations in method parameter values are made. 

Sampling Standard Deviation, σS,, is the theoretical true value of the variability of radionuclide 

concentration or radioactivity in space and time (i.e., the variation of the true but unknown 

concentrations from place to place and from time to time). The extent of the survey unit, the 

physical sizes of the measured material, and the choice of measurement locations affects the 

sampling standard deviation. 

Scaler is an electronic counter that displays the aggregate of a number of signals, which usually 

occur too rapidly to be recorded individually. In the context of radiological measurements, a 

scaler records the number of counts from a radiation detector over a specified time interval. See 

in this glossary ratemeter. 

Scenario A uses a null hypothesis that assumes the level of radioactivity associated with the 

M&E exceeds the action level. Scenario A is sometimes referred to as “presumed not to comply” 

or “presumed not clean.” 

Scenario B uses a null hypothesis that assumes the level of radioactivity associated with the 

M&E is less than or equal to the action level. Scenario B is sometimes referred to as 

“indistinguishable from background” or “presumed clean.” 

Secular Equilibrium is the condition in which the initial member of the decay series has a 

longer half-life than any subsequent members of the series. Secular equilibrium is achieved when 

the activities for all members of the decay series are equal to the activity of the precursor 

radionuclide. 

NUREG-1575, Supp. 1 GL-8 January 2009 
 Glossary 

Segregation is the process of separating or isolating from a main body or group. In the context 

of disposition surveys, segregation is based on the physical and radiological attributes of the 

M&E being investigated and is used to help control measurement method uncertainty. 

Sensitivity Coefficient for an input estimate, xi, used to calculate an output estimate, 

y = f (x1, x2, ..., xN), is the value of the partial derivative, ∂f/∂xi, evaluated at i = x1, x2,...,xN. The 

sensitivity coefficient represents the ratio of the change in y to a small change in xi. 

Sentinel Measurement is a biased measurement performed at a key location to provide 

information specific to the objectives of the initial assessment (IA). 

Significance Level is, in the context of a hypothesis test, a specified upper limit for the 

probability of a Type I decision error. 

Sign Test is a non-parametric statistical test used to evaluate radionuclide-specific disposition 

survey results if the radionuclide being measured is not present in background, or is present at 

such a small fraction of the action level as to be considered insignificant. 

Smear is a non-quantitative test for the presence of removable radioactive materials in which the 

suspected surface or area is wiped with a filter paper or other substance, which is then tested for 

the presence of radioactivity. The surface area tested may be related to the release criterion. 

Smear is also referred to as a smear test, swipe, or wipe. 

Source Efficiency is the ratio between the number of particles of a given type above a given 

energy emerging from the front face of a source or its window per unit time and the number of 

particles of the same type created or released within the source (for a thin source) or its 

saturation layer thickness (for a thick source) per unit time (ISO 1988). See also in this glossary 

instrument efficiency and total efficiency. 

Specific Activity is the radioactivity per unit mass for a specified radionuclide. 

Specificity is the ability of the measurement method to measure the radionuclide of concern in 

the presence of interferences. 

Spectrometry is a measurement across a range of energies. The measurement of alpha particles 

by energy is called alpha spectrometry. 

Spectroscopy is the measurement and analysis of electromagnetic spectra produced as the result 

of the emission or absorption of energy by various substances. The measurement of gamma-ray 

emissions from a substance is called gamma spectroscopy. 

Standard Operating Procedure (SOP) is a written document that details the method for an 

operation, analysis, or action with thoroughly prescribed techniques and steps, and that is 

officially approved as the method for performing certain routine or repetitive tasks (MARSSIM 

2002). 

Standard Deviation (of Measurement), σM, is a theoretical parameter describing the variability 

in the distribution of the measurement. See also in this glossary uncertainty (of measurement). 

January 2009 GL-9 NUREG-1575, Supp. 1 
Glossary MARSAME 

Standard Uncertainty is the uncertainty of a measured value expressed as an estimated standard 

deviation, often called a “1-sigma” (1σ) uncertainty (MARLAP 2004). The standard uncertainty 

of a value x is denoted by u(x). See also in this glossary uncertainty (of measurement). 

Standardized Initial Assessment is a set of instructions or questions that are used to perform 

the initial assessment, usually documented in a standard operating procedure. See also in this 

glossary initial assessment and standard operating procedure. 

Surficial Radioactive Material is radioactive material distributed on any of the surfaces of a 

solid object. Surficial radioactive material may be either removable by non-destructive means 

(such as casual contact, wiping, brushing, or washing) or fixed to the surface. 

Surrogate Measurement is a measurement where one radionuclide is quantified and used to 

demonstrate compliance with the release criterion for additional radionuclide(s) based on known 

or accepted relationships between the measured radionuclide and unmeasured radionuclide(s). 

Survey Unit for M&E is the specific lot, amount, or piece of M&E on which measurements are 

made to support a disposition decision concerning the same specific lot, amount, or piece of 

M&E. 

Total Efficiency is the product of the instrument efficiency and the source efficiency. See in this 

glossary instrument efficiency and source efficiency. 

Traceability is the “property of the result of a measurement or the value of a standard whereby it 

can be related to stated references, usually national or international standards, through an 

unbroken chain of comparisons all having stated uncertainties” (ISO 1996). 

  

Type I Decision Error occurs when the null hypothesis is rejected when it is actually true. The 

Type I decision error rate, or significance level, is represented by α. See in this glossary null 

hypothesis and significance level. 

Type II Decision Error occurs when the null hypothesis is not rejected when it is actually false. 

The Type II decision error rate is denoted by β. See in this glossary null hypothesis. 

Uncertainty (of Measurement), u(x), is a parameter, associated with the result of a 

measurement, x, that characterizes the dispersion of the values that could reasonably be 

attributed to the measurement of x. It is the estimated value of σ(x) obtained from the 

propagation of uncertainty. See also in this glossary See also in this glossary standard deviation 

(of measurement). 

Unrestricted Release is the removal of radiological regulatory controls from materials and 

equipment. See in this glossary release and clearance. 

Upper Bound of the Gray Region (UBGR) is the radionuclide concentration or level of 

radioactivity that corresponds with the highest value in the range where the consequence of 

decision errors is relatively minor. For Scenario A, the UBGR corresponds to the action level. 

For Scenario B, the UBGR corresponds to the discrimination limit. See in this glossary action 

level, discrimination limit, gray region, Scenario A, and Scenario B. 

NUREG-1575, Supp. 1 GL-10 January 2009 
 Glossary 

Volumetric Radioactive Material is radioactive material that is distributed throughout or within 

the materials or equipment being measured, as opposed to a surficial distribution. Volumetric 

radioactive material may be homogeneously (e.g., uniformly activated metal) or heterogeneously 

(e.g., activated reinforced concrete) distributed throughout the M&E. 

Wilcoxon Rank Sum (WRS) Test is a non-parametric statistical test used to evaluate 

disposition survey results when the radionuclide being measured is present in background by 

comparing the results to measurements performed using an appropriately chosen reference 

material. 

January 2009 GL-11 NUREG-1575, Supp. 1