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MARSSIM Overview

Professional Training Programs

Objectives

• Describe the Purpose of Multi-Agency Radiation Survey and Site Investigation Manual’s (MARSSIM) guidance

• Identify some of the limitations of MARSSIM

• Identify the seven steps of the Data Quality Objectives (DQOs) Process

• Identify the four stages of the data life cycle

• Explain the two types of derived concentration guideline levels (DCGLs)

• Identify the manner in which the site is classified

• Describe the role of the survey unit

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Objectives

• Explain the purpose of the relative shift

• Identify example situations where the Sign test and the Wilcoxon Rank Sum (WRS) test might be employed

• Explain the purpose of the scan

• Describe the minimum scan requirements in Class 1, 2, and 3 survey units

• Explain the purpose of making measurements/collecting samples

• Describe how the samples/measurements are distributed in Class 1, 2, and 3 survey units

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Objectives

• Explain the purpose of data verification

• Explain the purpose of data validation

• Identify some of the things that might be done during a preliminary data review

• Explain how the Sign test is performed

• Explain how the Wilcoxon Rank Sum test is performed

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General

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General

MARSSIM (NUREG 1575) • Intended to replace NUREG-5849 (only issued as a draft)

• Primarily provides guidance for the final status survey (FSS)

• Describes methods for characterizing contamination:

– On building surfaces

– In surface soil (e.g., top 15 cm)

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General

MARSSIM (NUREG 1575) • Does not address the characterization of materials and

equipment (M&E) released prior to license termination

• Does not provide guidance for characterizing contamination in subsurface soil

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General

Two Supplements to MARSSIM • MARSAME

A supplement to MARSSIM addressing the characterization of materials and equipment was finalized in January 2009, Multi-agency Radiation Survey and Assessment of Materials and Equipment (MARSAME).

• MARSAS

Another MARSSIM supplement, supposedly under development, will deal with contamination in subsurface soil the Multi-Agency Radiation Survey and Assessment of the Subsurface (MARSAS).

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General

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Three Related and Important NUREGs: • NUREG-1505

• NUREG-1507

• NUREG-1757 (3 volumes)

General

NUREG-1505 “A Nonparametric Statistical Methodology for the Design and Analysis of Final Status Decommissioning Surveys” • Covers much of the same material as MARSSIM but the

statistical issues are covered in more detail.

• In addition, it describes alternative ways to do things that are not in MARSSIM but are consistent with the MARSSIM philosophy.

• It is the major source of guidance regarding what is known as Scenario B (indistinguishable from background).

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General

NUREG-1507 “Minimum Detectable Concentrations with Typical Radiation Survey Instruments for Various Contaminants and Field Conditions” • A key issue in MARSSIM is the scan and measurement

minimum detectable concentrations (MDCs) for the survey instruments.

• NUREG-1505 supplements and expands upon the information in MARSSIM regarding the calculation of the MDCs.

• Also provides additional information concerning counting statistics and the use of ISO-7503 for quantifying surface contamination.

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General

NUREG-1757 “Consolidated NMSS Decommissioning Guidance Volume 2 Characterization, Survey and Determination of Radiological Criteria, ” • This three volume document provides NRC guidance on

several aspects of decommissioning.

• Volume 2 covers the FSS and the implementation of MARSSIM. Among the other topics addressed are dose modeling and the demonstration of as low as reasonably achievable (ALARA).

• The Appendices of Volume 2 are particularly useful.

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General

MARSSIM’s Advantages: • It is flexible.

• It uses non-parametric statistics.

• It can greatly reduce the number of measurements and/or samples.

When assessing contaminated soil, this can greatly reduce the cost of the survey, especially when the soil samples must be analyzed by radiochemistry.

For structural surveys, reducing the number of gross beta or alpha measurements might not be very advantageous.

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General

MARSSIM’s Disadvantages: • It only addresses surface contamination.

• It does not provide a good mechanism to address localized areas of contamination (e.g., hot spots and discrete pieces of radioactive material).

• The effective implementation of MARSSIM requires substantial communication between the regulator and the licensee.

• It is complicated and easy to misunderstand.

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General

MARSSIM is an honest attempt to assess radioactive contamination in a better way than has been done in the past.

MARSSIM is highly imperfect

When MARSSIM’s guidance seems inappropriate and/or when it does not provide sufficient guidance for a particular situation:

Rely on the DQO process and your best professional judgment while consulting with the regulator.

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General

Data Life Cycle There are four stages in the data life cycle:

• Planning phase

• Implementation phase

• Assessment phase

• Decision phase

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The Data Quality Objectives Process

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Data Quality Objectives

General • MARSSIM employs the DQO process in the planning phase

of the data life cycle.

• It is a seven step process/management tool to ensure that all the bases are covered during planning.

Step 1. State the problem: Identify the planning team, decision makers, deadlines, resources and a concise description of the problem.

Step 2. Identify the decision: For a FSS this might be “Is the level of residual contamination in a given survey unit below the release criteria?”

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Data Quality Objectives

Step 3. Identify inputs to the decision: Identify what specific questions have to be answered (e.g., “What physical characteristics of the site need to be evaluated,” “What chemical characteristics of the contamination need to be determined”). The means by which these questions will be answered are identified and listed. The information needed to establish the DCGLs is identified.

Step 4. Define the study boundaries: The areas of the site to be evaluated are defined and the time frame in which the survey will be performed is defined.

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Data Quality Objectives Step 5. Develop a decision rule: Identify the statistical method for

describing the residual activity (e.g., mean, median). Investigation levels are identified. The latter are measurements that if exceeded require some decision to be made as to the need for a more detailed investigation.

Step 6. Specify limits on decision errors: Estimate the likely variation in the measurements for the survey unit, identify the null hypothesis, and define the consequences of Type I and Type II errors in terms of health, political, resource issues. Specify the values for alpha and beta.

Step 7. Optimize the design of the survey for obtaining the data: Evaluate data collection design alternatives and select the best option.

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Radiation Survey and Site Investigation Process

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Overview: • Different types of radiological surveys can be performed at

a given site undergoing decommissioning. MARSSIM refers to this as the radiation survey and site investigation (RSSI) process, a subject it discusses in Chapters 2 and 5.

• MARSSIM describes each type of survey in some detail and even provides example checklists for each.

• Different names are used to describe these different survey types. Their “definitions” also vary.

• If some, or all, such surveys are performed at a given site, they are performed in the following order.

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Historic site assessment

Scoping survey

Characterization survey

Remedial action support survey

Final status survey

Verification survey

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Overview: • The historical site assessment (HSA) is not a radiological

survey. Nevertheless it can be just as important.

• It involves gathering and reviewing all the relevant historical data for the site.

• This includes reviewing documentation (e.g., records of radiological surveys, old aerial photographs) and talking to current and previous employees.

• Chapter three of MARSSIM is devoted to the HSA process.

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Overview: • To a large extent, the radiation survey and site investigation

process proceeds independently in different portions of the site.

In one area of the site, only the FSS might be performed. In other areas, the scoping, characterization, remedial action, final status, and verification surveys might be performed.

The FSS might be completed on one portion of the site while a scoping survey is being completed on an adjacent area.

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Overview: • The FSS is the only survey that is always performed on

impacted portions of the site. However, if an area is designated as non-impacted, even the FSS is omitted.

• With the exception of the verification survey, all of these surveys will be performed by the licensee or the licensee’s subcontractor.

• The verification survey would be performed by an independent organization under contract to the regulator.

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Planning the Final Status Survey

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Ten Steps for Planning a FSS 1. Determine the DCGLs for individual and multiple

nuclides.

2. Classify the site and identify survey units.

3. Determine whether Scenario A or B will be used and which statistical tests (e.g., Sign test or WRS test) will be deployed.

4. Determine whether the unity rule will be employed.

5. Choose the equipment and measurement protocols.

6. Determine the scan and measurement MDCs.

7. Determine the survey investigation levels.

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Ten Steps for Planning a FSS

8. Determine the acceptable probability (i.e., α and β) of Making Type I and Type II Errors. Specify an lower bound of the gray region (LBGR).

9. Determine the appropriate number of unbiased measurement/samples.

10. Create the reference grid and determine the sample locations.


1. DCGLs and the Release Criterion • A DCGL is the maximum allowable average level of

contamination in a defined area. Contamination at the DCGL is assumed to result in a dose at the release criterion.

• The release criterion established by the U.S. Nuclear Regulatory Commission (NRC) is 25 mrem (0.25 mSv) in a single year. All DCGLs for the NRC should equate to 25 mrem (0.25 mSv).

• Individual states may have lower release criteria (e.g., New Jersey at 15 mrem [0.15 mSv]).

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1. DCGLs – The Two Types • There are two basic types of DCGLs (the distinction is

somewhat arbitrary):

DCGLW The allowable average over the entire survey unit

DCGLEMC The allowable average over a defined area smaller than the survey unit; it can be thought of as the allowable average level in a “hot spot”

• DCGLs are defined for an individual nuclide or a specific mix of nuclides.

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1. DCGLs – Screening Levels vs. Site-Specific • The DCGLW for an individual nuclide might be a screening

level. Screening levels are relatively low conservative values. A licensee might choose to develop higher site-specific DCGLs that are more appropriate to a site’s conditions.

Screening levels for selected nuclides can be found in NUREG-1757 Volume 2. Screening levels for other nuclides can be calculated using the default parameters in the most recent version of the decontamination and decommissioning (DandD) code.

Site-specific (i.e., higher) DCGLWs can be calculated using the RESRAD or RESRAD-Build computer codes.

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1. DCGLs – the DCGLEMC

• The DCGLEMC is calculated as follows:

DCGLEMC = DCGLW × AF

• The area factor (AF), a number greater than or equal to 1, must be calculated using a computer code such as RESRAD or RESRAD-Build.

• The area factor, like the DCGLEMC, applies to a specified nuclide and specified area.

• The smaller the area, the larger the area factor.

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1. DCGLs – the DCGLEMC • There are tables of example area factors in MARSSIM

and NUREG-1505, but these are not to be used.

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1. DCGLs for Multiple Nuclides – Gross Beta/Alpha • Measurements of gross beta (or alpha) activity don’t

identify or distinguish the different nuclides emitting the betas (or alphas).

• If the ratio between the different nuclides is known, a less conservative (higher) value can be determined using an equation provided in MARSSIM.

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1. DCGLs for Multiple Nuclides – Gross Beta/Alpha

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 is the applicable DCGL for all the radionuclides

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷1 is the DCGL for Radionuclide 1 (e.g., Cs-137)

𝑓𝑓1 is the fraction of the activity contributed by Radionuclide 1

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷2 is the DCGL for Radionuclide 2 (e.g., Co-60)

𝑓𝑓2 is the fraction of the activity contributed by Radionuclide 2

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 =1

𝑓𝑓1𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷1

+ 𝑓𝑓2𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷2

+ ⋯+ 𝑓𝑓𝑛𝑛𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑛𝑛

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1. DCGLs for Multiple Nuclides – Gross Beta/Alpha • If the fraction of the total activity contributed by the

different radionuclides is not known, it is possible to use the DGCL for the most restrictive radionuclide.

In other words, assume that all the betas or alphas are emitted by the most restrictive nuclide (the nuclide with the lowest DCGL).

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1. DCGLs for Surrogate Nuclides – Radionuclides in Soil • Measurements of radionuclides in soil are more

expensive than gross alpha or beta measurements on structural surfaces.

• If a radionuclide in soil does not emit gamma rays (e.g., Sr-90) it cannot be analyzed directly by gamma spectrometry, and a radiochemical analysis might be necessary.

• Radiochemical analyses are even more expensive than analyses via gamma spectrometry.

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1. DCGLs for Surrogate Nuclides – Radionuclides in Soil • Analytical costs can be reduced if a ratio can be

established between the non-gamma emitter (the inferred nuclide [e.g., Sr-90]) and a gamma-emitting nuclide (the surrogate [e.g., Cs-137]).

• By lowering the DCGL for the surrogate (see next slide), we can account for the presence of the inferred nuclide.

• Some radiochemical analyses of the inferred nuclide must be performed to establish the ratio. But once that is done, it is only necessary to quantify the surrogate in the soil samples.

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1. DCGLs for Surrogate Nuclides – Radionuclides in Soil

DCGL𝑎𝑎𝑎𝑎𝑎𝑎 𝑔𝑔𝑠𝑠𝑔𝑔 = 11

DCGL𝑠𝑠𝑠𝑠𝑠𝑠 + R2

DCGL2 + ⋯ + R𝑛𝑛

DCGL𝑛𝑛

DCGLadj sur is the reduced DCGL for the surrogate radionuclide

DCGLsur is the unadjusted DCGL for the surrogate

DCGL2 is the DCGL for the inferred radionuclide

R2 is the ratio of the activity of the inferred nuclide (e.g., Sr-90) to the that of the surrogate radionuclide (e.g., Cs-137)

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2. Classify the Site and Identify Survey Units • Each area of the site is classified according to its potential

for contamination.

• If an area is potentially contaminated, it is classified as impacted.

• Impacted areas are either:

– Class 1

– Class 2

– Class 3

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2. Classify the Site and Identify Survey Units • Class 1 areas

Areas that have or had the potential for measurements above the DCGLW.

• Class 2 areas

Areas that have or had the potential for contamination, but few if any measurements should be above the DCGLW.

• Class 3 areas

Areas that have no potential for contamination, or if such a potential exists, measurements should not exceed a small fraction of the DCGLW.

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2. Classify the Site and Identify Survey Units • Non-impacted areas have never had any potential for

contamination.

Considerable justification is required to call a portion of the site non-impacted.

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2. Classify the Site and Identify Survey Units • The various areas of the site are divided into discrete

areas called survey units.

• The survey unit is the fundamental unit of compliance. For a site to be released, every survey unit must be shown to meet the release criterion.

• The size of the survey unit is linked to its class. The greater the potential for contamination, the smaller the survey unit.

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Planning the Final Status Survey 2. Classify the Site and Identify Survey Units

Class 1

Class 2

Class 3

fication of Impacted Areas Establishment of Survey Units

Class 3 Survey Unit

Class 1 Survey Unit

Class 1 Survey Unit

Class 1 Survey Unit

Class 1 Survey Unit

Class 1 Survey Unit

Class 1 Survey Unit

Class 2 Survey Unit

Class 2 Survey Unit

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3. Determine Whether Scenario A or B will be Used and Which Statistical Tests (e.g., Sign Test or WRS Test) will be Employed • Scenario A is used in the vast majority of cases.

It can be considered the default scenario.

• The Scenario A null hypothesis is that the survey unit does not meet the release criterion.

• In the standard MARSSIM application this hypothesis is tested by comparing the contaminant concentration in the survey unit with the DCGL.

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3. Determine Whether Scenario A or B will be Used and Which Statistical Tests (e.g., Sign Test or WRS Test) will be Employed • In Scenario B, the null hypothesis is that the survey unit

meets the release criterion.

• The most likely use of Scenario B is to demonstrate that measurements in the survey unit are indistinguishable from background.

• Scenario B is described in NUREG-1505 but not MARSSIM.

• Use Scenario B when: DCGLW is low, the nuclide is in background, background is variable, or the detection capability is poor.

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Planning the Final Status Survey 3. Determine Whether Scenario A or B will be Used and

Which Statistical Tests (e.g., Sign Test or WRS Test) will be Employed • MARSSIM recommends the use of two non-parametric

statistical tests: – Sign test

– WRS test

• These tests are easy to perform • They make few assumptions about the data distribution.

In the past, parametric statistics that assume a normal data distribution (e.g., student t test) have been used even though the data rarely exhibits a normal distribution.

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3. Determine Whether Scenario A or B will be Used and Which Statistical Tests (e.g., Sign Test or WRS Test) will be Employed • Sign test is used if the nuclide is not in background (or at

a very low level in background relative to the DCGL) and a nuclide-specific analysis is performed. For example, Co-60 in soil analyzed by gamma spectroscopy.

• WRS test is used if the nuclide is in background at a high level (relative to the DCGL) and a nuclide-specific analysis is performed. For example, Cs-137 in soil analyzed by gamma spectroscopy.

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3. Determine Whether Scenario A or B will be Used and Which Statistical Tests (e.g., Sign Test or WRS Test) will be Employed • If gross alpha or beta measurements are performed on

building surfaces, several options are available:

– Use the WRS test

– If the DCGL is quite high relative to background, use the Sign test and ignore (swallow) background

– Subtract background from the gross measurements in the survey unit and use the Sign test

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4. Determine Whether the Unity Rule will be Employed • The unity rule is used if more than one measurement (data

point) is obtained in each location or sample. For example:

– Co-60 and Cs-137 analyzed in each soil sample

– Both gross alpha and beta measurements performed at the same locations

The use of the unity rule affects way we determine the appropriate number of samples/measurements (via the relative shift) and the way the data is handled in the statistical tests.

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5. Choose the Equipment and Measurement Protocols The survey involves two fundamental activities:

1. Scan

– A scan will be performed to identify small areas of elevated activity (hot spots) that could exceed the release criteria.

– The detector is moved over the potentially contaminated surface.

2. Measurements and/or sample collection

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5. Choose the Equipment and Measurement Protocols There are two types of measurements and samples:

1. Unbiased

2. Biased

1. Unbiased samples or measurements

– Obtained to estimate the average level of contamination in the survey unit or employed in statistical test

– A secondary function of unbiased measurements in Class 1 areas is to locate “hot spots” (by accident)

– Locations selected in a random or systematic pattern

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5. Choose the Equipment and Measurement Protocols 2. Biased samples or measurements

– Obtained at locations of elevated activity identified in the scan, or

– Locations chosen based on professional judgment

Indoors: Floor drains, near work areas, cracks/joints, anchor bolts in floor, horizontal surfaces

Outdoors: Near loading docks, unusual depressions or mounds, animal burrows, fence line, areas where surface run-off could accumulate

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5. Choose the Equipment and Measurement Protocols

• Structural surfaces – instrumentation

For gross alpha or beta measurements and scans on walls, floors, etc., the gas-flow proportional counter is preferred.

• Soil – instrumentation

The sodium iodide (NaI) detector is preferred when scanning soil for gamma emitters.

Measurements of radionuclides in soil are typically obtained by collecting soil samples and analyzing the samples in the laboratory by gamma spectroscopy or radiochemistry.

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Scans: for finding hot spots

(scan coverage depends on survey unit class)

Measurements: for assessing mean concentration (number selected using relative shift)

Structural surfaces (indoor)

Scan for betas and/or alphas on surface

(e.g., gas flow proportional counter)

Measure beta and/or alpha activity on surface

(e.g., 1 min count with gas flow proportional counter)

Soil (outdoor)

Scan for gamma rays (e.g., NaI scintillator)

Collect soil sample and send to laboratory for analysis

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5. Choose the Equipment and Measurement Protocols • Class 1 survey units. 100% scan coverage.

Measurements/samples collected in systematic fashion (triangular pattern recommended).

• Class 2 survey units. 10–100% scan coverage. Measurements/samples collected in systematic fashion (triangular pattern recommended).

• Class 3 survey unit. Judgmental scan coverage. Measurements/samples collected in random fashion.

• Non-impacted area. Scanning and measurements not required.

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Measurement

or sample locations

Class 1 Class 2 Class 3

Class 1 Class 2 Class 3

Scan coverage 100%

10 - 100%

Judgmental

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5. Choose the Equipment and Measurement Protocols • MARSSIM states: “The results of smear samples should

not be used for determining compliance. Rather, they should be used as a diagnostic tool to determine if further investigation is necessary.”

For example, a series of repetitive smears might be taken on the same area in order to determine the fraction of the total activity that is removable. The calculation of the published screening levels assumed that only 10% of the total activity was removable.

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6. Determine the Scan and Measurement MDCs

• The goal is to have the MDC at or below 10–50% of the DCGLW. Failing that, it must be below the DCGLW.

• For planning purposes, the scan MDC must be at or below the DCGLEMC for Class 1 survey units.

• Guidance for calculating the MDCs can be found in Chapter 6 of MARSSIM and in NUREG-1507.

• Tables of typical measurement and scan MDCs can also be found in these documents.

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6. Determine the Scan and Measurement MDCs • One possible issue related to the calculation of scan and

measurement MDCs is how to determine the detector efficiency for multiple radionuclides.

This is known as a weighted efficiency:

Weighted efficiency = 𝑓𝑓1 𝐸𝐸1 + 𝑓𝑓2 𝐸𝐸2 +⋯ 𝑓𝑓𝑛𝑛 E𝑁𝑁

𝑓𝑓1 is the fraction of the total activity expected to be contributed by Radionuclide 1

𝐸𝐸1 is the detector efficiency for Radionuclide 1 𝑓𝑓2 is the fraction of the total activity expected to be contributed

by Radionuclide 2 𝐸𝐸2 is the detector efficiency for Radionuclide 2

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7. Determine the Survey Investigation Levels • The investigation level is the instrument response that

“trips” an investigation. A hot-spot might be outlined using chalk/paint or a flag might be stuck in the ground.

• The investigation level for a Class 1 survey unit might be the count rate that corresponds to the DCGLEMC.

• The investigation level for a Class 2 survey unit might be the count rate that corresponds to the DCGLW.

• The investigation level for a Class 3 survey unit might be the count rate that corresponds to a fraction of the DCGLW.

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • This is strictly related to the statistical tests that might be

performed on the unbiased measurements

• First, we must specify the null hypothesis which is the working assumption of the statistical test (e.g., “The average level of residual Cs-137 in the soil exceeds the DCGLW (action limit) of 10 pCi/g (370 Bq/kg)”).

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • When we perform the hypothesis test we will either:

– Reject the null hypothesis

– Fail to reject the null hypothesis

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Two Possible Errors if the Statistical Test is Performed

True average level of contamination above background

DCGL

Null hypothesis is false Type II error is possible (Type II error: incorrect failure to reject the null hypothesis)

Null hypothesis is true Type I error is possible (Type I error: incorrect rejection of the null hypothesis)

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • Two acceptable error rates must be specified: one for

Type I errors (alpha) and the other for Type II errors (beta).

• The only way to completely eliminate errors when we perform the statistical test is to have an infinite number of measurements.

• The lower the values selected for alpha and beta, the more samples or measurements that must be taken.

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • Alpha (aka significance level) is the acceptable probability

of a Type I error (null hypothesis is incorrectly rejected).

Alpha is defined at the DCGL.

• Given our example’s null hypothesis and DCGL, an alpha of 0.05 means there is a 5% chance that an actual concentration of 10 pCi/g (370 Bq/kg) will be incorrectly determined by the statistical test to be below 10 pCi/g (370 Bq/kg). This error is primarily the regulator’s concern.

• The regulator sets the value for alpha.

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • Beta is the acceptable probability of a Type II error

(incorrect failure to reject the null hypothesis).

• Given our example’s null hypothesis and action level, a beta of 0.1 would mean there is a 10% chance that the concentration will be incorrectly determined by the statistical test to be above 10 pCi/g (370 Bq/kg) when it is actually below 10 pCi/g (370 Bq/kg) at a value called the LBGR.

• The licensee selects the value for beta as well as the level of contamination at which beta applies, the LBGR.

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • Beta only has meaning when the LBGR is also specified.

• Most references recommend setting the LBGR at the expected median concentration of the contaminant.

Nevertheless, it is often better (more conservative) to set the LBGR at the expected average concentration since the latter is usually higher than the median.

• The LBGR is essentially a guess based on whatever information is available (e.g., characterization data).

• It is often incorrectly assumed that the LBGR must be set at one half the action level.

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors

• Setting a low beta increases the number of required samples/measurements.

• The required number of samples also increases as the LBGR is increased (gets closer to the DCGL).

In other words, the closer the actual level of contamination is to the DCGL, the more samples are required to limit the error rate.

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Actual level of Cs-137 contamination

1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

LBGR

Beta

DCGL

Prob

abili

ty o

f inc

orre

ctly

faili

ng

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DCGL

1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

LBGR

Gray region

Width of gray region = ∆ = DCGL - LBGR

Prob

abili

ty o

f inc

orre

ctly

faili

ng

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Prob

abili

ty o

f inc

orre

ctly

faili

ng 1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Few samples

Many samples


The number of samples or measurements determines the shape of the curve

DCGL

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Prob

abili

ty o

f inc

orre

ctly

faili

ng 1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

LBGR

If the expected level of contamination is high, set a high LBGR. This leads to the collection of many samples.


DCGL

74

Prob

abili

ty o

f inc

orre

ctly

faili

ng 1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

If the expected level of contamination is low, set a low LBGR. This leads to the collection of few samples.

LBGR


DCGL

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8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors • If beta (β) is the probability of a Type II error when

running a statistical test on the data from a “clean” survey unit where the actual average concentration is below the DCGL, what is 1 – β?

• 1 – β is the probability that the test will make the correct decision when the null hypothesis is false!

• 1 – β the probability of the correct decision, is known as “power,” and everyone wants power.

• The next slide shows a power curve.

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Prob

abili

ty o

f Cor

rect

Dec

ision

Po

wer

(1

– β)

1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Power curve

LBGR Actual level of Cs-137 contamination

DCGL

77


8. Determine the Acceptable Probability (i.e., 𝛂𝛂 and 𝛃𝛃) of Making Type I and Type II Errors

• If alpha (α) is the probability of a Type I error when running a test on the data from a “dirty” survey unit where the actual average concentration is above the DCGL, what is 1 – α?

1 – α is the probability that the test will make the correct decision when the null hypothesis is true!

• 1 – α, the probability of the correct decision, is known as the “confidence” or “specificity.”

78

Planning the Final Status Survey 9. Determine the Appropriate Number of Unbiased

Measurements/Samples • As indicated previously, the smaller the difference

between the DCGL and the LBGR, the greater the number of measurements that will be required to prove that the survey unit is clean (reject the null hypothesis).

• In addition, it should be obvious that the greater the spread of the measurements, the greater the chance that some will be above the DCGL and the greater the number of required measurements.

79

Planning the Final Status Survey 9. Determine the Number of Measurements/Samples

DCGL

Prob

abili

ty o

f inc

orre

ctly

faili

ng 1.0

0.1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2


The more measurements above the DCGL, the more likely a “clean” survey unit fails the statistical test.

Actual data distribution in

survey unit

Mean concentration in

survey unit

80

(DCGL – mean) large and sigma large 10 samples needed

(DCGL – mean) small and sigma large 30 samples needed

(DCGL – mean) very large and sigma small

5 samples needed

(DCGL – mean) small and sigma small 10 samples needed

81


Measurements/Samples • Calculate the relative shift as follows:

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑆𝑆𝑆𝑅𝑅𝑓𝑓𝑅𝑅 =Δ𝜎𝜎

=𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑊𝑊 − 𝐷𝐷𝐿𝐿𝐷𝐷𝑅𝑅

𝜎𝜎

The LBGR should be set at the expected average (or median) level of contamination in the survey unit.

𝜎𝜎 is the expected variability of the measurements

82


9. Determine the Appropriate Number of Unbiased Measurements/Samples If the unity rule is used, determine the inputs for the relative shift calculation as follows:

DCGL = 1

LBGR = expected concentration

DCGL +

expected concentrationDCGL

+ etc. nuclide 1 nuclide 2

nuclide 1 nuclide 2

83

σ = σ

DCGL +

σDCGL

+ etc. 2 2


9. Determine the Appropriate Number of Unbiased Measurements/Samples • Relative shifts greater than 1 generally result in a

reasonable required number of samples/measurements.

As the value goes below 1, the number of samples or measurements can skyrocket.

• The number of measurements necessary to meet the DQOs is found in Table 5.3 if the WRS test is to be used, or Table 5.5 if the Sign test is to be used.

• The numbers in these Tables are “padded” by 20% as a buffer.

84


Measurements/Samples • This method for determining the appropriate number of

samples/measurements is intended as guidance for the licensee. It is not a requirement.

• The licensee is free to use whatever number of measurements/samples they want to use.

Using too few measurements simply increases the chance that the data will fail the statistical test.

85


Measurements/Samples - Example The DCGL is 5000 (dpm/100 cm2 or Bq/cm2)

The expected average/median level of contamination is 2500. That is what we use as the LBGR.

The expected standard deviation of the measurements is 625 (dpm/100 cm2 or Bq/ cm2)

α is set by the regulator at 0.025

β is chosen to be 0.05

The WRS Test is to be performed

86


9. Determine the Appropriate Number of Unbiased Measurements/Samples - Example

From Table 5.3 on page 5-30 in MARSSIM (which is used for the WRS test), it is determined that 11 measurements are required in the survey unit (and the reference area) .

If the relative shift had been 3, the required number of measurements would have been 12, only 1 more than was required for a relative shift of 4.

=5000 − 2500

625

= 4

Relative Shift =DCGL − LBGR

σ

87


9. Determine the Appropriate Number of Unbiased Measurements/Samples • We have chosen the required number of unbiased

measurements to determine the mean and/or median concentration in the survey unit.

• Nevertheless, the unbiased measurements help to some degree in locating hotspots - one of them might happen to fall on a hotspot.

88


Measurements/Samples For Class 1 areas the scan MDC must be ≤ DCGLEMC

≤ DCGLW x AF

This criterion is always met if the scan MDC is < DCGLW

• In Class 1 survey units, additional samples or measurements are required if the scan MDC is above the DCGLEMC. This DCGLEMC is for the area bounded by four sampling (or measurement) points.

• The area bounded by four measurement points is the area of the survey unit divided by the number of measurements determined by the relative shift.

89


9. Determine the Appropriate Number of Unbiased Measurements/Samples

• If additional samples are required in Class 1 survey units because the scan MDC is above the DCGLEMC:

1. Divide the actual scan MDC by the DCGLW to calculate the new required area factor.

2. Determine the area (e.g., m2) that corresponds to this area factor.

3. Dividing this into the survey unit area gives the new required number of samples (or measurements).

90

Planning the Final Status Survey 10. Create the Reference Grid and Determine the

Coordinates for the Measurement/Sample Locations • The choice of reference grid is up to the licensee. One

desirable characteristic is that the reference grid be easily understood by everyone that will have to use it.

• For class 3 survey units, use random numbers to generate the x and y coordinates for all the measurement locations.

• For Class 1 and 2 survey units, use random numbers to generate the x and y coordinates for the starting point (origin) of the systematic measurements. The coordinates for the rest of the measurements are easily calculated.

91


Example:

The survey unit is 36 m x 50 m.

Two random numbers from MARSSIM: 0.154 and 0.766

The x coordinate for the sample is 36m x 0.154 = 5.5m

The y coordinate for the sample is 50m x 0.766 = 38.3m

Sample location is (5.5m, 38.3m)

0 10 20 30 0

10

20

30

40

50

y

x

10. Create the Reference Grid and Determine the Coordinates for the Measurement/Sample Locations

92

Planning the Final Status Survey 10. Create the Reference Grid and Determine the

Coordinates for the Measurement/Sample Locations

For triangular grids, the distance between measurement points (L) is:

𝐷𝐷 =𝐴𝐴

0.866𝑛𝑛

𝐴𝐴 is the area of the survey unit.

𝑛𝑛 is the required number of measurements/samples.

93

Assessing the Data

94

Assessing the Data

General • Data Assessment is described in MARSSIM as having three

components:

– Data Verification

– Data Validation

– Data Quality Assessment (DQA)

95

Assessing the Data

Data Verification (MARSSIM 9.3.1, NUREG-1576 Chapter 8)

• In essence, someone verifies that the laboratory did everything they were supposed to do.

• Data verification is always discussed in terms of a laboratory, but the concept can also applied to the surveyor in the field making measurements and collecting samples.

• For example, it should be determined that the lab used the selected analytical method, performed the requisite daily instrument performance checks, analyzed the appropriate quality control (QC) samples, participated in the appropriate inter-laboratory comparison studies, etc.

96

Assessing the Data

Data Validation (MARSSIM 9.3.2, App. N, NUREG-1576 Chapter 8) • Individual data points are reviewed and flagged as

necessary according to their usability. Typical flags:

J data point questionable

U data point below Critical Level or MDC

R data point rejected

• In validating data, the individual involved considers whether the number makes sense from an historical perspective, consideration of chemistry, the observed vs. expected ratios of different nuclides, etc.

97

Assessing the Data

Data Quality Assessment (MARSSIM 8.2) • MARSSIM describes the DQA process as having the

following five steps:

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

• One problem with the above is that the statistical tests were selected when the survey was being designed.

98

Assessing the Data

Data Quality Assessment • A better description of the DQA process might be:

1. Preliminary DQO and Data Review

2. Plot the Data

3. If Necessary, Perform the Sign Test/WRS Test

4. Perform an Elevated Measurement Comparison

5. Determine that the Total Dose from All Sources is Below the Release Criterion

99

Assessing the Data

1. Preliminary DQO and Data Review • Identify the number of valid measurements, lowest and

highest measurements, mean, median, standard deviation.

• Determine if the area classification appears correct.

• Was the requisite number of measurements made.

• Determine if the mean is above or below the LBGR.

• Survey unit fails if the average measurement (Sign test) or the difference between the average survey unit and reference area measurements (WRS test) exceeds the DCGLW. Note, does not include biased measurements, only those collected for the statistical tests.

100

Assessing the Data

1. Preliminary DQO and Data Review • A statistical test is not necessary if all the measurements

(Sign test), or the difference between the highest survey unit measurement minus the lowest background measurement (WRS test), is below the DCGLW. As before, this does not include judgmental and biased measurements.

• A statistical test is necessary if the average measurement (Sign test) or the difference between the average survey unit and reference area measurements (WRS test) is below the DCGLW but some measurements are above the DCGLW. This doesn’t include biased measurements.

101

Assessing the Data

2. Plot the Data (MARSSIM 8.4, NUREG-1505 4.2.2) • A posting plot is produced in which the measurements (Bq/kg,

pCi/g etc.) are indicated on a drawing of the survey unit at the locations where the measurements were taken

• A histogram might also be generated

102

Assessing the Data

3. If Necessary, Perform the Sign Test (MARSSIM 8.3, NUREG-1505 Chapter 5) This test only employs the unbiased random or systematic measurements, not judgmental or otherwise biased data.

• The total number of measurements being evaluated in the survey unit is N.

• The number of measurements below the DCGLW is the statistic S.

• If a measurement is the same as the DCGLW, it is not counted and the total number of measurements (N) is reduced by one.

103

Assessing the Data

3. If Necessary, Perform the Sign Test (MARSSIM 8.3, NUREG-1505 Chapter 5) • If S is above the appropriate critical value in Table I.3 of

Appendix I in MARSSIM, the Null Hypothesis (that the survey unit exceeds the release criterion) is rejected.

• If S is tied with or below the critical value (Table I.3 in MARSSIM App. I), we fail to reject the Null Hypothesis and a decision must be made as to how to proceed.

We say we “fail to reject the null hypothesis” rather than “accept the null hypothesis” because the conclusion is weak. It could result from inadequate data.

104

105

Assume that the DCGL is 140. Alpha is 0.025. N is 22. The total number of data points below the DCGL (140) is the statistic S. In this example, S = 17 This statistic is compared with the critical level for the Sign Test.

106

In this case, the statistic (17) exceeds the critical value (16). As such, the null hypothesis is rejected.

Assessing the Data

3. If Necessary, Perform the WRS Test (MARSSIM 8.4, NUREG-1505 Chapter 6) This test only employs the unbiased random or systematic measurements, not judgmental or otherwise biased data.

• Add the DCGLW to each reference area measurement.

• The adjusted reference area measurements are pooled with the survey unit measurements.

• The pooled measurements are ranked (sorted) from lowest to highest (1, 2, 3, etc.).

Tied values are assigned an average rank (e.g., 2.5).

107

Assessing the Data

3. If Necessary, Perform the WRS Test (MARSSIM 8.4, NUREG-1505 Chapter 6) • The sum of the ranks of the adjusted reference area

measurements is the statistic Wr.

• If Wr is above the appropriate critical value in Table I.4 of Appendix I in MARSSIM, the Null Hypothesis (that the survey unit exceeds the release criterion) is rejected.

• If Wr is tied with or below the critical value, we fail to reject the Null Hypothesis and a decision must be made as to how to proceed.

108

109

Assume that the DCGL is 160. Alpha is 0.025. n and m are 11. The sum of the ranks of the adjusted reference area data (in red) is the statistic W. In this example, W = 187 This statistic is compared with the critical level for the WRS Test.

Area Data Adjusted Data

Rank

R 49 209 22

R 35 195 12

R 45 205 17.5

R 45 205 17.5

R 41 201 14

R 44 204 16

R 48 208 21

R 37 197 13

R 46 206 19

R 42 202 15

R 47 207 20

S 104 104 9.5

S 94 94 4

S 98 98 6

S 99 99 7

S 90 90 1

S 104 104 9.5

S 95 95 5

S 105 105 11

S 93 93 3

S 101 101 8

S 92 92 2

110

In this case, the statistic (187) exceeds the critical value (156). As such, the null hypothesis is rejected.

Assessing the Data

4. Perform an Elevated Measurement Comparison (MARSSIM 8.5.1, NUREG-1505 Chapter 6)

• This test involves all the survey unit measurements, i.e., biased and unbiased measurements.

• Every measurement (above background) above the investigation level triggers an investigation.

• The elevated measurement comparison investigation typically occurs as soon as someone becomes aware that an investigation level has been exceeded.

111

Assessing the Data


• For hotspots, the investigation might involve confirming the initial measurement, determining the area of the contamination, determining the average concentration in the hot spot and determining the DCGLEMC for the hot spot. Remedial action would be performed as necessary.

• If a “hotspot,” is found that contains multiple nuclides, the investigation might demonstrate compliance by using a sum of the ratios approach as indicated on the next slide.

112

Assessing the Data


• The contamination in a hot spot does not exceed the release criterion if:

𝐴𝐴𝑅𝑅𝑅𝑅.𝐷𝐷𝐶𝐶𝑛𝑛𝐶𝐶.𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝐸𝐸𝐸𝐸

+𝐴𝐴𝑅𝑅𝑅𝑅.𝐷𝐷𝐶𝐶𝑛𝑛𝐶𝐶.𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝐸𝐸𝐸𝐸

+𝐴𝐴𝑅𝑅𝑅𝑅.𝐷𝐷𝐶𝐶𝑛𝑛𝐶𝐶.𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝐸𝐸𝐸𝐸

< 1

This only indicates that the potential dose from this one hot spot meets the release criterion. The dose from other contamination in the survey unit also has to be considered.

For this reason, an individual hot spot might be remediated even though its contribution to the total dose is well below the release criterion.

Nuclide 1 Nuclide 2 Nuclide 3

113

Assessing the Data

5. Determine that the Total Dose from All Sources is below the Release Criterion (MARSSIM 8.5.2, NUREG-1505 8.1) • Licensees often fail to assess the total dose due to all sources in

a survey unit.

• The recommended method to do so is to employ the sum of fractions approach as follows:

𝛿𝛿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑊𝑊

+(𝑅𝑅𝑅𝑅𝑅𝑅. 𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶. 𝑅𝑅𝑛𝑛 𝑆𝐶𝐶𝑅𝑅 𝑠𝑠𝑠𝑠𝐶𝐶𝑅𝑅𝑠 − 𝛿𝛿)𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝐸𝐸𝐸𝐸 𝑓𝑓𝐶𝐶𝑓𝑓 𝑆𝐶𝐶𝑅𝑅 𝑠𝑠𝑠𝑠𝐶𝐶𝑅𝑅𝑠

+(𝑅𝑅𝑅𝑅𝑅𝑅. 𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶. 𝑅𝑅𝑛𝑛 𝑆𝐶𝐶𝑅𝑅 𝑠𝑠𝑠𝑠𝐶𝐶𝑅𝑅𝑠 − 𝛿𝛿)𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝐸𝐸𝐸𝐸 𝑓𝑓𝐶𝐶𝑓𝑓 𝑆𝐶𝐶𝑅𝑅 𝑠𝑠𝑠𝑠𝐶𝐶𝑅𝑅 2

+ 𝑅𝑅𝑅𝑅𝐶𝐶. < 1

δ is the average concentration in the survey unit determined from the unbiased measurements

114

Decision Phase of the

Data Life Cycle

115

Decision Making

General • A decision as to how to proceed needs to be made when

things go wrong.

• In many cases, the regulator must approve the decision.

It is best if the various possibilities have been discussed ahead of time.

116

Decision Making

Things that Can Go Wrong and Require a Decision 1. The Survey Unit was Misclassified

2. The Average Concentration in the Survey Unit Exceeded the DCGL

3. The Survey Unit Failed the Statistical Test

4. The Survey Unit Failed the Elevated Measurement Comparison

5. The Total Dose from all Sources Exceeded the Release Criterion

117

Decision Making

1. The Survey Unit was Misclassified • Measurements above the DCGL in a Class 2 area or above

a “small fraction” of the DCGL in a Class 3 area indicate that the area might have been misclassified.

• If so, the area might have to be reclassified, subdivided into smaller survey units and resurveyed.

• The regulator might be flexible if there has not been a “pattern of misclassification” and the potential doses are small.

They might be satisfied with simply cleaning up the areas of concern.

118

Decision Making

2. The Average (Mean) Concentration in the Survey Unit Exceeded the DCGL • The entire survey unit (not isolated areas) might have to

be remediated and resurveyed.

• The DCGLW might have been too low.

Consider changing the input parameters in the dose modeling (e.g., RESRAD) and establish a higher DCGL.

• Release the site under restricted conditions.

119

Decision Making

3. The Survey Unit Failed the Statistical Test • The entire survey unit (not isolated areas) might have to

be remediated and resurveyed.

• The test might have been performed incorrectly – check to see if this was the case.

• The null hypothesis was not rejected because not enough measurements (or samples) were obtained.

If there is reason to believe that this is the case, the regulator might permit one round of “double sampling.”

120

Decision Making

3. The Survey Unit Failed the Statistical Test • Two reasons to believe not enough measurements were

taken:

The actual standard deviation of the measurements was greater than the standard deviation used in the calculation of the relative shift.

The average of the measurements was higher than the LBGR used in the calculation of the relative shift.

121

Decision Making

3. The Survey Unit Failed the Statistical Test • If double sampling is permitted, additional measurements

or samples are obtained at randomly selected locations.

These additional measurements are added to the pool of unbiased measurements obtained previously and the statistical test is redone.

• The regulator will only permit one round of double sampling.

122

Decision Making

123

3. The Survey Unit Failed the Statistical Test • The test might have failed to reject the null hypothesis

because the Reference Area measurements were lower than appropriate for the survey unit.

Assess the suitability of the reference area.

Decision Making

4. The Survey Unit Failed the Elevated Measurement Comparison

• The “hot spot(s)” might have to be remediated and resurveyed.

• The DCGLW might have been too low.

Consider changing the input parameters in the dose modeling (e.g., RESRAD) and establish new DCGLs.

• The area factor might have been too low.

Consider changing the input parameters in the dose modeling (e.g., RESRAD) used to calculate the area factor.

124

Possible Ways to Save Money

125


1. If the DCGLs are low and demonstrating compliance will be difficult, consider generating site-specific (i.e., higher) DCGLs using the appropriate RESRAD code.

2. If the background is low compared to the DCGLs, consider “swallowing background” and using the sign test on the gross measurements. This eliminates the need for reference area data.

3. When assessing gross alpha and/or beta activities for multiple nuclides, consider using the most restrictive DGCL. This eliminates the need to determine a ratio between the different nuclides, something that might prove difficult.

126


4. Many nuclides are present, and/or their analysis is expensive, it might not be necessary to do a detailed analysis of all of these nuclides.

The regulator might permit the licensee to forego a detailed consideration of those radionuclides that collectively contribute a small fraction (<10%) of the release criteria.

See Section 3.3 of NUREG-1757: “the sum of the dose contributions from all radionuclides and pathways considered insignificant should be no greater than 10% of the dose criteria.”

127


4. Nevertheless, the dose from ALL nuclides must be considered in order to demonstrate compliance.

One way to accomplish this would be to reduce the release criterion (and the DCGLs) by 10%. For example, the DCGLs of the nuclides being analyzed, the significant contributors to dose, would be based on a release criterion of 22.5 mrem (0.225 mSv) rather than 25 mrem (0.25 mSv).

Although the insignificant radionuclides would not be analyzed in the FSS, their potential dose has been accounted for!

Needless, to say, the regulator requires data to support the assertion that the dose from the radionuclides being ignored is less than 10% of the release criterion.

128


5. Minimize the number of survey units. This can be done by keeping the individual survey units as large as possible.

6. The regulator might give permission to combine multiple adjacent (Class 1 or 2) into a single supersized survey unit if there would be no reduction in the scan coverage and the number of samples collected.

7. Automate data management as much as possible. For example, employ spreadsheets in a suitable format for direct use in the FSS report.

129

Miscellaneous

130

Miscellaneous

1. Subsurface Contamination MARSSIM doesn’t provide guidance regarding the evaluation of subsurface contamination (below 15 cm).

However, NUREG-1757 volume 2 (G.2.1) suggests: “The DCGL may be based on the assumption that the residual radioactivity may be excavated some day and that mixing of the residual radioactivity will occur during excavation. . . . When the appropriate DCGLs and mixing volumes based on an acceptable site-specific dose assessment are established, the FSS is performed by taking core samples to the measured depth of the residual radioactivity.”

131

Miscellaneous

1. Subsurface Contamination “The number of cores to be taken is initially the number (N) required for the WRS or Sign test, as appropriate. The adjustment to the grid spacing for an elevated measurement comparison (EMC) is more complicated than for surface soils, because scanning is not applicable. The core samples should be homogenized over a soil thickness that is consistent with assumptions made in the dose assessment, typically not exceeding 1 meter in depth. The appropriate test (WRS or Sign) then is applied to the sample results. Triangular grids are recommended, because they are slightly more effective in locating areas of elevated concentrations.”

132

Miscellaneous

1. Subsurface Contamination “Site-specific EMCs may also need to be developed to demonstrate regulatory compliance. Generic guidance has not yet been developed for performing an EMC for subsurface samples; therefore, licensees should discuss this matter with NRC staff on a case-by-case basis.

The sampling approach described above may not be necessary if sufficient data to characterize the subsurface residual radioactivity are available from other sources. For example, for some burials conducted under prior NRC regulations, the records on the material buried may be sufficient to demonstrate compliance with the radiological criteria for license termination.”

133

Miscellaneous

2. Surface Water and Sediments MARSSIM doesn’t provide guidance regarding the evaluation of contaminated water or sediment. However, NUREG -1757 Volume 2 (F.6) states:

“The need for surface water samples should be evaluated on a case-by-case basis. Surveys for water should be based on appropriate environmental standards for water sampling. If the body of water is included in a larger survey unit, then sediment samples should be taken at sample locations selected by the normal method without taking the body of water into consideration.”

In other words, sediment could be treated as if it were soil.

134

Miscellaneous

2. Surface Water and Sediments

Pond

Two planned soil sampling locations fell on a pond (black circles).

A conservative approach would be to collect sediment samples at these two locations and analyze them as if they were soil. The dose due to contaminants in sediment

should be lower than the dose due to contaminants in soil.

135

Miscellaneous

3. Rubble, Debris, and Rocks MARSSIM doesn’t provide guidance regarding the evaluation of contaminated rubble debris or rocks. However, NUREG-1757 addresses this topic in Section G.2.2 of Appendix G:

“If the radioactivity is not substantially elevated, the rubble, debris, and rocks may be evaluated as part of a larger survey unit. When these materials will be evaluated as part of a larger survey unit and when they are found on a relatively small fraction of the area of a survey unit, the volumetric soil DCGL should be used uniformly throughout the survey unit. However, the reasonableness of modeling rocks and rubble as soil should be justified by the licensee.”

136

Miscellaneous

137

4. Embedded Piping MARSSIM doesn’t provide guidance regarding the evaluation of contamination in piping. However, NUREG-1757 Volume 2 addresses this topic in Section G.1.5 of Appendix G:

“Embedded piping is piping embedded in a durable material, typically concrete, that cannot be easily removed without significant effort and tools . . . The normal room surveys will adequately account for direct (external gamma) radiation from the pipes when the pipes are in place and undisturbed. The direct (external gamma) dose from the pipes will be in addition to the total effective dose equivalent from the residual radioactivity on surfaces in the room.”

Miscellaneous

4. Embedded Piping “It may also be necessary to take into consideration building that would disturb the piping as described in “Residual Radioactive Contamination from Decommissioning” NUREG/CR-5512, Volume 1. If this is done, the survey should be consistent with the dose modeling assumptions.”

To ensure that the material in the pipe cannot escape and pose an inhalation or ingestion hazard, the pipe could be filled with grout.

138

Miscellaneous

5. Ventilation Ducts MARSSIM doesn’t provide guidance regarding the evaluation of contamination in ventilation systems. However, NUREG-1757 Volume 2 addresses this topic in Section G.1.4 of Appendix G:

“External duct surfaces of ventilation ducts are surveyed as if they are a part of the building surface. For internal duct surfaces, surveys should be performed consistent with the dose modeling assumptions.”

“Consistent with the dose modeling assumptions” means that the survey method should be capable of measuring the source term used in the dose modeling - either a concentration (e.g., dpm/100 cm2, Bq/ cm2) or a total activity (e.g., mCi, MBq).

139

Miscellaneous

6. Sewer Systems, Waste Plumbing Systems, and Floor Drains While MARSSIM doesn’t consider the evaluation of contamination in sewers or drains, NUREG-1757 Volume 2 addresses this issue in Section G.1.3 of Appendix G:

“Residual radioactivity in sewer systems and floor drains generally does not contribute to the dose pathways in the building occupancy scenario or the residential scenario; thus, the dose from residual radioactivity in sewer pipes should be calculated using a site-specific scenario.”

140

The End

141

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