Draft

ADVISORY BOARD ON 

RADIATION AND WORKER HEALTH

National Institute for Occupational Safety and Health

SC&A EVALUATION OF NIOSH WHITE PAPER, “METHOD 

TO ASSESS INTERNAL DOSE USING GROSS ALPHA, BETA, 

AND GAMMA BIOASSAY AND AIR SAMPLING AT THE 

LAWRENCE BERKELEY NATIONAL LABORATORY” 

Contract No. 211-2014-58081 

SSCA-TR-2018-SP003, Revision 0 

Prepared by 

Robert Barton, III, CHP

Ron Buchanan, PhD, CHP 

Joyce Lipsztein, PhD

SC&A, Inc.

2200 Wilson Boulevard, Suite 300 

Arlington, VA 22201-3324 

Saliant, Inc.

5579 Catholic Church Road 

Jefferson, MD 21755 

April 2018 

DISCLAIMER 

This is a working document provided by the Centers for Disease Control and Prevention (CDC) technical 

support contractor, SC&A for use in discussions with the National Institute for Occupational Safety and 

Health (NIOSH) and the Advisory Board on Radiation and Worker Health (ABRWH), including its 

Working Groups or Subcommittees. Documents produced by SC&A, such as memorandum, white paper, 

draft or working documents are not final NIOSH or ABRWH products or positions, unless specifically 

marked as such. This document prepared by SC&A represents its preliminary evaluation on technical 

issues.

NOTICE: This document has been reviewed to identify and redact any information that is protected by 

the Privacy Act 5 U.S.C. § 552a and has been cleared for distribution. 
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SC&A, INC.: Technical Support for the Advisory Board on Radiation and 

Worker Health Review of NIOSH Dose Reconstruction Program

DOCUMENT TITLE: SC&A Evaluation of NIOSH White Paper, “Method to Assess 

Internal Dose Using Gross Alpha, Beta, and Gamma Bioassay and 

Air Sampling at the Lawrence Berkeley National Laboratory” 

DOCUMENT NUMBER/

DESCRIPTION:

SCA-TR-2018-SP003

REVISION NO.: 0 (Draft)

SUPERSEDES: N/A

EFFECTIVE DATE: April 18, 2018

TASK MANAGER Joe Fitzgerald, MS, MPH [signature on file]

PROJECT MANAGER: John Stiver, MS, CHP [signature on file]

DOCUMENT 

REVIEWER(S):

Joe Fitzgerald, MS, MPH [signature on file]

John Stiver, MS, CHP [signature on file]

Record of Revisions

Revision 

Number

Effective 

Date

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0 (Draft) 4/18/2018 Initial issue
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TABLE OF CONTENTS

Abbreviations and Acronyms ......................................................................................................... 4

1 Introduction and Background ................................................................................................. 5

2 Summary of White Paper ........................................................................................................ 5

2.1 Data Used in White Paper ............................................................................................... 5

2.2 Major Items Considered in White Paper ......................................................................... 5

3 SC&A’s Evaluation ................................................................................................................ 6

3.1 Items Not Addressed in White Paper .............................................................................. 6

3.1.1 Air Samples May Not Represent Concentrations Breathed by Workers .................... 6

3.1.2 Technical Issues with Gross Counting Data Conversion to Concentration for Use in 

Dose Reconstruction ................................................................................................... 8

3.1.3 Potentially Missed Radionuclides ............................................................................. 12

3.1.4 Incomplete Information in Claimants’ DOE Files .................................................... 12

4 Summary Conclusions .......................................................................................................... 16

5 References ............................................................................................................................. 16
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ABBREVIATIONS AND ACRONYMS

ABRWH Advisory Board on Radiation and Worker Health 

BZ breathing zone 

BZA breathing-zone air

C carbon 

cpm counts per minute

DOE U.S. Department of Energy 

dpm disintegrations per minutes 

Er erbium 

GA general air

GM Geiger-Müller tube 

H hydrogen 

I iodine 

ICRP International Commission on Radiological Protection

LBNL Lawrence Berkeley National Laboratory

LLNL Lawrence Livermore National Laboratory

MeV mega-electron volt 

MPC maximum permissible concentration

NIOSH National Institute for Occupational Safety and Health

NUMEC Nuclear Materials and Equipment Corporation 

ORAUT Oak Ridge Associated Universities Team

P phosphorus 

pCi/m3

 picocuries per cubic meter

SEC Special Exposure Cohort 

SRDB Site Research Database

TBD technical base document

U uranium 

USDHEW U.S. Department of Health Education and Welfare 
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1 INTRODUCTION AND BACKGROUND

On September 18, 2017, the National Institute for Occupational Safety and Health (NIOSH) 

issued a white paper, Method to Assess Internal Dose Using Gross Alpha, Beta, and Gamma 

Bioassay and Air Sampling at the Lawrence Berkeley National Laboratory (NIOSH 2017a, 

referred to as “the white paper”). This report is SC&A’s evaluation of that white paper.

2 SUMMARY OF WHITE PAPER

The purpose and overall methodology is summarized on page 6 of the white paper as follows: 

This white paper provides methods for assessing internal dose using gross alpha, 

gross beta, and gross gamma bioassay results and air sampling data at Lawrence 

Berkeley National Laboratory (LBNL). Methods are provided for assessing gross 

alpha, gross beta, and gross gamma results bioassay results that are below and 

above the minimum detectable activity. Air sampling data is used to assign 

internal dose from shorter-lived radionuclides that may not be detected by 

bioassay. In addition, a method is provided for assigning internal dose to an 

unmonitored worker using air sampling data. Example calculations are provided 

validating the proof of concept. This method would apply for 1962 and later. The 

methods described in this white paper will need to be implemented in an internal 

dose tool. 

The white paper essentially recommends a method of applying recorded air sample and bioassay

sample results, obtained using gross-count data (where the specific radionuclides may not 

identified), to the many possible radionuclides present at LBNL to derive intake values and then 

assigning the maximum feasible dose to the organ or tissue of interest for 1962 and later years. 

2.1 DATA USED IN WHITE PAPER

Table 1, page 10, is the main source of data used in the white paper. This table lists the 95th 

percentile annual air concentration for alpha and beta/gamma emitters in picocuries per cubic 

meter (pCi/m3) for the period 1962–1993. Additionally, the white paper addresses the use of 

bioassay data, if available. All intake and dose calculations in the white paper are based on air 

samples and bioassay samples. Note that the data in Table 1 are not radionuclide specific, and 

that the concentrations for beta emitters and gamma emitters are combined and not listed 

separately.

2.2 MAJOR ITEMS CONSIDERED IN WHITE PAPER

NIOSH considered the following items in the methodology used in the white paper: 

• Radionuclides with half-lives less than 20 hours (Table 2, page 11, for beta and gamma) 

• Radionuclides with half-lives between 20 hours and 100 days (Table 9, pages 20–22 for 

alpha, beta, and gamma emitters) 
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• Radionuclides with half-lives greater than 100 days (Table 4, page 14, for alpha emitters, 

Table 6, page 16, for beta emitters, and Table 8, page 19, for gamma emitters) 

• Branching factors of the emitted radiation (Table 3, page 12, for alpha emitters, Table 5, 

page 15, for beta emitters, and Table 7, page 17, for gamma emitters). 

3 SC&A’S EVALUATION

3.1 ITEMS NOT ADDRESSED IN WHITE PAPER

While the white paper did a commendable job of addressing many aspects of applying gross 

alpha, beta, and gamma results to dose reconstruction, SC&A found that the following technical 

issues were not sufficiently addressed.

3.1.1 Air Samples May Not Represent Concentrations Breathed by Workers 

The white paper states (NIOSH 2017a, page 7): 

These air samples were also called breathing-zone (BZ) air samples, but were 

actually work area air samples.

The limitations of work area air sampling were recognized in the early days at 

LBNL. Air samplers did not normally reflect general room area atmosphere, but 

only a closely circumscribed volume near the sampling head (Thaxter 1955). 

Lawrence Berkeley National Laboratory ([LBNL 1981]) noted: 

Our air samples are not good representative samples of breathing 

air. Air currents are so unpredictable that air even a few inches 

away from a person’s nose can be quite different from that which 

he breathes. Air samples give only a rough idea of an individual’s 

exposure, and for that reason, any radioactivity detected should be 

taken seriously, even if only a small fraction of MPC. [Emphasis 

added.]

A LBNL document, Air Monitoring – UCRL Berkeley, August 24, 1955 (Thaxter 1955) states: 

Possibly one of the most important limitations resides in the fact that the air 

sampled is that within a few inches of the sampling tube opening. Thus final 

readings do not normally reflect general room atmosphere but only a closely 

circumscribed volume near the sampling head. Thus a sampling at bench level 

will not tell you what the chemist breathed, necessarily.

The white paper refers to three types of air samples: room air, work area air (air sampler situated 

near the work), and BZ air. The use of the term “room air” is found once on page 91, the use of 

the term “work area air” is found twice on page 7, and the term “BZ air” is used throughout the 

white paper. It appears that the term BZ air is used when actually the air sample results could 

have been from room or work area sampling. 
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The fact that results from air sampling do not always represent BZ air concentrations has also 

been documented at other U.S. Department of Energy (DOE) sites and is the subject of 

International Commission on Radiological Protection (ICRP) Publication 130, Occupational 

Intakes of Radionuclides: Part 1 (ICRP 2015). Examples of concerns about representativeness 

include the following: 

1. The NUMEC site profile, ORAUT-TKBS-0041, Site Profile for Nuclear Materials and 

Equipment Corporation, Apollo and Parks Township, Pennsylvania, Revision 04 

(NIOSH 2017b), states (page 50): 

Fifty percent of the lapel air sample results at the Apollo site showed 

concentrations 7 times greater than stationary air samples. The median of 

the ratio of lapel BZA to GA concentration results was found to be about 7 

at the Apollo and Parks Township sites. 

2. Lawrence Livermore National Laboratory (LLNL) was a sister laboratory of LBNL and 

was also administered by the University of California. NIOSH’s evaluation of the LLNL 

air sampling program indicates that air monitoring data were not representative of the 

workers’ BZ air and were a factor in granting the LLNL Special Exposure Cohort (SEC) 

for the period 1974–1989. NIOSH stated in a 2016 presentation to the Advisory Board 

(NIOSH 2016): 

NIOSH has determined the available air monitoring data from Building 

251 may not be adequately representative of the worker breathing zones, 

and are consequently not considered sufficient for Building 251 dose 

reconstruction during the period 1974–1989. 

3. SC&A’s evaluation of using room air sampling at the Argonne National Laboratory-West 

is provided in a memorandum of July 14, 2016. In that memorandum, SC&A points out 

that room air samples do not generally represent the air concentration breathed by 

workers (SC&A 2016). 

4. ICRP Publication 130 (ICRP 2015, page 98) summarized the problem: 

Breathing zone measurements can vary significantly as they can be 

affected by measurement conditions such as orientation of the sampler 

with respect to source, on which lapel (right or left) the sampler is worn, 

design of the air sampling head, particle size, local air velocity and 

directions and sharp gradients in and around the breathing zone of 

workers. 

ICRP Publication 130 (ICRP 2015) cites studies that show that personal air sample data 

can be used to obtain satisfactory estimates of intakes for groups of workers. However, 

for individuals, personal air samplers (PAS) lack correlation between personal air 

sampler results and bioassay-based intake estimate for known acute exposures. Static air 

sampling can underestimate concentrations in air in the BZ of a worker; therefore, 
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personal air sampler/static air sampling air concentrations can vary from less than 1 up to 

50, depending on the nature of the work. 

Therefore, SC&A has the following finding: 

Finding 1: Air Samples May Not Represent Concentrations Breathed by Workers – It 

appears that the white paper uses the terms BZ, room air, and work area air samples 

interchangeably. However, as indicated in the quoted LBNL documents above, room air or work 

area air samples may not represent the radionuclide concentrations in the BZ of workers. This is 

a problem that has been recognized at other DOE sites and applies to LBNL also. The use of 

room air or work area air samples could lead to a sufficient underestimate of the worker’s intakes

when the worker is located close to the source and the air sampler is located elsewhere. 

3.1.2 Technical Issues with Gross Counting Data Conversion to Concentration for Use in 

Dose Reconstruction

It appears from the white paper that the air concentration values (pCi/m3) listed in Table 1, 

page 10, were obtained from LBNL records that listed the air concentration values in pCi/m3, or 

maximum permissible concentration (MPC) values (from which the pCi/m3 values were 

derived). The original counts per minute (cpm) results obtained from the counting equipment 

after counting the air samples were converted to disintegrations per minute (dpm). The pCi/m3, 

or percent of MPC, values were then obtained from these derived dpm values and the total air 

volume. The original conversion from cpm to dpm was based on an assumed radionuclide being 

counted, with an assigned efficiency at the time of counting using some standard for calibration 

(e.g., uranium-235 [U-235], strontium-90, etc.). The backscatter, sample self-absorption, air and 

window absorption, and detector intrinsic efficiency will vary depending on the radionuclide 

present, even if all the physical counting parameters remain constant. The many different 

radionuclides used to assign dose in the white paper have varying energies: approximate energy 

ranges are alpha 4.5 mega-electron volts (MeV) to 6.5 MeV, beta 0.010 MeV to 0.3 MeV median

energy (0.020 MeV to 2 MeV maximum energy), and photons from x-rays to approximately 

2 MeV. Therefore, the resulting counting efficiency (cpm to dpm conversion factor) would vary 

according to the energy of the radiation (i.e., radionuclide), creating an issue with using a 

recorded dpm value, obtained under an assumed radionuclide, and assigning it to another 

radionuclide with a different energy emission spectra. This same analysis applies to bioassay

results that were obtained from gross alpha, beta, or gamma counting of bioassay samples. 

SC&A analyzed the characteristics of the radiation detectors generally in use at the time the 

samples were counted and found that the type of radionuclide counted could influence the 

counting results by a factor of 2 to 10, depending on the energy and type of radiation (alpha, 

beta, or gamma) present. For the period of interest, it was assumed that the type of detectors in 

use were ionization chambers, gas-flow proportional counters, or Geiger-Müller (GM) tubes for 

gross alpha or beta/gamma counting. Sodium-iodide (NaI) or germanium-lithium (Ge(Li)) 

detectors would have been used for gamma-only counting. The following is a brief discussion of 

the dependence of the efficiency of a gross counting system as a function of the type and energy 

of the radiation emitted. 
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Alpha – Sample, Air, and Window Attenuation versus Energy of Alpha Particles

Alpha particles are helium nuclei with a +2 charge and have relatively low penetrating power 

compared to betas and photons. Therefore, material located between the point of emission inside 

the sample and the sensitive volume of the detector will decrease the chances of the alpha 

particle being counted. The more energetic the alpha particle, the more likely it will penetrate the 

sample material and air/window to reach the detector’s sensitive volume to be counted.

Therefore, all other physical parameters remaining constant, radionuclides with the more 

energetic alpha particles are more likely to be counted. Because of the relatively large mass and 

low penetrating power of alpha particles, backscatter from the sample holder and surrounding

structures is generally not an important function of alpha energy. 

Alpha particle energies for radionuclides listed in the white paper range from approximately 4.5 

MeV to 6.5 MeV. According to the information in the Radiological Health Handbook, Revised 

Edition (USDHEW 1970, page 125), the range of alpha particles in air (which would be a firstorder indicator for other materials as well) increases by a factor of approximately 2 when going 

from 4.5 MeV alphas to 6.5 MeV alphas. Therefore, if the detector was calibrated using a 

radionuclide standard emitting 6 MeV alphas but was actually counting an air or bioassay sample 

emitting 4.5 MeV alphas, the reported dpm could be understated for the lower energy (4.5 MeV)

alpha-emitting radionuclide because of the sample thickness and any air/window materials, if 

present.

Beta – Sample and Window Attenuation and Backscatter versus Energy of Beta Particles

Beta particles are energetic electrons with a -1 charge and have medium penetrating power 

compared to alpha particles and photons. Therefore, material located between the point of 

emission inside the sample and the sensitive volume of the detector will decrease the chances of 

the beta particle being counted. The more energetic the beta particle, the more likely it will 

penetrate the sample material and detector window to reach the detector’s sensitive volume to be 

counted. Therefore, all other physical parameters remaining constant, radionuclides with the 

more energetic beta particles are more likely to be counted. Additionally, because of the 

relatively small mass and medium penetrating power of beta particles, backscatter from the 

sample holder and surrounding structures is a function of beta energy. 

The beta particles listed in the white paper have a range of approximately 0.010 MeV to 

0.300 MeV of median energy (range of approximately 0.020 MeV to 2 MeV maximum energy).

Some Site Research Database (SRDB) documents cited in the white paper provide brief 

snapshots of the beta gross counting procedures used at LBNL during the period of interest. A 

1964 University of California at Berkeley Manual of Instructions, Automatic Processing of 

Airborne Activity Data (Peck 1964, pages 29 and 30), states that the beta/gamma counting for 

Filter-Queen air samples was performed using a GM tube and that the efficiency of the GM tube 

was a function of beta energy, with a median value of 0.6 MeV used for standardization (SC&A 

assumes that this means the beta calibration standard had a median beta energy of 0.6 MeV). The

manual gives the beta/gamma efficiency for the hand counter as 5% and the automatic counter as 

3%. For the stack membrane air filter, the manual gives the alpha efficiency as 47.4 % (no alpha 

energy provided), with a beta/gamma efficiency of 12.1%. For the stack charcoal samples, the

manual gives the beta/gamma efficiency as 12.1% ÷ 2 = 6.05%. These changes in efficiencies 
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indicate that the detector efficiency is an important function of the sample material and thickness 

and hence would be dependent on the particle’s energy.

A 1965 radiochemistry analysis report by the LBNL Health Chemistry Department (LBNL 1965, 

PDF page 15) states that the effective beta energy used in the analysis of the Filter Queen filters 

was 1.3 MeV (in contrast to the median beta energy of 0.6 MeV stated in Peck 1964). A 1966 

Health Chemistry Report of Special Analysis states that “There is a reasonably good fit, although 

our points lead to a slightly different shape [curve]. The discrepancy is very likely due to energy 

dependence in the response of our GM counter” (emphasis added) (LBNL 1966, PDF page 94).

The following summary indicates some of the parameters that determine the detection efficiency

of beta-particle counting systems. These parameters could influence the conversion of cpm to 

dpm for different energy beta particles (compared to the radionuclide standard used to calibrate 

the counting system). 

1. Sample self-absorption – The thicker the sample, the greater the amount of radioactive 

material it can contain. However, as the sample thickness increases, the fraction of the 

beta particles that escape the sample and interact with the detector’s sensitive volume 

decreases. Therefore, for a given sample thickness, the beta counting efficiency decreases 

with decreasing beta energy. Sample self-absorption can range from negligible for beta 

particles in the 1 to 2 MeV range to around 50% for beta particles of 0.1 MeV (Price 

1964, pages 132–134), depending on the sample material and thickness. 

2. Backscatter – Backscatter is the result of a beta particle initially traveling in the opposite 

direction from the detector but then being scattered back into the detector by the sample 

backing material. The backscatter factor can range from 1.0 to 2, depending on the 

atomic number and thickness of the backing material the energy of the beta particle. The 

backscatter factor increases with increasing beta particle energy, increasing backing 

thickness, or increasing backing atomic number (Price 1964, pages 131 and 132). 

3. Window thickness – The fraction of beta particles entering the detector’s sensitive 

volume is a function of the energy of the beta particle and the material and thickness of 

the detector wall or window. The transmission of beta particles can range from a few 

percent for 0.250 MeV (calcium-45) betas, to near 100% for 1.707 MeV (phosphorus-32 

[P-32]) betas (Price 1964, page 128) 

4. Detector intrinsic efficiency – Once a beta particle reaches the sensitive volume of the 

detector, the probability of detection of the particle is near unity. 

These beta counting parameters are all cumulative and decrease the system’s counting efficiency 

as the energy of the beta particle decreases. Therefore, if the detector was calibrated using a 

radionuclide standard emitting relatively energetic beta particles (such as 0.6 MeV betas) but was 

actually counting an air or bioassay sample emitting a lower energy (such as 0.2 MeV betas), the 

reported dpm could be understated for the lower energy (0.2 MeV) beta-emitting radionuclide 

because of different transmission for the lower energy beta particles. 
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Gamma – Counter Efficiency versus Energy of Gamma Radiation

Gamma radiation from a given radionuclide generally consists of one or several mono-energetic 

photons per disintegration. Because of the relatively larger penetrating power of photons 

compared to charged particles, factors such as backscatter, sample thickness, and air attenuation 

are generally secondary issues for gamma detection, whereas the efficiency of the detector is the 

main concern.

Gamma Counting Using Gas-Filled Detectors

Gamma detection by gas-filled detectors depends upon the ability of the photon to pass through 

any wall or window of the detector to create secondary ions in the sensitive volume of the 

detector. Therefore, the overall efficiency of the detector is dependent upon the energy of the 

photon, the wall or window material, and the intrinsic efficiency of the detector. An example of 

this dependence is illustrated in Price 1964, page 138, which shows that the intrinsic efficiency 

of a gas-filled detector for photons increases by a factor of approximately 10 as the photon 

energy increases from approximately 0.200 MeV to 2.5 MeV. Therefore, if the detector was 

calibrated using a standard emitting 1.0 MeV photons but was actually counting an air or 

bioassay sample emitting 0.200 MeV photons, the reported dpm would be understated for the 

lower energy (0.200 MeV) photon-emitting radionuclide.

Gamma Counting Using Solid Detectors

Beyond the energy where the photon penetrates the housing or window of a solid detector (e.g., 

sodium-iodide or germanium-lithium), the detection efficiency decreases with increasing energy. 

An example of this is illustrated in a 1973 LBNL report, in which the counts per gamma 

response decrease by a factor of approximately 100 when going from 0.140 MeV to 1.33 MeV 

gamma-ray energy (LBNL 1973, PDF page 17). Therefore, if the gross-gamma detector was 

calibrated using a radiation standard emitting 0.662 MeV photons (e.g., cesium-137) but was 

actually counting an air or bioassay sample emitting 1.33 MeV photons (e.g., cobalt-60), the 

reported dpm was be understated for the greater energy (1.33 MeV) photon-emitting

radionuclide in the sample.

Uncertainties Associated with Using Gross Count Data

These analyses indicate that the uncertainties associated with projecting intake values based on 

gross counting of air and/or bioassay samples are high and cannot be used to infer intakes and 

resulting doses with sufficient accuracy. Additionally, the uncertainties concerning the 

representativeness of air samples themselves are also high and were cited in NIOSH’s white 

paper and outlined in SC&A’s Finding 1 above. The combination of two results with a high 

degree of uncertainty does not yield results with lower uncertainty but instead creates a much 

larger overall uncertainty in attempting to project intake values.

The shortcomings of using gross counting of samples to project intakes were factors in granting 

the LLNL SEC for the period 1974–1989. NIOSH stated in a 2016 presentation to the Advisory 

Board (NIOSH 2016):
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NIOSH has determined that it has insufficient information to verify that the 

routine in vitro bioassay program for Building 251 workers (combinations of 

analyses for gross alpha in urine, gross beta in urine, and plutonium in urine) 

was adequately sensitive for detection of U-233 intakes during the period 1974–

1989. 

Therefore, SC&A has the following finding: 

Finding 2: Technical Issues and Uncertainties with Gross Counting Data Conversion to 

Concentration/Intake for Use in Dose Reconstruction – Applying an air sample activity or 

bioassay sample activity, in units of recorded dpm or picocuries from gross counting, to different 

radionuclides for determining air concentrations or intakes, as proposed in the white paper, could 

lead to an underestimate of the intake because detector efficiency is dependent on the energy of 

the emitted radiation, and hence, the radionuclide, leading to large uncertainties. 

3.1.3 Potentially Missed Radionuclides

As LBNL was a research laboratory with a wide range of projects and functions, it is difficult to 

ascertain that all the radionuclides that could result in assignable doses are included in the white 

paper. Although the white paper includes a large number of potential radionuclides, other 

radionuclides may have been present that were not accounted for in the white paper. For 

example, the white paper does not include any of the iodine radionuclides, but LBNL technical 

basis document (TBD) ORAUT-TKBS-0049, Revision 02, Site Profile for the Lawrence 

Berkeley National Laboratory (NIOSH 2010), includes radionuclides of iodine (i.e., iodine-123 

[I-123], I-125, I-129, and I-131) as potential intakes in many of the tables. Additionally, two 

1967 LBNL Health Chemistry reports (LBNL 1967a; LBNL 1967b) indicate that I-126 was 

being monitored and that 130 microcuries had been released. SC&A scanned the LBNL site 

profile and found other radionuclides listed that are not included in the white paper (e.g., erbium165 [Er-165], Er-169, fermium-237, rhodium-102, and scandium-93). 

Therefore, SC&A has the following observation: 

Observation 1: Potentially Missed Radionuclides – It has not been demonstrated that the 

radionuclides listed in the white paper are all-inclusive of the potential radionuclides intakes at 

LBNL that are needed for adequate dose reconstruction for 1962 forward. 

3.1.4 Incomplete Information in Claimants’ DOE Files

The Introduction section of the white paper indicates the following approach concerning internal 

exposure potential for LBNL workers post-1961 (NIOSH 2017a, page 6): 

Bioassay requests were generally made either once or twice per year for each 

employee in the bioassay program. Workers who worked with or in areas that 

contained unsealed radioactive materials typically received bioassays.... 

Therefore, based on the typical LBNL bioassay monitoring frequencies, a single 

bioassay result indicates that the worker had, at most, one year of internal 

exposure potential before the date of the bioassay.... All other employment 
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periods with no bioassay indicate a potential exposure to environmental levels 

only. [Emphasis added.]

Table 4-2, Section 4.0, of the LBNL TBD (NIOSH 2010), provides the annual environmental 

intake rates for gross alpha, gross beta, tritium, and carbon-14 (C-14) that would presumably be 

used for claims with no indication of internal monitoring. However, in the example dose 

reconstruction beginning on page 27 of the white paper, it appears that unmonitored occupational 

doses would be assigned using the air sampling approach for a worker whose employment was 

only partially monitored internally (in the white paper example, the unmonitored period is 

assumed to occur from January 1, 1967, through December 31, 1968). Based on the introductory 

statements in the white paper and the subsequent dose reconstruction example, it appears that 

NIOSH assumed that a partially monitored worker is still considered a radiation worker and 

assigned unmonitored occupational internal doses based on the air sampling approach rather than 

the environmental intakes contained in the LBNL site profile.

However, the reliance on internal monitoring criteria to make such determinations is dependent 

on the effectiveness of appropriately identifying all relevant claimant-specific internal 

monitoring records and the inclusion of those records in the claimant’s DOE-supplied dosimetry 

file. To evaluate the effectiveness of identifying internal monitoring records in the available 

claimant population, SC&A reviewed bioassay records captured by NIOSH and collected in 

SRDB References 21985 (LBNL 2006a), 21986 (LBNL 2006b), and 32378 through 32392 

(LBNL 2007a through 2007o).1

1 SC&A identified and reviewed additional bioassay references in the SRDB; however (with rare exception), these 

additional files contained duplicate records that were in a different format.

 SC&A then compared bioassay records for identified claimants 

in the captured references to the DOE-supplied monitoring records for those individuals. Those 

DOE-supplied monitoring records represent the primary resource available to NIOSH in making 

determinations on internal dose assignment for individual dose reconstruction.2

2 SC&A recognizes that NIOSH often supplements DOE-supplied monitoring records with additional captured 

records, such as the SRDB bioassay records referenced in this section. However, the completeness of such captured 

records has not been established; therefore, the reliance on supplemental records to fill in any gaps in an individual’s 

monitoring record remains questionable. 

 

SC&A identified 36 claimants among the captured bioassay records, which represented 719 

internal monitoring results. While most of these results were attributed to tritium bioassays for a 

single claim (496 of 719 total samples), the remaining bioassay results represented a combination 

of gross alpha/beta/gamma urinalysis with occasional phosphorous, uranium, or thorium in vitro 

sampling. Of the 36 identified claimants with internal monitoring in the captured SRDB 

references, seven claim files did not reflect any of the corresponding internal monitoring records 

in the DOE-supplied monitoring files. These seven claims are described in Table 1 of this report. 

In four of the seven cases, internal monitoring was indicated to have occurred in the introductory 

letter provided by LBNL. However, the dates of these samples were not provided so that a 

comparison of intake rates based on bioassay and workplace air sampling could be made. In at 

least one of these cases, the referenced bioassay sample did not reflect the actual urinalysis 

sample identified in captured SRDB records (see Claim D). In the remaining three of seven 

reviewed cases, DOE indicates the energy employee was not monitored internally. 

 
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Table 1. Description of Seven Claims with Deficiencies in Observed Internal Monitoring 

Records

Case 

Reference

(Claim 

Number)

Missing Sample Type* (reason) and 

Date

Additional Comments

A

( )

Alpha (periodic) – /1962

Alpha (unspecified) – /1963

Alpha (unspecified) – /1964

Alpha (unspecified) – /1965

Alpha (unspecified) – /1966

Alpha (periodic) – /1967

Introductory letter in DOE monitoring file 

indicates that six routine gross alpha 

samples were taken. However, no dates are 

provided and the specific records are not 

included. 

B

( ) Alpha (periodic) – /1964

Introductory letter in DOE monitoring file 

indicates a single alpha sample was 

analyzed. However, no date is provided 

and the specific record is not included. 

C

( )

Whole Body Count (unspecified) –

/1978

Beta/Gamma/P-32 (unspecified) – 

/1979

Beta/Gamma/P-32 (periodic) – /1980

Beta/Gamma/P32 (periodic) – /1981

Beta/Gamma/P-32 (periodic) – /1982

Beta/Gamma/P-32 (periodic) – /1983

DOE-supplied files indicate no internal 

monitoring exists for the claimant. 

D

( )

All (periodic) – /1972 

Introductory letter in DOE monitoring file 

indicates that only a gross alpha sample 

was conducted (no date supplied), and 

there was no indication of a gross beta or 

gross gamma analysis occurring. The 

observed sample was labelled as a 

“periodic sample.”
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Case 

Reference

(Claim 

Number)

Missing Sample Type* (reason) and 

Date

Additional Comments

E

( )

All/Uranium (periodic) – /1976

All/Uranium (periodic) – /1977

All/Uranium (periodic) – /1978

Alpha/Uranium (special) – /1978

All/Uranium (periodic) – /1978

All/Uranium (periodic) – /1979

All/Uranium (periodic) – /1980

Uranium (unspecified) – /1980

Uranium (unspecified) – /1980

All/Uranium (periodic) – /1981

C-14/H-3/P-32 (unspecified) - /1981

Alpha/Uranium (special) – /1982

All/Uranium (periodic) – /1982

All/Uranium (periodic) – /1983

Uranium (special) – /1984

Thorium (special) – /1984

All/Uranium (periodic) – /1985

All/Uranium (periodic) – /1986

All/Uranium (periodic) – /1987

Uranium (special) – /1988

All/Uranium (periodic) – /1989

All/Uranium (periodic) – /1990

DOE-supplied files indicate no internal 

monitoring exists for the claimant. 

F

( )

Thorium (periodic) – /1992

The introductory letter in DOE monitoring 

file indicates that a single bioassay exists; 

however, the record was not included. The 

letter indicates the bioassay sample should 

be included with “other medical records.” 

The sample was not located in Department 

of Labor case file. 

G

( ) Uranium (special) – /1986

DOE-supplied files indicate no internal 

monitoring exists for the claimant. 

Observed uranium urine bioassay result is 

labelled as a “special” sample.

* Sample type “All” designates a gross alpha/beta/gamma urinalysis.

In addition to the seven claims described in Table 1, another six reviewed claims had at least 

some of the identified bioassay sampling records omitted from their respective DOE-supplied 

monitoring records. While the dose reconstruction example provided by NIOSH demonstrates 

that these six additional claimants would still be assigned occupational missed doses based on 

workplace air sampling, the omitted bioassay records illustrate the potential completeness 

limitations observed in available claimant-specific monitoring records. Given these limitations, 

the presence of internal monitoring results in DOE-supplied monitoring records may not be an 

appropriate metric for determining whether to assign occupational internal doses (i.e., missed 

doses based on workplace air sampling) as opposed to the ambient environmental intakes 

developed in the LBNL TBD (NIOSH 2010). Therefore, SC&A has the following observations: 
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Observation 2: Bioassays in Claimant DOE Files May Not Be Indicative of Exposure 

Potential – Given the observed limitations in ascribing internal monitoring data to an individual 

claimant, the presence of internal monitoring results in individual claim files may not be an 

appropriate criterion for determining whether occupational intakes should be applied in lieu of 

ambient environmental intakes. An alternate approach of assigning occupational intakes, unless 

clear evidence exists of “little to no exposure potential,” would be more claimant favorable and 

consistent with dose reconstruction procedures for other sites. 

Observation 3: Bioassays in Claimant DOE Files May Not Be Complete Compared to 

LBNL Documents – SC&A’s sample analysis of 36 LBNL claims found that 13 of the DOE 

files for the 36 claims did not contain all the bioassay records indicated in the SRBD documents. 

4 SUMMARY CONCLUSIONS 

Although the white paper addressed many aspects of applying gross alpha, beta, and gamma 

results to dose reconstruction, SC&A found that some technical issues were not sufficiently 

addressed. SC&A’s two findings include: 

1. Air samples may not represent concentrations breathed by workers. 

2. Technical issues and uncertainties with gross counting data conversion to 

concentration/intake for use in dose reconstruction. 

SC&A had three observations: 

1. Potentially missed radionuclides. 

2. The bioassay records in claimant DOE files may not necessarily be indicative of exposure 

potential. 

3. The bioassay records in the claimant DOE files may not be complete compared to LBNL 

documents. 

5 REFERENCES

ICRP 2015. Occupational Intakes of Radionuclides: Part 1, ICRP Publication 130. Ann. ICRP 

44(2). 

LBNL 1965. Report of Special Analysis, Health Chemistry Department, Lawrence Berkeley 

National Laboratory, University of California, Berkeley, California. January 26, 1965. [SRDB 

Ref. ID 117675, PDF page 15] 

LBNL 1966. Report of Special Analysis: Fission Products in Samples off 11-7-66, Health 

Chemistry Department, Lawrence Berkeley National Laboratory, University of California, 

Berkeley, California. December 8, 1966. [SRDB Ref. ID 117675, PDF page 94] 
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LBNL 1967a. Report of Special Analysis: Environmental Gas Sampling, October 1967, Health 

Chemistry Department, Lawrence Berkeley National Laboratory, University of California, 

Berkeley, CA. November 16, 1967. [SRDB Ref. ID 117675, PDF page 139]

LBNL 1967b. Report of Special Analysis: Environmental Gas Sampling, November 1967, Health 

Chemistry Department, Lawrence Berkeley National Laboratory, University of California, 

Berkeley, CA. December 14, 1967. [SRDB Ref. ID 117675, PDF page 143]

LBNL 1973. Report of the Bioassay Laboratory, July 1972 through June 1973, Donner 

Laboratory, LBL-2448, C.T. Schmidt, T.W. Sargent, D.J. Yaeger, and A.deG. Low-Beer, 

Lawrence Berkeley National Laboratory, University of California, Berkeley, CA.

November 1973. [SRDB Ref. ID 71780]

LBNL 1981. The Environmental Health and Safety Department Operational Guide for the 

Health and Safety Representative, Lawrence Berkeley National Laboratory, University of 

California, Berkeley, CA. December 1981. [SRDB Ref. ID 21984]

LBNL 2006a. Individual Bioassay Reports for Employees Last Names Beginning with “A” 

(1965–1990), Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

January 13, 2006. [SRDB Ref. ID 21985]

LBNL 2006b. Terminated Bioassay Results of Employees with Last Names Beginning with “A” 

(1961–1983), Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

January 13, 2006. [SRDB Ref. ID 21986]

LBNL 2007a. Bioassay Cards January 4, 1960 – August 1, 1962 Card Numbers 1 through 407, 

Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. May 23, 2007. 

[SRDB Ref. ID 32378]

LBNL 2007b. Bioassay Cards Early Years (pre 1968) A through Z, Lawrence Berkeley National 

Laboratory, University of California, Berkeley, CA. May 23, 2007. [SRDB Ref. ID 32379]

LBNL 2007c. Bioassay Cards to October 183 A & B, Radio-Chemical Assay Request Cards 

1967–1983, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32380]

LBNL 2007d. Bioassay Cards to October 1983, C through F, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32381]

LBNL 2007e. Bioassay Cards to October 1983, G through Mc, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32382]

LBNL 2007f. Bioassay Cards to October 1983, Me through Z, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32383]
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LBNL 2007g. Bioassay Cards after October 1983, A through C, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32384] 

LBNL 2007h. Bioassay Cards after October 1983, D though G, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32385] 

LBNL 2007i. Bioassay Cards after October 1983, H through L, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32386] 

LBNL 2007j. Bioassay Cards after 1983, All of M, Radio-Chemical Assay Request Cards, 

Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. May 23, 2007. 

[SRDB Ref. ID 32387] 

LBNL 2007k. Bioassay Cards after October 1983, N through R, Radio-Chemical Assay Request 

Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA May 23, 

2007. [SRDB Ref. ID 32388] 

LBNL 2007l. Film Badge Area Readings throughout Site 1956–1957, Lawrence Berkeley 

National Laboratory, University of California, Berkeley, CA. May 22, 2007. [SRDB Ref. 

ID 32389] 

LBNL 2007m. Bioassay Cards after 1983, All of S, Radio-Chemical Assay Request Cards, 

Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. May 23, 2007. 

[SRDB Ref. ID 32390] 

LBNL 2007n. Bioassay Cards after October 1983, T through , Radio-Chemical Assay 

Request Cards, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA. 

May 23, 2007. [SRDB Ref. ID 32391] 

LBNL 2007o. Bioassay Cards after October 1983, through Z, and Radio-Chemical 

Assay Request Cards, Lawrence Berkeley National Laboratory, University of California, 

Berkeley, CA. May 23, 2007. [SRDB Ref. ID 32392] 

NIOSH 2010. Site Profile for the Lawrence Berkeley National Laboratory, ORAUT-TKBS0049, Revision 02, National Institute for Occupational Safety and Health, Cincinnati, OH. 

May 10, 2010. 

NIOSH 2016. Lawrence Livermore National Laboratory Special Exposure Cohort Evaluation 

Report, presentation to the Advisory Board on Radiation and Worker Health, Tampa, Florida. 

March 24, 2016. 

NIOSH 2017a. Method to Assess Internal Dose Using Gross Alpha, Beta, and Gamma Bioassay 

and Air Sampling at the Lawrence Berkeley National Laboratory [white paper], Revision 00, 

National Institute for Occupational Safety and Health, Cincinnati, OH. September 18, 2017. 
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Privacy Act 5 U.S.C. § 552a and has been cleared for distribution.

NIOSH 2017b. Site Profile for Nuclear Materials and Equipment Corporation, Apollo and Parks 

Township, Pennsylvania, ORAUT-TKBS-0041, Revision 04, National Institute for Occupational 

Safety and Health, Cincinnati, OH. March 13, 2017.

Peck 1964. J.S. Peck, Manual of Instructions, Automatic Processing of Airborne Activity Data, 

JSP-777-62, Revised, Lawrence Berkeley National Laboratory, University of California, 

Berkeley, CA. January 24, 1964. [SRDB Ref. ID 137854]

Price 1964. W.J. Price, Nuclear Radiation Detection, Second Edition, McGraw-Hill Book 

Company, New York. 1964.

SC&A 2016. Progress Report: ANL-W SEC-00224 Review, Strategy of Using the 10% Maximum 

Permissible Concentration Rule for Performing Dose Reconstruction, memorandum to Idaho 

National Laboratory Work Group, SC&A, Inc., Vienna, VA, and Saliant, Inc., Jefferson, MD. 

July 14, 2016.

Thaxter 1955. M.D. Thaxter, Health Chemistry, Air Monitoring – UCRL Berkeley, MDT-980-55.

August 24, 1955. [SRDB Ref. ID 21403, PDF pages 5–6]

USDHEW 1970. Radiological Health Handbook, Revised Edition, U.S. Department of Health 

Education and Welfare, Public Health Service, Rockville, MD. January 1970.