ASSESSMENT OF OCCUPATIONAL EXPOSURE DUE TO INTAKES OF RADIONUCLIDES Uncertainties and Performance Criteria

Interpretation of Measurement Results – Unit Objectives The objective of this unit to identify and define the criteria that are used to characterize the quality of the measurement process for both direct and indirect methods. It will also identify sources of uncertainty in measurement and interpretation and give an estimate of expected magnitudes. At the completion of this unit, the student should understand how to calculate Minimum Detectable Activity and establish adequate accuracy criteria for measurement bias and precision.

Interpretation of Measurement Results - Unit Outline Measurement Uncertainties Intake and Dose Assessment Uncertainties Performance Criteria: Accuracy Performance Criteria: Sensitivity MDAs - Examples

Measurement Uncertainties

Dose determination uncertainties Measurement Interpretation Direct or indirect measurements e(g)j m(t) Estimated intake Committed effective dose ?2 ?3 Body/organ content, M or Excretion rate, R ?1 Body/organ content, M or Excretion rate, R There are three sources of uncertainty in the internal dose assessment process individual monitoring measurements, ?1 assessment of intake from the measurements, ?2, and assessment of dose from the intake, ?3. The overall uncertainty in assessed dose is a combination of the uncertainties from these three sources. The uncertainty associated with each source and the effort required to quantify it depend on the radionuclides involved, the extent of information on the level of exposure and its time course, and the time of sampling or measurement.

Measurement uncertainties Usually most straightforward to estimate Counting statistics dominate at low activities For radionuclides that are, Easily detected, and In sufficient quantity, counting statistics are small compared to other uncertainties Systematic uncertainties are important Correction for activity remaining previously measured intakes may be necessary Generally, the uncertainties in the measurement are the most straightforward to estimate. When activity levels are low and close to the limit of detection, uncertainties due to counting statistics may dominate the overall uncertainty. For radionuclides that are easily detected and present in sufficient quantity, uncertainties due to counting statistics will be small compared to other sources of uncertainty. Consideration must also be given to systematic uncertainties in other parts of the measurement procedure, e.g. calibration, or for in vivo measurements, correction for body size.

Common measurement uncertainties Statistical counting errors Distribution in the body Absorption by overlying tissue (low energy photons) External contamination of the subject or measurement system Calibration errors Source activity Simulation accuracy Taking into account the main affecting sources of uncertainties in direct in vivo radioactivity measurements the typical values of uncertainties can be expected to be between 10% and 90% depending on the radionuclide involved and the monitoring method applied. This value can be even higher in case of measurement of transuranic elements Errors in assessment of distribution in the body, or even specific organs such as lung counting for plutonium, are highly dependent on several factors including: radionuclide, mode of intake, and time after intake. At higher levels – near or above the relevant limits – special counting can be performed to make improve the determination of body distribution. As indicated the absorption of overlying tissues is primarily an issue for lower energy photons (< 50-100 keV). If that is a concern, special techniques such as ultrasound measurements can be employed.

Estimated Direct Measurement uncertainties* Source of uncertainty Estimated magnitude 1 σ Chest wall thickness determination 15% to 300% worst case for 17 keV Geometry errors – Subject size and shape departure from single-size calibration model 10% for good geometries (I m arc, linear w/front /back counts) 15-20% for common geometries (linear w/counts from 1 side, 50 cm arc) 40% for poor geometries (detector in contact w/body) Positioning of subject 10-15% for whole body * From ANSI 13.30 (1996)

Typical uncertainties for assessing fission product isotopes* Source of Uncertainty Estimated Uncertainty Depth Length  Width Height-Weight Analysis Technique Calibration Counting Statistics Total Estimated Uncertainty 12% 5% 7% 3% 40% This illustrates typical estimated uncertainties for assessing fission product body burdens by in direct measurement. It can be seen in comparison with uncertainties for uranium and plutonium lung counting in the next two slides. * From Toohey, et al,

Typical uncertainties for U lung counting Source of Uncertainty Estimated Uncertainty Chest Depth Chest Wall Thickness Activity Location Detector Placement Subject Background Calibration Counting Statistics Total Estimated Uncertainty 12% 15% 5% 10% 40% 90% This illustrates typical estimated uncertainties for assessing uranium lung burdens by in vivo counting. The major impact derives from the low energy of the photons, and the lower body levels of activities. The main impact is on statistics. * From Toohey, et al,

Typical uncertainties for Pu lung counting Source of error Uncertainty Subject background 50% Counting statistics Chest wall thickness 40% Non-uniform distribution 70% Calibration 20% Overall uncertainty 110% The uncertainties become even greater for plutonium lung counting, primarily because of the very low photon energy – 17 keV. * From Toohey, et al,

Estimated Indirect Measurement uncertainties Several parameters contribute to indirect measurement uncertainties The uncertainty associated with most are highly variable Typical uncertainties associated with the radiochemistry are of the order of 3% More details can be found in the USDOE Laboratory Accreditation Program report – ANSI N 13.30 and ISO 12790-1

INTRODUCTION OF SF The recently developed IDEAS Guidelines for the assessment of internal doses from monitoring data suggest default measurement uncertainties (i.e. scattering factors, SF) to be used for different types of monitoring data. The SF values represent the geometric standard deviation of the distribution of all results, supposed to be approximated by log-normal distribution.

The IDEAS guidelines consider two types of uncertainty : INTRODUCTION OF SF The IDEAS guidelines consider two types of uncertainty : Type A : connected to counting statistic and decreasing with the increasing of activity and counting time (Poisson distribution) Type B : all other components of uncertainty also connected with inter and intra-subject variability (e.g. in excretion)

INTRODUCTION OF SF SF values are important. For these issues. They are needed to assess the uncertainty in the estimated intake and dose. They determine the relative weighting of data in fitting process and can effect the estimated intake when different types of monitoring data are used simultaneously. They enable rogue data to be identified objectively They enable objective (statistical) criteria (goodness-of-fit) to be calculated, which are used to determine whether the predictions of the biokinetic model (with a given set of parameter values) used to assess the intake and dose are inconsistent with the measurement data.

INTRODUCTION OF SF The IDEAS Guidelines assume the overall uncertainty on an individual monitoring value can be described in terms of a lognormal distribution and the SF is defined as the geometric standard deviation (GSD). This approximation is valid if Type A errors are relatively small (<30%). Thus, it is assumed that if the measurements could be repeated, hypothetically at the same time, then the distribution of the measurement results could be described by a lognormal distribution.

INTRODUCTION OF SF SF values depend on type of monitoring measurement. Default values are reported in the following slides. When the type A component of the uncertainty is small (< 30%) the type B component alone could be used for uncertainty.

SF default values for in-vivo measurements SF values depend on type of monitoring measurement. For in-vivo measurement types: In vivo measurements SF values (Type B uncertainty) Low photon energy E < 20 keV 2.1 Intermediate photon energy 20 keV < E < 100 keV 1.3 High photon energy E > 100 keV 1.2

SF default values for in-vivo measurements SF values depend on type of monitoring measurement. For in-vitro measurement types: In vitro measurements SF values (Type B uncertainty) URINE For HTO after inhalation 1.1 Normalized 24 h excretion 1.7 Spot urine data 2.0 FECES Inhalation (Pu-Am) 2.5 Wound (Pu) 3.1

Intake and Dose Assessment Uncertainties

Some sources of assessment uncertainty Mode of intake Physical and chemical form of material Particle size (AMAD) of the aerosol Time pattern of intake (acute vs. chronic) Errors in biokinetic and dosimetric models Individual variability in biokinetic and dosimetric parameters

Intake assessment uncertainties Difficult to quantify in routine monitoring - measurements are made at pre-determined times are unrelated to time of intakes Compromise between measurement interpretation quality and the practical limitations linked to measurement frequency Monitoring intervals should be selected so that underestimates due to unknown time of intake are ≤ 3 The uncertainty in the assessment of intake may be difficult to quantify in a routine monitoring programme, where measurements are made at pre-determined times, unrelated to the times of intakes. It is necessary to have a compromise between the quality of the interpretation of the measurement results and the practical limitations linked to the frequency of the measurements. A simple rule has to be defined to limit the possible error on estimate of intake due to the unknown time of exposure. Monitoring intervals should be selected so that any underestimation introduced by the unknown time of intake is no more than a factor of three.

Intake assessment uncertainties Practically, this is a maximum since the actual distribution of the exposure in time is unknown Statistically, the error is not systematically the same for all the assessments The random distribution of the exposure makes such an error clearly lower than a factor of 3 If intake occurs just before sampling or measurement, it could be overestimated ≥ 3 Indeed, in practice, this underestimate is a maximum because the actual distribution of the exposure in time is unknown, so that, statistically, the error is not systematically the same for all the assessments. The stochastic distribution of the exposure makes such an error clearly lower than a factor of three. If a substantial part of the intake occurs just before sampling or measurement, the intake could be overestimated by more than a factor of three.

Intake assessment uncertainties Particularly important for excreta monitoring  daily fractions excreted can change rapidly immediately after intake If a high result is found in routine monitoring, it would be appropriate to repeat the sampling or measurement a few days later  adjust the estimate of intake accordingly Samples could also be collected after a period of non-exposure, e.g. after weekend or holiday This may be particularly important in the case of excreta monitoring, since the fraction excreted each day may change rapidly with time in the period immediately following the intake. If an unexpectedly high result is found in a routine monitoring programme, it would be appropriate to repeat the sampling or measurement a few days later, and adjust the estimate of intake accordingly. Alternatively, if appropriate and if convenient, the sample could be collected or the measurement made after a period of non-exposure, for example after a weekend or holiday.

Assessment uncertainties Models used to describe radionuclide behavior are used to assess intake and dose Reliability of dose estimates depends on the accuracy of the models, and limitations on their application This will depend upon many factors, including: Knowledge of the time of intake, and Whether the intake was acute or chronic From an assessment of the activity in the whole body or in samples of tissues or excreta, the models used to describe the behaviour of radionuclides in the body are then used to assess intake and dose. The reliability of the estimates of dose therefore depend upon the accuracy of the models, and any limitations on their application in particular circumstances. This will depend upon many factors. In particular, knowledge of the time of the intake(s) and of whether the intake was acute or chronic is essential for a reliable dose estimate.

Assessment uncertainties If the sampling period does not enable the estimation of the biological half-life, assumption of a long body retention may lead to an underestimate of the intake and the committed effective dose The degree of over- or under-estimation of the dose depends on the body retention pattern When the sampling period does not enable the biological half-life of the radionuclide to be estimated, assuming a long period of retention in the body for the purpose of dose assessment may result in an underestimate of the intake, and hence of the committed effective dose. The degree of over- or under-estimation of the dose will depend upon the overall pattern of retention in the body.

Assessment uncertainties Radionuclide behavior in the body depends upon their physicochemical characteristics Particle size of inhaled radionuclides is a particularly important for influencing deposition in the respiratory system Gut absorption factor f1 substantially influences effective dose following ingestion The behaviour of radionuclides that enter the body by ingestion or inhalation will depend upon their physicochemical characteristics. For inhaled radionuclides, the particle size is particularly important in influencing deposition in the respiratory system, while for ingestion the gut absorption factor f1 can substantially influence effective dose.

Assessment uncertainties When exposures during routine monitoring are well within limits on intake, default parameters may be sufficient to assess intake If exposures approach or exceed these limits, more specific information on; Physical form and chemical form of the intake, and Characteristics of the individual, may be needed to improve the accuracy of the model predictions For routine monitoring when exposures are well within limits on intake, the default parameters recommended in the BSS may be sufficient for assessing intakes. For exposures approaching or exceeding these limits, however, more specific information on the physical and chemical form of the intake, and the characteristics of the individual, may be needed to improve the accuracy of the model predictions.

Intake fraction, m(t) depends on several factors 1 10-1 10-2 10-3 10-4 10-5 10-6 10-7 10-8 60Co, inhalation type M m(t) Whole body Lungs Urine Feces 10 100 1000 10000 This figure illustrates the factors which can contribute to the uncertainty in the intake fraction, m(t), a major component of uncertainty in determining intake from body content. The The value of the intake fraction depends on several factors: Intake pattern; Elapsed time from intake, Absorption rate; Deposition site; Particle size; and Mode of intake. Careful consideration needs to be given to each of these parameters to establish the most likely values before making a determination of M(t). Time after intake, d Intake pattern (acut. vs. chr.) Deposition site Time after intake Particle size Absorption rate (F, M or S) Mode of intake

Performance Criteria: Accuracy

Performance criteria Accuracy Bias (Systematic errors) How well can a given measurement be reproduced. Repeatability or Precision (Random errors) How close is the mean of a series of measurements to the true value Sensitivity (MDA) What is the lowest value of a quantity that can be measured? Two basic performance criteria are considered for direct and indirect measurements – Accuracy and Sensitivity. Accuracy, in turn has two components – Bias and Precision. Bias, which refers to the systematic errors, may be defined as, “The deviation of a single measured value of a random variable from a corresponding expected value, or a fixed mean deviation from the expected value that remains constant over replicated measurements within the statistical precision of the measurement. (Systematic error).” Precision, which refers to the random errors, is “A concept employed to describe dispersion of measurements with respect to a measure of location or central tendency.” Sensitivity refers to the minimum value of a quantity that can be detected – Minimum detectable activity (MDA).

Performance criteria - Bias Definition: where: Bri = relative bias for the ith measurement Ai = measured activity Aai = actual activity for the ith measurement

Performance criteria - Bias For a test or measurement category, Where: Br = Relative bias for the category n = number of replicate measurements

Performance criteria – Repeatability* Definition: where: SBr = measurement repeatability for the test or measurement category * Also termed Precision

Accuracy - How close is close enough? When the activity Aai is at or above the specified Minimum Testing Level (MTL), Relative bias, Br - 0.25  Br  +0.50 Relative repeatability, SBr SBr  0.40 These values used by ISO and USDOE Laboratory Accreditation Program Accuracy requirements for internal dosimetry have been addressed by only a few national regulatory bodies. In the United States, the Department of Energy emphasized its Laboratory Accreditation Program, DOELAP, for external and, now internal, dosimetry services. DOELAP accuracy requirements address measurement accuracy through specification of relative bias, Br and relative precision, SB, which together provide the required accuracy specification. Although other definitions may be found, bias is defined as, “The deviation of a single measured value of a random variable from a corresponding expected value, or a fixed mean deviation from the expected value that remains constant over replicated measurements within the statistical precision of the measurement. (Systematic error).”(22), and precision is “A concept employed to describe dispersion of measurements with respect to a measure of location or central tendency.”(22).

MTL Values for Direct Measurements Measurement Category Type Radionuclide MTL I. Transuranium elements via L x-rays Lung 238Pu 9 kBq II. Americium-241 241Am 0.1 kBq III. Thorium 234 234Th in equilibrium w/ parent 238U 0.5 kBq IV. Uranium-235 235U 30 kBq V. Fission and activation products Any two: 54Mn, 58Co, 60Co, 144Ce + 134Cs & 137Cs/137Ba 3 kBq VI. Fission and activation products Total body All of: 134Cs, 137Cs/137mBa, 60Co & 54Mn VII. Radionuclides in the thyroid Thyroid 131I or 125I The values MTL categories and values presented here are recommended both by ISO and the USDOE Laboratory Accreditation Program..

MTL Values for Indirect Measurements Measurement category Radionuclide MTL (per L or per sample) I. BETA activity: average energy < 100keV 3H, 14C 35S 228Ra 2 kBq 20 kBq 0.9 kBq II. BETA activity: average energy ≤ 100 keV 32P 89, 90Sr or 90Sr 4 Bq III. ALPHA activity: isotopic analysis 228,/230Th or 232Th 234/235U or 238U 237Np 238Pu or 239/240Pu 241Am 0.02 Bq 0.01 Bq IV. Elements (mass/volume) Uranium 20 μg V. GAMMA (photon) activity 137Cs/137mBa 60Co 125I 2 Bq 0.4 kBq

Accuracy - How close is close enough? ICRP Publication 75, General Principles for the Radiation Protection of Workers: For external dosimetry a factor of 1.5 at the limits (20 mSv/year) The overall uncertainty in the dose from internal exposure, is likely to be greater than for external exposure In searching for guidance on acceptable accuracy, ICRP Publication 75 is a starting point. It recognizes the problems and uncertainties associated with individual dose assessment, and sets a factor of 1.5 at the limits (20 mSv/year) as a starting point for external dosimetry. However, it is recognized that the problems encountered in internal dose assessment can be significantly greater….

Accuracy - How close is close enough? ICRP Publication 75, General Principles for the Radiation Protection of Workers: Sampling frequencies should be chosen to avoid errors due to intake uncertainties of more than about a factor 3 For less simple programs, e.g. for insoluble plutonium, total uncertainties may be about one order of magnitude. The recommendation is that the sampling frequencies for direct measurement, and/or indirect measurement such be chosen to avoid errors due to intake uncertainties of no more than about a factor of three. For more complicated situations, such as assessment of insoluble plutonium, the uncertainties can be about an order of magnitude. This also implies that a knowledge of the radiation hazard (nuclides, quantities, forms, etc.) is an a priori requirement.

Performance Criteria: Sensitivity

Two terms describe sensitivity Minimum Detectable Activity (MDA) (a priori) Minimum activity that can be detected Probability, α, of false positive (Type I error) Probability, β, of false negative (Type II error) Decision level, LC, (a posteriori) The total count value or final measurement of a statistical quantity, LC, at or above which the decision is made that the result is positive Minimum Detectable Activity (MDA) (detection limit, lower limit of detection, limit of detection) is a calculated a priori value which specifies the minimum body contribution (true activity) that can be detected by a defined measurement method, i.e. which would give the registered pulses below the decision threshold based on the Type I error () with given probability of a Type II (). The detection limit is an a priori value that can be compared with guideline values. Decision level (minimum significant activity, decision threshold, lower limit of decision, critical level ) is an a posteriori (i.e. after measurement) calculated value at which the decision can be made, with a given probability of Type I error (), for each measurement result whether the registered pulses include contributions by the body or are solely due to background (e.g. a peak of a gamma line of a nuclide in question). Otherwise, it should be assumed in each case that there is no sample contribution in the region of interest. If the decision rule is observed, a wrong decision that there is a real contribution in the region of interest when in fact only a background effect exists occurs with the probability,  (Type I error).

Confidence levels and k values α 1-β k 0.001 0.999 3.090 0.005 0.995 2.576 0.010 0.990 2.326 0.025 0.975 1.960 0.050 0.950 1.645 0.100 0.900 1.282 0.200 0.800 0.842 0.250 0.750 0.675 0.300 0.700 0.525 0.400 0.600 0.254 0.500

Standard Deviation where: s = standard deviation of a set of N measurements xi = ith measurement in the set x = mean of the set of measurements Estimate of the relative standard deviation for a single measurement: sB = standard deviation of the appropriate blank sample s0 = standard deviation of the net subject or sample count

Illustration of LC and MDA relationship Lc MDA 0 – Value of background distribution LC – The likelihood that the sample distribution characterized by LC was not really positive (false positive) is α MDA – The likelihood that a sample distribution characterized by the MDA will be missed (false negative) is β and is not really positive (false positive) is α sB α (b) (a) kαsB  s0 Because of the shape of the distributions, it should be stressed that these are not photopeaks. They represent the distribution of a large number of replicate measurements of the same quantity. (c) kβs0 Background Detected Not detected May be Will be

Minimum detectable activity - MDA Values assigned to MDA depends on the risk of making an error, false positive or false negative. Simplification: Assume β = α, and β = α = 0.05 Then kα = k1-β = 1.645 = k where: s0 = standard deviation of net subject counts K = efficiency T = subject counting time The minimum detectable activity (MDA), often termed the detection limit (LD), corresponds to the level of activity which is needed to ensure, with some chosen level of confidence β, that the net signal will be detected, according to the criterion that it exceed the MSA. The mathematical treatment is simplified, as in the following section, if β=σ, and by common convention 0.05 is adopted for both.

Minimum detectable activity - MDA where: sB1 = standard deviation in subject counts with no actual activity sB0 = standard deviation in unadjusted blank counts It can be assumed that sB1 = sB0 = s0, and m = 1 Then, s0 = sB2 = 1.415sB, where sB is the standard deviation of a total blank count

Minimum detectable activity - MDA For direct measurements, MDA becomes: For indirect measurements: where: R = chemical recovery λ = radiological decay constant Δt = elapsed time between reference time and time of count

Direct measurement MDAs* Measurement category Organ MDA I. Transuranium elements via x-rays Lungs 185 Bq/A II. 241Am 26 Bq III. 234Th 110 Bq IV. 235U 7.4 Bq V. Fission and activation products 740 Bq/A VI. Fission and activation products Whole body VII. Radionuclides in the thyroid Thyroid * From ANSI 13.30 A is the number of photons per nuclear transformation – L x-rays for transuranium elements, and gamma rays for fission and activation products Equivalent values of MDA and MDC (minimum detectable concentration) are presented in ICRP Publication 78. Those values are generally lower than those of ANSI 13.30. However, MDA depends on several factors, including counting geometry, analytical techniques and counting time. The difference ranges from very little (20 Bq for 241Am) to very significant (50 Bq for 137Cs compared with 890 Bq after correction for photon abundance). A short comparison follows: ICRP 78 ANSI 13.30* 238Pu 103 Bq 1.7103 Bq 239Pu 2103 Bq 4.0103 Bq 241Am 20 Bq 26 Bq 235U 200 Bq 7.4 Bq 58/60Co 100 Bq 740 Bq 137Cs 50 Bq 890 Bq 131I 100 Bq 830 Bq

Indirect measurement MDCs (urine)* Measurement Category Nuclide MDC I Beta - Average energy ≤ 100 keV 3H, 14C, 35S 147Pm 210Pb, 228Ra, 241Pu 370 Bq/L 0.37 Bq/L 0.19 Bq/L II. Beta – Average energy > 100 KeV 32P, 89/90Sr or 90Sr 131I 0.74 Bq/L 3.7 Bq/L III. Alpha – Isotopic specific measurements 210Po, 226Ra, 228/230/232Th, 234/235/238U 237Np, 238/239/240Pu, 241Am, 242/244Cm 3.7 mBq/L 2.2 mBq/L IV. Mass determination Uranium (natural) 5 μg/L V. Gamma or x-rays Emitters with photons ≤ 100 keV 2 Bq L-1/A VI. Gamma or x-rays Emitters with photons > 100 keV For urinalysis, the ICRP 78 values are typically a factor of 2-4 lower than those of ANSI 13.30. Differences in assumed parameters and analytical techniques can result in significant differences in the nominal MDC values. Those reported in Publication 78 are likely to represent MDCs that can be achieved under the best of conditions. Incidentally, the reason for the odd values in ANSI 13.30 (370, .37, 2.2, etc.) is not high accuracy, but the continuing use of the unit, Curie in the U.S. These quantities were established curies (e.g. 10 nCi/L) with a conversion to Becquerels (370 Bq/L). * From ANSI 13.30 A is the number of photons per nuclear transformation – L x-rays for transuranium elements, and gamma rays for fission and activation products

Indirect measurement MDAs (faeces)* Measurement Category Nuclide MDA VII. Alpha – Isotope specific measurements 234/235/238U, 228/230/232Th, 238/239/240Pu, 241Am 37 mBq/sample VIII. Beta – Average energy > 100 keV 89/90Sr or 90Sr 0.74 Bq/sample IX. Gamma or x-rays Emitters with photons ≤ 100 keV 2/A Bq/sample X. Gamma or x-rays Emitters with photons > 100 keV MDAs for faecal analysis reported ICRP Publication 78 are moderately to significantly lower than the ANSI 13.30 MDAs. ICRP78 presents the following for comparison: 234/235/238U and 228/232Th 10 mBq vs. 37 mBq 238/239/240Pu 1 mBq vs. 37 mBq 241Am 1 mBq vs. 37 mBq 58/60Co, 134/137Cs 1 mBq vs. ~2 mBq depending on photon abundance * Minimum detectable concentration - From ANSI 13.30 A is the number of photons per nuclear transformation – L x-rays for transuranium elements, and gamma rays for fission and activation products

MDAs – Examples

Determination of MDA - Example 90Sr by Beta Gas Flow Proportional Counting 20 reagent blanks were counted for 1 hour each – 3600 s Total counts 83 69 53 72 59 77 70 62 88 66 73 55 74 65 68 61

Determination of MDA - Example 90Sr by Beta Gas Flow Proportional Counting B = 67.4 counts SB =  [(Xi – 67.4)2/19] = 9.4 Counting efficiency, K = 0.36 Chemical yield = 0.81

Determination of MDA - Example Whole body counting for fission and activation products Radionuclide 137Cs 60Co Organ Body Lungs Counts in peak region - B 9 8 SB =B 3 2.8 Count time, T – s 600 Calibration factor, K 1.3510-4 2.9710-4 MDA - Bq 209 90

References HEALTH PHYSICS SOCIETY, Performance Criteria for Radiobioassy, An American National Standard, HPS N13.30-1996 (1996). INTERNATIONAL ATOMIC ENERGY AGENCY, Occupational Radiation Protection, Safety Guide No. RS-G-1.1, ISBN 92-0-102299-9 (1999). INTERNATIONAL ATOMIC ENERGY AGENCY, Assessment of Occupational Exposure Due to Intakes of Radionuclides, Safety Guide No. RS-G-1.2, ISBN 92-0-101999-8 (1999). INTERNATIONAL ATOMIC ENERGY AGENCY, Indirect Methods for Assessing Intakes of Radionuclides Causing Occupational Exposure, Safety Guide, Safety Reports Series No. 18, ISBN 92-0-100600-4 (2002). International Standards Organization, Radiation Protection – Performance Criteria for Radiobioassay – Part 1: General Principles, ISO TC 85/SC2 (1999).