In nuclear fuel fabrication, detailed knowledge of the inhalation exposure to uranium aerosols and the corresponding committed effective doses (CED) needs to be gained in order to ensure worker safety and regulatory compliance. Recent work has found evidence for increased lung cancer risk among uranium and plutonium workers (median, mean, and max lung absorbed doses of 2.4, 7.3, and 316 mGy, respectively) following inhalation intake (Grellier et al. 2017). It is thus important to ensure low intake levels and to accurately determine CED following inhalation exposure, which may be challenging due to confounding factors and uncertainties (ICRP 2007; Gilbert 2009; Harrison and Day 2008).
To accurately determine the CED following inhalation intake of uranium aerosols, the deposition of inhaled particles in the airways must be well understood. Particle deposition is commonly modeled using the Human Respiratory Tract Model (HRTM) designed by the International Commission on Radiological Protection (ICRP) (ICRP 1994, 2015). The model assumes a population of particles following a log-normal distribution, which can be described by the Activity Median Aerodynamic Diameter (AMAD) and its geometric standard deviation (GSD). The AMAD is the aerodynamic particle size where 50% of the total activity is associated with particles with an aerodynamic diameter greater than the AMAD (d50%). The GSD describes the spread of the activity size distribution. The frequency function is defined (eqn 1) as
where dA is the fraction of radioactivity in the size range between ln dae and ln dae + d ln dae, where dae is the aerodynamic particle diameter (Hinds 1999). The aerodynamic particle diameter of a non-spherical particle is defined as dae = de×[ρ×(ρ0χ)−1]0.5, where de is the diameter of a spherical particle with the same volume as the particle considered, ρ (g cm−3) is the density of the non-spherical particle, ρ0 is the reference density (1 g cm−3), and χ the dynamic shape factor (dimensionless, typically assumed to be 1.5) (Hinds 1999; ICRP 1994, 1997).
A GSD value of 1 describes a monodispersed aerosol, and a large GSD indicates a large spread in the activity size distribution. The equation is applicable for log-normal distributions only, which is typically assumed in the field of radiation protection. In the absence of detailed information, the ICRP recommends a default value of AMAD = 5 μm and GSD = 2.5 (ICRP 1994). These parameters indicate that 95% of the activity is associated with particles in the aerodynamic size range 0.8 μm (5 μm×2.5−2 = 0.8 μm) and 31 μm (5 μm×2.52 ≈ 31 μm) (Hinds 1999). The activity size distribution of aerosols is important for modeling deposition in the airways using the HRTM model; e.g., the alveolar deposition is expected to be 3% of the total inhaled activity if the AMAD is 15 μm, whereas the corresponding figure for an AMAD of 5 μm is 10%, thus increasing the lung equivalent dose (Hlung) (ICRP 2015).
The AMAD and GSD can be determined experimentally by sampling aerosols using cascade impactors. A common evaluation method is to plot the accumulated fraction of activity as a function of impactor cut-point in a log-probability graph. This method may give too much weight to early and late impaction stages (Hinds 1999). Alternatively, a nonlinear regression fit to the frequency function (eqn 1) can be applied to determine the AMAD and GSD (Thiel 2002).
The aerosol size distribution will affect the deposition pattern in the respiratory tract and, as a consequence, the biological fate of inhaled radioactive aerosols. Thus, the rate of excretion (e.g., via urine) and CED per inhaled unit of activity will vary with particle size (AMAD and GSD) in addition to the chemical composition/solubility class affecting particle dissolution rate in the respiratory tract.
Early studies have reported cascade impactor measurements of uranium aerosol AMADs in nuclear fuel fabrication. Schieferdecker et al. reported an AMAD of 8.2 μm (Schieferdecker et al. 1985). Thind reported an AMAD of 6.1 μm and GSD of 2.1 (Thind 1986). A more recent review paper stated an average AMAD of 6.2 μm with a GSD of 2.3 (Davesne and Blanchardon 2014). An earlier review paper stated that results below 1 μm were inconsistent, suggesting a bimodal size distribution (Dorrian and Bailey 1995). Available data are based on static sampling and might not be representative of the breathing zone of operators (Davesne and Blanchardon 2014). To the best of our knowledge, AMADs determined from cascade impactor sampling in the breathing zone of workers in nuclear fuel fabrication have not been published by open sources.
Uranium dioxide (UO2) powder for nuclear fuel and light-water reactors is typically produced from uranium hexafluoride (UF6) using integrated dry route (IDR) conversion or wet-route conversion via ammonium diuranate (ADU), alternatively via ammonium uranyl carbonate (AUC) (IAEA 2006). These production methods yield UO2 powder with different properties; e.g., the AUC route of conversion produces a UO2 powder consisting of coarser particles compared to the other methods (IAEA 2006). From a radiation protection perspective, process history is important since it affects the physicochemical properties of aerosols associated with a certain compound (Ansoborlo et al. 1999). From published data on uranium aerosols in nuclear fuel fabrication, it can be difficult to distinguish which production method was used. To the best of our knowledge, only one study has covered activity size distributions at a site using wet-route conversion via AUC and reported an average AMAD of 8.2 μm (Schieferdecker et al. 1985).
The aim of the present work is to evaluate uranium aerosol activity size distributions (with corresponding AMADs and GSDs) in the breathing zone of operators at a nuclear fuel fabrication plant using wet-route conversion via AUC. Portable cascade impactors were used to sample uranium aerosols in the operator breathing zone. In addition, static sampling was carried out at certain process steps. Activity size distributions were evaluated by assuming unimodal as well as bimodal distributions, and the impact on dosimetry calculations was evaluated based on the obtained results. The information in the present work is important in order to improve internal dosimetry, worker safety, and ensure regulatory compliance. Improved internal dosimetry might add to the overall knowledge about health effects associated with low-level exposure to ionizing radiation.
MATERIALS AND METHODS
Cascade impactors (Marple 298, Prod. No. SE298; Thermo Fisher Scientific, Waltham, MA), which operate at 2.0 L min−1 were used for aerosol sampling. The impactor used in the present work has eight impaction stages (A–H) with corresponding cut-points of 21.3, 14.8, 9.8, 6.0, 3.5, 1.6, 0.9, and 0.5 μm, respectively. The cut-point is defined as the aerodynamic diameter of particles having 50% probability of impaction at the given stage. The last stage is followed by a final filter to collect remaining particles. Gilian 5000 sampling pumps (Sensidyne, St. Petersburg, FL) were used with the impactors. The pumps were calibrated and flowrates checked using the methodology described in previous work, with flow rates showing variations typically <1% (Hansson et al. 2017). The small size of the impactor allows for sampling in the breathing zone, which was carried out by personnel carrying the impactor attached to the collar of the overalls throughout the work day. Static sampling was carried out by positioning the impactor as close as practically possible to work stations of particular interest.
Mixed cellulose ester membrane (MCE) (Thermo Scientific, SEC-290-MCE) was used as impaction substrate, and polyvinyl chloride (PVC) (SEF-290-P5) with a 5 μm pore size was used as final collection filter unless otherwise stated. Other authors have used an identical setup (Cheng et al. 2009). In the present work, impactor substrates were not coated to prevent particle bounce due to difficulties in reproducing application of thin, even layers of coating material. Furthermore, coating of substrates would distort comparison with planned in vitro dissolution rate experiments using the same sampling methodology. However, an attempt to investigate the presence of particle bounce was carried out by parallel sampling using cascade impactors with coated and uncoated substrates. The coating material (PRF Industrial Line Silicon Oil, Taerosol Oy, Kangasala, Finland) was sprayed onto each MCE substrate in a sweeping motion for 1-2 s at a distance of approximately 15 cm. Parallel sampling was carried out twice (three impactors per occasion) at a work station where scrap pellets are oxidized to triuranium octoxide (U3O8). Impaction stages G and H, as well as the final collection filters, were examined using scanning electron microscopy with energy-dispersive x-ray spectrometry (Phenom ProX, Phenom-World BV, Eindhoven, The Netherlands).
Sampling was carried out in the operator breathing zone at the four major workshops at the site: conversion, powder preparation, pelletizing, and burnable absorber (BA) pelletizing. At the conversion workshop, UO2 is formed from UF6 via AUC. At the powder preparation workshop, powder milling, powder blending, and oxidizing of waste materials is carried out. The pelletizing workshop produces UO2 pellets by pressing, sintering, and grinding. The BA pelletizing workshop is similar but uses milled UO2 powder, which is blended with Gd2O3. An overview of the main uranium flows is presented in Fig. 1. A more detailed description of the processes and typical operations has been described in previous work (Hansson et al. 2017).
Uranium material at the site is handled in batches, where the enrichment levels (mass-percent 235U) vary between 0.71% and 4.95% (average enrichment level 3.8%). The activity ratio 234U/238U increases approximately linearly with enrichment level. As a consequence, enrichment levels at the workshops may vary from day to day.
Sampling of the breathing zone was carried out on 18 occasions (five occasions at each workshop except for the BA pelletizing workshop, where only three occasions could be completed due to production-related circumstances). In addition to the breathing zone sampling, static sampling was carried out at 13 key locations at the different workshops:
- Conversion workshop: Static sampling at the transportation of AUC to the fluidizing bed furnaces and of the general workshop air;
- Powder preparation workshop: Static sampling at the stations for powder milling station, oxidizing of discarded pellets, oxidizing of waste from pellet grinding, humidity check (for criticality safety reasons) of UO2 powder and of the general workshop air;
- Pelletizing workshop: Static sampling at one of the pellet pressing stations, emptying of sintered pellets and of the general workshop air; and
- BA pelletizing workshop: Static sampling at the stations for UO2/Gd2O3 blending and oxidizing of waste from pellet grinding.
Sampling times were based on previous knowledge of airborne activity concentration levels and determined so as to collect sufficient amounts of radioactivity for reliable measurements but avoiding particle overload on the impaction substrates.
The total amount of alpha activity at all impactor substrates and final collection filters were measured for 20–24 h using an LB 790 10-Channel α-β Low-Level Counter (Berthold Technologies, USA, LLC, Oak Ridge, TN). Background levels were corrected for and frequently evaluated.
The breathing zone samples were also analyzed using alpha spectrometry in order to obtain uranium isotopic data. Samples were acid dissolved in concentrated HNO3 followed by aqua regia after addition of traceable amounts of 232U yield determinant (Isotrak, AEA Technology, PLC, Didcot, UK). Uranium was then extracted from 8 M HNO3 by tributyl phosphate (TBP) and back-extracted into demineralized water (Holm 1984) and finally electrodeposited onto stainless steel planchets (Hallstadius 1984). Measurements were carried out using passivated implanted planar silicon detectors (Canberra PIPS, Mirion Technologies Inc, San Ramon, CA), ORTEC (Oak Ridge, TN) OctêteR MCA system, and ORTEC MaestroR software. Counting times varied between 1–4 d.
The activity ratio from measurements by alpha spectrometry compared to total alpha activity followed a normal distribution with a mean of 1.01 and a standard deviation of 0.21. Two data points with very low activities deviated from the mean with more than 3 standard deviations and were considered outliers, resulting in a mean ratio of 0.99 with a standard deviation of 0.13. The agreement was considered satisfactory, and thus the remaining samples (static sampling) were not analyzed using alpha spectrometry.
Activity size distributions were evaluated using a unimodal approach (eqn 1). However, alpha spectrometry measurements revealed variable isotope ratios (234U/238U) during some sampling occasions, suggesting a multi-modal activity size distribution. Thus a bimodal activity size distribution was evaluated for each sampling occasion (eqn 2). A data reduction protocol developed by O’Shaughnessy and Raabe for sampling carried out with the Marple 298 impactor was used to evaluate activity size distributions (O’Shaughnessy and Raabe 2003). The protocol, which assumes a unimodal log-normal distribution (eqn 1), was modified as to model a bimodal activity size distribution:
where f and 1−f is the fraction of activity in the coarse and fine fraction, respectively. The same approach has been used by other authors investigating bimodal activity size distributions (Cheng et al. 2009).
The measured amount of radioactivity at each impaction substrate was corrected for sampling efficiency by applying a stage impaction efficiency of 0.52, 0.61, 0.78, 0.89, 0.95, 0.96, 0.97, and 0.99 for impactor stages A-H, respectively, as recommended by the manufacturer (Thermo Fisher 2009). Lower and upper bounds considered in the data reduction were 0.1 μm and 50 μm, respectively, as in previous studies (Cheng et al. 2009).
The AMADs, GSDs, and f for each sampling occasion were evaluated by applying a nonlinear least squares regression fit (Microsoft Excel 2010 Problem Solver) to the frequency function (eqn 1 and 2, respectively). Simulations were repeated in R (version 3.5.1., 2018-07-02) and included standard errors of the derived parameters.
Dose coefficients (CED per unit of inhalation intake) and predicted urinary excretion following inhalation of 1 Bq of 234U were evaluated. The software Integrated Modules for Bioassay Analysis (IMBA) Professional Plus (v. 4.1) was used (Birchall et al. 2007).
The HRTM has recently been revised by the ICRP; thus, new recommendations are available. For example, the particle transport model has been revised (ICRP 2015). Default absorption parameters for UO2 and U3O8 are now considered intermediate Type M/S (rapid fraction, fr = 0.03; rapid rate, sr = 1 d−1; slow rate ss = 5×10−4 d−1 and fractional absorption in the alimentary tract, fA = 6×10−4). Uranyl nitrate [UO2(NO3)2], uranium peroxide hydrate (UO4), ADU [(NH4)2U2O7] and uranium trioxide (UO3) are now considered intermediate Type F/M (fr = 0.8; sr = 1 d−1; ss = 0.01 d−1, and fA = 0.016) (ICRP 2017). AUC [(NH4)4UO2(CO3)3] is not included in ICRP Publication 137.
An add-on to IMBA has previously been used to model urinary excretion according to the ICRP 130 recommendations (Birchall et al. 2017). This add-on was acquired and used in the present work to model the urinary excretion following inhalation of 1 Bq of 234U according to the ICRP 130 particle transport model. Activity size distributions derived in the present work were used in combination with default absorption parameters, aerosol density (3 g cm−3), and shape factor (1.5) (ICRP 1994, 2015). For the conversion workshop Type F/M and Type M/S materials were considered. For remaining workshops, only Type M/S material was considered.
To the best of our knowledge, as of today, no software exists that allows for calculation of dose coefficients according to the ICRP 130 particle transport recommendations. Thus, dose coefficients were evaluated using the ICRP 66 particle transport model but with the revised ICRP 137 absorption parameters.
RESULTS AND DISCUSSION
Radioactivity measurements and activity size distributions
Radiometric data following alpha spectrometry of breathing zone samples are presented in Table 1, and radiometric data following alpha counting of static sampling are presented in Appendix A (Table A1). It is evident from Table 1 that isotope ratios (234U/238U) might vary across impactor stages during a single round of sampling (e.g., sampling 5 at the conversion workshop, sampling 1 at the powder preparation workshop, sampling 3 at the pelletizing workshop). This implies that at least two populations of particles with different size distributions and enrichment levels (i.e., originating from different batches of uranium material) might be present during a sampling occasion. Thus, a bimodal approach might be preferable to describe the data. Many sampling occasions showed uniform isotope ratios (e.g., sampling 1 at the pelletizing workshop). This does not contradict a bimodal distribution, as different process steps might be associated with different size distributions but identical enrichment levels (depending on material batch). It is also noteworthy from Table 1 that the concentration of sampled activity varies with up to an order of magnitude. This implies that day-to-day variations in exposure might be large, presumably due to variations in production and work tasks carried out.
Data from Table 1 are presented as fractions in Fig. 2 for easy comparison between the different workshops. Activity sampled at the conversion workshop was collected at the early impaction stages to a greater extent compared to the other workshops.
Activity size distributions
Each sampling occasion was evaluated by assuming both a unimodal and a bimodal activity size distribution. Results are presented in Table 2 (breathing zone sampling) and Table 3 (static sampling). The average parameters (Table 2) for each workshop were used to visually compare activity size distributions (Fig. 3).
From Tables 2 and 3 and Fig. 3, it is evident that a typical AMAD at the site is larger than the ICRP default distribution (AMAD = 5 μm, GSD = 2.5). Sampling in the breathing zone of the operators tended to generate more predominant coarse fractions (larger f) compared to static sampling. Static sampling can be difficult to carry out in a way that is representative of worker exposure due to positioning and the fact that operators carry out work at multiple positions during a work period. The distance to the source of particle dispersion is likely important since large particles settle more rapidly than small particles (Hinds 1999). Our results suggest that static sampling might underestimate the amount of activity present in the breathing zone and that the derived AMAD might be underestimated.
A bimodal activity size distribution appears to give a better curve fit compared to a unimodal fit (Fig. 3). This is in accordance with the observed variable isotope ratios for breathing zone sampling (Table 1). However, the AMAD and GSD for the fine fraction were difficult to quantify and were frequently not statistically significant (Tables 2 and 3). This was particularly obvious for the conversion workshop, where the fine fraction appeared to be very indistinct in comparison to the coarse fraction.
One could consider weighting the measurement points in the curve-fitting process (Kemmer and Keller 2010). An attempt was made using relative weighting for each of the samplings carried out at the pelletizing workshop. For the fine fraction, the mean AMAD increased from 3.2 μm to 5.0 μm and the mean GSD from 3.5 to 9.1, compared to the unweighted curve-fitting procedure. That large a GSD indicates a very broad peak. We believe that this is a case of overfitting data to the model, which might be misleading, in particular if the late impactor stages are affected by particle bounce. The effect on the coarse fraction was less prominent.
The derived AMADs in the present work are generally considerably higher than previously published data (Davesne and Blanchardon 2014). This might be due to aerosol characteristics associated with the particular production method. The wet-chemical AUC conversion process is known to generate a coarser UO2 powder compared to wet-chemical ADU conversion (Bergqvist and Sahle 2010; Palheiros et al. 2009). The sampling method (i.e., breathing zone vs. static sampling) can also play an important role.
The calculated dose coefficients (CED per unit of 234U inhalation intake) for breathing zone sampling at the four workshops are presented in Table 4. The dose coefficient associated with the ICRP default assumption (AMAD = 5 μm, GSD = 2.5) is included for comparison. Potential exposure at the workshops is associated with CED and Hlung coefficients being 21–51% and 12–29%, respectively, of those obtained for the default 5 μm and Type M/S assumption. Lower dose coefficients are expected since a high AMAD results in a greater fraction of activity depositing in the upper respiratory tract, resulting in a lower dose to the lung.
The resulting urinary excretion rate following inhalation intake of 1 Bq 234U is presented in Fig. 4. The urinary excretion rate associated with the ICRP default assumption (AMAD = 5 μm, GSD = 2.5) is included for comparison.
The expected urinary excretion per unit of inhalation intake 100 d after exposure was found to be 13–34% of that corresponding to the default assumption of AMAD 5 μm and Type M/S material. Lower excretion rates are expected since a high AMAD results in a smaller fraction of activity reaching the alveoli and thus the blood.
Although variable isotope ratios (Table 1) are a strong indicator of bimodal/multimodal activity size distributions, the parameters associated with the fine fractions proved difficult to be quantify as indicated by large uncertainties (Tables 2 and 3). Dose coefficients and excretion patterns are based on these derived parameters and must thus be interpreted with care.
Total analytical uncertainties due to counting statistics, detector efficiency (gross alpha counting only), tracer uncertainty, etc., were estimated to be <10%. The analytical uncertainties were considered lower than sampling-related uncertainties, including sample representativeness, particle bounce/roll-off, and impactor stage collection efficiency. Particle bounce/roll-off can be reduced by coating impaction substrates; e.g., by applying a thin layer of silicone oil. It is important to consider coating material, thickness, and homogeneity. To improve reproducibility, simplify sampling, and allow for easy comparison with planned in vitro dissolution rate studies, coating of the impaction substrates was not carried out in the present work except for a comparative measurement (Appendix B). Previous studies using the same type of impactor have concluded that some roll-off can be expected at impaction stages G and H (Rubow et al. 1987). Cheng et al. used the same type of impactor with coating applied on Stage A only. Stages G and H were uncoated, but the authors reported that bounce did not appear to be significant based on deposition patterns (Cheng et al. 2009). Another study concluded that measurements with uncoated impaction substrates tended to generate lower AMADs, although the difference was not statistically significant (Kirychuk et al. 2009). In the present work, the effect of coated and uncoated substrates was evaluated by parallel rounds of stationary sampling (Appendix B, Table B1 and B2, Fig. B1). Coating of substrates generated a somewhat lower fraction of activity on late impactor stages, in particular stages H and G, as well as the final collection filter. This might indicate that some sampling artefact, e.g. particle bounce, occurs. The presence of particles larger than expected compared to impactor stage cut-point (stages G-H and final collection filter) was verified using scanning electron microscopy for both coated and uncoated rounds of sampling. This might introduce a bias toward higher 1-f and GSD, and lower AMAD, for the fine fraction. However, modeled AMADs and GSDs remained similar for coated and uncoated rounds of sampling, regardless of whether a unimodal or bimodal approach was used (Fig. B1). We consider the effect of coating difficult to quantify due to variations between different rounds of samplings and uncertainties introduced in the coating procedure. It should be mentioned that particle bounce/roll-off is most pronounced at high particle loads (Fujitani et al. 2006). Particle loads in the present work were lower (< 0.1 mg assuming a specific activity of approximately 90 Bq mg−1) compared to other studies (approximately 0.2–0.9 mg) (Cheng et al. 2009).
Impactor stage collection efficiencies were determined by Rubow et al. (1987) using monodisperse aerosols. Early impaction stages are associated with lower collection efficiencies; thus, the impact on the overall uncertainty is greater when a large fraction of the activity is collected on early impaction stages. In the present work, at least 50% of the measured activity was typically collected at impaction stages A-C (collection efficiencies of 0.52, 0.61, and 0.78, respectively). The impact on derived AMADs, GSDs, and f for the pelletizing workshop was tested by modifying the collection efficiencies in the data reduction protocol with ± 20% for stages A-C. The impact on the derived f, AMAD, and GSD for the coarse fraction was less than 5%. The impact on the AMAD and GSD for the fine fraction was within uncertainties presented in Table 2.
In the present work, cascade impactors were used to evaluate activity size distributions at a nuclear fuel fabrication plant using wet-route AUC conversion. Sampling was carried out in the operator breathing zone at the four main workshops and at certain process steps. Variable 234U/238U isotope ratios indicated a multimodal rather than unimodal activity size distribution for breathing zone sampling. A bimodal distribution (coarse and fine fraction) was assumed. Most activity (75–88%) was associated with the coarse fraction (AMAD 15.2–18.9 μm). The AMAD of the fine fraction was 1.7–7.1 μm, but uncertainties were substantial. When static sampling was carried out, the coarse fraction consisted of a smaller fraction of the activity, and the AMAD was lower.
We conclude that although the parameters associated with the fine fractions were difficult to quantify, the presence of a fine fraction is nonetheless very important to consider in CED assessments. The predicted CED per unit of inhalation intake (234U) at the four main workshops was estimated to 1.6-2.6 μSv Bq−1 for the Type M/S solubility class, compared to 5.0 μSv Bq−1 when assuming an AMAD of 5 μm. The predicted urinary excretion of 234U per unit of inhaled activity at the four workshops at 100 d after intake was 13–34% of the excretion rate when an AMAD of 5 μm was assumed.
The findings in the present work allow for more realistic assumptions regarding activity size distributions, improving CED evaluations at the site. It also shows that internal dosimetry preferably should be based on breathing zone data rather than static sampling. Improved dosimetry can aid in radiation protection optimization and in future epidemiological studies. Future work includes determination of absorption parameters and fractional uptake to the alimentary tract.
The Swedish Radiation Safety Authority is acknowledged for funding the present work (grant number SSM2016-589-2). Westinghouse Electric Sweden AB is acknowledged for participation in the study and funding of the PhD program.
Special thanks go to Patrick O’Shaughnessy at the University of Iowa for help with the data reduction protocol, and to Jörgen Gustafsson and numerous colleagues at Westinghouse Electric Sweden AB for invaluable discussions. The operators at the site are thanked for assisting with sampling of aerosols.
Ansoborlo E, Henge-Napoli MH, Chazel V, Gibert R, Guilmette RA. Review and critical analysis of available in vitro dissolution tests. Health Phys 77:638–645; 1999. DOI 10.1097/00004032-199912000-00007.
Bergqvist H, Sahle W. Electron microscopy characterization and x-ray measurements of UO2 powder and UO2 based fuel pellet materials. Royal Institute of Technology, Stockholm, Sweden; FNM/ICT, KTH Report No. 100331; 2010.
Birchall A, Puncher M, Marsh JW, Davis K, Bailey MR, Jarvis NS, Peach AD, Dorrian MD, James AC. IMBA Professional Plus: a flexible approach to internal dosimetry. Radiat Protect Dosim 125:194–197; 2007. DOI 10.1093/rpd/ncl171.
Birchall A, Vostrotin V, Efimov A, Sokolova A, Suslova K, Zhdanov A, Schadilov A, Puncher M, Dorrian M-D, Napier B, Strom DJ, Scherpelz R, Miller S. The Mayak worker dosimetry system (MWDS-2013) for internally deposited plutonium: an overview. Radiat Protect Dosim 176:10–31; 2017. DOI 10.1093/rpd/ncx014.
Chang M, Kim S, Sioutas C. Experimental studies on particle impaction and bounce: effects of substrate design and material. Atmos Environ 33:2313–2322; 1999. DOI 10.1016/S1352-2310(99)00082-5.
Cheng YS, Kenoyer JL, Guilmette RA, Parkhurst MA. Physicochemical characterization of Capstone depleted uranium aerosols
II: particle size distributions as a function of time. Health Phys 96:266–75; 2009. DOI 10.1097/01.HP.0000290613.41486.cb.
Davesne E, Blanchardon E. Physico-chemical characteristics of uranium compounds: a review. Int J Radiat Biol 90:975–988; 2014. DOI 10.3109/09553002.2014.886796.
Dorrian MD, Bailey MR. Particle size distributions of radioactive aerosols
measured in workplaces. Radiat Protect Dosim 60:119–133; 1995. DOI 10.1097/HP.0b013e318287321d.
Fujitani Y, Hasegawa S, Fushimi A, Kondo Y, Tanabe K, Kobayashi S, Kobayashi T. Collection characteristics of low-pressure impactors with various impaction substrate materials. Atmos Environ 40:3221–3229; 2006. DOI 10.1016/j.atmosenv.2006.02.001.
Gilbert ES. Ionising radiation and cancer risks: what have we learned from epidemiology? Int J Radiat Biol 85:467–482; 2009. DOI 10.1080/09553000902883836.
Grellier J, Atkinson W, Berard P, Bingham D, Birchall A, Blanchardon E, Bull R, Guseva Canu I, Challeton-De Vathaire C, Cockerill R, Do MT, Engels H, Figuerola J, Foster A, Holmstock L, Hurtgen C, Laurier D, Puncher M, Riddell AE, Samson E, Thierry-Chef I, Tirmarche M, Vrijheid M, Cardis E. Risk of lung cancer mortality in nuclear workers
from internal exposure to alpha particle-emitting radionuclides. Epidemiol 28:675–684; 2017. DOI 10.1097/EDE.0000000000000684.
Hallstadius L. A method for the electrodeposition of actinides. Nucl Instrum Meth Phys Res 223:266–267; 1984. DOI 10.1016/0167-5087(84)90659-8.
Hansson E, Pettersson HBL, Fortin C, Eriksson M. Uranium aerosols
at a nuclear fuel fabrication plant: characterization using scanning electron microscopy and energy dispersive x-ray spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy 131:130–137; 2017. DOI 10.1016/j.sab.2017.03.002.
Harrison J, Day P. Radiation doses and risks from internal emitters. J. Radiol. Prot. 28:137–159; 2008. DOI 10.1088/0952-4746/28/2/R01.
Hinds WC. Aerosol technology: properties, behavior, and measurement of airborne particles. New York: Wiley & Sons; 1999.
Holm E. Review of alpha-particle spectrometric measurements of actinides. Int J of Applied Radiation and Isotopes 35(4):285–290; 1984. DOI 10.1016/0020-708X(84)90070-X.
International Atomic Energy Agency. The front end of the uranium fuel cycle. 50th IAEA General Conference, Documents [online]. 2006. Available at https://www.iaea.org/About/Policy/GC/GC50/GC50InfDocuments/English/gc50inf-3-att6[lowen]en.pdf
. Accessed 1 August 2018.
International Commission on Radiological Protection. Human respiratory tract model for radiological protection. Oxford: Pergamon Press; ICRP Publication 66; Ann ICRP 24(1–3); 1994.
International Commission on Radiological Protection. Individual monitoring for internal exposure of workers. Oxford: Pergamon Press; ICRP Publication 78; Ann ICRP 27(3–4); 1997.
International Commission on Radiological Protection. Recommendations of the International Commission on Radiological Protection. Oxford: Pergamon Press; ICRP Publication 103; Ann ICRP 37(2–4); 2007.
International Commission on Radiological Protection. Occupational intakes of radionuclides: part 1. Oxford: Pergamon Press; ICRP Publication 130; Ann ICRP 44(2); 2015.
International Commission on Radiological Protection. Occupational intakes of radionuclides: part 3. Oxford: Pergamon Press; ICRP Publication 137; Ann ICRP 46(3–4); 2017.
Kemmer G, Keller S. Nonlinear least-squares data fitting in Excel spreadsheets. Nature Protocols 5:267–281; 2010. DOI 10.1038/nprot.2009.182.
Kirychuk SP, Reynolds SJ, Koehncke N, Nakatsu J, Mehaffy J. Comparison of endotoxin and particle bounce in Marple cascade samplers with and without impaction grease. J Agromed 14:242–249; 2009. DOI 10.1080/10599240902845062.
O’Shaughnessy PT, Raabe OG. A comparison of cascade impactor data reduction methods. Aerosol Sci Technol 37:187–200; 2003. DOI 10.1080/02786820300956.
Palheiros F, Gonzaga R, Soares A. Comparative study of the different industrial manufacturing routes for UO2
pellet specifications through the wet process. NAC 2009: International nuclear atlantic conference Innovations in nuclear technology for a sustainable future, Brazil [online]. 2009. Available at https://inis.iaea.org/collection/NCLCollectionStore/_Public/41/057/41057359.pdf?r=1&r=1
. Accessed 17 February 2020.
Rubow K, Marple V, Olin J, Mccawley M. A personal cascade impactor: design, evaluation and calibration. Am Industrial Hygienists Assoc J 48:532–538; 1987. DOI 10.1080/15298668791385174.
Schieferdecker H, Dilger H, Doerfel H, Rudolph W, Anton R. Inhalation of U aerosols
fuel element fabrication. Health Phys 48:29–48; 1985.
Thermo Fisher Scientific Inc. Marple personal cascade impactors Series 290, instruction manual, part number 100065-00. 2009.
Thiel CG. Cascade impactor data and the lognormal distribution: nonlinear regression for a better fit. J Aerosol Med 15:369–378; 2002. DOI 10.1089/08942680260473443.
Thind KS. Determination of particle size for airborne UO2
dust at a fuel fabrication work station and its implication on the derivation and use of ICRP Publication 30 derived air concentration values. Health Phys 51:97–105; 1986.
APPENDIX A—RADIOMETRIC DATA FOR STATIC SAMPLING
APPENDIX B—PARALLEL SAMPLING