Plutonium Biokinetics in Human Body A. Luciani - Kit-Bibliothek - FZK

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As an example a practical application of the previous sensitivity analysis is shown in the following. The results of the sensitivity analysis for the respiratory tract allow to evaluate the effects of the modifying factors proposed by ICRP for the parameters describing the particle transport in the human respiratory tract. In the Annexe E of Publication 66 of ICRP [57] modifying factors Φ m are recommended by which the parameters of the respiratory model should be multiplied in order to consider alterations in the particle transport model due to factors such as age, smoking, pollutants, disease or pharmacological agents. The respiratory model parameters that are supposed to be altered are the clearance from the BB1 bronchial compartment and the bb1 bronchiolar compartment, for almost all the causes of clearance alteration, and the clearance from the two alveolar-interstitial compartments AI2 and AI3 together with the fraction deposited in AI1, for the effects of cigarette smoking. As suggested by the sensitivity analysis, only the cigarette smoking, affecting the clearance from the alveolar-interstitial compartments, would significantly alter the urinary excretion of Plutonium. Assuming the modifying factors proposed by ICRP and using the Model-b for Plutonium, it was calculated that smoking would cause an enhancement of the urinary excretion of type S Plutonium by 17 %, 43 % and 44 % at 100, 1,000 and 10,000 days post inhalation, respectively (see Figure 3.1.27). Daily urinary excretion [d -1 ] 1E-06 1E-07 Figure 3.1.27 Daily percentage urinary excretion of type S Plutonium for a smoker compared to a normal subject using the Model-b and the ICRP 66 respiratory tract model. 3.1.5.4 Uncertainty analysis Normal Subject Smoker 1 10 100 1000 10000 100000 Days post intake [d] Uncertainty analyses of models are generally performed in order to determine the uncertainty of a model’s predictions that results from imprecisely known input variables. In the specific context of the present work, the effects of incomplete knowledge [167] of the 128
transfer rates on the Plutonium urinary excretion and, to a lesser extent, on the fecal excretion and blood content are investigated. Nowadays different methods are available for performing an uncertainty analysis. Monte Carlo techniques are among the most common and effective procedures. This technique is based on performing a model’s multiple evaluations with probabilistically selected input, and then using the results of these evaluations to determine the uncertainty. In a more detailed way, Monte Carlo analysis involves four steps. 1. Selection of the range and distribution for each input variable. These selections will be used in the next step for generating a sample of values of the variable. The main problem in this step isthe choice of the distribution. If the analysis is just of exploratory nature, then rather crude but easily implemented statistical distributions can be considered (e.g. uniform, log-uniform or triangular). However if more realistic results are expected from the uncertainty analysis, then other distributions should be considered as well. 2. In a second step values must be sampled from the chosen distribution and for each input variable. The most widely used sampling procedures are random sampling, importance sampling and Latin hypercube sampling [168]. The result of this step is a n-dimensional vector of the form: [ ] i = 1,2,3,.......,m x i = x i1, x i2 ,x i3.....x in 129 equation 3.1.10 where n is the number of input variables (i.e. the number of transfer rates considered in the uncertainty analysis) and m is the size of the sample (i.e. the number of values sampled for each input variable). 3. In a third step the model is evaluated for a large number of sampling vectors and a sequence of results (y i) is generated in the following form: yi = f ( xi1, xi2 ,xi3.....x in ) = f xi ( ) i = 1,2,3,.......,m equation 3.1.11 4. In the last step the results of the previous evaluations are used as basis for the uncertainty analysis. One of the most common methods to represent the uncertainty is the mean value and a variance of the distribution of the generated values. In the particular case in which the output variable is described by a log-normal distribution, the geometric mean () as central parameter and the geometric standard deviation (S g(y)) as indicator of variability should be estimated according to the following expressions: ⎡ ⎢ < y >= Exp⎢ ⎢ ⎢ ⎣ ⎡ ⎢ Sg(y) = Exp⎢ ⎢ ⎢ ⎣ m ∑ i m ∑ i Logy i m ⎤ ⎥ m ⎥ ⎥ = m ∏ y i i ⎥ ⎦ ( Logyi − Log < y > ) 2 ⎤ ⎥ ⎥ m − 1 ⎥ ⎥ ⎦ equation 3.1.12 equation 3.1.13
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Forschungszentrum Karlsruhe in der
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Für diesen Bericht behalten wir un
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i ABSTRACT The biokinetic model pre
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ZUSAMMENFASSUNG Das von der Interna
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1 INDEX OF CONTENTS INDEX OF SYMBOL
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3 INDEX OF SYMBOLS Here a short glo
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Chapter 1 Introduction 5
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the duties of the Department for Ra
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adionuclide. Therefore only isotope
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stabilise the +4 oxidation state [1
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techniques adopted in the early pha
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present in spent and MOX reactor fu
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INTRAVENOUS INJECTION skin WOUND bl
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functions, defined as retained and
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the contamination event, of such as
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Table 1.3.2 Indicative 239 Pu detec
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The kinetics of Plutonium in the ki
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1.4.2 COMMENTS ON THE ICRP 67 MODEL
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Chapter 2 Data and measurements 31
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Therefore the radiolysis process, m
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Recent technologic developments in
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eu(t) = 0.19e −0.272t + 0.023e
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function for each of the considered
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the minimum detectable activity of
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2.2.2 AVAILABLE MEASUREMENTS Immedi
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Table 2.2.4 Daily fecal excretion o
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elements the spectral distribution
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The phoswich detector is based on t
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HPGe crystals are produced in cylin
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This quantity is normally expressed
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The peak is located in the region B
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To introduce the second quantity, t
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117 cm 3 h -1 for the incoming and
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Figure 2.3.8 The Lawrence Livermore
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three detectors are used. Two detec
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Table 2.3.4 Efficiency factors of p
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• the necessity of performing mea
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Table 2.3.7 Impulses in the measure
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2.4 ACTIVITY MEASUREMENTS IN BIOASS
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If more then one radionuclide is ma
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L A is the air layer in µgcm -2 ;
Page 88 and 89: The electrodeposition cell used for
Page 90 and 91: Table 2.4.1 238 Pu, 239 Pu and 241
Page 93 and 94: 3.1.1 MATHEMATICAL TOOLS 3.1.1.1 So
Page 95 and 96: calculating the amount of Plutonium
Page 97 and 98: 3.1.2 ANALYSIS OF THE ICRP 67 MODEL
Page 99 and 100: empirical curves given by Langham,
Page 101 and 102: the worst performance of the group.
Page 103 and 104: leave the total clearance from the
Page 105 and 106: Figure 3.1.4 shows that the use of
Page 107 and 108: 3.1.3 MODIFICATION OF ICRP 67 MODEL
Page 109 and 110: The structure of Polig’s skeleton
Page 111 and 112: Percentage daily urinary excretion
Page 113 and 114: of 0.693d -1 . The values of the F
Page 117 and 118: the basis of all the reference data
Page 119 and 120: Table 3.1.10 Transfer rates of the
Page 121 and 122: 3.1.4 VERIFICATION OF THE OPTIMIZED
Page 123 and 124: Table 3.1.13 Intakes and percentage
Page 125 and 126: soft tissue ST2 soft tissue ST0 sof
Page 127 and 128: For the routine application of Mode
Page 129 and 130: excreted by individuals aged sixty
Page 131 and 132: Table 3.1.20 Exponents and coeffici
Page 133 and 134: tract [57] to consider a contaminat
Page 135 and 136: The sensitivity coefficients for th
Page 137: Sensitivity coefficients S i Figure
Page 141 and 142: Geometric standard deviations rangi
Page 147 and 148: 3.2 APPLICATION OF THE OPTIMIZED MO
Page 149 and 150: lymph nodes, as in the LLNL phantom
Page 151 and 152: Americium in the lungs [Bq] 1000 10
Page 153 and 154: Daily urinary excretion [Bqd -1 ] 1
Page 155 and 156: long time is not yet described by t
Page 157 and 158: excretion that is one of the main i
Page 159 and 160: The comparison of model’s predict
Page 161 and 162: Americium in the lungs [Bq] 1000 10
Page 163 and 164: 153 CONCLUSIONS The optimized model
Page 165 and 166: Annex Quantities used in Radiologic
Page 167 and 168: present. As the radiation weighting
Page 169 and 170: w R is the radiation weighting fact
Page 171 and 172: 161 BIBLIOGRAPHY 1. Plutonium. W. K
Page 173 and 174: 29. Nenot, J.-C. and Stather, J. W.
Page 175 and 176: 55. International Atomic Energy Age
Page 177 and 178: 81. Crowley, J., Lanz, H., Scott, K
Page 179 and 180: 107. Hempelmann, L.H., Langham,W.H.
Page 181 and 182: 137. Breitestein, B.D., Newton, C.E
Page 183 and 184: 167. Harrison, J.D., Khursheed, A.,
Page 185 and 186: JOURNALS OWN PUBLICATIONS RELATED T
Page 187 and 188: 177 ACKNOWLEDGEMENTS Special thanks
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Name: Andrea Luciani Date and place
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Plutonium Biokinetics in Human Body A. Luciani - Kit-Bibliothek - FZK

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