a model for radio logical consequences of nuclear power plant atmospheric releases

8/8/2019 A Model for Radio Logical Consequences of Nuclear Power Plant Atmospheric Releases

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A model for radiological consequences of nuclear power plant operationalatmospheric releases

Cemil Kocar * , Cemal Niyazi So kmenHacettepe University, Department of Nuclear Engineering, Beytepe, Ankara 06800, Turkey

a r t i c l e i n f o

Article history:Received 14 June 2008Received in revised form27 October 2008Accepted 4 November 2008Available online 6 December 2008

Keywords:Nuclear reactorRadiationDoseRiskFood chain

a b s t r a c t

A dynamic dose and risk assessment model is developed to estimate radiological consequences of atmospheric emissions from nuclear power plants. Internal exposure via inhalation and ingestion,external exposure from clouds and radioactivity deposited on the ground are included in the model. Themodel allows to simulate interregional moves of people and multi-location food supply in the compu-tational domain. Any long-range atmospheric dispersion model which yields radionuclide concentrationsin air and on the ground at predetermined time intervals can easily be integrated into the model. Thesoftware developed is validated against radionuclide concentrations measured in different environ-mental media and dose values estimated after the Chernobyl accident. Results obtained using the modelcompare well with dose estimates and activities measured in foodstuffs and feedstuffs.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

Generally, the computer codes developed for estimation of radiological consequences due to routine releases from nuclearpower plants are based on annual average concentrations of radionuclides in air and on the ground (e.g., Hermann et al., 1984;Mayall et al., 1997; Chaki and Parks, 2000 ). These codes neglectradiation exposure changes due to seasonal variations of radionu-clides in the environment. For more realistic dose calculations, timedependency of the radionuclide transfer processes should be takeninto account, leading to dynamic modeling. In the dynamicmodeling, it is essential to consider seasonality in growing cycles of crops, feeding practices of domestic animals and human dietaryhabits.

In the late 1970s, such dynamic radio-ecological models cameinto scene. Existing dynamic radio-ecological models wereprimarily developed for nuclear power plant accidents; so, theirdatabases generally include radionuclides which have primeimportance for evaluating consequences of accidents (e.g., Kochand Tadmor, 1986; Whicker and Kirchner, 1987; Muller andProhl, 1993 ). For chronic exposures, some radioisotopes like 14Ccontribute signicantly to the doses. Those isotopes, their dose andrisk coefcients, and transfer parameters should be available in the

models for proper evaluation of operational radiologicalconsequences.

Where it is produced, it is consumed is the preferred approachwhile modeling foodstuff supply in the ingestion dose calculationsin existing softwares. However, in real life, different foodstuffs aresupplied from different geographical locations, and in addition,a typical individual may live at different geographical locations fordifferent durations because several activities such as education,business, or vacation may necessitate it. This situation may lead tosignicant changes in groundshine, cloudshine, and inhalationpathway doses for an individual. After each change of location, it islikely that foodstuff supply locations also change. For more realisticdose and risk assessments due to atmospheric releases duringa nuclear power plants routine operation, effects of multi-locationfood supply and interregional moves of people must be taken intoaccount.

To address the unique features of modeling operationalradiological consequences of nuclear power plants, a new soft-ware based on the dynamic radio-ecological model ( Muller andProhl, 1993 ) is coded. The software developed models all exposurepathways and time dependency and has a capability of calculatingdose and risks at a specied point as well as in the whole griddedcomputational domain. Multi-location food supply and interre-gional moves of people in the computational domain arepermitted. The software is validated against measured radionu-clide concentrations and estimated dose values presented in IAEA(1995) .

* Corresponding author. Tel.: 90 312 297 7300; fax: 90 312 299 2122.E-mail address: [email protected] (C. Kocar).

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Journal of Environmental Radioactivity

j ou rna l homepage : www.e l sev i e r. com/ loca t e / j env rad

0265-931X/$ see front matter 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.jenvrad.2008.11.003

Journal of Environmental Radioactivity 100 (2009) 8993
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2. Dose and risk modeling

The software developed has four main parts: (1) preprocessingatmospheric dispersion code results, (2) dose calculations, (3) riskcalculations, and (4) simulating multi-location food supply andinterregional moves of people.

The major pathways important to radiation exposure, imple-mented in the software, are

External exposure from radionuclides in the cloud (cloudshine)and radionuclides deposited on the ground (groundshine).Transfer of radionuclides through food chains and the subse-quent internal exposures of humans due to ingestion of contaminated foodstuffs.Internal exposure following inhalation of radionuclides duringpassage of the cloud.

The software developed receives air concentrations and grounddepositions of radionuclides corrected for radioactive decay atpredetermined time intervals. Measured values of radionuclideconcentrations in air and on the ground or output of any atmo-spheric dispersion software can be used as input. Shielding due tomigration of radionuclides into deep soil is considered. The migra-tion modeland itsconstants aretakenfrom Muller and Prohl (1993) .

Radionuclide concentrations accumulated on the ground andcorrected for decay are calculated by the following formula:

GC r p ; t j ; i GC r p ; t j1 ; iel it jt j1 a1 e

l 1 t a 2 el 2 t

GDr p ; t j; i (1)with GC (r p,t 1,i) GD(r p,t 1,i), where GC ground concentration of radionuclide i, attime step j and location r p (Bqm 2), GD depositionoutput from atmospheric dispersion software at time step j (Bqm 2),l i radioactive decay constant ith radionuclide (s 1), t j time at jthstep (s), l 1 migration rate ( l 1 126.15 s 1), l 2 migration rate(l 2 3.35 s 1), a1 contribution fraction of the migration rate(a1 0.36), a2 contributionfractionof themigration rate ( a2 0.64).

Once the time-dependent ground concentrations of radionu-clides are obtained, the groundshine dose, cloudshine dose, inha-lation dose, radionuclide concentrations in plants, radionuclideconcentrations in animal products, and the ingestion dose areevaluated by using the methodology discussed in Muller and Prohl(1993) .

The behavior of 3H and 14C in the ingestion pathway is handledin a special manner. It is assumed that activity concentrations inwater and in carbon in food and feed products at a given locationare in equilibrium with specic activities in atmospheric watervapor and carbon dioxide in airat time of harvest. It is also assumedthat plants obtain all their carbon from airborne carbon dioxide andthe animals obtain all their carbon through ingestion of plants.Concentration of 3H in vegetation is calculated as follows:

C lhr q 9AC hr qF lh

H (2)

where C lh(r q) concentration of 3H in plant type l at time of harvestat location r q (Bq kg 1); AC h(r q) concentration of 3H in air at timeof harvest at location r q (Bq m 3); F lh fraction of hydrogen in planttype l; H absolute humidity (L m 3).

In Eq. (2) , the factor 9 is introduced to convert 3H concentrationin environmental water to concentration in hydrogen. The absolutehumidity is taken to be 6 103 L m3 (IAEA, 2001).

Concentration of 3H in animal products is obtained from:

C mhr qC lh

r q

F mh

F h p (3)

where C mh(r q) concentration of 3H in animal product type m atlocation r q (Bq kg 1); F mh fraction of hydrogen in animal producttype m .

Concentration of 14C in crops from atmospheric contaminationis calculated as follows:

C lcr qAC cr qF lc

P c(4)

where C lc(r q) concentration of 14C in plant type l at time of harvestat location r q (Bq kg 1); AC c(r q) concentration of 14C in air at timeof harvest at location r q (Bq m 3); F lc fraction of carbon in planttype p; P c concentration of carbon in air (kg m 3).

Concentration of airborne carbon is assumed to be1.8 104 kg m 3, corresponding to an average atmospheric CO 2concentration of 370 ppm.

Concentration of 14C in animal products is calculated by thefollowing formula:

C mcr qC lcr qF mc

F lc(5)

where C mc(r q) concentration of 14C in animal product type m atlocation r q (Bq kg 1); F mc fraction of carbon in animal producttype m .

2.1. The models for multi-location food supply and interregionalmoves of people

Output of the developed software contains monthly pointwisedose and risk values at grid points of the computational domain.Doses and risks computed are saved separately for all exposurepathways as monthly values. Further, for the ingestion pathway,doses and risks calculated are saved separately for each simulatedfood category. This approach allows to model multi-location foodsupply and interregional moves for a simulated person.

Geographical coordinates where a simulated person resides inthe computational domain are dened throughout the simulationperiod. The residential period at each location is required as input.At each residential location, fractional contributions of food supplylocations for each foodstuff category are also to be input into themodels for multi-location food supply and interregional moves of people.

The total individual effective dose for a simulated person iscalculated by

Dsim ; tot XP

p 1X

K

i 1Dgsr p ; i Dcsr p ; i Dinh r p ; i

Ding r p ; i (6)where Dsim,tot total individual effective dose; P number of pla-ces where a simulated person lives during a simulation period.K number of months in a simulation period. Dgs(r p,i) ground-shine dose for a simulated person at location r p in the ith month.Dcs(r p,i) cloudshine dose for a simulated person at location r p inthe ith month. Dinh (r p,i) inhalation dose for a simulated person atlocation r p in the ith month.

The monthly ingestion dose for a simulated person, Ding (r p,i), isevaluated by the following equation:

Ding r p ; i XN

q 1X

M

j 1Fr p; q ; j; i Ding r q ; j; i (7)

where N number of locations where foodstuffs are supplied,

M number of foodstuff categories in diet of a simulated person,Fr ( p,q, j,i) fractional contribution of the qth location to the jth

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foodstuff category for calculation of monthly ingestion dose atlocation p, Ding (r q, j,i) ingestion dose contribution of the jthfoodstuff category in the ith month.

Calculations of mortality and morbidity risks to a simulatedperson are carried out by applying the same methodology used inthe dose calculations.

2.2. Risk calculations

It is common practice to estimate the cancer risk from intake of a radionuclide or external exposure to the radiation emitted from itas the product of a probability coefcient and an estimatedeffective dose to a typical adult. For example, a nominal cancerfatality probability coefcient of 0.05 Sv 1 is recommended in ICRP(1991) forall cancertypes combined.A betterestimate of the cancerrisk can be computed by using the United States EnvironmentalPollution Agency (EPA) risk coefcients. Detailed information onderivation of the EPA risk coefcients and their application can befound in EPA (1989, 1991,1994,1997 ) and Eckerman et al. (1999) .

In this study, the EPA risk coefcients are utilized to evaluateindividual and collective risks. The morbidity and mortality risksdue to radiation-induced cancers for ingestion, inhalation, cloudexposure, and ground exposure pathways are calculated by usingappropriate morbidity or mortality risk coefcients, instead of thedose conversion factors. The total morbidity or mortality risk isevaluated by summing all considered pathways.

3. Code structure and default modeling parameters

Developedsoftware is implemented in Fortran-90. All the arraysused in the software are dened as dynamic arrays; so, there is norestriction on extension of input parameters such as numbers of feedstuffs and foodstuffs. To the extent possible, the modelingparameters are put in data les instead of hard coding in thesoftware. Such an approach introduces exibility to simulatedifferent release conditions, environments, and numbers of feed-

stuffs and foodstuffs.Dose conversion factors for groundshine and cloudshine dose

calculations are taken from Eckerman and Rymann (1993) . Theinhalation rate and the dose conversion factors for inhalation andingestion pathways are taken from Eckerman et al. (1988) . Time-dependent translocation factors are based on Muller and Prohl(1993) . Default processing factors and storage times used in thesoftware are adopted from IAEA (1994) and Muller and Prohl(1993) , respectively.

Transfer factors from soil to plant and from feed to animalproduct are taken from Yu et al. (2001) . For sheep and goat milk,transfer factors 10 times higher than that forcow milk are assumed,as suggested by Johnson et al. (1988) . For lamb, chicken and goatmeat, the transfer is estimated from the feed-to-beef transfer factor

by correcting for the lower body mass. Correction factors areassumed to be 10 for lamb and goat meat and 100 for chicken.Fractions of hydrogen and carbon used in 3H and 14C models indifferent plant types and animal products are taken from Napieret al. (1988) .

The reduction of activity by physiological processes is describedby the metabolic turnover rate constant l b,m, as dened in CRRIScode package ( Hermann et al., 1984 ). Forall kinds of meat includingchicken, l b,m is assumed to be 5.73 107 s1 (equal to a half-life of 14 days); and, for milk and egg, 4.01 106 s1 (equal to a half-lifeof 2 days) ( Hermann et al., 1984 ).

4. Results and discussion

Following the Chernobyl accident, the International AtomicEnergy Authority established a coordinated research programme

on the validation of models for the transfer of radionuclides invarious environments. The programme, titled Validation of Envi-ronmental Model Predictions (VAMP), uses data on the environ-mental behavior of radionuclides, which became available asa result of the measurement programmes instituted in countries of the formerSoviet Union and in many European countries after April1986, to validate environmental model predictions. The detailedinformation about the VAMP, attended participants and theirresults are given in IAEA (1995) .

As the rst test exercise of VAMP, for modeling 137Cs contami-nation following the Chernobyl accident, data sets were collected

from various environmental media in the Central Bohemia (CB)region of the Czech Republic. Fourteen participants using thirteen

Table 1Predicted and measured concentrations of 137 Cs [Bqkg1] in various foodstuffs andfeedstuffs. (Measured and predicted values except current study predictions aretaken from IAEA (1995) .)

Foodstuff or feedstuff Time

Harvest 1986 Harvest 1987 Harvest 1988

Winter wheat Predictions

Current study 16.79 0.77 0.70Pyrima 8.3 0.12 0.12Lindoz 22.8 0.32 0.26Scraadlo-t 13.0 0.9 0.8Ternirbu 21.0 0.91 0.87Clrp 9.12 0.3 0.25Ecosys 24.0 0.3 0.3

MeasurementsMean 13.30 0.13 N.A.Lower 10.50 0.05 N.A.Upper 16.90 0.35 N.A.

Apple and pearsPredictions

Current study 34.30 0.75 0.68Pyrima 10.60 6.90 4.60Lindoz 5.0 3.0 2.0Scraadlo-t N.A. N.A. N.A.Ternirbu N.A. N.A. N.A.Clrp 2.03 0.01 0.01Ecosys 16.0 0.3 0.3

MeasurementsMean 26.20 N.A. 1.99Lower 20.70 N.A. 0.41Upper 33.0 N.A. 9.58

Leafy vegetablesPredictions

Current study 300.1 0.80 0.70Pyrima 352.5 5.32 3.51Lindoz 290.0 1.20 1.20Scraadlo-t 340.0 0.90 0.90Ternirbu 290.0 0.76 0.72Clrp 804.0 0.89 0.69Ecosys 2000.0 0.20 0.20

MeasurementsMean 240.0 N.A. N.A.Lower 63.9 N.A. N.A.Upper 909.0 N.A. N.A.

SilagePredictions

Current study 64.8 2.31 2.10Measurements

Mean 51.8 11.0 N.A.Lower 12.8 0.46 N.A.Upper 209.0 266.0 N.A.

Spring barleyPredictions

Current study 10.57 0.76 0.69Measurements

Mean 19.4 0.21 N.A.Lower 7.20 0.17 N.A.Upper 52.10 0.27 N.A.

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different computer codes participated in this test case, known asScenario CB. Ten out of those thirteen computer codes are timedependent, and three are time integrated. The ECOSYS software(Muller and Prohl, 1993 ) on which the software developed in thisstudy is based was one of the participated codes. The predictions of softwares participated and comparisons with data measured couldbe found in IAEA (1995) .

Scenario CB is selected to validate the model and softwaredeveloped in this study. The data collected within the framework of Scenario CB is summarized below.

General information containing topographic features andclimate data.Radionuclide concentration data in ground level air.Soil contamination data.Agricultural information.Demographic information.

In the simulation of Scenario CB, all model parameters are set todefault values in the software developed. Deposition amounts of 137Cs, annual consumption of main kinds of food products, feedingpractices, and times of seeding, setting, and harvesting in CentralBohemia are taken from IAEA (1995) . The 137Cs concentrations (inbeef, milk, winter wheat, fruits, leafy vegetables, silage, barley) anddoses (cloudshine, inhalation, groundshine, ingestion, and totaleffective) predicted by the software are compared with Scenario CBdata sets and with the results obtained by some Scenario CBparticipants, namely Pyrima, Lindoz, Scraadlo-t, Ternirbu, Clrp andEcosys ( IAEA, 1995 ). All the results are presented in Tables 1 and 2 .

As can be seen in Table 1 , 137Cs concentration in winter wheat,which was dominated by leaf contamination and translocation, is

slightly overestimated in 1986, falling in the uncertainty band. In1987, whenonly the root uptake wasdominant, an overestimation in137 Cs concentrations in winter wheat can be observed. The ScenarioCB participants also overestimate concentrations in winter wheat.

For fruit, it can be observed from Table 1 that the prediction of 1986 concentration of the current study is almostin the uncertaintyband while Scenario CB participants generally fail in predicting theconcentrations in fruit.

In case of leafy vegetables, the only available data measured isfrom the harvest of 1986, presented in Table 1 . The data exhibitsa large variability, thus making a meaningful comparison toodifcult.

Twotypesof animal feed, silage and spring barley, aretaken intoaccount for validation and verication. In case of silage, prediction

of 1986 concentration is fairly good. In 1987, where the root uptakeis the main route, the software developed underestimates the 137Cs

concentrations in silage, as seen in Table 1 . No observation data isavailable for the year 1988. For that year, Scenario CB participantsdid not submit any results for silage; therefore, no comparisoncould be given. For the harvest of 1986, the prediction for springbarley concentration is about two times lower than the measuredconcentration; however, it is four times higher for the subsequentyear.

Within the CB region, mean concentrations of 137Csin beef rangefrom 96 Bq kg 1 during June 1986 down to about 1 Bq kg 1 at theend of the rst quarter of 1989. Fig. 1 shows the predicted andmeasured values, together with the relevant uncertainties. Timedependency of 137Cs concentration in beef is predicted well by thesoftware developed and all the predictions fall in the uncertaintyband of the measurements.

Fig. 2 exhibits measured values of milk concentrations and thosepredicted by the current study, together with the associateduncertainties. The overall prediction is good and generally falls

within the 95% condence band. It should be noted that the soft-ware developed captures the rapid dynamic change between late1986 and early 1987.

During the last quarter of 1987 to the last quarter of 1988, theconcentration 137Cs in milk is overestimated. The main route tomilk concentrations during that year was root uptake of the 137Csby plants in 1987. However, the same trend cannot be observed in

Table 2Doses [ mSv] and risk values due to 137 Cs in the period 26/4/198627/4/1998. (Observed and predicted values except current study are taken from IAEA (1995) .)

Cloud Ground Inhalation Ingestion Total

Dose pathwaysCurrent study 0.017 43.6 4.1 100.2 152.9Pyrima 0.063 0.23 10.1 500.0 500.0Lindoz 0.037 28.0 2.1 60.0 87.0Scraadlo-t 0.054 80.0 4.1 150.0 234.0

Ternirbu 0.010 59.0 1.1 325.0 380.0Clrp 0.026 25.0 4.4 71.9 102.0Ecosys 0.016 27.0 5.2 140.0 170.0Observations

Mean 0.014 34.0 2.9 62.0 98.9Lower 0.0092 20.0 1.8 58.0 79.8Upper 0.022 52.0 4.6 79.0 135.6

Risk pathwaysCurrent study

Morbidity 1.40 1012 2.46 107 3.55 109 5.87 106 6.12 106

Mortality 1.23 1012 1.58 107 3.07 109 3.92 106 4.08 106

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Fig. 1. Average concentrations of 137

Cs in beef (vertical bars indicate the 95% con-dence intervals on the mean value of observations).

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beef concentrations. The overestimation in milk concentrations in1988 may be attributed to Scenario CB data sets for feeding regimeof lactating cows during this period.

Total effective individual dose and contributions of differentpathways can be seen in Table 2 . Compared to the estimations inScenario CB, the predictions of the current study are fairly good. Forthe ingestion dose, the result of the current study is out of con-dence interval and overestimated. This situation leads to the sametrend in the effective individual dose. The predictions for mortalityand morbidity risks by the software developed are also presentedin Table 2 ; however, no comparison can be made since no relevantdata is given in Scenario CB.

5. Conclusions

In summary, the developed software predicts well for theScenario CB exercise. It captures rapid dynamic changes of radio-nuclide concentrations in different food and feedstuffs. It can beconcluded that the results of the current study are slightlyconservative compared to the observations. The two pathways,ingestion and groundshine, contribute much more to the individualeffective dose and impose a greater risk than other pathways. Thisbehavior is an expected long-term effect of an accidental release of radionuclides in the absence of countermeasures.

There are many factors that contribute to the variability of theestimated results. Factors affecting the dose and risk values due toingestion and groundshine pathways are particularly important forlong-term effects of an accidental release or operational releases of

nuclear facilities. One of the major factors that affect estimateddose and risks is the geographical location of source of foodconsumed which varies considerably according to the type of foodin a country. Another major factor, especially important in coun-tries where social mobility is high, is the fact that a typical

individual lives in various locations fordifferent periods of time dueto socio-economical constraints or simply due to social and/orrecreational preferences. In this study, models for multi-locationfood supplyand interregional moves of people (including change induration and location of stay) are introduced. The application of themodels for multi-location food supply and interregional moves of people will be presented in another study.

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Fig. 2. Average concentrations of 137 Cs in milk (vertical bars indicate the 95% con-dence intervals on the mean value of observations).

C. Kocar, C.N. Sokmen / Journal of Environmental Radioactivity 100 (2009) 8993 93

a model for radio logical consequences of nuclear power plant atmospheric releases

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