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Journal of Environmental Radioactivity 100 (2009) 564–573

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Journal of Environmental Radioactivity

journal homepage: www.elsevier .com/locate/ jenvrad

Modelling remediation options for urban contamination situations

K.M. Thiessen a,*, K.G. Andersson b, T.W. Charnock c, F. Gallay d

a SENES Oak Ridge, Inc., Center for Risk Analysis, 102 Donner Drive, Oak Ridge, TN 37830, USAb Risø National Laboratory for Sustainable Energy, Technical University of Denmark, P.O. Box 49, DK-4000 Roskilde, Denmarkc Health Protection Agency (HPA), OX11 0RQ Chilton, Didcot, Oxfordshire, United Kingdomd Institut de Radioprotection et de Surete Nucleaire (IRSN), BP 17-92262 Fontenay-aux-Roses, Cedex, France

a r t i c l e i n f o

Article history:Received 17 November 2008Received in revised form5 March 2009Accepted 30 March 2009Available online 8 May 2009

Keywords:Urban contaminationRadioactivityModellingCountermeasuresRemediationDecontaminationDose reduction

* Corresponding author. Tel.: þ1 865 483 6111; faxE-mail address: kmt@senes.com (K.M. Thiessen).

0265-931X/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.jenvrad.2009.03.021

a b s t r a c t

The impact on a population from an event resulting in dispersal and deposition of radionuclides in anurban area could be significant, in terms of both the number of people affected and the economic costs ofrecovery. The use of computer models for assessment of urban contamination situations and remedialoptions enables the evaluation of a variety of situations or alternative recovery strategies in contexts ofpreparedness or decision-making. At present a number of models and modelling approaches are avail-able for different purposes. This paper summarizes the available modelling approaches, approaches formodelling countermeasure effectiveness, and current sources of information on parameters related tocountermeasure effectiveness. Countermeasure information must be applied with careful thought as toits applicability for the specific situation being modelled. Much of the current information base comesfrom the Chernobyl experience and would not be applicable for all types of situations.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction and background

Approximately 50% of the world’s population lives in urbansettings; in developed countries, 75% of the population is urbanized(UNPF, 2007). The impact on a population from an event resultingin dispersal and deposition of radionuclides in an urban area couldbe significant, in terms of both the number of people affected andthe economic costs of recovery. A vital part of both emergencypreparedness and recovery planning and optimization is theassessment of the radiological consequences of urban contamina-tion events – the fate and transport of the dispersed radionuclides,changes in radionuclide concentrations and external dose ratesover time, human exposures and doses, and the dose reductionsand economic costs expected with various remedial activities orcountermeasures. The use of computer models for such assess-ments enables the evaluation of a variety of situations or remedialoptions in the absence of an actual event or in the decision-makingprocess following an event. As with modelling for other types ofenvironmental assessments, the confidence to be placed in themodelling results depends on the degree to which the model

: þ1 865 481 0060.

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capabilities match the needs of the assessment and adequatelyreflect both the understanding of the physical processes involvedand the available database for parameter values (see for example,Thiessen et al., 1999).

The Urban Remediation Working Group of the InternationalAtomic Energy Agency’s EMRAS (Environmental Modelling forRadiation Safety) program was established in 2003 to addressissues of remediation assessment modelling for urban areascontaminated with dispersed radionuclides (IAEA, in press). TheWorking Group’s specific objective was to test and improve theprediction of dose rates and cumulative doses to humans for urbanareas contaminated with dispersed radionuclides, includingprediction of changes in radionuclide concentrations or dose ratesas a function of location and time and prediction of the reduction inradionuclide concentrations, dose rates, or doses expected to resultfrom various countermeasures or remediation efforts. The majoractivities of the Working Group included a review of modellingapproaches for the assessment of urban contamination andpotential remedial activities, along with two modelling exercisesfor selected urban contamination situations. The present papersummarizes recent international experience in modelling urbancontamination situations, with an emphasis on modelling specificremediation measures. The Working Group focused specifically onradiological aspects of countermeasures and remedial activities and

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573 565

has not addressed such aspects as economic, social, or environ-mental concerns.

2. Recent international experience in modelling urbancontamination situations

Much of the development of urban contamination modellinghas taken place in a context of concern about reactor accidents, forwhich external exposure to radionuclides deposited on urbansurfaces would generally be the most important exposure pathwayfor the affected population (Kelly, 1987). Therefore, this pathwayhas received more thorough attention than other pathways forurban contamination situations, although it should be noted thatfor some situations, additional exposure pathways (e.g., inhalation;home-grown food from kitchen gardens) should also be consid-ered. The first surveys of the consequences of accidental depositionof radionuclides in urban environments, in the early 1950s,assessed the shielding properties offered by buildings againstexternal exposure to the radiation emitted by radionuclidesdeposited on urban surfaces. The results from building contami-nation experiments, conducted in Nevada (United States) duringthe 1950s and 1960s, as well as in-depth research, allowed thedevelopment of the first computational methods. From the 1980s tothe present, new computational methods have emerged, especiallyin Europe, in order to expand the initial results to various types ofbuildings, and to incorporate the heterogeneous distribution ofradionuclides in an urban environment. These various methodsmay be based either on the point-kernel build-up method or onMonte Carlo simulations (Hedemann Jensen, 1985; Meckbach et al.,1988; Eged et al., 2006).

Studies of radionuclide transfers within the urban environmentin a post-accident situation began later, especially following theChernobyl accident (26 April 1986 in Ukraine). This subject had notbeen very well explored before the 1980s, since research on theconsequences of a nuclear accident had been primarily focused onrural areas (Andersson, 2006). Furthermore, before 1986, thedevelopment of radionuclide transfer models for an urban envi-ronment was impaired due to a lack of experimental data and to thecomplexity of the subject. Since Chernobyl, however, numerousmeasurement results have been published, thus allowing charac-terization of the phenomena impacting the spatial and temporalchanges in radionuclide distribution in an urban environment.

The Chernobyl accident also allowed the assessment of theimportance (at least for the case of a reactor accident) of otherexposure pathways for the population, in addition to externalexposure to particles deposited on urban surfaces, especiallyinternal exposure via inhalation of resuspended particles. Post-Chernobyl efforts also highlighted the need to identify and assessthe effectiveness of recovery strategies to allow the management ofmajor contamination of the urban environment (Brown et al., 1996,2005; Mobbs et al., 2005; Andersson, 1996; Andersson and Roed,1999; Andersson et al., 2003). Numerous actions aimed at thedecontamination of urban areas were implemented following thisaccident. The results of these experiments then allowed develop-ment of models for assessing post-accident rehabilitation strategiesfor urban areas, which, combined with radioecological and dosi-metric models, currently allow a comprehensive assessment of therisks for a population living in an urban area contaminated after anaccident.

A number of issues must be considered in modelling urbancontamination situations (Gallay, 2008), including characterizationof the contaminated urban environment, modelling of humanexposures and doses, and modelling of various rehabilitationstrategies (remedial activities or countermeasures). Each of these isdiscussed briefly below.

Characterization of a contaminated urban environment includesthe type of deposition, pre- and post-deposition contaminanttransport processes, the exposure pathways to be included, theradionuclides of concern and their physicochemical characteristics,the location of an individual with respect to contaminated surfaces,shielding factors to account for attenuation of activity by variousstructures, occupancy factors (proportion of time spent in specificlocations), and types of contaminated surfaces. For many situations,external exposure to radionuclides deposited on surfaces isexpected to be the most important pathway; for some situations,important pathways could include inhalation from the plume,inhalation of resuspended radionuclides, or deposition of radio-nuclides on skin. For reactor accidents such as Chernobyl, 137Cs isexpected to be the major radionuclide of concern for the long term,but omission of shorter-lived radionuclides can contribute toa significant underestimate of short-term dose.

Dosimetric models typically describe a ‘‘location’’ or ‘‘environ-ment’’ in terms of the structures and surfaces in the immediatevicinity of an individual and the contributions to that individual’sdose from each contaminated surface (Fig. 1). Urban environmentsare variously defined in terms of population density, land use (e.g.,residential or occupational), and the kinds and sizes of buildingsand surfaces. Surfaces vary with respect to their retention ofradionuclides and how effectively radionuclides are removed.Calculation of exposure and ultimately dose for an individualrequires both the location information and the relevant occupancyfactors.

Remediation of a contaminated urban environment is a complextopic, and some simplifications are necessary for modellingpurposes. However, at present, numerous relatively recent experi-mental results for post-event rehabilitation of a contaminatedurban environment allow identification of the main parameters tobe considered for modelling purposes and highlight the remaininggaps (Gallay, 2008; Brown et al., 2006):

� Parameters governing the deposition and distribution ofradionuclides on different surfaces (indoors, outdoors, and onhumans), especially for situations other than reactor accidents.� The long-term behaviour of radionuclides deposited on urban

surfaces, especially for situations other than reactor accidents.� The attenuation properties of the wide variety of buildings

encountered within urban environments worldwide, particu-larly for radionuclides other than those of primary importancein connection with a reactor accident release.� The effectiveness of post-event rehabilitation actions in case of

contamination from a release other than a reactor accident.� The global effectiveness of the various combinations of reha-

bilitation actions, including consideration of the physico-chemical characteristics of contaminants.� Treatment of uncertainty in model predictions for urban

contamination situations.

Furthermore, numerous parameter values used in currentmodels are associated with a high uncertainty, because these areoften average values of results from independent measurements, indifferent conditions, and on different types of materials (Eged et al.,2004). Results of calculations based on these values should there-fore be interpreted with caution, especially for the long termfollowing a contamination incident.

Certain modelling approaches in current use are relativelysimple and involve a limited number of parameters, while othersare much more complex. However, both types of approachesinclude advantages and drawbacks. Complex models require a largequantity of experimental data to provide values for the numerousparameters used. If this information is available, the complex

Fig. 1. Schematic drawing of an urban location illustrating the various surfaces that can contribute to the total dose rate to exposed persons. For example, a person spending timeoutdoors may receive a dose from grass, trees, paved surfaces such as streets and parking lots, and the roofs and outer walls of buildings. A person spending time indoors mayreceive a dose from the same surfaces (allowing for shielding effects of the building structure), plus any contaminated indoor surfaces.

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573566

models may generate very good predictions. However, the experi-mental data currently available show that the values of certainparameters may demonstrate relatively high variability, dependingon the types of release and urban environment being considered.Also, certain gaps in information have been identified with regardto the experimental results. Finally, the importance of technicalresources and human skills required, as well as the time requiredfor calculations, increases with the model complexity. In contrast,for simple models, a number of approximations are made in thecalculations, such that the uncertainty in their results may some-times be difficult to assess. Also, simple models are usually lessflexible than complex models, and may leave out importantprocesses and factors.

Selection of the type of model to be used is highly dependent onthe calculation objective, the information available, and the timeavailable to do the assessment. For example, models for use inemergency preparedness and planning could include detailedlocation-specific information such as building types and severaloptions for weather conditions at the time of a release, but useaverage weather conditions for assessment of the long-term impactof the release. At the beginning of the post-release phase in case ofan actual event, when few measurement results are available, theuse of simple models, applying relatively conservative assumptions,can provide initial indications to decision-makers with regard tothe exposure of the urban population living in the contaminatedarea. These initial modelling results can be used, for instance, toassess the opportunity to implement population-protective actionsin the post-release phase, such as temporary relocation. Realizationof the full potential of complex models may be difficult at this stage,as the large amount of input data required for calculations mightnot be available, although some models may be able to make use ofreal-time data for parameters such as meteorological variables(Andersson et al., 2008a,b).

As data become available, use of data assimilation techniquescan be applied to combine measurements with model results toimprove the predictive power of the assessment models (Kaiseret al., 2008). In the medium and long term after the depositionevent, results of measurement campaigns may allow a finerassessment of risks, based on calculations performed with morecomplex models. For example, a growing number of reliablemeasurements can be used for updating parameter values,reassessing initial estimates of deposition, or re-evaluating

a conceptual model. It may be necessary to use different models atdifferent stages of an assessment.

A number of models for assessing urban contamination havenow been developed in various countries (summarized in Table 1).These models are based largely on Chernobyl data, for whicha relatively rich feedback experience is now available. Modelcapabilities vary depending on the purposes for which the modelswere constructed. For example, while all are designed to provideexternal dose calculations, some also include inhalation of resus-pended materials. Some models include assessment of recoveryoptions (dose reductions due to remediation measures), and atleast three consider other aspects of remediation, such as costs,amounts of waste generated, and doses to workers. The radionu-clides considered by each model vary, as do the approaches tomodelling radionuclide transfers and the dosimetrical approaches.The Urban Remediation Working Group’s model intercomparisonexercises (IAEA, in press; Thiessen et al., 2008, 2009a,b) havepermitted an opportunity for more detailed evaluation of severalmodels in terms of their performance for defined modellingsituations.

3. Information sources for modelling of countermeasures

In order to model the effect and consequences of recoveryoptions, information about the options and their effectiveness isrequired. The many reports and papers about recovery counter-measures can be divided into two types. The first type includesthose that present basic data such as the results of experiments andfield trials, or real decontamination efforts following accidents suchas Chernobyl and Goiania (e.g., Roed et al., 2006; Fogh et al., 1999;Da Silva et al., 1991). The second type includes those that arecompilations or catalogues of recovery options generated for thepurposes of emergency preparedness at a national or internationallevel (e.g., Brown et al., 1996, 2005; Mobbs et al., 2005; Andersson,1996; Andersson and Roed, 1999; Andersson et al., 2003). As thesecond type generally refers back to the first type, they are anappropriate place to begin looking for information aboutcountermeasures.

A large number of recovery options are presently proposedwithin the compilations, but to a first approximation, these optionsmay be divided into a few categories:

Table 1Summary of main characteristics of computer codes for assessment of the recovery phase in urban areas (from Gallay, 2008).

Model CONDOa EXPURTb LCMT/RODOSc TEMAS Urban Model

Sponsoring organization Health Protection Agency (UnitedKingdom)

Health Protection Agency (UnitedKingdom)

European Community GSF (Germany) and CIEMAT (Spain)

References Charnock (2004), Charnock et al.(2003)

Jones and Singer (2003), Jones et al.(2006a)

Brown et al. (2000) Meckbach et al. (1999)

Purpose of model Multicriterion assessment ofrecovery options

External dose calculations andrecovery option assessment

External dose calculations andrecovery option assessment

External dose calculations andrecovery option multicriterionassessment

Parent code – CONDO – TEMASRadionuclides included 241Am, 140Ba, 140La, 60Co, 134,136,137Cs,

131I, 95Nb, 239Pu, 103,106Ru, 95Zr

241Am, 140Ba, 140La, 60Co, 134,136,137Cs,131I, 95Nb, 239Pu, 103,106Ru, 95Zr

Four groups represented by140Ba, 106Ru, 137Cs, 131I

134,137Cs, 90Sr

Radioecological approach – (realized by EXPURT) Dynamic compartment model – No compartmentsDosimetrical approachj – (realized by EXPURT) Global and complex approach Global and simple approach Local and complex approachNumber of

environments– 5 – 2

Inhalation (resuspension) Yes – – –Recovery options Yes Yes Yes YesOptimization of recovery

optionsCosts, wastes, time forimplementation, required skills andmaterial, additional doses to theworkers

– Costs, wastes, additional dosesto the workers

Yes

Model PARATId URGENTe JSP-5 Model METRO-Kf

Sponsoring organization Instutio de Radioproteçao eDosimetria (Brazil) and GSF(Germany)

RISØ National Laboratory(Denmark)

CEI – European Union Korea Atomic Energy ResearchInstitute (Republic of Korea)

References Rochedo et al. (1996, 1997) Andersson (1994) Golikov et al. (2002) Hwang et al. (2005)Purpose of model External dose calculations External dose calculations and

recovery option assessmentExternal dose calculations External dose calculations

Parent code – – – –Radionuclides included 137Cs (þrecent additions) 137Cs 134,137Cs, 132Te, 131,132I, 140Ba,

140La, 103Ru (þrecent additions)

137Cs, 131I, 106Ru

Radioecological approach Retention functions Dynamic compartment model Retention function formigration into soils

Dynamic compartment model

Dosimetrical approachj Global and complex approach Global and complex approach Global and simple approach Global and complex approachNumber of environments 9 4 – 7Inhalation

(resuspension)Yes – – –

Recovery options Yes Yes – –Optimization of recovery

options– – – –

Model MUDg RESRAD-RDDh ERMINi

Sponsoring organization Universidad Politecnica de Madrid(Spain)

Argonne National Laboratory (USA) European Community

References Gallego (2006) Yu et al. (2006) Jones et al. (2006b)Purpose of model Radionuclide transfers in urban

environment and to sewage systemOperation guidelines for use inemergency preparedness andresponse to an RDD incident; dosecalculations for multiple exposurepathways

External gamma and beta dosecalculations, calculation of dosefrom inhalation of resuspendedmaterial, recovery strategyassessment

Parent code MOIRA – –Radionuclides included 137Cs 137Cs, 241Am, 252Cf, 244Cm, 60Co,

192Ir, 210Po, 238Pu, 239Pu, 226Ra, 90Sr66 Radionuclides from theRODOS database, including allthose in EXPURT

Radioecological approach Dynamic compartment model Dynamic compartment model Retention functions, soilcolumn represented bya convective–dispersive model

Dosimetrical approachj – Global and complex approach Global and complex approachNumber of environments – 2 7Inhalation

(resuspension)– Yes Yes

Recovery options – Yes YesOptimization of recovery

options– Yes Costs, waste mass and activity,

work required, and additionaldoses to workers

a Software for estimating the consequences of decontamination options.b Exposure from Urban Radionuclide Transfer.c Late Countermeasures Module for terrestrial environments.d Program for the assessment of radiological consequences in a town and of intervention after a radioactive contamination.e Urban gamma exposure normative tool.f Model for evaluating the transient behaviour of radioactive materials in the Korean urban environment.g Model to investigate the migration of 137Cs in the urban environment and drainage and sewage treatment systems.h RESRAD – Radiological Dispersion Device.i EuRopean Model for INhabited areas.j Two methods of Monte Carlo simulation of photon transport are referred to as the ‘‘global’’ and ‘‘local’’ approaches (Eged et al., 2006; Brown et al., 2006). The global

approach calculates the air kerma rate at a central evaluation location due to all surfaces contributing to the air kerma rate at that location. The local approach calculates the airkerma per photon per unit area due to each specific deposition area (e.g., a roof) in the whole environment being considered.

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573568

(1) Reducing external exposure to radionuclides deposited onurban surfaces, including

� For hard surfaces, cleaning by various techniques or replace-ment of the surface;� For light soils, placing the contaminated layer deeper into the

soil profile or removing the top soil; and� For vegetation, grass cutting or plant removal.

(2) Reducing resuspension and both the subsequent doses frominhalation of resuspended material and also the spread ofradionuclides deposited on urban surfaces, by resurfacingcontaminated surfaces temporarily or permanently (‘‘tie-down’’ techniques).

Two types of recovery options may then be distinguished withinthe above categories: (1) Options that dilute or isolate the depos-ited activity within the compartment being considered or transferit to another compartment of the environment; and (2) Optionsthat remove activity from the urban environment but produceradioactive waste to be treated and stored.

Many of the compilation reports contain information relatingnot only to the effectiveness but also the cost and practicability ofcountermeasures, which would allow a decision-maker to evaluateand compare different recovery strategies. Some reports go furtherand apply the countermeasures to hypothetical situations usingurban models and stakeholder involvement in order to explore theconsequences of different strategies and to highlight less tangiblebut important considerations such as public acceptability (e.g.,Brown et al., 2003). Other reports are presented as a decisionframework, not only providing the information needed by thedecision-makers but also guiding them through the decision-making process, often using aids such as flow-charts and decisiontrees (e.g., Mobbs et al., 2005).

Inevitably these compilations draw information from earlierreports, updating, adding to, amalgamating and refining the datawithin. They also draw from the body of basic data, extrapolatingfrom the specific to the general where possible to allow counter-measures to be compared. In Europe, this process has culminated ina compendium (Brown et al., 2005) and a generic Europeanhandbook for the management of recovery options in contami-nated inhabited areas following a radiological incident, generatedunder the EC 6th Framework Program (Brown et al., 2007). Thecompendium includes 52 recovery options as well as counter-measures for the pre-release and release phases of an incident.

A common aim in the various compendia is to presenta consistent set of attributes for each recovery option. This allowscomparison among the countermeasures and use of situation-specific criteria in the selection of appropriate options. Brown et al.(2005) include the following attribute classes in a standard coun-termeasure template:

� Objectives of the option;� A short description of the option;� Constraints on its implementation;� Effectiveness;� Requirements;� Waste generated;� Doses received by those implementing the option;� Costs;� Side-effects;� Practical experience.

Each of these classes is further divided into a number of attri-butes. Most of the classes are fairly self-explanatory. Of particularinterest to the modelling of countermeasures is the effectiveness.

Brown et al. (2005) discuss countermeasure effectiveness interms of the following attributes:

� Reduction in contamination on the surface;� Reduction in surface dose rates;� Reduction in resuspension;� Averted doses;� Additional doses (doses to workers implementing the

countermeasure);� Factors influencing the effectiveness of the procedure

(technical);� Factors influencing the effectiveness of the procedure (social).

Some or all of these attributes are also found in the various othercompendia, but the information is derived primarily from Cher-nobyl data, and scenarios can be envisaged for which the datawould not be applicable. For instance, some plausible scenariosinvolving malicious dispersion of radionuclides would generatelarge particles that would be comparatively easy to remove fromimpermeable surfaces (Andersson et al., 2008b).

Whilst most of the attributes can be derived from experimentaldata or consideration of the practical details of the option, theattributes of averted doses and additional doses can be evaluatedonly using a model or, less practically, by the measurement of realpersonal doses following an incident. The averted doses depend notonly on the countermeasure, but also on the characteristics of thecontaminants deposited, the geometry and properties of thesurfaces in the environment, both natural and anthropogenicprocesses of contaminant redistribution, and the behaviour of thepopulation exposed. The additional doses depend on the abovefactors and on the nature of the countermeasure, such as the work-rate and where it places the worker within the contaminatedenvironment. These values are therefore not inputs to a model butcan be among the outputs.

Technical factors may also affect the global efficiency of therecovery strategy, as can be learned from the early decontamina-tion actions in Pripyat following the Chernobyl accident (Brownet al., 1996). The cleaning in this case was reported to have a loweffectiveness, attributable to a number of reasons, includingrecontamination of decontaminated areas by spread of radionu-clides deposited on nearby surfaces, lack of experience in imple-menting recovery options, lack of machinery designed specificallyfor implementing recovery options, and lack of waste disposal sites.In addition, sociological and psychological factors may influencethe choice of a recovery strategy. For example, the replacement ofdirt roads and the sowing of grass around dwellings was demandedby the public after Chernobyl, although these surfaces did notcontribute significantly to the doses received.

4. Methods of describing countermeasure effectiveness

4.1. Decontamination factor

The effectiveness of recovery options that decontaminate a surface can bedescribed with a decontamination factor (DF). A DF represents the efficiency ofremoving activity from a particular surface:

Activityafter

�Bq m�2

�¼ Activitybefore

�Bq m�2

�DF

(1)

Thus a countermeasure with a DF of 2 will reduce the contamination ona surface by 50%. A DF of 10 indicates a 90% reduction, and a DF of 100 indicates a 99%reduction. A DF quoted for a countermeasure strategy is the DF for the first appli-cation of a countermeasure. It is generally not reasonable to assume that the secondor subsequent applications will be equally as effective, particularly for cleaningtechniques.

A DF of 2 does not imply an overall dose reduction of 50%, as it depends howmuch the particular surface contributes to the total external dose from all surfaces.This in turn depends on the contamination on the surface and the relationship

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573 569

between where the population spend time and the location and orientation of thesurface.

The amount of information available on the effectiveness of decontaminationtechniques for specific elements is quite limited, with the exception of cesium andplutonium. Some additional information can be found on the effectiveness of fire-hosing surfaces contaminated with ruthenium, lanthanum, and barium, but littleelse (Brown et al., 2003). However, a large amount of information is needed withinmodel databases in order to account for various accident types and the corre-sponding large number of possible radionuclides that could be released, plus theirradioactive daughters. For radionuclides other than cesium and plutonium, it isusually assumed that they behave similarly to radionuclides with the same chemicalproperties, corrected by their respective half-lives. This approach is used within theUK model CONDO (Charnock et al., 2003). For 131I, information is also quite limited,but given its short half-life and high solubility in water, this radionuclide may notcontribute significantly to the doses to an urban population during the recoveryphase. In general, the physicochemical form of the contaminants at the time of thedispersion governs both their deposition and their subsequent natural or intentionalremoval from the different surfaces in an inhabited area.

A quoted DF must be interpreted correctly to ensure that it is used appropriatelywithin a model. For example, the DF for a countermeasure may change as a functionof time, becoming less effective at increasing times after the initial deposition. Thereare several reasons for this. Contamination may become increasingly tightly bondedwith the surface (e.g., pavement) as time progresses, especially in the case of cesiumdue to its particular affinity for building materials. In this situation, cleaning tech-niques such as fire-hosing become increasingly ineffective within a few days ofdeposition. However, the effectiveness of a removal technique such as road removalor roof replacement will not decrease with time. A second cause of DF timedependency is the movement of contamination to a part of the surface less affectedby the countermeasure. For example, material deposited on grass will in timemigrate to the base of a plant and into the leaf litter on the soil surface. A coun-termeasure such as mowing will take an increasingly smaller proportion of the totalmaterial that is on soil and grass as time continues.

For radionuclides such as barium, lanthanum, or ruthenium, the decrease withtime in the efficiency of fire-hosing contaminated urban surfaces will be less than forcesium, because they are less tightly bound to the deposition surface. Thus, DFs arealso sensitive to the type of radionuclides considered within a scenario and to thetype of contamination more generally. For example, strontium bound in a low-solubility matrix remained on the surface near the Chernobyl NPP until the matrixdissolved over a period of some years (Kashparov et al., 2004). Migration intoa surface can also be contaminant-specific, such that the DF for removal of a surfacelayer of a given thickness will depend on the radionuclide under consideration.

Time dependency is commonly described in qualitative terms. For example, forfire hosing of paved areas, Brown et al. (2005) state that ‘‘a decontamination factorbetween 2 and 4 can be achieved if this option is implemented within one week ofdeposition, and there has not been any significant rainfall. DFs at longer times will besignificantly lower unless the surface has not been subject to any traffic and therehas been no rainfall’’. The model user must consider which value in the range, if any,is most appropriate for the situation being addressed.

For the purposes of interpretation of the quoted DF, it is convenient to subdividedecontamination techniques into those that clean a surface of contamination andthose that remove a part of the surface and the contamination along with it. Forremoval techniques, high DFs are common. For example, Brown et al. (2005) givea DF of between 10 and 30 for ‘‘turf and topsoil removal’’. This means that some 90%to 97% of the contamination is removed. In practice, the techniques are likely to beless effective because some of the removed material may fall back onto the surface asit is taken away and because inevitably not all of the contaminated surface will beremoved.

Generally a surface removal technique will exhibit less time dependency thana cleaning technique. However, particularly in connection with late implementationof surface removal techniques, it is always recommended to first assess the depthprofile of the key contaminants in the material. For example, in an attempt in 1989 toreduce external dose in 93 Chernobyl-contaminated settlements of the Bryanskregion, the Russian army introduced the reasonable countermeasure of removinga topsoil layer, supposedly containing most of the contamination. However, at thattime, vertical soil profiles of radiocesium in the area were sometimes recorded topeak at a depth of some 5–10 cm (Roed et al., 1996, 1998). This means that anuncritical scraping off of the top, say, 5 cm layer would in fact remove a shielding soillayer containing very little contamination. Therefore, the dose rates in some caseswere found to increase, and the decontamination work was stopped (Anisimovaet al., 1994). As another example, migration into construction materials can increasesomewhat with time (Hubert et al., 1996; Sandalls, 1987), and the thickness ofsurface to be removed to obtain a given decontamination factor on such a surfacewill increase accordingly.

4.2. Dose rate reduction factor

A dose rate reduction factor (DRF) describes the reduction in external gammaand beta dose rate immediately above a contaminated surface following applicationof a countermeasure, excluding all dose rate contributions from other surfaces:

Dose rateafter

�Sv s�1

�¼ Dose ratebefore

�Sv s�1�DRF

(2)

A DRF can be used to describe the effectiveness of decontamination techniques,techniques that mix or bury the contamination in the soil column, and techniquesthat place shielding between the source and the population. A DRF can be radio-nuclide specific; this is particularly true for shielding techniques which, whenapplied to contamination including beta emitters and less energetic gamma emit-ters, will give a higher DRF than when applied to contamination involving higherenergy gamma emitters.

Quoted DRFs may be derived experimentally, from consideration of themechanics of the countermeasure, or by modelling or calculation methods. Forexample, Brown et al. (2005) assume for many decontamination techniques that theDRF is the same as the DF, based on consideration of the mechanics of the coun-termeasure. Derivation by modelling or calculation methods is particularly impor-tant for shielding techniques. Shielding options do not imply a reduction of thesurface contamination and are therefore characterized by a DF of 1, but since they areassociated with a reduction of the dose rate above the surface when the shield isintroduced, DRF will be greater than 1.

As with a DF, the DRF does not relate directly to external dose reduction, whichalso depends on how much that surface contributes to the overall external dose.

4.3. Resuspension reduction factor

A resuspension reduction factor (RRF) describes the reduction of respirablematerial available from a surface immediately following application ofa countermeasure:

Material availableafter

�Bq m�2

�¼ Material availablebefore

�Bq m�2

�RRF

(3)

An RRF can be used to describe the effectiveness of decontamination techniques,techniques that mix or bury the contamination in the soil column, techniques thatplace shielding between the source and the population, and tie-down techniques.

A resuspension reduction factor is a difficult quantity to measure or to use.Generally it will be an assumed value for the purposes of dose assessment, derivedfrom consideration of the countermeasure mechanics. For example, a conservativeassumption is that a decontamination technique has an RRF the same as the DF,because one might expect the fraction of material remaining following a decon-tamination technique to be that most difficult to remove and therefore not availablefor resuspension. It also assumes that all the remaining material is of a particle sizethat is respirable. According to the ICRP (1993), ‘‘inhaled particles larger than 10 mmwould be cleared rapidly by ciliary action’’. This, for instance, excludes sand particlesand most silt particles, to which contaminants may be attached. It would alsoexclude a large proportion of the initial particles deposited after detonation ofa ‘‘dirty bomb’’ (Andersson, 2005).

However, this will not be the case for ‘‘tie-down’’ techniques, which aredesigned exclusively to reduce resuspension, either temporarily or semi-perma-nently (Brown et al., 2003). Such techniques do not remove contamination but areemployed to reduce the hazard from and spread of contamination prior to theapplication of other recovery options. The DF associated with these ‘‘tie-down’’techniques is then unity, and the RRF must be evaluated independently.

As with a DF, the RRF does not relate directly to dose reduction, which alsodepends on how much that surface contributes to the overall resuspension dose.

4.4. Dose reduction

The dose reduction (DR) is the reduction in overall exposure from depositedmaterial within the environment, including external irradiation and inhalationpathways, and taking into account all the countermeasures that have been applied. ADR can be used to describe the effectiveness of all recovery options, includingoptions such as relocation or restriction of access. However, the reduction in dosewill be situation specific and dependent on the characteristics of the deposition, theenvironment and the population. Indeed, for many of the options described byBrown et al. (2005), no DR is quoted, because it is considered too situation depen-dent to usefully quantify (see Table 2).

The dose reduction may be established in a real situation by giving personaldosimeters to the population of the region. More commonly it is assessed usinga model. In order to interpret the DR, some knowledge of the model and how it wasused is required, for example, whether a specific radionuclide mix was assumed.Assumptions about occupancy (time spent in specific locations) are important inestimation of a DR.

4.5. Summary of countermeasure effectiveness

Table 2 gives examples of effectiveness information for a few selected coun-termeasures, based primarily on Chernobyl-related data. A longer list of examples isprovided in the Urban Remediation Working Group’s final report (IAEA, in press).

Table 2Selected examples of recovery countermeasure effectiveness (from Brown et al., 2005).

Recovery option Decontamination factor (DF) Dose rate reduction factor (DRF) Resuspension reduction factor (RRF) Dose reduction (DR)

Fire-hosing of buildings 1.3a Same as DF Same as DF Few %Roof replacement Effectively all Effectively all Effectively all 9–11%Vacuuming indoors 5–10b Same as DF Same as DF 15%c

Washing indoor surfaces 1.3–3b Same as DF Same as DF 5–10%c

Fire-hosing of paved areas 2–4a Same as DF Same as DF 5–10%Cutting and removal of grass 2–10a Same as DF Same as DF About 25%c

Topsoil and turf removal 10–30 Same as DF Same as DF 40%c,e; 65%d,e

Cover soil/grass with clean soil 1 4–5f Effectively all Situation dependentPloughing 1 2–5g >DRFh Situation dependentSnow removal 10–30 Same as DF Same as DF 35%c; 80%d

Removal of trees and shrubs Up to 50 Same as DF Same as DF 20%c

a DF can be achieved if this option is implemented within 1 week of deposition and before significant rain.b DF can be achieved if implemented within a few weeks of deposition.c DR achievable under dry deposition conditions.d DR achievable under wet deposition conditions.e DR assumes all grass/soil areas treated, not just large areas such as parks.f Example DRF for 137Cs, assuming clean soil to a depth of 10 cm, beta dose rates reduced by effectively 100%.g Gamma dose rate reduction dependent on energy, beta dose rates reduced by effectively 100%.h Significantly better at reducing resuspension than external dose rate.

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Brown et al. (2005) provide detailed information on these and othercountermeasures.

Effectiveness of recovery options has been broadly studied for the last 20 years,and a panel of reliable data is now available, even though data are better for someradionuclides or contaminants than for others. Effectiveness of combinations ofthese options is more difficult to assess, especially since field experiments show thatthe effect of combined recovery options is not simply the sum of the effects ofimplementing individual recovery options.

5. Using recovery countermeasure information in models

How a recovery option is included within a model depends onthe way the model works and the endpoints required from thatmodel. At the simplistic end, a DR can be applied to a calculateddose in the absence of specific consideration of countermeasures.This is essentially how the LCMT (Late Countermeasure ModuleTerrestrial) model in the RODOS nuclear emergency decisionsupport system works (see Brown et al., 2000). The LCMT modelapplies a library of DRs to the output of the dose module of RODOS.The DRs used were pre-calculated using an early version of theEXPURT model which considered scenarios involving a single

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generic environment type, countermeasures applied in isolation atone or two pre-selected times, and assumption of either dry or wetdeposition. Therefore, the user of LCMT is a step removed from theneed to interpret countermeasure effectiveness, as this has beendone in advance by the developers of the DR library and the LCMTinterface. The result is an application that is simple to use and ‘safe’for non-experts provided that they are familiar with the assump-tions of the pre-calculated scenarios as described in the LCMTdocumentation (Brown et al., 2000), but it is somewhat inflexible inthat the user is restricted to scenarios that have been pre-calculated.

At the more complex end, a DF or other measure of effectivenesscan be applied to a model that simulates the retention of radio-nuclides and calculates the dose contribution from each surfaceexplicitly. EXPURT is an example of this type of model (see Joneset al., 2006a). For this type of model, the user needs to interpreta quoted DF carefully before attempting to represent a particularcountermeasure. The following questions must be asked: Is the DFappropriate for the countermeasure one is intending to represent?Does it account for time dependency? Is it applicable to the

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Table 3Predicted total dose rate and assumed occupancy factors (fraction of time spent) forselected locations in Pripyat (IAEA, in press).

Location Predicted totaldose rate (mGy/h)a

Indoorworker

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Indoors at home 0.237 0.51 0.51Indoors at work 1.05 0.31 0.10Outdoors, soil surfaces 22.3 0.10 0.11Outdoors, soil surfaces (open areas) 65.6 0 0.23Outdoors, asphalt surfaces 7.97 0.08 0.05

a Total dose rate predicted by EXPURT for the specified location on 1 August 1986,in the absence of countermeasures (IAEA, in press).

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573 571

particular radionuclide contamination? Is it applicable to the wholesurface (e.g., all grass–soil surfaces or both grass and soil) or justa part (e.g., only large areas of grass but not small areas such asparks, or only grass and not the underlying soil)? The result isa model that is very flexible but not necessarily simple to use, andtherefore inappropriate for non-experts.

CONDO is an example of an application that embeds a model(EXPURT). By providing a front end it becomes easier and morerobust to use whilst retaining most of the flexibility (see Charnocket al., 2003). For example, CONDO applies a linear function torepresent time-dependent DFs in order to simulate the way somedecontamination methods become less effective with time. CONDOalso calculates the RRF for decontamination techniques and tie-down techniques in run-time, based on knowledge of the coun-termeasure mechanics and the time-dependent surface activityresults of EXPURT.

The present state of the art is represented by the decisionsupport systems ARGOS and RODOS, which are designed to eval-uate the overall dose-reduction capabilities of combined counter-measures (ARGOS, 2008; RODOS, 2008). Both of these systems willincorporate the ERMIN (EuRopean Model for INhabited areas)model to calculate external doses from contamination deposited ona variety of surfaces, as well as inhalation doses, for the interme-diate and late phases of an incident (Jones et al., 2006b; Anderssonet al., 2008a). In this context, ERMIN is intended to implementalmost any combination of countermeasures, including a specifictime of implementation (initiation of the countermeasure and theperiod of time required for its implementation) for each counter-measure and estimation of extra doses to remediation workers.ERMIN also will be able to assimilate measurement data as they

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become available, thereby permitting continual updating of themodel calculations.

6. Example of the implementation of countermeasures inmodelling

The following example with the EXPURT model (described inTable1) is drawn from the Urban Remediation Working Group’s first testexercise, which was based on Chernobyl fallout data in the town ofPripyat, Ukraine (IAEA, in press; Thiessen et al., 2008, 2009a). Thisexercise used measured deposition in Pripyat as input information,and participants were asked to predict radionuclide concentrationsand external dose rates at specified locations, contributions to thedose rates from individual surfaces and radionuclides, and annual andcumulative external doses to specified reference individuals. Predic-tions were requested for a ‘‘no action’’ situation (with no remedialmeasures) and for selected countermeasures.

Among the various countermeasures considered in the exercisewere cutting and removal of grass (performed 1 week after theinitial release) and high-pressure hosing of roads (performed 2weeks after the initial release). The EXPURT model used a decon-tamination factor (DF) of 8 for grass cutting and a DF of 7 for high-pressure hosing. Thus both countermeasures had similar values forDF and were carried out early in the post-accident period.

For high-pressure hosing, the dose rate reduction factor (DRF)for the dose rate attributable solely to paved areas was approxi-mately 7. However, the DRF for specific locations modelled in theexercise ranged from approximately 1.0 to 1.9, depending on theproportion of paved surfaces at the location and their contributionto the total dose rate (from all surfaces) at that location in theabsence of the countermeasure (Fig. 2).

For cutting and removal of grass, the DRF for dose rate attrib-utable to the top 1 cm of the soil/lawn surface was about 2.5–2.6 formost locations, or about a 60% reduction in the dose rate from thatsurface due to removal of the grass. For the total dose rate at specificlocations, however, the DRF for cutting and removal of grass rangedfrom about 1.1 to 1.8 (Fig. 2), depending on the contribution of thetop 1-cm soil/lawn layer to the total dose rate at the location in theabsence of the countermeasure. That contribution was approxi-mately 20–70% in this exercise.

Dose calculations in the exercise depended on both the totaldose rates at specific locations and the fraction of time assumed to

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K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573572

be spent at each location (occupancy factors). The exercise includeda reference indoor worker and a reference outdoor worker, whodiffered primarily in the amount of time spent indoors at work andoutdoors in open areas (Table 3). Cutting and removal of the grassresulted in a 40% reduction in the predicted dose over 20 years tothe outdoor worker (Fig. 3), but only about 30% for the indoorworker. High-pressure hosing of roads reduced the predicted 20-year dose to the indoor worker by 7–8%, but <2% for the outdoorworker. The importance of location and occupancy factors is illus-trated by the observation that the indoor worker in this exercise,even with no remedial action, has a substantially lower dose thanthe outdoor worker even with effective countermeasures (Fig. 3).

7. Summary and conclusions

The use of computer models for assessment of urban contami-nation situations and remedial options enables the evaluation ofa variety of situations or alternative recovery strategies in contextsof preparedness or decision-making. This can be highly valuable,for instance, in enabling selection of cost-effective remediationstrategies for a specific contamination situation and for estimatingin advance of any contaminating incident what the potentialhazards might be, so that a targeted operational preparedness canbe developed in time. At present a number of models and model-ling approaches are available for different purposes. Much of theinformation base, including parameter values, has come from theChernobyl experience and would not be applicable for all types ofurban contamination situations. However, the essential approachesto modelling countermeasures or remedial actions will be appli-cable. This paper has summarized these approaches and the pres-ently available information sources, pointing out weaknesses indata availability. In general, because assessment of the conse-quences of a radionuclide release in an urban area is quite complex,care is needed in the interpretation of model results. Counter-measure information in particular must be applied with carefulthought as to its applicability for the specific situation beingmodelled.

Acknowledgements

The activities of the Urban Remediation Working Group andpreparation of this manuscript have been supported in part by theU.S. Centers for Disease Control and Prevention under ORAURequisition No. 5-18266 under DOE Prime Contract DE-AC-05-06OR23100 and by the International Atomic Energy Agency.

References

Andersson, K.G., 1994. URGENT, a model for prediction of exposure from radio-cesium deposited in urban areas. Presented at the GSF/CEC/AIEA/GAST Work-shop on Dose Reconstruction in Bad Honnef, 6–9 June 1994.

Andersson, K.G., 1996. Evaluation of early phase nuclear accident clean-up proce-dures for Nordic residential areas. NKS Report NKS/EKO-5 (96) 18, ISBN:87-550-2250-2.

Andersson, K.G., 2005. Dose contributions from malicious radionuclide dispersionin an urban area. In: Radiological Protection in Transition. Proceedings of theXIV Regular Meeting of the Nordic Society for Radiation Protection, Rattvik,Sweden, 27–31 August 2005. SSI report 2005:15. ISSN: 0282-4434. SwedishRadiation Protection Authority, pp. 59–62.

Andersson, K.G., 2006. Radioactive contamination of urban areas (editorial). Journalof Environmental Radioactivity 85, 151–153.

Andersson, K.G., Roed, J., 1999. A Nordic preparedness guide for early clean-up inradioactively contaminated residential areas. Journal of Environmental Radio-activity 46, 207–233.

Andersson, K.G., Brown, J., Mortimer, K., Jones, J.A., Charnock, T., Thykier-Nielsen, S.,Kaiser, J.C., Proehl, G., Nielsen, S.P., 2008a. New developments to supportdecision-making in contaminated inhabited areas following incidents involvinga release of radioactivity to the environment. Journal of EnvironmentalRadioactivity 99, 439–454.

Andersson, K.G., Mikkelsen, T., Astrup, P., Thykier-Nielsen, S., Jacobsen, L.H., Schou-Jensen, L., Hoe, S.C., Nielsen, S.P., 2008b. Estimation of health hazards resultingfrom a radiological terrorist attack in a city. Radiation Protection Dosimetry 131(3), 297–307.

Andersson, K.G., Roed, J., Eged, K., Kis, Z., Voigt, G., Meckbach, R., Oughton, D.H.,Hunt, J., Lee, R., Beresford, N.A., Sandalls, F.J., 2003. Physical Countermeasures toSustain Acceptable Living and Working Conditions in Radioactively Contami-nated Residential Areas. Risø National Laboratory, Roskilde, Denmark. Risø-R-1396(EN).

Anisimova, L.I., Chesnokov, A.V., Govorun, A.P., Invanov, O.P., Potapov, B.N.,Shcherbak, S.B., Urutskoev, L.I., Kovalenko, V.I., Krivonosov, C.V.,Ponomarev, A.V., Ramzaev, V.P., Trusov, V.A., 1994. Analysis of some counter-measures efficiency based on radioecological data, deposit measurements andmodels for external dose formation. Case Study of Two Russian SettlementsYalovka and Zaborie. In: Annual Progress Report of the ECP4 Project Supportedby the European Commission. EMERCOM, Moscow, Russia.

ARGOS, 2008. ARGOS CBRN Whitepaper. Decision Support for EmergencyManagement. Available from: www.pdb.dk/argos.

Brown, J., Cooper, J.R., Jones, J.A., Flaws, L., McGeary, R., Spooner, J., 1996. Review ofDecontamination and Clean-up Techniques for Use in the UK following Acci-dental Releases of Radioactivity to the Environment. National RadiologicalProtection Board, Chilton. NRPB-288, DOE/RAS/96.009.

Brown, J., Mansfield, P.A., Smith, J.G., 2000. Modelling approach in the terrestrial latecountermeasures module, LCMT within RODOS 4.0, RODOS(WG3)-TN(99)-43.

Brown, J., Charnock, T.W., Morrey, M., 2003. DEWAR – Effectiveness of Decontam-ination Options, Wastes Arising and Other Practical Aspects of RecoveryCountermeasures in Inhabited Areas. Environment Agency R&D TechnicalReport P3-072/TR.

Brown, J., Andersson, K.G., Mortimer, K.T., 2005. Countermeasures for theManagement of Areas Contaminated after a Radiological Incident. HealthProtection Agency, UK. RPD-EA-5-2005, EURANOS(CAT1)-TN(05)-04.

Brown, J., Andersson, K.G., Jones, J.A., Meckbach, R., Muller, H., Roed, J., 2006.Requirements of future models for inhabited areas. Journal of EnvironmentalRadioactivity 85, 344–360.

Brown, J., Mortimer, K., Andersson, K., Duranova, T., Mrskova, A., Hanninen, R.,Ikaheimonen, T., Kirchner, G., Bertsch, V., Gallay, F., Reales, N., 2007. GenericHandbook for Assisting in the Management of Contaminated Inhabited Areas inEurope following a Radiological Emergency, Parts I–V. Final Report of the EC-EURANOS Project Activities CAT1RTD02 and CAT1RTD04. Radiation ProtectionDivision, Health Protection Agency, UK, p. 483.

Charnock, T., 2004. CONDO Version 3.1 User Guide, NRPB-EA/5/2004.Charnock, T., Brown, J., Jones, A.L., Oatway, W., Morrey, M., 2003. CONDO: Software

for Estimating the Consequences of Decontamination Options. Report forCONDO Version 2.1 (with associated Database Version 2.1). Chilton, NRPB-W43.

Da Silva, C.J., Delgado, J.U., Luiz, M.T.B., Cunha, P.G., de Barros, P.D., 1991. Consid-erations related to the decontamination of houses in Goiania: limitations andimplications. Health Physics 60 (1), 87–90.

Eged, K., Kis, Z., Semioschkina, N., Voigt, G., 2004. Evaluation of dose calculationmodels for inhabited areas applicable in nuclear consequence assessmentcodes. Technical Deliverable to EVANET TERRA (Evaluation and Network of EC-decision Support Systems in the Field of Terrestrial Radioecological Research,European Union).

Eged, K., Kis, Z., Voigt, G., 2006. Review of dynamical models for external dosecalculations based on Monte Carlo simulations in urbanised areas. Journal ofEnvironmental Radioactivity 85, 330–343.

Fogh, C.L., Andersson, K.G., Barkovsky, A.N., Mishine, A.S., Ponamarjov, A.V.,Ramzaev, V.P., Roed, J., 1999. Decontamination in a Russian settlement. HealthPhysics 76 (4), 421–430.

Gallay, F., 2008. Modelling the Rehabilitation of Contaminated Urban Environments.Bibliographic Survey. Institut de Radioprotection et de Surete Nucleaire. ReportIRSN/DEI/SESUC/08-70.

Gallego, E., 2006. MUD: a model to investigate the migration of 137Cs in the urbanenvironmentanddrainage andsewage treatment systems. Presentedat theNKSConference on Radioactive Contamination in Urban Areas, Roskilde (Denmark),7–9 May 2003. Journal of Environmental Radioactivity 85, 247–264.

Golikov, V.Yu., Balonov, M.I., Jacob, P., 2002. External exposure of the populationliving in areas of Russia contaminated due to the Chernobyl accident. RadiationEnvironment Biophysics 41, 185–193.

Hedemann Jensen, P., 1985. Shielding factors for gamma radiation from activitydeposited on structures and ground surfaces. Nuclear Technology 68, 29–39.

Hubert, P., Annisomova, L., Antsipov, G., Ramsaev, V., Sobotovich, V. (Eds.), 1996.Strategies of Decontamination. Final Report of Experimental CollaborationProject No. 4. European Commission, ISBN 92-827-5195-3.

Hwang, W.T., Kim, E.H., Jeong, H.J., Suh, K.S., Han, M.H., 2005. A model for radiologicalassessment due to an urban contamination following an accidental release intothe environment. In: Proceedings from the Second International Conference onRadioactivity in the Environment, Nice, France, 2–6 October 2005.

IAEA (International Atomic Energy Agency). Environmental Modelling of Remedi-ation of Urban Contaminated Areas. Report of the Urban Remediation WorkingGroup of the EMRAS (Environmental Modelling for Radiation Safety) Pro-gramme, in press.

ICRP (International Commission on Radiological Protection), 1993. ICRP Publication66: Human Respiratory Tract Model for Radiological Protection. PergamonPress, ISBN 0-08-041154-1.

Jones, J.A., Singer, L.N., 2003. Technical Description of EXPURT v3.0, NRPB-EA/11/2003.

K.M. Thiessen et al. / Journal of Environmental Radioactivity 100 (2009) 564–573 573

Jones, J.A., Singer, L.N., Brown, J., 2006a. The EXPURT model for calculating externalgamma doses from deposited material in inhabited areas. Journal of Environ-mental Radioactivity 85, 314–329.

Jones, J.A., Charnock, T., Singer, L., Roed, J., Andersson, K.G., Thykier-Nielsen, S.,Mikkelsen, T., Astrup, P., Kaiser, J.C., Muller, H., Prohl, G., Raskob, W., Hoe, S.,Jacobsen, L.H., Schou-Jensen, L., Gering, F., 2006b. Description of the modellingof transfer and dose calculations within ERMIN. Draft of Deliverable D5C2R2 ofthe CEC-EURANOS Project, EURANOS(CAT2)-TN(05)-04.

Kaiser, J.C., Gering, F., Andersson, K., Charnock, T., Hoe, S., Jacobsen, L.H., 2008.Correction of model predictions for the radioactive contamination of theenvironment with data assimilation. In: Strand, P., Brown, J., Jølle, T. (Eds.),Proceedings of the International Conference on Radioecology and Environ-mental Radioactivity, 15–20 June 2008, Bergen, Norway, Oral & Oral PosterPresentations, Part 1. Norwegian Radiation Protection Authority, Østerås,Norway, ISBN 978-82-90362-25-1, pp. 130–133.

Kashparov, V.A., Ahamdach, N., Zvarich, S.I., Yoschenko, V.I., Maloshtan, I.M.,Dewiere, L., 2004. Kinetics of dissolution of Chernobyl fuel particles in soil innatural conditions. Journal of Environmental Radioactivity 72, 335–353.

Kelly, G.N., 1987. The importance of the urban environment for accident conse-quences. Radiation Protection Dosimetry 21, 13–20.

Meckbach, R., Jacob, P., Paretzke, H.G., 1988. Gamma exposure due to radionuclidesdeposited in urban environments. Part I: Kerma rates from contaminated urbansurfaces. Radiation Protection Dosimetry 25, 167–179.

Meckbach, R., Muller, H., Moraleda, M., 1999. Techniques and Management Strate-gies for Environmental Restoration and their Ecological Consequences (TEMAS),Final Report-WP.1: Urban Environment, UE F14P-CT95-0021.

Mobbs, S., Nisbet, A., Brown, J., Mercer, J., Mortimer, K., Roberts, G., Hesketh, N.,2005. UK Recovery Handbook for Radiation Incidents: 2005. Health ProtectionAgency, Centre for Radiation, Chemical and Environmental Hazards, Chilton.HPA-RPD-002.

Rochedo, E.R.R., Conti, L.F.C., Paretzke, H.G., 1996. PARATI – a dynamic model forradiological assessments in urban areas: Part I, modelling of urban areas, theircontamination and radiation fields. Radiation Environment Biophysics 35, 243–261.

Rochedo, E.R.R., Conti, L.F.C., Paretzke, H.G., 1997. PARATI – a dynamic model forradiological assessments in urban areas: Part II, specifications of individuals andpopulations, their radiation exposure and variabilities. Radiation EnvironmentBiophysics 36, 183–193.

RODOS, 2008. Realtime Online Decision Support System for Nuclear EmergencyManagement. Homepage available from: www.rodos.fzk.de.

Roed, J., Lange, C., Andersson, K.G., Prip, H., Olsen, S., Ramzaev, V.P.,Ponomarjov, A.V., Barkovsky, A.N., Mishine, A.S., Vorobiev, B.F., Chesnokov, A.V.,Potapov, V.N., Shcherbak, S.B., 1996. Decontamination in a Russian Settlement,ISBN 87-550-2152-2, Risoe-R-870, p. 102.

Roed, J., Andersson, K.G., Barkovsky, A.N., Fogh, C.L., Mishine, A.S., Olsen, S.K.,Ponomarjov, A.V., Prip, H., Ramzaev, V.P., Vorobiev, B.F., 1998. MechanicalDecontamination Tests in Areas Affected by the Chernobyl Accident. RisøNational Laboratory, ISBN 87-550-2361-4. Risø-R-1029.

Roed, J., Andersson, K.G., Barkovsky, A.N., Fogh, C.L., Mishine, A.S., Ponamarjov, A.V.,Ramzaev, V.P.,2006. Reduction ofexternal dose in awet-contaminated housing areain Bryansk Region, Russia. Journal of Environmental Radioactivity 85, 265–279.

Sandalls, F.J., 1987. Removal of Radiocesium from Urban Surfaces Contaminated asa Result of a Nuclear Accident. Harwell AERE-R-2355.

Thiessen, K.M., Thorne, M.C., Maul, P.R., Prohl, G., Wheater, H.S., 1999. Modellingradionuclide distribution and transport in the environment. EnvironmentalPollution 100, 151–177.

Thiessen, K.M., Batandjieva, B., Andersson, K.G., Arkhipov, A., Charnock, T.W.,Gallay, F., Gaschak, S., Golikov, V., Hwang, W.T., Kaiser, J.C., Kamboj, S.,Steiner, M., Tomas, J., Trifunovic, D., Yu, C., Zelmer, R., Zlobenko, B., 2008.Improvement of modelling capabilities for assessing urban contamination: theEMRAS Urban Remediation Working Group. Applied Radiation and Isotopes 66,1741–1744.

Thiessen, K.M., Arkhipov, A., Batandjieva, B., Charnock, T.W., Gaschak, S., Golikov, V.,Hwang, W.T., Tomas, J., Zlobenko, B., 2009a. Modelling of a large-scale urbancontamination situation and remediation alternatives. Journal of EnvironmentalRadioactivity 100, 413–421.

Thiessen, K.M., Andersson, K.G., Batandjieva, B., Cheng, J.J., Hwang, W.T., Kaiser, J.C.,Kamboj, S., Steiner, M., Tomas, J., Trifunovic, D., Yu, C., 2009b. Modelling thelong-term consequences of a hypothetical dispersal of radioactivity in an urbanarea including remediation alternatives. Journal of Environmental Radioactivity100 (6), 445–455.

UNPF (United Nations Population Fund), 2007. State of World Population 2007:Unleashing the Potential of Urban Growth. United Nations Population Fund,New York, ISBN 978-0-89714-807-8. Available from: http://www.unfpa.org/swp/.

Yu, C., Cheng, J.J., Kamboj, S., Domotor, S., Wallo, A., 2006. Preliminary Report onOperational Guidelines Developed for Use in Emergency Preparedness andResponse to a Radiological Dispersal Device Incident, ANL/EVS/TM/06-4, DOE/HS-0001, December 2006.