radionuclide transport and dose assessment modelling in … · 2011-12-08 · radionuclide...

P O S I V A O Y

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Thomas H je rpe

Rober t Broed

November 2010

Work ing Repor t 2010 -79

Radionuclide Transport and Dose AssessmentModelling in Biosphere Assessment 2009

November 2010

Base maps: ©National Land Survey, permission 41/MML/10

Working Reports contain information on work in progress

or pending completion.

The conclusions and viewpoints presented in the report

are those of author(s) and do not necessarily

coincide with those of Posiva.

Thomas H jerpe

Saan io & R iekko la Oy

Robert Broed

Fac i l i a AB

Work ing Report 2010 -79

Radionuclide Transport and Dose AssessmentModelling in Biosphere Assessment 2009

RADIONUCLIDE TRANSPORT AND DOSE ASSESSMENT MODELLING IN BIOSPHERE ASSESSMENT 2009 ABSTRACT Following the guidelines set forth by the Ministry of Trade and Industry (now Ministry of Employment and Economy), Posiva is preparing to submit a construction license application for the final disposal spent nuclear fuel at the Olkiluoto site, Finland, by the end of the year 2012. Disposal will take place in a geological repository implemented according to the KBS-3 method. The long-term safety section supporting the license application will be based on a safety case that, according to the internationally adopted definition, will be a compilation of the evidence, analyses and arguments that quantify and substantiate the safety and the level of expert confidence in the safety of the planned repository. This report documents in detail the conceptual and mathematical models and key data used in the landscape model set-up, radionuclide transport modelling, and radiological consequences analysis applied in the 2009 biosphere assessment. Resulting environmental activity concentrations in landscape model due to constant unit geosphere release rates, and the corresponding annual doses, are also calculated and presented in this report. This provides the basis for understanding the behaviour of the applied landscape model and subsequent dose calculations. Keywords: Safety case, safety assessment, biosphere assessment, radionuclide transport, dose assessment.

RADIONUKLIDIEN KULKEUTUMIS- JA SÄTEILYANNOSMALLINNUS BIOSFÄÄRIARVIOINNISSA 2009 TIIVISTELMÄ Kauppa- ja teollisuusministeriön vuonna 2003 vahvistaman aikataulun mukaisesti Posiva on valmistautumassa käytetyn ydinpolttoaineen loppusijoituslaitoksen rakenta-mislupahakemuksen jättämiseen vuoden 2012 lopulla. Loppusijoituksen pitkäaikais-turvallisuus käsitellään lupahakemuksessa ns. turvallisuusperusteluna (engl. safety case), jolla kansainvälisesti omaksutun määritelmän mukaisesti tarkoitetaan kaikkea sitä teknis-tieteellistä aineistoa, analyysejä, havaintoja, kokeita, testejä ja muita todisteita, joilla perustellaan loppusijoituksen turvallisuus ja turvallisuudesta tehtyjen arvioiden luotettavuus. Vuonna 2008 Posiva esitti päivitetyn suunnitelman tuvallisuusperustelun muodostavasta aineistosta ja sen laatimisesta. Raportti kuvaa yksityiskohtaisesti konseptuaaliset ja matemaattiset mallit sekä keskeiset lähtötiedot, joita on käytetty vuoden 2009 biosfääriarvioinnin aluetason kulkeu-tumismallin rakentamisessa, radionuklidien kulkeutumismallinnuksessa ja radiologisten seuraamusten analyysissa. Tuloksena aluetason kulkeutumismallista saadut yksikkö-päästökohtaiset ympäristön radioaktiivisuuspitoisuudet ja niitä vastaavat vuosittaiset säteilyannokset esitetään niin ikään tässä raportissa. Tämä on perustana käytetyn alue-tason kulkeutumismallin ja annoslaskujen käyttäytymisen ymmärtämisessä. Avainsanat: Turvallisuusperustelu, turvallisuusanalyysi, biosfääriarviointi, radionuk-lidien kulkeutuminen, annoslaskenta.

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TABLE OF CONTENT ABSTRACT TIIVISTELMÄ

PART I ............................................................................................................................ 3

1 INTRODUCTION ..................................................................................................... 5 1.1 Olkiluoto site ..................................................................................................... 5 1.2 Safety case and biosphere assessment ........................................................... 6 1.3 This report ...................................................................................................... 10

2 BIOSPHERE ASSESSMENT MODELLING .......................................................... 11 2.1 Main concepts ................................................................................................ 11 2.2 Selection of radionuclides considered ............................................................ 14 2.3 Geosphere release rates ................................................................................ 15

3 SCREENING MODELS ......................................................................................... 19 3.1 Tier 1 .............................................................................................................. 19 3.2 Tier 2 .............................................................................................................. 21 3.3 Parameters and applied values ...................................................................... 26 3.4 Modelling platforms and tools ......................................................................... 28

4 THE LANDSCAPE MODEL ................................................................................... 37 4.1 Landscape model set-up ................................................................................ 39 4.2 Simplified release patterns ............................................................................. 40 4.3 Biosphere object modules .............................................................................. 49 4.4 The landscape model applied in the biosphere assessment 2009 ................. 59 4.5 Modelling platforms and tools ......................................................................... 73

5 RADIOLOGICAL CONSEQUENCES ANALYSIS ................................................. 75 5.1 Assessing doses to humans ........................................................................... 75 5.2 Assessing doses to other biota ...................................................................... 86 5.3 Compliance assessment ................................................................................ 99 5.4 Modelling platforms and tools ......................................................................... 99

6 SAFETY INDICATORS ....................................................................................... 101 6.1 Stylised well scenarios ................................................................................. 101 6.2 Modelling platforms and tools ....................................................................... 106

7 KNOWLEDGE QUALITY ASSESSMENT ........................................................... 119 7.1 Screening models ......................................................................................... 120 7.2 Landscape modelling ................................................................................... 125 7.3 Radiological consequence assessment ....................................................... 131 7.4 Safety indicators ........................................................................................... 139

PART II ....................................................................................................................... 145

8 SCREENING EVALUATION ............................................................................... 147 8.1 Constant unit geosphere releases ................................................................ 147 8.2 Repository assessment cases ...................................................................... 148

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9 CONSTANT RELEASES INTO THE BIOSPHERE .......................................... 159 9.1 Retained activity ........................................................................................ 159 9.2 Activity concentrations ............................................................................... 163 9.3 Annual doses to humans ........................................................................... 180

10 CONCLUSIONS ............................................................................................... 197

REFERENCES ........................................................................................................ 199

APPENDIX A – Biosphere object modules .............................................................. 209

APPENDIX B – Data for the Mäntykarinjärvi object ................................................. 225

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PART I

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1 INTRODUCTION

Posiva Oy (Posiva) was established in 1995 by the two Finnish nuclear power companies, Teollisuuden Voima Oyj (TVO) and Fortum Power and Heat Oy (Fortum), to implement the final disposal programme for spent nuclear fuel and to carry out the related research, technical design and development (RTD, or TKS, in Finnish). Other nuclear wastes are handled and disposed of by the power companies themselves. The spent nuclear fuel is planned to be disposed of in a KBS-3 type of repository to be constructed at a depth of about 400 metres in the crystalline bedrock at the Olkiluoto site. Currently, two variants of the KBS-3 method are under consideration, KBS-3V and KBS-3H. In KBS-3V, the canisters are emplaced vertically in individual deposition holes constructed in the floors of deposition tunnels. In KBS-3H, several canisters are emplaced horizontally in a system of 100-300 m long deposition drifts. In both variants, the canisters are surrounded by a swelling clay buffer material that separates them from the bedrock and, in the case of KBS-3H, also separates the canisters one from another along the deposition drifts. The KBS-3V deposition tunnels and other underground openings in both variants are to be backfilled with a low permeability material.

In 2001, the Finnish Parliament ratified the Government’s favourable Decision in Principle on Posiva’s application to locate a repository at Olkiluoto. This decision represents the milestone prior to entering the phase of confirming site characterisation. Following the guidelines set forth by the Ministry of Trade and Industry (now the Ministry of Employment and Economy), Posiva is preparing for the next step of the nuclear licensing of the repository, which involves submitting the construction licence application for a spent fuel repository by the end of 2012. A safety case will be produced to support the licence application. This report presents the models and data applied in the radionuclide transport modelling and dose assessment modelling in the biosphere assessment performed for the interim safety case of 2009, and will be updated for the safety case of 2012.

1.1 Olkiluoto site

Olkiluoto is a moderately sized island (currently an approximate area of 12 km2), on the coast of the Baltic Sea, separated from the mainland by a narrow strait (Figures 1-1). The Olkiluoto nuclear power plant, with two reactors in operation, and a repository for low- and intermediate-level waste are located on the western part of the island. The construction of a new reactor unit (OL3) is underway at the site. The repository for spent fuel will be constructed in the central-eastern parts of the island after the construction licence for the spent fuel repository has been obtained. The construction of an underground rock characterisation facility, called ONKALO, started in June 2004.

The areas considered in biosphere assessment are shown in Figure 1-2: the smaller purple rectangle, in which Olkiluoto Island is located, delineates the Model area considered in the safety assessment modelling, and the larger study area is the so-called Reference area, which is used for regional descriptions, especially for lakes and mires, which presently are scarce in the Model area.

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Figure 1-1. An overview map of Olkiluoto. Topographic database by the National Land Survey of Finland, map layout by Jani Helin/Posiva Oy.

1.2 Safety case and biosphere assessment

Posiva is currently producing a safety case to support the construction licence application for a KBS-3 type of repository at the Olkiluoto site. A safety case is a synthesis of evidence, analyses and arguments that quantify and substantiate the long-term safety, and the level of expert confidence in the safety, of a geological disposal facility for radioactive waste (IAEA 2006, NEA 2004, 2009). Posiva's plan for the safety case was initially prepared in 2004 (Vieno & Ikonen 2005), and has recently been revised (Posiva 2008). The first planning report introduced the Posiva safety case portfolio as the documentation management approach, facilitating a flexible and progressive development of the safety case; this approach is further developed in the current safety case plan.

A safety case includes a quantitative safety assessment, which is defined as the process of systematically analysing the ability of the disposal facility to provide the safety functions and to meet technical requirements, and evaluating the potential radiological hazards and compliance with the safety requirements (Posiva 2008). The present report is a supplementary report to the quantitative safety assessment.

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Figure 1-2. The full Reference area with locations of lakes and mires selected as reference objects. The Model area refers to the area included in the terrain and ecosystem development model, and the dependent radionuclide transport model. CORINE Land Cover 2000 classification by Finnish Environment Institute. Map layout by Jani Helin/Posiva Oy.

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1.2.1 Biosphere assessment

The overall aims of the biosphere assessment (BSA) in the safety case are:

� to describe the future, present, and relevant past conditions at, and prevailing processes in, the surface environment of the Olkiluoto site;

� to model the transport and fate of radionuclides hypothetically released from the repository through the geosphere to the surface environment; and

� to assess possible radiological consequences to humans and other biota.

The surface environment will evolve significantly on a timescale comparable to that of variations in the radionuclide release from the geosphere. For example, areas that are currently sea bottom will develop into terrestrial areas and lakes will be formed over a period of a few millennia. Hence, a steady-state approach, based on the current conditions at the site or on stylised future steady state conditions, such as the use of drinking water or irrigation wells in different assumed steady-state climate conditions, is not sufficient to meet current assessment purposes. The main approach in the present biosphere assessment is to develop a fully dynamic model for the development of the surface environments, radionuclide transport and radiological consequences analysis.

The time window adopted in the present assessment is the period over which the regulatory dose constraints are assumed to apply. It starts at the year of the emplacement of the first canister and lasts for ten millennia; covering the period from the year 2 020 to the year 12 020 in the Common Era1. Performing a biosphere assessment is a process, which can conceptually be divided into five main sub-processes (illustrated in Figure 1-3):

� Biosphere description (BSD) – performing environmental studies and monitoring, and the compilation of a description of the present properties and on-going processes at the Olkiluoto site.

� Terrain and ecosystems development – predicting the development of the topography, overburden, and hydrology at the site. In the future, flora and fauna will also be included in the predictions. This is called forecasting and is carried out by terrain and ecosystems development modelling (TESM).

� Landscape model set-up – defining the landscape model (LSM), which is a state-of-the-art time-dependent and site-specific radionuclide transport model used in the next sub-process.

1 Common Era (abbreviated as CE) is a designation for the world's most commonly used year-numbering system; the numbering of years is identical to the numbering used with Anno Domini (BC/AD) notation, 2010 being the current year. In the present report, all years, if not explicitly stated otherwise, are based on the Common Era system.

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Figure 1-3. Schematic illustration of the biosphere assessment process. The five major sub-process are marked in bold; the main activities (text under the components) are indicated by colours in the components. Selected key inputs and links are also included, especially regarding hydrological modelling.

� Radionuclide transport (RNT) modelling – defining the ecosystem-specific radionuclide transport models underlying the landscape model, and analysing the fate of radionuclides released from the geosphere. A screening approach is applied, to focus more detailed assessments on radionuclides that may have insignificant radiological consequences.

� Radionuclide consequences analysis (RCA) – estimating potential radiological consequences to humans and other biota and assessing compliance with regulatory requirements.

Reporting the biosphere assessment The Biosphere Assessment Portfolio was introduced in the Safety case planning report (Vieno & Ikonen 2005) and revised in Ikonen (2006). The biosphere assessment undertaken in 2009 is part of the interim safety case developed in 2009. The current biosphere assessment will produce four main reports, and several supporting reports, briefly described as follows.

Biosphere description report. The BSD-2009 report (Haapanen et al. 2009) provides a scientific synthesis of knowledge as to the current state of the surface environment and the main features of the past evolution at the site. Furthermore, it provides conceptual ecosystem models and assessment data to support the subsequent biosphere assessment sub-processes. Due to their extent, a large fraction of the data for the assessment was moved to a supplementary report (Ikonen et al. 2010a) The BSD-2009 is an update of Olkiluoto Biosphere description report 2006 (Haapanen et al. 2007), and will be further updated for the 2012 assessment.

Terrain and ecosystem development model report (TESM-2009). TESM-2009 (Ikonen et al. 2010b) provides a scientific synthesis of the expected development of the surface environments over the period for which the dose-based constraints apply. TESM-2009 is an update of TESM-2006, and will be further updated for the 2012 assessment.

Radiological consequences

analysis

Radionuclide transport modelling

Landscapemodel set-up

Terrain & ecosystem

development

Biosphere description

Integration of site dataProcesses Forecasting Transport modelling Compliance assessment

Safety indicators

Outcome

BIOSPHEREASSESSMENT

CLIMATIC ENVELOPE

Surface and near-surface hydrological modelling

Groundwater flow modelling

Near-field modelling Geosphere modelling

Env. studies Monitoring

Simplifiedrelease pattern

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Radionuclide transport and dose modelling in biosphere assessment in 2009 (this report). This report documents the conceptual and mathematical models and key data used in the landscape model set-up, radionuclide transport modelling, and radiological consequences analysis. The report also provides the basis for understanding the behaviour of the landscape model, most importantly by calculating results for stylised releases, such as long-term unit releases, into the biosphere. Key supporting reports are detailed model and modelling tool reports, such as Avila & Pröhl (2007), Avila & Bergström (2006) and Åstrand et al. (2005). The present report is also supported by a review report on concentration ratio and distribution coefficient data (Helin et al. 2010). The present report is an update, and extension of the similar report (Broed 2007b) produced for the KBS-3H safety assessment (Smith et al. 2007a), and will be further updated in 2012.

Biosphere assessment summary report. This report (Hjerpe et al. 2010) presents the biosphere calculation cases and applies them to results from the relevant repository calculation cases presented in the recent radionuclide release and transport report for the KBS-3V design, the RNT-2008 report (Nykyri et al. 2008). The fate of any radionuclides potentially released from the repository in the scenarios considered in RNT-2008 is discussed in the present report, along with the radiological consequences to humans and other biota. In addition, this report summarises the three above-mentioned main biosphere assessment reports. The BSA-2009 report is the first biosphere assessment report produced within the development of the Posiva safety case. The methodology and models applied in the present biosphere assessment are an update and an extension of the methodology and models applied in the biosphere analysis in the KBS-3H safety assessment (Broed et al. 2007). The BSA-2009 report will further be updated in 2012.

1.3 This report

The main purpose of this report is to ensure transparency of the radionuclide transport and dose assessment modelling in the biosphere assessment. It is a supporting report to the biosphere assessment 2009 report (Hjerpe et al 2010). This report is divided into two parts.

Part I starts with a brief presentation of the overall biosphere assessment process (chapter 2). This is followed by detailed presentations of the conceptual and mathematical models, as well as key data, used in the screening assessments (chapter 3), in the landscape modelling (chapter 4), and in the radionuclide consequences analysis (chapter 5). Thereafter, models and data used when deriving safety indicators from stylised well scenarios are documented (chapter 6). The final chapter in Part I (chapter 7) present the knowledge quality assessment performed for the screening models, landscape model, radiological consequences analysis, and safety indicators.

Part II presents and discusses results from applying the models and concepts described in Part I. The results from the screening evaluation (chapter 8) form the basis for screening out radionuclides considered to be insignificant for the biosphere assessment, from a radiological consequences point of view. Furthermore, this report provides the basis for understanding the behaviour of the landscape model, by analysing stylised geosphere releases, using long-term unit geosphere releases (chapter 9).

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2 BIOSPHERE ASSESSMENT MODELLING

Figure 2-1. Schematic description of the landscape model set-up, radionuclide transport modelling and radiological consequence analysis sub-processes in the biosphere assessment, see Figure 1-3 for the overall modelling process (SNSH: Surface and near surface hydrological modelling).

This report focus on models and data used in the safety assessment part of the biosphere assessment, which involves three of the sub-processes shown in Figure 1-3: landscape model (LSM) set-up, radionuclide transport (RNT) modelling and radiological consequence analysis (RCA). The models included in the first two sub-processes are discussed in detail in the Biosphere description report (Haapanen et al. 2009) and the Terrain and Ecosystem Development model report (Ikonen et al. 2010b).

This chapter briefly presents (section 2.1) the modelling concept of the safety assessment part of the biosphere assessment, as illustrated in Figure 2-1; the individual models and data that were used are discussed in more detail in the subsequent chapters. Further, the radionuclides that were considered are discussed in section 2.2 and the geosphere release rates applied are presented in section 2.3.

2.1 Main concepts

This section describes the main concepts applied when converting the radionuclide releases from the geosphere into quantities used for assessing compliance with regulatory dose constraints.

2.1.1 Landscape model set-up

The forecasted conditions in the surface environment at year 2 020, which is the assumed emplacement time of the first canister, define, in this context, the initial state of the biosphere. This is the starting point for the landscape modelling. The forecast is based on the terrain and ecosystem modelling and surface and near-surface hydrology

Radiological consequences

analysis

Radionuclide transport modelling

Landscapemodel set-up

Terrain & ecosystem

development

Biosphere description

Integration of site dataProcesses Forecasting Transport modelling Compliance assessment

Safety indicators

Outcome

BIOSPHEREASSESSMENT

CLIMATIC ENVELOPE

Surface and near-surface hydrological modelling

Groundwater flow modelling

Near-field modelling Geosphere modelling

Env. studies Monitoring

Simplifiedrelease pattern

BSO modules

Tier 1 & 2

Annual release

Safety indicators

Dose assessmentComplianceassessmentLSM set-up

Release pattern

SNSH

TESM

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modelling. These, in turn, are based on the latest available site-specific data and models, such as the terrain (elevation) model (Pohjola et al. 2009), the land uplift model (Vuorela et al. 2009) and the ecosystem models describing the present surface environment (Haapanen et al. 2009). Using the forecasts, sufficiently homogeneous2 sub-areas of the modelled area that could potentially receive radionuclides released from the repository are identified (these are called biosphere objects). To identify the biosphere objects, the release pattern is determined. This release pattern (section 4.2) is a stylised representation of the radionuclide release points from the geosphere to the biosphere, based on surface and near-surface hydrology modelling (Karvonen 2009a-c) and on deep groundwater modelling. Each biosphere object is described by one, or more, ecosystem types and one set of data, and is associated with a corresponding radionuclide transport model. The connections between the objects are derived from terrain forecasts for the period from the present (initial state) to the end of the assumed time window when regulatory dose constraints apply. The combination of the connected biosphere objects and the release pattern define the landscape model (section 4.4). The landscape model is a state-of-the art site-specific and time-dependent radionuclide transport model. Defining the initial state for the landscape model and how it develops with time is the main task of the landscape model set-up sub-process.

2.1.2 Radionuclide transport modelling

In the radionuclide transport modelling process, the fate of radionuclides potentially released to the biosphere is assessed. The main task of this process is to estimate the spatial and temporal distribution of each radionuclide in all biosphere objects included in the landscape model. A graded approach based on three tiers has been applied; this is further discussed in Hjerpe et al. (2010). Tier 1 and 2 (section 3) involve conducting generic evaluations to screen out radionuclides that have insignificant radiological consequences, using two levels of inherent pessimism, and only Tier 3 is based on the landscape model.

The ecosystem-specific radionuclide transport models used for the biosphere objects are called biosphere object modules (section 4.3) and, in the present assessment, include: forest, wetland, cropland, lake, river, coast, and sea. An important improvement in the models applied in the present assessment, as compare with models used in the KBS-3H safety assessment (Broed 2007b), is that they are consistent at the conceptual level, meaning that the structure of compartments3 is very similar in each module. This facilitates the coupling between ecosystems existing at the same time, and the transition

2 Sufficient homogeneity requires firstly that within the sub-area, the variation in properties does not affect significantly the parameter values of the respective object(s) in the radionuclide transport modelling and in the radionuclide consequence analysis. Secondly, the size should not be large enough to allow the inherently heterogeneous distribution of radionuclide concentration within the object to become significant in the dose calculations; in smaller objects the behaviour of an individual averages over these variations in the cause of the exposure in accordance with the dose concept. 3 The ecosystem-specific radionuclide transport models are based on a compartmental approach, where the radionuclides are assumed to be well-mixed within each compartment.

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over time from one ecosystem type to another due to biosphere development in time driven by land uplift and ecosystems succession processes.

2.1.3 Radiological consequences analysis

The activity concentrations in environmental media, obtained from the radionuclide transport modelling constitute the basis for assessing potential radiological consequences to humans and other biota. Assessing these consequences and putting them into the context of regulatory requirements are the main tasks of the radionuclide consequences analysis sub-process.

Radiological consequences to humans (section 5.1) are assessed by a prospective deterministic dose assessment process, based on recommendations from the ICRP (International Commission on Radiological Protection) (ICRP 2000, 2007b). The assessment is based on site-specific conditions and present regional land use; e.g., the exposed population size is limited by site-specific constraints on food production and by the availability of drinking water. During the time window of the biosphere assessment, the surface environment will undergo significant development and many generations may be exposed. This is taken into account by deriving the full annual dose 4distribution (the dose to each potentially exposed individual utilizing the contaminated area) for each generation.

Radiological consequences to other biota (section 5.2) are assessed by a deterministic prospective exposure assessment, in which typical absorbed dose rates to flora and fauna at the site are calculated. The guidance provided by the regulators on how to calculate typical absorbed dose rates to other biota is very limited; the approach developed and implemented in this report is considered by Posiva to meet the requirements in the Guide YVL D.5 (STUK 2009). However, it is acknowledged that this approach is still immature, as compare with the approach for assessing doses to humans, and, if found necessary, further development may be undertaken. The approach to identify typical absorbed dose rates is firstly, to identify the species that need to be include in the assessment, and secondly, for these to derive absorbed dose rates based on Tier 3 of the ERICA (Environmental Risk from Ionising Contaminants: Assessment and Management) methodology (Beresford et al. 2007). When selecting species to be included in the assessment (denoted assessment species), the first step is to identify their trophic roles (the position that the species occupy in the food web). The species in different trophic roles at the site have been identified in (Haapanen et al. 2009, chapters 4-7, especially section 4.1.6) and from these characteristic species have been selected to represent each trophic role. Here, ‘characteristic’ means most common within the limitations of available data, since analogue data are needed anyway. The choice of species for a trophic role is considered to matter less than the fact that the different roles 4 Annual dose refers to the sum of the effective dose arising from external radiation within the period of one year, and the committed effective dose from the intake of radioactive substances within the same period of time (GD 736/2008). In this report “dose” refers to effective dose, and “annual dose” refers to the annual effective dose, unless otherwise explicitly stated.

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in the ecosystem are covered. Moreover, the range of values used for properties of the selected assessment species also covers other species in the same trophic compartment; to a reasonable degree.

2.1.4 Safety indicators

The end results from the main assessment process described above are dose quantities directly used in the quantitative assessment of compliance with regulatory criteria. The conclusions made regarding compliance, based on these dose quantities, are further supported by using other lines of reasoning, here implemented by using a range of safety indicators. Safety indicators are used to support the safety case, by building understanding of, and confidence in, the outcome and conclusions of the safety assessment. In the biosphere assessment, they include both doses derived from robust, stylised scenarios, and other quantities (such as activity concentrations in environmental media) illustrating the behaviour of the biosphere, either as a whole or in relation to individual system components. Here, a distinction is made between safety indicators and complementary safety indicators, where the former includes quantities comparable to regulatory constraints, and the latter all other quantities derived for confidence building.

In the present assessment two safety indicators, in the form of annual doses, are derived, based on indicative stylised well scenarios: one for a drinking water well and one for an agricultural well. These safety indicators are further discussed in chapter 6. An important complementary safety indicator is the calculated activity concentrations in environmental media; these form the basis for the dose calculations. However, the activity concentrations themselves are useful when comparing with e.g. natural levels of radioactivity or the level of contamination in areas affected by accidents involving radioactive materials.

2.2 Selection of radionuclides considered

A novelty in the Posiva approach for the biosphere assessment is the implementation of a graded approach, aimed at reducing the number of radionuclides needed to be considered in Tier 3 (landscape modelling) of the graded approach described in section 3. The approach to selecting sets of nuclides to consider in the different modelling stages in the present assessment can be summarised as follows:

1. A full set of radionuclides is established, containing all radionuclides included in the calculated releases from the geosphere to the biosphere in the repository assessment for a KBS-3V repository (Nykyri et al. 2008), and KBS-3H assessment5 (Smith et al. 2007b). The full set of radionuclides is presented in Table 2-1 and is used in the screening evaluation. This set includes more

5 The main scope for the present assessment concerns the recent KBS-3V RNT-2008 analysis; however, selected geosphere releases from the KBS-3H assessments and previous KBS-3V assessments are included for comparison.

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radionuclides than the calculated releases from the geosphere; the reason for this is that some of the progeny radionuclides are explicitly modelled in the screening evaluation.

2. A subset of the full set of radionuclides is derived as the key set of radionuclides. The key radionuclides are selected based on a screening evaluation of a preliminary set of repository assessment cases. The key set of radionuclides comprises only radionuclides expected to be relevant for long-term safety (either themselves or their progeny radionuclides) and is used in the landscape modelling and when deriving safety indicators and focusing the research. The set is presented in Table 2-2, where it is further divided into:

� top priority radionuclides – either they or their progeny radionuclides are expected to dominate the dose in most biosphere calculation cases, especially the most realistic cases, and

� high priority radionuclides – either they or their progeny radionuclides are expected to give a significant contribution to the doses in some biosphere calculation cases, or even dominate in one or more sensitivity biosphere calculation cases. These are, in turn, further divided into three priority groups; these groups are mainly used to prioritise the site investigation and research resources in respect of radionuclides most relevant to long-term safety.

All actinides and all radionuclides in the naturally occurring decay chains are excluded from the key set of radionuclides since the applied screening evaluation is only valid for the time window where the dose constraints are assumed to apply and for the analysed repository assessment scenario with a single defective canister. In total only 11 radionuclides were identified as having top or high priority. However, this does not mean that it is only these 11 radionuclides, and their progeny radionuclides, that are important for long-term safety. A similar screening evaluation carried out beyond the dose assessment time window, or for different scenarios, would most likely return a different set of key radionuclides; in this case, Ra-226 and Pa-231 would certainly be regarded as key radionuclides (e.g., the rock shear cases in Nykyri et al. 2008).

2.3 Geosphere release rates

In previous reports presenting the landscape model (Broed 2007a,b), a unit release rate from the geosphere of 1 Bq/y for each radionuclide has been used during the whole considered time window. This is the base geosphere release rate used when analysing the landscape model in this report (section 9). In the present assessment Nominal Release Rates (NRRs) are considered in addition to the modelled geosphere release rates. The NRR are assumed to be constant continuous geosphere release rates for the key set of radionuclides. The NRRs are not estimates of the expected magnitude of the geosphere releases. Rather, they aim to capture a fairly realistic relationship between the geosphere release rate maxima for the key radionuclides in order to put the results in the context of well known differences in the magnitude of releases of different radionuclides. The selected NRRs are presented in Table 2-3 and are based on the modelled geosphere release rate maxima in a set of repository assessment cases from RNT-2008 (see Table 8-1). The NRR is used in the present assessment when deriving and comparing doses, such as doses from the stylised well scenarios. The purpose is to

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improve the focus on implications for long-term safety when comparing safety-related quantities such as radiation doses. It is considered a more informative approach than using unit release rates for all radionuclides and deriving (scenario-, ecosystem- or landscape-specific) dose conversion factors.

Table 2-1. Full set of radionuclides included in the screening evaluation in the biosphere assessment, their half-lives applied, their references (see table footnote), and which radioactive progenies are explicitly considered in the transport modelling

Nuclide Half-life (a) [y]

ENSDF citation: Progeny explicitly considered in RNT modelling of the screening evaluation

C-14 5.73E+03 NP A523,1 (1991) Cl-36 3.01E+05 NP A521,1 (1990) Ni-59 7.6E+04 NDS 69,733 (1993) Ni-63 1.00E+02 NDS 64,815 (1991) Se-79 1.13E+06 NDS 70,437 (1993) Sr-90 2.88E+01 NDS 82, 379 (1997) Y-90 Zr-93 1.53E+06 NDS 80, 1 (1997) Nb-93m

Mo-93 4.0E+03 NDS 80, 1 (1997) Nb-93m Nb-94 2.03E+04 NDS 66,1 (1992) Tc-99 2.11E+05 NDS 73,1 (1994)

Pd-107 6.5E+06 NDS 62,709 (1991) Sn-126 ~1E+05 (b) NDS 69,429 (1993) Sb-126

I-129 1.57E+07 NDS 77,631 (1996) Cs-135 2.3E+06 NDS 84, 115 (1998) Cs-137 3.01E+01 NDS 72,355 (1994)

Sm-151 9.0E+01 NDS 80, 263 (1997) Pb-210 2.23E+01 NDS 80, 263 (1997) Po-210 Ra-226 1.60E+03 NDS 77,433 (1996) Pb-210, Po-210 Th-229 7.34E+03 NDS 58,555 (1989) Th-230 7.54E+04 NDS 69,155 (1993) Ra-226, Pb-210, Po-210 Th-232 1.41E+10 NDS 63,139 (1991) Pa-231 3.28E+04 NDS 70,387 (1993) U-233 1.59E+05 NDS 59,263 (1990) Th-229 U-234 2.46E+05 NDS 71,181 (1994) Th-230, Ra-226, Pb-210, Po-210 U-235 7.04E+08 NDS 69,375 (1993) Pa-231 U-236 2.34E+07 NDS 63,183 (1991) Th-232 U-238 4.47E+09 NDS 53,601 (1988) U-234, Th-230, Ra-226, Pb-210, Po-210

Np-237 2.14E+06 NDS 74,461 (1995) U-233, Th-229 Pu-239 2.41E+04 NDS 66,839 (1992) U-235, Pa-231 Pu-240 6.56E+03 NDS 59,947 (1990) U-236, Th-232 Pu-242 3.73E+05 NDS 45,509 (1985) U-238, U-234, Th-230, Ra-226, Pb-210, Po-210

Am-241 4.32E+02 NDS 72,191 (1994) Np-237, U-233, Th-229 Am-243 7.37E+03 NDS 66,897 (1992) Pu-239, U-235, Pa-231 Cm-245 8.50E+03 NDS 67,153 (1992) Am-241, Np-237, U-233, Th-229 Cm-246 4.73E+03 NDS 84, 901 (1998) Pu-242, U-238, U-234, Th-230, Ra-226, Pb-210, Po-210

(a) Values are taken from Chu et al. 1999, which is based on the Evaluated Nuclear Structure File (ENSDF); ENSDF is updated and maintained by the National Nuclear Data Center at Brookhaven National Laboratory, (http://ie.lbl.gov/toi/);

(b) The value of 1E+05 is used in calculations.

17

Table 2-2. Key set of radionuclides in the present biosphere assessment; addressed in the landscape modelling.

Key set of radionuclides

Comment

Top priority C-14 Cl-36 I-129

The nuclides not screened out in the “most realistic” repository calculation cases.

High priority (I) Mo-93(a) Nb-94

Cs-135

The additional nuclides not screened out in the sensitivity repository calculation cases, see also section 8.2.

High priority (II) Ni-59 Se-79

Sr-90(a)

High priority (III) Pd-107 Sn-126(a)

(a) Radionuclides that decay to nuclides which are themselves radioactive. The activity build-ups of the progeny are not explicitly included in the radionuclide transport modelling, but taken into account in the dose calculations, assuming secular and environmental equilibrium with the parent radionuclides.

Table 2-3. Nominal Release Rates (NRR) applied in the present assessment.

Radionuclide NRR [Bq/y]

C-14 100

Cl-36, I-129 10

Ni-59, Se-79, Cs-135 1

Sr-90, Mo-93, Nb-94, Pd-107, Sn-126 0.01

19

3 SCREENING MODELS

This section describes the approach, and the models and data used in the screening evaluation (Tier 1 and Tier 2) within the graded approach to the biosphere assessment (Hjerpe et al. 2010). The quantity of interest in both tiers is the Risk Quotient (RQ), which is the calculated nuclide-specific dose rate divided by pre-selected Screening Dose Rates (SDR). The approach must be sufficiently cautious to ensure high degree of confidence that potential radiological consequences are below relevant regulatory requirements when the RQ is below 1. The SDR itself must be assigned a value below regulatory dose constraints; how much lower depends on the desired degree of conservatism in the screening evaluation. The approach here is to select the SDR low enough to allow Tier 1 and 2 to screen out individual radionuclides for which the calculated RQ is less than or equal to 1. Two SDRs are used throughout the evaluation: 10-5 mSv/y for humans, which is two orders of magnitudes below the lowest regulatory dose constraint, and 10 �Gy/h for the other biota, which is the default generic screening absorbed dose rate in the ERICA Tier 1 (see below) and is also recommended by the PROTECT project (Andersson et al. 2008).

The procedure for the screening evaluation is similar to the one recommended in IAEA (2001) for use in assessing the impact of discharges of radioactive substances to the environment, and the models are in line with recommendation by the ICRP (2000, 2007b) on how to conduct dose assessments. In Tier 1, an extremely cautious approach is taken, in which it is assumed that a hypothetical individual is maximally exposed over one year to the whole integrated release from the geosphere. If the RQ calculated for a specific radionuclide in Tier 1 is greater than 1, then it is necessary to continue to Tier 2 for this radionuclide. Otherwise, no further assessments are required for this radionuclide. In Tier 2, a screening model is applied that has a higher degree of realism than Tier 1 model, but it is still sufficiently cautious for screening purposes. The generic Tier 2 model includes three sub-models: one terrestrial, one aquatic and a well sub-model. The Tier 2 models do not require site-specific parameters and are therefore considered being generic models. If the RQ calculated for a specific radionuclide in Tier 2 is greater than 1, then it is necessary to consider that radionuclide in the site-specific landscape modelling (Tier 3). Otherwise, this radionuclide can be excluded from further assessment. The screening models and used data are documented in detail below; for clarity the applied radiological consequences analysis is described together with the radionuclide transport models.

3.1 Tier 1

Conceptual model The first tier is conceptually illustrated in Figure 3-1. It is designed to ensure extremely pessimistic RQs. Thus, radionuclides screened out at Tier 1 are indisputably insignificant for radiological consequences during the time window of the biosphere assessment. The screening assessment for humans is carried out for each released radionuclide by assessing doses for three exposure situations, where the whole integrated release from the geosphere is:

� totally routed to one person for intake by ingestion, � totally routed to one person for intake by inhalation, and � transferred to the ground surface and exposes one person externally.

20

Figure 3-1. Conceptual model of Tier 1. The environment box is shaded for the ingestion and inhalation pathways to emphasize that no environmental processes are considered – the source is directly inhaled/ingested (DCing, DCinh and DCext are the dose coefficients for ingestion, inhalation and external exposure, respectively).

When assessing the dose due to external exposure, it is pessimistically assumed that the whole released activity is transferred to one square meter of ground surface; then the external dose rate is, unrealistically, derived by multiplying the activity concentration in the contaminated surface by the dose coefficient for a source distributed over an infinite ground surface. Further, to derive the annual dose due to external exposure, it is assumed that the person is exposed to that dose rate during the whole year. The highest annual dose from the three exposure situations considered is then divided by the SDR, for humans, to obtain the RQhumans. Thus, Tier 1 is an extension of the integrated radiotoxicity flux (Becker et al. 2009) that may be used as an indicator of safety.

In the screening assessment for other biota the RQbiota are obtained by dividing estimated radionuclide concentrations in environmental media by radionuclide-specific environmental media concentration limits (EMCL), based on the ERICA integrated approach (section 5.2.1). The most penalising EMCL for different solid media (soil or sediment) and liquid media (freshwater and marine water) are used. These are denoted here as EMCLsolid and EMCLliquid, respectively.

Furthermore, it is pessimistically assumed that the habitat for the worst case reference organism has an activity concentration numerically equal to the total integrated activity, for each radionuclide, in the geosphere release. For example, if the integrated activity is 100 Bq and the worst case reference organism habitat is lake water, an activity concentration of 100 Bq/L in the lake water is assumed. The RQbiota is then determined as the highest ratio of the derived activity concentrations in solid media, or liquid media, and EMCLsolid, or EMCLliquid.

SOURCEIntegrated geosphere release – cautious time window

max

RQ

ENVIRONMENTNo processes considered

ENVIRONMENTPessimistic accumulation

ENVIRONMENTPessimistic accumulation

Humans Other biota

EMCLsolid EMCLliquid

RQbiotaRQhumans

IngestionDCing

InhalationDCinh

ExternalDCext

max SDRhumans

SDRbiota

21

In addition, to be even more certain that the RQs are not underestimated, the integrated geosphere release rates used are not corrected for any radioactive decay, but include build-up of progeny radionuclides. This is an unrealistic assumption that ensures that there is no potential for underestimating the contribution either from the parent or from its progeny. The integration takes places over a time window of 15 000 years (from year 2 020 to year 17 020), which is significantly longer than Posiva’s interpretation of the biosphere assessment time window where regulatory dose constraints apply. Thus, this means that one person, or one individual of the limiting reference organism for other biota, is exposed to everything released from the geosphere up to the year 17 020.

Mathematical model There is no transport processes to model in this tier. The only equations needed are deriving the different ratios discussed above.

3.2 Tier 2

Conceptual model The second tier is illustrated in Figure 3-2, and is designed to ensure defensible and very cautious RQs. In contrast to Tier 1, Tier 2 takes into account decay and in-growth of radionuclides and to some extent dispersion and accumulation processes in the biosphere, and thus the degree of realism is increased. The generic model applied (Figure 3-3) is still sufficiently cautious for the screening purpose. As in Tier 1, maximal exposures to the releases are used for estimating the RQ for each radionuclide. The exposure pathways considered are: inhalation, irradiation from radionuclides in the ground, ingestion of water and food. The following conservative assumptions are made for estimating annual doses to a hypothetical exposed individual:

� the individual stays during the whole year in a terrestrial object that has conservatively estimated high values of radionuclide concentrations in soil and air. This maximises the exposure via inhalation and external irradiation,

� hundred percent of the water consumed by the exposed individual comes from a water body with maximal radionuclide concentrations. This maximises the exposure via water ingestion, and

� hundred percent of the food ingested by the exposed individual has maximal values of the radionuclide concentrations. This maximises the exposure via food ingestion.

Maximum estimates of the radionuclide concentrations in air, soil, food and water are obtained by running the generic model, shown in Figure 3-3, with a constant release rate equal to the maximum geosphere release rate during the same time window as for Tier 1, i.e. for 15 000 years. The generic model includes three sub-models: a terrestrial object, a lake and a well. The following assumptions are made for obtaining conservative estimates of the radionuclide concentrations in environmental media:

� hundred percent of the releases are directed both to the well and the lake,

� hundred percent of the releases will also reach the soil of the terrestrial object, since losses other than runoff are not considered in the lake sub-model,

22

� the crops in the terrestrial objects are irrigated with water either from the lake or the well, whichever has maximum activity concentrations, and

� the areas of the lake and the terrestrial object were assumed equal to the areas needed to support the yearly demand of food by an individual (110 kgC/y) estimated by dividing the yearly food demand by conservative high values of the productivity of food (kgC/m2/y) in aquatic and terrestrial ecosystems. The small values of the assumed areas give small dilution volumes and therefore conservative estimates of the concentrations in water and soil are obtained.

Figure 3-2. Conceptual model of Tier 2 (DCing, DCinh and DCext are the dose coefficients for ingestion, inhalation and external exposure, respectively).

Figure 3-3. Conceptual diagram of the generic model used in Tier 2..

SOURCEGeosphere release rate maxima – cautious time window

IngestionDCing

InhalationDCinh

ExternalDCext

max

ENVIRONMENTGeneric ecosystem-specific radionuclide transport models

(terrestrial, lake, well)

EMCLsolid/liquid

HABITSCautious assumptions

sum

Other biotaHumans

RQ

RQbiota

RQhumansSDRhumans

SDRbiota

Geosphere releases

WELL

LAKE SOIL

Irrigation

RunoffRunoff

23

The radionuclide concentrations in the well water were obtained by dividing the release rates from the geosphere by a low value of the well capacity. Losses of radionuclides by sorption and other processes were conservatively neglected. This gives conservative estimates of the radionuclide concentrations in the well water. For calculation of doses via water ingestion and from irrigation of crops, the maximum value between the radionuclide concentrations in well and lake water were used.

Radionuclide concentrations in lake water were also conservatively estimated, by dividing the radionuclide inventory in the lake water by a conservatively estimated dilution volume (see above). Radionuclide losses from the lake water by processes other than runoff, like sedimentation and volatilization, were conservatively neglected. In estimating losses via runoff upstream water fluxes were neglected. Hence, the only losses considered were those by runoff caused by direct precipitation above the lake; and these were estimated using a low value of the runoff rate (m3/m2/y).

Radionuclide concentrations in soil were also conservatively estimated, by dividing the radionuclide inventory in the terrestrial object by a conservatively estimated soil volume (mass). The radionuclide retention in soil was maximised by: i) using a high value of the solid-liquid distribution coefficients (Kd), ii) neglecting upstream fluxes when estimating losses with runoff, iii) neglecting losses by other processes, such as volatilization of radionuclides and erosion. The radionuclide concentrations in air were obtained by multiplying the soil concentrations by a high value of the dust load (except for C-14 – see below). This assumes that all dust in the inhaled air has the same radionuclide concentration as the contaminated soil and gives therefore an overestimate of the air concentrations.

Radionuclide concentrations in aquatic and terrestrial food were estimated by multiplying the concentrations in lake water and soil, respectively, by high conservative values of the corresponding concentration ratios (CR). In the case of terrestrial food a contribution from irrigation was added. This contribution was estimated with the model described in (Bergström & Barkefors 2004) using conservative values for all model parameters. The highest value of the calculated food concentration, aquatic and terrestrial, was used for obtaining conservative estimates of the food ingestion doses.

In the case of C-14 the radionuclide concentrations were estimated using the specific activity models described in (Avila and Pröhl 2007). It is considered that these models provide conservative estimates, which can be used for screening purposes.

Mathematical model For all radionuclides doses by different exposure pathways were calculated using the equations presented in Avila & Bergström (2006). The input information for the dose calculations are the radionuclide concentrations in water, soil, air and food. For all radionuclides, except for C-14, these concentrations were calculated as described below. In the case of C-14 the concentrations were calculated using the models and equations presented in Avila & Pröhl (2007).

24

Radionuclide concentrations in soil Radionuclide concentrations in soil are used in the model for estimation of doses from external exposure, radionuclide concentrations in air and food. The activity concentrations in soil (Bq/kgdw) were calculated by dividing the radionuclide inventory in soil (Bq) by the area of the terrestrial object (m2), the depth (m) of the soil layer and the bulk density of the soil (kgdw/m3). The radionuclide inventory in soil was calculated by solving numerically the following ordinary differential equation:

��

�� !��!"#��$�%�� &� '(�)�� *�� '(�)�� + *�, � '(�)�, (3-1)

where,

Asoil[i] is the inventory of radionuclide i in soil [Bq], Asoil[j] is the inventory of radionuclide j (parent to radionuclide i) in soil [Bq], RelRate[i] is the release rate of the radionuclide i [Bq/y], �[i] is the decay constant of radionuclide i [1/y], �[i] is the decay constant of radionuclide i [1/y], runoff is the runoff rate [m/y], depthsoil is the soil depth [m], porsoil is the soil porosity [m3/m3], denssoil is the soil bulk density [kgdw/m3], Kd[i] is the distribution coefficient in soil of radionuclide i [m3/kgdw],

For radionuclides with decay chain an equation similar to the above is written for each radionuclide included in the chain. Hence, to obtain the inventory in soil of each of these radionuclides a system of ordinary differential equations has to be solved.

Radionuclide concentrations in air Radionuclide concentrations in air are used in the model for estimation of the doses from inhalation. The radionuclide activity concentrations in air (Bq/m3) were calculated by multiplying the radionuclide activity concentrations in soil (Bq/kgdw) by the dust concentrations, dust load, in air (kgdw/m3).

Radionuclide concentrations in well water Radionuclide concentrations in well water are used for calculation of doses from water ingestion and concentrations in food contaminated by irrigation. The radionuclide activity concentrations in well water (Bq/m3) were calculated by dividing the release rate (Bq/y) by the well capacity (m3/y).

Radionuclide concentrations in lake water Radionuclide concentrations in lake water are used for calculation of doses from water ingestion, concentrations in aquatic food and concentrations in food contaminated by irrigation. The radionuclide activity concentrations in lake water (Bq/m3) were calculated by dividing the radionuclide inventory (Bq) in lake water by the area of the lake (m2) and the depth of the lake (m). The radionuclide inventory in lake water was calculated by solving numerically the following ordinary differential equation:

��-."��

��-." � ')/0�� *�� ')/0�)�� + *�, � ')/0��, , (3-2)

25

where,

Alake[i] is the inventory of radionuclide i in water [Bq], Alake[j] is the inventory of radionuclide j (parent to radionuclide i) in water [Bq], RelRate[i] is the geosphere release rate of radionuclide i [Bq/y], �[i] is the decay constant of radionuclide i [1/y], �[j] is the decay constant of radionuclide j [1/y], runoff is the annual runoff [m3(m2/y)], depthlake is the lake depth [m],

For radionuclides with decay chain an equation similar to the above is written for each radionuclide included in the chain. Hence, to obtain the inventory in lake water of each of these radionuclides a system of ordinary differential equations has to be solved.

Radionuclide concentrations in terrestrial food The radionuclide concentrations in terrestrial food are used for calculations of doses from food ingestion. The radionuclide activity concentrations in terrestrial food are calculated with the following equation:

12345226�� 1234(�)�� 15/77�� + 8�%%�9�:�;��</��=��8>> � 1234��?/�� , (3-3)

where, Concsoil[i] is the activity concentration of radionuclide i in soil [Bq/kgdw], CFagg [i] is the aggregated concentration ratio from soil to food of radionuclide i

[kgdw/kgC], ConcirrWater[i] is the activity concentration of radionuclide i in irrigation water [Bq/m3], Nirr is the number of irrigation events per year [1/y], LAI is the leaf area index of the irrigated vegetation [m2/m2], StoCap is the storage capacity of water in the irrigated vegetation [m3/m2], Kret[i] is the coefficient of retention of radionuclide i on the surface of the irrigated vegetation [-].

The first term in the right side of the above equation corresponds to the concentration in terrestrial food that is directly or indirectly related with radionuclide concentrations in soil. To obtain a conservative estimate of this concentration a high value of the CFagg should be used. The selected CFagg should cover all pathways relating radionuclide concentrations in soil with radionuclide concentration in food: root uptake, contamination of plant surfaces from aerial deposition of resuspended radionuclides, contamination from rain splash, uptake of radionuclides by terrestrial animals via inhalation of resuspended radionuclides and ingestion of contaminated plants. The second term in the above equation corresponds to the concentration in terrestrial food arising from direct contamination of plant surfaces with irrigation water. As mentioned above, it is assumed that the water, from the lake or the well, with the highest concentration is used for irrigation. Other parameters needed to assess the concentration in plants from direct contamination via irrigation are explained in (Bergström & Barkefors 2004), where a full description of the model is provided.

26

Radionuclide concentrations in aquatic food The radionuclide concentrations in aquatic food are used for calculations of doses from food ingestion. The radionuclide activity concentrations in aquatic food are calculated by multiplying the radionuclide activity concentrations in lake water (Bq/m3) by a Bioconcentration Factor (BCF) for aquatic food (m3/kg C). To obtain a conservative value of the concentrations in aquatic food a high value of the BCF should be used taking into account all types of aquatic foods and types of aquatic ecosystems.

3.3 Parameters and applied values

This section presents and discusses the parameters used in the two screening models described above. First, a discussion focused on the most important parameters is included and thereafter the parameter values are presented (Table 3-1 to Table 3-7).

Screening Dose Rate (SDR). Two SDRs are used throughout the evaluation: 10-5 �Sv/y for human, which is two orders of magnitudes below the lowest regulatory dose constraint (STUK 2009, GD 736/2008), and 10 �Gy/h for other biota, which is the default generic screening absorbed dose rate in the ERICA Tier 1 (section 5.2.1) and recommended by the PROTECT project (Andersson et al. 2008).

Dose coefficient. The values are presented in Table 3-1 and Table 3-2. The dose coefficients6 for ingestion and inhalation are based on the values recommended by the International Commission on Radiological Protection (ICRP 1996) for adults7. Dose coefficients used in Tier 1 for external radiation are for radionuclides uniformly distributed on the ground surface, and an effectively infinite lateral extent of the contamination (based on Table III.3 in EPA 1993). Dose coefficients used in Tier 2 for external radiation are for radionuclides uniformly distributed to an infinite depth, and an effectively infinite lateral extent of the contamination, in soil (based on Table III.7 in EPA 1993). The dose coefficients for external radiation were extracted using the software Radiological Toolbox8.

Ingestion of food (intake rate of carbon). A cautious value of intake of carbon via ingestion of food for an adult male is used (155 kgC/y), derived by combining the value of intake of carbon via ingestion of food for an adult male based on Reference Man (110 kgC/y) with the spread in generalised habit data, here mean calorific intake averaged over one year for adult males from Smith & Jones (2003). Table 7 in Smith &

6 A synonym for dose per unit intake, but also used to describe other coefficients linking quantities or concentrations of activity to doses or dose rates, such as the external dose rate at a specified distance above a surface with a deposit of a specified activity per unit area of a radionuclide (IAEA 2007). 7 ICRP (2000) states that it is reasonable to calculate the annual dose averaged over the lifetime of the individuals, which means that it is not necessary to calculate doses to different age groups; this average can be adequately represented by the annual dose to an adult. 8 U.S. Nuclear Regulatory Commission Radiological Toolbox, (version 2.0.0, August 2006) (www.nrc.gov/about-nrc/regulatory/research/radiological-toolbox.html)

27

Jones (2003) indicates that, assuming the calorific intake average over one year is normally distributed, the 95th percentile intake is about 40% above the mean intake.

Ingestion of water (intake rate of water). For water intake rate, a cautious intake rate is derived based on reported 95th percentiles of average annual intakes. Ershow & Cantor (1989) cited by OEHHA (2000) report 95th percentiles annual intakes of total water (all food and beverage sources, as well as drinking water) and tap water (drinking water and tap water added in final home or restaurant preparation of beverages and foods) to be 1.296 and 0.904 m3, respectively. In the screening models, a rounded value of 1.3 m3 per year is used.

Effective mixing capacity. This parameter can be seen as the well capacity, divided by the number of pathways for the radionuclide release to reach the well water, multiplied by the total number of pathways for the release to take through the geosphere. The value of 30,000 m3/y is used in the screening evaluation. This value corresponds to the lower value of the effective dilution volume (same as effective mixing capacity) reported by STUK in their groundwater flow analysis (Kattilakoski & Suolanen 2000), where it is stated: “According to the groundwater flow analyses the effective dilution volume of the well seems to vary from 30,000 m3/y to 460,000 m3/y”. This value is more cautious than the value of 100,000 m3/y used in the well-scenarios.

Environmental media concentration limits (EMCL). EMCLs are derived for each radionuclide-reference organism combination by back-calculating from the screening dose rate (Beresford et al. 2007), here the default generic screening absorbed dose rate of 10 �Gy/h (see above). In the screening evaluation are only the most radiosensitive reference organism (lowest EMCLs) in solid media (soil and sediment) and liquid media (freshwater and marine water) considered; these are extracted from the ERICA Tool (version 1.0 May 2009) and listed in Table 3-3.

Solid-liquid distribution coefficient (Kd). Dissolved radionuclide ions can bind to solid surfaces by a number of processes often classified under the broad term of sorption. Models for the description of radionuclide sorption are mostly based on empirical solid-liquid distribution coefficient (Kd) values. This approach is the simplest sorption model available, where the Kd is the ratio of the concentration of radionuclide sorbed on a specified solid to the radionuclide concentration in a specified liquid phase at equilibrium (IAEA 2009). The distribution coefficients in soils used in Tier 2 (see Table 3-4) are mainly selected to highest of the 95th percentile, for any soil type, of the values reported in IAEA (2009) and Karlsson & Bergström (2002). The 95th percentiles for the distribution coefficient based on IAEA (2009) are calculated with LOGNORM4 (ver. 990920, http://qecc.pnl.gov/LOGNORM4.htm).

Bioconcentration factors. The bioconcentration factor (BCF), also called bioaccumulation factor, is the radionuclide concentration in biota (Bq/kgC) from all exposure pathways (including water, sediment and ingestion/dietary pathways) relative to that of water (Bq/m3). The values used in the current version of the screening model (Tier 2) are presented in Table 3-5, and are the same factors as in the previous biosphere analysis (Broed 2007b). The values are based on bioconcentration factors for lakes presented in Karlsson & Bergström (2002) and converted to units of kgC using the

28

methodology as presented in Avila & Bergström (2006). These values will be updated for the next biosphere assessment.

Aggregated concentration ratios (CFagg). The aggregated concentration ratios9 relate the radionuclide concentrations in the food produced in the terrestrial ecosystem (based on edible carbon contents) to the radionuclide concentrations in the soil. The ecosystem type specific factors are derived as the sum, over all possible food components of the diet, of the concentration ratios weighted with the fractional contribution of the various food products to the annual carbon intake (Avila 2006b). The factors used in Tier 2 are presented in Table 3-6, and are mainly selected as the values presented for forests and mires in Avila (2006b). These values will be updated for the biosphere assessment 2012.

3.4 Modelling platforms and tools

The mathematical models used in the screening evaluations were implemented and run in the software package Ecolego (www.ecolego.facilia.se). Ecolego (Avila et al. 2003) is a tool for model development, which supports all calculation methods that were used in Tier 1 and Tier 2, including the numerical solution of systems of ordinary differential equations (used in Tier 2). Testing of Ecolego has been carried out by performing comparisons with similar tools (Maul et al. 2003). The results of these tests have been satisfactory.

9 CRagg is identical with the quantity aggregated transfer factor (TFagg) used in Haapanen et al (2009), which earlier has been used for example in Avila (2006b), Broed et al. (2007), Bergström et al. (2008).

29

Table 3-1. Dose coefficients for ingestion and inhalation (ICRP 1996).

Nuclide

Ingestion [Sv/Bq]

Inhalation [Sv/Bq]

C-14 5.8E-10 5.8E-09 Cl-36 9.3E-10 7.3E-09 Ni-59 6.3E-11 4.4E-10 Ni-63 1.5E-10 1.3E-09 Se-79 2.9E-09 6.8E-09 Sr-90 2.8E-08 1.6E-07 Y-90 2.7E-09 1.5E-09

Zr-93 1.1E-09 2.5E-08 Mo-93 3.1E-09 2.3E-09

Nb-93m 1.5E-09 1.8E-09 Nb-94 1.7E-09 4.9E-08 Tc-99 6.4E-10 1.3E-08

Pd-107 3.7E-11 5.9E-10 Sn-126 4.7E-09 2.8E-08 Sb-126 2.4E-09 3.2E-09

I-129 1.1E-07 9.8E-09 Cs-135 2.0E-09 8.6E-09 Cs-137 1.3E-08 3.9E-08

Sm-151 9.8E-11 4.0E-09 Pb-210 6.9E-07 5.6E-06 Po-210 1.2E-06 4.3E-06 Ra-226 2.8E-07 9.5E-06 Th-229 4.9E-07 2.4E-04 Th-230 2.1E-07 1.0E-04 Th-232 2.3E-07 1.1E-04 Pa-231 7.1E-07 1.4E-04 U-233 5.1E-08 9.6E-06 U-234 4.9E-08 9.4E-06 U-235 4.7E-08 8.5E-06 U-236 4.7E-08 8.7E-06 U-238 4.5E-08 8.0E-06

Np-237 1.1E-07 5.0E-05 Pu-239 2.5E-07 1.2E-04 Pu-240 2.5E-07 1.2E-04 Pu-242 2.4E-07 1.1E-04

Am-241 2.0E-07 9.6E-05 Am-243 2.0E-07 9.6E-05 Cm-245 2.1E-07 9.9E-05 Cm-246 2.1E-07 9.8E-05

30

Table 3-2. Dose coefficients for external exposure (EPA 1993) due to spatially uniformly distributed radionuclides on the ground surface and to an infinite depth. The coefficients includes only radiations emitted by the indicated radionuclide and do not include consideration of the radiations emitted by radioactive decay products.

Nuclide Ground surface

[(Sv/h)/(Bq/m2)]

Infinite depth

[(Sv/h)/(Bq/m3)]

Nuclide Ground surface

[(Sv/h)/(Bq/m2)]

Infinite depth

[(Sv/h)/(Bq/m3)]

C-14 4.6E-17 2.1E-19 At-218 1.3E-14 9.4E-17 Cl-36 4.0E-14 4.8E-17 Rn-219 1.9E-13 5.5E-15 Ni-59 0 0 Rn-220 1.3E-15 4.1E-17 Ni-63 0 0 Rn-222 1.4E-15 4.2E-17 Se-79 5.9E-17 3.0E-19 Fr-221 1.0E-13 2.7E-15

Sr-90 5.9E-15 1.2E-17 Fr-223 2.8E-13 3.5E-15 Y-90 4.0E-13 7.7E-16 Ra-223 4.4E-13 1.1E-14

Zr-93 0 0 Ra-224 3.3E-14 9.1E-16 Mo-93 1.4E-14 8.0E-18 Ra-225 3.9E-14 1.7E-16

Nb-93m 2.5E-15 1.4E-18 Ra-226 2.2E-14 5.6E-16

Nb-94 5.4E-12 1.8E-13 Ra-228 0 0 Tc-99 2.3E-16 2.1E-18 Ac-225 5.3E-14 1.1E-15

Pd-107 0 0 Ac-227 5.1E-16 8.6E-18 Sn-126 1.7E-13 2.5E-15 Ac-228 3.4E-12 1.1E-13

Sb-126m 5.6E-12 1.7E-13 Th-227 3.5E-13 9.3E-15

Sb-126 9.8E-12 3.1E-13 Th-228 7.7E-15 1.4E-16 I-129 7.0E-14 1.8E-16 Th-229 2.8E-13 5.6E-15

Cs-135 9.7E-17 6.2E-19 Th-230 2.3E-15 2.1E-17 Cs-137 1.1E-14 1.6E-17 Th-231 5.6E-14 6.2E-16

Ba-137m 2.1E-12 6.5E-14 Th-232 1.6E-15 8.8E-18

Sm-151 1.3E-17 1.3E-20 Th-234 2.7E-14 4.1E-16 Tl-207 2.0E-13 4.4E-16 Pa-231 1.4E-13 3.4E-15 Tl-208 1.1E-11 4.2E-13 Pa-233 6.7E-13 1.8E-14 Tl-209 6.9E-12 2.4E-13 Pa-234 6.5E-12 2.1E-13 Pb-209 1.1E-14 1.5E-17 Pa-234m 3.9E-13 1.9E-15

Pb-210 7.7E-15 3.8E-17 Np-237 9.1E-14 1.3E-15 Pb-211 3.4E-13 5.6E-15 Np-239 5.5E-13 1.3E-14 Pb-212 4.9E-13 1.2E-14 Pu-239 1.0E-15 5.1E-18 Pb-214 8.6E-13 2.4E-14 Pu-240 2.2E-15 2.2E-18 Bi-210 1.3E-13 1.1E-16 Pu-241 6.2E-18 1.0E-19

Bi-211 1.6E-13 4.6E-15 Pu-242 1.8E-15 1.9E-18 Bi-212 8.1E-13 2.1E-14 Am-241 8.4E-14 7.2E-16 Bi-213 5.2E-12 1.8E-13 Am-243 1.7E-13 2.4E-15 Bi-214 5.2E-12 1.8E-13 U-233 2.2E-15 2.4E-17 Po-210 2.9E-17 9.5E-19 U-234 2.1E-15 6.6E-18

Po-211 2.7E-14 8.6E-16 U-235 5.0E-13 1.3E-14 Po-212 0 0 U-236 1.8E-15 3.4E-18 Po-214 2.9E-16 9.3E-18 U-237 4.4E-13 9.3E-15 Po-215 6.0E-16 1.8E-17 U-238 1.5E-15 1.5E-18 Po-216 5.8E-17 1.9E-18 Cm-245 2.9E-13 5.9E-15

Po-218 3.1E-17 1.0E-18 Cm-246 2.1E-15 1.6E-18 At-217 1.1E-15 3.2E-17

31

Table 3-3. EMCLs for the most radiosensitive reference organisms.

Nuclide Liquid media [Bq/L]

Solid media [Bq/kg]

C-14 6.5E+00 1.2E+01 Cl-36 1.1E+02 1.3E+01 Ni-59 5.7E+01 1.8E+05 Ni-63 4.2E+01 1.3E+05 Se-79 1.2E+01 5.0E+03 Sr-90 3.5E+00 7.1E+00

Nb-94 4.4E-03 1.1E+04 Tc-99 2.0E+00 2.9E+01 I-129 1.3E+01 1.8E+02

Cs-135 5.5E+00 1.9E+04 Cs-137 5.1E-02 3.1E+03 Pb-210 5.8E-02 1.2E+03 Ra-226 1.4E-02 7.2E+00 Th-230 1.8E-04 8.0E+01 Th-232 2.1E-04 9.4E+01 Np-237 3.0E-03 5.2E-03 Pu-239 9.3E-04 1.4E+01 Pu-240 9.3E-04 1.4E+01

Am-241 5.4E-04 5.1E+01 U-234 4.2E-02 3.2E-01 U-235 4.5E-02 3.5E-01 U-238 4.9E-02 3.7E-01

Cm-244 4.4E-04 3.9E+00

32

Tabl

e 3-

4. S

olid

-liqu

id d

istr

ibut

ion

coef

ficie

nts

(Kd)

in

soil

used

in

Tier

2.

Valu

es c

orre

spon

d to

the

95t

h pe

rcen

tile

of t

he a

ssum

ed

logt

rian

gula

r (L

ogT)

, tri

angu

lar

(T)

or lo

gnor

mal

(Lo

gN)

prob

abili

ty d

ensi

ty fu

nctio

n (P

DF)

. The

ref

eren

ces

(Ref

.) ar

e: (

1) K

arls

son

&

Berg

strö

m (2

002)

, (2)

IAEA

(200

9) a

nd (3

) She

ppar

d &

Thi

baul

t (19

90).

Ele

men

tV

alue

[m

3 /kg d

w]

PDF

Min

M

ode

Max

R

ef.

Nuc

lide

Val

ue

[m3 /k

g dw]

PDF(a

) M

in

Mod

e M

ax

Ref

C3.

4E-0

1 Lo

gT

7.0E

-03

7.0E

-02

7.0E

-01

(1)

I 1.

4E-0

1 Lo

gT

3.0E

-03

3.0E

-02

3.0E

-01

(1)

Cl

4.8E

-02

LogT

1.

0E-0

3 1.

0E-0

21.

0E-0

1 (1

) C

s 2.

1E+0

1 Lo

gN

3.

0E-0

1

(2)

Ni

3.9E

+00

LogT

2.

0E-0

1 1.

0E+0

07.

0E+0

0(1

) Sm

1.

4E+0

1 Lo

gT

3.0E

-01

3.0E

+00

3.0E

+01

(1)

Se9.

7E+0

0 Lo

gT

2.0E

-01

2.0E

+00

2.0E

+01

(1)

Pb

4.3E

+01

LogT

8.

0E+0

02.

0E+0

16.

0E+0

1(1

) Sr

4.7E

+01

T 4.

0E-0

3 2.

0E-0

16.

0E+0

1(1

) Po

2.

0E+0

2 Lo

gT

7.0E

-01

7.0E

+00

7.0E

+02

(1)

Y2.

6E+0

0

(3)

Ra

4.1E

+01

LogN

4.6E

-03

(2

) Zr

3.4E

+01

LogT

7.

0E-0

1 7.

0E+0

07.

0E+0

1(1

) T

h 4.

3E+0

2 Lo

gT

9.0E

+00

9.0E

+01

9.0E

+02

(1)

Mo

1.4E

-01

LogT

3.

0E-0

3 3.

0E-0

23.

0E-0

1 (1

) Pa

3.

4E+0

1 Lo

gT

7.0E

-01

7.0E

+00

7.0E

+01

(1)

Nb

9.7E

+00

LogT

2.

0E-0

1 2.

0E+0

02.

0E+0

1(1

) N

p 2.

2E+0

0 Lo

gT

5.0E

-01

1.0E

+00

3.0E

+00

(1)

Tc

1.0E

-02

LogN

4.9E

-04

(2

) Pu

9.

7E+0

0 Lo

gT

2.0E

-01

2.0E

+00

2.0E

+01

(1)

Pd3.

4E+0

0 Lo

gT

7.0E

-02

7.0E

-01

7.0E

+00

(1)

Am

4.

8E+0

2 Lo

gT

1.0E

+01

1.0E

+02

1.0E

+03

(1)

Sn7.

5E+0

0 Lo

gT

1.0E

+00

5.0E

+00

1.0E

+01

(1)

U

4.6E

+00

LogN

9.7E

-03

(2

) Sb

5.5E

-01

(3

) C

m

4.8E

+01

LogT

1.

0E+0

01.

0E+0

11.

0E+0

2(1

)

33

Tabl

e 3-

5. B

ioco

ncen

trat

ion

fact

ors f

or la

kes u

sed

in th

e aq

uatic

sub-

mod

el in

Tie

r 2.

Ele

men

t B

CF

[B

q/kg

C p

er B

q/m

3 ] R

efer

ence

N

uclid

e B

CF

[Bq/

kgC

per

Bq/

m3 ]

Ref

eren

ce

C

-(a)

- I

1.5E

+00

Bro

ed (2

007b

) C

l 3.

6E-0

1 B

roed

(200

7b)

Cs

7.3E

+01

Bro

ed (2

007b

) N

i 7.

3E-0

1 B

roed

(200

7b)

Sm

2.2E

-01

Bro

ed (2

007b

) Se

1.

5E+0

1 B

roed

(200

7b)

Pb

2.2E

+00

Bro

ed (2

007b

) Sr

4.

4E-0

1 B

roed

(200

7b)

Po

3.6E

-01

Bro

ed (2

007b

) Y

3.

0E+0

1 IA

EA (1

994)

R

a 3.

6E-0

1 B

roed

(200

7b)

Zr

1.5E

+00

Bro

ed (2

007b

) T

h 7.

3E-0

1 B

roed

(200

7b)

Mo

1.0E

+01

Bro

ed (2

007b

) Pa

7.

3E-0

2 B

roed

(200

7b)

Nb

2.2E

+00

Bro

ed (2

007b

) N

p 3.

6E-0

1 B

roed

(200

7b)

Tc

1.5E

-01

Bro

ed (2

007b

) Pu

2.

2E-0

1 B

roed

(200

7b)

Pd

7.3E

-01

Bro

ed (2

007b

) A

m

2.2E

-01

Bro

ed (2

007b

) Sn

2.

2E+0

1 B

roed

(200

7b)

U

7.3E

-02

Bro

ed (2

007b

) Sb

1.

0E+0

2 IA

EA (1

994)

C

m

2.2E

-01

Bro

ed (2

007b

) (a

) Cal

cula

ted

diff

eren

tly w

ith th

e C

-spe

cific

mod

el w

hich

doe

s not

relie

s on

BC

Fs

34

Tabl

e 3-

6. A

ggre

gate

d co

ncen

trat

ion

ratio

s (C

F agg

) for

fore

st u

sed

in te

rres

trial

sub-

mod

el in

Tie

r 2.

Ele

men

t C

Fag

g [B

q/kg

C p

er B

q/kg

dw]

Ref

eren

ce

Ele

men

t C

Fag

g [B

q/kg

C p

er B

q/kg

dw]

Ref

eren

ce

C

-(a)

Avi

la (2

006b

) I

4.0E

+00

Avi

la (2

006b

) C

l 3.

6E+0

1 A

vila

(200

6b)

Cs

5.5E

+01

Avi

la (2

006b

) N

i 1.

8E+0

0 A

vila

(200

6b)

Sm

1.0E

-01

Avi

la (2

006b

) Se

3.

1E+0

0 A

vila

(200

6b)

Pb

1.0E

-02

Avi

la (2

006b

) Sr

2.

4E+0

1 A

vila

(200

6b)

Po

5.2E

-01

Avi

la (2

006b

) Y

-(b

) -

Ra

7.4E

+00

Avi

la (2

006b

) Zr

1.

0E-0

2 A

vila

(200

6b)

Th

4.4E

-03

Avi

la (2

006b

) M

o 7.

0E+0

0 B

roed

(200

6b)

Pa

3.1E

-02

Avi

la (2

006b

) N

b 5.

2E-0

2 A

vila

(200

6b)

Np

1.9E

-02

Avi

la (2

006b

) T

c 6.

5E+0

0 A

vila

(200

6b)

Pu

1.0E

-04

Avi

la (2

006b

) Pd

2.

1E+0

0 A

vila

(200

6b)

Am

1.

1E-0

4 A

vila

(200

6b)

Sn

1.0E

+00

Avi

la (2

006b

) U

3.

1E-0

2 A

vila

(200

6b)

Sb

-(b)

- C

m

1.0E

-02

Avi

la (2

006b

)

(a) C

alcu

late

d di

ffer

ently

with

the

C-s

peci

fic m

odel

whi

ch d

oes n

ot re

lies o

n C

F agg

(b

) No

data

foun

d; w

ill b

e ad

dres

sed

in fu

ture

ass

essm

ent.

35

Tabl

e 3-

7. V

alue

s of r

adio

nucl

ide-

inde

pend

ent p

aram

eter

s use

d in

Tie

r 1 a

nd T

ier 2

of t

he sc

reen

ing

eval

uatio

n.

Para

met

er

A

pplie

d in

V

alue

U

nit

Ref

eren

ce

Com

men

t SD

Rhu

man

s Ti

er 1

, 2

1E-5

m

Sv/y

B

ased

on

STU

K (2

009)

Se

e m

ain

text

SD

Rbi

ota

Tier

1, 2

10

�G

y/h

And

erss

on e

t al.

2008

Se

e m

ain

text

T

ime

win

dow

Ti

er 1

, 2

2 02

0 –

17 0

20

y A

sses

smen

t dec

isio

n Se

lect

ed to

5 0

00 y

bey

ond

the

perio

d w

hen

dose

con

stra

ints

are

ass

umed

to a

pply

E

xpos

ureT

ime

Tier

1, 2

8,

760

h/y

Inta

ke r

ate

of w

ater

Ti

er 2

(wel

l, la

ke)

1.3

m3 /y

Er

show

& C

anto

r (19

89)

cite

d by

OEH

HA

(200

0)

See

mai

n te

xt

Inta

ke r

ate

of c

arbo

n Ti

er 2

(ter

rest

rial,

lake

) 15

5 kg

C/y

IC

RP

(200

2),

Smith

& Jo

nes (

2003

) Se

e m

ain

text

Inta

ke r

ate

of a

ir

(by

brea

thin

g)

Tier

2 (t

erre

stria

l) 1.

13

m3 /h

Sm

ith &

Jone

s (20

03)

Ave

rage

dai

ly a

ir br

eath

ed b

y an

adu

lt he

avy

wor

king

mal

e (2

h he

avy

exer

cise

, 10h

lig

ht

exer

cise

, 4h

rest

, sitt

ing,

8h

slee

p)

Mix

ing

capa

city

(e

ffec

tive)

Ti

er 2

(wel

l) 30

,000

m

3 /y

Ass

essm

ent d

ecis

ion

See

mai

n te

xt

Run

off

Tier

2

0.2

m3 /m

2 /y

Kar

lsso

n &

Ber

gströ

m

(200

0)

The

diff

eren

ce b

etw

een

prec

ipita

tion

and

evap

orat

ion

from

the

lake

cat

chm

ent a

rea

R

elea

se fr

actio

n (e

ffec

tive)

Ti

er 2

(ter

rest

rial,

lake

, C-1

4 on

ly)

1 -

Cau

tious

ass

umpt

ion

Frac

tion

of th

e C

-14

rele

ase

to th

e m

ixin

g la

yer

that

occ

urs i

n th

e pe

riod

whe

n ph

otos

ynth

esis

ca

n ta

ke p

lace

T

hick

ness

Ti

er 2

(lak

e)

0.5

M

Cau

tious

ass

umpt

ion

Dep

th o

f the

lake

Pr

oduc

tivity

Ti

er 2

(lak

e)

2.4E

-3

kgC

/m2 /y

A

vila

(200

6b)

Prod

uctio

n ra

te o

f edi

bles

from

the

lake

Pr

oduc

tivity

Ti

er 2

(ter

rest

rial)

0.22

kg

C /m

2 /y

Tabl

e 4-

1 in

Avi

la

(200

6b)

Prod

uctio

n ra

te o

f edi

bles

from

agr

icul

tura

l lan

d

Net

pri

mar

y pr

oduc

tion

Ti

er 2

(lak

e, C

-14

only

) 0.

185

kgC

/m2 /y

Li

ndbo

rg (2

005)

N

et p

rimar

y pr

oduc

tion

of o

rgan

ic c

ompo

unds

fr

om c

arbo

n di

oxid

e in

the

lake

N

et p

rim

ary

prod

uctio

n

Tier

2 (t

erre

stria

l, C

-14

only

) 0.

120

kgC

/m2 /y

Li

ndbo

rg (2

005)

N

et p

rimar

y pr

oduc

tion

of o

rgan

ic c

ompo

unds

fr

om c

arbo

n di

oxid

e in

agr

icul

tura

l lan

d

36

Tabl

e 3-

7 (c

ont’d

). Va

lues

of r

adio

nucl

ide-

inde

pend

ent p

aram

eter

s use

d in

tier

1 a

nd 2

of t

he sc

reen

ing

eval

uatio

n.

Para

met

er

A

pplie

d in

V

alue

U

nit

Ref

eren

ce

Com

men

t C

once

ntra

tion

(DIC

) Ti

er 2

(lak

e, C

-14

only

) 22

gC

/m3

Lind

borg

(200

5)

Con

cent

ratio

n of

dis

solv

ed in

orga

nic

carb

on in

the

lake

Thi

ckne

ss o

f soi

l lay

er

Tier

2 (t

erre

stria

l) 0.

3 M

Ta

ble

3-14

in B

ergs

tröm

et

al.

(199

9)

The

soil

laye

r con

tain

ing

the

soil

root

ing

zone

Den

sity

of s

oil

Tier

2 (t

erre

stria

l) 11

80

kgdw

/m3

Tabl

e 3-

3 in

Avi

la 2

006a

So

il bu

lk d

ensi

ty in

the

soil

root

ing

zone

Po

rosit

y of

soil

Tier

2 (t

erre

stria

l) 0.

2 m

3 /m3

Tabl

e 3-

3 in

Avi

la 2

006a

Th

e vo

lum

etric

wat

er c

onte

nt in

soil

Stor

age

capa

city

Ti

er 2

(ter

rest

rial)

4.0E

-4

m3 /m

2 Ta

ble

2-6

in B

ergs

tröm

et

al. (

2006

) U

pper

val

ue in

the

rang

e.

Con

cent

ratio

n Ti

er 2

(ter

rest

rial)

5.0E

-8

kgdw

/m3

IAEA

(200

1)

Con

cent

ratio

n of

dus

t in

air

Lea

f are

a in

dex

Tier

2 (t

erre

stria

l) 6

m2 /m

2 B

ergs

tröm

et a

l. 20

06

(Tab

le 2

-6)

Upp

er v

alue

in th

e ra

nge

of le

af a

rea

indi

ces f

or

vege

tabl

es.

Irri

gatio

n ev

ents

Ti

er 2

(ter

rest

rial)

5 L/

y B

ergs

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37

4 THE LANDSCAPE MODEL

The predicted conditions for the surface environment at year 2 020, upon the scheduled emplacement of the first canister, define, in this context, the initial state of the biosphere. This is the starting point for the set-up of the landscape model. The biosphere objects are delineated based on the forecasts from the TESM modelling. A biosphere object represents a continuous and sufficiently homogeneous sub-area within the modelled area that can potentially, directly or indirectly, receive radionuclides released from the repository. Each biosphere object is described by one, or more, ecosystem types and one set of data, and is associated with a corresponding radionuclide transport model. The connections between the objects are derived from terrain forecasts for the period from the initial state to the end of the assumed time window when regulatory dose constraints apply, thus at year 12 020. When delineating the biosphere objects, it is sufficient to include areas of the surface environment that will potentially be contaminated, either by direct release of radionuclides from the geosphere or by horizontal transport of radionuclides within the surface environment during the biosphere assessment time window. The spatial and temporal distribution of possible radionuclide release paths to the biosphere, the release pattern, is needed in order to identify which biosphere objects to include in the landscape model. The combination of the connected biosphere objects and the release pattern is the landscape model. The landscape model is thus a state-of-the- art, time-dependent and site-specific radionuclide transport model. The initial state for the landscape model and its development is defined as part of the landscape model set-up process. In order to present the models and data in a clear manner, the landscape model is here treated as three separate components:

� the landscape model set up, � the simplified release patterns, and � the biosphere objects modules.

The models, assumptions and data for the landscape model set-up (delineation and development of biosphere objects) are addressed in section 4.1, for the simplified release patterns in, section 4.2 and for the biosphere object modules in section 4.3. Then, in section 4.4, the resulting landscape model applied in the biosphere assessment 2009 is presented. Finally, in 4.5 is the modelling platform and tools documented. In addition, more details on the biosphere object modules and on the applied landscape model are found in Appendix A and B, respectively. In the biosphere assessment of 2009, the currently available site data is used to the fullest possible extent in the landscape model. Furthermore, significant amounts of site data are conveyed to the landscape modelling through the surface hydrology model and the terrain and ecosystem development models. Thus, a major part of the parameter data underpinning the landscape model is presented in detail in other reports related to the 2009 biosphere assessment, such in Haapanen et al. (2009), Ikonen et al. (2010a), Helin et al. (2010), Ikonen et al. (2010b) and Karvonen (2009c). However, some key data will have to be taken from the literature, for example the solid-liquid distribution coefficients (Kd) in soils and sediments and most of the concentration ratios to biota. These data are currently under acquisition and site-specific values will be provided for the next round of assessments. Parameter data origin from other BSA-2009 reports (especially site and regional data) are either summarised in the present report or given as an appropriate reference. Table 4-1 gives an overview of the key data input for the landscape model.

38

Table 4-1. Overview of key input data sets to the landscape modelling and the sources.

Landscape model component

Data set Source

Landscape model set-up

Spatio-temporal distribution of geosphere release locations in the biosphere

Groundwater flow simulations (Löfman & Poteri 2008)

Ecosystem types and geographical properties of each individual BSOs

UNTAMO (Ikonen et al. 2010b)

Water flow rates in rivers and lakes, thickness of compartments (soils, sediments)


Horizontal water flows (compartment specific) between individual BSOs

SNSH (Karvonen 2009c)

Vertical water flows between compartments within individual BSOs

SNSH (Karvonen 2009c)

Simplified release patterns

Spatio-temporal distribution of geosphere release locations in the biosphere

Groundwater flow simulations (Löfman & Poteri 2008)

Spatial properties (areas) of individual BSOs


BSO modules

Ecosystem-specific data (for example biomasses, production rates, loss rates, densities, irrigation habits)

Haapanen et al. (2009) and Ikonen et al. (2010a)

Element-specific data (concentration ratios, soil-liquid distribution coefficients)

Helin et al. (2010)

Parameter data for which site & regional data do not exist

Various literature sources

Figure 4-1. The allowed paths for ecosystem evolution in the landscape model. The dashed lines represent alternative paths. The directions of the paths may also be reversed, for example in the case of sea level rise.

lake (or river)coast wetland

forest

cropland

open sea

39

4.1 Landscape model set-up

Here are the applied types of ecosystems, assumptions regarding their development with time and assumptions when delineating and connecting the biosphere objects discussed.

Ecosystem types and their development In the present assessment, the following six main ecosystem types are included: lake, coast, river, forest, wetland and cropland. Furthermore, a wetland can include two different vegetation types: mire and reed. In addition to these ecosystem types, a separate system was implemented in the landscape model for the local atmosphere of all terrestrial ecosystems (for use in the plant-air gaseous exchange). The transitions between ecosystem types due to the evolution of the biosphere (e.g. when new terrestrial ecosystems are formed from the sea bottom due to land uplift) are also regulated in the landscape modelling, the allowed paths are presented in Figure 4-1.

Delineation of biosphere objects A key issue when delineating the biosphere objects is to assure that links can be established between the locations of possible radionuclide release points into the biosphere and to the biosphere objects in the landscape model (see also section 4.2). The spatial distribution of possible radionuclide release points into the biosphere is based on the findings from the groundwater flow simulations in the bedrock, as are summarised in the RNT-2008 report (Nykyri et al. 2008). The time window for the biosphere assessment is much less than the time window for radionuclide transport in the repository system presented in Nykyri et al. (2008). For this reason, radionuclide release paths with advective travel times of up to 15 000 years are included, and those with longer travel times are excluded from further consideration, as releases from the repository cannot reach the biosphere within the dose assessment time window along such paths.

A GIS toolbox customised for Posiva for TESM, named UNTAMO, has been developed (Ikonen et al. 2010b) and was used to delineate ecosystem types of biosphere objects for years 2 020 to 12 020 (in 500 year time steps). Nine different types of ecosystems were delineated for each time step; the six main types: coast, lakes (open water part), mires, forests, croplands and rivers. In addition, lakes include, in addition to the open water part, reed areas, parts dried out, and parts formed into mires. Certain rules for the delineated objects were also applied in order to not underestimate doses to the most exposed people. The most important rules are summarised as follow:

� an upper limit of 1.5 ha for the area of an individual forest object was applied in order to avoid excess numerical dispersion of radionuclides arising from treating individual objects as laterally homogeneous,

� an upper limit of 40 ha for the area of an individual irrigated cropland object was applied in order to avoid excess numerical dispersion of radionuclides arising from treating individual objects as laterally homogeneous,

� all terrestrial clay and gyttja areas, with a thickness of at least 50 cm, are delineated as croplands,

� irrigation water for croplands was taken from nearest river object, and

40

� small brooks were omitted from the model (e.g. forest objects are connected straight to the nearest water body)

The delineation procedure results in a list of identified biosphere objects, their ecosystem types and geographical properties (location and area), and how these properties change during the biosphere assessment time window. The landscape model set-up also includes the information regarding the radionuclide transport between biosphere objects and within biosphere objects. The transport between objects is based on the horizontal water flow results from the surface and near surface hydrological modelling (Karvonen 2009c). The transport within objects includes both object-specific results from TESM and data common for all biosphere objects of a certain ecosystem type; the latter data is not considered to be a part of the landscape model configuration and is addressed in section 4.3. The key object-specific (time-dependent) data from TESM and SNSHM are as follows (see also Appendix A):

� object areas, � thicknesses for each model compartment, � water content for soil compartments, � water fluxes between compartments, and � water fluxes to downstream object.

4.2 Simplified release patterns

The modelling of transport pathways for radionuclides released from the canister, through the near-field and geosphere was carried out separately from the transport modelling in the biosphere in the interim safety case. Although multiple flow pathways are identified in deep groundwater flow modelling, the transport of radionuclides through the geosphere was evaluated with a simplified model that considers only a single representative and non-dispersive pathway (Nykyri et al. 2008). Hence, the full information on spatial and temporal distribution of releases from the geosphere is not provided to the biosphere modelling in the present assessment; this will be improved by 2012. The connection between the geosphere transport modelling and the landscape model is achieved by deriving simplified release patterns (stylised representations of the radionuclide transport paths for the geosphere releases to the biosphere) based on surface and near-surface hydrology modelling and deep groundwater flow modelling. The derivation of the simplified release patterns is described below. 4.2.1 Radionuclide release paths

When delineating the biosphere objects in the landscape model, the findings from the groundwater flow simulations in the bedrock from six repository panels (Nykyri et al. 2008) were used. When deriving the simplified release pattern, only results for three repository panels, which are those needed for spent fuel from the units currently in operation or under construction, were applied (the 2006 repository layout, Kirkkomäki 2007). The present radionuclide transport modelling addressed results from Panels 1, 2 and 5 in Nykyri et al. (2008); denoted as Panels A, B and C in this report, respectively.

The starting points for each individual flow path from the repository (origin points) are distributed in the repository panels (Figure 4-2). The results from all realisations of the

41

groundwater flow simulations in the bedrock (Löfman & Poteri 2008) are included in relation to the three panels considered. Probability distributions describing the spatial distribution of local hydro-geological zones and background water-conducting features (discrete fracture network modelling, DFN) together with deterministic major hydro-geological zones were applied in the flow path simulations described above (Nykyri et al. 2008). The simulations were repeated for 21 realizations of stochastic local hydro-geological zones and background water-conducting features, in order to capture the variability between the realisations. The distribution of advective travel times from the origin point to the biosphere is given in the dataset for each path. For example, the advective travel times from Panel C to the biosphere on the southern side range from about 10 to 27 000 years, and on the northern side from about 10 to 6.4 million years. Figure 4-3 shows the spatial and temporal distribution of release paths into the biosphere with travel times up to 15 000 years. For the biosphere assessment, release paths with advective travel times beyond 15 000 years are excluded, as they cannot mediate any releases to the biosphere within the dose assessment time window.

The surface and near-surface hydrology model provides the advective travel times in the overburden (varying between 0 and 1 340 years). In principal, this could be taken into account when formulating the simplified release pattern to be used in the landscape modelling. However, the travel time in the overburden is currently not considered, firstly since the overburden model is still at the development stage and the uncertainties in the soil and sediment layer thicknesses are large and secondly, since it is cautious not to include any additional travelling time.

In geosphere transport modelling, which calculates the radionuclide release rates to be used in the biosphere assessment, the complex flow path system is represented as a single, representative and non-dispersive transport path, characterised by an integrated transport resistance (Nykyri et al. 2008, citing Poteri 2007). This simplification means that the flow paths are, in effect, treated as identical for geosphere transport modelling purposes. Thus, a full coupling between the calculated time-dependent activity release rates from a failed canister and the temporal and spatial distribution of possible radionuclide transport paths is not currently feasible. In reality, radionuclides released from a failed canister at a certain time will subsequently be dispersed in space and time, and will thus arrive at different locations in the biosphere and at different times.

Here, a stylised coupling between the modelled release rates and the deep groundwater modelling has been established to facilitate the connection between the geosphere release points and the landscape model. The only temporal aspect considered is the advective travel time for the characteristic single pathway used in the geosphere transport modelling. Hence, the stylised coupling described below results in releases from the canister occurring at a certain time ending up at different locations in the biosphere, but at the same time. The derived spatial and temporal distribution of transport paths (Figure 4-3) shows that the distribution of possible release paths to the biosphere does not vary greatly with time in the time window for biosphere assessment. Even though the number of possible pathways increases with time, the spatial pattern remains fairly constant. To simplify the implementation of a spatially distributed geosphere release, the pattern of release paths for each repository panel is treated as constant in time, and includes all pathways with travel times less than 15 000 years. Further, all pathways are assumed to have the same probability.

42

Figure 4-2. Origin points for flow paths from groundwater flow simulations presented in Nykyri et al. (2008), overlaid on the 2006 repository layout (Kirkkomäki 2007). The origin points from different model realisations are shown in the figure. Map layout by Ari Ikonen / Posiva Oy.

Figure 4-3. Release into the biosphere from Panel C for six intervals of the advective transport time (to the biosphere) for the first 15 000 years, based on groundwater flow simulations presented in Nykyri et al. 2008 and on the 2006 repository layout (Kirkkomäki 2007). Map layout by Ari Ikonen / Posiva Oy.

43

Connecting the releases paths to the landscape model The next step is to link the locations of the release points with specific biosphere objects in the landscape model. This is done based on the type of ecosystem in the landscape model coinciding with the location of the release point into the biosphere10. The results are summarised in Table 4-2 to Table 4-4 (see section 4.4 for more details on the names of the biosphere objects). Basically, the release goes to the object coinciding with the release point. In addition, when connecting a release point with a biosphere object (the compartment structures are addressed in section 4.3), the following assumptions are made:

� If the release point is at the perimeter of a lake, river or sea area, it goes to the surrounding terrestrial object.

� If the release point is labeled in the release simulations as being within a root zone, it is always taken to go to the respective terrestrial object even though it might coincide with a stream.

� If the release point is farther from a river but is labeled as being in the water body, e.g. in the middle of a cropland, this indicates a release to a predicted ditch or to subsurface drainage and the release is directed to the terrestrial object for conservatism.

� All release points to reed areas of lakes go into the water body, since the reed area does not provide food for humans (except waterfowl which is included in the open lake area in the dose assessment model).

� When a part of the object receiving releases is under the sea, all those releases are directed to the part already emerged from the sea.

10 Note that iteration is necessary between the modelling of the release paths in the overburden and delineation of the biosphere objects; as all potential release areas need to be captured by a biosphere object.

44

Table 4-2. Release-point distribution to different objects based on the surface and near-surface hydrology results for the primary repository panel A. The total number of points is less than in the original data due to exclusion of the paths with advective travel times longer than 15 000 years.

Side Type Biosphere object Number of points

Fraction [%]

North Cropland Kaunissaari W 2 0.03 Cropland Koivisto 4 0.06 Cropland Mäntykarinmaa 1 0.01 Forest/lake Valkiakarinjärvi (a) 10 0.14 Lake Mäntykarinjärvi 206 2.91 Lake Susijärvi 46 0.65 Lake Tankarienjärvi 959 13.57 Total 1228 17.37 South Cropland Koskelonpelto 10 0.14 Cropland Lepänmaa 3 0.04 Cropland Liiklanpellonjoki 10 0.14 Cropland Liiklanpelto 532 7.53 Forest Flutanmetsä E 4 0.06 Forest Flutanmetsä W 17 0.24 Lake Liiklanjärvi 5221 73.87 River * Tuomonjoki 15 0.21 River * Tuomonjoki 2 (b) 28 0.40 Total 5840 82.63 Objects marked in bold text 6918 97.88 Croplands, total 562 7.9 Forests, total 31 0.4 Lakes, total 6442 91.0 Rivers, total 43 0.6

(a) A lake is formed at a later stage as an extension of the river channel on forest land due to land tilting and formation of a damming threshold. The release is directed to the river water body (attracting the local flow paths) during the earlier stage.

(b) River forms at a later stage as the river channel shifts position due to land tilting, before this event the releases are directed to the cropland.

45

Table 4-3. Release-point distribution to different objects based on the surface and near-surface hydrology results for the primary repository panel B. The total number of points is less than in the original data due to exclusion of the paths with advective travel times longer than 15 000 years.


Fraction [%]

North Cropland Koivisto 1 0.01 Cropland Marikari 11 0.15 Cropland Mäntykarinmaa 3 0.04 Forest Kiskarinsivu 13 0.18 Forest Kiskarintaka 13 0.18 Forest Mäntykarinedus 3 0.04 Forest Pikkumetsä 1 0.01 Forest Satama 7 0.10 Forest Telakka 82 1.12 Forest/lake Valkiakarinjärvi (a) 2 0.03 Lake Mäntykarinjärvi 456 6.24 Lake Susijärvi 1 0.01 Lake Tankarienjärvi 6714 91.82 River Eurajoki W 1 0.01 Total 7308 99.95 South Lake Liiklanjärvi 4 0.05 Total 4 0.05 Objects marked in bold text 98.06 Croplands, total 15 0.2 Forests, total 121 1.7 Lakes, total 7177 98.1 Rivers, total 1 0.0


46

Table 4-4. Release-point distribution to different objects based on the surface and near-surface hydrology results for the primary repository panel C. The total number of points is less than in the original data due to exclusion of the paths with advective travel times longer than 15 000 years.


Fraction [%]

North Cropland Kaunissaari W 4 0.13 Cropland Koivisto 4 0.13 Cropland Mäntykarinmaa 9 0.29 Forest Kiskarinsivu 3 0.10 Forest Kiskarintaka 7 0.23 Forest Mäntykarinedus 22 0.71 Forest Telakka 1 0.03 Forest/lake Valkiakarinjärvi (a) 26 0.84 Lake Mäntykarinjärvi 484 15.73 Lake Susijärvi 64 2.08 Lake Tankarienjärvi 2105 68.41 River Lapinjoki S 1 0.03 Total 2732 88.79 South Cropland Liiklanpelto 21 0.68 Lake Liiklanjärvi 325 10.56 River Tuomonjoki 2 (b) 1 0.03 Total 347 11.28 Those on bold 96.78 % Croplands, total 38 1.2 % Forests, total 59 1.9 % Lakes, total 3004 96.8 % Rivers, total 2 0.1 %


(b) River forms at a later stage as the river channel shifts position due to land tilting, before this event the releases are directed to the cropland.

4.2.2 Formulation of simplified release patterns

The final step is to select a small number of simplified release patterns for use in the assessment, conceptualised based on the analysis and detailed results above. These are all analysed by radionuclide transport modelling and doses are derived for nominal release rates (presented in Hjerpe et al. 2010). Two types of simplified release patterns are selected: realistic release patterns and alternative release patterns. The realistic release patterns are considered to have the highest likelihood and are to be used in the realistic biosphere calculation cases. In the alternative release patterns, considered to have lower likelihood, the releases are targeted towards selected types of objects, in order to assess the impact of the outcome of the assessment due to uncertainties in the selection of the release pattern (i.e., analysed as sensitivity calculation cases).

47

Realistic release patterns For each panel considered, a realistic release pattern, based on the degree of realism, is selected to underpin the realistic biosphere calculation cases to analyse in the assessment (see section 2.1 in Hjerpe et al. 2010). When formulating a realistic release pattern, a high degree of realism is considered to have been reached when it captures a majority of the geosphere release points. By selecting just a few biosphere objects at least 97% of the geosphere release points, for releases from each repository panel, are captured by the landscape model. Distribution of the whole geosphere release between these few dominating objects can be done in several ways, since the distribution of releases between different release points in a release pattern is not well known. In the realistic release patterns, a simple approach is used: the release to each object is weighted by the relative number of release points due to releases from one repository panel. The formulation of the realistic patterns is presented below (Table 4-5). The realistic release patterns are all dominated by releases to aquatic objects (lakes). The high number of individually simulated release paths ending up in lakes suggests that water bodies do tend to draw groundwater flow, and thus releases, to them due to the hydraulic gradients.

Alternative patterns Even though the majority of geosphere release points may be linked to aquatic biosphere objects, points also exist that may be linked to terrestrial areas. Thus, routes direct to terrestrial objects cannot be totally ruled out, but may be considered to have a lower degree of realism. Consequently, alternative, less likely, release patterns may be formulated. The role of the alternative release patterns in the present assessment is to assess the sensitivity of the outcome of the assessment due to the selection of the release pattern.

Targeted releases to forests and croplands are selected as alternative patterns, primarily to assess the level of conservatism in the selected realistic patterns. The selection of forest and croplands is based on their characteristics regarding the food ingestion exposure pathway, which is expected to be most important; targeted releases to forest ecosystems are selected since forest objects resulted in the highest (biosphere object-specific) landscape dose maxima in the KBS-3H biosphere analysis (Broed et al. 2007). Thus, forest ecosystems may be the major contributor to the annual doses, at least to a few exposed persons, since forest objects generally can support only a few persons with food. Targeted releases to croplands are selected due to their high productivity of edibles. Thus, cropland ecosystems have the potential to expose a larger population via food ingestion. Furthermore, lakes are well represented in the realistic case. In the alternative patterns, it is assumed that the release either is entirely captured by forest objects or cropland objects. These patterns are divided into two variants: focused and dispersed. In the focused variants, the whole release is directed into a small number of, mostly a single, forest or cropland objects. In the dispersed variants, the whole release is distributed to all forest or cropland objects with at least one release point. The weighting of the releases to several objects is done, as for the realistic patterns, according to the number of release paths ending in each object.

48

Table 4-5. Simplified release pattern formulations, based on release from the three repository panels A, B or C. The percentages before the names of the biosphere objects specify the fraction of the geosphere release that is directed into the object.

Panel A Panel B Panel C

Realistic release patterns

Identifier Realistic-A (a) Realistic-B (a) Realistic-C (b) 75 % Liiklanjärvi

14 % Tankarienjärvi 8 % Liiklanpelto 3 % Mäntykarinjärvi

94 % Tankarienjärvi 6 % Mäntykarinjärvi

71 % Tankarienjärvi 16 % Mäntykarinjärvi 11 % Liiklanjärvi 2 % Susijärvi

Alternative release patterns

Identifier Forest_focused-A (c) Forest_focused-B (d) Forest_focused-C (e) Flutanmetsä W 100 %

Telakka 100 % Mäntykarinedus 100 %

Identifier Forest_dispersed-A (f) Forest_ dispersed-B (g) Forest_ dispersed-C (h) 81 % Flutanmetsä W

19 % Flutanmetsä E

71 % Telakka 11 % Kiskarinsivu 11 % Kiskarintaka 7 % Satama

76 % Mäntykarinedus 24 % Kiskarintaka

Identifier Cropland_focused-A (i) Cropland_focused-B (j) Cropland_focused-C (k) 98 % Liiklanpelto

2 % Koskelonpelto

100 % Marikari

100 % Mäntykarinmaa

Identifier Cropland_dispersed-A (l) Cropland_ dispersed-B (m) Cropland_ dispersed-C (n) 91.3 % Liiklanpelto

4.8 % Tuomonjoki2 1.7 % Koskelonpelto 1.7 % Liiklanpellonjoki 0.5 % Lepänmaa

73 % Marikari 20 % Mäntykarinmaa 7 % Koivisto

53 % Mäntykarinmaa 23.5 % Kaunissaari W 23.5 % Koivisto

(a) Captures 98% of all release points (b) Captures 97% of all release points (c) An over-estimation, captures only 0.24% of all release points (d) An over-estimation, captures only 1.1% of all release points (e) An over-estimation, captures only 0.7% of all release points (f) Still rather focused release, but no other forests available; captures 0.30% of all release points (g) Almost all forests receiving releases, capturing 1.6% of all points (h) Two most release-dominating forests, capturing 0.94% of all release points (i) Compromise between the most upstream and the dominating field, captures 7.7% of all release points (j) The release-dominating cropland capturing 0.15% of all release points, also located upstream (k) The release-dominating cropland capturing 0.3% of all release points (l) Only the southern croplands (northern croplands - total 1% of all release), captures 8.3% of all release

points (m) All croplands receiving releases, capturing 0.20% of all release points (n) All croplands receiving releases, capturing 0.55% of all release points

49

4.3 Biosphere object modules

The biosphere object modules used in the landscape model each represent a typical ecosystem identified to exist during any time-period in the evolution of the landscape. Based on performed sensitivity analysis (Broed 2007a,b) and internal auditing against available site-specific data and best scientific knowledge, the biosphere object modules have been updated. In the present assessment, biosphere object modules for the following ecosystem types are applied: lake, coast, river, forest, wetland and cropland. The compartment structures of the biosphere object modules and the underlying data basis are presented in this section; the mathematical structure (transport equations) are presented in Appendix A. The conceptual models are first discussed and then are used values for key parameters presented. The conceptual model for C-14 and its mathematical structure is documented in detail in Avila & Pröhl (2007) and is not discussed below.

4.3.1 Conceptual models

A great improvement in the models is that they are consistent on a conceptual level, meaning that the structure of compartments is very similar in all models. This will facilitate the coupling between ecosystems existing at the same time, and the transition between ecosystem types due to the evolution of the biosphere (e.g. when new terrestrial ecosystems are formed from the sea due to land uplift). All included ecosystem-specific models (forest, wetland, cropland, lakes, rivers, sea and coastal areas) could, in principle, be illustrated in on generic model. However, for clarity, the models are divided in a terrestrial and an aquatic model, presented in Figure 4-4 and Figure 4-5. Other conceptual improvements, compared to earlier Posiva biosphere analyses, implemented in the present assessments are:

� The transition between ecosystem types is modelled explicitly as a continuous process. To represent the transitions, new parameters are required, such as the growth rate of wetlands formed from lakes, the rate of succession from mires to forests, and that several parameters are given time dependent values.

� The vertical transport is explicitly included. This also requires new parameters, such as the advective water flows, diffusion coefficients, and specific properties of different compartments, like porosity, density, and distribution coefficients.

� The C-14 model based on specific activity approach (Avila & Pröhl 2007) is integrated into the landscape model.

It should be noted the release of radionuclides from the geosphere may be introduced in the transport model in several places (see Figure 4-4 and Figure 4-5). However, this is not treated in an arbitrary manor; the rules for which compartments that may get direct releases of radionuclides from the geosphere depend both on the radionuclide and the type of ecosystem, and can be summarised as follows:

� The default initial compartment for the release is deep soil (terrestrial) and deep sediment (aquatic)

� Only C-14 can be direct release into air. � Only Cropland has a direct release into crops, and rooted mineral soil; this is

through irrigation with contaminated water.

50

Figure 4-4. Conceptual radionuclide transport model for terrestrial ecosystems in the landscape model, forest (F), wetland (W) and cropland (C). The indices in the compartment names define for which ecosystem(s) they are valid.

Figure 4-5. Conceptual radionuclide transport model for aquatic ecosystems in the landscape model (lake, river, sea and coastal areas).

Deep soilFWC

Intermediate mineral soilFC

Rooted mineral soilFC CatotelmW

Green partsFW CropsC

AnimalsFWC

FungiF

WoodF

Dead woodF LitterF

AirFWC

HumusF AcrotelmW

Radionuclide release rates from the geosphere

Net RN flux out from the system

RN flux between compartments

Equilibrium relationship

Compartment possibly receiving direct release of radionuclides

Air to ground interface

Deep sediment

Intermediate sediment

Active layer (bio-active sediment)

Green parts

Animals

Water

Radionuclide release rates from the geosphere

Net RN flux out from the system

RN flux between compartments

Equilibrium relationship

Compartment possibly receiving direct release of radionuclides

Water to sediment interface

51

4.3.2 Key parameters and applied values

The major part of the parameter values applied in the BSO modules are site & regional data; these are discussed in detail in Haapanen et al. (2009), Helin et al. 2010 and Ikonen et al. 2010a. This section summarise the values applied for key parameters in the BSO modules. First are the parameters and values applied in several BSO modules presented (Table 4-6 to Table 4-9) and then are each module addressed separately. At the end of this section the C-14 model specific parameters are addressed.

Table 4-6. Applied soil and sediment type specific parameter values (Ikonen et al. (2010a).

Parameter Value Soil/Sediment type Ecosystem type Soil bulk density [kgdw/m3]

91 Peat (any)

150 Gyttja (recent mud/clay/detritus/gyttja)

(any)

1600 Clay Terrestrial(a) 1200 (any) cropland(b) 270 Clay aquatic 1850 Very fine sand (silt) (any) 1850 Fine sand (any) 1600 Sand (any) 1800 Fine-grained till (any) 1600 Washed till

(coarse-/medium-grained) (any)

Carbon concentration [kgC/kgdw]

0.03 (any) cropland(b)

0.51 Peat (any) 0.18 Gyttja (recent

mud/clay/detritus/gyttja) (any)

0.0018 Clay Terrestrial(a) 0.11 Clay aquatic 0.0018 Very fine sand (silt) (any) 0.0018 Fine sand (any) 0.0048 Sand (any) 0.002 Fine-grained till (any) 0.0014 Washed till

(coarse-/medium-grained) (any)

(a) All compartments except rooted mineral soil in Croplands (b) Valid for the compartment rooted mineral soil

52

Table 4-7. Applied soil-to-compartment concentration ratios [(Bq/kgdw)/(Bq/kgdw)] for terrestrial ecosystems (Ikonen et al. (2010a)).

Element

Soil-to- tree wood

tree foliage

understorey

crops (cereals)

Cl 6.2E+00 8.8E+00 2.4E+00 3.3E+01 I 1.3E-01 2.9E-01 2.2E-01 2.0E-02

Mo 2.0E-03 6.4E-01 1.0E+00 8.0E-01 Nb 1.7E-01 3.1E+00 4.0E+00 1.0E-02 Cs 1.6E-01 1.2E+00 2.6E+00 3.0E-02 Ni 1.7E-01 3.1E+00 4.0E+00 6.0E-02 Se 1.5E-01 1.8E-01 1.1E+00 1.1E+00 Sr 2.3E-01 1.1E+00 1.1E-01 3.1E-01 Y 1.8E-04 2.0E-02 5.0E-04 2.0E-05

Pd 1.7E-01 3.1E+00 4.0E+00 6.0E-02 Sn 3.1E-01 9.2E-02 1.1E-02 1.1E-02 Sb 2.5E-02 1.8E-03 1.8E-03 1.8E-03

Cr~Mo 5.0E-03 1.6E+00 2.4E+00 2.0E-04 Table 4-8. Soil-to-green parts (aquatic plants) concentration ratios [(Bq/kgdw)/(Bq/m3)] for aquatic ecosystems (Ikonen et al. (2010a)).

Element Freshwater Sea Element Freshwater Sea Cl 1.15 0.0036 Se 7.3 2.2

I 0.82 1.69 Sr 0.69 0.014 Mo 0.14 0.14 Y 1.5 4.3 Nb 1.4 1.4 Pd 1.5 4.3 Cs 2.2 1.1 Sn 0.59 0.59 Ni 1.5 4.3 Sb 0.59 0.59

Table 4-9. Parameter values for solid-liquid distribution coefficients (Kd) in soils and sediments [m3/kg]. The values are further discussed in detail in (Helin et al. 2010).

Element Loam (croplands)

Clay Till Sand Other mineral soil

Peat Sediment Suspended matter

Cl 0.0003 0.0002 0.0003 0.0004 0.0003 0.0058 0.0058 0.03I 0.0079 0.005 0.0092 0.0026 0.0092 0.0312 0.424 0.3Mo 0.128 0.116 0.116 0.116 0.116 0.121 0.166 1.7Nb 2.5 2.31 2.23 0.173 2.23 2 2 10Cs 3.37 5.2 1.02 0.535 1.87 0.343 0.337 10Ni 0.162 0.496 1.76 0.111 0.527 0.87 0.423 32Se 0.223 0.242 0.183 0.046 0.183 1.07 1.07 3.2Sr 0.048 0.035 0.013 0.019 0.031 0.097 0.011 0.316Y 0.022 0.022 0.022 0.023 0.023 0.312 0.312 3.1Pd (a) 0.18 0.27 0.18 0.09 0.18 0.67 0.67 6.7Sn (a) 0.45 0.67 4.1 0.15 1.6 1.6 1.6 16Sb (a) 0.061 0.14 0.099 0.017 0.062 0.075 0.075 0.75

(a) No site data available, values are taken from IAEA (2009).

53

Parameter values in forest objects Applied parameter values for forest objects are presented in Table 4-10 and Table 4-11. For elemental circulation, and thus radionuclide transport, in forests, according to applied models, the most important parameters are the annual production of wood, foliage and understorey (determining the biological storage) and concentration ratios (CR) from soil to understorey and foliage (determining the uptake). Furthermore, the biomasses of the forest compartments determine the concentrations and the transport implied by the CRs. The hydrological balance that greatly affects the circulation is simulated by the surface hydrology model, and thus transpiration and the intercepted fraction of precipitation by the canopy is of importance. Annual production of wood is given as a mean annual increment of stem wood (MAI, bark included), which is estimated by dividing stand volume by stand age at a given time. MAI reflects the site fertility, and it depends on tree species and the developmental stage of the stand. Here, derivation of MAI is based on the results published by Kuusela (1977) and Ilvessalo & Ilvessalo (1975); calculations were also based on measurements at the site (Saramäki & Korhonen 2005). Best estimate value for MAI was chosen as a mean value of MAIs from Ilvessalo & Ilvessalo (1975) and Kuusela (1977) matched to the intensively studied FET plots by UNTAMO site classes. Annual production of tree foliage was estimated to approximate 82.1 % of the annual stem wood production (Mälkönen 1974), and this proportion was used in calculations of tree foliage productivity based on the MAI. Annual production of understorey (above-ground parts) for UNTAMO site class 3 (herb-rich heath forest) was derived from measurements on FIP plots in 2008 (Haapanen 2009), based on six functional plant groups. For UNTAMO site class 2 (heath forest), values were derived from Mälkönen (1974).

The biomasses of the forest compartments are also important since they determine the concentrations and the transport implied by the CRs. The mean value of tree biomass (below and above-ground, all trees) are based on FET plot measurements (Saramäki & Korhonen 2005). The best estimate of average above-ground tree biomass was derived from stands of different ages representing different developmental stages of those stands. Mean value of biomass of other vegetation (below and above-ground, shrub-layer excluded) were derived from biomass estimates for forest compartments based on models by Muukkonen & Mäkipää (2006).

Concentration ratios from soil to understorey and foliage (Table 4-7) are important due to their great impact on the uptake. Site-specific concentration ratios from soil to wood, foliage and understorey; at the present phase of the programme, only some site-specific values for iodine can be given concerning the key elements. This data need to be complemented by literature to cover the uncertainties. The concentration ratios are based on samples from three monitoring plots at Olkiluoto: a Scots pine, a Norway spruce and a black alder stand (Haapanen 2009). To obtain a representative number of samples, and since the individual values do not much differ, the values for understorey are presented for two groups: Grasses and herbs and Dwarf shrubs.

Parameter values in wetland objects Applied parameter values for wetland objects are presented in Table 4-12. The most important parameters for radionuclide transport are the same as for forest objects.

54

Table 4-10. Forest type specific parameter values (Ikonen et al. (2010a)).

Parameter Compartment Class 1 Rocky

Class 2 Heat

Class 3 Herb-rich

heath

Unit

Biomass Tree wood 1.7 4.6 6.4 kgdw/m2 Biomass Foliage (tree) 0.9 2.5 3.5 kgdw/m2 Biomass Understorey 0.25 0.11 0.12 kgdw/m2 Biomass Litter 0.19(a) 0.5 0.62(a) kgdw/m2 Bioturbation rate 0.5 6.5 4.3 kgdw/m2/y Carbon concentration Humus 0.43 0.39 0.37 kgC/kgdw Density Humus 140 160 170 kgdw/m3 Loss rate to dead wood Tree wood 0.0013 0.0028 0.0031 1/y Loss rate to litter Foliage (tree) 0.21 0.32 0.34 1/y Loss rate to litter Understorey 0.31 0.4 0.38 1/y Net primary production 0.14 0.249 0.349 kgC/m2/y Production Tree wood 0.079 0.2 0.25 kgdw/m2/y Production Foliage (tree) 0.064 0.17 0.21 kgdw/m2/y Production Understorey 0.22(b) 0.096 0.063 kgdw/m2/y Thickness Humus 0.041 0.055 0.065 m

(a) Scaled from Class 2, based on the difference in foliage production (most of litter is from foliage; loss rates to litter are about the same; decay rate for the litter is independent from forest class due to lack of data) (b) Same production/biomass ratio applied as for class 2 (most similar)

Table 4-11. Parameter values for forest objects (Ikonen et al. (2010a)).

Parameter Compartment Value Unit Biomass Dead wood 0.158 kgdw/m2 Decomposition rate Dead wood 0.054 1/y Decomposition rate Litter 0.03 1/y Harvested fraction of tree foliage biomass 0 1/event Harvested fraction of tree wood biomass 0.89 1/event Rotation period of trees 100 y

55

Table 4-12. Parameter values for wetland objects (Ikonen et al. (2010a)).

Parameter Compartment Value Unit Biomass Tree wood 4.4 kgdw/m2 Biomass Foliage (tree) 2.4 kgdw/m2 Biomass Dead wood 0.158 kgdw/m2 Biomass Understorey 0.16 kgdw/m2 Bioturbation rate 6.1 kgdw/m2/y Carbon concentration Acrotelm 0.46 kgC/kgdw Decomposition rate Acrotelm 0.015 1/y Decomposition rate Dead wood 0.054 1/y Density Acrotelm 91 kgdw/m3 Harvested fraction of tree wood biomass 0.89 1/event Harvested fraction of tree foliage biomass 0 1/event Loss rate to dead wood Tree wood 0.0012 1/y Loss rate to litter Foliage (tree) 0.4 1/y Loss rate to litter Understorey 0.43 1/y Net primary production 0.17 kgC/m2/y Production Tree wood 0.17 kgdw/m2/y Production Foliage (tree) 0.17 kgdw/m2/y Production Understorey 0.75 kgdw/m2/y Rotation period of trees 100 y

Table 4-13. Parameter values for cropland objects (Ikonen et al. (2010a)).

Parameter Value Unit Comment Biomass 0.425 kgdw/m2 Based on cereals Bioturbation rate 2 kgdw/m2/y Erosion rate 1.25E-4 m3/m2/y For rooted mineral soil compartment Harvested fraction of biomass 0.57 kgdw/kgdw Based on cereals Height of vegetation 0.85 m Based on cereals Irrigation amount 0.03 m3/m2/event Based on cereals Irrigation frequency 1 event/y Based on cereals Leaf area index 1.5 m2/m2 Based on cereals Net primary production 0.18 kgC/m2/y Based on cereals Production 0.45 kgdw/m2/y Based on cereals Water storage capacity 2.0E-4 m3/m2

56

Parameter values in cropland objects Applied parameter values for cropland objects are presented in Table 4-13. For radionuclide transport in cropland objects in the landscape model, according to applied models, the most important parameters are:

� irrigation amount, � irrigation frequency, and � leaf area index

The irrigation rate (amount and frequency) is naturally a vital parameter, since radionuclides in the irrigation water is the main source in cropland ecosystems. The leaf area index (LAI) is the capacity for a plant to capture contaminants from the irrigation water, defined as half of the total green leaf area, i.e. one-sided area of broadleaves, in the plant canopy per unit ground area. This parameter has been identified as one of the most important parameters (Haapanen et al. 2009). Also the retention factor on plant surfaces, used when deriving the activity concentrations in the plants due to irrigation with contaminated water, is an important parameter. The same values as used in AgriWELL-2009, see Table 6-10, are used for croplands in the landscape model.

Parameter values in aquatic objects Applied parameter values for aquatic (lake, river, and coast) objects are presented in Table 4-14. For aquatic objects, the geometry and retention time (flow rates) are the most important parameters for radionuclide transport due to the rapid water exchange. The former is derived within the terrain development model, as is the latter for lakes and rivers. For coastal objects, the retention time is somewhat based on expert judgment in cases where water mass balance does not give a conclusive answer (two-directional flow across an interface between coastal objects) and is thus a matter of an assessment decision (assumption).

Parameter values specific for the C-14 model Due to that the fate of C-14 is modelled using a specific activity model (Avila & Pröhl 2007), it’s treated separately here. For radionuclide transport of C-14, according to applied models, the most important parameters are:

� wind speed, � mixing height, � net primary production, � irrigation amount and frequency, � dissolved inorganic carbon (DIC), � sedimentation rates, and � decomposition rate of exposed sediment

Wind speed determines in the model, together with the mixing height, the mixing volume for the C-14 release from the soil to the air – the pathway to assimilation by plants in the terrestrial systems. Thus, both these two parameters are significant in the terrestrial cases. A wind speed of 4.1 m/s at the height of 20 m is used.

57

The mixing height, or the height needed to supply the canopy with its CO2 demand, is basically dependent on the vegetation height and biomass: a well-developed canopy, which is able to assimilate daily 2–3 g CO2/m² soil in a sunny summer day during photosynthesis (Avila & Pröhl 2007 citing Geisler 1980), requires the CO2 that is contained in a 20-m layer from the ground surface. However, assigning a vegetation-dependent mixing height value is not feasible at the moment, and the values of 20 m and 10 m are chosen for forests and croplands in the assessment, respectively, as proposed in the model description report (Avila & Pröhl 2007).

Table 4-14. Parameter values for aquatic objects (Ikonen et al. (2010a)).

Parameter Compartment Value Unit

Lake-specific

Net primary production 0.064 kgC/m2/y Biomass Green parts 0.023 kgdw/m2 Production Green parts 0.029 kgdw/m2/y Suspended solids concentration 0.004 kgdw/m3 Dissolved inorganic carbon (DIC) 0.003 kgdw/m3 Sedimentation rate (gross) 1.1 kgdw/m2/y Resuspension rate 0.8 kgdw/m2/y Loss rate to active sediment Green parts 1 1/y

River-specific


Coast-specific


58

Net primary production. Primary production is the production of organic compounds from atmospheric or aquatic carbon dioxide, principally through the process of photosynthesis. Gross primary production is the rate at which an ecosystem's producers capture and store a given amount of chemical energy as biomass in a given length of time. Some fraction of this fixed energy is used by primary producers for cellular respiration and maintenance of existing tissues, the remaining fixed energy is referred to as net primary production. It is the rate at which all the plants in an ecosystem produce net useful chemical energy. Some net primary production goes toward growth and reproduction of primary producers, while some is consumed by herbivores. In the C-14 model implementations for forests and lakes, the net primary production is also a key parameter. For croplands, it has less of an effect on the model results due to the differences in contamination pathways; irrigation is more important for crops. Net primary production values for forests (UNTAMO site classes) are presented in Table 4-15 and further discussed in Ikonen et al. 2010a (section 5.2.3).

Irrigation amount and frequency are key parameters for croplands also in respect of C-14 transport; these are presented in Table 4-13.

Dissolved inorganic carbon. The C-14 release mixes with the stable DIC, and thus the DIC concentration has a significant role in determining the mixing rate and subsequent doses. Parameter values are presented in Table 4-14.

Sedimentation rate. As the DIC concentration regulates the mixing and availability of C-14 releases, sedimentation is a removal effect, mainly controlled by the sedimentation rate parameter. Sedimentation rates depend on winds, currents, upwelling and the productivity of system. Parameter values are presented in Table 4-14.

Decomposition rate of exposed sediment. Decomposition rate of organic matter in newly formed terrestrial object (relict) determines the rate on which the inherited radioactivity inventory is released. Gisi (1990) gives a value of 0.03 y-1 of soil organic matter amount, with a normal distribution of 0.03; 0.01 (mean; standard deviation). This is used further given the lack of better data, and a sufficiently large range should be chosen in the subsequent biosphere assessment to cover the uncertainties.

Table 4-15. Net primary production of forest vegetation by site class (from Table 5-9 in Ikonen et al. 2010a).

UNTAMO site class Net primary production [gC/m2/y]

1 Rocky forest 140 2 Heath forest 249 3 Herb-rich heath forest 349 5 Peatland forest Not available (a) (a) Reliable data not available due to scarce research.

59

4.4 The landscape model applied in the biosphere assessment 2009

This section describes the landscape model as implemented in the present assessment (more details are also given in Appendix B where one of the biosphere objects is described in detail). The landscape model is comprised of biosphere objects, which is a system of one or more ecosystem-specific BSO modules. Each biosphere object has an exchange of radionuclides during the assessment time window due to the fact that they together occupy the same modelled area, and that the individual ecosystem types inside the object often change over time due to the terrain development. When constructing biosphere objects in the landscape model, all sub-objects that will occur during the assessment time window were implemented and connected as a sequential chain with respect to any incoming flux of radionuclides. Consequently, all sub-objects within a biosphere object may not exist at all times. Each individual sub-object has a mechanism that instantaneously passes through any incoming flux of radionuclides when it does not exist (not yet formed or has been developed into another ecosystem type) and takes up incoming radionuclides if the ecosystem does exist. The incoming radionuclide flux could either originate from an upstream biosphere object, from a direct release of radionuclides, or a mix of both. Using this approach it is possible to describe the ecosystem development in time for all modelled biosphere objects. The inheritance of activity inventory from a shrinking ecosystem to a growing ecosystem was implemented by taking the areal rate of change divided by the area of the shrinking object.

A Simulink© library was created containing template BSO modules (section 4.3), one for each ecosystem type. This library was used in the implementation of the full landscape model in order to ascertain that all objects of the same type were exactly the same in its implementation in the final full version of the model. Also, using a library meant that any required change in an object of a given type could be executed at one single library object and then automatically the specific changes could be automatically propagated through the full landscape model on all objects of the same type. Furthermore, a Matlab script was written that contained information about the biosphere object types, names, and connectivity to other objects. This script built each of the objects in the model using the earlier BSO module library, setting up all the internal and external connections of each object. A second function of the Matlab script was to update each BSO module’s parameter values, i.e. assigning the object-specific data. The majority of the parameter values used was provided in MS Excel files, and could be parsed and imported directly using the script. However, a small fraction of parameter values were manually put in the script. The aim is to in the future have a system where all parameter values are assigned to biosphere objects automatically using a link to the biosphere assessment database (BSAdb).

4.4.1 Structure of the landscape model

The total number of biosphere objects in the landscape model is 70, and the total number of interconnected sub-objects is 166. The distribution of ecosystem types of sub-objects is presented in Table 4-16 and the number of structurally unique types of biosphere object, identified as being nine, is described in Table 4-17. Each biosphere object has been given a unique name and identification number; these are listed in Table 4-18, together with their types (as defined in Table 4-17). Figure 4-6 presents the landscape model with biosphere object types shown (with the codes as in Table 4-17);

60

the figure shows the final structure of the landscape model as implemented in PANDORA (see section 4.5). Each connecting line between any two biosphere objects represents the transfer of radionuclides. Figure 4-7 shows the landscape model schematically at the end of the biosphere assessment time window (year 12 020) overlaid on the terrain forecast. The present-day coastline is shown as a grey line and it can be seen from the figure that most of the objects are currently under sea.

Table 4-16. Distribution of ecosystem types in landscape model.

Ecosystem Type Objects in LSMForest 24 Wetland 19 Cropland 15 Lake 11 River 29 Coast 68 Total 166

Table 4-17. Types of biosphere object in the landscape model

Type Code Ecosystems Number of biosphere objects

1 F A single forest object 2 2 CA Agricultural land and coast 15 3 CR A river and coast 25 4 CF A forest and coast 14 5 CWmF Forest, wetland, and coast 3 6 CLWr Wetland, lake and coast 5 7 CLWrWmF Forest, wetland (reed-part), wetland

(mire-part), lake and coast 2

8 CLRWrWmF Forest, wetland (reed-part), wetland (mire-part), lake, river and coast

3

9 CLRWr Wetland (reed), river, lake and coast 1 Total 70

61

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63

Table 4-18: All biosphere objects in the landscape model with names, identification number and type (with the codes from Table 4-17).

Name ID Type Name ID Type Rummi_N 1 F Sivusuo 36 CFW Kangas 2 CA Janijärvi 37 CLRWWF Lapinjoki_S 3 CR Luoteisjoki_1 38 CR Niemelä 4 CF Luoteisjoki_2 39 CR Arinnotko 5 CF Luoteisjoki_3 40 CR

Katavansuo 6 CFW Luoteisjoki_4 41 CR Lapinjoki_N 7 CR Luoteisjoki_5 42 CR Martinsuo 8 CFW Kaukojoki_1 43 CR Itäranta 9 CA Kaukojoki_2 44 CR Eurajoki_E 10 CR Kaukojoki_3 45 CR

Eurajoki_W 11 CR Kaukolampi 46 CLW Kornamaa 12 CF Kaukojärvi 47 CLW Sivujoki 13 CR Rummi_S 48 F Kaunissaari_W 14 CA Raunela 49 CA Mustakarta 15 CF Syöpävuorenhaara 50 CR

Koivisto 16 CA Puhinmetsä 51 CF Pikkumetsä 17 CF Tuomonjoki 52 CR Tankarimetsä 18 CF Nanninmaa 53 CA Tankarienjarvi 19 CLW Koskelonpelto 54 CA Prinkka 20 CA Liiklanjärvi 55 CLW

Telakka 21 CF Lepanmaa 56 CA Satama 22 CF Flutanmetsä_W 57 CF Marikari 23 CA Flutanmetsä_E 58 CF Kiskarinsivu 24 CF Liponjärvi 59 CLWWF Kiskarintaka 25 CF Kaunisjoki 60 CR

Mäntykarinjärvi 26 CLWWF Eteläjärvi 61 CLRWWF Mäntykarinmaa 27 CA Lounaisjoki_1 62 CR Mäntykarinedus 28 CF Roopenmaa_3 63 CA Susijoki 29 CR Roopenmaa_1 64 CA Valkiakarinjärvi 30 CLRW Roopenmaa_2 65 CA

Susijärvi 31 CLW Lounaisjoki_2 66 CR Kallanjoki 32 CR Lounaisjoki_3 67 CR Kallanjärvi 33 CLRWWF Lounaisjoki_4 68 CR Nimetön_E 34 CR Ulkojoki 69 CR Nimetön_W 35 CR Liiklanpelto 70 CA

4.4.2 Technical implementation of the model

When implementing the biosphere objects the including sub-objects were connected as a sequential chain with respect to any incoming flux of radionuclides. Each individual ecosystem-specific sub-object has a mechanism that either passes through any incoming radionuclides if the actual ecosystem in question is not active/existing at a given time, or takes up incoming radionuclides if the ecosystem is active/existing. A simplified

64

example is illustrated in Figure 4-8. Each sub-object can either pass any incoming flux of radionuclides through to the next sub-object, or integrate the incoming flux, if the object is active. The incoming flux of radionuclides could either be flux from another sub-object (either from the same biosphere object, or from an upstream biosphere object), from a direct release of radionuclides, or a mix of both. Using this approach it was possible to describe all ecosystem successions in time, and for all the different biosphere objects in the landscape model. The example biosphere object in Figure 4-8 is initially a coastal object, so in the model implementation, any incoming flux of radionuclides will initially be passed through the lake and the wetland sub-objects. After a certain time, a lake with a surrounding wetland will start to form out of the coast area due to land uplift. After this point in time, the incoming flux will be input to the wetland sub-object, from which the runoff is going to the lake sub-object. From the lake, radionuclides will be flowing to the coast sub-object. When, and if, the coast object no longer exists, the outgoing flux of radionuclides will go to the next downstream biosphere object. The landscape model is complex, containing 166 interconnected sub-objects of various types of ecosystems. Due to the complexity and scope of the model, it was necessary to split the model into smaller sub-sets of biosphere objects, or “sub-models”. The full landscape model was first split into two main water flow paths, representing the water flow on the northern and southern part of the Olkiluoto Island respectively. A third western part was separated from the full landscape model, taking the sum of the fluxes from the northern and southern water flow paths as influx.

Figure 4-8. An example of a possible structure of a biosphere object (CLWr-type).

Air

WLReed Lake Coast

1)�Only coast object existing.

RN�loss�dueto�wind

RN�flux�outRN�transport

RN�Input

Air

WLReed Lake Coast

3)�Wetland with�associated air�part�formed.

Air

WLReed Lake Coast

2)�Lake�formed.

65

Figure 4-9. Schematic view of sub-models and their connections in the landscape model, using sub-sets of biosphere objects.

It was assumed that any releases of radionuclides to either the north or south part would not affect the other flow path (i.e. on the western side of the island where the two water flow paths merged). Finally, the north and south sub-models were further split into smaller sub-models (see Figure 4-9 and Table 4-19). The biosphere objects in each such sub-model was chosen so as to form a self-consisting group of objects, only being dependent on the incoming flux of radionuclides from any upstream objects. The sub-models were then simulated in order from east to west, i.e. in the down-stream direction. Each sub-model saved its outgoing flux of radionuclides to a file, used as input for the next downstream sub-model in the corresponding water flow path. By using this approach it was possible to get numerically stable solutions (by allowing the numerical ODE solvers to use optimal error tolerance settings for each individual sub-model), which also help to significantly reduce the overall required simulation time. One drawback using this approach was that since any given landscape sub-model (group of biosphere objects) can only depend on any upstream landscape sub-model, the water exchange between coastal ecosystem objects had to be assumed uni-directional between any two connected landscape sub-models (in the downstream direction), and the water used for irrigation had to be taken from any freshwater object either in the same biosphere object, or in one of the upstream objects (where the activity concentrations was saved as time-series). A Matlab script was developed that simulated each landscape sub-model and saved the output data to file, in sequence following the water flow path direction. To further enhance the performance of the simulations, each individual radionuclide was simulated independently in a batch mode (allowing the numerical solver to use optimal error tolerances for each individual radionuclide). The script automatically assigned the corresponding radionuclide dependent parameters during each batch iteration.

When deciding which biosphere objects to include in the different sub-models, care was taken to always include at least one freshwater object in the sub-models containing croplands. The objects used for irrigation are listed in Table 4-20. When several freshwater objects occurred in the same sub-model as croplands, a simplified approach of taking either the closest object for irrigation purposes, which can only be said to represent one (reasonable) alternative for where the irrigation water would be taken. Furthermore, checks were made to verify that the amount of irrigation water needed to supply one cropland did not exceed the yearly amount of water flowing through the freshwater object, and no such case was found. In the final implementation, only river objects were used for irrigation water; primarily because these existed over the full simulated time-period, whereas most lakes dries up, but also because the number of river objects in the model are so many more than lake objects.

66

Table 4-19: The biosphere objects included in each implemented sub-model.

Sub-model Biosphere objects Sub-model Biosphere objects

N1 1,2,3,4,5,6 S1 48,49,50,51,53 N2 7,8,9,10,12,13,14,15,16 S2 52,54,56,57,58,70 N3 11,17,20,21,22,23,24,25,27,28 S3 55,59 N4 19,26 S4 60,61,63 N5 29,30 S5 62,64,65,66 N6 31 S6 67,68 N7 32 W1 43,44,45 N8 33,36 W2 46,47,69 N9 34,35,37 N10 38,39,40,41,42

Table 4-20: Source objects for irrigation water. Irrigation water was for all cropland objects taken from a nearby located river in the same biosphere object.

Cropland Irrigation water source

Cropland Irrigation water source

Kangas Lapinjoki S Nanninmaa Syöpävorenhaara Itäranta Eurajoki E Koskelonpelto Tuomonjoki Koivisto Eurajoki E Liiklanpelto Tuomonjoki Kaunissari W Eurajoki E Lepenmaa Tuomonjoki Prinkka Eurajoki W Roopenmaa 1 Lounaisjoki 2 Marikari Eurajoki W Roopenmaa 2 Lounaisjoki 1 Mäntykarinmaa Eurajoki W Roopenmaa 3 Kaunisjoki Raunela Syöpävorenhaara

4.4.3 Ecosystem development

Table 4-21 to Table 4-24 shows the ecosystem changes over time in all biosphere objects. Table 4-25 presents the areas at the start and end of the biosphere assessment time window for each biosphere object, and in Figure 4-10 are the total areas of all biosphere sub-objects of different ecosystem types plotted as function of time.

67

Tabl

e 4-

21. E

cosy

stem

dev

elop

men

t in

bios

pher

e ob

ject

s dom

inat

ed b

y cr

opla

nd e

cosy

stem

s. B

is th

e Ba

ltic

coas

t, C

is c

ropl

and,

and

BC

is

the

Balti

c co

ast +

cro

plan

d (r

unof

f fro

m th

e cr

opla

nd to

the

coas

t).

Obj

ect

Y

ear

(AD

)

2020

2520

3020

3520

4020

4520

5020

5520

6020

6520

7020

7520

8020

8520

9020

9520

10020

10520

11020

11520

12020

Kan

gas

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Itär

anta

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Kau

niss

aari

WB

BB

CC

CC

CC

CC

CC

CC

CC

CC

CC

CK

oivi

sto

BB

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Prin

kka

BB

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Mar

ikar

iB

CB

CB

CC

CC

CC

CC

CC

CC

CC

CC

CC

CM

änty

kari

nmaa

BB

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Rau

nela

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Nan

ninm

aaB

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

CK

oske

lonp

elto

BB

CC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Lep

änm

aaB

BB

CC

CC

CC

CC

CC

CC

CC

CC

CC

CL

iikla

npel

toB

BC

BC

CC

CC

CC

CC

CC

CC

CC

CC

CC

Roo

penm

aa 3

BB

BB

BB

BB

BB

CC

CC

CC

CC

CC

CC

Roo

penm

aa 1

BB

BB

BB

BB

BC

BC

CC

CC

CC

CC

CC

CR

oope

nmaa

2B

BB

BB

BB

BB

BC

CC

CC

CC

CC

CC

C

68

Tabl

e 4-

22. E

cosy

stem

dev

elop

men

t in

bios

pher

e ob

ject

s do

min

ated

by

fore

st o

r w

etla

nd e

cosy

stem

s. B

is th

e Ba

ltic

coas

t, F

is fo

rest

, Wf

is w

etla

nd +

fore

st (r

unof

f fro

m th

e fo

rest

to th

e w

etla

nd),

and

BF

is B

altic

coa

st +

fore

st (r

unof

f fro

m th

e fo

rest

to th

e co

ast).

Obj

ect

Y

ear

(AD

)

2020

2520

3020

3520

4020

4520

5020

5520

6020

6520

7020

7520

8020

8520

9020

9520

10020

10520

11020

11520

12020

Rum

mi N

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FN

iem

elä

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FA

rinn

otko

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Kor

nam

aaB

FB

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Mus

taka

rta

BB

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Pikk

umet

säB

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FT

anka

rim

etsä

BB

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Tel

akka

BF

BF

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Sata

ma

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Kis

kari

nsiv

uB

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FK

iska

rint

aka

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FM

änty

kari

nedu

sB

BB

FF

FF

FF

FF

FF

FF

FF

FF

FF

FR

umm

i SF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Puhi

nmet

säB

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FFl

utan

met

sä W

BF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

Flut

anm

etsä

EB

FF

FF

FF

FF

FF

FF

FF

FF

FF

FF

FK

atav

ansu

oB

FW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fM

artin

suo

BF

FF

FF

FF

FF

FW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fSi

vusu

oB

BB

BB

FW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

fW

f

69

Tabl

e 4-

23. E

cosy

stem

dev

elop

men

t in

bios

pher

e ob

ject

s do

min

ated

by

rive

r ec

osys

tem

s. B

is th

e Ba

ltic

coas

t, R

is r

iver

, and

BR

is B

altic

co

ast +

rive

r (ru

noff

from

the

rive

r to

the

coas

t).

Obj

ect

Y

ear

(AD

)

2020

2520

3020

3520

4020

4520

5020

5520

6020

6520

7020

7520

8020

8520

9020

9520

10020

10520

11020

11520

12020

Lap

injo

ki S

BR

RR

RR

RR

RR

RR

RR

RR

RR

RR

RL

apin

jok

i NB

BR

RR

RR

RR

RR

RR

RR

RR

RR

RR

RE

uraj

oki E

BB

BR

RR

RR

RR

RR

RR

RR

RR

RR

RE

uraj

oki W

BB

BR

RR

RR

RR

RR

RR

RR

RR

RR

RSi

vujo

ki

BB

RB

RR

RR

RR

RR

RR

RR

RR

RR

RR

RSu

sijo

ki

BB

BB

RR

RR

RR

RR

RR

RR

RR

RR

RR

Kal

lanj

oki

BB

BB

BR

RR

RR

RR

RR

RR

RR

RR

RR

Nim

etön

EB

BB

BB

RR

RR

RR

RR

RR

RR

RR

RR

Nim

etön

WB

BB

BB

BR

BR

RR

RR

RR

RR

RR

RR

RR

Luo

teis

jok

i 1B

BB

BB

BB

BR

BR

RR

RR

RR

RR

RR

RR

Luo

teis

jok

i 2B

BB

BB

BB

BB

BR

RR

RR

RR

RR

RR

RL

uote

isjo

ki 3

BB

BB

BB

BB

BB

BR

RR

RR

RR

RR

RR

Luo

teis

jok

i 4B

BB

BB

BB

BB

BB

BR

RR

RR

RR

RR

RL

uote

isjo

ki 5

BB

BB

BB

BB

BB

BB

BR

BR

RR

RR

RR

RK

auk

ojok

i 1B

BB

BB

BB

BB

BB

BB

BB

RR

RR

RR

RK

auk

ojok

i 2B

BB

BB

BB

BB

BB

BB

BB

BR

RR

RR

RK

auk

ojok

i 3B

BB

BB

BB

BB

BB

BB

BB

BB

BR

BR

BR

BR

Syöp

ävuo

renh

aara

BR

RR

RR

RR

RR

RR

RR

RR

RR

RR

RT

uom

onjo

ki

BR

RR

RR

RR

RR

RR

RR

RR

RR

RR

RK

auni

sjok

iB

BB

BR

BR

RR

RR

RR

RR

RR

RR

RR

RR

Lou

nais

jok

i 1B

BB

BB

BR

BR

BR

BR

RR

RR

RR

RR

RR

RR

Lou

nais

jok

i 2B

BB

BB

BB

BB

BB

RR

RR

RR

RR

RR

Lou

nais

jok

i 3B

BB

BB

BB

BB

BB

RB

RR

RR

RR

RR

RR

Lou

nais

jok

i 4B

BB

BB

BB

BB

BB

BB

RB

RR

RR

RR

RR

Ulk

ojok

iB

BB

BB

BB

BB

BB

BB

BB

BB

BB

BB

70

Tabl

e 4-

24. E

cosy

stem

dev

elop

men

t in

the

bios

pher

e ob

ject

s th

at a

re d

omin

ated

by

lake

eco

syst

ems.

B is

Bal

tic c

oast

, L is

lake

, Lf

is

lake

+ s

ome

area

dri

ed in

to a

fore

st (r

unof

f fro

m th

e fo

rest

to th

e la

ke),

Lfm

is L

ake

+ s

ome

area

of w

etla

nd a

nd fo

rest

(run

off f

rom

the

fore

st -

> w

etla

nd -

> la

ke),

Wf

is w

etla

nd +

fore

st (

runo

ff fro

m fo

rest

to th

e w

etla

nd),

Wfr

is w

etla

nd in

mid

dle

of fo

rest

, riv

er r

unni

ng

thou

gh b

oth

(run

off f

ores

t ->

wet

land

-> ri

ver)

.

Obj

ect

Y

ear

(AD

)

2020

2520

3020

3520

4020

4520

5020

5520

6020

6520

7020

7520

8020

8520

9020

9520

10020

10520

11020

11520

12020

Tan

kar

ienj

ärvi

BB

BL

LL

LL

LL

LL

LL

LL

LL

LL

LM

änty

kar

injä

rvi

BB

BL

Lf

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Val

kia

kar

injä

rvi

BB

BB

Fr

Fr

Fr

Fr

Fr

Fr

Fr

Lf

Lf

Lf

Lf

Lf

Lf

Lf

Lf

Lf

Lf

Susi

järv

iB

BB

BL

LL

LL

LL

LL

LL

LL

LL

LL

Kal

lanj

ärvi

BB

BB

BL

LL

fL

fF

rW

frW

frW

frW

frW

frW

frW

frW

frW

frW

frW

frJa

nijä

rvi

BB

BB

BB

BL

LL

LF

rW

frW

frW

frW

frW

frW

frW

frW

frW

frK

auk

olam

piB

BB

BB

BB

BB

BB

BB

BB

BB

BB

BB

Kau

koj

ärvi

BB

BB

BB

BB

BB

BB

BB

BB

BB

BB

BL

iikla

njär

viB

BB

LL

LL

LL

LL

LL

LL

LL

LL

LL

Lip

onjä

rvi

BB

BL

Lf

Lf

Lf

Lf

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Lfm

Ete

läjä

rvi

BB

BB

BL

LL

LL

Fr

Wfr

Wfr

Wfr

Wfr

Wfr

Wfr

Wfr

Wfr

Wfr

Wfr

71

Table 4-25. The biosphere objects names, ecosystem types and areas at the start and end time steps in the radionuclide transport modelling.

Biosphere object Year 2 020 Ecosystem type

Area [m2]

Year 12 020 Ecosystem type

Area [m2]

Arinnotko coast 37600 forest 8606 forest 63 Coastal area coast 138324000 coast 106501000 Eteläjärvi coast 1652900 lake (dried out) 107800 lake (peat) 95900 river 11037 Eurajoki E coast 4659400 river 19709 Eurajoki W coast 49100 river 45835 Flutanmetsä E coast 18200 forest 9726 forest 2038 Flutanmetsä W coast 34700 forest 13485 Itäranta coast 23100 cropland 26200 cropland 11600 Janijärvi coast 944200 lake (dried out) 143800 lake (peat) 129800 river 214 Kallanjoki coast 1784800 river 27142 Kallanjärvi coast 2635900 river 53088 lake (dried out) 155600 lake (peat) 113300 Kangas coast 39500 cropland 32100 cropland 7400 Katavansuo coast 291300 wetland (mire) 164168 forest 5914 Kaukojoki 1 coast 1886300 river 77236 Kaukojoki 2 coast 3601700 river 54624 Kaukojoki 3 coast 4022400 coast 1700 river 87405 Kaukojärvi (a) coast 9387200 coast 2438900 Kaukolampi (a) coast 415800 coast 126700 Kaunisjoki coast 1093900 river 12982 Kaunissaari W coast 744200 cropland 347200 Kiskarinsivu coast 53100 forest 13095 Kiskarintaka coast 61000 forest 12087 Koivisto coast 1091600 cropland 228800 Kornamaa coast 42400 forest 12991 forest 8619 Koskelonpelto coast 664800 cropland 99200 Lapinjoki N coast 117500 river 23577 Lapinjoki S coast 146200 river 25088 Lepänmaa coast 110400 cropland 48000 Liiklanjärvi coast 933400 lake (water) 320602 lake (reed) 187702 Liiklanpelto coast 1605000 cropland 428400 Liponjärvi coast 15790400 lake (water) 5082743 lake (reed) 468543 lake (dried out) 18400 lake (peat) 17200 Lounaisjoki 1 coast 4053200 river 19723 Lounaisjoki 2 coast 2448800 river 17949 Lounaisjoki 3 coast 2542300 river 17729 Lounaisjoki 4 coast 3785700 river 19143

(a) Develops into a lake in the last time step (year 12 020) of the TESM forecast.

72

Table 4-25 cont’d. The biosphere objects names, ecosystem types and areas at the start and end time steps in the radionuclide transport modelling.

Biosphere object Year 2 020 Ecosystem type

Area [m2]

Year 12 020 Ecosystem type

Area [m2]

Luoteisjoki 1 coast 2436600 river 63972 Luoteisjoki 2 coast 2551800 river 62215 Luoteisjoki 3 coast 2481300 river 70760 Luoteisjoki 4 coast 4177800 river 60735 Luoteisjoki 5 coast 1936100 river 71174 Marikari coast 50400 cropland 44000 Marikari cropland 3500 Martinsuo coast 51100 wetland (mire) 30918 forest 3174 Mustakarta coast 10400 forest 9999 Mäntykarinedus coast 53700 forest 14526 Mäntykarinjärvi coast 2672700 lake (water) 300149 lake (reed) 211716 lake (dried out) 84200 lake (peat) 24100 Mäntykarinmaa coast 82500 cropland 57700 Nanninmaa coast 204500 cropland 32400 Niemelä coast 15600 forest 4354 Nimetön E coast 124300 river 43261 Nimetön W coast 836500 river 36956 Pikkumetsä coast 129900 forest 6032 Prinkka coast 638900 cropland 152500 Puhinmetsä coast 52500 forest 4781 Puhinmetsä forest 674 Raunela coast 49300 cropland 87400 Raunela cropland 46300 Roopenmaa 1 coast 304000 cropland 120200 Roopenmaa 2 coast 886900 cropland 119700 Roopenmaa 3 coast 274600 cropland 139900 Rummi N forest 11643 forest 11643 Rummi S forest 14990 forest 14990 Satama coast 3700 forest 5919 Satama forest 2262 Sivujoki coast 161200 Sivusuo coast 1000700 wetland (mire) 345136 forest 15261 Susijoki coast 368600 river 20422 Susijärvi coast 4905100 lake (water) 2105513 lake (reed) 161373 Syöpävuorenhaara coast 39700 river 288 Tankarienjärvi coast 2554200 lake (water) 1295125 lake (reed) 149666 Tankarimetsä coast 113200 forest 14809 Telakka coast 9600 forest 10020 Telakka forest 626 Tuomaanlahti coast 13672000 coast 5831700 Tuomonjoki coast 271900 river 11034 Ulkojoki (a) coast 407500 coast 181800 Valkiakarinjärvi coast 778400 lake (water) 101987 lake (reed) 101427

(a) Develops into a river in the last time step (year 12 020) of the TESM forecast.

73

.

Figure 4-10. Summed areas (m2) of all biosphere objects as function of time (excluding coastal objects due to their large areas compared to the other ecosystem types).


The library of biosphere object modules and the landscape model are implemented in the PANDORA toolbox (Åstrand et al. 2005), version 3.0. The PANDORA toolbox is developed for facilitating and creating radioecological models and is implemented as an add-on to Simulink© in the commercial Matlab/Simulink© (The versions used in the KBS-3V assessment are Matlab r2008b and Simulink v7.7).

0

200

400

600

800

1000

1200

1400

1600A

rea

[m2 ]

x 10

000

Year

Forest

Wetland

River

Cropland

Lake (reed)

Lake (peat)

Lake (dry area)

Lake (water)

75

5 RADIOLOGICAL CONSEQUENCES ANALYSIS

This section addresses the last sub-process in the biosphere assessment process, radiological consequences analysis (RCA). The models and concepts presented here are used to estimate potential consequences to humans and other biota due to the spatially distributed, time-dependent radionuclide-specific activity concentrations in environ-mental media. The activity concentrations in environmental media are calculated for each biosphere object of the landscape model using the radionuclide transport model. The focus here is on the main (dose) quantities to be used in assessing compliance with regulatory criteria and constraints related to radiation protection. For other quantities related to radiological consequences, such as the risk quotients in the screening evaluation and safety indicators, their radiological consequences analysis are addressed in their respective sections (section 3 for the screening models and section 6.1 for safety indicators).

5.1 Assessing doses to humans

The main objective of the assessment of doses to humans is to determine compliance with the regulatory dose constraints. The regulatory guideline includes constraints on the annual effective dose to the most exposed people and to other people. The effective dose is the tissue-weighted sum of the equivalent doses in all specified tissues and organs of the body, where the tissue weighting factor represents the relative contribution of that tissue or organ to the total health detriment11 resulting from uniform irradiation of the body. The equivalent doses are mean absorbed doses in each tissue or organ, weighted by a factor that depends on the radiation type (ICRP 2007a). Thus, effective dose is a quantity designed to reflect the amount of health detriment likely to result from the dose, based on current radiobiological, epidemiological and medical expertise.

Doses to the public cannot be measured directly and we are in any case concerned with doses that may occur in the future. Therefore, for the purpose of protection of the public, it is necessary to characterise an individual, either hypothetical or specific, whose dose can be used for determining compliance with the relevant dose constraint. The ICRP has provided guidance (ICRP 2007b) on how to assess dose to the individual for the purposes of establishing compliance with the ICRP recommendations (ICRP 2007a). However, ICRP states (ICRP 2007b) that the guidance for the protection of future individuals in the case of disposal of long-lived radioactive waste as provided in (ICRP 2000) remains valid. ICRP (2000) recommends, in the context of assessing doses to those most likely to receive the highest doses, that exposures should be assessed on

11 A concept used to quantify the harmful health effects of radiation exposure in different parts of the body; defined as a function of several factors, including incidence of radiation-related cancer or heritable effects, lethality of these conditions, quality of life, and years of life lost owing to these conditions (ICRP 2007a).

76

the basis of the mean annual doses received by the critical group12. Further, ICRP (2000) states that it is reasonable to calculate the annual dose averaged over the lifetime of the individuals, which means that it is not necessary to calculate doses to different age groups; this average can be adequately represented by the annual dose to an adult. This is valid in the cases in which radioactive contamination of the biosphere remains relatively constant over periods that are considerably longer than the human life span, as is expected to be the case for contamination due to releases from a geological repository.

The concept adopted in this report for assessing annual doses for the purpose of determining compliance with the regulatory dose criteria is based on the guidance in (ICRP 2007b) on how to assess the dose to the ‘representative person’ This is considered to be consistent with the ICRP (2000), since the ‘representative person’ is considered to be equivalent to an ‘average member of the critical group’ (ICRP 2007b, paragraph (i)) and the dose to the ‘representative person’ is considered to be the equivalent to the mean dose in the ‘critical group’ (ICRP 2007b, paragraph (25)). The dose assessment process is described below (sections 5.1.1 and 5.1.2), followed by the applied data (section 5.1.3).

5.1.1 General dose assessment process

The present assessment deals with estimation of future doses to persons whose exposure has not yet occurred, thus it is a prospective dose assessment. According to ICRP (2007b) dose assessment is a multistage process that can be summarized as follows.

� The first stage is to obtain information about the source, including data on the types and quantities of radionuclides and radiations emitted.

� The second stage is to obtain information about the environment, specifically the concentrations of radionuclides in environmental media arising from the source in question.

� The third stage of the process is to combine concentrations with habit data that are selected based on exposure scenarios of the relevant person or group. This includes data such as the amount of time spent in different radiation fields, information on the amount of food and water consumed or air breathed.

� The fourth stage is to use dose coefficients that either relate concentrations in air or soil to external exposure rates, or that convert a unit of intake into dose.

� The fifth and final stage is to sum the contributions from external and internal exposure as appropriate. It is useful to consider the stages separately.

12 A group of people representative of those individuals in the population expected to receive the highest annual dose, which is a small enough group to be relatively homogeneous with respect to age distribution, diet, and those aspects of behaviour that affect the annual doses received.

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In the biosphere assessment process, all but the first stage, of the ICRP dose assessment process are addressed (the first stage is addressed in other parts of the safety case). The second stage is addressed in the radionuclide transport modelling sub-process, and stages three to five in the radiological consequence analysis sub-process as discussed below.

Figure 5-1. General process for assessing doses to humans.

5.1.2 Dose assessment in the present assessment

In the present safety assessment, the dose assessment follows the general process described above, as illustrated by Figure 5-1. In this section key assumptions and data needs are presented for each component in Figure 5-1 (except for compliance assessment, which is addressed in section 5.3).

Environmental information The key information regarding the environment is obtained from landscape modelling, namely the geometric properties of biosphere objects in the landscape model (section 4.4) and the time-dependent and radionuclide-specific radioactivity concentrations in environmental media. In addition, information needed for deriving total productivity of edibles and aggregated concentration ratios of radionuclides from environmental media to edible food products, such as production rates of individual food products, is also presented.

The productivity of edibles is calculated by summing over all plant parts and animal products normally consumed by man. The original data, mostly in terms of fresh weight, from BSD-2009 are converted to kilograms of carbon. The carbon content is calculated based on the contents of proteins, carbohydrates and lipids (fat), as reported in the FINELI database of the National Institute for Health and Welfare (www.fineli.fi). The derivation of productivity of edibles and values are presented in detail in Ikonen et al. (2010a), section 6.2.

The aggregated concentration ratios (CFagg) are both radionuclide-specific and ecosystem-specific, and derived as the productivity-weighted average of the soil-to-edible concentration ratios for all edibles produced in the ecosystem in question. The concentration ratios for each type of edible are based on site and literature data. The aggregated concentration ratio also uses the carbon content in edibles, which is derived in the same way as described above for productivity. This approach for deriving aggregated concentration ratios describes the average transfer of radionuclides from soil to foodstuffs consumed in general, assuming that there is no preference for any

Environmental�information

Exposure�characteristics

Dose�calculation

Dose�identification

Compliance�assessment

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particular food. Thus, it gives a reasonable measure of the intake of radionuclides with food by people who fully satisfy their annual demand of food from one biosphere object, without making assumptions regarding details of their diets. The only assumption needed is whether to include an item or not in the list of edibles from each ecosystem. The derivation of aggregated concentration ratios (CFagg) and values are presented in detail in Helin et al. (2010), section 4.8.3.

Exposure characteristics A specific set of individual exposure characteristics13 is selected, including, among others, food and water intake rates. In general, diet, residence, and other information needed to estimate exposure can be referred to as ‘habit data’ (ICRP 2007b). When assigning the exposure characteristics, care has been exercised to prevent excessive conservatism; selecting extreme (cautious) percentile values for every variable could lead to a significant and unrealistic overestimation of the dose, and may unduly burden the outcome of the safety assessment. The key features in the selected exposure characteristics, and the selection of habit data, can be summarised as follows:

� the exposure characteristics aim at being reasonable and sustainable, in line with the concept of assessing doses to the representative person (ICRP 2007b),

� all main pathways are considered: ingestion of food, ingestion of water, inhalation and external exposure,

� the number of exposed persons is limited by the capability of the biosphere objects to produce food and drinking water, by the size of suitable residential areas from a present-day perspective, and by present demography,

� average present-day intake rates are assumed for the representative person, � cautious assumptions are made for occupancy data (e.g., time spent outdoors), � a very cautious assumption is made for usage of local resources (i.e., all foodstuffs

and water originate from contaminated areas), and � individuals have no preferences regarding food.

Dose calculation The dose calculation method applied follows the concept and equations described in Avila & Bergström (2006). The calculations are based on values of food energy intake (carbon) and water intake given by the ICRP for Reference Man (ICRP 1975, 2002). The doses arising from intake of contaminated water and food, inhalation of contaminated air, and external exposure from contaminated areas are calculated by using dose coefficients14. The dose coefficients for ingestion and inhalation are based on

13 The term ‘exposure characteristics’ is equivalent to the ICRP (2007) ‘exposure scenario’. This terminology is selected to avoid confusion with the term dose assessment scenario used in the present assessment. 14 A synonym for dose per unit intake, but also used to describe other coefficients linking quantities or concentrations of activity to doses or dose rates, such as the external dose rate at a specified distance above a surface with a deposit of a specified activity per unit area of a radionuclide (IAEA 2007).

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the values recommended by the International Commission on Radiological Protection (ICRP 1996) for adults. Dose coefficients for external radiation from radionuclides uniformly distributed to infinite depth, and an effectively infinite lateral extent of the contamination, in soil are used (based on Table III.7 in EPA 1993, extracted using the software Radiological Toolbox15).

Some radionuclides considered in the assessment form radioactive progeny, or daughter products, when undergoing radioactive decay. A series of radionuclides formed by successive radioactive decay is referred to as a decay chain, and the first member of the chain is referred to as the parent. The dose coefficients do not include the contribution to the dose from exposure or intake of other radionuclides that might be present as daughter products in the environment. Thus, coefficients for different members of a decay chain may be needed to be combined in order to reflect the growth of radioactive progeny in the environment. How this combination is done depends on the time period considered and the environmental behaviour of the radionuclides in question. For example, when considering external exposure to Cs-137 on a ground surface, the short-lived daughter Ba-137m (T½ = 2.5 m) is also present on the ground. Thus, the dose coefficients for Cs-137 and Ba-137m need to be combined. In this case, it can be assumed that, after only a few hours, Ba-137m will be in secular equilibrium with Cs-137. Thus, the dose coefficient for the decay chain with the parent Cs-137 can be obtained by adding the dose coefficient of Ba-137m, taking the branching factor in the radioactive decay into account, to the dose coefficient for Cs-137.

For intake of radionuclides, relatively long-lived, the contribution to the dose from intake of its short-lived radioactive progeny (defined here as daughter products with a half-life in the order of days or shorter) present in the environment is assumed to be insignificant compared with the dose from the parent. For this reason, the dose coefficients for parent radionuclides and their short-lived daughters does not need to be combined. For example, when considering internal exposure to Cs-137 in food products, the short-lived daughter Ba-137m is also present in the food, but it is sufficient to apply the dose coefficient for the Cs-137 in the dose calculation. However, after intake of parent radionuclide, the production and decay of short-lived daughters in the body may contribute significantly to the dose. For this reason, dose coefficients for ingestion and inhalation of radionuclides already include all contribution to the dose from growth of daughters in the body. The present assessment applies the above discussed environmental information and exposure characteristics discussed above to calculate landscape doses.

Landscape doses The present assessment applies the above discussed environmental information and exposure characteristics to calculate landscape doses. The landscape dose is defined as follows: 15 U.S. Nuclear Regulatory Commission Radiological Toolbox, (version 2.0.0, August 2006) (www.nrc.gov/about-nrc/regulatory/research/radiological-toolbox.html)

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� Landscape dose (EL) – the pathway-, radionuclide- and biosphere object-specific annual effective dose to an individual.

In the dose calculation, the exposure pathways inhalation and external exposure are combined into the same dose quantity (EL,IE), whereas doses due to food ingestion (EL,F) and water consumption (EL,W) are kept separate. The reason for this is that the exposures from external and inhalation pathways occur simultaneously at the same location, whereas it is not always the case that the consumed food and water originate from the same location. This approach gives the possibility to calculate the dose to a person who, for example, e.g. lives in a forest, drinks water from a nearby lake and eats food produced on nearby cropland. For the biosphere objects in the landscape model, landscape doses are calculated for the different pathways, as follows:

� Landscape dose from food ingestion (EL,F) is the product of the annual intake of a radionuclide (the activity concentration in the environmental media multiplied by the aggregated concentration ratio and the annual intake of carbon) and the corresponding dose coefficient for ingestion.

� Landscape dose from water consumption (EL,W) is the product of the annual intake of a radionuclide (the activity concentration in the water multiplied by the annual intake of drinking water) and the corresponding dose coefficient for ingestion. This dose is only calculated for lake and river objects.

� Landscape dose from inhalation and external exposure (EL,IE). This dose is the sum of doses due to inhalation and external exposure from terrestrial16 objects (forest, wetland and cropland). The contribution from inhalation is the product of the annual intake of a radionuclide via inhalation and the corresponding dose coefficient for inhalation. The contribution from external exposure is the product of the activity concentration in soil and the corresponding dose coefficient for external exposure.

It should be noted that when calculating EL,F and EL,W, the situation will occur that there is not enough available food, or water, in a biosphere object to satisfy one person’s annual demand. Then, the remaining part is added from another object. How this is implemented is addressed below in dose identification.

Dose identification Here, the pathway-specific dose contributions are combined to obtain the “total” dose to each person in the whole exposed population. The approach to identifying the all-pathway dose, denoted below as the annual landscape dose, EALD, to a person is to identify the dose maxima from each pathway, and sum these. Thus, the dose to the most exposed person is the sum of the doses from ingestion of the most contaminated food, ingestion of the most contaminated water, and the dose from inhalation and external

16 The potential contributions to the dose from inhalation and external exposure from aquatic objects are considered negligible for the radionuclides and activity levels of interest.

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exposure arising from staying all time in the biosphere object giving the highest dose. The annual landscape dose, EALD, is derived in the present assessment as follows:

EALD = max[EL,F] + max[EL,W] + max[EL,IE], (5-1)

When deriving EALD, it is also required that the annual consumption of food and water originates from the exposed area to as full an extent as possible. Thus, the dose may be a sum of contributions from different biosphere objects (or even from different biosphere object types). For example, if a forest is the object capable of producing food with highest “dose rate”, but not enough food to satisfy one person’s annual food demand, it is then assumed that this person eats everything produced from this forest; and the rest of the food the person needs is taken from the biosphere object producing food with the second highest “dose rate”.

In the current assessment, the approach is to derive EALD to each exposed individual. The distribution of annual landscape doses between the different individuals of an exposed population is referred to below as the dose distribution; the underlying assumptions and the procedure for deriving this distribution are explained below. The doses to each exposed person are derived, but information about the exposed population is also needed. The highest numbers of people that can satisfy their annual food demand, drinking water demand, and demand regarding residential areas, by utilising contaminated food, water and land are referred to as maximum sustainable populations; these are derived for each exposure pathway as follows:

� Maximum sustainable population – food ingestion, NF, is the carbon content in all contaminated edibles produced during one year divided by the average annual carbon demand by an individual.

� Maximum sustainable population – water intake, NW is derived as the available drinking water (from all contaminated surface water bodies) divided by the average annual water intake, and multiplied by a usage factor taking into account that not all available water is utilised as drinking water.

� Maximum sustainable population – inhalation and external exposure, NIE, is derived by dividing the total suitable residential area by an assumed population density. Here, only forest objects are included and the population density is cautiously selected as the highest present urban density in Finland (Helsinki).

It is cautiously assumed that the properties of the landscape model remain unaffected when humans utilise the area. This means, for example, that the amount of edibles annually produced is not affected, not even for forest areas that are assumed to be urbanised, and that the water balance is not affected by drinking part of the available water. These assumptions are particularly important to make since the dose assessment spans over many generations; the habits and use of natural resources of one generation are assumed not to affect the habits and use of natural resources of succeeding generations.

Procedure for deriving the dose distribution The derivation of the dose distribution is performed iteratively, starting by deriving the annual landscape dose to the most exposed person, and then subtracting what caused

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that person’s exposure from the landscape model (one person’s annual demand of food or water, or the size of the residential area). Then, the annual landscape dose to the second most exposed person is derived, taking into account what the most exposed person has “used” from the landscape. This procedure is repeated until all three pathways have been the dominating pathway in the annual landscape dose at least once, or until a pre-selected upper limit on the size of the exposed group is reached. The iterative procedure is described in more mathematical terms below, illustrated with an example.

� Step 1a. Derive max[EL,F]. Identify the highest EL,F in any biosphere object that is able to sustain >0 individuals (for example, EL,F,X from object X, that can sustain NF,X individuals with food); then:

i) if NF,X �1: max[EL,F]= EL,F,X and reduce NF,X by 1; proceed to Step 2a.

ii) if NF,X <1: proceed to Step 1b.

� Step 1b. Identify the highest EL,F in any biosphere object, excluding object X, that can sustain >0 individuals (for example, EL,F,Y from object Y, that can sustain NF,Y individuals); then:

i) if NF,X+NF,Y �1: max[EL,F]= NF,X×EL,F,X + (1–NF,Y)×EL,F,W. Then set NF,X to zero and reduce NF,Y by 1–NF,X; proceed to Step 2a.

ii) if NF,X+NF,W <1: redo Step 1b in an analogue way with more biosphere objects, until they together can sustain at least one individual with food.

� Step 2a and 2b. Derive max[EL,W]. Identify the highest EL,W in any biosphere object that is able to sustain >0 individuals with water (NW). Perform steps 2a and 2b in analogy with steps 1a and 1b; proceed to Step 3a.

� Steps 3a and 3b. Derive max[EL,IE]. Identify the highest EL,IE in any biosphere object that has a suitable residential able to sustain >0 individuals (NIE). Perform also steps 3a and 3b in analogy with steps 1a and 1b; proceed to Step 4.

� Step 4. Derive EALD. Sum the three highest pathway-specific doses, according to eq. (5-1), derived in steps 1 to 3 to obtain the annual landscape dose to the most exposed person, EALD(1); proceed to Step 5.

� Step 5. Repeat step 1 to step 4 to identify the EALD to the second most exposed person, EALD(2), third most exposed person EALD(3), etc. The procedure is iterated N times until:

i) each pathways-specific landscape dose (EL,F, EL,W and EL,IE) has been the dominating contributor to EALD at least once, or

ii) a pre-selected upper limit of N is reached.

The result from the procedure above is that EALD has been derived for N number of individuals. This group is defined as the exposed population. The upper limit of N is selected on the basis of the size of the current population in the Olkiluoto region. This approach to derive the dose distribution ensures that no potentially highly exposed

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groups are excluded. For example, if the potential residential areas (forest objects) are small, the maximum population living in the close vicinity of the site may be a small number. However, other contaminated objects may still be capable of producing food and water for a larger population, which is then accounted for in the dose distribution. Or, in other words, when food or water intake is the dominating pathway, no assumption is made regarding where the exposed person lives.

Identifying the dose to a representative person The dose distribution is used as the basis for identifying annual landscape doses to:

� a representative person from the most exposed group, which is the sub-group of the above identified exposed population that receives the highest doses, and

� a representative person for the other people in the exposed population.

ICRP (2007b) recommends using the 95th dose percentile as the basis for selection of the most exposed group. Further, ICRP recommends that in the case when relevant dose constraints might be exceeded by a few tens of people or more, the characteristics of these people need to be explored (ICRP 2007b). These recommendations are appropriate for probabilistic assessments, whereas the present dose assessment is deterministic. However, since a distribution of doses is derived, a similar approach for selecting the most exposed group is applied here. The most exposed group is defined as consisting of the smallest of: 1) the 5% most exposed persons in the dose distribution, and 2) a few tens of people, here selected as the 50 most exposed persons. As the final step, the annual landscape doses to the two representative persons are identified as follows:

� the annual landscape dose to a representative person within the most exposed group, Egroup, is the average EALD for all persons in the most exposed group.

� the annual landscape dose to a representative person among the other individuals in the exposed population, Epop, is the average EALD in the exposed population, excluding the most exposed group.

5.1.3 Parameters and applied values

Environmental information The derivation of the parameter values for productivity of edibles are documented in Ikonen et al. (2010a), section 6.2, and are summarised in Table 5-1. The derivation of parameter values for aggregated concentration ratios are documented in Helin et al. (2010), section 4.8.3 and are summarised in Table 5-2.

Table 5-1. Productivity of edibles [kgC/m2/y].

Total Berries Mushroom Game Fish Forest (class 1) 5.8E-4 2.5E-5 5.1E-4 4.1E-5 Forest (class 2) 6.9E-4 1.4E-4 5.1E-4 4.1E-5 Forest (class 3) 5.7E-4 1.6E-5 5.1E-4 4.1E-5 Wetland 1.7E-4 1.2E-4 2.6E-5 2.6E-5 Croplands (field vegetables) 1.5E-1 Lakes & rivers 5.3E-5 1.4E-7 5.3E-5 Coastal areas 8.5E-5 1.4E-7 8.5E-5

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Table 5-2. Element-specific aggregated concentration ratios for soils in forest, wetlands and croplands [(Bq/kgC)/(Bq/kqdw)], and for water in freshwaters and coastal areas [(Bq/kgC)/(Bq/m3)].

Nuclide Forest (class 1)

Forest (class 2)

Forest (class 3)

Wetland Cropland (a)

Freshwater Coast

Cl 3.8E+1 3.6E+1 3.8E+1 3.2E+1 5.3E-2 6.7E-1 4.2E-4 I 4.4E+0 3.7E+0 4.4E+0 1.3E+0 5.3E-2 7.2E-1 5.4E-2

Mo 1.5E+0 2.3E+0 1.4E+0 4.9E+0 2.1E+0 1.8E-2 1.5E-2 Nb 1.4E+0 2.2E+0 1.3E+0 4.7E+0 2.7E-2 4.9E-1 7.8E-1 Cs 2.2E+2 1.8E+2 2.2E+2 4.3E+1 8.0E-2 3.7E+1 3.7E-1 Ni 1.6E+0 3.3E+0 1.4E+0 8.9E+0 1.6E-1 4.8E-1 3.9E-1 Se 1.5E+0 1.4E+0 1.5E+0 1.1E+0 2.9E+0 2.4E+1 5.4E+1Sr 1.4E+0 2.0E+0 1.3E+0 5.5E+0 8.3E-1 2.2E+0 9.2E-2 Y 1.5E+0 2.3E+0 1.4E+0 4.9E+0 5.3E-5 4.9E-1 3.9E-1

Pd 1.6E+0 3.3E+0 1.4E+0 8.9E+0 1.6E-1 4.8E-1 3.9E-1 Sn 5.7E-2 5.2E-2 5.7E-2 7.0E-2 2.9E-2 2.6E+0 3.1E+1Sb 5.7E-2 5.2E-2 5.7E-2 7.0E-2 4.8E-3 2.5E+0 1.6E+0

(a) values for field vegetables

Exposure characteristics Ingestion of food (intake rate of carbon). The intake rate of carbon via ingestion of food is selected to 110 kgC/y. This value is applicable for an adult male, based on the Reference man (ICRP 1975, 2004). The carbon intake is estimated from the intake of protein, carbohydrates and fats given in ICRP (1975).

Ingestion of water (intake rate of water). The intake rate of water has been selected to 0.6 m3/y, which corresponds to a daily intake of about 1.6 L. This value does not include water consumed with food and is applicable for an adult male, based on the Reference man (ICRP 1975, 2004).

Inhalation rate. The air intake rate is selected to 1 m3/h, which is close to the highest value of 22 m3/day reported by ICRP (2004).

Exposure time for the pathways inhalation and external exposure is selected to 8760 h/y (corresponds to 24 h/day). Thus, for internal exposure due to inhalation of contaminated air and external exposure due to contaminated ground, outdoor exposure of 100 % of the time is assumed. In most cases this assumption gives cautious estimates, as the radionuclide contamination of the air mainly comes from resuspension of soil particles and the reduction of external exposure due to the shielding from dwellings when spending time indoors is not taken credit for.

Population density for suitable residential areas, used when deriving the maximum sustainable population for the inhalation and external exposure pathway, is selected to 3,000 persons/km2, which is somewhat higher than the present situation in Helsinki. At the end of 2008 were 578,961 persons living in Helsinki (Population Register Centre, www.vrk.fi) and the terrestrial area of Helsinki was 213.0 km2 (National Land survey of Finland, www.maanmittauslaitos.fi). Thus, each individual occupy an area of 333 m2.

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Maximum size of the exposed population, used when deriving the dose distribution, is selected to 6000 persons. This is approximately the present regional population; at the end of June 2009, 5901 persons were living in Eurajoki municipally (Population Register Centre, www.vrk.fi).

Usage factor is the fraction of the discharge of water from surface water bodies, assumed to be used as drinking water. A value of 1% is selected, assumed to be low enough not to disturb the water balance. However, to simplify the implementation of deriving the dose distribution, it is assumed that the maximum sustainable population due to water intake (NW) always is greater than the maximum size of the exposed population. Haapanen et al. (2009), Table 11-2, gives values for the discharge into the modelled area. The minimum values for the daily discharge and mean annual discharge are 0.03 and 4.2 m3/s, respectively. Then, when applying the usage factor of 1% and an average intake rate of drinking water of 0.6 m3/y, maximum sustainable populations due to water intake is about 15,000 and 2,100,000 persons, for the minimum daily and annual discharges, respectively.

Dose calculation Dose coefficients. The values used are the same values as in Tier 2 in the screening evaluation (section 3.3), and are presented in Table 5-3, limited to in the key set of radionuclides. The dose coefficients for ingestions are based on the values recommended by the International Commission on Radiological Protection (ICRP 1996) for adults. Contributions from radionuclide progeny are included. The dose coefficients for inhalation are also based on the values recommended by the International Commission on Radiological Protection (ICRP 1996) for adults. In ICRP (1996), three values are given, one for each class of absorption in the lungs: F (fast), M (moderate) and S (slow). The class resulting in the highest exposure was chosen for each radionuclide. The dose coefficients used for external radiation are due to radionuclides uniformly distributed to an infinite depth in soil. The (effective) dose coefficients are based on Tables III.3 and III.7 in EPA (1993), and extracted using the software Radiological Toolbox.

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Table 5-3. Dose coefficients for ingestion and inhalation (ICRP 1996) and for external exposure (EPA 1993) due to spatially uniformly distributed radionuclides on the ground surface and to an infinite depth. The coefficients marked with ‘+d’ include the contribution from the dose coefficient(s) from radioactive progeny (cf. Table 2-1).

Nuclide

Ingestion [Sv/Bq]

Inhalation [Sv/Bq]

External exposure [(Sv/h)/(Bq/m3)]

C-14 5.8E-10 5.8E-9 2.1E-19 Cl-36 9.3E-10 7.3E-9 4.8E-17 Ni-59 6.3E-11 4.4E-10 0 Se-79 2.9E-9 6.8E-9 3.0E-19

Sr-90+d 3.1E-8 1.6E-7 7.8E-16 Mo-93+d 4.6E-9 4.1E-9 9.4E-18

Nb-94 1.7E-9 4.9E-8 1.8E-13 Pd-107 3.7E-11 5.9E-10 0

Sn-126+d 5.0E-9 2.8E-8 2.2E-13 I-129 1.1E-7 9.8E-9 1.8E-16

Cs-135 2.0E-9 8.6E-9 6.2E-19

5.2 Assessing doses to other biota

The assessment by Posiva of radiological consequences to other biota is not as mature as for humans. This is also true internationally. In the current assessment, typical absorbed dose rates to flora and fauna of the types currently present at the site are estimated, mainly based on the ERICA integrated approach. The dose assessment process in the 2009 biosphere assessment is described in section 5.2.2, followed by the applied data (section 5.2.3); but first, the ERICA approach is summarised.

5.2.1 ERICA approach

The ERICA project (Beresford et al. 2007) was conducted under the EC 6th Framework Program. It aimed to provide an integrated approach to scientific, managerial and societal issues concerning the environmental effects of contaminants emitting ionising radiation, with emphasis on biota and ecosystems. Exposure of biota to radiation and transfer of radionuclides in the environment, are intimately linked. Exposure of biota to ionising radiation occurs when radionuclides, present naturally in the environment or released through man’s activities, decay releasing radiation of various types and energies. The pathways leading to exposure in aquatic and terrestrial ecosystems can be split into several categories (Brown 2009):

1. Inhalation of (re)suspended contaminated particles or gaseous radionuclides. This pathway is relevant for terrestrial animals and marine birds and mammals.

2. Contamination of fur, feathers and skin. This has both an external exposure component, e.g. � and �-emitting radionuclides on or near the epidermis cause irradiation of living cells beneath and an internal exposure component as contaminants are ingested and incorporated into the body of the animal.

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3. Ingestion of lower trophic plants and animals. This leads to direct irradiation of the digestive tract and internal exposure if the radionuclide becomes assimilated and distributed within the animal’s body.

4. Direct uptake from the water column, in the case of truly aquatic organisms (e.g. fish, molluscs, crustaceans), leading to direct irradiation of respiratory system, e.g. gills, and internal exposure if the radionuclide becomes assimilated and distributed within the animal’s body.

5. Intake of water contaminated by radionuclides through the gastrointestinal tract, i.e. the organism drinks water. The same exposure categories as discussed in point 3 are relevant here.

6. External exposure. This essentially occurs from exposure to �-irradiation and to a much lesser extent �-irradiation, originating from radionuclides present in the organism’s habitat. For microscopic organisms, irradiation from �-particles is also possible. The configuration of the source relative to the target clearly depends on the organism’s ecological characteristics and habitat. A benthic dwelling fish will, for example, be exposed to radiation from radionuclides present in the water column and deposited sediments, whereas a pelagic fish may only be exposed to the former.

In the context of European approaches (EPIC17, FASSET18 and ERICA), inhalation and contamination of fur, feathers and skin (exposure pathways (1) and (2) in the above list) have not been considered explicitly in the derivation of transfer parameters or dose-conversion coefficients. The ingestion and direct uptake from water pathways (points (3) and (4) in the above list) have been considered in so far as they relate to internal body burdens of contaminants normally under equilibrium conditions. Irradiation by unassimilated contaminants in the gastrointestinal tract has not been considered nor has exposure occurring due to the consumption of water (point (5) above). Finally, external exposures have been considered in some detail both in terms of contaminant transfer to terrestrial and aquatic habitats and from the dosimetric perspective. For utilisation within the impact assessment process, each (ERICA) reference organism (Table 5-4) has been assigned default attributes relating to radioecology and dosimetry, these being:

� Equilibrium concentration ratios

� Default occupancy factors

� Default ellipsoidal geometries (with the 3 primary axes defined) allowing dose-conversion factors to be defined.

17 EPIC (Environmental Protection from Ionising Contaminants in the Arctic). Funded under the European Commission’s Inco-Copernicus Programme.Contract No: ICA2-CT-2000-10032. 18 FASSET (Framework for Assessment of Environmental Impact). The FASSET project (contract N°: FIGE-CT-2000-00102) was launched in November 2000 under the EC 5th Framework Programme.

88

Table 5-4. ERICA Reference organisms. The corresponding ICRP Reference Animal or Plant (RAP) are indicated in italics within brackets (modified from Beresford et al. 2007).

Terrestrial Freshwater Marine Amphibian (frog) Amphibian (frog) Wading bird (duck) Bird (duck) Benthic fish Benthic fish (flat fish) Bird egg (duck egg) Bird (duck) Bivalve mollusc Detrivorous invertebrate Bivalve mollusc Crustacean (crab) Flying insect (bee) Crustacean Macroalgae (brown seaweed) Gastropod Gastropod Mammal Grass & herb (wild grass) Insect larvae Pelagic fish Lichen/bryophyte Mammal Phytoplankton Mammal (rat) Pelagic fish (salmonid/trout) Polychaete worm Larger mammal (deer) Phytoplankton Reptile Reptile Vascular plant Vascular plant Shrub Zooplankton Zooplankton Tree (pine) Sea anemone/true coral

(colony + polyp) Soil invertebrate (earthworm)

The ERICA Tool, which is a piece of software, has a structure based upon the ERICA tiered approach to assessing the radiological risk to other biota. The tiers can be summarised as (modified from Brown et al. 2008):

� Tier 1 assessments are based on environmental media concentrations, and use pre-determined environmental media concentration limits (EMCL) to estimate risk quotients (RQ).

� Tier 2 calculates absorbed dose rates, but allows examination and editing of most of the parameters used, including concentration ratios, distribution coefficients, percentage dry weight soil or sediment, dose conversion coefficients, radiation weighting factors and occupancy factors.

� Tier 3 offers the same flexibility as Tier 2, but allows the option of running the assessment probabilistically if the underling parameter probability distribution functions are defined.

The Tier 1 assessment is designed to be simple and cautious, requiring a minimum of input data. The default screening value (dose rate) in the ERICA Integrated Approach is derived from a species sensitivity distribution analysis performed on chronic exposure data in the FREDERICA database and is supported by other methods for determining predicted no-effect values. This selected value of 10 �Sv/h is also the recommended generic screening values by the PROTECT project (Anderssson et al. 2008). In ERICA Tier 1, environmental activity concentrations are compared to the Environmental Media Concentration Limit (EMCL). The EMCL is derived for each radionuclide-reference organism combination by back-calculating from the proposed screening dose rate. The EMCLs used in Tier 1 of the ERICA approach are utilised in the screening evaluation (section 3), and ERICA Tier 3 forms the basis for the current approach to deriving typical absorbed dose rates to the other biota, as described below.

89

5.2.2 Approach in Posiva’s biosphere assessment

To assess the consequences to other biota, typical absorbed dose rates to flora and fauna of the types currently present at the site are derived. The general approach is presented in Figure 5-2. One major difference regarding the other biota, compared with assessing doses to humans, is the wide variety of taxa. Consequently, the first task to carry out is to identify a group of assessment species (reference organisms; reference animals and plants, or RAPs). Then, as for humans, the assessment is a multistage process, and can be summarised as follows:

� Obtain information about the environment, specifically the estimated activity concentrations of radionuclides in environmental media, and identify assessment species and their specific geometrical data.

� Derive the internal concentrations in the biota to be assessed, by application of concentration ratios. Ingestion and inhalation are described through the use of aggregated concentration ratios.

� Calculate internal and external exposures. Following the methodology recently adopted internationally, a simplified (ellipsoidal, see Figure 5-3) geometry representative of the dimensions of the main body of the organism is assumed in the derivation of dose conversion coefficients. Species-specific occupancies are also considered.

� Sum the contributions from external and internal exposure as appropriate.

� Lastly, identify typical absorbed dose rates for reference organisms to be used in the compliance assessment.

Figure 5-2. General process for dose assessment for the other biota.

Environmental�radioactivity�

concentrations��(soil,�sediment,�water)

Total�ecosystem�exposure

Typical�absorbed�dose�rates

Internal�concentrations

External�exposure

Internal�exposure

Simplified�geometry�(ellipsoid)

Transfer�factors�(agg.)Concentration�ratios

Occupancy�habits Simplified�geometry�(ellipsoid)

90

Figure 5-3. Illustrative picture of the applied simplified geometry and the exposure pathways considered (external radiation; combined ingestion and inhalation); drawing by Ari Ikonen/Posiva Oy.

Dose calculation and identification The dose calculations were performed using a script implemented in Matlab, in order to handle the time-series of activity concentrations, where the DCC-values and the radiation weighing factors were obtained by using the ERICA tool. All environmental activity concentrations used as inputs to the dose calculations were from the landscape modelling. The activity concentrations were taken from all ecosystem objects and for all time-points. For each ecosystem the compartments used as inputs were: water and active sediment for aquatic objects, rooted mineral soil for croplands and rooted mineral soil and humus (weighted using the root fractions) for forest and wetlands.

The total dose rates were calculated for each assessment species, from each ecosystem object and for all time-points. For each time-point, the maximum dose rate for each assessment species was taken from all ecosystem objects. Finally the maximum over time for each assessment species was taken as the identified maximum dose rate. The calculation process applied for terrestrial and aquatic ecosystems are illustrated in Figure 5-4, and the corresponding equations are presented in Tables 5-5 and 5-6.

91

Figure 5-4. Diagram showing the calculation steps for obtaining the total dose rate for the assessment species from the terrestrial (top) and aquatic ecosystems (below).

Table 5-5. Equations applied when calculating the total dose rate for the assessment species from terrestrial ecosystems. Equations in Figure 5-4 Eq. 1 Cj

i = Csoil, i * CFij

Eq. 2 Djext, weighted, i = (DCCj

ext, InSoil, i * vjInSoil + DCCj

ext, OnSoil, i * vjOnSoil) * Csoil, i

Eq. 3 Djint, weighted, i = (wlow�, i * DCCj

int, low �, i + w��, i * DCCjint, ��, i + w�, i * DCCj

int, �, i) * Cji

Eq. 4 Djtotal, total = @ A��/)BC8

Parameter CFj

i Concentration factor for the i-th radionuclide and j-th organism [(Bq/kgfw)/(Bq/kgdw)] vj

InSoil Occupancy factor for the j-th organism in soil [-] vj

OnSoil Occupancy factor for the j-th organism on soil [-] wlow�, i Weighing factor of the low beta radiation for the i-th radionuclide [-] w��, i Weighing factor of the beta/gamma radiation for the i-th radionuclide [-] w�, i Weighing factor of the alpha radiation for the i-th radionuclide [-] Csoil, i Concentration of the i-th radionuclide in soil [Bq/kgdw] Cj

i Concentration of the i-th radionuclide in the j-th organism [Bq/kgfw] Dj

ext, i Weighted external dose rate from the i-th radionuclide in the j-th organism i [μGy/h] Dj

int, i Weighted internal dose rate from the i-th radionuclide in the j-th organism [μGy/h] Dj

total, i Weighted total dose rate from the i-th radionuclide in the j-th organism [μGy/h] Dj

total, total Weighted total dose rate from all N radionuclides in the j-th organism [μGy/h]

92

Table 5-6. Equations applied when calculating the total dose rate for the assessment species from aquatic ecosystems. Equations in Figure 5-4 Eq. 1 Ci

j = CFij * Cj

w, i

Eq. 2 Djext, weighted, i = (wlow�, i * DCCj

ext, low �, i + w��, i * DCCjext, ��, i) * vj

water +0.5 * vjwatsurf +0.5 *

vjsedsurf) * Cw, i + (0.5 * vj

sedsurf + vjsed) * Csed, i

Eq. 3 Djint, weighted, i = (wlow�, i * DCCj

int, low �, i + w��, i * DCCjint, ��, i + w�, i * DCCj

int, �, i) * Cji

Eq. 4 Djtotal, total = @ A��/)BC8

Parameter CFj

i Concentration factor for the i-th radionuclide and j-th organism [(Bq/kgfw)/(Bq/L)] vj

water Occupancy factor for the j-th reference organism in habitat water [-] vj

watsurf Occupancy factor for the j-th reference organism in habitat water surface [-] vj

sedsurf Occupancy factor for the j-th reference organism in habitat sediment surface [-] vj

sed Occupancy factor for the j-th reference organism in habitat sediment [-] wlow�, i Weighing factor of the low beta radiation for the i-th radionuclide [-] w��, i Weighing factor of the beta/gamma radiation for the i-th radionuclide [-] w�, i Weighing factor of the alpha radiation for the i-th radionuclide [-] Cw,i Concentration of the i-th radionuclide in water [Bq/m3] Csed, i Concentration of the i-th radionuclide in sediments [Bq/kgdw] Cj

i Concentration of the i-th radionuclide in the j-th ref. organism [Bq/kgfw] Dj

ext, i Weighted external dose rate from the i-th radionuclide in the j-th organism [μGy/h] Dj

int, i Weighted internal dose rate from the i-th radionuclide in the j-th organism [μGy/h] Dj

total, i Weighted total dose rate from the i-th radionuclide in the j-th organism [μGy/h] Dj

total, total Weighted total dose rate from all N radionuclides in the j-th organism [μGy/h] 5.2.3 Parameters and applied values

The selected assessment species and the parameter values used in the derivation of absorbed dose rates are presented here.

Assessment species and their properties The assessment species selected for the Olkiluoto site are summarised in Table 11-23 in Haapanen et al. (2009) and described in detail in Ikonen et al (2010a). The selected species are typical of the Olkiluoto terrestrial area, sea area or in the lakes in the Reference area; they are based on expert judgement, and partially on available data, and cover the significant trophy levels (roles) in the food webs of the ecosystems prevailing and expected at the site. Table 5-7 to Table 5-9 show the selected assessment species, and the respective ERICA reference organisms, and their properties used in the dose assessment. The body diameters and weights for the assessment species are collected from site data and literature, and interpreted as major axis lengths of an ellipsoid. The assessment species “tree” has been divided into the stem below the crown and the crown including the upper part of the stem.

Concentration ratios The values for concentration ratios are selected as the default values in the ERICA assessment tool (Beresford et al. 2007). These are justified in (Beresford et al. 2008, Hosseini et al. 2008). One exception is the concentration ratios to terrestrial plants, derived as a combination of site and literature data provided in Helin et al. 2010. The values are presented in Table 5-10 to Table 5-12.

93

Tabl

e 5-

7. S

elec

ted

terr

estr

ial a

sses

smen

t spe

cies

and

thei

r pro

pert

ies (

see

Ikon

en e

t al.

(201

0a) f

or re

fere

nces

to si

ze a

nd w

eigh

ts).

Ass

essm

ent s

peci

es

ER

ICA

ref

eren

ce

orga

nism

A

vera

ge b

ody

wei

ght [

kgFW

] W

idth

(a)

[m]

Hei

ght

[m]

Dep

th(b

)

[m]

Occ

upan

cy fa

ctor

s

Her

bivo

rous

inve

rtebr

ate,

Rin

glet

fly

ing

inse

ct

6.6E

-5

1.5E

-2

2.0E

-3

2.0E

-3

On

soil

100

%

Her

bivo

rous

bird

, Haz

el g

rous

e (c)

bird

4.

0E-1

1.

9E-1

1.

2E-1

1.

2E-1

O

n so

il 10

0 %

H

erbi

voro

us ro

dent

, Ban

k vo

le

mam

mal

(rat

) 2.

0E-2

7.

0E-2

3.

5E-2

3.

5E-2

O

n so

il 10

0 %

H

erbi

voro

us m

amm

al, M

ount

ain

hare

m

amm

al (r

at)

3.5E

+0

3.5E

-1

1.5E

-1

1.5E

-1

On

soil

100

%

Larg

e he

rbiv

orou

s mam

mal

, Moo

se

mam

mal

(dee

r)

3.5E

+2

1.7E

+0

7.5E

-1

5.0E

-1

On

soil

100

%

Om

nivo

rous

inve

rtebr

ate,

Ant

-

1.0E

-5

8.0E

-3

2.0E

-3

2.0E

-3

In so

il 10

0 %

O

mni

voro

us re

ptile

/am

phib

ian,

Com

mon

frog

am

phib

ian

4.0E

-2

4.5E

-2

3.0E

-2

2.5E

-2

In/o

n so

il 50

%/5

0%

Inse

ctiv

orou

s/om

nivo

rous

bird

, Hoo

ded

crow

(c)

bird

5.

3E-1

1.

8E-1

7.

0E-2

7.

0E-2

O

n so

il 10

0 %

O

mni

voro

us m

amm

al, R

ed fo

x

mam

mal

(rat

) 6.

0E+0

4.

5E-1

1.

3E-1

1.

3E-1

O

n so

il 10

0 %

La

rge

omni

voro

us m

amm

al, B

row

n be

ar, m

ale(d

) m

amm

al (d

eer)

2.

0E+2

2.

5E+0

8.

0E-1

6.

0E-1

In

/on

soil

50%

/50%

La

rge

omni

voro

us m

amm

al, B

row

n be

ar, f

emal

e(d)

mam

mal

(dee

r)

1.3E

+2

1.6E

+0

5.0E

-1

4.0E

-1

In/o

n so

il 50

%/5

0%

Car

nivo

rous

inve

rtebr

ate,

Car

abid

bee

tle

- 7.

7E-3

1.

0E-2

4.

0E-2

5.

0E-2

In

soil

100

%

Car

nivo

rous

rept

ile/a

mph

ibia

n, V

iper

re

ptile

1.

0E-1

5.

8E-1

2.

5E-2

2.

5E-2

O

n so

il 10

0 %

C

arni

voro

us b

ird, T

awny

ow

l (c)

bird

5.

2E-1

1.

8E-1

8.

0E-2

9.

0E-2

O

n so

il 10

0 %

C

arni

voro

us m

amm

al, A

mer

ican

min

k

mam

mal

(rat

) 1.

0E+0

2.

0E-1

6.

0E-2

6.

0E-2

O

n so

il 10

0 %

D

ecom

pose

r, Ea

rthw

orm

so

il in

verte

brat

e 4.

0E-3

1.

0E-2

4.

0E-3

4.

0E-3

In

soil

100

%

Mos

s, R

ed-s

tem

med

feat

her-

mos

s,

bryo

phyt

e 1.

0E-2

3.

5E-2

2.

0E-2

2.

0E-2

O

n so

il 10

0 %

Li

chen

, Rei

ndee

r lic

hen

liche

n 1.

0E-2

2.

0E-2

4.

0E-2

2.

0E-2

O

n so

il 10

0 %

H

erb (e

) , May

lily

he

rb

5.0E

-1

1.0E

-1

1.0E

-1

1.0E

-1

On

soil

100

%

Her

b (e) , B

rack

en

herb

5.

0E+1

5.

0E-1

6.

0E-1

5.

0E-1

O

n so

il 10

0 %

G

rass

, Wav

y ha

ir-gr

ass

gras

s 2.

5E+1

5.

0E-1

4.

0E-1

5.

0E-1

O

n so

il 10

0 %

Sh

rub,

Bilb

erry

sh

rub

1.0E

+0

1.5E

-1

2.0E

-1

1.5E

-1

On

soil

100

%

Tree

/ste

m o

f tre

e be

low

cro

wn

tre

e 4.

6E+1

1.

0E-1

4.

8E+0

1.

0E-1

O

n so

il 10

0 %

Tr

ee/c

row

n of

tree

(f)

tree

6.2E

+1

1.0E

+0

4.8E

+0

1.0E

+0

In a

ir 10

0 %

(a

) Cor

resp

onds

to th

e pa

ram

eter

leng

th in

Ikon

en e

t al (

2010

a) a

nd H

aapa

nen

et a

l. (2

009)

. (b

) Cor

resp

onds

to th

e pa

ram

eter

wid

th in

Ikon

en e

t al (

2010

a) a

nd H

aapa

nen

et a

l. (2

009)

. (c

) Hei

ght o

ver g

roun

d se

lect

ed to

3 m

. (d

) Spe

cific

cas

e du

e to

hib

erna

tion

(larg

e pa

rt of

the

year

in so

il). N

ot c

urre

ntly

pre

sent

at O

lkilu

oto,

but

the

site

is p

erip

hera

l to

the

pres

ent a

rea

of d

istri

butio

n.

(e) F

or h

erbs

, tw

o as

sess

men

t spe

cies

are

giv

en to

cov

er th

e va

riabi

lity.

(f

) Hei

ght o

ver g

roun

d se

lect

ed to

7 m

.

94

Tabl

e 5-

8. S

elec

ted

fres

hwat

er a

sses

smen

t spe

cies

for

Olk

iluot

o si

te a

nd th

eir

prop

ertie

s (s

ee Ik

onen

et a

l. (2

010a

) for

ref

eren

ces

to s

ize

and

wei

ghts

).

Ass

essm

ent s

peci

es

ER

ICA

re

fere

nce

orga

nism

Ave

rage

bod

y w

eigh

t [kg

FW]

Wid

th (a

) [m

] H

eigh

t [m

] D

epth

(b)

[m]

Occ

upan

cy fa

ctor

s

Phyt

opla

nkto

n, A

naba

ena

flos-

aqua

e ph

ytop

lank

ton

3.0E

-18

3.1E

-7

6.5E

-8

3.1E

-7

In w

ater

10

0 %

Ph

ytop

lank

ton,

Ana

baen

a le

mm

erm

anni

i ph

ytop

lank

ton

4.0E

-19

1.3E

-7

5.5E

-8

1.3E

-7

In w

ater

10

0 %

Ph

ytop

lank

ton,

Tab

ella

ria

fene

stra

ta

phyt

opla

nkto

n 6.

0E-1

5 7.

2E-7

3.

0E-6

6.

0E-6

In

wat

er

100

%

Phyt

opla

nkto

n, G

onyo

stom

um se

men

ph

ytop

lank

ton

3.0E

-11

3.4E

-5

5.5E

-5

3.4E

-5

In w

ater

10

0 %

V

ascu

lar p

lant

, Com

mon

reed

va

scul

ar p

lant

1.

5E+2

4.

0E-1

2.

0E+0

4.

0E-1

Se

dim

ent s

urfa

ce

100

%

Zoop

lank

ton,

Cla

doce

ra s

p.

zoop

lank

ton

2.0E

-6

1.6E

-3

1.6E

-3

1.6E

-3

In w

ater

10

0 %

In

sect

larv

ae, C

hiro

nom

us p

lum

osus

in

sect

larv

ae

1.5E

-4

1.6E

-2

1.6E

-3

1.6E

-2

Sedi

men

t 10

0 %

B

ival

ve m

ollu

sc, A

nodo

nta

sp.

biva

lve

mol

lusc

3.

0E-1

2.

0E-1

1.

0E-1

6.

7E-2

Se

dim

ent s

urfa

ce

100

%

Gas

tropo

d, a

snai

l, Ly

mna

ea p

ereg

ra

gast

ropo

d 4.

0E-4

8.

8E-3

1.

8E-2

8.

8E-3

Se

dim

ent s

urfa

ce

100

%

Gas

tropo

d, a

snai

l, Pl

anor

bis p

lano

rbis

ga

stro

pod

1.7E

-7

1.5E

-3

2.5E

-4

1.5E

-3

Sedi

men

t sur

face

10

0 %

C

rust

acea

n, C

rayf

ish

cr

usta

cean

1.

0E-2

1.

0E-1

3.

3E-2

1.

0E-2

Se

dim

ent s

urfa

ce

100

%

Ben

thic

fish

, Ruf

fe

(ben

thic

) fis

h 1.

3E-2

1.

2E-1

1.

2E-2

5.

8E-2

Se

dim

ent s

urfa

ce

100

%

Pela

gic

fish,

Ven

dace

(p

elag

ic) f

ish

5.0E

-2

1.5E

-1

2.1E

-2

3.8E

-2

In w

ater

10

0 %

A

mph

ibia

n, C

omm

on fr

og, m

ale

am

phib

ian

4.6E

-2

7.9E

-2

4.0E

-2

4.0E

-2

In w

ater

10

0 %

A

mph

ibia

n, C

omm

on fr

og, f

emal

e

amph

ibia

n 3.

8E-2

7.

2E-2

3.

6E-2

3.

6E-2

In

wat

er

100

%

Rep

tile,

Gra

ss sn

ake

re

ptile

1.

2E-1

6.

0E-1

3.

0E-2

3.

0E-2

In

wat

er

100

%

Bird

, Mal

lard

bi

rd

1.1E

+0

5.8E

-1

1.2E

-1

1.2E

-1

In w

ater

/Wat

er su

rfac

e 50

%/5

0%

Mam

mal

, Otte

r, sm

all

m

amm

al

3.0E

+0

5.0E

-1

1.3E

-1

1.3E

-1

In w

ater

/Wat

er su

rfac

e 50

%/5

0%

Mam

mal

, Otte

r, la

rge

m

amm

al

1.5E

+1

1.0E

+0

2.0E

-1

2.0E

-1

In w

ater

/Wat

er su

rfac

e 50

%/5

0%

(a)

Cor

resp

onds

to th

e pa

ram

eter

leng

th in

Ikon

en e

t al (

2010

a) a

nd H

aapa

nen

et a

l. (2

009)

. (b

) C

orre

spon

ds to

the

para

met

er w

idth

in Ik

onen

et a

l (20

10a)

and

Haa

pane

n et

al.

(200

9).

95

Tabl

e 5-

9. S

elec

ted

mar

ine

asse

ssm

ent s

peci

es fo

r O

lkilu

oto

site

and

thei

r pr

oper

ties

(see

Ikon

en e

t al.

(201

0a) f

or r

efer

ence

s to

siz

e an

d w

eigh

ts).

Ass

essm

ent s

peci

es

ER

ICA

ref

eren

ce

orga

nism

A

vera

ge b

ody

wei

ght [

kgFW

] W

idth

(a)

[m]

Hei

ght

[m]

Dep

th(b

) [m

] O

ccup

ancy

fact

ors

Phyt

opla

nkto

n, C

haet

ocer

os w

igha

mii

ph

ytop

lank

ton

3.0E

-13

7.0E

-6

8.1E

-6

1.2E

-5

In w

ater

10

0 %

Ph

ytop

lank

ton,

Aph

aniz

omen

on sp

. ph

ytop

lank

ton

1.0E

-12

1.0E

-4

4.7E

-6

4.7E

-6

In w

ater

10

0 %

M

acro

alga

e, C

lado

phor

a gl

omer

ata

m

acro

alga

e 6.

0E-4

2.

3E-3

2.

3E-1

2.

3E-3

In

wat

er

100

%

Vas

cula

r pla

nt, C

omm

on re

ed

vasc

ular

pla

nt

1.5E

+2

4.0E

-1

2.0E

+0

4.0E

-1

Sedi

men

t sur

face

10

0 %

Zo

opla

nkto

n C

lado

cera

sp.

zo

opla

nkto

n 1.

0E-3

1.

5E-2

1.

5E-2

1.

5E-2

In

wat

er

100

%

Ben

thic

mol

lusc

, Blu

e m

usse

l be

nthi

c m

ollu

sc

2.0E

-3

2.5E

-2

1.3E

-2

2.5E

-2

Sedi

men

t sur

face

10

0 %

B

enth

ic m

ollu

sc, B

altic

mac

oma

bent

hic

mol

lusc

1.

0E-3

2.

0E-2

1.

0E-2

2.

0E-2

Se

dim

ent s

urfa

ce

100

%

Cru

stac

ean,

Bal

tic p

raw

n

crus

tace

an

1.5E

-3

5.0E

-2

1.0E

-2

1.0E

-2

Sedi

men

t sur

face

10

0 %

B

enth

ic fi

sh, F

loun

der

(ben

thic

) fis

h 4.

5E-1

2.

8E-1

2.

8E-2

2.

8E-1

Se

dim

ent s

urfa

ce

100

%

Pela

gic

fish,

Bal

tic h

errin

g (p

elag

ic) f

ish

2.0E

-2

1.8E

-1

1.8E

-2

2.2E

-2

In w

ater

10

0 %

Po

lych

aete

wor

m, a

ragw

orm

po

lych

aete

wor

m

2.0E

-4

4.5E

-2

4.5E

-3

4.5E

-3

Sedi

men

t 10

0 %

B

ird, O

yste

rcat

cher

(w

adin

g) b

ird

4.8E

-1

4.5E

-1

8.9E

-2

8.9E

-2

In/o

n w

ater

50

/50%

M

amm

al, G

rey

seal

, mal

e

mam

mal

3.

0E+2

2.

9E+0

9.

7E-1

9.

7E-1

In

wat

er

100

%

Mam

mal

, Gre

y se

al, f

emal

e

mam

mal

1.

3E+2

1.

8E+0

6.

0E-1

6.

0E-1

In

wat

er

100

%

(a)

Cor

resp

onds

to th

e pa

ram

eter

leng

th in

Ikon

en e

t al (

2010

a) a

nd H

aapa

nen

et a

l. (2

009)

. (b

) C

orre

spon

ds to

the

para

met

er w

idth

in Ik

onen

et a

l (20

10a)

and

Haa

pane

n et

al.

(200

9).

96

Tabl

e 5-

10. C

once

ntra

tion

ratio

s for

terr

estr

ial a

sses

smen

t spe

cies

; all

units

in [k

g dw

,soil/k

g fw]

.

Ass

essm

ent

spec

ies

Cl

I M

o N

b C

s N

i Se

Sr

C

Pd

Sn

Rin

glet

3.

0E-1

3.

0E-1

3.

7E-1

5.

1E-4

5.

5E-2

8.

6E-3

1.

5E+0

6.

3E-2

4.

3E+2

8.

6E-3

6.

1E-2

H

azel

gro

use

7.0E

+0

4.0E

-1

2.7E

-1

1.9E

-1

7.5E

-1

7.2E

-2

6.3E

-2

5.5E

-1

1.3E

+3

7.2E

-2

6.2E

-2

Ban

k vo

le

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

2.9E

+0

7.2E

-2

6.3E

-2

1.7E

+0

1.3E

+3

7.2E

-2

3.9E

-2

Mou

ntai

n ha

re

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

2.9E

+0

7.2E

-2

6.3E

-2

1.7E

+0

1.3E

+3

7.2E

-2

3.9E

-2

Moo

se

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

2.9E

+0

7.2E

-2

6.3E

-2

1.7E

+0

1.3E

+3

7.2E

-2

3.9E

-2

Com

mon

frog

7.

0E+0

4.

0E-1

5.

7E-1

1.

9E-1

5.

4E-1

7.

2E-2

6.

3E-2

8.

3E-1

1.

3E+3

7.

2E-2

1.

2E-1

H

oode

d cr

ow

7.0E

+0

4.0E

-1

2.7E

-1

1.9E

-1

7.5E

-1

7.2E

-2

6.3E

-2

5.5E

-1

1.3E

+3

7.2E

-2

6.2E

-2

Ref

fox

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

2.9E

+0

7.2E

-2

6.3E

-2

1.7E

+0

1.3E

+3

7.2E

-2

3.9E

-2

Car

abid

bee

tle

1.8E

-1

1.6E

-1

3.7E

-1

5.1E

-4

8.9E

-2

6.5E

-2

1.5E

+0

9.0E

-3

4.3E

+2

6.5E

-2

2.9E

-2

Vip

er

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

3.6E

+0

7.2E

-2

6.3E

-2

1.2E

+1

1.3E

+3

7.2E

-2

6.2E

-2

Taw

ny o

wl

7.0E

+0

4.0E

-1

2.7E

-1

1.9E

-1

7.5E

-1

7.2E

-2

6.3E

-2

5.5E

-1

1.3E

+3

7.2E

-2

6.2E

-2

Am

eric

an m

ink

7.0E

+0

4.0E

-1

3.7E

-1

1.9E

-1

2.9E

+0

7.2E

-2

6.3E

-2

1.7E

+0

1.3E

+3

7.2E

-2

3.9E

-2

Earth

wor

m

1.8E

-1

1.6E

-1

3.7E

-1

5.1E

-4

8.9E

-2

6.5E

-2

1.5E

+0

9.0E

-3

4.3E

+2

6.5E

-2

2.9E

-2

May

lily

1.

2E+0

1.

1E-1

4.

8E-1

2.

0E+0

1.

3E+0

2.

0E+0

5.

5E-1

5.

5E-2

8.

9E+2

2.

0E+0

5.

5E-3

B

rack

en

1.2E

+0

1.1E

-1

4.8E

-1

2.0E

+0

1.3E

+0

2.0E

+0

5.5E

-1

5.5E

-2

8.9E

+2

2.0E

+0

5.5E

-3

Wav

y ha

ir-gr

ass

1.2E

+0

1.1E

-1

4.8E

-1

2.0E

+0

1.3E

+0

2.0E

+0

5.5E

-1

5.5E

-2

8.9E

+2

2.0E

+0

5.5E

-3

Bilb

erry

1.

2E+0

1.

1E-1

4.

8E-1

2.

0E+0

1.

3E+0

2.

0E+0

5.

5E-1

5.

5E-2

8.

9E+2

2.

0E+0

5.

5E-3

Tr

ee st

em

3.1E

+0

6.5E

-2

1.0E

-3

8.5E

-2

8.0E

-2

8.5E

-2

7.5E

-2

1.2E

-1

1.3E

+3

8.5E

-2

1.6E

-1

Tree

cro

wn

4.4E

+0

1.5E

-1

2.5E

-1

1.6E

+0

6.0E

-1

1.6E

+0

9.0E

-2

5.5E

-1

1.3E

+3

1.6E

+0

4.6E

-2

97

Tabl

e 5-

11. C

once

ntra

tion

ratio

s for

fres

hwat

er a

sses

smen

t spe

cies

; all

units

in [L

/kg f

w].

Ass

essm

ent s

peci

es

Cl

I M

o N

b C

s N

i Se

Sr

C

Pd

Sn

A

naba

ena

flos-

aqua

e 3.

6E+2

2.

3E+3

3.

5E+3

1.

0E+3

4.

7E+3

5.

0E+3

3.

6E+3

4.

0E+1

1.

8E+3

5.

0E+3

4.

9E+5

A

naba

ena

lem

mer

man

nii

3.6E

+2

2.3E

+3

3.5E

+3

1.0E

+3

4.7E

+3

5.0E

+3

3.6E

+3

4.0E

+1

1.8E

+3

5.0E

+3

4.9E

+5

Tabe

llaria

fene

stra

ta

3.6E

+2

2.3E

+3

3.5E

+3

1.0E

+3

4.7E

+3

5.0E

+3

3.6E

+3

4.0E

+1

1.8E

+3

5.0E

+3

4.9E

+5

Gon

yost

omum

sem

en

3.6E

+2

2.3E

+3

3.5E

+3

1.0E

+3

4.7E

+3

5.0E

+3

3.6E

+3

4.0E

+1

1.8E

+3

5.0E

+3

4.9E

+5

Com

mon

reed

3.

6E+2

3.

0E+2

4.

0E+3

8.

0E+2

1.

2E+3

5.

0E+1

1.

0E+3

2.

5E+2

4.

6E+3

5.

0E+1

1.

0E+3

C

lado

cera

3.

6E+2

1.

3E+3

3.

5E+3

1.

0E+3

1.

6E+3

5.

0E+3

6.

0E+3

6.

0E+1

4.

0E+3

5.

0E+3

2.

6E+4

C

hiro

nom

us p

lum

osus

5.

0E+1

4.

0E+2

1.

0E+3

3.

5E+2

1.

0E+4

5.

5E+2

7.

1E+3

2.

0E+2

7.

3E+3

5.

5E+2

1.

0E+4

A

nodo

nta

sp.

5.0E

+1

2.5E

+1

5.1E

+4

3.5E

+2

4.6E

+2

6.4E

+3

5.0E

+3

2.7E

+2

7.3E

+3

6.4E

+3

1.7E

+3

Lym

naea

per

egra

5.

0E+1

2.

5E+1

6.

0E+3

3.

5E+2

2.

8E+3

6.

4E+3

5.

0E+3

2.

7E+2

7.

3E+3

6.

4E+3

5.

0E+4

Pl

anor

bis p

lano

rbis

5.

0E+1

2.

5E+1

6.

0E+3

3.

5E+2

2.

8E+3

6.

4E+3

5.

0E+3

2.

7E+2

7.

3E+3

6.

4E+3

5.

0E+4

C

rayf

ish

5.0E

+1

4.0E

+2

1.0E

+4

3.5E

+2

1.0E

+4

5.5E

+2

7.1E

+3

2.0E

+2

7.3E

+3

5.5E

+2

1.0E

+4

Ruf

fe

8.2E

+1

1.8E

+2

9.8E

+2

2.3E

+2

6.3E

+3

1.0E

+2

2.0E

+2

1.7E

+1

4.6E

+3

1.0E

+2

3.0E

+2

Ven

dace

8.

2E+1

1.

8E+2

9.

8E+2

2.

3E+2

6.

3E+3

1.

0E+2

2.

0E+2

1.

7E+1

4.

6E+3

1.

0E+2

3.

0E+2

C

omm

on fr

og m

ale

8.2E

+1

1.3E

+2

9.8E

+2

2.3E

+2

9.3E

+3

1.0E

+2

2.0E

+2

1.7E

+1

7.3E

+3

1.0E

+2

3.0E

+2

Com

mon

frog

fem

ale

8.2E

+1

1.3E

+2

9.8E

+2

2.3E

+2

9.3E

+3

1.0E

+2

2.0E

+2

1.7E

+1

7.3E

+3

1.0E

+2

3.0E

+2

Gra

ss sn

ake

8.2E

+1

1.3E

+2

4.6E

+3

8.3E

+1

4.6E

+2

1.7E

+2

8.3E

+3

1.4E

+0

1.7E

+4

1.7E

+2

1.9E

+4

Mal

lard

8.

2E+1

1.

3E+2

9.

8E+2

2.

3E+2

3.

0E+3

1.

0E+2

2.

0E+2

1.

7E+1

7.

3E+3

1.

0E+2

3.

0E+2

O

tter s

mal

l 8.

2E+1

1.

3E+2

9.

8E+2

2.

3E+2

9.

3E+3

1.

0E+2

2.

0E+2

1.

7E+1

7.

3E+3

1.

0E+2

3.

0E+2

O

tter l

arge

8.

2E+1

1.

3E+2

9.

8E+2

2.

3E+2

9.

3E+3

1.

0E+2

2.

0E+2

1.

7E+1

7.

3E+3

1.

0E+2

3.

0E+2

98

Tabl

e 5-

12. C

once

ntra

tion

ratio

s for

mar

ine

asse

ssm

ent s

peci

es; a

ll un

its in

[L/k

g fw]

.

Ass

essm

ent s

peci

es

Cl

I M

o N

b C

s N

i Se

Sr

C

Pd

Sn

C

haet

ocer

os w

igha

mii

1.0E

+0

9.6E

+2

3.5E

+3

1.0E

+3

1.3E

+2

1.4E

+3

3.6E

+3

2.1E

+2

5.6E

+3

1.4E

+3

4.9E

+5

Aph

aniz

omen

on

1.0E

+0

9.6E

+2

3.5E

+3

1.0E

+3

1.3E

+2

1.4E

+3

3.6E

+3

2.1E

+2

5.6E

+3

1.4E

+3

4.9E

+5

Cla

doph

ora

glom

erat

a 8.

4E-1

4.

1E+3

3.

0E+4

6.

1E+2

1.

2E+2

7.

9E+2

2.

2E+2

4.

2E+1

8.

0E+3

7.

9E+2

1.

0E+3

C

omm

on re

ed

8.4E

-1

4.1E

+3

3.0E

+4

6.1E

+2

2.2E

+1

7.9E

+2

2.2E

+2

4.2E

+1

8.0E

+3

7.9E

+2

1.0E

+3

Cla

doce

ra sp

p 1.

0E+0

3.

0E+3

2.

2E+4

2.

2E+4

1.

1E+2

1.

0E+3

6.

0E+3

4.

6E+0

1.

0E+4

1.

0E+3

2.

6E+4

B

lue

mus

sel

4.7E

-2

1.4E

+1

1.1E

+4

8.5E

+2

6.6E

+1

6.4E

+3

5.0E

+3

1.2E

+2

1.0E

+4

6.4E

+3

1.7E

+3

Bal

tic m

acom

a 4.

7E-2

1.

4E+1

1.

1E+4

8.

5E+2

6.

6E+1

6.

4E+3

5.

0E+3

1.

2E+2

1.

0E+4

6.

4E+3

1.

7E+3

B

altic

pra

wn

5.6E

-2

3.6E

+0

2.2E

+4

1.0E

+2

4.1E

+1

5.5E

+2

7.1E

+3

1.3E

+1

1.0E

+4

5.5E

+2

1.0E

+4

Flou

nder

5.

6E-2

3.

6E+0

7.

4E+2

8.

3E+1

8.

6E+1

1.

7E+2

9.

3E+3

2.

3E+1

1.

2E+4

1.

7E+2

2.

0E+2

B

altic

her

ring

5.6E

-2

3.6E

+0

7.4E

+2

8.3E

+1

8.6E

+1

1.7E

+2

9.3E

+3

2.3E

+1

1.2E

+4

1.7E

+2

2.0E

+2

Rag

wor

m

5.0E

-2

1.4E

+1

2.2E

+4

8.5E

+2

1.8E

+2

4.2E

+3

4.5E

+3

4.7E

-1

1.0E

+4

4.2E

+3

1.0E

+4

Oys

terc

atch

er

3.0E

-2

6.8E

-1

4.6E

+3

8.3E

+1

4.6E

+2

1.7E

+2

8.3E

+3

1.4E

+0

1.7E

+4

1.7E

+2

1.9E

+4

Gra

y se

al m

ale

3.3E

-2

6.8E

-1

4.6E

+3

8.3E

+1

2.1E

+2

1.7E

+2

8.3E

+3

1.4E

+0

1.7E

+4

1.7E

+2

1.9E

+4

Gra

y se

al fe

mal

e 3.

3E-2

6.

8E-1

4.

6E+3

8.

3E+1

2.

1E+2

1.

7E+2

8.

3E+3

1.

4E+0

1.

7E+4

1.

7E+2

1.

9E+4

99

5.3 Compliance assessment

The main (dose) quantities to be used in assessing compliance with regulatory criteria and constraints related to radiation protection are briefly addressed below.

5.3.1 Requirements for humans

In order to assess compliance with the regulatory dose constraints for humans, the whole assessment time window needs to be considered. This is done by deriving, for each generation, the dose distribution and identifying the doses to representative persons (as described in section 5.1). Then,

� the highest value, over all generations, of Egroup is used to determine compliance with the regulatory dose constraints to the most exposed people, and

� the highest value, over all generations, of Epop is used to determine compliance with the regulatory dose constraints to other people.

5.3.2 Requirements for other biota

The approach adopted in the present assessment is to assess the exposures to other living species in terms of the typical absorbed dose rates to identified assessment species (Table 5-7 to Table 5-9). The approach to demonstrating that the environment is adequately protected and to assessing compliance with regulatory criteria is to compare the typical absorbed dose rates with internationally proposed screening values for the protection of biota against radiation in the environment. The screening values are used to screen out situations of no regulatory concern. Thus, if none of the typical absorbed dose rates that are calculated exceed the screening values, it can be stated, with a high degree of confidence, that any releases from the repository do not affect species of flora and fauna detrimentally. If derived typical absorbed dose rates do exceed the screening values, this means only that detrimental effects cannot be ruled out, and the assessment has to continue by performing a more detailed analysis to assess the level, and nature, of possible detrimental effects. The screening levels applied in the present assessment are the organism group-specific screening values recommended by the PROTECT project (Andersson et al. 2008). In that project, screening values were derived for three broad groups of organisms, recognising that each group contains organisms that are likely to have a range of radiosensitivities. The estimated screening values, in the form of absorbed dose rates, applied in the present assessment are 2 �Gy/h for vertebrates, 70 �Gy/h for plants, and 200 �Gy/h for invertebrates.


The methodology for deriving the dose distribution and identifying the annual landscape doses to representative persons from the most exposed group and for the other people in the exposed population was implemented and run in MATLAB®, version 7.9.0.529 (R2009b). The methodology for deriving typical absorbed dose rates to assessment species was implemented and calculated in MATLAB®, version 7.9.0.529 (R2009b) and ERICA. The input/output data from the ERICA Tool (version 1.0 May 2009) was handled with MS EXCEL files.

101

6 SAFETY INDICATORS

The landscape model approach (chapter 4) in conjunction with the dose assessment process (chapter 5) described above constitute the main approach for converting the radionuclide releases from the geosphere into dose quantities directly used in the quantitative compliance assessment. The conclusions made regarding compliance, based on these dose quantities, are further supported by safety indicators. In the present assessment two safety indicators, in the form of annual doses, are derived, based on indicative stylised well scenarios: one for a drinking water well and one for an agricultural well. The drinking water well is similar to the well scenario applied in previous safety assessments (Vieno 1994, 1997, Vieno & Nordman 1996, 1999, Broed et al. 2007, Nykyri et al. 2008). The definition of the agricultural well has been extended to also include watering cattle and irrigation of crops, and has earlier only been used in the KBS-3H safety analysis (Broed et al. 2007). These safety indicators are further discussed here.

6.1 Stylised well scenarios

Stylised well scenarios are used to estimate indicative hypothetical annual doses received by a representative member of the most exposed people by deriving dose conversion factors (DCF) and multiplying by the annual release rates from the geosphere. These safety indicators, the “well doses”, aim to support the decisions made in the compliance assessment regarding dose constraints. Well doses are also used to obtain indicative annual effective doses beyond the time window of biosphere assessment, where the regulatory constraints are based on activity fluxes from the geosphere; this is discussed in (Nykyri et al. 2008). In the following sections are the radionuclide transport models and the applied data presented. For clarity the radiological consequence analysis is presented together with the radionuclide transport models. In the present report, the well scenarios are used to derive doses for the nominal release rates (section 2.3).

6.1.1 Conceptual models

Conceptual models for the radionuclide transport and exposure pathways in the two scenarios WELL-2009 and AgriWELL-2009 are illustrated in Figure 6-1. The drinking water well (WELL) scenario was introduced in the Finnish assessment of deep repositories by Vieno (1994) and has been applied, with minor modifications, in safety assessments since 1996 (Vieno & Nordman 1996, Vieno 1997, Vieno & Nordman 1999, Broed et al. 2007, Smith et al. 2007b, Nykyri et al. 2008). The WELL scenario is very simple and robust; based only on the effective mixing capacity (mixing of the annual releases from the geosphere with the water in a well), and the annual intake of water (an adult male satisfies his annual demand for drinking water from well water).

The agricultural well (AgriWELL) scenario is an extension of the WELL scenario, taking more pathways into account. The AgriWELL scenario was first used in the KBS-3H biosphere analysis (Broed et al. 2007). In AgriWELL, it is assumed that the yield of the well is sufficiently high for human consumption, watering of livestock, and for irrigation of crops. Additional exposure pathways (see Figure 6-1) are consumption of irrigated crops and of animal products. The radionuclides in irrigation water contaminate the crops, and thus also the animal fodder, both by direct uptake of surface deposited activity and by secondary uptake via the roots.

102

Figure 6-1. Conceptual models for the radionuclide transport and exposure pathways to derive the dose conversion factors in the two well scenarios. The radionuclides in the well water also contaminate animal products due to the animals’ water consumption. AgriWELL utilises data for a fictive farm, with characteristics based on region-specific farm statistics (such as land-use, yields, and production). The AgriWELL scenario is applied to an adult male who satisfies his nutrient needs by eating and drinking products from the farm and drinking water from the same well as used in the WELL scenario.

6.1.2 Mathematical models

WELL-2009 The model for calculating the dose conversion factors for the WELL-2009 scenario, DCFWELL-2009, is identical to previous versions and extremely simple. The dose conversion factor for radionuclide i is calculated as follow:

A15?D99EFGGHB � :IJK<�#LB�DM�N , [(Sv/y)/(Bq/y)] (6-1)

where,

IW Intake rate of water [m3/y] DCing, i Dose coefficient for ingestion of radionuclide i [Sv/Bq] Emix Effective mixing capacity of the well [m3/y]

The effective mixing capacity is defined as the yield of the well divided by the fraction of geosphere release assumed to end up in the well.

AgriWELL-2009 The transport and exposure pathways for the AgriWELL scenario are described in Figure 6-1. The model for calculating the dose conversion factors for the AgriWELL-

Irrigation

Ingestion (drinking)

Ingestion (eating)

Wellwater

BerriesGreenhouse Kitchen gardenOutdoors

Vegetables

Livestock

SheepsPoultry Pigs Cows

DCFAgriWELL

Potatoes Silage/green fodderWheat

Cereals/grass/tubers

Humans

DCFWELL

Humans

103

2009 scenario, DCFAgriWELL-2009, is very similar to previous versions (AgriWELL-2007). The dose conversion factor for radionuclide i is calculated as follow:

A15�7�?D99EFGGH � O:I�@ P<#B�J:#Q# RJK<�#LB�DM�N [(Sv/y)/(Bq/y)] , (6-2)

where

Emix Effective mixing capacity of the well [y/m3]’ Cn,i Activity concentration of radionuclide i in edible n for unit activity

concentration in the irrigation water [(Bq/y)/(Bq/m3)] or [(Bq/y)/(Bq/kg)] IW Intake rate of water [m3/y] In Intake rate of edible n [m3/y] or [kg/y] n All different edibles included in the model DCing, i Dose coefficient for ingestion of radionuclide i [Sv/Bq]

The edibles are divided into two main categories: crops and animal products. The approaches to calculate the activity concentrations in these are describe below.

Activity concentrations in crops When deriving the activity concentrations in crops, Ccrop, the water applied by irrigation should, in principle, be divided into two fractions: one for the fraction intercepted and retained on plant surfaces and one passing the plant through to the soil. The fraction passing through to the soil is thus dependent on the amount of water applied at the irrigation event, and the storage capacity of the plant. In the applied model it is assumed that the amount of water used per irrigation event is constant and much greater than needed to saturate the storage capacity for a generic plant used (see Table 6.11), thus, only a small fraction will be retained on plant surfaces. The stylised assumptions are then made that every irrigation event fills the storage capacity of the plants, and 100% of the irrigation water passes through into the soil. The activity concentration in a certain crop type is derived as:

1S�� 1SB��+T1SB(�� [Bqfw/kg], (6-3)

Where Cc,root and Cc,surf are the activity concentration in crops due to root uptake and due to retention of water on the plant surface, respectively. The activity concentration in crops due to root uptake, Cc,root, due to contaminated irrigation water is calculated as:

1SB�� <�U< J

<VJ:��W�WKW�JX�EYWZJ[$ [Bqfw/kg], (6-4)

The transfer coefficient, TC, is calculated as (Bergström & Barkefors 2004):

\1 � ]YWJKW� J �� +

^�UKW�JX�EYWZJ[$ , (6-5)

The retention, _`a, is expressed as (Andersson et al. 1982):

�� =!J[$JX�EYWZ YWb , (6-6)

104

where,

Cr Concentration ratio, soil to plant [(Bq/kgfw)/(Bq/kgdw)] Irrtot Total irrigation amount [m3/(m2y)], Cw Activity concentration in the irrigation water (Bq/m3), R Runoff [m3/(m2y)] �t Porosity in the top soil [m3/m3] Dts Thickness of top soil layer [m] BioT Bioturbation [kg/(m2y)] �p Density of soil particles [kgdw/m3] Kd Solid-liquid distribution coefficient in soil [m3/kgdw]

The activity concentration of radionuclides in crops, Cc,surf, due to retention of contaminated irrigation water on the plant surface is based on the work by Bergström & Barkefors (2004) and is calculated as:

1SB(�� :��W�W:��"c"#W J

9�:J;d-$J=%"WJ<Ve$ J \f [Bq/kgfw] , (6-7)

where,

Irrtot Total irrigation amount [m3/(m2y)], Irrevent Water amount per irrigation event [m3/m2], LAI Leaf area index [m2/m2], Scap Water storage capacity in vegetation (m3/m2), Kret Element dependent retention factor (–). Cw Concentration in the irrigation water (Bq/m3), Yp Annual yield values (kgfw/m2), TL Translocation factor (for root crops only).

It should again be noted that Eq 6-7 is only valid when Irrevent is high enough to saturate the storage capacity.

Activity concentrations in animal products When deriving the activity concentrations in animal products (beef, pork, sheep meat, poultry, egg contents and milk) resulting concentrations in crops used as animal fodder, denoted CF below, are needed. The activity concentration in a certain animal product Canimal [Bqfw/kg] is derived as:

1/�g/) � 5� J Oh?/ + @ P1iB J hi/Qi R [Bqfw/kg], (6-8)

1/�g/) � 5g J �h?/ + @ X1i J hi/Zi [Bqfw/L], (6-9)

where,

Ff Translocation of intake (amount of an animal's daily intake of a radionuclide that is transferred to beef, pork, sheep meat, poultry, or egg contents) [d/kg],

Fm Translocation of intake (amount of an animal's daily intake of a radionuclide that is transferred to milk) [d/L],

h?/ Intake rate of water of animal a hi/ Intake rate of fodder F of animal a

105

CF Activity concentrations in crops used for fodder [Bqfw/kg],

The activity concentration CF is calculated with the model for Ccrop, described above.

6.1.3 Parameters and applied values

WELL-2009 As described above, only three parameters are needed to derive DCFWELL-2009.

Dose coefficients for ingestion, The values used are the same values as in Tier 2 in the screening evaluation (section 3.3); values are presented in Table 5-3.

Effective mixing capacity of the well, Emix, is unchanged since previous assessments. The value of 100 000 m3/y is used, based on the discussion at page 1 in Vieno (1994). This value is further and supported by the results in Kattilakoski & Suolanen (2000), in which it is stated: “According to the groundwater flow analyses the effective dilution volume of the well seems to vary from 30 000 m3/y to 460 000 m3/y…. In conservative considerations the value around 90 000 m3/y can be regarded as a representative expectation value of the effective dilution of the well.”

Ingestion of water (intake rate of water). The intake rate of water, IW, for the WELL-2009 scenario has been selected to 0.9 m3/y (corresponds to a daily intake of about 2.5 L). This value is based on high-consumer intake (95th percentile) of tap water (Ershow & Cantor 1989 cited in OEHHA 2000). It is a higher intake than applied in all previous versions of the drinking water well scenario. WELL-2007 (Broed et al. 2007) and earlier versions used a value of 0.5 m3/y. WELL-2008, applied in RNT-2008 (Nykyri et al. 2008), used a value of 0.6 m3/y, which was the same average-consumer intake rate of water as used in the dose calculations in the biosphere analysis in the KBS-3H safety studies (Broed et al 2007). The rationale for changing to the high-consumer intake rate of 0.9 m3/y is to be more confident in not underestimating the calculated doses in the drinking water well scenario, which aims to represents the annual dose the most exposed people.

AgriWELL-2009 The same values for the three parameters (DCing,i, Emix and IW) used in WELL-2009 are used in AgriWELL-2009. In addition to these, the derivation of DCFAgriWELL-2009, is based on a rather large amount of parameter data, of which the most important sets are presented below.

The fictive farm – AgriFARM. The AgriWELL-2009 model uses data for a fictive farm “AgriFARM”, which is based on average farm statistics (such as composition, land-use, yields, and production) for farms in the Satakunta region for the year 2004. The main source of information is the Yearbook of Farm Statistics 2005 (TIKE 2006). The key properties for AgriFARM-2009 are presented in Tables 6-1 to 6-3. The crop-specific water needs are mainly based on regional data derived from Pajula & Triipponen (2003), based on interviews with regional farmers. The irrigated fraction (Table 6-2) is the fraction of the total area that is irrigated on average (for example, if 70 % of the farms irrigate 100 % of a certain crop, the irrigated fraction is 0.7 for that

106

crop). For most of the crops there is no value found in the literature; for these, the value is cautiously selected to 100 %.

Dietary data (humans). The dose calculation in the AgriWELL scenario should ideally be based on region-specific dietary habits. However, this is an issue for future model versions. The version AgriWELL-2009 uses Swedish data for overall consumption (SJV 2006) and age-dependent consumption of different food groups (Karlsson & Aquilonius 2001), in combination to derive average intakes of different foodstuffs for an adult male. The results are then adapted to the foodstuffs produced at the fictive regional farm, contaminated by irrigation water; thus crops originating from non-irrigated fields are excluded (such as most cereals, berries and fruits). Which crops that are irrigated on AgriFARM-2009 are mainly based on regional data derived from Pajula & Triipponen (2003). The applied dietary data (annual consumptions of edibles) is summarised in Table 6-4.

Dietary data (animals). The applied annual consumptions of water and dry matter (feedstuffs) by the animals at the fictive farm are summarised in Table 6-5. The intake assumptions and data are based on IAEA (1994); the arithmetic mean is applied when a range is reported. IAEA (1994) report intake values for pigs with a weight of 110 kg. Here the reported values are assigned to fattening pigs, and value for smaller pigs is selected to 50 % of the reported values. Further, IAEA (1994) report intake values for laying hens and chickens. Here the values for laying hens are used, and the values for turkeys are derived by multiplying the values for laying hens with a factor of five (scaling with the approximately difference in weight). Further, the typical feedstuffs are based on Table X in IAEA (1994) and it is cautiously assumed that the animals only consume contaminated feedstuff.

Radionuclide transport data – root uptake The model for deriving the activity concentration in crops due to root uptake is described by equations 6-4 to 6-6. The applied parameter values are mainly taken from literature and are presented in the following tables:

� Table 6-6 – parameter values that are not element-specific,

� Table 6-7 – Solid-liquid distribution coefficients in soils (Kd),

� Table 6-8 – Soil to plant concentrations ratios (Cr), mainly derived from Karlsson & Bergström (2000, 2002), OPG (2002, 2004a), US DOE (2004) and Uchida et al. (2007).


The mathematical models used for deriving safety indicators based on the WELL-2009 and AGRIWELL-2009 scenarios were implemented in MS Excel (Microsoft Office Standard 2007, version 12.0.6425.1000).

107

Table 6-1. Summary of the land use in AgriFARM-2009.

Landuse Area [m2] Total area 735000 Arable land 309000 Total area under crops 282000 Total area under crops - subject to irrigation 75200

Table 6-2. Summary of the properties of crop production at AgriFARM-2009.

Crops - subject to irrigation

Area

[m2]

Water need

[m3/(m2y)]

Irrigated fraction

[%]

Yield

[kg/(m2y)]

Production

[kg/y] Spring wheat 17600 0.03 100 0.41 7216 Silage 27500 0.12 100 2.02 55550 Green fodder 600 0.12 100 0.75 450 Potatoes 9300 0.18 70(1) 2.98 27700 Sugar beet 14400 0.06 15(a) 3.75 54000 Peas 400 0.09 100 0.20 80 Vegetables (grown outdoors) – total

4950

Vegetables (grown in greenhouse) – total

45

Berries and fruits 200 Kitchen garden (own household) – total

200

Vegetables (grown outdoors)

Carrot 1645 0.09 100 3.66 6020 Beetroot 1180 0.09 100 3.35 3950 Swede 695 0.09 100 4.14 2875 Garden pea 475 0.09 100 0.44 210 Gherkin 375 0.30 100 3.67 1375 White cabbage 295 0.09 100 3.86 1140 Onion 170 0.09 100 2.06 350 Cauliflower 115 0.09 100 1.11 128 Greenhouse vegetables Tomato 33 0.30 100 30.8 1015.0 Cucumber 12 0.30 100 69.6 835.0 Berries & Fruits Strawberry 155 0.18 100 0.18 28.0 Kitchen garden Celeriac 67 90 100 1.92 129.0 Leek 67 90 100 1.72 115.0 Lettuce 66 90 100 1.18 78.0

(a) Based on Bergström & Barkefors (2004).

108

Table 6-3. Summary of the properties of animal products at AgriFARM-2009.

Animal products Individuals Production rate [kg/year/ind.]

Production (kg/y)

Meat Dairy cows 2 282 564 Other cows 4 282 1128 Fattening pigs (>50 kg) 11 84 924 Smaller pigs (<50 kg) 21 84 1764 Hens 62 1.6 99 Broilers 333 1.6 533 Turkeys 21 7.9 166.0 Sheep 1 21 21.0

Milk Dairy cows 2 7404 14808

Eggs Hens 62 18.1 1122.2

Table 6-4. Applied annual total consumption of edibles for an adult male in the Satakunta region.

Edible Amount Edible Amount Meat Cereals Beef and veal 27.0 kg Wheat-flour 52.8 kg Pork 38.7 kg Mutton 1.1 kg Milk products Poultry 16.0 kg Drinking milk 0.0883 m3 Fermented milk 0.0253 m3 Vegetables – grown in open Cream 8.0 kg Sugar beet (a) 208 kg Cheese 14.3 kg Carrot 9.3 kg Butter 12.2 kg Beetroot 2.1 kg Swede (rutabaga) 2.1 kg Water Celeriac 2.1 kg Drinking water 0.9 m3 Onions 6.4 kg Leek 1.2 kg Fruits and berries Cucumbers 3.9 kg Strawberry 4.6 kg Cauliflower 1.0 kg White cabbage 4.8 kg Potatoes Lettuce 5.2 kg Potatoes 78.1 kg Peas (incl. garden peas) 6.4 kg Eggs Vegetables – grown in greenhouse Eggs 11.3 kg Tomatoes 19.5 kg Gherkin 3.2 kg (a) The consumption of sugar beet is derived from the assumption of a 40 kg annual intake of sugar

(white sugar value) and sugar content in sugar beets of 18%. Further, no credit has been taken to losses of radionuclides do to food preparation for any of the edibles, which is a very conservative assumption for sugar, since most activity will be lost in the refinement process.

109

Table 6-5. Annual consumption of water and dry matter by animals at AgriFARM-2009, based on Tables X and XI in IAEA (1994), the percentages are the fractions of the total consumption originating from a certain feedstuff.

Cattle Water [L]

Dry matter [kg]

Typical feedstuff (from irrigated crops)

Dairy cows 27400 5880 Silage (50 %), green fodder (50 %) Other cows 14600 2630 Silage (50 %), green fodder (50 %) Fattening pigs (>50 kg) 2920 875 Cereals (50 %), potatoes (50 %) Smaller pigs (>50 kg) 1460 440 Cereals (50 %), potatoes (50 %) Hens 73 36 Cereals (100 %) Broilers 73 36 Cereals (100 %) Turkeys 365 180 Cereals (100 %) Sheep 2370 475 Silage (50 %), green fodder (50 %)

Table 6-6. Parameter values used when deriving activity concentrations in crops from root uptake in AgriWELL-2009.

Parameter Value Unit Reference Runoff 0.2 m3/(m2y) Karlsson & Bergström (2000) Porosity in the top soil 0.4 m3/m3 Karlsson & Bergström (2000) and

references therein Thickness (top soil layer)

0.3 m Bergström et al. (1999), Table 3-14

Bioturbation 2 kg/(m2y) Karlsson & Bergström (2000) and references therein

Density of soil particles 2400 kgdw/m3 Karlsson & Bergström (2000) and references therein

Total irrigation amount Crop dependent

m3/(m2y) Product of water need and irrigation fraction in Table 6-2

Table 6-7. Solid-liquid distribution coefficients (Kd) in soils when deriving activity concentrations in crops from root uptake in AgriWELL-2009.

Element Value [m3/kg]

Reference

Element Value[m3/kg]

Reference

C 0.001 SKB (2001) I 0.004 IAEA (2009) Cl 0.0005 IAEA (2009) Cs 0.53 IAEA (2009) Ni 0.13 IAEA (2009) Sm 0.24 IAEA (2009) Se 0.056 IAEA (2009) Pb 0.22 IAEA (2009) Sr 0.022 IAEA (2009) Po 0.1 IAEA (2009) Y 0.022 IAEA (2009) Ra 3.1 IAEA (2009) Zr 0.022 IAEA (2009) Th 0.7 IAEA (2009) Nb 1.5 IAEA (1994) Pa 0.54 IAEA (1994) Mo 0.038 IAEA (2009) U 0.012 IAEA (2009) Tc 0.00004 IAEA (2009) Np 0.014 IAEA (2009) Pd 0.055 IAEA (1994) Pu 0.4 IAEA (2009) Sn 1.6 IAEA (2009) Am 1 IAEA (2009) Sb 0.045 IAEA (1994) Cm 3.4 IAEA (2009)

110

Tabl

e 6-

8. S

oil t

o pl

ant c

once

ntra

tion

ratio

s [(B

q/kg

fw)/(

Bq/k

g dw)]

for c

rops

at A

griF

ARM

.200

9 us

ed w

hen

deri

ving

act

ivity

con

cent

ratio

ns

in c

rops

from

root

upt

ake

in A

griW

ELL-

2009

.

Ele

men

tSp

ring

whe

at

Sila

ge

Gre

en fo

dder

Po

tato

es

Suga

r be

et

Peas

C

arro

t B

eetr

oot

Swed

e G

arde

n pe

a C

0

0 0

0 0

0 0

0 0

0 C

l 2.

1E+1

4.

2E+0

1.

5E+1

2.

1E+0

4.

0E+0

1.

6E+0

1.

0E+0

5.

7E+0

5.

7E+0

1.

6E+0

N

i 5.

0E-3

2.

0E-1

2.

0E-1

2.

5E-3

4.

0E-2

2.

0E-2

4.

0E-2

4.

0E-2

4.

0E-2

2.

0E-2

Se

2.

8E-2

3.

3E-2

3.

3E-2

4.

6E-3

4.

0E+0

2.

0E+0

3.

5E-3

4.

0E+0

4.

0E+0

2.

0E+0

Sr

5.

7E-2

4.

6E-1

4.

6E-1

4.

2E-3

6.

0E-2

3.

0E-1

6.

0E-2

6.

0E-2

6.

0E-2

3.

0E-1

Y

7.4E

-5

- -

2.0E

-4

- -

- -

- -

Zr

9.

0E-4

1.

0E-3

1.

0E-3

2.

0E-4

2.

0E-4

1.

0E-4

2.

0E-4

2.

0E-4

2.

0E-4

1.

0E-4

N

b 4.

0E-3

1.

0E-3

1.

0E-3

1.

0E-3

1.

0E-3

5.

0E-4

1.

0E-3

1.

0E-3

1.

0E-3

5.

0E-4

M

o

2.6E

-1

6.5E

-2

6.5E

-2

3.4E

-2

2.0E

-1

8.0E

-2

2.0E

-1

2.0E

-1

2.0E

-1

8.0E

-2

Tc

1.4E

+0

5.9E

+0

5.9E

+0

5.0E

-2

1.7E

+1

1.1E

+0

5.0E

-2

1.7E

+1

8.7E

+0

1.1E

+0

Pd

3.0E

-2

2.0E

-1

2.0E

-1

4.0E

-2

4.0E

-2

2.0E

-2

4.0E

-2

4.0E

-2

4.0E

-2

2.0E

-2

Sn

5.9E

-3

3.5E

-2

3.5E

-2

1.4E

-3

5.0E

-2

6.0E

-2

5.0E

-2

5.0E

-2

5.0E

-2

6.0E

-2

Sb

- -

- -

- -

- -

- -

I 2.

2E-2

8.

7E-3

8.

7E-3

5.

0E-3

1.

0E-2

3.

0E-2

1.

0E-2

1.

0E-2

1.

0E-2

3.

0E-2

C

s 4.

3E-3

2.

8E-2

2.

8E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

Sm

9.5E

-5

1.0E

-2

1.0E

-2

2.5E

-4

4.0E

-5

3.0E

-3

4.0E

-5

4.0E

-5

4.0E

-5

3.0E

-3

Pb

2.1E

-3

3.9E

-3

3.9E

-3

1.1E

-4

4.0E

-3

1.0E

-3

4.0E

-3

4.0E

-3

4.0E

-3

1.0E

-3

Po

1.0E

-3

1.1E

-2

1.1E

-2

4.0E

-3

4.0E

-3

1.0E

-3

4.0E

-3

4.0E

-3

4.0E

-3

1.0E

-3

Ra

1.

3E-2

2.

1E-2

2.

1E-2

1.

3E-3

5.

4E-3

2.

6E-3

5.

4E-3

5.

4E-3

5.

4E-3

2.

6E-3

T

h

1.3E

-4

2.2E

-3

2.2E

-3

1.5E

-4

1.0E

-5

2.0E

-4

1.0E

-5

1.0E

-5

1.0E

-5

2.0E

-4

Pa

8.2E

-4

4.1E

-3

4.1E

-3

6.0E

-4

6.0E

-4

3.0E

-4

6.0E

-4

6.0E

-4

6.0E

-4

3.0E

-4

U

3.2E

-4

3.7E

-3

3.7E

-3

1.4E

-4

4.7E

-4

6.4E

-4

8.5E

-4

4.7E

-4

4.7E

-4

6.4E

-4

Np

3.8E

-3

1.3E

-2

1.3E

-2

1.4E

-3

2.0E

-3

4.5E

-3

5.6E

-3

2.0E

-3

2.0E

-3

4.5E

-3

Pu

1.6E

-5

2.2E

-4

2.2E

-4

3.0E

-5

3.0E

-5

2.0E

-5

3.0E

-5

3.0E

-5

3.0E

-5

2.0E

-5

Am

6.

5E-5

4.

6E-4

4.

6E-4

4.

0E-5

4.

0E-5

7.

0E-5

4.

0E-5

4.

0E-5

4.

0E-5

7.

0E-5

C

m

2.0E

-5

1.0E

-3

1.0E

-3

3.0E

-5

3.0E

-5

2.0E

-4

3.0E

-5

3.0E

-5

3.0E

-5

2.0E

-4

111

Tabl

e 6-

8 (c

ont’d

). So

il to

pla

nt c

once

ntra

tion

ratio

s [(

Bq/k

g fw)/(

Bq/k

g dw)]

for

cro

ps a

t Ag

riFA

RM.2

009

used

whe

n de

rivi

ng a

ctiv

ity

conc

entr

atio

ns in

cro

ps fr

om ro

ot u

ptak

e in

Agr

iWEL

L-20

09.

Ele

men

t G

herk

in

Whi

te c

abba

ge

Oni

on

Cau

liflo

wer

St

raw

berr

y C

eler

iac

Lee

k L

ettu

ce

Tom

ato

Cuc

umbe

r C

0

0 0

0 0

0 0

0 0

0 C

l 1.

0E+0

1.

4E+0

6.

0E-1

1.

4E+0

3.

0E+0

5.

7E+0

6.

0E-1

9.

6E-1

5.

0E-1

1.

0E+0

N

i 2.

0E-2

2.

0E-2

4.

0E-2

2.

0E-2

2.

0E-2

4.

0E-2

4.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

Se

8.

0E-4

3.

6E-3

2.

0E-3

3.

3E-3

2.

0E+0

4.

0E+0

4.

8E-3

9.

6E-4

1.

7E-3

8.

0E-4

Sr

3.

0E-1

3.

0E-1

6.

0E-2

3.

0E-1

3.

0E-1

6.

0E-2

6.

0E-2

3.

0E-1

3.

0E-1

3.

0E-1

Y

- -

- -

- -

- -

- -

Zr

1.

0E-4

1.

0E-4

2.

0E-4

1.

0E-4

1.

0E-4

2.

0E-4

2.

0E-4

1.

0E-4

1.

0E-4

1.

0E-4

N

b 5.

0E-4

5.

0E-4

1.

0E-3

5.

0E-4

5.

0E-4

1.

0E-3

1.

0E-3

5.

0E-4

5.

0E-4

5.

0E-4

M

o

8.0E

-2

8.0E

-2

2.0E

-1

8.0E

-2

8.0E

-2

2.0E

-1

2.0E

-1

8.0E

-2

8.0E

-2

8.0E

-2

Tc

2.0E

+1

1.4E

+0

5.0E

-2

2.0E

+1

2.0E

+1

9.5E

+0

5.0E

-2

1.6E

+1

2.0E

+1

2.0E

+1

Pd

2.0E

-2

2.0E

-2

4.0E

-2

2.0E

-2

2.0E

-2

4.0E

-2

4.0E

-2

2.0E

-2

2.0E

-2

2.0E

-2

Sn

6.0E

-2

6.0E

-2

5.0E

-2

6.0E

-2

6.0E

-2

5.0E

-2

5.0E

-2

6.0E

-2

6.0E

-2

6.0E

-2

Sb

- -

- -

- -

- -

- -

I 4.

0E-4

6.

8E-3

2.

9E-3

3.

0E-2

3.

0E-2

1.

0E-2

1.

0E-2

1.

0E-2

1.

6E-3

4.

0E-4

C

s 2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

2.

0E-2

Sm

3.0E

-3

3.0E

-3

4.0E

-5

3.0E

-3

3.0E

-3

4.0E

-5

4.0E

-5

3.0E

-3

3.0E

-3

3.0E

-3

Pb

1.0E

-3

1.0E

-3

4.0E

-3

1.0E

-3

1.0E

-3

4.0E

-3

4.0E

-3

1.0E

-3

1.0E

-3

1.0E

-3

Po

1.0E

-3

1.0E

-3

4.0E

-3

1.0E

-3

1.0E

-3

4.0E

-3

4.0E

-3

1.0E

-3

1.0E

-3

1.0E

-3

Ra

1.

6E-4

8.

4E-3

9.

8E-4

8.

4E-3

1.

0E-2

5.

4E-3

6.

3E-4

2.

3E-3

4.

4E-4

1.

6E-4

T

h

2.0E

-4

2.0E

-4

1.0E

-5

2.0E

-4

2.0E

-4

1.0E

-5

1.0E

-5

2.0E

-4

2.0E

-4

2.0E

-4

Pa

3.0E

-4

3.0E

-4

6.0E

-4

3.0E

-4

3.0E

-4

6.0E

-4

6.0E

-4

3.0E

-4

3.0E

-4

3.0E

-4

U

8.5E

-5

4.9E

-4

8.8E

-4

1.3E

-3

3.1E

-4

4.7E

-4

8.8E

-4

7.6E

-4

1.4E

-4

8.5E

-5

Np

1.3E

-3

2.8E

-3

1.4E

-3

4.0E

-3

1.5E

-3

2.0E

-3

1.2E

-2

4.0E

-3

5.0E

-4

1.3E

-3

Pu

2.0E

-5

2.0E

-5

3.0E

-5

2.0E

-5

2.0E

-5

3.0E

-5

3.0E

-5

2.0E

-5

2.0E

-5

2.0E

-5

Am

7.

0E-5

7.

0E-5

4.

0E-5

7.

0E-5

7.

0E-5

4.

0E-5

4.

0E-5

7.

0E-5

7.

0E-5

7.

0E-5

C

m

2.0E

-4

2.0E

-4

3.0E

-5

2.0E

-4

2.0E

-4

3.0E

-5

3.0E

-5

2.0E

-4

2.0E

-4

2.0E

-4

112

Radionuclide transport data – retention on crop surfaces The model for deriving the activity concentration in crops due to retention of contaminated irrigation water on crop surfaces is described by equation 6-7. The applied parameter values are mainly taken from literature and are presented in the following tables:

� Table 6-9 : parameter values that are not element-specific,

� Table 6-10 : retention factors (Kret),

� Table 6-11 : translocation factors for root crops (TL)

Translocation of intake. The amount of an animal's daily intake of a radionuclide that is transferred to animal products are shown in Table 6-12 (cow milk and beef), Table 6-13 (pork and sheep meat) and Table 6-14 (poultry and egg contents). The data basis for beef is the most complete one. The parameter values selected when data is lacking for other types of meat are values scaled from beef; the assumption is that value is proportional to the daily RN intake per kg of body weight. For data gaps in egg content, the same parameter values as for poultry is used.

Table 6-9. Parameter values used when deriving activity concentrations in crops from retention on crop surfaces in AgriWELL-2009.

Parameter Value Unit Reference Leaf area index (vegetables) 5 m2/m2 Bergström & Barkefors (2004) Leaf area index (root crops) 4 m2/m2 Bergström & Barkefors (2004) Water storage capacity 0.0003 m3/m2 Bergström & Barkefors (2004) Annual yield Crop dependent kg/(m2y) See Table 6-2 Water amount per irrigation event

0.030 m3/m2 Bergström & Barkefors (2004)

Total irrigation amount Crop dependent m3/(m2y) Product of water need and irrigation fraction in Table 6-2

113

Table 6-10. Retention factors used when deriving activity concentrations in crops from retention on crop surfaces in AgriWELL-2009. The values from Bergström et al. (2006) are based on the assumption the retention factors are 0.5 for anions, 1 for monions and 2 for cations.

Element Value [-]

Reference Comment

C 0.0 Expert judgement Not considered as a transport pathway Cl 0.5 Bergström et al. (2006) Ni 2 Expert judgement Se 2 Bergström et al. (2006) Sr 2 Duro et al. (2006) Y 1 Expert judgement Lack of data Zr 1 Duro et al. (2006) Nb 1 Duro et al. (2006) Mo 1 Expert judgement Lack of data Tc 0.5 Expert judgement Pd 1 Duro et al. (2006) Sn 1 Duro et al. (2006) Sb 1 Expert judgement Lack of data I 0.5 Bergström et al. (2006) Cs 1 Bergström et al. (2006) Sm 2 Duro et al. (2006) Assuming that the free cation Sm3+

dominates Pb 2 Expert judgement Po 2 Expert judgement Ra 2 Bergström et al. (2006) Th 2 Expert judgement Pa 1 Duro et al. (2006) U 2 Bergström et al. (2006) Np 2 OPG (2004b) Assuming Np(V) predominates in the water -

with the oxocation NpO2+ Pu 2 Agüero et al. (2006) Am 2 Duro et al. (2006) Assuming +3 cations dominate Cm 2 Duro et al. (2006)

114

Table 6-11. Translocation factors for root crops used when deriving activity concentrations in crops from retention on crop surfaces in AgriWELL-2009.

Element Value [-]

Reference Comment

C 0 Expert judgement Cl 0.1 Bergström et al. (2006) Ni 0.01 Karlsson & Bergström (2002) Se 0.1 Bergström et al. (2006) Sr 0.7 Karlsson & Bergström (2002) The higher value from Coughtrey et

al. 1985 is selected Y 0.1 - Lack of data Zr 0.1 Karlsson & Bergström (2002) Nb 0.15 Karlsson & Bergström (2002) The higher value from Coughtrey et

al. 1985 is selected Mo 0.1 Karlsson & Bergström (2002) Tc 0.1 Bergström et al. (2006) Pd 0.1 Karlsson & Bergström (2002) Sn 0.1 Karlsson & Bergström (2002) Sb 0.08 IAEA (1994) I 0.1 Bergström et al. (2006) Cs 0.2 Bergström et al. (2006) Sm 0.1 Karlsson & Bergström (2002) Pb 0.03 Karlsson & Bergström (2002) Po 0.1 Karlsson & Bergström (2002) Ra 0.1 Bergström et al. (2006) Th 0.1 Karlsson & Bergström (2002) Pa 0.1 Karlsson & Bergström (2002) U 0.1 Karlsson & Bergström (2002) Np 0.1 Bergström et al. (2006) Pu 0.02 Karlsson & Bergström (2002) Am 0.01 Karlsson & Bergström (2002) Cm 0.02 Karlsson & Bergström (2002)

115

Table 6-12. Translocation of intake used in AgriWELL-2009 for cow milk and beef.

Element Fm milk [d/L]

Reference Ff beef [d/kg]

Reference

C 1.0E-2 Karlsson & Bergström (2002)

0 Expert judgement

Cl 1.7E-2 IAEA (1994) 1.7E-2 IAEA (2009) Ni 9.5E-4 IAEA (2009) 5.0E-3 IAEA (1994) Se 5.2E-3 IAEA (2009) 1.5E-2 Karlsson & Bergström (2002) Sr 1.5E-3 IAEA (2009) 2.1E-3 IAEA (2009) Y - Lack of data 1.0E-3 IAEA (1994) Zr 7.1E-6 IAEA (2009) 1.2E-6 IAEA (2009) Nb 4.1E-7 IAEA (2009) 2.6E-7 IAEA (2009) Mo 1.5E-3 IAEA (2009) 1.0E-3 IAEA (2009) Tc 9.9E-4 OPG (2002) 8.5E-3 OPG (2002) Pd 1.0E-3 Karlsson & Bergström (2002) 1.0E-3 Karlsson & Bergström (2002) Sn 1.0E-3 Karlsson & Bergström (2002) 1.0E-2 Karlsson & Bergström (2002) Sb 5.2E-5 IAEA (2009) 1.2E-3 IAEA (2009) I 9.1E-3 IAEA (2009) 1.2E-2 IAEA (2009) Cs 6.1E-3 IAEA (2009) 3.0E-2 IAEA (2009) Sm 2.0E-5 Karlsson & Bergström (2002) 5.0E-3 Karlsson & Bergström (2002) Pb 3.3E-4 IAEA (2009) 9.3E-4 IAEA (2009) Po 2.3E-4 IAEA (2009) 5.0E-3 IAEA (1994) Ra 5.1E-4 IAEA (2009) 1.7E-3 IAEA (2009) Th 5.0E-6 Karlsson & Bergström (2002) 3.5E-4 IAEA (2009) Pa 5.0E-5 Karlsson & Bergström (2002) 1.0E-5 Karlsson & Bergström (2002) U 2.9E-3 IAEA (2009) 4.2E-4 IAEA (2009) Np 5.0E-6 IAEA (1994) 1.0E-3 IAEA (1994) Pu 1.0E-5 IAEA (2009) 6.0E-5 IAEA (2009) Am 4.2E-7 IAEA (2009) 5.0E-4 IAEA (2009) Cm 2.0E-5 Karlsson & Bergström (2002) 2.0E-5 Karlsson & Bergström (2002)

116

Table 6-13. Translocation of intake used for animal products – pork and sheep meat.

Element Ff pork [d/kg]

Reference Ff sheep meat [d/kg]

Reference

C 0 Expert judgement 0 Expert judgement Cl 3.1E-2 Scaled from beef 4.2E-2 Scaled from beef Ni 9.2E-3 Scaled from beef 1.2E-2 Scaled from beef Se 3.2E-1 IAEA (2009) 2.0E+0 Thorne et al. (2001b) Sr 3.6E-3 IAEA (2009) 1.7E-3 IAEA (1994) Y 1.8E-3 Scaled from beef 2.5E-3 Scaled from beef Zr 2.2E-6 Scaled from beef 3.0E-6 Scaled from beef Nb 2.0E-4 IAEA (1994) 3.0E-4 IAEA (1994) Mo 1.8E-3 Scaled from beef 2.5E-3 Scaled from beef Tc 1.5E-4 IAEA (1994) 7.5E-3 Thorne et al. (2001a) Pd 1.8E-3 Scaled from beef 2.5E-3 Scaled from beef Sn 1.8E-2 Scaled from beef 2.5E-2 Scaled from beef Sb 2.2E-3 Scaled from beef 3.0E-3 Scaled from beef I 4.1E-2 IAEA (2009) 3.0E-2 IAEA (2009) Cs 2.2E-1 IAEA (2009) 2.7E-1 IAEA (2009) Sm 9.2E-3 Scaled from beef 1.2E-2 Scaled from beef Pb 1.7E-3 Scaled from beef 7.1E-3 IAEA (2009) Po 9.2E-3 Scaled from beef 1.2E-2 Scaled from beef Ra 9.0E-4 OPG (2005) 9.0E-4 OPG (2005) Th 6.4E-4 Scaled from beef 8.7E-4 Scaled from beef Pa 1.8E-5 Scaled from beef 2.5E-5 Scaled from beef U 4.4E-2 IAEA (2009) 1.4E-3 Thorne et al. (2001c) Np 1.8E-3 Scaled from beef 2.5E-3 Scaled from beef Pu 8.0E-5 IAEA (1994) 5.3E-5 IAEA (2009) Am 1.7E-4 IAEA (1994) 1.1E-4 IAEA (2009) Cm 3.7E-5 Scaled from beef 5.0E-5 Scaled from beef

117

Table 6-14. Translocation of intake used for animal products – poultry and egg contents.

Element Ff poultry [d/kg]

Reference Ff egg contents [d/kg]

Reference

C 0 Expert judgement 0 Expert judgement Cl 2.0E+0 OPG (2004a) 2.0E+0 OPG (2004a) Ni 2.2E-2 Scaled from beef 2.2E-2 Same as for poultry Se 1.3E+01 IAEA (2009) 1.8E+01 IAEA (2009) Sr 2.3E-2 IAEA (2009) 8.8E-1 IAEA (2009) Y 1.0E-2 IAEA (1994) 2.0E-3 IAEA (1994) Zr 6.0E-5 IAEA (2009) 2.0E-4 IAEA (2009) Nb 3.0E-4 IAEA (2009) 1.0E-3 IAEA (2009) Mo 1.8E-1 IAEA (2009) 6.5E-1 IAEA (2009) Tc 1.3E-1 Thorne et al. (2001a) 1.3E+0 Thorne et al. (2001a) Pd 4.5E-3 Scaled from beef 4.5E-3 Same as for poultry Sn 3.5E-2 US DoE (2004) 5.9E-1 US DoE (2004) Sb 5.4E-3 Scaled from beef 5.4E-3 Same as for poultry I 1.0E-2 IAEA (2009) 2.4E+0 IAEA (2009) Cs 3.0E+0 IAEA (2009) 4.3E-1 IAEA (2009) Sm 2.2E-2 Scaled from beef 2.2E-2 Same as for poultry Pb 2.5E-2 US DoE (2004) 5.6E-2 US DoE (2004) Po 2.4E+0 IAEA (2009) 3.1E+0 IAEA (2009) Ra 1.3E-1 OPG (2005) 1.3E-1 OPG (2005) Th 5.9E-3 US DoE (2004) 3.5E-3 US DoE (2004) Pa 5.9E-3 US DoE (2004) 3.5E-3 US DoE (2004) U 7.5E-1 IAEA (2009) 1.1E+0 IAEA (2009) Np 3.6E-3 US DoE (2004) 3.4E-3 US DoE (2004) Pu 3.0E-3 IAEA (1994) 1.2E-3 IAEA (2009) Am 6.0E-3 IAEA (1994) 3.0E-3 IAEA (2009) Cm 9.0E-5 Scaled from beef 9.0E-5 Same as for poultry

119

7 KNOWLEDGE QUALITY ASSESSMENT

This chapter present the most the Knowledge quality assessment (KQA) performed for the sub-process of the biosphere assessment (BSA) process addressed in the present report: the screening models (section 7.1), landscape modelling (section 7.2), the radiological consequences analysis (section 7.3) and the safety indicators (section 7.4).

The KQA is an iterative process that spans all activities in the Biosphere assessment and in the safety case. Its aim is to foster communication of assumptions and uncertainties throughout the assessment chain in a systematic and comprehensive manner. The different aspects covered by the KQA, developed on the basis of Ikonen (2006), Hjerpe (2006), Broed (2007b), Broed et al. (2007) are as follows:

� Applied (and available) key data: sources and subsequent handling of data, where the data are used, are they fit for the purpose, how the data quality is assured and checked, what the impact of using available but not used data would have been, why such data have not been used.

� Main assumptions, their impact, potential for alternative interpretations.

� Main uncertainties in the input data delivered from other BSA sub-processes and those produced during the interpretation or modelling process, their cause, whether the uncertainty has been assessed, means to resolve and whether this would help in a further assessment.

� Sensitivity assessment and data quality: how sensitive the models are to the input data, confidence in an adequately high quality of the data and underlying process understanding.

� Overall consistency within the biosphere assessment, with previous versions, corresponding other models, assessments, and science in general.

� Overall knowledge quality. Overall statements regarding the confidence in applied models and data, level of conservatism, and the total uncertainty propagated to the next sub-process in the BSA (or next process in the overall safety case).

In the assessment of main assumptions, they are classified by their nature. The classification is presented in Table 7-1, which is a modified version of the approach of Swiss National Cooperative for the Disposal of Radioactive Waste (Nagra 2002).

The uncertainty in the product of a sub-process is divided into two categories: i) caused by uncertainty in the input data delivered from other BSA sub-processes and ii) caused by the uncertainty in the sub-process itself (here including also input data not originating from other biosphere assessment sub-processes). This approach is taken to better elucidate quality issues related to the products created by each component and to make the tracking of how uncertainties propagate during the rather complex modelling process easier.

120

Table 7-1. Classification system of assumptions, modified from (Nagra 2002).

Categorisation

Assumptions for broad characteristics and evolutionary path followed by the system and conceptualisation of phenomena LE Conceptual assumption corresponds to the likely/expected characteristics and

evolution of the system PCA Pessimistic conceptual assumption within the reasonably expected range of

possibilities WRP Within the range of possibilities but likelihood not currently possible to evaluate

– other (and sometimes more pessimistic) assumptions may not be unreasonable ST Stylised conceptualisation of system characteristics and evolution

Simplifications made for modelling purposes MS Modelling simplification — not significantly affecting numerical results CS Modelling simplification — intrinsically conservative CP Modelling simplification — conservative given the assumed model parameters

7.1 Screening models

The purpose of applying screening models is primarily to ensure that the level of detail of the assessment, especially the landscape modelling, is appropriate to the magnitude of the potential radiological consequences. The key considerations to ensure a high knowledge quality regarding the screening models is to apply models that are fit for the purpose in conjunction with assumptions and parameter values producing results that, with a high degree of confidence, undoubtedly overestimate potential radiological consequences. The numerical uncertainties in the results from the screening evaluation are of less importance, as long as, taking uncertainties into account, the results are undoubtedly overestimates of potential radiological consequences. Hence the KQA for the screening models focuses on applied data, main assumptions and overall consistency with the landscape modelling and internationally used models for screening purposes.

Applied key data The main approach to select parameter value data is to select cautious values from generic sources. Further, all applied data are compared to values selected in the landscape model and in previous biosphere analyses, in order to ensure that more cautious values are not applied elsewhere. For only one parameter used, the effective mixing capacity of the well in Tier 2, are site properties used as a basis. The key data, listed in detail in section 3.3, include data and data sets such as:

� screening dose rates for humans, cautiously selected such that there is a high degree of confidence that the potential radiological consequences are substantially, at least two orders of magnitude, below the regulatory dose constraints,

� screening dose rates for other biota, selected to be internationally applied dose rates recommended for screening purposes,

121

� dose coefficients for ingestion, inhalation and external exposure. The coefficients used for external radiation are selected under the geometrical cautious assumption that radionuclides are distributed on the ground surface,

� EMCL (Environmental Media Concentration Limit) for each radionuclide-reference organism combination, derived by back-calculating from the selected screening dose rate,

� solid-liquid distribution coefficients (Kd) in soil, selected as the highest 95th percentile values, for any soil type, of the values/distributions reported in IAEA (2009) and Karlsson & Bergström (2002),

� bioconcentration factors for lakes and aggregated concentration ratios for forest,

� productivity of edibles for lake and agricultural land,

� cautious exposure characteristics, (intake rates of food, water and air).

Main assumptions The screening model for humans, in the first tier, contains only a few, and extremely pessimistic, assumptions. The screening model for other biota, in the first tier, also contains very few, and extremely pessimistic, assumptions. The main assumptions are listed and categorised in Table 7-2 and Table 7-3 for Tier 1 and Tier 2, respectively.

Table 7-2. Main assumptions in the Tier 1 screening model.

Assumption Class Comment

A derived risk quotient for an individual radionuclide less than, or equal to, 1 is sufficient to screen out that particular radionuclide

CP The screening dose rates (both for humans and other biota) are considered to be selected low enough to allow the screening of individual radionuclides; even if all calculated RQs are equal to 1, the sum of all RQs would still correspond to an exposure leading to insignificant radiological consequences.

The whole integrated release from the geosphere is routed to one person either via ingestion or inhalation, or is transferred to the ground surface and exposes one person externally,

ST Extremely cautious stylised assumption, beyond any expected range of possibilities.

The three exposure situations are evaluated in parallel, and the situation resulting in the highest exposure is then the basis for the screening decision, which may differ for different radionuclides

ST

For releases transferred to the ground surface, dose coefficients due to radionuclides uniformly distributed on the ground surface is used.

PCA Cautious assumption, at the very limit of any expected range of possibilities.

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Table 7-2 cont’d. Main assumptions in the Tier 1 screening model.


The exposed individual stays during the whole year on the contaminated ground.

PCA Cautious assumption, at the very limit of any expected range of possibilities.

The dose calculation are based on exposure to geosphere release rates integrated over a long-time window

ST This is an extremely cautious assumption, beyond any expected range of possibilities.

The integrated geosphere release rates are not corrected for any radioactive decay, but including build-up of progeny radionuclides

ST This is an extremely cautious, even non-physical, assumption, beyond any expected range of possibilities.

The most radiosensitive reference organism (lowest EMCL) gets exposed

PCA

The habitat for the most radiosensitive reference organism has an activity concentration numerically equal to the total integrated activity in the geosphere release, for each radionuclide

ST This is an extremely cautious assumption, beyond any expected range of possibilities.

Table 7-3. Main assumptions in the Tier 2 screening model.


A RQ for an individual radionuclide less than, or equal to, 1 is sufficient to screen out that particular radionuclide

CP The screening dose rates (both for humans and other biota) are considered to be selected low enough to allow the screening of individual radionuclides; even if all calculated RQs are equal to 1, the sum of all RQs would still correspond to an exposure leading to insignificant radiological consequences.

The whole geosphere release is directed both to a well and a lake

ST This ensures conservative estimates of the radionuclide concentrations in environmental media

Losses other than runoff are not considered in the lake sub-model, the whole releases also reach the soil of the terrestrial object

ST This ensures conservative estimates of the radionuclide concentrations in environmental media

The sub-models are evaluated in parallel, and the situation with the highest exposure is then the basis for the radionuclide-specific screening decision

ST

No losses of radionuclides by sorption and other processes in the well sub-models

ST This ensures conservative estimates of the radionuclide concentrations in the well water

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Table 7-3 cont’d. Main assumptions in the Tier 2 screening model.


All dust in the inhaled air has the same radionuclide concentration as the contaminated soil

ST This assumption overestimate of the air concentrations

The crops in the terrestrial objects are irrigated with water either from the lake or the well, whichever has the maximum radionuclide concentrations

PCA This ensures conservative estimates of the radionuclide concentrations in environmental media

The areas of the lake and the terrestrial object were assumed equal to the areas needed to support the yearly demand of food by an individual; estimated by dividing the yearly food demand by conservative values of the productivity of food in aquatic and terrestrial ecosystems.

PCA The small values of the assumed areas give small dilution volumes and therefore conservative estimates of the concentrations in water and soil are obtained.

Constant geosphere release rates are used, selected to the maxima for each radionuclide, regardless if they occur at different times

PCA This ensures that the values of the radionuclide concentrations in air, soil, food and water are maximised

All water consumed by the exposed individual comes from a water body with maximal radionuclide concentrations

ST This maximises the exposure via water ingestion

All food ingested by the exposed individual has maximal values of the radionuclide concentrations

ST This maximises the exposure via food ingestion

The effective mixing capacity is selected to 30 000 m3

WRP This value is about three times lower than the value used in the well scenarios (section 6.1.3)

Selected parameter values cautiously selected, 95th percentiles values to as great extent as possible

PCA More cautious values, compared to the values used in the Tier 3 (the landscape model) are selected for parameters such as runoff, Kd, CR and intake rates

Radionuclide concentrations of C-14 were estimated using the specific activity models described in (Avila and Pröhl 2007)

PCA It is considered that these models provide conservative estimates, which can be used for screening purposes

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Main uncertainties The product in the screening evaluation is ultimately the screening decision: screened in or screened out (or if RQ is less than one or not). However, when assessing the uncertainties, the numerical value of RQ is considered as the product. Since the screening models are generic, they utilise very little input data delivered from other BSA sub-processes. The main uncertainty in the RQs derived in both Tier 1 and Tier 2 are dominated by uncertainties in the applied radionuclide-specific releases from the geosphere. The main uncertainty in the outcome of the screening models is that the screening decision is based only on RQhumans for some radionuclides; for nine radionuclides are EMCL values lacking in the used data set from ERICA.

Sensitivity assessment and data quality For Tier 1, it is considered that, since the conceptual model is so overly pessimistic, no sensitivity assessment is necessary and most of the used data is of sufficiently high quality. The only data set in need of improving is the EMCLs, by especially filling the gap of the radionuclides currently lacking EMCL values. Any comprehensive sensitivity assessment, including a sensitivity analysis, has not been performed for the Tier 2 model and its underlying sub-models. This will be done in future. The data used are mainly cautious values selected from generic sources. The quality of that data, for use in screening evaluations, is considered to be high.

Overall consistency The aim of the screening models is to produce very conservative (i.e. overestimated) estimates of the magnitude of the doses from a given release. The approach selected for the screening evaluation is similar to the approach recommended in IAEA (2001) for use in assessing the impact of discharges of radioactive substances to the environment, and the models are in line with the recommendation by the ICRP (2000, 2007b) on how to conduct a dose assessment. The data have been selected from generic sources for the purpose, and will be revisited for each assessment to ensure an adequate level of conservatism.

Overall knowledge quality The three-tiered graded approach, which includes the screening evaluation, has been recently developed and has been applied for the first time in the present assessment. The methodology will be evaluated, and possibly revised, in order to reach maturity by 2012. For Tier 1, the confidence is very high that the outcome of the screening evaluation is fit for purpose. Considering the results from the BSA-2009 biosphere assessment, the selection of models and data for Tier 2 is considered to result in a screening model that meet the intended goal; at least for the radionuclide release scenarios considered.

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7.2 Landscape modelling

This section presents the KQA of the landscape model applied in the present assessment. The KQA has been performed on the two parts of landscape modelling, the biosphere object modules and the landscape model set-up, and these are treated separately below.

7.2.1 Biosphere object modules

The biosphere object modules themselves are conventional compartment models with rather simple transport functions, as described in detail in section 4.3.

Applied key data The radionuclide transport modelling (the landscape model) includes hundreds of parameters; in addition to the most important data set, the radionuclide release rates from the geosphere, the key data sets and sources applied are listed in Table 7-4.

Main assumptions The individual biosphere objects modules include a number of assumptions; the key assumptions underpinning the modules are addressed in Table 7-5.

Main uncertainties Solving transport modelling problems with compartment modelling is a common approach; as the compartments have been defined with care, the largest uncertainties are arising from the parameter values aggregating a number of real-life processes into simplistic transfer factors necessitated by the computational demands. The product of the biosphere object modules is derived radionuclide-specific activity concentrations in environmental media in the biosphere objects in the landscape model. The main identified uncertainties in input data from other BSA sub-processes (mainly the BSD sub-process) are listed and assessed in Table 7-6.

Table 7-4. Key data sets used in the biosphere objects modules, where they are presented in this report and their sources.

Data set In this report Sources Radionuclide half lives Table 2-1 Chu et al. 1999 Soil and sediment physical properties (density, porosity and carbon concentration)

Table 4-6 Ikonen et al. 2010a

Concentration ratios Tables 4-7 and 4-8 Ikonen et al. 2010a Solid-liquid distribution coefficients in soils and sediments

Table 4-9 Helin et al. 2010

Standing biomass and production of biomass Tables 4-10 to 4-14 Ikonen et al. 2010a Irrigation amount and frequency Table 4-13 Ikonen et al. 2010a

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Table 7-5. Main assumptions in the biosphere object modules.


All BSOs The radionuclides are fully mixed within each compartment in the model within each modelled time step

ST Standard approach in the modelling of the transport and fate of radionuclides in the environment (cf. C-14 specific assumptions below).

All releases are in solute form in the groundwater

LE Gaseous releases of C-14 are assessed with the C-14 model

Radionuclides are treated as trace elements in the biosphere

LE That is, release rates of radionuclides are low enough to not disturb the transport or accumulation of natural elements

Terrestrial BSOs The geosphere releases are directed to the deep soil layer

PCA The vertical radionuclide transport between the compartments is then given by the surface and near surface hydrology modelling.

Aquatic BSOs The geosphere releases are directed to the deep sediment

PCA The vertical radionuclide transport between the compartments is then given by the surface and near surface hydrology modelling.

C-14 specific C-14 geosphere releases are fully mixed with the stable carbon

LE Isotopic equilibrium between C-14 and C-12 is achieved and a constant specific activity, that will be observed in all system components

The excess C-14/C-12 ratio is not affected by the other C-14 than released from the repository

LE

The C-14 geosphere release is readily transformed into bioavailable forms by microbial activity

LE

The specific activity of C-14 reduces as C-14 migrates away from the release source.

LE This is a direct consequence of the irreversibility of the isotopic dilution, i.e. re-concentration of C-14 will not occur once it has mixed with a certain amount of C-12 (Sheppard et al. 2006).

Radionuclides released to air are mixed in the whole area of the biosphere object.

LE From the kinetic point of view this is not a problem, as the air exchange rate should be much higher than the rate of variation of the area. However, since one object can have several ecosystem types there is an issue of which roughness to use.

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Table 7-6. Main uncertainties in the input data to the biosphere object modules from other BSA sub-processes.

Uncertainty Cause and assessment of uncertainty

Effect on the product

Means to reduce the uncertainty

From the BSD sub-process Concentration ratios for key elements

Gaps of, or low information density of, site data for key elements such as I and Cl

Uncertainties in the derived activity concentrations

More site data will be available for the 2012 assessment

Literature values exist – collection of a database initiated (Helin et al. 2010)

Solid-liquid distribution coefficients (Kd)

Lack of site-specific data for key elements such as I and Cl

Uncertainties in the derived activity concentrations

A research programme has been initiated in 2008

Literature values exist – collection of a database initiated (Helin et al. 2010)

Irrigation characteristics

Course information density - data integrated from whole Satakunta province

Uncertainties especially in the derived activity concentrations in crops

Irrigation data could be surveyed from areas close to Olkiluoto

Sensitivity assessment and data quality The sensitivity of the assessment models to changes in the value of an input parameter are explored by sensitivity analysis (e.g., Ekström & Broed 2006). Sensitivity analysis (SA) is a method applied to capture a quantitative dimension of the total uncertainty. In the previous biosphere analysis (Broed et al. 2007) a global sensitivity analysis was performed for each type of BSO module. The method of Morris (Morris 1991) was used for identifying the most important parameters, and Sobol's method (Sobol 1993) for quantitative sensitivity analysis was used to derive total sensitivity indices (TSI) for these parameters. This type of SA has not been performed on the BSO modules presented in this report. This will be done on the BSO modules applied in the 2012 assessment; these modules will presumably be same as in the present assessment, or with minor modifications.

The quality of the data recommended for the further assessment from the BSD sub-process has been evaluated with a quantified measure, the data quality index, which aims to capture the qualitative dimensions of the total uncertainty. The data quality index method is a further development from that of Broed (2007b) and Broed et al. (2007), and can be considered to be a simplified pedigree analysis (e.g., Ellis et al. 2000, Jeroen et al. 2002). The results of the data quality index evaluation repeat the pattern of uncertainties and lack of data discussed already earlier, but provide a more systematic measure than the non-quantified lists. To illustrate the applicability in the overall assessment, Figure 7-1 presents a plot of the data quality index against the

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Figure 7-1. Data Quality Index for key data related to the C-14 transport model against the sensitivity of the transport model to the parameter expressed by the Spearman Rank Correlation Coefficient (NPP: net primary production). respective sensitivity measure in the case of the C-14 transport model (Avila & Pröhl 2007). For each parameter given a site-relevant value, the corresponding Spearman rank correlation coefficient (SRCC) was taken from (Avila & Pröhl 2007). In the lower right corner of the figure, the data quality has been evaluated low and at the same time the model is relatively sensitive to small changes in the parameter value; parameters situated here would require immediate improvement. The further the individual parameters locate to the upper left corner on the plot, the higher the confidence in the model output is. Based on the data quality index, the best founded data at the moment are related to the forest and agricultural ecosystems, whereas data on lakes, coastal areas and especially rivers need improvement. These have been acknowledged readily in the biosphere description work (Haapanen et al. 2009; Ikonen et al. 2010a) to be addressed in the following version for the 2012 assessment.

Overall knowledge quality At the present, unlike with the other modules, the confidence in the model underlying the radionuclide transport from irrigation water to crops has not been assessed; since this model is originally developed for steady-state conditions, an evaluation of its fitness to the more dynamic landscape modelling shall be done by 2012.

7.2.2 Landscape model set-up

The KQA performed on the delineation of biosphere and the process of connecting them into a landscape model is discussed here.

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Applied key data The main data input is the forecast of the terrain and ecosystems development model and the results from the surface and near-surface hydrological modelling; the main data sets and sources applied are listed in Table 7-7.

Main assumptions The main assumptions made in the landscape model set-up are listed and categorised in Table 7-8.

Main uncertainties The main identified uncertainties are listed and assessed in Table 7-9.

Overall knowledge quality Generally, configuring the landscape model set-up is a rather robust step after the input data (terrain and ecosystem forecasts, surface and near-surface hydrological simulations and the release pattern from the geosphere) are available and well specified. There are a number of alternative development paths, which need to be handled by scenario approach, but producing adequate data basis, the calculation cases need to be propagated through the relevant other modelling steps - the overall uncertainties arise from the interplay of the various data sets more than from the landscape model set-up as such, even though some issues of discretisation always remain in compartment modelling.

Table 7-7. Key data sets used in the landscape model set-up and their sources.

Data set Sources

Ecosystem-types of biosphere objects

TESM (Ikonen et al. 2010b)

Geometrical properties of each biosphere object (areas) and its compartments (thicknesses)

TESM (Ikonen et al. 2010b)

Fluxes of water between the compartments within the biosphere objects

Based on site data, interpreted through the SNSH model (Karvonen 2009c)

Fluxes of water between biosphere objects Based on site data, interpreted through the SNSH model (Karvonen 2009c)

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Table 7-8. Main assumptions in the landscape model set-up.


The areas of individual forests and croplands were not allowed to be larger than a certain limit

CS To avoid excess numerical dispersion of radionuclides arising from treating individual objects as laterally homogeneous

Terrestrial areas with suitable soil (clay or gyttja/mud soils) of a thickness of at least 0.5 m are modelled as croplands

CP

In biosphere objects where an aquatic and a terrestrial ecosystem exists at the same time step, any direct geosphere releases are routed to the terrestrial part

CP

Inheritance of activity inventory from one shrinking ecosystem to a growing ecosystem is the areal rate of change divided by the area of the shrinking object

LE

Radionuclide releases to one main flow path of surface water do not affect the other main flow path

MS The model contains two main flow paths of surface water, representing the northern and southern part of the present-day Olkiluoto Island

Table 7-9. Main uncertainties in the input data to the landscape model set-up from other BSA sub-processes.

Uncertainty Cause and assessment of uncertainty

Effect on the product

Means to reduce the uncertainty

From the BSD and TESM sub-processes Ecosystem development path

The succession paths of "natural" ecosystems and how they are affected e.g. by clearing croplands or by rising water levels are uncertain

Uncertainties in time-dependent ecosystem types for the biosphere objects

Will be handled by scenario approach and will be further developed for the 2012 assessment

The spatial and temporal distribution of radionuclide pathways through the geosphere

The transport of radionuclides through the geosphere used a single representative pathway and did not fully utilise the identified multiple flow pathways

Uncertainties in the release pattern

Dependent on how the releases in the geosphere are modelled; will be improved for the 2012 assessment

The radionuclide transport pathways through the overburden

Not explicitly accounted for; the release patterns were based on the spatial and temporal distribution of exit points at the top of the geosphere

Uncertainties in the release pattern

Improvements will be made for the 2012 assessment

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7.3 Radiological consequence assessment

This section presents the KQA of the methodologies presented in this report to derive annual landscape doses (to humans) and typical absorbed dose rates (to other biota). The KQA has been performed on the two applied dose assessment processes and these are treated separately below.

7.3.1 Assessing doses to humans

The knowledge quality underpinning the radiological consequences analysis for humans is generally good. The areas to focus on are to strengthen the data basis for environmental information (productivity of edibles and aggregated concentration ratios) and exposure characteristics (implementing ranges of dietary profiles). This section presents the KQA performed for the dose assessment process presented in section 5.1.

Applied key data The key data sets used, and their sources, in the dose assessment for humans are:

� geometric properties of the landscape model (outcome of the TESM sub-process),

� radionuclide-specific radioactivity concentrations in environmental media (e.g., soil, sediment and water) – outcome of the landscape modelling sub-process,

� productivity of edibles – data recommended from the biosphere description sub-process,

� aggregated concentration ratios – data recommended from the biosphere description sub-process, and

� exposure parameters:

� food intake rate – based on ICRP Reference Man (ICRP 1975, 2002),

� water intake rate – based on ICRP Reference Man (ICRP 1975, 2002),

� dose coefficients for ingestion and inhalation – based on the values recommended by ICRP for adults (ICRP 1996), and

� dose coefficients for external radiation from radionuclides uniformly distributed to an infinite depth - Table III.7 in EPA (1993).

Main assumptions The main assumptions made in the landscape model set-up are listed and categorised in Table 7-10.

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Table 7-10. Main assumptions when assessing doses to humans.


General The concept of assess the dose to representative persons is assumed to be adequate

LE This methodology is applied when deriving doses for determining compliance to the regulatory dose criteria.

Environmental information The derivation of environmental information is unaffected by the behaviour of the exposed population.

ST This means, for example, that when the activity concentration in, and the productivity of food from, a forest object is derived, it is not taken into account that the same forest object is assumed to be urbanised in the dose assessment.

The productivity of edibles is calculated by summing over all plant parts and animal products normally consumed by man

WRP

Exposure characteristics All surface water bodies may be utilised as sources for drinking water and irrigation water

PCA

Individuals in the exposed population have no food preferences

WRP Applied when deriving aggregated concentration ratios to, without assuming details of diet, produce reasonable measures for the ingestion of food exposure pathway. The only assumption needed is whether to include something or not in the list of edibles from each ecosystem type.

Exposure pathways other than ingestion of food, ingestion of water, inhalation and external exposure are of minor importance

MS The annual doses to the exposed population are assumed to be dominated by these four pathway types.

In addition, ingestion of soil is partly considered when ingesting food, where no reduction in the intake of radionuclides due to pre-treatment of food, such as peeling potatoes, is taken credit for.

Other exposure pathways, such as swimming, boating, handling fishing equipment, inadvertent ingestion of soils or sediment, are assumed, for the radionuclides and activity levels of interest, not contribute significantly to the annual dose (see for example Bergström et al. 1999, Karlsson & Bergström 2000, Avila & Bergström 2006).

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Table 7-10 cont’d. Main assumptions when assessing doses to humans.


Exposure characteristics Average present-day intake rates of food and water are applied and assumed to be unchanged in future generations

ST This is a stylised assumption based on YVL D.5, which states that the dose assessment, in general, may assume that human habits, nutritional needs, and metabolism remains unchanged

Each exposed individual consumes only foodstuffs and water that are locally produced and contaminated.

PCA

The exposed individual stays during the whole year (and the whole life) on the contaminated ground.

ST This is a very cautious assumption, affecting the dose from inhalation and external exposure, beyond any expected range of possibilities. However, it does not affect the outcome much, since the other pathways dominate the annual landscape dose.

Dose calculations Dose calculations are based on food energy (carbon) intake

CP See discussion in (Avila & Bergström 2006).

Dose coefficients for external radiation from radionuclides uniformly distributed to an infinite depth in soil are used

MS/CS The calculated environmental radioactivity concentrations in soils are uniformly distributed to a finite depth, and with a finite lateral extent. Thus, the applied dose coefficients never underestimate the dose.

The volume of the soil dominating the dose increases with increasing energy of the emitted radiation, because the attenuation coefficient of the soil decreases with increasing energy. In practice this means that the assumption class is MS for radionuclides emitting low-energy radiation, and CS for radionuclides emitting high-energy radiation.

For intake of relatively long-lived radionuclides, the contribution to the dose from intake of its radioactive progeny present in the environment is not considered, when half-life of the progeny are in the order of days or shorter

MS For the radionuclide decay chains of interest, the dose is dominated by the dose arising from the parent radionuclides that are explicitly modelled.

Dose coefficients for inhalation are, for each radionuclide, selected from the class of absorption in the lungs resulting in the highest doses

PCA

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Table 7-10 cont’d. Main assumptions when assessing doses to humans.


Dose identification The annual landscape dose, EALD, is identified as the sum of dose maxima from each exposure pathway

PCA Thus, the EALD may be a sum of contributions from different biosphere objects (or even from different biosphere object types)

The annual consumption of food originate from the exposed area to as full extent as possible

PCA Thus, the food ingestion dose may be a sum of contributions from different biosphere objects (or even from different biosphere object types)

The most exposed group is the 5% of most exposed persons in the dose distribution, with a maximum of 50 persons

LE ICRP (2007b) recommends using the 95th

dose percentile as the basis for selection of the most exposed group. Further, ICRP recommends that in the case when relevant dose constraints might be exceeded by a few tens of people or more, the characteristics of these people need to be explored (ICRP 2007b)

Only forest objects are presumed to be used as residential areas

WRP Wetlands are presumed to not have suitable ground properties for building houses. Croplands are assumed to be exclusively used for cultivation (no areas are occupied by farm buildings).

The population density is selected as the highest present urban density in Finland (Helsinki)

PCA This assumption is made to assure that the dose from inhalation and external exposure is not underestimated.

The maximum sustainable population due to water intake is always greater than the maximum size of the exposed population.

LE

The dose distribution is derived by iteration until all three pathways have been the dominating pathway in the annual landscape dose at least once, or until a pre-selected upper limit on the size of the exposed group is reached.

PCA This approach derive the dose distribution, and limit the size of the exposed population, ensures that no potentially highly exposed groups are excluded, and ensure that average dose in the exposed population does not get underestimated if the least significant exposure pathway should be capable of supporting a large number of people but results in very low doses. This situation could happen for the water intake pathway for example if the most contaminated surface water is a large lake.

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Main uncertainties The overall uncertainty in the landscape doses is mainly due to the uncertainty in the estimated activity concentrations in environmental media from the foregoing landscape modelling sub-process. However, the radiological consequences analysis itself naturally also introduces uncertainties, but these are likely less dominant than the uncertainties in activity concentrations. In the present assessment, a few uncertainties in need of reduction have been identified; these are as follows:

� the estimates of productivity of edibles are derived from site or analogue site data, and larger uncertainties were found in quantification of the yield per unit area of berries, mushrooms and game, and

� the aggregated concentration ratios bear the uncertainties of the productivity estimates and, more strongly, the uncertainties in the concentration ratio values for the various edibles. The concentration ratios are derived from few actual data, as described in more detail in (Helin et al. 2010).

Sensitivity assessment and data quality The dose assessment process applied, especially the novel dose identification procedure, has been recently developed and is implemented here for the first time. A comprehensive sensitivity assessment has not yet been performed; this will be done for the 2012 assessment. The quality of the underlying data is generally good. Exposure parameters are based on high-quality data from ICRP and EPA, and the important geometrical properties of the landscape model are derived from plausible forecasts in the TESM sub-process. The main parameters identified for which there is a need to strengthen the data basis, and thus the quality, are productivity of edibles and aggregated concentration ratios.

Overall consistency The dose assessment process applied is based on the one used in the KBS-3H analysis (see Broed et al 2007). In the present assessment, the process has been refined to be more consistent with how dose assessments are commonly applied internationally. However, the concept of landscape doses is fairly new and has not been applied in many safety assessments internationally. In the development of the dose assessment approach, emphasis has been put on harmonising the dose assessment with international recommendations and to ensure that the outcome has an adequate level of conservatism.

Overall knowledge quality Calculating doses to individual humans from a given environmental concentration by an exposure pathway is a rather straightforward task and conventional models are used, shifting the question of level of confidence to the input data. However, as the spatial distribution of the contamination in various areas and consumables needs to be taken into account in the context of the site and the present way in which people use the area, the task becomes somewhat more complicated, i.e. how to define the exposed groups. The approach used does not aim to be realistic in the permutations of possible exposures, but assumes the highest plausible exposure from the remaining contamination after contaminations giving the highest exposure has been utilised by more exposed persons. In future assessments the reasonability of the dose distributions will be further discussed.

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There is an identified need to strengthen the data basis for environmental information (productivity of edibles and aggregated concentration ratios) and exposure characteristics. For berries and mushrooms, a site study has been initiated to improve the estimates of productivity of edibles, and for game a more rigorous approach will be included in the next Olkiluoto biosphere description. The concentration ratio values for the various edibles are, as said above, derived from only a few actual data. A wider literature review and analyses of samples acquired from the site will be included in the 2012 assessment. The exposure parameters are on one hand based on the ICRP Reference Man (intake rates and dose coefficients) and on the other on the regional population statistics. It is purposed to use national statistics, as well as regional information, for intake rates to define ranges of dietary profiles for the 2012 assessment, which is commonly used in many other dose assessments.

7.3.2 Assessing doses to other biota

The knowledge quality for the radiological consequences analysis for other biota has not yet reached the same level as for humans. This is as expected, since the radiation protection system for other biota is not yet as mature internationally as for humans. The approach to assess radiological consequences to other biota applied in the present assessment is considered to be in a fairly early maturity stage and will be revised for the 2012 assessment. This section presents the KQA performed for the dose assessment process presented in section 5.2.

Applied key data The main data sets used, and their sources, in the dose assessment for other biota are:

� radionuclide-specific radioactivity concentrations in environmental media (e.g., soil, sediment and water) – outcome of the landscape modelling sub-process,

� selection, sizes and weights of assessment species (Table 5-7 to Table 5-9) representing the diversity of biota at the site – made by expert judgement on the basis of site understanding in Haapanen et al. (2009),

� occupancy factors, i.e. fraction of time the assessment species spends in air, on soil, in soil, on water surface, in water column, on sediment surface or in sediment – default values from the ERICA assessment tool (Beresford et al. 2007) with some adjustments based on knowledge on the site (Table 5-7 to Table 5-9), and

� concentration ratios for the assessment species (Table 5-10 to Table 5-12) – default values from the ERICA assessment tool (Beresford et al. 2007); these are justified in (Beresford et al. 2008, Hosseini et al. 2008) with the exception of concentration ratios to terrestrial plants derived as a combination of site and literature data provided in Helin et al. 2010.

Main assumptions The main assumptions in the dose assessment for the other biota are listed and categorised in Table 7-11.

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Table 7-11. Main assumptions in the dose assessment for other biota.

Assumption Class Comment The ensemble of the selected assessment species is sufficient to derive estimates on "typical radiation exposures of terrestrial and aquatic populations" per the regulatory requirements (STUK 2009)

WRP The assessment species cover the significant trophic levels (roles) in the food webs of the ecosystems prevailing and expected at the site, thus cover the essential exposure situations and ranges of dose to fully developed entire organisms (within the limitations of the model)

All assessment species are represented by an ellipsoid

ST This is considered to be a suitable assumption for animals, but is acknowledged to be problematic for plants; alternative approaches will be considered for the 2012 assessment

Exposure pathways include external exposure from the surrounding media (soil, sediment and water) and internal exposure from the radionuclides transported from the media inside the organism

ST The modelled is using concentration ratios, thus also an equilibrium is assumed, but external exposure from other individuals of the same, or other, assessment species is not included

Internal radioactivity is evenly distributed in the (ellipsoidal) body of the organism

ST It is known that this is not totally valid especially to certain elements like iodine that accumulate in specific organs but the present assessment methodology lacks this level of detail; alternative approaches will be considered for the 2012 assessment

Occupancy in the compartments of habitat is handled in a stylised manner

CP In reality, the larger the species the more they move around the site; her it is assumed that the organism is always present in the single compartment

Different life stages of assessment species have not been considered – all have been assumed to be in the fully developed stage

ST It is acknowledged that in some specific cases earlier development stages may be more sensitive to the radiation as such or associated with higher accumulation of contaminants – this will be improved for the 2012 assessment

Assessment species have been considered as the whole organism,

ST It is acknowledged that specific parts may accumulate more radioactivity, be more sensitive to radiation or be crucial for the welfare of the whole organism (e.g. plant roots) – the approach will be improved for the 2012 assessment

The presence of a species does not prohibit the presence of any other species in the same biosphere object

ST In reality this is hardly the case due to competition and predator-prey relationships

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Table 7-11 cont’d. Main assumptions in the dose assessment for other biota.

Assumption Class Comment Individuals of assessment species stay all time the same biosphere object,

ST it has not been considered that some species live in both aquatic and terrestrial habitats (e.g. birds) and in some cases the type of habitat changes during the life cycle (e.g. in early development frogs live solely in the aquatic environment, whereas adult frogs live on the interface of terrestrial and aquatic environments or in wetter terrestrial environments)

The size of the home range for assessment species has not been considered

ST For example, moose may have a home range of tens of square kilometres but get the assumed exposure resulting from full-time presence in an object of half a hectare

Seasonal migration is not considered ST In reality, for example, many birds migrate away for winter and moose wander between summer and winter ranges

Main uncertainties The main uncertainties in the dose assessment for the other biota, taken that the assessment methodology is appropriate (see discussion below), are:

� concentration ratios – the data is from a generic compendium full of data gaps, though it is the best available, and does not fully match the selected species and the conditions at the Olkiluoto site (some improvement especially for terrestrial and aquatic plants is expected for the 2012 assessment as more site studies are completed), and

� sizes and weights of assessment species – the data are partly from scientific literature, but also to a large extent from popular nature books lacking reliable peer review, and a number of data gaps have been filled by expert judgement (improvement is expected for the 2012 assessment resulting from several ongoing and planned site studies).

Sensitivity assessment and data quality The process of estimating the typical absorbed doses to the other biota has been recently developed and is implemented here for the first time. A comprehensive sensitivity assessment has not yet been performed; it will be done for the 2012 assessment. However, the sensitivity of the dose assessment for the other biota has been studied in a project within the BIOPROTA framework (www.bioprota.com). Based on preliminary results that include also a case incorporating Olkiluoto data (based on activity concentrations predicted in Broed et al. 2007), in almost all cases the uncertainty in the concentration ratio has the largest affect on the doses, but in some specific cases of

139

small organisms and nuclides of certain range of radiation energy also the dose conversion coefficient, i.e. the size and weight of the assessment species, is central. A number of experts also performed a pedigree analysis within the BIOPROTA project; low scores were given to the quality of process understanding and/or data reflects the assumptions and uncertainties discussed above. However, to make final conclusions on Posiva's biosphere assessment, both the sensitivity and pedigree analyses need to be performed on the actual models and data used in the assessment in contrast to the more generic context of the BIOPROTA project. As reflected above, the quality of the underlying data varies rather much, but will be improved for the 2012 assessment as a number of site studies are completed and deliver suitable data.

Overall consistency The dose assessment for the other biota applied here is based on the ERICA integrated approach (Beresford et al. 2007) and on the test case of Smith & Robinson (2006). In the present assessment, the assessment process has been refined, but essentially it is similar than the earlier development and the main line of approaches applied internationally.

Overall knowledge quality The doses to the other biota can be stated to be uncertain, but almost certainly overestimated to the assessment species adopted. This is due to the fact that several conservative assumptions are combined. However, as reflected above, the issues of sensitive life stages or parts of organisms and detail differences in inter-/intra-species exposure geometry may produce less favourable dose/effect estimates. However, the assessment of doses to the other biota is in line with the recommendations of the international ERICA (Beresford et al. 2007) and PROTECT (Andersson et al. 2008) projects. The assessment methodology is still developing internationally.

Regarding the input data to the modelling, mainly the selection of the assessment species, their geometry, and concentration ratios from the environmental media to the organisms, the quality corresponds to recent international compendia. Mostly the data, especially the concentration ratios, are generic in nature, but improvements are expected for the 2012 assessment as ongoing and planned site studies provide a wider basis of site-specific data.

7.4 Safety indicators

Safety indicators are used to support the safety case, by building understanding of, and confidence in, the outcome and conclusions of the safety assessment. In the present assessment two safety indicators in the form of indicative annual doses received by a representative member of the most exposed group of people are derived, based on indicative stylised well scenarios: one for a drinking water well and one for an agricultural well. The KQA performed for the two well scenarios is presented here.

140

Applied key data Three principles underpin the strategy when selecting data for the well scenarios:

1. The parameter values should reflect the aim of the scenario; since safety indicators are expressed as annual doses to a representative member of the most exposed group, a comparable level of conservatism as in the landscape doses is strived for.

2. The well scenarios are intended to be regional-specific.

3. Since the well scenarios are used as an alternative line of reasoning to the landscape modelling approach, care is taken to avoid applying data from the same sources as used in the landscape modelling, especially for key parameters.

The input data for the models in the well scenarios can be divided into hydrological, radioecological and exposure characteristics; the key data for the three types of input data are presented in Table 7-12.

Main assumptions The main assumptions are listed and categorised in Table 7-13.

Table 7-12. Key data sets used in the well scenarios and their main sources.

Data set Main sources

Hydrological data Mixing capacity for the well Vieno (1994)

Radioecological dataDistribution coefficients IAEA (1994, 2009), SKB (2001)

Soil-to-plant concentration ratios Karlsson & Bergström (2000, 2002), OPG (2002, 2004a), US DOE (2004) and Uchida et al. (2007)

Retention factors Duro et al. (2006), expert judgement,

Translocation factors for root crops Karlsson & Bergström (2002), Bergström et al. (2006)

Root uptake model (not element-dependent)

Karlsson & Bergström (2000)

Surface retention model (not element-dependent)

Bergström & Barkefors (2004)

Translocation of intake (for animals)

IAEA (1994, 2009), Karlsson & Bergström (2002) and US DoE (2004)

Exposure characteristics Farm statistics Average data for the year 2004 in TIKE (2006)

Water need (for crops) Pajula & Triipponen (2003)

Water intake rate (humans) Ershow & Cantor (1989) cited in OEHHA (2000)

Dietary data (humans) SJV (2006) and Karlsson & Aquilonius (2001)

Dietary data (animals) IAEA (1994)

141

Table 7-13. Main assumptions when deriving safety indicators from the well scenarios.


General The yield of the well is sufficiently high for human consumption, watering of livestock, and for irrigation of crops,

LE

The effective mixing capacity is a representative expectation value adequate for cautious assessments

WRP

Radionuclide transport (only AgriWELL) Irrigation habits mainly based on interviews with regional farmers

WRP

Irrigated fraction assumed to be 100% for crops lacking data

CS The irrigated fractions for, especially, silage and green fodder are likely significantly lower due to the poor cost/benefit of irrigation

Every irrigation event fills the storage capacity of the plants, and 100% of the irrigation water passes through into the soil

MS This assumption means that the plants are irrigated with much more water than they can store, and that the small fraction of the total amount of irrigation water that is taken up by the above-ground parts of the plants is neglected in the root uptake model.

Cereals are assumed to consists only of spring wheat

PCA Spring wheat is the only cereal that are irrigated

Intake of water and feedstuff by animals are scaled by body weight

WRP Intake rates for turkeys have been derived as five-fold the values for hens. Further, it has been assumed that ‘smaller pigs’ consume half of what ‘fattening pigs’ consume.

The translocation of intake for animal products is proportional to the daily radionuclide intake per kg of body weight

ST Data gaps in translocation of intake for pig, broiler and sheep are filled by scaling from beef

Dose assessment A single exposure pathway – ingestion of water is assumed in the WELL scenarios

ST The use of a single exposure pathway aims to produce indicative annual doses that are extremely robust and transparent, and produce a dose quantity compatible with the “well-doses” presented in all earlier safety assessments made by Posiva

Multiple exposure pathways (ingestion of water, crops and animal product) are considered in the AgriWELL scenario.

ST These exposure pathways aim to produce indicative annual doses that are extremely robust and transparent, but to more comparable with the guide YVL D.5 regarding exposure pathways

142

Table 7-13 cont’d. Main assumptions when deriving safety indicators from the well scenarios.


Dose assessment All consumed water and food are contaminated.

PCA This applies both for humans and animals

The water intake rate is assumed to apply to a high-consumer

PCA The value applied aims to represent a “critical intake”, i.e., the intake of a representative member of a critical group of a larger group all utilising the contaminated well water as tap water.

Annual total consumption of food (meat, vegetables, cereals, milk products, potatoes, eggs, fruits and berries) corresponds to an average adult male consumer.

WRP

Any losses of activity due to food preparation is not taken into account

CS Decrease in radionuclide concentrations in food during preparation (washing, rinsing, peeling, boiling, canning, etc…) are cautiously neglected

Process efficiency (the ratio between the weight of the prepared food and the raw product) is not considered for meat and vegetables

MS The processing efficiency is generally high for meat and vegetables (~0.7-1.0), thus not affecting the radionuclide concentration in the prepared food so much.

Process efficiency (the ratio between the weight of the prepared food and the raw product) is considered for the diary products cream, cheese and butter

LS The processing efficiency is generally low for these products (~0.05-0.1), thus the radionuclide concentration is significantly higher in cream, cheese and butter compared to the milk.

The productions at the farm is corresponds to the arithmetic means of regional farm statistics

ST The indicative farm used aims at represent a cross-section of regional farms. It is recognised that there may be farms with a specialised production that could result in higher doses, either due to the need of more irrigation water and/or that the farm produces crops or animal products that accumulate the radioactivity in higher degree than the ones produced at the indicative farm.

143

Main uncertainties Since the well scenarios are used as an alternative line of reasoning, no uncertainties are propagated from other BSA sub-processes. The uncertainty in an annual dose estimated for a well scenario is dominated by uncertainties in the applied radionuclide-specific releases from the geosphere and uncertainties in the derived radionuclide-specific dose conversion factors. Further, the uncertainties in the dose conversion factors are dominated by the uncertainty in the assumed effective mixing volume of the well.

Other main uncertainties, but with less significance compared with the two above-mentioned, relate to the representativeness of the derived farm statistics and the dietary profile used for humans.

Sensitivity assessment and data quality No formal quantitative sensitivity analysis has yet been performed on the whole model used in the agricultural well scenario, and it is not necessary to perform one on the extremely simple expression used in the drinking water well scenario. Nevertheless, the obvious key parameter affecting the outcome (in terms of dose conversion factors) is the effective mixing capacity of the well19. This parameter has a direct impact on the dose due to ingestion of drinking water, and also a direct impact on the activity concentration in the irrigation water, which is the key parameter for the resulting activity concentration in crops and animal products, and thus also for the outcome.

The activity concentration in crops is dominated by uptake of activity through the roots due to irrigation water that has passed through into the soil. The model for root uptake is based on Bergström & Barkefors (2004), in which a sensitivity analysis was performed on the expression used for obtaining migration rates. The results show that for strongly sorbing nuclides (high distribution coefficient) bioturbation contributes more to migration than does the advection. When the distribution coefficient decreases, the advection contributes as well and the distribution coefficient and runoff begin to matter. Soil depth, porosity and density are the parameters for which the results are most sensitive. For nuclides with high mobility (low distribution coefficient), the outcome is most sensitive to parameters that affect the water turnover; such as runoff and soil depth.

Overall consistency The aim of the well scenarios in the present assessment is to derive safety indicators, which are estimates of indicative hypothetical annual doses received by a representative member of the most exposed group. This is consistent with how the well scenario has been applied in earlier Posiva assessments (for example in Smith et al. 2007b and Vieno & Nordman 1999). Prior to the development of the landscape modelling concept, these “well-doses” were been the primary quantity used in demonstrating compliance with

19 This is consistent with previous analysis of a range of deep disposal assessments (Pinedo et al. 1998) which formed the basis for the recommendation to study and understand better the geosphere-biosphere interface zone.

144

regulatory dose constraints. In the present assessment, and in future ones, the main purpose of safety indicators has been shifted, and now constitutes an alternative line of reasoning. Thus, it may be grounds to revise the aim of the safety indicators, especially regarding the level of conservatism. It would be more consistent with how stylised well scenarios are applied internationally if the derived safety indicators were a more cautious (representing an upper bound) estimate of the potential annual doses received by a representative member of the most exposed group.

Overall knowledge quality Generally, regarding the present aim of safety indicators, the overall knowledge quality is high. For plain drinking water, the model is very simple and only a single exposure pathway is considered. For the agricultural well, the exposure pathways form a more complicated system but their modelling is conventional in the dose assessments, and the adequacy of included pathways and transport of the radionuclides in the system is still relatively easy to check and justify. There are still some data gaps, especially for element-specific data, to be filled; and furthermore, the data basis underlying the exposure characteristics will be improved by 2012.

145

PART II

147

8 SCREENING EVALUATION

The screening evaluation has been applied on constant unit geosphere releases and to a set of repository calculation cases to identify the key set of radionuclides. The results are presented and discussed here.

8.1 Constant unit geosphere releases

The screening models have been applied to a constant unit (1 Bq/y) geosphere release during 15 000 years, for each radionuclide in the full set of radionuclides (Table 2-1) and the progeny of Sr-90, Zr-93, Mo-93, Sn-126 and Pb-210. The resulting risk quotients (RQ) are presented in Figure 8-1. The results show that the Tier 1 RQs are generally much higher than the Tier 2 RQs; the difference in RQs ranges from about two to eight orders of magnitude. This reflects the high aim of Tier 1, since it is intended to ensure extremely pessimistic RQs. A large fraction of the resulting RQs in Tier 2 are in the range from 10 to 0.01. Thus, in order for these radionuclides to not be screened out in Tier 2, the radionuclide-specific geosphere release rates needs to be in the range 0.1 to 100 Bq/y or higher.

148

Figure 8-1. Resulting RQs in the Tier 1 and 2 screening evaluation on constant geosphere releases (1 Bq/y for each radionuclide) during 15 000 years.

8.2 Repository assessment cases

The screening models have been applied on a set of repository calculation cases (RCCs) to form the basis for screening out the radionuclides considered to be insignificant for the biosphere assessment, from a radiological consequences point of view. Although the focus of the dose assessment is on the repository assessment from RNT-2008 (Nykyri et al. 2008), a few cases from the previous assessments (the KBS-3H safety assessment reported in Smith et al. 2007b, and TILA-99 reported in Vieno & Nordman 1999) are also included in the analysis. Not all RCCs are propagated to the biosphere assessment and analysed; the approach as to how RCCs are selected for the biosphere assessment is presented below

Selection of repository calculation cases Typically in the assessment of the radionuclide release and transport in the near-field and geosphere, a large number of repository calculation cases are identified and analysed in terms of radionuclide-specific near-field and geosphere release rates

1.E-05 1.E-03 1.E-01 1.E+01 1.E+03 1.E+05 1.E+07 1.E+09

Th-229Pa-231Th-232Pu-242Th-230Pu-239Pu-240

Am-241Cm-245Am-243Np-237

Cm-246U-233

Ra-226U-234U-236U-235U-238

Pb-210Po-210Nb-94I-129

Sn-126Zr-93Tc-99

Cs-135Cl-36Se-79C-14

Nb-93mMo-93Pd-107Cs-137

Sr-90Sb-126

Ni-59Sm-151

Ni-63Y-90

Risk Quotient (RQ)

Tier 2

Tier 1

149

(Nykyri et al. 2008, Smith et al. 2007b, Vieno & Nordman 1999). The treatment of a RCC in the biosphere assessment depends on the timing of geosphere release and the classification of the RCC. The following rules have been adopted:

� All RCC with zero geosphere release in the first 10 000 years are excluded, because they cannot lead to any radiological consequences within the time window when the regulatory dose constraints are assumed to apply.

� To avoid excessive conservatism in the outcome of the safety assessment, RCCs classified as “what if” cases and considered to result in pessimistic estimates of radionuclide releases from the geosphere in the first 10 000 years are not analysed in the biosphere assessment.

� RCCs with a geosphere release in the first 10 000 years, but with very low likelihood of occurring, are not analysed in the biosphere assessment.

Following these rules, the list of cases addressed in the current assessment contains 12 cases (see Table 8-1). All cases addressed here have their origin in the same type of repository scenario, assuming releases from a single canister with an initial penetrating defect at the time of emplacement. The full descriptions of the cases are presented in Nykyri et al. (2008), Smith et al. (2007b) and Vieno & Nordman (1999), for the RNT-2008, KBS-3H and TILA-99 cases, respectively.

It should be emphasised that the PD-EXPELL case also is included in the screening evaluation, which was the case with release of several radionuclides resulting in highest20 annual effective doses to most exposed persons in the KBS-3H safety studies (Broed et al. 2007). The reason for including this case is to better identify key radionuclides for a KBS-3 repository, regardless of design alternative. The assumptions underpinning the PD-EXPELL case (gas-induced displacement of radionuclide-contaminated water from the canister interior through the defect) are highly hypothetical when assessing a KBS-3V repository (Nykyri et al. 2008). Nonetheless, even though the likelihood of releases caused by this process is very small, a repository calculation case of this type (denoted GASexW) is included as a “what if” case for a KBS-3V repository (Nykyri et al. 2008). Consequently, following the rules above, the GASexW case is excluded from the present biosphere assessment, addressing a KBS-3V repository.

Resulting risk quotients The screening models have been applied to the repository calculation cases in Table 8-1 to screen out the radionuclides considered to be insignificant for the biosphere assessment, from a radiological consequences point of view. The 35 radionuclides in the full set and the progeny of Sr-90, Zr-93, Mo-93, Sn-126 and Pb-210 (cf. Table 2-1) have been analysed. The resulting RQs are presented in Table 8-2 from the Tier 1 evaluation and in Table 8-3 for the Tier 2 evaluation; the same data is plotted in Figures 8-2 to 8-5. 20 PD-VOL1 resulted in slightly higher dose maximum in the KBS-3H biosphere analysis. This case contains only a release of C-14, and, since C-14 is a key radionuclide also in many other cases, it is excluded from the screening analysis.

150

Of the 35 radionuclides included in the full set of radionuclides in the repository calculation cases, 11 are screened out at Tier 1, and a further 13 at Tier 2. The remaining 11 radionuclides, and their progeny, form the key set of radionuclides in the biosphere assessment. It is notable that all actinides and radionuclides in the naturally occurring decay chains are screened out. Further, it should also be noted that the generic screening absorbed dose rate for other biota is a factor of five higher than the lowest screening absorbed dose rate (2 �Gy/h for vertebrates) used in the compliance assessment; this will be taken into account in the future. However, the set of key radionuclides would have been the same if the environmental media concentration limits (EMCL) had been a factor five lower in the screening evaluation.

Table 8-1. Set of repository calculation cases included in the biosphere assessment.

Case name Case type Origin Sh1 Base case RNT-2008 Sh1-EPR Base case variant RNT-2008 Sh1-VVER Base case variant RNT-2008 PD-BC Base case KBS-3H SH-sal50 Base case (a) TILA-99 Sh1 Fd Sensitivity case RNT-2008 Sh1 Irf Sensitivity case RNT-2008 Sh1 Q Sensitivity case RNT-2008 Sh1 Sal Sensitivity case RNT-2008 Sh4 Sensitivity case RNT-2008 Sh4 Q Sensitivity case RNT-2008 PD-EXPELL Variant case KBS-3H

(a) This classification was not used in TILA-99 (Vieno & Nordman 1999); here the case most resembling Sh1 is selected.

151

Tabl

e 8-

2. R

Qs i

n th

e Ti

er 1

scre

enin

g ev

alua

tion

of th

e an

alys

ed re

posi

tory

cal

cula

tion

case

s.

Nuc

lide

Sh1

Sh1-

EPR

Sh

1-V

VE

R

Sh1

Fd

Sh1

Irf

Sh1

Q

Sh1

sal

Sh4

Sh4

Q

PD-B

C

PD-

EX

PEL

L

SHsa

l50

C-1

4 4.

65E+

7 4.

66E+

7 5.

03E+

7 6.

38E+

7 3.

02E+

6 6.

24E+

7 4.

65E+

7 5.

60E+

8 7.

55E+

8 5.

19E+

8 1.

52E+

10

1.32

E+8

Cl-3

6 4.

27E+

6 4.

30E+

6 4.

67E+

6 5.

89E+

6 6.

10E+

5 5.

60E+

6 4.

27E+

6 2.

67E+

7 3.

51E+

7 2.

70E+

8 9.

17E+

8 3.

51E+

7 N

i-59

4.21

E-13

4.

21E-

13

4.21

E-13

4.

23E-

13

5.

52E+

3 5.

12E+

1 5.

48E-

12

7.19

E+4

2.87

E-13

8.

02E-

4 2.

47E+

3 N

i-63

6.13

E-12

6.

13E-

12

6.14

E-12

6.

21E-

12

8.

30E-

9 8.

97E-

15

7.98

E-11

1.

08E-

7

Se-7

9 3.

20E+

0 3.

20E+

0 3.

34E+

0 3.

16E+

0 3.

20E+

0 5.

45E+

0 4.

72E+

4 2.

04E+

1 3.

48E+

1 3.

50E+

2 4.

32E+

6 3.

02E+

5 Sr

-90

8.88

E-9

8.88

E-9

8.88

E-9

8.88

E-9

8.88

E-9

1.31

E+3

6.70

E+2

1.16

E-7

1.70

E+4

5.05

E+4

Y-9

0 1.

50E-

10

1.50

E-10

1.

50E-

10

1.50

E-10

1.

50E-

10

2.20

E+1

1.13

E+1

1.95

E-9

2.87

E+2

8.53

E+2

Mo-

93

1.90

E+0

1.90

E+0

1.99

E+0

1.88

E+0

4.

44E+

0 8.

03E-

1 1.

21E+

1 2.

84E+

1 1.

48E+

2 1.

65E+

9

Zr-9

3 1.

14E-

16

1.15

E-16

1.

14E-

16

1.14

E-16

7.55

E-2

2.84

E-17

1.

49E-

15

9.83

E-1

1.09

E-16

2.

20E-

3

Nb-

93m

1.

10E+

0 1.

10E+

0 1.

15E+

0 1.

09E+

0

2.58

E+0

4.66

E-1

7.03

E+0

1.65

E+1

8.59

E+1

9.58

E+8

N

b-94

5.

92E-

3 5.

92E-

3 5.

92E-

3 5.

92E-

3

2.66

E+6

9.66

E-3

7.70

E-2

3.46

E+7

3.69

E-1

3.43

E+4

7.46

E+2

Tc-

99

7.05

E-18

7.

05E-

18

7.05

E-18

7.

05E-

18

7.13

E-18

8.

57E-

2 3.

60E-

17

9.18

E-17

1.

12E+

0 1.

08E-

16

1.70

E-3

Pd

-107

1.

81E-

1 1.

81E-

1 1.

81E-

1 1.

81E-

1 1.

81E-

1 7.

73E+

0 7.

48E-

1 2.

35E+

0 1.

01E+

2 1.

84E+

2 2.

55E+

6 1.

06E+

1 Sn

-126

9.

58E-

1 9.

58E-

1 9.

58E-

1 9.

58E-

1 9.

64E-

1 6.

62E+

1 1.

68E+

1 1.

25E+

1 8.

62E+

2 2.

59E+

1 1.

00E+

7 2.

77E+

7 Sb

-126

1.

53E-

2 1.

53E-

2 1.

53E-

2 1.

53E-

2 1.

54E-

2 1.

06E+

0 2.

69E-

1 1.

99E-

1 1.

38E+

1 4.

14E-

1 1.

60E+

5 4.

44E+

5 I-

129

1.09

E+7

1.21

E+7

1.19

E+7

1.50

E+7

1.08

E+7

1.34

E+7

1.09

E+7

6.80

E+7

8.37

E+7

4.55

E+8

1.44

E+9

1.63

E+8

Cs-

135

2.21

E-10

2.

30E-

10

2.34

E-10

3.

07E-

10

2.20

E-10

7.

90E+

5 1.

01E+

2 3.

08E-

9 1.

08E+

7 8.

61E-

11

2.01

E-4

6.00

E+3

Cs-

137

3.04

E-7

3.04

E-7

3.18

E-7

4.24

E-7

3.04

E-7

8.67

E-8

1.17

E-7

4.44

E-6

1.27

E-6

Po

-210

4.

52E-

13

2.63

E-12

6.

81E-

13

9.56

E-13

2.34

E+2

9.49

E-9

5.83

E-12

2.

98E+

3 2.

02E-

9

�Pb

-210

5.

89E-

13

3.42

E-12

8.

86E-

13

1.25

E-12

3.05

E+2

1.24

E-8

7.60

E-12

3.

89E+

3 2.

63E-

9

�R

a-22

6 9.

99E-

13

5.82

E-12

1.

50E-

12

2.11

E-12

5.23

E+2

2.17

E-8

1.29

E-11

6.

66E+

3 4.

50E-

9

�T

h-22

9 3.

27E-

18

5.51

E-17

3.

17E-

18

7.01

E-18

1.54

E+0

2.28

E-17

3.

22E-

17

2.00

E+1

2.88

E-16

�T

h-23

0 2.

05E-

18

1.07

E-17

4.

50E-

18

5.30

E-17

2.66

E-1

9.42

E-17

1.

73E-

16

3.46

E+0

2.68

E-17

�T

h-23

2 3.

20E-

23

3.34

E-23

2.

61E-

23

2.89

E-23

7.88

E-9

3.76

E-23

4.

17E-

22

1.03

E-7

9.66

E-23

�Pa

-231

1.

11E-

12

1.11

E-11

1.

51E-

12

2.00

E-12

4.18

E+4

1.11

E-12

1.

40E-

11

5.24

E+5

4.84

E-12

�U

-233

1.

95E-

19

1.07

E-18

1.

56E-

19

1.47

E-19

1.92

E-1

1.19

E-18

1.

03E-

18

2.49

E+0

1.47

E-17

�U

-234

2.

21E-

18

1.26

E-17

2.

70E-

18

5.70

E-19

3.68

E-2

6.38

E-17

5.

67E-

17

4.88

E-1

2.07

E-17

�U

-235

3.

92E-

19

4.42

E-19

4.

41E-

19

4.20

E-19

3.35

E-4

4.84

E-19

4.

91E-

18

4.37

E-3

3.62

E-18

�

152

Tabl

e 8-

2 (c

ont’d

). RQ

s in

the

Tier

1 sc

reen

ing

eval

uatio

n of

the

anal

ysed

repo

sito

ry c

alcu

latio

n ca

ses.

Nuc

lide

Sh1

Sh1-

EPR

Sh

1-V

VE

R

Sh1

Fd

Sh1

Irf

Sh1

Q

Sh1

sal

Sh4

Sh4

Q

PD-B

C

PD-

EX

PEL

L

SHsa

l50

U-2

36

1.06

E-17

1.

11E-

17

8.69

E-18

9.

41E-

18

5.

54E-

3 1.

44E-

17

1.39

E-16

7.

21E-

2 1.

97E-

16

�

U-2

38

4.52

E-17

2.

60E-

16

4.96

E-22

1.

17E-

17

4.

23E-

3 2.

14E-

17

1.12

E-17

5.

91E-

2 4.

99E-

18

�

Np-

237

2.49

E-17

1.

74E-

16

1.57

E-17

1.

10E-

18

1.

68E-

2 2.

29E-

17

1.33

E-18

2.

22E-

1 4.

44E-

18

�

Pu-2

39

1.67

E-12

1.

76E-

12

1.87

E-12

1.

83E-

12

9.

94E+

0 1.

33E-

12

2.15

E-11

1.

29E+

2 5.

20E-

11

�

Pu-2

40

1.10

E-12

1.

14E-

12

9.01

E-13

9.

52E-

13

2.

20E+

0 1.

22E-

12

1.43

E-11

2.

87E+

1 9.

35E-

11

�

Pu-2

42

1.58

E-14

1.

63E-

14

1.38

E-14

1.

46E-

14

1.

50E-

1 1.

12E-

14

2.06

E-13

1.

95E+

0 3.

37E-

13

�

Am

-241

8.

73E-

14

4.32

E-13

5.

00E-

14

5.34

E-15

9.86

E-1

1.13

E-13

1.

59E-

15

1.80

E+1

4.52

E-15

�A

m-2

43

8.97

E-15

1.

08E-

14

9.88

E-15

1.

00E-

14

1.

22E+

2 2.

95E-

12

1.15

E-13

1.

57E+

3 8.

73E-

16

�

Cm

-245

8.

55E-

14

4.23

E-13

4.

90E-

14

5.23

E-15

9.65

E-1

1.11

E-13

1.

56E-

15

1.76

E+1

2.51

E-15

� Ta

ble

8-3.

RQ

s in

the

Tier

2 sc

reen

ing

eval

uatio

n of

the

anal

ysed

repo

sito

ry c

alcu

latio

n ca

ses.

Nuc

lide

Sh1

Sh1-

EPR

Sh

1-V

VE

R

Sh1

Fd

Sh1

Irf

Sh1

Q

Sh1

sal

Sh4

Sh4

Q

PD-B

C

PD-

EX

PEL

L

SHsa

l50

C-1

4 4.

65E+

7 4.

66E+

7 5.

03E+

7 6.

38E+

7 3.

02E+

6 6.

24E+

7 4.

65E+

7 5.

60E+

8 7.

55E+

8 5.

19E+

8 1.

52E+

10

1.32

E+8

Cl-3

6 4.

27E+

6 4.

30E+

6 4.

67E+

6 5.

89E+

6 6.

10E+

5 5.

60E+

6 4.

27E+

6 2.

67E+

7 3.

51E+

7 2.

70E+

8 9.

17E+

8 3.

51E+

7 N

i-59

4.21

E-13

4.

21E-

13

4.21

E-13

4.

23E-

13

5.

52E+

3 5.

12E+

1 5.

48E-

12

7.19

E+4

2.87

E-13

8.

02E-

4 2.

47E+

3 N

i-63

6.13

E-12

6.

13E-

12

6.14

E-12

6.

21E-

12

8.

30E-

9 8.

97E-

15

7.98

E-11

1.

08E-

7

Se-7

9 3.

20E+

0 3.

20E+

0 3.

34E+

0 3.

16E+

0 3.

20E+

0 5.

45E+

0 4.

72E+

4 2.

04E+

1 3.

48E+

1 3.

50E+

2 4.

32E+

6 3.

02E+

5 Sr

-90

8.88

E-9

8.88

E-9

8.88

E-9

8.88

E-9

8.88

E-9

1.31

E+3

6.70

E+2

1.16

E-7

1.70

E+4

5.05

E+4

Y-9

0 1.

50E-

10

1.50

E-10

1.

50E-

10

1.50

E-10

1.

50E-

10

2.20

E+1

1.13

E+1

1.95

E-9

2.87

E+2

8.53

E+2

Mo-

93

1.90

E+0

1.90

E+0

1.99

E+0

1.88

E+0

4.

44E+

0 8.

03E-

1 1.

21E+

1 2.

84E+

1 1.

48E+

2 1.

65E+

9

Zr-9

3 1.

14E-

16

1.15

E-16

1.

14E-

16

1.14

E-16

7.55

E-2

2.84

E-17

1.

49E-

15

9.83

E-1

1.09

E-16

2.

20E-

3

Nb-

93m

1.

10E+

0 1.

10E+

0 1.

15E+

0 1.

09E+

0

2.58

E+0

4.66

E-1

7.03

E+0

1.65

E+1

8.59

E+1

9.58

E+8

N

b-94

5.

92E-

3 5.

92E-

3 5.

92E-

3 5.

92E-

3

2.66

E+6

9.66

E-3

7.70

E-2

3.46

E+7

3.69

E-1

3.43

E+4

7.46

E+2

Tc-

99

7.05

E-18

7.

05E-

18

7.05

E-18

7.

05E-

18

7.13

E-18

8.

57E-

2 3.

60E-

17

9.18

E-17

1.

12E+

0 1.

08E-

16

1.70

E-3

Pd

-107

1.

81E-

1 1.

81E-

1 1.

81E-

1 1.

81E-

1 1.

81E-

1 7.

73E+

0 7.

48E-

1 2.

35E+

0 1.

01E+

2 1.

84E+

2 2.

55E+

6 1.

06E+

1 Sn

-126

9.

58E-

1 9.

58E-

1 9.

58E-

1 9.

58E-

1 9.

64E-

1 6.

62E+

1 1.

68E+

1 1.

25E+

1 8.

62E+

2 2.

59E+

1 1.

00E+

7 2.

77E+

7

153

Tabl

e 8-

3 (c

ont’d

). RQ

s in

the

Tier

2 sc

reen

ing

eval

uatio

n of

the

anal

ysed

repo

sito

ry c

alcu

latio

n ca

ses.

Nuc

lide

Sh1

Sh1-

EPR

Sh

1-V

VE

R

Sh1

Fd

Sh1

Irf

Sh1

Q

Sh1

sal

Sh4

Sh4

Q

PD-B

C

PD-

EX

PEL

L

SHsa

l50

Sb-1

26

1.53

E-2

1.53

E-2

1.53

E-2

1.53

E-2

1.54

E-2

1.06

E+0

2.69

E-1

1.99

E-1

1.38

E+1

4.14

E-1

1.60

E+5

4.44

E+5

I-12

9 1.

09E+

7 1.

21E+

7 1.

19E+

7 1.

50E+

7 1.

08E+

7 1.

34E+

7 1.

09E+

7 6.

80E+

7 8.

37E+

7 4.

55E+

8 1.

44E+

9 1.

63E+

8 C

s-13

5 2.

21E-

10

2.30

E-10

2.

34E-

10

3.07

E-10

2.

20E-

10

7.90

E+5

1.01

E+2

3.08

E-9

1.08

E+7

8.61

E-11

2.

01E-

4 6.

00E+

3 C

s-13

7 3.

04E-

7 3.

04E-

7 3.

18E-

7 4.

24E-

7 3.

04E-

7 8.

67E-

8 1.

17E-

7 4.

44E-

6 1.

27E-

6

Po-2

10

4.52

E-13

2.

63E-

12

6.81

E-13

9.

56E-

13

2.

34E+

2 9.

49E-

9 5.

83E-

12

2.98

E+3

2.02

E-9

�

Pb-2

10

5.89

E-13

3.

42E-

12

8.86

E-13

1.

25E-

12

3.

05E+

2 1.

24E-

8 7.

60E-

12

3.89

E+3

2.63

E-9

�

Ra-

226

9.99

E-13

5.

82E-

12

1.50

E-12

2.

11E-

12

5.

23E+

2 2.

17E-

8 1.

29E-

11

6.66

E+3

4.50

E-9

�

Th-

229

3.27

E-18

5.

51E-

17

3.17

E-18

7.

01E-

18

1.

54E+

0 2.

28E-

17

3.22

E-17

2.

00E+

1 2.

88E-

16

�

Th-

230

2.05

E-18

1.

07E-

17

4.50

E-18

5.

30E-

17

2.

66E-

1 9.

42E-

17

1.73

E-16

3.

46E+

0 2.

68E-

17

�

Th-

232

3.20

E-23

3.

34E-

23

2.61

E-23

2.

89E-

23

7.

88E-

9 3.

76E-

23

4.17

E-22

1.

03E-

7 9.

66E-

23

�

Pa-2

31

1.11

E-12

1.

11E-

11

1.51

E-12

2.

00E-

12

4.

18E+

4 1.

11E-

12

1.40

E-11

5.

24E+

5 4.

84E-

12

�

U-2

33

1.95

E-19

1.

07E-

18

1.56

E-19

1.

47E-

19

1.

92E-

1 1.

19E-

18

1.03

E-18

2.

49E+

0 1.

47E-

17

�

U-2

34

2.21

E-18

1.

26E-

17

2.70

E-18

5.

70E-

19

3.

68E-

2 6.

38E-

17

5.67

E-17

4.

88E-

1 2.

07E-

17

�

U-2

35

3.92

E-19

4.

42E-

19

4.41

E-19

4.

20E-

19

3.

35E-

4 4.

84E-

19

4.91

E-18

4.

37E-

3 3.

62E-

18

�

U-2

36

1.06

E-17

1.

11E-

17

8.69

E-18

9.

41E-

18

5.

54E-

3 1.

44E-

17

1.39

E-16

7.

21E-

2 1.

97E-

16

�

U-2

38

4.52

E-17

2.

60E-

16

4.96

E-22

1.

17E-

17

4.

23E-

3 2.

14E-

17

1.12

E-17

5.

91E-

2 4.

99E-

18

�

Np-

237

2.49

E-17

1.

74E-

16

1.57

E-17

1.

10E-

18

1.

68E-

2 2.

29E-

17

1.33

E-18

2.

22E-

1 4.

44E-

18

�

Pu-2

39

1.67

E-12

1.

76E-

12

1.87

E-12

1.

83E-

12

9.

94E+

0 1.

33E-

12

2.15

E-11

1.

29E+

2 5.

20E-

11

�

Pu-2

40

1.10

E-12

1.

14E-

12

9.01

E-13

9.

52E-

13

2.

20E+

0 1.

22E-

12

1.43

E-11

2.

87E+

1 9.

35E-

11

�

Pu-2

42

1.58

E-14

1.

63E-

14

1.38

E-14

1.

46E-

14

1.

50E-

1 1.

12E-

14

2.06

E-13

1.

95E+

0 3.

37E-

13

�

Am

-241

8.

73E-

14

4.32

E-13

5.

00E-

14

5.34

E-15

9.86

E-1

1.13

E-13

1.

59E-

15

1.80

E+1

4.52

E-15

�A

m-2

43

8.97

E-15

1.

08E-

14

9.88

E-15

1.

00E-

14

1.

22E+

2 2.

95E-

12

1.15

E-13

1.

57E+

3 8.

73E-

16

�

Cm

-245

8.

55E-

14

4.23

E-13

4.

90E-

14

5.23

E-15

9.65

E-1

1.11

E-13

1.

56E-

15

1.76

E+1

2.51

E-15

�

154

Figure 8-2. Risk quotients from the Tier 1 evaluation of the repository calculation cases (for the radionuclides with the 20 highest RQ maxima).

1.E+01 1.E+03 1.E+05 1.E+07 1.E+09 1.E+11

C-14

Mo-93

I-129

Nb-93m

Cl-36

Nb-94

Sn-126

Cs-135

Se-79

Pd-107

Pa-231

Sb-126

Ni-59

Sr-90

Ra-226

Pb-210

Po-210

Am-243

Y-90

Pu-239

Risk Quotient (RQ)

PD-EXPELL Sh4 Q

Sh4 Sh1 sal

Sh1 Q Sh1 Irf

Sh1 Fd SHsal50

PD-BC Sh1-VVER

Sh1-EPR Sh1

155

Figure 8-3. Risk quotients from the Tier 1 evaluation of the repository calculation cases (for the radionuclides with the 19 lowest RQ maxima); the dashed green line is located at RQ=1.

1.E-09 1.E-07 1.E-05 1.E-03 1.E-01 1.E+01

Pu-240

Th-229

Am-241

Cm-245

Th-230

U-233

Pu-242

Tc-99

Zr-93

Cm-246

U-234

Np-237

U-236

U-238

U-235

Cs-137

Ni-63

Th-232

Sm-151

Risk Quotient (RQ)

PD-EXPELL Sh4 QSh4 Sh1 salSh1 Q Sh1 IrfSh1 Fd SHsal50PD-BC Sh1-VVERSh1-EPR Sh1

156

Figure 8-4. Risk quotients from the Tier 2 evaluation of the repository calculation cases (for the radionuclides with the 20 highest RQ maxima); the dashed green line is located at RQ=1.

1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06

Mo-93

I-129

Cl-36

Cs-135

C-14

Nb-93m

Se-79

Sn-126

Sr-90

Pd-107

Sb-126

Nb-94

Ni-59

Y-90

Ra-226

Po-210

Pa-231

Pb-210

Am-243

Tc-99

Risk Quotient (RQ)

PD-EXPELL Sh4 QSh4 Sh1 salSh1 Q Sh1 IrfSh1 Fd SHsal50PD-BC Sh1-VVERSh1-EPR Sh1

157

Figure 8-5. Risk quotients from the Tier 2 evaluation of the repository calculation cases (for the radionuclides with the 19 lowest RQ maxima).

1.E-16 1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04

Pu-239

Cm-245

Zr-93

U-233

Pu-240

Th-229

Am-241

Th-230

U-234

Np-237

Cm-246

Pu-242

Cs-137

U-236

U-238

U-235

Ni-63

Sm-151

Th-232

Risk Quotient (RQ)

PD-EXPELL

Sh4 Q

Sh4

Sh1 sal

Sh1 Q

Sh1 Irf

Sh1 Fd

SHsal50

PD-BC

Sh1-VVER

Sh1-EPR

Sh1

159

9 CONSTANT RELEASES INTO THE BIOSPHERE

This chapter presents the results from applying the landscape model (section 4.4) and the dose calculation and identification approach presented in section 5.1.2 on geosphere releases assumed to be constant throughout the biosphere assessment time window. Sections 9.1 and 9.2 presents the results from the landscape modelling, by applying a unit geosphere release rate (1 Bq/y) for each of the 11 radionuclides in the key set of radionuclides (Table 2-2). Section 9.3 presents resulting landscape doses both due to unit geosphere releases and nominal geosphere release rates (Table 2-3). Further, a comparison with derived safety indicators due to nominal release rates is included. The results in sections 9.2 and 9.3 are divided into two parts: first are results taking all biosphere objects into account presented and then are additional details regarding the Mäntykaarinjärvi object presented. The Mäntykarinjärvi object is selected to be studied in more details since it is one of the key objects regarding radiological consequences in the current safety assessment (section 7.4.1 in Hjerpe et al. 2010).

9.1 Retained activity

Figure 9-1 shows an example of how the activity is distributed in the different ecosystem types in the model, throughout the whole time window. In the beginning of the simulation the objects receiving direct releases from the geosphere are submerged, leading to that 100 % of the activity is found in coast objects. With time, due to the shoreline displacement, more activity will enter the landscape model in other types of objects, mainly lakes, and the release to marine water ceases.

At the end of the simulation time period, year 12 020, the total activity of each radionuclide that has entered the model is 10 kBq (a constant geosphere release of 1 Bq/y for 10,000 years). The activities retained in the landscape model at year 12 020 are presented in Figures 9-2 to 9-4. Here it is not necessary to show all three panel-specific results, it is sufficient to show one plot for each ecosystem type. This since the activity concentrations in the landscape model are close to, or in, equilibrium after 10,000 years of constant geosphere releases entering the model (cf Figure 9-1), and due to the fact that the landscape model itself is rather static (an inland site). There are small numerical differences in the results, but they would not be visible in the plots.

160

Figure 9-1. The distribution of the activity of Cl-36 between different ecosystem types.

Figure 9-2. Activity retained in all objects (the whole landscape model) at the end of the simulated time period (year 12 020); the total geosphere release for each radionuclide is 10,000 Bq.

Panel A

Panel B

Panel C

2020 4520 7020 9520 12020Year

All objects

Act

ivity

[Bq]

161

Figure 9-3. Activity retained in the terrestrial objects at the end of the simulated time period (year 12 020); the total geosphere release for each radionuclide is 10,000 Bq.

Forest objects

Cropland objects

Wetland objects

Act

ivity

[Bq]

Act

ivity

[Bq]

Act

ivity

[Bq]

162

Figure 9-4. Activity retained in the aquatic objects at the end of the simulated time period (year 12 020); the total geosphere release for each radionuclide is 10,000 Bq.

Lake objects

River objects

Coast objects

Act

ivity

[Bq]

Act

ivity

[Bq]

Act

ivity

[Bq]

163

9.2 Activity concentrations

The radionuclide-specific activity concentration maxima over all objects used in the dose calculations and the activity concentrations as function of time for the Mäntykaarinjärvi object are presented here.

9.2.1 Concentration maxima over all objects

Figure 9-5 and 9-6 show the activity concentration maxima in the compartments used in the dose calculations, i.e. the water columns for aquatic objects and the rooted mineral soil layer for terrestrial soils and the active layer in sediments, for constant geosphere release rates of 1 Bq/y. More details regarding the activity concentration maxima for each radionuclide are presented in Tables 9-1 to 9-15.

Figure 9-5. Activity concentration maxima in the water for freshwater objects (top) and coast objects (below), for constant geosphere release rates of 1 Bq/y for each radionuclide.

1E-10

1E-09

1E-08

1E-07

1E-06

1E-05

1E-04

1E-03

Act

ivity

conc

entr

atio

n [B

q/m

3 ] Panel A

Panel B

Panel C

1E-141E-131E-121E-111E-101E-091E-081E-071E-061E-05

Act

ivity

conc

entr

atio

n [B

q/m

3 ] Panel A

Panel B

Panel C

164

Figure 9-6. Activity concentration maxima in terrestrial soils (top), freshwater sediment (middle) and coast sediment (below), for constant geosphere release rates of 1 Bq/y for each radionuclide.

1E-11

1E-10

1E-09

1E-08

1E-07

1E-06

1E-05

Act

ivity

conc

entr

atio

n [B

q/kg

dw]

Panel A

Panel B

Panel C

1E-11

1E-10

1E-09

1E-08

1E-07

1E-06

1E-05

Act

ivity

conc

entr

atio

n [B

q/kg

dw]

Panel A

Panel B

Panel C

1E-11

1E-10

1E-09

1E-08

1E-07

1E-06

1E-05

Act

ivity

conc

entr

atio

n [B

q/kg

dw]

Panel A

Panel B

Panel C

165

Table 9-1. Activity concentration maxima in terrestrial soils (rooted mineral soil), constant unit geosphere release rates for each radionuclide (panel A).

Radionuclide Concentration [Bq/kgdw]

Year Biosphere object Ecosystem type

C-14 8.1E-7 11 660 Mäntykarinjärvi Wetland (mire) Cl-36 1.5E-7 11 520 Mäntykarinjärvi Wetland (mire) Cs-135 2.8E-8 12 020 Liiklanpelto Cropland I-129 1.1E-7 11 680 Mäntykarinjärvi Wetland (mire) Mo-93 3.1E-8 8 030 Liiklanpelto Cropland Nb-94 5.2E-8 11 520 Mäntykarinjärvi Wetland (mire) Ni-59 7.7E-8 11 420 Mäntykarinjärvi Wetland (mire) Pd-107 2.5E-7 11 420 Mäntykarinjärvi Wetland (mire) Se-79 1.4E-7 11 450 Mäntykarinjärvi Wetland (mire) Sn-126 1.3E-8 12 020 Liiklanpelto Cropland Sr-90 2.4E-10 2 620 Liiklanpelto Cropland

Table 9-2. Activity concentration maxima in sediments (active layer) of freshwater objects, constant unit geosphere release rates for each radionuclide (panel A).



C-14 6.8E-6 3 920 Kaunisjoki River Cl-36 8.5E-7 8 020 Tuomonjoki River Cs-135 1.0E-6 4 180 Kaunisjoki River I-129 1.3E-6 8 020 Tuomonjoki River Mo-93 1.3E-6 4 050 Kaunisjoki River Nb-94 2.0E-7 5 830 Kaunisjoki River Ni-59 8.5E-7 4 250 Kaunisjoki River Pd-107 6.9E-7 8 020 Tuomonjoki River Se-79 3.8E-7 5 000 Kaunisjoki River Sn-126 2.6E-7 5 580 Kaunisjoki River Sr-90 3.1E-8 3 560 Kaunisjoki River

Table 9-3. Activity concentration maxima in the water column of freshwater objects, constant unit geosphere release rates (panel A).

Radionuclide Concentration [Bq/m3]


C-14 3.9E-5 3 920 Kaunisjoki River Cl-36 1.4E-4 12 020 Liiklanjärvi Lake Cs-135 2.4E-7 4 180 Kaunisjoki River I-129 5.8E-6 4 250 Kaunisjoki River Mo-93 1.6E-6 4 050 Kaunisjoki River Nb-94 4.6E-8 5 830 Kaunisjoki River Ni-59 8.8E-8 4 250 Kaunisjoki River Pd-107 2.1E-7 8 020 Tuomonjoki River Se-79 2.4E-7 5 000 Kaunisjoki River Sn-126 4.2E-8 5 580 Kaunisjoki River Sr-90 3.4E-7 3 560 Kaunisjoki River

166

Table 9-4. Activity concentration maxima in sediments (active layer) of coast objects, constant unit geosphere release rates (panel A).

Radionuclide Concentration [Bq/kqdw]


C-14 2.9E-7 4 020 Kaunisjoki Coast Cl-36 1.1E-7 4 020 Kaunisjoki Coast Cs-135 1.7E-9 4 020 Kaunisjoki Coast I-129 1.2E-9 4 020 Kaunisjoki Coast Mo-93 4.3E-9 4 020 Kaunisjoki Coast Nb-94 6.2E-11 4 020 Kaunisjoki Coast Ni-59 1.1E-9 4 020 Kaunisjoki Coast Pd-107 5.0E-10 4 020 Kaunisjoki Coast Se-79 2.1E-10 4 020 Kaunisjoki Coast Sn-126 9.7E-11 4 020 Kaunisjoki Coast Sr-90 3.8E-10 3 520 Susijoki Coast

Table 9-5. Activity concentration maxima in the water column of coast objects, constant unit geosphere release rates (panel A).



C-14 9.0E-9 3 020 Liiklanpelto Coast Cl-36 1.4E-6 3 020 Liiklanpelto Coast Cs-135 4.5E-12 4 020 Kaunisjoki Coast I-129 6.3E-10 3 020 Liiklanpelto Coast Mo-93 3.9E-11 3 020 Liiklanpelto Coast Nb-94 1.4E-13 4 020 Kaunisjoki Coast Ni-59 2.8E-12 4 020 Kaunisjoki Coast Pd-107 1.2E-12 4 020 Kaunisjoki Coast Se-79 1.2E-12 3 020 Liiklanpelto Coast Sn-126 2.2E-13 4 020 Kaunisjoki Coast Sr-90 2.8E-10 3 520 Janijärvi Coast

Table 9-6. Activity concentration maxima in terrestrial soils (rooted mineral soil), constant unit geosphere release rates (panel B).

Radionuclide Concentration [Bq/mdw]


C-14 1.7E-6 11 660 Mäntykarinjärvi Wetland (mire) Cl-36 3.3E-7 11 520 Mäntykarinjärvi Wetland (mire) Cs-135 1.1E-8 11 330 Mäntykarinjärvi Wetland (mire) I-129 2.4E-7 11 680 Mäntykarinjärvi Wetland (mire) Mo-93 1.5E-8 4 800 Mäntykarinjärvi Wetland (mire) Nb-94 1.1E-7 11 520 Mäntykarinjärvi Wetland (mire) Ni-59 1.6E-7 11 420 Mäntykarinjärvi Wetland (mire) Pd-107 5.4E-7 11 420 Mäntykarinjärvi Wetland (mire) Se-79 2.9E-7 11 450 Mäntykarinjärvi Wetland (mire) Sn-126 1.1E-8 11 520 Mäntykarinjärvi Wetland (mire) Sr-90 1.9E-10 4 580 Mäntykarinjärvi Wetland (mire)

167

Table 9-7. Activity concentration maxima in sediments (active layer) of freshwater objects, constant unit geosphere release rates (panel B).



C-14 4.5E-7 3 710 Susijoki River Cl-36 1.1E-7 12 020 Tankarienjärvi Lake Cs-135 2.1E-8 12 020 Susijoki River I-129 2.0E-8 12 020 Susijoki River Mo-93 1.3E-8 5 110 Susijoki River Nb-94 8.7E-9 12 020 Susijoki River Ni-59 1.9E-8 12 020 Susijoki River Pd-107 1.9E-8 12 020 Susijoki River Se-79 1.6E-8 12 020 Susijoki River Sn-126 1.3E-8 12 020 Susijoki River Sr-90 5.7E-9 3 560 Susijoki River

Table 9-8. Activity concentration maxima in the water column of freshwater objects, constant unit geosphere release rates (panel B).



C-14 2.6E-6 3 710 Susijoki River Cl-36 4.0E-5 12 020 Tankarienjärvi Lake Cs-135 4.9E-9 12 020 Susijoki River I-129 1.3E-7 12 020 Susijoki River Mo-93 1.6E-8 5 110 Susijoki River Nb-94 2.0E-9 12 020 Susijoki River Ni-59 2.0E-9 12 020 Susijoki River Pd-107 6.3E-9 12 020 Susijoki River Se-79 1.0E-8 12 020 Susijoki River Sn-126 2.1E-9 12 020 Susijoki River Sr-90 6.3E-8 3 560 Susijoki River

Table 9-9. Activity concentration maxima in sediments (active layer) of coast objects, constant unit geosphere release rates (panel B).

Radionuclide Concentration [Bq/kqdw]


C-14 2.4E-7 3 520 Susijoki Coast Cl-36 8.7E-8 3 520 Susijoki Coast Cs-135 6.7E-10 4 020 Kallanjoki Coast I-129 4.9E-10 4 020 Kallanjoki Coast Mo-93 1.8E-9 3 520 Susijoki Coast Nb-94 3.6E-11 4 020 Kallanjoki Coast Ni-59 4.9E-10 4 020 Kallanjoki Coast Pd-107 2.5E-10 4 020 Kallanjoki Coast Se-79 1.2E-10 4 020 Kallanjoki Coast Sn-126 5.6E-11 4 020 Kallanjoki Coast Sr-90 2.2E-9 3 520 Susijoki Coast

168

Table 9-10. Activity concentration maxima in the water column of coast objects, constant unit geosphere release rates (panel B).



C-14 3.1E-8 3 520 Janijärvi Coast Cl-36 2.8E-7 3 020 Janijärvi Coast Cs-135 3.7E-12 3 520 Janijärvi Coast I-129 6.4E-11 3 020 Janijärvi Coast Mo-93 9.0E-11 3 520 Janijärvi Coast Nb-94 1.1E-13 3 520 Janijärvi Coast Ni-59 4.7E-13 3 520 Janijärvi Coast Pd-107 1.5E-12 3 520 Janijärvi Coast Se-79 1.4E-12 3 520 Janijärvi Coast Sn-126 9.3E-14 3 520 Janijärvi Coast Sr-90 1.6E-9 3 520 Janijärvi Coast

Table 9-11. Activity concentration maxima in terrestrial soils (rooted mineral soil), constant unit geosphere release rates (panel C).

Radionuclide Concentration [Bq/mdw]


C-14 4.3E-6 11 660 Mäntykarinjärvi Wetland (mire) Cl-36 8.3E-7 11 520 Mäntykarinjärvi Wetland (mire) Cs-135 2.7E-8 11 330 Mäntykarinjärvi Wetland (mire) I-129 6.0E-7 11 680 Mäntykarinjärvi Wetland (mire) Mo-93 3.7E-8 4 800 Mäntykarinjärvi Wetland (mire) Nb-94 2.8E-7 11 520 Mäntykarinjärvi Wetland (mire) Ni-59 4.1E-7 11 420 Mäntykarinjärvi Wetland (mire) Pd-107 1.4E-6 11 420 Mäntykarinjärvi Wetland (mire) Se-79 7.2E-7 11 450 Mäntykarinjärvi Wetland (mire) Sn-126 2.8E-8 11 520 Mäntykarinjärvi Wetland (mire) Sr-90 4.7E-10 4 580 Mäntykarinjärvi Wetland (mire)

Table 9-12. Activity concentration maxima in sediments (active layer) of freshwater objects, constant unit geosphere release rates (panel C).



C-14 9.9E-7 3 910 Kaunisjoki River Cl-36 1.0E-7 3 680 Kaunisjoki River Cs-135 1.5E-7 4 180 Kaunisjoki River I-129 1.2E-7 4 250 Kaunisjoki River Mo-93 1.9E-7 4 050 Kaunisjoki River Nb-94 2.9E-8 5 830 Kaunisjoki River Ni-59 1.2E-7 4 250 Kaunisjoki River Pd-107 8.4E-8 4 450 Kaunisjoki River Se-79 5.6E-8 5 000 Kaunisjoki River Sn-126 3.8E-8 5 580 Kaunisjoki River Sr-90 5.0E-9 3 560 Susijoki River

169

Table 9-13. Activity concentration maxima in the water column of freshwater objects, constant unit geosphere release rates (panel C).



C-14 5.7E-6 3 920 Kaunisjoki River Cl-36 3.0E-5 12 020 Tankarienjärvi Lake Cs-135 3.6E-8 4 180 Kaunisjoki River I-129 8.0E-7 4 250 Kaunisjoki River Mo-93 2.4E-7 4 050 Kaunisjoki River Nb-94 6.7E-9 5 830 Kaunisjoki River Ni-59 1.3E-8 4 250 Kaunisjoki River Pd-107 2.8E-8 4 450 Kaunisjoki River Se-79 3.5E-8 5 000 Kaunisjoki River Sn-126 6.2E-9 5 580 Kaunisjoki River Sr-90 5.5E-8 3 560 Susijoki River

Table 9-14. Activity concentration maxima in sediments (active layer) of coast objects, constant unit geosphere release rates (panel C).



C-14 2.1E-7 3 520 Susijoki Coast Cl-36 7.6E-8 3 520 Susijoki Coast Cs-135 6.7E-10 4 020 Kallanjoki Coast I-129 4.8E-10 4 020 Kallanjoki Coast Mo-93 1.6E-9 3 520 Susijoki Coast Nb-94 3.4E-11 4 020 Kallanjoki Coast Ni-59 4.8E-10 4 020 Kallanjoki Coast Pd-107 2.4E-10 4 020 Kallanjoki Coast Se-79 1.1E-10 4 020 Kallanjoki Coast Sn-126 5.4E-11 4 020 Kallanjoki Coast Sr-90 1.9E-9 3 520 Susijoki Coast

Table 9-15. Activity concentration maxima in the water column of coast objects, constant unit geosphere release rates (panel C).



C-14 2.8E-8 3 520 Janijärvi Coast Cl-36 2.6E-7 3 020 Janijärvi Coast Cs-135 3.4E-12 3 520 Janijärvi Coast I-129 5.8E-11 3 020 Janijärvi Coast Mo-93 8.4E-11 3 520 Janijärvi Coast Nb-94 1.0E-13 3 520 Janijärvi Coast Ni-59 4.4E-13 3 520 Janijärvi Coast Pd-107 1.4E-12 3 520 Janijärvi Coast Se-79 1.3E-12 3 520 Janijärvi Coast Sn-126 8.8E-14 3 520 Janijärvi Coast Sr-90 1.4E-9 3 520 Janijärvi Coast

170

9.2.2 Activity concentrations in the Mäntykarinjärvi object

Here are additional selected results for the Mäntykaarinjärvi object presented. Figures 9-7 to 9-10 present the activity concentrations as function of time in the seven compartments, for each of the 11 radionuclides in the key set of radionuclides. The results are here limited to releases from repository panel A. To also illustrate the differences between releases from the three repository panels are activity concentrations in Figures 9-11 to 9-19 for the compartments used in the dose calculations (same as in section 9.2.1), limited to the top priority radionuclides (C-14, Cl-36 and I-129).

171

Figure 9-7. Activity concentrations for C-14 (top), Cl-36 (middle), and I-129 (below) in Mäntykarinjärvi, for constant unit geosphere release rates for each radionuclide, assuming releases from panel A.

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

-14)

Year

Wetland acrotelm [Bq/kg]

Wetland reed [Bq/kg]

Forest soil [Bq/kg]

Lake sediment [Bq/kg]

Lake water [Bq/m3]

Coast sediment [Bq/kg]

Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

l-36)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (I

-129

)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

172

Figure 9-8. Activity concentrations for Mo-93 (top), Nb-94 (middle), and Cs-135 (below) in Mäntykarinjärvi, for constant unit geosphere release rates for each radionuclide, assuming releases from panel A.

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (M

o-93

)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (N

b-94

)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

s-13

5)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

173

Figure 9-9. Activity concentrations for Ni-59 (top), Se-79 (middle), and Sr-90 (below) in Mäntykarinjärvi, for constant unit geosphere release rates for each radionuclide, assuming releases from panel A.

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (N

i-59)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (S

e-79

)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (S

r-90

)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

174

Figure 9-10. Activity concentrations for Pd-107 (top) and Sn-126 (below) in Mäntykarinjärvi, for constant unit geosphere release rates for each radionuclide, assuming releases from panel A.

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (P

d-10

7)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

1.E-20

1.E-18

1.E-16

1.E-14

1.E-12

1.E-10

1.E-08

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (S

n-12

6)

Year



Forest soil [Bq/kg]


Lake water [Bq/m3]


Coast water [Bq/m3]

175

Figure 9-11. Activity concentrations [Bq/kgdw] of C-14 in selected forest and wetland compartments in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

Figure 9-12. Activity concentrations [Bq/kgdw] of Cl-36 in selected forest and wetland compartments in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

-14)

Year

Wetland acrotelm A

Wetland acrotelm B

Wetland acrotelm C

Wetland reed A

Wetland reed B

Wetland reed C

Forest soil A

Forest soil B

Forest soil C

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

l-36)

Year

Wetland acrotelm A

Wetland acrotelm B

Wetland acrotelm C

Wetland reed A

Wetland reed B

Wetland reed C

Forest soil A

Forest soil B

Forest soil C

176

Figure 9-13. Activity concentrations [Bq/kgdw] of I-129 in selected forest and wetland compartments in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

Figure 9-14. Activity concentrations of C-14 in lake water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates of for each radionuclide, assuming releases from panels A, B and C, respectively.

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (I

-129

)

Year

Wetland acrotelm A

Wetland acrotelm B

Wetland acrotelm C

Wetland reed A

Wetland reed B

Wetland reed C

Forest soil A

Forest soil B

Forest soil C

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

-14)

Year

Lake water A

Lake water B

Lake water C

Lake sediment A

Lake sediment B

Lake sediment C

177

Figure 9-15. Activity concentrations of Cl-36 in lake water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

Figure 9-16. Activity concentrations of I-129 in lake water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (C

l-36)

Year

Lake water A

Lake water B

Lake water C

Lake sediment A

Lake sediment B

Lake sediment C

1.E-19

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

2020 4520 7020 9520 12020

Act

ivity

con

cent

ratio

n (I

-129

)

Year

Lake water A

Lake water B

Lake water C

Lake sediment A

Lake sediment B

Lake sediment C

178

Figure 9-17. Activity concentrations of C-14 in coast water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

Figure 9-18. Activity concentrations of Cl-36 in coast water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively.

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

2020 2520 3020 3520

Act

ivity

conc

entr

atio

n (C

-14)

Year

Coast water A

Coast water B

Coast water C

Coast sediment A

Coast sediment B

Coast sediment C

1.E-13

1.E-12

1.E-11

1.E-10

1.E-09

2020 2520 3020 3520

Act

ivity

con

cent

ratio

n (C

l-36)

Year

Coast water A

Coast water B

Coast water C

Coast sediment A

Coast sediment B

Coast sediment C

179

Figure 9-19. Activity concentrations of I-129 in coast water [Bq/m3] and active sediment [Bq/kgdw] in the Mäntykarinjärvi object for constant unit geosphere release rates for each radionuclide, assuming releases from panels A, B and C, respectively. Table 9-16. Radionuclide-specific annual landscape dose maxima (Emax) and Egroup to a representative person for the most exposed group and the year the maxima occur, due to constant unit releases from the geosphere (1 Bq/y).

Radionuclide Emax [Sv]

Year

Emax [Sv]

Year

Emax [Sv]

Year

Panel A Panel B Panel C C-14 6.9E-14 2 620 9.0E-16 3 720 2.0E-15 3 920Cl-36 5.2E-13 11 620 9.2E-13 11 520 2.3E-12 11 470Cs-135 7.6E-14 12 020 1.6E-13 12 020 4.0E-13 12 020I-129 5.9E-12 12 020 1.3E-11 12 020 3.2E-11 12 020Mo-93 6.8E-15 12 020 1.4E-14 12 020 3.6E-14 12 020Nb-94 4.4E-15 11 420 8.4E-15 11 420 2.1E-14 11 420Ni-59 4.3E-16 11 320 9.1E-16 11 320 2.3E-15 11 320Pd-107 9.2E-16 11 320 2.0E-15 11 270 4.9E-15 11 320Se-79 9.4E-15 12 020 2.0E-14 12 020 5.0E-14 12 020Sn-126 6.2E-16 12 020 7.9E-17 11 520 2.0E-16 11 520Sr-90 5.7E-15 3 570 1.8E-15 12 020 4.3E-15 12 020Egroup 6.6E-12 12 020 1.4E-11 12 020 3.5E-11 12 020

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

2020 2520 3020 3520

Act

ivity

con

cent

ratio

n (I

-129

)

Year

Coast water A

Coast water B

Coast water C

Coast sediment A

Coast sediment B

Coast sediment C

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9.3 Annual doses to humans

The following sections presents the results from applying the dose calculation and identification approach presented in section 5.1.2 on the resulting activity concentrations in the biosphere objects. First are the landscape doses due to constant unit geosphere releases addressed (section 9.3.1), also including selected additional details regarding the Mäntykaarinjärvi object. Then are annual landscape doses due to nominal geosphere release rates presented (section 9.3.2), also including a comparison with derived safety indicators (annual doses obtained by applying the stylised well scenarios described in chapter 6). 9.3.1 Constant unit releases

Landscape dose maxima The results based on constant unit geosphere release rates presented below are as follows:

� Table 9-16 and Table 9-17 – Annual landscape dose maxima (both total and radionuclide-specific) to a representative person for the most exposed group and for other people,

� Table 9-18 – further information regarding the annual landscape dose maxima to a representative person for the most exposed group, such as the relative contribution from the considered exposure pathways and the biosphere objects most contributing to the dose from each pathway, and

� Table 9-19 – more information regarding the annual landscape dose maxima to a representative person for other people, such as the relative exposure pathway specific contributions and the biosphere objects most contributing to the dose from each pathway.

Table 9-17. Radionuclide-specific annual landscape dose maxima (Emax) and Epop to a representative person for other people, and the year the maxima occur, due to constant unit releases from the geosphere (1 Bq/y)

Radionuclide Emax [Sv]

Year

Emax [Sv]

Year

Emax [Sv]

Year

Panel A Panel B Panel C C-14 2.0E-14 3 920 9.0E-16 3 720 2.0E-15 3 920Cl-36 8.1E-14 12 020 2.6E-14 12 020 2.6E-14 12 020Cs-135 3.3E-16 12 020 7.0E-16 12 020 1.7E-15 12 020I-129 3.9E-13 4 270 5.7E-14 12 020 1.4E-13 12 020Mo-93 3.0E-15 4 070 6.4E-17 12 020 4.4E-16 4 070Nb-94 8.8E-15 12 020 1.3E-16 11 070 4.0E-16 11 070Ni-59 3.3E-18 4 270 7.8E-20 7 370 4.9E-19 4 270Pd-107 4.9E-18 8 020 8.9E-19 12 020 2.2E-18 12 020Se-79 4.3E-16 4 970 5.9E-17 12 020 1.5E-16 12 020Sn-126 7.6E-15 12 020 1.3E-17 6 020 1.0E-16 12 020Sr-90 5.4E-15 3 570 1.0E-15 3 570 7.9E-16 3 570Epop 4.4E-13 4 270 8.4E-14 12 020 1.7E-13 12 020

181

Table 9-18. Relative contributions from each exposure pathway, and dominating biosphere objects, to the annual landscape dose maxima to a representative person for the most exposed group, assuming constant unit geosphere releases.

Unit geosphere releases – Panel A Egroup 6.6E-12 Sv Year 12 020

Food ingestion contribution 97.8 % Dominating biosphere object(s) Mäntykaarinjärvi (wetland) (a, b)

Mäntykaarinjärvi (reed) (a)

Liklanjärvi (lake) (a) Mäntykaarinjärvi (forest) (b)

Water ingestion contribution 1.3 % Dominating object Liklanjärvi (lake)

Inhalation and external exposure contribution 1.0 % Dominating object Liiklanpelto (cropland)

Unit geosphere releases – Panel B Egroup 1.4E-11 Sv Year 12 020


Mäntykaarinjärvi (reed) (a) Mäntykaarinjärvi (forest) (b)

Water ingestion contribution 0.2 % Dominating object Tankarinjärvi (lake)

Inhalation and external exposure contribution 0.1 % Dominating object Mäntykaarinjärvi (wetland) Unit geosphere releases – Panel C Egroup 3.5E-11 Sv Year 12 020


Mäntykaarinjärvi (reed) (a) Mäntykaarinjärvi (forest) (b)


Inhalation and external exposure contribution 0.1 % Dominating object Mäntykaarinjärvi (wetland)

(a) Produces less edibles than one person’s annual demand (b) Developed from areas of the lake that have been overgrown by vegetation

182

Table 9-19. Relative contributions from each exposure pathway, and dominating biosphere objects, to the annual landscape dose maxima to a representative person for other people, assuming releases from Panel A.

Unit geosphere releases – Panel A Epop 4.4E-13 Sv Year 4 270

Food ingestion contribution 2.4 % Dominating biosphere object(s) Mäntykaarinjärvi (forest) (a)

Liiklanpelto (cropland)

Lepänmaa (cropland)

Koskelonpelto (cropland)

Water ingestion contribution 97.3 % Dominating object Kaunisjoki (river)

Inhalation and external exposure contribution 0.4 % Dominating object Liiklanpelto (cropland)

Unit geosphere releases – Panel B Epop 8.4E-14 Year 12 020



Inhalation and external exposure contribution 0.1 % Dominating object Mäntykaarinjärvi (wetland) (a)

Unit geosphere releases – Panel C Epop 1.7E-13 Year 12 020



Inhalation and external exposure contribution 0.3 % Dominating object Mäntykaarinjärvi (wetland) (a)

(a) Developed from areas of the lake that have been overgrown by vegetation

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Landscape dose over the whole time window The results based on constant unit geosphere release rates presented below are as follows:

� Figure 9-20 to Figure 9-22 – annual landscape doses as function of time (both total and radionuclide-specific) to a representative person for the most exposed group,

� Figure 9-23 to Figure 9-25 – annual landscape doses as function of time (both total and radionuclide-specific) to a representative person for other people,

� Figure 9-26 – the dose distributions, and

� Figure 9-27 to Figure 9-29 – Pathway-specific landscape doses due to the biosphere object Mäntykaarinjärvi.

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Figure 9-20. Total (Egroup) and radionuclide-specific annual landscape doses to a representative person for the most exposed group, for unit geosphere release rates, assuming releases from panel A.

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

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Cl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14

Sr90

Sn126

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Figure 9-21. Total (Egroup) and radionuclide-specific annual landscape doses to a representative person for the most exposed group, for unit geosphere release rates, assuming releases from panel B.

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

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Cl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14

Sr90

Sn126

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Figure 9-22. Total (Egroup) and radionuclide-specific annual landscape doses to a representative person for the most exposed group, for unit geosphere release rates, assuming releases from panel C.

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

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Cl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14

Sr90

Sn126

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Figure 9-23. Total (Epop) and radionuclide-specific annual landscape doses to a representative person for other people, for unit geosphere release rates, assuming releases from panel A.

1.E-20

1.E-19

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

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v]

Year

EgroupCl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14

Sr90

Sn126

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Figure 9-24. Total (Epop) and radionuclide-specific annual landscape doses to a representative person for other people, for unit geosphere release rates, assuming releases from panel B.

1.E-20

1.E-19

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

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v]

Year

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Cl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14

Sr90Sn126

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Figure 9-25. Total (Epop) and radionuclide-specific annual landscape doses to a representative person for other people, for unit geosphere release rates, assuming releases from panel C.

1.E-20

1.E-19

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

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v]

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Egroup

Cl36

I129

Cs135

Mo93

Pd107

Se79

Nb94

Ni59

C14Sr90Sn126

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Figure 9-26. The dose distributions for unit geosphere release rates, assuming releases from panel A (top), panel B (middle) and panel C (below).

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

1.E-18

1.E-17

1.E-16

1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10

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v]A

nnua

l lan

dsca

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[Sv]

Ann

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The Mäntykaarinjärvi object As for the presentation of landscape modelling results (section 9.2.2), the Mäntykarinjärvi object is also investigated in more details regarding landscape doses. The results above (Table 9-18 and Table 9-19) show that the biosphere object Mäntykaarinjärvi is the most dominating object for Egroup maxima, and also one of the most dominating objects for Epop maxima. Furthermore, it is generally the ingestion of food exposure pathway that dominates the contribution from Mäntykarinjärvi to the annual landscape dose maxima.

Figure 9-27 to Figure 9-29 show the landscape doses (EL) from Mäntykarinjärvi for the considered exposure pathways, limited to releases from repository panel C. The ranges of maximum sustainable populations for the different sub-objects also presented for the food ingestion pathway. It is evident (Figure 9-27) that the wetland mire sub-object is able to produce the most contaminated food. However, the production of edibles is so low that its total annual production can only satisfy 2-4 % of one person’s food demand.

Figure 9-27. Landscape dose from food ingestion (EL,F) due to the biosphere object Mäntykaarinjärvi, assuming releases from panel C. NF is the ranges of maximum sustainable population for the different sub-objects.

1E-20

1E-18

1E-16

1E-14

1E-12

1E-10

1E-08

2020 4020 6020 8020 10020 12020

Lan

dsca

pe d

ose,

EL,

F[S

v]

Year

Wetland (mire)

Wetland (reed)

Forest

Lake

Coast

0.02 – 0.04

0.3 – 0.4

66 – 740.1 – 0.2

1 – 2

NF

192

Figure 9-28. Landscape dose from water ingestion (EL,W) due to the lake sub-object within the biosphere object Mäntykaarinjärvi, assuming releases from panel C.

Figure 9-29. Landscape dose from inhalation and external exposure (EL,IE) due to the biosphere object Mäntykaarinjärvi, assuming releases from panel C.

1E-22

1E-21

1E-20

1E-19

1E-18

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Lan

dsca

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EL,

W[S

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Year

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1E-18

1E-17

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1E-14

1E-13

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dsca

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ose,

EL,

IE[S

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Year

Wetland (mire)

Wetland (reed)

Forest

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9.3.2 Nominal release rates

Here are the resulting annual landscape doses when applying constant NRRs (Table 2-3) presented. Furthermore, safety indicators are derived for NRR, based on the stylised well scenarios described in chapter 6; these are also compared to the annual landscape doses. Two issues are addressed here by deriving annual landscape doses using NRRs as input:

1. To derive an annual dose that may be used for comparison with the corresponding dose derived with the stylised well scenarios, and

2. To test if the outcome of the biosphere modelling (annual landscape doses) is linear in respect to applied geosphere release rates.

Landscape doses and safety indicators The resulting annual landscape dose maxima when applying a constant NRR is presented in Table 9-20. Further, the AgriWELL.2009 and WELL-2009 scenarios presented in section 6.1 (mainly aimed to be applied in assessment cases in the present assessment) are applied on the NRR to derive indicative annual doses for the; the results are presented in Table 9-21. As seen from the results, the resulting annual landscape dose maxima for the most exposed group are of the same order of magnitude as the derived safety indicators. Furthermore, the annual landscape dose maxima for other people are about two orders of magnitudes lower than the derived safety indicators.

Linearity of landscape doses The approach to dose assessment in the present work is to derive the dose distribution (section 5.1.2), not to derive single radionuclide-specific values for converting the geosphere release rates into annual dose. This latter approach may be called the landscape dose conversion factor (LDF) approach. One reason for adapting the dose distribution approach is that the landscape model, and the subsequent dose calculation,

Table 9-20. Annual landscape dose maxima to a representative person for the most exposed group (Egroup) and a representative person for other people (Epop), applying a constant NRR.

End-point Panel A Panel B Panel C Egroup 6.6E-12 1.4E-11 3.5E-11 Epop 4.4E-13 8.4E-14 1.7E-13

Table 9-21. Annual doses from the well scenarios applied on one NRR, the radionuclide specific doses from the three most contributing to the dose, and the relative contribution to the annual dose from the pathways included (drinking of water and intake of edibles).

Annual dose [Sv]

RN-specific dose [Sv]

Contribution to the annual dose

Water Food Agri-WELL-2009 1.6 x 10-11 I-129

C-14 Cl-36

9.9 x 10-12 5.2 x 10-13 8.4 x 10-14

61.4% 3.2% 0.5%

30.6% 1.6% 1.4%

WELL-2009 1.1 x 10-11 I-129

C-14 Cl-36

1.5 x 10-12 7.9 x 10-13 3.1 x 10-13

93.8 % 4.9 % 0.8 %

- - -

194

is rather complex. Thus, it cannot direct be assumed that the derived doses are direct proportional to the applied geosphere release rates, which is one of the key assumptions in the LDF approach. To test if the annual landscape doses are linear in respect to applied geosphere release rates, NRRs varying with six orders of magnitudes has been used as input to the landscape model and then the resulting activity concentrations used in assessing doses to humans. The resulting annual dose maxima are summarised in Table 9-22 and Table 9-23. As seen from the results in these tables, the annual landscape dose maxima show a total linear behaviour with the applied NRRs and they all are consistent when the maxima occur in the modelled time window. However, just evaluating the different dose maxima does not give a comprehensive picture. To further investigate the test linearity of annual landscape doses in respect to applied geosphere release rates, Egroup and Epop and their respective radionuclide-specific annual landscape doses are plotted in Figure 9-30 and Figure 9-31, for four different orders of magnitude of the nominal release rates (NRR). The plots are limited to the five radionuclides most contributing to Egroup and Epop and to releases from panels B. The results show that even though the dose maxima shows ideal linearity, the shapes of the landscape doses curves are in fact affected by the magnitude of the applied geosphere release rates. Especially C-14, and to some extent Cl-36 and Se-79, is affected. The width of the plateau from the landscape dose maxima, the slowly decreasing dose and to the step-wise rather large drop is getting narrower with increased geosphere release rates.

Table 9-22. Annual landscape dose maxima to a representative person for the most exposed group and the year the maxima occur, applying constant nominal release rates of varying orders of magnitude.

Release rate [xNRR]

Egroup [Sv]

Year

Egroup [Sv]

Year

Egroup [Sv]

Year

Panel A Panel B Panel C 1 6.5E-11 12 020 1.4E-10 12 020 3.4E-10 12 020

10 6.5E-10 12 020 1.4E-9 12 020 3.4E-9 12 020100 6.5E-09 12 020 1.4E-8 12 020 3.4E-8 12 020

1,000 6.5E-08 12 020 1.4E-7 12 020 3.4E-7 12 02010,000 6.5E-07 12 020 1.4E-6 12 020 3.4E-6 12 020

100,000 6.5E-06 12 020 1.4E-5 12 020 3.4E-5 12 0201,000,000 6.5E-05 12 020 1.4E-4 12 020 3.4E-4 12 020

Table 9-23. Annual landscape dose maxima to a representative person for other people and the year the maxima occur, applying constant nominal release rates of varying orders of magnitude.

Release rate [xNRR]

Epop [Sv]

Year

Epop [Sv]

Year

Epop [Sv]

Year

Panel A Panel B Panel C 1 6.0E-12 4 220 8.3E-13 12 020 1.7E-12 12 020

10 6.0E-11 4 220 8.3E-12 12 020 1.7E-11 12 020100 6.0E-10 4 220 8.3E-11 12 020 1.7E-10 12 020

1,000 6.0E-09 4 220 8.3E-10 12 020 1.7E-09 12 02010,000 6.0E-08 4 220 8.3E-09 12 020 1.7E-08 12 020

100,000 6.0E-07 4 220 8.3E-08 12 020 1.7E-07 12 0201,000,000 6.0E-06 4 220 8.3E-07 12 020 1.7E-06 12 020

195

Figure 9-30. Total (Egroup) and radionuclide-specific annual landscape doses to a representative person for the most exposed group, for nominal release rates (NRR) spanning six orders of magnitudes, assuming releases from panel B (for the five radionuclides most contributing to Egroup).

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

2020 4520 7020 9520 12020

EgroupC14Cl36Cs135I129Se79

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1.E-11

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1.E-09

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1.E-06

1.E-05 EgroupC14Cl36Cs135I129Se79

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1.E-13

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1.E-10

1.E-09

1.E-08


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Ann

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1xNRR

100xNRR

10,000xNRR

1,000,000xNRR

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Figure 9-31. Total (Epop) and radionuclide-specific annual landscape doses to a representative person for other people, for nominal release rates (NRR) spanning six orders of magnitudes, assuming releases from panel B (for the five radionuclides most contributing to Epop).

1.E-12

1.E-11

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1.E-09

1.E-08

1.E-07

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1.E-05

2020 4520 7020 9520 12020

EpopC14Cl36Cs135I129Se79

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1.E-13

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07 EpopC14Cl36Cs135I129Se79

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1.E-15

1.E-14

1.E-13

1.E-12

1.E-11

1.E-10


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1.E-16

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1.E-12


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100xNRR

10,000xNRR

1,000,000xNRR

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10 CONCLUSIONS

The report is a key supporting report for enhancing transparency and traceability of the radionuclide transport and dose assessment modelling preformed in the biosphere assessment 2009 (reported in Hjerpe et al 2010).

Part I of this report has presented in detail the conceptual and mathematical models applied the screening assessments, in the landscape modelling, in the radionuclide consequences analysis and when deriving safety indicators from stylised well scenarios. Also key data applied in all models are presented. The level of quality in the underlying knowledge basis for all models addressed in this report has also been assessed. The conclusions of the Knowledge quality assessment (KQA) performed is that the quality of, and the confidence in, the radionuclide transport and dose assessment modelling is generally high. However, some issues are identified were the knowledge quality needs improvement for the next assessment, these can be summarised as follows:

� the screening assessment is recently developed and has been applied for the first time in the present assessment; thus the concept, models and data needs to be evaluated, and are expected to reach maturity by 2012,

� the confidence in the model underlying the radionuclide transport from irrigation water to crops has not been assessed. This model is originally developed for steady-state conditions, an evaluation of its fitness to the more dynamic landscape modelling shall be done by 2012,

� the landscape model set-up is rather robust step after the input data is available and well specified. However, only one configuration has been applied in the present assessment. There are a number of alternative development paths, which need to be handled by scenario approach in the future. Producing adequate data basis to analyse identified scenarios needs to be propagated through the relevant preceding biosphere assessment sub-processes,

� the dose assessments, both for humans and other biota, have undergone major revisions compared to previously applied concepts; thus these parts needs to be evaluated, and are expected to reach maturity by 2012.

Part II of this report has presented results from applying the models and concepts described in Part I. The screening evaluation was applied on constant unit geosphere releases rates for each radionuclide and to a set of geosphere release rates resulting from analyses of repository calculation cases. The latter release rates were used to identify the key set of radionuclides to further analyse with landscape modelling. The results show that the screening evaluation is very useful, the number of radionuclides for further consideration decreased from 35 to 11. Thus it is considered that 14 of the radionuclides with a non-zero geosphere release rate for the analysed repository calculation cases, within the time window for biosphere assessment, do not contribute significantly to annual dose to humans or the absorbed dose rates for other biota.

Furthermore, this report provides the basis for understanding the behaviour of the landscape model and the sub-sequent calculations of doses to humans, by analysing with constant unit geosphere release rates. Resulting environmental activity concentrations in the landscape model, retained activities in different ecosystems, and

198

landscape doses have been calculated and presented in this report. These results are intended to give more insight on how the total model behaves, and especially on how different radionuclides behave in relation to each other throughout the rather complicated chain of models. Thus, the results cannot be used as ‘conversion factors’ to derive doses from any given geosphere release rate.

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209

APPENDIX A – Biosphere object modules

In this appendix, the mathematical structures regarding the biosphere object modules used to calculate the radionuclide transport in the landscape model are given. The input parameter values applied are addressed in section 4.3. The expressions for the transfer coefficients are all in unit of y-1 and are in the model multiplied with the corresponding source compartment activity inventory. By doing this the rate of change is obtained, in Bq/y. The biosphere object modules are schematically presented using box-diagrams showing the model compartments and the radionuclide transport paths.

The atmospheric air module The air module is used to take into consideration the gaseous releases of C-14 (Avila & Pröhl 2007) from soil to the mixing layer of terrestrial ecosystems, and the exchange between the mixing layer and the canopy. The box-diagram for the air module is presented in Figure A-1, the parameters included in Table A-1, and the expression used to derive the transfer coefficients in Table A-2.

The gas uptake process and the loss due processes are both dependant on the type of terrestrial ecosystem associated with the Air compartment, and its vegetation characteristics. The parameters Prod, hmix, and speed-wind are all thus dependant on which type of terrestrial ecosystem the air compartment belongs to.

Figure A-1. Box-diagram showing the radionuclide transports paths for the air module. Note: the gaseous release from a terrestrial object is included in the description for the terrestrial objects section later in this section.

210

Table A-1. Parameters used in the athomspheric air module

Name�in�model� Parameter�name Unit�

speed_wind_10� Wind speed at the height of 10 m m/y�

z0� Roughness length m�

conc_air_C� Carbon�12�content�in�air kg/m³�Area� Release�area m2�

hveg� Vegetation height m�

speed_wind� Wind speed at the vegetation height m/y�hmix� Mixing height m�Prod_<compartment>� Net primary production in the ecosystem kgC/m²/y�gasRelease_C� Soil�to�air�degassing�rate� kgC/m2/y�gasUptake Uptake�rate�of�gas�from�air 1/y�

Table A-2. Expressions for the transfer coefficients in Figure A-1.

Name� Source�compartment�

Target�compartment�

Transfer�Coefficient�[1/y]�

Air�exchange�rate Air� Elsewhere speed_wind/sqrt(Area/pi)�Gas�uptake�by�humus,�acrotelm�or�crops�

Air� Terrestrial�object Prod/(hmix*conc_air_C)�

Where

Speed_wind�=�speed_wind_10�*�log(hveg�/�z0)�/�log(10.0�/�z0)�

The forest module The box-diagram for the forest module is presented in Figure A-2, the parameters included in Table A-3, and the expression used to derive the transfer coefficients in Table A-4.

For each forest object in the landscape model, the soil type can change over time. The soil type decides the values of the soil density, the distribution coefficients, and the concentration of carbon in the soil. The following possible soil types were used to select the proper parameter values for each forest object: Bedrock, Fine till, Washed till, Gyttja, Ancylus clay, Sand or Tillage.

At any given time step, the soil type can be a mix of any of the available soil types. The actual parameter data used was a weighted average over the fractions of each soil type compared to the total forest objects area, using linear interpolation between the time-points (i.e. between every 500th years). Some parameters also have a dependency of the forest type. These parameters are the biomass, the production rate, the density and depth of the humus layer, and the bioturbation rate. The available forest types are: Heath forest, Herb-rich forest, Herb-rich heath forest and Rock forest. The same approach as for the soil types was used, using weighted averages over the fractions of each forest type of the total forest objects area, to obtain the effective value to use for each of the dependent parameters.

211

Figure A-2. Box-diagram with the radionuclide transport paths for the forest module.

212

Table A-3. Parameters used in the forest module. Name�in�model� Parameter name Unit�

BioTurb� Bioturbation�rate kgdw/m2/y�

Biomass_<compartment>� Biomass kgC/m2�CR_<from>_<to>� Concentration�ratios� (Bq/kgdw)�/�(Bq/kgdw)D� Diffusion�coefficient m2/y�DecompRate_Litter� Decomposition�rate�of�litter 1/y�DecompRate_TreeWood� Decomposition�rate�of�tree�wood 1/y�FractRoots_TreeFoliage_Humus� Fraction�of�roots�in�humus [�]�FractRoots_TreeFoliage_SoilMinRooted Fraction�of�roots�in�mineral�soil [�]�FractRoots_TreeWood_Humus� Fraction�of�roots�in�humus [�]�FractRoots_TreeWood_SoilMinRooted� Fraction�of�roots�in�mineral�soil [�]�FractRoots_Understorey_Humus� Fraction�of�roots�in�humus [�]�FractRoots_Understorey_SoilMinRooted Fractions�of�roots�in�mineral�soil [�]�Prod_<compartment> Production kgC/m2/y�Sat_<compartment>� Water�content�in�soil m3/m3�WFlux_From_Humus� Water�flux�to�downstream�object m/y�WFlux_<from>_<to>� Water�flux�between�compartments m/y�conc_Humus_C� Carbon�concentration�in�humus kgC/�kgdw�dens_<compartment> Soil�bulk�density kgdw/m

3�depth_<compartment> Soil�thickness m�gasRelease_C� Soil�to�air�degassing�rate� kgC/m2/y�gasUptake_<compartment>� Foliar�uptake�by�tree�foliage�or�

understory�1/y�

porosity_<compartment>� Porosity m3/m3�Kd_<compartment>� Solid�liquid�distribution�coefficient m3/kgdw�

213

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214

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e A

-4 c

ont’d

. Exp

ress

ions

for t

he tr

ansf

er c

oeffi

cien

ts in

Fig

ure

A-2.

Nam

e�Source�

compa

rtmen

t�Target�

compa

rtmen

t�Tran

sfer�Coe

fficient�[1

/y]�

Uptake_SM

R_Und

erstorey�

Rooted


Und

erstorey

CR_SoilM

inRo

oted

_Und

erstorey*Prod_

Und

erstorey*FractRo

ots_Und

erstorey_SoilM

inRo

oted

/�(den

s_SoilM

inRo

oted

*dep

th_SoilM

inRo

oted

)�Adv_SRM

_Hum

usRo

oted


Hum

usWFlux_SoilM

inRo

oted

_Hum

us/(de

pth_

SoilM

inRo

oted

*porosity

_SoilM

inRo

oted

*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)�Diff_SMR_

Hum

usRo

oted


Hum

us2.0*D/(de

pth_

SoilM

inRo

oted

^2.0*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)

BioT

urb_

SMR_

Hum

us�

Rooted


Hum

usBioTurb

Adv_SMR_

SMI

Rooted


Interm

ediate�

mineral�soil�

WFlux_Soil_Min_R

ootedToSoil_Min_interm/(de

pth_

SoilM

inRo

oted

*porosity

_SoilM

inRo

oted

*�Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)�Diff_SMR_

SMI

Rooted


Interm

ediate�

mineral�soil�

2.0*D/(de

pth_

SoilM

inRo

oted

^2.0*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)

FluxOut_SMR

Rooted


Dow

nstream

WFlux_From_SoilM

inRo

oted

/(de

pth_

SoilM

inRo

oted

*porosity

_SoilM

inRo

oted

*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)�Adv_SMI_SM

RInterm

ediate�

mineral�soil�

Rooted


WFlux_SoilM

inInterm

_SoilM

inRo

oted

/(de

pth_

SoilM

inInterm

*porosity

_SoilM

inInterm

*Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Diff_SMI_SM

RInterm

ediate�

mineral�soil�

Rooted


2.0*D/(de

pth_

SoilM

inInterm

^2.0*Sat_SoilM

inInterm

*R_SoilM

inInterm

)

Adv_SMI_SS

Interm

ediate�

mineral�soil�

Deep�soil

WFlux_SoilM

inInterm

_SoilSat/(de

pth_

SoilM

inInterm

*porosity

_SoilM

inInterm

*Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Diff_SMI_SS

Interm

ediate�

mineral�soil�

Deep�soil

2.0*D/(de

pth_

SoilM

inInterm

^2.0*Sat_SoilM

inInterm

*R_SoilM

inInterm

)

FluxOut_SMI

Interm

ediate�

mineral�soil�

Dow

nstream

WFlux_From_SoilM

inInterm

/(de

pth_

SoilM

inInterm

*porosity

_SoilM

inInterm

*Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Adv_SS_SM

IDeep�soil

Interm

ediate�

mineral�soil�

WFlux_SoilSat_SoilM

inInterm

/(de

pth_

SoilSat*p

orosity

_SoilSat*Sat_SoilSat*R

_SoilSat)

215

Tabl

e A

-4 c

ont’d

. Exp

ress

ions

for t

he tr

ansf

er c

oeffi

cien

ts in

Fig

ure

A-2.

Nam

e�Source�

compa

rtmen

t�Target�

compa

rtmen

t�Tran

sfer�Coe

fficient�[1

/y]�

Diff_SS_SM

IDeep�soil

Interm

ediate�

mineral�soil�

2.0*D/(de

pth_

SoilSat^2.0*Sat_SoilSat*R

_SoilSat)

FluxOut_SS

Deep�soil

Dow

nstream

WFlux_From_SoilSat/(de

pth_

SoilSat*p

orosity


_SoilSat)

GasUptake_TreeFoliage�(I) �

Air�

Tree�Foliage

Biom

ass_TreeFoliage/(�Biomass_Und

erstorey+�Biom

ass_TreeFoliage)*gasU

ptake

GasUptake_Und

erstorey

�(I)�

Air�

Und

erstorey

Biom

ass_Und

erstorey/(�Biomass_Und

erstorey+�Biom

ass_TreeFoliage)*gasU

ptake

whe

re

R_SoilM

inRo

oted

�=�1.0+Kd_

SoilM

inRo

oted

*den

s_SoilM

inRo

oted

/(po

rosity_SoilM

inRo

oted

*Sat_SoilM

inRo

oted

)�R_

SoilSat�=�1.0+K

d_SoilSat*d

ens_SoilSat/(po

rosity_SoilSat*Sat_SoilSat)�

R_Hum

us�=�1.0+K

d_Hum

us*d

ens_Hum

us/(po

rosity_H

umus*Sat_H

umus)�

R_SoilM

inInterm

�=�1.0+Kd_

SoilM

inInterm

*den

s_SoilM

inInterm

/(po

rosity_SoilM

inInterm

*Sat_SoilM

inInterm

)

I H

ere

is a

min

or m

ista

ke in

the

impl

emen

tatio

n. T

he sc

alin

g of

the

folia

r upt

ake

by tr

ee fo

liage

or u

nder

stor

y sh

ould

hav

e be

en b

y bi

omas

s pro

duct

ion,

not

the

biom

ass.

As a

n ex

ampl

e, u

sing

the

valu

es in

Tab

le B

-1, t

he sc

alin

g fa

ctor

s use

d ar

e 96

% a

nd 4

% fo

r tre

e fo

liage

and

und

erst

orey

, res

pect

ivel

y. T

he c

orre

ct fa

ctor

s sh

ould

hav

e be

en 6

4% a

nd 3

6% fo

r tre

e fo

liage

and

und

erst

orey

, res

pect

ivel

y. H

owev

er, t

his h

as in

sign

ifica

nt e

ffec

t on

resu

lting

dos

es, s

ince

thes

e ar

e ca

lcul

ated

from

th

e ac

tivity

con

cent

ratio

ns in

soil.

The

equ

atio

ns w

ill b

e co

rrec

ted

in th

e 20

12 a

sses

smen

t.

216

The aquatic module The box-diagram for the aquatic module is presented in Figure A-3, the parameters included in Table A-5, and the expression used to derive the transfer coefficients in Table A-6.

Figure A-3. Box-diagram with the radionuclide transport paths for the aquatic module.

Table A-5.Parameters used in the aquatic modules. Name�in�the�model� Parameter name Unit�

Area� Object�area m2�Biomass_GreenParts Biomass kgC/m2�CR_GreenParts� Concentration�ratio from�water�to�plants (Bq/kgdw)/(Bq/m

3)�Conc_SS� Suspended�solids�concentration kgdw/m3�D� Diffusion�coefficient m2/y�Kd_<compartment> Solid�liquid�distribution�coefficient m3/kg�Prod_GreenParts� Production kgC/m2/y�ResRate� Resuspension�rate kgdw/m

2/y�Sat_<compartment> Water�content�in�sediment m3/m3�SedRate� Sedimentation�rate�(gross) kgdw/m

2/y�WFluxOut� Water�flux�to�downstream�object m/y�WFlux_<from>_<to> Water�flux�between�compartments m/y�WFlux_From_<compartment>� Water�flux�to�downstream�object m/y�conc_water_DIC� DIC kgC/m3�dens_<compartment>� Soil�bulk�density kgdw/m

3�depth_ActSed� Sediment�thickness m�depth_DeepSed� Sediment�thickness m�depth_InterSed� Sediment�thickness m�depth_Water� Average�water�depth m�porosity_<compartment>� Porosity m3/m3�

217

Tabl

e A

-6. E

xpre

ssio

ns fo

r the

tran

sfer

coe

ffici

ents

in F

igur

e A-

3.

Nam

e�Source�com

partmen

tTarget�com

partmen

tTran

sfer�Coe

fficient�[1

/y]�

Uptake�

Water�

Green

�Parts

For�C�14:�Prod_

Green

Parts/conc_w

ater_D

IC/dep

th_W

ater*R

_Water

All�othe

r�nu

clides:�Prod_

Green

Parts*CR

_Green

Parts/�dep

th_W

ater*R

_Water�

Advection

Water�

Active�Sedimen

t(W

Flux_W

ater_A

ctSed/(dep

th_W

ater*R

_Water))

Sedimen

tation

Water�

Active�Sedimen

t((SedR

ate/de

pth_

Water)*(Kd_

SS/R_W

ater))

FluxOut�

Water�

Dow

nstream

WFluxOut/(Area*de

pth_

Water)

Sene

cense

Green

�Parts�

Active�Sedimen

t(Prod_

Green

Parts/Biom

ass_Green

Parts)

Advection

Active�Sedimen

t�Water

(WFlux_A

ctSed_

Water/(de

pth_

ActSed*

porosity_A

ctSed*Sat_ActSed*

R_ActSed))

Diffusion

Active�Sedimen

t�Water

(2.0*D

/(de

pth_

ActSed^

2.0*Sat_ActSed*

R_ActSed))

Resuspen

sion

Active�Sedimen

t�Water

(ResRa

te/(de

pth_

ActSed*

dens_A

ctSed))

Advection

Active�Sedimen

t�Interm

ediate�Sed

imen

t(W

Flux_A

ctSed_

InterSed

/(de

pth_

ActSed*

porosity_A

ctSed*

Sat_ActSed*

R_ActSed))

Diffusion

Active�Sedimen

t�Interm

ediate�Sed

imen

t(2.0*D

/(de

pth_

ActSed^

2.0*Sat_ActSed*

R_ActSed))

Net�

sedimen

tation

�Active�Sedimen

t�Interm

ediate�Sed

imen

t((SedR

ate�Re

sRate)/dep

th_A

ctSed)

FluxOut�

Active�Sedimen

t�Dow

nstream

WFlux_From_A

ctSed/(dep

th_A

ctSed*

porosity_A

ctSed*

Sat_ActSed*

R_ActSed)

Advection

Interm

ediate�Sed

imen

tActive�Sedimen

t(W

Flux_InterSed_

ActSed/(dep

th_InterSed*

porosity_InterSed*

Sat_InterSed

*R_InterSed))�

Diffusion

Interm

ediate�Sed

imen

tActive�Sedimen

t(2.0*D

/(de

pth_

InterSed

^2.0*Sat_InterSed*

R_InterSed

))Advection

Interm

ediate�Sed

imen

tDeep�Sedimen

t(W

Flux_InterSed_

DeepSed

/(de

pth_

InterSed

*porosity

_InterSed*

Sat_InterSed

*R_InterSed))�

Diffusion

Interm

ediate�Sed

imen

tDeep�Sedimen

t(2.0*D

/(de

pth_

InterSed

^2.0*Sat_InterSed*

R_InterSed

))FluxOut�

Interm

ediate�Sed

imen

tDow

nstream

WFlux_From_IntermSed/(dep

th_InterSed*

porosity_InterSed*

Sat_InterSed

*R_InterSed)

Advection

Deep�Sedimen

t�Interm

ediate�Sed

imen

t(W

Flux_D

eepSed

_InterSed/(dep

th_D

eepSed

*porosity

_DeepSed

*Sat_D

eepSed

*R_D

eepSed

))�

Diffusion

Deep�Sedimen

t�Interm

ediate�Sed

imen

t(2.0*D

/(de

pth_

DeepSed

^2.0*Sat_D

eepSed

*R_D

eepSed

))FluxOut�

Deep�Sedimen

t�Dow

nstream

WFlux_From_D

eepSed

/(de

pth_

DeepSed

*porosity

_DeepSed

*Sat_D

eepSed

*R_D

eepSed

)

Whe

re

R_Water�=�1.0+K

d_SS*C

onc_SS�

R_ActSed�=�1.0+Kd

_ActSed*

dens_A

ctSed/(porosity

_ActSed*

Sat_ActSed)�

R_InterSed

�=�1.0+K

d_InterSed

*den

s_InterSed

/(po

rosity_InterSed*

Sat_InterSed

)�R_

DeepSed

�=�1.0+K

d_DeepSed

*den

s_DeepSed

/(po

rosity_D

eepSed

*Sat_D

eepSed

)

218

The wetland module The box-diagram for the wetland module is presented in Figure A-4, the parameters included in Table A-7, and the expression used to derive the transfer coefficients in Table A-8.

Figure A-4. Box-diagram with the radionuclide transport paths for the wetland module.

219

Table A-7. Parameters used in the wetland module. Name�in�model� Parameter Unit�

Biomass_<compartment>� Biomass kgC/m2�CR_<from>_<to>� Concentration�ratio�from�soil�to�vegetation (Bq/kgdw)/(Bq/m

3)D� Diffusion�coefficient m2/y�DecompRate_<compartment>� Decomposition�rate 1/y�FractRoots_Acrotelm_TreeFoliage� Fraction�of�roots�in�acrotelm [�]�FractRoots_Acrotelm_TreeWood� Fraction�of�roots�in�acrotelm [�]�FractRoots_Catotelm_TreeFoliage� Fraction�of�roots�in�catotelm [�]�FractRoots_Catotelm_TreeWood� Fraction�of�roots�in�catotelm [�]�Kd_<compartment> Solid�liquid�distribution�coefficient m3/kg�Prod_<compartment> Production kgC/m2/y�Sat_<compartment> Water�content�in�soil m3/m3�WFlux_<from>_<to> Water�flux�between�compartments m/y�WFlux_From_<compartment>� Water�flux�to�downstream�object m/y�conc_acrotelm_C� Carbon�concentration�in�acrotelm kgC/m3�dens_<compartment> Soil�bulk�density kgdw/m

3�depth_<compartment>� Soil�thickness m�gasRelease_C� Soil�to�air�degassing�rate� kgC/m2/y�porosity_<compartment>� Porosity m3/m3�

220

Tabl

e A

-8. E

xpre

ssio

ns fo

r the

tran

sfer

coe

ffici

ents

in F

igur

e A-

4 Nam

e�Source�

compa

rtmen

t�Target�

compa

rtmen

t�Tran

sfer�Coe

fficient�[1

/y]�

Sene

sence_TreeFoliage�

Tree�Foliage

Acrotelm

Prod

_Green

Parts/Biom

ass_TreeFoliage

Sene

sence_TreeWoo

d�Tree�W

ood

Dead�Woo

dProd

_TreeW

ood/Biom

ass_TreeWoo

dSene

sence_Und

erstorey�

Und

erstorey

Acrotelm

Prod

_Und

erstorey/Biomass_Und

erstorey

Decom

position

Dead�Woo

dAcrotelm

Decom

pRate_DeadW

ood�

Uptake_Acrotelm_TreeFoliage�

Acrotelm

Tree�Foliage

CR_A

crotelm_TreeFoliage*Prod_

TreeFoliage*FractRo

ots_Acrotelm_TreeFoliage/�

(den

s_Acrotlm

*dep

th_A

crotelm)�

Uptake_Acrotelm_TreeW

ood�

Acrotelm

Tree�W

ood

CR_A

crotelm_TreeW

ood*

Prod

_TreeW

ood*

FractRoo

ts_A

crotelm_TreeW

ood/

(den

s_Acrotelm*d

epth_A

crotelm)�

Uptake_Acrotelm_U

nderstorey�

Acrotelm

Und

erstorey

CR_A

crotelm_TreeFoliage*Prod_

TreeFoliage*FractRo

ots_Acrotelm_TreeFoliage/

(den

s_Acrotelm*d

epth_A

crotelm)�

Adv_A

crotelm_C

atotelm�

Acrotelm

Catotelm

WFlux_A

crotelm_C

atotelm/(de

pth_

Acrotelm*p

orosity

_Acrotelm*Sat_A

crotelm*R

_Acrotelm)�

Diff_A

crotelm_C

atotelm�

Acrotelm

Catotelm

2.0*D/(de

pth_

Acrotelm^2.0*Sat_A

crotelm*R

_Acrotelm)

Decom

position

Acrotelm

Catotelm

Decom

pRate_Acrotelm�

Gaseo

usRe

lease�(*)

Acrotelm

Air

gasRelease_C

/(conc_acrotelm_C

*dep

th_A

crotelm)

FluxOut_�Acrotelm

Acrotelm

Dow

nstream

WFlux_From_A

crotelm/(de

pth_

Acrotelm*p

orosity

_Acrotelm*Sat_A

crotelm*R

_Acrotelm)�

Uptake_Ca

totelm

_TreeFoliage�

Catotelm

Tree�Foliage

CR_C

atotelm_TreeFoliage*Prod_

TreeFoliage*FractRo

ots_Catotelm

_TreeFoliage/

(den

s_Catotelm

*dep

th_C

atotelm)�

Uptake_Ca

totelm

_TreeW

ood�

Catotelm

Tree�W

ood

CR_C

atotelm_TreeW

ood*

Prod

_TreeW

ood*

FractRoo

ts_C

atotelm_TreeW

ood/

(den

s_Catotelm

*dep

th_C

atotelm)�

Uptake_Ca

totelm

_Und

erstorey�

Catotelm

Und

erstorey

CR_C

atotelm_TreeFoliage*Prod_

TreeFoliage*FractRo

ots_Catotelm

_TreeFoliage/

(den

s_Catotelm

*dep

th_C

atotelm)�

Adv_C

atotelm_A

crotelm�

Catotelm

Acrotelm

WFlux_C

atotelm_A

crotelm/(de

pth_

Catotelm

*porosity

_Catotelm*Sat_C

atotelm*R

_Catotelm)�

Diff_�atotelm_A

crotelm�

Catotelm

Acrotelm

2.0*D/(de

pth_

Catotelm

^2.0*Sat_C

atotelm*R

_Catotelm)

Adv_C

atotelm_�SatZon

e�Catotelm

SatZon

eWFlux_C

atotelm_SatZone

/(de

pth_

Catotelm

*porosity

_Catotelm*Sat_C

atotelm*R

_Catotelm)�

Diff_C

atotelm_�SatZon

e�Catotelm

SatZon

e2.0*D/(de

pth_

Catotelm

^2.0*Sat_C

atotelm*R

_Catotelm)

FluxOut_�Ca

totelm

Catotelm

Dow

nstream

WFlux_From_C

atotelm/(de

pth_

Catotelm

*porosity

_Catotelm*Sat_C

atotelm*R

_Catotelm)�

Adv_SatZo

ne_C

atotelm��

SatZon

eCatotelm

WFlux_SatZone

_Catotelm/(de

pth_

SatZon

e*po

rosity_SatZone

*Sat_SatZone

*R_SatZone

)Diff_�SatZon

e_Ca

totelm

��SatZon

eCatotelm

2.0*D/(de

pth_

SatZon

e^2.0*Sat_SatZon

e*R_

SatZon

e)FluxOut_�SatZon

eSatZon

eDow

nstream

WFlux_From_SatZone

/(de

pth_

SatZon

e*po

rosity_SatZone

*Sat_SatZone

*R_SatZone

)

221

whe

re

R_Acrotelm�=�1.0+K

d_Acrotelm*d

ens_Acrotelm/(po

rosity_A

crotelm*Sat_A

crotelm)�

R_Catotelm

�=�1.0+K

d_Catotelm

*den

s_Catotelm

/(po

rosity_C

atotelm*Sat_C

atotelm)�

R_SatZon

e�=�1.0+Kd

_SatZone

*den

s_SatZon

e/(porosity

_SatZone

*Sat_SatZone

)�(*)��

Expression

�is�valid�fo

r�C�14.�For�other�nuclides�a�single�coefficient�(gasRe

lease)�is�used�instead.

222

The cropland module The box-diagram for the cropland module is presented in Figure A-5, the parameters included in Table A-9, and the expression used to derive the transfer coefficients in Table A-10.

Figure A-5. Box-diagram with the radionuclide transport paths for the cropland module.

223

Table A-9. Parameters used in the cropland module. ModelName� Parameter Unit

BioTurb� Bioturbation�rate kgdw/m2/y�

CR_Crops� Concentration�ratio (Bq/kgdw)/(Bq/kgdw)�

hmix� Mixing�height m

D� Diffusion�coefficient m2/y�

Harvested_fraction� Harvested�fraction�of�biomass kgdw/kgdw�

Kd_<compartment>� Solid�liquid�distribution�coefficient m3/kg�

Prod_Crops� Production kgC/m2/y�

Sat_<compartment>� Water�content�in�soil m3/m3�

WFlux_From_<compartment>� Water�flux�to�downstream�object m/y

WFlux_<from>_<to>� Water�flux�between�compartments m/y

conc_SoilMinRooted� Carbon�concentration�in�soil kgC/m3�

conc_air_C� Carbon�concentration�in�air kgC/m3�

dens_<compartment>� Soil�bulk�density kgdw/m3�

depth_<compartment>� Soil�thickness m

gasRelease_C� Soil�to�air�degassing�rate� kgC/m2/y�

porosity_<compartment>� Porosity m3/m3�

release_gas� Soil�to�air�degassing�rate� 1/y

224

Tabl

e A

-10.

Exp

ress

ions

for t

he tr

ansf

er c

oeffi

cien

ts in

Fig

ure

A-5.

Nam

e�Source�com

partmen

tTarget�com

partmen

tTran

sfer�Coe

fficient�[1

/y]

Gaseo

us�uptake

Air�

Crop

sProd

_Crops/(�hmix*con

c_air_C)

Harvesting�remains

Crop

s�Ro

oted

�Mineral�Soil

(1�H

arvested

_fraction)

Harvesting

Crop

s�Used�in�dose�calculations

Harvested

_fraction

Gaseo

us�release

Rooted

�Mineral�Soil

Air

For�C�14:��gasRe

lease_C/(con

c_SoilM

inRo

oted

*dep

th_SoilM

inRo

oted

)Other:��release_gas�

Advection

Rooted

�Mineral�Soil

Interm

ediate�M

ineral�Soil

WFlux_Soil_Min_R

ootedToSoil_Min_interm/(de

pth_

SoilM

inRo

oted

*po

rosity_SoilM

inRo

oted

*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)�Diffusion

Rooted

�Mineral�Soil

Interm

ediate�M

ineral�Soil

2.0*D/(de

pth_

SoilM

inRo

oted

^2.0*Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)Bioturba

tion

Rooted

�Mineral�Soil

Interm

ediate�M

ineral�Soil

BioTurb

FluxOut�

Rooted

�Mineral�Soil

Dow

nstream

WFlux_From_SoilM

inRo

oted

/(de

pth_

SoilM

inRo

oted

*porosity

_SoilM

inRo

oted

*�Sat_SoilM

inRo

oted

*R_SoilM

inRo

oted

)�Uptake�

Rooted

�Mineral�Soil

Crop

sCR

_Crops*Prod_

Crop

s/(den

s_SoilM

inRo

oted

*dep

th_SoilM

inRo

oted

)Advection

Interm

ediate�M

ineral�Soil

Rooted

�Mineral�Soil

WFlux_SoilM

inInterm

_SoilM

inRo

oted

/(de

pth_

SoilM

inInterm

*po

rosity_SoilM

inInterm

*Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Diffusion

Interm

ediate�M

ineral�Soil

Rooted

�Mineral�Soil

2.0*D/(de

pth_

SoilM

inInterm

^2.0*Sat_SoilM

inInterm

*R_SoilM

inInterm

)Bioturba

tion

Interm

ediate�M

ineral�Soil

Rooted

�Mineral�Soil

BioTurb

Advection

Interm

ediate�M

ineral�Soil

Saturated�Soil

WFlux_SoilM

inInterm

_SoilSat/(de

pth_

SoilM

inInterm

*porosity

_SoilM

inInterm

*�Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Diffusion

Interm

ediate�M

ineral�Soil

Saturated�Soil

2.0*D/(de

pth_

SoilM

inInterm

^2.0*Sat_SoilM

inInterm

*R_SoilM

inInterm

)FluxOut�

Interm

ediate�M

ineral�Soil

Dow

nstream

WFlux_From_SoilM

inInterm

/(de

pth_

SoilM

inInterm

*porosity

_SoilM

inInterm

*Sat_SoilM

inInterm

*R_SoilM

inInterm

)�Advection

Saturated�Soil�

Interm

ediate�M

ineral�Soil

WFlux_SoilSat_SoilM

inInterm

/(de

pth_

SoilSat*p

orosity

_SoilSat*Sat_SoilSat*

R_SoilSat)�

Diffusion

Saturated�Soil�

Interm

ediate�M

ineral�Soil

2.0*D/(de

pth_

SoilSat^2.0*Sat_SoilSat*R

_SoilSat)

FluxOut�

Saturated�Soil�

Dow

nstream

WFlux_From_SoilSat/(de

pth_

SoilSat*p

orosity


_SoilSat)

Whe

re

R_SoilM

inRo

oted

�=�1.0+Kd_

SoilM

inRo

oted

*den

s_SoilM

inRo

oted

/(po

rosity_SoilM

inRo

oted

*Sat_SoilM

inRo

oted

)�R_

SoilM

inInterm

��=�1.0+K

d_SoilM

inInterm

*den

s_SoilM

inInterm

/(po

rosity_SoilM

inInterm

*Sat_SoilM

inInterm

)�R_

SoilSat�=�1.0+K

d_SoilSat*d

ens_SoilSat/(po

rosity_SoilSat*Sat_SoilSat)�

225

APPENDIX B – Data for the Mäntykarinjärvi object

All data used for all biosphere objects are documented in the biosphere assessment database; to present them in paper would be very space demanding. In order to illustrate the use of data and how the dynamics of a biosphere object is setup, all object specific data, and how the landscape and terrain evolution is handled mathematically is presented here for one selected biosphere object. The selected object is Mäntykarinjärvi, which is one of the key biosphere objects regarding radiological consequences in the current safety assessment (section 7.4.1 in Hjerpe et al. 2010). First, the involved BSO modules in this object, together with their connecting flow paths of radionuclides will be presented. The next part will show the data used, that is not given elsewhere in this report (i.e. the biosphere object specific data incl. the time-varying data).

The biosphere object Mäntykarinjärvi is of the LSM object type CLWWF, meaning that it consists of 5 separate interlinked BSO modules. These are of the ecosystem types: Forest, wetland (reed-part), wetland (mire-part), lake and coast. Figure B-1 shows the direction of inheritance of radionuclides, due to the different ecosystem sub-objects changing over time. For example as the coast area decreases over time, the inventory of radionuclides corresponding to the “disappeared” area is transferred to the lake part, which is increasing in area. The rate of the inheritance RN transfer is modelled as by taking the areal rate of change, i.e.:

j��/jUg� k

��/ ,

where the area is the area of the shrinking object. The Area is the difference in the area data over a 500 year period, and the Time is the 500 year time-step for which all time-dependent data is used in the model. The same approach is used for all radionuclide inheritances in the biosphere object as indicated in Figure B-1. The rules used for deciding in between which compartments the inherited radionuclide inventory should be copied are:

� Between aquatic objects: o Water, ActSed, IntermSed, and the DeepSed cocompartments are

transferred to the equivalent compartments of the aquatic object increasing in size.

� From an aquatic object to a terrestrial object: o From Water to Humus/Acrotelm, and from ActSed, IntermSed, and

DeepSed to Rooted Mineral Soil/Catotelm.

� Between terrestrial objects: o From a wetlands Acrotelm to Rooted Mineral Soil, and from a wetlands

Catotelm to Intermediate Mineral Soil.

No transfer to a cropland due to RN inheritance were used, since all cropland areas in the model were assumed constant over time. Figure 2 shows the time-evolution of the areas for the biosphere objects ecosystems. Initially only the coastal part exists, until year 3020 at which time a lake with a surrounding reed stand starts to form because of the land uplift. At year 4020 a forest part starts to form around the lake, and 500 years later a mire part starts to appear. All the ecosystem types, except the coast part, remain existing during the whole simulated time-interval.

226

Figure B-1: The radionuclide transport pathways; internal and external inputs and outputs of the Mäntykarinjärvi biosphere object.

Figure B-2: This figure shows the evolution of the ecosystem objects areas over time, in the biosphere object Mäntykarinjärvi.

Air Air Air

ForestWetlandMire

WetlandReed

Lake Coast

RN�release�from�the�geosphere

RN�loss�dueto�wind

RN�flux�out RN�transport RN�inheritanceRN�flux�in

227

Parameter data used in the forest part of the object. The forest soil types and forest types of Mäntykarinjärvi did not change over time. The forest type was heath, and the soil type distribution in the forest object was 35.63 % Bedrock type, 14.94 % Fine till type and 49.43 % Washed till. All other parameter values applied are shown in Tables B-1 to B-3. Table B-1. Constant parameter values used in the forest part of the Mäntykarinjärvi biosphere object.

Parameter� Value Unit

BioTurb� 6.5 kgdw/m2/y�

Biomass_TreeFoliage 2.5 kgC/m2

Biomass_TreeWood 4.6 kgC/m2

Biomass_Understorey 0.11 kgC/m2

D (diffusion�coefficient) 0.0158 m2/yDecompRate_Litter 0.03 1/yDecompRate_TreeWood 0.054 1/yFractRoots_Humus_TreeFoliage 0.59 [�]FractRoots_TreeFoliage_SoilMinRooted 0.41 [�]FractRoots_TreeWood_Humus 0.59 [�]FractRoots_TreeWood_SoilMinRooted 0.41 [�]FractRoots_Understorey_Humus 0.99 [�]FractRoots_Understorey_SoilMinRooted 0.01 [�]Prod_TreeFoliage� 0.17 kgC/m2/y�Prod_TreeWood� 0.2 kgC/m2/y�Prod_Understorey� 0.096 kgC/m2/y�conc_Humus_C� 0.39 kgC/m3

dens_Humus� 160 kgdw/m3

depth_Humus� 0.055 mdepth_SoilMinInterm 0.7 mdepth_SoilMinRooted 0.3 mgasRelease_C� 0.3 kgC/m2/y�porosity_Humus� 0.8 m3/m3

porosity_SoilMinInterm 0.5 m3/m3

porosity_SoilMinRooted 0.6 m3/m3

porosity_SoilSat� 0.4 m3/m3

228

Tabl

e B

-2. T

ime-

depe

nden

t par

amet

er v

alue

s for

the

fore

st p

art o

f the

Män

tyka

rinj

ärvi

bio

sphe

re o

bjec

t. Time�

�[y]�

Area

[m2 ]�

Sat_Hum

us[m

3 /m

3 ]�

Sat_SoilM

inRo

oted

[m3 /m

3 ]�

Sat_SoilM

inInterm

[m3 /m

3 ]�

Sat_SoilSat

[m3 /m

3 ]�

depth_

SoilSat

[m]�

4020�

75700

0.5200131

0.4275912

0.4009032

0.4263913

4.2275002

4520�

81000

0.5200131

0.4275474

0.4008873

0.4263857

4.2275004

5020�

81400

0.5200131

0.4275105

0.4008734

0.4263803

4.2275007

5520�

81800

0.5200131

0.4274882

0.4008642

0.4263774

4.2274999

6020�

81900

0.5200131

0.4274708

0.4008579

0.4263754

4.2275007

6520�

82400

0.5200131

0.4274676

0.4008564

0.4263751

4.2274997

7020�

82800

0.5200131

0.4274743

0.4008588

0.4263757

4.2275005

7520�

82800

0.5200131

0.4274918

0.4008656

0.4263778

4.2274996

8020�

82900

0.5200131

0.4275109

0.4008734

0.4263802

4.2275000

8520�

83100

0.5200131

0.4275450

0.4008859

0.4263852

4.2274997

9020�

83200

0.5200131

0.4275857

0.4009008

0.4263903

4.2300003

9520�

83800

0.5200131

0.4276361

0.4009193

0.4263969

4.2300005

10020�

83800

0.5200131

0.4276900

0.4009395

0.4264036

4.2300002

10520�

84100

0.5200131

0.4277506

0.4009613

0.4264116

4.2300003

11020�

84200

0.5200131

0.4278220

0.4009884

0.4264205

4.2300004

11520�

84200

0.5200131

0.4278996

0.4010161

0.4264310

4.2300002

12020�

84200

0.5200131

0.4279811

0.4010458

0.4264414

4.2324995

12520�

84400

0.5200131

0.4280677

0.4010794

0.4264527

4.2324999

229

Tabl

e B

-3. P

aram

eter

val

ues f

or th

e w

ater

flux

es u

sed

in th

e fo

rest

par

t of t

he M

änty

kari

njär

vi b

iosp

here

obj

ect.

Time�

Year�

�

Wflu

xHum

us�

SMR�

�[m/y]�

Wflu

x�SM

I�SM

R�[m

/y]�

Wflu

xSM

R�SM

I�[m

/y]�

Wflu

xSM

R�Hum

us�

[m/y]�

Wflu

xSM

I�SoilSat�

[m/y]�

Wflu

x�SoilSat�

SMI�

[m/y]�

Wflu

xFrom

�Hum

us�

[m/y]�

Wflu

xFrom

�SM

R�[m

/y]�

Wflu

xFrom

�SM

I�[m

/y]�

Wflu

x�From

�SoilSat�

[m/y]�

4020�

0.3965199

0.0951637�

0.1175587

0.0011544

0.0093173

0.0110749

0.0585166

0.1551240

0.0269861

0.0031550�

4520�

0.3965709

0.0951853�

0.1176931

0.0011545

0.0094088

0.0110422

0.0584627

0.1550328

0.0269734

0.0031222�

5020�

0.3966149

0.0952041�

0.1178093

0.0011546

0.0094886

0.0110148

0.0584162

0.1549542

0.0269625

0.0031060�

5520�

0.3966425

0.0952158�

0.1178821

0.0011546

0.0095383

0.0109974

0.0583871

0.1549049

0.0269558

0.0030897�

6020�

0.3966630

0.0952242�

0.1179359

0.0011547

0.0095759

0.0109852

0.0583654

0.1548683

0.0269508

0.0030896�

6520�

0.3966672

0.0952259�

0.1179467

0.0011547

0.0095835

0.0109827

0.0583611

0.1548609

0.0269498

0.0030896�

7020�

0.3966578

0.0952222�

0.1179222

0.0011547

0.0095652

0.0109874

0.0583709

0.1548776

0.0269521

0.0030732�

7520�

0.3966373

0.0952139�

0.1178684

0.0011546

0.0095275

0.0109996

0.0583926

0.1549142

0.0269571

0.0030733�

8020�

0.3966126

0.0952036�

0.1178034

0.0011546

0.0094822

0.0110144

0.0584186

0.1549582

0.0269631

0.0030732�

8520�

0.3965716

0.0951864�

0.1176952

0.0011545

0.0094067

0.0110391

0.0584620

0.1550315

0.0269733

0.0030730�

9020�

0.3965224

0.0951660�

0.1175656

0.0011544

0.0093165

0.0110688

0.0585139

0.1551195

0.0269855

0.0030730�

9520�

0.3964597

0.0951384�

0.1173990

0.0011543

0.0092009

0.0111062

0.0585802

0.1552316

0.0270011

0.0030565�

10020�

0.3963940

0.0951087�

0.1172238

0.0011541

0.0090805

0.0111459

0.0586496

0.1553490

0.0270173

0.0030564�

10520�

0.3963201

0.0950708�

0.1170223

0.0011540

0.0089451

0.0111906

0.0587276

0.1554812

0.0270357

0.0030564�

11020�

0.3962337

0.0950268�

0.1167873

0.0011538

0.0087870

0.0112428

0.0588189

0.1556357

0.0270573

0.0030560�

11520�

0.3961380

0.0949772�

0.1165259

0.0011536

0.0086117

0.0113006

0.0589200

0.1558069

0.0270811

0.0030394�

12020�

0.3960390

0.0949250�

0.1162555

0.0011534

0.0084318

0.0113609

0.0590246

0.1559838

0.0271057

0.0030391�

12520�

0.3959317

0.0948680�

0.1159621

0.0011531

0.0082375

0.0114267

0.0591379

0.1561756

0.0271322

0.0030390�

230

Parameter data used in the mire part of Mäntykarinjärvi All parameter values applied in the mire part of the Mäntykarinjärvi object are presented in Tables B-4 to B-6. Table B-4. Constant parameter values used in the mire part of the Mäntykarinjärvi biosphere object.

Parameter� Value Unit

Biomass_TreeFoliage 2.4 kgC/m2

Biomass_TreeWood 4.4 kgC/m2

Biomass_Understorey 0.16 kgC/m2

D(diffusion�coefficient) 0.0158 m2/y

DecompRate_Acrotelm 0.0485 1/y

DecompRate_DeadWood 0.0001 1/y

FractRoots_Acrotelm_TreeFoliage 0.38 [�]

FractRoots_Acrotelm_TreeWood 0.38 [�]

FractRoots_Acrotelm_Understorey 0.87 [�]

FractRoots_Catotelm_TreeFoliage 0.62 [�]

FractRoots_Catotelm_TreeWood 0.62 [�]

FractRoots_Catotelm_Understorey 0.13 [�]

Prod_TreeFoliage 0.17 kgC/m2/y

Prod_TreeWood 0.17 kgC/m2/y

Prod_Understorey 0.75 kgC/m2/y

conc_acrotelm_C 0.46 kgC/m3

dens_Acrotelm� 91 kgdw/m3

dens_Catotelm� 91 kgdw/m3

dens_SatZone� 2000 kgdw/m3

depth_Acrotelm 0.045 m

gasRelease_C� 0.3 kgC/m2/y

porosity_Acrotelm 0.95 m3/m3

porosity_Catotelm 0.85 m3/m3

porosity_SatZone 0.8 m3/m3

231

Table B-5. Time-dependent parameter values for the mire part of the Mäntykarinjärvi biosphere object. Time��[y]�

Area�[m2]�

depth_Catotelm�[m]�

depth_SatZone[m]�

Sat_Acrotelm[m3/m3]�

Sat_Catotelm�[m3/m3]�

Sat_SatZone[m3/m3]�

4520� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699635020� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699635520� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699636020� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699636520� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699637020� 12800� 0.3000000� 4.9274994 0.5200132 0.4349490� 0.41699637520� 20700� 0.3000000� 4.9274998 0.5200132 0.4349087� 0.41698778020� 24100� 0.3000000� 4.9275006 0.5200132 0.4348834� 0.41698268520� 24100� 0.3000001� 4.9275010 0.5200132 0.4348648� 0.41697849020� 24100� 0.3000000� 4.9274995 0.5200132 0.4348614� 0.41697789520� 24100� 0.2999999� 4.9275002 0.5200132 0.4348688� 0.416979210020� 24100� 0.3000001� 4.9275005 0.5200132 0.4348872� 0.416983310520� 24100� 0.3000000� 4.9274998 0.5200132 0.4349090� 0.416987711020� 24100� 0.3000000� 4.9274997 0.5200132 0.4349464� 0.416995511520� 24100� 0.3000000� 4.9300003 0.5200132 0.4349900� 0.417003212020� 24100� 0.3000000� 4.9300007 0.5200132 0.4350467� 0.417014212520� 24100� 0.2999999� 4.9300003 0.5200132 0.4351048� 0.4170258

Table B-6. Values for the water fluxes used in the mire part of the Mäntykarinjärvi biosphere object.

Time��[y]�

Wflux�Acrotelm�Catotelm�[m/y]�

Wflux�Catotelm�Acrotelm�[m/y]�

WfluxCatotelm�Satzone�[m/y]�

WfluxSatZone�Catotelm�[m/y]�

WfluxFrom�Acrotelm�[m/y]�

Wflux�From�Catotelm�[m/y]�

Wflux�From�SatZone�[m/y]�

4520� 0.4296443 0.0014457� 0.0896611 0.0916617 0.0992825 0.159937� 0.00297795020� 0.4296941 0.0014458� 0.089772 0.0916638 0.0992254 0.1598427� 0.00296195520� 0.4297256 0.0014459� 0.0898421 0.091665 0.0991891 0.1597829� 0.00294626020� 0.4297485 0.0014459� 0.0898928 0.0916658 0.0991628 0.1597395� 0.00294616520� 0.4297531 0.0014459� 0.0899029 0.0916659 0.0991575 0.1597308� 0.00294617020� 0.4297434 0.0014459� 0.0898816 0.0916656 0.0991687 0.1597492� 0.00293047520� 0.4297205 0.0014459� 0.0898309 0.0916649 0.0991949 0.1597925� 0.00293068020� 0.429693� 0.0014458� 0.0897697 0.0916636 0.0992266 0.1598447� 0.00293068520� 0.4296473 0.0014457� 0.0896681 0.0916618 0.0992791 0.1599314� 0.00293099020� 0.4295924 0.0014455� 0.0895468 0.0916603 0.0993422 0.1600355� 0.00293129520� 0.4295231 0.0014454� 0.0893934 0.0916579 0.0994219 0.1601668� 0.002915610020� 0.4294498 0.0014452� 0.0892305 0.0916548 0.0995061 0.1603057� 0.002915910520� 0.4293672 0.001445� 0.0890464 0.0916503 0.0996011 0.1604616� 0.002916511020� 0.4292707 0.0014448� 0.0888306 0.0916442 0.0997119 0.1606438� 0.002916911520� 0.4291647 0.0014445� 0.0885947 0.091639 0.0998338 0.1608441� 0.002901712020� 0.4290544 0.0014442� 0.0883499 0.0916339 0.0999605 0.1610523� 0.002902112520� 0.428935� 0.0014439� 0.0880842 0.091628 0.1000977 0.1612779� 0.0029023

232

Parameter data used in the reed part of Mäntykarinjärvi All parameter values applied in the mire part of the Mäntykarinjärvi object are presented in Tables B-7 to B-9. Table B-7. Constant parameter values used in the reed part of the Mäntykarinjärvi biosphere object. Parameter� Value Unit Parameter Value� Unit�

Biomass_TreeFoliage 2.4 kgC/m2 Prod_TreeFoliage 0.17� kgC/m2/y

Biomass_TreeWood� 4.4 kgC/m2 Prod_TreeWood 0.17� kgC/m2/y

Biomass_Understorey 0.16 kgC/m2 Prod_Understorey 0.75� kgC/m2/y

D� 0.0158 m2/y conc_acrotelm_C 0.46� kgC/m3

DecompRate_Acrotelm� 0.0485 1/y dens_Acrotelm 91� kgdw/m3

DecompRate_DeadWood� 0.0001 1/y dens_Catotelm 91� kgdw/m3

FractRoots_Acrotelm_TreeFoliage� 0.38 [�] dens_SatZone 2000� kgdw/m3

FractRoots_Acrotelm_TreeWood� 0.38 [�] depth_Acrotelm 0.045� m�

FractRoots_Acrotelm_Understorey� 0.87 [�] gasRelease_C 0.3� kgC/m2/y

FractRoots_Catotelm_TreeFoliage� 0.62 [�] porosity_Acrotelm 0.95� m3/m3�

FractRoots_Catotelm_TreeWood� 0.62 [�] porosity_Catotelm 0.85� m3/m3�

FractRoots_Catotelm_Understorey� 0.13 [�] porosity_SatZone 0.8� m3/m3�

Table B-8. Time-dependent parameter values for the reed part of the Mäntykarinjärvi biosphere object. Time��[y]�

Area�[m2]�

depth_Catotelm�[m]�

depth_SatZone[m]�

Sat_Acrotelm[m3/m3]�

Sat_Catotelm�[m3/m3]�

Sat_SatZone[m3/m3]�

3520� 258760� 0.7� 4.22 0.43 0.43� 0.43�4020� 232650� 0.7� 4.2275 0.43 0.43� 0.43�4520� 232850� 0.7� 4.2275 0.43 0.43� 0.43�5020� 231150� 0.7� 4.2275 0.43 0.43� 0.43�5520� 231550� 0.7� 4.2275 0.43 0.43� 0.43�6020� 229849� 0.7� 4.2275 0.43 0.43� 0.43�6520� 227149� 0.7� 4.2275 0.43 0.43� 0.43�7020� 227449� 0.7� 4.2275 0.43 0.43� 0.43�7520� 227049� 0.7� 4.2275 0.43 0.43� 0.43�8020� 227249� 0.7� 4.2275 0.43 0.43� 0.43�8520� 226749� 0.7� 4.2275 0.43 0.43� 0.43�9020� 224616� 0.7� 4.23 0.43 0.43� 0.43�9520� 224016� 0.7� 4.23 0.43 0.43� 0.43�10020� 221649� 0.7� 4.23 0.43 0.43� 0.43�10520� 219149� 0.7� 4.23 0.43 0.43� 0.43�11020� 218549� 0.7� 4.23 0.43 0.43� 0.43�11520� 218449� 0.7� 4.23 0.43 0.43� 0.43�12020� 211716� 0.7� 4.2325 0.43 0.43� 0.43�12520� 211716� 0.7� 4.2325 0.43 0.43� 0.43�

233

Table B-9. Values for the water fluxes used in the reed part of the Mäntykarinjärvi biosphere object.

Time��[y]�

Wflux�Acrotelm�Catotelm�[m/y]�

Wflux�Catotelm�Acrotelm�[m/y]�

WfluxCatotelmSatzone�[m/y]�

WfluxSatZone�Catotelm�[m/y]�

WfluxFrom�Acrotelm[m/y]�

Wflux�From�Catotelm�[m/y]�

Wflux�From�SatZone�[m/y]�

3520� 0� 0.0032604� 0 0.0030061 0 0� 0�

4020� 0� 0.0030943� 0 0.0028402 0 0� 0�

4520� 0� 0.0055572� 0 0.002723 0 0� 0�

5020� 0� 0.0071132� 0 0.0026352 0 0� 0�

5520� 0� 0.0077585� 0 0.0025766 0 0� 0�

6020� 0� 0.0077217� 0 0.0025474 0 0� 0�

6520� 0� 0.0076801� 0 0.0025376 0 0� 0�

7020� 0� 0.0076653� 0 0.0025474 0 0� 0�

7520� 0� 0.0076957� 0 0.0025766 0 0� 0�

8020� 0� 0.0077397� 0 0.0026254 0 0� 0�

8520� 0� 0.0077881� 0 0.002684 0 0� 0�

9020� 0� 0.0078726� 0 0.0027718 0 0� 0�

9520� 0� 0.0079372� 0 0.0028694 0 0� 0�

10020� 0� 0.008048� 0 0.0029768 0 0� 0�

10520� 0� 0.0081608� 0 0.0031037 0 0� 0�

11020� 0� 0.008306� 0 0.0032501 0 0� 0�

11520� 0� 0.0084675� 0 0.0034062 0 0� 0�

12020� 0� 0.0086389� 0 0.0035722 0 0� 0�

12520� 0� 0.0088183� 0 0.0037576 0 0� 0�

234

Parameter data used in the lake part of the Mäntykarinjärvi object. All parameter values applied in the lake part of the Mäntykarinjärvi object are presented in Tables B-10 to B-12.

Table B-10. Constant parameter values used in the lake part of the Mäntykarinjärvi biosphere object.

Parameter� Value� Unit Parameter Value� Unit�

Biomass_GreenParts� 0.023� kgC/m2 dens_InterSed 208� kgdw/m3�

D�(diffusion�coefficient)� 0.0158� m2/y depth_ActSed 0.3� m�

Prod_GreenParts 0.029� kgC/m2/y depth_InterSed 0.7� m�

ResRate� 0.8� kgdw/m2/y porosity_ActSed 0.9� m3/m3�

SedRate� 1.1� kgdw/m2/y porosity_DeepSed 0.8� m3/m3�

conc_SS� 0.004� kgdw/m3 porosity_InterSed 0.8� m3/m3�

conc_Water_DIC 0.003� kgdw/m3 RetTime 0.1� y�

dens_ActSed� 142� kgdw/m3 lakeDepth 4� m�

dens_DeepSed� 146� kgdw/m3 �

Table B-11. Time-dependent parameter values for the lake part of the Mäntykarinjärvi biosphere object.

Time��[y]�

Area�[m2]�

SatActSed�[m3/m3]�

SatIntermSed[m3/m3]�

SatDeepSed[m3/m3]�

DepthDeepSed

[m]�

Depth�Water�[m]��

3520� 376060� 0.43 0.43 0.43 4.22 0.54�

4020� 301050� 0.43 0.43 0.43 4.228 0.54�

4520� 298150� 0.43 0.43 0.43 4.228 0.54�

5020� 298350� 0.43 0.43 0.43 4.228 0.54�

5520� 298350� 0.43 0.43 0.43 4.228 0.54�

6020� 298449� 0.43 0.43 0.43 4.228 0.54�

6520� 298249� 0.43 0.43 0.43 4.228 0.54�

7020� 298549� 0.43 0.43 0.43 4.228 0.54�

7520� 298849� 0.43 0.43 0.43 4.228 0.54�

8020� 298949� 0.43 0.43 0.43 4.228 0.54�

8520� 299049� 0.43 0.43 0.43 4.228 0.54�

9020� 299116� 0.43 0.43 0.43 4.23 0.54�

9520� 298716� 0.43 0.43 0.43 4.23 0.54�

10020� 299449� 0.43 0.43 0.43 4.23 0.54�

10520� 299649� 0.43 0.43 0.43 4.23 0.54�

11020� 299749� 0.43 0.43 0.43 4.23 0.54�

11520� 299849� 0.43 0.43 0.43 4.23 0.54�

12020� 300149� 0.43 0.43 0.43 4.232 0.54�

12520� 300349� 0.43 0.43 0.43 4.232 0.54�

235

Tabl

e B

-12.

Val

ues f

or th

e w

ater

flux

es u

sed

in th

e la

ke p

art o

f the

Män

tyka

rinj

ärvi

bio

sphe

re o

bjec

t. Time�

�[y]�

Wflu

x�Water�

ActSed�

[m/y]�

Wflu

xActSed�

Water�

[m/y]�

Wflu

xActSed�

Interm

Sed�

[m/y]�

Wflu

xInterm

Sed�

ActSed�

[m/y]�

Wflu

x�Interm

Sed�

DeepSed

�[m

/y]�

Wflu

xDeepSed

�Interm

Sed�

[m/y]�

Wflu

xIn

[m3/y]�

Wflu

xOut

[m3/y]�

3520�

0�0.0033112

00.0031494

0�0.0030800

399490814

399591798

4020�

0�0.0190050

00.0058128

0�0.0029100

399509749

399591798

4520�

0�0.0167713

00.0048669

0�0.0027900

399512904

399591798

5020�

0�0.0153189

00.0043433

0�0.0027000

399512904

399591798

5520�

0�0.0146708

00.0041173

0�0.0026400

399512904

399591798

6020�

0�0.0146431

00.0040884

0�0.0026100

399512904

399591798

6520�

0�0.0146585

00.0040855

0�0.0026000

399538151

399620200

7020�

0�0.0146902

00.0041012

0�0.0026100

399538151

399620200

7520�

0�0.0147230

00.0041314

0�0.0026400

399538151

399620200

8020�

0�0.0147815

00.0041831

0�0.0026900

399538151

399620200

8520�

0�0.0148573

00.0042465

0�0.0027500

399538151

399620200

9020�

0�0.0149593

00.0043385

0�0.0028400

399538151

399617045

9520�

0�0.0150983

00.0044477

0�0.0029400

399699094

399781144

10020�

0�0.0152173

00.0045585

0�0.0030500

399777988

399860038

10520�

0�0.0153725

00.0046936

0�0.0031800

399771677

399853727

11020�

0�0.0155395

00.0048461

0�0.0033300

399771677

399853727

11520�

0�0.0157129

00.0050073

0�0.0034900

399771677

399853727

12020�

0�0.0158968

00.0051786

0�0.0036600

399771677

399853727

12520�

0�0.0161117

00.0053728

0�0.0038500

399771677

399853727

236

Parameter data used in the coast part of the object. All parameter values applied in the coast part of the Mäntykarinjärvi object are presented in Tables B-13 to B-15.

Table B-13. Constant parameter values used in the coast part of the Mäntykarinjärvi biosphere object.

Parameter Value Unit

Biomass_GreenParts 0.027 kgC/m2

D�(diffusion�coefficient) 0.0158 m2/y

Prod_GreenParts 0.033 kgC/m2/y

ResRate� 1.3 kgdw/m2/y

SedRate� 3.1 kgdw/m2/y

conc_SS� 0.003 kgdw/m3

conc_Water_DIC 0.013 kgdw/m3

dens_ActSed 142 kgdw/m3

dens_DeepSed 146 kgdw/m3

dens_InterSed 208 kgdw/m3

depth_ActSed 0.3 m

depth_InterSed 0.7 m

porosity_ActSed 0.9 m3/m3

porosity_DeepSed 0.8 m3/m3

porosity_InterSed 0.8 m3/m3

Table B-14. Time-dependent parameter values for the coast part of the Mäntykarinjärvi biosphere object.

Time��[y]�

Area�[m2]�

SatActSed�[m3/m3]�

SatIntermSed�[m3/m3]�

SatDeepSed[m3/m3]�

DepthDeepSed

[m]�

Depth�Water�[m]��

2020� 2672700� 0.43 0.43 0.43 4.22 5.1753�

2520� 2351300� 0.43 0.43 0.43 4.22 2.8284�

3020� 1317800� 0.43 0.43 0.43 4.22 1.2844�

Table B-15. Values for the water fluxes used in the coast part of the Mäntykarinjärvi biosphere object.

Time��[y]�

Wflux�Water�ActSed�[m/y]�

Wflux�ActSed�Water�[m/y]�

WfluxActSed�

IntermSed[m/y]�

WfluxIntermSedActSed�[m/y]�

WfluxIntermSedDeepSed�[m/y]�

Wflux�DeepSed�IntermSed�[m/y]�

WfluxOut�[m3/y]�

2020� 0� 0.001927� 0 0.001866 0 0.00184� 399591798�

2520� 0� 0.002363� 0 0.002298 0 0.00227� 399591798�

3020� 0� 0.002833� 0 0.002747 0 0.00271� 399591798�

237

Parameter data used in the air part of the biosphere object. All parameter values applied in the coast part of the Mäntykarinjärvi object are presented in Table B-16.

Table B-16. Constant parameter values used in air part of the forest, mire and reed objects.

Parameter� Value Value Value Unit� Forest Mire Reedhmix� 20 10 10 mconc_air_C� 0.000207 0.000207 0.000207 kgC/m3�Hveg� 2 1 1 mspeed_wind_20� 4.1 4.1 4.1 m/sz0 1 0.25 0.25 m

radionuclide transport and dose assessment modelling in … · 2011-12-08 · radionuclide...

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