Safety Case for the Disposal of Spent Nuclear Fuel at ... · EXAFS extended x-ray absorption fine...

$: Safety Case for the Disposal of Spent Nuclear Fuel at ... · EXAFS extended x-ray absorption fine structure f fraction FES frayed edge sites Fsorb fraction of sorption-available species$
POSIVA 2012-40

February 2014

POSIVA OY

Olki luoto

FI-27160 EURAJOKI, F INLAND

Phone (02) 8372 31 (nat. ) , (+358-2-) 8372 31 ( int. )

Fax (02) 8372 3809 (nat. ) , (+358-2-) 8372 3809 ( int. )

Paul Wersin, Mir jam Kiczka,

Dominic Rosch

Gruner AG, Switzer land

Michael Ochs, David Trudel

BMG Engineer ing Ltd, Switzer land

Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto

Radionuclide Solubility Limits andMigration Parameters for the Backfill

ISBN 978-951-652-220-6ISSN 1239-3096

Tekijä(t) – Author(s)

Paul Wersin, Mirjam Kiczka, Dominic Rosch Gruner AG, Switzerland Michael Ochs, David Trudel BMG Engineering Ltd, Switzerland

Toimeksiantaja(t) – Commissioned by

Posiva Oy

Nimeke – Title

SAFETY CASE FOR THE DISPOSAL OF SPENT NUCLEAR FUEL AT OLKILUOTO: RADIONUCLIDE SOLUBILITY LIMITS AND MIGRATION PARAMETERS FOR THE BACKFILL Tiivistelmä – Abstract

This report presents a geochemical database for the backfill considered at the Olkiluoto site. It includes solubility limits, diffusion and sorption data for radionuclides to be used in safety assessment.

The geochemical conditions assumed in the modelling are based on the six reference and bounding groundwaters defined and presented in Wersin et al. (2014). Based on these groundwaters, backfill porewaters were modelled for an averaged reference backfill consisting of compacted Friedland clay blocks (tunnel), Milos granules (foundation bed) and Milos pellets (wall, roof). Additional porewater calculations for individual and alternative backfill materials were performed and compared with the reference case. All geochemical calculations were performed using the PHREEQC code and the thermodynamic database Thermochimie v.7b developed by Andra, which builds on well established thermodynamic data, as recommended for example by the NEA database.

Radionuclide (RN) solubilities were calculated for the expected reference and bounding conditions in the reference backfill case. Reference solubility values are provided based on the calculated solubilities for the two reference waters. For each RN, an upper solubility limit is recommended considering the formal uncertainty (calculated from the thermodynamic uncertainties of the solid phase and the dominant solution species) and the geochemical uncertainty (given by the bounding water compositions). The derived solubilities for the backfill are compared with the parameters derived for the buffer and major differences are discussed.

In-situ effective diffusivities of neutral species and cations not sorbing via ion exchange are based on a compilation of HTO diffusion data in literature and their extrapolation to in-situ dry density. Effective diffusivities of anions and cations sorbing via ion exchange are derived by scaling the respective effective diffusivities for the buffer presented by Wersin et al. (2014) and the relative effective diffusivities of HTO in buffer and backfill. Diffusion-available porosities of neutral species and cations were assumed to equal the total porosity. Diffusion available porosities for anions were calculated using the relation of neutral to anion available diffusivity from the buffer (Wersin et al. 2014).

In-situ sorption values were mainly derived using the empirical approach of Bradbury & Baeyens (1997a, 2003) and Ochs & Talerico (2004), which was also applied in Wersin et al. (2014). If available, high quality batch sorption data for montmorillonite and illite were transferred to in-situ conditions using conversion factors for the different porewater chemistry. In case of lacking suitable and reliable experimental data, analogue considerations or expert judgment had to be applied. Sorption values for cations sorbing via cation exchange were modelled with a sorption model. Uncertainties were calculated as the product of the individual uncertainties associated with each conversion factor or the model. A best estimate and upper and lower limit Kd value for each porewater are recommended.

Avainsanat - Keywords

Geochemical database, porewater composition, Radionuclide (RN) solubilities, RN diffusion data, RN sorption data, backfill ISBN

ISBN 978-951-652-220-6 ISSN

ISSN 1239-3096 Sivumäärä – Number of pages

166 Kieli – Language

English

Posiva-raportti – Posiva Report Posiva Oy Olkiluoto FI-27160 EURAJOKI, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)

Raportin tunnus – Report code

POSIVA 2012-40

Julkaisuaika – Date

February 2014

Tekijä(t) – Author(s)

Paul Wersin, Mirjam Kiczka, Dominic Rosch Gruner AG, Switzerland Michael Ochs, David Trudel BMG Engineering Ltd, Switzerland

Toimeksiantaja(t) – Commissioned by

Posiva Oy

Nimeke – Title

TURVALLISUUSPERUSTELU KÄYTETYN POLTTOAINEEN LOPPUSIJOITUKSESTA OLKILUOTOON: RADIONUKLIDIEN KULKEUTUMISEN PARAMETRIT TÄYTEAINEESSA Tiivistelmä – Abstract

Tässä raportissa esitetään radionuklidien liukoisuusrajat sekä diffuusio- ja sorptiokertoimet täytölle. Näitä tietoja käytetään Olkiluotoon rakennettavan käytetyn polttoaineen loppusijoitustilan pitkäaikais-turvallisuusanalyysissä. Mallinnuksessa on oletettu suolaisen veden ja murtoveden kuvaavan eri ajanjaksoina vallitsevia pohjavesiolosuhteita. Sen lisäksi on häiriintyneinä olosuhteina huomioitu laimea karbonaattipitoinen vesi, erittäin suolainen vesi, korkean pH:n vesi ja jäätikön sulamisvesi (Grimsel). Keskiarvo referenssitäytölle, joka koostuu puristetuista Friedland-savi lohkoista (tunneli) Milos granuleista (lattian tasaus) ja Milos pelleteistä (seinät, katto), on mallinnettu näitä vesiä vastaavat huokosveden koostumukset, jotka on laskettu termodynaamisella mallilla. Kaikissa geokemiallisissa laskuissa käytettiin PHREEQC koodia ja Andran kehittämää Thermochimie v.7b termodynaamista tietokantaa. Tämä tietokanta perustuu vakiintuneeseen termodynaamiseen dataan, kuten esimerkiksi NEA TDB -tietokannassa suositellaan. Radionuklidien (RN) liukoisuudet laskettiin oletetuissa vallitsevissa ja häiriintyneissä pohjavesiolosuhteissa referenssitäyttöratkaisulle. Referenssiliukoisuusarvot perustuvat suolaiselle vedelle ja murtovedelle laskettuihin liukoisuuksiin. Jokaiselle nuklidille annettiin suositus liukoisuuden ylärajasta ottaen huomioon termo-dynaamiseen tietokantaan ja pohjavesien koostumukseen liittyvät epävarmuudet. In-situ efektiiviset diffuusiokertoimet neutraaleille nuklideille ja kationeille, jotka eivät sorboidu ioninvaihtomekanismilla perustuvat kirjallisuudesta koottuun HTO diffuusiodataan ja sen ekstrapoloimiseen in-situ kuivatiheyteen. Efektiiviset diffuusiokertoimet anioneille ja ioninvaihtomekanismilla sorboituville kationeille on saatu skaalaamalla vastaavista puskurin arvoista, jotka on esitetty Wersin et al. (2014) raportissa ja suhteelliselle efektiiviselle diffusiviteetille perustuen puskurin ja täytön HTO:n. Diffuusiohuokoisuus oletettiin neutraaleille molekyyleille ja kationeille samaksi kuin kokonaishuokoisuus. Anionien diffuusiokertoimet ja diffuusiohuokoisuudet määritettiin puskurin arvoista. In-situ sorptioarvot määritettiin käyttäen Bradburyn & Baeyensin (2003) empiiristä ratkaisutapaa ja huomioiden Ochsin & Talericon (2004) ehdottamat muutokset, jota käytettiin myös puskurin arvoja määritettäessä (Wersin et al. 2014). Jos korkealaatuista kokeellista tietoa oli saatavilla, se muunnettiin in-situ olosuhteita vastaavaksi konversiokertoimien avulla ottaen huomioon eroavuudet minerologiassa ja huokosveden kemiassa (pH ja RN spesiaatio). Jos soveltuvaa ja luotettavaa kokeellista tietoa ei ollut saatavilla, käytettiin hyväksi analogioita ja asiantuntija-arviointia. Kationinvaihtomekanismilla sorboituville kationeille mallinnettiin sorptioarvot. Epävarmuudet laskettiin yksittäisen epävarmuuden ja konversiokertoimen malliin liittyvien epävarmuuksien tulona. Kullekin vesityypille annetaan suositeltu sorptiokerroin sekä sen ylä- ja alaraja. Avainsanat - Keywords

Geokemiallinen tietokanta, huokosvesikoostumus, radionuklidien (RN) liukoisuus, RN diffuusio, RN sorptio, täyteaine. ISBN

ISBN 978-951-652-220-6 ISSN

ISSN 1239-3096 Sivumäärä – Number of pages

166 Kieli – Language

Englanti

Posiva-raportti – Posiva Report Posiva Oy Olkiluoto FI-27160 EURAJOKI, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)

Raportin tunnus – Report code

POSIVA 2012-40

Julkaisuaika – Date

Helmikuu 2014

1

TABLE OF CONTENTS ABSTRACT TIIVISTELMÄ

ABBREVIATIONS ........................................................................................................... 5

1 INTRODUCTION .................................................................................................... 7

1.1 Defining the geochemical system ................................................................. 8

1.1.1 Backfill material ................................................................................. 8

1.1.2 Geochemical model ........................................................................ 10

1.1.3 Modelled backfill porewater compositions ....................................... 13

1.1.4 Bulk properties of the backfill .......................................................... 19

1.2 Radionuclides of interest ............................................................................. 21

1.3 Thermodynamic database ........................................................................... 21

PART I - RADIONUCLIDE SOLUBILITIES ................................................................... 23

2 DERIVATION OF SOLUBILITY DATA .................................................................. 25

2.1 Background ................................................................................................. 25

2.2 Method ........................................................................................................ 25

2.3 Treatment of uncertainties .......................................................................... 26

2.4 Recommendation of "reference values" and "upper limit" ........................... 28

3 SOLUBILITY DATA............................................................................................... 29

3.1 Solubility of actinides ................................................................................... 29

3.1.1 Thorium (Th) ................................................................................... 29

3.1.2 Protactinium (Pa) ............................................................................ 30

3.1.3 Uranium (U) .................................................................................... 31

3.1.4 Neptunium (Np) ............................................................................... 32

3.1.5 Plutonium (Pu) ................................................................................ 33

3.1.6 Americium (Am) and Curium (Cm) .................................................. 34

3.2 Solubilities of the groups IA to VIIA ............................................................. 35

3.2.1 Carbon (C) ...................................................................................... 35

3.2.2 Radium (Ra) .................................................................................... 36

3.2.3 Caesium (Cs) .................................................................................. 36

3.2.4 Strontium (Sr) .................................................................................. 36

3.2.5 Selenium (Se) ................................................................................. 37

3.2.6 Tin (Sn) ........................................................................................... 38

3.2.7 Beryllium (Be) ................................................................................. 39

3.2.8 Iodine (I) .......................................................................................... 40

3.2.9 Chlorine (Cl) .................................................................................... 40

2

3.3 Solubilities of the transition metals .............................................................. 40

3.3.1 Zirconium (Zr) ................................................................................. 40

3.3.2 Nickel (Ni) ....................................................................................... 41

3.3.3 Niobium (Nb) ................................................................................... 42

3.3.4 Molybdenum (Mo) ........................................................................... 43

3.3.5 Technetium (Tc) .............................................................................. 44

3.3.6 Palladium (Pd) ................................................................................ 44

3.3.7 Silver (Ag) ....................................................................................... 45

3.4 Solubilities of the lanthanides ...................................................................... 46

3.4.1 Samarium (Sm) ............................................................................... 46

3.4.2 Europium (Eu) ................................................................................. 47

4 DISCUSSION OF SOLUBILITY DATA ................................................................. 49

4.1 Comparison with the other near-field solubilities ......................................... 49

4.2 Concluding remarks .................................................................................... 53

PART II - RADIONUCLIDE DIFFUSION AND SORPTION .......................................... 55

5 BACKGROUND ON RADIONUCLIDE MIGRATION ............................................ 57

5.1 Concepts and fundamental relations ........................................................... 57

5.2 Radionuclide diffusion: model concepts ...................................................... 57

6 RADIONUCLIDE DIFFUSION DATA .................................................................... 61

7 RADIONUCLIDE SORPTION IN COMPACTED CLAYS ...................................... 67

7.1 Sorption processes ..................................................................................... 67

7.2 Derivation of sorption data .......................................................................... 68

7.2.1 Selection of source data ................................................................. 69

7.2.2 Conversion factors .......................................................................... 70

7.2.3 Treatment of uncertainties .............................................................. 72

8 RADIONUCLIDE SORPTION DATA .................................................................... 75

8.1 Sorption values of actinides ........................................................................ 75

8.1.1 Thorium (Th) ................................................................................... 75

8.1.2 Protactinium (Pa) ............................................................................ 76

8.1.3 Uranium (U) .................................................................................... 77

8.1.4 Neptunium (Np) ............................................................................... 80

8.1.5 Plutonium (Pu) ................................................................................ 80

8.1.6 Americium (Am) and Curium (Cm) .................................................. 81

8.2 Sorption values of the groups IA to VIIA ..................................................... 83

8.2.1 Carbon (C) ...................................................................................... 83

8.2.2 Radium (Ra) and Strontium (Sr) ..................................................... 84

8.2.3 Caesium (Cs) .................................................................................. 84

3

8.2.4 Selenium (Se) ................................................................................. 85

8.2.5 Tin (Sn) ........................................................................................... 85

8.2.6 Beryllium (Be) ................................................................................. 86

8.2.7 Iodine (I) and Chlorine (Cl) .............................................................. 87

8.3 Sorption values of the transition metals ...................................................... 87

8.3.1 Zirconium (Zr) ................................................................................. 87

8.3.2 Nickel (Ni) ....................................................................................... 88

8.3.3 Niobium (Nb) ................................................................................... 89

8.3.4 Molybdenum (Mo) ........................................................................... 89

8.3.5 Technetium (Tc) .............................................................................. 91

8.3.6 Palladium (Pd) ................................................................................ 91

8.3.7 Silver (Ag) ....................................................................................... 92

8.4 Sorption values of the lanthanides .............................................................. 92

8.4.1 Europium (Eu) ................................................................................. 92

8.4.2 Samarium (Sm) ............................................................................... 93

9 DISCUSSION AND SUMMARY OF SORPTION DATA ....................................... 95

REFERENCES ........................................................................................................... 101

APPENDIX A: PARAMETERS OF THE GEOCHEMICAL SYSTEM .......................... 113

A.1 Groundwater compositions ........................................................................... 113

A.2 Properties of different backfill materials and MX-80 ..................................... 114

A.3 Alternative deposition tunnel backfill design of SKB .................................... 119

APPENDIX B: SORPTION DATA SHEETS ............................................................... 125

4

5

ABBREVIATIONS AC200 Na activated bentonite pellets from Milos

Andra French agency for the disposal of radioactive waste (Agence Nationale pour la Gestion des Déchets Radioactifs)

BET surface area

surface area determined by gas (N2) adsorption (developed by Brunauer-Emmett-Teller)

C concentration CEC cation exchange capacity CF conversion factor

CFmin conversion factor mineralogy

Da apparent diffusion coefficient (m2 s-1) DDL diffuse double layer

De effective diffusion coefficient (m2 s-1)

DIC dissolved inorganic carbon

DLVO theory theory for the stability of colloidal systems developed by Derjaguin, Landau, Verwey, Overbeek

EBS engineered barrier system EMDD effective montmorillonite dry density EXAFS extended x-ray absorption fine structure f fraction FES frayed edge sites

Fsorb fraction of sorption-available species

HLW high level waste HTO tritiated water IL interlayer JAEA Japan Atomic Energy Agency K hydraulic conductivity

KBS-3V multi barrier disposal concept developed by SKB with vertical emplacement of canisters in deposition holes

Kd mass distribution ratio between dissolved and sorbed species (sorption coefficient)

KR4/81/1 Borehole KR4, sampling depth 81 m (sample 1). KR4/861/1 Borehole KR4, sampling depth 861 m (sample 1). KR6/135/8 Borehole KR6, sampling depth 135 m (sample 8). KR20/465/1 Borehole KR20, sampling depth 465 m (sample 1). MINTEQ v.4 thermodynamic database available in PHREEQC MX-80 bentonite of Wyoming

Nagra Swiss agency for the disposal of radioactive waste (Nationale Genossenschaft für die Lagerung radioaktiver Abfälle)

NEA Nuclear Energy Agency NF Near field

6

PA performance assessment

pCO2 CO2 partial pressure

PHREEQC geochemical modelling program

Ps swelling pressure

PSI Paul Scherrer Institute RN radionuclide S/L ratio solid-liquid ratio SA safety assessment

SAz-1 montmorillonite from the Clay Minerals Society's Source Clay Repository

SIT Specific Interaction Theory

SKB Swedish Nuclear Fuel and Waste Management Co (Svensk Kärnbränslehantering)

SR-Can safety assessment of SKB

SWy1 montmorillonite from the Clay Minerals Society's Source Clay Repository

TDB thermodynamic database TDS total dissolved solids TOT tetrahedral-octahedral-tetrahedral layer of a clay mineral UF uncertainty factor

Wm water content

Xmont montmorillonite content

ε diffusion available porosity

ρacc grain density of accessory minerals

ρd or ρdry dry density (kg m3)

ρgrain grain density

ρs solid or grain density

7

1 INTRODUCTION

Safety assessment (SA) calculations depend fundamentally on the quality of the input parameters. Among these, radionuclide (RN) solubility, diffusion and sorption values are key parameters and are referred to as geochemical database. RN solubilities and sorption values are a direct function of the geochemical conditions, such as Eh, pH and solution composition. Sorption values and diffusivities further depend on the properties of the solid material. Consequently, the geochemical database is site specific and depends on the considered repository design as well as the expected evolution of groundwater compositions. The final disposal concept for spent nuclear fuel at the repository at Olkiluoto, Southwestern Finland, the KBS-3V design, is based on the use of multiple release barriers. The fuel is packed in copper canisters and embedded in vertical deposition holes filled with compacted bentonite. Once the canisters and buffer material are emplaced in the deposition holes, the deposition tunnels are backfilled with clay-based materials. The final barrier is provided by the crystalline bedrock. Recently, Wersin et al. (2014) presented an updated geochemical database for the canister and buffer solubility limits and migration parameters for the spent nuclear fuel repository to be constructed at Olkiluoto. It included RN solubilites for water inside a defective canister, bentonite porewater and water at the bentonite/host rock interface. RN diffusion and sorption values were derived for the bentonite buffer system, assuming MX-80 as reference buffer material. In the same context, Hakanen et al. (2014) compiled a geochemical database for the far-field, which includes RN sorption values for four rocks and some selected minerals representative for the Olkiluoto site. In the KBS-3V design the deposition tunnel backfill plays a further important role for RN retention and retardation. Thus, the present report presents RN solubilities, diffusion and sorption data for the backfill porewater. Backfill porewater compositions are modelled for the reference backfill design (Keto 2011) based on the expected evolution of groundwaters at Olkiluoto with time (Wersin et al. 2014) in analogy with the buffer porewaters. Calculations of the backfill porewater for the alternative tunnel backfill design of SKB are presented in Appendix A for comparison. They are not included in the main part of the report, because solubility and sorption parameters were only determined for the reference design. First, the geochemical system of the backfill and the modelling of the backfill porewaters are described. In the first main part, RN solubilities are presented and in the second main part, backfill specific RN diffusion and sorption values are derived. The concepts and applied procedures follow those used for the geochemical database of the canister and buffer solubility and migration parameters (Wersin et al. 2014). Nevertheless, for completeness, important theoretical background information, model assumptions and procedures are also described at the beginning of each part in this report.

8

1.1 Defining the geochemical system

1.1.1 Backfill material

In the current reference backfill design (Keto 2011), the backfill consists of three different materials1. The centre of the tunnel will be filled with compacted Friedland clay blocks with a high dry density. The tunnel floor consists of granules of Milos bentonite (e.g. DepCan, AC200) with a medium density. For the tunnel infill at the wall and roof, finer grained pellets of the same material with lower density are planned to be used1. The design is illustrated in Figure 1-1. The mineralogical composition, densities and porosities of these materials are presented in Appendix A.

Figure 1-1. Layout of the deposition tunnel backfill design (from Keto 2011). yellow: compacted Friedland clay blocks, orange: Milos pellets, grey: Milos granules.

Table 1-1 summarises the volumes and volume fractions2 of the three backfill materials. For the modelling of the porewater composition, these materials are averaged according to their volume fractions and an average backfill is defined.

1 These backfill materials were foreseen at the time when calculations started and do not represent the final design (Autio et al. 2013). In the final backfill design, Minelco granules for the floor and cebogel bentonite for wall and roof are foreseen. These are activated sodium bentonites and are similar in their composition to the AC200. 2 Note that the volume fractions for the different emplaced materials in Keto (2011) are slightly different than in Autio et al. 2013 where the “average” backfill is made up of 74 vol.% of blocks, 0.8 vol.% of foundation bed granules and 18vol. % of pellets. This slight differences have a negligible influence on calculated porewater chemistry for which these data are used here.

9

Table 1-1. Volume fractions of the different backfill materials (see footnote no. 2 on previous page).

Volumes (Memo B+Tech Paula Keto 2011) m3Fraction (-)

Blocks with gaps 11.36 0.69

Foundation Bed (Milos granules) 1.27 0.077

Wall/roof pellet (Milos pellets) 3.83 0.23

The (total) porosity (), solid/liquid ratio (S/L) and saturated density (sat) are derived from the following relationships.

s

dry

1 (1-1)

dry

ratioLS / (1-2)

ratio

ratiodrysat LS

LS

/

)/1(

(1-3)

where s is the solid or grain density. For the BET surface area and edge site concentrations used in the modelling, no values for Friedland clay or Milos bentonites are reported. These are taken here from MX-80 bentonite by scaling these values to the montmorillonite content. Thus, the edge sites of the illite component, which would only have a minor influence on the porewater composition, are not considered here. The system parameters and the mineralogical composition of the averaged reference backfill material are given in Table 1-2. Note that the montmorillonite mass fraction of the backfill is considerably lower than that of the buffer. But because of the higher compaction degree, the montmorillonite content per volume, often described as effective montmorillonite dry density (EMDD) is rather similar. The EMMD is defined as (setting the silica content to zero in equation 6 of Sato & Suzuki 2003):

3/24.1)7.2/72.1)486.01((1

72.1486.0

))1((1mMg

f

fEMDD

accdry

dry

(1-4) where f is the mass fraction of montmorillonite and acc is the grain density of the accessory minerals, taken to be 2.7 Mg/m3. This is slightly lower than the EMDD of the MX-80 buffer material (1.38 Mg/m3).

10

Table 1-2. Composition of the averaged reference backfill used in this report.

Parameter Value Reference

System parameters

S/L ratio (kg/L) 4.51 calculated

Porosity ε (-) 0.381 calculated

Dry density ρdry (kg/dm3) 1.72 calc. from Keto (2011), Hansen et al. (2009), Wimelius & Pusch (2008), Dixon et al. (2011)

Saturated density ρsat (kg/dm3) 2.10 calculated

Grain density ρs (kg/dm3) 2.78 calc. from Keto (2011), Kumpulainen et al.( 2011), Karnland et al. (2006)

BET surface area (m2/g) 20.40 scaled via MX-80

Internal surface area of montmorillonite (m2/g) 487 Appelo (2010)

Cation exchange capacity (CEC; eq/kg) 0.47 calc. from Keto (2011), Kumpulainen et al. (2011)

Surface site concentration (eq/kg) 0.0184 scaled via MX-80

Mineral composition (wt. %) calc. from Keto (2011), Kumpulainen et al. (2011)

Montmorillonite (Smectite) 48.6

Kaolinite 6.2

Illite 15.4

Mica 4.3

Quartz 16.1

Feldspar 1.1

Carbonate 2.2

Dolomite 0.3

Gypsum 1.9

Siderite 1.1

Pyrite 0.8

Tridymite 0.9

Goethite 0.3

Hematite 0.2

Magnetite 0.3

Anatase 0.2

Organic carbon 0.2

Sum % 99.9

Ion Exchanger composition (eq/kg) calc. from Keto (2011), Kumpulainen et al. (2011)

Ca 0.13

K 0.016

Mg 0.083

Na 0.24

Sum 0.47

1.1.2 Geochemical model

Geochemical conditions and their evolution are represented by the concept of reference groundwaters, which apply to specific time windows (Hellä et al. 2014). Reference waters are considered as the most plausible water composition for a specific time/climate window at Olkiluoto. To account for the uncertainty in groundwater chemistry, also so-called bounding waters were defined. As detailed in Wersin et al. (2014), from these groundwaters, two reference waters and four bounding waters as

11

listed below were defined for the near field which should represent the expected range of groundwater compositions including uncertainties in terms of ionic strength, alkalinity, Na, Ca and pH. The five original groundwater compositions are shown in Appendix A. Reference groundwaters:

saline water based on KR20/465/1 brackish water based on KR6/135/8

Bounding groundwaters: dilute carbonate rich brackish water, based on KR4/81/1 brine water, based on KR4/861/1 high alkaline water, based on the saline reference water titrated with Ca(OH)2 glacial melt water (Grimsel water).

The composition of the groundwater will evolve over time, mainly driven by changing climate conditions. These changes in the groundwater composition occur over long time scales compared with the transient state in the backfill. Therefore, calculations concerning the geochemical conditions in the backfill can be based on the assumption of complete mixing and equilibrium with the surrounding groundwater. This assumption of chemical equilibrium allows the application of thermodynamic data for the modelling of porewater compositions as well as solubility limits and RN speciation. The thermodynamic model for deriving backfill porewater compositions is based on the multiporosity anion exclusion model proposed for compacted saturated bentonite (Wersin et al. 2004). The porosity is filled with three water types: interlayer water (considered to part of the montmorillonite structure) which is devoid of anions, diffuse double layer water influenced by the charged external surfaces and the "free" water (Figure 1-2). The distribution of these three porosities depends on the compaction degree (or more precisely the effective montmorillonite dry density (EMDD)), the surface area and stacking number of the tetrahedral-octahedral-tetrahedral (TOT) layers and the thickness of the diffuse double layer (DDL) (in turn dependent on ionic strength). Parameters, including the internal surface area and stacking number of the montmorillonite flakes, and the DDL thickness are not well known and need to be estimated. Based on anion diffusion measurements and known anion porosities, these parameters were determined for Na-bentonite by a fitting procedure detailed in (Wersin et al. (2014, Appendix C). The corresponding values for the montmorillonite internal surface area and the stacking number are 487 m2/g and 4.8 respectively. For the DDL thickness, the anion free number, the multiplier for the Debye length needs to be derived. Too large DDL thickness values for this anion free number were obtained for the low ionic strength waters by this procedure, leading to negative "free" porosities. A distinctly lower anion free number of 2 was calculated by Bolt & de Haan (1979) for a variety of conditions, decreasing with ionic strength and when divalent cations are present. Because of this discrepancy we also lowered the anion free number to a value of 1.2, as proposed in Wersin et al. (2014). In that study, the approach for deriving the different porosities based on the concept of Appelo (2013) and Tournassat (2008), which is partly also described in Tournassat & Appelo (2011), is detailed. The corresponding water fractions for the six backfill porewaters are shown in Table 1-3.

12

Once the porosity fractions are determined, the thermodynamic model based on Wieland et al. (1994) and Wersin et al. (2004) is set up in a fairly straightforward manner with the PHREEQC code and the Andra/Thermochimie database (see description below). The model includes cation exchange reactions at interlayer sites and surface complexation reactions for protonation/deprotonation at external sites. The dissolution-precipitation of reactive accessory minerals is also accounted for: this includes calcite, quartz, kaolinite, gypsum and siderite. For siderite, a ten times lower solubility as predicted from the database is assumed. This is based on the experience gained from the geochemistry in the Opalinus clay and Callovo-Oxfordian formations, which contain notable contents of ferrous carbonate (e.g. Gaucher et al. 2009). Furthermore, calculations for Olkiluoto groundwaters show an undersaturation with respect to siderite by a factor of around ten. Fe(II) concentrations in the porewater based on the standard siderite solubility would be higher than in the present approach and thus presumably lead to an underestimation of RN solubilites, such as that of Se. The NaCl impurity in the material is used for initial equilibration of the DDL with the external surface. For most porewater types, the redox potential is assumed to be controlled by the sulphate/sulphide couple whose activities are determined by those in the corresponding groundwaters, in an analogue manner as has been assumed for the buffer (Wersin et al. 2014). Thus, the redox potential is assumed to be controlled by the activities of sulphur species from the surrounding groundwater (see Appendix A). An exception is the glacial melt water and the brine water, where the redox potential is assumed to be controlled by the ferrihydrite/Fe(II) and CO2/CH4 equilibrium, respectively (see Wersin et al. 2014 for details). All calculations are performed at 25 °C. The initial groundwater compositions (Appendix A) are first equilibrated with quartz and calcite at 25 °C before equilibration with the backfill. The temperature evolution in the backfill is expected to follow the temperature evolution of the far-field (Pastina & Hellä 2006). Thus, expected actual temperatures in the backfill will be close to 25 °C. Furthermore, by applying standard state conditions for all thermodynamic calculations, data uncertainties can be minimised.

13

external water

+

+

+

+

+

clayparticle

DDL

DDL

-

- -

-

-

-

-+

+

+

++

+

+

+

+

+++ +++ +++

+++

+++ +++ +++

+++

+

+

+

+

++

+

+

+

+

+

+

+

+

+

-

-

- + + -

1 23

12

3

interlayer water with exchanged cations

diffuse double layer with excess positive charge

charge balanced external porewater

1 nm

Figure 1-2. Different porewater types in bentonite according to model concept (Wersin et al. 2004).

1.1.3 Modelled backfill porewater compositions

The results for the six porewaters and exchange compositions are presented in Table 1-3. The pH is buffered at near-neutral to slightly alkaline values via buffering reactions (mineral dissolution/precipitation and surface protonation/deprotonation). Note that for the alkaline glacial melt water, a considerable pH drop in the backfill results. This arises from the coupled cation exchange and calcite / gypsum dissolution reactions. Because of the rather significant gypsum inventory in the backfill material, we consider justified to assume the presence of gypsum during the glaciation period. The gypsum pool in the backfill is expected to remain for considerable timescales, although uncertainties in gypsum depletion rates are rather large. As a sensitivity case we consider the absence of this phase (see below). As described above, the redox system in the modelling is constrained by the sulphate/sulphide activities from the surrounding saline, brackish, dilute, carbonate-rich and high alkaline groundwaters. For the glacial melt water and brine water, ferrihydrite/Fe(II) and CO2/CH4 equilibrium is assumed, respectively. Iron(II) in all porewaters is constrained by equilibrium with a FeCO3 phase, undersaturated by a factor of 10 relative to the siderite defined in the thermodynamic database. The resulting porewater compositions are highly supersaturated with regard to pyrite and slightly undersaturated relative to amorphous FeS (Table 1-3).

Comparison with porewaters of each emplaced material separately The porewater compositions are also calculated for the three emplaced materials assuming saturation of these with saline groundwater. The results are shown in Table 1-4 and compared with the averaged composition. The porewater composition of the Na-activated Milos bentonite pellets (AC200) is also shown.

14

Redox conditions are constrained as described for the averaged material in the above section. Note that for the DepCan materials, porewaters are undersaturated with siderite because of a lack of this phase according to the analytical data. This comparison shows that the porewater composition of the averaged backfill is rather similar to that of the Milos granules and pellets, but differs somewhat from that of the Friedland clay blocks. This is primarily explained by the absence of calcite in the latter material, which leads to undersaturated conditions relative to this phase. If calcite were present, Friedland clay porewater would display more similar compositions to the other waters.

Effect of interlayer water

A large uncertainty is the distribution of porosity fractions in the clay, namely the volume fraction of anion exclusion. In Table 1-4 the results of the averaged backfill assuming no interlayer water, thus assuming that the total porosity is available to chemical reactions, are also presented. The comparison with the standard case based on the multiporosity anion exclusion model, indicates a somewhat lower salinity for the interlayer-free case but otherwise rather similar compositions.

Effect of gypsum

A further uncertainty is the fate of gypsum with time present in the backfill. Calculations for the more evolved fresher water conditions, i.e. for the brackish water KR6/135/8, the dilute carbonate rich water KR4/81/1 and the glacial melt water, under the assumption of absence of gypsum are presented in Table 1-5. The comparison with the "standard" calculations generally indicate a fairly small difference, except for the dilute melt water (Grimsel) where, in the absence of gypsum, compositions and pH resemble more closely those of the groundwater. The shift to less alkaline conditions in the presence of gypsum results from buffering reactions including gypsum and calcite dissolution and cation exchange.

Effect of Na-activated infill material

The material type for the floor granules and for wall/roof pellets is not yet decided. In the "base case" DepCan was assumed to be the material of choice. Another possibility is Na-activated Milos bentonite (AC200). The main differences with regard to DepCan are its much higher sodium content at the exchanger and its slightly higher montmorillonite content. The results for AC200 as infill material are shown for saline, brackish and Grimsel waters and compared with those for DepCan as fill (Table 1-6). This comparison indicates some differences for the initial saline water case. This difference mainly arises from the different exchanger composition. For the other two waters, the differences are minor because the exchanger composition for these cases is assumed to be controlled by the concentrations in the groundwater.

Comparison with alternative backfill design

The results for the averaged alternative backfill material (IBECO RWC) are presented in Appendix A. The results are quite similar to those of the reference backfill in spite of the lower compaction degree. This highlights the effective buffering processes in the clay materials and the robustness of the derived solubility and sorption parameters.

15

Table 1-3. Reference and bounding water backfill porewater concentrations in mmol L-1 unless otherwise indicated.

Reference waters Bounding waters Saline water

KR20/465/1 Brackish

water KR6/135/8

Dilute, carbonate rich water, KR4/81/1

Brine water, KR4/861/1 (PSI db)

High alkaline water

Glacial melt water

(Grimsel water)

Free porewater

log p(CO2) -3,47 -2,70 -2,40 -4,61 -8,28 -5,48

pH 7,60 7,21 7,28 7,46 10,00 8,75

Eh (mV) -234 -201 -201 -270 -394 -297

Alkalinity (meq L-1)

0,23 0,73 1,53 0,10 3,40 0,08

Ionic Strength (meq L-1)

362,06 245,08 86,44 2953 350,52 46,58

Na 204,43 110,24 29,36 568 252,94 5,93

K 3,27 0,67 0,62 0,83 0,38 0,04

Mg 26,52 16,04 6,77 6,58 5,09 0,06

Ca 36,45 35,35 13,48 644 37,64 14,50

Cl 288,17 176,01 12,63 1862 288,05 0,22

SO42- 22,27 18,50 28,15 4,47 20,64 17,28

S-2 0,0056 0,0006 0,0003 - 0,0056 -

CO3 tot 0,364 0,865 1,686 0,069 0,014 0,044

Sr 0,170 0,167 0,025 2,8 0,256 0,106

Si 0,175 0,173 0,179 0,074 3,323 0,204

Mn 0,007 0,033 0,005 0,062 0,010 0,000

Fe 0,020 0,018 0,007 0,47 0,042 0,008

F 0,069 0,025 0,040 0,13 0,080 0,470

Br 0,868 0,265 0,023 6,69 0,886 0,000

B 0,164 0,089 0,038 0,13 0,188 0,000

Saturation index S.I.

Calcite 0,00 0,00 0,00 0,00 0,00 0,00

Siderite -1,00 -1,00 -1,00 -1,00 -1,00 -1,00

Pyrite 6,70 4,99 4,22 - 6,22 -

FeS(am) -0,32 -1,73 -2,50 - 2,16 -

Dolomite -0,02 -0,20 -0,11 -1,60 -0,75 -2,20

Gypsum 0,00 0,00 0,00 0,00 0,00 0,00

Kaolinite 0,00 0,00 0,00 0,00 0,00 0,00

Quartz 0,00 0,00 0,00 0,00 0,00 0,00

Exchanger CEC (eq L-1)

2,12 2,12 2,12 2,12 2,12 2,12

NaX (%) 50,7 35,1 17,6 49,6 64,0 4,3

CaX2 (%) 29,4 46,8 55,7 49,4 32,2 95,3

MgX2 (%) 16,7 17,2 25,2 0,8 3,4 0,4

KX (%) 3,2 0,8 1,5 0,2 0,4 0,1

Porosities IL % 42,8 42,9 42,8 42,9 42,9 42,8

Free % 39,6 35,7 9,8 50,1 39,3 9,8

DDL % 17,6 21,4 47,4 7,0 17,9 47,4

Edge sites (meq/L)

≡SOH 26,2 39,9 44,4 19,6 0,2 17,7

≡SOH2+ 0,6 1,9 2,8 0,3 0,0 0,2

≡SO- 56,1 41,1 35,7 63,1 81,9 65,0

16

Table 1-4. Backfill porewater concentrations of different emplaced backfill materials and average composition for the saline reference groundwater (KR20/465/1) case in mmol L-1 unless otherwise indicated. Eh is constrained by sulphate/sulphide equilibrium.

Friedland blocks

DepCaN granules

DepCaN pellets

averaged reference backfill

averaged backfill without

Interlayer

AC200 pellets

Free porewater

log p(CO2) -2,72 -3,42 -3,43 -3,47 -3,46 -3,70

pH 7,31 7,48 7,50 7,60 7,69 7,94

Eh (mV) -208 -227 -229 -234 -240 -254


0,83 0,19 0,24 0,23 0,34 0,34


582 343 299 362 257 336

Na 531 98,4 85,6 204 154 287

K 7,56 2,03 1,75 3,27 2,51 1,97

Mg 38,2 37,3 30,35 26,52 16,12 5,92

Ca 8,9 54,6 49,4 36 24,2 16,1

Cl 295 253 215,73 288 181 228,14

SO42- 168 15,0 15,3 22,3 28,0 52,1

S-2 0,006 0,006 0,006 0,006 0,006 0,006

CO3 tot 0,994 0,324 0,325 0,364 0,422 0,422

Sr 0,137 0,170 0,108 0,170 0,109 0,156

Si 0,165 0,170 0,172 0,175 0,180 0,195

Mn 0,005 0,007 0,004 0,007 0,004 0,006

Fe 0,020 0,026 0,023 0,020 0,013 0,009

F 0,058 0,062 0,041 0,069 0,044 0,059

Br 0,740 0,777 0,512 0,868 0,544 0,719

B 0,136 0,150 0,098 0,164 0,104 0,139


Calcite -0,69 0,00 0,00 0,00 0,00 0,00

Siderite -1,00 -1,04 -1,06 -1,00 -1,00 -1,00

Pyrite 6,76 6,78 6,73 6,70 6,58 0,00

FeS(am) -0,81 -0,31 -0,34 -0,32 -0,39 -0,37

Dolomite -0,58 -0,04 -0,08 -0,02 0,00 -0,28

Gypsum 0,00 0,00 0,00 0,00 0,00 0,00

Kaolinite 0,00 0,00 0,00 0,00 0,00 0,00

Quartz 0,00 0,00 0,00 0,00 0,00 0,00

Exchanger CEC (eq L-1) 2,30 1,88 1,19 2,12 2,12 1,37

NaX (%) 84,7 25,0 23,6 50,7 49,0 79,0

CaX2 (%) 2,2 47,6 50,1 29,4 31,0 14,0

MgX2 (%) 8,3 25,3 24,4 16,7 17,0 4,0

KX (%) 4,9 2,1 1,9 3,2 3,0 3,0

Porosities IL % 49,0 31,8 31,8 42,8 0,0 22,4

Free % 28,5 55,1 55,1 39,6 55,9 68,4

DDL % 22,5 13,1 13,1 17,6 44,1 9,2

Edge sites (meq/L)

≡SOH 48,3 22,6 14,1 26,2 14,8 10,2

≡SOH2+ 2,1 0,6 0,4 0,6 0,3 0,2

≡SO- 56,7 40,3 25,1 56,1 32,1 33,7

17

Table 1-5. Comparison of backfill porewater compositions for average backfill with and without gypsum for selected groundwater conditions. Eh is constrained by sulphate/sulphide equilibrium for brackish and dilute, carbonate-rich brackish waters and by ferrihydrite/Fe(II) for Grimsel water (see text). Concentrations in mmol L-1 unless otherwise indicated.

Brackish water with

CaSO4

Brackish water

no CaSO4

Dilute, carbonate rich

water with CaSO4


water no CaSO4

Grimsel water with

CaSO4

Grimsel water

no CaSO4

Free porewater

log p(CO2) -2,70 -2,70 -2,40 -2,40 -5,48 -5,48

pH 7,21 7,23 7,28 7,55 8,75 9,46

Eh (mV) -201 -205 -201 -227 -297 -339


0,73 0,76 1,53 2,54 0,08 0,33


245 224 86,4 23,3 46,6 1,73

Na 110 104,9 29,4 14,1 5,93 0,99

K 0,67 0,63 0,62 0,29 0,04 6,1E-03

Mg 16,0 13,9 6,77 1,02 0,06 9,5E-04

Ca 35,3 29,6 13,5 1,84 14,5 0,21

Cl 176 177 12,6 14,6 0,22 0,27

SO42- 18 7,45 28,2 1,44 17,3 1,1E-01

S-2 0,001 0,001 0,000 0,000 - -

CO3 tot 0,865 0,887 1,686 2,692 0,044 0,185

Sr 0,167 0,161 0,025 0,009 0,106 0,003

Si 0,173 0,174 0,179 0,183 0,204 0,269

Mn 0,033 0,032 0,005 0,004 0,000 0,000

Fe 0,018 0,015 0,007 0,002 0,008 0,001

F 0,025 0,025 0,040 0,047 0,470 0,608

Br 0,265 0,266 0,023 0,027 0,000 0,000

B 0,089 0,089 0,038 0,038 0,000 0,000


Calcite 0,00 0,00 0,00 0,00 0,00 0,00

Siderite -1,00 -1,00 -1,00 -1,00 -1,00 -1,00

Pyrite 4,99 4,88 4,22 3,51 - -

FeS(am) -1,73 -1,74 -2,50 -2,52 - -

Dolomite -0,20 -0,20 -0,11 -0,09 -2,20 -2,19

Gypsum 0,00 -0,41 0,00 -1,61 0,00 -3,24

Kaolinite 0,00 0,00 0,00 0,00 0,00 0,00

Quartz 0,00 0,00 0,00 0,00 0,00 0,00


NaX (%) 35,1 35,3 17,6 18,2 4,3 4,5

CaX2 (%) 46,8 46,5 55,7 54,5 95,3 95,1

MgX2 (%) 17,2 17,4 25,2 25,8 0,4 0,4

KX (%) 0,8 0,8 1,5 1,5 0,1 0,1

Porosities IL % 42,9 42,8 42,8 42,8 42,8 42,8

Free % 35,7 35,7 9,8 9,8 9,8 9,8

DDL % 21,4 21,4 47,4 47,4 47,4 47,4

Edge sites (meq/L)

≡SOH 39,9 39,9 44,4 45,3 17,7 33,8

≡SOH2+ 1,9 1,9 2,8 3,0 0,2 1,2

≡SO- 41,1 41,2 35,7 34,6 65,0 48,1

18

Table 1-6. Comparison of results in mmol L-1 unless otherwise indicated with two different Milos fill materials (DepCan and AC200). Eh is constrained by sulphate/sulphide equilibrium for saline and brackish waters and by ferrihydrite/Fe(II) for Grimsel water (see text).

Saline water

DepCan fill

Saline water AC200

fill

Brackish water

DepCan fill

Brackish water

AC200 fill

Grimsel water

DepCan fill

Grimsel water

AC200 fill

Free porewater log p(CO2) -3,47 -3,71 -2,70 -2,70 -5,48 -5,48

pH 7,60 7,97 7,21 7,21 8,75 8,75

Eh (mV) -234 -257 -201 -201 -297 -297


0,23 0,38 0,73 0,73 0,084 0,084


362 420 245 246 46,6 46,6

Na 204 359,6 110,2 110 5,93 5,95

K 3,27 3,70 0,666 0,667 0,04 0,04

Mg 26,5 17,3 16,0 16,1 0,060 0,060

Ca 36,5 14,7 35,3 35,5 14,5 14,5

Cl 288 293 176 177 0,217 0,218

SO42- 22,3 66,8 18,5 18,5 17,3 17,3

S-2 0,006 0,006 0,001 0,001 - 0,000

CO3 tot 0,364 0,463 0,865 0,864 0,044 0,044

Sr 0,170 0,164 0,167 0,168 0,106 0,108

Si 0,175 0,199 0,173 0,173 0,204 0,204

Mn 0,007 0,007 0,033 0,033 0,000 0,000

Fe 0,020 0,008 0,018 0,018 0,008 0,008

F 0,069 0,070 0,025 0,025 0,470 0,471

Br 0,868 0,875 0,265 0,266 0,000 0,000

B 0,164 0,164 0,089 0,089 0,000 0,000


Calcite 0,00 0,00 0,00 0,00 0,00 0,00

Siderite -1,00 -1,00 -1,00 -1,00 -1,00 -1,00

Pyrite 6,70 6,70 4,99 5,00 - -

FeS(am) -0,32 -0,32 -1,73 -1,72 - -

Dolomite -0,02 -0,02 -0,20 -0,20 -2,20 -2,20

Gypsum 0,00 0,00 0,00 0,00 0,00 0,00

Kaolinite 0,00 0,00 0,00 0,00 0,00 0,00

Quartz 0,00 0,00 0,00 0,00 0,00 0,00


NaX (%) 50,7 80,0 35,1 35,0 4,3 4,5

CaX2 (%) 29,4 8,3 46,8 47,0 95,3 95,0

MgX2 (%) 16,7 8,0 17,2 17,0 0,4 0,4

KX (%) 3,2 3,7 0,8 1,0 0,1 0,1

Porosities IL % 42,8 44,3 42,8 44,3 42,8 44,3

Free % 39,6 37,4 35,7 33,3 9,8 6,7

DDL % 17,6 18,3 21,4 22,4 47,4 49,0

Edge sites (meq/L)

≡SOH 26,2 17,5 39,9 41,9 17,7 18,6

≡SOH2+ 0,6 0,2 1,9 2,0 0,2 0,3

≡SO- 56,1 69,3 41,1 43,1 65,0 68,1

19

1.1.4 Bulk properties of the backfill

The bulk properties, such as swelling pressure (Ps) and hydraulic conductivity (k) of compacted swelling clays can be related to their montmorillonite content, respectively to their EMDD (e.g. Dixon et al. 2011, p.41). According to the empirical Ps-EMDD relationship given for example in the recent study on several potential buffer and backfill materials emplaced in the ABM test at Äspö (Kumpulainen & Kiviranta 2011), the Ps of the averaged reference backfill material can be estimated. Note that these materials were saturated in the borehole with artificial Äspö water (I 0.2 meq/L) as illustrated in Figure 1-3, this relationship leads to a swelling pressure for the average backfill of about 4.8 MPa and to a hydraulic conductivity of about 210-13 m/s.

Figure 1-3. Swelling pressure (upper figure) and hydraulic conductivity (lower figure) vs. effective montmorillonite dry density (EMDD) for various potential buffer and backfill materials (Wyoming MX-80 with buffer block subsample 11AP, Milos Dep-CaN with buffer block subsample 15CP, Asha bentonite from Kutch region, India with buffer block subsample 14AP). The corresponding values for the reference backfill mixture (pink) according to regression curve are also shown (figures modified from Kumpulainen & Kiviranta 2011).

20

A similar relationship has been derived by Karnland (2010). It relates the montmorillonite content (Xmont), expressed as the ratio of the mass montmorillonite and the mass solids; and the water content (Wm), expressed as the ratio of the mass water and the mass solids, to the swelling pressure. The relationship of Xmont/Wm

2 vs Ps for a number of bentonite materials is shown in Figure 1-4. Note that at low Xmont/Wm

2, Na-bentonites (shown as a red ellipse) diverge from the relationship, which, according to Karnland (2010), can be explained by the DLVO theory. At higher compaction degree, both Na and Ca bentonites follow the same trend. As suggested from the experimental Friedland clay data, the trend curve for this material is slightly below the average curve (shown as green hatched line in Figure 1-4). Based on the "Friedland curve", the swelling pressure for the average reference backfill is about 3 MPa and that of the Friedland blocks about 10 MPa. As the upper limit, the trend for all studied materials from Karnland (2010) is taken, which would yield a swelling pressure of about 10 MPa for the averaged reference backfill.

backfill mixture

Friedland blocks

Friedland curve

Figure 1-4. Swelling pressure vs Xmont/Wm2 for various bentonites and Friedland clay

(see text). The corresponding data for the reference backfill mixture (pink) and the emplaced Friedland blocks (green) also shown (figure modified from Karnland 2010). Investigated bentonites: Czech Dnesice (Dn), Rokle (Ro), Skalna (Sk), Strance (St); Danish Holmehus (Ho), Rösnäs (Rö); German Friedland clay (Fr); Greece Milos: IBECO RWC (Mi); Indian Ashapura (Ku) (Ku39=ASHA505, Ku40=ASHA229, the number denotes the different grades), US Wyoming (Wy) MX-80. The extensions denote the reference material (R1) and the ion exchanged fractions (Na, Ca). In summary, the swelling pressure expected for the reference backfill ranges from about 3 to 10 MPa, based on the relationships shown above. It is important to note that this statement refers to the averaged material, i.e. assuming complete homogenisation. This of course cannot be a priori expected. For the purpose here, however, the main interest lies in the derivation of porewater chemistry. As shown from the sensitivity analysis, the applied approach is robust, and thus porewater compositions are not much affected by small density variations. The largest effect lies in the changing composition of the

21

surrounding groundwater. By applying the concept of reference and bounding waters, the uncertainty in the groundwater evolution is accounted for.

1.2 Radionuclides of interest

The inventory of radionuclides (RN) inside the spent fuel canisters comprises fission products of uranium and plutonium and activation products from neutron absorption (Pastina & Hellä 2010). The set of RN investigated in this report is based on the RN inventory summarised in Pastina & Hellä (2010). For the purpose of the safety case 2012 screening calculations have been used to limit the number of radionuclides in the inventory that need to be considered in modelling radionuclide release and transport and by eliminating those that could have no conceivable safety impact (Assessment of Radionculide Releases report), the same screened elements are included in this report. It includes 33 RN of 22 elements, which were also used in previous safety assessments for example of Grivé et al. (2008). In addition to the RN set investigated by Grivé et al. (2008) we also provide data for beryllium, silver as well as iodine and chlorine, which are key elements affecting dose (e.g., Nykyri et al. 2008). Previously, Eu has not been included in SA considerations. However, it serves as an analogue for Sm sorption data and is therefore included in the new solubility and sorption thermodynamic database. The main groups of elements are:

actinides (Th, Pa, U, Np, Pu, Am, Cm) elements of the groups IA-VIIA (C, Ra, Cs, Sr, Se, Sn, I, Cl, Be) transition metals (Zr, Ni, Nb, Mo, Tc, Pd, Ag) lanthanides (Sm, Eu).

1.3 Thermodynamic database

The derivation of RN solubilities and sorption values hinges on a reliable and consistent thermodynamic database. For this work, we apply the database Thermochimie v.7b developed by Andra (Andra 2009a), which is described in Duro et al. (2012). This database is designed to deal with various aspects of radioactive waste disposal including the determination of radioelement aqueous speciation and solubility, the study of the geochemical evolution, the assessment of the process of cement degradation and the assessment of the process of canister corrosion. 25 radioelements are included in this database. The database builds on well established thermodynamic data, such as for example those recommended by the NEA and the Nagra/PSI database (Hummel et al. 2002), on which solubility calculations of Grivé et al. (2008) were based.

As indicated in Duro et. al (2012), the Andra/Thermochimie database contains a rather comprehensive set of temperature dependent thermodynamic data although gaps are still manifested in enthalpy and/or entropy values, where literature sources are scarce (Duro et al. 2012). Further important uncertainties include metal carbonate complexation data (as also reflected in the NEA data) and data at pH > 10. For the purpose of this study, only the uncertainty related to carbonate complexation is important, in particular for actinides. For these elements, the uncertainty can be estimated to a large extent from the uncertainty in logK values reported in the Andra/Thermochimie database.

22

23

PART I - RADIONUCLIDE SOLUBILITIES

24

25

2 DERIVATION OF SOLUBILITY DATA

2.1 Background

Radionuclide (RN) solubilities or - in more general terms - RN concentration limits represent an important chemical constraint for safety assessment (SA) calculations. They are defined here for the backfill porewater in the reference backfill case. The concept of solubilities for SA is well established and is based on chemical thermodynamics. Thus, in principle, the aqueous RN concentration is controlled by the most insoluble RN-containing solid for given chemical conditions. However, kinetic considerations with regard to the precipitation of the solid must be accounted for as well. This particularly holds for insoluble actinides and lanthanides, where, for conservative reasons, the X-ray amorphous more soluble hydroxide forms rather than the more crystalline less soluble ones are assumed to control the aqueous concentrations. Moreover, for redox sensitive elements (e.g. U, Np, Se), redox kinetics, which are highly system-specific, need to be accounted for. The general concept and RN specific considerations for the selection of the solubility limiting solid phase and the upper "pessimistic" solubility limit follow the argumentation in Wersin et al. (2014). Thus, the applied methods and chosen solubility limiting phases for the backfill porewater are generally the same as for the other 3 NF compartments, the water inside a defective canister, groundwater at the bentonite-host rock interface and porewater in the buffer. Nevertheless, a summary of the methods and reasons for the selection of solubility limiting phases and upper limits are presented for completeness. The calculated RN solubility limits for the backfill porewater are finally compared with the corresponding solubility limits for the other three water types of the near field, the water in a defective canister, bentonite buffer porewater and water at the buffer/host rock interface. Major differences are discussed with respect to the different water chemistry expected in the deposition tunnel backfill and the other near field compartments. RN solubilities for the backfill have not been considered in previous SA of Posiva (Pastina & Hellä 2010). In this former SA, RN solubilities were only applied for the case of selected groundwaters inside the canister with redox conditions determined by the corrosion products of the cast iron insert (data of Grivé et al. 2008) and bentonite waters with redox conditions determined by equilibrium with pyrite or siderite (see Smith et al. 2007). Therefore no comparison with previously applied solubility limits is provided in the present report. A comparison between previously used RN solubilities in the groundwater and the updated geochemical database of the near field, including a discussion of different approaches and thermodynamic data, is provided in Wersin et al. (2014).

2.2 Method

The procedure for deriving RN concentration limits is rather well established and can be summarised as:

26

1) Derive backfill porewater composition based on the reference and bounding groundwaters as outlined in chapter 1.

2) Calculate solubilities for RN based on thermodynamic database. This procedure included the following steps: First, the potential solubility controlling phases in the database were checked and the appropriate one(s) selected. Second, the equilibrium calculations with the selected solid(s) were carried out.

3) Calculate "thermodynamic" uncertainties and derive "reference values" and "upper limit". Derive solubilities for "special" RN: Ra, C-14(inorg), Se.

The thermodynamic calculations were performed with the PHREEQC code assuming a temperature of 25 °C in all calculations. The justification for calculations at 25 °C only is given in section 1.1. For some elements with a high solubility, PHREEQC intrinsic factors made it necessary to stabilise the Eh by adjusting the hydrogen partial pressure. In the PHREEQC code no direct fixation of the Eh is possible and the Eh is used to adjust for charge balance.

An implicit assumption in the calculations was that no microbially-induced sulphate reduction would occur in the backfill, which was ensured by decoupling sulphate from redox reactions. Experimental data for the backfill, which support this assumption, are lacking so far, but microbially-induced sulphate reduction is known to be suppressed, or at least severely limited in compacted bentonite (e.g. Masurat et al. 2010). The high density and rather high EMDD value (section 1.1.1) suggest that microbial activity will also be limited in the backfill material composed of Friedland Clay and Milos-type bentonite. However, there is uncertainty with regard to homogenisation of the backfill and the possibility of lower density regions at the backfill/rock interface, where microbially-induced sulphate reduction could occur (Performance Assessment report). For a number of transition metals forming insoluble sulphides (e.g. Ni, Co), the absence of the sulphate-sulphide reaction results in higher (and thus more pessimistic) solubilities. For some elements (e.g. Sr, Ra) forming insoluble sulphate phases, the opposite effect, namely increasing solubility upon sulphate reduction, would arise. However, from the large range in sulphate concentrations in the considered reference and bounding waters, the omission of sulphate reduction is not expected to lead to an underestimation of solubilities for these elements.

Phosphate concentrations in the groundwaters are low, about 10-8 to 10-7 M (see table A-1 in Appendix A). Formation and precipitation of RN-phosphates are therefore conservatively ignored in the solubility calculations.

2.3 Treatment of uncertainties

A central aspect in deriving solubilities is the estimate of uncertainties. In particular, in addition to the "best estimate" (termed "reference values" here) it is important to present a "pessimistic" estimate or "upper limit", which accounts for both data and conceptual uncertainty (e.g. Andersson 1999). This is not a trivial task and requires, besides a transparent and traceable treatment, a certain deal of (subjective) expert judgment (e.g. Berner 2002; Wersin & Schwyn 2004). For most elements, the uncertainty treatment is approximated by estimating two types of uncertainties, namely the "thermodynamic" and "geochemical" uncertainty.

27

"Thermodynamic" uncertainty

The formal thermodynamic uncertainty (formal uncertainty in the following) is estimated from the uncertainty in the solubility constant and that of the main species in solution. If the required uncertainty data are available and the different uncertainties of the logK constants are independent, the Gaussian error propagation method can be used (Grenthe et al. 1992). A simplified form of the general formula for error propagation is given in equation 2-1.

2

1

2

N

iYi

ix Y

X (2-1)

The formula to calculate the standard deviation can be simplified if the resulting variable X is a function of the sum of the variables Yi.

222

12

2211 21 : YYx ccYcYcX

(2-2) The total concentration of a radionuclide is the result of summing up the distribution of the total solubility over different complexes. Therefore, the error propagation is calculated by summing up the uncertainties of the solubility product of the solubility controlling phase, and the formation constant of the main aqueous complex. It should be emphasised that in many cases these uncertainties are highly correlated (e.g. Hummel & Berner 2002) and thus the error propagation often leads to an overestimation of the uncertainty in total dissolved concentration. Identification of such correlations requires inspection beyond the thermodynamic database and careful evaluation of the original experimental data. This is beyond the scope of this exercise. There is also some "thermodynamic" uncertainty introduced by the selection of the data in the database itself. This includes the selection or omission of complexes and solid phases or the extrapolation of the experimental data to standard conditions. In the canister and buffer solubility limits and migration parameters database report this uncertainty was qualitatively assessed by applying the alternative Nagra/PSI database for selected RN, where underlying thermodynamic data differs (Wersin et al. 2014). The results supported the reliability of the applied Andra/Thermochimie database and recommended solubilities were in general based on the standard Andra/Thermochimie database. Thus, a repetition of this exercise was not deemed necessary for the derivation of backfill solubility data. However, for some RN with complex geochemistry, a discussion on underlying thermodynamic data and neglected complexes is included in the text. There is yet another type of uncertainty, the logK uncertainty induced by the extrapolation method from zero to the ionic strength of the water to be considered. In the Andra/Thermochimie database, the logK data for charged species is extrapolated by the Davies equation and for uncharged species by the Setchenow equation (Andra 2009b; Parkhurst & Appelo 1999). The error induced by the ionic strength extrapolation is largely accounted for in the reported logK uncertainty up to an ionic strength of about 0.3 M, thus for all waters except for the brine water. In the canister and buffer solubility

28

limits and migration parameters database report (Wersin et al. 2014) the error introduced to the brine water by the Davies method was evaluated for selected RN by comparison with the SIT data implemented in the Andra/Thermochimie database. Resulting solubilities agreed fairly well (less than one order of magnitude) and thus provided support for the general application of the simplistic extrapolation procedure. Final recommended solubility data in the canister and buffer solubility limits and migration parameters database were therefore based on the standard Davies ionic strength correction for all waters and this approach is adopted in the present report as well. Hence, no additional calculations with the SIT data were performed for the backfill porewater solubilities.

"Geochemical" uncertainty

The uncertainty in the geochemical conditions (in particular pH, Eh, CO2 concentration) is separately evaluated by defining reference and bounding waters. The geochemical uncertainty depends on the timing of the scenario considered and the uncertainty in estimated chemical evolution of the geosphere. This is accounted for to a large extent by defining the reference and bounding groundwaters as a function of the climatic evolution. For many elements, the "geochemical" uncertainty - the uncertainty resulting from variations in chemical composition is larger than the "thermodynamic" uncertainty.

2.4 Recommendation of "reference values" and "upper limit"

Provided that good thermodynamic data for a given RN are available, the derivation of the solubilities for the two reference waters (saline KR20/465/1 and brackish KR6/135/8) is straightforward. The calculated solubilities for these reference waters are termed here as "reference values" for the water inside the canister and for the water at the buffer/rock interface. In case of insufficient reliable data, reference values are estimated from data of a chemically analogous element, or if this is not possible, they are estimated by expert judgement. For safety assessment, it is important to carry out calculations under pessimistic assumptions. For this purpose, an upper (solubility) limit is proposed which considers both formal and "geochemical" uncertainty. The estimate of the overall uncertainty is a difficult task and depends strongly on the information available and the specific characteristics of the radioelement. Moreover, the water composition is not constant with time, but is influenced by the climatic evolution. Because of the large uncertainty in that evolution, we propose one (pessimistic) upper limit for all times. For most elements, this upper limit corresponds to the highest solubility of the six water considered and accounting for the formal uncertainty. In cases where this results in unrealistically high solubilities, an element-specific procedure based on geochemical reasoning was adopted. Thus, for some elements (e.g. Am, Sn) only the "geochemical" uncertainty, but not the formal uncertainty was considered. For elements where the derived solubilities are higher than 210-3 M, they are described as "unlimited".

29

3 SOLUBILITY DATA

3.1 Solubility of actinides

3.1.1 Thorium (Th)

According to the Andra/Thermochimie database, Th solubility is controlled by ThO2 (mcr). We conservatively chose the amorphous hydrous oxide ThO2·2H2O(am) as solubility controlling solid phase. The redox state is Th(IV) for all conditions considered. Dissolved thorium in most of the waters is mainly present as Th(OH)4(aq) and Th(OH)3CO3

- complexes. In a carbonate rich water, Th(OH)3CO3- and

Th(OH)2(CO3)22- are clearly the dominating complexes (see speciation data sheet in

Appendix B). As pointed out in previous assessments (Hummel & Berner 2002; Duro et al. 2006; Grivé et al. 2008), the uncertainty in the Th carbonate complexation constants is rather large and the dominant contributor to the overall formal uncertainty. Whereas the Nagra/PSI database only includes the Th(OH)3(CO)3

- and Th(CO3)56- complexes, the

Andra/Thermochimie database includes complexation constants for a variety of Th-hydroxo-carbonate complexation constants, as determined by Altmaier et al. (2006). The thermodynamic data of Altmaier et al. (2006) has also been recommended by NEA (Rand et al. 2009), although the Th(OH)3(CO3)

- complex was not included by NEA. Its inclusion in the Andra/Thermochimie database however represents a conservative approach. Formation of solubility increasing ternary Ca-Th(IV)-OH complexes is limited to pH > 14 (Altmaier et al. 2008) and can therefore be neglected for all in-situ conditions. Reference values: 2.210-9 M and 3.510-9 M for saline water and for brackish water, respectively. Upper limit: Adding the formal uncertainty to the dilute, carbonate rich water yields 2.9810-8 M. Table 3-1. Solubilities of thorium (Th) for backfill porewater as calculated with the Andra/Thermochimie database.

Thorium (Th)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1

High alkaline Glacial melt water

Solubility controlling solid phase

Solubility (mol L-1) Andra db 2.2E-09 3.5E-09 6.4E-09 9.2E-10 1.5E-09 1.7E-09

Uncertainty based on Andra db (± log10 unit) 0.58 0.67 0.67 0.58 0.58 0.58

Th(OH)4 (am)

Reference water Bounding water

30

3.1.2 Protactinium (Pa)

In general, the thermodynamic data for Pa are scarce and uncertain. The element may occur in the Pa(V) and Pa(IV) state, but it is generally assumed that the Pa(V) is stabilised under repository-type environments (e.g. Berner 2002). Table 3-2 summarises Pa solubilities calculated with Pa2O5 (s) as the solubility limiting phase. For most waters, PaO2(OH) is the dominant species and only in the high alkaline water the PaO2(OH)2

- species becomes dominant, resulting in a somewhat higher solubility. Due to the incomplete logK uncertainty data, the formal thermodynamic uncertainty cannot be calculated. However, the uncertainty in the thermodynamic data is large and clearly dominates the overall uncertainty. In view of this unsatisfactory situation, Berner (2002) proposed a conservative "best estimate" of 10-8 M for Nagra's HLW disposal concept, based entirely on experimental data of JAEA reported in Yui et al. (1999) and a comparison with the solubilities of other actinides. Calculations with the Andra/Thermochimie database result in lower solubilities (Table 3-2). Because of the uncertainty of the solubility controlling solid Pa2O5, whose data are derived from an old reference (Baes & Mesmer 1976), we conservatively adapt the reference value proposed by Berner (2002). Reference values: 110-8 M for saline water and for brackish water. Upper limit: In view of the scarcity of the data, no estimate of the formal uncertainty is possible. We arbitrarily assume a large uncertainty of 2 log-units for the upper limit, thus yielding 10-6 M. Table 3-2. Solubilities of protactinium (Pa) for the backfill porewaters as calculated with the Andra/Thermochimie database. These data are only for comparison. A conservative choice is applied for reference values and upper limits. No overall uncertainties are provided, because no uncertainty is available for the solubility controlling solid phase (Pa2O5 (s)).

Protactinium (Pa)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


solubility controlling solid phaseSolubility (mol L-1) Andra db 1.3E-09 1.8E-09 1.7E-09 8.6E-10 6.3E-09 1.3E-09

Uncertainty based on Andra db (± log10 unit) - - - - - -


Pa2O5

31

3.1.3 Uranium (U)

The oxidation state of U is highly dependent on redox conditions. Under the redox conditions of interest, U(IV), U(V)3, and U(VI) species may form stable complexes. Besides redox equilibria, redox kinetics need to be considered, in particular with regard to the precipitating uranium phase. Wersin et al. (2014) provide a detailed discussion on experimental data in literature, indicating that uranyl having possibly been generated by radiolysis in the fuel will be reduced to insoluble UO2 form when entering the canister environment. Thus, the U oxidation state in the backfill porewater can be assumed to be thermodynamically determined by the prevailing redox-conditions. According to the thermodynamic calculations, U(OH)4 is the predominant species for all waters. Only in the dilute carbonate rich water, a significant fraction of U is expected to form uranyl carbonate complexes. From this argumentation and in accordance with the solubility calculations for the other water compartments of the near field, UO2 in the amorphous form (UO2:H2O(am)) from the Andra/Thermochimie database was chosen as the solubility limiting phase. In the glacial melt water, solubility is controlled by uranophane (Ca(UO2)2(SiO3OH)2:5H2O. UO2 solubilities experimentally determined by Ollila (2008) at different pH, ionic strength and experimental duration were lower than the calculated solubilities for UO2:H2O(am) under the experimental conditions. This is probably related to the higher crystallinity of the solid in the experiment relative to the amorphous phase taken from the Andra/Thermochimie database. Recently, stability constants for ternary earth alkaline (Ca, Mg) uranyl carbonate complexes have been reported (Dong & Brooks 2006; 2008), which are not implemented in the NEA data and not included in the Andra/Thermochimie database. The updated NEA TDB of uranium (Guillamont et al. 2003) noted these complexes but the authors did not accept them in the TDB. In the U solubility calculations for the near field (Wersin et al. 2014), a comparison of solubilities calculated with the standard Andra/Thermochimie database and with inclusion these ternary earth alkaline uranyl carbonate complexes in the database was performed. These showed that the inclusion of the complexes led to strongly increased solubilities, which are not in line with natural uranium concentrations inferred from natural analogue studies (see e.g. Wersin & Schwyn 2004). We acknowledge however that there remains uncertainty with regard to the relevance of ternary calcium uranyl carbonate complexes for Olkiluoto-type waters. Thus, we calculated the U solubilities using the standard Andra/Thermochimie database. The formal uncertainty is calculated from the individual uncertainties of the solid phase (UO2:H2O(am): 1.09 log-units; uranophane: 5.06 log-units), the most abundant aqueous complex U(OH)4 (1.4 log-units), the redox reaction (0.04 log units) and the carbonate formation (0.14 log-units).

3 The stability of pentavalent U is still controversial. According to the new NEA database (Guillaumot et al. 2003), U(V) complexes are stable and recommended to be included for thermodynamic calculations, in spite of the uncertainty of their stability.

32

Reference values: 3.110-9 M for saline water and 3.8 10-9 M for brackish water. Upper limit: Adding the formal uncertainty to the water with the highest solubility, the carbonate rich water yields a value of 3.5 10-7 M. Table 3-3. Solubilities of uranium (U) for backfill porewater as calculated with the Andra/Thermochimie database.

Uranium (U)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


solubility controlling solid phase

Uranophane




UO2:H2O (am)

3.1.4 Neptunium (Np)

Under the redox conditions of interest, neptunium may occur as tetravalent and pentavalent species. From a thermodynamic viewpoint, Np(IV) is the dominant oxidation state, as revealed from equilibria calculations carried out for recent safety assessments (e.g. Berner 2002; Duro et al. 2006; Grivé et al. 2008). Kinetic data supporting the reduction of oxidised Np(V) in the presence of reduced Fe are more scarce than for U(VI). Recent Japanese studies (Nakata et al. 2002; 2004) support the reduction of Np(V) to Np(IV) in the presence of Fe(II), even in the case of homogenous solutions. This gives support for the assumption that neptunium in the EBS will behave according to thermodynamic predictions. From the Np(IV) solids, the neptunium oxide NpO2(s) is the solubility controlling phase according to the Andra/Thermochimie database. For conservative reasons, the amorphous neptunium hydroxide (NpO2:2H2O (am)) was selected as the solubility limiting phase. According to the thermodynamic calculations, Np(OH)4 with tetravalent neptunium is the dominant aqueous species for all waters. The formal uncertainty is calculated from the logK uncertainty of the solid phase (0.5) and of the Np(OH)4 complex (1.0). Reference values: 9.510-10 M for saline water and 1.0 10-9 M for brackish water. Upper limit: Adding the formal uncertainty to the solubility of the dilute, carbonate rich bounding water yields a value of 1.4 10-8 M.

33

Table 3-4. Solubilities of neptunium (Np) for backfill porewater with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Neptunium (Np)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1





NpO2:2H2O(am)

3.1.5 Plutonium (Pu)

Plutonium occurs in the tri- and tetravalent oxidation state in reducing environments. Its solubility is controlled by the tetravalent Pu oxide PuO2 (s) over a wide range of redox conditions, according to the Andra/Thermochimie database. For conservative reasons, the more soluble hydrous plutonium oxide PuO2:2H2O(am) is selected as the solubility limiting phase. The calculations indicate the trivalent species Pu(SO4)

+ (logK uncertainty 0.66) and Pu(CO3)

+ (logK uncertainty 0.86) as dominant aqueous species for most waters. In the brine water, Pu(OH)2

+ (logK uncertainty 0.3) is the most abundant species. In the high alkaline and glacial melt waters, the tetravalent Pu(OH)4 species (logK uncertainty 0.5) predominates. Formation of solubility increasing ternary Ca-Pu(IV)-(OH) complexes only occur at pH above 11 and Ca concentration > 2 M (Altmaier et al. 2008) and therefore do not play a role under in-situ conditions. The solubility is generally low, reaching the highest values for the dilute carbonate rich water. The formal uncertainty is calculated based on the individual uncertainties of the solid phase (0.6 log-units), of the dominant aqueous complex (see above) and the redox equilibrium Pu4+/Pu3+ (0.67 log-units). Reference values: 7.8 10-10 M for saline water and 6.3 10-9 M for brackish water. Upper limit: Taking the highest solubility obtained for the dilute carbonate rich water and accounting for its formal uncertainty yields 9.910-8 M.

34

Table 3-5. Solubilities of plutonium (Pu) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Plutonium (Pu)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




PuO2:2H2O (am)


3.1.6 Americium (Am) and Curium (Cm)

Americium and curium are chemically very similar. Because of that, the calculated data of americium are also used for curium. Under considered redox conditions, the only oxidation state is Am(III). Various solid Am phases may form under the considered conditions. Thermodynamic calculations with the Andra/Thermochimie database indicate the stability of Am(III) carbonate hydroxide phases Am(CO3)3(OH)(am) and Am(CO3)2Na5H2O. The crystalline hydroxide and carbonate hydroxide forms are conservatively excluded from the solubility considerations. The main aqueous complexes are AmOSi(OH)3

2+ and Am(CO3)+. Only in the high

alkaline water, Am(OH)2+ predominates. Based on the Andra/Thermochimie database

and the predominant aqueous speciation, the chosen solubility controlling phase for the brine water KR4/861/1, the high alkaline water and the glacial melt water is Am(OH)3(am), for the dilute carbonate rich water it is Am(CO3)(OH) (am) and for all other waters it is Am(CO3)2Na:5H2O(s). Am(OH)3 (aq) species do not play a significant role under in-situ conditions. Hence, solubility increasing formation of ternary Ca-Am/Cm-OH complexes as observed for Cm at pH 11 (Rabung et al. 2008) can be neglected. The "geochemical" uncertainty is more relevant than the thermodynamic one, as indicated from the variation in solubility in the different waters (Table 3-6). The increase in solubility is related to the carbonate complexation for the more carbonate rich waters, and to the first hydrolysis complex in case of the alkaline water. In the formal "thermodynamic" uncertainty calculation, the uncertainty of the solubility controlling phase and the most abundant aqueous complex formation is included. Reference values: 1.010-5 M for saline water and 1.1 10-5 M for brackish water. Upper limit: The highest solubility of about 3.9 10-5 M is obtained for the brine water. A slightly lower value is obtained if the calculated formal uncertainty is considered for the brackish and saline reference waters. We propose the solubility in the brine water to be used as an upper limit, due to the considerably larger geochemical than thermodynamic uncertainty.

35

Table 3-6. Solubilities of americium (Am) and curium (Cm) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Americium (Am) and Curium (Cm)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


solubility controlling solid phase

Am(CO3)

(OH) (am)

uncertainty solid phase (± log10 unit) 1.0


uncertainty main complex (± log10 unit) 0.1 0.1 0.4 0.1 0.7 0.1

total uncertainty based on Andra db (± log10 unit) 0.52 0.52 1.08 0.81 1.06 0.81

Am(OH)3 (am)

0.5 0.8

Bounding waterReference water

Am(CO3)2Na:5H2O

3.2 Solubilities of the groups IA to VIIA

3.2.1 Carbon (C)

Most carbon-14 released from the spent fuel and the zircaloy cladding is expected to occur in organic form (Yim & Caron 2006). Organic carbon: The solubility of organic carbon species is conservatively assumed to be unlimited in view of the large uncertainty with regard to the organic C-14 species released from the spent nuclear fuel and the activated metals of the canister (Johnson & Schwyn 2008). Inorganic carbon: For inorganic carbon solubility is fixed by the porewater composition which is in equilibrium with calcite. Reference values: 3.6 10-4 M for saline water and 8.6 10-4 M for brackish water. Upper limit: A high dissolved carbonate concentration of 1.7 10-3 M occurs in the dilute carbonate rich brackish water. Table 3-7. Solubilities of inorganic carbon for backfill porewaters.

Carbon (Cinorg)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


Solubility controlling solid phase


Uncertainty based on Andra db (± log10 unit)

Calcite

negligible


36

3.2.2 Radium (Ra)

Radium is chemically very similar to barium. Therefore it associates with solids including barium by forming solid solutions. In particular, the coprecipitation of Ra with Ba and the formation of (Rax,Ba1-x)SO4 solid solutions4 are well established (Bruno et al. 2007) and have been recently investigated (Bosbach et al. 2010). As with the other waters in the near field (Wersin et al. 2014), reference solubilities were calculated assuming equilibrium with a Ba-Ra sulphate solid solution. For the derivation of the upper pessimistic limit, formation of pure RaSO4 is considered, resulting in Ra solubilities 3-4 orders higher than for the solid-solution. The inventory of (stable) barium in the spent nuclear fuel is largely in excess compared to that of active Ra-226 which is formed by decay of thorium and uranium isotopes. For the calculation of the Ra solubility, the highest Ra/Ba ratio calculated for different fuels over time of 3.2·10-4 was applied (see Wersin et al. 2014) Reference values: 3.2 10-11 M for saline water and brackish water. Upper limit: Due to conceptual uncertainties related to the solid solution with barium, the solubility of pure RaSO4 for the worst case (brine water) is taken. This gives a value of 3.6 10-5 M. Table 3-8. Solubilities of radium (Ra) for backfill porewaters.

Radium (Ra)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


Solubility controlling solid phaseSolubility (mol L-1) Andra db 3.2E-11 3.2E-11 1.6E-11 1.0E-08 3.4E-11 1.7E-11


RaSO4

Ra(x)Ba(1-x)SO4 x=0.000329


3.2.3 Caesium (Cs)

For caesium, there is no sparingly soluble salt which would form under Olkiluoto-type conditions. Therefore, the solubility of Cs is assumed to be unlimited for all cases.

3.2.4 Strontium (Sr)

In the backfill porewaters, celestite (SrSO4) is the solubility limiting phase for all cases based on the Andra/Thermochimie database. The most dominant dissolved species is

4 x is the mole fraction of Ra in the solid solution.

37

Sr2+. Depending on the water composition, SrCl+ and SrSO4 can make up a substantial fraction of the speciation. The formal uncertainties related to the solubility of celestite are small compared with the geochemical uncertainty. For Sr, the most important solubility controlling factor is the sulphate concentration, which is assumed to be in equilibrium with gypsum. As for all calculations, the reduction of sulphate to sulphide is suppressed. If microbial-mediated sulphate reduction were to occure, solubility of Sr would increase until strontianite (SrCO3) would exert the solubility control. In the brine water, the high Ca concentration leads to very low SO4

2- concentrations in equilibrium with gypsum. Thus, Sr solubilities in the brine water are much larger than for all other waters. Reference values: 3.710-4 M for saline water and 3.5 10-4 M for brackish water. Upper limit: Considering the high solubility of Sr in the brine water (3.210-2 M), we propose the upper limit to be unlimited. Table 3-9. Solubilities of strontium (Sr) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database. Values in italics only have informative character and are considered unlimited.

Strontium (Sr)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




Bounding water

Celestite

Reference water

3.2.5 Selenium (Se)

Selenium may occur in many oxidation states: VI, IV, 0, -I, -II. Under reducing conditions, Se(O), and Se(-II) prevail due to their thermodynamic stability under these conditions (Séby et al. 2001). The solubility of Se under reducing conditions is affected by the presence of iron with insoluble FeSex phases forming. Moreover, Se may be incorporated in iron sulphides as solid solution (Vaughan & Craig 1987; Cutter 1989). The reduction of oxidised Se is slow and kinetically controlled. In particular, Se(VI) may be stabilised under reducing conditions for long times. As pointed out in Wersin et al. (2014), experimental data suggest the reduction of oxidised Se species in the presence of zero-valent iron and reduced iron minerals. Furthermore, there is experimental evidence of Se(IV) reduction to insoluble compounds in the presence of pyrite and FeS (Breynaert et al. 2008; Bruggeman et al. 2011). Given the overall experimental picture and the notable concentration (0.8 %) of pyrite in the backfill, we deem justified to estimate Se solubilities from thermodynamic considerations.

38

Under the Eh conditions defined, Se(-II) is the stable form, albeit the anionic HSe- species. Solubility control is exerted by the very insoluble FeSe2 according to the Andra/Thermochimie database. This is also supported by experimental findings of Cui et al. (2009). However, the possibility of formation of more soluble less crystalline FeSex or Se(0) species cannot be ruled out, as suggested from the experimental study of Iida et al. (2007). The formal uncertainty is calculated from the individual uncertainties of the FeSe2 formation (2.73 log-units) and of the dominant solution species (HSe-: 0.4 log-units, Se4

2-:1.3 log-units). Reference values: 4.210-10 M for saline water and 2.8 10-10 M for brackish water Upper limit: Due to the uncertainties in the redox potentials, the large formal uncertainty of 2.76 log units is added to the highest solubility obtained for the brine water. This results in a value of 2.9 10-6 M, which we propose as an upper limit. Table 3-10. Solubilities of selenium (Se) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Selenium (Se)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




FeSe2


3.2.6 Tin (Sn)

Tin occurs in the tetravalent oxidation state under the redox conditions of interest. Sn(OH)4 and Sn(OH)5

- are the most abundant aqueous complexes. In the high alkaline waters, Sn(OH)6

2- becomes more important than Sn(OH)4. The amorphous oxide SnO2(am) is selected as the solubility controlling solid based on the Andra/Thermochimie database.

The formal uncertainty is calculated from the uncertainty of the equilibrium constant and the formation constant of the most abundant aqueous species. This uncertainty is relatively small compared to the range of geochemical uncertainties found for the bounding waters, and is therefore neglected for the derivation of the upper limit. The highest solubility is found for the high alkaline waters in which the negatively charged Sn(OH)5

- is the dominant species.

Reference values: 7.410-8 M for saline water and 5.8 10-8 M for brackish water.

Upper limit: We propose 1.410-5 M obtained for the high alkaline water as an upper limit.

39

Table 3-11. Solubilities of tin (Sn) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Tin (Sn)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




SnO2 (am)


3.2.7 Beryllium (Be)

No thermodynamic data for Be are given in the Andra Thermochimie v7b (Andra 2009a) database nor in the Nagra/PSI database. Beryllium thermodynamic data were therefore taken from the Minteq v.4 database and implemented in the Andra Thermochimie v.7b database. The implementation of the Be data in the Thermochimie v7b database rather than the direct application of the Minteq v4 database assured the consistency of the modelled backfill pore/ground-water chemistry with all other performed calculations for the near-field (Wersin et al. 2014). According to the Minteq v4 thermodynamic data, the Be-hydroxide Be(OH)2 (beta) is the least soluble mineral under all canister-, pore- and groundwater conditions. However, kinetic considerations with regard to the precipitation of the solid must be accounted for as well. Therefore, we conservatively selected the more soluble X-ray amorphous hydroxide Be(OH)2(am) as the solubility limiting phase instead of the crystalline alpha and beta form. Speciation and solubility of Be under in-situ conditions are controlled by pH via the hydrolysis of Be. Complexation of Be by F-, Cl-, CO3

2- or SO42- only plays a

subordinate role, with the exception of the brine water, were BeF+ and BeF2 complexes contribute 13.5%. For Be, no logK uncertainties for the solid phases and aqueous species are available. Thus no formal thermodynamic uncertainty could be calculated. However, speciation and solubility of Be under in-situ conditions are controlled by pH. The large range in pH described by the reference and bounding waters results in a geochemical uncertainty, which presumably exceeds the formal thermodynamic uncertainty. In combination with the conservatively selected solubility limiting phase we therefore deem it justified to base the upper solubility limit on the geochemical uncertainty alone. Reference values: 2.510-6 M for saline water and 5.710-6 M for brackish water. Upper limit: The highest solubility of 5.710-6 M is obtained for the brackish water which is selected as an upper limit.

40

Table 3-12. Solubilities of beryllium (Be) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database..

Be solubility backfill

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1


Solubility limiting solid phase

Solubility (mol L-1) Minteq database with Andra db

2.5E-06 5.7E-06 4.9E-06 3.9E-06 1.8E-06 6.1E-07

Uncertainty based on Andra db (± log10 unit) - - - - - -


Be(OH)2 (am)

3.2.8 Iodine (I)

Within the spent nuclear fuel, iodine is present as the anion I-. Salts of I- with e.g. Ca, Mg or K are highly soluble and the solubility of I has therefore to be regarded as unlimited for all reference and boundary backfill porewaters.

3.2.9 Chlorine (Cl)

Natural occurring chloride (Cl-) is the dominant anion in all reference and bounding waters. The release of radiogenic Cl will therefore be negligible in comparison with the natural Cl concentrations. The solubility of Cl in all reference and bounding backfill waters has to be regarded as unlimited.

3.3 Solubilities of the transition metals

3.3.1 Zirconium (Zr)

Under the studied system, zirconium remains in the tetravalent state. According to the Andra/Thermochimie database, the solubility of Zr(IV) can potentially be controlled by the x-ray amorphous phases Zr(OH)4(am, aged) or Zr(OH)4(am, fresh), or by the crystalline phase ZrO2. As assumed in a general fashion, solubility control by the amorphous phase is conservatively favoured. As for the waters of the near field (Wersin et al. 2014) Zr(OH)4(am, aged) is chosen as the solubility controlling phase because calculations with Zr(OH)4(am, fresh) yield unrealistically high zirconium concentrations. Based on the speciation calculations with the Andra/Thermochimie database, Zr(OH)4(aq) is the only relevant aqueous complex for Zr under the considered conditions. The formation of Zr-carbonate complexes only influences solubility at HCO3-concentrations above 5 mM, as shown in the experiments at pH 9 of Pouchon et al. (2001). Recent findings on the effect of Ca on the solubility of Zr demonstrated that Ca interacts with the Zr(OH)6

2- octahedron, and therefore only influences Zr solubility at pH > 10 (Altmaier et al. 2008).

41

The logK uncertainty of the formation of Zr(OH)4(aq) is rather large (1.7). Together with the uncertainty of the solubility constant of the solid phase an uncertainty of 1.71 logK units ((1.72+0.22)1/2 = 1.71) is calculated. All waters show very similar solubilities. Reference values: 1.710-8 M for saline and brackish water. Upper limit: Adding to the highest solubility value (1.810-8 M), the formal uncertainty results in an upper limit of 9.210-7 M. Table 3-13. Solubilities of zirconium (Zr) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Zirconium (Zr)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




Zr(OH)4 (am,aged)


3.3.2 Nickel (Ni)

Nickel occurs in the divalent oxidation state under the conditions of interest. Under the considered conditions, the solubility controlling phase is Ni(OH)2(s) based on the Andra/Thermochimie database. As a mineral formed at high temperatures, the mineral gaspeite (NiCO3(s)) is not considered as solubility controlling phase. For pH values in the range of 7-8, the free Ni2+ is the dominant aqueous species, with the exception of the brine water, where NiCl+ contributes 63 %. At higher pH values the nickel hydroxide Ni(OH)2 becomes the most abundant aqueous complex. The formal thermodynamic uncertainty is low compared to the geochemical uncertainty reflected in the different water compositions and is therefore neglected in the determination of the upper limit. Reference values: 3.110-4 M for saline water and 1.7 10-3 M for brackish water. Upper limit: The highest value of 1.7 10-3 M obtained for the brackish water is taken as an upper limit.

42

Table 3-14. Solubilities of nickel (Ni) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Nickel (Ni)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1





Ni(OH)2(s)

3.3.3 Niobium (Nb)

Niobium in natural waters is only present in the pentavalent oxidation state. The solubility of niobium is controlled by Nb2O5(s) according to the Andra/Thermochimie database. The solubility of the solid phase depends on pH, thus showing larger solubilities at higher pH. The thermodynamic data in the Andra/Thermochimie database are derived from solubility and hydrolysis data of Peiffert et al. (1997, unpublished Andra report). This work has recently been published (Peiffert et al. 2010). According to these data, the main aqueous species is Nb(OH)6

- at near-neutral pH conditions. For the high alkaline water and glacial melt water, Nb(OH)7

2-(aq) dominates over Nb(OH)6-

(aq). At high pH, Ca-niobate may limit solubilities, as shown by Talerico et al. (2004). These authors applied experimental data for the derivation of an empirical relationship between Nb solubilities, Ca concentrations and pH. Due to the rather preliminary nature of the underlying data, we conservatively do not account for the potential precipitation of Ca-niobate. Reference values: 3.810-7 M for saline water and 1.510-7 M for brackish water. Upper limit: Because of the high solubility in the high alkaline water (2.910-3 M), we propose this value to be unlimited.

43

Table 3-15. Solubilities of niobium (Nb) for backfill porewaters with the associated formal uncertainty, where available as calculated with the Andra/Thermochimie database.Values in italics have informative character and are considered as unlimed.

Niobium (Nb)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1



Uncertainty based on Andra db (± log10 unit) 1.21 1.21


Nb2O5(s)

no uncertainty for the dominat species

3.3.4 Molybdenum (Mo)

The solubility of molybdenum is dependent on the pH and the redox potential of the water. Under Eh conditions of interest the hexavalent state predominates in solution. Despite the hexavalent state in solution, the solubility of molybdenum at near-neutral pH is controlled by MoO2(s), according to the Andra/Thermochimie database. Above a pH of 8.6, however, the hexavalent CaMoO4(s) is the solubility controlling solid. From the thermodynamic data the formal uncertainty cannot be calculated because no logK uncertainty for the formation of the aqueous complex is reported. But, as stated above, the strong dependency of pH and Eh on the solubility makes quantitative estimates of uncertainty difficult. Moreover, at higher pH conditions, the solubility is very sensitive to the calcium concentrations. The solubility calculations presented were made under the assumption of calcite equilibrium. Due to these uncertainties, we recommend an unlimited upper limit. Reference values: 1.310-7 M for saline water and 4.710-8 M for brackish water. Upper limit: Unlimited due to uncertainties caused by the strong Eh and pH dependency. Table 3-16. Solubilities of molybdenum (Mo) for backfill porewaters.

Molybdenum (Mo)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1





MoO2(s)

no uncertainty for the aqeous complex available

CaMoO4(s)

44

3.3.5 Technetium (Tc)

Technetium is a redox sensitive element with prevailing oxidation states of +VII and +IV in natural waters. Under reducing conditions, the tetravalent state is thermodynamically stable and its solubility is controlled by insoluble Tc(IV) hydroxides. On the other hand, Tc(VII) forms soluble anionic complexes and is highly mobile. The reduction of Tc(VII) in the presence of reduced iron (e.g. magnetite) is rapid, as has been experimentally shown (Cui & Eriksen 1996; Lee & Bondietti 1983). Thus, the assumption of rapid reduction of Tc(VII) possibly released by the fuel via radiolysis to Tc(IV) is deemed justified. The oxidation state of Tc in the backfill porewater can therefore be expected to be controlled by prevailing redox conditions. Calculations with Andra/Thermochimie database indicate the crystalline technetium dioxide TcO2(cr) as solubility controlling solid phase. For conservative reasons, the more amorphous form TcO2:1.63H2O(s) is chosen as the solubility controlling solid phase. The aqueous speciation is dominated by TcO(OH)2(aq). For the glacial meltwater, which displays the least reducing conditions of all waters, the oxidised species TcO4

- makes up a minor fraction of the aqueous speciation. The uncertainty for the most important equilibrium TcO2:1.63H2O(s) ↔ TcO(OH)2(aq) + 0.63 H2O is reported with 0.5 logK units. Based on the minor contribution of other species, their uncertainties can be neglected. Reference values: 3.710-9 M for saline water and 3.8 10-9 M for brackish water. Upper limit: Adding the formal uncertainty to the highest value (alkaline water) yields a value of 1.2510-8 M. Table 3-17. Solubilities of technetium (Tc) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Technetium (Tc)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




TcO2:1.63H2O(s)


3.3.6 Palladium (Pd)

According to thermodynamic predictions metallic palladium is the most insoluble form (Berner 2002). The formation of this phase however may be kinetically restricted. In this case, Pd(II) oxide or hydroxide will form. As for the other waters in the near field, we conservatively assume the formation of the more soluble hydrated phase Pd(OH)2(s) over the oxide PdO2(s). The main species is Pd(OH)2(aq) except for high ionic

45

strengths, where according to the standard Andra/Thermochimie database the PdCl42-

(aq) complex becomes important. The estimated uncertainty for the solubility of Pd(OH)2 has been reported to be in the order of 0.5 log-units (Hummel et al. 2002, Berner 2002). Reference values: 3.710-6 M for saline water and 3.8 10-6 M for brackish water. Upper limit: The high ionic strength for the brine backfill porewater generates a solubility of 8.410-5 M, which is probably too high and induced by the crude ionic strength correction procedure and the high fraction of chloride. Nevertheless, this value is taken as an upper limit, but without addition of the formal uncertainty, which has been reported to be in the order of 0.5 log-units for the solubility of Pd(OH)2 (Hummel et al. 2002, Berner 2002) and 0.2 for the formation of PdCl4

2-. Table 3-18. Solubilities of palladium (Pd) for backfill porewaters with the associated formal uncertainty. With the exception of the brine water, the uncertainty is only based on the uncertainty of the solid phase, because of missing thermodynamic uncertainty for the dominant solution species.

Palladium (Pd)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




Pd(OH)2 (s)


3.3.7 Silver (Ag)

The solubility of silver is likely controlled by the precipitation of AgCl(cr) according to the Andra/Thermochimie database, if the precipitation of AgS is not accounted for. The latter phase would result in a much lower solubility. As for the other waters in the near field (Wersin et al. 2014), we conservatively ignore the possibility of AgS formation. The formation of metallic silver is difficult to defend due to slow kinetics. For the considered system, silver is only present in the monovalent oxidation state. The aqueous speciation is dominated by chloride complexes, except in the glacial melt water, where the Ag+ is the dominant species. The thermodynamic uncertainty on the AgCl(s) solubility product is very small and the solubility of AgCl is well known from the Ag/AgCl electrode. The variations in ionic strength in the different waters are more relevant in terms of uncertainty. The highest solubility occurs in the brine water because of the formation of chloride complexes. The obtained value however is probably too high and results from the ionic strength extrapolation method. Nevertheless, it is proposed as an upper limit.

46

Reference values: 2.810-5 M for saline water and 1.010-5 M for brackish water. Upper limit: 3.010-4 M which is the solubility calculated for the brine water. Table 3-19. Solubilities of silver (Ag) for backfill porewaters.

Silver (Ag)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




AgCl (cr)


no uncertainty for the solution species available

3.4 Solubilities of the lanthanides

3.4.1 Samarium (Sm)

Samarium is not redox sensitive and occurs in the trivalent oxidation state. The solubility mainly depends on the pH value, carbonate, silicate and chloride concentrations. In carbonate rich waters, Sm(CO3)

+(aq) is the dominant aqueous complex. For high alkaline waters, samarium hydroxides become more important. Light lanthanides (La to Eu) form mixed hydroxocarbonate solids (Spahiu & Bruno 1995). Based on the Andra/Thermochimie database, solubility of Samarium is controlled by SmOHCO3(cr). For conservative reasons, the more soluble amorphous form SmOHCO3:0.5H2O is chosen as the solubility controlling solid phase. Reference values: 4.310-7 M for saline water and 7.210-7 M for brackish water. Upper limit: The highest value is obtained for the brine water. Adding the formal uncertainty of 0.69 log units results in 1.110-5 as an upper limit. Table 3-20. Solubilities of samarium (Sm) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Samarium (Sm)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




SmOHCO3:0.5H20(cr)


47

3.4.2 Europium (Eu)

Europium occurs mainly in the trivalent oxidation state and to a minor extent in the divalent state. The dependence from the different system parameters and the solubility limiting phase is equal to that of samarium. Reference values: 1.510-7 M for saline water and 2.610-7 M for brackish water. Upper limit: The highest value is obtained for the brine water. Adding the formal uncertainty results in 1.410-5 M as an upper limit. Table 3-21. Solubilities of europium (Eu) for backfill porewaters with the associated formal uncertainty as calculated with the Andra/Thermochimie database.

Europium (Eu)

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1




Eu(CO3)(OH):0.5H2O(cr)


48

49

4 DISCUSSION OF SOLUBILITY DATA

4.1 Comparison with the other near-field solubilities

The comparisons of the RN solubilities for the backfill porewaters with those of the waters in the water in a defective canister, the free porewater in the bentonite buffer and the groundwater at the buffer/host rock interface are presented in Table 4-1 to 4-4. Generally, the geochemical uncertainty described by the definition of the six reference and bounding waters is larger than the differences between the four near-field water types. For some RN however, the solubility in the backfill porewater differs significantly from those in the corresponding water of the other near-field water compartments. The main differences were found in the dilute carbonate rich and glacial melt water cases. The pH in the backfill porewaters "dilute, carbonate rich" (7.28) and "glacial melt water" (8.75) are significantly lower than the pH in the corresponding waters of the other compartments (7.5-7.7 and >9.6, respectively). For Sn and Nb, this result in lower solubilities for glacial melt water in the backfill compared with the other compartments due to the strong increase in solubility at alkaline pH. In contrast, Ni solubility decreases with increasing pH and Ni solubility in the backfill dilute carbonate rich and glacial melt water are approximately one order of magnitude higher than in the other compartments. Linked to the pH, lower carbonate concentrations in the dilute carbonate rich and glacial melt water backfill porewaters were modelled compared with the other compartments. The lower CO3 concentration affects the solubilities of those RN, forming carbonate complexes in particular of actinides and lanthanides. For Cm/Am, Eu and Sm, where the solubility is control by carbonate-hydroxo solid phases, the lower CO3

2- concentration results in higher solubilities for the backfill porewaters. Note that Am/Cm solubility in the glacial melt water is controlled by the hydroxide. The higher solubility in the backfill is therefore a direct pH effect. Thorium solubility is controlled by Th(OH)4 and the lower proportion of Th-carbonate complexes in solution therefore leads to the lower Th solubility in the dilute carbonate rich backfill water. The same effect can be observed for U solubility. Despite the slightly higher redox potential in the backfill dilute carbonate rich water, the reduced stabilisation of the U(VI) by carbonate complexation results in a higher proportion of U(IV) and thus lower U solubility in the backfill. Sulphate concentrations in the backfill porewater are, with the exception of the saline water, up to two orders of magnitude higher than in the other water types. Radium solubility is constrained by either Ra(x)Ba(1-x)SO4 solid-solution formation or, in the conservative approach, by precipitation of a pure RaSO4 phase. In both cases, the higher SO4

2- concentration leads to the lower concentration limit of Ra in the backfill porewaters. Strontium solubility in all backfill porewaters is controlled by celestite (SrSO4) according to the Andra/Thermochimie database. In the other compartments of the near field, for some waters strontianite (SrCO3) is the solubility controlling solid phase. Thus, differences in Sr solubility between backfill porewater and other near-field water types result from different sulphate and carbonate concentrations and changing

50

solubility limiting solid phases. Microbial-mediated sulphate reduction is omitted in all calculations. If it would occur, a decrease in the sulphate concentration would result in higher Ra and Sr solubilities in all water types (except were strontianite is the solubility controlling phase). However, given the conservative approach for the derivation of the upper solubility limit of Ra (3.6·10-5 M for poor RaSO4) and the unlimited Sr solubility, the effect of locally occurring sulphate reduction would be within the expected geochemical uncertainty. Selenium concentrations in the backfill porewaters are lower than in the other near-field water compartments for the high alkaline and glacial melt water case. Selenium solubilities were modelled with SeFe2 as solubility limiting solid phase. Thus, Se solubilities were directly influenced by the higher Fe2+ concentration in the backfill porewater. The higher Fe2+ concentrations arise from the slightly lower Eh and pH in the backfill water. Table 4-1. Comparison of actinide solubilities in the backfill porewater with those in canister-, ground- and buffer porewater of the near field (Wersin et al. 2014). Solubilities differing by more than one order of magnitude between the compartments are highlighted in yellow.

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1

High alkaline

Glacial melt water

Upper limit

canister 2.7E-09 4.2E-09 1.3E-08 1.1E-09 1.5E-09 2.1E-09 8.8E-07

groundwater 3.3E-09 6.3E-09 2.0E-08 1.2E-09 1.5E-09 1.9E-09 1.2E-06

buffer 3.6E-09 3.6E-09 1.6E-08 1.1E-09 1.5E-09 2.1E-09 7.5E-08

backfill 2.2E-09 3.5E-09 6.4E-09 9.2E-10 1.5E-09 1.7E-09 3.0E-08canister 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-06groundwater 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-06buffer 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-06backfill 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.0E-06canister 4.1E-09 2.4E-08 9.4E-09 2.1E-09 3.0E-09 8.7E-09 3.0E-07groundwater 3.3E-09 6.3E-09 2.0E-08 1.2E-09 1.5E-09 1.9E-09 6.0E-08

buffer 3.5E-09 3.7E-09 1.2E-08 1.7E-09 3.0E-09 6.9E-10 1.5E-07backfill 3.1E-09 3.8E-09 5.6E-09 1.8E-09 2.9E-09 3.1E-09 3.5E-07canister 9.6E-10 1.0E-09 1.2E-09 6.8E-10 9.5E-10 1.0E-09 1.3E-08groundwater 1.0E-09 1.1E-09 1.3E-09 7.0E-10 9.5E-10 1.0E-09 1.3E-08buffer 9.1E-10 1.0E-09 1.2E-09 5.5E-10 9.5E-10 1.0E-09 2.1E-08backfill 9.5E-10 1.0E-09 1.1E-09 6.1E-10 9.2E-10 9.9E-10 1.4E-08canister 1.2E-10 4.3E-10 1.8E-09 1.2E-09 1.3E-11 1.4E-11 3.0E-08groundwater 7.4E-09 1.1E-08 5.8E-09 3.4E-08 1.3E-11 1.4E-11 4.5E-07buffer 8.4E-10 5.7E-09 2.4E-09 4.9E-09 1.3E-11 1.4E-11 9.9E-08backfill 7.8E-10 6.3E-09 7.5E-09 3.5E-09 1.3E-11 1.4E-11 2.3E-07canister 1.7E-06 6.0E-06 1.9E-06 2.7E-05 1.2E-08 3.3E-08 2.7E-05

groundwater 1.1E-05 5.2E-06 2.3E-06 6.8E-05 1.2E-08 2.3E-08 6.8E-05buffer 4.9E-07 6.3E-06 1.1E-06 9.2E-06 1.1E-08 3.3E-08 9.2E-06backfill 1.0E-05 1.1E-05 8.4E-06 3.9E-05 1.2E-08 7.0E-07 3.9E-05

Cm, Am III

Pa V

U IV

Np IV

Pu III


Th IV

51

Table 4-2. Comparison of solubilities RN of the groups IA to VIIA in the backfill porewater with those in canister-, ground- and buffer porewater of the near field (Wersin et al. 2014). Solubilities differing by more than one order of magnitude between the compartments are highlighted in yellow.

Saline KR20/465/1

Brackish KR6/135/8

Dilute, carbonate

rich KR4/81/1

Brine KR4/861/1

High alkaline

Glacial melt water

Upper limit (mol/l)

canister 5.2E-04 1.1E-03 unlim. 2.5E-05 9.3E-06 2.5E-04 unlim.groundwater 7.7E-04 1.7E-03 unlim. 1.0E-04 9.5E-06 1.9E-04 unlim.buffer 9.3E-04 9.0E-04 unlim. unlim. unlim. 2.8E-04 unlim.backfill 3.6E-04 8.6E-04 1.7E-03 8.1E-05 1.4E-05 4.4E-05 1.7E-03

Corg all waters unlim. unlim. unlim. unlim. unlim. unlim. unlim.canister 1.6E-11 6.7E-11 7.2E-11 2.5E-08 1.8E-09 4.2E-10 8.7E-05groundwater 1.7E-09 6.7E-11 7.7E-11 2.4E-08 1.7E-09 4.1E-10 8.6E-05buffer 1.4E-11 5.7E-11 5.1E-11 2.8E-08 1.7E-09 2.4E-10 1.1E-04

backfill 3.2E-11 3.2E-11 1.6E-11 1.0E-08 3.4E-11 1.7E-11 3.6E-05canister unlim. unlim. unlim. unlim. unlim. unlim. unlim.groundwater unlim. unlim. unlim. unlim. unlim. unlim. unlim.buffer unlim. unlim. unlim. unlim. unlim. unlim. unlim.backfill unlim. unlim. unlim. unlim. unlim. unlim. unlim.canister 1.3E-04 7.4E-04 1.8E-04 unlim. unlim. 2.0E-05 unlim.groundwater 5.0E-03 8.7E-04 1.8E-04 unlim. unlim. 2.7E-05 unlim.buffer 1.0E-03 6.7E-04 5.1E-04 unlim. 3.2E-04 1.6E-05 unlim.backfill 3.7E-04 3.5E-04 1.3E-04 unlim. 3.8E-04 1.4E-04 unlim.

canister 5.8E-10 5.9E-11 3.3E-10 8.2E-09 2.0E-07 5.9E-09 3.4E-07groundwater 1.7E-09 4.9E-10 4.7E-10 1.2E-08 5.9E-09 1.3E-08 1.0E-06buffer 1.4E-09 4.3E-10 4.5E-10 1.5E-08 5.8E-09 4.9E-09 8.1E-07backfill 4.2E-10 2.8E-10 3.5E-10 5.1E-09 8.5E-10 4.3E-10 2.9E-06canister 1.1E-07 6.3E-08 7.6E-08 7.0E-08 1.3E-05 4.2E-06 1.3E-05groundwater 5.9E-08 5.7E-08 6.9E-08 3.9E-08 1.3E-05 3.3E-06 1.3E-05buffer 8.4E-08 5.9E-08 8.1E-08 3.4E-08 1.2E-05 2.8E-06 1.2E-05backfill 7.4E-08 5.8E-08 6.2E-08 3.6E-08 1.4E-05 4.3E-07 1.4E-05canister 1.4E-06 4.4E-06 2.2E-06 1.7E-06 1.7E-06 7.6E-07 4.4E-06

groundwater 6.3E-06 7.1E-06 3.0E-06 7.4E-06 1.7E-06 9.3E-07 7.4E-06buffer 1.9E-06 6.0E-06 2.1E-06 4.8E-06 1.7E-06 8.6E-07 6.0E-06backfill 2.5E-06 5.7E-06 4.9E-06 3.9E-06 1.8E-06 6.1E-07 5.7E-06canister unlim. unlim. unlim. unlim. unlim. unlim. unlim.groundwater unlim. unlim. unlim. unlim. unlim. unlim. unlim.buffer unlim. unlim. unlim. unlim. unlim. unlim. unlim.backfill unlim. unlim. unlim. unlim. unlim. unlim. unlim.canister unlim. unlim. unlim. unlim. unlim. unlim. unlim.

groundwater unlim. unlim. unlim. unlim. unlim. unlim. unlim.buffer unlim. unlim. unlim. unlim. unlim. unlim. unlim.backfill unlim. unlim. unlim. unlim. unlim. unlim. unlim.

I

Cl

Sr II

Se -II

Sn IV

Be II

Cinorg

Ra II

Cs I


52

Table 4-3. Comparison of transition metal solubilities in the backfill porewater with those in canister-, ground- and buffer porewater (Wersin et al. 2014). Solubilities differing by more than one order of magnitude between the compartments are highlighted in yellow.


Saline KR20/465/1

Brackish KR6/135/8


KR4/81/1

Brine KR4/861/1

High alkaline

Glacial melt

water

Upper limit

(mol/l)

Zr IV

canister 1,7E-08 1,8E-08 1,8E-08 1,2E-08 1,7E-08 1,8E-08 9,2E-07

groundwater 1,7E-08 1,8E-08 1,8E-08 1,2E-08 1,7E-08 1,8E-08 9,2E-07

buffer 1,5E-08 1,7E-08 1,8E-08 9,8E-09 1,7E-08 1,8E-08 9,2E-07

backfill 1,7E-08 1,7E-08 1,8E-08 1,0E-08 1,7E-08 1,8E-08 9,2E-07

Ni II



buffer 1,9E-04 1,5E-03 1,0E-04 4,4E-04 1,2E-07 1,3E-07 1,5E-03


Nb V

canister 9,5E-07 1,9E-07 3,4E-07 5,2E-07 unlim. 1,3E-04 unlim.

groundwater 1,5E-07 1,2E-07 2,4E-07 9,7E-08 unlim. 2,8E-04 unlim.

buffer 6,1E-07 1,5E-07 4,1E-07 1,2E-07 unlim. 2,1E-04 unlim.

backfill 3,8E-07 1,5E-07 1,6E-07 1,4E-07 unlim. 1,0E-05 unlim.

Mo VI

canister 3,1E-06 2,4E-06 1,6E-07 1,1E-10 4,2E-06 1,0E-04 unlim.

groundwater 8,8E-09 2,3E-08 5,2E-08 1,1E-11 4,1E-06 9,9E-05 unlim.

buffer 3,3E-07 3,7E-08 1,3E-07 8,4E-12 4,1E-06 1,0E-04 unlim.

backfill 1,3E-07 4,7E-08 7,0E-08 1,3E-11 4,9E-06 6,1E-06 unlim.

Tc IV



buffer 3,4E-09 3,8E-09 4,0E-09 2,2E-09 4,6E-09 4,4E-09 1,4E-08


Pd II



buffer 3,4E-06 3,9E-06 4,0E-06 1,5E-04 3,8E-06 4,0E-06 1,5E-04


Ag I



buffer 3,0E-05 1,4E-05 1,0E-06 3,3E-04 1,0E-05 9,9E-07 1,5E-04


53

Table 4-4. Comparison of lanthanide solubilities in the backfill porewater with those in canister-, ground- and buffer porewater (Wersin et al. 2014). Solubilities differing by more than one order of magnitude between the compartments are highlighted in yellow.


Saline KR20/465/1

Brackish KR6/135/8


KR4/81/1

Brine KR4/861/1

High alkaline

Glacial melt

water

Upper limit

(mol/l)

Sm III



buffer 1,7E-07 6,3E-07 1,3E-07 8,6E-07 2,4E-06 5,8E-09 2,1E-05


Eu III



buffer 6,2E-08 2,3E-07 5,3E-08 4,7E-07 1,3E-06 2,2E-09 2,9E-05


4.2 Concluding remarks

The RN solubility database for the backfill represents a supplement to the other near-field solubilities presented in Wersin et al. (2014). General concepts, thermodynamic source data and calculation approaches were adopted in order to provide a consistent geochemical database for SA. Thus, quality control measures, such as the comparison with the alternative Nagra/PSI database or the alternative high ionic strength correction with the SIT approach, also provide confidence in the here presented solubility data for the backfill. However, remaining uncertainties associated with the thermodynamic approach, i.e. the nature of the solubility limiting phase and the carbonate complexation of actinides and lanthanides, also hold for the data presented here. The uncertainty in the solubility limiting solid phase was taken into account by the conservative selection of the kinetically favoured less crystalline phase, if available. Nevertheless, more experimental work regarding solid solution formation or carbonate complexation could help to reduce the geochemical uncertainty and lower best estimate solubilities for some RN. RN solubilities were calculated for the porewater in the averaged reference backfill material. Comparison of the porewater compositions derived for the individual backfill components and the alternative backfill design of SKB demonstrated the robustness of the derived porewater composition (see Table 1-4 and Appendix A). The geochemical uncertainty introduced by the reference and bounding groundwaters clearly outweighed the uncertainty associated with the different backfill materials. This also became evident in the comparison of RN solubilities in the different near-field compartments (section 4.1). RN solubilities generally span a larger range over the reference and bounding waters of one near-field compartment than for one water type over the different near-field compartments. Thus, solubilities derived here for the averaged reference backfill can be considered a good approximation for the backfill. The effect of groundwater or of backfill derived dissolved humic substances on RN speciation and thus solubilities has not been considered, due to the lack of site specific

54

data. Discussion of this topic in Wersin et al. (2014) led to the conclusion that groundwater derived humic substances will have only a minor effect except for the trivalent lanthanides and actinides. However, considering the conservative uncertainty treatment and the associated large range of solubilities, it was deemed justified to ignore the effect of humic and fulvic substances on RN solubilities. The organic carbon content within the different backfill materials does not exceed 0.4 %, but additional organic matter might be introduced in construction materials such as concrete admixtures. Thus, some more detailed investigations on the content and nature of organic matter in the Olkiluoto groundwater and backfill materials would be necessary and should be included in future SA.

55

PART II - RADIONUCLIDE DIFFUSION AND SORPTION

56

57

5 BACKGROUND ON RADIONUCLIDE MIGRATION

5.1 Concepts and fundamental relations

The migration of radionuclides (RN) through the tunnel backfill follows the same principles as RN migration in the buffer. The discussion given in Wersin et al. (2014) is not completely repeated here, but the main issues are summarised. The transport of most RN is constrained by (slow) diffusion and is further retarded by sorption to the clay surfaces. Radionuclide transport models applied in safety assessment are generally based on the simple formalism of Fick's laws for one-dimensional steady-state and time-dependent RN mass transfer. The latter is formulated for the linear case as follows:

2

2

dx

CdD

t

Ca

(5-1)

with dd

ea K

DD

(5-2)

where Da is the apparent diffusion coefficient (m2s-1), d the clay or rock dry density (kgm-3); and Kd the mass distribution ratio representing partitioning of RN mass between dissolved species and sorbed species at position x (m). The term +dKd is often referred to as the rock capacity factor. In practice, transport through the backfill is modelled with the aid of eq. 5-2, requiring the assignment of De, and Kd values for each RN. The diffusion parameters De and are commonly obtained from small-scale through-diffusion experiments using non-sorbing or weakly sorbing tracers, such as HTO, Cl- or Na+. Kd values on the other hand can either be obtained from diffusion data or from batch sorption tests as outlined in section 7. It should be born in mind that there are some implicit assumptions in this approach: The first assumption is that the magnitude of sorption is not dependent on RN concentration, which is generally valid at the low RN concentrations of concern. The second is that sorption is reversible, which is not clearly established in all cases. The third assumption concerns the transport process itself: the RN are presumed to migrate solely by diffusion, independently of other species, thus multicomponent diffusion effects and other transport processes, such as electromigration processes, are neglected. This assumption is also valid for RN diffusing through a compact clay matrix at trace concentrations under more or less constant conditions. Inherent in this assumption is that each species may diffuse at its own rate. Thus, a value for the diffusivity and diffusion-accessible porosity has to be defined for each RN.

5.2 Radionuclide diffusion: model concepts

From tracer through-diffusion experiments it can be inferred that anions show lower diffusive fluxes than neutral tracers (e.g. HTO), whereas simple (uncomplexed) cations, such as Na+, Sr2+ and Cs+, tend to show higher diffusive fluxes. It is established that these effects are primarily related to the negative charge in the narrow pore spaces

58

stemming from the negatively charged clay surfaces (NEA 2011). The magnitude of the electrostatic effects depends on the composition of the external solution and on the clay type, and is more pronounced for montmorillonite than for illite (Glaus et al. 2010). To explain the mentioned anion and cation diffusion behaviour, different model concepts have been developed. This diversity of concepts is in turn related to significant uncertainties regarding the microstructure and nanostructure of compacted clays and especially regarding the nature and distribution of pore types. The different model concepts can be grouped accordingly into single- and multi-porosity models. It should be pointed out that most work to date has been conducted on montmorillonitic clays and less is known about the respective effects in illite. The single-porosity models all consider a more or less homogeneous pore space to be present in compacted clay:

The conventional pore model (as typically applied for performance assessment calculations) does not take into account the electrostatic effects, and the diffusion of simple anions and cations is handled by a reduction of accessible porosity and a lower De for anions and an increased De for cations (see e.g. Yu & Neretnieks 1997, Schwyn 2003, Ochs & Talerico 2004). I.e., two different mechanisms need to be invoked, which is not very satisfying from the viewpoint of process understanding.

Donnan-equilibrium models (Birgersson & Karnland 2009) treat compacted smectite clay like a homogeneous solution containing immobile negative charges (clay platelets) inside a semi-permeable membrane. This model successfully describes swelling pressure development as a function of compaction and salinity, as well as the diffusion of some cations and anions.

Sato et al. (1995) and Ochs et al. (2001) developed a diffuse layer model considering the distribution of ions between bulk solution, diffuse layer (extending from planar surfaces) and ion exchange (or surface complexation) sites. The model is able to describe enhanced cation diffusion and anion exclusion in compacted bentonite based on cation excess and anion deficit in the diffuse layer. However, this model used significant simplifications (such as not explicitly accounting for the truncation of the diffuse layer) which are partly remedied in a modified version used by Tachi et al. (2009a, 2010).

Recently, several multi-porosity models have been formulated, which generally consider diffusion in the different porosity/porewater compartments "free" water, diffuse layer water and interlayer water, each with their own physicochemical properties (e.g. Appelo & Wersin 2007; Bourg et al. 2006). While these models use different concepts of pore characteristics, all are based on the concept of exclusion of anions from a part of the total porosity, thus leading to lower diffusion porosities and diffusion coefficients relative to neutral and cationic species. The enhanced diffusion of cations on the other hand is taken into account by the increased diffusional gradient of cations between the diffuse layer and the free water (e.g. Appelo & Wersin 2007), by an additional diffusion pathway in the interlayer (e.g. Bourg et al. 2007) or by an increased mobility close to the surface, often termed surface diffusion (e.g. Gimmi et al. 2010). Following the latter approach, Gimmi & Kosakowski (2011) inferred from an evaluation of a large set of effective diffusion data in the literature that cations sorbed to

59

clay surfaces exhibit a cation specific surface mobility, which is inversely proportional to the sorption affinity. According to the authors, this surface mobility would contribute to diffusive fluxes exceeding the diffusive flux expected from the sorption coefficients. It should be pointed out that in the context of diffusive transport, sorption of alkali and alkaline earth elements at the planar (siloxane) surface of clay minerals has to be viewed as an accumulation at or near the negatively charged planar surface. While it is not finally established to which extent this should be interpreted as actual sorption (immobilisation) at ion exchange sites vs. an excess of cations in the diffuse layer, most experiments can only be interpreted by assuming some of the interlayer cations to be mobile (cf. NEA, 2011 and references above).

60

61

6 RADIONUCLIDE DIFFUSION DATA

The approach chosen for the derivation of diffusion data for the backfill is grounded in the geochemical model used for porewater calculations and in the lack of experimental diffusion data for the backfill. As already pointed out in Wersin et al. (2014), the geochemical model concept underlying the calculation of the porewater composition defines the partitioning of ions into the various porewater compartments (e.g. free water, interlayer water). This partitioning ultimately defines the mass-flux of all charged species in the pore space with respect to an inert and uncharged tracer such as HTO. As described in chapter 1.1.2, the same geochemical model as in case of the buffer has been used for the backfill. Therefore, the concepts of anion and cation diffusion used for the buffer are directly adopted from Wersin et al. (2014). To our knowledge, there are no diffusion data for Friedland clay or whole Milos bentonite. Because of the lack of reference data regarding diffusion as well as actual physical properties, De values for the backfill were derived by scaling De values derived for similar material. For this purpose, the values derived by Wersin et al. (2014) for the MX-80 clay are an obvious choice: the contacting groundwaters are the same as in the present case and the geochemical model underlying their derivation of diffusion data is consistent with the model used for the present report (see chapter 1.1.2). Further, the composition of the average backfill as given in Table 1-2 is considered, i.e. no variations are taken into account. Scaling De values derived for the composition of the buffer to the backfill could be done in different ways, for example based on the dry density or clay content of the buffer and backfill materials. However, it is not clear in this context how the contribution of the (calculated) illite content should be evaluated. Therefore, it was decided to perform scaling directly on the basis of the diffusion of HTO. Specifically, the following steps were carried out: First, the recommended diffusion data for HTO in the buffer were re-evaluated for the average backfill. The basis for this is provided by the summary of experimental diffusion data given in Figure 8-1 of the report by Wersin et al. (2014). This figure is reproduced in Figure 6-1 and shows effective diffusivities (De) of HTO in compacted montmorillonite and bentonite as a function of dry density at room temperature. A consistent diffusion behaviour as a function of dry density can be seen. It is further evident that the clays with a high smectite content result is lower De of HTO at any given dry density than Kunigel-V1, which is the only clay with a low smectite content (45-50% ). Further, the data in Figure 6-1 do not show a significant difference in the effective diffusivity coefficients of Na or Ca bentonites. While some studies comparing Na- and Ca-clays (e.g. Gonzalez Sanchez et al. 2008) report significant higher diffusion coefficients for the Ca-form, these do not exceed the diffusivities observed overall for Na-bentonites/montmorillonites. Hence, the trend of De vs. dry density is considered valid for both Na- and Ca-clays. The effective diffusion of non-charged species is further not significantly dependent on the ionic strength of the porewater (e.g. Gonzalez Sanchez et al. 2008; Melkior et al.

62

2009; Glaus et al. 2010), but shows a clear dependency on temperature, see below (eq. 6-1). It is assumed that Kunigel-V1 with a smectite content of about 48-50% is a reasonably conservative analogue material for the backfill. The diffusive resistance of the illite and kaolinite components in the backfill is neglected in a first approximation, since the predicted content of these minerals in the backfill is rather low. While the effective diffusivity of HTO in pure illite and kaolinite is higher than in pure montmorillonite by about factor 10 (Gonzalez-Sanchez et al. 2008; Glaus et al. 2010), there is no significant difference in HTO diffusion in low grade bentonite (e.g. Kunigel-V1 see Figure 6-1) and clay rocks containing montmorillonite and illite (see e.g. Bazer-Bachi et al. 2007). Further, in the absence of any other evidence, the same dependency of De on dry density as in case of the buffer is assumed. These relations are shown in Figure 6-1. For the specified dry density of 1720 kg/m3, a De of 9.0×10–11 m2/s can be estimated as realistic value. In view of the lack of diffusion data for the specified backfill material, a pessimistic De is proposed as well. The pessimistic De is proposed as 2.0×10–10 m2/s, which is oriented along the highest values for Kunigel-V1 shown in Figure 6-1, using a small additional margin of uncertainty. This value is thought to take into account any uncertainty related to neglecting the effect of illite and kaolinite, but not any uncertainties related to the actual backfill composition. The corresponding diffusion-available porosity is taken to be the same as the physical porosity (0.38, see chapter 1.1). Second, the diffusion parameters for anions and cations sorbing via ion exchange (Cs, Ra, Sr) as recommended for the buffer in Wersin et al. (2014) are scaled with regard to the parameters for HTO, assuming that the same ratios of De(HTO)/De(Cs), De(HTO)/De(Ra), De(HTO)/De(Sr), and De(HTO)/De(anions) as derived for the buffer would be valid for the backfill. Scaling was done on the basis of the best estimate De for HTO of 9.0×10–11 m2/s. In case of the anions, the De values for the buffer are taken from Table 8-4 in Wersin et al. (2014), which represent upper limit values. This is considered sufficiently cautious, and no additional conservatism (e.g. by scaling using the pessimistic value for HTO) is introduced. The corresponding diffusion-available porosities diff are scaled from the respective values proposed in Wersin et al. (2014) for each porewater assuming that the relation diff(HTO)/diff(anion) is the same for buffer and backfill in case of each porewater. Anions in the present sense include all true and non-sorbing anions (Se(-II), iodide, chloride), but do not include labile RN complexes that are negatively charged (such as negatively charged RN-hydroxo or -carbonate species) or sorbing anions such as molybdate, which may overcome anion exclusion effects in the sorption process. The De values for Cs, Sr and Ra are also based on the values given in Table 8-4 of Wersin et al. (2014). Following their recommendation, the scaled diffusivities are to be used in combination with lower limit Kd values. Again, this is considered sufficiently conservative, and no additional scaling on the basis of the pessimistic value for HTO is done. Note that the derivation of diffusion parameters for these cations in the buffer (Wersin et al. 2014) is based on the concept by Gimmi & Kosakowski (2011), which

63

implies a direct relationship between the diffusion and the ion-exchanged concentration (i.e., the concentration sorbed in the interlayer). This coupling leads to low De values when sorption (i.e., accumulation in the interlayer) is low. As a result, the calculated De values for Sr and Ra would fall below both the realistic and pessimistic values for HTO in case of the brine, saline and brackish waters, and the De for Cs would be lower than the values for HTO in case of the brine water. While this is consistent with the model concept of Gimmi & Kosakowski (2011), it was decided to assign the pessimistic De for HTO in all of these cases. This was done for reasons of conservatism, but also to acknowledge the relations observed when a more conventional pore model is used (where an enhancement of cation diffusion is typically linked to a De greater than that of HTO). Further, the same diffusion-accessible porosity as for HTO is proposed for Cs, Sr and Ra in analogy to the approach taken for the buffer (Wersin et al. 2014). For all other cationic/hydrolysable radionuclides (Th, Pa, U, Np, Pu, Am, Cm, Eu, Sm, Sn, Zr, Ni, Co, Nb, Tc, Pd, Ag in the relevant oxidation states, and for Mo, see above), the same realistic and pessimistic De and diff values as for HTO are proposed in analogy to the approach followed in the case of the buffer (Wersin et al. 2014). This applies also to 14C in the form of CH4 or other neutral organic species. The diffusion parameters for these RNs are to be used in combination with the independently derived Kd values. The recommended diffusion parameters are summarised in Table 6-1. They generally correspond to a temperature of 25 °C. As pointed out in chapter 1.1.2, this is close to the temperature expected as relevant for the backfill. With regard to the effect of temperature on diffusion, the approach by Wersin et al. (2014) is adopted. They observed an exponential increase with increasing temperature according to

0.03)T0.026(ee eC)(0DC)(TD ]s [m -12 (6-1)

This would imply a factor of 1.5 for a temperature increase from 25 °C to 40 °C. Similarly, the approach of Wersin et al. (2014) is accepted with regard to a possible influence of Ca-clay vs. Na-clay: they assessed that the selected data are valid for both ionic forms. This is consistent with the examples Ca-clays shown in Figure 6-1, as well as with further data for e.g the Ca-form of Kunigel-V1 (Mihara 2000; not shown). It is further pointed out that the backfill is expected to exist mainly in the Na-form (Table 1-2). As a result of the chosen scaling approach, all De values as well as diffusion-available porosities are very similar to those recommended for the buffer by Wersin et al. (2014). For Cs, Ra and Sr, where diffusion parameters are coupled to Kd following the approach of Gimmi & Kosakowski (2011), the lower limit Kd values to be used in combination with the diffusion parameters are also given in the table. Because of the strong sorption of Cs to illite, these are much higher for Cs than for the alkali earth elements. Note that the strong sorption of Cs on illite is mainly due to the frayed edge sites (FES) located at the edge surface of illite, not to a particularly strong sorption at the planar surface. Thus, this is not inconsistent with the model concept of Gimmi & Kosakowski (2011).

64

y = 3E-09e-0.0022x

R2 = 0.70

1.E-12

1.E-11

1.E-10

1.E-09

0 500 1000 1500 2000 2500

De

(m2

s-1

)

dry density (kg m-3)

Montmorillonite Milos (Na) (Gonzales Sanchez et al., 2008) Montmorillonite Milos (Na) (Glaus et al., 2007,2010)

Montmorillonite Milos (Ca) (Gonzales Sanchez et al., 2008) MX-80 (Na) (Melkior et al., 2009)

MX-80 (Na) (Neretnieks, 1982) MX-80 (Na) (Eriksen, 1982)

MX-80 (Na) (Pocachard et al. 2000) MX-80 (Ca) (Melkior et al., 2009)

MX-80 (APW) (Goutelard & Charles, 2004) MX-80 (APW) (Melkior et al., 2004, 2009)

MX-80 (APW) (Brouard et al., 2004) MX-80 (Cs) (Melkior et al., 2009)

Kunipia-F (Na) (Kozaki et al. 1998) Kunipia-F (Na) (Sato & Suzuki, 2003)

Kunipia-F (Na) (Suzuki et al., 2004) Avonlea (Na) (Choi & Oscarson, 1996)

Avonlea (Ca) (Choi & Oscarson, 1996) FEBEX-bentonite (Ca,Mg) (Garcia-Guttierez et al., 2001,2004)

Kunigel-V1 (Na) (Sato & Suzuki, 2003) Kunigel-V1 (Na) (Kato et al., 1995)

Kunigel-V1 (APW) (Sato, 1998a)

Figure 6-1. Effective diffusivities (De) of HTO in compacted montmorillonite and bentonite as a function of dry density at room temperature (modified from figure 8-1 of Wersin et al. 2014). Vertical black lines indicate the target (solid) and bounding (dashed) dry densities of the buffer, the vertical blue line indicates the target dry density of the backfill. The regression line for the buffer (black) and the corresponding line for the backfill (blue solid) are also indicated; the dashed blue line indicates the pessimistic case for the backfill (see text).

65

Table 6-1. Recommended De values, diffusion-available porosities and in case of Cs+, Sr2+ and Ra2+ lower limit Kd values (to be used in combination with diffusion parameters) for safety assessment. Values where the conservative De(HTO) is recommended for Ra, Sr and Cs instead of the nominally calculated values are indicated in italics.

saline water KR20/465/1

brackish water KR6/135/8

dilute, carbonate rich water KR4/81/1

brine water KR4/861/1

high alkaline water

glacial melt water

anions

De (m2/s) 9.5E-12 7.4E-12 2.0E-12 3.0E-11 9.5E-12 3.5E-13

diff 0.10 0.07 0.01 0.15 0.10 0.01

Sr, Ra

De (m2/s) 2.0E-10 2.0E-10 1.2E-09 2.0E-10 2.1E-10 9.5E-09

diff 0.38 0.38 0.38 0.38 0.38 0.38

Kd (m3/kg) 5.9E-04 9.8E-04 2.4E-03 3.8E-05 6.3E-04 3.7E-03

Cs

De (m2/s) 2.8E-10 9.5E-10 2.0E-09 2.0E-10 1.3E-09 2.9E-09

diff 0.38 0.38 0.38 0.38 0.38 0.38

Kd (m3/kg) 1.3E-01 6.1E-01 5.5E-01 4.0E-01 1.1E+00 6.6E+00

HTO, other cations, neutral species

De (m2/s), best estimate 9.0E-11 9.0E-11 9.0E-11 9.0E-11 9.0E-11 9.0E-11

De (m2/s), upper limit 2.0E-10 2.0E-10 2.0E-10 2.0E-10 2.0E-10 2.0E-10

diff 0.38 0.38 0.38 0.38 0.38 0.38

66

67

7 RADIONUCLIDE SORPTION IN COMPACTED CLAYS

7.1 Sorption processes

The average composition of the backfill is described in chapter 1.1. It is similar to the composition of bentonite (see report by Wersin et al. 2014) in the sense that the mineralogy is dominated by clay minerals. Therefore, similar sorption processes as in compacted bentonite can be expected in a broad sense. The only major qualitative difference in comparison to bentonite is the presence of a significant fraction of illite in the backfill. As further detailed below, a small fraction of the ion exchange sites present on illite differ from those on smectite-type clay minerals, offering additional sorption sites for a few elements. With regard to the RN considered here, this is only relevant for Cs. Sorption of RN and other solutes on montmorillonite and illite, and to some degree on related materials such as bentonite and some clay rocks has been studied in batch systems in the last 20 years and fairly good understanding of the basic sorption processes exists. In this regard it is worth to point out the systematic experimental work of Bradbury & Baeyens (e.g. Bradbury & Baeyens 1997a; 2003; 2011). While their work is restricted to simple systems (pure minerals/inert electrolyte solutions), it provides at least a minimum set of sorption data (such as a sorption edges on smectite and illite) for many of the relevant radionuclides. For a range of radionuclides, no other comparable datasets are available. The above-mentioned research has established the importance of two fundamental sorption processes occurring on different surfaces of clay minerals (e.g. Stumm & Morgan 1996; NEA 2011):

Cation exchange takes place at the siloxane (basal or planar) surfaces of clay minerals, which are well crystallised and are chemically rather inert (possess no surface functional groups). However, isostructural substitutions in the clay mineral lattice give rise to an excess permanent negative charge at these surfaces, which is compensated by exchangeable cations. The majority of such ion exchange sites are nearly identical on smectite and illite. In addition, illite possesses a small number of exchange sites located at the edge of clay platelets (frayed edge sites, FES) which can only be accessed by ions that are easily dehydrated (mainly Cs+, K+) due to steric restrictions.

Surface complexation (or ligand exchange in case of anions) takes place at the edge sites of the montmorillonite surface. Here, the crystal structure of the clay platelets is interrupted and the broken bonds form surface hydroxo-groups (similar to a metal oxide surface).

An illustrative example of these two processes is given by the sorption of divalent metals, such as Ni(II). At low pH, the Ni2+ ion sorbs via cation exchange. In parallel with hydrolysis, Ni begins to interact with the edge hydroxo-groups and sorbs via surface complexation under neutral and alkaline conditions. Whereas the cation exchange process leads to moderate sorption and is strongly dependent on salinity

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(more precisely, on the concentration of competing cations) but not on pH, the surface complexation process is strongly dependent on pH but (almost) not on ionic strength and leads to strong sorption for the conditions of interest in PA. Both processes can be fairly well modelled with simple thermodynamic mass-action models including aqueous speciation as well as cation exchange and surface complexation sub-models (NEA, 2011). The relationships and parameters relevant for the present derivation of Kd values (e.g. sorption capacity, sorption values) have been evaluated from disperse batch systems. To date, only a few studies have tested whether the data derived from disperse conditions also holds for compacted systems (see NEA 2011, Montavon et al. 2006, 2009, Van Loon & Glaus 2008, Tachi et al. 2010). While the results from the different studies do not yield an unambiguous picture, they suggest that sorption is equal to or possibly even stronger in compacted systems. This was also concluded recently by NEA (2011) on the basis of the available literature information. However, there is still a lack of data under compacted conditions for more strongly sorbing RN (e.g. lanthanides, actinides). In addition to the experimental evidence, the validity of applying sorption data from disperse conditions to compacted systems can also be tested by comparing batch data with diffusion data on compacted samples under sufficiently similar chemical conditions. This can be done by deducing Kd values from diffusion data using experimental Da values and estimated De values (see chapter 5) according to eq. 5-2. Bradbury & Baeyens (2002b) conducted a preliminary comparison exercise using experimental batch Kd data and diffusion-derived Kd values (estimated on the basis of Da and De values from Sato & Yui 1997; Sato 1998b) for Cs(I), Ni(II), Sm(III), Am(III), Zr(IV) and Np(V) and found a remarkable agreement within the experimental uncertainty. Ochs & Talerico (2004) used independent, experimental Da values from a variety of literature sources to evaluate the consistency of their recommended Kd and De values. They also found fairly good agreement within the estimated uncertainty of the data. It needs to be pointed out that there is only a limited amount of reliable diffusion data available that are suitable for such comparisons. This is because of the often poorly constrained geochemical boundary conditions in diffusion experiments and the limited resolution of the experimental in-diffusion profiles, especially in case of strongly sorbing tracers.

7.2 Derivation of sorption data

Following the approach taken by Wersin et al. (2014) as well as in recent safety assessments, such as Nagra's project Opalinus Clay (Nagra 2002) and SKB's projects SR-Can and SR-Site (SKB 2006, 2010), the RN sorption data derived in the present report are principally based on well constrained sorption data obtained in batch experiments (disperse conditions) which are extrapolated to various predicted in-situ conditions of the buffer. The underlying derivation procedures are based on the approach of Bradbury & Baeyens (1997a, 2003) and Ochs & Talerico (2004). The latter modified the procedure of Bradbury & Baeyens (1997a, 2003) to account for the sorption of complexed RN

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(more precisely, for the formation of ternary surface species). The three main steps in these approaches include

(i) the data selection, which follows a defined hierarchy of data sources; (ii) the conversion of the selected data to the predicted in-situ conditions (in

particular the corresponding pore water conditions) by the use of conversion factors (CF);

(iii) the estimation of uncertainties, using uncertainty factors (UF) that correspond to the conversion factors used.

These three processes are briefly summarised below. The derivation of RN-specific sorption data, expressed in terms of a best estimate (recommended) Kd value and corresponding upper/lower limits is discussed for each RN in chapter 8. Data sheets for each RN, including the speciation in the experimental systems as well as in the reference and bounding in-situ porewaters, source data information, CF and UF are provided in Appendix B. The recommended best estimate Kd values and the corresponding lower limits for all reference and bounding porewaters are summarised in Table 9-1. In principle, sorption data for the conditions of interest could also be derived from thermodynamic sorption models, as for example proposed by Bradbury & Baeyens (2011b) for a number of RN. While these models are principally accepted as ideal tools for such a purpose, it needs to be realised that most of the presently available models have been parameterised for simple systems (such as pure montmorillonite in an inert electrolyte solution) only. This means that application to the complex in-situ conditions would therefore represent a very significant extrapolation outside the range of calibration, which is not recommended (NEA 2005, 2011). Therefore, the more empirical approach of using conversion factors was adopted for the present purpose.

7.2.1 Selection of source data

In contrast to the buffer material (MX-80) addressed by Wersin et al. (2014), the backfill materials of concern here, Friedland Clay and Milos bentonite, are not well known. Moreover, the reference composition of the backfill represents a (theoretical) average composition of these two clay materials. Accordingly, no experimental sorption data are available for this specific material. Therefore, focus was placed on data for the two main components of the backfill material, montmorillonite and illite. For the same reason, data for a specific material, such as MX-80, were not given more weight than data from generic systems. The quality of the underlying experiments was evaluated to ensure the selection of the best available data. Special respect was given to the numerous sorption edges determined by Bradbury & Baeyens (2005, 2009) on montmorillonite and illite under nearly identical conditions. For elements, for which no reliable experimental data exist, data of chemical analogue elements were used. Where neither experimental data nor chemical analogues are available, the selection of sorption data was based on conservative expert judgment. It is acknowledged that a notable amount of data exists for clay rocks, such as Opalinus Clay (Switzerland) and the Callovo-Oxfordien formation (France), which could also

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have been considered. However, the related experiments are often carried out in the presence of complex (synthetic) porewaters, and sorption edges are not as commonly determined as in simple systems. Therefore, it is more difficult to transfer such data to the in-situ conditions for the backfill, which would introduce additional uncertainties in the data derivation. Clay rocks may also contain further minerals (carbonates, possibly oxide minerals) and organic matter, which makes it more difficult to interpret the data and again, to transfer the data to the smectite/illite mix assumed for the backfill. For these reasons, the data determined for the simpler systems were generally preferred.

7.2.2 Conversion factors

Because the magnitude of Kd for a given RN depends strongly on pH and other chemical conditions, it is necessary to convert the original sorption data to the reference conditions of the repository site. This has been done on the basis of conversion factors, following the approach of Bradbury & Baeyens (1997a, 2003), Ochs & Talerico (2004) and Wersin et al. (2014). In the following, these factors are briefly described. Conversion mineralogy (CFmin): This conversion factor takes into account the difference in sorption capacity between the minerals used in the selected experimental systems and the reference backfill composition. Since the backfill contains two sorption-relevant minerals (smectite, illite), source data for both minerals were considered. Because the average properties of the backfill are not well known, the mineralogy conversion factor CFmin was based directly on the respective fractional smectite and illite content (fsmectite, fillite).

CFmin(smectite) = fsmectite backfill / fsmectite data source (7-1a)

CFmin(illite) = fillite backfill / fillite data source (7-1b) Implicit to this approach is the assumption that the sorption capacity of the different smectite and illite samples are the same in terms of surface complexation and ion exchange. We feel that less uncertainty is introduced in this way in comparison to using an assumed CEC for the smectite and illite fraction of the backfill (and further assuming that the CEC and surface complexation capacity follow the same proportionality in all cases). Conversion pH (CFpH): Where available, sorption data at the pH of the reference backfill porewaters were chosen, in which case no pH conversion is required. Otherwise the slope of additional sorption edge data (RN sorption vs pH) was used to correct for the difference in pH values. The pH correction factor is calculated as the ratio of the distribution coefficient at the pH of the in-situ porewater, Kd(pH porewater), and at the pH of the measurements used as data source, Kd(pH data source):

CFpH = Kd(pH porewater) / Kd(pH data source) (7-2) Conversion speciation (CFspeciation): Dissolved ligands and major cations can have a significant influence on radionuclide sorption. Therefore, scaling of Kd to the predicted

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in-situ conditions should in principle be carried out in all cases where the source data correspond to a solution composition that is significantly different from that of the reference porewater (which is nearly always the case). Only thermodynamic sorption models are capable of consistently taking into account all speciation effects, but as pointed out above, most of these models are not sufficiently well calibrated to date. Therefore, the same type of empirical approach as in case of the other conversion factors was used. The speciation correction factor accounts for the competition between sorption sites and dissolved species for any given RN and is defined as the ratio of the fraction of sorption-available species in the reference porewater Fsorb(porewater) and in the solution of the sorption experiments Fsorb(data source).

CFspec = Fsorb(porewater) / Fsorb(data source) (7-3) Following the argumentation of Ochs & Talerico (2004), Fsorb can be described as the fraction of the total amount of RNs (RNtot) that has not formed sorption-competitive complexes (RNcomplexed).

Fsorb = (RNtot – RNcomplexed) / RNtot (7-4) Changes in the speciation and sorption behaviour based on the hydrolysis of the RN are already accounted for in the pH conversion factor and were therefore not considered in the speciation conversion factor. A special case is the complexation with dissolved carbonate. Several elements, especially IV- and higher-valent actinides, are known or suspected to form mixed hydroxy-carbonate complexes in aqueous solutions (see e.g. Guillaumont et al. 2003). By analogy, the formation of ternary RN-carbonate surface complexes at clay edge sites can be assumed. For some cases, the existence of such complexes has been confirmed by spectroscopy (e.g. for UVI sorption on iron oxide by Bargar et al. 2000). Therefore, it is highly questionable in these cases whether the presence of carbonate should be counted as a competitive factor or not. This situation is complicated further by the uncertainties regarding the existence and importance of various simple and mixed actinide(IV)-carbonate complexes (see discussions in Hummel et al. 2002; Guillaumont et al. 2003). Therefore, scaling of Kd to account for speciation was carefully evaluated. As a basis for further evaluation, CFspec was calculated twice in these cases, i) taking competition by formation of aqueous complexes involving carbonate ions fully into account or ii) neglecting this effect. Conversion disperse compacted systems: As pointed out in section 7.1, the presently available evidence indicates that sorption in compacted systems is not lower than in batch experiments with dispersed material. Therefore, this conversion factor is taken as unity in all cases. Overall conversion data source backfill: To calculate overall Kd, sorption by the smectite and illite components were first evaluated separately. Overall Kd was then calculated according to the fractions of smectite and illite in the backfill.

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Kd, backfill = Kd, backfill(smectite) + Kd, backfill(illite) (7-5)

where

Kd, backfill(smectite) = CFtotal(smectite) × Kd, data source(smectite) (7-6a)

Kd, backfill(illite) = CFtotal(illite) × Kd, data source(illite) (7-6b)

Based on the individual conversion factors explained above, the overall conversion factor for converting original (experimental) sorption data to the backfill reference conditions is defined for the smectite and illite component as:

CFtotal(smectite) = CFmin(smectite) × CFpH(smectite) × CFspec(smectite) (7-7a)

CFtotal(illite) = CFmin(illite) × CFpH(illite) × CFspec(illite) (7-7b)

7.2.3 Treatment of uncertainties

Uncertainties were generally handled in the same way as described in Bradbury & Baeyens (2003). As they point out, there is no obvious reason for choosing between quantifying uncertainties as an error on a linear scale (i.e., Kd ± error) vs. a logarithmic scale (i.e., log Kd ± log error). Following the procedure chosen in NEA (2005), the latter type of representation was adopted. The following uncertainty factors (log errors) are considered:

1. Experimental uncertainty of source data (UFexperiment) For good quality experimental data, an uncertainty of log Kd ± 0.2 log units is proposed based on NEA (2005). This gives an uncertainty factor: UFexperiment = 1.6

2. Uncertainty of sorption model calculations (UFmodel) To sorption data obtained by model predictions, e.g. for Ra, Sr, Cs, the following uncertainty was accepted from Wersin et al. (2014): UFmodel = 6 to 8

3. Uncertainty of mineralogy conversion (UFmin) Wersin et al. (2014) used an UFmin of 1.3 based on uncertainties in CEC measurements. Even though the present conversion is based directly on clay content, this UF is kept to acknowledge the differences in sorption capacity between different clays. UFmin = 1.3

4. Uncertainty of pH conversion (UFpH) This is only needed where CFpH ≠ 1, i.e., where scaling to in-situ pH had to be done via additional data and equation 7-3. Because CFpH contains uncertainties of two Kd values (which are read off at pH-data source and in-situ pH, respectively), an uncertainty of log experimental Kd ± 2×0.2 log units is used. This gives an uncertainty factor: UFpH = 2.6

5. Uncertainty of speciation conversion (UFspeciation)

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Following the evaluation by Hummel & Berner (2002), who propose a factor of 2 between highest and lowest value, the following uncertainty factor is used: UFspeciation = 1.4 Note, however, that this is the uncertainty associated with the use of a given, self-consistent TDB. If certain species are missing or erroneous, uncertainties could be much higher. Also, this UF does not take into account any inappropriate evaluation of CFspeciation.

6. Uncertainty due to use of chemical analogues (UFanalogy) Where the Kd for a given RN was derived on the basis of analogy data, the UFexperiment was increased in most cases to 2.5 (corresponding approximately to UFexperiment × UFspeciation). In case of less obvious analogies, this was increased to a factor of 4.

7. Uncertainty of conversion disperse compacted systems (UFdisperse-compacted) As the conversion factor disperse compacted systems was set to unity, the corresponding uncertainty factor may be viewed as a somewhat conservative measure. A factor corresponding to a loss of available sorption capacity of 50 % upon compaction would give UFdisperse-compacted = 2.

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8 RADIONUCLIDE SORPTION DATA

8.1 Sorption values of actinides

8.1.1 Thorium (Th)

Thorium occurs exclusively in the tetravalent oxidation state in normal aqueous solutions. Like other tetravalent actinides, Th hydrolyses strongly. In the absence of other ligands, the uncharged Th(OH)4 complex is the dominating species over a very wide pH range. Th also forms stable complexes with carbonate and/or mixed hydroxy-carbonate species (their predicted importance is also dependent on the TDB used). The calculated solubility of Th in the in-situ reference and bounding porewaters is given in chapter 3.1.1. The sorption of Th on smectite is evaluated on the basis of several datasets measured by Bradbury and Baeyens on standard Wyoming montmorillonite (SWy-1) and MX-80:

Bradbury & Baeyens (2011a) measured sorption edges on Na-SWy1-montmorillonite at trace Th concentrations in 0.1 and 1.0 M NaClO4. These data show that Th sorption reaches a nearly constant plateau at pH values between 5 and 11.

Bradbury & Baeyens (2011a) measured sorption isotherms for Th(IV) on MX-80 in a synthetic bentonite porewater (pH 7.2-7.7).

In principle, the data obtained on SWy-1 would be preferable as source data, because reference data can be extracted directly for relevant pH values (see chapter 7.2). However, the speciation data sheet (see Appendix B) shows that the largest uncertainty for in-situ Kd values is associated with the formation of aqueous Th-hydroxy-carbonate complexes and the possible formation of ternary Th-carbonate surface complexes. On the other hand, the nearly constant sorption edge data demonstrate that the pH conversion is of less relevance in case of Th. Therefore, the use of the MX-80 data (at a different pH but similar speciation) is judged to be less problematic than the use of the SWy-1 data (at the correct pH, but based on an entirely different speciation). The isotherm on MX-80 shows that thorium sorption is independent of the Th concentration between 510-12 M and 210-8 M. For this concentration range, an average sorption value of 63 m3/kg can be extracted from the data. As shown in the speciation sheet (Appendix B), the formation of aqueous Th-hydroxy-carbonate complexes has a notable effect on CFspeciation. Following the argumentation in section 7.2, it is assumed that ternary Th-carbonate surface complexes would form in the presence of dissolved carbonate, compensating the competition by dissolved Th-carbonate species. This is supported by the relative similarity of experimental Kd values for the sorption isotherm (> 35 % Th-hydroxo-carbonate complexes) and the Kd values in the carbonate free sorption edge measurements. The sorption edge data on SWy-1 are considered for pH conversion. With the exception of the highly alkaline water and the Grimsel melt water, CFpH is unity; for the higher pH values, a slight rise of the sorption edges is acknowledged.

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The data for the illite component of the backfill are based on a sorption edge on Na-illite (purified illite du Puy) in 0.1 M NaClO4 (Bradbury & Baeyens 2009). These data indicate an even flatter sorption plateau than the data for SWy-1. The same approach as for smectite was followed in terms of handling speciation. Together, the fractional contributions of smectite and illite result in a Kd value of 124 m3/kg for highly alkaline and glacial melt water, and of a Kd value of 110 m3/kg for all other porewaters with a lower pH. The small difference is entirely due to the data for smectite. As both the illite data as well as the hydrolysis behaviour of Th do not point to a difference in sorption in the range of pH 7-10, a Kd value of 110 m3/kg is recommended throughout. The upper limit is calculated accordingly as 644 m3/kg. The lower limit Kd (4-7 m3/kg) is recommended conservatively considering the contribution by smectite alone. Table 8-1. Thorium Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 40.82 40.82 40.82 40.82 54.43 54.43 Kd illite 70 70 70 70 70 70 Kd backfill calculated 110 110 110 110 124 124 Kd recommended 110 110 110 110 110 110 upper limit calculated 644 644 644 644 1230 1230 upper limit recommended 644 644 644 644 644 644 lower limit calculated 19 19 19 19 19 19 lower limit recommended 7 7 7 7 4 4

8.1.2 Protactinium (Pa)

Speciation calculations (Appendix B) indicate that under in-situ porewater conditions, Pa is almost exclusively present in the pentavalent oxidation state. For Pa(V), Bradbury & Baeyens (2006, 2009) determined sorption edges on Na-montmorillonite (SWy-1) and illite (purified Illite du Puy), respectively. In both cases, no speciation conversion factor is required, since only hydrolytic Pa(V)-species are of importance. In this situation, any change in the speciation of Pa is fully accounted for by the pH sorption edge. The experimental as well as the resulting estimated Kd values for both smectite and illite are constant as a function of pH. Sorption of Pa(V) has almost the same magnitude on illite and smectite. Considering the sum of the fractional contribution of both minerals results in a Kd value of 57 m3/kg for all porewater conditions, with upper and lower limits of 334 m3/kg and 7 m3/kg. The lower limit is based conservatively on the contribution of smectite alone.

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Table 8-2. Protactinium Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 40 40 40 40 40 40

Kd illite 17 17 17 17 18 18

Kd backfill calculated 57 57 57 57 57 57

Kd recommended 57 57 57 57 57 57

upper limit 334 334 334 334 334 334

lower limit calculated 10 10 10 10 10 10

8.1.3 Uranium (U)

The dominant stable oxidation states of uranium in aqueous solutions are the tetra- and hexavalent state. U(IV) and U(VI) show an entirely different chemical behaviour in terms of aqueous chemistry, which results in a generally significantly higher sorption of U(IV) compared with U(VI). The speciation calculations for the in-situ porewaters (appendix B) show that uranium exists predominantly in the tetravalent state. U(VI) is only significant in case of the dilute, carbonate-rich water. U(V) generally plays a minor role. In-situ sorption values for U were calculated for two different scenarios: First, values are calculated based on the predominant speciation of U under the

expected reducing conditions. Second, it is assumed that U exists exclusively in the hexavalent state in all

porewaters.

Note that the speciation of U(VI) differs slightly between the two cases (see speciation sheets in Appendix B). This is not relevant when CFspeciation without carbonate is used, however. For the first scenario, the total Kd value is calculated as the weighted sum of the contribution of the different oxidation states to sorption. These contributions are first derived separately for each porewater condition and U oxidation state, as follows: a relevant contribution of U(VI) is considered only in case of the dilute, carbonate-

rich water; U(V) is not considered to be stable in the long term, but to disproportionate to U(IV)

and U(VI) eventually; because of the higher sorption of U(IV) in comparison to U(VI), it is assumed to

completely dominate Kd in all porewaters except the dilute, carbonate-rich water.

For the scenario where U exists in the hexavalent state, the Kd values calculated for U(VI) apply directly (i.e., it is assumed that uranium exists to 100 % as U(VI)).

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Very few experimental data on U(IV) sorption on bentonite/montmorillonite or illite exist in the literature, and the available data show a relatively large scatter. This is presumably related to experimental difficulties, especially with regard to maintaining strongly reducing conditions during the experiments. We therefore followed the approach by Wersin et al. (2014) and chose Th(IV) as an analogue for U(IV). The values derived for Th were directly accepted. Data for U(VI) are taken from different literature sources. The speciation calculations show that in the presence of dissolved carbonate, aqueous carbonate and hydroxo-carbonate complexes are very important for U(VI). Moreover, the formation of ternary U(VI)-carbonate surface complexes has been established by EXAFS studies (Bargar et al. 2000). Therefore, preference was given to reliable sorption data that were obtained in the presence of different concentrations of dissolved carbonate. For U(VI) sorption on smectite, such datasets are available: The Kd values for the porewaters with high carbonate concentrations (saline,

brackish, and dilute carbonate-rich water) were derived on the basis of sorption edges measured by Pabalan & Turner (1997) on Na-SAz-1 montmorillonite in 0.1 M NaNO3 under different carbonate concentrations. From the data corresponding to atmospheric pCO2, relatively low Kd values are derived (Appendix B).

The Kd values for the carbonate-poor porewaters (high alkaline and glacial melt water) were derived form sorption edges of U(VI) on different smectites in simple electrolytes and in the absence of carbonate. There are several studies in the literature that report such data. While the different data appear to be reliable based on the experimental procedures, they scatter by about an order of magnitude. Overall, the sorption edges measured for a range of ionic strength values by Turner et al. (1996) on a soil-derived Na-smectite and by Bradbury & Baeyens (2005) on Na-SWy-1 were considered as the most representative. From their sorption edges, average Kd values of 18 m3/kg at pH 10 and of 30 m3/kg at pH 8.75 are extracted.

For the derivation of the Kd value for the brine water all three above-mentioned experimental datasets were evaluated. Kd values were first derived separately using the data from the experiments with and without carbonate present (see sorption datasheet for U(VI)-oxidising conditions in Appendix B). As recommended Kd, the average of the derived values using CFspeciation excl. carbonate complexes is selected.

In case of illite, no reliable and systematic sorption data for U(VI) obtained in the presence of controlled carbonate concentrations were found. Therefore, the sorption edge measured by Bradbury & Baeyens (2009) on Na-illite was used for data derivation. The selected source data are from the high end of the range of experimental data in the neutral pH-range, considering the overall trend of the measured sorption edge and corresponding model curve. Based on the evidence discussed above regarding the formation of U(VI)-carbonate surface species, the conversion factor excluding the effect of carbonate was applied. Under reducing conditions, the recommended Kd values for uranium are the same as for thorium, except in the case of the dilute, carbonate-rich water, where the presence of a significant fraction of U(VI) leads to a lower Kd. For the lower limits, an increased UF is used to account for the higher uncertainty. Under oxidising conditions, Kd is predicted

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to be about half an order of magnitude lower, and is surprisingly constant as a function of pH. However, this is regarded as consistent with the trends of increasing hydrolysis but decreasing complexation with carbonate (due to lower carbonate concentration) with increasing porewater pH. Table 8-3. Uranium Kd values, recommended values and upper and lower limits for reference and bounding porewaters; reducing conditions.

Kd (m3/kg)





high alkaline water

glacial melt water

U(VI)

Kd smectite n.d. n.d. 1.5 n.d. n.d. n.d.

Kd illite n.d. n.d. 17.5 n.d. n.d. n.d.

Kd backfill calculated 19.0

upper limit 110.7

lower limit 3.3

fraction U(VI) of total U 0.4

Kd adjusted for fraction U(VI) 8

upper limit adjusted for fraction U(VI)

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lower limit adjusted for fraction U(VI)

1

U(IV): analogy with Th Kd 110 110 110 110 110 110

upper limit 644 644 644 644 644 644

lower limit 4 4 4 4 2 2

fraction U(IV) 1.00 1.00 0.59 1.00 1.00 1.00

Kd adjusted for fraction U(IV) 110 110 65 110 110 110

upper limit adjusted for fraction U(IV)

644 644 382 644 644 644

lower limit adjusted for fraction U(IV)

4 4 3 4 2 2

Uranium (total) Kd recommended 110 110 73 110 110 110

upper limit 644 644 427 644 644 644


Table 8-4. Uranium(VI) Kd values, recommended values and upper and lower limits for reference and bounding porewaters; oxidising conditions.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 1.0 1.5 1.5 9.8 9.0 15.0

Kd illite 17 17 17 16 7 11

Kd backfill calculated 18.5 19.0 19.0 26.0 16.0 26.0

Kd recommended 18.5 19.0 19.0 26.0 16.0 26.0

upper limit calculated 140 144 111 197 121 197


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8.1.4 Neptunium (Np)

The speciation calculations indicate that only Np(IV) is relevant in the case of all in-situ porewaters. As in the case of U(IV), Kd values are recommended in direct analogy with Th. This is preferred over the use of the few data for Np(IV) that are available in the literature. In comparison to the Th data used, the potential data for Np(IV) are generally associated with greater uncertainty due to the need for maintaining strongly reducing conditions. It further seems that experimental difficulties due to low solubility and the tendency to form colloids may be larger in the case of Np(IV). Table 8-5. Neptunium Kd values, recommended values and upper and lower limits for reference and bounding porewaters (analogy with Th).

Kd (m3/kg)





high alkaline water

glacial melt water

Kd recommended 110 110 110 110 110 110



8.1.5 Plutonium (Pu)

The speciation calculations indicate that plutonium exists in the tri- and tetravalent oxidation state under the in-situ porewater conditions, while the penta- and hexavalent oxidation states are of no relevance. Following the approach used for uranium, the total Kd value is calculated as the weighted sum of the contribution of the different Pu oxidation states to overall sorption. These contributions are first derived separately for Pu(III) and Pu(IV) at each porewater condition. Following the same line of reasoning given above for Np, Kd values for Pu(III) and Pu(IV) are derived in direct analogy with similar actinides that can be assigned a definite oxidation state. Specifically, Am was used as an analogue for Pu(III) and Th as an analogue for Pu(IV). This approach is preferred in comparison to an attempt of using directly the available sorption data for Pu. These are not very systematic and considered less reliable, mainly due to the substantial uncertainty in the redox state of Pu. The uncertainty regarding oxidation states is corroborated by the fact that it is not sufficient to find data where the presence of oxidised Pu species can be excluded, but where reliable information regarding the ratio of Pu(III)/Pu(IV) is available. On the other hand, the use of the above-mentioned analogues allows deriving a Kd according to the distribution of oxidation states predicted for each porewater. As in case of Np(IV), direct analogy with Th was used for Pu(IV). Because of the different speciation of Pu(III) and Am (see speciation sheet for Pu in Appendix B), Kd values for Pu(III) were derived on the basis of the selected experimental data for Am while explicitly considering the speciation of Pu(III). The predicted higher sorption of Am in comparison to Th leads to higher calculated Kd for the porewaters where Pu(III) is the dominating oxidation state. Since complexation with chloride does not appear to be relevant for Pu(III), the calculated values for the

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brine solution are comparatively high (see Pu speciation and sorption datasheets). The upper limit calculated through this approach is considered unreasonably high in some cases, however. This may be due to an overestimation of uncertainties by assuming a multiplicative contribution of all the individual uncertainty factors. It is therefore proposed to use the lowest upper limit (calculated for the high alkaline water) throughout. The lower limit is accepted as calculated. Table 8-6. Plutonium Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Pu(III): analogy with Am

Kd smectite 153 139 101 251 251 248

Kd illite 54 48 35 88 55 55


upper limit 1205 1091 796 1973 1785 1761

lower limit 36 32 23 58 53 52

fraction Pu(III) 9.83E-01 9.98E-01 9.98E-01 9.98E-01 2.50E-02 5.89E-02

Kd adjusted for fraction Pu(III) 203 187 136 338 8 18

upper limit adjusted for fraction Pu(III) 1185 1088 795 1969 45 104

lower limit adjusted for fraction Pu(III) 35 32 23 58 1 3

Pu(IV): analogy with Th Kd 110 110 110 110 110 110

upper limit 644 644 644 644 644 644


fraction Pu(IV) 1.70E-02 2.37E-03 2.18E-03 2.00E-03 9.75E-01 9.41E-01

Kd adjusted for fraction Pu(IV) 2 0.26 0.24 0.22 107 104 upper limit adjusted for fraction Pu(IV)

11 2 1 1 627 606

lower limit adjusted for fraction Pu(IV)

0.08 0.01 0.01 0.01 2 2

Plutonium (total) Kd recommended 205 187 137 338 115 121


upper limit recommended 672 672 672 672 672 672

lower limit 35 32 23 58 4 5

8.1.6 Americium (Am) and Curium (Cm)

Based on the close similarity of the aqueous chemistry of Am and Cm, Wersin et al. (2014) proposed a single set of Kd values for both elements. This approach is followed for the present report as well. Only few reliable datasets are available in the literature with regard to the sorption of Am and Cm on smectite and illite. Gorgeon (1994) and Bradbury & Baeyens (2006) report sorption edges of Am on Na-smectite in simple electrolyte solutions. We selected the data by Bradbury & Baeyens (2006) as reference because of the uncertainties

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regarding the concentration of dissolved carbonate (and of the composition of exchangeable ions) in the experiments by Gorgeon (1994). As already pointed out in the report by Wersin et al. (2014), the two datasets agree quite well, but sorption data on Ca-smectite (also by Bradbury & Baeyens 2006) are up to one order of magnitude lower at pH below about 8. At pH ≥ 8, the two datasets start to approach each other. Sorption isotherms of Cm are available from Rabung et al. (2005) for Ca-SWy-1 in simple Ca-electrolyte solution and from Grambow et al. (2006) for MX-80 in synthetic porewaters. The data by Rabung et al. (2005) roughly follow the data by Bradbury & Baeyens (2006) for the Ca-system. The data by Grambow et al. (2006) show a much less pronounced sorption edge; they are similar to the data for Ca-smectite at pH ≈ 7, but are by nearly an order of magnitude lower in the range of pH 8-10. For the sorption of Am/Cm on illite, systematic datasets for Am by Gorgeon (1994) and Bradbury & Baeyens (2009) and for Cm by Rabung et al. (2005) were found. These studies all used Illite du Puy as a source for illite: Bradbury & Baeyens (2009) and Rabung et al. (2005) used purified material converted to the Na-form. In case of the study by Gorgeon (1994), the degree of purification and the composition of exchangeable ions are not completely clear, and the aforementioned uncertainty regarding carbonate concentration applies here as well. The data by Rabung et al. (2005) and Bradbury & Baeyens (2009) are in good agreement, while the data by Gorgeon (1994) are up to an order of magnitude lower, especially at pH ≈ 7 and lower. At pH ≥ 8, the data are in good agreement. For the derivation of recommended data, several aspects need to be considered. First, the speciation of Am and Cm calculated for the different porewaters (see chapter 3 and speciation sheets in Appendix B) shows the generally significant effect of carbonate complexation. It follows that CFspeciation will be of major importance. No mixed hydroxo-carbonate complexes form in the porewaters, according to the TDB employed. Still, we decided to use CFspeciation without considering carbonate species to be competitive to sorption, following the procedure used for the +IV-valent actinides and the argumentation of Wersin et al. (2014). Ochs & Talerico (2004) also used this procedure, based on a comparison of apparent diffusion coefficients calculated with Kd values based on CFspeciation excluding/including competition by Am-carbonate species with independent experimental diffusion data. Second, considering the unresolved issues in the overall data situation for smectite, the following approach was followed: The Kd values for the smectite component were derived separately based on the sorption edge of Am on Na-SWy-1 by Bradbury & Baeyens (2006) and on the sorption edge for Cm on MX-80 by Grambow et al. (2006). The Kd values for the illite component were calculated from the data by Bradbury & Baeyens (2009). The overall Kd for the backfill was again calculated as the weighted sum of the contribution by smectite and illite. The smectite contribution for the best estimates was based on the sorption edge data by Bradbury & Baeyens (2006). To acknowledge a possible larger uncertainty than suggested by this dataset, the lower limits were derived

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on the basis of the smectite data by Grambow et al. (2006), who measured the lowest sorption for Am/Cm. As the upper limit for all conditions, the lowest upper limit Kd value (calculated for the brine water) is proposed. This is a subjective decision based on our judgement that some of the formally calculated upper limits are unreasonably high (presumably due to overestimating the combined effect of the individual UFs). Table 8-7. Americium: Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 88 132 166 56 198 75

Kd illite 31 46 58 20 44 17


Kd recommended 119 178 224 75 242 92




Table 8-8. Curium: Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 16 22 28 9 38 32

Kd illite not available not available not available not available not available not available


Kd recommended 119 178 224 75 242 92

upper limit 439 439 439 439 439 439


8.2 Sorption values of the groups IA to VIIA

8.2.1 Carbon (C)

For 14C in the form of carbonate ions, isotopic exchange rather than an actual sorption process can be presumed, following the argumentation by Wersin et al. (2014). Applying the same approach, the best estimate sorption value is calculated by assuming that 0.27 % of calcite is exchangeable. The upper and lower limits were based on 1 % and 0.1 % exchangeable calcite, respectively. In case that 14C occurs in the form of methane or simple organic acids, a Kd of zero is proposed.

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Table 8-9. Inorganic carbon (Cinorg.): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd isotopic exchange 1.6E-03 6.9E-04 3.5E-04 7.3E-03 4.4E-02 1.3E-02

upper limit 6.0E-03 2.5E-03 1.3E-03 2.7E-02 1.6E-01 4.9E-02

lower limit 6.0E-04 2.5E-04 1.3E-04 2.7E-03 1.6E-02 4.9E-03

8.2.2 Radium (Ra) and Strontium (Sr)

Kd values for these elements were calculated separately for each backfill porewater using the approach and the thermodynamic model documented in Wersin et al. (2014). This model is the same as has been used for calculating the porewater chemistry (chapter 1 and appendix A). To account for the increased model uncertainties at very low ionic strength, the UF for the two dilute bounding waters was increased (for a detailed discussion see Wersin et al. 2014). As may be expected based on their similar chemistry, the predicted sorption of Ra and Sr is nearly identical. To be consistent with the recommended diffusion coefficients (see section 6), the values calculated for Sr are proposed for both elements. Table 8-10. Strontium: Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd recommended 3.53E-03 5.86E-03 1.90E-02 2.26E-04 3.80E-03 2.96E-02

UF total 6 6 8 6 6 8

upper limit 2.12E-02 3.52E-02 1.52E-01 1.35E-03 2.28E-02 2.37E-01

lower limit 5.89E-04 9.77E-04 2.38E-03 3.76E-05 6.33E-04 3.70E-03

8.2.3 Caesium (Cs)

The Kd values for Cs were calculated using the approach documented in Wersin et al. (2014) in combination with the thermodynamic model for illite by Bradbury & Baeyens (2000). Table 8-11. Caesium (Cs): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd sorption model 7.84E-01 3.66E+00 3.29E+00 2.40E+00 6.29E+00 3.96E+01

UF total 6 6 6 6 6 6

upper limit 4.70E+00 2.20E+01 1.97E+01 1.44E+01 3.78E+01 2.38E+02

lower limit 1.31E-01 6.11E-01 5.48E-01 4.00E-01 1.05E+00 6.60E+00

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8.2.4 Selenium (Se)

According to the speciation calculations, Se in the backfill porewaters exists exclusively in the reduced (-II) oxidation state. Since no sorption data are available for Se in the -II oxidation state, a Kd of zero is proposed. No sorption datasheet is provided.

8.2.5 Tin (Sn)

In normal aqueous solutions, Sn is present only in the tetravalent oxidation state. At pH ≤ 7.6, the neutral Sn(OH)4 species dominates, whereas at higher pH the hydrolytic Sn(OH)5

- or Sn(OH)62- species prevail (see Sn sorption data sheet).

There are several studies in the literature that indicate extremely high sorption on smectite. The most systematic datasets are sorption edges from about pH 4-10 measured by Bradbury & Baeyens (2003) on purified SWy-1 Na-montmorillonite in 0.1 M NaClO4 and by Oda et al. (1999) on untreated Kunigel-V1 bentonite in 0.01 M NaCl. Within the (relatively large) experimental scatter, these two datasets are almost identical and indicate constantly high Kd values of 1000 m3/kg for about pH 4-7, decreasing to ~150 m3/kg at pH 10. Of the two datasets, the study by Bradbury & Baeyens (2003) is preferred based on the more straightforward speciation of Sn in their system. It is noted that the data for Sn in the near field buffer report (Wersin et al. 2014) are based on an analogy with Th rather than on the data for Sn sorption on smectite given in Bradbury & Baeyens (2005). It is argued that the initial concentration of Sn may have exceeded the solubility limit in the underlying experiments. While this cannot be excluded based on calculated solubilities, there are other arguments suggesting that the data should be valid: The initial concentration in the experiments by Oda et al. (1999), is about half an

order of magnitude lower, and they observe similarly high sorption. In a re-appraisal of their in-house database, Bradbury & Baeyens (2011a) still

evaluate their data for Sn sorption on smectite as reliable. In the absence of clear evidence with regard to experimental shortcomings, the direct use of the data for Sn by Bradbury & Baeyens (2005) was preferred over the use of an analogy. Regarding Sn sorption on illite, only the data by Bradbury & Baeyens (2009) on Na-illite (Illite du Puy) in 0.1 M NaClO4 were found. Recommended values were derived by calculating the fractional contribution by the illite and smectite components of the backfill. As can be seen in the Sn sorption data sheet, the resulting values are extremely high and largely determined by the smectite fraction. As this is very unusual in comparison to the behaviour of most other RN considered here, and because the questions regarding Sn sorption on smectite are not completely answered, the recommended values are based on a conservative approach: it is proposed to use the lowest values calculated for any porewater throughout. The lowest Kd (101 m3/kg) is calculated for the high alkaline water. Using this value for all other porewaters incidentally gives a set of values very similar to Th, which is also reasonable. Bounding values are proposed accordingly.

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Table 8-12. Tin (Sn): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 501 501 501 501 79 199

Kd illite 70 70 70 70 22 44


Kd recommended 101 101 101 101 101 101



lower limit 86 86 86 86 14 34

lower limit recommended 14 14 14 14 14 14

8.2.6 Beryllium (Be)

Be has a much larger first hydrolysis constant compared with the other RN of group II. Thus, Be in solution is mainly present as hydrolysed Be and the contribution of free Be2+ is negligible (appendix B). Therefore, sorption via cation exchange is not very likely for Be and an adaptation of the sorption model of Sr and Ra is not advisable. Thus, as for the buffer (Wersin et al. 2014) and for the far-field (Hakanen et al. 2014), Be sorption data are derived by the conversion factor approach applying the experimental data of You et al. (1989) for best estimate calculation. You et al. (1989) reported Be sorption data for illite and montmorillonite in river and seawater. In these experiments, the addition of carrier-free Be-7 isotopes resulted only in a small concentration increase. Kd values determined for illite and montmorillonite in river water as well as illite in sea water varied between 178 and 360 m3/kg in the pH between 6 and 10. The lowest value of 178 was selected as source data for Be sorption on the illite component of the backfill as well as for Be sorption on the montmorillonite component in dilute, carbonate rich and glacial melt water conditions. Beryllium sorption on montmorillonite in sea water was found to be lower with an experimental Kd around 50 m3 kg-1 (You et al. 1989). This value was conservatively selected as montmorillonite source data for the other four porewaters. For the derivation of in-situ sorption data, only the free Be2+ and the hydrolysed Be species were considered as sorbing. The final best estimate in-situ Kd was calculated using the fraction of montmorillonite and illite in the averaged backfill. The upper limit Kd are calculated as the best estimate Kd × UF of 6.3. Following the procedure applied in the near-field report, the in-situ sorption data based on analogue considerations with Ni are recommended to be used as lower limit. This conservative analogue approach was adapted to account for the rather large uncertainty in the source data, where only limited information on experimental conditions were available, and significantly lower sorption values found for bentonite by Ramesh et al. (2002). Ni was proposed as Be analogue by Nagra for the Opalinus Clay (Bradbury & Baeyens 2003b), because the chemical properties of Be resemble more the ones of the divalent transition metals rather than the other alkaline earth metals. However, the speciation of Ni and Be in the reference and bounding porewaters differ significantly

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and the higher first hydrolysis constant would, according to the Linear Free Energy Relationship, suggest a stronger sorption of the Be complex than of Ni. We thus deem it justified to use the Ni-analogue derived Kd values as lower limit without additional consideration of an UF. These lower limit Kd values were calculated using a CF speciation factor considering the Ni-sorbing fraction and the hydrolysed Be species. The negatively charged Be(OH)3

- species at high pH was treated as non-sorbing, since Ni(OH)3

- only plays a very subordinate role. Table 8-13. Beryllium (Be): Kd values, recommended values and upper and lower limits for reference and bounding porewaters. Best estimates are based on experimental data of You et al. (1989) and lower limits on analogue considerations with Ni.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 25.8 25.8 78.8 23.1 27.2 84.2

Kd illite 26.0 26.0 25.0 23.2 27.4 26.7

Kd calculated 52 52 104 46 55 111

Kd recommended 52 52 104 46 55 111

upper limit 327 325 652 290 345 698 lower limit based on Ni analogue 1.9 1.9 1.8 14.5 0.2 5.1

8.2.7 Iodine (I) and Chlorine (Cl)

Iodine and chlorine are nearly exclusively present as the iodide and chloride anions (I–, Cl–). Sorption of these anions on clay materials is typically observed to be very low, or zero. For both anions, the argumentation of Wersin et al. (2014) is followed here. Considering the negligible anion exchange capacity of clays at neutral to alkaline pH, a Kd of 0 for all in-situ porewater conditions is proposed. In case of iodide, a Kd value of ~510-4 m3/kg determined on MX-80 at pH of 7.5 (Bradbury & Baeyens, 2003) is acknowledged as an upper limit. No corresponding value is given for radioactive chloride in view of the high background concentrations of stable Cl in most porewaters. No sorption data sheets are provided.

8.3 Sorption values of the transition metals

8.3.1 Zirconium (Zr)

Zirconium exists exclusively in the +IV oxidation state in normal aqueous solutions. The speciation calculations using the Andra/Thermochimie database indicate that all dissolved Zr is present as the neutral Zr(OH)4 species. No experimentally determined sorption data of Zr on relevant clay materials were found in the literature. Therefore, it is proposed to base the data for Zr on a direct analogy with Th(IV). While there are questions regarding the possible relevance of a Zr(OH)5

– species above neutral pH (see Bradbury & Baeyens 2003; Ochs & Talerico 2004; Hummel et al. 2002), this would not influence the derivation of Kd in the simple

88

conversion factor model. The use of an analogy is accounted for by an increased uncertainty factor (see 7.2.3). Table 8-14. Zirconium (Zr): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd recommended 110 110 110 110 110 110

upper limit 644 644 644 644 1230 1230


8.3.2 Nickel (Ni)

Ni exists only in the divalent oxidation state in aqueous solutions. In the backfill porewaters, Ni exists as the free Ni2+ ions plus as a variety of hydrolytic species and complexes with chloride, carbonate and sulphur-ligands, depending on the specific porewater composition. With regard to Ni sorption on clays, a relatively large number of systematic and careful studies are available. Baeyens & Bradbury (1997b, see also Bradbury & Baeyens, 2005) measured Ni sorption edges as well as isotherms on Na-montmorillonite (SWy-1) in 0.01 M to 0.1 M NaClO4 solutions. Tertre et al. (2005) determined sorption edges on Na-smectite obtained from MX-80 in 0.025 M and 0.5 M NaClO4. While both studies show roughly the same trend of sorption as a function of pH, there are differences in the magnitude of sorption. Further, the sorption edges for different ionic strength on SWy-1 fall together at pH ≥ 9, whereas the curves measured by Tertre et al. (2005) do not converge (i.e., Kd is always higher in 0.025 M NaClO4 than in 0.5 M NaClO4). Thus, the two studies give a different indication of the relative importance of ion exchange and surface complexation as a function of pH. Sorption edges on Ca-SWy-1 in different NaClO4 solutions were measured by Bradbury & Baeyens (1999). These data agree with the data discussed above on Na-smectite within the overall scatter. Sorption edges on Na-illite in different NaClO4 solutions are available from Bradbury & Baeyens (2009) and from Poinssot et al. (1999). Within the experimental scatter mentioned above, these data are identical to those measured for smectite. Therefore, only one set of reference data, valid for both smectite and illite, was selected. CFmin then takes on the value representing the entire clay mineral fraction of the backfill material (see Ni sorption datasheet). CFspeciation is calculated in a straightforward way, since there is no indication of ternary surface complexes.

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Table 8-15. Nickel (Ni): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite/illite 1.0 1.5 1.2 0.7 6.3 4.0

Kd recommended 1.0 1.5 1.2 0.7 6.3 4.0

upper limit 6.0 9.0 6.7 4.2 36.9 23.3

lower limit 0.2 0.3 0.2 0.1 1.1 0.7

8.3.3 Niobium (Nb)

In aqueous solutions, niobium exists exclusively in the pentavalent oxidation state. Speciation calculations with the Andra/Thermochimie database indicate that under the in-situ porewater conditions, niobium is present mainly as Nb(OH)6

– and Nb(OH)72−.

Very few sorption data for Nb are available. The most systematic dataset, to our knowledge, is given in Andra's Dossier 2005 (Andra 2005a), where a sorption edge on smectite extracted from MX-80 is given. For the pH range 7-7.5, a Kd value of 10-30 m3/kg can be extracted. For pH 8, Ikeda & Amaya (1998) report some sorption measurements on bentonite in seawater with Kd values around 20-25 m3/kg. No data for illite were found. Due to the scarcity of data, pessimistic estimates of 3 m3/kg for the pH range 7-8 and of 1 m3/kg for pH ≥ 8 were selected conservatively for the present purpose. Upper and lower limit were estimated as 20 m3/kg and 0.3 m3/kg, also directly on the basis of the available data. No sorption datasheet is given. Table 8-16. Niobium (Nb): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water


upper limit 20 20 20 20 20 20

lower limit 0.3 0.3 0.3 0.3 0.3 0.3

8.3.4 Molybdenum (Mo)

The speciation calculations indicate that Mo exists exclusively in the hexavalent oxidation state as uncomplexed MoO4

2– anion (molybdate anion) under all relevant conditions. Typically, such oxo-anions are expected to sorb only weakly. In case of molybdate sorption on clay minerals, several studies can be found in the literature, with partly conflicting information. Motta & Miranda (1989) measured sorption isotherms of molybdate in 0.01 M NaCl and at 25 °C as well as 40 °C on montmorillonite at pH ≈ 4-4.5, and on illite at pH 8.2-8.9. The isotherm for

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montmorillonite shows a very steep and not well defined linear region. From this part, a Kd of about 0.3-1 m3/kg can be extracted. The isotherm for illite is less steep and gives a Kd of about 0.005 m3/kg. Assuming roughly the same magnitude of sorption on montmorillonite and illite at the same pH, this would imply a decrease of sorption from pH 4 to 8.9 by roughly two orders of magnitude. Such a decrease of sorption with increasing pH is typical for anion sorption by ligand exchange. Goldberg et al. (1996) measured the sorption of molybdate on montmorillonite and kaolinite as a function of pH in 0.1 M NaCl. Significant sorption was mainly seen between up to about pH 5, with sorption increasing in the order kaolinite < illite < montmorillonite. With increasing pH, they observed a drastic decrease of sorption. At pH 7, sorption could not be distinguished from zero. Hakanen et al. (2014) recently also determined the sorption of Mo(VI) on kaolinite and illite as a function of pH in a fresh and saline reference water representative for the Olkiluoto site. For kaolinite, they observed fairly strong and nearly constant sorption in the pH range 7-9 for both water types (average Kd 0.03 m3/kg). For illite, a clear decrease of sorption with pH and a stronger dependency on the water type was observed: in the fresh water, Kd ranged from about 0.1 m3/kg at pH 7 to about 0,02 m3/kg at

pH 9; in the saline water, Kd ranged from about 0.04 m3/kg at pH 7 to about 0,001 m3/kg

at pH 9. Together, the above evidence is considered sufficient to demonstrate sorption of molybdate up to high pH and in the presence of saline solutions. To derive Kd values for the backfill, the dataset by Hakanen et al. (2014) for illite in the saline water is selected as reference data for both illite and smectite. This dataset shows a pH-dependence of sorption that is similar to the observations by Motta & Miranda (1989). Due to the constant speciation of Mo, only the fractional contribution by the smectite plus illite component of the backfill has to be considered. To acknowledge the observation of roughly zero sorption at pH 7 by Goldberg et al. (1996), whose study appears to be systematic and of good quality, an additional (arbitrary) UF of 5 was used to calculate the lower limit. Table 8-17. Molybdenum (Mo): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite/illite 9.6.E-03 1.9.E-02 1.9.E-02 1.2.E-02 1.3.E-04 9.6.E-04

Kd recommended 9.6E-03 1.9E-02 1.9E-02 1.2E-02 1.3E-04 9.6E-04

upper limit 4.0.E-02 8.0.E-02 8.0.E-02 4.8.E-02 5.3.E-04 4.0.E-03

lower limit 4.6.E-04 9.2.E-04 9.2.E-04 5.5.E-04 6.2.E-06 4.6.E-05

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8.3.5 Technetium (Tc)

Speciation calculations with the Andra/Thermochimie database indicate that under in-situ porewater conditions, Tc will almost exclusively occur in the tetravalent state, mainly as the neutral TcO(OH)2. No relevant and reliable sorption data for Tc(IV) were found in the literature. The scarcity of data is presumably related to the difficulty of avoiding traces of Tc(VII), which could significantly affect sorption results. We therefore propose to use direct analogy with Th(IV) for the derivation of in-situ Kd values. An additional uncertainty (UF = 4) due to analogue use is considered in the derivation of the lower limit, because the effect of the differences in Th vs Tc(IV) speciation on sorption is not clear. Table 8-18. Technetium (Tc): Kd values, recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd recommended 110 110 110 110 110 110

upper limit 644 644 644 644 644 644


8.3.6 Palladium (Pd)

Pd exists in aqueous solutions in the divalent state. It forms strong complexes with chloride, but has also a pronounced tendency to hydrolyse. Therefore, strong sorption (binding to surface OH– groups) can be assumed, and the Kd values derived on the basis of reference data for Ni (see below) are viewed as pessimistic. No relevant and reliable sorption data were found for palladium in the literature. Following Wersin et al. (2014), in-situ Kd values are therefore based on analogy considerations with Ni. This was done by applying the conversion factors calculated for Pd (see Pd sorption datasheet in Appendix B) to the reference sorption data selected for Ni. In this way, the strong complexation of Pd with chloride in the brine water is taken explicitly into account. Table 8-19. Palladium (Pd): Kd values recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite/illite 1.9 1.9 2.0 0.05 6.6 5.9

Kd recommended 1.9 1.9 2.0 0.05 6.6 5.9

upper limit 11.3 11.3 11.5 0.30 38.4 34.6

lower limit 0.3 0.3 0.3 9.0.E-03 1.1 1.0

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8.3.7 Silver (Ag)

Silver exists as a monovalent cation in most aqueous solutions. Hydrolysis is very weak, but Ag ions form very stable complexes with soft ligands such as chloride or sulphide. Accordingly, speciation calculations with the Andra/Thermochimie database show that in all in-situ porewaters, except the glacial melt water, Ag will be present in the form of various Ag-chloro and Ag-hydrosulphide complexes. All of these are assumed as non-sorbing according to the used conversion factor for speciation. Hence, Ag is considered as non-sorbing (Kd = 0) in all in-situ porewaters except the glacial melt water. In the glacial melt water, the presence of some free Ag+ ions is predicted. Therefore, a Kd different from zero can be expected. Hakanen et al. (2014) for rock and Wersin et al. (2014) for the buffer based sorption values for Ag on the data by Khan et al. (1995). Since no other data source could be found, their approach was used also for the backfill. Conservatively neglecting the steep sorption edge in the pH region of 7.5 to 9, a value of 3.2×10-2 m3/kg can be extracted from an adsorption isotherm at pH 6.5. The overall clay content of the bentonite used by Khan et al. (1995) is estimated as about 98 % on the basis of the reported clay and silt fractions; this value is consistent with the given CEC. Since no data for illite were found, CFmin in chosen to represent the total clay fraction of the backfill material. Table 8-20. Silver (Ag): Kd values recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd recommended 0 0 0 0 0 0.0148

upper limit 0.224

lower limit 0.001

8.4 Sorption values of the lanthanides

8.4.1 Europium (Eu)

In aqueous solutions, Eu exists in the trivalent state. Speciation calculations indicate that under in-situ porewater conditions, Eu-carbonate complexes can be extremely important. While no aqueous mixed hydroxo-carbonate complexes are predicted according to the TDB used, there is a large uncertainty regarding the possible formation of ternary Eu-carbonate surface complexes, as in case of the actinides. The sorption of Eu on smectite was evaluated from the data by Marques-Fernandes et al. (2008) for Na-SWy-1 and by Bradbury & Baeyens (2002) for Ca-SWy-1. Sorption appears to be slightly lower on the Ca-montmorillonite, but both datasets show good agreement at pH 7.5 and higher. Marques-Fernandes et al. (2008) determined sorption edges in carbonate-free solutions and in the presence of different levels of dissolved carbonate. The highest carbonate concentration (20 mM) lies significantly above the levels expected for the porewaters; the corresponding sorption data indicate some reduction of sorption but are not entirely conclusive. A comparison of the datasets

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obtained in carbonate-free solutions and in the presence of atmospheric pCO2 clearly indicates two effects: Up to pH 8, the presence of carbonate has no influence on sorption. At higher pH, the presence of carbonate leads to a decrease of sorption; however,

the decrease of sorption as a function of pH is less steep than what would be expected if carbonate had a purely competitive effect. Presumably, the negative effect on sorption caused by the formation of dissolved Eu-carbonate complexes at pH ≥ 8 is partly compensated by the formation of ternary Eu-carbonate surface complexes.

Accordingly, source data were extracted from the carbonate-free experiments for pH values < pH 8 and from the experiments including carbonate for the porewaters with higher pH values. CFspeciation was in all cases calculated without considering a competitive effect of Eu-carbonate complexation. Use of CFspeciation including carbonate complexes was also tested for the two porewaters with pH values > 8, but resulted in highly variable Kd values (i.e., in a very high sensitivity of predicted sorption on carbonate concentration). This is not realistic, however, as shown by the data of Marques-Fernandes et al. (2008). As for other RN, the measurements by Bradbury & Baeyens (2009) on illite were used as source data for the illite component. No reliable source data obtained in the presence of carbonate were found. Data derivation was done again on the basis of CFspeciation excluding carbonate complexes. Table 8-21. Europium (Eu): Kd values recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water

Kd smectite 24 32 36 8 0.3 10

Kd illite 12 16 18 4 48 17



upper limit 212 284 316 66 282 156


8.4.2 Samarium (Sm)

Data for Sm were directly based on the results obtained for the close analogue Eu (see above). This choice reflects the data situation, which is much better in case of Eu. To acknowledge the additional uncertainty that may be introduced by the use of analogues, the lower limit Kd is calculated using an increased uncertainty factor.

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Table 8-22. Samarium (Sm): Kd values recommended values and upper and lower limits for reference and bounding porewaters.

Kd (m3/kg)





high alkaline water

glacial melt water


upper limit 212 284 316 66 282 156


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9 DISCUSSION AND SUMMARY OF SORPTION DATA

A summary of all recommended Kd values and of the corresponding lower limits is given in Table 9-1. The lower limits are calculated through the use of uncertainty factors that take into account uncertainties in

the original experimental data, the sorption capacity of both backfill and of the clays used in the selected

experimental studies, the pH calculated for each backfill porewater and measured in the experimental

studies, the formal uncertainty in the RN speciation calculated for the porewaters and

experimental solutions (i.e., the uncertainty due to errors in the underlying thermodynamuic data),

the uncertainty associated with calculations through sorption models, where applicable,

an uncertainty due to application of data from disperse systems to the compacted backfill, which is conservatively assumed,

additional uncertainties due to the use of analogy considerations, where relevant. Details regarding the uncertainty factors are given in chapter 7.2.3. The individual uncertainty factors are used in a multiplicative way, not considering any internal compensation of errors. Therefore, the overall uncertainty factor and the resulting lower limits are considered to be rather conservative.

Some general issues need to be discussed that are not addressed directly through the uncertainty factors. These concern different aspects of the solid and solution composition as well as of the data derivation procedure.

As pointed out in the report, the mineralogical composition of the backfill is estimated, and the properties of the underlying materials (Friedland Clay, Milos bentonite) are not very well known. In lack of more detailed information, the backfill composition as given in Table 1-2 is accepted for the derivation of sorption values. As explained in chapter 7.2, the overall Kd for a given RN on the backfill is composed of the fractional Kd calculated for the smectite and illite component of the backfill. It can be taken from the data sheets in Appendix B that the illite component contributes substantially to the overall sorption of a number of RN. It follows that the derived sorption values are significantly dependent on the calculated illite content of the backfill. On the other hand, sorption by the kaolinite component was neglected for all RN. The influence of illite on overall sorption is illustrated in the data sheets in Appendix B, where the Kd values calculated for the smectite and illite component are listed separately. It can be seen that entirely neglecting the illite (and kaolinite) component would still lead to sorption values that are higher than the recommended lower limits, in most cases by a significant factor. An exception are some values for U(VI) under oxidising conditions, which is a conservative case overall. Analogously to the solid composition, the calculated porewater chemistry is critical with respect to the derivation of Kd, and the values listed in Table 9-1 are only valid

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with respect to the respective porewater compositions. Basic uncertainties in this respect are handled by considering several bounding water compositions. Not included in these bounding water compositions is the presence of humic substances and possibly other types of DOC (see chapter 4.2). For the buffer, Wersin et al. (2014) conclude that possible effects on sorption are likely restricted to trivalent metals and that sorption would be reduced by less than order of magnitude. This can also be assumed for the backfill.

Similarly, Wersin et al. (2014) estimated that the high availability of surface sites in compacted clay would render competition effects not relevant in case of most RN. For the most sensitive RN (Sr, Ra, Cs) competition effects are fully taken into account through the applied ion exchange models.

The calculated speciation of RN is also dependent on a reliable thermodynamic database. While an uncertainty factor is included to address the formal error (uncertainty associated with the thermodynamic data) that may be introduced with the calculations, it is generally assumed that the TDB is correct, complete, and representing the state of the art. No provisions are made for possible further uncertainties, such as species that may be missing from the TDB.

The sorption values in this report generally correspond to ambient temperature. An increase in temperature could also influence RN speciation and thereby sorption. The influence of temperature on aqueous speciation is discussed in chapter 2.3. With regard to sorption, a situation similar to the buffer can be expected, where Wersin et al. (2014) point out that the effect of the variability in the bounding porewaters will be much larger than the effect of temperature. Moreover, the temperature variation in the backfill is much smaller than in the buffer.

There is a fundamental uncertainty regarding as to how RN sorption competition by complexation in solution and the possible formation of ternary RN-surface complexes should be handled. In principle, this is a relevant topic regarding all ligands present, but mainly concerns RN complexes with carbonate in the present case, due to the high affinity for carbonate for many RN and the proven existence of ternary RN surface complexes (see chapter 7.2.2). Following the approach taken in Ochs & Talerico (2004) and Wersin et al. (2014), this question was approached by calculating Kd twice, i) taking competition by formation of aqueous complexes involving carbonate ions fully into account (i.e., neglecting ternary surface complex formation) or ii) completely neglecting this competitive effect (i.e., assuming ternary surface complexes to fully compensate the competition by aqueous complexation). While it is obvious that either approach represents a great simplification, more appropriate approaches are not available in the absence of reliable sorption models. Following the critical comparison of calculated sorption and corresponding experimental diffusion data by Ochs & Talerico (2004), approach ii) was generally preferred. However, results for both methods are given in Appendix B. It can be seen that the effect of either approach depends strongly on the pH and aqueous carbonate concentration in both porewater and experimental solution.

In Table 9-2, the best estimate Kd values for the backfill are compared with corresponding values for the buffer. It can be seen that the values for most RN agree relatively well between buffer and backfill, even though the mineralogy of the clay as

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well as the aqueous chemical conditions are not identical (see chapters 1 and 4). However, there is a clear trend towards higher values for the backfill. This can be explained mainly by the contribution of illite, which appears to be a better sorbent for most RN than smectite. This is also evident from the experimental values listed in the data sheets in Appendix B. The main differences in sorption values are observed in the following cases: In case of U, there is a major difference in the values proposed for the glacial melt

water. This can be largely explained by a difference in the speciation of U, which is dominated by U(IV) in case of the backfill and by U(VI) in case of the buffer. Moreover, Wersin et al. (2014) selected very pessimistic source data to calculate Kd for U(VI), which result in a much lower calculated Kd than the experimental data by Bradbury & Baeyens (2005) and Turner et al. (1996) selected for the present evaluation of U(VI) sorption (see chapter 8.1.3).

Cs in both cases was calculated by an ion exchange model. Higher values for the backfill due to contribution of illite were derived.

In case of the Kd values for Sn at high pH, the main reason for the difference in the values for backfill and buffer is the different interpretation of the experimental sorption data, see chapter 8.2.5.

For Tc in equilibrium with the high-pH porewaters, significantly higher Kd values are proposed in case of the backfill. In case of both buffer and backfill, sorption values for Tc are derived on the basis of a chemical analogy with Th for the porewaters with near neutral pH. For the two high-pH porewaters, Wersin et al. (2014) considered experimental Tc sorption data which results in a much lower Kd. However, the data by Berry et al. (2007) show very large scatter (over 3 orders of magnitude at pH 8) and also differ from the Th data of Bradbury & Baeyens (2003a) at this pH (the lowest values for Tc are < 0.1 m3/kg) For the present report, the proposed analogy with Th was used for all porewaters, therefore.

The difference in the values for Pd in brine water is mostly due to the difference in Kd for Ni, which was used in both cases as analogue element. This difference is based on the respective source data used. The source data for Ni sorption on the buffer were taken from experiments with MX-80 (which is foreseen for the buffer, see Wersin et al. 2014), whereas source data from more generic smectite and illite were used for the backfill (chapter 8.3.2). An additional influence stems from differenced in the Pd speciation in the buffer- and backfill-equilibrated brine waters.

In summary, the derived sorption values are approximately similar to those derived by Wersin et al. (2014) for the buffer and show similar trends as a function of porewater composition. In many cases, higher Kd values are predicted for the backfill than for the buffer, because sorption of many elements appears to be stronger on illite than on smectite. Where illite sorption is not taken explicitly into account due to lacking data, the Kd predicted for the backfill is lower than the corresponding value for the buffer because the overall clay mineral content is lower in the backfill. The quality of data derivation could be improved by making more site-specific data available, and in particular by a better characterisation of the foreseen clay materials. To handle the aqueous as well as surface speciation of RN in a straightforward way, reliable sorption models would have to be developed. In turn, this would require the measurement of systematic sorption data under the relevant geochemical conditions.

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Table 9-1. Summary of recommended best estimate Kd values and lower limits for reference and bounding porewaters. Minimum values are indicated in bold.

RN / comment (values in m3/kg)




brine water, KR4/861/1, PSI database

high alkaline water

glacial melt water, (Grimsel water)

Th Kd 110 110 110 110 110 110 lower limit 7 7 7 7 4 4 Pa(V) Pa(IV) not relevant for porewaters Kd 57 57 57 57 57 57 lower limit 7 7 7 7 7 7 U (reducing conditions): Kd based on U(IV) and U(VI), U(V) assumed to be of no relevance Kd 110 110 73 110 110 110 lower limit 4 4 4 4 2 2 U(VI), oxidising conditions Kd 18.5 19.0 19.0 26.0 16.0 26.0 lower limit 2 3 3 3 2 3 Np(IV) Kd 110 110 110 110 110 110 lower limit 4 4 4 4 2 2 Pu: Kd based on Pu(III) and Pu(IV), Pu(V, VI) not relevant Kd 205 187 137 338 115 121 lower limit 35 32 23 58 4 5 Am Kd 119 178 224 75 242 92 lower limit 3 4 5 2 7 6 Cm Kd 119 178 224 75 242 92 lower limit 3 4 5 2 7 6 Corg Kd 0 0 0 0 0 0 lower limit Cinorg Kd 1.6E-03 6.9E-04 3.5E-04 7.3E-03 4.4E-02 1.3E-02 lower limit 6.0E-04 2.5E-04 1.3E-04 2.7E-03 1.6E-02 4.9E-03 Eu(III) Kd 36 49 54 11 48 27 lower limit 6 8 9 2 8 5 Sm Kd 36 49 54 11 48 27 lower limit 4 5 6 1 5 3 Ra: results of ion exchange modelling Kd 3.53E-03 5.86E-03 1.90E-02 2.26E-04 3.80E-03 2.96E-02 lower limit 5.89E-04 9.77E-04 2.38E-03 3.76E-05 6.33E-04 3.70E-03 Sr: results of ion exchange modelling Kd 3.53E-03 5.86E-03 1.90E-02 2.26E-04 3.80E-03 2.96E-02 lower limit 5.89E-04 9.77E-04 2.38E-03 3.76E-05 6.33E-04 3.70E-03 Cs: results of ion exchange modelling Kd 7.84E-01 3.66E+00 3.29E+00 2.40E+00 6.29E+00 3.96E+01 lower limit 1.31E-01 6.11E-01 5.48E-01 4.00E-01 1.05E+00 6.60E+00 Sn(IV) Kd 101 101 101 101 101 101 lower limit 14 14 14 14 14 14 Be(II) Kd 52 52 104 46 55 111 lower limit 1.9 1.9 1.8 14.5 0.2 5.1 I, Cl, Se(-II) Kd 0 0 0 0 0 0 Zr Kd 110 110 110 110 110 110 lower limit 4 4 4 4 2 2 Ni Kd 1.0 1.5 1.2 0.7 6.3 4.0 lower limit 0.2 0.3 0.2 0.1 1.1 0.7 Mo(VI) Kd 9.6E-03 1.9E-02 1.9E-02 1.2E-02 1.3E-04 9.6E-04 lower limit 4.6E-04 9.2E-04 9.2E-04 5.5E-04 6.2E-06 4.6E-05 Tc(IV) Kd 110 110 110 110 110 110 lower limit 3 3 3 3 1 1 Pd(II) Kd 1.9 1.9 2.0 0.05 6.6 5.9 lower limit 0.3 0.3 0.3 9.0.E-03 1.1 1.0 Ag Kd 0 0 0 0 0 0.0148 lower limit 0.001 Nb Kd 3 3 3 3 1 1 lower limit 0.3 0.3 0.3 0.3 0.3 0.3

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Table 9-2. Comparison of best estimate Kd values for backfill and buffer Values differing by an order of magnitude or more are highlighted.

RN / comment (values in m3/kg)




brine water, KR4/861/1, PSI database

high alkaline water

glacial melt water, (Grimsel water)

Th backfill 110 110 110 110 110 110 buffer 63 63 63 63 63 63 Pa(V) backfill 57 57 57 57 57 57 buffer 81 81 81 81 81 81 U (reducing conditions):. backfill 110 110 73 110 110 110 buffer 48 52 18 62 63 0.056 U(VI), oxidising conditions backfill 18.5 19.0 19.0 26.0 16.0 26.0 buffer – – – – – – Np(IV) backfill 110 110 110 110 110 110 buffer 63 63 63 63 63 63 Pu backfill 205 187 137 338 115 121 buffer 22 99 66 84 23 24 Am backfill 119 178 224 75 242 92 buffer 50 32 79 20 137 135 Cm backfill 119 178 224 75 242 92 buffer 50 32 79 20 137 135 Eu(III) backfill 36 49 54 11 48 27 buffer 20 11 34 2.9 89 115 Sm backfill 36 49 54 11 48 27 buffer 18 10 34 3.5 88 113 Ra: results of ion exchange modelling backfill 3.53E-03 5.86E-03 1.90E-02 2.26E-04 3.80E-03 2.96E-02 buffer 1.5E-03 8.7E-03 1.1E-01 1.4E-04 1.8E-02 8.5E-01 Sr: results of ion exchange modelling backfill 3.53E-03 5.86E-03 1.90E-02 2.26E-04 3.80E-03 2.96E-02 buffer 1.5E-03 8.7E-03 1.1E-01 1.4E-04 1.8E-02 8.5E-01 Cs: results of ion exchange modelling backfill 7.84E-01 3.66E+00 3.29E+00 2.40E+00 6.29E+00 3.96E+01 buffer 6.2E-02 2.1E-01 4.3E-01 2.4E-02 2.8E-01 6.2E-01 Sn(IV) backfill 101 101 101 101 101 101 buffer 31 50 39 45 0.24 1.14 Be(II) backfill 52 52 104 46 55 111 buffer 39 39 122 33 42.2 133 I, Cl, Se(-II) backfill 0 0 0 0 0 0 buffer 0 0 0 0 0 0 Zr backfill 110 110 110 110 110 110 buffer 63 63 63 63 63 63 Ni backfill 1.0 1.5 1.2 0.7 6.3 4.0 buffer 0.34 0.24 0.57 0.11 3.15 3.15 Mo(VI) backfill 9.6E-03 1.9E-02 1.9E-02 1.2E-02 1.3E-04 9.6E-04 buffer 7.5E-03 2.1E-02 7.5E-03 1.5E-02 1.5E-04 3.4E-04 Tc backfill 110 110 110 110 110 110 buffer 63 63 63 63 2 2 Pd backfill 1.9 1.9 2.0 0.05 6.6 5.9 buffer 0.7 0.27 0.63 5E-03 3.12 3.14 Ag backfill 0 0 0 0 0 0.0148 buffer 0 0 0 0 0 0.021 Nb backfill 3 3 3 3 1 1 buffer 5.43 5.43 5.43 5.43 7.81 1.81

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Acknowledgements: We greatly appreciate the scientific support of Tony Appelo (Amsterdam) for his valuable comments of an earlier note on the porewater model. The work benefited from fruitful discussions and review with the SAFCA team, in particular Margit Snellman, Pirjo Hellä, Barbara Pastina, Paul Smith, Mikko Nykyri and Aimo Hautojärvi, and also from Martti Hakanen (University of Helsinki). The manuscript benefited from review comments of Martin Glaus (PSI). We thank Eric Giffaut (Andra) for providing the version 7b of the Thermochimie database.

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113

Appendix A: Parameters of the geochemical system

A.1 Groundwater compositions

Table A-1. Concentrations in mmol L-1 unless otherwise indicated, measured data Saline Brackish SO4 Fresh/

brackish HCO3

Brine Glacial melt water

KR20/465/1 KR6/135/8 KR4/81/1 KR4/861/1 Grimsel

Depth (z, m) -360.71 -101.77 -69.97 -818.75 -

TDS (mg/L) 10544 7225 1122 69114 0.00

CB (%) 0.97 0.010 3.76 0.15 -

pH 7.4 7.6 7.8 7.8 9.6

Alkalinity 0.66 1.82 4.57 0.20 0.45

DIC 0.55 1.86 4.87 0.083 -

SO4 0.21 4.79 0.96 <0.01 0.061

Cl 180.54 113.12 9.90 1212.98 0.16

Na 114.83 76.56 13.14 424.10 0.69

K 0.28 0.49 0.25 0.56 0.0050

Ca 32.44 16.22 1.35 391.72 0.14

Mg 2.55 7.40 0.74 4.52 6.2E-04

Sr 0.16 0.092 0.0057 1.84 0.0020

SiO2 0.17 0.18 0.20 0.09 0.25

Mn 0.0058 0.022 0.0035 0.040 5.0E-06

Fe 0.0023 0.0057 0.0081 0.036 3.0E-06

S2-tot 0.0056 6.24E-04 3.12E-04 <0.016 -

F 0.053 0.016 0.032 0.084 0.36

Br 0.55 0.17 0.018 4.36 -

NH4 - 0.018 0.042 0.0017 -

PO4 - 3.16E-04 1.68E-03 4.21E-04 -

B 0.120 0.057 0.027 0.083 -

N2 (g, ml/L) 73.00 71.00 41.50 187.00 -

H2 (g, µl/L) <2.3 2.50 0.23 -

He (g, ml/L) 7.20 0.08 0.02 20.70 -

CH4 (g, ml/L) 137.00 0.08 0.71 928.00 -

114

A.2 Properties of different backfill materials and MX-80

MX-80Parameter Value Reference

System parameters

S/L ratio (kg/L) 3.65

Porosity ε (-) 0.43

Dry density ρdry (kg/dm3) 1.57

Saturated density ρsat (kg/dm3) 2.00

Grain density ρs (kg/dm3) 2.76

BET surface area (m2/g) 31.5 Bradbury and Baeyens (1997b)

Internal surface area of montmorillonite (m2/g) 487 Appelo (2010)

Cation exchange capacity (CEC; eq/kg) 0.787 Bradbury and Baeyens (2002a)Surface site concentration (eq/kg) 0.0284 Wieland et al. (1994)

Mineral composition (wt. %) Müller-Vonmoos & Kahr (1983)Montmorillonite (Smectite) 75

Kaolinite 1

Illite

Mica 1

Quartz 15.2

Feldspar 7

Carbonate 1.4

Dolomite -

Gypsum 0.4 Bradbury and Baeyens (2002)Siderite 0.5

Pyrite 0.3

Organic carbon 0.4

Sum 95.2

Ion Exchanger composition eq fraction Bradbury and Baeyens (2002)

Ca 0.0840

K 0.0170

Mg 0.0510

Na 0.8480

Sum 1.00EMDD effective clay dry density (kg/dm3) 1.38

115

Friedland blocks (SH Friedland)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 2.028 Hansen et al. (2009)


Grain density ρs (kg/dm3) 2.793 Kumpulainen et al. (2011)

BET surface area (m2/g) 15.96

Internal surface area of montmorillonite (m2/g) 487.00

Cation exchange capacity (CEC; eq/kg) 0.31 Kumpulainen et al. (2011)

Surface site concentration (eq/kg) 0.0144

Mineral composition (wt. %) Kumpulainen et al. (2011)

Montmorillonite (Smectite) 38

Kaolinite 9

Illite 20

Mica 4

Quartz 23

Feldspar 0.9

Carbonate

Dolomite

Gypsum 2

Siderite 1.6

Pyrite 0.8

Organic carbon 0.27

Sum 98.67

Ion Exchanger composition (eq/kg) Kumpulainen et al. (2011)

Ca 0.0062

K 0.0155

Mg 0.0279

Na 0.2573Sum 0.3069EMDD effective mont. dry density (kg/dm3) 1.442

116

Milos granules (floor) (ABM DepCaN)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 1.25 Äspö tests, Wimelius & Pusch (2008)


Grain density ρs (kg/dm3) 2.751 Karnland et al. (2006)





Mineral composition (wt. %) (ABM DepCaN) Kumpulainen et al. (2011)


Kaolinite

Illite 5

Mica 5

Quartz 0.7

Feldspar 1.5

Carbonate 7

Dolomite 1

Gypsum 1.6

Siderite

Pyrite 0.9

Tridymite 2.8

Goethite 1

Hematite 0.5

Magnetite 1

Anatase 0.5

Organic carbon 0.02

Sum 99.12


Ca 0.418

K 0.016

Mg 0.205

Na 0.189

Sum 0.83

EMDD effective mont. dry density (kg/dm3) 1.03

117

Milos pellets (wall/roof) (ABM DepCaN)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 0.95 Äspö & Riihimäki tests, e.g. Dixon et al. (2011)


Grain density ρs (kg/dm3) 2.751 Karnland et al. (2006)





Mineral composition (wt. %) (ABM DepCaN) Kumpulainen et al. (2011)


Kaolinite

Illite 5

Mica 5

Quartz 0.7

Feldspar 1.5

Carbonate 7

Dolomite 1

Gypsum 1.6

Siderite

Pyrite 0.9

Tridymite 2.8

Goethite 1

Hematite 0.5

Magnetite 1

Anatase 0.5

Organic carbon 0.02

Sum 99.12


Ca 0.418

K 0.016

Mg 0.205

Na 0.189

Sum 0.83


118

Milos granules (floor) (AC-200)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 1.25 Äspö tests, Wimelius & Pusch (2008)




Internal surface area of montmorillonite (m2/g) 487



Mineral composition (wt. %) (AC-200) Kumpulainen et al. (2011)


Kaolinite

Illite 2.1

Mica 4.7

Quartz 0.2

Feldspar

Carbonate 5.8

Dolomite 0.4

Gypsum 1.2

Siderite

Pyrite 1.4

Tridymite 1.7

Goethite 0.4

Hematite 0.7

Magnetite 0.8

Anatase 0.3

Organic carbon 0

Sum 100.1


Ca 0.057

K 0.019

Mg 0.076

Na 0.798

Sum 0.95


119

Milos pellets (wall/roof) (AC-200)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 0.95 Äspö & Riihimäki tests, e.g. Dixon (2011)







Mineral composition (wt. %) (AC-200) Kumpulainen et al. (2011)


Kaolinite

Illite 2.1

Mica 4.7

Quartz 0.2

Feldspar

Carbonate 5.8

Dolomite 0.4

Gypsum 1.2

Siderite

Pyrite 1.4

Tridymite 1.7

Goethite 0.4

Hematite 0.7

Magnetite 0.8

Anatase 0.3

Organic carbon 0

Sum 100.1


Ca 0.057

K 0.019

Mg 0.076

Na 0.798

Sum 0.95


A.3 Alternative deposition tunnel backfill design of SKB

The alternative deposition tunnel backfill design of SKB proposed for SR-Site consists of low-grade Milos bentonite (Milos B). For the purpose of this study, the volumes of the three different compartments (blocks, floor granules, pellet infill) are assumed to be the same as for the reference design (and are not taken from the SKB design). For the IBECO RWC BF 04 material, no measured grain density is reported. We calculated this parameter with the values of the initial state of the backfill proposed in the main report of the SR-Site project (SKB 2011, Table 5-21).

120

3Mg/m79.20.46-1

504.1

1

drys

(A-1) The EMDD is calculated to be:

3/126.17.2/49.1)58.01((1

49.158.0

)1((1mMg

f

fEMDD

accdry

dry

(A-2)

Averaged alternative SKB backfillParameter Value Reference

System parameters

S/L ratio (kg/L) 3.21 calculated

Porosity ε (-) 0.46 calculated

Dry density ρdry (kg/dm3) 1.490765492 calculated

Saturated density ρsat (kg/dm3) 1.96 calculated

Grain density ρs (kg/dm3) 2.785 calculated

BET surface area (m2/g) 24.36 calculated

Internal surface area of montmorillonite (m2/g) 487.00 Appelo (2010)

Cation exchange capacity (CEC; eq/kg) 0.73 calculated

Surface site concentration (eq/kg) 0.0220 calculated

Mineral composition (wt. %) calculated

Montmorillonite (Smectite) 58

Kaolinite 0

Illite 6

Mica 0

Quartz 0

Feldspar 7.1

Carbonate 8

Dolomite 16

Gypsum 0.8

Siderite 0

Pyrite 0

Tridymite 0

Goethite 0

Hematite 1.3

Magnetite 1.7

Anatase 0

Organic carbon 0.38

Sum % 99.28

Ion Exchanger composition (eq/kg) calculated

Ca 0.296

K 0.020

Mg 0.350

Na 0.066

Sum 0.73

EMDD effective clay dry density (kg/dm3) 1.13

121

IBECO RWC blocks (IBECO RWC BF 04)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 1.7 SKB (2010, TR-10-16)


Grain density ρs (kg/dm3) 2.785 TR-11-01,pdry/(1-ε)=pk; dk=1.504/(1-0.46)



Cation exchange capacity (CEC; eq/kg) 0.73 Olsson and Karnland (2009, R-09-53)


Mineral composition (wt. %) IBECO RWC BF 04 Olsson and Karnland (2009, R-09-53)Montmorillonite (Smectite) 58

Kaolinite

Illite 6

Mica

Quartz

Feldspar 7.1

Carbonate 8

Dolomite 16

Gypsum 0.8

Siderite

Pyrite

Tridymite

Goethite

Hematite 1.3

Magnetite 1.7

Anatase

Organic carbon 0.38

Sum 99.28

Ion Exchanger composition (eq/kg) Olsson and Karnland (2009, R-09-53)

Ca 0.296

K 0.020

Mg 0.350

Na 0.066

Sum 0.73


122

IBECO RWC granules (floor) (IBECO RWC BF 04)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 1.25 same as Posiva


Grain density ρs (kg/dm3) 2.785 TR-11-01, pdry/(1-ε)=pk; ddry=1.504/(1-0.46)






Kaolinite

Illite 6

Mica

Quartz

Feldspar 7.1

Carbonate 8

Dolomite 16

Gypsum 0.8

Siderite

Pyrite

Tridymite

Goethite

Hematite 1.3

Magnetite 1.7

Anatase

Organic carbon 0.38

Sum 99.28


Ca 0.296

K 0.020

Mg 0.350

Na 0.066

Sum 0.73


123

IBECO RWC pellets (wall/roof) (IBECO RWC BF 04)Parameter Value Reference

System parameters



Dry density ρdry (kg/dm3) 0.95 same as Posiva


Grain density ρs (kg/dm3) 2.785 TR-11-01, pdry/(1-ε)=pk; ddry=1.504/(1-0.46)






Kaolinite

Illite 6

Mica

Quartz

Feldspar 7.1

Carbonate 8

Dolomite 16

Gypsum 0.8

Siderite

Pyrite

Tridymite

Goethite

Hematite 1.3

Magnetite 1.7

Anatase

Organic carbon 0.38

Sum 99.28


Ca 0.296

K 0.020

Mg 0.350

Na 0.066

Sum 0.73


Table on next page: SKB backfill porewater concentrations in mmol L-1 unless otherwise indicated. Eh is constrained by sulphate/sulphide equilibrium for saline, brackish and dilute, carbonate-rich and high alkaline waters, by ferrihydrite/Fe(II) for glacial melt water, and by CO2/CH4 for brine water.

124

Saline water KR20/465/1

Brackish water KR6/135/8


water, KR4/81/1

Brine water, KR4/861/1(PSI db)

High alkaline water

Glacial melt water (Grimsel

water)

log p(CO2) -3.43 -2.70 -2.40 -4.61 -8.28 -5.48

pH 7.49 7.23 7.28 7.50 10.00 8.75

Eh (mV) -228 -202 -201 -271 -394 -297

Alkalinity (meq

L-1)0.22 0.76 1.54 0.10 3.34 0.08

Ionic Strength

(meq L-1)374 231 86.2 2669 346 46.5

Na 50 106 29.2 525 247 5.93

K 2.56 0.64 0.62 0.75 0.37 0.04

Mg 70.5 14.9 6.75 6.05 5.00 0.06

Ca 52.0 33.1 13.4 579 37.6 14.5

Cl 263 163 12.13 1686 283 0.20

SO42- 17.1 19.1 28.2 4.76 20.5 17.3

S-2 5.6E-03 6.2E-04 3.1E-04 - 5.6E-03 -

CO3 tot 3.5E-01 8.8E-01 1.7E+00 6.6E-02 1.4E-02 4.4E-02

Sr 1.7E-01 1.5E-01 2.3E-02 2.6E+00 2.5E-01 1.0E-01

Si 1.7E-01 1.7E-01 1.8E-01 7.9E-02 3.3E+00 2.0E-01

Mn 6.8E-03 3.1E-02 4.5E-03 5.6E-02 9.9E-03 9.4E-06

Fe 2.8E-02 1.7E-02 6.7E-03 3.9E-01 4.2E-02 7.7E-03

F 6.3E-02 2.3E-02 3.9E-02 1.2E-01 7.9E-02 4.5E-01

Br 8.0E-01 2.5E-01 2.2E-02 6.1E+00 8.7E-01 0.0E+00

B 1.5E-01 8.3E-02 3.6E-02 1.2E-01 1.8E-01 0.0E+00

Calcite 0.00 0.00 0.00 0.00 0.00 0.00

Siderite -1.00 -1.00 -1.00 -1.00 -1.00 -1.00

Pyrite 6.80 4.98 4.22 - 6.22 -

FeS(am) -0.28 -1.74 -2.34 - 2.16 -

Dolomite 0.25 -0.21 -0.11 -1.59 -0.76 -2.20

Gypsum 0.00 0.00 0.00 0.00 0.00 0.00

Kaolinite 0.00 0.00 0.00 0.00 0.00 0.00

Quartz 0.00 0.00 0.00 0.00 0.00 0.00

CEC (eq L-1) 2.34 2.34 2.34 2.34 2.34 2.34

NaX (f) 0.12 0.35 0.18 0.49 0.63 0.04

CaX2 (f) 0.41 0.47 0.56 0.50 0.33 0.95

MgX2 (f) 0.44 0.17 0.25 0.01 0.04 0.00

KX (f) 0.02 0.01 0.01 0.00 0.00 0.00

IL % 36.21 36.21 36.21 36.00 36.00 36.00

Free % 48.84 45.51 23.75 58.00 49.00 24.00

DDL % 14.95 18.27 40.03 6.00 15.00 40.00

Exc

han

ger

Po

rosi

ties

Fre

e p

ore

wat

erS

atu

rati

on

Ind

ex S

.I.Reference waters Bounding waters

125

Appendix B: Sorption data sheets

126

126

127

Th

(IV

) K

d-d

eri

vati

on

Sys

tem

Da

ta S

ou

rce

Su

bst

rate

MX

-80

So

luti

on

sali

ne

wa

ter

KR

20/4

65/1

bra

ckis

h w

ate

r K

R6/

135/

8d

ilu

te,

carb

on

ate

ri

ch w

ate

r K

R4/

81/1

bri

ne

wa

ter,

K

R4/

861/

1, P

SI

da

tab

ase

hig

h a

lka

lin

e

wa

ter

gla

cia

l m

elt

w

ate

r, (

Gri

mse

l w

ate

r)

SB

PW

, p

H 7

.6

Ref

eren

ceB

radb

ury

& B

aeye

ns (

2003

)

Ex

pe

rim

en

tal

Kd

(m

3 /kg)

6363

6363

6363

cons

t. K

d fo

r re

leva

nt p

H-v

alue

s

UF

-exp

erim

enta

l Kd

1.6

1.6

1.6

1.6

1.6

1.6

Co

nve

rsio

n M

ine

ralo

gy

(CF

-min

)sm

ectit

e co

nten

t%

48.6

48.6

48.6

48.6

48.6

48.6

75U

F-m

in1.

31.

31.

31.

31.

31.

3C

F-m

in0.

650.

650.

650.

650.

650.

65C

on

vers

ion

pH

(C

F-p

H)

pH7.

607.

217.

287.

469.

998.

757.

60

Kd

for

0.1

M N

aClO

4 (p

H c

onve

rsio

n)1)

300

300

300

300

400

400

300

CF

-pH

1.00

1.00

1.00

1.00

1.33

1.33

UF

-pH

con

vers

ion

1.0

1.0

1.0

1.0

2.6

2.6

Co

nve

rsio

n S

pe

cia

tio

n (

CF

-sp

ec)

UF

-spe

ciat

ion

1.4

1.4

1.4

1.4

1.4

1.4

F-s

orb/

incl

. C

O3-

com

pl.

0.67

0.44

0.25

0.98

1.00

0.95

0.64

CF

-F-s

orb/

incl

. C

O3-

com

pl.

1.04

0.69

0.39

1.51

1.55

1.48

F-s

orb/

excl

. C

O3-

com

pl.

1.00

1.00

1.00

1.00

1.00

1.00

1.00

CF

-F-s

orb/

excl

. C

O3-

com

pl.

1.00

1.00

1.00

1.00

1.00

1.00

Co

nve

rsio

n t

ot

CF

in

cl.

carb

on

ate

0.68

0.45

0.25

0.98

1.34

1.27

Pre

dic

ted

Kd

: C

F i

ncl

. ca

rbo

na

te(m

3 /kg)

42.6

328

.15

15.8

661

.78

84.3

080

.31

UF

-tot

al5.

85.

85.

85.

815

.115

.1

Kd

uppe

r lim

it(m

3 /kg)

248.

2616

3.93

92.3

635

9.81

1276

.54

1216

.10

Kd

low

er li

mit

(m3 /k

g)7.

319

4.83

32.

723

10.6

085.

567

5.30

4

Co

nve

rsio

n t

ot

CF

ex

cl.

carb

on

ate

0.65

0.65

0.65

0.65

0.86

0.86

Pre

dic

ted

Kd

: C

F e

xcl

. ca

rbo

na

te(m

3 /kg)

40.8

240

.82

40.8

240

.82

54.4

354

.43

UF

-tot

al5.

85.

85.

85.

815

.115

.1

Kd

uppe

r lim

it(m

3 /kg)

237.

7623

7.76

237.

7623

7.76

824.

2382

4.23

Kd

low

er li

mit

(m3 /k

g)7.

017.

017.

017.

013.

593.

59

(1)

Kd

valu

es fr

om T

h(IV

) so

rptio

n ed

ges

on S

Wy-

1 m

ontm

orill

onite

in 0

.1 M

NaC

lO4

(Bra

dbur

y an

d B

aeye

ns,

200

3)

Re

fere

nce

Tu

nn

el

Ba

ckfi

lla

vera

ge

ba

ckfi

ll

127

128

128

129

129

130

130

131

131

132

. U s

pe

cia

tio

n (

red

uci

ng

co

nd

itio

ns)

Sys

tem

Su

bst

rate

Na

-SA

z-1

Na

-Ill

ite

So

luti

on

sali

ne

wa

ter

KR

20/4

65/1

bra

ckis

h w

ate

r K

R6/

135/

8d

ilu

te,

carb

on

ate

ri

ch w

ate

r K

R4/

81/1

bri

ne

wa

ter,

K

R4/

861/

1, P

SI

da

tab

ase

hig

h a

lka

lin

e

wa

ter

gla

cia

l m

elt

w

ate

r, (

Gri

mse

l w

ate

r)

0.1

M N

aN

O3

0,1

M N

aC

lO4,

pH

7-1

0

Ref

eren

ceP

abal

an &

Tur

ner

(199

7)B

radb

ury

& B

aeye

ns (

2009

)

pH

7.60

7.21

7.28

7.46

10.0

08.

757.

287-

10p

e-3

.97

-3.4

0-3

.40

-5.3

6-6

.68

-5.0

12.

94

U (

tota

l)(m

ol/k

g)3.

09E

-09

3.81

E-0

95.

59E

-09

2.07

E-0

92.

93E

-09

3.14

E-0

91.

01E

-09

only

hyd

roly

sis

of U

(VI)

U(I

V)

U(O

H) 4

(mol

/kg)

2.91

E-0

92.

99E

-09

3.10

E-0

92.

04E

-09

2.92

E-0

93.

13E

-09

––

U(V

)U

O2

+(m

ol/k

g)1.

31E

-10

4.71

E-1

04.

42E

-10

4.27

E-1

22.

55E

-13

1.03

E-1

1–

–

U(V

I)U

O2(O

H) 3

-(m

ol/k

g)1.

60E

-13

1.45

E-1

32.

26E

-13

7.13

E-1

79.

69E

-12

3.33

E-1

22.

33E

-11

.

UO

2(O

H) 2

(mol

/kg)

3.44

E-1

37.

92E

-13

1.16

E-1

22.

09E

-16

8.31

E-1

46.

10E

-13

1.18

E-1

0

UO

2(O

H)+

(mol

/kg)

1.03

E-1

35.

59E

-13

6.25

E-1

39.

52E

-17

9.88

E-1

71.

06E

-14

6.47

E-1

1

UO

2(C

O3)

(mol

/kg)

1.02

E-1

21.

39E

-11

4.00

E-1

15.

90E

-17

3.84

E-1

81.

59E

-14

3.27

E-1

0

UO

2(C

O3) 2

2-

(mol

/kg)

6.81

E-1

28.

49E

-11

5.00

E-1

07.

69E

-18

2.50

E-1

71.

09E

-13

3.40

E-1

0

UO

2(C

O3) 3

4-

(mol

/kg)

1.86

E-1

12.

00E

-10

1.47

E-0

92.

59E

-20

6.67

E-1

71.

24E

-13

2.20

E-1

1

(UO

2)2(C

O3)(

OH

) 3-

(mol

/kg)

5.02

E-1

66.

00E

-15

2.60

E-1

41.

84E

-23

1.14

E-1

91.

51E

-16

9.06

E-1

1

∑ c

om

pe

titi

ve U

co

mp

lex

es

(U-c

mp

) w

ith

U-C

O3

(mol

/kg)

2.64

E-1

12.

99E

-10

2.01

E-0

96.

67E

-17

9.56

E-1

72.

49E

-13

7.79

E-1

0

U t

ot-

(U-c

mp

)(m

ol/k

g)3.

06E

-09

3.51

E-0

93.

59E

-09

2.07

E-0

92.

93E

-09

3.14

E-0

92.

30E

-10

(U t

ot-

(U

-cm

p))

/ U

to

t(F

-so

rb/i

ncl

. C

O3-c

om

pl.

)0.

990.

920.

641.

001.

001.

000.

23

UO

2(C

O3)

(mol

/kg)

1.02

E-1

21.

39E

-11

4.00

E-1

15.

90E

-17

3.84

E-1

81.

59E

-14

3.27

E-1

0

UO

2(C

O3) 2

2-

(mol

/kg)

6.81

E-1

28.

49E

-11

5.00

E-1

07.

69E

-18

2.50

E-1

71.

09E

-13

3.40

E-1

0

UO

2(C

O3) 3

4-

(mol

/kg)

1.86

E-1

12.

00E

-10

1.47

E-0

92.

59E

-20

6.67

E-1

71.

24E

-13

2.20

E-1

1

(UO

2)2(C

O3)(

OH

) 3-

(mol

/kg)

5.02

E-1

66.

00E

-15

2.60

E-1

41.

84E

-23

1.14

E-1

91.

51E

-16

9.06

E-1

1

∑ c

om

pe

titi

ve U

co

mp

lex

es

(U-c

mp

) w

ith

ou

t U

-CO

3

(mol

/kg)

0.00

E+

000.

00E

+00

0.00

E+

000.

00E

+00

0.00

E+

000.

00E

+00

0.00

E+

00

U t

ot-

(U-c

mp

) w

ith

ou

t U

-CO

3(m

ol/k

g)3.

09E

-09

3.81

E-0

95.

59E

-09

2.07

E-0

92.

93E

-09

3.14

E-0

91.

01E

-09

(U t

ot-

(U

-cm

p))

/ U

to

t(F

-so

rb/e

xcl

. C

O3-

com

pl.

)1.

001.

001.

001.

001.

001.

001.

00

Re

fere

nce

Tu

nn

el

Ba

ckfi

lla

vera

ge

ba

ckfi

llD

ata

So

urc

e

132

133

133

134

134

135

. U(V

I) s

pe

cia

tio

n (

ox

idis

ing

co

nd

itio

ns)

Sys

tem

Su

bst

rate

Na

-SW

y-1,

0.1

M N

aC

lO4

Na

-Ill

ite

So

luti

on

sali

ne

wa

ter

KR

20/4

65/1

bra

ckis

h w

ate

r K

R6/

135/

8d

ilu

te,

carb

on

ate

ri

ch w

ate

r K

R4/

81/1

bri

ne

wa

ter,

K

R4/

861/

1, P

SI

da

tab

ase

hig

h a

lka

lin

e

wa

ter

gla

cia

l m

elt

w

ate

r, (

Gri

mse

l w

ate

r)

pH

7.6

pH

7.2

1p

H 7

.28

pH

7.4

6p

H 8

.75-

100.

1 M

Na

ClO

4,

pH

7-1

0

Ref

eren

ceB

radb

ury

& B

aeye

ns (

2005

)B

radb

ury

& B

aeye

ns (

2009

)

pH

7.60

7.21

7.28

7.46

10.0

08.

757.

607.

217.

287.

468.

75-1

07-

10p

e4.

004.

004.

004.

004.

094.

004.

003.

182.

944.

00

U(V

I)(m

ol/k

g)1.

00E

-09

1.00

E-0

91.

00E

-09

1.00

E-0

91.

00E

-09

1.00

E-0

91.

01E

-09

1.01

E-0

91.

01E

-09

1.01

E-0

9on

ly h

ydro

lysi

son

ly h

ydro

lysi

s

UO

22+

(mol

/kg)

5.04

E-1

45.

34E

-14

5.96

E-1

51.

42E

-12

4.56

E-1

91.

39E

-15

7.83

E-1

42.

01E

-12

3.09

E-1

3

UO

2(O

H) 2

(mol

/kg)

1.46

E-1

12.

78E

-12

5.82

E-1

34.

61E

-10

8.36

E-1

21.

43E

-10

3.18

E-1

11.

36E

-10

1.18

E-1

06.

57E

-11

UO

2(O

H) 3

-(m

ol/k

g)6.

79E

-12

5.09

E-1

31.

14E

-13

1.57

E-1

09.

74E

-10

7.81

E-1

01.

31E

-11

2.28

E-1

12.

33E

-11

1.97

E-1

1

UO

2(O

H)+

(mol

/kg)

4.37

E-1

21.

96E

-12

3.14

E-1

32.

10E

-10

9.94

E-1

52.

47E

-12

8.33

E-1

28.

73E

-11

6.47

E-1

12.

38E

-11

UO

2(O

H) 4

2-

(mol

/kg)

4.85

E-1

61.

44E

-17

3.11

E-1

83.

17E

-15

1.75

E-1

15.

53E

-13

7.72

E-1

65.

47E

-16

8.39

E-1

6

UO

2(C

O3) 3

4-(m

ol/k

g)6.

70E

-10

6.60

E-1

07.

29E

-10

1.52

E-1

56.

70E

-15

2.71

E-1

14.

62E

-10

5.46

E-1

12.

20E

-11

2.64

E-1

0U

O2(

CO

3) 2

2-(m

ol/k

g)2.

59E

-10

2.86

E-1

02.

49E

-10

1.50

E-1

22.

51E

-15

2.44

E-1

13.

99E

-10

2.83

E-1

03.

40E

-10

4.33

E-1

0U

O2(

CO

3)

(mol

/kg)

4.10

E-1

14.

76E

-11

2.01

E-1

13.

85E

-11

3.86

E-1

63.

65E

-12

8.78

E-1

13.

75E

-10

3.27

E-1

01.

82E

-10

(UO

2) 2

(CO

3)(O

H) 3

-(m

ol/k

g)8.

52E

-13

7.23

E-1

46.

55E

-15

2.64

E-1

11.

15E

-15

8.13

E-1

23.

32E

-12

2.46

E-1

19.

06E

-11

1.03

E-1

1

UO

2SiO

(OH

) 3+

(mol

/kg)

1.97

E-1

28.

96E

-13

1.47

E-1

36.

73E

-11

4.60

E-1

51.

14E

-12

UO

2(S

O4)

(mol

/kg)

5.39

E-1

45.

97E

-14

2.40

E-1

41.

25E

-13

4.73

E-1

95.

03E

-15

UO

2(S

O4) 2

2-(m

ol/k

g)5.

76E

-15

5.80

E-1

54.

59E

-15

2.01

E-1

64.

87E

-20

5.95

E-1

6

UO

2F+

(mol

/kg)

9.40

E-1

43.

99E

-14

1.17

E-1

46.

33E

-12

1.19

E-1

84.

22E

-14

UO

2F2

(mol

/kg)

9.77

E-1

51.

66E

-15

1.17

E-1

56.

09E

-13

1.72

E-1

96.

00E

-14

UO

2Cl+

(mol

/kg)

5.85

E-1

54.

03E

-15

4.28

E-1

73.

01E

-12

5.33

E-2

02.

10E

-19

∑ c

om

pe

titi

ve U

co

mp

lex

es

(U-c

mp

) w

ith

U-C

O3

(mol

/kg)

9.73

E-1

09.

95E

-10

9.99

E-1

01.

44E

-10

1.54

E-1

46.

45E

-11

9.52

E-1

07.

36E

-10

7.79

E-1

08.

89E

-10

U t

ot-

(U-c

mp

)(m

ol/k

g)2.

66E

-11

5.37

E-1

21.

03E

-12

8.56

E-1

01.

00E

-09

9.35

E-1

05.

66E

-11

2.72

E-1

02.

30E

-10

1.20

E-1

0(U

to

t- (

U-c

mp

)) /

U t

ot

(F-s

orb

/in

cl.

CO

3-co

mp

l.)

0.03

0.01

0.00

10.

861.

000.

940.

060.

270.

230.

12

UO

2(C

O3) 3

4-(m

ol/k

g)6.

70E

-10

6.60

E-1

07.

29E

-10

1.52

E-1

56.

70E

-15

2.71

E-1

14.

62E

-10

5.46

E-1

12.

20E

-11

2.64

E-1

0

UO

2(C

O3) 2

2-(m

ol/k

g)2.

59E

-10

2.86

E-1

02.

49E

-10

1.50

E-1

22.

51E

-15

2.44

E-1

13.

99E

-10

2.83

E-1

03.

40E

-10

4.33

E-1

0

UO

2(C

O3)

(mol

/kg)

4.10

E-1

14.

76E

-11

2.01

E-1

13.

85E

-11

3.86

E-1

63.

65E

-12

8.78

E-1

13.

75E

-10

3.27

E-1

01.

82E

-10

(UO

2) 2

(CO

3)(O

H) 3

-(m

ol/k

g)8.

52E

-13

7.23

E-1

46.

55E

-15

2.64

E-1

11.

15E

-15

8.13

E-1

23.

32E

-12

2.46

E-1

19.

06E

-11

1.03

E-1

1

UO

2SiO

(OH

) 3+

(mol

/kg)

1.97

E-1

28.

96E

-13

1.47

E-1

36.

73E

-11

4.60

E-1

51.

14E

-12

UO

2(S

O4)

(mol

/kg)

5.39

E-1

45.

97E

-14

2.40

E-1

41.

25E

-13

4.73

E-1

95.

03E

-15

UO

2(S

O4) 2

2-(m

ol/k

g)5.

76E

-15

5.80

E-1

54.

59E

-15

2.01

E-1

64.

87E

-20

5.95

E-1

6

UO

2F+

(mol

/kg)

9.40

E-1

43.

99E

-14

1.17

E-1

46.

33E

-12

1.19

E-1

84.

22E

-14

UO

2F2

(mol

/kg)

9.77

E-1

51.

66E

-15

1.17

E-1

56.

09E

-13

1.72

E-1

96.

00E

-14

UO

2Cl+

(mol

/kg)

5.85

E-1

54.

03E

-15

4.28

E-1

73.

01E

-12

5.33

E-2

02.

10E

-19

∑ c

om

pe

titi

ve U

co

mp

lex

es

(U-c

mp

) w

ith

ou

t U

-CO

3

(mol

/kg)

2.14

E-1

21.

01E

-12

1.89

E-1

37.

74E

-11

4.60

E-1

51.

24E

-12

0.00

E+

000.

00E

+00

####

###

0.00

E+

00

U t

ot-

(U-c

mp

) w

ith

ou

t U

-CO

3(m

ol/k

g)9.

98E

-10

9.99

E-1

01.

00E

-09

9.23

E-1

01.

00E

-09

9.99

E-1

01.

01E

-09

1.01

E-0

91.

01E

-09

1.01

E-0

9

(U t

ot-

(U

-cm

p))

/ U

to

t(F

-so

rb/e

xcl

. C

O3-c

om

pl.

)1.

001.

001.

000.

921.

001.

001.

001.

001.

001.

00

Pab

alan

& T

urne

r (1

997)

Na

-SA

z-1,

0.1

M N

aN

O3

Re

fere

nce

Tu

nn

el

Ba

ckfi

lla

vera

ge

ba

ckfi

llD

ata

So

urc

e

135

136

136

137

137

138

138

139

139

140

140

141

141

142

142

143

143

144

144

145

145

146

146

147

147

148

148

149

149

150

150

151

151

152

152

153

153

154

154

155

155

156

156

157

157

158

158

159

159

160

160

161

Ag

(I)

spe

cia

tio

n

Sys

tem

Da

ta S

ou

rce

Su

bst

rate

Be

nto

nit

eS

olu

tio

ng

laci

al

me

lt w

ate

r, (

Gri

mse

l w

ate

r)0.

01 M

Ag

NO

3, p

H 2

-10

Ref

eren

ceK

han

et a

l. (1

995)

pH

8.75

2-10

pe

-5.0

3

Ag

(mol

/kg)

1.00

E-1

1on

ly h

ydro

lysi

s

Ag

+(m

ol/k

g)7.

06E

-12

Ag

CO

3-

(mol

/kg)

2.56

E-1

5

Ag

Cl

(mol

/kg)

1.94

E-1

2

Ag

Cl 2

-(m

ol/k

g)4.

31E

-14

Ag

Cl 3

-2(m

ol/k

g)1.

45E

-17

Ag

Cl 4

-3(m

ol/k

g)1.

14E

-20

Ag

(SO

4)-

(mol

/kg)

9.43

E-1

3

∑ c

om

pe

titi

ve A

g c

om

ple

xe

s(E

u-c

mp

) w

ith

Ag

-CO

3

(mol

/kg)

2.93

E-1

2

Ag

to

t-(A

g-c

mp

)(m

ol/k

g)7.

07E

-12

(Ag

to

t- (

Ag

-cm

p))

/ A

g t

ot

(F-s

orb

/in

cl.

CO

3-c

om

pl.

)0.

711.

00

Ag

CO

3-

(mol

/kg)

2.56

E-1

5

Ag

Cl

(mol

/kg)

1.94

E-1

2

Ag

Cl 2

-(m

ol/k

g)4.

31E

-14

Ag

Cl 3

-2(m

ol/k

g)1.

45E

-17

Ag

Cl 4

-3(m

ol/k

g)1.

14E

-20

Ag

(SO

4)-

(mol

/kg)

9.43

E-1

3

∑ c

om

pe

titi

ve A

g c

om

ple

xe

s(A

g-c

mp

) w

ith

ou

t A

g-C

O3

(mol

/kg)

2.93

E-1

2

Ag

to

t-(A

g-c

mp

) w

ith

ou

t A

g-C

O3

(mol

/kg)

7.07

E-1

2

(Ag

to

t- (

Ag

-cm

p))

/ A

g t

ot

(F-s

orb

/ex

cl.

CO

3-co

mp

l.)

0.71

1.00

Re

fere

nce

Tu

nn

el

Ba

ckfi

lla

vera

ge

ba

ckfi

ll

161

162

162

163

163

164

164

165

165

166

166


LIST OF REPORTS

POSIVA-REPORTS 2012

_______________________________________________________________________________________

POSIVA 2012-01 Monitoring at Olkiluoto – a Programme for the Period Before Repository Operation Posiva Oy ISBN 978-951-652-182-7 POSIVA 2012-02 Microstructure, Porosity and Mineralogy Around Fractures in Olkiluoto

Bedrock Jukka Kuva (ed.), Markko Myllys, Jussi Timonen, University of Jyväskylä Maarit Kelokaski, Marja Siitari-Kauppi, Jussi Ikonen, University of Helsinki Antero Lindberg, Geological Survey of Finland Ismo Aaltonen, Posiva Oy ISBN 978-951-652-183-4

POSIVA 2012-03 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Design Basis 2012 Posiva Oy ISBN 978-951-652-184-1 POSIVA 2012-04 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Performance Assessment 2012 Posiva Oy ISBN 978-951-652-185-8 POSIVA 2012-05 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Description of the Disposal System 2012 Posiva Oy ISBN 978-951-652-186-5 POSIVA 2012-06 Olkiluoto Biosphere Description 2012 Posiva Oy ISBN 978-951-652-187-2 POSIVA 2012-07 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Features, Events and Processes 2012 Posiva Oy ISBN 978-951-652-188-9 POSIVA 2012-08 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Formulation of Radionuclide Release Scenarios 2012 Posiva Oy ISBN 978-951-652-189-6


POSIVA 2012-09 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Assessment of Radionuclide Release Scenarios for the Repository System 2012 Posiva Oy ISBN 978-951-652-190-2 POSIVA 2012-10 Safety case for the Spent Nuclear Fuel Disposal at Olkiluoto - Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-191-9 POSIVA 2012-11 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Complementary Considerations 2012 Posiva Oy ISBN 978-951-652-192-6 POSIVA 2012-12 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Synthesis 2012 Posiva Oy ISBN 978-951-652-193-3 POSIVA 2012-13 Canister Design 2012 Heikki Raiko, VTT ISBN 978-951-652-194-0 POSIVA 2012-14 Buffer Design 2012 Markku Juvankoski, VTT ISBN 978-951-652-195-7 POSIVA 2012-15 Backfill Design 2012 Posiva Oy ISBN 978-951-652-196-4 POSIVA 2012-16 Canister Production Line 2012 – Design, Production and Initial State of the Canister Heikki Raiko (ed.), VTT Barbara Pastina, Saanio & Riekkola Oy Tiina Jalonen, Leena Nolvi, Jorma Pitkänen & Timo Salonen, Posiva Oy ISBN 978-951-652-197-1 POSIVA 2012-17 Buffer Production Line 2012 – Design, Production, and Initial State of the Buffer Markku Juvankoski, Kari Ikonen, VTT Tiina Jalonen, Posiva Oy ISBN 978-951-652-198-8


POSIVA 2012-18 Backfill Production Line 2012 - Design, Production and Initial State of the Deposition Tunnel Backfill and Plug Paula Keto (ed.), Md. Mamunul Hassan, Petriikka Karttunen, Leena Kiviranta, Sirpa Kumpulainen, B+Tech Oy Leena Korkiala-Tanttu, Aalto University Ville Koskinen, Fortum Oyj Tiina Jalonen, Petri Koho, Posiva Oy Ursula Sievänen, Saanio & Riekkola Oy ISBN 978-951-652-199-5 POSIVA 2012-19 Closure Production Line 2012 - Design, Production and Initial State of Underground Disposal Facility Closure Ursula Sievänen, Taina H. Karvonen, Saanio & Riekkola Oy David Dixon, AECL Johanna Hansen, Tiina Jalonen, Posiva Oy ISBN 978-951-652-200-8 POSIVA 2012-20 Representing Solute Transport Through the Multi-Barrier Disposal System by Simplified Concepts Antti Poteri. Henrik Nordman, Veli-Matti Pulkkanen, VTT Aimo Hautojärvi, Posiva Oy Pekka Kekäläinen, University of Jyväskylä, Deparment of Physics ISBN 978-951-652-201-5 POSIVA 2012-21 Layout Determining Features, their Influence Zones and Respect Distances at the Olkiluoto Site Tuomas Pere (ed.), Susanna Aro, Jussi Mattila, Posiva Oy Henry Ahokas & Tiina Vaittinen, Pöyry Finland Oy Liisa Wikström, Svensk Kärnbränslehantering AB ISBN 978-951-652-202-2 POSIVA 2012-22 Underground Openings Production Line 2012 – Design, Production and Initial State of the Underground Openings Posiva Oy ISBN 978-951-652-203-9 POSIVA 2012-23 Site Engineering Report Posiva Oy ISBN 978-951-652-204-6 POSIVA 2012-24 Rock Suitability Classification, RSC-2012 Tim McEwen (ed.), McEwen Consulting Susanna Aro, Paula Kosunen, Jussi Mattila, Tuomas Pere, Posiva Oy Asko Käpyaho, Geological Survey of Finland Pirjo Hellä, Saanio & Riekkola Oy ISBN 978-951-652-205-3 POSIVA 2012-25 2D and 3D Finite Element Analysis of Buffer-Backfill Interaction Martino Leoni, Wesi Geotecnica Srl ISBN 978-951-652-206-0


POSIVA 2012-26 Climate and Sea Level Scenarios for Olkiluoto for the Next 10,000 Years Natalia Pimenoff, Ari Venäläinen & Heikki Järvinen, Ilmatieteen laitos ISBN 978-951-652-207-7 POSIVA 2012-27 Geological Discrete Fracture Network Model for the Olkiluoto Site, Eurajoki, Finland: version 2.0 Aaron Fox, Kim Forchhammer, Anders Pettersson, Golder Associates AB Paul La Pointe, Doo-Hyun Lim, Golder Associates Inc. ISBN 978-951-652-208-4 POSIVA 2012-28 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Data Basis for the Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-209-1 POSIVA 2012-29 Safety Case For The Disposal of Spent Nuclear Fuel at Olkiluoto - Terrain and Ecosystems Development Modelling in the Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-210-7 POSIVA 2012-30 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Surface and Near-surface Hydrological Modelling in the Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-211-4 POSIVA 2012-31 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Radionuclide Transport and Dose Assessment for Humans in the Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-212-1 POSIVA 2012-32 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Dose Assessment for the Plants and Animals in the Biosphere Assessment BSA-2012 Posiva Oy ISBN 978-951-652-213-8 POSIVA 2012-33 Underground Openings Line Demonstrations Stage 1, 2012 ISBN 978-951-652-214-5 POSIVA 2012-34 Seismic Activity Parameters of the Olkiluoto Site Jouni Saari, ÅF-Consult Oy ISBN 978-951-652-215-2


POSIVA 2012-35 Inspection of Disposal Canisters Components Jorma Pitkänen, Posiva Oy ISBN 978-951-652-216-9 POSIVA 2012-36 Analyses of Disposal Canister Falling Accidents Juha Kuutti, Ilkka Hakola, Stephania Fortino, VTT ISBN 978-951-652-217-6 POSIVA 2012-37 Long-Term Safety of the Maintenance and Decommissioning Waste of the Encapsulation Plant Olli Nummi, Jarkko Kyllönen, Tapani Eurajoki, Fortum Power and Heat ISBN 978-951-652-224-4 POSIVA 2012-38 Human Factors in NDT of the EB-Weld ISBN 978-951-652-225-1 POSIVA 2012-39 Safety case for the disposal of spent nuclear fuel at Olkiluoto: Radionuclide solubility limits and migration parameters for the canister and the buffer. Paul Wersin,Mirjam Kiczka,Dominic Rosch, Gruner AG, Switzerland ISBN 978-951-652-219-0 POSIVA 2012-40 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto: Radionuclide Solubility Limits and Migration Parameters for the Backfill. Paul Wersin,Mirjam Kiczka,Dominic Rosch, Gruner AG, Switzerland Michael Ochs, David Trudel, BMG Engineering Ltd, Switzerland ISBN 978-951-652-220-6

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