ASSESSMENT OF SORPTION AND DIFFUSION IN THE ROCK MATRIX IN THE
NUMO SAFETY CASE
T. Hamamotoa, S. Shibutania, K. Ishidaa, K. Fujisakia, M. Yamadaa, Y. TachibaaNuclear Waste Management Organization of Japan (NUMO)bJapan Atomic Energy Agency (JAEA)

Mita NN Bldg. 1-23, Shiba 4-Chome, Minato-ku, Tokyo 108-0014 Japanb
4-33, Muramatsu, Tokai-mura, Naka-gun, Ibaraki, 319-1194 Japan

Key words; Geological disposal; Performance assessment; Radionuclide migration; Sorption; Diffusion

Abstract
NUMO has developed a generic safety case to demonstrate the feasibility and safety of geological disposal ofhigh-level radioactive waste (HLW) and low and intermediate level waste generated from reproccessing and
MOX fabrication (named TRU waste) in Japan. In this pre-siting safety case, the performance assessment was
carried out for the repositories tailored to site descriptive models developed for three representative rock groups(plutonic, Neogene sedimentary and Pre-Neogene sedimentary rocks). Radionuclide migration parameters inrocks, i.e. distribution coefficients and effective diffusion coefficients, were derived to allow performanceassessment for a range of scenarios. The values of these parameters were given statistically from data for certain
rock types. The data were extracted from the latest sorption and diffusion database, with interpretation based on
the speciation by thermodynamic modeling using relevant groundwater chemistry. In the performanceassessment, the parameters assessed as the most realistic were used for a dose calculation of a “likely” scenario.
The variations of the parameters to account for the uncertainties were analyzed in “less-likely” scenarios.
1. Introduction

1.1 NUMO safety case
NUMO has developed a safety case (NUMO-SC)
to confirm a generic demonstration of feasibility ofgeological disposal of HLW and TRU waste in Japanand extend this to consider the geological settings
that may result from the volunteering approach tositing. A consequence of volunteer siting is that thedesign of the repository has to be tailored to thegeological setting encountered.
For the long-term performance assessment after the
closure of the repository, parameters to characterise
the migration of nuclides in the geologicalenvironment relevant to each host rocks have to be
defined. We first develop the relevant models ofgroundwater chemistry for each host rocks, thenassess key parameters such as solubility, speciation,
distribution coefficient (Kd) and effective diffusion
coefficient (De) in the engineered material and rocks.
This paper describes the setting of Kd and De of three
representative host rocks for target radionuclides for
performance assessment.
1.2. The engineered repository
For disposal of HLW, two different designs of theengineered barrier system layout have beendeveloped (Fig. 1), although these both feature a

massive steel overpack, a bentonite-based buffer andbackfill (Ref. 1). HLW will be co-disposed with TRU
waste, which will be placed in concrete vaults infilledwith a cementitious material. Following theclassification concept (Ref. 2), 4 groups of TRU
waste packages was defined. For some TRU waste
groups, a bentonite-based buffer layer is also
included (Fig. 2). TRU waste group 3 contains large
amount of nitrate which influences the radionuclide
migration.
VerticalBackfill(Bentonite-sand mixture)

Shotcrete

Buffer
Overpack
Vitrified waste

Shotcrete

Horizontal
Buffer
Overpack
Vitrified waste

Backfill(Bentonite-sand mixture)

Fig. 1. Cross section of HLW drift.
(modified from Ref. 1)

6th East Asia Forum on Radwaste Management Conference
November 27-29, 2017, Osaka, JapanTABLE 1. Chemical composition of model
groundwater. High salinity (HS) and Low salinity(LS) groundwater are set for each rocks.

Backfill(Bentonite-sand mixture)
Shotcrete
Structure andwastesPlutonic rocks

Drain

Gr.1 and Gr.2

HS

LS

7.6

8.2

6.5

8.4

6.3

8.2

Eh (mV)

-260

-300

-170

-280

-170

-290

Na

1.7×10-2

3.1×10-3

2.2×10-1

2.8×10-3

2.2×10-1

2.8×10-3

Ca

1.6×10-2

4.0×10-4

3.5×10-3

2.3×10-4

3.5×10-3

2.3×10-4

K

1.0×10-4

1.6×10-5

3.2×10-3

3.0×10-5

3.2×10-3

3.0×10-5

Mg

6.2×10-5

8.2×10-6

5.0×10-3

1.5×10-5

5.0×10-3

1.5×10-5Fe

4.9×10-7

9.0×10-7

3.3×10-5

8.5×10-7

3.3×10-5

8.5×10-7Al

2.8×10-7

7.9×10-7

1.3×10-9

2.2×10-8

2.4×10-9

4.3×10-8Si

3.0×10-4

3.2×10-4

6.6×10-4

7.5×10-4

9.2×10-4

1.0×10-3

S

2.0×10-5

7.3×10-6

4.1×10-6

1.2×10-4

4.1×10-6

1.2×10-4

C

2.2×10-4

9.5×10-4

4.0×10-2

1.7×10-3

4.7×10-2

1.7×10-3

Cl

4.9×10-2

2.3×10-3

2.1×10-1

1.1×10-3

2.1×10-1

1.1×10-3

F

1.3×10-4

5.7×10-4

6.5×10-6

1.9×10-4

6.5×10-6

1.9×10-4

B

2.7×10-4

4.6×10-6

1.0×10-2

4.6×10-6

1.0×10-2

4.6×10-6

P

5.3×10-6

6.5×10-7

5.9×10-6

5.6×10-6

5.9×10-6

5.6×10-6N

2.8×10-5

2.0×10-5

1.0×10-2

2.5×10-6

1.0×10-2

2.5×10-6Br

3.9×10-5

4.3×10-6

8.0×10-4

4.4×10-6

8.0×10-4

4.4×10-6

I

5.5×10-6

7.9×10-6

1.8×10-4

3.9×10-6

1.8×10-4

3.9×10-6

Gr.3 and Gr.4

Fig. 2. Cross section of TRU waste drift.
(modified from Ref. 1)
1.3. Site descriptive models
In this pre-siting safety case, site descriptivemodels are developed for representative rock groups(plutonic, Neogene sedimentary and Pre-Neogene
sedimentary rocks) in Japan (Ref. 3). The nationwide
geoscientific information suggested that plutonic
rocks and Pre-Neogene sedimentary rocks arecharacterised by the network of faults/fractures whereflow
predominatly
occurswhile
Neogenesedimentary rocks are characterised by the permeable
sedimentary layers and water conducting fracture
systems (Ref. 3). As the result of the repository
design based on the site descriptive models, therepository of the plutonic rocks and the both
sedimentary rocks are assumed to be located in
granite and mudstone, respectively (Ref. 3). Higher
porosity mudstone in Neogene sedimentary rocks
than that in Pre-Neogene sedimentary rocks inresponse to the site descriptive models.
Groundwater chemistry reflects its origins, as
rainwater, seawater, deep-seated fluids, etc., together
with processes along its flow path, including mixing
and rock-water interactions. It is thus difficult to set
the groundwater chemistry based on specification of
the host rock alone. In NUMO-SC, two salinity typesof groundwater chemistry for each host rock are
specified based on average values from the limited
number of high quality measurements of relevant
deep groundwaters (Refs. 4 to 8) to understand awide variation in groundwater chemistry (salinity) inJapan. However, there is no high quality
measurements of deep groundwater for the
Pre-Neogene sedimentary rocks so the Neogene
sedimentary groundwater chemistry data aresubstituted for the setting of the Pre-Neogene
sedimentary groundwater. Sampling groundwaterfrom deep underground is inherently problematic,
introducing errors due to artefacts such as degassing,and hence considerations of the equilibrium ofmineral such as quartz, feldspar, mica, carbonate
minerals and the redox equilibrium withthermodynamic calculation are coupled. Table 1
show the resulting groundwater chemistries.
3. Scenario analysis
In performance assessment, scenarios aredeveloped based on the state-of-the-art understanding

Pre-Neogene
sedimentary rocks
HS
LS

pH

EDZ

BufferNeogene sedimentary
rocks
HS
LS

Element concentration[mol/dm3]of the evolution of repository system. At the first stepof scenario development regarding to theradionuclide migration in the host rock, we extract
FEPs (feature, event, process) which may influence it.
The FEP analysis showed the retention ofradionuclides to host rocks might be inhibited bynitrate from the TRU waste and high pH plume from
cement porewater. Note that we do not assess the
microbes effects in this generic safety case because of
its high regionality.
Reactive transport analyses have been conducted tounderstand the processes, such as transport of
perturbing species and alteration of rock. The results
show the high pH plume from cementitious materials
have low effect on the sorption and diffusion becauseof pore clogging at the boundary of cement and rock.
We assume no influence of high pH plume in thelikely scenario and less-likely scenarios.
Radionuclide migration near the TRU waste group
3 containing nitrate is assessed considering the nitrateeffect in the likely case. The nitrate may not influence
the groudwater in the pathway of radionuclides fromthe other wastes because the nitrate waste is locatedat the downstream of the other waste. In less-likely
scenario, the nitrate effect on the migrationi of theradionuclides from the waste is evaluated in case thatnitrate transports to the pathway of radionuclides
considering the uncertainties on the assessment of
nitrate transport.
4. Parameter setting
4.1 Effective diffusion coefficient
De for rocks has been reported to depend onelectric charge of species and porosity of host rock.
Since De is well known to have temperaturedependency, correction is made according to the
expected temperature at each repository depth. The
temperature of 30°C for the Neogene sedimentary

6th East Asia Forum on Radwaste Management Conference
November 27-29, 2017, Osaka, Japan

1E-08〇cation (●: Reference, ●: Lower limit)□neutral (■: Reference, ■: Lower limit)△anion (▲: Reference, ▲: Lower limit)

1E-09

De (m 2 /s)rocks and 45°C for the plutonic rocks and the
Pre-Neogene sedimentary rocks is set.
De values for specified conditions are derived fromempirical equations based on measured data from the
JAEA-DDB (Diffusion Database) (Ref. 9). The
trends of De of the plutonic rock and the porosity
showed no clear dependency with electric charged
states of elements (Fig. 3, Ref. 10). Thus, De valuesof the plutonic rocks at 25°C are obtained from thefollowing relation: equation (1).

y = 1.1×10 -13 x 2.2

1E-10
1E-11
1E-12

y = 1.9×10-14 x2.0

1E-13

1E-14
1

De = 8.55×10-13・ε1.3

Pre-Neogene sedimentary rocks(ε = 3.5%)

10

Neogene sedimentary rocks(ε = 24.5%)100

porosity, ε (%)(1)

where ε is the porosity.
On the other hand, the dataset in the sedimentaryrocks show the relations of electric charge ofdiffusing species and De (Fig. 4). This is consistent
with the setting of De for clay rocks assessed in
SGT-E2 report (Ref. 11). De values of the both
sedimentary rocks at 25°C are obtained from thefollowing relations: equation (2)-(4).
Cations;
Neutral species;
Anions;y = 3.5×10 -13 x1.6

De = 1.1×10-13・ε2.2 (2)De = 3.5×10-13・ε1.6 (3)De = 2.0×10-14・ε2.0 (4)Fig. 4 The relation of De (m2/s) of the mudstone at
25°C and porosity (%).The reference values (orange)
and the lower limits (green) are set with the porosityof 3.5% and 24.5% for the Pre-Neogene
sedimenatary rock and the Neogene sedimentary rock,respectively.
TABLE 2. De (m2/s) dataset corrected for therepository temperature for high salinity groundwater(HS) and low salinity groundwater (LS) of three
representative host rocks.
plutonic rocks

where ε is the porosity.
Table 2 show the De values corrected for therepository temperature. The Neogene sedimentaryrocks are more porous than Pre-Neogene sedimentaryrocks so the De values of Neogene sedimentary rocks
are higher. De values of some elements, such as Co,
Ni, Zr, Pd, Sn and Pu (in addition Am, Ac and Cm
for Neogene sedimentary groundwater), for highsalinity sedimentary groundwater and low salinity
one are different reflecting their speciation in the
groundwater.
1E-08
1E-09

GW typeC(inorg)C(org)
Cl
Co
Ni
Se
Sr
Zr
Nb
Mo
Tc
Pd
Sn
I
Cs
Pb
Ra
Ac
Th
Pa
U
Np
Pu
Am
Cm

HS
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12LS
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12

Neogene sedimentary
rocks
HS
LS
1×10-11
1×10-11
6×10-11
6×10-11
1×10-11
1×10-11
1×10-10
6×10-11
1×10-10
6×10-11
1×10-11
1×10-11-10
1×10
1×10-10-11
1×10
6×10-11
1×10-11
1×10-11
1×10-11
1×10-11
6×10-11
6×10-11
1×10-10
6×10-11
6×10-11
1×10-11
1×10-11
1×10-11
1×10-10
1×10-10
6×10-11
6×10-11
1×10-10
1×10-10-10
1×10
1×10-11-11
1×10
1×10-11
1×10-11
1×10-11
1×10-11
1×10-11
1×10-11
1×10-11
1×10-10
1×10-11
1×10-10
1×10-11
1×10-10
1×10-11

Pre-Neogene
sedimentary rocks
HSLS
3×10-13
3×10-13
4×10-12
4×10-12
3×10-13
3×10-13
2×10-12
4×10-12
2×10-12
4×10-12
3×10-13
3×10-13-12
2×10
2×10-12-13
3×10
4×10-12
3×10-13
3×10-13
3×10-13
3×10-13
4×10-12
4×10-12
2×10-12
4×10-12
4×10-12
3×10-13
3×10-13
3×10-13
2×10-12
2×10-12
4×10-12
4×10-12
2×10-12
2×10-12-12
2×10
2×10-12-13
3×10
3×10-13
3×10-13
3×10-13
3×10-13
3×10-13
3×10-13
3×10-13
2×10-12
3×10-13
2×10-12
2×10-12
2×10-12
2×10-12De (m 2 /s)

1E-10
1E-11
1E-12
1E-13
1E-14
1E-15

1E-16
0.01

0.1

Plutonic rock(ε = 0.8) 1

10

100

Porosity, ε (%)

Fig. 3 The relation of De (m2/s) of the granitic rock at
25°C and porosity (%). The reference value (orange)
and the lower limit (green) are set with the porosityof 0.8%. (Modified from Ref. 10)

4.2 Distribution coefficient
The Kd setting approach was developed by the
integration of three methods; direct use of measured
Kd data extracted from the sorption database,semi-quantitative estimation procedures by scalingdifferences between experiment and performanceassessment conditions, and thermodynamic sorption
models (Ref. 12). An trial of the integrated Kd setting
method for the Kd of granitic rock for Cs and Am
indicated that Kd can be quantitatively evaluated by
all approaches when adequate data and models areavailable (Ref. 12). In this paper, the dataset for the
all host rocks based on the direct use of measured
data was adopted because of the limitation of data
under the situation of this generic stage.

6th East Asia Forum on Radwaste Management Conference
November 27-29, 2017, Osaka, JapanTABLE 3. Kd (m3/kg) dataset for high salinity
groundwater (HS) and low salinity groundwater (LS)of three representative host rocks.
GW typeC(inorg)C(org)
Cl
Co
Ni
Se
Sr
Zr
Nb
Mo
Tc
Pd
Sn
I
Cs
Pb
Ra
Ac
Th
Pa
U
Np
Pu
Am
Cm

Plutonic rocks(Ref. 9)
HS
LS
0
0
0
0
0
0
0.1
0.1
0.1
0.1
0.001
0.001
0.008
0.008
0.8
0.8
0.4
0.4
0
0
8
8
0.6
0.6
10
10
0
0
0.04
0.04
1
1
0.1
0.1
1
1
0.3
0.3
2
2
0.3
0.3
0.3
0.3
0.3
0.3
1
1
1
1

Neogene sedimentary
rocks
HS
LS
0
0
0
0
0
0
0.5
3
0.5
3
0.05
0.04
0.07
0.2
0
2
6
6
0
0
0
0.01
3
1
100
100
0
0
0.1
1
0
2
0.03
0.3
20
200
5
30
2
2
-6
2×10
6
5
30
5
30
20
200
20
200

Pre-Neogene
sedimentary rocks
HS
LS
0
0
0
0
0
0
0.5
3
0.5
3
0.05
0.04
0.07
0.2
0
2
6
6
0
0
0
0.01
3
1
100
100
0
0
0.1
1
0
2
0.03
0.3
20
200
5
30
2
2
-6
2×10
6
5
30
5
30
20
200
20
200

In this situation, a large uncertainty on geologicalenvironment remained. For plutonic rocks, thedatasets have the same value for two types ofgroundwater chemistry. Sorption of the alkali andalkaline earth elements generally have an dependency
on ionic strength, but the both plutonic groundwater
have so similar ionic strength that the defference ofthe Kd is negligible. For the high salinit ysedimentary groundwater, lower sorption of the alkali
and alkaline earth elements (Sr, Cs and Ra) are
estimated because these elements assumed to beadsorbed by ion exchange. Moreover, high carbonate
concentration (ca. 40 mM) indicates the low sorptionof many elements (Co, Ni, Zr, Tc, Pb, Ac, Th, U, Np,
Pu, Am and Cm) due to carbonate complexation. No
sorption of Zr, Tc and Pb are conservatively set
because of the limitation of adequate data to evaluate
the effect of such high carbonate concentration
although these elements are usually assumed to be
somewhat adsorbed to host rocks. Note that relatively
low sorption of U under high salinity Neogene
groundwater, due to the stabilization of U(VI) by
forming carbonate complexation, may be somewhat
over-conservative because the value is estimatedfrom the data under oxidizing condition.
The results show low sorption in plutonic and highsorption in sedimentary rock except for the species
associated with carbonate. This trend can beinterpreted by the fact that mudstone usually contains
large amount of clay minerals which have good
retardation properties.
4.3 Parameter variability and uncertainty
Kd and De values are set from the dataset extracted
from the database. The range of the values dataset is
extracted from relatively wide conditions due to ahigh uncertainty of geological environment. The Kdvalue of the element which has large number of data,such as Sr, exhibits a log-normal distribution
approximately (Fig. 3). Thus, the 95% confidence
interval is estimated as the variation of Kd and De
values. The lower limit of the 95% confidence
interval is used in the less-likely scenario (see Table
4 and 5).
Uncertainties on the scenarios have also been
considered in less-likely scenarios. As described in
section 3, nitrate is assumed to influence the waterchemistry of near-field pathways for radionuclides
from all of the TRU waste in the less-likely scenario
of nitrate transport. Because there is less amount ofdata obtained under high nitrate concentration, theeffect of nitrate is evaluated with the reduction
factors for Kd (Ref. 13). These factors are acquiredby analysis of the effect of ionic strength from the
JAEA-SDB and the effect of complex formation
using the sorption dependency of sodium nitrateconcentration for tuff. The nitrate reduction factors
are shown as Table 6. The factors of Co, Ni, Pd andPb are estimated considering the association withammonia/ammonium. The factors which assessed forthe highest concentration of ammonia areconservatively used because it is hard to estimate theconcentration of ammonia produced by the nitrate
reduction bacteria.

35: Histogram of Kd of Sr: Log-normal distribution

30
25

Frequency

Kd values for host rocks are logarithmic meanvalues of data in JAEA-SDB (Sorption Database)obtained under similar conditions to the groundwaterchemistry. In particular, pH, ionic strength, redox
condition and carbonate concentration are consideredin the data extracting process. Table 3 show the Kd
datasets.

20

15
10
5
0

-2

-1.6

-1.2

-0.8
-0.4
0
log Kd [m3 /kg]

0.4

0.8

Fig. 3 Histogram of the logarithmic value of
mudstone Kd (m3/kg) of Sr. The red line show the
log-normal distribution with the mean value andstandard deviation of the dataset.

6th East Asia Forum on Radwaste Management Conference
November 27-29, 2017, Osaka, JapanTABLE 4. Lower De (m2/s) dataset corrected for therepository temperature for high salinity groundwater(HS) and low salinity groundwater (LS) of three
representative host rocks including 95% confidence
interval.
plutonic rocks
GW typeC(inorg)C(org)
Cl
Co
Ni
Se
Sr
Zr
Nb
Mo
Tc
Pd
Sn
I
Cs
Pb
Ra
Ac
Th
Pa
U
Np
Pu
Am
Cm

HS
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14LS
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14

Neogene sedimentary
rocks
HS
LS
1×10-12
1×10-12-12
3×10
3×10-12
1×10-12
1×10-12
3×10-12
3×10-12
3×10-12
3×10-12
1×10-12
1×10-12
3×10-12
3×10-12
1×10-12
3×10-12
1×10-12
1×10-12-12
1×10
1×10-12-12
3×10
3×10-12
3×10-12
3×10-12
3×10-12
1×10-12
1×10-12
1×10-12
3×10-12
3×10-12
3×10-12
3×10-12-12
3×10
3×10-12-12
3×10
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
1×10-12
3×10-12
1×10-12
3×10-12
1×10-12
3×10-12
1×10-12Pre-Neogene
sedimentary rocks
HSLS
5×10-14
5×10-14-14
9×10
9×10-14
5×10-14
5×10-14
9×10-14
9×10-14
9×10-14
9×10-14
5×10-14
5×10-14
9×10-14
9×10-14
5×10-14
9×10-14
5×10-14
5×10-14-14
5×10
5×10-14-14
9×10
9×10-14
9×10-14
9×10-14
9×10-14
5×10-14
5×10-14
5×10-14
9×10-14
9×10-14
9×10-14
9×10-14-14
9×10
9×10-14-14
9×10
9×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
5×10-14
9×10-14
5×10-14
9×10-14
9×10-14
9×10-14
9×10-14TABLE 5. Lower Kd (m3/kg) dataset for high salinity
groundwater (HS) and low salinity groundwater (LS)of three representative host rocks including 95%
confidence interval.

GW typeC(inorg)C(org)
Cl
Co
Ni
Se
Sr
Zr
Nb
Mo
Tc
Pd
Sn
I
Cs
Pb
Ra
Ac
Th
Pa
U
Np
Pu
Am
Cm

Plutonic rocks(Ref. 10)
HS
LS
0
0
0
0
0
0
0.006
0.006
0.006
0.006
0.00030.00030.00050.0005
0.1
0.1
0.05
0.05
0
0
0.3
0.3
0.04
0.04
0.5
0.5
0
0
0.001
0.001
0.2
0.2
0.01
0.01
0.07
0.07
0.008
0.008
0.009
0.009
0.008
0.008
0.008
0.008
0.008
0.008
0.07
0.07
0.07
0.07

Neogenesedimentary rocks
HS
LS
0
0
0
0
0
0
0.4
0.8
0.4
0.8
0.05
0.02
0.02
0.01
0
2
6
6
0
0
0
0.01
0.7
0.07
60
60
0
0
0.005
0.04
0
1
0.01
0.1
10
100
5
20
2
2
8×10-8
4
5
20
5
20
10
100
10
100

Pre-Neogene
sedimentary rocks
HS
LS
0
0
0
0
0
0
0.4
0.8
0.4
0.8
0.05
0.02
0.02
0.01
0
2
6
6
0
0
0
0.01
0.7
0.07
60
60
0
0
0.005
0.04
0
1
0.01
0.1
10
100
5
20
2
2
8×10-8
4
5
20
5
20
10
100
10
100

TABLE 6. Nitrate reduction factor of Kd for all
representative groundwater for 1 – 6 M and 0.1 – 1 M
nitrate (Ref. 13).
Reduction factor (Ref. 13)
1-6M nitrate
0.1-1M nitrate
Co

10000

10000

Ni
Sr

10000
1000

10000
100

Pd
Cs
Pb
Ra

100
1000
1000
1000

100
100
1000
100

5. Conclusion
As part of the performance assessment in
NUMO-SC, the Kd and De of rocks and theirvariables and uncertainties are estimated reflecting
the characteristics of site descriptive models andperformance assessment scenarios. The results show
low sorption and fast diffusion of plutonic rocks, highsorption and slow diffusion in Neogene sedimentaryrocks and high sorption and fast diffusion in
Pre-Neogene sedimentary rocks, relatively. However,the uncertainties on perturbing species, such ascarbonate and nitrate, indicate that this trend canchange. In particular, the limitation of sorption data
under high carbonate concentration makes large
uncertainties. NUMO is plannning to promote
experimental stueies at high carbonate concentrationin reducing conditions.
In this paper, the uncertainties are assessed basedon output from speciation calculation and simple
statistical analysis of sorption and diffusion data.
NUMO will implement a structured program to
improve understanding and to develop more rigorous
models using long-term experiments under realistic
conditions coupled to appropriate natural analogues.
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6th East Asia Forum on Radwaste Management Conference
November 27-29, 2017, Osaka, Japan