Attachment 2 of Groundwater Pathway Appendix US Ecology EIS

Groundwater Pathway Uncertainty Analysis in Support of the Performance Assessment for the US Ecology Low-Level Radioactive

Waste Facility

Arthur S. Rood May 10,2000, Revised May 24, 2000

Introduction us Ecology operates a low-level facility on leased land from the U.S. Department of Energy's

Hanford Reservation located near Richland Washington. This report documents a parametric uncertainty analysis of the groundwater pathway in support of the low-level waste perfonnance assessment for this facility. Calculations are performed for two cover designs; the site soils alternative cover and an enhanced cover. The site soils cover provides the least amount of protection from infiltrating water and therefore, concentrations are expected to error on the conservative side. The enhanced cover is the most restrictive in terms of limiting percolation, therefore, concentrations are expected to be substantially lower with this cover compared to the site soils cover. Distributions of predicted groundwater concentrations reported in this document will be used in a stochastic all pathway dose assessment to be perfonned by Washington State Department of Health (WDOH) personnel. The work was funded by WDOH the under contract number N08344.

Methodology and Input Distributions Deterministic groundwater pathway calculations were originally performed using GWSCREEN

Version 2.4a (Rood 1994). Version 2.5 of the code (Rood 1999), which includes several enhancements and the ability to perfonn Monte Carlo uncertainty analysis, was used to calculate distributions of groundwater concentrations for the uncertainty analysis. The conceptual model for the site soils cover is illustrated in Figure I. The enhanced cover used a slightly different conceptual model and is described in the Enhanced Cover Methodology section. The disposal area as measured perpendicular to groundwater flow is 518 m by 382 m and 10.6 m deep. Radioactive waste is assumed to be homogeneously mixed with backfilled soil; placed in the disposal trench; and covered by 8 to II feet of native soil. Percolating water leaches radionuc1ides from the disposal trench and they are transported through an 82 m unsaturated zone to an aquifer. Concentrations in the aquifer are then evaluated at a receptor well located on the downgradient edge of the disposal area during the time frame from the present out to 10,000 years.

Model input for the site soils cover is listed in Table I. Model input for the enhanced cover was basically the same, except infiltration was treated somewhat differently and is discussed in the section on Enhanced Cover Methodology. Radionuc1ide inventories are provided in Table 2. Nominal values for parameters used in the detenninistic runs and distributions assigned to them were provided by WDOH. However, some of the distributions were modified by the author. A discussion of each distribution follows.

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Attachment 2 of Groundwater Pathway Appendix US Ecology EIS

Groundwater Pathway Uncertainty Analysis in Support of the Performance Assessment for the US Ecology Low-Level Radioactive

Waste Facility

Arthur S. Rood May 10,2000, Revised May 24, 2000

Introduction us Ecology operates a low-level facility on leased land from the U.S. Department of Energy's

Hanford Reservation located near Richland Washington. This report documents a parametric uncertainty analysis of the groundwater pathway in support of the low-level waste perfonnance assessment for this facility. Calculations are performed for two cover designs; the site soils alternative cover and an enhanced cover. The site soils cover provides the least amount of protection from infiltrating water and therefore, concentrations are expected to error on the conservative side. The enhanced cover is the most restrictive in terms of limiting percolation, therefore, concentrations are expected to be substantially lower with this cover compared to the site soils cover. Distributions of predicted groundwater concentrations reported in this document will be used in a stochastic all pathway dose assessment to be perfonned by Washington State Department of Health (WDOH) personnel. The work was funded by WDOH the under contract number N08344.

Methodology and Input Distributions Deterministic groundwater pathway calculations were originally performed using GWSCREEN

Version 2.4a (Rood 1994). Version 2.5 of the code (Rood 1999), which includes several enhancements and the ability to perfonn Monte Carlo uncertainty analysis, was used to calculate distributions of groundwater concentrations for the uncertainty analysis. The conceptual model for the site soils cover is illustrated in Figure I. The enhanced cover used a slightly different conceptual model and is described in the Enhanced Cover Methodology section. The disposal area as measured perpendicular to groundwater flow is 518 m by 382 m and 10.6 m deep. Radioactive waste is assumed to be homogeneously mixed with backfilled soil; placed in the disposal trench; and covered by 8 to II feet of native soil. Percolating water leaches radionuc1ides from the disposal trench and they are transported through an 82 m unsaturated zone to an aquifer. Concentrations in the aquifer are then evaluated at a receptor well located on the downgradient edge of the disposal area during the time frame from the present out to 10,000 years.

Model input for the site soils cover is listed in Table I. Model input for the enhanced cover was basically the same, except infiltration was treated somewhat differently and is discussed in the section on Enhanced Cover Methodology. Radionuc1ide inventories are provided in Table 2. Nominal values for parameters used in the detenninistic runs and distributions assigned to them were provided by WDOH. However, some of the distributions were modified by the author. A discussion of each distribution follows.

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Page 2

Receptor Well

Arthur S. Rood Washington State Department of Health

Infiltration

Unsaturated Zone (82.3 m)

Saturated Zone

x

AdvectiOn and Dispersion

Leachate

Plug Flol.IDispe!·sion

Groundwater Flow

(15 m)

y

Figure 1. Conceptual model for the release and transport of radionuclides from the disposal trench to the unsaturated and saturated zone.

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Page 2

Receptor Well

Arthur S. Rood Washington State Department of Health

DISPOSAL AREA

Source length{518

Unsaturated Zone (82.3 m)

Saturated Zone

x

AdvectiOn and Dispersion

Leachate

Plug Flovv/Di"p';sion

Groundwater Flow

(15 m)

y

Figure 1. Conceptual model for the release and transport of radionuclides from the disposal trench to the unsaturated and saturated zone.

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Groundwater Pathway Uncertainty Analysis for Performance Assessment Page 3

Table L Input Parameters and Distributions for the GWSCREEN Simnlations

Parameter Name (OWSCREEN

Variable)

Length (AL)

Width (WA)

Percolation rate (PERC)a

Thickness (THICKS)

Bulk density of source (RHOS)

Depth to aquifer (DEPTH)

Bulk density of unsaturated zone

(RHOU)

Dispersivity in unsaturated z.one

(AXU)

Longitudinal dispersivity (AX)

Transverse dispersivity (A Y)

Aquifer thickness (B)

Darcy velocity in aquifer (U)

Porosity of aquifer

Bulk density of aquifer (RHOA)

Distribution coefficient, Cl (KDS)

Distribution coefficient, I (KDS)

Distribution coefficient, Tc (KDS)

Distribution coefficient, U (KDS)

Distribution coefficient, Th (KDA)

Distribution coefficient, Ra (KDA)

Distribution coefficient, Pb (KDA)

Distribution coefficient, Pa Q<DAl

Distribution coefficient, Ac (KDA)

Nominal Value

518m

382m

0.02 my-I

1O.6m

1.26 g cm-3

82.3m

O.Om

275m

5.0m

15m

32.9 my-I

0.1

1.6gcm-3

0.3 mL g-I

0.OmLg-1

0.6 mLg-1

40mLg-1

8 mLg-1

2000 mL g-I

0.6 mLg-1

IOOmLg-1

Description and distribution assigned

Length of source parallel to groundwater flow, fixed

Width of source perpendicular to groundwater flow, fixed

Triangular distribution, minimum"" O.QJS m y-l, most likely = 0.02 m y-l,

maximum = 0.05 my-I

Thickness of source, fixed

Triangular distribution, minimum:l.008, most IikeJy=I.26, maximum=1.512

gcm-3

Fixed value

Triangular distribution, minimum:1.52. most likely=1.6, maximum=1.98 g

cm-3

Truncated nonnal distribution, mean =4.0 m, standard deviation=2.0 m,

minimum=O.O m, maximum:6.0 m

Triangular distribution, minimum==I3.75 m, most likely=~27.5 m,

maximum=41.25 m

Triangular distribution, minimum'=2.5 m, most likeJr-S.O m, maximum=7.5

m

Fixed value

Truncated lognormal distribution, geometric mean=32.9 m y-l, geometric

standard deviation=2.33, minimurIF'3.0 m y_l, maximum=250 m y-l

Triangular distribution, minimum=O.97, most likeJy:::O.l, maximum=0.103

Triangular distribution, nrinimurrr--1.52, most likely=1.6, maximum=1.98 g

cm-3

Truncated normal distribution, mean = 0.01 mL g:-l, standard deviation =

0.26 mL g-l, minimum = 0.0 mL g-I, maximum = 0.6 mL g-I

Truncated lognormal distribution, geometric mean = 0.3 mL g-l, geometric

standard deviation =2.3, minimum = 02 mL g-I, maximum = 15 mL g-1

Truncated normal distribution. mean = 0.01 mL g-I. standard deviation =

0.26 mL g-I, minimum = 0.0 mL g-I, maximum = 0.6 mL g-I

Truncated lognonnal distribution, geometric mean == 3.0 mL g-l, geometric

standard deviation = 3.65, minimum = 0.1 mLg-1, maximum = 79 mLg-1

Truncated lognormal distribution, geometric mean == 600 mL g-I, geometric

standard deviation =2.1. minimum = 40 rnL g-I, maximum = 2000 mL g-l

Truncated 10gnomIal distribution, geometric mean = 20 mL g-l, geometric

standard deviation =2.0, minimum == 5 mL g-I, maximum = 173 mL g-I

Unifonn distribution; miriimum 2000 rnL g-l, maximum 6000 mL g-l

Truncated lognormal distribution. geometric mean = 0.6 mL g-l, geometric

standard deviation == 3.65, minimum = 0.01 mLg-1, maximum = 79 rnL g-l

Truncated lognormal distribution, geometric mean = 100 mL g-l, geometric

standard deviation = 1.9, minimum = 60 mLg-J, maximum = 1330 mL g-l

Solubility limit, U (SL) I mg L -I Triangular distribution minimum == 1.0, most likely = 25., maximum = 50.

a) Site soils cover percolation. See text for discussion on moisture contents as related to percolation .. Enhanced cover percolation is discussed

in another section.

b) The distribution presented here was for the site soils cover. For the enhanced cover the distribution was described by triangular distribution

having a minimum value of2.4, most likely value of 15, and a maximum value of22 mLg-l. For the site soils cover, it was necessary to

use the Uranium Ka values to preserve correlation during sampling. See text for additional explanation.

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Groundwater Pathway Uncertainty Analysis for Performance Assessment Page 3

Table L Input Parameters and Distributions for the GWSCREEN Simnlations

Parameter Name (OWSCREEN

Variable)

Length (AL)

Width (WA)

Percolation rate (PERC)a

Thickness (THICKS)

Bulk density of source (RHOS)

Depth to aquifer (DEPTH)

Bulk density of unsaturated zone

(RHOU)

Dispersivity in unsaturated z.one

(AXU)

Longitudinal dispersivity (AX)

Transverse dispersivity (A Y)

Aquifer thickness (B)

Darcy velocity in aquifer (U)

Porosity of aquifer

Bulk density of aquifer (RHOA)

Distribution coefficient, Cl (KDS)

Distribution coefficient, I (KDS)

Distribution coefficient, Tc (KDS)

Distribution coefficient, U (KDS)

Distribution coefficient, Th (KDA)

Distribution coefficient, Ra (KDA)

Distribution coefficient, Pb (KDA)

Distribution coefficient, Pa Q<DAl

Distribution coefficient, Ac (KDA)

Nominal Value

518m

382m

0.02 my-I

1O.6m

1.26 g cm-3

82.3m

O.Om

275m

5.0m

15m

32.9 my-I

0.1

1.6gcm-3

0.3 mL g-I

0.OmLg-1

0.6 mLg-1

40mLg-1

8 mLg-1

2000 mL g-I

0.6 mLg-1

IOOmLg-1

Description and distribution assigned

Length of source parallel to groundwater flow, fixed

Width of source perpendicular to groundwater flow, fixed

Triangular distribution, minimum"" O.QJS m y-l, most likely = 0.02 m y-l,

maximum = 0.05 my-I

Thickness of source, fixed

Triangular distribution, minimum:l.008, most IikeJy=I.26, maximum=1.512

gcm-3

Fixed value

Triangular distribution, minimum:1.52. most likely=1.6, maximum=1.98 g

cm-3

Truncated nonnal distribution, mean =4.0 m, standard deviation=2.0 m,

minimum=O.O m, maximum:6.0 m

Triangular distribution, minimum==I3.75 m, most likely=~27.5 m,

maximum=41.25 m

Triangular distribution, minimum'=2.5 m, most likeJr-S.O m, maximum=7.5

m

Fixed value

Truncated lognormal distribution, geometric mean=32.9 m y-l, geometric

standard deviation=2.33, minimurIF'3.0 m y_l, maximum=250 m y-l

Triangular distribution, minimum=O.97, most likeJy:::O.l, maximum=0.103

Triangular distribution, nrinimurrr--1.52, most likely=1.6, maximum=1.98 g

cm-3

Truncated normal distribution, mean = 0.01 mL g:-l, standard deviation =

0.26 mL g-l, minimum = 0.0 mL g-I, maximum = 0.6 mL g-I

Truncated lognormal distribution, geometric mean = 0.3 mL g-l, geometric

standard deviation =2.3, minimum = 02 mL g-I, maximum = 15 mL g-1

Truncated normal distribution. mean = 0.01 mL g-I. standard deviation =

0.26 mL g-I, minimum = 0.0 mL g-I, maximum = 0.6 mL g-I

Truncated lognonnal distribution, geometric mean == 3.0 mL g-l, geometric

standard deviation = 3.65, minimum = 0.1 mLg-1, maximum = 79 mLg-1

Truncated lognormal distribution, geometric mean == 600 mL g-I, geometric

standard deviation =2.1. minimum = 40 rnL g-I, maximum = 2000 mL g-l

Truncated 10gnomIal distribution, geometric mean = 20 mL g-l, geometric

standard deviation =2.0, minimum == 5 mL g-I, maximum = 173 mL g-I

Unifonn distribution; miriimum 2000 rnL g-l, maximum 6000 mL g-l

Truncated lognormal distribution. geometric mean = 0.6 mL g-l, geometric

standard deviation == 3.65, minimum = 0.01 mLg-1, maximum = 79 rnL g-l

Truncated lognormal distribution, geometric mean = 100 mL g-l, geometric

standard deviation = 1.9, minimum = 60 mLg-J, maximum = 1330 mL g-l

Solubility limit, U (SL) I mg L -I Triangular distribution minimum == 1.0, most likely = 25., maximum = 50.

a) Site soils cover percolation. See text for discussion on moisture contents as related to percolation .. Enhanced cover percolation is discussed

in another section.

b) The distribution presented here was for the site soils cover. For the enhanced cover the distribution was described by triangular distribution

having a minimum value of2.4, most likely value of 15, and a maximum value of22 mLg-l. For the site soils cover, it was necessary to

use the Uranium Ka values to preserve correlation during sampling. See text for additional explanation.

K-Spar Inc. ScienUHcConsu/Ung 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 4 Arthur S. Ro

Washington State Department of He a

Table 2 Radionuclide Inventories used in Performance Assessment

Radionuclide Invento!2: {Ci} CI-36 4.907 1-129 6.012 Tc-99 67.1 U-238 22,280 U-235 14,680

Percolation Rate and Moisture Content for Site Soils Cover

A range of percolation rates through the waste for the site soils cover were provided by WDOH. This upper bound limiting case assumed conditions where the amount of percolation would increase due to

such changes as subsidence or decrease in vegetation. The nominal value of 20 mm y-l was taken as the most likely' value that reflected typical background conditions. A triangular distribution then was

assigned having a minimum of 15 mm y 1 and a maximum of 50 mm y-l.

0.14

0.12

0.10

0.02

o.oo.j....,~~~~~~~~~~~~_~~~_~_~_~_~~

~ ~ ~ ~ ~ ~ ~ ~ : : ~ : : : : ~ : : ~ : ~ Percolation Rate (em y.l)

MW5-1

___ MW5-2

MW5-3

MW&4

--MW>S

--MWS<

-o-MW5-7

--MW"

.. MWS-9

MWS-l0

MW8-1

MWs.2

MW6-3

MW1()..1

MW10-2

MW10..J

_MW10-4

.- MW10-5

MW10-6

MW1().7

Figure 2. Moisture content, as a function of percolation, for the 22 moisture characteristic curves provided by WDOH Khaleel and Freeman (1995). Van Genuchten fitting parameters also from Khaleel and Freeman (1995).

Moisture content is a function of the percolation rate under unsaturated conditions. GWSCREEN Version 2.5 has the capability to calculate moisture content internally by proViding the code with the Van Genuchten fitting parameters for moisture characteristic curves. Moisture characteristic curves for soils

1 The value which, if exists, occurs most often in a distribution. Also called the mode.

K-Spar Inc. ScieoUDc CooSU/DOII 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 4 Arthur S. Ro

Washington State Department of He a

Table 2 Radionuclide Inventories used in Performance Assessment

Radionuclide Invento!2: {Ci} CI-36 4.907 1-129 6.012 Tc-99 67.1 U-238 22,280 U-235 14,680

Percolation Rate and Moisture Content for Site Soils Cover

A range of percolation rates through the waste for the site soils cover were provided by WDOH. This upper bound limiting case assumed conditions where the amount of percolation would increase due to

such changes as subsidence or decrease in vegetation. The nominal value of 20 mm y-l was taken as the most likely' value that reflected typical background conditions. A triangular distribution then was

assigned having a minimum of 15 mm y 1 and a maximum of 50 mm y-l.

0.14

0.12

0.10

0.02

0.00 .j....,~~~~""~~~~~c:-'C-~"C'-:c-~-~-~--:c~ ~ ~ ~ ~ ~ ~ ~ ~ : : ~ : : : : ~ : : ~ : ~

Percolation Rate (em y.l)

MW5-1

___ MW5-2

MW5-3

MW&4

--MW>S

--MWS<

-o-MW5-7

--MW"

.. MWS-9

MWS-l0

MW8-1

MWs.2

MW6-3

MW1()..1

MW10-2

MW10..J

_MW10-4

.- MW10-5

MW10-6

MW1().7

Figure 2. Moisture content, as a function of percolation, for the 22 moisture characteristic curves provided by WDOH Khaleel and Freeman (1995). Van Genuchten fitting parameters also from Khaleel and Freeman (1995).

Moisture content is a function of the percolation rate under unsaturated conditions. GWSCREEN Version 2.5 has the capability to calculate moisture content internally by proViding the code with the Van Genuchten fitting parameters for moisture characteristic curves. Moisture characteristic curves for soils

1 The value which, if exists, occurs most often in a distribution. Also called the mode.

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Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 5

within the Hanford Reservation have been measured and published (Khalee1 and Freeman 1995). Twenty-three moisture characteristic curves representing sandy soils at the US Ecology facility were provided by WDOH. Moisture content as a function of percolation was then calculated for all moisture characteristic curves (Figure 2). The Moisture Characteristic curve selected was based on the single curve that gave best fit to the geometric mean of the moisture content versus percolation rate for all the curves (Curve MW5-7 on Figure 2). The geometric mean of the moisture content (considering all curves) varied

from 0.049 to 0.057 for percolation rates between 15 and 50 rnm y 1 The curve selected had the

following Van Genuchten fitting parameters: a = 7.51 m-l, n = 2.298, saturated hydraulic conductivity =

1710 my-I, total porosity = 0.2724, and residual moisture content = 0.0321. The values for the curve were not selected by any numerical technique that would for example, find the curve that best matched the mean value by computing sum of squares. oft he residuals. The curve selected was simply "eyeballed" from Figure 2. If the moisture content showed greater variability within the percolation range, then numeric techniques would have been warranted. However, moisture content only varied by 16% between the driest and wettest conditions and a more precise method of estimating the curve would probably have little impact on overall results. The curve is intended to represent the bulk characteristics of the soil at the site. In reality, these characteristics are spatially variable. However, the GWSCREEN model in not capable of incorporating such variability into a simulation.

Uncertainty in the Van Genuchten fitting parameters themselves was not considered. To consider uncertainty in the fitting parameters, confidence intervals around each of the fitting parameters for the 23 curves would have to have been provided. These data were not available. It is not correct to generate distributions of fitting parameters from the 23 curves provided because a) parameters are correlated with one another and b) the 23 curves represent natural variability and not uncertainty.

Bulk Densities

Nominal values for the bulk density were provided by WDOH. These values were assumed to vary

by ± 20%. The nominal value for the source was 1.26 g cm-3 and 1.6 g cm-3 for the unsaturated Zone

and aquifer. Twenty-percent of the nominal value was 0.252 g cm-3 for the source and 0.32 g cm-3 for the unsaturated zone and aquifer. A triangular distribution was assigned having a most likely value equivalent to the nominal value, a minimum value equal to the nominal value minus 20% of the nominal value, and a maximum value equal to the nominal value plus 20% ofthe nominal value.

Dispersivity

Nominal values for longitudinal and transverse dispersivity in the aquifer were provided by WDOH. These values were assumed to vary by ± 50%. The nominal value for the longitudinal dispersivity was 27.5 m and 5 m for the transverse dispersivity. Fifty-percent of the nominal value was 13.75 m for the longitudinal dispersivity and 2.5 m for the transverse dispersivity. A triangular distribution was assigned having a most likely value equivalent to the nominal value, a minimum value equal to the nominal value minus 50% of the nominal value, and a maximum value equal to the nominal value plus 50% of the nominal value.

Dispersivity in the unsaturated zone was not provided by WDOH and the nominal value and distribution was assigned by the author. Deterministic calculations were performed using a version of GWSCREEN (version 2.4a) that assumed plug flow (no dispersion). Therefore, by default, zero dispersivity was assumed in the deterministic case. A value for the longitudinal dispersivity was

K-Spar Inc. SeieRtiUe CURSU/Uog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

1

1

1

1

:1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 5

within the Hanford Reservation have been measured and published (Khalee1 and Freeman 1995). Twenty-three moisture characteristic curves representing sandy soils at the US Ecology facility were provided by WDOH. Moisture content as a function of percolation was then calculated for all moisture characteristic curves (Figure 2). The Moisture Characteristic curve selected was based on the single curve that gave best fit to the geometric mean of the moisture content versus percolation rate for all the curves (Curve MW5-7 on Figure 2). The geometric mean of the moisture content (considering all curves) varied

from 0.049 to 0.057 for percolation rates between 15 and 50 rnm y 1 The curve selected had the

following Van Genuchten fitting parameters: a = 7.51 m-l, n = 2.298, saturated hydraulic conductivity =

1710 my-I, total porosity = 0.2724, and residual moisture content = 0.0321. The values for the curve were not selected by any numerical technique that would for example, find the curve that best matched the mean value by computing sum of squares. oft he residuals. The curve selected was simply "eyeballed" from Figure 2. If the moisture content showed greater variability within the percolation range, then numeric techniques would have been warranted. However, moisture content only varied by 16% between the driest and wettest conditions and a more precise method of estimating the curve would probably have little impact on overall results. The curve is intended to represent the bulk characteristics of the soil at the site. In reality, these characteristics are spatially variable. However, the GWSCREEN model in not capable of incorporating such variability into a simulation.

Uncertainty in the Van Genuchten fitting parameters themselves was not considered. To consider uncertainty in the fitting parameters, confidence intervals around each of the fitting parameters for the 23 curves would have to have been provided. These data were not available. It is not correct to generate distributions of fitting parameters from the 23 curves provided because a) parameters are correlated with one another and b) the 23 curves represent natural variability and not uncertainty.

Bulk Densities

Nominal values for the bulk density were provided by WDOH. These values were assumed to vary

by ± 20%. The nominal value for the source was 1.26 g cm-3 and 1.6 g cm-3 for the unsaturated Zone

and aquifer. Twenty-percent of the nominal value was 0.252 g cm-3 for the source and 0.32 g cm-3 for the unsaturated zone and aquifer. A triangular distribution was assigned having a most likely value equivalent to the nominal value, a minimum value equal to the nominal value minus 20% of the nominal value, and a maximum value equal to the nominal value plus 20% ofthe nominal value.

Dispersivity

Nominal values for longitudinal and transverse dispersivity in the aquifer were provided by WDOH. These values were assumed to vary by ± 50%. The nominal value for the longitudinal dispersivity was 27.5 m and 5 m for the transverse dispersivity. Fifty-percent of the nominal value was 13.75 m for the longitudinal dispersivity and 2.5 m for the transverse dispersivity. A triangular distribution was assigned having a most likely value equivalent to the nominal value, a minimum value equal to the nominal value minus 50% of the nominal value, and a maximum value equal to the nominal value plus 50% of the nominal value.

Dispersivity in the unsaturated zone was not provided by WDOH and the nominal value and distribution was assigned by the author. Deterministic calculations were performed using a version of GWSCREEN (version 2.4a) that assumed plug flow (no dispersion). Therefore, by default, zero dispersivity was assumed in the deterministic case. A value for the longitudinal dispersivity was

K-Spar Inc. SeieRtiUe CURSU/Uog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

1

1

1

:1

1

1

1

1

1

I' I,

I I i

i'

Page 6 Arthur S. Rooe

Washington State Department ofHealtl:

calculated using the equations developed by Xu and Eckstein (1995) and reported in the GWSCREEN Version 2.5 user's manual (Rood 1999). The longitudinal dispersivity (aL) is given by

( )2.414

aL=0.8310g lO L (1 )

where L is the length of the domain in the longitudinal direction and equivalent to the depth to the aquifer. Using the depth to the aquifer of 82.3 m, Equation I gives an estimate of the longitudinal dispersivity of -4 m. The dispersivity was assumed to be normally distributed, having a mean of 4 m and have a standard deviation of 2 m. This gives roughly an upper bound aL value of 8 m and a lower bound of

Zero. For conservatism the distribution was truncated on the upper end at 6.0 m because the greater the dispersivity, the lower the peak concentration. On the lower end, the minimum value of the distribution was truncated by 0.0 m because negative dispersivity is not plausible.

Darcy Velocity and Porosity Originally, data was provided for this parameter in the form of the average linear velocity as required

byGWSCREEN version 2.4a. The average linear velocity is the Darcy velocity divided by the porosity. In GWSCREEN version 2.5, the Darcy velocity is input. The Darcy velocity can be approximated by Darcy's Law, which states:

dh v=K,at dL

where

v = the Darcy velocity (m y I),

Ksat= the saturated hydraulic conductivity (m y-I),

dh/dx= the hydraulic gradient (mm-I).

(2)

A range of 0.04 to 8.0 m d-I (14.6 to 2920 m yl) was provided byWDOH for the average linear

velocity with a median2 value of 0.9 m 0-1 (329 IIi y-I). These data were based on a range of saturated

hydraulic conductivities from 730-73,000 m y-I (Connelly, M.P et ai, 1992) and hydraulic gradients that range from 0.002 to 0.004 (Cole et ai, 1997). The hydraulic gradient data was based on projections for the year 2350 by which time there will no longer be mounding of the water table due to injection practices

upgradient. The median value for saturated hydraulic conductivity was 10,950 my-I. The distribution assigned to the Darcy velocity was generated by assuming a lognormal distribution for saturated hydraulic conductivity and a triangular distribution for hydraulic gradient. The median value for the saturated hydraulic conductivity was taken to represent the geometric mean, and a geometric standard deviation was fit such that the 1% and 99% value was near that of the given range. A value of2.4 was the best fit. The midpoint in range of hydraulic gradient was taken to be the most likely value of a triangular distribution (0.003) with a minimum and maximum value of 0.002 and 0.004 respectively. Sampling was performed using the Crystal Ball® software (Decisioneering 1996). The output distribution had a

geometric mean of32.9 m y-I and a geometric standard deviation of2.33. The distribution was truncated

by the minimum (! 4.6 m y-I ) and maximum (2920 m y-I) values because these are the minimum and

2 The value ntidway (in terms of order) between the smallest and the largest possible value in a distribution

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I

I

I I

I,

Page 6 Arthur S. Rooe

Washington State Department of Rea Itt

calculated using the equations developed by Xu and Eckstein (1995) and reported in the GWSCREEN Version 2.5 user's manual (Rood 1999). The longitudinal dispersivity (aL) is given by

( )2.414

aL=0.8310g lO L (1 )

where L is the length of the domain in the longitudinal direction and equivalent to the depth to the aquifer. Using the depth to the aquifer of 82.3 m, Equation I gives an estimate of the longitudinal dispersivity of -4 m. The dispersivity was assumed to be normally distributed, having a mean of 4 m and have a standard deviation of 2 m. This gives roughly an upper bound aL value of 8 m and a lower bound of

Zero. For conservatism the distribution was truncated on the upper end at 6.0 m because the greater the dispersivity, the lower the peak concentration. On the lower end, the minimum value of the distribution was truncated by 0.0 m because negative dispersivity is not plausible.

Darcy Velocity and Porosity Originally, data was provided for this parameter in the form of the average linear velocity as required

byGWSCREEN version 2.4a. The average linear velocity is the Darcy velocity divided by the porosity. In GWSCREEN version 2.5, the Darcy velocity is input. The Darcy velocity can be approximated by Darcy's Law, which states:

dh v=K,at dL

where

v = the Darcy velocity (m y I),

Ksat= the saturated hydraulic conductivity (m y-I),

dh/dx= the hydraulic gradient (mm-I).

(2)

A range of 0.04 to 8.0 m d-I (14.6 to 2920 m yl) was provided byWDOH for the average linear

velocity with a median2 value of 0.9 m 0-1 (329 IIi y-I). These data were based on a range of saturated

hydraulic conductivities from 730-73,000 m y-I (Connelly, M.P et ai, 1992) and hydraulic gradients that range from 0.002 to 0.004 (Cole et ai, 1997). The hydraulic gradient data was based on projections for the year 2350 by which time there will no longer be mounding of the water table due to injection practices

upgradient. The median value for saturated hydraulic conductivity was 10,950 my-I. The distribution assigned to the Darcy velocity was generated by assuming a lognormal distribution for saturated hydraulic conductivity and a triangular distribution for hydraulic gradient. The median value for the saturated hydraulic conductivity was taken to represent the geometric mean, and a geometric standard deviation was fit such that the 1% and 99% value was near that of the given range. A value of2.4 was the best fit. The midpoint in range of hydraulic gradient was taken to be the most likely value of a triangular distribution (0.003) with a minimum and maximum value of 0.002 and 0.004 respectively. Sampling was performed using the Crystal Ball® software (Decisioneering 1996). The output distribution had a

geometric mean of32.9 m y-I and a geometric standard deviation of2.33. The distribution was truncated

by the minimum (! 4.6 m y-I ) and maximum (2920 m y-I) values because these are the minimum and

2 The value ntidway (in terms of order) between the smallest and the largest possible value in a distribution

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. maximum Darcy velocity values calculated using the range of saturated hydraulic conductivity (730-

73,000 my-I) and hydraulic gradient (0.002 to 0.004). The distribution assigned to the porosity in the aquifer was provided by WDOH. A nominal value of

. 0.1 was given and reported to vary by ± 3% (Cole et a1. 1997). A triangular distribution was assigned having a most likely value of 0.1 and a minimum of 0.97 and maximum of 0.103.

Distribution Coefficients and Solubility Distribution coefficients (Kd) were derived from Kincaid et a1. (1998) who obtained Kci values from

the literature and site-specific measurements. These distribution coefficients were used in the U.S. DOE composite performance assessment for the 200 Areas on the Hanford Reservation (Kincaid et a1. (1998)). These data included a recommended conservative value, "best estimate" value, and a range. In general, the "best estimate" value was used to represent either the median or most likely value. The type of distribution assigned was then based on the range of values. Three distributions were used: trunc~ted lognormal, triangular, al)d truncated normal. If the range between the minimum and maximum values exceeded an order of magnitude, then truncated lognormal distributions were assigned. Triangular distributions were assigned to distribution coefficients if the range between minimum and maximum values was less than an order of magnitude. Truncated normal distributions were assigned if the distribution coefficient value included 0 in its range. Truncated normal distributions were assigned to C1-36 and Tc-99.

Because GWSCREEN assumes correlation between parent and progeny of the same element, the first significant daughter ofU-238 (U-234) is not sampled and assumes the distribution coefficients sampled for U-238. Therefore, the first daughter for all the other nuclides had to have identical distribution coefficients to that of their parent. This only made a difference in the case ofU-235, the only other nuclide with daughter products in this analysis. In order to keep the same random number sequence, Pa-231 (the first significant daughter) was assigned a distribution coefficient distribution identical to that of its parent. For the enhanced cover, a different procedure was used for the Monte Carlo analysis and it was not necessary to set the Pa-231 K" to the uranium K". Therefore, for the enhanced cover, the Pa-23 1 K" was assigned a distribution using data from Kincaid et a1. (1998).

At the direction ofWDOH, solubility limited releases for I, Cl, and Tc were not to be considered.

Therefore a solubility limit of 1 x 106 mg L -1 was used for these elements which essentially disables consideration of a solubility release. In any case, there was insufficient mass of these elements such that solubility would become an issue.

Distributions of uranium solubility provided by WDOH were based on data in DOE 1994. Uranium is

classified as moderately soluble and given a best estimate value of 25 mg L -1. Unpublished work done at

Hanford indicates uranium solubility maybe as low as 1 mg L -1 (oral communication with R; J. Seme 1999 and Serne et al. 1999). This value was assumed to be the lower bound of a triangular distribution. A

most likely and maximum value of 25 mg L -1 and 50 mg L -1 respectively were then assigned to the distribution.

Methodology Modifications for the Enhanced Cover

The conceptual model for the enhanced cover (Figure 3) assumes the emplacement of a cover restricts water flow through the waste but does not restrict water movement in the unsaturated zone underlying the waste. Under these conditions, radionuclides leaching from the waste are governed by

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. maximum Darcy velocity values calculated using the range of saturated hydraulic conductivity (730-

73,000 my-I) and hydraulic gradient (0.002 to 0.004). The distribution assigned to the porosity in the aquifer was provided by WDOH. A nominal value of

. 0.1 was given and reported to vary by ± 3% (Cole et a1. 1997). A triangular distribution was assigned having a most likely value of 0.1 and a minimum of 0.97 and maximum of 0.103.

Distribution Coefficients and Solubility Distribution coefficients (Kd) were derived from Kincaid et a1. (1998) who obtained Kci values from

the literature and site-specific measurements. These distribution coefficients were used in the U.S. DOE composite performance assessment for the 200 Areas on the Hanford Reservation (Kincaid et a1. (1998)). These data included a recommended conservative value, "best estimate" value, and a range. In general, the "best estimate" value was used to represent either the median or most likely value. The type of distribution assigned was then based on the range of values. Three distributions were used: trunc~ted lognormal, triangular, al)d truncated normal. If the range between the minimum and maximum values exceeded an order of magnitude, then truncated lognormal distributions were assigned. Triangular distributions were assigned to distribution coefficients if the range between minimum and maximum values was less than an order of magnitude. Truncated normal distributions were assigned if the distribution coefficient value included 0 in its range. Truncated normal distributions were assigned to C1-36 and Tc-99.

Because GWSCREEN assumes correlation between parent and progeny of the same element, the first significant daughter ofU-238 (U-234) is not sampled and assumes the distribution coefficients sampled for U-238. Therefore, the first daughter for all the other nuclides had to have identical distribution coefficients to that of their parent. This only made a difference in the case ofU-235, the only other nuclide with daughter products in this analysis. In order to keep the same random number sequence, Pa-231 (the first significant daughter) was assigned a distribution coefficient distribution identical to that of its parent. For the enhanced cover, a different procedure was used for the Monte Carlo analysis and it was not necessary to set the Pa-231 K" to the uranium K". Therefore, for the enhanced cover, the Pa-23 1 K" was assigned a distribution using data from Kincaid et a1. (1998).

At the direction ofWDOH, solubility limited releases for I, Cl, and Tc were not to be considered.

Therefore a solubility limit of 1 x 106 mg L -1 was used for these elements which essentially disables consideration of a solubility release. In any case, there was insufficient mass of these elements such that solubility would become an issue.

Distributions of uranium solubility provided by WDOH were based on data in DOE 1994. Uranium is

classified as moderately soluble and given a best estimate value of 25 mg L -1. Unpublished work done at

Hanford indicates uranium solubility maybe as low as 1 mg L -1 (oral communication with R; J. Seme 1999 and Serne et al. 1999). This value was assumed to be the lower bound of a triangular distribution. A

most likely and maximum value of 25 mg L -1 and 50 mg L -1 respectively were then assigned to the distribution.

Methodology Modifications for the Enhanced Cover

The conceptual model for the enhanced cover (Figure 3) assumes the emplacement of a cover restricts water flow through the waste but does not restrict water movement in the unsaturated zone underlying the waste. Under these conditions, radionuclides leaching from the waste are governed by

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r i"

Arthur S. Ro. Page 8 Washington State Department of Heal

infiltration through the cover, but their movement through the unsaturated zone to the aquifer is not. Tha is, radionuclides move at the same rate through the unsaturated zone as if no cover was in place.

---....... Water flow around cover and unsaturated zone

Water flow through cover and waste

--1--+ Waste

Unsaturated Zone

Figure 3. Conceptual model for water flow around and through an engineered cover.

In reality, the placement of an engineered cover will certainly create an "infiltration shadow" underneath the disposal site. At depth, water infiltrating from outside the cover will migrate horizontally and eventually mix with the vertical flow of water that travelled through the cover and the waste. The vertical extent of the shadow is not known but could be estimated using additional model calculations or field studies. The conceptual model described here conservatively assumes the shadow is minimal in vertical extent. Therefore, in this conceptual model, the engineered cover only has the effect of reducing the mass flux of radionuclides through the waste and does not affect transit times in the unsaturated zone. Water fluxes in the unsaturated zone are therefore equivalent to the background percolation rate from natural recharge estimated to be 5 rom y-' (Kincaid et aI, 1998; Wood et aI, 1996).

Mathematical Model and Implementation

The GWSCREEN Version 2.5 conceptual model (Rood 1999a) does not allow for different Darcy velocities in the source and unsaturated zone. Therefore, some modifications of the input were necessary to implement the "no shadow effect" on transport of radionuclides in the unsaturated zone. The Darcy velocity in the unsaturated zone only affects the mean transit time in the unsaturated zone. While the concentrations of the leachate may change with changes in the Darcy velocity, this effect makes no difference in the aquifer model because the aquifer model uses only the mass flux from the unsaturated zone and not the leachate concentration.

Given that changes in the Darcy velocity in the unsaturated zone affects only the mean unsaturated transit time, then our goal is to adjust the transport parameters such that the water transit time reflects natural percolation and not infiltration through the site cover. The mean unsaturated transit time is given by

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, !

I,

Arthur S. Ro. Page 8 Washington State Department of Heal

infiltration through the cover, but their movement through the unsaturated zone to the aquifer is not. Tha is, radionuclides move at the same rate through the unsaturated zone as if no cover was in place.

---...... Water flow around cover and unsaturated zone ...•..•..•.... ~

Engineered Cover Water flow through cover and waste

'2 --l---+ Waste

Unsaturated Zone

Figure 3. Conceptual model for water flow around and through an engineered cover.

In reality, the placement of an engineered cover will certainly create an "infiltration shadow" underneath the disposal site. At depth, water infiltrating from outside the cover will migrate horizontally and eventually mix with the vertical flow of water that travelled through the cover and the waste. The vertical extent of the shadow is not known but could be estimated using additional model calculations or field studies. The conceptual model described here conservatively assumes the shadow is minimal in vertical extent. Therefore, in this conceptual model, the engineered cover only has the effect of reducing the mass flux of radionuclides through the waste and does not affect transit times in the unsaturated zone. Water fluxes in the unsaturated zone are therefore equivalent to the background percolation rate from natural recharge estimated to be 5 rom y-' (Kincaid et aI, 1998; Wood et aI, 1996).

Mathematical Model and Implementation

The GWSCREEN Version 2.5 conceptual model (Rood 1999a) does not allow for different Darcy velocities in the source and unsaturated zone. Therefore, some modifications of the input were necessary to implement the "no shadow effect" on transport of radionuclides in the unsaturated zone. The Darcy velocity in the unsaturated zone only affects the mean transit time in the unsaturated zone. While the concentrations of the leachate may change with changes in the Darcy velocity, this effect makes no difference in the aquifer model because the aquifer model uses only the mass flux from the unsaturated zone and not the leachate concentration.

Given that changes in the Darcy velocity in the unsaturated zone affects only the mean unsaturated transit time, then our goal is to adjust the transport parameters such that the water transit time reflects natural percolation and not infiltration through the site cover. The mean unsaturated transit time is given by

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Groundwater Pathway Uncertainty Analysis for Performance Assessment

where x B =

=

=

unsaturated thickness (m) moisture content in unsaturated zone Darcy velocity in unsaturated zone (m y-') retardation factor (nuclide specific, unit less) mean unsaturated transit time (y).

Page 9

(3)

We want to adjust one of the parameters in Equation I so that t is equivalent to the mean unsaturated transit time for background infiltration. Because we want to use a Darcy velocity for the waste that reflects infiltration through the cap, and we cannot change that velocity for the unsaturated zone (using the surface or buried source model), then the logical parameter to adjust is the unsaturated thickness. Solving Equation 1 for x and substituting tb" for t, ~'" for B, and Veo,", for v, gives the unsaturated thickness to use in the simulation for a specific cover.

where: x

likg Voover

tb",

=

= =

unsaturated thickness to use in the simulation for a specific cover (m) moisture content in unsaturated zone for background infiltration Darcy velocity through the cover (m y-') mean unsaturated transit time for background infiltration (y).

(4)

Mean unsaturated transit time for background infiltration was 752 y for a non-sorbing radionuclide (Kd = 0; Rd = I) and is based on 0.005 m y-l infiltration, 82.3 m unsaturated thickness, and 0.0457 moisture content.

GWSCREEN Version 2.5 allows for dispersion in the unsaturated zone and therefore, the unsaturated dispersivity must also be adjusted.To simulate the same dispersion effects for different unsaturated thickness given a stochastic unsaturated dispersivity, the Peclet number is kept constant for each· simulation. The Peelet number relates the ratio of advection to dispersion and is given by

(5)

where aL = the longitudinal dispersivity (m). lfthe Pecletnumber is determined frrst for an unsaturated thickness of 82.3 m and a given value of aL> then an effective dispersivity can be calculated for a different unsaturated thickness such that the relative effect of dispersivity is preserved. The method was tested by calculating the radionuclide flux as a function of time from the disposal facility for the enhanced cover. The flux was then put into GWSCREEN as a user-defmed source, the percolation rate was set at background (5 mm y-l), and the unsaturated thickness was set at the nominal value of 82.3 m. The testing method just described could have also been used to calculate groundwater concentrations for the stated conditions. It is more explicit than the method described by Equation 3-5, but also more laborious to implement. Both methods gave identical results.

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1

1

·1

1

1

1

1

1

11

1

1

1

1

1

1

1

1

1

1

1

Groundwater Pathway Uncertainty Analysis for Performance Assessment

where x B =

=

=

unsaturated thickness (m) moisture content in unsaturated zone Darcy velocity in unsaturated zone (m y-') retardation factor (nuclide specific, unit less) mean unsaturated transit time (y).

Page 9

(3)

We want to adjust one of the parameters in Equation 1 so that t is equivalent to the mean unsaturated transit time for background infiltration. Because we want to use a Darcy velocity for the waste that reflects infiltration through the cap, and we cannot change that velocity for the unsaturated zone (using the surface or buried source model), then the logical parameter to adjust is the unsaturated thickness. Solving Equation 1 for x and substituting fb" for f, ~'" for B, and Veo,", for v, gives the unsaturated thickness to use in the simulation for a specific cover.

where: x

likg Voover

fb",

=

= =

unsaturated thickness to use in the simulation for a specific cover (m) moisture content in unsaturated zone for background infiltration Darcy velocity through the cover (m y-') mean unsaturated transit time for background infiltration (y).

(4)

Mean unsaturated transit time for background infiltration was 752 y for a non-sorbing radionuclide (Kd = 0; Rd = I) and is based on 0.005 m y-l infiltration, 82.3 m unsaturated thickness, and 0,0457 moisture content.

GWSCREEN Version 2.5 allows for dispersion in the unsaturated zone and therefore, the unsaturated dispersivity must also be adjusted, To simulate the same dispersion effects for different unsaturated thickness given a stochastic unsaturated dispersivity, the Peclet number is kept constant for each· simulation. The Peelet number relates the ratio of advection to dispersion and is given by

(5)

where aL = the longitudinal dispersivity (m). lfthe Pecletnumber is determined frrst for an unsaturated thickness of 82.3 m and a given value of aL> then an effective dispersivity can be calculated for a different unsaturated thickness such that the relative effect of dispersivity is preserved, The method was tested by calculating the radionuclide flux as a function of time from the disposal facility for the enhanced cover. The flux was then put into GWSCREEN as a user-defmed source, the percolation rate was set at background (5 mm y-l), and the unsaturated thickness was set at the nominal value of 82.3 m. The testing method just described could have also been used to calculate groundwater concentrations for the stated conditions. It is more explicit than the method described by Equation 3-5, but also more laborious to implement. Both methods gave identical results,

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1

·1

1

1

1

1

1

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1

Page 10 Arthur S. RDDd

WashingtDn State Department .of Health

The distributiDn .of percDlatiDn rates was provided by WDOH. Three values were prDvided: a minimum (0.01 mm y-l), a most likely (0.5 mm y-l), and a maximum (10 mm y-l). The weight .of the distributiDn was tD faU in the 0.5 to 3 mm y-l. Given these data, a custDm distributiDn was cDnstructed in Crystal BaU (Figure 4).

ImplementatiDn .of the stochastic analysis fDr the enhanced CDver required writing an external prDgram because the Monte Carlo sampling features DfGWSCREEN cDuld nDt be used. A script file written in the Perl prDgramming language (Attachment A) was used tD (I) read sequentiaUy frDm an external file, a parameter value for each realization, (2) calculate a new unsaturated thickness and dispersivity as described by equations 3 thrDugh 5, (3) write a GWSCREEN input file and execute GWSCREEN, and (4) extract results (concentratiDns) frDm the .output file and stDre. Except fDr the percDlatiDn rate and Pa-23 I Kd , aU other parameter distributions were identical tD that used in the site sDils CDver analysis.

B rJJ C Q)

o >,

;=:

15 ro

..Q

e 0..

1.00E-S 2.S1E-3 S.01E-3 7.50E-3 1.00E-2

Percolation Rate (m y1)

Figure 4. Probability density functiDn fDr enhanced cover percDlatiDn rate. Cumulative prDbability at each of the inflection pDints is indicated by the percentage value in the bDX.

Results for the Site Soils Cover TWD types .of analysis were run. The first calculated the peak concentratiDn and time .of peak

cDncentratiDn for aU 5 nuclides. These results are summarized in Figures 5-9 which show the relatiDnship between peak CDncentration and peak time. The peak time is the time at which the peak cDncentratiDn in the aquifer .occurred at the receptor well. In general, we see a decrease in the peak cDncentratiDn with increasing peak time. This trend is probably a reflectiDn of the percDlation rate and the distributiDn cDefficient. Higher percolation rates result in higher cDntaminant fluxes tD the grDundwater and shDrter cDntaminant travel times resulting in higher aquifer concentratiDns. The distributiDn cDefficient is inversely related tD the peak cDncentration. The distributiDn cDefficient is inversely related tD the peak cDncentratiDn. Higher distribution coefficients translate intD more mass in the sDlid phrase which results in lDwer pDre water cDncentratiDns and lDnger contaminant travel times.

The deterministic values shDwn in Figures 5-9 are the peak concentrations generated using the nDminal parameter values listed in Table I. For CI-36, 1-129, and Tc-99, the deterministic value is .on the

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\' ,',

Page 10 Arthur S. RDVd

WashingtDn State Department .of Health

The distributiDn .of percDlativn rates was provided by WDOH. Three values were prDvided: a minimum (0.01 mm y-l), a most likely (0.5 mm y-l), and a maximum (10 mm y-l). The weight .of the distributiDn was tD faU in the 0.5 to 3 mm y-l. Given these data, a custvm distributiDn was cDnstructed in Crystal BaU (Figure 4).

ImplementatiDn .of the stochastic analysis fDr the enhanced cvver required writing an external prDgram because the Monte Carlo sampling features DfGWSCREEN cvuld nDt be used. A script file written in the Perl prvgramming language (Attachment A) was used tD (I) read sequentiaUy frDm an external file, a parameter value for each realization, (2) calculate a new unsaturated thickness and dispersivity as described by equations 3 thrDugh 5, (3) write a GWSCREEN input file and execute GWSCREEN, and (4) extract results (concentrativns) frDm the .output file and stDre. Except fDr the percDlatiDn rate and Pa-23 I Kd, aU other parameter distributions were identical tD that used in the site sDils CDver analysis.

B rJJ C Q)

o >,

;=:

15 ro

..Q

e 0..

1.00E-S 2.S1E-3 S.01E-3 7.50E-3 1.00E-2

Percolation Rate (m y1)

Figure 4. Probability density functiDn fDr enhanced cover percvlatiDn rate. Cumulative prDbability at each of the inflection pvints is indicated by the percentage value in the bDX.

Results for the Site Soils Cover TWD types .of analysis were run. The first calculated the peak concentratiDn and time .of peak

cDncentratiDn for aU 5 nuclides. These results are summarized in Figures 5-9 which show the relatiDnship between peak CDncentration and peak time. The peak time is the time at which the peak cDncentratiDn in the aquifer .occurred at the receptor well. In general, we see a decrease in the peak cDncentratiDn with increasing peak time. This trend is probably a reflectivn of the percvlation rate and the distributiDn cDefficient. Higher percolation rates result in higher cDntaminant fluxes tD the grDundwater and shDrter cDntaminant travel times resulting in higher aquifer concentratiDns. The distributiDn cDefficient is inversely related tv the peak cvncentration. The distributivn cvefficient is inversely related tD the peak cvncentratiDn. Higher distribution coefficients translate intD more mass in the sDlid phrase which results in lDwer pDre water cDncentrativns and lDnger contaminant travel times.

The deterministic values shvwn in Figures 5-9 are the peak concentrations generated using the nDminal parameter values listed in Table I. For CI-36, 1-129, and Tc-99, the deterministic value is .on the

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high-end of the distribution of peak concentrations (i.e., relatively high peak concentrations). For the uranium isotopes, the deterministic value is on the lower end of the distribution of peak concentrations (i.e., relatively low peak concentrations). The difference between the fission/activation products (CI-36, 1-129, Tc-99) and the uranium isotopes is believed to be due to choice of the nominal distribution coefficients and solubility limits respectively. For the fission/activation products, the nominal values chosen for the distribution coefficients were the smallest values (more conservative) in the distribution. Consequently, high peak concentrations were calculated for the fission/activation product deterministic runs. For the uranium isotopes, solubility controls the release and the value chosen as the nominal value

(I mg L-I) was on the low-end (less conservative) of the distribution. In the case where solubility controls the release, pore water concentration in the source is directly related to the solubility limit. Consequently, aquifer concentrations are also lower because lower pore water concentrations in the source result in lower contaminant fluxes to the groundwater.

CI-36

1.00E-05.----------------------------

000

o 0 0

10' 1,000 10,000

Peak Time (years)

Figure 5. Peak concentration in the aquifer at the receptor well as afunction of the time of peak concentration (peak time) for CI-36. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table I.

As stated earlier, the solubility limit c.ontrolled many of the releases for uranium isotopes. Solubility limited releases result in a flat concentration versus time curves. Figure 10 shows the concentration

versus time curve for U-238 using the nominal values. Note that the concentration is the same (7.0 x 10-9

Ci m-3) from year 4180 to year 16,721,680. The peak finding routine in GWSCREEN when the solubility limit controls the release locates the peak time anywhere within the flat portion of the curve. Therefore, the time of peak concentration in Figures 8 and 9 for uranium isotopes could have occurred earlier or later than shown including earlier than 10,000 years.

For CI-36 and Tc-99, peak concentrations occurred between 100 and 10,000 years in all cases. Iodine-129 peaked for the most part between 1,000 and 10,000 years, however some peaks occurred after 10,000 years.

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high-end of the distribution of peak concentrations (i.e., relatively high peak concentrations). For the uranium isotopes, the deterministic value is on the lower end of the distribution of peak concentrations (i.e., relatively low peak concentrations). The difference between the fission/activation products (CI-36, 1-129, Tc-99) and the uranium isotopes is believed to be due to choice of the nominal distribution coefficients and solubility limits respectively. For the fission/activation products, the nominal values chosen for the distribution coefficients were the smallest values (more conservative) in the distribution. Consequently, high peak concentrations were calculated for the fission/activation product deterministic runs. For the uranium isotopes, solubility controls the release and the value chosen as the nominal value

(I mg L-I) was on the low-end (less conservative) of the distribution. In the case where solubility controls the release, pore water concentration in the source is directly related to the solubility limit. Consequently, aquifer concentrations are also lower because lower pore water concentrations in the source result in lower contaminant fluxes to the groundwater.

CI-36

1.00E-05,---------------------------

000

o 0 0

10' 1,000 10,000

Peak Time (years)

Figure 5. Peak concentration in the aquifer at the receptor well as afunction of the time of peak concentration (peak time) for CI-36. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table I.

As stated earlier, the solubility limit c.ontrolled many of the releases for uranium isotopes. Solubility limited releases result in a flat concentration versus time curves. Figure 10 shows the concentration

versus time curve for U-238 using the nominal values. Note that the concentration is the same (7.0 x 10-9

Ci m-3) from year 4180 to year 16,721,680. The peak finding routine in GWSCREEN when the solubility limit controls the release locates the peak time anywhere within the flat portion of the curve. Therefore, the time of peak concentration in Figures 8 and 9 for uranium isotopes could have occurred earlier or later than shown including earlier than 10,000 years.

For CI-36 and Tc-99, peak concentrations occurred between 100 and 10,000 years in all cases. Iodine-129 peaked for the most part between 1,000 and 10,000 years, however some peaks occurred after 10,000 years.

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Page 12 Arthur S. Rood

Washington State Department of Health

Concentrations of uranium progeny were not shown in Figures 8 and 9. In general, progeny were found to be oflittle importance because little ingrowth occurs during the 10,000 year time frame (see Table 4 and discussion in next paragraph).

1-129

1.00e'OO,.-------------:co---------------

1.00E_10-l-_~-~_~~~~-_-~~~~~--~~~_~~

100 1.000 10,000 100,000

Peak Time (years)

Fignre 6_ Peak concentration in the aquifer at the receptor well as a function of the time of peak concentration (peak time) for 1-129. Deterministic value shown was the peak concentration generated using the nominal parameter values list.ed in Table 1.

The second analysis involved calculating the concentration of all nuclides at selected points in time. Based on the results of the first analysis, the following times were selected: 200, 300, 400, 500, 900, 1,000,1,100,1,500,2,000,2,500,5,000, and 10,000 years. These times were selected on the basis of the results shown in Figures 5-9 and were intended to bracket the maximum concentration observed for all nuclides. Recall that for the uranium isotopes, the concentration versus time curve was flat because the solubility limit controls the release. Therefore, the concentration obtained at say, year 5,000 would be the same as obtained at year 10,000_

Each of the 500 realizations3 was saved in a Excel™ spreadsheet (TIMECONCXLS). It is "important to sample directly from the realizations when performing the stochastic all pathway dose assessment because each realization for a given nuclide is correlated with the corresponding realization of the other nuclides. These correlations are present in the water flow and transport parameters (percolation rate, moisture content, bulk density, porosity, unsaturated and saturated dispersivity, and Darcy velocity). Uncorrelated parameters are the distribution coefficients and solubility limits. The spreadsheet also contains the sampled values for each parameter. These values are summarized in Table 3. Table 4 contains the maximum and mean concentrations from the 500 GWSCREEN realizations for each nuclide at selected output times. Note that the uranium progeny concentrations are typically lower than their

3 In a later calculation, number of realizations was increased to 1000. Results were put into the spreadsheet,

TIMECONC1000.xLS.

K-Sparlnc.SciBn~J,7CCausu/~ng

493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

) I

'I

Page 12 Arthur S. Rood

Washington State Department of Health

Concentrations of uranium progeny were not shown in Figures 8 and 9. In general, progeny were found to be oflittle importance because little ingrowth occurs during the 10,000 year time frame (see Table 4 and discussion in next paragraph).

1·129

1.00e'OO,.-------------:co---------------

o Deterministic Value o ~ 0 ______

o '::°oooo~ '1- 1.00E-07t----------;:o~~~__.'___;;_-."..----------­E

!2. g :c jg 1.00E-08 ~----------".2..-''''''~ir.&ia'i':O'If;;-'l1'Jil'l-o,o_.e..,---'o'__;;:___::_---c 0

" " c o

" '" o i. 1.00E-09t-_____________ ~ ___ ~~,...::..o -----o 0 0

Peak Time (years)

Fignre 6. Peak concentration in the aquifer at the receptor well as a function of the time of peak concentration (peak time) for 1-129. Deterministic value shown was the peak concentration generated using the nominal parameter values list.ed in Table 1.

The second analysis involved calculating the concentration of all nuclides at selected points in time. Based on the results of the first analysis, the following times were selected: 200, 300, 400, 500, 900, 1,000,1,100,1,500,2,000,2,500,5,000, and 10,000 years. These times were selected on the basis of the results shown in Figures 5-9 and were intended to bracket the maximum concentration observed for all nuclides. Recall that for the uranium isotopes, the concentration versus time curve was flat because the solubility limit controls the release. Therefore, the concentration obtained at say, year 5,000 would be the same as obtained at year 10,000.

Each of the 500 realizations3 was saved in a Excel™ spreadsheet (TIMECONCXLS). It is 'important to sample directly from the realizations when performing the stochastic all pathway dose assessment because each realization for a given nuclide is correlated with the corresponding realization of the other nuclides. These correlations are present in the water flow and transport parameters (percolation rate, moisture content, bulk density, porosity, unsaturated and saturated dispersivity, and Darcy velocity). Uncorrelated parameters are the distribution coefficients and solubility limits. The spreadsheet also contains the sampled values for each parameter. These values are summarized in Table 3. Table 4 contains the maximum and mean concentrations from the 500 GWSCREEN realizations for each nuclide at selected output times. Note that the uranium progeny concentrations are typically lower than their

3 In a later calculation, number of realizations was increased to 1000. Results were put into the spreadsheet,

TIMECONC1000.xLS.

K-Sparlnc.SciBn~J,7CCausu/~ng

493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 13

parent concentrations by an order of magnitude or more. Therefore, dose contributions from radi.oactive progeny are not anticipated to be significant.

Attachment B contains the GWSCREEN input and output files. Output files are presented for runs using the nominal input values. Please refer to the Excel spreadsheet, TIMECONC.xLS, for output from all 500 realizations.

Tc..g9

1.00E-04,---------------------------o

o o o 0 0

100 1.000 10.000

Peak Time (years)

Figure 7. Peak concentration as a function of the time of peak concentration (peak time) for Tc-99. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table I.

K-Spar Inc. Sc;eoUficCoosuHin!l 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

--------------......... ..... Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 13

parent concentrations by an order of magnitude or more. Therefore, dose contributions from radi.oactive progeny are not anticipated to be significant.

Attachment B contains the GWSCREEN input and output files. Output files are presented for runs using the nominal input values. Please refer to the Excel spreadsheet, TIMECONC.xLS, for output from all 500 realizations.

Tc..g9

1.00E-04,.----------------------------o

o o o 0 0

100 1.000 10.000

Peak Time (years)

Figure 7. Peak concentration as a function of the time of peak concentration (peak time) for Tc-99. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table I.

K-Spar Inc. Sc;eoUficCoosuHin!l 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 14

,-E §. " 0

~ c ~ u

" 0

" '" m ~ a.

U-238

Arthur S. Roc Washington State Department of Heall

1.00E.{l4,,------------------------------Deterrnilnilstic Value

1.00E-05

0

1.00E-OO

0

goo o

o

to,COO 100.000 1,000,000 10,000,000

Peak Time (years)

Figure 8. Peak concentration as a function of the time of peak concentration (peak time) for U-238. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table 1.

U-235

l.00E-04r_----------------------------

o Detennin;stic Value

00

0 0

o 0 0

00

0 0 0 0

0 0 0

• o

Peak Time (years)

Figure 9. Peak concentration as a function of the time of peak concentration (peak time) forU-235. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table 1.

K-Spar Inc. ScienlificCoosu/Uog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 14

U-238

o o

Arthur S. Roc Washington State Department of Heall

Dellemninisti'c Value

o o o

1.1XlE-08~--------------------"-----,,"-

o

Peak Time (years)

Figure 8. Peak concentration as a function of the time of peak concentration (peak time) for U-238. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table 1.

U-235

1.00E-04r-_--------------------------

o Detennin;stic Value

00

0 o 0 ., 00

0

0 0 0 0

0

• o

Peak Time (years)

Figure 9. Peak concentration as a function of the time of peak concentration (peak time) forU-235. Deterministic value shown was the peak concentration generated using the nominal parameter values listed in Table 1.

K-Spar Inc. ScienlificCoosu/Uog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment

BE-009

~ 7E-009 E U 6E-009 -c o . SE-009 ~ ... -c ., o c

8 00 .., N ::,

4E-009

3E-009

2E-009

1E-009

·Page 15

4120 4160 4200 4240 4280 4320 16,721,600 16,721,800 16,722,000

Time (years)

Figure 10. Uranium-238 concentration as a function of time. Values were generated using the nominal values for U-238 listed in Tablel. Concentrations between the years 4320 and 16,721,500 are not shown. The concentration remained the same during those years.

Table 3. Summary of the Sampled Parameters for 500 Realizations (If GWSCREEN.

Parameters Min Max Mean Median PERC (my-I) 1.54E-02 4.88E-02 2.86E-02 2.71E-02 AX(m) 1.39E+OI 4.05E+OI 2.75E+Ol 2.75E+OI AY(m) 2.60E+OO 7.34E+00 5.0IE+00 5.03E+OO AXU(m) O.OOE+OO 6.00E+OO 3.80E+00 3. 84E+00 U(my-I) 3.27E+00 2.50E+02 4.75E+Ol 3.17E+Ol RHOS (g cm-3) 1.02E+00 1.50E+00 1.26E+00 1.27E+OO RHOU (g cm-3) 1.53E+00 1.96E+00 1.70E+00 1.68E+00 RHOA (g cm-3) 1.54E+00 1.97E+00 1.70E+00 1.68E+00 .

PORS 9.72E-02 1.03E-Ol 1.00E-Ol 1.00E-Ol CL~(mLg-!) O.OOE+OO 6.00E-Ol 9.57E-02 O.OOE+OO I ~ (mL g-!) 2.00E-Ol 4.94E+00 6.70E-01 4. 82E-0 1 Tc~ (mLg-!) O.OOE+OO 6.00E-Ol 9.57E-02 O.OOE+OO U~(mLg-I) 1.00E-Ol 7.93E+OI 6.06E+00 2.83E+OO Th~(mLg-!) 5.54E+Ol 2.00E+03 7.48E+02 6.01E+02 Ra~(mLg-I) 5.00E+00 1.43E+02 2.72E+Ol 2.12E+Ol Pb~(mLg-!) 2.00E+03 6.00E+03 4.00E+03 3.91E+03 Pa~ (mLg-I) 1.00E-Ol 2.00E+Ol 1.26E+00 6.25E-Ol Ac~(mLg-!) 6.00E+Ol 6.29E+02 1.28E+02 1.00E+02 Sol U (mg L-!2 1.98E+00 4.93E+Ol 2.49E+Ol 2.45E+Ol

K-Spar Inc. ScleoHfic Consu/Hnu 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment

BE-009

~ 7E-009 E U 6E-009 -c o . SE-009 ~ ... -c ., o c

8 00 .., N ::,

4E-009

3E-009

2E-009

1E-009

·Page 15

4120 4160 4200 4240 4280 4320 16,721,600 16,721,800 16,722,000

Time (years)

Figure 10. Uranium-238 concentration as a function of time. Values were generated using the nominal values for U-238 listed in Tablel. Concentrations between the years 4320 and 16,721,500 are not shown. The concentration remained the same during those years,

Table 3. Summary of the Sampled Parameters for 500 Realizations (If GWSCREEN.

Parameters Min Max Mean Median PERC (my-I) 1.54E-02 4.88E-02 2.86E-02 2.71E-02 AX(m) 1.39E+OI 4.05E+OI 2.75E+Ol 2.75E+OI AY(m) 2.60E+OO 7.34E+00 5.0IE+00 5.03E+OO AXU(m) O.OOE+OO 6.00E+OO 3.80E+00 3. 84E+00 U(my-I) 3.27E+00 2.50E+02 4.75E+Ol 3.17E+Ol RHOS (g cm-3) 1.02E+00 1.50E+00 1.26E+00 1.27E+OO RHOU (g cm-3) 1.53E+00 1.96E+00 1.70E+00 1.68E+00 RHOA (g cm-3) 1.54E+00 1.97E+00 1.70E+00 1.68E+00 .

PORS 9.72E-02 1.03E-Ol 1.00E-Ol 1.00E-Ol CL~(mLg-!) O.OOE+OO 6.00E-Ol 9.57E-02 O.OOE+OO I ~ (mL g-!) 2.00E-Ol 4.94E+00 6.70E-01 4. 82E-0 1 Tc~ (mLg-!) O.OOE+OO 6.00E-Ol 9.57E-02 O.OOE+OO U~(mLg-I) 1.00E-Ol 7.93E+OI 6.06E+00 2.83E+OO Th~(mLg-!) 5.54E+Ol 2.00E+03 7.48E+02 6.01E+02 Ra~(mLg-I) 5.00E+00 1.43E+02 2.72E+Ol 2.12E+Ol Pb~(mLg-!) 2.00E+03 6.00E+03 4.00E+03 3.91E+03 Pa~ (mLg-I) 1.00E-Ol 2.00E+Ol 1.26E+00 6.25E-Ol Ac~(mLg-!) 6.00E+Ol 6.29E+02 1.28E+02 1.00E+02 Sol U (mg L-!2 1.98E+00 4.93E+Ol 2.49E+Ol 2.45E+Ol

K-Spar Inc. ScleoHfic Consu/Hnu 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

I

1

1

1

1

1

1

1

1

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1 Ii

Ii 1'1 I

Page 16

Maximum

Mean

Arthur S. Roo, Washington State Department of Healtl

Table 4. Maximum and Mean Conceutration from the 500 GWSCREEN Realizations at Selected Output Times for the Site Soils Cover

Time

(Years)

CI-36 1-129 Tc-99 U-23S U-234 Th-230 Ra-226 Pb-210 U·235 Pa-23 I Ac-227

(Cilm') (Ci/m') (Cilm') (Citm') (Cilm') (Citm') (Cilm') (Citm') (Cilm') (Ci";,') (Cilm')

200 I.E-06 2,E-1O 2.E-05 7.E-1O 4.E-13 4.E-20 4.E-20 3.E-22 5.E-09 2.E-11 I.E-14

300 I.E-06 S.E-09 2.E-05 6.E-OS 5.E-1I S.E-IS I.E-17 I.E-19 4.E-07 3.E-09 2.E-12

400 7.E-07 4.E-OS I.E-05 4.E-07 4.E-1O I.E-I 6 2.E-16 I.E-IS 2.E-06 2.E-OS I.E-II

500 3.E-07 S.E-OS 3.E-06 7.E-07· I.E-09 6.E-16 7.E-16 6.E-18 5.E-06 5.E-OS 3.E-1I

900 4.E-07 I.E-07 5.E-06 I.E-06 3.E-09 7.E-15 I.E-14 5.E-17 7.E-06 I.E-07 2.E-1O

1000 5.E-07 I.E-07 7.E-06 I.E-06 3.E-09 9.E-15 2.E-14 7.E-17 7.E-06 2.E-07 2.E-1O

1100 3.E-07 I.E-07 4.E-06 I.E-06 4.E-09 I.E-14 3.E-14 I.E-16 7.E-06 2.E-07 2.E-1O

1500 I.E-07 2.E-07 2.E-06 I.E-06 5.E-09 2.E-14 9.E-14 2.E·16 7.E·06 2.E-07 4.E-1O

2000 I.E-07 I.E-07 I.E-06 I.E-06 6.E-09 5.E-14 2.E-13 I.E-15 7.E·06 3.E-07 I.E-09

2500 I.E-07 2.E-07 I.E-06 I.E-06 S.E-09 I.E-13 6.E-13 4.E-15 7.E-06 4.E-07 3.E-09

5000 2.E-OS S.E-OS 3.E-07 I.E-06 2.E-08 7.E-13 2.E-1I 4.E-14 7.E.06 7.E-07 S.E-09

10000 I.E-IO 2.E-OS I.E-09 2.E-06 4.E-OS I.E-II 5.E-10 I.E-12 I.E-05 2.E-06 6.E-08

200 I.E-07 4.E-13 I.E-06 2.E-12 9.E-16 I.E-22 I.E-22 6.E-25 I.E·II 4.E-14 3.E-17

300 4.E-08 2.E-1I 6.E-07 I.E-IO I.E-13 3.E-20 4.E-20 2.E-22 I.E-09 6.E-12 5.E-15

400 2.E-OS 2.E-1O 2.E-07 I.E-09 I.E-J2 4.E-19 6.E-19 4.E-21 6.E-09 5.E-1I 5.E-14

500 I.E-OS 7.E-1O 2.E-07 2.E-09 3.E-12 2.E-IS 3.E-IS 2.E-20 I.E-OS 2.E-1O 2.E-13

900 I.E-OS 6.E-09 I.E-07 7.E-09 2.E-1I 3.E-17 8.E-17 4.E-19 5.E-OS 9.E-10 I.E-12

1000 I.E-OS S.E-09 I.E-07 S.E-09 2.E-1I 5.E-17 I.E-16 6.E-19 5.E-OS I.E-09 2.E-12

1100 9.E-09 9.E-09 I.E-07 9.E-09 3.E-1I 7.E-17 2.E-16 9.E-19 6.E-08 I.E-09 3.E-12

1500 5.E-09 I.E-OS 7.E-OS I.E-OS 5.E-1I 2.E-16 9.E-16 4.E-IS S.E-OS 2.E-09 6.E-12

2000 3.E-09 I.E-OS 5.E-OS 2.E-OS I.E-IO 7.E-16 4.E-15· 2.E-17 I.E-07 5.E-09 2.E-1I

2500 2.E-09 I.E-OS 3.E-OS 2.E-OS 2.E-1O 2.E-15 I.E-14 6.E-17 2.E-07 8.E-09 4.E-1I

5000 3.E-1O 4.E-09 4.E-09 6.E-OS S.E-IO 3.E-14 4.E-13 2.E-15 4.E-07 4.E-OS 3.E-1O

10000 5.E-13 7.E-1O 7.E-12 I.E-07 4.E-09 5.E-13 S.E-12 4.E-14 S.E-07 2.E-07 2.E-09

Results for the Enhanced Cover For the enhanced cover simulation, a single analysis was perfonned where concentrations were

output at specific times and saved in a Microsoft Excel™ spreadsheet (CONCENTRATIONJeLS). A summary of these data are presented in Table 5 for the 5th, 50th, and 95th percentiles. Concentrations were substantially lower compared to those for the site soils cover. Concentrations were lower because; a) decreased leaching from the waste trench due to lower percolation through the waste, and b) longer water travel times in the unsaturated zone delayed the arrival ofI-129, U-238 and U-235.

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Page 16

Maximum

Mean

Arthur S. Roo, Washington State Department of Healtl

Table 4. Maximum and Mean Conceutration from the 500 GWSCREEN Realizations at Selected Output Times for the Site Soils Cover

Time

(Years)

200

300

400

CI-36 1-129 Tc-99 U-23S U-234 Th-230 Ra-226 Pb-210 U·235 Pa-23 I Ac-227

(Cilm') (Ci/m') (Cilm') (Citm') (Cilm') (Citm') (Cilm') (Citm') (Cilm') (Cil,;,') (Cilm')

I.E-06 2.E-1O 2.E-05 7.E-1O 4.E-13 4.E-20 4.E-20 3.E-22 5.E-09 2.E-1I I.E-14

I.E-06 S.E-09 2.E-05 6.E-OS 5.E-1I S.E-IS I.E-17 I.E-19 4.E-07 3.E-09 2.E-12

7.E-07 4.E-OS I.E-05 4.E-07 4.E-1O LE-16 2.E-16 I.E-IS 2.E-06 2.E-OS I.E-II

500 3.E-07 S.E-OS 3.E-06 7.E-07· I.E-09 6.E-16 7.E-16 6.E-18 5.E-06 5.E-OS 3.E-1I

900 4.E-07 LE-07 5.E-06 I.E-06 3.E-09 7.E-15 I.E-14 5.E-17 7.E-06 I.E-07 2.E-1O

1000 5.E-07 I.E-07 7.E-06 I.E-06 3.E-09 9.E-15 2;E-14 7.E-17 7.E-06 2.E-07 2.E-1O

1100 3.E-07 I.E-07 4.E-06 I.E-06 4.E-09 I.E-14 3.E-14 I.E-16 7.E-06 2.E-07 2.E-1O

1500 LE-07 2.E-07 2.E-06 I.E-06 5.E-09 2.E-14 9.E-14 2.E·16 7.E·06 2.E-07 4.E-1O

2000 I.E-07 LE-07 I.E-06 I.E-06 6.E.Q9 5.E-14 2.E-13 I.E-15 7.E·06 3.E-07 I.E-09

2500 I.E-07 2.E-07 I.E-06 LE-06 S.E-09 I.E-13 6.E-13 4.E-15 7.E-06 4.E-07 3.E-09

5000 2.E-OS S.E-OS 3.E-07 LE-06 2.E-08 7.E-13 2.E-1I 4.E-14 7.E.06 7£-07 S.E-09

10000 I.E-IO 2.E-OS I.E-09 2.E-06 4.E-OS I.E-II 5£-10 LE-12 I.E-05 2.E-06 6.E-08

200 I.E-07 4.E-13 I.E-06 2£-12 9.E-16 I.E-22 I.E-22 6.E-25 I.E·II 4£-14 3.E-17

300 4.E-08 2.E-1I 6.E-07 I.E-IO I.E-13 3.E-20 4.E-20 2.E-22 I.E-09 6.E-12 5.E-15

400 2.E-OS 2.E-1O 2£-07 LE-09 I.E-J2 4.E-19 6.E-19 4.E-21 6.E-09 5.E-1I 5.E-14

500 I.E-OS 7.E-1O 2.E-07 2.E-09 3.E-12 2.E-IS 3.E-IS 2.E-20 I.E-OS 2.E-1O 2£-13

900 I.E-OS 6.E-09 LE-07 7.E-09 2.E-1I 3.E-17 8.E-17 4.E-19 5.E-OS 9.E-10 I.E-12

1000 I.E-OS S.E-09 I.E-07 S.E-09 2.E-1I 5.E-17 LE-16 6.E-19 5.E-OS I.E-09 2.E-12

1100 9.E-09 9.E-09 LE.Q7 9.E-09 3.E-1I 7.E-17 2.E-16 9.E-19 6.E-08 I.E-09 3.E-12

1500 5.E-09 I.E-OS 7.E-OS I.E-OS 5.E-1I 2.E-16 9.E-16 4.E-IS S.E-OS 2.E-09 6.E-12

2000 3.E-09 I.E-OS 5.E-OS 2.E-OS I.E-IO 7.E-16 4.E-15· 2.E-17 I.E-07 5.E-09 2.E-1I

2500 2.E-09 I.E-OS 3.E-OS 2.E-OS 2.E-1O 2.E-15 I.E-14 6.E-17 2.E-07 8.E.Q9 4.E-1!

5000 3.E-1O 4.E-09 4.E-09 6.E-OS S.E-IO 3.E-14 4.E-13 2.E-15 4.E.Q7 4.E-OS 3£-10

10000 5.E-13 7.E-1O 7.E-12 I.E-07 4£-09 5.E-13 S.E-12 4.E-14 S.E-07 2.E-07 2.E-09

Results for the Enhanced Cover For the enhanced cover simulation, a single analysis was performed where concentrations were

output at specific times and saved in a Microsoft Excel™ spreadsheet (CONCENTRATIONJeLS). A summary of these data are presented in Table 5 for the 5th, 50th, and 95th percentiles. Concentrations were substantially lower compared to those for the site soils cover. Concentrations were lower because; a) decreased leaching from the waste trench due to lower percolation through the waste, and b) longer water travel times in the unsaturated zone delayed the arrival ofI-129, U-238 and U-235.

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Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 17

Table 5. Percentiles of Concentration from 1000 GWSCREEN Realizations at Selected Ontpnt Times for the Enhanced Cover

Percentile

5%

50%

95%

Time

(years)

400

500

CI-36 1-l29 Tc-99 U-238 U-234 Th-230 Ra-226 Pb-21O U-235 Pa-231 Ac-227

(Ci m-') (ei m-3) (Ci m-') (ei m-') (Ci m-') (ei m-')

O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

600 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

700 O.E+OO O.E+OO O.E+OO O.E+OO 0.E+00 O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO

800 O.E+OO O.E+OO· O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo

900 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO

1000 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

11 00 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

1500 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2000 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2500 3.E-20 O.E+OO 4.E-19 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO ·O.E+OO O.E+OO O.E+OO

5000

10000

400

500

600

700

800

900

1000

1100

1500

2000

2500

5000

lOOOO

400

500

2.E-19 O.E+OO 2.E-18 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

I.E-34 O.E+OO 2.E-33 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

6.E-12 O.E+OO 8.E-11 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

3.E-1O O.E+OO 4.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO

2.E-09 O.E+OO 2.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

4.E-09 O.E+OO 5.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO

6.E-09 O.E+OO 9.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO

8.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-09 O.E+OO 9.E-08 O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

3.E-09 O.E+OO 4.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

6.E-1O O.E+OO 9.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oO O.E+OO

2.E-1O 8.E-20 2.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-12 9.E-13 9.E-11 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2.E-14 5.E-1O 2.E-13 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO

7.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO . O.E+OO O.E+OO

. 3.E-08 O.E+OO 3.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO

600 5.E-08 O.E+OO 7.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

700 8.E-08 O.E+OO I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

800 I.E-07 O.E+OO I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

900

lOOO

llOO

1500

2000

2500

5000

lOOOO

I.E-07 3.E-19 I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

9.E-08 6.E-18 I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO 0.E+00 O.E+OO

7.E-08 6.E-17 9.E-07 O.E+OO O.E+OO O.E+OO O.E+OO· O.E+OO O.E+OO O.E+OO O.E+OO

3.E-08 4.E-14 4.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+oo O.E+OO O.E+OO O.E+OO

2.E-08 3.E-12 2.E-07 5.E-23 3.E-25 I.E-30 2.E-29 5.E-32 2.E-22 4.E-25 I.E-26

I.E-08 4.E-11 2.E-07 I.E-20 I.E-22 5.E-28 5.E-27 3.E-29 8.£-20 I.E-22 5.E-24

9.E-09 4.E-09 I.E-07 9.E-16 I.E-17 2.E-22 3.E-21 2.E-23 6.E-15 2.E-17 9.E-19

5.E-09 8.E-09 7.E-08 3.E-14 9.E-16 4.E-20 6.E-19 4.E-21 2.E-13 2.E·15 8.E-17

K"8parlnc.Scienn7.rcCoRSUIUng 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Groundwater Pathway Uncertainty Analysis for Perfonnance Assessment Page 17

Table 5. Percentiles of Concentration from 1000 GWSCREEN Realizations at Selected Ontpnt Times for the Enhanced Cover

Percentile

5%

50%

95%

Time

(years)

400

500

CI-36 1-129 Tc-99 U-23S U-234 Th-230 Ra-226 Pb-21O U-235 Pa-231 Ac-227

(Ci m-') (ei m-3) (ei m-') (ei m-') (ei m-') (ei m-')

O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

600 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

700 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

800 O.E+OO O.E+OO· O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

900 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

1000 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

II 00 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

1500 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2000 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2500 3.E-20 O.E+OO 4.E-19 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO -O.E+OO O.E+OO O.E+OO

5000

10000

400

500

600

700

800

900

1000

1100

1500

2000

2500

5000

lOOOO

400

500

2.E-I9 O.E+OO 2.E-IS O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

I.E-34 O.E+OO 2.E-33 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

6.E-12 O.E+OO S.E-ll O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

3.E-1O O.E+OO 4.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2.E-09 O.E+OO 2.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

4.E-09 O.E+OO 5.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

6.E-09 O.E+OO 9.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

8.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-09 O.E+OO 9.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

3.E-09 O.E+OO 4.E-08 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

6.E-IO O.E+OO 9.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+oO O.E+OO

2.E-1O S.E-20 2.E-09 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-I2 9.E-13 9.E-ll O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2.E-14 5.E-1O 2.E-13 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-09 O.E+OO I.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO . O.E+OO O.E+OO

. 3.E-OS O.E+OO 3.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

600 5.E-08 O.E+OO 7.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

700 8.E-08 O.E+OO I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

800 I.E-07 O.E+OO I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

900

lOOO

llOO

1500

2000

2500

5000

lOOOO

I.E-07 3.E-I9 I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

9.E-08 6.E-IS I.E-06 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

7.E-OS 6.E-17 9.E-07 O.E+OO O.E+OO O.E+OO O.E+OO· O.E+OO O.E+OO O.E+OO O.E+OO

3.E-08 4.E-I4 4.E-07 O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO O.E+OO

2.E-08 3.E-I2 2.E-07 5.E-23 3.E-25 I.E-30 2.E-29 5.E-32 2.E-22 4.E-25 l.E-26

I.E-08 4.E-1l 2.E-07 l.E-20 l.E-22 5.E-28 5.E-27 3.E-29 8.E-20 l.E-22 5.E-24

9.E-09 4.E-09 l.E-07 9.E-I6 l.E-I7 2.E-22 3.E-21 2.E-23 6.E-I5 2.E-17 9.E-I9

5.E-09 S.E-09 7.E-08 3.E-14 9.E-I6 4.E-20 6.E-I9 4.E-21 2.E-13 2.E-IS 8.E-I7

K"8parlnc.Scienn7.rcCoRSUIUng 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 18

References

Arthur S. Rood Washington State Department of Health

Decisioneering, 1996. Crystal Ball Version 4.0. Decisioneering Inc. Aurora, Colorado.

DOE 1994. Remedial Investigation and Feasibility Study Report for the Environmental Restoration Disposal Facility. DOEIRL-93-99 Rev 1. U.S. Department of Energy, Richland Field Office, Richland Washington.

Cole, c.R., S.K. Wrstner, M.P Bergeron, M.D. Williams, P.D. Thorne, 1997. Three-Dimensional Analysis of Future Groundwater Flow Conditions. PNNL-I1801, Pacific Northwest Laboratory, Richland, Washington.

Khaleel R., and E. J. Freeman, 1995. Variability and Scaling of Hydrologic Properties for 200 Area Hanford Soils, Hanford Site. WHC-EP-0883, Westinghouse Hanford Company, Richland, Washington

Kincaid, C.T., M.P. Bergeron, C.R. Cole, M.D. Freshley, N.L. Hassig, V.G. Johnson, D.l. Kaplan, R.J. Serne et aI., 1998. Composite Analysis for Low-Level Waste Disposal in the 200 Area Plateau of the Hanford Site. PNNL-I1800, Pacific Northwest Laboratories, Richland Washington.

Rood, A. S., 1994. GWSCREEN: A Semi-Analytical Modelfor Assessment of the Groundwater pathway from Suiface or Buried Contamination, Theory and User's Manual. EGG-GEO-l 0797, Idaho National Engineering and Environmental Laboratory, Idaho Fans, Idaho.

Rood, A. S., 1999. GWSCREEN: A Semi-Analytical Model for Assess~ent of the Groundwater pathway from Suiface or Buried Contamination, Theory and User's Manual Version 2.5. INEELIEXT-98-00750, Rev 1 February, Idaho National Engineering and Enviromnental Laboratory, Idaho Fans, Idaho.

Serne, J.R., 1999. Pacific Northwest Laboratory, personal communication with M. Dunkelman.

Serne, J .R., D.S. Burke, and KM. Krupka, 1999. Uranium Solubility Tests in Support of Solid Waste Burial. PNNL-llxxx. Pacific Northwest Laboratory, Richland, Washington

Xu, M. and Y. Eckstein, 1996. "Use of Weighted Least-Squares Method in Evaluation of the Relationship Between Dispersivity and Field Scale." Ground Water 33(6): pp. 905-908.

K-5par Inc. ScieoUUcCoosulUog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net

Page 18

References

Arthur S. Rood Washington State Department of Health

Decisioneering, 1996. Crystal Ball Version 4.0. Decisioneering Inc. Aurora, Colorado.

DOE 1994. Remedial Investigation and Feasibility Study Report for the Environmental Restoration Disposal Facility. DOEIRL-93-99 Rev 1. U.S. Department of Energy, Richland Field Office, Richland Washington.

Cole, c.R., S.K. Wrstner, M.P Bergeron, M.D. Williams, P.D. Thorne, 1997. Three-Dimensional Analysis of Future Groundwater Flow Conditions. PNNL-I1801, Pacific Northwest Laboratory, Richland, Washington.

Khaleel R., and E. J. Freeman, 1995. Variability and Scaling of Hydrologic Properties for 200 Area Hanford Soils, Hanford Site. WHC-EP-0883, Westinghouse Hanford Company, Richland, Washington

Kincaid, C.T., M.P. Bergeron, C.R. Cole, M.D. Freshley, N.L. Hassig, V.G. Johnson, D.l. Kaplan, R.J. Serne et aI., 1998. Composite Analysis for Low-Level Waste Disposal in the 200 Area Plateau of the Hanford Site. PNNL-I1800, Pacific Northwest Laboratories, Richland Washington.

Rood, A. S., 1994. GWSCREEN: A Semi-Analytical Modelfor Assessment of the Groundwater pathway from Suiface or Buried Contamination, Theory and User's Manual. EGG-GEO-l 0797, Idaho National Engineering and Environmental Laboratory, Idaho Fans, Idaho.

Rood, A. S., 1999. GWSCREEN: A Semi-Analytical Model for Assess~ent of the Groundwater pathway from Suiface or Buried Contamination, Theory and User's Manual Version 2.5. INEELIEXT-98-00750, Rev 1 February, Idaho National Engineering and Enviromnental Laboratory, Idaho Fans, Idaho.

Serne, J.R., 1999. Pacific Northwest Laboratory, personal communication with M. Dunkelman.

Serne, J .R., D.S. Burke, and KM. Krupka, 1999. Uranium Solubility Tests in Support of Solid Waste Burial. PNNL-llxxx. Pacific Northwest Laboratory, Richland, Washington

Xu, M. and Y. Eckstein, 1996. "Use of Weighted Least-Squares Method in Evaluation of the Relationship Between Dispersivity and Field Scale." Ground Water 33(6): pp. 905-908.

K-5par Inc. ScieoUUcCoosulUog 493 N 4154 E, Rigby, Idaho 83442 (208) 745-1288 email asr@srv.net