United States
Environmental Protection
Agency
                                  Model

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                                                  EPA/600/R-02/051b
                                                       August 2002
Predicting  Attenuation  of Viruses

      During  Percolation in Soils


          2. User's  Guide to the

      Virulo  1.0  Computer Model

                      William G. Lyon
       ManTech Environmental Research Services Corporation
                    Ada, Oklahoma 74820

                     Barton R. Faulkner
          Subsurface Protection and Remediation Division
          National Risk Management Research Laboratory
                    Ada, Oklahoma 74820

                      Faruque A. Khan
                   U.S. EPA Headquarters
                   Washington, D.C. 20460

                    Sandip Chattopadhyay
                  Battelle Memorial Institute
                    Columbus, Ohio 43230

                      Jerome B. Cruz
             Washington State Department of Ecology
         Toxics Cleanup Program, Northwest Regional Office
                  Bellevue, Washington 98008

                  EPA Contract 68-C-98-138

                       Project Officer
                     Georgia A. Sampson
          Subsurface Protection and Remediation Division
          National Risk Management Research Laboratory
                    Ada, Oklahoma 74820

          National Risk Management Research Laboratory
              Office of Research and Development
              U.S. Environmental Protection Agency
                    Cincinnati, Ohio 45268     /yy
                                             Printed with vegetable-based ink on
                                             paper that contains a minimum ot
                                             50% post-consumer fiber content
                                             processed chlorine tree.

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                                Notice
    The U.S. Environmental  Protection Agency through  its Office of Research
and Development funded and managed the research described here through in-
house  efforts  and under  Contract  68-C-98-138 to ManTech  Environmental
Research Services Corporation. It has been subjected to the Agency's peer and
administrative  review and has been approved for  publication as an  EPA
document.  Use of trade  names  or  commercial products does  not constitute
endorsement or recommendation for  use.

    All  research projects making  conclusions or recommendations based on
environmental data and funded by the U.S. Environmental Protection Agency are
required to participate in the Agency Quality Assurance Program. This project
was conducted under an approved Quality Assurance  Project Plan. The proce-
dures specified in this plan were used without exception.  Information on the plan
and documentation of the quality assurance activities and results are available
from the Principal Investigator.

    Virulo  and  the user's guide have been subjected to  the Agency's peer and
administrative  review and  have  been  approved for  publication as an  EPA
document.   Virulo is made available on an as-is basis without guarantee or
warranty of any kind,  express or implied.  Neither the United States Government
(U.S. EPA), ManTech Environmental Research  Services Corporation, Battelle
Memorial  Institute, Washington State Department of Ecology, nor any of the
authors or reviewers accept any liability resulting from  the use  of Virulo,  and
interpretation of the predictions of the model are the sole responsibility of the
user.

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                              Foreword
    The U.S. Environmental Protection Agency is  charged by Congress with
protecting the Nation's land, air, and water resources.  Under a mandate of
national environmental laws, the Agency  strives to formulate and implement
actions leading to a compatible balance between human activities and the ability
of natural systems to support and  nurture life.  To meet this mandate, EPA's
research program is providing data and technical support for solving environmen-
tal problems today and building a science knowledge base necessary to manage
our ecological resources wisely, understand how pollutants affect our health, and
prevent or reduce environmental risks in the future.

    The National Risk Management Research Laboratory is the Agency's center
for investigation of technological and management approaches for preventing and
reducing risks from pollution that threatens human health and the environment.
The focus of the Laboratory's research program is  on  methods and their cost-
effectiveness for  prevention and control  of pollution to air, land, water, and
subsurface  resources; protection of  water quality in  public water  systems;
remediation of contaminated sites, sediments and ground water; prevention and
control of indoor air pollution; and restoration  of ecosystems.  NRMRL collabo-
rates  with both public and private  sector  partners to  foster technologies that
reduce the cost of compliance and to anticipate emerging problems.  NRMRL's
research provides solutions to environmental problems by: developing and
promoting technologies that protect and improve the  environment; advancing
scientific and engineering information to support regulatory and policy decisions;
and providing the technical support and information transfer to ensure implemen-
tation of environmental  regulations and strategies  at the  national, state, and
community levels.

    This publication has been produced as part of the Laboratory's strategic long-
term research plan.   It is  published and  made  available  by EPA's  Office of
Research and Development to assist the user community and to link researchers
with their clients.

    EPA's Office  of Water is currently promulgating a  Ground Water Rule to
ensure water supplies are safe from  contamination by viruses. States may be
required to conduct hydrogeologic sensitivity assessments to predict whether a
particular aquifer is vulnerable to pathogens. This work presents a User's Guide
for  Virulo, a  user-friendly predictive screening model for  virus attenuation above
aquifers. It is hoped this model will be a useful tool for state regulators, utilities,
and development planners.
                                  len G. Schmelling, Acting
                             Subsurface Protection and Remedition Division
                             National Risk Management Research Laboratory

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                              Abstract
    In the EPA document Predicting Attenuation of Viruses During Percolation in
Soils  1.  Probabilistic Model the conceptual,  theoretical,  and  mathematical
foundations for a predictive  screening model were presented.  In this  current
volume we present a User's  Guide for the computer model that implements the
probabilistic model. The  model is a predictive screening model called  Virulo, so
named because of its use of the Monte Carlo method for predicting virus fate and
transport. This document presents a general overview of the parameters used
and how they can be modified to suit a particular predictive modeling scenario. In
addition,  a non-technical overview of the conceptual modeling approach is given.
Some example applications of Virulo are  presented.
                                   IV

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                                    Contents
Foreword	Hi
Abstract	iv
Figures	vi
Tables	vii
Acknowledgments	vi'ti
1   Introduction	1
    1.1 What does Virulo do?	,	,	1
    1.2 What information is needed to run Virulo?	1
2   The Virulo  User Interface	6
    2.1 Soil and Flow Parameters	, 7
    2.2 Virus Parameters	8
3   Example Applications	9
4   References	14
5   Website  References	14

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                                       Figures
1.1       Schematic depiction of physical  role of microscopic parameters discussed
         in detail in Predicting Attenuation of Viruses During Percolation in Soils.
         1. Probabilistic Model.	3
1.2       Schematic depiction of physical role of macroscopic parameters discussed
         in detail in Predicting Attenuation of Viruses During Percolation in Soils.
         1. Probabilistic Model.	3
1.3       Schematic depiction of physical role of the equilibrium partitioning coefficient,
         discussed in detail in Predicting Attenuation of Viruses During Percolation in Soils.
         1. Probabilistic Model.	4
1.4       Schematic depiction of how the Monte Carlo method works. Each realization is a
         random sample of input parameters. The collection of all realizations is the
         ensemble, from which inference  is made on the  output	5
2.1       Default flow parameters upon first opening Virulo (Windows™ Version)	6

2.2       Default virus parameters upon first opening Virulo (Linux (WindowMaker) Version). 7
2.3       Flow input panel  showing pull-down list of soil textures (Mac OS X Version)	8

2.4       Virus input panel showing pull-down list of virus-texture defaults
         (Linux (WindowMaker} Version)	8

3.1       Virus property panel showing start of simulation (Linux (WindowMaker) Version)	10
3.2       Histogram for a single simulation of default case Clay, Polio
         (Linux (WindowMaker) Version)	10

3.3       Histogram for a single simulation of default loamy sand,  hepatitis A-Clay
         (Linux (WindowMaker) Version)	11

3.4       Histogram for a single simulation of default sandy loam with 6m mean set
         to 0.2 and its standard deviation set to 0.05, hepatitis A-Clay
         (Linux (WindowMaker) Version)	12
                                           VI

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                                       Tables
1.1       Summary of Notation Used in the Virulo Input Data Windows	2

3.1       Failures to Achieve 4-log10 Attenuation Results for Various
         Soil Textures for a Single Virulo Simulation Run of 106 Iterations	13
                                           VII

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                      Acknowledgments
   The  authors wish to  acknowledge  Martha Williams, Computer Sciences
Corp., who typeset this document. We also thank Liping Pang, of the Institute of
Environmental Science  & Research Limited,  Christchurch,  New Zealand,  for
many helpful comments and suggestions which improved the utility of the Virulo
model.
                               Vtll

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                                   Section 1.  Introduction
Understanding the fate and transport of viruses and pathogens in variably saturated soil and aquifer materials is
important in predicting potential risk to human health from human and animal wastes.  The U.S. EPA Office of Water
proposed the Ground Water Rule (U.S. EPA 2000), which will reduce exposure to pathogenic viruses and bacteria
associated with human and animal wastes that might contaminate Public Water Supply wells. As part of the Ground
Water Rule, public water supply systems  that do not meet disinfectant treatment requirements will be required to
conduct source water monitoring if the system produces water from sensitive aquifers1.

The  U.S. EPA Office of Research and Development has developed  a virus fate and transport model (Virulo) that
analyzes virus leaching from sorption sites in variably saturated soil and aquifer materials.  Virulo predicts attenuation
to total viruses as they pass through  a soil layer. One use of the model could be to provide estimates of attenuation
of viruses percolating through a candidate barrier layer of a particular soil textural type. However the model has many
other potential uses such as predicting virus attenuation through land spreading of wastewater during ground-water
recharge operations  or for predicting the effects  of high density septic tank discharge on  ground-water quality.

1.1  What does Virulo do?
Virulo is a one-dimensional, variably saturated, ground-water flow and contaminant transport model.  By computing
analytical  solution equations that  describe advection,  dispersion, viral sorption, inactivation,  and  mass transfer,
ensembles of outputs are  produced using random sampling of  certain parameters  over specified ranges and
distributions through  use of the Monte Carlo method.
Virulo generates the number of cases in  which  a certain level2 of virus attenuation  was or was not achieved. As
discussed in detail in Predicting Attenuation of Viruses During Percolation in Soils.  1. Probabilistic Model (hereafter
referred to as "Part 1"), attenuation is defined as the ratio of total mass leaving the layer  to the total mass entering
it (Figure 1.1). Part 1 contains a detailed description of the system being modeled and the theoretical development of
Virulo.
Virulo is a screening model only. It was developed to fill a niche for users who are expected to have only a small
amount of information available for  a site of interest. Default values, categorized  by qualitative descriptions  are
emphasized. For more complete investigations of a site, numerical models such as  Viralt (Park et al. 1992),  Virtus
(Yates et al. 1991), or Canvas (Park et al. 1991) should be considered. These models allow handling of heterogeneity,
and  other factors affecting transport  and fate which are conceptually greatly simplified  in  Virulo.

1.2  What information is needed to run Virulo?

The  transport  component of the model primarily uses data from soil textural3 and thickness information (perhaps
derived from boring  logs) and soil survey  reports for a particular site of interest.  Human virus data at  present are
limited to  polio, hepatitis A, reovirus 3, coxsackievirus,  and echovirus.
Many parameters can  be defaulted  to standardized  values on the input panels.  Default parameters can also  be
overridden by the user when more accurate values are available for a particular site.

The  default description of soil water content used internally is a climate-free estimate based on the assumption of a
uniform random distribution  of water content values chosen from values between the residual water saturation and
complete  water saturation for a given soil texture (Table 1.1).   This distribution is used  in the Virulo Monte Carlo
calculations. The user currently has  the option of overriding this distribution, and inserting a water content (e.g., the
estimated field capacity or wilt point for  the given  soil texture) directly.  This allows the user to gain a better
appreciation for the effect of water content on virus breakthrough at the bottom of the soil layer of interest.
 1 Sensitive aquifers include karst, fractured bedrock, and gravel aquifers.
 2 The targeted level of attenuation is adjustable by the user. 4-log10 (99.99%) represents the current default level.
 3 The 12 USDA textural categories are used in Virulo to supply default parameters. See, for example, http;//www,agronomy.psu.edu/
  Courses/SOILS101/Labs/texture.html.

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Table 1.1     Summary of Notation Used in the Virulo Input Data Windows
Parameter
Description
Units
Flow Parameters Window (given in sequence):
6r
em
es
"og10(K.)
Iog10(a>
logio(n)
P
rP
C<*
T
L
Residual water content, the water content that is
approached at highly negative capillary pressure, where the
water remaining is that held by the soil's capillary forces
Water content, the volume fraction containing water
Saturated water content, equal to the porosity
Saturated hydraulic conductivity, the hydraulic conductivity
the soil would have if 6m = 6S
van Genuchten parameter'
van Genuchten parameter"
Soil dry bulk density
Mean soil particle radius
Hydrodynamic dispersivity
Mean soil temperature, used for computing molecular
dispersivity of the virus, a component of dispersion
Thickness of candidate barrier layer
m3 nr3
m3 nr3
m3 nr3
Iog10(mtr1)
log10(m-1)
log,0
gnr3
m
m
°C
m
Virus Parameters Window (given in sequence)
P
l°9io (*)
Iog10 (A*)
K
K*
rv
Cmax
Kd
Exponential decay exponent in source term
Mobile virus inactivation rate constant, rate of inactivation of
suspended viruses
Solid-sorbed virus inactivation rate constant, rate of
inactivation of soil-adsorbed viruses
Mass transfer coefficient, mobile phase to solid-sorbed
phase1
Mass transfer coefficient, mobile phase to air-water
interface sorbed *
Mean virus radius
Maximum virus concentration in source term, arbitrary units
Equilibrium partitioning coefficient for viruses sorbing onto
soil particles
h'1
lo&oth-1)
Iog10(rr1)
mh'1
mh-1
m
[e.g., PFU L1]
m3g-1
*the van Genuchten relation is  I Oa—Br
n
fthe rate itself is computed in Virulo by k = K ar where ar is the water-solid interfacial area.



*the rate itself is computed in Virulo by fc° =  /r°ar0 where aJ* is the water-air interfacial area.

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 Figure 1.1 shows schematically, at the microscopic level, the interaction among the various parameters and the
 descriptions of their physical basis are also described in detail in Part 1.
                          •max—*
                              C(in)
C(in)=e-P(time)
                            C(out)
                                      time
 Figure 1.1   Schematic depiction of physical role of microscopic parameters discussed in detail in Predicting
            Attenuation of Viruses During Percolation in Soils, 1. Probabilistic Model,

                                              q=^(Ks,n,a,er,es,em)
Figure 1.2   Schematic depiction of physical role of macroscopic parameters discussed in detail in Predicting
            Attenuation of Viruses During Percolation in Soils. 1. Probabilistic Model.

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                         Viruses suspended
                         in water
Viruses adsorbed
to soil
Figure 1 2 shows the macroscopic parameters that describe advective flux and dispersion of viruses as they pass through
the soil layer of interest. Figure 1.3 illustrates the role of  Kd, the equilibrium partitioning coefficient.


Figure 1 3    Schematic depiction of physical role of the equilibrium partitioning coefficient, discussed in detail in
             Predicting Attenuation of Viruses During  Percolation in Soils. 1, Probabilistic Model.

Virulo makes use of a conceptual model that simplifies the types and rates of natural processes that govern variably
saturated ground water flow and virus transport. As noted in Part 1,  Virulo employs the following simplifications and
assumptions:
 •  One-dimensional, vertical,  uniform, variably saturated,  ground-water flow

 •  Gravity drainage only (no abrupt change in capillary pressure in the soil)
 •  Random soil water content representing random, instantaneous  recharge from precipitation
 •  There is no soil water hysteresis and water content is random, rather than cyclical, wetting and drying (the results
    are very sensitive  to the water content)
 •  Variably saturated ground-water flow through uniform layers of porous media without preferential flow pathways

 •  Virus transport may be simulated by linear sorption typical  of dissolved contaminants  rather than by colloidal
    filtration theory specific to colloidal  particles
 •  Measured laboratory, rather than field, values of virus transport and survival are suitable for use in the simulations

 •  Parameter values are displayed with significant figures not warranted by the actual measurements available to the
    user to allow use of a variety of data sources with varying significant figures. The user should recognize that he
    results are suitable for informational purposes only; i.e., four log  removal could be undistmguishable from two log
    removal or six log removal given available  data and theory
 The figures are merely meant to illustrate the role of the parameters  used  in Virulo. For a complete description of the
 processes they represent, Part  1 should be studied.
 In order to quantify uncertainty in  model outputs, Virulo employs the Monte Carlo method (Figure 1.4). The method
 works  in  Virulo by assuming the values of the  parameters vary randomly in one of two possible ways:
  • the input parameters vary  randomly and  independently of each  other, but they follow a  normal  distribution
    described by the mean and standard deviation; or,
  • the input parameters vary randomly, but dependently with a subset of the other  parameters, and they follow a
    multivariate normal distribution described by their means and their variance-covanance matrix.

 The  parameters in Virulo which vary according to the latter are the hydraulic parameters 9  68, log  K , Iog10 a log  n.
 This is important to the user  because their standard deviations cannot be modified because Virulo employs the
 variance-covanance matrix, computed a priori for each of  the twelve USDA soil types. The current version does no
 allow the user  to modify  the  underlying matrix  used in the  dependent conditional simulations for these input
 parameters. Furthermore, modifying  the mean values of  these  five parameters is not recommended because it is
 dubious  to  assume the variance-covariance  matrix would  remain unchanged, and presumably the greater the
 magnitude  of the adjustment in the mean, the  greater the error in the existing variance-covariance matrix, The user

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                                                realizations       ensemble
                                           123...              ...n
                  input parameter 1
                  input parameter 2
                  input parameter 3
                  input parameter 4
                               output <
Figure 1.4    Schematic depiction of how the Monte Carlo method works. Each realization is a random sample of
             input parameters. The collection of all realizations is the ensemble, from which inference is made on
             the output.

is advised not to change these values unless they have a complete understanding of the way conditional dependence is
simulated in Virulo, as described in Part 1, and they are completely able to justify the change made.

The Monte Carlo method uses computer-generated random numbers. In models such as Virulo, the Monte Carlo method
works as follows:
    I.   A randomly generated set of input parameters is created and the model is run to produce an output.
    11.  Step I is repeated many times, building up a list of output values corresponding to each random set of input
       parameters.
    III. After suitably many such runs (enough runs so that the user is confident the full range of possible inputs
       has been covered), inference is made on the output.
In the case of Virulo, the outputs (attenuation values) are plotted on a logarithmic scale as a right-truncated histogram.
The user is free to adjust any of the parameters provided they can justify doing so. Furthermore, a user may wish to
run Virulo by assuming one or more of the parameters is deterministic (i.e., does not vary). This is done by simply
setting the standard  deviation for the parameter to zero. Note that some of the parameters are given as the Iog10 of
its value. This is  because these parameters are either known  to follow, or are thought to follow the lognormal
distribution, hence the user should be  careful to note this if the values are adjusted from the defaults.

In addition to the special case of the conditional dependence of some of the input parameters, another special case
should be discussed here: The value of the water content will depend not only on soil type, but on many external
factors, such as climate, time of year, presence of irrigation, and evapotranspiration.  Indeed, for  most sites users will
not have definitive information about its value. Therefore, Virulo offers two ways to model its value:

 *   it varies in a uniformly random manner between the residual water content at the lower end, and the saturated
    water content  at the upper end (this is the default);

 •   the user can input a value and a standard deviation, as is done with the other independent  parameters.

As is discussed in  Part 1, the parameters to which the output is most sensitive are the hydraulic parameters. This may
be viewed as fortunate, because these parameters and  their variation are better studied and documented at present,
than the parameters directly related to microscopic adsorption and  decay.

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                            Section 2.   The  Virulo User Interface
The main data input windows for Virulo consist of two panels, one denoted "Flow Parameters," and one denoted "Virus
Parameters." These two panels are displayed on the following two pages as Figures 2.1 and 2.2. Despite the names, both
panels contain some parameters that straddle more than one category. For example, the Flow Parameters panel contains
some soil parameters, and the Virus Parameters panel contains some virus properties that are specifically for a certain soil
texture. Nevertheless, the rough subdivision into two panels clusters together parameters controlling flow, and parameters
controlling virus inactivation and sorption.
                                                                                 (-tofllO)
                        Parameter
                                        Mean
                                                       Sid. Deviation
                                                                      Unfts
8ffl
Iog10a
P
T
L
day •»"

0.100993333
0.0
0.515332222
•2.085670553
0.276202632
0.113751456
1290000.0
9.95E-5
8.75E-5
11.7
0.5
0010708176
0-0
Q.Q852B0028
0475140674
0,129474299
0.015201357
168000.0
6.15E-5
1.0E-4
7.38
1.0E-4

MW *
0 Untform(y Random
nftnf
N>010(mh -')
Iofl10( tn -' }
loglO(-)
gm-1
m
m
Celsius
These parameters modeled as conditionally dependent by
varlance-covariance matrix, so the standard deviations are
not editable by the user.
Figure 2.1    Default flow parameters upon first opening Virulo (Windows™ Version).
The input variables are denoted by symbols, which reflect common usage in the published literature.  In case of doubt,
resting the cursor on the symbol opens an identity box, which gives a short description of the variable denoted by the
symbol.

When first opened, Virulo has default values corresponding to clay, and polio-clay entered for each of the parameters,
and for the  standard deviation to be associated with the parameter values. On the far right side, the dimensions for
the entered parameter are indicated.  In order to run Virulo, appropriate values for the parameters  expressed  in the
necessary units must be entered in each of the active  boxes.  This can be accomplished in one of two ways.

Method One is simply the use of the built-in default options as they stand. At the bottom of each panel is a box that
activates a  pull-down list of default options for a given  soil texture, selection of which will  automatically fill  in all the
appropriate parameters.  This makes the program very easy to run for simple cases.

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Figure 2.2    Default virus parameters upon first opening Viruio (Linux (WindowMaker) Version).

Method Two involves the more laborious filling in of the parameters one by one based on a detailed knowledge of the site.
These parameters might be obtained independently (see Schijven and Hassanizadeh, 2000), from the compilation of
Breidenbach et al., 20004. Great care must be taken to convert all parameters into the exact units specified by the
input panel.

Finally, we should mention that Methods One and Two can be combined. Method One can be used to fill in most of the
parameters, leaving only a few parameters to be changed to fit the particular situation prevailing on the site of interest. The
parameters can be changed repeatedly, and are not finalized until the program is actually run.

Note that in Figure 2.1 the "uniformly random" box opposite 0m has been checked as the default. If this box is unchecked,
then the 6m parameter and its standard deviation may be edited.  Various water contents consistent with the values of 0r
and  0e may be inserted here, e.g., values for the field capacity and wilt point for the given texture.  It is recommended that
the standard deviation be kept very small (e.g., 0.001) for benchmark calculations of this type.

The value of the threshold attenuation can be set by the user by typing it in the text field at the right side of the toolbar of
the main window frame.  This is the level of attenuation being considered. Attenuation less than this value is considered an
exceedance for that particular Monte Carlo  run.  In this way the user can set what constitutes an exceedance, for their
purposes. A vertical red bar will be displayed in the histogram  panel indicating the threshold attenuation chosen. By
default, this value is set to 4, indicating a threshold  of "4-log attenuation." This value is often mentioned in the context of
proposed hydrogeologic sensitivity assessments, as well as in Comprehensive Performance Evaluations for public water
systems. In the future,  such performance evaluations will  consider ground-water systems. The value is completely
arbitrary, and is offered  as a way for the user to make judgments or inferences from the model runs.

2.1    Soil and Flow Parameters

In the following Figure 2.3, the flow input panel is shown with the pull-down list of default textures. The clay default option
was selected, and the program has inserted  all the appropriate parameters for an "average" clay. The basis for selecting
these default values is given in Faulkner et al., 2002.
The user will notice that  when the model is running on their computer some of the parameter values are highlighted in red,
some in regular black and white, and some of the  standard deviations are grayed-out. Holding the  cursor on any red-
highlighted value will produce a message box indicating that "Modifying this mean value is not recommended. See model
4 We anticipate making a data compilation available in the future as a separate document.

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documentation." Study has shown that the model results are highly sensitive to these red-highlighted parameters.  The
grayed-out standard deviations indicate that these values cannot be changed.  Only those parameters and standard
deviations appearing in regular black and white should ordinarily be changed.
                                                    Vlrule 1.0
                                        Sta^             nirtitmldALltnintlonlt); ["
Par* meter
e,
em
es
'09i0Ks
|0910«
Iog10n
P
fp
V-2
T

clayloam [
loam g|
loamysand P^
[sand \
Uandvclawlojm . ^1
u«»n
0.100993333
0.0
0.51S332222
-2.08S670553
0,276202682
0.113751456
1290000.0
9.95E-5
8.7SE-S
11.7

0.5

Ad. Devlillon
0.010708176
0.0
0.085260028
0.475140674
0.129474299
0.015201357
16SOOO.O
6.15E-5
l.OE-4
7.38

l.OE-4

Unit!
mm-'
0 Uniformly Random
mm-'
loglW m h -> )
logHXm-')
loglM.)
ym-'
m
m
Celsius

m

                     [[sandyloam
                      sill
                      slltloam
                      siltyclay
                      slltydayloam
Figure 2.3   Flow input panel showing pull-down list of soil textures (Mac OS X Version).

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2.2    Virus Parameters

In the following Figure 2.4, the virus input panel is shown with the pull-down list of virus and soil textures. The polio-clay
default option was selected, and the program has inserted all the appropriate parameters for an "average" clay.  The basis
for selecting these  default values  as well  as guidance for  selecting  custom values for a real site  are given  in
Breidenbachetal.,2000.
Figure 2.4   Virus input panel showing pull-down list of virus-texture defaults (Linux (WindowMaker) Version).
                                                     10

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                              Section 3.   Example Applications
Following entry of the default clay parameters,  and the default polio-clay parameters, one can proceed to run the
simulation.  Figure 3.1 displays how the simulation is set to run, just after selection of the virus parameters. One can
also proceed by clicking on the green arrow button.  Typical run times  are approximately 3 minutes5.
The simulation generates two kinds of  output.   The graphical histogram output, which is displayed continuously
developing during the simulation, is shown below in final form as Figure 3.2.  The vertical red bar separates cases of
failure to attenuate (left of the bar) from successfully attenuated cases (right of the bar). The horizontal axis is the
negative base-10 logarithm of the attenuation (A). The  negative is  used merely to allow the user to intuitively view
increasing attenuation to the right,  decreasing attenuation to the left.  In the simulation shown, zero cases of failure
were calculated in 106 iterations; thus, the probability of failure to achieve 4-log10 attenuation of poliovirus under all the
prevailing assumptions about texture, water content, etc. is very low.  If  it were necessary to estimate this probability
more precisely, further simulations could be  run and accumulated until  several cases of failure were seen.  This is
done by checking the Retain and Accumulate box on the histogram panel. When this box is checked, results from the
simulation are included in  the next simulation. This can be done as many times as needed in order to  produce an
acceptable histogram of outputs.
Probability Output as text can also be obtained by clicking on the appropriate tab. The output contains a complete list
of all parameter values assumed in the simulation.  This text  information can readily be  clip-boarded into another
program for  formatting and printing.  Below we display  this text output information (edited to manuscript format) for
the clay, polio-clay simulation, and random water content:
Output from Virulo Model:
    Input parameters used:
    Parameter: Mean Value (Standard Deviation) units:
    Soil Parameters:
      Residual water content: 0.1(0.01)   m3 rrr3
      Water content: random between Residual water content and  Saturated water content.
      Saturated water content: 0.52(0.09)  m3 rrr3
      Hydrodynamic dispersivity: 8.75E-5(1.0E-4)  m
      Log van Genuchten's alpha:  0.28(0.13) Iog10( m "1 )
      Log van Genuchten's n: 0.11(0.02) Iog10( . )
      Temperature  of soil (for computing molecular diffusivity): 11.7(7.38)  Celsius
      Soil bulk density: 1290000.0(168000.0) g m "3
      Mean soil particle radius: 9.95E-5(6.15E-5) m
      Log Saturated hydraulic conductivity: -2.09(0.48)  Iog10( m h "1 )
      Thickness of  proposed barrier: 0.5(0.1) m
    Virus Parameters:
      Coefficient of exponential decay of input concentration: 0.1(1.0E-11)   Ir1
      Log Mobile Virus Inactivation Rate: 0.605(0.608)  Iog10(  rr1 )
      Log Solid-sorbed Virus Inactivation Rate:  0.304(0.608) Iog10( Ir1 )
  For the example above, run times were typically 2 minutes 43 seconds for 10s iterations on a Pentium III system with 128 Mb RAM
  operating under Windows98, and no other programs running concurrently.
                                                     11

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                                       0.515332222
                                       —•—a,^—«-™m

                                       -2.085670553


                                       0.276202682


                                       0.113751456
Figure 3.1   Virus property panel showing start of simulation (Linux (WindowMaker) Version).
Figure 3.2    Histogram for a single simulation of default case Clay, Polio (Linux (WindowMaker) Version).
                                                       12

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     Mobile to solid-sorbed mass transfer coeff.: 0.00134(0.0018) m rr1
     Mobile to air-sorbed mass transfer coeff.: 0.00927(0.0018) m rr1
     Radius of virus: 1.375E-8(1.25E-9) m
     Maximum virus concentration entering proposed barrier: 10.0(0.01) any
     Mobile to solid-sorbed equilibrium partition coeff.: 7.2E-4(9.74E-4) m3 g -1

The probability of failure to achieve 4.0-log attenuation from 1000000 Monte Carlo runs was 0:1000000.

The last entry gives the probability of failure to achieve successful reduction of virus concentration. In the computed
case, this is apparently a relatively low probability6, and such a clay barrier would, therefore, under the assumed
moisture conditions seem fairly adequate to  prevent poliovirus contamination of an aquifer beneath it.

Figure 3.3 shows the  histogram result for a default loamy sand with hepatitis A, Kd value for clays. In this case the
histogram shows a distinct mode in the neighborhood of 60-log attenuation. By observing the shape of the histogram
the user can obtain information about the distribution of attenuation values and how they behave in the vicinity of the
chosen threshold attenuation. The maximum of 300-log is close to the numerical double precision limit of most modern
math  processors.

Figure 3.4 shows the output for hepatitis A with a default sandy loam soil. However, in this case the volumetric water
content was set to 0.2, with a standard deviation of 0.05, instead of varying in a uniformly random way. It shows the
dramatically different results that can be obtained if information is known about the water content. In this case the
mode is very close to zero, and the probability  of failure is very high.
                                             ^ummjM^s^ssmEam^
                           Exceedances ,  Runs
                           24$  '    t 18397
Figure 3.3   Histogram for a single simulation of default loamy sand, hepatitis A-Clay (Linux (WindowMaker)
             Version).
6 Further accumulated simulations yielded ca. 7 chancein 1.2 million. Note thatVirulo offers no guidance regarding what constitutes an
  "acceptable risk" in any given case. This judgment requires additional considerations well beyond the scope of the simple transport
  model described here.
                                                     13

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Figure 3.4   Histogram for a single simulation of default sandy loam with e  mean set to 0.2 and its standard
             deviation  set to 0.05, hepatitis A-Clay (Linux (Window/Maker) version).

To estimate the ability of a clay layer to attenuate poliovirus under wetter conditions, the simulation was run with the water
content set to the approximate field capacity of clay7.
Further Output from Virulo Model with 6m = Field Capacity
    Input parameters used:
    Parameter: Mean Value(Standard Deviation) units:
Soil Parameters:
    Log van Genuchten's alpha: 0.28(0.13)  Iog10( m "1)
    Residual water content: 0.1 (0.01) m3 nr3
    Water content: 0.47 (0.00) m3 m*
    Saturated water content: 0.52(0.09) m3 rrr3
    Thickness of proposed barrier: 0.5(0.1)  m
    Hydrodynamic dispersivity: 8.75E-5(1 .OE-4) m
    Log van Genuchten's n: 0.11 (0.02)  Iog10(.)
    Temperature of soil  (for computing molecular diffusivity): 11.7(7.38) Celsius
    Soil bulk density: 1290000.0(168000.0) grrr3
    Mean soil particle radius: 9.95E-5(6.15E-5) m
    Log Saturated hydraulic conductivity: -2.09(0.48) Iog10( m rr1)
7 This was calculated as ca. 0.9091 xs, where the value 0.9091 is the water saturation fraction at the field capacity for centroid clay
  texture (64.83% Clay, 16.55% Silt, 1*8.62% Sand) calculated using the method on the website, http://www.bsyse.wsu.edu/saxton/
  soilwater/
                                                       14

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Virus Parameters:
    Coefficient of exponential decay of input concentration: 0.1(1 .OE-11)  rr1
    Log Mobile Virus Inactivation Rate: 0.605(0.608) Iog10( h/1)
    Log Solid-sorbed Virus Inactivation Rate: 0.304(0.608)  Iog10{ h-1)
    Mobile to solid-sorbed mass transfer coeff.: 0.00134(0.0018) m h "1
    Mobile to air-sorbed mass transfer coeff.: 0.00927(0.0018) m h -1
      Radius of virus: 1.375E-8(1.25E-9) m
     Maximum virus concentration entering proposed barrier: 10.0(0.01) any
     Mobile to solid-sorbed equilibrium partition coeff.: 7.2E-4(9.74E-4) m3 g ~1
     Saturated water content: 0.515332222 (0.085260028) m3 nr3
     Thickness of proposed barrier: 0.5 (1 .OE-4) m
      Hydrodynamic dispersivity: 8.75E-5 (1 .OE-4)  m
     Log van Genuchten's n:  0.113751456 (0.015201357)  Iog10(.)
     Temperature: 11.7 (7.38) °C
     Soil bulk density: 1290000.0 (168000.0) g nr3
      Mean soil particle radius: 9.95E-5 (6.15E-5)  m
     Log Saturated hydraulic  conductivity: -2.085670553 (0.475140674) Iog10( m rr1)

The probability of failure to achieve 4.0-log attenuation from  1000000 Monte Carlo runs was 60:1000000.
Again, the last entry gives the probability of failure to achieve successful reduction  of virus concentration.  Here the
choice of a water content constantly equal to the field capacity yields a substantially higher probability of failure to
attenuate.
Further Results
Runs for the thirteen  possible default data sets are  summarized in the table  below.  For clay and sand textures,
1000000 iterations were sufficient to estimate the probability of failure.  For the silt texture, even the accumulated
results from four simulations  did not detect any failure cases.
Other mixed cases  are shown in the table. These were computed using the textural based, flow parameters with a
set of virus parameters. For example, the clay loam flow parameters were used with the polio-clay virus parameters.

Table 3.1     Failures to Achieve 4-log10 Attenuation Results for Various Soil Textures for a Single Virulo Simulation
             Run of 106 Iterations
Texture
Clay,em=UD
Clay, 6m = FC = 0.468
Clay loam, 6m = UD
Loam
Loamy sand
Sand,6m = UD
Sand,0m = FC = 0.133
Sand, 6m =0.35
Poliovirus
0
60
0
1
35
236
0
98,358
Hepatitis A
11
31009
6
3
158
864
1
277,415
Reovirus 3
13
68383
7
9
265
684
0
232,229
Coxsackievirus
15
79880
1.0
7
332
655
0
228,397
Echovirus
18
54778
10
8
219
612
0
220,645
                UD = Uniform Distribution between 6rand
                FC = Field Capacity Moisture Content
                                                     15

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                                   Section 4.   References
Breidenbach, P., S. Chattopadhyay, and W.G. Lyon. 2000. Survival and Transport of Viruses in the Subsurface, An
    Environmental Handbook, a report prepared for WA-RE-1-5 under EPA Contract 68-C-98-138, September 15,
    2000, 273 pp.
Faulkner, B.R., W.G.  Lyon,  F.A. Khan, and S. Chattopadhyay.  2002.  Predicting  Attenuation of Viruses  During
    Percolation in Soils: 1.  Probabilistic Model. EPA/600/R-02/051a.
Park, N.S., T.N. Blandford, and  P.S. Huyakorn. 1992. Viralt Version 2.1, A Modular  Semi-Analytical and Numerical
    Transport Model for Simulating Viral Transport in Ground Water. Documentation  and User's Guide.
Park, N.S., T.N. Blandford, and P.S. Huyakorn. 1991. Canvas Version 1.0, A Composite Analytical-Numerical Model
    for Viral and Solute Transport Simulation, Documentation and User's Guide.
Schijven, J.F., and S.M. Hassanizadeh. 2000. Removal of viruses by soil passage: overview of modeling, processes
    and  parameters.  Crit. Rev.  Environ. Sci. Technol. 30(1):49-127.
Yates, M.V., S.R. Yates, and Y.  Ouyang. 1991. A Model of Virus Transport in Unsaturated Soil. EPA/600/2-91/062.
U.S. EPA. 2000. National Primary Drinking Water Regulations: Ground Water Rule. Fed. Regis. 65(91) :30193-30274.
                              Section 5.    Website References
Centroid Compositions
Rosetta Database
Soil Texture Calculator
Soil Texture Triangle
UNSODA and RETC
http://www.  epa.gov/superfund/programs/risk/airmodel/guide.pdf
http://www.ussl.ars.usda.gov/MODELS/Rosetta/rosetta.htm
http://www.bsyse.wsu.edu/saxton/soilwater/
http://www.agronomy.psu.edu/Courses/SOILS101/Labs/texture.html
http://www.ussl.ars.usda.aov/
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