Predicting              of
Viruses Durin
ON         VUUOCTO LSUMMV2
2, User's Guide to ne
Virulo 1.0 Compute: 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       'V .-.,".,-

                       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 Recycied/Recyciabie
                                         {A-J~ Printed with vegetable-based ink on
                                         T"\  \\ paper that contains a minimum of
                                              50% post-consumer fiber content

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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  Ihe 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, EBattelle
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 Viru/o, 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.
                             Stephen G. Schmelling,  Acting
                             Subsurface Protection and Re/nedition Division
                             National Risk Management Resea/ch 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	,	 iii
Abstract	iv
Figures	vi
Tables	,	 vn
Acknowledgments 	viii
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
33       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 fora Single Virulo Simulation Run of 106 Iterations	13

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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.
                                VIM

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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.
  Sensitive aquifers include karst, fractured bedrock, and gravel aquifers
  The targeted level of attenuation is adjustable by the user 4-logw (99 99%) represents the current default level
  The 12 USDA textural categories are used in Virulo to supply default parameters. See. for example, httpV/www.agronomy psu edu/
  Courses/SO/LSI 01/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):
0,
em
es
iog10(Ks)
iog,0(°0
logic (n)
P
rP
«z
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 nv3
m3 m 3
rn3 m 3
log,0 (m h'1)
Iog10(nr1)
log,0
g m"3
rn
rn
°C
m
Virus Parameters Window (given in sequence)
P
iog,c (A)
iog,0 (A*)
K
K
ru
cmax
Kd
Exponential decay exponent in source term
Mobile virus inactivation rate constant, rate of mactivation of
suspended viruses
Solid-sorbed virus inactivation rate constant, rate of
inactivation of soil-adsorbed viruses
Mass transfer coefficient, mobile phase to solid-sorbed
phasef
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 sorbmg onto
soil particles
h'1
iog,0 (h-1)
loglD(rr1)
m h'1
mh-1
m
[e.g , PFU I'1]
mj g-1
 *the van Genuchten relation is  I 6S— 9r
                                                       J_
                                                       n
 rthe rate itself is computed in Virulo by k = K aT where a_  is the water-solid intertacial area.

 *the rate itself is computed in Virulo by k° - /cc'ar0  where a'5  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.
-m<
t
t
'
IX
C(in) V^-
time
1"
C(in) =
S
/
i
soil /
layer *
\
\
                             C(out)

                                                                      A*
                                       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.
 \
a2
                                                  = /(Ks,n,a,0r,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 adsorbed
                         viruses suspended      ^qf-*^         .      ,
                         in water          —T*	to so.l
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 thai 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 the
    results are suitable for informational purposes only; i e., four log  removal  could be undistinguishable 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
    mullivariate normal distribution described by  their  means and their variance-covariance matrix.

The parameters in Virulo which vary according to the latter are the hydraulic parameters  8r 6s, Iog10 K., Iog10  a, Iog10n.
This is important to  the user  because  their  standard deviations cannot  be modified  because Virulo employs the
variance-covariance matrix, computed a priori for each of the twelve USDA soil types  The current version does not
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
            123...             ...n
                                                                     ensemble
                  input parameter 1
                  input parameter 2
                  input parameter 3
                  input parameter 4 <
output
                   t  •
                                                         I I
                                                            I T * t
                                                                        A
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.
    II.  Step I is repeated many times,  building up a list of output values corresponding to each random set of input
       parameters.
    Ill  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 mam 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.
File Edit Run  Help   > Start Simulation

}IRJ Flow Parameters  '. '. Virus Parameters
                                                        <4 Stop

                                                       J]j Histogram ~^2- Probability
                                          12900000


                                          905E-5


                                          8 75E-5


                                          11 7
                                0010708176   _


                                00              • Uniformly Random


                                0085260028   _


                                0475140674


                                0129474299   _


                                0015201357   _


                                1C80000


                                G 1 5E-5


                                1 OE-4





                                I OE-4
                          clay
                                          These parameters modeled as conditionally dependent by
                                          variance-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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                       Parameter
                       File Edit Run Help  ^> Stan Simulation   ^ Stop  Threshold Attenuation (E):  4  (-loglO)

                        •MM            --              jw         —
                        BMg Flow Parameters   '.'  virus Parameters  Jj| Histogram  ^^ Probability
                                      Mean
                                                    Std. Deviation


                                                     1 OE-11


                                                     0 F.08


                                                     (-' F08


                                                     'J NO 18
Units


h -'


Iogl0(h-')


Iogl0(h-')


m h ~l


m h -'


m


any


m3 g -'
                        polio-clay
Figure 2.2   Default virus parameters upon first opening Virulo (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 alLparametersJnto the exact units specified jjy.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 6mhas been checked as the default.  If this box is unchecked,
then the 9m parameter and its standard deviation may be edited. Various water contents  consistent with the values of 8r
and  9 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 thai 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
  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.
                       Virulo
                              Flow Parameters   O"  Virus Parameters    J|fl| Histoqram   ^^Probability
                      stlt'oam
                      silwclav
                      s.ltyo jy .CM
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
Breidenbach et al., 2000.
                 file Edit Run Help   > Start Simulation     ^ Stop  Threshold Attenuation (E):  4  (-loglO)

                  ESSJ Flow Parameters   '.'Virus Parameters   Jltjj Histogram   .~^ Probability

                 Parameter             Mean                Std. Deviation         Units
                  polio-clay
                 polio-clay
                 Ipolio-silt
                 Ipolio-sand
                 (hepatitis A-clay
                 hepatitis A-sand
                                     0 1


                                     0 605


                                     0 204


                                     0 001J4


                                     0 00927


                                     1 375E-8


                                     10 0


                                     7 2E-4
1 OE-11


0 608


0 608


0 0018


0 0013


1 25E-9


f) 01


9 74E-4
h -'

loglu
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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 m3
      Water content: random between Residual water content and  Saturated water content.
      Saturated water content:  0.52(0 09)   m3 nr3
      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)   h1
      Log Mobile Virus Inactivation Rate: 0 605(0 608)  Iog10( h1 )
      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 106 iterations on a Pentium III system with 128 Mb RAM
  operating under Wmdows98, and no other programs running concurrently
                                                     11

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File Ed
&K r
Paramet
/
Two w
to star
Simula








clay
1
t Run Help J> Start Simulation 4 Stop Threshold Attenuation (E): 4 (-loglO)
la Jr 5JJ»"imul»t!on UJ pjrimewrs jjjjl Histogram 7££. Probability
/^ttop Simulation "^ 	
T'/ Mean Std. Deviation Units
i) 0010708176 m'm-3
ays
t a 0 oo oo
tion.
	 0 0085200028
i ;,; r 0475140674
1 ,,; tt 0 129474299
1 \: I 0015201357
,) 1290000 0 168000 0
1 9 y5E-5 € 15E-5
,< 875E-5 1 OE-4
11 " 7 38
0 5 1 OE-4
''
v Uniformlv Random
m3 m -s
Iogl0(m h -' )
Iogl0(m -' )
Iogl0(,>
gm -3
m
m
Celsius
m

Figure 3.1    Virus property panel  showing start of simulation (Linux (Window/Maker) Version).
                         File Edit Run  Help  J> start Simulation    ^ Stop  Threshold Attenuation (e): 4  (-loglO)

                              Flow Parameters   '. .  Virus Parameters   j|j Histogram   ZJ|; Probability

                                                    • Retain and Accumulate

                                    Right Truncated Histogram
                                                         Vertical red bar displaying threshold level
                                                         of attenuation (E) specified by user.
                                                             Number of exceedances in this
                                                             simulation of the threshold level
                                                             specified by user.

                                                             Number of runs of random input
                                                             parameters in this  simulation.
                                                                 300
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 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

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
                       File Edit Run Help  > Start Simulation    ^ Stop  Threshold Attenuation (6): 20  (-loglO)

                       jKJfjj] Flow Parameters ]  '. .  Virus Parameters ;  J||))|] Histogram  I^jj; Probability

                                                 Retain and Accumulate


                                Rignt Truncated Histogram
                                            Mode of the histogram.
                                                           300
Figure 3.3    Histogram for a single simulation of default loamy sand,  hepatitis A-Clay (Linux (WindowMaker)
              Version).
  Further accumulated simulations yielded ca 1 chance in 1 2 million. Note that Virulo 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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                       File Edit Run Help   > Start Simulation
                          I Flow Parameters
                         300-
                         280'
                         260-
                         240
                        c 220-
                        0 20°
                        IJ 180-
                        N 15°
                        T 140-
                         120
                         100-
                         80
                         60 -
                         40
                         20
                         °  0
                       i^ Stop   Threshold Attenuation (E):  4  (-loglO)

           Virus Parameters j  J||| Histogram  ."^ Probability i
                 Retain and Accumulate

Right Truncated Histogram
                                              150
                                            -log A
                                        Runs
                                        237602
                                                            300
Figure 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 (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 0m = 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 rtr3
    Water content: 0.47 (0.00) m3 m3
    Saturated water content: 0.52(0 09)  m3 rrr3
    Thickness of proposed barrier: 0.5(0.1) m
    Hyclrodynamic 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) 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)
  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.bs_y_.sej/y_su.edu/saxton/
  soilwater/
                                                         14

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Virus Parameters:
    Coefficient of exponential decay of input concentration: 0.1(1 .OE-11)  h1
    Log Mobile Virus Inactivation Rate: 0.605(0.608) Iog10( h1)
    Log Solid-sorbed Virus Inactivation Rate: 0.304(0.608) Iog10( h 1)
    Mobile to solid-sorbed mass transfer coeff: 0.00134(0.0018) mh 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 m3
     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 m3
     Mean  soil particle  radius: 9.95E-5 (6.15E-5)  m
     Log Saturated hydraulic conductivity: -2.085670553 (0.475140674) Iog10( m h1 )

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, 6m=UD
Clay, 6m= FC = 0.468
Clay loam, 9m = UD
Loam
Loamy sand
Sand, 6m= UD
Sand, 8m= 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
10
7
332
655
0
228,397
Echovirus
18
54778
10
8
219
612
0
220,645
                UD = Uniform Distribution between 6r and
                FC = Field Capacity Moisture Content
                                                     15

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                                   Section 4.   References
Breidenbach, P., S Chattopadhyay, andWG. 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        http://www.  epa.gov/superfund/programs/risk/airmodel/guide.pdf
Rosetta Database            http://www.ussl.ars.usda.gov/MODELS/Rosetta/rosetta.htm
Soil Texture Calculator        http://www.bsyse.wsu.edu/saxton/soilwater/
Soil Texture Triangle          http://www.agronomy.psu.edu/Courses/SOILS101/Labs/texture.html
UNSODA and RETC          http.//www.ussl.ars.usda.gov/
                                                  17

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