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/
17
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