Water Research 197 (2021) 117040

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Water Research
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Virus transport from drywells under constant head conditions:
A modeling study
Salini Sasidharan a,b,∗, Scott A. Bradford b, Jiří Šimůnek a, Stephen R. Kraemer caDepartment of Environmental Sciences, University of California, Riverside, CA 92521, USA
United States Department of Agriculture, Agricultural Research Service, Sustainable Agricultural Water Systems Unit, Davis, CA 95616, USAcU.S. Environmental Protection Agency, Oﬃce of Research and Development, San Francisco, CA 94105, USAba r t i c l e

i n f oArticle history:
Received 7 October 2020
Revised 24 January 2021
Accepted 10 March 2021
Available online 12 March 2021
Keywords:
Vadose zone
Managed aquifer recharge
Drywell
Virus
Water Quality
HYDRUS (2D/3D)

a b s t r a c t
Many arid and semi-arid regions of the world face challenges in maintaining the water quantity andquality needs of growing populations. A drywell is an engineered vadose zone inﬁltration device widelyused for stormwater capture and managed aquifer recharge. To our knowledge, no prior studies havequantitatively examined virus transport from a drywell, especially in the presence of subsurface heterogeneity. Axisymmetric numerical experiments were conducted to systematically study virus fate from adrywell for various virus removal and subsurface heterogeneity scenarios under steady-state ﬂow conditions from a constant head reservoir. Subsurface domains were homogeneous or had stochastic heterogeneity with selected standard deviation (σ ) of lognormal distribution in saturated hydraulic conductivity
and horizontal (X) and vertical (Z) correlation lengths. Low levels of virus concentration tailing can occur even at a separation distance of 22 m from the bottom of the drywell, and 6-log10 virus removal
was not achieved when a small detachment rate (kd1 =1 × 10− 5 min− ¹) is present in a homogeneous
domain. Improved virus removal was achieved at a depth of 22 m in the presence of horizontal lenses(e.g., X=10 m, Z=0.1 m, σ =1) that enhanced the lateral movement and distribution of the virus. In contrast, faster downward movement of the virus with an early arrival time at a depth of 22 m occurred
when considering a vertical correlation in permeability (X=1 m, Z=2 m, σ =1). Therefore, the generalassumption of a 1.5–12 m separation distance to protect water quality may not be adequate in some instances, and site-speciﬁc microbial risk assessment is essential to minimize risk. Microbial water quality
can potentially be improved by using an in situ soil treatment with iron oxides to increase irreversible
attachment and solid-phase inactivation.
© 2021 Elsevier Ltd. All rights reserved.

1. Introduction
Groundwater is the primary source of freshwater supplies tobillions of people worldwide (Gleeson et al., 2012; Taylor et al.,2013). However, microbiological contamination of groundwater(Feighery et al., 2013; Gunnarsdottir et al., 2013; Kauppinen et al.,2018; Pitkänen et al., 2011; Powell et al., 2003) causes waterborne disease outbreaks worldwide and is a growing problem
in the United States (Barwick et al., 20 0 0; Dowd et al., 1998;
Fong et al., 2007; Keswick 1980; Levy et al., 1998). Sixty-four percent of the drinking water diseases between 1989 and 2002 in
the U.S. were related to microbially contaminated groundwater(Fong et al., 2007). According to the World Health Organization,∗
Corresponding author at: USDA, ARS, Salinity Laboratory, Riverside, CA 92507,
United States.
E-mail addresses: salinis@ucr.edu, salini.a.sasidharan@gmail.com (S. Sasidharan).

https://doi.org/10.1016/j.watres.2021.117040
0043-1354/© 2021 Elsevier Ltd. All rights reserved.

contaminated drinking water can transmit diseases that are estimated to cause 485,0 0 0 diarrheal deaths each year around theworld (WHO 2019). The Center for Disease Control and Prevention(CDC) has indicated that 17 waterborne pathogens (bacteria, parasites, and viruses) caused 7.15 million illnesses, 6630 deaths, and$3.33 billion in direct healthcare costs in 2014. CDC estimates thateach year 1 in 44 people gets sick from waterborne diseases in
the U.S. (CDC 2014). Therefore, groundwater quality management
has become an essential public policy (Abdalla et al., 1992), andthe U.S. Environmental Protection Agency (EPA) has established theGround Water Rule (EPA 2002) to help prevent waterborne disease
outbreaks caused by contamination of groundwater in the U.S.
Viruses are generally considered to be the most dangerous microbial pathogen in groundwater because they may have a very
low infectious dose. For example, the infectious dose for rotavirus strain CJN is 1, poliovirus is 2–20, norovirus is 10–100,
and echovirus type 12 is 3.3 × 102 PFU (Yezli and Otter 2011).

S. Sasidharan, S.A. Bradford, J. Šimůnek et al.

Water Research 197 (2021) 117040

It has been estimated that 65% of the waterborne-disease outbreaks associated with groundwater contamination are caused byenteric viruses (Benedict et al., 2017; Blackburn et al., 2004;
Brunkard et al., 2011; Yates et al., 1985). Viruses are often introduced into the subsurface through sewage and wastewater eﬄuents, septic tanks, and landﬁlls (Yates et al., 1985) and eventually reach groundwater sources (Fong et al., 2007; Gabrieli et al.,2009; Locas et al., 2007) where they pose a potential threat to
public health. Most human viruses have limited viability in thesubsurface given residence times (Regnery et al., 2017; Sidhu andToze 2012; Sidhu et al., 2010), and aquifers are often used to “polish” drinking water through soil aquifer treatment (Elkayam et al.,2018; Fox 2020; Hermes et al., 2019; Maliva 2020; Morrison et al.,2020).
Managed aquifer recharge (MAR) is a cross-cutting technology(Sprenger et al., 2017) that can be used to enhance groundwater
recharge. It can be accomplished through a variety of approaches
such as inﬁltration basins or trenches (Błażejewski et al., 2018;
Chahar et al., 2012; Heilweil and Watt 2011), aquifer storage, transfer, and recovery (ASTR) (Antoniou et al., 2017; Dillon et al., 1999;
Pavelic et al., 2004), ﬂooding land (Flood-MAR) (Alam et al., 2020;
Chinnasamy et al., 2018; Scherberg et al., 2014), ﬂooding agricultural land (Ag-MAR) (Dahlke et al., 2018; Ghasemizade et al.,2019; Niswonger et al., 2017), and vadose zone recharge techniques like drywells or vadose zone pits/trench/small diameter
well (Edwards 2017; Händel et al., 2014; Liang et al., 2018;
Qi et al., 2020; Sasidharan et al., 2020). Various water sources areused in MAR, including snowmelt, streamﬂow, stormwater, recycled wastewater, excess irrigation water in agricultural ﬁelds, winter ﬂoodwater, groundwater, and reclaimed water (NGWA 2014;
NRC 2008). Vadose zone recharge wells have been used successfully for many years in Scottsdale, AZ, using treated recycled water (City of Scottsdale 2020). Contamination of groundwater by stormwater drainage wells has been reported across the
U.S. (Cadmus 1991, 1996, 1999; Haney et al., 1989; Michael 1997;
Orr 1993; USEPA 1997; Wilde 1994). The microbial risks from MAR
techniques, therefore, need to be assessed to ensure adequate protection of groundwater quality and public health.
Microbial risk assessment is an evaluation process to determine
the likelihood of adverse human health effects following exposure
to pathogenic microorganisms from various sources (Soller 2009).
One of the commonly advocated methods for assessing microbial
risks in recycled water systems is static Quantitative Microbial Risk
Assessment (QMRA) (NRMMC-EPHC-AHMC 2006; Soller and Eisenberg 2008; WHO 1993). Static QMRA focuses on estimating theprobability of infection to an individual due to a single exposure
event (Soller and Eisenberg 2008). The QMRA approach requires
quantitative input data for pathogen occurrence in the source water and their removal through treatment barriers (NRMMC-EPHCAHMC 2006; WHO 1993). Appropriate quantiﬁcation of input data
and the selection of numerical values for each variable is critical for a successful risk assessment. The parameters for QMRA include the evaluation of pathogen source, concentration, an infectious dose of individual pathogen, dose-response assessment to estimate a risk of response (infection, illness, or death), human behavior (water consumption pattern and the daily intake volume),disease incidence of the local community as well as operational
design and performance of the treatment barriers (Blokker et al.,2014; QMRAWiki 2014). When MAR is used as a treatment barrier
and recharge option for groundwater management, the parameterization requires an understanding of pathogen transport and removal processes in the aquifer. The residence time in an aquifer
has been found to play a pivotal role in allowing biological andchemical processes to improve the water quality (Dillon et al.,
2003; Toze et al., 2010). The residence time for a drywell depends
on the setback distance between the drywell and a drinking wa-

ter well (City of Portland 2008; City of Portland 2015; EPA 1999;
Washington State Department of Ecology 2006). However, current
regulatory guidelines to protect groundwater quality require an additional separation distance between the bottom of the drywelland the local groundwater table of 1.5 to 13 m (City of Portland
2008; City of Portland 2015; EPA 1999; Washington State Department of Ecology 2006).
Several ﬁeld- and laboratory-scale experiments and numericalmodeling approaches have been used to study virus removal andassess risk during MAR (Anders and Chrysikopoulos 2008; Jin and
Flury 2002; Powelson and Gerba 1994; Stevenson et al., 2015).
Toze et al. (2010) conducted an in-situ decay study at a MAR
site, and 1 log reduction for rotavirus was calculated as a function of residence time to be 34 days (Toze et al., 2010). However, the MAR guideline evaluates the effectiveness of aquifer treatment (expressed as log reductions) to meet the human healthbased microbial target of 1 × 10−6 DALYs (disability-adjusted life
years, 6-log10 ) per person per year as a function of residence
time (NRMMC-EPHC-AHMC 2006; Page et al., 2018). Current guidelines for MAR have only considered the inactivation of virusesin the liquid-phase as a reliable mechanism for virus removal
but have neglected irreversible attachment and solid-phase inactivation of viruses (Abu-Ashour et al., 1994; Dillon et al., 2008).
Therefore, the QMRA approach based on liquid-phase inactivation of viruses may underestimate the virus removal during MAR
operations.
Major processes controlling the transport and removal ofviruses in porous media are advection, dispersion, attachment, detachment, and ﬁrst-order inactivation (both liquid and solid-phase)(Schijven and Hassanizadeh 20 0 0; Schijven et al., 2002). The rate
coeﬃcient for attachment, detachment, liquid, and solid-phase inactivation is sensitive to many physicochemical factors such as
ﬂow dynamics, porous media properties, solution chemistry, temperature, residence time, and pathogen physicochemical properties(Molnar et al., 2015; Schijven and Hassanizadeh 20 0 0). The relative importance of these processes will determine the transportpotential of viruses at MAR sites (Regnery et al., 2017; Sidhu et al.,2015; Toze et al., 2010). Therefore, several physicochemical and environmental factors may need to be optimized to ensure adequate
virus removal during MAR operations. However, no previous studies have investigated a range of virus removal parameters and theirimpact on groundwater quality when assessing the microbial risk
from a drywell operation.
The local hydrogeology and subsurface heterogeneity at a MAR
site will inﬂuence the residence time, which, in turn, affects
the groundwater quality. Subsurface heterogeneity, such as stratiﬁed or aggregated soils (i.e., low and high conductive soil texture), their horizontal and vertical (process such as fractures,
faults, macropores, cracks, and other preferential ﬂow paths) distribution, and connectivity, signiﬁcantly impacts water ﬂow inthe subsurface (Dagan 2012; Gelhar 1986; Mantoglou and Gelhar
1987; Schilling et al., 2017; Xie et al., 2014; Yeh et al., 1985a;
Yeh et al., 1985b). Our previous studies investigated the inﬂuence of stochastic subsurface heterogeneity parameters on cumulative inﬁltration and recharge volumes, early and late arrival times,
and arrival points from a drywell under constant head conditions(Sasidharan et al., 2019; Sasidharan et al., 2020). Preferential ﬂow
paths or high permeable vertical or horizontally extended soil layers (Arnaud et al., 2015; Bradford et al., 2017) can lead to theearly arrival of pathogenic microbes at the water table or nearby
drinking water wells. Therefore, an understanding of the inﬂuenceof subsurface heterogeneity on virus transport during the operation of a drywell is essential when assessing water quality. However, ﬁeld-scale transport experiments using pathogenic or indicator viruses poses many technical challenges in quantifying ﬂow
and transport processes, and introduced pathogens create an un2

S. Sasidharan, S.A. Bradford, J. Šimůnek et al.

Water Research 197 (2021) 117040

acceptable risk to human health. Therefore, previous researchers
have mainly conducted column scale virus experiments and inversely determined transport and fate parameters (Frohnert et al.,2014; Sasidharan et al., 2017; Schijven et al., 2002; Torkzaban et al.,2006). This information can subsequently be used in numerical experiments to investigate expected virus behavior at the ﬁeld-scale(Torkzaban et al., 2019) or speciﬁc MAR sites. Nevertheless, such
efforts were not made before for a drywell operation.
The objective of this study is to demonstrate the applicationof numerical experiments using HYDRUS (2D/3D) software coupled with column laboratory-scale data to understand the inﬂuence of subsurface heterogeneity on virus transport, where ﬁeldscale studies are not feasible. Constant head water ﬂow simulations were conducted to obtain steady-state initial conditions in
the ﬂow domain to represent the worst-case scenario for virustransport. Additional constant head simulations for virus transport
were considered in a drywell to understand the effect of various
removal parameters on virus transport under homogenous and different subsurface heterogeneity conditions.

plication are given as:



∂θ C
∂
∂C
∂ qi Cθ Dwi j=−− θ μl C − θ k a C
∂t
∂ xi
∂ xi
∂ xi
+ pb kd1 S1 + pb kd2 S2

(1a)

∂ S1
= θ fr kaC − pb kd1 S1 − pb μs S1
∂t

(1b)

∂ S2
= θ (1 − fr )kaC − pb kd1 S1 − pb μs S2
∂t

(1c)

pb

where t [T; T denotes the dimension of time] is time, C [NL−3 ;N denotes the virus concentration in pore-water, L denotes thedimension of length] is the aqueous phase virus concentration,
ρb [ML−3 ; M denotes the dimension of mass] is the bulk density, θ is the volumetric water content [-], S [NM−1 ] is the solidphase virus concentration, Dw[L2 T−1 ;] is the dispersion coeﬃcientij
tensor for the liquid-phase (longitudinal dispersivity, DL =6 m andtransverse dispersivity, DT =0.5 m were used), qi [LT−1 ] is the i thcomponent of the Darcian ﬂuid ﬂux density, xi [L] (i = 1, 2) are
the spatial coordinates, ka [T−1 ] is the total virus attachment rate
coeﬃcient, μl [T−1 ] is the liquid-phase virus inactivation rate, μs[T−1 ] is the solid-phase virus inactivation rate, kd [T−1 ] is the virus
detachment rate coeﬃcient, and subscripts 1 and 2 on parametersindicate the solid-phase sites.
In this study, virus attachment to and detachment from thesolid surface were modeled as ﬁrst-order processes. The value ofka was divided between the kinetic attachment sites using the reversible fraction, fr , in Eqs. (1b) and (1c) (Bradford et al., 2016).
Reversible attachment ( fr ka ) occurs on site 1 because kd1 =10−5
min−1 , whereas irreversible attachment, (1 − fr )ka , occurs on site
2 when kd2 = 0 min−1 . The same value of μs was assumed for bothsites 1 and 2. According to ﬁltration theory, the value of ka is related to the average ﬂow velocity as (Yao et al., 1971):

2. Materials and methods
2.1. Mathematical model for water ﬂow and virus transport
HYDRUS (2D/3D) was used to numerically solve the 2dimensional axisymmetric form of the Richards equation with thevan Genuchten (1980) and Mualem (1976) unsaturated soil hydraulic functions (Mualem 1976; van Genuchten 1980). The input
parameters, initial conditions, and boundary conditions employedin these numerical experiments were generally based on resultsfrom drywells located in Fort Irwin, CA (Sasidharan et al., 2020).
In brief, the water level in the Fort Irwin drywell was continuously monitored using an in-situ water level sensor and remote
data acquisition techniques. The falling head phase of a ﬁlling anddraining cycle in the drywell during a storm event was selected
for further analysis (Sasidharan et al., 2018a; Sasidharan et al.,2020). The observed falling head data from the drywell wasused in conjunction with HYDRUS (2D/3D) and the reservoir
boundary condition module to inversely optimize the saturated
soil hydraulic conductivity (Ks = 0.046 m day− ¹) and the shapeparameter in the soil water retention function (α =9.17 m− ¹)(Sasidharan et al., 2018a; Šimůnek et al., 2018). The residual
soil water content (θ r =0.043), the saturated soil water content(θ s =0.39), and the pore-size distribution parameter in the soil water retention function (n = 2.76) were derived from the measured
particle size distribution (silt-clay-fraction) and the Rosetta software (Schaap et al., 2001; Segal et al., 2009), whereas the tortuosity parameter in the hydraulic conductivity function (l) was set
to 0.5.
Sasidharan et al. (2018b) showed in column experiments that asmall fraction of the injected virus was reversibly retained and notinactivated, and the remaining fraction was irreversibly retained
and inactivated on the solid phase. A one-site retention model did
not account for the reversible virus fraction and observed low concentration tailing, whereas a model with two kinetic retention sites
and separate solid phase inactivation rates was needed to describe
this behavior (Fig. S1). Two-site kinetic retention may occur due tospatial variability in the ﬂow ﬁeld, mass transfer, strength of adhesive, and retention mechanisms (Schijven and Simunek, 2002).
In this work, the HYDRUS (2D/3D) computer code was used tosimulate virus transport from a drywell under various virus removal and subsurface conditions (Šejna et al., 2014). This model
solves the two-dimensional form of the advection-dispersion equation with two kinetic retention sites having different attachment
and detachment coeﬃcients and solid-phase inactivation rates. The
aqueous phase and solid-phase mass balance equations for this ap-

ka =3 (1 − n )
αηv
2 dc

(2)

where n is the porosity, dc [L] is the median grain diameter, v[LT−1 ] is the average pore-water velocity, η [−] is the singlecollector contact eﬃciency representing the ratio of the numberof virus approaching the collector to the number of virus striking
a collector (Tufenkji and Elimelech 2004), α [−] is the sticking efﬁciency representing the ratio of the rate of virus that sticks to acollector to the rate at which they strike the collector. In this study,
the correlation equation developed by Messina et al. (2015) wasused to predict η as a function of parameters such as Peclet number and virus size (50 nm). The value of dc in Eq. (2) was determined from the saturated hydraulic conductivity (Ks ) using the
Kozeny–Carman equation (Bear 1972) as follows:

dc = Ui K μ  180 1 − n 2
(
)
s
pw g

n3

(3)

where ρw [M L−3 ] is the density of water, μ [M L−1 T−1 ] is the dynamic viscosity of water, g [L T−2 ] is the acceleration due to gravity, and Ui is the uniformity coeﬃcient that was taken to be 4.
2.2. Numerical experiments
A constant head recharge simulation was conducted for 365
days to achieve steady-state ﬂow conditions (Sasidharan et al.,2020). The ﬂow domain and water ﬂow boundary conditions areshown in Fig. 1. Boundaries shown in blue were assigned a no-ﬂow
boundary condition (BC), drywell’s screened boundaries shown in
red are constant head BCs that increase in a linear manner with3

S. Sasidharan, S.A. Bradford, J. Šimůnek et al.

Water Research 197 (2021) 117040

Fig. 1. The drywell geometry, dimensions, initial condition, the boundary conditions, and the X and Z-correlation length scales (virus BTC sampling location depth corresponding to the Z-axis) for the 2D-axisymmetrical ﬂow domain for the water ﬂow and solute transport. The detailed drywell geometry and the water ﬂow dynamics were
presented in our previous study (Sasidharan et al., 2018a).

depth, and the green bottom boundary was a free drainage BC.
Note that the free drainage condition assumes that the water table is deeper than 60 m, such that the capillary fringe does not
impact the simulation results. The initial condition (IC) was speciﬁed in terms of the soil water pressure head h (x, z). In this study,
the ﬁnal pressure head from a recharge simulation (in which theinitial condition was set as a constant pressure head of −0.5 m forthe entire ﬂow domain (Sasidharan et al., 2020) was imported into
HYDRUS (2D/3D) and employed as the initial condition (e.g., Fig.
S2). The steady-state ﬂow ﬁeld (i.e., calculate water contents andﬂuxes) for the virus transport simulations was determined fromthis IC.
The ﬂow domain was discretized using a two-dimensional triangular ﬁnite element mesh with the MESHGEN tool available
within HYDRUS (2D/3D) (Šejna et al., 2014). The mesh was reﬁned
such that the size of elements was smaller (0.05–0.25 m) near
the drywell (where inﬁltration is simulated), and the grid size was
gradually increased with radial distance from the drywell, with amaximum element size of 0.5–0.75 m. The quality of the ﬁnite
element mesh was assessed by ensuring the value of mass balance error reported by HYDRUS (2D/3D) at the end of simulation
was always below 1% for water ﬂow and 2-3% for solute transport(Brunetti et al., 2017). The unit of time in the simulations was set

in minute and the initial time step was 0.001 min. The minimum
and maximum time steps were set at 0.0 0 01 min and 1 min, respectively. The maximum number of iterations was set at 10, thewater content tolerance was set at 0.0 0 01 [-], and pressure head
tolerance was set at 0.01 [m].
The solute transport boundary conditions are also shown in
Fig. 1. In brief, a no-ﬂux virus transport boundary condition was
assigned to the upper boundary. The nodes representing the rightand lower left sides of the ﬂow domain were set to no ﬂux boundaries. The nodes at z = −8.7 to −38 m (drywell sidewall) and thebottom boundary (z=−60 m) were assigned a third-type (Cauchy,
mixed, or solute ﬂux) boundary condition since the solute ﬂux
along a boundary is speciﬁed. This type of boundary condition
is mass conservative. When the water ﬂux is zero or directed
out of the region, third-type boundary conditions are automatically switched to second type (Neumann) boundary conditions(Šimunek et al., 2012). The initial concentration of viruses at theﬂow domain was set as zero, the initial input concentration of injection water from drywell (C0 ) was set as 1, and the breakthroughcurve (BTC) at a speciﬁed location on the ﬂow domain was determined as C/C0 .
Numerical experiments were conducted using HYDRUS (2D/3D)
to determine the appropriate groundwater separation distance for4

S. Sasidharan, S.A. Bradford, J. Šimůnek et al.

Water Research 197 (2021) 117040

Table 1The virus removal parameters employed in the numerical experiments and the corresponding ﬂow domains and scenarios.
kd

∗

μl

∗

μs

∗

0

0

0

0

1

0
1 × 10− 5
1 × 10− 5

00
1 × 10− 5

0
0
0

0
1
1

1
0
0

1 × 10− 4

1 × 10− 5

1 × 10− 5

1 × 10− 5

1

0

1 × 10− 5

1 × 10− 5

0

0

0.1

0.9

1 × 10− 5

1 × 10− 5

0

1 × 10− 5

0.1

0.9

−5

−5

−5

0.1

0.9

Flow Domain

Scenario∗

Set IA

Homogeneous

Irreversible Attachment[( fr = 0) + α]

Set IBSet II

Homogeneous

Detachment [ ( f r = 1 ) + α + kd1 ]Liquid Inactivation[ ( f r = 1 ) + α + kd1 + μl ]Solid-Phase Inactivation[ ( f r = 1 ) + α + kd1 + μl + μs ) ]Reversible Fraction[( f r = 0.1) + α + kd1 ]Reversible Fraction+ μl [( f r = 0.1) + α + kd1 + μl ]Reversible Fraction+ μl + μs [( f r = 0.1) + α+kd1 + μl + μs ]Detachment [( f r = 1 ) + α + kd1 ]0 1 × 10− 5
1 × 10− 4
1 × 10− ³
0.47 0.03
1 × 10− 4
1 × 10− 4

Set III

Set IV

X=10, Z=0.1, σ =1
X=1, Z=0.1, σ =1
X=0.1, Z=0.1, σ =1
X=1, Z=1, σ =1
X=1, Z=2, σ =1
X=1, Z=0.1, σ =0.5
X=1, Z=0.1, σ =0.25
X=10, Z=0.1, σ =1
X=1, Z=2, σ =1
X=10, Z=0.1, σ =1
X=1, Z=2, σ =1∗αExperiment∗

1 × 101 × 101 × 10−5

1 × 10fr∗(1 − f r )1 × 10− 4

1 × 10− 5

0

0

1

0

Detachment [( f r = 1 ) + α + kd1 ]1 × 10− 4

1 × 10− 5

0

0

1

0

Reversible Fraction+ μl + μs [( f r = 0.1) + α+kd1 + μl + μs ]1 × 10− 5

1 × 10− 5

1 × 10− 5

1 × 10− 5

0.1

0.9

sticking eﬃciency (α ), solid-phase inactivation (μs ), liquid-phase inactivation (μl ), detachment coeﬃcient (kd ), a reversible fraction ( f r ), an irreversible fraction (1 − f r ).

Table 2The PRD1 virus removal parameters used in numerical experiments Set VA and Set VB, corresponding ﬂow domains, and the separation distance to obtain 6-log10 virus
removal.

Experiment

Input Parameters

Reference∗

Set VA (Sasidharan et al., 2018b)(Column Scale Experiment)

Set VB (Schijven et al., 1999)(Field Scale Experiment)∗αStochastic
Heterogeneity

μs

∗

μl

∗

kd1

∗

[-]

[min−1 ][min−1 ][min−1 ][min−1 ]8.1 × 10− ²

4.2 × 10− 5

1.6 × 10− 6

4.8 × 10− 8

1.0 × 10− ¹²

8.6 × 10− 4

4.7 × 10− 5

8.3 × 10− 5

1.7 × 10− 6

NA

∗

kd2

Separation distancefor 6-log10 removal[m]Homogeneous
X=10, Z=0.1, σ =1
X=1, Z=2, σ =1
Homogeneous

1
1
1.8
6.5

X=10, Z=0.1, σ =1
X=1, Z=2, σ =1

6.8
7.9

sticking eﬃciency (α ), solid-phase inactivation (μs ), liquid-phase inactivation (μl ), detachment coeﬃcient for site 1 and site 2 (kd1 and kd2 ).

the safe operation of a drywell to minimize groundwater contamination at the water table from a virus that is 50 nm in size. Steadystate water ﬂow occurred in the simulation domain (a conservative
ﬂow scenario), while a 90-day step pulse of viruses was added tothe drywell followed by 275-days of virus-free water. Virus BTCs
were determined at depths of 1, 4, 6, 13, and 22 m below thebottom of the drywell (corresponding nodes at the left boundaryof the ﬂow domain were selected to represent a monitoring welllocated next to the drywell, Fig. 1). A range of virus removal parameters was selected to optimize virus transport during the drywell operation. Tables 1 and 2 show a summary of virus transport and fate parameter combinations employed in all simulations.
The parameter ranges were selected to encompass reported values in the literature (Blanc and Nasser 1996; Bradford et al., 2016;
Schijven and Hassanizadeh 20 0 0; Yahya et al., 1993), and justiﬁcation for inclusion or non-inclusion of different transport processes
are explained in section S1.1 of the Supporting Information. Fig. 2
illustrates various virus removal processes in porous media considered in this study.

2.2.1. Homogeneous domain
Set I and II simulations (Table 1) were conducted assuming ahomogeneous domain, and hydraulic parameters were measured
for the Fort Irwin, CA soil, as described in Section 2.1. Set IA simulations investigated the effect of low values of irreversible attachment by varying α from 0 to 1 × 10− ³. Additional simulations(Set IB, Table 1) were conducted to demonstrate the beneﬁt of insitu iron oxide treatment near the drywell on virus removal using
higher values of α =0.03 and 0.47 (Ryan et al., 1999).
Set II simulations were conducted to illustrate expected virus transport and removal when α =1 × 10−4
and other fate parameters were considered. Seven scenarios were considered and are referred to as the irreversible
attachment[ ( f r = 0 ) + α ],detachment[( fr = 1 ) + α + kd1 ],liquidinactivation[( fr = 1 ) + α + kd1 + μl ],solid-phaseinactivation[( fr = 1 ) + α + kd1 + μl + μs ) ],reversible
fraction[( fr = 0.1) + α + kd1 ],reversible
fraction+μl[( fr = 0.1) + α + kd1 + μl ], and reversible fraction + μl + μs[( fr = 0.1) + α + kd1 + μl + μs ] scenarios (Table 1).
5

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Water Research 197 (2021) 117040

bacteriophage PRD1 at 20 °C (Sasidharan et al., 2018b). Set VB
simulations (Table 2) were conducted using ﬁeld-scale PRD1 removal parameters from a dune recharge site in the Netherlands(Schijven et al., 1999).
3. Result and discussions
3.1. Homogeneous soil proﬁles
3.1.1. Set I - irreversible attachment
Numerical experiments were conducted to better understandthe transport behavior of a virus from a drywell under steady-statewater ﬂow conditions. Virus removal in soils has been reported
to be controlled by attachment (Sasidharan et al., 2018b). Set IA
simulations (irreversible attachment scenario) were therefore conducted to investigate the effect of low levels of α . Low values of α
were employed in these simulations to provide a best-case (greatest) virus transport scenario to assess the potential risk to humanhealth.
Fig. 3A and B show the virus concentration (log scale) in theﬂow domain after 90 and 365 days, respectively, of steady-statewater ﬂow when fr =0, kd1 =μl =μs =0 min− ¹, and α =0, 1 × 10−5 ,1 × 10−4 , and 1 × 10− ³. Note that readers could refer to Fig. S3that explains various terms used in a representative BTC graph tointerpret data easily for all the BTCs presented in this manuscript.
Recall that the drywell contained virus for 90 days, and then virusfree water for another 275 days. The highest virus concentration(red) reached the bottom boundary during the 90-day pulse injection when α =0. However, the virus distribution around the drywell
rapidly decreased with an increase in α and with the elution time(comparison of Fig. 3A and B) due to irreversible attachment. Notethat the virus concentration in the domain pore-water was almost
completely eliminated after 365 days when α =1 × 10− ³.
Fig. 4 shows the corresponding virus breakthrough curves (BTC)(C/C0 ) on a log-scale at depths of 1, 4, 6, 13, and 22 m below
the bottom of the drywell. The virus concentration was higher
than 6-log10 removal at 22 m after 90 days when α =0, 1 × 10−5 ,and 1 × 10−4 . In comparison, the virus concentration was lessthan 6-log10 at 6 m when α =1 × 10− ³. However, the virus concentration declined to less than 6-log10 after 365 days when
α =1 × 10−5 , 1 × 10−4 , and 1 × 10− ³. The time for this observed decline at 4 m after 90 days pulse varies from 214, 15,
and 1 day for α =1 × 10−5 , 1 × 10−4 , and 1 × 10− ³, respectively. Prolonged concentration tailing was observed in BTCs eventhough detachment was not considered. This tailing behavior was
more pronounced when the value of α was smaller. This occurs
because of physical nonequilibrium from the velocity distribution
in the ﬂow domain (Fig. S4A). In addition, ka (Eq. (2)) is also afunction of the ﬂow velocity (Johnson et al., 2007; Shen et al.,2010; Syngouna and Chrysikopoulos 2013; Syngouna et al., 2017;
Torkzaban et al., 2007).
The treatment of soil material with metal oxides such as zerovalent iron, goethite, manganese oxide, and iron hydroxide has
been found to greatly enhance the removal of microbial pathogens(Ahammed and Meera 2010; Kim et al., 2008; Li et al., 2014).
Therefore, Set IB simulations were conducted to assess the potential beneﬁt of iron oxide coating of soil adjacent to a drywell on
virus removal. Reported values of α =0.03 and 0.47 for virus removal on iron oxide coated sand were employed for this purpose(Ryan et al., 1999). A 6-log10 virus removal was obtained aroundthe drywell within <1.5 m and 0.43 m in the horizontal and vertical direction, respectively, when α =0.47 (Fig. S5A), and within<1.9 m and 0.43 m when α =0.03 (Fig. S5B). These distances aremuch smaller than those shown in other simulation scenarios inthis paper. These results suggest that incorporating ex-situ treatment and/or in-situ treatment zones into drywell designs may be

Fig. 2. The schematic of various virus removal processes during transport in porous
media that were applied in the numerical experiments presented in this study.

2.2.2. Heterogeneous domain
Additional numerical experiments were conducted to investigate the inﬂuence of subsurface heterogeneity on virus fate from adrywell. Natural geological processes produce high permeable horizontal lenses and layers in soil texture and preferential water ﬂow
pathways in the vertical direction (cracks, micropores, faults, andother preferential ﬂow paths). The HYDRUS (2D/3D) computer software has an option to generate stochastic distributions of hydraulicconductivity (α K ) and pressure head (α h ) scaling factors using theMiller-Miller similitude approximation (Miller and Miller 1956).
The detailed methodology that was used to produce stochastic domains in HYDRUS (2D/3D) is explained in our previous studies(Sasidharan et al., 2019; Sasidharan et al., 2020) and is only brieﬂy
discussed in section S1.2 of the supporting information.
The following hypothetical combinations of scaling factors
with variable σ (σ =0.25, 0.5, 1: when X=1 m and Z=0.1 m),
X (X =0.1, 1, 10 m when σ =1 and Z=0.1 m), and Z (Z=0.1, 1,
2 m when σ =1 and X=1) were used in virus transport simulations. Similar parameter ranges have been measured in the ﬁeld
(Freeze 1975; Sudicky and MacQuarrie 1989) and employed in published modeling studies for other applications (Sasidharan et al.,2019; Sasidharan et al., 2020). Set III simulations (Table 1) considered the detachment scenario for all seven stochastic domains.
Set IV simulation (Table 1) only considered the detachment and reversible fraction + μs + μl scenarios for stochastic domains X=1,
Z=2, σ =1 and X=10, Z=0.1, σ =1 under steady-state ﬂow conditions. Additional drywell simulations were conducted to demonstrate the application and upscaling of virus parameters obtained
from a laboratory-scale column to ﬁeld-scale studies. Set VA simulations (Table 2) were conducted using virus removal parameters taken from saturated sand-packed column experiments using
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Water Research 197 (2021) 117040

Fig. 3. The virus removal (log scale) for the irreversible attachment scenario at various sticking eﬃciencies (α )=0, 1 × 10−5 , 1 × 10−4 , and 1 × 10−3 when detachment
coeﬃcient (kd )=0 in a homogeneous domain (Fort Irwin Soil) after 90 days of a continuous pulse of virus (A) and the 365th day of steady-state virus-free water ﬂow (B).

Fig. 4. The virus breakthrough curve (log scale) for the irreversible attachment scenario when sticking eﬃciency (α )=0 (A), 1 × 10−5 (B), 1 × 10−4 (C), and 1 × 10−3 (D) at
depths=1, 4, 6, 13, and 22 m when the detachment coeﬃcient (kd )=0 in a homogeneous domain (Fort Irwin Soil) over 365 days (90 days of a continuous pulse of virus and275-days of steady-state virus-free water ﬂow) simulation.

needed to ensure that recharge water from a drywell is of acceptable microbial quality. Additional research is warranted to address
this issue.

concentration (log scale) in the ﬂow domain after 90 and 365 days,
respectively.
Note that all Set II simulations were nearly identical to the irreversible attachment scenario (Fig. S6A) for the ﬁrst 90 days of virus
injection. In this case, results were controlled by the same value of
α =1 × 10−4 . In contrast, pronounced differences occur after virus
injection stopped, and elution continued (>90 days). Low levels ofvirus concentration tailing can occur during this time interval asa result of slow detachment (kd1 =1 × 10−5 m min− ¹) and subsequent migration of viruses. Note that nonequilibrium viral masstransfer plays a larger role in concentration tailing (Fig. 5A) thanphysical nonequilibrium (Fig. 4C) when α =1 × 10−4 . The concentration tailing level and slope depend on the presence of liquidand solid-phase inactivation and the fraction of reversible sites. In

3.1.2. Set II - reversible attachment
Virus removal in the soil below a drywell depends on other parameters besides α . Set II simulations were conducted to illustrate
the expected transport and removal behavior when α =1 × 10−4
and other virus fate parameters ( fr , kd1 , μl , and μs ) were varied.
Fig. 5A–F shows the virus BTCs on a log-scale at depths of 1, 4,
6, 13, and 22 m below the bottom of the drywell when considering detachment, liquid inactivation, solid-phase inactivation, reversible
fraction, reversible fraction + μl , and reversible fraction + μl + μs
scenarios, respectively. Figs. S6A–S6F show the corresponding virus
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Water Research 197 (2021) 117040

Fig. 5. The virus breakthrough curve (log scale) at depths=1, 4, 6, 13, and 22 m for the detachment (A), liquid inactivation (B), solid-phase inactivation (C), reversible fraction(D), reversible fraction+μs (E), and reversible fraction+μs +μl (F) scenarios. The Irreversible attachment scenario is presented as the doted (brown) line. The 6-log10 removal
is shown as the dashed blue line. Table 1 (Set I and Set II) shows the corresponding virus removal parameters. . (For interpretation of the references to colour in this ﬁgurelegend, the reader is referred to the web version of this article.)

general, liquid (Fig. 5B) and especially solid-phase (Fig. 5C) inactivation reduce the level and slope of concentration tailing in comparison to no inactivation (Fig. 5A). Higher levels and less steep
slopes of concentration tailing also occur for larger reversible fractions (Fig. 5A, B, C in comparison to Fig. 5D, E, and F, respectively).
The relative importance of concentration tailing also changes withdepth. The peak concentration of the BTC (<90 days) is greaterthan the level of concentration tailing at lower depths (>90 days).
In contrast, the peak concentration of the BTC can sometimes belower than the level of concentration tailing at greater depths(e.g., Fig. 5A). This occurs because the level of concentration tailing is relatively stable with depth, whereas the peak BTC concentration strongly decreases with increasing depth due to attachment(Fig. 4).

ulations by importing the initial pressure head (Fig. S7). Fig. 6
shows the virus concentration distribution (log scale) after 365days for Set III (detachment scenario) simulations. Increasing theX-correlation length (Fig. 6A, B, and C) produces a wider distribution (in the lateral direction), whereas increasing the Z-correlation
length (Fig. 6D and E) creates a greater vertical distribution of
the virus. Fig. S8 shows the virus BTC (log scale) at 3 m and22 m for all the stochastic parameter combinations. Parameters
X=1, Z=2, σ =1 produced the earliest arrival time and highest
concentration at a depth of 22 m, whereas parameters X=10,
Z=0.1, σ =1 resulted in the latest arrival time and lowest concentration among the stochastic parameter combinations (Fig. S8).
Sasidharan et al. (2020) demonstrated that the arrival time ofrecharge water at the water table highly depends on the stochastic heterogeneity. For example, the arrival time was 34 days in the
X=1, Z=2, σ =1 domain, 66 days in the X=10, Z=0.1, σ =1 domain,and 100 days in the X=1, Z=0.1, σ =0.25 domain. The presenceof highly permeable vertical lenses facilitates the faster downward
movement of water. In contrast, the domain with high permeable
horizontal lenses has increased water spreading in the lateral direction. A small value of σ leads to a more uniform and lower permeability in the domain, which takes longer to ﬁll before the wet-

3.2. Heterogeneous soil proﬁles
3.2.1. Set III - detachment scenario
Additional numerical experiments were conducted to better understand the inﬂuence of stochastic heterogeneity (X=0.1, 1, 10,
Z=0.1, 1, 2, and σ =0.25, 0.5, 1) on virus transport during drywell operation. Steady-state water ﬂow occurred during these sim8

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Water Research 197 (2021) 117040

Fig. 6. The virus concentration distribution proﬁle (log scale) for heterogeneous X=10, Z=0.1, σ =1 (horizontal lens) (A), X=1, Z=0.1, σ =1 (B), X=0.1, Z=0.1, σ =1 (C), X=1,
Z=1, σ =1 (D), X=1, Z=2, σ =1 (thick vertical lens) (E), X=1, Z=0.1, σ =0.5 (F), and X=1, Z=0.1, σ =0.25 (G)) Fort Irwin soil ﬂow domains at 365 day after 90 days of acontinuous pulse of virus transport and 275-days of steady-state water ﬂow simulation for detachment scenario.

the homogeneous and heterogeneous (X=1, Z=2, σ =1, and X=10,
Z=0.1, σ =1) domains. Fig. 9A shows that the virus concentration
remained <6-log10 for the X=10, Z=0.1, σ =1 domain during theﬁrst 30 days of simulation, whereas the concentration rapidly increased >6-log10 after 30 days (Fig. 9B). Similarly, the virus concentration remained <6-log10 in both homogeneous and X=10,
Z=0.1, σ =1 domains (Fig. 9C). However, the concentration was >6log10 during the 15–90 day injection period and declined rapidly
in the homogeneous domain, but the concentration always stayed<6-log10 in the X=10, Z=0.1, σ =1 stochastic domain.

ting front arrives at the bottom boundary. Besides, the velocity distribution is also a function of stochastic parameters that lead to thephysical nonequilibrium transport of viruses (Figs. S4B and S4C).
Therefore, Figs. 6 and S8 conﬁrm that the vertical ﬂow path facilitates virus transport. In contrast, horizontally extended high permeable layers will initially promote the lateral distribution and removal of viruses before the viruses arrive at the water table, evenwhen a low detachment rate is present.
3.2.2. Set IV - Best-case and worst-case scenario
Set IV simulations only considered stochastic domains X=1,
Z=2, σ =1 and X=10, Z=0.1, σ =1 under the worst-case (detachment) and best-case (reversible fraction + μs + μl ) removal scenarios. Fig. 7A & C and Fig. 8A & C show the virus concentration
distribution in the X=1, Z=2, σ =1 and X=10, Z=0.1, σ =1 domains,respectively, after 365 days. Similarly, Fig. 7B & D and Figs. 8B &D show the corresponding virus BTC at depths of 1, 4, 6, 13, and
22 m of the X=1, Z=2, σ =1 and X=10, Z=0.1, σ =1 domains, respectively. The virus concentration was above 6-log10, even at a
depth of 22 m in both domains in the worst-case (detachment) removal scenario. The tail concentration increased due to slow detachment and subsequent migration of viruses. In comparison to
Fig. 8B (X=10, Z=0.1, σ =1), the X=1, Z=2, σ =1 domain (Fig. 7B)
produced rapidly increasing concentration tailing in the deep vadose zone (13 and 22 m) due to the presence of higher velocities
from permeable vertical lenses. For the best-case (reversible fraction+ μs + μl ) scenario, the 6-log10 removal was achieved at a depthof 22 m of the X=10, Z=0.1, σ =1 domain over 365 days. Figs. 7D
and 8D show that the time required to achieve 6-log10 removal after 90 days pulse at a depth of 13 m was 185 days and 105 days
for the X=1, Z=2, σ =1, and for X=10, Z=0.1, σ =1 ﬂow domains,respectively.
Fig. 9 shows virus BTCs for the worst-case (Fig. 9A and B) andbest-case (Fig. 9C and D) removal scenarios at a depth of 22 m of

3.2.3. Set v - Real-case scenarios
Sections 3.1 and 3.2 demonstrated the importance of sitespeciﬁc subsurface heterogeneity and virus removal parameters inmicrobial risk assessment. Parameters employed in those simulations purposely represent worst-case removal scenarios to show
the maximum microbial risk potential associated with drywell
operation. Set VA simulations were conducted using experimentally measured viral removal parameters to evaluate several realcase scenarios using measured model parameters from ﬁeld-scale(Schijven et al., 1999) or column-scale (Sasidharan et al., 2018b)
studies. Table 2 provides a summary of PRD1 removal parametersfrom these studies used in Set VA simulations and the separation
distance required to achieve a 6-log10 removal. A 6-log10 removalof PRD1 could be achieved within 1.8 m using column parameters(Sasidharan et al., 2018), whereas 7.9 m was needed when usingﬁeld-scale parameters (Schijven et al., 1999). The difference in thetravel distance for the PRD1 virus based on both column-scale andﬁeld-scale experiments can be explained by values of α . These observations suggest that site-speciﬁc measurements of virus transport and fate parameters ought to be employed to improve estimates of separation distance and microbial risk. For example, sitespeciﬁc virus removal parameters could be determined in smallscale column studies, and then employed in ﬁeld-scale modeling
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Water Research 197 (2021) 117040

Fig. 7. The virus concentration distribution for the worst-case (detachment) (A) and best-case (reversible fraction + μs + μl ) (C) removal scenarios in the “X=1, Z=2, andσ =1 ﬂow domain. The virus breakthrough curve at depths=1, 4, 6, 13, and 22 m in the “X=1, Z=2, and σ =1 ﬂow domain for the worst-case (B) and best-case (D) removal
scenarios.

Fig. 8. The virus concentration distribution for the worst-case (detachment) (A) and best case (reversible fraction + μs + μl ) (C) removal scenarios in the “X=10, Z=0.1, andσ =1 ﬂow domain. The virus breakthrough curve at depths=1, 4, 6, 13, and 22 m in the “X=10, Z=0.1, and σ =1 ﬂow domain for the worst-case (B) and best-case (D)removal scenarios.

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Water Research 197 (2021) 117040

Fig. 9. The virus breakthrough curves for the worst-case (detachment) removal scenario at 22 m as a function of time during 30 days (A) and 365 days (B). The virus
breakthrough curves for the best-case (reversible fraction + μs + μl ) removal scenario at 22 m as a function of time during 15 days (C) and 365 days (D).

studies that account for site-speciﬁc hydraulic properties and subsurface heterogeneity. This approach would eliminate the need fortedious, costly, and time-consuming experimental determination ofvirus transport and fate in the ﬁeld.
Note that virus concentrations for all the presented simulationsin this study were collected from a single location at the speciﬁed
depth (Fig. 1) to represent the monitoring well next to the drywell, which is the current common ﬁeld scale practice (worst-casescenario). In comparison, Fig. S9 compares BTCs for virus collectedfrom a single point location (one monitoring well) with integrated
values through the entire free-drainage boundary at 22 m below
drywell. The integrated BTC concentration was always lower thanthe point location. Consequently, the BTC for a point located close
to the drywell will better predict the risk associated with an earlyarrival time than an integrated BTC. Even though collecting an integrated BTCs for the cone of inﬁltration of a drywell at a ﬁeld site
is not practical, this study points out the beneﬁt of installing andsampling multiple monitoring wells around the cone of inﬁltration(Fig. S9). Such ﬁeld scale practice will help determine site-speciﬁcgroundwater microbial quality, set back distance to drinking water
wells, and minimize under and overestimation of the risk from adrywell.

Washington State Department of Ecology 2006). The results from
Figs. 4–9, S6, S8, S9, and S10 clearly demonstrate that these regulatory standards may sometimes be inadequate to achieve a 6-log10
removal of viruses even at a depth of 22 m below the bottom of adrywell. The virus concentration was highly dependent on α during virus injections (<90 days) but was then controlled by small
rates of detachment. Previous researchers have assumed that thethreat from virus detachment was minimal (Schijven et al., 1999).
However, this study showed that small α and kd values can lead toprolonged migration and release of viruses that can adversely impact groundwater quality even for a deep water table. The threatof virus transport will be mitigated for larger α , smaller reversible
fractions and detachment rates, and higher solid and liquid inactivation rates. Additional research is warranted to better understandand quantify these site-speciﬁc parameters that contribute to longterm concentration tailing. This information could be used in MAR
guidelines and engineering approaches to minimize microbial contamination of groundwater.
Figs. 7 and 8 conﬁrm that the separation distance to achieve 6log10 virus removal is a function of both virus removal parametersand stochastic subsurface heterogeneity at a speciﬁc site. The presence of a vertically extended lens facilitates the rapid transport ofviruses and result in an early arrival at the groundwater table. In
contrast, horizontally extended lenses increase the lateral spreading and the travel time, enhancing virus removal by irreversible
attachment and solid-phase inactivation, and limited vertical migration. Therefore, these factors should be considered in microbial
risk assessment and separation distance requirements. The generalized use of a constant separation distance may overestimate virus

4. Future implications on regulatory standard
Current regulatory standards for drywells only require 1.5 to
13 m separation distance between the bottom of the drywelland the local groundwater table for various cities across the
U.S. (City of Portland 2008; City of Portland 2015; EPA 1999;
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Water Research 197 (2021) 117040

removal via vadose zone treatment and potentially lead to groundwater contamination.
Fig. 9 indicates that the general assumption of a 1.5–12 m
separation distance may not be adequate in some instances, andthe timing of the water quality monitoring can signiﬁcantly underestimate the potential future groundwater viral contamination.
These observations are consistent with reported waterborne disease outbreaks in highly heterogeneous (e.g., fractured and Karst)
subsurface environments (Borchardt et al., 2011; Wallender et al.,2014), predictions of contaminant transport in stochastic systems(Mantoglou and Gelhar 1987; Yeh et al., 1985a; Yeh et al., 1985b),and simulated variations in recharge arrival times and locations
below a drywell (Sasidharan et al., 2020). However, we acknowledge that the microbial risk from recharged groundwater to public health depends on various factors such as the exposure pathways, volume of injection and microbial concentration, frequencyof exposure, the infectivity of the pathogen, setback distance tothe drinking water well, removal within the treatment train framework, and post-treatment of recovered water. In contrast, thisstudy explicitly points out the potential for groundwater contamination when vadose zone treatment is considered as the primary
treatment barrier during MAR operation.

Declaration of Competing Interest
None.
CRediT authorship contribution statement
Salini Sasidharan: Investigation, Methodology, Software, Formal
analysis, Validation, Data curtion, Writing – original draft, Visualization. Scott A. Bradford: Conceptualization, Software, Resources,
Writing – review & editing, Supervision, Project administration. JiříŠimůnek: Conceptualization, Software, Resources, Writing – review& editing, Supervision, Project administration. Stephen R. Kraemer: Writing – review & editing, Project administration.
Acknowledgments
Funding for this research was partially provided by the U.S. Environmental Protection Agency (US EPA) through an interagency
agreement with the United States Department of Agriculture (EPA
DW-012–92465401; ARS 60–2022–7–002), hatch funds CA-R-ENS7274-H and CA-R-ENS-5047-RR from Dr Jiří Šimůnek, University ofCalifornia, Riverside, and USDA ARS National Program 211 from Dr.
Scott A. Bradford, USDA Salinity Lab, Riverside. The authors would
like to thank anonymous reviewers and Drs. Phil Berger and Mohamed Hantush for their constructive comments during the internal EPA review. The views expressed in this article are those of
the authors and do not necessarily represent the views or policiesof the U.S. Environmental Protection Agency. The mention of commercial products does not constitute an endorsement.

5. Conclusions
Numerical modeling was used to study virus transport from adrywell and the potential risk of groundwater contamination under various virus removal and subsurface heterogeneity scenarios.
The separation distance between the bottom of the drywell andthe bottom of the ﬂow domain to achieve a 6-log10 removal of thevirus was typically much larger than the currently recommended
guideline of 1.5 to 13 m. Higher risks of groundwater contamination from prolonged virus concentration tailing in the deep vadosezone occurred for lower values of the sticking eﬃciency and liquid and solid-phase inactivation rates, and higher detachment rates
and reversible fractions. Conversely, the risk from viruses can be
minimized by increasing the sticking eﬃciency, solid-phase inactivation, and irreversible fraction of the soil around the drywell. For
example, in-situ treatment with iron oxide with high sticking eﬃciency was found to create 6-log10 virus removal within 1.9 m fromthe drywell.
Sasidharan et al. (2017a, 2018b) demonstrated that up to 4.6 log
removal of virus could be achieved by increasing the storage orresidence time in a column experiment at 20 °C. Therefore, virus
risk is also expected to be minimized by transient drywell operation that increases the residence time for irreversible attachment
and solid-phase inactivation. Virus transport under transient conditions also involves additional processes under unsaturated conditions, such as retention at the air-water interface or remobilization during water imbibition. Therefore, transient water ﬂow
simulations were not considered in this study but warrants additional research. Site-speciﬁc sub-surface heterogeneity and hydraulic property will also inﬂuence virus transport. Horizontallyextended lenses with high permeability enhance the lateral distribution and removal of the virus. Conversely, vertically extended
high permeable lenses such as fractures and preferential ﬂow pathways facilitate virus transport and will result in the rapid arrivalof viruses at the groundwater table. This research illustrates thatsite-speciﬁc virus removal parameters obtained from the ﬁeld- orcolumn-scale experiments can be employed in ﬁeld-scale modeling studies to assess risks from virus contamination and determine needed separation distances. This same approach and ﬁndings are expected to be applicable for other MAR systems (e.g., AgMAR and inﬁltration basins), as well as for septic tanks and leachﬁelds, when vadose zone transport is considered as a treatment
barrier.

Supplementary materials
Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.watres.2021.117040.
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