Agricultural Water Management 131 (2014) 17–29

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Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat

Simulation of the redistribution and fate of contaminants fromsoil-injected animal slurry
M.G. Mostofa Amin a,∗ , Jirka Šimůnek b , Mette Lægdsmand aab

Department of Agroecology, Aarhus University, Blichers Alle 20, DK-8830 Tjele, Denmark
Department of Environmental Sciences, University of California, Riverside, CA 92521, USAa r t i c l e

i n f oArticle history:
Received 23 January 2013
Accepted 10 September 2013
Keywords:
Contamination
Simulation
Nitrogen
Microorganisms
Hormones
Leaching
HYDRUSa b s t r a c t
Spreading of contaminants from land-applied animal slurry may create hazard for both soil and water
environments. Both the leaching and persistence of the contaminants is controlled by the redistribution of
the contaminants immediately after application, while the redistribution is inﬂuenced by site conditions(here different slurry dry matter content and soil texture). HYDRUS-2D was used to simulate the redistribution of water, chloride, mineral N, Salmonella Typhimurium Bacteriophage 28B (phage), Escherichia coli,
and steroid hormone estrogens near the slurry injection slit over a 50-day period after slurry injection
at two ﬁeld sites (Silstrup and Estrup) in Denmark to estimate the controlling transport and reaction
parameters based on ﬁeld measurements of the contaminants. The calibrated model was thereafter used
to predict the leaching potential into the subsoil. The simulations conﬁrmed that the higher water contents measured in the slurry application slit were due to a change in the hydraulic parameters. Chloride
was redistributed considerably beyond the sampled soil proﬁle at Estrup, but not at Silstrup, which had
lower hydraulic conductivities than Estrup. Average size of the microorganisms affected their mobility;
the bigger the size, the higher was the effect of the site conditions. The sorption coefﬁcient of estrogens
for slurry-amended soil was remarkably lower than that for unamended soil. The study suggests that
dissolved organic carbon retained in slurry can facilitate the transport of contaminants. E. coli, phage, andestrogens were vulnerable to leaching from the very ﬁrst precipitation event after the slurry application,
whereas mineral N started to leach when NO3 -N accumulated. Model predictions suggest that there arepotential risks of leaching of these contaminants from the top soil to the subsoil associated with theland-injected slurry.
© 2013 Elsevier B.V. All rights reserved.

1. Introduction
The spreading of pathogens, steroid hormones, and nutrientsfrom land-applied animal slurry is a hazard for the aquatic environment (Doltra and Munoz, 2010; Guber et al., 2005; Jenkins et al.,2009; Laegdsmand et al., 2009; Lee et al., 2007). Pathogenic contamination of ready-to-eat types of crops or drinking water can cause
various diseases in humans (van Overbeek et al., 2010). Estrogenic
hormones are endocrine disrupters and can affect the reproductive
systems of aquatic life even at a very low concentration (1–2 ng/L)(Lahnsteiner et al., 2006). Release of nutrients from agriculturalland into surface water can contribute to eutrophication and have

∗ Corresponding author. Current address: Department of Irrigation and Water
Management, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
Tel.: +880 91 67401x2859; fax: +880 91 61510; mobile: +880 1712 536494.
E-mail addresses: aminmgmbau@yahoo.com, maminbau@yahoo.com(M.G.M. Amin), jiri.simunek@ucr.ca (J. Šimůnek), mel@fertilitech.com(M. Lægdsmand).
0378-3774/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.agwat.2013.09.002

impact on aquatic ecology. Heavy rainfall after a slurry applicationis the most frequent cause for the release of slurry-borne contaminants to water bodies (Jenkins et al., 2008; Kjaer et al., 2007).
Pathogens from the top soil can move with both surface runoff andinﬁltrating water through preferential pathways, avoiding the normal ﬁltering capacity of soils (Coffey et al., 2010; Guber et al., 2007;
Jenkins et al., 2006; Lee et al., 2003). Although estrogenic hormones
readily sorb to soils, they have nevertheless been found in subsurface water draining from manure-amended soils (Kjaer et al., 2007;
Laegdsmand et al., 2009).
The redistribution process of contaminants depends on theirchemical characteristics that can inﬂuence their leaching andpersistence in soils. The redistribution and transport processes
under ﬁeld conditions are complex, particularly when the contaminants are reactive and degradable such as steroid hormones andpathogens. The redistribution process of water and solutes depends
on the gradients of the soil water potential and solute concentrations, respectively, and attachment–detachment processes. In ﬁeld
conditions one frequently encounters temporal and spatial variations in water contents, ﬂow velocities, dispersion, and dilution

18

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29(Schijven and Šimůnek, 2002). A small change in bulk density andporosity caused by the trafﬁc of agricultural equipment can leadto considerable differences in soil hydraulic conductivities (Coquet
et al., 2005). Soil column experiments with disturbed and homogeneous soils under steady-state water ﬂow conditions may not
represent ﬁeld conditions properly (Kjaer et al., 2007). Furthermore, the redistribution process for contaminants applied withliquid manure can be different than when they enter the soil environment with irrigation water (Guber et al., 2005). The immobile
organic matter originating from slurry can change the hydraulicproperties of the soil in and around the slits through which slurry
is injected (Hoorman and Shipitalo, 2006; Petersen et al., 1996;
Petersen and Andersen, 1996). Presence of slurry may also alter
the sorption capacity of both organic and inorganic particles ofsoil with alkaline pH and high carbon and salt content (Lucas andJones, 2009). Additionally, slurry enriched with dissolved organiccarbon (DOC) can enhance DOC-mediated transport of contaminants (Stumpe and Marschner, 2007). The combined effects of thechemical, physical, and microbiological changes following slurry
application on the fate of contaminants are therefore important toinvestigate at a slurry-injected ﬁeld site.
Calibration of transport parameters through simulation of theﬁeld observation data helps to understand the effect of the properties of slurry and soil on the fate of different slurry-borne
contaminants. Various models are used to simulate and assess thetransport and fate of contaminants in soils. A numerical model is atool to investigate various processes intensively and elaborately
in an inexpensive way. HYDRUS (2D/3D) (Šimůnek et al., 2006)
is a physically based mechanistic model that solves the Richards
equation for water ﬂow and a convection–dispersion equation forsolute transport (Šimůnek et al., 2008). The model has been usedduring the last few years to simulate the transport of soil water(Hassan et al., 2010; Kandelous and Šimůnek, 2010; Ma et al., 2010),
salts (Roberts et al., 2009), nitrates (Crevoisier et al., 2008; Doltra
and Munoz, 2010; Wang et al., 2010), microorganisms (Jiang et al.,2010; Schijven and Šimůnek, 2002), and organic contaminants(Cheviron and Coquet, 2009; Pang et al., 2000) in variably-saturated
soil formations in a variety of soil geometries and irrigation systems. The model was chosen, as it is capable of simulating reactive
and colloidal transport in two dimensions. It was hypothesizedthat the parameters obtained in simulating the redistribution andtransport of constituents in the slurry-injected soil would be different compared to unamended soil or those observed in laboratory
because of the presence of slurry organic matter, salts and alteredsoil hydraulic properties. The objective of the study was (a) toinvestigate how effectively the redistribution process of different
contaminants injected into soils with animal slurry follows thegeneral transport theory incorporated in simulation models, (b) tocompare the parameters obtained in this study with those available
in literature, and (c) to understand the details of the redistribution
process beyond the boundary of our ﬁeld experiment.

2. Materials and methods

Top
layer

A B

C

D

E

F

4 cm

6 cm

Bottomlayer

9-10 cm

Ground surfaceA

6-7 cmSlurry slit

2.5 cm
85 cm

12.5 cm

Water table

Fig. 1. Cross sectional view of the area between two neighbouring slurry slits (A).
The shaded area is the modelled area and areas denoted A–F are the sampling
sections.

used at both sites. All constituents used in the study were slurryborne, except for spiked phages. In April 2009, slurry was applied
during the ﬁrst week, and spring barley sown in the second week,at both sites. Three slurry injection slits and ﬁve sampling positions
per injection slit were randomly selected and marked with labelled
ﬂexible sticks immediately after slurry injection. Three intact soil
proﬁles, one for each slit, with a 20 cm × 4 cm surface area, having
the slurry slit in the middle and a total depth of 15 cm (Silstrup) or
17 cm (Estrup) were sampled on days 0, 1, 6, 18, and 46/49 after theapplication (Fig. 1). Each proﬁle was subdivided into a top layer (aslurry injection depth) and a bottom layer (the rest of the proﬁledepth). The top layer was again subdivided horizontally into four
sections (A–D): section A (a 4 cm wide slurry slit) in the middle, andsequentially sections B (2 cm wide), C (3 cm), and D (3 cm) at bothsides of section A. The bottom layer was subdivided into sections
E and F. Only one side of the slurry slit is shown in Fig. 1. Samples taken from sections B at both sides of the slit were thoroughly
mixed to prepare one sample. Similar mixing was done for samples
from sections C, D, E, and F, assuming symmetrical distribution of
all contaminants. The gravimetric soil water content in each thoroughly mixed fresh subsample was analyzed by drying (105 ◦ C for24 h), chloride concentrations by ion chromatography (Metrohm
AG, Switzerland), NH4 -N and NO3 -N by an Auto-analyzer III Digital Colorimeter (Bran & Luebbe, Germany), viable E. coli by plate
counting on E. coli Petriﬁlms (3 M a/s, Denmark), and phages by adouble-agar layer method as described by Amin et al. (2013). Estrogens (estradiol and estrone) concentrations were determined by
GC–MS/MS technique described in detail by Hansen et al. (2011).
Hydrometeorological data were collected using the micrometeorological and hydrological instruments installed at both ﬁeld sites.
The meteorological data at two ﬁeld sites during the ﬁeld experiment is shown in Fig. 2.

2.1. Field experiment2.2. Simulation studies
This study was conducted at two experimental sites, Silstrup(56◦ 56 N, 8◦ 39 E) and Estrup (55◦ 30 N, 11◦ 45 E), of the Danish Pesticide Leaching Assessment Program. Soil properties for bothsites are given in Table 1. Different types of slurry (at Silstrup: 6.38%
dry matter content, 2.8% C, 2.95 g NH4 -N/L; at Estrup: 0.8% dry matter content, 0.4% C, 2.34 g NH4 -N/L) were injected at two ﬁeld sites
directly into pre-ploughed soil at a 25-cm spacing with an average
injection depth of 9 (Silstrup) or 10 cm (Estrup). While slurry wasapplied at different rates, a similar N application (125 kg N/ha) was

HYDRUS (2D/3D) (Šimůnek et al., 2006, 2008) was used tosimulate the redistribution and persistence of slurry-borne contaminants in soils during a 50-day period following a direct
injection of slurry. Only one half of the symmetrical soil proﬁlebetween two adjacent slurry-injected slits (Fig. 1) was simulated
to reduce calculation time. The simulated area included the entire
vadose zone from the soil surface to the groundwater table situatedin 1 m depth.

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

19

Table 1
Soil physico-chemical parameters at Silstrup and Estrup.
Site

Horizon

Sand (%)

Silt (%)

Clay (%)

Textural class (USDA)

OCa (%)

b a (g/cm3 )CECa (cmol/kg)

pH

Silstrup

A
B

48
45

25
25

27
30

Sandy clay loam
Sandy clay loam

2.1
0.5

1.34
1.51

20.7
19.8

6.7
6.7

Estrup

A
B

61
58

24
26

15
16

Sandy loam
Sandy loam

2.8
0.8

1.27
1.61

15.1
10.2

7.1
6.6

a

OC, b , and CEC represent organic carbon, dry bulk density, and cation exchange capacity, respectively.

A deterministic approach, based on the numerical solution of the
Richards equation by means of linear ﬁnite elements, was used tosimulate soil water movement. The redistribution of all solutes was
simulated by numerically solving the convection–dispersion equation. The Crank-Nicholson time weighting scheme and the Galerkinﬁnite elements space-weighting scheme were chosen to solve theconvection–dispersion equations for all contaminants. The impact
of tortuosity on diffusion was also considered in the model. The general convection–dispersion equation solved by HYDRUS includes
sorption, volatilization, the sink term for root solute uptake, andzero- and ﬁrst-order reactions, which can have different rates in
the gaseous, liquid, and/or solid phases.

2.3.1. Soil water content
The redistribution of water in unsaturated soils was simulatedusing the Richards equation for variably saturated conditions:







− Wr

Precipitation (mm)

16
14
12
10
8
6
4
2
0

10 15 20 25 30 35 40 45

Estrup

0

16
14
12
10
8
6
4
2
0

Temperature (oC)Silstrup

5

∂C
Dij
∂xj



−

16
14
12
10
8
6
4
2
0

∂qi C
∂xi

(2)

where Dij is the dispersion coefﬁcient tensor (cm2 /day) deﬁned as(Bear, 1972):

(1)

qi qj
|q|

+ Dd ıij

5 10 15 20 25 30 35 40 45
Day after slurry application

Fig. 2. Meteorological data at Silstrup and Estrup during the ﬁeld experimental
period.

(3)

and C represents the chloride concentration (mg/cm3 ), qi is the ith
component of the volumetric ﬂux density (cm/day), Dd represents
the molecular diffusion coefﬁcient in free water (cm2 /day),  is atortuosity factor [–], |q| is the absolute value of the Darcian ﬂuid ﬂux
density (cm/day), ıij is the Kronecker delta function, and L and T
are the longitudinal and transverse dispersivity (cm), respectively.
2.3.3. Mineral N
The transformation of NH4 -N into nitrate was simulatedby HYDRUS using a solute chain reaction option with ﬁrstorder kinetic reaction parameters (Bolado-Rodriguez et al., 2010;
Persicani et al., 1996). The sorption process was described using asimple instantaneous adsorption isotherm. All these processes canbe described by the following equations:
SNH4 = Kd CNH4

∂CNH4
∂
=
∂t
∂xi

(4)



∂CNH4
Dij
∂xj


−

∂qi CNH4
∂SNH4
− B
∂xi
∂t

− l CNH4 − s B SNH4 − Wr cNH4S

Temperature (oC)

Precipitation (mm)Precipitation
Air tempeature at 2 m
Soil temperature at 10 cm

0Dij = L |q|ıij + (L − T )where  is the water content (cm3 /cm3 ); t is time (day); xi is thespatial coordinate (cm), with x1 as the vertical and x2 as the horizontal spatial coordinate perpendicular to the injection slit (two

16
14
12
10
8
6
4
2
0

2.3.2. Chloride
Chloride is assumed to be conservative in the soil, neglecting
the low sorption and the root uptake of chloride. Therefore, theretardation factor is one, the sorption and decay constants are zeroand the redistribution of chloride can be described by the following
convection–dispersion equation.

∂C
∂
=
∂t
∂xi

2.3. Theory and equations∂∂∂hK(h) KijA+ KizA
=
∂t
∂xi
∂xj

dimensional problem); h is the pressure head (cm); K is thehydraulic conductivity (cm/day); KijA is a component of a dimensionless anisotropy tensor; and Wr is the root water uptake(cm3 /cm3 /day).

(5)

where SNH4 (mg/g) represents the adsorbed concentration, Kd isdistribution coefﬁcient (for linear adsorption) (cm3 /g), CNH4 is theNH4 -N concentration in the soil solution (mg/cm3 ), B is the dry
bulk density (g/cm3 ), l and s are the ﬁrst-order rate constantsin the soil solution (day−1 ) and in the solid phase (day−1 ), respectively, Wr is the sink term representing root water uptake from theRichards equation, and cNH4s is the NH4 -N concentration associatedwith the sink term (mg/cm3 ).
NO3 -N dynamics in this study was simulated using the following
convection–dispersion equation∂CNO3
∂
=
∂t
∂xi



Dij

∂CNO3
∂xj



−

∂qi CNO3+ s B SNH4 + l CNH4∂xi− ld CNO3 − Wr cNO3S

(6)

20

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

where CNO3 nitrate concentration in soil solution (mg/cm3 ), ld
is the ﬁrst-order rate constants in the soil solution (day−1 ), andcNO3s is the NO3 -N concentration associated with the root water
uptake (mg/cm3 ). The last term of Eq. (6) represents a passive
uptake of NO3 -N. Nitriﬁcation of NH4 -N is assumed to be the solesource of NO3 -N in the soil, thus, all other reactive processes, suchas mineralization, were neglected in our relatively short-duration
experiment. The nitriﬁcation process from NH4 -N to NO3 -N was
represented in the governing equation for NO3 -N (6) as a zero-orderproduction reaction term.
2.3.4. E. coli and phages
An attachment-detachment model, in which the attachment
coefﬁcient was calculated using the ﬁltration theory, was used for
simulating the transport of E. coli and phages. The HYDRUS model
considers reversible adsorption of microorganisms to two types ofkinetic sites, with one process usually representing attachment tothe solid phase and the other straining (Bradford et al., 2006). The
ﬁltration theory determines the attachment coefﬁcient based on
the diameter of both soil particles and microorganisms. It is possible to assign a different blocking function for each sorption site.
Sorption sites may have different sticking efﬁciencies and ﬁrstorder entrainment rates. The code considers different ﬁrst-order
rates in both the liquid and solid phases representing inactivation
processes. Microorganism can be removed from the liquid phase byﬁrst-order inactivation and/or attachment to the solid phase. Fromthe solid phase they can be removed by the ﬁrst-order entrainment
and inactivation. The two-site attachment-detachment model isincorporated in the convection–dispersion equation as describedby Hassanizadeh and Schijven (2000).
2.3.5. Estrogens
Estrogens are highly sorptive to the soil matrix and produce
metabolites in the soil environment. Estradiol quickly degrades
to estrone, but estrone has a lower degradation rate (Stumpe andMarschner, 2007). The instantaneous or time dependent sorption
and ﬁrst-order transformation reaction of estradiol into estrone
can be described by solving the convective–dispersive equations
as described by Casey et al. (2005).
2.4. Initial and boundary conditions
Soil hydraulic and transport parameters used as model input are
shown in Table 2. Measured background values of the soil watercontent and different contaminants before slurry application wereused as initial conditions (Table 3). The initial soil water content
in the area saturated by applied slurry was speciﬁed as the watercontent at saturation ( s ). Similarly, all contaminants were speciﬁed in the model using initial conditions in the slurry-applied area.
A variable ﬂux condition (calculated from precipitation and actual
evapotranspiration data) was applied to the upper boundary of thesimulated zone. Actual evapotranspiration was calculated using theequation given by Allen et al. (1998). A zero ﬂux condition wasapplied at the two vertical boundaries, considering the symmetrical distribution of water and slurry components and therefore
no gradients over the boundaries. At the bottom boundary (theposition of the groundwater table), a constant saturation condition was assumed. The depth of the water table (and the simulatedzone) during the experimental period was set to 1 m, based on ﬁeld
observations. For solute transport, the third-type Cauchy boundary
condition was chosen at the top and bottom of the soil proﬁle. It
was assumed that no contaminant was entering with precipitationand leaving with evaporation.

2.5. Calibration of parameters
The parameters of the soil water retention curves for two soil
types required by the model were estimated from measured soil
texture and bulk density using the Rosetta Lite software (Schaap
et al., 2001) implemented in HYDRUS. Soil properties were determined by the standard methods (Amin et al., 2012). The bulk
density in the top layer was lower than in the lower layer becauseof the breakup of soil structure during tillage and slurry injection
operations. The soil hydraulic properties in slurry hotspots weredifferent from the bulk soil, due to the added slurry organic matter. We assumed that the slurry organic matter only changed thehydraulic properties in section A after the slurry application. The
inverse solution option of HYDRUS was therefore used to calculate
modiﬁed soil hydraulic properties of the soil affected by the slurry
application from the observed water contents. As a result, threeslightly different sets of hydraulic properties were used for eachﬁeld site (top soil, lower soil, and slurry hotspot). The soil hydraulicparameters of the van Genuchten–Mualem model (van Genuchten,1980) used in HYDRUS are given in Table 2.
The longitudinal and transversal dispersivities for two soil types
were manually optimized using the observed chloride redistribution data under the assumption that the ratio between longitudinal
and transverse dispersivities was 10 (Pang et al., 2000). The
optimized dispersivity values were then used for simulating theredistribution of other solutes. The parameters for different contaminants were estimated either from the available experimental
data or from the reported values in the literature.

2.6. Statistical analysis
Agreement between predicted and observed data was evaluatedby some frequently used model performance criteria, which can be
expressed as (Wang et al., 2010):

Table 2
Soil hydraulic and transport parameters used as model input for HYDRUS.
Soil

r a

s a

˛b (1/cm)nb

Ks c (cm/d)lc

L d (cm)T d (cm)

e

0.07
0.11
0.13
0.10
0.10
0.10

0.46
0.46
0.46
0.46
0.50
0.48

0.058
0.055
0.032
0.060
0.050
0.030

1.48
1.48
1.40
1.46
1.46
1.30

8
8
8
62
65
63

0.5
0.5
0.5
0.5
0.5
0.5

12
12
12
10
12
10

1
1
1
1
1
1

S1
S2
S3
E1e
E2
E3
a

 r and  s are the residual and saturated water contents, respectively.
˛ and n are empirical parameters determining the shape of the hydraulic functions.
c
Ks is the saturated hydraulic conductivity. l is a pore-connectivity parameter.
dL and T represent longitudinal and transverse dispersivities, respectively.
e
Letters S and E in soil names refer to Silstrup and Estrup, respectively, and numbers to soil horizons: 1 for lower layer, 2 for top layer, and 3 for injection slit.
bM.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

21

Table 3
Initial and background values of different slurry constituents used in the model.
Sitea
SS
SB
ES
EB
a
b
c

SWCb
cm3 /cm3

Chloride

0.46
0.30
0.48
0.30

0.69
0.035
0.28
0.025

NH4 -N
mg/cm3NO3 -N

0.98
0.004
0.80
0.004

0.008
0.008
0.007
0.007

Phage
PFU/cm3

E. coli
CFU/cm3

E2c

2.0 × 1050
2.0 × 1050

1.7 × 1040
2 × 104
0

16
0.04
1.2
0.03

E1c
ng/cm3
21
0
6.3
0

S and E represent Silstrup and Estrup, respectively, and subscripts S and B the slurry saturated zone and the rest of the modelled area, respectively.
SWC, soil water content.
E2 and E1 represent estradiol and estrone, respectively.

(i) Root mean square error (RMSE): n
(Pi − Oi )2
RMSE = 
i=1

n

(7)

(ii) Coefﬁcient of model determination (CD):
CD =

2
n
(Oi − Ō)i=12n
(P − Ō)
i=1 i

(8)

(iii) Nash–Sutcliffe modelling efﬁciency (EF):EF = 1 −n(Oi − Pi )2i=1
2n(Oi − Ō)i=1

considerably by the site conditions. The redistribution of estrogens was much more affected than that of the microorganisms.
It may be due to the high estrogen sorption capacity of slurryorganic matter (Amin et al., 2012). The immobile slurry dry mattersorbed estrogens and thus retarded redistribution than did for the
microorganisms and mineral N. However, it was somewhat unclear
what factors are more important for the redistribution processes.
A simulation technique was therefore used to estimate different
transport parameters such as sorption coefﬁcient, inactivation rateof microorganisms, degradation of estrogens, nitriﬁcation and denitriﬁcation rates. The calibrated model was then used to investigate
the environmental fate (leaching risks) of the slurry-borne constituents.

(9)

where n is the total number of observations, Oi and Pi are theobserved and predicted values of the ith data point, respectively,
and Ō is the observed mean.
All three indices indicate the model accuracy. The RMSE’s closeness to 0 indicates the low average error in the model, however inabsolute terms (same unit as variable). The CD value gives the total
variance of the observed values explained by the predicted dataand should be close to 1 to indicate high accuracy of the model.
Modelling efﬁciencies (EF) can vary from −∞ to 1, but the closer
the model efﬁciency is to 1, the more accurate the model is. A
zero value of EF indicates that the model predictions are no better predictors than the mean of the observed data, while a negative
efﬁciency occurs when the observed mean is a better predictor thanthe model. In addition to the indices mentioned above the coefﬁcient of determination (R2 ) was calculated. Values of R2 indicate if
the precision of the model is high or low. If precision is high wewill have a value of R2 that is close to one, meaning that there isa strong correlation between the predicted and observed data, but
the relation may not be 1:1 (Tedeschi, 2006). Hence, it is only relevant to discuss the precision in relation to model evaluation whenaccuracy is high.
3. Results and discussion
Field observation data showed that the redistribution processof aqueous and solid phase of the slurry was considerably affectedby the site conditions (different slurry dry matter content and soil
texture). The soil water content in slurry hotspot remained higher
than that in the surrounding soil even after seven weeks of theslurry injection. The redistribution of chloride followed the similar trend of soil water content during the ﬁrst two measurements
due to convective transport and its nonreactive nature. On theother hand, nitriﬁcation and N balance appeared different for different conditions throughout the experimental period. Mineral N
disappeared from Estrup before the last measurement after sevenweeks of the slurry injection but not from Silstrup. Virus redistribution was only slightly affected, whereas E. coli was affected

3.1. Parameter values
3.1.1. Chloride
Dispersivity is a sensitive parameter in estimating the leaching of contaminants through the vadose zone into groundwater(Vanderborght and Vereecken, 2007). The optimized value of longitudinal dispersivity was 12 cm at Silstrup and 10 cm at Estrup.
Transverse dispersivity of 1 cm was assumed for both sites. Similarvalues of transverse dispersivities were reported in literature (e.g.,
Schijven and Šimůnek, 2002; Vanderborght and Vereecken, 2007).
3.1.2. Mineral N
The molecular diffusion of NH4 -N was set to 1.25 cm2 /day. Similar values (0.85–1.65 cm2 /day) for this parameter were used by
Hassan et al. (2010) and Wang et al. (2010). The presence andvolatilization of NH3 was not considered individually, but it was
included into the overall decay constant used in simulations toaccount for otherwise neglected factors. The rate coefﬁcient of
nitriﬁcation in the soil usually varies between 0.02 and 0.3 day−1
depending on soil properties, water content, ammonium content,
air content, and biochemical activity (Bolado-Rodriguez et al., 2010;
Crevoisier et al., 2008; Hanson et al., 2006). The ﬁrst-order rate constant for ammonium nitriﬁcation was assumed to be 0.05 day−1for both sites, given the low temperature during the study period(6–11 ◦ C at Silstrup and 8–15 ◦ C at Estrup). The Kd value for NH4 N in the model was considered to be 1.1 cm3 /g for both soils.
The Kd values reported by different authors were in the range of
0.2–4 cm3 /g (Hanson et al., 2006; Jellali et al., 2010). The ﬁrstorder rate denitriﬁcation constant for NO3 -N was assumed to be
0.08 day−1 . This value was taken from the range 0.001–0.08 day−1
used by Hassan et al. (2010) and Wang et al. (2010). Nitrate sorption to the soil matrix was assumed to be negligible with regards
to other processes (Crevoisier et al., 2008).
3.1.3. Phage
The d50 for the Silstrup and Estrup soils was 0.08 mm and
0.2 mm, respectively. The average size of the phage was considered
0.1 ␮m (Schijven et al., 2002; Taylor et al., 2004). The ﬁrst-orderdecay constant for both attached and free phases was assumed tobe 0.05–0.07 day−1 , based on the experimental results. This value

22

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

could vary given different conditions, such as soil type, water content, temperature, etc. Schijven and Šimůnek (2002) optimized thevalue between 0.01 and 0.08 day−1 . The detachment coefﬁcient was
assumed to be 0.08 day−1 at both sites as the centre value reported
in the literature. The detachment coefﬁcient for phage was foundin the literature to be between 0.001 and 0.18 day−1 (e.g., Schijven
et al., 2002).
3.1.4. E. coli
The average size of the E. coli was assumed to be 1 ␮m
(Pachepsky et al., 2006; Taylor et al., 2004). The ﬁrst-order decay
constant for both attached and free phase E. coli was found tobe 0.1–0.12 day−1 at both sites. Jiang et al. (2010) used this value
between 0.3 and 0.8 day−1 . This value was a little lower in our study
because of the low temperature during the whole study period. The
detachment coefﬁcient for E. coli was assumed 0.05 day−1 at bothsites.
3.1.5. Estrogens
The sorption coefﬁcient for both estradiol and estrone was calculated as 11 ml/g for the soil at Silstrup and 14 ml/g for the soil
at Estrup, based on the organic carbon content in the soil afterslurry application. Since the redistribution of estrogens takes placein an environment dominated by slurry liquid the Kd value wascalculated using the Koc obtained for the soil-slurry environment(presence of slurry-DOC in the aqueous phase) by Amin et al.
(2012). The study suggests that DOC present in slurry can facilitate transport of contaminants, and that the sorption coefﬁcientestimated using the laboratory batch experiment can underestimate the leaching risks of slurry-borne contaminants. The same Kd
value was used for both estradiol and estrone, as they have usuallysimilar sorptivity (Caron et al., 2010). The ﬁrst-order rate constantfor both estrone and estradiol was assumed to be the same atboth sites (0.08 day−1 ). This value was in the lower range reported
in literature, probably due to low temperatures during the study
period. The value was found to be 0.007–1.8 day−1 for estradiol and
0.09–0.14 day−1 for estrone in literature (e.g., Das et al., 2004).
3.2. Model performance
The HYDRUS code simulated the redistribution process of different components and their persistence in soil with reasonably
high model efﬁciency (Table 4). The EF values for the simulation ofwater, chloride, and mineral N were between 0.92 and 0.96 for Silstrup, and between 0.91 and 0.95 for Estrup. Redistribution andsurvival of both phages and E. coli were simulated with similar
efﬁciency (0.86–0.94) at both sites. Transformation of estradiol toestrone, and their redistribution and persistence were predictedwith higher model efﬁciency (EF = 0.89–0.94) at Estrup, but withlower model efﬁciency (0.72–0.75) at Silstrup. The CD values werealso close to 1, with only a few exceptions. The R2 values between
predicted and observed values for all simulations were higher than0.89, except for estrogens at Silstrup (0.72–0.84) indicating thatprecision of the predictions was also high. Other users of HYDRUS
achieved similar or higher ranges of model efﬁciency for simulating
the transport/sorption processes of water and solutes (e.g., Unoldet al., 2009). The relationship between the observed and predictedvalues of each studied component will be discussed in the following
sections.
3.2.1. Soil water content
After the calibration procedure, we found good agreement
between the observed and predicted soil water contents at different positions of the soil proﬁle at both ﬁeld sites (Fig. 3). Higher
accuracies in simulating soil water content at ﬁeld condition werealso obtained using HYDRUS by Kandelous and Šimůnek (2010)(R2 = 0.77–0.98) and Zhou et al. (2007) (R2 = 0.81–0.99). The slurry
injection slit (section A) had a higher water holding capacity due
to the high content of organic matter. The simulation conﬁrmedthat the persistently increased soil water content in the injection
slit could be described by the changed hydraulic parameters in thispart of the soil. The short-term effect of immobile slurry organic
matter on soil hydraulic parameters can considerably affect theenvironmental fate of contaminates in the slurry injection zone,such as the survival of microorganisms, nutrient dynamics, anddegradation of organic contaminants. Although the increased water
holding capacity of the slurry slit will have diminished graduallywith time, the soil hydraulic parameters used in the model weretime-independent, as HYDRUS does not have an option for timevariable soil hydraulic properties. Consequently, the water content
in section A during the ﬁnal part of the experiment was slightly
overestimated (Fig. 3). Deviations between predicted and simulated values of soil water contents, observed especially in sections
E and F, were likely caused by natural heterogeneity of ﬁeld properties.
3.2.2. Chloride
Some observed values of chloride had a high standard error due
to heterogeneous soil conditions and the likely presence of macropore ﬂow (Fig. 3). There were some uncertainties in the uniformityof the slurry application and solid–liquid ratio along the slurry slit.
However, the average values of six sampling positions probably
provided a representative result. The simulated and measured chloride concentrations seem to be in agreement. However, agreements
between simulated and measured values were relatively lower for
most distant sections of D, E, and F. Deviations between predicted
and observed values were higher at the Silstrup site because of theuncertainty caused by the preferential ﬂow in this clay rich soil.
3.2.3. Mineral N
NH4 -N concentrations at different sampling times at both sites
were simulated well when adsorption, nitriﬁcation, and dissipation processes were considered. Model predictions were close toobserved values in most cases (Fig. 3). Like the soil water contents,
which remained near saturation for the ﬁrst three sampling times
in section A at Silstrup, the NH4 -N concentrations in this section
remained at high values, indicating low initial nitriﬁcation. Whenwater contents decreased, NH4 -N concentrations started to dissipate noticeably. Moreover, the actual nitriﬁcation rate may havebeen different in the different sections due to different oxygen contents in the different sections. The oxygen content in section A may
also have changed over time with changes in the nitriﬁcation rate.
Since the nitriﬁcation rate in the model was time-independent,
the model predictions deviated slightly from observed values forsome sampling times at both sites (Fig. 3). Due to these reasons,the model underestimated the NH4 -N concentration in section A
on day 6 at Silstrup. The model could not predict the higher NH4 N concentration in section D on day 18 at Silstrup and in section
F on day 6 at Estrup (Fig. 3). The agreements between some ofthe predicted and observed NO3 -N concentrations were low inthe beginning of the experiments at Silstrup and at the end at
Estrup.
3.2.4. Phage
It appears that the attachment coefﬁcient calculated by the ﬁltration theory based on the average soil particle diameter and theaverage phage size gave a good accuracy. Simulated phage concentrations were within the range of observed values in most cases(Fig. 4). However, there were some comparatively higher deviations between observed and predicted values in sections C–F at
Silstrup. The transport of phage through the soil matrix to sections D and F was probably very low, especially in the ﬁne-grained

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

23

Fig. 3. Observed and predicted soil water contents, chloride, NH4 -N, and NO3 -N concentrations at Silstrup (left) and Estrup (right) (different colour symbols represent
different soil sections as indicated in the legend).

24

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

Table 4
Performance indicators for the model simulating different components.
Site

Silstrup

Component

R2

RMSEa

CD

EF

R2

RMSE

CD

EF

Water (cm3 /cm3 )Chloride (mg/cm3 )NH4 -N (mg/cm3 )NO3 -N (mg/cm3 )Phage (PFU/cm3 )E. coli (CFU/cm3 )Estradiol (ng/cm3 )Estrone (ng/cm3 )

0.94
0.92
0.96
0.96
0.94
0.92
0.84
0.72

0.01
0.03
0.05
0.02
9867
968
1.9
3.8

1.12
0.93
1.14
1.05
0.98
1.28
0.80
1.13

0.94
0.92
0.96
0.96
0.94
0.92
0.75
0.72

0.92
0.94
0.95
0.92
0.94
0.89
0.94
0.96

0.01
0.01
0.04
0.03
16,713
1305
0.12
0.42

0.96
1.20
1.09
1.08
1.32
0.98
0.86
0.84

0.91
0.93
0.95
0.91
0.90
0.86
0.89
0.94

a

Estrup

The unit of RMSE (root mean square error) is the same as the unit of the corresponding slurry constituent.

soil at Silstrup. The possibility of macropore ﬂow was higher at
Silstrup, due to the high clay content. Because of the very highconcentration gradient, even insigniﬁcant macropore ﬂow could
have caused considerable variation in concentrations in sections
D, E, and F. At Estrup, the model slightly underestimated the phage

concentrations in sections A–B in the beginning of the experiment,but overestimated them in sections C–F in some of the cases (Fig. 4).
For simpliﬁcation, the same ﬁrst-order decay rate was used for all
sections, although it was perhaps slightly different between theslurry slit and other sections.

Fig. 4. Observed and predicted phages, E. coli and estrogens (E2 is for estradiol and E1 for estrone) concentrations at Silstrup (left) and Estrup (right) (different colour symbols
represent different soil sections as indicated in the legend).

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

Chloride (C/Co)

day 0

day 1
day 6
day 18day 46

NO3-N (C/Co)

NH4-N (C/Co)

1.0
0.8
0.6
0.4
0.2
0.0

1.0
0.8
0.6

0.4
0.2

0.0
0 2 4 6 8 1012

1.0
0.8
0.6
0.4
0.2
0.0

0 2 4 6 8 1012
Phage (C/Co)

E. coli (C/Co)

1.0
0.8
0.6
0.4
0.2
0.0
0 2 4 6 8 1012

0 2 4 6 8 1012
1.0
0.8
0.6
0.4
0.2
0.0

Estrone (C/Co)

3.2.6. Estrogens
The redistribution and persistence of estradiol and estrone indifferent soil sections at Estrup were simulated well when theirsorption, degradation, and transformations processes were considered. Low contact time due to the redistribution process and thepresence of soluble organic matter in the slurry liquid decreased
the sorption in the slurry slit. Stumpe and Marschner (2010) suggested that the presence of dissolved organic carbon in the liquid
phase can decrease sorption to the solid phase. Casey et al. (2005)
found a low Koc value for a modelling study of a leaching experiment and argued that low contact time during transport could be
responsible for the low Koc value. When the Kd value speciﬁc forthe slurry environment was used, we found a reasonable agreement between observations and simulations. However, there weresome considerable differences between the observed and predictedvalues at Silstrup (Fig. 4), as is also suggested by the indices inTable 4. In a few cases, the model overestimated the estrogenconcentration in section B and E, but underestimated it in section C. Concentrations in section C were quite high at only one or
two times, which cannot be explained using the calculated Kd values. The increased concentration in section C was likely not due
to matrix ﬂow. Other processes, such as slurry-estrogens interactions and preferential ﬂow, may have led to the enhanced transport(Arnon et al., 2008). The agreements between observed and predicted values were generally much lower at Silstrup compared to
Estrup.

Legend

1.0
0.8
0.6
0.4
0.2
0.0
0 2 4 6 8 1012

Estradiol (C/Co)

3.2.5. E. coli
As with phages, E. coli concentrations in different sections andat different times were, in most cases, predicted with high accuracy(Fig. 4). The transport of E. coli through the soil was much lower at
Silstrup, due to ﬁne grained soil and immobile slurry dry matter. In
sections D, E, and F at Silstrup, the measured concentrations werevery low during the ﬁrst three sampling events and under the detection limit after that. The model overestimated E. coli concentrations
for some of these cases at Silstrup (Fig. 4). The model could not estimate concentrations as high as observed in section B on the ﬁrst
sampling day. Due to uncertainties in survival rates in different sections and due to preferential ﬂow, relatively high deviations wereobserved at low concentration values at both sites. Although thesurvival rate was actually lower at Silstrup than at Estrup, becauseof the relatively dry conditions, an average rate was considered
for both sites in order to generalize the model, and to predict theleaching potential of E. coli for normal weather conditions.

25

0 2 4 6 8 1012

Distance (cm)

1.0
0.8
0.6
0.4
0.2
0.0
0 2 4 6 8 1012
Distance (cm)Fig. 5. Ratio of the predicted to the initial concentrations of different slurry constituents at a cross-section at a 5-cm soil depth at Silstrup.

3.3. Horizontal redistribution
Simulating transport processes under ﬁeld conditions is more
complex than for simpliﬁed laboratory setups. For example, thehigh clay content of the Silstrup soil may have resulted in the development of cracks under unsaturated conditions and during tillage
operations. Preferential ﬂow through macropores, ﬁssures, and/or
voids created by crop roots is difﬁcult to predict (e.g., Šimůnek
et al., 2003). However, since the values of the statistical indicesindicate that the model simulates the observed values, the simulated results were used to understand the processes that occurredin between and beyond the observation events. The temporal andspatial dynamics of different contaminants around slurry-injection
slit at both sites predicted by HYDRUS is shown in Figs. 5 and 6.
Chloride transport in the horizontal direction was much slower
at Silstrup than Estrup, and there was still a concentration gradient
after 46 days (Figs. 5 and 6). The redistribution of slurry was slower
in the sandy clay loam soil at Silstrup, due to low hydraulic conductivity, compared to conditions in the sandy loam soil at Estrup.
The simulation showed that the downward ﬂux of chloride fromthe observed soil proﬁle was not considerable at Silstrup. Chloride

concentrations at the end of the experiment at Estrup were close
to soil background values due to the faster redistribution as well as
leaching from the proﬁle.
NH4 -N redistribution was primarily within 6–7 cm at both sites
due to sorption processes and nitriﬁcation (Figs. 5 and 6). On
the other hand, NO3 -N redistributed through the entire observed
proﬁle at both sites. The redistribution of NO3 -N and chloride was
not exactly the same because NO3 -N took time to accumulate bynitriﬁcation and its concentrations decreased with time due tocrop uptake. Denitriﬁcation was considered the same at both ﬁeld
sites, as they had the same crop and very similar soil moisture(0.29–0.41 cm3 /cm3 at Silstrup and 0.27–0.36 cm3 /cm3 at Estrup)
and weather conditions. At the end of the experiment the mineral
nitrogen almost disappeared at Estrup, whereas the peak concentration was observed at Silstrup (Figs. 5 and 6). This suggests thatthe leaching of N from the sampled soil proﬁle at Estrup was higher
compared to Silstrup.
Phages primarily redistributed up to the distance of 8 cm, with
only a few travelling beyond this distance in the horizontal direction (Fig. 5). At Silstrup, concentrations decreased at a rate of about

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

Chloride (C/Co)

26

1.0
0.8
0.6
0.4
0.2
0.0

Legend
day 0
day 1
day 6
day 18day 49

NO3-N (C/Co)

1.0
0.8
0.6
0.4
0.2
0.0

Phage (C/Co)

1.0
0.8
0.6
0.4
0.2
0.0

1.0
0.8
0.6
0.4
0.2
0.0

Estrone (C/Co)

NH4-N (C/Co)

0 2 4 6 8 10 12

1.0
0.8
0.6
0.4
0.2
0.0

E. coli (C/Co)

0 2 4 6 8 1012
1.0
0.8
0.6
0.4
0.2
0.0

0 2 4 6 8 10 12

Estradiol (C/Co)

0 2 4 6 8 10 12
1.0
0.8
0.6
0.4
0.2
0.0
0 2 4 6 8 10 12Distance (cm)

0 2 4 6 8 10 12

0 2 4 6 8 10 12Distance (cm)Fig. 6. Ratio of the predicted to the initial concentrations of different slurry constituents at a cross-section at a 5-cm soil depth at Estrup.

one log scale for every 2 cm travelled (data not shown). Consequently, a very low concentration was observed at a distance of
10 cm from the slurry slit at this site. On the other hand, the redistribution was higher at Estrup due to relatively larger pore sizes.
Phages travelled as far as 12 cm at Estrup (Fig. 6). E. coli redistribution was much slower in the ﬁne-grained soil at Silstrup. Almost all
E. coli remained within 8 cm from the slurry slit (Fig. 5). It seems thatthe redistribution of E. coli in sandy loam soil at Estrup was muchhigher. They travelled up to a distance of 12 cm in the horizontal
direction.
The transport of estrogens through the soil matrix was very
low because of their sorptivity. They remained mainly within the
distance of 5–6 cm. The values of Kd were higher at Estrup thanat Silstrup, due to the higher content of the soil organic carbon at Estrup (Table 1). Estrogens can be transported in soils
with the mobile DOC in the liquid phase of the injected slurry,
as estrogens readily sorb to the organic carbon. However, underrelatively dry conditions, the slurry-DOC can also be adsorbed
to soil particles during redistribution (Stumpe and Marschner,2010).
The simulations show that assuming a homogeneous distribution of the contaminants originating from injected-slurry whenestimating the leaching or fate of these contaminants may resultin errors, especially for microorganisms and estrogens.

3.4. Leaching of contaminants to the subsoil
Model predictions of the microorganism transport were supported by the analysis of the drainage leachates collected fromthe two ﬁeld sites during the experimental period. No E. coli
was detected in the drainage water during the entire season at
Silstrup, and only a few viable E. coli were detected at Estrup after
the rain events on days 35–38 (Bech et al., 2010). The leachingpotential of mineral N, phages, E. coli, and estrogens from the top
20 cm of the soil proﬁle was evaluated using HYDRUS and theparameters established above for the last 10 years of precipitationand evapotranspiration data for the respective sites (Table 5).
Leached amounts varied in different years for all contaminants, but
the correlation between the amount leached and the total amountof precipitation was very low. The total leaching was primarily
inﬂuenced by the time and intensity of precipitation, especially forthe spring application of slurry in Denmark (Fig. 7). Precipitation
during the ﬁrst week after the slurry application was not very
effective in leaching of mineral N, as N still remained in the formof less mobile NH4 -N. Only after N was transformed to NO3 -N bynitriﬁcation could it be leached by the inﬁltrating water. Hanson
et al. (2006) reported that NH4 -N remained concentrated aroundthe application line at all times for all fertigation strategies, becauseof soil adsorption and subsequent fast nitriﬁcation and/or root
uptake, whereas nitrate moved continuously downwards during
the ﬁrst 28-day period. The peak ﬂux of mineral N at a 20-cm soil
depth was not observed in most cases until at least 10 days after theslurry application (Fig. 7). The lowest N leaching at both sites wasfound in 2007, because of a very dry ﬁrst 70 days at Silstrup and 40
days at Estrup. Leaching in 2005 at Silstrup was also low because of
low seasonal precipitation and 15 continuous rainless days during
the peak time (15–30 days after the slurry application) of NO3 -N
accumulation. In 2001, a continuous water ﬂux during the peak
period of NO3 -N accumulation leached the highest quantity ofmineral N at Silstrup. At Estrup, the highest quantity of N leached
was observed in 2008 when the site received some heavy precipitation events (>30 mm/day) during the ﬁrst 30 days after the slurry
application. Leaching in 2003 was higher than in 2001 and 2002 at
Estrup due to the timing of the high intensity precipitations (Fig. 7).
The average leaching potentials for 6 months after application from the top 20 cm of the soil for phage and E. coli
were 12.2 × 104 PFU/m2 and 2.1 × 104 CFU/m2 at Silstrup and3.3 × 106 PFU/m2 and 0.58 × 106 CFU/m2 at Estrup, respectively.
Leaching of both phage and E. coli was signiﬁcantly higher in the
sandy loam soil at Estrup than in the sandy clay loam soil at Silstrup(Table 5). The ﬂux of phage and E. coli substantially decreased withan increase in the number of rainless days just after application(Fig. 7). Phages and E. coli were much more susceptible to leachingfor the precipitation events occurring during the ﬁrst week after theslurry application. Leaching of both microorganisms at Silstrup was
low in 2002, 2005, and 2007 because the 6, 3, and 7 days, respectively, after slurry application were rainless. On the other hand, thehighest leaching was found in 2001 when Silstrup received considerable precipitation the very ﬁrst day after slurry application.
Leaching was also high at Estrup in 2003 because of high precipitation during the ﬁrst 3–4 weeks after the slurry application, whereasleaching was very low in 2002 when there was no precipitation
during the ﬁrst several weeks (Fig. 7).
The leached mass of estrogens from the topsoil varied between
100 and 1380 ng/m2 (average 0.12%) at Silstrup and 40–620 ng/m2(average 0.4%) at Estrup (Table 5). Although this seems low, it
should be noted that estrogens can affect aquatic ecology at very
low concentration levels (1–2 ng/L). As with phage and E. coli, thetiming of precipitation events was very important in estrogens
leaching (Fig. 7). If soils get dry after the slurry application, contaminants concentration in liquid phase becomes high which in turn

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

27

Table 5Predicted total leaching of different contaminants below 0.20 m depth during 6 months after the slurry application using 10-year precipitation data at Silstrup and Estrup.
Year

2008
2007
2006
2005
2004
2003
2002
2001
2000
1999

Estrup

PPTa(mm)

Drainage(mm)

Na(102 mg/m2 )Phage
E. coli(104 PFU/m2 ) (104 CFU/m2 )Esa(102 ng/m2 )

PPT
(mm)

Drainage(mm)

N(102 mg/m2 )Phage(104 PFU/m2 )E. coli(104 CFU/m2 )Es(102 ng/m2 )

448
427
360
281
459
417
373
522
322
548

341
321
246
189
331
303
297
396
199
370

2.8
1.8
7.6
2.4
7.6
10.4
3.4
21.6
3.4
13.4

3.8
4.2
4.5
1.5
8.2
20.1
4.8
42.6
6.7
25.6

2.2
1.0
6.4
2.6
5.2
4.4
1.2
13.8
1.2
7.2

569
594
359
382
499
419
420
482
318
444

446
461
253
265
373
307
336
374
201
314

9.8
3.8
9.0
7.8
5.2
7.0
5.2
5.0
4.4
4.4

883
94
550
301
309
442
54
270
187
197

254
5
94
26
38
94
4
28
15
19

6.2
0.6
5.4
3.6
4.2
3.0
0.4
3.4
2.4
2.6

0.44
0.04
1.80
0.24
1.84
1.22
0.34
11.66
0.66
2.92

PPT, N, and Es represent precipitation, mineral N, and total estrogens, respectively.

Flux (unit /cm/day)

Flux (unit/cm/day)

Flux (mg/cm/day)

Water flux (cm2/day)a

Silstrup

0

2001

2002

2003

Time (day)Time (day)Time (day)60

120 180 0

60

120 180 0

60

120 180

0
-5
-10
-15
-20
-25
-30
0.0
-0.2
-0.4

-0.6
-0.8
-1.0
0

Chloride

-500
-1000
-1500

E. coli

-2000

Phage

-2500

-0.02

-0.07

-0.12

MineralN(mg)
Estrogen(ng)

Fig. 7. Simulated ﬂuxes of different contaminants through the soil depth of 20 cm during 6 months after the slurry application at Estrup in three years (minus sign in the Yaxis indicating downward ﬂux).

28

M.G.M. Amin et al. / Agricultural Water Management 131 (2014) 17–29

increases the sorption in simulation process and also in real ﬁeld
condition so the effect of subsequent precipitation is less important for leaching. In these ﬁeld sites in 2005–2006, estrogens weredetected in sub-surface drainage water after some heavy precipitation events (Kjaer et al., 2007).
4. Conclusions
The calibrated HYDRUS 2D was capable of predicting the redistribution and persistence of slurry-borne contaminants in the soil.
The simulation conﬁrmed that the persistently increased soil watercontent in the injection slit could be described by the changed
hydraulic parameters in this part of the soil. Comparison between
simulated and observed results suggests that the N dynamics wereslightly different for different sections sampled. E. coli redistribution was much affected by the site conditions compared to phage
because of the larger size of E. coli. The redistribution of estrogens,assuming linear sorption and degradation and with Kd values calculated using the Koc value of similar soil under similar soil-slurry
environment, was simulated well at Estrup, but less so at Silstrup.
Contaminants persisted for up to 4–6 months, and leached continually during this period at varying rates depending on their chemical
behaviour and the intensity and frequency of precipitation events.
Mineral N started to transport as soon as it was transformed fromNH4 -N to NO3 -N and it took more than a week to accumulate aconsiderable amount of NO3 -N in the soil. In contrast, the vulnerability to leaching of E. coli, phage, and estrogens decreased
rapidly with time of subsequent precipitations as their concentrations decreased with time due to degradation, mineralization, anddie off.
Acknowledgements
This study was supported by the Pathos Project funded by theStrategic Research Council of Denmark (ENV 2104-07-0015) and
Grundfoss New Business A/S. We would also like to thank Anita
Forslund, Tina Bech and Martin Hansen of Copenhagen University
and Preben Olsen of Aarhus University, as well as the Danish Pesticide Leaching Assessment Program, Denmark for their cooperation
during the ﬁeld experiment.
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