Published online August 10, 2017

Special Section: Soil Variability
and Biogeochemical Fluxes

Core Ideas• Eleven years of Zn and Cu movement in an Oxisol amended with pig
slurry (PS) was modeled.
• Root growth and water uptake
effects were successfully added in
HYDRUS simulations.
• Concentration profiles of Zn were
described better than those of Cu.
• DOC-facilitated Cu transport needs
to be considered when modeling
Cu transport in soils.
• Zn concentrations will not be a
problem during the next 50 yr in PSamended soils.

F.J.K. Mallmann, D.R. dos Santos,
M.A. Bender, E. Gubiani, and J.P.G.
Minella, Dep. of Soils, Federal Univ. of
Santa Maria, 97105-900 Santa Maria,
RS, Brazil; F.J.K. Mallmann, Dep. ofAgricultural Sciences, Regional Integrated Univ. of High Uruguay andMissions (URI), 98400-000 Frederico
Westphalen, RS, Brazil; M. da Veiga,
EPAGRI, P.O. Box 116, 89620‑000, Campos Novos, SC, Brazil; F. van Oort,
INRA-AgroParisTech, UMR 1402, Ecosys, RD10, F-78026 Cedex, France;
J. Šimůnek, Dep. of Environmental
Sciences, Univ. of California, Riverside,
CA 92521. *Corresponding author(fabiojkmallmann@gmail.com).
Received 17 Jan. 2017.
Accepted 13 May 2017.
Citation: Mallmann, F.J.K., D.R. dos
Santos, M.A. Bender, E. Gubiani, M. da
Viega, J.P.G. Minella, F. van Oort, andJ. Šimůnek. Modeling zinc and copper
movement in an Oxisol under longterm pig slurry amendments. Vadose
Zone J. doi:10.2136/vzj2017.01.0011© Soil Science Society of America

5585 Guilford Rd., Madison, WI 53711 USA.
All rights reserved.

Modeling Zinc and Copper
Movement in an Oxisol underLong-Term Pig Slurry Amendments

Fábio Joel Kochem Mallmann,* Danilo Rheinheimer dos
Santos, Marcos Antonio Bender, Elci Gubiani, Milton da
Veiga, Jean Paolo Gomes Minella, Folkert van Oort,
and Jirka Šimůnek

Increases in Zn and Cu concentrations in soils amended with pig slurry (PS)can be described using numerical models. Our main objective was tovalidate that the HYDRUS-1D model is able to numerically describe profile
concentrations and long-term vertical transport of Zn and Cu in a clay soil
(Oxisol) cultivated under annual cropping in a no-till system and contaminated by successive doses of PS amendments. We first used a modelingapproach that had previously been validated for an Alfisol. Then, we additionally also evaluated the effects of root growth and root water uptake on
the transport of trace metals (TMs). Finally, we carried out 50-yr-long prospective simulations for different doses of PS amendments. Consideration of root
growth and root water uptake processes in HYDRUS-1D simulations improved
the description of measured field Zn concentrations. Although the correspondence between simulated and measured Cu concentrations was not
as good as for Zn, we performed prospective simulations for both elements.
Future scenarios that considered large PS doses showed large increases inconcentrations of both TMs in the soil surface layer. The feasibility of using PS
amendments on agricultural Oxisols will be limited by Cu because the soil
Cu threshold concentration is exceeded in approximately 29 yr. Moreover,
the total loads of both TMs allowed on agricultural soils are reached very fast
when large rates are used, especially for Cu (19 yr), indicating that the longterm disposal of PS on agricultural soils should be done at low doses. These
conclusions are probably conservative because our model did not consider
potential leaching of TMs from the surface soil into deeper soil layers by dissolved organic C facilitated transport.
Abbreviations: DOC, dissolved organic carbon; HPS, high-dose pig slurry; LPS, low-dose
pig slurry; PS, pig slurry; SWRC, soil water retention curve; TM, trace metal.

Applications of animal manure on soils to increase their fertility is an ancient
agricultural practice. In modern agriculture, pig slurry (PS) is an important source ofanimal manure. Since the 1970s, an increase in intensive and confined swine commercial
operations has generated huge volumes of PS. In many areas, agricultural soils have beenused as PS disposal media and have received successive applications of PS, frequently athigh rates.
Such amendment practices may lead to environmental contamination because PS often
contains high concentrations of trace metals (TMs) such as Zn and Cu. Trace metal
concentrations may vary widely, depending on the manure dry mass and the animal diet,because Zn and Cu are used as additives to improve the productivity index of swine (Liet al., 2005). Some researchers have reported total amounts of Zn and Cu in PS as highas 1225 and 528 mg kg−1, respectively, on a dry-mass basis (Ogiyama et al., 2005). The
continuous long-term application of PS amendments at the soil surface will promote theaccumulation of TMs within the first few centimeters of the soil profile, especially insoils managed under no-till with the absence of mechanical plowing (Veiga et al., 2012;

Vadose Zone Journal | Advancing Critical Zone ScienceGirotto et al., 2010a; Berenguer et al., 2008; L’Herroux et al.,1997). Moreover, the mobility of TMs, notably Cu, is low in soils(McBride, 1994). Considering that the input of animal wastes insoils can increase the organic matter content, which generally tends
to decrease the TM solubility and mobility in soils (Mantovi et al.,2003), an even greater surface accumulation of Zn and Cu could
be expected. Consequently, the continuous application of PS on
soils raises questions about the risks of future environmental pollution of soils and surface and subsurface waters. Problems may
arise due to runoff in regions with frequent high-intensity rainfall
and a consequent TM transfer to surficial water bodies (Xue et al.,2003) or due to ecotoxicity of TMs for plants (Yadav, 2010) andmicroorganisms (Kuperman and Carreiro, 1997), with great implications for the wider environment. As mentioned by Mantovi et al.
(2003), applications of animal farming manures must therefore beviewed as a controlled agronomic practice of fertilization and not
as a simple way of disposal.
It is essential to study and monitor temporal increases in Zn and
Cu concentrations in soils in relation to the intensity of PS applications. Depending on soil and manure conditions, the maximum
TM concentration values, established by regulatory bodies for
agricultural soils, may be rapidly reached and restrict the disposalof organic amendments in certain areas. Different soil properties
and external factors affect the accumulation and depth redistributions of TMs in soils and their transfer to surface runoff. These
factors include soil characteristics, such as pH (Gäbler, 1997),
texture, organic matter and Fe oxide contents (Venditti et al.,2000), climatic conditions such as rainfall and evapotranspiration (Visser et al., 2012), land use (Fernandez et al., 2007; Baizeand van Oort, 2014), and anthropogenic practices such as quality of PS amendments and their metal concentrations (Girotto etal., 2010a). Furthermore, Zn and Cu show different affinities forthe charge sites of soil, which affect their mobility in soils. Trace
metals form outer sphere complexes with permanent negative
charges of 2:1 phyllosilicate minerals, and most of them tend toform inner sphere complexes with variable surface charges (organic
matter, oxyhydroxides, and 1:1 clay minerals). Copper shows a highaffinity for variable charge sites, especially of organic matter and at
neutral or alkaline pH, while Zn can form inner and outer spherecomplexes, the latter prevailing in acid soils (Weber et al., 1991).
Mathematical models are helpful in describing the mobility of
TMs in soils (Rheinheimer et al., 2007). Many of them have beendeveloped to simulate water flow and solute transfer processes
between the soil surface and groundwater (Carrillo-González etal., 2006). The models can provide a base for designing future
experiments and for interpreting and understanding trends andrelationships in observed field data. They can be powerful tools indeveloping alternatives for soil and water management strategies(Šimůnek and de Vos, 1999). Thus, predictive simulations estimating environmental contamination risks caused by TM additions
by successive PS applications in soils can help in developing better

VZJ | Advancing Critical Zone Sciencestrategies for manure management and support the adoption of
public policies for swine producers.
However, a prerequisite to the use of any model and interpretationof its simulated results is model validation. For instance, Legout etal. (2009) discussed that before a model can be used, its predictive
capacity and the validity of its assumptions need to be evaluated. A
model needs to show that it can faithfully reproduce phenomena
studied under controlled laboratory conditions or observed undernatural conditions. Only after verifying the correct functionalityof the model can long-term predictions be made.
Recently, Mallmann et al. (2012a, 2012b, 2014) carried out
studies that involved modeling of TM migration in sandy soils.
However, they did not consider the effects of plant roots in theirsimulations. Schoups and Hopmans (2002) discussed the effectsof roots on solute movement in soils, highlighting the importance of considering this factor in modeling studies. Roots may
form macropores, which can enhance the downward movementof TMs due to preferential flow. On the other hand, among
many other rhizosphere processes important for TM mobility insoils, root water uptake may reduce water fluxes and TM leaching through the soil profile (Robinson et al., 2009). Moreover,roots may stabilize TMs in the soil and affect the soil pH, redox
potential, and biomass, activity, and diversity of microorganisms,
and influence the speciation of TMs. Therefore, changes in the
downward movement of TMs within the root zone are expected
when roots are considered in simulations.
It is also well known that the soil’s clay and oxide contents, among
other factors, affect its hydro-physical and chemical characteristics
and consequently the potential for TM adsorption (Lang Burak etal., 2013). In clayey soils, such as Oxisols, the adsorption capacity
for TMs is higher than in sandy soils (Mattias, 2006). Soil texture
governs to a large extent the soil’s water retention capacity andhydraulic conductivity, which are larger and smaller, respectively,in clay soils than in sandy soils (Reichert et al., 2009; Tindall
and Kunkel, 1999). Consequently, although less TM leaching is
expected in clay soils, the quantification and better understandingof these processes is necessary. Therefore, it is important to validate solute transport models for different types of soils, to verify
the influence of plant roots on simulation results, and to increase
the range of model applicability for estimating TM movement incontaminated soils.
Our main objective was to validate that the HYDRUS-1Dmodel is able to numerically describe profile concentrations andlong-term vertical transport of Zn and Cu in a clay soil (Oxisol)
cultivated under annual cropping in a no-till system contaminatedby successive doses of PS amendments. For the model validation,
we first tested the transport model previously used by Mallmann etal. (2012b) for an Alfisol and then additionally also considered theeffects of roots on TM transport in the soil. Finally, we performed
p. 2 of 14

50-yr-long simulations to predict the accumulation of Zn and Cu
in the soil surface layers and their downward movement in the soil
profile toward the groundwater for different PS application rates.

66Materials and Methods
Experimental Site, Soil,and Other Characteristics

The study was conducted at the demonstration field of the
Cooperativa Regional Agropecuária de Campos Novos(Copercampos), in Campos Novos, Santa Catarina State, Brazil(27°21¢56² S, 51°15¢33² W, 896 m altitude), with periodical PS
applications. The soil, developed from the intermediate effusive
rock saprolites of the Serra Geral Formation, is classified as a
Rhodic Hapludox (Soil Survey Staff, 2003) or a Rhodic Ferralsol(IUSS Working Group WRB, 2014) and referred to as Latossolo
Vermelho Distroférrico típico in the Brazilian System of Soil
Classification (Empresa Brasileira de Pesquisa Agropecuária,2004). At the beginning of the experiment, the 0- to 20-cm soil
layer had the following chemical characteristics: 5.8 pH in water(1:1 v/v); 45 g kg−1 organic matter; 0.0, 5.0, 47.0, and 29.0 molc m−3of exchangeable Al, K, Ca, and Mg, respectively; and 10.0, 1.7, and10.5 g m−3 of extractable P, Zn, and Cu, respectively (Veiga et al.,2012). Exchangeable Al, Ca, and Mg were extracted with 1.0 M
KCl solution; K and P were extracted using Mehlich-1 solution(0.0125 M H2SO4 + 0.05 M HCl); and Cu and Zn were extractedwith 0.1 M HCl solution (Tedesco et al., 1995). The climate ishumid mesothermal with mild summers, the Cfb type according
to the Köppen classification, with annual precipitation ranging
from 1460 to 1820 mm and annual average temperatures from
15.8 to 17.9°C (Pandolfo et al., 2002).
The experiment started in 2000, on a field previously managed witha crop rotation and no-till system for >10 yr. Treatments consisted offour different annual PS doses: 0, 50, 100, and 200 m3 ha−1, divided
into two surface applications: half before winter crop sowing andthe other half before summer crop sowing. These treatments wereapplied to three field replicates in a randomized block design, withplots measuring 6 by 5 m. More details can be found in Veiga et al.
(2012). During 11 yr of the experiment, 22 PS applications wereapplied, using manure obtained from an anaerobic lagoon reservoir
at a pig finishing farm, with the dry mass content ranging from 0.40
to 10.27% and an average of 2.62%.
The number of crop cultivations during the 11 yr of the experiment was the same as the number of PS applications. Seeding was
performed by direct drilling and followed a 3-yr crop rotation
scheme: black oat (Avena strigosa Schreb.) + common vetch (Vicia
sativa L.)–corn (Zea mays L.), black oat–soybean [Glycine max (L.)
Merr.], and black oat–common bean (Phaseolus vulgaris L.). The
average production for the 0, 50, 100, and 200 m3 ha−1 treatments
was 5188, 6908, 7025, and 7046 kg ha−1, respectively, of the dry

VZJ | Advancing Critical Zone Science

mass for the winter crops and 4919, 5729, 5789, and 6024 kg ha−1,
respectively, for the summer crops.

Soil and Solution Sample Collection
The soil and solution samples were collected in June of 2011.
Disturbed soil samples were collected from 10 layers, at depths of
0 to 5, 5 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to
40, and 40 to 60 cm, for the treatments with PS doses of 0, 50, and
200 m3 ha−1 yr−1, hereafter referred to as the 0-, 50-, and 200-m3
treatments, respectively. The soil samples were dried, sieved at 2.0mm, and stored in plastic pots prior to chemical analyses. In thecontrol plots (the 0-m3 treatment), soil solution samples were collected using tension lysimeters at three depths: 20, 40, and 80 cm.
For the determination of the soil hydro-physical characteristics,
soil samples were collected in two different places located between
the experimental blocks from six soil layers: depths of 0 to 5, 5
to 10, 10 to 20, 20 to 30, 30 to 40, and 40 to 60 cm. These locations had the same soil characteristics and received the same
management as the control plot (the 0-m3 treatment). First, 10
undisturbed soil samples were collected from each soil layer foreach type of analysis: the saturated hydraulic conductivity (Ks),the bulk density (Bd), and the soil water retention curve (SWRC)
for soil water tensions <100 kPa. Second, disturbed bulk samples
were collected for the measurement of soil texture and the SWRC
for soil water tensions >500 kPa.

Soil Hydro-Physical Analysis
The saturated hydraulic conductivity, Ks, was measured on undisturbed soil samples, fully saturated with water for 48 h, using afalling-head permeameter linked to the Ksat computer software(Gubiani et al., 2010). This procedure consists of measuring thetime needed for a specific volume of water, contained between theupper and lower limits of a water supply tube, to pass through thesoil sample. The Ks was then calculated as suggested by Hillel (1998).
Undisturbed soil samples, also fully saturated for 48 h, were used
for SWRC measurements. These samples were subjected to successive tensions of 1, 6, and 10 kPa in a sand tank (Reinert andReichert, 2006) and to 33 and 100 kPa in a Richards chamber.
Gravimetric water contents at higher tensions (500, 1000, and1500 kPa) were determined using the dew point potentiometer
method on disturbed soil samples. The paired tension and watercontent values of the SWRC were used to fit the a, n1, and mparameters of the van Genuchten model (van Genuchten, 1980).
The soil Bd was calculated by dividing the dry mass of soil contained in the cylinder (dried in a stove at 105°C to constant weight)
by the cylinder volume. The soil texture was measured following
the Soil Conservation Service (1972) procedure.

Soil Chemical Analysis
All chemical analyses were performed on disturbed soil samples
collected in the experimental plots. Soil pH (in water, 1:1 v/v) was
p. 3 of 14

determined following the methodology described by Tedesco et al.
(1995), and the organic C with an elemental CHNS autoanalyzer.
Total soil concentrations of Zn and Cu were measured using an
atomic absorption spectrometer after sample digestion according
to the USEPA (2007) procedure. Solution concentrations of TMs
were determined directly using an inductively coupled plasma optical emission spectrometer on samples collected in the field.
Soil desorption isotherm curves for Zn and Cu were determinedusing ethylenediaminetetraacetic acid (EDTA) extractions at
0.05 mol L−1 (pH 6.0), with a soil/solution ratio of 1:10 (Bermondet al., 1998). The contact times between the soil and EDTA solution were 1, 5, 15, 30, 60, 120, 300, 660, 1020, and 1440 min.
The amounts of TMs extracted from the soil by EDTA are similar
to the anthropogenic metal additions (Labanowski et al., 2008).
Labanowski et al. (2008) also suggest that EDTA extractions areindicative of long-term predictions of TM migration to different
soil depths, which meets the goals of this work.
The adsorption isotherm curves of TMs were determined using
the procedure described by Mattias (2006), adding 10 mL of a
0.0025 mol L−1 Ca(NO3)2 solution containing different concentrations of Zn or Cu (0, 2.5, 5, 10, 20, 40, 60, 80, 120, 160, 200,
240, 280, 320, and 360 mg L−1) to 0.5 g of soil. These solution

concentrations were set to reach the maximum adsorption capacity of the soil.

Climate Data
Daily meteorological data from May 2000 to June 2011, i.e.,
precipitation, air temperature, air relative humidity, wind speed,
and radiation, were obtained from the Meteorological Station of
EPAGRI (Empresa de Pesquisa Agropecuária e Extensão Ruralde Santa Catarina), in Campos Novos, SC. The data, except for
precipitation, were used to calculate the daily potential evapotranspiration of the crops (ETp) cultivated during the experiment using
the Penman–Monteith equation (Allen et al., 1998).
Daily and cumulative values of measured precipitation and calculated transpiration and evaporation between the beginning of
the experiment and soil sampling (1 May 2000 and 15 June 2011,
respectively) are shown in Fig. 1. Cumulative precipitation andevapotranspiration (transpiration + evaporation) were 23,267 and8649 mm, respectively, corresponding to average annual values of2090 and 777 mm yr−1, respectively.

Solute Transport Simulations

Model and Parameterization Details
HYDRUS-1D (Šimůnek et al., 2016) was used to simulate unsaturated water flow and the transport of Zn and Cu in a vertical soil

Fig. 1. Daily and cumulative values of precipitation, transpiration, and evaporationfrom 1 May 2000 to 15 June 2011 usedin simulations with HYDRUS-1D for an
experiment with different pig slurry doses
conducted on an Oxisol in Campos Novos,
SC, Brazil.

VZJ | Advancing Critical Zone Sciencep. 4 of 14

profile. The van Genuchten–Mualem analytical models were used
to describe soil water retention curves and unsaturated hydraulicconductivity functions (van Genuchten, 1980). The soil residual(θr) and saturated (θs) water contents and parameters a and n1 foreach soil layer were obtained by fitting the van Genuchten model
to retention data determined in the laboratory using the SWRC
software (Dourado Neto et al., 2000). The pore connectivity–tortuosity factor (l) was set in all simulations and for all soil layers to
0.5, as recommended by Mualem (1976).

than the Langmuir isotherm because it better fit the adsorption
data, reaching R 2 values >0.98 when comparing estimated andmeasured concentrations.
From the Zn and Cu desorption curves, the b and l parameters of
the Fangueiro kinetic model (Fangueiro et al., 2005) were determined. The b parameter is the fraction of soil sorption sites that
instantaneously adsorb TMs to the functional groups (at equilibrium), and the l parameter is the kinetic desorption constant of
the nonequilibrium sites (1 − b) of the soil, both calculated according to Mallmann et al. (2012b). The values of the parameters usedin the HYDRUS-1D simulations are listed in Table 1.

The initial total and solution concentrations of Zn and Cu ofeach soil layer used in HYDRUS-1D (1 May 2000) were set
according to measured values in the control plot. Based on thesevalues and on the average value of the n parameter determinedfrom the adsorption curves fitted with the Freundlich nonlinear
isotherm, the K F values for both TMs for each soil layer were
calculated. The Freundlich sorption isotherm was used rather

Trace Metal Inputs
During model validation simulations, the total amounts of Zn
and Cu added to the soil surface via PS amendments were determined as the difference between the TM mass of a particular

Table 1. Hydro-physical and chemical soil parameters at 10 depths of the control plot in the pig slurry experiment located in Campos Novos, Santa
Catarina State, Brazil, and initial values required for simulations with HYDRUS-1D.
Parameter†

0–5 cm

5–10 cm

10–15 cm

15–20 cm

20–25 cm

25–30 cm

30–35 cm

35–40 cm

40–50 cm

50–60 cm

pH

4.94

4.81

4.72

4.57

4.48

OC, g kg−1

4.47

4.50

4.52

4.61

4.70

39.4

34.6

32.3

Clay, g kg−1

662

679

686

31.4

30.7

29.2

27.7

27.4

25.8

23.6

684

671

689

711

735

762

Silt, g kg−1

318

307

786

303

305

319

302

281

258

231

Sand, g kg−1

207

20

Bd, g cm−3

1.03

14

11

11

10

9

8

7

7

7

1.06

1.08

1.08

1.06

1.06

1.04

1.04

1.02

Ks, cm dia−1
θs, cm3 cm−3
θr, cm3 cm−3

1.02

117.3

79.1

91.4

91.4

117.3

117.3

127.0

127.0

77.9

77.9

0.60

0.59

0.58

0.58

0.59

0.59

0.60

0.60

0.61

0.61

, cm−1

0.26

0.27

0.28

0.28

0.27

0.27

0.24

0.24

0.25

0.25

0.251

0.296

0.489

0.489

0.412

0.412

0.326

0.326

0.415

0.415

n1

1.335

1.340

1.299

1.299

1.308

1.308

1.344

1.344

1.322

1.322

l

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

98.5

99.1

101.3

108.3

109.5

Soil

aVG

Zinc
Total, mg kg−1

101.5

99.1

95.6

99.6

100.3

Solution, mg L−1

0.036

0.036

0.036

0.036

0.032

0.032

0.032

0.032

0.035

0.035

b

0.233

0.110

0.057

0.025

0.013

0.007

0.007

0.005

0.010

0.014

l, 10 −5 d−1

5.623

4.374

3.159

2.160

1.257

1.407

0.759

0.810

0.992

2.078

KF

11.78

11.51

11.11

11.58

12.53

12.31

12.39

12.67

12.81

12.95

n

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

0.62

118.7

118.7

118.7

124.2

125.6

CopperTotal, mg kg−1

113.2

110.3

110.3

113.1

115.9

Solution, mg L−1

0.00370.00370.00370.00370.00360.00360.00360.00360.00510.0051

b

0.181

0.128

0.096

0.075

0.070

0.063

0.059

0.053

0.053

0.057

l, 10 −5 d−1

4.888

4.783

4.552

4.272

4.128

4.030

4.068

3.983

3.984

4.385

KF

0.51

0.50

0.50

0.51

0.53

0.54

0.54

0.54

0.51

0.51

n

0.34

0.34

0.34

0.34

0.34

0.34

0.34

0.34

0.34

0.34

† OC, organic carbon; Bd, bulk density; Ks, saturated hydraulic conductivity; θs, saturated water content; θr, residual water content; aVG and n1, retention parameters;l, pore-connectivity factor; b, fraction of sorption sites with instantaneous sorption of heavy metals; l, kinetic constant for sorption sites 1 − b; KF and n, parametersof the Freundlich isotherm for the b sites (calculated with total and solution concentrations in mol g−1 and mol L−1, respectively).

VZJ | Advancing Critical Zone Science

p. 5 of 14

treatment and a corresponding mass of the 0-m3 treatment evaluated across the 60-cm depth using the soil bulk density (Table
1) and the TM total concentration. These amounts accounted
for 43.0 and 27.3 kg ha−1 of Zn and Cu, respectively, in the
50-m 3 treatment and for 120.9 and 71.7 kg ha−1 of Zn and Cu,
respectively, in the 200-m 3 treatment (Fig. 2). Temporal distributions of TM additions to the soil are shown in Fig. 2. The
amounts of Zn and Cu added to the soil in each application
were proportional to the dry mass content of the manure usedin 22 amendments.

Model Validation
First, the Zn and Cu vertical transport was simulated in the
PS-amended soil profiles for two different doses (50 and
200 m 3 ha−1 yr−1) during 4063 d (1 May 2000–15 June 2011,
between the beginning of the experiment and the soil sampling
date, respectively), testing the modeling approach used and validated by Mallmann et al. (2012a, 2012b). The model is based ona two-site nonequilibrium solute transport model. In this solutetransport model, the sorption on the equilibrium fraction ofsorption sites (the b parameter) follows the Freundlich isothermand on the kinetic fraction of sorption sites (1 − b) the first-orderkinetic reaction (with a kinetic rate constant l). The kinetic
rate constant l was derived from the EDTA-determined rates
and was validated by Mallmann et al. (2012a, 2012b). In thismodeling approach, water uptake by plant roots and corresponding transpiration were not considered. Consequently, all water
losses through the soil surface to the atmosphere occurred only
via evaporation. This modeling approach is hereafter referred
to as Model I.

Second, root water uptake and root growth modules, available in
HYDRUS-1D (Šimůnek et al., 2012a), were used in simulations.
The modeling approach that considers root water uptake and root
growth is hereafter referred to as Model II. In this second modeling approach, root water uptake was simulated using the modelof Feddes et al. (1978). Root growth data for all crops cultivatedduring the experiment, i.e., the rooting depths on each day (Fig.
3), were set to average values for different phenological stages ofparticular crops. These data were obtained from other experiments
and agricultural areas cultivated under similar pedoclimatic conditions. Additionally, the solute uptake of TMs by plant roots wasneglected because the amounts of Zn and Cu absorbed by plants is
usually very small. For example, Mallmann et al. (2012b) showedthat cumulative removals of TMs by harvested crops in an 8-yr pig
slurry amendment experiment represented only 0.33 to 1.13 and0.05 to 0.12% of the total Zn and Cu masses, respectively, in the
0- to 60-cm soil depth. Furthermore, calculated potential evapotranspiration rates (ETp) were divided into potential evaporation(Ep) and transpiration (Tp) rates using Beer’s law, as suggested byŠimůnek et al. (2012a), based on the soil cover fraction (SCF):
E p = ETp (1- SCF)

[1]

Tp = ETp ´SCF

[2]This resulted in cumulative evaporation and transpiration of
3434 and 5215 mm, respectively, and annual averages of 308.5
and 468.5 mm yr−1 (0.85 and 1.28 mm d−1), respectively (Fig. 1).
The modeling results for both Models I and II were evaluated using
the coefficient of determination (R 2). The regression
lines for simulated and measured concentrations wereforced to pass through the origin so that the set ofvalues from the simulations was equal to the measured
ones and not just proportional.

Future Scenarios
The HYDRUS-1D model that best fit the experimental
data, i.e., the model that considered root water uptake
and root growth (Model II), was then extended to simulate an additional 50 yr of pig slurry applications (until
15 June 2061). This prospective modeling, which considered the same PS doses of 50 and 200 m3 ha−1 yr−1
used in the experiment, represented the low-dose (LPS)
and high-dose (HPS) scenarios.

Fig. 2. (A) Zinc and (B) Cu amounts (bars are applications and lines are cumulativevalues) applied at the soil surface via pig slurry in two treatments (50 and 200 m3 ha−1yr−1) in the experiment conducted on an Oxisol in Campos Novos, SC, Brazil.

VZJ | Advancing Critical Zone ScienceFor the simulations of future scenarios with
HYDRUS-1D, the same parameters as in the validation
simulations were used. The data set of precipitation,
evaporation, transpiration, root growth, and Zn and
Cu additions from the first 10 yr of the model validation simulations were sequentially used five times.
During the 50-yr simulations, the total amount of
p. 6 of 14

the 50-m3 treatment and 223.6 mg kg−1 for the 200-m3 treatment, differing only by −6.2 and 4.1%, respectively, from theconcentrations measured at sampling (Table 2). For the othernine soil layers, the differences between simulated and measured
concentrations of Zn were even smaller in the 50-m3 treatment,with differences less than 3.3% (the 35–40-cm layer). For the
200-m3 treatment, these differences did not exceed 4.4% (the5–10-cm layer; Table 2). When comparing simulated and measured concentrations for both treatments, the R 2 values were0.942 and 0.988 for the 50- and 200-m3 treatments, respectively(Fig. 4).

Fig. 3. Daily values of the rooting depth for different crops cultivated in
an experiment from 1 May 2000 to 15 June 2011 with different pig slurry
doses conducted on an Oxisol in Campos Novos, SC, Brazil. These values
were used in the simulations with HYDRUS-1D.

precipitation was 105,201 mm, the total amounts of added Zn
and Cu were 196 and 129 kg ha−1, respectively, for the LPS scenario and 560 and 342 kg ha−1, respectively, for the HPS scenario,
whereas cumulative evaporation and transpiration were 15,619 and23,390 mm, respectively.

The simulated Cu concentrations in the soil profile after 22 PS
amendments fitted measured concentrations less well. Table 2
shows that the simulated Cu concentrations in the 0- to 5-cm
layer were 15.1 and 17.0% higher than the measured concentrations for the 50- and 200-m3 treatments, respectively. The R 2
values of 0.674 for the 50-m3 treatment and 0.839 for the 200-m3
treatment (Fig. 4) were much lower than for Zn, especially for
the LPS scenario.

Model II with Root Water Uptake
and Root Growth
When root water uptake and root growth were considered in the
HYDRUS-1D simulations, soil concentrations of Zn and Cu differed slightly from the results obtained by Model I. The largest
differences were observed in the surface soil layer (0–5 cm), whereconcentrations of Zn and Cu reached values of 144.9 and 156.1 mg
kg−1, respectively, for the 50-m3 treatment and 217.8 and 201.1 mg
kg−1, respectively, for the 200-m3 treatment at the end of simulations.
When compared with the measured concentrations, the simulated
Zn concentrations differed by −7.7 and 1.4% for the 50- and 200-m3
treatments, respectively, while for Cu the simulated and measured
concentrations differed by 14.6 and 15.7%, respectively (Table 3).

66Results

Simulations for Model Validation

Model I without Root Water Uptake
Concentrations of Zn estimated using Model I were found tobe very similar to the measured ones. In the soil’s surface layer(0–5 cm), the simulated Zn concentrations were 147.3 mg kg−1 for

Table 2. Zinc and Cu concentrations measured (Mea) in soil profiles after 11 yr of pig slurry amendments and treatments with annual doses of 50 and
200 m3 ha−1 and the corresponding concentrations simulated using HYDRUS-1D and the chemical modeling approach of Mallmann et al. (2012a)(SM). Also shown is the percentage difference between simulated and measured concentrations (Dif).
Zinc

Copper

Dose 50 m3 ha−1
SM

Dose 200 m3 ha−1

SM

Dose 200 m3 ha−1

Soil layer

Mea

Dif

Mea

Dif

Mea

Dif

Mea

cm

— mg kg−1 —%— mg kg−1 —%— mg kg−1 —%— mg kg−1 —

%

0–5

157.0

147.3

−6.2

214.7

223.6

4.1

136.2

156.8

15.1

173.8

203.3

17.0

5–10

117.2

115.3

−1.7

146.0

152.4

4.4

123.4

116.9

−5.2

145.3

149.9

3.2

10–15

99.9

100.3

0.4

115.5

116.9

1.2

114.8

110.3

−3.9

125.6

115.4

−8.1

15–20

98.8

101.0

2.3

108.2

108.0

−0.1

116.3

113.1

−2.7

122.8

113.1

−7.9

20–25

100.5

101.0

0.6

107.0

104.3

−2.5

117.7

115.9

−1.5

120.0

115.9

−3.4

25–30

98.2

98.9

0.7

103.7

101.0

−2.6

119.1

118.7

−0.4

121.4

118.7

−2.2

30–35

99.9

99.3

−0.6

104.8

100.5

−4.1

119.1

118.7

−0.3

121.4

118.7

−2.2

35–40

98.2

101.5

3.3

105.3

102.4

−2.8

120.5

118.8

−1.4

120.7

118.8

−1.5

40–50

106.2

108.4

2.0

109.9

109.3

−0.5

124.8

123.9

−0.7

124.9

123.9

−0.8

50–60

109.1

109.5

0.4

111.6

110.1

−1.3

126.2

125.6

−0.5

126.3

125.6

−0.5

VZJ | Advancing Critical Zone Science

SM

Dose 50 m3 ha−1

SM

Dif

p. 7 of 14

30 cm for the 50- and 200-m3 treatments, respectively. For Cu, a considerable increase was observed
only in the upper soil profile depths of 0 to 10 and
0 to 15 cm, respectively.
The consideration of processes of root water uptake
and root growth in the HYDRUS-1D simulations(Model II) resulted in simulated profile concentrations of Zn and Cu that generally better fit themeasured TM concentrations than the results
obtained by Model I (without root processes). The
R 2 values obtained by Model II were 0.905 and0.993 for Zn and 0.685 and 0.853 for Cu for the
50- and 200-m3 treatments, respectively (Fig. 5).

Simulations of Future Scenarios
Pig Slurry Applications
at Low Doses

Simulations of the LPS scenario predicted that
additions of Zn and Cu during the 61 yr resulted
in an increase in the TM mass in the 0- to 60-cm
profile by 30 and 20%, respectively. Total Zn and
Cu concentrations at the soil surface (0–5 cm)increased by 127 and 132%, reaching 231 (Fig. 6)
Fig. 4. The regression analysis for measured and simulated (A,B) Zn and (C,D) Cu conand 262 mg kg−1 (Fig. 7), respectively. Within thecentrations in soils (an Oxisol located in Campos Novos, SC, Brazil) amended with pig
3−1−1
top 20 cm of the cultivated layer, the total Zn conslurry at rates of (A,C) 50 and (B,D) 200 m ha yr . Measured values are the total concentrations obtained using the USEPA (2007) procedure. Simulated values were obtained
centrations reached 185, 146, and 129 mg kg−1 inusing the Model I parameterization. Dotted line is the 1:1 line.
soil layers of 5 to 10, 10 to 15, and 15 to 20 cm (Fig.
6), respectively, corresponding to mass increases ofWhen comparing the Zn and Cu concentrations simulated using
87, 52, and 30%, respectively. For Cu, the simulated concentraModel II (Table 3) with the corresponding initial concentrations reached 201, 149, and 122 mg kg−1 (Fig. 7), corresponding
tions (Table 1), no considerable increase in Zn concentrations
to Cu mass increases of 82, 35, and 8%, respectively. Considerable−1(>2.0 mg kg ) was observed in soil layers deeper than 15 andincreases (>10%) in Cu concentrations in the profile amended with
Table 3. Zinc and Cu concentrations measured (Mea) in soil profiles after 11 yr of pig slurry amendments and treatments with annual doses of 50 and
200 m3 ha−1 and the corresponding concentrations simulated using HYDRUS-1D and the chemical modeling approach of Mallmann et al. (2012a),while additionally considering root water uptake and root growth modules (SMR). Also shown is the percentage difference between simulated andmeasured concentrations (Dif).
Zinc

Copper

Dose 50 m3 ha−1
SMR

Dose 200 m3 ha−1

SMR

Dose 200 m3 ha−1

Soil layer

Mea

Dif

Mea

Dif

Mea

Dif

Mea

cm

— mg kg−1 —%— mg kg−1 —%— mg kg−1 —%— mg kg−1 —

%

0–5

157.0

144.9

−7.7

214.7

217.8

1.4

136.2

156.1

14.6

173.8

201.1

15.7

5–10

117.2

115.6

−1.4

146.0

152.3

4.4

123.4

117.6

−4.7

145.3

150.7

3.7

10–15

99.9

101.0

1.1

115.5

118.0

2.1

114.8

110.4

−3.9

125.6

116.7

−7.1

15–20

98.8

101.4

2.6

108.2

108.9

0.7

116.3

113.1

−2.7

122.8

113.1

−7.9

20–25

100.5

101.3

0.8

107.0

105.0

−1.9

117.7

115.9

−1.5

120.0

115.9

−3.4

25–30

98.2

99.1

0.9

103.7

101.6

−2.0

119.1

118.7

−0.4

121.4

118.7

−2.2

30–35

99.9

99.4

−0.5

104.8

100.9

−3.7

119.1

118.7

−0.3

121.4

118.7

−2.2

35–40

98.2

101.6

3.4

105.3

102.7

−2.5

120.5

118.9

−1.4

120.7

118.9

−1.5

40–50

106.2

108.4

2.1

109.9

109.7

−0.1

124.8

123.9

−0.7

124.9

123.9

−0.8

50–60

109.1

109.5

0.4

111.6

110.5

−1.0

126.2

125.6

−0.5

126.3

125.6

−0.5

VZJ | Advancing Critical Zone Science

SMR

Dose 50 m3 ha−1

SMR

Dif

p. 8 of 14

low PS doses were observed only in the top 15 cm,
accompanied by variations smaller than 1% in soil
layers below 20 cm. For Zn, considerable mass
increases in the LPS scenario were observed in theupper 30 cm of the soil, while in the 30- to 60-cm
soil layer increases were in the range of 6 to 8%.
Solution concentrations of Zn increased in all soil
layers during the LPS scenario simulation, reachinga maximum value of 0.22 mg L−1 in the surface layer(Fig. 6). However, at the bottom of the soil profile(a depth of 60 cm), the Zn concentrations remained
below 0.06 mg L−1 during the entire simulated
period (Fig. 8). Simulations also showed that thehighest Cu solution concentrations appeared in the
0- to 5-cm layer (0.11 mg L−1), while increasing Cu
concentrations were predicted only in the four upper
layers (Fig. 7 and 8). Simulations predicted losses of
46.4 kg ha−1 of Zn and 5.4 kg ha−1 of Cu for depths
below 60 cm during the simulated 61 yr.

Pig Slurry Applications
at High Doses
The initial mass of Zn and Cu in the 60-cm soil
Fig. 5. The regression analysis for measured and simulated (A,B) Zn and (C,D) Cu conprofile increased by 91 and 54%, respectively, during
centrations in soils (an Oxisol located in Campos Novos, SC, Brazil) amended with pig
the 61 yr simulated in the HPS scenario. Totalslurry at rates of (A,C) 50 and (B,D) 200 m3 ha−1 yr−1. Measured values are the total concentrations obtained using the USEPA (2007) procedure. Simulated values were obtained
Zn concentrations reached 419, 331, 244, andusing the Model II (with root water uptake and root growth) parameterization. Dotted−1
196 mg kg at the end of simulations in soil layers
line is the 1:1 line.
of 0 to 5, 5 to 10, 10 to 15, and 15 to 20 cm, respectively (Fig. 6). Such concentrations imply 313, 234,
155, and 96% increases, respectively. Estimated Zn concentrations
during the entire simulated period, leading to a Cu transfer of only
in deeper soil layers also showed consistent increases, with the 405.5 kg ha−1 to soil layers below 60 cm (Fig. 8).
to 50-cm layer showing the smallest gain of 28%.
For Cu, the HPS scenario resulted in total concentrations of372, 317, 257, and 204 mg kg−1 in 2061 in the upper four soil
layers, respectively (Fig. 7), which corresponded to 228, 188, 133,
and 80% more Cu than at the beginning in 2000. Moreover, thedeepest soil layer with a consistent increase (11%) in the total concentration of Cu was the 25- to 30-cm depth, while deeper soil
layers showed only very small variations (<2%).
In the HPS scenario, the soil solution concentrations were highest in the surface layer, reaching maximum concentrations of
0.65 mg L−1 for Zn (Fig. 6) and 0.37 mg L−1 for Cu (Fig. 7). All
soil layers in the 60-cm profile showed increases in the Zn soil
solution concentration after 61 yr, including at the profile bottom(60 cm deep), where it increased from 0.035 mg L−1 in 2000 to
0.169 mg L−1 in 2061 (Fig. 8). Such concentrations predicted a
total Zn loss of 100.4 kg ha−1 from the soil profile to layers below
the depth of 60 cm. On the other hand, the Cu solution concentrations did not change below the depth of 30 cm. Thus, solution
concentrations of this TM remained constant at the profile bottom

VZJ | Advancing Critical Zone Science

66Discussion

Model Validation and Effect of Rootson Trace Metal Movements in Soil

The HYDRUS-1D simulations using Model I, which neglected
the effects of roots on TM movement, produced results that werein very good agreement with the measured Zn concentrations in
the PS field experiment for both evaluated treatments. Tables 1
and 2 indicate that simulated Zn concentrations were comparable to concentrations observed under field conditions. However,simulated results using Model I were less good for Cu than for Zn.
Simulated Cu concentrations in deeper soil layers were notably
lower than measured under field conditions. Therefore, we are able
to validate Model I only for Zn for this PS-amended soil.
It is important to mention that Model I was already validated
under other conditions, where the type of TM contaminants, thesoil type, and climate characteristics were different. One of thevalidation studies (Mallmann et al., 2012a) was done for Zn andp. 9 of 14

4 and 5). In comparison to Model I, the main effectof considering root processes in Model II on TM
transport was to increase the amounts of Zn and
Cu transferred from the upper soil layer (0–5 cm)
to the two underlying layers (5–10 and 10–15 cm)
and to reduce surface accumulation.

Fig. 6. Zinc (A,B) total and (C,D) solution concentrations in six soil layers simulated from2000 to 2061 using HYDRUS-1D for future scenarios under (A,C) low and (B,D) highrates of pig slurry amendments applied at the soil surface of an Oxisol located in Campos
Novos, SC, Brazil. The vertical dotted gray line indicates the time of soil sampling.

Pb in a 50-yr simulation for two agricultural arable soils locatedin northern France, classified as Eutric Cambisols, with an average clay content of 110 g kg−1, receiving atmospheric fallout ofZn and Pb from a metal smelter complex during the past century.
The second validation study was performed for Zn and Cu inan agricultural Alfisol in southern Brazil, with approximately
150 g kg−1 of clay in the A and E horizons, submitted to successive
PS amendments for 8 yr (Mallmann et al., 2012b). In this latter
work, simulated Cu inputs were similarly less incorporated into
deeper soil layers and estimated Cu concentration profiles fitted
less well the measured Cu field concentrations than was observedfor Zn. However, these differences were smaller than our findingsin this study for a clayey Oxisol. It is likely that in an Oxisol otherprocesses, besides the TM sorption–desorption processes that areconsidered in the Model I approach, play a significant and important role in Cu transport in the soil.
In order to better describe field conditions, the processes of root
water uptake and root growth were considered in the second set ofsimulations with HYDRUS-1D, denoted as Model II. Thus, the
HYDRUS-1D projects for Model I served as a basis for Model
II, which required additional information about root processes.
Results of these simulations with Model II produced a better correspondence with measured Zn and Cu field concentrations (Fig.

VZJ | Advancing Critical Zone ScienceConsidering that all water that flows through thesoil contains solutes to some extent, the reduction of
TM accumulation at the soil’s surface is mainly due
to a different way in which water is returned to theatmosphere. While in Model I the water transfer tothe atmosphere all occurred through the soil surface
via evaporation, in Model II approximately 60% ofwater was transferred via transpiration. In this case,water did not flow all the way to the soil surface
before being transferred to the atmosphere but was
partly absorbed by roots in the soil rooting zone
(ranging from 5–40-cm depth, depending on thecrop). The upward movement of Zn and Cu fromlower depths to the 0- to 5-cm soil layer decreased,
while water absorption by roots in deeper layers
forced downward flow of water and solutes. Thishypothesis is consistent with the study of Schoups
and Hopmans (2002), in which the important
effects of water uptake by plants on root zone concentrations and fluxes and travel times of water andsolutes in soil were discussed.

The surface accumulation of TMs in simulations that consideredroot water uptake and growth was smaller than in simulations that
did not consider these processes. The differences in Zn mass were
1.2 and 3.0 kg ha−1 and in Cu mass were 0.3 and 1.2 kg ha−1 inthe 50- and 200-m3 treatments, respectively. This is equivalent
to 2.8 and 2.5% of Zn and to 1.1 and 1.7% of Cu, respectively,
added to the soil during simulations. Because Model II improved
modeling results for Zn compared with Model I, we consider it
to be validated for Zn. However, the improvements in predicting
Cu concentrations by Model II were not sufficient to consider themodel to be validated. Therefore, in order to obtain better resultsfor Cu transport in the PS-amended soil, more parameters need
to be adjusted or additional processes need to be considered in the
HYDRUS-1D simulations.
The movement of TMs in the soil profile associated with solid
particles, i.e., colloidal or colloid-facilitated transport, is of varying importance depending on the chemistry of the metals (notably
Cu and Pb) and the pedo-geochemical conditions (van Oort et al.,2006; Citeau et al., 2003). Failure to account for such colloidal
processes during solute transport simulations may lead to a severe
underestimation of the transport and the potential risk assessment for considered TMs (Šimůnek et al., 2006). Considering
the findings of Girotto et al. (2010b) that Cu in water leached
p. 10 of 14

from a PS-amended soil occurred predominantly(approximately 75%) in particulate forms, thecolloid-facilitated transport probably represents aprocess that is not well described by the validation
simulations for Cu movement in the case of the
PS-amended Oxisol studied here. Such particulate
movement is less relevant in simulations of Zn transport in the soil profile because Zn movement in soils
is considered to occur mainly (³85%) in a dissolved,
free ion form (Citeau et al., 2003).
Pig slurry amendments also add organic matter(OM) to soils, which can be an important factor
involved in TM movement in the soil profile, especially for Cu (as discussed above). Organic mattermay not be completely humified (Moral et al., 2005)
and may be subject to microbial degradation that
could cause a release of dissolved organic C (DOC)
into the soil solution. Because DOC has a highaffinity with Cu, it may form soluble Cu–DOC
complexes that can enhance the Cu concentration
in solution (Díaz-Barrientos, 2003) and the leaching and transfer into deeper soil layers (Madrid;
Díaz-Barrientos, 1998). Accounting for the presence
Fig. 7. Copper (A,B) total and (C,D) solution concentrations in six soil layers simulatedof OM and DOC in the model could improve thefrom 2000 to 2061 using HYDRUS-1D for future scenarios under (A,C) low and (B,D)
correspondence between simulated and observedhigh rates of pig slurry amendments applied at the soil surface of an Oxisol located in CamCu concentrations in the soil profiles. Using adsorppos Novos, SC, Brazil. The vertical dotted gray line indicates the time of soil sampling.
tion isotherms measured on disturbed soil samples
is another factor that may affect the results of transShort- and Long-Term Feasibility
port models. Soil disturbance increases exposure of its functionalof Pig Slurry Amendments
groups and the contact between soil mineral particles and equilibrium solutions, favoring Zn and Cu adsorption and overestimating
Future scenarios simulated for estimating Zn transport in thethe accumulation of TMs on the soil surface in the simulations.
PS-amended Oxisol showed that in the short term, e.g., 10 yr after
soil sampling (June 2011), the total concentrations of this TM reachedAccording to Šimůnek et al. (2006), models
that can simulate various mechanisms controlling colloid and solute transport, as well theirmutual interactions and interactions with thesolid phase, are essential for improving the predictions of colloid-facilitated transport of solutes invariably saturated porous media. Therefore, it islikely that prediction of Cu concentrations in the
PS-amended Oxisol could be improved using the
C-Ride module (Šimůnek et al., 2012b), available
for both HYDRUS-1D and HYDRUS (2D/3D)
software packages, that incorporates various processes associated with colloid and colloid-facilitatedsolute transport in variably saturated porous media.
However, this module requires a significant number
Fig. 8. (A) Soil solution concentrations of Zn and Cu at the bottom of the soil profile andof additional parameters, e.g., Cu sorption to DOC(B) the metal losses from the soil profile. The HYDRUS-1D simulations were carried out
and the fraction of total C in DOC forms, which
for a 61-yr period and for scenarios with amendments of low and high (50 and 200 m3
are currently unknown and which would requireha−1 yr−1, respectively) pig slurry rates on an Oxisol in Campos Novos, SC, Brazil. The
verticaldotted gray line indicates the time of soil sampling.
new studies for their correct estimation.

VZJ | Advancing Critical Zone Sciencep. 11 of 14

170 and 275 mg kg−1, respectively, in the 0- to 5-cm soil layer (Fig.
6) and 129 and 184 mg kg−1, respectively, in the 0- to 20-cm soil
layer (Fig. 7) for the LPS and HPS scenarios. The Zn concentrations
thus remained far below 450 mg kg−1, which is the Zn thresholdvalue established by the Conselho Nacional do Meio Ambiente(CONAMA), the Brazilian council for investigation in agriculturalsoils (Conselho Nacional do Meio Ambiente, 2009). At the same time,
the long-term scenarios predicted a great increase in the Zn surface
accumulation, especially for the HPS scenario. Total concentrationsin the 0- to 5-cm soil layer during the last 5 yr were very high and led
to a concentration in the 0- to 20-cm layer of 297 mg kg−1. In thisway, no potential risks related to Zn environmental problems werepredicted during the simulated scenarios. However, this also meansthat if PS additions were done at the same HPS rate after 2061, andthe Zn concentration in the 0- to 20-cm layer increased at the samerate, the soil would exceed its Zn threshold concentration in about anadditional 50 yr. Besides, if we consider the Brazilian law that establishes the amounts of TMs that can be added to agricultural soils via
sewage sludge, the maximum addition of Zn of 445 kg ha−1 (Conselho
Nacional do Meio Ambiente, 2006) from PS would be reached in
the HPS scenario in 2038, i.e., 27 yr after the beginning of the future
50-yr simulation. Therefore, this law can limit the feasibility of PS
amendments to agricultural soils when added at high rates, whereas
in the LPS scenario the maximum addition of Zn was not reached.
Considering Zn concentrations in the soil solution, neither scenariopresented significant problems to the environment or to the related
Brazilian limits for groundwater (1.05 mg L−1; Conselho Nacional
do Meio Ambiente, 2009) in both the short and long term. However,
the amounts of Zn transferred to layers below a depth of 60 cm
during the simulated 61 yr were equivalent to approximately 15%(LPS) and 19% (HPS) of the total amounts of this TM added tothe soil via PS. This was a consequence of increases in the soil solution concentrations at the profile bottom (Fig. 8), which indicatesa transport of the exogenous Zn throughout theentire 0- to 60-cm soil profile.
Significant accumulation of Zn on the soil
surface, albeit at lower concentrations than inthis Oxisol, was also detected by Mallmann etal. (2012b) in simulations predicting future scenarios for an Alfisol amended with high doses ofPS. Moreover, the Zn losses below the depth of
60 cm were higher in the Alfisol than the Oxisol.
These differences between the two soil types are
ascribed mainly to the lower clay and oxide contents and, consequently, the more coarse-grained
texture of the Alfisol, which exhibited a lower
tendency for heavy metal adsorption than finegrained soils (Bradl, 2004).

overestimated Cu soil surface accumulation, we still performed
simulations of the movement of this TM for future scenarios.
These results may help to guide the establishment of management
strategies for PS amendments. Simulations for the HPS scenario
indicated that in 18 yr after 2011 the Cu concentrations in the
0- to 20-cm soil surface layer would exceed the Brazilian threshold for Cu concentration in agricultural soils, which is 200 mg
kg−1 (Conselho Nacional do Meio Ambiente, 2009) (Fig. 9). If
the manure doses are applied to the soil at low rates (LPS scenario),the concentration of Cu in this soil layer will remain below thethreshold value for at least another 50 yr, extending the feasibility of this Oxisol receiving successive disposals of PS on the soil
surface (Fig. 9). Indeed, the maximum load of Cu of 137 kg ha−1
(Conselho Nacional do Meio Ambiente, 2006) that is allowed tobe added to agricultural soils was not reached in the LPS scenario,
while in the HPS scenario this limit was reached already in 2019.
Therefore, when the goal is long-term availability of agriculturalsoils for PS disposal, the applications should be done at low doses.
The HYDRUS-1D simulations also estimated very low concentrations of Cu in the soil solution during 61 yr in both scenarios. As
shown in Fig. 8, the solution concentration at the profile bottom
did not change during this period. This indicates that Cu introduced on the soil surface with PS amendments was not transferredto the deepest layers of the soil profile. These results agree with thefindings of Mallmann et al. (2012b) for an Alfisol and confirm
again the higher soil affinity for adsorption of Cu than Zn, independently if the soil has high (Nascimento and Fontes, 2004) orlow (Arias et al., 2005) clay contents.
Considering only the soil contamination caused by TMs, thelong-term feasibility of PS amendments in the clayey Oxisol ofCampos Novos is viable only if PS amendments are done at low
rates. However, even then the successive applications of the manure

Although the chemical model was not validated for Cu transport in the Oxisol because it

Fig. 9. (A) Zinc and (B) Cu total concentrations in the upper 0- to 20-cm soil layer simulatedusing HYDRUS-1D during a 61-yr period for scenarios with amendments of low and high
(50 and 200 m3 ha−1 yr−1, respectively) pig slurry rates on an Oxisol in Campos Novos, SC.
The red line corresponds to the threshold value for agricultural soils. The vertical dotted grayline indicates the time of soil sampling.

VZJ | Advancing Critical Zone Science

p. 12 of 14

at the soil surface will increase soil concentrations of Cu until theyreach the threshold value established by Conselho Nacional do
Meio Ambiente (2009), making the Oxisol unusable for agricultural production. Therefore, after validating the transport model
for Cu transport in an Oxisol, future studies should be carried out
about the effect of soil tillage on reducing the TM accumulationon the soil surface and thus on extending the time suitable for PS
amendments of agricultural soils, similarly to those performed by
Mallmann et al. (2014) for an Alfisol.

66Conclusion

A transport model (Model I) constructed in HYDRUS-1D, andalready validated by Mallmann et al (2012b) for a coarse textured
Alfisol, was evaluated in this study to simulate the vertical movement of Zn and Cu in a clayey Oxisol subjected to PS amendmentswith low and high doses for 11 yr. The simulated total Zn concentrations throughout the soil profile provided a good fit with thecorresponding measured values for both treatments, leading to thevalidation of this transport model also for the clayey Oxisol andincreasing its applicability to a larger range of soil types. However,estimated concentrations of Cu were found to be less satisfactory,
overestimating surface Cu accumulation. Therefore, the transportmodel was not validated for Cu transport in a clayey Oxisol.
Consideration of root water uptake and root growth processes
in the original transport model (Model II) improved the correspondence between both simulated and measured Zn and Cu
concentrations. However, the improvement was not sufficient toallow the validation of this solute transport model for Cu. Thus,
we recommend in future studies to additionally also account forthe colloid-facilitated movement of TMs, increasing the rangeof transport processes considered in the simulations. However,models that consider such processes, such as the C-Ride moduleof HYDRUS-1D, require many additional parameters, such as
those characterizing sorption of TMs on particulate matter and/or
DOC, which will need to be measured experimentally.
Simulations of future scenarios that considered continued PS amendments of the Oxisol during the next 50 yr do not indicate that Zn
accumulation in the soil or Zn leaching to groundwater would exceed
the threshold values of the Brazilian law under either low or highapplication rates. However, these simulations also indicate that thefeasibility of PS amendments to the agricultural Oxisol will be limited due to Cu. Although the transport model was not validated forCu, several trends could be extracted from simulations of the fate ofthis TM in the soil. First, the threshold value can be reached approximately after 29 yr when high doses of PS amendments are applied tothe unamended Oxisol. Second, no problems were observed during
the next 50 yr when the PS amendments were made at low rates.
Moreover, the total loads of both TMs allowed by the law on agricultural soils were reached very fast when high rates were used, especiallyfor Cu (19 yr). Therefore, for the long-term feasibility of PS disposal to

VZJ | Advancing Critical Zone Science

agricultural soils, such as this Oxisol, the manure applications should
be done at low doses. These conclusions are probably conservative
because our model did not consider potential leaching of TMs fromthe surface soil into deeper soil layers by DOC-facilitated transport.

Acknowledgments

F.J.K. Mallmann acknowledges CAPES and CNPq for postgraduate scholarships. We thank
Copercampos for allowing this research to be done in its experimental area and EPAGRI for
providing the soil samples, the historical data about the pig slurry experiment, and the meteorological data for Campos Novos, SC.

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