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Chlorotoluron mobility in compost amended soil
Soil & Tillage Research 118 (2012) 88–96
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Chlorotoluron mobility in compost amended soil Radka Kodesˇova´ a,*, Martin Kocˇa´rek a, Tereza Hajkova´ a,b, Martina Hy´bler a, Ondrˇej Dra´bek a, Vı´t Kodesˇ a,b a Czech University of Life Sciences Prague, Faculty of Agrobiology, Food and Natural Resources, Department of Soil Science and Soil Protection, Kamy´cka´ 129, CZ-16521 Prague 6, Czech Republic b Czech Hydrometeorological Institute, Department of Water Quality, Na Sˇabatce 17, CZ-14306 Prague 4, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 July 2011 Received in revised form 22 October 2011 Accepted 25 October 2011 Available online 25 November 2011
Knowledge about the impact that various organic amendments have on the behavior of pesticides in soils is essential when assessing groundwater contamination risk. The aim of this study was to evaluate the impact of a compost amendment on chlorotoluron mobility in the A horizon of the Luvic Chernozem. Soil was mixed with compost material and placed into plastic cylinders. Eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (from 1 to 8% of mixture weight) were prepared. Chlorotoluron solution was applied on the top of the soil samples and a rainfall simulator was used to apply distilled water on the soil surfaces with controlled infiltration rates. Measured pressure heads at 3 positions, water outflow from the bottom, and final chlorotoluron concentrations within the soil samples were used to optimize soil hydraulic and herbicide transport parameters via numerical optimization using the HYDRUS-1D code. Optimized soil hydraulic parameters did not show noticeable changes (demonstrating an improvement of soil hydro-physical properties) with increasing compost fraction. Final chlorotoluron distribution in soil columns and estimated transport parameters also showed high variability. However, results indicated that while chlorotoluron mobility decreased up to a compost fraction of 6%, herbicide mobility noticeably increased in G (7%) samples and slightly increased in H (8%) samples. These finding corresponded to herbicide adsorption studied using a batch experiment. Multiple linear regressions revealed that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which were mostly properties of soil, but could be affected by compost composition as well) was documented. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Soil properties amendment Organic compost Hydraulic function Pesticide transport Chlorotoluron
1. Introduction Groundwater contamination caused by pesticides used in agriculture is an environmental problem worldwide. Groundwater contamination depends on many factors and conditions. It has been shown that the addition of organic matter into soil may improve soil physical and chemical properties such as aggregation, degree of compaction, hydraulic properties, etc. (Zebarth et al., 1999; Franzluebbers, 2002; Pagliai et al., 2004; Garcı´a-Orenes et al., 2005; Tejada and Gonzalez, 2006; Tejada et al., 2009; Hemmat et al., 2010) and decrease pesticide mobility in soils (Briceno et al., 2007). The impact of different organic amendments (various organic composts, agro-industrial waste, urban waste, mushroom compost, animal manure, sewage sludge, etc.) on various pesticides’ adsorption characteristics in amended soils was presented in several studies. The positive influence of added organic matter on pesticide adsorption, e.g. negative impact on pesticide mobility, was mostly
* Corresponding author. Tel.: +420 2 24 38 25 92; fax: +420 2 34 38 18 36. E-mail address: [email protected] (R. Kodesˇova´). 0167-1987/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.still.2011.10.014
reported among others by Rodriguez-Rubio et al. (2006), Dolaptsoglou et al. (2007), Delgado-Moreno et al. (2007, 2010), Ghosh and Singh (2009), Marin-Benito et al. (2009), Filipe et al. (2010) and Lima et al. (2010). A considerable decrease of pesticide leaching from amended soils due to soil physical properties improvement and higher pesticide adsorption in soil was documented by Konomi et al. (2005) and Fenoll et al. (2011). Conversely, an increase of pesticide mobility due to the higher content of dissolved organic carbon caused by soil organic matter amendment (which depended on organic carbon source) was documented among others by Baskaran et al. (1996), Celis et al. (1998), Graber et al. (2001), Yang et al. (2005) and Cabrera et al. (2007). Chlorotoluron is one of the more widely used herbicides. The positive impact of organic carbon content on herbicide adsorption in soils was mostly documented. The Freundlich adsorption coefficient (kF) values positively correlated with soil organic carbon content (Hiller et al., 2008). A linear relationship between kF and organic carbon content was proved (Meyer-Windel et al., 1997). Adsorption increased with increasing N content (Gao et al., 2007). A multiple linear equation was used to evaluate the relationship between the kF coefficient, and organic carbon content and CaCO3 content
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(Kodesˇova´ et al., 2011). Yang et al. (2005) documented greater chlorotoluron adsorption in soils without dissolved organic matter. The addition of dissolved organic carbon significantly increased the mobility of chlorotoluron and the total concentration of chlorotoluron in the leachate in columns (Song et al., 2008). Adsorption into the aggregates was lower compared to the batch experiment (van Beinum et al., 2005; Villaverde et al., 2009). The impact of diffusionlimited sorption on pesticide availability for leaching was documented in results of lysimetr experiment (van Beinum et al., 2006). Chlorotoluron transport was experimentally studied under field conditions and mathematically simulated by Kocˇa´rek et al. (2005, 2010) and Kodesˇova´ et al. (2004, 2005, 2008). Chlorotoluron transport was also physically and mathematically modeled in soil columns under laboratory conditions (Kodesˇova´ et al., 2009). Studies by Kocˇa´rek et al. (2005, 2010) and Kodesˇova´ et al. (2004, 2005, 2008, 2009) showed that herbicide transport was frequently affected by preferential flow caused by soil aggregation, biopores and soil fraction. The chlorotoluron leaching from some soils was also enhanced by reduced herbicide inflow into the soil aggregates due to the presence of organic and clay coatings covering the surface of soil aggregates. In general a moderate mobility of this herbicide and reasonable potential to contaminate ground water was documented. Soil improvement with respect to the reduction of groundwater contamination risk was investigated in this study. The study focuses on the evaluation of the compost impact on chlorotoluron transport in the compost amended soil samples under laboratory conditions. The main aims were: (i) to assess the impact that compost amendments have on water flow and consequently on numerically optimized soil hydraulic properties, (ii) to assess the impact that compost amendments have on pesticide leaching and retention in soils, and consequently on estimated/measured properties characterizing herbicide transport, (iii) to elucidate chlorotoluron behavior in soil with respect to soil physical and chemical properties. 2. Materials and methods 2.1. Preparation of compost amended soil mixtures Soil from the A horizon of the Luvic Chernozem (parent material loess) characterized as loam – clay loam was mixed with compost
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material. Compost was prepared according to a standard methodology as used by the Research Institute of Agricultural Engineering in Prague. The main compost components were grass, leaves, wooden chips, recycled previously slightly decomposed compost material, and a limited amount of straw. The main compost properties were 24.6% of Cox, 1.2% of N and pHH2 O 7.9. Eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (from 1 to 8% of mixture weight) were prepared. Wet soil and compost materials were homogenized as much as possible before mixing, but no other special treatment of the materials was used. Prepared mixtures were first stored for two weeks in plastic bags and then packed into plastic cylinders (diameter of 15 cm and height of 18 cm). Three samples were prepared for each mixture. Samples were kept wet with no vegetation in a greenhouse for 9 months. Two samples were then used to perform a water and solute transport experiment, which is described below. One sample was used to determine basic physical and chemical soil properties. The following basic chemical and physical soil properties were obtained using standard laboratory procedures under a constant laboratory temperature of 20 8C: the soil pHH2 O and pHKCl (International Organization of Standardization, 1994), the exchangeable acidity (EA) (Hendershot et al., 1993), the cation exchange capacity (CEC) (Bower and Hatcher, 1966), the soil hydrolytic acidity (HA) (Klute, 1996), the basic cation saturation (BCS) (difference between CEC and HA), the sorption complex saturation (SCS) (percentage of BCS in CEC), the oxidable organic carbon content (Cox) (Skjemstad and Baldock, 2008), the CaCO3 content (Looppert and Suarez, 1996), the soil salinity (Rhoades, 1996), the particle density (rs) (Flint and Flint, 2002), and the particle size distribution (fractions of clay, silt and sand) (Gee and Or, 2002). The final mixture properties are shown in Table 1. Three undisturbed 100-cm3 soil samples were also taken from each mixture sample to measure the bulk density and saturated soil water content. Despite precautions taken when preparing homogeneous soil and compost materials (which were next used for mixing) no substantial gradual changes of soil mixture properties were obtained. One of the reasons may be soil, compost and soil mixtures not being fully homogenized due to the large volumes of the materials. A second reason may be an uneven properties development in soil columns during 9 months in the greenhouse caused by various densification when packing, different soil water
Table 1 Physical and chemical soil properties for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight): pHKCl, pHH2 O , exchangeable acidity (EA), cation exchange capacity (CEC), hydrolytic acidity (HA), basic cation saturation (BCS), sorption complex saturation (SCS), oxidable organic carbon content (Cox), CaCO3 content, salinity, sand, silt and clay content, soil particle density (rs). Mixture
A B C D E F G H Mixture
A B C D E F G H
pHKCl
pHH2 O
EA
CEC
HA
BCS
SCS
(–)
(–)
(mmol + kg1)
(mmol + kg1)
(mmol + kg1)
(mmol + kg1)
(%)
6.41 6.32 6.44 6.41 6.42 6.36 6.57 6.59
6.77 6.83 6.75 6.8 6.67 6.66 6.68 6.7
2.21 1.51 1.46 1.57 1.08 1.64 1.46 1.27
267.50 275.00 247.50 321.67 320.83 270.83 262.50 334.17
10.68 11.81 10.68 11.06 9.17 12.00 11.62 10.40
256.82 263.19 236.82 310.61 311.66 258.83 250.88 323.77
96.01 95.71 95.68 96.56 97.14 95.57 95.57 96.89
Cox
CaCO3
Salinity
Sand
Silt
Clay
rs
(%)
(%)
(mS cm1)
(%)
(%)
(%)
(g cm3)
1.82 2.11 2.05 2.14 2.36 2.26 2.42 2.40
0.12 0.02 0.04 0.12 0.25 0.15 0.09 0.08
39.00 46.27 38.03 39.60 43.13 51.45 45.67 40.55
23 23 20 24 20 24 20 20
40 37 40 38 42 38 41 41
37 40 40 38 38 38 39 39
2.59 2.60 2.61 2.55 2.59 2.58 2.54 2.56
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content during this period, and possibly also by different biological activity in various soil columns. The most evident change was an increasing Cox content with increasing compost fraction. Values of pHH2 O and pHKCl showed a decreasing and increasing trend, respectively, with increasing compost fraction. Values of cation exchange capacity also indicated an increasing trend with increasing compost fraction. 2.2. Water flow and solute transport experiment Two samples of each mixture were transported into the laboratory where micro-tensiometers Tensior 5 (UMS GmbH, Munich, 2005) for pressure head measurements were installed 6.5, 11 and 15.5 cm below the soil surface. Cementation was used to prevent water flow and solute transport between the soil core and plastic cylinder. 20 cm3 of chlorotoluron solution (actual chlorotoluron concentration was measured for each experiment) was applied with an infiltration rate of 0.113 cm min1 on the top of the soil samples. One liter of chlorotoluron solution was prepared prior to the experiments. The corresponding amount of Syncuran 80 DP (which should contain 80% of chlorotoluron) was used to achieve a concentration of 200 mg cm3. 40 cm3 of previously prepared solution was carefully mixed before each experiment. Part of the solution was used for application into the soil column and part was used to analyze actually applied chlorotoluron concentration. The
rainfall simulator was used 4 min after the chlorotoluron application to supply distilled water on the soil tops with controlled infiltration rates. Infiltration rate varied depending on infiltration ability of each soil sample. The same amount of water (2550 cm3) was applied at all soil columns. The duration of the infiltration experiment is shown in Table 2. Water outflow and solute concentration from the bottom of the soil sample were monitored in time. Outflow duration and total solution outflow is again documented in Table 2. Solute concentrations were determined using High Performance Liquid Chromatography (HPLC). One day after the infiltration, all soil samples were cut into 9 layers (thickness of 2 cm). The sub-samples were air dried and the mass of soil in each layer was measured. The soil was then ground and sieved through a 2-mm sieve. The total amount of chlorotoluron remaining in each soil layer was ascertained as follows. 5 g of dry soil was placed into the centrifuge cuvette. 5 ml of methanol was added and the centrifuge cuvette was placed for 22 h into the shaking apparatus. Analyzed soil samples were then centrifuged for 30 min at 13,800 rotations per minute. The chlorotoluron concentration in the methanol extract was again measured using HPLC. The chlorotoluron concentration in the soil sample was expressed as the total amount of solute per unit of mass of dry soil (mg g1). Finally, chlorotoluron recovery was found by comparing the amount of applied solute with the sum of solute leached at the bottom of the column and remaining in the soil (Table 3).
Table 2 Characteristics of water flow experiments perform in soil columns for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Soil column
Infiltration
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2
Total outflow (cm3)
Outflow
Beginning (min)
Ending (min)
Beginning (min)
Ending (min)
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
44.62 46.58 39.72 47.65 62.93 58.92 50.55 70.32 56.83 49.65 69.78 62.37 61.97 63.00 67.77 71.53
12.27 11.75 7.42 10.58 8.68 13.30 8.03 16.80 13.20 10.65 15.72 19.65 22.52 17.37 17.05 16.82
52.12 58.00 48.58 51.38 71.72 75.58 54.60 74.38 59.92 54.13 74.00 71.78 69.95 76.12 77.42 80.50
2082.09 2241.19 1916.11 2142.55 2115.09 2162.16 1898.58 2153.17 2080.06 2187.23 2001.16 2003.57 1909.30 1851.73 2109.56 2064.91
Table 3 Chlorotoluron balance in soil columns for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Soil column
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2
Applied concentrations
Total amount applied
Content in ouflow
Content in soil
Content in outflow and soil
Chlorotoluron recovery
(mg cm3)
(mg)
(mg)
(mg)
(mg)
(%)
181.2 187.3 176.4 189.5 185.9 188.1 196.4 201.7 189.9 192.0 177.8 182.7 177.4 179.1 194.0 198.2
3623.6 3745.6 3527.2 3789.3 3717.5 3761.2 3929.0 4034.0 3797.7 3840.4 3555.6 3654.7 3548.3 3581.4 3880.9 3963.4
74.4 68.7 26.0 20.0 83.3 8.7 66.5 16.4 15.9 10.9 81.8 6.9 17.1 51.2 32.4 10.7
3443.6 2659.9 2138.0 2624.4 2696.2 2664.1 3815.3 3707.3 3771.8 3634.4 3540.8 3181.3 3130.3 3157.8 2798.0 2800.8
3518.0 2728.6 2164.0 2644.4 2779.5 2672.8 3881.8 3723.8 3787.6 3645.2 3622.6 3188.2 3147.4 3209.0 2830.4 2811.5
97.09 72.85 61.35 69.79 74.77 71.06 98.80 92.31 99.73 94.92 101.88 87.24 88.70 89.60 72.93 70.94
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2.3. Numerical evaluation of experimental data 2.3.1. Water flow model Water flow in the soil profile may be simulated using the singleporosity model in HYDRUS-1D (Sˇimu˚nek et al., 2008; Sˇimu˚nek and van Genuchten, 2008). The Richards equation, describing the onedimensional isothermal Darcian flow in a variably saturated rigid porous medium, is used in the model: @u @ @h ¼ K ðhÞ þ K ðhÞ (1) @t @z @z where u is the volumetric soil water content [L3L3], h is the pressure head [L], K is the hydraulic conductivity [LT1], t is time [T], and z is the vertical axis [L]. Eq. (1) is solved for the entire flow domain using one set of soil water retention and hydraulic conductivity functions. Analytical expressions proposed by van Genuchten (1980) for the soil water retention curve, u(h), and the hydraulic conductivity function, K(u), are used in the model:
ue ¼
uðhÞ ur 1 ¼ m ; us ur 1 þ jahjn
ue ¼ 1;
h<0
(2)
h0
h i2 l 2=m m KðuÞ ¼ K s ue 1 ð1 ue Þ ;
KðuÞ ¼ K s ;
h<0
(3)
h0
where ue is the effective soil water content [–], Ks is the saturated hydraulic conductivity [LT1], ur and us are the residual and saturated soil water contents [L3L3], respectively, l is the poreconnectivity parameter [–], a is reciprocal of the air entry pressure, [L1], and n [–] is related to the slope of the retention curve at the inflection point, and m = 1 1/n [–]. 2.3.2. Solute transport model The advection-dispersion equation for solute transport is used in the model: @uc @rd s @ @c @qc þ ¼ F uD (4) @t @t @z @z @z where c [ML3] and s [MM1] are solute concentrations in the liquid and solid phases, respectively, q is the volumetric water flux density [LT1], rd is the soil bulk density [ML3], D is the dispersion coefficient [L2T1], and F describes zero- and first-order rate reactions [ML3T1]. Assuming the equilibrium solute adsorption, the adsorption isotherm relating the adsorbed concentration, s, and liquid concentration, c, may be described using the Freundlich equation: s ¼ kF cb
(5) b
b
where kF [L3 ;1 M1] and b [–] are empirical coefficients. 2.3.3. Estimation of soil hydraulic properties The single-porosity model in HYDRUS-1D was first used to analyze transient flow data (cumulative outflow and pressure heads at three positions) to obtain the parameters of both soil hydraulic functions (soil water retention and unsaturated hydraulic conductivity functions) that were described using the van Genuchten model (Eqs. (2) and (3)). Initial conditions within the soil sample were set, based on pressure heads measured at the beginning of the experiments. Pressure heads at monitored
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locations were set to measured values, while pressure heads between, above, and below were interpolated and/or extrapolated. Since the soil samples were stored in plastic bags in a cold environment 1 week before the experiments, the steady state conditions were assumed and the linear pressure head distributions were applied. The upper boundary condition was defined using an initial infiltration pulse of 0.113 cm min1 and for 1 min duration, followed by a controlled irrigation (simulated rainfall) rate. The bottom boundary condition was defined using a seepage face. Since measured saturated water contents, us, did not indicate any trend, the average value (0.550 cm3 cm3) was set for all soil columns. The remaining soil hydraulic parameters (i.e., ur, a, n, and Ks) were optimized. In all cases the pore connectivity parameter was assumed to be equal to an average value for many soils (l = 0.5) (Mualem, 1976) since it was found (not shown) that optimization of the l parameter increased uncertainty for all optimized parameters. The objective function, OF, (Sˇimu˚nek et al., 1998) in HYDRUS-1D, representing deviations between the measured and calculated space-time variables (e.g., cumulative flux versus time across a bottom boundary, and pressure heads observed at 3 depths), was minimized during the parameter estimation process. 2.3.4. Estimation of solute transport parameters The single-porosity model in HYDRUS-1D was then again used to simulate observed solute transport in all soil columns. Initial and boundary water flow conditions were defined as before. Soil hydraulic parameters obtained from previous inversions of water flow data were fixed. In addition, a chlorotoluron concentration, which was measured for each experiment, was specified for the initial infiltration pulse (1 min). A zero concentration gradient was defined at the bottom of the sample. Bulk density was set to the measured average value (1.16 g cm3). Molecular diffusion was assumed to be zero. No herbicide degradation (e.g. no zero- or firstorder rate reactions) was assumed during the short term experiment. The other parameters were estimated. Since a low solute outflow at the soil column bottoms was mostly observed (see below Table 3), solute transport parameters could not be estimated using measured outflow concentrations versus time as it is usually completed (Kodesˇova´ et al., 2009). Therefore the final chlorotoluron contents within the soil samples were used in optimizations. The final chlorotoluron concentration in soil water was calculated using the total measured herbicide content in analyzed soil layers (Fig. 1), the final simulated water content within the soil sample, and the adsorption isotherms. Chlorotoluron adsorption in different soil mixtures was studied using the standard laboratory procedure and adsorption isotherms were described using the Freundlich Eq. (5) (Table 4). The same procedure as described by Kodesˇova´ et al. (2011) was used. 10 g
Table 4 Parameters of the Freundlich (kF, b) adsorption isotherms for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Mixture
b
kF for b = 0.76
(cm3 mg1 g1)
(–)
(cm3 mg1 g1)
5.70 5.61 5.08 5.59 5.52 5.91 5.89 5.52
0.71 0.74 0.73 0.76 0.77 0.77 0.76 0.80
4.93 5.39 4.61 5.47 5.63 6.08 5.71 5.60
kF b
A B C D E F G H
b
b
b
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Fig. 1. Chlorotoluron distribution, expressed as total amount (adsorbed on soil particles and dissolved in soil water) per mass unite of dry soil, within the soil sample (two repetitions) at the end of the experiment for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight).
of air dried, ground and sieved (2-mm) soil were placed into the 50 cm3 glass bottle. 20 cm3 of solution of a known chlorotoluron concentration was added to the glass bottle. The bottle was shaken for 24 h using the shaking apparatus at 20 8C. Five initial pesticide concentrations and three replicates of each concentration were applied for each soil. Solutions were prepared using Syncuran 80 DP to obtain the following approximate initial concentrations: cini = 1, 2, 5, 10 and 25 mg cm3. The actual initial (cini,a) and final equilibrium pesticide concentrations (ceq) in solutes (mg cm3) were measured using the HPLC technique. The pesticide concentration adsorbed on soil particles (s) was expressed as the amount of solute per mass unit (mg g1) using the mass balance in the suspension. Data points of the adsorption isotherms were given by the final pesticide concentration c = ceq and s. The Freundlich equation (Eq. (5)) was used to fit data points of the adsorption isotherms. However, due to the Eq. (5) nonlinearity a three linear equations (b = 1, kF = 3, 4, and 5 cm3 g1) were applied for each soil column to calculate final
chlorotoluron concentrations in soil water, then to simulate herbicide transport in soil, and to estimate the longitudinal dispersivity using these final chlorotoluron concentrations in soil water. The lower values of the kF values in comparison with the measured kF values were used due to the linearity of the equation replacing the exponentional shape of the measured isotherms (which acceptably approximated herbicide adsorption within a simulated ration of herbicide concentration in soil water). The second reason was that the lower adsorption had been expected due to the soil material aggregation (possibly causing lower herbicide sorption into aggregates, which was before documented by van Beinum et al. (2005, 2006) and Villaverde et al. (2009)) and dynamic nature of the experiment (e.g. nonequilibrium adsorption during the experiment documented by Kodesˇova´ et al. (2009)). The objective function in HYDRUS-1D, representing deviations between the measured and calculated chlorotoluron concentrations in soil water, was minimized during the parameter estimation process.
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3. Results and discussion 3.1. Water flow and solute transport experiments The infiltration and outflow durations, and total volumes of water captured below the soil samples are shown in Table 2. It is evident that the infiltration durations (e.g. time that was necessary to infiltrate the same amount of water 2550 cm3), which was controlled by soil infiltration ability, increased with the compost content. Outflow durations correspondingly increased as well. The total outflow at the bottom varied randomly with increasing compost content. It depended on initial soil water content and soil retention ability. Chlorotoluron balance in soil columns are shown in Table 3. Table 3 shows the actually applied chlorotoluron concentration, the total amount of applied chlorotoluron for each soil sample, chlorotoluron amount in outflow solution, chlorotoluron content in soil at the end of the experiment, sum of both (in outflow and in soil) and chlorotoluron recovery (in %). It is evident that in some cases a weak correspondence between applied and recovered herbicide was achieved. The reasons may be: herbicide loss when cutting the soil sample, experimental error when preparing the soil sample for chlorotoluron extraction (no complete soil homogenization, herbicide loss when grinding etc.), chlorotoluron degradation, no full extraction of herbicide from soil, and an analytical error. Despite this fact, the following conclusion can be made based on obtained data. Table 3 shows that a very low solute leaching at the soil column bottoms was observed in all cases. The total amount of leached herbicide varied randomly with increasing compost content. Chlorotoluron mostly remained in soil columns, which is also evident on final distributions of chlorotoluron concentrations (Fig. 1). While the highest chlorotoluron concentrations were mostly found in the surface layers of all soil mixtures, very low concentrations were mostly recovered in the bottom layers. Similar herbicide distributions for each pair of soil mixtures were observed. Fig. 1 shows that herbicide mobility decreased with increasing compost content (up to 6% of compost content – mixture F), which was indicated by increasing chlorotoluron retention in the top part of the soil column and decreasing chlorotoluron content in the bottom part of the soil column. For mixtures with compost content above 6% the herbicide mobility increased with increasing compost content. In some cases herbicide transport was possibly moderately (D1, G1, G2) or slightly (A2, E1, H1, H2) influenced by preferential flow. The largest
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impact is evident in G soil samples. We suppose that this soil mixture was probably affected by biological activities (which could originate in storage bags before soil packing into the columns) to a greater degree than other soil samples, which caused the formation of preferential pathways. However, these pathways did not noticeably increase herbicide leaching from the soil columns. 3.2. Numerical evaluation of experimental data 3.2.1. Estimation of soil hydraulic properties One example of measured and simulated pressure heads at depths of 6.5, 11 and 15.5 cm below the soil surface, cumulative water outflow at the soil column bottom (both documenting an excellent agreement of measured and simulated data), and applied infiltration at the soil surface is shown in Fig. 2. Resulting parameters of the van Genuchten soil hydraulic functions (ur, a, n, Ks) are shown in Table 5. The R2 values and objective functions showed that measured transient flow data (pressure heads at depths of 6.5, 11 and 15.5 cm below the soil surface and cumulative water outflow at the soil column bottom) were successfully approximated using HYDRUS-1D in all cases. This agreement was due to the measured transient water flow data, which did not indicate any occurrence of preferential flow (which is in contrast to some of the observed chlorotoluron distributions in soil columns). Resulting van Genuchten parameters varied randomly with the increasing compost content despite the experiment durations demonstrating an increasing trend with increasing compost content. No apparent impact of compost adding was thus observed based on these numerical analyses. 3.2.2. Estimation of solute transport parameters One example of measured and simulated chlorotoluron distribution within the soil column at the end of the experiment, expressed as total chlorotoluron content per mass unit of dry soil, is shown in Fig. 3 and exhibits a weaker agreement between the measured and simulated values. Resulting values of longitudinal dispersivity, DL, estimated via numerical inversion using the HYDRUS-1D program are shown in Table 6. The R2 values and objective functions showed that, in comparison to simulated water flow discussed above, less successful fitting of measured data (chlorotoluron concentrations in soil water within the soil sample at the end of the experiment) were reached. The weakest fitting of measured data were obtained for D and G soil samples, where herbicide transport was probably affected by the preferential flow
Fig. 2. Example (column A1, compost fraction 1% of mixture weight) of measured and simulated (using HYDRUS-1D) pressure heads in depths of 6.5 (T1), 11 (T2) and 15.5 (T3) cm below the soil surface, cumulative water outflow at the soil column bottom, and applied infiltration at the soil surface.
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Table 5 Parameters of the van Genuchten soil hydraulic functions (ur, a, n, Ks, us = 0.55 cm3 cm3) obtained using numerical inversion with HYDRUS-1D for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight), (R2 – coefficient of determination, OF – objective function, index 1 – cumulative boundary flux across the bottom boundary, index 2 – pressure head measurements at 3 observation points). Soil column
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2 a b
ur
a
n
Ks
(cm3 cm3)
(cm1)
(–)
(cm min1)
0.261 0.034a 0.205 0.003 0.023 0.059 0.006 0.020 0.005 0.002 0.245 0.073 0.302 0.028 0.165 0.027 0.139 0.017 0.203 0.087 0.292 0.049 0.330 0.013 0.084 0.005 0.131 0.016 0.316 0.055 0.161 0.054
0.074 0.004 0.035 0.001 0.027 0.003 0.034 1.966 0.019 0.001 0.051 0.010 0.012 0.001 0.007 0.0003 0.209 0.054 0.157 0.033 0.035 0.008 0.059 0.005 0.021 0.001 0.053 0.007 0.057 0.006 0.278 0.03
1.97 0.26 1.42 0.014 4.52 1.62 2.5 0.49 6.02 0.83 1.99 0.34 4.12 0.70 4.14 0.91 1.5b 1.43 0.16 1.78 0.33 1.99 0.29 1.44 0.02 1.34 0.01 2.26 0.47 1.35 0.07
1.33 1.18 0.33 0.006 1.46 0.83 0.50 0.11 1.15 0.39 0.35 0.07 0.50 0.21 0.39 0.11 22.56 13.65 6.48 4.49 0.64 0.35 0.69 0.10 0.27 0.02 0.20 0.03 0.45 0.11 13.12 1.27
R2
OF1
OF2
0.987 0.962 0.839 0.877 0.906 0.966 0.919 0.891 0.982 0.975 0.974 0.992 0.976 0.983 0.965 0.982
0.015 0.006 0.039 0.078 0.014 0.002 0.053 0.033 0.008 0.009 0.005 0.005 0.006 0.030 0.004 0.005
0.019 0.050 0.221 0.167 0.125 0.047 0.103 0.127 0.027 0.038 0.032 0.010 0.030 0.023 0.054 0.023
95% confidence interval. Not optimized due to the computational instability.
(which was not however documented on water flow data). Despite that, the following conclusions could be made based on the presented results. When the same adsorption isotherms were applied for all soil samples, a decreasing trend of longitudinal dispersivity, DL, values was obtained with an increasing compost content up to 6% (F samples), above this value DL values increased. This corresponded to the observed chlorotoluron distributions within the soil samples (Fig. 1). Since the lowest herbicide mobility was observed in the F samples, the lowest DL values for F samples had to be obtained, which was the only parameter controlling simulated herbicide transport (and opposite). While the lower DL values reduced solute spreading in soils, higher DL values increased solute penetration into greater depths. The lowest value of the objective function indicates the best fit of measured data for each soil sample. Assuming only the best fits for all soil samples, DL values varied randomly with compost content (range between 2.17 and 7.54 cm), and kF values showed an increasing trend with increasing compost content up to 5%
Fig. 3. Example (column A1, compost fraction 1% of mixture weight) of measured and simulated (using HYDRUS-1D) chlorotoluron distribution, expressed as total amount (adsorbed on soil particles and dissolved in soil water) per mass unite of dry soil, within the soil sample at the end of the experiment, assuming 3 liner (b = 1) adsorption isotherms with various kF coefficients.
(E soil samples) and a decreasing trend with increasing compost content up to 8% (H soil samples). This approximately corresponded to the observed chlorotoluron mobility within the soil samples (Fig. 1) and measured adsorption isotherms (Table 4). The preferential flow did not affect herbicide transport as intensively as in the study by Kodesˇova´ et al. (2009). Despite a limited impact of preferential flow, which was noticeable in some soil samples, a single-porosity model was sufficient for describing water flow and herbicide transport in soil columns. 3.3. Chlorotoluron behavior in soils with respect to soil physical and chemical soil properties Results showed that herbicide transport decreased with increasing compost content (up to 6% of compost content – mixture F), and increased with increasing compost content for mixtures with compost content above 6%. Since numerical analysis illustrated no evident impact of compost addition on water flow characteristics, variability of herbicide mobility was caused dominantly by herbicide behavior (mostly adsorption) in soils. Findings are partly in contrast to results documenting a positive compost impact on pesticides sorption (e.g. negative impact on their mobility) (see studies mentioned in the introduction). The negative impact of dissolved organic carbon on chlorotoluron adsorption as discussed by Yang et al. (2005) and Song et al. (2008) could be suggested. However, this effect cannot be documented based on the data presented in our study. Previous studies by Meyer-Windel et al. (1997), Hiller et al. (2008) and Kodesˇova´ et al. (2011) showed that the Freundlich adsorption coefficient, kF, for chlorotoluron (e.g. its mobility in soils) depends mostly on the organic carbon content in soils. A linear relationship between the kF value and organic carbon content was confirmed in these studies. Kodesˇova´ et al. (2011) also showed that better predictions of the kF values from other soil properties were reached when the organic carbon content and the CaCO3 content were included in the multiple linear regression equation. Gao et al. (2007) documented that chlorotoluron adsorption increased with increasing N content. Data presented here showed that the kF value evaluated for all mixtures, observed herbicide mobility in soil columns, and parameters characterizing herbicide mobility resulting from numerical analysis did not depend solely on the organic carbon content (e.g. compost content)
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95
Table 6 Longitudinal dispersions (DL) obtained using numerical inversion with HYDRUS-1D for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight) assuming 3 liner (b = 1) adsorption isotherms with various kF coefficients (SC – soil column, R2 – coefficient of determination, OF – objective function). SC
A1
B1
C1
D1
E1
F1
G1
H1
a b
kF
DL
(cm3 g1)
(cm)
3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5
3.08 0.05a 4.36 0.09 6.33 0.18 5.25 0.07 9.23 0.23 13.99 0.46 4.06 0.07 6.07 0.07 8.47 0.22 3.37 0.44 4.88 0.13 7.24 0.44 2.05 0.10 2.85 0.11 3.75 0.12 1.86 0.11 2.54 0.10 3.43 0.12 3.55 0.20 4.55 0.09 6.52 0.11 3.52 0.11 5.08 0.14 7.21 0.21
R2
0.912 0.912 0.910 0.884 0.935 0.894 0.808 0.974 0.961 0.117 0.639 0.573 0.647 0.668 0.670 0.632 0.650 0.653 0.096 0.020 0.018 0.732 0.733 0.716
OF
SC
0.129 0.105b 0.294 0.076 0.387 1.033 0.088 0.045 0.297 1.195 1.041 1.010 0.521 0.369 0.331 0.542 0.395 0.348 1.537 1.245 1.072 0.323 0.272 0.410
A2
B2
C2
D2
E2
F2
G2
H2
kF
DL
(cm3 g1)
(cm)
3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5
3.64 0.36 5.94 0.36 8.30 0.45 3.82 0.09 6.52 0.15 10.27 0.29 2.17 0.10 3.78 0.15 6.34 0.23 3.29 0.11 4.86 0.13 6.91 0.17 2.51 0.11 3.73 0.12 5.29 0.14 1.61 0.11 2.64 0.09 4.09 0.12 4.84 0.26 7.54 0.16 11.49 0.31 3.14 0.10 4.67 0.14 6.78 0.23
R2
OF
0.739 0.737 0.730 0.866 0.868 0.839 0.756 0.718 0.661 0.377 0.377 0.371 0.758 0.771 0.762 0.825 0.824 0.799 0.475 0.694 0.429 0.782 0.779 0.759
0.502 0.290 0.291 0.145 0.237 0.582 0.253 0.335 0.564 0.692 0.625 0.656 0.424 0.255 0.249 0.299 0.192 0.223 0.780 0.563 0.624 0.231 0.257 0.473
95% confidence interval. The best fit of measured data.
as would be expected from studies by Meyer-Windel et al. (1997) and Hiller et al. (2008). Therefore a similar analysis as performed by Kodesˇova´ et al. (2011) was done in our study. The average b coefficient from all b values (Table 4) was calculated and the Freundlich equation with the fixed average b value (=0.76) was used again to fit experimental data points and to obtain a new set of kF values (Table 4). The multiple linear regressions were then used to define the relationship between the kF coefficient (for the fixed b value) and other measured physical and chemical soil properties. The following relationship was obtained: kF ðcm3b mg1b g1 Þ ¼ 40:3 þ 3:64 Cox ð%Þ 0:483 clay content ð%Þ 3:67pHKCl 5:47CaCO3 ð%Þ
(6)
The order of soil parameters in this equation reflects the statistical significance of the variables. Eq. (6) explained 98.973% of the variability in the kF value. The standard deviation of the residuals was 0.072. Since all p-values belonging to independent variables were lower than 0.01, the statistical significance at the 99% confidence level was achieved for all of them. Eq. (6) clearly shows that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which are characteristics of soil, but may be affected by compost composition as well) was documented. It should be pointed out that Eq. (6) applies only for this soil and its mixtures with compost. To provide a general relationship, which would characterize herbicide adsorption in soils a larger range of soil properties and correspondingly larger variation of the kF values would be necessary (as in study by Kodesˇova´ et al., 2011). However, since the correlation is so close and observation in some aspects corresponds to previously obtained results (Kodesˇova´ et al., 2011), this relationship may indicate properties impact of chlorotoluron adsorption in studied soil mixtures (especially in this case, when adsorption variation is
given only by variation of the soils of similar mineralogical composition. 4. Conclusions The possibility to improve the soil physical, hydraulic and chemical properties using organic compost was evaluated in this study. Despite precautions taken when preparing homogeneous soil and compost materials (which were next used for mixing) no substantial gradual changes of soil mixture properties (pHKCl, pHH2 O , exchangeable acidity, cation exchange capacity, hydrolytic acidity, basic cation saturation, sorption complex saturation, oxidable organic carbon content, CaCO3 content, salinity, sand, silt and clay content, soil particle density) were obtained with increasing compost content. The most evident change was increasing Cox content with increasing compost fraction. Values of pHH2 O and pHKCl showed a decreasing and increasing trend, respectively, with increasing compost fraction. Values of cation exchange capacity also indicated an increasing trend with increasing compost fraction. Measured physical properties (bulk density and saturated soil water content) and soil hydraulic parameters, resulting from the numerical analysis of measured transient water flow data, did not show noticeable changes (demonstrating soil hydro-physical properties improvement) with increasing compost fraction. Final chlorotoluron distributions in soil columns and estimated transport parameters also showed high variability. However results indicated a decreasing trend of chlorotoluron mobility up to the compost fraction of 6%. Above this value, herbicide mobility noticeably (7%) and slightly (8%) increased. These findings correspond to herbicide adsorption studied using a batch experiment on all soil mixtures. Multiple linear regressions revealed that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which were
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mostly characteristics of soil, but could be affected by compost composition as well) was documented. Acknowledgement Authors acknowledge the financial support of the Grant Agency of the Czech Republic (Grant No. GA CR 526/08/0434) and the Ministry of Education, Youth and Sports of the Czech Republic (Grant Nos. 2B06095 and MSM 6046070901). Authors also acknowledge P. Plı´va and G. Sˇedivcova´ from the Research Institute of Agricultural Engineering in Prague for providing and preparing compost amended soil samples for the laboratory experiments. References Baskaran, S., Bolan, N.S., Rahman, A., Tillman, R.W., 1996. Effect of exogenous carbon on the sorption and movement of atrazine and 2,4-D by soils. Aust. J. Soil Res. 34 (4), 609–622. Bower, C.A., Hatcher, J.T., 1966. Simultaneous determination of surface area and cation-exchange capacity. Soil Sci. Soc. Am. Proc. 30, 525–527. Briceno, G., Palma, G., Duran, N., 2007. Influence of organic amendment on the biodegradation and movement of pesticides. Crit. Rev. Environ. Sci. Technol. 37 (3), 233–271. Cabrera, A., Cox, L., Velarde, P., Koskinen, W.C., Cornejo, J., 2007. Fate of diuron and terbuthylazine in soils amended with two-phase olive oil mill waste. J. Agric. Food Chem. 55 (12), 4828–4834. Celis, R., Barriuso, E., Houot, S., 1998. Sorption and desorption of atrazine by sludgeamended soil: dissolved organic matter effects. J. Environ. Qual. 27 (6), 1348– 1356. Delgado-Moreno, L., Pena, A., Almenbdros, G., 2010. Contribution by different organic fractions to triazines sorption in Calcaric Regosol amended with raw and biotransformed olive cake. J. Hazard. Mater. 174 (1–3), 93–99. Delgado-Moreno, L., Sanchez-Moreno, L., Pena, A., 2007. Assessment of olive cake as soil amendment for the controlled release of triazine herbicides. Sci. Total Environ. 378 (1–2), 119–123. Dolaptsoglou, C., Karpouzas, D.G., Menkissoglu-Spiroudi, U., Eleftherohorinos, I., Voudrias, E.A., 2007. Influence of different organic amendments on the degradation, metabolism, and adsorption of terbuthylazine. J. Environ. Qual. 36 (6), 1793–1802. Fenoll, J., Ruiz, E., Flores, P., Vela, N., Hellin, P., Navarro, S., 2011. Use of farming and agro-industrial wastes as versatile barriers in reducing pesticide leaching through soil columns. J. Hazard. Mater. 187 (1–3), 206–212. Filipe, O.M.S., Vidal, M.M., Scherer, H.W., Schneider, R.J., Duarte, A.C., Esteves, V.I., Santos, E.B.H., 2010. Effect of long term organic amendments on adsorptiondesorption of thiram onto a luvisol soil derived from loess. Chemosphere 80 (3), 293–300. Flint, A.L., Flint, L.E., 2002. Particle Density. In: Dane, J.H., Topp, G.C. (Eds.), Methods of Soil Analysis, Part 4 - Physical Methods. Soil Science Society of America, Inc., Madison, USA, pp. 229–240. Franzluebbers, A.J., 2002. Water infiltration and soil structure related to organic matter and its stratification with depth. Soil Tillage Res. 66, 97–205. Gao, M.L., Dai, S.G., Ma, Y.M., Zhang, P., 2007. Impact of atrazine and nitrogen fertilizers on the sorption of chlorotoluron in soil and model sorbents. J. Environ. Sci. 19 (3), 327–331. ˜ o, J., Go´mez, I., Garcı´a-Orenes, F., Guerrero, C., Mataix-Solera, J., Navarro-Pedren Mataix-Beneyto, J., 2005. Factors controlling the aggregate stability and bulk density in two different degraded soils amended with biosolids. Soil Tillage Res. 2 (1), 65–76. Gee, G.W., Or, D., 2002. Particle-Size Analysis. In: Dane, J.H., Topp, G.C. (Eds.), Methods of Soil Analysis, Part 4 - Physical Methods. Soil Science Society of America, Inc., Madison, USA, pp. 255–294. Ghosh, R.K., Singh, N., 2009. Effect of organic manure on sorption and degradation of azoxystrobin in soil. J. Agric. Food Chem. 57 (2), 632–636. Graber, E.R., Dror, I., Bercovich, F.C., Rosner, M., 2001. Enhanced transport of pesticides in a field trial with treated sewage sludge. Chemosphere 44, 805–811. Hemmat, A., Aghilinategh, N., Rezainejad, Y., Sadeghi, M., 2010. Long-term impacts of municipal solid waste compost, sewage sludge and farmyard manure application on organic carbon, bulk density and consistency limits of a calcareous soil in central Iran. Soil Tillage Res. 108 (1–2), 43–50. Hendershot, W.H., Lalande, H., Duquette, M., 1993. Soil Reaction and Exchangeable Acidity. In: Carter, M.R. (Ed.), Soil Sampling and Method of Analysis, Canadian Society of Soil Science. Lewis Publisher, USA, pp. 141–185. Hiller, E., Krascsenits, Z., Cernansky, S., 2008. Sorption of acetochlor, atrazine, 2,4-D, chlorotoluron, MCPA, and trifluralin in six soils from Slovakia. Bull. Environ. Contam. Toxicol. 80 (5), 412–416. International Organization of Standardization, 1994. Standard of Soil Quality Determination of pH (ISO 10390:1994). Klute, A., 1996. Methods of Soil Analysis. Agronomy Monograph 9. American Society of Agronomy, Madison, WI.
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Soil & Tillage Research journal homepage: www.elsevier.com/locate/still
Chlorotoluron mobility in compost amended soil Radka Kodesˇova´ a,*, Martin Kocˇa´rek a, Tereza Hajkova´ a,b, Martina Hy´bler a, Ondrˇej Dra´bek a, Vı´t Kodesˇ a,b a Czech University of Life Sciences Prague, Faculty of Agrobiology, Food and Natural Resources, Department of Soil Science and Soil Protection, Kamy´cka´ 129, CZ-16521 Prague 6, Czech Republic b Czech Hydrometeorological Institute, Department of Water Quality, Na Sˇabatce 17, CZ-14306 Prague 4, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 July 2011 Received in revised form 22 October 2011 Accepted 25 October 2011 Available online 25 November 2011
Knowledge about the impact that various organic amendments have on the behavior of pesticides in soils is essential when assessing groundwater contamination risk. The aim of this study was to evaluate the impact of a compost amendment on chlorotoluron mobility in the A horizon of the Luvic Chernozem. Soil was mixed with compost material and placed into plastic cylinders. Eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (from 1 to 8% of mixture weight) were prepared. Chlorotoluron solution was applied on the top of the soil samples and a rainfall simulator was used to apply distilled water on the soil surfaces with controlled infiltration rates. Measured pressure heads at 3 positions, water outflow from the bottom, and final chlorotoluron concentrations within the soil samples were used to optimize soil hydraulic and herbicide transport parameters via numerical optimization using the HYDRUS-1D code. Optimized soil hydraulic parameters did not show noticeable changes (demonstrating an improvement of soil hydro-physical properties) with increasing compost fraction. Final chlorotoluron distribution in soil columns and estimated transport parameters also showed high variability. However, results indicated that while chlorotoluron mobility decreased up to a compost fraction of 6%, herbicide mobility noticeably increased in G (7%) samples and slightly increased in H (8%) samples. These finding corresponded to herbicide adsorption studied using a batch experiment. Multiple linear regressions revealed that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which were mostly properties of soil, but could be affected by compost composition as well) was documented. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Soil properties amendment Organic compost Hydraulic function Pesticide transport Chlorotoluron
1. Introduction Groundwater contamination caused by pesticides used in agriculture is an environmental problem worldwide. Groundwater contamination depends on many factors and conditions. It has been shown that the addition of organic matter into soil may improve soil physical and chemical properties such as aggregation, degree of compaction, hydraulic properties, etc. (Zebarth et al., 1999; Franzluebbers, 2002; Pagliai et al., 2004; Garcı´a-Orenes et al., 2005; Tejada and Gonzalez, 2006; Tejada et al., 2009; Hemmat et al., 2010) and decrease pesticide mobility in soils (Briceno et al., 2007). The impact of different organic amendments (various organic composts, agro-industrial waste, urban waste, mushroom compost, animal manure, sewage sludge, etc.) on various pesticides’ adsorption characteristics in amended soils was presented in several studies. The positive influence of added organic matter on pesticide adsorption, e.g. negative impact on pesticide mobility, was mostly
* Corresponding author. Tel.: +420 2 24 38 25 92; fax: +420 2 34 38 18 36. E-mail address: [email protected] (R. Kodesˇova´). 0167-1987/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.still.2011.10.014
reported among others by Rodriguez-Rubio et al. (2006), Dolaptsoglou et al. (2007), Delgado-Moreno et al. (2007, 2010), Ghosh and Singh (2009), Marin-Benito et al. (2009), Filipe et al. (2010) and Lima et al. (2010). A considerable decrease of pesticide leaching from amended soils due to soil physical properties improvement and higher pesticide adsorption in soil was documented by Konomi et al. (2005) and Fenoll et al. (2011). Conversely, an increase of pesticide mobility due to the higher content of dissolved organic carbon caused by soil organic matter amendment (which depended on organic carbon source) was documented among others by Baskaran et al. (1996), Celis et al. (1998), Graber et al. (2001), Yang et al. (2005) and Cabrera et al. (2007). Chlorotoluron is one of the more widely used herbicides. The positive impact of organic carbon content on herbicide adsorption in soils was mostly documented. The Freundlich adsorption coefficient (kF) values positively correlated with soil organic carbon content (Hiller et al., 2008). A linear relationship between kF and organic carbon content was proved (Meyer-Windel et al., 1997). Adsorption increased with increasing N content (Gao et al., 2007). A multiple linear equation was used to evaluate the relationship between the kF coefficient, and organic carbon content and CaCO3 content
R. Kodesˇova´ et al. / Soil & Tillage Research 118 (2012) 88–96
(Kodesˇova´ et al., 2011). Yang et al. (2005) documented greater chlorotoluron adsorption in soils without dissolved organic matter. The addition of dissolved organic carbon significantly increased the mobility of chlorotoluron and the total concentration of chlorotoluron in the leachate in columns (Song et al., 2008). Adsorption into the aggregates was lower compared to the batch experiment (van Beinum et al., 2005; Villaverde et al., 2009). The impact of diffusionlimited sorption on pesticide availability for leaching was documented in results of lysimetr experiment (van Beinum et al., 2006). Chlorotoluron transport was experimentally studied under field conditions and mathematically simulated by Kocˇa´rek et al. (2005, 2010) and Kodesˇova´ et al. (2004, 2005, 2008). Chlorotoluron transport was also physically and mathematically modeled in soil columns under laboratory conditions (Kodesˇova´ et al., 2009). Studies by Kocˇa´rek et al. (2005, 2010) and Kodesˇova´ et al. (2004, 2005, 2008, 2009) showed that herbicide transport was frequently affected by preferential flow caused by soil aggregation, biopores and soil fraction. The chlorotoluron leaching from some soils was also enhanced by reduced herbicide inflow into the soil aggregates due to the presence of organic and clay coatings covering the surface of soil aggregates. In general a moderate mobility of this herbicide and reasonable potential to contaminate ground water was documented. Soil improvement with respect to the reduction of groundwater contamination risk was investigated in this study. The study focuses on the evaluation of the compost impact on chlorotoluron transport in the compost amended soil samples under laboratory conditions. The main aims were: (i) to assess the impact that compost amendments have on water flow and consequently on numerically optimized soil hydraulic properties, (ii) to assess the impact that compost amendments have on pesticide leaching and retention in soils, and consequently on estimated/measured properties characterizing herbicide transport, (iii) to elucidate chlorotoluron behavior in soil with respect to soil physical and chemical properties. 2. Materials and methods 2.1. Preparation of compost amended soil mixtures Soil from the A horizon of the Luvic Chernozem (parent material loess) characterized as loam – clay loam was mixed with compost
89
material. Compost was prepared according to a standard methodology as used by the Research Institute of Agricultural Engineering in Prague. The main compost components were grass, leaves, wooden chips, recycled previously slightly decomposed compost material, and a limited amount of straw. The main compost properties were 24.6% of Cox, 1.2% of N and pHH2 O 7.9. Eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (from 1 to 8% of mixture weight) were prepared. Wet soil and compost materials were homogenized as much as possible before mixing, but no other special treatment of the materials was used. Prepared mixtures were first stored for two weeks in plastic bags and then packed into plastic cylinders (diameter of 15 cm and height of 18 cm). Three samples were prepared for each mixture. Samples were kept wet with no vegetation in a greenhouse for 9 months. Two samples were then used to perform a water and solute transport experiment, which is described below. One sample was used to determine basic physical and chemical soil properties. The following basic chemical and physical soil properties were obtained using standard laboratory procedures under a constant laboratory temperature of 20 8C: the soil pHH2 O and pHKCl (International Organization of Standardization, 1994), the exchangeable acidity (EA) (Hendershot et al., 1993), the cation exchange capacity (CEC) (Bower and Hatcher, 1966), the soil hydrolytic acidity (HA) (Klute, 1996), the basic cation saturation (BCS) (difference between CEC and HA), the sorption complex saturation (SCS) (percentage of BCS in CEC), the oxidable organic carbon content (Cox) (Skjemstad and Baldock, 2008), the CaCO3 content (Looppert and Suarez, 1996), the soil salinity (Rhoades, 1996), the particle density (rs) (Flint and Flint, 2002), and the particle size distribution (fractions of clay, silt and sand) (Gee and Or, 2002). The final mixture properties are shown in Table 1. Three undisturbed 100-cm3 soil samples were also taken from each mixture sample to measure the bulk density and saturated soil water content. Despite precautions taken when preparing homogeneous soil and compost materials (which were next used for mixing) no substantial gradual changes of soil mixture properties were obtained. One of the reasons may be soil, compost and soil mixtures not being fully homogenized due to the large volumes of the materials. A second reason may be an uneven properties development in soil columns during 9 months in the greenhouse caused by various densification when packing, different soil water
Table 1 Physical and chemical soil properties for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight): pHKCl, pHH2 O , exchangeable acidity (EA), cation exchange capacity (CEC), hydrolytic acidity (HA), basic cation saturation (BCS), sorption complex saturation (SCS), oxidable organic carbon content (Cox), CaCO3 content, salinity, sand, silt and clay content, soil particle density (rs). Mixture
A B C D E F G H Mixture
A B C D E F G H
pHKCl
pHH2 O
EA
CEC
HA
BCS
SCS
(–)
(–)
(mmol + kg1)
(mmol + kg1)
(mmol + kg1)
(mmol + kg1)
(%)
6.41 6.32 6.44 6.41 6.42 6.36 6.57 6.59
6.77 6.83 6.75 6.8 6.67 6.66 6.68 6.7
2.21 1.51 1.46 1.57 1.08 1.64 1.46 1.27
267.50 275.00 247.50 321.67 320.83 270.83 262.50 334.17
10.68 11.81 10.68 11.06 9.17 12.00 11.62 10.40
256.82 263.19 236.82 310.61 311.66 258.83 250.88 323.77
96.01 95.71 95.68 96.56 97.14 95.57 95.57 96.89
Cox
CaCO3
Salinity
Sand
Silt
Clay
rs
(%)
(%)
(mS cm1)
(%)
(%)
(%)
(g cm3)
1.82 2.11 2.05 2.14 2.36 2.26 2.42 2.40
0.12 0.02 0.04 0.12 0.25 0.15 0.09 0.08
39.00 46.27 38.03 39.60 43.13 51.45 45.67 40.55
23 23 20 24 20 24 20 20
40 37 40 38 42 38 41 41
37 40 40 38 38 38 39 39
2.59 2.60 2.61 2.55 2.59 2.58 2.54 2.56
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content during this period, and possibly also by different biological activity in various soil columns. The most evident change was an increasing Cox content with increasing compost fraction. Values of pHH2 O and pHKCl showed a decreasing and increasing trend, respectively, with increasing compost fraction. Values of cation exchange capacity also indicated an increasing trend with increasing compost fraction. 2.2. Water flow and solute transport experiment Two samples of each mixture were transported into the laboratory where micro-tensiometers Tensior 5 (UMS GmbH, Munich, 2005) for pressure head measurements were installed 6.5, 11 and 15.5 cm below the soil surface. Cementation was used to prevent water flow and solute transport between the soil core and plastic cylinder. 20 cm3 of chlorotoluron solution (actual chlorotoluron concentration was measured for each experiment) was applied with an infiltration rate of 0.113 cm min1 on the top of the soil samples. One liter of chlorotoluron solution was prepared prior to the experiments. The corresponding amount of Syncuran 80 DP (which should contain 80% of chlorotoluron) was used to achieve a concentration of 200 mg cm3. 40 cm3 of previously prepared solution was carefully mixed before each experiment. Part of the solution was used for application into the soil column and part was used to analyze actually applied chlorotoluron concentration. The
rainfall simulator was used 4 min after the chlorotoluron application to supply distilled water on the soil tops with controlled infiltration rates. Infiltration rate varied depending on infiltration ability of each soil sample. The same amount of water (2550 cm3) was applied at all soil columns. The duration of the infiltration experiment is shown in Table 2. Water outflow and solute concentration from the bottom of the soil sample were monitored in time. Outflow duration and total solution outflow is again documented in Table 2. Solute concentrations were determined using High Performance Liquid Chromatography (HPLC). One day after the infiltration, all soil samples were cut into 9 layers (thickness of 2 cm). The sub-samples were air dried and the mass of soil in each layer was measured. The soil was then ground and sieved through a 2-mm sieve. The total amount of chlorotoluron remaining in each soil layer was ascertained as follows. 5 g of dry soil was placed into the centrifuge cuvette. 5 ml of methanol was added and the centrifuge cuvette was placed for 22 h into the shaking apparatus. Analyzed soil samples were then centrifuged for 30 min at 13,800 rotations per minute. The chlorotoluron concentration in the methanol extract was again measured using HPLC. The chlorotoluron concentration in the soil sample was expressed as the total amount of solute per unit of mass of dry soil (mg g1). Finally, chlorotoluron recovery was found by comparing the amount of applied solute with the sum of solute leached at the bottom of the column and remaining in the soil (Table 3).
Table 2 Characteristics of water flow experiments perform in soil columns for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Soil column
Infiltration
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2
Total outflow (cm3)
Outflow
Beginning (min)
Ending (min)
Beginning (min)
Ending (min)
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
44.62 46.58 39.72 47.65 62.93 58.92 50.55 70.32 56.83 49.65 69.78 62.37 61.97 63.00 67.77 71.53
12.27 11.75 7.42 10.58 8.68 13.30 8.03 16.80 13.20 10.65 15.72 19.65 22.52 17.37 17.05 16.82
52.12 58.00 48.58 51.38 71.72 75.58 54.60 74.38 59.92 54.13 74.00 71.78 69.95 76.12 77.42 80.50
2082.09 2241.19 1916.11 2142.55 2115.09 2162.16 1898.58 2153.17 2080.06 2187.23 2001.16 2003.57 1909.30 1851.73 2109.56 2064.91
Table 3 Chlorotoluron balance in soil columns for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Soil column
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2
Applied concentrations
Total amount applied
Content in ouflow
Content in soil
Content in outflow and soil
Chlorotoluron recovery
(mg cm3)
(mg)
(mg)
(mg)
(mg)
(%)
181.2 187.3 176.4 189.5 185.9 188.1 196.4 201.7 189.9 192.0 177.8 182.7 177.4 179.1 194.0 198.2
3623.6 3745.6 3527.2 3789.3 3717.5 3761.2 3929.0 4034.0 3797.7 3840.4 3555.6 3654.7 3548.3 3581.4 3880.9 3963.4
74.4 68.7 26.0 20.0 83.3 8.7 66.5 16.4 15.9 10.9 81.8 6.9 17.1 51.2 32.4 10.7
3443.6 2659.9 2138.0 2624.4 2696.2 2664.1 3815.3 3707.3 3771.8 3634.4 3540.8 3181.3 3130.3 3157.8 2798.0 2800.8
3518.0 2728.6 2164.0 2644.4 2779.5 2672.8 3881.8 3723.8 3787.6 3645.2 3622.6 3188.2 3147.4 3209.0 2830.4 2811.5
97.09 72.85 61.35 69.79 74.77 71.06 98.80 92.31 99.73 94.92 101.88 87.24 88.70 89.60 72.93 70.94
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2.3. Numerical evaluation of experimental data 2.3.1. Water flow model Water flow in the soil profile may be simulated using the singleporosity model in HYDRUS-1D (Sˇimu˚nek et al., 2008; Sˇimu˚nek and van Genuchten, 2008). The Richards equation, describing the onedimensional isothermal Darcian flow in a variably saturated rigid porous medium, is used in the model: @u @ @h ¼ K ðhÞ þ K ðhÞ (1) @t @z @z where u is the volumetric soil water content [L3L3], h is the pressure head [L], K is the hydraulic conductivity [LT1], t is time [T], and z is the vertical axis [L]. Eq. (1) is solved for the entire flow domain using one set of soil water retention and hydraulic conductivity functions. Analytical expressions proposed by van Genuchten (1980) for the soil water retention curve, u(h), and the hydraulic conductivity function, K(u), are used in the model:
ue ¼
uðhÞ ur 1 ¼ m ; us ur 1 þ jahjn
ue ¼ 1;
h<0
(2)
h0
h i2 l 2=m m KðuÞ ¼ K s ue 1 ð1 ue Þ ;
KðuÞ ¼ K s ;
h<0
(3)
h0
where ue is the effective soil water content [–], Ks is the saturated hydraulic conductivity [LT1], ur and us are the residual and saturated soil water contents [L3L3], respectively, l is the poreconnectivity parameter [–], a is reciprocal of the air entry pressure, [L1], and n [–] is related to the slope of the retention curve at the inflection point, and m = 1 1/n [–]. 2.3.2. Solute transport model The advection-dispersion equation for solute transport is used in the model: @uc @rd s @ @c @qc þ ¼ F uD (4) @t @t @z @z @z where c [ML3] and s [MM1] are solute concentrations in the liquid and solid phases, respectively, q is the volumetric water flux density [LT1], rd is the soil bulk density [ML3], D is the dispersion coefficient [L2T1], and F describes zero- and first-order rate reactions [ML3T1]. Assuming the equilibrium solute adsorption, the adsorption isotherm relating the adsorbed concentration, s, and liquid concentration, c, may be described using the Freundlich equation: s ¼ kF cb
(5) b
b
where kF [L3 ;1 M1] and b [–] are empirical coefficients. 2.3.3. Estimation of soil hydraulic properties The single-porosity model in HYDRUS-1D was first used to analyze transient flow data (cumulative outflow and pressure heads at three positions) to obtain the parameters of both soil hydraulic functions (soil water retention and unsaturated hydraulic conductivity functions) that were described using the van Genuchten model (Eqs. (2) and (3)). Initial conditions within the soil sample were set, based on pressure heads measured at the beginning of the experiments. Pressure heads at monitored
91
locations were set to measured values, while pressure heads between, above, and below were interpolated and/or extrapolated. Since the soil samples were stored in plastic bags in a cold environment 1 week before the experiments, the steady state conditions were assumed and the linear pressure head distributions were applied. The upper boundary condition was defined using an initial infiltration pulse of 0.113 cm min1 and for 1 min duration, followed by a controlled irrigation (simulated rainfall) rate. The bottom boundary condition was defined using a seepage face. Since measured saturated water contents, us, did not indicate any trend, the average value (0.550 cm3 cm3) was set for all soil columns. The remaining soil hydraulic parameters (i.e., ur, a, n, and Ks) were optimized. In all cases the pore connectivity parameter was assumed to be equal to an average value for many soils (l = 0.5) (Mualem, 1976) since it was found (not shown) that optimization of the l parameter increased uncertainty for all optimized parameters. The objective function, OF, (Sˇimu˚nek et al., 1998) in HYDRUS-1D, representing deviations between the measured and calculated space-time variables (e.g., cumulative flux versus time across a bottom boundary, and pressure heads observed at 3 depths), was minimized during the parameter estimation process. 2.3.4. Estimation of solute transport parameters The single-porosity model in HYDRUS-1D was then again used to simulate observed solute transport in all soil columns. Initial and boundary water flow conditions were defined as before. Soil hydraulic parameters obtained from previous inversions of water flow data were fixed. In addition, a chlorotoluron concentration, which was measured for each experiment, was specified for the initial infiltration pulse (1 min). A zero concentration gradient was defined at the bottom of the sample. Bulk density was set to the measured average value (1.16 g cm3). Molecular diffusion was assumed to be zero. No herbicide degradation (e.g. no zero- or firstorder rate reactions) was assumed during the short term experiment. The other parameters were estimated. Since a low solute outflow at the soil column bottoms was mostly observed (see below Table 3), solute transport parameters could not be estimated using measured outflow concentrations versus time as it is usually completed (Kodesˇova´ et al., 2009). Therefore the final chlorotoluron contents within the soil samples were used in optimizations. The final chlorotoluron concentration in soil water was calculated using the total measured herbicide content in analyzed soil layers (Fig. 1), the final simulated water content within the soil sample, and the adsorption isotherms. Chlorotoluron adsorption in different soil mixtures was studied using the standard laboratory procedure and adsorption isotherms were described using the Freundlich Eq. (5) (Table 4). The same procedure as described by Kodesˇova´ et al. (2011) was used. 10 g
Table 4 Parameters of the Freundlich (kF, b) adsorption isotherms for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight). Mixture
b
kF for b = 0.76
(cm3 mg1 g1)
(–)
(cm3 mg1 g1)
5.70 5.61 5.08 5.59 5.52 5.91 5.89 5.52
0.71 0.74 0.73 0.76 0.77 0.77 0.76 0.80
4.93 5.39 4.61 5.47 5.63 6.08 5.71 5.60
kF b
A B C D E F G H
b
b
b
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Fig. 1. Chlorotoluron distribution, expressed as total amount (adsorbed on soil particles and dissolved in soil water) per mass unite of dry soil, within the soil sample (two repetitions) at the end of the experiment for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight).
of air dried, ground and sieved (2-mm) soil were placed into the 50 cm3 glass bottle. 20 cm3 of solution of a known chlorotoluron concentration was added to the glass bottle. The bottle was shaken for 24 h using the shaking apparatus at 20 8C. Five initial pesticide concentrations and three replicates of each concentration were applied for each soil. Solutions were prepared using Syncuran 80 DP to obtain the following approximate initial concentrations: cini = 1, 2, 5, 10 and 25 mg cm3. The actual initial (cini,a) and final equilibrium pesticide concentrations (ceq) in solutes (mg cm3) were measured using the HPLC technique. The pesticide concentration adsorbed on soil particles (s) was expressed as the amount of solute per mass unit (mg g1) using the mass balance in the suspension. Data points of the adsorption isotherms were given by the final pesticide concentration c = ceq and s. The Freundlich equation (Eq. (5)) was used to fit data points of the adsorption isotherms. However, due to the Eq. (5) nonlinearity a three linear equations (b = 1, kF = 3, 4, and 5 cm3 g1) were applied for each soil column to calculate final
chlorotoluron concentrations in soil water, then to simulate herbicide transport in soil, and to estimate the longitudinal dispersivity using these final chlorotoluron concentrations in soil water. The lower values of the kF values in comparison with the measured kF values were used due to the linearity of the equation replacing the exponentional shape of the measured isotherms (which acceptably approximated herbicide adsorption within a simulated ration of herbicide concentration in soil water). The second reason was that the lower adsorption had been expected due to the soil material aggregation (possibly causing lower herbicide sorption into aggregates, which was before documented by van Beinum et al. (2005, 2006) and Villaverde et al. (2009)) and dynamic nature of the experiment (e.g. nonequilibrium adsorption during the experiment documented by Kodesˇova´ et al. (2009)). The objective function in HYDRUS-1D, representing deviations between the measured and calculated chlorotoluron concentrations in soil water, was minimized during the parameter estimation process.
R. Kodesˇova´ et al. / Soil & Tillage Research 118 (2012) 88–96
3. Results and discussion 3.1. Water flow and solute transport experiments The infiltration and outflow durations, and total volumes of water captured below the soil samples are shown in Table 2. It is evident that the infiltration durations (e.g. time that was necessary to infiltrate the same amount of water 2550 cm3), which was controlled by soil infiltration ability, increased with the compost content. Outflow durations correspondingly increased as well. The total outflow at the bottom varied randomly with increasing compost content. It depended on initial soil water content and soil retention ability. Chlorotoluron balance in soil columns are shown in Table 3. Table 3 shows the actually applied chlorotoluron concentration, the total amount of applied chlorotoluron for each soil sample, chlorotoluron amount in outflow solution, chlorotoluron content in soil at the end of the experiment, sum of both (in outflow and in soil) and chlorotoluron recovery (in %). It is evident that in some cases a weak correspondence between applied and recovered herbicide was achieved. The reasons may be: herbicide loss when cutting the soil sample, experimental error when preparing the soil sample for chlorotoluron extraction (no complete soil homogenization, herbicide loss when grinding etc.), chlorotoluron degradation, no full extraction of herbicide from soil, and an analytical error. Despite this fact, the following conclusion can be made based on obtained data. Table 3 shows that a very low solute leaching at the soil column bottoms was observed in all cases. The total amount of leached herbicide varied randomly with increasing compost content. Chlorotoluron mostly remained in soil columns, which is also evident on final distributions of chlorotoluron concentrations (Fig. 1). While the highest chlorotoluron concentrations were mostly found in the surface layers of all soil mixtures, very low concentrations were mostly recovered in the bottom layers. Similar herbicide distributions for each pair of soil mixtures were observed. Fig. 1 shows that herbicide mobility decreased with increasing compost content (up to 6% of compost content – mixture F), which was indicated by increasing chlorotoluron retention in the top part of the soil column and decreasing chlorotoluron content in the bottom part of the soil column. For mixtures with compost content above 6% the herbicide mobility increased with increasing compost content. In some cases herbicide transport was possibly moderately (D1, G1, G2) or slightly (A2, E1, H1, H2) influenced by preferential flow. The largest
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impact is evident in G soil samples. We suppose that this soil mixture was probably affected by biological activities (which could originate in storage bags before soil packing into the columns) to a greater degree than other soil samples, which caused the formation of preferential pathways. However, these pathways did not noticeably increase herbicide leaching from the soil columns. 3.2. Numerical evaluation of experimental data 3.2.1. Estimation of soil hydraulic properties One example of measured and simulated pressure heads at depths of 6.5, 11 and 15.5 cm below the soil surface, cumulative water outflow at the soil column bottom (both documenting an excellent agreement of measured and simulated data), and applied infiltration at the soil surface is shown in Fig. 2. Resulting parameters of the van Genuchten soil hydraulic functions (ur, a, n, Ks) are shown in Table 5. The R2 values and objective functions showed that measured transient flow data (pressure heads at depths of 6.5, 11 and 15.5 cm below the soil surface and cumulative water outflow at the soil column bottom) were successfully approximated using HYDRUS-1D in all cases. This agreement was due to the measured transient water flow data, which did not indicate any occurrence of preferential flow (which is in contrast to some of the observed chlorotoluron distributions in soil columns). Resulting van Genuchten parameters varied randomly with the increasing compost content despite the experiment durations demonstrating an increasing trend with increasing compost content. No apparent impact of compost adding was thus observed based on these numerical analyses. 3.2.2. Estimation of solute transport parameters One example of measured and simulated chlorotoluron distribution within the soil column at the end of the experiment, expressed as total chlorotoluron content per mass unit of dry soil, is shown in Fig. 3 and exhibits a weaker agreement between the measured and simulated values. Resulting values of longitudinal dispersivity, DL, estimated via numerical inversion using the HYDRUS-1D program are shown in Table 6. The R2 values and objective functions showed that, in comparison to simulated water flow discussed above, less successful fitting of measured data (chlorotoluron concentrations in soil water within the soil sample at the end of the experiment) were reached. The weakest fitting of measured data were obtained for D and G soil samples, where herbicide transport was probably affected by the preferential flow
Fig. 2. Example (column A1, compost fraction 1% of mixture weight) of measured and simulated (using HYDRUS-1D) pressure heads in depths of 6.5 (T1), 11 (T2) and 15.5 (T3) cm below the soil surface, cumulative water outflow at the soil column bottom, and applied infiltration at the soil surface.
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Table 5 Parameters of the van Genuchten soil hydraulic functions (ur, a, n, Ks, us = 0.55 cm3 cm3) obtained using numerical inversion with HYDRUS-1D for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight), (R2 – coefficient of determination, OF – objective function, index 1 – cumulative boundary flux across the bottom boundary, index 2 – pressure head measurements at 3 observation points). Soil column
A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2 a b
ur
a
n
Ks
(cm3 cm3)
(cm1)
(–)
(cm min1)
0.261 0.034a 0.205 0.003 0.023 0.059 0.006 0.020 0.005 0.002 0.245 0.073 0.302 0.028 0.165 0.027 0.139 0.017 0.203 0.087 0.292 0.049 0.330 0.013 0.084 0.005 0.131 0.016 0.316 0.055 0.161 0.054
0.074 0.004 0.035 0.001 0.027 0.003 0.034 1.966 0.019 0.001 0.051 0.010 0.012 0.001 0.007 0.0003 0.209 0.054 0.157 0.033 0.035 0.008 0.059 0.005 0.021 0.001 0.053 0.007 0.057 0.006 0.278 0.03
1.97 0.26 1.42 0.014 4.52 1.62 2.5 0.49 6.02 0.83 1.99 0.34 4.12 0.70 4.14 0.91 1.5b 1.43 0.16 1.78 0.33 1.99 0.29 1.44 0.02 1.34 0.01 2.26 0.47 1.35 0.07
1.33 1.18 0.33 0.006 1.46 0.83 0.50 0.11 1.15 0.39 0.35 0.07 0.50 0.21 0.39 0.11 22.56 13.65 6.48 4.49 0.64 0.35 0.69 0.10 0.27 0.02 0.20 0.03 0.45 0.11 13.12 1.27
R2
OF1
OF2
0.987 0.962 0.839 0.877 0.906 0.966 0.919 0.891 0.982 0.975 0.974 0.992 0.976 0.983 0.965 0.982
0.015 0.006 0.039 0.078 0.014 0.002 0.053 0.033 0.008 0.009 0.005 0.005 0.006 0.030 0.004 0.005
0.019 0.050 0.221 0.167 0.125 0.047 0.103 0.127 0.027 0.038 0.032 0.010 0.030 0.023 0.054 0.023
95% confidence interval. Not optimized due to the computational instability.
(which was not however documented on water flow data). Despite that, the following conclusions could be made based on the presented results. When the same adsorption isotherms were applied for all soil samples, a decreasing trend of longitudinal dispersivity, DL, values was obtained with an increasing compost content up to 6% (F samples), above this value DL values increased. This corresponded to the observed chlorotoluron distributions within the soil samples (Fig. 1). Since the lowest herbicide mobility was observed in the F samples, the lowest DL values for F samples had to be obtained, which was the only parameter controlling simulated herbicide transport (and opposite). While the lower DL values reduced solute spreading in soils, higher DL values increased solute penetration into greater depths. The lowest value of the objective function indicates the best fit of measured data for each soil sample. Assuming only the best fits for all soil samples, DL values varied randomly with compost content (range between 2.17 and 7.54 cm), and kF values showed an increasing trend with increasing compost content up to 5%
Fig. 3. Example (column A1, compost fraction 1% of mixture weight) of measured and simulated (using HYDRUS-1D) chlorotoluron distribution, expressed as total amount (adsorbed on soil particles and dissolved in soil water) per mass unite of dry soil, within the soil sample at the end of the experiment, assuming 3 liner (b = 1) adsorption isotherms with various kF coefficients.
(E soil samples) and a decreasing trend with increasing compost content up to 8% (H soil samples). This approximately corresponded to the observed chlorotoluron mobility within the soil samples (Fig. 1) and measured adsorption isotherms (Table 4). The preferential flow did not affect herbicide transport as intensively as in the study by Kodesˇova´ et al. (2009). Despite a limited impact of preferential flow, which was noticeable in some soil samples, a single-porosity model was sufficient for describing water flow and herbicide transport in soil columns. 3.3. Chlorotoluron behavior in soils with respect to soil physical and chemical soil properties Results showed that herbicide transport decreased with increasing compost content (up to 6% of compost content – mixture F), and increased with increasing compost content for mixtures with compost content above 6%. Since numerical analysis illustrated no evident impact of compost addition on water flow characteristics, variability of herbicide mobility was caused dominantly by herbicide behavior (mostly adsorption) in soils. Findings are partly in contrast to results documenting a positive compost impact on pesticides sorption (e.g. negative impact on their mobility) (see studies mentioned in the introduction). The negative impact of dissolved organic carbon on chlorotoluron adsorption as discussed by Yang et al. (2005) and Song et al. (2008) could be suggested. However, this effect cannot be documented based on the data presented in our study. Previous studies by Meyer-Windel et al. (1997), Hiller et al. (2008) and Kodesˇova´ et al. (2011) showed that the Freundlich adsorption coefficient, kF, for chlorotoluron (e.g. its mobility in soils) depends mostly on the organic carbon content in soils. A linear relationship between the kF value and organic carbon content was confirmed in these studies. Kodesˇova´ et al. (2011) also showed that better predictions of the kF values from other soil properties were reached when the organic carbon content and the CaCO3 content were included in the multiple linear regression equation. Gao et al. (2007) documented that chlorotoluron adsorption increased with increasing N content. Data presented here showed that the kF value evaluated for all mixtures, observed herbicide mobility in soil columns, and parameters characterizing herbicide mobility resulting from numerical analysis did not depend solely on the organic carbon content (e.g. compost content)
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Table 6 Longitudinal dispersions (DL) obtained using numerical inversion with HYDRUS-1D for eight mixtures (A, B, C, D, E, F, G and H) of various compost fractions (1, 2, 3, 4, 5, 6, 7 and 8% of mixture weight) assuming 3 liner (b = 1) adsorption isotherms with various kF coefficients (SC – soil column, R2 – coefficient of determination, OF – objective function). SC
A1
B1
C1
D1
E1
F1
G1
H1
a b
kF
DL
(cm3 g1)
(cm)
3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5
3.08 0.05a 4.36 0.09 6.33 0.18 5.25 0.07 9.23 0.23 13.99 0.46 4.06 0.07 6.07 0.07 8.47 0.22 3.37 0.44 4.88 0.13 7.24 0.44 2.05 0.10 2.85 0.11 3.75 0.12 1.86 0.11 2.54 0.10 3.43 0.12 3.55 0.20 4.55 0.09 6.52 0.11 3.52 0.11 5.08 0.14 7.21 0.21
R2
0.912 0.912 0.910 0.884 0.935 0.894 0.808 0.974 0.961 0.117 0.639 0.573 0.647 0.668 0.670 0.632 0.650 0.653 0.096 0.020 0.018 0.732 0.733 0.716
OF
SC
0.129 0.105b 0.294 0.076 0.387 1.033 0.088 0.045 0.297 1.195 1.041 1.010 0.521 0.369 0.331 0.542 0.395 0.348 1.537 1.245 1.072 0.323 0.272 0.410
A2
B2
C2
D2
E2
F2
G2
H2
kF
DL
(cm3 g1)
(cm)
3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5 3 4 5
3.64 0.36 5.94 0.36 8.30 0.45 3.82 0.09 6.52 0.15 10.27 0.29 2.17 0.10 3.78 0.15 6.34 0.23 3.29 0.11 4.86 0.13 6.91 0.17 2.51 0.11 3.73 0.12 5.29 0.14 1.61 0.11 2.64 0.09 4.09 0.12 4.84 0.26 7.54 0.16 11.49 0.31 3.14 0.10 4.67 0.14 6.78 0.23
R2
OF
0.739 0.737 0.730 0.866 0.868 0.839 0.756 0.718 0.661 0.377 0.377 0.371 0.758 0.771 0.762 0.825 0.824 0.799 0.475 0.694 0.429 0.782 0.779 0.759
0.502 0.290 0.291 0.145 0.237 0.582 0.253 0.335 0.564 0.692 0.625 0.656 0.424 0.255 0.249 0.299 0.192 0.223 0.780 0.563 0.624 0.231 0.257 0.473
95% confidence interval. The best fit of measured data.
as would be expected from studies by Meyer-Windel et al. (1997) and Hiller et al. (2008). Therefore a similar analysis as performed by Kodesˇova´ et al. (2011) was done in our study. The average b coefficient from all b values (Table 4) was calculated and the Freundlich equation with the fixed average b value (=0.76) was used again to fit experimental data points and to obtain a new set of kF values (Table 4). The multiple linear regressions were then used to define the relationship between the kF coefficient (for the fixed b value) and other measured physical and chemical soil properties. The following relationship was obtained: kF ðcm3b mg1b g1 Þ ¼ 40:3 þ 3:64 Cox ð%Þ 0:483 clay content ð%Þ 3:67pHKCl 5:47CaCO3 ð%Þ
(6)
The order of soil parameters in this equation reflects the statistical significance of the variables. Eq. (6) explained 98.973% of the variability in the kF value. The standard deviation of the residuals was 0.072. Since all p-values belonging to independent variables were lower than 0.01, the statistical significance at the 99% confidence level was achieved for all of them. Eq. (6) clearly shows that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which are characteristics of soil, but may be affected by compost composition as well) was documented. It should be pointed out that Eq. (6) applies only for this soil and its mixtures with compost. To provide a general relationship, which would characterize herbicide adsorption in soils a larger range of soil properties and correspondingly larger variation of the kF values would be necessary (as in study by Kodesˇova´ et al., 2011). However, since the correlation is so close and observation in some aspects corresponds to previously obtained results (Kodesˇova´ et al., 2011), this relationship may indicate properties impact of chlorotoluron adsorption in studied soil mixtures (especially in this case, when adsorption variation is
given only by variation of the soils of similar mineralogical composition. 4. Conclusions The possibility to improve the soil physical, hydraulic and chemical properties using organic compost was evaluated in this study. Despite precautions taken when preparing homogeneous soil and compost materials (which were next used for mixing) no substantial gradual changes of soil mixture properties (pHKCl, pHH2 O , exchangeable acidity, cation exchange capacity, hydrolytic acidity, basic cation saturation, sorption complex saturation, oxidable organic carbon content, CaCO3 content, salinity, sand, silt and clay content, soil particle density) were obtained with increasing compost content. The most evident change was increasing Cox content with increasing compost fraction. Values of pHH2 O and pHKCl showed a decreasing and increasing trend, respectively, with increasing compost fraction. Values of cation exchange capacity also indicated an increasing trend with increasing compost fraction. Measured physical properties (bulk density and saturated soil water content) and soil hydraulic parameters, resulting from the numerical analysis of measured transient water flow data, did not show noticeable changes (demonstrating soil hydro-physical properties improvement) with increasing compost fraction. Final chlorotoluron distributions in soil columns and estimated transport parameters also showed high variability. However results indicated a decreasing trend of chlorotoluron mobility up to the compost fraction of 6%. Above this value, herbicide mobility noticeably (7%) and slightly (8%) increased. These findings correspond to herbicide adsorption studied using a batch experiment on all soil mixtures. Multiple linear regressions revealed that other properties (not only organic carbon content) play a noticeable role in pesticide adsorption in soils. A negative impact of pHKCl (which was positively affected by compost addition), clay content, and CaCO3 content (which were
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