Phytolith transport in sandy sediment: Experiments and modeling

Geoderma 151 (2009) 168–178

Contents lists available at ScienceDirect

Geoderma j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o d e r m a

Phytolith transport in sandy sediment: Experiments and modeling Olga Fishkis ⁎, Joachim Ingwersen, Thilo Streck University of Hohenheim, Institute of Soil Science and Land Evaluation, Biogeophysics, D-70593 Stuttgart, Germany

a r t i c l e

i n f o

Article history: Received 3 March 2008 Received in revised form 4 March 2009 Accepted 3 April 2009 Available online 12 May 2009 Keywords: Phytoliths Soil Plant silica Colloid transport Modeling

a b s t r a c t Phytoliths are minerals of amorphous silicon dioxide forming in cell lumina and intercellular spaces of living plants. Because of their morphological specificity and their stability in soils phytoliths have been widely used in paleoenvironmental reconstructions. However, the mechanisms of phytolith displacement in soil are not well understood, which causes uncertainties in the interpretation of phytolith data. The objectives of the present study were (1) to determine phytolith transport in sandy sediment under different rainfall, and (2) to assess the long-term phytolith displacement with moving water by modeling. Phytoliths extracted from Phragmites australis were added to the upper 1-cm layer of columns packed with medium sand. Two rainfall regimes 1) low-frequency irrigation (40 mm × 2 times per month), and 2) high-frequency irrigation (40 mm × 8 times per month) were simulated over a period of 5 months. At the end of this period, the distribution of phytoliths was examined in the columns. The weighted mean travel distance of phytoliths was 2.7 ± 1.6 mm and 3.7 ± 0.2 mm at the low-frequency and high-frequency treatments, respectively. Under the high-frequency irrigation 22% of the applied phytoliths were leached from the application layer. Two modeling approaches were tested against the experimental data of the high-frequency irrigation treatment. The first model was a convection–dispersion model with attachment, detachment and straining terms. In the second model, reversibility of straining was additionally taken into account. Both models resulted in an equally good agreement with observations. However, the long-term predictions calculated with the two approaches were contradictory. The first model showed no significant translocation after a period of thousand years, whereas the second model predicted that phytoliths would on average be displaced by 19 cm. We hence conjecture that our set of observations was not sufficient to distinguish between two models. Long-term experiments on phytolith transport in undisturbed soil are required to ascertain proper modeling of phytolith transport in a sandy soil. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Phytoliths (syn: plant silica or plant opal) are minerals of amorphous silicon dioxide (opal A; SiO2·nH2O) forming in cell lumina or intercellular spaces of living plants (Jones and Handreck, 1967; Rovner, 1983; Piperno, 1988). Numerous plant families of Angiosperms and Gymnosperms and several families of Pteridophytes are known as phytolith producers with phytolith content ranging from 0.1% to 16% (Piperno, 1988; Marschner, 1995; Epstein, 1999). Silica accumulation in plant tissues is controlled by the uptake of dissolved Si through the roots (Jones and Handreck, 1967; Motomura et al., 2004). Besides Si, phytoliths contain minor quantities of organic carbon occluded and some other elements – Al, Fe and others (Bartoli and Wilding, 1980). Through the decomposition of litter, phytoliths are released and become mineral constituents of soils. Phytolith content in soil typically shows a maximum at the surface (0.1–3% by mass) and declines continuously with depth.

⁎ Corresponding author. Tel.: +49 71145922466. E-mail address: ofi[email protected] (O. Fishkis). 0016-7061/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2009.04.003

Because of their specific morphology many phytoliths can be attributed to taxonomic groups at least on a family level (Rovner, 1983; Piperno, 1988). Diagnostic features of soil phytoliths are widely used in paleoenvironmental reconstructions (Golyeva et al., 1995; Piperno and Becker, 1996; Alexandre et al., 1999; Fisher et al., 1995; Golyeva and Alexandrovskij 1999; Horrocks et al., 2000; Blinnikov et al., 2002; Delhon et al., 2003; Gallego et al., 2004; Blecker et al., 1997). Although the mean diameter of plant silica ranges from 0.1 μm to 200 μm, only phytoliths with mean diameter ≥5 μm can be used for plant diagnostics (Piperno, 1988). Interestingly, the radiocarbon dating of phytoliths ≥5 μm extracted from a clayey Oxisol under tropical rain forest in the Central Amazonia (Piperno and Becker, 1996) and an Alfisol under desert grassland in southwestern North America (McClaran and Umlauf, 2000) showed that phytoliths mean residence time in soil may exceed 5000 years. On the other hand, there is evidence that plant silica is an important source for dissolved Si in soil (Farmer et al., 2005; Derry et al., 2005; Wüst and Bustin, 2003; Meunier et al., 1999). According to calculations carried out by Alexandre et al. (1997) 92.5% of phytolith input in equatorial rainforest ecosystem is rapidly dissolved, while 7.5% of phytolith input contributes to the stable silica pool. Paleolandscape reconstruction by the use of soil phytoliths is usually based on the

O. Fishkis et al. / Geoderma 151 (2009) 168–178

analysis of their morphology in different depths. Changes in phytolith morphology with depth are interpreted as changes in the vegetation cover in the past (Piperno and Becker, 1996; Fisher et al., 1995; Golyeva et al., 1995; McClaran and Umlauf, 2000; Gallego et. al., 2004). This approach, which works well in loess–paleosol sequences (buried soils separated by sediment deposits of different ages), can be applied to recent soils only if the age of soil phytoliths increases with soil depth. There is still high uncertainty concerning mechanisms and velocity of phytolith transport in soil. The mechanisms of phytolith translocation are supposed to be bioturbation and downward percolation (Clarke, 2003; Runge, 1999; Farmer et al., 2005). However there are only few studies on the relative contribution of these mechanisms to phytolith translocation in soil (Hart and Humphreys, 1997; Humphreys et al., 2003; Hart 2003). Based on distribution patterns of phytoliths, morphological diversity of phytoliths versus depth and the percentage of faunal channels in different soil types they concluded bioturbation to be a primary phytolith transport mechanism in Acrisol and Lixisol, whereas downward displacement in Podsol could only partly be attributed to bioturbation. The understanding of mechanisms of phytolith translocation in soil is of great interest regarding the analysis of soil phytoliths. Thus, low morphological diversity of phytoliths versus soil depth could be expected in soil environments with primary contribution of bioturbation in phytolith distribution, whereas stratification of phytoliths of different ages versus depth would be significant if any downward transport process is prevailing. Up to now there are still strong discrepancies in the literature concerning the extent of downward translocation of phytoliths. Alexandre et al. (1997) reported phytolith translocation to the depth of 220 cm in a ferrallitic soil profile with slight accumulation found in impermeable clay layer at a depth of 130–140 cm; while according to Fisher et al. (1995) and Rovner (1983) phytoliths do not readily move downward. Further research is required to quantify the rate and mechanisms of phytolith translocation in soils. The present study aimed at quantifying the vertical displacement of phytoliths with percolating water in a sandy soil. Because the direct examination of phytolith transport in natural soils would be imprecise, due to the presence of indigenous phytoliths, we used phytolith-free sandy sediments to study possible displacement. The second objective was to model phytolith transport in order to better understand the processes involved and to assess the long-term displacement of phytoliths in sandy soils by leaching. 2. Materials and methods 2.1. Experimental design The leaves and stems of Phragmites australis were ashed at 450 °C in a muffle furnace. The ash was analysed for phytolith concentration and size distribution using a polarized light microscope (SM-LUX, Leitz Wetzlar, Germany). The number of phytoliths with mean diameters between 5 and 63 µm was 3.38 × 107 ± 0.4 × 107 per gram of ash. Eighty-seven percent of the phytoliths had diameters between 5 and 35 µm. Each of the other size groups (35–63 μm, 63–100 μm, and 100–300 μm) composed about 4% of the total number. Phytolith images were taken with a scanning electron microscope LEO 420 (LEO Electron Microscopy Ltd, UK) (Fig. 1). The sandy sediment was taken from a pit near Aalen, Germany (48°44′19″N, 10°10′49″E). The texture was medium sand (92% sand, 3% silt and 5% clay) with d50 = 442 µm. Texture was determined by the pipette method (Dane and Topp, 2002). The moisture retention curve of the sediment was measured using the hanging water column and pressure plate method (Dane and Topp, 2002). The sediment had been checked for absence of phytoliths using the procedure given below. The sediment was passed through a 2 mm sieve, homogenized and filled to a height of 23 cm in the 6 polyvinyl chloride (PVC) columns (diameter

169

Fig. 1. Experimental design: irrigation scheme (A), column slicing (B).

10.3 cm; length 25.5 cm). The columns were evenly compacted to a bulk density of 1.4 g cm− 3. At the bottom of each column a 100-micron stainless steel sieve was placed above a water drain opening to prevent washout of the solid phase. Before starting the experiment the columns were saturated from the bottom to the top with 0.01 M CaCl2 and redrained, so that a seepage face boundary condition was established. Next, the 4.5 g ash of P. australis (3.38 × 107 ± 0.4 × 107 phytoliths per gram) was added to the upper 1-cm layer of each column. Two rainfall regimes differing in frequency of rainfall events were simulated: 1) “low-frequency” irrigation with 4 cm rainfall applied once per 14 days and 2) “high-frequency” irrigation with 4 cm rainfall applied four times per 14 days. For irrigation a solution of 0.01 M CaCl2 was applied for a period of 20 min at a rate of 0.2 cm/min. The duration of the experiment was 5 months resulting in 400 mm and 1600 mm cumulative water flux for low-frequency and highfrequency treatments, respectively. Each treatment was carried out in triplicates. The solution was pumped to a rain simulator equipped with 25 needles to ensure homogeneous application of water over the column area (Fig. 2A). At the beginning of the experiment the surface of each column was covered with a 2-cm layer of polyethylene spheres to minimize evaporation loss. Before each irrigation step, leachates were collected, filtrated and analyzed for phytoliths. The water dynamics in the columns was modeled using the Hydrus 1D code (Version 3.00, Simunek et al., 2005). The van Genuchten parameters were estimated from the moisture retention curve (van Genuchten, 1980) by nonlinear regression analysis using SPSS 8.0 (SPSS, Chicago, IL). Saturated hydraulic conductivity of 0.66 cm min− 1 was estimated by a pedotransfer function provided by Hydrus 1D (Schaap et al., 2001). With both water regimes (Fig. 3) at the soil surface the water content was 0.22 cm3 cm− 3 between rainfall events rising to 0.39 cm3 cm− 3 during irrigation. At the bottom of the column the soil was saturated (as checked by tensiometry) and had a water content of 0.43 cm3 cm− 3. After the end of the transport experiment, columns were sliced into 13 layers following the scheme presented in Fig. 2B. Phytoliths were extracted from soil using a procedure described in Coil et al.

170

O. Fishkis et al. / Geoderma 151 (2009) 168–178

Fig. 2. SEM images of the phytolith in ash of Phragmites australis: A) trapeziform, B) elongate, C) unidentified amorphous silica, D) cuneiform.

(2003) omitting treatment with HCl. The sample was deflocculated with NaH2PO4 and passed through a 300-μm sieve. Particles smaller than 5 μm were removed by a sedimentation using Stokes' law:
 ρ − ρ p m 2
t = gd 18μ

ð1Þ

where υt (m s− 1) is the settling velocity, g (m s− 2) is the acceleration of gravity, μ (g m− 1s− 1) is the viscosity of medium, d (m) is the diameter of the spherical object, ρp (g m− 3) is the density of particle, and ρm (g m− 3) is the density of medium. The fraction 5–300 μm was subjected to sink-float analysis using heavy liquid, sodium heteropolytungstate diluted to a specific gravity of 2.1 g/cm3, for separation of phytoliths from other minerals. According to standard procedure phytoliths are extracted with heavy liquid of 2.3 g/cm3 because the density of phytoliths ranges

from 1.5 g/cm3 to 2.3 g/cm3 and density of the most of other minerals is about 2.65 g/cm3 (Piperno, 1988). In our study we used the heavy liquid density of 2.1 g/cm3 for reducing the contamination of the sample with crystalline mineral particles of the silt fraction, which usually float along with phytoliths. The absolute number of extracted particles with diameters of 5– 63 μm was measured using a particle counter (Coulter Multisizer II, Beckmann, Germany). Because the extracted particles contained not only phytoliths but also other minerals, measurements by particle counter were followed by microscope viewing (250×) to determine the phytolith fraction of all particles extracted. For this purpose a weighted sample of particles extracted was mounted on the microscopic slide in glycerin and viewed with a polarized light microscope (SM-LUX, Leitz Wetzlar, Germany). The percentage of phytoliths of all particles extracted was estimated from 1000 grains observed. Finally, phytolith concentration in each layer was calculated.

O. Fishkis et al. / Geoderma 151 (2009) 168–178

171

The variance components were estimated using the restricted maximum likelihood method. Wald-type F-tests were used to test fixed effects. 2.2. Modeling approach To model the transport of phytoliths we adapted approaches from bacteria and colloid transport studies (McDowell-Boyer et al., 1986; Ryan and Elimelech, 1996; Sen and Khilar, 2006; Foppen and Schijven, 2006). Particle transport in packed homogeneous soil columns is typically described by the advection–dispersion model including attachment and detachment terms that account for the particle exchange between solid and liquid phase (Hornberger et al., 1992; Bolster et al., 1999; Harter et al. 2000; Kim and Corapcioglu, 2002). In this approach, particle transport in soils is controlled by downward advective–dispersive translocation, colloid deposition on soil matrix and colloid detachment by mobilization of attached particles by moving water. Bradford et al. (2003) extended this approach by including straining – capture of moving colloids in pores or pore constrictions that are too narrow for particle passaging. 2

Fig. 3. Water content dynamics in sand columns during a rainfall event (0–20 min).

The weighted mean transport depth of phytoliths was computed as follows: X=

13 X i=0

Ni xi

=

13 X

Ni

ð2Þ

i=0

where X (cm) is the mean transport distance, xi (cm) is the transport distance in the i-th layer (calculated as the difference between the middepth of the i-th layer and the mid-depth of the surface layer into which the phytoliths had been mixed), and Ni (N cm− 3) is the number of phytoliths in the i-th layer. Based on their length phytoliths were grouped into four size classes: b35 μm, 35–63 μm, 63–100 μm, and N100 μm. The frequency of each size class was determined using a polarized light microscope from 100 phytoliths observed for the three upper sediment layers. Student's t-test was used to check the differences in mean travel depths between irrigation treatments. The linear mixed model approach for repeated measurements was applied to analyze the effect of phytolith size on phytolith transport for each irrigation treatment. According to the mixed linear model approach (Schabenberger and Pierce, 2002; Piepho et al., 2004) phytolith counts were modeled as: Nijk = μ + ci + dj + eij + sk + ðdsÞjk + fijk;

i = 1; ::3; j = 1; ::3; k = 1; 2 ð3Þ

where Nijk is the log count of phytoliths, μ is the grand mean count, ci is the effect of the replicate column, dj is the effect of the soil depth (j = 1: 0–1 cm; j = 2: 1–1.5 cm; j = 3: 1.5–2 cm), sk is the effect of phytolith size (k = 1 mean diameter b63 µm; k = 2 mean diameter N63 µm), eij is the random variable – experimental error corresponding to soil layers within a single column with E[fijk] = 0 and Var[fijk] =σe2 and fijk is the residual error with E[fijk] = 0 and Var[fijk] =σf2. To account for autocorrelation between measurements obtained from different depths within a single column autoregressive correlation structure was fitted to eij.
 Cov eij ; eij V = σ 2e ρ j j − j Vj
 Cov fijk ; fij Vk = σ 2f ρ j j − j Vj

ð4Þ

Because in our case we have 3 layers, j − j′ was either 1 or 2, promoting higher correlation for neighboured layers.

AC A C AC ρ =
λ 2 −
− ka C + kd Satt − kstr ψC At Ax θ Ax

ð5Þ

ASatt θ = − kd Satt + ka C ρ At

ð6Þ

ASstr θ = kstr ψC ρ At

ð7Þ

Here C (N cm− 3) is the particle concentration in the liquid phase, Satt (N g− 1) stands for the concentration of particles reversibly attached to the solid phase, Sstr (N g− 1) denotes the concentration of particles strained in the solid phase, θ (cm3 cm− 3) is the volumetric water content, t is time (d) and x is the depth (cm). The symbol υ (cm d− 1) stands for the pore water velocity, that is the ratio of water flux density and θ. λ (cm) is the particle dispersivity and ka (d− 1), kd (d− 1) and kstr (d− 1) are attachment, detachment and straining coefficient, respectively. The function ψ is a dimensionless straining function.  ψ=

d50 + x d50

−β

ð8Þ

where d50 (cm) is the median grain size diameter of the soil, x (cm) is the distance from the system inlet and (–) is an empirical coefficient set to 0.43 (Bradford et al., 2003). 2.2.1. Theoretical background Advection (second term on right-hand side of Eq. (5)) is the process of particle transport with moving water in which particles are suspended. The higher the flow rate and the higher the concentration of particles suspended in the aqueous phase the higher is the particle transport by advection. Hydrodynamic dispersion (first term of the Eq. (5)) accounts for temporal and spatial spreading of particles around their average transport velocity caused by different geometry of soil pores and velocity distribution within single pores. Detachment is the mobilization of particles by fluid flowing through granular media (Ryan and Elimelech, 1996; Bai and Tien, 1997; Bergendahl and Grasso, 2003). Particles may be detached either due to changes in surface interaction between particle and media grain or by an increase in hydrodynamic shear. The former can be achieved by changes in ionic strength, ionic composition or pH of solution, the latter by an increase of the flow rate. Attachment or reversible particle retention by porous media may occur by means of sedimentation or net attractive interaction between particle and media grain (McDowellBoyer, 1986; Foppen and Schijven, 2006). Sedimentation or

172

O. Fishkis et al. / Geoderma 151 (2009) 168–178

Table 1 Input parameters for irreversible and reversible straining models used to simulate phytolith spatial distribution in sand columns presented in Fig. 6. Parameter/variable

Symbol

Source

Value

Unit

Bulk density Volumetric water Water flux density Dispersivity Attachment coefficient Detachment coefficient Straining coefficient Detachment coefficient 2b Pore velocity

ρ θ q λ ka kd kstr kd′ v

Measured pF curve Measured Literature Fitted Fitted Calculateda Calculated (0.01 kd) Calculated (q/θ)

1.5 0.415 0.2 0.1 − − 2.64 – 0.48

g cm− 3 cm3 cm− 3 cm min− 1 cm min− 1 min− 1 min− 1 min− 1 cm min− 1

a b

Bradford et al. (2003); see modeling approach. Detachment coefficient for particles strained.

gravitational settling of particles in fluid increases with particle density and diameter according to Stokes' law (McDowell-Boyer, 1986, Wan et al., 1995). Net surface interaction between like-charged particle and media grains will lead to attachment if the attractive London van der Waals forces overcome electrostatic double layer repulsion, which is controlled mainly by the surface charge of both particles and media grains (Ryan and Elimelech, 1996; Foppen and Schijven, 2006). A theoretical framework for predicting attachment coefficients based on physicochemical properties of particles, media grain and carrying fluid is provided by filtration theory (Yao et al., 1971; Rajagopalan and Tien, 1976). The contribution of straining in the overall deposition depends on the ratio between particle d and media grain diameter d50 (Herzig et al. 1970; Bradford et al., 2002, 2003; Foppen and Schijven, 2006). Straining becomes significant if d/ d50 ≥ 0.005 (Bradford et al., 2003). Transport of colloids with diameter less than 3 μm is controlled mostly by surface interaction and diffusion whereas filtration of particles with diameter larger than 30 μm is attributed to straining and sedimentation (Herzig et al. 1970). Transport of medium particles with a diameter of 3–30 µm is influenced by surface interactions as well as by straining and sedimentation. According to this classification, phytoliths used in our study (5–63 μm) can be attributed to medium and large particles, influenced more or less by straining and sedimentation. Two models were tested to simulate phytolith transport in sandy sediment. Firstly, phytolith transport was modeled by the advection– dispersion model with attachment, detachment and irreversible straining terms – Eqs. (5)–(7) (Bradford et al., 2003), and secondly, by the advection–dispersion model with attachment, detachment and reversible straining terms – Eqs. (9)–(11): AC A2 C AC ρ ρ =
λ 2 −
− ka C + kd Satt − kstr ψC + kVd Sstr At Ax θ θ Ax

ð9Þ

ASatt θ = − kd Satt + ka C ρ At

ð10Þ

ASstr θ = kstr ψC − k Vd Sstr ρ At

ð11Þ

where k′d(d− 1) is the detachment coefficient for particles strained and all other variables and constants are the same as defined before. Thus, in contrast to the first model, the second model accounts for reversibility of straining. This seems to be reasonable for modeling long-term transport processes in soils because of temporal variability of soil structure. Even if the total macroporosity and pore size distribution in intact sandy soil do not change significantly over time the spatial distribution of pores will vary due to formation of secondary pores through earthworms' activity, degradation and formation of soil aggregates and development of root system (Hayashi et al., 2006). If the spatial location of soil pore channels changes over time, strained particles cannot be irreversibly captured.

Both sets of equations were solved numerically by the Runge– Kutta method of fourth order using the differential equation solver package Berkeley Madonna 8.1 (Macey and Oster, 2000). To simulate long-term transport the models were modified to account for root water uptake resulting in decrease of water flux density with soil depth. According to a simplified approach proposed by Streck and Richter (1997) the pore water velocity v was written as vðzÞ =

1
q − T ð1 − expð − ωzÞÞ θ inf

ð12Þ

where T is the total root uptake (cm d− 1), q (cm d− 1) denotes the flux density of the infiltrating water, z (cm) is the soil depth, and ω is an empirical parameter set to 0.06 cm− 1 (Streck and Richter, 1997). The simulation scenario included the following yearly input data: annual precipitation (1000 mm), sum of surface evaporation and interception (200 mm), annual root uptake (460 mm) (46% of the total rainfall – corresponding to the transpiration in a coniferous forest in temperate climate zone of west/central Europe (Lerch, 1991, p.128)). The rainfall rate was set to 0.2 cm min− 1 (strong shower). Median diameter of_ the sandy soil was d50 = 442 µm and mean phytolith diameter d = 17 µm. Finally the phytolith distribution in a sand was calculated over periods of 100, 500, 1000 years. 2.2.2. Model parameters and statistical analysis Parameters and further model input are listed in Table 1. The attachment and detachment coefficients were fitted to experimental data of the “high-frequency” treatment by means of a modified Gauss–Newton method of minimizing least-squares objective function using the computer code UCODE 1.08 (Poeter and Hill, 1998). The detachment coefficient for particles strained was assumed to be two orders of magnitude lower to that of attached particles k′d = 0.01 kd. The straining coefficient was estimated using an empirical relationship established by Bradford et al. (2003):  kstr = 269:7

d d50

1:42

2

r = 0:93

ð13Þ

where d (cm) is the mean diameter of colloids/particles. It has often been assumed that particle dispersivity is equal to the dispersivity of a conservative tracer like Br− (Brusseau et al., 2005; Cortis et al., 2006). However, direct measurements of particle dispersion show that particle dispersivity decreases with increase in particle size (Auset and Keller, 2004). Because our dataset was not large enough to estimate phytolith dispersivity together with attachment and detachment coefficient by means of a least-square optimisation, we took dispersivity values from the literature. It turns out that colloid dispersivity values as obtained from colloid breakthrough experiments with packed sand columns range between 0.1 and 0.5 cm (Bradford et al., 2002, 2003, 2006; Brush et al., 1999; Johnson et al. 1996). Because phytoliths are larger than colloids for which dispersivity values were reported we used the lowest value in the reported range (0.1 cm). All remaining model parameters were determined experimentally. The flow rate of 0.2 cm/min and the soil volumetric water content 0.415 cm3 cm− 3 were assumed to be steady during irrigation. Statistics for estimated parameters and standard error of regression were computed in UCODE 1.8 (Poeter and Hill, 1998). The model efficiency, which is the proportion of variance explained by the model, was calculated to evaluate the model performance. 3. Results At the end of the experiment the recovery of phytoliths from the sandy sediments was 95 ± 33% (where error is the standard deviation) for low-frequency irrigation and 113 ± 87% for high-frequency irrigation. Phytolith overall recovery in effluents for high-frequency irrigation was 7 × 10− 4 ± 6 × 10− 4%.

O. Fishkis et al. / Geoderma 151 (2009) 168–178

173

Fig. 4. Phytolith concentration [N ⁎ cm− 3] versus depth A; Percentage of phytoliths recovered B. Error bars indicate a standard deviation of observed values.

In all columns phytolith concentration decreased exponentially with soil depth within the first upper 3-cm of the sand columns (Fig. 4A). Below that depth the phytolith concentration approached a rather constant value. Eighty-three percent of phytoliths recovered at the low-frequency irrigation treatment and 78% of phytoliths recovered at the high-frequency irrigation treatment were found in the layer of application (0–1 cm, Fig. 4B). The second layer (1–1.5 cm) contained 16% and 20% for low and high irrigation, respectively. Thus 98–99% of phytoliths recovered were found within the upper 1.5 cm. The columns that experienced high-frequency irrigation contained only 0.6% of the total phytolith recovery below the 5-cm depth, therefore the columns of the low-frequency irrigation treatment were analyzed only to the depth of 5 cm to reduce the time of analysis. The weighted mean transport distance was 2.23±1.13 mm and 3.01± 0.19 mm under low-frequency and high-frequency irrigation, respectively. Although there is a trend to larger transport distance under higher

irrigation, the hypothesis on deeper phytolith transport under highfrequency irrigation could not be confirmed at the 95% significance level because of high variability among replicates of the low-frequency treatment. Size distribution of phytoliths with soil depth was determined for the three upper layers, which contained more than 99% of the phytoliths recovered at the end of the experiment. A slight decrease in the fraction of large particles and an increase in the fraction of small particles with depth were found for the high-frequency irrigation treatment (Fig. 5). However the effect of phytolith size on transport was not significant for both treatments according to mixed linear model analysis. The p-value for the interaction between size and depth was found to be 0.34. Thus, our results do not provide unambiguous evidence on faster transport of smaller phytoliths. Both models gave a similar fit to the observations explaining 97% of the total variance (Table 2). The comparison of both fits (Fig. 6) shows

Fig. 5. Phytolith size distribution versus sand depth at high-frequency irrigation (40 × 40 mm in 5 months) (A) and low-frequency irrigation (10 × 40 mm in 5 months) (B). Error bars indicate a standard deviation of observed values.

174

O. Fishkis et al. / Geoderma 151 (2009) 168–178

Table 2 Model statistics: parameter uncertainty and model performance for irreversible and reversible straining models. Parameter estimation

Irreversible straining model Reversible straining model

Model performance

Parameters

Mean

SE

t-value

ka (min− 1) kd (min− 1) ka (min− 1) kd (min− 1)

9.13 0.0072 8.97 0.0068

6.89 0.0058 6.09 0.0046

1.33 1.24 1.45 1.48

Standard error of the regression

r2

1.24

0.97

1.23

0.97

that the implementation of mobilization for strained phytoliths did not have a significant effect on the agreement between simulation and experimental data. The parameter and model performance statistics are about the same for both models (Table 2). The low t-ratios of the estimated parameters indicate rather high uncertainty of parameter estimates. This is certainly due to the low number of observations and their high variability. The transport of phytoliths to the depths between 1.5 and 2.5 mm was systematically overestimated (Fig. 6). The relevance of the phytolith downward displacement by moving water on a Holocene time scale was assessed through long-term model predictions. Phytolith distribution simulated after a period of 2, 100, 500 and 1000 years is presented in Fig. 7A. The irreversible straining model shows very slight translocation of phytoliths after a period of 100 years with no significant changes afterwards. Thereby, after a period of 1000 years 99% of phytoliths input were irreversibly retained in the upper 5-cm layer. The long-term predictions of the reversible straining model gave completely different results compared to those of irreversible straining model (Fig. 7A, B). Under the same simulation scenario a mean travel distance of phytoliths in sandy soil was 1.5, 9 and 19 cm

after 100, 500 and 1000 years, respectively, indicating significant displacement of phytoliths by leaching. 4. Discussion The high variability of the recovery values needs to be stated, which however is rather common for experiments on colloid quantification in replicate columns designed similarly (Darnault et al., 2004; Brush et al., 1999). This variability was probably due to error propagation associated with the multistep phytolith extraction procedure. An alternative method of phytoliths quantification would be required to get more precise data on phytolith concentration. Staining of phytoliths with fluorescent dye could be an alternative, which may facilitate phytolith quantification in sediment or soil without extraction (Hodson et al., 1994). Exponential decrease with depth similar to the one observed in our study for phytoliths (Fig. 4A, B) was reported recently for latex colloids (d= 3.2 µm) (Bradford et al., 2002), C. parvum oocysts (d =5 µm) (Nasser et al., 2003; Darnault et al. 2004) and Giardia cysts (d=10 µm) (Bradford et al., 2006) in packed sand. Harter et al (2000) observed, on contrary, an increase of C. parvum oocysts with depth in an experiment in which flow rates were twice as high as in our experiment, while the d/d50 ratio was lower by one order of magnitude. Bradford et al. (2002) showed that the spatial distribution pattern of colloids in sand is very sensitive to d/d50 ratio and, hence, to contribution of straining. They observed exponential-like distribution patterns at high values of d/d50 (0.009– 0.02) and a linear decrease or even homogenous depth distribution at low values of d/d50 (≤0.004). According to these findings the exponential distribution pattern observed in our study together with high d/d50 ratio −0.04 indicates that straining process contributes to phytolith transport. Phytolith recovery in the effluent showed that just a negligible part of phytoliths was leached from the column. A review of studies on transport

Fig. 6. Simulated and measured phytolith concentration versus sand depth. Irreversible straining model (A) and reversible straining model (B). Error bars indicate a standard deviation of observed values.

O. Fishkis et al. / Geoderma 151 (2009) 168–178

175

Fig. 7. Simulated phytolith transport over periods of 100, 500 and 1000 years. Irreversible straining model (A) and reversible straining model (B). Phytolith content in each 1-cm layer is given as percentage of the initial input.

of particles comparable with the phytoliths as used in the present study with respect to size and surface charge shows high variability of colloidal breakthrough in saturated sand even for a single colloidal type (Table A1, Appendix A). Negligible breakthrough was reported for transport of protozoan pathogen C. parvum oocysts through saturated sandy sediments and intact loamy sand (Nasser et al., 2003; Hijnen et al., 2005). However, much higher transport was observed for the same pathogen in a column experiment with saturated packed sand by Bradford and Bethahar (2005) and Cortis et al. (2006) (Table A1, Appendix A). They reported a recovery of 11 to 51% of oocysts in effluent. The higher recovery values observed by Bradford and Bethahar (2005) are probably due to the lower ionic strength (1 mM) used compared with the present study (10 mM), which may have resulted in enhanced repulsion between hydroxylated quartz sand surfaces and oocysts and therefore reduced contribution of attractive surface interaction to particle deposition. The experiments conducted by Cortis et al. (2006) were performed with higher flow intensity and lower ratio of d/d50 compared with the present study, promoting enhanced breakthrough. Two orders of magnitude increase in oocysts recovery in effluent was caused by one order of magnitude increase in flow velocity (Harter et al., 2000, Table A1, Appendix A). Positive effect of flow rate enhancement on recovery in effluents was also observed in other studies (Hijnen et al., 2005; Jacobson et al., 1997). Based on the percentage of phytoliths found below the application layer (Fig. 4B) and the values of weighted mean transport distance we can summarize our results on magnitude of phytolith transport in sand. Twenty-two percent of phytoliths recovered were displaced from the

application layer in course of transport experiment of five-month duration under 1600 mm water flux. The weighted mean transport distance was 2.23±1.13 mm and 3.01±0.19 mm under low-frequency and highfrequency irrigation treatments, respectively. Hence, our experimental data give evidence for phytolith displacement through the pores of homogenous sand in absence of bioturbation. These results could not be directly transferred to natural sandy soils, firstly because the sandy sediment used in the present study did not contain organic substances and had just minor amount of clay, which are expected to aggregate with phytoliths and therefore reduce their mobility and secondly because of the lack of natural soil structure in our experiments. Comparative studies on bacteria transport in intact and packed soil showed less pronounced transport of particles in packed soil compared with undisturbed soil columns (Smith et al.,1985; van Elsas et al.,1991). According to Smith et al. (1985) the bacteria (E. coli) transport efficiency in intact soils of different types did not give any correlation with soil texture or bulk density but was highly correlated with soil structure. Hence we expect that phytolith transport in intact sandy soil with a preserved network of interconnected pores would be more intense than in our experiment. The performance of the model obtained by assuming irreversible straining was very similar to that assuming reversible straining. In contrast, long-term predictions by two models were contradictory. According to the irreversible straining model phytolith transport by leaching was negligible, whereas according to reversible straining model phytolith mean travel distance after 1000 years was 19 cm. These discrepancies occurred because the straining reversibility term

176

O. Fishkis et al. / Geoderma 151 (2009) 168–178

to aggregate with phytoliths and therefore reduce their mobility and secondly because of the lack of natural soil structure in our experiments. As packed soil columns usually exhibit less pronounced transport of particles compared to undisturbed soil columns (Smith et al., 1985; van Elsas et al.,1991), we expect that phytolith transport in undisturbed sandy soil would be more pronounced under the same experimental conditions. Irreversible straining model adapted from Bradford et al. (2003) as well as the same model but with reversible straining term resulted in equally good agreement with observations. However, long-term predictions of the models gave contradictory results. The irreversible straining model showed that after a period of 1000 years 99% of phytoliths would be irreversibly retained within 5 cm depth, whereas the reversible straining model indicated significant translocation of phytoliths with a mean travel distance of 19 cm. Because the experimental data obtained in our study did not allow us to distinguish between reversible and irreversible straining models of phytolith transport, more long-term experiments on phytolith transport in undisturbed soils are needed to understand and quantify phytolith transport in soil.

implemented in the second model takes effect just on the long time scale. Although we suppose that straining reversibility contributes to the phytolith transport in natural soils our short-time experiment was not suited to distinguish between the two models discussed. Consequently, further experiments are required to discriminate between the models in order to produce reliable long-term predictions of phytolith displacement in soil. We suppose that the best alternative would be observations on phytolith distribution in intact soils obtained under vegetation of known age (10–100 years). Desired experimental conditions could be found using, for example, forestry reports with documented chronology of plantations. The study area should fulfill following requirements: first, the modern plantation of known age produces phytoliths of specific morphology, second, these phytoliths of specific morphology had not been produced by the vegetation that existed on the area before and third, earthworms activity can be ignored (e.g., pine plantations on a sandy soil). 5. Summary and conclusion The transport of phytoliths was studied in packed sand columns exposed to periodic irrigation over a period of 5 months. The weighted mean travel distance of phytoliths was 2.23±1.13 mm and 3.01±0.19 mm at low-frequency irrigation and high-frequency irrigation, respectively. Twenty-two percent of phytoliths applied to the 1-cm upper layer of the columns were displaced from the application layer after 1600 mm of simulated rainfall. Hence, our data provide evidence for the displacement of phytoliths in sandy sediment by leaching in absence of bioturbation. These results could not be directly transferred to natural sandy soils, firstly because of lack of organic and clay substances, which are expected

Acknowledgments We thank the German Academic Exchange Service (DAAD) and Hohenheim University for financial support. We are very grateful to Erhard Strohm for the technical assistance. We express our special thanks to Konstantin Pustovoytov for collaborative assistance and initiating the concept of the present research project. Thanks to Mehdi Zarei for taking the SEM images of phytoliths and Hans-Peter Piepho for his help with statistical analysis.

Appendix A. Supplementary data

Table A1 A review of studies on particle breakthrough in saturated sand. Particle characteristics

Experimental design

Recovery Source in leachates

Type

Mean diameter (μm)

ζ-potential at pH7 (mV)

Ratio of particle diameter to median diameter of media grain (d/d50)

Flow rate (cm min− 1)

Ionic strength (M)

Number of pore volumes applieda suspension eluant

Phytolith Giardia intestinalis cysts

17 11

− 20 to − 30b − 12d

0.04 0.06

0.2 0.06

0.01 0.0065

0 5

16.8 45

0.0007 0

11 10 10 10 5 5 5 5 5 5 5 5 5 5 5 5 5

− 12 − 12 − 12 − 12 − 6 to − 19d − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19 − 6 to − 19

0.02 0.07 0.03 0.01 0.03 0.01 nd 0.03 0.01 0.01 0.004 0.004 nd 0.01 0.01 0.01 0.01 0.03

0.02 0.1 0.1 0.1 0.06 0.06 0.008 0.45 0.044 0.45 0.044 0.45 1.76 0.5 0.5 0.5 0.5 0.1

0.012 0.001 0.001 0.001 0.0065 0.012 nd 0.003 0.003 0.003 0.003 0.003 0 0.1 0.009 0.003 0.00001 0.001

5 1.9 1.9 1.9 5 5 0 2.5 2.5 2.5 2.5 2.5 1 1.5 1.5 1.5 1.5 2

45 5.1 5.1 5.1 45 45 5 5.5 5.5 5.5 5.5 5.5 3 2000 2000 2000 2000 4.3

0 0 0.4 1.8 0.01 0 0.01 0.7 0.23 20 10 69 48 11 22 51 15 14

5 5 3.6 3.6 3.6 4.1

− 6 to − 19 − 6 to − 19 − 30 − 30 − 30 − 60

0.014 0.007 0.02 0.02 0.02 0.02

0.1 0.1 2.52 2.52 2.52 2.52

0.001 0.001 0.001 0.003 0.01 0.001

2 2 8 8 8 8

4.3 4.3 10 10 10 10

21 43 41 7 4 41

C. parvum oocysts

Latex particle a

Present studyc Hijnen et al. (2005)

Bradford et al. (2006)

Hijnen et al. (2005) Nasser et al. (2003) Harter et al. (2000)

Brush et al. (1999) Cortis et al. (2006)

Bradford and Bettahar (2005)

Tufenkji et al., 2004

Tufenkji et al., 2004

Number of pore volumes is calculated as water flux density multiplied by duration of irrigation divided by the product of column length and water content. b Fraysse et al., 2009. c Sand was saturated just on the bottom whereas on the surface the saturation level varied between 51 and 88% during irrigation. d Dai and Boll (2003).

O. Fishkis et al. / Geoderma 151 (2009) 168–178

References Alexandre, A., Meunier, J.D., Lézine, A.M., Vincens, A., Schwartz, D., 1997. Phytoliths: indicators of grassland dynamics during the late Holocene in intertropical Africa. Palaeogeogr., Palaeoclimatol., Palaeoecol. 136, 213–229. Alexandre, A., Meunier, J.D., Mariotti, A., Soubies, F., 1999. Late Holocene phytoliths and carbon-isotope record from a latosol at Salitre, South-Central Brazil. Quarternary Research 51 (2), 187–194. Auset, M., Keller, A.A., 2004. Pore-scale processes that control dispersion of colloids in saturated porous media. Water Resources Research 40 (3), W03503. Bai, R., Tien, C., 1997. Particle detachment in deep bed filtration. Journal of Colloid Interface Science 186, 307–317. Bartoli, F., Wilding, L.P., 1980. Dissolution of biogenic opal as a function of its physical and chemical properties. Soil Science Society of America 44, 873–878. Bergendahl, J.A., Grasso, D., 2003. Mechanistic basis for particle detachment from granular media. Environ. Sci. Technol. 37, 2317–2322. Blecker, S.W., Yonker, C.M., Olson, C.G., Kelly, E.F., 1997. Paleopedologic and geomorphic evidence for Holocene climate variation, Shortgrass Steppe, Colorado, USA. Geoderma 76, 113–130. Blinnikov, M., Busacca, A., Whitlock, C., 2002. Reconstruction of the late Pleistocene grassland of the Columbia basin, Washington, USA, based on phytolith records in loess. Palaeogeography, Palaeoclimatology, Palaeoecology 177 (1–2), 77–101. Bolster, C.H., Mills, A.L., Hornberger, G.M., Herman, J.S., 1999. Spatial distribution of bacteria following miscible displacement experiments in intact cores. Water Resources Research 35 (6), 1797–1807. Bradford, S.A., Bettahar, M., 2005. Straining, attachment, and detachment of Cryptosporidium oocysts in saturated porous media. Journal of Environmental Quality 34 (2), 469–478. Bradford, S.A., Simunek, J., Bettahar, M., van Genuchten, M.Th., Yates, S.R., 2003. Modeling colloid attachment, straining, and exclusion in saturated porous media. Environ. Sci. Technol. 37, 2242–2250. Bradford, S.A., Yates, S.R., Bettahar, M., Simunek, J., 2002. Physical factors affecting the transport and fate of colloids in saturated porous media. Water Resources Research 38 (12), 1327 WR001340. Bradford, S.A., Tadassa, Y.F., Pachepsky, Y., 2006. Transport of Giardia and manure suspensions in saturated porous media. Journal of Environmental Quality, 35, 749–757. Brush, C.F., Ghiorse, W.C., Anguish, L.J., Parlange, J.Y., Grimes, H.G., 1999. Transport of Cryptosporidium parvum oocysts through saturated columns. Journal of Environmental Quality 28 (3), 809–815. Brusseau, M.L., Oleen, J.K., Santamaria, J., Cheng, L., Orosz-Coghlan, P., Chetochine, A.S., Blanford, W.J., Rykwalder, P., Gerba, C.P., 2005. Transport of microsporidium Encephalitozoon intestinales spores in sandy porous media. Water Research 39, 3636–3642. Clarke, J., 2003. The occurrence and significance of biogenic opal in the regolith. Earth Science Review 60, 175–194. Coil, J., Korstanje, M.A., Archer, S., Hastorf, C.A., 2003. Laboratory goals and consideration for multiple microfossil extraction in archaeology. Journal of Archchaeological Science 30, 991–1008. Cortis, A., Harter, T., Hou, L.L., Atwill, E.R., Packman, A.I., Green, P.G., 2006. Transport of Cryptosporidium parvum in porous media: long-term elution experiments and continuous time random walk filtration modeling. Water Resources Research 42 (12), W12S13. Dai, X., Boll, J., 2003. Evaluation of attachment of Cryptosporidium parvum and Giardia lambia to soil particles. Journal of Environmental Quality 32, 296–304. Dane, J.H., Topp, G.C. (Eds.), 2002. Methods of Soil Analysis. Part 4. Physical Methods. Soil Science Society America, Book Series, vol. 5. Madison, WI, pp. 272–278. 6. Darnault, C.J.G., Steenhuis, T.S., Garnier, P., Kim, Y.J., Jenkins, M.B., Ghiorse, W.C., Baveye, P.C., Parlange, J.Y., 2004. Preferential flow and transport of Cryptosporidium parvum oocysts through the vadose zone: experiments and modeling. Vadose Zone Journal 3 (1), 262–270. Delhon, C., Alexandre, A., Berger, J.F., Thiebault, S., Brochier, J.L., Meunier, J.D., 2003. Phytolith assemblages as a promising tool for reconstructing Mediterranean Holocene vegetation. Quarternary Research 59, 48–60. Derry, L.A., Kurtz, A.C., Ziegler, K., Chadwick, O.A., 2005. Biological control of terrestrial silica cycling and export fluxes to watersheds. Nature 433 (7027), 728–731. van Elsas, J.D., Trevors, J.T., van Overbeek, L.S., 1991. Influence of soil properties on the vertical movement of genetically-marked Pseudomonas fluorescens through large soil microcosms. Biology and Fertility of Soils 10, 249–255. Epstein, E., 1999. Silicon. Annual Review of Plant Physiology and Plant Molecular Biology 50, 641–664. Farmer, V.C., Delbos, E., Miller, J.D., 2005. The role of phytolith formation and dissolution in controlling concentrations of silica in soil solutions and streams. Geoderma 127 (1–2), 71–79. Fisher, R.F., Newell Bourne, C., Fisher, W.F., 1995. Opal phytoliths as an indicator of the foristics of prehistoric grasslands. Geoderma 68 (4), 243–255. Foppen, J.W.A., Schijven, J.F., 2006. Evaluation of data from the literature on the transport and survival of Escherichia coli and thermotolerant coliforms in aquifers under saturated conditions. Water Research 40 (3), 401–426. Fraysse, F., Pokrovsky, O.S., Schott, J., Meunier, J.D., 2009. Surface chemistry and reactivity of plant phytoliths in aqueous solutions. Chemical Geology 258 (3–4), 197–206. Gallego, L., Distel, R.A., Camina, R., Iglesias, R.M.R., 2004. Soil phytoliths as evidence for species replacement in grazed rangelands of central Argentina. Ecography 27 (6), 725–732.

177

Gol'eva, A.A., Aleksandrovskij, A.L., 1999. The application of phytolith analysis for solving problems of soil genesis and evolution. Eurasian Soil Science 32 (8), 884–891. Golyeva, A.A., Aleksandrovskiy, A.L., Tselishcheva, L.K., 1995. Phytolithic analysis of Holocene paleosoils. Eurasian Soil Science 27 (2), 46–56. Hart, D.M., Humphreys, G.S., 1997. The mobility of phytoliths in soils; pedological considerations. First European meeting on phytolith research. In: Pinilla, A., JuanTresserras, J., Machado, M.J. (Eds.), The State-of-the-art of Phytoliths in Soils and Plants. Centro de Clencias Medioambientales Monograph, Madrid, pp. 93–100. Hart, D.M., 2003. The influence of soil fauna on phytolith distribution in an Australian soil. Papers from a conference held at the ANU, August 2001, Canberra Australia. Phytolith and starch research in the Australian–Pacific–Asian regions: the state of the art. Terra Australis, vol. 19, pp. 83–91. Harter, T., Wagner, S., Atwill, E.R., 2000. Colloid transport and filtration of Cryptosporidium parvum in sandy soils and aquifer sediments. Environmental Science Technol. 34 (1), 62–70. Hayashi, Y., Ken’ichirou, K., Mizuyama, T., 2006. Changes in pore size distribution and hydraulic properties of forest soil resulting from structural development. Journal of Hydrology 331 (1–2), 85–102. Herzig, J.P., Leclerc, D.M., LeGoff, P., 1970. Flow of suspension through porous media— application in deep bed filtration. Industrial and Engineering Chemistry 62 (1), 8–35. Hijnen, W.A.M., Brouwer-Hanzens, A.J., Charles, K.J., Medema, G.J., 2005. Transport of MS2 phage, Escherichia coli, Clostridium perfringens, Cryptosporidium parvum and Giardia intestinalis in a gravel and a sandy soil. Environmental Science and Technology 39 (20), 7860–7868. Hodson, M.J., Smith, R.J., van Blaaderen, A., Crafton, T., ÒNeill, C.H., 1994. Detecting plant silica fibres in animal tissue by confocal fluorescence microscopy. Annals of Occupational Hygiene 38 (2), 149–160. Hornberger, G.M., Mills, A.L., Herman, J.S., 1992. Bacterial transport in porous media: evaluation of a model using laboratory observations. Water Resource Research 28, 915–923. Horrocks, M., Deng, Y., Ogden, J., Sutton, D.G., 2000. A reconstruction of the history of Holocene sand dune on Great Barrier Island, northern New Zealand, using pollen and phytolith analyses. Journal of Biogeography 2 (6), 1269–1277. Humphreys, G.S., Hart, D.M., Simons, N.A., Field, R.J., 2003. Phytoliths as indicator of process in soils. Papers from a conference held at the ANU, August 2001, Canberra Australia. Phytolith and Starch Research in the Australian–Pacific–Asian Regions: The State of the Art. Terra australis, vol. 19, pp. 93–104. Jacobson, O.H., Moldrup, P., Larsen, C., Konnerup, L., Petersen, L.W., 1997. Particle transport in macropores of undisturbed soil columns. Journal of Hydrology 196, 185–203. Johnson, P.R., Sun, N., Elimelech, M., 1996. Colloid transport in geochemically heterogeneous porous media. Environmental Science and Technology 30, 3284–3293. Jones, L.P.H., Handreck, K.A., 1967. Silica in soils, plants, and animals. Advances in Agronomy 19, 107–149. Kim, S.B., Corapcioglu, M.Y., 2002. Vertical transport of Cryptosporidium parvum oocysts through sediments. Environ. Technol. 23, 1435–1446. Lerch, G., 1991. Pflanzenökologie. Akademie Verlag GmbH, Berlin, p. 128. Macey, R., Oster, G., 2000. Berkley Madonna v. 8.0.1. http://www.berkeleymadonna. com. Marschner, H., 1995. Mineral Nutrition of Higher Plants, 2nd edn. Academic press, San Diego, pp. 418–428. McClaran, M.P., Umlauf, M., 2000. Desert grassland dynamics estimated from carbon isotopes in grass phytoliths and soil organic matter. Journal of Vegetation Science 11, 71–76. McDowell-Boyer, L.M., Hunt, J.R., Sitar, N., 1986. Particle transport through porous media. Water Resource. Research 22, 1901–1921. Meunier, J.D., Colin, F., Alarcon, C., 1999. Biogenic silica storage in soils. Geology 27, 835–838. Motomura, H., Fujii, T., Suzuki, M., 2004. Silica deposition in relation to ageing of leaf tissues in Sasa veitchii (Carriere) rehder (Poaceae: Bambusoideae). Annals of Botany 93 (3), 235–248. Nasser, A.M., Huberman, Z., Zelberman, A., Greenfeld, S., 2003. Die-off and retardation of Cryptosporidium spp. oocysts in loamy soil saturated with secondary effluent. Water Science and Technology 3 (4), 253–259. Piepho, H.P., Büchse, A., Richter, C., 2004. A mixed modelling approach for randomized experiments with repeated measures. Journal of Agronomy and Crop Science 190, 230–247. Piperno, D., Becker, P., 1996. Vegetation history of a site in the central Amazon basin derived from phytolith and charcoal records from natural soils. Quaternary Research 45, 202–209. Piperno, D.R., 1988. Phytolith Analysis. An Archaeological and Geological Perspective. Academic Press, London. Poeter, E.P., Hill, M.C., 1998. Documentation of UCODE: a computer code for universal inverse modeling. Water-Resources Investigations Reports 98-4080. U.S. Geological Survey. Rajagopalan, R., Tien, N.C., 1976. Trajectory analysis of deep-bed filtration with the sphere-incell porous media model. Am. Inst. Chem. Ing., 22, 523–533. Rovner, I., 1983. Plant opal phytolith analysis. Advances in Archaeological Method and Theory 6, 225–266. Runge, F., 1999. The opal phytolith inventory of soils in central Africa—quantities, shapes, classification, and spectra. Review of Palaeobotany and Palynology 107, 23–53. Ryan, J.N., Elimelech, M., 1996. Colloid mobilization and transport in groundwater. Colloids and Surfaces A-Physicochemical and Engineering Aspects 107, 1–56.

178

O. Fishkis et al. / Geoderma 151 (2009) 168–178

Schaap, M.G., Leij, F.J., van Genuchten, M.T.h., 2001. Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251, 163–176. Schabenberger, O., Pierce, F.J., 2002. Contemporary Statistical Models for the Plant and Soil Sciences. CRC Press, LLC. Sen, T.K., Khilar, K.C., 2006. Review on subsurface colloids and colloid-associated contaminant transport in saturated porous media. Advances in Colloid Interface Science 119, 71–96. Simunek, J., Sejna, M., van Genuchten, M.T., 2005. The HYDRUS-1D Software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. University of California, Riverside, Research reports, p. 240. Smith, M.S., Thomas, G.W., White, R.E., Ritonga, D., 1985. Transport of Escherichia coli trough intact and disturbed soil columns. Journal of Environmental Quality 14 (1), 1985. Streck, T., Richter, J., 1997. Heavy metal displacement in a sandy soil at the field scale. II. Modeling. Journal of Environmental Quality 26, 56–62.

Tufenkji, N., Miller, G.F., Ryan, J.N., Harvey, R.W., Elimelech, M., 2004. Transport of Cryptosporidium oocysts in porous media: role of straining and physicochemical filtration. Environmental Science & Technology 38 (22), 5932–5938. van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science of American Journal 44, 892–898. Wan, J.M., Tokunaga, T.K., Tsang, C.F., 1995. Bacterial Sedimentation through a porous medium. Water Resources Research 31 (7), 1627–1636. Wüst, R.A.J., Bustin, R.M., 2003. Opaline and Al–Si phytoliths from a tropical mire system of West Malaysia: abundance, habit, element composition, preservation and significance. Chemical Geology 200, 267–292. Yao, K.M., Habibian, M.T., O'Melia, C.R., 1971. Water and wastewater filtration: concepts and applications. Environment Science and Technology 5, 1105–1111.