Description of mutual interactions between silicon and phosphorus in Andisols by mathematical and mechanistic models

Chemosphere 131 (2015) 164–170

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Description of mutual interactions between silicon and phosphorus in Andisols by mathematical and mechanistic models Paula Cartes a,b,⇑, Mara Cea a, Alejandra Jara a,b, Antonio Violante c, María de la Luz Mora a,b a

Scientific and Technological Bioresource Nucleus, Universidad de La Frontera, Casilla 54-D, Temuco, Chile Departamento de Ciencias Químicas y Recursos Naturales, Universidad de La Frontera, Casilla 54-D, Temuco, Chile c Dipartimento di Agraria, Università Degli Studi di Napoli Federico II, Naples, Italy b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t

 Si and P sorption were studied on two

Andisols with contrasting physicochemical properties.  Mathematical and mechanistic models modeled Si and P sorption data.  Competition between Si and P was not symmetrical in binary systems.

a r t i c l e

i n f o

Article history: Received 24 October 2014 Received in revised form 27 January 2015 Accepted 25 February 2015 Available online 31 March 2015 Handling Editor: X. Cao Keywords: Andisol Phosphorus Silicon Sorption Freundlich equation Constant Capacitance Model

a b s t r a c t The Freundlich model and the Constant Capacitance Model (CCM) were used to describe silicon (Si) and phosphorus (P) sorption, both individually and for binary P–Si systems, on two Andisols with different chemical properties: Freire soil (FS) and Piedras Negras soil (PNS). Silicon sorption kinetics were examined through the Elovich equation, revealing that the initial sorption rate was 16 times greater in PNS. The Freundlich equation provides a good fit to the sorption data for both Andisols. When compared with FS, larger Si sorption capacity and lower Si affinity for the surface sites were observed in PNS; nevertheless, Si sorption decreased in both soils as P sorption increased. Slight reductions in P sorption capacity due to the presence of Si were found, whereas there was no apparent effect on P bonding intensity. The CCM was able to describe Si adsorption, and potentiometric titrations support that Si seems to be specifically sorbed mainly onto sites of negative charge. Comparable log Kint Si values were obtained for both soils, indicating that Si was bound on similar sites. Phosphorus sorption was well described by int the CCM, and log Kint P denoted strong interactions of P with the surface sites. For binary systems, log KP int did not vary with increasing Si concentration; comparatively, log KSi scarcely decreased with increasing P concentration in PNS, but a 28% reduction was found in FS at the highest initial P concentration. Ó 2015 Elsevier Ltd. All rights reserved.

Abbreviations: FS, Freire soil; PNS, Piedras Negras soil; CCM, Constant Capacitance Model.

⇑ Corresponding author at: Scientific and Technological Bioresource Nucleus, Universidad de La Frontera, Casilla 54-D, Temuco, Chile. E-mail address: [email protected] (P. Cartes). http://dx.doi.org/10.1016/j.chemosphere.2015.02.059 0045-6535/Ó 2015 Elsevier Ltd. All rights reserved.

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P. Cartes et al. / Chemosphere 131 (2015) 164–170

1. Introduction Accurate estimation of the fate of macroelements or microelements in soils is critical in developing effective strategies for fertilization, and in assessing their impact on the environment. Even though silicon (Si) is one of the most abundant elements in Earth’s crust, it is not currently considered an essential element for vascular plants (Epstein, 1999; Ma and Takahashi, 2002). Nevertheless, there is increasing evidence for the benefits of Si in terms of plant growth and development, especially under stressful growth conditions (Ma and Yamaji, 2006; Van Bockhaven et al., 2013; Wu et al., 2013). It is well known that weathering of primary or secondary minerals releases soluble Si into the soil. Additionally, biogenic forms (phytoliths) represent another important sink and source of Si in soils (Farmer et al., 2005). Monosilicic acid (H4SiO04), the form of available Si in the soil–plant system, is slightly dissociated to silicate ions below pH 9 (Dietzel, 2000). Silicate can be complexed by metals and organic compounds in soil solution (Matichencov and Bocharnikova, 2001). It is also specifically sorbed by ligand exchange onto iron (Fe) and aluminium (Al) oxides/hydroxides (Hansen et al., 1994; Dietzel, 2002), forming innersphere complexes (Parfitt, 1978; Hiemstra et al., 2007; Violante, 2013) in a pH-dependent process (Beckwith and Reeve, 1963). The extent of Si sorption is governed by the presence of anions, such as phosphate, that compete for sorption sites, with the consequent effect on the electrical charge of mineral surfaces (Barrow, 1989). Several studies have investigated silicate sorption in soils in either single or binary mixtures with phosphate. The majority of these studies have focused on describing anion sorption using mathematical Freundlich or Langmiur models (e.g. McKeague and Cline, 1963; Huang et al., 2006; Lee and Kim, 2007). Even though these models are empirical, they have provided a useful approach for modelling quantity/intensity relationships of silicate sorption in different soils. However, to date there are few reports that evaluate both the influence of silicate on phosphate sorption in ashderived soils such as Andisols (Hartono, 2008), and the reciprocal effect of phosphate on silicate in these variable-charge soils (Ma and Takahashi, 1990). Moreover, surface complexation models, which provide a mechanistic description for sorption processes, have not yet been applied to describe competition between silicate and phosphate in soils. Such models could provide valuable information on binary silicate-phosphate mixtures in terms of surface species, chemical reactions, equilibrium constant expressions, surface activity coefficients, and mass and charge balance in a wide range chemical conditions. The aim of this research is to investigate mutual interactions between silicon and phosphorus through the application of the Freundlich equation and the Constant Capacitance Model (CCM) to two Andisols with differing chemical properties. 2. Materials and methods 2.1. Soil samples Soil samples from two Andisols of Southern Chile, belonging to the Freire (38°500 0900 S and 72°410 3900 W) and the Piedras Negras (40°230 4700 S and 72°320 3500 W) Series, were collected at 0–20 cm depth, air-dried at room temperature and sieved (<2 mm). These soils were selected for their contrasting chemical properties, as previously described by Cartes et al. (2009). 2.2. Chemical characterization of soil samples The soil samples’ chemical composition was determined according to the methodology described by Sadzawka et al.

(2006). Soil pH was measured by potentiometry in a soil/solution (H2O) suspension (ratio: 1:2.5). Phosphorus was extracted by the Olsen bicarbonate method and analysed by the method of Murphy and Riley (1962). Organic matter was estimated after wet digestion with a modified Walkley–Black procedure. Exchangeable cations (Ca, Mg, Na and K) were extracted with 1 M NH4Ac at pH 7.0 and analysed using flame atomic absorption spectrophotometry (FAAS). Exchangeable aluminium and silicon were extracted with 1 M KCl and analysed by FAAS. 2.3. Potentiometric titration The surface charge behaviour in both soils was evaluated by acid–base titration in a N2 atmosphere, using 0.1 N NaOH and 0.1 N HCl, according to the methodology described by Cea et al. (2005). Briefly, 3 g of the soil was placed in a vessel containing 100 mL of 0.001, 0.01, or 0.1 M NaCl as the background solution. Titrations were carried out by adding 0.2 mL increments of titrant with at least a 20-min reaction time between additions to allow the pH to stabilize. The surface charge behaviour of the soil was evaluated in the presence of Si (2 mM) using a similar technique. All titration experiments were conducted in duplicate. 2.4. Sorption experiments 2.4.1. Silicon sorption kinetics Batch experiments were carried out to determine the equilibrium time for Si sorption on Freire soil (FS) and Piedras Negras soil (PNS). For each soil, duplicate 1.0 g soil samples were placed in 50mL polypropylene centrifuge tubes and equilibrated with 20 mL of 0.1 M NaCl containing 2 mM of Si (as Na2SiO4, Merck reagent). Soil suspensions were equilibrated in a thermo-regulated shaker at 25 ± 1 °C for 0.5, 1, 2, 4, 8, 12, 24, 48 and 72 h. During the course of the experiments, the pH of the solutions was adjusted to 5.0 with dilute HCl or NaOH. After the incubation time, samples were centrifuged at 10,000 g for 10 min and aliquots of supernatants were taken for Si analysis. The difference between the initial and final amounts of Si in solution was taken as the amount of Si sorbed by each soil. Silicon concentration in the supernatant was determined by FAAS at 251.6 nm. Experimental data were modelled by the first-order reaction Elovich equation (1):

qt ¼ q0 þ ð1=bÞ lnðabÞ þ ð1=bÞ ln t

ð1Þ 1

1

where a is the sorption initial rate constant (mmol kg h ), b is the desorption rate constant, and qt and q0 are the amounts of sorption at time t (h) and time zero, respectively (Arora and Chahal, 2002). 2.4.2. Silicon and phosphorus competitive sorption experiments Silicon and P sorption experiments on FS and PNS were conducted in binary systems at pH 5.0, using a soil:solution ratio of 1:20, and 0.1 M NaCl as the background electrolyte. We made two sets of separate experiments. In the first set, the initial Si (as Na2SiO4, Merck reagent) concentrations were: 0, 0.05, 0.15, 0.25, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 and 5.0 mM, and each of these concentrations was tested with 0, 0.5, 1.0 or 2.0 mM of P (as Na2HPO4, Merck reagent). In the second set, we used various initial P concentrations (0, 0.05, 0.15, 0.25, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 and 5.0 mM), each combined with 0, 0.5, 1.0 or 2.0 mM of Si. Soil suspensions were equilibrated in a thermoregulated shaker at 25 ± 1 °C, and the samples were collected at the equilibrium time previously defined by our Si kinetic studies. In all the experiments, the pH of the solution was adjusted with HCl or NaOH. After equilibration, the suspensions were centrifuged (10,000 g for 10 min), and the supernatants were sampled for Si and P analyses. The amount of anion sorbed was calculated as

P. Cartes et al. / Chemosphere 131 (2015) 164–170

the difference between the initial and final concentrations of the anion in the equilibrium solution. Silicon concentration was determined by FAAS, and P by the method of Murphy and Riley (1962). 2.4.3. Mathematical model Sorption data from the competition experiments were fitted to the Freundlich equation (2):

x ¼ K f C 1=n

ð2Þ

where x is the amount of sorbed Si or P, C is the Si or P equilibrium concentration in solution and Kf and 1/n are coefficients, where Kf indicates the amount of solute sorbed when the solution equilibrium concentration is 1. It is sometimes stated that the Freundlich equation coefficients have empirical significance, but Van Bladel and Moreale (1977) suggested that Kf and 1/n are correlated with the capacity and intensity of sorption, respectively. Freundlich coefficients (Kf and 1/n) were determined by non-linear regression based on equilibrium batch results using Origin 7.5 (OriginLab Corporation, Northampton, MA 01060). 2.4.4. Mechanistic model The Capacitance Constant Model (CCM) was used in this study. The assumptions and limitations of the model are discussed in detail by Goldberg (1992). The surface complexation constants were derived from experimental data using the computer program FITEQL 3.2 (Herbelin and Westall, 1996). Parameter values required for the model are (i) specific surface area (SA) of soils (SAFS = 160 m2 g1 and SAPNS = 148 m2 g1), determined by a gravimetric method based on the retention of ethylene glycol monoethyl ether (EGME) (Heilman et al., 1965) and (ii) capacitance, fixed at C = 1.5 F m2. The surface ionization constants were estimated simultaneously from potentiometric titrations, and electrolyte complexation constants were optimized using FITEQL 3.2. The surface site density (NS) was also optimized using FITEQL 3.2. The site concentration, Nt (M), was calculated as:

Nt ¼ SA NS C S 1:66  106

ð3Þ

where CS is the solid concentration (g L1) and NS represents the number of sites per nm2

Na; consequently, Al concentration was low in the exchange complex, rendering Al saturation lower than 2%. PNS had pH 5.14 and was classified as strongly acidic; this soil presented a higher content of organic matter (20%), lower concentrations of exchangeable cations and greater Al saturation (11%) than FS. Both soils exhibited similar levels of Si, but Olsen-P was greater in FS (28 mg kg1) than in PNS (6 mg kg1). The surface charge behaviour in both Andisols was characterized potentiometrically by titrations (data not shown). These curves followed the typical pattern for variable charge surfaces: as soil pH increases, the net surface charge becomes more negative as the dissociation of X-OH sites is enhanced. According to Sposito (1981), the common cross-over point of titration curves at different ionic strengths represents the point of zero salt effect (PZSE). The PZSE reached values of 4.59 and 4.70 for FS and PNS, respectively, indicating the predominance of negative charge at the pH values used in our sorption experiments.

3.2. Silicon sorption kinetics In our study, Si sorption on PNS was quite rapid, with about 56% of the total Si sorbed in the first 30 min, compared with 38% for FS (Fig. 1a and b). Sorption continued slowly in both soils, reaching an apparent equilibrium after 24 h; more than 95% of the maximum sorption had occurred after 72 h. These results are consistent with

25

(a)

20

Si sorbed (mmol kg -1 )

166

15 α = 424 β = 0.46

10

Log Keq = 2.9 2

r = 0.85

5

3. Results and discussion 0 0

3.1. Physico-chemical characterization of soils

20

40

60

80

Time (h) FS and PNS differed greatly in their chemical properties, as shown in Table 1. FS presented an organic matter content of about 15%, moderate acidity (pH 5.76) and high levels of Ca, Mg, K and

25

(b)

Parameter pHH20 Organic Matter (%) P (mg kg1) Ca (cmol + kg1) Mg (cmol + kg1) K (cmol + kg1) Na (cmol + kg1) Al (cmol + kg1) Sum of cations (cmol + kg1) ECEC (cmol + kg1) Al Sat. (%) Si KCl (mg kg1) ECEC: Effective cation exchange capacity.

FS

PNS

5.76 ± 0.19 14.77 ± 0.46 28 ± 1 7.38 ± 0.03 1.24 ± 0.01 0.97 ± 0.07 0.17 ± 0.02 0.19 ± 0.02 9.76 ± 0.10 9.95 ± 0.09 1.91 ± 0.18 18 ± 2

5.14 ± 0.01 20.05 ± 0.15 6±1 2.08 ± 0.04 0.30 ± 0.01 0.21 ± 0.02 0.05 ± 0.02 0.33 ± 0.02 2.64 ± 0.03 2.97 ± 0.04 11.22 ± 0.41 14 ± 2

Si sorbed (mmol kg -1 )

20 Table 1 Chemical properties of the two Andisols from Southern Chile: Freire soil (FS) and Piedras Negras soil (PNS). Values represent the mean of three replicates ± standard deviation.

15 α = 6,842 β = 0.54

Log Keq = 4.1

10

2

r = 0.92 5

0 0

20

40

60

80

Time (h) Fig. 1. Silicon sorption kinetics on FS (a) and PNS (b) modelled by the Elovich equation at an initial concentration of 2 mM, pH 5.0, and using 0.1 M NaCl as the background electrolyte.

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those reported by Delstanche et al. (2009) and Hansen et al. (1994) for Si sorption on ferrihydrite. A fast sorption of Si was observed in both studies in the first few hours, followed by slower sorption requiring more than two days to reach equilibrium. The authors explained their results by an initial rapid interaction of monosilicic acid with external oxide surface sites, followed by slower diffusive penetration. The Elovich equation has been commonly used to describe the sorption kinetics of different elements/compounds in soils (e.g. Dimirkou, 1994; Arora and Chahal, 2002; Rezaei et al., 2014). The Si sorption kinetics on FS and PNS were well described by the Elovich equation, with R2 values of 0.85 and 0.92, respectively. According to the Elovich parameters, the initial rate of Si sorption on PNS (a = 6,842) was about 16 times greater than the rate on FS (a = 424), indicating the possible occurrence of other surface phenomena in addition to adsorption. Differences in this parameter have been attributed to differential diffusion into pores, sorbate site preferences or surface precipitation of ions (Pigna et al., 2006). On the other hand, the Si desorption rate for PNS (b = 0.54) was only 1.2 times higher than that observed for FS (b = 0.46), suggesting that Si sorption occurred on similar sites or by similar mechanisms in both soils. 3.3. Mathematical modelling of sorption in Si–P binary systems All Si sorption isotherms were adequately described by the Freundlich equation, with R2 P 0.82 and P0.98 for FS and PNS, respectively (Table 2), indicating that the sorption curves were Ltype. These isotherms are well characterized by an initial slope that is independent of sorbate concentration in solution; this fact demonstrates the high affinity of sorbate for the sorption sites (Giles et al., 1974; Sposito, 1984). Thus, without P addition, Si sorption increased with increasing equilibrium concentration in both soils, and we did not observe a plateau in the sorption process. This fact could be attributed to a multilayer Si formation at the soil surface, as reported by Iler (1979), since silicic acid polymerization probably occurs under acidic conditions (Dietzel, 2000; Falcone, 2006) and at Si concentrations exceeding 2 mM (Ma et al., 2001). The 1/n Freundlich parameter indicated a higher affinity of Si for the sorption sites in FS (0.46) than in PNS (0.67). Nevertheless, in both soils, 1/n increased with the addition of P, indicating that the affinity for Si diminishes as P sorption progresses (Table 2). Comparatively, Si sorption was more affected by P in FS than in PNS, as demonstrated by the Kf parameter. For FS, the extent of Si sorption was lowered by 66% when 2 mM of P were added, compared with 42% for PNS (Table 3). The decrease in the amount of sorbed Si could stem from direct competition with P for binding sites. It could be also a consequence of the increased difficulty in approaching the mineral surfaces for Si due to both the changes in electrical charges and the steric effects caused by increasing surface coverage by P. Even though Si exhibited a greater bonding intensity in FS, the Si sorption capacity of PNS appeared higher.

Table 2 Freundlich parameters (± standard error) obtained from Si sorption isotherms in the presence of P at pH 5.0 for FS and PNS. P (mM)

Kf

1/n

R2

FS

0 0.5 1.0 2.0

17.06 ± 0.64 12.54 ± 1.01 10.07 ± 1.23 5.77 ± 0.86

0.46 ± 0.02 0.48 ± 0.07 0.49 ± 0.08 0.57 ± 0.09

0.95 0.88 0.82 0.83

PNS

0 0.5 1.0 2.0

22.05 ± 0.60 17.67 ± 0.90 14.53 ± 0.47 12.83 ± 0.47

0.67 ± 0.03 0.72 ± 0.05 0.73 ± 0.03 0.77 ± 0.03

0.97 0.97 0.98 0.98

Table 3 Efficiency (%) of P (or Si) to depress Si (or P) sorption on FS and PNS at pH 5.0. P (mM)

P efficiency (%)

Si (mM)

Si efficiency (%)

FS

0.5 1.0 2.0

26.9 40.9 66.1

0.5 1.0 2.0

4.7 5.6 5.6

PNS

0.5 1.0 2.0

19.9 34.4 42.1

0.5 1.0 2.0

0.7 0.7 1.4

This result could be partially explained by the higher proportion of Al in the exchange complex of PNS, which could lead to increased formation of hydroxyaluminosilicate precipitates by condensation of silicic acid at hydroxyl bridges and/or Al-OH on aluminium hydroxides (Exley et al., 2002). In addition, under acidic soil conditions, Al apparently stabilizes polymeric silicic acid against depolymerisation (Dietzel, 2002). The phosphorus sorption isotherm was scarcely affected by Si at pH 5.0 (Table 4). In the binary mixtures, 1/n did not vary, denoting that the addition of Si had no effect on the P bonding intensity. Furthermore, P sorption capacity was greater in PNS than in FS, and we detected a slight decrease in Kf with increasing Si additions. This decrease in P sorption was about 6% in FS, but almost negligible in PNS (Table 3). Differences between the soils’ intrinsic P sorption capacity could be explained by the higher organic matter content of PNS. Organic matter seems to indirectly increase the P sorption in soils (López-Hernández and Burnham, 1974; Vistoso et al., 2009), by either inhibiting Al-oxides’ crystallization (Borggaard et al., 1990) or generating partial dissolution of allophane that renders new aluminol groups to sorb P (Hanudin et al., 2014). Likewise, variations in the effect of Si on the amount of sorbed P could be related to the different organic matter content of FS and PNS, since Al-organic matter complexes exhibit limited reactivity by Si (Saito and Shoji, 1984), thus reducing the ability of Si as a competitor. In other results, Kf values indicated that, in both soils, P was sorbed to a larger extent than Si; P sorption intensity was also much higher than for Si, as revealed by 1/n values. Considering the chemistry in solution of Si and P, these results were as expected because maximum sorption of Si arises at pH values above 9.0, while maximum P sorption occurs in acidic rather than alkaline media (Hingston et al., 1967; Obihara and Russell, 1972). 3.4. Mechanistic modelling of sorption in Si–P binary systems The Freundlich model determines some empirical parameters that can be used for comparative purposes. However, it is not capable of generating information on the mechanisms involved in the sorption process. In our study, the CCM was used to describe the mechanisms involved in Si or P adsorption on FS and PNS. This

Table 4 Freundlich parameters (± standard error) obtained from P sorption isotherms in the presence of Si at pH 5.0 for FS and PNS. Si (mM)

Kf

1/n

R2

FS

0 0.5 1.0 2.0

106.63 ± 1.15 102.92 ± 1.23 101. 53 ± 1.12 101.40 ± 1.75

0.30 ± 0.01 0.30 ± 0.01 0.31 ± 0.01 0.30 ± 0.01

0.99 0.99 0.99 0.98

PNS

0 0.5 1.0 2.0

142.94 ± 3.90 142.06 ± 2.27 141.61 ± 3.62 140.61 ± 4.07

0.27 ± 0.01 0.25 ± 0.01 0.27 ± 0.01 0.28 ± 0.01

0.96 0.98 0.97 0.96

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P. Cartes et al. / Chemosphere 131 (2015) 164–170

50

6

Si Si + 0.5 mM P Si + 1.0 mM P Si + 2.0 mM P

(a)

0 mM Si 2 mM Si

4 2

cmol kg-1

Si sorbed (mmol kg -1 )

40

(a)

30

0 -2

20

-4 10 -6

3

4

5

0 0

1

2

3

4

7

8

6

-1

Si in solution (mmol L )

Si Si + 0.5 mM P Si + 1.0 mM P Si + 2.0 mM P

(b)

(b)

2

cmol kg -1

40

9

0 mM Si 2 mM Si

4

50

Si sorbed (mmol kg -1 )

6

pH

30

0 -2 -4

20

-6 3

10

4

5

6

7

8

9

pH Fig. 3. Potentiometric titration curves for FS (a) and PNS (b) at 0 or 2 mM Si and constant ionic strength (0.1 M NaCl).

0 0

1

2

3

4

Si in solution (mmol L-1 )

For modelling Si experimental data, monodentate ((4) and (5)) and bidentate ((6) and (7)) surface complexes were used as previously reported in Fourier Transform Infrared (FTIR) and Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy studies for Si sorption on Fe oxides (Hiemstra et al., 2007; Yang et al., 2008). The FITEQL 3.2 program was used for log Kint Si optimization. Thus, at pH 5.0, the net reaction in terms of Si species in soluint tion and the surface charge of FS (pKint a1 = 4.24; pKa2 = 7.88) and PNS int int (pKa1 = 4.32; pKa2 = 6.98) can be written as follows:

Fig. 2. Fit of the Constant Capacitance Model (CCM) to Si sorption data from binary systems (Si–P) at pH 5.0 and using 0.1 M NaCl as the background electrolyte: FS (a) and PNS (b).

model considered the formation of surface complexes between functional groups of minerals and organic matter (AAlOH, ASiOH, AFeOH ACOOH, AOH, ANH2) and molecules of silicic acid (H4SiO04; pKa1 = 9.8) and phosphate (H2PO 4 ; pKa1 = 2.1, pKa2 = 7.2).

Table 5 Surface complexation constants describing Si or P sorption in single and binary systems on FS and PNS using CCM. Surfacecomplex

FS

PNS

Nt (sites nm Single systems Si complex @XOSi(OH)3 @(XO)2Si(OH)2 P complex @XOPO(OH)2 @XOPO2OHBinary systems Si complex @XOSi(OH)3

P complex @XOPO(OH)2/@XOPO2OH

P (mM) 0 0 Si (mM) 0 0 P (mM) 0 0.5 1.0 2.0 Si (mM) 0 0.5 1.0 2.0

Values of SOS/DF indicate the goodness of fit of the model.

2

)

log K

int

SOS/DF

Nt (sites nm2)

log Kint

SOS/DF

0.38

1.39 3.34

40.7

0.41

1.44 3.35

44.7

0.38

4.47 1.46

1.2

0.41

7.24 2.11

1.5

1.46 1.24 1.17 1.05

44.7 49.3 50.5 42.4

1.50 1.50 1.47 1.43

44.6 48.8 49.9 49.7

4.47/1.46 4.47/1.45 4.46/1.46 4.45/1.46

1.2 40.2 40.5 41.8

7.24/2.11 7.24/2.10 7.24/2.11 7.24/2.12

1.5 38.5 39.7 39.8

0.38

0.41

0.38

0.41

P. Cartes et al. / Chemosphere 131 (2015) 164–170

The log Kint Si values obtained were similar to those reported by Davis et al. (2002), but lower than those modelled by Hansen et al. (1994) and Jordan et al. (2007) for Si complexation on Feoxides. These differences can be attributed to the modification of surface site reactivity in FS and PNS due to the presence of heterogeneous surface groups belonging to different minerals or organic matter. We also found that log Kint Si values scarcely differed between FS and PNS; this fact denotes that, in both soils, Si was bound on similar surface sites, forming either monodentate or bidentate complexes. Likewise, data from potentiometric titrations in the presence of Si (Fig. 3a and b) indicated log Kint Si values of about 1.52 (FS) and 1.55 (PNS), which support the values shown in Table 5. These data showed that Si appears to be predominantly sorbed on negatively charged sites by ligand exchange. In fact, while Si sorption proceeded, OH- ions from the surface were released, resulting in an increase of solution pH as evidenced by the stoichiometry of the reactions shown in equations [5] and [7]. 2 Phosphorus (H2PO 4 and HPO4 ) sorption on FS and PNS was well described using the CCM as shown by the low SOS/DF values (Fig. 4a and b; Table 5). For each P species, two types of monodentate complexes were considered (Eq. (8)–(11)) according to Goldberg and Sposito (1984a):

100

(a) P sorbed (mmol kg -1 )

80

60

40

P P + 0.5 mM Si P + 1.0 mM Si P + 2.0 mM Si

20

0 0.0

0.2

0.4

0.6

0.8

1.0

-1

P in solution (mmol L ) 100

(b) P sorbed (mmol kg -1 )

80

60

40

P P + 0.5 mM Si P + 1.0 mM Si P + 2.0 mM Si

20

0.2

0.4

0.6

0.8

@XOH þ H2 PO4 ! @XOPOðOHÞ2 þ OH

ð8Þ

@XO þ H2 PO4 þ Hþ ! @XOPOðOHÞ2 þ OH

ð9Þ

  @XOH þ HPO2 4 ! @XOPO2 OH þ OH

ð10Þ

  þ @XO þ HPO2 4 þ H ! @XOPO2 OH þ OH

ð11Þ

log Kint P

0 0.0

169

1.0

-1

P in solution (mmol L ) Fig. 4. Fit of the CCM to P sorption data from binary systems (Si–P) at pH 5.0 and using 0.1 M NaCl as the background electrolyte: FS (a) and PNS (b).

Monodentate complexes

@XOH þ H4 SiO4 ! @XOSiðOHÞ3 þ H2 O

ð4Þ

@XO þ H4 SiO4 ! @XOSiðOHÞ3 þ OH

ð5Þ

Bidentate complexes

2@XOH þ H4 SiO4 ! @ðXOÞ2 SiðOHÞ2 þ 2H2 O

ð6Þ

2@XO þ H4 SiO4 ! @ðXOÞ2 SiðOHÞ2 þ 2OH

ð7Þ

where XOH represents surface hydroxyl groups on oxides, clay minerals and soil organic matter. The CCM was able to describe the Si sorption data (Fig. 2a and b) considering both monodentate and bidentate complexes, although the proportion of bidentate complexes was less than 3% at the range of initial Si concentrations used here. Table 5 shows the optimized parameters for the surface complexation of Si (log Kint Si ) and the respective SOS/DF values. SOS/DF values provide information about the goodness of fit for the model; according to Herbelin and Westall (1996), values up to 20 generally indicate a reasonable fit to the data. The lack of a better fit to our Si experimental data can be attributed to the fact that the CCM describes specific adsorption (i.e. ligand exchange), but not phenomena such as precipitation and/or polymerization reactions, including the formation of either aluminosilicates or Si polymers at the soil surface, as discussed above (e.g. in Section 3.2 and 3.3).

The values denote the dominance of @XOPO(OH)2 complexes over @XOPO2OH at this soil pH (Table 5). The values of this parameter also show that the interaction of P with the surface sites was higher in PNS than in FS, which is consistent with the greater P sorption that occurred on PNS, as shown in Table 4. The log Kint P values are in accordance with those reported for mineral oxides (Goldberg and Sposito, 1984a) and soils (Goldberg and Sposito, 1984b). In binary Si/P systems, we only included monodentate complexes for Si sorption since the bidentate complexes did not represent more than 3%, as stated above. The log Kint Si values for Si sorption on PNS in the presence of P showed a slight reduction with increasing P concentration (Table 5); conversely, in FS, log Kint Si diminished by 28% at the highest initial P concentration (2.0 mM). Thus, from our results, it can be inferred that P and Si are sorbed on common sites in both soils; however, PNS displays additional available sites for P sorption as a consequence of its greater organic matter content, which reduces the need to compete with Si for the same sorption sites. On the other hand, the log Kint P values did not vary with increasing Si concentration in solution. The lack of effect of Si on P sorption could be explained by (i) the greater affinity of P for the organo-mineral surfaces as described by 1/n Freundlich parameters (Table 2 v/s Table 4) and (ii) the higher rate of sorption of P compared to Si on Andisols (Hartono, 2008), which creates steric hindrance for Si, thus limiting competition. 4. Conclusions Freire (FS) and Piedras Negras (PNS) soils differ greatly in their ability to sorb Si and P, and the amount of anion sorbed on these variable-charge soils was influenced by their chemical composition. Kinetic (Elovich), mathematical (Freundlich) and mechanistic (CCM) approaches denote that Si was sorbed on similar sites in both soils, but other phenomena (i.e. precipitation of

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hydroxyaluminosilicates at high Al levels in the exchange complex and/or polymerization of silicic acid into mineral surfaces at Si concentrations exceeding 2 mM) may enhance the amount of Si fixed on acidic Andisols. In binary systems, competition between Si and P was not symmetrical in that P was a better competitor than Si, while the effect of Si on P sorption was almost negligible. Even though Si and P sorption occurred on similar sites in both soils, the greater organic matter content of PNS provided extra sites for P sorption, thus considerably reducing the competition with Si in this soil. Likewise, the high P affinity of organo-mineral surfaces, as well as the large amount of P sorbed compared with Si, support the possibility that the reduction in the capacity of Si sorption could be the product of not only direct competition between anions, but also steric hindrance for Si approaching mineral surfaces as P sorption proceeds. These findings could be of great interest for agricultural systems developed on acidic Andisols, since interactions between Si and P due to the application of phosphate fertilizers are expected, and could result in improved availability of Si in soil solution for plant production. Acknowledgements This work was supported by the FONDECYT Project 1120901. References Arora, S., Chahal, D.S., 2002. Modelling boron adsorption kinetics in benchmark soils of Punjab, India. 17th WCSS, 14–21 August, Thailand. Barrow, N.J., 1989. Testing a mechanistic model. IX. Competition between anions for sorption by soil. J. Soil Sci. 40, 415–425. Beckwith, R.S., Reeve, R., 1963. Studies on soluble silica in soils. I. The sorption of silicic acid by soils and minerals. Aust. J. Soil Res. 1, 157–168. Borggaard, O.K., Jørgensen, S.S., Møberg, J.P., Raben-Lange, B., 1990. Influence of organic matter on phosphate adsorption by aluminium and iron oxides in sandy soils. J. Soil Sci. 41, 443–449. Cartes, P., Jara, A., Demanet, R., Mora, M.L., 2009. Urease activity and nitrogen mineralization kinetics as affected by temperature and urea input rate in Southern Chilean Andisols. Rev. Cienc. Suelo Nutr. 9, 69–82. Cea, M., Seaman, J.C., Jara, A.A., Mora, M.L., Diez, M.C., 2005. Describing chlorophenol sorption on variable-charge soil using the triple-layer model. J. Colloid Interf. Sci. 292, 171–178. Davis, C.C., Chen, H.-W., Edwards, M., 2002. Modeling silica sorption to iron hydroxide. Environ. Sci. Technol. 36, 582–587. Delstanche, S., Opfergelt, S., Cardinal, D., Elsass, F., André, L., Delvaux, B., 2009. Silicon isotopic fractionation during adsorption of aqueous monosilicic acid onto iron oxide. Geochim. Cosmochim. Ac. 73, 923–934. Dietzel, M., 2000. Dissolution of silicates and the stability of polysilicic acid. Geochim. Cosmochim. Ac. 64, 3275–3281. Dietzel, M., 2002. Interaction of polysilicic and monosilicic acid with mineral surfaces. In: Stober, I., Bucher, K. (Eds.), Water-Rock Interaction. Kluwer Academic Publishers, The Netherlands, pp. 207–235. Dimirkou, A., Ioannou, A., Mitsios, J., Doula, M., Deligianni, Ch., 1994. Kinetics of potassium adsorption by Entisols of Greece. Commun. Soil Sci. Plan. 25, 1417– 1430. Epstein, E., 1999. Silicon. Annu. Rev. Plant Phys. 50, 641–664. Exley, C., Schneider, C., Doucet, F.J., 2002. The reaction of aluminium with silicic acid in acidic solution: an important mechanism in controlling the biological availability of aluminium? Coordin. Chem. Rev. 228, 127–135. Falcone, J.S., 2006. Silica in biology. In: Bergna, H.E., Roberts, W.O. (Eds.), Colloidal Silica Fundamentals and Applications. CRC Taylor and Francis Group, LLC, pp. 765–778. 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, 71–79. Giles, C.H., Smith, D., Huitson, A., 1974. A general treatment and classification of the solute adsorption isotherm. I. Theor. J. Colloid Interf. Sci. 47, 755–765. Goldberg, S., Sposito, G., 1984a. A chemical model of phosphate adsorption by soils: I. Reference oxide minerals. Soil Sci. Soc. Am. J. 48, 772–778. Goldberg, S., Sposito, G., 1984b. A chemical model of phosphate adsorption by soils: II. Noncalcareous soils. Soil Sci. Soc. Am. J. 48, 779–783. Goldberg, S., 1992. Use of surface complexation models in soil chemical systems. Adv. Agron. 47, 233–329.

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