Modelling the pH dependency of dissolved calcium and aluminium in O, A and B horizons of acid forest soils

Geoderma 206 (2013) 85–91

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Modelling the pH dependency of dissolved calcium and aluminium in O, A and B horizons of acid forest soils P. Gruba a,⁎, J. Mulder b, S. Brożek a a b

Department of Forest Soil Science, Agricultural University of Krakow, Al. 29-go Listopada 46, PL-31-425 Krakow, Poland Department of Plant and Environmental Sciences, Norwegian University of Life Science, P.O. Box 5003, N-1432 Aas, Norway

a r t i c l e

i n f o

Article history: Received 13 April 2012 Received in revised form 16 March 2013 Accepted 21 April 2013 Available online 25 May 2013 Keywords: Calcium Aluminium CECe Forest soils Cation exchange Model

a b s t r a c t The molar ratio of the calcium (Ca) and aluminium (Al) activities (Ca/Al ratio) in soil water is an important indicator for forest health conditions, with values less than 1 indicating negative conditions for forest growth. Here we used and calibrated a simple model, with only modest parameter requirement, to describe the Ca activity in soil water of acid forest soils according to the Gaines–Thomas exchange equation. Next, the model was combined with the Al(OH)3 equilibrium model, applicable at pH > pHthreshold, and the recently developed Al solubility model (based on organic complexation theory, applicable at pH b pHthreshold) proposed by Gruba and Mulder (2008). The pHthreshold is the pH of the soil above which the Al(OH)3 equilibrium model and below which the organic complexation model control the Al solubility. Calibration of the Gaines–Thomas selectivity coefficient for calcium–aluminium exchange resulted in LogKCa-Al(gt) close to zero. A combined model, including the Gaines–Thomas equation and an aluminium solubility model, was tested subsequently for internal consistency, using a set of 32 samples each from A and B horizons of forest soils (Cambisols) located in Southern Poland (pHH2O range 3.3–5.4). The observed relationship between pHH2O and CaX for a large number of O, A and B horizons from Polish lowland soils, as reported in a second data set, was in accordance with the general curvi-linear pattern predicted by the models. The observations in data set II also indicate that at any given CaX, the value of pHH2O increased in the order O b A b B horizon. Probably, this order is explained by the increase in pHKCl and the decrease in Ca activity. Model predictions suggested that molar Ca/Al b 1 occurred at levels of exchangeable Ca smaller than 4%. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Calcium (Ca) depletion in forest soils is a natural pedogenic process that is accelerated by harvest and leaching, due to acidic deposition (Huntington, 2003). Calcium is an important element in forest tree tissue affecting disease resistance, wound repair, frost hardiness, and lignin synthesis (McLaughlin and Wimmer, 1999. Schaberg et al. (2006) emphasised the importance of Ca and aluminium (Al) for the health of sugar maple and highlighted the vulnerability of sugar maple stands to declines in growth and vigor following continued anthropogenic Ca depletion. On the other hand, Ca addition led to improvement of the resistance of red spruce to abiotic injuries (Hawley et al., 2006). Lawrence et al. (1997)) claim that the recent decline of Ca in forest soils is exacerbated by acidification-induced increase in Al on the soil exchanger, thus limiting the retention of Ca released from litter decomposition and mineral weathering. Elevated ratios of Al to Ca concentration in soil water may cause inhibition of Ca uptake by roots, and the ratio of Ca or base cations (BC = ⁎ Corresponding author. E-mail address: [email protected] (P. Gruba). 0016-7061/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.geoderma.2013.04.036

Ca + Mg + K) to Al concentration in soil water is often used as an indicator of Al stress (de Wit et al., 2010; Kinraide, 2003; Sverdrup and Warfvinge, 1993). Although some studies question the use of Ca/Al ratios for a rigorous assessment of the critical load (Hansen et al., 2007), evidence exists that Al and Ca influence both tree growth (Cronan and Grigal, 1995; van Schöll et al., 2004) and forest floor plant condition and diversity (Närhi et al., 2011). Because of the importance of the Ca to Al ratio in soil water for forest health, and forest understory composition, there is a great need for models that describe and predict Ca and Al solubility in forest soils for a wide range of soil conditions, based on commonly available soil parameters. In the last decades a number of mechanistic models were developed (e.g. WHAM (Tipping, 1996)), which allow accurate prediction of the activity of Ca and Al in soil water. However, these models require a large number of parameters, which are not always available. This is often a problem, particularly if many sites have to be assessed simultaneously. Therefore, more simple approaches, using less data-intensive models, remain important. Several models exist based on either an empirical equation for the Al solubility (e.g. Reuss et al., 1990) or equilibrium with Al(OH)3 (Cosby et al., 1985; Sverdrup et al., 1995). In addition, Ca solubility in acid forest

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P. Gruba et al. / Geoderma 206 (2013) 85–91

soils is generally modelled by cation exchange (Reuss, 1983; Reuss et al., 1990). Here, we combine the recently developed Al solubility model proposed by Gruba and Mulder (2008), which applies at relatively low pH, together with the common Al(OH)3 equilibrium model (operating at somewhat higher pH values), and the Ca solubility model according to the Gaines–Thomas cation exchange equation. This combination of models, applied to the pH range from about 3 to 5.4, was calibrated using batch equilibrium data in a background of 0.001 M NaCl of A and B horizons from acidic forest soils in southern Poland. We tested the internal consistency of the models to describe the Ca activity. In addition, we tested the parameterized model with respect to its ability to describe the observed relationship between exchangeable Ca and pHH2O, using data from a large number of O, A and B horizons from forest soils in much of the Polish lowlands. For these forest soils we also used the models to predict the molar Ca/Al concentration ratios in soil water. 2. Theoretical development The basic equation for Ca–Al exchange may be written as (McBride, 1994): 3þ

2Al

þ 3CaX ¼ 3Ca

2þ

þ 2AlX

ð1Þ

where X represents the cation exchange complex. Earlier, Reuss et al. (1990) showed that the Gaines Thomas equation represents a good approximation of the Ca–Al exchange in forest soils from North America. According to the Gaines–Thomas exchange equation


K Ca−A1 ðgtÞ

3 Ca2þ ½A1X 2 ¼  2 A13þ ½CaX 3

ð2Þ

where (Ca 2+) and (Al3+) are molar activities in solution of Ca and Al ions, respectively and square brackets indicate charge (+) fractions of exchangeable Al and Ca. In a logarithmic form this becomes: "

2þ

3þ

LogKCa−AlðgtÞ ¼ 3LogCa −2LogAl

# CaX3 −Log : AlX2

ð3Þ

Values for LogKCa–Al(gt) reported in the literature vary considerably. For British soils, Bache (1974) obtained values between 2.23 and 3.59 for subsoils, 1.33 and 2.12 for loamy topsoils, − 0.90 and 1.22 for sandy soils and 1.25 for sphagnum peat. Reuss et al. (1990) estimated overall LogKCa–Al(gt) = 2.9. For some Finish Podzols Nissinen et al. (1999) obtained LogKAl–Ca(gt) of − 2.08, − 0.78 and 0.32 for O, A and B horizons, respectively. For German forest soils, Prenzel and Schulte-Bisping (1995) reported LogKAl–Ca(gt) ranging from − 0.3 to 0.3, irrespective of horizon. Note that computation of LogKCa–Al(gt) requires that the free Al activity in solution is known. The activity of Al 3+ in Eqs (2) and (3) is controlled by two different mechanisms, which operate in different pH ranges. In their study, Reuss et al. (1990) assumed a common empirical expression for the solubility of Al in the entire pH range of all acid soil horizons.

n 3þ þ = H Al ¼ KSO

ð4Þ

where it was assumed that n = 1.6 and LogKSO = 2.3 However, in a recent study Gruba and Mulder (2008) found that in soils with pHH2O greater than about 4.2, the Al activity is best predicted assuming gibbsite equilibrium: LogAl þ n′pH ¼ LogKSO

ð5Þ

where n′, the reaction stoichiometry equals 3.0 and LogKSO at laboratory temperature is 8.3 (Berggren and Mulder, 1995). In combination of Eq. (5) with the Gaines–Thomas equation for Ca–Al exchange (Eq. (3)) we derive the following expression for the logarithm of the Ca 2+ activity in this pH range: 2þ

LogCa

" # 1 2 1 CaX3 ¼ LogKCaAlðgtÞ −2pH þ LogKSO þ Log : 3 3 3 AlX2

ð6Þ

For more acid soils (pHH2O less than about 4.2), Gruba and Mulder (2008) derived an empirical Al-organic matter complexation model to predict the activity of Al in solution: 3þ

LogAl

¼ −npH þ 0:82pHKCl −2:18

ð7Þ

where n = 1.27. To predict the activity of Al 3+, this model only requires knowledge of pHKCl (pHKCl expresses the saturation of organic matter with Al; for details see Gruba and Mulder, 2008) of the soil and the pH of soil water. Combining this equation with the Gaines– Thomas expression (Eq. (3)) results in: 2þ

LogCa

¼

" # 3 1 2n 2 1 CaX : ð8Þ LogKCaAlðgtÞ − pH þ ð0:82pHKCl −2:18Þ þ Log 3 3 3 3 AlX2

Thus, Eqs. (6) and (8) allow the estimation of the Ca activity in equilibrium solutions in all acid soil horizons. The threshold pH above which Eq. (6) is applicable and below which Eq. (8) may be used, can easily be found, as the right-hand sides of Eqs (6) and (8) are equal in this point. Thus  2 2n 2
−2pHthreshold þ LogKSO ¼ − pHthreshold þ 0:82pHKCl −2:18 ð9Þ 3 3 3 Rearranging gives: pHthreshold ¼ 6:06−0:47pHKCl :

ð10Þ

In Eq (10) only pHKCl is a variable, differing greatly both within and between A and B horizons (Table 1). Therefore, also the values for pHthreshold vary significantly. 3. Materials and methods The models as given in Eqs. (6) and (8), to be applied to two different pH ranges (greater and smaller than pHthreshold, respectively), were parameterized, using equilibrium composition data of suspensions (see below: Batch equilibrium experiment). Model parameterization required calibration with respect to KCa–Al(gt) only. Suspensions were from A and B horizons from acid forest soils (Dystric Cambisols) in southern Poland. Soils were derived from three different parent materials and with two types of forest vegetation (i.e. Norway spruce and mixed forest). General soil characteristics were reported previously by Gruba and Mulder (2008). In addition, we included data of suspensions of A and B horizons from Eutric and Dystric Cambisols under silver fir. The latter are from sites in Central Beskidy mountains and their foothills, respectively, in southern Poland. Experimental data were obtained from batch equilibrium experiments in the laboratory, conducted at room temperature. Data are indicated as set I, and include 32 A and 32 B horizons. Following calibration, the models were used to calculate the equilibrium activity of Ca in suspension to test for internal consistency of the predictions. The relationship between pHH2O and CaX as described by the calibrated models was investigated using a large data set of forest soils in the lowlands of Poland (set II). In addition, we used the models to predict the Ca/Al ratio in soil water for all horizons of this data set. In total 926 samples from 343 soil profiles were used in set II. Samples

P. Gruba et al. / Geoderma 206 (2013) 85–91 Table 1 Selected properties of the A and B horizons from the set I. Ct refers to the soil's total content of organic carbon. The subscript ex indicates exchangeable cations, CECe is the effective cation exchange capacity of the soil, EA represents exchangeable acidity and LogKCa–Al(gt) is the logarithm of the Gaines–Thomas selectivity constant (Eq. (2)) for Ca–Al exchange. pHthreshold is explained in the text (Eq. (11)). The concentrations of Ca2+ and Al3+ refer to activities measured in the 10−3 NaCl equilibrium solution. SD indicates standard deviation. Properties A horizons Clay Ct pHH2O pHKCl pH⁎ pHtreshold Caex Alex Hex BCex EA CECe Ca2+ Al3+ LogKCa–Al(gt) B horizons Clay Ct pHH2O pHKCl pH⁎ pHtreshold Caex Alex Hex BCex EA CECe Ca2+ Al3+ LogKCa–Al(gt)

Unit

N

Average

Median

Min.

Max.

SD

g kg−1

32 32 32 32 32 32 32 32 32 32 32 32 32 32 32

160 62.6 3.90 3.14 4.08 4.55 0.92 8.61 0.98 1.49 9.61 11.06 71.14 29.75 −0.15

150 58.1 3.87 3.18 3.98 4.53 0.45 8.86 0.62 0.88 9.78 10.70 48.50 25.06 −0.27

80 30.0 3.30 2.60 3.64 4.09 0.16 2.32 0.00 0.45 2.76 6.41 11.41 3.32 −1.68

350 190.0 4.95 4.10 4.75 4.81 5.47 15.78 5.75 6.52 16.87 17.93 498.45 83.32 2.04

60 33.9 0.33 0.34 0.30 0.16 1.23 2.90 1.13 1.44 2.84 2.86 90.74 18.63 0.74

32 32 32 32 32 32 32 32 32 32 32 32 32 32 32

150 20.5 4.42 3.73 4.66 4.27 0.51 7.15 0.32 0.89 7.47 8.31 48.42 33.17 0.09

140 20.7 4.40 3.80 4.66 4.24 0.25 6.36 0.22 0.47 6.57 7.76 29.02 32.88 −0.02

50 7.1 3.30 2.60 4.10 4.00 0.07 1.56 0.00 0.22 1.64 2.32 6.32 1.44 −1.71

350 41.4 5.40 4.30 5.50 4.81 4.90 15.80 1.38 6.43 15.95 16.54 217.08 83.32 1.96

70 9.3 0.39 0.35 0.33 0.17 0.97 3.68 0.29 1.17 3.81 3.72 52.01 24.12 1.00

cmol(+)kg−1

μmol L−1

g kg−1

cmol(+)kg−1

μmol L−1

⁎ pH of supernatant obtained after filtration of soil-background electrolyte suspension, see chapter 3.2.

include O – (forest floor and surface peats), as well as A (including A, AB and E horizons) – and B horizons, mostly from Cambisols, podzols and Luvisols (WRB, 2006). According to USDA classification the soils are largely Eutrochrepts, Dystrochrepts Haplohumods, Haplorhods, Ferrods and Hapludalfs.

87

calculated as the difference between exchangeable acidity and exchangeable H +. 3.2. Batch equilibrium experiments Batch equilibrium experiments were performed using the A and B horizons of set I and 0.001 M NaCl as a background electrolyte. For each of the 64 soil samples (32 A and 32 B horizons, respectively) we made suspension using 4 g sample and 40 ml of electrolyte. The suspensions were equilibrated in PVC centrifuge tubes at 8 °C for 7 days and shaken manually twice a day. After 7 days suspensions were centrifuged (1500 g) for 30 min and then filtered using 0.45 μm millipore membrane filters. Also centrifugation was conducted at 8 °C. In the supernatant we determined pH (combination electrode), and the concentration of Ca, which was determined by AAS. Quickly reacting aluminium (Alqr), including all monomeric inorganic Al, except fluoride complexes, was measured using Clarke et al.'s (1992) method using FIA. The activities of Ca 2 + and Al 3 + were calculated from their concentrations after ionic strength and temperature correction using MINTEQ (Allison et al., 1991). 4. Results and discussion The investigated soil samples of set “I” were relatively rich in clay and organic matter (Table 1). Previously, Gruba and Mulder (2008) showed that in these soils organic matter is the main cation exchanger, with only a minor contribution of the clay fraction. The A and B horizons were acidic, poor in exchangeable Ca and rich in exchangeable Al. The average fraction of Ca2+ of the sum of exchangeable cations (CaX) was similar in both horizons (8 and 6% in the A and B horizons, respectively). The fraction of exchangeable Al (AlX) was greatest in the B horizon (78 and 88% of CEC for the A and B, respectively). 4.1. The Gaines–Thomas equation for Ca–Al exchange The Ca concentration obtained after equilibration of soil samples with 10 −3 mol L −1 NaCl was greater in the A than in the B horizon. The reverse was true for the Al concentration. The pH of the equilibrium solution was smaller in the A horizon than in the B horizon (Table 1). For the A horizons pHNaCl was smaller than pHthreshold in

3.1. Laboratory analyses In all cases, soil samples were air dried and sieved to pass 2 mm. The pH of air-dried soils was measured electrochemically with a combination electrode in suspension with distilled water and 1 M KCl, respectively (1:2.5 mass to volume ratio) after 24 h equilibration. The content of total soil organic carbon (Ct) was measured by a LECO CNS 2000 carbon analyzer. As only carbonate-free samples were chosen for analysis, the Ct was assumed to be organic carbon (Ct = Corg). Exchangeable calcium (Caex) and other cations (magnesium (Mgex), potassium (Kex) and sodium (Naex)) required for calculation of the effective cation exchange capacity (CECe) were extracted with 1 M NH4-acetate and determined by atomic absorption spectroscopy (AAS) (Jackson, 1958). KCl-extractable Al (assumed to represent exchangeable Al) was determined by extracting 40 g of soil with 100 ml 1 M KCl for 1 h, followed by filtration (millipore 0.45 μm), using potentiometric titration of the supernatant to pH 8.2 (exchangeable acidity). Of the extracts, 25 mL sub-samples were titrated potentiometrically to pH 8.2 without and with the addition of 0.8 M NaF. Results were used to calculate exchangeable acidity and exchangeable H +, respectively (Ostrowska et al., 1991). AlKCl was

Fig. 1. Map of Poland. Crosses represent the sampled sites (set II). For explanation of black dots see the text, Section 4.2.

88

P. Gruba et al. / Geoderma 206 (2013) 85–91

"

2þ

LogCa

Fig. 2. Observed and modelled Log Ca2+ (data set I). Predictions were made using Eqs. (11) and (12) (see text).

26 out of 32 cases. For the B horizon only 7 cases of 32 cases had pH b pHthreshold. In conditions where pHthreshold b pH Eq. (9) applies. From the data in the equilibrium experiment we obtained averages of LogKCa–Al(gt) for the A and B horizons of − 0.15 and 0.09, respectively. Due to its small average values, LogKCa–Al(gt) contributed little to the right-hand sides of Eqs. (6) and (8), so that they were simplified to Eqs. (11) (pH > pHthreshold) and (12) (pH b pHthreshold), respectively. " 2þ

LogCa

¼ −2pH þ 5:5 þ 0:33Log

CaX3 AlX2

# ð12Þ

LogCa 2+ was computed for all 64 suspensions according to Eq. (11) or (12) (depending on pH), and compared with measured values (Fig. 2). Results shown in Fig. 2 indicate that the equations described the Ca 2+ activity quite well (R 2 = 0.68), despite the large range of observations, which spans two orders of magnitude. The uncertainty in LogCa 2+ is a consequence of the uncertainties in the input parameters of the models: pH, pHKCl, LogKCa–Al(gt), Log KSO and the fraction of exchangeable cations (CaX and AlX). Especially, relatively high standard deviation suggests that observed variability of LogKCa–Al(gt), assumed to be constant, may considerably contribute to the model's uncertainty. Predictions using the Reuss et al.'s (1990) model (Eq. 4) showed a considerably greater scatter when plotted against observations (R 2 = 0.34; results not shown). According to the presented model we may describe the pH of any soil sample in a range from pHthreshold to 5.4 (upper limit of the model parameterization), as: " # 1 1 1 1 CaX3 2þ pH ¼ − LogCa þ LogKCaAlðgtÞ þ LogKSO þ Log 2 6 3 6 AlX2

ð13Þ

and at pH b pHthreshold as

# ð11Þ

3

CaX ¼ −0:85pH þ 0:55pHKCl −1:45 þ 0:33Log AlX2

pH ¼ −

" # 3  3 1 1
1 CaX 2þ LogCa þ LogKCaAlðgtÞ þ 0:82pHKCl −2:18 þ Log : 2n 2n n 2n AlX2

ð14Þ

Table 2 Selected chemical properties of O, A and B horizons from lowland forest soils in Poland. Abbreviations are as indicated in Table 1. Properties O horizons Clay C pHH2O pHKCl pHtreshold Caex Alex BCex EA CECe A horizons Clay C pHH2O pHKCl pHtreshold Caex Alex BCex EA CECe

Fig. 3. Relationships between CaX and pHH2O in the A (a) and B (b) horizons of set I. Lines are computed for each sample using Eqs. (13) and (14) for pH range over and below pH threshold, respectively.

B horizons Clay C pHH2O pHKCl pHtreshold Caex Alex BCex EA CECe

Units

N

Average

Median

Min.

Max.

SD

g kg−1

0 188 188 188 188 188 188 188 188 188

– 316.9 3.85 2.99 4.65 8.22 8.19 12.62 11.06 23.68

– 331.9 3.74 2.90 4.70 6.08 7.00 8.54 9.75 20.05

– 73.7 2.88 2.20 3.98 0.15 0.35 0.26 0.70 2.46

– 498.3 5.19 4.42 5.03 82.21 35.40 89.97 43.50 91.09

– 112.0 0.44 0.46 0.22 13.02 5.77 13.72 7.15 12.61

188 410 410 410 410 410 410 410 410 410

60 27.4 4.31 3.54 4.40 1.36 3.23 1.82 3.45 5.27

60 19.0 4.30 3.50 4.42 0.32 2.35 0.54 2.59 3.85

0 2.9 3.13 2.34 3.78 0.01 0.40 0.02 0.08 0.29

340 201.7 5.40 4.85 4.96 15.83 41.82 19.14 42.39 53.98

60 26.3 0.47 0.45 0.21 2.41 3.77 3.01 3.56 5.39

168 328 328 328 328 328 328 328 328 328

80 4.6 4.63 3.98 4.19 0.80 2.23 1.16 2.31 3.46

60 2.3 4.60 4.00 4.18 0.12 1.28 0.20 1.33 1.95

0 0.8 3.50 2.30 3.48 0.01 0.02 0.01 0.05 0.11

610 65.0 5.39 4.49 4.98 8.86 21.30 11.81 21.50 24.75

90 9.0 0.39 0.37 0.17 1.69 2.77 2.27 2.80 3.91

cmol(+) kg−1

g kg−1

cmol(+) kg−1

g kg−1

cmol(+) kg−1

P. Gruba et al. / Geoderma 206 (2013) 85–91

Fig. 4. Relationship between pHH2O (with upper limit set up as 5.4) and calcium saturation of CECe (CaX) according to Eqs. (13) and (14). Circles, triangles and squares, represent the O, A and B horizons, respectively. Filled symbols (for the A and B horizons only) are samples from set I used for the model parameterization. Curves indicate ranges (highest and lowest position) of curves shown in Fig. 3 for the A (green) and B (red) horizons, respectively.

Fig. 5. Measured and modelled values of Ca2+ to Al3+ ratio. Data for the set I.

89

Because LogKCa–Al(gt) values were close to zero (Table 1), the pH in equilibrium solution depended on CaX, AlX and Ca2+ activities. This was true for all soil horizons irrespective of equilibrium pH (Eqs. (13) and (14)). For the acidic horizons, having pH b pHthreshold, equilibrium pH in soil water depended in addition on pHKCl (Eq. (14)). The strong, negative correlation between AlX and CaX (not shown), and the characteristic range of Ca 2+ activity and pHKCl values for the different horizons (Table 1), resulted in a horizon-specific relationship between pH and CaX. Since the average equilibrium activity of Ca 2+ was greater in A than in B horizons, and the reverse was true for pHKCl, a given CaX value resulted in smaller pH in the A than in the B horizon. A family of curvi-linear relationships between CaX and pH for A and B horizons is illustrated in Fig. 3. Previously, also Reuss (1983), Bloom and Grigal (1985) and Reuss et al. (1990) reported a curvi-linear relationship between pH (measured in 0.002 M CaCl2) and Ca (or base cation) saturation (CaX), based on experimental data in the laboratory. In addition, a curvi-linear relationship between CaX and soil pH was observed for a large data set of German soil samples (Prenzel and Schulte-Bisping, 1995). However, in data set from Germany the authors did not observe the shift between the horizons. We investigated the relationship between observed pHH2O and CaX for a large number of forest soil O, A and B horizons throughout Poland (set II; Fig. 1) and compared this with the modelled relationships for A and B horizons, based on data from set I. To avoid extrapolation, the data were limited to pH range b5.4. Selected characteristics for the soils of set II are presented in Table 2. The Caex values of the soil horizons were highly variable. In general, the investigated soils were poor in exchangeable Ca, most of the samples from mineral horizons (68% of A and 83% of B) having Caex b 1 cmol(+) kg−1. Calcium-rich horizons (more than 10 cmol(+) kg−1) constituted about 2 and 0% of total number of samples from the A or B horizons, respectively. The Caex values in O horizons were significantly larger, only 5% of total number of samples had Caex b 1 cmol(+) kg − 1, while 34% of samples had Caex > 10 cmol(+) kg − 1. The soil horizons exhibited a large variation in pHKCl values and thus in pHthreshold. Therefore, the prediction of the equilibrium pH of the soil suspensions required the use of both Eqs. (13) and (14).

Fig. 6. Predicted values of Ca/Al ratio as a function of CaX. Data for the set II.

90

P. Gruba et al. / Geoderma 206 (2013) 85–91

Fig. 4 presenting observations of pH against CaX for A and B horizons from set II confirms the curvilinear relationship as predicted by the models (Fig. 3). Although not calibrated for the O horizon, also data for this horizon exhibit a curvi-linear relationship, albeit with a shift to lower pH values. Assuming a general validity of the models (Eqs. (13) and (14)), also for acid O horizons, the increase in pH at a given CaX in the order O horizon b A horizon b B horizon, may be due to the increase in pHKCl and the often observed decrease in Ca activity in acid soils (declining importance of Ca cycling at depth; data not shown). Note that this general pattern is clearly visible, despite the fact that the soil samples represent a broad range of parent materials, and vegetation covers. 4.2. Prediction of the Ca/Al ratio in soil water The model proposed here can be applied for estimation of Ca 2+/ Al 3+ molar ratio. Fig. 5 indicates that for data set I the model describes the Ca/Al molar ratio well (r 2 = 0.77) over a large range of observations. Next, we applied the models to predict the Ca/Al ratio for set II assuming that pHH2O equals the in situ pH of soil water. The predicted molar ratio of Ca to Al activity in soil water (Ca/Al ratio), important for the assessment of critical loads in forest soils (Fig. 6), suggests values well below 1 (Cronan and Grigal, 1995) for CaX b 0.04. Values for Ca/Al b 1 are generally considered harmful to forest vegetation. Estimated Ca/Al ratios in the mineral A or E horizons of set II were generally greater than 1, except for E horizons of fifty sites, indicated in Fig. 1 as black dots. Five of these 50 sites where the model predicted Ca/Al b 1 are podzols under spruce stands in mountain areas; the other 45 sites are podzols located in lowlands (Fig. 1). Values of Ca/ Al in E or A horizons were positively correlated with the total Ca content in parent material (Fig. 7). This suggests that the Ca/Al is strongly influenced by the availability of Ca in the parent material. However, other explanatory variables may occur, including acid rain, which may be responsible for Ca depletion. Ca/Al ratios b 1 only occur in parent materials with total Ca b 1000 mg/kg, viz. dominated by quartz. 5. Conclusions A model was developed and parameterized describing the activity of H +, Ca 2+ and Al 3+ activities in equilibrium solutions of forest soils. The model is based on the Gaines–Thomas equation for Ca–Al exchange in combination with either the Al(OH)3 solubility model (at pHthreshold > pHH2O) or the organic complexation model proposed by Gruba and Mulder (2008) (at pHH2O b pHthreshold). The model describes a family of curvilinear relationships between Ca saturation of the cation exchange sites (CaX) and pHH2O, with values of pHH2O

Fig. 7. Relationship between predicted values of Ca/Al ratio in the A horizons and the total content of calcium in the parent material (C horizons) of a variety of Polish soils (digested in HClO4; Brożek, unpublished data). Both axes have logarithmic scale, and dashed line indicates the critical Ca/Al ratio of 1.

at a given CaX increasing in the order O b A b B horizon. This order is probably due to increase of pHKCl and decrease in Ca 2+ activity. The model was used to predict the Ca to Al ratio in forest soil water; a parameter which is commonly used to assess the risk of forest damage due to soil acidification. Results suggest that only in horizons with total CaX b 0.04, do molar Ca/Al ratios decrease to values less than 1. In Polish forest soils, such conditions are met at Ca contents in the parent material (C horizon) less than 1000 mg/kg.

Acknowledgements This work was supported by the Polish-Norwegian Research Fund (PNRF-68-AI-1/07).

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