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Amorphous silica analysis in terrestrial runoff samples
Geoderma 167-168 (2011) 228–235
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Amorphous silica analysis in terrestrial runoff samples Wim Clymans a,⁎, Gerard Govers a, Bas Van Wesemael b, Patrick Meire c, Eric Struyf c a b c
Department of Earth and Environmental Sciences, Leuven Sustainable Earth Research Centre, K.U. Leuven, B-3001 Heverlee, Belgium Geography Department, Université catholique de Louvain, Place Louis Pasteur 3, B-1348 Louvain-la-Neuve, Belgium Department of Biology, Ecosystem Management Research Group, University Antwerp, B-2610 Wilrijk, Belgium
a r t i c l e
i n f o
Article history: Received 10 August 2010 Received in revised form 30 June 2011 Accepted 23 July 2011 Available online 2 November 2011 Keywords: Amorphous silica Solid to solution ratio Error compensation Biogeochemical cycle Biological control
a b s t r a c t The correct analysis of amorphous silica concentration (CASi) in natural waters is crucial if one wants to correctly quantify terrestrial and/or riverine ASi fluxes. Soil ASi measurements are conducted with a constant solid to solution ratio (σ). As the suspended particulate matter concentration (CSPM), and therefore σ, cannot be exactly known a priori in river samples. It is important to understand how variations in σ effect analysed CASi. The objectives of this paper are (i) to investigate whether and how variations in σ values affect measured CASi in river runoff samples and (ii) to investigate whether or not it is possible to define a range of σ within which CASi in runoff and/or soil samples can be accurately measured. For the laboratory experiment 30 runoff samples with a wide range of CSPM, typical for the Belgian Loam Belt, were prepared and analysed using the alkaline digestion method (0.1 M Na2CO3). Our study confirmed that the alkaline digestion method proposed by DeMaster can be used for runoff samples provided that σ is within certain limits: at very low σ (b 0.1 kg m−3), subsample heterogeneity results in high variability of measured CASi while at higher σ values (N 0.8 kg m−3) incomplete dissolution of ASi as well as the reduction of mineral dissolution rates results in underestimated CASi. As both errors compensate one another, the range of applicable σ-values can be extended above the theoretically correct limit (1.6 kg m −3). The finding that reliable measurements can be made within a relatively wide range of σ values (0.1 ≤ σ ≤ 1.6 kg m −3) is important. It is now possible to propose a method for the measurement of ASi in runoff samples. We make recommendations for ASi analysis distinguishing samples with a low and high CSPM. For samples with a low CSPM (≤ 1.6 kg m−3) the standard procedure is proposed while for samples with a high CSPM (N 1.6 kg m−3) an adapted procedure is proposed, analogue to that for soil samples. However, one should be aware that the range and limits for σ proposed here may depend on the type of sediment to be analysed: it is therefore recommended to evaluate the performance of the method again before it is used in other environments. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved.
1. Introduction Silicon (Si) is the second most abundant element in the Earth's crust after oxygen (Iler, 1979). Various forms of Si occur in the natural environment: dissolved silicate (DSi), crystalline or mineral silicates fractions (e.g. quartz, plagioclase, and clay minerals) and amorphous Si (ASi). ASi consists of biogenic silica (BSi) and a non-crystalline inorganic fraction (ISi) (Saccone et al., 2007; Sauer et al., 2006). Most studies concerning ASi dynamics have focused on the marine environment (DeMaster, 2002; Ragueneau et al., 2006; Tréguer et al., 1995). Due to minimal interference of non-biological ASi in marine environments, ASi in marine studies has generally been referred to as BSi. In the ocean and coastal zones, Si is an essential nutrient for diatom production and the related carbon sequestration (Brzezinski, 1985; Dugdale et al., 1995).
⁎ Corresponding author. Tel.: + 32 16326406; fax: + 32 16322980. E-mail address: [email protected] (W. Clymans).
There is growing evidence illustrating important biological Si cycling in terrestrial ecosystems (Meunier et al., 1999; Saccone et al., 2008; Van Cappellen, 2003). Large amounts of ASi are stored in terrestrial soils, mainly in the form of plant siliceous bodies called phytoliths (Alexandre et al., 1997). Dissolution of the ASi in terrestrial soils has been shown to control DSi export from catchments dominated by wetlands (Struyf et al., 2010), forests (Gérard et al., 2008) and grasslands (Blecker et al., 2006). The terrestrial and marine Si cycle are linked through riverine fluxes of Si, which replenish the Si lost to the deep oceans due to burial of diatom shells (Laruelle et al., 2009). Conley (1997) demonstrated that ASi transport from the continents constitutes a substantial part (16%) of bio-available Si fluxes into the ocean. Due to this important terrestrial-ocean link in the global Si cycle, the study of ASi in terrestrial soils and rivers is now receiving growing attention (Street-Perrott and Barker, 2008; Struyf and Conley, 2009). Smis et al. (in press) showed that ASi in arable catchments can attribute up to 40% of bio-reactive Si-transport (ASi and DSi). During the last decades, aquatic scientists have developed several techniques to asses ASi quantities in marine sediments (Müller and
0016-7061/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2011.07.033
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
Schneider, 1993). For soils, quantification of amorphous silica was mainly based on time demanding physical separation techniques, exploiting the differences in density between silicate minerals and phytoliths (Saccone et al., 2007). A single study mentioned the application of wet digestion methods to remove amorphous material from soils (Jackson et al., 1950). However, over recent years the wet alkaline digestion technique has become the standard technique for ASi determination in marine as well as in soil research (DeMaster, 1991; Saccone et al., 2008; Sauer et al., 2006). Saccone et al. (2007) tested the applicability and validity of commonly used acid and alkaline digestion techniques in soil sciences. They concluded that alkaline techniques were more appropriate than acid techniques for analysing soil samples, as acid techniques were incapable of dissolving all ASi. Originally a single step procedure was used to determine ASi concentrations (CASi) in sediments (Hurd, 1972; Mortlock and Froelich, 1989). However, such a technique is only appropriate for samples with a low clay content. When the sample's clay content is significant, a correction for mineral dissolution is necessary (Gehlen and Van Raaphorst, 1993). DeMaster (1981) described a sequential alkaline digestion method using Na2CO3 as a weak base (0.1 M). This method is based on two assumptions (1) there is a difference in dissolution rate between ASi and mineral silica (MSi) (Van Cappellen, 2003), and (2) the dissolution of ASi is a surface process (Koning et al., 2002). In practise, the method consists of a time course extraction using 0.1 M Na2CO3 at 80–85 °C, over a total period of 6 h, during which the extract is sampled and analysed several times. All ASi dissolves within the first two hours of analysis, while aluminosilicates release Si at a constant rate during the whole extraction time (Fig. 1). Extrapolating the Si release during the mineral phase back to the intercept is assumed to correct for mineral dissolution of Si. The method of DeMaster is now the de facto standard for ASi analysis in aquatic environments (Struyf et al., 2010). Understanding the linkage between the terrestrial Si pool and Si transport in the aquatic continuum not only requires assessment of ASi and DSi in the soil reservoir, but also of Si fluxes between the terrestrial and marine environment. Calculation of these riverine Si fluxes requires the measurement of water discharge as well as ASi (CASi) and DSi (CDSi) concentrations in runoff. CDSi is generally measured spectrophotometrically (Mortlock and Froelich, 1989). However, the analysis for ASi of water/suspended sediment samples poses specific problems due to the fact that the suspended particle matter concentration (CSPM) and therefore the solid to solution ratio (σ), i.e. the ratio between the amount of sediment that is present (as measured by weighing the oven-dried filters) and the amount of Na2CO3 solution used in the analysis, is variable and unknown. CSPM in runoff and river water may vary between 0.01 and 100 kg m −3 (Steegen et al., 2000).
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Clearly, accurate estimation of ASi fluxes requires an accurate measurement of CASi for different σ values as the SPM in the river water is not a priori known. However, the effect of σ on the measurement of ASi is still unclear. Loassachan and Tada (2008) carried out ASi measurements using various σ values. They found that the extractable Si per unit of sediment continued to increase with decreasing σ. Using a series of samples consisting of a known proportion of silica gel and kaolin, they concluded that extractions should be made using a fixed σ value of 2 kg m −3: using lower σ values led to an overestimation of CASi as measured CASi continued to rise with decreasing σ while ASi recovery was incomplete at higher σ values. However, the σ value they proposed is significantly higher than the one originally proposed by DeMaster (1981); 0.2– 0.6 kg m −3. Gehlen and Van Raaphorst (1993) noted that ASi measurements in marine sediments were not affected by the value of σ, as long as it remained within the range indicated (0.5–2.5 kg m −3) and they proposed to use a value as close as possible to the upper boundary to minimise experimental error. The different findings of Loassachan and Tada (2008) appear to contradict the findings of Gehlen and Van Raaphorst (1993) and suggest that all measurements should be carried out using a constant σ value to be determined beforehand. The latter would pose significant problems for measuring CASi in runoff. The availability of a method allowing the correct measurement of CASi is crucial if one wants to quantify correctly terrestrial and/or riverine ASi fluxes. As the CSPM cannot be exactly known a priori this requires that the effect of σ on measured CASi is understood and that the effects of variations in σ can be accounted for. The objectives of this paper are therefore (i) to investigate whether and how variations in σ values affect measured CASi in river runoff samples and (ii) to investigate whether or not it is possible to define a range of σ within which CASi in runoff and/or soil samples can be accurately measured using De Master's technique. 2. Materials and methods 2.1. Sample preparation 30 runoff samples with a wide range of CSPM were prepared by mixing a known amount of oven-dried (50°) soil, sieved at 2 mm, with a known quantity of distilled water. CSPM of artificial water– sediment mixtures varied between 0.1 and 100 kg m −3: this is the range which is generally encountered in field situations (Steegen et al., 2000). The soil used in the experiments was silty loamy subsoil sampled in central Belgium (Fig. 2, loc. 7). Grain size analysis using laser diffraction showed that the soil had 7.8% clay (b0.002 mm), 79% silt (0.002–0.063 mm) and 13.2% sand (0.063–2 mm). Organic matter content was ~0.94% (n = 5). The ASi content of the soil, as measured with the standard De Master method (Saccone et al., 2007) at a fixed σ value of 1.2 kg m −3 was 1.8 ± 0.3 mg g −1 (or 0.18 ± 0.03 wt.%, n = 5). Six other agricultural fields were sampled to check the representativeness of the reference soil sample (Fig. 2). Average ASi content varied between 1.33 and 1.82 mg g −1. 2.2. Laboratory analysis
Fig. 1. Illustration of the methodology used to account for the simultaneous dissolution of mineral silicates during the ASi extractions (after DeMaster (1981) and Koning et al. (2002)).
CASi in the water–sediment mixtures was measured using the wet chemical digestion method in hot alkali (Saccone et al., 2006). For each sample 5 and 25 ml of the well-mixed water–sediment mixture was passed through a 0.45 μm membrane-filter (Porafil®). The filter was air-dried for 24 h. ASi was extracted from the filters in a flatbottom digestion vessel with 25 ml of a 0.1 M Na2CO3 solution at 80 °C in a shaking (120 rpm) hot water bath for 2.5 h. 10 ml of the extract was filtered again at 0.45 μm (Chromafil® A-45/25) and cooled to 4 °C to stop dissolution. The filtrate was analysed colorimetrically for total extracted silica concentration (CTXSi) on a SAN ++
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Fig. 2. Location of the sampling sites in the Belgian Loam Belt. (7 = reference site).
analyzer (Skalar®, detection limit 0.01 ppm SiO2). Blank extractions were subtracted to account for DSi release from recipients and chemicals. The fact that most ASi dissolves completely within 2 h of digestion in hot alkali allows determining the interference of mineral silicates during the extraction by extrapolating the DSi release back through time to the intercept to correct for mineral dissolution (Saccone et al., 2006) (Fig. 1). In this study, 30% of the samples were sequentially extracted, i.e. an additional analysis of the sample after a heating period of 4 and 6 h was carried out. For each sequentially extracted sample, the slope of the linear relation between CTXSi and extraction time was determined based on CTXSi values after 2.5, 4 and 6 h of extraction. The slope (MDS) derived from this relation was assumed to represent mineral dissolution and CASi equals the y-intercept of the mineral dissolution line, i.e. the extrapolated value at time zero (Conley, 1998).
3. Results In Fig. 3, total extracted silica concentrations measured after 2.5 h of extraction time (CTXSi2.5) are plotted against σ. We found a poor relationship between CASi and σ for one series of measurements (A25) for σ b 1.6 kg m −3 (Fig. 8). The A25 measurement series differs significantly from all other series (Table 1) indicating incoherence. Therefore we did not include this series in our further statistical analysis. However, implications and possible reasons for this incoherence will be discussed later in the paper.
2.3. Statistical analysis In order to test for reproducibility, extraction procedures for 5 and 25 ml filtration volume were conducted simultaneously by two different lab-entities in the Dept. of Biology of the University of Antwerp (A) and in the Dept. of Earth and Environmental Sciences of the Katholieke Universiteit Leuven (L). These four measurements series are referred to as A5, L5, A25 and L25. Afterwards, the relations retrieved were compared statistically by introducing a dummy variable which allows to test the hypothesis of coincidence, i.e. whether or not the regression relationships retrieved are significantly different (Wonnacott and Wonnacott, 1977). All statistical analyses were performed using the SAS statistical package (Sas-Institute, 2002– 2003).
Fig. 3. CTXSi2.5 vs. σ; dashed lines indicate the limits between the different domains (see text).
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
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Table 1 Dummy analysis for CASi vs σ regressions at a significance level of 0.05 in the range 0.1 b σ ≤ 1.6 kg m−3. Series
Dummy
N
F-value
p-value
Sign. difference
L5 vs A5 L5 vs L25 L5 vs A25 A5 vs A25 A5 vs L25 A25 vs L25
L5 = 0 and A5 = 1 L5 = 0 and L25 = 1 L5 = 0 and A25 = 1 A5 = 0 and A25 = 1 A5 = 0 and L25 = 1 A25 = 0 and L25 = 1
28 25 25 25 25 22
3.3 1.3 25.9 17.9 0.8 28.6
0.0528 0.3025 0.0001 0.0001 0.4791 0.0001
No No Yes Yes No Yes
Over the total range of σ three domains can be distinguished: – Domain (1), σ≤0.8 kg m−3: CTXSi2.5 increases linearly with σ: CTXSi2.5 = 76.3*σ (R²= 0.92, n =38), – Domain (2), 0.8 b σ ≤ 4 kg m−3: CTXSi2.5 increases degressively with σ and a power function can be fitted: CTXSi2.5 = 72.1*σ0.72 (R² = 0.91, n = 32), – Domain (3), σ≥4 kg m−3: CTXSi2.5 remains constant: 225±44 μM (n=40). When MDS is plotted against σ, the same three domains can again be distinguished (Fig. 4): (1) a linear relationship for low σ values (≤0.8 kg m−3): MDS = 0.0044*σ (R² = 0.89, n = 17), (2) a degressive increase for intermediate values of σ (0.8b σ ≤ 4 kg m−3) where the relationship can be described through a power function: MDS = 0.0037*σ0.61 (R² = 0.64, n = 14) and (3) a highly variable MDS when σ exceeds 4 kg m−3. We used the linear relationship between MDS and σ described above to calculate CASi using De Master's method for σ values below 0.8 kg m −3. A clear linear relationship between CASi and σ: CASi = 52.6*σ (R² = 0.78; n = 30) is found (Fig. 5). 4. Discussion 4.1. Solution domains The analysis of our data reveals important limitations of the alkaline extraction procedure at high σ values. Complete dissolution of ASi should lead to a linear relationship over the whole range of σ values between CTXSi2.5 and MDS on the one hand and σ on the other hand. For our samples, these relationships were only linear when σ was below 0.8 kg m −3. Above this threshold value, both CTXSi2.5 and MDS do no longer increase linearly with σ indicating
Fig. 5. CASi vs. σ for σ b 0.8 kg m−3.
incomplete dissolution. The fact that CTXSi2.5 does no longer increase linearly with σ is explained by the fact that the ASi present within the sample is no longer completely dissolved within 2.5 h, when too much sediment is present. MDS is also expected to increase linearly with σ as mineral dissolution should be proportional to the amount of sediment (and therefore mineral silica) present in the sample. However, when σ exceeds a value of 0.8 kg m −3 the coupling is no longer linear, indicating that, similar to the dissolution of ASi, the dissolution of MSi becomes increasingly impeded with increasing sediment amounts. The reduction of dissolution rates with increasing σ values may have two causes. First, Si dissolution is a surface process (Koning et al., 2002): as σ increase, it is no longer certain that all sediment surfaces are free and can contribute to the dissolution process. Second, saturation may occur with an increasing amount of sediment and dissolvable Si in the sample: the total potential extractable amount of silica with the 0.1 M Na2CO3 alkaline extraction procedure is gradually reached. The absence of any clear relationship between σ on the one hand and CTXSi2.5 and MDS on the other hand once σ exceeds 4 kg m −3 is a clear indication that saturation is indeed reached. For the lowest σ values (b0.1 kg m −3) we observed a large variability in our results: this probably indicates that the accuracy limits of the method have been reached (Fig. 6a). Measurements errors with respect to the quantity of sediment on the filters become too large to allow for an accurate assessment of CTXSi2.5 and hence CASi concentrations. It is therefore not surprising that such low σ values are discouraged for alkaline digestion procedures (Flower, 1993). However, contrary to Loassachan and Tada (2008) we did not notice any significant variation in CASi with σ when the latter varied between 0.1 and 0.8 kg m −3 (Fig. 6b). Furthermore, Fig. 6a also shows that the calculated ASi content start to decrease significantly only when σ exceeds ca. 1.6 kg m −3. At first sight this is surprising, given the fact that Si dissolution becomes clearly incomplete when σ exceeds a much lower threshold value of 0.8 kg m −3. This is due to compensating error effects which are discussed in the next paragraph. 4.2. Error compensation
Fig. 4. MDS vs. σ; dashed lines indicate limits between domains for the CTXSi2.5 vs. σ relationship (Fig. 2, see also text).
Our results indicate that we underestimate (1) total extracted silica concentrations (CTXSi2.5, μM) and (2) mineral dissolution slopes (MDS) once a threshold σ value of 0.8 kg m −3 is exceeded.
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Fig. 6. Amorphous silica content (mg-Si g−1) vs. σ (kg m−3) for (a) all samples and (b) for 0.1 ≤ σ ≤ 0.8 kg m−3.
Both CTXSi2.5 and MDS are used to calculate ASi concentrations (CASi, μM): CASi ¼ CTXSit −
MDS ⋅t ⋅60 0:028
ð1Þ
with; – CTXSit = total extracted silica concentrations (μM) at time t (h) – MDS = mineral dissolution slope – t = duration of extraction (h) and, coefficients 60 and 0.028 to convert to minutes and molar weight of Si respectively. From Eq. 1 it is clear that an underestimation of CTXSi2.5 results in an underestimation of CASi while underestimating the MDS evokes an overestimation of CASi. Thus both errors compensate each other to a certain extent. In order to gain insight in these counteracting effects, we calculated CASi values based on Eq. 1 using values for CTXSi2.5 and MDS obtained from the empirical curves for each domain. We compared these empirical values with the theoretical values that would be obtained by simply extrapolating the linear relationships between σ and both CTXSi2.5 and MDS obtained for domain 1. Fig. 7 shows the empirical values and four curves: one curve (solid) represents unbiased results, assuming no measurement errors, which results in a linear relationship between σ and CASi having a slope of 52.8 which was determined experimentally using data for 0.1 ≤ σ ≤ 0.8 kg m −3 (theoretical) and one curve (dash–dotted) which was constructed using the empirical relationships between σ, CTXSi2.5 and MDS for each domain (empirical). The other curves illustrate the over- and underestimation effect. If we use the empirical MDS and the theoretical CTXSi2.5 (from domain 1) relationships, an overestimation occurs (dashed) while if we use the empirical CTXSi2.5 and the theoretical MDS (from domain 1) relationships, underestimation occurs (dotted). Fig. 7 shows, that the theoretical data do show a linear relationship over the whole range of σ values while the empirical data show a degressive trend. However, the empirical values do not significantly deviate from the linear trend when σ ≤ 1.6 due to the fact that errors on CTXSi2.5 and MDS compensate. Comparing the theoretical and empirical relationships, it can be seen that the error due to bias in the domain 0.8 b σ ≤ 1.6 varies between 0.1 and 11%. From 1.6 kg m −3 onward the empirically observed CASi concentration varies significantly. Residues, theoretical fit minus empirical values, are systematically exceeding acceptable variance (Fig. 7). De facto, the error compensation
effect extends the suitable range of σ-values to determine reliable CASi above the theoretically correct limit. Fig. 8 shows the empirical CASi vs. σ for σ ≤ 1.6 kg m −3. For all measurement series, except A25 there was a very good match between the empirical and the theoretical values predicted on the basis of observed linear relations in domain 1. The three other measurements series are coincident (Table 1) and an overall regression can be fitted: CASi = 52*σ (R² = 0.98, n = 39). This is comparable with the theoretical curve CASi = 52.8*σ. We do not have a solid explanation as to why the results for the A25 series are different from those of the three other series. The fact that the measured values are consistently lower than the theoretically expected values may indicate that small variations in treatment (e.g. subsampling, filtration, shaking) result in noticeable discrepancies which indicates the necessity of using a strongly standardised sample treatment procedure.
Fig. 7. (a) CASi vs. σ: comparing (1) empirical values, and (2) theoretical based curves: theoretical (solid), overestimation (dashed), underestimation (dotted) and empirical data (dash–dotted); (b) residue (theoretical–empirical data) vs. σ value.
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
Fig. 8. CASi vs. σ within 0.1 b σ ≤ 1.6 kg m−3 for the empirical L5, A5 and L25 series.
4.3. Effect of σ on ASi measurements Several studies already investigated the effect of σ on ASi measurements, also using different solutions and solution concentrations. Table 2 gives an overview of these studies with the range of σ values that were tested and/or recommended. Recommended values for σ range between 0.2 and 5 kg m −3. Some of this variation may be attributed to the different types of samples tested as well as the strength of the solution used. Liu et al. (2002) found that σ values of up to 5 kg m −3 can be used for marine samples (with a high ASi content) in combination with a high solution concentration (2 M Na2CO3). However, all other studies recommend maximum values equal to or below 2.5 kg m −3 and several authors (DeMaster, 1981; Loassachan and Tada, 2008) indicate that recovery may be incomplete at higher σ values. Our study does not only confirm that incomplete recovery indeed prevents the accurate measurement of CASi when σ is too high but also clarifies the mechanisms leading to systematic errors at high σ values. At σ values exceeding 0.8 kg m −3 both the recovery of ASi becomes incomplete and the dissolution rate of MSi is reduced. As these errors compensate, overall measurement bias remains limited as long as σ is below 1.6 kg m −3. The latter value is still somewhat lower than the values recommended by other authors, even when using low solution concentrations (0.1 M). Most likely, the maximum theoretically correct σ value that can safely be used is to some extent dependent on the sample type. Given that Si dissolution is a surface process, one may expect that the maximum σ value may be lower for samples having a high specific surface area, i.e. samples with a significant clay content such as the samples used in this study. Analogue the extent of acceptable error compensation will vary with sample characteristics. Similarly the type and concentration of the solution that is used may also play a role: however, systematic studies on the effects of solution type and concentration are hitherto lacking. Contrary to Loassachan and Tada (2008) and Liu et al. (2002) but similarly to Gehlen and Van Raaphorst (1993) we found that, at low σ values, measured ASi content (per g of sample) remained constant. Thus, contrary to what was proposed by Loassachan and Tada (2008), it appears possible to make valid measurements over a range of solid to solution ratios. The latter is important as the solid to solution ratio cannot always be accurately determined a priori for runoff samples. The reasons for these different results are not entirely clear, but it is interesting to note that Loassachan and Tada (2008) calculated a
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recovery percentage exceeding 100% for small σ values. Also, both Loassachan and Tada (2008) and Liu et al. (2002) observed that measured ASi concentration decreased rapidly and systematically with σ for σ values b 1 kg m −3. Loassachan and Tada (2008) attribute this phenomenon “to the increase of the relative reactive surface area” with decreasing σ. Considering that samples in this study were ground before analysis, it seems possible that the relatively small samples used (minimum 5 mg) contained relatively high amounts of very fine mineral particles that were much more reactive then the bulk mineral material. The relatively higher abundance of such material in the small samples would not only explain why the calculated recovery percentage may exceed 100% but also why they observed that the Si dissolution curves were convex downward rather than linear for most of their samples. Other studies support the hypothesis that a highly reactive pool of very fine particles may be present in the mineral fraction. Müller and Schneider (1993) measured mineral dissolution rates in a strong base solution and found that rapid initial dissolution occurred, after which dissolution rates became constant. They attributed this phenomenon to a grain size effect, i.e. the preferential dissolution of very fine particles. In our study the samples were not ground and small samples were prepared by subsampling a volume of water into which a larger volume of sediment sample was mixed. Our samples were all prepared from a single soil sample so that grain size composition was constant. In natural runoff we could expect significant variations in grain size and in mineral composition due to the size selectivity of the processes involved (Beuselinck et al., 2000). The enrichment in clay-minerals, typical at low sediment concentrations (Wang et al., 2010), may lead to variations in mineral dissolution rates and hence to measurement bias. In other environments sediments differ in mineralogical composition depending on the parent material or soil type. This influences ASi measurements as various clay-types have different mineral dissolution rates (Koning et al., 2002). Further research efforts to reveal the grain size and mineralogical dependency of ASi measurements are therefore necessary. 4.4. Method adaption As CSPM in runoff samples may vary between 0.01 and 100 kg m −3 (Steegen et al., 2000) and the standard method is limited for σ (≤1.6 kg m −3), an alternative method for runoff samples with a high CSPM and therefore high σ is proposed. We propose a simple adaptation of the filtration method, similar to the method used by Saccone et al. (2007) for soil samples. Filter a substantial amount of subsample (100–300 ml) at 2.5 μm using a large filter (e.g. using Whatman 42 filters). The rest of the sample may be used for other analyses. Samples are subsequently air-dried, removed from the filter by gentle brushing and homogenised. Homogenisation should be done with mortar and pestle. Although for the reference sample mill grounding didn't show significant different CASi, it is discouraged because higher CASi could be obtained from the exposure of new surfaces available for dissolution (Conley and Schelske, 2001). A fixed quantity of filtrated sample (25–30 10 −6 kg) is then used during the extraction procedure. This implies that we are working with a constant σ value (1–1.25 kg m −3) which is within the suitable range (i.e. 0.1 ≤ σ ≤ 1.6 kg m −3) as retrieved from our experiments. Although our analysis shows that runoff samples may also be analysed without bias at lower σ, results can be expected to be more reliable at higher σ, as measurements errors become relatively less important. The amorphous silica content we determined for our reference soil (1.8 ± 0.3 mg-Si g −1, n = 5) is somewhat higher than the average ASi content we found for the artificial runoff samples we created from this soil (1.65 ± 0.4 mg-Si g −1, n = 30), but the difference was not significant for the reference sample nor the six other agricultural sites (p b 0.02). Therefore this method proves to be precise and
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Table 2 Overview of studies measuring ASi in sediments at various σ values. Extractant refers to the solution used as digestive during extraction procedure. Reference
Sample type
Tested σ (kg m−3)
Recommended σ (kg m−3)
Extractant
Remarks
Gehlen and Van Raaphorst, 1993
Marine sediments
2.5
2.5
0.1 M Na2CO3
Conley and Schelske, 2001
Sediments
0.5–1
0.75
0.1 M Na2CO3
Loassachan and Tada, 2008
Marine sediments
0.1–8.0
2
0.1 M Na2CO3
Kamatani and Oku, 2000
Siliceous micro-organisms and aluminosilicates
0.5–0.75
0.5–0.75
Liu et al., 2002
Marine sediments
0.25–6.25
1–5
Diverse: 0.1 M and 0.5 M Na2CO3, and 0.1 and 0.2 M NaOH 2 M Na2CO3
DeMaster, 1981
Marine
0.2–0.6
0.2–0.6
0.1 M Na2CO3
Conley, 1998
Freshwater and coastal sediments
0.2–2.5
No
Diverse: range 0.1–2 M Na2CO3 and 0.2–1 M NaOH
Saccone et al., 2008
Soil
0.75
No
0.094 M Na2CO3
Mortlock and Froelich, 1989 Müller and Schneider, 1993
Marine sediments
0.625–5
See remark
2 M Na2CO3
Siliceous micro-organisms and clay minerals
0.20–0.42
No
0.1, 0.5 and 1 M NaOH
Have previous tested (unpublished data) for three different σ values: 2.5, 1.25, 0.625 kg m−3 and concludes that variability is low (7%), to minimise effects of heterogeneity, the highest σ is used: 2.5 kg m−3. No sign. differences in ASi extracted has been found for σ-values between 0.5 and 1 kg m−3, a consistent weight is recommended Optimal σ of 2 kg m−3: 100% recovery b 1 over contribution clay mineral derived extractable Si ≥ overestimation N 2 incomplete dissolution ≥ underestimation b 0.5 kg m−3 gives lower accuracy and N 0.75 kg m−3 lower recovery, the tested values correspond with recommended (based on previous work; Kamatani, 1980). Linear relation between CASi and σ: a σ of 1–5 kg m−3 during extraction is necessary to minimise uncertainty in ASi measurements Generally accepted as pioneer for sequential alkaline extraction techniques It was interlaboratory comparison and every laboratory used its own σ, Conley legitimise this by referring to Gehlen and Van Raaphorst (1993) to be within this accepted range. Reproducibility was checked with replicates: standard errors : 0.01 to 3.48 g kg−1 Single step — Adapted in order that no more then 25 mg of pure biogenic opal is present A grain size effect is suggested, with preferential dissolution of ultra-fine particles during initial extraction period. Homogeneity is important because small masses are used.
Sediments, silica gel and clay minerals
1
No
0.5 M NaOH
Koning et al., 2002
delivers reliable results for the Belgian Loam Belt when σ values are too high. Results of this alternative method are insensitive to incomplete dissolution. Instead of forming thin layers on the filtrate, sediments are loose particles within the solution. Therefore incomplete dissolution due to partial-surface exposure is minimal. CTXSi2.5 and MDS aren't underestimated and will result in accurate results without error compensation. This is true until buffer capacity of the solution is reached. The advantages of the original method with extraction from filters are that (1) only a limited amount of sample is necessary without a lot of pre-treatment and (2) it is a reproducible and easy method within a large range of σ values. Disadvantages are (1) underestimation of CASi at high σ values and (2) increasing variability towards very low σ values. A general disadvantage with sequential leaching is that the linear slope and the extrapolated intercept value are based only on few measurements in time, usually three to four. With such a low number of data points, potential outliers are difficult to identify and the uncertainty on the extrapolated amorphous silica value can be large. Continuous monitoring of silica would overcome a large part of these problems (Müller and Schneider, 1993).
5. Conclusion and recommendations Our study confirmed that the alkaline (0.1 M Na2CO3) digestion method proposed by DeMaster can be used for runoff samples provided that the solid to solution ratio (σ) is within certain limits: at very low σ (b0.1 kg m −3), subsample heterogeneity results in high variability of measured CASi while at higher σ values (N0.8 kg m −3) incomplete dissolution of ASi as well as the reduction of mineral dissolution rates results in underestimated CASi. As both errors
compensate one another, the range of applicable σ-values can be extended above the theoretically correct limit (1.6 kg m −3). We found that CASi can be determined reliably within a considerable range of σ. While this finding confirms the results from some studies, it differs from observations made by other researchers who found that measured ASi concentrations continued to rise with decreasing σ. This may be due to the absence of a fine reactive mineral fraction in our samples. The finding that reliable measurements can be made within a relatively wide range of σ values (0.1 ≤ σ ≤ 1.6 kg m −3) is important as it is now possible to propose a method for the measurement of ASi in runoff samples and derive some recommendations for ASi analysis, making a distinction between samples with a low and high CSPM. (i) Samples with low CSPM (≤1.6 kg m −3): stir sample thoroughly take a subsample of 5 or 25 ml and use standard procedure (see Materials and methods). Determine CSPM afterwards and use this to determine σ. (ii) Samples with high CSPM (N1.6 kg m −3, water is no longer clear): stir sample thoroughly and take a substantial subsample (100–300 ml) and use adapted procedure (see Discussion). However, one should be aware that the range and limits for σ proposed here may depend on the type of sediment to be analysed: it is therefore recommended to evaluate the performance of the method again before it is used in other environments. Acknowledgements The authors thank both the ECOBE laboratory of University Antwerp and Environmental Science laboratory of K.U. Leuven for sample analysis. Wim Clymans would like to thank the Flemish Agency for
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
the promotion of Innovation by Science and Technology (IWT) for funding his personal promotion grant. Eric Struyf would like to thank FWO (Research Foundation Flanders) for personal post doc research funding. We would like to acknowledge Belgian Science Policy (BELSPO) for funding project “LUSi: Land use changes and silica fluxes in the Scheldt river basin” and FWO for funding project “Tracking the biological control on Si mobilisation in upland ecosystems” (Project nr. G014609N). References Alexandre, A., Meunier, J., Colin, F., Koud, J., 1997. Plant impact on the biogeochemical cycle of silicon and related weathering processes. Geochim Cosmochim Ac 61 (3), 677–682. Beuselinck, L., Steegen, A., Govers, G., Nachtergaele, J., Takken, I., Poesen, J., 2000. Characteristics of sediment deposits formed by intense rainfall event in small catchments in the Belgian Loam Belt. Geomorphology 32, 69–82. Blecker, S.W., McCulley, R.L., Chadwick, O.A., Kelly, E.F., 2006. Biologic cycling of silica across a grassland bioclimosequence. Global Biogeochem. Cycles 20 (3), GB3023. Brzezinski, M., 1985. The SI–C–N ratio of marine diatoms — interspecific variability and the effect of some environmental variables. J. Phycol. 21 (3), 347–357. Conley, D., 1997. Riverine contribution of biogenic silica to the oceanic silica budget. Limnol. Oceanogr. 42 (4), 774–777. Conley, D., 1998. An interlaboratory comparison for the measurement of biogenic silica in sediments. Mar. Chem. 63 (1–2), 39–48. Conley, D., Schelske, C., 2001. Biogenic silica. In: Smol, J., Birks, H., Last, W. (Eds.), Tracking Environmental Change Using Lake Sediments: Biological Methods and Indicators. Kluwer Academic Press, pp. 281–293. DeMaster, D., 1981. The supply and accumulation of silica in the marine-environment. Geochim. Cosmochim. Acta 45 (10), 1715–1732. DeMaster, D., 1991. Measuring biogenic silica in marine-sediments and suspended matter. Mar. Part. Anal.Charact 63, 363–367. DeMaster, D., 2002. The accumulation and cycling of biogenic silica in the Southern Ocean: revisiting the marine silica budget. Deep. Sea. Res II 49 (16), 3155–3167. Dugdale, R., Wilkerson, F., Minas, H., 1995. The role of a silicate pump in driving new production. Deep. Sea. Res I 42 (5), 697–719. Flower, R., 1993. Diatom preservation — experiments and observations on dissolution and breakage in modern and fossil material. Hydrobiologia 269, 473–484. Gehlen, M., Van Raaphorst, W., 1993. Early diageneis of silica in sandy north-seas sediments — quantification of the solid-phase. Mar. Chem. 42 (2), 71–83. Gérard, F., Mayer, K., Hodson, M., Ranger, J., 2008. Modelling the biogeochemical cycle of silicon in soils: application to a temperate forest ecosystem. Geochim. Cosmochim. Acta 72 (3), 741–758. Hurd, D., 1972. Factors affecting solution rate of biogenic opal in seawater. Earth Planet. Sci. Lett. 15 (4), 411–417. Iler, R., 1979. The Chemistry of Silica. Wiley-Interscience, New York. Jackson, M., Whittig, L., Pennington, R., 1950. Seggragation procedure for the mineralogical analysis of soils. Proc. Soil Sci. Soc. Am 14, 77–81. Kamatani, A., 1980. Determination of biogenic silica in marine sediment. La Mer Bull. Soc. Fr.-Jpn. Oceanogr. 18, 63–68. Kamatani, A., Oku, O., 2000. Measuring biogenic silica in marine sediments. Mar. Chem. 68 (3), 219–229. Koning, E., Epping, E., Van Raaphorst, W., 2002. Determining biogenic silica in marine samples by tracking silicate and aluminium concentrations in alkaline leaching solutions. Aquat. Geochem. 8 (1), 37–67. Laruelle, G.G., Roubeix, V., Sferratore, A., Brodherr, B., Ciuffa, D., Conley, D.J., Dürr, H.H., Garnier, J., Lancelot, C., Le Thi Phuong, Q., Meunier, J.D., Meybeck, M.,
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Michalopoulos, P., Moriceau, B., Ní Longphuirt, S., Loucaides, S., Papush, L., Presti, M., Ragueneau, O., Regnier, P., Saccone, L., Slomp, C.P., Spiteri, C., Van Cappellen, P., 2009. Anthropogenic perturbations of the silicon cycle at the global scale: Key role of the land–ocean transition. Global Biogeochem. Cycles 23. doi:10.1029/ 2008GB003267. Liu, S., Ye, X., Zhang, J., Zhao, Y., 2002. Problems with biogenic silica measurement in marginal seas. Mar. Geol. 192 (4), 383–392. Loassachan, N., Tada, K., 2008. Effect of solid to solution ratio on biogenic silica determination in coastal sediments. J. Oceanogr. 64 (5), 657–662. Meunier, J., Colin, F., Alarcon, C., 1999. Biogenic silica storage in soils. Geology 27 (9), 835–838. Mortlock, R., Froelich, P., 1989. A simple method for the rapid-determination of biogenic opal in pelagic marine-sediments. Deep. Sea. Res A 36 (9), 1415–1426. Müller, P., Schneider, R., 1993. An automated leaching method for the determination of opal in sediments and particulate matter. Deep. Sea. Res I 40 (3), 425–444. Ragueneau, O., Conley, D., Ni Longphuirt, S., Slomp, C., LEYNAERT, A., 2006. A review of the Si biogeochemical cycle in coastal waters: I. Diatoms in coastal food webs and the coastal Si cycle. In: Ittekkot, V., Unger, D., Humborg, C., Tac An, N. (Eds.), The silicon cycle: Human Perturbations and Impacts on Aquatic Systems. Island Press, Washington DC. Saccone, L., Conley, D., Sauer, D., 2006. Methodologies for amorphous silica analysis. J. Geochem. Explor. 88 (1–3), 235–238. Saccone, L., Conley, D., Koning, E., Sauer, D., Sommer, M., Kaczorek, D., Blecker, S., Kelly, E., 2007. Assessing the extraction and quantification of amorphous silica in soils of forest and grassland ecosystems. Eur. J. Soil. Sci. 58 (6), 1446–1459. Saccone, L., Conley, D., Likens, G., Bailey, S., Buso, D., Johnson, C., 2008. Factors that control the range and variability of amorphous silica in soils in the Hubbard Brook experimental forest. Soil. Sci. Soc. Am. J. 72 (6), 1637–1644. Sas-Institute, 2002–2003. SAS Release 9.1.3. In: Sas-Institute (Ed.), Cary, NC, USA. Sauer, D., Saccone, L., Conley, D., Herrmann, L., Sommer, M., 2006. Review of methodologies for extracting plant-available and amorphous Si from soils and aquatic sediments. Biogeochemistry 80 (1), 89–108. Smis, A., Van Damme, S., Struyf, E., Clymans, W., van Wesemael, B., Frot, E., Vandevenne, F., Van, Hoestenberghe, Govers, G., Meire, P., in press. A trade-off between dissolved and amorphous silica transport during peak-flow events (Scheldt River basin, Belgium): impacts of precipitation intensity on terrestrial Si dynamics in strongly cultivated catchments. Biogeochemistry. doi:10.1007/s10533-010-9527-1. Steegen, A., Govers, G., Nachtergaele, J., Takken, I., Beuselinck, L., Poesen, J., 2000. Sediment export by water from an agricultural catchment in the Loam Belt of central Belgium. Geomorphology 33 (1–2), 25–36. Street-Perrott, F.A., Barker, P.A., 2008. Biogenic silica: a neglected component of the coupled global continental biogeochemical cycles of carbon and silicon. Earth Surf. Proc. Land 33 (9), 1436–1457. Struyf, E., Conley, D., 2009. Silica: an essential nutrient in wetland biogeochemistry. Front. Ecol. Environ. 7 (2), 88–94. Struyf, E., Mörth, M., Humborg, C., Conley, D., 2010. An Enormous Bio-silica Pool in Boreal Wetlands. . JGR 115. doi:10.1029/2010JG001324. Tréguer, P., Nelson, D., Vanbennekom, A., DeMaster, D., Leynaert, A., Queguiner, B., 1995. The silica balance in the world ocean — a reestimate. Science 268 (5209), 375–379. Van Cappellen, P., 2003. Biomineralization and global biogeochemical cycles. Rev. Mineral. Geochem. 54, 357–381. Wang, Z., Govers, G., Steegen, A., Ckymans, W., Van den Putte, A., Langhans, C., Merckx, R., Van Oost, K., 2010. Catchment-scale carbon redistribution and delivery by water erosion in an intensively cultivated area. Geomorphology 124, 65–74. Wonnacott, T., Wonnacott, R., 1977. Introductory Statistics, 3th ed. Wiley, New York.
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Amorphous silica analysis in terrestrial runoff samples Wim Clymans a,⁎, Gerard Govers a, Bas Van Wesemael b, Patrick Meire c, Eric Struyf c a b c
Department of Earth and Environmental Sciences, Leuven Sustainable Earth Research Centre, K.U. Leuven, B-3001 Heverlee, Belgium Geography Department, Université catholique de Louvain, Place Louis Pasteur 3, B-1348 Louvain-la-Neuve, Belgium Department of Biology, Ecosystem Management Research Group, University Antwerp, B-2610 Wilrijk, Belgium
a r t i c l e
i n f o
Article history: Received 10 August 2010 Received in revised form 30 June 2011 Accepted 23 July 2011 Available online 2 November 2011 Keywords: Amorphous silica Solid to solution ratio Error compensation Biogeochemical cycle Biological control
a b s t r a c t The correct analysis of amorphous silica concentration (CASi) in natural waters is crucial if one wants to correctly quantify terrestrial and/or riverine ASi fluxes. Soil ASi measurements are conducted with a constant solid to solution ratio (σ). As the suspended particulate matter concentration (CSPM), and therefore σ, cannot be exactly known a priori in river samples. It is important to understand how variations in σ effect analysed CASi. The objectives of this paper are (i) to investigate whether and how variations in σ values affect measured CASi in river runoff samples and (ii) to investigate whether or not it is possible to define a range of σ within which CASi in runoff and/or soil samples can be accurately measured. For the laboratory experiment 30 runoff samples with a wide range of CSPM, typical for the Belgian Loam Belt, were prepared and analysed using the alkaline digestion method (0.1 M Na2CO3). Our study confirmed that the alkaline digestion method proposed by DeMaster can be used for runoff samples provided that σ is within certain limits: at very low σ (b 0.1 kg m−3), subsample heterogeneity results in high variability of measured CASi while at higher σ values (N 0.8 kg m−3) incomplete dissolution of ASi as well as the reduction of mineral dissolution rates results in underestimated CASi. As both errors compensate one another, the range of applicable σ-values can be extended above the theoretically correct limit (1.6 kg m −3). The finding that reliable measurements can be made within a relatively wide range of σ values (0.1 ≤ σ ≤ 1.6 kg m −3) is important. It is now possible to propose a method for the measurement of ASi in runoff samples. We make recommendations for ASi analysis distinguishing samples with a low and high CSPM. For samples with a low CSPM (≤ 1.6 kg m−3) the standard procedure is proposed while for samples with a high CSPM (N 1.6 kg m−3) an adapted procedure is proposed, analogue to that for soil samples. However, one should be aware that the range and limits for σ proposed here may depend on the type of sediment to be analysed: it is therefore recommended to evaluate the performance of the method again before it is used in other environments. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved.
1. Introduction Silicon (Si) is the second most abundant element in the Earth's crust after oxygen (Iler, 1979). Various forms of Si occur in the natural environment: dissolved silicate (DSi), crystalline or mineral silicates fractions (e.g. quartz, plagioclase, and clay minerals) and amorphous Si (ASi). ASi consists of biogenic silica (BSi) and a non-crystalline inorganic fraction (ISi) (Saccone et al., 2007; Sauer et al., 2006). Most studies concerning ASi dynamics have focused on the marine environment (DeMaster, 2002; Ragueneau et al., 2006; Tréguer et al., 1995). Due to minimal interference of non-biological ASi in marine environments, ASi in marine studies has generally been referred to as BSi. In the ocean and coastal zones, Si is an essential nutrient for diatom production and the related carbon sequestration (Brzezinski, 1985; Dugdale et al., 1995).
⁎ Corresponding author. Tel.: + 32 16326406; fax: + 32 16322980. E-mail address: [email protected] (W. Clymans).
There is growing evidence illustrating important biological Si cycling in terrestrial ecosystems (Meunier et al., 1999; Saccone et al., 2008; Van Cappellen, 2003). Large amounts of ASi are stored in terrestrial soils, mainly in the form of plant siliceous bodies called phytoliths (Alexandre et al., 1997). Dissolution of the ASi in terrestrial soils has been shown to control DSi export from catchments dominated by wetlands (Struyf et al., 2010), forests (Gérard et al., 2008) and grasslands (Blecker et al., 2006). The terrestrial and marine Si cycle are linked through riverine fluxes of Si, which replenish the Si lost to the deep oceans due to burial of diatom shells (Laruelle et al., 2009). Conley (1997) demonstrated that ASi transport from the continents constitutes a substantial part (16%) of bio-available Si fluxes into the ocean. Due to this important terrestrial-ocean link in the global Si cycle, the study of ASi in terrestrial soils and rivers is now receiving growing attention (Street-Perrott and Barker, 2008; Struyf and Conley, 2009). Smis et al. (in press) showed that ASi in arable catchments can attribute up to 40% of bio-reactive Si-transport (ASi and DSi). During the last decades, aquatic scientists have developed several techniques to asses ASi quantities in marine sediments (Müller and
0016-7061/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2011.07.033
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
Schneider, 1993). For soils, quantification of amorphous silica was mainly based on time demanding physical separation techniques, exploiting the differences in density between silicate minerals and phytoliths (Saccone et al., 2007). A single study mentioned the application of wet digestion methods to remove amorphous material from soils (Jackson et al., 1950). However, over recent years the wet alkaline digestion technique has become the standard technique for ASi determination in marine as well as in soil research (DeMaster, 1991; Saccone et al., 2008; Sauer et al., 2006). Saccone et al. (2007) tested the applicability and validity of commonly used acid and alkaline digestion techniques in soil sciences. They concluded that alkaline techniques were more appropriate than acid techniques for analysing soil samples, as acid techniques were incapable of dissolving all ASi. Originally a single step procedure was used to determine ASi concentrations (CASi) in sediments (Hurd, 1972; Mortlock and Froelich, 1989). However, such a technique is only appropriate for samples with a low clay content. When the sample's clay content is significant, a correction for mineral dissolution is necessary (Gehlen and Van Raaphorst, 1993). DeMaster (1981) described a sequential alkaline digestion method using Na2CO3 as a weak base (0.1 M). This method is based on two assumptions (1) there is a difference in dissolution rate between ASi and mineral silica (MSi) (Van Cappellen, 2003), and (2) the dissolution of ASi is a surface process (Koning et al., 2002). In practise, the method consists of a time course extraction using 0.1 M Na2CO3 at 80–85 °C, over a total period of 6 h, during which the extract is sampled and analysed several times. All ASi dissolves within the first two hours of analysis, while aluminosilicates release Si at a constant rate during the whole extraction time (Fig. 1). Extrapolating the Si release during the mineral phase back to the intercept is assumed to correct for mineral dissolution of Si. The method of DeMaster is now the de facto standard for ASi analysis in aquatic environments (Struyf et al., 2010). Understanding the linkage between the terrestrial Si pool and Si transport in the aquatic continuum not only requires assessment of ASi and DSi in the soil reservoir, but also of Si fluxes between the terrestrial and marine environment. Calculation of these riverine Si fluxes requires the measurement of water discharge as well as ASi (CASi) and DSi (CDSi) concentrations in runoff. CDSi is generally measured spectrophotometrically (Mortlock and Froelich, 1989). However, the analysis for ASi of water/suspended sediment samples poses specific problems due to the fact that the suspended particle matter concentration (CSPM) and therefore the solid to solution ratio (σ), i.e. the ratio between the amount of sediment that is present (as measured by weighing the oven-dried filters) and the amount of Na2CO3 solution used in the analysis, is variable and unknown. CSPM in runoff and river water may vary between 0.01 and 100 kg m −3 (Steegen et al., 2000).
229
Clearly, accurate estimation of ASi fluxes requires an accurate measurement of CASi for different σ values as the SPM in the river water is not a priori known. However, the effect of σ on the measurement of ASi is still unclear. Loassachan and Tada (2008) carried out ASi measurements using various σ values. They found that the extractable Si per unit of sediment continued to increase with decreasing σ. Using a series of samples consisting of a known proportion of silica gel and kaolin, they concluded that extractions should be made using a fixed σ value of 2 kg m −3: using lower σ values led to an overestimation of CASi as measured CASi continued to rise with decreasing σ while ASi recovery was incomplete at higher σ values. However, the σ value they proposed is significantly higher than the one originally proposed by DeMaster (1981); 0.2– 0.6 kg m −3. Gehlen and Van Raaphorst (1993) noted that ASi measurements in marine sediments were not affected by the value of σ, as long as it remained within the range indicated (0.5–2.5 kg m −3) and they proposed to use a value as close as possible to the upper boundary to minimise experimental error. The different findings of Loassachan and Tada (2008) appear to contradict the findings of Gehlen and Van Raaphorst (1993) and suggest that all measurements should be carried out using a constant σ value to be determined beforehand. The latter would pose significant problems for measuring CASi in runoff. The availability of a method allowing the correct measurement of CASi is crucial if one wants to quantify correctly terrestrial and/or riverine ASi fluxes. As the CSPM cannot be exactly known a priori this requires that the effect of σ on measured CASi is understood and that the effects of variations in σ can be accounted for. The objectives of this paper are therefore (i) to investigate whether and how variations in σ values affect measured CASi in river runoff samples and (ii) to investigate whether or not it is possible to define a range of σ within which CASi in runoff and/or soil samples can be accurately measured using De Master's technique. 2. Materials and methods 2.1. Sample preparation 30 runoff samples with a wide range of CSPM were prepared by mixing a known amount of oven-dried (50°) soil, sieved at 2 mm, with a known quantity of distilled water. CSPM of artificial water– sediment mixtures varied between 0.1 and 100 kg m −3: this is the range which is generally encountered in field situations (Steegen et al., 2000). The soil used in the experiments was silty loamy subsoil sampled in central Belgium (Fig. 2, loc. 7). Grain size analysis using laser diffraction showed that the soil had 7.8% clay (b0.002 mm), 79% silt (0.002–0.063 mm) and 13.2% sand (0.063–2 mm). Organic matter content was ~0.94% (n = 5). The ASi content of the soil, as measured with the standard De Master method (Saccone et al., 2007) at a fixed σ value of 1.2 kg m −3 was 1.8 ± 0.3 mg g −1 (or 0.18 ± 0.03 wt.%, n = 5). Six other agricultural fields were sampled to check the representativeness of the reference soil sample (Fig. 2). Average ASi content varied between 1.33 and 1.82 mg g −1. 2.2. Laboratory analysis
Fig. 1. Illustration of the methodology used to account for the simultaneous dissolution of mineral silicates during the ASi extractions (after DeMaster (1981) and Koning et al. (2002)).
CASi in the water–sediment mixtures was measured using the wet chemical digestion method in hot alkali (Saccone et al., 2006). For each sample 5 and 25 ml of the well-mixed water–sediment mixture was passed through a 0.45 μm membrane-filter (Porafil®). The filter was air-dried for 24 h. ASi was extracted from the filters in a flatbottom digestion vessel with 25 ml of a 0.1 M Na2CO3 solution at 80 °C in a shaking (120 rpm) hot water bath for 2.5 h. 10 ml of the extract was filtered again at 0.45 μm (Chromafil® A-45/25) and cooled to 4 °C to stop dissolution. The filtrate was analysed colorimetrically for total extracted silica concentration (CTXSi) on a SAN ++
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Fig. 2. Location of the sampling sites in the Belgian Loam Belt. (7 = reference site).
analyzer (Skalar®, detection limit 0.01 ppm SiO2). Blank extractions were subtracted to account for DSi release from recipients and chemicals. The fact that most ASi dissolves completely within 2 h of digestion in hot alkali allows determining the interference of mineral silicates during the extraction by extrapolating the DSi release back through time to the intercept to correct for mineral dissolution (Saccone et al., 2006) (Fig. 1). In this study, 30% of the samples were sequentially extracted, i.e. an additional analysis of the sample after a heating period of 4 and 6 h was carried out. For each sequentially extracted sample, the slope of the linear relation between CTXSi and extraction time was determined based on CTXSi values after 2.5, 4 and 6 h of extraction. The slope (MDS) derived from this relation was assumed to represent mineral dissolution and CASi equals the y-intercept of the mineral dissolution line, i.e. the extrapolated value at time zero (Conley, 1998).
3. Results In Fig. 3, total extracted silica concentrations measured after 2.5 h of extraction time (CTXSi2.5) are plotted against σ. We found a poor relationship between CASi and σ for one series of measurements (A25) for σ b 1.6 kg m −3 (Fig. 8). The A25 measurement series differs significantly from all other series (Table 1) indicating incoherence. Therefore we did not include this series in our further statistical analysis. However, implications and possible reasons for this incoherence will be discussed later in the paper.
2.3. Statistical analysis In order to test for reproducibility, extraction procedures for 5 and 25 ml filtration volume were conducted simultaneously by two different lab-entities in the Dept. of Biology of the University of Antwerp (A) and in the Dept. of Earth and Environmental Sciences of the Katholieke Universiteit Leuven (L). These four measurements series are referred to as A5, L5, A25 and L25. Afterwards, the relations retrieved were compared statistically by introducing a dummy variable which allows to test the hypothesis of coincidence, i.e. whether or not the regression relationships retrieved are significantly different (Wonnacott and Wonnacott, 1977). All statistical analyses were performed using the SAS statistical package (Sas-Institute, 2002– 2003).
Fig. 3. CTXSi2.5 vs. σ; dashed lines indicate the limits between the different domains (see text).
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231
Table 1 Dummy analysis for CASi vs σ regressions at a significance level of 0.05 in the range 0.1 b σ ≤ 1.6 kg m−3. Series
Dummy
N
F-value
p-value
Sign. difference
L5 vs A5 L5 vs L25 L5 vs A25 A5 vs A25 A5 vs L25 A25 vs L25
L5 = 0 and A5 = 1 L5 = 0 and L25 = 1 L5 = 0 and A25 = 1 A5 = 0 and A25 = 1 A5 = 0 and L25 = 1 A25 = 0 and L25 = 1
28 25 25 25 25 22
3.3 1.3 25.9 17.9 0.8 28.6
0.0528 0.3025 0.0001 0.0001 0.4791 0.0001
No No Yes Yes No Yes
Over the total range of σ three domains can be distinguished: – Domain (1), σ≤0.8 kg m−3: CTXSi2.5 increases linearly with σ: CTXSi2.5 = 76.3*σ (R²= 0.92, n =38), – Domain (2), 0.8 b σ ≤ 4 kg m−3: CTXSi2.5 increases degressively with σ and a power function can be fitted: CTXSi2.5 = 72.1*σ0.72 (R² = 0.91, n = 32), – Domain (3), σ≥4 kg m−3: CTXSi2.5 remains constant: 225±44 μM (n=40). When MDS is plotted against σ, the same three domains can again be distinguished (Fig. 4): (1) a linear relationship for low σ values (≤0.8 kg m−3): MDS = 0.0044*σ (R² = 0.89, n = 17), (2) a degressive increase for intermediate values of σ (0.8b σ ≤ 4 kg m−3) where the relationship can be described through a power function: MDS = 0.0037*σ0.61 (R² = 0.64, n = 14) and (3) a highly variable MDS when σ exceeds 4 kg m−3. We used the linear relationship between MDS and σ described above to calculate CASi using De Master's method for σ values below 0.8 kg m −3. A clear linear relationship between CASi and σ: CASi = 52.6*σ (R² = 0.78; n = 30) is found (Fig. 5). 4. Discussion 4.1. Solution domains The analysis of our data reveals important limitations of the alkaline extraction procedure at high σ values. Complete dissolution of ASi should lead to a linear relationship over the whole range of σ values between CTXSi2.5 and MDS on the one hand and σ on the other hand. For our samples, these relationships were only linear when σ was below 0.8 kg m −3. Above this threshold value, both CTXSi2.5 and MDS do no longer increase linearly with σ indicating
Fig. 5. CASi vs. σ for σ b 0.8 kg m−3.
incomplete dissolution. The fact that CTXSi2.5 does no longer increase linearly with σ is explained by the fact that the ASi present within the sample is no longer completely dissolved within 2.5 h, when too much sediment is present. MDS is also expected to increase linearly with σ as mineral dissolution should be proportional to the amount of sediment (and therefore mineral silica) present in the sample. However, when σ exceeds a value of 0.8 kg m −3 the coupling is no longer linear, indicating that, similar to the dissolution of ASi, the dissolution of MSi becomes increasingly impeded with increasing sediment amounts. The reduction of dissolution rates with increasing σ values may have two causes. First, Si dissolution is a surface process (Koning et al., 2002): as σ increase, it is no longer certain that all sediment surfaces are free and can contribute to the dissolution process. Second, saturation may occur with an increasing amount of sediment and dissolvable Si in the sample: the total potential extractable amount of silica with the 0.1 M Na2CO3 alkaline extraction procedure is gradually reached. The absence of any clear relationship between σ on the one hand and CTXSi2.5 and MDS on the other hand once σ exceeds 4 kg m −3 is a clear indication that saturation is indeed reached. For the lowest σ values (b0.1 kg m −3) we observed a large variability in our results: this probably indicates that the accuracy limits of the method have been reached (Fig. 6a). Measurements errors with respect to the quantity of sediment on the filters become too large to allow for an accurate assessment of CTXSi2.5 and hence CASi concentrations. It is therefore not surprising that such low σ values are discouraged for alkaline digestion procedures (Flower, 1993). However, contrary to Loassachan and Tada (2008) we did not notice any significant variation in CASi with σ when the latter varied between 0.1 and 0.8 kg m −3 (Fig. 6b). Furthermore, Fig. 6a also shows that the calculated ASi content start to decrease significantly only when σ exceeds ca. 1.6 kg m −3. At first sight this is surprising, given the fact that Si dissolution becomes clearly incomplete when σ exceeds a much lower threshold value of 0.8 kg m −3. This is due to compensating error effects which are discussed in the next paragraph. 4.2. Error compensation
Fig. 4. MDS vs. σ; dashed lines indicate limits between domains for the CTXSi2.5 vs. σ relationship (Fig. 2, see also text).
Our results indicate that we underestimate (1) total extracted silica concentrations (CTXSi2.5, μM) and (2) mineral dissolution slopes (MDS) once a threshold σ value of 0.8 kg m −3 is exceeded.
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Fig. 6. Amorphous silica content (mg-Si g−1) vs. σ (kg m−3) for (a) all samples and (b) for 0.1 ≤ σ ≤ 0.8 kg m−3.
Both CTXSi2.5 and MDS are used to calculate ASi concentrations (CASi, μM): CASi ¼ CTXSit −
MDS ⋅t ⋅60 0:028
ð1Þ
with; – CTXSit = total extracted silica concentrations (μM) at time t (h) – MDS = mineral dissolution slope – t = duration of extraction (h) and, coefficients 60 and 0.028 to convert to minutes and molar weight of Si respectively. From Eq. 1 it is clear that an underestimation of CTXSi2.5 results in an underestimation of CASi while underestimating the MDS evokes an overestimation of CASi. Thus both errors compensate each other to a certain extent. In order to gain insight in these counteracting effects, we calculated CASi values based on Eq. 1 using values for CTXSi2.5 and MDS obtained from the empirical curves for each domain. We compared these empirical values with the theoretical values that would be obtained by simply extrapolating the linear relationships between σ and both CTXSi2.5 and MDS obtained for domain 1. Fig. 7 shows the empirical values and four curves: one curve (solid) represents unbiased results, assuming no measurement errors, which results in a linear relationship between σ and CASi having a slope of 52.8 which was determined experimentally using data for 0.1 ≤ σ ≤ 0.8 kg m −3 (theoretical) and one curve (dash–dotted) which was constructed using the empirical relationships between σ, CTXSi2.5 and MDS for each domain (empirical). The other curves illustrate the over- and underestimation effect. If we use the empirical MDS and the theoretical CTXSi2.5 (from domain 1) relationships, an overestimation occurs (dashed) while if we use the empirical CTXSi2.5 and the theoretical MDS (from domain 1) relationships, underestimation occurs (dotted). Fig. 7 shows, that the theoretical data do show a linear relationship over the whole range of σ values while the empirical data show a degressive trend. However, the empirical values do not significantly deviate from the linear trend when σ ≤ 1.6 due to the fact that errors on CTXSi2.5 and MDS compensate. Comparing the theoretical and empirical relationships, it can be seen that the error due to bias in the domain 0.8 b σ ≤ 1.6 varies between 0.1 and 11%. From 1.6 kg m −3 onward the empirically observed CASi concentration varies significantly. Residues, theoretical fit minus empirical values, are systematically exceeding acceptable variance (Fig. 7). De facto, the error compensation
effect extends the suitable range of σ-values to determine reliable CASi above the theoretically correct limit. Fig. 8 shows the empirical CASi vs. σ for σ ≤ 1.6 kg m −3. For all measurement series, except A25 there was a very good match between the empirical and the theoretical values predicted on the basis of observed linear relations in domain 1. The three other measurements series are coincident (Table 1) and an overall regression can be fitted: CASi = 52*σ (R² = 0.98, n = 39). This is comparable with the theoretical curve CASi = 52.8*σ. We do not have a solid explanation as to why the results for the A25 series are different from those of the three other series. The fact that the measured values are consistently lower than the theoretically expected values may indicate that small variations in treatment (e.g. subsampling, filtration, shaking) result in noticeable discrepancies which indicates the necessity of using a strongly standardised sample treatment procedure.
Fig. 7. (a) CASi vs. σ: comparing (1) empirical values, and (2) theoretical based curves: theoretical (solid), overestimation (dashed), underestimation (dotted) and empirical data (dash–dotted); (b) residue (theoretical–empirical data) vs. σ value.
W. Clymans et al. / Geoderma 167-168 (2011) 228–235
Fig. 8. CASi vs. σ within 0.1 b σ ≤ 1.6 kg m−3 for the empirical L5, A5 and L25 series.
4.3. Effect of σ on ASi measurements Several studies already investigated the effect of σ on ASi measurements, also using different solutions and solution concentrations. Table 2 gives an overview of these studies with the range of σ values that were tested and/or recommended. Recommended values for σ range between 0.2 and 5 kg m −3. Some of this variation may be attributed to the different types of samples tested as well as the strength of the solution used. Liu et al. (2002) found that σ values of up to 5 kg m −3 can be used for marine samples (with a high ASi content) in combination with a high solution concentration (2 M Na2CO3). However, all other studies recommend maximum values equal to or below 2.5 kg m −3 and several authors (DeMaster, 1981; Loassachan and Tada, 2008) indicate that recovery may be incomplete at higher σ values. Our study does not only confirm that incomplete recovery indeed prevents the accurate measurement of CASi when σ is too high but also clarifies the mechanisms leading to systematic errors at high σ values. At σ values exceeding 0.8 kg m −3 both the recovery of ASi becomes incomplete and the dissolution rate of MSi is reduced. As these errors compensate, overall measurement bias remains limited as long as σ is below 1.6 kg m −3. The latter value is still somewhat lower than the values recommended by other authors, even when using low solution concentrations (0.1 M). Most likely, the maximum theoretically correct σ value that can safely be used is to some extent dependent on the sample type. Given that Si dissolution is a surface process, one may expect that the maximum σ value may be lower for samples having a high specific surface area, i.e. samples with a significant clay content such as the samples used in this study. Analogue the extent of acceptable error compensation will vary with sample characteristics. Similarly the type and concentration of the solution that is used may also play a role: however, systematic studies on the effects of solution type and concentration are hitherto lacking. Contrary to Loassachan and Tada (2008) and Liu et al. (2002) but similarly to Gehlen and Van Raaphorst (1993) we found that, at low σ values, measured ASi content (per g of sample) remained constant. Thus, contrary to what was proposed by Loassachan and Tada (2008), it appears possible to make valid measurements over a range of solid to solution ratios. The latter is important as the solid to solution ratio cannot always be accurately determined a priori for runoff samples. The reasons for these different results are not entirely clear, but it is interesting to note that Loassachan and Tada (2008) calculated a
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recovery percentage exceeding 100% for small σ values. Also, both Loassachan and Tada (2008) and Liu et al. (2002) observed that measured ASi concentration decreased rapidly and systematically with σ for σ values b 1 kg m −3. Loassachan and Tada (2008) attribute this phenomenon “to the increase of the relative reactive surface area” with decreasing σ. Considering that samples in this study were ground before analysis, it seems possible that the relatively small samples used (minimum 5 mg) contained relatively high amounts of very fine mineral particles that were much more reactive then the bulk mineral material. The relatively higher abundance of such material in the small samples would not only explain why the calculated recovery percentage may exceed 100% but also why they observed that the Si dissolution curves were convex downward rather than linear for most of their samples. Other studies support the hypothesis that a highly reactive pool of very fine particles may be present in the mineral fraction. Müller and Schneider (1993) measured mineral dissolution rates in a strong base solution and found that rapid initial dissolution occurred, after which dissolution rates became constant. They attributed this phenomenon to a grain size effect, i.e. the preferential dissolution of very fine particles. In our study the samples were not ground and small samples were prepared by subsampling a volume of water into which a larger volume of sediment sample was mixed. Our samples were all prepared from a single soil sample so that grain size composition was constant. In natural runoff we could expect significant variations in grain size and in mineral composition due to the size selectivity of the processes involved (Beuselinck et al., 2000). The enrichment in clay-minerals, typical at low sediment concentrations (Wang et al., 2010), may lead to variations in mineral dissolution rates and hence to measurement bias. In other environments sediments differ in mineralogical composition depending on the parent material or soil type. This influences ASi measurements as various clay-types have different mineral dissolution rates (Koning et al., 2002). Further research efforts to reveal the grain size and mineralogical dependency of ASi measurements are therefore necessary. 4.4. Method adaption As CSPM in runoff samples may vary between 0.01 and 100 kg m −3 (Steegen et al., 2000) and the standard method is limited for σ (≤1.6 kg m −3), an alternative method for runoff samples with a high CSPM and therefore high σ is proposed. We propose a simple adaptation of the filtration method, similar to the method used by Saccone et al. (2007) for soil samples. Filter a substantial amount of subsample (100–300 ml) at 2.5 μm using a large filter (e.g. using Whatman 42 filters). The rest of the sample may be used for other analyses. Samples are subsequently air-dried, removed from the filter by gentle brushing and homogenised. Homogenisation should be done with mortar and pestle. Although for the reference sample mill grounding didn't show significant different CASi, it is discouraged because higher CASi could be obtained from the exposure of new surfaces available for dissolution (Conley and Schelske, 2001). A fixed quantity of filtrated sample (25–30 10 −6 kg) is then used during the extraction procedure. This implies that we are working with a constant σ value (1–1.25 kg m −3) which is within the suitable range (i.e. 0.1 ≤ σ ≤ 1.6 kg m −3) as retrieved from our experiments. Although our analysis shows that runoff samples may also be analysed without bias at lower σ, results can be expected to be more reliable at higher σ, as measurements errors become relatively less important. The amorphous silica content we determined for our reference soil (1.8 ± 0.3 mg-Si g −1, n = 5) is somewhat higher than the average ASi content we found for the artificial runoff samples we created from this soil (1.65 ± 0.4 mg-Si g −1, n = 30), but the difference was not significant for the reference sample nor the six other agricultural sites (p b 0.02). Therefore this method proves to be precise and
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Table 2 Overview of studies measuring ASi in sediments at various σ values. Extractant refers to the solution used as digestive during extraction procedure. Reference
Sample type
Tested σ (kg m−3)
Recommended σ (kg m−3)
Extractant
Remarks
Gehlen and Van Raaphorst, 1993
Marine sediments
2.5
2.5
0.1 M Na2CO3
Conley and Schelske, 2001
Sediments
0.5–1
0.75
0.1 M Na2CO3
Loassachan and Tada, 2008
Marine sediments
0.1–8.0
2
0.1 M Na2CO3
Kamatani and Oku, 2000
Siliceous micro-organisms and aluminosilicates
0.5–0.75
0.5–0.75
Liu et al., 2002
Marine sediments
0.25–6.25
1–5
Diverse: 0.1 M and 0.5 M Na2CO3, and 0.1 and 0.2 M NaOH 2 M Na2CO3
DeMaster, 1981
Marine
0.2–0.6
0.2–0.6
0.1 M Na2CO3
Conley, 1998
Freshwater and coastal sediments
0.2–2.5
No
Diverse: range 0.1–2 M Na2CO3 and 0.2–1 M NaOH
Saccone et al., 2008
Soil
0.75
No
0.094 M Na2CO3
Mortlock and Froelich, 1989 Müller and Schneider, 1993
Marine sediments
0.625–5
See remark
2 M Na2CO3
Siliceous micro-organisms and clay minerals
0.20–0.42
No
0.1, 0.5 and 1 M NaOH
Have previous tested (unpublished data) for three different σ values: 2.5, 1.25, 0.625 kg m−3 and concludes that variability is low (7%), to minimise effects of heterogeneity, the highest σ is used: 2.5 kg m−3. No sign. differences in ASi extracted has been found for σ-values between 0.5 and 1 kg m−3, a consistent weight is recommended Optimal σ of 2 kg m−3: 100% recovery b 1 over contribution clay mineral derived extractable Si ≥ overestimation N 2 incomplete dissolution ≥ underestimation b 0.5 kg m−3 gives lower accuracy and N 0.75 kg m−3 lower recovery, the tested values correspond with recommended (based on previous work; Kamatani, 1980). Linear relation between CASi and σ: a σ of 1–5 kg m−3 during extraction is necessary to minimise uncertainty in ASi measurements Generally accepted as pioneer for sequential alkaline extraction techniques It was interlaboratory comparison and every laboratory used its own σ, Conley legitimise this by referring to Gehlen and Van Raaphorst (1993) to be within this accepted range. Reproducibility was checked with replicates: standard errors : 0.01 to 3.48 g kg−1 Single step — Adapted in order that no more then 25 mg of pure biogenic opal is present A grain size effect is suggested, with preferential dissolution of ultra-fine particles during initial extraction period. Homogeneity is important because small masses are used.
Sediments, silica gel and clay minerals
1
No
0.5 M NaOH
Koning et al., 2002
delivers reliable results for the Belgian Loam Belt when σ values are too high. Results of this alternative method are insensitive to incomplete dissolution. Instead of forming thin layers on the filtrate, sediments are loose particles within the solution. Therefore incomplete dissolution due to partial-surface exposure is minimal. CTXSi2.5 and MDS aren't underestimated and will result in accurate results without error compensation. This is true until buffer capacity of the solution is reached. The advantages of the original method with extraction from filters are that (1) only a limited amount of sample is necessary without a lot of pre-treatment and (2) it is a reproducible and easy method within a large range of σ values. Disadvantages are (1) underestimation of CASi at high σ values and (2) increasing variability towards very low σ values. A general disadvantage with sequential leaching is that the linear slope and the extrapolated intercept value are based only on few measurements in time, usually three to four. With such a low number of data points, potential outliers are difficult to identify and the uncertainty on the extrapolated amorphous silica value can be large. Continuous monitoring of silica would overcome a large part of these problems (Müller and Schneider, 1993).
5. Conclusion and recommendations Our study confirmed that the alkaline (0.1 M Na2CO3) digestion method proposed by DeMaster can be used for runoff samples provided that the solid to solution ratio (σ) is within certain limits: at very low σ (b0.1 kg m −3), subsample heterogeneity results in high variability of measured CASi while at higher σ values (N0.8 kg m −3) incomplete dissolution of ASi as well as the reduction of mineral dissolution rates results in underestimated CASi. As both errors
compensate one another, the range of applicable σ-values can be extended above the theoretically correct limit (1.6 kg m −3). We found that CASi can be determined reliably within a considerable range of σ. While this finding confirms the results from some studies, it differs from observations made by other researchers who found that measured ASi concentrations continued to rise with decreasing σ. This may be due to the absence of a fine reactive mineral fraction in our samples. The finding that reliable measurements can be made within a relatively wide range of σ values (0.1 ≤ σ ≤ 1.6 kg m −3) is important as it is now possible to propose a method for the measurement of ASi in runoff samples and derive some recommendations for ASi analysis, making a distinction between samples with a low and high CSPM. (i) Samples with low CSPM (≤1.6 kg m −3): stir sample thoroughly take a subsample of 5 or 25 ml and use standard procedure (see Materials and methods). Determine CSPM afterwards and use this to determine σ. (ii) Samples with high CSPM (N1.6 kg m −3, water is no longer clear): stir sample thoroughly and take a substantial subsample (100–300 ml) and use adapted procedure (see Discussion). However, one should be aware that the range and limits for σ proposed here may depend on the type of sediment to be analysed: it is therefore recommended to evaluate the performance of the method again before it is used in other environments. Acknowledgements The authors thank both the ECOBE laboratory of University Antwerp and Environmental Science laboratory of K.U. Leuven for sample analysis. Wim Clymans would like to thank the Flemish Agency for
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the promotion of Innovation by Science and Technology (IWT) for funding his personal promotion grant. Eric Struyf would like to thank FWO (Research Foundation Flanders) for personal post doc research funding. We would like to acknowledge Belgian Science Policy (BELSPO) for funding project “LUSi: Land use changes and silica fluxes in the Scheldt river basin” and FWO for funding project “Tracking the biological control on Si mobilisation in upland ecosystems” (Project nr. G014609N). References Alexandre, A., Meunier, J., Colin, F., Koud, J., 1997. Plant impact on the biogeochemical cycle of silicon and related weathering processes. Geochim Cosmochim Ac 61 (3), 677–682. Beuselinck, L., Steegen, A., Govers, G., Nachtergaele, J., Takken, I., Poesen, J., 2000. Characteristics of sediment deposits formed by intense rainfall event in small catchments in the Belgian Loam Belt. Geomorphology 32, 69–82. Blecker, S.W., McCulley, R.L., Chadwick, O.A., Kelly, E.F., 2006. Biologic cycling of silica across a grassland bioclimosequence. Global Biogeochem. Cycles 20 (3), GB3023. Brzezinski, M., 1985. The SI–C–N ratio of marine diatoms — interspecific variability and the effect of some environmental variables. J. Phycol. 21 (3), 347–357. Conley, D., 1997. Riverine contribution of biogenic silica to the oceanic silica budget. Limnol. Oceanogr. 42 (4), 774–777. Conley, D., 1998. An interlaboratory comparison for the measurement of biogenic silica in sediments. Mar. Chem. 63 (1–2), 39–48. Conley, D., Schelske, C., 2001. Biogenic silica. In: Smol, J., Birks, H., Last, W. (Eds.), Tracking Environmental Change Using Lake Sediments: Biological Methods and Indicators. Kluwer Academic Press, pp. 281–293. DeMaster, D., 1981. The supply and accumulation of silica in the marine-environment. Geochim. Cosmochim. Acta 45 (10), 1715–1732. DeMaster, D., 1991. Measuring biogenic silica in marine-sediments and suspended matter. Mar. Part. Anal.Charact 63, 363–367. DeMaster, D., 2002. The accumulation and cycling of biogenic silica in the Southern Ocean: revisiting the marine silica budget. Deep. Sea. Res II 49 (16), 3155–3167. Dugdale, R., Wilkerson, F., Minas, H., 1995. The role of a silicate pump in driving new production. Deep. Sea. Res I 42 (5), 697–719. Flower, R., 1993. Diatom preservation — experiments and observations on dissolution and breakage in modern and fossil material. Hydrobiologia 269, 473–484. Gehlen, M., Van Raaphorst, W., 1993. Early diageneis of silica in sandy north-seas sediments — quantification of the solid-phase. Mar. Chem. 42 (2), 71–83. Gérard, F., Mayer, K., Hodson, M., Ranger, J., 2008. Modelling the biogeochemical cycle of silicon in soils: application to a temperate forest ecosystem. Geochim. Cosmochim. Acta 72 (3), 741–758. Hurd, D., 1972. Factors affecting solution rate of biogenic opal in seawater. Earth Planet. Sci. Lett. 15 (4), 411–417. Iler, R., 1979. The Chemistry of Silica. Wiley-Interscience, New York. Jackson, M., Whittig, L., Pennington, R., 1950. Seggragation procedure for the mineralogical analysis of soils. Proc. Soil Sci. Soc. Am 14, 77–81. Kamatani, A., 1980. Determination of biogenic silica in marine sediment. La Mer Bull. Soc. Fr.-Jpn. Oceanogr. 18, 63–68. Kamatani, A., Oku, O., 2000. Measuring biogenic silica in marine sediments. Mar. Chem. 68 (3), 219–229. Koning, E., Epping, E., Van Raaphorst, W., 2002. Determining biogenic silica in marine samples by tracking silicate and aluminium concentrations in alkaline leaching solutions. Aquat. Geochem. 8 (1), 37–67. Laruelle, G.G., Roubeix, V., Sferratore, A., Brodherr, B., Ciuffa, D., Conley, D.J., Dürr, H.H., Garnier, J., Lancelot, C., Le Thi Phuong, Q., Meunier, J.D., Meybeck, M.,
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