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Quantification of carbon derived from lignite in soils using mid-infrared spectroscopy and partial least squares
Organic Geochemistry 32 (2001) 831–839 www.elsevier.nl/locate/orggeochem
Quantification of carbon derived from lignite in soils using mid-infrared spectroscopy and partial least squares C. Rumpel a,c,*, L.J. Janik b, J.O. Skjemstad b, I. Ko¨gel-Knabner c a
Department of Soil Protection and Recultivation, Brandenburg Technical University, PO Box 10 13 44, D-03013 Cottbus, Germany b CSIRO, Land and Water, Waite Road, Urrbrae, PMB 2, Glen Osmond, Adelaide, SA 5064, Australia c Lehrstuhl fu¨r Bodenkunde, Technische Universita¨t Mu¨nchen, 85350 Freising-Weihenstephan, Germany Received 4 July 2000; accepted 5 March 2001 (returned to author for revision 1 February 2001)
Abstract The total organic carbon (TOC) of many recultivated mine soils is composed of a fraction that is lignite-derived as well as a fraction that is derived from recent plant litter. In these soils, precise quantification of the lignite contribution to the TOC content can only be achieved with expensive and time consuming methods. In the present study, we tested diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in combination with multivariate data analysis [partial least squares (PLS)] as a rapid and inexpensive means of quantifying the lignite contribution to the TOC content of soil samples. The conceptual approach included analysis of samples with different lignite content (bulk soil and particle size fractions) by DRIFT-spectroscopy and 14C activity measurements. Afterwards, with both data sets a calibration curve was established by PLS and the lignite content predicted from the DRIFT spectra. A good fit was obtained between this approach and the radiocarbon analysis. Loading factors showed that this prediction was based on structural differences between the two organic matter types. We conclude that DRIFT spectroscopy can be used in combination with multivariate data analysis for the differentiation of carbon derived from lignite and carbon derived from recent organic matter in soils. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Lignite; Organic matter; DRIFT spectroscopy; 14C activity; Partial least squares
1. Introduction In the Lusatian mining district in the Eastern part of Germany, recultivated mine soils can contain more than one organic matter type. Organic matter (OM) derived from lignite is often present in the spoil material from open-cast mining. In the former German Democratic Republic these substrates were ameliorated with lignitederived ash and planted with coniferous or broad-leaved trees. During the course of soil development recent OM derived from plant material accumulated and became intimately mixed with the lignite from the spoil (Rumpel
* Corresponding author at third address. Tel.: +49-355-781162; fax: +49-355-78-1170. E-mail address: [email protected] (C. Rumpel).
et al., 1998). Characterization of the recent OM is absolutely necessary when studying soil development because the quantity and properties of OM derived from plant material influence most physical and chemical soil characteristics. Quantitative information about its contribution can be obtained by 14C activity measurements (Rumpel et al., 2000a). Using 13C cross polarization magic angle spinning nuclear magnetic resonance (CPMAS NMR) spectroscopy, chemical structures characteristic of recently formed OM can be identified in the OM mixture in the upper few centimeters of the soil (Rumpel et al., 2000a). However, these methods are expensive and timeconsuming, so it was considered appropriate to test infrared (IR) spectroscopy in combination with multivariate data analysis as a rapid and inexpensive means to observe structural differences between lignite and recently formed OM and to quantify the contribution of lignite-derived carbon.
0146-6380/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0146-6380(01)00029-8
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Fourier transformed infrared analyses, combined with a wide variety of chemical methods, are traditionally used to examine complex organic substances in the solid state. It is especially useful for elucidating degradation pathways of litter compounds (Gianfranco et al., 1995; Wershaw et al., 1996), occurring during composting (Inbar et al. 1989) and high energy UV photo-oxidation (Skjemstad et al., 1993). Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has several advantages over the traditional KBr pellet pressing technique (Fuller and Griffiths, 1978; Baes and Bloom, 1989). It is more rapid (spectra of a powdered sample can be obtained in less than a minute) and needs little sample preparation (grinding is sufficient) while being superior for showing the presence of inorganic compounds in soils (Nguyen et al., 1991). The interpretation of the spectra is difficult, however, because of the increasing influence of inorganic soil components with profile depth and overlap between individual absorption bands (Hempfling et al., 1987). Some of these problems can be overcome by using spectral subtraction of digital IR spectra (Painter et al., 1981; Skjemstad et al., 1993), but unfortunately the infrared peaks of soils and OM are often too complex to provide simple visual quantitative data (Janik et al., 1995). To interpret the wealth of information provided by the IR spectra, chemometrical methods such as multivariate data analysis have been used to determine quantitatively the content of special compound classes, e.g. fat, protein, polysaccharides and microbes (Gordon et al., 1993; Luinge et al., 1993). Multivariate data analysis is often associated with regression modeling, i.e. to model the relationships between two sets of measurements. Any data matrix can be represented as just a few bi-linear projections, principal components (PC) or partial least squares (PLS) in latent variables (Wold, 1989). The use of PLS focuses on predictive modeling and the technique was successfully applied in soil science to predict soil properties from their mid-infrared spectra (Janik et al., 1995; Janik and Skjemstad, 1995). In this study, DRIFT spectroscopy was used to characterize the OM component of rehabilitated mine soils containing substantial amounts of lignite-derived carbon. The objective of this study was to predict the TOC fraction that is lignite-derived of a soil sample from its IR spectrum by the use of partial least squares (PLS).
spoil bank. The spoil material was rich in lignite-derived carbon (up to 50 g/kg Corg) from actual lignite coal fragments as well as originally disseminated lignitic phytoclast particles which were present in the fine particle size fractions of the mud and shale material. Prior to afforestation the parent substrate was ameliorated using lignite ash and NPK fertilizer (Katzur and HauboldRosar, 1996). As the trees became older, OM from the decomposition of plant material accumulated and was incorporated into the mineral soil to form an initial surface soil horizon (Ai horizon, AG Boden, 1994). Additionally, airborne lignite dust was deposited on the soil surface until the beginning of the 1990s (Heinsdorf and To¨lle, 1993) . Due to the dark colour of the parent material (Cv) macroscopic visual differentiation of recently formed OM and lignite was not possible. Horizon designations were applied according to the German Soil Science Classification (AG Boden, 1994). Prior to sampling an inventory of the chemical parameters of the sites was carried out. Following these studies, one representative soil profile was established at each site. Samples were taken from the forest floor, the Ai and Cv horizon of soil profiles under a chronosequence of pine [Black pine (Pinus nigra) 11 years and Scots pine (Pinus sylvestris) 20 and 30 years], and a red oak stand (Quercus rubra, 36 years). A soil chronosequence is characterized by similar conditions for soil development (i.e. geology, slope, climate, vegetation), the only difference being the time of soil development. Additionally, a mine soil containing substantial amounts of airborne lignite-derived contamination was sampled under 25 year old red oak (Quercus rubra). After sampling the samples were thoroughly homogenized. 2.2. Sample pre-treatment and particle size fractionation The samples of the mineral soil were air dried and the fraction >2 mm removed by dry sieving. The pH measurements were carried out with a glass electrode in the supernatant of a 1:2.5 w/vol mixture of soil and water. A particle size fractionation of the Ai horizon under the 36 year old red oak was carried out using ultrasonic dispersion, sieving and sedimentation techniques as described by Schmidt et al. (1999). The total organic carbon (TOC) content of solid samples was determined with a Leco CHN 1000 analyser. Chemical parameters of all samples are listed in Table 1.
2. Materials and methods 2.1. Soil samples
2.3. 14C activity measurements for the determination of lignite carbon
The soils are located in the Lusatian mining district in east Germany. They developed on sandy spoil material from a lignite-bearing sequence of Miocene age (16 million years old), which was excavated during mining operations and had been relocated and deposited at a
Radiocarbon acitvity measurements of the soil samples were carried out using the conventional macrotechnique of liquid scintillation as described by BeckerHeidmann et al. (1988). A soil sample containing 6 g carbon was burned and the carbon was subjected to
C. Rumpel et al. / Organic Geochemistry 32 (2001) 831–839 Table 1 Assignments of the main IR absorbtion bands of humic substances (after Stevenson, 1994, modified) Frequency (cm1)
Assignment
3400–3300 2940–2900 1725–1720 1660–1630
O–H stretching Aliphatic C–H stretching C¼O stretching of COOH C¼O stretching of amide groups (amide I band) Aromatic C¼C COOsymmetric stretching, N–H deformation, C¼N stretching (amide II band) Aliphatic C–H OH deformation and C–O stretching of phenolic OH, C–H deformation of CH2 and CH3 C–O stretching and OH deformation of COOH C–O stretching of polysaccharide
1620–1600 1590–1517 1460–1450 1400–1390
1280–1200 1170–950
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wide-band mercury-cadmium-telluride (MCT) detector operating at two scans per second. The infrared spectra were recorded by the diffuse reflectance Fourier transform (DRIFT) technique using a Harrick off-axis diffuse reflectance accessory (DRA-3SO). A KBr blank was subtracted from the recorded spectra. Subspectra of the organic matter components were obtained by subtracting the spectrum of the mineral component from the spectrum of the whole soil. The mineral component spectra were obtained after ignition of the sample at 350 C overnight. This temperature was found to be adequate to burn all OM. The assignments of FTIR bands were chosen according to Stevenson (1994) (Table 1). The spectral files were transformed into GRAMS/ 386TM format for PLS manipulation. The computer program PLSPLUSTM (GRAMS/386TM Galactic. NH) was used for PLS-1 analysis (Haaland and Thomas, 1988). Reduced portions of the spectra, from 3780 to 740 cm1 using an eight point average, were pre-processed with baseline correction and mean centering.
3. Results and discussion benzene synthesis. After 6 weeks, the 14C activity of the benzene was recorded with a scintillation spectrometer (Packard Tri Carb Model 3320). The 14C activity was corrected for isotopic fractionation, according to Stuiver and Pollach (1977). Therefore part of the sample was combusted, the CO2 purified and its d 13C determined in an isotope ratio mass spectrometer (MTA 250, Fa Finnigan). The data were corrected with the d13C values for isotopic effects. Radiocarbon decay is a statistical process and the 14C activity was obtained as mean with standard deviation. Lignite carbon is several million years old and therefore no longer shows any 14C activity (‘dead carbon’). The percentage of dead carbon in a sample can be obtained from the 14C activity measurements by Eq. (1) (Rumpel et al., 1998): Lignite ð% total CÞ ¼ 10014 C activity
ð1Þ
2.4. DRIFT spectroscopy and PLS analysis Sample preparation and DRIFT spectroscopy were performed as described by Janik et al. (1995). Samples were poured into 10 mm diameter stainless steel cups and the top surfaces of the powders were leveled. Spectra were recorded for infrared measurements from 4000 to 500 cm1 at 1.92 cm1 intervals and a resolution of 4 cm1. The measurements were carried out on a Digilab FTS-80 rapid-scan Fourier transform spectrometer by using a germanium-coated KBr beam-splitter, a high intensity globar source and a liquid nitrogen cooled
3.1. Chemical parameters of the soil samples and lignite content determined with 14C activity The total carbon content (TOC) was relatively high for all samples studied (Table 2). In the Cv horizon, this was almost exclusively lignite-related carbon (Rumpel et al., 1998). In the first few centimeters of the Ai horizon at least 42% of the TOC was derived from lignite. Comparing all sites, a decrease of the relative contribution of lignite-derived carbon with increasing age was observed in the Ai horizons (Table 2). This illustrates the increasing accumulation of OM originating from plant material during mine soil development (Rumpel et al., 1999). Lignite-derived carbon was present in all particle size fractions (Table 2), but its highest contents occurred in the 63-20 and <2 mm fraction. Here, the lignite contribution amounted to 42 and 48% of the TOC. These data are in contrast with the results obtained by Schmidt et al. (1996), who studied the impact of airborne lignitederived contamination on particle size fractions of a Mollisol. The latter authors found most lignite-derived carbon in the coarse size fractions. Our data indicate that lignite in the parent substrate is not allocated to well defined fractions but has a heterogeneous distribution among the particle size fractions (Rumpel et al., 2000b). Lignite-derived carbon was found to increase the contribution of aromatic and aliphatic carbon species in the OM (Rumpel et al., 2000a). Thus a lignite contribution has changed the chemical composition of all samples studied. It should be possible to observe the changes in structure by IR spectroscopy.
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cm1 due to aromatic species (Stevenson, 1994). These spectra were somewhat unlike the infrared spectra observed by other authors for humic substances and compost samples (Gerasimowicz and Blyer, 1985; Inbar et al, 1989; Gressel et al., 1995). Many of the frequencies identified for the humic and compost substances were similar to those for organic matter of lignite-containing mine soils, but their relative proportions were different and the peaks in the spectra region from 1700–1200 cm1 were better resolved. The infrared spectra of the lignite-containing soils show that lignite and plantderived OM occur in intimate mixture in these soils. Both samples have a low lignite content (Table 2), and coupled with the extreme overlap of the infrared peaks,
3.2. DRIFT-spectra of samples high and low in carbon Fig. 1 depicts the DRIFT spectra of two samples with contrasting TOC content (no. 12, 23.8%C and no. 4, 2.8%C) for comparison. Both spectra are characterized by strong peaks due to hydroxyl (3405 cm1), alkyl (2925–2927 and 2856–2858 cm1) and amide (1658– 1652 cm1). The assignment of the peaks at 2179–2115 cm1 is uncertain but may be attributed to the carbohydrate overtone frequency occurring near 1050 cm1. Samples which were low in TOC corresponded to a reduction in the amide peak intensities but were characterized by an enhancement of a shoulder near 1728 cm1 due to carboxylic acid, and another near 1561
Table 2 Chemical parameters of the soil samples. Years refer to the age of the trees, i.e. the length of period during which the vegetation existed Study site Red oak (36 years)
Red oak (25 years) Black pine (11 years) Scots pine (20 years) Scots pine (30 years)
Sample no. 1 2 3 4 5 6 7 8
Horizon
Sampling depth (cm)
pH (H2O)
TOCa (mg/g)
lignite carbon (% of total TOC)
Oh Ai Cv Ai Ai Ai Ai Ai
2–0 0–5 100 0–2 0–2 0–1 1–3 0–5
6.6 6.8 3.1 6.0 5.6 5.0 5.8 5.5
224 110 37 28 102 80 46 160
131b 421 966 461 812 641 802 611
51 123 94 238 208 160
321 291 481 201 272 421
Particle size fractions of the Ai horizon under 36 year old red oak (carbon recovery was 95%) 630–200 mm 9 Ai 0–5 n.b 200–63 mm 10 Ai 0–5 n.b 63–20 mm 11 Ai 0–5 n.b 20.0–6.3 mm 12 Ai 0–5 n.b 6.3–2.0 mm 13 Ai 0–5 n.b < 2 mm 14 Ai 0–5 n.b a b
TOC=total organic carbon. S.D. accounts for variability of 14C activity measurements.
Fig. 1. DRIFT spectra for high TOC (sample no. 12) and low TOC (sample no. 4).
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Fig. 2. DRIFT spectra for samples rich in lignite-derived carbon (nos. 3, nos. 5 and nos. 7).
would make visual assignments of signals to lignite difficult, if not impossible. Fig. 2 shows spectra of high lignite/TOC ratio samples (nos. 3, 5 and 7, all >75% ratio). The carbon in these samples is dominated by alkyl (2924, 2855 cm1), carboxylic acids (1717 cm1), aromatic (1638, 1566 cm1) and carboxylate species (near 1441–1420 cm1) (Fuller and Smyrl, 1990). The peak near 1256 cm1 is unassigned. The dilution of the lignite in sample no.3 (subsoil, 96% lignite) by possible amide carbon in samples no. 5 and 7 (both surface soil horizons with recent carbon contribution, Table 2) is in accord with its lower recent carbon contribution (4%), compared to nos. 5 and 7 of approximately 20%. It is apparent from Figs. 1 and 2, that prediction of lignite below a level of 95% lignite contribution to TOC would require statistical methods. 3.3. Mid infrared partial least squares (PLS) PLS analysis is a method that can be used to simplify a data matrix when many descriptors are available. It decomposes spectral and soil property data into a set of eigenvectors and their scores (Haaland and Thomas, 1988). Two-dimensional plots of the first two to three scores depict the major distribution of the samples within the calibration set, somewhat analogous to performing a principle component discriminant analysis. PLS analysis was used in this study to cross-validate the prediction of the lignite-derived fraction of TOC as obtained by 14C activity measurements. The TOC content of lignite-containing soils can be considered to be from two major sources; recent OM from plant inputs (primarily trees), and lignite-derived carbon. Recent OM can be further sub-divided into such groups as alkyl, proteinaceous, carboxyl, carbohydrate and aromatic species (Ko¨gelKnabner, 1997). It would therefore be an advantage to partition the PLS analysis into at least the two major carbon sources (recent OM and lignite), and also con-
sider the distribution of the various carbon species in the recent soil OM, to achieve a meaningful description of the carbon in these soils. PLS predictions of carbon from both of these pools is simple and has the advantage of providing qualitative information as to the characteristics of the carbon species, in addition to a purely quantitative aspect. The resulting PLS regression by crossvalidation, for both recent OM and lignite/TOC ratio, can be considered highly significant for the 14 calibration samples. Prediction of r2 of 0.79 and standard error of cross-validation (SECV) of 29 g/kg were achieved for recent OM, and an r2 of 0.84 and SECV of 10 g/kg for lignite (Fig. 3, Table 3). This SECV is considered to be comparable (10% of the maximum range) to the range of standard errors obtained for other soil properties by Janik et al. (1995). For TOC, the precision was higher, with an r2 of 0.86 and an SECV of 23 g/kg for three factors. With the 14 samples, which were included in the analysis, a good correlation was obtained between values predicted from PLS analysis and data from 14C activity measurements. 3.4. Qualitative analysis using PLS PLS also generates loading weight vectors that explain the qualitative variance in the independent data, correlating either positively or negatively with the observed spectral data. Positive peaks in the first few loading weights indicate a positive correlation of the infra-red vibration of the active molecular species with OM derived from plant material or lignite. Of the 5–7 factors used for optimum determination of the recent OM and lignite/ TOC components, the first few loading weights are the most important for providing qualitative information on those species correlating most highly with the components in question. The first three loading weights for recent OM are presented in Fig. 4. These are characteristic of recent soil OM, e.g. amide (1682 and 1532 cm1),
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plant lignin (1512 cm1) and alkyl (2939, 2860 cm1) for the first loading weight, alkyl (2939, 2860 cm1), plant lignin (1512 cm1) and carbohydrate (2005, 1136 and
1094 cm1) for the second, and alkyl (2939, 2860 cm1), plant lignin (1512 cm1) and carboxylic acid (1729 cm1) for the third (Janik and Skjemstad, 1995).
Fig. 3. Regression between the lignite/TOC ratios obtained by radiocarbon dating versus values predicted by infrared PLS crossvalidation. Table 3 Actual and infra-red PLS predicted values of recent OM–C and the lignite/TOC ratio (as percent) for the 14 sample calibration set. The regression r2 and standard error of cross-validation (SECV) are also presented Sample no.
Actual OM–C (mg/g)
Predicted OM–C (mg/g)
Actual lignite (% of TOC)
Predicted lignite (% of TOC)
1 2 3 4 5 6 7 8 9 10 11 12 13 14
195 64 1 15 19 29 9 62 35 87 49 190 152 93
164 118 30 25 11 67 20 60 20 67 48 146 137 143
13 42 96 46 81 64 80 61 32 29 48 20 27 42
18 42 113 43 73 67 65 56 49 36 38 22 41 52
r2 SECV
0.79 29
0.84 10
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The assignments can be confirmed by examining the score distributions presented in Fig. 5 for recent OM. Samples high in recent OM, e.g. no. 1, 2, 12 and nos. 13, are strong in the first loading, i.e. strong in amide. These samples are related to the Ai horizon where recent OM accumulated during soil development (Rumpel et al., 1999). Samples high in loading weight 2, such as sample no. 7, are high in carbohydrate, while samples nos. 3 and 6 with a high loading weight 3 are assumed to be high in carboxylic acids. This is consistent with the low pH of these samples which is due to the acid condition in the lower depth of lignite-containing mine soils.
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In contrast, the loading weights for lignite (Fig. 6) are dominated by three strong groups of peaks: alkyl (2939, 2860 cm1), 1792 cm1 of uncertain assignment but possibly due to C¼O stretching vibrations in the aromatic lignite structure, and 2342 cm1 due to iron oxide. The sharp peaks near 3695–3620 cm1 are undoubtedly due to kaolinite. The score plots, presented in Fig. 7 for lignite, suggests that it is a combination of the first and second loadings which are most strongly associated with lignite. Thus high lignite/TOC samples e.g. nos. 8, 6 and #7, are placed in the top right-hand corner of the score map, i.e. positive and strong in both factor 1 and 2, and
Fig. 4. Loading weights 1 (a), 2 (b) and 3 (c) for PLS cross-validation analysis of recent OM–C for the 14 calibration samples.
Fig. 5. Loading scores 2 versus 1, and scores 3 versus 1 for PLS cross-validation analysis of recent OM–C for the 14 calibration samples.
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Fig. 6. Loading weights 1 (a), 2 (b), and 3 (c) for PLS cross-validation analysis of lignite/TOC ratios for the 14 calibration samples.
4. Conclusions
Fig. 7. Loading scores 2 versus 1 for PLS cross-validation analysis of lignite for the 14 calibration samples.
therefore strong in the species giving rise to the 1792 cm1 peak. These results illustrate that the prediction model developed by PLS is not a simple correlation, but is based on chemical and structural differences between lignite and recent OM originating from plant material. Therefore it is appropriate to quantify lignite-derived carbon in lignite containing mine soils by the use of PLS based on its chemistry as revealed by DRIFT spectroscopy.
In this study DRIFT spectroscopy and 14C activity measurements of 14 soil samples were used to determine the fraction of TOC in lignite-containing soils that is lignite-derived. DRIFT-spectra showed that Ai horizons of lignite-rich mine soils contain recently formed OM with large amounts of amide. The lignite content of samples containing a mixture of lignite-derived carbon and carbon derived from plant material can be quantified from DRIFT-spectra by using a predictive model PLS. By interpretation of the factor loadings we concluded that this prediction was not a simple correlation but based on structural differences between lignite and OM derived from plant material as observed by DRIFT spectroscopy. Once a calibration curve is established, DRIFT spectroscopy in combination with PLS data analysis can serve as a valuable tool for the quantification of the fraction of TOC that is lignite-derived in soils containing organic matter mixtures.
Acknowledgements We thank the Deutsche Forschungsgemeinschaft for financial support. P. Becker-Heidmann is acknowledged for the 14C activity measurements. We also thank R. Tyson and W. Prickel for valuable comments on the manuscript. Associate Editor—A.C. Cook
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Quantification of carbon derived from lignite in soils using mid-infrared spectroscopy and partial least squares C. Rumpel a,c,*, L.J. Janik b, J.O. Skjemstad b, I. Ko¨gel-Knabner c a
Department of Soil Protection and Recultivation, Brandenburg Technical University, PO Box 10 13 44, D-03013 Cottbus, Germany b CSIRO, Land and Water, Waite Road, Urrbrae, PMB 2, Glen Osmond, Adelaide, SA 5064, Australia c Lehrstuhl fu¨r Bodenkunde, Technische Universita¨t Mu¨nchen, 85350 Freising-Weihenstephan, Germany Received 4 July 2000; accepted 5 March 2001 (returned to author for revision 1 February 2001)
Abstract The total organic carbon (TOC) of many recultivated mine soils is composed of a fraction that is lignite-derived as well as a fraction that is derived from recent plant litter. In these soils, precise quantification of the lignite contribution to the TOC content can only be achieved with expensive and time consuming methods. In the present study, we tested diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in combination with multivariate data analysis [partial least squares (PLS)] as a rapid and inexpensive means of quantifying the lignite contribution to the TOC content of soil samples. The conceptual approach included analysis of samples with different lignite content (bulk soil and particle size fractions) by DRIFT-spectroscopy and 14C activity measurements. Afterwards, with both data sets a calibration curve was established by PLS and the lignite content predicted from the DRIFT spectra. A good fit was obtained between this approach and the radiocarbon analysis. Loading factors showed that this prediction was based on structural differences between the two organic matter types. We conclude that DRIFT spectroscopy can be used in combination with multivariate data analysis for the differentiation of carbon derived from lignite and carbon derived from recent organic matter in soils. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Lignite; Organic matter; DRIFT spectroscopy; 14C activity; Partial least squares
1. Introduction In the Lusatian mining district in the Eastern part of Germany, recultivated mine soils can contain more than one organic matter type. Organic matter (OM) derived from lignite is often present in the spoil material from open-cast mining. In the former German Democratic Republic these substrates were ameliorated with lignitederived ash and planted with coniferous or broad-leaved trees. During the course of soil development recent OM derived from plant material accumulated and became intimately mixed with the lignite from the spoil (Rumpel
* Corresponding author at third address. Tel.: +49-355-781162; fax: +49-355-78-1170. E-mail address: [email protected] (C. Rumpel).
et al., 1998). Characterization of the recent OM is absolutely necessary when studying soil development because the quantity and properties of OM derived from plant material influence most physical and chemical soil characteristics. Quantitative information about its contribution can be obtained by 14C activity measurements (Rumpel et al., 2000a). Using 13C cross polarization magic angle spinning nuclear magnetic resonance (CPMAS NMR) spectroscopy, chemical structures characteristic of recently formed OM can be identified in the OM mixture in the upper few centimeters of the soil (Rumpel et al., 2000a). However, these methods are expensive and timeconsuming, so it was considered appropriate to test infrared (IR) spectroscopy in combination with multivariate data analysis as a rapid and inexpensive means to observe structural differences between lignite and recently formed OM and to quantify the contribution of lignite-derived carbon.
0146-6380/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0146-6380(01)00029-8
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Fourier transformed infrared analyses, combined with a wide variety of chemical methods, are traditionally used to examine complex organic substances in the solid state. It is especially useful for elucidating degradation pathways of litter compounds (Gianfranco et al., 1995; Wershaw et al., 1996), occurring during composting (Inbar et al. 1989) and high energy UV photo-oxidation (Skjemstad et al., 1993). Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has several advantages over the traditional KBr pellet pressing technique (Fuller and Griffiths, 1978; Baes and Bloom, 1989). It is more rapid (spectra of a powdered sample can be obtained in less than a minute) and needs little sample preparation (grinding is sufficient) while being superior for showing the presence of inorganic compounds in soils (Nguyen et al., 1991). The interpretation of the spectra is difficult, however, because of the increasing influence of inorganic soil components with profile depth and overlap between individual absorption bands (Hempfling et al., 1987). Some of these problems can be overcome by using spectral subtraction of digital IR spectra (Painter et al., 1981; Skjemstad et al., 1993), but unfortunately the infrared peaks of soils and OM are often too complex to provide simple visual quantitative data (Janik et al., 1995). To interpret the wealth of information provided by the IR spectra, chemometrical methods such as multivariate data analysis have been used to determine quantitatively the content of special compound classes, e.g. fat, protein, polysaccharides and microbes (Gordon et al., 1993; Luinge et al., 1993). Multivariate data analysis is often associated with regression modeling, i.e. to model the relationships between two sets of measurements. Any data matrix can be represented as just a few bi-linear projections, principal components (PC) or partial least squares (PLS) in latent variables (Wold, 1989). The use of PLS focuses on predictive modeling and the technique was successfully applied in soil science to predict soil properties from their mid-infrared spectra (Janik et al., 1995; Janik and Skjemstad, 1995). In this study, DRIFT spectroscopy was used to characterize the OM component of rehabilitated mine soils containing substantial amounts of lignite-derived carbon. The objective of this study was to predict the TOC fraction that is lignite-derived of a soil sample from its IR spectrum by the use of partial least squares (PLS).
spoil bank. The spoil material was rich in lignite-derived carbon (up to 50 g/kg Corg) from actual lignite coal fragments as well as originally disseminated lignitic phytoclast particles which were present in the fine particle size fractions of the mud and shale material. Prior to afforestation the parent substrate was ameliorated using lignite ash and NPK fertilizer (Katzur and HauboldRosar, 1996). As the trees became older, OM from the decomposition of plant material accumulated and was incorporated into the mineral soil to form an initial surface soil horizon (Ai horizon, AG Boden, 1994). Additionally, airborne lignite dust was deposited on the soil surface until the beginning of the 1990s (Heinsdorf and To¨lle, 1993) . Due to the dark colour of the parent material (Cv) macroscopic visual differentiation of recently formed OM and lignite was not possible. Horizon designations were applied according to the German Soil Science Classification (AG Boden, 1994). Prior to sampling an inventory of the chemical parameters of the sites was carried out. Following these studies, one representative soil profile was established at each site. Samples were taken from the forest floor, the Ai and Cv horizon of soil profiles under a chronosequence of pine [Black pine (Pinus nigra) 11 years and Scots pine (Pinus sylvestris) 20 and 30 years], and a red oak stand (Quercus rubra, 36 years). A soil chronosequence is characterized by similar conditions for soil development (i.e. geology, slope, climate, vegetation), the only difference being the time of soil development. Additionally, a mine soil containing substantial amounts of airborne lignite-derived contamination was sampled under 25 year old red oak (Quercus rubra). After sampling the samples were thoroughly homogenized. 2.2. Sample pre-treatment and particle size fractionation The samples of the mineral soil were air dried and the fraction >2 mm removed by dry sieving. The pH measurements were carried out with a glass electrode in the supernatant of a 1:2.5 w/vol mixture of soil and water. A particle size fractionation of the Ai horizon under the 36 year old red oak was carried out using ultrasonic dispersion, sieving and sedimentation techniques as described by Schmidt et al. (1999). The total organic carbon (TOC) content of solid samples was determined with a Leco CHN 1000 analyser. Chemical parameters of all samples are listed in Table 1.
2. Materials and methods 2.1. Soil samples
2.3. 14C activity measurements for the determination of lignite carbon
The soils are located in the Lusatian mining district in east Germany. They developed on sandy spoil material from a lignite-bearing sequence of Miocene age (16 million years old), which was excavated during mining operations and had been relocated and deposited at a
Radiocarbon acitvity measurements of the soil samples were carried out using the conventional macrotechnique of liquid scintillation as described by BeckerHeidmann et al. (1988). A soil sample containing 6 g carbon was burned and the carbon was subjected to
C. Rumpel et al. / Organic Geochemistry 32 (2001) 831–839 Table 1 Assignments of the main IR absorbtion bands of humic substances (after Stevenson, 1994, modified) Frequency (cm1)
Assignment
3400–3300 2940–2900 1725–1720 1660–1630
O–H stretching Aliphatic C–H stretching C¼O stretching of COOH C¼O stretching of amide groups (amide I band) Aromatic C¼C COOsymmetric stretching, N–H deformation, C¼N stretching (amide II band) Aliphatic C–H OH deformation and C–O stretching of phenolic OH, C–H deformation of CH2 and CH3 C–O stretching and OH deformation of COOH C–O stretching of polysaccharide
1620–1600 1590–1517 1460–1450 1400–1390
1280–1200 1170–950
833
wide-band mercury-cadmium-telluride (MCT) detector operating at two scans per second. The infrared spectra were recorded by the diffuse reflectance Fourier transform (DRIFT) technique using a Harrick off-axis diffuse reflectance accessory (DRA-3SO). A KBr blank was subtracted from the recorded spectra. Subspectra of the organic matter components were obtained by subtracting the spectrum of the mineral component from the spectrum of the whole soil. The mineral component spectra were obtained after ignition of the sample at 350 C overnight. This temperature was found to be adequate to burn all OM. The assignments of FTIR bands were chosen according to Stevenson (1994) (Table 1). The spectral files were transformed into GRAMS/ 386TM format for PLS manipulation. The computer program PLSPLUSTM (GRAMS/386TM Galactic. NH) was used for PLS-1 analysis (Haaland and Thomas, 1988). Reduced portions of the spectra, from 3780 to 740 cm1 using an eight point average, were pre-processed with baseline correction and mean centering.
3. Results and discussion benzene synthesis. After 6 weeks, the 14C activity of the benzene was recorded with a scintillation spectrometer (Packard Tri Carb Model 3320). The 14C activity was corrected for isotopic fractionation, according to Stuiver and Pollach (1977). Therefore part of the sample was combusted, the CO2 purified and its d 13C determined in an isotope ratio mass spectrometer (MTA 250, Fa Finnigan). The data were corrected with the d13C values for isotopic effects. Radiocarbon decay is a statistical process and the 14C activity was obtained as mean with standard deviation. Lignite carbon is several million years old and therefore no longer shows any 14C activity (‘dead carbon’). The percentage of dead carbon in a sample can be obtained from the 14C activity measurements by Eq. (1) (Rumpel et al., 1998): Lignite ð% total CÞ ¼ 10014 C activity
ð1Þ
2.4. DRIFT spectroscopy and PLS analysis Sample preparation and DRIFT spectroscopy were performed as described by Janik et al. (1995). Samples were poured into 10 mm diameter stainless steel cups and the top surfaces of the powders were leveled. Spectra were recorded for infrared measurements from 4000 to 500 cm1 at 1.92 cm1 intervals and a resolution of 4 cm1. The measurements were carried out on a Digilab FTS-80 rapid-scan Fourier transform spectrometer by using a germanium-coated KBr beam-splitter, a high intensity globar source and a liquid nitrogen cooled
3.1. Chemical parameters of the soil samples and lignite content determined with 14C activity The total carbon content (TOC) was relatively high for all samples studied (Table 2). In the Cv horizon, this was almost exclusively lignite-related carbon (Rumpel et al., 1998). In the first few centimeters of the Ai horizon at least 42% of the TOC was derived from lignite. Comparing all sites, a decrease of the relative contribution of lignite-derived carbon with increasing age was observed in the Ai horizons (Table 2). This illustrates the increasing accumulation of OM originating from plant material during mine soil development (Rumpel et al., 1999). Lignite-derived carbon was present in all particle size fractions (Table 2), but its highest contents occurred in the 63-20 and <2 mm fraction. Here, the lignite contribution amounted to 42 and 48% of the TOC. These data are in contrast with the results obtained by Schmidt et al. (1996), who studied the impact of airborne lignitederived contamination on particle size fractions of a Mollisol. The latter authors found most lignite-derived carbon in the coarse size fractions. Our data indicate that lignite in the parent substrate is not allocated to well defined fractions but has a heterogeneous distribution among the particle size fractions (Rumpel et al., 2000b). Lignite-derived carbon was found to increase the contribution of aromatic and aliphatic carbon species in the OM (Rumpel et al., 2000a). Thus a lignite contribution has changed the chemical composition of all samples studied. It should be possible to observe the changes in structure by IR spectroscopy.
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cm1 due to aromatic species (Stevenson, 1994). These spectra were somewhat unlike the infrared spectra observed by other authors for humic substances and compost samples (Gerasimowicz and Blyer, 1985; Inbar et al, 1989; Gressel et al., 1995). Many of the frequencies identified for the humic and compost substances were similar to those for organic matter of lignite-containing mine soils, but their relative proportions were different and the peaks in the spectra region from 1700–1200 cm1 were better resolved. The infrared spectra of the lignite-containing soils show that lignite and plantderived OM occur in intimate mixture in these soils. Both samples have a low lignite content (Table 2), and coupled with the extreme overlap of the infrared peaks,
3.2. DRIFT-spectra of samples high and low in carbon Fig. 1 depicts the DRIFT spectra of two samples with contrasting TOC content (no. 12, 23.8%C and no. 4, 2.8%C) for comparison. Both spectra are characterized by strong peaks due to hydroxyl (3405 cm1), alkyl (2925–2927 and 2856–2858 cm1) and amide (1658– 1652 cm1). The assignment of the peaks at 2179–2115 cm1 is uncertain but may be attributed to the carbohydrate overtone frequency occurring near 1050 cm1. Samples which were low in TOC corresponded to a reduction in the amide peak intensities but were characterized by an enhancement of a shoulder near 1728 cm1 due to carboxylic acid, and another near 1561
Table 2 Chemical parameters of the soil samples. Years refer to the age of the trees, i.e. the length of period during which the vegetation existed Study site Red oak (36 years)
Red oak (25 years) Black pine (11 years) Scots pine (20 years) Scots pine (30 years)
Sample no. 1 2 3 4 5 6 7 8
Horizon
Sampling depth (cm)
pH (H2O)
TOCa (mg/g)
lignite carbon (% of total TOC)
Oh Ai Cv Ai Ai Ai Ai Ai
2–0 0–5 100 0–2 0–2 0–1 1–3 0–5
6.6 6.8 3.1 6.0 5.6 5.0 5.8 5.5
224 110 37 28 102 80 46 160
131b 421 966 461 812 641 802 611
51 123 94 238 208 160
321 291 481 201 272 421
Particle size fractions of the Ai horizon under 36 year old red oak (carbon recovery was 95%) 630–200 mm 9 Ai 0–5 n.b 200–63 mm 10 Ai 0–5 n.b 63–20 mm 11 Ai 0–5 n.b 20.0–6.3 mm 12 Ai 0–5 n.b 6.3–2.0 mm 13 Ai 0–5 n.b < 2 mm 14 Ai 0–5 n.b a b
TOC=total organic carbon. S.D. accounts for variability of 14C activity measurements.
Fig. 1. DRIFT spectra for high TOC (sample no. 12) and low TOC (sample no. 4).
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Fig. 2. DRIFT spectra for samples rich in lignite-derived carbon (nos. 3, nos. 5 and nos. 7).
would make visual assignments of signals to lignite difficult, if not impossible. Fig. 2 shows spectra of high lignite/TOC ratio samples (nos. 3, 5 and 7, all >75% ratio). The carbon in these samples is dominated by alkyl (2924, 2855 cm1), carboxylic acids (1717 cm1), aromatic (1638, 1566 cm1) and carboxylate species (near 1441–1420 cm1) (Fuller and Smyrl, 1990). The peak near 1256 cm1 is unassigned. The dilution of the lignite in sample no.3 (subsoil, 96% lignite) by possible amide carbon in samples no. 5 and 7 (both surface soil horizons with recent carbon contribution, Table 2) is in accord with its lower recent carbon contribution (4%), compared to nos. 5 and 7 of approximately 20%. It is apparent from Figs. 1 and 2, that prediction of lignite below a level of 95% lignite contribution to TOC would require statistical methods. 3.3. Mid infrared partial least squares (PLS) PLS analysis is a method that can be used to simplify a data matrix when many descriptors are available. It decomposes spectral and soil property data into a set of eigenvectors and their scores (Haaland and Thomas, 1988). Two-dimensional plots of the first two to three scores depict the major distribution of the samples within the calibration set, somewhat analogous to performing a principle component discriminant analysis. PLS analysis was used in this study to cross-validate the prediction of the lignite-derived fraction of TOC as obtained by 14C activity measurements. The TOC content of lignite-containing soils can be considered to be from two major sources; recent OM from plant inputs (primarily trees), and lignite-derived carbon. Recent OM can be further sub-divided into such groups as alkyl, proteinaceous, carboxyl, carbohydrate and aromatic species (Ko¨gelKnabner, 1997). It would therefore be an advantage to partition the PLS analysis into at least the two major carbon sources (recent OM and lignite), and also con-
sider the distribution of the various carbon species in the recent soil OM, to achieve a meaningful description of the carbon in these soils. PLS predictions of carbon from both of these pools is simple and has the advantage of providing qualitative information as to the characteristics of the carbon species, in addition to a purely quantitative aspect. The resulting PLS regression by crossvalidation, for both recent OM and lignite/TOC ratio, can be considered highly significant for the 14 calibration samples. Prediction of r2 of 0.79 and standard error of cross-validation (SECV) of 29 g/kg were achieved for recent OM, and an r2 of 0.84 and SECV of 10 g/kg for lignite (Fig. 3, Table 3). This SECV is considered to be comparable (10% of the maximum range) to the range of standard errors obtained for other soil properties by Janik et al. (1995). For TOC, the precision was higher, with an r2 of 0.86 and an SECV of 23 g/kg for three factors. With the 14 samples, which were included in the analysis, a good correlation was obtained between values predicted from PLS analysis and data from 14C activity measurements. 3.4. Qualitative analysis using PLS PLS also generates loading weight vectors that explain the qualitative variance in the independent data, correlating either positively or negatively with the observed spectral data. Positive peaks in the first few loading weights indicate a positive correlation of the infra-red vibration of the active molecular species with OM derived from plant material or lignite. Of the 5–7 factors used for optimum determination of the recent OM and lignite/ TOC components, the first few loading weights are the most important for providing qualitative information on those species correlating most highly with the components in question. The first three loading weights for recent OM are presented in Fig. 4. These are characteristic of recent soil OM, e.g. amide (1682 and 1532 cm1),
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plant lignin (1512 cm1) and alkyl (2939, 2860 cm1) for the first loading weight, alkyl (2939, 2860 cm1), plant lignin (1512 cm1) and carbohydrate (2005, 1136 and
1094 cm1) for the second, and alkyl (2939, 2860 cm1), plant lignin (1512 cm1) and carboxylic acid (1729 cm1) for the third (Janik and Skjemstad, 1995).
Fig. 3. Regression between the lignite/TOC ratios obtained by radiocarbon dating versus values predicted by infrared PLS crossvalidation. Table 3 Actual and infra-red PLS predicted values of recent OM–C and the lignite/TOC ratio (as percent) for the 14 sample calibration set. The regression r2 and standard error of cross-validation (SECV) are also presented Sample no.
Actual OM–C (mg/g)
Predicted OM–C (mg/g)
Actual lignite (% of TOC)
Predicted lignite (% of TOC)
1 2 3 4 5 6 7 8 9 10 11 12 13 14
195 64 1 15 19 29 9 62 35 87 49 190 152 93
164 118 30 25 11 67 20 60 20 67 48 146 137 143
13 42 96 46 81 64 80 61 32 29 48 20 27 42
18 42 113 43 73 67 65 56 49 36 38 22 41 52
r2 SECV
0.79 29
0.84 10
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The assignments can be confirmed by examining the score distributions presented in Fig. 5 for recent OM. Samples high in recent OM, e.g. no. 1, 2, 12 and nos. 13, are strong in the first loading, i.e. strong in amide. These samples are related to the Ai horizon where recent OM accumulated during soil development (Rumpel et al., 1999). Samples high in loading weight 2, such as sample no. 7, are high in carbohydrate, while samples nos. 3 and 6 with a high loading weight 3 are assumed to be high in carboxylic acids. This is consistent with the low pH of these samples which is due to the acid condition in the lower depth of lignite-containing mine soils.
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In contrast, the loading weights for lignite (Fig. 6) are dominated by three strong groups of peaks: alkyl (2939, 2860 cm1), 1792 cm1 of uncertain assignment but possibly due to C¼O stretching vibrations in the aromatic lignite structure, and 2342 cm1 due to iron oxide. The sharp peaks near 3695–3620 cm1 are undoubtedly due to kaolinite. The score plots, presented in Fig. 7 for lignite, suggests that it is a combination of the first and second loadings which are most strongly associated with lignite. Thus high lignite/TOC samples e.g. nos. 8, 6 and #7, are placed in the top right-hand corner of the score map, i.e. positive and strong in both factor 1 and 2, and
Fig. 4. Loading weights 1 (a), 2 (b) and 3 (c) for PLS cross-validation analysis of recent OM–C for the 14 calibration samples.
Fig. 5. Loading scores 2 versus 1, and scores 3 versus 1 for PLS cross-validation analysis of recent OM–C for the 14 calibration samples.
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Fig. 6. Loading weights 1 (a), 2 (b), and 3 (c) for PLS cross-validation analysis of lignite/TOC ratios for the 14 calibration samples.
4. Conclusions
Fig. 7. Loading scores 2 versus 1 for PLS cross-validation analysis of lignite for the 14 calibration samples.
therefore strong in the species giving rise to the 1792 cm1 peak. These results illustrate that the prediction model developed by PLS is not a simple correlation, but is based on chemical and structural differences between lignite and recent OM originating from plant material. Therefore it is appropriate to quantify lignite-derived carbon in lignite containing mine soils by the use of PLS based on its chemistry as revealed by DRIFT spectroscopy.
In this study DRIFT spectroscopy and 14C activity measurements of 14 soil samples were used to determine the fraction of TOC in lignite-containing soils that is lignite-derived. DRIFT-spectra showed that Ai horizons of lignite-rich mine soils contain recently formed OM with large amounts of amide. The lignite content of samples containing a mixture of lignite-derived carbon and carbon derived from plant material can be quantified from DRIFT-spectra by using a predictive model PLS. By interpretation of the factor loadings we concluded that this prediction was not a simple correlation but based on structural differences between lignite and OM derived from plant material as observed by DRIFT spectroscopy. Once a calibration curve is established, DRIFT spectroscopy in combination with PLS data analysis can serve as a valuable tool for the quantification of the fraction of TOC that is lignite-derived in soils containing organic matter mixtures.
Acknowledgements We thank the Deutsche Forschungsgemeinschaft for financial support. P. Becker-Heidmann is acknowledged for the 14C activity measurements. We also thank R. Tyson and W. Prickel for valuable comments on the manuscript. Associate Editor—A.C. Cook
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