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Quantitative estimation of peat, brown coal and lignite humic acids using chemical parameters, 1H-NMR and DTA analyses
Bioresource Technology 88 (2003) 189–195
Quantitative estimation of peat, brown coal and lignite humic acids using chemical parameters, 1H-NMR and DTA analyses O. Francioso a
a,*
, C. Ciavatta a, D. Montecchio a, V. Tugnoli b, S. S anchez-Cortes c, C. Gessa a
Dipartimento di Scienze e Tecnologie Agroambientali, Universit a degli Studi di Bologna, V.le Fanin 40, Bologna 40126, Italy b Dipartimento di Biochimica, Universit a degli Studi di Bologna, Via Belmeloro 8/2, Bologna 40127, Italy c Instituto de Estructura de la Materia, CSIC, Serrano 121, Madrid 28006, Spain Received 8 July 2002; received in revised form 28 October 2002; accepted 8 December 2002
Abstract Humic acids extracted from peats (P), brown coals (BC) and lignites (L), were characterized using different (chemical, 1 H-nuclear magnetic resonance spectroscopy and differential thermal analysis) techniques. Fourteen variables were obtained from these analyses and only five were selected because uncorrelated in multiple partial correlation. The chosen variables were C concentration, aliphatic and aromatic components and the heat of reaction of the second exothermic peak. The multivariate discriminant analysis was performed on these variables and a discriminant function was obtained which was able to efficiently separate the P, BC and L. This function enables simple predictions on samples of unknown origin. The straightforward method proposed and the results obtained are discussed. Ó 2003 Elsevier Science Ltd. All rights reserved. Keywords: Peat; Brown coal; Lignite; Chemical analysis; 1 H-NMR; DTA; Multivariate statistical analysis
1. Introduction Geochemical transformations of organic carbon (C) in aquatic and terrestrial ecosystems are important starting points for genesis of peats (P), the formation of brown coals (BC) and lignites (L) from which coal matures. Humification, considered to be the most important chemical process in geochemical transformations, is mainly involved during the early formation stage of these materials (Teichm€ uller and Teichm€ uller, 1975). For instance, during coalification, a gradual change in the quantity and chemical composition of humic substances (HS) (Lawson and Steward, 1989) has been observed. Due to favorable properties of HS there is a great interest in understanding the depositional fate of HS during coalification. Although characterization of HS from coals has been useful for interpreting the chemical pathways during coalification (Ibarra and Juan, 1985; Gonzalez-Vila * Corresponding author. Tel.: +39-51-2096205; fax: +39-512096203. E-mail address: [email protected] (O. Francioso).
et al., 1994), there is little information about the quantitative relation between the distribution of functional groups and structure during that process. Due to the complexity of HS, these relationships could follow a non-linear trend. The present practice to characterize HS is to use chemical parameters describing the quantity of organic carbon, humification parameters (Ciavatta et al., 1989; Alianello et al., 1999), and the relationship between atomic ratios (Ibarra and Juan, 1985; Lu et al., 2000). These methods do not provide a holistic view representing the complexity of the coalification process. The need to obtain structural details during the diagenesis of natural organic carbon (Ibarra et al., 1996; Gonzalez-Vila et al., 1994) or the structural changes which may take place in humification (Francioso et al., 2001; Cavani et al., 2003) led to the use of some additional analytical techniques. 1 H-nuclear magnetic resonance spectroscopy (1 H-NMR) and differential thermal analysis (DTA) might be an ideal approach for chemical structural investigation of complex macromolecules such as HS. The quantitative distribution of the hydrogens obtained using the NMR technique may complement
0960-8524/03/$ - see front matter Ó 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0960-8524(03)00004-X
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the qualitative information about the structural units (Wilson et al., 1983; Lee et al., 1998; Francioso et al., 2000). In addition, thermal analysis has been proposed as a method to characterize the genesis of coal (Sheppard and Forgeron, 1987; Juntgen, 1984; C ß etinkaya and Y€ ur€ um, 2000; Mazumdar, 2000) and of HS from different environments (Geyer et al., 2000; DellÕAbate et al., 2002). The assumption is that the thermoanalysis yields volatile molecules that are representative of the macromolecular network. The chemical changes taking place in the humification process seem to be quantitative rather than qualitative. These changes are generally processed using various multivariate statistical techniques (Geyer et al., 2000; Chapman et al., 2001). In fact the number of pertinent variables is high and usually univariate statistical analysis seems unable to cope with the complexity of the measurements. On the other hand multivariate factor analysis has two weaknesses: (i) the choice of variables on which to apply the statistical procedure and (ii) the robustness of inference from the analysis results. The aim of the present work was to quantify the chemical and structural changes of the humic acid (HA) originated from P, BC and L using different analytical techniques (chemical, 1 H-NMR and DTA) and, moreover, to construct a multivariate analysis procedure which might allow a simple and robust interpretation of the data. 2. Methods HA extraction: The standard samples used in this study were taken from the collection of the Dept. of
Agro-Environmental Science & Technologies of Bologna University. The HAs were extracted from samples of (i) 5 P; (ii) 5 BC; and (iii) 5 L (Table 1). Additional chemical characteristics were reported in detail by Cavani et al. (2003). Briefly, 2 g of air-dried and finely ground samples were extracted under N2 with 100 ml of 0.5 M NaOH and stirred for 24 h. The suspension was centrifuged at 5000g for 30 min and then filtered through a 0.45 lm filter using a Minitan S System (Millipore, Bedford MA, USA). The solution was acidified with 5 M HCl to pH < 2 to precipitate the HAs and was subsequently centrifuged at 5000g for 20 min in order to eliminate the supernatant. The HAs were dissolved with NaOH 0.5 M to produce a Na-humate. The Na-humates were dialyzed against Millipore water, using a tubing (Cellu Sep H1-USA) with a cut-off of 1000 Da, until a neutral pH was achieved, and were then freeze-dried. Elemental analysis: Elemental analysis (C, H, N, S, O) was carried out using an Elemental Analyzer––Model EA 1108 (Carlo Erba Milan, Italy). Organic carbon: The procedures for quantifying the total extracted organic carbon (TEC) and the percentage of humified carbon (HuC), both relevant characters of HS, have been described by Ciavatta et al. (1989, 1991), Cavani et al. (2003). Potentiometric titration: Samples were prepared by dissolving 12 mg of the freeze-dried HA in 20 ml of Milli-Q Millipore water containing 0.05 M NaCl to keep the ionic strength constant. The pH was adjusted to pH 3 by adding about 1 ml of 0.05 M HCl. The solutions were titrated to pH 10.5 with 0.05 M NaOH using a VIT 90 Titrator Radiometer (Radiometer, Copenhagen,
Table 1 Results for HA samples from different rank coals Variables
C H N S O )COOH Phe-OH TEC HuC Har c Hro c Hal c Exo1 Exo2
Rank
(%) dafa ;b ’’ ’’ ’’ ’’ meq/mg meq/mg (%)b ’’
lV s/mg lV s/mg
P avg se n¼5
BC avg se n¼5
L avg se n¼5
44 1.3 5.07 0.24 1.57 0.19 0.82 0.65 49 0.9 429 10 224 13 23 1.5 17 1.0 18 1.5 28 2.1 46 3.9 2185 366 2871 1667
53 2.6 4.00 0.13 1.22 0.09 0.40 0.09 41 2.6 404 21 350 37 27 6.8 25 6.5 38 2.3 6.2 0.8 56 2.2 783 240 6814 2040
57 1.3 3.88 0.20 0.90 0.05 1.14 0.71 36 1.63 368 14 297 14 16 4.0 9.40 0.81 23 1.0 15 1.5 62 2.6 1048 354 2895 1881
Listed are the chemical parameters, H-species in H-NMR spectra and DTA data (Exo1 and Exo2 are the first and second exothermic peak). a (Daf) dry ash free basis. b Values expressed in percentage on dry matter. c Percentage of H estimated by integrating the spectra.
O. Francioso et al. / Bioresource Technology 88 (2003) 189–195
DK). The potentiometric titrations were carried out in triplicate at 25 °C, under N2 flow and the delivery range was 10 ll min1 (0.01). The titration data were processed using the PGAUSS computer program (Gessa et al., 1994). 1 H-NMR spectroscopy: The samples were prepared by dissolving HA (20 mg) in 0.5 ml of 0.5 M sodium deuteroxide. The spectra were recorded with a Bruker ACF 250 spectrometer using a 5 mm multinuclear probe. 1 H spectra were accumulated with 16 K data point, one pulse sequence, 40° pulse angle, 3 s relaxation delay and a sweep width of 2.5 kHz. To obtain a satisfactory signal to noise ratio 1000–2000 scans were needed. Gated irradiation was applied between acquisitions to presaturate the residual water peak. Sodium 3-trimethylsilyl-propionate-2,2,3,3-d4 (TSP) was added to the samples to provide a chemical shift standard. Quantitative estimates of hydrogen in various structural groups were obtained by integrating the area of the 1 H-NMR spectra. The spectra were divided into three main regions: aromatic hydrogens (Har ) from 6.0 to 8.0 ppm; H attached to oxygen groups in carbon a (Hro ) from 4.2 to 3.0 ppm, and aliphatic H (Hal ) from 3.0 to 0.5 ppm. In accordance with results obtained by Wilson et al. (1983) the Hro region was attributed to protons largely arising from polysaccharides. Wilson et al. (1983), Gil-Sotres et al. (1994) and Simpson et al. (1997) showed that the Hal region might be affected by the presence of protons attached to aromatic rings in a, b and c positions. DTA: DTA was carried out using a TG-DTA92 instrument (SETARAM, France). About 10 mg of lyophilized HA was weighted on an alumina crucible and isothermally heated to 30 °C for 10 min under air flow (5 l min1 ) and then heated from 30 to 700 °C in air static atmosphere. The heating rate was 10 °C min1 . Calcined caolinite was used as the reference material. The area under the DTA curves, which is proportional to the heat
191
of the reaction, was calculated by the TG-DTA92B program (SETARAM, France). Statistical analysis: The data from the elemental analyses, functional groups, 1 H-NMR and DTA were used for statistical analysis. Mean values and multiple partial correlation (MPC) coefficients of the different variables were estimated (Snedecor and Cochran, 1967). The MPC coefficient measures the relationship between two variables while checking for possible effects of the other variables. The discriminant analysis (DA) was performed on uncorrelated variables (i) to determine statistically significant differences among groups, (ii) to establish a procedure for classification, (iii) to determine which independent variables account for most of the differences among groups analyzed. The Statgraphics version 5 plus (Statistical graphics system by Statistical Graphics Corporation) was used for our calculations.
3. Results and discussion 3.1. Elemental and functional groups analyses The elemental analysis (C, H, N, S, O) and functional groups concentration are shown in Table 1. These parameters are frequently utilized to characterize the soil organic matter as demonstrated by Tissot and Welte (1984), Stevenson (1994). Some parameters such as the C or O, –COOH groups and HuC, progressively changed in HA according to their maturation rank and they can be considered representative during the humification process. Differences between means indicated that the data of C from P is statistically significant when compared to that of BC and L (Table 2). These changes could be interpreted as different chemical and microbiological processes involved in the formation of BC and L with respect to P samples. In agreement with the trend observed for C, the O content varied from 48% to 36%
Table 2 Evaluation of the differences between means of analyzed parameters in P, BC and L Variables
P–BC
t
P–L
t
BC–L
t
C H N S O )COOH Phe-OH TEC HuC Har Hro Hal Exo1 Exo2
)9.18 1.07 n.s. n.s. 7.2 n.s. )126.4 n.s. n.s. n.s. 22.09 n.s. 1400 n.s.
)3.05 3.91 n.s. n.s. 2.51 n.s. )3.18 n.s. n.s. n.s. 8.80 n.s. 3.19 n.s.
)13.30 1.18 0.67 n.s. 12.4 61 )73.6 n.s. 7.6 )5.10 13.63 )15.47 n.s. n.s.
)6.95 3.77 3.30 n.s. 6.53 3.50 )3.92 n.s 5.86 )3.70 5.60 )4.21 n.s. n.s.
n.s. n.s. 0.32 n.s. n.s. n.s. n.s. n.s. 16 14.33 )8.46 n.s. n.s. n.s.
n.s. n.s. 2.88 n.s. n.s. n.s. n.s. n.s. 2.44 5.71 )5.63 n.s. n.s. n.s.
Number of observations, n ¼ 5; n:s: ¼ P > 0:05; t ¼ studentÕs t. P 6 0:05; P 6 0:01; P 6 0:005.
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resulting in statistically significant differences when P is compared to the BC and L samples. The H content is not considered important in the humification process discussion. We found that the H content decreased with humification rank and the differences between groups were significant when P was compared to the BC and L samples (Table 2). The N content decreased as the maturation rank increased (Table 1). How the N can persist in mature materials is an important consideration. Knicker et al. (1996) and Bonnett (1996) demonstrated the existence of resistant forms of N in HA attributing its presence to heterocyclic N compounds such as porphyrins and metalloporphyrins. N, the central element of porphyrin structures, was involved mainly in the complexation of metals. In this case the N content was statistically significant when L was compared to BC and P samples (Table 2). The quantifications of carboxylic acids (COOH) and phenolic-OH (phe-OH) groups are shown in Table 1. The P samples showed a higher COOH concentration with respect to the BC and L samples (Lawson and Steward, 1989; Stefanova et al., 1993). The differences between groups were statistically significant when P was compared to L samples (Table 2). It is important to evaluate these functional groups in the materials analyzed because they give an accurate estimate of the reactivity and the humification rank of the HA. The focal point of the coalification process is the decrease of COOH, methoxyl and carbonyl groups in HAs and an increase in phe-OH groups during humification (Lawson and Steward, 1989; Stefanova et al., 1993). Hatcher et al. (1989a,b) (using a number of different analytical techniques) showed that during the coalification stages-lignin to L, chemical reactions promote the cleavage of aryl ether bonds in lignin derivates to produce phe-OH. We found a lower concentration of phe-OH groups in P samples compared to BC and L samples. There are no statistically significant differences between BC and L samples (Table 2). Since the COOH group content was higher with respect to that of phe-OH in BC and L samples, we can assume that HA originated from parent materials with a low rank of coalification. The changes observed in HuC were statistically significant when L was compared to P and BC samples. Apparently the TEC does not seem to be significant in the quantification of the differences among these materials. 3.2. Differential thermal analysis Each thermogram was characterized by an endothermic peak (around 120 °C) assigned to the deydratation, and two exothermic oxidation peaks: (i) the first peak (Exo1) is considered to be the result of the thermal degradation of polysaccharides, decarboxylation of acidic groups and dehydration of hydroxylate aliphatic
structures (Sheppard and Forgeron, 1987; DellÕAbate et al., 2002); (ii) the second peak (Exo2) is related to the breakdown of aromatic structures and cleavage of the C–C bond (Sheppard and Forgeron, 1987; Provenzano and Senesi, 1999; DellÕAbate et al., 2002). The combustion reaction of the first exothermic peak (292 °C) in P samples was significantly higher ðP 6 0:05Þ than the second peak (382 °C) (Table 1). The accumulation of recent C can influence the thermal profiles of P samples, characteristic of carbohydrate molecules and not of ‘‘typical’’ lignin structures (Ishiwatari et al., 1983). On this basis we can deduce that these materials might be composed of parent materials with a low rank of coalification. The combustion reaction of the first exothermic peak (290 °C) in BC samples was significantly lower ðP 6 0:05Þ than the second peak (455 °C) (Table 1). The more intense combustion reaction of the second peak is due to cracking of higher molecular weight polynuclear systems (Sheppard and Forgeron, 1987). This effect is particularly evident in the BC sample purchased from the International Humic Substances Society (IHSS) where the oxidation temperatures of both peaks were higher compared to the P samples. The combustion reactions of two exothermic peaks at 285 °C and at 437 °C in L samples did not show statistically significant differences. However, the differences among samples were statistically significant when L was compared to P and BC (Table 2).
3.3. 1 H-NMR spectroscopy The percentage of the subdivisions within the three regions of the HA spectra are shown in Table 1. The main changes in the spectra can be attributed to HA aging. P samples showed a prominent region (Hro ), corresponding to about 30% that was attributed to the sugar-like and polyethers components (Francioso et al., 2001). The presence of these components was also supported by the intense exothermic peak at 292 °C of DTA of P samples. The aliphatic region (Hal ) was mainly characterized by terminal methyl groups as described in previous papers by the same authors (Francioso et al., 1996, 2001). Although the Har region (6.0–8.0 ppm) was broad and intense, it represented about 20% of the total integrated area. Har and Hal regions of P samples were statistically significant when compared to those of L samples (Table 2). On the other hand, the Hro regions were statistically significant when compared to those of BC and L samples (Table 2). BC samples were characterized by a strong decrease of the sugar-like component (at about 6%) with respect to P samples. The low concentration of these components was also supported by DTA of BC samples. This
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Table 3 MPC coefficients between investigated variables C H N S O COOH Phe-OH TEC HuC Har Hro Hal Exo1 Exo2
C
H
N
S
O
COOH
Phe-OH
TEC
HuC
Har
0.700 0.236 0.664 )0.825 )0.528 0.403 )0.681 0.675 )0.302 0.644 0.144 )0.712 )0.121
)0.560 )0.980 0.875 0.755 )0.593 0.961 )0.957 0.229 )0.916 0.072 0.971 0.400
)0.569 0.474 0.712 0.129 0.532 )0.635 0.375 )0.515 )0.344 0.643 0.315
0.861 0.773 )0.576 0.977 )0.974 0.221 )0.933 0.089 0.957 0.386
)0.586 0.495 )0.912 0.917 )0.202 0.881 )0.179 )0.859 )0.500
0.120 )0.698 0.752 )0.163 0.609 0.229 )0.757 )0.270
0.586 )0.481 0.200 )0.640 0.320 0.544 )0.00
0.987 )0.215 0.260 0.933 )0.931 0.280 )0.170 0.090 0.635 )0.928 0.945 )0.367 )0.479 0.473 0.424
Hro
Hal
Exo1
0.197 0.937 0.300
0.04 )0.516 )0.274
Exo2
P 6 0:05.
region showed statistically significant differences when compared to L samples (Table 2). The Har and Hal regions in BC samples were about 38% and 56%, respectively. With regard to the differences between regions, only Har was statistically different when compared to those of L samples (Table 2). L samples were characterized by a high percentage of Hal (about 62%) when compared to other regions. It can be supposed that the Hal region can be affected by the presence of protons attached to an aromatic ring in a, b and c position in accordance with NMR spectra obtained by Wilson et al. (1983), Gil-Sotres et al. (1994) and Simpson et al. (1997). The presence of a higher percentage of Hro with respect to BC samples seems to be related to polysaccharides and lignin derivatives that can survive in the early coalification (Bates and Hatacher, 1989). The low aromaticity found in these samples (about 23%) may suggest that it is the earliest stage of coalification (Hatacher, 1988).
The DA analysis was performed on the uncorrelated variables, Har , Hal , Exo2, phe-OH and C since in practical applications, correlations between XÕs usually have the effect of making the discriminant function less accurate (Snedecor and Cochran, 1967). It is to be noticed that these variables are measures of the aromatic and aliphatic component in HS (Lawson and Steward, 1989; Hatcher and Clifford, 1997). Two statistically significant discriminating functions ðP < 0:05Þ were obtained (Fig. 1). The first DA function accounts for 80% of the variance between groups while the second DA function accounts for about 20% (Table 4). The FisherÕs linear discriminant function coefficients for each group enable us to classify the observations into groups. In addition the standardized coefficients of functions indicate the partial contribution of each variable to the different levels of discriminant. In particular the first function was 1:31742 C þ 1:1153 phe-OH þ
3.4. Multivariate analysis for chemical, DTA and NMR parameters The depositional HS during coalification can be studied using a multivariate analysis of variables which are presumably affected by such a process. A preliminary linear correlation analysis had shown that most of the Pearson product moment correlation coefficients ðrÞ between each pair of variables were statistically significant. To estimate the spurious and/or suppressive portions that exist in paired ðrÞ coefficients, a MPC was performed; such information can be applied in order to improve the prediction power of the variables under consideration (Snedecor and Cochran, 1967). The MPC results suggested that some variables were highly correlated among them ðP < 0:05Þ while others were not (Table 3).
Fig. 1. Binary diagram of two discriminant function F1 and F2 obtained from the analysis performed on HA from P, BC and L data. F1 accounts for 80% between groups variance.
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Table 4 DA for indipend variables C, phe-OH, Har , Hal , Exo2
Acknowledgements
Discriminant function
Eigenvalue
Relative percentage
Canonical correlation
1 2
8.19765 2.03811
80.09 19.91
0.944 0.819
Function derived
Wilks lambda
Chi-square
DF
P
1 2
0.035 0.329
33.301 11.11
10 4
0.0002 0.02
0:577604 Har 0:816597 Hal þ 0:0888662 Exo2. The relative magnitude of these coefficients indicates how the independent variables are being used to discriminate amongst the groups. The C and phe-OH coefficients seem to have a higher weight in predictions. The second function ð0:174692 C 0:151624 phe-OH þ 1:45972 Har 1:41506 Hal 0:0449892 Exo2Þ maximized the differences between the dependent variables. From all the observations used to fit the model, 93.333% were correctly classified. A sample of BC in particular was incorrectly predicted as belonging to that group. The diversity of such a sample might be explained by considering its uncertain classification.
4. Conclusions Quantitative differentiation of three matrices, P, BC and L, based on five HA variables is a method to investigate the humification process during coalification. The DA analysis performed on the five variables (C, phe-OH, Har , Hal , and Exo2, selected in relation to their independence) has given enough quantitative information to distinguish different HA extracted from P, BC and L. The five variables are also an estimate of the coalification process (Lawson and Steward, 1989; Hatcher and Clifford, 1997). On the basis of the results obtained from DA, it is reasonable to assume that only the five variables have provided a quantitative estimation of the differences between P, BC and L, even having discarded the remaining set of variables considered. This suggests that the univariate method, based on a single variable analysis, can be misleading when different data sets are investigated. The DA functions, obtained using five variables of five samples for each coal, are sufficient to give a general characterization of the coals. In fact by applying the DA analysis it is possible to obtain discriminant functions which allow us to predict the best classification method of unknown samples. The advantages of the proposed DA analysis are a consequence of the linear relation between variables before and after the analysis. The test of the differences between coals is simple and robust and may be referred directly to the initial observed data.
This research was supported with funds provided by the ‘‘Ispettorato Centrale delle Repressioni Frodi’’ of the Italian Ministery of the Agricultural and Forestry Politics (MiPAF). References Alianello, F., DellÕOrco, S., Benedetti, A., Sequi, P., 1999. Identification of primary substrates in organo-mineral fertilizers by means of isoelectric focusing. Commun. Soil Sci. Plant Anal. 30, 2169–2181. Bates, A.L., Hatacher, P.G., 1989. Solid-State 13 C-NMR studies of a large fossil gymnosperm form the Yallourn Open Cut, Latrobe Valley, Australia. Org. Geochem. 14, 609–617. Bonnett, R., 1996. Porphyrins in coals. Int. J. Coal Geol. 32, 137–149. Cavani, L., Ciavatta, C., Gessa, C., 2003. Identification of organic matter from peat, leonardite and lignite fertilisers using humification parameters and electrofocusing. Bioresource Technol. 86, 45–52. C ur€ um, Y., 2000. Oxidative pyrolysis of Turkish lignites ß etinkaya, S., Y€ in air up to 500 °C. Fuel Process. Technol. 67, 177–189. Chapman, S.J., Campbell, C.D., Fraser, A.R., Pruri, G., 2001. FTIR spectroscopy of peat in and bordering Scots pine woodland: relationship with chemical and biological properties. Soil Biol. Biochem. 33, 1193–1200. Ciavatta, C., Govi, M., Vittori Antisari, L., Sequi, P., 1989. Determination of organic carbon in soil and fertilizer. Commun. Soil Sci. Plant Anal. 20, 759–773. Ciavatta, C., Govi, M., Vittori Antisari, L., Sequi, P., 1991. Determination of organic carbon in aqueous extracts of soil and fertilizers. Commun. Soil Sci. Plant Anal. 22, 795–807. DellÕAbate, M.T., Benedetti, A., Trinchera, A., Dazzi, C., 2002. Humic substances along the profile of two Typic Haploxerert. Geoderma 107, 281–296. Francioso, O., Sanchez-Cortes, S., Tugnoli, V., Ciavatta, C., Sitti, L., Gessa, C., 1996. Infrared, Raman and nuclear magnetic resonance (1 H, 13 C and 31 P) spectroscopic study of fractions of peat humic acids. Appl. Spectrosc. 50, 1165–1174. Francioso, O., Ciavatta, C., Sanchez-Cortes, S., Tugnoli, V., Sitti, L., Gessa, C., 2000. Spectroscopic characterization of soil organic matter in long-term amendment trials. Soil Sci. 165, 495–504. Francioso, O., Sanchez-Cortes, S., Tugnoli, V., Marzadori, C., Ciavatta, C., 2001. Spectroscopic study (DRIFT, SERS and 1 HNMR) of peat, leonardite and lignite humic substances. J. Mol. Struct. 565, 481–485. Gessa, C., Deiana, S., Maddau, V., Manunza, B., Rausa, R., 1994. Binding of proton and metal ions in humic acids. Test of the bimodal Gaussian model. Agrochimica 38, 85–91. Geyer, W., Hemidi, F.A.-H., Bruggemann, L., Hanschmann, G., 2000. Investigation of soil humic substances from different environments using TG–FT–IR and multivariate analysis. Thermochim. Acta 361, 139–146. Gil-Sotres, F., Saa, A., Trasar-Cepeda, M.C., Leir os, M.C., Zech, W., 1994. Spectroscopic (13 C-NMR, 1 H-NMR and IR) and chemical characterization of mine soil humic acids. In: Senesi, N., Miano, T.M (Eds.), Humic Substances in the Global Environment and Implications on Human Health. Elsevier Science Amsterdam, London, pp. 601–606. Gonzalez-Vila, F.J., del Rio, J.C., Almedros, G., Martin, F., 1994. Structure relationship between humic fractions from peat and lignites from Miocene Granada Basin. Fuel 73, 215–221. Hatacher, P.G., 1988. Dipolar-dephasing 13 C-NMR studies of decomposed wood and coalified xylem tissue: evidence for chemical structural changes associated with defunctionalization of lignin structural units during the coalification. Energy Fuels 1, 40–58.
O. Francioso et al. / Bioresource Technology 88 (2003) 189–195 Hatcher, P.G., Clifford, D.J., 1997. The organic geochemistry of coal: from plant materials to coal. Org. Geochem. 27, 251–274. Hatcher, P.G., Lerch, H.E., Verheyen, T.V., 1989a. Organic geochemical studies of transformation of gymnosperm xylem during peatification and coalification to subbituminous coal. Int. J. Coal Geol. 13, 65–97. Hatcher, P.G., Wilson, M.A., Vassallo, A.M., Lerch, H.E., 1989b. Studies of angiospermeous wood in Australian brown coal by nuclear magnetic resonance and analytical pyrolysis: new insights into the early coalification process. In: Lyons, P.C., Alpern, B. (Eds.), Coal: Classification, Coalification, Mineralogy, Traceelement Chemistry, and Oil and Gas Potential, International Journal of Coal Geology, vol. 13. pp. 99–126. Ibarra, J.V., Juan, R., 1985. Structural changes humic acids during the coalification process. Fuel 64, 650–656. Ibarra, J.V., Munoz, E., Moliner, R., 1996. FTIR study of the evolution of coal structure during the colaification process. Org. Geochem. 24, 725–735. Ishiwatari, R., H€ anninen, K., Lehito, O., Tirronen, A., Tuhkanen, M., M€ alk€ onen, P., Pohjala, E., 1983. Chemical degradation and oxidation studies of Finnish Peat and of Peat humic fractions. Qualitative analysis. In: Fuchsman, C.H., Spigarelli, S.A (Eds.), International Symposium on Peat utilization Bemidji State University center for environmental Studies. pp. 457–465. Knicker, H., Almendros, G., Gonzalez-Vila, F.J., Martin, F., Ludemann, H.D., 1996. 13 C- and 15 N-NMR spectroscopic examination of the transformation of organic nitrogen in plant biomass during thermal treatment. Soil Biol. Biochem. 28, 1053–1060. Lawson, G.J., Steward, D., 1989. Coal humic acids. In: Aiken, G.R., McKnight, D.R., Wershaw, R.L., MacCarthy, P.C. (Eds.), Humic Substances II. Wiley-Interscience, New York, pp. 641–686. Lee, G., Wilson, M.A., Young, B.R., 1998. The application of the ‘‘Watergate’’ suppression technique for analyzing humic substances by nuclear magnetic resonance. Org. Geochem. 28, 549–559.
195
Lu, X.Q., Hanna, J.V., Johnson, W.D., 2000. Source indicators of humic substances: an elemental composition, solid state 13 C-NMR and Py-GC/MS study. Appl. Geochem. 15, 1019– 1033. Juntgen, H., 1984. Review of the kinetics of pyrolysis and hydropyrolysis in relation to the chemical constitution of coal. Fuel 63, 731– 737. Mazumdar, B.K., 2000. Thermochemical history in the genetic path of coal: derivation of a novel correlation for heat of combustion. Fuel 79, 1267–1276. Provenzano, M.R., Senesi, N., 1999. Thermal properties of standard and reference humic substances by differential scanning calorimetry. J. Anal. Calorimetry 57, 517–526. Sheppard, J.D., Forgeron, D.W., 1987. Differential thermogravimetry of peat fractions. Fuel 66, 232–236. Simpson, A.J., Boersma, R.E., Kingery, W.L., Hicks, R.P., Hayes, M.H.B., 1997. Applications of NMR spectroscopy for studies of the molecular compositions of humic substances. In: Hayes, M.H.B, Wilson, W.S. (Eds.), Humic Substances, Peat and Sludge: Health and Environmental Aspects. The Royal Society of Chemistry, Cambridge, pp. 46–63. Snedecor, G.W., Cochran, W.G., 1967. Statistical Methods. The Iowa State University Press, Iowa USA. Stefanova, D.M., Velinova, S.P., Marinov, Nikolova, R., 1993. The composition of lignite humic acids. Fuel 72, 681–684. Stevenson, F., 1994. Humus Chemistry: Genesis, Composition, Reactions. Wiley, Interscience, New York. Teichm€ uller, M., Teichm€ uller, R., 1975. StachÕs Texbook of Coal Petrology. Gebruder Borntrager, Berlin (Chapter 2). Tissot, B.P., Welte, D.H., 1984. Petroleum Formation and Occurrence, second ed. Springer-Verlag, Berlin. Wilson, M.A., Collin, P.J., Tate, K.R., 1983. 1 H-NMR nuclear magnetic resonance spectroscopy of soil humic acid. J. Soil Sci. 34, 297–304.
Quantitative estimation of peat, brown coal and lignite humic acids using chemical parameters, 1H-NMR and DTA analyses O. Francioso a
a,*
, C. Ciavatta a, D. Montecchio a, V. Tugnoli b, S. S anchez-Cortes c, C. Gessa a
Dipartimento di Scienze e Tecnologie Agroambientali, Universit a degli Studi di Bologna, V.le Fanin 40, Bologna 40126, Italy b Dipartimento di Biochimica, Universit a degli Studi di Bologna, Via Belmeloro 8/2, Bologna 40127, Italy c Instituto de Estructura de la Materia, CSIC, Serrano 121, Madrid 28006, Spain Received 8 July 2002; received in revised form 28 October 2002; accepted 8 December 2002
Abstract Humic acids extracted from peats (P), brown coals (BC) and lignites (L), were characterized using different (chemical, 1 H-nuclear magnetic resonance spectroscopy and differential thermal analysis) techniques. Fourteen variables were obtained from these analyses and only five were selected because uncorrelated in multiple partial correlation. The chosen variables were C concentration, aliphatic and aromatic components and the heat of reaction of the second exothermic peak. The multivariate discriminant analysis was performed on these variables and a discriminant function was obtained which was able to efficiently separate the P, BC and L. This function enables simple predictions on samples of unknown origin. The straightforward method proposed and the results obtained are discussed. Ó 2003 Elsevier Science Ltd. All rights reserved. Keywords: Peat; Brown coal; Lignite; Chemical analysis; 1 H-NMR; DTA; Multivariate statistical analysis
1. Introduction Geochemical transformations of organic carbon (C) in aquatic and terrestrial ecosystems are important starting points for genesis of peats (P), the formation of brown coals (BC) and lignites (L) from which coal matures. Humification, considered to be the most important chemical process in geochemical transformations, is mainly involved during the early formation stage of these materials (Teichm€ uller and Teichm€ uller, 1975). For instance, during coalification, a gradual change in the quantity and chemical composition of humic substances (HS) (Lawson and Steward, 1989) has been observed. Due to favorable properties of HS there is a great interest in understanding the depositional fate of HS during coalification. Although characterization of HS from coals has been useful for interpreting the chemical pathways during coalification (Ibarra and Juan, 1985; Gonzalez-Vila * Corresponding author. Tel.: +39-51-2096205; fax: +39-512096203. E-mail address: [email protected] (O. Francioso).
et al., 1994), there is little information about the quantitative relation between the distribution of functional groups and structure during that process. Due to the complexity of HS, these relationships could follow a non-linear trend. The present practice to characterize HS is to use chemical parameters describing the quantity of organic carbon, humification parameters (Ciavatta et al., 1989; Alianello et al., 1999), and the relationship between atomic ratios (Ibarra and Juan, 1985; Lu et al., 2000). These methods do not provide a holistic view representing the complexity of the coalification process. The need to obtain structural details during the diagenesis of natural organic carbon (Ibarra et al., 1996; Gonzalez-Vila et al., 1994) or the structural changes which may take place in humification (Francioso et al., 2001; Cavani et al., 2003) led to the use of some additional analytical techniques. 1 H-nuclear magnetic resonance spectroscopy (1 H-NMR) and differential thermal analysis (DTA) might be an ideal approach for chemical structural investigation of complex macromolecules such as HS. The quantitative distribution of the hydrogens obtained using the NMR technique may complement
0960-8524/03/$ - see front matter Ó 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0960-8524(03)00004-X
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the qualitative information about the structural units (Wilson et al., 1983; Lee et al., 1998; Francioso et al., 2000). In addition, thermal analysis has been proposed as a method to characterize the genesis of coal (Sheppard and Forgeron, 1987; Juntgen, 1984; C ß etinkaya and Y€ ur€ um, 2000; Mazumdar, 2000) and of HS from different environments (Geyer et al., 2000; DellÕAbate et al., 2002). The assumption is that the thermoanalysis yields volatile molecules that are representative of the macromolecular network. The chemical changes taking place in the humification process seem to be quantitative rather than qualitative. These changes are generally processed using various multivariate statistical techniques (Geyer et al., 2000; Chapman et al., 2001). In fact the number of pertinent variables is high and usually univariate statistical analysis seems unable to cope with the complexity of the measurements. On the other hand multivariate factor analysis has two weaknesses: (i) the choice of variables on which to apply the statistical procedure and (ii) the robustness of inference from the analysis results. The aim of the present work was to quantify the chemical and structural changes of the humic acid (HA) originated from P, BC and L using different analytical techniques (chemical, 1 H-NMR and DTA) and, moreover, to construct a multivariate analysis procedure which might allow a simple and robust interpretation of the data. 2. Methods HA extraction: The standard samples used in this study were taken from the collection of the Dept. of
Agro-Environmental Science & Technologies of Bologna University. The HAs were extracted from samples of (i) 5 P; (ii) 5 BC; and (iii) 5 L (Table 1). Additional chemical characteristics were reported in detail by Cavani et al. (2003). Briefly, 2 g of air-dried and finely ground samples were extracted under N2 with 100 ml of 0.5 M NaOH and stirred for 24 h. The suspension was centrifuged at 5000g for 30 min and then filtered through a 0.45 lm filter using a Minitan S System (Millipore, Bedford MA, USA). The solution was acidified with 5 M HCl to pH < 2 to precipitate the HAs and was subsequently centrifuged at 5000g for 20 min in order to eliminate the supernatant. The HAs were dissolved with NaOH 0.5 M to produce a Na-humate. The Na-humates were dialyzed against Millipore water, using a tubing (Cellu Sep H1-USA) with a cut-off of 1000 Da, until a neutral pH was achieved, and were then freeze-dried. Elemental analysis: Elemental analysis (C, H, N, S, O) was carried out using an Elemental Analyzer––Model EA 1108 (Carlo Erba Milan, Italy). Organic carbon: The procedures for quantifying the total extracted organic carbon (TEC) and the percentage of humified carbon (HuC), both relevant characters of HS, have been described by Ciavatta et al. (1989, 1991), Cavani et al. (2003). Potentiometric titration: Samples were prepared by dissolving 12 mg of the freeze-dried HA in 20 ml of Milli-Q Millipore water containing 0.05 M NaCl to keep the ionic strength constant. The pH was adjusted to pH 3 by adding about 1 ml of 0.05 M HCl. The solutions were titrated to pH 10.5 with 0.05 M NaOH using a VIT 90 Titrator Radiometer (Radiometer, Copenhagen,
Table 1 Results for HA samples from different rank coals Variables
C H N S O )COOH Phe-OH TEC HuC Har c Hro c Hal c Exo1 Exo2
Rank
(%) dafa ;b ’’ ’’ ’’ ’’ meq/mg meq/mg (%)b ’’
lV s/mg lV s/mg
P avg se n¼5
BC avg se n¼5
L avg se n¼5
44 1.3 5.07 0.24 1.57 0.19 0.82 0.65 49 0.9 429 10 224 13 23 1.5 17 1.0 18 1.5 28 2.1 46 3.9 2185 366 2871 1667
53 2.6 4.00 0.13 1.22 0.09 0.40 0.09 41 2.6 404 21 350 37 27 6.8 25 6.5 38 2.3 6.2 0.8 56 2.2 783 240 6814 2040
57 1.3 3.88 0.20 0.90 0.05 1.14 0.71 36 1.63 368 14 297 14 16 4.0 9.40 0.81 23 1.0 15 1.5 62 2.6 1048 354 2895 1881
Listed are the chemical parameters, H-species in H-NMR spectra and DTA data (Exo1 and Exo2 are the first and second exothermic peak). a (Daf) dry ash free basis. b Values expressed in percentage on dry matter. c Percentage of H estimated by integrating the spectra.
O. Francioso et al. / Bioresource Technology 88 (2003) 189–195
DK). The potentiometric titrations were carried out in triplicate at 25 °C, under N2 flow and the delivery range was 10 ll min1 (0.01). The titration data were processed using the PGAUSS computer program (Gessa et al., 1994). 1 H-NMR spectroscopy: The samples were prepared by dissolving HA (20 mg) in 0.5 ml of 0.5 M sodium deuteroxide. The spectra were recorded with a Bruker ACF 250 spectrometer using a 5 mm multinuclear probe. 1 H spectra were accumulated with 16 K data point, one pulse sequence, 40° pulse angle, 3 s relaxation delay and a sweep width of 2.5 kHz. To obtain a satisfactory signal to noise ratio 1000–2000 scans were needed. Gated irradiation was applied between acquisitions to presaturate the residual water peak. Sodium 3-trimethylsilyl-propionate-2,2,3,3-d4 (TSP) was added to the samples to provide a chemical shift standard. Quantitative estimates of hydrogen in various structural groups were obtained by integrating the area of the 1 H-NMR spectra. The spectra were divided into three main regions: aromatic hydrogens (Har ) from 6.0 to 8.0 ppm; H attached to oxygen groups in carbon a (Hro ) from 4.2 to 3.0 ppm, and aliphatic H (Hal ) from 3.0 to 0.5 ppm. In accordance with results obtained by Wilson et al. (1983) the Hro region was attributed to protons largely arising from polysaccharides. Wilson et al. (1983), Gil-Sotres et al. (1994) and Simpson et al. (1997) showed that the Hal region might be affected by the presence of protons attached to aromatic rings in a, b and c positions. DTA: DTA was carried out using a TG-DTA92 instrument (SETARAM, France). About 10 mg of lyophilized HA was weighted on an alumina crucible and isothermally heated to 30 °C for 10 min under air flow (5 l min1 ) and then heated from 30 to 700 °C in air static atmosphere. The heating rate was 10 °C min1 . Calcined caolinite was used as the reference material. The area under the DTA curves, which is proportional to the heat
191
of the reaction, was calculated by the TG-DTA92B program (SETARAM, France). Statistical analysis: The data from the elemental analyses, functional groups, 1 H-NMR and DTA were used for statistical analysis. Mean values and multiple partial correlation (MPC) coefficients of the different variables were estimated (Snedecor and Cochran, 1967). The MPC coefficient measures the relationship between two variables while checking for possible effects of the other variables. The discriminant analysis (DA) was performed on uncorrelated variables (i) to determine statistically significant differences among groups, (ii) to establish a procedure for classification, (iii) to determine which independent variables account for most of the differences among groups analyzed. The Statgraphics version 5 plus (Statistical graphics system by Statistical Graphics Corporation) was used for our calculations.
3. Results and discussion 3.1. Elemental and functional groups analyses The elemental analysis (C, H, N, S, O) and functional groups concentration are shown in Table 1. These parameters are frequently utilized to characterize the soil organic matter as demonstrated by Tissot and Welte (1984), Stevenson (1994). Some parameters such as the C or O, –COOH groups and HuC, progressively changed in HA according to their maturation rank and they can be considered representative during the humification process. Differences between means indicated that the data of C from P is statistically significant when compared to that of BC and L (Table 2). These changes could be interpreted as different chemical and microbiological processes involved in the formation of BC and L with respect to P samples. In agreement with the trend observed for C, the O content varied from 48% to 36%
Table 2 Evaluation of the differences between means of analyzed parameters in P, BC and L Variables
P–BC
t
P–L
t
BC–L
t
C H N S O )COOH Phe-OH TEC HuC Har Hro Hal Exo1 Exo2
)9.18 1.07 n.s. n.s. 7.2 n.s. )126.4 n.s. n.s. n.s. 22.09 n.s. 1400 n.s.
)3.05 3.91 n.s. n.s. 2.51 n.s. )3.18 n.s. n.s. n.s. 8.80 n.s. 3.19 n.s.
)13.30 1.18 0.67 n.s. 12.4 61 )73.6 n.s. 7.6 )5.10 13.63 )15.47 n.s. n.s.
)6.95 3.77 3.30 n.s. 6.53 3.50 )3.92 n.s 5.86 )3.70 5.60 )4.21 n.s. n.s.
n.s. n.s. 0.32 n.s. n.s. n.s. n.s. n.s. 16 14.33 )8.46 n.s. n.s. n.s.
n.s. n.s. 2.88 n.s. n.s. n.s. n.s. n.s. 2.44 5.71 )5.63 n.s. n.s. n.s.
Number of observations, n ¼ 5; n:s: ¼ P > 0:05; t ¼ studentÕs t. P 6 0:05; P 6 0:01; P 6 0:005.
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resulting in statistically significant differences when P is compared to the BC and L samples. The H content is not considered important in the humification process discussion. We found that the H content decreased with humification rank and the differences between groups were significant when P was compared to the BC and L samples (Table 2). The N content decreased as the maturation rank increased (Table 1). How the N can persist in mature materials is an important consideration. Knicker et al. (1996) and Bonnett (1996) demonstrated the existence of resistant forms of N in HA attributing its presence to heterocyclic N compounds such as porphyrins and metalloporphyrins. N, the central element of porphyrin structures, was involved mainly in the complexation of metals. In this case the N content was statistically significant when L was compared to BC and P samples (Table 2). The quantifications of carboxylic acids (COOH) and phenolic-OH (phe-OH) groups are shown in Table 1. The P samples showed a higher COOH concentration with respect to the BC and L samples (Lawson and Steward, 1989; Stefanova et al., 1993). The differences between groups were statistically significant when P was compared to L samples (Table 2). It is important to evaluate these functional groups in the materials analyzed because they give an accurate estimate of the reactivity and the humification rank of the HA. The focal point of the coalification process is the decrease of COOH, methoxyl and carbonyl groups in HAs and an increase in phe-OH groups during humification (Lawson and Steward, 1989; Stefanova et al., 1993). Hatcher et al. (1989a,b) (using a number of different analytical techniques) showed that during the coalification stages-lignin to L, chemical reactions promote the cleavage of aryl ether bonds in lignin derivates to produce phe-OH. We found a lower concentration of phe-OH groups in P samples compared to BC and L samples. There are no statistically significant differences between BC and L samples (Table 2). Since the COOH group content was higher with respect to that of phe-OH in BC and L samples, we can assume that HA originated from parent materials with a low rank of coalification. The changes observed in HuC were statistically significant when L was compared to P and BC samples. Apparently the TEC does not seem to be significant in the quantification of the differences among these materials. 3.2. Differential thermal analysis Each thermogram was characterized by an endothermic peak (around 120 °C) assigned to the deydratation, and two exothermic oxidation peaks: (i) the first peak (Exo1) is considered to be the result of the thermal degradation of polysaccharides, decarboxylation of acidic groups and dehydration of hydroxylate aliphatic
structures (Sheppard and Forgeron, 1987; DellÕAbate et al., 2002); (ii) the second peak (Exo2) is related to the breakdown of aromatic structures and cleavage of the C–C bond (Sheppard and Forgeron, 1987; Provenzano and Senesi, 1999; DellÕAbate et al., 2002). The combustion reaction of the first exothermic peak (292 °C) in P samples was significantly higher ðP 6 0:05Þ than the second peak (382 °C) (Table 1). The accumulation of recent C can influence the thermal profiles of P samples, characteristic of carbohydrate molecules and not of ‘‘typical’’ lignin structures (Ishiwatari et al., 1983). On this basis we can deduce that these materials might be composed of parent materials with a low rank of coalification. The combustion reaction of the first exothermic peak (290 °C) in BC samples was significantly lower ðP 6 0:05Þ than the second peak (455 °C) (Table 1). The more intense combustion reaction of the second peak is due to cracking of higher molecular weight polynuclear systems (Sheppard and Forgeron, 1987). This effect is particularly evident in the BC sample purchased from the International Humic Substances Society (IHSS) where the oxidation temperatures of both peaks were higher compared to the P samples. The combustion reactions of two exothermic peaks at 285 °C and at 437 °C in L samples did not show statistically significant differences. However, the differences among samples were statistically significant when L was compared to P and BC (Table 2).
3.3. 1 H-NMR spectroscopy The percentage of the subdivisions within the three regions of the HA spectra are shown in Table 1. The main changes in the spectra can be attributed to HA aging. P samples showed a prominent region (Hro ), corresponding to about 30% that was attributed to the sugar-like and polyethers components (Francioso et al., 2001). The presence of these components was also supported by the intense exothermic peak at 292 °C of DTA of P samples. The aliphatic region (Hal ) was mainly characterized by terminal methyl groups as described in previous papers by the same authors (Francioso et al., 1996, 2001). Although the Har region (6.0–8.0 ppm) was broad and intense, it represented about 20% of the total integrated area. Har and Hal regions of P samples were statistically significant when compared to those of L samples (Table 2). On the other hand, the Hro regions were statistically significant when compared to those of BC and L samples (Table 2). BC samples were characterized by a strong decrease of the sugar-like component (at about 6%) with respect to P samples. The low concentration of these components was also supported by DTA of BC samples. This
O. Francioso et al. / Bioresource Technology 88 (2003) 189–195
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Table 3 MPC coefficients between investigated variables C H N S O COOH Phe-OH TEC HuC Har Hro Hal Exo1 Exo2
C
H
N
S
O
COOH
Phe-OH
TEC
HuC
Har
0.700 0.236 0.664 )0.825 )0.528 0.403 )0.681 0.675 )0.302 0.644 0.144 )0.712 )0.121
)0.560 )0.980 0.875 0.755 )0.593 0.961 )0.957 0.229 )0.916 0.072 0.971 0.400
)0.569 0.474 0.712 0.129 0.532 )0.635 0.375 )0.515 )0.344 0.643 0.315
0.861 0.773 )0.576 0.977 )0.974 0.221 )0.933 0.089 0.957 0.386
)0.586 0.495 )0.912 0.917 )0.202 0.881 )0.179 )0.859 )0.500
0.120 )0.698 0.752 )0.163 0.609 0.229 )0.757 )0.270
0.586 )0.481 0.200 )0.640 0.320 0.544 )0.00
0.987 )0.215 0.260 0.933 )0.931 0.280 )0.170 0.090 0.635 )0.928 0.945 )0.367 )0.479 0.473 0.424
Hro
Hal
Exo1
0.197 0.937 0.300
0.04 )0.516 )0.274
Exo2
P 6 0:05.
region showed statistically significant differences when compared to L samples (Table 2). The Har and Hal regions in BC samples were about 38% and 56%, respectively. With regard to the differences between regions, only Har was statistically different when compared to those of L samples (Table 2). L samples were characterized by a high percentage of Hal (about 62%) when compared to other regions. It can be supposed that the Hal region can be affected by the presence of protons attached to an aromatic ring in a, b and c position in accordance with NMR spectra obtained by Wilson et al. (1983), Gil-Sotres et al. (1994) and Simpson et al. (1997). The presence of a higher percentage of Hro with respect to BC samples seems to be related to polysaccharides and lignin derivatives that can survive in the early coalification (Bates and Hatacher, 1989). The low aromaticity found in these samples (about 23%) may suggest that it is the earliest stage of coalification (Hatacher, 1988).
The DA analysis was performed on the uncorrelated variables, Har , Hal , Exo2, phe-OH and C since in practical applications, correlations between XÕs usually have the effect of making the discriminant function less accurate (Snedecor and Cochran, 1967). It is to be noticed that these variables are measures of the aromatic and aliphatic component in HS (Lawson and Steward, 1989; Hatcher and Clifford, 1997). Two statistically significant discriminating functions ðP < 0:05Þ were obtained (Fig. 1). The first DA function accounts for 80% of the variance between groups while the second DA function accounts for about 20% (Table 4). The FisherÕs linear discriminant function coefficients for each group enable us to classify the observations into groups. In addition the standardized coefficients of functions indicate the partial contribution of each variable to the different levels of discriminant. In particular the first function was 1:31742 C þ 1:1153 phe-OH þ
3.4. Multivariate analysis for chemical, DTA and NMR parameters The depositional HS during coalification can be studied using a multivariate analysis of variables which are presumably affected by such a process. A preliminary linear correlation analysis had shown that most of the Pearson product moment correlation coefficients ðrÞ between each pair of variables were statistically significant. To estimate the spurious and/or suppressive portions that exist in paired ðrÞ coefficients, a MPC was performed; such information can be applied in order to improve the prediction power of the variables under consideration (Snedecor and Cochran, 1967). The MPC results suggested that some variables were highly correlated among them ðP < 0:05Þ while others were not (Table 3).
Fig. 1. Binary diagram of two discriminant function F1 and F2 obtained from the analysis performed on HA from P, BC and L data. F1 accounts for 80% between groups variance.
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Table 4 DA for indipend variables C, phe-OH, Har , Hal , Exo2
Acknowledgements
Discriminant function
Eigenvalue
Relative percentage
Canonical correlation
1 2
8.19765 2.03811
80.09 19.91
0.944 0.819
Function derived
Wilks lambda
Chi-square
DF
P
1 2
0.035 0.329
33.301 11.11
10 4
0.0002 0.02
0:577604 Har 0:816597 Hal þ 0:0888662 Exo2. The relative magnitude of these coefficients indicates how the independent variables are being used to discriminate amongst the groups. The C and phe-OH coefficients seem to have a higher weight in predictions. The second function ð0:174692 C 0:151624 phe-OH þ 1:45972 Har 1:41506 Hal 0:0449892 Exo2Þ maximized the differences between the dependent variables. From all the observations used to fit the model, 93.333% were correctly classified. A sample of BC in particular was incorrectly predicted as belonging to that group. The diversity of such a sample might be explained by considering its uncertain classification.
4. Conclusions Quantitative differentiation of three matrices, P, BC and L, based on five HA variables is a method to investigate the humification process during coalification. The DA analysis performed on the five variables (C, phe-OH, Har , Hal , and Exo2, selected in relation to their independence) has given enough quantitative information to distinguish different HA extracted from P, BC and L. The five variables are also an estimate of the coalification process (Lawson and Steward, 1989; Hatcher and Clifford, 1997). On the basis of the results obtained from DA, it is reasonable to assume that only the five variables have provided a quantitative estimation of the differences between P, BC and L, even having discarded the remaining set of variables considered. This suggests that the univariate method, based on a single variable analysis, can be misleading when different data sets are investigated. The DA functions, obtained using five variables of five samples for each coal, are sufficient to give a general characterization of the coals. In fact by applying the DA analysis it is possible to obtain discriminant functions which allow us to predict the best classification method of unknown samples. The advantages of the proposed DA analysis are a consequence of the linear relation between variables before and after the analysis. The test of the differences between coals is simple and robust and may be referred directly to the initial observed data.
This research was supported with funds provided by the ‘‘Ispettorato Centrale delle Repressioni Frodi’’ of the Italian Ministery of the Agricultural and Forestry Politics (MiPAF). References Alianello, F., DellÕOrco, S., Benedetti, A., Sequi, P., 1999. Identification of primary substrates in organo-mineral fertilizers by means of isoelectric focusing. Commun. Soil Sci. Plant Anal. 30, 2169–2181. Bates, A.L., Hatacher, P.G., 1989. Solid-State 13 C-NMR studies of a large fossil gymnosperm form the Yallourn Open Cut, Latrobe Valley, Australia. Org. Geochem. 14, 609–617. Bonnett, R., 1996. Porphyrins in coals. Int. J. Coal Geol. 32, 137–149. Cavani, L., Ciavatta, C., Gessa, C., 2003. Identification of organic matter from peat, leonardite and lignite fertilisers using humification parameters and electrofocusing. Bioresource Technol. 86, 45–52. C ur€ um, Y., 2000. Oxidative pyrolysis of Turkish lignites ß etinkaya, S., Y€ in air up to 500 °C. Fuel Process. Technol. 67, 177–189. Chapman, S.J., Campbell, C.D., Fraser, A.R., Pruri, G., 2001. FTIR spectroscopy of peat in and bordering Scots pine woodland: relationship with chemical and biological properties. Soil Biol. Biochem. 33, 1193–1200. Ciavatta, C., Govi, M., Vittori Antisari, L., Sequi, P., 1989. Determination of organic carbon in soil and fertilizer. Commun. Soil Sci. Plant Anal. 20, 759–773. Ciavatta, C., Govi, M., Vittori Antisari, L., Sequi, P., 1991. Determination of organic carbon in aqueous extracts of soil and fertilizers. Commun. Soil Sci. Plant Anal. 22, 795–807. DellÕAbate, M.T., Benedetti, A., Trinchera, A., Dazzi, C., 2002. Humic substances along the profile of two Typic Haploxerert. Geoderma 107, 281–296. Francioso, O., Sanchez-Cortes, S., Tugnoli, V., Ciavatta, C., Sitti, L., Gessa, C., 1996. Infrared, Raman and nuclear magnetic resonance (1 H, 13 C and 31 P) spectroscopic study of fractions of peat humic acids. Appl. Spectrosc. 50, 1165–1174. Francioso, O., Ciavatta, C., Sanchez-Cortes, S., Tugnoli, V., Sitti, L., Gessa, C., 2000. Spectroscopic characterization of soil organic matter in long-term amendment trials. Soil Sci. 165, 495–504. Francioso, O., Sanchez-Cortes, S., Tugnoli, V., Marzadori, C., Ciavatta, C., 2001. Spectroscopic study (DRIFT, SERS and 1 HNMR) of peat, leonardite and lignite humic substances. J. Mol. Struct. 565, 481–485. Gessa, C., Deiana, S., Maddau, V., Manunza, B., Rausa, R., 1994. Binding of proton and metal ions in humic acids. Test of the bimodal Gaussian model. Agrochimica 38, 85–91. Geyer, W., Hemidi, F.A.-H., Bruggemann, L., Hanschmann, G., 2000. Investigation of soil humic substances from different environments using TG–FT–IR and multivariate analysis. Thermochim. Acta 361, 139–146. Gil-Sotres, F., Saa, A., Trasar-Cepeda, M.C., Leir os, M.C., Zech, W., 1994. Spectroscopic (13 C-NMR, 1 H-NMR and IR) and chemical characterization of mine soil humic acids. In: Senesi, N., Miano, T.M (Eds.), Humic Substances in the Global Environment and Implications on Human Health. Elsevier Science Amsterdam, London, pp. 601–606. Gonzalez-Vila, F.J., del Rio, J.C., Almedros, G., Martin, F., 1994. Structure relationship between humic fractions from peat and lignites from Miocene Granada Basin. Fuel 73, 215–221. Hatacher, P.G., 1988. Dipolar-dephasing 13 C-NMR studies of decomposed wood and coalified xylem tissue: evidence for chemical structural changes associated with defunctionalization of lignin structural units during the coalification. Energy Fuels 1, 40–58.
O. Francioso et al. / Bioresource Technology 88 (2003) 189–195 Hatcher, P.G., Clifford, D.J., 1997. The organic geochemistry of coal: from plant materials to coal. Org. Geochem. 27, 251–274. Hatcher, P.G., Lerch, H.E., Verheyen, T.V., 1989a. Organic geochemical studies of transformation of gymnosperm xylem during peatification and coalification to subbituminous coal. Int. J. Coal Geol. 13, 65–97. Hatcher, P.G., Wilson, M.A., Vassallo, A.M., Lerch, H.E., 1989b. Studies of angiospermeous wood in Australian brown coal by nuclear magnetic resonance and analytical pyrolysis: new insights into the early coalification process. In: Lyons, P.C., Alpern, B. (Eds.), Coal: Classification, Coalification, Mineralogy, Traceelement Chemistry, and Oil and Gas Potential, International Journal of Coal Geology, vol. 13. pp. 99–126. Ibarra, J.V., Juan, R., 1985. Structural changes humic acids during the coalification process. Fuel 64, 650–656. Ibarra, J.V., Munoz, E., Moliner, R., 1996. FTIR study of the evolution of coal structure during the colaification process. Org. Geochem. 24, 725–735. Ishiwatari, R., H€ anninen, K., Lehito, O., Tirronen, A., Tuhkanen, M., M€ alk€ onen, P., Pohjala, E., 1983. Chemical degradation and oxidation studies of Finnish Peat and of Peat humic fractions. Qualitative analysis. In: Fuchsman, C.H., Spigarelli, S.A (Eds.), International Symposium on Peat utilization Bemidji State University center for environmental Studies. pp. 457–465. Knicker, H., Almendros, G., Gonzalez-Vila, F.J., Martin, F., Ludemann, H.D., 1996. 13 C- and 15 N-NMR spectroscopic examination of the transformation of organic nitrogen in plant biomass during thermal treatment. Soil Biol. Biochem. 28, 1053–1060. Lawson, G.J., Steward, D., 1989. Coal humic acids. In: Aiken, G.R., McKnight, D.R., Wershaw, R.L., MacCarthy, P.C. (Eds.), Humic Substances II. Wiley-Interscience, New York, pp. 641–686. Lee, G., Wilson, M.A., Young, B.R., 1998. The application of the ‘‘Watergate’’ suppression technique for analyzing humic substances by nuclear magnetic resonance. Org. Geochem. 28, 549–559.
195
Lu, X.Q., Hanna, J.V., Johnson, W.D., 2000. Source indicators of humic substances: an elemental composition, solid state 13 C-NMR and Py-GC/MS study. Appl. Geochem. 15, 1019– 1033. Juntgen, H., 1984. Review of the kinetics of pyrolysis and hydropyrolysis in relation to the chemical constitution of coal. Fuel 63, 731– 737. Mazumdar, B.K., 2000. Thermochemical history in the genetic path of coal: derivation of a novel correlation for heat of combustion. Fuel 79, 1267–1276. Provenzano, M.R., Senesi, N., 1999. Thermal properties of standard and reference humic substances by differential scanning calorimetry. J. Anal. Calorimetry 57, 517–526. Sheppard, J.D., Forgeron, D.W., 1987. Differential thermogravimetry of peat fractions. Fuel 66, 232–236. Simpson, A.J., Boersma, R.E., Kingery, W.L., Hicks, R.P., Hayes, M.H.B., 1997. Applications of NMR spectroscopy for studies of the molecular compositions of humic substances. In: Hayes, M.H.B, Wilson, W.S. (Eds.), Humic Substances, Peat and Sludge: Health and Environmental Aspects. The Royal Society of Chemistry, Cambridge, pp. 46–63. Snedecor, G.W., Cochran, W.G., 1967. Statistical Methods. The Iowa State University Press, Iowa USA. Stefanova, D.M., Velinova, S.P., Marinov, Nikolova, R., 1993. The composition of lignite humic acids. Fuel 72, 681–684. Stevenson, F., 1994. Humus Chemistry: Genesis, Composition, Reactions. Wiley, Interscience, New York. Teichm€ uller, M., Teichm€ uller, R., 1975. StachÕs Texbook of Coal Petrology. Gebruder Borntrager, Berlin (Chapter 2). Tissot, B.P., Welte, D.H., 1984. Petroleum Formation and Occurrence, second ed. Springer-Verlag, Berlin. Wilson, M.A., Collin, P.J., Tate, K.R., 1983. 1 H-NMR nuclear magnetic resonance spectroscopy of soil humic acid. J. Soil Sci. 34, 297–304.