Molecular composition and chemometric differentiation and classification of soil organic matter in Podzol B-horizons

GEODER~ ELSEVIER

Geoderma 76 (1997) 193-219

Molecular composition and chemometric differentiation and classification of soil organic matter in Podzol B-horizons H. Wilcken, C. Sorge, H.-R. Schulten * In.~titut Fre~'enius, Chemical and Biological Laboratories. hn Maisel 14, 65232. Taunusstein. Germany

Received 6 November 1996: accepted 18 December 1996

Abstract

Whole soils from nine different Podzol B-horizons were analysed by wet-chemistry, solid-state cross-polarization/magic-angle spinning (CPMAS) I~C-NMR spectroscopy and pyrolysis-field ionization mass spectrometry (Py-FIMS). The wet-chemical analyses referred to site-specific contents of polysaccharides, lipids, lignins, fulvic acids, humic acids and humins in the organic matter of each horizon. All CPMAS t3C-NMR spectra tbr soils were characterized by intense signals from O-alkyl carbon and alkyl carbon. Aryl carbon and carboxyl carbon were less abundant. Correspondingly, the Py-FIMS spectra were dominated by signals from carbohydrates and lipids, especially sterols such as ethylcholestapentaene, ethylcholestatetraene, dehydroergosterol, ergosterol, stigmasterol, taraxerone and a-tocopherol. Lower relative abundances were registered for lignins and alkylaromatics. Both intact and microbially altered lignins accumulated in the B-horizons. Temperature-resolved Py-FIMS enabled two organic matter pools with different thermal stability to be detected. The thermolabile pool (evolution under 450°C) consists mainly of carbohydrates, sterols and N-containing compounds, whereas the thermostable pool (evolution above 450°C) is largely made up of condensed lignins, lipids and alkylaromatics. To visualize differences and/or similarities between the nine Podzol B-horizons according to their organic matter composition, the data sets obtained by wet-chemistry, CPMAS ~3C-NMR spectroscopy and Py-FIMS were evaluated by chemometric methods. Using principal component analysis (PCA), neither the wet-chemical data nor the ~C-NMR spectra enabled the Podzol B-horizons to be classified according to vegetation or Podzol type. In contrast, PCA of 200 FISHER-weighted Py-FIMS signals clearly separated the B-horizons according to the composition of SOM. Soils with weak Podzol features are characterized mainly by signals from carbohydrates,

• Corresponding author. Tel.: +49 6128 744-250: Fax: +49 6128 744-890. 0016-7061/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII SO01 6-7061(96)00107-3

194

H. Wilcken ef al. / Geo&,nna 76 ~19971 193 219

phenols/lignin monomers, fatty acids, and constituents of plant waxes (e.g., nonaeosanedione. nonacosanediol). Haplic Podzols show strong signals from lignin dimers, hmg-cham lipids and sterols. The results obtained by cluster analysis (CA} of the 200 FISHER-weighted Py-FIMS signals were visually well correlated with those derived from PCA. Both chemometric techniques enabled the classification of the nine Podzol B-horizons according to their degree of podzolization. Kev~ord.s: mol¢cular-chmmcal~lructurc: chemometrics: ciusmr analysis: CPMAS ~C-NMR spectroscop~.: pattern recognition: P,Mzol: principalcomponentanalyqs: pyrolysis-mas~,spectrometry:soil clas~dtication:soil organic rnancr: v~,et-chcmicaldata

1. Introduction Podzolization involves the mobilization, translocation and accumulation both of inorganic and organic soil constituents, leading to the formation in the subsurface of humus-rich (Bh) and sesquioxide-rich (Bs) horizons or combinations of both. Different explanations have been advanced to account for the formation of the B-horizons in Podzols. Wiechmann (1978), for example, proposed that Podzol B-horizons became enriched with high-molecular weight organic substances by filtration from percolating liquids and by sorption onto Fe- and M-oxides. The podzolization process was described by de Coninck (1980) as follows. In soils, mobile organic substances are formed during breakdown of plant remains in surface horizons. If enough polyvalent cations, especially of AI and Fe, are available in situ, the mobile organic substances formed are immediately immobilized and no migration occurs. If, however, the amounts of AI a n d / o r Fe available are insufficient for complete immobilization, these sesquioxide cations are complexed by the mobile compounds and transported downward in the soil profile. Immobilization may occur at some depth through supplementary fixation of cations, through desiccation, or on arrival at a level with different ionic concentration. However, little detailed information is available to date on the molecular composition of the organic matter that accumulates in B-horizons. Analysis has mostly been restricted to extracted humic substances and the results may not be directly transferable to the organic matter in whole soils. Advances in instrumental analysis, notably spectroscopic methods have enabled analyses of whole soils to be carried out. Thus. soil organic matter (SOM) can now be investigated directly following drying and milling in soils, soil aggregates, particle-size and density-fractions, that is, without prior extractions, saving time and labor. Modern methods, especially, CPMAS ~C-NMR spectroscopy (e.g., Fri.ind and Lhdemann, 1989; Wilson. 1989; Baldock et al., 1992) and pyrolysis-field ionization mass spectrometry (Py-FIMS) (e.g., Schulten, 1987, 1993. 1996) have proved very useful for the structural characterization of organic matter in whole soils. Moreover, using pyrolysis-methylation/mass spectrometry polar structures rich with carboxyl and hydroxyl groups have been identified in the organic matter of whole soil of a Canadian Podzol Bh-horizon (Schulten and Sorge. 1995) and intercalated in clays (Schulten et al.. 1996). Although wet-chemical methods still can provide important supplementary data on SOM composition, there is little doubt that for contemporary work in soil science, in particular the

H. Wilcken et al. / Geodernut 76 (1997) 193-219

195

combination of analytical methods will expand the knowledge on the structure and formation of SOM (Schnitzer, 1991). Previously, the integrated analytical approach including wet-chemistry, CPMAS '3C-NMR spectroscopy and Py-FIMS has been successfully applied to the analysis of organic matter in forest humus layers (Hempfling et al., 1987), agricultural soils (Schulten and Hempfling, 1992) and podzol horizons (Beyer et al., 1993a,b, 1996a,b; Beyer, 1996). So far, however, the structural characterization and classification of SOM in Podzol B-horizons of different origin has not been assessed by chemometric methods. Preliminary organic matter analyses using Py-FIMS were carried out previously as a contribution to a more comprehensive understanding of podzolization. Thus, it was shown that Py-FIMS can be used for direct characterization of dissolved organic matter (DOM) in percolating water from Podzol profiles (Hempfling and Schulten. 1990a). Guggenberger ct al. (1994) postulated that hydrophobic acids served as direct precursors of organic, plant-derived materials in illuvial horizons. These acids showed high concentrations of carboxyl and hydroxyl groups, microbially altered lignins and lignocellulose. Sorge et al. (1994) analysed the organic matter of the whole soil and the particle-size fractions taken from a Canadian Podzol Bh-horizon using Py-FIMS. In the whole soil, and in the silt and clay-size fractions, the organic matter showed characteristic signals from lipids, alkylaromatics, carbohydrates as well as intact and microbially altered lignins. For the sand fraction, higher relative intensities of carbohydrates. phenols/lignin monomers and lower relative intensities of dimeric lignin units were found as for the other fractions. Thus the question arises whether these observations are generally applicable to Podzol B-horizons. Due to the complexity of SOM, analyses using modern instrumental methods produce a vast amount of data which are difficult to correlate, handle, and preserve. This problem may be overcome by the chemometric approach by which large data sets can be visualized, and from which similarities and differences can be extracted so that even an observer who has no special experience can gain an insight into the data. Chemometric evaluation techniques in soil science were first used by Bracewell and Robertson (1984) in analyzing the composition of SOM from A-horizons of different soil types and Schulten et al. (1988) discriminating horizons in a soil profile by pattern recognition. More recently. Hempfling et al. (1991) interpreted their Py-FIMS spectra of spruce needles, litter materials, roots and forest soils using principal component analysis (PCA). The application of PCA and cluster analysis allowed Schulten and Hempfling (1992) to discriminate between agricultural soils under different soil managements in terms of molecular differences in humus composition. In this paper we combine wet-chemical methods, CPMAS '3C-NMR spectroscopy and Py-FIMS and subsequently apply chemometric evaluation techniques to characterize the organic matter in whole soils of nine Podzol B-horizons. The objectives of the integrated approach are to 1. examine and validate the current concepts of composition of SOM in Podzol B-horizons at the molecular scale: 2. apply chemometric techniques for the evaluation of similarities and differences in the composition of SOM in Podzol B-horizons: and 3. contribute to a more comprehensive understanding of Podzol tbrmation.

196

H. Wih'ken et al. / Geoderma 76 ( 19971 193-219

2. Materials and methods 2.1. Soils The nine soil samples were taken from six sites in northern Germany. Vegetation. soil type. depth, Munsell color, soil texture, pH-value and cation exchange capacity (CEC) of each horizon are given in Table I. The soil types were characterized according to Spaargaren (1994). The texture was determined with a combined sieving ( > 63 ~m) and pipette analysis. The Ph-value was measured in 0.01 M CaCI,. CEC was determined at Ph 8.2 according to Mehlich. Prior to the wet-chemical and spectroscopic analyses, all soils were air-dried and finely ground with an agate mortar (particle size < 0.5 ram). 2.2. Wet-chemistry Organic carbon (C,,~) was determined by dry combustion in a Str6hlein apparatus. Proteins were estimated as 6.25 × a-NH ,-N according to Stevenson and Cheng (1970). Fat and waxes were extracted with ethanol/benzene, free sugars and starch with 0.05 M H,SO~, hemicellulosc with 2c~ HCI and cellulose with 72c£ H : S O 4. Lignins were estimated as 7 × OCH~ (conifers. monocotyledons) following the Zeisl-Pregl method (Gattermann and Wieland, 1982). The mobile fulvic acids (tuFA) were extracted with 0.05 M H2SO ~. Fulvic (FA) and humic acids (HA) were extracted with 0.1 M NaOH from which the HA were separated by precipitation with 0.1 M H2SO 4. Humins (Hu) were isolated in the sediments by "sulfacetolysis" according to Springer (1943). More detailed descriptions of the methods are given by Beyer and Blume (1990). Pyrophosphate extractable C and Fe were determined by shaking 0.2 g sample in 20 ml 0.1 M NatP20 7 (Ph 10) for 16 h. Aiiquots of 5 ml were used for C determination by dry combustion. Fe was determined by ICP-spectroscopy. The mean coefficients of variation for two replicates were 6c~ (C~,,) and 3% (FeF,~). 2.3. Solid-state cross-polarization~magic-angle spinning (CPMAS) I~C-NMR spectroscopy The CPMAS ~3C-NMR spectra were obtained with a BRUKER MSL 300 spectrometer at a frequency of 75.4 MHz. Chemical shift values ( 6 ) are reported relative to tetramethylsilane (TMS). According to Wilson (1987) the spectra were divided into four regions each of which covers a range of chemical shift: 6 = 0 - 4 6 ppm (alkyl carbon). 6 = 46-110 ppm (O-aikyl carbon). 6 = 110-160 ppm (aryl carbon) and ~ = 160-210 ppm (carboxyl carbon). The integrated areas for each range can be correlated directly to the carbon concentration (Friind and IAidemann. 1989). 2.4. Pyrolysis-lield ionization mass spectromet O" (Pv-FIMS) The Py-FIMS spectra were recorded on a double-focusing FINNIGAN MAT 731 mass spectrometer. Between 4.9 and 11.7 mg of soil was weighed in quartz crucibles

197

H. Wilcken et al. / G e o d e r m a 76 (1997) 1 9 3 - 2 1 9

(J

E, r~',

~6 ~

"t"

r,':

--

r-.:

~

~

--

"I"

r..:

E o>.

_cr~

~>-

,,..:

~

uq,.

>.

>.

>.

o~_

~

>

-

-5_

~

c-j

--

~ I

0

.aJ

E~

~..~

P.

E

-r

2 E

.c_

C <

E

v

<

"r <

198

H. Wih'ken et al. / Geoderma 76 ( 1997/ / 93- 2 / 9

and introduced directly into the high vacuum of the ionization source (Schulten et al., 1987). During the time- and temperature-resolved pyrolysis the samples were heated from 100°C to 700°C at a heating rate of 0.5°C per second. Scans were recorded in I 0°C intervals using a mass range from m / z 15 to m / , 600. These data were integrated to obtain summed spectra using the FINNIGAN MAT SS 200 data system (Bremen. Germany). Three replicates per sample were analyzed and averaged to one survey spectrum. The spectra were normalized to I m g soil. Recently, a comprehensive survey of the methodology for the analyses of soil extracts and soils has been published (Schnitzer and Schulten. 1995). In order to be able to evaluate differences in SOM composition by Py-FIMS spectra. a selection of characteristic nominal masses (biomarkers) were selected. These assignments were based on studies of model compounds, plant materials, extracted humic fractions and soils using low- and high-resolution Py-FIMS and Curie-point pyrolysis gas chromatography/mass spectrometry (Py-GC/MS; Schnitzer and Schulten, 1992: Sorge ct al., 1993a). In the present Py-FIMS investigation, the selected nominal masses of eight biomarkers were summed: carbohydrates, phenols/lignin monomers, lignin dimers, lipids, sterols, alkylaromatics, N-compounds, and amino acids/peptides. Particularly pyrolysis-GC/MS in combination with nitrogen-selective detection allowed the identification of about 100 N-containing pyrolyzable soil components and shed light on the aliphatic and aromatic molecular structures of the "unknown organic nitrogen" (Schulten et al., 1995). 2.5. Chemometric data et'aluation As the data sets obtained by' wet-chemistry and CPMAS ~C-NMR spectroscopy, contain only a small number of variables, these sets were used without data reduction by' FISHER weighting (see below). To compensate for different scaling units, all sets were scaled according to the autoscaling method proposed by Kvalheim (1985). To reduce the Py-FIMS data sets (800 variables), the FISHER weights of all signals were calculated. Thus, the mass signals with low inner-group but high outer-group variances could be selected. The standardization of the Py-F1MS spectra and the mass signal selection (weighting) by FISHER ratios were carried out using the FINNIGAN MAT SS 200 data system (Bremen, Germany) on a DEC pdp-II computer (Massachusetts, USA). The chemometric evaluation methods were run on an IBM-compatible 486/33 PC using the PONTOS© software (Utah, USA) tbr principal component analysis and the SYSTAT(("~ software (Illinois, USA) for cluster analysis. 2.5.1. Principal component analysis (PCA) PCA enables similarities and/or differences between a large number of data sets (e.g., wet-chemical data sets, t3C-NMR spectra sets, Py-F1MS spectra sets) to be visualized. Every value of a data set is regarded as a variable (x,,) in a multivariate space. Thus, each spectrum is represented in a multivariate coordinate system by one single point. As this can lead to non-interpretable coordinate systems with more than 500 axes, PCA is used. The mathematical routines reduce the number of necessary

H. Wih'ken et aL / Geoderma 76 (1997) 193-219

199

variables by calculating new axes that are linear combinations of the old axes. The new axes are called principal components (PC): PCi = a ~ x j + a j 2 x 2 + . . " + ot,,x,

(1)

The a factors are Ioadings representing the importance of a variable for the new PC. The first PC contains the largest information part (variance), the second the largest information part of the rest etc.. Thus, only a small number of score plots is required to present the analytical results (Brereton, 1992; Malinowski and Howery. 1980). To explain the differences between the Py-FIMS spectra in the PCA score plots by mass signals factor rotation was used. By rotating a window of 10° on the spanned space of two PCs and calculating the sum of squares of the Ioadings present in the window (VAR.~): VARy, = ~ a f

(2)

/=1

a new polar plot is obtained (variance diagram). These variance diagrams indicate differences in the molecular composition of SOM in the B-horizons by maxima. The maxima can be explained by Py-FI mass signals. In addition, rotated factor spectra containing the original variables (mass signals) were computed. They explain the position of the spectra dots in the PC space and thus differences between the spectra by certain Py-FI mass signals. More detailed mathematical backgrounds are published by Windig et al. (1981. 1982) and Windig and Meuzelaar (1984). 2.5.2. Cluster analysis (CA)

CA is based on distance calculations. As the distances in a multivariate space can be computed only in one dimension, CA techniques are a powerful tool for similarity searches. The most common method is the calculation of the Euclidian distance, dc. between two objects (measurements) i and j: d,, =

V/I_~ I( -Ifi~ k

.r,~ )-

(3)

where x,k is the value of variable k for object i. and p the number of measured variables The resultant plot is a dendogram (Bratchell, 1989: Varmuza, 1980).

3. Results and discussion 3.1. Wet-chemistr3'

The wet-chemical results for the Podzol B-horizons investigated are presented in Table 2. The Cotg concentrations are similar (0.92-1.63%) except for horizon 5 with 8% Co~g. Between 17.9% and 46.5% of Corg was accounted for by litter material (protein + fats/waxes + sugar/starch + hemicellulose + cellulose + lignin). Correspondingly,

204)

H. Wilcken et al. / Geodenna 76 (1997) 1 9 3 - 2 1 9

+--++, ?+

E

++";

+

t+e~

,,....,

c.+

,~ t-... ~

•"4

+~

+~

•..,,

r'-i

+".',

,--! m

¢--1

~1 +

++%

,-,+-: ,+,..-~ ~

r,~.

+-t.

t+-i

- -

~I

i"-4

_

~

.-t ,~ ~+ q

,.r: r-: ~

-I"

tr+

+~

o

- -

-~

.~.-=&

{"

r-J ~

<~

~:

r~

+'+

+'~,

-- r~ r i ..~

+-+, +-; ~

) E

~

~

- - ~i ~i - - ~

--

~

~

) E2

.E

,++

o

.t

;5 -C

?

,'-, --

N

~

--

~

--

3C

--

--

--

.+=

H. Wih'ken et al. / Geoderma 76 (1997) 193-219

201

from 53.5ck to 82.1% of Corg was found in humic fractions (MFA + FA + HA + Hu). Whereas the protein contents were similar in all horizons, significant differences were found for fats/waxes, carbohydrates, and lignins. Especially, high lignin contents were determined for horizons 4 to 7. Probably large amounts of undecomposed or partially decomposed roots were enriched. Horizons 7 to 9 were characterized by high contents of fats/waxes. The composition of humic fractions varied significantly between the horizons. In horizons 1, 7 and 8 HA exceeded the sum of MFA + FA. This suggests that conditions prevailing in these horizons favor the elimination of fulvic acid components, either by complete degradation or incorporation into HA polymers. In horizons 4 and 9 the ratio HA to MFA + FA was equal. In the remaining horizons (2, 3, 5, 6) FA were dominant. Typically FA have a greater solubility and mobility compared to HA, especially in acid media, and are therefore often enriched in Podzol B-horizons (e.g.. Wiechmann. 1978: de Coninck, 1980). In the investigated Podzol Bh-horizons, the ratio FA : HA does not depend on the podzolization degree. The wet-chemical analyses refer to a heterogeneous and site-specific composition of SOM in the B-horizons of Podzols indicating differences in soil environment. Distinct relations between the vegetation or the Podzol type and the chemical composition of the organic matter in the B-horizons are not discernible.

3.2. CPMAS I¢C-NMR spectroscopy The CPMAS ~3C-NMR spectra of the Podzol B-horizons are similar (not shown). The integrated peak areas for the selected carbon species are presented in Table 3. The dominant signals are due to alkyl carbon and O-alkyl carbon. The alkyl carbon signal arises mainly from aliphatic alkanes, fatty acids and waxes. The O-alkyl carbon signal represents methoxyl carbon in lignins, alkyl-amino carbon, oxygenated carbon in carbohydrates and dioxygenated carbon in cellulose. Additionally, the carbon of pyra-

Table 3 Result.,, of peak area integration in the CPMAS J~C-NMR spectra of aline B-horizons Horizon

Chemical shift ~5 (ppm)

No. '*

0-46 Alkyl C (c~ TPA) h

46-110 O-Alkyl C (~2; TPA)

110-160 Aryl C (% TPA)

160-210 Carboxyl C 1~7~TPA)

I

32 29 22 37 27 25 38 36 41

39 43 34 39 42 49 28 30 35

19 19 25 2O 22 17 24 26 15

10 lg 5 O I0 I0 8 9

2 3 4 5 6 7 8 9

" The horizon numbers are explained in Table I. h Total peak area.

9

202

H. Wih'ken et al. / (;eodenna 76 (1997) 193-219

noid hexose monomers in polymers like cellulose and the ring carbon of pentose monomers in polymers like hemicellulose give resonances in the range of O-alkyl carbon (Wilson. 1987). Signals of aryl carbon and carboxyl carbon are less pronounced. The aryl carbon signal originates from lignins or alkylaromatics. The carboxyl carbon signal is due to carboxyl groups in aliphatic acids and benzenecarboxylic acids of SOM. It is likely that the ~3C-NMR spectra are influenced by the presence of paramagnetic Fe in the samples, in particular in those with close Cp, "Fev~ ratios (Table I). Using Py-FIMS Schulten and Leinweber (1995) showed that lipids, alkylaromatics and lignin dimers were preferentially associated with pedogenic Fe and AI in particle-size fractions of a Humic Gleysol. Therefore, differences in the integrated peak areas of the ~C-NMR spectra may also originate from different C : F e ratios (Arshad et al., 1988) and preferential bonds of some C species with paramagnetic Fe. Besides. the CPMAS ~C-NMR spectra generally indicate the accumulation of organic substances rich in alkyl and O-alkyl structures in Podzol B-horizons.

3.3. Pyrolysis-field ionization mass spectromett T Between 1.7% and 22.2% of soil weight was volatilized. The contents of SOM were obtained by multiplying C,~ by two. assuming 50% C,,,~ in SOM. These values were significantly correlated with the weights of volatilized mattcr ( r = 0.963 . . . . ) and the generated total ion intensity (r = 0 . 8 8 5 ' ). Thus. increasing SOM contents induce higher percentages of volatilized matter and higher total ion intensities (Tll). This was also observed in a set of 115 samples of plant materials, humic substances, particle-size fractions and whole soils with known organic carbon concentrations (Sorge et al., 1993b). Linear correlation and regression analyscs revealed that larger C concentrations result in a significant increase in percentage of volatilized matter and TII per mg sample weight. Py-FIMS spectra and the thermograms of the nine Podzol B-horizons are shown in Fig. 1 (horizons I to 5) and Fig. 2 (horizons 6 to 9). The numbering of the horizons is explained in Table I.

3.3. I. Pv-FI mass spectra The Py-FI mass spectra of horizons 1 t~) 6 are characterized by distinct signals for carbohydrates ( m / z 82, 96, 110. 126. 144). The remaining horizons (7 to 9) show relatively weak signals for carbohydrates. Signals for phenols ( m / z 94, 108, I10, 124) and monomeric lignin units such as derivatives of coniferyl alcohol ( m / z 124, 150, 152, 164, 166, 180, 182). syringyl units ( m / z 168, 192, 194, 198), sinapyl aldehyde ( m / z 208) and sinapyl alcohol ( m / z 210) are discernible particularly for horizons 1 to 6. Likewise, signals for lignin dimers at m / z 270, 284, 296, 300, 310. 312. 314, 326, 330, 340, 342, 356 (phenylcoumaran structures), m / z 244, 246. 260, 272, 274, 286, 300. 312. 316, 328 (biphenyl structures), n,/z 272. 302, 332 (diarylpropane structures) and m / z 298, 328, 358. 418 (resinol-type structures) are intense for horizons I to 6. These signals provide direct evidence lk)r the accumulation of mostly intact lignin subunits to Podzol B-horizons. In all B-horizons alkylaromatics are indicated by C2-C22 alkylbenzenes (starting with m / z 106), Cj-C~ alkylnaphthalenes ( m / z 142, 156, 170, 184) and C I - C a

H. Wih'ken et a l. / Geoderma 76 (1997) 193-2 / 9

203

1.6-

1.2 0.8

/ II

0.4

,.

,l 1O0

I~:~,' 200

l

300

400 390

18

~2

,oo ~oo 5o0 ,oo 500

600

700

O8 58 I

82/.1°

III hll~.

o.,

126

E

0

1O0

272 ; 308

2o82~.4,jii,

14o 4

~

342 ,III

"~ 200

-

|

436 300 258280

400

/:

/

h-~L/

•/

I

.L~_ I

,oo 360560 700 Temperature [°C]

500

600

700

E~ x

i1'0 ,56,94

0.8

C

~1jJi~338

'

-

(J

..................... (n E c

0

, .........

, . . . . ~. . . .

rein .roture

1O0

200

300

400 392

500

600

700

I O0

200

300

400 394

500

600

700

4.8 ..3.2 16

-61 0

.2,

~

~"

I L

-

3,01d*l

"

'

Temperoture [oc]

0

1O0

200 rn/z

300

400

500

600

700

Fig. 1. Summed and averaged Py-FIMS spectra of ( I ) the Bsh-horizon Krumesse, (2) the Bhs-horizon Krumesse, (3) the Bsh-horizon Norderstapel, (4) the Bsh-horizon Norderstapel, and (5) the Bsh-horizon Siiderstapel. The descriptions of the sampling sites and soil properties arc given in Table I. The total ion intensities (Tll) in the thermograms ( i n . i s ) were normalized in counts X l0 ~ per mg sample.

alkylphenanthrenes (m/z 192, 206, 220, 234). Likewise, all horizons are characterized by intense signals from homologous series of Cm-C20 alkyldiesters (starting with m/z 202), C2()-C3. aikenes/alkanes (starting with m/z 208 and 282. respectively) and

204

H. Wih'ken et al. / Geoderma 76 ( 19971 193- 2/9

40

436 I (¢-,'~4

392

i -t

.~^,

/]

!

J o

100

200

300

400

500

800

700

500

600

700

500

600

700

424 20

15 E

10 5

x

0

o3

100

200

300

400

E

(J

4.24

>~

C C

6

I

4 2 0

100

200

300

"00

392 436

1 8 ;0 143 ! I 0

I

I

''

406 i

96

110

1O0

308 348 280 230 2.]1011~L~ I ~ 1 ~ L 200 m/z

500

400

I

452

~

. . . 100 300 500 700 Temperoture [°C}

500 .~

600

7()0

Fig. 2. Summed and averaged Py-FIMS spectra of (6) the Bsh-horizon Radau, (7) the Bh-horizon Aukrug, (8) the Bbsh-horizon Aukrug and (9) the Bs-hori/,on Hannover. The total ion intensities (TII) in the thermograms (insets) were normalized in counts x 10 ~ per mg samplc.

CI6-C~ 4 fatty acids (starling with m / z 256). These compounds derive from natural waxes that are preserved in SOM (Schulten and Schnitzel 1990. 1991). The relatively high intensities of mass signals around m / z 390 show that these signals do not only

H. Wilckenet al. / Geoderma76 (1997) 193-219

205

reflect members of the above homologous series but are also indicative of sterois such as ethylcholestapentaene ( m / z 390), ethylcholestatetraene ( m / z 392), dehydroergosterol ( m / z 394), ergosterol ( m / z 396), stigmasterol ( m / z 414), taraxerone ( m / z 424) and ot-tocopherol ( m / z 430). Relatively intense signals from dehydroergosterol and ergosterol were also observed in whole soil and particle-size fractions of a Canadian Podzol B-horizon (Sorge et al., 1994) and in Pleistocene moraines of different ages (Leinweber et al., 1996b). Based on the assignments of Py-FIMS signals to SOM biomarkers, the summed relative ion intensities (in percent of TII per mg soil) of carbohydrates, phenols/lignin monomers, lignin dimers, lipids, sterols, alkylaromatics, N-compounds and amino acids/peptides are shown in Fig. 3. These histograms illustrate the different contributions of the eight biomarkers to the SOM composition in the Podzol B-horizons investigated. Between 44% and 71% of the TII per mg soil was attributed to them. For the horizons 1 to 6 higher proportions of carbohydrates, phenols/lignin monomers and alkylaromatics are clearly discernible. The proportions of lignin dimers, lipids, sterols, N-compounds and amino acids/peptides are site-specific.

3.3.2. Thermograms The thermograms of total ion intensity (TII) indicate two temperature ranges in each soil horizon for the thermal degradation of SOM. Consequently, two organic matter pools with a different thermal stability accumulate in Podzol B-horizons. The thermola-

100q--

G ~ Carbohydrates ~

80

I

F-

Phenols + LJgnin Monomers Lignin Dimers kipids Alkylaromotics N-Compounds Sterols Peptides

60-

i

4.0.

i

c C. > ",S, o

¥

20 ¸

I

k\\\l 1

2

3

4 Sample

5

6

!

8

9

Number

Fig. 3. Summedrelativeabundances(percentageof TII) of eight importantbiomarkersof SOM in whole soils of the Podzol B-horizons.The numberingcorrespondsto those in Table I.

206

H. Wih'ken et al. / Geoderma 76 (1997) 193-219

bile pool is volatilized from 280°C to approximately 420°C with intensity maxima between 350°C and 380°C. Above this first temperature range the TII increases again up to a maximum around 500°C (thermostable pool). The thermal decomposition of SOM is largely completed at 600°C. Significant differences between the horizons appear in the absolute TII values per mg soil and in the intensity relations between the thermolabile and thermostable pool. Temperature-resolved Py-FIMS enables the temperature resolution of the biomarker signals. Accordingly, the thermolabile organic matter is mostly due to carbohydrates. sterols and amino acids/peptides. The thermostable pool reflects especially phenols/lignin monomers, lignin dimers, lipids and alkylaromatics. Phenols/lignin monomers are additional constituents of the thermolabile pool in the organic matter of the horizons 1, 6 and 9. Bimodal curves for lipids were registered in the horizons 1, 5.6, 7 and 8. The composition of the thermostable and the thermolabile part of SOM is not the same in all soils, but shows site-specific modifications. For the whole soil of a Canadian B-horizon (Sorge et al,, 1994), temperature-resolved Py-FIMS showed only one temperature range for the thermal degradation of organic matter with a maximum between 380°C and 460°C. However. the temperature resolution of the biomarker signals indicated a thermolabile pool of mainly carbohydrates, phenols/lignin monomers and free lipids and a thermostable pool of predominantly condensed lignins, bound lipids and alkylaromatics. Except for the sand fraction, the concentration of thermolabile carbohydrates, phenols/lignin monomers and N-containing compounds having thermolabile bonds increased with decreasing particle size. In contrast, thermostable bonds characteristic of lignin dimers and alkylaromatics were present in all fractions. Bimodal curves of lipids indicated two bond types tbr this biomarker (Sorge et al., 1994). Thus, two pools of organic matter with different thermal stability and molecular composition appear to be a characteristic feature of SOM in Podzol B-horizons. In comparison, the thermal properties of illuvial organic matter corresponded to the thermal degradation behavior of DOM percolating through Podzol profiles (Hempfling and Schulten, 1990a). 3.4. Chemometric data etal,lation

The results of wet-chemical analyses (Table 2), CPMAS L~C-NMR spectroscopy (Table 3) and Py-FIMS (Figs. 1 and 2) were evaluated by chemometric methods in order to obtain an objective picture of similarities and differences in composition of SOM in Podzol B-horizons. ,¢.4.1. Principal component analysis ( PCA ) 3.4.1.1. Classification and differentiation o f the Podzol B-horizons. In a first step, PCA

was used to test whether the applied analytical methods could differentiate and classify Podzol B-horizons according to their SOM composition. For the wet-chemical results, the first and second PC of the nine data sets (5". of variance = 59.1%) discriminate horizons 1 to 4 and 6 (Fig. 4a). Horizons 5 and 7 to 9, however, form a single cluster and were not separated as their data were too similar.

H. Wih'ken et al. / Geoderma 76 (19971 193-219

(a)

207

PC 1 / PC 2 Score Plot 25

20

15

10

05 Pnncipal Component 2

3

00

i

-05

-10 2

4 -15 '. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -25 -20 -15 -10 -05

1

0.0

05

10

1.5

20

Pnncipal Comlxment 1

(b)

PC 1 / PC 2 Score Plot 200

150

100 !

i, 050 r Principal Component 2

! 000 r

-050 i

i -1 O0 L . . . . . . . . . . . . . . . . . . . . . . . . -15 -tO -05

87

9

5 6.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . O0 05 tO l5

20

,......... 25 30

PrincipalComponent I

Fig. 4. (a) Score plot o f the first versus the second principal c o m p o n e n t calculated from wet-chemical data sets o f nine Podzol B-horizons (Table 2). (b) Score plot o f the first versus the second principal c o m p o n e n t calculated from C P M A S J3C-NMR data sets o f nine Podzol B-horizons (Table 3). For numbers see Table I.

208

H. Wilcken et al. / Geoderma 76 (1997) 193- 219

With respect to NMR spectroscopy, plotting the scores of the first and second PC obtained from the integrated peak areas in the nine NMR spectra (x; of variance = 84.4%), indicate that horizons 1, 2, 4 and horizons 5 to 9 are similar in each case (Fig. 4b). Horizon 3 is an outlier due to the relatively strong signal for carboxyl carbon in this soil. Podzol B-horizons could not be differentiated or classified according to vegetation or Podzol type (Table I) by either the wet-chemical data sets or the integrated peak areas of the CPMAS t3C-NMR spectra. For the nine Pv-FI mass spectra, PCA was performed with 200 FISHER-weighted signals. The score plot PC1/PC2 which contains the highest proportion of information ('s" of variance = 80.9%, Fig. 5a), clearly discriminates the B-horizons. The numbers in Fig. 5a correspond to the numbering of the horizons in Table I. Horizons 7 and 8, which were separated from the other samples by values for PC1 lower than - 0 . 9 , had the widest Ci,: :Fee` values (11.4 and 7.3) which may originate from organic matter from the Calluna vegetation and low proportions of available Fe due to very low clay and silicate contents (Table 1). The spectrum of the positive part of PC 1 (horizons I to 5) is characterized mainly by signals from carbohydrates (e.g., m / - 82, 96, II0), phenols/lignins (e.g.. m/z 124, 208, 244, 270), fatty acids (e.g., m/z 256), cholestanol ( m / : 388) and ethylcholestatetraene (m/z 392) (Fig. 6a). The negative part of PCI (horizons 6 to 9) contains only higher molecular signals > m/z 394, which can be assigned to long-chain lipids and sterols. The spectrum of the positive part of PC2 (not shown) is dominated by signals of carbohydrates and lignins, whereas the signals > m/z 300 are situated nearly completely in the negative part. Apparently, podzolization leads to the accumulation of two qualitative types of SOM. The first type contains significant amounts of plant debris besides recalcitrant lipids. The second type is dominated by long-chain lipids and sterols. Carbohydrates and lignins of plant origin are scarcely incorporated into the second type of SOM. The mechanisms underlying the formation of these two types SOM in Podzol B-horizons are as yet unknown. Differences in the composition of SOM in B-horizons with podzol features can be related to the composition of the organic precursors (e.g., Calluna tulgaris: horizons 5, 7. 8 vs. Picea abies: horizons 1, 2, 6), to the composition of mineral matrix (e.g., sands: horizons 1, 2, 5, 7, 8 vs. silts: horizons 6, 9) and to the time factor of podzolization (e.g., recent podzolization: horizons 1.2, 7, 8 vs. fossil podzolization: horizons 3, 4, 5). As shown in Fig. 5b, a classification of the B-horizons according to the degree of podzolization can be achieved by plotting PCI versus PC3 (,Y-, of variance = 75.4c7~). With the exception of one outlier for horizon 1 (see lower right comer), the points of repeated measurements for each horizon are close together, and well separated from one another. This indicates the good reproducibility of the measurements. PC3 displays a distinct sample gradation in the sense Spodo-Dystric Cambisol (horizon 6), SpodoStagnic Gleysol (horizon 9) --+ Haplic Podzols (horizons 1, 2, 3, 4, 7. 8) ---, Cumulic Anthrosol (Plaggensol over fossil Haplic Podzol) (horizon 5). Fig. 6b shows the corresponding factor spectrum of PC3. The positive signals in this spectrum are responsible for the positive shift into the direction of PC3 in Fig. 5b (e.g., horizon 5). This is mainly due to fatty acids (e.g., m/z 256), lignin dimers (e.g., m/z 244, 270) and sterols (e.g., m/z 388, 392, 394, 396, 424). Signals for carbohydrates (m/z 82, 96, 110, 144), phenols/lignin monomers (m/z 124, 150) and fatty acids (m/z 438, 452)

H. Wilcken et al. / Geoderma 76 ( 19971 193-2 / 9

(a)

209

PC I / PC 2 Score Plot 24 20 16 12

?

7

I

0g

04

Pnncipal

3 12 22

8

I

8

O0

2

Component 2 -04

8

66

4

6

-08

5 44

55 5

4

-12

9 -16

9

9

9 -20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . )............................................................ -24 -21 -18 -15 -1,2 -09 -06 -03 00 03 06 09 12 Principal Component I

(b)

PC I / PC 3 Score Plot 22

55 18

5

14 I0 06 Prmcapal Component 3

7

(12

2

4 888

I

8 4

-02

44

I

-06 -'I 9

-10 ] 9

9

:13 13 3 3

9

-14 1 -18 ! -22 =*........ , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -2,4 -2 I -1 8 -15 -! 2 -09 -06 -03 O0

~ .......... 03 06

, . . . . . . .I 09 I2

Pnncipal Component I

Fig. 5. (a) Score plot o f the first versus the s e c o n d principal c o m p o n e n t c a l c u l a t e d f r o m 200 F I S H E R - w e i g h t e d P y - F I M S signals o f nine Podzol B-horizons. (b) Score plot o f the first v e r s u s the third principal c o m p o n e n t c a l c u l a t e d f r o m 200 F I S H E R - w e i g h t e d P y - F I M S signals o f nine Podzol B-horizons. For n u m b e r s see T a b l e I.

H. Wilcken et al./Geoderma 76 ( 1997; 193-219

2 I0

(a)

PC I Spectrum 0 60 i 44

96

0 50 i • I10

040 1 i 030 "

82i

020 !

:

244 208

oloi Covarlanoe

000

27o

388 392

i

[

]

,~.,.-i.,1~r,,,

1

~...........

-0 IO -0 2O -0 30

410

-040

42-$

-0 50 ! -060

L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1l0 190 270 350 430 510

590

670

.dz

PC 3 Spectrum

(b) 0 5(I

396 040

394

0 30

38~

+

0 21) ] I• i

o+ t ....... :

-OlO

-0 20

!oo6

', 244 270

0 I0 ~ Covaflancc

424

i

~7

r ...... '

82,

+ +

.....

, , .25d

Ii!

,

In

,~

.,- . . . . . .

t ..............

124

96110

436

-0 30

-040 -050 :- . . . . . . . . ' . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 110 190 270 350

~. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 5 l0 590

670

m/z Fig. 6. (a) Factor spectrum of the first principal c o m p o n e n t calculated from 200 F I S H E R - w e i g h t e d Py-FIMS signals of nine Podzol B-horizons. (b) Factor spectrum of the third principal c o m p o n e n t calculated from 200 F I S H E R - w e i g h t e d Py-FIMS signals of nine P(xtzol B-horizons.

H. Wilckenet al. / Geoderma76 (1997) 193-219

211

characterize the negative part of PC3 (e.g., horizon 6). Accordingly, soils with stronger podzol features are characterized especially by the accumulation of lignins, special fatty acids and sterols in B-horizons.

3.4.1.2. Selection of characteristic mass signals for each horizon. In a second step, in order to optimize the information content of the PC I/PC3 score plot and to select specific Py-FIMS signals for each B-horizon, the PC3 axis was rotated until its orientation coincided with the axis of each B-horizon. if all horizons were separated from each other, each horizon would have a specific rotated PC3 spectrum, which contains the characteristic mass signals for the position of a horizon within the PC 1/3 plane. However, all horizons were not completely separated by PCA of their Py-FIMS spectra in that the rotated PC3 spectra of horizons 1 and 3 as well as of horizons 6 and 9 agree. The 10 most intense positive and negative signals of the rotated PC3 spectra of the B-horizons are given in Table 4. The positive signals indicate which signals are more intense in a Py-F1MS spectra, while the negative signals describe the opposite. Horizon 5 is dominated by lipid signals. They can be assigned to sterols ( m / z 394-ethylcholestatriene, m / z 396-cholesterol, m / z 388-cholestanol, m / z 392-ethylcholestatetraene, m / z 424-taraxenone), fatty acids (e.g., m / z 256-Cj6 fatty acid) and long-chain aliphatics ( m / z 268-Ct9 alkane, m / z 406-C,9 alkene). Two signals for lignins ( m / z 244, 270) were selected as characteristic for this horizon. Similarly, horizons 1 to 4 are characterized especially by m / z 244. 268, 270, 388, 392 and 394. Horizons 7 and 8 are characterized by signals of other long-chain aliphatics, fatty acids and sterois (e.g., C3o alkene/alkane, m / z 420, 422 dehydrostigmasterol, m / z 410 C,~ fatty acid and/or taraxerone ( m / z 424), taraxerol ( m / z 426)). Aside from giving signals for carbohydrates and phenols/lignin monomers (not listed in Table 4) the rotated PC3 spectra of horizons 6 and 9 with weaker Podzol features show relatively intense signals for long-chain fatty acids ( m / z 438, 452), alkenes/alkanes ( m / z 434, 450) and thermostable aliphatic constituents of plant waxes (nonacosanedione ( m / z 436), nonacosanediol ( m / z 440)). Some signals of the B-horizons are specific to the vegetation. Investigations of needles and raw humus of Picea abies by pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) showed relative intense signals for m / z 416 (stigmastanol), talc 430 (a-tocopherol), m / z 436 (nonacosanedione) and m / z 440 (nonacosanediol) (Simmleit and Schulten, 1989: Hempfling and Schulten, 1990b). The pyrolysis gas chromatography/mass spectrometry chromatogram of Ericaceae rootlets is characterized by relative intense signals of m / z 410 (squalene, friedoolean-2-ene) and m / z 424 (taraxerone) (van Smeerdijk and Boon, 1987). In the present investigation of Podzol B-horizons the same signals were observed especially for soils under pine (horizons 1, 2, 6) or heath (horizons 5, 7. 8) vegetation. All data sets have also been analysed using discriminant analysis (DA). This method which can be regarded as double stage PCA (Hoogerbrugge et al., 1983) enables the differentiation of complex data clusters by determining well discriminating factors. The application of DA for the described data sets revealed no new information. For the wet-chemical results, horizons 5 and 7 to 9 were separated but for all other score plots the clusters were displayed closer together. The obtained discriminant spectra were

212

H. Wilcken et al. / Geoderma 76 ( 1997J / 9 3 - 2 / 9

w

+--

w

w

~

3 +_

"7

.,~

.

z

r

~

"I"

"I"

"-l"

Z

+ . .-p

Z . +.1+. .-p

, . +

~

+

N

e+-

~11

-1"

"1"

-1"

,-'t

~.

w

w

w

w

.-p

"I"

~

e+-,

B

A

r. e-i

...2"

- -

.2 N

=_

+~

.'2

P.

"1"

_

~

~,

-I+

~,

~

i~

~c

H. Wilcken et al. / Geoderma 76 (19971 193-219

213

__1

hi__ _

i

I m _ .

-7

I

C mL~

f

i

Fig. 7. Dendograms resulting from hierarchial cluster analyses of (a) wet-chemical data sets. (b) CPMAS L~C-NMR data sets and (c) Py-FIMS spectra of the Podzol B-horizons. For numbers see Table I.

identical to the shown PC spectra. Therefore no detailed interpretation of the DA results are given as PCA has proved its suitability for the Podzol data analysis.

3.4.2. Cluster analysis (CA) The CA results are shown in Fig. 7a-c for the wet-chemical data sets (a), the CPMAS k~C-NMR spectra (b) and the 200 FISHER-weighted Py-FIMS signals (c). The CA of the wet-chemical data sets shows increasing similarities for the horizons 7 to 9. The CA of the CPMAS J3C-NMR spectra refers to similarities among horizons 5 to 9. Additionally, horizons 1, 2 and 4 are clustered. Horizon 3 is an outlier. The CA of the Py-FIMS signals was calculated with 200 FISHER-weighted signals. Accordingly, horizons I to 3 are similar although one replicate measurement of horizon 1 is far out. Horizons 4, 6, 9 and horizons 7, 8 are clustered in each case. Horizon 5 is an outlier. These clusters correlate visually well with the PCA of the Py-FIMS signals. The clusters obtained by CPMAS t~C-NMR spectra do not correspond completely to those derived from the Py-FIMS signals. According to CPMAS Z3C-NMR spectra the SOM composition in horizon 3 is different from that of the other horizons. However, the

214

H. Wih'ken et al. / Geoderma 76 11997) 193-219

CA of the Py-FIMS signals indicate strong similarities among horizons 1 to 3. The ~3C-NMR spectrum of horizon 3 shows a relatively strong signal tbr carboxyl carbon. Substances with underivatized carboxyl groups have a low volatility and can be partly decarboxylated during pyrolysis. Thus, highly-polar structures, such as benzenecarboxylic acids and long-chain fatty acids, are decarboxylated, and are not directly visible by conventional pyrolysis (Martin et al., 1994). 3.5. Soil organic matter in Podzol B-horizons

The information obtained by wet-chemistry, CPMAS ~C-NMR spectroscopy and Py-FIMS on the molecular composition of SOM in Podzol B-horizons can be summarized as follows. Wet-chemical analyses are site-specific and reveal contents of litter material and humic fractions with an enrichment of fats/waxes in some B-horizons. CPMAS ~~C-NMR spectroscopy indicates the accumulation of organic substances rich in alkyl and O-alkyl structures in Podzol B-horizons. Py-FIMS detects a wide variety o1" molecular subunits of SOM. The organic matter that accumulates in the B-horizons of German Podzols consists primarily of long-chain lipids and sterols, carbohydrates, lignins, and alkylaromatics. The translocation of these compounds into Podzol B-horizons is in agreement with recent Py-FIMS investigations of a Canadian Podzol B-horizon by Sorge et al. (1994). Moreover the nine German counterparts similarly contain thermolabile and thermostable pools of SOM. The requirement for the accumulation of lipids, alkylaromatics, carbohydrates and lignins in P ~ z o l subsoils is their translocation through the soil profile. Investigating DOM in the leachates of forest humus profiles by Py-FIMS, Hempfling and Schulten (1990a) and Guggenberger et al. (1994) found, in addition to intensive carbohydraterelated peaks, distinct signals of lipid-derived structures, lignins and alkylaromatics. These analyses prove the translocation of the organic substances identified into Podzol B-horizons from the raw humus layer. Generally the recalcitrant lipid-derived structures, alkylaromatics and lignins are accumulated in Podzol B-horizons. The accumulation of carbohydrates is site-specific. Applying pyrolysis/methylation to the whole soil sample of a Canadian Podzol Bh-horizon (Schulten and Sorge, 1995), the methylated forms of C2-C~, ~ monocarboxylic acids, C4-C~ o dicarboxylic acids, benzenecarboxylic acids, phenolic acids, lignin subunits, benzendiols, benzentriols and furancarboxylic acids were identified. The highly polar carboxyl and hydroxy groups are only insufficiently detectable by conventional pyrolysis. Especially the lipids, lignin subunits and alkylaromatics identified by conventional Py-FIMS of the nine German Podzol B-horizons in reality have more, additional carboxyl groups. This has been confirmed by recent methylation-pyrolysis mass spectrometry investigations of clay-particles (Schulten et al.. 1996) and whole soils (Schulten and Sorge, 1995). Whereas monocarboxylic acids, dicarboxylic acids and lignin subunits are original SOM constituents, benzenecarboxylic acids, benzoldiols, benzoltriols and phenolic acids are connected mainly by ester linkages to the macromolecular network of SOM. Analysing the particle-size fractions of the soil of a Canadian Podzol Bh-horizon, the high accumulation of SOM in clay, fine silt and medium silt indicated a high importance of intramolecular SOM bonds (Sorge et al., 1994).

H. Wilcken et al. / Geoderma 76 (1997) 193-219

215

Recently, three-dimensional models of a humic acids and organo-mineral complexes were proposed (Schulten, 1995a,b) which show bonding between the structural subunits of SOM (e.g., ester linkages) and the surface of the mineral matrix. Structural subunits with non-esterified carboxyl groups explain the low pH value, the considerably hydrophilicity and the known interactions of podzolic organic matter with iron and aluminium (de Coninck, 1980). Modeling of biological (Schulten and Schnitzer. 1995) and anthropogenic substances (Schulten, 1996; Leinweber et ai., 1996a) displayed their occlusion in humic and organo-mineral complexes and was explained by the voids existing in humic structures and their capacity to immobilize these substances by metal cations, e.g., Fe 3., AI 3÷ (Leinweber and Schuiten, 1994) and/or hydrogen bonding. Geometry optimization by semi-empirical calculations confirmed that both types of bonds, i.e., inter- (humic-inorganic) and especially intra-molecular bonding (humic-humic) are prominent in podzols.

4. Conclusions

The role of fulvic acids in the formation of Podzol B-horizons has been discussed at length in the literature. The wet-chemical analyses, which showed relatively large proportions of fulvic acids in the nine B-horizons with Podzol features, located in Northwest Germany, supported these hypotheses. However, there are also Podzol B-horizons in which humic acids are more abundant than fulvic acids. Since the chemical meaning of this operationally defined SOM fractions is not clear, the application of ~3C-NMR and Py-FIMS improved the knowledge of SOM composition in samples with Podzol features on a molecular basis. These two methods showed in good agreement that SOM was largely composed of alkyl- (long-chain lipids, sterols, aikyl chains of alkylaromatics) and O-alkyl-C (C in lignins and carbohydrates). Since these substances were also found in the Bh-horizon of a Podzol-Bh-horizon from Canada (Sorge et al., 1994) and in percolating DOM in forest soils in Germany (Hempfling and Schulten, 1990a; Guggenberger et al., 1994), their contribution to the formation of illuvial B-horizons is very likely, independent of geographic location, plant cover. mineral matrix and time of podzolization. Obviously these factors account for their site-specific differences between the SOM composition of the samples, which were indicated by all analytical methods in the present study. Only the application of chemometric evaluations of Py-FIMS data enabled the objective and unequivocal differentiation and classification of the nine samples under study according to their SOM composition. As also supported by experience from Braceweli and Robertson (1984; Bracewell and Robertson, 1987a; Bracewell and Robertson, 1987b), who investigated organic surfaces and A- and B-horizons from Scottish Podzols using Curie-point pyrolysis-mass spectrometry (m/z 40-300) and PCA, this approach seems to be most suitable for a better knowledge of relationships between soil genesis and SOM. Substantial advances in inorganic chemistry of Podzol formation have been reported (Ugolini et al., 1987; Ugolini et al., 1988). In addition, the presented integrated analytical approach including wet-chemistry, ~3C-NMR spectroscopy, Py-FIMS and chemometrics (i.e., pattern recognition, principal component and cluster analyses)

216

H. Wih'ken et al. / Geoderma 76 (1997) 193- 219

appears as a powerful tool not only for fingerprinting but also for objective and mathematically reproducible differentiation and classification of SOM in Podzols (soil quality). Moreover, a better understanding of the molecular structure of this complex organic matter in whole soils as well as the degree of podzolization can be expected.

Acknowledgements This work was financially supported by the Deutsche Forschungsgemcinschaft, Bonn-Bad Godesberg (projects Schu 4 1 6 / 3 and Schu 4 1 6 f 18-3). The authors thank Dr. l+. Beyer, University of Kiel, Germany. tbr his collaboration, in particular lk)r generous supply of the soil samples and wet-chemical analyses. Dr. R. Ffiind and Prof. Dr. H.-D. LiJdemann, University of Regensburg, Germany. for providing the CPMAS ~3C-NMR analyses+ and Prof. Dr. M. Statheropoulos, National Technical University of Athens. Greece, for supplying a beta-test-version of the software-package PONTOS©. Wc are very grateful to Dr. P. Leinweber, University of Osnabriick. Vechta. Germany, for the determination of pyrophosphate extractable C and Fe and constructive discussions.

References Arshad. M.A.. Ripmeester. J.A. and Schnitzer, M.. 1988. Attempts to improve ,,olid state ~C-NMR spectra of whole mineral soils. Can. J. Soil Sci.. 68: 593-602. Baldock, J.A.. Oades, J.M.. Waters. A.(L, Xie. P.. Vassallo. A.M. and WiLson. M.A., 1992. Aspects of the chemical structure of soil organic materials as revealed by solid-state t~C-NMR spectroscop>. Biogeochemistry. Ib: 1-42. Beyer, L., 1996. Soil organic matter composition of sp¢~ic horizons in P(xtzols of thc Northwest German l,owcr Plain. Sci. Total Environ.. 181 : 167-180. Bcycr, L. and Blume. H-P.. 1990. Eigenschaften and Entstehung der Hurnuski,irper typischcr Wald- und AckerbBden Schleswig-Holsteins+ Z. Pflanzenernfihr. Bodenk.. 153: 61-68. Beyer, L.. Sorge, C. and Schulten. H.-R., 1993a. Studies on the composition of soil organic matter in terrestrial soils by wet-chemical methods and pyrolysis-field ionization mass spectrometr3.. J. Anal. Appl. Pyrolysis. 2 7 : 1 6 9 - 185. Beyer. L., Schulten. H.-R., Friind, R. and Irmler, U.. 19936. Formation and properties of organic matter in a forest .,,oil+ as revealed by its biological activity, wet-chemical analysis, CPMAS :3C-NMR spectroscop) and pyrolysis-field ionization mass spectrometry. Soil Biol. Biochem.. 25: 587-596. Beycr, I,., Blume, H.-P.. Ahlsdorf, B., Sorge. C. and Schulten, H.-R., 1996a. Soil organic matter composition and pesticide I:xmding in sand)' soils in relation to groundwater protection in the North-West German Lower Plain. Biol. Fen. Soils, 23: 266-272. Beyer. 1+., Friind. R., Wachendorf. C.. Knicker, H.. Sorge. C.. Kiibbemann. C., Schulten. H.-R.. Liidemann. H.-D. and Blumc, H.-P., 1996b. A simplc wet-chemical extraction procedurc to characterize soil organic matter (SOM): 3. Resultx of vegetation, crop litter, and forest litter in Comparison to data as re',ealed with CPMAS I~C-NMR spectro.scopy and pyrolysis-field ionization mass spectrometry. Commun. Soil Sci. Plant Anal.. 27: 2243-2264. Bratchell. N. 1989. Cluster anal3,sis. Chemometr. lntell. Lab. Syst., 6: 105-125. Bracewell. J.M. and Robertson, G.W., 1984. Characteristics of soil organic matter in temperate soils by Curie-point pyrolysis-mass spectrometry. 1. Organic matter variations with drainage and mull humification in A horizons. J. Soil Sci.. 35: 549--558, Bracewell. J.M. and Robertson, G.W.. 1987a. Characteristics of soil organic matter in temperate ,,oils h 5+

H. Wilcken et al. / Geoderma 76 (19971 193-219

217

Curie-point pyrolysis-mass spectrometry. II. The effect of drainage and illuviation in B horizons. J. Soil Sci., 38: 191-198. Bracewell, J.M. and Robertson, G.W.. 1987b. Characteristics of soil organic matter in temperate soils by Curie-point pyrolysis-mass spectrometry. III. Transformations occurring in surface organic horizons. Geoderma. 40: 333-34,-I.. Brereton, R.G.. 1992. Multivariate Pattern Recognition in Chemometrics. Elsevier, Amsterdam. de Coninck, F., 1980. Major mechanisms in formation of spodic horizons. Getu.lerma. 24: 1(11-128. Friind, R. and Liidemann, H.-D., 1989. The quantitative analysis of solution and CPMAS-U3C-NMR spectra of humic material. Sci. Total Environ.. 117/I 18: 157-168. Gattermann, L. and Wieland, H., 1982. Die Praxis des organischen Chemikers. De Gruyter, Berlin. Guggenberger. G.. Zech, W. and Schulten, H-R.. 1994. Formation and mobilization pathways of dissolved organic matter - Evidence from chemical structural studies of organic matter fractions in acid forest floor solutions. Org. Geochem.. 21: 51-66. Hempfling, R. and Schulten, H.-R., 19t~)a. Direct chemical characterization of dissolved organic matter in water by pyrolysis-field ionization mass spectrometry. Int. J. Environ. Anal. Chem.. 43: 55-62. Hempfling. R. and Schulten, H.-R.. 1990b. Chemical characterization of the organic matter in forest soils by Curie-point pyrolysis-GC/MS and pyrolysis-field ionization mass spectrometry. Org. Gee,t h e m . 15: 131-145. Hcmpfling. R., Ziegler, F.. Zech. W. and Schulten. H.-R.. 1987. Litter decomposition and humification in acidic forest soils studied by chemical degradation, IR and NMR spectroscopy and pyrolysis-field ionization mass spectrometry. Z. Pflanzenern,ihr. Bodenk.. 150: 179-186. Hcmpfling, R.. Simmleit, N. and Schulten, H.-R.. 1991. Characterization and chemodynamics of plant constituents during maturation, senescence and humus genesis in spruce ecosystems. Biogeochemistry. 13: 27-60. Hoogerbruggc, R., Willig. S.J. and Kistemaker. P.G., 1983. Discriminant analysis by double stage principal component analysis. Anal. Chem., 55:1710-1712. Kvalheim. O.M., 1985. Scaling of analytical data. Anal. C h i m Acta. 177: 71-79. Leinweber. P. and Schulten. H.-R., 1994. Zur Bedeutung pedogener Oxide ['tir die Bindung organischer Substanzen. Mitteilgn. Dtsch. Bodenkundl. Gesellsch.. 74: 383-386. Leinweber. P., Blumenstein. O. and Schulten, H.-R.. 1996a. Soil organic matter composition in sewage farms - Investigations by carbon-13 NMR and pyrolysis-lield ionization mass spectrometry. Eur. J. Soil Sci.. 47: 71-80. Leinweber, P.. Jordan. E. and Schulten. H.-R., 1996b. Molecular characterization of soil organic matter in Pleistocene moraines from the Bolivian Andes. Geoderma. 72: 133-148. Malinowski. E,R. and Howery. D.G.. 198(I. Factor Analysis in Chemistry. Wiley & Sons, New York. Martin. F., Gonzfilez-Vila, F.J., del Rio, J.C. and Verdejo, T., 1994. Pyrolysis derivatization of humic substances I. Pyrolysis of fulvic acids in the presence of tetramethylammonium hydroxide. J. Anal. Appl. Pyrolysis, 28:71-80. Schnitzer. M., 1991. Soil organic matter - the next 75 years. Soil Sci.. 151: 41-58. Schnitzer, M. and Schulten, H.-R., 1992. The analysis of soil organic matter by pyrolysis-field ionization mass spectrometry. Soil Sci. Soc. Am. J.. 56:1811-1817. Schnitzer. M. and Schulten. H.-R., 1995. The analysis of organic matter in soil extracts and soils by pyrolysis-mass spectrometry. Adv. Agron.. 55: 167-217. Schulten. H.-R., 1987. Pyrolysis and soft ionization mass spectrometry, of aquatic/terrestrial humic substances and whole soils. J. Anal. Appl. Pyrolysis, 12: 149-186. Schulten. H.-R.. 1993. Analytical pyrolysis of humic substances and soils: Geochemical, agricultural and ecological consequences. J. Anal. Appl. Pyrolysis. 25: 97-122. Schulten. H.-R., 1995a. The three-dimensional structure of soil organo-mineral complexes studied by analytical pyrolysis. J. Anal. Appl. Pyrolysis. 3 2 : I 1 1 - 1 2 6 . Schulten. H.-R.. 1995b. The three-dimensional structure of humie substances and soil organic matter studied by computational chemistry. Fresenius J. Anal. Chem.. 351: 74-82. Schulten, H.-R.. 1996. Direct pyrolysis-mass spectrometry of soils - A direct tool in agriculture, ecology. forestry, and soil science. In: S. Yamasaki and T.W. Boutton (Editors). Mass Spectrometry of Soils. Marcel Dekker. New York. Chapter 14. pp. 373-436.

218

H. Wilcken et al. / Geodenna 76 (1997) 193- 219

Schulten, H.-R. and Hempfling. R.. 1992. Influence of agricultural soil management on humus composition arid dynamics: classical and modern analytical techniqucs. Plant Soil, 142:259-271. Schulten, H.-R. and Leinweber. P.. 1995. Dithionite-citrate-bicarb,,~nate extractable organic matter in particlesize fractions of a Haplaquoll. Soil Sci. Soc. Am. J.. 59:1019-1027 Schuhen. H.-R. and Schnitzer. M.. 19~,8). Aliphatics in soil organic matter in l'inc-clay fraction~,. Soil Sci. Sc~.'. Am. J.. 54: 98-105. Schulten, H.-R. and Schnitzer. M., 1991. Supercritical carbon dioxide extraction of long-chain aliphatics from two soils. Soil Sci. Soc. Am. J.. 55:1603-161 I. Schulten. H.-R. and Schnitzer, M., 1995. Three-dimensional models for humic acids and soil organic matter. Naturwissenschaften, 82: 487-498. Schulten. H-R. and Sorge. C.. 1995. Pyrolysis methylation-mass spectrometr2,' of whole soils. Eur. J. Soil Sci.. 46: 567-579. Schulten, H.-R.. Simmleit. N. and Miiller. R., 1987. High-temperature. high-sensitivity pyrolysis-field ionization mass spectrometry. Anal. Chem.. 59: 2903-29(18. Schulten. H.-R., Hempfling, R. and Zcch. W., 1988. Discriminating horizons in a moder profile by field ionization mass spectrometry and pattern recognition. Geodenna, 41: 211-222. Schulten. H.-R., Sorge, C. and Schnitzer, M.. 1995. Structural studies on soil nitrogen by Curie-point pyrolysis-gas chromatography/mass spectrometry and nitrogen selective detection. Biol, Fcrtil. Soils. 20: 174-184. Schulten. H.-R.. l,cinweber. P. and Theng. B.K.G., 1996. Characterization of organic matter in an interlayer clay-organic complex from soil b~, pyrolysis methylation-mass spectrometry. Geoderma, 69: 105-118. Simmleit, N. and Schuhen. H.-R., 1989. Thermal degradation of spruce necdles. Chemosphere, 18:1855-1869. Sorge, C.. Schnitzer, M. and Schulten. H.-R., 1993a. ln-~,ource pyrolysis-field ionization mass spectrometr,, and Curie-point pyrolysis-gas chromatography/mass spectrometr) of amino acids in humic substances and soils. Biol. Fertil. Soils, 16:I00-110. Sorge. C,. Mtiller. R., Leinweber. P. and Schuhen. H.-R., 1993b. Pyrolysis-mass spectrometry' of whole soils. litter materials and humic substances: statistical evaluation of sample weight, residue, volatilized matter and total ion intensity. Frcsenius ]. Anal. Chem.. 346: 697-703. Sorgc, C.. Schnitzer, M., Leinweber. P. and Schulten. H.-R.. 1994. Molecular-chemical composition of organic matter in whole soil and particle-size fractions of a Spodosol by pyrolysis-field ionization mass spectromc try. Soil Sci.. 158: 189-203. Spaargaren. O.C.. 1994. World Relcrence Basc for Soil Resources: Int. Soc. Soil Ski.: I:cx~d & Drug Organization, United Nations. Wageningen/Romc. pp. I--161. Springer, U., 1943. Beitrag zur Fraktionierung der echten Huminstofl~:. Z. Pflanzcnern~,ihr. Bodenk.. 32: 129-146. Stevenson, [:.J. and Cheng. C.N.. 1970. Amino acids in sediments: Recovery by acid hydrolysis and quantitative estimation by a colorimctric procedure. Geochim. Cosmochim Acta. 31: 77-88. tJgolini. F.C., Stoner. M.G. and Marrett. D.J.. 1987. Arctic pedogenesis: I. Evidence for contemporar3/ podzolization. Soil Sci.. 144: 90-100. Ugolini. F.C.. Dahlgren, R.. Shoji. S. and lto. T., 1988. An example of andosolization and p.odzolization us rc,.ealed by soil solution studies, southern Hakk~'cda. N.E. Japan. Soil Sci.. 145: 111-125. van Smeerdijk. D. and Boon, J.J., 1987. Characterization of subfossil Sphagnum leaves, rootlets of Ericaceac and their peat by pyrol',sis-high-resolution gas chromatography-mass spectrometry. J. Anal. Appl. Pyrol'.,six, I I: 377-402. Varmuza, K., 1980. Lecture notes in chemistry - Bd. 21: Pattern recognition in chemistry. Springer, Berlin. Wiechmann. H,. 1978. Stoffverlagerung in Podsolen. Hohenheimer Arb. 94. Ulmer, Stuttgart. Wilson, M.A.. 1987. NMR Techniques and Applications in Geochemist~ and Soil Chemistry., Pergamon Press, Oxford. Wilson, M.A.. 1989. Solid-state nuclear magnetic resonance spectroscopy of humic substances: Basic concepts and techniques. In: M.H.B. Hayes. P. MacCarthy. R.L. Malcolm and R.S. Swift (Editors). Humic Substances 11. In Search of structure. Wiley, Chichester. pp. 309-338. Windig. W.. Kistemaker, P.G. and Haverkamp, J., 1981/82. Chemical interpretation of differences in pyrolysis-mass spectra of simulated mixtures of biopolymers by factor analysis with graphical rotation. J. Anal. Appl. Pyrolysis, 3: 199..-212.

H. Wilcken et al. / Geoderma 76 (1997) 193-219

219

Windig, W., Kistemaker, P.G. and Haverkamp, J., 1981/82. Chemical interpretation of differences in pyrolysis-mass spectra of simulated mixtures of biopolymers by factor analysis with graphical rotation. J. Anal. Appl. Pyrolysis, 3: 199-212. Windig, W. and Meuzelaar, H.L.C., 1984. Nonsupervi~d numerical component extraction from pyrolysis mass spectra of complex mixtures. Anal. Chem.. 56: 2297-2303.