Journal of Analytical and Applied Pyrolysis, 8 (1985) 473-482

Elsevier Science Publishers B.V.. Amsterdam - Printed in The Netherlands 473

CHARACTERIZATION OF MARINE FULVIC AND HUMIC ACIDS BY PYROLYSIS-MASS SPECTROMETRY

ADAM ZSOLNAY*,*

Code 333, NORDA, NSTL, MS 39529 (U.S.A.)

GEORGE R. HARVEY

Atlantic Oceanographic and Meteorological Laboratories, NOAA, 4301 Rickenbacker Causeway, Miami, FL 33149 (U.S.A.)

SUMMARY

Pyrolysis-mass spectrometry was used to characterize marine fulvic and humic acid material. Although there was a great deal of similarity in the mass spectra, they could readily be differentiated by principal coordinate analysis. As the marine fulvic acids aged, they became more similar to the marine humic acids. This would support the conclusion that there is a direct and gradual transition between marine fulvic and humic acid material.

The pattern of pyrolyzate fragments, which were relatively unique to the near-surface marine fulvic acids, was most similar to non-amino acid, nitrogen-containing material, while the fragments characteristic of the marine humic acids showed similarities to both proteins and carboxylic acids. These patterns could be used to develop a “humification” index for marine fulvic acids.

INTRODUCTION

The not readily characterizable organic material in seawater constitutes a very large percentage of the total organic matter in that environment. At present there is no universally accepted nomenclature for these compounds. However, in this paper the terms “marine fulvic acids” and “marine humic acids” will be used. This is in keeping with a previous paper [l]. Both classes of compounds are obtained by base extraction, but the marine fulvic acids are also soluble after the pH has been readjusted into the acidic range [l].

By definition, this material is difficult to analyze by the normal analytical techniques applied in marine chemistry. Pyrolysis (Py) in combination with

* Present address: Institut fir Gkologische Chemie, Gesellschaft fur Strahlen- und Umwelt- forschung mbH, Munich, F.R.G.

0165-2370/85/$03.30 0 1985 Elsevier Science Publishers B.V.

gas chromatography (GC) and/or mass spectrometry (MS) has been used successfully to study soil and terrestrial humic materials for many years (e.g., ref. 2-4). Therefore, there is reason to believe that similar techniques can be used to analyse marine humic materials. Wilson et al. [5] used Py-GC-MS to analyze coastal as well as fresh water and terrestrial humic material. However, the research reported here is the first attempt to analyze and characterize pelagic marine fulvic and humic acids by Py-MS.

EXPERIMENTAL

Sample collection and preparation

Samples were obtained from the following locations and depths: marine fulvic acids from pristine oligotrophic ocean water at 20 m, marine humic acids from green productive water off San Blas, FL, U.S.A. at 1 m, marine humic acids from polluted water off Galveston, TX, U.S.A. at 1 m, marine fulvic acids from Mississippi outflow during a spring bloom at 1 m and marine fulvic acids from the 5°C water in the open Gulf of Mexico at 2000 m. The water from the last sample had an approximate age of 100-200 years. Details about the sampling and extraction procedures were given by Harvey et al. [l].

Pyrolysis-mass spectromtry

A small amount (less than 0.1 mg) of each sample was placed in a small quartz boat (26 x 2.5 mm I.D.). The boats were inserted in a Chemical Data System Pyroprobe (Series 120) and pyrolyzed for 20 s at a nominal tempera- ture of 800°C. A thermocouple near the sample indicated that the real temperature was 580°C and that it was attained after 13 s. However, in analytical pyrolysis for fingerprinting purposes reproducibility is more im- portant than actual temperature [6].

The pyrolyzates were swept with 99.99% methane gas, which had passed through an oxygen and water scrubber, into a Finnigan 4500 mass spec- trometer. The flow-rate was unknown but adjusted to maintain a pressure of 1 Torr (133 Pa) in the ionizer. The interface was at 180°C transfer lines at 220°C and the ionizer at 140°C. Ionization of the pyrolyzates was effected with methane, ionized by electrons at 130 eV. This resulted in chemical ionization of the pyrolyzates. The potential advantage over electron-impact ionization is that there is less fragmentation, making it more likely that unique features of the original organic material in the samples are retained in the spectra. Generally, chemical ionization with methane results in proto- nated pyrolyzate ions (M + 1). However, n-alkanes are ionized with hydride abstraction (M - 1).

The ions were filtered by a quadrupole and counted with an electron multiplier at a potential of - 1500 V. The scan-rate was 60-260 a.m.u. over 1 s, and all MS data were processed by the Finnigan INCOS data system. The background was automatically subtracted from the mass spectra. The nominal masses were also assigned automatically using perfluorotributyl- amine (FC43) reference spectra, acquired the same day as the analyses. All the spectra obtained from the pyrolysis of any one sample were averaged into one spectrum, which was then transferred to a Tektronix 4052 computer for additional processing. All analyses were carried out in triplicate. Details for the selection of the above parameters are given elsewhere [7].

Standards of various commercially available compounds were also analyzed in the same manner as the environmental samples.

Mathematical analysis

All the environmental spectra were placed in a matrix, and average peak intensities less than 1% of the largest average peak intensity were rejected. In order to emphasize the smaller mass peaks, all the spectra were square root transformed. The mass peaks were then normalized so that the sum of all peaks equaled 100. A typical spectrum is shown in Fig. 1.

The next step was the reduction of dimensionality to make the data easier to understand and to handle mathematically [8]. There were several ways to accomplish this. One could have worked either in variable or sample space. As the data matrix in this study would have had over 150 dimensions in variable (m/z) space and only 5 in sample space, it was decided to transpose

Fig. 1. Mass spectrum obtained from the pyrolysis of marine fulvic acids from an open ocean environment. The data were square root transformed and normalized.

476

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Fig. 2. Outline of the mathematical approach used. P.C. indicates principal coordinate analysis. The asterisk indicates that a non-linear map was used to display the results.

the data matrix and to do the data reduction in sample or I-space [9]. A similarity matrix of correlation coefficients was calculated and a principal coordinate analysis carried out based on this matrix. This analysis is of exactly the same form as principal component analysis [8]. Marriott [8] stated further, “It is in numerical taxonomy that the duality between individuals and variates is most important. Here, the number of characters observed is often large . . . In these circumstances, principal coordinates are preferable to principal components, and have exactly similar applications”.

The new coordinate system, similarly to principal components, has the most variation in the first dimension with the second dimension being orthogonal to the first and having as much of the remaining variation as possible. The other dimensions are constructed in a similar manner until all the variation is accounted for in a fully orthogonal system. Each sample correlates with each principal coordinate to a greater or lesser degree called the factor loadings [lo]. These factor loadings can be used to “name” the principal coordinates (reification). Often this task is made clearer by rotating the matrix in such a way as either to maximize or the minimize the factor loadings on any one principal coordinate. One commonly used technique is varimax rotation [ll].

Spectra, analogous to the mass spectra, can be obtained by calculating the factor scores of the a.m.u.s on the principal coordinates derived from sample space [lo]. In this way it is possible to see what type of material is represented by each principal coordinate. Furthermore, these pseudo-spectra can then be compared with the spectra of standards to obtain information

411

about the identity of the organic material, which they represent. An ad- vantage of the mathematical approach used here (Fig. 2) is that no sophisti- cated software or hardware was required.

RESULTS AND DISCUSSION

There is considerable similarity between the mass spectra of the average near-surface marine fulvic acids and those of the marine humic acids (see Figs. 3 and 4, respectively). For example, the chief homologous series in both instances is representative of carboxylic acids (m/z 111-223). However, a plot of the loadings of the marine fulvic acid and marine humic acid samples on the first two principal coordinates indicates that, despite the varied sources of the samples, the near-surface marine fulvic acids can readily be separated from the near-surface marine humic acids (Fig. 5). The first two principal coordinates account for over 94% of the variation in the original data. Interestingly, the older marine fulvic acid material was more similar to the marine humic acids than to the near-surface fulvic acids. This would indicate that marine fulvic acids upon ageing are transformed into marine humic acid material. This is shown more clearly after a varimax rotation (Fig. 6). There are not enough data here to confirm this, but it would be in agreement with earlier work [l].

Reification of the rotated principal coordinates is straightforward, and all subsequent analyses were carried out with this rotated matrix. The first principal coordinate represents material that is largely unique to the marine humic acids whil the second principal coordinate is indicative of compounds

78 94 110 126 142 158 174 198 206 222 238 254

M/Z

Fig. 3. Mass spectrum showing the average values of all the near-surface marine fulvic acid samples. The data were square root transformed and normalized.

478

Fig. 4. As Fig. 3, but for the near-surface marine humic acid samples.

that are characteristic of “pure” open-ocean marine fulvic acids. The factor scores of the a.m.u.s on the first (hunk) principal coordinates are shown in Fig. 7. As all the loadings were positive, only the positive part of the factor

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Fig. 5. Plot of loadings on the first two principal coordinates. The first principal coordinate accounted for 87.3% of the total variance and the second for 7.1%. 1, Open ocean marine fulvic acid; 2, marine humic acid off the Florida coast; 3, marine humic acid off the Texas coast; 4, marine fulvic acid in the Mississippi River outflow; 5, older marine fulvic acid material from 2000 m in the Gulf of Mexico.

479

4

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Fig. 6. As Fig. 5 after varimax rotation. The first principal coordinate now accounted for 50.8% of the total variance and the second for 43.98.

scores needs to be considered. It must be emphasized that this does not represent the bulk of the marine humic acids, but only that portion which most separates them from the marine fulvic acids. Several homologous series appear to be present.

’ 6

1 1

62

Fig. 7. Scores of a.m.u.s on the first (hunk) principal coordinate. Only the positive values are shown, as only positive loadings were present (cf., Fig. 6).

Fig. 8. As Fig. 7, for the second (fulvic) principal coordinate.

Similarly, the pattern of material most indicative of the marine fulvic acids is represented in the second (fulvic) principal coordinate (Fig. 8). No homologous series are readily apparent. With exception of m/z 77, most of

Fig. 9. Non-linear mapping of the factor loadings on the first ten principal coordinates extracted from a data matrix consisting of the factor score spectra (Gl and G2) shown in Figs. 7 and 8, and 21 spectra obtained from standards. The axes are in arbitrary units with a mapping error of 0.035 unit. Pl, albumin; P2, thyroglobulin; P3, concanvalin A; P4, gelatin; P5, polyleucine; Al, cystine; A2, hydroxyproline; A3, lysine; A4, tryptophan; A5, leucine; A6, isoleucine; A7, glycine; Sl, fructose; S2, dextran; S3, agarose; S4, glucose; Fl, glycolic acid; F2, myristic acid; Ul, salmon DNA; U2, herring DNA; and Nl, pyridine.

481

the prominent peaks appear to be non-amino acid, nitrogen-containing compounds.

To assist in the tentative classification of the above factors, the factor scores were added to a matrix consisting of the data obtained by pyrolyzing known standards in the same way as the environmental samples (Fig. 2). However, ten principal coordinates had to be extracted in order to account for over 90% of the total variability present. This would be impossible to illustrate with an ordinary scatter plot. Therefore, a non-linear mapping technique was used to illustrate the relative distance between the various sample loadings in this ten-dimensional principal coordinate space [12,13].

Generally, similar classes of compounds cluster well on the non-linear map (Fig. 9). Using a nearest neighbour approach, it appears that the factor score pattern indicative of marine humic acids is most similar to proteins and carboxylic acids, while the pattern indicative of marine fulvic acid material is most sin$lar to the non-ammo acid, nitrogen-containing pyridine. This information could be used to develop an index to measure the degree to which different marine fulvic acids have been “humified”. A similar ap- proach was used to show the relative diagentic history of the organic material in a deep-sea sediment [14].

ACKNOWLEDGEMENTS

This work was supported by the U.S. Naval Ocean Research and Devel- opment Acivity, by the Office of Marine Pollution Assessment, U.S. Na- tional Oceanic and Atmospheric Administration (NOAA) as project ROME (Role of Organics in the Marine Environment), and by the Gesellschaft fur Strahlen- und Umweltforschung mbH, Munich (F.R.G.).

REFERENCES

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chim. Acta, 47 (1983) 497. 6 W.J. Irwin, J. Anal. Appl. Pyrol., 1 (1979) 3. 7 A. Zsolnay, J. Anal. Appl. Pyrol., 4 (1982) 47. 8 F.H.C. Marriott, The Interpretation of Multiple Observations, Acedemic Press, London,

1974,117 pp. 9 P.H.A. Sneath and R.R. Sokal, Numerical Taxonomy, Freeman, San Francisco, CA, 1973,

573 pp. 10 R.J. Rummel, Applied Factor Analysis, Northwestern University Press, Evanston, IL,

1970, 617 pp.

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11 H.F. Kaiser, Psychometrika, 23 (1958) 187. 12 J.B. Kruskal, Psychometrika, 29 (1964) 115. 13 J.B. Kruskal, Psychometrika, 29 (1964) 1. 14 A. Zsolnay, Mar. Geol., in press.