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Persistence of soil organic matter as a function of its molecular Composition as revealed by Py‐GC/MS

AutorAlmendros Martín, Gonzalo ; Hernández, Zulimar ; Sanz Perucha, Jesús ; Rodríguez-Sánchez, Sonia ; Jiménez González, M. A.; González-Pérez, José Antonio
Fecha de publicaciónnov-2016
EditorSociedad Española de Cromatografía y Técnicas Afines
CitaciónAdvances in chromatography and related techniques: Book of Absracts, 166-166 (2016)
ResumenThere is a large controversy on whether the resilience of soil organic matter (SOM) depends on physical protection by soil minerals or on its intrinsic molecular composition [1]. In the case study of 30 volcanic soils from Tenerife (Spain), the dependent variables (DV) were total amount of SOM and TMC (total mineralization coefficient, CO2 released under incubation). This research focuses on estimating DVs using SOM molecular descriptors obtained by pyrolysis‐gas chromatography‐mass spectrometry (Py‐GC/MS) using a Pyrojector® device (SGE instruments) on a GC/MS Finnigan Trace GC Ultra, a Trace DSQ MS and an HP‐1 column. Up to 47 of the 102 major Py compounds were selected as descriptors (independent variables, IVs) attending to its normal distribution and few missing values. The relationships between DVs and normalized GC peaks (IVs) were examined by three approaches: (a) Partial least squares regression (PLSR) checking different pretreatments (standardization of IVs as total or relative abundances, mean centering, variance scale, and logarithmic or square root transformations). Spurious models were discarded after repeating PLSR models with the randomized DV; (b) Pearson’s correlation coefficients between each DV and the 47 IVs; comparison with other indices computed during PLSR: the variable Importance in the projection (VIP), b coefficients, W weights and factor loadings [2]; (c) comparing soil subsets representing opposed levels of the DVs: The 30 soils were ordered in decreasing DVs values and two subsets were considered, e.g., above vs below the median of the DV (or in the uppermost Q1 quartile vs Q4), i.e., soils ‘behaving as Csinks’ vs ‘soils with ‘poor C sequestration potential’. Differences between subsets were checked by ANOVA and plotted as subtraction values. The results from PLSR demonstrated that both SOM concentration and TMC can be predicted (P< 0.05) from the relative abundance of 47 major pyrolysis compounds. Although no cause‐to‐effect is inferred from this fact, it makes evident that SOM molecular composition differs in terms of its resistance to biodegradation. For rapid perceptual identification of the results, the indices for the IVs: VIPs (a), Pearson’s r2 (b), average differences between DV subsets (c), were represented in the z axis in 3D plots (as contour plots, in the figure) the coordinates in the basal plane being atomic H/C and O/C ratios of the Py compounds in a classical Van Krevelen diagram [3]. Applying a moving average algorithm to z values, we obtain clusters (showing gradients of compounds with similar stoichiometry) in some way illustrating structural domains of the SOM (carbohydrate‐and lignin‐derived, condensed lipid…). The figure illustrates the structural components prevailing in C‐depleted and in C‐rich soils (– or + values shown in red and green, respectively, for b & c approaches). In the case of the VIPs (a), which is a positive index, previous checking of b or c plots is required for its complete interpretation. The 3 approaches coincided in pointing out that the SOM levels parallel the accumulation of lignin‐ and carbohydrate‐derived structures, and the depletion of condensed polyalkyl structures; in other words, the larger the quantity, the lowest the quality of the SOM in our soils. Practically the same pattern, but with signs swapped, was obtained for the TMC. Judging the approaches (a–c) in terms of workload and time consumed, and despite PLSR is frequently invoked to have outstanding potential to extract underlying information, we found approach (c) the most simple and intuitive. In the other cases, IVs with VIPs> 1 were not always correlated (P< 0.05) with the corresponding DV or, in the case of Pearson’s coefficients, careful control of outliers is required.
[1] M.W.I. Schmidt et al., Nature 478 (2011) 49–56. [2] R.A Viscarra‐Rossel, Chemometrics and Intelligent Laboratory Systems 90 (2008) 72–83. [3] D.W. Van Krevelen, Fuel 29 (1950) 269–284
DescripciónPóster presentado en la XVI Reunión Científica de la Sociedad Española de Cromatografía y Técnicas Afines (SECyTA2016) P‐ENV‐19
Eds: González-Pérez, José Antonio.-- Almendros Martín, Gonzalo.-- González-Vila, Francisco Javier.-- Rosa Arranz, José M. de la
Versión del editorhttp://hdl.handle.net/10261/139608
URIhttp://hdl.handle.net/10261/140415
ISBN978-84-617-6155-5
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