1000 resultados para Peat geochemistry


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Coupled analyses of n-alkane biomarkers and plant macrofossils from a peat plateau deposit in the northeast European Russian Arctic were carried out to assess the effects of past hydrology on the molecular contributions of plants to the peat. The n-alkane biomarkers accumulated over 9.6 kyr of local paleohydrological changes in this complex peat profile in which a succession of vegetation changes occurred during a transition from a wet fen to a relatively dry peat plateau bog. This study shows that the contribution of the n-C31 alkane from rootlets to peat layers rich in fine and dark roots is important. The results further indicate that the n-alkane Paq and n-C23/n-C29 biomarker proxies that have been useful to reconstruct past water table levels in many peat deposits can be misleading when the contributions of Betula and Sphagnum fuscum to the peat are large. Under these conditions, the C23/(C27 + C31) n-alkane ratio seems to correct for the presence of Betula and S. fuscum and provides a better description for the relative amounts of moisture. The average chain length (ACL) n-alkane proxy also appears to be a good paleohydrology proxy in having larger values during dry and cold conditions in this Arctic bog setting.

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We determined the distribution of lipids (n-alkanes and n-alkan-2-ones) in present-day peat-formingplants in the RoñanzasBog in northernSpain. Consistent with the observation of others, most Sphagnum (moss) species alkanes maximized at C23, whereas the other plants maximized at higher molecular weight (C27 to C31). We show for the first time that plants other than seagrass and Sphagnum moss contain n-alkan-2-ones. Almost all the species analysed showed an n-alkan-2-one distribution between C21 and C31 with an odd/even predominance, maximizing at C27 or C29, except ferns, which maximized at lower molecular weight (C21–C23). We also observed that microbial degradation can be a major contributor to the n-alkan-2-one distribution in sediments as opposed to a direct input of ketones from plants

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Acknowledgements This work was funded by the projects HAR2013-43701-P (Spanish Economy and Competitiveness Ministry) and CGL2010-20672 (Spanish Ministry of Science and Innovation). This research was also partially developed with Xunta de Galicia funding (grants R2014/001 and GPC2014/009). N. Silva-Sánchez is currently supported by a FPU pre-doctoral grant (AP2010-3264) funded by the Spanish Government. We are grateful to Ana Moreno, Mariano Barriendos and Gerardo Benito who kindly provide us data included in Figure 5a. We also want to thank constructive comments from two anonymous reviewers.

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Acknowledgements This work was funded by the projects HAR2013-43701-P (Spanish Economy and Competitiveness Ministry) and CGL2010-20672 (Spanish Ministry of Science and Innovation). This research was also partially developed with Xunta de Galicia funding (grants R2014/001 and GPC2014/009). N. Silva-Sánchez is currently supported by a FPU pre-doctoral grant (AP2010-3264) funded by the Spanish Government. We are grateful to Ana Moreno, Mariano Barriendos and Gerardo Benito who kindly provide us data included in Figure 5a. We also want to thank constructive comments from two anonymous reviewers.

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The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).