Effect of climatic variability in the soil organic matter composiion studied by analytical pyrolysis
In present days there is a growing concern about the progress of desertification in different areas of the world. The unsuitable management of the soils and the change of land use may increase the desertification risk. On the other hand, desertification is typically associated with the decrease of soil organic matter (SOM) levels with the consequent loss of fertility in the soil. Presumably, all these aspects are reflected in the molecular composition of SOM. Previous studies have evidenced that a correlation exists between the carbon sequestration efficiency and the relative abundance of specific SOM constituents, e.g., alkane homologous series [1] or lignin-derived methoxyphenols [2]. This study aims to identify molecular descriptors of the SOM composition, which are responsive for the impact of climate, quantified with bioclimatic indices defining a continuous gradient between wet and dry areas. A total of 33 soil samples were collected from different areas of Spain. The studied soils presented a large variability in their chemical and physical properties, and were developed under different geological substrate and vegetation type. The sampling was carried out in the topsoil (0–10 cm) where the SOM content is higher. In order to assess desertification levels we used the De Martonne aridity index. This index was calculated from the annual average rainfall and annual average temperature for each soil sampling point. The SOM was analyzed by pyrolysis - gas chromatography mass spectrometry (Py-GC/MS) of whole soil samples. A total of 193 pyrolysis compounds were identified, and used as predictor variables in Partial Least Squares (PLS) regression models forecasting the De Martonne aridity index. In order to assess desertification levels we used the De Martonne aridity index. This index was calculated from the annual average rainfall and annual average temperature for each soil sampling point. The SOM was analyzed by pyrolysis - gas chromatography mass spectrometry (Py-GC/MS) of whole soil samples. A total of 193 pyrolysis compounds were identified, and used as predictor variables in Partial Least Squares (PLS) regression models forecasting the De Martonne aridity index. The results showed that a significant prediction of this index (R = 0.869) exclusively using the information provided by Py-GC/MS analysis of the corresponding soils is possible.
| Main Authors: | , , , , , |
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| Format: | póster de congreso biblioteca |
| Published: |
Society of Environmental Toxicology and Chemistry
2018-10-02
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| Online Access: | http://hdl.handle.net/10261/171566 |
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