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通过傅里叶变换红外光谱(FT-IR)和X射线分析改进土壤碳酸盐测定

Improved soil carbonate determination by FT-IR and X-ray analysis.

作者信息

Bruckman Viktor J, Wriessnig Karin

机构信息

Austrian Academy of Sciences (ÖAW), Commission for Interdisciplinary Ecological Studies (KIOES), Dr. Ignaz Seipel-Platz 2, 1010 Vienna, Austria.

出版信息

Environ Chem Lett. 2013 Mar;11(1):65-70. doi: 10.1007/s10311-012-0380-4. Epub 2012 Sep 1.

DOI:10.1007/s10311-012-0380-4
PMID:23459253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3582815/
Abstract

In forest soils on calcareous parent material, carbonate is a key component that influences both chemical and physical soil properties and thus fertility and productivity. At low organic carbon contents, it is difficult to distinguish between organic and inorganic carbon, e.g. carbonates, in soils. The common gas-volumetric method to determine carbonate has a number of disadvantages. We hypothesize that a combination of two spectroscopic methods, which account for different forms of carbonate, can be used to model soil carbonate in our region. Fourier transform mid-infrared spectroscopy was combined with X-ray diffraction to develop a model based on partial least squares regression. Results of the gas-volumetric Scheibler method were corrected for the calcite/dolomite ratio. The best model performance was achieved when we combined the two analytical methods using four principal components. The root mean squared error of prediction decreased from 13.07 to 11.57, while full cross-validation explained 94.5 % of the variance of the carbonate content. This is the first time that a combination of the proposed methods has been used to predict carbonate in forest soils, offering a simple method to precisely estimate soil carbonate contents while increasing accuracy in comparison with spectroscopic approaches proposed earlier. This approach has the potential to complement or substitute gas-volumetric methods, specifically in study areas with low soil heterogeneity and similar parent material or in long-term monitoring by consecutive sampling.

摘要

在以钙质母质为基础的森林土壤中,碳酸盐是一种关键成分,它会影响土壤的化学和物理性质,进而影响土壤肥力和生产力。在土壤有机碳含量较低时,很难区分土壤中的有机碳和无机碳,如碳酸盐。常用的测定碳酸盐的气量法有诸多缺点。我们假设,两种能分析不同形态碳酸盐的光谱方法相结合,可用于模拟我们研究区域的土壤碳酸盐含量。将傅里叶变换中红外光谱与X射线衍射相结合,基于偏最小二乘回归建立了一个模型。气量法(谢伊布勒法)的结果根据方解石/白云石比例进行了校正。当我们使用四个主成分将这两种分析方法结合起来时,模型性能最佳。预测的均方根误差从13.07降至11.57,全交叉验证解释了碳酸盐含量94.5%的方差。这是首次将所提出的方法结合起来用于预测森林土壤中的碳酸盐,提供了一种简单方法来精确估算土壤碳酸盐含量,同时与早期提出的光谱方法相比提高了准确性。这种方法有可能补充或替代气量法,特别是在土壤异质性低、母质相似的研究区域,或在通过连续采样进行的长期监测中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/82674cd4e9ec/10311_2012_380_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/68129d3d5f19/10311_2012_380_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/e89a208ec2e0/10311_2012_380_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/dcc427ef6238/10311_2012_380_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/82674cd4e9ec/10311_2012_380_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/68129d3d5f19/10311_2012_380_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/e89a208ec2e0/10311_2012_380_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/dcc427ef6238/10311_2012_380_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcd/3582815/82674cd4e9ec/10311_2012_380_Fig4_HTML.jpg

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