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Using pyrolysis molecular beam mass spectrometry to characterize soil organic carbon in native prairie soils

机译:使用热解分子束质谱法表征原生草原土壤中的有机碳

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The goal of this study was to test if analytical pyrolysis coupled with molecular beam mass spectrometry and multivariate statistical analyses could provide a rapid and accurate methodology to identify and quantify soil carbon fractions and understand the fundamental chemistry that distinguishes these fractions. We analyzed soil organic carbon (SOC) contained in well-characterized agricultural soils with pyrolysis molecular beam mass spectrometry (py-MBMS) and then determined correlations between the mass spectra and associated soil characterization data. Both soil carbon chemistry and the organic forms in which SOC is sequestered (soil microbial biomass (SMBC), particulate organic matter carbon (POM C), and mineral-associated carbon (Cmin C)) were assessed by multivariate statistical analyses to discover existing correlations and if they could be developed into estimative models. The sample set consisted of well-characterized soils collected from native prairie sites in the western U.S. Corn Belt and Great Plains: 11 sites located within 8 midwestern states (CO, NE, IA, ND, MT, TX, MO, and MN). Sample characterization parameters included site, depth, %SOC, POM C, Cmin C, SMBC, and SOC calendar age (determined from 14C age). Correlations were found for samples collected across this large geographic region (at or greater than 0.90) for SOC, POM C, Cmin C, and SMBC. Soil organic carbon calendar age derived from radiocarbon-14 dating could be estimated for ustollic soils from MT, NE, and CO. These soils also contained deeper and younger eolian layers, whose ages were correctly estimated with this technique. The Py-MBMS analysis additionally showed that soils developed from water-sorted sediments on a tilled-floor lake plain (lacustrine soils) were significantly different from the other samples.
机译:这项研究的目的是测试分析热解结合分子束质谱和多元统计分析是否可以提供一种快速,准确的方法来鉴定和定量土壤碳组分,并了解区分这些组分的基本化学方法。我们使用热解分子束质谱法(py-MBMS)分析了特征明确的农业土壤中所含的土壤有机碳(SOC),然后确定了质谱图与相关土壤特征数据之间的相关性。通过多元统计分析评估了土壤碳化学和隔离SOC的有机形式(土壤微生物生物量(SMBC),颗粒有机物碳(POM C)和矿物相关碳(Cmin C)),以发现现有的相关性以及是否可以将其发展为估算模型。样本集由从美国西部玉米带和大平原的原始草原站点收集的特征明确的土壤组成:位于中西部8个州(CO,NE,IA,ND,MT,MT,TX,MO和MN)的11个站点。样品表征参数包括部位,深度,%SOC,POM C,Cmin C,SMBC和SOC日历年龄(由14C年龄确定)。在整个较大地理区域(等于或大于0.90)处收集的SOC,POM C,Cmin C和SMBC的样本之间存在相关性。可以估算出MT,NE和CO的固溶土壤从radiocarbon-14测年得出的土壤有机碳日历年龄。这些土壤还包含更深和更年轻的风积层,用这种技术可以正确估计其年龄。 Py-MBMS分析还表明,从耕种到湖底平原上的水分类沉积物发育的土壤(湖床土壤)与其他样品显着不同。

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