Magrini, Kim (National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO, 80401; Phone: 303-384-7706; Fax: 303-384-6363; Email: kim_magrini@nrel.gov)

Rapid Identification and Quantification of Soil Organic Carbon Forms Using Pyrolysis Molecular Beam Mass Spectrometry

K.A. Magrini *

A critical need exists to better understand both the amount of soil organic matter (SOM) as a result of land use and management practices and its chemical (and structural/molecular) composition. Rapid quantitative analysis of soil carbon and SOM is required for assessing and monitoring managed agricultural and forest soils to establish carbon sequestration baselines, uptake, and retention. This need is not currently being met in carbon sequestration studies and instrumentation and methodologies must be developed so that SOM inventories can be measured and quantitated in the terrestrial biosphere. We are using analytical pyrolysis coupled with molecular beam mass spectrometry (py-MBMS) and multivariate statistical analysis to rapidly analyze (5-minutes) and quantify SOM in well-characterized agricultural soils (from eleven Midwestern states) provided by the United States Department of Agriculture (USDA) National Soils Laboratory in Lincoln, NE. Multivariate statistical analysis of the mass spectral and characterization data demonstrate that carbon contained in the particulate organic matter (POM), mineral (Cmin), and microbial biomass (SMBC) soil fractions can be measured as a metric expressed as mg-g fraction/g soil. Figure 1 shows principal component analysis of mass spectra from the 0-5 cm depth increment of prairie soils under varied management practice. We have used this technique to assess impacts on SOM accumulation in agricultural soils under the USDA’s Conservation Reserve Program (CRP) management and unambiguously show that eighteen-year old CRP soils have not yet reached native SOM or total carbon content. Additional work with forest soils subjected to periodic disturbance shows that soil chemistry, depths, and location can easily be distinguished based on mass spectral signatures. We are using these results to develop data based models to predict soil carbon content in SMBC, POM, and Cmin soil fractions that can then be used in assessing carbon sequestration pathways and progress