Quantitative Soil Carbon Analysis with in Situ Laser-Induced Breakdown Spectroscopy by Multivariate Analysis

The Earth's oceans, forests, agricultural lands and other natural areas absorb about half of the carbon dioxide emitted from anthropogenic sources. Terrestrial carbon sequestration strategies are immediately available to bridge the gap between current terrestrial sequestration capacity and high-capacity geologic sequestration projects available in 10 to 20 years. Terrestrial carbon sequestration strategies consist of implementing land management practices aimed at decreasing CO2 emitted into the atmosphere and developing advanced measurement tools to inventory and monitor carbon processes in soils and biota. Laser-Induced Breakdown Spectroscopy (LIBS) is one of the analytical tools used to determine the total soil carbon in samples within the Big Sky and Southwest Carbon Sequestration Regional Partnerships. LIBS involves focusing a Nd:YAG laser operating at 1064nm onto the surface of the sample. The laser ablates material from the surface, generating an expanding plasma containing electronically excited ions, atoms, and small molecules. As these electronically excited species relax back to the ground state, they emit light at wavelengths characteristic of the species present in the sample. Some of this emission is directed into one of three dispersive spectrometers. The experiments discussed in this paper were completed with a person portable LIBS instrument designed and built at Los Alamos National Laboratory that uses a Kigre Laser (25mJ/pulse) and an Ocean Optics HR2000 dispersive spectrometer. This instrument was used to probe samples collected from Illinois (no-till loam), Michigan (no-till clay), and North Dakota (reduced-till sand). A new multivariate analysis technique was employed to extract concentrations to 0.5%C with significantly greater statistical accuracy than conventional univariate techniques. These MVA techniques appear to completely compensate for these matrix effects because the analysis identifies the correlations between the spectra (independent variables), the individual elements of interest (dependent variables such as Si) as well as the other elements in the matrix.