UNDERSTANDING CHAR AND TERRA PRETA SOIL CHEMISTRY FROM PYROLYSIS MASS SPECTROMETRIC ANALYSIS. K. Magrini, S. Czernik, R. Evans

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1 UNDERSTANDING CHAR AND TERRA PRETA SOIL CHEMISTRY FROM PYROLYSIS MASS SPECTROMETRIC ANALYSIS K. Magrini, S. Czernik, R. Evans October 6, 8

2 OUTLINE Rapid SOC Analysis Py-MBMS Instrument and Methods Multivariate Analysis Results Characterized and Managed Soils Terra Preta Soils Conclusions

3 MOLECULAR BEAM MASS SPECTROMETRY Combustion mode provides C, N, and S concentrations Pyrolysis mode provides SOC chemistry Handles large number of samples (- samples/day) Analysis time is short (-5 minutes)

5 5 5 Pyrolysis MS Traces #9 # 985 985-5 cm

4 py-reactor Transportable MBMS 5-mg Soil Samples Total Ion Intensity, Arbitrary Units Pyrolysis MS Traces #9 # cm -5 cm 5.6% C.6% C #7 985 # # 5-5 cm % C 5-5 cm 5-5 cm # #8 #.5% C.65% C cm 5- cm 5- cm.9% C.79% C.59% C 6 8 Time, Min.

5 Soil Samples Heated Quartz Reactor e- Q Q Q (P+) Collisions (D+) Detector Argon Collision Gas Total Ion Intensity, Arbitrary Units CID provides a mass spectrum of a specific fragment ion for unique identification i.e. Mass 96 is fragmented to identify ergosterol as the parent species # cm 5.6% C # cm.6% C #7 985 # # 5-5 cm % C 5-5 cm 5-5 cm # #8 #.5% C.65% C cm 5- cm 5- cm.9% C.79% C.59% C 6 8 Time, Min. Correlate with other spectroscopic techniques MIR, NIR, DRIFTS Correlate with other characterization data Build comprehensive SOC database for research use Ion Intensity , m/z

6 PRINCIPAL COMPONENT ANALYSIS AND PLS MODELING A projection method that helps to visualize information PCA helps to determine which samples are different and which variables are contributing to the difference Helps to determine groupings Helps quantify the amount of useful information PLS builds predictive models with quantitative information

7 Py-MBMS SOC ANALYSIS Analytical pyrolysis coupled with molecular beam mass spectrometry (py-mbms) and multivariate statistics for rapid soil organic carbon (SOC) chemistry analysis of varied, characterized soils to establish breadth of application RESULTS Forest soils: distinguish disturbance,depth and location Agro forest: SOC accumulation with poplar rotation (litter and root inputs with depth) Grassland CRP soils: distinguish management impacts Native prairie soils: quantitating SMBC, SOC, POM C, Cmin, fractions Agricultural management for SOC accumulation (switchgrass) Terra preta chemistry

8 Native prairie soils: quantitating SMBC, SOC, POM C, C min Soil characterization data PARAMETER RANGE Depth (cm) -5 SOC (wt %).-9.8 SMBC (µg/g soil).6-9 POM C (g/g soil).-7.7 Cmin C (g/g soil).6-.6 Calendar Age (YBP) -657 yrs

9 Predicted Y Measured Y Predicted Y Lacustrine soils ESTIMATING SOC SOC PLS model for all samples Lacustrine soils different regression Coefficients Lignin and carbohydrates 5 7 Measured Y 5 Fatty acids m/z Complex SOC spectra containing lignin, fatty acids ergosterol, and carbohydrate species SOC model regression coefficients Magrini et al., 7.

10 Predicted Y Measured Predicted ESTIMATING SMBC SMBC PLS model for all samples Regression Coefficients Measured Y X Variables 9 Palmitic acid: 7, 87,, 5, 9,, 57, 7, 85, 99,, 7, 56 SMBC biomarker SMBC model regression coefficients Magrini et al., 7.

11 ESTIMATING SMBC FROM MASS Measured Predicted Predicted Y Measured Y PLS model regressing SMBC vs. mass 56 Single mass has potential to be used as an SMBC biomarker Magrini et al., 7.

12 SUMMARY OF PLS MODELS CONSRUCTED WITH SAMPLE CHARACTERIZATION DATA Variable # of Samples Correlation # Principal Components Slope RMSEP % Carbon POM C C min SMBC C Calendar Age.9.8 Depth SOC SMBC POM C C min Calendar Age -5 cm.-9.8 wt%.6-9 µg/g.-7.7 µg/g.6-. g/g -6 yrs K. A. Magrini, R. F. Follett, J. Kimble, M. F. Davis, and E. Pruessner.7. Soil Science.

PCA shows land may need to be in CRP longer to recover to native standard SOC levels. CRP Impacts on SOC Kimble - - - -. wt% Increasing total soil carbon <.

13 Can we determine sequestration potentials of soil types and management impacts? cm Scores PC Increasing SOM Native Continuous 8-yr No Till CRP Till PC - - Samples from Idaho farm in CRP 8 years. PCA shows land may need to be in CRP longer to recover to native standard SOC levels. CRP Impacts on SOC Kimble wt% Increasing total soil carbon <.5 wt% PC Native 5- cm Scores CRP CT PC PC Native CRP - cm Scores CT NT NT PC CRP and CT soils likely influenced by specific crop roots at 5- cm NT and CT: corn (shorter roots) CRP: switchgrass (longer roots) AT - cm, CT and CRP soils have (CT-short roots) and switchgrass (CRP-long roots) crops. Possibly seeing SOC inputs from each root type

14 Model for SOC ESTIMATION IN CRP SOILS 5.E-.E Regression Coefficients.E-.E-.E-.E+ -.E- -.E- -.E Carbohydrates 6, 7, 8, Monoaromatics 78, 9, 6 Phenolics 9, Fatty Acid Fragments (-CH -) amu units X-Variables Plant-derived Fatty Acids Linoleic Linolenic Stearic Microbial Contributions?

15 5 PC AKRON, CO SOIL FRACTIONS Scores NH NH NH NH NH NH NH NH WSWS HA WS WS WS WS WS HA HA Native HA HA WS - PC X-expl: 56%,9% WS: whole soil HA: humic acid NH: non hydrolyzable HA WS WS Cropped WS WS WS HA

16 8 Ion Intensity Aromatics Fatty 56 Non-hydrolyzable soil fraction Less complex spectrum Enriched in aromatics Masses at 7, 8, 9,,, 5, 6, 8: may indicate fatty acids likely derived from microbial biomass during decomposition acids Mass spectra of Akron, CO whole soil and non hydrolysable fraction OH m/z 9 N H M/z 67 OH O M/z 6 CH OMe m/z Ion Intensity amu CO Aromatics Akron, CO Whole Soil Complex spectrum Lignin and carbohydrates present,, 8, 5 amu "Lignin" m/z

17 Py-MBMS SHOWS: Fractions have different chemistry Humic acid similar spectrum to whole soil; enriched mass 9 Lignin fragments and microbial products present in whole soil, humic acid, and humin fractions Non acid hydrolyzable fraction contains fatty acids. Less complex spectra than the whole soil and other fractions similar to terra preta

18 Principal Component Scores Terra Preta C. Steiner TP samples

19 Principal Component Loadings Terra Preta

20 Principal Component Scores Terra Preta

21 Principal Component Loadings Terra Preta Palmitic Acid Microbial Biomass Markers (n-acetyl glucosamine)

22 Terra Preta and Forest Soils amu Forest Soil Terra Preta Ion Intensity m/z

23 8 NH and TP spectra are similar to each other and to highly charred biomass Ion Intensity Aromatics m/z , 9-6 Non hydrolyzable fraction AK, CO 8 Fatty acids Terra preta

24 TERRA PRETA MASS SPECTRUM , Ion Intensity Proteins Fatty Acids m/z

25 CONCLUSIONS Py-MBS rapidly characterizes SOC species Data can be used to estimate SOC and SMB contents Terra Preta soils have less SOM complexity and are similar to non hydrolyzable soil fractions Working on characterizing black carbon Potting Soil (PS) PS + NPK PS + 5% Char Figure : Corn plant roots 5 days post germination in potting soil (PS), PS and NPK addition, PS and 5% peanut char.

26 ACKNOWLEDGEMENTS NREL LDRD Program USDA NRCS and ARS USDA FOREST SERVICE BOISE-CASCADE CHRIS STEINER

Soil Organic Matter. Unit 2. Forms of Soil Organic Matter: OM OC x (assumes 30% C) (1.72 typically used as a conversion factor)

Soil Organic Matter. Unit 2. Forms of Soil Organic Matter: OM OC x (assumes 30% C) (1.72 typically used as a conversion factor) Unit 2 Soil Organic Matter OM OC x 1.7-2.0 (assumes 30% C) (1.72 typically used as a conversion factor) Histosol Alfisol Spodosol Forms of Soil Organic Matter: - dissolved (soil solution): DOM Nonliving