Measurement of biochar properties, including aromatic carbon, and monitoring the concentration of charred material in soil using NIR spectroscopy

Transcription

1 Measurement of biochar properties, including aromatic carbon, and monitoring the concentration of charred material in soil using NIR spectroscopy BAMBANG H. KUSUMO With the collaboration of P. BISHOP, R. CALVELO PEREIRA, M. CAMPS ARBESTAIN, C.B. HEDLEY, M.J. HEDLEY, A.F. MAHMUD, and T. WANG

2 Introduction Conversion of biomass to biochar increases the residence time of C in soil relative to that of the original feedstock It is proposed as a strategy able to reduce the release of CO 2 to the atmosphere Techniques and protocols are required to monitor and confirm biochar stability in soil over time Biomass Biochar Uniform Biochar Application Biochar Massey University Farm

3 Introduction Application rates of biochar to soil will reflect cost of production, transport and application ( 10 t ha -1 ~ 1% topsoil) NIRS techniques have been successfully used to measure C in soil. But not yet widely used to measure biochar properties In situ NIRS Measurement Landcare Research Palmerston North, NZ

4 Questions? 1. Can NIRS be used to measure biochar stability parameters, e.g., aromatic C and molar H/C org ratio? 2. Can NIRS be used to measure biochar concentration in soil at low (narrow range) to high (wide range) rates of application? 3. Can NIRS discriminant analysis be used to detect biochar in soil?

5 1 st Study 1. Can NIRS be used to measure biochar stability parameters, e.g., aromatic C and molar H/C org ratio?

6 Materials and Methods Eucalyptus Pine Willow Poplar Manure Biosolid Eucalyptus Leaves Chicken manure Poultry litter Paper sludge 25 biochar samples were analysed Pyrolysis 250, 350, 450, 550 C The biochar samples were scanned Spectral pre-processing (ParLes) PCA analysis (ParLes) Discriminant Analysis (MINITAB 16) PLSR was used to build calibration models (ParLes; Viscarra Rossel, 2008)

7 Reflectance 0.9 Higher maximum pyrolysis temperatures 0.8 reduced NIR reflectance - Pyrolysis 0.7 removes water and alcohols from feedstock 0.6 Vis-NIR Spectra of 25 Biochars OH Absorption Water Absorption MAe250 MAe350 BSe550 MAe450 BSe250 BSe350 BSe450 EuW400A EuW550A EuW EuW550 EuL400A EuL550A 0.4 PS550A PL400 PL550A 0.3 CM400 CM550A PI PO-400 WI o C PO-550 WI-550 M-C-1 M-C Wavelength (nm)

8 PCA Score Plot of Biochar Spectral data based on: Feedstock Temperature of Pyrolysis Unknown May contain wood/plant tissue Unknown o C PC2 (8.9%) Manure or may contain decomposed plant tissue PC1 (37.2%) Feedstock Biosolid+Wood Leaves Manure Manure+Wood Paper sludge Wood PC2 (8.9%) o C o C PC1 (37.2%) Temperature Unknown Aromatic C H/C org > 65% < 35% PC2 (8.9%) % Aromatic C < 35% > 65% 35-65% PC2 (8.9%) H/Corg Ratio < 0.7 > PC1 (37.2%) PC1 (37.2%)

9 Linear Discriminant Analysis of Spectral Biochar Data based on Temperature Observation Predicted group From group Square distance Proba bility Archaeological charcoal 1 (M-C-1) Archaeological charcoal 2 (M-C-2) 350 o C 250 o C 250 o C o C o C o C o C o C o C o C o C o C True Group All Groups Put into group 250 o C 350 o C 400 o C 450 o C 550 o C 250 o C o C o C o C o C Total number Correct number Proportion (%)

10 NIRS-Predicted Aromatic C (%) NIRS can predict biochar Aromatic C (using band region: nm) Vis-NIR Spectroscopy 13 C NMR Spectroscopy y = x R² = RMSE = RPD = C NMR-Measured Aromatic C (%)

11 NIRS-Predicted H/C org Atomic Ratio NIRS-Predicted fa Fixed C NIRS ( nm bands) can predict Fixed C, H/C org Atomic Ratio, and Fraction of Aromaticity (fa) NIRS-Predicted Fixed C (%) y = x R² = RMSE = RPD = Measured Fixed C (%) H/C org Fraction of Aromaticity y = x R² = RMSE = RPD = Measured H/C org Atomic Ratio y = x R² = RMSE = RPD = Measured fa

12 NIRS ( nm bands) can predict C org, H O & Ash content, Volatile Matter content, O/C org atomic ratio, and ph

13 Correlation matrix among the reference data of biochar properties r ph Corg (%) N (%) H (%) Ash (%) O (%) VM (%) FC (%) H /Corg O/Corg fa Aromatic C (%) ph * * * * Corg (%) * * * * N (%) H (%) * * * * Ash (%) * * * * O (%) * * VM (%) * * FC (%) * * * * * H /Corg * * * * O/Corg * * * * fa * * * * Aromatic C (%) * * * * * VM = volatile matter, FC = fixed carbon, fa = fraction of aromaticity

14 Independent NIRS prediction between Aromatic C and fraction of aromaticity (fa) Fraction of aromaticity Aromatic C Aromatic C & fa; r = Aromatic C prediction; R 2 = fa prediction; R 2 = 0.921

15 Aromatic Carbon

16 VIP Bands of Various Biochar Properties

17 2 nd Study 2. Can NIRS be used to measure biochar concentration in soil at low (narrow range) to high (wide range) rates of application?

18 Materials and Methods Soils/substrate: - Alfisol (silt-loam Tokomaru) - Entisol (sandy soil Motuiti) - Quartz Pine-350 Biosolids- 550 ph C % N % Ash % Vol Matter % Fixed C % Narrow range; 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5% Biochar Wide range; 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 75, 100% Biochar

19 Marked change in soil spectral reflectance when 5% biochar was added

20 Biochar Spectra UV-Vis-NIR NMR Aromatic C * * * * Fraction of Aromaticity (BS-550) = 87% (PI-350) = 73% Total of Aromatic C (BS-550) = g kg -1 (PI-350) = g kg -1

21 Using NIR ( nm) Silt Loam Soil Predicting Wide Range (0-100%) Biochar Concentration in Soil using NIRS Using Vis-NIR Bands Using NIR ( nm) Sandy Soil & nm Using NIR ( nm) Better prediction when soil + biochar types were separated

22 Can biochar, with low concentration (0-2.5%) in soil, be predicted well using NIRS ( & nm)? & nm Silt Loam Soil & nm & nm Sandy Soil Finely Ground the Sandy Soil & nm & nm Finely Ground the Sandy Soil

23 VIP Bands and PLSR Coefficients Very important bands ( , & )

24 Effect of band region selection and grinding on prediction accuracy for one soil and biochar type Sample Bands Used Number of Samples Number of Factors For PLSR model Leave-one-out cross-validation R 2 RMSECV RPD Motuiti (coarse) + BS-550 UV-Vis-NIR *) Motuiti (coarse) + BS-550 Vis-NIR **) Motuiti (coarse) + BS-550 NIR ***) Motuiti (coarse) + BS & nm Sample Bands Used Number of Samples Number of Factors For PLSR model Leave-one-out cross-validation R 2 RMSECV RPD Motuiti (fine) # + BS-550 UV-Vis-NIR *) Motuiti (fine) # + BS-550 Vis-NIR **) Motuiti (fine) # + BS-550 NIR ***) Motuiti (fine) # + BS & nm *) UV-Vis-NIR bands = ; **) Vis-NIR bands = nm; ***) NIR bands = nm # Finely ground Motuiti soil = passed through mm sieve

25 Effect of band region selection and grinding on prediction accuracy of two soils and biochars Sample Bands Used Number of Samples Number of Factors for PLSR model Leave-one-out cross-validation R 2 RMSECV RPD Tokomaru (fine)+pi-350 and Motuiti (fine) # +BS-550 UV-Vis-NIR *) Tokomaru (fine) +PI-350 and Motuiti (fine) # +BS-550 Vis-NIR **) Tokomaru (fine)+pi-350 and Motuiti (fine) # +BS-550 NIR ***) Tokomaru (fine) +PI-350 and Motuiti (fine) # +BS & nm *) UV-Vis-NIR bands = ; **) Vis-NIR bands = nm; ***) NIR bands = nm # Finely ground Motuiti soil = passed through mm sieve

26 3 rd Study 3. Can NIRS detect biochar in situ in field soil?

27 BIOCHAR Materials and Methods BIOCHAR BIOCHAR BIOCHAR Core samples were scanned every 1 cm

28 Spectra with and without biochar 6_22-BC* Spectrum containing biochar

29 Spectra with and without biochar 19_18-BC* 19_17-BC*

30 PCA Score Plot of 1690 Pre-Processed Spectral Data ( & nm) with and without Biochar PC2 (5.5% variance) NIL BC (Biochar) Group BC NIL PC1 (65.3% variance) Marked influence of biochar in soil spectral reflectance

31 R to Log(1/R) Wavelet detrending Smoothing (Savitzky-Golay 1 st Derivative Mean Centre Response Variables (e.g. NIL, biochar) Raw Spectral Data Pre- Processed PCA Scores of Principal Components Discriminant Analysis Result of Classification Summary of Classification Put into group True Group All Biochar NIL groups Biochar 26 1 NIL Total number Correct number Correct proportion (%) Summary of Classification; Separate Calibration and validation set Calibration set (1000 spectra) Validation set (690 spectra) Put into group True Group True Group Biochar NIL All Biochar NIL All Biochar NIL Total number Correct number Correct proportion (%)

32 Measured vs. Predicted Total C from the Soil Cores (using selected band region: & nm)

33 NIRS prediction on cores without biochar NIRS prediction on cores with biochar Effect of added biochar on soil C (elemental analysis data) Effect of added biochar on soil C (NIRSpredicted data) Biochar

34 CONCLUSIONS NIR reflectance spectroscopy has a potential use for predicting biochar C aromaticity and other biochar properties Successful prediction of molar H/C org indicates that NIRS can be used as a quick assessment for C stability NIRS was able to predict biochar at low (narrow range, 0 2.5% BC) and high (wide range, 0 100% BC) application rates Grinding the coarse soil and selecting the important bands ( & nm) improved the prediction accuracy NIRS Discriminant Analysis can be used to detect biochar in field soil

35 THANK YOU

36 C2_46 is grouped into containing biochar Core C2