Sustainable Agriculture Flagship

1. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy Les J. Janik, Sean Forrester, Jason K. Kirby, Michael J. McLaughlin, José M. Soriano-Disla, Clemens Reimann Sustainable Agriculture Flagship 05 December 2013 EGS Geochemistry Expert Group, FAO Headquarters (Rome)

2. Background Solid-solution partitioning coefficients (Kd values) 2 | • Assessment of potential risks posed by metals (mobile and bioavalable fraction) • Mobile fraction might affect organisms, biological processes and be leached • Laborious determination. A reliable, cheap and quick method is needed Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

3. Background MIR-PLSR as an alternative for Kd assessment 3 | Mid-infrared light absorbed by molecules in soil containing C-H, N-H, O-H, C-O, C-N, C-C, N-O, Al-O, Fe-O and Si-O bonds Spectrum determined by the chemical nature of the soil: absorbance peaks at specific wave numbers related to soil compounds MIR-active compounds influence Kd Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

4. Objectives 4 | • MIR diffuse reflectance infrared Fourier transform (DRIFT)-PLSR method to develop predictive models for Kd values using 500 GEMAS soil samples for: • Metallic cations Ag+, Co2+, Cu2+, Mn2+, Ni2+, Pb2+, Sn4+, and Zn2+ • Metal and metalloid oxoanions MoO42-, Sb(OH)6-, SeO42-, TeO42-, VO3-, and uncharged boron H3BO30 • Use these models to predict Kd values for the complete GEMAS data set of 4313 soil samples Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

5. Material and methods Soil samples and MIR scanning • GEMAS agricultural and grazing land soil samples (n = 4813) • Soil sieved at <2 mm and oven dried at 40ºC • Perkin-Elmer Spectrum One • Fourier Transform infrared spectrometer • Diffuse reflectance spectra • Range: 4000-500/cm • Resolution 8 /cm 5 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

6. Material and methods Selection of samples and determination Kd experimental values • N = 500 by “APSpectroscopy StdSelect” application (Unscrambler™ 9.8) • Single point soluble metal or radioactive isotope spike. Rates chosen to be in linear region of sorption curve and closer to ecotoxicity thresholds (PNECs) and predicted exposure concentrations (PECs) (OECD, 2000) 6 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

7. Material and methods Infrared models 7 | Model development: Partial Least Squares (Unscrambler V 9.8) Calibration models trained by “leave-one-out” cross-validation Models used to predict samples in the 4313 unknown samples Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

8. Material and methods Statistical assessment of model and predictions • The uncertainty of Kd value prediction of unknown soil samples expressed as empirical ‘deviation’ values (Unscrambler) • <0.2 Excellent spectral fit of the unknowns with the model • 0.2-0.4 Good spectral fit of the unknowns with the model • 0.4-0.6 Marginal spectral fit of the unknowns with the model • >0.6 Poor spectral fit of the unknowns with the model 8 | • PLSR models reported in terms of: • Coefficient of determination: R2 • Root mean square error of the CV (RMSECV). • Residual predictive deviation (RPD)=standard deviation/RMSECV <1.5: poor; 1.5-2.0: indicator quality; 2.0-3.0: good quality; >3.0 analytical quality (Chang et al., 2001; Janik et al., 2009) Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

9. Results and discussion: Cations 9 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

10. Results and discussion Prediction maps cations: example of Ni Grassland Arable (From Janik et al.,2014, Fig. 11.1, p.186) (From Janik et al., 2014, Fig. 11.1, p.186) Lower strength in northern Europe, rest more variable with highest in southern and eastern Europe. Patterns associated to pH induced by climate (mainly rainfall) and parent material. 10 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

11. Results and discussion: Cations Figure. Histograms of the distribution of log-transformed Kd (L/kg) deviation values for the Class 1 metals for calibration (dark) and predicted “Unknown” (light) using PLSR (DRIFT+pH). Janik et al., 2014 (submitted) Few unknowns with deviation values >0.6: unknowns predicted with similar accuracy to calibration samples 11 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

12. Results and discussion Anions 12 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

13. Results and discussion Prediction maps oxoanions: example of Mo Arable Grassland (From Janik et al.,2014, Fig. 11.2, p.187) (From Janik et al., 2014, Fig. 11.2, p.187) Opposite patterns to Ni, negatively related to pH More variability, especially southern and eastern Europe Lowest for eastern Spain. Highest in western Iberian peninsula, Dinarides 13 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

14. Results and discussion Figure: Histograms of the distribution of log-transformed Kd (L/kg) deviation values for the anionic metals for calibration (dark) and predicted “Unknown” (light) using PLSR (DRIFT+pH). Janik et al., 2014 (submitted) Few unknowns with deviation values >0.6: unknowns predicted with similar accuracy to calibration samples 14 | Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

15. Conclusions 15 | • The MIR-PLSR (plus pH) technique is suitable for Kd prediction with models dependent on the metal under study: • Good for cationic metals (Co2+, Mn2+, Ni2+ , Pb2+ and Zn2+) and oxoanions (MoO42-, Sb(OH)6-, TeO42-): RPD > 2.0 and R2 > 0.74 • Indicator quality for H3BO30 and VO3-: RPD > 1.5 and R2 > 0.62 • Unsuccessful for Ag+, Cu2+, Sn4+ and SeO42-: RPD < 1.5 and R2 < 0.46 • Capability further expanded by the possibility of predicting Kd values in the field using DRIFT hand-held spectrometers. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al.

16. Acknowledgements 16 | • Cathy Fiebiger (CSIRO L&W) • Government of Valencia (Conselleria de Educación) for a Post-Doctoral Fellowship Prediction of PBI by mid-infrared reflectance spectroscopy | Soriano-Disla et al.

17. GEMAS – The Project Team

18. Thank you CSIRO Land and Water Jose Martin Soriano Disla (PhD) Tel.: +61883038425 E-mail: jose.sorianodisla@csiro.au Website: www.clw.csiro.au Sustainable Agriculture Flagship References

19. References SLIDE 8: Chang, C.W., Laird, D.A., Mausbach, M.J. and Hurburgh C.R., J., 2001. Near-infrared reflectance spectroscopy - Principal components regression analyses of soil properties. Soil Sci. Soc. Am. J., 65:480-490. Janik, L.J., Forrester, S.T. & Rawson, A., 2009. The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemometrics and Intelligent Laboratory Systems, 97, 179-188. SLIDES 10, 13: Janik, L.J., Forrester, S., Kirby, J.K., McLaughlin, M.J., Soriano-Disla, J.M. & Reimann, C., 2014. Prediction of metal and metalloid partioning coefficients (Kd) in soil using Mid-Infrared diffuse reflectance spectroscopy. Chapter 11 In: C. Reimann, M. Birke, A. Demetriades, P. Filzmoser & P. O’Connor (Editors), Chemistry of Europe's agricultural soils – Part B: General background information and further analysis of the GEMAS data set. GeologischesJahrbuch (Reihe B 103), Schweizerbarth, 183-188. SLIDES 6: OECD, 2000. OECD guideline for the testing of chemicals. Section 1. Physical-chemical properties. Test No. 106. Adsorption-desorption using a batch equilibrium method. Organisation for Economic Cooperation and Development Publishing, 44 pp. SLIDES 11, 14: Janik, L., Forrester, S., Kirby, J.K., McLaughlin, M.J., Soriano-Disla, J.M., Reimann, C. & The GEMAS Project Team, 2014a. GEMAS: Prediction of solid-solution partitioning coefficients (Kd) for cationic metals in soils using mid-infrared diffuse reflectance spectroscopy. Science of the Total Environment (submitted). Janik, L., Forrester, S., Soriano-Disla, J.M., Kirby, J.K., McLaughlin, M.J., Reimann, C. & The GEMAS Project Team, 2014b.GEMAS: Prediction of solid-solution phase partitioning coefficients (Kd) for boric acid and oxyanions in soils using mid-infrared diffuse reflectance spectroscopy. Science of the Total Environment(submitted). 19 | Prediction of PBI by mid-infrared reflectance spectroscopy | Soriano-Disla et al.