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

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. Background Solid-solution partitioning coefficients (K d values) 2 |

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 K d Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Background MIR-PLSR as an alternative for K d assessment 3 |

4. MIR diffuse reflectance infrared Fourier transform (DRIFT)-PLSR method to develop predictive models for K d values using 500 GEMAS soil samples for: Metallic cations Ag +, Co 2+, Cu 2+, Mn 2+, Ni 2+, Pb 2+, Sn 4+, and Zn 2+ Metal and metalloid oxoanions MoO 4 2-, Sb(OH) 6 -, SeO 4 2-, TeO 4 2-, VO 3 -, and uncharged boron H 3 BO 3 0 Use these models to predict K d 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. Objectives 4 |

5. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Material and methods Soil samples and MIR scanning 5 | 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

6. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Material and methods Selection of samples and determination K d experimental values 6 | 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)

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. Material and methods Infrared models 7 |

8. PLSR models reported in terms of: Coefficient of determination: R 2 Root mean square error of the CV (RMSECV). Residual predictive deviation (RPD)=standard deviation/RMSECV 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. Material and methods Statistical assessment of model and predictions 8 | The uncertainty of K d 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

9. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion: Cations 9 | Metal Range Median (L/kg) Class pH R 2 log-K d (DRIFT)log-K d (DRIFT+pH) R2R2 RMSERPDR2R2 RMSERPD Zn 2-20,276 10.840.780.472.10.930.273.7 1737 Mn 1-14,288 10.840.700.791.80.880.502.9 1195 Co 3-15,739 10.71 0.621.90.830.482.4 2285 Ni 4-3925 10.590.720.351.90.870.242.8 549 Pb 10-339,624 10.570.700.481.90.840.352.6 10,939 Cu 23-8589 20.260.400.301.30.460.281.4 1643 Sn 60-22,079 20.150.320.471.20.320.471.2 2500 Ag 159-4655 20.050.330.241.20.350.231.2 2623

10. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion Prediction maps cations: example of Ni 10 | Grassland Arable 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. (From Janik et al.,2014, Fig. 11.1, p.186)

11. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion: Cations 11 | Figure. Histograms of the distribution of log-transformed K d (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

12. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion Anions 12 | Element Range Median (L/kg) Class pH R 2 log-K d (DRIFT)log-K d (DRIFT+pH) R2R2 RMSERPDR2R2 RMSERPD Te 0.32-2443 10.620.720.521.90.790.452.2 193 Mo 0.70-7078 10.430.630.481.70.750.382.1 41.7 Sb 0.51-5494 10.260.640.311.70.740.272.0 67.9 V 0.35-11507 10.090.610.391.60.620.391.6 596 B 0.38-51.88 10.130.660.191.70.680.181.8 2.15 Se 0.58-6339 20.300.430.561.30.430.561.3 2.20

13. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion Prediction maps oxoanions: example of Mo 13 | Grassland Arable 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 (From Janik et al.,2014, Fig. 11.2, p.187)

14. Prediction of metal and metalloid partitioning coefficients (Kd) in soil using mid-infrared diffuse reflectance spectroscopy| Janik et al. Results and discussion 14 | Figure: Histograms of the distribution of log-transformed K d (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

15. The MIR-PLSR (plus pH) technique is suitable for K d prediction with models dependent on the metal under study: Good for cationic metals (Co 2+, Mn 2+, Ni 2+, Pb 2+ and Zn 2+ ) and oxoanions (MoO 4 2-, Sb(OH) 6 -, TeO 4 2- ): RPD > 2.0 and R 2 > 0.74 Indicator quality for H 3 BO 3 0 and VO 3 - : RPD > 1.5 and R 2 > 0.62 Unsuccessful for Ag +, Cu 2+, Sn 4+ and SeO 4 2- : RPD < 1.5 and R 2 < 0.46 Capability further expanded by the possibility of predicting K d 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. Conclusions 15 |

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. Acknowledgements 16 |

17. GEMAS – The Project Team

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

19. Prediction of PBI by mid-infrared reflectance spectroscopy | Soriano-Disla et al. References 19 | 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. Geologisches Jahrbuch (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 (K d ) 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 (K d ) for boric acid and oxyanions in soils using mid-infrared diffuse reflectance spectroscopy. Science of the Total Environment (submitted).