sustainable agriculture flagship

Prediction of metal and metalloid partitioning coefficients (Kd) 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 2013 EGS Geochemistry Expert Group, FAO Headquarters (Rome)

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• 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.

BackgroundSolid-solution partitioning coefficients (Kd values)

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phasesolution

phase solid

MM

Kd

• 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


BackgroundMIR-PLSR as an alternative for Kd assessment

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• 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-, TeO4

2-,

VO3-, and uncharged boron H3BO3

0

• Use these models to predict Kd values for the complete GEMAS

data set of 4313 soil samples


Objectives

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Material and methodsSoil samples and MIR scanning

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• 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


Material and methodsSelection of samples and determination Kd experimental values

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• 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)

• 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


Material and methods

Infrared models

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• 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)


Material and methodsStatistical assessment of model and predictions

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• 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


Results and discussion: Cations

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MetalRangeMedian(L/kg)

ClasspHR2

log-Kd (DRIFT) log-Kd (DRIFT+pH)

R2 RMSE RPD R2 RMSE RPD

Zn2-20,276

1 0.84 0.78 0.47 2.1 0.93 0.27 3.71737

Mn1-14,288

1 0.84 0.70 0.79 1.8 0.88 0.50 2.91195

Co3-15,739

1 0.71 0.71 0.62 1.9 0.83 0.48 2.42285

Ni4-3925

1 0.59 0.72 0.35 1.9 0.87 0.24 2.8549

Pb10-339,624

1 0.57 0.70 0.48 1.9 0.84 0.35 2.610,939

Cu23-8589

2 0.26 0.40 0.30 1.3 0.46 0.28 1.41643

Sn60-22,079

2 0.15 0.32 0.47 1.2 0.32 0.47 1.22500

Ag159-4655

2 0.05 0.33 0.24 1.2 0.35 0.23 1.22623


Results and discussionPrediction maps cations: example of Ni

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GrasslandArable

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) (From Janik et al., 2014, Fig. 11.1, p.186)


Results and discussion: Cations

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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


Results and discussionAnions

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ElementRangeMedian(L/kg)

ClasspHR2

log-Kd (DRIFT) log-Kd (DRIFT+pH)

R2 RMSE RPD R2 RMSE RPD

Te0.32-2443

1 0.62 0.72 0.52 1.9 0.79 0.45 2.2193

Mo0.70-7078

1 0.43 0.63 0.48 1.7 0.75 0.38 2.141.7

Sb0.51-5494

1 0.26 0.64 0.31 1.7 0.74 0.27 2.067.9

V0.35-11507

1 0.09 0.61 0.39 1.6 0.62 0.39 1.6596

B0.38-51.88

1 0.13 0.66 0.19 1.7 0.68 0.18 1.82.15

Se0.58-6339

2 0.30 0.43 0.56 1.3 0.43 0.56 1.32.20


Results and discussionPrediction maps oxoanions: example of Mo

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GrasslandArable

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) (From Janik et al., 2014, Fig. 11.2, p.187)


Results and discussion

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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

• 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-, TeO4

2-): 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.


Conclusions

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• 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

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GEMAS – The Project Team

Thank you

Sustainable Agriculture Flagship

CSIRO Land and Water

Jose Martin Soriano Disla (PhD)

Tel.: +61883038425

E-mail: [email protected]

Website: www.clw.csiro.au

ReferencesReferences

Prediction of PBI by mid-infrared reflectance spectroscopy | Soriano-Disla et al.

References

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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 (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).

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