Quantification of multiple soil trace elements by combining portable X-ray fluorescence and reflectance spectroscopy

Abstract

Several combinations of proximal sensors, portable x-ray fluorescence spectroscopy (pXRF), visible-near-infrared spectroscopy (vis-NIR), and mid-infrared spectroscopy (MIR) are assessed to quantify multiple soil trace elements (TE) with reliable accuracy. A total 622 topsoil samples (0–20 cm depth) collected at regular 8 km spacing across southern New Zealand were analysed for TE concentrations by a reference laboratory method and scanned with pXRF, vis-NIR, and MIR. The laboratory results and spectral information were used to develop robust models that enable prediction for soil concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn). Chemometric approaches including spectral data fusion, partial least squares – support vector machine (PLS-SVM) model fusion, and model averaging were tested to obtain optimal quantitative TE predictions in the samples. Validation of PLS-SVM based models on held-out samples showed the combination of pXRF and MIR performed optimally for predicting As, Cr, and Pb concentrations with concordance correlation coefficient (CCC) of 0.97 and root mean square error (RMSE) of 0.57 mg As/kg, 1.14 mg Cr/kg, and 1.05 mg Pb/kg respectively. Fusion of pXRF and vis-NIR data performed optimally for quantifying Cu, Ni, and Zn concentrations with CCC of 0.98 and RMSE of 1.09 mg Cu/kg, 0.63 mg Ni/kg, and 3.90 mg Zn/kg respectively. For Cd and Hg, Granger-Ramanathan model averaging of outputs from PLS-SVM based pXRF, vis-NIR, and MIR models performed optimally with CCC > 0.80 and RMSE of 0.03 mg Cd/kg and 0.01 mg Hg/kg respectively. This study showed that quantitative prediction models for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn can be successfully implemented at local- and national-scale and for long-term monitoring of soil trace elements at concentrations below pXRF detection limits and with reduced matrix interference from organic matter than from individual techniques alone.