Prediction of As, Cd, Cr, Hg, Ni, and Se Concentrations in Organic Amendments Using Portable X-ray Fluorescence and Multivariate Modeling  
[PERSON]  
1 Instituto de Recurso Naturales y Agrobiologia de Sevilla (IRNAS-CSIC), Avda, Reina Mercedes 10, 41012 Sevilla, Spain; [EMAIL_ADDRESS] (F.A.-P.); [EMAIL_ADDRESS] (P.B.-D.)1 Instituto de Recurso Naturales y Agrobiologia de Sevilla (IRNAS-CSIC), Avda, Reina Mercedes 10, 41012 Sevilla, Spain; [EMAIL_ADDRESS] (F.A.-P.); pburgo@irnase.cs  
Carbon (SOC, texture (clay and sand contents), and cation exchange capacity (CEC) can be inferred from these elementary concentrations using chemometric procedures, such as multiple linear regression models [2]. Other authors [7] have used various multivariate models to predict the geographic variation of certain parameters. Despite the possibilities of this technique, its use for the analysis of organic amendments, such as compost, sewage sludge, and organic fertilizers, has been limited [8; 9; 10; 11; 12; 13]. Only a few elements (Ca, Cr, Cu, Fe, K, Mn, P, and Zn) have shown potential for quantitative determination in dry samples [11], and only qualitative results were obtained for a few more. In a 2016 review, [PERSON] et al. [14] indicated that the XRF analyzer was only used to analyze Cd, Cu, Cr, Fe, Mn, Ni, Pb, and Zn in 3-10% of samples of household waste fractions. The standardized method EN16424 [15; 16] has been developed for waste materials at a screening level. A semi-quantitative analysis for the screening of residues, sludge, and soil samples has also been developed in standard EN15309 [17] but through using desktop equipment and specific sample preparation. The organic matter in the sample matrix produces matrix effects that affect the precision of the results obtained and forces specific calibrations to be made for the organic materials [17]. This may be of particular relevance for the elements considered since some of them may accumulate in the organic fraction. Keeping those limitations in mind, [PERSON] et al. [8] recommend the technique for qualitative analysis in waste treatment processes as the first line of quality control in waste treatment plants and landfills. In soils, [PERSON] et al. [7] show that multivariate modeling can compensate for some of the shortcomings of the pXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making pXRF instruments capable of predicting elemental concentrations at comparable levels of accuracy to conventional laboratory analyses. In line with the aforementioned for soils, two recent investigations have shown that some properties of organic amendments can be predicted from elementary pXRF readings. [PERSON] et al. [18] used elemental data as a proxy for the prediction of compost salinity, and using principal component regression models, calculated the compost electrical conductivity and pH values with a good correlation between the measured and predicted datasets. [PERSON] et al. [19] obtained calibration functions using multiple linear regression, which allowed for the quantitative determination of the aqua-regia-extractable contents of Fe, K, P, S, Zn, Cu, Pb, Sr, Cr, and Mn, as well as the organic matter content, and a semi-quantitative assessment of Al, Ca, V, Ba, Ni, and As contents. The pXRF technique has a wide range of quantifications but its detection limits were poorer than those of desktop XRF analyzers, ICP, or atomic absorption spectroscopies commonly used for elementary analysis. The usual levels in composts and organic amendments of the elements Cd, Co, Hg, Mg, Na, Sb, Se, and Sn are not attainable using pXRF, and for As, Ba, Ni, Cr, and V, only a fraction of all samples can be measured. In these matrices, elements were usually determined using the aqua-regia-extractable form [20; 21], which constitutes a difference with the measurement made with pXRF that corresponds to the total content.