## 4 Results  
The mining waste samples were enriched in total Fe\({}_{2}\)O\({}_{3}\) (min-max: 10.06-28.60 wt %; median: 16.4 wt %) and MgO (10.64-23.86 wt %; 20.2 wt %), and depleted in Al\({}_{2}\)O\({}_{3}\) (3.37-14.14 wt %; 6.52 wt %) and K\({}_{2}\)O (0.04-1.37 wt %; 0.08 wt %) compared to the adjacent topsoil from the surrounding areas (Figure 4). The LOI was large in both sample groups within the mining waste (4.45-16.52 wt %), in comparison with the surrounding areas (9.81-16.98 wt %). The CaO and LOI increased in the subsurface samples with depth (exceeding 20 wt % from P1), coupled with a decrease of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) (Supplementary File, Figures S1 and S3). The trend shows the approaching to the local bedrock, which is the carbonated serpentinite. The subsurface samples from P2, instead, show limited variations relative to the surface samples, with the exception of Fe\({}_{2}\)O\({}_{3}\), which decreased with depth, from 25.99 to 12.65 wt %. Among other elements, the concentrations of Ni, Pb, Sc, and Zn were similar within the mining waste and the surrounding areas. Conversely, the concentrations of Cr, Co, Cu, and V were enriched in the mining waste area compared to the surrounding areas (Figure 4). The subsurface samples from the profile P1 showed a slight increasing in Co, Cr, and Cu concentrations up to \(-\)50 cm depth (Figure S2). At the greater depths, concentration of Co and Cu decreased, with the exception of Cr, which reached its maximum concentration (around 2500 ppm) at 160 cm depth. The Co, Cr, and Cu concentrations at the site P2 did not display variations, being more than 100 ppm, 1000 ppm, and 1750 ppm, respectively (Figure S4). Statistical analysis was performed to evaluate the variability of the geochemical elements. One of the sensitive elements in RS detection is iron, because of the iron absorption features at multiple bands in VNIR [38]. Therefore, the correlation coefficient of iron with different elements was studied (Table 1). The choice of iron was based on [31]: the heavy metal carriers in Vigonzano are Fe-clay minerals (smectite and chlorite), thus making total Fe\({}_{2}\)O\({}_{3}\) a proxy for metal occurrence. We used this knowledge on the Fe\({}_{2}\)O\({}_{3}\)-metals relationships in our statistical approach. Outliers were excluded for cerium data. The band ratios for the iron mineral detection were tested, and only that showed the medium-high (>0.5) correlation coefficient with the major and trace element concentrations were chosen. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Correlation Coefficient with Iron**} & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Num. Samples**} \\  & **Oxides Band Ratio 4 (665 nm)/2** & & & & \\  & **(490 nm)** & & & & \\ \hline Chromium (ppm) & \(-\)0.761 & 1233 & 1965 & 308 & 42 \\ Copper (ppm) & \(-\)0.563 & 1615 & 5715 & 29 & 42 \\ Cobalt (ppm) & \(-\)0.672 & 93 & 158 & 38 & 42 \\ Iron (\%) & \(-\)0.720 & 15 & 29 & 7 & 42 \\ \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Correlation Coefficient with Ferrous**} & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Num. Samples**} \\  & **(1610 nm)** & & & & \\ \hline Cerium (ppm) & +0.771 & 28 & 86 & 1.5 & 41 \\ Niobium (ppm) & +0.718 & 6.5 & 17 & 0 & 42 \\ Rubidium (ppm) & +0.818 & 34.68 & 143 & 1.5 & 42 \\ Lanthanum (ppm) & +0.771 & 13.51 & 36 & 1.5 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical analysis and the correlation coefficient between band ratios and concentration of different elements. Figure 4: Major (**A**) and selected trace element (**B**) compositions from surface samples of the mining waste and surrounding areas. Box size is Q1–Q3; horizontal line is the median; whiskers represent the non-outlier range; empty circles are outliers and stars (*) are extreme outliers.