Although arsenic (As) toxicity in soil vary depending on its chemical forms and oxidation states, regulatory limits for this compartment rely on total As content. Conventional methods of total As determination are expensive and time-consuming. The development of predictive techniques might enable a speditive assessment of As contamination in those scenarios, such as thermal spring sites, where exposure to the metalloid poses a threat to human health. The objective of this study was to assess the suitability of Visible Near Infrared spectrophotometry for predicting the total As content in highly calcareous thermal spring soils and the same aim was pursued for those elements (i.e. Al, Fe and Mn) the chemistry of which is tightly connected with that of As. A Partial Least Square approach, including cross-validation and external independent test, was used to relate the concentrations of the target elements to spectral data. The most accurate prediction was found for As with Pearson's coefficient, RMSE, RPD and SEP being equal to 0.94, 69.65, 2.9 and 66.99, respectively. Less accurate predictions were found for Al (r = 0.88; RMSE = 11014; RPD = 1.96; SEP = 11014), Fe (r = 0.93; RMSE = 6921.1; RPD = 2.45; SEP = 6462.4), and Mn (r = 0.92; RMSE = 542.01; RPD = 2.43; SEP = 529.79).
Keywords: As polluted soil; Hot springs; Hyperspectral spectrophotometry; Speditive soil analysis; Visible near infrared.
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