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).

尽管砷在土壤中的毒性根据其化学形式和氧化态而异，但该隔室的管理限制取决于总砷含量。测定总砷的常规方法既昂贵又费时。预测技术的发展可能使得能够在那些情况下（例如温泉场所）对砷污染进行评估，例如在温泉中，暴露于准金属对人类健康构成威胁。这项研究的目的是评估可见近红外分光光度法预测高钙质温泉土壤中总砷含量的适用性，并且针对化学性质严格的那些元素（即Al，Fe和Mn）追求相同的目标。与As有关。偏最小二乘方法，包括交叉验证和外部独立测试，用于将目标元素的浓度与光谱数据相关联。对于As的皮尔逊系数，RMSE，RPD和SEP分别等于0.94、69.65、2.9和66.99，发现了最准确的预测。发现Al的预测较不准确（r = 0.88; RMSE = 11014; RPD = 1.96; SEP = 11014）， Fe（r = 0.93; RMSE = 6921.1; RPD = 2.45; SEP = 6462.4）和Mn（r = 0.92; RMSE = 542.01; RPD = 2.43; SEP = 529.79）。