Abstract Rapid, inexpensive, and equally reliable estimates of potentially toxic elements are a necessity; portable X-ray fluorescence (pXRF) spectrometry is a handy tool to help achieve such. The current study sought to compare multiple linear regression with three regularized regression models [Ridge, Lasso, and ElasticNet (ENET)] for the estimation of total arsenic (As) using pXRF datasets in polluted temperate floodplain soils of Přibram, Czech Republic. A total of 158 surface (0–25 cm) floodplain surface soil samples were collected from a specific site in Přibram. Models were evaluated separately and compared based on mean absolute error (MAE), root mean squared error (RMSE) and the coefficient of determination (R2). All four models were able to predict As with good accuracy (MAE and RMSE values of 0.02 and 0.03 mg/kg, respectively, and R2 values ranging from 0.94 to 0.95). As measured via pXRF as well as predicted via the four regression models produced similar spatial variability as shown by the standard laboratory-measured As using ordinary kriging and Conditional Gaussian Simulations (CGS), although the latter produced more details of As spatial distribution in floodplain soils. Future research should include other auxiliary predictors (e.g., soil physicochemical properties, other various sensor data) as well as cover a wider range of soils to improve model robustness.

中文翻译：

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便携式 X 射线荧光光谱法对漫滩土壤中砷估计和绘图的多元方法的比较

摘要 对潜在有毒元素进行快速、廉价且同样可靠的估计是必要的；便携式 X 射线荧光 (pXRF) 光谱法是帮助实现这一目标的便捷工具。当前的研究试图将多元线性回归与三个正则化回归模型 [Ridge、Lasso 和 ElasticNet (ENET)] 进行比较，以使用捷克共和国 Přibram 受污染的温带洪泛区土壤中的 pXRF 数据集估算总砷 (As)。从 Přibram 的一个特定地点收集了总共 158 个表面（0-25 厘米）漫滩表面土壤样本。分别评估模型并根据平均绝对误差 (MAE)、均方根误差 (RMSE) 和决定系数 (R2) 进行比较。所有四个模型都能够以良好的准确度预测 As（MAE 和 RMSE 值分别为 0.02 和 0.03 mg/kg，和 R2 值范围从 0.94 到 0.95）。通过 pXRF 测量以及通过四个回归模型预测产生了类似的空间变异性，如标准实验室测量的 As 所示，使用普通克里金法和条件高斯模拟 (CGS)，尽管后者产生了漫滩土壤中 As 空间分布的更多细节. 未来的研究应包括其他辅助预测因子（例如，土壤物理化学特性、其他各种传感器数据）以及覆盖更广泛的土壤以提高模型的稳健性。尽管后者产生了漫滩土壤中砷空间分布的更多细节。未来的研究应包括其他辅助预测因子（例如，土壤物理化学特性、其他各种传感器数据）以及覆盖更广泛的土壤以提高模型的稳健性。尽管后者产生了漫滩土壤中砷空间分布的更多细节。未来的研究应包括其他辅助预测因子（例如，土壤物理化学特性、其他各种传感器数据）以及覆盖更广泛的土壤以提高模型的稳健性。