Metallic tungsten (W) is a highly dense material of increasing importance to the U.S. Army as a strategic, non-radioactive replacement for depleted uranium. While there is a growing body of evidence regarding the mechanistic behavior of ionic W (formed after the spontaneous oxidation of metal) in the environment, predicting its environmental fate remains challenging, owing to the widespread geochemical heterogeneity of soils. Therefore, we developed W adsorption prediction models by creating different functional “compositions” of the chemical and physical characteristics for different soil “types” (a non-specific yet commonly used to term to designate different soils). A relatively small dataset consisting of twenty soils (possessing six different soil “types” from across the U.S.) were evaluated for W adsorption behavior. Physical and chemical soil data were separated into water-extracted (WE), bulk, and particle-size distribution (PSD) compositions, and center log-ratio (clr) transformed. Classification models built using extremely randomized trees (ERT) showed that the compositions' accuracies were WE > Bulk > PSD at the Order and Suborder levels. W's adsorption isotherms were constructed using batch equilibrium experiments and modeled against the Langmuir model, where S_max = calculated adsorption maximum, K_1/L = inverse Langmuir affinity coefficient. Afterward, both the ERT and ensemble, or stacked, ERT models (by addition of Order and/or Suborder taxonomic labels as ensemble classifiers) were developed for predicting the S_max and K_1/L parameters based on the different compositions. In general, model accuracies were substantially increased by the addition of the labels (stacked models). Feature importance calculations pointed to a wide range of potential chemical mechanisms simultaneously controlling W adsorption, laying the groundwork for more detailed in-situ elemental speciation studies. Overall, this work showcased a new technological capability allowing for accurately predicting W adsorption on a wide variety of morphological soil designations.

Capsule: This work found that soil morphological designations greatly improved the accuracy of Langmuir adsorption predictions of CoDA-transformed characterization data.

金属钨 (W) 是一种高密度材料，作为贫化铀的战略性非放射性替代品，对美国陆军来说越来越重要。虽然越来越多的证据表明离子 W（金属自发氧化后形成）在环境中的机械行为，但由于土壤广泛的地球化学异质性，预测其环境归宿仍然具有挑战性。因此，我们通过为不同的土壤“类型”（一种非特异性但通常用于指定不同土壤的术语）创建化学和物理特性的不同功能“组成”来开发 W 吸附预测模型。对包含 20 种土壤（拥有来自美国各地的六种不同土壤“类型”）组成的相对较小的数据集进行了 W 吸附行为的评估。(clr)转换。使用极端随机树 (ERT) 构建的分类模型表明，在 Order 和 Suborder 级别上，组合的准确度是 WE > Bulk > PSD。W 的吸附等温线是使用批次平衡实验构建的，并针对 Langmuir 模型建模，其中 S _max  = 计算的吸附最大值，K _1/L  = 反 Langmuir 亲和系数。之后，开发了 ERT 和集成或堆叠 ERT 模型（通过添加顺序和/或子分类标签作为集成分类器）来预测 S _max和 K _1/L基于不同成分的参数。通常，通过添加标签（堆叠模型）可以显着提高模型精度。特征重要性计算指出了同时控制 W 吸附的多种潜在化学机制，为更详细的原位元素形态研究奠定了基础。总体而言，这项工作展示了一种新技术能力，可以准确预测各种形态土壤名称上的 W 吸附。

胶囊：这项工作发现，土壤形态名称大大提高了对 CoDA 转换表征数据的 Langmuir 吸附预测的准确性。