Soil washing is employed to prevent the issue of Cr re-oxidation following the remediation of Cr-contaminated soil by transferring contaminants from the soil to the wash solution through the dissolving action. Nevertheless, rapidly screening effective washing agents remains challenging. This study conducted batch experiments to gather dataset on key factors (soil properties, Cr sequential extraction and type of washing agent) and Cr content after washing with 250 experimental data points. We developed 12 machine learning models, among which the light gradient boosting machine model excelled in predicting Cr content after washing, determining soil suitability for washing. Furthermore, the adaptive boosting and random forest models preferably predicted Cr content after various washing agents treatments, facilitating optimal detergent identification. Based on the feature analysis, soil pH, exchangeable potassium, reactive iron oxides, and Cr sequential extraction can account for most of the differences in washing treatment. To further validate our model, we used an additional 60 experimental data points, including soil properties with pH values beyond the range of the initial 250 data points. The R value of the predicted fit to the actual results was 0.801. This study provides the feasibility of improving the efficiency of technical remediation of Cr-contaminated soils.

中文翻译：

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通过机器学习对铬污染土壤的快速判别策略进行建模

土壤冲洗用于通过溶解作用将污染物从土壤转移到冲洗溶液中，防止铬污染土壤修复后出现铬再氧化问题。然而，快速筛选有效的洗涤剂仍然具有挑战性。本研究通过批量实验收集了关键因素（土壤性质、Cr 顺序提取和洗涤剂类型）和洗涤后 Cr 含量的数据集，共 250 个实验数据点。我们开发了 12 个机器学习模型，其中光梯度增强机器模型在预测水洗后的 Cr 含量、确定土壤是否适合水洗方面表现出色。此外，自适应增强和随机森林模型可以更好地预测各种洗涤剂处理后的 Cr 含量，从而有助于最佳的洗涤剂识别。根据特征分析，土壤pH、交换性钾、活性铁氧化物和Cr顺序提取可以解释冲洗处理中的大部分差异。为了进一步验证我们的模型，我们使用了额外的 60 个实验数据点，包括 pH 值超出最初 250 个数据点范围的土壤特性。预测与实际结果拟合的 R 值为 0.801。本研究为提高铬污染土壤技术修复效率提供了可行性。