Quantifying the impact of factors on soil available arsenic using machine learning

Arsenic (As) can accumulate in edible plant parts and thus pose a serious threat to human health. Identifying the contributions of various factors to soil available As is crucial for evaluating environmental risks. However, research quantitatively assessing the importance of soil properties on available As is scarce. In this study, we utilized 442 datasets covering total As, available As, and properties of farmland soils. The five machine learning models were employed to predict soil available As content, and the model with the best predictive performance was selected to calculate the importance of soil properties on available As and interpret the model results. The Random Forest model exhibited the best predictive performance, with R² for the test set of dryland and paddy fields being 0.83 and 0.82 respectively, while also outperforming other machine learning models in terms of accuracy. Concurrently, evaluating the contribution of soil properties to soil available As revealed that increases in soil total arsenic, pH, organic matter (OM), and cation exchange capacity (CEC) led to higher soil available As content. Among these factors, soil total As had the greatest impact, followed by CEC. The influence of pH on soil available As was greater in dryland compared to OM, while in paddy fields, it was smaller than OM (p < 0.01). Sensitivity analysis results indicated that reducing soil total As content had the greatest effect on available As. In both dryland and paddy field soils, reducing soil total As had the most pronounced effect on available As, leading to reductions of 10.09% and 8.48%, respectively. Therefore, prioritizing the regulation of soil total As and CEC is crucial in As contamination management practices to alter As availability in farmland soils.