Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging. In this study, integrating HM influx from atmospheric deposition, a boosted regression tree model with an average R > 0.8 was obtained to predict accumulation of Pb, As, and Cd in wheat grain across a smelting region. The atmospheric deposition serves as the dominant factor influencing the accumulation of Pb (28.2 %) and As (31.2 %) in wheat grain, but shows a weak influence on Cd accumulation (12.1 %). The contents of available HM in soil affect HM accumulation in wheat grain more significantly than their total contents in soil with relative importance rates of Pb (14.4 % > 8.2 %), As (30.9 % > 4.0 %), and Cd (55.0 % > 16.9 %), respectively. Marginal effect analysis illustrates that HM accumulation in wheat grain begins to intensify when Pb content in atmospheric dust reaches 5140 mg/kg and available Cd content in soil exceeds 1.15 mg/kg. The path analysis rationalizes the cascading effects of distances from study sites to smelting factories on HM accumulation in wheat grain via negatively influencing atmospheric HM deposition. The study provides data support and a theoretical basis for the sustainable development of non-ferrous metal smelting industry, as well as for the restoration and risk management of HM-contaminated soils.