Quantifying the influencing factors and multi-factor interactions affecting cadmium accumulation in limestone-derived agricultural soil using random forest (RF) approach

Abstract

Cadmium (Cd) is a highly toxic heavy metal that occurs widely in the environment and poses extensive threats to human health, animals, and plants. This study aims to identify and apportion multi-source and multi-phase Cd pollution from natural and anthropogenic inputs using ensemble models that include random forest (RF) in agricultural soils on Karst areas. The contributions of natural and anthropogenic factors to Cd accumulation were quantitatively assessed using the RF machine learning method. The results revealed that the main influencing factors were pH, organic carbon (Corg), and elevation. Moreover, the interaction effects of pH and Corg on distance and elevation were also quantified and visualised. It is observed that pH and Corg had stronger effects on soil Cd concentration than that of distance when pH > 7.02 and Corg > 1.53. In other words, higher Cd content in the soil along roadways may be caused by the interaction of distance, pH and Corg, with pH and Corg playing the dominant role in our case. Moreover, the maximum contribution of a single factor, elevation, to Cd concentration was about 0.13 mg/kg, and its interactions reached 1.082 mg/kg and 0.83 mg/kg, respectively, when combined with pH and Corg at 194.0 m. However, with increasing elevation, pH and Corg gradually took over the leading roles. This result not only gives us a quantitative understanding of the relationship between the factors that affect soil cadmium accumulation, but also provides an accurate method for source apportionment of heavy metals in soil.

Keywords

• Cadmium
• China
• Karst soil
• Machine learning
• Quantitative interaction analysis
• Spatial distribution