Abstract
Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.
Highlights
Soil contamination arouse from potentially toxic element (PTE) such as chromium (Cr), lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni) in agricultural land has raised serious concerns worldwide [1,2,3,4,5,6,7], inChina, which has experienced rapid industrialization and urbanization in the past four decades [8,9,10,11,12,13]
Ni in farmland soils in the region under survey were higher than their background contents in China
The model developed using random forest (RF) significantly outperformed the Multiple linear regression (MLR), support vector machines (SVM), and Cubist models, and could efficiently predict the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, with an R2 varying between 0.58 and 0.79 and root mean square error (RMSE) varying between 0.03 and
Summary
Soil contamination arouse from potentially toxic element (PTE) such as chromium (Cr), lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni) in agricultural land has raised serious concerns worldwide [1,2,3,4,5,6,7], inChina, which has experienced rapid industrialization and urbanization in the past four decades [8,9,10,11,12,13]. Apart from natural weathering from parent soil materials, anthropogenic activities (including industrial waste production, sewage irrigation, agricultural inputs, mining, and smelting) are a major source of PTE accumulation in farmland soils [14,15,16]. Rice is one of the most important staple food crops worldwide. China is the world’s largest producer of rice, and rice is the staple food for majority of the Chinese population, in Southern China. PTEs pollution in soil and their subsequent accumulation in these crops, and related health risks of human exposure have been extensively studied around the world [30,31,32,33,34,35]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
