Identification of factors controlling heavy metals/metalloid distribution in agricultural soils using multi-source...

Understanding the factors that controlling the agricultural soil heavy metals/metalloids distribution is vital for cropland soil remediation and management. For this objective, 227 agricultural soils were sampled in the Guanzhong Plain, China, to measure the concentration of five heavy metals (Pb, Cd, Ni, Zn, and Cu) and one metalloid (As) by X-ray fluorescence spectrometer, meanwhile, 24 possible influencing factors to agricultural soil heavy metals/metalloid distribution were collected and grouped into three categories. A sequential multivariate statistical analysis was carried out to provide insight into the controlling factors of soil heavy metals/metalloid distribution, then stepwise multiple linear regression (SMLR) and partial least squares regression (PLS) were used to predict heavy metals/metalloid concentrations in agricultural soil based on the result of soil heavy metals/metalloid controlling factors identification. The results demonstrated the types of soil and land use did not have a substantial effect on soil heavy metals/metalloid distribution, except Zn and Cu. The soil properties category played a major role in influencing the soil heavy metals/metalloid concentration. The concentrations of Mn and Fe, which are the main constitute elements of soil inorganic colloid, were the most significant factors, followed by the concentrations of P, K and Ca. Soil pH and soil organic matter (SOM) content, which are often considered as important factors for soil heavy metals/metalloid distribution, were not important in the present study. The SMLR was more effective than the PLS for predicting soil heavy metals/metalloid content. The results of this study enlighten that future soil heavy metals/metalloid contamination treatment in regions with high pH and low SOM content should concentrate on inorganic colloid particles, which have strong adsorption capacity for soil heavy metals/metalloid and are environmentally friendly. Moreover, the combination of successive multivariate statistical analysis and SMLR provide an effective tool to predict and monitor agricultural soil heavy metals/metalloid distribution, and facilitate the improvement of environmental and territorial management.