Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution.

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

Development of a new method framework to estimate the nonlinear and interaction relationship between environmental factors and soil heavy metals.

The intricate and multifaceted nature of soil system profoundly influences the highly complex and often nonlinear changes that soil heavy metals (HM) undergo. Spatial heterogeneity, location and scale variability, and the interaction and superposition among environmental drivers challenged researchers to determine the sophisticated nature of soil HMs changes at the regional scale. This study aims to develop a new method framework and selects Ningbo as the case study to apportion the environmental factors responsible for soil HMs pollution that include Cd, Cr, Pb, Hg, As, Cu, Zn and Ni, focusing on nonlinearity and interaction. We harnessed the Random Forest model to apportion the environmental drivers of soil HM change. The directionality and shape of the nonlinear relationship between HMs and their individual contributors were derived by Partial Dependence Plots. The interactions of multiple drivers were quantitatively assessed by the Conditional Inference Tree. Our results demonstrated that soil HMs in the study area varied spatially. Soil HMs pollution was mitigated by natural factors and anthropogenic factors. The main influencing factors were pH, soil parent material type, enterprise activities, and agricultural application. The effects of some factors on soil HMs showed a monotonic linear trend, but some have apparent threshold effects. The direction of influence on soil HMs will shift when pH and phosphate fertilizer reach a specific value. The addition of enterprises in the area would rarely have an impact on the HMs pollution once it reached around 2 per km2 because of the industrial agglomeration. Soil HM concentrations were mainly from multi-pollutants and were governed by a combination of environmental factors. Our study provided managers and policymakers with site-specific and definite guidelines for preventing and controlling soil HM pollution.

Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops.

Heavy metal pollution is a notable threat to agricultural production. Soil heavy metal pollution can cause potential ecological risk (ERI), and crop heavy metal pollution can cause human health risk (HRI). However, most previous studies partially focused on heavy metal pollution in soil or crop but often neglected the relationship between them. Actually, soil heavy metal can pollute crops to some extent, while not all heavy metal pollution in crops comes from soil. The inner relationship of pollution risk in soil-crop system is worth attention. In this study, we selected Ningbo as the study region and used sample data to assess both soil and crop heavy metal risks, in order to explore the differences between heavy metal contamination risks in soil and crops as well as the relationships between heavy metal contents in soil and crops. Our results showed that Hg was the most polluted heavy metal in soil, which led to the highest ecological risk in Jiangbei (Comprehensive ERI = 567) with the maximum ERI of Hg (430). However, As in crops contributed the most to health risk and caused the highest health risk in Fenghua (HRI = 10) with the largest contribution of 64.5%. Such differences of pollution risk assessment indicated that the contents of the same heavy metal were inconsistent in soil and crops. Our results further showed that the heavy metals in soil had the greatest influence on Zn in crops. Pb and Cr in soil had synergistic effects on the crop absorption of Zn, whereas As, Hg and Cu played antagonistic roles in the crop absorption of Zn. Our study confirms that heavy metals in soil would variously influence heavy metals in crops and the interaction of heavy metals is very important for pollution risk control, which have been largely ignored yet.