Spatial Distribution and Migration Characteristics of Heavy Metals in Grassland Open-Pit Coal Mine Dump Soil Interface

Elemental composition and potential health risk of vegetable cultivated in residential area situated close to abandoned gold mine dump: Characteristics of soil quality on the vegetables

The presence of toxic metals in residential areas near abandoned gold mine tailings is a major environmental issue. This study mainly aimed to investigate the elemental distribution of both toxic and essential elements in soils and leafy vegetables (Brassica oleracea) collected from eight different sites around the Davidsonville residential area, located closer to the abandoned Princess gold mine dump, Johannesburg, South Africa. The nutritional value of vegetables in the human diet was determined to assess their value to their health. The vegetables contained metals in the following descending order: Ca > Mg > Ca > Sb > Pb > Fe > Mo > Cr > Se > As > V > Ni > Co > Cd. The bioaccumulation factor (BAF) revealed that vegetables tend to accumulate most metals even (toxic) during the transfer and translocation process. Based on the recommended daily allowance (%RDA) the vegetables showed to contribute 152%, 84% and 75% toward RDA for Se, V and Ca, respectively for most adults and these play a role in human metabolic activities. The vegetables were found to be a good source of essential elements (Ca, Mg, Ni, Na, Fe) but with some traces of toxic metals such as Pb, As and Sb. Based on the health risk assessment, the vegetable posed an adverse health hazard for human consumption due to metals with high HRI >1.

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. ENVIRONMENTAL IMPLICATION: This study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

Pollution Risk Prediction for Cadmium in Soil from an Abandoned Mine Based on Random Forest Model

It is highly uncertain as to the potential risk of toxic metal(loid)s in abandoned mine soil. In this study, random forest was used to predict the risk of cadmium pollution in the soils of an abandoned lead/zinc mine. The results showed that the random forest model is stable and precise for the pollution risk prediction of toxic metal(loid)s. The mean of Cd, Cu, Tl, Zn, and Pb was 6.02, 1.30, 1.18, 2.03, and 2.08 times higher than the soil background values of China, respectively, and their coefficients of variation were above 30%. As a case study, cadmium in the mine soil had "slope" hazard characteristics while the ore sorting area was the major source area of cadmium. The theoretical values of the random forest model are similar to the practical values for the ore sorting area, metallogenic belt, riparian zone, smelting area, hazardous waste landfill, and mining area. The potential risk of soil Cd in the ore sorting area, metallogenic belt, and riparian zone are extremely high. The tendency of pollution risk migrates significantly both from the ore sorting area to the smelting area and the mining area, and to the hazardous waste landfill. The correlation of soil pollution risk is significant between the mining area, the smelting area, and the riparian zone. The results suggested that the random forest model can effectively evaluate and predict the potential risk of the spatial heterogeneity of toxic metal(loid)s in abandoned mine soils.