Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram

Abstract

Heavy metal pollution is a major environmental problem facing humankind. Locating the source and distribution of heavy metal pollutants around mines can provide a scientific basis for environmental control. The structure effect and random effect of a semivariogram can be used to determine the reason for spatial differences in the heavy metal content in surface soil, and the coefficient of variation and regression analysis can be used to confirm that the verification accuracy meets the geostatistical requirements. According to the maximum difference method, the content of heavy metals in the surface soil of the mining area is higher than that of the surroundings, and Cu and Zn levels are higher than the background values for Inner Mongolia. In the present case, Zn, Mn, Pb, Cr, Ni, and Cu levels exceeded the background values for the surroundings of the study area by 65.10%, 53.72%, 52.17%, 46.24%, 33.08%, and 29.49%, respectively. The results show that human activities play a decisive role in the spatial distribution of heavy metals, leading to their spatial distribution in the form of “core periphery”. This distribution pattern was significantly affected by the slope, NDVI value, and the distance from the mining area, but the spatial distribution of Pb was significantly related to high-grade roads. The research methods and conclusions have reference significance for the sources and spatial distribution characteristics of heavy metal pollution in similar mining areas and provide a target for the prevention and control of environmental pollution in the study area.

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

“…Meanwhile, the proportion of serious degradation in the subsidence center was 5.64% by trend analysis method, which was twice and five times that in the subsidence edge and non-subsidence zones. It can be found that the coefficient of variation and trend analysis methods have the same development trend in the mining areas, and combining the two methods to monitor the surface vegetation can better analyze the vegetation characteristics [8,21].…”

“…Increasing evidence suggests that Landsat NDVI can effectively monitor the temporal and spatial variation characteristics of surface vegetation in seven open-pit coal mines, such as Pingshuo, Curragh, Appalachian, Keshutang, Jiazibei, Yimin, and Shengli. Meanwhile, the spatial variability of soil and vegetation characteristics can be expressed by the coefficient of variation [2,[17][18][19][20][21]; therefore, it can be known from the above literature survey that most of the studies mainly analyzed the surface vegetation of opencast coal mines, less attention has been placed on the surface vegetation degradation caused by underground coal mining, and synchronous monitoring of regional surface subsidence and vegetation degeneration researches are relatively limited [3,22]. In particular, there are few studies on coal mining and its effect on surface vegetation in Xishan Coalfield of Shanxi Province.…”

Contamination and Health Risk Assessment of Heavy Metals in Soil and Ditch Sediments in Long-Term Mine Wastes Area

“…The calculated semi-variance function values can be fitted by a series of theoretical models and characterized by nugget variance (C0), Sill (C0+C) and range, which represent the measurement error or spatial variation, the maximum variance between data pairs and the farthest distance of correlation between graphic parameters, respectively. The nugget to sill (N:S ) ratio [C0/(C0+C)] of ≤0.25 means strong spatial dependency, which indicate the variation of heavy metals mainly affected by the structural effect of the natural environment; the ratio remains between 0.25 and 0.75 which means moderate spatial dependency, indicating the variation of heavy metals mainly affected by the joint action of the natural environment factors and the random factors of human activities; and the ratio of ≥0.75 suggests weak spatial dependency, which indicates the variation of heavy metals mainly affected by human activities [ 20 , 21 ]. Main natural factors include climate, parent material, topography and soil properties, and human activities including fertilization, farming measures and cropping systems.…”

Geostatistical Assessment of Heavy Metals and Nutrients Availability in Soil of Oil Palm Plantation Affected by Bauxite Mining

“…Geostatistics and geographic information system (GIS) techniques have been widely employed to quantify the spatial distribution of soil properties to reduce uncertainties, minimising costs and to identify pollution sources, especially in areas of mined soil (Chen et al, 2021;Kulikova et al, 2019). It has additional advantages in research as its analysis includes the characteristics of space and time changes.…”