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

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...

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Main Authors: Guoqing Chen, Yong Yang, Xinyao Liu, Mingjiu Wang
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/10/5/290
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spelling doaj-142586eb69a1421a8de9d331b550bf7e2021-05-31T23:05:20ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-05-011029029010.3390/ijgi10050290Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on SemivariogramGuoqing Chen0Yong Yang1Xinyao Liu2Mingjiu Wang3College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010011, ChinaCollege of Grassland, Resources and Environment, Inner Mongolia Agriculture University, Hohhot 010011, ChinaCollege of Grassland, Resources and Environment, Inner Mongolia Agriculture University, Hohhot 010011, ChinaCollege of Grassland, Resources and Environment, Inner Mongolia Agriculture University, Hohhot 010011, ChinaHeavy 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.https://www.mdpi.com/2220-9964/10/5/290maximum difference methodsemivariogramkriging interpolationspatial variabilityopencast mine areaheavy metals
collection DOAJ
language English
format Article
sources DOAJ
author Guoqing Chen
Yong Yang
Xinyao Liu
Mingjiu Wang
spellingShingle Guoqing Chen
Yong Yang
Xinyao Liu
Mingjiu Wang
Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
ISPRS International Journal of Geo-Information
maximum difference method
semivariogram
kriging interpolation
spatial variability
opencast mine area
heavy metals
author_facet Guoqing Chen
Yong Yang
Xinyao Liu
Mingjiu Wang
author_sort Guoqing Chen
title Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
title_short Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
title_full Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
title_fullStr Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
title_full_unstemmed Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram
title_sort spatial distribution characteristics of heavy metals in surface soil of xilinguole coal mining area based on semivariogram
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2021-05-01
description 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.
topic maximum difference method
semivariogram
kriging interpolation
spatial variability
opencast mine area
heavy metals
url https://www.mdpi.com/2220-9964/10/5/290
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