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<p>The large-scale nonferrous metal smelting activities involved in industrialization processes have raised serious environmental concerns, particularly heavy metals (HMs) pollution in soil, which poses significant threats to ecosystems and human health. This study comprehensively examined the horizontal and vertical spatial distribution patterns of HMs in soils at the 0–100 cm depth in the historical slag storage area of Laibin Chemical Plant, Guangxi Province. The site is representative of HM pollution resulting from long-term inadequately managed smelting activities in slag accumulation areas in southern China. The inverse distance weighting (IDW) interpolation, geostatistical techniques (Voronoi method, Moran’s I), and Pearson correlation analysis were integrated to quantify the horizontal and vertical spatial distribution, variability, and migration characteristics of the target HMs. The spatial analysis of HMs (and metalloids) shows that soil quality is severely threatened by Cd, Cr, Pb, Zn, As, and Ni. Cd, Pb, Zn, As are the main pollutants. All HMs (and metalloids) exhibit marked spatial heterogeneity and a “patchy aggregation” pattern due to intense human and industrial activities. The vertical movement of HMs suggests pollutants are primarily concentrated in the 0–10 cm soil layer. Different contaminants had various migration depths, with migration mobility ranking as follows: As &gt; Zn &gt; Cd &gt; Pb &gt; Cr &gt; Ni. Spatial variability analysis reveals that Zn, Pb, and As have substantial local spatial variation. Their average vertical variation coefficients are 0.55, 0.884, and 0.607, respectively. This study introduces a novel method that combines horizontal-vertical spatial heterogeneity, variability, and migration depth quantification, differing from conventional approaches that typically focus on single-dimensional distribution—providing a comprehensive analysis of HM (and metalloid) pollution in nonferrous metal smelting sites and supporting targeted layered remediation and precise hotspot pollution control.</p> Graphical Abstract <p></p>

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Spatial Distribution and Risk Assessment of Heavy Metal Pollution at an Abandoned Smelting Site in Guangxi Province of China

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Bodong Xiong,
  • Lizhu Hou,
  • Wajahat Waseem,
  • Weihua Cui,
  • Muhammad Asad ghafoor

摘要

The large-scale nonferrous metal smelting activities involved in industrialization processes have raised serious environmental concerns, particularly heavy metals (HMs) pollution in soil, which poses significant threats to ecosystems and human health. This study comprehensively examined the horizontal and vertical spatial distribution patterns of HMs in soils at the 0–100 cm depth in the historical slag storage area of Laibin Chemical Plant, Guangxi Province. The site is representative of HM pollution resulting from long-term inadequately managed smelting activities in slag accumulation areas in southern China. The inverse distance weighting (IDW) interpolation, geostatistical techniques (Voronoi method, Moran’s I), and Pearson correlation analysis were integrated to quantify the horizontal and vertical spatial distribution, variability, and migration characteristics of the target HMs. The spatial analysis of HMs (and metalloids) shows that soil quality is severely threatened by Cd, Cr, Pb, Zn, As, and Ni. Cd, Pb, Zn, As are the main pollutants. All HMs (and metalloids) exhibit marked spatial heterogeneity and a “patchy aggregation” pattern due to intense human and industrial activities. The vertical movement of HMs suggests pollutants are primarily concentrated in the 0–10 cm soil layer. Different contaminants had various migration depths, with migration mobility ranking as follows: As > Zn > Cd > Pb > Cr > Ni. Spatial variability analysis reveals that Zn, Pb, and As have substantial local spatial variation. Their average vertical variation coefficients are 0.55, 0.884, and 0.607, respectively. This study introduces a novel method that combines horizontal-vertical spatial heterogeneity, variability, and migration depth quantification, differing from conventional approaches that typically focus on single-dimensional distribution—providing a comprehensive analysis of HM (and metalloid) pollution in nonferrous metal smelting sites and supporting targeted layered remediation and precise hotspot pollution control.

Graphical Abstract