Pollution characteristics and source identification of farmland soils in Pb–Zn mining areas through an integrated approach

Lu, Dingtian; Zhang, Chaolan; Zhou, Zirui; Huang, Dan; Qin, Chaoke; Nong, Zexi; Ling, Caiyuan; Zhu, Yuqi; Chai, Xingle

doi:10.1007/s10653-022-01355-0

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…Using the methods in reference [3] and reference [4] as experimental comparison methods, verify the specific numerical levels of pixel translation differences in remote sensing images under the application of the three methods, as shown in Figure 1. Analyzing Figure 1, the probability of pixel distortion in remote sensing images was the lowest.…”

Section: Resultsmentioning

confidence: 99%

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Identification method of regional soil erosion characteristics based on drone low altitude remote sensing technology

Xie,

Duan,

Yin

et al. 2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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In order to accurately identify the characteristics of regional soil erosion, this paper proposes a method for identifying the characteristics of regional soil erosion based on unmanned aerial vehicle low altitude remote sensing technology. By installing high-resolution sensors on drones, multispectral remote sensing images of the target area are obtained, and advanced image processing and analysis methods are used to extract features related to soil erosion. The experimental results show that the regional soil erosion feature recognition method based on drone low altitude remote sensing technology can accurately identify the types of soil erosion, providing strong support for soil and water conservation and ecological environment protection. The conclusion indicates that this technology has the advantages of efficiency, accuracy, and flexibility, and can be widely applied in regional soil erosion monitoring and assessment, providing scientific basis for sustainable development.

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Section: Resultsmentioning

confidence: 99%

“…Select the t orthogonal feature vector with the highest eigenvalue in the covariance matrix to construct an orthogonal basis, and project the vector existing in the original space into t principal component to perform denoising on highresolution remote sensing data [3][4].…”

Section: Use ()mentioning

confidence: 99%

Identification method of regional soil erosion characteristics based on drone low altitude remote sensing technology

Xie,

Duan,

Yin

et al. 2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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“…Previous research has indicated that the heavy metal levels in cultivated soils in numerous regions surpass the acceptable limits, indicating a tendency for pollutants to accumulate. Commonly detected pollutants include copper (Cu), mercury (Hg), arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), and zinc (Zn) [17][18][19][20]. The primary approaches for assessing heavy metal pollution in arable soils include the single factor pollution index and the Nemero comprehensive pollution index [21].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China

Liu,

Shen,

Fan

et al. 2024

Agronomy

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The impacts of heavy metal pollution in arable soil on agricultural production, environmental health, and the wellbeing of urban and rural residents cannot be overlooked. It has become a significant bottleneck in achieving comprehensive rural revitalization. To accurately grasp the characteristics of heavy metal pollution in suburban cultivated soil, Tangwang Village (a suburb of Huainan City) was subjected to scrutiny. The contents of heavy metals (Hg, Cu, Hg, As, Pb, Cr, Cd, and Zn) in the topsoil of cultivated land in this area were detected, and their spatial distribution characteristics were analyzed using inverse distance spatial interpolation. (1) After conducting a comprehensive analysis and thorough examination of the PMF model sources, it was determined that Cu, Cd, and Zn exhibit a direct correlation with agricultural practices, collectively contributing to a cumulative percentage of 21.10%. Meanwhile, Cr is derived from a combination of sources, including both natural parent materials and human activities, accounting for a total proportion of 24.45%. Notably, lead emissions from automobile exhausts constitute a significant source, while arsenic is primarily associated with dispersed factories and their respective operations, contributing to respective proportions of 36.38% and 18.07%. It is evident that agricultural practices, transportation, and industrial activities are the main reasons for heavy metal pollution in arable soil. (2) The evaluation of geological accumulation indicators reveals that the level of soil arsenic accumulation pollution is mild to moderate (1.199). On the other hand, the cumulative pollution level of Cd, Hg, Cr, and Cu was relatively low (0.462→0.186), whereas the levels of Pb and Zn were below the threshold. (3) The assessment of the ecological risk index revealed that the predominant elements posing potential ecological risks in the investigated region were Hg, As, and Cd, with average Ei values of E(Hg) = 86.81, E(As) = 80.67, and E(Cd) = 67.83, respectively. (4) The human health risk assessment revealed significant differences in the single non-carcinogenic risk values of heavy metals generated by different exposure pathways, with oral ingestion > dermal contact > oral nasal inhalation. Children were more susceptible to the toxic effects of heavy metals compared to adults. Both As and Cr caused an increased risk of cancer in both children and adults, which is a matter of great concern. The results of this study contribute to a more accurate description of the sources of heavy metals in farmland soil. This study indicates that the application of PMF for soil source analysis yields clear results that can be further applied. This research also has potential policy significance as it can help to improve the sustainability of ecosystems by coordinating both environmental and human activities.

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“…Identifying potential sources related to mine activities for heavy metal pollution is vitally important for controlling the priority pollutants to regulate soil heavy metal pollution. In recent years, an increasing number of studies have focused on using mathematical models and statistical analysis to identify the potential sources of heavy metals in soils surrounding typical mining areas [18][19][20][21][22][23]. In addition to mine activities, land use is also an important factor affecting concentration and distribution of heavy metals in the soil [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Heavy Metals in Soil around a Typical Antimony Mine Area of China: Pollution Characteristics, Land Cover Influence and Source Identification

Tang

Wang

et al. 2023

IJERPH

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To understand contamination characteristics and identify sources of heavy metals in soil affected by complex mine activities, a detailed survey of soil heavy metals from different land cover types was investigated around the Xikuangshan (XKS) antimony mine in south-central China. Soil samples had average concentrations of Sb, As, Cd, Cr, Hg, Pb, Cu, Zn and Ni exceeding their background level in the Hunan province. Sb, As and Cd were the main pollutants. A total of 86.8% of samples were severely polluted, characterized by the Nemerow’s comprehensive index, and 68.4% of samples were of very high potential ecological risk, primarily contributed by Sb, Cd and Hg. Among different land cover patterns, Hg, Pb and Cd concentrations showed a statistically significant difference. The application of Pearson correlation, principal component analysis (PCA) and hierarchical cluster analysis (HCA) combined with spatial interpolation GIS mapping revealed that Ni, Cr and Cu were mainly from natural parent materials, whereas other heavy metals were related to anthropogenic sources. Pb, As and Hg were mainly derived from smelting processes of sulfide minerals in the XKS area. The agricultural practice is the main factor for the accumulation of Cd and Zn, and sphalerite smelting also contributed to high Zn concentrations. Particularly, spatial variation of soil Sb concentrations was affected by multiple factors of complex antimony mine activities related to mining, beneficiation and smelting in the XKS area. These results are useful for the prevention and reduction of heavy metal contamination in soils by various effective measures in typical regions affected by antimony mine activities.

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Pollution characteristics and source identification of farmland soils in Pb–Zn mining areas through an integrated approach

Cited by 9 publications

References 39 publications

Identification method of regional soil erosion characteristics based on drone low altitude remote sensing technology

Identification method of regional soil erosion characteristics based on drone low altitude remote sensing technology

Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China

Heavy Metals in Soil around a Typical Antimony Mine Area of China: Pollution Characteristics, Land Cover Influence and Source Identification

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