Spatial characteristics of heavy metal pollution and the potential ecological risk of a typical mining area: A case study in China

Chen, Yixian; Jiang, Xiaosan; Wang, Yong; Zhuang, Dafang

doi:10.1016/j.psep.2017.10.008

Cited by 124 publications

(56 citation statements)

References 29 publications

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“…Therefore, they may have been derived from natural processes such as soil erosion and rock weathering. This hypothesis is also supported by previous study [28,46].…”

Section: Identification Of the Sources Of Heavy Metalssupporting

confidence: 92%

“…Many measures have been used in literature to assess heavy metal pollution in sediment, e.g., the geoaccumulation index, the potential ecological risk index, the enrichment factor, etc. [45][46][47]. In this study, we used I geo and RI to assess heavy metal pollution in the sediment of the Zhuzhou Reach with reference to the concentration of Dongting Lake sediments as the background.…”

Section: Assessment Of Heavy Metal Pollutionmentioning

confidence: 99%

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Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China

et al. 2020

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Background: The Xiangjiang River is an important drinking water resource for the Hunan Province of China. It is crucial to ascertain the pollution status, influencing factors, ecological risks, and possible sources of heavy metals in the sediments of the Xiangjiang River. Sediment is both a source and a sink of heavy metals in aquatic ecosystems. In this study, surface sediment was collected from the Zhuzhou Reach of the Xiangjiang River and eight heavy metals were investigated. Results:In all sediment samples, all eight heavy metals were detected and their average concentration fell in the order of Zn > Pb > As > Cu > Cr > Ni > Cd > Co. Assessment shows extremely serious Cd pollution and a very high potential ecological risk from Cd. According to correlation analysis and principal component analysis (PCA), As, Cu, Ni, Pb, and Zn originate from industrial wastewater and mineral smelting activities, whereas Co, Cr, and Ni come from natural sources. Redundancy analysis (RDA) reveals that the organic matter content and the particle size of the sediment have a certain influence on the enrichment of heavy metals. Conclusion:Among all eight examined heavy metals in the surveyed area, the content of Zn, Pb, and As is the highest, and that of Cd and Co is the lowest. Despite a low level of absolute content, the Cd in sediment already renders a high ecological risk and thus calls for urgent attention. Anthropogenic activities are the main source of heavy metals in the sediment. The distribution of heavy metals may also be influenced by sediment properties. The results provide guidance for controlling heavy metal pollution and protecting drinking water sources in the Xiangjiang River.

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“…Therefore, they may have been derived from natural processes such as soil erosion and rock weathering. This hypothesis is also supported by previous study [28,46].…”

Section: Identification Of the Sources Of Heavy Metalssupporting

confidence: 92%

Section: Assessment Of Heavy Metal Pollutionmentioning

confidence: 99%

Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China

et al. 2020

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“…The contribution rate of Hg to the comprehensive ecological risk of PTEs was 79.9%-84.2%; as such, the potential ecological risk of Hg in soil should be a matter of concern. This is consistent with the findings of Yuan et al [52], and Chen et al [53] also reached the same conclusion. This may be due to the fact that single factor ecological risk is associated with PTE toxicity.…”

Section: Soil As and Hg Safety Limitssupporting

confidence: 92%

As-Hg Compound Pollution: Rice Growth, Yield, and Environmental Safety Limits

Cao

Islam

et al. 2020

Sustainability

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With rapid economic and industrial development, the potentially toxic element (PTE) pollution of farmlands in China has become increasingly serious. Based on the Hakanson potential ecological risk index (RI) and national food pollutant limit standards (GB 2762-2017), the effects of exogenous potentially toxic elements (PTEs), i.e., arsenic (As) and mercury (Hg), on rice growth were studied. Common varieties of hybrid (YY 538, CY 84) and conventional (XS 134, JH 218) rice were grown in pots having representative paddy soil from Zhejiang Province, China. The results showed that As and Hg in soil significantly inhibited growth; however, the plant height of hybrid rice was less affected. Further, there were significant correlations between concentrations of PTEs in soil and rice grains; however, hybrid rice had a stronger ability to absorb and accumulate PTEs. The comprehensive potential ecological RI of soil PTEs was 39.4–89.9, which is below the level of ‘minor ecological hazard’, and the contribution rate of Hg was 79.9%–84.2%; as such, Hg posed the greatest potential ecological risk in the study area. The ecological risk limits of As and Hg in the safe production of different rice varieties decreased in the order XS 134 > JH 218 > YY 538 > CY 84. The ecological risk limits of As and Hg for conventional rice were higher, and the risk limits of As and Hg for hybrid rice were lower, than the limit of the soil environmental quality assessment index in the national “Soil quality control criteria for soil pollution in agricultural land (for trial implementation) (Trial)” (GB15618-2018). The health risk limit of soil As in the typical paddy soil of China decreased in the order XS 134 > JH 218 > CY 84 > YY 538. The health risk limit of Hg decreased in the order XS 134 > JH 218 > YY 538 > CY 84. The health risk limit of As was lower, whereas the risk limit of Hg was higher, than the limit of the soil environmental quality evaluation index in the national “Soil quality control criteria for soil pollution in agricultural land (for trial implementation) (Trial)”(GB15618-2018).

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“…Traditional interpolation methods, such as the Generalized Kriging Method, Polynomial Method, and the Inverse Distance Weighting Method (IDW) have been extensively employed to transform the experimental point data of sample locations into thematic maps that show the spatial distribution [27]. The polynomial method and IDW method are comparatively easy and quick to evaluate, but the spatial variability and relations between the nodes cannot be estimated with them, moreover, these methods allow a partial accuracy of estimation.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

Shaheen

Iqbal

2018

Sustainability

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Abstract:In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr), cadmium (Cd), and lead (Pb), at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF) function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples) at three depths (0-15 cm, 15-30 cm, and 60-90 cm). The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC), organic matter (OM), pH, phosphorus (P), potassium (K), and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC) had a significant (p ≤ 0.05) negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P) in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of Cr, Cd, and Pb revealed spatial variability regarding subsoil horizons. Significant contamination was discovered near the Deg drain and the Bed Nallah irrigated area that indicated a high Cr topsoil contamination, and in a homogenous pattern in Cd and Pb (p < 0.05). Consequently, different soil management strategies can be adopted in an industrial irrigated area to reduce the contamination load of heavy metals in soil.

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Spatial characteristics of heavy metal pollution and the potential ecological risk of a typical mining area: A case study in China

Cited by 124 publications

References 29 publications

Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China

Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China

As-Hg Compound Pollution: Rice Growth, Yield, and Environmental Safety Limits

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

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