Spatial distribution and source analysis of heavy metals in soils influenced by industrial enterprise distribution: Case study in Jiangsu Province

Wang, Yazhu; Duan, Xuejun; Wang, Lei

doi:10.1016/j.scitotenv.2019.134953

Cited by 229 publications

(76 citation statements)

References 47 publications

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“…Through the enrichment of soil nutrients, they have effectively improved the soil quality by absorbing a certain concentration of heavy metal elements in soil. Nevertheless, its content of heavy metals is slightly higher than grassland (Wang et al, 2020). In the woodland, the lamellar structure of plant community can weaken the effects of leaching and soil erosion, leading to enhanced physicochemical properties of soil.…”

Section: Effects Of Urbanization On Heavy Metals In Soilmentioning

confidence: 99%

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Yang

Kai

et al. 2021

Preprint

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The urbanization is regarded as the major factor related to human activities that may interfere with the natural ecosystem. In this study, we have selected the wetland of East Dongting Lake as the research area. We have collected 180 soil samples (within the range of 0–20 cm, and 20cm-40cm), and we have measured the contents of their physicochemical properties (including salinity, pH value, soil particle composition, soil organic carbon, nitrate nitrogen and rapidly available phosphorus) as well as heavy metal elements (including As, Hg, Cd, Pb, Ni and Cr). We have adopted the methods of multivariate statistical analysis and inverse distance weighted (IDW) interpolation, so as to to reveal the sources and distribution characteristics of heavy metal content in soil in the research area. By adopting the potential ecological risk index (PERI) method proposed by Hakanson, we intend to assess the PERI values of heavy metals. Our research findings have shown that: (1) 8 sorts of heavy metals have shown positive correlation with each other. As, Hg and Zn have shown a significantly positive correlation with SOC (P < 0.01); As, Ni, Cr and Zn have shown a significantly positive correlation with AP (P < 0.01); As and Pb have shown a significantly positive correlation with Clay (P < 0.01); whereas Hg and Zn have shown a significantly negative correlation with Silt (P < 0.01); As and Pb have shown a significantly negative correlation with Sand (P < 0.01). (2) During urbanization, the elements of Cd, Ni, As, Hg and Pb might be enriched due to agricultural and industrial activities, whereas the use of fertilizers and pesticides constitute one of the major factors contributing to the increase of Cd and Pb contents in soil. (3) Influenced by the varying land patterns and with exception to Cu, the Fe-normalized concentrations have shown significant variations among different types of land use (P < 0.05). Specifically, there is a significantly higher level of Cd, Zn, Pb and Hg contents in the agricultural land than other types of land use, whereas there is a slightly higher level of heavy metal content in the mudflats than that in the grassland. In addition, the content of heavy metals in woodland remains relatively stable, and with exception to As, the content of heavy metals in woodland is the lowest among the five types of land. (4) The average value of the comprehensive PERI in heavy metals amounts to 555.03, representing a strong degree of ecological risks. Specifically, the proportion of sampling points of Cd that have a high level of ecological risks amounts to 36.51%, which is the primary element contributing to heavy metal pollution in the region, especially in the agricultural land.

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Section: Effects Of Urbanization On Heavy Metals In Soilmentioning

confidence: 99%

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Yang

Kai

et al. 2021

Preprint

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“…Natural sources are mainly related to trace elements in the crust [9], while anthropogenic sources are mainly related to metal smelting, fuel and coal combustion, coal mining, and agricultural fertilization [10,11]. To identify sources and understand the spatial variation of heavy metals in soils, multivariate statistics combined with geostatistical methods were generally used [12][13][14]. Typically, geostatistical methods are applied to study the spatial distribution characteristics of heavy metals in soils.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, geostatistical methods are applied to study the spatial distribution characteristics of heavy metals in soils. e ordinary kriging method is the main interpolation method frequently used to study the spatial distribution characteristics of heavy metals in soils [12,15]; it is based on measured data and a semivariogram model to predict unknown sites [12,15,16]. e US Environmental Protection Agency's positive matrix factorization (EPA's PMF) model is widely applied to evaluate the sources of heavy metals in soils.…”

Section: Introductionmentioning

confidence: 99%

Concentrations, Spatial Distributions, and Sources of Heavy Metals in Surface Soils of the Coal Mining City Wuhai, China

Liu

Zhang

et al. 2020

Journal of Chemistry

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Various studies have shown that soils surrounding mining areas are seriously polluted by heavy metals. In this study, 58 topsoil samples were systematically collected throughout the coal mining city Wuhai, located within the Inner Mongolia Autonomous Region of China. The concentrations of As, Hg, Cr, Ni, Cu, Zn, Cd, and Pb in these samples were measured and statistically analyzed. The mean concentrations of all heavy metals were lower than their Grade I values defined by the Chinese Soil Quality Standard. However, the mean concentrations of individual heavy metals in many samples exceeded their background values. The spatial distribution of heavy metals was analyzed by the ordinary kriging interpolation method. The positive matrix factorization model was used to ascertain contamination sources of the eight heavy metals and to apportion the contribution of each source. The most severely polluted area was the Wuhushan mine site in the Wuda district of Wuhai. Our results showed that coal mining strongly affected heavy metal contamination of the local soils. Results of source apportionment indicated that contributions from industrial activities, atmospheric deposition, agricultural activities, and natural sources were 31.3%, 26.3%, 21.9%, and 20.5%, respectively. This clearly demonstrates that anthropogenic activities have markedly higher contribution rates than natural sources to heavy metal pollution in soils in this area.

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“…Accurate identification of the extent of polluted areas could contribute to the more efficient management of soils contaminated by PTEs and reduce health risks and financial costs. Interpolation methods such as inverse distance weighting (IDW) and ordinary kriging (OK) have been widely employed to explore the spatial distribution of PTEs [38][39][40][41][42][43]. Recently, a new empirical Bayesian kriging (EBK) method developed by Krivoruchko (2012) [44] has been introduced to estimate the spatial pattern of target variables in the fields of public health [45], meteorology [46], and geology [47].…”

Section: Introductionmentioning

confidence: 99%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

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Spatial distribution and source analysis of heavy metals in soils influenced by industrial enterprise distribution: Case study in Jiangsu Province

Cited by 229 publications

References 47 publications

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Impact Imposed by Urbanization on Soil Heavy Metal Content of Lake Wetland and Evaluation of Ecological Risks in East Dongting Lake in China

Concentrations, Spatial Distributions, and Sources of Heavy Metals in Surface Soils of the Coal Mining City Wuhai, China

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

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