Characterization of Land Contaminated by Past Heavy Metal Mining Using Geostatistical Methods

Ersoy, A.; Yünsel, Tayfun Yusuf; Çetin, Mahmut

doi:10.1007/s00244-003-2269-5

Cited by 40 publications

(14 citation statements)

References 18 publications

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“…Rodríguez et al (2008) characterized and mapped the spatial variability patterns of seven topsoil heavy metals (Cr, Ni, Pb, Cu, Zn, Hg and Cd) within the Ebro river basin in Spain by Multivariate Factorial Kriging. Ersoy et al (2004) determined the extent and severity of the pollution levels on land contaminated by past mining activity through using geostatistical techniques.…”

Section: Introductionmentioning

confidence: 99%

Cadmium, lead, and arsenic contamination in paddy soils of a mining area and their exposure effects on human HEPG2 and keratinocyte cell-lines

Xue

Shi

et al. 2017

Environmental Research

177

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A mining district in south China shows significant metal(loid) contamination in paddy fields. In the soil, Pb, Cd and As were 516.7, 11.7 and 35.1 mg•kg-1 respectively. The content of Cd exceeded the environmental quality standard for agricultural soils in China by approximately 82%. The contents of Pb, Cd and As in rice were 22.7, 1.1 and 0.7 mg•kg-1 respectively. These concentrations are also higher than the agricultural industry standard of China for rice. The elevated contents of Pb, Cd and As detected in soils around the factories, indicated that their spatial distribution was influenced by anthropogenic activity. Greater concentrations of Cd in rice appeared in the northwest region of the factories, indicating that the spatial distribution of heavy metals was affected by natural factors. The metals affected the viability of HepG2 and KERTr cells, which decreased with increasing metal concentration. Co-exposure to heavy metals (Pb+Cd) increased the metals (Pb or Cd)-mediated MT protein induction in both human HepG2 and KERTr cells. Increased levels of MT protein will lead to greater risk of carcinogenic manifestations, and it is likely that chronic exposure to metals may increase the risk to human health. Nevertheless, when co-exposure to two or more metals occur (such as As+Pb), they may have an antagonistic effect thus reducing the toxic effects of each other.

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Section: Introductionmentioning

confidence: 99%

Cadmium, lead, and arsenic contamination in paddy soils of a mining area and their exposure effects on human HEPG2 and keratinocyte cell-lines

Xue

Shi

et al. 2017

Environmental Research

177

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“…Based on the result of preliminary grid sampling analysis (optimal number of samples, 121; assumed threshold value, 1.5), one AAPF (shown in Figure 6(A)) from a total of 150 randomly generated AAPFs is selected as one example to continually perform the geostatistical analysis. The analysis basically consists of three steps: (1) exploratory data analysis; (2) structural analysis (variogram model selection, parameter determination, and model validation); and (3) prediction (Kriging estimation error and simulations) [35]. A histogram of the sampling data on this randomly selected AAPF is illustrated in Figure 6(B).…”

Section: Geostatistical (Kriging) Analysismentioning

confidence: 99%

Cost-Effective Strategy for the Investigation and Remediation of Polluted Soil Using Geostatistics and a Genetic Algorithm Approach

Cui¹,

Yoneda²,

Shimada³

et al. 2016

JEP

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The geostatistical technique of Kriging has extensively been used for the investigation and delineation of soil heavy metal pollution. Kriging is rarely used in practical circumstances, however, because the parameter values are difficult to decide and relatively optimal locations for further sampling are difficult to find. In this study, we used large numbers of assumed actual polluted fields (AAPFs) randomly generated by unconditional simulation (US) to assess the adjusted total fee (ATF), an assessment standard developed for balancing the correct treatment rate (CTR) and total fee (TF), based on a traditional strategy of systematic (or uniform) grid sampling (SGS) and Kriging. We found that a strategy using both SGS and Kriging was more cost-effective than a strategy using only SGS. Next, we used a genetic algorithm (GA) approach to find optimal locations for the additional sampling. We found that the optimized locations for the additional sampling were at the joint districts of polluted areas and unpolluted areas, where abundant SGS data appeared near the threshold value. This strategy was less helpful, however, when the pollution of polluted fields showed no spatial correlation.

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“…erefore, unreclaimed abandoned mine sites may accumulate PTMs in soil and threaten environmental and public health. PTM pollution near abandoned mine sites may vary greatly as a function of numerous variables, which include types and total amounts of ores/minerals mined, materials/chemicals used for processing and treatment, duration since abandonment, major routes of metal transport, and types of vegetation [12]. Additionally, many individual metal species exhibit distinctively different environmental behaviors [12].…”

Section: Introductionmentioning

confidence: 99%

“…PTM pollution near abandoned mine sites may vary greatly as a function of numerous variables, which include types and total amounts of ores/minerals mined, materials/chemicals used for processing and treatment, duration since abandonment, major routes of metal transport, and types of vegetation [12]. Additionally, many individual metal species exhibit distinctively different environmental behaviors [12]. PTM bioavailability, transport, and spatial distribution in soils are a complex phenomenon, affected by many factors such as total concentration [13,14], pH [15], organic matter [16], texture [17,18], cation exchange capacity (CEC) [19], and redox reactions [6,20].…”

Section: Introductionmentioning

confidence: 99%

Using Spatial Regression to Model Potentially Toxic Metal (PTM) Mobility Based on Physicochemical Soil Properties

Magno

Nandi

Luffman

2019

Applied and Environmental Soil Science

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Mining processes generate waste rock, tailings, and slag that can increase potentially toxic metal (PTM) concentrations in soils. Un-reclaimed, abandoned mine sites are particularly prone to leaching these contaminants, which may accumulate and pose significant environmental and public health concerns. e characterization and spatial delineation of PTMs in soils is vital for risk assessment and soil reclamation. Bumpus Cove, a once active mining district of eastern Tennessee, is home to at least 47 abandoned, un-reclaimed mines, all permanently closed by the 1950s. is study evaluated soil physicochemical properties, determined the spatial extent of PTMs (Zn, Mn, Cu, Pb, and Cd), and examined the influence of soil properties on PTM distribution in Bumpus Cove, TN. Soil samples (n � 52) were collected from a 0.67 km 2 study area containing 6 known abandoned Pb, Zn, and Mn mines at the headwaters of Bumpus Cove Creek. Samples were analyzed for Zn, Mn, Cu, Pb, and Cd by microwave-assisted acid digestion and flame atomic absorption spectrometry (FAAS) (12-1,354 mg/kg Zn, 6-2,574 mg/kg Mn, 1-65 mg/kg Cu, 33-2,271 mg/kg Pb, and 7-40 mg/kg Cd). Of the measured PTMs, only Pb exceeds permissible limits in soils. In addition to the PTM analyses, soil physical (texture, moisture content, and bulk density) and chemical (pH, cation exchange capacity (CEC), and total organic carbon (TOC)) properties were evaluated. Spatially weighted multivariate regression models developed for all PTMs using soil physicochemical properties produced improved results over ordinary least squares (OLS) regression models. Models for Zn (R 2 � 0.71) and Pb (R 2 � 0.69) retained covariates e pH , moisture content, and CEC (Zn), and pH and CEC (Pb). is study will help define PTM concentration and transport and provide a reference for state and local entities responsible for contaminant monitoring in Bumpus Cove, TN.

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Characterization of Land Contaminated by Past Heavy Metal Mining Using Geostatistical Methods

Cited by 40 publications

References 18 publications

Cadmium, lead, and arsenic contamination in paddy soils of a mining area and their exposure effects on human HEPG2 and keratinocyte cell-lines

Cadmium, lead, and arsenic contamination in paddy soils of a mining area and their exposure effects on human HEPG2 and keratinocyte cell-lines

Cost-Effective Strategy for the Investigation and Remediation of Polluted Soil Using Geostatistics and a Genetic Algorithm Approach

Using Spatial Regression to Model Potentially Toxic Metal (PTM) Mobility Based on Physicochemical Soil Properties

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