Geostatistical and multivariate analysis of soil heavy metal contamination near coal mining area, Northeastern India

Reza, S. K.; Baruah, Utpal; Singh, Surendra; Das, T. H.

doi:10.1007/s12665-014-3797-1

Cited by 81 publications

(31 citation statements)

References 35 publications

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“…Ni with 24.56 mg/kg is the fourth prominent element, reaching SQGs [33,34]. A similar upraise in concentration is observed in China (29.1 mg/kg) [36,37] and India (83.7 mg/kg) [38,39]. Afterwards, Se (27 mg/kg) is an emerging element, especially in tailings and soils, which is comparable with Iran (23 mg/kg) [40,41], Spain (13mg/kg), and Vietnam (12mg/kg) [46,47].…”

Section: Overall Elemental Concentration and Physical Parameterssupporting

confidence: 65%

“…Cd, a well carcinogenic element, has higher concentrations, like tailings have 3.52 mg/kg and soils have 7.33 mg/kg, while others areas of the Indus basin have lower concentrations of 0.4 mg/kg and 0.3 mg/kg [35]. A higher concentration is comparable with neighboring countries like China (11.0 mg/kg) [36,37] and India (3.82 mg/kg) [38,39]. Sediments of Spain areas also show higher concentration of 6.59 mg/kg [42,43].…”

Section: Overall Elemental Concentration and Physical Parametersmentioning

confidence: 99%

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Anthropogenic Effects of Coal Mining on Ecological Resources of the Central Indus Basin, Pakistan

Khan

Akhter

Gabriel

et al. 2020

IJERPH

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Water is essential for life, agriculture, and industrialization; however, a rapid increase in population is constantly causing water scarcity and pollution in Pakistan. Mining activities produce the potential toxic element (PTE) accumulation, which lead to unnatural enrichment, ecological pollution, and environmental degradation. The ecological resources impeded by the PTEs cause serious abnormalities in the population through dermal contact, inhalation, and digestion. Mining induced anthropogenic activities are well-known causes of contamination of ecological resources. The produced effluents have drastic effects by changing the physical, chemical, and biological properties of the concerned resources. The Central Indus Basin is a well-known coal regime, where more than 160 mines are active at present. The samples that were collected from the mine water, groundwater, surface water, and the soil were analyzed by atomic absorption and elemental determination analysis (EDA) for an assessment of their quality and the presence of PTEs. The results were correlated with available quality standards, including the World Health Organization (WHO), National Standard of Drinking Water Quality (NSDWQ), World Wildlife Fund (WWF), and Sediment Quality Guidelines (SQGs). These analyses showed the noticeable anthropogenic concentration of PTEs, like iron, cadmium, sulphur, and copper, which can degrade the quality of resources in the Central Indus Basin and have adverse effects on human health. An excessive amount of acid mine drainage (AMD) draws attention to some suitable active or passive treatments for disposal from mines to avoid degradation of ecological resources in the Central Indus Basin of Pakistan.

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Section: Overall Elemental Concentration and Physical Parameterssupporting

confidence: 65%

Section: Overall Elemental Concentration and Physical Parametersmentioning

confidence: 99%

Anthropogenic Effects of Coal Mining on Ecological Resources of the Central Indus Basin, Pakistan

Khan

Akhter

Gabriel

et al. 2020

IJERPH

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“…However, compositing soil samples prior to chemical analyses allowed for a greater generalization of chemical loads in the study area via kriging. Whether our small sample size was sufficient for accurately interpolating values across the entire study area is unclear; although, empirical Bayesian kriging models are typically accurate with small datasets [ 57 , 58 ] and our cross-validation results suggested that prediction accuracy for each chemical was high [ 58 , 62 – 64 ]. Additionally, RMSSDE and MSDE values indicated that the kriging models accurately estimated the variance in the predictions, though nominally poorer for mercury and TEQs.…”

Section: Discussionmentioning

confidence: 99%

“…We applied all semi-variograms that are available under empirical Bayesian kriging, which are spatial autocorrelation models that predict the relationship of concentrations as a function of distance and direction. We selected the best semi-variogram for each chemical via cross-validation, based on the following goodness-of-fit measures: mean error (ME), mean standardized error (MSDE), mean standard error (MSE), root-mean-square error (RMSE), and root-mean-square standardized error (RMSSDE) [ 58 , 62 – 64 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

et al. 2020

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Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

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“…Juan and Lee used three nonparametric Kriging methods to delineate heavy metal contaminated soils [ 19 ]. Reza used the ordinary Kriging to estimate the spatial distribution of heavy metals in the soils of surrounding agricultural fields affected by mine drainage [ 20 ]. Hu et al studied on spatial distribution of farmland soil heavy metals using BP neural network [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

Jia

Zhou

et al. 2017

IJERPH

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Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

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Geostatistical and multivariate analysis of soil heavy metal contamination near coal mining area, Northeastern India

Cited by 81 publications

References 35 publications

Anthropogenic Effects of Coal Mining on Ecological Resources of the Central Indus Basin, Pakistan

Anthropogenic Effects of Coal Mining on Ecological Resources of the Central Indus Basin, Pakistan

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

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