Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana

Manyiwa, Trust; Ultra, Venecio; Rantong, Gaolathe; Opaletswe, Khumo Ace; Gabankitse, Gorataone; Taupedi, Sekgele Bobby; Gajaje, Katumelo

doi:10.1007/s10653-021-00918-x

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…4 In recent time, Ni pollution in soil has become critical due to the incessant release of Ni via steel industries, the application of phosphate fertilizers, mining, processing of metals and sewage sludge discharge. According to Freedman and Hutchinson 6 and Manyiwa et al, 7 the main topsoil pollution source immediate a vicinity and adjacent environments is principally caused by Ni-Cu based smelter and mines.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Kebonye

John

2021

Preprint

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Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium(Ca), magnesium(Mg), potassium(K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well although its estimated root mean square error (RMSE) (235.974) and mean absolute error (MAE) (166.946) were higher when compared with the other methods applied. Conversely, the hybridized model of empirical bayesian kriging -multiple linear regression (EBK-MLR) performed poorly as indicated by the measured coefficient of determination value below 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the best model, with low RMSE (95.479) and MAE (77.368) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modeling technique was visualized using self-organizing map. The clustered neurons of the hybridized model CakMg -EBK-SVMR component plane showed a diverse color pattern predicting the concentration of Ni in the urban and peri urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

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Section: Introductionmentioning

confidence: 99%

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Kebonye

John

2021

Preprint

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“…The natural sources of nickel include volcanic eruptions, vegetation, forest fires, and geological processes; however, anthropogenic sources include steel industry Ni/Cd batteries, electroplating, arc welding, diesel oil and fuel oil, and atmospheric nickel accumulation from coal combustion and waste and sludge incineration 7 , 8 . According to Freedman and Hutchinson 9 and Manyiwa et al 10 the primary source of topsoil pollution in the immediate vicinity and adjacent environments is principally caused by Ni-Cu based smelter and mines. The topsoil around the Ni–Cu refinery in Sudbury, Canada, had the highest levels of Ni pollution, up to 26,000 mg/kg 11 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Agyeman

Kebonye

John

et al. 2022

Sci Rep

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Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

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“…This study deals with dust fractions of waste materials and soil collected in and near the mining district of Selebi‐Phikwe, located in the semi‐arid east of Botswana, where Ni‐Cu ores were mined and processed by a pyrometallurgical operation. Previous studies in this area were focused on the distribution of the metals in the soils and vegetation (Ekosse, 2005; Ekosse et al., 2003; Manyiwa et al., 2022; Motswaiso et al., 2019; Ultra & Manyiwa, 2021), and on the geochemistry, environmental impacts, and potential rehabilitation of the contaminated mine tailing sites (Schippers et al., 2007; Schwartz & kgomanyane, 2008; Sracek et al., 2018; Ultra, 2020; Ultra & Manyiwa, 2021). The human health issues in the Selebi‐Phikwe area were only evaluated using the old statistical data from the 1990 s and the statistical processing of questionnaires acquired during the early 2000 s, when the mine and smelter were still in operation, and indicated that mine workers suffered from different symptoms and illnesses such as influenza, headaches, chest pain, and coughing (Asare & Darkoh, 2001; Ekosse, 2008).…”

Section: Introductionmentioning

confidence: 99%

Contaminant Binding and Bioaccessibility in the Dust From the Ni‐Cu Mining/Smelting District of Selebi‐Phikwe (Botswana)

et al. 2022

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We studied the dust fractions of the smelting slag, mine tailings, and soil from the former Ni‐Cu mining and processing district in Selebi‐Phikwe (eastern Botswana). Multi‐method chemical and mineralogical investigations were combined with oral bioaccessibility testing of the fine dust fractions (<48 and <10 μm) in a simulated gastric fluid to assess the potential risk of the intake of metal(loid)s contaminants. The total concentrations of the major contaminants varied significantly (Cu: 301–9,600 mg/kg, Ni: 850–7,000 mg/kg, Co: 48–791 mg/kg) but were generally higher in the finer dust fractions. The highest bioaccessible concentrations of Co, Cu, and Ni were found in the slag and mine tailing dusts, where these metals were mostly bound in sulfides (pentlandite, pyrrhotite, chalcopyrite). On the contrary, the soil dusts exhibited substantially lower bioaccessible fractions of these metals due to their binding in less soluble spinel‐group oxides. The results indicate that slag dusts are assumed to be risk materials, especially when children are considered as a target group. Still, this exposure scenario seems unrealistic due to (a) the fencing of the former mine area and its inaccessibility to the local community and (b) the low proportion of the fine particles in the granulated slag dump and improbability of their transport by wind. The human health risk related to the incidental ingestion of the soil dust, the most accessible to the local population, seems to be quite limited in the Selebi‐Phikwe area, even when a higher dust ingestion rate (280 mg/d) is considered.

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Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana

Cited by 11 publications

References 23 publications

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Contaminant Binding and Bioaccessibility in the Dust From the Ni‐Cu Mining/Smelting District of Selebi‐Phikwe (Botswana)

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