Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review

Agyeman, Prince Chapman; Ahado, Samuel; Borůvka, Luboš; Biney, James Kobina Mensah; Sarkodie, Vincent Yaw Oppong; Kebonye, Ndiye Michael; John, Kingsley

doi:10.1007/s10653-020-00742-9

Cited by 23 publications

(18 citation statements)

References 58 publications

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“…Multivariate statistics techniques (e.g., principal component analysis, correlation analysis) are powerful tools for pollution source assessments [ 4 , 24 ]. These techniques were applied for the investigation of pollutants in soil, sediment [ 25 , 26 , 27 , 28 ], and natural waters [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Anthropogenic Impact and Distribution of Potentially Toxic and Rare Earth Elements in Lake Sediments from North-Eastern Romania

Soroaga

Amarandei

Negru

et al. 2022

Toxics

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Chemical analysis was performed on sediment samples collected in two sampling sessions (July and October) from Podu Iloaiei Dam Lake, one of the most important water resources used for aquaculture in north-eastern Romania. The concentration of 15 trace elements (TEs), 8 refractory elements (REs), and 15 rare earth elements (REEs)—determined using inductively coupled plasma mass spectrometry—showed variability largely dependent of the sampling points and collection time. Manganese was the most abundant TE, V and Cr were the most abundant REs, while Ce was one of the most abundant REEs. The cerium negative anomaly and Gd positive anomaly were observed in the Chondrite-normalized distributions. In October, the Ce anomaly showed significant negative correlation with Mn, emphasizing the water body oxidation potential. The identified positive Gd anomaly was most likely associated with the use of Gd-chelating agents in magnetic resonance imaging in Iasi, the largest medical hub in north-eastern Romania. Principal component analysis extracted three factors explaining 96.0% of the observed variance, i.e., rock weathering, leaching from soil surface, contributions from urban stormwater and atmospheric deposition (50.9%), pedological contributions (23.7%), and mixed anthropogenic sources (e.g., traffic, waste discharge, agricultural activities; 21.4%). The evaluation of pollution indices highlighted low and moderate degrees of contamination for most of the elements and a considerable degree of contamination for Cd. Assigned Cd sources included fertilizers and pesticides used in the near agricultural areas or the high traffic road located near the lake. Since contamination of aquatic ecosystems with harmful elements is a human health concern, further monitoring of specific vectors in the food chain of the investigated dam lake will be of the utmost importance.

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Section: Introductionmentioning

confidence: 99%

Assessment of the Anthropogenic Impact and Distribution of Potentially Toxic and Rare Earth Elements in Lake Sediments from North-Eastern Romania

Soroaga

Amarandei

Negru

et al. 2022

Toxics

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“…Therefore, it is understandable that the amount of pedological research has increased rapidly in recent decades [6,7]. With population growth and economic development, soil environmental problems in many countries have become increasingly prominent [8][9][10]. Among soil's geochemical components, potentially toxic elements (PTEs) are the main pollutants in farmland soils [11,12].…”

Section: Introductionmentioning

confidence: 99%

Potentially Toxic Elements in Oasis Agricultural Soils Caused by High-Intensity Exploitation in the Piedmont Zone of the Tianshan Mountains, China

Liu

Abuduwaili

2021

Agriculture

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Considering the pollution of potentially toxic elements (PTEs) in the soils of China, the present study analyzed the current state and influencing factors of PTEs in oasis soils using the model of absolute principal component score–multiple linear regression in the piedmont zone of the Tianshan Mountains. The possible non-carcinogenic and carcinogenic risks of PTEs at current concentrations were also explored using a human-health risk-assessment model. The results suggested that the extent to which potentially toxic elements in the soils of different geographical units in the study area is affected by human activities varies considerably. The PTEs Cd and As in the soils of the Yili River Watershed were the most strongly influenced by human activities, reaching levels of 40% and 59%, respectively. However, in the Bortala River Watershed, Cu, Cd, and As were the most strongly influenced by human activities, reaching levels of 33%, 64%, and 76%, respectively. Geographical units with a high degree of economic development (e.g., the Yili River Watershed) had, in contrast, low levels of PTE pollution caused by human activities, which may be related to the regional economic development structure. The human health risk assessment showed that the non-carcinogenic and carcinogenic risks of PTEs are currently below the threshold. However, increasing the arsenic content to 1.78 times the current level in the Bortala River Watershed would lead to carcinogenic risk. For the Yili River Watershed, a 3.33-fold increase in the arsenic content above its current level would lead to a carcinogenic risk. This risk should be addressed, and targeted environmental-protection measures should be formulated. The present research results will provide important decision support for regional environmental protection.

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“…Hitherto, spatial prediction of potentially toxic elements (PTEs) such as Ni in the soil using the conventional means has been laborious and time-consuming. The advent of digital soil mapping (DSM) and its success chalked 8 in this present time has improved predictive soil mapping (PSM) tremendously. Predictive soil mapping, or DSM, according to Minasny and McBratney, 9 has proven to be a prominent soil science subdiscipline.…”

Section: Introductionmentioning

confidence: 99%

“…Even though one is superior to the other, the combination of the two has increased the accuracy level in mapping or prediction in DSM. 8 Woodcock and Gopal 19 Finke 20 ; Pontius and Cheuk, 21 and Grunwald 22 have commented on the imperfection and some error that exist in predictive soil mapping. Soil scientists have tried a variety of techniques to optimize the effectiveness, precision of mapping and prediction in DSM.…”

Section: Introductionmentioning

confidence: 99%

“…The incorporation of uncertainty and validation is one of the many different facets that have been integrated into DSM to optimize effectiveness as well as decrease the imperfection. Nevertheless, Agyeman et al, 8 outlined that the act of validation and the uncertainty that come with creation of map and prediction should be validated independently to enhance map quality. Recently there have been a new trend in DSM that foster the combination of geostatitics and MLA in mapping and prediction.…”

Section: Introductionmentioning

confidence: 99%

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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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Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review

Cited by 23 publications

References 58 publications

Assessment of the Anthropogenic Impact and Distribution of Potentially Toxic and Rare Earth Elements in Lake Sediments from North-Eastern Romania

Assessment of the Anthropogenic Impact and Distribution of Potentially Toxic and Rare Earth Elements in Lake Sediments from North-Eastern Romania

Potentially Toxic Elements in Oasis Agricultural Soils Caused by High-Intensity Exploitation in the Piedmont Zone of the Tianshan Mountains, China

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

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