Assessment of selected heavy metal concentrations in soils from a mining area in Minna, Niger state

2021 ◽  
Vol 193 (3) ◽  
Author(s):  
S. O. Okonkwo ◽  
J. O. Jacob ◽  
Y. A. Iyaka ◽  
A. Inobeme
Keyword(s):  
Heavy Metal ◽  
Mining Area ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations

Spatial Structure Features and Distributing Rules of Soil Heavy Metal Concentrations of Antimony Mining Area

2012 ◽  
Vol 253-255 ◽  
pp. 1063-1068
Author(s):  
Bo Zhi Ren ◽  
Yi Zhou ◽  
Zhou Hong Tao ◽  
Lu Jing Cheng
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Structure ◽  
Influence Factors ◽  
Theoretical Models ◽  
Mining Area ◽  
Hunan Province ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations ◽  
Gis Tools

The spatial structure features and distributing rules of six heavy metals of antimony mining area, Cr, Cu, Ni, Zn, As, Cd and their main influence factors were researched using geostatistics combined with GIS tools. The results showed that: six kinds of heavy metal elements presented lognormal distribution. The mean of six kinds of heavy metal concentrations were higher than soil background value of Hunan province. Cd, Zn and Pb have a good correlation with each other, but the correlation between the rest elements was not obvious. The semivariogram theoretical models of six heavy metals in the study area were well fitted. The concentrations of Sb, As, Zn and Cd exhibited strong spatial autocorrelations, whereas the concentrations of Hg and Pb presented moderate spatial autocorrelations. The production activities of antimony smelter and dressing plant within the study area were the main factors for the high heavy metal concentrations of the study area.

scholarly journals Estimating the heavy metal concentrations in topsoil in the Daxigou mining area, China, using multispectral satellite imagery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yun Yang ◽  
Qinfang Cui ◽  
Peng Jia ◽  
Jinbao Liu ◽  
Han Bai
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Back Propagation ◽  
Mining Area ◽  
Shaanxi Province ◽  
Geochemical Data ◽  
M5 Model Tree ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations ◽  
Propagation Network

AbstractA precise estimation of the heavy metal concentrations in soils using multispectral remote sensing technology is challenging. Herein, Landsat8 imagery, a digital elevation model, and geochemical data derived from soil samples are integrated to improve the accuracy of estimating the Cu, Pb, and As concentrations in topsoil, using the Daxigou mining area in Shaanxi Province, China, as a case study. The relationships between the three heavy metals and soil environmental factors were investigated. The optimal combination of factors associated with the elevated concentrations of each heavy metal was determined combining correlation analysis with collinearity tests. A back propagation network optimised using a genetic algorithm was trained with 80% of the data for samples and subsequently employed to estimate the heavy metal concentrations in the area. The validation results show that the RMSE of the proposed model is lower than those of the existing linear model and rule-based M5 model tree. From the spatial distribution map of the three metals concentrations using the proposed method, there are findings that high concentrations of the heavy metals studied occur in the mining area, across the slag storage area, on the sides of the road used for transporting ore materials, and along the base of slopes in the area. These findings are consistent with the survey results in the field. The validation and findings validate the effectiveness of the proposed method.

Heavy metal concentrations in roadside soils of Lithuania's highways

Geologija ◽  
2008 ◽  
Vol 50 (4) ◽  
pp. 237-245 ◽  
Author(s):  
Audronė Jankaitė ◽  
Pranas Baltrėnas ◽  
Agnė Kazlauskienė
Keyword(s):  
Heavy Metal ◽  
Roadside Soils ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations

scholarly journals Lead smelting alters wheat flour heavy metal concentrations and health risks

2021 ◽  
Author(s):  
Liping Li ◽  
Yuqing Zhang ◽  
James A. Ippolito ◽  
Weiqin Xing ◽  
Chen Tu
Keyword(s):  
Heavy Metal ◽  
Wheat Flour ◽  
Health Risks ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations ◽  
Lead Smelting
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