scholarly journals Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

2018 ◽  
Vol 10 (3) ◽  
pp. 799 ◽  
Author(s):  
Asma Shaheen ◽  
Javed Iqbal
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Random Forest ◽  
Soil Profiles ◽  
Mobility Assessment ◽  
Boruta Algorithm

Feasibility of using environmental covariates and machine learning to predict the spatial variability of selected heavy metals in soils

2020 ◽  
Author(s):  
Mojtaba Zeraatpisheh ◽  
Rouhollah Mirzaei ◽  
Younes Garosi ◽  
Ming Xu ◽  
Gerard B.M. Heuvelink ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Random Forest ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Prediction Performance ◽  
Anthropogenic Sources ◽  
Environmental Covariates

<p>Heavy metal contamination in soil is a major environmental issue intensified by rapid industrial and population growth. Understanding the spatial distribution of soil contamination by heavy metals in the ecosystem is a necessary precondition to monitor soil health and to assess the ecological risks. The main sources of heavy metals in soil are natural and anthropogenic sources. Natural sources are typically released of heavy metals from rock by weathering and atmospheric precipitation. Anthropogenic sources are related to industrialization, rapid urbanization, agricultural practices, and military activities. We analyzed a total of 358 topsoil samples (0–30 cm) collected in Golestan province in the northeast of Iran based on a regular square grid networks with 1,700 squares each sized 2.5 km²(random sampling within the grid). From these samples, we determined the spatial distribution of Cd, Cu, Ni, Zn, and Pb using random forest (RF). A multi-spectral image (Landsat 8), and environmental derivatives calculated from terrain attributes, climatic parameters, parent material, land use maps, distances to mine sectors, main roads, industrial sites, and rivers were used as covariates to predict the spatial distribution of concentrations of heavy metals. The multi-collinearity of the predictors was examined by the variance inflation factor (VIF), and a feature selection process (genetic algorithm) was applied to avoid noise and optimize the selected input variables for the final model. The predictive accuracy of RF model was assessed by the mean prediction error (ME), root mean squared error (RMSE), and coefficient of determination (R<sup>2</sup>) using 5-fold cross-validation technique. The results showed that the concentration levels (mg kg<sup>-1</sup>) of Cd, Cu, Pb, Ni, and Zn varied from 0.02 to 2.75, 9.70 to 93.70, 6.80 to 114.20, 9.50 to 93.20, and 25.10 to 417.4, respectively. The best prediction performance was for Ni (RMSE=9.9 mg kg<sup>-1 </sup>and R<sup>2</sup>=56.6%), and the lowest prediction performance for Cd (RMSE=0.4 mg kg<sup>-1 </sup>and R<sup>2</sup>=28.0%). Environmental covariates that control soil moisture and water flow along with climatic factors were the most important variables to define the spatial distribution of soil heavy metals. We conclude that the RF model using easily accessible environmental covariates is a promising, cost-effective and fast approach to monitor the spatial distribution of heavy metal contamination in soils.</p><p><strong>Keywords:</strong> Heavy metals; digital soil mapping; machine learning; random forest; spatial variation; soil pollution.</p>

scholarly journals Profile Distribution and Migration of Heavy Metals in the Soils of the Kursk Agglomeration (Model Experiments)

2020 ◽  
Vol 15 (1) ◽  
pp. 60-68
Author(s):  
N. P. Nevedrov
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Temporal Dynamics ◽  
Laboratory Evaluation ◽  
Soil Profiles ◽  
Model Soil ◽  
Lead And Zinc ◽  
Soil Solutions ◽  
And Migration ◽  
Kinetics Of

Aim. Laboratory evaluation of the characteristics of spatial distribution and migration of heavy metals (HM) in model soil profiles of varied genesis through measurement of the electrokinetic potential of soil solutions. Material and Methods. Undisturbed soils of forest parks landscapes and continental floodplain meadows of the Kursk agglomeration were studied. Experiments were carried out in laboratory conditions. The short‐term temporal dynamics were studied of vertical distribution and migration of the introduced HMs in model soil columns which imitated soil profiles. Results. Analysis of the kinetics of soil solutions and of lysimeter waters of control and polluted samples showed that the model profile of typical dark‐gray soil has the least capacity to capture lead ions from polluted soil solutions. Minimum sorption capacity with respect to zinc was found to be characteristic of sod‐podzol illuvialferruginous soil profiles. Maximum ability to deposit the HMs under analysis (Zn and Pb) was shown in leached chernozem medium loamy soils. Conclusion. The dynamics and kinetics of lead and zinc in soils of the Kursk agglomeration differ significantly and depend on a number of soil factors. In the soils studied, the spatial distribution and the intensity of migration of lead and zinc were determined by the capacity and contrast indices of the internal soil geochemical barriers. Inhibition of the processes of vertical migration of Pb and Zn in the model soil profiles was observed in those rich in humusified humus‐accumulative genetic horizons as well as in mineral horizons with highly contrasting acid‐base and redox conditions. Adsorption zones of lead and zinc are formed with a significant increase in granulometric texture and a decrease in pHKCl.

scholarly journals Correction: Spatial distribution of heavy metals in the West Dongting Lake floodplain, China

2020 ◽  
Vol 22 (5) ◽  
pp. 1306-1306
Author(s):  
Dong Peng ◽  
Ziyu Liu ◽  
Xinyue Su ◽  
Yaqian Xiao ◽  
Yuechen Wang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Dongting Lake ◽  
The West

Correction for ‘Spatial distribution of heavy metals in the West Dongting Lake floodplain, China’ by Dong Peng et al., Environ. Sci.: Processes Impacts, 2020, DOI: 10.1039/c9em00536f.

Spatial distribution and geochemistry of heavy metals in soils: A case study from the NE area of Vaslui county, Romania

2017 ◽  
Vol 176 ◽  
pp. 20-32 ◽  
Author(s):  
Traian Ungureanu ◽  
Gabriel Ovidiu Iancu ◽  
Mitică Pintilei ◽  
Marian Marius Chicoș
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Heavy Metals In Soils
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