The distribution of elements in soil fractions affects their mobility and availability and thus their potential beneficial or harmful impact on ecosystems, biota and humans. Different mineralogical and chemical characteristics of soil influence elemental distribution. In the present study, chemical speciation of macro and micro elements (Al, Fe, Mn, K, Cd, Cr, Cu, Li, Ba, Ni, Pb and Zn) in unpolluted soils of different types, collected from the territory of the Republic of Serbia, were analysed by sequential extraction procedure. The impact of the physicochemical soil properties on the content, distribution, mobility and availability of elements was investigated. Principal component analysis was employed for the evaluation and characterization of the experimental data, understanding of the relationships between soil properties and the distribution, affiliation and connection of the elements. Finally, an artificial neural network (ANN) model was developed to explore the applicability of... this approach for the prediction of the elemental distribution based on soil properties. Good agreement between the model and the experimental results implied that the ANN could be considered as a useful tool for control and prediction purposes.
Keywords:
Uncontaminated soil / Soil properties / Sequential extraction / Element distribution / Pattern recognition techniques / Artificial neural network
Source:
Journal of Geochemical Exploration, 2016, 165, 71-80
TY - JOUR
AU - Marković, Jelena P.
AU - Jović, Mihajlo D.
AU - Smičiklas, Ivana D.
AU - Pezo, Lato
AU - Šljivić-Ivanović, Marija Z.
AU - Onjia, Antonije
AU - Popović, Aleksandar R.
PY - 2016
UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3261
AB - The distribution of elements in soil fractions affects their mobility and availability and thus their potential beneficial or harmful impact on ecosystems, biota and humans. Different mineralogical and chemical characteristics of soil influence elemental distribution. In the present study, chemical speciation of macro and micro elements (Al, Fe, Mn, K, Cd, Cr, Cu, Li, Ba, Ni, Pb and Zn) in unpolluted soils of different types, collected from the territory of the Republic of Serbia, were analysed by sequential extraction procedure. The impact of the physicochemical soil properties on the content, distribution, mobility and availability of elements was investigated. Principal component analysis was employed for the evaluation and characterization of the experimental data, understanding of the relationships between soil properties and the distribution, affiliation and connection of the elements. Finally, an artificial neural network (ANN) model was developed to explore the applicability of this approach for the prediction of the elemental distribution based on soil properties. Good agreement between the model and the experimental results implied that the ANN could be considered as a useful tool for control and prediction purposes.
PB - Elsevier Science Bv, Amsterdam
T2 - Journal of Geochemical Exploration
T1 - Chemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approach
EP - 80
SP - 71
VL - 165
DO - 10.1016/j.gexplo.2016.03.004
ER -
@article{
author = "Marković, Jelena P. and Jović, Mihajlo D. and Smičiklas, Ivana D. and Pezo, Lato and Šljivić-Ivanović, Marija Z. and Onjia, Antonije and Popović, Aleksandar R.",
year = "2016",
abstract = "The distribution of elements in soil fractions affects their mobility and availability and thus their potential beneficial or harmful impact on ecosystems, biota and humans. Different mineralogical and chemical characteristics of soil influence elemental distribution. In the present study, chemical speciation of macro and micro elements (Al, Fe, Mn, K, Cd, Cr, Cu, Li, Ba, Ni, Pb and Zn) in unpolluted soils of different types, collected from the territory of the Republic of Serbia, were analysed by sequential extraction procedure. The impact of the physicochemical soil properties on the content, distribution, mobility and availability of elements was investigated. Principal component analysis was employed for the evaluation and characterization of the experimental data, understanding of the relationships between soil properties and the distribution, affiliation and connection of the elements. Finally, an artificial neural network (ANN) model was developed to explore the applicability of this approach for the prediction of the elemental distribution based on soil properties. Good agreement between the model and the experimental results implied that the ANN could be considered as a useful tool for control and prediction purposes.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Geochemical Exploration",
title = "Chemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approach",
pages = "80-71",
volume = "165",
doi = "10.1016/j.gexplo.2016.03.004"
}
Marković, J. P., Jović, M. D., Smičiklas, I. D., Pezo, L., Šljivić-Ivanović, M. Z., Onjia, A.,& Popović, A. R.. (2016). Chemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approach. in Journal of Geochemical Exploration
Elsevier Science Bv, Amsterdam., 165, 71-80.
https://doi.org/10.1016/j.gexplo.2016.03.004
Marković JP, Jović MD, Smičiklas ID, Pezo L, Šljivić-Ivanović MZ, Onjia A, Popović AR. Chemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approach. in Journal of Geochemical Exploration. 2016;165:71-80.
doi:10.1016/j.gexplo.2016.03.004 .
Marković, Jelena P., Jović, Mihajlo D., Smičiklas, Ivana D., Pezo, Lato, Šljivić-Ivanović, Marija Z., Onjia, Antonije, Popović, Aleksandar R., "Chemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approach" in Journal of Geochemical Exploration, 165 (2016):71-80,
https://doi.org/10.1016/j.gexplo.2016.03.004 . .
