TY  - JOUR
AU  - Stojić, Nataša
AU  - Pezo, Lato
AU  - Lončar, Biljana
AU  - Pucarević, Mira
AU  - Filipović, Vladimir
AU  - Prokić, Dunja
AU  - Ćurčić, Ljiljana
AU  - Štrbac, Snežana
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/7193
AB  - The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r(2)) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.
PB  - MDPI
T2  - Toxics
T1  - Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation
VL  - 11
IS  - 3
DO  - 10.3390/toxics11030269
UR  - conv_1093
ER  - 

@article{
author = "Stojić, Nataša and Pezo, Lato and Lončar, Biljana and Pucarević, Mira and Filipović, Vladimir and Prokić, Dunja and Ćurčić, Ljiljana and Štrbac, Snežana",
year = "2023",
abstract = "The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r(2)) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.",
publisher = "MDPI",
journal = "Toxics",
title = "Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation",
volume = "11",
number = "3",
doi = "10.3390/toxics11030269",
url = "conv_1093"
}

Stojić, N., Pezo, L., Lončar, B., Pucarević, M., Filipović, V., Prokić, D., Ćurčić, L.,& Štrbac, S.. (2023). Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation. in Toxics
MDPI., 11(3).
https://doi.org/10.3390/toxics11030269
conv_1093

Stojić N, Pezo L, Lončar B, Pucarević M, Filipović V, Prokić D, Ćurčić L, Štrbac S. Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation. in Toxics. 2023;11(3).
doi:10.3390/toxics11030269
conv_1093 .

Stojić, Nataša, Pezo, Lato, Lončar, Biljana, Pucarević, Mira, Filipović, Vladimir, Prokić, Dunja, Ćurčić, Ljiljana, Štrbac, Snežana, "Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation" in Toxics, 11, no. 3 (2023),
https://doi.org/10.3390/toxics11030269 .,
conv_1093 .

TY  - JOUR
AU  - Stojić, Nataša
AU  - Štrbac, Snežana
AU  - Ćurčić, Ljiljana
AU  - Pucarević, Mira
AU  - Prokić, Dunja
AU  - Stepanov, Jasna
AU  - Stojić, Gordan
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/7174
AB  - This study aimed to investigate the impact of road and rail traffic on the soil through the analysisof the presence of heavy metals in soil samples collected next to a busy highway, local roads, andnext to an active railway line. Results showed that cars emitted higher levels of heavy metals thantrains. Soil samples near the highway had higher levels of Cu, Ni, and Hg. The values of thecalculated indices like geo-accumulation index, potential toxicity response index, ecological riskfactor, contamination factor, pollution load index, Nemerow’s pollution index, and degree ofcontamination confirm that the soil samples sampled near the highway are the most polluted andhighway have the greatest negative impact on the soil environment. These results suggest thatcontrolling car emissions through strict regulations and promoting public transportation couldeffectively reduce the heavy metal concentrations in soil, particularly from highway emissions.
PB  - Elsevier
T2  - Transportation Research Part D
T1  - Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars
VL  - 125
SP  - 103966
DO  - 10.1016/j.trd.2023.103966
ER  - 

@article{
author = "Stojić, Nataša and Štrbac, Snežana and Ćurčić, Ljiljana and Pucarević, Mira and Prokić, Dunja and Stepanov, Jasna and Stojić, Gordan",
year = "2023",
abstract = "This study aimed to investigate the impact of road and rail traffic on the soil through the analysisof the presence of heavy metals in soil samples collected next to a busy highway, local roads, andnext to an active railway line. Results showed that cars emitted higher levels of heavy metals thantrains. Soil samples near the highway had higher levels of Cu, Ni, and Hg. The values of thecalculated indices like geo-accumulation index, potential toxicity response index, ecological riskfactor, contamination factor, pollution load index, Nemerow’s pollution index, and degree ofcontamination confirm that the soil samples sampled near the highway are the most polluted andhighway have the greatest negative impact on the soil environment. These results suggest thatcontrolling car emissions through strict regulations and promoting public transportation couldeffectively reduce the heavy metal concentrations in soil, particularly from highway emissions.",
publisher = "Elsevier",
journal = "Transportation Research Part D",
title = "Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars",
volume = "125",
pages = "103966",
doi = "10.1016/j.trd.2023.103966"
}

Stojić, N., Štrbac, S., Ćurčić, L., Pucarević, M., Prokić, D., Stepanov, J.,& Stojić, G.. (2023). Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars. in Transportation Research Part D
Elsevier., 125, 103966.
https://doi.org/10.1016/j.trd.2023.103966

Stojić N, Štrbac S, Ćurčić L, Pucarević M, Prokić D, Stepanov J, Stojić G. Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars. in Transportation Research Part D. 2023;125:103966.
doi:10.1016/j.trd.2023.103966 .

Stojić, Nataša, Štrbac, Snežana, Ćurčić, Ljiljana, Pucarević, Mira, Prokić, Dunja, Stepanov, Jasna, Stojić, Gordan, "Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars" in Transportation Research Part D, 125 (2023):103966,
https://doi.org/10.1016/j.trd.2023.103966 . .

TY  - JOUR
AU  - Štrbac, Snežana
AU  - Stojić, Nataša
AU  - Lončar, Biljana
AU  - Pezo, Lato
AU  - Ćurčić, Ljiljana
AU  - Prokić, Dunja
AU  - Pucarević, Mira
PY  - 2023
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/7173
AB  - Purpose To anticipate the impact of illegal landfills, development of new models should become a part of environmental risk management strategies. One of such approaches includes applications of the artificial neural network (ANN). The main objective of this study was to elucidate the impact of illegal landfilling on the surrounding soil environment and human health, as well as to establish an artificial neural network (ANN) models for predicting the hazards of illegal landfilling as an effective tool in decision-making and environmental risk management.Methods The identification of heavy metals source in soil was performed by principal component analysis (PCA). To assess the sensitivity of the soil ecosystem to heavy metal concentrations, Soil Quality standards and quantitative indices were used. The possible health effects were valued using the average daily doses (ADDs), hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR). ANN modeling was used for the prediction of heavy metal concentrations in the soil based on landfill size, municipality size, the number of residents, plant species, soil, and landform types.Results The average values of the pollution indexes for Cd were in the moderately contaminated and very high contamina tion categories. The HQ values were lower than the safe level. Cr and Pb posed a significant CR for adults and children, and Ni for children. The ANN models have exhibited good generalization power and accurately predicted the output parameters with a high value of the coefficient of determination.Conclusion Concerning heavy metal concentrations, illegal landfills near agricultural soil have a significant impact on the soil ecosystem and people’s health. The developed ANN models can be applied generally to anticipate the heavy metal concentrations in soil, according to the before mentioned input parameters, with high accuracy.
PB  - Springer Nature
T2  - Journal of Soils and Sediments
T1  - Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
VL  - 23
IS  - 9
DO  - 10.1007/s11368-023-03637-1
ER  - 

@article{
author = "Štrbac, Snežana and Stojić, Nataša and Lončar, Biljana and Pezo, Lato and Ćurčić, Ljiljana and Prokić, Dunja and Pucarević, Mira",
year = "2023",
abstract = "Purpose To anticipate the impact of illegal landfills, development of new models should become a part of environmental risk management strategies. One of such approaches includes applications of the artificial neural network (ANN). The main objective of this study was to elucidate the impact of illegal landfilling on the surrounding soil environment and human health, as well as to establish an artificial neural network (ANN) models for predicting the hazards of illegal landfilling as an effective tool in decision-making and environmental risk management.Methods The identification of heavy metals source in soil was performed by principal component analysis (PCA). To assess the sensitivity of the soil ecosystem to heavy metal concentrations, Soil Quality standards and quantitative indices were used. The possible health effects were valued using the average daily doses (ADDs), hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR). ANN modeling was used for the prediction of heavy metal concentrations in the soil based on landfill size, municipality size, the number of residents, plant species, soil, and landform types.Results The average values of the pollution indexes for Cd were in the moderately contaminated and very high contamina tion categories. The HQ values were lower than the safe level. Cr and Pb posed a significant CR for adults and children, and Ni for children. The ANN models have exhibited good generalization power and accurately predicted the output parameters with a high value of the coefficient of determination.Conclusion Concerning heavy metal concentrations, illegal landfills near agricultural soil have a significant impact on the soil ecosystem and people’s health. The developed ANN models can be applied generally to anticipate the heavy metal concentrations in soil, according to the before mentioned input parameters, with high accuracy.",
publisher = "Springer Nature",
journal = "Journal of Soils and Sediments",
title = "Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach",
volume = "23",
number = "9",
doi = "10.1007/s11368-023-03637-1"
}

Štrbac, S., Stojić, N., Lončar, B., Pezo, L., Ćurčić, L., Prokić, D.,& Pucarević, M.. (2023). Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach. in Journal of Soils and Sediments
Springer Nature., 23(9).
https://doi.org/10.1007/s11368-023-03637-1

Štrbac S, Stojić N, Lončar B, Pezo L, Ćurčić L, Prokić D, Pucarević M. Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach. in Journal of Soils and Sediments. 2023;23(9).
doi:10.1007/s11368-023-03637-1 .

Štrbac, Snežana, Stojić, Nataša, Lončar, Biljana, Pezo, Lato, Ćurčić, Ljiljana, Prokić, Dunja, Pucarević, Mira, "Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach" in Journal of Soils and Sediments, 23, no. 9 (2023),
https://doi.org/10.1007/s11368-023-03637-1 . .