Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
Samo za registrovane korisnike
2023
Autori
Štrbac, SnežanaStojić, Nataša
Lončar, Biljana
Pezo, Lato
Ćurčić, Ljiljana
Prokić, Dunja
Pucarević, Mira
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
Soil quality / Heavy metals / Illegal landfills / Ecological risks / Health risksIzvor:
Journal of Soils and Sediments, 2023, 23, 9Izdavač:
- Springer Nature
Finansiranje / projekti:
- European Union’s Horizon Europe Project GREENLand —Twinning Microplastic-free Environment under grant agreement number 101079267
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (RS-MESTD-inst-2020-200032)
Institucija/grupa
IHTMTY - 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 . .