Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia
Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/13/4220 |
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| author | Mohamed A. Yassin Bassam Tawabini Abdulaziz Al-Shaibani John Adedapo Adetoro Mohammed Benaafi Ahmed M. AL-Areeq A. G. Usman S. I. Abba |
| author_facet | Mohamed A. Yassin Bassam Tawabini Abdulaziz Al-Shaibani John Adedapo Adetoro Mohammed Benaafi Ahmed M. AL-Areeq A. G. Usman S. I. Abba |
| author_sort | Mohamed A. Yassin |
| collection | DOAJ |
| description | Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil. |
| first_indexed | 2024-03-09T04:00:35Z |
| format | Article |
| id | doaj.art-9502a831ff32424dbb4e290b87db9666 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T04:00:35Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj.art-9502a831ff32424dbb4e290b87db96662023-12-03T14:13:54ZengMDPI AGMolecules1420-30492022-06-012713422010.3390/molecules27134220Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi ArabiaMohamed A. Yassin0Bassam Tawabini1Abdulaziz Al-Shaibani2John Adedapo Adetoro3Mohammed Benaafi4Ahmed M. AL-Areeq5A. G. Usman6S. I. Abba7Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaMinistry of Environment, Water, and Agriculture, Riyadh 11195, Saudi ArabiaCentre for Environmental Management and Control, Enugu Campus, University of Nigeria, Nsukka 410001, NigeriaInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaOperational Research Centre in Healthcare, Near East University, TRNC, Mersin 10, Nicosia 99138, CyprusInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaUnconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.https://www.mdpi.com/1420-3049/27/13/4220artificial intelligencetrace metalstopsoilspatial distributionSaudi Arabia |
| spellingShingle | Mohamed A. Yassin Bassam Tawabini Abdulaziz Al-Shaibani John Adedapo Adetoro Mohammed Benaafi Ahmed M. AL-Areeq A. G. Usman S. I. Abba Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia Molecules artificial intelligence trace metals topsoil spatial distribution Saudi Arabia |
| title | Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia |
| title_full | Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia |
| title_fullStr | Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia |
| title_full_unstemmed | Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia |
| title_short | Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia |
| title_sort | geochemical and spatial distribution of topsoil hms coupled with modeling of cr using chemometrics intelligent techniques case study from dammam area saudi arabia |
| topic | artificial intelligence trace metals topsoil spatial distribution Saudi Arabia |
| url | https://www.mdpi.com/1420-3049/27/13/4220 |
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