Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques

Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. T...
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Autor*in:	El-Sharkawy, Mahmoud [verfasserIn] Li, Jian Kamal, Nourhan Mahmoud, Esawy Omara, Alaa El-Dein Du, Daolin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Pollution indices Soil quality Biochemical activity Anthropic activity ANN models

Anmerkung:	© The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of soil science and plant nutrition - [Cham] : Springer International Publishing, 2010, 23(2023), 4 vom: 03. Nov., Seite 6510-6526
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:4 ; day:03 ; month:11 ; pages:6510-6526

Links:	Volltext

DOI / URN:	10.1007/s42729-023-01507-w

Katalog-ID:	SPR054096774

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allfields	10.1007/s42729-023-01507-w doi (DE-627)SPR054096774 (SPR)s42729-023-01507-w-e DE-627 ger DE-627 rakwb eng El-Sharkawy, Mahmoud verfasserin aut Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213 Li, Jian (orcid)0000-0002-7871-7816 aut Kamal, Nourhan aut Mahmoud, Esawy aut Omara, Alaa El-Dein aut Du, Daolin aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 03. Nov., Seite 6510-6526 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526 https://dx.doi.org/10.1007/s42729-023-01507-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 03 11 6510-6526
spelling	10.1007/s42729-023-01507-w doi (DE-627)SPR054096774 (SPR)s42729-023-01507-w-e DE-627 ger DE-627 rakwb eng El-Sharkawy, Mahmoud verfasserin aut Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213 Li, Jian (orcid)0000-0002-7871-7816 aut Kamal, Nourhan aut Mahmoud, Esawy aut Omara, Alaa El-Dein aut Du, Daolin aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 03. Nov., Seite 6510-6526 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526 https://dx.doi.org/10.1007/s42729-023-01507-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 03 11 6510-6526
allfields_unstemmed	10.1007/s42729-023-01507-w doi (DE-627)SPR054096774 (SPR)s42729-023-01507-w-e DE-627 ger DE-627 rakwb eng El-Sharkawy, Mahmoud verfasserin aut Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213 Li, Jian (orcid)0000-0002-7871-7816 aut Kamal, Nourhan aut Mahmoud, Esawy aut Omara, Alaa El-Dein aut Du, Daolin aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 03. Nov., Seite 6510-6526 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526 https://dx.doi.org/10.1007/s42729-023-01507-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 03 11 6510-6526
allfieldsGer	10.1007/s42729-023-01507-w doi (DE-627)SPR054096774 (SPR)s42729-023-01507-w-e DE-627 ger DE-627 rakwb eng El-Sharkawy, Mahmoud verfasserin aut Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213 Li, Jian (orcid)0000-0002-7871-7816 aut Kamal, Nourhan aut Mahmoud, Esawy aut Omara, Alaa El-Dein aut Du, Daolin aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 03. Nov., Seite 6510-6526 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526 https://dx.doi.org/10.1007/s42729-023-01507-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 03 11 6510-6526
allfieldsSound	10.1007/s42729-023-01507-w doi (DE-627)SPR054096774 (SPR)s42729-023-01507-w-e DE-627 ger DE-627 rakwb eng El-Sharkawy, Mahmoud verfasserin aut Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213 Li, Jian (orcid)0000-0002-7871-7816 aut Kamal, Nourhan aut Mahmoud, Esawy aut Omara, Alaa El-Dein aut Du, Daolin aut Enthalten in Journal of soil science and plant nutrition [Cham] : Springer International Publishing, 2010 23(2023), 4 vom: 03. Nov., Seite 6510-6526 (DE-627)661265102 (DE-600)2611093-3 0718-9516 nnns volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526 https://dx.doi.org/10.1007/s42729-023-01507-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 23 2023 4 03 11 6510-6526
language	English
source	Enthalten in Journal of soil science and plant nutrition 23(2023), 4 vom: 03. Nov., Seite 6510-6526 volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526
sourceStr	Enthalten in Journal of soil science and plant nutrition 23(2023), 4 vom: 03. Nov., Seite 6510-6526 volume:23 year:2023 number:4 day:03 month:11 pages:6510-6526
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Pollution indices Soil quality Biochemical activity Anthropic activity ANN models
isfreeaccess_bool	false
container_title	Journal of soil science and plant nutrition
authorswithroles_txt_mv	El-Sharkawy, Mahmoud @@aut@@ Li, Jian @@aut@@ Kamal, Nourhan @@aut@@ Mahmoud, Esawy @@aut@@ Omara, Alaa El-Dein @@aut@@ Du, Daolin @@aut@@
publishDateDaySort_date	2023-11-03T00:00:00Z
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author	El-Sharkawy, Mahmoud
spellingShingle	El-Sharkawy, Mahmoud misc Pollution indices misc Soil quality misc Biochemical activity misc Anthropic activity misc ANN models Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques
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topic_title	Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques Pollution indices (dpeaa)DE-He213 Soil quality (dpeaa)DE-He213 Biochemical activity (dpeaa)DE-He213 Anthropic activity (dpeaa)DE-He213 ANN models (dpeaa)DE-He213
topic	misc Pollution indices misc Soil quality misc Biochemical activity misc Anthropic activity misc ANN models
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title_full	Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques
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title_sort	assessing and predicting soil quality in heavy metal-contaminated soils: statistical and ann-based techniques
title_auth	Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques
abstract	Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract This study aimed to evaluate the pollution status of soils contaminated with heavy metals using different statistical techniques and artificial neural network (ANN) modeling. Soil samples were collected from eight sites irrigated with drainage water in the Middle Nile Delta region, Egypt. The concentrations of Cd, As, Cr, Co, and V elements, along with soil biochemical characteristics, were measured. Pollution indices were selected based on data availability and their relevance for evaluation. Self-organizing map (SOM), simple regression analysis, analysis of variances, and principal component analysis (PCA) were employed to assess soil quality and predict the soil ecological status. Relying solely on heavy metal concentrations is insufficient for accurately interpreting soil pollution levels. The sites were classified as moderate to highly polluted. The simple regression analysis detected a substantial correlation between the Potential Ecological Risk (PER) index and cadmium and cobalt concentration. The PCA demonstrated positive correlations between Geoaccumulation Index (Igeo), modified contamination degree, and PER. The ANN model developed using the contamination factor (CF) as target and heavy metals’ concentrations as inputs exhibited a high correlation coefficient (R = 0.9974). The SOM analysis based on PER values allowed for the prediction of other variables’ behavior, including Igeo, Pollution Load Index (PLI), microbial count (TC), and dehydrogenase activity (DHA), in the tested soil. This study provides valuable insights into the evaluation of pollution indices in contaminated soils and identifies potential approaches for treating polluted soils to safeguard public health and enhance soil quality. The combined use of statistical techniques and ANN modeling can assess and predict soil pollution status, enabling decision-making for soil management practices. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques
url	https://dx.doi.org/10.1007/s42729-023-01507-w
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author2	Li, Jian Kamal, Nourhan Mahmoud, Esawy Omara, Alaa El-Dein Du, Daolin
author2Str	Li, Jian Kamal, Nourhan Mahmoud, Esawy Omara, Alaa El-Dein Du, Daolin
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doi_str	10.1007/s42729-023-01507-w
up_date	2024-07-03T23:54:43.302Z
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Assessing and Predicting Soil Quality in Heavy Metal-Contaminated Soils: Statistical and ANN-Based Techniques

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