Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China
Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in...
Ausführliche Beschreibung
Autor*in: |
Zhou, Jian [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2007 |
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Anmerkung: |
© Springer-Verlag 2007 |
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Übergeordnetes Werk: |
Enthalten in: Environmental geology - Berlin : Springer, 1975, 54(2007), 2 vom: 28. Juni, Seite 373-380 |
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Übergeordnetes Werk: |
volume:54 ; year:2007 ; number:2 ; day:28 ; month:06 ; pages:373-380 |
Links: |
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DOI / URN: |
10.1007/s00254-007-0824-5 |
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Katalog-ID: |
SPR003057933 |
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520 | |a Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. | ||
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700 | 1 | |a Wu, Kai |4 aut | |
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10.1007/s00254-007-0824-5 doi (DE-627)SPR003057933 (SPR)s00254-007-0824-5-e DE-627 ger DE-627 rakwb eng Zhou, Jian verfasserin aut Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2007 Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 Ma, Dongsheng aut Pan, Jiayong aut Nie, Wenming aut Wu, Kai aut Enthalten in Environmental geology Berlin : Springer, 1975 54(2007), 2 vom: 28. Juni, Seite 373-380 (DE-627)253722586 (DE-600)1459034-7 1432-0495 nnns volume:54 year:2007 number:2 day:28 month:06 pages:373-380 https://dx.doi.org/10.1007/s00254-007-0824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4338 AR 54 2007 2 28 06 373-380 |
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10.1007/s00254-007-0824-5 doi (DE-627)SPR003057933 (SPR)s00254-007-0824-5-e DE-627 ger DE-627 rakwb eng Zhou, Jian verfasserin aut Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2007 Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 Ma, Dongsheng aut Pan, Jiayong aut Nie, Wenming aut Wu, Kai aut Enthalten in Environmental geology Berlin : Springer, 1975 54(2007), 2 vom: 28. Juni, Seite 373-380 (DE-627)253722586 (DE-600)1459034-7 1432-0495 nnns volume:54 year:2007 number:2 day:28 month:06 pages:373-380 https://dx.doi.org/10.1007/s00254-007-0824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4338 AR 54 2007 2 28 06 373-380 |
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10.1007/s00254-007-0824-5 doi (DE-627)SPR003057933 (SPR)s00254-007-0824-5-e DE-627 ger DE-627 rakwb eng Zhou, Jian verfasserin aut Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2007 Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 Ma, Dongsheng aut Pan, Jiayong aut Nie, Wenming aut Wu, Kai aut Enthalten in Environmental geology Berlin : Springer, 1975 54(2007), 2 vom: 28. Juni, Seite 373-380 (DE-627)253722586 (DE-600)1459034-7 1432-0495 nnns volume:54 year:2007 number:2 day:28 month:06 pages:373-380 https://dx.doi.org/10.1007/s00254-007-0824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4338 AR 54 2007 2 28 06 373-380 |
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10.1007/s00254-007-0824-5 doi (DE-627)SPR003057933 (SPR)s00254-007-0824-5-e DE-627 ger DE-627 rakwb eng Zhou, Jian verfasserin aut Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2007 Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 Ma, Dongsheng aut Pan, Jiayong aut Nie, Wenming aut Wu, Kai aut Enthalten in Environmental geology Berlin : Springer, 1975 54(2007), 2 vom: 28. Juni, Seite 373-380 (DE-627)253722586 (DE-600)1459034-7 1432-0495 nnns volume:54 year:2007 number:2 day:28 month:06 pages:373-380 https://dx.doi.org/10.1007/s00254-007-0824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4338 AR 54 2007 2 28 06 373-380 |
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10.1007/s00254-007-0824-5 doi (DE-627)SPR003057933 (SPR)s00254-007-0824-5-e DE-627 ger DE-627 rakwb eng Zhou, Jian verfasserin aut Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2007 Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 Ma, Dongsheng aut Pan, Jiayong aut Nie, Wenming aut Wu, Kai aut Enthalten in Environmental geology Berlin : Springer, 1975 54(2007), 2 vom: 28. Juni, Seite 373-380 (DE-627)253722586 (DE-600)1459034-7 1432-0495 nnns volume:54 year:2007 number:2 day:28 month:06 pages:373-380 https://dx.doi.org/10.1007/s00254-007-0824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 GBV_ILN_4035 GBV_ILN_4037 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4338 AR 54 2007 2 28 06 373-380 |
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Zhou, Jian |
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Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China Sediment Sample (dpeaa)DE-He213 Inductively Couple Plasma Mass Spectrometry (dpeaa)DE-He213 Riverine Sediment (dpeaa)DE-He213 Potential Toxic Element (dpeaa)DE-He213 Multivariate Statistical Approach (dpeaa)DE-He213 |
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Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China |
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Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China |
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Zhou, Jian Ma, Dongsheng Pan, Jiayong Nie, Wenming Wu, Kai |
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application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in yangzhong, china |
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Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China |
abstract |
Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. © Springer-Verlag 2007 |
abstractGer |
Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. © Springer-Verlag 2007 |
abstract_unstemmed |
Abstract Multivariate statistical approach is used to identify the sources of heavy metals (Bi, Cd, Co, Cr, Mn, Pb, U, V, and Zn) in surface water and freshly deposited riverine sediment samples in Yangzhong city, China. The metal concentration data for the water and sediment samples are reported in terms of basic statistical parameters and metal-to-metal correlations. In both surface water and sediment samples, significant correlations are observed between some metals. Principal component analysis and cluster analysis distinguishes factors of lithogenic and anthropogenic origin. Bismuth, Cd, Co, and Pb (Co only for water samples) contents are controlled by the regional lithogenic high background factor; Co, Mn, U, and V (Co only for sediment samples) are interpreted to be mainly inherited from soil parent materials, while Cr, Zn, and Mn in the two kinds of samples are recognized as the tracer of industrial pollution. Obvious similarity between factor loadings of the two kinds of samples is observed, evidencing that metal variability in the two kinds of samples is controlled by the same sources. Statistical analysis agrees with discussion based on background value and field survey of point-source pollutant affected sediment, making this statistical discussion more convincing. © Springer-Verlag 2007 |
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Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China |
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