Contamination analysis of groundwater in Coimbatore district, India: a statistical approach
Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster...
Ausführliche Beschreibung
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| Autor*in: |
Jebastina, Nalini [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 75(2016), 22 vom: 15. Nov. |
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| Übergeordnetes Werk: |
volume:75 ; year:2016 ; number:22 ; day:15 ; month:11 |
| Links: |
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| DOI / URN: |
10.1007/s12665-016-6253-6 |
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SPR026723972 |
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| 520 | |a Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. | ||
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| 650 | 4 | |a Coimbatore |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cluster analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Prince Arulraj, G. |4 aut | |
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10.1007/s12665-016-6253-6 doi (DE-627)SPR026723972 (SPR)s12665-016-6253-6-e DE-627 ger DE-627 rakwb eng Jebastina, Nalini verfasserin aut Contamination analysis of groundwater in Coimbatore district, India: a statistical approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Prince Arulraj, G. aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 22 vom: 15. Nov. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:22 day:15 month:11 https://dx.doi.org/10.1007/s12665-016-6253-6 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_105 GBV_ILN_110 GBV_ILN_120 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2336 GBV_ILN_2360 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 22 15 11 |
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10.1007/s12665-016-6253-6 doi (DE-627)SPR026723972 (SPR)s12665-016-6253-6-e DE-627 ger DE-627 rakwb eng Jebastina, Nalini verfasserin aut Contamination analysis of groundwater in Coimbatore district, India: a statistical approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Prince Arulraj, G. aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 22 vom: 15. Nov. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:22 day:15 month:11 https://dx.doi.org/10.1007/s12665-016-6253-6 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_105 GBV_ILN_110 GBV_ILN_120 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2336 GBV_ILN_2360 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 22 15 11 |
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10.1007/s12665-016-6253-6 doi (DE-627)SPR026723972 (SPR)s12665-016-6253-6-e DE-627 ger DE-627 rakwb eng Jebastina, Nalini verfasserin aut Contamination analysis of groundwater in Coimbatore district, India: a statistical approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Prince Arulraj, G. aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 22 vom: 15. Nov. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:22 day:15 month:11 https://dx.doi.org/10.1007/s12665-016-6253-6 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_105 GBV_ILN_110 GBV_ILN_120 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2336 GBV_ILN_2360 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 22 15 11 |
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10.1007/s12665-016-6253-6 doi (DE-627)SPR026723972 (SPR)s12665-016-6253-6-e DE-627 ger DE-627 rakwb eng Jebastina, Nalini verfasserin aut Contamination analysis of groundwater in Coimbatore district, India: a statistical approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Prince Arulraj, G. aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 22 vom: 15. Nov. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:22 day:15 month:11 https://dx.doi.org/10.1007/s12665-016-6253-6 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_105 GBV_ILN_110 GBV_ILN_120 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2336 GBV_ILN_2360 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 22 15 11 |
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10.1007/s12665-016-6253-6 doi (DE-627)SPR026723972 (SPR)s12665-016-6253-6-e DE-627 ger DE-627 rakwb eng Jebastina, Nalini verfasserin aut Contamination analysis of groundwater in Coimbatore district, India: a statistical approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Prince Arulraj, G. aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 75(2016), 22 vom: 15. Nov. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:75 year:2016 number:22 day:15 month:11 https://dx.doi.org/10.1007/s12665-016-6253-6 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_105 GBV_ILN_110 GBV_ILN_120 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_2336 GBV_ILN_2360 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 75 2016 22 15 11 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR026723972</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401020201.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12665-016-6253-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026723972</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12665-016-6253-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jebastina, Nalini</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Contamination analysis of groundwater in Coimbatore district, India: a statistical approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. 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Jebastina, Nalini |
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Contamination analysis of groundwater in Coimbatore district, India: a statistical approach Multivariate statistical analysis (dpeaa)DE-He213 Hydrogeochemical regimes (dpeaa)DE-He213 Coimbatore (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 |
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contamination analysis of groundwater in coimbatore district, india: a statistical approach |
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Contamination analysis of groundwater in Coimbatore district, India: a statistical approach |
| abstract |
Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. © Springer-Verlag Berlin Heidelberg 2016 |
| abstractGer |
Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. © Springer-Verlag Berlin Heidelberg 2016 |
| abstract_unstemmed |
Abstract Coimbatore district is facing problems of groundwater depletion, and the quality of groundwater deteriorates to a greater extent. This study aims to determine the contaminants in groundwater and the sources of contamination in Coimbatore district. Multivariate statistical analysis, cluster analysis (CA) and principal component analysis (PCA) were performed on water quality data of twenty-seven samples collected within Coimbatore district (India), during the pre-monsoon period of the years 2012 and 2013 to study the hydrogeochemical regimes. The correlation between major ions was obtained from PCA, and the factor scores from PCA were used to classify the samples by CA. The possible factors which cause contamination were identified. The electrical conductivity distribution shows that the northern taluks of the district are much more polluted than the southern taluks due to leaching of ions. In the year 2012, the ions dominance was in the order of $ Mg^{2+} $ > $ Na^{+} $ > $ Ca^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $− and in the year 2013 the ions dominance was in the order of $ Na^{+} $ > $ Ca^{2+} $ > $ Mg^{2+} $ > $ K^{+} $ = $ Cl^{−} $ > $ HCO_{3} $− > $ SO_{4} $2− > $ NO_{3} $−. The statistical results reveal that carbonate dissolution, silicate weathering, ion exchange, leaching of industrial effluents and leaching of fertilizers from agricultural lands alter the groundwater chemistry. CA classified the sampling sites into three groups based on the nature of pollution. Eastern side sampling sites are clustered together, which has more ion concentration due to the flow of groundwater from west to east and more residence time. © Springer-Verlag Berlin Heidelberg 2016 |
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Contamination analysis of groundwater in Coimbatore district, India: a statistical approach |
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| score |
7.4013157 |