A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater
Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic proce...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Afrifa, George Y. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Modeling earth systems and environment - Berlin : Springer, 2015, 8(2022), 4 vom: 25. Juni, Seite 4975-4983 |
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| Übergeordnetes Werk: |
volume:8 ; year:2022 ; number:4 ; day:25 ; month:06 ; pages:4975-4983 |
| Links: |
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| DOI / URN: |
10.1007/s40808-022-01415-5 |
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| Katalog-ID: |
SPR048404195 |
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| 100 | 1 | |a Afrifa, George Y. |e verfasserin |0 (orcid)0000-0003-3141-1876 |4 aut | |
| 245 | 1 | 2 | |a A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
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| 520 | |a Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). | ||
| 650 | 4 | |a Gamma mixture model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Natural background level |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Anthropogenic activities |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Groundwater nitrate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Iterative outlier removal techniques |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Ansah-Narh, Theophilus |4 aut | |
| 700 | 1 | |a Doe, Caroline |4 aut | |
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| 700 | 1 | |a Sakyi, Patrick A. |4 aut | |
| 700 | 1 | |a Chegbeleh, Larry P. |4 aut | |
| 700 | 1 | |a Yidana, Sandow M. |4 aut | |
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10.1007/s40808-022-01415-5 doi (DE-627)SPR048404195 (SPR)s40808-022-01415-5-e DE-627 ger DE-627 rakwb eng Afrifa, George Y. verfasserin (orcid)0000-0003-3141-1876 aut A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 Ansah-Narh, Theophilus aut Doe, Caroline aut Loh, Yvonne S. A. aut Sakyi, Patrick A. aut Chegbeleh, Larry P. aut Yidana, Sandow M. aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 8(2022), 4 vom: 25. Juni, Seite 4975-4983 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 https://dx.doi.org/10.1007/s40808-022-01415-5 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_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_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_4393 GBV_ILN_4700 AR 8 2022 4 25 06 4975-4983 |
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10.1007/s40808-022-01415-5 doi (DE-627)SPR048404195 (SPR)s40808-022-01415-5-e DE-627 ger DE-627 rakwb eng Afrifa, George Y. verfasserin (orcid)0000-0003-3141-1876 aut A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 Ansah-Narh, Theophilus aut Doe, Caroline aut Loh, Yvonne S. A. aut Sakyi, Patrick A. aut Chegbeleh, Larry P. aut Yidana, Sandow M. aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 8(2022), 4 vom: 25. Juni, Seite 4975-4983 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 https://dx.doi.org/10.1007/s40808-022-01415-5 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_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_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_4393 GBV_ILN_4700 AR 8 2022 4 25 06 4975-4983 |
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10.1007/s40808-022-01415-5 doi (DE-627)SPR048404195 (SPR)s40808-022-01415-5-e DE-627 ger DE-627 rakwb eng Afrifa, George Y. verfasserin (orcid)0000-0003-3141-1876 aut A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 Ansah-Narh, Theophilus aut Doe, Caroline aut Loh, Yvonne S. A. aut Sakyi, Patrick A. aut Chegbeleh, Larry P. aut Yidana, Sandow M. aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 8(2022), 4 vom: 25. Juni, Seite 4975-4983 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 https://dx.doi.org/10.1007/s40808-022-01415-5 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_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_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_4393 GBV_ILN_4700 AR 8 2022 4 25 06 4975-4983 |
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10.1007/s40808-022-01415-5 doi (DE-627)SPR048404195 (SPR)s40808-022-01415-5-e DE-627 ger DE-627 rakwb eng Afrifa, George Y. verfasserin (orcid)0000-0003-3141-1876 aut A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 Ansah-Narh, Theophilus aut Doe, Caroline aut Loh, Yvonne S. A. aut Sakyi, Patrick A. aut Chegbeleh, Larry P. aut Yidana, Sandow M. aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 8(2022), 4 vom: 25. Juni, Seite 4975-4983 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 https://dx.doi.org/10.1007/s40808-022-01415-5 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_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_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_4393 GBV_ILN_4700 AR 8 2022 4 25 06 4975-4983 |
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10.1007/s40808-022-01415-5 doi (DE-627)SPR048404195 (SPR)s40808-022-01415-5-e DE-627 ger DE-627 rakwb eng Afrifa, George Y. verfasserin (orcid)0000-0003-3141-1876 aut A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 Ansah-Narh, Theophilus aut Doe, Caroline aut Loh, Yvonne S. A. aut Sakyi, Patrick A. aut Chegbeleh, Larry P. aut Yidana, Sandow M. aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 8(2022), 4 vom: 25. Juni, Seite 4975-4983 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 https://dx.doi.org/10.1007/s40808-022-01415-5 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_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_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_4393 GBV_ILN_4700 AR 8 2022 4 25 06 4975-4983 |
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Enthalten in Modeling earth systems and environment 8(2022), 4 vom: 25. Juni, Seite 4975-4983 volume:8 year:2022 number:4 day:25 month:06 pages:4975-4983 |
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Gamma mixture model Natural background level Anthropogenic activities Groundwater nitrate Iterative outlier removal techniques Densu Basin |
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Afrifa, George Y. @@aut@@ Ansah-Narh, Theophilus @@aut@@ Doe, Caroline @@aut@@ Loh, Yvonne S. A. @@aut@@ Sakyi, Patrick A. @@aut@@ Chegbeleh, Larry P. @@aut@@ Yidana, Sandow M. @@aut@@ |
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The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. 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Afrifa, George Y. |
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Afrifa, George Y. misc Gamma mixture model misc Natural background level misc Anthropogenic activities misc Groundwater nitrate misc Iterative outlier removal techniques misc Densu Basin A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
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A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater Gamma mixture model (dpeaa)DE-He213 Natural background level (dpeaa)DE-He213 Anthropogenic activities (dpeaa)DE-He213 Groundwater nitrate (dpeaa)DE-He213 Iterative outlier removal techniques (dpeaa)DE-He213 Densu Basin (dpeaa)DE-He213 |
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A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
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A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
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Afrifa, George Y. Ansah-Narh, Theophilus Doe, Caroline Loh, Yvonne S. A. Sakyi, Patrick A. Chegbeleh, Larry P. Yidana, Sandow M. |
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gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{no}}_{3}}^{-}%$–%${\mathrm{n}}%$ in groundwater |
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A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
| abstract |
Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
| abstractGer |
Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
| abstract_unstemmed |
Abstract The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The various sources of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles in determining NBLs. Another constraint on NBL estimation is choosing the right statistical technique. In this paper, the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ levels of groundwater and the high-risk zones in the Densu Basin were evaluated. The evaluation was done using the Gamma mixture probability distribution and the iterative outlier removal technique. We also considered the strengths and weaknesses of these two models by assuming the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration is coming from a single source. The Gamma mixture model was used to identify the sub-populations in the %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ data set and also, estimate the optimal parameters for the hidden clusters. The initial component with the lower %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ concentration was considered as the NBL. This was measured at %$2.56\pm 2.56%$ mg/L, whereas considering a single source the iterative technique recorded the NBL at %$5.6\pm 5.3%$ mg/L. Assuming the groundwater contamination is from a single source, then the iterative method introduces an error of %$3.1 \pm 2.8%$ mg/L in the NBL estimation. The result suggests that the Gamma mixture model is more robust in estimating pollution with multiple sources (that is, natural and human-induced sources). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 |
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| title_short |
A gamma mixture model-based approach for the estimation of natural background levels of %${{\mathrm{NO}}_{3}}^{-}%$–%${\mathrm{N}}%$ in groundwater |
| url |
https://dx.doi.org/10.1007/s40808-022-01415-5 |
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| author2 |
Ansah-Narh, Theophilus Doe, Caroline Loh, Yvonne S. A. Sakyi, Patrick A. Chegbeleh, Larry P. Yidana, Sandow M. |
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Ansah-Narh, Theophilus Doe, Caroline Loh, Yvonne S. A. Sakyi, Patrick A. Chegbeleh, Larry P. Yidana, Sandow M. |
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| doi_str |
10.1007/s40808-022-01415-5 |
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2024-07-03T18:59:37.415Z |
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| score |
7.397312 |