Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil
Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected para...
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| Autor*in: |
Passos, Jéssica Bandeira de Melo Carvalho [verfasserIn] Teixeira, David Bruno de Sousa |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental monitoring and assessment - Springer International Publishing, 1981, 193(2021), 3 vom: 15. Feb. |
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| Übergeordnetes Werk: |
volume:193 ; year:2021 ; number:3 ; day:15 ; month:02 |
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| DOI / URN: |
10.1007/s10661-021-08918-1 |
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OLC2123679747 |
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| 520 | |a Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. | ||
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10.1007/s10661-021-08918-1 doi (DE-627)OLC2123679747 (DE-He213)s10661-021-08918-1-p DE-627 ger DE-627 rakwb eng 333.7 VZ Passos, Jéssica Bandeira de Melo Carvalho verfasserin (orcid)0000-0002-0473-5384 aut Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. Principal component analysis Hierarchical cluster analysis Monitoring network Brazilian watershed Teixeira, David Bruno de Sousa (orcid)0000-0003-4808-6328 aut Campos, Jasmine Alves (orcid)0000-0002-6214-9471 aut Lima, Rafael Petruceli Coelho (orcid)0000-0003-4699-2677 aut Fernandes-Filho, Elpídio Inácio (orcid)0000-0002-9484-1411 aut da Silva, Demetrius David (orcid)0000-0001-9666-7421 aut Enthalten in Environmental monitoring and assessment Springer International Publishing, 1981 193(2021), 3 vom: 15. Feb. (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:193 year:2021 number:3 day:15 month:02 https://doi.org/10.1007/s10661-021-08918-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 193 2021 3 15 02 |
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10.1007/s10661-021-08918-1 doi (DE-627)OLC2123679747 (DE-He213)s10661-021-08918-1-p DE-627 ger DE-627 rakwb eng 333.7 VZ Passos, Jéssica Bandeira de Melo Carvalho verfasserin (orcid)0000-0002-0473-5384 aut Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. Principal component analysis Hierarchical cluster analysis Monitoring network Brazilian watershed Teixeira, David Bruno de Sousa (orcid)0000-0003-4808-6328 aut Campos, Jasmine Alves (orcid)0000-0002-6214-9471 aut Lima, Rafael Petruceli Coelho (orcid)0000-0003-4699-2677 aut Fernandes-Filho, Elpídio Inácio (orcid)0000-0002-9484-1411 aut da Silva, Demetrius David (orcid)0000-0001-9666-7421 aut Enthalten in Environmental monitoring and assessment Springer International Publishing, 1981 193(2021), 3 vom: 15. Feb. (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:193 year:2021 number:3 day:15 month:02 https://doi.org/10.1007/s10661-021-08918-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 193 2021 3 15 02 |
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333.7 VZ Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil Principal component analysis Hierarchical cluster analysis Monitoring network Brazilian watershed |
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Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil |
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Passos, Jéssica Bandeira de Melo Carvalho Teixeira, David Bruno de Sousa Campos, Jasmine Alves Lima, Rafael Petruceli Coelho Fernandes-Filho, Elpídio Inácio da Silva, Demetrius David |
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multivariate statistics for spatial and seasonal quality assessment of water in the doce river basin, southeastern brazil |
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Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil |
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Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 |
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Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 |
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Abstract This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources. © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 |
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Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil |
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Teixeira, David Bruno de Sousa Campos, Jasmine Alves Lima, Rafael Petruceli Coelho Fernandes-Filho, Elpídio Inácio da Silva, Demetrius David |
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