Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil

2020 ◽  
Vol 27 (28) ◽  
pp. 35303-35318 ◽  
Author(s):  
Micael de Souza Fraga ◽  
Guilherme Barbosa Reis ◽  
Demetrius David da Silva ◽  
Hugo Alexandre Soares Guedes ◽  
Abrahão Alexandre Alden Elesbon
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Surface Water Quality ◽  
Minas Gerais ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Doce River

The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil

2018 ◽  
Vol 190 (8) ◽  
Author(s):  
Giovanna Moura Calazans ◽  
Carolina Cristiane Pinto ◽  
Elizângela Pinheiro da Costa ◽  
Anna Flávia Perini ◽  
Sílvia Corrêa Oliveira
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Network Monitoring ◽  
Surface Water Quality ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

Application of Multivariate Statistical Methods and Water Quality Index for the Evaluation of Surface Water Quality in the Cunas River Basin, Peru

2021 ◽  
Vol 18 (4) ◽  
pp. 19-27
Author(s):  
Henry Dominguez Franco ◽  
María Custodio ◽  
Richard Peñaloza ◽  
Heidi De la Cruz
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Ccme Wqi

Watershed management requires information that allows the intervention of possible sources that affect aquatic systems. Surface water quality in the Cunas river basin (Peru) was evaluated using multivariate statistical methods and the CCME-WQI water quality index. Twenty-seven sampling sites were established in the Cunas River and nine sites in the tributary river. Water samples were collected in two contrasting climatic seasons and the CCME-WQI was determined based on physicochemical and bacteriological parameters. The PCA generated three PC with a cumulative explained variation of 78.28 %. The generalised linear model showed strong significant positive relationships (p < 0.001) of E. coli with Fe, nitrate, Cu and TDS, and a strong significant negative relationship (p < 0.001) with pH. Overall, the CCME-WQI showed the water bodies in the upper reaches of the Cunas River as good water quality (87.07), in the middle reaches as favourable water quality (67.65) and in the lower reaches as poor water quality (34.86). In the tributary, the CCME-WQI showed the water bodies as having good water quality (82.34).

scholarly journals Correction to: The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil

2018 ◽  
Vol 190 (9) ◽  
Author(s):  
Giovanna Moura Calazans ◽  
Carolina Cristiane Pinto ◽  
Elizângela Pinheiro da Costa ◽  
Anna Flávia Perini ◽  
Sílvia Corrêa Oliveira
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Network Monitoring ◽  
Surface Water Quality ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

Surface Water Quality Assessment of Lis River Using Multivariate Statistical Methods

2012 ◽  
Vol 223 (9) ◽  
pp. 5549-5561 ◽  
Author(s):  
Judite S. Vieira ◽  
José C. M. Pires ◽  
Fernando G. Martins ◽  
Vítor J. P. Vilar ◽  
Rui A. R. Boaventura ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Lis River

Temporal and spatial trend analysis of surface water quality in the Doce River basin, Minas Gerais, Brazil

2021 ◽  
Author(s):  
Micael de Souza Fraga ◽  
Demetrius David da Silva ◽  
Guilherme Barbosa Reis ◽  
Hugo Alexandre Soares Guedes ◽  
Abrahão Alexandre Alden Elesbon
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Trend Analysis ◽  
River Basin ◽  
Surface Water Quality ◽  
Minas Gerais ◽  
Spatial Trend ◽  
Temporal And Spatial ◽  
Doce River

scholarly journals Surface water quality assessment using hybrid multivariate statistical analysis and geographic information system based on integrated water quality index model for Maozhou River Basin, Guangdong, China

2021 ◽  
Author(s):  
Tao Lin ◽  
Huiqing Yu ◽  
Qi Wang ◽  
Lin Hu ◽  
Jing Yin
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Surface Water Quality ◽  
Water Quality Evaluation ◽  
Multivariate Statistical ◽  
Index Model ◽  
Water Ecosystem ◽  
Water Quality Status

Abstract The river is a vital component of the water ecosystem in both urban and rural regions. However, its rapidly increasing pollutants are posing a severe threat for water ecosystem security. Using Multivariate statistical technique and Integrated water quality index model (IWQI) to evaluate surface water quality and its spatial distribution based on Geographic information system (GIS). This combinatorial model have been proved to be a feasible tool for evaluating surface water quality at large-scale basin. This study analyzed the spatio-temporal variations of surface water quality, which were determined monthly from samples collected in the Maozhou River Basin Guangdong Province, China from 2018 to 2020. The results demonstrated that the surface water quality status of in the Maozhou River Basin has been steadily improved during the study period. The surface water quality of 82.17% of monitoring site reached the water quality target of function zones (surface water quality of the class V standards), with the IWQI values ranging from 12.118 to 3.650. By the end of 2019, black-odorous water in Maozhou River basin has disappeared from our sight. By 2020, the water quality status of the Maozhou River Basin has been steadily maintained at “Medium and good” level, and the main background pollutants for the water quality target of function zones is NH3-N. However, the some area in which the surface water quality still need to further improve is estuary and southwest tributary in the basin. This finding calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. It is concluded that this combined surface water quality evaluation model is more efficient and reasonable for surface water quality evaluation at a larger scale. It can provide scientific foundation for the water ecosystem management and planning in efficiently managing and evaluating surface water quality at river or basin scales.

scholarly journals Spatial-temporal analysis of the surface water quality of the Pará River Basin through statistical techniques

2019 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Josiani Cordova de Oliveira ◽  
Kelly Prado Maia ◽  
Nara Linhares Borges de Castro ◽  
Sílvia Maria Alves Corrêa Oliveira
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Statistical Tests ◽  
Surface Water Quality ◽  
Minas Gerais ◽  
State Legislation ◽  
Historical Series ◽  
The Impact

Water quality issues are a growing concern due to the the recent intensification of urbanization and industrialization. This paper evaluates and compares the surface water quality of the ten sub-basins of the Pará River, located in the São Francisco River Basin, Minas Gerais, and evaluates the impact of seasonality and the compliance with the current limits of state legislation. The surface water quality monitoring database of the Institute of Water Management of Minas Gerais (Igam) was used, and 18 parameters were analyzed from a historical series from 2008 to 2016, totaling 16,651 observations. First, the descriptive statistics of the parameters were calculated, considering each sub-basin separately. Then, for the temporal and spatial analysis, the Kruskal-Wallis nonparametric statistical tests were applied, followed by the multiple comparison test, with an alpha level of 5%, due to the asymmetric behavior of the data. Thus, it was possible to compare water quality of the sub-basins in rainy and dry seasons and to identify which parameters were responsible for the greater degradation. In the compliance analysis to the current limits of state legislation, it was identified that all of the sub-basins were out of the specified range for at least one of the evaluated parameters. Finally, the seasonality analysis exposed significant differences in the parameters of dissolved oxygen, turbidity, total suspended solids, total solids and water temperature, where it was shown that there was a worsening of water quality in the rainy season for most sub-basins.

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