scholarly journals Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India

2020 ◽  
Author(s):  
MohdYawar Ali Khan ◽  
Jie Wen
Keyword(s):  
Heavy Metals ◽  
Surface Water ◽  
Statistical Methods ◽  
Anthropogenic Activities ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Ganges Basin ◽  
River Ganges

scholarly journals Data Analysis of Beach Sands’ Chemical Analysis Using Multivariate Statistical Methods and Heavy Metal Distribution Maps: The Case of Moonlight Beach Sands, Kemer, Antalya, Turkey

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1538 ◽  
Author(s):  
Fusun Yalcin
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Data Analysis ◽  
Chemical Analysis ◽  
Statistical Methods ◽  
Risk Index ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Distribution Maps ◽  
Beach Sands

Multivariate statistical methods are widely used in several disciplines of fundamental sciences. In the present study, the data analysis of the chemical analysis of the sands of Moonlight Beach in the Kemer region was examined using multivariate statistical methods. This study consists of three parts. The multivariate statistical analysis tests were described in the first part, then the pollution indexes were studied in the second part. Finally, the distribution maps of the chemical analyses and pollution indexes were generated using the obtained data. The heavy metals were mostly observed in location K1, while they were sorted as follows based on their concentrations: Mg > Fe > Al > Ti > Sr > Mn > Cr > Ni > Zn > Zr > Cu > Rb. Also, strong positive correlations were found between Si, Fe, Al, K, Ti, P. According to the results of factor analysis, it was found that four factors explained 83.5% of the total variance. On the other hand, the coefficient of determination (R2) was calculated as 63.6% in the regression model. Each unit increase in the value of Ti leads to an increase of 0.022 units in the value of Si. Potential Ecological Risk Index analysis results (RI < 150) revealed that the study area had no risk. However, the locations around Moonlight Beach are under risk in terms of Enrichment Factor and Contamination Factor values. The index values of heavy metals in the anomaly maps and their densities were found to be successful; and higher densities were observed based on heavy metal anomalies.

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

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).

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

Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil

2018 ◽  
Vol 190 (7) ◽  
Author(s):  
Darlan Daniel Alves ◽  
Roberta Plangg Riegel ◽  
Daniela Müller de Quevedo ◽  
Daniela Montanari Migliavacca Osório ◽  
Gustavo Marques da Costa ◽  
...  
Keyword(s):  
Water Pollution ◽  
Surface Water ◽  
Statistical Methods ◽  
Southern Brazil ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Surface Water Pollution
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