Characterization of water quality in shrimp ponds of different sizes and with different management regimes using multivariate statistical analysis

2005 ◽  
Vol 13 (6) ◽  
pp. 501-518 ◽  
Author(s):  
A. Milstein ◽  
M. S. Islam ◽  
M. A. Wahab ◽  
A. H. M. Kamal ◽  
S. Dewan
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Shrimp Ponds ◽  
Management Regimes

Assessment of water quality and Algae growth for the Ganwol reservoir using multivariate statistical analysis

2019 ◽  
Vol 18 (2) ◽  
pp. 217-230
Author(s):  
Zihan Liu ◽  
Jin Chul Joo ◽  
Eun Bi Kang ◽  
Jin Ho Kim ◽  
Sae-Eun Oh ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Algae Growth

scholarly journals Assessing water quality of Nen River, the neighboring section of three provinces, using multivariate statistical analysis

2018 ◽  
Vol 67 (8) ◽  
pp. 779-789 ◽  
Author(s):  
Duoying Zhang ◽  
Guochen Zheng ◽  
Shufeng Zheng ◽  
Wenbo Guan ◽  
Wenjun Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

scholarly journals Identification of the dominant hydrogeochemical processes and characterization of potential contaminants in groundwater in Qingyuan, China, by multivariate statistical analysis

RSC Advances ◽  
2018 ◽  
Vol 8 (58) ◽  
pp. 33243-33255 ◽  
Author(s):  
Zuobing Liang ◽  
Jianyao Chen ◽  
Tao Jiang ◽  
Kun Li ◽  
Lei Gao ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Water Source ◽  
Hydrogeochemical Processes ◽  
Karst Aquifers ◽  
Multivariate Statistical ◽  
Drinking And Irrigation ◽  
Karst Areas

In karst areas, groundwater is an important water source for drinking and irrigation purposes; however, karst aquifers are vulnerable and recovery from damage is difficult.

scholarly journals Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mansoor A. Baluch ◽  
Hashim Nisar Hashmi
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quality Parameters ◽  
Anthropogenic Source ◽  
Indus River ◽  
Multivariate Statistical ◽  
Quality Of Water ◽  
The Impact

Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

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