Effects of phreatophyte removal on water quality in the Gila River phreatophyte project area, Graham County, Arizona, with a section on statistical analysis

1977 ◽  
Author(s):  
R.L. Laney ◽  
H.W. Hjalmarson
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Project Area ◽  
Gila River

Evaluation of Water Quality Characteristics in the Nakdong River using Statistical Analysis

2012 ◽  
Vol 45 (11) ◽  
pp. 1157-1168 ◽  
Author(s):  
Kil Yong Choi ◽  
Toe Hyo Im ◽  
Jae Woon Lee ◽  
Se Uk Cheon
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Quality Characteristics ◽  
Nakdong River ◽  
The Nakdong River

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 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.

scholarly journals Using Statistical Analysis to Assess Urban Groundwater in Beni Mellal City (Morocco)

2019 ◽  
Vol 2019 ◽  
pp. 1-22
Author(s):  
Mohamed El Baghdadi ◽  
Radouane Medah ◽  
Amal Jouider
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Electric Conductivity ◽  
Water Samples ◽  
Urban Groundwater ◽  
City Center ◽  
Guideline Values ◽  
Beni Mellal ◽  
Total Dissolved Solid ◽  
The City

The study was carried out in a shallow phreatic aquifer in the piedmont zone between the Atlas Mountains and Tadla plain in Morocco. This study is carried out using physicochemical analyses with statistical analysis (CA and PCA) to show variability of groundwater hydrochemical parameters beneath Beni Mellal city in order to know spatial variability of water quality under urban activities. Total dissolved solid shows large variation from 355 mg/L to 918 mg/L with high values recorded, as electric conductivity, in the city center. High sulfate content is intercepted also in the old city center with values exceeding the threshold in the Moroccan guideline. Sulfate ions are often suspected of having an anthropogenic origin. All water samples show a dominance of Ca against Mg (Ca/Mg: 1.08–6.25) and HCO3 against SO4 (HCO3/SO4: 0.29–6.92). For most of the trace elements, the measured concentrations were far below the standard values except Al and Fe in some samples which exceed all guideline values. PCA of all dataset highlights eight factors with eigenvalues higher than 1 that explained about 80.34% of the total variance. The first two components PC1 and PC2 explained about 41.14% of the total cumulative variance and were responsible for 24.25% and 16.89% of the variance for each one, respectively. The component PC1 is mostly correlated with electric conductivity, TDS, and chloride. The component PC2 was highly correlated with Ca, Cr, and Zn. The dendrogram at a linkage distance of about 10.5 leads to dividing the diagram into three clusters of water samples, C1, C2, and C3. Cluster C1 shows a medium content of EC, HCO3, and NO3 and low content of TDS, Ca, Mg, Na, K, SO4, and Ba compared with C2 and C3. C1 samples show the lowest ion content, resulting probably from the minimal time of residence within the aquifer with low rock interactions. Cluster C2 regroups samples with high content of Ca, Mg, K, SO4, Al, and Cr, medium content of TDS and Na, and low content of EC, HCO3, NO3, and Cl. Samples in cluster C3 have more content of heavy metal (Cd, Fe, Mn, and Ni), CE, TDS, Ca, Mg, Na, HCO3, NO3, and Cl, with low content of Cr and Al and medium values of K and SO4. We recommended the monitoring and follow-up of the water quality under the city and the repair of pipes especially in the downtown area to limit unwanted infiltration. Spatial autocorrelation used with variograms and Moran'I leads to conclude that groundwater parameters varied differently according to the direction, which means that the semivariance depended on direction and distance between samples.

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