scholarly journals Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques

2016 ◽  
Vol 4 (4) ◽  
pp. 284-292 ◽  
Author(s):  
Ahmed Barakat ◽  
Mohamed El Baghdadi ◽  
Jamila Rais ◽  
Brahim Aghezzaf ◽  
Mohamed Slassi
Keyword(s):  
Water Quality ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Variation ◽  
Seasonal Water Quality ◽  
Quality Variation

scholarly journals Application of environmetric methods to investigate control factors on water quality

2017 ◽  
Vol 43 (3) ◽  
pp. 17-23 ◽  
Author(s):  
Hülya Boyacioglu ◽  
Hayal Boyacioglu
Keyword(s):  
Water Quality ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Reservoir Water ◽  
Control Factors ◽  
Natural Factors ◽  
Run Off ◽  
Water Quality Variation ◽  
Low Levels ◽  
Quality Variation

AbstractIn the study, environmetric methods were successfully performed a) to explore natural and anthropogenic controls on reservoir water quality, b) to investigate spatial and temporal differences in quality, and c) to determine quality variables discriminating three reservoirs in Izmir, Turkey. Results showed that overall water quality was mainly governed by “natural factors” in the whole region. A parameter that was the most important in contributing to water quality variation for one reservoir was not important for another. Between summer and winter periods, difference in arsenic concentrations were statistically significant in the Tahtalı, Ürkmez and iron concentrations were in the Balçova reservoirs. Observation of high/low levels in two seasons was explained by different processes as for instance, dilution from runoff at times of high flow seeped through soil and entered the river along with the rainwater run-off and adsorption. Three variables “boron, arsenic and sulphate” discriminated quality among Balçova & Tahtalı, Balçova & Ürkmez and two variables “zinc and arsenic” among the Tahtalı & Ürkmez reservoirs. The results illustrated the usefulness of multivariate statistical techniques to fingerprint pollution sources and investigate temporal/spatial variations in water quality.

scholarly journals Evaluation of the Swat River, Northern Pakistan, water quality using multivariate statistical techniques and water quality index (WQI) model

2020 ◽  
Vol 27 (31) ◽  
pp. 38545-38558 ◽  
Author(s):  
Shah Jehan ◽  
Ihsan Ullah ◽  
Sardar Khan ◽  
Said Muhammad ◽  
Seema Anjum Khattak ◽  
...  
Keyword(s):  
Water Quality ◽  
Health Risk ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Statistical Techniques ◽  
Northern Pakistan ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Poor Water Quality ◽  
Swat River

Abstract This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were collected and analyzed for physicochemical parameters including heavy metals (HM). The mean concentrations of physicochemical parameters and HM were within the drinking water guideline values set by the World Health Organization (WHO 2011) except 34%, 60%, and 56% of copper (Cu), nickel (Ni), and lead (Pb), respectively. Pollution sources were identified by various multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicating different origins both naturally and anthropogenically. Results of the water quality index (WQI) ranged from 13.58 to 209 with an average value of 77 suggesting poor water quality for drinking and domestic purposes. The poor water quality was mainly related to high sodium (alkalinity) and salinity hazards showing > 27% and 20% water samples have poor alkalinity and salinity hazards, respectively. Hazard quotient (HQ) and hazard index (HI) were used to determine the health risk of HM in the study area. For water-related health risk, HQingestion, HQdermal, and HI values were > 1, indicating noncarcinogenic health risk (NCR) posed by these HM to the exposed population.

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