Enhanced monitoring of water quality variation in Nakdong River downstream using multivariate statistical techniques

2015 ◽  
Vol 57 (27) ◽  
pp. 12508-12517 ◽  
Author(s):  
Minsoo Kim ◽  
Yejin Kim ◽  
Hyosoo Kim ◽  
Wenhua Piao ◽  
Changwon Kim
Keyword(s):  
Water Quality ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Nakdong River ◽  
Multivariate Statistical ◽  
Water Quality Variation ◽  
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.

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