Process identification by principal component analysis of river water-quality data

This study investigates the temporal water quality variations and pollution sources identification in Merbok River using principal component analysis. The variables analyzed include As, Cd, Pb, Fe, Cr, Mn, Zn, Ni, Ca, Mg, Na, K, NH4, F, Cl, Br, NO2, NO3, SO4, PO4, pH, BOD, DO, COD, turbidity, and salinity. These variables were analyzed using inductively coupled plasma mass spectrometry, ion chromatography, and YSI multiprobe. Principal component analysis (PCA) was utilized to evaluate the variations of the most significant water quality parameters and identify the probable source of the pollutants. From the results of PCA, 86% of the total variations were observed in the water quality data with strong dominance of toxic heavy metals (As, Pb, and Cr), parameters associated with industrial discharge, domestic inputs, overland runoff (NH4, pH, BOD, DO, COD), agrochemicals (NO2, NO3, SO4, PO4), and weathering of basement rocks (Ca, Mg, Cl, F, K, and Na). Most of these parameters were present in concentrations exceeded the reference standards limits used in this study, indicating pollution of the river water. Together with the presence of microbial contamination, the results suggest potential human health risk due to water uses, fish and shellfish consumption. Moreover, the results revealed that anthropogenic activities and weathering were the main sources of pollutants in Merbok River.

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Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽

10.3390/w13243634 ◽

2021 ◽

Vol 13 (24) ◽

pp. 3634

Author(s):

Zoltan Horvat ◽

Mirjana Horvat ◽

Kristian Pastor ◽

Vojislava Bursić ◽

Nikola Puvača

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Multivariate Analysis ◽

Cross Sections ◽

Principal Component ◽

Component Analysis ◽

Danube River ◽

Quality Data ◽

Water Quality Data ◽

The Danube River

This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

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Comments on Ouyang, Y., Evaluation of river water quality monitoring stations by principal component analysis. Water Research 39 (2005) 2621–2635

Water Research ◽

10.1016/j.watres.2006.07.001 ◽

2006 ◽

Vol 40 (16) ◽

pp. 3141-3143 ◽

Cited By ~ 3

Author(s):

V. Zitko

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

River Water ◽

Principal Component ◽

Water Quality Monitoring ◽

Component Analysis ◽

River Water Quality ◽

Quality Monitoring ◽

Water Research ◽

Monitoring Stations

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Processes governing river water quality identified by principal component analysis

Hydrological Processes ◽

10.1002/hyp.1091 ◽

2002 ◽

Vol 16 (16) ◽

pp. 3113-3130 ◽

Cited By ~ 40

Author(s):

I. Haag ◽

B. Westrich

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

River Water ◽

Principal Component ◽

Component Analysis ◽

River Water Quality

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Analysis of Water Quality for Marinculture in Moro, Karimun, Riau Islands with Principal Component Analysis

Journal of Aquaculture and Fish Health ◽

10.20473/jafh.v9i2.17192 ◽

2020 ◽

Vol 9 (2) ◽

pp. 143

Author(s):

Wiyoto Wiyoto ◽

Irzal Effendi

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Quality Data ◽

Multiple Linear Regressions ◽

Water Quality Data ◽

Seawater Quality ◽

Linear Regressions ◽

Surface Water Samples

Finding a good location is of important aspects in mariculture. This can be done by evaluating the water quality data. The aims of the study were to evaluate the seawater quality at Moro, Karimun, Riau Islands and to analyze its compatibility for mariculture by using principal component analysis (PCA) and multiple linear regressions. Generally, seawater qualities in the study area were in the tolerance range for mariculture. Surface water samples were collected from five different sampling points around Moro Sea. PCA results demonstrated that there were eleven variation factors which explained 95.4% of the total variance. In addition, based on PCA and multiple linear regressions, four water quality predictors for environmental quality could be identified, that is nitrite (NO2), temperature, pH and dissolved oxygen. Multiple linear regressions showed that the contribution of each parameter to the water quality was significant (R2=1, P < 0.05).

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MODELLING IKPOBA RIVER WATER QUALITY USING PRINCIPAL COMPONENT ANALYSIS (PCA) METHOD

Journal of Civil Engineering Science and Technology ◽

10.33736/jcest.1083.2019 ◽

2019 ◽

Vol 10 (1) ◽

pp. 59-74 ◽

Cited By ~ 1

Author(s):

Ehizonomhen Solomon Okonofua ◽

Ifeanyi Benjamin Nwadialo ◽

M. O. Ekun

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

River Water ◽

Principal Component ◽

Treatment Method ◽

Component Analysis ◽

River Water Quality ◽

Brewery Wastewater ◽

Two Samples ◽

Intense Activity

This paper examined the effects of brewery wastewater on the quality of water in Ikpoba River which has experienced significant pollution over the years, with the intention of determining the main pollutant in the river water. Samples were recovered from eight (8) different locations covering a total distance of 750 m: one sample from upstream at 150 m from the effluent discharge location, two samples from effluents discharge point and five samples from downstream location at 150 m interval. Samples were taken twice monthly in March, May and July, 2014 during period of intense activity of production. The physcio-chemical analyses of the twenty-five (25) selected parameters were calculated and values obtained were used to calculate the water Quality index of the river. The results indicated that Ikpoba River is severely polluted (WQI = -5429792.89, in SN1, March, 2014) as a result of untreated brewery effluent hence Principal Component Analysis (PCA) was applied to determine the parameter that contributes mainly to the pollution and those that contributed minimally. Evaluation of the PCA results shows that the only reoccurring parameter is Copper hence it is concluded that Copper is the only component factor that influences the river water quality throughout the period under study. Therefore, it is strongly recommended that any proposed treatment method must be targeted at the removal of copper in addition to other factors of high contributory effects.

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