Processes governing river water quality identified by principal component analysis

2002 ◽  
Vol 16 (16) ◽  
pp. 3113-3130 ◽  
Author(s):  
I. Haag ◽  
B. Westrich
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Water ◽  
Principal Component ◽  
Component Analysis ◽  
River Water Quality

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

2019 ◽  
Vol 10 (1) ◽  
pp. 59-74 ◽  
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.

scholarly journals The Langat River water quality index based on principal component analysis

2013 ◽  
Author(s):  
Zalina Mohd Ali ◽  
Noor Akma Ibrahim ◽  
Kerrie Mengersen ◽  
Mahendran Shitan ◽  
Hafizan Juahir
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
River Water Quality ◽  
Langat River

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

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