scholarly journals Relationship between Water Quality Environment and Landuse in an Agricultural Watershed during Snowmelt Period. Principal Component Analysis of River Water Quality.

2002 ◽  
Vol 15 (4) ◽  
pp. 391-398 ◽  
Author(s):  
Keiji UNOKI ◽  
Tetuaki NAGASAWA ◽  
Takashi INOUE ◽  
Tadao YAMAMOTO
2019 ◽  
Vol 10 (1) ◽  
pp. 59-74 ◽  
Author(s):  
Ehizonomhen Solomon Okonofua ◽  
Ifeanyi Benjamin Nwadialo ◽  
M. O. Ekun

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.


2013 ◽  
Author(s):  
Zalina Mohd Ali ◽  
Noor Akma Ibrahim ◽  
Kerrie Mengersen ◽  
Mahendran Shitan ◽  
Hafizan Juahir

2013 ◽  
Vol 409-410 ◽  
pp. 208-213
Author(s):  
Mei Liu ◽  
Wen Qian Shi ◽  
Jun Lu

According to many uncertain problems of river eutrophication, a Bayesian hierarchical model was established to predict water quality. We applied the hierarchical method to assess river water quality in an agricultural watershed in eastern China. The procedure followed was developing a hierarchical model relating eutrophication response - the level of chlorophyll (Chla). Through Principal Component Analysis (PCA), five factors strong related with Chla were selected to establish Bayesian hierarchical model to predict the water quality. Results showed that Bayesian hierarchical model was very realistic. Furthermore, in Bayesian perspective, predictions expressed as probabilities, rather than a single value, involving more uncertainty information, can be essential to environmental management and decision-making.


Author(s):  
Petr Praus

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.


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