A multivariate analysis of water quality in Lake Naivasha, Kenya

2015 ◽  
Vol 66 (2) ◽  
pp. 177 ◽  
Author(s):  
Jane Ndungu ◽  
Denie C. M. Augustijn ◽  
Suzanne J. M. H. Hulscher ◽  
Bernard Fulanda ◽  
Nzula Kitaka ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Close Association ◽  
Sampling Site ◽  
Principal Component ◽  
Component Analysis ◽  
Northern Region ◽  
Lake Naivasha ◽  
Water Quality Status ◽  
Quality Status

Water quality information in aquatic ecosystems is crucial in setting up guidelines for resource management. This study explores the water quality status and pollution sources in Lake Naivasha, Kenya. Analysis of water quality parameters at seven sampling sites was carried out from water samples collected weekly from January to June and biweekly from July to November in 2011. Principal component analysis (PCA) and cluster analysis (CA) were used to analyse the dataset. Principal component analysis showed that four principal components (PCA-1 to PCA-4) explained 94.2% of the water quality variability. PCA-1 and PCA-2 bi-plot suggested that turbidity in the lake correlated directly to nutrients and iron with close association with the sampling site close to the mouth of Malewa River. Three distinct clusters were discerned from the CA analysis: Crescent Lake, a more or less isolated crater lake, the northern region of the lake, and the main lake. The pollution threat in Lake Naivasha includes agricultural and domestic sources. This study provides a valuable dataset on the current water quality status of Lake Naivasha, which is useful for formulating effective management strategies to safeguard ecosystem services and secure the livelihoods of the riparian communities around Lake Naivasha, Kenya.

scholarly journals A preliminary study of marine water quality status using principal component analysis at three selected mangrove estuaries in East Coast Peninsular Malaysia

2017 ◽  
Vol 13 (4) ◽  
pp. 764-768 ◽  
Author(s):  
Saiful Iskandar Khalit ◽  
Mohd Saiful Samsudin ◽  
Azman Azid ◽  
Kamaruzzaman Yunus ◽  
Muhammad Amar Zaudi ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
East Coast ◽  
Principal Component ◽  
Component Analysis ◽  
Peninsular Malaysia ◽  
Marine Water ◽  
Mangrove Estuary ◽  
Water Quality Status ◽  
Quality Status

This research presents marine water quality status in three different mangrove estuaries. The objective of this study is to evaluate the surface water quality of three estuaries in east coast Peninsular Malaysia. The parameters measured were Dissolved Oxygen (DO), pH, Biochemical Oxygen Demand (BOD), total dissolved solid (TDS), ammonium (NH4-N), turbidity (TUR), total suspended solid (TSS) and coliform. Monthly sampling was performed during the dry season, from June 2016 until September 2016. Data were analysed using principal component analysis (PCA). PCA yielded two PCs where VF1 forms strong factor loadings for pH, NH4-N, SAL, and TDS signifying saltwater intrusion in mangrove area. VF2 designed strong factors of BOD, TUR and Coliform and strong negative loading of DO indicating anthropogenic pollutions in the area. This study output will be a baseline setting for future studies in mangrove estuary marine water quality. Mangrove marine water samples of future monitoring studies in mangrove estuary will benefit by enabling understanding of pollution loading and coastal water quality. It is essential to plan a workable water quality modelling as powerful tool to simulate marine water quality and forecast future consequences to facilitate mangrove biodiversity conservation.

Water Quality Evaluation for Tributaries of Danjiangkou Reservoir with Principal Component Analysis

2014 ◽  
Vol 955-959 ◽  
pp. 3586-3594 ◽  
Author(s):  
Xiao Kang Xin ◽  
Wei Yin ◽  
Hai Yan Jia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Water Quality Evaluation ◽  
Mean Values ◽  
Danjiangkou Reservoir ◽  
Research Task ◽  
Pca Method ◽  
Water Quality Status

To reflect the water quality status of Danjiangkou reservoir tributaries, identify the main pollution factors, and compare pollution degree between tributaries. Principal component analysis (PCA) method is used to assess and explicate research task with annual mean values for 11 main water quality indicators of 16 priority tributaries measured in 2013. The results show that: The main pollution factors of Danjiangkou reservoir are oxygen-consuming pollutants and heavy metal, and the former is the dominant one. Shending, Sihe, Jianghe, Jianhe and Danjiang are heavily-polluted tributaries while Duhe, Jiangjun, Taogou, Hanjiang and Taohe are lightly-polluted tributaries.

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.

Assessment of water quality using principal component analysis: a case study of the Marrecas stream basin in Brazil

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Alexandre Teixeira de Souza ◽  
Lucas Augusto T. X. Carneiro ◽  
Osmar Pereira da Silva Junior ◽  
Sérgio Luís de Carvalho ◽  
Juliana Heloisa Pinê Américo-Pinheiro
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis
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