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Article << Previous     |     Next >>   Contents Vol 66(2)

A multivariate analysis of water quality in Lake Naivasha, Kenya

Jane Ndungu A B C E, Denie C. M. Augustijn A, Suzanne J. M. H. Hulscher A, Bernard Fulanda D, Nzula Kitaka B and Jude M. Mathooko B

A University of Twente, PO BOX 217, 7500 AE Enschede, The Netherlands.
B Egerton University, PO Box 536, Njoro, Kenya.
C Kenya Marine and Fisheries Research Institute, PO Box 81651-80100, Mombasa, Kenya.
D Pwani University, PO BOX 195-80108, Kilifi, Kenya.
E Corresponding author. Email: jandungu@gmail.com

Marine and Freshwater Research 66(2) 177-186 http://dx.doi.org/10.1071/MF14031
Submitted: 6 November 2013  Accepted: 9 May 2014   Published: 31 October 2014

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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.

Additional keywords: cluster analysis, physico-chemical parameters, pollution, principal component analysis.


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