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Article

Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia

1
Faculty of Agriculture and Environmental Sciences, University of Rostock, 18051 Rostock, Germany
2
Faculty of Biosystems and Water Resource Engineering, Institute of Technology, Hawassa University, Hawassa P.O. Box 05, Ethiopia
3
Center for Ethiopian Rift Valley studies, Hawassa University, Hawassa P.O. Box 05, Ethiopia
*
Author to whom correspondence should be addressed.
Academic Editor: Jorge Rodríguez-Chueca
Appl. Sci. 2021, 11(19), 8991; https://doi.org/10.3390/app11198991
Received: 2 August 2021 / Revised: 20 September 2021 / Accepted: 23 September 2021 / Published: 27 September 2021
(This article belongs to the Special Issue Water Quality Modelling, Monitoring and Mitigation)
The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na+), and potassium ion (K+) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed. View Full-Text
Keywords: monitoring; mitigations; spatial and temporal variabilities; principal component analysis; cluster analysis; discriminant analysis; water quality; pollution; correlation monitoring; mitigations; spatial and temporal variabilities; principal component analysis; cluster analysis; discriminant analysis; water quality; pollution; correlation
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MDPI and ACS Style

Lencha, S.M.; Ulsido, M.D.; Muluneh, A. Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia. Appl. Sci. 2021, 11, 8991. https://doi.org/10.3390/app11198991

AMA Style

Lencha SM, Ulsido MD, Muluneh A. Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia. Applied Sciences. 2021; 11(19):8991. https://doi.org/10.3390/app11198991

Chicago/Turabian Style

Lencha, Semaria M., Mihret D. Ulsido, and Alemayehu Muluneh. 2021. "Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia" Applied Sciences 11, no. 19: 8991. https://doi.org/10.3390/app11198991

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