Using Multiple Correspondence Analysis to Identify Types of Pollution in Water Resources of Lukula Region

Document Type : Original Article

Authors

1 President Joseph Kasa-Vubu University, Faculty of Engineering, Boma, Democratic Republic of the Congo

2 Regional School of Water (ERE), University of Kinshasa (UNIKIN), Kinshasa, Democratic Republic of the Congo

3 Higher Pedagogical Institute of Inongo, Agroveterinary Sciences, Exact Sciences stream, Inongo, Democratic Republic of the Congo

4 University of Kinshasa, Faculty of Science and Technology, Kinshasa, Democratic Republic of the Congo5Higher Institute of Agronomic Studies, Mangaly and Ministry of Mines, Kinshasa, Democratic Republic of Congo

5 Higher Institute of Agronomic Studies, Mangaly, and Ministry of Mines, Kinshasa, Democratic Republic of Congo

6 University of Kinshasa, Faculty of Science and Technology, Kinshasa, Democratic Republic of the Congo

7 Laboratory of Chemistry and Geochemistry, Center for Geological and Mining Research, B.P.898 Kinshasa Gombe, Democratic Republic of the Congo

8 University of Quebec in Abitibi-Temiscamingue, School of Engineering, Rouyn-Noranda, Canada.

9 University of Kinshasa, Faculty of Agronomic Sciences, Department of Phytotechnics, Kinshasa, Democratic Republic of the Congo

Abstract

Water quality degradation is a major environmental and public health issue, particularly in developing countries where communities depend on untreated groundwater and spring water for domestic use. In the Lukula region (Kongo Central Province, Democratic Republic of the Congo), limited access to safe drinking water increases the risk of exposure to physicochemical and microbiological contamination. This study aimed to characterize the quality of spring waters in the Lukula Health Zone and to identify the main types of pollution affecting these resources using multivariate statistical techniques. Water samples were collected from ten springs during two sampling campaigns conducted in April and December 2018. Physicochemical parameters (pH, temperature, electrical conductivity, total dissolved solids, turbidity, alkalinity, hardness, major ions, and nutrients) and microbiological indicators (total viable counts and total coliforms) were analyzed. Data were processed using Principal Component Analysis (PCA), Hierarchical Ascending Clustering (HAC), and Multiple Correspondence Analysis (MCA). The results revealed significant spatial variability in water quality. PCA indicated that the first two principal components explained 64.1% of the total variance, with the first component primarily associated with mineralization, alkalinity, hardness, bicarbonates, calcium, and magnesium, and the second primarily associated with chloride, sodium, potassium, and sulfate. HAC classified the springs into three hydrochemical groups reflecting differences in mineralization and salinity. MCA identified multiple pollution patterns, including mineral pollution, nutrient enrichment, microbiological contamination, sediment-related pollution, and anthropogenic impacts. Springs such as Kizinga, Kiyangi I, Kinduka, and Kilonde showed the highest contamination levels. These findings demonstrate the value of multivariate statistical methods for water-quality assessment and provide a scientific basis for monitoring, pollution control, and sustainable water-resource management in the Lukula region.

Keywords

References

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Journal of Hydraulic and Water Engineering
Volume 3, Issue 1
July 2025
Pages 109-131

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