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Statistical Assessment of Water Quality Indicators for Pollution Status Identification of River Kaduna in Niger State, Nigeria

Authors: Okibe FG, Yahaya IA, Onoyima CC, Ajibola VO, Agbaji EB, Afolayan MO

ABSTRACT

Due to the multivariate nature of environmental data, statistical analysis is used to decipher any structure within the data and bring out hidden information about quality of environmental samples. This research applied statistical analysis to identify pollution status of flood-prone River Kaduna at Kede Niger State, Nigeria. Samples were collected in March and September of the same year and analysed for selected water quality parameters. The data was subjected to basic statistics, cluster analysis (CA), principal component analysis (PCA), correlation analysis, and discriminant analysis (DA). Descriptive statistics show that the following parameters were above the World Health Organisation (WHO) limit: Temperature (30.12±0.54 oC and 30.53±0.99 oC), Turbidity (23.21±2.79 NTU and 26.42±4.25 NTU), chemical oxygen demand (COD) (34.96±6.84 mg/L and 33.81±7.14 mg/L), biochemical oxygen demand (BOD) (17.96±3.52 mg/L and 18.67±3.44 mg/L), TBC (93.33±14.4 CFU/100 cm3 and 88.37±16.64 CFU/100 cm3), nitrate (12.2±2.28 mg/L and 11.68±1.54 mg/L), Fe (0.32±0.25 and 1.10±0.62 mg/L) for March and September respectively. pH, TDS, Conductivity, DO, Alkalinity, Phosphate, Sulphate, Ca, Mg, K, Na, and Chloride were within the WHO limit, while Cu, Cd, Cr, and Zn were below detection limit. PCA identified four components for March and September respectively. Cluster analysis also reveals four distinct clusters of variables for both sampling seasons, while DA shows pH, BOD, Alkalinity Ca, Mg, Na and Sulphate as the only significant predictors for March while TDS, TBC and Ca are the only significant predictors for September. The study shows that there was significant loading of the quality parameter after flooding.


Affiliations: Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria.
Keywords: Water Quality Indicators, Statistical Analysis, Correlation, Cluster, Principal Component Analysis
Published date: 2019/12/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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