Statistical evaluation of surface water quality parameters: the extent of industrial effluent pollution in urban settlement

ABSTRACT: BACKGROUND AND OBJECTIVES: Industrial effluent contamination of water resources has emerged as a major challenge in developing and densely populated countries like Nigeria where river systems are the primary means for disposal of waste, especially the effluents from surrounding industries. This study seeks to determine the extent of surface water pollution from industrial effluent discharge. METHODS: Principal Component Analysis (PCA) and analysis of variance (ANOVA) were applied on the surface water quality data to identify the pollution sources and their contribution toward water quality variation. Water samples were collected from 4 sampling locations along the Onne-Okirika river. FINDING: The characteristics of the river showed a mean concentration of biochemical Oxygen Demand (BOD = 19.73 mg/l), Chemical Oxygen Demand (COD = 54.53 mg/l), Ammonia (NH3 = 21.00 mg/l), Dissolved Oxygen (DO = 5.78 mg/l) and temperature (28.40ᵒC) which varied significantly among sampling locations. Site 1, 2, and 3 showed a high level of NH3 and Chemical Oxygen Demand while site 4 (the control) has slightly high salinity when compared. The extent of pollution could be classified as; site 1 (High Pollution), site 2 (Moderate Pollution), and site 3 (Low Pollution). CONCLUSION: Sources of pollution include effluents from industrial plants such as fertilizer plants and oil refineries due to the high level of NH3. While the higher Chemical Oxygen Demand level is an indication of a greater amount of biodegradable organic material, which reduced the amount of Dissolved Oxygen. Additionally, a reduction in Dissolved Oxygen level can lead to an anaerobic condition, which is insidious to aquatic life forms. Therefore, the state environmental regulatory agency should ensure compliance monitoring of the industry’s activities.

Keywords:

Extent , Industrial effluent , pollution , Surface water quality , Principal Component Analysis (PCA)