Authors : Nwogbu Peter Chinedu; Chima Daniel Azubuike; Utobo Ruth Chinaza; Nwigwe Simon; Nwozaku Basil Odinaka; Ngwuta Amuche Daniel; Utobo Maria Ginika; Utobo Martha Kelechi

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/52thn5dp

Scribd : https://tinyurl.com/mb549sk8

DOI : https://doi.org/10.38124/ijisrt/26mar449

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Biofouling is a significant operational challenge in wastewater treatment systems, caused by the accumulation of microorganisms and organic matter on treatment infrastructure, which reduces efficiency and increases maintenance costs. This study evaluates the biofouling potential at the Davyhulme Wastewater Treatment Works by analysing temporal water quality trends, spatial hydrological conditions, and machine learning–based predictive models. Key water quality indicators examined include Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), ammoniacal nitrogen, suspended solids, arsenic, and chloroform using monitoring data collected between January and April 2025. Statistical analysis revealed high variability in organic loading, particularly for BOD and COD, which recorded mean values of 97.9 mg/L and 282 mg/L respectively, with peak concentrations reaching 317 mg/L and 694 mg/L. Time-series analysis showed that biofouling risk is episodic, driven by short-term spikes in organic matter that promote microbial growth and biofilm formation. Correlation analysis further indicated strong relationships between oxygen-demand parameters, highlighting organic loading as a key driver of biofouling risk. Spatial analysis of upstream hydrological infrastructure demonstrated that variations in flow velocity and catchment characteristics influence nutrient accumulation and create stagnation zones favourable for microbial attachment. To assess risk levels, K-Means clustering classified water quality conditions into low, moderate, and high biofouling risk categories, with high-risk clusters strongly associated with elevated BOD and COD concentrations. A Decision Tree classification model achieved a predictive accuracy of 98%, confirming that pollutant concentration levels are strong predictors of biofouling risk. Overall, the study demonstrates that integrating statistical analysis, geospatial intelligence, and machine learning provides an effective predictive framework for proactive biofouling management and improved wastewater treatment system resilience.

Keywords : Biofouling, Wastewater Treatment, Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Machine Learning, Geospatial Analysis, Water Quality Monitoring.

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