Hydrochemistry and Predictive Modelling of Water Quality in Ogun and Oshun Rivers, Southwestern Nigeria
DOI:
https://doi.org/10.54536/ajee.v4i1.6041Keywords:
Compliance Analysis, Decision Tree Regression, Hydrochemistry, Nigeria, Ogun River, Oshun River, Seasonal Variation, Water Quality ModellingAbstract
Rivers in tropical Africa face increasing pressures from rapid urbanization, agricultural intensification, and weak wastewater management, yet systematic assessments of their hydrochemistry and predictive modelling remain limited. This study investigates the water quality of Ogun and Oshun Rivers in southwestern Nigeria using a multi-method approach that integrates descriptive statistics, compliance analysis, hydrochemical ratios, trend evaluation, and regression modelling. Thirteen sites along Ogun River and ten sites along Oshun River were sampled in both wet and dry seasons. Key parameters analyzed included pH, major cations (Na⁺, Ca²⁺, Mg²⁺, K⁺), anions (Cl⁻, SO₄²⁻, PO₄³⁻), total dissolved solids (TDS), turbidity, and iron. Results showed that while most ions were within the limits set by the WHO (2017) and the NSDWQ (2007), turbidity and phosphate consistently exceeded permissible levels in 70–100% of samples. Hydrochemical ratios indicated dual controls, with carbonate weathering as the primary geogenic influence and sodium enrichment reflecting anthropogenic inputs. Trend analysis revealed significantly higher Na⁺, PO₄³⁻, and TDS in the dry season (p < 0.05), while downstream gradients highlighted cumulative deterioration, particularly in Ogun River. Predictive modelling demonstrated that sodium is the strongest predictor ion, and decision tree regression outperformed linear and polynomial models, achieving R² values above 0.95 in dry season datasets. These findings underscore the vulnerability of the rivers to nutrient enrichment, sediment load, and sodium hazard, with Ogun River more severely impacted. Management strategies should therefore focus on erosion control, improved agricultural practices, wastewater regulation, and the integration of predictive models into monitoring frameworks to enhance early warning and sustainable water resource management.
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Copyright (c) 2025 Abiodun B. Laniyan, Opeyemi O. Ogunyinka, Olaide I. Afolabi, Oluwaseun A. Odusanya, Adebayo S. Oyefusi, Olugbenga S. Adebukola
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