Theoretical and Practical Perspectives on Mathematical Modelling of Water Contaminants in Klang River: Analysis and Insights into Pollution Dynamics

Abstract

Understanding pollutant transport dynamics in fluvial systems and formulating effective environmental management strategies necessitate robust water quality modelling. This study develops a mathematical framework to simulate the transport and transformation of key water pollutants—Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total Dissolved Solids (TDS)—within the Klang River, Selangor, Malaysia. The model incorporates multiple pollution sources and temporal variations in contaminant inputs to represent real-world conditions. The governing system of differential equations, which encapsulates pollutant advection, diffusion, degradation, and external influxes, is numerically solved using MATLAB. Three principal analyses are performed: (i) Sensitivity Analysis to determine critical parameters influencing pollutant concentration dynamics; (ii) Scenario Analysis to assess the impact of varying pollution levels, including industrial effluents and seasonal fluctuations; and (iii) Stability Analysis to evaluate long-term pollutant behaviour and the river’s intrinsic self-purification capacity. Simulation results indicate that temporal variations in pollutant loading, and degradation rates significantly influence water quality trends. Elevated industrial discharges substantially increase BOD and COD concentrations, heightening the risk of hypoxic conditions and ecological degradation. Additionally, model predictions suggest that under specific conditions, pollutant concentrations may reach equilibrium, signifying a dynamic balance between natural attenuation processes and continuous external inputs. The study’s findings offer valuable insights into aquatic pollution control and sustainable water resource management. They provide a scientific basis for policy recommendations concerning wastewater treatment, industrial discharge regulations, and riverine ecosystem preservation. Future research will incorporate empirical water quality datasets to further refine model validation and enhance predictive accuracy.