Mathematical Modelling for Performance Prediction of Ex-Mining Lake Water Phytoremediation by Scirpus Grossus

This study explores the use of Scirpus grossus for phytoremediation of ex-mining lake water, offering a potential low-cost alternative to conventional wastewater treatment. The focus is on removing contaminants such as total iron, total nitrate, total sulfate, total phosphorus, electrical conductivity, chemical oxygen demand, turbidity, and pH. Over 28 days, the ex-mining lake water was treated with S. grossus to assess contaminant removal, with the results analyzed using a mathematical model in Microsoft Excel. The model simulated exponential reductions in pollutants and increases in pH, with absorption coefficients calculated for each parameter. The study found that S. grossus effectively reduced contaminants, with the most significant removal of total iron at 95.45%. The pH of the water increased from 2.61 (acidic) to 6.29 (neutral), improving its suitability for aquatic life. The predicted removal rates closely matched the observed data, suggesting that the model is reliable for forecasting phytoremediation outcomes. Overall, the study confirms that S. grossus is a highly effective species for cleaning ex-mining lake water, offering a sustainable and cost-effective solution for industrial wastewater treatment. The findings encourage further research into the scalability, long-term effectiveness, and integration of this technique with other wastewater management strategies.