ABSTRACT

Air pollution is a major environmental concern that affects human health, ecosystems, and climate. Understanding and predicting the dispersion of pollutants is essential for environmental assessment and pollution control strategies. This study presents a computational approach to modelling pollutant dispersion using the Gaussian Plume Model implemented in Python. The model considers key parameters such as stack height, wind speed, emission rate, and diffusion coefficients, which significantly influence pollutant spread in the atmosphere. A Python-based simulation framework is developed to analyse these effects through contour plots, providing a clear visualization of how pollutants disperse under different environmental conditions. The results indicate that higher stacks reduce ground-level pollution, stronger winds dilute pollutants over longer distances, increased emissions intensify pollution levels, and greater diffusion leads to wider dispersion. This study demonstrates the effectiveness of Python for atmospheric pollution modelling and provides insights for air quality management and regulatory policies.