This research develops a computational approach for predicting the dispersion of radioactive effluents in the air, addressing environmental protection and public health concerns. In accordance with regulatory requirements, facility operators in Indonesia must report data on radioactive effluents to BAPETEN and establish limit values for the release of radioactivity to ensure radiation protection. Accurate simulations of the dispersion patterns of radioactive effluents are crucial. A Gaussian dispersion model, widely used in environmental health and atmospheric studies, was applied in this research.

The aim was to develop a computational application to simulate the dispersion of radionuclide particulates and assess their potential impact on human health. The methodology involved the use of Python for model implementation, incorporating atmospheric stability classifications (Pasquill method), lateral and vertical dispersion coefficients, and the development of the computational modeling application DISCHEV (Particle Dispersion in Air Based on Python). The application was rigorously tested, with verification comparing the results to theoretical expectations and outputs from established models. The simulation results demonstrated a minimal deviation of 0.0000% when compared to Microsoft Excel calculations, indicating high accuracy.

The findings highlight the effectiveness of computational models in predicting radioactive effluent dispersion and their potential for enhancing environmental quality, radiological safety, and health risk assessments. The successful development of DISCHEV showcases the power of advanced programming techniques in improving environmental protection strategies. Future applications could further refine these models for more precise and dynamic simulations of radioactive emissions.