Evaluating the Potential of Random Forest Regression for PM2.5 Modeling Within Environmental Safety Decision Support Systems

Progressive urbanization and increased air pollution emissions pose a significant challenge for modern safety engineering, especially in terms of forecasting and mitigating environmental risks¹. This paper presents a machine learning-based approach to modeling PM2.5 particulate matter concentrations using open environmental and meteorological data from the OpenWeatherMap platform. The aim of the study was to develop a model to support decision-making in environmental safety systems through early detection of potential air pollution episodes. The study used a Random Forest model with parameter optimization and cross-validation, as well as time feature transformations to account for the cyclical nature of atmospheric phenomena. The analysis of the results showed a high correlation between the predictions and the actual PM2.5 concentrations, with a coefficient of determination R² above 0.8 in most of the analyzed time intervals. The results confirm the effectiveness of the proposed approach in identifying trends and environmental anomalies that may pose a threat to public health. The developed model can be a component of intelligent environmental safety management systems² and a basis for further research on the integration of artificial intelligence with IoT infrastructure and urban air quality monitoring systems.