Forecasting air pollution concentrations based on atmospheric conditions and seasonality in urban environments

This paper aims to examine the impact of seasonality on a monthly basis in the context of forecasting PM10 levels using machine learning methods based on weather conditions. Predicting air pollution concentrations is crucial for public health. In this study, these pollutants are analyzed using predictive modeling. The research hypothesis of this paper is that seasonality influences the prediction accuracy of machine learning models forecasting air pollution levels. The study explores selected applications of machine learning methods for predicting air pollution levels and analyzes seasonal variations in pollutant concentrations. Data for this analysis were obtained from measurement stations in a highly urbanized region of Central Europe. The study demonstrated that, when forecasting air pollutant concentrations based on atmospheric conditions, the models achieved higher R² values in winter than in summer, yet exhibited greater prediction errors during colder months compared to warmer periods. Further analysis requires the inclusion of additional environmental factors to better reflect their influence on the accuracy of predictions. The results underscore the importance of seasonality in forecasting air pollution based on weather conditions, demonstrate its impact on the performance of machine learning models, and suggest directions for future research aimed at improving predictive accuracy and model generalization.