Application of machine learning methods to the prediction of NO2 concentration in the air environment

Air quality significantly impacts public health, with nitrogen dioxide (NO2) being a key pollutant linked to respiratory and cardiovascular diseases. In this study, we developed a machine learning model to accurately predict hourly NO2 concentrations in Ternopil, Ukraine, using readily available meteorological and temporal data. The model was trained on a large dataset and tested using data from the Ecocity monitoring station, known for recording NO2 levels exceeding legal limits. By employing neural networks, the model demonstrated high accuracy in predicting NO2 concentrations, with the error of 3.9% and 1.4%, respectively, in the test samples. Our findings underscore the potential of machine learning techniques to enhance air quality monitoring and forecasting, particularly in urban areas with limited resources. This approach offers a valuable tool for real-time pollution management and public health protection.