Abstract
The prediction of emissions to air is a crucial and complex task for environmental monitoring and air quality management. Accurate forecasting is essential for the timely adoption of mitigation measures and for ensuring regulatory compliance. However, traditional statistical methods often perform inadequately because they poorly capture non-linear dependencies, intricate interactions between variables, and long-term temporal patterns, all of which ultimately decrease forecasting accuracy. The work presents an emission prediction software module based on a neural network with LSTM architecture. The input factors used were the concentrations of the main pollutants (NO, NO2, SO2, CO, solid particles) as well as meteorological indicators including air temperature, humidity and flow rate. Data provided by the operating enterprises, including 39,803 lines with increments of 20 minutes, were pre-processed: cleared from skips, normalized parameters and forming training sequences of 72 steps, corresponding to the daily interval. Additional exploration analysis was performed, which revealed the presence of expressed daily and weekly cycles, as well as correlations between weather conditions and concentrations of pollutants. The built model showed the ability to reproduce emission dynamics with acceptable accuracy, which is confirmed by MSE 0.87 and R2 0.86 values. The developed module is integrated into the current monitoring system and provides a user-friendly interface for building real-time forecasts. The results are consistent with current research, but the work is applied as a tool used in practical activities. In the future, it is planned to expand the set of factors and explore the possibilities of using ensemble architecture to improve the accuracy and robustness of forecasts.