DEVELOPMENT OF A NEURAL NETWORK-BASED MODULE FOR FORECASTING ATMOSPHERIC POLLUTANT EMISSIONS
DOI:
https://doi.org/10.37943/24WXPA8545Keywords:
neural networks, air pollution forecasting, atmospheric pollutant emissions, environmental monitoring, predictive modelingAbstract
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.
References
Guo, Q., He, Z., Li, S., Li, X., Meng, J., Hou, Z., ... & Chen, Y. (2020). Air pollution forecasting using artificial and wavelet neural networks with meteorological conditions. Aerosol and Air Quality Research, 20(6), 1429-1439.
Petrić, V., Hussain, H., Časni, K., Vuckovic, M., Schopper, A., Andrijić, Ž. U., ... & Lovrić, M. (2024). Ensemble machine learning, deep learning, and time series forecasting: improving prediction accuracy for hourly concentrations of ambient air pollutants. Aerosol and Air Quality Research, 24(12), 230317.
Chen, M., Xu, P., Liu, Z., Liu, F., Zhang, H., & Miao, S. (2025). Air pollution prediction based on optimized deep learning neural networks: PSO-LSTM. Atmospheric Pollution Research, 16(3), 102413.
Bhalgat, P., Pitale, S., & Bhoite, S. (2019). Air quality prediction using machine learning algorithms. International Journal of Computer Applications Technology and Research, 8(9), 367-370.
He, Z., & Guo, Q. (2024). Comparative analysis of multiple deep learning models for forecasting monthly ambient PM2. 5 concentrations: A case study in Dezhou City, China. Atmosphere, 15(12), 1432.
Gueye, A., Niang, S. A. A., Diallo, I., Dramé, M. S., Diallo, M., & Younous, A. A. (2025). On the Application of Long Short-Term Memory Neural Network for Daily Forecasting of PM2. 5 in Dakar, Senegal (West Africa). Sustainability, 17(12), 5421.
Guo, Q., He, Z., & Wang, Z. (2025). Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City. Scientific Reports, 15(1), 6798.
Chen, C., Qiu, A., Chen, H., Chen, Y., Liu, X., & Li, D. (2023). Prediction of pollutant concentration based on spatial–temporal attention, ResNet and ConvLSTM. Sensors, 23(21), 8863.
Wang, X., Zhang, S., Chen, Y., He, L., Ren, Y., Zhang, Z., ... & Zhang, S. (2024). Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM. Scientific Reports, 14(1), 17841.
Wu, Y. (2024). Time-Series Forecasting of PM₂.₅ and PM₁₀ Concenntrations Using a Dual-Channel Deep Learning Model Integrating Visual and Numerical Data. Sensors, 25(1), 95.
Chadalavada, S., Faust, O., Salvi, M., Seoni, S., Raj, N., Raghavendra, U., ... & Acharya, R. (2025). Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review. Environmental Modelling & Software, 185, 106312.
Liang, Y., Xia, Y., Ke, S., Wang, Y., Wen, Q., Zhang, J., ... & Zimmermann, R. (2023, June). Airformer: Predicting nationwide air quality in china with transformers. In Proceedings of the AAAI conference on artificial intelligence (Vol. 37, No. 12, pp. 14329-14337).
Alléon, A., Jauvion, G., Quennehen, B., & Lissmyr, D. (2020). PlumeNet: Large-scale air quality forecasting using a convolutional LSTM network. arXiv preprint arXiv:2006.09204.
Bai, X., Zhang, N., Cao, X., & Chen, W. (2024). Prediction of PM2. 5 concentration based on a CNN-LSTM neural network algorithm. PeerJ, 12, e17811.
Özüpak, Y., Alpsalaz, F., & Aslan, E. (2025). Air Quality Forecasting Using Machine Learning: Comparative Analysis and Ensemble Strategies for Enhanced Prediction. Water, Air, & Soil Pollution, 236(7), 464.
Kristiani, E., Lin, H., Lin, J. R., Chuang, Y. H., Huang, C. Y., & Yang, C. T. (2022). Short-term prediction of PM2. 5 using LSTM deep learning methods. Sustainability, 14(4), 2068.
Zhang, Q., Han, Y., Li, V. O., & Lam, J. C. (2022). Deep-AIR: A hybrid CNN-LSTM framework for fine-grained air pollution estimation and forecast in metropolitan cities. IEEE access, 10, 55818-55841.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR).
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