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<p>Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air quality remains a critical concern due to pollutants such as particulate matter (PM), which pose serious risks to public health and ecological systems. Effective control of PM emissions and accurate forecasting of their concentrations are essential for improving air quality and supporting public health interventions. This paper presents four advanced deep learning-based forecasting methods: extended long short-term memory network (xLSTM), Kolmogorov-Arnold network (KAN), temporal convolutional network (TCN), and variational autoencoder (VAE). Using data from eight cities in Poland, we evaluate our methods’ ability to predict particulate matter concentrations through extensive experiments, utilizing statistical hypothesis testing and error metrics such as mean absolute error (MAE) and root mean square error (RMSE). Our findings demonstrate that these methods achieve high prediction accuracy, significantly outperforming several state-of-the-art algorithms. The proposed forecasting framework offers practical applications for policymakers and public health officials by enabling timely interventions to decrease pollution impacts and enhance urban air quality management.</p>

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Forecasting the concentration of the components of the particulate matter in Poland using neural networks

  • 期刊论文

  • 发表日期：

    2025年3月21日

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  • Jarosław Bernacki

摘要

Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air quality remains a critical concern due to pollutants such as particulate matter (PM), which pose serious risks to public health and ecological systems. Effective control of PM emissions and accurate forecasting of their concentrations are essential for improving air quality and supporting public health interventions. This paper presents four advanced deep learning-based forecasting methods: extended long short-term memory network (xLSTM), Kolmogorov-Arnold network (KAN), temporal convolutional network (TCN), and variational autoencoder (VAE). Using data from eight cities in Poland, we evaluate our methods’ ability to predict particulate matter concentrations through extensive experiments, utilizing statistical hypothesis testing and error metrics such as mean absolute error (MAE) and root mean square error (RMSE). Our findings demonstrate that these methods achieve high prediction accuracy, significantly outperforming several state-of-the-art algorithms. The proposed forecasting framework offers practical applications for policymakers and public health officials by enabling timely interventions to decrease pollution impacts and enhance urban air quality management.