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Towards Digitalization for Air Pollution Detection: Forecasting Information System of the Environmental Monitoring

Author

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  • Kyrylo Vadurin

    (Department of Computer Engineering and Electronics, Kremenchuk Mykhailo Ostrohradskyi National University, 20 Universytetska Str., 39600 Kremenchuk, Ukraine)

  • Andrii Perekrest

    (Department of Computer Engineering and Electronics, Kremenchuk Mykhailo Ostrohradskyi National University, 20 Universytetska Str., 39600 Kremenchuk, Ukraine)

  • Volodymyr Bakharev

    (Department of Computer Engineering and Electronics, Kremenchuk Mykhailo Ostrohradskyi National University, 20 Universytetska Str., 39600 Kremenchuk, Ukraine)

  • Vira Shendryk

    (Department of Information Technologies, Sumy State University, 116 Kharkivska Str., 40007 Sumy, Ukraine)

  • Yuliia Parfenenko

    (Department of Information Technologies, Sumy State University, 116 Kharkivska Str., 40007 Sumy, Ukraine)

  • Sergii Shendryk

    (Department of Cybernetics and Informatics, Sumy National Agrarian University, 160 Herasyma Kondratieva Str., 40000 Sumy, Ukraine)

Abstract

This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting information system for integrating data processing, analysis, and visualization to improve environmental monitoring practices is described in this article. The system utilizes machine learning models (ARIMA and BATS) for time series forecasting, automatically selecting the optimal model based on accuracy metrics. Spatial analysis employing inverse distance weighting (IDW) provides insights into pollutant distribution, while correlation analysis identifies relationships between pollutants. The system was tested using retrospective data from the Kremenchuk agglomeration (2007–2024), demonstrating its ability to forecast air quality parameters and identify areas exceeding maximum permissible pollutant concentrations. Results indicate that BATS often outperforms ARIMA for several key pollutants, highlighting the importance of automated model selection. The developed system offers a cost-effective solution for local municipalities, enabling data-driven decision-making, optimized monitoring network placement, and improved alignment with European Union environmental standards.

Suggested Citation

  • Kyrylo Vadurin & Andrii Perekrest & Volodymyr Bakharev & Vira Shendryk & Yuliia Parfenenko & Sergii Shendryk, 2025. "Towards Digitalization for Air Pollution Detection: Forecasting Information System of the Environmental Monitoring," Sustainability, MDPI, vol. 17(9), pages 1-36, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3760-:d:1639475
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    References listed on IDEAS

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    1. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
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