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

Kyrylo Vadurin, Andrii Perekrest, Volodymyr Bakharev, Vira Shendryk (), Yuliia Parfenenko and Sergii Shendryk
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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

Sustainability, 2025, vol. 17, issue 9, 1-36

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.

Keywords: sustainable development; environmental monitoring; air pollution; emissions; climate change mitigation; forecasting; machine learning; information system; spatial analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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