Predictability of Air Pollutants Based on Detrended Fluctuation Analysis: Ekibastuz Сoal-Mining Center in Northeastern Kazakhstan
Kuchanskyi O. Biloshchytskyi A. Andrashko Y. Neftissov A. Biloshchytska S. Bronin S.
July 2025Multidisciplinary Digital Publishing Institute (MDPI)
Urban Science
2025#9Issue 7
Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO2, SO2, PM2.5, and PM10, as well as current mass consumption of CO, NO, NO2, SO2, dust, and NOx. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas.
air pollution , coal-fired power station , detrended fluctuation analysis , Hurst exponent , long-term memory , predictability index
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Department of Computational and Data Science, Astana IT University, Astana, 010000, Kazakhstan
Department of Information Control Systems and Technologies, Uzhhorod National University, Uzhhorod, 88000, Ukraine
Department of Biomedical Cybernetics, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, 03056, Ukraine
Department of Administration, Astana IT University, Astana, 010000, Kazakhstan
Department of Information Technology, Kyiv National University of Construction and Architecture, Kyiv, 03037, Ukraine
Department of System Analysis and Optimization Theory, Uzhhorod National University, Uzhhorod, 88000, Ukraine
Research and Innovation Center “Industry 4.0”, Astana IT University, Astana, 010000, Kazakhstan
Institute of Social Sciences and Applied Informatics, Vilnius University, Kaunas, 44280, Lithuania
Department of Computational and Data Science
Department of Information Control Systems and Technologies
Department of Biomedical Cybernetics
Department of Administration
Department of Information Technology
Department of System Analysis and Optimization Theory
Research and Innovation Center “Industry 4.0”
Institute of Social Sciences and Applied Informatics
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