INTEGRATED MODEL FOR FORECASTING TIME SERIES OF ENVIRONMENTAL POLLUTION PARAMETERS

Abstract

The quality of life in large urban areas is considerably diminished by air pollution, with major contributors being motor vehicles, industrial activities, and fossil fuel combustion. A major contributor to air pollution is coal-fired and thermal power plants, which are commonly found in emerging markets. In Astana, Kazakhstan, a rapidly expanding city's significant reliance on coal for heating and considerable building exacerbate air pollution. This research is essential for improving urban development practices that support sustainable growth in rapidly expanding cities. Using time series data from four monitoring stations in Astana using fractal R/S analysis, the study looks at long-term patterns in air pollutant levels, especially PM10 and PM2.5. The stations' Hurst exponents were determined to be 0.723, 0.548, 0.442, and 0.462. Additionally, the flow window method was used to study the Hurst exponent's dynamic behavior. The findings showed that one station's pollution levels had long-term memory, which suggests that the time series is persistent. While anti-persistence was noted in the third and fourth sites, data from the second station indicated nearly random behavior. The Hurst exponent values explain the October 2021 spike in pollution levels, which is probably caused by thermal power plants close to the city. The fractal analysis of time series could serve as an indicator of environmental conditions in a given region, with persistent pollution trends potentially aiding in predicting critical pollution events. Anti-persistence or temporary pollution spikes may be influenced by the observation station's proximity to pollution sources. Overall, the findings suggest that fractal time series analysis can act as a valuable tool for monitoring environmental health in urban areas.

Keywords

• urban air pollution
• r/s analysis
• time series analysis
• hurst exponent
• pm10
• pm2.5