Key driving factors and model predictions of the characteristics of air pollution in the urban area of Changchun, a typical city on the Northeast China Plain

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Abstract

In this study, we used monitoring data on six representative pollutants, meteorological data, and socioeconomic data from January 2018 to December 2023 to clarify the spatiotemporal characteristics of air pollution in Changchun, Jilin Province, China, and to establish a short-term prediction model. First, correlation analysis (CA) was used to identify the key factors affecting pollution characteristics. Then, wavelet analysis (WA) was applied to capture the major periodic coupling characteristics of the time series, thereby analyzing the variation law of air quality. The results showed that the six air pollutants in Changchun exhibited significant seasonal differences. The peak concentration of O₃ occurred in summer, while the peaks of other pollutants were concentrated in winter and spring. The average air quality index (AQI) exhibited a downward, overall fluctuating trend, with PM_2.5 and PM₁₀ as the major pollutants. Urbanization rate and green space coverage area were identified as key social driving factors, while temperature, relative humidity, and wind speed were identified as key meteorological driving factors. Furthermore, WA revealed a coupling phenomenon between the major periods of the AQI sequence and the PM_2.5 and PM₁₀ sequences. Additionally, the low-frequency wavelet signals showed an overall downward trend, confirming that composite pollution is a typical feature of air pollution in Changchun, while the regional air quality is gradually improving. A short-term AQI forecasting equation based on ordinary least squares (OLS) was constructed. Unlike previous AQI prediction models that used only meteorological factors as independent variables, this model included "AQI data of the same period in the previous year" as a key independent variable. Verification showed that the model's fit outperformed that of traditional meteorologically driven models, yielding more accurate short-term AQI predictions for Changchun, driven by the following two major innovations. First, combined wavelet and CA comprehensively determined the periodic coupling characteristics between AQI and major pollutants, providing an efficient tool for analyzing the causes of composite pollution. Second, incorporating historical AQI data into the OLS prediction model mitigated the limitation of traditional models that rely solely on meteorological factors.

Keywords

urban air pollution / short-term air quality index / socioeconomic factors / meteorological factors / Northeast China Plain

CHANGE HISTORY

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