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Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors

Author

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  • Yuyi Zhang

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Saint Petersburg 198504, Russia)

  • Qiushi Sun

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Saint Petersburg 198504, Russia)

  • Jing Liu

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Saint Petersburg 198504, Russia)

  • Ovanes Petrosian

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Saint Petersburg 198504, Russia)

Abstract

Long-term forecasting and analysis of PM2.5, a significant air pollution source, is vital for environmental governance and sustainable development. We evaluated 10 machine learning and deep learning models using PM2.5 concentration data along with environmental variables. Employing explainable AI (XAI) technology facilitated explainability and formed the basis for factor analysis. At a 30-day forecasting horizon, ensemble learning surpassed deep learning in performance, with CatBoost emerging as the top-performing model. For forecasting horizons of 90 and 180 days, Bi-SLTM and Bi-GRU, respectively, exhibited the highest performance. Through an analysis of influencing factors by SHAP, it was observed that PM10 exerted the greatest impact on PM2.5 forecasting. However, this effect was particularly pronounced at higher concentrations of CO. Conversely, at lower CO concentrations, the impact of increased PM10 concentrations on PM2.5 was limited. Hence, it can be inferred that CO plays a pivotal role in driving these effects. Following CO, factors such as “dew point” and “temperature” were identified as influential. These factors exhibited varying levels of linear correlation with PM2.5, with temperature showing a negative correlation, while PM10, CO, and dew point generally demonstrated positive correlations with PM2.5.

Suggested Citation

  • Yuyi Zhang & Qiushi Sun & Jing Liu & Ovanes Petrosian, 2023. "Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:19-:d:1303016
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    References listed on IDEAS

    as
    1. Sultan Ayoub Meo & Faris Jamal Almutairi & Abdulelah Adnan Abukhalaf & Adnan Mahmood Usmani, 2021. "Effect of Green Space Environment on Air Pollutants PM2.5, PM10, CO, O 3 , and Incidence and Mortality of SARS-CoV-2 in Highly Green and Less-Green Countries," IJERPH, MDPI, vol. 18(24), pages 1-11, December.
    2. Zhiyu Fan & Qingming Zhan & Chen Yang & Huimin Liu & Meng Zhan, 2020. "How Did Distribution Patterns of Particulate Matter Air Pollution (PM 2.5 and PM 10 ) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
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    More about this item

    Keywords

    PM2.5 forecasting; explainable AI; influencing factors;
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