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A Dynamic Multiple Equation Approach for Forecasting PM2.5 Pollution in Santiago, Chile

Author

Listed:
  • Stella Moisan

    (Universidad de Talca, Chile)

  • Rodrigo Herrera

    (Universidad de Talca, Chile)

  • Adam Clements

    (QUT)

Abstract

A methodology based on a system of dynamic multiple linear equations is proposed that incorporates hourly, daily and annual seasonal characteristics to predict hourly pm2.5 pollution concentrations for 11 meteorological stations in Santiago, Chile. It is demonstrated that the proposed model has the potential to match or even surpass the accuracy of other linear and nonlinear forecasting models in terms of fit and predictive ability. In addition, the model is successful in predicting various categories of high concentration events, up to 76% of mid-range and 100% of extreme-range events as an average across all stations. This forecasting model is considered a useful tool for government authorities to anticipate critical episodes of air quality so as to avoid the detrimental impacts economic and health impacts of extreme pollution levels.

Suggested Citation

  • Stella Moisan & Rodrigo Herrera & Adam Clements, 2017. "A Dynamic Multiple Equation Approach for Forecasting PM2.5 Pollution in Santiago, Chile," NCER Working Paper Series 117, National Centre for Econometric Research.
    • Handle: RePEc:qut:auncer:2017_01
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    File URL: http://www.ncer.edu.au/papers/documents/WP117.pdf
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    References listed on IDEAS

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    Cited by:

    1. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    2. Clements, Adam & Hurn, Stan & Volkov, Vladimir, 2021. "A simple linear alternative to multiplicative error models with an application to trading volume," Working Papers 2021-06, University of Tasmania, Tasmanian School of Business and Economics.
    3. Pei Du & Jianzhou Wang & Wendong Yang & Tong Niu, 2022. "A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 64-85, January.
    4. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    5. Xiang Xu, 2020. "Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 117-125, March.
    6. Zhongfei Li & Kai Gan & Shaolong Sun & Shouyang Wang, 2023. "A new PM2.5 concentration forecasting system based on AdaBoost‐ensemble system with deep learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 154-175, January.
    7. Du, Ruijin & Li, Jingjing & Dong, Gaogao & Tian, Lixin & Qing, Ting & Fang, Guochang & Dong, Yujuan, 2020. "Percolation analysis of urban air quality: A case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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    Keywords

    Air quality; Particulate matter; Dynamic multiple equations;
    All these keywords.

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