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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Atmosphere and Climate
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1442644

Short-term PM 2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives

Provisionally accepted
Hasnain Iftikhar Hasnain Iftikhar 1*Moiz Qureshi Moiz Qureshi 2Justyna Zywiołek Justyna Zywiołek 3Javier Linkolk López-Gonzales Javier Linkolk López-Gonzales 4
  • 1 Quaid-i-Azam University, Islamabad, Pakistan
  • 2 Department of Statistics, Govt: Degree College Tandojam, Hyderabad, Pakistan
  • 3 Other, Czestochowa, Silesian Voivodeship, Poland
  • 4 Other, Lima, Peru

The final, formatted version of the article will be published soon.

    Particulate matter with a diameter of 2.5 microns or less (PM 2.5 ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM 2.5

    Keywords: Air Pollution, concentration, short-term PM 2.5 forecasting, single time series models, ensemble time series models, sustainable development, Early warning system, Decision Making

    Received: 02 Jun 2024; Accepted: 23 Aug 2024.

    Copyright: © 2024 Iftikhar, Qureshi, Zywiołek and López-Gonzales. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hasnain Iftikhar, Quaid-i-Azam University, Islamabad, Pakistan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.