IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p1101-d1579846.html
   My bibliography  Save this article

A Review on PM 2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges

Author

Listed:
  • Peng-Yeng Yin

    (Information Technology and Management Program, Ming Chuan University, No. 5 De-Ming Road, Gui-Shan District, Taoyuan City 333321, Taiwan)

Abstract

Air pollution has long been one of the most life-threatening issues which has led to massive amounts of premature human death due to fatal diseases and environmental disasters. Several Sustainable Development Goals (SDGs) set up by the United Nations coincide with the solutions for air pollution reduction. To reach a sustainable future, researchers have conducted many theoretical analyses or case studies of air pollution at different places on the globe and proposed prudent strategies for obtaining an equilibrium between socioeconomic development and air pollution reduction. This research selected a substantial number of articles and existing review papers published between 2013 and 2024 and organized these publications into subfields. This research was focused on filling the gap between existing reviews and the state-of-the-art technologies in the last decade. To be informative and contextual, this review presented a pathway for readers to comprehend the research in three contiguous phases of air pollution analysis, from compositional apportionment and mass prediction of pollution to disclosing associations between pollution concentration and natural or anthropogenic factors. At the end of this review, the author highlighted several research fields which have been overlooked in previous reviews but will be increasingly important in the future.

Suggested Citation

  • Peng-Yeng Yin, 2025. "A Review on PM 2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges," Sustainability, MDPI, vol. 17(3), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1101-:d:1579846
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/1101/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/3/1101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    2. Peng-Yeng Yin, 2024. "Mining Associations between Air Quality and Natural and Anthropogenic Factors," Sustainability, MDPI, vol. 16(11), pages 1-22, May.
    3. Mateusz Zareba & Szymon Cogiel & Tomasz Danek & Elzbieta Weglinska, 2024. "Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation," Energies, MDPI, vol. 17(11), pages 1-13, June.
    4. Li, Shaoshuai & Li, Zhigang & Ni, Jinlan & Yuan, Jia, 2023. "Growing pains for others: Using holidays to identify the pollution spillover between China and South Korea," China Economic Review, Elsevier, vol. 77(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Tomasz Gorzelnik & Marek Bogacki & Robert Oleniacz, 2024. "Identification of Factors Influencing Episodes of High PM 10 Concentrations in the Air in Krakow (Poland) Using Random Forest Method," Sustainability, MDPI, vol. 16(20), pages 1-23, October.
    4. Izabela Rojek & Dariusz Mikołajewski & Krzysztof Galas & Adrianna Piszcz, 2025. "Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities," Energies, MDPI, vol. 18(2), pages 1-19, January.
    5. 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.
    6. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    7. 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.
    8. 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.
    9. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1101-:d:1579846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.