IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i19p8721-d1495077.html
   My bibliography  Save this article

Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China

Author

Listed:
  • Panagiotis Fazakis

    (Air Pollution Laboratory, Department of Mechanical Engineers, School of Engineers, University of West Attica, P. Ralli & 250 Thivon Str., Aegaleo, GR-12244 Athens, Greece)

  • Konstantinos Moustris

    (Air Pollution Laboratory, Department of Mechanical Engineers, School of Engineers, University of West Attica, P. Ralli & 250 Thivon Str., Aegaleo, GR-12244 Athens, Greece)

  • Georgios Spyropoulos

    (Air Pollution Laboratory, Department of Mechanical Engineers, School of Engineers, University of West Attica, P. Ralli & 250 Thivon Str., Aegaleo, GR-12244 Athens, Greece
    Soft Energy Applications & Environmental Protection Laboratory, University of West Attica, P. Ralli & 250 Thivon Str., Aegaleo, GR-12244 Athens, Greece)

Abstract

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with an aerodynamic diameter of 2.5 μm (PM 2.5 ) and 10 μm (PM 10 ), nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO 2 ), and ozone (O 3 ) needs constant monitoring, as they consist of the main cause for many diseases. Based on the existence of statutory limits from the World Health Organization (WHO) for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict air pollutant concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural network (ANN) models. In the frame of a specific work, a significant number of ANNs are developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. The results showed that ANNs present a remarkable prognostic ability in order to forecast air pollution levels in an urban environment.

Suggested Citation

  • Panagiotis Fazakis & Konstantinos Moustris & Georgios Spyropoulos, 2024. "Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China," Sustainability, MDPI, vol. 16(19), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8721-:d:1495077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/19/8721/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/19/8721/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valentin Simoncic & Christophe Enaux & Séverine Deguen & Wahida Kihal-Talantikite, 2020. "Adverse Birth Outcomes Related to NO 2 and PM Exposure: European Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 17(21), pages 1-70, November.
    2. Alireza Sarraf Shirazi & Ian Frigaard, 2021. "SlurryNet: Predicting Critical Velocities and Frictional Pressure Drops in Oilfield Suspension Flows," Energies, MDPI, vol. 14(5), pages 1-20, February.
    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. Wenzheng Zhou & Xin Ming & Yunping Yang & Yaqiong Hu & Ziyi He & Hongyan Chen & Yannan Li & Xiaojun Zhou & Ping Yin, 2022. "Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015–2020," IJERPH, MDPI, vol. 19(4), pages 1-15, February.
    2. Xinping Li & Nailiang Li & Xiang Lei & Ruotong Liu & Qiwei Fang & Bin Chen, 2023. "Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System," Energies, MDPI, vol. 16(4), pages 1-13, February.
    3. Marcelle Virginia Canto & Mònica Guxens & Anna García-Altés & Maria José López & Marc Marí-Dell’Olmo & Javier García-Pérez & Rebeca Ramis, 2023. "Air Pollution and Birth Outcomes: Health Impact and Economic Value Assessment in Spain," IJERPH, MDPI, vol. 20(3), pages 1-14, January.
    4. Valentin Simoncic & Séverine Deguen & Christophe Enaux & Stéphanie Vandentorren & Wahida Kihal-Talantikite, 2022. "A Comprehensive Review on Social Inequalities and Pregnancy Outcome—Identification of Relevant Pathways and Mechanisms," IJERPH, MDPI, vol. 19(24), pages 1-43, December.
    5. Marcos A. Rangel & Romina Tomé, 2022. "Health and the Megacity: Urban Congestion, Air Pollution, and Birth Outcomes in Brazil," IJERPH, MDPI, vol. 19(3), pages 1-26, January.

    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:16:y:2024:i:19:p:8721-:d:1495077. 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.