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

Author

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  • 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 1-15, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8721-:d:1495077
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    References listed on IDEAS

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    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.
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