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

Panagiotis Fazakis, Konstantinos Moustris () and Georgios Spyropoulos
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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

Sustainability, 2024, vol. 16, issue 19, 1-14

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.

Keywords: artificial neural networks; atmospheric pollution; predictive model; pollutant forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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