Version 1
: Received: 25 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (10:24:27 CEST)
How to cite:
Fazakis, P.; Moustris, K.; Spyropoulos, G. Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China. Preprints2024, 2024081822. https://doi.org/10.20944/preprints202408.1822.v1
Fazakis, P.; Moustris, K.; Spyropoulos, G. Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China. Preprints 2024, 2024081822. https://doi.org/10.20944/preprints202408.1822.v1
Fazakis, P.; Moustris, K.; Spyropoulos, G. Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China. Preprints2024, 2024081822. https://doi.org/10.20944/preprints202408.1822.v1
APA Style
Fazakis, P., Moustris, K., & Spyropoulos, G. (2024). Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China. Preprints. https://doi.org/10.20944/preprints202408.1822.v1
Chicago/Turabian Style
Fazakis, P., Konstantinos Moustris and Georgios Spyropoulos. 2024 "Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China" Preprints. https://doi.org/10.20944/preprints202408.1822.v1
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 2.5μm (PM2.5) and 10μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist the main cause for many diseases. Based on the existence of statutory limits, by the World Health Organization (WHO), for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that will have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict the air pollutants concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural networks (ANNs) models. In the frame of the specific work, a significant number of ANNs was 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. Results showed that ANNs present a re-markable prognostic ability in order to forecast the air pollution levels in an urban environment.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
