| Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
|---|---|---|
| Article Number | 02004 | |
| Number of page(s) | 8 | |
| Section | Pollution and Waste, Weather and Climate | |
| DOI | https://doi.org/10.1051/e3sconf/202458302004 | |
| Published online | 25 October 2024 | |
Air quality assessment model
1 Bauman Moscow State Technical University, 105005 Moscow, Russia
2 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
3 Russian State Agrarian University - Timiryazev Moscow Agricultural Academy (RSAU- MAA Named after K.A. Timiryazev), 127434 Moscow, Russia
* Corresponding author: sofaglu2000@mail.ru
This study examines the application of machine learning methods to predict air quality in Brisbane, Australia. The main attention is paid to the creation of a model capable of predicting the concentration of PM10 suspended particles based on meteorological data. In the course of the work, a statistical analysis of the factors influencing the level of pollution was carried out, and a random forest model was developed and tested. The results showed that the model is able to explain about 69% of the variation in PM10 concentration, and also identified key meteorological parameters such as air temperature and wind speed that have the greatest impact on the concentration of pollutants. The data obtained can be used to improve the monitoring and management of air quality in cities, which in the future may contribute to reducing the harmful effects of pollution on public health.
© The Authors, published by EDP Sciences, 2024
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