Means for monitoring, modeling and predicting the concentration of urban air pollution by microparticles

Air pollution is one of the biggest threats to the environment and humans. Due to meteorological and transport factors, industrial activity and emissions of power plants are the main agents of air pollution. Therefore, environmental authorities are focused on the effects of air pollution and the development of guidelines to minimize it. The main objective of this study is to design a system that uses a machine learning approach for predicting urban air pollution by analyzing a set of data on air pollutants, PM2.5 particulate matter in particular. A linear controlled machine learning algorithm, which has a RMSE error value of 31.29 and a Decision Forest Regression algorithm with an RMSE value of 29.26, is used for predictions. The system is developed on a web-based platform and is accessible for mobile phones; it is user-friendly and represents the values of air pollutant concentration with PM2.5 particles and the values of the air quality index. Values of PM2.5 particle concentration are dependent on other sources and background levels, which indicates the importance of localized factors for understanding spatio-temporal model of air pollution at intersections and supporting individuals making decisions in the field of regulating and controlling pollution in cities.

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