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Unveiling Seasonal Air Pollution Patterns with Data Mining

Abstract:

Air pollution is one of the issues of great importance and relevance for different government institutions worldwide due to its harmful effects on human health and the environment. Considering that, constant monitoring of behavior in the atmosphere is essential. For this reason, this article aims to perform the seasonal analysis of atmospheric pollution through data mining grouped monthly. Therefore, data on atmospheric pollutants was collected through meteorological stations in different areas of Cuenca, Ecuador, in 2018. This data includes information on the primary air pollutants, such as ozone (O<inf>3</inf>), carbon monoxide (CO), sulfur dioxide (SO<inf>2</inf>), nitrogen dioxide (NO<inf>2</inf>), and particulate matter sized 2.5 microns (PM<inf>2.5</inf>). Consequently, when applying data mining techniques and algorithms, it was observed that the concentration of the studied atmospheric pollutants tends to show variable behavior throughout the year, with values above normal behavior occurring at certain times. This study offers important information about the air quality of Cuenca, providing a scientific basis for making decisions that support improving the quality of life in urban areas and implementing measures that reduce air pollution in the city.