Univariate Time-Series Analysis to Analyze the Particles PM2.5 and PM10 Using ARIMA Model

Without air, mankind would not be able to live a long life. The continuous development of society has affected the air quality. This has become an important factor that has become part of the monitoring and forecasting of air quality in developing and developed nations. Particulate matter (PM) PM
2.5
and PM
10
are the main components of air pollution that contribute to climate change and human health. Exposure to these contaminants over an extended period of time can lead to a host of health problems, including asthma and heart disease. Among the other factors that contribute to air quality determination are emissions from factories and other sources. In agriculture, the presence of particulate matter on the plant's stomata can negatively affect the plant's yield. This issue can also cause food security concerns. Prediction and forecasting led to the development of strategies to address the various health effects of air pollutants. This paper demonstrates how the ARIMA model provides forecasting and prediction values, allowing to act accordingly.