Time Series Forecasting of Air Quality: A Case Study of Sofia City

Abstract

Air pollution has a significant impact on human health and the environment, causing cardiovascular disease, respiratory infections, lung cancer and other diseases. Understanding the behavior of air pollutants is essential for adequate decisions that can lead to a better quality of life for citizens. Air quality forecasting is a reliable method for taking preventive and regulatory actions. Time series analysis produces forecasting models, which study the characteristics of the data points over time to extrapolate them in the future. This study explores the trends of air pollution at five air quality stations in Sofia, Bulgaria. The data collected between 2015 and 2019 is analyzed applying time series forecasting. Since the time series analysis works on complete data, imputation techniques are used to deal with missing values of pollutants. The data is aggregated by granularity periods of 3 h, 6 h, 12 h, 24 h (1 day). The AutoRegressive Integrated Moving Average (ARIMA) method is employed to create statistical analysis models for the prediction of pollutants’ levels at each air quality station and for each granularity, including carbon oxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine particles (PM2.5). In addition, the method allows us to find out whether the pollutants’ levels exceed the limits prescribed by the World Health Organization (WHO), as well as to investigate the correlation between levels of a given pollutant measured in different air quality stations.