Time Series Analysis and Forecasting of Air Pollution Particulate Matter (PM2.5): An SARIMA and Factor Analysis Approach

Abstract

Current development of Pakistan's economy, transportation and industry with the improvement of urbanization, environmental pollution problems have gradually become prominent, but this is contrary to people's vision of pursuing a high-quality life. Now the problem of haze, photochemical problems in the air, and global warming is already a key issue of global concern. This is focused on the ambient air quality of Lahore city of Pakistan. The study reveals that the particulate matter in the Lahore season (PM2.5, PM10) exceeds Pakistan's National Environmental Quality Standards (NEQS). Correlation study suggests the positive correlation between the particulate matter and other mass concentration particles like Ozone (O3), Nitrogen Oxide (NO), Sulphur Dioxide (SO2). Higher values of CO/NO suggest that mobile sources are one of the major factors of this increase in NO. Further estimation of backward trajectory is done by the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model which provides the path of those particles in the last year period and the source of origin is from Afghanistan. This study provides in depth analysis of all factors of air pollutants by correlation between those factors. Prediction of future concentration of PM2.5 is predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which gives the increasing value of PM2.5 in next year and provides the lowest and highest predicts (more than 100 μg/m3).