Examining Air Pollution Continuity in Tehran Province using Markov Chain Model

Abstract

The Air Quality Index is a numerical tool used to quantify air pollution levels and classify pollution severity. It plays a vital role in ensuring healthcare system stability by understanding air pollution's dynamic behavior and shifts in pollution intensity. To analyze the probabilistic transition between pollution severity levels, a Markov Chain model was utilized. This study examined six air pollution states (Clean, Healthy, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous) across 12 stations in Tehran's northern, southern, eastern, western, and central regions from 2018 to 2022. Results revealed that the western and southern areas exhibited the highest pollution levels, with over 44% and 34% of instances corresponding to unhealthy indices, respectively. In contrast, northern Tehran consistently maintained cleaner air quality. Probability transition matrices highlighted the greatest stability continuity in healthy air quality across all regions. Transitioning between clean/healthy air to very unhealthy/hazardous air was minimal, with hazardous air quality almost absent in most stations, except for the west and south (25% stability). The probability of continued unhealthy air quality in these areas reached 60%, indicating heightened pollution. The findings of transition probability matrices indicated that the western and southern regions had the highest likelihood of sustained pollution, while the northern region consistently maintained cleaner air. The probability of continuous clean air in the west was below 30%, while transitioning from very unhealthy/hazardous air to unhealthy air reached 50%. Conversely, the northern Tehran region exhibited over 40% stability for unhealthy air quality and over 50% for clean and healthy air.

Keywords

• air pollution
• air quality index
• markov chain
• transmission probability