Exploring regional air pollution transition dynamics: A multi-state markov model approach

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Air pollution, measured by the Air Quality Index (AQI), poses a global health risk.
• Understanding regional air pollution transition dynamics is crucial but remains poorly understood.
• This study investigates transitions between different air quality states.

Purpose of the Study:

• To analyze regional air pollution transition dynamics across various air quality states.
• To compare air quality improvement and deterioration patterns in Asia, Africa, and Europe.
• To provide data-driven insights for pollution mitigation strategies.

Main Methods:

• Weekly average Air Quality Index (AQI) data from 19 countries (Asia, Africa, Europe) from January to September 2024.
• Categorization of AQI into Good, Unhealthy, and Very Unhealthy states based on international standards.
• Application of a multi-state Markov model to assess state transitions and sojourn times.

Main Results:

• Asia and Africa show more frequent air quality deterioration with low transition rates from "Very Unhealthy" states.
• Europe exhibits greater air quality stability and improvement compared to Asia and Africa.
• Sojourn times in "Good" state: Europe (7.68 weeks), Asia (6.80 weeks); "Very Unhealthy" state persistence: Africa (0.95 weeks), Asia (3.36 weeks).

Conclusions:

• Asia and Africa experience prolonged pollution incidents, necessitating focused interventions.
• Europe demonstrates more effective pollution control measures.
• Regional AQI transition trends offer valuable guidance for policymakers to develop targeted mitigation strategies.