Using Sequence Pattern Mining to Analyze the Associations among Air Quality Monitoring Stations in Southern Taiwan

Air pollution has the characteristics of multiple sources, various scales, and cross-regional distribution, making it impossible to be solved solely through pollution prevention and treatment in individual counties or cities. Therefore, for the study of air pollution, Sequence pattern mining is suitable to analyze the relationship between air pollution and regions. Due to the fact that most previous research has mainly used the level intervals of individual air pollutants and their monitoring values as the basis for regional correlations, rarely utilizing the level intervals of multiple air pollutants along with the monitoring values of a single air pollutant as the basis for regional relationships, this study uses the data of PM
2.5
, PM
10
, and 8-hour moving average of ozone (O
3
-8Hr) with Air Quality Index (AQI) sub-indices greater than 101 in the southern region of Taiwan from 2020 to 2022 as the basis, aiming to uncover relationships in the southern region of Taiwan and contribute to future air quality management.