| Summary: | A correlation analysis of pollutant variables provides comprehensive information on dependency behaviour and is thus useful in relating the risk and consequences of pollution events. However, common correlation measurements fail to capture the various properties of air pollution data, such as their non-normal distribution, heavy tails, and dynamic changes over time. Hence, they cannot generate highly accurate information. To overcome this issue, this study proposes a combination of the Generalized Autoregressive Conditional Heteroskedasticity model, Generalized Pareto distribution, and stochastic copulas as a tool to investigate the dependence structure between the PM<sub>10</sub> variable and other pollutant variables, including CO, NO<sub>2</sub>, O<sub>3</sub>, and SO<sub>2</sub>. Results indicate that the dynamic dependence structure between PM<sub>10</sub> and other pollutant variables can be described with a ranking of PM<sub>10</sub>–CO > PM<sub>10</sub>–SO<sub>2</sub> > PM<sub>10</sub>–NO<sub>2</sub> > PM<sub>10</sub>–O<sub>3</sub> for the overall time paths (<i>δ</i>) and the upper tail (<i>τ<sup>U</sup></i>) or lower tail (<i>τ<sup>L</sup></i>) dependency measures. This study reveals an evident correlation among pollutant variables that changes over time; such correlation reflects dynamic dependency.
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