Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach
1Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia.
Summary
This study introduces duration and severity measures for unhealthy air pollution events. A copula model effectively analyzes these characteristics, providing valuable insights for risk mitigation and planning.
Area of Science:
- Environmental Science
- Atmospheric Science
- Statistics
Background:
- Unhealthy air pollution events pose significant risks.
- Characterizing these events requires advanced statistical methods.
- Existing methods may not fully capture the complexities of pollution event duration and severity.
Purpose of the Study:
- To propose duration (D) and severity (S) measures for unhealthy air pollution events.
- To apply a copula model for evaluating the bivariate characteristics of these events.
- To develop statistical measurements for a comprehensive description of air pollution events.
Main Methods:
- Derivation of duration and severity measures from pollution data.
- Application of a bivariate copula model to analyze dependency structures.
- Calculation of statistical measures: Kendall's τ, conditional probabilities, joint and conditional return periods.
Main Results:
- The copula model effectively addresses structural dependency and non-identical marginal distributions.
- Key statistical measurements were successfully derived from air pollution data.
- A case study in Klang, Malaysia, demonstrated the model's applicability.
Conclusions:
- Copula modeling provides a robust statistical tool for analyzing air pollution event characteristics.
- The proposed measures and methods offer valuable information for risk assessment and mitigation strategies.
- This approach enhances understanding and management of unhealthy air pollution events.
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