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Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

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  • Nurulkamal Masseran

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia)

Abstract

This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall’s τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events.

Suggested Citation

  • Nurulkamal Masseran, 2021. "Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8751-:d:617540
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    1. Genest C. & Boies J-C., 2003. "Detecting Dependence With Kendall Plots," The American Statistician, American Statistical Association, vol. 57, pages 275-284, November.
    2. Falk, Michael & Padoan, Simone A. & Wisheckel, Florian, 2019. "Generalized Pareto copulas: A key to multivariate extremes," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    3. Nurulkamal Masseran & Saiful Izzuan Hussain, 2020. "Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    4. Steffen Grønneberg & Nils Lid Hjort, 2014. "The Copula Information Criteria," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 436-459, June.
    5. J. Shiau, 2006. "Fitting Drought Duration and Severity with Two-Dimensional Copulas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 795-815, October.
    6. Fatih Tosunoglu & Ibrahim Can, 2016. "Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(3), pages 1457-1477, July.
    7. Jong-Min Kim & Namgil Lee & Xingyao Xiao, 2019. "Directional dependence between major cities in China based on copula regression on air pollution measurements," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-23, March.
    8. Kojadinovic, Ivan & Yan, Jun, 2010. "Modeling Multivariate Distributions with Continuous Margins Using the copula R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i09).
    9. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    10. Christian Genest & Jean‐François Quessy & Bruno Rémillard, 2006. "Goodness‐of‐fit Procedures for Copula Models Based on the Probability Integral Transformation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 337-366, June.
    11. Bruce W. Schmeiser & Ram Lal, 1982. "Bivariate Gamma Random Vectors," Operations Research, INFORMS, vol. 30(2), pages 355-374, April.
    12. Royen, T., 1991. "Expansions for the multivariate chi-square distribution," Journal of Multivariate Analysis, Elsevier, vol. 38(2), pages 213-232, August.
    13. Fadhilah Yusof & Foo Hui-Mean & Jamaludin Suhaila & Zulkifli Yusof, 2013. "Characterisation of Drought Properties with Bivariate Copula Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4183-4207, September.
    14. Huard, David & Evin, Guillaume & Favre, Anne-Catherine, 2006. "Bayesian copula selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 809-822, November.
    15. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    16. Ji-Eun Choi & Dong Wan Shin, 2019. "Three regime bivariate normal distribution: a new estimation method for co-value-at-risk, CoVaR," The European Journal of Finance, Taylor & Francis Journals, vol. 25(18), pages 1817-1833, December.
    17. Sudesh Pundir & R. Amala, 2015. "Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2671-2685, December.
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    Cited by:

    1. Mohd Sabri Ismail & Nurulkamal Masseran & Mohd Almie Alias & Sakhinah Abu Bakar, 2024. "Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach," Mathematics, MDPI, vol. 12(4), pages 1-23, February.
    2. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    3. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2022. "Statistical Modeling on the Severity of Unhealthy Air Pollution Events in Malaysia," Mathematics, MDPI, vol. 10(16), pages 1-15, August.

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