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Power-law behaviors of the severity levels of unhealthy air pollution events

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  • Nurulkamal Masseran

    (Universiti Kebangsaan Malaysia)

Abstract

The severity level of air pollution refers to the cumulative effect of unhealthy air pollutant index (API) values during certain air pollution events. High severity levels indicate a negative effect on human health, a disruption in economic activities, and possible disastrous consequences on the environmental ecosystem. This study investigated the power-law behaviors of air pollution events with high severity levels. Three types of power-law models were used to analyze the API data in Klang, Malaysia. The results revealed that the continuous power-law distribution is a reliable model for describing the power-law behaviors that occur in the upper tail of the API distribution. Air pollution events with severity levels greater than a threshold of 1221 were found to exhibit power-law behaviors. On this basis, this study suggest that authorities exercise vigilance with respect to pollution incidents with severity levels exceeding the 1221 threshold.

Suggested Citation

  • Nurulkamal Masseran, 2022. "Power-law behaviors of the severity levels of unhealthy air pollution events," 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. 112(2), pages 1749-1766, June.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:2:d:10.1007_s11069-022-05247-5
    DOI: 10.1007/s11069-022-05247-5
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    References listed on IDEAS

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    1. Muhammad Aslam Mohd Safari & Nurulkamal Masseran & Kamarulzaman Ibrahim, 2019. "On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(10), pages 1886-1902, July.
    2. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    3. Bruce Malamud & Donald Turcotte, 1999. "Self-Organized Criticality Applied to Natural Hazards," 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. 20(2), pages 93-116, November.
    4. Chunshui Lin & Ru-Jin Huang & Darius Ceburnis & Paul Buckley & Jana Preissler & John Wenger & Matteo Rinaldi & Maria Christina Facchini & Colin O’Dowd & Jurgita Ovadnevaite, 2018. "Extreme air pollution from residential solid fuel burning," Nature Sustainability, Nature, vol. 1(9), pages 512-517, September.
    5. Wang, Qizhen, 2019. "Multifractal characterization of air polluted time series in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 167-180.
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    Cited by:

    1. 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.
    2. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.

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