Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
J Environ Manage. 2020 Jun 15;264:110429. doi: 10.1016/j.jenvman.2020.110429. Epub 2020 Mar 20.
Intensity-duration-frequency (IDF) curves can serve as useful tools in risk assessment of extreme environmental events. Thus, this study proposes an IDF approach for evaluating the risk of expected occurrences of extreme air pollution as measured by an air pollution index (API). Hourly data of Klang city in Malaysia from 1997 to 2016 are analyzed. For each year, a block maxima size is determined based on four different monsoon seasons. Generalized extreme value (GEV) distribution is used as a model to represent the probabilistic behavior of maximum intensity of the API, which is derived from each block. Based on the GEV model, the IDF curves are developed to estimate the extreme pollution intensities that correspond to various duration hours and return periods. Considering the IDF curves, we found that for any duration hour, the magnitude of pollution intensity tends to be high in parallel with increasing return periods. In fact, a high-intensity pollution event that poses a high risk of affecting the environment is less frequent than low-intensity pollution. In conclusion, the IDF curves provide a good basis for decision makers to assess the expected risk of extreme pollution events in the future.
强度-持续时间-频率（IDF）曲线可作为评估极端环境事件风险的有用工具。因此，本研究提出了一种 IDF 方法，用于评估以空气污染指数（API）衡量的极端空气污染预期发生的风险。分析了马来西亚 1997 年至 2016 年期间克拉朗市的每小时数据。对于每一年，根据四个不同的季风季节确定一个块极大值大小。广义极值（GEV）分布被用作表示从每个块得出的 API 最大强度的概率行为的模型。基于 GEV 模型，开发了 IDF 曲线以估计对应于各种持续时间小时和重现期的极端污染强度。考虑到 IDF 曲线，我们发现，对于任何持续时间小时，随着重现期的增加，污染强度的大小趋于升高。事实上，对环境造成高影响风险的高强度污染事件比低强度污染事件发生的频率更低。总之，IDF 曲线为决策者提供了一个很好的基础，用于评估未来极端污染事件的预期风险。
