The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.

极端污染事件的发生严重影响人类健康，环境生态系统和国民经济。为了更好地理解此问题，必须有效设计有关这些事件行为的风险评估，以预测其发生的可能性。在这项研究中，我们建议使用强度-持续时间-频率（IDF）技术来描述污染强度（i）与其持续时间（d）和恢复期（T）的关系。）。作为案例研究，我们使用了马来西亚巴生市的数据。IDF曲线的构建涉及确定极端污染事件的部分持续时间序列的过程。基于PDS数据，使用广义帕累托分布（GPD）表示其概率行为。根据拟合的GPD模型，确定与各种返回时间相对应的污染强度的估计返回时间和IDF曲线。结果表明，巴生市的污染强度随着返回期之间时间间隔的增加而趋于增加。尽管IDF曲线针对不同的回报期显示出不同的幅度，但所有曲线均显示出相似的增长趋势。实际上，更长的回收期与更高的污染强度估算有关。根据研究结果，