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建立不健康空气污染事件特征模型:一种 Copula 方法。

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

机构信息

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

出版信息

Int J Environ Res Public Health. 2021 Aug 19;18(16):8751. doi: 10.3390/ijerph18168751.

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 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.

摘要

本研究提出了持续时间(D)和严重程度(S)度量的概念,这些概念是从不良空气污染事件中得出的。同时,还提出了应用 Copula 模型来评估与持续时间和严重程度特征相关的不良空气污染事件。由持续时间和严重程度表示的二元标准表明它们具有结构依赖性、长尾和非同边际分布。Copula 方法可以提供一个很好的统计工具来处理这些问题,并从空气污染数据中提取有价值的信息。基于 Copula 模型,提出了几个统计度量来描述不良空气污染事件的特征,包括 Copula 的 Kendall 相关系数、给定持续时间下空气污染严重程度的条件概率、联合 OR/AND 重现期,以及条件 D|S 和条件 S|D 重现期。在马来西亚巴生进行了一项基于空气污染数据指数的案例研究。结果表明,Copula 方法有助于为规划和减轻不良空气污染事件的风险提供有价值的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3b9/8393697/7526097a86e6/ijerph-18-08751-g001.jpg

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