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京津冀地区霾极端事件的协同变化及其极端依赖性模式识别。

The haze extreme co-movements in Beijing-Tianjin-Hebei region and its extreme dependence pattern recognitions.

机构信息

School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China.

Department of Statistics, University of Wisconsin Madison, Madison, WI, USA.

出版信息

Sci Prog. 2020 Apr-Jun;103(2):36850420916315. doi: 10.1177/0036850420916315.

Abstract

Extreme haze was often observed at many locations in Beijing-Tianjin-Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014-2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing-Tianjin-Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze's appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

摘要

京津冀地区在短时间内多个地点会出现极端霾污染天气,这种极端同时发生和极端依赖现象在统计学中被称为极端协同和极端相依。本文采用尾部商相关系数来探讨京津冀地区霾污染的时空极端相依模式。利用 2014-2018 年逐小时 PM2.5 站级数据,结果表明,站与站之间的尾部商相关系数随月份增加而增大。具体而言,第四季度同时极端相依性较强,此时霾污染也较严重;第一季度霾污染同样严重,但极端霾仅表现出强烈的同时极端相依性,且存在时间滞后效应。基于此,本文研究了两个特殊场景,即最严重极端霾的并发/极端相依性及其滞后效应。识别出同时遭受极端霾或存在一定时间滞后且同时极端相依性最强的城市群,以及这些城市的极端协同作用及其发生的原因,对改善京津冀地区 PM2.5 的联合防控具有重要意义。滞后效应的重要性也反映在极端霾出现的先后顺序上,这对于设置由极端霾首次出现触发的预警系统机制特别有用。这种先后顺序也有助于研究霾的传输路径,从而可以制作更精确的气象模型,以改善该地区的霾预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0be7/10452795/8ac841e53160/10.1177_0036850420916315-fig1.jpg

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