长株潭城市群PM2.5 多尺度演化的EEMD 和多重分形分析

大气与环境光学学报 ›› 2022, Vol. 17 ›› Issue (3): 304-316.

长株潭城市群PM2.5 多尺度演化的EEMD 和多重分形分析

杜娟1;2, 刘春琼3, 吴波1, 张娇1, 黄毅1, 史凯3∗

1 吉首大学数学与统计学院, 湖南吉首 416000; 2 洪江高新技术产业开发区 (洪江市) 管理委员会, 湖南怀化 418000; 3 吉首大学生物资源与环境科学学院, 湖南吉首 416000

收稿日期:2020-07-21 修回日期:2022-03-27 出版日期:2022-05-28 发布日期:2022-05-28
通讯作者: E-mail: shikai@jsu.edu.cn
作者简介:杜娟 (1995 - ), 女, 贵州铜仁人, 硕士研究生, 主要从事应用统计方面的研究。 E-mail: dumin0014@163.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金项目, 52160024), Natural Science Foundation of Hunan Province, China (湖南省自然科学基金项目, 2020JJ4504), Hunan Provincial Innovation Foundation for Postgraduate, China (湖南省研究生科研创新项目, CX20190872)

Multiscale ensemble empirical mode decomposition and multifractal approach of PM2.5 evolution in Changsha-Zhuzhou-Xiangtan Urban Agglomeration

DU Juan1;2, LIU Chunqiong3, WU Bo1, ZHANG Jiao1, HUANG Yi1, SHI Kai3∗

1 College of Mathematic and Statistics, Jishou University, Jishou 416000, China; 2 Management Commission of High-Tech Industrial Development Zone in Hongjiang, Huaihua 418000, China; 3 College of Biology and Environmental Science, Jishou University, Jishou 416000, China

Received:2020-07-21 Revised:2022-03-27 Published:2022-05-28 Online:2022-05-28

摘要/Abstract

摘要： 为解析长株潭地区 PM2:5 演化的多尺度特征, 阐释其演化的主要动力机制, 提出了一种集合经验模态分解 (EEMD) 和多重分形消除趋势波动分析 (MFDFA) 的新模型, 研究了该区域 2015 年 1 月 1 日至 2019 年 12 月 31 日 PM2:5 浓度的动力演化。利用 EEMD 方法获得了各城市 PM2:5 的高频模态以及趋势项, 趋势项结果表明 PM2:5 浓度呈下降趋势, 而 PM2:5 的高频模态反映了 PM2:5 浓度波动的非线性特征。进一步采用 MFDFA 方法对其高频累加模态进行分析, 研究表明 PM2:5 高频分量存在较强的多重分形特征。此外, 还利用相位随机替代法和随机重构法研究了其多重分形的主要来源, 结果表明 PM2:5 浓度波动在不同时间尺度内的长期持续作用是造成高浓度 PM2:5 污染涌现的主要动力因素。最后, 讨论了气象条件对其高频分量多重分形强度的影响, 结果发现, 相对于其他季节, 冬季 PM2:5 高频模态的多重分形强度更强。分析表明, 尽管该区域通过大气污染行动计划已取得积极的污染控制效果, 但在冬季, 污染物演化的长期持续动力机制对 PM2:5 高频模态的演化发挥着更加主导的控制作用, 不同时间尺度上 PM2:5 非线性长期持续动力机制导致冬季仍有高浓度 PM2:5 涌现的风险, 甚至出现更为严重的污染。本研究结果对于区域 PM2:5 多时间尺度演化动力特征的研究以及大气污染预测预警机制的建立具有重要意义。

关键词: PM2:5, 集合经验模态分解, 多重分形消除趋势波动分析, 高频模态, 多重分形, 长期持续性

Abstract: To analyze the multi-scale characteristics of PM2:5 in Changsha-Zhuzhou-Xiangtan region, China, and explain the main dynamic mechanism of PM2:5 evolution, a novel ensemble empirical mode decomposition and multifractal detrended fluctuation analysis (EEMD-MFDFA) model is proposed, and then the dynamic evolution of PM2:5 hourly average concentrations in Xiangtan, Changsha and Zhuzhou from January 1, 2015 to December 31, 2019 is studied. Through EEMD, the high-frequency modes and trend terms of PM2:5 are obtained for the three cities. The results of the trend term show a decreasing trend of PM2:5 concentrations, and the high-frequency mode of PM2:5 reflects the nonlinear characteristics of PM2:5 concentration fluctuation. Furthermore, MFDFA method is used to analyze the high-frequency cumulative mode of PM2:5. The results indicate that the high-frequency mode of PM2:5 has strong multifractal characteristics. In addition, the main sources of multifractal characteristics are studied by using shuffling procedure and phase randomization. The results indicate that the long-term persistence of PM2:5 concentration fluctuation in different time scales is the main dynamic factor for the emergence of high concentration PM2:5. Finally, the influence of meteorological conditions on the multifractal strength of high-frequency modes of PM2:5 is discussed. The results show that the multifractal strength of PM2:5 in winter was stronger than that in other seasons. The analysis shows that although the air pollution in Changsha-Zhuzhou-Xiangtan Urban Agglomeration has been effectively controlled through the air pollution action plan, in winter, the long-term persistence mechanism of air pollution plays a more dominant role in controlling the PM2:5 evolutions, and the non-linear long-term persistent mechanism of PM2:5 at different time scales can lead to the risk of emergence of high concentration PM2:5 in winter, and even more serious air pollution events. The results have great significance for studying the dynamic characteristics of regional PM2:5 multi-scale evolution and forecasting haze.

Key words: PM2:5, ensemble empirical mode decomposition, multifractal detrended fluctuation analysis, highfrequency modes, multifractal, long-term persistence

中图分类号:

X513

杜娟, 刘春琼, 吴波, 张娇, 黄毅, 史凯∗. 长株潭城市群PM2.5 多尺度演化的EEMD 和多重分形分析[J]. 大气与环境光学学报, 2022, 17(3): 304-316.

DU Juan, LIU Chunqiong, WU Bo, ZHANG Jiao, HUANG Yi, SHI Kai∗. Multiscale ensemble empirical mode decomposition and multifractal approach of PM2.5 evolution in Changsha-Zhuzhou-Xiangtan Urban Agglomeration[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(3): 304-316.

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