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长株潭城市群PM2.5 多尺度演化的EEMD 和多重分形分析
引用本文: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
作者姓名:杜 娟  刘春琼  吴 波  张 娇  黄 毅  史 凯∗
作者单位: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
基金项目:Supported by National Natural Science Foundation of China (国家自然科学基金项目, 52160024), Natural Science Foundation of HunanProvince, 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)
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  
收稿时间:2020-07-21
修稿时间:2022-03-27

Multiscale ensemble empirical mode decomposition andmultifractal approach of PM2.5 evolution inChangsha-Zhuzhou-Xiangtan Urban Agglomeration
DU Juan,LIU Chunqiong,WU Bo,ZHANG Jiao,HUANG Yi,SHI Kai∗. Multiscale ensemble empirical mode decomposition andmultifractal approach of PM2.5 evolution inChangsha-Zhuzhou-Xiangtan Urban Agglomeration[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(3): 304-316
Authors:DU Juan  LIU Chunqiong  WU Bo  ZHANG Jiao  HUANG Yi  SHI Kai∗
Affiliation: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
Abstract:To analyze the multi-scale characteristics of PM2:5 in Changsha-Zhuzhou-Xiangtan region, China, andexplain the main dynamic mechanism of PM2:5 evolution, a novel ensemble empirical mode decomposition andmultifractal detrended fluctuation analysis (EEMD-MFDFA) model is proposed, and then the dynamic evolution ofPM2:5 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 threecities. The results of the trend term show a decreasing trend of PM2:5 concentrations, and the high-frequency modeof PM2:5 reflects the nonlinear characteristics of PM2:5 concentration fluctuation. Furthermore, MFDFA method isused to analyze the high-frequency cumulative mode of PM2:5. The results indicate that the high-frequency mode ofPM2:5 has strong multifractal characteristics. In addition, the main sources of multifractal characteristics are studiedby using shuffling procedure and phase randomization. The results indicate that the long-term persistence of PM2:5concentration fluctuation in different time scales is the main dynamic factor for the emergence of high concentrationPM2:5. Finally, the influence of meteorological conditions on the multifractal strength of high-frequency modes ofPM2:5 is discussed. The results show that the multifractal strength of PM2:5 in winter was stronger than that in otherseasons. The analysis shows that although the air pollution in Changsha-Zhuzhou-Xiangtan Urban Agglomerationhas been effectively controlled through the air pollution action plan, in winter, the long-term persistence mechanismof 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 PM2:5in winter, and even more serious air pollution events. The results have great significance for studying the dynamiccharacteristics of regional PM2:5 multi-scale evolution and forecasting haze.
Keywords:PM2:5  ensemble empirical mode decomposition  multifractal detrended fluctuation analysis  highfrequency modes  multifractal  long-term persistence  
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