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灰霾消散前后PM_(10)浓度大幅波动的多重分形分析 被引量:9

Multifractal Characterization of Sharp Fluctuations about PM_(10) Before and After the Haze
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摘要 该研究采用多重分形消除趋势波动分析法,对成都一次灰霾污染过程中,PM10浓度在灰霾消散前后的多重分形特征进行分析,研究表明灰霾消散前后PM10浓度均具有多重分形特征。进一步运用相位随机替代法与随机重构法,对导致PM10浓度多重分形特征的动力原因进行分析。结果表明重度灰霾期间,长期记忆机制在PM10演化中均占据了主导控制作用;灰霾消散期间,虽然降水过程使得PM10多重分形特征的动力来源有所变化,但长期持续机制仍是多重分形特征的主要动力来源。尽管从表观上来看,大气降水过程显著降低了大气PM10浓度,但由于其内在动力机制并未得到本质的破坏,长期记忆机制仍是PM10演化的内在动力机制,从而可能导致未来特定气象条件下出现高浓度PM10污染,形成灰霾,后续监测数据证实了该论断。研究结果对于PM10浓度演化动力特征的研究以及灰霾预测预警机制的建立具有实际的参考意义。 Multifractal detrended fluctuation analysis method was used to research the multifractal characteristics of PM10 before and after the haze in Chengdu, Sichuan Province. It was found that the PM10 shows multifractal characteristics. Furthermore, the sources of mulfifractal characteristic were analyzed, through shuffling procedure and phase randomization procedure, it showed that multifractal characteristic of PM10 was due to long-term memory during the haze.Although the precipitation changes the source of multifractal characteristics of PM10 after the haze, it does not destroy the system about PM10 evolution fundamentally, long-term memory is still the main source of power for the multifractal characteristic. The concentration of PM10 is reduced significantly by precipitation, but its inherent motive mechanism has not been damaged, which may lead to a high concentration of PM10 in the future with a specific weather, and this assertion is confirmed by the follow-up data. The results have the theoretical and practical significance for studying PM10 of the haze and forecasting haze.
出处 《环境科学与技术》 CAS CSCD 北大核心 2016年第1期140-146,共7页 Environmental Science & Technology
基金 国家自然科学基金地区基金项目(41465010) 湖南省自然科学基金青年人才培养联合基金项目(13JJB012) 湖南省教育厅科学研究青年项目(13B089) 吉首大学校级科研项目资助(JGY201412)
关键词 复杂系统 长期持续性 多重分形消除趋势波动分析法 PM10 多重分形 complex system long-term memory multifractal detrended fluctuation analysis method PM10 multifractality
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