成都市大气消光系数时间序列随机特性分析

对不同湿度条件下消光系数序列演变特性的正确认知是构建大气颗粒物湿度订正模型的前提和基础。利用成都市人民南路4段环境监测站所提供的2013年6月到2014年5月逐时（降雨天除外）细颗粒物（PM2.5）浓度监测数据以及相应的地面能见度、相对湿度观测数据，反演得该区域相应时段单位质量消光系数时间序列。简要论述了消光系数吸湿过程中的复杂演变性及已有湿度订正模型的非普适性；基于相空间重构理论确定该时间序列的最佳延迟时间f和最佳嵌入维数m，据此计算出饱和关联维数、最大Lyapunov指数以及Kolmogorov熵特征量，其结果显示该序列具有低维混沌的特征；应用Cao方法排除其为非线性序列的可能性；结合替代数据法论证得成都市单位质量消光系数时间序列为随机序列。该研究结论不仅明晰了单位质量消光系数序列的特性，还为大气颗粒物湿度订正模型的改进奠定理论基础。

Abstract

A correct understanding of the evolution characteristics of the extinction coefficient series under different humidity conditions is the premise and foundation to establish the correction model of atmospheric particulate matter humidity. Using the data of mass concentration of PM2.5 (excluding rainy days), together with the homologous surface visibility and relative humidity data from June 2013 to May 2014 at 4th Section, South Renmin Road of Chengdu, the corresponding unit mass extinction coefficient time series is retrieved. The complex evolution of the extinction coefficient during moisture absorption is briefly discussed, and the non-universality of the existing humidity correction model is exemplified. Based on the reconstruction of phase space theories, the optimal delay time f and the embedding dimension m are determined, and several characteristic quantities, including the saturation correlation dimension, the maximum Lyapunov index and the Komogorove entropy, are also calculated. The results show that this series has the characteristics of low dimensional chaos. The Cao method is applied to excluding the possibility that the unit mass extinction coefficient time series is nonlinear. By adopting the surrogate data method, the time series is finally proved to be stochastic. The results not only clarify the characteristics of the unit mass extinction coefficient series, but also establish the theoretical foundation for the improvement of the atmospheric particulate matter humidity.

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孙欢欢, 倪长健, 崔蕾, 王超. 成都市大气消光系数时间序列随机特性分析[J]. 光学学报, 2016, 36(8): 0801001. Sun Huanhuan, Ni Changjian, Cui Lei, Wang Chao. Stochastic Characteristic Analysis of Time Series of Extinction Coefficient in Chengdu[J]. Acta Optica Sinica, 2016, 36(8): 0801001.

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