关中地区PM2.5污染过程的主要参数特征和EMD统计分析

为了掌握关中地区的污染过程特征，并为关中地区预警预报提供理论支撑，利用2014—2017年关中地区五市（西安市、咸阳市、宝鸡市、渭南市、铜川市）ρ（PM_2.5）数据，对该地区PM_2.5污染过程的峰值质量浓度、持续时间等特征进行统计分析，并用EMD（经验模态分解法）分解海平面气压观测数据，对PM_2.5污染过程的统计结果进行解释.结果表明:①关中地区ρ（PM_2.5）分布在时间和空间上均具有显著的区域相关和时间同步特征.各城市的ρ（PM_2.5）日均值较接近，相差范围为2~15 μg/m3.②污染过程持续时间的统计表明，冬季污染过程持续时间（11~15 d）相对较长，夏季污染过程持续时间（7~9 d）相对较短；PM_2.5污染过程的峰值质量浓度分析表明，各城市中度及以上等级的污染频次差异较大，最大值出现在咸阳市，为16次，最小值出现在铜川市，为9次.③利用EMD算法对气压数据进行分解后发现，第4模态（IMF4）的震荡频率变化是关中地区各城市不同季节污染过程持续时间存在明显差异的主要原因.研究显示，单站气压的EMD模态分解可以较好地解释关中地区的污染物浓度特征. 

Primary Parameter Characteristics of PM2.5 Pollution Process and EMD Analysis in Guanzhou Area

In order to determine the characteristics of the PM_2.5 pollution process in Guanzhong Area and provide theoretical support for early warning and forecasting system, the peak mass concentration of PM_2.5 and the duration of the pollution process were analyzed by using the data of five cities (Xi'an, Xianyang, Baoji, Weinan and Tongchuan City) in Guanzhong Area from 2014 to 2017. The empirical mode decomposition (EMD) algorithm was used to decompose the sea-level pressure data to explain the statistical results gained from PM_2.5 pollution process. The results revealed that: (1) The distribution and concentration of PM_2.5 in the Guanzhong cities had significant regional correlation and time synchronization characteristics. The daily averaged PM_2.5 concentration of the five cities within four years was closed to each other. The difference was between 2-15 μg/m3. (2) The statistical results of pollution duration showed that the Guanzhong Area had a relatively long pollution process in winter, lasting for 11-15 d, while the pollution process in summer was short, lasting for 7-9 d. The statistical results of the peak PM_2.5 mass concentration during the pollution processes indicated that the frequency of pollution levels in the cities was different, especially at moderate pollution levels (and above). Xianyang City had the highest frequency of 16 times, while Tongchuan City had the lowest frequency of 9 times. (3) After using the EMD algorithm to decompose the pressure data, the variation in the oscillation frequency of the fourth mode (IMF4) was found, which accounted for the significant difference in the duration of pollution processes between different cities in different seasons. In conclusion, the EMD modal decomposition of single-station air pressure can better explain the pollutant concentration characteristics in the Guanzhong Area.