在讨论大气可降水量（PWV）与细颗粒物（PM_2.5）之间的相关性时，传统方法未能很好地顾及连续数据中包含的非雾霾天气信息的影响，为此本文提出2个数据选取标准——时间标准和空气质量指数（AQI）等级标准，用于获取雾霾期间对应的PWV和PM_2.5序列。为解决数据筛选后不连续的问题，引入一种非参数性质的Spearman秩相关系数ρ，在北京市2014~2016年雾霾多发期，分析不同AQI等级对应时段的非连续等距的PWV和PM_2.5序列的相关性可知，筛选后3 a的ρ在第1、4季度的均值分别为0.661 3和0.628 0，整体均值为0.644 7，表明雾霾天气下PWV和PM_2.5序列具有单调正向的相关性，而传统分析方法（未筛选）下两者的相关系数均较小，表明在对数据进行选取后的分析结果更具针对性和准确性。

When discussing the correlation between precipitable water vapor (PWV) and fine particulate matter (PM_2.5), traditional methods do not consider the impact of non-haze weather information contained in continuous data. So, this paper presents two data extraction standards: time standard and air quality index (AQI) level standard, which are used to obtain the corresponding PWV and PM_2.5 sequence during haze. In order to solve the problem of discontinuity after data filtering, a non-parametric Spearman rank correlation coefficient（ρ） is introduced in this paper. By dealing with the discontinuous and non-equidistant PWV and PM_2.5 sequence in different levels of the air quality during the haze-prone period in Beijing from 2014 to 2016, we learn that the average value after filtering of the first and fourth quarters in the three years is 0.661 3 and 0.628 0 respectively, and the overall average is 0.644 7, which indicates that the PWV and PM_2.5 sequence have a monotonous and positive correlation under hazy weather. The correlation coefficients of both are small under the traditional analysis method (unfiltered), indicating that the analysis result is more specific and accurate after the data is selected.