基于PCT方法的京津冀冬季PM_(2.5)重污染天气型分析

Analysis of synoptic pattern on PM heavy pollution over the Beijing-Tianjin-Hebei region in winter based on PCT

T模态斜交主成分分析法(PCT)分析的天气过程时间尺度越长,该算法的优势越明显,天气分型结果也更完整,可信度越高.利用京津冀地区2014年冬季—2019年冬季(每年12月—翌年2月)的环境监测资料,以区域平均PM_(2.5)日均值大于150μg·m-3为标准,筛选出72个京津冀地区PM_(2.5)重污染日,采用ERA5提供的0.25°×0.25°气象再分析资料,应用PCT算法将72个PM_(2.5)重污染日海平面气压场客观地分为高压前部型、锋前低压型、高压后部型、均压场型和弱低压型5种类型,分别占总PM_(2.5)重污染天数的34.72%、20.83%、16.67%、16.67%和11.11%.另外,对2017年2月12—16日京津冀地区PM_(2.5)重污染过程的分析表明,重污染天气过程中随着逐日天气型的演变,污染物浓度特征、近地面风场和大气污染物污染传输路径均发生相应变化.

The PCT algorithm could provide more complete and more credible classifications of weather-patterns with longer observational data. According to the national standard of PM_(2.5) heavy pollution with the daily PM_(2.5) concentration exceeding 150 μg·m-3, 72 heavy PM_(2.5) pollution days were identified from urban air quality monitoring measurements over the Beijing-Tianjin-Hebei(BTH) region during winters from 2014 to 2019. By employing the PCT(Principle Components in T-mode) method with 0.25°×0.25° mean sea level pressure provided by ERA5 reanalysis data of meteorology, the synoptic patterns of the heavy PM_(2.5) heavy pollution days were objectively classified with five weather patterns:(1)FH(front of high pressure),(2)DCF(depression in front of cold front),(3)RH(rear of high pressure),(4)UP(uniform pressure), and(5)WL(weak low pressure) respectively accounting for 34.72%, 20.83%, 16.67%, 16.67%, 11.11% of total heavy pollution days over the BTH region. Furthermore, an analysis on a PM_(2.5) heavy pollution process in the BTH region over February 12—16, 2017 revealed that the PM_(2.5) heavy pollution levels, air pollutant concentrations, near-surface winds and air pollutant transport pathways were changed accordingly with the evolution of synoptic patterns during a heavy air pollution event.