研究报告
陆汇丞,马翠平,赵天良,孟凯,郑小波,李嘉鼎,路佩瑶,刘华英.基于PCT方法的京津冀冬季PM2.5重污染天气型分析[J].环境科学学报,2021,41(3):898-904
基于PCT方法的京津冀冬季PM2.5重污染天气型分析
- Analysis of synoptic pattern on PM2.5 heavy pollution over the Beijing-Tianjin-Hebei region in winter based on PCT
- 基金项目：国家自然科学基金（No.41830965，91744209，91644223）；江苏省研究生科研与实践创新计划项目（No.SJCX20_0304）
- 作者
- 单位
- 陆汇丞
- 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点实验室, 南京 210044
- 马翠平
- 河北省环境气象中心, 石家庄 050021
- 赵天良
- 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点实验室, 南京 210044
- 孟凯
- 河北省环境气象中心, 石家庄 050021
- 郑小波
- 贵州山地环境气候研究所, 贵阳 550002
- 李嘉鼎
- 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点实验室, 南京 210044
- 路佩瑶
- 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点实验室, 南京 210044
- 刘华英
- 南京信息工程大学长望学院, 南京 210044
- 摘要：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PM2.5日均值大于150 μg·m-3为标准，筛选出72个京津冀地区PM2.5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PM2.5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PM2.5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PM2.5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.
- Abstract：The PCT algorithm could provide more complete and more credible classifications of weather-patterns with longer observational data. According to the national standard of PM2.5 heavy pollution with the daily PM2.5 concentration exceeding 150 μg·m-3, 72 heavy PM2.5 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 PM2.5 heavy pollution days were objectively classified with five weather patterns:1FH (front of high pressure), 2DCF(depression in front of cold front), 3RH(rear of high pressure), 4UP(uniform pressure), and 5WL(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 PM2.5 heavy pollution process in the BTH region over February 12-16, 2017 revealed that the PM2.5 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.
- Key words：PCT method Beijing-Tianjin-Hebei (BTH) heavy air pollution synoptic pattern classification
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