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2019年西安首场雾霾PM2.5关键特征的综合诊断
引用本文:李博,裴成章,王楠,闫庆,狄慧鸽,刘晶晶,屈姣,华灯鑫.2019年西安首场雾霾PM2.5关键特征的综合诊断[J].环境科学学报,2020,40(11):4048-4059.
作者姓名:李博  裴成章  王楠  闫庆  狄慧鸽  刘晶晶  屈姣  华灯鑫
作者单位:西安理工大学机械与精密仪器工程学院,西安710048;中国气象局大气化学重点开放实验室,北京100081,西安理工大学机械与精密仪器工程学院,西安710048,陕西省气象台,西安710014,西安理工大学机械与精密仪器工程学院,西安710048,西安理工大学机械与精密仪器工程学院,西安710048,西安理工大学机械与精密仪器工程学院,西安710048,西安科技大学安全科学与工程学院,西安710054,西安理工大学机械与精密仪器工程学院,西安710048
基金项目:国家自然科学基金(No.41627807,61575160);中国气象局大气化学重点开放实验室开放课题(No.2020B03)
摘    要:通过对WRF-Chem(Weather Research and Forecasting Model Coupled to Chemistry)环境模式模拟资料、HYSPLIT(HYbrid Single Particle Lagrangian Integrated Trajectory Model)前/后向气团轨迹资料、环境站监测资料,以及西安理工大学(Xi''an University of Technology,简称XUT)多波长激光雷达、米散射激光雷达、能见度仪、粒谱仪等观测资料的综合诊断,探讨了2019年1月初发生在西安的雾霾过程(记为首场雾霾)PM2.5组分、分布及传输特征,旨在为雾霾气溶胶研究提供有益的个例积累.定性、定量双重检验表明,Chem模式较成功复制了此次雾霾气溶胶过程.利用这些可靠的模式数据分析表明,PM2.5中碳气溶胶的主要组分为有机碳,约占85%,强盛期气溶胶各组分随高度增加均呈递减趋势,各组分近地面浓度最高.通过对两类不同方法获取的消光系数对比分析表明,相比于模式数据,激光雷达数据具有更高的垂直分辨率,因此,更善于描述消光廓线的细节特征.通过对多源资料的综合诊断最终揭示出,"北风涌"是雾霾消散的关键影响因子,沿铜川-西安-山阳一带存在着污染物传输的重要路径,雾霾由此体现出自北向南依次消散的特征.

关 键 词:西安  2019年首场雾霾  PM2.5关键特征  多源资料  综合诊断  北风涌
收稿时间:2019/8/20 0:00:00
修稿时间:2019/11/22 0:00:00

The characteristics of PM2.5 about the component, distribution and transmission during the first fog-haze process in Xi'an in 2019 by using SACDM
LI Bo,PEI Chengzhang,WANG Nan,YAN Qing,DI Huige,LIU Jingjing,QU Xiao,HUA Dengxin.The characteristics of PM2.5 about the component, distribution and transmission during the first fog-haze process in Xi'an in 2019 by using SACDM[J].Acta Scientiae Circumstantiae,2020,40(11):4048-4059.
Authors:LI Bo  PEI Chengzhang  WANG Nan  YAN Qing  DI Huige  LIU Jingjing  QU Xiao  HUA Dengxin
Affiliation:1. School of Mechanical and Precision Instrument Engineering, Xi''an University of Technology, Xi''an 710048;2. Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100081;The Meteorological Observatory of Shaanxi Province, Xi''an 710014;School of Safety Science and Engineering, Xi''an University of Science and Technology, Xi''an 710054
Abstract:Based on the multi-source dataset including the American WRF-Chem (Weather Research and Forecasting Model Coupled to Chemistry) data, the American HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory Model) data, the Chinese environmental station observation data, and the special observation data (the multi-wavelength lidar, the Mie scattering lidar, the visibility meter, and particle size spectrometer) collected from Xi''an University of Technology (XUT), the key characteristics of PM2.5 about the component, distribution and transmission during the first fog-haze episode occurred in early January 2019 in Xi''an were studied by using the Synthetically Analysis and Classifying Diagnosis Method (SACDM). The paper aimed at providing the features of the fog-haze aerosols in the typical case. The fog-haze process was successfully copied by WRF-Chem according to both qualitative and quantitative test, and the characteristics on the component and distribution of PM2.5 were discovered by analyzing the model data. The main carbon-aerosol component of PM2.5 was organic carbon, with a value of 85%. In the mature phase of fog-haze, the aerosol decreased with the increase of height, and got a zero at about 5 km. With a higher vertical resolution than the model data, the lidar data were better at describing the detailed features of the profile of extinction coefficient. And the double fog-haze layers including the surface ground and 3.7 km layer were discovered according to the lidar data. The multi-source data showed that the north-wind surges played a more important role in the transmission process of PM2.5, and a key pollution channel from Tongchuan to Xi''an to Shanyang was discovered. Therefore, the first fog-haze disappeared with an important phenomenon of south-last and north-first.
Keywords:Xi''an  the first fog-haze process in 2019  the key characterize on the component  distribution and transmission of PM2  5  multi-source dataset  synthetically analysis and classifying diagnosis method (SACDM)  north-wind surges
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