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成都市一次典型空气重污染过程特征及成因分析
引用本文:郭倩,汪嘉杨,周子航,翟庆伟.成都市一次典型空气重污染过程特征及成因分析[J].环境科学学报,2018,38(2):629-639.
作者姓名:郭倩  汪嘉杨  周子航  翟庆伟
作者单位:成都信息工程大学资源环境学院, 成都 610225,成都信息工程大学资源环境学院, 成都 610225,成都市环境科学研究院, 成都 610225,成都信息工程大学资源环境学院, 成都 610225
基金项目:国家自然科学基金(No.51209024);国家社会科学基金(No.13BGL009)
摘    要:为了研究成都市冬季空气重污染过程的成因,以2015年12月26日—2016年1月6日成都市一次典型重污染天气过程为例,基于HYSPLIT后向轨迹模式结合全球资料同化系统(Global Data Assimilation System,GDAS)气象数据和成都市7个监测站的AQI、PM_(2.5)、PM10、NO2质量浓度数据,使用气象分析、轨迹聚类(Cluster Analysis)、潜在源贡献因子法(Potential Source Contribution Function,PSCF)和浓度权重轨迹法(Concentration Weighted Trajectory,CWT),分析了此次过程的气象特征、轨迹输送特征和污染物潜在来源分布.结果表明,此次污染天气过程是以PM_(2.5)为主要污染物,其次为PM10、NO2.2015年12月30日14:00左右是此次污染天气过程各站点PM_(2.5)、PM10浓度到达峰值的时刻.缺少北方冷空气南下,四川盆地内空气水平运动弱,以及扩散条件差的静稳天气形势是导致此次大气污染过程成都市污染物累积的原因,而冷空气活动是改善这种天气形势的关键.污染过程辐射逆温层的形成对当时污染物浓度增长有促进作用,但随着每日生消、加强减弱,其并不是最终导致重污染天气形成的关键因素.川东北的广元、绵阳、德阳等地区和成都本地及其南向的眉山、雅安等地区是此次过程主要的潜在源区,这些地区人口较密集,工业较发达,且沿地形走向而分布.

关 键 词:PM2.5  聚类分析  潜在源贡献因子法  浓度权重轨迹  成都
收稿时间:2017/6/19 0:00:00
修稿时间:2017/8/2 0:00:00

Characteristics and reason analysis of a typical heavy air pollution event in Chengdu
GUO Qian,WANG Jiayang,ZHOU Zihang and ZHAI Qingwei.Characteristics and reason analysis of a typical heavy air pollution event in Chengdu[J].Acta Scientiae Circumstantiae,2018,38(2):629-639.
Authors:GUO Qian  WANG Jiayang  ZHOU Zihang and ZHAI Qingwei
Institution:College of Resource and Environment, Chengdu University of Information Technology, Chengdu 610225,College of Resource and Environment, Chengdu University of Information Technology, Chengdu 610225,Chengdu Institute of Environmental Science, Chengdu 610225 and College of Resource and Environment, Chengdu University of Information Technology, Chengdu 610225
Abstract:In order to study the causes of a heavy air pollution process in Chengdu, a typical heavily polluted weather event from 26th December 2015 to 6th January 2016 was taken as an example in this work. The meteorological characteristics, trajectory transport characteristics and potential source distribution of pollutants were analyzed based on the meteorological data of global data assimilation system (GDAS) and the AQI, PM2.5, PM10, NO2 concentration data from 7 monitoring stations in Chengdu. HYSPLIT backward simulation model was used here to analysis the data. Several methods were used to analysis the characteristics and causes of the heavy pollution process from different perspectives, such as meteorological analysis, cluster analysis, potential source contribution function (PSCF) and concentration-weighted trajectory (CWT). The results show that PM2.5 was the main pollutant of the process, followed by PM10, NO2. The highest concentration of PM2.5 and PM10 appeared at~14:00 on December 30th, 2015. Lacking of the north cold air activities, weak atmospheric horizontal motion and poor diffusion conditions lead to the accumulation of pollutants in Chengdu. Therefore, the cold air activities from high latitudes are the key to improve the weather situation. Though the presence of inversion layer has an effect on the growth of PM2.5 concentration, it isn''t the key to improve the weather situation. Northeastern regions such as Guangyuan, Mianyang, Deyang, southern regions of Meishan, Ya''an and others, and Chengdu local area, are the major contributors to increase pollutants concentrations and the main potential source of this process. These areas are densely populated, developed in industry and distributed along the terrain.
Keywords:PM2  5  cluster analysis  potential source contribution function(PSCF)  concentration-weighted trajectory (CWT)  Chengdu
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