摘要
利用Traj Stat软件和全球资料同化系统数据,计算了2014—2016年银川市逐日72 h气流后向轨迹,并采用聚类分析方法,结合银川市同期PM^(10)和PM^(2.5)质量浓度数据,分析了银川年及四季气流轨迹特征及其对银川颗粒物浓度的影响.同时,运用潜在源贡献因子分析法(PSCF)和浓度权重轨迹分析法(CWT),探讨了影响银川颗粒物质量浓度的潜在源区及不同源区对银川颗粒物质量浓度的贡献.结果表明,输送距离最长、高度最高、移速最快的西北气流轨迹占总轨迹的比例最高,达66.7%,且气团移动速度和高度与轨迹距离呈正比;输送高度较低、距离最短、移速最慢的北方气流轨迹占总轨迹数的24.3%;东南气团占总轨迹数的9%,输送距离和移速介于前两者之间,但输送高度较西北气流和北方气流低.四季各类气流轨迹变化特征与年变化特征基本一致,春、秋、冬三季,中、短距离西北气流占气流轨迹总数的比例最高,夏季东南气流占比最高,且夏季南方气流和北方气流占比较春、秋两季高,冬季未出现南方气流和北方气流,春季和冬季气流轨迹输送距离普遍比夏季和秋季长;春、夏、秋三季,偏南气流的输送高度均最低,四季长距离西北气流的输送高度均最高.年及四季都表现为西北气流轨迹对应的银川PM_(10)和PM_(2.5)平均浓度均较高,是影响银川颗粒物质量浓度的最重要输送路径,其次是东南气流轨迹,北方气流轨迹对银川颗粒物浓度影响较小.PSCF和CWT分析发现,位于新疆、甘肃、蒙古国、内蒙古、青海的西北源区及四川、陕西的东南源区是影响银川PM_(10)和PM_(2.5)浓度的两个主要潜在源区,各季节区域范围有所差异.
In this study,first,the daily 72 h back trajectories are calculated in Yinchuan during the period of 2014—2016 using the Traj Stat software and the global data assimilation system. And then,the back trajectory cluster analysis method combined with PM10 and PM2.5 concentration data observed during the same period are employed to understand the impacts of different trajectories on the concentration of particles over Yincuan in the whole year and different seasons. Meanwhile,using potential source contributing factor analysis method and concentration weight trajectory analysis method,the potential source regions and the contribution of different source regions to the particle concentrations in Yinchuan are analyzed. The results show that the northwest airflow trajectory with the longest distance,the highest altitude,and the fastest moving velocity accounts for the highest proportion of the total trajectory number,up to 66.7%,and the air mass movement speed and altitude is proportional to the trajectory distance,the northern airflow trajectory with low altitude,shortest distance and the slowest moving velocity accounts for 24.3%,and the southeast air mass accounts for 9%,transportation distance and velocity are between the first two,but the transportation height is lower than the northwest and northern airflow. The variation characteristics of airflow trajectories in the four seasons are basically the same as those in the year. The distribution range of air flow in spring and winter was generally longer than in summer and autumn,and the transport height of southerly air was lowest in spring,summer and autumn. The transportation height of long distancenorth-west air was highest in all four seasons. In spring,autumn and winter,northwest airflow with middle or short trajectory distance accounts for the highest proportion of total airflow,while the southwest airflow accounts for the highest proportion in summer,and the southern and the northern airflow are higher in spring and autumn than in summer,but there is no southern and northern flow in winter. The transportation distance of air trajectory in spring and winter is generally longer than in summer and autumn. The transportation height of the northwest airflow is slowest in spring,summer and autumn,while the height of longest northwest airflow is highest in the four seasons. The analysis of the concentration loadings proves that the air mass from northwest are the main directions influencing particles concentration of Yinchuan in the year and different seasons,followed by the southeast,and the north are the directions for the minimum particles concentration loading. The PSCF and CWT results indicate that northwest source area and southeast source area are the two main potential source regions that have great influence on PM10 and PM2.5 concentrations of Yinchuan,but the range of region is different for different seasons.
作者
严晓瑜
缑晓辉
武万里
黄峰
杨军
刘玉兰
YAN Xiaoyu;GOU Xiaohui;WU Wanli;HUANG Feng;YANG Jun;LIU Yulan(Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Charaeteristie Agriculture in Arid Regions, CMA, Yinehuan 750000;Ningxia Key Lab of Meteorological Disaster Prevention and Reduction, Yinchuan 750000)
出处
《环境科学学报》
CAS
CSCD
北大核心
2018年第5期1727-1738,共12页
Acta Scientiae Circumstantiae
基金
国家自然科学基金(No.41765006)~~
关键词
大气颗粒物
后向轨迹聚类分析
潜在源贡献
浓度权重轨迹
银川
atmospheric particles
back trajectory cluster analysis
potential source contribution
concentration weighted trajectory
Yinchuan
