周述学,王兴,弓中强,石春娥. 2017. 长江三角洲西部地区PM2.5输送轨迹分类研究[J]. 气象学报, 75(6):996-1010, doi:10.11676/qxxb2017.071
长江三角洲西部地区PM2.5输送轨迹分类研究
Transport patterns of PM2.5 in the western Yangtze River Delta district, China
投稿时间:2017-03-02  修订日期:2017-05-27
DOI:10.11676/qxxb2017.071
中文关键词:  长三角西部  后向轨迹  聚类分析  天气形势  PM2.5浓度
英文关键词:Western of Yangtze River Delta  Back trajectory  Cluster analysis  Weather pattern  PM2.5
基金项目:安徽省自然科学基金(1608085MD84)、华东区域气象科技协同创新基金(QYHZ201401)、公益性行业(气象)科研专项(GYHY201206011)。
作者单位E-mail
周述学 安徽省气象科学研究所, 安徽省大气科学与卫星遥感重点实验室, 合肥, 230031
寿县国家气候观象台, 寿县, 232200 
 
王兴 合肥市气象局, 合肥, 230000  
弓中强 安徽省气象台, 合肥, 230031  
石春娥 安徽省气象科学研究所, 安徽省大气科学与卫星遥感重点实验室, 合肥, 230031
寿县国家气候观象台, 寿县, 232200 
shichune@sina.com 
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中文摘要:
      长三角4个省会(直辖市)城市(上海、南京、合肥、杭州)中,合肥与南京的PM2.5浓度演变有较高的一致性。应用聚类分析的方法对2013-2015年合肥非降水日(日降水量低于10 mm)100 m高度(代表近地层)和1000 m高度(代表边界层中上部)的72 h后向轨迹进行分类,结合合肥2013-2015年PM2.5日均浓度资料,探讨近地层和边界层中上部输送轨迹与长三角西部PM2.5浓度的关系。近地层和边界层中上部分别得到7组和6组不同的后向轨迹;不同输送轨迹对应的PM2.5浓度、重污染(重度以上污染,PM2.5日均浓度大于150 μg/m3)天数、能见度、地面风速、相对湿度等都有显著不同,尤其是在近地层。100 m高度,平均长度最短、来向偏东的轨迹组对应的PM2.5浓度均值最高(约是组内均值最低值的2倍)、重污染天数最多,且占比最高(30%),重污染日对应的气流在过去72 h下降高度均值仅28 m,明显低于其他PM2.5污染等级日;来向偏西北、长度较短的轨迹组,PM2.5浓度均值和重污染天数为第2高,这一类轨迹占比14%,气流到达本地前存在明显的下沉运动,反映了远距离输送加剧本地PM2.5重污染的特征。这两类轨迹常对应PM2.5日均浓度的上升。PM2.5平均浓度最低的2个轨迹组分别是来自东北和西南的较长轨迹组,所占比例分别为6.4%和10.3%,这2类轨迹往往对应着PM2.5日均浓度下降。1000 m高度的结果与100 m高度结果类似,但PM2.5平均浓度的组间差异不及100 m高度,与2001-2005年PM10浓度与输送轨迹的关系不同。对3 a中84个重污染日两个高度的后向轨迹进行聚类,近地层和边界层中上部各得到7类和6类PM2.5重污染日的天气形势。近地层92%的重污染日对应的海平面气压形势场上,从华北到华东属于均压区,气压梯度小,轨迹来向以偏东到偏北方向为主,垂直方向延伸高度在950 hPa以下。1000 m高度,77%的重污染日属于相对较短的轨迹组,对应的850 hPa高度场特征为从中国西北(新疆)到东南受高压控制,长三角或位于高压底部,或位于两高压之间的均压区。这对PM2.5浓度预报有较好的指示意义。
英文摘要:
      The variations of daily PM2.5 concentration in the two capital cities (Hefei and Nanjing) in western Yangtze River Delta district are highly correlated. To investigate the impact of transport pattern on PM2.5 concentration in this area, the cluster analysis was used to categorize the daily 72 h back trajectories of Hefei at 100 m above ground level (AGL), which represents near surface, and 1000 m AGL, which represents the mid-high level of the boundary layer, on days with rainfall lower than 10 mm during 3 a period from 2013 to 2015. The back trajectories were divided into seven groups at 100 m and six groups at 1000 m. The relationship between PM2.5 concentration and the transport pattern was studied based on the results of cluster analysis in combination with daily averages of PM2.5 concentration, horizontal visibility, surface wind speed and relative humidity at ground level. The results are as follows: (1) The statistical results of PM2.5 concentration, visibility, wind speed, and relative humidity in different clusters were evidently different at both 100 and 1000 m levels. (2) At 100 m, the highest cluster-mean PM2.5 concentration, which was almost double of the lowest value, the severe PM2.5 pollution (daily mean PM2.5 concentration >150 μg/m3) and the lowest daily average visibility were found in the cluster with the shortest cluster-mean trajectory mainly coming from the east. More than 60% of total severe pollution days fell within this cluster, which accounted for about 30% of total days (the biggest percentage among all clusters). The air mass in this cluster moved to the studied area with very weak descending motions during the past 72 h, especially in those severe pollution days, the averaged descending height was only 28 m. The second highest daily mean PM2.5 concentration and the number of severe PM2.5 pollution days fell within the cluster with short trajectories from the northwest. The trajectories in this cluster accounted for 14% of the total. In thisv cluster, the air mass moved to the studied area with evident downward motion, indicating that the long-range transport of pollutants intensified local PM2.5 pollution. According to daily changes in PM2.5 concentration, the above two clusters usually corresponded to increasing daily average PM2.5 concentration. The two clusters with the lowest cluster-mean PM2.5 concentration had long trajectories from the northeast and southwest, which accounted for 6.4% and 10.3% of the total. They corresponded to decreasing daily average PM2.5 concentrations. (3) The results of statistics with clustering of trajectories at 1000 m were similar to those at 100 m. However, the differences in PM2.5 concentration among clusters were smaller than those at 100 m, and different from those for PM10 at the beginning of the 2000s. (4) The back trajectories on 84 severe PM2.5 pollution days during 2013-2015 were divided into seven groups by cluster analysis at 100 m and six groups at 1000 m. The distributions of sea level pressure and geo-potential height, which were conducive to the accumulation of fine particles in the western Yangtze River Delta, were obtained by composite analysis. At 100 m, around 92% of trajectories of severe PM2.5 pollution days were quite short, corresponding to slow moving weather systems. In vertical direction, those trajectories were below 950 hPa during the past 48 h, indicating that the transport mainly occurred in the near surface layer without evident upward or downward motions. Correspondingly, the sea-level pressure was homogeneous from North China to East China. At 1000 m, around 77% of severe pollution days belonged to short trajectory groups, while the broad region from Northwest China (Xinjiang) to Southeast China was controlled by high pressure systems at 850 hPa, and Anhui is located in the bottom of high pressure system or between two high systems. The results may be helpful for the forecast of PM2.5 pollution.
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