To characterize the air pollution relative to season and identify potential pollution sources in Tuanbowa of Tian-jin, HYSPLIT model and meteorological data of global data assimilation system ( GDAS) were used and the cluster method was applied to invert the tracks of airflows arriving at Tuanbowa of Tianjin during the period from December 2012 to No-vember 2013, and the tracks were classified by season. Moreover, the daily monitoring data of NO2 and SO2 during the pe-riod were taken into account in analysis of effects of the airflow tracks on concentrations of pollutants in Tuanbowa of Tian-jin relative to season. The potential source contribution function ( PSCF) analysis method and concentration-weighted traj-ectory ( CWT) analysis method were applied to simulate contributions of the potential sources and CWT of NO2 and SO2 relative to season. Results show that airflows coming in from different directions had apparent different impacts on distribu-tion of potential NO2 and SO2 sources. The airflow tracks corresponding to the daily mean highest values of NO2 and SO2 concentrations were concentrated mainly in winter and autumn. In winter, the daily highest concentration of NO2 and SO2 was 115 and 179 μg·m-3 , respectively, with airflows coming mainly from northwest while in autumn, it was 81 and 116μg·m-3 , respectively, with airflows coming mainly from northwest and southeast. PSCF and CWT of NO2 and SO2 in Tu-anbohu were distributed in a similar pattern, with the highest concentration of NO2 and SO2 appearing in the surroundings of Beijing and Tianjin and areas around the coal industries in Hebei Province, which demonstrate that they are the main potential source areas of the two main pollutants. Compared with the PSCT analysis method, the CWT method may be used to quantitatively simulate concentrations of the pollutants in the potential source area, and the potential source areas simu-lated with the CWT method are more concentrated than those with the PSCF method, regardless of season, which helps i-dentify potential source areas more accurately.%为研究天津清洁站点团泊洼不同季节大气污染物的污染特征和可能来源区域,利用HYSPLIT模型和全球资料同化系统(GDAS)气象数据,采取聚类方法对2012年12月—2013年11月期间抵达天津团泊洼的气团轨迹进行模拟并按不同季节分类.结合该期间NO2和SO2日均浓度监测数据,分析了不同季节气流轨迹对团泊洼污染物浓度的影响.利用潜在源贡献(PSCF)因子分析法和浓度权重轨迹(CWT)分析法分别模拟不同季节NO2和SO2潜在源的贡献作用和浓度权重轨迹.结果表明,不同方向气流轨迹对团泊洼NO2和SO2潜在源区分布的影响存在显著差异.团泊洼NO2和SO2日均最高浓度值对应的气流轨迹均集中在冬季和秋季.冬季日浓度最大值分别为115和179μg·m-3,气流主要来自团泊洼的西北方向;秋季日均浓度值分别为81和116μg·m-3,气流主要来自西北和东南方向.团泊洼NO2和SO2的PSCF与CWT分布特征类似,最高值主要集中在北京和天津周边、河北省的煤炭工业区附近,是团泊洼这2种污染物最大的潜在源区.与PSCF分析法相比,CWT分析法能定量模拟潜在源区污染物的浓度数值,而且采用CWT法模拟的各季节潜在源区均比PSCF法的模拟区域更为集中,有助于更精确确定污染物的潜在源区.
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