摘要
利用南京大学城市大气环境观测站(32°03′20″N,118°46′32″E)2011年1~12月一氧化碳(CO)连续观测资料,分析南京市CO浓度变化特征;利用后向轨迹模式和聚类分析方法研究影响南京市的主要气团及其化学性质;基于MOPITT资料分析南京市CO的垂直分布.研究表明,南京市CO的年均浓度为(757.5±410.5)×10-9.CO浓度具有明显日变化特征,早上8:00浓度最高,下午16:00浓度最低.CO日变化具有季节差异性,春季最为明显,夏季幅度最小.一周之中CO在周五的浓度最高.CO存在明显季节变化,冬季1月浓度最高,夏季6月浓度最低.HYSPLIT4把影响该观测站的主要气团分为6类,其中来自江苏南部、浙江、上海的气团的污染物浓度最高,对南京市CO浓度贡献最大;源于西伯利亚高原,伴随强冷空气迅速向南移动的气团对南京市CO贡献最小.卫星数据分析结果表明,南京市夏季CO的垂直分布与其他3个季节有较大差异.与地面观测站相比,卫星反演的CO地面浓度要明显偏低.
Using the continuous measurements of carbon monoxide (CO) at Urban Atmospheric Environment Observation Station (32°03′20″N,118°46′32″E) of Nanjing University from January to December 2011, the concentration characteristics of CO was investigated. Backward trajectory and cluster analysis were used to isolate air masses reaching Nanjing with different chemical characteristics. The satellite data from MOPITT was used to analyze vertical distribution of CO at Nanjing. Studies revealed that the annual mean concentration of CO was (757.5±410.5)×10 9. CO exhibited significant diurnal variation with the peak around 8:00am and the trough around 16:00pm. Diurnal variations in four seasons were different, which was the largest in spring and the smallest in summer. As to weekly variation of CO, the highest concentration occurred on Friday. There was an obviously seasonal cycle of CO, with maximum in January and minimum in June. Backward trajectories arriving at Nanjing were divided into 6categories using HYSPLIT4model and cluster technique. The results indicated that CO level in the air masses from south of Jiangsu Province, Zhejiang Province and Shanghai City was the highest. The air masses from Siberian Plateau, fast transport to Nanjing, were the cleanest. The vertical variation of CO in summer was different from that in other three seasons at Nanjing. Compared with the ground-based observation, retrieved CO concentration near surface was significantly lower.
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2013年第9期1577-1584,共8页
China Environmental Science
基金
国家"973"项目(2010CB950704
2010CB428503)
国家科技部公益行业(气象)科研专项(GYHY201206011-1)
国家科技支撑项目(2011BAK21B03)
国家人才培养基金(J1103410)