• 关键词：PM_2.5质量浓度  时间变化  气象要素  后向轨迹  PSCF

• 基金项目：国家重点研发计划项目（No.2016YFA0602003）；国家自然科学基金（No.41590873，41805096，41705118）；江苏省高等学校自然科学研究项目（No.18KJB170011）；上海市大气颗粒物污染防治重点实验室开放课题（No.FDLAP18006）

• 卢文
• 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点开放实验室, 南京 210044

• 王红磊
• 1. 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点开放实验室, 南京 210044;2. 上海市大气颗粒物污染防治重点实验室, 上海 200000

• 朱彬
• 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点开放实验室, 南京 210044

• 施双双
• 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点开放实验室, 南京 210044

• 康晖
• 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶-云-降水重点开放实验室, 南京 210044

• 摘要：利用2014-2016年南京江北地区PM_2.5质量浓度和气象要素的小时数据，并结合HYSPLIT模式后向轨迹聚类分析和PSCF法分析了PM_2.5质量浓度的污染特征及其主要影响因素和主要来源特征.结果表明：2014-2016年PM_2.5质量浓度呈逐年下降趋势，下降幅度约为17.40%，由2014年的62.1 μg·m^-3下降至2016年的51.2 μg·m^-3，能见度由2014年的5.8 km上升至2016年6.6 km.PM_2.5质量浓度存在显著的月变化和季节变化特征，1月浓度最高，可达93.0 μg·m^-3；8月浓度最低，仅为38.8 μg·m^-3；冬季浓度最高，可达76.8 μg·m^-3，夏季浓度最低，仅为47.1 μg·m^-3.不同季节日变化均为单峰型分布.气象要素对PM_2.5质量浓度的影响较大，不同相对湿度下能见度和PM_2.5质量浓度具有较好的拟合关系.霾和非霾天PM_2.5质量浓度的阈值为15 μg·m^-3.不同季节的主导气团不同，春季主导气团为偏北气流和偏东气流，占比分别为43.50%和30.80%；夏季主导气团以东部气流为主，占比约为68.22%；秋季和冬季主导气团为来自北方的气流，总占比分别为83.52%和100%；偏北内陆气团PM_2.5质量浓度较大，偏东海洋性气团PM_2.5质量浓度较低.PM_2.5质量浓度潜在源区春冬季潜在源区范围较大，夏秋季潜在源区范围较小，季节变化显著.春季潜在来源主要分布在安徽、江西北部、江苏南部和浙江北部等地区，夏秋季分布在安徽东部、浙江北部和江苏南部等地区，冬季分布在安徽、河南东部，山东和江苏等地区.

• Abstract：Based on the hourly monitoring data including meteorological elements and PM_2.5 mass concentration in the north suburb of Nanjing during 2014 to 2016, PM_2.5 mass concentration variations and their influencing factors were analyzed by using a cluster analysis derived from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Potential Source Contribution Function Analysis (PSCF) method. Results show that the mass concentration of PM_2.5 has a downward trend from a level of 62.1 μg·m^-3 (2014) to 51.2 μg·m^-3 (2016), with a amplitude decline of about 17.4%. The visibility rose from 5.8 km in 2014 to 6.6 km in 2016. There present significantly monthly and seasonal changes for PM_2.5 concentration, who were the highest in January (93.0 μg·m^-3) and the lowest in August (39 μg·m^-3), and were the highest in winter (76.8 μg·m^-3) and the lowest in summer (47.1 μg·m^-3). The diurnal variation of PM_2.5 in different seasons showed a unimodal distribution. Meteorological factors have a great impact on PM_2.5 mass concentration, It's calculated to have a good fitting relationship between visibility and PM_2.5 under different relative humidity. The threshold value of PM_2.5 on haze days and non-haze days was 15 μg·m^-3 uniformly. Dominant air masses varied in different seasons. It's dominated by northerly and easterly airflows in spring, accounting for 43.50% and 30.80% of the total airflows respectively, by easterly airflows in summer,with a proportion of 68.22%, and by northerly airflow in autumn and winter with a fraction of 83.52% and 100.00%, respectively. The PM_2.5 concentrations were comparably large under the control of northly airflows from inland and were low under the easterly airflows originated from ocean areas. The range of the potential source areas of PM_2.5 revealed a significantly seasonal variation with a relatively large area in spring and winter and a small area in summer and autumn. The potential source areas of PM_2.5 mass concentration are mainly distributed in Anhui, northern Jiangxi, southern Jiangsu and northern Zhejiang in spring; in eastern Anhui, northern Zhejiang and southern Jiangsu in summer and autumn, and in Anhui, eastern Henan, Shandong and Jiangsu in winter.

• Key words：PM_2.5 mass concentration  temporal variation  meteorological elements  back trajectory  PSCF

• 
  摘要点击次数： 1066 全文下载次数： 1908