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广西十万大山背景点PM2.5中非极性有机气溶胶组成及来源解析
引用本文:邢佳莉,曹芳,王谦,张煜娴,章炎麟.广西十万大山背景点PM2.5中非极性有机气溶胶组成及来源解析[J].环境科学,2022,43(6):2895-2905.
作者姓名:邢佳莉  曹芳  王谦  张煜娴  章炎麟
作者单位:南京信息工程大学应用气象学院, 耶鲁大学-南京信息工程大学大气环境中心, 气候与环境变化国际合作联合实验室, 南京 210044;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, 气象灾害教育部重点实验室, 南京 210044;江苏省农业气象重点实验室, 南京信息工程大学应用气象学院, 南京 210044
基金项目:国家自然科学基金项目(41977185,41977305,41761144056);江苏省双创团队;江苏省杰出青年基金项目(BK20180040)
摘    要:大气细颗粒物(PM2.5)中的非极性化合物包括多环芳烃(PAHs)和正构烷烃(n-alkanes)等,通常用于识别污染来源,且对人体健康和环境有很重要的影响.为探究广西背景点PM2.5中非极性有机气溶胶的污染特征及来源,于2017年11月至2018年10月,对野外采集的PM2.5样品分析了其中17种多环芳烃和20种正构烷烃.结果表明,多环芳烃和正构烷烃全年的平均值分别为(4.28±4.25)ng·m-3和(13.7±14.72)ng·m-3;季节变化规律均是:冬季(7.86±5.19)ng·m-3和(27.51±16.9)ng·m-3]>春季(2.73±1.76)ng·m-3和(7.64±4.71)ng·m-3]>秋季(2.34±1.45)ng·m-3和(7.01±4.55)ng·m-3]>夏季(1.91±1.67)ng·...

关 键 词:非极性化合物  多环芳烃(PAHs)  正构烷烃(n-alkanes)  后向轨迹分析  主成分分析
收稿时间:2021/10/8 0:00:00
修稿时间:2021/11/16 0:00:00

Organic Aerosols and Source Analysis of Fine Particles in the Background of Shiwanda Mountain, Guangxi
XING Jia-li,CAO Fang,WANG Qian,ZHANG Yu-xian,ZHANG Yan-lin.Organic Aerosols and Source Analysis of Fine Particles in the Background of Shiwanda Mountain, Guangxi[J].Chinese Journal of Environmental Science,2022,43(6):2895-2905.
Authors:XING Jia-li  CAO Fang  WANG Qian  ZHANG Yu-xian  ZHANG Yan-lin
Institution:International Joint Laboratory on Climate and Environment Change (ILCEC), Yale-NUIST Center on Atmospheric Environment, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract:The non-polar compounds in atmospheric fine particles (PM2.5) mainly include polycyclic aromatic hydrocarbons (PAHs) and normal alkanes (n-alkanes), etc., which are usually used to identify the source of the air pollution and have a great important impact on human health and the environment. To study the pollution characteristics and sources of non-polar organic aerosols in the background point PM2.5 in Guangxi, from November 2017 to October 2018, 17 types of PAHs and 20 types of n-alkanes were analyzed on PM2.5 samples collected in the field. It was found that the annual average concentrations of PAHs and n-alkanes were 4.28±4.25 ng·m-3 and 13.7±14.72 ng·m-3, respectively. The seasonal change was as follows:winter(7.86±5.19) ng·m-3, (27.51±16.90) ng·m-3]>spring(2.73±1.76) ng·m-3, (7.64±4.71) ng·m-3]>autumn(2.34±145) ng·m-3, (7.01±4.55) ng·m-3]>summer(1.91±1.67) ng·m-3, (3.98±3.12) ng·m-3]. In PAHs, 5-ring and 6-ring molecules accounted for more than 60%, followed by low- and medium-ring molecules (4-ring and 3-ring). The high molecular weight of n-alkanes was relatively high (C29>C31>C27), and the odd and even carbon numbers were significantly different. In addition, combined with the feature ratio method, principal component analysis method, and backward trajectory joint verification, it was found that 41.5% of non-organic aerosols in winter were affected by maritime traffic emissions and ocean source transportation, and 36.7% of the pollution was explained by the coal burning and local biomass burning; 25.2% of the pollution in spring came from biomass combustion and transportation, and 45.0% was attributed to marine transportation in the southern part of the study area and higher plant wax emissions pollution; 53.4% of pollution in summer came from polluted ship emissions, and 10.6% of pollution came from transportation in Southwest Thailand Source transportation. The organic aerosols at the background sites in Guangxi were affected by the combined effects of local emissions and transmission sources.
Keywords:non-polar compound  polycyclic aromatic hydrocarbons(PAHs)  normal alkanes(n-alkanes)  backward trajectory analysis  principal component analysis
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