基于数值模式的细颗粒物来源解析  

Source apportionment of fine particles based on combined numerical model and receptor model

作　　者：陈璞珑 王体健[1] 谢晓栋[1] 汤莉莉[2] 徐少才 王静 Pulong Chen;Tijian Wang;Xiaodong Xie;Lili Tang;Shaocai Xu;Jing Wang(School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China;Environmental Monitoring Center ofJiangsu, Nanjing 210009, China;Environmental Monitorine Center of Oinedao, Oingdao 266000, China)

机构地区：[1]南京大学大气科学学院,南京210023 [2]江苏省环境监测中心站,南京210009 [3]青岛市环境监测中心站,青岛266000

出　　处：《科学通报》2018年第18期1829-1838,共10页Chinese Science Bulletin

基　　金：国家重点研发计划(2016YFC0208504;2016YFC0203303);国家自然科学基金(91544230;41575145;41621005)资助

摘　　要：传统的颗粒物来源解析是通过受体采样和化学组分分析开展,该方法主要适用于有限的采样点位和时段.为了提高颗粒物来源解析结果的时空分辨率,发展了以数值模式和受体模型相结合的颗粒物来源解析技术.基于南京大学自主研发的区域大气环境模式(RegAEMS)和基于正定矩阵因子分解法的受体模型(PMF),以2014年南京青年奥林匹克运动会(简称青奥会)为例,开展了细颗粒物的来源解析研究.结果表明,RegAEMS可以较好地模拟南京市PM_(2.5)浓度及其主要化学组分,与同期基于手工采样分析的结果基本相当.进一步利用PMF模型计算不同类型排放源的贡献,发现南京青奥会期间(2014年7~9月)PM_(2.5)的来源依次是二次有机气溶胶(25.9%)、燃煤(16.5%)、硫酸盐(14.5%)、硝酸盐(12.6%)、机动车尾气(12.0%)、扬尘(11.7%)和工业生产(6.9%).比较发现,本方法解析出来的PM_(2.5)主要排放源贡献与基于离线采样分析的源解析结果基本一致.此外,基于数值模式和受体模型的源解析结果还反映出了青奥会中期电厂燃煤和工业生产的排放对颗粒物的贡献要明显低于青奥会前期和后期,表明青奥会期间对工业生产和电厂燃煤的污染控制措施起到了有效作用.本研究所发展的将数值模型和受体模型相结合的颗粒物来源解析方法还可以实现对未来重污染天气下的颗粒物来源贡献分析,从而为大气重污染应急管控提供科学依据.In recent years, atmospheric particulate matter has become the primary air pollutant in most cities of China. In order to support an efficient control for particles reduction, it is of great importance to investigate the contribution of different sources to particulate matter. Source apportionment has been a conventional technique for seeking the emission sources of particulate matter. There are many ways to investigate the source of particles, such as receptor models, emission inventories, trajectory analysis, dispersion models, photochemical models and source models. Receptor models were shown to be an effective tool for source apportionment. As one of the most popular receptor models, the Positive Matrix Factorization（PMF） model estimates the sources contribution rate based on chemical characteristics of particulate samples. Traditionally, apportioning various sources of particulate matter is mainly through offline chemical composition analysis in combination with measurements in receptor regions. This method is limited by observational periods and locations and is applicable only for historical events. So researches on the source apportionment of PM2.5 with high spatial and temporal resolution in urban scale would be of great significance to control air pollution scientifically and improve urban air quality. The aim of this paper is to develop a source apportionment method of fine particulate matter based on a combination of numerical air quality model and receptor model. This method will simulate the chemical components of fine particulate matter accurately and evaluate the source contributions quantitatively at the same time. In this study, a new method is developed based on a numerical model（RegA EMS model） and a receptor model（PMF） to enhance the temporal and spatial resolutions of apportioning particulate matter sources. This method is applied to the period during the Youth Olympic Games（YOG） in Nanjing for apportioning fine particulate matter sources. With Reg AEMS, the concentrations of

关 键 词：细颗粒物 来源解析 受体模型 区域大气环境模式 南京青年奥林匹克运动会 

分 类 号：X513[环境科学与工程—环境工程]