基于数值模式的细颗粒物来源解析
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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 PM_(2.5) 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 PM_(2.5) and its main chemical compositions are simulated. We find that the simulations agree well with the results from the offline chemical composition analysis. Using PMF model, the components of fine particulate matter during the 2014 YOG(July to September) are identified, which include secondary organic aerosols(25.9%), combustion dust(16.5%), secondary sulfate aerosols(14.5%), secondary nitrate aerosols(12.6%), vehicle exhaust(12.0%), dusts(11.7%), and industrial activities(6.9%). Such results are in good agreement with sampling measurements. This study suggests that the contributions from combustion dust and industrial activities are lower during YOG than before and after YOG, indicating that the control measures of industrial activities during YOG are effective in reducing air pollution. This study suggests that integrated numerical modeling and receptor modeling can effectively assess contributions of different pollution sources, and thus be useful to source apportionments of heavy pollution events, providing scientific basis for emergency controls of air pollution. This finding will be useful for the local government to create efficient control strategies to reduce emissions of different sources of PM_(2.5).
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 PM_(2.5) 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 PM_(2.5) and its main chemical compositions are simulated. We find that the simulations agree well with the results from the offline chemical composition analysis. Using PMF model, the components of fine particulate matter during the 2014 YOG(July to September) are identified, which include secondary organic aerosols(25.9%), combustion dust(16.5%), secondary sulfate aerosols(14.5%), secondary nitrate aerosols(12.6%), vehicle exhaust(12.0%), dusts(11.7%), and industrial activities(6.9%). Such results are in good agreement with sampling measurements. This study suggests that the contributions from combustion dust and industrial activities are lower during YOG than before and after YOG, indicating that the control measures of industrial activities during YOG are effective in reducing air pollution. This study suggests that integrated numerical modeling and receptor modeling can effectively assess contributions of different pollution sources, and thus be useful to source apportionments of heavy pollution events, providing scientific basis for emergency controls of air pollution. This finding will be useful for the local government to create efficient control strategies to reduce emissions of different sources of PM_(2.5).
引文
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14 Zhang Q,Streets D G,Carmichael G R,et al.Asian emissions in 2006 for the NASA INTEX-B mission.Atmos Environ,2009,9:5131-5153
15 Chen P,Wang T,Dong M,et al.Characterization of major natural and anthropogenic source profiles for size-fractionated PM in Yangtze River Delta.Sci Total Environ,2017,598:135-145
16 Coulter C T.EPA-CMB8.2 User’s Manual,EPA-452/R-04-011.US Environmental Protection Agency,Research Triangle Park,NC,2004
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18 Watson J G.Overview of receptor model principles.J Air Pollut Contr Assn,1984,34:619-623
19 Chen P,Wang T,Lu X B,et al.Source apportionment of size-fractionated particles during the 2013 Asian Youth Games and the 2014Youth Olympic Games in Nanjing,China.Sci Total Environ,2017,579:860-870
2 Huang Z X,Li M,Li L,et al.Progress in research of single particle aerosol mass spectrometey(in Chinese).J Shanghai Univ(Nat Sci),2011,17:562-566[黄正旭,李梅,李磊,等.单颗粒气溶胶质谱仪研究进展.上海大学学报(自然科学版),2011,17:562-566]
3 Li L,Tan G B,Zhang L,et al.Analysis of diesel exhaust particles using single particle aerosol mass spectrometry(in Chinese).Chin JAnal Chem,2013,41:1831-1836[李磊,谭国斌,张莉,等.运用单颗粒气溶胶质谱仪分析柴油车排放颗粒物.分析化学,2013,41:1831-1836]
4 Zhang Y M,Sun J Y,Zhang X Y,et al.Application of aerosol mass spectrometer in the characterization of ambient aerosol(in Chinese).Phys Test Chem Anal B:Chem Anal,2011.47:1371-1376[张养梅,孙俊英,张小曳,等.气溶胶质谱仪在大气气溶胶特征研究中的应用.理化检验:化学分册,2011,47:1371-1376]
5 Wu W S,Wang T.On the performance of a semi-continuous PM2.5 sulphate and nitrate instrument under loadings of particulate and sulphur dioxide.Atmos Environ,2007,41:5442-5455
6 Dong H B,Zeng L M,Hu M,et al.Technical note:The application of an improved gas and aerosol collector for the ambient air pollutants in China.Atmos Chem Phys,2012,12:10519-10533
7 Du H,Kong L,Cheng T,et al.Insights into summertime haze pollution events over Shanghai based on online water-soluble ionic composition of aerosols.Atmos Environ,2011,45:5131-5137
8 Huang X F,He L Y,Hu M,et al.Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an aerodyne high-resolution aerosol mass spectrometer.Atmos Chem Phys,2010,10:8933-8945
9 Sun Y L,Zhang Q,Schwab J J,et al.Characterization of the sources and processes of organic and inorganic aerosols in New York city with a high-resolution time-of-flight aerosol mass spectrometer.Atmos Chem Phys,2010,10:2215-2227
10 Sun Y,Wang Z,Fu P,et al.The impact of relative humidity on aerosol composition and evolution processes during wintertime in Beijing,China.Atmos Environ,2013,77:927-934
11 Wang T J,Hu Z Y,Xie M.Atmospheric sulfur deposition onto different ecosystems over China.Environ Geochem Health,2004,26:169-177
12 Deng J J.Study on the formation mechanism and forecasting methodology on haze weather in Yangtze River Delta(in Chinese).Doctor Dissertation.Nanjing:Nanjing University,2011[邓君俊.长三角地区霾天气形成机理和预报方法研究.博士学位论文.南京:南京大学,2011]
13 Zhu J L,Wang T J,Deng J J,et al.An emission inventory of air pollutants from crop residue burning Yangtze River Delta region and its application in simulation of a heavy haze weather process(in Chinese).Acta Sci Circumst,2012,32:3045-3055[朱佳雷,王体健,邓君俊,等.长三角地区秸秆焚烧污染物排放清单及其在重霾污染天气模拟中的应用.环境科学学报,2012,32:3045-3055]
14 Zhang Q,Streets D G,Carmichael G R,et al.Asian emissions in 2006 for the NASA INTEX-B mission.Atmos Environ,2009,9:5131-5153
15 Chen P,Wang T,Dong M,et al.Characterization of major natural and anthropogenic source profiles for size-fractionated PM in Yangtze River Delta.Sci Total Environ,2017,598:135-145
16 Coulter C T.EPA-CMB8.2 User’s Manual,EPA-452/R-04-011.US Environmental Protection Agency,Research Triangle Park,NC,2004
17 Watson J G,Cooper J A,Huntzicker J J.The effective variance weighting for least squares calculations applied to the mass balance receptor model.Atmos Environ,1984,18:1347-1355
18 Watson J G.Overview of receptor model principles.J Air Pollut Contr Assn,1984,34:619-623
19 Chen P,Wang T,Lu X B,et al.Source apportionment of size-fractionated particles during the 2013 Asian Youth Games and the 2014Youth Olympic Games in Nanjing,China.Sci Total Environ,2017,579:860-870