基于受体模型和源成分谱的缺失组分反演算法
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摘要
针对目前在线监测仪器无法监测Al、Si标识组分可能增加源解析结果不确定性的问题,基于正交矩阵因子分解法模型和实测源成分谱反演Al、Si受体数据,提出缺失组分反演-嵌套PMF算法.设置周期性和非周期性模拟实验,利用PMF模型对缺失Al、Si受体数据、含反演Al、Si受体数据和含Al、Si受体数据分别进行源解析评估,并将源解析结果与真实值进行对比分析,验证缺失组分反演-嵌套PMF算法的可靠性.研究结果表明对于周期性扰动模拟实验,缺失Al、Si对解析土壤源、机动车、燃煤源有较大的影响,反演Al、Si能优化源解析结果,降低源解析结果的不确定性.
Source markers are very important for apportioning the particulate matter sources. However, some markers like aluminum and silicium could not be measured by online instruments,which might increase the uncertainty of source apportionment results. To figure out this problem, this work proposed a new inverse method to estimate Al and Si concentrations base on PMF(Positive Matrix Factorization) and measured source profiles. Several simulation experiments was designed to estimate the performance of the new method. Three input data, including data without Al and Si, data with reversed Al and Si, data with Al and Si, were setup and run separately by PMF, the calculated source profiles and contributions were compared with the corresponding true values. The results show that running model without Al and Si data increases uncertainties of results, and the new method can improve the model performance, for some cases.
Source markers are very important for apportioning the particulate matter sources. However, some markers like aluminum and silicium could not be measured by online instruments,which might increase the uncertainty of source apportionment results. To figure out this problem, this work proposed a new inverse method to estimate Al and Si concentrations base on PMF(Positive Matrix Factorization) and measured source profiles. Several simulation experiments was designed to estimate the performance of the new method. Three input data, including data without Al and Si, data with reversed Al and Si, data with Al and Si, were setup and run separately by PMF, the calculated source profiles and contributions were compared with the corresponding true values. The results show that running model without Al and Si data increases uncertainties of results, and the new method can improve the model performance, for some cases.
引文
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[19]Peré-Trepat E, Kim E, Paatero P, et al. Source apportionment of time and size resolved ambient particulate matter measured with a rotating DRUMimpactor[J].AtmosphericEnvironment,2007,41(28):5921-5933.
[20]陈刚,刘佳媛,皇甫延琦,等.合肥城区PM10及PM2.5季节污染特征及来源解析[J].中国环境科学, 2016,36(7):1938-1946.ChenG,LiuJY,HuangfuY,etal.Seasonalvariationsandsource apportionmentofambientPM10andPM2.5aturbanareaofHefei,China[J]. China Environmental Science, 2016,36(7):1938-1946.
[21]王琴,张大伟,刘保献,等.基于PMF模型的北京市PM2.5来源的时空分布特征[J].中国环境科学, 2015,35(10):2917-2924.Wang Q, Zhang D W, Liu B X, et al. Spatial and temporal variations of ambient PM2.5 source contributions using positive matrix factorization[J]. China Environmental Science, 2015,35(10):2917-2924.
[22]王苏蓉,喻义勇,王勤耕,等.基于PMF模式的南京市大气细颗粒物源解析[J].中国环境科学, 2015,35(12):3535-3542.Wang S Y, Yu Y Y, Wang Q G, et al. Source apportionment of PM2.5 in NanjingbyPMF[J].ChinaEnvironmentalScience,2015,35(12):3535-3542.
[23]张彩艳,吴建会,张普,等.成都市冬季大气颗粒物组成特征及来源变化趋势[J].环境科学研究, 2014,27(7):782-789.Zhang C Y, Wu J H, Zhang P, et al. Particulate composition and source apportionmenttrendsinwinterinChengdu[J].Researchof Environmental Sciences, 2014,27(7):782-789.
[24]Shi G L, Zeng F, Li X, et al. Estimated contributions and uncertainties ofPCA/MLR-CMBresults:Sourceapportionmentforsyntheticand ambientdatasets[J].AtmosphericEnvironment,2011,45(17):2811–2819.
[25]PaateroP,TapperU.Positivematrixfactorisation:anon-negative factor model with optimal utilisation of error estimates of data values[J]. Environmetrics, 1994,5:111-126.
[26]Amato F, Polfi M, Escrig A, et al. Quantifying road dust resuspension in urban environment by Multilinear Engine:a comparison with PMF2[J]. Atmospheric Environment, 2009,43:2770-2780.
[27]AmatoF,HopkePK.SourceapportionmentoftheambientPM2.5acrossSt.Louisusingconstrainedpositivematrixfactorization[J].Atmospheric Environment, 2012,46:329-37.
[28]NguyenQT,SkovH,SorensenLL,etal.Sourceapportionmentof particlesatStationNord,NorthEastGreenlandduring2008~2010usingCOPREMandPMFanalysis[J].AtmosphericChemistryand Physics, 2013,13:35-49.
[29]PantP,HarrisonRM.Estimationofthecontributionofroadtraffic emissions to particulate matter concentrations from field measurements:A review[J]. Atmospheric Environment, 2013,77:78-97.
[30]Wongphatarakul V, Friedlander S K, Pinto J P. A comparative study of PM2.5 ambient aerosol chemical databases[J]. Environmental Science&Technology, 1998,32:3926-3934.
[31]JavitzHS,WatsonJG,RobinsonN.Performanceofthechemical massbalancemodelwithsimulatedlocal-scaleaerosols[J].Atmospheric Environment, 1988,22:2309-2322.
[2]韩力慧,张鹏,张海亮,等.北京市大气细颗粒物污染与来源解析研究[J].中国环境科学, 2015,36(11):3203-3210.HanLH,ZhangP,ZhangHL,etal.Pollutionandsource apportionmentofatmosphericfineparticlesinBeijing[J].China Environmental Science, 2015,36(11):3203-3210.
[3]张延君,郑玫,蔡靖,等.PM2.5源解析方法的比较与评述[J].科学通报, 2015,2:109-121.ZhangYJ,ZhengM,CaiJ,etal.Comparisonandoverviewof PM2.5sourceapportionmentmethods(inChinese).ChinaScience Bulletin, 2015,2:109-121.
[4]朱坦,吴琳,毕晓辉,等.大气颗粒物源解析受体模型优化技术研究[J].中国环境科学, 2010,30(7):865-870.Zhu T, Wu L, Bi X H, et al. Improving receptor models for ambient air particulatemattersourceapportionment[J].ChinaEnvironmental Science, 2010,30(7):865-870.
[5]Du H H, Kong L D, Cheng T T, et al. Insights into summertime haze pollutioneventsoverShanghaibasedononlinewater-solubleionic composition of aerosols[J]. Atmospheric Environment, 2011,45:5131–5137.
[6]Vodi?ka P, Schwarz J, Cusack M, et al. Detailed comparison of OC/EC aerosolatanurbanaruralCzechbackgroundsiteduringsummer winter[J]. Science of the Total Environment, 2015,518:424-433.
[7]ZhangYM,ZhangXY,SunJY,etal.Chemicalcompositionmass sizedistributionofPM1atanelevatedsiteincentraleastChina[J].Atmospheric Chemistry and Physics, 2014,14:12237-12249.
[8]李令军,王占山,张大伟,等.2013~2014年北京大气重污染特征研究[J].中国环境科学, 2016,36(1):27-35.Li L, Wang Z, Zhang D, et al. Analysis of heavy air pollution episodes inBeijingduring2013~2014[J].ChinaEnvironmentalScience,2016,36(1):27-35.
[9]徐虹,肖致美,孔君,等.天津市冬季典型大气重污染过程特征[J].中国环境科学, 2017,37(4):1239-1246.XuH,XiaoZh,KongJ,etal.Characteristicofatmosphericheavy pollutionepisodesinWinterofTianjin[J].ChinaEnvironmental Science, 2017,37(4):1239-1246.
[10]GaoJ,PengX,ChenG,etal.Insightsintothechemical characterizationandsourcesofPM2.5inBeijingata1-htime resolution[J]. Science of the Total Environment, 2015,542:162-171.
[11]Peng X, Shi G L, Gao J, et al. Characteristics and sensitivity analysis of multiple-time-resolved source patterns of PM2.5 with real time data usingMultilinearEngine2[J].AtmosphericEnvironment.2016,139:113-121.
[12]WangM,ShaoM,ChenW,etal.Atemporallyspatiallyresolved validationofemissioninventoriesbymeasurementsofambient volatileorganiccompoundsinBeijing[J].China.Atmospheric Chemistry and Physics, 2014,14:5871-5891.
[13]HemannJG,BrinkmanGL,DuttonSJ,etal.Assessingpositive matrixfactorizationmodelfit:anewmethodtoestimateuncertainty andbiasinfactorcontributionsatthemeasurementtimescale[J].Atmospheric Chemistry and Physics, 2009,9(2):497-513.
[14]ZhouJB,XiongY,XingZY,etal.Characterizingandsourcing ambientPM2.5,overkeyemissionregionsinChinaII:Organic molecular markers and CMB modeling[J]. Atmospheric Environment2017,163:57-64.
[15]Kim E, Hopke P K. Improving source identification of fine particles in aruralnortheasternUSareautilizingtemperature-resolvedcarbon fractions[J].JournalofGeophysicalResearch-Atmospheres,2004,109(D09204).
[16]Liu W, Wang Y H, Russell A, et al. Enhanced source identification of southeastaerosolsusingtemperature-resolvedcarbonfractionsand gasphasecomponents[J].AtmosphericEnvironment,2006,40(2):S445-S466.
[17]Puig R,àvila A, Soler A. Sulphur isotopes as tracers of the influence ofacoal-firedpowerplantonaScotspineforestinCatalonia(NE Spain)[J]. Atmospheric Environment, 2008,42(4):733-745.
[18]Shi G L, Li X, Feng Y C, et al. Combined source apportionment, using positivematrixfactorization-chemicalmassbalanceandprincipal component analysis/multiple linear regression-chemical mass balance models[J]. Atmospheric Environment, 2009,43(18):2929-2937.
[19]Peré-Trepat E, Kim E, Paatero P, et al. Source apportionment of time and size resolved ambient particulate matter measured with a rotating DRUMimpactor[J].AtmosphericEnvironment,2007,41(28):5921-5933.
[20]陈刚,刘佳媛,皇甫延琦,等.合肥城区PM10及PM2.5季节污染特征及来源解析[J].中国环境科学, 2016,36(7):1938-1946.ChenG,LiuJY,HuangfuY,etal.Seasonalvariationsandsource apportionmentofambientPM10andPM2.5aturbanareaofHefei,China[J]. China Environmental Science, 2016,36(7):1938-1946.
[21]王琴,张大伟,刘保献,等.基于PMF模型的北京市PM2.5来源的时空分布特征[J].中国环境科学, 2015,35(10):2917-2924.Wang Q, Zhang D W, Liu B X, et al. Spatial and temporal variations of ambient PM2.5 source contributions using positive matrix factorization[J]. China Environmental Science, 2015,35(10):2917-2924.
[22]王苏蓉,喻义勇,王勤耕,等.基于PMF模式的南京市大气细颗粒物源解析[J].中国环境科学, 2015,35(12):3535-3542.Wang S Y, Yu Y Y, Wang Q G, et al. Source apportionment of PM2.5 in NanjingbyPMF[J].ChinaEnvironmentalScience,2015,35(12):3535-3542.
[23]张彩艳,吴建会,张普,等.成都市冬季大气颗粒物组成特征及来源变化趋势[J].环境科学研究, 2014,27(7):782-789.Zhang C Y, Wu J H, Zhang P, et al. Particulate composition and source apportionmenttrendsinwinterinChengdu[J].Researchof Environmental Sciences, 2014,27(7):782-789.
[24]Shi G L, Zeng F, Li X, et al. Estimated contributions and uncertainties ofPCA/MLR-CMBresults:Sourceapportionmentforsyntheticand ambientdatasets[J].AtmosphericEnvironment,2011,45(17):2811–2819.
[25]PaateroP,TapperU.Positivematrixfactorisation:anon-negative factor model with optimal utilisation of error estimates of data values[J]. Environmetrics, 1994,5:111-126.
[26]Amato F, Polfi M, Escrig A, et al. Quantifying road dust resuspension in urban environment by Multilinear Engine:a comparison with PMF2[J]. Atmospheric Environment, 2009,43:2770-2780.
[27]AmatoF,HopkePK.SourceapportionmentoftheambientPM2.5acrossSt.Louisusingconstrainedpositivematrixfactorization[J].Atmospheric Environment, 2012,46:329-37.
[28]NguyenQT,SkovH,SorensenLL,etal.Sourceapportionmentof particlesatStationNord,NorthEastGreenlandduring2008~2010usingCOPREMandPMFanalysis[J].AtmosphericChemistryand Physics, 2013,13:35-49.
[29]PantP,HarrisonRM.Estimationofthecontributionofroadtraffic emissions to particulate matter concentrations from field measurements:A review[J]. Atmospheric Environment, 2013,77:78-97.
[30]Wongphatarakul V, Friedlander S K, Pinto J P. A comparative study of PM2.5 ambient aerosol chemical databases[J]. Environmental Science&Technology, 1998,32:3926-3934.
[31]JavitzHS,WatsonJG,RobinsonN.Performanceofthechemical massbalancemodelwithsimulatedlocal-scaleaerosols[J].Atmospheric Environment, 1988,22:2309-2322.