大气颗粒物来源解析复合受体模型的研究和应用
|
摘要
受体模型是大气颗粒物来源解析的一个重要计算工具。受体模型的种类很多,主要分为source known类受体模型和source unknown类受体模型两大类。前者主要以化学质量平衡模型(CMB)为代表；后者主要以主成分分析／多元线性回归模型(PCA/MLR-CMB)、正定因子分解模型(PMF)等为代表。在这些模型的应用过程中,都普遍遇到一个重要的问题——共线性问题带来的干扰。
共线性问题是指参与模型计算的源类型中,有两种以上的源成分谱相似。当共线性问题存在时,使用CMB模型进行解析,会得到负值的解析结果；使用PCA/MLR或PMF模型解析时,共线源类会混在一个因子里被提取出来。
本论文的研究结果表明,对CMB受体模型而言,源—受体体系的不匹配性是导致共线性问题产生干扰的最根本原因。如果体系的匹配程度较高,那么即便有共线性源存在,也能得到理想的解析结果。
基于上述思想,本论文提出了主成分分析/多元线性回归—化学质量平衡复合受体模型(PCA/ MLR-CMB)和非负主成分回归化学质量平衡受体模型。这两种模型分别对受体和源的信息加以净化,从而降低共线性问题带来的干扰。
为验证这两种模型的准确性,本论文建立了模拟受体。使用这两种模型对模拟受体进行解析。
对于PCA/MLR-CMB复合受体模型,本论文使用来自真实的源成分谱构建了模拟数据。构建模拟数据的成分谱中,扬尘、土壤风尘、煤烟尘的成分谱共线性强烈,如果使用传统CMB模型则无法得到理想结果。因此使用复合模型对模拟数据进行解析。结果表明,模型的拟合值接近真实值,说明模型的结果是理想的。
接下来,使用PCA/MLR-CMB复合受体模型对成都市和太原市受体进行了解析,并把解析结果同传统CMB模型的解析结果进行比较。结果表明,由于有共线性源类的存在,传统CMB模型的解析结果有负值的产生,不可被接受；而PCA/MLR-CMB复合受体模型则得到了理想的结果。表明复合模型在实际应用中是可行的。
对于NCPCRCMB复合受体模型,本论文使用来自真实的源成分谱构建了100条受体成分谱,并对源和受体成分谱在一定范围内进行了扰动。接着对这100条受体成分谱进行解析,对拟合值和设定值的差异进行了评估。结果表明,NCPCRCMB复合受体模型是可行的。
接下来使用NCPCRCMB复合受体模型分别对无锡、银川、天津和济南的受体样品进行了解析,得到了理想的结果,表明,NCPCRCMB复合受体模型在实际应用中是可行的
Receptor model is a useful tool for air source apportionment study. Receptor model included two kinds:souce known model and source unknown model. Chemical mass balance model (CMB) is an important model for source known model which needs both of information of source and receptor; principal component analysis/multiple linear regression model (PCA/MLR) and positive matrix factorization model (PMF) belong to source unknown model which only need receptor information. There are defferent strengthen and weakeness for these models. Genarally, collinearity problem is an important problem for these models.
Collinearity problem means that there were more than two source categories have similar source profiles. When the collinearity problem is presented, CMB model often obtains negative results; on the other hand, for PCA/MLR and PMF model, the collinear sources usually be extracted in one factor.
According to our study, for CMB model, the compatibility between receptor and source is the key reason to resolve the collinearity problem. If the source and receptor are compatible, the acceptable results can be obtained by CMB model even if the collinearity problem was presented.
In this study, principal component analysis/multiple linear regression-Chemical mass balance (PCA/MLR-CMB) combined modle and Nonnegative Constrained Principal Component Regression Chemical Mass Balance (NCPCRCMB) model are developed to resolve collienarity problem.
In order to access the results of the combined models, the synthetic receptor datasets were developed and studied by combined model.
For PCA/MLR-CMB combined model, the actual source profiles which obtained from real world were applied to construct the synthetic datasets. Among the actual source profiles, resuspended dust, soil dust and coal combustion are the collinear sources. So, if the synthetic datasets were studied by CMB model, negative results would obtained. The PCA/MLR-CMB combined model was applied to study the synthetic datasets. The estimated results by combined model were close to the true values.
Next, the PCA/MLR-CMB combined model was applied to study the ambient datasets from Chengdu and Taiyuan cities. The difference between the results of PCA/MLR-CMB and CMB models were discussed. The negative results were obtained by CMB model due to the presenting of collinearity problem; while for PCA/MLR-CMB model, acceptable results were obtained. The conclusion indicates that the PCA/MLR-CMB model is feasible.
For NCPCRCMB model,100 receptor profiles were developed by actual source profiles. The sources and receptor profiles randomly perturbed in order to make the sources and receptor incompatible. The synthetic receptors were studied by NCPCRCMB model, then the difference between estimated results and true values were discussed. The acceptable results were obtained.
Next, the NCPCRCMB model were applied to study the ambient datasets from Xuxi, Yinchuan, Tianjin and Ji'nan. The acceptable results show that the NCPCRCMB model is feasible.
共线性问题是指参与模型计算的源类型中,有两种以上的源成分谱相似。当共线性问题存在时,使用CMB模型进行解析,会得到负值的解析结果；使用PCA/MLR或PMF模型解析时,共线源类会混在一个因子里被提取出来。
本论文的研究结果表明,对CMB受体模型而言,源—受体体系的不匹配性是导致共线性问题产生干扰的最根本原因。如果体系的匹配程度较高,那么即便有共线性源存在,也能得到理想的解析结果。
基于上述思想,本论文提出了主成分分析/多元线性回归—化学质量平衡复合受体模型(PCA/ MLR-CMB)和非负主成分回归化学质量平衡受体模型。这两种模型分别对受体和源的信息加以净化,从而降低共线性问题带来的干扰。
为验证这两种模型的准确性,本论文建立了模拟受体。使用这两种模型对模拟受体进行解析。
对于PCA/MLR-CMB复合受体模型,本论文使用来自真实的源成分谱构建了模拟数据。构建模拟数据的成分谱中,扬尘、土壤风尘、煤烟尘的成分谱共线性强烈,如果使用传统CMB模型则无法得到理想结果。因此使用复合模型对模拟数据进行解析。结果表明,模型的拟合值接近真实值,说明模型的结果是理想的。
接下来,使用PCA/MLR-CMB复合受体模型对成都市和太原市受体进行了解析,并把解析结果同传统CMB模型的解析结果进行比较。结果表明,由于有共线性源类的存在,传统CMB模型的解析结果有负值的产生,不可被接受；而PCA/MLR-CMB复合受体模型则得到了理想的结果。表明复合模型在实际应用中是可行的。
对于NCPCRCMB复合受体模型,本论文使用来自真实的源成分谱构建了100条受体成分谱,并对源和受体成分谱在一定范围内进行了扰动。接着对这100条受体成分谱进行解析,对拟合值和设定值的差异进行了评估。结果表明,NCPCRCMB复合受体模型是可行的。
接下来使用NCPCRCMB复合受体模型分别对无锡、银川、天津和济南的受体样品进行了解析,得到了理想的结果,表明,NCPCRCMB复合受体模型在实际应用中是可行的
Receptor model is a useful tool for air source apportionment study. Receptor model included two kinds:souce known model and source unknown model. Chemical mass balance model (CMB) is an important model for source known model which needs both of information of source and receptor; principal component analysis/multiple linear regression model (PCA/MLR) and positive matrix factorization model (PMF) belong to source unknown model which only need receptor information. There are defferent strengthen and weakeness for these models. Genarally, collinearity problem is an important problem for these models.
Collinearity problem means that there were more than two source categories have similar source profiles. When the collinearity problem is presented, CMB model often obtains negative results; on the other hand, for PCA/MLR and PMF model, the collinear sources usually be extracted in one factor.
According to our study, for CMB model, the compatibility between receptor and source is the key reason to resolve the collinearity problem. If the source and receptor are compatible, the acceptable results can be obtained by CMB model even if the collinearity problem was presented.
In this study, principal component analysis/multiple linear regression-Chemical mass balance (PCA/MLR-CMB) combined modle and Nonnegative Constrained Principal Component Regression Chemical Mass Balance (NCPCRCMB) model are developed to resolve collienarity problem.
In order to access the results of the combined models, the synthetic receptor datasets were developed and studied by combined model.
For PCA/MLR-CMB combined model, the actual source profiles which obtained from real world were applied to construct the synthetic datasets. Among the actual source profiles, resuspended dust, soil dust and coal combustion are the collinear sources. So, if the synthetic datasets were studied by CMB model, negative results would obtained. The PCA/MLR-CMB combined model was applied to study the synthetic datasets. The estimated results by combined model were close to the true values.
Next, the PCA/MLR-CMB combined model was applied to study the ambient datasets from Chengdu and Taiyuan cities. The difference between the results of PCA/MLR-CMB and CMB models were discussed. The negative results were obtained by CMB model due to the presenting of collinearity problem; while for PCA/MLR-CMB model, acceptable results were obtained. The conclusion indicates that the PCA/MLR-CMB model is feasible.
For NCPCRCMB model,100 receptor profiles were developed by actual source profiles. The sources and receptor profiles randomly perturbed in order to make the sources and receptor incompatible. The synthetic receptors were studied by NCPCRCMB model, then the difference between estimated results and true values were discussed. The acceptable results were obtained.
Next, the NCPCRCMB model were applied to study the ambient datasets from Xuxi, Yinchuan, Tianjin and Ji'nan. The acceptable results show that the NCPCRCMB model is feasible.
引文
[1.冯银厂，《关于花雪之灵平衡受体模型应用中若干技术问题的研究》.南开大学博士论文。
2.王帅杰,朱坦,《大气颗粒物源解析技术研究进展》.环境污染治理技术与设备,2002.08：11-15.
3.冯银厂,白志鹏,朱坦,《大气颗粒物二重源解析技术原理与应用》.环境科学,2002.23：106-108.
4. Glover, D.M., P.K. Hopke, S.J. Vermette, et al., SOURCE APPORTIONMENT WITH SITE SPECIFIC SOURCE PROFILES. Journal of the Air & Waste Management Association,1991. 41(3):294-305.
5. Hopke, P.K., N. Gao, and M.D. Cheng, COMBINING CHEMICAL AND METEOROLOGICAL DATA TO INFER SOURCE AREAS OF AIRBORNE POLLUTANTS. Chemometrics and Intelligent Laboratory Systems,1993.19(2):187-199.
6. Watson, J.G., J.C. Chow, Z.Q. Lu, et al., CHEMICAL MASS-BALANCE SOURCE APPORTIONMENT OF PM(10) DURING THE SOUTHERN CALIFORNIA AIR-QUALITY STUDY. Aerosol Science and Technology,1994.21(1):1-36.
7. Juvela, M., K. Lehtinen, and P. Paatero, The use of Positive Matrix Factorization in the analysis of molecular line spectra. Monthly Notices of the Royal Astronomical Society,1996. 280(2):616-626.
8. Rachdawong, P. and E.R. Christensen, Determination of PCB sources by a principal component method with nonnegative constraints. Environmental Science & Technology,1997. 31(9):2686-2691.
9. Xie, Y.L., P.K. Hopke, and P. Paatero, Positive matrix factorization applied to a curve resolution problem. Journal of Chemometrics,1998.12(6):357-364.
10. Hopke, P.K., Y.L. Xie, and P. Paatero, Mixed multiway analysis of airborne particle composition data. Journal of Chemometrics,1999.13(3-4):343-352.
11. Polissar, A.V., P.K. Hopke, P. Paatero, et al. The aerosol at Barrow, Alaska:long-term trends and source locations. Atmospheric Environment,1999.33(16):2441-2458.
12. Xie, Y.L., P.K. Hopke, P. Paatero, et al. Locations and preferred pathways of possible sources of Arctic aerosol. Atmospheric Environment,1999.33(14):2229-2239.
13. Xie, Y.L., P.K. Hopke, P. Paatero, et al. Identification of source nature and seasonal variations of Arctic aerosol by the multilinear engine. Atmospheric Environment,1999. 33(16):2549-2562.
14. Chueinta, W., P.K. Hopke, and P. Paatero, Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmospheric Environment,2000.34(20):3319-3329.
15. Mugica, V., E. Vega, G. Sanchez, et al. Contribution of food cooking emissions to the presence of non-methane hydrocarbons in the Mexico City atmosphere. Air Pollution Viii, 2000.8:283-291.
16. Engelbrecht, J.P., L. Swanepoel, J.C. Chow, et al., PM2.5 and PMIO concentrations from the Qalabotjha low-smoke fuels macro-scale experiment in South Africa. Environmental Monitoring and Assessment,2001.69(1):1-15.
17. Li, W.W., R. Orquiz, J.H. Garcia, et al. Analysis of temporal and spatial dichotomous PM air samples in the El Paso-Cd. Juarez air quality basin. Journal of the Air & Waste Management Association,2001.51(11):1551-1560.
18. Polissar, A.V., P.K. Hopke, and J.M. Harris, Source regions for atmospheric aerosol measured at Barrow, Alaska. Environmental Science & Technology,2001.35(21):4214-4226.
19. Mugica, V., J. Watson, E. Reyes, et al., Receptor model source apportionment of nonmethane hydrocarbons in Mexico City. TheScientificWorldJOURNAL,2002.2(Cited April 5,2002): 844-860.
20. Ho, K.F.L., S.C.; Chan, C.K.; Yu, J.C.; Chow, J.C.; Yao, X.H., Characterization of chemical species in PM2.5 and PM10 aerosols in Hong Kong. Atmospheric Environment,2003.37: 31-39.
21. Chueinta, W., P.K. Hopke, and P. Paatero, Multilinear model for spatial pattern analysis of the measurement of haze and visual effects project. Environmental Science & Technology,2004. 38(2):544-554.
22. Han, Y.J., T.M. Holsen, P.K. Hopke, et al., Identification of source locations for atmospheric dry deposition of heavy metals during yellow-sand events in Seoul, Korea in 1998 using hybrid receptor models. Atmospheric Environment,2004.38(31):5353-5361.
23. Zhao, W.X. and P.K. Hopke, Source apportionment for ambient particles in the San Gorgonio wilderness. Atmospheric Environment,2004.38(35):5901-5910.
24. Ogulei, D., P.K. Hopke, L.M. Zhou, et al., Receptor modeling for multiple time resolved species:The Baltimore, supersite. Atmospheric Environment,2005.39(20):3751-3762.
25. Gao, N., A.E. Gildemeister, K. Krumhansl, et al., Sources of fine particulate species in ambient air over Lake Champlain Basin, VT. Journal of the Air & Waste Management Association,2006.56(11):1607-1620.
26. Hopke, P.K., Recent developments in receptor modeling. Journal of Chemometrics,2003. 17(5):255-265.
27. Thurston, G.D. and J.D. Spengler, A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON. Atmospheric Environment,1985.19(1):9-25.
28. Kim, E. and P.K. Hopke, Re:Source apportionment of fine particles in Washington, DC, utilizing temperature-resolved carbon fractions (vol 54, pg 1011,2004). Journal of the Air & Waste Management Association,2004.54(8):1011-1012.
29. Kim, E. and P.K. Hopke, Improving source identification of fine particles in a rural northeastern US area utilizing temperature-resolved carbon fractions. Journal of Geophysical Research-Atmospheres,2004.109(D9).
30. Kim, E. and P.K. Hopke, Identification of fine particle sources in mid-Atlantic US area. Water Air and Soil Pollution,2005.168(1-4):391-421.
31. Henry, R.C., Duality in multivariate receptor models. Chemometrics and Intelligent Laboratory Systems,2005.77(1-2):59-63.
32. 许禄,《化学计量学方法》.科学出版社.
33. Shi, G.L., Y.C. Feng, J.H. Wu, et al., Source Identification of Polycyclic Aromatic Hydrocarbons in Urban Particulate Matter of Tangshan, China. Aerosol and Air Quality Research,2009.9(3):309-315.
34. Formenti, P., H.J. Annegarn, P. Prati, et al., Source apportionment by receptor models in a study of Geneva aerosol via streaker sampling and PIXE analysis. Physica Medica,1997. 13(3):101-109.
35. Prati, P., A. Zucchiatti, F. Lucarelli, et al., Source apportionment near a steel plant in Genoa (Italy) by continuous aerosol sampling and PIXE analysis. Atmospheric Environment,2000. 34(19):3149-3157.
36. Bruno, P., M. Caselli, G. de Gennaro, et al., Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model. Fresenius Journal of Analytical Chemistry,2001.371(8):1119-1123.
37 Kulshrestha, U.C., M. Jain, R. Sekar, et al., Chemical characteristics and source apportionment of aerosols over Indian Ocean during INDOEX-1999. Current Science,2001. 80:180-185.
38. Lucarelli, F., P.A. Mando, S. Nava, et al., One-year study of the elemental composition and source apportionment of PM10 aerosols in Florence, Italy. Journal of the Air & Waste Management Association,2004.54(11):1372-1382.
39. Gupta, A.K. and K. Karar, Source apportionment of PM/sub 10/at residential and industrial sites of an urban region of Kolkata, India. Atmospheric Research,2007.84(1):30-41.
40. Karar, K. and A.K. Gupta, Source apportionment of PM10 at residential and industrial sites of an urban region of Kolkata, India. Atmospheric Research,2007.84(1):30-41.
41.Tiwari. S.,U.C. Kulshrestha, and B. Padmanabhamurty, Monsoon rain chemistry and source apportionment using receptor modeling in and around National Capital Region (NCR) of Delhi, India. Atmospheric Environment,2007.41(27):5595-5604.
42. Viana, M., X. Querol, T. Gotschi, et al., Source apportionment of ambient PM2.5 at five Spanish centres of the European Community Respiratory Health Survey (ECRHS Ⅱ). Atmospheric Environment,2007.41(7):1395-1406.
43. Sofowote, U.M., B.E. McCarry, and C.H. Marvin, Source apportionment of PAH in Hamilton Harbour suspended sediments:Comparison of two factor analysis methods. Environmental Science & Technology,2008.42(16):6007-6014.
44. Tauler, R., M. Viana, X. Querol, et al., Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies. Atmospheric Environment,2009. 43(26):3989-3997.
45. Harrison, R.M., D.J.T. Smith, and L. Luhana, Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, UK. Environmental Science & Technology,1996.30(3):825-832.
46. Guo, H., T. Wang, I.J. Simpson, et al., Source contributions to ambient VOCs and CO at a rural site in Eastern China. Atmospheric Environment,2004.38(27):4551-4560.
47. Park, S.S. and Y.J. Kim, Source contributions to fine particulate matter in an urban atmosphere. Chemosphere,2005.59(2):217-226.
48. Kim, E. and P.K. Hopke, Characterization of fine particle sources in the Great Smoky Mountains area. Science of the Total Environment,2006.368(2-3):781-794.
49. Kim, E. and P.K. Hopke, Source characterization of ambient fine particles in the Los Angeles basin. Journal of Environmental Engineering and Science,2007.6(4):343-353.
50. Kim, E., P.K. Hopke, and E.S. Edgerton, Source identification of Atlanta aerosol by positive matrix factorization. Journal of the Air & Waste Management Association,2003.53(6): 731-739.
51. Kim, E., P.K. Hopke, and E.S. Edgerton, Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Atmospheric Environment,2004.38(20):3349-3362.
52. Kim, E., P.K. Hopke, T.V. Larson, et al., Factor analysis of Seattle fine particles. Aerosol Science and Technology,2004.38(7):724-738.
53. Kim, E., P.K. Hopke, J.P. Pinto, et al., Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis. Environmental Science & Technology,2005.39(11):4172-4179.
54. Kim, E., T.V. Larson, P.K. Hopke, et al., Source identification of PM2.5 in an arid Northwest US City by positive matrix factorization. Atmospheric Research,2003.66(4):291-305.
55. Hopke, P.K., The use of source apportionment for air quality management and health assessments. Journal of Toxicology and Environmental Health-Part a-Current Issues,2008. 71(9-10):555-563.
56. Hopke, P.K., K. Ito, T. Mar, et al., PM source apportionment and health effects:1. Intercomparison of source apportionment results. Journal of Exposure Science and Environmental Epidemiology,2006.16(3):275-286.
57. Lee, E., C.K. Chan, and P. Paatero, Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmospheric Environment,1999. 33(19):3201-3212.
58. Paterson, K.G., J.L. Sagady, and D.L. Hooper, Analysis of air quality data using positive matrix factorization. Environmental Science & Technology,1999.33(4):635-641.
59. Miller, S.L., M.J. Anderson, E.P. Daly, et al., Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data. Atmospheric Environment,2002.36(22):3629-3641.
60. Buzcu, B., M.P. Fraser, P. Kulkarni, et al., Source identification and apportionment of fine particulate matter in Houston, TX, using positive matrix factorization. Environmental Engineering Science,2003.20(6):533-545.
61. Lewis, C.W., G.A. Norris, T.L. Conner, et al., Source apportionment of phoenix PM2.5 aerosol with the Unmix receptor model. Journal of the Air & Waste Management Association, 2003.53(3):325-338.
62. Ramadan, Z., B. Eickhout, X.H. Song, et al., Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems,2003.66(1):15-28.
63. Jorquera, H. and B. Rappengluck, Receptor modeling of ambient VOC at Santiago, Chile. Atmospheric Environment,2004.38(25):4243-4263.
64. Latella, A., G. Stani, L. Cobelli, et al., Semicontinuous GC analysis and receptor modelling for source apportionment of ozone precursor hydrocarbons in Bresso, Milan,2003. Journal of Chromatography A,2005.1071(1-2):29-39.
65. Sarnat, J.A., A. Marmur, M. Klein, et al., Fine particle sources and cardiorespiratory morbidity:An application of chemical mass balance and factor analytical source-apportionment methods. Environmental Health Perspectives,2008.116(4):459-466.
66. Aiken, A.C., D. Salcedo, M.J. Cubison, et al., Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban super site (TO)-Part 1:Fine particle composition and organic source apportionment. Atmospheric Chemistry and Physics, 2009.9(17):6633-6653.
67. Lanz, V.A., M.R. Alfarra, U. Baltensperger, et al., Source apportionment of submicron organic aerosols at an urban site by factor analytical modelling of aerosol mass spectra. Atmospheric Chemistry and Physics,2007.7(6):1503-1522.
68. Reff, A., S.I. Eberly, and P.V. Bhave, Receptor modeling of ambient particulate matter data using positive matrix factorization:Review of existing methods. Journal of the Air & Waste Management Association,2007.57(2):146-154.
69. Shrivastava, M.K., R. Subramanian, W.F. Rogge, et al., Sources of organic aerosol:Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models. Atmospheric Environment,2007.41(40):9353-9369.
70. Ulbrich, I.M., M.R. Canagaratna, Q. Zhang, et al., Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data. Atmospheric Chemistry and Physics,2009.9(9):2891-2918.
71. Chen, L.W.A., B.G. Doddridge, R.R. Dickerson, et al., Origins of fine aerosol mass in the Baltimore-Washington corridor:Implications from observation, factor analysis, and ensemble air parcel back trajectories. Atmospheric Environment,2002.36(28):4541-4554.
72. Larsen, R.K. and J.E. Baker, Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere:A comparison of three methods. Environmental Science & Technology, 2003.37(9):1873-1881.
73. Mukerjee, S., G.A. Norris, L.A. Smith, et al., Receptor model comparisons and wind direction analyses of volatile organic compounds and submicrometer particles in an arid binational, urban air shed. Environmental Science & Technology,2004.38(8):2317-2327.
74. Hu, S.H., R. McDonald, D. Martuzevicius, et al., UNMIX modeling of ambient PM2.5 near an interstate highway in Cincinnati, OH, USA. Atmospheric Environment,2006.40:S378-S395.
75. Olson, D.A., G.A. Norris, R.L. Seila, et al., Chemical characterization of volatile organic compounds near the World Trade Center:Ambient concentrations and source apportionment. Atmospheric Environment,2007.41(27):5673-5683.
76. Martello, D.V., N.J. Pekney, R.R. Anderson, et al., Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis. Journal of the Air & Waste Management Association,2008. 58(3):357-368.
77. Olson, D.A. and G.A. Norris, Chemical characterization of ambient particulate matter near the World Trade Center:Source apportionment using organic and inorganic source markers. Atmospheric Environment,2008.42(31):7310-7315.
78. Callen, M.S., M.T. de la Cruz, J.M. Lopez, et al., Comparison of receptor models for source apportionment of the PM10 in Zaragoza (Spain). Chemosphere,2009.76(8):1120-1129.
79. Huang, F., X.Q. Wang, L.P. Lou, et al., Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Research, 2010.44(5): 1562-1572.
80. Paatero, P., Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems,1997.37:23-35.
81. Liu, W., P.K. Hopke, Y.J. Han, et al., Application of receptor modeling to atmospheric constituents at Potsdam and Stockton, NY. Atmospheric Environment,2003.37(36): 4997-5007.
82. US Environmental Protection Agency, EPA-CMB8 Users Manual. US EPA, office of Air Quality Planning and Standards Research Triangle Park, NC,2005.
83. Watson, J.G., J.C. Chow, D.H. Lowenthal, et al., Aerosol chemical and optical properties during the Mt. Zirkel Visibility Study. Journal of Environmental Quality,2001.30(4): 1118-1125.
84. Watson, J.G., J.C. Chow, and J.E. Houck, PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere,2001.43(8):1141-1151.
85. Watson, J.G., J.C. Chow, J.L. Bowen, et al., Air quality measurements from the Fresno Supersite. Journal of the Air & Waste Management Association,2000.50(8):1321-1334.
86. Chow, J.C., J.G. Watson, R.L. Berglund, et al., Diesel engines:Environmental impact and control-A critical review introduction. Journal of the Air & Waste Management Association, 2001.51(6):807-808.
87. Chow, J.C., L.W.A. Chen, J.G. Watson, et al., PM/sub 2.5/chemical composition and spatiotemporal variability during the California Regional PM/sub 10//PM/sub 2.5/Air Quality Study (CRPAQS). Journal of Geophysical Research-Part D-Atmospheres,2006. 111(D10):17 pp.
88. Seigneur, C., Louis, J. F., Hopke, P. K., Grosjean, D., Review of air quality models for particulate matter. Document Number CP015-97-1b.,1997.
89. Hopke, P.K., Receptor modeling in environmental chemistiy.1985.
90. Brinkman, G., Vance, G., Hannigan, M. P., Milford, J. B., Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data. Environmental Science and Technology,2006.40:1892-1901.
91. Lioy, P.J., Zelenka, M. P., Cheng, M. D., Reiss, N. M. and Wilson, W., The effect of sampling duration on the ability to resolve source types using factor analysis. Atmospheric Environment,1989.23:239-254.
92. Okamoto, S., Hayashi, M., Nakajima, M., Kainuma, Y. and Shiozawa, K., A factor analysis-multiple regression model for source apportionment of suspended particulate matter. Atmospheric Environment,1990.42:6114-6130.
93. Mamane, Y., Perrino, C., Yossef, O. and Catrambone, M., Source characterization of fine and coarse particles at the East Mediterranean coast. Atmospheric Environment,2008.24A: 2089-2097.
94. Maykut, N.N., Lewtas, J., Kim, E., Larson, T. V., Source apportionment of PM2.5 at an urban improve site in Seattle, Washington. Environmental Science and Technology,2003.37: 5135-5142.
95. Henry, R.C., Dealing with near collinearity in chemical mass balance receptor models. Atmospheric Environment,1992.26A:933-938.
96. Watson, J.G., Chen, L. W. A., Chow, J. C, Doraiswamy, P. and Lowenthal, D. H., Source apportionment, Findings from the US supersites program. J Air Waste Manage. Assoc.,2008. 58:265-288.
97. Shi, G.L., Li, X., Wu, T., Feng, Y.C., Bi, X.H., Wu, J.H. and Zhu, T., Effects of Unknown Source on the Collinearity problem in CMB Model.237th ACS National Meeting, Salt Lake City, UT, America,2009.
98. Zeng, F., Shi, G.L., Li, X., Feng, Y.C., Wu, J.H., Xue, Y.H., Bi, X.H., Application of a combined model to study the source apportionment of PM10 in Taiyuan, China. Aerosol and Air Quality Research,2010.10:177-184.
99. 安阳市大气颗粒物源解析研究报告,安阳市环境监测站,南开大学环境科学与工程学院.
100. Shi, G.L., X. Li, Y.C. Feng, et al., Combined source apportionment, using positive matrix factorization-chemical mass balance and principal component analysis/multiple linear regression-chemical mass balance models. Atmospheric Environment,2009.43(18): 2929-2937.
101. Dermentzoglou, M., E. Manoli, D. Voutsa, et al., Sources and patterns of polycyclic aromatic hydrocarbons and heavy metals in fine indoor particulate matter of Greek houses. Fresenius Environmental Bulletin,2003.12(12):1511-1519.
102. Astel, A., B. Walna, I. Kurzyca, et al., Application of chemometry to the comparison of atmospheric precipitation pollution profiles in urban and ecologically protected areas. Chemia Analityczna,2006.51(3):377-389.
103. Hong, H., H. Yin, X. Wang, et al., Seasonal variation of PM10-bound PAHs in the atmosphere of Xiamen, China. Atmospheric Research,2007.85(3-4):429-441.
104. Akyuz, M. and H. Cabuk, Particle-associated polycyclic aromatic hydrocarbons in the atmospheric environment of Zonguldak, Turkey. Science of the Total Environment,2008. 405(1-3):62-70.
105. Mao, T., Y.S. Wang, J. Jiang, et al., The vertical distributions of VOCs in the atmosphere of Beijing in autumn. Science of the Total Environment,2008.390(1):97-108.
106. Pandolfi, M., M. Viana, M.C. Minguillon, et al., Receptor models application to multi-year ambient PM10 measurements in an industrialized ceramic area:Comparison of source apportionment results. Atmospheric Environment,2008.42(40):9007-9017.
107. Sharma, H., V.K. Jain, and Z.H. Khan, Atmospheric polycyclic aromatic hydrocarbons (PAHs) in the urban air of Delhi during 2003. Environmental Monitoring and Assessment,2008. 147(1-3):43-55.
108. Viana, M., J.M. Lopez, X. Querol, et al., Tracers and impact of open burning of rice straw residues on PM in Eastern Spain. Atmospheric Environment,2008.42(8):1941-1957.
109. Ciaparra, D., E. Aries, M.J. Booth, et al., Characterisation of volatile organic compounds and polycyclic aromatic hydrocarbons in the ambient air of steelworks. Atmospheric Environment, 2009.43(12):2070-2079.
110. Guo, H., A.J. Ding, K.L. So, et al., Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution. Atmospheric Environment, 2009.43(6):1159-1169.
111. Han, B., Z.P. Bai, G.H. Guo, et al., Characterization of PM 10 fraction of road dust for polycyclic aromatic hydrocarbons (PAHs) from Anshan, China. Journal of Hazardous Materials,2009.170(2-3):934-940.
112. Jacquemin, B., T. Lanki, T. Yli-Tuomi, et al., Source category-specific PM2.5 and urinary levels of Clara cell protein CC16. The ULTRA study. Inhalation Toxicology,2009.21(13): 1068-1076.
113. Liu, Y, L. Chen, Q.H. Huang, et al., Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China. Science of the Total Environment,2009.407(8):2931-2938.
114. Moreno, N., M. Viana, M. Pandolfi, et al., Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods. Journal of Environmental Management,2009.91(1):133-141.
115. Vega, E., D. Lowenthal, H. Ruiz, et al., Fine Particle Receptor Modeling in the Atmosphere of Mexico City. Journal of the Air & Waste Management Association,2009.59(12):1417-1428.
116. Wang, L.L., Z.F. Yang, J.F. Niu, et al., Characterization, ecological risk assessment and source diagnostics of polycyclic aromatic hydrocarbons in water column of the Yellow River Delta, one of the most plenty biodiversity zones in the world. Journal of Hazardous Materials, 2009.169(1-3):460-465.
117. Allen, A.G., A.A. Cardoso, A.G. Wiatr, et al., Influence of Intensive Agriculture on Dry Deposition of Aerosol Nutrients. Journal of the Brazilian Chemical Society,2010.21(1): 87-97.
118. Byrd, T., M. Stack, and A. Furey, The assessment of the presence and main constituents of particulate matter ten microns (PM10) in Irish, rural and urban air. Atmospheric Environment,2010.44(1):75-87.
119. Caggiano. R., M. Macchiato, and S. Trippetta, Levels, chemical composition and sources of fine aerosol particles (PM1) in an area of the Mediterranean basin. Science of the Total Environment,2010.408(4):884-895.
120. Contini, D., A. Genga, D. Cesari, et al., Characterisation and source apportionment of PM10 in an urban background site in Lecce. Atmospheric Research,2010.95(1):40-54.
121. He. J. and R. Balasubramanian, Semi-volatile organic compounds (SVOCs) in ambient air and rainwater in a tropical environment:Concentrations and temporal and seasonal trends. Chemosphere,2010.78(6):742-751.
122. Hellebust, S., A. Allanic, I.P. O'Connor, et al., The use of real-time monitoring data to evaluate major sources of airborne particulate matter. Atmospheric Environment,2010.44(8): 1116-1125.
123. Liu, X.H., M.Z. Xu, Z.F. Yang, et al., Sources and risk of polycyclic aromatic hydrocarbons in Baiyangdian Lake, North China. Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering,2010.45(4):413-420.
124. Perrone, M.G., M. Gualtieri, L. Ferrero, et al., Seasonal variations in chemical composition and in vitro biological effects of fine PMfrom Milan. Chemosphere,2010.78(11):1368-1377.
125. Almeida, S.M., C.A. Pio, M.C. Freitas, et al., Source apportionment of fine and coarse particulate matter in a sub-urban area at the Western European Coast. Atmospheric Environment,2005.39(17):3127-3138.
126. Fang, G.C., Y.S. Wu, P.P.C. Fu, et al., Metallic elements study of fine and coarse particulates using a versatile air pollutant system at a traffic sampling site. Atmospheric Research,2005. 75(1-2):1-14.
127. Garcia, J.H., W.W. Li, N. Cardenas, et al., Determination of PM2.5 sources using time-resolved integrated source and receptor models. Chemosphere,2006.65(11): 2018-2027.
128. Viana, M., X. Querol, A. Alastuey, et al., Identification of PMsources by principal component analysis (PCA) coupled with wind direction data. Chemosphere,2006.65(11):2411-2418.
129. Ilacqua, V., O. Hanninen, K. Saarela, et al., Source apportionment of population representative samples of PM2.5 in three European cities using structural equation modelling. Science of the Total Environment,2007.384:77-92.
130. John, K., S. Karnae, K. Crist, et al., Analysis of trace elements and ions in ambient fine particulate matter at three elementary schools in Ohio. Journal of the Air & Waste Management Association,2007.57(4):394-406.
131. Magalhaes, D., R.E. Bruns, and P.D. Vasconcellos, Polycyclic aromatic hydrocarbons as sugarcane burning tracers:A statistical approach. Quimica Nova,2007.30(3):577-581.
132. Meza-Figueroa, D., M. De la O-Villanueva, and M.L. De la Parra, Heavy metal distribution in dust from elementary schools in Hermosillo, Sonora, Mexico. Atmospheric Environment, 2007.41(2):276-288.
133. Mudge, S.M., Multivariate statistical methods in environmental forensics. Environmental Forensics,2007.8(1-2):155-163.
134. Caggiano, R., M. Macchiato, M. Ragosta, et al., Trace elements in daily collected PM1 samples and source identification. Aaas08:2nd Advanced Atmospheric Aerosol Symposium, 2008.16:169-176.
135. Gupta, A.K., K. Karar, S. Ayoob, et al., Spatio-temporal characteristics of gaseous and particulate pollutants in an urban region of Kolkata, India. Atmospheric Research,2008. 87(2):103-115.
136. Mari, M., M. Nadal, M. Schuhmacher, et al., Monitoring PCDD/Fs, PCBs and metals in the ambient air of an industrial area of Catalonia, Spain. Chemosphere,2008.73(6):990-998.
137. Mintz, R. and R.D. McWhinney, Characterization of volatile organic compound emission sources in Fort Saskatchewan, Alberta using principal component analysis. Journal of Atmospheric Chemistry,2008.60(1):83-101.
138. Morillo, E., A.S. Romero, L. Madrid, et al., Characterization and sources of PAHs and potentially toxic metals in urban environments of sevilla (Southern Spain). Water Air and Soil Pollution,2008.187(1-4):41-51.
139. Ravindra, K., R. Sokhi, and R. Van Grieken, Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation. Atmospheric Environment,2008.42(13): 2895-2921.
140. Sanchez, M., S. Karnae, and K. John, Source characterization of volatile organic compounds affecting the air quality in a coastal urban area of South Texas. Int J Environ Res Public Health,2008.5(3):130-8.
141. Shi, B.B., Q.H. Deng, J.D. Li, et al., Source apportionment and chemical composition of PM10 in university classrooms of Changsha, China. First International Conference on Building Energy and Environment, Proceedings Vols 1-3,2008:848-855.
142. Singh, K.P., A. Malik, R. Kumar, et al., Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere. Environmental Monitoring and Assessment,2008.136:183-196.
143. Srivastava, A., S. Gupta, and V.K. Jain, Source apportionment of total suspended particulate matter in coarse and fine size ranges over Delhi. Aerosol and Air Quality Research,2008. 8(2):188-200.
144. Viana, M., M. Pandolfi, M.C. Minguillon, et al., Inter-comparison of receptor models for PM source apportionment:Case study in an industrial area. Atmospheric Environment,2008. 42(16):3820-3832.
145. Bi, X.H., Y.C. Feng, J.H. Wu, et al., Source apportionment of PM10 in six cities of northern China. Atmospheric Environment,2007.41(5):903-912.
146. Zhao, P.S., Y.C. Feng, and T. Zhu, Characterizations of resuspended dust in six cities of north China. Abstracts of Papers of the American Chemical Society,2006.231:147-ENVR.
147. Yakovleva, E., P.K. Hopke, and L. Wallace, Receptor modeling assessment of particle total exposure assessment methodology data. Environmental Science & Technology,1999.33(20): 3645-3652.
148. Bullock, K.R., R.M. Duvall, G.A. Norris, et al., Evaluation of the CMB and PMF models using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmospheric Environment,2008.42(29):6897-6904.
149. Chang, C.-M., L.-N. Chang, C.-Z. Lin, et al.,2007 coal fugitive dust monitoring and survey of the Taichung thermal power plant. Monthly Journal of Taipower's Engineering,2008:66-77.
150. Hanlim, L., S.S. Park, K.W. Kim, et al., Source identification of PM/sub 2.5/ particles measured in Gwangju, Korea. Atmospheric Research,2008:199-211.
151. Song, Y., W. Dai, M. Shao, et al., Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China. Environmental Pollution,2008.156(1): 174-183.
152. Wang, W.C., K.S. Chen, S.J. Chen, et al., Characteristics and receptor modeling of atmospheric PM2.5 at urban and rural sites in Pingtung, Taiwan. Aerosol and Air Quality Research,2008.8(2):112-129.
153. Ying, Q., J. Lu, A. Kaduwela, et al., Modeling air quality during the California Regional PM10/PM2.5 Air Quality Study (CPRAQS) using the UCD/CIT Source Oriented Air Quality Model-Part Ⅱ. Regional source apportionment of primary airborne particulate matter. Atmospheric Environment,2008.42(39):8967-8978.
154. Brinkman, G., Vance, G., Hannigan, M. P., Milford, J. B., Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data. Environmental Science and Technology,2006.40:1892-1901.
155. Christensen, W.F. and R.F. Gunst, Measurement error models in chemical mass balance analysis of air quality data. Atmospheric Environment,2004.38(5):733-744.
156. 成都市大气颗粒物源解析研究报告,成都市环境监测站,南开大学环境科学与工程学院.
157. 太原市大气颗粒物源解析研究报告,太原市环境监测站,南开大学环境科学与工程学院.
158. Rachdawong P.; Christensen, E.R., Determination of PCB sources by a principal component method with nonnegative constraints. Environmental Science & Technology,1997.31: 2686-2691.
159. Shi, G.L., Y.C. Feng, F. Zeng, et al., Use of a Nonnegative Constrained Principal Component Regression Chemical Mass Balance Model to Study the Contributions of Nearly Collinear Sources. Environmental Science & Technology,2009.43(23):8867-8873.
160. Javitz, H.S., Watson, J.G., Robinson, N., Performance of the chemical mass balance model with simulated local-scale aerosols. Atmospheric Environment,1988.22:2309-2322.
161. 无锡市大气颗粒物源解析研究报告,无锡市环境监测站,南开大学环境科学与工程学院.
162. 银川市大气颗粒物源解析研究报告,银川市环境监测站,南开大学环境科学与工程学院.
163. 天津市大气颗粒物源解析研究报告,天津市环境监测站,南开大学环境科学与工程学院.
2.王帅杰,朱坦,《大气颗粒物源解析技术研究进展》.环境污染治理技术与设备,2002.08：11-15.
3.冯银厂,白志鹏,朱坦,《大气颗粒物二重源解析技术原理与应用》.环境科学,2002.23：106-108.
4. Glover, D.M., P.K. Hopke, S.J. Vermette, et al., SOURCE APPORTIONMENT WITH SITE SPECIFIC SOURCE PROFILES. Journal of the Air & Waste Management Association,1991. 41(3):294-305.
5. Hopke, P.K., N. Gao, and M.D. Cheng, COMBINING CHEMICAL AND METEOROLOGICAL DATA TO INFER SOURCE AREAS OF AIRBORNE POLLUTANTS. Chemometrics and Intelligent Laboratory Systems,1993.19(2):187-199.
6. Watson, J.G., J.C. Chow, Z.Q. Lu, et al., CHEMICAL MASS-BALANCE SOURCE APPORTIONMENT OF PM(10) DURING THE SOUTHERN CALIFORNIA AIR-QUALITY STUDY. Aerosol Science and Technology,1994.21(1):1-36.
7. Juvela, M., K. Lehtinen, and P. Paatero, The use of Positive Matrix Factorization in the analysis of molecular line spectra. Monthly Notices of the Royal Astronomical Society,1996. 280(2):616-626.
8. Rachdawong, P. and E.R. Christensen, Determination of PCB sources by a principal component method with nonnegative constraints. Environmental Science & Technology,1997. 31(9):2686-2691.
9. Xie, Y.L., P.K. Hopke, and P. Paatero, Positive matrix factorization applied to a curve resolution problem. Journal of Chemometrics,1998.12(6):357-364.
10. Hopke, P.K., Y.L. Xie, and P. Paatero, Mixed multiway analysis of airborne particle composition data. Journal of Chemometrics,1999.13(3-4):343-352.
11. Polissar, A.V., P.K. Hopke, P. Paatero, et al. The aerosol at Barrow, Alaska:long-term trends and source locations. Atmospheric Environment,1999.33(16):2441-2458.
12. Xie, Y.L., P.K. Hopke, P. Paatero, et al. Locations and preferred pathways of possible sources of Arctic aerosol. Atmospheric Environment,1999.33(14):2229-2239.
13. Xie, Y.L., P.K. Hopke, P. Paatero, et al. Identification of source nature and seasonal variations of Arctic aerosol by the multilinear engine. Atmospheric Environment,1999. 33(16):2549-2562.
14. Chueinta, W., P.K. Hopke, and P. Paatero, Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmospheric Environment,2000.34(20):3319-3329.
15. Mugica, V., E. Vega, G. Sanchez, et al. Contribution of food cooking emissions to the presence of non-methane hydrocarbons in the Mexico City atmosphere. Air Pollution Viii, 2000.8:283-291.
16. Engelbrecht, J.P., L. Swanepoel, J.C. Chow, et al., PM2.5 and PMIO concentrations from the Qalabotjha low-smoke fuels macro-scale experiment in South Africa. Environmental Monitoring and Assessment,2001.69(1):1-15.
17. Li, W.W., R. Orquiz, J.H. Garcia, et al. Analysis of temporal and spatial dichotomous PM air samples in the El Paso-Cd. Juarez air quality basin. Journal of the Air & Waste Management Association,2001.51(11):1551-1560.
18. Polissar, A.V., P.K. Hopke, and J.M. Harris, Source regions for atmospheric aerosol measured at Barrow, Alaska. Environmental Science & Technology,2001.35(21):4214-4226.
19. Mugica, V., J. Watson, E. Reyes, et al., Receptor model source apportionment of nonmethane hydrocarbons in Mexico City. TheScientificWorldJOURNAL,2002.2(Cited April 5,2002): 844-860.
20. Ho, K.F.L., S.C.; Chan, C.K.; Yu, J.C.; Chow, J.C.; Yao, X.H., Characterization of chemical species in PM2.5 and PM10 aerosols in Hong Kong. Atmospheric Environment,2003.37: 31-39.
21. Chueinta, W., P.K. Hopke, and P. Paatero, Multilinear model for spatial pattern analysis of the measurement of haze and visual effects project. Environmental Science & Technology,2004. 38(2):544-554.
22. Han, Y.J., T.M. Holsen, P.K. Hopke, et al., Identification of source locations for atmospheric dry deposition of heavy metals during yellow-sand events in Seoul, Korea in 1998 using hybrid receptor models. Atmospheric Environment,2004.38(31):5353-5361.
23. Zhao, W.X. and P.K. Hopke, Source apportionment for ambient particles in the San Gorgonio wilderness. Atmospheric Environment,2004.38(35):5901-5910.
24. Ogulei, D., P.K. Hopke, L.M. Zhou, et al., Receptor modeling for multiple time resolved species:The Baltimore, supersite. Atmospheric Environment,2005.39(20):3751-3762.
25. Gao, N., A.E. Gildemeister, K. Krumhansl, et al., Sources of fine particulate species in ambient air over Lake Champlain Basin, VT. Journal of the Air & Waste Management Association,2006.56(11):1607-1620.
26. Hopke, P.K., Recent developments in receptor modeling. Journal of Chemometrics,2003. 17(5):255-265.
27. Thurston, G.D. and J.D. Spengler, A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON. Atmospheric Environment,1985.19(1):9-25.
28. Kim, E. and P.K. Hopke, Re:Source apportionment of fine particles in Washington, DC, utilizing temperature-resolved carbon fractions (vol 54, pg 1011,2004). Journal of the Air & Waste Management Association,2004.54(8):1011-1012.
29. Kim, E. and P.K. Hopke, Improving source identification of fine particles in a rural northeastern US area utilizing temperature-resolved carbon fractions. Journal of Geophysical Research-Atmospheres,2004.109(D9).
30. Kim, E. and P.K. Hopke, Identification of fine particle sources in mid-Atlantic US area. Water Air and Soil Pollution,2005.168(1-4):391-421.
31. Henry, R.C., Duality in multivariate receptor models. Chemometrics and Intelligent Laboratory Systems,2005.77(1-2):59-63.
32. 许禄,《化学计量学方法》.科学出版社.
33. Shi, G.L., Y.C. Feng, J.H. Wu, et al., Source Identification of Polycyclic Aromatic Hydrocarbons in Urban Particulate Matter of Tangshan, China. Aerosol and Air Quality Research,2009.9(3):309-315.
34. Formenti, P., H.J. Annegarn, P. Prati, et al., Source apportionment by receptor models in a study of Geneva aerosol via streaker sampling and PIXE analysis. Physica Medica,1997. 13(3):101-109.
35. Prati, P., A. Zucchiatti, F. Lucarelli, et al., Source apportionment near a steel plant in Genoa (Italy) by continuous aerosol sampling and PIXE analysis. Atmospheric Environment,2000. 34(19):3149-3157.
36. Bruno, P., M. Caselli, G. de Gennaro, et al., Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model. Fresenius Journal of Analytical Chemistry,2001.371(8):1119-1123.
37 Kulshrestha, U.C., M. Jain, R. Sekar, et al., Chemical characteristics and source apportionment of aerosols over Indian Ocean during INDOEX-1999. Current Science,2001. 80:180-185.
38. Lucarelli, F., P.A. Mando, S. Nava, et al., One-year study of the elemental composition and source apportionment of PM10 aerosols in Florence, Italy. Journal of the Air & Waste Management Association,2004.54(11):1372-1382.
39. Gupta, A.K. and K. Karar, Source apportionment of PM/sub 10/at residential and industrial sites of an urban region of Kolkata, India. Atmospheric Research,2007.84(1):30-41.
40. Karar, K. and A.K. Gupta, Source apportionment of PM10 at residential and industrial sites of an urban region of Kolkata, India. Atmospheric Research,2007.84(1):30-41.
41.Tiwari. S.,U.C. Kulshrestha, and B. Padmanabhamurty, Monsoon rain chemistry and source apportionment using receptor modeling in and around National Capital Region (NCR) of Delhi, India. Atmospheric Environment,2007.41(27):5595-5604.
42. Viana, M., X. Querol, T. Gotschi, et al., Source apportionment of ambient PM2.5 at five Spanish centres of the European Community Respiratory Health Survey (ECRHS Ⅱ). Atmospheric Environment,2007.41(7):1395-1406.
43. Sofowote, U.M., B.E. McCarry, and C.H. Marvin, Source apportionment of PAH in Hamilton Harbour suspended sediments:Comparison of two factor analysis methods. Environmental Science & Technology,2008.42(16):6007-6014.
44. Tauler, R., M. Viana, X. Querol, et al., Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies. Atmospheric Environment,2009. 43(26):3989-3997.
45. Harrison, R.M., D.J.T. Smith, and L. Luhana, Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, UK. Environmental Science & Technology,1996.30(3):825-832.
46. Guo, H., T. Wang, I.J. Simpson, et al., Source contributions to ambient VOCs and CO at a rural site in Eastern China. Atmospheric Environment,2004.38(27):4551-4560.
47. Park, S.S. and Y.J. Kim, Source contributions to fine particulate matter in an urban atmosphere. Chemosphere,2005.59(2):217-226.
48. Kim, E. and P.K. Hopke, Characterization of fine particle sources in the Great Smoky Mountains area. Science of the Total Environment,2006.368(2-3):781-794.
49. Kim, E. and P.K. Hopke, Source characterization of ambient fine particles in the Los Angeles basin. Journal of Environmental Engineering and Science,2007.6(4):343-353.
50. Kim, E., P.K. Hopke, and E.S. Edgerton, Source identification of Atlanta aerosol by positive matrix factorization. Journal of the Air & Waste Management Association,2003.53(6): 731-739.
51. Kim, E., P.K. Hopke, and E.S. Edgerton, Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Atmospheric Environment,2004.38(20):3349-3362.
52. Kim, E., P.K. Hopke, T.V. Larson, et al., Factor analysis of Seattle fine particles. Aerosol Science and Technology,2004.38(7):724-738.
53. Kim, E., P.K. Hopke, J.P. Pinto, et al., Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis. Environmental Science & Technology,2005.39(11):4172-4179.
54. Kim, E., T.V. Larson, P.K. Hopke, et al., Source identification of PM2.5 in an arid Northwest US City by positive matrix factorization. Atmospheric Research,2003.66(4):291-305.
55. Hopke, P.K., The use of source apportionment for air quality management and health assessments. Journal of Toxicology and Environmental Health-Part a-Current Issues,2008. 71(9-10):555-563.
56. Hopke, P.K., K. Ito, T. Mar, et al., PM source apportionment and health effects:1. Intercomparison of source apportionment results. Journal of Exposure Science and Environmental Epidemiology,2006.16(3):275-286.
57. Lee, E., C.K. Chan, and P. Paatero, Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmospheric Environment,1999. 33(19):3201-3212.
58. Paterson, K.G., J.L. Sagady, and D.L. Hooper, Analysis of air quality data using positive matrix factorization. Environmental Science & Technology,1999.33(4):635-641.
59. Miller, S.L., M.J. Anderson, E.P. Daly, et al., Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data. Atmospheric Environment,2002.36(22):3629-3641.
60. Buzcu, B., M.P. Fraser, P. Kulkarni, et al., Source identification and apportionment of fine particulate matter in Houston, TX, using positive matrix factorization. Environmental Engineering Science,2003.20(6):533-545.
61. Lewis, C.W., G.A. Norris, T.L. Conner, et al., Source apportionment of phoenix PM2.5 aerosol with the Unmix receptor model. Journal of the Air & Waste Management Association, 2003.53(3):325-338.
62. Ramadan, Z., B. Eickhout, X.H. Song, et al., Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems,2003.66(1):15-28.
63. Jorquera, H. and B. Rappengluck, Receptor modeling of ambient VOC at Santiago, Chile. Atmospheric Environment,2004.38(25):4243-4263.
64. Latella, A., G. Stani, L. Cobelli, et al., Semicontinuous GC analysis and receptor modelling for source apportionment of ozone precursor hydrocarbons in Bresso, Milan,2003. Journal of Chromatography A,2005.1071(1-2):29-39.
65. Sarnat, J.A., A. Marmur, M. Klein, et al., Fine particle sources and cardiorespiratory morbidity:An application of chemical mass balance and factor analytical source-apportionment methods. Environmental Health Perspectives,2008.116(4):459-466.
66. Aiken, A.C., D. Salcedo, M.J. Cubison, et al., Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban super site (TO)-Part 1:Fine particle composition and organic source apportionment. Atmospheric Chemistry and Physics, 2009.9(17):6633-6653.
67. Lanz, V.A., M.R. Alfarra, U. Baltensperger, et al., Source apportionment of submicron organic aerosols at an urban site by factor analytical modelling of aerosol mass spectra. Atmospheric Chemistry and Physics,2007.7(6):1503-1522.
68. Reff, A., S.I. Eberly, and P.V. Bhave, Receptor modeling of ambient particulate matter data using positive matrix factorization:Review of existing methods. Journal of the Air & Waste Management Association,2007.57(2):146-154.
69. Shrivastava, M.K., R. Subramanian, W.F. Rogge, et al., Sources of organic aerosol:Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models. Atmospheric Environment,2007.41(40):9353-9369.
70. Ulbrich, I.M., M.R. Canagaratna, Q. Zhang, et al., Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data. Atmospheric Chemistry and Physics,2009.9(9):2891-2918.
71. Chen, L.W.A., B.G. Doddridge, R.R. Dickerson, et al., Origins of fine aerosol mass in the Baltimore-Washington corridor:Implications from observation, factor analysis, and ensemble air parcel back trajectories. Atmospheric Environment,2002.36(28):4541-4554.
72. Larsen, R.K. and J.E. Baker, Source apportionment of polycyclic aromatic hydrocarbons in the urban atmosphere:A comparison of three methods. Environmental Science & Technology, 2003.37(9):1873-1881.
73. Mukerjee, S., G.A. Norris, L.A. Smith, et al., Receptor model comparisons and wind direction analyses of volatile organic compounds and submicrometer particles in an arid binational, urban air shed. Environmental Science & Technology,2004.38(8):2317-2327.
74. Hu, S.H., R. McDonald, D. Martuzevicius, et al., UNMIX modeling of ambient PM2.5 near an interstate highway in Cincinnati, OH, USA. Atmospheric Environment,2006.40:S378-S395.
75. Olson, D.A., G.A. Norris, R.L. Seila, et al., Chemical characterization of volatile organic compounds near the World Trade Center:Ambient concentrations and source apportionment. Atmospheric Environment,2007.41(27):5673-5683.
76. Martello, D.V., N.J. Pekney, R.R. Anderson, et al., Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis. Journal of the Air & Waste Management Association,2008. 58(3):357-368.
77. Olson, D.A. and G.A. Norris, Chemical characterization of ambient particulate matter near the World Trade Center:Source apportionment using organic and inorganic source markers. Atmospheric Environment,2008.42(31):7310-7315.
78. Callen, M.S., M.T. de la Cruz, J.M. Lopez, et al., Comparison of receptor models for source apportionment of the PM10 in Zaragoza (Spain). Chemosphere,2009.76(8):1120-1129.
79. Huang, F., X.Q. Wang, L.P. Lou, et al., Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Research, 2010.44(5): 1562-1572.
80. Paatero, P., Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems,1997.37:23-35.
81. Liu, W., P.K. Hopke, Y.J. Han, et al., Application of receptor modeling to atmospheric constituents at Potsdam and Stockton, NY. Atmospheric Environment,2003.37(36): 4997-5007.
82. US Environmental Protection Agency, EPA-CMB8 Users Manual. US EPA, office of Air Quality Planning and Standards Research Triangle Park, NC,2005.
83. Watson, J.G., J.C. Chow, D.H. Lowenthal, et al., Aerosol chemical and optical properties during the Mt. Zirkel Visibility Study. Journal of Environmental Quality,2001.30(4): 1118-1125.
84. Watson, J.G., J.C. Chow, and J.E. Houck, PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere,2001.43(8):1141-1151.
85. Watson, J.G., J.C. Chow, J.L. Bowen, et al., Air quality measurements from the Fresno Supersite. Journal of the Air & Waste Management Association,2000.50(8):1321-1334.
86. Chow, J.C., J.G. Watson, R.L. Berglund, et al., Diesel engines:Environmental impact and control-A critical review introduction. Journal of the Air & Waste Management Association, 2001.51(6):807-808.
87. Chow, J.C., L.W.A. Chen, J.G. Watson, et al., PM/sub 2.5/chemical composition and spatiotemporal variability during the California Regional PM/sub 10//PM/sub 2.5/Air Quality Study (CRPAQS). Journal of Geophysical Research-Part D-Atmospheres,2006. 111(D10):17 pp.
88. Seigneur, C., Louis, J. F., Hopke, P. K., Grosjean, D., Review of air quality models for particulate matter. Document Number CP015-97-1b.,1997.
89. Hopke, P.K., Receptor modeling in environmental chemistiy.1985.
90. Brinkman, G., Vance, G., Hannigan, M. P., Milford, J. B., Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data. Environmental Science and Technology,2006.40:1892-1901.
91. Lioy, P.J., Zelenka, M. P., Cheng, M. D., Reiss, N. M. and Wilson, W., The effect of sampling duration on the ability to resolve source types using factor analysis. Atmospheric Environment,1989.23:239-254.
92. Okamoto, S., Hayashi, M., Nakajima, M., Kainuma, Y. and Shiozawa, K., A factor analysis-multiple regression model for source apportionment of suspended particulate matter. Atmospheric Environment,1990.42:6114-6130.
93. Mamane, Y., Perrino, C., Yossef, O. and Catrambone, M., Source characterization of fine and coarse particles at the East Mediterranean coast. Atmospheric Environment,2008.24A: 2089-2097.
94. Maykut, N.N., Lewtas, J., Kim, E., Larson, T. V., Source apportionment of PM2.5 at an urban improve site in Seattle, Washington. Environmental Science and Technology,2003.37: 5135-5142.
95. Henry, R.C., Dealing with near collinearity in chemical mass balance receptor models. Atmospheric Environment,1992.26A:933-938.
96. Watson, J.G., Chen, L. W. A., Chow, J. C, Doraiswamy, P. and Lowenthal, D. H., Source apportionment, Findings from the US supersites program. J Air Waste Manage. Assoc.,2008. 58:265-288.
97. Shi, G.L., Li, X., Wu, T., Feng, Y.C., Bi, X.H., Wu, J.H. and Zhu, T., Effects of Unknown Source on the Collinearity problem in CMB Model.237th ACS National Meeting, Salt Lake City, UT, America,2009.
98. Zeng, F., Shi, G.L., Li, X., Feng, Y.C., Wu, J.H., Xue, Y.H., Bi, X.H., Application of a combined model to study the source apportionment of PM10 in Taiyuan, China. Aerosol and Air Quality Research,2010.10:177-184.
99. 安阳市大气颗粒物源解析研究报告,安阳市环境监测站,南开大学环境科学与工程学院.
100. Shi, G.L., X. Li, Y.C. Feng, et al., Combined source apportionment, using positive matrix factorization-chemical mass balance and principal component analysis/multiple linear regression-chemical mass balance models. Atmospheric Environment,2009.43(18): 2929-2937.
101. Dermentzoglou, M., E. Manoli, D. Voutsa, et al., Sources and patterns of polycyclic aromatic hydrocarbons and heavy metals in fine indoor particulate matter of Greek houses. Fresenius Environmental Bulletin,2003.12(12):1511-1519.
102. Astel, A., B. Walna, I. Kurzyca, et al., Application of chemometry to the comparison of atmospheric precipitation pollution profiles in urban and ecologically protected areas. Chemia Analityczna,2006.51(3):377-389.
103. Hong, H., H. Yin, X. Wang, et al., Seasonal variation of PM10-bound PAHs in the atmosphere of Xiamen, China. Atmospheric Research,2007.85(3-4):429-441.
104. Akyuz, M. and H. Cabuk, Particle-associated polycyclic aromatic hydrocarbons in the atmospheric environment of Zonguldak, Turkey. Science of the Total Environment,2008. 405(1-3):62-70.
105. Mao, T., Y.S. Wang, J. Jiang, et al., The vertical distributions of VOCs in the atmosphere of Beijing in autumn. Science of the Total Environment,2008.390(1):97-108.
106. Pandolfi, M., M. Viana, M.C. Minguillon, et al., Receptor models application to multi-year ambient PM10 measurements in an industrialized ceramic area:Comparison of source apportionment results. Atmospheric Environment,2008.42(40):9007-9017.
107. Sharma, H., V.K. Jain, and Z.H. Khan, Atmospheric polycyclic aromatic hydrocarbons (PAHs) in the urban air of Delhi during 2003. Environmental Monitoring and Assessment,2008. 147(1-3):43-55.
108. Viana, M., J.M. Lopez, X. Querol, et al., Tracers and impact of open burning of rice straw residues on PM in Eastern Spain. Atmospheric Environment,2008.42(8):1941-1957.
109. Ciaparra, D., E. Aries, M.J. Booth, et al., Characterisation of volatile organic compounds and polycyclic aromatic hydrocarbons in the ambient air of steelworks. Atmospheric Environment, 2009.43(12):2070-2079.
110. Guo, H., A.J. Ding, K.L. So, et al., Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution. Atmospheric Environment, 2009.43(6):1159-1169.
111. Han, B., Z.P. Bai, G.H. Guo, et al., Characterization of PM 10 fraction of road dust for polycyclic aromatic hydrocarbons (PAHs) from Anshan, China. Journal of Hazardous Materials,2009.170(2-3):934-940.
112. Jacquemin, B., T. Lanki, T. Yli-Tuomi, et al., Source category-specific PM2.5 and urinary levels of Clara cell protein CC16. The ULTRA study. Inhalation Toxicology,2009.21(13): 1068-1076.
113. Liu, Y, L. Chen, Q.H. Huang, et al., Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China. Science of the Total Environment,2009.407(8):2931-2938.
114. Moreno, N., M. Viana, M. Pandolfi, et al., Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods. Journal of Environmental Management,2009.91(1):133-141.
115. Vega, E., D. Lowenthal, H. Ruiz, et al., Fine Particle Receptor Modeling in the Atmosphere of Mexico City. Journal of the Air & Waste Management Association,2009.59(12):1417-1428.
116. Wang, L.L., Z.F. Yang, J.F. Niu, et al., Characterization, ecological risk assessment and source diagnostics of polycyclic aromatic hydrocarbons in water column of the Yellow River Delta, one of the most plenty biodiversity zones in the world. Journal of Hazardous Materials, 2009.169(1-3):460-465.
117. Allen, A.G., A.A. Cardoso, A.G. Wiatr, et al., Influence of Intensive Agriculture on Dry Deposition of Aerosol Nutrients. Journal of the Brazilian Chemical Society,2010.21(1): 87-97.
118. Byrd, T., M. Stack, and A. Furey, The assessment of the presence and main constituents of particulate matter ten microns (PM10) in Irish, rural and urban air. Atmospheric Environment,2010.44(1):75-87.
119. Caggiano. R., M. Macchiato, and S. Trippetta, Levels, chemical composition and sources of fine aerosol particles (PM1) in an area of the Mediterranean basin. Science of the Total Environment,2010.408(4):884-895.
120. Contini, D., A. Genga, D. Cesari, et al., Characterisation and source apportionment of PM10 in an urban background site in Lecce. Atmospheric Research,2010.95(1):40-54.
121. He. J. and R. Balasubramanian, Semi-volatile organic compounds (SVOCs) in ambient air and rainwater in a tropical environment:Concentrations and temporal and seasonal trends. Chemosphere,2010.78(6):742-751.
122. Hellebust, S., A. Allanic, I.P. O'Connor, et al., The use of real-time monitoring data to evaluate major sources of airborne particulate matter. Atmospheric Environment,2010.44(8): 1116-1125.
123. Liu, X.H., M.Z. Xu, Z.F. Yang, et al., Sources and risk of polycyclic aromatic hydrocarbons in Baiyangdian Lake, North China. Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering,2010.45(4):413-420.
124. Perrone, M.G., M. Gualtieri, L. Ferrero, et al., Seasonal variations in chemical composition and in vitro biological effects of fine PMfrom Milan. Chemosphere,2010.78(11):1368-1377.
125. Almeida, S.M., C.A. Pio, M.C. Freitas, et al., Source apportionment of fine and coarse particulate matter in a sub-urban area at the Western European Coast. Atmospheric Environment,2005.39(17):3127-3138.
126. Fang, G.C., Y.S. Wu, P.P.C. Fu, et al., Metallic elements study of fine and coarse particulates using a versatile air pollutant system at a traffic sampling site. Atmospheric Research,2005. 75(1-2):1-14.
127. Garcia, J.H., W.W. Li, N. Cardenas, et al., Determination of PM2.5 sources using time-resolved integrated source and receptor models. Chemosphere,2006.65(11): 2018-2027.
128. Viana, M., X. Querol, A. Alastuey, et al., Identification of PMsources by principal component analysis (PCA) coupled with wind direction data. Chemosphere,2006.65(11):2411-2418.
129. Ilacqua, V., O. Hanninen, K. Saarela, et al., Source apportionment of population representative samples of PM2.5 in three European cities using structural equation modelling. Science of the Total Environment,2007.384:77-92.
130. John, K., S. Karnae, K. Crist, et al., Analysis of trace elements and ions in ambient fine particulate matter at three elementary schools in Ohio. Journal of the Air & Waste Management Association,2007.57(4):394-406.
131. Magalhaes, D., R.E. Bruns, and P.D. Vasconcellos, Polycyclic aromatic hydrocarbons as sugarcane burning tracers:A statistical approach. Quimica Nova,2007.30(3):577-581.
132. Meza-Figueroa, D., M. De la O-Villanueva, and M.L. De la Parra, Heavy metal distribution in dust from elementary schools in Hermosillo, Sonora, Mexico. Atmospheric Environment, 2007.41(2):276-288.
133. Mudge, S.M., Multivariate statistical methods in environmental forensics. Environmental Forensics,2007.8(1-2):155-163.
134. Caggiano, R., M. Macchiato, M. Ragosta, et al., Trace elements in daily collected PM1 samples and source identification. Aaas08:2nd Advanced Atmospheric Aerosol Symposium, 2008.16:169-176.
135. Gupta, A.K., K. Karar, S. Ayoob, et al., Spatio-temporal characteristics of gaseous and particulate pollutants in an urban region of Kolkata, India. Atmospheric Research,2008. 87(2):103-115.
136. Mari, M., M. Nadal, M. Schuhmacher, et al., Monitoring PCDD/Fs, PCBs and metals in the ambient air of an industrial area of Catalonia, Spain. Chemosphere,2008.73(6):990-998.
137. Mintz, R. and R.D. McWhinney, Characterization of volatile organic compound emission sources in Fort Saskatchewan, Alberta using principal component analysis. Journal of Atmospheric Chemistry,2008.60(1):83-101.
138. Morillo, E., A.S. Romero, L. Madrid, et al., Characterization and sources of PAHs and potentially toxic metals in urban environments of sevilla (Southern Spain). Water Air and Soil Pollution,2008.187(1-4):41-51.
139. Ravindra, K., R. Sokhi, and R. Van Grieken, Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation. Atmospheric Environment,2008.42(13): 2895-2921.
140. Sanchez, M., S. Karnae, and K. John, Source characterization of volatile organic compounds affecting the air quality in a coastal urban area of South Texas. Int J Environ Res Public Health,2008.5(3):130-8.
141. Shi, B.B., Q.H. Deng, J.D. Li, et al., Source apportionment and chemical composition of PM10 in university classrooms of Changsha, China. First International Conference on Building Energy and Environment, Proceedings Vols 1-3,2008:848-855.
142. Singh, K.P., A. Malik, R. Kumar, et al., Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere. Environmental Monitoring and Assessment,2008.136:183-196.
143. Srivastava, A., S. Gupta, and V.K. Jain, Source apportionment of total suspended particulate matter in coarse and fine size ranges over Delhi. Aerosol and Air Quality Research,2008. 8(2):188-200.
144. Viana, M., M. Pandolfi, M.C. Minguillon, et al., Inter-comparison of receptor models for PM source apportionment:Case study in an industrial area. Atmospheric Environment,2008. 42(16):3820-3832.
145. Bi, X.H., Y.C. Feng, J.H. Wu, et al., Source apportionment of PM10 in six cities of northern China. Atmospheric Environment,2007.41(5):903-912.
146. Zhao, P.S., Y.C. Feng, and T. Zhu, Characterizations of resuspended dust in six cities of north China. Abstracts of Papers of the American Chemical Society,2006.231:147-ENVR.
147. Yakovleva, E., P.K. Hopke, and L. Wallace, Receptor modeling assessment of particle total exposure assessment methodology data. Environmental Science & Technology,1999.33(20): 3645-3652.
148. Bullock, K.R., R.M. Duvall, G.A. Norris, et al., Evaluation of the CMB and PMF models using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmospheric Environment,2008.42(29):6897-6904.
149. Chang, C.-M., L.-N. Chang, C.-Z. Lin, et al.,2007 coal fugitive dust monitoring and survey of the Taichung thermal power plant. Monthly Journal of Taipower's Engineering,2008:66-77.
150. Hanlim, L., S.S. Park, K.W. Kim, et al., Source identification of PM/sub 2.5/ particles measured in Gwangju, Korea. Atmospheric Research,2008:199-211.
151. Song, Y., W. Dai, M. Shao, et al., Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China. Environmental Pollution,2008.156(1): 174-183.
152. Wang, W.C., K.S. Chen, S.J. Chen, et al., Characteristics and receptor modeling of atmospheric PM2.5 at urban and rural sites in Pingtung, Taiwan. Aerosol and Air Quality Research,2008.8(2):112-129.
153. Ying, Q., J. Lu, A. Kaduwela, et al., Modeling air quality during the California Regional PM10/PM2.5 Air Quality Study (CPRAQS) using the UCD/CIT Source Oriented Air Quality Model-Part Ⅱ. Regional source apportionment of primary airborne particulate matter. Atmospheric Environment,2008.42(39):8967-8978.
154. Brinkman, G., Vance, G., Hannigan, M. P., Milford, J. B., Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data. Environmental Science and Technology,2006.40:1892-1901.
155. Christensen, W.F. and R.F. Gunst, Measurement error models in chemical mass balance analysis of air quality data. Atmospheric Environment,2004.38(5):733-744.
156. 成都市大气颗粒物源解析研究报告,成都市环境监测站,南开大学环境科学与工程学院.
157. 太原市大气颗粒物源解析研究报告,太原市环境监测站,南开大学环境科学与工程学院.
158. Rachdawong P.; Christensen, E.R., Determination of PCB sources by a principal component method with nonnegative constraints. Environmental Science & Technology,1997.31: 2686-2691.
159. Shi, G.L., Y.C. Feng, F. Zeng, et al., Use of a Nonnegative Constrained Principal Component Regression Chemical Mass Balance Model to Study the Contributions of Nearly Collinear Sources. Environmental Science & Technology,2009.43(23):8867-8873.
160. Javitz, H.S., Watson, J.G., Robinson, N., Performance of the chemical mass balance model with simulated local-scale aerosols. Atmospheric Environment,1988.22:2309-2322.
161. 无锡市大气颗粒物源解析研究报告,无锡市环境监测站,南开大学环境科学与工程学院.
162. 银川市大气颗粒物源解析研究报告,银川市环境监测站,南开大学环境科学与工程学院.
163. 天津市大气颗粒物源解析研究报告,天津市环境监测站,南开大学环境科学与工程学院.