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標題: 受體模式CMB與PMF之比較與驗證
A Study On the Comparison Of Two Receptor Models:Chemical Mass Balance Model and Positive Matrix Factorization Model
作者: 梁志鋒
Liang, Jyh-Feng
關鍵字: receptor model;受體模式;CMB;PMF;source profile;Asian dust storm;CMB;PMF;污染源組成;沙塵暴
出版社: 環境工程學系所
引用: 1.Baker, D.R. and L.M. Diana, “Simple method for fitting data when both variables have uncertainties”, The American Journal of Physics, Vol. 42, pp. 224-226(1974). 2.Belsley, D.A., E. Kuh, R.E. Welsch, Regression Diagnostics: Indentifying Influential Data and Sources of Collinearity(1980). 3.Blifford I.H. and Meeker G.O.”A factor analysis model of large scale pollution.”, Atmospheric Environment, Vol. 1, pp. 147-157(1967). 4.Brown, S.G. and H.R. Hafner, Multivariate Receptor Modeling Workbook. Prepared for the U.S. EPA, Office Research and Development, Research Triangle Park, NC(2005). 5.Chan, Y.C., R.W. Simpson, G.H. Mctainsh , P.D. Vowles, D.D. Cohen, G.M. Bailey, “Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modeling.”, Atmospheric Environment, Vol. 33, pp. 3251-3268(1999). 6.Chen, K.S., C.F. Lin, and Y.M. Chou., ”Determination of Source Contribution to Ambient PM2.5 in Kaohsiung, Taiwan, Using a Receptor Model”, Journal of the Air and Waste Management Association, Vol. 51, pp. 489-498(2001). 7.Chen, S.J., L.T. Hsieh, M.J. Kao, Lin, W.Y., Huang, K.L., and C.C. Lin, “Characteristics of particles sampled in southern Taiwan during the Asian dust storm periods in 2000 and 2001.”, Atmospheric Environment, Vol. 38, pp. 5925-5934(2004). 8.Chen, W.C., C.S. Wang, and C.C. Wei, ”An Assessment of Source Contribution to Ambient Aerosols in Central Taiwan”, Journal of the Air and Waste Management Association, Vol. 47, pp. 501-509(1997). 9.Cheng, T., D. Lu , G. Wang and Y. Xu , “Chemical characteristics of Asian dust aerosol from Hunshan Dake Sandland in Northern China.”, Atmospheric Environment, Vol. 39, pp. 2903-2911(2005). 10.Chio, C.P., M.T. Cheng and C.F. Wang, “Source Apportionment to PM10 in Different Air Quality Conditions for Taichung Urban and Coastal Areas, Taiwan”, Atmospheric Environment , Vol. 38, pp. 6893-6905(2004). 11.Chow J.C, “Measurement methods to determine compliance with ambient air quality standards for suspended particles. Journal of Air and Waste Management Association. Vol. 45, pp. 320-382(1995). 12.Chow, J. C., D. Fairley, J. G. Watson, R. Demandel, E. M. Fujita, D. H. Lowenthal, Z. Lu, C. A. Frazier, G. Long, and J. Cordova, “Source Apportionment of Wintertime PM10 at San Jose, Calif”, Journal of Environmental Engineering, pp. 378-387(1995). 13.Chow, J. C., C. S. Liu, J. Cassmassi, J. G. Watson, Z. Lu, and L. C. Prichett, “A Neighborhood-Scale Study of PM10 Source Contirbutions in Rubidoux, California”, Atmospheric Environment, Vol. 26A, No. 4, pp. 693-706(1992). 14.Chow, J.C., J.G. Watson, L.W. Richards, D.L. Hasse, C. McDade, D.L. Dietrich, D. Moon and C. Sloane. “The 1989-90 Phoenix PM10 study.” Volume II: Source apportionment (1991). 15.Duce, R.A., Sources, distributions, and fluxes of mineral aerosols and their relationship to climate. In: Charlson,R.J., Heintzenberg, J. (Eds.), Aerosol Forcing of Climate. Wiley, Chichester, UK, pp. 43-22(1995). 16.Han, J.S., K.J. Moon, S.J. Lee, Y.J. Kim, S.Y. Ryu, S.S. Cliff, and S.M. YI, “Size-resolved source apportionment of ambient particles by positive matrix factorization”, Atmospheric Chemistry and Physics, Vol. 5, pp. 5223-5252 (2005). 17.Henry, R.C, Multivariate Receptor Models, In : Receptor Modeling for Air Quality Management, P.K. Hopke ,ed., Elsevier Science Publishers, Amsterdam, 117-147 (1991). 18.Hopke, P.K., Receptor Model In Environmental Chemistry, John Wiley & Sons, Inc., New York (1985). 19.Hopke, P. K., “Recent developments in receptor modeling,” Journal of Chemometrics, Vol. 17, pp. 255-265 (2003). 20.Huang, S., K.A. Rahn, and R. Arimoto, “Testing and optimizing two factor-analysis techniques on aerosol at Narragansett, Rhode Island”, Atmospheric Environment, Vol.33, pp. 2169-2185(1999). 21.Jimenez, J., C.F. Wu , C. Claiborn , T. Gould , C.D. Simpson , T. Larson , L.-J.S. Liu, “Agricultural burning smoke in eastern Washington-Part I: Atmospheric characterization .”, Atmospheric Environment, Vol. 40, pp. 639-650(2006). 22.Kim E., P.K. Hopke, and Y. Qin. “Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment.” Journal of Air and Waste Management Association, Vol. 55, pp. 1190-1199(2005). 23.Larson, C.L., and Hanson R.J., Solving Least-squares Problems, Prentitce-Hall, Englewood Cliffs, NJ(1974). 24.Lee E., C.K. Chan, and P. Paatero, ”Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong”, Atmospheric Environment, Vol. 33, pp. 3201-3212.(1999). 25.Liu, W., Y. Wang, A. Russell, E.S. Edgerton, “Atmospheric aerosol over two urban-rural pairs in the southeastern United States: Chemical composition and possible sources.”, Atmospheric Environment, Vol. 39, pp. 4453–4470 (2005). 26.Ma, C.J., M. Kasahara, R. Hőller, and T. Kamiya, “Characteristics of single particles sampled in Japan during the Asian dust storm period”, Atmospheric Environment, Vol. 35, pp. 2707-2714(2001). 27.Malinowski, E.R., Factor Analysis in Chemistry ,Wiley, New York, 2nd Ed(1991). 28.Metzger, K. B., P. E. Tolbert, M. Klein, J. L. Peel, W. D. Flanders, K. Todd, J. A. Mulholland, P. B. Ryan, and H. Frumkin, “Ambient air pollution and cardiovascular emergency department visits”, Epedemiology , Vol. 15, pp. 46-56(2004). 29.Miller, M.S., S.K. Friedlander and G.M. Hidy, “A Chemical Element Balance for the Pasadena Aerosol”, Journal of Colloid Interface Science, Vol. 39, pp. 165-176(1972). 30.Milne, J.W., D.B. Roberts, S.J. Walk, and D.J. William. Source of Sydney brown haze. The urban atmosphere-Sydney, A case study(1982). 31.Mori, I., M. Nishikawa, T. Tanimura and H. Quan, “Change in size distribution and chemical composition of kosa (Asian dust) aerosol during long-range transport.” Atmospheric Environment, Vol. 37, pp. 4253-4263 (2003). 32.National Acid Deposition Program(NADP). Ammual Data Summary. Precipitation Chemistry in the United States. 1992. National Research Ecology Laboratory, Colorado State University, Fort Collins, Colorado, pp. 480(2003). 33.Olmez, I., A. Sheffield, G. E. Gordon, J. E. Houck, L.C. Pritchett, J. A. Cooper, T. G. Dzubay, and R. L. Bennent, “Composition of Particles from Selected Sources in Philadelphia for Receptor Modeling Applications”, Journal of the Air Pollution Control Association , Vol. 38, No. 11, pp. 1392- 1402 (1988). 34.Ogulei, D., P. K. Hopke, L. Zhou, P. Paatero, S.S. Park, and J. M. Ondov, “Receptor modeling for multiple time resovled species: The Baltimore supersite”, Atmospheric Environment, Vol. 39, pp. 3751-3762.(2005). 35.Paatero, P. and Tapper U., “Analysis of Different Models of Factor Analysis as Least Squares Fit Problems”, Chemometrics Inteligent Laboratory Systems, Vol. 18, pp. 183-194 (1993). 36.Paatero, P. and Tapper U., “Positive matrix factorization : a non-negative factor model with optimal utilization of error estimates of data values”, Environmetrics, Vol. 5, pp. 111-126(1994). 37.Paatero, P., “Least squares formulation of robust nonnegative factor analysis,” Chemometrics Intelligent Laboratory Systems, Vol. 38, pp. 223 -242(1997). 38.Paatero P., “The multilinear engine –a table –driven least squares program for solving multilinear program, including the n-way parallel factor analysis model”, Journal of Computational and Graphical Statistics, Vol. 8, pp. 854- 888(1999). 39.Pattero, P., Hopke, P.K., “Discarding or downweighting high-noise variables in factor analytic models.”, Analytica Chimica Acta , Vol. 490, pp. 227-289 (2003). 40.Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman, “Increased particulate air pollution and the triggering of myocardial infarction”, Circulation, Vol. 103, pp. 2810-2815(2001). 41.Peterson K.G., J.L. Sagady, D.L. Hooper, S.B. Bertman, M.A. Carroll, and P.B. Shepson, “Analysis of air quality data using positive matrix factorization ”, Environmental Science Technology, Vol. 33, pp. 635-641 (1999). 42.Polissar A.V., P.K. Hopke, P. Paatero, Y.J. Kaufman, D.K. Hall, B.A. Bodhaine , E.G. Dutton, and J.M. Harris, “The aerosol at Barrow , Alaska: long-term trends and source locations”, Atmospheric Environment, Vol. 33, pp. 2441-2458(1999). 43.Pope, C. A. I., R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito,and G. D. Thurston, “Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution”, Journal of the American Medical Association. Vol. 287, pp. 1123-1141(2002). 44.Ramadan Z., X-H. Song, and P.K. Hopke, “Identification of sources of Phoenix aerosol by positive matrix factorization”, Atmospheric Environment, Vol. 34, pp. 3319-3329(2000). 45.Rolph, G. D., Real-time Environment Applications and Display sYstem (READY) Website ( ,NOAA Air Resources Laboratory, Silver Spring, MD (2003). 46.Salvador, P., B. Artinano, D. G. Alonso, X. Querol and A. Alastuey, “Identification and Characterisation of Sources of PM10 in Madrid(Spain) by Statistical Methods”, Atmospheric Environment, Vol. 38, pp. 435-447 (2004). 47.Schauer, J. J., W. F. Rogge, L. M. Hildemann, M. A. Mazurek, G.. R. Cass, B. R. R. T. Simoneit, “Source apportionment of airborne particulate matter using organic compounds as tracers,” Atmospheric Environment, Vol. 30, pp. 3837-3855 (1996). 48.Song, Y., Y. Zhang, S. Xie , L. Zeng , M. Zheng , L.G. Salmon, M. Shao and S. Slanina , “Source apportionment of PM2.5 in Beijing by positive matrix factorization.”, Atmospheric Environment, Vol. 40, pp. 1526-1537(2006). 49.Uematsu, M., R.A. Duce , J.M. Prospero, L. Chen, J.T. Merrill and R.L. McDonald , “Transport of mineral aerosol from Asia over the North Pacific Ocean.” Journal of Geophysical Research , Vol. 88, pp. 5343-5352 (1983). 50.U.S.EPA ,”Receptor Model Source Composition Library”, Environmental Protection Agency Research Triangle Park, NC., EPA-450/4-85-002 (1984). 51.U.S.EPA, Code of Federal Regulations. Part 50. National primary and secondary ambient air quality standards. Available on the Internet at /epacfr40/ (1998). 52.U.S.EPA, Impact of April 2001 Asian Dust Event on Particulate Matter Concentrations in the United States. National Air Quality And Emissions Trends Report, Special Studies, /air/ airtrends/ asian-dust4.pdf (2003). 53.U.S.EPA, Receptor Model Technical Series, Volume III(1989 Revision). CMB7 User’s Manual. Report Number EPA-450/4-90-004. Office of Air Quality Planning and Standards, Research Triangle Park, NC (1990). 54.Wang, S. and T. Larson, “Partial Least Squares Regression—A New Receptor Model”, The 8th Annual Conference on Air Pollution Control Technology, pp. 145-154. 55.Watson, J.G., ”Chemical Elemental Balance Receptor Model Methodology for Assessing the Sources of Fine and Total Suspended Particulate Matter in Portland, Oregon” , Ph.D. Dissertation, Oregon Graduate Center, Beaverton, Oregon(1979). 56.Watson, J.G., J.A. Copper and J.J. Huntzicker, “The Effective Variance Weighting Weighting For least Square Calculations Applied to the Mass Balance Receptor Model.” Atmospheric Environment, Vol. 18, pp. 1347-1355 (1984). 57.Watson J.G, E.M. Fujita, J.C. Chow, B. Zielinska, L.W. Richards, W. Neff, and D. Dietrich. Northern front range air quality study. Final report prepared for Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, by Desert Research Insitite, Reno, NV, STI-996410-1772-FR, June(1998). 58.Watson, J.G., J.C. Chow, “Source characterization of major emission sources in the Imperial and Mexicali valleys along the US/Mexico border”, Sciences of the Total Environment. Vol. 276, pp. 33-47(2001). 59.Watson, J.G., Robinson, N.F., Chow, J.C., Henry, R.C., Kim, B. M., Pace, T.G., Meyer, E.L. and Nguyen, O., “The USEPA/DRI Chemical mass Balance Receptor Model, CMB7.0”, Environmental Software, Vol. 5, pp. 38-49 (1990). 60.Xie Y.L., P.K. Hopke, P. Paatero, L.A. Barrie and S.M. Li, “Identification of source nature and seasonal variations of Arctic aerosol by the multilinear engine.” , Atmospheric Environment, Vol. 33, pp. 2549-2562(1999). 61.Zheng, M., L.G. Salmon, J.J. Schauer, L. Zeng, C.S. Kiang, Y. Zhang, and G.R. Cass, “Seasonal trends in PM2.5 source contribution in Beijing, China.”, Atmospheric Environment, Vol. 39, pp. 3967-3976 (2005). 62.大甲鎮公所,網址: (2006)。 63.中山醫學大學公共衛生學系,「臺中縣懸浮微粒特性及其污染來源調查研究計畫」,臺中縣環保局(2003)。 64.王秋森,「石化工廠產生的粒狀空氣污染物的受體模式之建立」,行政院國家科學委員會專題研究計畫,NSC 83-0421-B-002-318Z(1994)。 65.行政院環保署,「空氣品質保護25年紀實(1975-2000)」,台北(2000)。 66.行政院環保署,空氣品質監測網─沙塵暴,網址: /default. aspx? pid=b0301&cid=b0301(2006)。 67.行政院農業委員會水土保持局,整合性網際網路地理資訊系統,。 68.洪維恩 編著,旗標出版有限公司,民國94年,Matlab 7 程式設計。 69.邱嘉斌,「受體模式化學質量平衡法共線問題特性探討」,碩士論文,國立中興大學環境工程研究所,台中(1994)。 70.邱嘉斌,「台灣中部都會與沿海區域PM2.5及PM2.5-10氣膠化學組成及污染源貢獻量之研究」,博士論文,國立中興大學環境工程研究所,台中(2005)。 71.蔣本基、沈世宏、楊末雄、王竹方、魏耀輝、張勝祺、陳堯中、王碧、林志鴻、鐘瑞源等,「台北地區交通污染源與營建工程對空氣品質影響之研究及受體模式之確立」,行政院環境保護署報告(1989)。 72.蔣本基、楊末雄、王竹方、張勝祺、魏耀輝、周仲島、望熙榮、鄭曼婷、詹長全、王秋森、杜悅元等,「台灣北、中部地區受體模式建立與應用研究(一)」,行政院環境保護署報告,EPA-82-E3F1-09-01(1993)。 73.鄭曼婷、邱嘉斌、陳紀倫、王景良,「南高屏地區空氣品質總量悹至研究─子計畫E2:既有指紋資料庫之彙整與受體模式在南高屏地區之應用」,行政院環境保護署研究報告,EPA-87-FA42-03-F5(1998)。 74.鄭曼婷、程萬里、張艮輝、林沛練、莊秉潔、王竹方、郭崇義、林宗嵩、王重傑、王景祥、白曛綾,「中部地區空氣污染總量管制技術資料建立與應用」,行政院環境保護署研究報告,EPA-89-FA11-03-231(2000)。 75.鄭曼婷、陳昭忞、邱嘉斌,「鍋爐煙道排放PM2.5及PM10微粒之特性與化學組成」,國科會專題研究計劃期末報告,NSC 90-2211-E-005-014(2002)。 76.賴沛君,「應用CMB受體模式分析懸浮微粒高污染事件之研究」,碩士論文,國立中興大學環境工程研究所,台中(2004)。
本研究應用化學質量平衡法(CMB)與正矩陣因子法(PMF)兩受體模式推估大甲地區污染來源。PM2.5和PM¬2.5~10的樣本數據各有33組,其中有4組樣本受到沙塵暴事件的影響,在研究中將嘗試利用受體模式予以解析。模式CMB是由Matlab程式語言撰寫,並利用條件指數(Codition Index)和π矩陣做為污染源組成共線性判定方法,其優點在於明確定義了共線性判定準則,並增加污染源組成選取之彈性,而PMF模式則採用美國環保署開發之軟體EPA PMF 1.1 。
比對兩模式推估的結果,在PM2.5污染貢獻量方面,兩模式皆解析出交通排放、農廢燃燒、硫酸銨、硝酸銨、地殼物質、焚化爐、燃油鍋爐等7個污染源,其中交通排放在PM2.5中貢獻量為最大,CMB模式推估的貢獻量約佔57 %,PMF推估的貢獻量約35 %,農廢燃燒、硫酸銨、硝酸銨則位居污染貢獻量的第二至第四順位,分別佔CMB與PMF模式中總貢獻量的44.3 %、48.4 %;在PM2.5~10污染貢獻量方面,兩模式皆有解析出交通排放、地殼物質、海鹽飛沫、硝酸銨、焚化爐、農廢燃燒等6個污染源,且解析結果皆以交通排放和地殼物質為前兩大污染源,CMB模式推估佔總貢獻量的74%,PMF模式推估則佔總貢獻量的61%。其中CMB模式對交通排放的貢獻量推估為52%仍高於PMF模式推估貢獻量的35%。至於沙塵暴的貢獻量只有CMB模式解析出,其貢獻量只有整體的3.4 %,而PMF模式則無解析出。

Two receptor models, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF), are applied to estimate the source contributions of TaChia area in this study. There are thirty three samples of PM2.5 and PM2.5~10 , respectively. And four samples are impacted by Asian Dust Storm. This research will analysis the source contribution of Asian Dust Storm by receptor model. CMB model is written by Matlab program language. Condition Index and π matrix are used to identify the collinearity of source profiles by CMB model. Their advantages are that collinearity of source profiles are defined definitely and source profiles can be chosen flexible. PMF model is used the EPA PMF 1.1 version developed by USEPA.
The results of two models are compared. Vehicle emissions, vegetative burning, ammonium sulfate, ammonium nitrate, crustal materials, incinerator, oil-fired boiled are analyzed to the source contributions of PM2.5 by two receptor models. Vehicle emissions are the major source contributions of PM2.5, and it was estimated about 57 % and 35 % of PM2.5 by CMB model and PMF model. The second to fourth source contributions are vegetative burning, ammonium sulfate, ammonium nitrate, and they accounts for 44.3 % and 48.4 % of total source contributions to CMB model and PMF model, respectively. Six sources include vehicle emissions, crustal materials, marine spray, ammonium nitrate, incinerator, vegetative burning are resolved to the source contribution of PM2.5~10 by two receptor models. The results show that vehicle emission and crustal materials are primary and secondary source contributions of PM2.5~10. They accounts for 74 % and 61 % of total source contributions to PM2.5~10 according to the results obtained from CMB model and PMF model, respectively. Vehicle emissions estimated by CMB model are still 52 % of total source contribution higher than 35 % estimated by PMF model. The contribution of Asian dust storm is only resolved by CMB model, and it accounts for 3.4 % of total source contributions. Instead, PMF model can't resolve the contribution of Asian dust storm.
In conclusion, the major sources identified by the two receptor models are the same. The reason why high differences of contributions to vehicle emissions may be the source profile collected from the foreign area and it is not proper for the characteristics of vehicle emissions for TaChia area. Incinerator and crustal materials are low percentages of total contributions and their regression coefficient(r2) are low of the results between CMB model and PMF model. The reasons may be due to the incompleteness of profiles and a lack of local-specific profiles. In addition, a lack of samples to Asian dust storm, PMF model can't resolve the source contribution of Asian dust storm.
其他識別: U0005-2808200613561900
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