Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)

Korean Society for Atmospheric Environment (한국대기환경학회)
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Estimation of Quantitative Source Contribution of Ambient PM-10 Using the PMF Model

PMF모델을 이용한 대기 중 PM-10 오염원의 정량적 기여도 추정

  • 황인조 (경희대학교 산학협력기술연구원) ;
  • 김동술 (경희대학교 환경응용화학대학 대기오염연구실 및 환경연구센터)
  • Published : 2003.12.01
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Abstract

In order to maintain and manage ambient air quality, it is necessary to identify sources and to apportion its sources for ambient particulate matters. The receptor methods were one of the statistical methods to achieve reasonable air pollution strategies. Also, receptor methods, a field of chemometrics, is based on manifold applied statistics and is a statistical methodology that analyzes the physicochemical properties of gaseous and particulate pollutant on various atmospheric receptors, identifies the sources of air pollutants, and quantifies the apportionment of the sources to the receptors. The objective of this study was 1) after obtaining results from the PMF modeling, the existing sources of air at the study area were qualitatively identified and the contributions of each source were quantitatively estimated as well. 2) finally efficient air pollution management and control strategies of each source were suggested. The PMF model was intensively applied to estimate the quantitative contribution of air pollution sources based on the chemical information (128 samples and 25 chemical species). Through a case study of the PMF modeling for the PM-10 aerosols, the total of 11 factors were determined. The multiple linear regression analysis between the observed PM-10 mass concentration and the estimated G matrix had been performed following the FPEAK test. Finally the regression analysis provided quantitative source contributions (scaled G matrix) and source profiles (scaled F matrix). The results of the PMF modeling showed that the sources were apportioned by secondary aerosol related source 28.8 ％, soil related source 16.8％, waste incineration source 11.5%, field burning source 11.0%, fossil fuel combustion source 10%, industry related source 8.3％, motor vehicle source 7.9%, oil/coal combustion source 4.4％, non-ferrous metal source 0.3％. and aged sea- salt source 0.2％, respectively.

Keywords

References

  1. 기상연보(1999) 한국기상청
  2. 기상연보(2000) 한국기상청
  3. 기상연보(2001) 한국기상청
  4. 김관수, 황인조, 검동술 (2001) 수원지역 대기 중 PM-IO 오염원의 정량명가를 위한 수용방법론의 개발， 한국대기환경학회지，17(2), 119-131
  5. 김동술, 황인조 (2002) 미세먼지의 발생 메카니즘 벚 미세먼지 오염원의 추정 방법론, 공기청정기술, 15(1),38-53
  6. 대기환경연구회(2000) 대기환경개론,통화기술
  7. 수원시 (2002) http://www.suwon.ne.kr
  8. 이태정, 김동술 (1997) 수원지역 입자상 오염물질의 오염원 기여도의 추정, 한국대기보전학회지, 13(4),285296
  9. 황인조, 김동술 (1997) Submicron 부유분진의 화학적 조성및 분포에 관한 연구, 한국대기보전학회지, 14(1),11-23
  10. 황인조, 김동술 (2003) PMF 모델을 이용한 대기 중 PM-I0 오염원 확인, 한국대기보전학회지,19(6), 701 - 717
  11. 황인조, 김태오, 김동술(2001) PMF 방법론을 이용한 수원지역 PM 一10의 오염원 확인, 한국대기환경학회지, 7(2), 133-145
  12. Anttila, P., P. Paatero, U. Tapper, and O. Jarvinen (1995) Source identification of bulk wet deposition in Finland by positive matrix factorization, Atmospheric Environment, 29(14), 1705-1718
  13. Appel, B.R. (1993) Atmospheric sample analysis and sampling artifacts, in Aerosol measurement principles. techniques. and applications. Edited by K. Willeke and P.A. Baron, Van Nostrand Reinhold, New York, 233-259
  14. Budiansky, S. (1980) Dispersion modeling, Environ. Sci. & Techno!., 14,370-374
  15. Chan, Y.C., R.W. Simpson, G.H. Mctainsh, P.O. Vowles, D.O. Cohen, and G.M. Bailey (1999) Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modeling, Atmospheric Environment, 33(19), 3251-3268
  16. Chow, J.C., C.S. Liu, J. Cassmassi, J.G. Watson, Z. Lu, and L.c. Pritchett (1992) A neighborhood-scale study of PMIO source contributions in Rubidox, CA, Atmospheric Environment, 26A(4), 693 - 706
  17. Chow, J.C., J.G. Watson, D.H. Lowenthal, P.A. Solomon, K.L. Magliano, S.D. Ziman, and L.W. Richards (1992a) PM10 source apportionment in California's San Joaquin Valley, Atmospheric Environment, 26A(18), 3335-3354
  18. Cooper, J.A. and J.G. Watson (1980) Receptor oriented methods of air particulate source apportionment, JAPCA, 30(10),1116-1125
  19. Gordon, G.E. (1988) Receptor models, Environ. Sci. & Technol., 22(10), 1132- 1142 https://doi.org/10.1021/es00175a002
  20. Hidy, G.M. and S.K. Friedlander (1972) The nature of the Los Angeles aerosol, Proceedings of the 2nd International Clean Air Congress. Academic Press, New York, 391-404
  21. Hopke, P.K. (2000) A guide to Positive Matrix Factorization, in Workshop on UNMIX and PMF as applied to PM2.5. Edited by R.D. Willis, RTP. NC, EPA 600/ A-00/048
  22. Lee, E., C.K. Chan, and P. Paatero (1999) Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong, Atmospheric Environment, 33(19), 3201-3212 https://doi.org/10.1016/S1352-2310(99)00113-2
  23. Link, D.D., P.J. Water, and H.M. Kingston (1998) Development and validation of the new EPA microwaveassisted leach method 3051 A, Environ. Sci. & Technol., 32(22), 3628-3632
  24. Miller, M.S., S.K. Friedlander, and G.M. Hidy (1972) A chemical element balance for the Pasadena aerosol, J. Colloid and Inter. Inter. Science, 39(1), 165-176 https://doi.org/10.1016/0021-9797(72)90152-X
  25. Paatero, P. and U. Tapper (1994) Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, 5, 111 -126
  26. Polissar, A.V., P.K. Hopke, and R.D. Poirot (2001) Atmospheric aerosol over Vermont: Chemical composition and sources, Environ. Sci. & Techno!., 35(23), 4604-4621
  27. Polissar, A.V., P.K. Hopke, P. Paatero, W.C. MaIm, and J.F. Sisler (1998) Atmospheric aerosol over Alaska, 2. Elemental composition and sources, J. of Geophysical Research, 103(015), 19045-19057 https://doi.org/10.1029/98JD01212
  28. Ramadan, Z., X.H. Song, and P.K. Hopke (2000) Identification of sources of Phoenix aerosol by positive matrix factorization, Air & Waste Manage. Assoc., 50(8), 1308-1320