Bayesian treatment of a chemical mass balance receptor model with multiplicative error structure

Atmospheric Environment

Volumn 43, Issue 3, 2009, Pages 510-519

  Keats, Andrew ^a   Cheng, Man Ting ^b   Yee, Eugene ^c   Lien, Fue Sang ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

^b NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

^c DEFENCE RESEARCH AND DEVELOPMENT CANADA   (Canada)

Author keywords

Bayesian inference; Chemical mass balance; Hamiltonian Markov chain Monte Carlo; Multiplicative error; Receptor model; Source apportionment

Indexed keywords

BAYESIAN INFERENCE; CHEMICAL MASS BALANCE; MULTIPLICATIVE ERROR; RECEPTOR MODEL; SOURCE APPORTIONMENT;

BAYESIAN NETWORKS; ENVIRONMENTAL PROTECTION AGENCY; HAMILTONIANS; INFERENCE ENGINES; MARKOV PROCESSES; MATHEMATICAL MODELS; MEATS; MONTE CARLO METHODS; PARTICULATE EMISSIONS; PERMANENT MAGNETS; PROBABILITY; UNCERTAIN SYSTEMS; UNCERTAINTY ANALYSIS;

PROBABILITY DENSITY FUNCTION;

BAYESIAN ANALYSIS; MARKOV CHAIN; MASS BALANCE; MODELING; MONTE CARLO ANALYSIS; PARTICULATE MATTER;

CALIFORNIA; FRESNO; NORTH AMERICA; UNITED STATES;

EID: 58049156025     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2008.10.031     Document Type: Article

References (30)

1. Billheimer D. Compositional receptor modeling. Environmetrics 12 (2001) 451-467
2. x, and THC in Taipei. Atmospheric Environment 30 (1996) 25-33
3. Chio C.-P., Cheng M.-T., and Wang C.-F. Source apportionment to PM10 in different air quality conditions for Taichung urban and coastal areas, Taiwan. Atmospheric Environment 38 (2004) 6893-6905
4. 10 source apportionment in California's San Joaquin Valley. Atmospheric Environment 26A (1992) 3335-3354
5. Christensen W.F., and Gunst R.F. Measurement error models in chemical mass balance analysis of air quality data. Atmospheric Environment 38 (2004) 733-744
6. Coulter C.T. EPA-CMB8.2. Users Manual EPA-452/R-04-011 (Dec 2004), Office of Air Quality Planning & Standards, Research Triangle Park, NC
7. Duane S., Kennedy A.D., Pendleton B.J., and Roweth D. Hybrid Monte Carlo. Physics Letters B 195 (1987) 216-222
8. Dunkley J., Bucher M., Ferreira P.G., Moodley K., and Skordis C. Fast and reliable MCMC for cosmological parameter estimation. Monthly Notices of the Royal Astronomical Society 356 (2005) 925-936
9. In: Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall/CRC, London
10. ® Support (2005), Cambridge University Press, Cambridge
11. Hajian A. Efficient cosmological parameter estimation with Hamiltonian Monte Carlo technique. Physical Review D 75 (2007) 083525 11 pages
12. Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
13. Jaynes E.T. Probability Theory: The Logic of Science (2003), Cambridge University Press, Cambridge
14. Kashiwagi N. Chemical mass balance when an unknown source exists. Environmetrics 15 (2004) 777-796
15. Kim B.M., and Henry R.C. Diagnostics for determining influential species in the chemical mass balance receptor model. Journal of the Air & Waste Management Association 49 (1999) 1449-1455
16. Limpert E., Stahl W.A., and Abbt M. Log-normal distributions across the sciences: keys and clues. Bioscience 51 (2001) 341-352
17. Lingwall J.W., Christensen W.F., and Reese C.S. Dirichlet based Bayesian multivariate receptor modeling. Environmetrics 18 (2008) 618-629
18. MacKay D.J.C. Information Theory, Inference, and Learning Algorithms (2003), Cambridge University Press, Cambridge
19. Marmur A., Mulholland J.A., and Russell A.G. Optimized variable source-profile approach for source apportionment. Atmospheric Environment 41 (2007) 493-505
20. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Equations of state calculations by fast computing machine. Journal of Chemical Physics 21 (1953) 1087-1091
21. Neal, R.M., 1993. Probabilistic Inference Using Markov Chain Monte Carlo Methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto.
22. Park E.S., Guttorp P., and Henry R.C. Multivariate receptor modeling for temporally correlated data by using MCMC. Journal of the American Statistical Association 96 (2001) 1171-1183
23. Park E.S., Oh M.-S., and Guttorp P. Multivariate receptor models and model uncertainty. Chemometrics and Intelligent Laboratory Systems 60 (2002) 49-67
24. R Development Core Team, 2005. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN: 3-900051-07-0. Available from: .
25. Seinfeld J.H., and Pandis S.N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (2006), John Wiley and Sons, Hoboken
26. Sivia D.S., and Skilling J. Data Analysis: A Bayesian Tutorial (2006), Oxford University Press
27. Slob W. Uncertainty analysis in multiplicative models. Risk Analysis 14 (1994) 571-576
28. Tarantola A. Elements for Physics: Qualities, Quantities, and Intrinsic Theories (2006), Springer, Berlin
29. Watson J.G., and Chow J.C. Source characterization of major emission sources in the Imperial and Mexicali Valleys along the US/Mexico border. The Science of the Total Environment 276 (2001) 33-47
30. Watson J.G., Robinson N.F., Chow J.C., Henry R.C., Kim B.M., Pace T.G., Meyer E.L., and Nguyen Q. The USEPA/DRI Chemical Mass Balance receptor model, CMB 7.0. Environmental Software 5 (1990) 38-49