請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/45513
標題: Bayesian treatment of a chemical mass balance receptor model with multiplicative error structure
作者: Keats, A.
鄭曼婷
Cheng, M.T.
Yee, E.
Lien, F.S.
關鍵字: Receptor model
Chemical mass balance
Bayesian inference
Multiplicative error
Source apportionment
Hamiltonian Markov chain
Monte Carlo
source apportionment
monte-carlo
uncertainty
pm10
期刊/報告no:: Atmospheric Environment, Volume 43, Issue 3, Page(s) 510-519.
摘要: The chemical mass balance (CMB) receptor model is commonly used in source apportionment studies as a means for attributing measured airborne particulate matter (PM) to its constituent emission sources. Traditionally, error terms (e.g., measurement and source profile uncertainty) associated with the model have been treated in an additive sense. In this work, however, arguments are made for the assumption of multiplicative errors, and the effects of this assumption are realized in a Bayesian probabilistic formulation which incorporates a 'modified' receptor model. One practical, beneficial effect of the multiplicative error assumption is that it automatically precludes the possibility of negative source contributions, without requiring additional constraints on the problem. The present Bayesian treatment further differs from traditional approaches in that the source profiles are inferred alongside the source contributions. Existing knowledge regarding the source profiles is incorporated as prior information to be updated through the Bayesian inferential scheme. Hundreds of parameters are therefore present in the expression for the joint probability of the source contributions and profiles (the posterior probability density function, or PDF), whose domain is explored efficiently using the Hamiltonian Markov chain Monte Carlo method. The overall methodology is evaluated and results compared to the US Environmental Protection Agency's standard CMB model using a test case based on PM data from Fresno, California. (c) 2008 Elsevier Ltd. All rights reserved.
URI: http://hdl.handle.net/11455/45513
ISSN: 1352-2310
文章連結: http://dx.doi.org/10.1016/j.atmosenv.2008.10.031
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