Abstract
The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.
Original language | American English |
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Qualification | Doctor of Philosophy |
State | Published - 2 May 2006 |
Externally published | Yes |
Keywords
- air pollution
- pollution source apportionment
- source attribution
- statistics
EGS Disciplines
- Statistics and Probability