Pollution Source Direction Identification: Embedding dispersion models to solve an inverse problem
William F. Christensen, Brigham Young University
Monday, October 26, 2009
4:00 p.m., 223 Weber
We develop a Bayesian method for identifying pollution source directions which combines deterministic and stochastic models. We frame source direction identification as an inverse problem, embedding the deterministic dispersion model AERMOD directly into the likelihood function. AERMOD’s fast computation time allows us to run the model at each iteration of the MCMC, thereby creating a simulated likelihood function and obviating the need for an emulator. The method is flexible enough to identify multiple source directions for cases in which a species or source type of interest is emitted at more than one location, and Reversible Jump MCMC is used to evaluate the appropriate number of sources. Source direction identification is an important part of the pollution source apportionment (PSA) problem, which entails identifying and describing pollution sources and their contributions.