Pollution source direction identification: Embedding dispersion models to solve an inverse problem

Basil Williams, William F. Christensen, C. Shane Reese

    Research output: Contribution to journalArticlepeer-review


    We develop a Bayesian method for identifying pollution source directions that combines deterministic and stochastic models. We frame the source direction identification as an inverse problem, embedding the deterministic dispersion model American Meteorological Society/United States Environmental Protection Agency Regulatory Model (AERMOD) directly into the likelihood function. AERMOD's fast computation time allows us to run the model at each iteration of the Markov chain Monte Carlo (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 problem, which entails identifying and describing pollution sources and their contributions.

    Original languageEnglish (US)
    Pages (from-to)962-974
    Number of pages13
    Issue number8
    StatePublished - Dec 2011


    • Bayesian hierarchical model
    • Circular data
    • Computer model
    • Deterministic model
    • Pollution source apportionment
    • Wind direction

    ASJC Scopus subject areas

    • Statistics and Probability
    • Ecological Modeling


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