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

Abstract

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.

A New Traceability Method for Sudden Water Pollution Accidents

“…such as a coupling method was proposed based on the best perturbation and regularization to identify multi-point pollution sources with fractional diffusion equation [6], genetic algorithm and simulated annealing algorithm were used to identify groundwater pollution source [7], probability density function and MCMC were used to reconstruct or identify pollution sources [8], and so on. But there are many uncertainties in practice, such as the uncertainty of measurement data, which will make w traceability problems become a complex "ill-posed" problems [9][10][11]. Regularization and optimization method can only obtain point estimation of the unknown parameters; the probability statistics method has strong randomness.…”

Statistical-Physical Estimation of Pollution Emission

“…Ghosh et al (2010) studied formation and deformation of atmospheric concentrations of total nitrate using the empirical chemical relationship and dynamic statistical models. Williams et al (2011) estimated the pollution source direction using the dispersion model, treating the computer model outputs as given. Although proven to be useful, these efforts do not incorporate enough physical knowledge as to the pollution generation process.…”