Source estimation methods: a survey

Methods for source estimation available in the literature are surveyed. Trajectory calculations are often used for determining the source-receptor relationships. Forward trajectories in the atmosphere are irreversible due to the effects of flow deformation, turbulence, and convection. A single backward trajectory is generally inadequate to trace the transport history of a sampling volume and identify the location of its source origin. Among the source-finding methods considered are Gifford's inverted plume technique for estimating the total concentration at a point from a set of upwind sources, source footprint and “area of influence” analysis for measured concentrations, and chemical mass balance methods suitable for detecting unknown chemical sources in urban areas.

Application of air quality models for determining the source distribution is an emerging science. Both forward- and backward-modeling methods are currently being developed. Forward-modeling methods use forward running transport and dispersion models or CFD codes, which are run many times and the resulting dispersion field is compared against the actual data from multiple sensors. Such methods are based on Bayesian updating/inference, Kalman filtering, and Markov chain Monte-Carlo techniques. Utilizing sensor and meteorological data, inverse modeling methods solve the adjoint of a transport and dispersion model to estimate the source distribution, and reverse diffusion methods consider diffusion along backward trajectories to estimate the upwind area of influence for each sensor measurement. Lagrangian stochastic dispersion models, which calculate the ensemble-mean dispersion quantities from the trajectories of a large number of fluid particles representing the pollutant mass, are well suited for both forward and backward modeling. The need for characterizing the uncertainties in source estimation using atmospheric dispersion models is emphasized.