Event reconstruction for line source releases

Event reconstruction in the context of atmospheric dispersion of airborne contaminants has the goal of determining the source parameters based on information from a limited number of concentration and meteorological sensors. The pollutant sensors provide concentrations or dosages at different locations and times which can be used to calculate most probable strength, location and possibly start and end times of the release. Development of faster and more accurate algorithms is important for applications in a case of accidental or intentional releases, since it could help reduce the populace's exposure to dangerous airborne contaminants, plan evacuation routes, and help assess the magnitude of clean up.

In recent years there had been a boom in event reconstructions research, also known as source inversion. A lot of different approaches had been used, among others optimization and Bayesian statistics where dispersion models applied ranged from fast Gaussian plume models to slower but more accurate CFD models. Many of the source inversion codes to date apply to point source releases. In this research, we develop methods for back-calculating potential line source(s). We have developed an event reconstruction method that uses a segmented Gaussian plume model, information on the local road network, and atmospheric conditions to calculate the most probable strength and location of the line source release.