Achievable accuracy in parameter estimation of a Gaussian plume dispersion model

The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength, size) and environmental parameters (wind speed, direction, atmospheric stability conditions). A sensor network is at disposal to measure the concentration of biological pathogen or chemical substance within the plume. This paper presents a theoretical analysis of the best achievable accuracy in estimation of Gaussian plume model parameters. Numerical results illustrate how parameter estimation accuracy depends on sensor measurement accuracy, the density of sensors and the quality of (prior) meteorological advice. The theoretical bounds are compared with empirical errors obtained using an importance sampling parameter estimation technique.