Utilizing state estimation to determine the source location for a contaminant

In the event of an atmospheric contaminant release, it is crucial to ascertain the source information for the contaminant, both for mitigation purposes and to predict subsequent transport and dispersion. Here, obtaining part of this information, namely the contaminant source location, is accomplished by adopting a state estimation approach for instantaneous and continuous contaminant releases. The relevant state components that we exploit here are the contaminant cloud’s axis and spread. For an instantaneous release, we can adopt a Lagrangian approach to obtain the source location by extrapolating state observations back to the initial state. In contrast, the formulation for a continuous release cannot adopt this strictly Lagrangian approach because a steady flow of contaminants implies that the contaminant cloud is statistically stationary with respect to the sensor grid. Therefore, the concentration data are averaged in time and a hybrid Lagrangian/Eulerian framework is used to determine the average state. It is shown that with these frameworks it is possible to ascertain the contaminant source location for both dense and sparse sensor grids. An advantage of these algorithms is that no meteorological input is required. The algorithms in the form presented here, are relevant for short-range transport and dispersion. However, the source term estimation method presented here can be extended to longer-range applications by relaxing assumptions on the contaminant atmospheric transport and dispersion.

► Lagrangian approach improves efficiency for contaminant source location estimation.
► Instantaneous sources can be handled with a purely Lagrangian approach.
► Continuous sources require a combined Lagrangian/Eulerian approach.
► An advantage of these algorithms is that no meteorological input is required.