(43ah) A Hybrid Model for Characterizing the Source of Hazardous Material Release

On Sept 10, 2012 Los Angeles Fire department and 911 operators received many complaints about a strong foul smell. The complaint covered an area of 10,000 square miles. There were many speculations for the source of the release; sewage, chemical spill, pipeline carrying natural gas. However, after many hours of investigation by South Coast Air Quality Management it was discovered that the source of the foul odor was the dead fish churned up by the thunderstorm, occurred that night, at the Salton Sea. Salton Sea is a 376-square-mile saltwater lake about 150 miles southwest of Los Angeles.  The fact that the odor remained strong up to 150 miles downwind of the source was very unusual.  

Fortunately the above case did not pose any health effects. But there may be events where extremely hazardous chemicals or biological agents are involved. Locating the source and amount of release for predicting the geographical extent of the exposure in a timely manner is, therefore, essential to take an effective response action for evacuation or shelter-in-place. So the problem is finding the source of contaminant given measurements of concentration at sensors scattered throughout a geographical domain. This is an inverse problem to the usual forward dispersion calculation in which the source location and release rate is known and the concentrations at sensors positions are predicted. There are many publications in the area of source characterization; location, and amount of chemical being released. The two mostly cited methods are; Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling methodology; and adjoint advection- diffusion equation. The Bayesian methodology fits well for application to atmospheric dispersion where ambient and gas sensor data are stochastic in nature, however, it takes a long time to converge.

In this paper, we briefly describe a simple least-square based method for source characterization using Gaussian plume model. We apply the Bayesian method to address the same problem and compare the results of the two methodologies for few selected release scenarios as well as a set of field data. Subsequently, we will present a hybrid model using Bayesian inference which utilizes the result of the simple least- square model as a seed for narrowing the search domain and reducing the time for locating source of chemical release.  We present the improvement in the run time using the hybrid model which makes it suitable for emergency response and provides a reasonably accurate prediction of source characterization.  Furthermore, we will evaluate model sensitivity to plume meander, number of sensors, and weather conditions involved in the study.