A Hybrid Model for Characterizing the Source of Hazardous Material Release

摘要

On Sept 10, 2012, Los Angeles Fire department and 911 operators received many complaintsabout a strong foul smell. The complaint covered an area of 10,000 square miles. There weremany speculations for the source of the release; sewage, chemical spill, pipeline carrying naturalgas. However, after many hours of investigation by South Coast Air Quality Management it wasdiscovered that the source of the foul odor was dead fish at the Salton Sea churned up by athunderstorm that had occurred that evening. The Salton Sea is a 376-square-mile saltwater lakeabout 150 miles southwest of Los Angeles. The odor remained strong for up to 150 milesdownwind of the source, and quite unusual. Fortunately the above case did not pose any healtheffects.It is common for industrial sites carrying hazardous chemicals to receive calls from surroundingcommunities complaining of a chemical presence in the ambient air. Following a complaint,industrial sites must work promptly check and verify if the odor source is related to a chemicalrelease on their property, especially when there are other industrial sites in the vicinity, formitigating and reducing the extent of the possible hazard. Locating the source and amount ofrelease 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.With only basic knowledge of meteorological information; wind speed, wind direction, andstability along with two or more gas concentration measurements, it is possible to quickly framethe zone of chemical release. This is an inverse problem to the usual forward dispersioncalculation in which the source location and release rate is known and the concentrations atsensors positions are predicted. There are many publications in the area of sourcecharacterization; location, and amount of chemical being released. The two most cited methodsare: Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling methodology, andadjoint advection- diffusion equation. The Bayesian methodology fits well for application to