Source term estimation using noble gas and aerosol samples

Abstract

Algorithms that estimate the location, time, and magnitude of a point-source atmospheric release using remotely sampled air concentrations typically use data for a single chemical or radioactive isotope. A Bayesian algorithm is presented that uses data from multiple radioactive isotopes that are all released in the same short-duration event. Data from noble gas and aerosol samplers can be used simultaneously in the model. Application to a large synthetic data set using four isotopes shows the new algorithm generally gives more accurate location and time estimates than a comparable model using a single isotope.