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Emissions of harmful substances into the atmo- sphere are a serious environmental concern. In order to un- derstand and predict their effects, it is necessary to estimate the exact quantity and timing of the emissions from sen- sor measurements taken at different locations. There are a number of methods for solving this problem. However, these existing methods assume Gaussian additive errors, making them extremely sensitive to outlier measurements. We first show that the errors in real-world measurement data sets come from a heavy-tailed distribution, i.e., include outliers. Hence, we propose robustifying the existing inverse meth- ods by adding a blind outlier-detection algorithm. The im- proved performance of our method is demonstrated on a real data set and compared to previously proposed methods. For the blind outlier detection, we first use an existing al- gorithm, RANSAC, and then propose a modification called TRANSAC, which provides a further performance improve- ment.

A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection