For the past ten years, much of the research in hyperspectral image data exploitation techniques has been focused on detection of ground targets. As a passive remote sensing technique, hyperspectral imagers have performed reasonably well in detecting the presence of a variety of objects; from crop species to land mines to mineral deposits to vehicles under camouflage. These often promising results have prompted new studies of hyperspectral remote sensing for other applications - including atmospheric monitoring. Should technologies like hyperspectral imaging prove effective in emission source monitoring, organizations interested in environmental assessment could transition from inspection using hand-held analytical instruments to a truly standoff technique. In this paper, we evaluate the utility of a set of hyperspectral exploitation techniques applied to the task of gas detection. This set of techniques is a sampling of approaches that have appeared in the literature, and all of the methods discussed have demonstrated utility in the reflective regime. Specifically, we look at signature-based detection, anomaly detection, transformations (i.e. rotations) of the spectral space, and even dedicated band combinations and scatter plots. Using real LWIR hyperspectral data recently collected on behalf of the US Environmental Protection Agency, we compare performance in detecting three different industrial gases.