A number of groups have proposed similar techniques for emission localization and quantification in this setting. Some of these solutions rely on a large sensor network (20 plus), which would be cost prohibitive in practice , or on long term (multiple hours to days) aggregates, which would be unable to identify short-lived events, even if they are large . Additionally, many solutions rely on the Gaussian plume atmospheric dispersion model for modeling atmospheric transport . The Gaussian plume model is optimized for far-field applications with distance scales much larger than the typical production site and assumes steady state wind conditions over short (∼10 minute) time intervals, an assumption that breaks down in many practical scenarios where wind conditions are variable. On the other end of the complexity spectrum, some solutions rely on large eddy simulations for modeling atmospheric transport . These simulations are far more accurate than the Gaussian plume model but require special expertise to operate and implement for specific sites, are computationally expensive, and are not generally publicly available. Finally, none of the solutions discussed here attempt to perform emission event detection, and hence they are all tested in rounds with known start and end times . Note that proprietary solutions are developing rapidly in the private sector, which we are unable to assess.