The event detection, localization, and quantification framework proposed here contributes a number of advancements to this body of literature. First, the framework includes a novel spike detection algorithm that creates a local background estimate for each spike identified in the concentration time series. This algorithm is used in multiple steps of the broader framework. Second, the framework requires no information on emission start or end times to operate. The framework can be run continuously and will only estimate emission location and rate if an emission event is detected. Third, the framework uses a Gaussian puff atmospheric transport model that provides a balance between simpler models whose assumptions are rarely met in practice (e.g., the Gaussian plume) and more complex simulations that are computationally expensive and may require customization for different locations. Fourth, with the goal of providing rapid alerts, the framework can provide a localization and quantification estimate using as little as 15 minutes of data. Finally, the framework does not require an expansive sensor network, but rather can operate using only as many sensors as are required to surround all potential emission sources on the site (approximately 4-8 sensors). Note that the proposed framework can be applied to data from any sensor network that provides point-in-space methane concentration, wind speed, and wind direction measurements.