Novel techniques for characterization of hydrocarbon emission sources in the Barnett Shale

Changes in ambient atmospheric hydrocarbon concentrations can have both short-term and long-term effects on the atmosphere and on human health. Thus, accurate characterization of emissions sources is critically important. The recent boom in shale gas production has led to an increase in hydrocarbon emissions from associated processes, though the exact extent is uncertain. As an original quantification technique, a model airplane equipped with a specially-designed, open-path methane sensor was flown multiple times over a natural gas compressor station in the Barnett Shale in October 2013. A linear optimization was introduced to a standard Gaussian plume model in an effort to determine the most probable emission rate coming from the station. This is shown to be a suitable approach given an ideal source with a single, central plume. Separately, an analysis was performed to characterize the nonmethane hydrocarbons in the Barnett during the same period. Starting with ambient hourly concentration measurements of forty-six hydrocarbon species, Lagrangian air parcel trajectories were implemented in a meteorological model to extend the resolution of these measurements and achieve domain-fillings of the region for the period of interest. A self-organizing map (a type of unsupervised classification) was then utilized to reduce the dimensionality of the total multivariate set of grids into characteristic one-dimensional signatures. By also introducing a self-organizing map classification of the contemporary wind measurements, the spatial hydrocarbon characterizations are analyzed for periods with similar wind conditions. The accuracy of the classification is verified through assessment of observed spatial mixing ratio enhancements of key species, through site-comparisons with a related long-term study, and through a random forest analysis (an ensemble learning method of supervised classification) to determine the most important species for defining key classes. The hydrocarbon classification is shown to have performed very well in identifying expected signatures near and downwind-of oil and gas facilities with active permits, which showcases this method's usefulness for future regional hydrocarbon source-apportionment analyses.